Annexes to the Inventory of U.S. GHG
Emissions and Sinks
The following nine annexes provide additional information related to the material presented in the main body
of this report as directed in the UNFCCC Guidelines on Reporting and Review (UNFCCC 2014). Annex I contains an analysis
of the key categories of emissions discussed in this report and a review of the methodology used to identify those key
categories. Annex 2 describes the methodologies used to estimate C02 emissions from fossil fuel combustion, the carbon
content of fossil fuels, and the amount of carbon stored in products from non-energy uses of fossil fuels. Annex 3
discusses the methodologies used for a number of individual source categories in greater detail than was presented in
the main body of the report and includes explicit activity data and emission factor tables. Annex 4 presents the IPCC
reference approach for estimating C02 emissions from fossil fuel combustion. Annex 5 addresses the criteria for the
inclusion of an emission source or sink category and discusses some of the sources that are excluded from U.S.
estimates. Annex 6 provides a range of additional information that is relevant to the contents of this report. Annex 7
provides data on the uncertainty of the emission estimates included in this report. Annex 8 provides information on the
QA/QC methods and procedures used in the development of the Inventory, including responses to UNFCCC reviews.
Finally, Annex 9 provides an overview of GHGRP data use in the Inventory.
Annexes to the Inventory of U.S. GHG Emissions and Sinks	1
ANNEX 1 Key Category Analysis	3
ANNEX 2 Methodology and Data for Estimating C02 Emissions from Fossil Fuel Combustion	60
2.1.	Methodology for Estimating Emissions of C02 from Fossil Fuel Combustion	60
2.2.	Methodology for Estimating the Carbon Content of Fossil Fuels	85
2.3.	Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil Fuels	126
ANNEX 3 Methodological Descriptions for Additional Source or Sink Categories	156
3.1.	Methodology for Estimating Emissions of CH4, N20, and Indirect Greenhouse Gases from Stationary
Combustion	156
3.2.	Methodology for Estimating Emissions of CH4, N20, and Indirect Greenhouse Gases from Mobile Combustion
and Methodology for and Supplemental Information on Transportation-Related Greenhouse Gas Emissions
	164
3.3.	Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel Consumption	215
3.4.	Methodology for Estimating CH4 Emissions from Coal Mining	221
3.5.	Methodology for Estimating CH4, C02, and N20 Emissions from Petroleum Systems	229
3.6.	Methodology for Estimating CH4, C02, and N20 Emissions from Natural Gas Systems	235
3.7.	Methodology for Estimating C02, CH4, and N20 Emissions from the Incineration of Waste	244
3.8.	Methodology for Estimating Emissions from International Bunker Fuels used by the U.S. Military	251
3.9.	Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances	258
3.10.	Methodology for Estimating CH4 Emissions from Enteric Fermentation	290
3.11	Methodology for Estimating CH4 and N20 Emissions from Manure Management	320
3.12.	Methodologies for Estimating Soil Organic C Stock Changes, Soil N20 Emissions, and CH4 Emissions and from
Agricultural Lands (Cropland and Grassland)	356
3.13.	Methodology for Estimating Net Carbon Stock Changes in Forest Ecosystems and Harvested Wood Products
for Forest Land Remaining Forest Land and Land Converted to Forest Land as well as Non-C02 Emissions from
Forest Fires	409
3.14.	Methodology for Estimating CH4 Emissions from Landfills	447
ANNEX 4 IPCC Reference Approach for Estimating C02 Emissions from Fossil Fuel Combustion	470
ANNEX 5 Assessment of the Sources and Sinks of Greenhouse Gas Emissions Not Included	481
ANNEX 6 Additional Information	494
6.1.	Global Warming Potential Values	494
6.2.	Ozone Depleting Substance Emissions	503
6.3.	Sulfur Dioxide Emissions	505
6.4.	Complete List of Source and Sink Categories	507
6.5.	Constants, Units, and Conversions	508
6.6.	Abbreviations	511
6.7.	Chemical Formulas	518
A-1

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ANNEX 7 Uncertainty	523
7.1.	Overview	523
7.2.	Methodology and Results	524
7.3.	Information on Uncertainty Analyses by Source and Sink Category	532
7.4.	Reducing Uncertainty and Planned Improvements	532
ANNEX 8 QA/QC Procedures	535
8.1.	Background	535
8.2.	Purpose	535
8.3.	Assessment Factors	537
8.4.	Responses to Review Processes	539
ANNEX 9 Use of EPA Greenhouse Gas Reporting Program in Inventory	541
A-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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ANNEX 1 Key Category Analysis
The United States has identified national key categories based on the estimates presented in this report. The
2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories (IPCC 2006)
describes a key category as a "[category] that is prioritized within the national inventory system because its estimate has
a significant influence on a country's total inventory of greenhouse gases in terms of the absolute level, the trend, or the
uncertainty in emissions and removals." By definition, key categories are sources or sinks that have the greatest
contribution to the absolute overall level of national emissions in any of the years covered by the time series. In addition,
when an entire time series of emission estimates is prepared, a determination of key categories must also account for
the influence of the trends of individual categories. Therefore, a trend assessment is conducted to identify source and
sink categories for 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 Volume 1, Chapter 4 of the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006), includes:
•	Approach 1 (including both level and trend assessments);
•	Approach 2 (including both level and trend assessments, and incorporating uncertainty analysis); and
•	Qualitative approach.
This Annex presents an analysis of key categories, both for sources only and also for sources and sinks (i.e.,
including Land Use, Land-Use Change and Forestry LULUCF); discusses Approach 1, Approach 2, and qualitative
approaches used to identify key categories for the United States; provides level and trend assessment equations; and
provides a brief evaluation of IPCC's quantitative methodologies for defining key categories. The UNFCCC common
reporting format (CRF) reporting software generates Table 7, which also identifies key categories using an Approach 1
analysis based on the default disaggregation approach provided in Volume 1, Chapter 4, Table 4.1 of the 2006 IPCC
Guidelines. The disaggregation of categories presented in CRF Table 7 and this annex vary but the results of the key
category analysis are consistent. As suggested by the UN reporting guidelines, the United States key category analysis
uses the IPCC suggested aggregation level as the basis for the analysis, but in some cases the disaggregation does differ.
Differences arise from implementation of special considerations identified in Table 4.1. As stated in section 4.2 in
Volume 1, Chapter 4 of the 2006 IPCC Guidelines, "...countries using Approach 2 will probably choose the same level of
aggregation that was used for the uncertainty analysis." In order to retain consistency in the categorization with the
uncertainty analysis, the aggregation level for this analysis (i.e. Approach 1, 2 etc.) does reflect some (e.g., for l.A.l, 3.A,
3.B)	but not all special considerations such as disaggregating for significant subcategories, fuel types, and/or carbon
pools for the following categories: Fuel Combustion Activities—Water-borne Navigation (l.A.3.d), Fuel Combustion
Activities—Other Sectors (1.A.4), Fugitive Emissions from Fuels -Oil (l.B.2.a) and Natural Gas (l.B.2.b), Petrochemical
and Carbon Black Production (2.B.8), Direct and Indirect N20 Emissions (3.D.1 and 3.D.2), land use categories (4.A, 4.B,
4.C,	4.D, 4.E, and 4.F), Solid Waste Disposal (5.A) and Wastewater (5.D). Most other differences stem from additional
disaggregation to subcategories consistent with the uncertainty analysis, including within Fuel Combustion Activities-
Other Sectors (l.A.4.a Commercial/Institutional and l.A.4.b Residential), Fossil Fuel Combustion—Non-Specified
Stationary (l.A.5.a Incineration of Waste, Non-Energy Use of Fossil Fuels, and U.S. Territories) and Mobile (l.A.5.b
Military), Biomass Burning (4.A(V) Forest Fires and 4.C(V) Grass Fires), and Biological Treatment of Solid Waste (5.B.1
Composting and 5.B.2 Anaerobic Digestion at Biogas Facilities). As EPA disaggregates the uncertainty analysis, it will
reflect these special considerations in aggregation levels of the key category analysis. Finally, in addition to conducting
Approach 1 and 2 level and trend assessments, a qualitative assessment of categories, as described in the 2006 IPCC
Guidelines, was conducted to capture any key categories that were not identified by either quantitative method. For this
Inventory, no additional categories were identified using criteria recommend by IPCC, but EPA continues to review its
qualitative assessment on an annual basis.
Table A-l presents the key categories for the United States (including and excluding LULUCF categories) using
emissions and uncertainty data in this report and ranked according to their sector and C02 Eq. emissions in 2019. The
table also indicates the criteria used in identifying these categories (i.e., level, trend, Approach 1, Approach 2, and/or
qualitative assessments).
A-3

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Table A-l: Key Categories for the United States (1990 and 2019)


Approach 1
Approach 2



Level
Trend Level
Trend
Level
Trend Level
Trend

CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2019 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
Energy
l.A.3.b C02 Emissions








from Mobile
C02
•
• •
•
•
• •
•
1,510.5
Combustion: Road








l.A.l C02 Emissions from








Stationary Combustion
co2






973.5
- Coal - Electricity






Generation








l.A.l C02 Emissions from








Stationary Combustion
co2






616.0
- Gas - Electricity






Generation








1.A.2 C02 Emissions from








Stationary Combustion
co2
•
• •
•
•
• •
•
503.3
- Gas - Industrial








l.A.4.b C02 Emissions








from Stationary
co2






275.3
Combustion - Gas -






Residential








1.A.2 C02 Emissions from








Stationary Combustion
co2
•
• •
•
•
• •
•
269.7
- Oil - Industrial








l.A.4.a C02 Emissions








from Stationary
co2






192.8
Combustion - Gas -






Commercial








l.A.3.a C02 Emissions








from Mobile
co2
•
• •
•
•
•

178.5
Combustion: Aviation








1.A.5 C02 Emissions from








Non-Energy Use of
co2
•
• •
•
•
• •
•
128.8
Fuels








l.A.4.b C02 Emissions








from Stationary
Combustion - Oil -
co2
•
• •
•

•

61.5
Residential








A-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------


Approach 1
Approach 2



Level
Trend Level
Trend
Level
Trend Level
Trend

CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2019 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
l.A.4.a C02 Emissions








from Stationary
C02






55.3
Combustion - Oil -






Commercial








l.A.3.e C02 Emissions








from Mobile
C02
•
• •
•



53.7
Combustion: Other








1.A.2 C02 Emissions from








Stationary Combustion
C02
•
• •
•
•
• •
•
49.5
- Coal - Industrial








l.B.2.a C02 Emissions








from Petroleum
C02
•
• •
•
•
• •
•
47.3
Systems








l.B.2.b C02 Emissions








from Natural Gas
C02
•
•




37.2
Systems








1.A.3.C C02 Emissions








from Mobile
C02
•
•




37.1
Combustion: Railways








l.A.3.d C02 Emissions








from Mobile
C02
•
•




32.1
Combustion: Marine








1.A.5 C02 Emissions from








Stationary Combustion
C02
•
•




19.5
- Oil - U.S. Territories








l.A.l C02 Emissions from








Stationary Combustion
C02






16.2
- Oil - Electricity






Generation








l.A.5.b C02 Emissions








from Mobile
C02


•



5.3
Combustion: Military








1.A.5 C02 Emissions from








Stationary Combustion
C02




•

2.5
- Gas - U.S. Territories








l.A.4.a C02 Emissions
C02






1.6
from Stationary






A-5

-------


Approach 1
Approach 2



Level
Trend Level
Trend
Level
Trend Level
Trend

CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2019 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
Combustion - Coal -








Commercial








l.A.4.b C02 Emissions








from Stationary
C02






NO
Combustion - Coal -






Residential








l.B.2.b CH4 Emissions








from Natural Gas
ch4
•
• •
•
•
• •
•
157.6
Systems








l.B.l Fugitive Emissions
ch4






47.4
from Coal Mining






l.B.2.a CH4 Emissions








from Petroleum
ch4
•
• •
•
•
• •
•
39.1
Systems








1.B.2 CH4 Emissions from








Abandoned Oil and Gas
ch4



•
•

6.6
Wells








l.A.4.b CH4 Emissions








from Stationary
Combustion -
ch4



•
•

4.6
Residential








l.A.3.b CH4 Emissions








from Mobile
ch4




•

0.9
Combustion: Road








l.A.l N20 Emissions from








Stationary Combustion
- Coal - Electricity
n2o



•
•

16.7
Generation








l.A.3.b N20 Emissions








from Mobile
n2o
•
• •
•
•
•
•
9.3
Combustion: Road








Industrial Processes and Product Use
2.C.1 C02 Emissions from








Iron and Steel








Production &
co2
•
• •
•
•
• •
•
41.3
Metallurgical Coke








Production








A-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------


Approach 1
Approach 2



Level
Trend Level
Trend
Level
Trend Level
Trend

CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2019 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
2.A.1 C02 Emissions from
C02






40.9
Cement Production






2.B.8 C02 Emissions from








Petrochemical
C02
•
• •
•



30.8
Production








2.B.3 N20 Emissions from
N20






5.3
AdipicAcid Production






2.F.1 Emissions from








Substitutes for Ozone








Depleting Substances:
HFCs, PFCs
•
• •
•
•
• •
•
133.4
Refrigeration and Air








conditioning








2.F.4 Emissions from








Substitutes for Ozone
HFCs, PFCs






16.3
Depleting Substances:






Aerosols








2.F.2 Emissions from








Substitutes for Ozone
HFCs, PFCs






16.1
Depleting Substances:






Foam Blowing Agents








2.F.3 Emissions from








Substitutes for Ozone
HFCs, PFCs






"> Q
Depleting Substances:






Z.O
Fire Protection








2.F.5 Emissions from








Substitutes for Ozone
HFCs, PFCs






2.0
Depleting Substances:






Solvents








2.G.1 SF6 Emissions from








Electrical Transmission
sf6
•
• •
•

•
•
4.2
and Distribution








2.B.9HFC-23 Emissions








from HCFC-22
HFCs
•
• •
•

•
•
3.7
Production








2.C.3 PFC Emissions from
PFCs






1 Q
Aluminum Production






l.o
Agriculture
A-7

-------


Approach 1
Approach 2



Level
Trend Level
Trend
Level
Trend Level
Trend

CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2019 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
3.G C02 Emissions from
Liming
C02




•

2.4
3.A.1 CH4 Emissions from








Enteric Fermentation:
ch4
•
• •
•
•
•

172.3
Cattle








3.B.1 CH4 Emissions from








Manure Management:
ch4
•
• •
•

•
•
35.4
Cattle








3.B.4 CH4 Emissions from








Manure Management:
ch4
•
•




26.9
Other Livestock








3.C CH4 Emissions from
ch4






15.1
Rice Cultivation






3.D.1 Direct N20








Emissions from
Agricultural Soil
n2o
•
• •
•
•
• •
•
290.4
Management








3.D.2 Indirect N20








Emissions from Applied
n2o
•
• •
•
•
• •
•
54.2
Nitrogen








Waste
5.A CH4 Emissions from
ch4






114.5
Landfills






5.D CH4 Emissions from
Wastewater T reatment
ch4
•
•

•


18.4
5.D N20 Emissions from
n2o






26.4
Wastewater T reatment






Land Use, Land-Use Change, and Forestry
4.E.2 Net C02 Emissions








from Land Converted to
co2

•
•

•
•
79.2
Settlements








4.B.2 Net C02 Emissions








from Land Converted to
co2

•


•

54.2
Cropland








4.C.1 Net C02 Emissions








from Grassland
co2




•
•
14.5
Remaining Grassland








A-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------


Approach 1
Approach 2



Level
Trend Level
Trend
Level
Trend Level
Trend

CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2019 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
4.B.1 Net C02 Emissions








from Cropland
C02

•
•

•
•
(14.5)
Remaining Cropland








4.C.2 Net C02 Emissions








from Land Converted to
C02

•
•

•
•
(23.2)
Grassland








4.A.2 Net C02 Emissions








from Land Converted to
C02

•


•

(99.1)
Forest Land








4.E.1 Net C02 Emissions








from Settlements
C02

•
•

•
•
(124.1)
Remaining Settlements








4.A.1 Net C02 Emissions








from Forest Land
C02

•
•

•
•
(691.8)
Remaining Forest Land








4.A.1 CH4 Emissions from
Forest Fires
ch4


•



9.5
Subtotal Without LULUCF
6,398.6
Total Emissions Without LULUCF
6,558.3
Percent of Total Without LULUCF
98%
Subtotal With LULUCF
5,598.0
Total Emissions With LULUCF
5,769.1
Percent of Total With LULUCF
97%
Note: Parentheses indicate negative values (or sequestration).
NO (Not Occurring)
A-9

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Table A-2 provides a complete listing of source categories by IPCC sector, along with notations on the criteria
used in identifying key categories, without LULUCF sources and sinks. Similarly, Table A-3 provides a complete listing of
source and sink categories by IPCC sector, along with notations on the criteria used in identifying key categories,
including LULUCF sources and sinks. The notations refer specifically to the year(s) in the Inventory time series (i.e., 1990
to 2019) in which each source or sink category reached the threshold for being a key category based on either a Tier 1 or
Tier 2 level assessment.
Table A-2: U.S. Greenhouse Gas Inventory Source Categories without LULUCF


2019 Emissions
Key
ID
Level in which
CRF Source/Sink Category
Greenhouse Gas
(MMT COz Eq.)
Category
Criteria3
year(s)b
Energy
l.A.3.b C02 Emissions from Mobile
C02
1,510.5
•
Li Ti L2 T2
1990, 2019
Combustion: Road





l.A.l C02 Emissions from Stationary
co2
973.5
•
Li Ti L2 T2
1990, 2019
Combustion - Coal - Electricity





Generation





l.A.l C02 Emissions from Stationary
co2
616.0
•
Li Ti L2 T2
1990, 2019
Combustion - Gas - Electricity





Generation





1.A.2 C02 Emissions from Stationary
co2
503.3
•
Li Ti L2 T2
1990, 2019
Combustion - Gas - Industrial





l.A.4.b C02 Emissions from
co2
275.3
•
Li Ti L2 T2
1990, 2019
Stationary Combustion - Gas -





Residential





1.A.2 C02 Emissions from Stationary
co2
269.7
•
Li Ti L2 T2
1990, 2019
Combustion - Oil - Industrial





l.A.4.a C02 Emissions from
co2
192.8
•
Li Ti L2 T2
1990, 2019
Stationary Combustion - Gas -





Commercial





l.A.3.a C02 Emissions from Mobile
co2
178.5
•
Li Ti L2
1990, 2019
Combustion: Aviation





1.A.5 C02 Emissions from Non-
co2
128.8
•
Li Ti L2 T2
1990, 2019
Energy Use of Fuels





l.A.4.b C02 Emissions from
co2
61.5
•
Li Ti T2
1990,, 2019,
Stationary Combustion - Oil -





Residential





l.A.4.a C02 Emissions from
co2
55.3
•
Li Ti
1990,, 2019,
Stationary Combustion - Oil -





Commercial





l.A.3.e C02 Emissions from Mobile
co2
53.7
•
Li Ti
1990,, 2019,
Combustion: Other





1.A.2 C02 Emissions from Stationary
co2
49.5
•
Li Ti L2 T2
1990, 2019
Combustion - Coal - Industrial





l.B.2.a C02 Emissions from
co2
47.3
•
Li Ti L2 T2
2019
Petroleum Systems





l.B.2.b C02 Emissions from Natural
co2
37.2
•
Li
1990,, 2019,
Gas Systems





1.A.3.C C02 Emissions from Mobile
co2
37.1
•
Li
1990,, 2019,
Combustion: Railways





l.A.3.d C02 Emissions from Mobile
co2
32.1
•
Li
1990,, 2019,
Combustion: Marine





1.A.5 C02 Emissions from Stationary
co2
19.5
•
Li
1990,, 2019,
Combustion - Oil - U.S. Territories





l.A.l C02 Emissions from Stationary
co2
16.2
•
Li Ti L2 T2
1990
Combustion - Oil - Electricity





Generation





1.A.5 C02 Emissions from
co2
11.5



Incineration of Waste





A-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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CRF Source/Sink Category
2019 Emissions	Key	ID	Level in which
Greenhouse Gas (MMT C02 Eq.) Category Criteria3	year(s)b
l.A.5.b C02 Emissions from Mobile
Combustion: Military
1.A.5 C02 Emissions from Stationary
Combustion - Gas - U.S.
Territories
1.A.5 C02 Emissions from Stationary
Combustion - Coal - U.S.
Territories
l.A.4.a C02 Emissions from
Stationary Combustion - Coal -
Commercial
l.A.l C02 Emissions from Stationary
Combustion - Geothermal Energy
1.B.2 C02 Emissions from
Abandoned Oil and Gas Wells
l.A.4.b C02 Emissions from
Stationary Combustion - Coal -
Residential
l.B.2.b CH4 Emissions from Natural
Gas Systems
l.B.l Fugitive Emissions from Coal
Mining
l.B.2.a CH4 Emissions from
Petroleum Systems
1.B.2 CH4 Emissions from
Abandoned Oil and Gas Wells
l.B.l Fugitive Emissions from
Abandoned Underground Coal
Mines
l.A.4.b CH4 Emissions from
Stationary Combustion -
Residential
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
l.A.4.a CH4 Emissions from
Stationary Combustion -
Commercial
l.A.l CH4 Emissions from Stationary
Combustion - Gas - Electricity
Generation
l.A.3.e CH4 Emissions from Mobile
Combustion: Other
l.A.3.b CH4 Emissions from Mobile
Combustion: Road
l.A.3.d CH4 Emissions from Mobile
Combustion: Marine
l.A.l CH4 Emissions from Stationary
Combustion - Coal - Electricity
Generation
1.A.3.C CH4 Emissions from Mobile
Combustion: Railways
1.A.5 CH4 Emissions from Stationary
Combustion - U.S. Territories
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
C02	5.3
C02	2.5	•	T2
C02	2.5
C02	1.6	•	T,
C02	0.4
C02	+
C02	0.0	•	T2
CH4	157.6	•	U T1 L2 T2 1990,2019
CH4	47.4	•	U T1 L2 T2 1990,2019
CH4	39.1	•	UTtLzTz 1990,2019
CH4	6.6	•	L2	19902,20192
CH4	5.9
CH4	4.6	•	L2	19902,20192
CH4	1.5
CH4	1.2
CH4	1.1
CH4	0.9
CH4	0.9	•	T2
CH4	0.4
CH4	0.2
CH4	0.1
CH4	+
CH4	+
A-11

-------
CRF Source/Sink Category
Greenhouse Gas
2019 Emissions	Key	ID	Level in which
(MMT C02 Eq.) Category Criteria3	year(s)b
l.A.l CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
l.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity
Generation
l.A.5.b CH4 Emissions from Mobile
Combustion: Military
1.A.5 CH4 Emissions from
Incineration of Waste
l.A.l N20 Emissions from
Stationary Combustion - Coal -
Electricity Generation
l.A.3.b N20 Emissions from Mobile
Combustion: Road
l.A.3.e N20 Emissions from Mobile
Combustion: Other
l.A.l N20 Emissions from
Stationary Combustion - Gas -
Electricity Generation
1.A.2 N20 Emissions from
Stationary Combustion -
Industrial
l.A.3.a N20 Emissions from Mobile
Combustion: Aviation
l.A.4.b N20 Emissions from
Stationary Combustion -
Residential
l.A.4.a N20 Emissions from
Stationary Combustion -
Commercial
1.A.5 N20 Emissions from
Incineration of Waste
1.A.3.C N20 Emissions from Mobile
Combustion: Railways
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
1.A.5 N20 Emissions from
Stationary Combustion - U.S.
Territories
l.B.2.a N20 Emissions from
Petroleum Systems
l.A.l N20 Emissions from
Stationary Combustion - Wood -
Electricity Generation
l.B.2.b N20 Emissions from Natural
Gas Systems
l.A.l N20 Emissions from
Stationary Combustion - Oil -
Electricity Generation
l.A.5.b N20 Emissions from Mobile
Combustion: Military	
CH4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
16.7
9.3
6.5
4.4
2.5
1.6
0.9
0.3
0.3
0.3
0.2
0.1
Li Ti L2 T2
19902, 20192
1990
Industrial Processes and Product Use
2.C.1 C02 Emissions from Iron and
Steel Production & Metallurgical
Coke Production
C02
41.3
Li Ti L2 T2 1990, 2019
A-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
CRF Source/Sink Category
2019 Emissions	Key	ID	Level in which
Greenhouse Gas (MMT C02 Eq.) Category Criteria3	year(s)b
2.A.1 C02 Emissions from Cement
Production
2.B.8 C02 Emissions from
Petrochemical Production
2.B.1 C02 Emissions from Ammonia
Production
2.A.2 C02 Emissions from Lime
Production
2.A.4 C02 Emissions from Other
Process Uses of Carbonates
2.B.10 C02 Emissions from Urea
Consumption for Non-Ag
Purposes
2.B.10 C02 Emissions from Carbon
Dioxide Consumption
2.C.3 C02 Emissions from Aluminum
Production
2.B.7 C02 Emissions from Soda Ash
Production
2.C.2 C02 Emissions from Ferroalloy
Production
2.B.6 C02 Emissions from Titanium
Dioxide Production
2.A.3 C02 Emissions from Glass
Production
2.C.6 C02 Emissions from Zinc
Production
2.B.10 C02 Emissions from
Phosphoric Acid Production
2.C.5 C02 Emissions from Lead
Production
2.B.5 C02 Emissions from Silicon
Carbide Production and
Consumption
2.C.4 C02 Emissions from
Magnesium Production and
Processing
2.B.8 CH4 Emissions from
Petrochemical Production
2.C.2 CH4 Emissions from Ferroalloy
Production
2.B.5 CH4 Emissions from Silicon
Carbide Production and
Consumption
2.C.1 CH4 Emissions from Iron and
Steel Production & Metallurgical
Coke Production
2.B.2 N20 Emissions from Nitric
Acid Production
2.B.3 N20 Emissions from Adipic
Acid Production
2.G.3 N20 Emissions from Product
Uses
2.B.4 N20 Emissions from
Caprolactam, Glyoxal, and
Glyoxylic Acid Production
C02	40.9	•	L,	1990,, 2019,
C02	30.8	•	L, T,	1990,, 2019,
C02	12.3
C02	12.1
C02	7.5
C02	6.2
C02	4.9
C02	1.9
C02	1.8
C02	1.6
C02	1.5
C02	1.3
C02	1.0
C02	0.9
C02	0.5
C02	0.2
C02	+
CH4	0.3
CH4 +
CH4	+
CH4	+
N20	10.0
N20	5.3	•	T,
N20	4.2
N20	1.4
A-13

-------
CRF Source/Sink Category
Greenhouse Gas
2019 Emissions
(MMT C02 Eq.)
Key
Category
ID
Criteria3
Level in which
year(s)b
2.E N20 Emissions from Electronics	N20
Industry
2.F.1 Emissions from Substitutes for	HFCs, PFCs
Ozone Depleting Substances:
Refrigeration and Air conditioning
2.F.4 Emissions from Substitutes for	HFCs, PFCs
Ozone Depleting Substances:
Aerosols
2.F.2 Emissions from Substitutes for	HFCs, PFCs
Ozone Depleting Substances:
Foam Blowing Agents
2.E PFC, HFC, SF6, and NF3 Emissions	HiGWPs
from Electronics Industry
2.F.3 Emissions from Substitutes for	HFCs, PFCs
Ozone Depleting Substances: Fire
Protection
2.F.5 Emissions from Substitutes for	HFCs, PFCs
Ozone Depleting Substances:
Solvents
2.G.1 SF6 Emissions from Electrical	SF6
Transmission and Distribution
2.B.9 HFC-23 Emissions from HCFC-	HFCs
22 Production
2.C.3 PFC Emissions from Aluminum	PFCs
Production
2.C.4 SF6 Emissions from	SF6
Magnesium Production and
Processing
2.C.4 HFC-134a Emissions from	HFCs
Magnesium Production and
Processing	
0.2
133.4
16.3
16.1
2.8
2.0
4.4
4.2
3.7
1.8
0.9
0.1
Li Ti L2 T2
Ti L2 T2
Ti
T 2
T 2
Li Ti T2
Li Ti T2
Li Ti
2019
20192
1990,
1990,
1990,
Agriculture
3.H C02 Emissions from Urea
Fertilization
3.G C02 Emissions from Liming
3.A.1 CH4 Emissions from Enteric
Fermentation: Cattle
3.B.1 CH4 Emissions from Manure
Management: Cattle
3.B.4 CH4 Emissions from Manure
Management: Other Livestock
3.C CH4 Emissions from Rice
Cultivation
3.A.4 CH4 Emissions from Enteric
Fermentation: Other Livestock
3.F CH4 Emissions from Field
Burning of Agricultural Residues
3.D.1 Direct N20 Emissions from
Agricultural Soil Management
3.D.2 Indirect N20 Emissions from
Applied Nitrogen
3.B.1 N20 Emissions from Manure
Management: Cattle
3.B.4 N20 Emissions from Manure
Management: Other Livestock
C02
C02
CH4
ch4
ch4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
5.3
2.4
172.3
35.4
26.9
15.1
6.3
0.4
290.4
54.2
15.4
4.2
T2
L, T, L2
L, T, T2
L,
L2
1990, 2019
2019,
2019,
19902, 20192
L, Ti L2 T2 1990, 2019
Li Ti L2 T2 1990, 2019
A-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------


2019 Emissions
Key
ID
Level in which
CRF Source/Sink Category
Greenhouse Gas
(MMT C02 Eq.)
Category
Criteria3
year(s)b
3.F N20 Emissions from Field
N20
0.2



Burning of Agricultural Residues





Waste
5.A CH4 Emissions from Landfills
ch4
114.5
•
Li Ti L2 T2
1990, 2019
5.D CH4 Emissions from Wastewater
ch4
18.4
•
Li L2
1990
Treatment





5.B.1 CH4 Emissions from
ch4
2.3



Composting





5.B.2 CH4 Emissions from Anaerobic
ch4
0.2



Digestion at Biogas Facilities





5.D N20 Emissions from
n2o
26.4
•
Li L2 T2
19902, 2019
Wastewater T reatment





5.B.1 N20 Emissions from
n2o
2.0



Composting





Note: LULUCF sources and sinks are not included in this analysis.
+ Does not exceed 0.05 MMT C02 Eq.
a If the source is a key category for both Liand L2 (as designated in the ID criteria column), it is a key category for both
assessments in the years provided unless noted by a subscript, in which case it is a key category for that assessment in that
year only (e.g., 19902 designates a source is a key category for the Approach 2 assessment only in 1990).
b Emissions from these sources not included in emission totals.
Table A-3: U.S. Greenhouse Gas Inventory Source Categories with LULUCF





Level in

Greenhouse
2019 Emissions
Key
ID
which
CRF Source/Sink Category
Gas
(MMT COz Eq.)
Category
Criteria3
year(s)b
Energy
l.A.3.b C02 Emissions from Mobile
C02
1,510.5
•
Li Ti L2 T2
1990, 2019
Combustion: Road





l.A.l C02 Emissions from Stationary
co2
973.5
•
Li Ti L2 T2
1990, 2019
Combustion - Coal - Electricity





Generation





l.A.l C02 Emissions from Stationary
co2
616.0
•
Li Ti L2 T2
1990,, 2019
Combustion - Gas - Electricity Generation





1.A.2 C02 Emissions from Stationary
co2
503.3
•
Li Ti L2 T2
1990, 2019
Combustion - Gas - Industrial





l.A.4.b C02 Emissions from Stationary
co2
275.3
•
Li Ti L2
1990, 2019
Combustion - Gas - Residential





1.A.2 C02 Emissions from Stationary
co2
269.7
•
Li Ti L2 T2
1990, 2019
Combustion - Oil - Industrial





l.A.4.a C02 Emissions from Stationary
co2
192.8
•
Li Ti L2 T2
1990, 2019
Combustion - Gas - Commercial





l.A.3.a C02 Emissions from Mobile
co2
178.5
•
Li Ti L2
1990, 2019
Combustion: Aviation





1.A.5 C02 Emissions from Non-Energy Use
co2
128.8
•
Li Ti L2 T2
1990, 2019
of Fuels





l.A.4.b C02 Emissions from Stationary
co2
61.5
•
Li Ti
1990,, 2019,
Combustion - Oil - Residential





l.A.4.a C02 Emissions from Stationary
co2
55.3
•
Li Ti
1990,, 2019,
Combustion - Oil - Commercial





l.A.3.e C02 Emissions from Mobile
co2
53.7
•
Li Ti
1990,, 2019,
Combustion: Other





1.A.2 C02 Emissions from Stationary
co2
49.5
•
Li Ti L2 T2
1990, 2019,
Combustion - Coal - Industrial





l.B.2.a C02 Emissions from Petroleum
co2
47.3
•
Li Ti L2 T2
2019
Systems





A-15

-------




Level in

Greenhouse
2019 Emissions
Key ID
which
CRF Source/Sink Category
Gas
(MMT C02 Eq.)
Category Criteria3
year(s)b
l.B.2.b C02 Emissions from Natural Gas
C02
37.2
• L,
1990,, 2019,
Systems




1.A.3.C C02 Emissions from Mobile
C02
37.1
• L,
1990,, 2019,
Combustion: Railways




l.A.3.d C02 Emissions from Mobile
C02
32.1
• L,
1990,, 2019,
Combustion: Marine




1.A.5 C02 Emissions from Stationary
C02
19.5
• L,
1990,, 2019,
Combustion - Oil - U.S. Territories




l.A.l C02 Emissions from Stationary
C02
16.2
Li T, T2
1990,
Combustion - Oil - Electricity Generation




1.A.5 C02 Emissions from Incineration of
C02
11.5


Waste




l.A.5.b C02 Emissions from Mobile
C02
5.3
Ti

Combustion: Military




1.A.5 C02 Emissions from Stationary
C02
2.5


Combustion - Gas - U.S. Territories




1.A.5 C02 Emissions from Stationary
C02
2.5


Combustion - Coal - U.S. Territories




l.A.4.a C02 Emissions from Stationary
C02
1.6
Ti

Combustion - Coal - Commercial




l.A.l C02 Emissions from Stationary
C02
0.4


Combustion - Geothermal Energy




1.B.2 C02 Emissions from Abandoned Oil
C02
+


and Gas Wells




l.A.4.b C02 Emissions from Stationary
C02
0.0
T 2

Combustion - Coal - Residential




l.B.2.b CH4 Emissions from Natural Gas
ch4
157.6
Li Ti L2 T2
1990, 2019
Systems




l.B.l Fugitive Emissions from Coal Mining
ch4
47.4
Li Ti L2 T2
1990, 2019,
l.B.2.a CH4 Emissions from Petroleum
ch4
39.1
Li Ti L2 T2
1990, 2019
Systems




1.B.2 CH4 Emissions from Abandoned Oil
ch4
6.6
• l2
19902, 20192
and Gas Wells




l.B.l Fugitive Emissions from Abandoned
ch4
5.9


Underground Coal Mines




l.A.4.b CH4 Emissions from Stationary
ch4
4.6
• l2
19902, 20192
Combustion - Residential




1.A.2 CH4 Emissions from Stationary
ch4
1.5


Combustion - Industrial




l.A.4.a CH4 Emissions from Stationary
ch4
1.2


Combustion - Commercial




l.A.l CH4 Emissions from Stationary
ch4
1.1


Combustion - Gas - Electricity Generation




l.A.3.e CH4 Emissions from Mobile
ch4
0.9


Combustion: Other




l.A.3.b CH4 Emissions from Mobile
ch4
0.9


Combustion: Road




l.A.3.d CH4 Emissions from Mobile
ch4
0.4


Combustion: Marine




l.A.l CH4 Emissions from Stationary
ch4
0.2


Combustion - Coal - Electricity




Generation




1.A.3.C CH4 Emissions from Mobile
ch4
0.1


Combustion: Railways




A-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
CRF Source/Sink Category
Greenhouse 2019 Emissions	Key	ID
Gas	(MMT C02 Eq.) Category Criteria3
Level in
which
year(s)b
1.A.5 CH4 Emissions from Stationary
Combustion - U.S. Territories
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
l.A.l CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
l.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity Generation
l.A.5.b CH4 Emissions from Mobile
Combustion: Military
1.A.5 CH4 Emissions from Incineration of
Waste
l.A.l N20 Emissions from Stationary
Combustion - Coal - Electricity
Generation
l.A.3.b N20 Emissions from Mobile
Combustion: Road
l.A.3.e N20 Emissions from Mobile
Combustion: Other
l.A.l N20 Emissions from Stationary
Combustion - Gas - Electricity Generation
1.A.2 N20 Emissions from Stationary
Combustion - Industrial
l.A.3.a N20 Emissions from Mobile
Combustion: Aviation
l.A.4.b N20 Emissions from Stationary
Combustion - Residential
l.A.4.a N20 Emissions from Stationary
Combustion - Commercial
1.A.5 N20 Emissions from Incineration of
Waste
1.A.3.C N20 Emissions from Mobile
Combustion: Railways
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
1.A.5 N20 Emissions from Stationary
Combustion - U.S. Territories
l.B.2.a N20 Emissions from Petroleum
Systems
l.A.l N20 Emissions from Stationary
Combustion - Wood - Electricity
Generation
l.B.2.b N20 Emissions from Natural Gas
Systems
l.A.l N20 Emissions from Stationary
Combustion - Oil - Electricity Generation
l.A.5.b N20 Emissions from Mobile
Combustion: Military
CH4
ch4
ch4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
16.7
9.3
6.5
4.4
2.5
1.6
0.9
0.3
0.3
0.3
0.2
0.1
Li Ti T2
19902
1990,
Industrial Processes and Product Use
2.C.1 C02 Emissions from Iron and Steel
Production & Metallurgical Coke
Production
2.A.1 C02 Emissions from Cement
Production
C02
C02
41.3
40.9
L, T, L2 T2
1990, 2019,
1990,, 2019,
A-17

-------
CRF Source/Sink Category
Greenhouse 2019 Emissions	Key	ID
Gas	(MMT C02 Eq.) Category Criteria3
Level in
which
year(s)b
2.B.8 C02 Emissions from Petrochemical
Production
2.B.1 C02 Emissions from Ammonia
Production
2.A.2 C02 Emissions from Lime Production
2.A.4 C02 Emissions from Other Process
Uses of Carbonates
2.B.10 C02 Emissions from Urea
Consumption for Non-Ag Purposes
2.B.10 C02 Emissions from Carbon Dioxide
Consumption
2.C.3 C02 Emissions from Aluminum
Production
2.B.7 C02 Emissions from Soda Ash
Production
2.C.2 C02 Emissions from Ferroalloy
Production
2.B.6 C02 Emissions from Titanium Dioxide
Production
2.A.3 C02 Emissions from Glass Production
2.C.6 C02 Emissions from Zinc Production
2.B.10 C02 Emissions from Phosphoric Acid
Production
2.C.5 C02 Emissions from Lead Production
2.B.5 C02 Emissions from Silicon Carbide
Production and Consumption
2.C.4 C02 Emissions from Magnesium
Production and Processing
2.B.8 CH4 Emissions from Petrochemical
Production
2.C.2 CH4 Emissions from Ferroalloy
Production
2.B.5 CH4 Emissions from Silicon Carbide
Production and Consumption
2.C.1 CH4 Emissions from Iron and Steel
Production & Metallurgical Coke
Production
2.B.2 N20 Emissions from Nitric Acid
Production
2.B.3 N20 Emissions from Adipic Acid
Production
2.G.3 N20 Emissions from Product Uses
2.B.4 N20 Emissions from Caprolactam,
Glyoxal, and Glyoxylic Acid Production
2.E N20 Emissions from Electronics
Industry
2.F.1 Emissions from Substitutes for Ozone
Depleting Substances: Refrigeration and
Air conditioning
2.F.4 Emissions from Substitutes for Ozone
Depleting Substances: Aerosols
2.F.2 Emissions from Substitutes for Ozone
Depleting Substances: Foam Blowing
Agents
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
CH4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
HFCs, PFCs
HFCs, PFCs
HFCs, PFCs
30.8
12.3
12.1
7.5
6.2
4.9
1.9
1.8
1.6
1.5
1.3
1.0
0.9
0.5
0.2
+
0.3
L1T1
1990,, 2019,
10.0
5.3
4.2
1.4
0.2
133.4
16.3
16.1
T,
L, T, L2 T2
t,t2
T,
2019
A-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------




Level in

Greenhouse
2019 Emissions
Key ID
which
CRF Source/Sink Category
Gas
(MMT COz Eq.)
Category Criteria3
year(s)b
2.F.3 Emissions from Substitutes for Ozone
HFCs, PFCs
2.8


Depleting Substances: Fire Protection




2.F.5 Emissions from Substitutes for Ozone
HFCs, PFCs
2.0


Depleting Substances: Solvents




2.E PFC, HFC, SF6, and NF3 Emissions from
HiGWPs
4.4


Electronics Industry




2.G.1 SF6 Emissions from Electrical
sf6
4.2
Li Ti T2
1990,
Transmission and Distribution




2.B.9 HFC-23 Emissions from HCFC-22
HFCs
3.7
Li Ti T2
1990,
Production




2.C.3 PFC Emissions from Aluminum
PFCs
1.8
Li Ti
1990,
Production




2.C.4 SF6 Emissions from Magnesium
sf6
0.9


Production and Processing




2.C.4 HFC-134a Emissions from Magnesium
HFCs
0.1


Production and Processing




Agriculture
3.H C02 Emissions from Urea Fertilization
C02
5.3


3.G C02 Emissions from Liming
C02
2.4


3.A.1 CH4 Emissions from Enteric
ch4
172.3
Li Ti L2
1990, 2019
Fermentation: Cattle




3.B.1 CH4 Emissions from Manure
ch4
35.4
Li Ti T2
2019,
Management: Cattle




3.B.4 CH4 Emissions from Manure
ch4
26.9
• Li
2019,
Management: Other Livestock




3.C CH4 Emissions from Rice Cultivation
ch4
15.1
• l2
19902, 20192
3.A.4 CH4 Emissions from Enteric
ch4
6.3


Fermentation: Other Livestock




3.F CH4 Emissions from Field Burning of
ch4
0.4


Agricultural Residues




3.D.1 Direct N20 Emissions from
n2o
290.4
Li Ti L2 T2
1990, 2019
Agricultural Soil Management




3.D.2 Indirect N20 Emissions from Applied
n2o
54.2
Li Ti L2 T2
1990, 2019
Nitrogen




3.B.1 N20 Emissions from Manure
n2o
15.4


Management: Cattle




3.B.4 N20 Emissions from Manure
n2o
4.2


Management: Other Livestock




3.F N20 Emissions from Field Burning of
n2o
0.2


Agricultural Residues




Waste
5.A CH4 Emissions from Landfills
ch4
114.5
Li Ti L2 T2
1990, 2019
5.D CH4 Emissions from Wastewater
ch4
18.4
• Li
2019,
Treatment




5.B.1 CH4 Emissions from Composting
ch4
2.3


5.B.2 CH4 Emissions from Anaerobic
ch4
0.2


Digestion at Biogas Facilities




5.D N20 Emissions from Wastewater
n2o
26.4
Li L2 T2
19902, 2019
Treatment




5.B.1 N20 Emissions from Composting
n2o
2.0


Land Use, Land-Use Change, and Forestry
4.E.2 Net C02 Emissions from Land	C02	79.2	•	L, Ti L2 T2 1990,2019
Converted to Settlements
A-19

-------
CRF Source/Sink Category
Greenhouse
Gas
2019 Emissions
(MMT C02 Eq.)
Key
Category
ID
Criteria3
Level in
which
year(s)b
4.B.2 Net C02 Emissions from Land
Converted to Cropland
4.C.1 Net C02 Emissions from Grassland
Remaining Grassland
4.D.2 Net C02 Emissions from Land
Converted to Wetlands
4.D.1 Net C02 Emissions from Coastal
Wetlands Remaining Coastal Wetlands
4.B.1 Net C02 Emissions from Cropland
Remaining Cropland
4.C.2 Net C02 Emissions from Land
Converted to Grassland
4.A.2 Net C02 Emissions from Land
Converted to Forest Land
4.E.1 Net C02 Emissions from Settlements
Remaining Settlements
4.A.1 Net C02 Emissions from Forest Land
Remaining Forest Land
4.A.1 CH4 Emissions from Forest Fires
4.D.1 CH4 Emissions from Coastal Wetlands
Remaining Coastal Wetlands
4.C.1 CH4 Emissions from Grass Fires
4.D.2 CH4 Emissions from Land Converted
to Coastal Wetlands
4.A.4 CH4 Emissions from Drained Organic
Soils
4.D.1 CH4 Emissions from Peatlands
Remaining Peatlands
4.A.1 N20 Emissions from Forest Fires
4.E.1 N20 Emissions from Settlement Soils
4.A.1 N20 Emissions from Forest Soils
4.C.1 N20 Emissions from Grass Fires
4.D.1 N20 Emissions from Coastal
Wetlands Remaining Coastal Wetlands
4.A.4 N20 Emissions from Drained Organic
Soils
4.D.1 N20 Emissions from Peatlands
Remaining Peatlands
C02
C02
C02
C02
C02
C02
C02
C02
C02
CH4
ch4
ch4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
n2o
n2o
54.2
14.5
(+)
(+)
(+)
(+)
(+)
(+)
(+)
9.5
3.8
0.3
0.2
6.2
2.4
0.5
0.3
0.1
0.1
L-, L2	1990, 2019
L2 T2	19902, 20192
Li Ti L2 T2
Li Ti L2 T2
Li L2
Li T, L2 T2
Li T, L2 T2
Ti
1990, 20192
2019
1990, 2019
1990, 2019
1990, 2019
Note: Parentheses indicate negative values (or sequestration).
+ Does not exceed 0.05 MMT C02 Eq.
a If the source is a key category for both Liand L2 (as designated in the ID criteria column), it is a key category for both
assessments in the years provided unless noted by a subscript, in which case it is a key category only for that assessment in
only that year (e.g., 19902 designates a source is a key category for the Approach 2 assessment only in 1990).
b Emissions from these sources not included in emission totals.
Approach for Evaluation of Key Categories
Level Assessment
When using an Approach 1 for the level assessment, a predetermined cumulative emissions threshold is used to
identify key categories. When source and sink categories are sorted in order of decreasing absolute emissions, those that
fall at the top of the list and cumulatively account for 95 percent of emissions are considered key categories. The 95
percent threshold in the 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) was designed to
establish a general level where the key category analysis covers approximately 90 percent of inventory uncertainty.
A-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
Including the Approach 2 provides additional insight into why certain source and sink categories are considered
key, and how to prioritize inventory improvements. In the Approach 2, the level assessment for each category from the
Approach 1 is multiplied by its percent relative uncertainty. If the uncertainty reported is asymmetrical, the absolute
value of the larger uncertainty is used. When source and sink categories are sorted in decreasing order of this
calculation, those that fall at the top of the list and cumulatively account for 90 percent of emissions are considered key
categories. The key categories identified by the Approach 2 level assessment may differ from those identified by the
Approach 1 assessment. The final set of key categories includes all source and sink categories identified as key by either
the Approach 1 or the Approach 2 assessment, keeping in mind that the two assessments are not mutually exclusive. The
uncertainty associated with C02 from mobile combustion is applied to each mode's emission estimate. Uncertainty
associated with CH4 emissions from waste incineration nor certain F-GHGs, photovoltaics (PV), micro-electro-mechanical
systems (MEMS) devices (MEMs), and Heat Transfer Fluids (HTFs) from the Electronics Industry because an uncertainty
analysis was not conducted.
It is important to note that a key category analysis can be sensitive to the definitions of the source and sink
categories. If a large source or sink category is split into many subcategories, then the subcategories may have
contributions to the total inventory that are too small for those source categories to be considered key. Similarly, a
collection of small, non-key source categories adding up to less than 5 percent of total emissions could become key
source categories if those source categories were aggregated into a single source or sink category. The United States has
attempted to define source and sink categories by the conventions that would allow comparison with other international
key category analyses, while still maintaining the category definitions that constitute how the emissions estimates were
calculated for this report. As such, some of the category names used in the key category analysis may differ from the
names used in the main body of the report. Additionally, the United States accounts for some source categories,
including fossil fuel feedstocks, international bunkers, and emissions from U.S. Territories, that are derived from unique
data sources using country-specific methodologies.
Table A-4 through Table A-7 contain the 1990 and 2019 level assessments for both with and without LULUCF
sources and sinks, and contain further detail on where each source falls within the analysis. Approach 1 key categories
are shaded dark gray. Additional key categories identified by the Approach 2 assessment are shaded light gray.
Trend Assessment
Approach 1 for trend assessment is defined as the product of the source or sink category level assessment and
the absolute difference between the source or sink category trend and the total trend. In turn, the source or sink
category trend is defined as the change in emissions from the base year to the current year, as a percentage of current
year emissions from that source or sink category. The total trend is the percentage change in total inventory emissions
from the base year to the current year.
Thus, the source or sink category trend assessment will be large if the source or sink category represents a large
percentage of emissions and/or has a trend that is quite different from the overall inventory trend. To determine key
categories, the trend assessments are sorted in descending order, so that the source or sink categories with the highest
trend assessments appear first. The trend assessments are summed until the threshold of 95 percent is reached; all
categories that fall within that cumulative 95 percent are considered key categories.
For Approach 2, the trend assessment for each category from Approach 1 is multiplied by its percent relative
uncertainty. If the uncertainty reported is asymmetrical, the larger uncertainty is used. When source and sink categories
are sorted in decreasing order of this calculation, those that fall at the top of the list and cumulatively account for 90
percent of emissions are considered key categories. The key categories identified by the Approach 2 trend assessment
may differ from those identified by the Approach 1 assessment. The final set of key categories includes all source and
sink categories identified as key by either the Approach 1 or the Approach 2 assessment, keeping in mind that the two
assessments are not mutually exclusive.
Table A-8 and Table A-9 contain the 1990 through 2019 trend assessment for both with and without LULUCF
sources and sinks, and contain further detail on where each source falls within the analysis. Approach 1 key categories
are shaded dark gray. Additional key categories identified by the Approach 2 assessment are shaded light gray.
A-21

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



Approach 1


Approach 2

Greenhouse
1990 Estimate
Level
Cumulative

Level
CRF Source/Sink Category
Gas
(MMT C02 Eq.) Assessment
Total
Uncertainty3
Assessment
l.A.l C02 Emissions from Stationary
C02
1,546.5
0.24
0.24
10%
0.023
Combustion - Coal - Electricity






Generation






l.A.3.b C02 Emissions from Mobile
C02
1,157.4
0.18
0.42
6%
0.011
Combustion: Road






1.A.2 C02 Emissions from Stationary
C02
408.8
0.06
0.48
7%
0.005
Combustion - Gas - Industrial






1.A.2 C02 Emissions from Stationary
C02
287.2
0.04
0.53
20%
0.009
Combustion - Oil - Industrial






3.D.1 Direct N20 Emissions from
N20
272.5
0.04
0.57
31%
0.013
Agricultural Soil Management






l.A.4.b C02 Emissions from Stationary
C02
237.8
0.04
0.61
7%
0.003
Combustion - Gas - Residential






l.A.3.a C02 Emissions from Mobile
C02
187.4
0.03
0.64
6%
0.002
Combustion: Aviation






l.B.2.b CH4 Emissions from Natural Gas
ch4
186.9
0.03
0.67
15%
0.004
Systems






5.A CH4 Emissions from Landfills
ch4
176.6
0.03
0.69
22%
0.006
l.A.l C02 Emissions from Stationary
co2
175.4
0.03
0.72
5%
0.001
Combustion - Gas - Electricity






Generation






3.A.1 CH4 Emissions from Enteric
ch4
158.4
0.02
0.74
18%
0.004
Fermentation: Cattle






1.A.2 C02 Emissions from Stationary
co2
157.8
0.02
0.77
16%
0.004
Combustion - Coal - Industrial






l.A.4.a C02 Emissions from Stationary
co2
142.0
0.02
0.79
7%
0.002
Combustion - Gas - Commercial






1.A.5 C02 Emissions from Non-Energy
co2
112.8
0.02
0.81
45%
0.008
Use of Fuels






2.C.1 C02 Emissions from Iron and Steel
co2
104.7
0.02
0.82
19%
0.003
Production & Metallurgical Coke






Production






l.A.4.b C02 Emissions from Stationary
co2
97.8
0.02
0.84
6%
0.001
Combustion - Oil - Residential






l.A.l C02 Emissions from Stationary
co2
97.5
0.02
0.85
8%
0.001
Combustion - Oil - Electricity






Generation






l.B.l Fugitive Emissions from Coal
ch4
96.5
0.01
0.87
20%
0.003
Mining






l.A.4.a C02 Emissions from Stationary
co2
74.3
0.01
0.88
5%
0.001
Combustion - Oil - Commercial






l.B.2.a CH4 Emissions from Petroleum
ch4
48.9
0.01
0.89
29%
0.002
Systems






2.B.9 HFC-23 Emissions from HCFC-22
HFCs
46.1
0.01
0.90
10%
0.001
Production






3.D.2 Indirect N20 Emissions from
N20
43.4
0.01
0.90
154%
0.010
Applied Nitrogen






l.A.3.d C02 Emissions from Mobile
C02
39.3
0.01
0.91
6%
<0.001
Combustion: Marine






l.A.3.b N20 Emissions from Mobile
N20
37.7
0.01
0.91
19%
0.001
Combustion: Road






l.A.3.e C02 Emissions from Mobile
C02
36.0
0.01
0.92
6%
<0.001
Combustion: Other






A-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------



Approach 1


Approach 2

Greenhouse
1990 Estimate Level
Cumulative

Level
CRF Source/Sink Category
Gas
(MMT C02
Eq.) Assessment
Total
Uncertainty3
Assessment
1.A.3.C C02 Emissions from Mobile
C02
35.5
0.01
0.93
6%
<0.001
Combustion: Railways






2.A.1 C02 Emissions from Cement
C02
33.5
0.01
0.93
6%
<0.001
Production






l.B.2.b C02 Emissions from Natural Gas
C02
32.0
<0.01
0.94
19%
0.001
Systems






2.G.1 SF6 Emissions from Electrical
sf6
23.2
<0.01
0.94
18%
0.001
Transmission and Distribution






2.B.8 C02 Emissions from Petrochemical
C02
21.6
<0.01
0.94
6%
<0.001
Production






2.C.3 PFC Emissions from Aluminum
PFCs
21.5
<0.01
0.95
7%
<0.001
Production






1.A.5 C02 Emissions from Stationary
C02
21.2
<0.01
0.95
11%
<0.001
Combustion - Oil - U.S. Territories






5.D CH4 Emissions from Wastewater
ch4
20.2
<0.01
0.95
38%
0.001
Treatment






l.A.l N20 Emissions from Stationary
n2o
20.1
<0.01
0.96
48%
0.001
Combustion - Coal - Electricity






Generation






3.B.4 CH4 Emissions from Manure
ch4
19.3
<0.01
0.96
20%
0.001
Management: Other Livestock






5.D N20 Emissions from Wastewater
n2o
18.7
<0.01
0.96
209%
0.006
Treatment






3.B.1 CH4 Emissions from Manure
ch4
17.9
<0.01
0.96
20%
0.001
Management: Cattle






3.C CH4 Emissions from Rice Cultivation
ch4
16.0
<0.01
0.97
149%
0.004
2.B.3 N20 Emissions from Adipic Acid
n2o
15.2
<0.01
0.97
5%
<0.001
Production






l.A.5.b C02 Emissions from Mobile
co2
13.6
<0.01
0.97
6%
<0.001
Combustion: Military






2.B.1 C02 Emissions from Ammonia
co2
13.0
<0.01
0.97
11%
<0.001
Production






2.B.2 N20 Emissions from Nitric Acid
N20
12.1
<0.01
0.98
5%
<0.001
Production






l.A.4.a C02 Emissions from Stationary
co2
12.0
<0.01
0.98
15%
<0.001
Combustion - Coal - Commercial






2.A.2 C02 Emissions from Lime
co2
11.7
<0.01
0.98
2%
<0.001
Production






3.B.1 N20 Emissions from Manure
N20
11.2
<0.01
0.98
24%
<0.001
Management: Cattle






l.B.2.a C02 Emissions from Petroleum
co2
9.7
<0.01
0.98
41%
0.001
Systems






5.C.1 C02 Emissions from Incineration of
co2
8.1
<0.01
0.98
27%
<0.001
Waste






l.B.l Fugitive Emissions from
ch4
7.2
<0.01
0.98
22%
<0.001
Abandoned Underground Coal Mines






2.C.3 C02 Emissions from Aluminum
co2
6.8
<0.01
0.99
2%
<0.001
Production






1.B.2 CH4 Emissions from Abandoned Oil
ch4
6.8
<0.01
0.99
219%
0.002
and Gas Wells






2.A.4 C02 Emissions from Other Process
co2
6.3
<0.01
0.99
15%
<0.001
Uses of Carbonates






3.A.4 CH4 Emissions from Enteric
ch4
6.3
<0.01
0.99
18%
<0.001
Fermentation: Other Livestock






A-23

-------
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 1990 Estimate Level Cumulative	Level
Gas (MMT C02 Eq.) Assessment Total Uncertainty3 Assessment
l.A.4.b CH4 Emissions from Stationary
Combustion - Residential
1.A.3.b	CH4 Emissions from Mobile
Combustion: Road
2.C.4	SF6 Emissions from Magnesium
Production and Processing
1.A.3.e	N20 Emissions from Mobile
Combustion: Other
3.G	C02 Emissions from Liming
2.G.3	N20 Emissions from Product Uses
2.B.10 C02 Emissions from Urea
Consumption for Non-Ag Purposes
2.E	PFC, HFC, SF6, and NF3 Emissions
from Electronics Industry
1.A.2 N20 Emissions from Stationary
Combustion - Industrial
1.A.4.b	C02 Emissions from Stationary
Combustion - Coal - Residential
3.B.4	N20 Emissions from Manure
Management: Other Livestock
3.H C02 Emissions from Urea
Fertilization
2.C.2	C02 Emissions from Ferroalloy
Production
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
1.A.3.a	N20 Emissions from Mobile
Combustion: Aviation
2.B.4	N20 Emissions from Caprolactam,
Glyoxal, and Glyoxylic Acid Production
2.A.3 C02 Emissions from Glass
Production
2.B.10 C02 Emissions from Phosphoric
Acid Production
2.B.10 C02 Emissions from Carbon
Dioxide Consumption
2.B.7 C02 Emissions from Soda Ash
Production
2.B.6 C02 Emissions from Titanium
Dioxide Production
l.A.4.a CH4 Emissions from Stationary
Combustion - Commercial
l.A.4.b N20 Emissions from Stationary
Combustion - Residential
1.A.3.e	CH4 Emissions from Mobile
Combustion: Other
2.C.6	C02 Emissions from Zinc
Production
1.A.l	C02 Emissions from Stationary
Combustion - Geothermal Energy
2.C.5	C02 Emissions from Lead
Production
1.A.5 C02 Emissions from Stationary
Combustion - Coal - U.S. Territories
CH4
ch4
sf6
n2o
C02
N20
C02
HiGWPs
N20
C02
N20
C02
C02
CH4
n2o
n2o
C02
co2
co2
co2
co2
ch4
n2o
ch4
co2
co2
co2
co2
5.2
5.2
5.2
4.8
4.7
4.2
3.8
3.6
3.1
3.0
2.8
2.4
2.2
1.8
1.7
1.7
1.5
1.5
1.5
1.4
1.2
1.1
1.0
0.7
0.6
0.5
0.5
0.5
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
235%
46%
13%
40%
111%
24%
14%
6%
201%
NE
24%
43%
12%
48%
66%
32%
4%
21%
5%
9%
13%
141%
219%
86%
21%
NA
16%
19%
0.002
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
Approach 1	Approach 2
Greenhouse 1990 Estimate Level Cumulative	Level
CRF Source/Sink Category	Gas (MMT C02 Eq.) Assessment Total Uncertainty3 Assessment
5.C.1 N20 Emissions from Incineration of N20	0.5	<0.01	1.00	325%	<0.001
Waste
1.A.4.a	N20 Emissions from Stationary	N20	0.4	<0.01	1.00	174%	<0.001
Combustion - Commercial
5.B.1 CH4 Emissions from Composting	CH4	0.4	<0.01	1.00	50%	<0.001
3.F CH4 Emissions from Field Burning of	CH4	0.4	<0.01	1.00	18%	<0.001
Agricultural Residues
2.B.5	C02 Emissions from Silicon Carbide	C02	0.4	<0.01	1.00	9%	<0.001
Production and Consumption
l.A.3.dCH4 Emissions from Mobile	CH4	0.4	<0.01	1.00	38%	<0.001
Combustion: Marine
5.B.1N20 Emissions from Composting	N20	0.3	<0.01	1.00	50%	<0.001
l.A.l N20 Emissions from Stationary	N20	0.3	<0.01	1.00	47%	<0.001
Combustion - Gas - Electricity
Generation
l.A.l CH4 Emissions from Stationary	CH4	0.3	<0.01	1.00	10%	<0.001
Combustion - Coal - Electricity
Generation
l.A.3.d N20 Emissions from Mobile	N20	0.3	<0.01	1.00	28%	<0.001
Combustion: Marine
1.A.3.C	N20 Emissions from Mobile	N20	0.3	<0.01	1.00	71%	<0.001
Combustion: Railways
2.B.8	CH4 Emissions from Petrochemical	CH4	0.2	<0.01	1.00	57%	<0.001
Production
2.F.4	Emissions from Substitutes for	HFCs, PFCs	0.2	<0.01	1.00	65%	<0.001
Ozone Depleting Substances: Aerosols
3.F	N20 Emissions from Field Burning of	N20	0.2	<0.01	1.00	17%	<0.001
Agricultural Residues
l.A.l CH4 Emissions from Stationary	CH4	0.1	<0.01	1.00	2%	<0.001
Combustion - Gas - Electricity
Generation
l.A.l N20 Emissions from Stationary	N20	0.1	<0.01	1.00	9%	<0.001
Combustion - Oil - Electricity
Generation
1.A.3.cCH4 Emissions from Mobile	CH4	0.1	<0.01	1.00	25%	<0.001
Combustion: Railways
l.A.3.aCH4 Emissions from Mobile	CH4	0.1	<0.01	1.00	89%	<0.001
Combustion: Aviation
1.A.5 N20 Emissions from Stationary	N20	0.1	<0.01	1.00	197%	<0.001
Combustion - U.S. Territories
1.A.5	CH4 Emissions from Stationary	CH4	+	<0.01	1.00	55%	<0.001
Combustion - U.S. Territories
2.E	N20 Emissions from Electronics	N20	+	<0.01	1.00	9%	<0.001
Industry
2.B.5 CH4 Emissions from Silicon Carbide	CH4	+	<0.01	1.00	9%	<0.001
Production and Consumption
2.C. 1CH4 Emissions from Iron and Steel	CH4	+	<0.01	1.00	19%	<0.001
Production & Metallurgical Coke
Production
2.C.2 CH4 Emissions from Ferroalloy	CH4	+	<0.01	1.00	12%	<0.001
Production
l.A.l CH4 Emissions from Stationary	CH4	+	<0.01	1.00	9%	<0.001
Combustion - Oil - Electricity
Generation
A-25

-------
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 1990 Estimate Level Cumulative	Level
Gas (MMT C02 Eq.) Assessment Total Uncertainty3 Assessment
1.B.2.a	N20 Emissions from Petroleum
Systems
5.B.2 CH4 Emissions from Anaerobic
Digestion at Biogas Facilities
2.F.1	Emissions from Substitutes for
Ozone Depleting Substances:
Refrigeration and Air conditioning
1.B.2	C02 Emissions from Abandoned Oil
and Gas Wells
2.F.2	Emissions from Substitutes for
Ozone Depleting Substances: Foam
Blowing Agents
l.B.2.b N20 Emissions from Natural Gas
Systems
1.A.l	N20 Emissions from Stationary
Combustion - Wood - Electricity
Generation
2.C.4	C02 Emissions from Magnesium
Production and Processing
l.A.5.b CH4 Emissions from Mobile
Combustion: Military
l.A.5.b N20 Emissions from Mobile
Combustion: Military
l.A.l CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
5.C.1 CH4 Emissions from Incineration of
Waste
1.A.5	C02 Emissions from Stationary
Combustion - Gas - U.S. Territories
2.F.3	Emissions from Substitutes for
Ozone Depleting Substances: Fire
Protection
2.F.5 Emissions from Substitutes for
Ozone Depleting Substances: Solvents
2.C.4 HFC-134a Emissions from
Magnesium Production and Processing
N20
ch4
HFCs, PFCs
C02
HFCs, PFCs
N20
n2o
C02
CH4
n2o
ch4
ch4
C02
HFCs, PFCs
HFCs, PFCs
HFCs
0.0
0.0
0.0
0.0
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
41%
50%
14%
219%
9%
19%
2%
6%
46%
19%
2%
NE
17%
17%
24%
19%
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
Note: LULUCF sources and sinks are not included in this analysis.
+ Does not exceed 0.05 MMT C02 Eq.
NE (Not Estimated)
NA (Not Available)
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and
always positive.
Table A-5:1990 Key Category Approach 1 and Approach 2 Analysis—Level Assessment, with LULUCF
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 1990 Estimate Level Cumulative	Level
Gas (MMTC02Eq.) Assessment Total Uncertainty3 Assessment
l.A.l C02 Emissions from Stationary
C02
1,546.5
0.20
0.20
10%
0.020
Combustion - Coal - Electricity






Generation






l.A.3.b C02 Emissions from Mobile
C02
1,157.4
0.15
0.36
6%
0.009
Combustion: Road






A-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------



Approach 1


Approach 2

Greenhouse
1990 Estimate
Level
Cumulative

Level
CRF Source/Sink Category
Gas
(MMT C02 Eq.)
Assessment
Total
Uncertainty3
Assessment
4.A.1 Net C02 Emissions from Forest
C02
787.6
0.10
0.46
11%
0.011
Land Remaining Forest Land






1.A.2 C02 Emissions from Stationary
C02
408.8
0.05
0.51
7%
0.004
Combustion - Gas - Industrial






1.A.2 C02 Emissions from Stationary
C02
287.2
0.04
0.55
20%
0.008
Combustion - Oil - Industrial






3.D.1 Direct N20 Emissions from
N20
272.5
0.04
0.59
31%
0.011
Agricultural Soil Management






l.A.4.b C02 Emissions from Stationary
C02
237.8
0.03
0.62
7%
0.002
Combustion - Gas - Residential






l.A.3.a C02 Emissions from Mobile
C02
187.4
0.02
0.64
6%
0.002
Combustion: Aviation






l.B.2.b CH4 Emissions from Natural
ch4
186.9
0.02
0.67
15%
0.004
Gas Systems






5.A CH4 Emissions from Landfills
ch4
176.6
0.02
0.69
22%
0.005
l.A.l C02 Emissions from Stationary
co2
175.4
0.02
0.71
5%
0.001
Combustion - Gas - Electricity






Generation






3.A.1 CH4 Emissions from Enteric
ch4
158.4
0.02
0.73
18%
0.004
Fermentation: Cattle






1.A.2 C02 Emissions from Stationary
co2
157.8
0.02
0.75
16%
0.003
Combustion - Coal - Industrial






l.A.4.a C02 Emissions from Stationary
co2
142.0
0.02
0.77
7%
0.001
Combustion - Gas - Commercial






1.A.5 C02 Emissions from Non-Energy
co2
112.8
0.01
0.79
45%
0.007
Use of Fuels






4.E.1 Net C02 Emissions from
co2
109.6
0.01
0.80
96%
0.014
Settlements Remaining Settlements






2.C.1 C02 Emissions from Iron and
co2
104.7
0.01
0.82
19%
0.003
Steel Production & Metallurgical






Coke Production






4.A.2 Net C02 Emissions from Land
co2
98.2
0.01
0.83
11%
0.001
Converted to Forest Land






l.A.4.b C02 Emissions from Stationary
co2
97.8
0.01
0.84
6%
0.001
Combustion - Oil - Residential






l.A.l C02 Emissions from Stationary
co2
97.5
0.01
0.85
8%
0.001
Combustion - Oil - Electricity






Generation






l.B.l Fugitive Emissions from Coal
ch4
96.5
0.01
0.87
20%
0.003
Mining






l.A.4.a C02 Emissions from Stationary
co2
74.3
0.01
0.88
5%
0.001
Combustion - Oil - Commercial






4.E.2 Net C02 Emissions from Land
co2
62.9
0.01
0.89
34%
0.003
Converted to Settlements






4.B.2 Net C02 Emissions from Land
co2
51.8
0.01
0.89
103%
0.007
Converted to Cropland






l.B.2.a CH4 Emissions from Petroleum
ch4
48.9
0.01
0.90
29%
0.002
Systems






2.B.9 HFC-23 Emissions from HCFC-22
HFCs
46.1
0.01
0.90
10%
0.001
Production






3.D.2 Indirect N20 Emissions from
N20
43.4
0.01
0.91
154%
0.009
Applied Nitrogen






l.A.3.d C02 Emissions from Mobile
C02
39.3
0.01
0.92
6%
<0.001
Combustion: Marine






A-27

-------



Approach 1


Approach 2

Greenhouse
1990 Estimate
Level
Cumulative

Level
CRF Source/Sink Category
Gas
(MMT C02 Eq.)
Assessment
Total
Uncertainty3
Assessment
l.A.3.b N20 Emissions from Mobile
N20
37.7
<0.01
0.92
19%
0.001
Combustion: Road






l.A.3.e C02 Emissions from Mobile
C02
36.0
<0.01
0.93
6%
<0.001
Combustion: Other






1.A.3.C C02 Emissions from Mobile
C02
35.5
<0.01
0.93
6%
<0.001
Combustion: Railways






2.A.1 C02 Emissions from Cement
C02
33.5
<0.01
0.93
6%
<0.001
Production






l.B.2.b C02 Emissions from Natural
C02
32.0
<0.01
0.94
19%
0.001
Gas Systems






4.B.1 Net C02 Emissions from
C02
23.2
<0.01
0.94
601%
0.018
Cropland Remaining Cropland






2.G.1 SF6 Emissions from Electrical
sf6
23.2
<0.01
0.94
18%
0.001
Transmission and Distribution






2.B.8 C02 Emissions from
co2
21.6
<0.01
0.95
6%
<0.001
Petrochemical Production






2.C.3 PFC Emissions from Aluminum
PFCs
21.5
<0.01
0.95
7%
<0.001
Production






1.A.5 C02 Emissions from Stationary
C02
21.2
<0.01
0.95
11%
<0.001
Combustion - Oil - U.S. Territories






5.D CH4 Emissions from Wastewater
ch4
20.2
<0.01
0.96
38%
0.001
Treatment






l.A.l N20 Emissions from Stationary
n2o
20.1
<0.01
0.96
48%
0.001
Combustion - Coal - Electricity






Generation






3.B.4 CH4 Emissions from Manure
ch4
19.3
<0.01
0.96
20%
0.001
Management: Other Livestock






5.D N20 Emissions from Wastewater
n2o
18.7
<0.01
0.96
209%
0.005
Treatment






3.B.1 CH4 Emissions from Manure
ch4
17.9
<0.01
0.97
20%
<0.001
Management: Cattle






3.C CH4 Emissions from Rice
ch4
16.0
<0.01
0.97
149%
0.003
Cultivation






2.B.3 N20 Emissions from Adipic Acid
Production
1.A.5.b	C02 Emissions from Mobile
Combustion: Military
2.B.1	C02 Emissions from Ammonia
Production
2.B.2 N20 Emissions from Nitric Acid
Production
1.A.4.a	C02 Emissions from Stationary
Combustion - Coal - Commercial
2.A.2	C02 Emissions from Lime
Production
3.B.1	N20 Emissions from Manure
Management: Cattle
l.B.2.a C02 Emissions from Petroleum
Systems
4.C.1	Net C02 Emissions from	C02	8.3	<0.01	0.98	1,066%	0.012
Grassland Remaining Grassland
5.C.	1C02 Emissions from Incineration	C02	8.1	<0.01	0.98	27%	<0.001
of Waste
4.D.1 Net C02 Emissions from	C02	7.4	<0.01	0.98	64%	0.001
Wetlands Remaining Wetlands
N20	15.2	<0.01
C02	13.6	<0.01
C02	13.0	<0.01
N20	12.1	<0.01
C02	12.0	<0.01
C02	11.7	<0.01
N20	11.2	<0.01
C02	9.7	<0.01
0.97	5%	<0.001
0.97	6%	<0.001
0.97	11%	<0.001
0.98	5%	<0.001
0.98	15%	<0.001
0.98	2%	<0.001
0.98	24%	<0.001
0.98	41%	0.001
A-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 1990 Estimate Level Cumulative	Level
Gas (MMTC02Eq.) Assessment Total Uncertainty3 Assessment
1.B.l	Fugitive Emissions from
Abandoned Underground Coal
Mines
2.C.3	C02 Emissions from Aluminum
Production
1.B.2	CH4 Emissions from Abandoned
Oil and Gas Wells
2.A.4	C02 Emissions from Other
Process Uses of Carbonates
3.A.4	CH4 Emissions from Enteric
Fermentation: Other Livestock
4.C.2	Net C02 Emissions from Land
Converted to Grassland
l.A.4.b CH4 Emissions from Stationary
Combustion - Residential
1.A.3.b	CH4 Emissions from Mobile
Combustion: Road
2.C.4	SF6 Emissions from Magnesium
Production and Processing
1.A.3.e	N20 Emissions from Mobile
Combustion: Other
3.G	C02 Emissions from Liming
2.G.3	N20 Emissions from Product
Uses
2.B.10 C02 Emissions from Urea
Consumption for Non-Ag Purposes
4.D.1	CH4 Emissions from Coastal
Wetlands Remaining Coastal
Wetlands
2.E	PFC, HFC, SF6, and NF3 Emissions
from Electronics Industry
1.A.2 N20 Emissions from Stationary
Combustion - Industrial
1.A.4.b	C02 Emissions from Stationary
Combustion - Coal - Residential
3.B.4	N20 Emissions from Manure
Management: Other Livestock
3.H	C02 Emissions from Urea
Fertilization
2.C.2	C02 Emissions from Ferroalloy
Production
4.E.1	N20 Emissions from Settlement
Soils
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
1.A.3.a	N20 Emissions from Mobile
Combustion: Aviation
2.B.4	N20 Emissions from
Caprolactam, Glyoxal, and Glyoxylic
Acid Production
2.A.3 C02 Emissions from Glass
Production
2.B.10 C02 Emissions from Phosphoric
Acid Production
CH4
C02
CH4
C02
CH4
C02
CH4
ch4
sf6
n2o
C02
n2o
co2
ch4
HiGWPs
N20
C02
N20
C02
co2
n2o
ch4
n2o
n2o
co2
co2
7.2
6.8
6.8
6.3
6.3
6.2
5.2
5.2
5.2
4.8
4.7
4.2
3.8
3.7
3.6
3.1
3.0
2.8
2.4
2.2
2.0
1.8
1.7
1.7
1.5
1.5
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
22%
2%
219%
15%
18%
148%
235%
46%
13%
40%
111%
24%
14%
30%
6%
201%
NE
24%
43%
12%
56%
48%
66%
32%
4%
21%
<0.001
<0.001
0.002
<0.001
<0.001
0.001
0.002
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-29

-------
CRF Source/Sink Category
Approach 1
Greenhouse 1990 Estimate Level Cumulative
Gas (MMTC02Eq.) Assessment Total
Uncertainty3
Approach 2
Level
Assessment
2.B.10 C02 Emissions from Carbon
Dioxide Consumption
2.B.7 C02 Emissions from Soda Ash
Production
2.B.6 C02 Emissions from Titanium
Dioxide Production
l.A.4.a CH4 Emissions from Stationary
Combustion - Commercial
l.A.4.b N20 Emissions from Stationary
Combustion - Residential
4.A.1 CH4 Emissions from Forest Fires
1.A.3.e	CH4 Emissions from Mobile
Combustion: Other
2.C.6	C02 Emissions from Zinc
Production
4.A.1	N20 Emissions from Forest Fires
1.A.l	C02 Emissions from Stationary
Combustion - Geothermal Energy
2.C.5	C02 Emissions from Lead
Production
1.A.5 C02 Emissions from Stationary
Combustion - Coal - U.S. Territories
5.C.1	N20 Emissions from Incineration
of Waste
4.D.2	Net C02 Emissions from Land
Converted to Wetlands
1.A.4.a	N20 Emissions from Stationary
Combustion - Commercial
5.B.1	CH4 Emissions from Composting
3.F	CH4 Emissions from Field Burning
of Agricultural Residues
2.B.5	C02 Emissions from Silicon
Carbide Production and
Consumption
l.A.3.d CH4 Emissions from Mobile
Combustion: Marine
5.B.1 N20 Emissions from Composting
l.A.l N20 Emissions from Stationary
Combustion - Gas - Electricity
Generation
l.A.l CH4 Emissions from Stationary
Combustion - Coal - Electricity
Generation
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
1.A.3.C	N20 Emissions from Mobile
Combustion: Railways
4.D.2	CH4 Emissions from Land
Converted to Coastal Wetlands
2.B.8	CH4 Emissions from
Petrochemical Production
2.F.4 Emissions from Substitutes for
Ozone Depleting Substances:
Aerosols
C02
C02
C02
CH4
n2o
ch4
ch4
C02
N20
C02
C02
C02
N20
C02
n2o
ch4
ch4
co2
ch4
n2o
n2o
ch4
n2o
n2o
ch4
ch4
HFCs, PFCs
1.5
1.4
1.2
1.1
1.0
0.9
0.7
0.6
0.6
0.5
0.5
0.5
0.5
0.4
0.4
0.4
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
5%
9%
13%
141%
219%
18%
86%
21%
14%
NA
16%
19%
325%
37%
174%
50%
18%
9%
38%
50%
47%
10%
28%
71%
30%
57%
65%
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------



Approach 1


Approach 2

Greenhouse
1990 Estimate
Level
Cumulative

Level
CRF Source/Sink Category
Gas
(MMT COz Eq.)
Assessment
Total
Uncertainty3
Assessment
3.F N20 Emissions from Field Burning
N20
0.2
<0.01
1.00
17%
<0.001
of Agricultural Residues






4.D.1 N20 Emissions from Coastal
n2o
0.1
<0.01
1.00
116%
<0.001
Wetlands Remaining Coastal






Wetlands






4.A.4 N20 Emissions from Drained
n2o
0.1
<0.01
1.00
128%
<0.001
Organic Soils






l.A.l CH4 Emissions from Stationary
ch4
0.1
<0.01
1.00
2%
<0.001
Combustion - Gas - Electricity






Generation






4.A.1 N20 Emissions from Forest Soils
n2o
0.1
<0.01
1.00
318%
<0.001
4.C.1 N20 Emissions from Grass Fires
n2o
0.1
<0.01
1.00
146%
<0.001
l.A.l N20 Emissions from Stationary
n2o
0.1
<0.01
1.00
9%
<0.001
Combustion - Oil - Electricity






Generation






4.C.1 CH4 Emissions from Grass Fires
ch4
0.1
<0.01
1.00
146%
<0.001
1.A.3.C CH4 Emissions from Mobile
ch4
0.1
<0.01
1.00
25%
<0.001
Combustion: Railways






l.A.3.a CH4 Emissions from Mobile
ch4
0.1
<0.01
1.00
89%
<0.001
Combustion: Aviation






1.A.5 N20 Emissions from Stationary
n2o
0.1
<0.01
1.00
197%
<0.001
Combustion - U.S. Territories






1.A.5 CH4 Emissions from Stationary
ch4
+
<0.01
1.00
55%
<0.001
Combustion - U.S. Territories






2.E N20 Emissions from Electronics
n2o
+
<0.01
1.00
9%
<0.001
Industry






2.B.5 CH4 Emissions from Silicon
ch4
+
<0.01
1.00
9%
<0.001
Carbide Production and






Consumption






2.C.1 CH4 Emissions from Iron and
ch4
+
<0.01
1.00
19%
<0.001
Steel Production & Metallurgical






Coke Production






2.C.2 CH4 Emissions from Ferroalloy
ch4
+
<0.01
1.00
12%
<0.001
Production






l.A.l CH4 Emissions from Stationary
ch4
+
<0.01
1.00
9%
<0.001
Combustion - Oil - Electricity






Generation






l.B.2.a N20 Emissions from Petroleum
n2o
+
<0.01
1.00
41%
<0.001
Systems






5.B.2 CH4 Emissions from Anaerobic
ch4
+
<0.01
1.00
50%
<0.001
Digestion at Biogas Facilities






2.F.1 Emissions from Substitutes for
HFCs, PFCs
+
<0.01
1.00
14%
<0.001
Ozone Depleting Substances:






Refrigeration and Air conditioning






4.A.4 CH4 Emissions from Drained
ch4
+
<0.01
1.00
80%
<0.001
Organic Soils






1.B.2 C02 Emissions from Abandoned
co2
+
<0.01
1.00
219%
<0.001
Oil and Gas Wells






4.D.1 CH4 Emissions from Peatlands
ch4
+
<0.01
1.00
78%
<0.001
Remaining Peatlands






2.F.2 Emissions from Substitutes for
HFCs, PFCs
+
<0.01
1.00
9%
<0.001
Ozone Depleting Substances: Foam






Blowing Agents






l.B.2.b N20 Emissions from Natural
N20
+
<0.01
1.00
19%
<0.001
Gas Systems






A-31

-------
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 1990 Estimate Level Cumulative	Level
Gas (MMTC02Eq.) Assessment Total Uncertainty3 Assessment
1.A.l	N20 Emissions from Stationary
Combustion - Wood - Electricity
Generation
2.C.4	C02 Emissions from Magnesium
Production and Processing
l.A.5.b CH4 Emissions from Mobile
Combustion: Military
4.D.1	N20 Emissions from Peatlands
Remaining Peatlands
l.A.5.b N20 Emissions from Mobile
Combustion: Military
1.A.l	CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
5.C.1	CH4 Emissions from Incineration
of Waste
2.C.4	HFC-134a Emissions from
Magnesium Production and
Processing
1.A.5	C02 Emissions from Stationary
Combustion - Gas - U.S. Territories
2.F.3	Emissions from Substitutes for
Ozone Depleting Substances: Fire
Protection
2.F.5 Emissions from Substitutes for
Ozone Depleting Substances:
Solvents
N20
C02
CH4
n2o
n2o
ch4
ch4
HFCs
C02
HFCs, PFCs
HFCs, PFCs
0.0
0.0
0.0
0.0
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
2%
6%
46%
53%
19%
2%
NE
19%
17%
17%
24%
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
+ Does not exceed 0.05 MMT C02 Eq.
NE (Not Estimated)
NA (Not Available)
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and
always positive.
Table A-6: 2019 Key Category Approach 1 and Approach 2 Analysis—Level Assessment, without LULUCF
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 2019 Estimate Level Cumulative	Level
Gas (MMT C02 Eq.) Assessment Total Uncertainty3 Assessment
l.A.3.b C02 Emissions from Mobile
C02
1,510.5
0.23
0.23
6%
0.014
Combustion: Road






l.A.l C02 Emissions from Stationary
C02
973.5
0.15
0.38
10%
0.014
Combustion - Coal - Electricity






Generation






l.A.l C02 Emissions from Stationary
C02
616.0
0.09
0.47
5%
0.005
Combustion - Gas - Electricity






Generation






1.A.2 C02 Emissions from Stationary
C02
503.3
0.08
0.55
7%
0.006
Combustion - Gas - Industrial






3.D.1 Direct N20 Emissions from
N20
290.4
0.04
0.59
31%
0.014
Agricultural Soil Management






l.A.4.b C02 Emissions from Stationary
C02
275.3
0.04
0.64
7%
0.003
Combustion - Gas - Residential






1.A.2 C02 Emissions from Stationary
C02
269.7
0.04
0.68
20%
0.008
Combustion - Oil - Industrial






A-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
CRF Source/Sink Category
Greenhouse
Gas
Approach 1
2019 Estimate Level
(MMT C02 Eq.) Assessment
Cumulative
Total
Uncertainty3
Approach 2
Level
Assessment
l.A.4.a C02 Emissions from Stationary
C02
192.8
0.03
0.71
7%
0.002
Combustion - Gas - Commercial






l.A.3.a C02 Emissions from Mobile
C02
178.5
0.03
0.73
6%
0.002
Combustion: Aviation






3.A.1 CH4 Emissions from Enteric
ch4
172.3
0.03
0.76
18%
0.005
Fermentation: Cattle






l.B.2.b CH4 Emissions from Natural Gas
ch4
157.6
0.02
0.78
15%
0.004
Systems






2.F.1 Emissions from Substitutes for
HFCs, PFCs
133
0.02
0.80
14%
0.003
Ozone Depleting Substances:






Refrigeration and Air conditioning






1.A.5 C02 Emissions from Non-Energy
C02
128.8
0.02
0.82
45%
0.009
Use of Fuels






5.A CH4 Emissions from Landfills
ch4
114.5
0.02
0.84
22%
0.004
l.A.4.b C02 Emissions from Stationary
C02
61.5
0.01
0.85
6%
0.001
Combustion - Oil - Residential






l.A.4.a C02 Emissions from Stationary
C02
55.3
0.01
0.86
5%
<0.001
Combustion - Oil - Commercial






3.D.2 Indirect N20 Emissions from
N20
54.2
0.01
0.87
154%
0.013
Applied Nitrogen






l.A.3.e C02 Emissions from Mobile
C02
53.7
0.01
0.88
6%
0.001
Combustion: Other






1.A.2 C02 Emissions from Stationary
C02
49.5
0.01
0.88
16%
0.001
Combustion - Coal - Industrial






l.B.l Fugitive Emissions from Coal
ch4
47.4
0.01
0.89
20%
0.001
Mining






l.B.2.a C02 Emissions from Petroleum
C02
47.3
0.01
0.90
41%
0.003
Systems






2.C.1 C02 Emissions from Iron and Steel
C02
41.3
0.01
0.90
19%
0.001
Production & Metallurgical Coke






Production






2.A.1 C02 Emissions from Cement
C02
40.9
0.01
0.91
6%
<0.001
Production






l.B.2.a CH4 Emissions from Petroleum
ch4
39.1
0.01
0.92
29%
0.002
Systems






l.B.2.b C02 Emissions from Natural Gas
C02
37.2
0.01
0.92
19%
0.001
Systems






1.A.3.C C02 Emissions from Mobile
C02
37.1
0.01
0.93
6%
<0.001
Combustion: Railways






3.B.1 CH4 Emissions from Manure
ch4
35.4
0.01
0.93
20%
0.001
Management: Cattle






l.A.3.d C02 Emissions from Mobile
C02
32.1
<0.01
0.94
6%
<0.001
Combustion: Marine






2.B.8 C02 Emissions from Petrochemical
C02
30.8
<0.01
0.94
6%
<0.001
Production






3.B.4 CH4 Emissions from Manure
ch4
26.9
<0.01
0.95
20%
0.001
Management: Other Livestock






5.D N20 Emissions from Wastewater
n2o
26
<0.01
0.95
209%
0.008
Treatment






1.A.5 C02 Emissions from Stationary
C02
19.5
<0.01
0.95
11%
<0.001
Combustion - Oil - U.S. Territories






5.D CH4 Emissions from Wastewater
Treatment
ch4
18.4
<0.01
0.96
38%
0.001
A-33

-------
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 2019 Estimate Level Cumulative	Level
Gas (MMT C02 Eq.) Assessment Total Uncertainty3 Assessment
1.A.l	N20 Emissions from Stationary
Combustion - Coal - Electricity
Generation
2.F.4	Emissions from Substitutes for
Ozone Depleting Substances: Aerosols
1.A.l	C02 Emissions from Stationary
Combustion - Oil - Electricity
Generation
2.F.2	Emissions from Substitutes for
Ozone Depleting Substances: Foam
Blowing Agents
3.B.1	N20 Emissions from Manure
Management: Cattle
3.C CH4 Emissions from Rice Cultivation
2.B.1 C02 Emissions from Ammonia
Production
2.A.2 C02 Emissions from Lime
Production
5.C.1 C02 Emissions from Incineration of
Waste
2.B.2 N20 Emissions from Nitric Acid
Production
1.A.3.b	N20 Emissions from Mobile
Combustion: Road
2.A.4	C02 Emissions from Other Process
Uses of Carbonates
1.B.2 CH4 Emissions from Abandoned Oil
and Gas Wells
1.A.3.e	N20 Emissions from Mobile
Combustion: Other
3.A.4	CH4 Emissions from Enteric
Fermentation: Other Livestock
2.B.10	C02 Emissions from Urea
Consumption for Non-Ag Purposes
l.B.l Fugitive Emissions from
Abandoned Underground Coal Mines
3.H	C02 Emissions from Urea
Fertilization
1.A.5.b	C02 Emissions from Mobile
Combustion: Military
2.B.3	N20 Emissions from Adipic Acid
Production
2.B.10 C02 Emissions from Carbon
Dioxide Consumption
1.A.4.b	CH4 Emissions from Stationary
Combustion - Residential
2.E	PFC, HFC, SF6, and NF3 Emissions
from Electronics Industry
1.A.l	N20 Emissions from Stationary
Combustion - Gas - Electricity
Generation
2.G.1	SF6 Emissions from Electrical
Transmission and Distribution
2.G.3 N20 Emissions from Product Uses
N20
16.7
<0.01
0.96
HFCs, PFCs	16.3
C02
HFCs, PFCs	16.1
N20
ch4
C02
C02
C02
N20
n2o
C02
CH4
n2o
ch4
C02
ch4
co2
co2
n2o
co2
ch4
HiGWPs
N20
sf6
n2o
15.4
15.1
12.3
12.1
11.5
10.0
9.3
7.5
6.6
6.5
6.3
6.2
5.9
5.3
5.3
5.3
4.9
4.6
4.4
4.4
4.2
4.2
<0.01	0.96
16.2	<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.96
<0.01	0.97
0.97
0.97
0.97
0.97
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
48%
65%
8%
9%
24%
149%
11%
2%
27%
5%
19%
15%
219%
40%
18%
14%
22%
43%
6%
5%
5%
235%
6%
47%
18%
24%
0.001
0.002
<0.001
<0.001
0.001
0.003
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.002
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.002
<0.001
<0.001
<0.001
<0.001
A-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 2019 Estimate Level Cumulative	Level
Gas (MMT C02 Eq.) Assessment Total Uncertainty3 Assessment
3.B.4 N20 Emissions from Manure
Management: Other Livestock
2.B.9 HFC-23 Emissions from HCFC-22
Production
2.F.3	Emissions from Substitutes for
Ozone Depleting Substances: Fire
Protection
1.A.5 C02 Emissions from Stationary
Combustion - Gas - U.S. Territories
1.A.5 C02 Emissions from Stationary
Combustion - Coal - U.S. Territories
1.A.2	N20 Emissions from Stationary
Combustion - Industrial
3.G	C02 Emissions from Liming
5.B.1 CH4 Emissions from Composting
5.B.1 N20 Emissions from Composting
2.F.5	Emissions from Substitutes for
Ozone Depleting Substances: Solvents
2.C.3 C02 Emissions from Aluminum
Production
2.B.7 C02 Emissions from Soda Ash
Production
2.C.3 PFC Emissions from Aluminum
Production
l.A.3.a N20 Emissions from Mobile
Combustion: Aviation
1.A.4.a	C02 Emissions from Stationary
Combustion - Coal - Commercial
2.C.2	C02 Emissions from Ferroalloy
Production
1.A.2	CH4 Emissions from Stationary
Combustion - Industrial
2.B.6	C02 Emissions from Titanium
Dioxide Production
2.B.4 N20 Emissions from Caprolactam,
Glyoxal, and Glyoxylic Acid Production
2.A.3 C02 Emissions from Glass
Production
l.A.4.a CH4 Emissions from Stationary
Combustion - Commercial
1.A.l	CH4 Emissions from Stationary
Combustion - Gas - Electricity
Generation
2.C.6	C02 Emissions from Zinc
Production
l.A.3.e CH4 Emissions from Mobile
Combustion: Other
l.A.3.b CH4 Emissions from Mobile
Combustion: Road
1.A.4.b	N20 Emissions from Stationary
Combustion - Residential
2.C.4	SF6 Emissions from Magnesium
Production and Processing
2.B.10 C02 Emissions from Phosphoric
Acid Production
N20
HFCs
HFCs, PFCs
C02
C02
N20
C02
CH4
n2o
HFCs, PFCs
C02
C02
PFCs
N20
C02
C02
CH4
C02
N20
C02
CH4
ch4
co2
ch4
ch4
n2o
sf6
co2
4.2
3.7
2.8
2.5
2.5
2.5
2.4
2.3
2.0
2.0
1.9
1.8
1.8
1.6
1.6
1.6
1.5
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.9
0.9
0.9
0.9
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
24%
10%
17%
17%
19%
201%
111%
50%
50%
24%
2%
9%
7%
66%
15%
12%
48%
13%
32%
4%
141%
2%
21%
86%
46%
219%
13%
21%
<0.001
<0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-35

-------
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 2019 Estimate Level Cumulative	Level
Gas (MMT C02 Eq.) Assessment Total Uncertainty3 Assessment
2.C.5	C02 Emissions from Lead
Production
3.F	CH4 Emissions from Field Burning of
Agricultural Residues
l.A.3.d CH4 Emissions from Mobile
Combustion: Marine
l.A.l C02 Emissions from Stationary
Combustion - Geothermal Energy
1.A.4.a	N20 Emissions from Stationary
Combustion - Commercial
2.B.8	CH4 Emissions from Petrochemical
Production
5.C.1 N20 Emissions from Incineration of
Waste
1.A.3.C	N20 Emissions from Mobile
Combustion: Railways
2.E	N20 Emissions from Electronics
Industry
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
1.A.l	CH4 Emissions from Stationary
Combustion - Coal - Electricity
Generation
3.F	N20 Emissions from Field Burning of
Agricultural Residues
5.B.2 CH4 Emissions from Anaerobic
Digestion at Biogas Facilities
2.B.5	C02 Emissions from Silicon Carbide
Production and Consumption
1.A.3.C	CH4 Emissions from Mobile
Combustion: Railways
2.C.4	HFC-134a Emissions from
Magnesium Production and Processing
1.A.5 N20 Emissions from Stationary
Combustion - U.S. Territories
l.B.2.a N20 Emissions from Petroleum
Systems
1.A.5 CH4 Emissions from Stationary
Combustion - U.S. Territories
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
l.A.l N20 Emissions from Stationary
Combustion - Wood - Electricity
Generation
1.B.2.a	N20 Emissions from Natural Gas
Systems
2.C.2	CH4 Emissions from Ferroalloy
Production
2.B.5 CH4 Emissions from Silicon Carbide
Production and Consumption
2.C.1 CH4 Emissions from Iron and Steel
Production & Metallurgical Coke
Production
1.B.2 C02 Emissions from Abandoned Oil
and Gas Wells
C02
CH4
ch4
C02
N20
ch4
n2o
n2o
n2o
n2o
ch4
n2o
ch4
C02
ch4
HFCs
N20
n2o
ch4
ch4
n2o
n2o
ch4
ch4
ch4
co2
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
16%
18%
38%
NA
174%
57%
325%
71%
9%
28%
10%
17%
50%
9%
25%
19%
197%
41%
55%
89%
2%
19%
12%
9%
19%
219%
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------



Approach 1


Approach 2

Greenhouse
2019 Estimate
Level
Cumulative

Level
CRF Source/Sink Category
Gas
(MMT C02 Eq.) Assessment
Total
Uncertainty3
Assessment
l.A.l N20 Emissions from Stationary
N20
+
<0.01
1.00
9%
<0.001
Combustion - Oil - Electricity






Generation






l.A.l CH4 Emissions from Stationary
ch4
+
<0.01
1.00
2%
<0.001
Combustion - Wood - Electricity






Generation






2.C.4 C02 Emissions from Magnesium
co2
+
<0.01
1.00
6%
<0.001
Production and Processing






l.A.l CH4 Emissions from Stationary
ch4
+
<0.01
1.00
9%
<0.001
Combustion - Oil - Electricity






Generation






l.A.5.b CH4 Emissions from Mobile
ch4
+
<0.01
1.00
46%
<0.001
Combustion: Military






l.A.5.b N20 Emissions from Mobile
n2o
+
<0.01
1.00
19%
<0.001
Combustion: Military






5.C.1 CH4 Emissions from Incineration of
ch4
+
<0.01
1.00
NE
<0.001
Waste






l.A.4.b C02 Emissions from Stationary
co2
0.0
<0.01
1.00
NE
<0.001
Combustion - Coal - Residential






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




+ Does not exceed 0.05 MMT C02 Eq.






NE (Not Estimated)






NA (Not Available)






a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and
always positive.






Table A-7: 2019 Key Category Approach 1 and Approach 2 Analysis—Level Assessment with LULUCF




Approach 1


Approach 2

Greenhouse
2019 Estimate
Level
Cumulative

Level
CRF Source/Sink Category
Gas
(MMT COz Eq.)
Assessment
Total
Uncertainty3
Assessment
l.A.3.b C02 Emissions from Mobile
C02
1,510.5
0.20
0.20
6%
0.012
Combustion: Road






l.A.l C02 Emissions from Stationary
C02
973.5
0.13
0.32
10%
0.012
Combustion - Coal - Electricity






Generation






4.A.1 Net C02 Emissions from Forest
C02
691.8
0.09
0.41
11%
0.010
Land Remaining Forest Land






l.A.l C02 Emissions from Stationary
C02
616.0
0.08
0.49
5%
0.004
Combustion - Gas - Electricity






Generation






1.A.2 C02 Emissions from Stationary
C02
503.3
0.07
0.56
7%
0.005
Combustion - Gas - Industrial






3.D.1 Direct N20 Emissions from
n2o
290.4
0.04
0.60
31%
0.012
Agricultural Soil Management






l.A.4.b C02 Emissions from Stationary
co2
275.3
0.04
0.63
7%
0.003
Combustion - Gas - Residential






1.A.2 C02 Emissions from Stationary
co2
269.7
0.04
0.67
20%
0.007
Combustion - Oil - Industrial






l.A.4.a C02 Emissions from Stationary
co2
192.8
0.03
0.69
7%
0.002
Combustion - Gas - Commercial






l.A.3.a C02 Emissions from Mobile
co2
178.5
0.02
0.72
6%
0.001
Combustion: Aviation






3.A.1 CH4 Emissions from Enteric
ch4
172.3
0.02
0.74
18%
0.004
Fermentation: Cattle






A-37

-------



Approach 1


Approach 2

Greenhouse
2019 Estimate
Level
Cumulative

Level
CRF Source/Sink Category
Gas
(MMT C02 Eq.)
Assessment
Total
Uncertainty3
Assessment
l.B.2.b CH4 Emissions from Natural
ch4
157.6
0.02
0.76
15%
0.003
Gas Systems






2.F.1 Emissions from Substitutes for
HFCs, PFCs
133.4
0.02
0.78
14%
0.002
Ozone Depleting Substances:






Refrigeration and Air conditioning






1.A.5 C02 Emissions from Non-Energy
C02
128.8
0.02
0.79
45%
0.008
Use of Fuels






4.E.1 Net C02 Emissions from
C02
124.1
0.02
0.81
96%
0.016
Settlements Remaining Settlements






5.A CH4 Emissions from Landfills
ch4
114.5
0.01
0.82
22%
0.003
4.A.2 Net C02 Emissions from Land
C02
99.1
0.01
0.84
11%
0.001
Converted to Forest Land






4.E.2 Net C02 Emissions from Land
C02
79.2
0.01
0.85
34%
0.003
Converted to Settlements






l.A.4.b C02 Emissions from Stationary
C02
61.5
0.01
0.85
6%
<0.001
Combustion - Oil - Residential






l.A.4.a C02 Emissions from Stationary
C02
55.3
0.01
0.86
5%
<0.001
Combustion - Oil - Commercial






4.B.2 Net C02 Emissions from Land
C02
54.2
0.01
0.87
103%
0.007
Converted to Cropland






3.D.2 Indirect N20 Emissions from
N20
54.2
0.01
0.88
154%
0.011
Applied Nitrogen






l.A.3.e C02 Emissions from Mobile
C02
53.7
0.01
0.88
6%
<0.001
Combustion: Other






1.A.2 C02 Emissions from Stationary
C02
49.5
0.01
0.89
16%
0.001
Combustion - Coal - Industrial






l.B.l Fugitive Emissions from Coal
ch4
47.4
0.01
0.90
20%
0.001
Mining






l.B.2.a C02 Emissions from Petroleum
C02
47.3
0.01
0.90
41%
0.003
Systems






2.C.1 C02 Emissions from Iron and
C02
41.3
0.01
0.91
19%
0.001
Steel Production & Metallurgical






Coke Production






2.A.1 C02 Emissions from Cement
C02
40.9
0.01
0.91
6%
<0.001
Production






l.B.2.a CH4 Emissions from Petroleum
ch4
39.1
0.01
0.92
29%
0.001
Systems






l.B.2.b C02 Emissions from Natural
C02
37.2
<0.01
0.92
19%
0.001
Gas Systems






1.A.3.C C02 Emissions from Mobile
C02
37.1
<0.01
0.93
6%
<0.001
Combustion: Railways






3.B.1 CH4 Emissions from Manure
ch4
35.4
<0.01
0.93
20%
0.001
Management: Cattle






l.A.3.d C02 Emissions from Mobile
C02
32.1
<0.01
0.94
6%
<0.001
Combustion: Marine






2.B.8 C02 Emissions from
C02
30.8
<0.01
0.94
6%
<0.001
Petrochemical Production






3.B.4 CH4 Emissions from Manure
ch4
26.9
<0.01
0.94
20%
0.001
Management: Other Livestock






5.D N20 Emissions from Wastewater
n2o
26.4
<0.01
0.95
209%
0.007
Treatment






4.C.2 Net C02 Emissions from Land
C02
23.2
<0.01
0.95
148%
0.004
Converted to Grassland






1.A.5 C02 Emissions from Stationary
C02
19.5
<0.01
0.95
11%
<0.001
Combustion - Oil - U.S. Territories






A-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------



Approach 1


Approach 2

Greenhouse
2019 Estimate
Level
Cumulative

Level
CRF Source/Sink Category
Gas
(MMT COz Eq.)
Assessment
Total
Uncertainty3
Assessment
5.D CH4 Emissions from Wastewater
ch4
18.4
<0.01
0.95
38%
0.001
Treatment






l.A.l N20 Emissions from Stationary
n2o
16.7
<0.01
0.96
48%
0.001
Combustion - Coal - Electricity






Generation






2.F.4 Emissions from Substitutes for
HFCs, PFCs
16.3
<0.01
0.96
65%
0.001
Ozone Depleting Substances:






Aerosols






l.A.l C02 Emissions from Stationary
C02
16.2
<0.01
0.96
8%
<0.001
Combustion - Oil - Electricity






Generation






2.F.2 Emissions from Substitutes for
HFCs, PFCs
16.1
<0.01
0.96
9%
<0.001
Ozone Depleting Substances: Foam






Blowing Agents






3.B.1 N20 Emissions from Manure
N20
15.4
<0.01
0.97
24%
<0.001
Management: Cattle






3.C CH4 Emissions from Rice
ch4
15.1
<0.01
0.97
149%
0.003
Cultivation






4.B.1 Net C02 Emissions from
C02
14.5
<0.01
0.97
601%
0.011
Cropland Remaining Cropland






4.C.1 Net C02 Emissions from
C02
14.5
<0.01
0.97
1066%
0.020
Grassland Remaining Grassland






2.B.1 C02 Emissions from Ammonia
C02
12.3
<0.01
0.97
11%
<0.001
Production






2.A.2 C02 Emissions from Lime
C02
12.1
<0.01
0.97
2%
<0.001
Production






5.C.1 C02 Emissions from Incineration
C02
11.5
<0.01
0.98
27%
<0.001
of Waste






2.B.2 N20 Emissions from Nitric Acid
N20
10.0
<0.01
0.98
5%
<0.001
Production






4.A.1 CH4 Emissions from Forest Fires
ch4
9.5
<0.01
0.98
18%
<0.001
l.A.3.b N20 Emissions from Mobile
n2o
9.3
<0.01
0.98
19%
<0.001
Combustion: Road






4.D.1 Net C02 Emissions from
C02
8.0
<0.01
0.98
64%
0.001
Wetlands Remaining Wetlands






2.A.4 C02 Emissions from Other
C02
7.5
<0.01
0.98
15%
<0.001
Process Uses of Carbonates






1.B.2 CH4 Emissions from Abandoned
ch4
6.6
<0.01
0.98
219%
0.002
Oil and Gas Wells






l.A.3.e N20 Emissions from Mobile
n2o
6.5
<0.01
0.98
40%
<0.001
Combustion: Other






3.A.4 CH4 Emissions from Enteric
ch4
6.3
<0.01
0.98
18%
<0.001
Fermentation: Other Livestock






4.A.1 N20 Emissions from Forest Fires
n2o
6.2
<0.01
0.98
14%
<0.001
2.B.10 C02 Emissions from Urea
C02
6.2
<0.01
0.99
14%
<0.001
Consumption for Non-Ag Purposes






l.B.l Fugitive Emissions from
ch4
5.9
<0.01
0.99
22%
<0.001
Abandoned Underground Coal






Mines






3.H C02 Emissions from Urea
C02
5.3
<0.01
0.99
43%
<0.001
Fertilization






l.A.5.b C02 Emissions from Mobile
C02
5.3
<0.01
0.99
6%
<0.001
Combustion: Military






2.B.3 N20 Emissions from Adipic Acid
N20
5.3
<0.01
0.99
5%
<0.001
Production
A-39

-------
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 2019 Estimate Level Cumulative	Level
Gas (MMTC02Eq.) Assessment Total Uncertainty3 Assessment
2.B.10 C02 Emissions from Carbon
Dioxide Consumption
1.A.4.b	CH4 Emissions from Stationary
Combustion - Residential
2.E	PFC, HFC, SF6, and NF3 Emissions
from Electronics Industry
1.A.l	N20 Emissions from Stationary
Combustion - Gas - Electricity
Generation
2.G.1	SF6 Emissions from Electrical
Transmission and Distribution
2.G.3	N20 Emissions from Product
Uses
3.B.4	N20 Emissions from Manure
Management: Other Livestock
4.D.1	CH4 Emissions from Coastal
Wetlands Remaining Coastal
Wetlands
2.B.9 HFC-23 Emissions from HCFC-22
Production
2.F.3	Emissions from Substitutes for
Ozone Depleting Substances: Fire
Protection
1.A.5 C02 Emissions from Stationary
Combustion - Gas - U.S. Territories
1.A.5 C02 Emissions from Stationary
Combustion - Coal - U.S. Territories
1.A.2	N20 Emissions from Stationary
Combustion - Industrial
3.G	C02 Emissions from Liming
4.E.1	N20 Emissions from Settlement
Soils
5.B.1	CH4 Emissions from Composting
5.B.1 N20 Emissions from Composting
2.F.5	Emissions from Substitutes for
Ozone Depleting Substances:
Solvents
2.C.3 C02 Emissions from Aluminum
Production
2.B.7 C02 Emissions from Soda Ash
Production
2.C.3 PFC Emissions from Aluminum
Production
l.A.3.a N20 Emissions from Mobile
Combustion: Aviation
1.A.4.a	C02 Emissions from Stationary
Combustion - Coal - Commercial
2.C.2	C02 Emissions from Ferroalloy
Production
1.A.2	CH4 Emissions from Stationary
Combustion - Industrial
2.B.6	C02 Emissions from Titanium
Dioxide Production
C02
CH4
HiGWPs
N20
sf6
n2o
n2o
ch4
HFCs
HFCs, PFCs
C02
C02
N20
C02
N20
ch4
n2o
HFCs, PFCs
C02
C02
PFCs
N20
C02
C02
CH4
C02
4.9
4.6
4.4
4.4
4.2
4.2
4.2
3.8
3.7
2.8
2.5
2.5
2.5
2.4
2.4
2.3
2.0
2.0
1.9
1.8
1
1
1
1
1
1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
5%
235%
6%
47%
18%
24%
24%
30%
10%
17%
17%
19%
201%
111%
56%
50%
50%
24%
2%
9%
7%
66%
15%
12%
48%
13%
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 2019 Estimate Level Cumulative	Level
Gas (MMTC02Eq.) Assessment Total Uncertainty3 Assessment
2.B.4 N20 Emissions from
Caprolactam, Glyoxal, and Glyoxylic
Acid Production
2.A.3 C02 Emissions from Glass
Production
l.A.4.a CH4 Emissions from Stationary
Combustion - Commercial
1.A.l	CH4 Emissions from Stationary
Combustion - Gas - Electricity
Generation
2.C.6	C02 Emissions from Zinc
Production
l.A.3.e CH4 Emissions from Mobile
Combustion: Other
l.A.3.b CH4 Emissions from Mobile
Combustion: Road
1.A.4.b	N20 Emissions from Stationary
Combustion - Residential
2.C.4	SF6 Emissions from Magnesium
Production and Processing
2.B.10 C02 Emissions from Phosphoric
Acid Production
2.C.5	C02 Emissions from Lead
Production
4.A.1 N20 Emissions from Forest Soils
3.F	CH4 Emissions from Field Burning
of Agricultural Residues
l.A.3.d CH4 Emissions from Mobile
Combustion: Marine
l.A.l C02 Emissions from Stationary
Combustion - Geothermal Energy
1.A.4.a	N20 Emissions from Stationary
Combustion - Commercial
2.B.8	CH4 Emissions from
Petrochemical Production
4.C.1	N20 Emissions from Grass Fires
5.C.1	N20 Emissions from Incineration
of Waste
4.C.1	CH4 Emissions from Grass Fires
1.A.3.C	N20 Emissions from Mobile
Combustion: Railways
2.E	N20 Emissions from Electronics
Industry
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
l.A.l CH4 Emissions from Stationary
Combustion - Coal - Electricity
Generation
3.F	N20 Emissions from Field Burning
of Agricultural Residues
5.B.2	CH4 Emissions from Anaerobic
Digestion at Biogas Facilities
4.D.2	CH4 Emissions from Land
Converted to Coastal Wetlands
N20
C02
CH4
ch4
C02
CH4
ch4
n2o
sf6
C02
C02
n2o
ch4
ch4
co2
n2o
ch4
n2o
n2o
ch4
n2o
n2o
n2o
ch4
n2o
ch4
ch4
1.4
1.3
1.2
1.1
1.0
0.9
0.9
0.9
0.9
0.9
0.5
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
32%
4%
141%
2%
21%
86%
46%
219%
13%
21%
16%
318%
18%
38%
NA
174%
57%
146%
325%
146%
71%
9%
28%
10%
17%
50%
30%
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-41

-------
CRF Source/Sink Category
Approach 1	Approach 2
Greenhouse 2019 Estimate Level Cumulative	Level
Gas (MMTC02Eq.) Assessment Total Uncertainty3 Assessment
2.B.5 C02 Emissions from Silicon
Carbide Production and
Consumption
4.D.1 N20 Emissions from Coastal
Wetlands Remaining Coastal
Wetlands
4.A.4 N20 Emissions from Drained
Organic Soils
1.A.3.C	CH4 Emissions from Mobile
Combustion: Railways
2.C.4	HFC-134a Emissions from
Magnesium Production and
Processing
1.A.5 N20 Emissions from Stationary
Combustion - U.S. Territories
l.B.2.a N20 Emissions from Petroleum
Systems
1.A.5 CH4 Emissions from Stationary
Combustion - U.S. Territories
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
l.A.l N20 Emissions from Stationary
Combustion - Wood - Electricity
Generation
4.D.2 Net C02 Emissions from Land
Converted to Wetlands
4.A.4 CH4 Emissions from Drained
Organic Soils
1.B.2.b	N20 Emissions from Natural
Gas Systems
2.C.2	CH4 Emissions from Ferroalloy
Production
2.B.5 CH4 Emissions from Silicon
Carbide Production and
Consumption
2.C.1 CH4 Emissions from Iron and
Steel Production & Metallurgical
Coke Production
1.B.2 C02 Emissions from Abandoned
Oil and Gas Wells
l.A.l N20 Emissions from Stationary
Combustion - Oil - Electricity
Generation
4.D.1 CH4 Emissions from Peatlands
Remaining Peatlands
1.A.l	CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
2.C.4	C02 Emissions from Magnesium
Production and Processing
l.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity
Generation
4.D.1 N20 Emissions from Peatlands
Remaining Peatlands
C02
N20
n2o
ch4
HFCs
N20
n2o
ch4
ch4
n2o
C02
CH4
n2o
ch4
ch4
ch4
co2
n2o
ch4
ch4
co2
ch4
n2o
0.2
0.1
0.1
0.1
0.1
0.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
9%
116%
128%
25%
19%
197%
41%
55%
89%
2%
37%
80%
19%
12%
9%
19%
219%
9%
78%
2%
6%
9%
53%
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------



Approach 1


Approach 2

Greenhouse
2019 Estimate
Level
Cumulative

Level
CRF Source/Sink Category
Gas
(MMT C02 Eq.)
Assessment
Total
Uncertainty3
Assessment
l.A.5.b CH4 Emissions from Mobile
ch4
+
<0.01
1.00
46%
<0.001
Combustion: Military






l.A.5.b N20 Emissions from Mobile
n2o
+
<0.01
1.00
19%
<0.001
Combustion: Military






5.C.1 CH4 Emissions from Incineration
ch4
+
<0.01
1.00
NE
<0.001
of Waste






l.A.4.b C02 Emissions from Stationary
co2
0.0
<0.01
1.00
NE
<0.001
Combustion - Coal - Residential






+ Does not exceed 0.05 MMT C02 Eq.
NE (Not Estimated)
NA (Not Available)
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and
always positive.
Table A-8:1990-2019 Key Category Approach 1 and 2 Analysis—Trend Assessment, without LULUCF


1990 Estimate 2019 Estimate Approach 1
Approach 2
%


Greenhouse
(MMT COz
(MMT COz
Trend
Trend
Contribution Cumulative
CRF Source/Sink Category
Gas
Eq.)
Eq.)
Assessment
Assessment
to Trend
Total
l.A.l C02 Emissions from
C02
1,546.5
973.5
0.09
0.009
23.1
23
Stationary Combustion -







Coal - Electricity







Generation







l.A.l C02 Emissions from
co2
175.4
616.0
0.07
0.003
16.8
40
Stationary Combustion -







Gas - Electricity







Generation







l.A.3.b C02 Emissions
co2
1,157.4
1,510.5
0.05
0.003
12.8
53
from Mobile







Combustion: Road







2.F.1 Emissions from
HFCs, PFCs
+
133.4
0.02
0.003
5.1
58
Substitutes for Ozone







Depleting Substances:







Refrigeration and Air







conditioning







1.A.2 C02 Emissions from
C02
157.8
49.5
0.02
0.003
4.3
62
Stationary Combustion -







Coal - Industrial







1.A.2 C02 Emissions from
C02
408.8
503.3
0.01
0.001
3.4
65
Stationary Combustion -







Gas - Industrial







l.A.l C02 Emissions from
C02
97.5
16.2
0.01
0.001
3.2
69
Stationary Combustion -







Oil - Electricity







Generation







2.C.1 C02 Emissions from
C02
104.7
41.3
0.01
0.002
2.5
71
Iron and Steel







Production &







Metallurgical Coke







Production







5.A CH4 Emissions from
ch4
176.6
114.5
0.01
0.002
2.5
74
Landfills







l.B.l Fugitive Emissions
ch4
96.5
47.4
0.01
0.002
2.0
76
from Coal Mining







l.A.4.a C02 Emissions
C02
142.0
192.8
0.01
0.001
1.9
77
A-43

-------


1990 Estimate 2019 Estimate Approach 1
Approach 2
%

Greenhouse
(MMT C02
(MMT C02
Trend
Trend
Contribution Cumulative
CRF Source/Sink Category
Gas
Eq.)
Eq.)
Assessment
Assessment
to Trend Total
from Stationary






Combustion - Gas -






Commercial






2.B.9 HFC-23 Emissions
HFCs
46.1
3.7
0.01
0.001
1.7 79
from HCFC-22






Production






l.A.4.b C02 Emissions
C02
97.8
61.5
0.01
<0.001
1.5 81
from Stationary






Combustion - Oil -






Residential






l.B.2.a C02 Emissions
C02
9.7
47.3
0.01
0.002
1.4 82
from Petroleum






Systems






l.A.4.b C02 Emissions
C02
237.8
275.3
0.01
<0.001
1.3 83
from Stationary






Combustion - Gas -






Residential






l.B.2.b CH4 Emissions
ch4
186.9
157.6
0.01
0.001
1.3 85
from Natural Gas






Systems






l.A.3.b N20 Emissions
n2o
37.7
9.3
<0.01
0.001
1.1 86
from Mobile






Combustion: Road






1.A.2 C02 Emissions from
C02
287.2
269.7
<0.01
0.001
0.9 87
Stationary Combustion -






Oil - Industrial






l.A.4.a C02 Emissions
C02
74.3
55.3
<0.01
<0.001
0.8 87
from Stationary






Combustion - Oil -






Commercial






2.C.3 PFC Emissions from
PFCs
21.5
1.8
<0.01
<0.001
0.8 88
Aluminum Production






2.G.1 SF6 Emissions from
sf6
23.2
4.2
<0.01
0.001
0.7 89
Electrical Transmission






and Distribution






3.B.1 CH4 Emissions from
ch4
17.9
35.4
<0.01
0.001
0.7 90
Manure Management:






Cattle






l.A.3.e C02 Emissions
C02
36.0
53.7
<0.01
<0.001
0.7 90
from Mobile






Combustion: Other






2.F.2 Emissions from
HFCs, PFCs
+
16.1
<0.01
<0.001
0.6 91
Substitutes for Ozone






Depleting Substances:






Foam Blowing Agents






2.F.4 Emissions from
HFCs, PFCs
0.2
16.3
<0.01
0.002
0.6 91
Substitutes for Ozone






Depleting Substances:






Aerosols






1.A.5 C02 Emissions from
C02
112.8
128.8
<0.01
0.001
0.5 92
Non-Energy Use of Fuels






3.D.1 Direct N20
N20
272.5
290.4
<0.01
0.001
0.5 92
Emissions from






Agricultural Soil






Management






A-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------


1990 Estimate 2019 Estimate Approach 1
Approach 2
%


Greenhouse
(MMT C02
(MMT C02
Trend
Trend
Contribution Cumulative
CRF Source/Sink Category
Gas
Eq.)
Eq.)
Assessment
Assessment
to Trend
Total
l.A.3.a C02 Emissions
C02
187.4
178.5
<0.01
<0.001
0.5
93
from Mobile







Combustion: Aviation







3.A.1 CH4 Emissions from
ch4
158.4
172.3
<0.01
<0.001
0.4
93
Enteric Fermentation:







Cattle







l.B.2.a CH4 Emissions
ch4
48.9
39.1
<0.01
<0.001
0.4
94
from Petroleum







Systems







l.A.4.a C02 Emissions
co2
12.0
1.6
<0.01
<0.001
0.4
94
from Stationary







Combustion - Coal -







Commercial







2.B.3 N20 Emissions from
N20
15.2
5.3
<0.01
<0.001
0.4
95
Adipic Acid Production







3.D.2 Indirect N20
N20
43.4
54.2
<0.01
0.002
0.4
95
Emissions from Applied







Nitrogen







2.B.8 C02 Emissions from
co2
21.6
30.8
<0.01
<0.001
0.3
95
Petrochemical







Production







l.A.5.b C02 Emissions
co2
13.6
5.3
<0.01
<0.001
0.3
96
from Mobile







Combustion: Military







l.A.3.d C02 Emissions
co2
39.3
32.1
<0.01
<0.001
0.3
96
from Mobile







Combustion: Marine







3.B.4 CH4 Emissions from
ch4
19.3
26.9
<0.01
<0.001
0.3
96
Manure Management:







Other Livestock







5.D N20 Emissions from
n2o
18.7
26.4
<0.01
0.002
0.3
96
Wastewater Treatment







2.A.1 C02 Emissions from
co2
33.5
40.9
<0.01
<0.001
0.3
97
Cement Production







2.C.3 C02 Emissions from
co2
6.8
1.9
<0.01
<0.001
0.2
97
Aluminum Production







l.B.2.b C02 Emissions
co2
32.0
37.2
<0.01
<0.001
0.2
97
from Natural Gas







Systems







l.A.3.b CH4 Emissions
ch4
5.2
0.9
<0.01
<0.001
0.2
97
from Mobile







Combustion: Road







2.C.4 SF6 Emissions from
sf6
5.2
0.9
<0.01
<0.001
0.2
97
Magnesium Production







and Processing







3.B.1 N20 Emissions from
n2o
11.2
15.4
<0.01
<0.001
0.2
98
Manure Management:







Cattle







l.A.l N20 Emissions from
n2o
0.3
4.4
<0.01
<0.001
0.2
98
Stationary Combustion -







Gas - Electricity







Generation







l.A.l N20 Emissions from
n2o
20.1
16.7
<0.01
<0.001
0.1
98
Stationary Combustion -







Coal - Electricity







A-45

-------
CRF Source/Sink Category
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
Greenhouse (MMTC02 (MMT C02 Trend	Trend Contribution Cumulative
Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
Generation
2.B.10	C02 Emissions from
Carbon Dioxide
Consumption
5.C.1 C02 Emissions from
Incineration of Waste
1.A.4.b	C02 Emissions
from Stationary
Combustion - Coal -
Residential
3.H	C02 Emissions from
Urea Fertilization
2.F.3	Emissions from
Substitutes for Ozone
Depleting Substances:
Fire Protection
1.A.5	C02 Emissions from
Stationary Combustion -
Gas - U.S. Territories
2.B.10	C02 Emissions from
Urea Consumption for
Non-Ag Purposes
2.B.2	N20 Emissions from
Nitric Acid Production
3.G	C02 Emissions from
Liming
5.D CH4 Emissions from
Wastewater Treatment
1.A.5	C02 Emissions from
Stationary Combustion -
Oil - U.S. Territories
2.F.5	Emissions from
Substitutes for Ozone
Depleting Substances:
Solvents
1.A.5 C02 Emissions from
Stationary Combustion -
Coal - U.S. Territories
5.B.1 CH4 Emissions from
Composting
5.B.1 N20 Emissions from
Composting
l.A.3.e N20 Emissions
from Mobile
Combustion: Other
1.B.l	Fugitive Emissions
from Abandoned
Underground Coal
Mines
3.B.4	N20 Emissions from
Manure Management:
Other Livestock
3.C CH4 Emissions from
Rice Cultivation
2.A.4	C02 Emissions from
C02
C02
C02
C02
C02
N20
C02
CH4
C02
HFCs, PFCs
C02
CH4
n2o
n2o
ch4
n2o
ch4
C02
1.5
8.1
3.0
C02	2.4
HFCs, PFCs	0.0
0.0
3.8
12.1
4.7
20.2
21.2
0.0
0.5
0.4
0.3
4.8
7.2
2.8
16.0
6.3
4.9
11.5
0.0
5.3
2.8
2.5
6.2
10.0
2.4
18.4
19.5
2.0
2.5
2.3
2.0
6.5
5.9
4.2
<0.01 <0.001	0.1
<0.01 <0.001	0.1
<0.01 <0.001	0.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01 <0.001
<0.01 <0.001
<0.01 <0.001
<0.01 <0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.01	<0.001
<0.01	<0.001
<0.01	<0.001
<0.01	<0.001
<0.001
<0.01 <0.001
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
98
98
98
98
98
99
99
99
99
99
99
99
99
99
99
99
99
99
15.1	<0.01 <0.001	<0.1	100
7.5	<0.01 <0.001	<0.1	100
A-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
CRF Source/Sink Category
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
Greenhouse (MMTC02 (MMT C02 Trend	Trend Contribution Cumulative
Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
Other Process Uses of
Carbonates
2.B.1 C02 Emissions from
Ammonia Production
l.A.l CH4 Emissions from
Stationary Combustion -
Gas - Electricity
Generation
1.A.3.C	C02 Emissions
from Mobile
Combustion: Railways
2.E	PFC, HFC, SF6, and NF3
Emissions from
Electronics Industry
1.A.4.b	CH4 Emissions
from Stationary
Combustion -
Residential
2.B.10	C02 Emissions from
Phosphoric Acid
Production
1.A.2	N20 Emissions from
Stationary Combustion -
Industrial
2.C.2	C02 Emissions from
Ferroalloy Production
2.C.6 C02 Emissions from
Zinc Production
2.B.7 C02 Emissions from
Soda Ash Production
1.A.2	CH4 Emissions from
Stationary Combustion -
Industrial
2.B.4	N20 Emissions from
Caprolactam, Glyoxal,
and Glyoxylic Acid
Production
1.B.2	CH4 Emissions from
Abandoned Oil and Gas
Wells
2.A.3	C02 Emissions from
Glass Production
1.A.3.e	CH4 Emissions
from Mobile
Combustion: Other
2.B.6	C02 Emissions from
Titanium Dioxide
Production
2.A.2 C02 Emissions from
Lime Production
2.B.5 C02 Emissions from
Silicon Carbide
Production and
Consumption
2.E N20 Emissions from
C02
CH4
C02
CH4
C02
N20
C02
C02
C02
CH4
n2o
ch4
C02
ch4
co2
co2
co2
n2o
13.0
0.1
35.5
HiGWPs	3.6
5.2
1.5
3.1
2.2
0.6
1.4
1.8
1.7
6.8
1.5
0.7
I.2
II.7
0.4
12.3	<0.01 <0.001	<0.1	100
1.1	<0.01 <0.001	<0.1	100
37.1	<0.01 <0.001	<0.1
4.4
4.6
0.9
2.5
1.6
1.0
1.8
1.5
1.4
6.6
1.5
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
100
100
100
100
100
100
100
100
100
100
100
1.3	<0.01 <0.001	<0.1	100
0.9	<0.01 <0.001	<0.1	100
100
12.1	<0.01 <0.001	<0.1	100
0.2	<0.01 <0.001	<0.1	100
0.2
<0.01 <0.001	<0.1
100
A-47

-------
CRF Source/Sink Category
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
Greenhouse (MMTC02 (MMT C02 Trend	Trend Contribution Cumulative
Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
Electronics Industry
l.A.l C02 Emissions from
Stationary Combustion -
Geothermal Energy
5.B.2 CH4 Emissions from
Anaerobic Digestion at
Biogas Facilities
5.C.1 N20 Emissions from
Incineration of Waste
l.A.4.b N20 Emissions
from Stationary
Combustion -
Residential
l.A.4.a CH4 Emissions
from Stationary
Combustion -
Commercial
l.A.l CH4 Emissions from
Stationary Combustion -
Coal - Electricity
Generation
1.A.3.a	N20 Emissions
from Mobile
Combustion: Aviation
2.B.8	CH4 Emissions from
Petrochemical
Production
2.G.3	N20 Emissions from
Product Uses
3.A.4	CH4 Emissions from
Enteric Fermentation:
Other Livestock
1.A.l	N20 Emissions from
Stationary Combustion -
Oil - Electricity
Generation
2.C.4	HFC-134a Emissions
from Magnesium
Production and
Processing
l.A.3.d N20 Emissions
from Mobile
Combustion: Marine
l.A.4.a N20 Emissions
from Stationary
Combustion -
Commercial
3.F	CH4 Emissions from
Field Burning of
Agricultural Residues
l.A.3.a CH4 Emissions
from Mobile
Combustion: Aviation
l.B.2.a N20 Emissions
from Petroleum
C02
CH4
n2o
n2o
ch4
ch4
n2o
ch4
n2o
ch4
n2o
HFCs
N20
n2o
ch4
ch4
n2o
0.5
0.5
1.0
1.1
0.3
1.7
0.2
4.2
6.3
0.1
0.0
0.3
0.4
0.4
0.1
0.4
0.2
0.3
0.9
1.2
0.2
1.6
0.3
0.1
0.2
0.3
0.4
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
100
100
<0.01 <0.001	<0.1	100
<0.01 <0.001	<0.1	100
100
100
100
100
4.2	<0.01 <0.001	<0.1	100
6.3	<0.01 <0.001	<0.1	100
100
100
100
100
100
100
100
A-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
CRF Source/Sink Category
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
Greenhouse (MMTC02 (MMT C02 Trend	Trend Contribution Cumulative
Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
Systems
3.F N20 Emissions from
Field Burning of
Agricultural Residues
2.B.5 CH4 Emissions from
Silicon Carbide
Production and
Consumption
l.A.l N20 Emissions from
Stationary Combustion -
Wood - Electricity
Generation
1.A.3.C N20 Emissions
from Mobile
Combustion: Railways
1.A.l	CH4 Emissions from
Stationary Combustion -
Oil - Electricity
Generation
2.C.5	C02 Emissions from
Lead Production
2.C.1 CH4 Emissions from
Iron and Steel
Production &
Metallurgical Coke
Production
l.A.3.d CH4 Emissions
from Mobile
Combustion: Marine
1.B.2.b	N20 Emissions
from Natural Gas
Systems
2.C.2	CH4 Emissions from
Ferroalloy Production
1.A.5 N20 Emissions from
Stationary Combustion -
U.S. Territories
1.A.3.C CH4 Emissions
from Mobile
Combustion: Railways
1.A.5 CH4 Emissions from
Stationary Combustion -
U.S. Territories
l.A.l CH4 Emissions from
Stationary Combustion -
Wood - Electricity
Generation
l.A.5.b CH4 Emissions
from Mobile
Combustion: Military
l.A.5.b N20 Emissions
from Mobile
Combustion: Military
1.B.2 C02 Emissions from
Abandoned Oil and Gas
N20
ch4
n2o
n2o
ch4
C02
CH4
ch4
n2o
ch4
n2o
ch4
ch4
ch4
ch4
n2o
co2
0.2
0.3
0.5
0.4
0.1
0.1
0.2
0.3
0.5
0.4
0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001
<0.01 <0.001
<0.1
<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
100
100
100
100
100
<0.01 <0.001	<0.1	100
<0.01 <0.001	<0.1	100
100
100
+	<0.01 <0.001	<0.1	100
0.1	<0.01 <0.001	<0.1	100
100
100
100
100
100
100
A-49

-------
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
Greenhouse (MMTC02 (MMT C02 Trend	Trend Contribution Cumulative
CRF Source/Sink Category	Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
Wells
5.C.1 CH4 Emissions from
Incineration of Waste
2.C.4 C02 Emissions from
Magnesium Production
and Processing
Note: LULUCF sources and sinks are not included in this analysis.
+ Does not exceed 0.05 MMT C02 Eq.
Table A-9:1990-2019 Key Category Approach 1 and 2 Analysis—Trend Assessment, with LULUCF


1990 Estimate 2019 Estimate Approach 1
Approach 2
%
CRF Source/Sink
Greenhouse
(MMT COz
(MMTCOz Trend
Trend
Contribution Cumulative
Category
Gas
Eq.)
Eq.) Assessment
Assessment
to Trend Total
l.A.l C02 Emissions
C02
1,546.5
973.5 0.08
0.008
21.2 21
from Stationary





Combustion - Coal -





Electricity Generation





l.A.l C02 Emissions
co2
175.4
616.0 0.06
0.003
15.8 37
from Stationary





Combustion - Gas -





Electricity Generation





l.A.3.b C02 Emissions
co2
1,157.4
1,510.5 0.04
0.003
12.2 49
from Mobile





Combustion: Road





2.F.1 Emissions from
HFCs, PFCs
+
133.4 0.02
0.002
^r
LO
CO
Substitutes for Ozone





Depleting Substances:





Refrigeration and Air





conditioning





1.A.2 C02 Emissions
C02
157.8
49.5 0.01
0.002
00
LO
o
from Stationary





Combustion - Coal -





Industrial





4.A.1 Net C02 Emissions
C02
787.6
691.8 0.01
0.001
3.8 62
from Forest Land





Remaining Forest Land





1.A.2 C02 Emissions
C02
408.8
503.3 0.01
0.001
3.2 65
from Stationary





Combustion - Gas -





Industrial





l.A.l C02 Emissions
C02
97.5
16.2 0.01
0.001
CO
o
cn
00
from Stationary





Combustion - Oil -





Electricity Generation





2.C.1 C02 Emissions from
C02
104.7
41.3 0.01
0.002
2.3 70
Iron and Steel





Production &





Metallurgical Coke





Production





5.A CH4 Emissions from
ch4
176.6
114.5 0.01
0.002
2.3 73
Landfills





l.B.l Fugitive Emissions
ch4
96.5
47.4 0.01
0.001
^r
r-.
CO
i
from Coal Mining





ch4
co2
<0.01
<0.01
<0.001
<0.001
<0.1
<0.1
100
100
A-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
CRF Source/Sink	Greenhouse (MMTC02 (MMT C02 Trend Trend Contribution Cumulative
Category	Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
l.A.4.a C02 Emissions
C02
142.0
192.8
0.01
<0.001
1.8
76
from Stationary







Combustion - Gas -







Commercial







2.B.9 HFC-23 Emissions
HFCs
46.1
3.7
0.01
0.001
1.5
78
from HCFC-22







Production







l.B.2.a C02 Emissions
C02
9.7
47.3
<0.01
0.002
1.3
79
from Petroleum







Systems







l.A.4.b C02 Emissions
C02
97.8
61.5
<0.01
<0.001
1.3
80
from Stationary







Combustion - Oil -







Residential







l.A.4.b C02 Emissions
C02
237.8
275.3
<0.01
<0.001
1.3
82
from Stationary







Combustion - Gas -







Residential







l.B.2.b CH4 Emissions
ch4
186.9
157.6
<0.01
0.001
1.1
83
from Natural Gas







Systems







l.A.3.b N20 Emissions
n2o
37.7
9.3
<0.01
0.001
1.0
84
from Mobile







Combustion: Road







1.A.2 C02 Emissions
C02
287.2
269.7
<0.01
0.001
0.7
85
from Stationary







Combustion - Oil -







Industrial







2.C.3 PFC Emissions from
PFCs
21.5
1.8
<0.01
<0.001
0.7
85
Aluminum Production







l.A.4.a C02 Emissions
C02
74.3
55.3
<0.01
<0.001
0.7
86
from Stationary







Combustion - Oil -







Commercial







2.G.1 SF6 Emissions from
sf6
23.2
4.2
<0.01
<0.001
0.7
87
Electrical Transmission







and Distribution







3.B.1 CH4 Emissions from
ch4
17.9
35.4
<0.01
<0.001
0.6
87
Manure Management:







Cattle







l.A.3.e C02 Emissions
C02
36.0
53.7
<0.01
<0.001
0.6
88
from Mobile







Combustion: Other







4.C.2 Net C02 Emissions
co2
6.2
23.2
<0.01
0.003
0.6
88
from Land Converted







to Grassland







2.F.2 Emissions from
HFCs, PFCs
+
16.1
<0.01
<0.001
0.6
89
Substitutes for Ozone







Depleting Substances:







Foam Blowing Agents







2.F.4 Emissions from
HFCs, PFCs
0.2
16.3
<0.01
0.001
0.6
90
Substitutes for Ozone







Depleting Substances:







Aerosols







A-51

-------


1990 Estimate 2019 Estimate Approach 1
Approach 2
%

CRF Source/Sink
Greenhouse
(MMT C02
(MMT C02
Trend
Trend
Contribution Cumulative
Category
Gas
Eq.)
Eq.)
Assessment
Assessment
to Trend
Total
4.E.2 Net C02 Emissions
C02
62.9
79.2
<0.01
0.001
0.6
90
from Land Converted







to Settlements







3.D.1 Direct N20
N20
272.5
290.4
<0.01
0.001
0.5
91
Emissions from







Agricultural Soil







Management







1.A.5 C02 Emissions
C02
112.8
128.8
<0.01
0.001
0.5
91
from Non-Energy Use







of Fuels







4.E.1 Net C02 Emissions
C02
109.6
124.1
<0.01
0.002
0.5
92
from Settlements







Remaining







Settlements







3.A.1 CH4 Emissions from
ch4
158.4
172.3
<0.01
<0.001
0.4
92
Enteric Fermentation:







Cattle







l.A.3.a C02 Emissions
co2
187.4
178.5
<0.01
<0.001
0.4
93
from Mobile







Combustion: Aviation







l.A.4.a C02 Emissions
co2
12.0
1.6
<0.01
<0.001
0.4
93
from Stationary







Combustion - Coal -







Commercial







3.D.2 Indirect N20
N20
43.4
54.2
<0.01
0.002
0.4
93
Emissions from







Applied Nitrogen







l.B.2.a CH4 Emissions
ch4
48.9
39.1
<0.01
<0.001
0.4
94
from Petroleum







Systems







2.B.3 N20 Emissions
n2o
15.2
5.3
<0.01
<0.001
0.4
94
from Adipic Acid







Production







2.B.8 C02 Emissions
co2
21.6
30.8
<0.01
<0.001
0.3
94
from Petrochemical







Production







4.B.1 Net C02 Emissions
co2
23.2
14.5
<0.01
0.007
0.3
95
from Cropland







Remaining Cropland







4.A.1 CH4 Emissions from
ch4
0.9
9.5
<0.01
<0.001
0.3
95
Forest Fires







l.A.5.b C02 Emissions
co2
13.6
5.3
<0.01
<0.001
0.3
95
from Mobile







Combustion: Military







l.A.3.d C02 Emissions
co2
39.3
32.1
<0.01
<0.001
0.3
96
from Mobile







Combustion: Marine







3.B.4 CH4 Emissions from
ch4
19.3
26.9
<0.01
<0.001
0.3
96
Manure Management:







Other Livestock







5.D N20 Emissions from
n2o
18.7
26.4
<0.01
0.002
0.3
96
Wastewater







Treatment







A-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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CRF Source/Sink
Category
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
Greenhouse (MMT C02 (MMT C02 Trend	Trend Contribution Cumulative
Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
2.A.1 C02 Emissions	C02
from Cement
Production
4.C.1 Net C02 Emissions	C02
from Grassland
Remaining Grassland
4.A.1	N20 Emissions	N20
from Forest Fires
2.C.3 C02 Emissions from	C02
Aluminum Production
1.B.2.b C02 Emissions	C02
from Natural Gas
Systems
1.A.3.b	CH4 Emissions	CH4
from Mobile
Combustion: Road
2.C.4	SF6 Emissions from	SF6
Magnesium
Production and
Processing
3.B.1	N20 Emissions	N20
from Manure
Management: Cattle
l.A.l N20 Emissions	N20
from Stationary
Combustion - Gas -
Electricity Generation
1.A.l	N20 Emissions	N20
from Stationary
Combustion - Coal -
Electricity Generation
2.B.10	C02 Emissions	C02
from Carbon Dioxide
Consumption
5.C.1	C02 Emissions from	C02
Incineration of Waste
1.A.4.b	C02 Emissions	C02
from Stationary
Combustion - Coal -
Residential
3.H	C02 Emissions from	C02
Urea Fertilization
2.F.3	Emissions from	HFCs, PFCs
Substitutes for Ozone
Depleting Substances:
Fire Protection
1.A.5	C02 Emissions	C02
from Stationary
Combustion - Gas -
U.S. Territories
2.B.10	C02 Emissions	C02
from Urea
Consumption for Non-
Ag Purposes
33.5
8.3
0.6
6.8
32.0
5.2
5.2
11.2
0.3
20.1
1.5
8.1
3.0
2.4
0.0
0.0
3.8
40.9
14.5
0.9
0.9
4.4
4.9
11.5
0.0
5.3
2.8
2.5
6.2
<0.01 <0.001
<0.01	0.009
6.2	<0.01 <0.001
1.9	<0.01 <0.001
37.2	<0.01 <0.001
<0.01 <0.001
<0.01 <0.001
<0.01 <0.001
<0.01 <0.001
<0.01 <0.001
0.3
0.2
0.2
0.2
0.2
0.2
0.2
15.4	<0.01 <0.001	0.1
<0.01 <0.001	0.1
16.7	<0.01 <0.001	0.1
0.1
<0.01	<0.001	0.1
<0.01	<0.001	0.1
<0.01	<0.001	0.1
<0.01	<0.001	0.1
0.1
0.1
96
97
97
97
97
97
97
98
98
98
98
98
98
98
98
99
99
A-53

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CRF Source/Sink
Category
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
Greenhouse (MMT C02 (MMT C02 Trend	Trend Contribution Cumulative
Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
2.B.2	N20 Emissions	N20	12.1	10.0
from Nitric Acid
Production
3.G	C02 Emissions from	C02	4.7	2.4
Liming
1.A.5	C02 Emissions	C02	0.5	2.5
from Stationary
Combustion - Coal -
U.S. Territories
2.F.5	Emissions from	HFCs, PFCs	0.0	2.0
Substitutes for Ozone
Depleting Substances:
Solvents
5.D CH4 Emissions from	CH4	20.2	18.4
Wastewater
Treatment
1.A.5 C02 Emissions	C02	21.2	19.5
from Stationary
Combustion - Oil - U.S.
Territories
4.B.2	Net C02 Emissions	C02	51.8	54.2
from Land Converted
to Cropland
5.B.1	CH4 Emissions from	CH4	0.4	2.3
Composting
5.B.1 N20 Emissions	N20	0.3	2.0
from Composting
l.A.3.e N20 Emissions	N20	4.8	6.5
from Mobile
Combustion: Other
l.B.l Fugitive Emissions	CH4	7.2	5.9
from Abandoned
Underground Coal
Mines
3.B.4	N20 Emissions	N20	2.8	4.2
from Manure
Management: Other
Livestock
1.A.3.C	C02 Emissions	C02	35.5	37.1
from Mobile
Combustion: Railways
3.C CH4 Emissions from	CH4	16.0
Rice Cultivation
2.A.4	C02 Emissions	C02	6.3
from Other Process
Uses of Carbonates
1.A.l	CH4 Emissions from	CH4	0.1	1.1
Stationary Combustion
- Gas - Electricity
Generation
2.B.1C02	Emissions	C02	13.0	12.3
from Ammonia
Production
<0.01 <0.001
0.1
<0.01	<0.001
<0.01	<0.001
<0.01	<0.001
<0.01	<0.001
<0.01	<0.001
<0.01	<0.001
<0.01	<0.001
<0.01
<0.001
0.1
0.1
0.1
0.1
0.1
0.1
0.1
<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
99
<0.01 <0.001	0.1	99
<0.01 <0.001	0.1	99
99
99
99
99
99
99
99
99
99
99
15.1	<0.01 <0.001	<0.1	100
7.5	<0.01 <0.001	<0.1	100
100
100
A-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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CRF Source/Sink
Category
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
Greenhouse (MMT C02 (MMT C02 Trend	Trend Contribution Cumulative
Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
2.E PFC, HFC, SF6, and
NFs Emissions from
Electronics Industry
1.A.4.b	CH4 Emissions
from Stationary
Combustion -
Residential
2.B.10	C02 Emissions
from Phosphoric Acid
Production
1.A.2	N20 Emissions
from Stationary
Combustion -
Industrial
2.C.2	C02 Emissions from
Ferroalloy Production
4.D.1 Net C02 Emissions
from Wetlands
Remaining Wetlands
4.D.2 Net C02 Emissions
from Land Converted
to Wetlands
2.C.6 C02 Emissions from
Zinc Production
4.E.1 N20 Emissions
from Settlement Soils
4.A.1 N20 Emissions
from Forest Soils
2.B.7 C02 Emissions
from Soda Ash
Production
1.A.2	CH4 Emissions from
Stationary Combustion
- Industrial
2.B.4	N20 Emissions
from Caprolactam,
Glyoxal, and Glyoxylic
Acid Production
2.A.2 C02 Emissions
from Lime Production
2.A.3 C02 Emissions
from Glass Production
1.B.2	CH4 Emissions from
Abandoned Oil and
Gas Wells
2.B.6	C02 Emissions
from Titanium Dioxide
Production
l.A.3.e CH4 Emissions
from Mobile
Combustion: Other
4.C.1 N20 Emissions
from Grass Fires
4.C.1 CH4 Emissions from
Grass Fires
HFCs, PFCs	3.6
CH4
C02
N20
C02
C02
C02
C02
N20
n2o
C02
CH4
n2o
C02
co2
ch4
co2
ch4
n2o
ch4
5.2
1.5
3.1
2.2
7.4
0.4
0.6
2.0
0.1
1.4
1.8
1.7
11.7
1.5
6.8
1.2
0.7
0.1
0.1
4.4
4.6
0.9
2.5
1.6
8.0
1.0
2.4
0.5
1.8
1.5
1.4
12.1
1.3
6.6
1.5
0.9
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.01 <0.001
<0.01 <0.001
<0.01 <0.001
<0.001
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.01 <0.001	<0.1
100
100
100
100
<0.01 <0.001	<0.1	100
<0.01 <0.001	<0.1	100
100
100
100
100
100
100
100
100
100
100
100
100
0.3	<0.01 <0.001	<0.1	100
0.3	<0.01 <0.001	<0.1	100
A-55

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CRF Source/Sink
Category
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
Greenhouse (MMT C02 (MMT C02 Trend	Trend Contribution Cumulative
Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
2.B.5 C02 Emissions	C02
from Silicon Carbide
Production and
Consumption
2.E N20 Emissions from	N20
Electronics Industry
4.A.2	Net C02 Emissions	C02
from Land Converted
to Forest Land
l.A.l C02 Emissions	C02
from Stationary
Combustion -
Geothermal Energy
5.B.2	CH4 Emissions from	CH4
Anaerobic Digestion at
Biogas Facilities
5.C.1 N20 Emissions	N20
from Incineration of
Waste
l.A.4.a CH4 Emissions	CH4
from Stationary
Combustion -
Commercial
l.A.4.b N20 Emissions	N20
from Stationary
Combustion -
Residential
l.A.l CH4 Emissions from	CH4
Stationary Combustion
- Coal - Electricity
Generation
1.A.3.a	N20 Emissions	N20
from Mobile
Combustion: Aviation
2.B.8	CH4 Emissions from	CH4
Petrochemical
Production
4.D.2 CH4 Emissions	CH4
from Land Converted
to Coastal Wetlands
1.A.l	N20 Emissions	N20
from Stationary
Combustion - Oil -
Electricity Generation
2.C.4	HFC-134a	HFCs
Emissions from
Magnesium
Production and
Processing
2.G.3 N20 Emissions	N20
from Product Uses
4.D.1 CH4 Emissions	CH4
from Coastal Wetlands
Remaining Coastal
Wetlands
0.4
0.2
<0.01 <0.001	<0.1
100
+	0.2	<0.01 <0.001	<0.1	100
98.2	99.1	<0.01 <0.001	<0.1	100
0.5
0.5
1.1
1.0
0.3
1.7
0.2
0.2
0.1
0.0
4.2
3.7
0.4
0.2
0.3
1.2
0.9
0.2
1.6
0.3
0.2
0.1
4.2
3.8
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
100
100
100
100
100
100
100
100
100
100
100
<0.01 <0.001	<0.1	100
<0.01 <0.001	<0.1	100
A-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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CRF Source/Sink
Category
1990 Estimate 2019 Estimate Approach 1 Approach 2 %
Greenhouse (MMT C02 (MMT C02 Trend	Trend Contribution Cumulative
Gas	Eq.)	Eq.) Assessment Assessment to Trend Total
l.A.3.d N20 Emissions	N20
from Mobile
Combustion: Marine
3.F CH4 Emissions from	CH4
Field Burning of
Agricultural Residues
l.A.4.a N20 Emissions	N20
from Stationary
Combustion -
Commercial
3.A.4 CH4 Emissions from	CH4
Enteric Fermentation:
Other Livestock
l.A.3.a CH4 Emissions	CH4
from Mobile
Combustion: Aviation
1.B.2.a	N20 Emissions	N20
from Petroleum
Systems
3.F	N20 Emissions from	N20
Field Burning of
Agricultural Residues
2.B.5	CH4 Emissions from	CH4
Silicon Carbide
Production and
Consumption
1.A.3.C N20 Emissions	N20
from Mobile
Combustion: Railways
1.A.l	N20 Emissions	N20
from Stationary
Combustion - Wood -
Electricity Generation
2.C.5	C02 Emissions from	C02
Lead Production
1.A.l	CH4 Emissions from	CH4
Stationary Combustion
- Oil - Electricity
Generation
2.C.	1 CH4 Emissions from	CH4
Iron and Steel
Production &
Metallurgical Coke
Production
l.A.3.d CH4 Emissions	CH4
from Mobile
Combustion: Marine
4.D.1	N20 Emissions	N20
from Coastal Wetlands
Remaining Coastal
Wetlands
l.B.2.b N20 Emissions	N20
from Natural Gas
Systems
0.3
0.4
0.4
6.3
0.1
0.2
0.3
0.5
0.4
0.1
0.2
0.4
0.3
6.3
0.2
0.3
0.5
0.4
0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01
<0.01
<0.01
<0.001
<0.001
<0.001
<0.1
<0.1
<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
<0.01 <0.001	<0.1
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
A-57

-------
CRF Source/Sink
Category
1990 Estimate 2019 Estimate Approach 1
Greenhouse (MMT C02 (MMT C02 Trend
Gas	Eq.)	Eq.) Assessment
Approach 2 %
Trend Contribution Cumulative
Assessment to Trend Total
2.C.2 CH4 Emissions from
Ferroalloy Production
1.A.5 N20 Emissions
from Stationary
Combustion - U.S.
Territories
1.A.3.C CH4 Emissions
from Mobile
Combustion: Railways
4.D.1 CH4 Emissions
from Peatlands
Remaining Peatlands
1.A.5 CH4 Emissions from
Stationary Combustion
-	U.S. Territories
l.A.l CH4 Emissions from
Stationary Combustion
-	Wood - Electricity
Generation
4.A.4 N20 Emissions
from Drained Organic
Soils
l.A.5.b CH4 Emissions
from Mobile
Combustion: Military
4.D.1 N20 Emissions
from Peatlands
Remaining Peatlands
l.A.5.b N20 Emissions
from Mobile
Combustion: Military
4.A.4	CH4 Emissions from
Drained Organic Soils
1.B.2	C02 Emissions
from Abandoned Oil
and Gas Wells
5.C.1	CH4 Emissions from
Incineration of Waste
2.C.4	C02 Emissions from
Magnesium
Production and
Processing	
CH4
n2o
ch4
ch4
ch4
ch4
n2o
ch4
n2o
n2o
ch4
C02
CH4
C02
0.1
0.1
0.1
0.1
0.1
0.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001	<0.1	100
<0.001	<0.1	100
<0.001	<0.1
<0.001	<0.1
<0.001	<0.1
<0.001	<0.1
<0.001	<0.1
<0.001	<0.1
<0.001	<0.1
<0.001	<0.1
<0.001
<0.001
<0.001
<0.001
<0.1
<0.1
<0.1
<0.1
100
100
100
100
100
100
100
100
100
100
100
100
+ Does not exceed 0.05 MMT C02 Eq.
A-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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References
IPCC (2006) Volume 1, Chapter 4: Methodological Choice and Identification of Key Categories, 2006IPCC Guidelines for
National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories Programme, The Intergovernmental
Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Negara, and K. Tanabe (eds.). Hayman, Kanagawa,
Japan.
A-59

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ANNEX 2 Methodology and Data for Estimating
CO2 Emissions from Fossil Fuel Combustion
2.1. Methodology for Estimating Emissions of CO2 from Fossil Fuel Combustion
Carbon dioxide (C02) emissions from fossil fuel combustion were estimated using a "bottom-up" methodology
characterized by eight steps. These steps are described below.
Step 1: Determine Total Fuel Consumption by Fuel Type and Sector
The bottom-up methodology used by the United States for estimating C02 emissions from fossil fuel
combustion is conceptually similar to the approach recommended by the Intergovernmental Panel on Climate Change
(IPCC) for countries that intend to develop detailed, sector-based emission estimates in line with a Tier 2 method in the
2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). Total consumption data and adjustments to
consumption are presented in Columns 2 through 13 of Table A-10.
Adjusted consumption data for years 1990,1995, 2000, 2005, and 2010-2019 are presented in Columns 2
through 8 of Table A-12 through Table A-24 with totals by fuel type in Column 8 and totals by end-use sector in the last
rows.1 Fuel consumption data for the bottom-up approach were obtained directly from the Energy Information
Administration (EIA) of the U.S. Department of Energy. These data were first gathered in physical units, and then
converted to their energy equivalents (see Annex 6.5 Constants, Units, and Conversions). The EIA data were collected
through a variety of consumption surveys at the point of delivery or use and qualified with survey data on fuel
production, imports, exports, and stock changes. Individual data elements were supplied by a variety of sources within
EIA. Most information was taken from published reports, although some data were drawn from unpublished energy
studies and databases maintained by EIA.
Energy use data were aggregated by sector (i.e., residential, commercial, industrial, transportation, electric
power, and U.S. Territories), primary fuel type (e.g., coal, natural gas, and petroleum), and secondary fuel type (e.g.,
motor gasoline, distillate fuel). The 2019 total adjusted energy consumption across all sectors, including U.S. Territories,
and energy types was 72,207.8 trillion British thermal units (TBtu), as indicated in the last entry of Column 13 in Table A-
10. This total excludes fuel used for non-energy purposes and fuel consumed as international bunkers, both of which
were deducted in earlier steps.
Electricity use information was allocated to each sector based on ElA's distribution of electricity retail sales to
ultimate customers (i.e., residential, commercial, industrial, and other). Because the "other" fuel use includes sales to
both the commercial and transportation sectors, ElA's limited transportation electricity use data were subtracted from
"other" electricity use and reported separately, and the remaining "other" electricity use was consequently combined
with the commercial electricity data. Further information on these electricity end uses is described in ElA's Monthly
Energy Review (EIA 2020b). Within the transportation sector, electricity use from electric vehicle charging in commercial
and residential locations, not specifically reported by EIA, was calculated and re-allocated from the residential and
commercial sectors to the transportation sector, for the years 2010 to present. The methodology for estimating
electricity consumption by electric vehicles is outlined in Browning (2018).
There are also three basic differences between the consumption data presented in Table A-10 and Table A-12
through Table A-24 and those recommended in the IPCC (2006) emission inventory methodology.
First, consumption data in the U.S. Inventory are presented using higher heating values (HHV)2 rather than the
lower heating values (LHV)3 reflected in the IPCC (2006) emission inventory methodology. This convention is followed
because data obtained from EIA are based on HHV. Of note, however, is that EIA renewable energy statistics are often
1	Adjusted consumption data for other years in the time series are available along with all other data tables for this report on U.S. EPA's
homepage at .
2	Also referred to as gross calorific values (GCV).
3	Also referred to as net calorific values (NCV).
A-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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published using LHV. The difference between the two conventions relates to the treatment of the heat energy that is
consumed in the process of evaporating the water contained in the fuel. The simplified convention used by the
International Energy Agency for converting from HHV to LHV is to multiply the energy content by 0.95 for petroleum and
coal and by 0.9 for natural gas.
Second, while ElA's energy use data for the United States includes only the 50 U.S. states and the District of
Columbia, the data reported to the United Nations Framework Convention on Climate Change (UNFCCC) are to include
energy use within U.S. Territories. Therefore, estimates for U.S. Territories4 were added to domestic consumption of
fossil fuels. Energy use data from U.S. Territories are presented in Column 7 of Table A-12 through Table A-24. It is
reported separately from domestic sectoral consumption, because it is collected separately by EIA with no sectoral
disaggregation.
Third, there were a number of modifications made in this report that may cause consumption information
herein to differ from figures given in the cited literature. These are (1) the reallocation of select amounts of coking coal,
petroleum coke, natural gas, residual fuel oil, and other oil (>401 degrees Fahrenheit) for processes accounted for in the
Industrial Processes and Product Use chapter, (2) corrections for synthetic natural gas production, (3) subtraction of
other fuels used for non-energy purposes, and (4) subtraction of international bunker fuels. These adjustments are
described in the following steps.
Step 2: Subtract uses accounted for in the Industrial Processes and Product Use chapter.
Portions of the fuel consumption data for seven fuel categories—coking coal, distillate fuel, industrial other
coal, petroleum coke, natural gas, residual fuel oil, and other oil (>401 degrees Fahrenheit)—were reallocated to the
Industrial Processes and Product Use (IPPU) chapter, as these portions were consumed as raw materials during non-
energy related industrial processes. Emissions from these fuels used as raw materials are presented in the Industrial
Processes and Product Use chapter and are removed from the energy and non-energy use estimates within the Energy
chapter.
•	Coking coal is used as a raw material (specifically as a reducing agent) in the blast furnace process to
produce iron and steel, lead, and zinc and therefore is not used as a fuel for this process.
•	Similarly, petroleum coke is used in multiple processes as a raw material and is thus not used as a fuel in
those applications. The processes in which petroleum coke is used include (1) ferroalloy production, (2)
aluminum production (for the production of C anodes and cathodes), (3) titanium dioxide production (in
the chloride process), (4) ammonia production, and (5) silicon carbide.
•	Natural gas consumption is used for the production of ammonia, and blast furnace and coke oven gas used
in iron and steel production.
•	Residual fuel oil and other oil (>401 degrees Fahrenheit) 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.
Examples of iron and steel production adjustments in allocating emissions in Energy and IPPU sectors:
The consumption of coking coal, natural gas, distillate fuel, and coal used in iron and steel production are
adjusted within the Energy chapter to avoid double counting of emissions from consumption of these fuels during
activities in IPPU related sectors. These fuels are adjusted based on activity data utilized in calculating emissions
estimates within the Iron and Steel Production section. Iron and steel production is an industrial process in which coal
coke is used as a raw material rather than as a fuel;5 as such, the total use of industrial coking coal, as reported by EIA, is
adjusted downward to account for this consumption within the iron and steel category. In this case, if the reported
amount of coking coal used in these processes is greater than the amount of coking coal consumption reported by the
4	Fuel consumption by U.S. Territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake Island, and other U.S. Pacific
Islands) is included in this report.
5	In addition to iron and steel, lead and zinc production are also industrial processes in which coal coke is used as a raw
material. Iron and steel, lead and zinc production accounts for the major portion of consumption of coal coke in the United
States.
A-61

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EIA, the excess amount of coking coal used in these processes that is greater than the amount reported from
consumption is subtracted from the industrial other coal fuel type.
In 2019,17,924 thousand tons of coking coal were consumed,6 resulting in an Energy sector adjustment of 382
TBtu. Natural gas, fuel oil, and coal are other fossil fuels also used in the production of iron and steel; therefore, the
consumption of these fuels in industrial processes is subtracted from the industrial fossil fuel combustion sector to
account for the amount of fuel used in the iron and steel calculation. In 2019, the iron and steel industry consumed
2,465 tons of coal (bituminous), 49,238 million ft3 of natural gas, and 2,321 thousand gallons of distillate fuel as fuel. This
resulted in Energy chapter adjustments of roughly 59 TBtu for coal, 51 TBtu for natural gas, and less than 1 TBtu for
distillate fuel. In addition, an additional 132 TBtu is adjusted to account for coking coal consumed for industrial processes
other than iron and steel, lead, and zinc production in 2019.
Step 3: Adjust for Conversion of Fossil Fuels and Exports
First, ethanol has been added to the motor gasoline stream for many years, but prior to 1993 this addition was
not captured in EIA motor gasoline statistics. Starting in 1993, ethanol was included in gasoline statistics. Carbon dioxide
emissions from ethanol added to motor gasoline are not included specifically in summing energy sector totals. Net
carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for LULUCF, therefore, fuel
consumption estimates are adjusted to remove ethanol. Thus, motor gasoline consumption statistics given in this report
exclude ethanol and may be slightly lower than in EIA sources for finished gasoline that includes ethanol.
Second, EIA distillate fuel oil consumption statistics include "biodiesel" and "other renewable diesel fuel"
consumption starting in 2009. Carbon dioxide emissions from biodiesel and other renewable diesel added to diesel fuel
are not included specifically in summing energy sector totals. Net carbon fluxes from changes in biogenic carbon
reservoirs are accounted for in the estimates for LULUCF, therefore, fuel consumption estimates are adjusted to remove
biodiesel and other renewable diesel fuel. Thus, distillate fuel oil consumption statistics for the transportation sector in
this report may be slightly lower than in EIA sources.
Third, a portion of industrial "other" coal that is accounted for in EIA coal combustion statistics is actually used
to make "synthetic natural gas" via coal gasification at the Dakota Gasification Plant, a synthetic natural gas plant. The
plant produces synthetic natural gas and byproduct C02. Since October 2000, a portion of the C02 produced by the coal
gasification plant has been exported to Canada by pipeline. The energy in this synthetic natural gas enters the natural gas
distribution stream, however it is accounted for in EIA coal combustion statistics.7 The exported C02 is not emitted to the
atmosphere in the United States, and therefore the energy associated with the amount of C02 exported is subtracted
from industrial other coal.
Step 4: Adjust Sectoral Allocation of Distillate Fuel Oil and Motor Gasoline
EPA conducted a separate bottom-up analysis of transportation fuel consumption based on data from the
Federal Highway Administration (FHWA). The FHWA data indicated that the amount of distillate and motor gasoline
consumption allocated to the transportation sector in the EIA statistics should be adjusted. Therefore, for the estimates
presented in the U.S. Inventory, the transportation sector's distillate fuel and motor gasoline consumption were adjusted
to match the value obtained from the bottom-up analysis. As the total distillate and motor gasoline consumption
estimate from EIA are considered to be accurate at the national level, the distillate and motor gasoline consumption
totals for the residential, commercial, and industrial sectors were adjusted proportionately.
Step 5: Subtract Consumption for Non-Energy Use
U.S. aggregate energy statistics include consumption of fossil fuels for non-energy purposes. Depending on the
end-use, non-energy uses of fossil fuels can result in long term storage of some or all of the C contained in the fuel. For
example, asphalt made from petroleum can sequester up to 100 percent of the C contained in the petroleum feedstock
for extended periods of time. Other non-energy fossil fuel products, such as lubricants or plastics also store C, but can
lose or emit some of this C when they are used and/or burned as waste. As the emission pathways of C used for non-
energy purposes are vastly different than fuel combustion, these emissions are estimated separately in the Carbon
6	Coking coal includes non-imported coke consumption from the iron and steel, lead, and zinc industries.
7	To avoid double-counting, ElA's MER statistics account for supplemental gaseous fuels (including synthetic natural gas) in their
primary energy category (i.e., coal, petroleum, or biomass) (EIA 2020b).
A-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
Emitted in Products from Non-Energy Uses of Fossil Fuels section in this chapter. Therefore, the amount of fuels used for
non-energy purposes, shown in Table A-25, was subtracted from total fuel consumption.
Step 6: Subtract Consumption of International Bunker Fuels
Emissions from international transport activities, or international bunker fuel consumption, are not included in
national totals and instead reported separately, as required by the IPCC (2006) and UNFCCC (2014) inventory reporting
guidelines. EIA energy statistics, however, include these bunker fuels jet fuel for aircraft, and distillate fuel oil and
residual fuel oil for marine shipping as part of fuel consumption by the transportation end-use sector. Therefore, the
amount of consumption for international bunker fuels was estimated and subtracted from total fuel consumption (see
Table A-26). Emissions from international bunker fuels have been estimated separately and not included in national
totals..8
Step 7: Determine the C Content of All Fuels
The C content of combusted fossil fuels was estimated by multiplying adjusted energy consumption (Columns 2
through 8 of Table A-12 through Table A-24) by fuel-specific C content coefficients (see Table A-27 and Table A-28) that
reflect the amount of C per unit of energy in each fuel. The C content coefficients used in the Inventory were derived in
part by EIA and EPA from detailed fuel information and are similar to the C content coefficients contained in the IPCC's
default methodology (IPCC 2006), with modifications reflecting fuel qualities specific to the United States.
For geothermal electricity production, C content was estimated by multiplying net generation for each geotype
(see Table A-31) by technology-specific C content coefficients (see Table A-27). For industrial energy and non-energy
hydrocarbon gas liquids (HGL)9 consumption, annually variable C contents were estimated by multiplying annual energy
and non-energy consumption for each HGL component (e.g., ethane, ethylene, propane, propylene) by its respective C
content coefficient (see Table A-28).
Step 8: Estimate C02 Emissions
Actual C02 emissions in the United States were summarized by major fuel (i.e., coal, petroleum, natural gas,
geothermal) and consuming sector (i.e., residential, commercial, industrial, transportation, electric power, and U.S.
Territories). Emission estimates are expressed in million metric tons of carbon dioxide equivalents (MMT C02 Eq.). To
convert from C content to C02 emissions, the fraction of C that is oxidized was applied. This fraction was 100 percent
based on guidance in IPCC (2006).
To determine total emissions by final end-use sector, emissions from electric power were distributed to each
end-use sector according to its share of aggregate electricity use (see Table A-29). This pro-rated approach to allocating
emissions from electric power may overestimate or underestimate emissions for particular sectors due to differences in
the average C content of fuel mixes burned to generate electricity.
To provide a more detailed accounting of emissions from transportation, fuel consumption data by vehicle type
and transportation mode were used to allocate emissions by fuel type calculated for the transportation end-use sector.
Additional information on the allocation is available in Annex 3.1.
Box A-l: Uses of Greenhouse Gas Reporting Program Data in Reporting Emissions from Industrial Sector Fossil
Fuel Combustion
As described in the calculation methodology, total fossil fuel consumption for each year is based on aggregated
end-use sector consumption published by the EIA. The availability of facility-level combustion emissions through EPA's
Greenhouse Gas Reporting Program (GHGRP) has provided an opportunity to better characterize the industrial sector's
energy consumption and emissions in the United States, through a disaggregation of ElA's industrial sector fuel
consumption data from select industries.
8	Refer to the International Bunker Fuels section of the Energy chapter and Annex 3.3 for a description of the methodology for
distinguishing between international and domestic fuel consumption.
9	EIA defines HGL as "a group of hydrocarbons including ethane, propane, normal butane, isobutane, and natural gasoline, and their
associated olefins, including ethylene, propylene, butylene, and isobutylene" (EIA 2020b).
A-63

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For EPA's GHGRP 2010 through 2019 reporting years, facility-level fossil fuel combustion emissions reported
through EPA's GHGRP were categorized and distributed to specific industry types by utilizing facility-reported NAICS
codes (as published by the U.S. Census Bureau). As noted previously in this report, the definitions and provisions for
reporting fuel types in EPA's GHGRP include some differences from the Inventory's use of EIA national fuel statistics to
meet the UNFCCC reporting guidelines. The IPCC has provided guidance on aligning facility-level reported fuels and fuel
types published in national energy statistics, which guided this exercise..10
As with previous Inventory reports, this year's effort represents an attempt to align, reconcile, and coordinate
the facility-level reporting of fossil fuel combustion emissions under EPA's GHGRP with the national-level approach
presented in this report. Consistent with recommendations for reporting the Inventory to the UNFCCC, progress was
made on certain fuel types for specific industries and has been included in the Common Reporting Format (CRF) tables
that are submitted to the UNFCCC along with this report..11 The efforts in reconciling fuels focus on standard, common
fuel types (e.g., natural gas, distillate fuel oil) where the fuels in ElA's national statistics aligned well with facility-level
GHGRP data. For these reasons, the current information presented in the CRF tables should be viewed as an initial
attempt at this exercise. Additional efforts will be made for future Inventory reports to improve the mapping of fuel
types, and examine ways to reconcile and coordinate any differences between facility-level data and national statistics.
This year's analysis includes the full time series presented in the CRF tables. Analyses were conducted linking
GHGRP facility-level reporting with the information published by EIA in its MECS data in order to disaggregate the full
1990 through 2019 time series in the CRF tables. It is believed that the current analysis has led to improvements in the
presentation of data in the Inventory, but further work will be conducted, and future improvements will be realized in
subsequent Inventory reports. This includes incorporating the latest MECS data as it becomes available.
10	See Section 4 "Use of Facility-Level Data in Good Practice National Greenhouse Gas Inventories" of the IPCC meeting report, and
specifically the section on using facility-level data in conjunction with energy data, available at: .
11	See .
A-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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Table A-10: 2019 Energy Consumption Data by Fuel Type (TBtu) and Adjusted Energy Consumption Data
1	2	3	4	5	6	7	8	9	10	11	12	13

Total Consumption (TBtu)a
Adjustments (TBtu)b
Total Adjusted
Consumption
(TBtu)
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Bunker Fuel
Unadjusted NEU Consumption
Ind. Trans. Terr.
Total Coal
NO
16.7
659.0
NO
10,181.2
27.1
10,884.0

141.5
10,742.5
Residential Coal
NO





NO


NO
Commercial Coal

16.7




16.7


16.7
Industrial Other Coal


526.9



526.9

9.5
517.4
Transportation Coal



NO


NO


NO
Electric Power Coal




10,181.2

10,181.2


10,181.2
U.S. Territory Coal (bit)





27.1
27.1


27.1
Natural Gas
5,204.9
3,645.1
10,245.4
1,035.2
11,645.4
48.2
31,824.2

730.8
31,093.5
Total Petroleum
904.3
787.4
8,639.7
26,309.2
188.6
279.8
37,108.9
1,618.6
5,024.8 132.1 10.7
30,322.7
Asphalt & Road Oil


843.9



843.9

843.9

Aviation Gasoline



23.4


23.4


23.4
Distillate Fuel Oil
398.7
277.3
1,021.5
6,624.8
53.9
80.3
8,456.5
136.3
5.8
8,314.4
Jet Fuel



3,608.0
NA
30.8
3,638.8
1,146.1

2,492.7
Kerosene
10.3
1.7
2.0


0.5
14.5


14.5
LPG (Propane)
495.2
172.0

7.5


674.8


674.8
HGL


2,887.4


6.4
2,893.8

2,758.8
135.0
Lubricants


117.6
132.1

1.0
250.7

117.6 132.1 1.0

Motor Gasoline

333.6
245.3
15,381.1

105.0
16,064.9


16,064.9
Residual Fuel

2.7

532.2
58.8
46.2
639.9
336.2

303.7
Other Petroleum










AvGas Blend Components


(1.2)



(1.2)


(1.2)
Crude Oil










MoGas Blend Components










Misc. Products


180.2


9.6
189.8

180.2 9.6

Naphtha (<401 deg. F)


396.7



396.7

396.7

Other Oil (>401 deg. F)


234.1



234.1

234.1

Pentanes Plus


334.6



334.6

166.6
167.9
Petroleum Coke

0.2
602.3

75.9

678.4

56.4
622.0
Still Gas


1,533.5



1,533.5

158.7
1,374.7
Special Naphtha


95.6



95.6

95.6

Unfinished Oils


135.9



135.9


135.9
Waxes


10.4



10.4

10.4

Geothermal




52.8

52.8


52.8
Total (All Fuels)
6,109.1
4,449.3
19,544.1
27,344.4
22,067.9
355.1
79,869.9
1,618.6
5,897.1 132.1 10.7
72,211.5
Note: Parentheses indicate negative values.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-65

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Table A-ll: 2019 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
16.7
517.4
NO
10,181.2
27.1
10,742.5
NO
1.6
49.5
NO
973.5
2.5
1,027.1
Residential Coal
NO





NO
NO





NO
Commercial Coal

16.7




16.7

1.6




1.6
Industrial Other Coal


517.4



517.4


49.5



49.5
Transportation Coal



NO


NO



NO


NO
Electric Power Coal




10,181.2

10,181.2




973.5

973.5
U.S. Territory Coal (bit)





27.1
27.1





2.5
2.5
Natural Gas
5,204.9
3,645.1
9,514.7
1,035.2
11,645.4
48.2
31,093.5
275.3
192.8
503.3
54.8
616.0
2.5
1,644.6
Total Petroleum
904.3
787.4
3,614.9
24,558.5
188.6
269.1
30,322.7
61.5
55.3
269.7
1,762.5
16.2
19.5
2,184.6
Asphalt & Road Oil














Aviation Gasoline



23.4


23.4



1.6


1.6
Distillate Fuel Oil
398.7
277.3
1,015.6
6,488.6
53.9
80.3
8,314.4
29.6
20.6
75.3
481.1
4.0
6.0
616.4
Jet Fuel



2,461.9
NA
30.8
2,492.7



177.8
NA
2.2
180.0
Kerosene
10.3
1.7
2.0


0.5
14.5
0.8
0.1
0.1


+
1.1
LPG (Propane)
495.2
172.0

7.5


674.8
31.1
10.8

0.5


42.4
HGL


128.6


6.4
135.0


8.2


0.4
8.7
Lubricants














Motor Gasoline

333.6
245.3
15,381.1

105.0
16,064.9

23.6
17.3
1,086.8

7.4
1,135.1
Residual Fuel

2.7

196.0
58.8
46.2
303.7

0.2

14.7
4.4
3.5
22.8
Other Petroleum














AvGas Blend Components


(1.2)



(1.2)


(0.1)



(0.1)
Crude Oil














MoGas Blend Components














Misc. Products














Naphtha (<401 deg. F)














Other Oil (>401 deg. F)














Pentanes Plus


167.9



167.9


11.2



11.2
Petroleum Coke

0.2
545.9

75.9

622.0

+
55.7

7.8

63.5
Still Gas


1,374.7



1,374.7


91.7



91.7
Special Naphtha














Unfinished Oils


135.9



135.9


10.1



10.1
Waxes














Geothermal




52.8

52.8




0.4

0.4
Total (All Fuels)
6,109.1
4,449.3
13,647.0
25,593.7
22,067.9
344.4
72,211.5
336.8
249.7
822.5
1,817.2
1,606.0
24.6
4,856.7
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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Table A-12: 2018 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans. Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
18.7
569.0
NO 12,053.0
27.1
12,667.9
NO
1.8
54.4
NO
1,152.9
2.5
1,211.6
Residential Coal
NO




NO
NO





NO
Commercial Coal

18.7



18.7

1.8




1.8
Industrial Other Coal


569.0


569.0


54.4



54.4
Transportation Coal



NO

NO



NO


NO
Electric Power Coal



12,053.0

12,053.0




1,152.9

1,152.9
U.S. Territory Coal (bit)




27.1
27.1





2.5
2.5
Natural Gas
5,174.4
3,638.3
9,334.9
962.2 10,912.1
48.1
30,070.0
273.8
192.5
494.0
50.9
577.4
2.5
1,591.2
Total Petroleum
943.6
733.3
3,544.4
24,606.6 260.4
269.1
30,357.4
64.2
51.4
265.2
1,765.6
22.2
19.5
2,188.2
Asphalt & Road Oil













Aviation Gasoline



22.4

22.4



1.5


1.5
Distillate Fuel Oil
428.8
273.0
1,052.9
6,476.5 80.6
80.3
8,392.1
31.8
20.2
78.1
480.2
6.0
6.0
622.2
Jet Fuel



2,386.0 NA
30.8
2,416.8



172.3
NA
2.2
174.5
Kerosene
8.2
1.3
1.6

0.5
11.7
0.6
0.1
0.1


+
0.9
LPG (Propane)
506.5
176.0

7.7

690.2
31.8
11.1

0.5


43.4
HGL


127.3

6.4
133.7


8.1


0.4
8.6
Lubricants













Motor Gasoline

279.5
205.6
15,527.5
105.0
16,117.7

19.8
14.5
1,097.1

7.4
1,138.8
Residual Fuel

3.1

186.5 78.3
46.2
314.0

0.2

14.0
5.9
3.5
23.6
Other Petroleum













AvGas Blend Components


(1.6)


(1.6)


(0.1)



(0.1)
Crude Oil













MoGas Blend Components













Misc. Products













Naphtha (<401 deg. F)













Other Oil (>401 deg. F)













Pentanes Plus


112.7


112.7


7.5



7.5
Petroleum Coke

0.4
569.8
101.5

671.6

+
58.2

10.4

68.6
Still Gas


1,445.3


1,445.3


96.4



96.4
Special Naphtha













Unfinished Oils


30.9


30.9


2.3



2.3
Waxes













Geothermal



54.5

54.5




0.4

0.4
Total (All Fuels)
6,118.0
4,390.3
13,448.3
25,568.8 23,279.9
344.4
73,149.7
338.1
245.7
813.6
1,816.6
1,752.9
24.6
4,991.4
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-67

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Table A-13: 2017 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
20.7
614.1
NO
12,622.2
27.1
13,284.1
NO
2.0
58.7
NO
1,207.1
2.5
1,270.2
Residential Coal
NO





NO
NO





NO
Commercial Coal

20.7




20.7

2.0




2.0
Industrial Other Coal


614.1



614.1


58.7



58.7
Transportation Coal



NO


NO



NO


NO
Electric Power Coal




12,622.2

12,622.2




1,207.1

1,207.1
U.S. Territory Coal (bit)





27.1
27.1





2.5
2.5
Natural Gas
4,563.5
3,272.9
8,872.4
798.6
9,555.2
48.1
27,110.6
241.5
173.2
469.5
42.3
505.6
2.5
1,434.6
Total Petroleum
766.1
808.9
3,512.7
24,250.4
217.7
269.1
29,824.9
51.9
56.8
261.9
1,740.2
18.9
19.5
2,149.2
Asphalt & Road Oil














Aviation Gasoline



20.9


20.9



1.4


1.4
Distillate Fuel Oil
327.0
244.0
905.0
6,322.2
54.7
80.3
7,933.2
24.2
18.1
67.0
468.3
4.1
6.0
587.6
Jet Fuel



2,378.1
NA
30.8
2,408.9



171.8
NA
2.2
174.0
Kerosene
8.4
1.2
1.1


0.5
11.2
0.6
0.1
0.1


+
0.8
LPG (Propane)
430.7
155.7

7.1


593.5
27.1
9.8

0.4


37.3
HGL


175.6


6.4
182.0


11.2


0.4
11.6
Lubricants














Motor Gasoline

403.7
295.7
15,302.8

105.0
16,107.2

28.5
20.9
1,081.8

7.4
1,138.7
Residual Fuel

3.8

219.3
65.8
46.2
335.0

0.3

16.5
4.9
3.5
25.2
Other Petroleum














AvGas Blend Components


(0.2)



(0.2)


(+)



(+)
Crude Oil














MoGas Blend Components














Misc. Products














Naphtha (<401 deg. F)














Other Oil (>401 deg. F)














Pentanes Plus


87.0



87.0


5.8



5.8
Petroleum Coke

0.5
553.0

97.2

650.8

0.1
56.5

9.9

66.4
Still Gas


1,419.0



1,419.0


94.7



94.7
Special Naphtha














Unfinished Oils


76.4



76.4


5.7



5.7
Waxes














Geothermal




54.3

54.3




0.4

0.4
Total (All Fuels)
5,329.6
4,102.4
12,999.2
25,049.0
22,449.5
344.4
70,274.0
293.4
232.0
790.1
1,782.4
1,732.0
24.6
4,854.5
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
Table A-14: 2016 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans. Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
23.7
661.6
NO 12,996.4
34.9
13,716.6
NO
2.3
63.2
NO
1,242.0
3.2
1,310.7
Residential Coal
NO




NO
NO





NO
Commercial Coal

23.7



23.7

2.3




2.3
Industrial Other Coal


661.6


661.6


63.2



63.2
Transportation Coal



NO

NO



NO


NO
Electric Power Coal



12,996.4

12,996.4




1,242.0

1,242.0
U.S. Territory Coal (bit)




34.9
34.9





3.2
3.2
Natural Gas
4,505.8
3,223.5
8,769.1
757.2 10,301.3
63.6
27,620.6
238.4
170.5
463.9
40.1
545.0
3.4
1,461.3
Total Petroleum
799.2
834.5
3,551.6
23,980.8 243.9
267.7
29,677.7
54.4
58.7
265.4
1,719.8
21.5
19.4
2,139.2
Asphalt & Road Oil













Aviation Gasoline



20.5

20.5



1.4


1.4
Distillate Fuel Oil
355.7
266.8
939.9
6,129.2 54.9
80.2
7,826.6
26.4
19.8
69.6
454.2
4.1
5.9
580.0
Jet Fuel



2,298.8 NA
29.6
2,328.3



166.0
NA
2.1
168.2
Kerosene
13.7
2.1
2.3

0.4
18.4
1.0
0.2
0.2


+
1.3
LPG (Propane)
429.9
150.0

7.1

587.0
27.0
9.4

0.4


36.9
HGL


227.4

6.3
233.7


14.5


0.4
14.9
Lubricants













Motor Gasoline

410.8
287.2
15,352.9
105.0
16,155.9

29.0
20.3
1,084.8

7.4
1,141.5
Residual Fuel

4.4

172.4 70.7
46.2
293.7

0.3

12.9
5.3
3.5
22.1
Other Petroleum













AvGas Blend Components


(0.3)


(0.3)


(+)



(+)
Crude Oil













MoGas Blend Components













Misc. Products













Naphtha (<401 deg. F)













Other Oil (>401 deg. F)













Pentanes Plus


56.5


56.5


3.8



3.8
Petroleum Coke

0.3
591.4
118.3

710.1

+
60.4

12.1

72.5
Still Gas


1,438.6


1,438.6


96.0



96.0
Special Naphtha













Unfinished Oils


8.6


8.6


0.6



0.6
Waxes













Geothermal



54.0

54.0




0.4

0.4
Total (All Fuels)
5,305.0
4,081.7
12,982.4
24,738.0 23,595.6
366.2
71,068.9
292.8
231.6
792.5
1,759.9
1,808.9
26.0
4,911.5
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-69

-------
Table A-15: 2015 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
31.1
733.9
NO
14,138.3
44.9
14,948.1
NO
3.0
70.0
NO
1,351.4
4.1
1,428.5
Residential Coal
NO





NO
NO





NO
Commercial Coal

31.1




31.1

3.0




3.0
Industrial Other Coal


733.9



733.9


70.0



70.0
Transportation Coal



NO


NO



NO


NO
Electric Power Coal




14,138.3

14,138.3




1,351.4

1,351.4
U.S. Territory Coal (bit)





44.9
44.9





4.1
4.1
Natural Gas
4,776.9
3,315.6
8,678.5
744.8
9,926.5
57.4
27,499.8
252.7
175.4
459.1
39.4
525.2
3.0
1,454.9
Total Petroleum
938.9
937.9
3,580.7
23,435.1
276.0
303.8
29,472.5
64.6
66.2
268.2
1,679.8
23.7
22.1
2,124.5
Asphalt & Road Oil














Aviation Gasoline



21.1


21.1



1.5


1.5
Distillate Fuel Oil
483.1
315.5
1,018.0
6,170.4
70.4
78.8
8,136.2
35.8
23.4
75.5
457.5
5.2
5.8
603.2
Jet Fuel



2,181.9
NA
36.0
2,217.9



157.6
NA
2.6
160.2
Kerosene
10.1
1.4
1.7


0.1
13.4
0.7
0.1
0.1


+
1.0
LPG (Propane)
445.7
148.0

6.6


600.3
28.0
9.3

0.4


37.7
HGL


243.3


6.2
249.5


15.5


0.4
15.9
Lubricants














Motor Gasoline

468.6
321.4
14,998.5

113.0
15,901.4

33.1
22.7
1,058.6

8.0
1,122.4
Residual Fuel

4.0

56.6
93.9
69.6
224.0

0.3

4.2
7.0
5.2
16.8
Other Petroleum














AvGas Blend Components


(0.3)



(0.3)


(+)



(+)
Crude Oil














MoGas Blend Components














Misc. Products














Naphtha (<401 deg. F)














Other Oil (>401 deg. F)














Pentanes Plus


80.9



80.9


5.4



5.4
Petroleum Coke

0.5
600.8

111.7

713.0

0.1
61.3

11.4

72.8
Still Gas


1,332.9



1,332.9


88.9



88.9
Special Naphtha














Unfinished Oils


(17.8)



(17.8)


(1.3)



(1.3)
Waxes














Geothermal




54.3

54.3




0.4

0.4
Total (All Fuels)
5,715.9
4,284.6
12,993.1
24,180.0
24,395.0
406.1
71,974.7
317.3
244.6
797.3
1,719.2
1,900.6
29.2
5,008.3
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
Table A-16: 2014 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
40.2
833.0
NO
16,427.4
43.8
17,344.4
NO
3.8
79.2
NO
1,568.6
4.0
1,655.7
Residential Coal
NO





NO
NO





NO
Commercial Coal

40.2




40.2

3.8




3.8
Industrial Other Coal


833.0



833.0


79.2



79.2
Transportation Coal



NO


NO



NO


NO
Electric Power Coal




16,427.4

16,427.4




1,568.6

1,568.6
U.S. Territory Coal (bit)





43.8
43.8





4.0
4.0
Natural Gas
5,242.5
3,571.9
8,817.9
759.7
8,361.7
60.6
26,814.3
277.7
189.2
467.0
40.2
442.9
3.2
1,420.2
Total Petroleum
1,003.2
558.1
3,567.9
23,266.4
295.5
295.7
28,986.9
68.9
39.4
268.2
1,668.0
25.3
21.4
2,091.2
Asphalt & Road Oil














Aviation Gasoline



21.7


21.7



1.5


1.5
Distillate Fuel Oil
500.0
334.4
1,273.2
6,002.9
82.2
65.6
8,258.3
37.1
24.8
94.4
445.3
6.1
4.9
612.6
Jet Fuel



2,054.3
NA
35.0
2,089.3



148.4
NA
2.5
150.9
Kerosene
13.7
2.0
2.8


0.1
18.7
1.0
0.1
0.2


+
1.4
LPG (Propane)
489.5
160.5

7.2


657.1
30.8
10.1

0.4


41.3
HGL


177.9


6.4
184.3


11.4


0.4
11.8
Lubricants














Motor Gasoline

52.7
205.6
15,103.0

126.7
15,488.0

3.7
14.5
1,066.6

8.9
1,093.8
Residual Fuel

7.9

77.4
95.1
61.9
242.4

0.6

5.8
7.1
4.7
18.2
Other Petroleum














AvGas Blend Components


(0.1)



(0.1)


(+)



(+)
Crude Oil














MoGas Blend Components














Misc. Products














Naphtha (<401 deg. F)














Other Oil (>401 deg. F)














Pentanes Plus


44.5



44.5


3.0



3.0
Petroleum Coke

0.5
592.1

118.2

710.8

0.1
60.5

12.1

72.6
Still Gas


1,352.4



1,352.4


90.2



90.2
Special Naphtha














Unfinished Oils


(80.6)



(80.6)


(6.0)



(6.0)
Waxes














Geothermal




54.2

54.2




0.4

0.4
Total (All Fuels)
6,245.7
4,170.2
13,218.9
24,026.1
25,138.7
400.1
73,199.7
346.5
232.4
814.5
1,708.2
2,037.2
28.7
5,167.5
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-71

-------
Table A-17: 2013 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
41.4
836.8
NO
16,450.6
30.8
17,359.6
NO
3.9
79.5
NO
1,571.3
2.8
1,657.6
Residential Coal
NO





NO
NO





NO
Commercial Coal

41.4




41.4

3.9




3.9
Industrial Other Coal


836.8



836.8


79.5



79.5
Transportation Coal



NO


NO



NO


NO
Electric Power Coal




16,450.6

16,450.6




1,571.3

1,571.3
U.S. Territory Coal (bit)





30.8
30.8





2.8
2.8
Natural Gas
5,022.9
3,379.8
8,512.5
887.3
8,376.3
58.2
26,237.0
266.4
179.2
451.4
47.0
444.2
3.1
1,391.3
Total Petroleum
916.8
580.6
4,075.6
22,613.7
255.2
298.8
28,740.7
62.8
41.1
303.4
1,622.8
22.4
21.7
2,074.1
Asphalt & Road Oil














Aviation Gasoline



22.4


22.4



1.5


1.5
Distillate Fuel Oil
445.1
311.2
1,138.8
5,804.1
55.4
71.9
7,826.5
33.0
23.1
84.5
430.5
4.1
5.3
580.5
Jet Fuel



2,036.9
NA
30.0
2,067.0



147.1
NA
2.2
149.3
Kerosene
8.3
1.0
1.5


0.1
10.9
0.6
0.1
0.1


+
0.8
LPG (Propane)
463.5
151.6

6.9


622.0
29.1
9.5

0.4


39.1
HGL


294.0


6.3
300.3


18.7


0.4
19.2
Lubricants














Motor Gasoline

92.1
606.2
14,542.0

117.5
15,357.8

6.5
42.9
1,028.0

8.3
1,085.7
Residual Fuel

24.4

201.4
77.2
72.9
375.9

1.8

15.1
5.8
5.5
28.2
Other Petroleum














AvGas Blend Components


(0.4)



(0.4)


(+)



(+)
Crude Oil














MoGas Blend Components














Misc. Products














Naphtha (<401 deg. F)














Other Oil (>401 deg. F)














Pentanes Plus


47.5



47.5


3.2



3.2
Petroleum Coke

0.4
600.9

122.5

723.7

+
61.4

12.5

73.9
Still Gas


1,370.6



1,370.6


91.4



91.4
Special Naphtha














Unfinished Oils


16.7



16.7


1.2



1.2
Waxes














Geothermal




53.8

53.8




0.4

0.4
Total (All Fuels)
5,939.8
4,001.8
13,424.9
23,501.0
25,135.8
387.8
72,391.2
329.1
224.2
834.3
1,669.8
2,038.3
27.6
5,123.3
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
Table A-18: 2012 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
43.6
822.6
NO
15,821.2
36.9
16,724.3
NO
4.1
78.2
NO
1,511.7
3.4
1,597.5
Residential Coal
NO





NO
NO





NO
Commercial Coal

43.6




43.6

4.1




4.1
Industrial Other Coal


822.6



822.6


78.2



78.2
Transportation Coal



NO


NO



NO


NO
Electric Power Coal




15,821.2

15,821.2




1,511.7

1,511.7
U.S. Territory Coal (bit)





36.9
36.9





3.4
3.4
Natural Gas
4,242.1
2,959.5
8,195.6
779.8
9,286.8
49.2
25,512.9
225.1
157.0
434.9
41.4
492.8
2.6
1,353.8
Total Petroleum
832.6
550.7
3,909.3
22,513.0
214.2
373.0
28,392.9
57.3
39.1
293.6
1,619.0
18.3
27.2
2,054.5
Asphalt & Road Oil














Aviation Gasoline



25.1


25.1



1.7


1.7
Distillate Fuel Oil
429.3
316.1
1,123.3
5,761.2
52.4
63.5
7,745.9
31.8
23.4
83.3
427.1
3.9
4.7
574.3
Jet Fuel



1,985.2
NA
39.1
2,024.3



143.4
NA
2.8
146.2
Kerosene
7.7
1.2
2.0


0.6
11.6
0.6
0.1
0.1


+
0.8
LPG (Propane)
395.6
135.5

7.1


538.2
24.9
8.5

0.4


33.8
HGL


280.1


11.1
291.2


17.9


0.7
18.6
Lubricants














Motor Gasoline

66.1
432.2
14,523.3

131.4
15,153.0

4.7
30.7
1,030.4

9.3
1,075.1
Residual Fuel

31.4

211.1
76.7
127.3
446.5

2.4

15.8
5.8
9.6
33.5
Other Petroleum














AvGas Blend Components


(+)



(+)


(+)



(+)
Crude Oil














MoGas Blend Components














Misc. Products














Naphtha (<401 deg. F)














Other Oil (>401 deg. F)














Pentanes Plus


42.5



42.5


2.8



2.8
Petroleum Coke

0.4
649.0

85.1

734.4

+
66.3

8.7

75.0
Still Gas


1,320.2



1,320.2


88.1



88.1
Special Naphtha














Unfinished Oils


60.1



60.1


4.5



4.5
Waxes














Geothermal




53.1

53.1




0.4

0.4
Total (All Fuels)
5,074.7
3,553.8
12,927.5
23,292.8
25,375.3
459.0
70,683.2
282.4
200.3
806.7
1,660.4
2,023.3
33.2
5,006.2
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-73

-------
Table A-19: 2011 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
61.7
905.8
NO
18,035.2
36.9
19,039.5
NO
5.8
86.0
NO
1,722.4
3.4
1,817.6
Residential Coal
NO





NO
NO





NO
Commercial Coal

61.7




61.7

5.8




5.8
Industrial Other Coal


905.8



905.8


86.0



86.0
Transportation Coal



NO


NO



NO


NO
Electric Power Coal




18,035.2

18,035.2




1,722.4

1,722.4
U.S. Territory Coal (bit)





36.9
36.9





3.4
3.4
Natural Gas
4,804.6
3,216.1
7,871.5
733.5
7,712.2
27.1
24,365.0
255.1
170.7
417.9
38.9
409.4
1.4
1,293.5
Total Petroleum
1,033.9
670.1
3,900.8
22,673.6
295.0
391.8
28,965.1
71.1
47.9
293.0
1,632.3
25.8
28.5
2,098.6
Asphalt & Road Oil














Aviation Gasoline



27.1


27.1



1.9


1.9
Distillate Fuel Oil
522.7
391.5
1,227.3
5,775.9
63.7
59.3
8,040.4
38.8
29.0
91.0
428.2
4.7
4.4
596.1
Jet Fuel



2,029.9
NA
47.1
2,077.0



146.6
NA
3.4
150.0
Kerosene
18.5
3.2
3.6


1.1
26.4
1.4
0.2
0.3


0.1
1.9
LPG (Propane)
492.6
142.5

7.3


642.4
31.0
9.0

0.5


40.4
HGL


159.6


9.0
168.7


10.2


0.6
10.8
Lubricants














Motor Gasoline

79.0
455.9
14,575.5

147.7
15,258.1

5.6
32.4
1,035.7

10.5
1,084.2
Residual Fuel

53.7
46.9
258.0
93.1
127.5
579.2

4.0
3.5
19.4
7.0
9.6
43.5
Other Petroleum














AvGas Blend Components


+



+


+



+
Crude Oil














MoGas Blend Components














Misc. Products














Naphtha (<401 deg. F)














Other Oil (>401 deg. F)














Pentanes Plus


27.6



27.6


1.8



1.8
Petroleum Coke

0.2
600.3

138.3

738.8

+
61.3

14.1

75.4
Still Gas


1,323.4



1,323.4


88.3



88.3
Special Naphtha














Unfinished Oils


56.1



56.1


4.2



4.2
Waxes














Geothermal




52.3

52.3




0.4

0.4
Total (All Fuels)
5,838.5
3,947.9
12,678.1
23,407.1
26,094.7
455.7
72,421.9
326.2
224.5
796.8
1,671.2
2,158.1
33.4
5,210.1
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
Table A-20: 2010 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
69.7
993.0
NO
19,133.5
36.9
20,233.0
NO
6.6
94.2
NO
1,827.2
3.4
1,931.4
Residential Coal
NO





NO
NO





NO
Commercial Coal

69.7




69.7

6.6




6.6
Industrial Other Coal


993.0



993.0


94.2



94.2
Transportation Coal



NO


NO



NO


NO
Electric Power Coal




19,133.5

19,133.5




1,827.2

1,827.2
U.S. Territory Coal (bit)





36.9
36.9





3.4
3.4
Natural Gas
4,878.1
3,164.7
7,685.4
719.0
7,527.6
27.8
24,002.6
258.9
168.0
407.9
38.2
399.5
1.5
1,274.0
Total Petroleum
1,103.3
697.9
3,918.8
23,032.1
370.3
409.6
29,532.0
75.8
49.9
294.7
1,658.7
31.4
29.9
2,140.6
Asphalt & Road Oil














Aviation Gasoline



27.0


27.0



1.9


1.9
Distillate Fuel Oil
544.4
379.2
1,108.5
5,729.1
79.7
66.4
7,907.3
40.4
28.1
82.3
425.2
5.9
4.9
586.8
Jet Fuel



2,097.5
NA
36.6
2,134.0



151.5
NA
2.6
154.1
Kerosene
29.1
4.8
7.3


7.5
48.7
2.1
0.4
0.5


0.5
3.6
LPG (Propane)
529.8
140.0

7.5


677.3
33.3
8.8

0.5


42.6
HGL


148.0


17.6
165.5


9.5


1.1
10.6
Lubricants














Motor Gasoline

111.8
559.7
14,898.8

112.9
15,683.2

7.9
39.8
1,059.3

8.0
1,115.0
Residual Fuel

61.7
32.2
272.2
154.1
168.7
688.8

4.6
2.4
20.4
11.6
12.7
51.7
Other Petroleum














AvGas Blend Components


(0.2)



(0.2)


(+)



(+)
Crude Oil














MoGas Blend Components














Misc. Products














Naphtha (<401 deg. F)














Other Oil (>401 deg. F)














Pentanes Plus


78.4



78.4


5.2



5.2
Petroleum Coke

0.3
633.0

136.6

770.0

+
64.6

13.9

78.6
Still Gas


1,324.0



1,324.0


88.3



88.3
Special Naphtha














Unfinished Oils


28.0



28.0


2.1



2.1
Waxes














Geothermal




51.9

51.9




0.4

0.4
Total (All Fuels)
5,981.4
3,932.2
12,597.2
23,751.0
27,083.3
474.3
73,819.5
334.8
224.5
796.8
1,696.9
2,258.6
34.8
5,346.3
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-75

-------
Table A-21: 2005 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
8.4
97.0
1,246.0
NO
20,737.2
32.7
22,121.4
0.8
9.3
117.8
NO
1,982.8
3.0
2,113.7
Residential Coal
8.4





8.4
0.8





0.8
Commercial Coal

97.0




97.0

9.3




9.3
Industrial Other Coal


1,246.0



1,246.0


117.8



117.8
Transportation Coal



NO


NO



NO


NO
Electric Power Coal




20,737.2

20,737.2




1,982.8

1,982.8
U.S. Territory Coal (bit)





32.7
32.7





3.0
3.0
Natural Gas
4,946.4
3,073.2
7,330.7
623.9
6,014.5
24.3
22,013.1
262.2
162.9
388.6
33.1
318.9
1.3
1,167.0
Total Petroleum
1,366.4
760.7
4,617.6
25,371.2
1,222.1
712.4
34,050.4
95.9
54.9
346.4
1,825.6
98.0
51.6
2,472.3
Asphalt & Road Oil














Aviation Gasoline



35.4


35.4



2.4


2.4
Distillate Fuel Oil
769.1
402.9
1,123.8
6,193.8
114.5
136.5
8,740.6
57.4
30.1
83.9
462.6
8.5
10.2
652.8
Jet Fuel



2,621.7
NA
65.5
2,687.2



189.3
NA
4.7
194.1
Kerosene
83.8
21.6
39.1


5.8
150.2
6.1
1.6
2.9


0.4
11.0
LPG (Propane)
513.5
131.6

28.2


673.3
32.3
8.3

1.8


42.3
HGL


281.9


73.6
355.4


18.0


4.7
22.7
Lubricants














Motor Gasoline

88.6
689.5
16,235.7

200.2
17,213.9

6.3
48.8
1,150.1

14.2
1,219.4
Residual Fuel

115.8
237.4
256.4
876.5
230.9
1,717.0

8.7
17.8
19.3
65.8
17.3
128.9
Other Petroleum














AvGas Blend Components


8.3



8.3


0.6



0.6
Crude Oil














MoGas Blend Components














Misc. Products














Naphtha (<401 deg. F)














Other Oil (>401 deg. F)














Pentanes Plus


98.9



98.9


6.6



6.6
Petroleum Coke

0.3
706.6

231.1

938.0

+
72.1

23.6

95.8
Still Gas


1,429.4



1,429.4


95.4



95.4
Special Naphtha














Unfinished Oils


2.8



2.8


0.2



0.2
Waxes














Geothermal




50.1

50.1




0.5

0.5
Total (All Fuels)
6,321.2
3,931.0
13,194.3
25,995.1
28,024.0
769.4
78,235.0
358.9
227.1
852.9
1,858.6
2,400.1
55.9
5,753.5
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
Table A-22: 2000 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use


Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
11.4
91.9
1,361.6
NO
20,220.2
4.6
21,689.6
1.1
8.8
128.5
NO
1,926.4
0.4
2,065.1
Residential Coal
11.4





11.4
1.1





1.1
Commercial Coal

91.9




91.9

8.8




8.8
Industrial Other Coal


1,361.6



1,361.6


128.5



128.5
Transportation Coal



NO


NO



NO


NO
Electric Power Coal




20,220.2

20,220.2




1,926.4

1,926.4
U.S. Territory Coal (bit)





4.6
4.6





0.4
0.4
Natural Gas
5,104.6
3,251.5
8,659.3
672.0
5,293.4
12.7
22,993.5
270.8
172.5
459.4
35.7
280.8
0.7
1,219.8
Total Petroleum
1,425.4
766.7
3,747.6
24,297.3
1,144.1
575.2
31,956.4
99.8
55.3
281.7
1,756.7
88.5
41.3
2,323.2
Asphalt & Road Oil














Aviation Gasoline



36.3


36.3



2.5


2.5
Distillate Fuel Oil
775.2
420.7
1,000.1
5,442.4
174.7
87.5
7,900.6
58.0
31.5
74.8
406.9
13.1
6.5
590.7
Jet Fuel



2,700.3
NA
68.6
2,768.9



195.0
NA
5.0
200.0
Kerosene
94.6
29.7
15.6


2.4
142.2
6.9
2.2
1.1


0.2
10.4
LPG (Propane)
555.6
150.6

11.9


718.1
34.9
9.5

0.7


45.1
HGL


393.8


91.8
485.6


25.3


5.9
31.2
Lubricants














Motor Gasoline

74.1
249.9
15,663.0

186.3
16,173.3

5.3
17.8
1,118.2

13.3
1,154.6
Residual Fuel

91.6
184.1
443.5
870.8
138.6
1,728.5

6.9
13.8
33.3
65.4
10.4
129.8
Other Petroleum














AvGas Blend Components


3.8



3.8


0.3



0.3
Crude Oil














MoGas Blend Components














Misc. Products














Naphtha (<401 deg. F)














Other Oil (>401 deg. F)














Pentanes Plus


172.9



172.9


11.6



11.6
Petroleum Coke

0.2
697.3

98.6

796.2

+
71.2

10.1

81.3
Still Gas


1,431.2



1,431.2


95.5



95.5
Special Naphtha














Unfinished Oils


(401.2)



(401.2)


(29.7)



(29.7)
Waxes














Geothermal




48.1

48.1




0.5

0.5
Total (All Fuels)
6,541.4
4,110.2
13,768.5
24,969.3
26,705.8
592.4
76,687.5
371.7
236.5
869.5
1,792.3
2,296.2
42.4
5,608.6
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-77

-------
Table A-23: 1995 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use

Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec. Terr.
Total
Total Coal
17.5
116.8
1,557.0
NO
17,466.3
4.7
19,162.2
1.7
11.2
147.2
NO
1,659.9 0.4
1,820.4
Residential Coal
17.5





17.5
1.7




1.7
Commercial Coal

116.8




116.8

11.2



11.2
Industrial Other Coal


1,557.0



1,557.0


147.2


147.2
Transportation Coal



NO


NO



NO

NO
Electric Power Coal




17,466.3

17,466.3




1,659.9
1,659.9
U.S. Territory Coal (bit)





4.7
4.7




0.4
0.4
Natural Gas
4,954.2
3,096.0
8,725.9
724.0
4,302.0

21,802.0
262.8
164.2
462.8
38.4
228.2
1,156.4
Total Petroleum
1,259.3
724.1
3,754.6
21,529.8
754.5
323.7
28,346.1
88.7
52.4
279.9
1,542.4
58.7 23.3
2,045.4
Asphalt & Road Oil













Aviation Gasoline



39.6


39.6



2.7

2.7
Distillate Fuel Oil
789.7
418.0
965.3
4,383.3
108.0
62.5
6,726.8
58.4
30.9
71.4
324.2
8.0 4.6
497.5
Jet Fuel



2,428.8
NA
57.2
2,486.0



172.2
NA 4.1
176.3
Kerosene
74.3
22.1
15.4


2.0
113.9
5.4
1.6
1.1

0.1
8.3
LPG (Propane)
395.3
108.9

17.8


521.9
24.9
6.8

1.1

32.8
HGL


277.8


35.6
313.5


17.8

2.3
20.1
Lubricants













Motor Gasoline

33.5
370.4
14,273.1

84.5
14,761.5

2.4
26.3
1,013.1
6.0
1,047.7
Residual Fuel

141.5
286.2
387.3
566.0
81.9
1,462.8

10.6
21.5
29.1
42.5 6.1
109.8
Other Petroleum













AvGas Blend Components


5.3



5.3


0.4


0.4
Crude Oil


14.5



14.5


1.1


1.1
MoGas Blend Components













Misc. Products













Naphtha (<401 deg. F)













Other Oil (>401 deg. F)













Pentanes Plus


170.3



170.3


11.4


11.4
Petroleum Coke

0.1
600.7

80.6

681.4

+
61.3

8.2
69.6
Still Gas


1,369.5



1,369.5


91.4


91.4
Special Naphtha













Unfinished Oils


(320.9)



(320.9)


(23.8)


(23.8)
Waxes













Geothermal




45.6

45.6




0.4
0.4
Total (All Fuels)
6,231.0
3,936.9
14,037.5
22,253.7
22,568.4
328.4
69,355.9
353.1
227.8
889.9
1,580.8
1,947.2 23.7
5,022.5
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
Table A-24: 1990 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1	2	3	4	5	6	7	8	9	10	11	12	13	14	15



Adjusted Consumption (TBtu)a



Emissionsb (MMT CO2 Eq.) from Energy Use

Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec. Terr.
Total
Total Coal
31.1
124.5
1,668.2
NO
16,261.0
5.4
18,090.1
3.0
12.0
157.8
NO
1,546.5 0.5
1,719.8
Residential Coal
31.1





31.1
3.0




3.0
Commercial Coal

124.5




124.5

12.0



12.0
Industrial Other Coal


1,668.2



1,668.2


157.8


157.8
Transportation Coal



NO


NO



NO

NO
Electric Power Coal




16,261.0

16,261.0




1,546.5
1,546.5
U.S. Territory Coal (bit)





5.4
5.4




0.5
0.5
Natural Gas
4,486.6
2,679.6
7,712.8
679.2
3,308.5

18,866.7
237.8
142.0
408.8
36.0
175.4
1,000.0
Total Petroleum
1,375.8
1,022.6
3,846.2
19,977.2
1,289.4
294.8
27,805.9
97.8
74.3
287.2
1,433.1
97.5 21.2
2,011.2
Asphalt & Road Oil













Aviation Gasoline



45.0


45.0



3.1

3.1
Distillate Fuel Oil
959.3
525.5
1,098.3
3,554.8
96.5
56.4
6,290.8
70.9
38.9
81.2
262.9
7.1 4.2
465.2
Jet Fuel



2,590.1
NA
48.6
2,638.7



184.2
NA 3.5
187.7
Kerosene
63.9
11.8
12.3


2.0
90.0
4.7
0.9
0.9

0.1
6.6
LPG (Propane)
352.6
102.4

22.9


477.9
22.2
6.4

1.4

30.0
HGL


227.1


33.4
260.5


14.6

2.1
16.7
Lubricants













Motor Gasoline

153.0
254.8
13,464.1

75.9
13,947.7

10.9
18.1
958.9
5.4
993.3
Residual Fuel

229.8
364.1
300.3
1,162.6
78.5
2,135.3

17.3
27.3
22.6
87.3 5.9
160.3
Other Petroleum













AvGas Blend Components


0.2



0.2


+


+
Crude Oil


50.9



50.9


3.8


3.8
MoGas Blend Components


53.7



53.7


3.8


3.8
Misc. Products













Naphtha (<401 deg. F)













Other Oil (>401 deg. F)













Pentanes Plus


126.1



126.1


8.4


8.4
Petroleum Coke


591.2

30.4

621.5


60.4

3.1
63.5
Still Gas


1,436.5



1,436.5


95.8


95.8
Special Naphtha













Unfinished Oils


(369.0)



(369.0)


(27.3)


(27.3)
Waxes













Geothermal




52.7

52.7




0.5
0.5
Total (All Fuels)
5,893.5
3,826.6
13,227.1
20,656.4
20,911.6
300.2
64,815.4
338.6
228.3
853.8
1,469.1
1,820.0 21.7
4,731.5
Note: Parentheses indicate negative values.
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-25), and
international bunker fuel consumption (see Table A-26).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-79

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Table A-25: Unadjusted Non-Energy Fuel Consumption (TBtu)
Sector/Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Industry
4,644.2
5,153.6
5,575.9
5,482.5
4,735.1
4,663.1
4,599.2
4,829.2
4,724.8
5,022.6
5,106.3
5,378.9
5,789.2
5,897.1
Industrial Coking Coal
0.0
37.8
53.5
80.4
64.8
60.8
132.5
119.9
49.3
122.4
89.6
113.0
124.8
132.1
Industrial Other Coal
7.6
10.5
11.5
11.0
9.6
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
Natural Gas to Chemical














Plants, Other Uses
305.9
371.0
401.7
270.4
310.0
314.0
319.3
324.6
330.6
431.8
532.0
631.1
730.8
730.8
Asphalt & Road Oil
1,170.2
1,178.2
1,275.7
1,323.2
877.8
859.5
826.7
783.3
792.6
831.7
853.4
849.2
792.8
843.9
HGL
1,302.2
1,651.6
1,759.3
1,659.5
1,899.9
1,954.4
1,983.7
2,155.2
2,142.8
2,216.8
2,257.1
2,329.7
2,677.4
2,758.8
Lubricants
186.3
177.8
189.9
160.2
135.9
127.4
118.3
125.1
130.7
142.1
135.1
124.9
121.9
117.6
Pentanes Plus
125.2
169.0
171.6
98.1
77.7
27.3
42.2
47.1
44.2
80.2
56.1
86.4
111.8
166.6
Naphtha (<401 deg. F)
347.8
373.0
613.5
698.7
490.6
487.3
453.9
517.8
442.6
428.1
420.0
436.2
447.1
396.7
Other Oil (>401 deg. F)
753.9
801.0
722.2
708.0
452.5
388.5
287.2
223.9
247.2
229.0
222.5
262.9
239.1
234.1
Still Gas
36.7
47.9
17.0
67.7
147.8
163.6
160.6
166.7
164.5
162.2
166.1
163.8
166.9
158.7
Petroleum Coke
123.1
120.6
98.7
186.9
61.0
62.4
67.8
62.4
61.4
62.5
61.2
57.0
58.9
56.4
Special Naphtha
107.1
70.8
97.4
62.5
26.1
22.6
14.7
100.0
106.1
99.3
93.6
100.3
92.0
95.6
Other (Wax/Misc.)














Distillate Fuel Oil
7.0
6.8
11.7
11.7
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
Waxes
33.3
40.6
33.1
31.4
17.1
15.1
15.3
16.5
14.8
12.4
12.8
10.2
12.4
10.4
Miscellaneous Products
137.8
97.1
119.2
112.8
158.7
164.7
161.6
171.2
182.7
188.9
191.3
198.8
198.0
180.2
Transportation
176.0
167.9
179.4
151.3
154.8
148.4
135.4
143.4
149.4
162.8
154.4
142.0
137.0
132.1
Lubricants
176.0
167.9
179.4
151.3
154.8
148.4
135.4
143.4
149.4
162.8
154.4
142.0
137.0
132.1
U.S. Territories
50.8
55.4
140.8
114.9
27.4
14.6
17.6
10.5
10.7
10.3
10.5
10.7
10.7
10.7
Lubricants
0.7
2.0
3.1
4.6
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Prod.)
50.1
53.4
137.7
110.3
26.4
13.6
16.6
9.5
9.6
9.3
9.5
9.6
9.6
9.6
Total
4,871.1 5,377.0 5,896.1 5,748.7 4,917.3 4,826.1 4,752.2 4,983.1 4,884.9 5,195.8 5,271.1 5,531.5 5,936.9 6,039.9
Note: These values are unadjusted non-energy fuel use provided by EIA. They have not yet been adjusted to remove petroleum feedstock exports and processes
accounted for in the Industrial Processes and Product Use chapter.
Table A-26: International Bunker Fuel Consumption (TBtu)
Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Aviation Jet Fuel
Marine Residual Fuel Oil
Marine Distillate Fuel Oil
539.4
715.7
158.0
703.4
523.2
125.7
880.1
444.1
85.9
853.1
581.0
126.9
865.4
619.8
128.2
919.9
518.4
107.4
916.3
459.5
91.7
931.6
379.8
75.4
987.8
369.2
82.0
1,022.3
406.8
113.5
1,051.1
450.7
117.5
1,103.2
445.3
121.3
1,146.8
417.6
134.4
1,146.1
336.2
136.3
Total
1,413.1
1,352.3
1,410.0
1,561.0
1,613.4
1,545.7
1,467.4
1,386.9
1,439.0
1,542.6
1,619.3
1,669.9
1,698.8
1,618.6
Note: Further information on the calculation of international bunker fuel consumption of aviation jet fuel is provided in Annex 3.3 Methodology for Estimating Emissions
from Commercial Aircraft Jet Fuel Consumption.
A-80 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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Table A-27: Key Assumptions for Estimating CO2 Emissions

C Content Coefficient
Fuel Type
(MMT C/QBtu)
Coal

Residential Coal
(See footnote b)
Commercial Coal
(See footnote b)
Industrial Coking Coal
(See footnote b)
Industrial Other Coal
(See footnote b)
Electric Power Coal
(See footnote b)
U.S. Territory Coal (bit)
25.14
Natural Gas

Pipeline Natural Gas
(See footnote b)
Petroleum

Asphalt & Road Oil
20.55
Aviation Gasoline
18.86
Distillate Fuel Oil No. 1
19.98
Distillate Fuel Oil No. 2a
(See footnote b)
Distillate Fuel Oil No. 4
20.47
Jet Fuel
(See footnote b)
Kerosene
19.96
LPG (Propane)
17.15
HGL (Energy Use)
(See footnote b)
HGL (Non-Energy Use)
(See footnote b)
Lubricants
20.20
Motor Gasoline
(See footnote b)
Residual Fuel Oil No. 5
19.89
Residual Fuel Oil No. 6a
20.48
Other Petroleum

AvGas Blend Components
18.87
Crude Oil
(See footnote b)
MoGas Blend Components
(See footnote b)
Misc. Products
(See footnote b)
Misc. Products (Territories)
20.00
Naphtha (<401 deg. F)
18.55
Other Oil (>401 deg. F)
20.17
Pentanes Plus
18.24
Petroleum Coke
27.85
Still Gas
18.20
Special Naphtha
19.74
Unfinished Oils
(See footnote b)
Waxes
19.80
Geothermal

Flash Steam
2.18
Dry Steam
3.22
Binary
0.00
Binary/Flash Steam
0.00
a Distillate fuel oil No. 2 and residual fuel oil No. 6 are used in the
C02 from fossil fuel combustion calculations, and other oil types are
presented for informational purposes only. An additional discussion
on the derivation of these carbon content coefficients is presented
in Annex 2.2.
b These coefficients vary annually due to fluctuations in fuel quality
(see Table A-28).
Sources: C coefficients from EIA (2009), EPA (2010), and EPA (2020).
A-81

-------
Table A-28: Annually Variable eContent Coefficients by Year(MMT C/QBtu)
Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Residential Coal3
26.19
26.13
26.00
26.04
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
26.11
Commercial Coal
26.19
26.13
26.00
26.04
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
26.11
Industrial Coking Coal
25.53
25.57
25.63
25.60
25.58
25.57
25.57
25.58
25.57
25.57
25.57
25.56
25.59
25.59
Industrial Other Coal
25.81
25.79
25.74
25.79
25.86
25.88
25.94
25.93
25.95
26.00
26.03
26.06
26.08
26.07
Electric Power Coal
25.94
25.92
25.98
26.08
26.05
26.05
26.06
26.05
26.04
26.07
26.06
26.08
26.09
26.08
Pipeline Natural Gas
14.46
14.47
14.47
14.46
14.48
14.48
14.47
14.46
14.45
14.43
14.43
14.43
14.43
14.43
HGL (Energy Use)
17.51
17.51
17.52
17.45
17.43
17.40
17.41
17.39
17.41
17.43
17.43
17.45
17.45
17.47
HGL (Non-Energy Use)
17.24
17.25
17.22
17.19
16.93
16.84
16.86
16.88
16.86
16.89
16.85
16.84
16.82
16.85
Motor Gasoline
19.42
19.36
19.47
19.32
19.39
19.38
19.35
19.28
19.26
19.25
19.27
19.28
19.27
19.27
Jet Fuel
19.40
19.34
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
MoGas Blend














Components
19.42
19.36
19.33
19.36
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
Misc. Products
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Unfinished Oils
20.15
20.21
20.22
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
Crude Oil
20.15
20.21
20.22
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
Distillate Fuel Oil No. 2
20.17
20.17
20.39
20.37
20.24
20.22
20.22
20.23
20.23
20.22
20.21
20.20
20.22
20.22
a EIA discontinued collection of residential sector coal consumption data in 2008, because consumption of coal in the residential sector is extremely limited. Therefore,
the number cited here is developed from commercial/institutional consumption.
Source: Coal C content coefficients calculated from USGS (1998), PSU (2010), Gunderson (2019), IGS (2019), ISGS (2019), EIA (1990 through 2001), EIA (2001 through
2020a), and EIA (2001 through 2020b); pipeline natural gas C content coefficients calculated from EIA (2020b) and EPA (2010); petroleum carbon contents from EPA
(2010), EIA (1994), EIA (2009), EPA (2020), and ICF (2020). See Annex 2.2 for information on how these C content coefficients are calculated.
Table A-29: Electricity Consumption by End-Use Sector (Billion Kilowatt-Hours)
End-Use Sector
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Residential
924
I 1,043
1,192
1,359
1,446
1,423
1,374
1,394
1,407
1,403
1,410
1,377
1,466
1,437
Commercial
838
953
1,159
1,275
1,330
1,328
1,327
1,337
1,352
1,361
1,367
1,353
1,381
1,360
Industrial
1,070
1,163
1,235
1,169
1,103
1,124
1,123
1,129
1,136
1,128
1,117
1,125
1,145
1,146
Transportation3
5
5
5
8
8
8
8
8
9
9
9
10
11
12
Total
2,837
3,164
3,592
3,811
3,887
3,883
3,832
3,868
3,903
3,900
3,902
3,864
4,003
3,954
Note: Does not include the U.S. Territories.
a Includes electricity used for electric vehicle charging in the residential and commercial sectors.
Source: Retail sales of electricity to end-users obtained from EIA (2020b). Industrial electricity consumption includes direct use.
A-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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Table A-30: Electric Power Generation by Fuel Type (Percent)
Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Coal
54.1%
52.7%
53.3%
51.1%
46.0%
43.5%
38.6%
40.2%
39.9%
34.2%
31.4%
30.9%
28.4%
24.2%
Natural Gas
10.7%
13.1%
14.2%
17.5%
22.7%
23.5%
29.1%
26.4%
26.3%
31.6%
32.7%
30.9%
34.0%
37.3%
Nuclear
19.9%
21.1%
20.7%
20.0%
20.3%
20.0%
19.8%
20.2%
20.3%
20.4%
20.6%
20.8%
20.1%
20.4%
Renewables
11.3%
10.9%
8.8%
8.3%
10.0%
12.2%
11.9%
12.5%
12.8%
13.0%
14.7%
16.8%
16.8%
17.6%
Petroleum
4.1%
2.1%
2.9%
3.0%
0.9%
0.7%
0.5%
0.6%
0.7%
0.7%
0.6%
0.5%
0.6%
0.4%
Other Gases3
+%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
Net Electricity














Generation (Billion














kWh)b
2,905
3,197
3,643
5 3,902 /
3,971
3,947
3,888
3,901
3,936
3,917
3,917
3,877
4,017
3,962
Note: Does not include electricity generation from purchased steam as the fuel used to generate the steam cannot be determined. Does not include non-renewable
waste (i.e., municipal solid waste from non-biogenic sources, and tire-derived fuels).
Source: EIA (2020b).
+ Does not exceed 0.05 percent.
a Other gases include blast furnace gas, propane gas, and other manufactured and waste gases derived from fossil fuels.
b Represents net electricity generation from the electric power sector. Excludes net electricity generation from commercial and industrial combined-heat-and-power and
electricity-only plants. Net electricity generation differs from the total presented in Table A-29 (i.e., end-use consumption of electricity) due to electricity transmitted
across U.S. borders, as well as transmission and distribution losses.
Table A-31: Geothermal Net Generation by Geotype (Billion Kilowatt-Hours)
Geotype
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Binary
0.08
0.28
0.24
0.68
2.41
2.16
2.43
2.75
3.12
3.36
3.62
3.56
3.84
4.34
Flash Steam
6.15
1 7.63
7.43
7.93
6.83
7.17
7.02
7.03
6.92
7.00
6.65
6.69
6.39
5.92
Dry Steam
9.21
5.47
6.43
6.09
5.98
5.98
6.11
6.00
5.84
5.56
5.55
5.67
5.73
5.21
Total
15.43
13.38
14.09
14.69
15.22
15.32
15.56
15.77
15.88
15.92
15.83
15.93
15.97
15.47
Source: EIA (2020a).
A-83

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References
Browning, L. (2018). Updated Methodology for Estimating Electricity Use from Highway Plug-In Electric Vehicles.
Technical Memo, October 2018.
EIA (2020a) Form EIA-923 detailed data with previous form data (EIA-906/920), Energy Information Administration, U.S.
Department of Energy. Washington, DC. DOE/EIA. March 2020.
EIA (2020b) Monthly Energy Review. November 2020, Energy Information Administration, U.S. Department of Energy,
Washington, D.C. DOE/EIA-0035(2019/11).
EIA (2001 through 2020a) Annual Coal Report, U.S. Department of Energy, Energy Information Administration.
Washington, D.C. DOE/EIA 0584.
EIA (2001 through 2020b) Annual Coal Distribution Report, Energy Information Administration, U.S. Department of
Energy. Washington, D.C. DOE/EIA.
EIA (2009) Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
DOE/EIA-0384(2008).
EIA (1990 through 2001) Coal Industry Annual, U.S. Department of Energy, Energy Information Administration.
Washington, D.C. DOE/EIA 0584.
EIA (1994) Emissions of Greenhouse Gases in the United States 1987-1992, Energy Information Administration, U.S.
Department of Energy. Washington, D.C. November 1994. DOE/EIA 0573.
EPA (2020) EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Updated Gasoline and Diesel Fuel C02
Emission Factors - Memo.
EPA (2010) Carbon Content Coefficients Developed for EPA's Mandatory Reporting Rule. Office of Air and Radiation,
Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
Gunderson, J. (2019) Montana Coal Sample Database. Data received 28 February 2019 from Jay Gunderson, Montana
Bureau of Mines & Geology.
ICF (2020) Potential Improvements to Energy Sector Hydrocarbon Gas Liquid Carbon Content Coefficients. Memorandum
from ICF to Vincent Camobreco, U.S. Environmental Protection Agency. December 7, 2020.
Illinois State Geological Survey (ISGS) (2019) Illinois Coal Quality Database, Illinois State Geological Survey.
Indiana Geological Survey (IGS) (2019) Indiana Coal Quality Database 2018, Indiana Geological Survey.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
Pennsylvania State University (PSU) (2010) Coal Sample Bank and Database. Data received by SAIC 18 February 2010
from Gareth Mitchell, The Energy Institute, Pennsylvania State University.
UNFCCC (2014) Report of the Conference of the Parties on its nineteenth session, held in Warsaw from 11 to 23
November 2013. (FCCC/CP/2013/10/Add.3). January 31, 2014. Available online at:
.
USGS (1998) CoalQual Database Version 2.0, U.S. Geological Survey.
A-84 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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2.2. Methodology for Estimating the Carbon Content of Fossil Fuels
This sub-annex presents the background and methodology for estimating the carbon (C) content of fossil fuels
combusted in the United States. The C content of a particular fossil fuel represents the maximum potential emissions to
the atmosphere if all C in the fuel is oxidized during combustion. The C content coefficients used in this report were
developed using methods first outlined in the U.S. Energy Information Administration's (EIA) Emissions of Greenhouse
Gases in the United States: 1987-1992 (1994) and were developed primarily by EIA. EPA has updated many of the C
content coefficients based on carbon dioxide (C02) emission factors developed for the Mandatory Reporting of
Greenhouse Gases Rule, signed in September 2009 (EPA 2009b, 2010). In addition, EPA has revised many of the C
content coefficients to vary annually across the time series to account for the annual variability in carbon content (or
composition) of each fuel type as it is consumed in the United States (ICF 2020; USGS 1998; PSU 2010; Gunderson 2019;
IGS 2019; ISGS 2019; Martel and Angello 1977; ASTM 1985; NIPER 1990 through 2009; Green & Perry ed. 2008;
Wauquier ed. 1995; EPA (2009b; 2010; 2013; 2020b); and EIA (1994; 2008a; 2009a; 2010; 2020c; 1990 through 2001;
2001 through 2020a; 2001 through 2020b)). This sub-annex presents a time-series analysis of changes in U.S. C content
coefficients for coal, petroleum products, and natural gas. A summary of C content coefficients used in this report
appears in Table A-32.
Though the methods for estimating C contents for coal, natural gas, and petroleum products differ in their
details, they each follow the same basic approach. First, because C coefficients are presented in terms of mass per unit
energy (i.e., million metric tons C per quadrillion Btu or MMT C/QBtu), those fuels that are typically described in
volumetric units (i.e., petroleum products and natural gas) are converted to units of mass using an estimated density.
Second, C contents are derived from fuel sample data, using descriptive statistics to estimate the C share of the fuel by
weight. The heat content of the fuel is then estimated based on the sample data, or where sample data are unavailable
or unrepresentative, by default values that reflect the characteristics of the fuel as defined by market requirements. A
discussion of each fuel appears below.
The C content of coal is described first; approximately one-quarter of all U.S. C emissions from fossil fuel
combustion are associated with coal consumption. The methods and sources for estimating the C content of natural gas
are provided next. Approximately one-third of U.S. greenhouse gas emissions from fossil fuel combustion are
attributable to natural gas consumption. Finally, this sub-annex examines C contents of petroleum products. U.S. energy
use statistics account for more than 20 different petroleum products.
A-85

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Table A-32: Carbon Content Coefficients Used in this Report (MMT Carbon/QBtu)
Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Coal














Residential Coala'b
26.19
26.13
26.00
26.04
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
26.11
Commercial Coal3
26.19
26.13
26.00
26.04
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
26.11
Industrial Coking Coal3
25.53
25.57
25.63
25.79
25.58
25.57
25.57
25.58
25.57
25.57
25.57
25.56
25.59
25.59
Industrial Other Coal3
25.81
25.79
25.74
26.08
25.86
25.88
25.94
25.93
25.95
26.00
26.03
26.06
26.08
26.07
Utility Coal3'c
25.94
25.92
25.98
26.04
26.05
26.05
26.06
26.05
26.04
26.07
26.06
26.08
26.09
26.08
Pipeline Natural Gasd
14.46
14.47
14.47
14.46
14.48
14.48
14.47
14.46
14.45
14.43
14.43
14.43
14.43
14.43
Petroleum














Asphalt and Road Oil
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
Aviation Gasoline
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
Distillate Fuel Oil No. 2
20.17
20.17
20.39
20.37
20.24
20.22
20.22
20.23
20.23
20.22
20.21
20.20
20.22
20.22
Jet Fuel3
19.40
19.34
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
Kerosene
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
LPG (Propane)
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
HGL (Energy Use)d
17.51
17.51
17.52
17.45
17.43
17.40
17.41
17.39
17.41
17.43
17.43
17.45
17.45
17.47
HGL (Non-Energy Use)d
17.24
17.25
17.22
17.19
16.93
16.84
16.86
16.88
16.86
16.89
16.85
16.84
16.82
16.85
Lubricants
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
Motor Gasolined
19.42
19.36
19.47
19.32
19.39
19.38
19.35
19.28
19.26
19.25
19.27
19.28
19.27
19.27
Residual Fuel No. 6
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
Other Petroleum














Av. Gas Blend Comp.
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
Mo. Gas Blend Compc
19.42
19.36
19.33
19.36
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
Crude Oild
20.15
20.21
20.22
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
Misc. Productse
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Other Petroleum Liquids
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
Naphtha (<401 deg. F)
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
Other Oil (>401 deg. F)
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
Pentanes Plus
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
Petroleum Coke
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
Still Gas
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
Special Naphtha
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
Unfinished Oilsd
20.15
20.21
20.22
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
Waxes
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
Geothermalf














Flash
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
Steam
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
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a C contents vary annually based on changes in annual mix of production and end-use consumption of coal from each producing state.
b EIA discontinued collection of residential sector coal consumption data in 2008, because consumption of coal in the residential sector is extremely limited. Therefore,
starting in 2008, the number cited here is developed from commercial/institutional consumption.
c Content for utility coal used in the electric power calculations. All coefficients based on higher heating value. Higher heating value (gross heating value) is the total
amount of heat released when a fuel is burned. Coal, crude oil, and natural gas all include chemical compounds of carbon and hydrogen. When those fuels are burned,
the carbon and hydrogen combine with oxygen in the air to produce C02 and water. Some of the energy released in burning goes into transforming the water into
steam and is usually lost. The amount of heat spent in transforming the water into steam is counted as part of gross heat content. Lower heating value (net heating
value), in contrast, does not include the heat spent in transforming the water into steam. Using a simplified methodology based on International Energy Agency
defaults, higher heating value can be converted to lower heating value for coal and petroleum products by multiplying by 0.95 and for natural gas by multiplying by
0.90. Carbon content coefficients are presented in higher heating value because U.S. energy statistics are reported by higher heating value.
d C contents vary annually based on changes in fuel composition.
e The miscellaneous products category reported by EIA is assumed to be mostly petroleum refinery sulfur compounds that do not contain carbon (EIA 2019).
f C contents based on geotype (i.e., flash steam and dry steam) were obtained from EPA's Emissions & Generation Resource Integrated Database (eGRID) 2018 Technical
Support Document (EPA 2020a). C contents were obtained in pounds C02/megawatt hour and were applied to net generation by geotype (in megawatt hours) from EIA
(2020a). C contents were converted to MMT Carbon/QBtu in this table. C contents for binary and binary/flash geotypes are zero and have been excluded from this
table.
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Coal
Although the IPCC (2006) guidelines provide C contents for coal according to rank, it was necessary to develop C
content coefficients by consuming sector to match the format in which coal consumption is reported by EIA. Because the
C content of coal varies by the state in which it was mined and by coal rank, and because the sources of coal for each
consuming sector vary by year, the weighted average C content for coal combusted in each consuming sector also varies
over time. A time series of C contents by coal rank and consuming sector appears in Table A-33..12
Methodology
The methodology for developing C contents for coal by consuming sector consists of four steps. An additional
step has been taken to calculate C contents by coal rank to facilitate comparison with IPCC default values.
Step 1: Determine Carbon Contents by Rank and by State of Origin
Carbon contents by rank and state of origin are estimated on the basis of 8,672 coal samples, 6,588 of which
were collected by the U.S. Geological Survey (USGS) (1998), 504 samples that come from the Pennsylvania State
University database (PSU 2010), and the remainder from individual State Geological Surveys. Samples obtained directly
from individual State Geological Surveys include 908 samples from the Montana Bureau of Mines & Geology (Gunderson
2019), 745 samples from the Indiana Geological Survey Coal Quality Database (IGS 2019), and 460 samples from the
Illinois State Geological Survey (ISGS 2019). Because the data obtained directly from the State Geological Surveys for
these three states included both samples collected by the USGS and additional samples, these data were used to
determine C content coefficients for these states instead of the USGS and Pennsylvania State University data.
These coal samples are classified according to rank and state of origin. For each rank in each state, the average
heat content and C content of the coal samples are calculated based on the proximate (heat) and ultimate (percent
carbon) analyses of the samples. Dividing the C content (reported in pounds of C02) by the heat content (reported in
million Btu or MMBtu) yields an average C content coefficient. This coefficient is then converted into units of MMT
C/QBtu.
Step 2: Determine Weighted Average Carbon Content by State
Carbon contents by rank and origin calculated in Step 1 are then weighted by the annual share of state
production that was each rank. State production by rank is obtained from the EIA. This step yields a single carbon
content per state that varies annually based on production by coal type. However, most coal-producing states
produce only one rank of coal. For these states the weighted factor equals the carbon content calculated in Step 1
and is constant across the time series.
Step 3: Allocate Sectoral Consumption by State of Origin
U.S. energy statistics.13 through 2019 provide data on the origin of coal used in four areas: 1) the electric
power industry, 2) industrial coking, 3) all other industrial uses, and 4) the residential and commercial end-use
sectors.14 Because U.S. energy statistics do not provide the distribution of coal rank consumed by each consuming
sector, it is assumed that each sector consumes a representative mixture of coal ranks from a particular state that
matches the mixture of all coal produced in that state during the year. Thus, the weighted state-level factor
developed in Step 2 is applied.
12	For a comparison to earlier estimated carbon contents see Chronology and Explanation of Changes in Individual Carbon Content
Coefficients of Fossil Fuels near the end of this Annex.
13	U.S. Energy Information Administration (EIA). Annual Coal Distribution Report (2001-2019b); Coal Industry Annual (1990-2001).
14	In 2008, EIA began collecting and reporting data on commercial and institutional coal consumption, rather than residential and
commercial consumption. Thus, the residential/commercial coal coefficient reported in Table A-32 for 2009 to the present represents
the mix of coal consumed by commercial and institutional users. Currently, only an extremely small amount of coal is consumed in the
U.S. residential sector.
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Step 4: Weight Sectoral Carbon Contents to Reflect the Rank and State of Origin of Coal Consumed
Sectoral C contents are calculated by multiplying the share of coal purchased from each state by the state's
weighted C content estimated in Step 2. The resulting partial C contents are then totaled across all states to generate a
national sectoral C content.
Csector — Sstatel*Cstatel Sstate2*Cstate2 +.... + Sstate50*Cstate50
where,
Csector =	The C content by consuming sector;
Sstate =	The portion of consuming sector coal consumption attributed to production from a
given state;
Cstate =	The estimated weighted C content of all ranks produced in a given state.
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Table A-33: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank (MMT C/QBtu) (1990-2019)
Consuming Sector
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Electric Power
25.94
25.92
25.98
26.08
26.05
26.05
26.06
26.05
26.04
26.07
26.06
26.08
26.09
26.08
Industrial Coking
25.53
25.57
25.63
25.60
25.58
25.57
25.57
25.58
25.57
25.57
25.57
25.56
25.59
25.59
Other Industrial
25.81
25.79
25.74
; 25.79
25.86
25.88
25.94
25.93
25.95
26.00
26.03
26.06
26.08
26.07
Residential/ Commercial3
26.19
26.13
26.00
; 26.04
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
26.11
Coal Rankb
Anthracite
28.28
28.28
28.28
, 28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
Bituminous
25.38
25.42
25.45
25.45
25.42
25.42
25.41
25.41
25.41
25.40
25.40
25.40
25.41
25.41
Sub-bituminous
26.46
26.47
26.46
| 26.48
26.47
26.49
26.49
26.49
26.49
26.49
26.49
26.20
26.49
26.49
Lignite
26.58
26.59
26.61
26.62 _
26.63
26.61
26.61
26.62
26.63
26.66
26.64
26.67
26.76
26.75
a In 2008, EIA began collecting consumption data for commercial and institutional consumption rather than commercial and residential consumption.
b Emission factors for coal rank are weighted based on production in each state.
Sources: C content coefficients calculated from USGS (1998), PSU (2010), Gunderson (2019), IGS (2019), ISGS (2019), EIA (1990 through 2001; 2001 through
2020a; 2001 through 2020b).
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Step 5: Develop National-Level Carbon Contents by Rank for Comparison to IPCC Defaults
Although not used to calculate emissions, national-level C contents by rank are more easily compared to C
contents of other countries than are sectoral C contents. This step requires weighting the state-level C contents by rank
developed under Step 1 by overall coal production by state and rank. Each state-level C content by rank is multiplied by
the share of national production of that rank that each state represents. The resulting partial C contents are then
summed across all states to generate an overall C content for each rank.
Nrank — Prankl * Crankl + Prank2 * Crank2 "K..+ Prankn* Crankn
where,
Nrank =	The national C content by rank;
Prank =	The portion of U.S. coal production of a given rank attributed to each state; and
Crank =	The estimated C content of a given rank in each state.
Data Sources
The ultimate analysis of coal samples was based on 8,672 coal samples, 6,588 of which are from USGS (1998),
504 from the Pennsylvania State University Coal Database (PSU 2010), and the remainder from individual State
Geological Surveys. Samples obtained directly from individual State Geological Surveys include 908 samples from the
Montana Bureau of Mines & Geology (Gunderson 2019), 745 samples from the Indiana Geological Survey Coal Quality
Database (IGS 2019), and 460 samples from the Illinois State Geological Survey (ISGS 2019). Because the data obtained
directly from the State Geological Surveys for these three states included both samples collected by the USGS and
additional samples, these data were used to determine C content coefficients for these states instead of the USGS and
Pennsylvania State University data. Data contained in the USGS's CoalQual Database are derived primarily from samples
taken between 1973 and 1989, and were largely reported in State Geological Surveys. Data in the PSU Coal Database are
mainly from samples collected by PSU since 1967 and are housed at the PSU Sample Bank. Additional samples that were
not contained in the USGS's CoalQual Database, many of which were more recent samples taken after 1989, were
obtained directly from the State Geological Surveys for Montana, Illinois, and Indiana. Whole-seam channel samples
provided by PSU, Illinois, and Indiana, and both whole-seam channel and drill core samples provided by Montana, were
included in the development of carbon factors.
Data on coal consumption by sector and state of origin, as well as coal production by state and rank, were
obtained from EIA. ElA's Annual Coal Report (EIA 2001 through 2020a) is the source for state coal production by rank
from 2001 through 2019. In prior years, EIA reported this data in its Coal Industry Annual (EIA 1990 through 2001). Data
for coal consumption by state of origin and consuming sector for 2001 through 2019 was obtained from the ElA's Annual
Coal Distribution Report (EIA 2001 through 2020b). For 1990 through 2000, end-use data was obtained from the Coal
Industry Annual (EIA 1990 through 2001).
Uncertainty
Carbon contents vary considerably by state. Bituminous coal production and sub-bituminous coal production
represented 47.2 percent and 45.0 percent of total U.S. supply in 2019, respectively. Of the states that have been
producing bituminous coal since 1990, state average C content coefficients for bituminous coal vary from a low of 85.58
kg C02 per MMBtu in Texas to a high of 96.36 kg C02 per MMBtu in Arkansas. The next lowest average emission factor
for bituminous coal is found in Missouri (91.71 kg C02 per MMBtu). In 2019, Missouri production accounted for less than
0.1 percent of overall bituminous production. More than 50 percent of bituminous coal was produced in three states in
2019: West Virginia, Kentucky, and Pennsylvania, and this share has remained fairly constant since 1990. These three
states show a variation in C content for bituminous coals of +0.7 percent, based on more than 2,000 samples (see Table
A-34).
Similarly, the C content coefficients for sub-bituminous coal range from 91.29 kg C02 per MMBtu in Utah to
98.10 kg C02 per MMBtu in Alaska. However, Utah has no recorded production of sub-bituminous coal since 1990.
Production of sub-bituminous coal in Alaska has made up less than 0.7 percent of total sub-bituminous production since
1990, with even this small share declining over time. Wyoming has represented between 75 percent and 90 percent of
total sub-bituminous coal production in the United States throughout the time series (1990 through 2019). Thus, the C
content coefficient for Wyoming (97.21 kg C02 per MMBtu), based on 503 samples, dominates the national average.
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The interquartile range of C content coefficients among samples of sub-bituminous coal in Wyoming was +1.5
percent from the mean. Similarly, this range among samples of bituminous coal from West Virginia, Kentucky, and
Pennsylvania was +1.2 percent or less for each state. The large number of samples and the low variability within the
sample set of the states that represent the predominant source of supply of U.S. coal suggest that the uncertainty in this
factor is very low, on the order of +1.0 percent.
For comparison, J. Quick (2010) completed an analysis similar in methodology to that used here, in order to
generate national average C emission factors as well as county-level factors. This study's rank-based national average
factors have a maximum deviation from the factors developed in this Inventory report of 0.78 percent, which is for sub-
bituminous (range: -0.32 to +0.78 percent). This corroboration further supports the assertion of minimal uncertainty in
the application of the rank-based factors derived for the purposes of this Inventory.
Table A-34: Variability in Carbon Content Coefficients by Rank Across States (Kilograms CO2 Per MMBtu)
State
Number of Samples
Bituminous
Sub-bituminous
Anthracite
Lignite
Alabama
951
92.84
-
-
99.10
Alaska
91
98.32
98.09
-
98.65
Arizona
15
93.94
97.34
-
-
Arkansas
77
96.36
-
-
94.97
Colorado
317
94.37
96.52
-
101.10
Georgia
35
95.00
-
-
-
Idaho
1
-
94.90
-
-
Illinois
460
92.53
-
-
-
Indiana
745
92.30
-
-
-
Iowa
100
91.87
-
-
-
Kansas
29
90.91
-
-
-
Kentucky
897
92.61
-
-
-
Louisiana
1
-
-
-
96.01
Maryland
47
94.29
-
-
-
Massachusetts
3
-
-
114.82
-
Michigan
3
-
-
-
92.87
Mississippi
8
-
-
-
98.18
Missouri
111
91.71
-
-
-
Montana
908
96.01
96.61
-
98.34
Nebraska
6
103.59
-
-
-
Nevada
2
94.41
-
-
99.86
New Mexico
185
94.28
94.88
103.92
-
North Dakota
202
-
93.97
-
99.47
Ohio
674
91.84
-
-
-
Oklahoma
63
92.33
-
-
-
Pennsylvania
849
93.33
-
103.68
-
Tennessee
61
92.81
-
-
-
Texas
64
85.58
94.19
-
94.46
Utah
169
95.75
91.29
-
-
Virginia
465
93.51
-
98.54
-
Washington
18
94.53
97.35
102.53
106.55
West Virginia
612
93.84
-
-
-
Wyoming
503
94.80
97.21
-
-
U.S. Average
8,672
93.46
96.01
102.15
98.95
Note: Indicates no sample data available. Average is weighted by number of samples.
Sources: Calculated from USGS (1998) and PSU (2010), Gunderson (2019), IGS (2019), and ISGS (2019).
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Natural Gas
Natural gas is predominantly composed of methane (CH4), which is 75 percent C by weight and contains 14.2
MMT C/QBtu (higher heating value), but it may also contain many other compounds that can lower or raise its overall C
content. These other compounds may be divided into two classes: (1) natural gas liquids (NGLs) and (2) non-hydrocarbon
gases. The most common NGLs are ethane (C2H6), propane (C3HS), butane (C4Hi0), and, to a lesser extent, pentane (C5Hi2)
and hexane (C6Hi4). Because the NGLs have more C atoms than CH4 (which has only one), their presence increases the
overall C content of natural gas. NGLs have a commercial value greater than that of CH4, and therefore are usually
separated from raw natural gas at gas processing plants and sold as separate products. Ethane is typically used as a
petrochemical feedstock, propane and butane have diverse uses, and natural gasoline.15 contributes to the
gasoline/naphtha "octane pool," used primarily to make motor gasoline.
Raw natural gas can also contain varying amounts of non-hydrocarbon gases, such as C02, nitrogen, helium and
other noble gases, and hydrogen sulfide. The share of non-hydrocarbon gases is usually less than 5 percent of the total,
but there are individual natural gas reservoirs where the share can be much larger. The treatment of non-hydrocarbon
gases in raw gas varies. Hydrogen sulfide is always removed. Inert gases are removed if their presence is substantial
enough to reduce the energy content of the gas below pipeline specifications (see Step 1, below). Otherwise, inert gases
will usually be left in the natural gas. Because the raw gas that is usually flared (see Step 2, below) contains NGLs and
C02, it will typically have a higher overall C content than gas that has been processed and moved to end-use customers
via transmission and distribution pipelines.
Methodology
The methodology for estimating the C contents of pipeline and flared natural gas can be described in five steps.
Step 1: Define pipeline-quality natural gas
In the United States, pipeline-quality natural gas is required to have an energy content greater than 970 Btu per
cubic foot, but less than 1,100 Btu per cubic foot. Hydrogen sulfide content must be negligible. Typical pipeline-quality
natural gas is about 95 percent CH4, 3 percent NGLs, and 2 percent non-hydrocarbon gases, of which approximately half
is C02.
However, there remains a range of gas compositions that are consistent with pipeline specifications. The
minimum C content coefficient for natural gas would match that for pure CH4, which equates to an energy content of
1,005 Btu per standard cubic foot. Gas compositions with higher or lower Btu content tend to have higher C emission
factors, because the "low" Btu gas has a higher content of inert gases (including C02 offset with more NGLs), while "high"
Btu gas tends to have more NGLs.
Step 2: Define flared gas
Every year, a certain amount of natural gas is flared in the United States. There are several reasons that gas is
flared:
•	There may be no market for some batches of natural gas, the amount may be too small or too variable, or the
quality might be too poor to justify treating the gas and transporting it to market (such is the case when gas
contains large shares of C02). Most natural gas that is flared for these reasons is "rich" associated gas, with
relatively high energy content, high NGL content, and a high C content.
•	Gas treatment plants may flare substantial volumes of natural gas because of "process upsets," because the gas
is "off spec," or possibly as part of an emissions control system. Gas flared at processing plants may be of
variable quality.
Data on the energy content of flare gas, as reported by states to EIA, indicate an average energy content of
1,130 Btu per standard cubic foot (EIA 1994). Flare gas may have an even higher energy content than reported by EIA
since rich associated gas can have energy contents as high as 1,300 to 1,400 Btu per cubic foot.
15 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.
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Step 3: Determine a relationship between carbon content and heat content
A relationship between C content and heat content may be used to develop a C content coefficient for natural
gas consumed in the United States. In 1994, EIA examined the composition (including C contents) of 6,743 samples of
pipeline-quality natural gas from utilities and/or pipeline companies in 26 cities located in 19 states. To demonstrate that
these samples were representative of actual natural gas "as consumed" in the United States, their heat content was
compared to that of the national average. For the most recent year, the average heat content of natural gas consumed in
the United States was -1,036 Btu per cubic foot, and has varied by less than 1 percent (1,025 to 1,037 Btu per cubic foot)
over the past 10 years. Meanwhile, the average heat content of the 6,743 samples was 1,027 Btu per cubic foot, and the
median heat content was 1,031 Btu per cubic foot. Thus, the average heat content of the sample set falls well within the
typical range of natural gas consumed in the United States, suggesting that these samples continue to be representative
of natural gas "as consumed" in the United States. The average and median composition of these samples appear in
Table A-35.
Table A-35: Composition of Natural Gas (Percent)
Compound
Average
Median
Methane
93.07
95.00
Ethane
3.21
2.79
Propane
0.59
0.48
Higher Hydrocarbons
0.32
0.30
Non-hydrocarbons
2.81
1.43
Higher Heating Value (Btu per cubic foot)
1,027
1,031
Source: Gas Technology Institute (1992).
Carbon contents were calculated for a series of sub-samples based on their C02 content and heat content.
Carbon contents were calculated for the groups of samples with less than 1.0 percent (n=5,181) and less than 1.5
percent C02 only (n=6,522) and those with less than 1.0 or 1.5 percent C02 and less than 1,050 Btu/cf (n=4,888 and
6,166, respectively). These stratifications were chosen to exclude samples with C02 content and heat contents outside
the range of pipeline-quality natural gas. In addition, hexane was removed from the samples since it is usually stripped
out of raw natural gas before delivery because it is a valuable natural gas liquid used as a feedstock for gasoline. The
average carbon contents for the four separate sub-samples are shown below in Table A-36.
Table A-36: Carbon Content of Pipeline-Quality Natural Gas by CO2 and Heat Content (MMT C/QBtu)
Sample	Average Carbon Content
Full Sample	14.48
<	1.0% C02	14.43
<	1.5% C02	14.47
<	1.0 % C02 and <1,050 Btu/cf	14.42
<	1.5 % C02 and <1,050 Btu/cf	14.47
Source: EPA (2010).
Step 4. Apply carbon content coefficients developed in Step 3 to pipeline natural gas
A regression analysis was performed on the sub-samples in to further examine the relationship between carbon
(C) content and heat content (both on a per cubic foot basis). The regression used carbon content as the dependent
variable and heat content as the independent variable. The resulting R-squared values.16 for each of the sub-samples
ranged from 0.79 for samples with less than 1.5 percent C02 and under 1,050 Btu/cf to 0.91 for samples containing less
than 1.0 percent C02 only. However, the sub-sample with less than 1.5 percent C02 and 1,050 Btu/cf was chosen as the
representative sample for two reasons. First, it most accurately reflects the range of C02 content and heat content of
pipeline quality natural gas. Secondly, the R-squared value, although it is the lowest of the sub-groups tested, remains
16 R-squared represents the percentage of variation in the dependent variable (in this case carbon content) explained by variation in the
independent variables.
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relatively high. This high R-squared indicates a low percentage of variation in C content as related to heat content. The
regression for this sub-sample resulted in the following equation:
C Content = (0.011 x Heat Content) + 3.5341
This equation was used to estimate the annual predicted carbon content of natural gas from 1990 to 2019
based on the ElA's national average pipeline-quality gas heat content for each year (EIA 2020a). The table of average C
contents for each year is shown below in Table A-37.
Table A-37: Carbon Content Coefficients for Natural Gas (MMT Carbon/QBtu)
Fuel Type
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Natural Gas
14.46
14.47
14.47
14.48
14.48
14.47
14.46
14.45
14.43
14.43
14.43
14.43
14.43
Source: Calculated from EPA (2010) and EIA (2020a).
Figure A-l: Carbon Content for Samples of Pipeline-Quality Natural Gas Included in the Gas Technology Institute
Database
10.0
	 = National Average

1&C . ."
i	r
970 990 1,010 1,030 1,050 1,070
Energy Content (Btu per Cubic Foot)
1,090 1,110
1
1,130
Source: EIA (1994) Energy Information ^drninislratbn, Emissions of Greenhouse Gases in tie United States 1987-1992, U.S. Department of
Energy, Washington, DC, Ncwembsr, 1994, B0E/EIAG573, Appendix A.
Natural gas suppliers may achieve the same overall energy content from a wide variety of methane, higher
hydrocarbon, and non-hydrocarbon gas combinations. Thus, the plot reveals large variations in C content for a single Btu
value. In fact, the variation in C content for a single Btu value may be nearly as great as the variation for the whole
sample. As a result, while energy content has some predictive value, the specific energy content does not substantially
improve the accuracy of an estimated C content coefficient beyond the +5.0 percent offered with the knowledge that it
is of pipeline-quality.
The plot of C content also reveals other interesting anomalies. Samples with the lowest emissions coefficients
tend to have energy contents of about 1,000 Btu per cubic foot. They are composed of almost pure CH4. Samples with a
greater proportion of NGLs (e.g., ethane, propane, and butane) tend to have energy contents greater than 1,000 Btu per
cubic foot, along with higher emissions coefficients. Samples with a greater proportion of inert gases tend to have lower
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energy content, but they usually contain C02 as one of the inert gases and, consequently, also tend to have higher
emission coefficients (see left side of Figure A-l).
For the full sample (n=6,743), the average C content of a cubic foot of gas was 14.48 MMT C/QBtu. Additionally,
a regression analysis using the full sample produced a predicted C content of 14.49 MMT C/QBtu based on a heat
content of 1,029 Btu/cf (the average heat content in the United States for the most recent year). However, these two
values include an upward influence on the resulting carbon content that is caused by inclusion in the sample set of the
samples that contain large amounts of inert carbon dioxide and those samples with more than 1,050 Btu per cubic foot
that contain an unusually large amount of NGLs. Because typical gas consumed in the United States does not contain
such a large amount of carbon dioxide or natural gas liquids, a C content of 14.43 MMT C/QBtu (see Table A-37), based
on samples with less than 1.5 percent carbon dioxide and less than 1,050 Btu per cubic foot, better represents the
pipeline-quality fuels typically consumed.
Furthermore, research was done on C02 emission factors for fuel gas used by upstream oil and gas producers in
order to determine whether a different C02 emission factor for fuel gas used in offshore oil and gas production than the
emission factor for the processed gas that enters the transmission, storage and distribution networks used in power and
industrial plants and by other users is warranted. It was determined that a different factor was not warranted as natural
gas carbon content is based on the heating value of the gas and EIA reports that the heat content of dry natural gas
produced (which is used in upstream oil and gas production) is the same value as natural gas consumed in downstream
operations (EIA 2020a). Therefore, the same carbon factor is used for all natural gas consumption including upstream
operations.
Petroleum
There are four critical determinants of the C content coefficient for a petroleum-based fuel:
•	The density of the fuel (e.g., the weight in kilograms of one barrel of fuel);
•	The fraction by mass of the product that consists of hydrocarbons, and the fraction of non-
hydrocarbon impurities;
•	The specific types of "families" of hydrocarbons that make up the hydrocarbon portion of the fuel;
and
•	The heat content of the fuel.
where,
Cfuel	—	(Dfuel* SfUel) / Efuel
Quel	=	The C content coefficient of the fuel
DfUei	=	The density of the fuel
SfUei	=	The share of the fuel that is C
Efuei	=	The heat content of the fuel
Most of the density, carbon share, or heat contents applied to calculate the carbon coefficients for petroleum
products that are described in this sub-Annex and applied to this emissions Inventory were updated in 2010 for the 1990
through 2008 Inventory report. These changes have been made where necessary to increase the accuracy of the
underlying data or to align the petroleum properties data used in this report with that developed for use in EPA's
Mandatory Reporting of Greenhouse Gases Rule (EPA 2009b).
Petroleum products vary between 5.6 degrees API gravity.17 (dense products such as asphalt and road oil) and
247 degrees (ethane). This is a range in density of 60 to 150 kilograms per barrel, or +50 percent. The variation in C
content, however, is much smaller (+5 to 7 percent) for products produced by standard distillation refining: ethane is 80
17 API gravity is an arbitrary scale expressing the gravity or density of liquid petroleum products, as established by the American
Petroleum Institute (API). The measuring scale is calibrated in terms of degrees API. The higher the API gravity, the lighter the
compound. Light crude oils generally exceed 38 degrees API and heavy crude oils are all crude oils with an API gravity of 22 degrees or
below. Intermediate crude oils fall in the range of 22 degrees to 38 degrees API gravity. API gravity can be calculated with the following
formula: API Gravity = (141.5/Specific Gravity) - 131.5. Specific gravity is the density of a material relative to that of water. At standard
temperature and pressure, there are 62.36 pounds of water per cubic foot, or 8.337 pounds water per gallon.
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percent C by weight, while petroleum coke is 90 to 92 percent C. This tightly bound range of C contents can be explained
by basic petroleum chemistry (see below). Additional refining can increase carbon contents. Calcined coke, for example,
is formed by heat treating petroleum coke to about 1600 degrees Kelvin (calcining), to expel volatile materials and
increase the percentage of elemental C. This product can contain as much as 97 to 99 percent carbon. Calcined coke is
mainly used in the aluminum and steel industry to produce C anodes.
Petroleum Chemistry
Crude oil and petroleum products are typically mixtures of several hundred distinct compounds, predominantly
hydrocarbons. All hydrocarbons contain hydrogen and C in various proportions. When crude oil is distilled into
petroleum products, it is sorted into fractions by the boiling temperature of these hundreds of organic compounds.
Boiling temperature is strongly correlated with the number of C atoms in each molecule. Petroleum products consisting
of relatively simple molecules and few C atoms have low boiling temperatures, while larger molecules with more C
atoms have higher boiling temperatures.
Products that boil off at higher temperatures are usually denser, which implies greater C content as well.
Petroleum products with higher C contents, in general, have lower energy content per unit mass and higher energy
content per unit volume than products with lower C contents. Empirical research led to the establishment of a set of
quantitative relationships between density, energy content per unit weight and volume, and C and hydrogen content.
Figure A-2 compares C content coefficients calculated on the basis of the derived formula with actual C content
coefficients for a range of crude oils, fuel oils, petroleum products, and pure hydrocarbons. The actual fuel samples were
drawn from the sources described below in the discussions of individual petroleum products.
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Figure A-2: Estimated and Actual Relationships Between Petroleum Carbon Content Coefficients and
Hydrocarbon Density
24 -i
¦ Fiefo rmate
¦ Lig ht Refo rmate
Heavy Pefo rmate
Propylene
n-butane |. butane
-)- Propane
"I- = Paraffin Hydrocarbo re
16
0 15 30 45 60 75 90 105 120 135 150
Hydrocarbon Density (API Gravity)
Source: Carton content factors for paraffins are cafcutat&d based on the properties of hydrocarbons in V. Gutfirie (ed.), Petroleum Products
Handbook (New York: McGraw Hill, 1960) p. 33. Carbon content factors from other petroleum products are drawn from sources described
below. Relationship between density and emission factors based on the relationship between density and energy content in U.S. Department of
Commerce, National Bureau of Standards, Thermal Properties of Petrole Urn Products, Miscellaneous Publication, No. 97 (Washington, DC.,
1929), pp. 16-21, and relationship between energy content and fuel composition inS. Ringen, J. Lanum, and F.P. Miknis, "Calculating Heatirg
Values from the Elemental Composition of Fossil Fuels,1 Fuel, Vol. 5§ (January 1979), p.69.
The derived empirical relationship between C content per unit heat and density is based on the types of
hydrocarbons most frequently encountered. Petroleum fuels can vary from this relationship due to non-hydrocarbon
impurities and variations in molecular structure among classes of hydrocarbons. In the absence of more exact
information, this empirical relationship offers a good indication of C content.
Most fuels contain a certain share of non-hydrocarbon material. This is also primarily true of crude oils and fuel
oils. The most common impurity is sulfur, which typically accounts for between 0.5 and 4 percent of the mass of most
crude oils, and can form an even higher percentage of heavy fuel oils. Some crude oils and fuel oils also contain
appreciable quantities of oxygen and nitrogen, typically in the form of asphaltenes or various acids. The nitrogen and
oxygen content of crude oils can range from near zero to a few percent by weight. Lighter petroleum products have
much lower levels of impurities, because the refining process tends to concentrate all of the non-hydrocarbons in the
residual oil fraction. Light products usually contain less than 0.5 percent non-hydrocarbons by mass. Thus, the C content
of heavy fuel oils can often be several percent lower than that of lighter fuels, due entirely to the presence of non-
hydrocarbons.
Variations in Hydrocarbon Classes
Hydrocarbons can be divided into five general categories, each with a distinctive relationship between density
and C content and physical properties. Refiners tend to control the mix of hydrocarbon types in particular products in
order to give petroleum products distinct properties. The main classes of hydrocarbons are described below.
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Non-hydrocarbon Impurities

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Paraffins. Paraffins are the most common constituent of crude oil, usually comprising 60 percent by mass.
Paraffins are straight-chain hydrocarbons with the general formula CnH2n+2. Paraffins include ethane (C2H6), propane
(CsHs), butane (C4Hi0), and octane (CsHis). As the chemical formula suggests, the C content of the paraffins increases with
their C number: ethane is 79.89 percent C by weight, octane 84.12 percent. As the size of paraffin molecules increases,
the C content approaches the limiting value of 85.7 percent asymptotical (see Figure A-3).
Cycloparaffins. Cycloparaffins are similar to paraffins, except that the C molecules form ring structures rather
than straight chains, and consequently require two fewer hydrogen molecules than paraffins. Cycloparaffins always have
the general formula CnH2n and are 85.63 percent C by mass, regardless of molecular size.
Olefins. Olefins are a very reactive and unstable form of paraffin: a straight chain with two carbon atoms double
bonded together (thus are unsaturated) compared to the carbon atoms in a paraffin (which are saturated with
hydrogen). They are never found in crude oil but are created in moderate quantities by the refining process. Gasoline, for
example, may contain between 2 and 20 percent olefins. They also have the general formula CnH2n, and hence are also
always 85.63 percent C by weight. Propylene (C3H6), a common intermediate petrochemical product, is an olefin.
Aromatics. Aromatics are very reactive hydrocarbons that are relatively uncommon in crude oil (10 percent or
less). Light aromatics increase the octane level in gasoline, and consequently are deliberately created by catalytic
reforming of heavy naphtha. Aromatics also take the form of ring structures with some double bonds between C atoms.
The most common aromatics are benzene (C6H6), toluene (C7HS), and xylene (CsHi0). The general formula for aromatics is
CnH2n-6. Benzene is 92.26 percent C by mass, while xylene is 90.51 percent C by mass and toluene is 91.25 percent C by
mass. Unlike the other hydrocarbon families, the C content of aromatics declines asymptotically toward 85.7 percent
with increasing C number and density (see Figure A-3).
Polynuclear Aromatics. Polynuclear aromatics are large molecules with a multiple ring structure and few
hydrogen atoms, such as naphthalene (Ci0Hs and 93.71 percent C by mass) and anthracene (Ci4Hi0 and 97.7 percent C).
They are relatively rare but do appear in heavier petroleum products.
Figure A-3 illustrates the share of C by weight for each class of hydrocarbon. Hydrocarbon molecules containing
2 to 4 C atoms are all natural gas liquids; hydrocarbons with 5 to 10 C atoms are predominantly found in naphtha and
gasoline; and hydrocarbon compounds with 12 to 20 C atoms comprise "middle distillates," which are used to make
diesel fuel, kerosene and jet fuel. Larger molecules which can be vacuum distilled may be used as lubricants, waxes, and
residual fuel oil or cracked and blended into the gasoline or distillate pools.
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Figure A-3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number
100 -I
95 -
¦ Paraffins
t Cyclo paraffins
~ Aro matics
90 -
£
_|3)
"iii
§:
>¦
-D
C
o
X!
£
o
t
ill
2
aj 80
85
Eie nz e ne ~
Toluene ~l
Xy le re
Cyclo pentane
~t
~ t.
n- pe ntane • ¦
"B uta ne
1 Pro pare
1 Ethane
ttt
75
Me t ha re
Gaso Iine Jet Fie I
LPG N ap ht ha Ke ro 3 e ne Diesel
Lube Oil Fuel Oil
T
~T~
15
T
10	15	20	25
Number of Carbo n A.to rns in Mo leoule
30
35
Source: J.M. Hunt, Pfer-afeifn G'socfiaiTijsfjya.r>^ Gsabg/ (San Francisco, CA, W.H. Freeman and Company, 1979), pp. 31^37.
If nothing is known about the composition of a particular petroleum product, assuming that it is 85.7 percent C
by mass is not an unreasonable first approximation. Since denser products have higher C numbers, this guess would be
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 twenty separate petroleum products and
product categories. The C contents, heat contents, and density for each product are provided below in Table A-38. A
description of the methods and data sources for estimating the key parameters for each individual petroleum product
appears below.
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Table A-38: Carbon Content Coefficients and Underlying Data for Petroleum Products


Gross Heat of



Carbon Content
Combustion
Density
Percent
Fuel
(MMTC/QBtu)
(MMBtu/Barrel)
(API Gravity)
Carbon
Motor Gasoline
19.27
(See a)
(See a)
(See a)
LPG (Propane)
17.15
3.841
155.3
81.80
HGL (Energy Use)b
17.47
(See b)
(See b)
(See b)
HGL (Non-Energy Use)b
16.85
(See b)
(See b)
(See b)
Jet Fuel
19.70
5.670
42.0
86.30
Distillate Fuel No. 1
19.98
5.822
35.3
86.40
Distillate Fuel No. 2
20.22
(See c)
(See c)
(See c)
Distillate Fuel No. 4
20.47
6.135
23.2
86.47
Residual Fuel No. 5
19.89
5.879
33.0
85.67
Residual Fuel No. 6
20.48
6.287
15.5
84.67
Asphalt and Road Oil
20.55
6.636
5.6
83.47
Lubricants
20.20
6.065
25.7
85.80
Naphtha (< 400 deg. F)c
18.55
5.248
62.4
84.11
Other Oil (>400 deg. F)c
20.17
5.825
35.8
87.30
Aviation Gasoline
18.86
5.048
69.0
85.00
Kerosene
19.96
5.670
35.3
86.40
Petroleum Coke
27.85
6.024
-
92.28
Special Naphtha
19.74
5.248
52.0
84.75
Petroleum Waxes
19.80
5.537
43.3
85.30
Still Gas
18.20
6.000
-
77.70
Crude Oil
20.31
5.800
31.2
85.49
Unfinished Oils
20.31
5.825
31.2
85.49
Miscellaneous Products
0.00
5.796
31.2
0.00
Pentanes Plus
18.24
4.638
81.3
83.70
Note: Indicates no sample data available. For carbon content coefficients that are annually variable, 2019 values are shown.
a Calculation of the carbon content coefficient for motor gasoline in 2008 uses separate higher heating values for conventional
and reformulated gasoline of 5.253 and 5.150, respectively (EIA 2008a). Densities and carbon shares (percent carbon) are
annually variable and separated by both fuel formulation and grade, see Motor Gasoline and Blending Components, below, for
details.
b HGL is a blend of multiple paraffinic hydrocarbons: ethane, propane, isobutane, and normal butane, and their associated
olefins: ethylene, propylene, isobutylene, and butylene, each with their own heat content, density, and C content, see Table A-
40.
c Petrochemical feedstocks have been split into naphthas and other oils for this Inventory report. Parameters presented are for
naphthas with a boiling temperature less than 400 degrees Fahrenheit. Other oils are petrochemical feedstocks with higher
boiling points. They are assumed to have the same characteristics as distillate fuel oil no. 2.
Sources: EIA (1994); EIA (2009a); EPA (2020b); and EPA (2010).
Motor Gasoline and Motor Gasoline Blending Components
Motor gasoline is a complex mixture of relatively volatile hydrocarbons with or without small quantities of
additives, blended to form a fuel suitable for use in spark-ignition engines.18 "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 22 percent of all U.S. C02 emissions. EIA collects consumption data (i.e., "petroleum products supplied" to end-
users) for several types of finished gasoline over the 1990 through 2019 time period: regular, mid-grade, and premium
18 Motor gasoline, as defined in ASTM Specification D 4814 or Federal Specification VV-G-1690C, is characterized as having a boiling
range of 122 degrees to 1S8 degrees Fahrenheit at the 10-percent recovery point to 36S degrees to 374 degrees Fahrenheit at the 90-
percent recovery point.
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conventional gasoline (all years) and regular, mid-grade, and premium reformulated gasoline (November 1994 to 2019).
Leaded and oxygenated gasoline are not separately included in the data used for this report.19
The American Society for Testing and Materials (ASTM) standards permit a broad range of densities for gasoline, ranging
from 50 to 70 degrees API gravity, or 111.52 to 112.65 kilograms per barrel (EIA 1994), which implies a range of possible
C and energy contents per barrel. The density of motor gasoline across grades and formulations for 1990-2008 is taken
from the National Institute of Petroleum and Energy Research. Values from 2008 have been used as a proxy for 2009
through 2019.
The density of motor gasoline increased across all grades through 1994, partly as a result of the leaded gasoline
phase-out. In order to maintain the "anti-knock" quality and octane ratings of gasoline in the absence of lead, the
portion of aromatic hydrocarbons blended into gasoline through the refining process was increased. As discussed above,
aromatic hydrocarbons have a lower ratio of hydrogen to C than other hydrocarbons typically found in gasoline, and
therefore increase fuel density.
The trend in gasoline density was reversed beginning in 1996 with the development of fuel additives that raised
oxygen content. In 1995, a requirement for reformulated gasoline in non-attainment areas implemented under the Clean
Air Act Amendments further changed the composition of gasoline consumed in the United States. Through 2005, methyl
tertiary butyl ether (MTBE), ethanol, ethyl tertiary butyl ether (ETBE), and tertiary amyl methyl ether (TAME) were added
to reformulated and sometimes to conventional gasoline to boost its oxygen content, reduce its toxics impacts and
increase its octane. The increased oxygen reduced the emissions of carbon monoxide and unburned hydrocarbons.
These oxygen-rich blending components are also much lower in C than standard gasoline. The average gallon of
reformulated gasoline consumed in 2005 contained over 10 percent MTBE and 0.6 percent TAME (by volume). The
characteristics of reformulated fuel additives appear in Table A-39.
Table A-39: Characteristics of Major Reformulated Fuel Additives
Additive
Density (Degrees
API)
Carbon Share (Percent)
MTBE
58.6
68.13
ETBE
58.5
70.53
TAME
51.2
70.53
DIPE
62.7
70.53
Ethanol (100%)
45.8
52.14
Source: EPA (2009b).
Since 2005, due to concerns about the potential environmental consequences of the use of MTBE in fuels, there
has been a shift away from the addition of MTBE, TAME, ETBE, and DIPE and towards the use of ethanol as a fuel
oxygenate..20 Ethanol, also called ethyl alcohol, is an anhydrous alcohol with molecular formula C2H5OH. Ethanol has a
lower C share than other oxygenates, approximately 52 percent compared to about 70 percent for MTBE and TAME. The
density of ethanol was calculated by fitting density data at 10-degree intervals to a polynomial of order two and then
using the fit to interpolate the value of the density at 15 degrees Celsius. A common fuel mixture of 10 percent
denatured ethanol (denatured by 2 percent hydrocarbons) and 90 percent gasoline, known as E10, is widely used in the
United States and does not require any modification to vehicle engines or fuel systems. The federal Renewable Fuel
Standard (RFS) program requires a certain volume of renewable fuel, including ethanol, be blended into the national fuel
supply.21 Ethanol blends up to E85 (85 percent ethanol, 15 percent gasoline) are in use in the United States but can only
be used in specially designed vehicles called flexible fuel vehicles (FFVs). Most ethanol fuel in the United States is
produced using corn as feedstock,.22 although production pathways utilizing agricultural waste, woody biomass and other
resources are in development.
"Oxygenated gasoline volumes are included in the conventional gasoline data provided by EIA from 2007 onwards. Leaded gasoline
was included in total gasoline by EIA until October 1993.
2°The annual motor gasoline carbon contents that are applied for this Inventory do not include the carbon contributed by the ethanol
contained in reformulated fuels. Ethanol is a biofuel, and net carbon fluxes from changes in biogenic carbon reservoirs in croplands are
accounted for in the estimates for Land Use, Land-Use Change and Forestry.
21 See .
22See .
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Methodology for Years 1990-1999:
Step 1. Disaggregate U.S. gasoline consumption by grade and type
Separate monthly data for U.S. sales to end users of finished gasoline by product grade and season for both
standard gasoline and reformulated gasoline were obtained from the EIA.
Step 2. Develop carbon content coefficients for each grade and type
Annual C content coefficients for each gasoline grade, type, and season are derived from four parameters for
each constituent of the finished gasoline blend: the volumetric share of each constituent,.23 the density of the
constituent, share of the constituent.24 that is C; and the energy content of a gallon of the relevant formulation of
gasoline. The percent by mass of each constituent of each gasoline type was calculated using percent by volume data
from the National Institute for Petroleum and Energy Research (NIPER) and the density of each constituent.
The ether additives listed in Table A-39 are accounted for in both reformulated fuels and conventional fuels, to
the extent that they were present in the fuel. From 2006 onward, reformulated fuel mass percentages are calculated
from their constituents, net of the share provided by ethanol. C content coefficients were then derived from the
calculated percent by mass values by weighting the C share of each constituent by its contribution to the total mass of
the finished motor gasoline product.
Step 3. Weight overall gasoline carbon content coefficient for consumption of each grade and type
The C content for each grade, type, and season of fuel is multiplied by the share of annual consumption
represented by the grade and fuel type during the relevant time period. Individual coefficients are then summed and
totaled to yield an overall C content coefficient for each year.
Methodology for Years 2000-Present:
Step 1. Disaggregate U.S. gasoline consumption by grade and type
Separate monthly data for U.S. sales to end users of finished gasoline by product grade and season for both
standard gasoline and reformulated gasoline were obtained from the EIA. The EIA publishes prime supplier sales volumes
of motor gasoline by type (conventional, oxygenated, and reformulated) and by grade (regular, midgrade and premium)
for each month from 1983 to present (EIA 2020c). Gasoline sold in May through August was assumed to be summer
grade, gasoline sold in September was assumed to be half summer and half winter grade, and gasoline sold in other
months was assumed to be winter grade.
Step 2. Develop carbon content coefficients for each grade and type
Fuel properties are gathered through the Alliance of North American Fuel Survey (NAFS) published by the
Alliance of Automobile Manufacturers (AAM), an association which is now part of the Alliance for Automotive
Innovation. This fuel survey is conducted twice per year, in January and July, and includes measured properties of both
regular and premium gasoline. While the exact number of samples vary by year and location, fuel samples are drawn
from multiple retail locations in each of over 20 U.S. cities for each biannual survey.
The carbon content and net heating value are calculated according to ASTM D3343, Standard Test Method for
the Estimation of Hydrogen Content of Aviation Fuels, and ASTM D3338, Standard Test Method for the Net Heat of
Combustion of Aviation Fuels, respectively using fuel properties inputs from the NAFS for each year and season. These
methods use a correlation between the measured fuel distillation range, API gravity, and aromatic content to estimate
the hydrogen content and net heating values. The C content of hydrocarbon fuel calculated according to ASTM D3343
applies to hydrocarbon containing fuels only and is not applicable towards oxygenated fuel blends. However, recently
EPA has proposed an amendment to 40 CFR §600.113-12, containing equations allowing for the estimation of base fuel
blendstock properties using the bulk oxygenated fuel properties. This technique is applied here for oxygenated gasoline
calculations.
23 Calculations account for the properties of the individual constituents of gasoline, including, as applicable to the fuel grade and type:
aromatics (excluding benzene), olefins, benzene, saturates, MTBE, TAME, ETBE, DIPE and ethanol.
24Saturates are assumed to be octane and aromatics are assumed to be toluene.
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The fuels sampled in the NAFS by AAM are assumed to be representative of the seasonal fuels sold throughout
the U.S. Also, the method of calculation of the fuel properties of the hydrocarbon fraction ofthe fuel from blended fuel
properties was developed for Tier 3 certification test fuels, and not commercial fuel blends as used here.
Data Sources
Data for the density of motor gasoline were derived from NIPER (1990 through 2009). Data on the
characteristics of reformulated gasoline, including C share, were also taken from NIPER (1990 through 2009) and Alliance
of North American Fuel Survey (NAFS) published by the Alliance of Automobile Manufacturers (AAM), an association
which is now part ofthe Alliance for Automotive Innovation.
Standard heat contents for motor gasoline of 5.222 MMBtu per barrel conventional gasoline and 5.150 MMBtu
per barrel reformulated gasoline.25 were adopted from EIA (2009a).
Uncertainty
The uncertainty underlying the C content coefficients for motor gasoline has three underlying sources: (1) the
uncertainty in the averages published by NIPER, (2) uncertainty in the C shares assumed in the EPA's analysis to be
representative ofthe constituent hydrocarbon classes within gasoline (aromatics, olefins and saturates), and (3)
uncertainty in the heat contents applied.
A variable number of samples are used each year to determine the average percent by volume share of each
hydrocarbon within each grade, season and formulation of gasoline that were obtained from NIPER through 2009. The
total number of samples analyzed for each seasonal NIPER report varies from approximately 730 to over 1,800 samples
over the period from 1990 through 2009. The number of samples analyzed that underlie the calculation of the average
make-up of each seasonal formulation and grade varies from approximately 50 to over 400, with the greatest number of
samples each season being of conventional, regular or premium gasoline. Further, not all sample data submitted to
NIPER contains data for each ofthe properties, such that the number of samples underlying each constituent average
value for each season, grade and formulation may be variable within the single gasoline type (e.g., of the 1,073 samples
for which some data was obtained for gasoline sold in Winter 1995 through 1996, benzene content was provided for all
samples, while olefin, aromatic and saturate content was provided for just 736 of those samples).
The distribution of sample origin collected for the NIPER report and the calculation of national averages are not
reflective of sales volumes. The publication of simple, rather than sales-weighted averages to represent national average
values increases the uncertainty in their application to the calculation of carbon content factors for the purposes of this
Inventory. Further, data for each sample is submitted voluntarily, which may also affect their representativeness.
Additionally, because the simple average constituent shares are calculated based upon data that have been
renormalized to account for the share of ethers and alcohols, total average volume shares may not equal 100 percent.
The simple average for each hydrocarbon constituent is contained within a range of values that are as wide as
-63.0/+74.5 percent of the mean across the Winter 2007 through 2008 and -51.3/+49.6 percent across the Summer 2008
samples of conventional, regular grade gasoline. However, these wide ranges exist for benzene, which generally
accounts for only 1 percent, by volume, of each gallon. In contrast, saturates, the class of hydrocarbon that contribute
the largest share, by volume, ranges only -6.5/+6.4 percent for the same set of winter samples and -8.8/+15.7 percent
for the summer samples.
Secondly, EPA's calculation of C content factors for each gasoline type includes the following assumptions: for
the purposes of assigning a carbon share to each compound in the blend, aromatic content (other than benzene) is
assumed to be toluene and saturated hydrocarbons are assumed to be octane. All olefins have the same carbon share
because they all have a molecular formula in the form CnH2n, so the C share applied to the olefin portion of the total
gasoline blend does not increase the level of uncertainty in the calculation. These assumptions are based upon the use of
octane and octane isomers as the primary saturates and toluene as the primary non-benzene aromatic in U.S. motor
gasoline blends. The octane rating of a particular blend is based upon the equivalent iso-octane to heptane ratio, which
is achieved through significant octane content relative to the other saturates. Aside from benzene, U.S. gasolines will
include toluene as a major aromatic component, so toluene may be assumed a reasonable representative of total non-
benzene aromatic content (EPA 2009a).
25 The reformulated gasoline heat content is applied to both reformulated blends containing ethers and those containing ethanol.
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For each hydrocarbon category, the assumed C content lies within a range of possible values for all such
hydrocarbons. Among saturated hydrocarbons, the C share of octane (84.12 percent) is at the high end of the range
while ethane represents the low end of the range (79.89 percent C). Total saturates constitute from 40 to 95 percent by
volume of a given gasoline blend. For aromatics, toluene (91.25 percent C) lies in the middle of the possible range. This
range is bounded by cumene (89.94 percent C) and naphthalene (93.71 percent C). Total aromatics may make up
between 3 and 50 percent by volume of any given gasoline blend. The range of these potential values contributes to the
uncertainty surrounding the final calculated C factors.
However, as demonstrated above in Figure A-3, the amount of variation in C content of gasoline is restricted by
the compounds in the fuel to +4 percent. Further, despite variation in sampling survey response, sample size and
annually variable fuel formulation requirements, the observed variation in the annual weighted motor gasoline
coefficients estimated for this Inventory is +0.8 percent over 1990 through 2019.
The third primary contributor to uncertainty is the assumed heat content. The heat contents are industry
standards established many years ago. The heat contents are standard conversion factors used by EIA to convert
volumetric energy data to energy units. Because the heat contents of fuels change over time, without necessarily and
directly altering their volume, the conversion of known volumetric data to energy units may introduce bias. Thus, a more
precise approach to estimating emissions factors would be to calculate C content per unit of volume, rather than per unit
of energy. Adopting this approach, however, makes it difficult to compare U.S. C content coefficients with those of other
nations.
The changes in density of motor gasoline over the last decade suggest that the heat content of the fuels is also
changing. However, that change within any season grade has been less than 1 percent over the decade. Of greater
concern is the use of a standardized heat content across grades that show a variation in density of +1.5 percent from the
mean for conventional gasoline and +1.0 percent for reformulated fuels.
Jet Fuel
Jet fuel is a refined petroleum product used in jet aircraft engines. There are two classes of jet fuel used in the
United States: "naphtha-based" jet fuels and "kerosene-based" jet fuels. In 1989,13 percent of U.S. consumption was
naphtha-based fuel, with the remainder kerosene-based jet fuel. In 1993, the U.S. Department of Defense began a
conversion from naphtha-based JP-4 jet fuel to kerosene-based jet fuel, because of the possibility of increased demand
for reformulated motor gasoline limiting refinery production of naphtha-based jet fuel. By 1996, naphtha-based jet fuel
represented less than one-half of one percent of all jet fuel consumption. The C content coefficient for jet fuel used in
this report prior to 1996 represents a consumption-weighted combination of the naphtha-based and kerosene-based
coefficients. From 1996 to 2019, only the kerosene-based portion of total consumption is considered significant.
Methodology
Step 1. Estimate the carbon content for naphtha-based jet fuels
Because naphtha-based jet fuels are used on a limited basis in the United States, sample data on its
characteristics are limited. The density of naphtha-based jet fuel (49 degrees) was estimated as the central point of the
acceptable API gravity range published by ASTM. The heat content of the fuel was assumed to be 5.355 MMBtu per
barrel based on EIA industry standards. The C fraction was derived from an estimated hydrogen content of 14.1 percent
(Martel and Angello 1977), and an estimated content of sulfur and other non-hydrocarbons of 0.1 percent.
Step 2. Estimate the carbon content for kerosene-based jet fuels
The density of kerosene-based jet fuels was estimated at 42 degrees API and the carbon share at 86.3 percent.
The density estimate was based on 38 fuel samples examined by NIPER. Carbon share was estimated on the basis of a
hydrogen content of 13.6 percent found in fuel samples taken in 1959 and reported by Martel and Angello, and on an
assumed sulfur content of 0.1 percent. The ElA's standard heat content of 5.670 MMBtu per barrel was adopted for
kerosene-based jet fuel.
Step 3. Weight the overall jet fuel carbon content coefficient for consumption of each type of fuel (1990-1995
only)
For years 1990 through 1995, the C content for each jet fuel type (naphtha-based, kerosene-based) is
multiplied by the share of overall consumption of that fuel type, as reported by EIA (2009a). Individual coefficients are
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then summed and totaled to yield an overall C content coefficient. Only the kerosene-based C coefficient is reflected in
the overall jet fuel coefficient for 1996 through 2019.
Data Sources
Data on the C content of naphtha-based jet fuel was taken from C.R. Martel and L.C. Angello (1977). Data on
the density of naphtha-based jet fuel was taken from ASTM (1985). Standard heat contents for kerosene and naphtha-
based jet fuels were adopted from EIA (2009a). Data on the C content of kerosene-based jet fuel is based on C.R. Martel
and L.C. Angello (1977) and the density is derived from NIPER (1993).
Uncertainty
Variability in jet fuel is relatively small with the average C share of kerosene-based jet fuel varying by less than
+1 percent and the density varying by +1 percent. This is because the ratio of fuel mass to useful energy must be tightly
bounded to maximize safety and range. There is more uncertainty associated with the density and C share of naphtha-
based jet fuel because sample data were unavailable and default values were used. This uncertainty has only a small
impact on the overall uncertainty of the C content coefficient for jet fuels, however, because naphtha-based jet fuel
represents a small and declining share of total jet fuel consumption in the United States and is treated as negligible when
calculating C content factors for 1996 onward.
Distillate Fuel
Distillate fuel is a general classification for diesel fuels and fuel oils. Products known as No. 1, No. 2, and No. 4
diesel fuel are used in on-highway diesel engines, such as those in trucks and automobiles, as well as off-highway
engines, such as those in railroad locomotives and agricultural machinery. No. 1, No. 2, and No. 4 fuel oils are also used
for space heating and electric power generation.
Methodology
For this Inventory, separate C coefficients have been estimated for each of the three distillates, although the
level of aggregation of U.S. energy statistics requires that a single coefficient is used to represent all three grades in
inventory calculations. In past Inventories, the emission coefficient was only determined for distillate No. 2. Distillate No.
2 remains the representative grade applied to the distillate class for calculation purposes. Coefficients developed for No.
1 and No. 4 distillate are provided for informational purposes. The C share for distillate No. 1 and No. 4 is drawn from
Perry's Chemical Engineers' Handbook, 8th Ed. (Green & Perry 2008). Each C share was combined with individual heat
contents of 5.822 and 6.135 MMBtu per barrel, respectively for distillates No. 1 and No. 4, and densities of 35.3 and 23.2
degrees API to calculate C coefficients for each distillate type.
The carbon content of diesel fuel No. 2 is calculated according to ASTM D3343, Standard Test Method for the
Estimation of Hydrogen Content of Aviation Fuels, and ASTM D3338, Standard Test Method for the Net Heat of
Combustion of Aviation Fuels, using fuel properties inputs from the Alliance of North American Fuel Survey (NAFS) data
for each year and season. These methods use a correlation between the measured fuel distillation range, API gravity, and
aromatic content to estimate the hydrogen content.
Data Sources
Fuel properties for distillate No. 2 were derived from diesel surveys taken by the Alliance of Automobile
Manufacturers, an association which is now part of the Alliance for Automotive Innovation. The density of distillate fuel
oil No. 4 is taken from Perry's Chemical Engineer's Handbook, 8th Ed. (Green & Perry, ed. 2008), Table 24-6.
Heat contents are adopted from EIA (2020a), and carbon shares for distillates No. 1 and No. 4 are from Perry's
Chemical Engineers' Handbook (Green & Perry, ed. 2008), Table 24-6.
Uncertainty
The primary source of uncertainty for the estimated C content of distillate fuel is the selection of No. 2 distillate
as the typical distillate fuel oil or diesel fuel. No. 2 fuel oil is generally consumed for home heating. No. 1 distillate is
generally less dense and if it is consumed in large portions for mobile sources, the application of the C content estimated
for No. 2 for this report is likely to be too high when applied to both No. 1 and No. 2 distillates. The opposite is true of
the application of a coefficient based upon the properties of No. 2 to the consumption of No. 4 distillate, which is of a
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significantly higher density and thus, has a higher C coefficient despite its lower C share. The overall effect on uncertainty
from applying a single factor will depend on the relative annual consumption of each distillate.
The densities applied to the calculation of each carbon factor are an underlying a source of uncertainty. While
the density of No. 1 distillate is based upon just four samples, the factor applied to all distillates in the Inventory
estimates (that for No. 2 oil) is based on a much larger sample size (144). Given the range of densities for these three
distillate fuel classes (0.1342 to 0.1452 MT/bbl at 60 degrees F), the uncertainty associated with the assumed density of
distillate fuels is predominately a result of the use of No. 2 to represent all distillate consumption. There is also a small
amount of uncertainty in the No. 2 distillate density itself. This is due to the possible variation across seasonal diesel
formulations and fuel grades and between stationary and transport applications within the No. 2 distillate classification.
The range of the density of the samples of No. 2 diesel (regular grade, 15 ppm sulfur) is ± 2.5 percent from the mean,
while the range in density across the small sample set of No. 1 diesel is -2.1 to +1.6 percent of the mean. Samples from
AAM (2009) of Premium No. 2 diesel (n=5) and higher sulfur (500 ppm S) regular diesel (n=2), each have nominally higher
average densities (+1.3 percent and +0.6 percent, respectively) than do the low-sulfur regular diesel samples that
underlie the density applied in this Inventory.
The use of the 144 AAM samples to define the density of No. 2 distillate (and those four samples used to define
that of No. 1 distillate) may introduce additional uncertainty because the samples were collected from just one season of
on-road fuel production (Winter 2008). Despite the limited sample frame, the average No. 2 density calculated from the
samples is applied to the calculation of a uniform C coefficient applicable for all years of the Inventory and for all types of
distillate consumption. The ASTM standards for each grade of diesel fuel oil do not include a required range in which the
density must lie, and the density (as well as heat content and carbon share) may vary according to the additives in each
seasonal blend and the sulfur content of each sub-grade.
However, previous studies also show relatively low variation in density across samples of No. 2 and across all
distillates, supporting the application of a single No. 2 density to all U.S. distillate consumption. The average density
calculated from samples analyzed by the EIA in 1994 (n=7) differs only very slightly from the value applied for the
purposes of this Inventory (-0.12 percent for No. 2 distillate). Further, the difference between the mean density applied
to this Inventory (No. 2 only) and that calculated from EIA samples of all distillates, regardless of grade, is also near zero
(-0.06 percent, based on n=14, of distillates No. 1, No. 2 and No. 4 combined).
A C share of 87.30 percent is applied to No. 2 distillate, while No. 1 and No. 4 have C shares estimated at 86.40
and 86.47 percent, respectively. Again, the application of parameters specific to No. 2 to the consumption of all three
distillates contributes to an increased level of uncertainty in the overall coefficient and emissions estimate and its broad
application. For comparison, four No. 1 fuel oil samples obtained by EIA (1994) contained an average of 86.19 percent C,
while seven samples No. 2 fuel oil from the same EIA analysis showed an average of 86.60 percent C. Additionally, three
samples of No. 4 distillate indicate an average C share of 85.81 percent. The range of C share observed across the seven
No. 2 samples is 86.1 to 87.5 percent, and across all samples (all three grades, n=14) the range is 85.3 to 87.5 percent C.
There also exists an uncertainty of +1 percent in the share of C in No. 2 based on the limited sample size.
Residual Fuel
Residual fuel is a general classification for the heavier oils, known as No. 5 and No. 6 fuel oils, that remain after
the distillate fuel oils and lighter hydrocarbons are distilled away in refinery operations. Residual fuel conforms to ASTM
Specifications D 396 and D 975 and Federal Specification VV-F-815C. No. 5, a residual fuel oil of medium viscosity, is also
known as Navy Special and is defined in Military Specification MIL-F-859E, including Amendment 2 (NATO Symbol F-770).
It is used in steam-powered vessels in government service and inshore power plants. No. 6 fuel oil includes Bunker C fuel
oil and is used for the production of electric power, space heating, vessel bunkering, and various industrial purposes.
In the United States, electric utilities purchase about one-third of the residual oil consumed. A somewhat larger
share is used for vessel bunkering, and the balance is used in the commercial and industrial sectors. The residual oil
(defined as No. 6 fuel oil) consumed by electric utilities has an energy content of 6.287 MMBtu per barrel (EIA 2008a)
and an average sulfur content of 1 percent (EIA 2001). This implies a density of about 17 degrees API.
Methodology
Because U.S. energy consumption statistics are available only as an aggregate of No. 5 and No. 6 residual oil, a
single coefficient must be used to represent the full residual fuel category. As in earlier editions of this report, residual
fuel oil has been defined as No. 6 fuel oil, due to the majority of residual consumed in the United States being No. 6.
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However, for this report, a separate coefficient for fuel oil No. 5 has also been developed for informational purposes.
Densities of 33.0 and 15.5 degrees API were adopted when developing the C content coefficients for Nos. 5 and 6,
respectively (Wauquier, J.-P., ed. 1995; Green & Perry, ed. 2008).
The estimated C share of fuel oil No. 5 is 85.67 percent, based on an average of 12 ultimate analyses of samples
of fuel oil (EIA 1994). An average share of C in No. 6 residual oil of 84.67 percent by mass was used, based on Perry's, 8th
Ed. (Green & Perry, ed. 2008).
Data Sources
Data on the C share and density of residual fuel oil No. 6 were obtained from Green & Perry, ed. (2008). Data
on the C share of fuel oil No. 5 was adopted from EIA (1994), and the density of No. 5 was obtained from Wauquier, J.-P.,
ed. (1995). Heat contents for both No. 5 and No. 6 fuel oil are adopted from EPA (2009b).
Uncertainty
Beyond the application of a C factor based upon No. 6 oil to all residual oil consumption, the largest source of
uncertainty in estimating the C content of residual fuel centers on the estimates of density. Fuel oils are likely to differ
depending on the application of the fuel (i.e., power generation or as a marine vessel fuel). Slight differences between
the density of residual fuel used by utilities and that used in mobile applications are likely attributable to non-sulfur
impurities, which reduce the energy content of the fuel, but do not greatly affect the density of the product. Impurities
of several percent are commonly observed in residual oil. The extent of the presence of impurities has a greater effect on
the uncertainty of C share estimation than it does on density. This is because these impurities do provide some Btu
content to the fuel, but they are absent of carbon. Fuel oils with significant sulfur, nitrogen and heavy metals contents
would have a different total carbon share than a fuel oil that is closer to pure hydrocarbon. This contributes to the
uncertainty of the estimation of an average C share and C coefficient for these varied fuels.
The 12 samples of residual oil (EIA 1994) cover a density range from 4.3 percent below to 8.2 percent above the
mean density. The observed range of C share in these samples is -2.5 to +1.8 percent of the mean. Overall, the
uncertainty associated with the C content of residual fuel is probably +1 percent.
Hydrocarbon Gas Liquids (HGL)
EIA identifies four categories of paraffinic hydrocarbons (i.e., ethane, propane, isobutane, and n-butane) and
four categories of olefinic hydrocarbons (i.e., ethylene, propylene, isobutylene, and butylene) as HGL. HGL also includes
pentanes plus, or natural gasoline, but this category is calculated separately from other HGL components in this report.
Because each of these compounds is a pure paraffinic or olefinic hydrocarbon, their C shares are easily derived by taking
into account the atomic weight of C (12.01) and the atomic weight of hydrogen (1.01). Thus, for example, the C share of
propane, CsHs, is 81.71 percent. The densities and heat contents of the compounds are also well known, allowing C
content coefficients to be calculated directly. Table A-40 summarizes the physical characteristic of HGL.
Table A-40: Physical Characteristics of Hydrocarbon Gas Liquids





Carbon Content

Chemical
Density (Barrels
Carbon Content
Energy Content
Coefficient (MMT
Compound
Formula
Per Metric Ton)
(Percent)
(MMBtu/Barrel)
C/QBtu)
Ethane
c2h6
11.55
80.00
2.783
16.25
Propane
CsHs
12.76
81.80
3.841
17.15
Isobutane
C4H10
11.42
82.80
4.183
17.71
n-butane
C4H10
10.98
82.30
4.354
17.66
Ethylene
C2H4
11.07
85.71
2.436
17.99
Propylene
c3H6
12.45
85.71
3.835
17.99
Isobutylene
C4Hs
10.68
85.71
4.355
18.78
Butylene
C4Hs
10.70
85.71
4.377
18.74
Source: Densities - CRC Handbook of Chemistry and Physics (2008/09) and EPA (2009c); Carbon Contents - derived from
the atomic weights of the elements EPA (2013); Energy Contents - EIA (2020a). All values are for the compound in liquid
form. The density and energy content of ethane are for refrigerated ethane (-89 degrees C). Values for n-butane are for
pressurized butane (-25 degrees C).
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Methodology
Step 1. Assign carbon content coefficients to each pure paraffinic compound
Based on their known physical characteristics, a C content coefficient is assigned to each compound contained
in the U.S. energy statistics category, HGL.
Step 2. Weight individual HGL coefficients for share of fuel use consumption
A C content coefficient for HGL used as fuel is developed based on the consumption mix of the individual
compounds reported in U.S. energy statistics, excluding pentanes plus, which is calculated separately.
Step 3. Weight individual HGL coefficients for share of non-fuel use consumption
The mix of HGL consumed for non-fuel use differs significantly from the mix of HGL that is combusted. EIA
(2020a) states that HGL consumption in the residential, commercial, and transportation sector is 100 percent propane,
therefore a constant, non-weighted propane C content coefficient is applied to HGL (LPG - Propane) in these sectors.
While the majority of HGL consumed for fuel use in the industrial sector is propane, ethane is the largest component of
HGL used for non-fuel applications. C content coefficients for HGL used for fuel use and non-fuel applications are
developed based on the consumption mix of the individual compounds reported in U.S. energy statistics.
Step 4. Weight the carbon content coefficients for fuel use and non-fuel use by their respective shares of
consumption
The changing shares of HGL fuel use and non-fuel use consumption appear below in Table A-41.
Data Sources
Data on C share was derived via calculations based on atomic weights of each element of the four individual
compounds densities are from the CRC Handbook of Chemistry and Physics, 89th Education. The energy content of each
HGL is from EIA (2020a). HGL consumption was based on data obtained from EIA (2020b). Non-fuel use of HGL was
obtained from EIA (2020b).
Uncertainty
Because HGL consists of pure paraffinic and olefinic compounds whose density, heat content, and C share are
physical constants, there is limited uncertainty associated with the C content coefficient for this petroleum product. Any
uncertainty is associated with the collection of data tabulating fuel- and non-fuel consumption in U.S. energy statistics.
This uncertainty is likely less than +3 percent.
Table A-41: Industrial Sector Consumption and Carbon Content Coefficients of Hydrocarbon Gas Liquids, 1990-
2019

1990
1995
2000
J 2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Energy Consumption (QBtu)
Fuel Use
0.96
0.78
1.95
1.00
9.21
9.09
9.26
10.28
9.52
9.65
9.43
9.10
9.85
9.78
Ethane
0.02
0.02
0.03
0.02
0.03
0.03
0.04
0.04
0.04
0.04
0.04
0.05
0.05
0.06
Propane
0.60
0.47
1.19
0.64
6.28
6.28
6.38
7.15
6.42
6.38
6.16
5.92
6.55
6.41
Butane
0.11
0.07
0.17
0.09
0.50
0.33
0.37
0.57
0.57
0.62
0.41
0.19
0.17
0.25
Isobutane
0.02
0.04
0.14
0.02
0.12
0.15
0.19
0.28
0.29
0.46
0.60
0.65
0.75
0.88
Ethylene
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Propylene
0.21
0.17
0.42
0.23
2.25
2.28
2.28
2.26
2.19
2.18
2.23
2.27
2.29
2.20
Butylene
+
+
+
+
0.02
+
+
(0.02)
0.01
(0.03)
(0.01)
0.02
0.04
+
Isobutylene
+
+
+
+
+
0.01
+
(+)
(+)
(+)
+
+
(+)
+
Non-Fuel Use
1.88
2.35
2.61
2.48
1.80
1.85
1.88
2.04
2.03
2.10
2.13
2.19
2.51
2.58
Ethane
0.48
0.61
0.71
0.63
0.88
0.96
0.96
1.01
1.05
1.09
1.15
1.26
1.50
1.56
Propane
0.89
1.09
1.17
1.21
0.57
0.57
0.57
0.64
0.58
0.57
0.55
0.53
0.59
0.58
Butane
0.16
0.17
0.17
0.16
0.11
0.07
0.08
0.13
0.13
0.14
0.09
0.04
0.04
0.06
Isobutane
0.03
0.10
0.14
0.04
0.03
0.03
0.04
0.06
0.07
0.10
0.14
0.15
0.17
0.20
Ethylene
+
+
+
+
0.01
0.01
+
+
+
+
+
+
+
+
Propylene
0.31
0.38
0.41
0.43
0.20
0.21
0.20
0.20
0.20
0.20
0.20
0.20
0.21
0.20
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Butylene	+	+	+	+	+ + +(+)+ (0.01) (+) + 0.01 +
Isobutylene	+	+	+	+	+ + +(+)(+)(+) + + (+) +
Carbon Content (MMT C/QBtu)
Fuel Use 17.51
17.51
17.52
17.45
17.43
17.40
17.41
17.39
17.41
17.43
17.43
17.45
17.45
17.47
Non-Fuel Use 17.24
17.25
17.22
17.19
16.93
16.84
16.86
16.88
16.86
16.89
16.85
16.84
16.82
16.85
Notes: "+" indicates a value less than 0.01 QBtu. Parentheses indicate negative values.
Sources: Fuel use of HGL based on data from EIA (2020b). Non-fuel use of HGL from (EIA 2020b). Volumes converted using the
energy contents provided in Table A-40. C contents from EPA (2013).
Aviation Gasoline
Aviation gasoline is used in piston-powered airplane engines. It is a complex mixture of relatively volatile
hydrocarbons with or without small quantities of additives, blended to form a fuel suitable for use in aviation
reciprocating engines. Fuel specifications are provided in ASTM Specification D910 and Military Specification MIL-G-5572.
Aviation gas is a relatively minor contributor to greenhouse gas emissions compared to other petroleum products,
representing approximately 0.1 percent of all consumption.
The ASTM standards for boiling and freezing points in aviation gasoline effectively limit the aromatics content
to a maximum of 25 percent (ASTM D910). Because weight is critical in the operation of an airplane, aviation gas must
have as many Btu per pound (implying a lower density) as possible, given other requirements of piston engines such as
high anti-knock quality.
Methodology
A C content coefficient for aviation gasoline was calculated on the basis of the EIA standard heat content of
5.048 MMBtu per barrel. This implies a density of approximately 69 degrees API gravity or 5.884 pounds per gallon,
based on the relationship between heat content and density of petroleum liquids, as described in Thermal Properties of
Petroleum Products (DOC 1929). To estimate the share of C in the fuel, it was assumed that aviation gasoline is 87.5
percent iso-octane, 9.0 percent toluene, and 3.5 percent xylene. The maximum allowable sulfur content in aviation
gasoline is 0.05 percent, and the maximum allowable lead content is 0.1 percent. These amounts were judged negligible
and excluded for the purposes of this analysis. This yielded a C share of 85.00 percent and a C content coefficient of
18.86 MMT C/QBtu.
Data Sources
Data sources include ASTM (1985). A standard heat content for aviation gas was adopted from EIA (2009a).
Uncertainty
The relationship used to calculate density from heat content has an accuracy of five percent at 1 atm. The
uncertainty associated with the C content coefficient for aviation gasoline is larger than that for other liquid petroleum
products examined because no ultimate analyses of samples are available. Given the requirements for safe operation of
piston-powered aircraft the composition of aviation gas is well bounded, and the uncertainty of the C content coefficient
is likely to be +5 percent.
Still Gas
Still gas, or refinery gas, is composed of light hydrocarbon gases that are released as petroleum is processed in
a refinery. The composition of still gas is highly variable, depending primarily on the nature of the refining process and
secondarily on the composition of the product being processed. Petroleum refineries produce still gas from many
different processes. Still gas can be used as a fuel or feedstock within the refinery, sold as a petrochemical feedstock, or
purified and sold as pipeline-quality natural gas. For the purposes of this Inventory, the coefficient derived here is only
applied to still gas that is consumed as a fuel. In general, still gas tends to include large amounts of free hydrogen and
methane, as well as smaller amounts of heavier hydrocarbons. Because different refinery operations result in different
gaseous by-products, it is difficult to determine what represents typical still gas.
Methodology
The properties of still gas used to calculate the carbon content are taken from the literature. The carbon share
of still gas was calculated from its net calorific value and carbon content from IPCC (2006). This calculation yields a
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carbon share of 77.7 percent. The density of still gas was estimated to be 0.1405 metric tons per barrel based on its heat
content (EIA 2008a) and the relationship between heat content and density that is described by the U.S. Department of
Commerce, Bureau of Standards (DOC 1929).
Data Sources
The carbon share of still gas is calculated from data provided by IPCC (2006). Density is estimated at 0.1405
metric tons per barrel, approximately 28.3 degrees API, based on the heat content of 6.00 MMbtu/barrel of still gas from
EIA (2009a).
Uncertainty
The EIA obtained data on four samples of still gas. Table A-42 below shows the composition of those samples.
Table A-42: Composition, Energy Content, and Carbon Content Coefficient for Four Samples of Still Gas
Sample
Hydrogen
Methane
Ethane
Propane
Btu Per Cubic
Carbon Content

(%)
(%)
(%)
(%)
Foot
(MMT C/QBtu)
One
12.7
28.1
17.1
11.9
1,388
17.51
Two
34.7
20.5
20.5
6.7
1,143
14.33
Three
72.0
12.8
10.3
3.8
672
10.23
Four
17.0
31.0
16.2
2.4
1,100
15.99
Sources: EIA (2008b).
Because the composition of still gas is highly heterogeneous, the C content coefficient for this product is highly
uncertain. Gas streams with a large, free-hydrogen content are likely to be used as refinery or chemical feedstocks.
Therefore, the sample cited above with the very high H content of 72 percent (and the lowest calculated C content) is
less likely to be representative of the still gas streams to which the calculated coefficient is applied. The C content
coefficient used for this report is probably at the high end of the plausible range given that it is higher than the greatest
sample-based C content in Table A-42.
Asphalt
Asphalt is used to pave roads. Because most of its C is retained in those roads, it is a small source of carbon
dioxide emissions. It is derived from a class of hydrocarbons called "asphaltenes," which are abundant in some crude oils
but not in others. Asphaltenes have oxygen and nitrogen atoms bound into their molecular structure, so that they tend
to have lower C contents than do other hydrocarbons.
Methodology
Ultimate analyses of twelve samples of asphalts showed an average C content of 83.47 percent. The EIA
standard Btu content for asphalt of 6.636 MMBtu per barrel was assumed. The ASTM petroleum measurement tables
show a density of 5.6 degrees API or 8.605 pounds per gallon for asphalt. Together, these variables generate C content
coefficient of 20.55 MMT C/QBtu.
Data Sources
A standard heat content for asphalt was adopted from EIA (2009b). The density of asphalt was determined by
the ASTM (1985). C share is adopted from analyses in EIA (2008b).
Uncertainty
The share of C in asphalt ranges from 79 to 88 percent by weight. Also present in the mixture are hydrogen and
sulfur, with shares by weight ranging from seven to 13 percent for hydrogen, and from trace levels to eight percent for
sulfur. Because C share and total heat content in asphalts do vary systematically, the overall C content coefficient is likely
to be accurate to +5 percent.
Lubricants
Lubricants are substances used to reduce friction between bearing surfaces, or incorporated into processing
materials used in the manufacture of other products, or used as carriers of other materials. Petroleum lubricants may be
produced either from distillates or residues. Lubricants include all grades of lubricating oils, from spindle oil to cylinder
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oil to those used in greases. Lubricant consumption is dominated by motor oil for automobiles, but there is a large range
of product compositions and end uses within this category.
Methodology
The ASTM Petroleum Measurement tables give the density of lubricants at 25.6 degrees API, or 0.1428 metric
tons per barrel. Ultimate analysis of a single sample of motor oil yielded a C content of 85.80 percent. A standard heat
content of 6.065 MMBtu per barrel was adopted from EIA. These factors produce a C content coefficient of 20.20 MMT
C/QBtu.
Data Sources
A standard heat content was adopted from the EIA (2009b). The carbon content of lubricants is adopted from
ultimate analysis of one sample of motor oil (EPA 2009a). The density of lubricating oils was determined by ASTM (1985).
Uncertainty
Uncertainty in the estimated C content coefficient for lubricants is driven by the large range of product
compositions and end uses in this category combined with an inability to establish the shares of the various products
captured under this category in U.S. energy statistics. Because lubricants may be produced from either the distillate or
residual fractions during refineries, the possible C content coefficients range from 19.89 MMT C/QBtu to 21.48 MMT
C/QBtu or an uncertainty band from -1.5 percent to +1.4 percent of the estimated value.
Petrochemical Feedstocks
U.S. energy statistics distinguish between two different kinds of petrochemical feedstocks: those with a boiling
temperature below 400 degrees Fahrenheit, generally called "naphtha," and those with a boiling temperature 401
degrees Fahrenheit and above, referred to as "other oils" for the purposes of this Inventory.
Methodology
The C content of these petrochemical feedstocks are estimated independently according to the following steps.
Step 1. Estimate the carbon content coefficient for naphtha
Because reformed naphtha is used to make motor gasoline (hydrogen is released to raise aromatics content
and octane rating), "straight-run" naphtha is assumed to be used as a petrochemical feedstock. Ultimate analyses of five
samples of naphtha were examined and showed an average C share of 84.11 percent. A density of 62.4 degrees API
gravity was taken from the Handbook of Petroleum Refining Processes, 3rd ed. (Meyers 2004). The standard EIA heat
content of 5.248 MMBtu per barrel is used to estimate a C content coefficient of 18.55 MMT C/QBtu.
Step 2. Estimate the carbon content coefficient for petrochemical feedstocks with a boiling temperature 400
degrees Fahrenheit and above ("other oils")
The boiling temperature of this product places it into the "middle distillate" fraction in the refining process, and
EIA estimates that these petrochemical feedstocks have the same heat content as distillate fuel No. 2. Thus, the C
content coefficient of 20.17 MMT C/QBtu used for distillate fuel No. 2 is also adopted for this portion of the
petrochemical feedstocks category.
Data Sources
Naphthas: Data on the C content was taken from Unzelman (1992). Density is from Meyers (2004). A standard
heat content for naphthas was adopted from EIA (2009a). Other oils: See Distillate Fuel, Distillate No.2.
Uncertainty
Petrochemical feedstocks are not so much distinguished on the basis of chemical composition as on the identity
of the purchaser, who are presumed to be a chemical company, or a petrochemical unit co-located on the refinery
grounds. Naphthas are defined, for the purposes of U.S. energy statistics, as those naphtha products destined for use as
a petrochemical feedstock. Because naphthas are also commonly used to produce motor gasoline, there exists a
considerable degree of uncertainty about the exact composition of petrochemical feedstocks.
Different naphthas are distinguished by their density and by the share of paraffins, isoparaffins, olefins,
naphthenes and aromatics contained in the oil. Naphtha from the same crude oil fraction may have vastly different
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properties depending on the source of the crude. Two different samples of Egyptian crude, for example, produced two
straight run naphthas having naphthene and paraffin contents (percent volume) that differ by 18.1 and 17.5 percent,
respectively (Matar and Hatch 2000).
Naphthas are typically used either as a petrochemical feedstock or a gasoline feedstock, with lighter paraffinic
naphthas going to petrochemical production. Naphthas that are rich in aromatics and naphthenes tend to be reformed
or blended into gasoline. Thus, the product category encompasses a range of possible fuel compositions, creating a
range of possible C shares and densities. The uncertainty associated with the calculated C content of naphthas is
primarily a function of the uncertainty that underlies the average carbon share calculation, which is based on a limited
number of samples. Two additional samples cited by the EIA (1994) have a range of 83.80 to 84.42 percent C.
The uncertainty of the C content for other oils is based upon the assumption of distillate oil No. 2 as a product
representative of the ill-defined classification of "other oils," and from the calculation of the C content of No. 2 itself (see
"Distillate Fuels," above). While No. 2 distillate is used as a proxy for "other oils" for the purposes of this Inventory's
carbon coefficient, important differences exist between these two petroleum products, contributing some uncertainty to
the cross-application. Other oils are defined herein as those "oils with a boiling range equal to or greater than 401
degrees F that are generally intended for use as a petrochemical feedstock and are not defined elsewhere." For
comparison, various material safety data sheets (MSDSs) published by producers of distillate No. 2 indicate a boiling
range for this product of 320 to 700 degrees Fahrenheit. The relatively open definition of the classification "other oils"
leaves room for potentially significant variation in the heating value, density and carbon share properties of each
feedstock oil having a boiling point above 400 degrees Fahrenheit, creating a large band of uncertainty beyond that
associated with the C factor for distillate No. 2.
Kerosene
A light petroleum distillate that is used in space heaters, cook stoves, and water heaters and is suitable for use
as a light source when burned in wick-fed lamps, kerosene is drawn from the same petroleum fraction as jet fuel.
Kerosene is generally comparable to No. 1 distillate oil.
Methodology
The average density and C share of kerosene are assumed to be the same as those for distillate No. 1 since the
physical characteristics of the products are very similar. Thus, a density of 35.3 degrees API and average C share of 86.40
percent were applied to a standard heat content for distillate No. 1 of 5.825 MMBtu per barrel to yield a C content
coefficient of 19.96 MMT C/QBtu.
Data Sources
A standard heat content for distillate No. 1 was adopted from EIA (2009a).
Uncertainty
Uncertainty in the estimated C content for kerosene is driven by the selection of distillate No. 1 as a proxy for
kerosene. If kerosene is more like kerosene-based jet fuel, the true C content coefficient is likely to be some 1.3 percent
lower. If kerosene is more aptly compared to No. 2 distillate oil, then the true C content coefficient is likely to be about
1.1 percent higher. While kerosene is a light petroleum distillate, like distillate No. 1, the two oil classes have some
variation in their properties. For example, the boiling range of kerosene is 250 to 550 degrees Fahrenheit, whereas No. 1
oils typically boil over a range from 350 to 615 degrees Fahrenheit. The properties of individual kerosenes will vary with
their use and particular crude origin, as well. Both kerosene and fuel oil No. 1 are primarily composed of hydrocarbons
having 9 to 16 carbon atoms per molecule. However, kerosene is a straight-run No. 1 fuel oil, additional cracking
processes and additives contribute to the range of possible fuels that make up the broader distillate No. 1 oil category.
Petroleum Coke
Petroleum coke is the solid residue by-product of the extensive processing of crude oil. It is a coal-like solid,
usually has a C content greater than 90 percent, and is used as a boiler fuel and industrial raw material.
Methodology
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Ultimate analyses of two samples of petroleum coke showed an average C share of 92.28 percent. The ASTM
standard density of 9.543 pounds per gallon was adopted and the EIA standard energy content of 6.024 MMBtu per
barrel assumed. Together, these factors produced an estimated C content coefficient of 27.85 MMT C/QBtu.
Data Sources
C content was derived from two samples from Martin, S.W. (1960). The density of petroleum coke was taken
from the ASTM (1985). A standard heat content for petroleum coke was adopted from EIA (2009a).
Uncertainty
The uncertainty associated with the estimated C content coefficient of petroleum coke can be traced to two
factors: the use of only two samples to establish C contents and a standard heat content which may be too low.
Together, these uncertainties are likely to bias the C content coefficient upwards by as much as 6 percent.
Special Naphtha
Special naphtha is defined as a light petroleum product to be used for solvent applications, including
commercial hexane and four classes of solvent: (1) Stoddard solvent, used in dry cleaning; (2) high flash point solvent,
used as an industrial paint because of its slow evaporative characteristics; (3) odorless solvent, most often used for
residential paints; and (4) high solvency mineral spirits, used for architectural finishes. These products differ in both
density and C percentage, requiring the development of multiple coefficients.
Methodology
The method for estimating the C content coefficient of special naphtha includes three steps.
Step 1. Estimate the carbon content coefficient for hexane
Hexane is a pure paraffin containing 6 C atoms and 14 hydrogen atoms; thus, it is 83.63 percent C. Its density is
83.7 degrees API or 5.477 pounds per gallon and its derived C content coefficient is 21.40 MMT C/QBtu.
Step 2. Estimate the carbon contents of non-hexane special naphthas
The hydrocarbon compounds in special naphthas are assumed to be either paraffinic or aromatic (see
discussion above). The portion of aromatics in odorless solvents is estimated at less than 1 percent, Stoddard and high
flash point solvents contain 15 percent aromatics and high solvency mineral spirits contain 30 percent aromatics (Boldt
and Hall 1977). These assumptions, when combined with the relevant densities, yield the C content factors contained in
Table A-43, below.
Table A-43: Characteristics of Non-hexane Special Naphthas
Special Naphtha
Aromatic Content
(Percent)
Density
(Degrees API)
Carbon Share
(Percent Mass)
Carbon Content
(MMT C/QBtu)
Odorless Solvent
1
55.0
84.51
19.41
Stoddard Solvent
15
47.9
84.44
20.11
High Flash Point
15
47.6
84.70
20.17
Mineral Spirits
30
43.6
85.83
20.99
Sources: EIA (2008b) and Boldt and Hall (1977).
Step 3. Develop weighted carbon content coefficient based on consumption of each special naphtha
EIA reports only a single consumption figure for special naphtha. The C contents of the five special naphthas are
weighted according to the following formula: approximately 10 percent of all special naphtha consumed is hexane; the
remaining 90 percent is assumed to be distributed evenly among the four other solvents. The resulting emissions
coefficient for special naphthas is 19.74 MMT C/QBtu.
Data Sources
A standard heat content for special naphtha was adopted from EIA (2009a). Density and aromatic contents
were adopted from Boldt and Hall (1977).
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Uncertainty
The principal uncertainty associated with the estimated C content coefficient for special naphtha is the
allocation of overall consumption across individual solvents. The overall uncertainty is bounded on the low end by the C
content of odorless solvent and on the upper end by the C content of hexane. This implies an uncertainty band of-1.7
percent to +8.4 percent.
Petroleum Waxes
The ASTM standards define petroleum wax as a product separated from petroleum that is solid or semi-solid at
77 degrees Fahrenheit (25 degrees Celsius). The two classes of petroleum wax are paraffin waxes and microcrystalline
waxes. They differ in the number of C atoms and the type of hydrocarbon compounds. Microcrystalline waxes have
longer C chains and more variation in their chemical bonds than paraffin waxes.
Methodology
The method for estimating the C content coefficient for petroleum waxes includes three steps.
Step 1. Estimate the carbon content of paraffin waxes
For the purposes of this analysis, paraffin waxes are assumed to be composed of 100 percent paraffinic
compounds with a chain of 25 C atoms. The resulting C share for paraffinic wax is 85.23 percent and the density is
estimated at 45 degrees API or 6.684 pounds per gallon.
Step 2. Estimate the carbon content of microcrystalline waxes
Microcrystalline waxes are assumed to consist of 50 percent paraffinic and 50 percent cycloparaffinic
compounds with a chain of 40 C atoms, yielding a C share of 85.56 percent. The density of microcrystalline waxes is
estimated at 36.7 degrees API, based on a sample of 10 microcrystalline waxes found in the Petroleum Products
Handbook (Martin, S.W. 1960).
Step 3. Develop a carbon content coefficient for petroleum waxes by weighting the density and carbon content
of paraffinic and microcrystalline waxes
A weighted average density and C content was calculated for petroleum waxes, assuming that wax
consumption is 80 percent paraffin wax and 20 percent microcrystalline wax. The weighted average C content is 85.30
percent, and the weighted average density is 6.75 pounds per gallon. ElA's standard heat content for waxes is 5.537
MMBtu per barrel. These inputs yield a C content coefficient for petroleum waxes of 19.80 MMT C/QBtu.
Data Sources
Density of paraffin wax was taken from ASTM (1985). Density of microcrystalline waxes was derived from 10
samples found in Guthrie (1960). A standard heat content for petroleum waxes was adopted from EIA (2009a).
Uncertainty
Although there is considerable qualitative uncertainty associated with the allocation of petroleum waxes and
microcrystalline waxes, the quantitative variation in the C contents for all waxes is limited to + 1 percent because of the
nearly uniform relationship between C and other elements in petroleum waxes broadly defined.
Crude Oil, Unfinished Oils, and Miscellaneous Products
U.S. energy statistics include several categories of petroleum products designed to ensure that reported
refinery accounts "balance" and cover any "loopholes" in the taxonomy of petroleum products. These categories include
crude oil, unfinished oils, and miscellaneous products. Crude oil is rarely consumed directly, miscellaneous products
account for less than one percent of oil consumption, and unfinished oils are a balancing item that may show negative
consumption. For C accounting purposes, it was assumed that all unfinished oils have the same C content as crude oil.
The miscellaneous products category reported by EIA includes miscellaneous products that are not reported elsewhere
in the EIA data set. According to EIA recovered sulfur compounds from petroleum and natural gas processing, and
potentially carbon black feedstock could be reported in this category. Recovered sulfur has no carbon content and would
not be reported in the Inventory. Based on this information, the miscellaneous products category reported by EIA was
assumed to be mostly petroleum refinery sulfur compounds that do not contain carbon (EIA 2019). Therefore, the
carbon content for miscellaneous products was assumed to be zero across the time series.
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Methodology
EIA reports on the average density and sulfur content of U.S. crude oil purchased by refineries. To develop a
method of estimating C content based on this information, results of ultimate analyses of 182 crude oil samples were
collected. Within the sample set, C content ranged from 82 to 88 percent C, but almost all samples fell between 84
percent and 86 percent C. The density and sulfur content of the crude oil data were regressed on the C content,
producing the following equation:
Percent C = 76.99 + (10.19 x Specific Gravity) + (-0.76 x Sulfur Content)
Absent the term representing sulfur content, the equation had an R-squared of only 0.35.26 When C content
was adjusted to exclude sulfur, the R-squared value rose to 0.65. While sulfur is the most important non-hydrocarbon
impurity, nitrogen and oxygen can also be significant, but they do not seem to be correlated with either density or sulfur
content. Restating these results, density accounts for about 35 percent of the variation in C content, impurities account
for about 30 percent of the variation, and the remaining 35 percent is accounted for by other factors, including
(presumably) the degree to which aromatics and polynuclear aromatics are present in the crude oil. Applying this
equation to the 2008 crude oil quality data (30.21 degrees API and 1.47 percent sulfur) produces an estimated C content
of 84.79 percent. Applying the density and C content to the EIA standard energy content for crude oil of 5.800 MMBtu
per barrel produced an emissions coefficient of 20.31 MMT C/QBtu.
Data Sources
Carbon content was derived from 182 crude oil samples, including 150 samples from U.S. National Research
Council (1927). A standard heat content for crude oil was adopted from EIA (2009a).
Uncertainty
The uncertainty of the estimated C content for crude oil centers on the 35 percent of variation that cannot be
explained by density and sulfur content. This variation is likely to alter the C content coefficient by +3 percent. Since
unfinished oils and miscellaneous products are impossible to define, the uncertainty of applying a crude oil C content is
likely to be bounded by the range of petroleum products described in this chapter at +10 percent.
Chronology and Explanation of Changes in Individual Carbon Content Coefficients of Fossil Fuels
The following section describes changes to carbon content coefficients of fossil fuels, organized by the calendar
year in which the update was implemented. Additional information on which Inventory year these changes appear is
provided within each section.
Coal
Original 1994 Analysis
A set of 5,426 coal samples from the EIA coal analysis file were used to develop C content estimates. The results
from that sample set appear below in Table A-44. The EIA Coal Analysis File was originally developed by the U.S. Bureau
of Mines and contained over 60,000 coal samples obtained through numerous coal seams throughout the United States.
Many of the samples were collected starting in the 1940s and 1950s through the 1980s and analyzed in U.S. government
laboratories. The coefficients developed in 1994 were in use for the 1990 through 2000 Inventory and are provided in
Table A-44.
Table A-44: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank, 1990 - 2000 (MMT
C/QBtu)	
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Consuming Sector
Electric Power	25.68 25.69 25.69 26.71 25.72 25.74 25.74 25.76 25.76 25.76 25.76
Industrial Coking	25.51 25.51 25.51 25.51 25.52 25.53 25.55 25.56 25.56 25.56 25.56
Other Industrial	25.58 25.59 25.62 25.61 25.63 25.63 25.61 25.63 25.63 25.63 25.63
26 R-squared represents the percentage of variation in the dependent variable (in this case carbon content) explained by variation in
the independent variables.
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Residential/Commercial 25.92 26.00 26.13 25.97 25.95 26.00 25.92 26.00 26.00 26.00 26.00
Coal Rank
Anthracite
28.13
28.13
28.13
28.13
28.13
28.13
28.13
28.13
28.13
28.13
28.13
Bituminous
25.37
25.37
25.37
25.37
25.37
25.37
25.37
25.37
25.37
25.37
25.37
Sub-bituminous
26.24
26.24
26.24
26.24
26.24
26.24
26.24
26.24
26.24
26.24
26.24
Lignite
26.62
26.62
26.62
26.62
26.62
26.62
26.62
26.62
26.62
26.62
26.62
Sources: Emission factors by consuming sector from B.D. Hong and E.R. Slatnick, "Carbon Dioxide Emission Factors for Coal,
"U.S. EIA, Quarterly Coal Report, January-March 1994 (Washington, DC, 1994); and emission factors by rank from Science
Applications International Corporation, Analysis of the Relationship Between Heat and Carbon Content of U.S. Fuels: Final Task
Report, Prepared for the U.S. EIA, Office of Coal, Nuclear, Electric and Alternative Fuels (Washington, DC 1992).
Subsequent Updates
In 2002 a database compiled by the U.S. Geological Survey (USGS), CoalQual 2.0 (1998), was adopted to update
the analysis. The updated sample set included 6,588 coal samples collected by the USGS and its state affiliates between
1973 and 1989. The decision to switch to the sample data contained in the USGS CoalQual database from the EIA
database was made because the samples contained in the USGS database were collected and analyzed more recently
than those obtained by EIA from the Bureau of Mines. The updated methodology first appeared in the 1990-2004
Inventory. The methodology employed for these estimates has remained unchanged since 2002,27 however, the
underlying coal data sample set has been updated over the years to integrate new data sets as they became available.
In 2010 sample data from the Energy Institute at Pennsylvania State University (504 samples) were added to
the 6,588 USGS samples to create a new database of 7,092 samples. The new coefficients developed in the 2010 update
were first implemented for the 1990 through 2008 Inventory.
In 2019 sample data from the Montana Bureau of Mines & Geology (908 samples), the Illinois State Geological
Survey Coal Quality Database (460 samples), and the Indiana Geological Survey Coal Quality Database (745 samples)
were used to calculate updated carbon contents by rank for Montana, Illinois, and Indiana. Combining revised carbon
contents for these three states with the carbon contents for all other states calculated from the USGS and Pennsylvania
State University samples yielded updated national average carbon contents by coal rank and end-use sector. The new
coefficients developed in the 2019 update were first implemented for the 1990 through 2017 Inventory.
In 2021, carbon content coefficients for industrial coking coal were updated to be annually variable to align
with the variability of other sectors and coal ranks. The new coefficients developed were first implemented for the
current 1990 through 2019 Inventory. See Table A-32 and Table A-33 for the carbon content coefficients values used in
this Inventory.
Natural Gas
Original 1994 Analysis
Prior to the 1990 through 2008 Inventory, descriptive statistics were used to stratify 6,743 samples of pipeline
quality natural gas by heat content and then to determine the average C content of natural gas at the national average
heat content (EIA 1994). The same coefficient was applied to all pipeline natural gas consumption for all years, because
U.S. energy statistics showed a range of national average heat contents of pipeline gas of only 1,025 to 1,031 Btu per
cubic foot (1 percent) from 1990 through 1994. A separate factor was developed in the same manner for all flared gas.
Previously, a weighted national average C content was calculated using the average C contents for each sub-sample of
gas that conformed with an individual state's typical cubic foot of natural gas since there is regional variation in energy
content. The result was a weighted national average of 14.47 MMT C/QBtu.
2010 and 2019 Updates
A revised analytical methodology introduced in 2010 underlies the natural gas C coefficients used in this report.
This methodology was first implemented in the 1990 through 2008 Inventory. The revised analysis conducted in 2010
used the same set of samples, but utilized a regression equation, as described above, of sample-based heat content and
27 In 2009, the analysis of the USGS Coal Qual data was updated to make a technical correction that affected the value for
lignite and those sectors which consume lignite. The updated coefficients resulting from this correction were first implemented
for the 1990 through 2007 Inventory.
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carbon content data in order to calculate annually variable national average C content coefficients based on annual
national average heat contents for pipeline natural gas and for flare gas. In addition, the revised analysis calculated an
average C content from all samples with less than 1.5 percent C02 and less than 1,050 Btu/cf (samples most closely
approximating the makeup of pipeline quality natural gas).
In 2019, this analysis was updated again to calculate annually variable national average C content coefficients
for years 2009 through 2017 in the time series using heat contents published in EIA (2019). The resulting average was
14.43 MMT C/QBtu, which is slightly less than the previous weighted national average of 14.47 MMT C/QBtu. The 2019
update was first implemented in the 1990 through 2017 Inventory. The average C contents from the 1994 calculations
are presented in Table A-45 below for comparison.
Table A-45: Carbon Content of Pipeline-Quality Natural Gas by Energy Content (MMT C/QBtu)
Sample	Average Carbon Content
GRI Full Sample	14.51
Greater than 1,000 Btu	14.47
1,025 to 1,035 Btu	14.45
975 to 1,000 Btu	14.73
1,000 to 1,025 Btu	14.43
1,025 to 1,050 Btu	14.47
1,050 to 1,075 Btu	14.58
1,075 to 1,100 Btu	14.65
Greater than 1,100 Btu	14.92
Weighted National Average	14.47
Source: EIA (1994).
Petroleum Products
2010 Update
All of the petroleum product C coefficients except that for Aviation Gasoline Blending Components were
updated in 2010 for the 1990 through 2008 Inventory and held constant through the current Inventory. EPA updated
these factors to better align the fuel properties data that underlie the Inventory factors with those published in EPA's
Mandatory Reporting of Greenhouse Gases Rule (EPA 2009b), Suppliers of Petroleum Products (MM) and Stationary
Combustion (C) subparts. The coefficients that were applied in previous reports are provided in Table A-46 below.
Specifically, each of the coefficients used in this report have been calculated from updated density and C share data,
largely adopted from analyses undertaken for the Greenhouse Gas Reporting Rule (EPA 2009b). In some cases, the heat
content applied to the conversion to a carbon-per-unit-energy basis was also updated. Additionally, the category Misc.
Products (U.S. Territories), which is based upon the coefficients calculated for crude oil, was allowed to vary annually
with the crude oil coefficient. The petrochemical feedstock category was eliminated because the constituent products-
naphthas and other oils—are estimated independently. Further, although the level of aggregation of U.S. energy
statistics currently limits the application of coefficients for residual and distillate fuels to these two generic
classifications, individual coefficients for the five major types of fuel oil (Nos. 1, 2,4, 5 and 6) were estimated and are
presented in Table A-38 above. Each of the C coefficients applied in previous Inventories are provided below for
comparison (Table A-46).
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Table A-46: Carbon Content Coefficients and Underlying Data for Petroleum Products

Carbon Content
Gross Heat of Combustion
Density

Fuel
(MMT C/QBtu)
(MMBtu/Barrel)
(API Gravity)
Percent Carbon
Motor Gasoline
19.27
5.220
59.1
86.60
LPG (Propane)
17.15
3.841
155.3
81.80
HGL (Energy Use)a
17.47
(See b)
(See b)
(See b)
HGL (Non-Energy Use)a
16.85
(See b)
(See b)
(See b)
Jet Fuel
19.33
5.670
42.0
86.30
Distillate Fuel
19.95
5.825
35.5
86.34
Residual Fuel
21.49
6.287
11.0
85.68
Asphalt and Road Oil
20.62
6.636
5.6
83.47
Lubricants
20.24
6.065
25.6
85.80
Petrochemical Feedstocks
19.37
5.248c
67.lc
84.11c
Aviation Gas
18.87
5.048
69.0
85.00
Kerosene
19.72
5.670
41.4
86.01
Petroleum Coke
27.85
6.024
-
92.28
Special Naphtha
19.86
5.248
51.2
84.76
Petroleum Waxes
19.81
5.537
43.3
85.29
Still Gas
17.51
6.000
-
-
Crude Oil
20.33
5.800
30.5
85.49
Unfinished Oils
20.33
5.825
30.5
85.49
Miscellaneous Products'1
0.00
0.00
30.5
85.49
Pentanes Plus
18.24
4.620
81.7
83.70
Natural Gasoline
18.24
4.620
81.7
83.70
Note: Indicates no sample data available.
a HGL is a blend of multiple paraffinic and olefinic hydrocarbons: ethane, propane, isobutane, and normal butane, each with
their own heat content, density and C content, see Table A-40.
b Heat, density, and percent carbon values are provided separately for ethane, and isobutene, butane, ethylene, isobutylene,
and butylene.
c Parameters presented are for naphthas with a boiling temperature less than 400 degrees Fahrenheit. Petrochemical
feedstocks with higher boiling points are assumed to have the same characteristics as distillate fuel.
d The miscellaneous products category reported by EIA is assumed to be mostly petroleum refinery sulfur compounds that do
not contain carbon (EIA 2019).
Sources: EIA (1994), EIA (2008a), EPA (2009c), EPA (2020b), ICF (2020).
Additional revisions to the Inventory's C coefficients since 1990 are detailed below.
Jet Fuel
1995 Update
Between 1994 and 1995, the C content coefficient for kerosene-based jet fuel was revised downward from
19.71 MMT C/QBtu to 19.33 MMT C/QBtu. This downward revision was the result of a shift in the sample set used from
one collected between 1959 and 1972 and reported on by Martel and Angello in 1977 to one collected by Boeing in 1989
and published by Hadaller and Momenthy in 1990. The downward revision was a result of a decrease in density, as well
as slightly lower C shares than in the earlier samples. However, the assumed heat content is unchanged because it is
based on an EIA standard and probably yields a downward bias in the revised C content coefficient. The coefficient
revised in 1995 was first implemented in the 1990 through 2007 Inventory.
2010 Update
The coefficient was revised again for the 1990 through 2008 Inventory, returning to Martel and Angello and
NIPER as the source of the carbon share and density data, respectively, for kerosene-based fuels. This change was made
in order to align the coefficients used for this report with the values used in EPA's Mandatory Reporting of Greenhouse
Gases Rule (EPA 2009b). The return to the use of the Martel and Angello and NIPER coefficients was deemed more
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appropriate for the Rule as it was considered a more conservative coefficient given the uncertainty and variability in
coefficients across the types of jet fuel in use in the United States.
Hydrocarbon Gas Liquids (HGL)
Summary of Previous Updates
The C content coefficient of HGL is updated annually to reflect changes in the consumption mix of the
underlying compounds: ethane; propane; isobutane; normal butane; ethylene; propylene; isobutylene; and butylene.
According to EIA, LPG is a subset of HGL, which include the paraffinic compounds: ethane; propane; isobutane; and
normal butane. In 1994, EIA included pentanes plus—assumed to have the characteristics of hexane—in the mix of
compounds broadly described as LPG. In 1995, EIA removed pentanes plus from this fuel category. Because pentanes
plus is relatively rich in C per unit of energy, its removal from the consumption mix lowered the C content coefficient for
LPG from 17.26 MMT C/QBtu to 16.99 MMT C/QBtu. In 1998, EIA began separating LPG consumption into two
categories: energy use and non-fuel use and providing individual coefficients for each. Because LPG for fuel use typically
contains higher proportions of propane than LPG for non-fuel use, the C content coefficient for fuel use was 1.8 to 2.5
percent higher than the coefficient for non-fuel use in previous inventories (see Table A-46).
However, in 2010 the assumptions that underlie the selection of density and heat content data for each pure
LPG compound were updated, leading to a significant revision of the assumed properties of ethane. In 2010, the physical
characteristics of ethane, which constitutes over 90 percent of LPG consumption for non-fuel uses, were updated to
reflect ethane that is in (refrigerated) liquid form. Previously, the share of ethane was included using the density and
energy content of gaseous ethane. Table A-47, below, compares the values applied for each of the compounds under the
two sets of coefficient calculations, those used in the 1990 through 2007 Inventory and those used in the 1990 through
2008 Inventory to the 1990 through 2018 Inventory. The C share of each pure compound was also updated by using
more precise values for each compound's molecular weight.
Due in large part to the revised assumptions for ethane, the weighted C content for non-fuel use was now
higher than that of the weighted coefficient for fuel use, which is dominated by the consumption of more dense
propane. Under the revised assumptions, each annual weighted coefficient for non-fuel LPG consumption is 1.2 to 1.7
percent higher each year than is that for LPGs consumed for fuel (energy) uses.
Table A-47: Physical Characteristics of Liquefied Petroleum Gases


1990-2007
2010 Update
1990-2007
2010 Update
1990-2007
2010 Update






C Content
C Content

Chemical
Density
Density
Energy Content
Energy Content
Coefficient
Coefficient
Compound
Formula
(bbl / MT)
(bbl / MT)
(MMBtu/bbl)
(MMBtu/bbl)
(MMT C/QBtu)
(MMT C/QBtu)
Ethane
c2h6
16.88
11.55
2.916
3.082
16.25
17.16
Propane
C3Hs
12.44
12.76
3.824
3.836
17.20
16.76
Isobutane
C4H10
11.20
11.42
4.162
3.974
17.75
17.77
n-butane
C4H10
10.79
10.98
4.328
4.326
17.72
17.75
Sources: Updated: Densities - CRC Handbook of Chemistry and Physics, 89th Ed. (2008/09); Energy Contents - EPA (2009b). All
values are for the compound in liquid form. The density and energy content of ethane are for refrigerated ethane (-89 degrees
C). Values for n-butane are for pressurized butane (-25 degrees C). Values in previous editions of this Inventory: Gurthrie
(1960).
2020 Update
The coefficients were revised again for the 1990 through 2019 Inventory. This change was made in order to
align the coefficients used for this report with the updated heat content values used in ElA's energy data statistics (EIA
2020a; EIA 2020b). EIA (2020a) states, "LPG is a subset of HGL, which include the paraffinic compounds: ethane;
propane; isobutane; and normal butane," therefore the Inventory revised the fuel type classification of LPG to HGL to
indicate this fuel types includes both paraffinic and olefinic compounds. Furthermore, EIA (2020a) states that HGL
consumption in the residential, commercial, and transportation sectors is 100 percent propane. Therefore, a constant,
non-weighted propane C content coefficient is applied to HGL consumption in these sectors and is referred to as "LPG -
Propane" throughout the Inventory.
The mix of HGL consumed for non-fuel use differs significantly from the mix of HGL that is combusted. C
content coefficients for HGL used for fuel use and non-fuel applications were developed based on the consumption mix
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of the individual compounds reported in U.S. energy statistics (EIA 2020b) for industrial fuel use and industrial non-fuel
use across the Inventory timeseries. The C content of each HGL was obtained from EPA (2013) and applied to the fuel
use and non-fuel use consumption of each compound. The carbon content coefficient for industrial fuel use and
industrial non-fuel use HGL was then calculated through a weighted average that accounts for the consumption
proportion for each paraffinic and olefinic compound and their associated C contents (ICF 2020).
Distillate Fuel
2021 Updates
The carbon content of diesel fuel is calculated according to ASTM D3343,28 Standard Test Method for the
Estimation of Hydrogen Content of Aviation Fuels using fuel properties inputs from the NAFS for each year and season.
This method uses a correlation between the measured fuel distillation range, API gravity, and aromatic content to
estimate the hydrogen content (Browning 2020).29
Motor Gasoline
Summary of Previous Updates
The C content coefficient for motor gasoline varies annually based on the density of and proportion of additives
in a representative sample of motor gasoline examined each year. However, in 1997 EIA began incorporating the effects
of the introduction of reformulated gasoline into its estimate of C content coefficients for motor gasoline. This change
resulted in a downward step function in C content coefficients for gasoline of approximately 0.3 percent beginning in the
1990 through 1995 Inventory. In 2005 through 2006 reformulated fuels containing ethers began to be phased out
nationally. Ethanol was added to gasoline blends as a replacement oxygenate, leading to another shift in gasoline density
(see Table A-38), in the list and proportion of constituents that form the blend and in the blended C share based on those
constituents.
28	ASTM International, ASTM D3343-16, Standard Test Method for Estimation of Hydrogen Content of Aviation Fuels,
https://www.astm.org/Standards/D3343.htm
29	As equations are based on assuming hydrocarbon containing fuels only, C % is 100 - H %.
A-121

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Table A-48: Carbon Content Coefficients for Petroleum Products, 1990-2007 (MMT C/QBtu)
Fuel Type
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Petroleum














Asphalt and Road Oil
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
Aviation Gasoline
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
Distillate Fuel Oil
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
Jet Fuel3
19.40
19.34
19.33
19.33
19.33
19.33
19.33
19.33
19.33
19.33
19.33
19.33
19.33
19.33
Kerosene
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
LPG (energy use)a
17.21
17.20
17.20
17.18
17.23
17.25
17.20
17.21
17.20
17.21
17.20
17.19
17.19
17.18
LPG (non-energy use)a
16.83
16.87
16.86
16.88
16.88
16.84
16.81
16.83
16.82
16.84
16.81
16.81
16.78
16.76
Lubricants
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
Motor Gasoline3
19.41
19.38
19.36
19.35
19.33
19.33
19.34
19.34
19.35
19.33
19.33
19.33
19.33
19.33
Residual Fuel
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
Other Petroleum














AvGas Blend Components
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
MoGas Blend Components3
19.41
19.38
19.36
19.35
19.33
19.33
19.34
19.34
19.35
19.33
19.33
19.33
19.33
19.33
Crude Oil3
20.16
20.23
20.25
20.24
20.24
20.19
20.23
20.29
20.30
20.28
20.33
20.33
20.33
20.33
Misc. Products3
20.16
20.23
20.25
20.24
20.24
20.19
20.23
20.29
20.30
20.28
20.33
20.33
20.33
20.33
Misc. Products (Terr.)
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
Naphtha (<401 deg. F)
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
Other Oil (>401 deg. F)
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
Pentanes Plus
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
Petrochemical Feed.
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
Petroleum Coke
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
Still Gas
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
Special Naphtha
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
Unfinished Oils3
20.16
20.23
20.25
20.24
20.24
20.19
20.23
20.29
20.30
20.28
20.33
20.33
20.33
20.33
Waxes
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
Other Wax and Misc.
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
a C contents vary annually based on changes in fuel composition.
2021 Updates
The annual C content of gasoline over the time series of the Inventory was determined using a combination of two data sources (Browning 2020). The first is
the measured properties of both regular and premium gasoline from the Alliance of North American Fuel Survey (NAFS). The second is the prime supplier sales volumes
of motor gasoline by type and grade from the EIA.
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References
AAM (2009) Diesel Survey. Alliance of Automobile Manufacturers, Winter 2008.
API (1990 through 2008) Sales of Natural Gas Liquids and Liquefied Refinery Gases, American Petroleum Institute.
ASTM (1985) ASTM and Other Specifications for Petroleum Products and Lubricants, American Society for Testing and
Materials. Philadelphia, PA.
Boldt, K. and B.R. Hall (1977) Significance of Tests for Petroleum Products, Philadelphia, PA, American Society for Testing
and Materials, p. 30.
Browning, L. (2020). GHG Inventory EF Development Using Certification Data. Technical Memo, September 2020.
Chemical Rubber Company (CRC) (2008/2009), Handbook of Chemistry and Physics, 89th Ed., editor D. Lide, Cleveland,
OH: CRC Press.
DOC (1929) Thermal Properties of Petroleum Products, U.S. Department of Commerce, National Bureau of Standards.
Washington, D.C. pp. 16-21.
EIA (2020a) Monthly Energy Review, November 2020, Energy Information Administration, U.S. Department of Energy,
Washington, D.C. DOE/EIA-0035(2020/11).
EIA (2020b) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
EIA (2020c) Prime Supplier Sales Volume, U.S. Department of Energy, Washington, D.C. Available online at
.
EIA (2019) Personal communication between EIA and ICF on November 11, 2019.
EIA (2001 through 2020a) Annual Coal Report, U.S. Department of Energy, Energy Information Administration.
Washington, D.C. DOE/EIA0584.
EIA (2001 through 2020b) Annual Coal Distribution Report, U.S. Department of Energy, Energy Information
Administration. Washington, D.C. DOE/EIA.
EIA (2008a) Monthly Energy Review, September 2006 and Published Supplemental Tables on Petroleum Product detail.
Energy Information Administration, U.S. Department of Energy, Washington, D.C. DOE/EIA-0035(2007/9).
EIA (2008b) Documentation for Emissions of Greenhouse Gases in the United States 2006. DOE/EIA-0638(2006). October
2008.
EIA (2009a) Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
DOE/EIA-0384(2008).
EIA (2009b) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
EIA (2001) Cost and Quality of Fuels for Electric Utility Plants 2000, Energy Information Administration. Washington, D.C.
August 2001. Available online at .
EIA (1990 through 2001) Coal Industry Annual, U.S. Department of Energy, Energy Information Administration.
Washington, D.C. DOE/EIA 0584.
EIA (1994) Emissions of Greenhouse Gases in the United States 1987-1992, Energy Information Administration, U.S.
Department of Energy. Washington, D.C. November 1994. DOE/EIA 0573.
EIA (1993) Btu Tax on Finished Petroleum Products, Energy Information Administration, Petroleum Supply Division
(unpublished manuscript, April 1993).
EPA (2020a) The Emissions & Generation Resource Integrated Database (eGRID) 2018 Technical Support Document. Clean
Air Markets Division, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
EPA (2020b) EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Updated Gasoline and Diesel Fuel
C02 Emission Factors - Memo.
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EPA (2013) Memo: Table of Final 2013 Revisions to the Greenhouse Gas Reporting Rule, Amendments to Table C-l to 40
CFR part 98, subpart C: Table C—1 to Subpart C—Default C02 Emission Factors and High Heat Values for Various
Types of Fuel. Available online at: .
EPA (2010) Carbon Content Coefficients Developed for EPA's Inventory of Greenhouse Gases and Sinks. Office of Air and
Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
EPA (2009a) "Industry Overview and Current Reporting Requirements for Petroleum Refining and Petroleum Imports,"
Petroleum Product Suppliers Technical Support Document for the Proposed Mandatory Reporting Rule. Office of Air
and Radiation. January 30, 2009.
EPA (2009b) Mandatory Reporting of Greenhouse Gases Rule. Federal Register Docket ID EPA-HQ-OAR-2008-0508-2278,
September 30, 2009.
EPA (2009c) Technical Support Document, Petroleum Products and Natural Gas Liquids: Definitions, Emission Factors,
Methods and Assumptions. Final Rule for Mandatory Reporting of Greenhouse Gases. September 15, 2009. Available
online at: < https://www.epa.gov/sites/production/files/2015-07/documents/subpartmmproductdefinitions.pdf>.
Gas Technology Institute (1992) Database as documented in W.E. Liss, W.H. Thrasher, G.F. Steinmetz, P. Chowdiah, and
A. Atari, Variability of Natural Gas Composition in Select Major Metropolitan Areas of the United States. GRI-
92/0123. March 1992.
Green & Perry, ed. (2008) Perry's Chemical Engineers' Handbook, 8th Ed. New York, NY, McGraw-Hill.
Gunderson, J. (2019) Montana Coal Sample Database. Data received 28 February 2019 from Jay Gunderson, Montana
Bureau of Mines & Geology.
Guthrie, V.B., ed. (1960) Characteristics of Compounds, Petroleum Products Handbook, p.3-3. New York, NY, McGraw-
Hill.
Hadaller, O.J. and A.M. Momenthy (1990) The Characteristics of Future Fuels, Part 1, "Conventional Heat Fuels". Seattle,
WA, Boeing Corp. September 1990. pp. 46-50 (2006).
ICF (2020) Potential Improvements to Energy Sector Hydrocarbon Gas Liquid Carbon Content Coefficients. Memorandum
from ICF to Vincent Camobreco, U.S. Environmental Protection Agency. December 7, 2020.
Illinois State Geological Survey (ISGS) (2019) Illinois Coal Quality Database, Illinois State Geological Survey.
Indiana Geological Survey (IGS) (2019) Indiana Coal Quality Database 2018, Indiana Geological Survey.
Intergovernmental Panel on Climate Change (IPCC) 2006IPCC Guidelines for National Greenhouse Gas Inventories.
Prepared by the National Greenhouse Gas Inventories Programme (Japan, 2006).Matar, S. and L. Hatch (2000)
Chemistry of Petrochemical Processes, 2nd Ed. Gulf Publishing Company: Houston.
Martel, C.R., and L.C. Angello (1977) "Hydrogen Content as a Measure of the Combustion Performance of Hydrocarbon
Fuels," in Current Research in Petroleum Fuels, Volume I. New York, NY, MSS Information Company, p. 116.
Martin, S.W. (1960) "Petroleum Coke," in Virgil Guthrie (ed.), Petroleum Processing Handbook, New York, NY, McGraw-
Hill, pp. 14-15.
Meyers (2004) Handbook of Petroleum Refining Processes, 3rd ed., NY, NY: McGraw Hill.
National Institute for Petroleum and Energy Research (NIPER) (1990 through 2009) Motor Gasolines, Summer and Motor
Gasolines, Winter.
NIPER (1993) C. Dickson, Aviation Turbine Fuels, 1992, NIPER-179 PPS93/2 (Bartlesville, OK: National Institute for
Petroleum and Energy Research, March 1993).
Pennsylvania State University (PSU) (2010) Coal Sample Bank and Database. Data received by SAIC 18 February 2010
from Gareth Mitchell, The Energy Institute, Pennsylvania State University.
Quick, Jeffrey (2010) "Carbon Dioxide Emission Factors for U.S. Coal by Origin and Destination," Environmental Science &
Technology, Forthcoming.
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U.S. National Research Council (1927) International Critical Tables of Numerical Data, Physics, Chemistry, and
Technology, New York, NY, McGraw-Hill.
Unzelman, G.H. (1992) "A Sticky Point for Refiners: FCC Gasoline and the Complex Model," Fuel Reformulation,
July/August 1992, p. 29.
USGS (1998) CoalQual Database Version 2.0, U.S. Geological Survey.
Wauquier, J., ed. (1995) Petroleum Refining, Crude Oil, Petroleum Products and Process Flowsheets (Editions Technip
Paris, 1995) pg. 225, Table 5.16.
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2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil
Fuels
Carbon (C) storage associated with the non-energy use of fossil fuels was calculated by multiplying each fuel's
potential emissions (i.e., each fuel's total C content) by a fuel-specific storage factor, as listed in Table A-49. The
remaining C—i.e., that which is not stored—is emitted. This sub-annex explains the methods and data sources employed
in developing the storage factors for (1) petrochemical feedstocks (industrial other coal, natural gas for non-fertilizer
uses, hydrocarbon gas liquids (HGL), pentanes plus, naphthas, other oils, still gas, special naphtha), (2) asphalt and road
oil, (3) lubricants, and (4) waxes. The storage factors.30 for the remaining other (industrial coking coal, petroleum coke,
distillate fuel oil, and other petroleum) non-energy fuel uses are either based on values recommended for use by IPCC
(2006), or when these were not available, assumptions based on the potential fate of C in the respective non-energy use
(NEU) products.
Table A-49: Fuel Types and Percent of C Stored for Non-Energy Uses
Sector/Fuel Type	Storage Factor (%)
Industry
Industrial Coking Coal3	10%
Industrial Other Coalb	62%
Natural Gas to Chemical Plants'5	62%
Asphalt & Road Oil	100%
HGLb	62%
Lubricants	9%
Pentanes Plusb	62%
Naphtha (<401 deg. F)b	62%
Other Oil (>401 deg. F)b	62%
Still Gasb	62%
Petroleum Cokec	30%
Special Naphthab	62%
Distillate Fuel Oil	50%
Waxes	58%
Miscellaneous Products'1	0%
Transportation
Lubricants	9%
U.S. Territories
Lubricants	9%
Other Petroleum (Misc. Prod.)	10%
a Includes processes for which specific coking coal consumption and emission factor data are not available. Consumption of
coking coal for production of iron and steel is covered in the Industrial Processes and Product Use chapter.
b The storage factor listed is the value for 2019. As described in this annex, the factor varies over time.
c Assumes petroleum coke consumption is for pigments. Consumption of petroleum coke for production of primary aluminum
anodes, electric arc furnace anodes, titanium dioxide, ammonia, urea, and ferroalloys is covered in the Industrial Processes
and Product Use chapter.
d The miscellaneous products category reported by EIA is assumed to be mostly petroleum refinery sulfur compounds that do
not contain carbon (EIA 2019).
The following sections describe the non-energy uses in greater detail, outlining the methods employed and
data used in estimating each storage factor. Several of the fuel types tracked by EIA are used in organic chemical
synthesis and in other manufacturing processes and are referred to collectively as "petrochemical feedstocks." Because
the methods and data used to analyze them overlap, they are handled as a group and are discussed first. Discussions of
the storage factors for asphalt and road oil, lubricants, waxes, and other products follow.
3°Throughout this section, references to "storage factors" represent the proportion of carbon stored.
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Petrochemical Feedstocks
Petrochemical feedstocks—industrial other coal, natural gas for non-fertilizer uses.,31 HGL, pentanes plus,
naphthas, other oils, still gas, special naphtha—are used in the manufacture of a wide variety of man-made chemicals
and products. Plastics, rubber, synthetic fibers, solvents, paints, fertilizers, pharmaceuticals, and food additives are just a
few of the derivatives of these fuel types. Chemically speaking, these fuels are diverse, ranging from simple natural gas
(i.e., predominantly CH4) to heavier, more complex naphthas and other oils.32
After adjustments for (1) use in industrial processes and (2) net exports, these eight fuel categories constituted
approximately 261.1 MMT C02 Eq., or 73 percent, of the 357.5 MMT C02 Eq. of non-energy fuel consumption in 2019.
For 2019, the storage factor for the eight fuel categories was 62 percent. In other words, of the net consumption, 62
percent was destined for long-term storage in products—including products subsequently combusted for waste
disposal—while the remaining 38 percent was emitted to the atmosphere directly as C02 (e.g., through combustion of
industrial by-products) or indirectly as C02 precursors (e.g., through evaporative product use). The indirect emissions
include a variety of organic gases such as volatile organic compounds (VOCs) and carbon monoxide (CO), which
eventually oxidize into C02 in the atmosphere. The derivation of the storage factor is described in the following sections.
Methodology and Data Sources
The petrochemical feedstocks storage factor is equal to the ratio of C stored in the final products to total C
content for the non-energy fossil fuel feedstocks used in industrial processes, after adjusting for net exports of
feedstocks. One aggregate storage factor was calculated to represent all eight fuel feedstock types. The feedstocks were
grouped because of the overlap of their derivative products. Due to the many reaction pathways involved in producing
petrochemical products (or wastes), it becomes extraordinarily complex to link individual products (or wastes) to their
parent fuel feedstocks.
Import and export data for feedstocks were obtained from the Energy Information Administration (EIA) for the
major categories of petrochemical feedstocks. ElA's Petroleum Supply Annual publication tracks imports and exports of
petrochemical feedstocks, including HGL,33 and naphthas (i.e., most of the large volume primary chemicals produced by
petroleum refineries). These imports and exports are already factored into the U.S. fuel consumption statistics. However,
EIA does not track imports and exports of chemical intermediates and products produced by the chemical industry (e.g.,
xylenes, vinyl chloride), which are derived from the primary chemicals produced by the refineries. These products
represent very large flows of C derived from fossil fuels (i.e., fossil C), so estimates of net flows not already considered in
ElA's dataset were developed for the entire time series from 1990 to 2019.
The approach to estimate imports and exports involves three steps, listed here and then described in more
detail below:
Step 1. Identify commodities derived from petrochemical feedstocks, and calculate net import/export for
each.
Step 2. Estimate the C content for each commodity.
Step 3. Sum the net C imports/exports across all commodities.
Step 1 relies heavily on information provided by the National Petrochemical and Refiners Association (NPRA)
and U.S. Bureau of the Census (BoC) trade statistics published by the U.S. International Trade Commission (USITC). NPRA
provided a spreadsheet of the ten-digit BoC Harmonized Tariff Schedule (HTS) Commodity Codes used to compile import -
31	Natural gas used as a petrochemical feedstock includes use in production of methanol. The storage factor developed for
petrochemical feedstocks includes emissions from the use of products. Therefore, it is assumed that emissions from the combustion of
methanol used in biodiesel are captured here and not reported as part of biodiesel combustion emissions.
32	Naphthas are compounds distilled from petroleum containing 4 to 12 carbon atoms per molecule and having a boiling point less than
401 degrees Fahrenheit. "Other oils" are distillates containing 12 to 25 carbon atoms per molecule and having a boiling point greater
than 401 degrees Fahrenheit.
33	HGL (formerly referred to as liquefied petroleum gas, or LPG) are hydrocarbons that occur as gases at atmospheric pressure and as
liquids under higher pressures. HGLs include paraffins, such as ethane, propane, butanes, and pentanes plus, and HGLs include olefins,
such as ethylene, propylene, and butylene. Adjustments were made in the current Inventory report to HGL activity data, carbon
content coefficients, and heat contents HGL.
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export data for periodic reports issued to NPRA's membership on trade issues. Additional feedstock commodities were
identified by HTS code in the BoC data system and included in the net import/export analysis.
One of the difficulties in analyzing trade data is that a large portion of the outputs from the refining industry are
fuels and fuel components, and it was difficult to segregate these from the outputs used for non-energy uses. The NPRA-
supplied codes identify fuels and fuel components, thus providing a sound basis for isolating net imports/exports of
petrochemical feedstocks. Although MTBE and related ether imports are included in the published NPRA data, these
commodities are not included in the total net imports/exports calculated here, because it is assumed that they are fuel
additives and do not contribute to domestic petrochemical feedstocks. Net exports of MTBE and related ethers are also
not included in the totals, as these commodities are considered to be refinery products that are already accounted for in
the EIA data. Imports and exports of commodities for which production and consumption data are provided by EIA (e.g.,
butane, ethylene, and liquefied petroleum gases) are also not included in the totals, to avoid double-counting.
Another difficulty is that one must be careful to assure that there is not double-counting of imports and exports
in the data set. Other parts of the mass balance (described later) provide information on C flows, in some cases based on
production data and in other cases based on consumption data. Production data relates only to production within the
country; consumption data incorporates information on imports and exports as well as production. Because many
commodities are emissive in their use, but not necessarily their production, consumption data is appropriately used in
calculations for emissive fates. For purposes of developing an overall mass balance on U.S. non-energy uses of C, for
those materials that are non-emissive (e.g., plastics), production data is most applicable. And for purposes of adjusting
the mass balance to incorporate C flows associated with imports and exports, it was necessary to carefully review
whether or not the mass balance already incorporated cross-boundary flows (through the use of consumption data), and
to adjust the import/export balance accordingly.
The BoC trade statistics are publicly available.34 and cover a complete time series from 1990 to 2019. These
statistics include information on imports and exports of thousands of commodities. After collecting information on
annual flows of the more than 100 commodities identified by NPRA, Step 2 involves calculating the C content for each
commodity from its chemical formula. In cases where the imports and exports were expressed in units of volume, rather
than mass, they were converted to mass based on the commodities' densities.
Step 3 involves summing the net C imports/exports across all commodities. The results of this step are shown in
Table A-50. As shown in the table, the United States has been a net exporter of chemical intermediates and products
throughout the 1990 to 2019 period.
Table A-50: Net Exports of Petrochemical Feedstocks, 1990-2019 (MMT CO2 Eq.)

1990
2005
2010
2015
2016
2017
2018
2019
Net Exports
12.0
6.5
7.3
5.5
12.7
13.9
17.1
21.0
After adjusting for imports and exports, the C budget is adjusted for the quantity of C that is used in the
Industrial Processes and Product Use sector of the Inventory. Fossil fuels used for non-energy purposes in industrial
processes—and for which C emissions and storage have been characterized through mass balance calculations and/or
emission factors that directly link the non-energy use fossil fuel raw material and the industrial process product—are not
included in the non-energy use sector. These industrial processes (and their non-energy use fossil fuel raw materials)
include iron and steel (coal coke), primary aluminum (petroleum coke), titanium oxide (petroleum coke), ferroalloys
(petroleum coke), carbon black (petroleum coke and other oils), silicon carbide (petroleum coke), and ammonia and urea
(petroleum coke and natural gas).
For each year of the Inventory, the total C content of non-energy uses was calculated by starting with the EIA
estimate of non-energy use, and reducing it by the adjustment factor for net exports (see Table A-50) and non-energy
use reported in the Industrial Processes and Product Use (IPPU) sector to yield net domestic fuel consumption for non-
energy. The balance was apportioned to either stored C or emissive C, based on a storage factor.
The overall storage factor for the feedstocks was determined by developing a mass balance on the C in
feedstocks, and characterizing products, uses, and environmental releases as resulting in either storage or emissions. The
total C in the system was estimated by multiplying net domestic consumption for non-energy by the C content of each of
the feedstocks (i.e., industrial other coal, natural gas for non-fertilizer uses, HGL, pentanes plus, naphthas, other oils, still
34See the U.S. International Trade Commission (USITC) Trade Dataweb at .
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gas, special naphtha). Carbon content values for the fuel feedstocks are discussed in the Estimating Emissions from Fossil
Fuel Combustion and Estimating the Carbon Content from Fossil Fuel Combustion Annexes.
Next, C pools and releases in a variety of industrial releases, energy recovery processes, and products were
characterized. The C fate categories are plastics, energy recovery, synthetic rubber, synthetic fibers, organic solvents, C
black, detergents and personal cleansers, industrial non-methane volatile organic compound (NMVOC) emissions,
hazardous waste incineration, industrial toxic chemical (i.e., TRI) releases, pesticides, food additives, antifreeze and
deicers (glycols), and silicones.35
The C in each product or waste produced was categorized as either stored or emitted. The aggregate storage
factor is the C-weighted average of storage across fuel types. As discussed later in the section on uncertainty, the sum of
stored C and emitted C (i.e., the outputs of the system) exceeded total C consumption (i.e., the inputs to the system) for
some years in the time series. To address this mass imbalance, the storage factor was calculated as C storage divided by
total C outputs (rather than C storage divided by C inputs).
Note that the system boundaries for the storage factor do not encompass the entire life-cycle of fossil-based C
consumed in the United States insofar as emissions of C02from waste combustion are accounted for separately in the
Inventory and are discussed in the Incineration of Waste section of the Energy chapter.
The following sections provide details on the calculation steps, assumptions, and data sources employed in
estimating and classifying the C in each product and waste shown in Table A-51. Summing the C stored and dividing it by
total C outputs yields the overall storage factor, as shown in the following equation for 2019:
Overall Storage Factor = C Stored / (C Stored + C Emitted + C Unaccounted for) =
161.8 MMT C02 Eq. / (161.8 + 63.0 + 36.3) MMT C02 Eq. = 62%
Table A-51: C Stored and Emitted by Products from Feedstocks in 2019 (MMT CO2 Eq.)

C Stored
C Emitted
Product/Waste Type
(MMT CO? Eq.)
(MMT CO? Eq.)
Industrial Releases
0.1
5.7
TRI Releases
0.1
1.0
Industrial VOCs
NA
3.4
Non-combustion CO
NA
0.5
Hazardous Waste Incineration
NA
0.9
Energy Recovery
NA
44.4
Products
161.7
12.8
Plastics
140.3
NA
Synthetic Rubber
12.9
NA
Antifreeze and Deicers
NA
1.0
Abraded Tire Rubber
NA
0.2
Food Additives
NA
1.1
Silicones
0.5
NA
Synthetic Fiber
7.8
NA
Pesticides
0.2
0.3
Soaps, Shampoos, Detergents
NA
4.7
Solvent VOCs
NA
5.6
Total	161.8	63.0
Note: Totals may not sum due to independent rounding.
NA (Not Applicable)
The C unaccounted for is the difference between the C accounted for (discussed below) and the total C in the
Total U.S. Petrochemical consumption, which are the potential carbon emissions from all energy consumption in Non-
Energy Use.
35 For the most part, the releases covered by the U.S. Toxic Release Inventory (TRI) represent air emissions or water discharges
associated with production facilities. Similarly, VOC emissions are generally associated with production facilities. These emissions could
have been accounted for as part of the Waste chapter, but because they are not necessarily associated with waste management, they
were included here. Toxic releases are not a "product" category, but they are referred to as such for ease of discussion.
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The three categories of C accounted for in the table are industrial releases, energy recovery, and products. Each
is discussed below.
Industrial Releases
Industrial releases include toxic chemicals reported through the Toxics Release Inventory (TRI), industrial
emissions of volatile organic compounds (VOCs), CO emissions (other than those related to fuel combustion), and
emissions from hazardous waste incineration.
TRI Releases
Fossil-derived C is found in many toxic substances released by industrial facilities. The TRI, maintained by EPA,
tracks these releases by chemical and environmental release medium (i.e., land, air, or water) on a biennial basis (EPA
2000b). By examining the C contents and receiving media for the top 35 toxic chemicals released, which account for 90
percent of the total mass of chemicals, the quantity of C stored and emitted in the form of toxic releases can be
estimated.
The TRI specifies releases by chemical, so C contents were assigned to each chemical based on molecular
formula. The TRI also classifies releases by disposal location as either off-site or on-site. The on-site releases are further
subdivided into air emissions, surface water discharges, underground injection, and releases to land; the latter is further
broken down to disposal in a RCRA Subtitle C (i.e., hazardous waste) landfill or to "Other On-Site Land Disposal."36 The C
released in each disposal location is provided in Table A-52.
Each on-site classification was assigned a storage factor. A 100 percent storage factor was applied to disposition
of C to underground injection and to disposal to RCRA-permitted landfills, while the other disposition categories were
assumed to result in an ultimate fate of emission as C02 (i.e., a storage factor of zero was applied to these categories).
The release allocation is not reported for off-site releases; therefore, the approach was to develop a C-weighted average
storage factor for the on-site C and apply it to the off-site releases.
For the remaining 10 percent of the TRI releases, the weights of all chemicals were added and an average C
content value, based upon the top 35 chemicals' C contents, was applied. The storage and emission allocation for the
remaining 10 percent of the TRI releases was carried out in the same fashion as for the 35 major chemicals.
Data on TRI releases for the full 1990 through 2019 time series were not readily available. Since this category is
small (less than 1 MMT C emitted and stored), the 1998 value was applied for the entire time series.
Table A-52:1998 TRI Releases by Disposal Location (kt CO2 Eq.)

Carbon Stored
Carbon Emitted
Disposal Location
(kt CO? Eq.)
(kt CO? Eq.)
Air Emissions
NA
924
Surface Water Discharges
NA
6.7
Underground Injection
89.4
NA
RCRA Subtitle C Landfill Disposal
1.4
NA
Other On-Site Land Releases
NA
15.9
Off-site Releases
6.4
36
Total
97.2
982.6
Note: Totals may not sum due to independent rounding.
NA (Not Applicable)
Volatile Organic Compound Emissions from Industrial Processes and Solvent Evaporation Emissions
Data on annual non-methane volatile organic compound (NMVOC) emissions were obtained (EPA 2020) and
disaggregated based on EPA (2003), which has been published on the National Emission Inventory (NEI) Air Pollutant
Emission Trends web site. The 1990 through 2019 Trends data include information on NMVOC emissions by end-use
36 Only the top nine chemicals had their land releases separated into RCRA Landfills and Other Land Disposal. For the remaining
chemicals, it was assumed that the ratio of disposal in these two categories was equal to the carbon-weighted average of the land
disposal fate of the top nine chemicals (i.e., 8 percent attributed to RCRA Landfills and 92 percent in the "Other" category).
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category; some of these fall into the heading of "industrial releases" in Table A-51 above, and others are related to
"product use;" for ease of discussion, both are covered here. The end-use categories that represent "Industrial NMVOC
Emissions" include some chemical and allied products, certain petroleum related industries, and other industrial
processes. NMVOC emissions from solvent utilization (product use) were considered to be a result of non-energy use of
petrochemical feedstocks. These categories were used to distinguish non-energy uses from energy uses; other categories
where VOCs could be emitted due to combustion of fossil fuels were excluded to avoid double counting.
Because solvent evaporation and industrial NMVOC emission data are provided in tons of total NMVOCs,
assumptions were made concerning the average C content of the NMVOCs for each category of emissions. The
assumptions for calculating the C fraction of industrial and solvent utilization emissions were made separately and differ
significantly. For industrial NMVOC emissions, a C content of 85 percent was assumed. This value was chosen to reflect
the C content of an average volatile organic compound based on the list of the most abundant NMVOCs provided in the
Trends Report. The list contains only pure hydrocarbons, including saturated alkanes (C contents ranging from 80 to 85
percent based upon C number), alkenes (C contents approximately 85 percent), and some aromatics (C contents
approximately 90 percent, depending upon substitution).
An EPA solvent evaporation emissions dataset (Tooly 2001) was used to estimate the C content of solvent
emissions. The dataset identifies solvent emissions by compound or compound category for six different solvent end-use
categories: degreasing, graphic arts, dry cleaning, surface coating, other industrial processes, and non-industrial
processes. The percent C of each compound identified in the dataset was calculated based on the molecular formula of
the individual compound (e.g., the C content of methylene chloride is 14 percent; the C content of toluene is 91 percent).
For solvent emissions that are identified in the EPA dataset only by chemical category (e.g., butanediol derivatives) a
single individual compound was selected to represent each category, and the C content of the category was estimated
based on the C content of the representative compound. The overall C content of the solvent evaporation emissions for
1998, estimated to be 56 percent, is assumed to be constant across the entire time series.
The results of the industrial and solvent NMVOC emissions analysis are provided in Table A-53 for 1990 through
2019. Industrial NMVOC emissions in 2019 were 3.4 MMT C02 Eq. and solvent evaporation emissions in 2019 were 5.6
MMTC02 Eq.
Table A-53: Industrial and Solvent NMVOC Emissions

1990
1995
2000
2005
2015
2016
2017
2018
2019
Industrial NMVOCsa









NMVOCs ('000 Short Tons)
1,279
1,358
802
825
1,349
1,277
1,205
1,205
1,205
Carbon Content(%)
85%
85%
85%
85%
85%
85%
85%
85%
85%
Carbon Emitted (MMT C02






3.4
3.4
3.4
Eq.)
3.6
3.8
2.3
2.3
3.8
3.6



Solvent Evaporation*1









Solvents ('000 Short Tons)
5,750
6,183
4,832
4,245
3,025
2,999
2,972
2,972
2,972
Carbon Content(%)
56%
56%
56%
56%
56%
56%
56%
56%
56%
Carbon Emitted (MMT C02









Eq.)
10.8
11.6
9.0
7.9
5.7
5.6
5.6
5.6
5.6
a Includes emissions from chemical and allied products, petroleum and related industries, and other industrial processes
categories.
b Includes solvent usage and solvent evaporation emissions from degreasing, graphic arts, dry cleaning, surface coating, other
industrial processes, and non-industrial processes.
Non-Combustion Carbon Monoxide Emissions
Carbon monoxide (CO) emissions data were also obtained from the NEI data (EPA 2020) and disaggregated
based on EPA (2003). There are three categories of CO emissions in the report that are classified as process-related
emissions not related to fuel combustion. These include chemical and allied products manufacturing, metals processing,
and other industrial processes. Some of these CO emissions are accounted for in the Industrial Processes and Product
Use section of this report and are therefore not accounted for in this section. These include total C emissions from the
primary aluminum, titanium dioxide, iron and steel, and ferroalloys production processes. The total C (CO and C02)
emissions from oil and gas production, petroleum refining, and asphalt manufacturing are also accounted for elsewhere
in this Inventory. Biogenic emissions (e.g., pulp and paper process emissions) are accounted for in the Land Use, Land-
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Use Change and Forestry chapter and excluded from calculation of CO emissions in this section. Those CO emissions that
are not accounted for elsewhere are considered to be by-products of non-fuel use of feedstocks and are thus included in
the calculation of the petrochemical feedstocks storage factor. Table A-54 lists the CO emissions that remain after taking
into account the exclusions listed above.
Table A-54: Non-Combustion Carbon Monoxide Emissions

1990
1995
2000
2005
2015
2016
2017
2018
2019
CO Emissions ('000 Short Tons)
489
481
623
461
389
358
327
327
327
Carbon Emitted (MMT C02 Eq.)
0.7
0.7
» 0.9
0.7
0.6
0.5
0.5
0.5
0.5
Note: Includes emissions from chemical and allied products, petroleum and related industries, metals processing, and other
industrial processes categories.
Hazardous Waste Incineration
Hazardous wastes are defined by the EPA under the Resource Conservation and Recovery Act (RCRA).37
Industrial wastes, such as rejected products, spent reagents, reaction by-products, and sludges from wastewater or air
pollution control, are federally regulated as hazardous wastes if they are found to be ignitable, corrosive, reactive, or
toxic according to standardized tests or studies conducted by EPA.
Hazardous wastes must be treated prior to disposal according to the federal regulations established under the
authority of RCRA. Combustion is one of the most common techniques for hazardous waste treatment, particularly for
those wastes that are primarily organic in composition or contain primarily organic contaminants. Generally speaking,
combustion devices fall into two categories: incinerators that burn waste solely for the purpose of waste management,
and boilers and industrial furnaces (BIFs) that burn waste in part to recover energy from the waste. More than half of the
hazardous waste combusted in the United States is burned in BIFs; because these processes are included in the energy
recovery calculations described below, they are not included as part of hazardous waste incineration.
EPA's Office of Solid Waste requires biennial reporting of hazardous waste management activities, and these
reports provide estimates of the amount of hazardous waste burned for incineration or energy recovery. EPA stores this
information in its Resource Conservation and Recovery Act (RCRA) Information system (EPA 2013a), formerly reported in
its Biennial Reporting System (BRS) database (EPA 2000a; 2009; 2015a; 2016a; 2018; 2021). Combusted hazardous
wastes are identified based on EPA-defined management system types M041 through M049 (incineration). Combusted
quantities are grouped into four representative waste form categories based on the form codes reported in the BRS:
aqueous liquids, organic liquids and sludges, organic solids, and inorganic solids. To relate hazardous waste quantities to
C emissions, "fuel equivalent" factors were derived for hazardous waste by assuming that the hazardous wastes are
simple mixtures of a common fuel, water, and noncombustible ash. For liquids and sludges, crude oil is used as the fuel
equivalent and coal is used to represent solids.
Fuel equivalent factors were multiplied by the tons of waste incinerated to obtain the tons of fuel equivalent.
Multiplying the tons of fuel equivalent by the C content factors (discussed in the Estimating the Carbon Content from
Fossil Fuel Combustion Annex) yields tons of C emitted. Implied C content is calculated by dividing the tons of C emitted
by the associated tons of waste incinerated.
Waste quantity data for hazardous wastes were obtained from EPA's RCRA Information/BRS database for
reporting years 1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2019 (EPA
2000a; 2009; 2013a; 2015a; 2016a; 2018; 2021). 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-55).
Regulations require incinerators to achieve at least 99.99 percent destruction of organics; this formed the basis for
assuming the fraction of C oxidized. Emissions from hazardous waste incineration in 2019 were 0.9 MMT C02 Eq. Table A-
56 lists the C02 emissions from hazardous waste incineration.
37 [42 U.S.C. §6924, SDWA §3004]
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Table A-55: Assumed Composition of Combusted Hazardous Waste by Weight (Percent)
Waste Type	Water (%) Noncombustibles (%) Fuel Equivalent (%)
Aqueous Waste
90
5
5
Organic Liquids and Sludges
40
20
40
Organic Solids
20
40
40
Inorganic Solids
20
70
10
Table A-56: CO2 Emitted from Hazardous Waste Incineration (MMT CO2 Eq.)

1990
1995
2000
2005
2015
2016
2017
2018
2019
C02 Emissions
1.1
1.7
1.4
1.5
0.9
0.9
0.9
0.9
0.9
Energy Recovery
The amount of feedstocks combusted for energy recovery was estimated from data included in ElA's
Manufacturers Energy Consumption Survey (MECS) for 1991, 1994, 1998, 2002, 2006, 2010, 2014, and 2018 (EIA 1994;
1997; 2001; 2005; 2010; 2013b; 2017; 2021). Some fraction of the fossil C exiting refineries and designated for use for
feedstock purposes actually ends up being combusted for energy recovery (despite the designation of feedstocks as a
"non-energy" use) because the chemical reactions in which fuel feedstocks are used are not 100 percent efficient. These
chemical reactions may generate unreacted raw material feedstocks or generate by-products that have a high energy
content. The chemical industry and many downstream industries are energy-intensive and often have boilers or other
energy recovery units on-site, and thus these unreacted feedstocks or by-products are often combusted for energy
recovery. Also, as noted above in the section on hazardous waste incineration, regulations provide a strong incentive—
and in some cases require—burning of organic wastes generated from chemical production processes.
Information available from the MECS include data on the consumption for energy recovery of "other" fuels in
the petroleum and coal products, chemicals, primary metals, nonmetallic minerals, and other manufacturing sectors.
These "other" fuels include refinery still gas; waste gas; waste oils, tars, and related materials; petroleum coke, coke
oven and blast furnace gases; scrap tires; liquor or black liquor; woodchips and bark; and other uncharacterized fuels.
Fuel use of petroleum coke is included separately in the fuel use data provided annually by EIA, and energy recovery of
coke oven gas and blast furnace gas (i.e., by-products of the iron and steel production process) is addressed in the Iron
and Steel production section in the Industrial Processes and Product Use chapter. Consumption of refinery still gas in the
refinery sector is also included separately in the fuel use data from EIA. The combustion of scrap tires in cement kilns,
lime kilns, and electric arc furnaces is accounted for in the Waste Incineration chapter; data from the Rubber
Manufacturers Association (RMA 2009a) were used to subtract out energy recovery from scrap tires in these industries.
Consumption of net steam, assumed to be generated from fossil fuel combustion, is also included separately in the fuel
use data from EIA. Therefore, these categories of "other" fuels are addressed elsewhere in the Inventory and not
considered as part of the petrochemical feedstocks energy recovery analysis. Liquor or black liquor and woodchips and
bark are assumed to be biogenic fuels, in accordance with IPCC (2006), and therefore are not included in the Inventory.
The remaining categories of fuels, including waste gas; waste oils, tars, and related materials; and other uncharacterized
fuels are assumed to be petrochemical feedstocks burned for energy recovery (see Table A-57). The conversion factors
listed in Annex 2.1 were used to convert the Btu values for each fuel feedstock to MMT C02. Petrochemical feedstocks
combusted for energy recovery corresponded to 42.5 MMT C02 Eq. in 1991, 35.1 MMT C02 Eq. in 1994, 58.0 MMT C02
Eq. in 1998, 70.6 MMT C02 Eq. in 2002, 74.7 MMT C02 Eq. in 2006, 41.3 MMT C02 Eq. in 2010, 45.6 MMT C02 Eq. in
2014, and 44.4 MT C02 Eq in 2018. Values for petrochemical feedstocks burned for energy recovery for years between
1991 and 1994, between 1994 and 1998, between 1998 and 2002, between 2002 and 2006, between 2007 and 2010,
between 2011 and 2013, and between 2015 and 2017 have been estimated by linear interpolation. The value for 1990 is
assumed to be the same as the value for 1991, and the value 2019 is assumed to be the same as the value for 2018
(Table A-58).
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Table A-57: Summary of 2018 MECS Data for Other Fuels Used in Manufacturing/Energy Recovery (Trillion Btu)



Waste
Refinery Still
Net
Other
Subsectorand Industry
NAICS CODE
Waste Gasa
Oils/Tarsb
Gasc
Steamd
Fuelse
Printing and Related Support
323
0
0
0
0
0
Petroleum and Coal Products
324
0
2
1,394
191
76
Chemicals
325
402
6
0
310
116
Plastics and Rubber Products
326
0
0
0
0
0
Nonmetallic Mineral Products
327
0
9
0
0
18
Primary Metals
331
3
0
0
10
3
Fabricated Metal Products
332
0
0
0
0
2
Machinery
333
0
0
0
0
1
Computer and Electronic Products
334
0
0
0
0
0
Electrical Equip., Appliances,






Components
335
0
0
0
0
0
Transportation Equipment
336
1
0
0
1
5
Furniture and Related Products
337
0
0
0
0
5
Miscellaneous
339
0
0
0
0
1
Total (Trillion Btu)

406
17
1,394
511
227
Average C Content (MMT/QBtu)

18.14
20.62
17.51
0
19.37
Fraction Oxidized

1
1
1
0
1
Total C (MMT)

7.36
0.35
24.41
0.00
4.40
Total C (MMT) (ex. still gas from






refining)

7.36
0.35
0.00
0.00
4.40
NA (Not Applicable)
a C content: Waste Gas is assumed to be same as naphtha <401 deg. F.
b C content: Waste Oils/Tars is assumed to be same as asphalt/road oil.
c Refinery "still gas" fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate.
d Net steam fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate.
e C content: "Other" is assumed to be the same as petrochemical feedstocks.
Table A-58: Carbon Emitted from Fuels Burned for Energy Recovery (MMT CO2 Eq.)

1990
1995
2000
2005
2015
2016
2017
2018
2019
C Emissions
42.5
40.8
J 64.3
73.7
45.3
45.0
44.7
44.4
44.4
Products
More C is found in products than in industrial releases or energy recovery. The principal types of products are
plastics; synthetic rubber; synthetic fiber; C black; pesticides; soaps, detergents, and cleansers; food additives; antifreeze
and deicers (glycols); silicones; and solvents. Solvent evaporation was discussed previously along with industrial releases
of NMVOCs; the other product types are discussed below.
Plastics
Data on annual production of plastics through 2005 were taken from the American Plastics Council (APC), as
published in Chemical & Engineering News and on the APC and Society of Plastics Industry (SPI) websites, and through
direct communication with the APC (APC 2000, 2001, 2003 through 2006; SPI 2000; Eldredge-Roebuck 2000). Data for
2006 through 2019 were taken directly or derived from the American Chemistry Council (ACC 2007 through 2020b
supplemented by Vallianos 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020). In 2009, the American
Chemistry Council consolidated the resin categories for which it reports plastics production. Production numbers in the
original categories were provided via personal correspondence for 2009, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
2018, and 2019 (Vallianos 2011; 2012; 2013; 2014; 2015; 2016; 2017; 2018; 2019; 2020). Production figures for the
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consolidated resin categories in 2010 were linearly interpolated from 2009 and 2011 data. Production was organized by
resin type (see Table A-59) and by year.
Several of the resin categories included production from Canada and/or Mexico, in addition to the U.S. values
for part of the time series. The production data for the affected resins and years were corrected using an economic
adjustment factor, based on the percent of North American production value in this industry sector accounted for by the
United States (Chemistry Industry Association of Canada 2020, Bank of Canada 2020). A C content was then assigned for
each resin. These C contents were based on molecular formulae and are listed in Table A-60 and Table A-61. In cases
where the resin type is generic, referring to a group of chemicals and not a single polymer (e.g., phenolic resins, other
styrenic resins), a representative compound was chosen. For other resins, a weighted C content of 75 percent was
assumed (i.e., it was assumed that these resins had the same content as those for which a representative compound
could be assigned).
There were no emissive uses of plastics identified, so 100 percent of the C was considered stored in products.
As noted in the chapter, an estimate of emissions related to the combustion of these plastics in the municipal solid waste
stream can be found in the Incineration of Waste section of the Energy chapter; those emissions are not incorporated in
the mass balance for feedstocks (described in this annex) to avoid double-counting.
Table A-59: 2019 Plastic Resin Production (MMT dry weight) and C Stored (MMT CO2 Eq.)

2019 Production3
Carbon Stored
Resin Type
(MMT dry weight)
(MMT CO? Eq.)
Epoxy
0.2
0.7
Polyester
0.6
1.5
Urea
1.2
1.5
Melamine
0.1
0.1
Phenolic
1.6
4.5
Low-Density Polyethylene (LDPE)
3.2
10.1
Linear Low-Density Polyethylene (LLDPE)
8.6
27.1
High Density Polyethylene (HDPE)
9.4
29.4
Polypropylene (PP)
6.7
21.0
Acrylonitrile-butadiene-styrene (ABS)
0.5
1.6
Other Styrenicsb
0.5
1.7
Polystyrene (PS)
1.7
5.6
Nylon
0.4
1.0
Polyvinyl chloride (PVC)C
6.7
9.5
Thermoplastic Polyester
3.1
7.2
All Other (including Polyester (unsaturated))
6.5
17.9
Total	51.1	140.3
Note: Totals may not sum due to independent rounding.
a Production estimates provided by the American Chemistry Council include Canadian production
for Urea, Melamine, Phenolic, LDPE, LLDPE, HDPE, PP, ABS, SAN, Other Styrenics, PS, Nylon, PVC,
and Thermoplastic Polyester, and Mexican production for PP, ABS, SAN, Other Styrenics, Nylon,
and Thermoplastic Polyester. Values have been adjusted to account just for U.S. production.
b Includes Styrene-acrylonitrile (SAN).
c Includes copolymers.
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Table A-60: Assigned C Contents of Plastic Resins (% by weight)
Resin Type
C Content
Source of C Content Assumption
Epoxy
76%
Typical epoxy resin made from epichlorhydrin and bisphenol A
Polyester (Unsaturated)
63%
Poly (ethylene terephthalate) (PET)
Urea
34%
50% carbamal, 50% N-(hydroxymethyl) urea3
Melamine
29%
Trimethylol melamine3
Phenolic
77%
Phenol
Low-Density Polyethylene (LDPE)
86%
Polyethylene
Linear Low-Density Polyethylene (LLDPE)
86%
Polyethylene
High Density Polyethylene (HDPE)
86%
Polyethylene
Polypropylene (PP)
86%
Polypropylene
Acrylonitrile-Butadiene-Styrene (ABS)
85%
50% styrene, 25% acrylonitrile, 25% butadiene
Styrene-Acrylonitrile (SAN)
80%
50% styrene, 50% acrylonitrile
Other Styrenics
92%
Polystyrene
Polystyrene (PS)
92%
Polystyrene
Nylon
65%
Average of nylon resins (see Table A-)
Polyvinyl Chloride (PVC)
38%
Polyvinyl chloride
Thermoplastic Polyester
63%
Polyethylene terephthalate
All Other
75%
Weighted average of other resin production
a Does not include alcoholic hydrogens.
Table A-61: 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 (RMA). The market information is presented in the report 2017 U.S.
Scrap Tire Management Summary (RMA 2018), while the tire composition information is from the "Scrap Tires, Facts and
Figures" section of the organization's website (RMA 2009). Data on synthetic rubber in other products (durable goods,
nondurable goods, and containers and packaging) were obtained from EPA's Municipal Solid Waste in the United States
reports (1996 through 2003a, 2005, 2007b, 2008, 2009a, 2011a, 2013b, 2014, 2016b, 2019) and detailed unpublished
backup data for some years not shown in the Characterization of Municipal Solid Waste in the United States reports
(Schneider 2007). The abraded rubber from scrap passenger tires was assumed to be 2.5 pounds per scrap tire, while the
abraded rubber from scrap commercial tires was assumed to be 10 pounds per scrap tire. Data on abraded rubber
weight were obtained by calculating the average weight difference between new and scrap tires (RMA 2018). Import and
export data were obtained from the published by the U.S. International Trade Commission (U.S. International Trade
Commission 1990 through 2019).
A C content for synthetic rubber (90 percent for tire synthetic rubber and 85 percent for non-tire synthetic
rubber) was assigned based on the weighted average of C contents (based on molecular formula) by elastomer type
consumed in 1998, 2001, and 2002 (see Table A-62). The 1998 consumption data were obtained from the International
Institute of Synthetic Rubber Producers (IISRP) press release Synthetic Rubber Use Growth to Continue Through 2004,
Says IISRP and RMA (IISRP 2000). The 2001 and 2002 consumption data were obtained from the IISRP press release, IISRP
Forecasts Moderate Growth in North America to 2007 (IISRP 2003).
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The rubber in tires that is abraded during use (the difference between new tire and scrap tire rubber weight)
was considered to be 100 percent emitted. Other than abraded rubber, there were no emissive uses of scrap tire and
non-tire rubber identified, so 100 percent of the non-abraded amount was assumed stored. Emissions related to the
combustion of rubber in scrap tires and consumer goods can be found in the Incineration of Waste section of the Energy
chapter.
Table A-62: 2002 Rubber Consumption (kt) and C Content (%)
Elastomer Type
2002 Consumption (kt)a
C Content
SBR Solid
768
91%
Polybutadiene
583
89%
Ethylene Propylene
301
86%
Polychloroprene
54
59%
NBR Solid
84
77%
Polyisoprene
58
88%
Others
367
88%
Weighted Average
NA
90%
Total
2,215
NA
Note: Totals may not sum due to independent rounding.
NA (Not Applicable)
a Includes consumption in Canada.
Synthetic Fibers
Annual synthetic fiber production data were obtained from the ACC, as published in the Guide to the Business
of Chemistry (ACC 2020a), and the Fiber Economics Bureau, as published in Chemical & Engineering News (FEB 2001,
2003, 2005, 2007, 2009, 2010, 2011, 2012, 2013). For acrylic fiber, the most recent data available were for 2012, so it
was assumed that the 2013, 2014, 2015, 2016, 2017, 2018, and 2019 consumption was equal to that of 2012. For
polyester, nylon, and olefin, the most recent data were for 2019. These data are organized by year and fiber type. For
each fiber, a C content was assigned based on molecular formula (see Table A-63). For polyester, the C content for poly
(ethylene terephthalate) (PET) was used as a representative compound. For nylon, the average C content of nylon 6 and
nylon 6.6 was used, since these are the most widely produced nylon fibers. Cellulosic fibers, such as acetate and rayon,
have been omitted from the synthetic fibers' C accounting displayed here because much of their C is of biogenic origin
and carbon fluxes from biogenic compounds are accounted for in the Land Use, Land-Use Change and Forestry chapter.
These fibers account for only 4 percent of overall fiber production by weight.
There were no emissive uses of fibers identified, so 100 percent of the C was considered stored. Note that
emissions related to the combustion of textiles in municipal solid waste are accounted for under the Incineration of
Waste section of the Energy chapter.
Table A-63: 2019 Fiber Production (MMT), C Content (%), and C Stored (MMT CO2 Eq.)

Production

C Stored
Fiber Type
(MMT)
C Content
(MMT CO? Eq.)
Polyester
1.3
63%
2.9
Nylon
0.5
64%
1.2
Olefin
1.1
86%
3.6
Acrylic
+
68%
0.1
Total
3.0
NA
7.8
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 MMT.
NA (Not Applicable)
Pesticides
Pesticide consumption data were obtained from the 1994/1995, 1996/1997, 1998/1999, 2000/2001,
2006/2007, and 2008-2012 Pesticides Industry Sales and Usage Market Estimates (EPA 1998,1999, 2002, 2004, 2011b,
2017) reports. The most recent data available were for 2012, so it was assumed that the 2013 through 2019
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consumption was equal to that of 2012. Active ingredient compound names and consumption weights were available for
the top 25 agriculturally-used pesticides and top 10 pesticides used in the home and garden and the
industry/commercial/government categories. The report provides a range of consumption for each active ingredient; the
midpoint was used to represent actual consumption. Each of these compounds was assigned a C content value based on
molecular formula. If the compound contained aromatic rings substituted with chlorine or other halogens, then the
compound was considered persistent and the C in the compound was assumed to be stored. All other pesticides were
assumed to release their C to the atmosphere. Over one-third of 2012 total pesticide active ingredient consumption was
not specified by chemical type in the Sales and Usage report (EPA 2017). This unspecified portion of the active ingredient
consumption was treated as a single chemical and assigned a C content and a storage factor based on the weighted
average of the known chemicals' values.
Table A-64: Active Ingredient Consumption in Pesticides (Million lbs.) and C Emitted and Stored (MMT CO2 Eq.)
in 2012

Active Ingredient
C Emitted
C Stored
Pesticide Usea
(Million lbs.)
(MMT CO? Eq.)
(MMT CO? Eq.)
Agricultural Uses
606.0
0.2
0.1
Non-Agricultural Uses
58.0
+
+
Home & Garden
39.5
+
+
Industry/Gov't/Commercial
28.0
+
+
Other
342.0
0.1
0.1
Total
1,006.0
0.3
0.2
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 MMT C02 Eq.
a 2012 estimates (EPA 2017).
Soaps, Shampoos, and Detergents
Cleansers—soaps, shampoos, and detergents—are among the major consumer products that may contain
fossil C. All of the C in cleansers was assumed to be fossil-derived, and, as cleansers eventually biodegrade, all of the C
was assumed to be emitted. The first step in estimating C flows was to characterize the "ingredients" in a sample of
cleansers. For this analysis, cleansers were limited to the following personal household cleaning products: bar soap,
shampoo, laundry detergent (liquid and granular), dishwasher detergent, and dishwashing liquid. Data on the annual
consumption of household personal cleansers were obtained from the U.S. Census Bureau 1992,1997, 2002, 2007, 2012
Economic Census (U.S. Bureau of the Census 1994,1999, 2004, 2009, 2014). Production values, given in terms of the
value of shipments, for 1990 and 1991 were assumed to be the same as the 1992 value; consumption was interpolated
between 1992 and 1997, 1997 and 2002, 2002 and 2007, and 2007 and 2012; production for 2013 through 2019 was
assumed to equal the 2012 value. Cleanser production values were adjusted by import and export data to develop U.S.
consumption estimates.
Chemical formulae were used to determine C contents (as percentages) of the ingredients in the cleansers.
Each product's overall C content was then derived from the composition and contents of its ingredients. From these
values the mean C content for cleansers was calculated to be 21.9 percent.
The Census Bureau presents consumption data in terms of quantity (in units of million gallons or million
pounds) and/or terms of value (thousands of dollars) for eight specific categories, such as "household liquid laundry
detergents, heavy duty" and "household dry alkaline automatic dishwashing detergents." Additionally, the report
provides dollar values for the total consumption of "soaps, detergents, etc.—dry" and "soaps, detergents, etc.—liquid."
The categories for which both quantity and value data are available is a subset of total production. Those categories that
presented both quantity and value data were used to derive pounds per dollar and gallons per dollar conversion rates,
and they were extrapolated (based on the Census Bureau estimate of total value) to estimate the total quantity of dry
and liquid38 cleanser categories, respectively.
Next, the total tonnage of cleansers was calculated (wet and dry combined) for 1997. Multiplying the mean C
content (21.9 percent) by this value yielded an estimate of 4.6 MMT C02 Eq. in cleansers for 1997. For all subsequent
38 A density of 1.05 g/mL—slightly denser than water—was assumed for liquid cleansers.
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years, it was assumed that the ratio of value of shipments to total carbon content remained constant. For 1998 through
2019, value of shipments was adjusted to 1997 dollars using the producer price index for soap and other detergent
manufacturing (Bureau of Labor Statistics 2020). The ratio of value of shipments to carbon content was then applied to
arrive at total carbon content of cleansers. Estimates are shown in Table A-65.
Table A-65: C Emitted from Utilization of Soaps, Shampoos, and Detergents (MMT CO2 Eq.)

1990
1995
2000
2005
2015
2016
2017
2018
2019
C Emissions
3.6
4.2
4.5
6.7
4.8
4.7
4.7
4.7
4.7
Antifreeze and Deicers
Glycol compounds, including ethylene glycol, propylene glycol, diethylene glycol, and triethylene glycol, are
used as antifreeze in motor vehicles, deicing fluids for commercial aircraft, and other similar uses. These glycol
compounds are assumed to ultimately enter wastewater treatment plants where they are degraded by the wastewater
treatment process to C02 or to otherwise biodegrade to C02. Glycols are water soluble and degrade rapidly in the
environment (Howard 1993).
Annual production data for each glycol compound used as antifreeze and deicers were obtained from the Guide
to the Business of Chemistry (ACC 2020a) and the EPA Chemical Data Access Tool (CDAT) (EPA 2014). Import and export
data were used to adjust annual production data to annual consumption data. The percentage of the annual
consumption of each glycol compound used for antifreeze and deicing applications was estimated from Chemical Profiles
data published on The Innovation Group website.39 and from similar data published in the Chemical Market Reporter,
which became ICIS Chemical Business in 2005.40 Production data for propylene glycol, diethylene glycol, and triethylene
glycol are no longer reported in the Guide to the Business of Chemistry, so data from ICIS Chemical Business on total
demand was used with import and export data to estimate production of these chemicals. ICIS last reported total
demand for propylene glycol and diethylene glycol in 2006, and triethylene glycol demand in 2007. EPA reported total
U.S. production of propylene glycol, diethylene glycol, and triethylene glycol in 2012 in the CDAT (EPA 2014). Total
demand for these compounds for 2012 was calculated from the 2012 production data using import and export data.
Demand for propylene glycol and diethylene glycol was interpolated for years between 2006 and 2012, and demand for
triethylene glycol was interpolated for years between 2007 and 2012, using the calculated 2012 total demand values for
each compound and the most recently reported total demand data from ICIS. Values for 2014, 2015, 2016, 2017, 2018,
and 2019 for these compounds were assumed to be the same as the 2012 values.
The glycol compounds consumed in antifreeze and deicing applications is assumed to be 100 percent emitted
as C02. Emissions of C02 from utilization of antifreeze and deicers are summarized in Table A-66.
Table A-66: C Emitted from Utilization of Antifreeze and Deicers (MMT CO2 Eq.)

1990
1995
2000
2005
2015
2016
2017
2018
2019
C Emissions
1.2
1.4
1.5
1.2
1.0
1.0
1.0
1.1
1.0
Food Additives
Petrochemical feedstocks are used to manufacture synthetic food additives, including preservatives, flavoring
agents, and processing agents. These compounds include glycerin, propylene glycol, benzoic acid, and other compounds.
These compounds are incorporated into food products, and are assumed to ultimately enter wastewater treatment
plants where they are degraded by the wastewater treatment processes to C02 or to otherwise biodegrade to C02.
Certain food additives, e.g., glycerin, are manufactured both from petrochemical feedstocks and from biogenic
feedstocks. Food additives that are derived from biogenic feedstocks are accounted for in the Land Use, Land-Use
Change and Forestry chapter.
Annual production data for food additive compounds were obtained from the Guide to the Business of
Chemistry (ACC 2020a). Historical values for adipic acid, acetic acid, and maleic anhydride were adjusted according to the
most recent data in the 2019 Guide to the Business of Chemistry. Import and export data were used to adjust annual
production data to annual consumption data. The percentage of the annual consumption of food additive compounds
39	See .
40	See .
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was estimated from Chemical Profiles data published on The Innovation Group website.41 and from similar data published
in the Chemical Market Reporter, which became ICIS Chemical Business in 2005.42 Production data for several food
additive compounds are no longer reported in the Guide to the Business of Chemistry, so data from ICIS Chemical
Business on total demand was used with import and export data to estimate production of these chemicals.
ICIS last reported total demand for glycerin and benzoic acid in 2007, and demand for propionic acid in 2008.
Total demand for dipropylene glycol was last reported by ICIS in 2004. ICIS last reported cresylic acid demand in 1999.
EPA reported total U.S. production of these compounds in 2012 in the CDAT (EPA 2014). Total demand for these
compounds for 2012 was calculated from the 2012 production data using import and export data. Demand for each of
these compounds was interpolated for years between the most recently reported total demand data from ICIS and 2012,
using the calculated 2012 total demand values for each compound. Values for 2014, 2015, 2016, 2017, 2018, and 2019
for these compounds were assumed to be the same as the 2012 values.
The consumption of synthetic food additives is assumed to be 100 percent emitted as C02. Emissions of C02
from utilization of synthetic food additives are summarized in Table A-67.
Table A-67: C Emitted from Utilization of Food Additives (MMT CO2 Eq.)

1990
1995
2000
2005
2015
2016
2017
2018
2019
C Emissions
0.6
0.7
0.7
00
O
1.1
1.1
1.1
1.1
1.1
Silicones
Silicone compounds (e.g., polymethyl siloxane) are used as sealants and in manufactured products. Silicone
compounds are manufactured from petrochemical feedstocks including methyl chloride. It is assumed that
petrochemical feedstocks used to manufacture silicones are incorporated into the silicone products and not emitted as
C02 in the manufacturing process. It is also assumed that the C contained in the silicone products is stored, and not
emitted as C02.
Import and export data were used to adjust annual production data to annual consumption data. The
percentage of the annual consumption of each silicone manufacturing compound was estimated from Chemical Profiles
data published on The Innovation Group website and from similar data published in the Chemical Market Reporter,
which became ICIS Chemical Business in 2005.43 ICIS last reported production of methyl chloride in 2007. EPA reported
total U.S. production of methyl chloride in 2012 in the CDAT (EPA 2014). Total consumption of methyl chloride for 2012
was calculated from the 2012 production data using import and export data. Production of methyl chloride was
interpolated for years between 2007 and 2012, using the calculated 2012 total production value for methyl chloride and
the most recently reported total production data from ICIS. The production values for 2014, 2015, 2016, 2017, 2018 and
2019 were assumed to be the same as the 2012 value.
The consumption of silicone manufacturing compounds is assumed to be 100 percent stored, and not emitted
as C02. Storage of silicone manufacturing compounds is summarized in Table A-68.
Table A-68: C Stored in Silicone Products (MMT CO2 Eq.)	

1990
1995
2000
2005
2015
2016
2017
2018
2019
C Storage
0.3
0.4
0.4
0.4
0.5
0.5
0.5
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 2019. 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
41	See .
42	See .
43	See .
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estimates were determined using assumptions based on source category knowledge. Uncertainty estimates for
production data (the majority of the variables) were assumed to exhibit a normal distribution with a relative error of ±20
percent in the underlying EIA estimates, plus an additional ±15 percent to account for uncertainty in the assignment of
imports and exports. An additional 10 percent (for a total of ±45 percent) was applied to the production of other oils
(>401 degrees Fahrenheit) to reflect the additional uncertainty in the assignment of part of the production quantity to
industrial processes. A relatively narrow uniform distribution ±1 percent to ±15 percent, depending on the fuel type) was
applied to each C coefficient.
The Monte Carlo analysis produced a storage factor distribution with a standard deviation of 7 percent and the
95 percent confidence interval of 50 percent and 73 percent. This compares to the calculated Inventory estimate of 62
percent. The analysis produced a C emission distribution with a standard deviation of 28.5 MMT C02 Eq. and 95 percent
confidence limits of 56.4 and 160.4 MMT C02 Eq. This compares with a calculated Inventory estimate of 99.3 MMT C02
Eq.
The apparently tight confidence limits for the storage factor and C storage probably understate uncertainty, as
a result of the way this initial analysis was structured. As discussed above, the storage factor for feedstocks is based on
an analysis of six fates that result in long-term storage (e.g., plastics production), and eleven that result in emissions
(e.g., volatile organic compound emissions). Rather than modeling the total uncertainty around all 17 of these fate
processes, the current analysis addresses only the storage fates, and assumes that all C that is not stored is emitted. As
the production statistics that drive the storage factors are relatively well-characterized, this approach yields a result that
is probably biased toward understating uncertainty.
As far as specific sources of uncertainty, there are several cross-cutting factors that pervade the
characterization of C flows for feedstocks. The aggregate storage factor for petrochemical feedstocks (industrial other
coal, natural gas for non-fertilizer uses, HGL, pentanes plus, naphthas, other oils, still gas, special naphtha) is based on
assuming that the ultimate fates of all of these fuel types—in terms of storage and emissions—are similar. In addition,
there are uncertainties associated with the simplifying assumptions made for each end use category C estimate.
Generally, the estimate for a product is subject to one or more of the following uncertainties:
•	The value used for estimating the C content has been assumed or assigned based upon a representative
compound.
•	The split between C storage and emission has been assumed based on an examination of the environmental
fate of the products in each end use category.
•	Environmental fates leading to emissions are assumed to operate rapidly, i.e., emissions are assumed to occur
within one year of when the fossil C enters the non-energy mass balance. Some of the pathways that lead to
emissions as C02 may actually take place on a time-scale of several years or decades. By attributing the
emissions to the year in which the C enters the mass balance (i.e., the year in which it leaves refineries as a
non-energy fuel use and thus starts being tracked by EIA), this approach has the effect of "front-end loading"
the emission profile.
Another cross-cutting source of uncertainty is that for several sources the amount of C stored or emitted was
calculated based on data for only a single year. This specific year may not be representative of storage for the entire
Inventory period. Sources of uncertainty associated with specific elements of the analysis are discussed below.
Import and export data for petrochemical feedstocks were obtained from EIA, the National Petroleum Refiners
Association, and the BoC for the major categories of petrochemical feedstocks (EIA 2001; NPRA 2001; and U.S. Bureau of
the Census 2017). The complexity of the organic chemical industry, with multiple feedstocks, intermediates, and subtle
differences in nomenclature, makes it difficult to ensure that the adjustments to the EIA data for imports and exports is
accurate and the approach used here may underestimate or overestimate net exports of C.
Oxidation factors have been applied to non-energy uses of petrochemical feedstocks in the same manner as for
energy uses. However, for those fuels where IPCC storage factors are used, this "oxidation factor" may be inherent in the
storage factor applied when calculating emissions from non-energy consumption, which would result in a double-
counting of the unoxidized C. Oxidation factors are small corrections, on the order of 1 percent, and therefore
application of oxidation factors to non-energy uses may result in a slight underestimation of C emissions from non-
energy uses.
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The major uncertainty in using the TRI data is the possibility of double counting emissions that are already
accounted for in the NMVOC data (see above) and in the storage and emission assumptions used. The approach for
predicting environmental fate simplifies some complex processes, and the balance between storage and emissions is
very sensitive to the assumptions on fate. Extrapolating from known to unknown characteristics also introduces
uncertainty. The two extrapolations with the greatest uncertainty are: (1) that the release media and fate of the off-site
releases were 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 C02 emissions from solvent evaporation and industrial
NMVOC emissions are in the estimates of (a) total emissions and (b) their C content. Solvent evaporation and industrial
NMVOC emissions reported by EPA are based on a number of data sources and emission factors, and may underestimate
or overestimate emissions. The C content for solvent evaporation emissions is calculated directly from the specific
solvent compounds identified by EPA as being emitted, and is thought to have relatively low uncertainty. The C content
for industrial emissions has more uncertainty, however, as it is calculated from the average C content of an average
volatile organic compound based on the list of the most abundant measured NMVOCs provided in EPA (2002a).
Uncertainty in the hazardous waste combustion analysis is introduced by the assumptions about the
composition of combusted hazardous wastes, including the characterization that hazardous wastes are similar to
mixtures of water, noncombustibles, and fuel equivalent materials. Another limitation is the assumption that all of the C
that enters hazardous waste combustion is emitted—some small fraction is likely to be sequestered in combustion ash-
but given that the destruction and removal efficiency for hazardous organics is required to meet or exceed 99.99
percent, this is a very minor source of uncertainty. C emission estimates from hazardous waste should be considered
central value estimates that are likely to be accurate to within +50 percent.
The amount of feedstocks combusted for energy recovery was estimated from data included in the
Manufacturers Energy Consumption Surveys (MECS) for 1991, 1994,1998, 2002, 2006, 2010, 2014, and 2018 (EIA 1994,
1997, 2001, 2005, 2010, 2013b, 2017, 2021). MECS is a comprehensive survey that is conducted every four years and
intended to represent U.S. industry as a whole, but because EIA does not receive data from all manufacturers (i.e., it is a
sample rather than a census), EIA must extrapolate from the sample. Also, the "other" fuels are identified in the MECS
data in broad categories, including refinery still gas; waste gas; waste oils, tars, and related materials; petroleum coke,
coke oven and blast furnace gases; and other uncharacterized fuels. Moreover, the industries using these "other" fuels
are also identified only in broad categories, including the petroleum and coal products, chemicals, primary metals,
nonmetallic minerals, and other manufacturing sectors. The "other" fuel consumption data are reported in BTUs (energy
units) and there is uncertainty concerning the selection of a specific conversion factor for each broad "other" fuel
category to convert energy units to mass units. Taken as a whole, the estimate of energy recovery emissions probably
introduces more uncertainty than any other element of the non-energy analysis.
Uncertainty in the C storage estimate for plastics arises primarily from four factors. First, production of some
plastic resins is not tracked directly and must be estimated based on other market data. Second, the raw data on
production for several resins include Canadian and/or Mexican production and may overestimate the amount of plastic
produced from U.S. fuel feedstocks; this analysis includes adjustments to "back out" the Canadian and Mexican values,
but these adjustments are approximate. Third, the assumed C content values are estimates for representative
compounds, and thus do not account for the many formulations of resins available. This uncertainty is greater for resin
categories that are generic (e.g., phenolics, other styrenics, nylon) than for resins with more specific formulations (e.g.,
polypropylene, polyethylene). Fourth, the assumption that all of the C contained in plastics is stored ignores certain end
uses (e.g., adhesives and coatings) where the resin may be released to the atmosphere; however, these end-uses are
likely to be small relative to use in plastics.
The quantity of C stored in synthetic rubber only accounts for the C stored in scrap tire synthetic rubber. The
value does not take into account the rubber stored in other durable goods, clothing, footwear, and other non-durable
goods, or containers and packaging. This adds uncertainty to the total mass balance of C stored. There are also
uncertainties as to the assignment of C content values; however, they are much smaller than in the case of plastics.
There are probably fewer variations in rubber formulations than in plastics, and the range of potential C content values is
much narrower. Lastly, assuming that all of the C contained in rubber is stored ignores the possibility of volatilization or
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degradation during product lifetimes. However, the proportion of the total C that is released to the atmosphere during
use is probably negligible.
A small degree of uncertainty arises from the assignment of C content values in textiles; however, the
magnitude of this uncertainty is less than that for plastics or rubber. Although there is considerable variation in final
textile products, the stock fiber formulations are standardized and proscribed explicitly by the Federal Trade
Commission.
For pesticides, the largest source of uncertainty involves the assumption that an active ingredient's C is either
zero percent stored or 100 percent stored. This split is a generalization of chemical behavior, based upon active-
ingredient molecular structure, and not on compound-specific environmental data. The mechanism by which a
compound is bound or released from soils is very complicated and can be affected by many variables, including the type
of crop, temperature, application method, and harvesting practice. Another smaller source of uncertainty arises from the
C content values applied to the unaccounted for portion of active ingredient. C contents vary widely among pesticides,
from 7 to 77 percent, and the remaining pesticides may have a chemical make-up that is very different from the 49
pesticides that have been examined. Additionally, pesticide consumption data were only available for 1987,1993,1995,
1997,1999, 2001, 2007, 2009, and 2012; the majority of the time series data were interpolated or held constant at the
latest (2012) value. Another source of uncertainty is that only the "active" ingredients of pesticides are considered in the
calculations; the "inactive" ingredients may also be derived from petrochemical feedstocks.
It is important to note that development of this uncertainty analysis is a multi-year process. The current
feedstocks analysis examines NEU fuels that end in storage fates. Thus, only C stored in pesticides, plastics, synthetic
fibers, synthetic rubbers, silicones, and TRI releases to underground injection and Subtitle C landfills is accounted for in
the uncertainty estimate above. In the future this analysis will be expanded to include the uncertainty surrounding
emitted fates in addition to the storage fates. Estimates of variable uncertainty will also be refined where possible to
include fewer assumptions. With these major changes in future Inventories, the uncertainty estimate is expected to
change, and likely increase. An increase in the uncertainty estimate in the coming years will not indicate that the
Inventory calculations have become less certain, but rather that the methods for estimating uncertainty have become
more comprehensive; thus, potential future changes in the results of this analysis will reflect a change in the uncertainty
analysis, not a change in the Inventory quality.
Asphalt and Road Oil
Asphalt is one of the principal non-energy uses of fossil fuels. The term "asphalt" generally refers to a mixture
of asphalt cement and a rock material aggregate, a volatile petroleum distillate, or water. For the purposes of this
analysis, "asphalt" is used interchangeably with asphalt cement, a residue of crude oil. Though minor amounts of C are
emitted during production, asphalt has an overall C storage factor of almost 100 percent, as discussed below.
Paving is the primary application of asphalt cement, comprising 86 percent of production. The three types of
asphalt paving produced in the United States are hot mix asphalt (HMA), cut-backs, and emulsified asphalt. HMA, which
makes up 90 percent of total asphalt paving (EPA 2001), contains asphalt cement mixed with an aggregate of rock
materials. Cut-back asphalt is composed of asphalt cement thinned with a volatile petroleum distillate (e.g., naphtha).
Emulsified asphalt contains only asphalt cement and water. Roofing products are the other significant end use of asphalt
cement, accounting for approximately 14 percent of U.S. production (Kelly 2000). No data were available on the fate of C
in asphalt roofing; it was assumed that it has the same fate as C in asphalt paving applications.
Methodology and Data Sources
A C storage factor was calculated for each type of asphalt paving. The fraction of C emitted by each asphalt type
was multiplied by consumption data for asphalt paving (EPA 2001) to estimate a weighted average C storage factor for
asphalt as a whole.
The fraction of C emitted by HMA was determined by first calculating the organic emissions (volatile organic
compounds [VOCs], carbon monoxide [CO], polycyclic aromatic hydrocarbons [PAHs], hazardous air pollutants [HAPs],
and phenol) from HMA paving, using emission factors reported in EPA (2001) and total HMA production.44The next step
was to estimate the C content of the organic emissions. This calculation was based on the C content of CO and phenol,
44The emission factors are expressed as a function of asphalt paving tonnage (i.e., including the rock aggregate as well as the asphalt
cement).
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and an assumption of 85 percent C content for PAHs and HAPs. The C content of asphalt paving is a function of (1) the
proportion of asphalt cement in asphalt paving, assumed to be 8 percent asphalt cement content based on EPA (2001),
and (2) the proportion of C in asphalt cement. For the latter factor, all paving types were characterized as having a mass
fraction of 85 percent C in asphalt cement, based on the assumption that asphalt is primarily composed of saturated
paraffinic hydrocarbons. By combining these estimates, the result is that over 99.6 percent of the C in asphalt cement
was retained (i.e., stored), and less than 0.4 percent was emitted.
Cut-back asphalt is produced in three forms: rapid, medium, and slow cure. The production processes for all
three forms emit C primarily from the volatile petroleum distillate used in the process as a diluent to thin the asphalt
cement so that it can be applied more readily (EPA 2001).
A mass balance on C losses from asphalt was constructed by first estimating the amount of carbon emitted as
VOCs. Values for medium cure asphalt are used to represent all cut-back asphalt. The average weight of distillates used
in medium cure cut-back asphalt (35 percent) is multiplied by the loss rate (as emissions of VOCs) of 70 percent from the
Emissions Inventory Guidebook to arrive at an estimate that 25 percent of the diluent is emitted (Environment Canada
2006). Next, the fraction of C in the asphalt/ diluent mix that is emitted was estimated, assuming 85 percent C content;
this yields an overall storage factor of 93.5 percent for cut-back asphalt.
One caveat associated with this calculation is that it is possible that the carbon flows for asphalt and diluent
(volatile petroleum distillate) are accounted for separately in the EIA statistics on fossil fuel flows, and thus the mass
balance calculation may need to re-map the system boundaries to correctly account for carbon flows. EPA plans to re-
evaluate this calculation in the future.
It was assumed that there was no loss of C from emulsified asphalt (i.e., the storage factor is 100 percent)
based on personal communication with an expert from Akzo Nobel Coatings, Inc. (James 2000).
Data on asphalt and road oil consumption and C content factors were supplied by EIA. Hot mix asphalt
production and emissions factors, and the asphalt cement content of HMA were obtained from Hot Mix Asphalt Plants
Emissions Assessment Report from EPA's AP-42 (EPA 2001) publication. The consumption data for cut-back and
emulsified asphalts were taken from a Moulthrop, et al. study used as guidance for estimating air pollutant emissions
from paving processes (EIIP 2001). "Asphalt Paving Operation" AP-42 (EPA 2001) provided the emissions source
information used in the calculation of the C storage factor for cut-back asphalt. The storage factor for emulsified asphalt
was provided by Alan James of Akzo Nobel Coatings, Inc. (James 2000).
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the asphalt C storage factor and the quantity of C stored in asphalt in 2019. The Tier 2
analysis was performed to allow the specification of probability density functions for key variables, within a
computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments
of uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty
estimates were determined using assumptions based on source category knowledge. Uncertainty estimates for asphalt
production were assumed to be ±20 percent, while the asphalt property variables were assumed to have narrower
distributions. A narrow uniform distribution, with maximum 5 percent uncertainty (± 5 percent) around the mean, was
applied to the C content coefficient.
The Monte Carlo analysis produced a tight distribution of storage factor values, with the 95 percent confidence
interval of 99 percent and 100 percent. This compares to the storage factor value used in the Inventory of 99.6 percent.
The analysis produced a C emission distribution with a standard deviation of 0.1 and 95 percent confidence limits of 0.1
MMT C02 Eq. and 0.6 MMT C02 Eq. This compares to an Inventory calculated estimate of 0.3 MMT C02 Eq.
The principal source of uncertainty is that the available data are from short-term studies of emissions
associated with the production and application of asphalt. As a practical matter, the cement in asphalt deteriorates over
time, contributing to the need for periodic re-paving. Whether this deterioration is due to physical erosion of the cement
and continued storage of C in a refractory form or physicochemical degradation and eventual release of C02 is uncertain.
Long-term studies may reveal higher lifetime emissions rates associated with degradation.
Many of the values used in the analysis are also uncertain and are based on estimates and professional
judgment. For example, the asphalt cement input for hot mix asphalt was based on expert advice indicating that the
range is variable—from about 3 to 5 percent—with actual content based on climate and geographical factors (Connolly
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2000). Over this range, the effect on the calculated C storage factor is minimal (on the order of 0.1 percent). Similarly,
changes in the assumed C content of asphalt cement would have only a minor effect.
The consumption figures for cut-back and emulsified asphalts are based on information reported for 1994.
More recent trends indicate a decrease in cut-back use due to high VOC emission levels and a related increase in
emulsified asphalt use as a substitute. This change in trend would indicate an overestimate of emissions from asphalt.
Future improvements to this uncertainty analysis, and to the overall estimation of a storage factor for asphalt,
include characterizing the long-term fate of asphalt.
Lubricants
Lubricants are used in industrial and transportation applications. They can be subdivided into oils and greases,
which differ in terms of physical characteristics (e.g., viscosity), commercial applications, and environmental fate.
According to EIA (2020), the C content from U.S. production of lubricants in 2019 was approximately 5.1 MMT C. Based
on apportioning oils and greases to various environmental fates, and characterizing those fates as resulting in either
long-term storage or emissions, the overall C storage factor was estimated to be 9.2 percent; thus, emissions in 2019
were about 4.6 MMT C, or 16.9 MMT C02 Eq.
Methodology and Data Sources
For each lubricant category, a storage factor was derived by identifying disposal fates and applying assumptions
as to the disposition of the C for each practice. An overall lubricant C storage factor was calculated by taking a
production-weighted average of the oil and grease storage factors.
Oils
Regulation of used oil in the United States has changed dramatically over the past 20 years.45 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-69 provides an estimated allocation of the fates of lubricant oils (Rinehart 2000), along with an
estimate of the proportion of C stored in each fate. The ultimate fate of the majority of oils (about 84 percent) is
combustion, either during initial use or after collection as used oil. Combustion results in 99 percent oxidation to C02
(EllP 1999), with correspondingly little long-term storage of C in the form of ash. Dumping onto the ground or into storm
sewers, primarily by "do-it-yourselfers" who change their own oil, is another fate that results in conversion to C02 given
that the releases are generally small and most of the oil is biodegraded (based on the observation that land farming-
application to soil—is one of the most frequently used methods for degrading refinery wastes). In the landfill
environment, which tends to be anaerobic within municipal landfills, it is assumed that 90 percent of the oil persists in an
undegraded form, based on analogy with the persistence of petroleum in native petroleum-bearing strata, which is also
anaerobic. Re-refining adds a recycling loop to the fate of oil. Re-refined oil was assumed to have a storage factor equal
to the weighted average for the other fates (i.e., after re-refining, the oil would have the same probability of combustion,
landfilling, or dumping as virgin oil), that is, it was assumed that about 97 percent of the C in re-refined oil is ultimately
oxidized. Because of the dominance of fates that result in eventual release as C02, only about 3 percent of the C in oil
lubricants goes into long-term storage.
45 For example, the U.S. EPA "RCRA (Resource Conservation and Recovery Act) On-line" web site ()
has over 50 entries on used oil regulation and policy for 1994 through 2000.
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Table A-69: Commercial and Environmental Fate of Oil Lubricants (Percent)
Fate of Oil	Portion of Total Oil	C Stored
Combusted During Use
20%
0.2%
Not Combusted During Use
80%
2.7%
Combusted as Used Oil3
64%
0.6%
Dumped on the ground or in storm sewers
6%
NA
Landfilled
2%
1.8%
Re-refined into lube oil base stock and other products
8%
0.2%
Weighted Average
NA
2.9%
NA (Not Applicable)
a For example, in boilers or space heaters.
Greases
Table A-70 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.
Table A-70: Commercial and Environmental Fate of Grease Lubricants (Percent)	

Portion of Total

Fate of Grease
Grease
C Stored
Combusted During Use
5%
0.1%
Not Combusted During Use
95%
81.7%
Landfilled
90%
77.0%
Dumped on the ground or in storm sewers
10%
4.8%
Weighted Average
NA
81.8%
Having derived separate storage factors for oil and grease, the last step was to estimate the weighted average
for lubricants as a whole. No data were found apportioning the mass of lubricants into these two categories, but the U.S.
Census Bureau does maintain records of the value of production of lubricating oils and lubricating greases. These were
retrieved from the relevant industry series summaries from the 1997 Economic Census (U.S. Bureau of the Census 1999).
Assuming that the mass of lubricants can be allocated according to the proportion of value of production (92 percent oil,
8 percent grease), applying these weights to the storage factors for oils and greases (3 percent and 82 percent) yields an
overall storage factor of 9.2 percent.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the lubricants weighted average C storage factor and the quantity of C emitted from
lubricants in 2019. The Tier 2 analysis was performed to allow the specification of probability density functions for key
variables, within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or
expert judgments of uncertainty were not available directly from the information sources for the activity variables; thus,
uncertainty estimates were determined using assumptions based on source category knowledge. Uncertainty estimates
for oil and grease variables were assumed to have a moderate variance, in triangular or uniform distribution. Uncertainty
estimates for lubricants production were assumed to be rather high (±20 percent). A narrow uniform distribution, with 6
percent uncertainty (± 6 percent) around the mean, was applied to the lubricant C content coefficient.
The Monte Carlo analysis produced a storage factor distribution with the 95 percent confidence interval of 4
percent and 18 percent. This compares to the calculated Inventory estimate of 9.2 percent. The analysis produced a C
emission distribution approximating a normal curve with a standard deviation of 1.4 and 95 percent confidence limits of
14.0 MMT C02 Eq. and 19.6 MMT C02 Eq. This compares to an inventory-calculated estimate of 16.9 MMT C02 Eq.
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The principal sources of uncertainty for the disposition of lubricants are the estimates of the commercial use,
post-use, and environmental fate of lubricants, which, as noted above, are largely based on assumptions and judgment.
There is no comprehensive system to track used oil and greases, which makes it difficult to develop a verifiable estimate
of the commercial fates of oil and grease. The environmental fate estimates for percent of C stored are less uncertain,
but also introduce uncertainty in the estimate.
The assumption that the mass of oil and grease can be divided according to their value also introduces
uncertainty. Given the large difference between the storage factors for oil and grease, changes in their share of total
lubricant production have a large effect on the weighted storage factor.
Future improvements to the analysis of uncertainty surrounding the lubricants C storage factor and C stored
include further refinement of the uncertainty estimates for the individual activity variables.
Waxes
Waxes are organic substances that are solid at ambient temperature, but whose viscosity decreases as
temperature increases. Most commercial waxes are produced from petroleum refining, though "mineral" waxes derived
from animals, plants, and lignite (coal) are also used. An analysis of wax end uses in the United States, and the fate of C
in these uses, suggests that about 42 percent of C in waxes is emitted, and 58 percent is stored.
Methodology and Data Sources
The National Petroleum Refiners Association (NPRA) considers the exact amount of wax consumed each year by
end use to be proprietary (Maguire 2004). In general, about thirty percent of the wax consumed each year is used in
packaging materials, though this percentage has declined in recent years. The next highest wax end use, and fastest
growing end use, is candles, followed by construction materials and firelogs. Table A-71 categorizes some of the wax end
uses, which the NPRA generally classifies into cosmetics, plastics, tires and rubber, hot melt (adhesives), chemically
modified wax substances, and other miscellaneous wax uses (NPRA 2002).
Table A-71: Emissive and Non-emissive (Storage) Fates of Waxes: Uses by Fate and Percent of Total Mass
Use
Emissive
Non-emissive
Packaging
6%
24%
Non-packaging
36%
34%
Candles
18%
2%
Construction Materials
4%
14%
Firelogs
7%
+
Cosmetics
1%
2%
Plastics
1%
2%
Tires/Rubber
1%
1%
Hot Melts
1%
1%
Chemically Modified
+
1%
Other
2%
9%
Total	42%	58%
+ Does not exceed 0.5 percent.
A C storage factor for each wax end use was estimated and then summed across all end uses to provide an
overall C storage factor for wax. Because no specific data on C contents of wax used in each end use were available, all
wax products are assumed to have the same C content. Table A-72 categorizes wax end uses identified by the NPRA and
lists the estimated C storage factor of each end use.
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Table A-72: Wax End-Uses by Fate, Percent of Total Mass, Percent C Stored, and Percent of Total C Mass Stored

Percent of Total
Percent of C
Percent of Total
Use
Wax Mass
Stored
C Mass Stored
Packaging
30%
79%
24%
Non-Packaging



Candles
20%
10%
2%
Construction Materials
18%
79%
14%
Firelogs
7%
1%
+
Cosmetics
3%
79%
2%
Plastics
3%
79%
2%
Tires/Rubber
3%
47%
1%
Hot Melts
3%
50%
1%
Chemically Modified
1%
79%
1%
Other
12%
79%
9%
Total
100%
NA
58%
Notes: Totals may not sum due to independent rounding. Estimates of percent stored are based on ICF professional judgment.
+ Does not exceed 0.5 percent.
NA (Not Applicable)
Source mass percentages: NPRA (2002).
Emissive wax end-uses include candles, firelogs (synthetic fireplace logs), hotmelts (adhesives), matches, and
explosives. At about 20 percent, candles consume the greatest portion of wax among emissive end uses. As candles
combust during use, they release emissions to the atmosphere. For the purposes of the Inventory, it is assumed that 90
percent of C contained in candles is emitted as C02. In firelogs, petroleum wax is used as a binder and as a fuel, and is
combusted during product use, likely resulting in the emission of nearly all C contained in the product. Similarly, C
contained in hotmelts is assumed to be emitted as C02 as heat is applied to these products during use. It is estimated
that 50 percent of the C contained in hot melts is stored. Together, candles, firelogs, and hotmelts constitute
approximately 30 percent of annual wax production (NPRA 2002).
All of the wax utilized in the production of packaging, cosmetics, plastics, tires and rubber, and other products
is assumed to remain in the product (i.e., it is assumed that there are no emissions of C02 from wax during the
production of the product). Wax is used in many different packaging materials including wrappers, cartons, papers,
paperboard, and corrugated products (NPRA 2002). Davie (1993) and Davie et al. (1995) suggest that wax coatings in
packaging products degrade rapidly in an aerobic environment, producing C02; however, because packaging products
ultimately enter landfills typically having an anaerobic environment, most of the C from this end use is assumed to be
stored in the landfill.
In construction materials, petroleum wax is used as a water repellent on wood-based composite boards, such
as particle board (IGI 2002). Wax used for this end-use should follow the life-cycle of the harvested wood used in
product, which is classified into one of 21 categories, evaluated by life-cycle, and ultimately assumed to either be
disposed of in landfills or be combusted (EPA 2003).
The fate of wax used for packaging, in construction materials, and for most remaining end uses is ultimately to
enter the municipal solid waste (MSW) stream, where it is either combusted or sent to landfill for disposal. Most of the C
contained in these wax products will be stored. It is assumed that approximately 21 percent of the C contained in these
products will be emitted through combustion or at landfill. With the exception of tires and rubber, these end-uses are
assigned a C storage factor of 79 percent.
Waxes used in tires and rubber follow the life cycle of the tire and rubber products. Used tires are ultimately
recycled, landfilled, or combusted. The life-cycle of tires is addressed elsewhere in this annex as part of the discussion of
rubber products derived from petrochemical feedstocks. For the purposes of the estimation of the C storage factor for
waxes, wax contained in tires and rubber products is assigned a C storage factor of 47 percent.
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 2019. A Tier 2 analysis
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was performed to allow the specification of probability density functions for key variables, within a computational
structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of uncertainty
were not available directly from the information sources for the activity variables; thus, uncertainty estimates were
determined using assumptions based on source category knowledge. Uncertainty estimates for wax variables were
assumed to have a moderate variance, in normal, uniform, or triangular distribution; uniform distributions were applied
to total consumption of waxes and the C content coefficients.
The Monte Carlo analysis produced a storage factor distribution, whose 95 percent confidence interval values
fell within the range of 47 percent and 67 percent. This compares to the calculated Inventory estimate of 57.8 percent.
The analysis produced an emission distribution, with the 95 percent confidence interval values of 0.2 MMT C02 Eq. and
0.6 MMT C02 Eq. This compares with a calculated Inventory estimate of 0.3 MMT C02 Eq., which falls within the range of
95 percent confidence limits established by this quantitative uncertainty analysis. Uncertainty associated with the wax
storage factor is considerable due to several assumptions pertaining to wax imports/exports, consumption, and fates.
Miscellaneous Products
Miscellaneous products are defined by the U.S. Energy Information Administration as: "all finished [petroleum]
products not classified elsewhere, e.g., petrolatum; lube refining by-products (e.g., aromatic extracts and tars);
absorption oils; ram-jet fuel; petroleum rocket fuel; synthetic natural gas feedstocks; and specialty oils."
Methodology and Data Sources
The "miscellaneous products" category reported by EIA includes miscellaneous products that are not reported
elsewhere in the EIA data set. The EIA does not have firm data concerning the amounts of various products that are
being reported in the "miscellaneous products" category; however, EIA has indicated that recovered sulfur compounds
from petroleum and natural gas processing, and potentially also carbon black feedstock could be reported in this
category. Recovered sulfur has no carbon content and would not be reported in the NEU calculation or elsewhere in the
Inventory. Based on this information, the miscellaneous products category reported by EIA was assumed to be mostly
petroleum refinery sulfur compounds that do not contain carbon (EIA 2019). Therefore, the carbon content for
miscellaneous products was updated to be zero across the time series. This resulted in recalculating historical emissions
from 1990 through 2018.
Other Non-Energy Uses
The remaining fuel types use storage factors that are not based on U.S.-specific analysis. For industrial coking
coal and distillate fuel oil, storage factors were taken from Marland and Rotty (1984). These factors are 0.1 and 0.5,
respectively.
IPCC does not provide guidance on storage factors for the remaining fuel types (petroleum coke and other
petroleum), and assumptions were made based on the potential fate of C in the respective NEUs. Specifically, the
storage factor for petroleum coke is 0.3, based on information from Huurman (2006) indicating that petroleum coke is
used in the Netherlands for production of pigments, with 30 percent being stored long-term. Carbon dioxide emissions
from carbide production are implicitly accounted for in the storage factor calculation for the non-energy use of
petroleum coke. The "other petroleum" category is reported by U.S. Territories and accounts mostly for the same
products as miscellaneous products, but probably also includes some asphalt, known to be non-emissive. The exact
amount of asphalt or any of the other miscellaneous products is confidential business information, but based on
judgment, the storage factor for this category was estimated at 0.1.
For all these fuel types, the overall methodology simply involves multiplying C content by a storage factor,
yielding an estimate of the mass of C stored. To provide a complete analysis of uncertainty for the entire NEU
subcategory, the uncertainty around the estimate of "other" NEUs was characterized, as discussed below.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the weighted average of the remaining fuels' C storage factors and the total quantity of C emitted from
these other fuels in 2019. A Tier 2 analysis was performed to allow the specification of probability density functions for
key variables, within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses
or expert judgments of uncertainty were not available directly from the information sources for some of the activity
variables; thus, uncertainty estimates were determined using assumptions based on source category knowledge. A
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uniform distribution was applied to coking coal consumption, while the remaining consumption inputs were assumed to
be normally distributed. The C content coefficients were assumed to have a uniform distribution; the greatest
uncertainty range of 10 percent (± 10 percent) around the Inventory value, was applied to coking coal. C coefficients for
distillate fuel oil ranged from 18.5 to 21.1 MMT C/QBtu. The fuel-specific storage factors were assigned wide triangular
distributions indicating greater uncertainty.
The Monte Carlo analysis produced a storage factor distribution with 95 percent confidence limits of 8 percent
and 81 percent. This compares to the Inventory calculation of weighted average (across the various fuels) storage factor
of about 11.3 percent. The analysis produced an emission distribution, with the 95 percent confidence limit of 2.5 MMT
C02 Eq. and 13.9 MMT C02 Eq. This compares with the Inventory estimate of 12.0 MMT C02 Eq., which falls closer to the
upper boundary of the 95 percent confidence limit. The uncertainty analysis results are driven primarily by the very
broad uncertainty inputs for the storage factors.
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References
ACC (2020a) "Guide to the Business of Chemistry, 2020," American Chemistry Council.
ACC (2020b) "U.S. Resin Production & Sales 2019 vs. 2018." Available online at:
.
ACC (2017) "U.S. Resin Production & Sales 2016 vs. 2015."
ACC (2016) "U.S. Resin Production & Sales 2015 vs. 2014."
ACC (2015) "U.S. Resin Production & Sales: 2014 vs. 2013," American Chemistry Council. Available online at:
.
ACC (2014) "U.S. Resin Production & Sales: 2013 vs. 2012," American Chemistry Council. Available online at:
.
ACC (2007 through 2011) "PIPS Year-End Resin Statistics: Production, Sales and Captive Use." Available online at:
.
APC (2003 through 2006) "APC Year-End Statistics."
APC (2001) as cited in ACS (2001) "Production: slow gains in output of chemicals and products lagged behind U.S.
economy as a whole" Chemical & Engineering News.
APC (2000) Facts and Figures, Chemical & Engineering News, June 26, 2000.
Bank of Canada (2020) Financial Markets Department Year Average of Exchange Rates. Available online at:
.
Bank of Canada (2019) Financial Markets Department Year Average of Exchange Rates. Available online at:
.
Bank of Canada (2017) Financial Markets Department Year Average of Exchange Rates.
Bank of Canada (2016) Financial Markets Department Year Average of Exchange Rates.
Bank of Canada (2013) Financial Markets Department Year Average of Exchange Rates.
Bank of Canada (2012) Financial Markets Department Year Average of Exchange Rates.
Bank of Canada (2009) Financial Markets Department Year Average of Exchange Rates.
Bureau of Labor Statistics (2020) Producer Price Index Industry Data: Soap and Other Detergent Manufacturing. Available
online at: .
Chemistry Industry Association of Canada (2020) Economic Review of Chemistry. Available online at:
.
Davie, I.N., J.P. Winter, and R.P. Varoney (1995) "Decomposition of Coated Papers from a Quick Service Restaurant."
Technical Association for Pulp and Paper Industry Journal. Vol 78 (5): 127-130.
Davie, I.N. (1993) "Compostability of Petroleum Wax-based Coatings." Technical Association for Pulp and Paper Industry
Journal. Vol 76 (2): 167-170.
EIA (2021) EIA Manufacturing Consumption of Energy (MECS) 2018. U.S. Department of Energy, Energy Information
Administration, Washington, D.C.
EIA (2020) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, November 2020. Energy
Information Administration, U.S. Department of Energy, Washington, D.C. DOE/EIA-0035 (2020/11).
EIA (2019) Personal communication between EIA and ICF on November 11, 2019.
A-151

-------
EIA (2017) EIA Manufacturing Consumption of Energy (MECS) 2014. U.S. Department of Energy, Energy Information
Administration, Washington, D.C.
EIA (2013b) EIA Manufacturing Consumption of Energy (MECS) 2010. U.S. Department of Energy, Energy Information
Administration, Washington, D.C.
EIA (2009) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
Available online at .
EIA (2010) EIA Manufacturing Consumption of Energy (MECS) 2006, U.S. Department of Energy, Energy Information
Administration, Washington, D.C.EIA (2005) EIA Manufacturing Consumption of Energy (MECS) 2002, U.S.
Department of Energy, Energy Information Administration, Washington, D.C.
EIA (2001) EIA Manufacturing Consumption of Energy (MECS) 1998, U.S. Department of Energy, Energy Information
Administration, Washington, D.C.
EIA (1997) EIA Manufacturing Consumption of Energy (MECS) 1994, U.S. Department of Energy, Energy Information
Administration, Washington, D.C.
EIA (1994) EIA Manufacturing Consumption of Energy (MECS) 1991, U.S. Department of Energy, Energy Information
Administration, Washington, D.C.
Eldredge-Roebuck (2000) Personal communication between Joe Casola, ICF Consulting and Brandt Eldredge-Roebuck,
American Plastics Council, 11 July 2000.
EIIP (2001) "Area Sources" Asphalt Paving, Emissions Inventory Improvement Program: State and Territorial Air Pollution
Program Administrators/Association of Local Air Pollution Control Officials and U.S. EPA, EIIP Document Series Vol.
Ill, Ch. 17. (STAPPA/ALAPCO/EPA), Washington D.C., January 2001. Available online at
.
EIIP (1999) Methods for Estimating Greenhouse Gas Emissions from Combustion of Fossil Fuels. Emissions Inventory
Improvement Program: State and Territorial Air Pollution Program Administrators/Association of Local Air Pollution
Control Officials and U.S. Environmental Protection Agency, EIIP Document Series Volume VIII, Chapter 1,
STAPPA/ALAPCO/EPA, Washington, D.C. August 2000.
Environment Canada (2006) Emissions Inventory Guidebook vl.3. Criteria Air Contaminants Division: Quebec, Canada.
Available online at: .
EPA (2020) "1970 - 2019 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory (NEI)
Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards, April 2020. Available online at:
.
EPA (2019) Advancing Sustainable Materials Management: 2016 and 2017 Data Tables. Office of Land and Emergency
Management, U.S. Environmental Protection Agency. Washington, D.C. Available online at:
.
EPA (2013a, 2015a, 2016a, 2018, 2021) RCRAInfo, Biennial Report, Generation and Management (GM) Form (Section 2 -
Onsite Management) and Waste Received from Offsite (WR) Form.
EPA (2017) EPA's Pesticides Industry Sales and Usage, 2008-2012 Market Estimates. Available online at:
.
EPA (2016b) Advancing Sustainable Materials Management: 2014 Facts and Figures Fact Sheet. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, D.C. Available online at:
.
EPA (2014) Chemical Data Access Tool (CDAT). U.S. Environmental Protection Agency, June 2014. Available online at <
https://chemview.epa.gov/chemview>. Accessed January 2015.
EPA (1996 through 2003a, 2005, 2007b, 2008, 2009a, 2011a, 2013b, 2014) Municipal Solid Waste in the United States:
Facts and Figures. Office of Solid Waste and Emergency Response, U.S. Environmental Protection Agency,
A-152 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
Washington, D.C. Available online at: .
EPA (2009) Biennial Reporting System (BRS) Database. U.S. Environmental Protection Agency, Envirofacts Warehouse.
Washington, D.C. Available online at .
EPA (2006) Air Emissions Trends - Continued Progress Through 2005. U.S. Environmental Protection Agency, Washington
D.C. December 19, 2006.
EPA (2004) EPA's Pesticides Industry Sales and Usage, 2000 and 2001 Market Estimates. Available online at <
https://nepis.epa.gov/Exe/ZyPDF.cgi/3000659P.PDF?Dockey=3000659P.PDF>. Accessed September 2006.
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and the
Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (2002) EPA's Pesticides Industry Sales and Usage, 1998 and 1999 Market Estimates, table 3.6. Available online at
. Accessed July 2003.
EPA (2001) AP 42, Volume I, Fifth Edition. Chapter 11: Mineral Products Industry. Available online at
.
EPA (2000a) Biennial Reporting System (BRS). U.S. Environmental Protection Agency, Envirofacts Warehouse.
Washington, D.C. Available online at .
EPA (2000b) Toxics Release Inventory, 1998. U.S. Environmental Protection Agency, Office of Environmental Information,
Office of Information Analysis and Access, Washington, D.C. Available online at
.
EPA (1999) EPA's Pesticides Industry Sales and Usage, 1996-1997 Market Estimates and Available online at:
.
EPA (1998) EPA"s Pesticides Industry Sales and Usage, 1994-1995 Market Estimates. Available online at
.
FEB (2013) Fiber Economics Bureau, as cited in C&EN (2013) Lackluster Year for Chemical Output: Production stayed flat
or dipped in most world regions in 2012. Chemical &Engineering News, American Chemical Society, 1 July. Available
online at: .
FEB (2012) Fiber Economics Bureau, as cited in C&EN (2012) Too Quiet After the Storm: After a rebound in 2010, chemical
production hardly grew in 2011. Chemical &Engineering News, American Chemical Society, 2 July. Available online
at: .
FEB (2011) Fiber Economics Bureau, as cited in C&EN (2011) Output Ramps up in all Regions. Chemical & Engineering
News, American Chemical Society, 4 July. Available online at: .
FEB (2010) Fiber Economics Bureau, as cited in C&EN (2010) Output Declines in U.S., Europe. Chemical & Engineering
News, American Chemical Society, 6 July. Available online at: .
FEB (2009) Fiber Economics Bureau, as cited in C&EN (2009) Chemical Output Slipped In Most Regions. Chemical &
Engineering News, American Chemical Society, 6 July. Available online at: .
FEB (2007) Fiber Economics Bureau, as cited in C&EN (2007) Gains in Chemical Output Continue. Chemical & Engineering
News, American Chemical Society. July 2, 2007. Available online at: .
FEB (2005) Fiber Economics Bureau, as cited in C&EN (2005) Production: Growth in Most Regions. Chemical & Engineering
News, American Chemical Society, 11 July. Available online at: .
FEB (2003) Fiber Economics Bureau, as cited in C&EN (2003) Production Inches Up in Most Countries. Chemical &
Engineering News, American Chemical Society, 7 July. Available online at: .
A-153

-------
FEB (2001) Fiber Economics Bureau, as cited in ACS (2001) Production: slow gains in output of chemicals and products
lagged behind U.S. economy as a whole Chemical & Engineering News, American Chemical Society, 25 June.
Available online at: .
Financial Planning Association (2006) Canada/US Cross-Border Tools: US/Canada Exchange Rates. Available online at:
. Accessed August 16, 2006.
Gosselin, Smith, and Hodge (1984) Clinical Toxicology of Commercial Products. Fifth Edition, Williams & Wilkins,
Baltimore.
Huurman, J.W.F. (2006) Recalculation of Dutch Stationary Greenhouse Gas Emissions Based on sectoral Energy Statistics
1990-2002. Statistics Netherlands, Voorburg, The Netherlands.
IGI (2002) 100 Industry Applications. The International Group Inc.
IISRP (2003) "IISRP Forecasts Moderate Growth in North America to 2007" International Institute of Synthetic Rubber
Producers, Inc. New Release; available online at: .
IISRP (2000) Synthetic Rubber Use Growth to Continue Through 2004, Says IISRP and RMA. International Institute of
Synthetic Rubber Producers press release.
INEGI (2006) Produccion bruta total de las unidades economicas manufactureras por Subsector, Rama, Subrama y Clase
de actividad. Available online at:
. Accessed
August 15.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds.; Institute for Global Environmental
Strategies (IGES). Hayama, Kanagawa, Japan.
James, A. (2000) Personal communication between Suzanne Bratis of ICF International and Alan James of Akzo Nobel
Coatings, Inc. July 2000. (Tel: 614-294-3361).
Kelly (2000) Personal communication between Tom Smith, ICF Consulting and Peter Kelly, Asphalt Roofing
Manufacturers Association, August 2000.
Maguire (2004) Personal communication with J. Maguire, National Petrochemicals and Refiners Association. August -
September 2004.
Marland, G., and R.M. Rotty (1984) Carbon dioxide emissions from fossil fuels: A procedure for estimation and results for
1950-1982, Tellus 36b:232-261.
NPRA (2002) North American Wax - A Report Card.
Rinehart, T. (2000) Personal communication between Thomas Rinehart of U.S. Environmental Protection Agency, Office
of Solid Waste, and Randall Freed of ICF International. July 2000. (Tel: 703-308-4309).
RMA (2018) 2017 U.S. Scrap Tire Management Summary. Rubber Manufacturers Association, Washington, D.C. July
2018.
RMA (2016) 2015 U.S. Scrap Tire Management Summary. Rubber Manufacturers Association, Washington, D.C. August
2016.
RMA (2014) 2013 U.S. Scrap Tire Management Summary. Rubber Manufacturers Association, Washington, D.C.
November 2014.
RMA (2011) U.S. Scrap Tire Management Summary: 2005-2009. Rubber Manufacturers Association, Washington, D.C.
October 2011, updated September 2013.
RMA (2009) "Scrap Tire Markets: Facts and Figures - Scrap Tire Characteristics." Available online at:
. Accessed 17 September 2009.
Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah Shapiro of ICF
International, January 10, 2007.
A-154 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

-------
SPI (2000) The Society of the Plastics Industry Website, http://www.plasticsindustry.org/industry/stat3.htm, Accessed 28
June 2000.
U.S. Bureau of the Census (1994, 1999, 2004, 2009, 2014) 1992, 1997, 2002, 2007, 2012 Economic Census. Available
online at
.
U.S. International Trade Commission (1990 through 2019) "Interactive Tariff and Trade DataWeb: Quick Query."
Available online at . Accessed September 2020.
Vallianos, Jean (2020) Personal communication between Katie O'Malley of ICF and Jean Vallianos of the American
Chemistry Council, November 19, 2020.
Vallianos, Jean (2019) Personal communication between Katie O'Malley of ICF and Jean Vallianos of the American
Chemistry Council, October 3, 2019.
Vallianos, Jean (2018) Personal communication between Drew Stilson of ICF and Jean Vallianos of the American
Chemistry Council, October 5, 2018.
Vallianos, Jean (2017) Personal communication between Drew Stilson of ICF and Jean Vallianos of the American
Chemistry Council, November 1, 2017.
Vallianos, Jean (2016) Personal communication between Drew Stilson of ICF and Jean Vallianos of the American
Chemistry Council, November 17, 2016.
Vallianos, Jean (2015) Personal communication between Tyler Fitch of ICF International and Jean Vallianos of the
American Chemistry Council, December 20, 2015.
Vallianos, Jean (2014) Personal communication between Sarah Biggar of ICF International and Jean Vallianos of the
American Chemistry Council, November 13, 2014.
Vallianos, Jean (2013) Personal communication between Sarah Biggar of ICF International and Jean Vallianos of the
American Chemistry Council, November 8, 2013.
Vallianos, Jean (2012) Personal communication between Ben Eskin of ICF International and Jean Vallianos of the
American Chemistry Council, September 14, 2012.
Vallianos, Jean (2011) Personal communication between Joe Indvik of ICF International and Jean Vallianos of the
American Chemistry Council, January 4, 2011.
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ANNEX 3 Methodological Descriptions for
Additional Source or Sink Categories
3.1. Methodology for Estimating Emissions of CH4, N2O, and Indirect Greenhouse
Gases from Stationary Combustion
Estimates of ChU and N2O Emissions
Methane (CH4) and nitrous oxide (N20) emissions from stationary combustion were estimated using methods
from the Intergovernmental Panel on Climate Change (IPCC). Estimates were obtained by multiplying emission factors—
by sector and fuel type—by fossil fuel and wood consumption data. This "top-down" methodology is characterized by
two basic steps, described below. Data are presented in Table A-73 through Table A-78.
Step 1: Determine Energy Consumption by Sector and Fuel Type
Energy consumption from stationary combustion activities was grouped by sector: industrial, commercial,
residential, electric power, and U.S. Territories. For CH4 and N20 emissions from industrial, commercial, residential, and
U.S. Territories, estimates were based upon consumption of coal, gas, oil, and wood. Energy consumption and wood
consumption data for the United States were obtained from the Energy Information Administration's (EIA) Monthly
Energy Review, November 2020 (EIA 2020). Because the United States does not include U.S. Territories in its national
energy statistics, fuel consumption data for U.S. Territories were collected from ElA's International Energy Statistics
database (EIA 2017) and Jacobs (2010).46 Fuel consumption for the industrial sector was adjusted to subtract out
construction and agricultural use, which is reported under mobile sources.47 Construction and agricultural fuel use was
obtained from EPA (2019) and the Federal Highway Administration (FHWA) (1996 through 2019). The energy
consumption data by sector were then adjusted from higher to lower heating values by multiplying by 0.90 for natural
gas and wood and by 0.95 for coal and petroleum fuel. This is a simplified convention used by the International Energy
Agency (IEA). Table A-73 provides annual energy consumption data for the years 1990 through 2019.
In this Inventory, the energy consumption estimation methodology for the electric power sector used a Tier 2
methodology as fuel consumption by technology-type for the electric power sector was estimated based on the Acid
Rain Program Dataset (EPA 2021). Total fuel consumption in the electric power sector from EIA (2020) was apportioned
to each combustion technology type and fuel combination using a ratio of fuel consumption by technology type derived
from EPA (2021) data. The combustion technology and fuel use data by facility obtained from EPA (2021) were only
available from 1996 to 2019, so the consumption estimates from 1990 to 1995 were estimated by applying the 1996
consumption ratio by combustion technology type from EPA (2021) to the total EIA (2020) consumption for each year
from 1990 to 1995.
Step 2: Determine the Amount of CH4 and N20 Emitted
Activity data for industrial, commercial, residential, and U.S. Territories and fuel type for each of these sectors
were then multiplied by default Tier 1 emission factors to obtain emission estimates. Emission factors for the residential,
commercial, and industrial sectors were taken from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC 2006). These N20 emission factors by fuel type (equivalent across sectors) were also assumed for U.S. Territories.
The CH4 emission factors by fuel type for U.S. Territories were estimated based on the emission factor for the primary
sector in which each fuel was combusted. Table A-74 provides emission factors used for each sector and fuel type. For
the electric power sector, emissions were estimated by multiplying fossil fuel and wood consumption by technology- and
fuel-specific Tier 2 IPCC emission factors shown in Table A-75. Emission factors were taken from U.S. EPA publications on
46	U.S. Territories data also include combustion from mobile activities because data to allocate U.S. Territories' energy use were
unavailable. For this reason, CH4 and N20 emissions from combustion by U.S. Territories are only included in the stationary combustion
totals.
47	Though emissions from construction and farm use occur due to both stationary and mobile sources, detailed data was not available
to determine the magnitude from each. Currently, these emissions are assumed to be predominantly from mobile sources.
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emissions rates for combustion sources, and EPA's Compilation of Air Pollutant Emission Factors, AP-42 (EPA 1997) for
combined cycle natural gas units. The EPA factors were in large part used in the 2006IPCC Guidelines as the factors
presented.
Estimates of NOx, CO, and NMVOC Emissions
Emissions estimates for NOx, CO, and NMVOCs were obtained from data published on the National Emission
Inventory (NEI) Air Pollutant Emission Trends web site (EPA 2020b) and disaggregated based on EPA (2003).
For indirect greenhouse gases, the major source categories included coal, fuel oil, natural gas, wood, other fuels
(i.e., bagasse, liquefied petroleum gases, coke, coke oven gas, and others), and stationary internal combustion, which
includes emissions from internal combustion engines not used in transportation. EPA periodically estimates emissions of
NOx, CO, and NMVOCs by sector and fuel type using a "bottom-up" estimating procedure. In other words, the emissions
were calculated either for individual sources (e.g., industrial boilers) or for many sources combined, using basic activity
data (e.g., fuel consumption or deliveries) as indicators of emissions. The national activity data used to calculate the
individual categories were obtained from various sources. Depending upon the category, these activity data may include
fuel consumption or deliveries of fuel, tons of refuse burned, raw material processed, etc. Activity data were used in
conjunction with emission factors that relate the quantity of emissions to the activity.
The basic calculation procedure for most source categories presented in EPA (2003) and EPA (2020) is
represented by the following equation:
Ep,s = As x EFp,s x (1 - Cp.s/100)
where,
E
= Emissions
P
= Pollutant
s
= Source category
A
= Activity level
EF
= Emission factor
C
= Percent control efficiency
EPA currently derives the overall emission control efficiency of a category from a variety of sources, including
published reports, the 1985 National Acid Precipitation and Assessment Program (NAPAP) emissions inventory, and
other EPA databases. The U.S. approach for estimating emissions of NOx, CO, and NMVOCs from stationary combustion
as described above is similar to the methodology recommended by IPCC.
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Table A-73: Fuel Consumption by Stationary Combustion for Calculating CH4 and N2O Emissions (TBtu)
Fuel/End-Use
Sector
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Coal
19,637
20,912
23,087
22,966
20,733
19,535
16,941
17,828
17,806
15,455
14,268
13,772
13,155
11,115
Residential
31
17
11
8
0
0
0
0
0
0
0
0
0
0
Commercial
124
117
92
97
70
62
44
41
40
31
24
21
19
17
Industrial
1,668
1,557
1,362
1,246
993
906
823
837
833
734
662
614
569
517
Electric Power
17,807
19,216
21,618
21,582
19,633
18,531
16,038
16,919
16,889
14,645
13,547
13,110
12,540
10,553
U.S. Territories3
5
5
5
33
37
37
37
31
44
45
35
27
27
27
Petroleum
6,089
5,627
6,486
6,758
5,035
4,776
4,457
4,549
4,137
4,614
4,265
4,017
4,132
4,140
Residential
1,376
1,259
1,425
1,366
1,103
1,034
833
917
1,003
939
799
766
944
904
Commercial
1,023
724
767
761
698
670
551
581
558
938
834
809
733
787
Industrial
2,599
2,460
2,449
2,915
2,412
2,407
2,413
2,568
2,124
2,261
2,204
2,101
2,093
2,137
Electric Power
797
860
1,269
1,003
412
273
288
185
157
173
159
71
93
42
U.S. Territories3
295
324
575
712
410
392
373
299
296
304
268
269
269
269
Natural Gas
17,255
19,340
20,923
20,937
22,913
23,315
24,605
25,130
25,924
26,536
26,565
26,137
28,943
29,965
Residential
4,487
4,954
5,105
4,946
4,878
4,805
4,242
5,023
5,242
4,777
4,506
4,563
5,174
5,205
Commercial
2,680
3,096
3,252
3,073
3,165
3,216
2,960
3,380
3,572
3,316
3,224
3,273
3,638
3,645
Industrial
7,713
8,726
8,659
7,331
7,685
7,871
8,196
8,513
8,818
8,679
8,769
8,872
9,335
9,515
Electric Power
2,376
2,564
3,894
5,562
7,157
7,396
9,158
8,156
8,231
9,707
10,003
9,381
10,747
11,552
U.S. Territories3
0
0
13
24
28
27
49
58
61
57
64
48
48
48
Wood
2,095
2,252
2,138
1,963
2,046
2,055
1,989
2,160
2,209
2,127
2,057
2,014
2,105
2,094
Residential
580
520
420
430
541
524
438
572
579
513
442
425
517
529
Commercial
66
72
71
70
72
69
61
70
76
79
84
84
84
84
Industrial
1,442
1,652
1,636
1,452
1,409
1,438
1,462
1,489
1,495
1,476
1,474
1,442
1,438
1,413
Electric Power
7
8
11
11
25
24
28
30
60
59
57
62
66
68
U.S. Territories
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
Note: Totals may not sum due to independent rounding.
NE (Not Estimated)
3 U.S. Territories coal is assumed to be primarily consumed in the electric power sector, natural gas in the industrial sector, and petroleum in the transportation sector.
A-158 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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Table A-74: Cm and N2O Emission Factors by Fuel Type and Sector (g/GJ)a
Fuel/End-Use Sector	CH4	N2Q
Coal
Residential
Commercial
Industrial
U.S. Territories
Petroleum
Residential
Commercial
Industrial
U.S. Territories
Natural Gas
Residential
Commercial
Industrial
U.S. Territories
Wood
Residential
Commercial
Industrial
U.S. Territories
NA (Not Applicable)
a GJ (Gigajoule) = 109 joules. One joule = 9.486xl0 4 Btu.
Table A-75: CH4 and N2O Emission Factors by Technology Type and Fuel Type for the Electric Power Sector
(g/GJ )a	
Technology Configuration	CH4	N20
Liquid Fuels
Residual Fuel Oil/Shale Oil Boilers Normal Firing	0.8	0.3
Tangential Firing	0.8	0.3
Gas/Diesel Oil Boilers Normal Firing	0.9	0.4
Tangential Firing	0.9	0.4
Large Diesel Oil Engines >600 hp (447kW)	4.0	NA
Solid Fuels
Pulverized Bituminous Combination Boilers Dry Bottom, wall fired	0.7	5.8
Dry Bottom, tangentially fired	0.7	1.4
Wet bottom	0.9	1.4
Bituminous Spreader Stoker Boilers With and without re-injection	1.0	0.7
Bituminous Fluidized Bed Combustor Circulating Bed	1.0	61
Bubbling Bed	1.0	61
Bituminous Cyclone Furnace	0.2	0.6
Lignite Atmospheric Fluidized Bed	NA	71
Natural Gas
Boilers	1.0	0.3
Gas-Fired Gas Turbines >3MW	3.7	1.3
Large Dual-Fuel Engines	258	NA
Combined Cycle	3.7	1.3
Peat
Peat Fluidized Bed Combustion Circulating Bed	3.0	7.0
Bubbling Bed	3.0	3.0
Biomass
Wood/Wood Waste Boilers	11.0	7.0
Wood Recovery Boilers	LO	LO^
NA (Not Applicable)
aIbid.
300	1.5
10	1.5
10	1.5
1	1.5
10	0.6
10	0.6
3	0.6
5	0.6
5	0.1
5	0.1
1	0.1
1	0.1
300	4.0
300	4.0
30	4.0
NA	NA
A-159

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Table A-76: NOx Emissions from Stationary Combustion (kt)
Sector/Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Electric Power
6,045
5,792
4,829
3,434
2,226
1,893
1,779
1,666
1,603
1,419
1,234
1,049
1,032
902
Coal
5,119
5,061
4,130
2,926
1,896
1,613
1,516
1,419
1,366
1,209
1,051
894
879
769
Fuel Oil
200
87
147
114
74
63
59
55
53
47
41
35
34
30
Natural gas
513
510
376
250
162
138
129
121
117
103
90
76
75
66
Wood
NA
NA
36
29
19
16
15
14
13
12
10
9
9
8
Other Fuels3
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Internal Combustion
213
134
140
115
75
63
60
56
54
48
41
35
35
30
Industrial
2,559
2,650
2,278
1,515
1,087
1,048
1,016
984
952
921
890
859
859
859
Coal
530
541
484
342
245
237
229
222
215
208
201
194
194
194
Fuel Oil
240
224
166
101
73
70
68
66
64
62
60
57
57
57
Natural gas
877
999
710
469
336
324
314
305
295
285
275
266
266
266
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
119
111
109
76
55
53
51
50
48
46
45
43
43
43
Internal Combustion
792
774
809
527
378
364
353
342
331
320
309
299
299
299
Commercial
671
607
507
490
456
548
535
521
448
444
440
537
537
537
Coal
36
35
21
19
15
15
14
14
14
14
13
13
13
13
Fuel Oil
88
94
52
49
38
37
37
37
36
35
34
33
33
33
Natural gas
181
210
161
155
120
118
117
116
115
112
108
105
105
105
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
366
269
273
267
284
378
366
354
283
283
284
386
386
386
Residential
749
813
439
418
324
318
315
312
310
301
292
283
283
283
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oilb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
42
44
21
20
16
16
15
15
15
15
14
14
14
14
Other Fuels3
707
769
417
398
308
302
300
297
295
286
278
269
269
269
Total
10,023
9,862
8,053
5,858
4,092
3,807
3,645
3,483
3,313
3,084
2,856
2,728
2,711
2,581
Note: Totals may not sum due to independent rounding.
NA (Not Applicable)
3 Other Fuels include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2019).
b Residential coal, fuel oil, and natural gas emissions are included in the Other Fuels category (EPA 2019).
A-160 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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Table A-77: CO Emissions from Stationary Combustion (kt)
Sector/Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Electric Power
329
337
439
582
693
710
694
678
661
618
575
532
532
532
Coal
213
227
221
292
347
356
348
340
331
310
288
267
267
267
Fuel Oil
18
9
27
37
44
45
44
43
42
39
36
34
34
34
Natural gas
46
49
96
122
145
149
146
142
139
130
121
112
112
112
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
NA
NA
31
43
51
52
51
50
48
45
42
39
39
39
Internal Combustion
52
52
63
89
106
108
106
104
101
94
88
81
81
81
Industrial
797
958
1,106
1,045
853
872
861
851
840
806
771
737
737
737
Coal
95
88
118
115
94
96
95
94
93
89
85
81
81
81
Fuel Oil
67
64
48
42
34
35
34
34
33
32
31
29
29
29
Natural gas
205
313
355
336
274
281
277
274
270
259
248
237
237
237
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
253
270
300
295
241
247
244
241
238
228
218
208
208
208
Internal Combustion
177
222
285
257
209
214
212
209
206
198
189
181
181
181
Commercial
205
211
151
166
140
142
134
127
120
124
128
133
133
133
Coal
13
14
14
14
12
12
12
11
10
11
11
12
12
12
Fuel Oil
16
17
17
19
16
16
15
14
13
14
14
15
15
15
Natural gas
40
49
83
91
77
78
74
70
66
68
71
73
73
73
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
136
132
36
41
35
35
34
32
30
31
32
33
33
33
Residential
3,668
3,877
2,644
2,856
2,416
2,446
2,319
2,192
2,065
2,140
2,215
2,291
2,291
2,291
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oilb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
3,430
3,629
2,416
2,615
2,212
2,239
2,123
2,007
1,890
1,959
2,028
2,097
2,097
2,097
Other Fuels3
238
248
228
241
204
207
196
185
174
181
187
193
193
193
Total
5,000
5,383
4,340
4,648
4,103
4,170
4,009
3,847
3,686
3,688
3,690
3,692
3,692
3,692
Note: Totals may not sum due to independent rounding.
NA (Not Applicable)
3 Other Fuels include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2019).
b Residential coal, fuel oil, and natural gas emissions are included in the Other Fuels category (EPA 2019).
A-161

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Table A-78: NMVOC Emissions from Stationary Combustion (kt)
Sector/Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Electric Power
43
40
56
44
38
37
36
35
34
33
31
29
29
29
Coal
24
26
27
21
18
18
17
17
16
16
15
14
14
14
Fuel Oil
5
2
4
3
3
3
3
3
3
3
2
2
2
2
Natural Gas
2
2
12
10
8
8
8
8
8
7
7
6
6
6
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
NA
NA
2
1
1
1
1
1
1
1
1
1
1
1
Internal Combustion
11
9
11
8
7
7
7
7
7
6
6
6
6
6
Industrial
165
187
157
120
100
101
101
100
99
100
101
101
101
101
Coal
7
5
9
8
7
7
7
7
7
7
7
7
7
7
Fuel Oil
11
11
9
6
5
5
5
5
5
5
5
5
5
5
Natural Gas
52
66
53
41
34
34
34
34
34
34
34
34
34
34
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
46
45
27
22
18
19
19
18
18
18
19
19
19
19
Internal Combustion
49
60
58
43
36
36
36
36
35
36
36
36
36
36
Commercial
18
21
28
33
40
42
40
39
35
36
36
47
47
47
Coal
1
1
1
1
+
+
+
+
+
+
+
+
+
+
Fuel Oil
3
3
4
2
2
2
2
2
1
1
1
1
1
1
Natural Gas
7
10
14
9
7
7
7
6
6
6
6
6
6
6
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
8
8
9
22
31
32
31
31
28
28
28
40
40
40
Residential
686
725
837
518
399
419
389
358
327
324
322
319
319
319
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oilb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
651
688
809
502
386
406
376
346
317
314
311
308
308
308
Other Fuels3
35
37
27
17
13
14
13
12
11
11
10
10
10
10
Total
912
973
1,077
716
576
599
566
532
497
493
489
496
496
496
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.5 kt.
NA (Not Applicable)
a "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2019).
b Residential coal, fuel oil, and natural gas emissions are included in the "Other Fuels" category (EPA 2019).
A-162 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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References
EIA (2020) Monthly Energy Review, November 2020, Energy Information Administration, U.S. Department of Energy,
Washington, D.C. DOE/EIA-0035(2020/11).
EPA (2021) Acid Rain Program Dataset 1996-2019. Office of Air and Radiation, Office of Atmospheric Programs, U.S.
Environmental Protection Agency, Washington, D.C.
EPA (2020) "Criteria pollutants National Tier 1 for 1970 - 2019." National Emissions Inventory (NEI) Air Pollutant
Emissions Trends Data. Office of Air Quality Planning and Standards, April 2020. Available online at:
.
EPA (2019) MOtor Vehicle Emissions Simulator (MOVES) 2014b. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at: .
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and the
Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency. Research Triangle Park, NC. October 1997.
FHWA (1996 through 2019) Highway Statistics. Federal Highway Administration, U.S. Department of Transportation,
Washington, D.C. Report FHWA-PL-96-023-annual. Obtained from Tiffany Presmy at FHWA.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
Jacobs, G. (2010) Personal communication. Gwendolyn Jacobs, Energy Information Administration and Rubaab Bhangu,
ICF International. U.S. Territories Fossil Fuel Consumption. Unpublished. U.S. Energy Information Administration.
Washington, D.C.
A-163

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3.2. Methodology for Estimating Emissions of CH4, IVhO, and Indirect Greenhouse
Gases from Mobile Combustion and Methodology for and Supplemental
Information on Transportation-Related Greenhouse Gas Emissions
Estimating CO2 Emissions by Transportation Mode
Transportation-related C02 emissions, as presented in the C02 Emissions from Fossil Fuel Combustion section of
the Energy chapter, were calculated using the methodology described in Annex 2.1. This section provides additional
information on the data sources and approach used for each transportation fuel type. As noted in Annex 2.1, C02
emissions estimates for the transportation sector were calculated directly for on-road diesel fuel and motor gasoline
based on data sources for individual modes of transportation (considered a bottom up approach). For most other fuel
and energy types (aviation gasoline, residual fuel oil, natural gas, LPG, and electricity), C02 emissions were calculated
based on transportation sector-wide fuel consumption estimates from the Energy Information Administration (EIA 2020a
and EIA 2020d) and apportioned to individual modes (considered a "top down" approach). Carbon dioxide emissions
from commercial jet fuel use are obtained directly from the Federal Aviation Administration (FAA 2021), while C02
emissions from other aircraft jet fuel consumption is determined using a top down approach.
Based on interagency discussions between EPA, EIA, and FHWA beginning in 2005, it was agreed that use of
"bottom up" data would be more accurate for diesel fuel and motor gasoline consumption in the transportation sector,
based on the availability of reliable data sources. A "bottom up" diesel calculation was first implemented in the 1990
through 2005 Inventory, and a bottom-up gasoline calculation was introduced in the 1990 through 2006 Inventory for
the calculation of emissions from on-road vehicles. Estimated motor gasoline and diesel consumption data for on-road
vehicles by vehicle type come from FHWA's Highway Statistics, Table VM-1 (FHWA 1996 through 2019),48 and are based
on federal and state fuel tax records. These fuel consumption estimates were then combined with estimates of fuel
shares by vehicle type from DOE's Transportation Energy Data Book Annex Tables A.l through A.6 (DOE 1993 through
2018) to develop an estimate of fuel consumption for each vehicle type (i.e., passenger cars, light-duty trucks, buses,
medium- and heavy-duty trucks, motorcycles). The on-road gas and diesel fuel consumption estimates by vehicle type
were then adjusted for each year so that the sum of gasoline and diesel fuel consumption across all on-road vehicle
categories matched the fuel consumption estimates in Highway Statistics' Table MF-27 (FHWA 1996 through 2019). This
resulted in a final "bottom up" estimate of motor gasoline and diesel fuel use by vehicle type, consistent with the FHWA
total for on-road motor gasoline and diesel fuel use.
A primary challenge to switching from a top-down approach to a bottom-up approach for the transportation
sector relates to potential incompatibilities with national energy statistics. From a multi-sector national standpoint, EIA
develops the most accurate estimate of total motor gasoline and diesel fuel supplied and consumed in the United States.
EIA then allocates this total fuel consumption to each major end-use sector (residential, commercial, industrial and
transportation) using data from the Fuel Oil and Kerosene Sales (FOKS) report for distillate fuel oil and FHWA for motor
gasoline. However, the "bottom-up" approach used for the on-road and non-road fuel consumption estimate, as
described above, is considered to be the most representative of the transportation sector's share of the EIA total
consumption. Therefore, for years in which there was a disparity between ElA's fuel allocation estimate for the
transportation sector and the "bottom-up" estimate, adjustments were made to other end-use sector fuel allocations
(residential, commercial and industrial) in order for the consumption of all sectors combined to equal the "top-down"
EIA value.
In the case of motor gasoline, estimates of fuel use by recreational boats come from the Nonroad component
of EPA's MOVES2014b model (EPA 2019a), and these estimates, along with those from other sectors (e.g., commercial
sector, industrial sector), were adjusted for years in which the bottom-up on-road motor gasoline consumption estimate
exceeded the EIA estimate for total gasoline consumption of all sectors. Similarly, to ensure consistency with ElA's total
48 In 2011 FHWA changed its methods for estimating vehicle miles traveled (VMT) and related data. These methodological changes included
how vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. These changes were first
incorporated forthe 1990 through 2008 Inventory and applied to the 2007 to 2019 time period. This resulted in large changes in VMT and fuel
consumption data by vehicle class, thus leading to a shift in emissions among on-road vehicle classes. For example, the category "Passenger
Cars" has been replaced by "Light-duty Vehicles-Short Wheelbase" and "Other 2 axle-4 Tire Vehicles" has been replaced by "Light-duty Vehicles,
Long Wheelbase." This change in vehicle classification has moved some smaller trucks and sport utility vehicles from the light truck category to
the passenger vehicle category in this emission inventory. These changes are reflected in a large drop in light-truck emissions between 2006
and 2007.
A-164 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019

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diesel estimate for all sectors, the diesel consumption totals for the residential, commercial, and industrial sectors were
adjusted proportionately.
Estimates of diesel fuel consumption from rail were taken from: the Association of American Railroads (AAR
2008 through 2019) for Class I railroads, the American PublicTransportation Association (APTA 2007 through 2019 and
APTA 2006) and Gaffney (2007) for commuter rail, the Upper Great Plains Transportation Institute (Benson 2002 through
2004), Whorton (2006 through 2014), and Railinc (2014 through 2019) for Class II and III railroads, and the U.S.
Department of Energy's Transportation Energy Data Book (DOE 1993 through 2018) for passenger rail. Class II and III
railroad diesel consumption is estimated by applying the historical average fuel usage per carload factor to yearly
carloads.
Estimates of diesel fuel consumption from ships and boats were taken from ElA's Fuel Oil and Kerosene Sales
(1991 through 2019).
As noted above, for fuels other than motor gasoline and diesel, ElA's transportation sector total was
apportioned to specific transportation sources. For jet fuel, estimates come from: FAA (2021) for domestic and
international commercial aircraft, and DLA Energy (2020) for domestic and international military aircraft. General
aviation jet fuel consumption is calculated as the difference between total jet fuel consumption as reported by EIA and
the total consumption from commercial and military jet fuel consumption. Commercial jet fuel C02 estimates are
obtained directly from the Federal Aviation Administration (FAA 2021), while C02 emissions from domestic military and
general aviation jet fuel consumption is determined using a top down approach. Domestic commercial jet fuel C02 from
FAA is subtracted from total domestic jet fuel C02 emissions, and this remaining value is apportioned among domestic
military and domestic general aviation based on their relative proportion of energy consumption. Estimates for biofuels,
including ethanol and biodiesel, were discussed separately in Section 3.2 Carbon Emitted from Non-Energy Uses of Fossil
Fuels under the methodology for Estimating C02 from Fossil Combustion, and in Section 3.11 Wood Biomass and Ethanol
Consumption, and were not apportioned to specific transportation sources. Consumption estimates for biofuels were
calculated based on data from the Energy Information Administration (EIA 2020a).
Table A-79 displays estimated fuel consumption by fuel and vehicle type. Table A-80 displays estimated energy
consumption by fuel and vehicle type. The values in both tables correspond to the figures used to calculate C02
emissions from transportation. Except as noted above, they are estimated based on EIA transportation sector energy
estimates by fuel type, with activity data used to apportion fuel consumption to the various modes of transport. The
motor gasoline and diesel fuel consumption volumes published by EIA and FHWA include ethanol blended with gasoline
and biodiesel blended with diesel. Biofuels blended with conventional fuels were subtracted from these consumption
totals in order to be consistent with IPCC methodological guidance and UNFCCC reporting obligations, for which net
carbon fluxes in biogenic carbon reservoirs in croplands are accounted for in the estimates for Land Use, Land-Use
Change and Forestry chapter, not in Energy chapter totals. Ethanol fuel volumes were removed from motor gasoline
consumption estimates for years 1990 through 2019. Biodiesel fuel volumes were removed from diesel fuel consumption
volumes for years 2001 through 2019, as there was negligible use of biodiesel as a diesel blending competent prior to
2001. The subtraction or removal of biofuels blended into motor gasoline and diesel were conducted following the
methodology outlined in Step 2 ("Remove Biofuels from Petroleum") of the ElA's Monthly Energy Review (MER) Section
12 notes.
In order to remove the volume of biodiesel blended into diesel fuel, the 2009 to 2019 biodiesel and renewable
diesel fuel consumption estimates from EIA (2020a) were subtracted from the transportation sector's total diesel fuel
consumption volume (for both the "top-down" EIA and "bottom-up" FHWA estimates). To remove the ethanol blended
into motor gasoline, ethanol energy consumption data sourced from MER Table 10.2b - Renewable Energy Consumption:
Industrial and Transportation Sectors (EIA 2020a) were subtracted from the total EIA and FHWA transportation motor
gasoline energy consumption estimates. Total ethanol and biodiesel consumption estimates are shown separately in
Table A-81.49
49 Note that the refinery and blender net volume inputs of renewable diesel fuel sourced from ElA's Petroleum Supply Annual (PSA) differs from
the biodiesel volume presented in Table A-81. The PSA data is representative of the amount of biodiesel that refineries and blenders added to
diesel fuel to make low level biodiesel blends. This is the appropriate value to subtract from total diesel fuel volume, as it represents the
amount of biofuel blended into diesel to create low-level biodiesel blends. The biodiesel consumption value presented in Table A-79 is
representative of the total biodiesel consumed and includes biodiesel components in all types of fuel formulations, from low level (<5%) to high
level (6-20%, 100%) blends of biodiesel. This value is sourced from MER Table 10.4 and is calculated as biodiesel production plus biodiesel net
imports minus biodiesel stock exchange.
A-165

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Table A-79: Fuel Consumption by Fuel and Vehicle Type (million gallons unless otherwise specified)
Fuel/Vehicle Type
1990
1995
2000
2009a
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Motor Gasolineb
-------
Buses
Rail
+	+	+	+	2	2	1	1	1	1	2	5	8	9
4,751 4,975? 5,382 7,768 7,745 7,770 7,531 8,080 8,517 8,725 9,034 9,624 10,661 11,654
+ Does not exceed 0.05 trillion cubic feet
a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2019 time period. These methodological changes include how on-road
vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class
between 2006 and 2007.
b Figures do not include ethanol blended in motor gasoline or biodiesel blended into distillate fuel oil. Net carbon fluxes associated with ethanol are accounted for in the Land
Use, Land-Use Change and Forestry chapter. This table is calculated with the heat content for gasoline without ethanol (from Table A.l in the EIA Monthly Energy Review)
rather than the annually variable quantity-weighted heat content for gasoline with ethanol, which varies by year.
c Gasoline and diesel highway vehicle fuel consumption estimates are based on data from FHWA Highway Statistics Table MF-21, MF-27, and VM-1 (FHWA 1996 through 2019).
Data from Table VM-1 is used to estimate the share of consumption between each on-road vehicle class. These fuel consumption estimates are combined with estimates of fuel
shares by vehicle type from DOE's TEDB Annex Tables A.l through A.6 (DOE 1993 through 2018).
d Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
e Class II and Class III diesel consumption data for 2014-2019 is estimated by applying the historical average fuel usage per carload factor to the annual number of carloads.
f Estimated based on EIA transportation sector energy estimates by fuel type, with bottom-up activity data used for apportionment to modes. Transportation sector natural gas
and LPG consumption are based on data from EIA (2020a). In previous Inventory years, data from DOE TEDB was used to estimate each vehicle class's share of the total natural
gas and LPG consumption. Since TEDB does not include estimates for natural gas use by medium and heavy-duty trucks or LPG use by passenger cars, EIA Alternative Fuel
Vehicle Data (Browning 2017) is now used to determine each vehicle class's share of the total natural gas and LPG consumption. These changes were first incorporated in the
2016 Inventory and apply to the 1990 through 2019 time period.
g Fluctuations in reported fuel consumption may reflect data collection problems.
h Million kilowatt-hours
' Electricity consumption by passenger cars, light-duty trucks (SUVs), and buses is based on plug-in electric vehicle sales data and engine efficiencies, as outlined in Browning
(2018a). In prior Inventory years, C02 emissions from electric vehicle charging were allocated to the residential and commercial sectors. They are now allocated to the
transportation sector. These changes were first incorporated in the 2017 Inventory and applied to the 2010 through 2019 time period.
Table A-80: Energy Consumption by Fuel and Vehicle Type (TBtu)
Fuel/Vehicle Type
1990
1995
2000
2009a
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Motor Gasolineb
-------
Medium- and Heavy-














Duty Trucks
2,576
3,223
4,186
4,460
4,652
4,625
4,642
4,683
4,840
4,947
4,984
5,152
5,272
5,325
Recreational Boats
37
37
37
37
36
35
35
34
34
36
37
37
38
39
Ships and Non-














Recreational Boats
91
161
190
116
112
149
115
117
100
178
148
136
127
102
Raile
480
536
569
486
525
543
532
537
562
536
489
507
531
500
Jet Fuelf
2,590
2,429
2,700
2,134
2,097
2,030
1,985
2,037
2,054
2,182
2,299
2,378
2,386
2,462
Commercial Aircraft
1,562
1,638
1,981
1,699
1,611
1,629
1,611
1,624
1,638
1,692
1,711
1,819
1,843
1,908
General Aviation














Aircraft
545
454
427
241
314
256
224
274
241
319
430
403
393
403
Military Aircraft
484
337
293
194
173
145
150
138
175
171
158
156
150
151
Aviation Gasoline'
45
40
36
27
27
27
25
22
22
21
20
21
22
23
General Aviation














Aircraft
45
40
36
27
27
27
25
22
22
21
20
21
22
23
Residual Fuel Oilf-B
300
387
443
186
272
258
211
201
77
57
172
219
186
196
Ships and Boats
300
387
443
186
272
258
211
201
77
57
172
219
186
196
Natural Gasf
679
724
672
715
719
734
780
887
760
745
757
799
962
1,035
Passenger Cars
+
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Light-Duty Trucks
+
+
0.4
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Medium- and Heavy-














Duty Trucks
+
+
0.2
0.3
0.3
0.3
0.4
0.4
0.5
0.6
0.7
0.7
0.8
0.8
Buses
+
+
3
15
15
15
15
15
15
17
16
18
18
18
Pipelines
679
724
668
699
703
718
765
872
744
727
740
780
943
1,016
LPGf
23
18
12
28
7
7
7
7
7
7
7
7
8
8
Passenger Cars
0.1
0.1
0.1
0.4
+
+
+
+
0.1
0
0
+
+
+
Light-Duty Trucks
3
2
2
7
2
1
1
1
2
1
1
1
1
1
Medium- and Heavy-














Duty Trucks
18
14
9
16
4
5
6
5
5
4
5
5
5
5
Buses
1
1
1
5
1
1
1
1
1
1
1
1
1
2
Electricity11
3
3
3
4
5
4
4
4
4
4
4
4
5
5
Passenger Cars
+
+
+
+
+
+
0.1
0.2
0.4
0.5
0.6
0.8
1.2
1.4
Light-Duty Trucks
+
+
+
+
+
+
+
+
+
+
0.1
0.1
0.2
0.2
Buses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Rail
3
3
3
4
4
4
4
4
4
4
3
3
3
3
Total
20,659
22,257
24,973
23,619
23,756
23,411
23,297
23,505
24,031
24,184
24,742
25,053
25,573
25,598
+ Does not exceed 0.05 TBtu
a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2019 time period. These methodological changes include how on-road vehicles
are classified, moving from a system based on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class between 2006
and 2007.
A-168 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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b Figures do not include ethanol blended in motor gasoline or biodiesel blended into distillate fuel oil. Net carbon fluxes associated with ethanol are accounted for in the Land Use,
Land-Use Change and Forestry chapter.
c Gasoline and diesel highway vehicle fuel consumption estimates are based on data from FHWA Highway Statistics Table MF-21, MF-27, and VM-1 (FHWA 1996 through 2019). Data
from Table VM-1 is used to estimate the share of consumption between each on-road vehicle class. These fuel consumption estimates are combined with estimates of fuel shares
by vehicle type from DOE's TEDB Annex Tables A.l through A.6 (DOE 1993 through 2018).
d Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
e Class II and Class III diesel consumption data for 2014-2019 is estimated by applying the historical average fuel usage per carload factor to the annual number of carloads.
f Estimated based on EIA transportation sector energy estimates, with bottom-up data used for apportionment to modes. Transportation sector natural gas and LPG consumption
are based on data from EIA (2020a). In previous Inventory years, data from DOE TEDB was used to estimate each vehicle class's share of the total natural gas and LPG
consumption. Since TEDB does not include estimates for natural gas use by medium and heavy-duty trucks or LPG use by passenger cars, EIA Alternative Fuel Vehicle Data
(Browning 2017) is now used to determine each vehicle class's share of the total natural gas and LPG consumption. These changes were first incorporated in the 2016 Inventory
and apply to the 1990-2019 time period,
s Fluctuations in reported fuel consumption may reflect data collection problems. Residual fuel oil for ships and boats data is based on EIA (2020a).
h Electricity consumption by passenger cars, light-duty trucks (SUVs), and buses is based on plug-in electric vehicle sales data and engine efficiencies, as outlined in Browning
(2018a). In Inventory years prior to 2017, C02 emissions from electric vehicle charging were allocated to the residential and commercial sectors. They are now allocated to the
transportation sector. These changes were first incorporated in the 2017 Inventory and apply to the 2010 through 2019 time period.
Table A-81: Transportation Sector Biofuel Consumption by Fuel Type (million gallons)
Fuel Type
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Ethanol
699
1,290
1,556
10,072
11,833
11,972
11,997
12,154
12,758
12,793
13,261
13,401
13,573
13,589
Biodiesel
NA
NA
NA
322
260
886
899
1,429
1,417
1,494
2,085
1,985
1,904
1,813
NA (Not Available)
Note: According to the MER, there was no biodiesel consumption prior to 2001.
A-169
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Estimates of CH4 and N20 Emissions
Mobile source emissions of greenhouse gases other than C02 are reported by transport mode (e.g., road, rail,
aviation, and waterborne), vehicle type, and fuel type. Emissions estimates of CH4 and N20 were derived using a
methodology similar to that outlined in the 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006).
Activity data were obtained from a number of U.S. government agencies and other publications. Depending on
the category, these basic activity data included fuel consumption and vehicle miles traveled (VMT). These estimates were
then multiplied by emission factors, expressed as grams per unit of fuel consumed or per vehicle mile.
Methodology for On-Road Gasoline and Diesel Vehicles
Step 1: Determine Vehicle Miles Traveled by Vehicle Type, Fuel Type, and Model Year
VMT by vehicle type (e.g., passenger cars, light-duty trucks, medium- and heavy-duty trucks,50 buses, and
motorcycles) were obtained from the FHWA's Highway Statistics (FHWA 1996 through 2019).51 As these vehicle
categories are not fuel-specific, VMT for each vehicle type was disaggregated by fuel type (gasoline, diesel) so that the
appropriate emission factors could be applied. VMT from Highway Statistics Table VM-1 (FHWA 1996 through 2019) was
allocated to fuel types (gasoline, diesel, other) using historical estimates of fuel shares reported in the Appendix to the
Transportation Energy Data Book, Tables A.5 andA.6 (DOE 1993 through 2018). These fuel shares are drawn from
various sources, including the Vehicle Inventory and Use Survey, the National Vehicle Population Profile, and the
American PublicTransportation Association. Fuel shares were first adjusted proportionately such that gasoline and diesel
shares for each vehicle/fuel type category equaled 100 percent of national VMT. VMT for alternative fuel vehicles (AFVs)
was calculated separately, and the methodology is explained in the following section on AFVs. Estimates of VMT from
AFVs were then subtracted from the appropriate total VMT estimates to develop the final VMT estimates by vehicle/fuel
type category.52 The resulting national VMT estimates for gasoline and diesel on-road vehicles are presented in Table A-
82 and Table A-83, 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 30 model years shown for 2019 in Table A-84.
This distribution was derived by weighting the appropriate age distribution of the U.S. vehicle fleet according to vehicle
registrations by the average annual age-specific vehicle mileage accumulation of U.S. vehicles. Age distribution values
were obtained from EPA's MOBILE6 model for all years before 1999 (EPA 2000) and EPA's MOVES2014b model for years
2009 forward (EPA 2019a).53 Age-specific vehicle mileage accumulations were also obtained from EPA's MOVES2014b
model (EPA 2019a).54
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-90 through Table A-93. 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
50	Medium- and heavy-duty trucks correspond to FHWA's reporting categories of single-unit trucks and combination trucks. Single-unit trucks
are defined as single frame trucks that have 2-axles and at least 6 tires or a gross vehicle weight rating (GVWR) exceeding 10,000 lbs.
51	In 2011 FHWA changed its methods for estimated vehicle miles traveled (VMT) and related data. These methodological changes included
how vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. These changes were first
incorporated for the 1990 through 2008 Inventory and apply to the 2007 to 2019 time period. This resulted in large changes in VMT data by
vehicle class, thus leading to a shift in emissions among on-road vehicle classes. For example, the category "Passenger Cars" has been replaced
by "Light-duty Vehicles-Short Wheelbase" and "Other 2 axle-4Tire Vehicles" has been replaced by "Light-duty Vehicles, Long Wheelbase." This
change in vehicle classification has moved some smaller trucks and sport utility vehicles from the light truck category to the passenger vehicle
category in this emission inventory. These changes are reflected in a large drop in light-truck emissions between 2006 and 2007.
52	In Inventories through 2002, gasoline-electric hybrid vehicles were considered part of an "alternative fuel and advanced technology"
category. However, vehicles are now only separated into gasoline, diesel, or alternative fuel categories, and gas-electric hybrids are now
considered within the gasoline vehicle category.
53	Age distributions were held constant for the period 1990 to 1998, and reflect a 25-year vehicle age span. EPA (2020b) provides a variable age
distribution and 31-year vehicle age span beginning in year 1999.
54	The updated vehicle distribution and mileage accumulation rates by vintage obtained from the MOVES2014b model resulted in a decrease in
emissions due to more miles driven by newer light-duty gasoline vehicles.
A-170 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Tier 1, EPA Tier 2, and EPA Tier 3 refer to U.S. emission regulations and California Air Resources Board (CARB) LEV, CARB
LEVI I, and CARB LEVII refer to California emissions regulations, rather than control technologies; however, each does
correspond to particular combinations of control technologies and engine design. EPA Tier 2 and Tier 3 and its
predecessors EPA Tier 1 and Tier 0 as well as CARB LEV, LEVII, and LEVIN apply to vehicles equipped with three-way
catalysts. The introduction of "early three-way catalysts," and "advanced three-way catalysts," as described in the
Revised 1996IPCC Guidelines, roughly correspond to the introduction of EPA Tier 0 and EPA Tier 1 regulations (EPA
1998).55 EPA Tier 2 regulations affect vehicles produced starting in 2004 and are responsible for a noticeable decrease in
N20 emissions compared EPA Tier 1 emissions technology (EPA 1999b). EPA Tier 3 regulations affect vehicles produced
starting in 2017 and are fully phased in by 2025. ARB LEVII regulations affect California vehicles produced starting in 2004
while ARB LEVIN affect California vehicles produced starting in 2015.
Emission control technology assignments for light- and heavy-duty conventional fuel vehicles for model years
1972 (when regulations began to take effect) through 1995 were estimated in EPA (1998). Assignments for 1998 through
2019 were determined using confidential engine family sales data submitted to EPA (EPA 2020c). Vehicle classes and
emission standard tiers to which each engine family was certified were taken from annual certification test results and
data (EPA 2019d). This information was used to determine the fraction of sales of each class of vehicle that met EPA Tier
0, EPA Tier 1, EPA Tier 2, EPA Tier 3 and CARB LEV, CARB LEVII and CARB LEVII standards. Assignments for 1996 and 1997
were estimated based on the fact that EPA Tier 1 standards for light-duty vehicles were fully phased in by 1996. Tier 2
began initial phase-in by 2004. EPA Tier 3 began initial phase-in by 2017 and CARB LEV III standards began initial phase-in
by 2015.
Step 3: Determine CH4 and N20 Emission Factors by Vehicle, Fuel, and Control Technology Type
Methane and N20 emission factors (in grams of CH4 and N20 per mile) for gasoline and diesel on-road vehicles
utilizing EPA Tier 2, EPA Tier 3, and CARB LEV, LEVII, and LEVIN technologies were developed by Browning (2019). These
emission factors were calculated based upon annual certification data submitted to EPA by vehicle manufacturers.
Emission factors for earlier standards and technologies were developed by ICF (2004) based on EPA, CARB and
Environment and Climate Change Canada laboratory test results of different vehicle and control technology types. The
EPA, CARB and Environment and Climate Change Canada tests were designed following the Federal Test Procedure (FTP).
The procedure covers three separate driving segments, since vehicles emit varying amounts of GHGs depending on the
driving segment. These driving segments are: (1) a transient driving cycle that includes cold start and running emissions,
(2) a cycle that represents running emissions only, and (3) a transient driving cycle that includes hot start and running
emissions. For each test run, a bag was affixed to the tailpipe of the vehicle and the exhaust was collected; the content
of this bag was later analyzed to determine quantities of gases present. The emission characteristics of driving Segment 2
was used to define running emissions. Running emissions were subtracted from the total FTP emissions to determine
start emissions. These were then recombined based upon MOBILE6.2's ratio of start to running emissions for each
vehicle class to approximate average driving characteristics.
Step 4: Determine the Amount of CH4 and N20 Emitted by Vehicle, Fuel, and Control Technology Type
Emissions of CH4 and N20 were then calculated by multiplying total VMT by vehicle, fuel, and control
technology type by the emission factors developed in Step 3.
Methodology for Alternative Fuel Vehicles (AFVs)
Step 1: Determine Vehicle Miles Traveled by Vehicle and Fuel Type
VMT for alternative fuel and advanced technology vehicles were calculated from "Updated Methodology for
Estimating CH4 and N20 Emissions from Highway Vehicle Alternative Fuel Vehicles" (Browning 2017). Alternative Fuels
include Compressed Natural Gas (CNG), Liquid Natural Gas (LNG), Liquefied Petroleum Gas (LPG), Ethanol, Methanol,
Biodiesel, Hydrogen and Electricity. Most of the vehicles that use these fuels run on an Internal Combustion Engine (ICE)
powered by the alternative fuel, although many of the vehicles can run on either the alternative fuel or gasoline (or
diesel), or some combination.56 Except for electric vehicles and plug-in hybrid vehicles, the alternative fuel vehicle VMT
55	Forfurther description, see "Definitions of Emission Control Technologies and Standards" section of this annex below.
56	Fuel types used in combination depend on the vehicle class. For light-duty vehicles, gasoline is generally blended with ethanol and diesel is
blended with biodiesel; dual-fuel vehicles can run on gasoline or an alternative fuel - either natural gas or LPG - but not at the same time, while
A-171
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were calculated using the Energy Information Administration (EIA) Alternative Fuel Vehicle Data. The EIA data provides
vehicle counts and fuel use for fleet vehicles used by electricity providers, federal agencies, natural gas providers,
propane providers, state agencies and transit agencies, for calendar years 2003 through 2015. For 1992 to 2002, EIA Data
Tables were used to estimate fuel consumption and vehicle counts by vehicle type. These tables give total vehicle fuel
use and vehicle counts by fuel and calendar year for the United States over the period 1992 through 2010. Breakdowns
by vehicle type for 1992 through 2002 (both fuel consumed and vehicle counts) were assumed to be at the same ratio as
for 2003 where data existed. For 1990,1991, 2018 and 2019, fuel consumed by alternative fuel and vehicle type were
extrapolated based on a regression analysis using the best curve fit based upon R2 using the nearest five years of data.
For the current Inventory, counts of electric vehicles (EVs) and plug-in hybrid-electric vehicles (PHEVs) were
taken from data compiled by the Hybridcars.com from 2010 to 2018 (Hybridcars.com, 2019). For 2019, EV and PHEV
sales were taken from Wards Intelligence U.S. Light Vehicle Sales Report (Ward Intelligence, 2020). EVs were divided into
cars and trucks using confidential engine family sales data submitted to EPA (EPA 2020c). Fuel use per vehicle for
personal EVs and PHEVs were assumed to be the same as those for the public fleet vehicles surveyed by EIA and
provided in their data tables.
Because AFVs run on different fuel types, their fuel use characteristics are not directly comparable. Accordingly,
fuel economy for each vehicle type is expressed in gasoline equivalent terms, i.e., how much gasoline contains the
equivalent amount of energy as the alternative fuel. Energy economy ratios (the ratio of the gasoline equivalent fuel
economy of a given technology to that of conventional gasoline or diesel vehicles) were taken from the Argonne National
Laboratory's GREET2019 model (ANL 2020). These ratios were used to estimate fuel economy in miles per gasoline gallon
equivalent for each alternative fuel and vehicle type. Energy use per fuel type was then divided among the various
weight categories and vehicle technologies that use that fuel. Total VMT per vehicle type for each calendar year was
then determined by dividing the energy usage by the fuel economy. Note that for AFVs capable of running on
both/either traditional and alternative fuels, the VMT given reflects only those miles driven that were powered by the
alternative fuel, as explained in Browning (2017). VMT estimates for AFVs by vehicle category (passenger car, light-duty
truck, medium-duty and heavy-duty vehicles) are shown in Table A-84, while more detailed estimates of VMT by control
technology are shown in Table A-85.
Step 2: Determine CH4 and N20 Emission Factors by Vehicle and Alternative Fuel Type
Methane and N20 emission factors for alternative fuel vehicles (AFVs) were calculated using Argonne National
Laboratory's GREET model (ANL 2020) and are reported in Browning (2018). These emission factors are shown in Table
A-95 and Table A-96.
Step 3: Determine the Amount of CH4 and N20 Emitted by Vehicle and Fuel Type
Emissions of CH4 and N20 were calculated by multiplying total VMT for each vehicle and fuel type (Step 1) by
the appropriate emission factors (Step 2).
Methodology for Non-Road Mobile Sources
Methane and N20 emissions from non-road mobile sources were estimated by applying emission factors to the
amount of fuel consumed by mode and vehicle type.
Activity data for non-road vehicles include annual fuel consumption statistics by transportation mode and fuel
type, as shown in Table A-89. Consumption data for ships and boats (i.e., vessel bunkering) were obtained from DHS
(2008) and EIA (1991 through 2019) for distillate fuel, and DHS (2008) and EIA (2020a) for residual fuel; marine transport
fuel consumption data for U.S. Territories (EIA 2017) were added to domestic consumption, and this total was reduced
by the amount of fuel used for international bunkers.57 Gasoline consumption by recreational boats was obtained from
the NONROAD component of EPA's MOVES2014b model (EPA 2019a). Annual diesel consumption for Class I rail was
obtained from the Association of American Railroads (AAR 2008 through 2019), diesel consumption from commuter rail
was obtained from APTA (2007 through 2019) and Gaffney (2007), and consumption by Class II and III rail was provided
flex-fuel vehicles are designed to run on E85 (85 percent ethanol) or gasoline, or any mixture of the two in between. Heavy-duty vehicles are
more likely to run on diesel fuel, natural gas, or LPG.
57 See International Bunker Fuels section of the Energy chapter.
A-172 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
by Benson (2002 through 2004) and Whorton (2006 through 2014).58 It is estimated that an average of 41 gallons of
diesel consumption per Class II and III carload originated from 2000-2009 based on carload data reported from AAR
(2008 through 2019) and fuel consumption data provided by Whorton, D. (2006 through 2014). Class II and Class III diesel
consumption for 2014-2019 is estimated by multiplying this average historical fuel usage per carload factor by the
number of shortline carloads originated each year (Raillnc 2014 through 2019). Diesel consumption by commuter and
intercity rail was obtained from DOE (1993 through 2018). Data on the consumption of jet fuel and aviation gasoline in
aircraft were obtained from EIA (2020a) and FAA (2021), as described in Annex 2.1: Methodology for Estimating
Emissions of C02 from Fossil Fuel Combustion, and were reduced by the amount allocated to international bunker fuels
(DLA 2020 and FAA 2021). Pipeline fuel consumption was obtained from EIA (2007 through 2019) (note: pipelines are a
transportation source but are stationary, not mobile sources). Data on fuel consumption by non-transportation mobile
sources were obtained from the NONROAD component of EPA's MOVES2014b model (EPA 2019a) for gasoline and diesel
powered equipment, and from FHWA (1996 through 2019) for gasoline consumption by off-road trucks used in the
agriculture, industrial, commercial, and construction sectors.59Specifically, this Inventory uses FHWA's Agriculture,
Construction, and Commercial/Industrial MF-24 fuel volumes along with the MOVES NONROAD model gasoline volumes
to estimate non-road mobile source CH4 and N20 emissions for these categories. For agriculture, the MF-24 gasoline
volume is used directly because it includes both off-road trucks and equipment. For construction and
commercial/industrial gasoline estimates, the 2014 and older MF-24 volumes represented off-road trucks only;
therefore, the MOVES NONROAD gasoline volumes for construction and commercial/industrial are added to the
respective categories in the Inventory. Beginning in 2015, this addition is no longer necessary since the FHWA updated
its method for estimating on-road and non-road gasoline consumption. Among the method updates, FHWA now
incorporates MOVES NONROAD equipment gasoline volumes in the construction and commercial/industrial categories.
Emissions of CH4 and N20 from non-road mobile sources were calculated using the updated 2006 IPCC Tier 3
guidance and estimates of activity from EPA's MOVES2014b model. CH4 and N20 emission factors were calculated from
engine certification data by engine and fuel type and weighted by activity estimates calculated by MOVES2014b to
determine overall emission factors in grams per kg of fuel consumed by fuel type.
Estimates of NOx, CO, and NMVOC Emissions
The emission estimates of NOx, CO, and NMVOCs from mobile combustion (transportation) were obtained from
EPA's National Emission Inventory (NEI) Air Pollutant Emission Trends web site (EPA 2020b). This EPA report provides
emission estimates for these gases by fuel type using a procedure whereby emissions were calculated using basic activity
data, such as amount of fuel delivered or miles traveled, as indicators of emissions. Table A-99 through Table A-101
provides complete emission estimates for 1990 through 2019.
Table A-82: Vehicle Miles Traveled for Gasoline On-Road Vehicles (billion miles)
Year
Passenger
Cars
Light-Duty
Trucks
Heavy-Duty
Vehicles3
Motorcycles
1990
1,391.4
554.8
25.8
9.6
1991
1,341.9
627.8
25.4
9.2
1992
1,355.1
683.4
25.1
9.6
1993
1,356.8
721.0
24.9
9.9
1994
1,387.7
739.2
25.3
10.2
1995
1,421.0
763.0
25.1
9.8
1996
1,455.1
788.6
24.5
9.9
1997
1,489.0
821.7
24.1
10.1
1998
1,537.1
837.7
24.1
10.3
1999
1,559.6
868.3
24.3
10.6
2000
1,592.2
887.6
24.2
10.5
2001
1,620.1
906.0
24.0
9.6
2002
1,650.0
926.8
23.9
9.6
58	Diesel consumption from Class II and Class III railroad were unavailable for 2014-2017. Diesel consumption data for 2014-2018 is estimated
by applying the historical average fuel usage per carload factor to the annual number of carloads.
59	"Non-transportation mobile sources" are defined as any vehicle or equipment not used on the traditional road system, but excluding aircraft,
rail and watercraft. This category includes snowmobiles, golf carts, riding lawn mowers, agricultural equipment, and trucks used for off-road
purposes, among others.
A-173
-------
2003
1,663.6
944.1
24.3
9.6
2004
1,691.2
985.5
24.6
10.1
2005
1,699.7
998.8
24.8
10.5
2006
1,681.9
1,038.6
24.8
12.0
2007b
2,093.7
562.8
34.2
21.4
2008
2,014.5
580.9
35.0
20.8
2009
2,005.4
592.5
32.5
20.8
2010
2,015.4
597.4
32.3
18.5
2011
2,035.7
579.6
30.2
18.5
2012
2,051.8
576.8
30.5
21.4
2013
2,062.5
578.7
31.2
20.4
2014
2,059.3
612.4
31.7
20.0
2015
2,133.7
606.1
31.8
19.6
2016
2,176.3
630.8
32.7
20.4
2017
2,203.9
629.1
33.8
20.1
2018
2,212.7
636.4
34.7
20.1
2019
2,231.6
641.1
34.2
19.7
Notes: In 2015, EIA changed its methods for estimating AFV fuel consumption. These methodological changes included how
vehicle counts are estimated, moving from estimates based on modeling to one that is based on survey data. EIA now
publishes data about fuel use and number of vehicles for only four types of AFV fleets: federal government, state government,
transit agencies, and fuel providers. These changes were first incorporated in the 1990 through 2014 Inventory and apply to
the 1990 through 2019 time period. This resulted in large reductions in AFV VMT, thus leading to a shift in VMT to
conventional on-road vehicle classes. Gasoline and diesel highway vehicle mileage are based on data from FHWA Highway
Statistics Table VM-1 (FHWA 1996 through 2019). These mileage consumption estimates are combined with estimates of fuel
shares by vehicle type from DOE's TEDB Annex Tables A.l through A.6 (DOE 1993 through 2018).
a Heavy-Duty Vehicles includes Medium-Duty Trucks, Heavy-Duty Trucks, and Buses.
b In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2019 time period. These
methodological changes include how on-road vehicles are classified, moving from a system based on body-type to one that is
based on wheelbase. This resulted in large changes in VMT data by vehicle class between 2006 and 2007.
Source: Derived from FHWA (1996 through 2019), DOE (1990 through 2018), Browning (2018a), and Browning (2017).
Table A-83: Vehicle Miles Traveled for Diesel On-Road Vehicles (billion miles)
Year
Passenger
Cars
Light-Duty
Trucks
Heavy-Duty
Vehicles3
1990
16.9
19.7
125.7
1991
16.3
21.6
129.5
1992
16.5
23.4
133.7
1993
17.9
24.7
140.7
1994
18.3
25.3
150.9
1995
17.3
26.9
159.1
1996
14.7
27.8
164.7
1997
13.5
29.0
173.8
1998
12.4
30.5
178.9
1999
9.4
32.6
185.6
2000
8.0
35.2
188.4
2001
8.1
37.0
191.5
2002
8.3
38.9
196.8
2003
8.4
39.7
199.7
2004
8.5
41.4
202.1
2005
8.5
41.9
203.4
2006
8.4
43.5
202.2
2007b
10.5
23.4
281.7
2008
10.1
24.2
288.0
2009
10.0
24.7
267.5
2010
10.1
24.9
265.7
2011
10.1
23.7
245.2
A-174 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
2012
10.2
23.5
247.5
2013
10.1
23.2
249.9
2014
10.1
24.6
254.3
2015
10.4
24.3
254.6
2016
10.4
24.9
258.3
2017
10.6
24.9
268.2
2018
10.6
25.3
276.0
2019
10.7
25.6
272.3
Note: Gasoline and diesel highway vehicle mileage are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996
through 2019). These mileage consumption estimates are combined with estimates of fuel shares by vehicle type from DOE's
TEDB Annex Tables A.l through A.6 (DOE 1993 through 2018).
a Heavy-Duty Vehicles includes Medium-Duty Trucks, Heavy-Duty Trucks, and Buses.
b In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2019 time period. These
methodological changes include how on-road vehicles are classified, moving from a system based on body-type to one that is
based on wheelbase. This resulted in large changes in VMT data by vehicle class between 2006 and 2007.
Source: Derived from FHWA (1996 through 2019), DOE (1993 through 2018), and Browning (2017), Browning (2018a).
A-175
-------
Table A-84: Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (billion miles)
Passenger	Light-Duty	Heavy-Duty
Year	Cars	Trucks	Vehicles3
1990
0.0
0.1
0.4
1991
0.0
0.1
0.4
1992
0.0
0.1
0.3
1993
0.0
0.1
0.4
1994
0.1
0.1
0.4
1995
0.1
0.1
0.4
1996
0.1
0.1
0.4
1997
0.1
0.1
0.4
1998
0.1
0.1
0.4
1999
0.1
0.1
0.4
2000
0.1
0.2
0.5
2001
0.1
0.2
0.6
2002
0.2
0.3
0.7
2003
0.1
0.3
0.8
2004
0.2
0.2
0.9
2005
0.2
0.3
1.3
2006
0.2
0.4
2.3
2007
0.2
0.4
2.8
2008
0.2
0.4
2.5
2009
0.2
0.4
2.6
2010
0.2
0.4
2.2
2011
0.5
0.9
5.9
2012
0.9
1.0
6.0
2013
1.8
1.4
9.1
2014
2.7
1.4
9.1
2015
3.7
1.5
9.7
2016
4.9
2.3
13.3
2017
6.2
2.6
12.8
2018
9.1
3.0
12.4
2019
11.7
3.3
11.6
Note: In 2017, estimates of alternative fuel vehicle mileage for the last ten years were revised to reflect updates made to EIA
data on alternative fuel use and vehicle counts. These changes were incorporated into this year's Inventory and apply to the
2005 to 2019 time period.
a Heavy Duty-Vehicles includes medium-duty trucks, heavy-duty trucks, and buses.
Source: Derived from Browning (2017), Browning (2018a), and EIA (2019h).
A-176 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-85: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (10s Miles)
Vehicle Type/Year
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Light-Duty Cars
3.7
60.2
105.8
227.6
232.6
524.5
911.3
1,801.3
2,727.2
3,727.4
4,929.8
6,208.7
9,056.1
11,725.7
Methanol-Flex
+
45.9
14.3
+
+
+
+
+
+
+
+
+
+
+
Fuel ICE














Ethanol-Flex Fuel
ire
+
0.3
19.6
90.3
114.8
111.2
139.8
163.0
127.2
110.4
124.5
87.0
85.6
70.5
CNG ICE
+
0.1
5.2
10.8
10.1
10.8
11.1
12.1
11.6
11.7
13.7
12.9
14.0
14.0
CNG Bi-fuel
+
0.2
16.9
9.3
7.4
6.6
4.1
3.2
2.3
1.7
1.4
1.5
1.8
2.2
LPG ICE
1.1
1.1
1.1
1.5
+
0.1
0.1
0.3
3.1
7.6
6.1
0.9
0.5
0.2
LPG Bi-fuel
2.7
2.8
2.8
1.7
1.2
0.3
0.2
0.2
0.1
0.1
0.1
+
+
+
Biodiesel (BD100)
+
+
1.1
41.5
34.3
127.1
156.6
274.1
298.0
337.5
501.3
512.1
521.2
507.9
NEVs
+
9.4
43.6
71.2
61.5
102.9
98.9
103.8
113.2
124.3
83.8
89.9
86.4
80.2
Electric Vehicle
+
0.3
1.3
1.2
1.3
113.8
263.5
768.6
1,438.8
2,200.3
2,921.4
3,810.8
6,097.1
8,488.8
SI PHEV-
+
+
+
+
2.0
51.7
237.0
476.0
732.7
933.7
1,276.5
1,692.0
2,247.5
2,559.8
Electricity














Fuel Cell Hydrogen
+
+
+
+
+
0.1
0.1
0.1
0.1
0.1
1.1
1.4
2.0
2.0
Light-Duty Trucks
72.7
87.7
168.2
390.5
362.3
873.1
957.3
1,421.4
1,430.5
1,477.1
2,258.4
2,646.0
3,007.5
3,325.0
Ethanol-Flex Fuel
ire
+
0.3
21.9
96.4
121.7
135.4
180.1
213.6
208.8
218.2
279.1
418.4
411.9
495.8
CNG ICE
+
0.1
5.3
9.1
8.0
8.6
8.9
8.7
7.6
6.6
5.8
8.9
6.5
7.7
CNG Bi-fuel
+
0.4
44.3
20.4
19.0
18.2
14.8
16.1
19.3
20.3
26.3
24.3
28.9
29.4
LPG ICE
21.0
24.9
25.9
12.1
9.7
9.6
5.9
6.3
7.3
7.5
7.3
7.9
8.4
7.9
LPG Bi-fuel
51.7
61.2
63.6
26.8
23.8
12.4
4.9
5.9
21.8
8.7
6.5
7.9
9.0
8.1
LNG
+
+
0.1
0.2
+
+
+
+
+
+
+
0.1
0.1
0.1
Biodiesel (BD100)
+
+
3.3
220.9
175.7
685.5
736.3
1,152.2
1,132.5
1,172.2
1,615.9
1,540.6
1,481.5
1,371.0
Electric Vehicle
+
0.8
3.8
4.6
4.3
3.1
6.2
18.4
32.5
35.0
271.8
533.4
847.9
1,102.3
SI PHEV-
+
+
+
+
+
+
+
+
0.4
8.2
45.7
104.4
213.4
302.6
Electricity














Fuel Cell Hydrogen
+
+
+
+
+
0.3
0.2
0.2
0.3
0.3
+
+
+
+
Medium-Duty
255.4
249.9
244.6
636.7
476.2
1,510.3
1,574.3
2,503.3
2,519.8
2,670.0
3,741.2
3,590.8
3,448.3
3,258.3
Trucks














CNG ICE
+
+
0.8
5.7
5.6
7.5
8.9
9.3
10.4
11.7
12.5
13.9
14.9
16.6
CNG Bi-fuel
+
0.1
7.8
6.6
6.3
6.1
6.8
7.1
9.5
10.2
11.3
12.3
13.9
15.1
LPG ICE
215.6
210.8
192.5
33.0
29.0
27.1
25.6
23.6
22.7
17.9
16.0
14.8
12.1
9.8
LPG Bi-fuel
39.9
39.0
35.6
6.4
7.8
7.0
9.4
10.0
12.7
9.5
11.5
12.5
12.9
13.5
LNG
+
+
+
+
+
+
+
0.1
+
0.1
0.1
0.2
0.3
0.3
Biodiesel (BD100)
+
+
7.8
585.1
427.5
1,462.6
1,523.5
2,453.2
2,464.4
2,620.7
3,689.7
3,536.9
3,394.2
3,203.0
Heavy-Duty T rucks
104.4
102.0
115.4
1,323.6
1,103.5
3,663.7
3,666.0
5,795.9
5,771.2
6,133.6
8,613.1
8,268.9
7,977.3
7,353.3
A-177
-------
Neat Methanol ICE
+
+
+

+
+
+
+
+
+
+
+
+
+
Neat Ethanol ICE
+
+
+
2.9
3.6
5.7
9.1
12.6
15.0
20.2
23.9
11.1
7.3
7.7
CNG ICE
+
+
0.9
3.2
3.4
3.4
3.9
4.7
5.2
7.3
9.4
8.5
10.5
10.8
LPG ICE
98.1
95.9
87.5
39.9
33.0
34.7
22.5
22.2
18.0
16.8
15.4
13.6
11.5
9.1
LPG Bi-fuel
6.3
6.2
5.6
4.1
4.3
6.3
4.9
5.2
2.2
2.1
2.1
2.1
2.0
1.7
LNG
+
+
+
1.2
1.5
1.6
1.6
1.4
1.9
2.0
1.6
1.6
1.4
1.3
Biodiesel (BD100)
+
+
21.4
1,272.2
1,057.7
3,612.0
3,624.0
5,749.7
5,728.9
6,085.2
8,560.7
8,232.1
7,944.6
7,322.7
Buses
20.0
38.7
140.3
664.5
673.1
761.9
754.5
823.8
824.0
906.5
922.8
987.9
995.1
982.8
Neat Methanol ICE
6.4
10.4
+
+
+
+
+
+
+
+
+
+
+
+
Neat Ethanol ICE
+
4.8
0.1
+
+
+
0.1
0.1
2.7
3.6
1.4
1.0
0.5
0.2
CNG ICE
+
1.1
100.2
560.7
584.2
614.6
606.6
627.9
627.6
705.2
654.5
723.5
734.8
741.6
LPG ICE
13.2
12.7
11.5
7.2
6.5
3.9
3.8
4.0
4.4
3.2
4.4
5.2
4.9
5.1
LNG
0.4
8.5
22.3
34.7
35.5
38.1
39.7
28.4
26.2
21.0
17.5
10.7
6.8
3.3
Biodiesel (BD100)
+
+
0.9
57.5
42.5
100.4
101.0
160.0
159.3
168.8
236.7
227.1
218.9
197.1
Electric
+
1.1
5.2
4.4
4.5
4.5
3.0
3.1
3.6
3.9
7.2
19.2
27.8
33.9
Fuel Cell Hydrogen
+
+
+
+
+
0.3
0.3
0.3
0.3
0.8
1.1
1.3
1.4
1.6
Total VMT
456.3
538.6
774.2
3,242.8
2,847.7
7,333.5
7,863.3
12,345.7
13,272.8
14,914.6
20,465.3
21,702.2
24,484.4
26,645.2
Note: Throughout the rest of this Inventory, medium-duty trucks are grouped with heavy-duty trucks; they are reported separately here because these two categories may run
on a slightly different range of fuel types. In 2017, estimates of alternative fuel vehicle mileage for the last ten years were revised to reflect updates made to EIA data on
alternative fuel use and vehicle counts. These changes were incorporated into this year's Inventory and apply to the 2005 to 2019 time period. Source: Derived from Browning
(2017), Browning (2018a), and EIA (2019h).
+ Does not exceed 0.05 million vehicle miles traveled.
A-178 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-86: Age Distribution by Vehicle/Fuel Type for On-Road Vehicles,3 2019
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MC
0
7.0%
7.7%
6.5%
9.8%
8.0%
6.1%
7.1%
1
7.0%
7.8%
6.5%
9.8%
8.1%
6.0%
7.0%
2
7.1%
7.8%
6.4%
9.9%
8.1%
5.9%
7.0%
3
7.1%
7.8%
6.3%
9.8%
8.0%
5.8%
6.5%
4
6.8%
7.4%
6.1%
9.6%
7.6%
5.6%
5.9%
5
6.7%
6.9%
5.5%
9.3%
7.1%
5.1%
5.4%
6
6.3%
6.3%
4.9%
8.8%
6.6%
4.5%
4.8%
7
6.0%
5.8%
4.4%
8.4%
6.1%
4.1%
4.3%
8
3.7%
3.8%
2.4%
5.1%
4.0%
2.5%
3.5%
9
4.0%
3.2%
1.6%
4.8%
2.2%
1.7%
3.1%
10
3.6%
2.3%
1.4%
3.1%
1.9%
2.0%
3.2%
11
4.4%
3.7%
2.7%
0.3%
4.5%
2.9%
5.7%
12
4.7%
3.7%
2.4%
0.2%
3.8%
5.7%
5.1%
13
4.1%
3.6%
3.4%
3.3%
4.6%
4.8%
4.8%
14
3.6%
3.5%
2.6%
2.0%
3.7%
4.4%
4.2%
15
3.0%
3.2%
3.2%
1.1%
3.1%
3.0%
3.6%
16
2.7%
2.7%
2.8%
1.2%
2.6%
2.6%
3.1%
17
2.3%
2.4%
2.7%
1.1%
2.1%
2.1%
2.7%
18
1.9%
2.0%
2.2%
0.6%
2.2%
2.7%
2.3%
19
1.7%
1.8%
4.3%
0.5%
1.1%
4.1%
1.8%
20
1.2%
1.4%
4.0%
0.3%
1.5%
3.2%
1.4%
21
1.0%
1.1%
1.7%
0.2%
0.5%
2.2%
1.2%
22
0.8%
0.9%
3.0%
0.1%
0.7%
2.0%
1.1%
23
0.7%
0.6%
1.8%
0.1%
0.5%
1.8%
1.0%
24
0.6%
0.6%
2.4%
0.1%
0.4%
2.2%
0.7%
25
0.5%
0.5%
1.9%
0.0%
0.2%
1.6%
0.9%
26
0.4%
0.4%
1.5%
0.0%
0.2%
1.2%
0.7%
27
0.3%
0.3%
1.1%
0.0%
0.2%
0.8%
0.6%
28
0.3%
0.2%
0.9%
0.1%
0.1%
0.8%
0.5%
29
0.2%
0.2%
1.2%
0.0%
0.1%
1.0%
0.4%
30
0.3%
0.2%
2.1%
0.0%
0.1%
1.6%
0.3%
Total
100.0%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
Note: This year's Inventory includes updated vehicle population data based on the MOVES2014b Model.
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks),
HDGV (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty
diesel vehicles), and MC (motorcycles).
Source: EPA (2019a).
Table A-87: Annual Average Vehicle Mileage Accumulation per Vehicle3 (miles)
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MCb
0
13,414
15,161
17,663
13,414
15,161
41,952
7,720
1
13,159
14,876
17,666
13,159
14,876
41,527
4,122
2
12,884
14,555
17,670
12,884
14,555
41,008
3,119
3
12,590
14,203
17,669
12,590
14,203
40,964
2,578
4
12,280
13,824
16,724
12,280
13,824
38,890
2,231
5
11,954
13,421
15,782
11,954
13,421
36,611
1,984
6
11,617
12,998
14,839
11,617
12,998
34,435
1,799
7
11,269
12,558
13,897
11,269
12,558
33,049
1,652
8
10,913
12,106
12,074
10,913
12,106
37,287
1,528
9
10,550
11,647
10,309
10,550
11,647
36,330
1,428
10
10,183
11,181
9,749
10,183
11,181
37,001
1,343
11
9,815
10,716
9,890
9,815
10,716
24,963
1,266
12
9,446
10,253
8,122
9,446
10,254
33,019
1,204
A-179
-------
13
9,080
9,799
7,911
9,080
9,799
27,000
1,142
14
8,718
9,354
6,515
8,718
9,354
24,819
1,088
15
8,361
8,924
6,018
8,361
8,924
19,678
1,042
16
8,014
8,513
5,045
8,014
8,513
18,278
1,004
17
7,676
8,123
4,844
7,676
8,123
16,178
965
18
7,351
7,761
4,448
7,351
7,761
14,926
926
19
7,040
7,429
4,855
7,040
7,429
14,515
895
20
6,746
7,130
4,537
6,746
7,130
13,128
865
21
6,472
6,869
4,141
6,472
6,870
12,555
841
22
6,217
6,650
3,691
6,217
6,650
9,151
818
23
5,985
6,477
3,467
5,985
6,477
9,480
772
24
5,779
6,352
3,428
5,779
6,353
7,739
726
25
5,599
6,280
3,028
5,599
6,280
6,476
679
26
5,448
6,266
3,024
5,448
6,266
5,996
625
27
5,329
6,266
2,333
5,329
6,266
5,336
579
28
5,243
6,266
2,337
5,243
6,266
4,758
548
29
5,192
6,266
2,122
5,192
6,266
3,806
509
30
5,192
6,266
2,149
5,192
6,266
3,142
471
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks),
HDGV (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty
diesel vehicles), and MC (motorcycles).
b Because of a lack of data, all motorcycles over 12 years old are considered to have the same emissions and travel
characteristics, and therefore are presented in aggregate.
Source: EPA (2019a).
Table A-88: VMT Distribution by Vehicle Age and Vehicle/Fuel Type,3 2019
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MC
0
8.78%
9.73%
10.65%
11.25%
9.97%
9.08%
24.91%
1
8.57%
9.63%
10.69%
10.97%
9.86%
8.95%
13.26%
2
8.50%
9.43%
10.45%
10.89%
9.64%
8.56%
9.96%
3
8.26%
9.15%
10.35%
10.57%
9.35%
8.47%
7.64%
4
7.82%
8.46%
9.41%
10.01%
8.64%
7.72%
6.01%
5
7.44%
7.67%
8.11%
9.52%
7.82%
6.62%
4.91%
6
6.84%
6.84%
6.73%
8.76%
6.98%
5.49%
3.96%
7
6.30%
6.08%
5.70%
8.10%
6.23%
4.85%
3.25%
8
3.71%
3.77%
2.75%
4.76%
3.95%
3.37%
2.41%
9
3.96%
3.08%
1.54%
4.36%
2.13%
2.14%
2.04%
10
3.40%
2.12%
1.29%
2.71%
1.75%
2.65%
1.98%
11
4.04%
3.33%
2.47%
0.23%
3.92%
2.61%
3.31%
12
4.13%
3.16%
1.85%
0.15%
3.19%
6.74%
2.80%
13
3.46%
2.90%
2.49%
2.58%
3.67%
4.66%
2.52%
14
2.94%
2.71%
1.60%
1.52%
2.86%
3.87%
2.10%
15
2.30%
2.40%
1.81%
0.77%
2.26%
2.12%
1.71%
16
1.98%
1.93%
1.30%
0.84%
1.83%
1.72%
1.41%
17
1.61%
1.65%
1.23%
0.75%
1.40%
1.21%
1.19%
18
1.27%
1.30%
0.91%
0.41%
1.42%
1.45%
0.97%
19
1.11%
1.09%
1.92%
0.31%
0.68%
2.14%
0.75%
20
0.78%
0.85%
1.68%
0.16%
0.89%
1.51%
0.55%
21
0.58%
0.62%
0.64%
0.14%
0.31%
0.97%
0.45%
22
0.48%
0.51%
1.04%
0.05%
0.36%
0.66%
0.42%
23
0.37%
0.35%
0.57%
0.05%
0.26%
0.61%
0.35%
24
0.35%
0.32%
0.78%
0.04%
0.18%
0.60%
0.25%
25
0.26%
0.27%
0.53%
0.00%
0.10%
0.38%
0.27%
26
0.21%
0.19%
0.42%
0.02%
0.11%
0.25%
0.20%
A-180 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
27	0.17%	0.14%	0.25%	0.02%	0.10%	0.16%	0.15%
28	0.14%	0.12%	0.20%	0.04%	0.06%	0.13%	0.12%
29	0.11%	0.10%	0.24%	0.01%	0.04%	0.13%	0.09%
3	0	0.15%	0.10%	0.41%	0.01%	0.04%	0.18%	0.07%
Total	100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%
Note: Estimated by weighting data by data in Table A-87. This year's Inventory includes updated vehicle population data based
on the MOVES2014b model that affects this distribution.
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks),
HDGV (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty
diesel vehicles), and MC (motorcycles).
A-181
-------
Table A-89: Fuel Consumption for Non-Road Sources by Fuel Type (million gallons unless otherwise noted)
Vehicle Type/Year
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Aircraft3
19,560
18,320
20,304
16,030
15,762
15,262
14,914
15,274
15,397
16,338
17,198
17,790
17,860
18,431
Aviation Gasoline
374
329
302
221
225
225
209
186
181
176
170
174
186
195
Jet Fuel
19,186
17,991
20,002
15,809
15,537
15,036
14,705
15,088
15,217
16,162
17,028
17,616
17,674
18,236
Commercial Aviationb
11,569
12,136
14,672
12,588
11,931
12,067
11,932
12,031
12,131
12,534
12,674
13,475
13,650
14,132
Ships and Boats
4,826
5,932
6,544
4,201
4,693
4,833
4,239
4,175
3,191
3,652
4,235
4,469
4,190
4,074
Diesel
1,156
1,661
1,882
1,395
1,361
1,641
1,389
1,414
1,284
1,881
1,680
1,593
1,525
1,342
Gasoline
1,611
1,626
1,636
1,498
1,446
1,401
1,372
1,349
1,323
1,325
1,335
1,344
1,352
1,355
Residual
2,060
2,646
3,027
1,308
1,886
1,791
1,477
1,413
584
445
1,219
1,532
1,313
1,377
Construction/Mining














Equipment11














Diesel
4,317
4,718
5,181
5,885
5,727
5,650
5,533
5,447
5,313
5,200
5,483
5,978
6,262
6,464
Gasoline
472
437
357
583
678
634
651
1,100
710
367
375
375
385
387
CNG (million cubic feet)
5,082
5,463
6,032
6,378
6,219
6,121
5,957
5,802
5,598
5,430
5,629
6,018
6,204
6,321
LPG
22
24
27
27
26
25
24
24
23
22
23
25
26
27
Agricultural Equipment11














Diesel
3,514
3,400
3,278
3,938
3,942
3,876
3,932
3,900
3,925
3,862
3,760
3,728
3,732
3,742
Gasoline
813
927
652
676
692
799
875
655
644
159
168
168
160
129
CNG (million cubic feet)
1,758
1,712
1,678
1,677
1,647
1,600
1,611
1,588
1,590
1,561
1,517
1,503
1,502
1,507
LPG
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Rail
3,461
3,864
4,106
3,535
3,807
3,999
3,921
4,025
4,201
4,020
3,715
3,832
3,996
3,756
Diesel
3,461
3,864
4,106
3,535
3,807
3,999
3,921
4,025
4,201
4,020
3,715
3,832
3,996
3,756
Othere














Diesel
2,095
2,071
2,047
2,375
2,450
2,523
2,639
2,725
2,811
2,832
2,851
2,919
3,027
3,110
Gasoline
4,371
4,482
4,673
5,291
5,525
5,344
5,189
5,201
5,281
5,083
5,137
5,178
5,238
5,287
CNG (million cubic feet)
20,894
22,584
25,035
28,163
29,891
32,035
35,085
37,436
39,705
38,069
37,709
38,674
40,390
41,474
LPG
1,412
1,809
2,191
2,130
2,165
2,168
2,181
2,213
2,248
2,279
2,316
2,408
2,526
2,616
Total (gallons)
44,863
45,984
49,361
44,671
45,467
45,113
44,099
44,740
43,745
43,815
45,261
46,871
47,402
48,024
Total (million cubic feet)
27,735
29,759
32,745
36,218
37,757
39,755
42,653
44,826
46,893
45,060
44,854
46,194
48,097
49,301
Note: This year's Inventory uses the NONROAD component of MOVES2014b for years 1999 through 2019.
a For aircraft, this is aviation gasoline. For all other categories, this is motor gasoline.
b Commercial aviation, as modeled in FAA's AEDT, consists of passenger aircraft, cargo, and other chartered flights.
c Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
d Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment,
and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
Sources: AAR (2008 through 2019), APTA (2007 through 2019), BEA (2018), Benson (2002 through 2004), DHS (2008), DOC (1991 through 2019), DLA (2020), DOE (1993 through
2018), DOT (1991 through 2019), EIA (2002), EIA (2007b), EIA (2020a), EIA (2007 through 2019), EIA (1991 through 2019), EPA (2020b), FAA (2021), Gaffney (2007), and Whorton
(2006 through 2014).
A-182 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-90: Emissions Control Technology Assignments for Gasoline Passenger Cars (Percent of VMT)
Model
Years
Non-
catalyst Oxidation EPA Tier 0 EPA Tier 1 CARB LEV
CARB LEV 2 EPA Tier 2
CARB LEV 3 EPA Tier 3
1973-1974
100%
-
-
-
-
-
-
-
1975
20%
80%
-
-
-
-
-
-
1976-1977
15%
85%
-
-
-
-
-
-
1978-1979
10%
90%
-
-
-
-
-
-
1980
5%
88%
7%
-
-
-
-
-
1981
-
15%
85%
-
-
-
-
-
1982
-
14%
86%
-
-
-
-
-
1983
-
12%
88%
-
-
-
-
-
1984-1993
-
-
100%
-
-
-
-
-
1994
-
-
80%
20%
-
-
-
-
1995
-
-
60%
40%
-
-
-
-
1996
-
-
40%
54%
6%
-
-
-
1997
-
-
20%
68%
12%
-
-
-
1998
-
-
<1%
82%
18%
-
-
-
1999
-
-
<1%
67%
33%
-
-
-
2000
-
-
-
44%
56%
-
-
-
2001
-
-
-
3%
97%
-
-
-
2002
-
-
-
1%
99%
-
-
-
2003
-
-
-
<1%
85%
2%
12%
-
2004
-
-
-
<1%
24%
16%
60%
-
2005
-
-
-
-
13%
27%
60%
-
2006
-
-
-
-
18%
35%
47%
-
2007
-
-
-
-
4%
43%
53%
-
2008
-
-
-
-
2%
42%
56%
-
2009
-
-
-
-
<1%
43%
57%
-
2010
-
-
-
-
-
44%
56%
-
2011
-
-
-
-
-
42%
58%
-
2012
-
-
-
-
-
41%
59%
-
2013
-
-
-
-
-
40%
60%
-
2014
-
-
-
-
-
37%
62%
1%
2015
-
-
-
-
-
33%
56%
11% <1%
2016
-
-
-
-
-
25%
50%
18% 6%
2017
-
-
-
-
-
14%
1%
29% 56%
2018
-
-
-
-
-
7%
0%
42% 52%
2019
-
-
-
-
-
3%
0%
44% 53%
Note: Detailed descriptions of emissions control technologies are provided in the following section of this Annex. In 2016,
historical confidential vehicle sales data was re-evaluated to determine the engine technology assignments. First several light-
duty trucks were re-characterized as heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales
data. Second, which emission standards each vehicle type was assumed to have met were re-examined using confidential sales
data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and
therefore were not included in the engine technology breakouts. For this Inventory, HEVs are now classified as gasoline vehicles
across the entire time series.
- (Not Applicable)
Sources: EPA (1998), EPA (2019d), and EPA (2020c).
Table A-91: Emissions Control Technology Assignments for Gasoline Light-Duty Trucks (Percent of VMT)a
Model	Non-
Years	catalyst Oxidation EPA Tier 0 EPA Tier 1 CARB LEVb CARB LEV 2 EPA Tier 2 CARB LEV 3 EPA Tier 3
1973-1974	100%	....	.	.	.	.
1975	30%	70%	...	.	.	.	.
1976	20%	80%	...	.	.	.	.
1977-1978 25% 75% ... . . . .
A-183
-------
1979-1980	20%	80%	...	.	.	.	.
1981	- 95%	5%	-	-	-	-	-	-
1982	- 90%	10%	......
1983	- 80%	20%	......
1984	- 70%	30%	......
1985	- 60% 40%	......
1986	- 50%	50%	......
1987-1993	-	5%	95%	......
1994	-	- 60%	40%	.....
1995	-	- 20%	80%	.....
1996	-	-	- 100%	.....
1997	-	-	- 100%	.....
1998	-	-	-	87%	13%	....
1999	-	-	-	61%	39%	....
2000	-	-	-	63%	37%	....
2001	-	-	-	24%	76%	....
2002	-	-	-	31%	69%	....
2003	-	-	-	25%	69%	-	6%
2004	-	-	-	1%	26%	8%	65%
2005	....	17%	17%	66%
2006	....	24%	22%	54%
2007	....	14%	25%	61%
2008	-	-	-	-	<1%	34%	66%
2009	.....	34%	66%
2010	.....	30%	70%
2011	.....	27%	73%
2012	.....	24%	76%
2013	.....	31%	69%
2014	.....	26%	73%	1%
2015	.....	22%	72%	6%
2016	.....	20%	62%	16%	2%
2017	....	-	9%	14%	28% 48%
2018	....	-	7%	-	38%	55%
201	9	-	-	-	-	-	3%	0%	44%	53%
Note: In 2016, historical confidential vehicle sales data was re-evaluated to determine the engine technology assignments. First
several light-duty trucks were re-characterized as heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and
confidential sales data. Second, which emission standards each vehicle type was assumed to have met were re-examined using
confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative
fueled vehicles and therefore were not included in the engine technology breakouts. For this Inventory, HEVs are now classified
as gasoline vehicles across the entire time series.
- (Not Applicable)
a Detailed descriptions of emissions control technologies are provided in the following section of this Annex.
b The proportion of LEVs as a whole has decreased since 2001, as carmakers have been able to achieve greater emission
reductions with certain types of LEVs, such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a carmaker can
reduce the total number of LEVs they need to build to meet a specified emission average for all of their vehicles in a given
model year.
Sources: EPA (1998), EPA (2019d), and EPA (2020c).
Table A-92: Emissions Control Technology Assignments for Gasoline Heavy-Duty Vehicles (Percent of VMT)a	
Model	Non-
Years Uncontrolled catalyst Oxidation EPA Tier 0 EPA Tier 1 CARB LEVb CARB LEV 2 EPA Tier 2 CARB LEV 3 EPA Tier 3
<1980	100%	.....	.	.	.	.
1981-1984	95%	-	5%	-	-	-	-	-	-	-
1985-1986	- 95%	5%	-	-	-	-	-	-	-
1987	- 70% 15% 15%	-	-	-	-	-	-
A-184 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
1988-1989	- 60% 25% 15%	-	-	-	-	-	-
1990-1995	- 45% 30% 25%	-	-	-	-	-	-
1996	-	- 25% 10% 65%	-	....
1997	-	- 10%	5% 85%	-	....
1998	...	. ioo%	-	....
1999	...	. 98%	2%	-	-	-	-
2000	...	. 93%	7%	-	-	-	-
2001	...	. 78%	22%	-	-	-	-
2002	...	. 94%	6%	-	-	-	-
2003	...	. 85%	14%	-	1%
2004	..... 33%	- 67%
2005	.....	15%	-	85%
2006	.....	50%	-	50%
2007	.....	.	27%	73%
2008	.....	.	46%	54%
2009	.....	.	45%	55%
2010	.....	.	24%	76%
2011	.....	-	7%	93%
2012	.....	.	17%	83%
2013	.....	.	17%	83%
2014	.....	.	19%	81%
2015	.....	.	31%	64%	5%
2016	.....	.	24%	10%	21% 44%
2017	.....	-	8%	8%	39% 45%
2018	.....	-	13%	-	35% 52%
201	9	-	-	-	-	-	-	10%	-	40% 50%
Note: In 2016, historical confidential vehicle sales data was re-evaluated to determine the engine technology assignments. First
several light-duty trucks were re-characterized as heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and
confidential sales data. Second, which emission standards each vehicle type was assumed to have met were re-examined using
confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative
fueled vehicles and therefore were not included in the engine technology breakouts. For this Inventory, HEVs are now classified
as gasoline vehicles across the entire time series.
- (Not Applicable)
a Detailed descriptions of emissions control technologies are provided in the following section of this Annex.
b The proportion of LEVs as a whole has decreased since 2000, as carmakers have been able to achieve greater emission
reductions with certain types of LEVs, such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a manufacturer
can reduce the total number of LEVs they need to build to meet a specified emission average for all of their vehicles in a given
model year.
Sources: EPA (1998), EPA (2019d), and EPA (2020c).
Table A-93: Emissions Control Technology Assignments for Diesel On-Road Vehicles and Motorcycles
Vehicle Type/Control Technology	Model Years
Diesel Passenger Cars and Light-Duty Trucks
Uncontrolled	1960-1982
Moderate control	1983-1995
Advanced control	1996-2006
Aftertreatment	2007-2019
Diesel Medium- and Heavy-Duty Trucks and Buses
Uncontrolled	1960-1989
Moderate control	1990-2003
Advanced control	2004-2006
Aftertreatment	2007-2019
Motorcycles
Uncontrolled	1960-1995
Non-catalyst controls	1996-2019
Note: Detailed descriptions of emissions control technologies are provided in the following section of this Annex.
A-185
-------
Source: EPA (1998) and Browning (2005).
Table A-94: Emission Factors for Cm and N2O for On-Road Vehicles
N2O	ch7
Vehicle Type/Control Technology	(g/mi)	(g/mi)
Gasoline Passenger Cars


EPA Tier 3
0.0015
0.0055
ARB LEVIN
0.0012
0.0045
EPA Tier 2
0.0048
0.0072
ARB LEV II
0.0043
0.0070
ARB LEV
0.0205
0.0100
EPA Tier la
0.0429
0.0271
EPA Tier 0a
0.0647
0.0704
Oxidation Catalyst
0.0504
0.1355
Non-Catalyst Control
0.0197
0.1696
Uncontrolled
0.0197
0.1780
Gasoline Light-Duty Trucks


EPA Tier 3
0.0012
0.0092
ARB LEVIN
0.0012
0.0065
EPA Tier 2
0.0025
0.0100
ARB LEV II
0.0057
0.0084
ARB LEV
0.0223
0.0148
EPA Tier la
0.0871
0.0452
EPA Tier 0a
0.1056
0.0776
Oxidation Catalyst
0.0639
0.1516
Non-Catalyst Control
0.0218
0.1908
Uncontrolled
0.0220
0.2024
Gasoline Heavy-Duty Vehicles


EPA Tier 3
0.0063
0.0252
ARB LEVIN
0.0136
0.0411
EPA Tier 2
0.0015
0.0297
ARB LEV II
0.0049
0.0391
ARB LEV
0.0466
0.0300
EPA Tier la
0.1750
0.0655
EPA Tier 0a
0.2135
0.2630
Oxidation Catalyst
0.1317
0.2356
Non-Catalyst Control
0.0473
0.4181
Uncontrolled
0.0497
0.4604
Diesel Passenger Cars


Aftertreatment
0.0192
0.0302
Advanced
0.0010
0.0005
Moderate
0.0010
0.0005
Uncontrolled
0.0012
0.0006
Diesel Light-Duty Trucks


Aftertreatment
0.0214
0.0290
Advanced
0.0014
0.0009
Moderate
0.0014
0.0009
Uncontrolled
0.0017
0.0011
Diesel Medium- and Heavy-Duty


Trucks and Buses


Aftertreatment
0.0431
0.0095
Advanced
0.0048
0.0051
Moderate
0.0048
0.0051
Uncontrolled
0.0048
0.0051
Motorcycles


A-186 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Non-Catalyst Control	0.0069	0.0672
Uncontrolled	0.0087	0.0899
a The categories "EPA Tier 0" and "EPA Tier 1" were substituted for the early three-way catalyst and advanced three-way
catalyst categories, respectively, as defined in the 2006 IPCC Guidelines. Detailed descriptions of emissions control
technologies are provided at the end of this Annex.
Source: ICF (2006b and 2017a).
A-187
-------
Table A-95: Emission Factors for N2O for Alternative Fuel Vehicles (g/mi)

1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Light-Duty Cars














Methanol-Flex Fuel ICE
0.035
0.035
0.034
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
0.005
Ethanol-Flex Fuel ICE
0.035
0.035
0.034
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
0.005
CNG ICE
0.021
0.021
0.027
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
0.005
CNG Bi-fuel
0.021
0.021
0.027
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
0.005
LPG ICE
0.021
0.021
0.027
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
0.005
LPG Bi-fuel
0.021
0.021
0.027
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
0.005
Biodiesel (BD100)
0.001
0.001
0.001
0.001
0.001
0.004
0.008
0.012
0.015
0.019
0.019
0.019
0.019
0.019
Light-Duty Trucks














Ethanol-Flex Fuel ICE
0.068
0.069
0.072
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.012
0.011
0.009
CNG ICE
0.041
0.041
0.058
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.012
0.011
0.009
CNG Bi-fuel
0.041
0.041
0.058
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.012
0.011
0.009
LPG ICE
0.041
0.041
0.058
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.015
0.014
0.013
0.012
LPG Bi-fuel
0.041
0.041
0.058
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.015
0.014
0.013
0.012
LNG
0.041
0.041
0.058
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.012
0.011
0.009
Biodiesel (BD100)
0.001
0.001
0.001
0.001
0.001
0.005
0.009
0.013
0.017
0.021
0.021
0.021
0.021
0.021
Medium Duty Trucks














CNG ICE
0.002
0.002
0.003
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
CNG Bi-fuel
0.002
0.002
0.003
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
LPG ICE
0.055
0.055
0.069
0.043
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
LPG Bi-fuel
0.055
0.055
0.069
0.043
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
LNG
0.002
0.002
0.003
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
Biodiesel (BD100)
0.002
0.002
0.003
0.003
0.003
0.011
0.019
0.027
0.035
0.043
0.043
0.043
0.043
0.043
Heavy-Duty Trucks
Neat Methanol ICE
0.040
0.040
0.049
0.034
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
Neat Ethanol ICE
0.002
0.040
0.049
0.034
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
CNG ICE
0.045
0.002
0.002
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
LPG ICE
1.229
0.045
0.049
0.032
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
LPG Bi-fuel
0.002
0.045
0.049
0.032
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
LNG
0.002
0.002
0.002
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
Biodiesel (BD100)
0.040
0.002
0.002
0.002
0.002
0.010
0.018
0.027
0.035
0.043
0.043
0.043
0.043
0.043
Buses














Neat Methanol ICE
0.045
0.045
0.058
0.040
0.032
0.032
0.032
0.032
0.032
0.032
0.032
0.032
0.032
0.032
Neat Ethanol ICE
0.045
0.045
0.058
0.040
0.032
0.032
0.032
0.032
0.032
0.032
0.032
0.032
0.032
0.032
CNG ICE
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
LPG ICE
0.051
0.051
0.058
0.038
0.030
0.028
0.025
0.022
0.020
0.017
0.017
0.017
0.017
0.017
LNG
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
A-188 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Biodiesel (BD100)	O.OQ2	0.002	0.002	0.002 0.002 0.011 0.019 0.027 0.035 0.043 0.043 0.043 0.043 0.043
Note: When driven in all-electric mode, plug-in electric vehicles have zero tailpipe emissions. Therefore, emissions factors for battery electric vehicle (BEVs) and the electric
portion of plug-in hybrid electric vehicles (PHEVs) are not included in this table.
Source: Developed by ICF (Browning 2017) using ANL (2020).
Table A-96: Emission Factors for Cm for Alternative Fuel Vehicles (g/mi)

1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Light-Duty Cars














Methanol-Flex Fuel ICE
0.034
0.034
0.019
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
0.008
0.008
0.008
Ethanol-Flex Fuel ICE
0.034
0.034
0.019
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
0.008
0.008
0.008
CNG ICE
0.489
0.489
0.249
0.153
0.153
0.139
0.126
0.113
0.100
0.086
0.085
0.083
0.082
0.081
CNG Bi-fuel
0.489
0.489
0.249
0.153
0.153
0.139
0.126
0.113
0.100
0.086
0.085
0.083
0.082
0.081
LPG ICE
0.049
0.049
0.025
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
0.008
0.008
0.008
LPG Bi-fuel
0.049
0.049
0.025
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
0.008
0.008
0.008
Biodiesel (BD100)
0.002
0.002
0.002
0.001
0.001
0.007
0.013
0.018
0.024
0.030
0.019
0.030
0.030
0.030
Light-Duty Trucks














Ethanol-Flex Fuel ICE
0.051
0.051
0.053
0.033
0.033
0.029
0.025
0.021
0.017
0.013
0.013
0.013
0.012
0.012
CNG ICE
0.728
0.725
0.709
0.349
0.332
0.292
0.251
0.210
0.170
0.129
0.127
0.125
0.123
0.121
CNG Bi-fuel
0.728
0.725
0.709
0.349
0.332
0.292
0.251
0.210
0.170
0.129
0.127
0.125
0.123
0.121
LPG ICE
0.073
0.072
0.071
0.035
0.033
0.029
0.025
0.021
0.017
0.013
0.013
0.013
0.012
0.012
LPG Bi-fuel
0.073
0.072
0.071
0.035
0.033
0.029
0.025
0.021
0.017
0.013
0.013
0.013
0.012
0.012
LNG
0.728
0.725
0.709
0.349
0.332
0.292
0.251
0.210
0.170
0.129
0.127
0.125
0.123
0.121
Biodiesel (BD100)
0.005
0.005
0.005
0.002
0.001
0.007
0.012
0.018
0.023
0.029
0.029
0.029
0.029
0.029
Medium Duty Trucks














CNG ICE
6.800
6.800
6.800
6.800
6.800
6.280
5.760
5.240
4.720
4.200
4.200
4.200
4.200
4.200
CNG Bi-fuel
6.800
6.800
6.800
6.800
6.800
6.280
5.760
5.240
4.720
4.200
4.200
4.200
4.200
4.200
LPG ICE
0.262
0.262
0.248
0.023
0.021
0.020
0.018
0.017
0.016
0.014
0.014
0.014
0.014
0.014
LPG Bi-fuel
0.262
0.262
0.248
0.023
0.021
0.020
0.018
0.017
0.016
0.014
0.014
0.014
0.014
0.014
LNG
6.800
6.800
6.800
6.800
6.800
6.280
5.760
5.240
4.720
4.200
4.200
4.200
4.200
4.200
Biodiesel (BD100)
0.004
0.004
0.004
0.002
0.002
0.004
0.005
0.006
0.008
0.009
0.009
0.009
0.009
0.009
Heavy-Duty Trucks














Neat Methanol ICE
0.296
0.296
0.095
0.136
0.151
0.136
0.120
0.105
0.090
0.075
0.075
0.075
0.075
0.075
Neat Ethanol ICE
0.296
0.296
0.095
0.136
0.151
0.136
0.120
0.105
0.090
0.075
0.075
0.075
0.075
0.075
CNG ICE
4.100
4.100
4.100
4.100
4.100
4.020
3.940
3.860
3.780
3.700
3.700
3.700
3.700
3.700
LPG ICE
0.158
0.158
0.149
0.014
0.013
0.013
0.013
0.013
0.013
0.013
0.013
0.013
0.013
0.013
LPG Bi-fuel
0.158
0.158
0.149
0.014
0.013
0.013
0.013
0.013
0.013
0.013
0.013
0.013
0.013
0.013
LNG
4.100
4.100
4.100
4.100
4.100
4.020
3.940
3.860
3.780
3.700
3.700
3.700
3.700
3.700
Biodiesel (BD100)
0.012
0.012
0.005
0.005
0.005
0.006
0.007
0.007
0.008
0.009
0.009
0.009
0.009
0.009
Buses














Neat Methanol ICE
0.086
0.086
0.067
0.068
0.075
0.067
0.060
0.052
0.045
0.037
0.032
0.027
0.022
0.016
A-189
-------
Neat Ethanol ICE
CNG ICE
LPG ICE
LNG
Biodiesel (BD100)
0.086
0.086
0.067
0.068
0.075
0.067
0.060
0.052
0.045
0.037
0.032
0.027
0.022
0.016
18.800
18.800
18.800
18.800
18.800
17.040
15.280
13.520
11.760
10.000
10.000
10.000
10.000
10.000
0.725
0.725
0.686
0.063
0.058
0.053
0.048
0.044
0.039
0.034
0.034
0.034
0.034
0.034
18.800
18.800
18.800
18.800
18.800
17.040
15.280
13.520
11.760
10.000
10.000
10.000
10.000
10.000
0.004
0.004
0.003
0.002
0.002
0.004
0.005
0.006
0.008
0.009
0.009
0.009
0.009
0.009
Note: When driven in all-electric mode, plug-in electric vehicles have zero tailpipe emissions. Therefore, emissions factors for battery electric vehicle (BEVs) and the electric
portion of plug-in hybrid electric vehicles (PHEVs) are not included in this table.
Source: Developed by ICF (Browning 2017) using ANL (2020).
Table A-97: Emission Factors for N2O Emissions from Non-Road Mobile Combustion (g/kg fuel)
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Ships and Boats
Residual Fuel Oil
Gasoline
2 Stroke
4 Stroke
Distillate Fuel Oil
Rail
Diesel
Aircraft
Jet Fuel
Aviation Gasoline
Agricultural
Equipment3
Gasoline-Equipment
2 Stroke
4 Stroke
Gasoline-Off-road
Trucks
Diesel-Equipment
Diesel-Off-Road
Trucks
CNG
LPG
Construction/Mining
Equipment'5
Gasoline-Equipment
2 Stroke
4 Stroke
0.09
0.021
0.002
0.054
0.08
0.10
0.04
0.055
0.543
0.54
0.282
0.288
0.036
0.250
0.079
0.461
0.09
0.021
0.003
0.054
0.08
0.10
0.04
0.060
0.550
0.55
0.282
0.288
0.036
0.250
0.082
0.486
0.09
0.021
0.003
0.054
0.08
0.10
0.04
0.064
0.558
0.56
0.282
0.288
0.036
0.250
0.084
0.509
0.09
0.024
0.003
0.054
0.08
0.10
0.04
0.091
0.618
0.62
0.282
0.288
0.042
0.277
0.120
0.582
0.09
0.025
0.003
0.054
0.08
0.10
0.04
0.092
0.624
0.62
0.282
0.288
0.043
0.281
0.121
0.584
0.09
0.025
0.003
0.054
0.08
0.10
0.04
0.092
0.629
0.63
0.282
0.288
0.044
0.283
0.121
0.587
0.09
0.025
0.003
0.054
0.08
0.10
0.04
0.092
0.634
0.63
0.282
0.288
0.044
0.285
0.121
0.590
0.09
0.026
0.003
0.054
0.08
0.10
0.04
0.092
0.638
0.64
0.282
0.288
0.044
0.287
0.121
0.591
0.09
0.026
0.003
0.054
0.08
0.10
0.04
0.092
0.642
0.64
0.282
0.288
0.044
0.288
0.121
0.592
0.09
0.026
0.003
0.054
0.08
0.10
0.04
0.092
0.644
0.64
0.282
0.288
0.044
0.290
0.121
0.594
0.09
0.027
0.003
0.054
0.08
0.10
0.04
0.092
0.647
0.65
0.282
0.288
0.044
0.291
0.121
0.595
0.09
0.027
0.003
0.054
0.08
0.10
0.04
0.092
0.650
0.65
0.282
0.288
0.045
0.292
0.121
0.596
0.09
0.027
0.003
0.054
0.08
0.10
0.04
0.092
0.653
0.65
0.282
0.288
0.045
0.293
0.121
0.596
0.09
0.027
0.003
0.054
0.08
0.10
0.04
0.092
0.656
0.66
0.282
0.288
0.045
0.294
0.121
0.597
A-190 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Gasoline-Off-road





Trucks
0.46
0.49
0.51
0.58
0.58
Diesel-Equipment
0.274
0.274
0.274
0.274
0.274
Diesel-Off-Road





Trucks
0.288
0.288
0.288
0.288
0.288
CNG
0.036
0.036
0.036
0.038
0.038
LPG
0.250
0.250
0.250
0.279
0.283
Lawn and Garden Equipment




Gasoline-Residential





2 Stroke
0.053
0.056
0.060
0.084
0.084
4 Stroke
0.401
0.421
0.447
0.526
0.529
Gasoline-





Commercial





2 Stroke
0.066
0.069
0.072
0.102
0.102
4 Stroke
0.427
0.464
0.498
0.553
0.554
Diesel-Residential





Diesel-Commercial
0.271
0.271
0.270
0.270
0.270
LPG
0.250
0.250
0.250
0.291
0.297
Airport Equipment





Gasoline





4 Stroke
0.597
0.616
0.631
0.734
0.741
Diesel
0.285
0.285
0.285
0.285
0.285
LPG
0.250
0.250
0.250
0.295
0.299
Industrial/Commercial





Equipment





Gasoline





2 Stroke
0.055
0.060
0.064
0.092
0.092
4 Stroke
0.457
0.485
0.510
0.583
0.587
Diesel
0.270
0.270
0.270
0.271
0.271
CNG
0.036
0.036
0.036
0.042
0.043
LPG
0.250
0.250
0.250
0.285
0.291
Logging Equipment





Gasoline





2 Stroke
0.082
0.085
0.087
0.124
0.124
4 Stroke
0.445
0.453
0.463
0.518
0.522
Diesel
0.287
0.287
0.287
0.287
0.287
Railroad Equipment





Gasoline





4 Stroke
0.442
0.464
0.485
0.561
0.562
0.59
0.59
0.59
0.59
0.59
0.59
0.60
0.60
0.60
0.274
0.274
0.274
0.274
0.274
0.274
0.274
0.274
0.274
0.288
0.288
0.288
0.288
0.288
0.288
0.288
0.288
0.288
0.038
0.039
0.039
0.039
0.040
0.040
0.041
0.042
0.042
0.287
0.290
0.293
0.295
0.298
0.300
0.303
0.305
0.306
0.085
0.085
0.085
0.085
0.085
0.085
0.085
0.085
0.085
0.532
0.534
0.535
0.536
0.537
0.537
0.537
0.538
0.537
0.102
0.102
0.102
0.102
0.102
0.102
0.102
0.102
0.102
0.555
0.557
0.558
0.558
0.558
0.559
0.559
0.559
0.559
0.270
0.270
0.270
0.270
0.270
0.270
0.270
0.270
0.270
0.302
0.306
0.307
0.309
0.310
0.311
0.311
0.312
0.312
0.750
0.755
0.758
0.760
0.762
0.763
0.764
0.764
0.764
0.285
0.285
0.285
0.285
0.285
0.285
0.285
0.285
0.285
0.304
0.307
0.308
0.310
0.311
0.311
0.312
0.312
0.312
0.092
0.092
0.092
0.092
0.092
0.092
0.092
0.092
0.092
0.590
0.593
0.594
0.595
0.596
0.597
0.598
0.599
0.600
0.272
0.272
0.273
0.273
0.273
0.272
0.272
0.272
0.272
0.043
0.044
0.044
0.044
0.044
0.044
0.044
0.044
0.044
0.297
0.303
0.305
0.307
0.308
0.309
0.310
0.311
0.311
0.124
0.124
0.124
0.124
0.124
0.124
0.124
0.124
0.124
0.528
0.535
0.542
0.549
0.554
0.557
0.559
0.562
0.563
0.287
0.287
0.287
0.287
0.287
0.287
0.287
0.287
0.287
0.564
0.565
0.566
0.567
0.567
0.568
0.568
0.568
0.568
A-191
-------
Diesel
0.243
0.243
0.243
0.243
0.243
0.243
0.243
0.243
0.243
0.243
0.243
0.243
0.243
0.243
LPG
0.250
0.250
0.250
0.274
0.276
0.283
0.287
0.291
0.294
0.297
0.299
0.303
0.305
0.306
Recreational














Equipment














Gasoline














2 Stroke
0.059
0.060
0.060
0.063
0.064
0.064
0.065
0.066
0.067
0.068
0.069
0.070
0.071
0.072
4 Stroke
0.719
0.732
0.743
0.790
0.790
0.792
0.793
0.793
0.793
0.794
0.794
0.794
0.795
0.795
Diesel
0.236
0.236
0.236
0.236
0.236
0.236
0.236
0.236
0.236
0.236
0.236
0.236
0.236
0.236
LPG
0.250
0.250
0.250
0.263
0.265
0.267
0.270
0.272
0.274
0.276
0.278
0.281
0.283
0.285
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
Source: IPCC (2006) and Browning, L (2018b), EPA (2019a).
Table A-98: Emission Factors for ChU Emissions from Non-Road Mobile Combustion (g/kg fuel)

1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Ships and Boats














Residual Fuel Oil
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
Gasoline














2 Stroke
1.259
1.259
1.270
1.443
1.465
1.489
1.514
1.537
1.557
1.578
1.598
1.615
1.629
1.642
4 Stroke
0.720
0.721
0.725
0.757
0.760
0.764
0.769
0.773
0.778
0.783
0.789
0.794
0.798
0.802
Distillate Fuel Oil
2.010
2.010
2.010
2.010
2.010
2.010
2.010
2.010
2.010
2.010
2.010
2.010
2.010
2.010
Rail














Diesel
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
Aircraft














Jet Fuelc
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Aviation Gasoline
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
Agricultural Equipment3














Gasoline-Equipment














2 Stroke
2.111
2.141
2.284
3.288
3.294
3.294
3.294
3.294
3.294
3.294
3.294
3.294
3.294
3.293
4 Stroke
0.997
1.000
1.014
1.125
1.135
1.143
1.153
1.160
1.167
1.172
1.176
1.182
1.187
1.193
Gasoline-Off-road Trucks
0.997
1.000
1.014
1.125
1.135
1.143
1.153
1.160
1.167
1.172
1.176
1.182
1.187
1.193
Diesel-Equipment
0.303
0.303
0.303
0.303
0.303
0.303
0.303
0.303
0.303
0.303
0.303
0.303
0.303
0.303
Diesel-Off-Road Trucks
0.310
0.310
0.310
0.310
0.310
0.310
0.310
0.310
0.310
0.310
0.310
0.310
0.310
0.310
CNG
2.293
2.293
2.293
2.698
2.762
2.798
2.819
2.834
2.845
2.853
2.858
2.861
2.861
2.860
LPG
0.175
0.175
0.175
0.194
0.197
0.198
0.200
0.201
0.202
0.203
0.204
0.205
0.205
0.206
Construction/Mining














Equipment'5














Gasoline-Equipment














2 Stroke
2.924
2.945
3.033
4.329
4.339
4.341
4.341
4.341
4.341
4.341
4.341
4.341
4.341
4.340
A-192 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
4 Stroke
0.875
0.885
0.926
1.058
1.063
Gasoline-Off-road Trucks
0.875
0.885
0.926
1.058
1.063
Diesel-Equipment
0.295
0.295
0.295
0.295
0.295
Diesel-Off-Road Trucks
0.310
0.310
0.310
0.310
0.310
CNG
2.293
2.293
2.293
2.424
2.444
LPG
0.175
0.175
0.175
0.195
0.198
Lawn and Garden





Equipment





Gasoline-Residential





2 Stroke
1.99
2.01
2.14
3.01
3.03
4 Stroke
0.76
0.77
0.81
0.96
0.96
Gasoline-Commercial





2 Stroke
2.46
2.49
2.60
3.65
3.65
4 Stroke
0.83
0.84
0.91
1.01
1.01
Diesel-Residential





Diesel-Commercial
0.29
0.29
0.29
0.29
0.29
LPG
0.18
0.18
0.18
0.20
0.21
Airport Equipment





Gasoline





4 Stroke
1.11
1.12
1.15
1.33
1.35
Diesel
0.31
0.31
0.31
0.31
0.31
LPG
0.18
0.18
0.18
0.21
0.21
Industrial/Commercial





Equipment





Gasoline





2 Stroke
2.13
2.16
2.29
3.29
3.29
4 Stroke
0.87
0.88
0.93
1.06
1.07
Diesel
0.29
0.29
0.29
0.29
0.29
CNG
2.29
2.29
2.29
2.72
2.75
LPG
0.18
0.18
0.18
0.20
0.20
Logging Equipment





Gasoline





2 Stroke
3.03
3.05
3.13
4.47
4.47
4 Stroke
0.82
0.82
0.84
0.94
0.95
Diesel
0.31
0.31
0.31
0.31
0.31
Railroad Equipment





Gasoline





4 Stroke
0.84
0.84
0.88
1.02
1.02
Diesel
0.26
0.26
0.26
0.26
0.26
1.068
1.073
1.075
1.077
1.080
1.082
1.083
1.085
1.085
1.068
1.073
1.075
1.077
1.080
1.082
1.083
1.085
1.085
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.310
0.310
0.310
0.310
0.310
0.310
0.310
0.310
0.310
2.468
2.488
2.511
2.531
2.556
2.600
2.653
2.695
2.730
0.201
0.204
0.205
0.207
0.209
0.211
0.213
0.214
0.215
3.05
3.06
3.06
3.06
3.06
3.06
3.06
3.06
3.06
0.97
0.97
0.97
0.97
0.98
0.98
0.98
0.98
0.98
3.65
3.65
3.65
3.65
3.65
3.65
3.66
3.66
3.66
1.01
1.01
1.01
1.01
1.02
1.02
1.02
1.02
1.02
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.21
0.21
0.22
0.22
0.22
0.22
0.22
0.22
0.22
1.36
1.37
1.38
1.38
1.39
1.39
1.39
1.39
1.39
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.21
0.21
0.22
0.22
0.22
0.22
0.22
0.22
0.22
3.30
3.30
3.30
3.30
3.30
3.30
3.30
3.30
3.30
1.07
1.08
1.08
1.08
1.08
1.09
1.09
1.09
1.09
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.29
2.78
2.81
2.82
2.83
2.84
2.84
2.84
2.85
2.85
0.21
0.21
0.21
0.22
0.22
0.22
0.22
0.22
0.22
4.47
4.47
4.47
4.47
4.47
4.47
4.47
4.47
4.47
0.96
0.97
0.99
1.00
1.01
1.01
1.02
1.02
1.02
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
1.03
1.03
1.03
1.03
1.03
1.03
1.03
1.03
1.03
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
A-193
-------
LPG
0.18
0.18
0.18
0.19
0.19
0.20
0.20
0.20
0.21
0.21
0.21
0.21
0.21
0.21
Recreational Equipment














Gasoline














2 Stroke
1.28
1.29
1.29
1.35
1.37
1.38
1.40
1.42
1.44
1.46
1.48
1.50
1.52
1.53
4 Stroke
1.15
1.15
1.17
1.24
1.24
1.24
1.25
1.25
1.25
1.25
1.25
1.25
1.25
1.54
Diesel
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
LPG
0.18
0.18
0.18
0.18
0.19
0.19
0.19
0.19
0.19
0.19
0.20
0.20
0.20
0.20
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
Source: IPCC (2006) and Browning, L (2018b), EPA (2019a).
Table A-99: NOx Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Gasoline On-Road
5,746
4,560
3,812
; 2,966
2,724
2,805
2,647
2,489
2,332
2,122
1,751
1,670
1,463
1,326
Passenger Cars
3,847
2,752
2,084
\ 1,618
1,486
1,530
1,444
1,358
1,272
1,158
955
911
798
723
Light-Duty Trucks
1,364
1,325
1,303
j 1,026
942
970
915
861
806
734
605
578
506
459
Medium- and Heavy-














Duty Trucks and Buses
515
469
411
j 311
286
294
278
261
245
223
184
175
154
139
Motorcycles
20
14
13
11
10
10
10
9
9
8
6
6
5
5
Diesel On-Road
2,956
3,493
3,803
j 2,665
2,448
2,520
2,379
2,237
2,095
1,907
1,573
1,501
1,315
1,191
Passenger Cars
39
19
7
j 5
4
4
4
4
4
3
3
3
2
2
Light-Duty Trucks
20
12
6
4
4
4
4
4
3
3
3
2
2
2
Medium- and Heavy-














Duty Trucks and Buses
2,897
3,462
3,791
2,656
2,439
2,512
2,370
2,229
2,088
1,900
1,568
1,495
1,310
1,187
Alternative Fuel On-














Road3
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
Non-Road
2,160
2,483
2,584
2,166
2,118
1,968
1,883
1,797
1,712
1,605
1,416
1,392
1,345
1,345
Ships and Boats
402
488
506
448
438
407
389
372
354
332
293
288
278
278
Rail
338
433
451
400
391
363
348
332
316
296
261
257
248
248
Aircraft15
25
31
40
32
32
29
28
27
26
24
21
21
20
20
Agricultural Equipment0
437
478
484
392
383
356
340
325
309
290
256
252
243
243
Construction/Mining














Equipment
641
697
697
563
550
511
489
467
445
417
368
362
349
349
Other0
318
357
407
332
324
301
288
275
262
246
217
213
206
206
Total
10,862
10,536
10,199
7,797
7,290
7,294
6,909
6,523
6,138
5,634
4,739
4,563
4,123
3,862
Notes: The source of this data is the National Emissions Inventory. Updates to estimates from MOVES2014b is a change that affects the emissions time series. Totals may not
sum due to independent rounding.
IE (Included Elsewhere)
A-194 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
a N0X emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial
equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
Table A-100: CO Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Gasoline On-Road
98,328
74,673
60,657
24,515
25,235
24,442
23,573
22,704
21,834
20,864
17,995
17,435
15,898
15,070
Passenger Cars
60,757
42,065
32,867
13,659
14,060
13,618
13,134
12,649
12,165
11,625
10,026
9,714
8,858
8,396
Light-Duty Trucks
29,237
27,048
24,532
9,758
10,044
9,729
9,383
9,037
8,690
8,304
7,162
6,940
6,328
5,998
Medium- and Heavy-














Duty Trucks and Buses
8,093
5,404
3,104
1,042
1,073
1,039
1,002
965
928
887
765
741
676
641
Motorcycles
240
155
154
57
58
57
55
53
51
48
42
40
37
35
Diesel On-Road
1,696
1,424
1,088
376
387
375
361
348
335
320
276
267
244
231
Passenger Cars
35
18
7
3
3
3
2
2
2
2
2
2
2
2
Light-Duty Trucks
22
16
6
2
2
2
2
2
2
2
2
2
1
1
Medium- and Heavy-














Duty Trucks and Buses
1,639
1,391
1,075
371
382
370
357
343
330
316
272
264
241
228
Alternative Fuel On-














Road3
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
Non-Road
19,337
21,533
21,814
14,365
13,853
13,488
12,981
12,474
11,966
11,451
10,518
10,421
10,448
10,448
Ships and Boats
1,559
1,781
1,825
1,182
1,140
1,109
1,068
1,026
984
942
865
857
859
859
Rail
85
93
90
58
56
54
52
50
48
46
42
42
42
42
Aircraft15
217
224
245
151
145
141
136
131
125
120
110
109
110
110
Agricultural Equipment0
581
628
626
401
386
376
362
348
334
319
293
291
291
291
Construction/Mining














Equipment
1,090
1,132
1,047
672
648
631
607
583
560
535
492
487
489
489
Othere
15,805
17,676
17,981
11,903
11,479
11,176
10,756
10,335
9,915
9,488
8,715
8,635
8,657
8,657
Total
119,360
97,630
83,559
39,256
39,475
38,305
36,915
35,525
34,135
32,635
28,789
28,124
26,590
25,749
Notes: The source of this data is the National Emissions Inventory. Updates to estimates from MOVES2014b is a change that affects the emissions time series. Totals may not
sum due to independent rounding.
IE (Included Elsewhere)
a CO emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial
equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
A-195
-------
Table A-101: NMVOCs Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Gasoline On-Road
8,110
5,819
4,615
2,384
2,393
2,485
2,342
2,200
2,058
1,929
1,626
1,570
1,395
1,295
Passenger Cars
5,120
3,394
2,610
1,332
1,336
1,388
1,308
1,229
1,149
1,077
908
877
779
723
Light-Duty Trucks
2,374
2,019
1,750
926
929
965
910
854
799
749
631
610
542
503
Medium- and Heavy-Duty














Trucks and Buses
575
382
232
115
115
120
113
106
99
93
78
76
67
62
Motorcycles
42
24
23
12
12
13
12
11
11
10
8
8
7
7
Diesel On-Road
406
304
216
115
116
120
113
106
100
93
79
76
68
63
Passenger Cars
16
8
3
2
2
2
2
2
1
1
1
1
1
1
Light-Duty Trucks
14
9
4
2
2
2
2
2
2
2
1
1
1
1
Medium- and Heavy-Duty














Trucks and Buses
377
286
209
112
112
116
110
103
96
90
76
74
65
61
Alternative Fuel On-Roada
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
Non-Road
2,415
2,622
2,398
2,150
2,082
1,957
1,837
1,717
1,597
1,435
1,168
1,112
1,080
1,080
Ships and Boats
608
739
744
660
639
600
564
527
490
440
358
341
331
331
Rail
33
36
35
32
31
29
27
26
24
21
17
17
16
16
Aircraft15
28
28
24
17
17
16
15
14
13
12
9
9
9
9
Agricultural Equipment0
85
86
76
65
63
60
56
52
49
44
36
34
33
33
Construction/Mining














Equipment
149
152
130
113
109
103
96
90
84
75
61
58
57
57
Other0
1,512
1,580
1,390
1,263
1,223
1,149
1,079
1,008
938
843
686
653
634
634
Total
10,932
8,745
7,230
4,650
4,591
4,562
4,293
4,023
3,754
3,458
2,873
2,758
2,543
2,437
Notes: The source of this data is the National Emissions Inventory. Updates to estimates from MOVES2014b is a change that affects the emissions time series. Totals may not
sum due to independent rounding.
IE (Included Elsewhere)
a NMVOC emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial
equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
A-196 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Definitions of Emission Control Technologies and Standards
The N20 and CH4 emission factors used depend on the emission standards in place and the corresponding level
of control technology for each vehicle type. Table A-90 through Table A-93 show the years in which these technologies or
standards were in place and the penetration level for each vehicle type. These categories are defined below and were
compiled from EPA (1993, 1994a, 1994b, 1998, 1999) and IPCC/UNEP/OECD/IEA (1997).
Uncontrolled
Vehicles manufactured prior to the implementation of pollution control technologies are designated as
uncontrolled. Gasoline passenger cars and light-duty trucks (pre-1973), gasoline heavy-duty vehicles (pre-1984), diesel
vehicles (pre-1983), and motorcycles (pre-1996) are assumed to have no control technologies in place.
Gasoline Emission Controls
Below are the control technologies and emissions standards applicable to gasoline vehicles.
Non-catalyst
These emission controls were common in gasoline passenger cars and light-duty gasoline trucks during model
years (1973-1974) but phased out thereafter, in heavy-duty gasoline vehicles beginning in the mid-1980s, and in
motorcycles beginning in 1996. This technology reduces hydrocarbon (HC) and carbon monoxide (CO) emissions through
adjustments to ignition timing and air-fuel ratio, air injection into the exhaust manifold, and exhaust gas recirculation
(EGR) valves, which also helps meet vehicle NOx standards.
Oxidation Catalyst
This control technology designation represents the introduction of the catalytic converter, which was the most
common technology in gasoline passenger cars and light-duty gasoline trucks made from 1975 to 1980 (cars) and 1975 to
1985 (trucks). This technology was also used in some heavy-duty gasoline vehicles between 1982 and 1997. The two-way
catalytic converter oxidizes HC and CO, significantly reducing emissions over 80 percent beyond non-catalyst-system
capacity. One reason unleaded gasoline was introduced in 1975 was due to the fact that oxidation catalysts cannot
function properly with leaded gasoline.
EPA Tier 0
This emission standard from the Clean Air Act was met through the implementation of early "three-way"
catalysts, a technology used in gasoline passenger cars and light-duty gasoline trucks beginning in the early 1980s which
remained common until 1994. This more sophisticated emission control system improves the efficiency of the catalyst by
converting CO and HC to C02 and H20, reducing NOx to nitrogen and oxygen, and using an on-board diagnostic computer
and oxygen sensor. In addition, this type of catalyst includes a fuel metering system (carburetor or fuel injection) with
electronic "trim" (also known as a "closed-loop system"). New cars with three-way catalysts met the Clean Air Act's
amended standards (enacted in 1977) of reducing HC to 0.41 g/mile by 1980, CO to 3.4 g/mile by 1981 and NOx to 1.0
g/mile by 1981.
EPA Tier 1
This emission standard created through the 1990 amendments to the Clean Air Act limited passenger car NOx
emissions to 0.4 g/mi, and HC emissions to 0.25 g/mi. These bounds respectively amounted to a 60 and 40 percent
reduction from the EPA Tier 0 standard set in 1981. For light-duty trucks, this standard set emissions at 0.4 to 1.1 g/mi
for NOx, and 0.25 to 0.39 g/mi for HCs, depending on the weight of the truck. Emission reductions were met through the
use of more advanced emission control systems applied to light-duty gasoline vehicles beginning in 1994. These
advanced emission control systems included advanced three-way catalysts, electronically controlled fuel injection and
ignition timing, EGR, and air injection.
EPA Tier 2
This emission standard was specified in the 1990 amendments to the Clean Air Act, limiting passenger car NOx
emissions to 0.07 g/mi on average and aligning emissions standards for passenger cars and light-duty trucks.
A-197
-------
Manufacturers can meet this average emission level by producing vehicles in 11 emission "Bins," the three highest of
which expired in 2006. These emission standards represent a 77 to 95 percent reduction in emissions from the EPA Tier 1
standard set in 1994. Emission reductions were met through the use of more advanced emission control systems and
lower sulfur fuels and applied to vehicles beginning in 2004. These advanced emission control systems include improved
combustion, advanced three-way catalysts, electronically controlled fuel injection and ignition timing, EGR, and air
injection.
EPA Tier 3
These standards begin in 2017 and will fully phase-in by 2025, although some Tier 3-compliant vehicles were
produced prior to 2017. This emission standard reduces both tailpipe and evaporative emissions from passenger cars,
light-duty trucks, medium-duty passenger vehicles, and some heavy-duty vehicles. It is combined with a gasoline sulfur
standard that will enable more stringent vehicle emissions standards and will make emissions control systems more
effective.
CARB Low Emission Vehicles (LEV)
This emission standard requires a much higher emission control level than the Tier 1 standard. Applied to light-
duty gasoline passenger cars and trucks beginning in small numbers in the mid-1990s, LEV includes multi-port fuel
injection with adaptive learning, an advanced computer diagnostics systems and advanced and close coupled catalysts
with secondary air injection. LEVs as defined here include transitional low-emission vehicles (TLEVs), low emission
vehicles, ultra-low emission vehicles (ULEVs). In this analysis, all categories of LEVs are treated the same due to the fact
that there are very limited CH4 or N20 emission factor data for LEVs to distinguish among the different types of vehicles.
Zero emission vehicles (ZEVs) are incorporated into the alternative fuel and advanced technology vehicle assessments.
CARB LEVII
This emission standard builds upon ARB's LEV emission standards. They represent a significant strengthening of
the emission standards and require light trucks under 8500 lbs gross vehicle weight meet passenger car standards. It also
introduces a super ultra-low vehicle (SULEV) emission standard. The LEVII standards decreased emission requirements
for LEV and ULEV vehicles as well as increasing the useful life of the vehicle to 150,000. These standards began with 2004
vehicles. In this analysis, all categories of LEVI Is are treated the same due to the fact that there are very limited CH4 or
N20 emission factor data for LEVIIs to distinguish among the different types of vehicles. Zero emission vehicles (ZEVs) are
incorporated into the alternative fuel and advanced technology vehicle assessments.
CARB LEVIII
These standards begin in 2015 and are fully phased in by 2025, although some LEVIII-compliant vehicles were
produced prior to 2017. LEVIII set new vehicle emissions standards and lower the sulfur content of gasoline, considering
the vehicle and its fuel as an integrated system. These new tailpipe standards apply to all light-duty vehicles, medium
duty and some heavy-duty vehicles. Zero emission vehicles (ZEVs) are incorporated into the alternative fuel and
advanced technology vehicle assessments.
Diesel Emission Controls
Below are the three levels of emissions control for diesel vehicles.
Moderate control
Improved injection timing technology and combustion system design for light- and heavy-duty diesel vehicles
(generally in place in model years 1983 to 1995) are considered moderate control technologies. These controls were
implemented to meet emission standards for diesel trucks and buses adopted by the EPA in 1985 to be met in 1991 and
1994.
Advanced control
EGR and modern electronic control of the fuel injection system are designated as advanced control
technologies. These technologies provide diesel vehicles with the level of emission control necessary to comply with
standards in place from 1996 through 2006.
A-198 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Aftertreatment
Use of diesel particulate filters (DPFs), oxidation catalysts and NOx absorbers or selective catalytic reduction
(SCR) systems are designated as aftertreatment control. These technologies provide diesel vehicles with a level of
emission control necessary to comply with standards in place from 2007 on.
Supplemental Information on GHG Emissions from Transportation and Other Mobile Sources
This section of this Annex includes supplemental information on the contribution of transportation and other
mobile sources to U.S. greenhouse gas emissions. In the main body of the Inventory report, emission estimates are
generally presented by greenhouse gas, with separate discussions of the methodologies used to estimate C02, N20, CH4,
and HFC emissions. Although the Inventory is not required to provide detail beyond what is contained in the body of this
report, the IPCC allows presentation of additional data and detail on emission sources. The purpose of this sub-annex,
within the Annex that details the calculation methods and data used for non-C02 calculations, is to consolidate all
transportation estimates presented throughout the report.
This section of this Annex reports total greenhouse gas emissions from transportation and other (non-
transportation) mobile sources in C02 equivalents, with information on the contribution by greenhouse gas and by
mode, vehicle type, and fuel type. Additional analyses were conducted to develop estimates of C02 from non-
transportation mobile sources (e.g., agricultural equipment, construction/mining equipment, recreational vehicles), and
to provide more detailed breakdowns of emissions by source.
Estimation of CO2 from Non-Transportation Mobile Sources
The estimates of N20 and CH4 from fuel combustion presented in the Energy chapter of the Inventory include
both transportation sources and other mobile sources. Other mobile sources include construction/mining equipment,
agricultural equipment, vehicles used off-road, and other sources that have utility associated with their movement but
do not have a primary purpose of transporting people or goods (e.g., snowmobiles, riding lawnmowers, etc.). Estimates
of C02 from non-transportation mobile sources, based on EIA fuel consumption estimates, are included in the industrial
and commercial sectors of the Inventory. In order to provide comparable information on transportation and mobile
sources, Table A-102 provides estimates of C02 from these other mobile sources, developed from the Nonroad
component of EPA's MOVES2014b model and FHWA's Highway Statistics. These other mobile source estimates were
developed using the same fuel consumption data utilized in developing the N20 and CH4 estimates (see Table A-89). Note
that the method used to estimate fuel consumption volumes for C02 emissions from non-transportation mobile sources
for the supplemental information presented in Table A-102, Table A-104, and Table A-105 differs from the method used
to estimate fuel consumption volumes for C02 in the industrial and commercial sectors in this Inventory, which include
C02 emissions from all non-transportation mobile sources (see Section 3.1 for a discussion of that methodology).
A-199
-------
Table A-102: CO2 Emissions from Non-Transportation Mobile Sources (MMT CO2 Eq.)
Fuel Type/ Vehicle Type
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Agricultural Equipment3
43.4
43.1
39.9
46.5
46.6
46.8
48.0
45.8
45.9
41.1
40.2
39.8
39.8
39.7
Construction/Mining














Equipment15
48.9
52.7
57.4
66.1
65.3
64.0
62.9
65.9
61.1
57.0
60.0
65.1
68.2
70.3
Other Sources0
69.6
72.2
76.3
83.8
86.6
85.8
85.9
87.0
88.8
87.4
88.3
89.9
92.3
94.1
Total
161.9
168.0
173.6
196.3
198.4
196.6
196.8
198.7
195.9
185.6
188.4
194.8
200.3
204.1
Note: The method used to estimate C02 emissions in this supplementary information table differs from the method used to estimate C02 in the industrial and commercial
sectors in the Inventory, which include C02 emissions from all non-transportation mobile sources (see Section 3.1 for the methodology for estimating C02 emissions from fossil
fuel combustion in this Inventory). The current Inventory uses the Nonroad component of MOVES2014b for years 1999 through 2019.a Includes equipment, such as tractors
and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
c "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial
equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
A-200 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Estimation of HFC Emissions from Transportation Sources
In addition to C02, N20 and CH4 emissions, transportation sources also result in emissions of HFCs. HFCs are
emitted to the atmosphere during equipment manufacture and operation (as a result of component failure, leaks, and
purges), as well as at servicing and disposal events. There are three categories of transportation-related HFC emissions:
Mobile air-conditioning represents the emissions from air conditioning units in passenger cars, light-duty trucks, and
heavy-duty vehicles; Comfort Cooling represents the emissions from air conditioning units in passenger trains and buses;
and Refrigerated Transport represents the emissions from units used to cool freight during transportation.
Table A-103 below presents these HFC emissions. Table A-104 presents all transportation and mobile source
greenhouse gas emissions, including HFC emissions.
A-201
-------
Table A-103: HFC Emissions from Transportation Sources (MMT CO2 Eq.)
Vehicle Type
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Mobile AC
+
19.4
55.2
68.2
64.7
58.6
52.7
46.7
43.4
40.5
36.9
33.3
31.0
28.8
Passenger Cars
+
11.2
28.0
29.9
27.5
23.9
20.6
17.2
15.8
14.7
13.2
11.4
10.4
9.3
Light-Duty Trucks
+
7.8
25.6
35.2
34.1
31.6
29.2
26.5
24.7
23.0
21.1
19.2
18.1
16.9
Heavy-Duty Vehicles
+
0.5
1.6
3.0
3.1
3.0
2.9
2.9
2.9
2.8
2.7
2.6
2.6
2.6
Comfort Cooling for Trains and Buses
+
+
0.1
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
School and Tour Buses
+
+
0.1
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Transit Buses
+
+
+
+
+
+
+
+
0.1
0.1
0.1
0.1
0.1
0.1
Rail
+
+
+
+
+
+
+
+
+
+
+
+
+
0.1
Refrigerated Transport
+
0.2
0.8
2.4
2.9
3.4
3.9
4.4
4.9
5.4
5.9
6.4
6.9
7.4
Medium- and Heavy-Duty Trucks
+
0.1
0.4
1.4
1.6
1.8
2.1
2.3
2.5
2.7
2.9
3.1
3.3
3.5
Rail
+
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Ships and Boats
+
+
0.3
0.9
1.2
1.5
1.7
2.0
2.3
2.6
2.9
3.3
3.6
3.9
Total
+
19.6
56.2
71.1
68.1
62.4
57.1
51.6
48.8
46.3
43.3
40.1
38.5
36.7
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 MMT C02 Eq.
A-202 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Mode/Vehicle
Type/Fuel Type
Table A-104 presents estimates of greenhouse gas emissions from an expanded analysis including all
transportation and additional mobile sources, as well as emissions from electricity generation by the consuming
category, in C02 equivalents. In total, transportation and non-transportation mobile sources emitted 2,092 MMT C02 Eq.
in 2019, an increase of 23 percent from 1990.60 Transportation sources account for 1,880.5 MMT C02 Eq. while non-
transportation mobile sources account for 211.5 MMT C02 Eq. These estimates include HFC emissions for mobile AC,
comfort cooling for trains and buses, and refrigerated transport. These estimates were generated using the estimates of
C02 emissions from transportation sources reported in Section 3.1 C02 Emissions from Fossil Fuel Combustion, and CH4
emissions and N20 emissions reported in the Mobile Combustion section of the Energy chapter; information on HFCs
from mobile air conditioners, comfort cooling for trains and buses, and refrigerated transportation from the Substitution
of Ozone Depleting Substances section of the IPPU chapter; and estimates of C02 emitted from non-transportation
mobile sources reported in Table A-102 above.
Although all emissions reported here are based on estimates reported throughout this Inventory, some
additional calculations were performed in order to provide a detailed breakdown of emissions by mode and vehicle
category. In the case of N20 and CH4, additional calculations were performed to develop emission estimates by type of
aircraft and type of heavy-duty vehicle (i.e., medium- and heavy-duty trucks or buses) to match the level of detail for C02
emissions. N20 estimates for both jet fuel and aviation gasoline, and CH4 estimates for aviation gasoline were developed
for individual aircraft types by multiplying the emissions estimates for each fuel type (jet fuel and aviation gasoline) by
the portion of fuel used by each aircraft type (from FAA 2021 and DLA 2020). Emissions of CH4 from jet fuels are no
longer considered to be emitted from aircraft gas turbine engines burning jet fuel A at higher power settings. This update
applies to the entire time series.61 Recent research indicates that modern aircraft jet engines are typically net consumers
of methane (Santoni et al. 2011). Methane is emitted at low power and idle operation, but at higher power modes
aircraft engines consume methane. Over the range of engine operating modes, aircraft engines are net consumers of
methane on average. Based on this data, CH4 emission factors for jet aircraft were reported as zero to reflect the latest
emissions testing data.
Similarly, N20 and CH4 estimates were developed for medium- and heavy-duty trucks and buses by multiplying
the emission estimates for heavy-duty vehicles for each fuel type (gasoline, diesel) from the Mobile Combustion section
in the Energy chapter, by the portion of fuel used by each vehicle type (from DOE 1993 through 2018). Carbon dioxide
emissions from non-transportation mobile sources are calculated using data from the NONROAD component of EPA's
MOVES2014b model (EPA 2019a). Otherwise, the table and figure are drawn directly from emission estimates presented
elsewhere in the Inventory, and are dependent on the methodologies presented in Annex 2.1 (for C02), Chapter 4, and
Annex 3.9 (for HFCs), and earlier in this Annex (for CH4 and N20).
Transportation sources include on-road vehicles, aircraft, boats and ships, rail, and pipelines (note: pipelines are
a transportation source but are stationary, not mobile, emissions sources). In addition, transportation-related
greenhouse gas emissions also include HFC released from mobile air-conditioners and refrigerated transport, and the
release of C02from lubricants (such as motor oil) used in transportation. Together, transportation sources were
responsible for 1,880.5 MMT C02 Eq. in 2019.
On-road vehicles were responsible for about 74 percent of all transportation and non-transportation mobile
greenhouse gas emissions in 2019. Although passenger cars make up the largest component of on-road vehicle
greenhouse gas emissions, medium- and heavy-duty trucks have been the primary sources of growth in on-road vehicle
emissions. Between 1990 and 2019, greenhouse gas emissions from passenger cars increased by 19 percent, while
emissions from light-duty trucks decreased by one percent. Meanwhile, greenhouse gas emissions from medium- and
60	Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan, Turbojet and Turboprop
Engines," EPA-420-R-09-901, May 27, 2009 (see ).
61	In 2011 FHWA changed how they defined vehicle types for the purposes of reporting VMT for the years 2007 to 2010. The old approach to
vehicle classification was based on body type and split passenger vehicles into "Passenger Cars" and "Other 2 Axle 4-Tire Vehicles." The new
approach is a vehicle classification system based on wheelbase. Vehicles with a wheelbase less than or equal to 121 inches are counted as
"Light-duty Vehicles-Short Wheelbase." Passenger vehicles with a wheelbase greater than 121 inches are counted as "Light-duty Vehicles -
Long Wheelbase." This change in vehicle classification has moved some smaller trucks and sport utility vehicles from the light truck category to
the passenger vehicle category in this Inventory. These changes are reflected in a large drop in light-truck emissions between 2006 and 2007.
A-203
-------
heavy-duty trucks increased 93 percent between 1990 and 2019, reflecting the increased volume of total freight
movement and an increasing share transported by trucks.
Greenhouse gas emissions from aircraft decreased four percent between 1990 and 2019. Emissions from
military aircraft decreased 66 percent between 1990 and 2019. Commercial aircraft emissions rose 27 percent between
1990 and 2007 then dropped 4 percent from 2007 to 2019, a change of approximately 22 percent between 1990 and
2019.
Non-transportation mobile sources, such as construction/mining equipment, agricultural equipment, and
industrial/commercial equipment, emitted approximately 211.5 MMT C02 Eq. in 2019. Together, these sources emitted
more greenhouse gases than ships and boats, and rail combined. Emissions from non-transportation mobile sources
increased, growing approximately 26 percent between 1990 and 2019. Methane and N20 emissions from these sources
are included in the "Mobile Combustion" section and C02 emissions are included in the relevant economic sectors.
Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Gas
Table A-105 presents estimates of greenhouse gas emissions from transportation and other mobile sources
broken down by greenhouse gas. As this table shows, C02 accounts for the vast majority of transportation greenhouse
gas emissions (approximately 97 percent in 2019). Emissions of C02 from transportation and mobile sources increased by
389 MMT C02 Eq. between 1990 and 2019. In contrast, the combined emissions of CH4 and N20 decreased by 30.8 MMT
C02 Eq. over the same period, due largely to the introduction of control technologies designed to reduce criteria
pollutant emissions.62 Meanwhile, HFC emissions from mobile air-conditioners and refrigerated transport increased from
virtually no emissions in 1990 to 36.7 MMT C02 Eq. in 2019 as these chemicals were phased in as substitutes for ozone
depleting substances. It should be noted, however, that the ozone depleting substances that HFCs replaced are also
powerful greenhouse gases, but are not included in national greenhouse gas inventories per UNFCCC reporting
requirements.
Greenhouse Gas Emissions from Freight and Passenger Transportation
Table A-106 and Table A-107 present greenhouse gas estimates from transportation, broken down into the
passenger and freight categories. Passenger modes include light-duty vehicles, buses, passenger rail, aircraft (general
aviation and commercial aircraft), recreational boats, and mobile air conditioners, and are illustrated in Table A-106.
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-107. Commercial aircraft do carry some freight, in
addition to passengers, and emissions have been split between passenger and freight transportation. The amount of
commercial aircraft emissions to allocate to the passenger and freight categories was calculated using BTS data on
freight shipped by commercial aircraft, and the total number of passengers enplaned. Each passenger was considered to
weigh an average of 150 pounds, with a luggage weight of 50 pounds. The total freight weight and total passenger
weight carried were used to determine percent shares which were used to split the total commercial aircraft emission
estimates. The remaining transportation and mobile emissions were from sources not considered to be either freight or
passenger modes (e.g., construction/mining and agricultural equipment, lubricants).
The estimates in these tables are derived from the estimates presented in Table A-104. In addition, estimates of
fuel consumption from DOE (1993 through 2018) were used to allocate rail emissions between passenger and freight
categories.
In 2019, passenger transportation modes emitted 1,276.8 MMT C02 Eq., while freight transportation modes
emitted 569.8 MMT C02 Eq. Between 1990 and 2019, the percentage growth of greenhouse gas emissions from freight
sources was 63 percent, while emissions from passenger sources grew by 13 percent. This difference in growth is due
largely to the rapid increase in emissions associated with medium- and heavy-duty trucks.
62 The decline in CFC emissions is not captured in the official transportation estimates.
A-204 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-104: Total U.S. Greenhouse Gas Emissions from Transportation and Mobile Sources (MMT CO2 Eq.)
Percent
Change
Mode / Vehicle Type / Fuel	1990-
Type
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2019
Transportation Total3
1,529.8
1,670.3
1,917.2
1,800.8
1,806.8
1,773.5
1,753.5
1,756.3
1,790.5
1,798.4
1,834.3
1,851.8
1,883.0
1,880.5
23%
On-Road Vehicles
1,206.8
1,342.0
1,557.8
1,517.5
1,515.2
1,483.9
1,473.1
1,466.2
1,514.3
1,510.5
1,534.6
1,540.7
1,563.9
1,555.7
29%
Passenger Cars
639.6
629.9
685.8
774.0
762.7
753.3
746.2
739.2
753.0
752.5
763.5
760.6
770.3
762.3
19%
Gasolineb
631.7
610.8
654.1
740.4
731.4
725.3
721.4
717.7
732.6
733.0
745.4
744.0
754.3
747.0
18%
Dieselb
7.9
7.9
3.7
3.6
3.8
4.1
4.1
4.1
4.2
4.3
4.3
4.4
4.4
4.6
-41%
AFVsc
+
+
+
+
+
0.1
0.1
0.2
0.4
0.5
0.7
0.8
1.2
1.4
NA
HFCs from Mobile AC
0.0
11.2
28.0
29.9
27.5
23.9
20.6
17.2
15.8
14.7
13.2
11.4
10.4
9.3
NA
Light-Duty Trucks
326.7
425.2
506.7
343.5
339.6
322.6
316.1
312.1
331.9
320.9
330.2
324.3
325.7
323.1
-1%
Gasolineb
315.1
402.4
460.7
295.7
292.7
277.9
273.8
272.6
293.2
283.8
294.8
290.7
293.0
290.9
-8%
Dieselb
11.5
14.9
20.3
12.1
12.6
13.0
13.0
12.8
13.9
13.9
14.2
14.2
14.3
15.0
31%
AFVsc
0.2
0.2
0.1
0.5
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.3
0.3
60%
HFCs from Mobile AC
0.0
7.8
25.6
35.2
34.1
31.6
29.2
26.5
24.7
23.0
21.1
19.2
18.1
16.9
NA
Medium- and Heavy-Duty















Trucks
230.3
275.9
352.3
379.7
393.3
387.8
389.0
393.3
406.4
413.9
417.9
431.4
442.1
444.4
93%
Gasolineb
38.5
35.8
36.4
42.3
42.0
38.5
38.1
38.8
39.9
39.3
40.4
41.2
42.2
40.3
5%
Dieselb
190.7
238.6
313.2
331.9
346.4
344.1
345.5
348.9
360.8
368.7
371.5
384.0
393.5
397.5
108%
AFVsc
1.2
0.9
0.6
1.0
0.3
0.4
0.4
0.4
0.4
0.4
0.5
0.5
0.5
0.5
-58%
HFCs from Refrigerated















Transport and Mobile















ACe
0.0
0.6
2.0
4.4
4.7
4.8
5.0
5.2
5.3
5.5
5.5
5.7
5.9
6.1
NA
Buses
8.5
9.2
11.1
16.2
16.0
16.7
17.8
17.8
19.2
19.6
19.1
20.6
22.0
22.2
162%
Gasolineb
0.3
0.4
0.4
0.7
0.7
0.7
0.8
0.8
0.9
0.9
0.9
0.9
1.1
1.0
193%
Dieselb
8.0
8.7
10.3
13.7
13.6
14.4
15.5
15.4
16.8
17.1
16.7
18.0
19.3
19.4
142%
AFVsc
0.1
0.1
0.3
1.4
1.2
1.1
1.1
1.1
1.1
1.1
1.1
1.2
1.3
1.3
1257%
HFCs from Comfort















Cooling
0.0
+
0.1
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
NA
Motorcycles
1.7
1.8
1.9
4.1
3.6
3.5
4.0
3.8
3.8
3.7
3.9
3.8
3.8
3.6
111%
Gasolineb
1.7
1.8
1.9
4.1
3.6
3.5
4.0
3.8
3.8
3.7
3.9
3.8
3.8
3.6
111%
Aircraft
189.2
176.7
199.4
157.4
154.8
149.9
146.5
150.1
151.3
160.5
169.0
174.8
175.5
181.1
-4%
General Aviation Aircraft
42.9
35.8
35.9
21.2
26.7
22.5
19.9
23.6
20.9
26.8
35.1
33.3
32.8
33.7
-22%
Jet Fuel'
39.8
33.0
33.4
19.4
24.8
20.6
18.2
22.0
19.4
25.3
33.7
31.8
31.2
32.0
-19%
Aviation Gasoline
3.2
2.8
2.6
1.9
1.9
1.9
1.8
1.6
1.5
1.5
1.5
1.5
1.6
1.7
-48%
Commercial Aircraft
110.9
116.3
140.6
120.6
114.4
115.7
114.3
115.4
116.3
120.1
121.5
129.2
130.8
135.4
22%
Jet Fuel'
110.9
116.3
140.6
120.6
114.4
115.7
114.3
115.4
116.3
120.1
121.5
129.2
130.8
135.4
22%
A-205
-------
Military Aircraft
35.3
24.5
22.9
15.5
13.7
11.7
12.2
11.1
14.1
13.6
12.4
12.3
11.9
12.0
-66%
Jet Fuel'
35.3
24.5
22.9
15.5
13.7
11.7
12.2
11.1
14.1
13.6
12.4
12.3
11.9
12.0
-66%
Ships and Boatsd
47.0
58.8
65.8
39.1
44.9
46.4
40.3
39.7
29.1
33.8
40.8
43.9
41.2
40.4
-14%
Gasoline
14.4
14.3
14.5
12.4
11.8
11.4
11.1
10.9
10.6
10.7
10.7
10.8
10.9
10.9
-25%
Distillate Fuel
9.7
15.0
17.4
11.6
11.3
14.0
11.4
11.5
10.2
16.2
14.0
13.2
12.6
10.7
10%
Residual Fuele
22.8
29.4
33.7
14.1
20.7
19.6
16.0
15.3
5.9
4.3
13.1
16.7
14.2
14.9
-35%
HFCs from Refrigerated















Transport6
+
+
0.3
0.9
1.2
1.5
1.7
2.0
2.3
2.6
2.9
3.3
3.6
3.9
NA
Rail
39.0
43.1
46.6
41.1
44.0
45.2
43.9
44.5
46.4
44.1
40.3
41.5
43.3
40.7
4%
Distillate Fuel'
35.8
40.0
43.0
36.4
39.3
40.6
39.8
40.2
42.1
40.2
36.6
37.9
39.7
37.4
5%
Electricity
3.1
3.1
3.5
4.5
4.5
4.3
3.9
4.1
4.1
3.8
3.5
3.4
3.4
3.1
2%
Other Emissions from Rail















Electricity Use®
0.1
0.1
+
+
+
+
+
+
+
+
+
0.1
0.1
+
-100%
HFCs from Comfort Cooling
0.0
+
+
+
+
+
+
+
+
+
+
+
+
0.1
NA
HFCs from Refrigerated















Transport6
0.0
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
NA
Pipelines'1
36.0
38.4
35.5
37.1
37.3
38.1
40.6
46.2
39.4
38.5
39.2
41.3
49.9
53.7
49%
Natural Gas
36.0
38.4
35.5
37.1
37.3
38.1
40.6
46.2
39.4
38.5
39.2
41.3
49.9
53.7
49%
Other Transportation
11.8
11.3
12.1
8.5
10.4
10.0
9.1
9.6
10.0
11.0
10.4
9.6
9.2
8.9
-25%
Lubricants
11.8
11.3
12.1
8.5
10.4
10.0
9.1
9.6
10.0
11.0
10.4
9.6
9.2
8.9
-25%
Non-Transportation Mobile'















Total
167.3
173.8
179.5
203.7
206.0
204.1
204.3
206.3
203.4
192.4
195.4
201.9
207.6
211.5
26%
Agricultural Equipment1''
44.8
44.6
41.2
48.0
48.2
48.4
49.7
47.3
47.5
42.4
41.4
41.0
41.0
40.8
-9%
Gasoline
7.7
8.6
6.1
6.2
6.4
7.3
8.0
6.0
5.9
1.4
1.5
1.5
1.5
1.2
-85%
Diesel
37.1
35.9
34.9
41.7
41.7
41.0
41.6
41.3
41.5
40.8
39.7
39.4
39.5
39.6
7%
CNG
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
-13%
LPG
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-9%
Construction/Mining















Equipment'^
50.3
54.2
59.0
68.1
67.3
66.0
64.9
68.1
63.1
58.7
61.8
67.0
70.2
72.3
44%
Gasoline
4.4
4.0
3.3
5.4
6.2
5.8
5.9
10.0
6.4
3.3
3.4
3.4
3.5
3.5
-21%
Diesel
45.5
49.7
55.2
62.2
60.6
59.7
58.5
57.6
56.2
54.9
57.9
63.1
66.2
68.3
50%
CNG
0.3
0.3
0.3
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.4
26%
LPG
0.1
0.1
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.2
21%
Other Equipment''1
72.2
74.9
79.3
87.6
90.5
89.7
89.7
90.9
92.9
91.3
92.2
93.9
96.4
98.4
36%
Gasoline
40.6
41.2
43.2
48.3
50.1
48.4
47.0
46.9
47.6
45.7
46.3
46.6
47.2
47.6
17%
Diesel
22.1
21.8
21.8
25.1
25.9
26.7
27.9
28.8
29.7
29.9
30.1
30.8
32.0
32.9
49%
CNG
1.2
1.3
1.4
1.6
1.7
1.8
2.0
2.1
2.2
2.2
2.1
2.2
2.3
2.4
101%
LPG
8.3
10.7
12.9
12.6
12.8
12.9
12.9
13.1
13.3
13.5
13.7
14.3
15.0
15.5
86%
A-206 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Transportation and Non-
Transportation Mobile
Total1	1,697.1 1,844.1 2,096.7 2,004.5 2,012.7 1,977.6 1,957.8 1,962.6 1,993.9 1,990.8 2,029.6 2,053.7 2,090.6 2,092.0 23%
Notes: Increases to CH4 and N20 emissions from mobile combustion relative to previous Inventories are largely due to updates made to the Motor Vehicle Emissions Simulator
(MOVES2014b) model that is used to estimate on-road gasoline vehicle distribution and mileage across the time series, as well as non-transportation mobile fuel consumption.
See Section 3.1 "CH4 and N20 from Mobile Combustion" for more detail. This year's Inventory uses the NONROAD component of MOVES2014b for years 1999 through 2019. In
2016, historical confidential vehicle sales data were re-evaluated to determine the engine technology assignments. First, several light-duty trucks were re-characterized as
heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales data. Second, the emission standards each vehicle type was assumed to have met
were re-examined using confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and
therefore not included in the engine technology breakouts. For this Inventory, HEVs are classified as gasoline vehicles across the entire time series.
+ Does not exceed 0.05 MMT C02 Eq.
NA (Not Applicable), as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.
a Not including emissions from international bunker fuels.
b Gasoline and diesel highway vehicle fuel consumption estimates used to develop C02 estimates in this Inventory are based on data from FHWA Highway Statistics Table MF-21,
MF-27 and VM-1 (FHWA 1996 through 2019). Data from Table VM-1 are used to estimate the share of fuel consumption between each on-road vehicle class. For mobile CH4 and
N20 emissions estimates, gasoline and diesel highway vehicle mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2019). These
fuel consumption and mileage estimates are combined with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables A.l through A.6 (DOE 1993 through 2018).
c In 2017, estimates of alternative fuel vehicle mileage for the last ten years were revised to reflect updates made to EIA data on alternative fuel use and vehicle counts. These
changes were incorporated into this year's Inventory and apply to the 2005 to 2019 time period.
d Fluctuations in emission estimates reflect data collection problems. Note that CH4 and N20 from U.S. Territories are included in this value, but not C02 emissions from U.S.
Territories, which are estimated separately in the section on U.S. Territories.
e Domestic residual fuel for ships and boats is estimated by taking the total amount of residual fuel and subtracting out an estimate of international bunker fuel use.
f Class II and Class III diesel consumption data for 2014 to 2019 is not available. Diesel consumption data for 2014-2019 is estimated by applying the historical average fuel usage
per carload factor to the annual number of carloads.
g Other emissions from electricity generation are a result of waste incineration (as the majority of municipal solid waste is combusted in "trash-to-steam" electricity generation
plants), electrical transmission and distribution, and a portion of Other Process Uses of Carbonates (from pollution control equipment installed in electricity generation plants).
h Includes only C02 from natural gas used to power natural gas pipelines; does not include emissions from electricity use or non-C02 gases.
' Note that the method used to estimate C02 emissions from non-transportation mobile sources in this supplementary information table differs from the method used to estimate
C02 in the industrial and commercial sectors in the Inventory, which include C02 emissions from all non-transportation mobile sources (see Section 3.1 for the methodology for
estimating C02 emissions from fossil fuel combustion in this Inventory).
> Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
k Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
1 "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial
equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
A-207
-------
Table A-105: Transportation and Mobile Source Emissions by Gas (MMT CO2 Eq.)
Percent
Change

1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
1990-2019
C02a
1,645.9
1,763.1
1,981.4
1,897.9
1,910.2
1,882.1
1,870.2
1,882.4
1,918.6
1,920.0
1,962.9
1,991.1
2,030.7
2,034.9
24%
n2o
44.7
55.2
54.2
32.0
31.1
29.7
27.3
25.6
23.7
21.7
20.8
19.8
18.8
18.0
-60%
ch4
6.4
6.1
4.8
3.3
3.3
3.3
3.0
2.9
2.7
2.6
2.5
2.5
2.4
2.4
-63%
HFC
+
19.6
56.2
71.1
68.1
62.4
57.1
51.6
48.8
46.3
43.3
40.1
38.5
36.7
NA
Totalb
1,697.0
1,844.0
2,096.6
2,004.3
2,012.6
1,977.5
1,957.7
1,962.5
1,993.8
1,990.7
2,029.5
2,053.6
2,090.5
2,092.0
23%
Notes: Gasoline and diesel highway vehicle fuel consumption estimates used to develop C02 estimates in this Inventory are based on data from FHWA Highway Statistics Table
MF-21, MF-27 and VM-1 (FHWA 1996 through 2019). Data from Table VM-1 is used to estimate the share of fuel consumption between each on-road vehicle class. For mobile
CH4 and N20 emissions estimates, gasoline and diesel highway vehicle mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through
2019). These fuel consumption and mileage estimates are combined with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables A.l through A.6 (DOE 1993
through 2018).
In 2016, historical confidential vehicle sales data was re-evaluated to determine the engine technology assignments. First several light-duty trucks were re-characterized as
heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales data. Second, the emission standards each vehicle type was assumed to have met
were re-examined using confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and
therefore not included in the engine technology breakouts. For this Inventory, HEVs are classified as gasoline vehicles across the entire time series.
+ Does not exceed 0.05 MMT C02 Eq.
NA (Not Applicable), as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.
a The method used to estimate C02 emissions from non-transportation mobile sources in this supplementary information table differs from the method used to estimate C02 in
the industrial and commercial sectors in the Inventory, which include C02 emissions from all non-transportation mobile sources (see Section 3.1 for the methodology for
estimating C02 emissions from fossil fuel combustion in this Inventory).
b Total excludes other emissions from electricity generation and CH4 and N20 emissions from electric rail.
A-208 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2019
2,500
Passenger Cars/Motorcycles
I Medium- and Heavy-Duty Trucks and Buses
I Boats/Ships, Rail, and Pipelines
I Non-Transportation Mobile Sources
I Light-Duty Trucks
Aircraft
Mobile AC, Refrig. Transport, Lubricants
2,000
1,500
r->.
00
CD
i
1
1
o
o
o
(N
(N
CM
A-209
-------
Table A-106: Greenhouse Gas Emissions from Passenger Transportation (MMT CO2 Eq.)
Vehicle Type
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Percent
Change
2019 1990-2019
On-Road Vehiclesa'b
976.5
1,066.1
1,205.5
1,137.8
1,121.9
1,096.1
1,084.0
1,072.9
1,107.9
1,096.6
1,116.7
1,109.3
1,121.8
1,111.3
14%
Passenger Cars
639.6
629.9
685.8
774.0
762.7
753.3
746.2
739.2
753.0
752.5
763.5
760.6
770.3
762.3
19%
Light-Duty Trucks
326.7
425.2
506.7
343.5
339.6
322.6
316.1
312.1
331.9
320.9
330.2
324.3
325.7
323.1
-1%
Buses
LO
00
9.2
11.1
16.2
16.0
16.7
17.8
17.8
19.2
19.6
19.1
20.6
22.0
22.2
162%
Motorcycles
1.7
1.8
1.9
4.1
3.6
3.5
4.0
3.8
3.8
3.7
3.9
3.8
3.8
3.6
111%
Aircraft
134.6
132.0
152.2
125.2
124.8
122.1
118.5
123.1
120.9
130.5
139.8
144.1
144.9
149.8
11%
General Aviation
42.9
35.8
35.9
21.2
26.7
22.5
19.9
23.6
20.9
26.8
35.1
33.3
32.8
33.7
-22%
Commercial















Aircraft
91.7
96.2
116.3
103.9
98.0
99.6
98.6
99.5
100.0
103.6
104.7
110.7
112.1
116.1
27%
Recreational Boats
17.2
17.1
17.3
15.2
14.5
14.0
13.7
13.5
13.4
13.6
10.7
10.8
10.9
10.9
-37%
Passenger Rail
4.4
4.5
5.2
6.2
6.2
6.0
5.5
5.8
5.7
5.4
5.2
5.1
5.0
4.8
10%
Total
1,132.7
1,219.7
1,380.2
1,284.3
1,267.3
1,238.2
1,221.8
1,215.3
1,248.0
1,246.1
1,272.4
1,269.3
1,282.6
1,276.8
13%
Notes: Data from DOE (1993 through 2018) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates. This year's
Inventory uses the NONROAD component of MOVES2014b for years 1999 through 2019. In 2017, estimates of alternative fuel vehicle mileage for the last ten years were
revised to reflect updates made to EIA data on alternative fuel use and vehicle counts. These changes were incorporated into this year's Inventory and apply to the 2005 to
2019 time period.
In 2016, historical confidential vehicle sales data were re-evaluated to determine the engine technology assignments. First, several light-duty trucks were re-characterized as
heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales data. Second, the emission standards each vehicle type was assumed to have met
were re-examined using confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and
therefore not included in the engine technology breakouts. For this Inventory, HEVs are classified as gasoline vehicles across the entire time series.
a The current Inventory includes updated vehicle population data based on the MOVES2014b Model.
b Gasoline and diesel highway vehicle fuel consumption estimates used to develop C02 estimates in this Inventory are based on data from FHWA Highway Statistics Table MF-21,
MF-27 and VM-1 (FHWA 1996 through 2019). Data from Table VM-1 is used to estimate the share of fuel consumption between each on-road vehicle class. For mobile CH4 and
N20 emissions estimates, gasoline and diesel highway vehicle mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2019).
These fuel consumption and mileage estimates are combined with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables A.l through A.6 (DOE 1993 through
2018).
Table A-107: Greenhouse Gas Emissions from Domestic Freight Transportation (MMT CO2 Eq.)
By Mode
1990
1995
2000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Percent
Change
1990-2019
Trucking3'15
230.3
275.4
350.7
376.6
390.3
384.7
386.1
390.3
403.6
411.1
415.2
428.7
439.5
441.8
92%
Freight Rail
34.5
38.6
41.4
34.9
37.8
39.2
38.4
38.7
40.6
38.7
35.1
36.4
38.2
35.9
4%
Ships and Non-
29.8
41.7
48.5
23.9
30.5
32.5
26.6
30.8
19.4
14.9
16.4
20.2
18.0
19.0
-36%
Recreational Boats
A-210 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Pipelines0	36.0	38.4	35.5	37.1 37.3 38.1 40.6 46.2 39.4 38.5 39.2 41.3 49.9 53.7	49%
Commercial Aircraft	19.2	20.1	24.3	16.7 16.3 16.0 15.8 15.9 16.2 16.5 16.8 18.4 18.7 19.3	1%
Total	349.8 414.3 500.4 489.3 512.2 510.5 507.4 521.9 519.2 519.6 522.7 545.1 564.4 569.8	63%
Notes: Data from DOE (1993 through 2018) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates. This year's
Inventory uses the NONROAD component of MOVES2014b for years 1999 through 2019. In 2017, estimates of alternative fuel vehicle mileage for the last ten years were
revised to reflect updates made to EIA data on alternative fuel use and vehicle counts. These changes were incorporated into this year's Inventory and apply to the 2005 to
2019 time period.
In 2016, historical confidential vehicle sales data were re-evaluated to determine the engine technology assignments. First, several light-duty trucks were re-characterized as
heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales data. Second, the emission standards each vehicle type was assumed to have met
were re-examined using confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and
therefore not included in the engine technology breakouts. For this Inventory, HEVs are classified as gasoline vehicles across the entire time series.
a The current Inventory includes updated vehicle population data based on the MOVES2014b Model.
b Gasoline and diesel highway vehicle fuel consumption estimates used to develop C02 estimates in this Inventory are based on data from FHWA Highway Statistics Table MF-21,
MF-27 and VM-1 (FHWA 1996 through 2019). Data from Table VM-1 is used to estimate the share of fuel consumption between each on-road vehicle class. For mobile CH4 and
N20 emissions estimates, gasoline and diesel highway vehicle mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2019).
These fuel consumption and mileage estimates are combined with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables A.l through A.6 (DOE 1993 through
2018).
c Pipelines reflect C02 emissions from natural gas powered pipelines transporting natural gas.
A-211
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References
AAR (2008 through 2019) Railroad Facts. Policy and Economics Department, Association of American Railroads,
Washington, D.C.
ANL (2020) The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model (GREET2020). Argonne
National Laboratory. October 2020. Available at .
APTA (2007 through 2019) Public Transportation Fact Book. American Public Transportation Association, Washington,
D.C. Available online at: .
APTA (2006) Commuter Rail National Totals. American Public Transportation Association, Washington, D.C. Available
online at: .
BEA (2018) Table 1.1.6. Real Gross Domestic Product Chained 2012 Dollars. Bureau of Economic Analysis (BEA), U.S.
Department of Commerce, Washington, D.C. September 2018. Available online at:
.
Benson, D. (2002 through 2004) Unpublished data. Upper Great Plains Transportation Institute, North Dakota State
University and American Short Line & Regional Railroad Association.
Browning (2019) Updated On-highway CH4 and N20 Emission Factors for GHG Inventory. Memorandum from ICF to Sarah
Roberts and Justine Geidosch, Office of Transportation and Air Quality, U.S. Environmental Protection Agency.
September 2019.
Browning, L. (2018a). Updated Methodology for Estimating Electricity Use from Highway Plug-In Electric Vehicles.
Technical Memo, October 2018.
Browning, L. (2018b). Updated Non-Highway CH4 and N20 Emission Factors for U.S. GHG Inventory. Technical Memo,
November 2018.
Browning, L. (2017) "Updated Methodology for Estimating CH4 and N20 Emissions from Highway Vehicle Alternative Fuel
Vehicles". Technical Memo, October 2017.
Browning, L. (2005) Personal communication with Lou Browning, Emission control technologies for diesel highway
vehicles specialist, ICF.
DHS (2008) Email Communication. Elissa Kay, Department of Homeland Security and Joe Aamidor, ICF International.
January 11, 2008.
DLA Energy (2020) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense Energy
Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, D.C.
DOC (1991 through 2019) Unpublished Report of Bunker Fuel Oil Laden on Vessels Cleared for Foreign Countries. Form-
563. Foreign Trade Division, Bureau of the Census, U.S. Department of Commerce. Washington, D.C.
DOE (1993 through 2018) Transportation Energy Data Book Edition 39. Office of Transportation Technologies, Center for
Transportation Analysis, Energy Division, Oak Ridge National Laboratory. ORNL-5198.
DOT (1991 through 2019) Airline Fuel Cost and Consumption. U.S. Department of Transportation, Bureau of
Transportation Statistics, Washington, D.C. DAI-10. Available online at: .
EEA (2009J EMEP/EAA Air Pollutant Emission Inventory Guidebook. European Environment Agency, Copenhagen,
Denmark. 
EIA (2020a) Monthly Energy Review, November 2020, Energy Information Administration, U.S. Department of Energy,
Washington, D.C. DOE/EIA-0035(2019/11).
EIA (2020d) "Natural gas prices, production, consumption, and exports increased in 2019." Today in Energy. Available
online at: .
EIA (2020f) Natural Gas Annual 2019. Energy Information Administration, U.S. Department of Energy. Washington, D.C.
DOE/EIA-0131(06).
A-212 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
EIA (2019h) Alternative Fuels Data Tables. Energy Information Administration, U.S. Department of Energy. Washington,
D.C. Available online at: .
EIA (1991 through 2019) Fuel Oil and Kerosene Sales. Energy Information Administration, U.S. Department of Energy.
Washington, D.C. Available online at: .
EIA (2017) International Energy Statistics 1980-2016. Energy Information Administration, U.S. Department of Energy.
Washington, D.C. Available online at: .
EPA (2019aJ. Motor Vehicle Emissions Simulator (MOVES) 2014b. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at: .
EPA (2020c) Confidential Engine Family Sales Data Submitted to EPA By Manufacturers. Office of Transportation and Air
Quality, U.S. Environmental Protection Agency.
EPA (2019d) Annual Certification Test Results Report. Office of Transportation and Air Quality, U.S. Environmental
Protection Agency. Available online at: .
EPA (2020b) "1970 - 2019 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available online at:
.
EPA (2000) Mobile6 Vehicle Emission Modeling Software. Office of Mobile Sources, U.S. Environmental Protection
Agency, Ann Arbor, Michigan.
EPA (1999) Regulatory Announcement: EPA's Program for Cleaner Vehicles and Cleaner Gasoline. Office of Mobile
Sources. December 1999. EPA420-F-99-051. Available online at:
.
EPA (1998) Emissions of Nitrous Oxide from Highway Mobile Sources: Comments on the Draft Inventory of U.S.
Greenhouse Gas Emissions and Sinks, 1990-1996. Office of Mobile Sources, Assessment and Modeling Division, U.S.
Environmental Protection Agency. August 1998. EPA420-R-98-009.
FAA (2021) Personal Communication between FAA and John Steller, Mausami Desai, and Vincent Camobreco for aviation
emissions estimates from the Aviation Environmental Design Tool (AEDT). March 2021.
FHWA (1996 through 2019) Highway Statistics. Federal Highway Administration, U.S. Department of Transportation,
Washington, D.C. Report FHWA-PL-96-023-annual. Available online at:
.
FHWA (2015) Off-Highway and Public-Use Gasoline Consumption Estimation Models Used in the Federal Highway
Administration, Publication Number FHWA-PL-17-012. Available online at:
.
Gaffney, J. (2007) Email Communication. John Gaffney, American PublicTransportation Association and Joe Aamidor, ICF
International. December 17, 2007.
HybridCars.com (2019). Monthly Plug-In Electric Vehicle Sales Dashboard, 2010-2019. Available online at
.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K.
Tanabe (eds.)]. Hayama, Kanagawa, Japan.
ICF (2004) Update of Methane and Nitrous Oxide Emission Factors for On-Highway Vehicles. Final Report to U.S.
Environmental Protection Agency. February 2004.
Raillnc (2014 through 2019) Raillnc Short line and Regional Traffic Index. Carloads Originated Year-to-Date. December
2019. Available online at: < https://www.railinc.com/rportal/railinc-indexes>.
A-213
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Santoni, G., B. Lee, E. Wood, S. Herndon, R. Miake-Lye, S Wofsy, J. McManus, D. Nelson, M. Zahniser (2011) Aircraft
emissions of methane and nitrous oxide during the alternative aviation fuel experiment. Environ Sci Technol. 2011
Aug 15; 45(16):7075-82.
Wards Intelligence (2019-2020) U.S. Light Vehicle Sales Report. August 2020
Whorton, D. (2006 through 2014) Personal communication, Class II and III Rail energy consumption, American Short Line
and Regional Railroad Association.
A-214 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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3.3. Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel
Consumption
IPCCTier 3B Method: Commercial aircraft jet fuel burn and carbon dioxide (C02) emissions estimates were
developed by the U.S. Federal Aviation Administration (FAA) using radar-informed data from the FAA Enhanced Traffic
Management System (ETMS) for 2000 through 2019 as modeled with the Aviation Environmental Design Tool (AEDT).
This bottom-up approach is built from modeling dynamic aircraft performance for each flight occurring within an
individual calendar year. The analysis incorporates data on the aircraft type, date, flight identifier, departure time,
arrival time, departure airport, arrival airport, ground delay at each airport, and real-world flight trajectories. To
generate results for a given flight within AEDT, the radar-informed aircraft data is correlated with engine and aircraft
performance data to calculate fuel burn and exhaust emissions. Information on exhaust emissions for in-production
aircraft engines comes from the International Civil Aviation Organization (ICAO) Aircraft Engine Emissions Databank
(EDB). This bottom-up approach is in accordance with the Tier 3B method from the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories.
International Bunkers: The IPCC guidelines define international aviation (International Bunkers) as emissions
from flights that depart from one country and arrive in a different country. Bunker fuel emissions estimates for
commercial aircraft were developed for this report for 2000 through 2019 using the same radar-informed data modeled
with AEDT. Since this process builds estimates from flight-specific information, the emissions estimates for commercial
aircraft can include emissions associated with the U.S. territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin
Islands, Wake Island, and other U.S. Pacific Islands). However, to allow for the alignment of emissions estimates for
commercial aircraft with other data that is provided without the U.S. territories, this annex includes emissions estimates
for commercial aircraft both with and without the U.S. territories included.
Time Series and Analysis Update: The FAA incrementally improves the consistency, robustness, and fidelity of
the C02 emissions modeling for commercial aircraft, which is the basis of the Tier3B inventories presented in this report.
While the FAA does not anticipate significant changes to the AEDT model in the future, recommended improvements are
limited by budget and time constraints, as well as data availability. For instance, previous reports included reported
annual C02 emission estimates for 2000 through 2005 that were modeled using the FAA's System for assessing Aviation's
Global Emissions (SAGE). That tool and its capabilities were significantly improved after it was incorporated and evolved
into AEDT. For this report, the AEDT model was used to generate annual C02 emission estimates for 2000, 2005, 2010,
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, and 2019 only. The reported annual C02 emissions values for 2001
through 2004 were estimated from the previously reported SAGE data. Likewise, C02 emissions values for 2006 through
2009 were estimated by interpolation to preserve trends from past reports.
Commercial aircraft radar data sets are not available for years prior to 2000. Instead, the FAA applied a Tier3B
methodology by developing Official Airline Guide (OAG) schedule-informed estimates modeled with AEDT and great
circle trajectories for 1990, 2000 and 2010. The ratios between the OAG schedule-informed and the radar-informed
inventories for the years 2000 and 2010 were applied to the 1990 OAG scheduled-informed inventory to generate the
best possible C02 inventory estimate for commercial aircraft in 1990. The resultant 1990 C02 inventory served as the
reference for generating additional 1995-1999 emissions estimates, which were established using previously available
trends. International consumption estimates for 1991-1999 and domestic consumption estimates for 1991-1994 are
calculated using fuel consumption estimates from the Bureau of Transportation Statistics (DOT 1991 through 2013),
adjusted based on the ratio of DOT to AEDT data.
Notes on the 1990 C02 Emissions Inventory for Commercial Aircraft: There are uncertainties associated with
the modeled 1990 data that do not exist for the modeled 2000 to 2019 data. Radar-based data is not available for 1990.
The OAG schedule information generally includes fewer carriers than radar information, and this will result in a different
fleet mix, and in turn, different C02 emissions than would be quantified using a radar-based data set. For this reason, the
FAA adjusted the OAG-informed schedule for 1990 with a ratio based on radar-informed information. In addition, radar
trajectories are also generally longer than great circle trajectories. While the 1990 fuel burn data was adjusted to
address these differences, it inherently adds greater uncertainty to the revised 1990 commercial aircraft C02 emissions
as compared to data from 2000 forward. Also, the revised 1990 C02 emissions inventory now reflects only commercial
aircraft jet fuel consumption, while previous reports may have aggregated jet fuel sales data from non-commercial
aircraft into this category. Thus, it would be inappropriate to compare 1990 to future years for other than qualitative
purposes.
A-215
-------
The 1990 commercial aircraft C02 emissions inventory is approximately [18] % lower than the 2019 C02
emissions inventory. It is important to note that the distance flown increased by more than [63] % over this 29 year
period and that fuel burn and aviation activity trends over the past two decades indicate significant improvements in
commercial aviation's ability to provide increased service levels while using less fuel.63
Methane Emissions: Contributions of methane (CH4) emissions from commercial aircraft are reported as zero.
Years of scientific measurement campaigns conducted at the exhaust exit plane of commercial aircraft gas turbine
engines have repeatedly indicated that CH4 emissions are consumed over the full mission flight envelope (Aircraft
Emissions of Methane and Nitrous Oxide during the Alternative Aviation Fuel Experiment, Santoni et al., Environ. Sci.
Technol., 2011,45, 7075-7082). As a result, the U.S. Environmental Protection Agency published that "...methane is no
longer considered to be an emission from aircraft gas turbine engines burning Jet A at higher power settings and is, in
fact, consumed in net at these higher powers" (Recommended Best Practice for Quantifying Speciated Organic Gas
Emissions from Aircraft Equipped with Turbofan, Turbojet and Turboprop Engines, EPA-420-R-09-901, May 27, 2009,
http://www.epa.gov/otaq/aviation.htm). In accordance with the following statements in the 2006 IPCC Guidelines (IPCC
2006), the FAA does not calculate CH4 emissions for either the domestic or international bunker commercial aircraft jet
fuel emissions inventories. "Methane (CH4) may be emitted by gas turbines during idle and by older technology engines,
but recent data suggest that little or no CH4 is emitted by modern engines." "Current scientific understanding does not
allow other gases (e.g., N20 and CH4) to be included in calculation of cruise emissions" (IPCC 1999).
Results: For each inventory calendar year the graph and table below include four jet fuel burn values. These
values are comprised of domestic and international fuel burn totals for the US 50 States and the U.S. 50 States +
Territories. Data are presented for domestic defined as jet fuel burn from any commercial aircraft flight departing and
landing in the U.S. 50 States and for the U.S. 50 States + Territories. The data presented as international is respective of
the two different domestic definitions, and represents flights departing from the specified domestic area and landing
anywhere in the world outside of that area.
Note that the graph and table present more fuel burn for the international US 50 States + Territories than for
the international U.S. 50 States. This is because the flights between the 50 states and U.S. Territories are "international"
when only the 50 states are defined as domestic, but they are "domestic" for the U.S. 50 States + Territories definition.
Figure A-5 is a scatter plot showing commercial aviation fuel burn in kilograms for the United States and
territories from 1990 to 2019. Commercial aviation fuel burn has been relatively stable with a slight positive trend
throughout the time series, with the exception of small decreases following the economic recessions in 2001 and 2008.
63 Additional information on the AEDT modeling process is available at:

A-216 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Figure A-5: Commercial Aviation Fuel Burn for the United States and Territories
5.00E+10
4.50E+10
Commercial Aviation Fuel Burn
for the United States and Territories
4.00E+10
3.50E+10
M
^ 3.00E+10
3
CQ
2.50E+10
2.00E+10
m
3 1.50E+10

¦
o
~
~
1.00E+10
5.00E+09
0.00E+00
O tH
f(l t tn U3 N CO Ol
OTHrMm^inuDi^-cxjeriO^rMrotLriinr^c
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-------

Domestic U.S. 50 States
5,129,493,877
12,493
1,687
37,604,800,905
118.6

International U.S. 50 States
1,159,535,153
5,326
719
16,030,792,741
50.6
2003a
Domestic U.S. 50 States and U.S. Territories
5,288,138,079
12,942
1,747
38,956,861,262
122.9

International U.S. 50 States and U.S. Territories
1,155,218,577
5,328
719
16,038,632,384
50.6

Domestic U.S. 50 States
5,197,102,340
12,658
1,709
38,100,444,893
120.2

International U.S. 50 States
1,174,818,219
5,396
728
16,242,084,008
51.2
2004a
Domestic U.S. 50 States and U.S. Territories
5,371,498,689
13,146
1,775
39,570,965,441
124.8

International U.S. 50 States and U.S. Territories
1,173,429,093
5,412
731
16,291,460,535
51.4

Domestic U.S. 50 States
5,279,027,890
12,857
1,736
38,701,048,784
122.1

International U.S. 50 States
1,193,337,698
5,481
740
16,498,119,309
52.1
2005
Domestic U.S. 50 States and U.S. Territories
6,476,007,697
13,976
1,887
42,067,562,737
132.7

International U.S. 50 States and U.S. Territories
1,373,543,928
5,858
791
17,633,508,081
55.6

Domestic U.S. 50 States
6,370,544,998
13,654
1,843
41,098,359,387
129.7

International U.S. 50 States
1,397,051,323
5,936
801
17,868,972,965
56.4
2006a
Domestic U.S. 50 States and U.S. Territories
5,894,323,482
14,426
1,948
43,422,531,461
137

International U.S. 50 States and U.S. Territories
1,287,642,623
5,939
802
17,877,159,421
56.4

Domestic U.S. 50 States
5,792,852,211
14,109
1,905
42,467,943,091
134

International U.S. 50 States
1,309,488,994
6,015
812
18,103,932,940
57.1
2007a
Domestic U.S. 50 States and U.S. Territories
6,009,247,818
14,707
1,986
44,269,160,525
139.7

International U.S. 50 States and U.S. Territories
1,312,748,383
6,055
817
18,225,718,619
57.5

Domestic U.S. 50 States
5,905,798,114
14,384
1,942
43,295,960,105
136.6

International U.S. 50 States
1,335,020,703
6,132
828
18,456,913,646
58.2
2008a
Domestic U.S. 50 States and U.S. Territories
5,475,092,456
13,400
1,809
40,334,124,033
127.3

International U.S. 50 States and U.S. Territories
1,196,059,638
5,517
745
16,605,654,741
52.4

Domestic U.S. 50 States
5,380,838,282
13,105
1,769
39,447,430,318
124.5

International U.S. 50 States
1,216,352,196
5,587
754
16,816,299,099
53.1
2009a
Domestic U.S. 50 States and U.S. Territories
5,143,268,671
12,588
1,699
37,889,631,668
119.5

International U.S. 50 States and U.S. Territories
1,123,571,175
5,182
700
15,599,251,424
49.2

Domestic U.S. 50 States
5,054,726,871
12,311
1,662
37,056,676,966
116.9

International U.S. 50 States
1,142,633,881
5,248
709
15,797,129,457
49.8
2010
Domestic U.S. 50 States and U.S. Territories
5,652,264,576
11,931
1,611
35,912,723,830
113.3

International U.S. 50 States and U.S. Territories
1,474,839,733
6,044
816
18,192,953,916
57.4

Domestic U.S. 50 States
5,554,043,585
11,667
1,575
35,116,863,245
110.8

International U.S. 50 States
1,497,606,695
6,113
825
18,398,996,825
58
2011
Domestic U.S. 50 States and U.S. Territories
5,767,378,664
12,067
1,629
36,321,170,730
114.6

International U.S. 50 States and U.S. Territories
1,576,982,962
6,496
877
19,551,631,939
61.7

Domestic U.S. 50 States
5,673,689,481
11,823
1,596
35,588,754,827
112.3

International U.S. 50 States
1,596,797,398
6,554
885
19,727,043,614
62.2
2012
Domestic U.S. 50 States and U.S. Territories
5,735,605,432
11,932
1,611
35,915,745,616
113.3

International U.S. 50 States and U.S. Territories
1,619,012,587
6,464
873
19,457,378,739
61.4

Domestic U.S. 50 States
5,636,910,529
11,672
1,576
35,132,961,140
110.8

International U.S. 50 States
1,637,917,110
6,507
879
19,587,140,347
61.8
2013
Domestic U.S. 50 States and U.S. Territories
5,808,034,123
12,031
1,624
36,212,974,471
114.3

International U.S. 50 States and U.S. Territories
1,641,151,400
6,611
892
19,898,871,458
62.8

Domestic U.S. 50 States
5,708,807,315
11,780
1,590
35,458,690,595
111.9

International U.S. 50 States
1,661,167,498
6,657
899
20,036,865,038
63.2
2014
Domestic U.S. 50 States and U.S. Territories
5,825,999,388
12,131
1,638
36,514,970,659
115.2

International U.S. 50 States and U.S. Territories
1,724,559,209
6,980
942
21,008,818,741
66.3

Domestic U.S. 50 States
5,725,819,482
11,882
1,604
35,764,791,774
112.8

International U.S. 50 States
1,745,315,059
7,027
949
21,152,418,387
66.7
2015
Domestic U.S. 50 States and U.S. Territories
5,900,440,363
12,534
1,692
37,727,860,796
119

International U.S. 50 States and U.S. Territories
1,757,724,661
7,227
976
21,752,301,359
68.6

Domestic U.S. 50 States
5,801,594,806
12,291
1,659
36,997,658,406
116.7

International U.S. 50 States
1,793,787,700
7,310
987
22,002,733,062
69.4
2016
Domestic U.S. 50 States and U.S. Territories
5,929,429,373
12,674
1,711
38,148,578,811
120.4
A-218 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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International U.S. 50 States and U.S. Territories
1,817,739,570
7,453
1,006
22,434,619,940
70.8

Domestic U.S. 50 States
5,827,141,640
12,422
1,677
37,391,339,601
118

International U.S. 50 States
1,839,651,091
7,504
1,013
22,588,366,704
71.3
2017
Domestic U.S. 50 States and U.S. Territories
6,264,650,997
13,475
1,819
40,560,206,261
128

International U.S. 50 States and U.S. Territories
1,944,104,275
7,841
1,059
23,602,935,694
74.5

Domestic U.S. 50 States
6,214,083,068
13,358
1,803
40,207,759,885
126.9

International U.S. 50 States
1,912,096,739
7,755
1,047
23,343,627,689
73.6
2018
Domestic U.S. 50 States and U.S. Territories
6,408,870,104
13,650
1,843
41,085,494,597
129.6

International U.S. 50 States and U.S. Territories
2,037,055,865
8,178
1,104
24,616,382,063
77.7

Domestic U.S. 50 States
6,318,774,158
13,425
1,812
40,410,478,534
127.5

International U.S. 50 States
2,066,756,708
8,254
1,114
24,843,232,462
78.4
2019
Domestic U.S. 50 States and U.S. Territories
6,617,450,051
14,132
1,908
42,536,399,715
134.2

International U.S. 50 States and U.S. Territories
2,084,393,888
8,173
1,103
24,601,971,697
77.6

Domestic U.S. 50 States
6,617,074,577
14,131
1,908
42,535,165,758
134.2

International U.S. 50 States
2,008,158,986
7,973
1,076
23,997,773,004
75.7
NA (Not Applicable)
a Estimates for these years were derived from previously reported tools and methods.
A-219
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References
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K.
Miwa, T. Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
IPCC (1999) Aviation and the Global Atmosphere. Intergovernmental Panel on Climate Change. [J.E. Penner, et al.
(eds.)]. Cambridge University Press. Cambridge, United Kingdom.
Santoni, G., B. Lee, E. Wood, S. Herndon, R. Miake-Lye, S Wofsy, J. McManus, D. Nelson, M. Zahniser (2011)
Aircraft emissions of methane and nitrous oxide during the alternative aviation fuel experiment. Environ Sci
Technol. 2011 Aug 15; 45(16):7075-82.
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3.4. Methodology for Estimating CH4 Emissions from Coal Mining
EPA uses an IPCC Tier 3 method for estimating CH4 emissions from underground mining and an IPCC Tier 2
method for estimating CH4 emissions from surface mining and post-mining activities (for both coal production from
underground mines and surface mines). The methodology for estimating CH4 emissions from coal mining consists of two
steps:
•	Estimate emissions from underground mines. These emissions have two sources: ventilation systems and
degasification systems. They are estimated using mine-specific data, then summed to determine total CH4
liberated. The CH4 recovered and used is then subtracted from this total, resulting in an estimate of net
emissions to the atmosphere.
•	Estimate emissions from surface mines and post-mining activities. This step does not use mine-specific
data; rather, it consists of multiplying coal-basin-specific coal production by coal-basin-specific gas content
and an emission factor.
Step 1: Estimate CH4 Liberated and CH4 Emitted from Underground Mines
Underground mines generate CH4 from ventilation systems and degasification systems. Some mines recover
and use the generated CH4, thereby reducing emissions to the atmosphere. Total CH4 emitted from underground mines
equals the CH4 liberated from ventilation systems, plus the CH4 liberated from degasification systems, minus CH4
recovered and used.
Step 1.1: Estimate CH4 Liberated from Ventilation Systems
All coal mines with detectable CH4 emissions use ventilation systems to ensure that CH4 levels remain within
safe concentrations. Many coal mines do not have detectable levels of CH4; others emit several million cubic feet per day
(MMCFD) from their ventilation systems. On a quarterly basis, the U.S. Mine Safety and Health Administration (MSHA)
measures CH4 concentration levels at underground mines. MSHA maintains a database of measurement data from all
underground mines with detectable levels of CH4 in their ventilation air (MSHA 2020).64 Based on the four quarterly
measurements, MSHA estimates average daily CH4 liberated at each of these underground mines.
For 1990 through 1999, average daily CH4 emissions from MSHA were multiplied by the number of days in the
year (i.e., coal mine assumed in operation for all four quarters) to determine the annual emissions for each mine. For
2000 through 2019, the average daily CH4 emissions were multiplied by the number of days corresponding to the
number of quarters the mine vent was operating. For example, if the mine vent was operational in one out of the four
quarters, the average daily CH4 emissions were multiplied by 92 days.
Total ventilation emissions for a particular year are estimated by summing emissions from individual mines.
Since 2011, the nation's "gassiest" underground coal mines—those that liberate more than 36,500,000 cubic
feet of CH4 per year (about 17,525 MT C02 Eq.)—have been required to report to EPA's GHGRP (EPA 2020).65 Mines that
report to EPA's GHGRP must report quarterly measurements of CH4 emissions from ventilation systems; they have the
option of recording their own measurements, or using the measurements taken by MSHA as part of that agency's
quarterly safety inspections of all mines in the U.S. with detectable CH4 concentrations.
Since 2013, ventilation emission estimates have been calculated based on both EPA's GHGRP,66 data submitted
by underground mines, and on mine-specific CH4 measurement data obtained directly from MSHA for the remaining
mines. The MSHA measurement data are used to determine the average daily emission rate for all mines in the reporting
year. The CH4 liberated from ventilation systems is estimated by summing the emissions from the mines reporting to
EPA's GHGRP and emissions based on MSHA measurements for the remaining mines not reporting to EPA's GHGRP.
64	MSHA records coal mine methane readings with concentrations of greater than 50 ppm (parts per million) methane. Readings below
this threshold are considered non-detectable.
65	Underground coal mines report to EPA under subpart FF of EPA's GHGRP (40 CFR part 98). In 2019, 65 underground coal mines
reported to the program.
66	In implementing improvements and integrating data from EPA's GHGRP, the EPA followed the latest guidance from the IPCC on the
use of facility-level data in national inventories (IPCC 2011).
A-221
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Table A-109: Mine-Specific Data Used to Estimate Ventilation Emissions
Year Individual Mine Data Used
1990	All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3
1991	1990 Emission Factors Used Instead of Mine-Specific Data
1992	1990 Emission Factors Used Instead of Mine-Specific Data
1993
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a



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



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



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



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



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



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



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



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



2002
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3



2003
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a



2004
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a



2005
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3



2006
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3



2007
All
Mines with Detectable Emissions (Assumed to Account for 100% of Total)



2008
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)b



2009
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)b



2010
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)b



2011
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)b



2012
All
Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)b



2013
All
Mines with Detectable Emissions and GHGRP reported data (Assumed to account
for
100%
of Total)
2014
All
Mines with Detectable Emissions and GHGRP reported data (Assumed to account
for
100%
of Total)
2015
All
Mines with Detectable Emissions and GHGRP reported data (Assumed to account
for
100%
of Total)
2016
All
Mines with Detectable Emissions and GHGRP reported data (Assumed to account
for
100%
of Total)
2017
All
Mines with Detectable Emissions and GHGRP reported data (Assumed to account
for
100%
of Total)
2018
All
Mines with Detectable Emissions and GHGRP reported data (Assumed to account
for
100%
of Total)
2019
All
Mines with Detectable Emissions and GHGRP reported data (Assumed to account
for
100%
of Total)
a Factor derived from a complete set of individual mine data collected for 1997.
b Factor derived from a complete set of individual mine data collected for 2007.
Step 1.2: Estimate CH4 Liberated from Degasification Systems
Coal mines use several types of degasification systems to remove CH4, including pre-mining vertical and
horizontal wells (to recover CH4 before mining) and post-mining vertical wells and horizontal boreholes (to recover CH4
during mining of the coal seam). Post-mining gob wells and cross-measure boreholes recover CH4 from the overburden
(i.e., gob area) after mining of the seam (primarily in longwall mines).
Nineteen mines employed degasification systems in 2019, and 17 of these mines reported the CH4 liberated
through these systems to the EPA's GHGRP (EPA 2020). Thirteen of the 19 mines with degasification systems had
operational CH4 recovery and use projects, and the other six reported emitting CH4 from degasification systems to the
atmosphere. Several of the mines venting CH4 from degasification systems use a small portion of the gas to fuel gob well
blowers or compressors in remote locations where electricity is not available. However, this CH4 use is not considered to
be a formal recovery and use project.
Degasification information reported to EPA's GHGRP by underground coal mines is the primary source of data
used to develop estimates of CH4 liberated from degasification systems. Data reported to EPA's GHGRP were used
exclusively to estimate CH4 liberated from degasification systems at 14 of the 19 mines that used degasification systems
in 2019.
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Degasification volumes for the life of mined-through, pre-mining wells are attributed to the mine as emissions
in the year in which the well is mined through.67 EPA's GHGRP does not require gas production from virgin coal seams
(coalbed methane) to be reported by coal mines under subpart FF. Most pre-mining wells drilled from the surface are
considered coalbed methane wells and are reported under another subpart of the program (subpart W, "Petroleum and
Natural Gas Systems"). As a result, for the five mines with degasification systems that include pre-mining wells that were
mined through in 2019, EPA's GHGRP information was supplemented with historical data from state gas well production
databases and mine-specific information regarding the dates on which pre-mining wells were mined through (GSA 2020;
DMME 2020; WVGES 2020; JWR 2010; El Paso 2009; ERG 2020). For pre-mining wells, the cumulative CH4 production
from the well is totaled using gas sales data and is considered liberated from the mine's degasification system the year in
which the well is mined through.
Reports to EPA's GHGRP with CH4 liberated from degasification systems are reviewed for errors in reporting.
For some mines, GHGRP data are corrected for the Inventory based on expert judgment. Common errors include
reporting CH4 liberated as CH4 destroyed and vice versa. Other errors include reporting CH4 destroyed without reporting
any CH4 liberated by degasification systems. In the rare cases where GHGRP data are inaccurate and gas sales data are
unavailable, estimates of CH4 liberated are based on historical CH4 liberation rates.
Step 1.3: Estimate CH4 Recovered from Ventilation and Degasification Systems, and Utilized or Destroyed
(Emissions Avoided)
There were 13 active coal mines with operational CH4 recovery and use projects in 2019, including one mine
that had two recovery and use projects. Thirteen of these projects involved degasification systems and one involved
ventilation air methane (VAM). Eleven of these mines sold the recovered CH4 to a pipeline, including one mine that used
CH4 to fuel a thermal coal dryer. One mine used CH4 to heat mine ventilation air (data was unavailable for estimating CH4
recovery at this mine). One mine destroyed the recovered CH4 (VAM) using Regenerative Thermal Oxidation (RTO)
without energy recovery.
The CH4 recovered and used (or destroyed) at the thirteen coal mines described above for which data were
available were estimated using the following methods:
•	EPA's GHGRP data was exclusively used to estimate the CH4 recovered and used from seven mines that
deployed degasification systems in 2019. Based on weekly measurements of gas flow and CH4
concentrations, the GHGRP summary data for degasification destruction at each mine were added
together to estimate the CH4 recovered and used from degasification systems. Reports to EPA's GHGRP are
reviewed for errors in reporting. For some mines, GHGRP data are corrected for the Inventory based on
expert judgment (see further discussion in Step 1.2).
•	State sales data were used to estimate CH4 recovered and used from the remaining five mines that
deployed degasification systems in 2019 (DMME 2020; GSA 2020). These five mines intersected pre-mining
wells in 2019. Supplemental information was used for these mines because estimating CH4 recovery and
use from pre-mining wells requires additional data (data not reported under subpart FF of EPA's GHGRP;
see discussion in step 1.2 above) to account for the emissions avoided prior to the well being mined
through. The 2019 data came from state gas production databases (DMME 2020; GSA 2020; WVGES 2020),
as well as mine-specific information on the timing of mined-through, pre-mining wells (JWR 2010; El Paso
2009, ERG 2019-2020). For pre-mining wells, the cumulative CH4 production from the wells was totaled
using gas sales data, and was considered to be CH4 recovered and used from the mine's degasification
system in the year in which the well was mined through.
•	For the single mine that employed VAM for CH4 recovery and use, the estimates of CH4 recovered and used
were obtained from the mine's offset verification statement (OVS) submitted to the California Air
Resources Board (CARB) (McElroy OVS 2020).
Step 2: Estimate CH4 Emitted from Surface Mines and Post-Mining Activities
Mine-specific data are not available for estimating CH4 emissions from surface coal mines or for post-mining
activities. For surface mines, basin-specific coal production data obtained from the Energy Information Administration's
67 A well is "mined through" when coal mining development or the working face intersects the borehole or well.
A-223
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Annual Coal Report are multiplied by basin-specific gas contents and a 150 percent emission factor (to account for CH4
from over- and under-burden) to estimate CH4 emissions (King 1994; Saghafi 2013). For post-mining activities, basin-
specific coal production data are multiplied by basin-specific gas contents and a mid-range 32.5 percent emission factor
accounting for CH4 desorption during coal transportation and storage (Creedy 1993). Basin-specific in situ gas content
data were compiled from AAPG (1984) and USBM (1986). Beginning in 2006, revised data on in situ CH4 content and
emission factors have been used (EPA 1996, 2005).
Step 2.1: Define the Geographic Resolution of the Analysis and Collect Coal Production Data
The first step in estimating CH4 emissions from surface mining and post-mining activities is to define the
geographic resolution of the analysis and to collect coal production data at that level of resolution. The analysis is
conducted by coal basin as defined in Table A-110, which presents coal basin definitions by basin and by state.
The Energy Information Administration's Annual Coal Report (EIA 2020) includes state- and county-specific
underground and surface coal production by year. To calculate production by basin, the state-level data are grouped into
coal basins using the basin definitions listed in Table A-110. For two states—West Virginia and Kentucky—county-level
production data are used for the basin assignments because coal production occurred in geologically distinct coal basins
within these states. Table A-lll presents the coal production data aggregated by basin.
Step 2.2: Estimate Emission Factors for Each Emissions Type
Emission factors for surface-mined coal were developed from the in situ CH4 content of the surface coal in each
basin. Based on analyses conducted in Canada and Australia on coals similar to those present in the United States (King
1994; Saghafi 2013), the surface mining emission factor used was conservatively estimated to be 150 percent of the in
situ CH4 content of the basin. Furthermore, the post-mining emission factors used were estimated to be 25 to 40 percent
of the average in situ CH4 content in the basin. For this analysis, the post-mining emission factor was determined to be
32.5 percent of the in situ CH4 content in the basin. Table A-112 presents the average in situ content for each basin,
along with the resulting emission factor estimates.
Step 2.3: Estimate CH4 Emitted
The total amount of CH4 emitted from surface mines and post-mining activities is calculated by multiplying the
coal production in each basin by the appropriate emission factors.
Table A-110 lists each of the major coal mine basins in the United States and the states in which they are
located. As shown in Figure A-6, several coal basins span several states. Table A-lll shows annual underground, surface,
and total coal production (in short tons) for each coal basin. Table A-112 shows the surface, post-surface, and post-
underground emission factors used for estimating CH4 emissions for each of the categories. For underground mines,
Table A-113 presents annual estimates of CH4 emissions for ventilation and degasification systems, and CH4 recovered
and used. Table A-114 presents annual estimates of total CH4 emissions from underground, post-underground, surface,
and post-surface activities.
Table A-110: Coal Basin Definitions by Basin and by State
Basin
States
Northern Appalachian Basin
Maryland, Ohio, Pennsylvania, West Virginia North
Central Appalachian Basin
Kentucky East, Tennessee, Virginia, West Virginia South
Warrior Basin
Alabama, Mississippi
Illinois Basin
Illinois, Indiana, Kentucky West
South West and Rockies Basin
Arizona, California, Colorado, New Mexico, Utah
North Great Plains Basin
Montana, North Dakota, Wyoming
West Interior Basin
Arkansas, Iowa, Kansas, Louisiana, Missouri, Oklahoma, Texas
Northwest Basin
Alaska, Washington
State
Basin
Alabama
Warrior Basin
Alaska
Northwest Basin
Arizona
South West and Rockies Basin
Arkansas
West Interior Basin
California
South West and Rockies Basin
Colorado
South West and Rockies Basin
A-224 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Illinois
Illinois Basin
Indiana
Illinois Basin
Iowa
West Interior Basin
Kansas
West Interior Basin
Kentucky (east)
Central Appalachian Basin
Kentucky (west)
Illinois Basin
Louisiana
West Interior Basin
Maryland
Northern Appalachian Basin
Mississippi
Warrior Basin
Missouri
West Interior Basin
Montana
North Great Plains Basin
New Mexico
South West and Rockies Basin
North Dakota
North Great Plains Basin
Ohio
Northern Appalachian Basin
Oklahoma
West Interior Basin
Pennsylvania
Northern Appalachian Basin
Tennessee
Central Appalachian Basin
Texas
West Interior Basin
Utah
South West and Rockies Basin
Virginia
Central Appalachian Basin
Washington
Northwest Basin
West Virginia South
Central Appalachian Basin
West Virginia North
Northern Appalachian Basin
Wyoming
North Great Plains Basin
Figure A-6: Locations of U.S. Coal Basins
Coalbed Methane Fields, Lower 48 States
North Central
Coal. Region
ir River
Wind River Ba<
Wyoming (
Michigan
Northern
Appalachian
mnah-Carbori Basin
„Park Basin
ireater Green,1
Riyer Basip"*
Illinois
'V Basin
Piceance^Ba'sfi
CtrerokeelPlatform
Deep River
Basin
/ San Juah B0Sinl
Basin |
Miles
Black Warrior
JK, Basin
Southwestern
Coal Region
Terlingua
''..Field /'
Coalbed Methane Fields
Source: Energy Information Administration based on data from USGS and various published studies
Updated; April 8, 2009
A-225
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Table A-lll: Annual Coal Production (Thousand Short Tons)
Basin
1990
2005
2015
2016
2017
2018
2019
Underground Coal Production
423,556
368,612
306,821 252,106
273,129
275,361
267,373
N. Appalachia
103,865
111,151
103,579
94,685
97,741
97,070
97,905
Cent. Appalachia
198,412
123,082
53,230
39,800
46,053
45,306
39,957
Warrior
17,531
13,295
9,897
7,434
10,491
12,199
11,980
Illinois
69,167
59,180
96,361
76,578
80,855
85,416
81,061
S. West/Rockies
32,754
60,866
33,762
26,413
30,047
25,387
27,257
N. Great Plains
1,722
572
9,510
6,776
7,600
9,777
9,213
West Interior
105
465
482
420
343
206
0
Northwest
0
0
0
0
0
0
0
Surface Coal Production
602,753
762,190
588,736 475,407
500,782
480,080
438,445
N. Appalachia
60,761
28,873
13,200
8,739
9,396
9,219
8,476
Cent. Appalachia
94,343
112,222
37,530
26,759
31,796
33,799
32,742
Warrior
11,413
11,599
6,437
5,079
4,974
5,523
4,841
Illinois
72,000
33,703
27,359
21,707
22,427
21,405
18,591
S. West/Rockies
43,863
42,756
26,019
18,951
19,390
19,599
18,394
N. Great Plains
249,356
474,056
436,929 350,898
372,874
362,664
329,164
West Interior
64,310
52,262
40,084
42,342
38,966
26,969
25,261
Northwest
6,707
6,720
1,177
932
959
902
975
Total Coal Production
1,026,309
1,130,802
895,557 727,514
773,911
755,442
705,818
N. Appalachia
164,626
140,023
116,779 103,424
107,137
106,289
106,381
Cent. Appalachia
292,755
235,305
90,760
66,558
77,848
79,105
72,700
Warrior
28,944
24,894
16,334
12,513
15,464
17,723
16,822
Illinois
141,167
92,883
123,720
98,285
103,282
106,821
99,652
S. West/Rockies
76,617
103,622
59,781
45,364
49,437
44,987
45,652
N. Great Plains
251,078
474,629
446,438 357,675
380,474
372,441
338,376
West Interior
64,415
52,727
40,566
42,763
39,309
27,175
25,261
Northwest
6,707
j 6,720
1,177
932
959
902
975
Notes: Totals may not sum due to independent rounding.





Table A-112: Coal Underground, Surface, and Post-Mining CH4 Emission Factors (ft3 per Short Ton)



Surface
Underground
Surface Post-Mining
Post-Mining


Average
Average

Mine
Surface Underground
Basin
In Situ Content
In Situ Content
Factors
Factors
Factors
Northern Appalachia

59.5
138.4

89.3
19.3
45.0
Central Appalachia (WV)

24.9
136.8

37.4
8.1
44.5
Central Appalachia (VA)

24.9
399.1

37.4
8.1
129.7
Central Appalachia (E KY)

24.9
61.4

37.4
8.1
20.0
Warrior

30.7
266.7

46.1
10.0
86.7
Illinois

34.3
64.3

51.5
11.1
20.9
Rockies (Piceance Basin)

33.1
196.4

49.7
10.8
63.8
Rockies (Uinta Basin)

16.0
99.4

24.0
5.2
32.3
Rockies (San Juan Basin)

7.3
104.8

11.0
2.4
34.1
Rockies (Green River Basin)

33.1
247.2

49.7
10.8
80.3
Rockies (Raton Basin)

33.1
127.9

49.7
10.8
41.6
N. Great Plains (WY, MT)

20.0
15.8

30.0
6.5
5.1
N. Great Plains (ND)

5.6
15.8

8.4
1.8
5.1
West Interior (Forest City, Cherokee Basins)
34.3
64.3

51.5
11.1
20.9
West Interior (Arkoma Basin)

74.5
331.2

111.8
24.2
107.6
West Interior (Gulf Coast Basin)

11.0
127.9

16.5
3.6
41.6
Northwest (AK)

16.0
160.0

24.0
5.2
52.0
Northwest (WA)

16.0
47.3

24.0
5.2
15.4
Sources: 1986 USBM Circular 9067, Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins-, U.S.
DOE Report DOE/METC/83-76, Methane Recovery from Coalbeds: A Potential Energy Source; 1986-1988 Gas Research
Institute Topical Report, A Geologic Assessment of Natural Gas from Coal Seams; 2005 U.S. EPA Draft Report, Surface Mines
Emissions Assessment.
A-226 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Table A-113: Underground Coal Mining ChU Emissions (Billion Cubic Feet)
Activity
1990
2005
2015
2016
2017
2018
2019
Ventilation Output
112
75
84
76
78
73
62
Adjustment Factor for Mine Data
98%
98%
100%
100%
100%
100%
100%
Adjusted Ventilation Output
114
77
84
76
78
73
62
Degasification System Liberated
54
47
43
42
42
47
42
Total Underground Liberated
168
124
127
119
121
120
104
Recovered & Used
(14)
(37)
(34)
(34)
(36)
(39)
(33)
Total
154
87
93
85
84
81
72
Table A-114: Total Coal Mining Cm Emissions (Billion Cubic Feet)
Activity

1990
2005
2015 2016
2017
2018 2019

Underground Mining

154
87
93
85
84
81 72

Surface Mining

22
25
18
14
15
15 13

Post-Mining (Underground)
19
16
12
10
11
11 11

Post-Mining (Surface]

5
5
4
3
3
3 3

Total

200
133
127
112
114
110 98

vlotes: Totals may not sum due to independent rounding. Parentheses indicate negative values.


fable A-115: Total Coal Mining ChU Emissions by State (Million Cubic Feet)



State
1990
2005
2015
2016

2017
2018
2019
Alabama
32,097
15,789
12,675
10,752

11,044
12,119
9,494
Alaska
50
42
34
27

28
26
28
Arizona
151
161
91
72

83
87
51
Arkansas
5
+
559
247

770
71
0
California
1
0
0
0

0
0
0
Colorado
10,187
13,441
3,248
2,272

1,940
1,616
1,730
Illinois
10,180
6,488
10,547
11,034

8,513
6,530
5,661
Indiana
2,232
3,303
6,891
6,713

6,036
6,729
6,807
Iowa
24
0
0
0

0
0
0
Kansas
45
11
12
2

0
0
0
Kentucky
10,018
6,898
6,378
4,880

4,636
4,636
2,264
Louisiana
64
84
69
56

42
129
36
Maryland
474
361
171
131

152
113
119
Mississippi
0
199
176
161

146
165
151
Missouri
166
37
9
15

15
16
12
Montana
1,373
1,468
1,353
1,004

1,102
1,172
1,038
New Mexico
363
2,926
2,648
1,954

1,728
1,360
1,446
North Dakota
299
306
294
287

294
303
276
Ohio
4,406
3,120
2,718
1,998

1,473
1,342
1,283
Oklahoma
226
825
736
867

2,407
2,317
116
Pennsylvania
21,864
18,605
19,554
17,932

19,662
20,695
23,528
Tennessee
276
115
40
27

14
23
17
Texas
1,119
922
721
783

730
498
468
Utah
3,587
4,787
1,737
788

678
629
811
Virginia
46,041
8,649
6,396
6,692

7,663
7,051
6,959
Washington
146
154
0
0

0
0
0
West Virginia
48,335
29,745
36,460
32,309

33,122
28,686
25,711
Wyoming
6,671
14,745
13,624
10,812

11,497
13,201
10,409
Total
200,399
133,182
127,139
111,815

113,777
109,515
98,416
Note: The emission estimates provided above are inclusive of emissions from underground mines, surface mines and post-
mining activities. The following states have neither underground nor surface mining and thus report no emissions as a result of
coal mining: Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Maine, Massachusetts, Michigan, Minnesota, Nebraska,
Nevada, New Hampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, South Carolina, South Dakota,
Vermont, and Wisconsin.
+ Does not exceed 0.5 million cubic feet.
A-227
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References
AAPG (1984) Coalbed Methane Resources of the United States. AAPG Studies in Geology Series #17.
Creedy, D.P. (1993) Chemosphere. Vol. 26, pp. 419-440.
DMME (2020) DGO Data Information System. Department of Mines, Minerals and Energy of Virginia. Available online at
.
EIA (2020) Annual Coal Report 2019. Table 1. Energy Information Administration, U.S. Department of Energy.
El Paso (2009) Shoal Creek Mine Plan, El Paso Exploration & Production.
EPA (2020) Greenhouse Gas Reporting Program (GHGRP) 2019 Envirofacts. Subpart FF: Underground Coal Mines.
Available online at .
EPA (2005) Surface Mines Emissions Assessment. Draft. U.S. Environmental Protection Agency.
EPA (1996) Evaluation and Analysis of Gas Content and Coal Properties of Major Coal Bearing Regions of the United
States. U.S. Environmental Protection Agency. EPA/600/R-96-065.
ERG (2019-2020). Correspondence between ERG and Buchanan Mine.
Geological Survey of Alabama State Oil and Gas Board (GSA) (2020) Well Records Database. Available online at
.
IPCC (2011) Use of Models and Facility-Level Data in Greenhouse Gas Inventories. Report of IPCC Expert Meeting on Use
of Models and Measurements in Greenhouse Gas Inventories 9-11 August 2010, Sydney, Australia. Eds: Eggleston
H.S., Srivastava N., Tanabe K., Baasansuren J., Fukuda M. IGES.
JWR (2010) No. 4 & 7 Mines General Area Maps. Walter Energy: Jim Walter Resources.
King, B. (1994) Management of Methane Emissions from Coal Mines: Environmental, Engineering, Economic and
Institutional Implication of Options, Neil and Gunter Ltd., Halifax, March 1994.
McElroy OVS (2020) Marshall County VAM Abatement Project Offset Verification Statement submitted to California Air
Resources Board, July 2019.
MSHA (2020) Data Transparency at MSHA. Mine Safety and Health Administration. Available online at
.
Mutmansky, Jan M., and Yanbei Wang (2000) Analysis of Potential Errors in Determination of Coal Mine Annual Methane
Emissions. Department of Energy and Geo-Environmental Engineering, Pennsylvania State University. University
Park, PA.
Saghafi, Abouna (2013) Estimation of fugitive emissions from open cut coal mining and measurable gas content, 13th
Coal Operators' Conference, University of Wollongong, The Australian Institute of Mining and Metallurgy & Mine
Managers Association of Australia, 2013, 306-313.
USBM (1986) Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins. Circular 9067, U.S.
Bureau of Mines.
West Virginia Geological & Economic Survey (WVGES) (2020) Oil & Gas Production Data. Available online at
.
A-228 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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3.5. Methodology for Estimating CH4, CO2, and N2O Emissions from Petroleum
Systems
For details on the emissions, emission factors, activity data, data sources, and methodologies for each year
from 1990 to 2019 please see the spreadsheet file annexes for the current (i.e., 1990 to 2019) Inventory, available at
https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-svstems.
As described in the main body text on Petroleum Systems, the Inventory methodology involves the calculation
of CH4, C02, and N20 emissions for approximately 100 emissions sources, and then the summation of emissions for each
petroleum systems segment. The approach for calculating emissions for petroleum systems generally involves the
application of emission factors to activity data.
Emission Factors
Table 3.5-2, Table 3.5-7, and Table 3.5-10 show CH4, C02, and N20 emissions, respectively, for all sources in
Petroleum Systems, for all time series years. Table 3.5-3, Table 3.5-8, and Table 3.5-11 show the CH4, C02, and N20
average emission factors, respectively, for all sources in Petroleum Systems, for all time series years. These emission
factors are calculated by dividing net emissions by activity. Therefore, in a given year, these emission factors reflect the
estimated contribution from controlled and uncontrolled fractions of the source population.
Additional detail on the basis for emission factors used across the time series is provided in Table 3.5-4, Table
3.5-9, Table 3.5-12, and below.
In addition to the Greenhouse Gas Reporting Program (GHGRP), key references for emission factors for CH4 and
non-combustion-related C02 emissions from the U.S. petroleum industry include a 1999 EPA/Radian report Methane
Emissions from the U.S. Petroleum Industry (EPA/Radian 1999), which contained the most recent and comprehensive
determination of CH4 emission factors for CH4-emitting activities in the oil industry at that time, a 1999 EPA/ICF draft
report Estimates of Methane Emissions from the U.S. Oil Industry (EPA/ICF 1999) which is largely based on the 1999
EPA/Radian report, and a detailed study by the Gas Research Institute and EPA Methane Emissions from the Natural Gas
Industry (EPA/GRI 1996). These studies still represent best available data in many cases—in particular, for the early years
of the time series.
Data from studies and EPA's GHGRP (EPA 2020b) allows for emission factors to be calculated that account for
adoption of control technologies and emission reduction practices. For several sources, EPA has developed control
category-specific emission factors from recent data that are used over the time series (paired with control category-
specific activity data that fluctuates to reflect control adoption over time). For oil well completions with hydraulic
fracturing, controlled and uncontrolled emission factors were developed using GHGRP data. For associated gas, separate
emission estimates are developed from GHGRP data for venting and flaring. For oil tanks, emissions estimates were
developed for large and small tanks with flaring or VRU control, without control devices, and with upstream
malfunctioning separator dump valves. For pneumatic controllers, separate estimates are developed for low bleed, high
bleed, and intermittent controllers. For chemical injection pumps, the estimate is calculated with an emission factor
developed with GHGRP data, which is based on the previous GRI/EPA factor but takes into account operating hours.
Some sources in Petroleum Systems that use methodologies based on GHGRP data use a basin-level aggregation
approach, wherein EPA calculates basin-specific emissions and/or activity factors for basins that contribute at least 10
percent of total annual emissions (on a C02 Eq. basis) from the source in any year—and combines all other basins into
one grouping. This methodology is applied for associated gas venting and flaring and miscellaneous production flaring.
Produced Water CH4 estimates were incorporated into the 1990-2019 Inventory. Emissions are calculated using annual
produced water quantities (Enverus 2021, Kansas Department of Health and Environment 2020, Ohio Environmental
Protection Agency 2020, Oklahoma Department of Environmental Quality 2020, Pennsylvania Department of
Environmental Protection 2020, Illinois Office of Oil and Gas Resource Management 2020, Indiana Division of Oil and Gas
2020, West Virginia Department of Environmental Protection 2020, and EIA 2020) and an emission factor from EPA's
Nonpoint Oil and Gas Emission Estimation Tool (EPA 2017).
For the refining segment, EPA has directly used the GHGRP data for all emission sources for recent years (2010
forward) (EPA 2020b) and developed source level throughput-based emission factors from GHGRP data to estimate
emissions in earlier time series years (1990-2009). For some sources within refineries, EPA continues to apply the
historical emission factors for all time series years. All refineries have been required to report CH4, C02, and N20
emissions to GHGRP for all major activities since 2010. The national totals of these emissions for each activity were used
A-229
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for the 2010 to 2019 emissions. The national emission totals for each activity were divided by refinery feed rates for
those four Inventory years to develop average activity-specific emission factors, which were used to estimate national
emissions for each refinery activity from 1990 to 2009 based on national refinery feed rates for each year (EPA 2015b).
Offshore emissions are taken from analysis of the Gulfwide Emission Inventory Studies and GHGRP data (BOEM
2020a-d; EPA 2020a; EPA 2020b). Emission factors are calculated for offshore facilities located in the Gulf of Mexico,
Pacific, and Alaska regions.
When a C02-specific emission factor is not available for a source, the C02 emission factors were derived from
the corresponding source CH4emission factors. The amount of C02 in the crude oil stream changes as it passes through
various equipment in petroleum production operations. As a result, four distinct stages/streams with varying C02
contents exist. The four streams that are used to estimate the emissions factors are the associated gas stream separated
from crude oil, hydrocarbons flashed out from crude oil (such as in storage tanks), whole crude oil itself when it leaks
downstream, and gas emissions from offshore oil platforms. For this approach, C02 emission factors are estimated by
multiplying the existing CH4emissions factors by a conversion factor, which is the ratio of C02 content to methane
content for the particular stream. Ratios of C02 to CH4 volume in emissions are presented in Table 3.5-1.
N20 emission factors were calculated using GHGRP data. For each flaring emission source calculation
methodology that uses GHGRP data, the existing source-specific methodology was applied to calculate N20 emission
factors.
1990-2019 Inventory updates to emission factors
Summary information for emission factors for sources with revisions in this year's Inventory is below. The
details are presented in a memorandum,68 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for
Produced Water Emissions (Produced Water memo) (EPA 2021), as well as the "Recalculations Discussion" section of the
main body text.
In the production segment, EPA developed a new calculation methodology to estimate for produced water
emissions from oil wells. Previous Inventories did not include emissions from produced water from oil wells. EPA's
considerations for this source are documented in the Produced Water memo.
Activity Data
Table 3.5-5 shows the activity data for all sources in Petroleum Systems, for all time series years. Additional
detail on the basis for activity data used across the time series is provided in Table 3.5-6, and below.
For many sources, complete activity data were not available for all years of the time series. In such cases, one
of three approaches was employed. Where appropriate, the activity data were calculated from related statistics using
ratios developed based on EPA 1996, and/or GHGRP data. For major equipment (equipment leak categories), pneumatic
controllers, and chemical injection pumps, GHGRP subpart W data were used to develop activity factors (i.e., count per
well) that are applied to calculated activity in recent years; to populate earlier years of the time series, linear
interpolation is used to connect GHGRP-based estimates with existing estimates in years 1990 to 1995. In other cases,
the activity data were held constant from 1990 through 2014 based on EPA (1999). Lastly, the previous year's data were
used when data for the current year were unavailable. For offshore production in the GOM, the number of active major
and minor complexes are used as activity data. For offshore production in the Pacific and Alaska region, the activity data
are region-specific production. The activity data for the total crude transported in the transportation segment are not
available, therefore the activity data for the refining sector (i.e., refinery feed in 1000 bbl/year) was also used for the
transportation sector, applying an assumption that all crude transported is received at refineries. In the few cases where
no data were located, oil industry data based on expert judgment were used. In the case of non-combustion C02 and N20
emission sources, the activity factors are the same as for CH4emission sources. In some instances, where recent time
series data (e.g., year 2018) are not yet available, year 2017 or prior data were used as proxy.
Methodology for well counts and events
EPA used Drillinglnfo and Prism, production databases maintained by Enverus Inc. (Enverus 2021), covering U.S.
oil and natural gas wells to populate time series activity data for active oil wells, oil wells drilled, and oil well completions
68 Stakeholder materials including EPA memoranda for the current (i.e., 1990 to 2019) Inventory are available at
.
A-230 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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and workovers with hydraulic fracturing. For more information on Enverus data processing, please see Annex 3.6
Methodology for Estimating CH4, C02, and N20 from Natural Gas Systems.
1990-2019 Inventory updates to activity data
Summary information for activity data for sources with revisions in this year's Inventory is below. The details
are presented in a memorandum,,69 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for
Produced Water Emissions (Produced Water memo) (EPA 2021), as well as the "Recalculations Discussion" section of the
main body text.
In the production segment, EPA developed a new calculation methodology to estimate for produced water
emissions from oil wells. Annual produced water quantities were used as activity data. Previous Inventories did not
include emissions from produced water from oil wells. EPA's considerations for this source are documented in the
Produced Water memo.
Methane, Carbon Dioxide, and Nitrous Oxide Emissions by Emission Source for Each Year
Annual CH4, C02, and N20 emissions for each source were calculated by multiplying the activity data for each
year by the corresponding emission factor. These annual emissions for each activity were then summed to estimate the
total annual CH4, C02, and N20 emissions, respectively. Emissions at a segment level are shown in Table 3.5-2, Table 3.5-
7, and Table 3.5-10.
Refer to the 1990-2019 Inventory section at https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
systems for the following data tables, in spreadsheet format:
•	Table 3.5-1: Ratios of C02 to CH4 Volume in Emissions from Petroleum Production Field Operations
•	Table 3.5-2: CH4 Emissions (kt) for Petroleum Systems, by Segment and Source, for All Years
•	Table 3.5-3: Average CH4 Emission Factors (kg/unit activity) for Petroleum Systems Sources, for All Years
•	Table 3.5-4: CH4 Emission Factors for Petroleum Systems, Data Sources/Methodology
•	Table 3.5-5: Activity Data for Petroleum Systems Sources, for All Years
•	Table 3.5-6: Activity Data for Petroleum Systems, Data Sources/Methodology
•	Table 3.5-7: C02 Emissions (kt) for Petroleum Systems, by Segment and Source, for All Years
•	Table 3.5-8: Average C02 Emission Factors (kg/unit activity) for Petroleum Systems Sources, for All Years
•	Table 3.5-9: C02 Emission Factors for Petroleum Systems, Data Sources/Methodology
•	Table 3.5-10: N20 Emissions (kt) for Natural Gas Systems, by Segment and Source, for All Years
•	Table 3.5-11: Average N20 Emission Factors (kg/unit activity) for Natural Gas Systems Sources, for All Years
•	Table 3.5-12: N20 Emission Factors for Natural Gas Systems, Data Sources/Methodology
•	Table 3.5-13: Annex 3.5 Electronic Tables - References
69 Stakeholder materials including EPA memoranda for the current (i.e., 1990 to 2019) Inventory are available at
.
A-231
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References
AOGCC (2020a) List of wells. Alaska Oil and Gas Conservation Commission (AOGCC). Available online at:
.
AOGCC (2020b) Oil and Gas Production. Alaska Oil and Gas Conservation Commission (AOGCC). Available online at:
.
API (1989) Aboveground Storage Tank Survey report prepared by Entropy Limited for American Petroleum Institute, April
1989.
API (1995) API 4615: Emission Factors For Oil and Gas Production Operations. American Petroleum Institute.
Washington, DC.
API (1996) API 4638: Calculation Workbook For Oil And Gas Production Equipment Fugitive Emissions. American
Petroleum Institute. Washington, DC.
API (2000) API 4697: Production Tank Emissions Model - A Program For Estimating Emissions From Hydrocarbon
Production Tanks - E&P Tank Version 2.0. American Petroleum Institute. Washington, DC.
API (2003) Basic Petroleum Data Book, 1990-2003. American Petroleum Institute. Washington, DC.
BOEM (2005) Year 2005 Gulfwide Emission Inventory Study. Each GEI study is available online:
.
BOEM (2008) Year 2008 Gulfwide Emission Inventory Study. Each GEI study is available online:
.
BOEM (2011) Year 2011 Gulfwide Emission Inventory Study. Each GEI study is available online:
.
BOEM (2014) Year 2014 Gulfwide Emission Inventory Study. Each GEI study is available online:
.
BOEM (2020a) BOEM Platform Structures Online Query. Available online at:
.
BOEM (2020b) BOEM Oil and Gas Operations Reports - Part A (OGOR-A). Production Data for 1947 to 2019. Download
"Production Data" online at: .
BOEM (2020c) BOEM Oil and Gas Operations Reports - Part A (OGOR-A). Production Data for 1996 to 2019. Available
online at: .
BOEM (2020d) BOEM Oil and Gas Operations Reports - Part B (OGOR-B). Flaring volumes for 1996 to 2019. Available
online at: 
CA/DOC (2019) Annual Oil and Gas Reports for 1990-2018. State Oil and Gas Supervisor, California Department of
Conservation. Available online at:

CAPP (1992) Canadian Association of Petroleum Producers (CAPP), A Detailed Inventory of CH4 and VOC Emissions from
Upstream Oil & Gas Operations in Alberta. March 1992.
Enverus (2021) March 2021 Download. Dl Desktop® Enverus Drillinglnfo, Inc.
EIA (2020a) Monthly Energy Review, 1995-2019 editions. Energy Information Administration, U.S. Department of Energy.
Washington, DC. Available online at: .
EIA (2020b) Petroleum Supply Annual. 2001-2019 editions. U.S Department of Energy Washington, DC. Available online
at: .
EIA (2020c) Refinery Capacity Report, 2005-2019 editions. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available online at: .
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EIA (2020d) 1981-Current: Energy Information Administration estimates published in the Petroleum Supply Annual and
Petroleum Supply Monthly reports. Energy Information Administration, U.S. Department of Energy. Washington, DC.
Available online at: .
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency. Research Triangle Park, NC. October 1997.
EPA (2015a) Inventory of U.S. GHG Emissions and Sinks 1990-2013: Revision to Well Counts Data. Available online at:
.
EPA (2015b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Refineries Emissions Estimate.
Available online at: .
EPA (2016a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas and Petroleum
Production Emissions. Available online at: .
EPA (2017a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015: Revisions to Natural Gas and Petroleum
Production Emissions. Available online at: .
EPA (2017) 2017 Nonpoint Oil and Gas Emission Estimation Tool, Version 1.2. Prepared for U.S. Environmental Protection
Agency by Eastern Research Group, Inc. (ERG). October 2019.
EPA (2018a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Additional Revisions Under
Consideration. Available online at: .
EPA (2018b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to Create Year-Specific
Emissions and Activity Factors. Available online at: .
EPA (2018c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to C02 Emissions Estimation
Methodologies. Available online at: .
EPA (2019a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Other Updates Considered for 2019 and
Future GHGIs. .
EPA (2019b) Greenhouse Gas Reporting Program. U.S. Environmental Protection Agency. Data reported as of August 4,
2019.
EPA (2020a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2018: Update for Offshore Production
Emissions. Available online at: .
EPA (2020b) Greenhouse Gas Reporting Program. U.S. Environmental Protection Agency. Data reported as of September
26, 2020.
EPA (2021) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for Produced Water Emissions.
Available online at: .
EPA/GRI (1996) Methane Emissions from the Natural Gas Industry. Prepared by Harrison, M., T. Shires, J. Wessels, and R.
Cowgill, eds., Radian International LLC for National Risk Management Research Laboratory, Air Pollution Prevention
and Control Division, Research Triangle Park, NC. EPA-600/R-96-080a.
EPA/ICF (1999) Estimates of Methane Emissions from the U.S. Oil Industry (Draft Report). Prepared by ICF International.
Office of Air and Radiation, U.S. Environmental Protection Agency. October 1999.
EPA/Radian (1999) Methane Emissions from the U.S. Petroleum Industry. Prepared by Radian International. U.S.
Environmental Protection Agency. February 1999.
Illinois Office of Oil and Gas Resources Management (2020) State-level petroleum production quantities.
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Indiana Division of Oil & Gas (2020) State-level petroleum production quantities.
Kansas Department of Health and Environment (2020) County-level produced water quantities.
LA/DNR (2020) Production Data. Louisiana Department of Natural Resources. Available online at:

OGJ (2020) Special Report: Pipeline Economics, 2005-2020 Editions. Oil & Gas Journal, PennWell Corporation, Tulsa, OK.
Available online at: .
Ohio Environmental Protection Agency (2020) Well-level produced water quantities.
Oklahoma Department of Environmental Quality (2020) Well-level produced water quantities.
Pennsylvania Department of Environmental Protection (2020) Well-level produced water quantities.
Radian/API (1992) "Global Emissions of Methane from Petroleum Sources." American Petroleum Institute, Health and
Environmental Affairs Department, Report No. DR140, February 1992.
TRC (2020) Oil & Gas Production Data Query. Texas Railroad Commission. Available online at:
.
WCUS (2016) Waterborne Commerce of the United States, Part 5: National Summaries, 2000-2016 Editions. United
States Army Corps of Engineers. Washington, DC, July 20, 2015. Latest edition available online at:
.
West Virginia Department of Environmental Protection (2020) State-level petroleum production quantities.
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3.6. Methodology for Estimating CH4, CO2, and N2O Emissions from Natural Gas
Systems
For details on the emissions, emission factors, activity data, data sources, and methodologies for each year
from 1990 to 2019 please see the spreadsheet file annexes for the current (i.e., 1990 to 2019) Inventory, available at
https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
As described in the main body text on Natural Gas Systems, the Inventory methodology involves the calculation
of CH4, C02, and N20 emissions for over 100 emissions sources, and the summation of emissions for each natural gas
sector stage. The approach for calculating emissions for natural gas systems generally involves the application of
emission factors to activity data. For many sources, the approach uses technology-specific emission factors or emission
factors that vary over time and take into account changes to technologies and practices, which are used to calculate net
emissions directly. For others, the approach uses what are considered "potential methane factors" and reduction data to
calculate net emissions.
Emission Factors
Table 3.6-1, Table 3.6-10, and Table 3.6-14 show CH4, C02, and N20 emissions, respectively, for all sources in
Natural Gas Systems, for all time series years. Table 3.6-2, Table 3.6-12, and Table 3.6-15 show the CH4, C02, and N20
average emission factors, respectively, for all sources in Natural Gas Systems, for all time series years. These emission
factors are calculated by dividing net emissions by activity. Therefore, in a given year, these emission factors reflect the
estimated contribution from controlled and uncontrolled fractions of the source population and any source-specific
reductions (see below section "Reductions Data"); additionally, for sources based on the GRI/EPA study, the values take
into account methane compositions from GTI 2001 adjusted year to year using gross production for National Energy
Modeling System (NEMS) oil and gas supply module regions from the EIA. These adjusted region-specific annual CH4
compositions are presented in Table 3.6-3 (for general sources), Table 3.6-4 (for gas wells without hydraulic fracturing),
and Table 3.6-5 (for gas wells with hydraulic fracturing).
Additional detail on the basis for the CH4, C02, and N20 emission factors used across the time series is provided
in Table 3.6-6, Table 3.6-13, Table 3.6-16, and below.
Key references for emission factors for CH4 and non-combustion-related C02 emissions from the U.S. natural
gas industry include the 1996 Gas Research Institute (GRI) and EPA study (EPA/GRI1996), the Greenhouse Gas Reporting
Program (GHGRP) (EPA 2020c), and others.
The EPA/GRI study developed over 80 CH4 emission factors to characterize emissions from the various
components within the operating stages of the U.S. natural gas system for base year 1992. Since the time of this study,
practices and technologies have changed. This study still represents best available data in many cases—in particular, for
early years of the time series.
Data from studies and EPA's GHGRP (EPA 2020c) allows for emission factors to be calculated that account for
adoption of control technologies and emission reduction practices. For some sources, EPA has developed control
category-specific emission factors from recent data that are used over the time series (paired with control category-
specific activity data that fluctuates to reflect control adoption over time). In other cases, EPA retains emission factors
from the EPA/GRI study for early time series years (1990 to 1992), applies updated emission factors in recent years (e.g.,
2011 forward), and uses interpolation to calculate emission factors for intermediate years. For some sources, EPA
continues to apply the EPA/GRI emission factors for all time series years, and accounts for emission reductions through
data reported to Gas STAR or estimated based on regulations (see below section "Reductions Data"). For many sources
in the exploration and production segments, EPA has used GHGRP data to calculate net emission factors and establish
source type and/or control type subcategories:
• For gas well completions and workovers with hydraulic fracturing, separate emissions estimates were
developed for hydraulically fractured completions and workovers that vent, flared hydraulic fracturing
completions and workovers, hydraulic fracturing completions and workovers with reduced emissions
completions (RECs), and hydraulic fracturing completions and workovers with RECs that flare.
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•	For gas well completions without hydraulic fracturing, separate emissions estimates were developed for
completions that event and completions that flare.
•	For liquids unloading, separate emissions estimates were developed for wells with plunger lifts and wells
without plunger lifts.
•	For condensate tanks, emissions estimates were developed for large and small tanks with flaring or VRU
control, without control devices, and with upstream malfunctioning separator dump valves.
•	For pneumatic controllers, separate estimates are developed for low bleed, high bleed, and intermittent
controllers.
•	Chemical injection pumps estimates are calculated with an emission factor developed with GHGRP data,
which is based on the previous GRI/EPA factor but takes into account operating hours.
For most sources in the processing, transmission and storage, and distribution segments, net emission factors have been
developed for application in recent years of the time series, while the existing emission factors are applied in early time
series years. When a C02-specific emission factor is not available for a source, the C02 emission factors were derived
from the corresponding source CH4 emission factors using default gas composition data. C02 emission factors are
estimated by multiplying the CH4 emission factors by the ratio of the C02-to-CH4 gas content. This approach is applied for
certain sources in the natural gas production, gas processing (only for early time series years), transmission and storage,
and distribution segments. The default gas composition data are specific to segment and are provided in Table 3.6-11.
The default values were derived from EPA/GRI (1996), EIA (1994), and GTI (2001).
N20 emission factors were calculated using GHGRP data. For each flaring emission source calculation
methodology that uses GHGRP data, the source-specific methodology use to estimate C02 was applied to calculate N20
emission factors.
1990-2019 Inventory updates to emission factors
Summary information for emission factors for sources with revisions in this year's Inventory is below. The
details are presented in memoranda,70 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for
Natural Gas Customer Meter Emissions (EPA 2021a) and Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-
2019: Update for Produced Water Emissions (EPA 2021b), as well as the "Recalculations Discussion" section of the main
body text.
In the production segment, EPA updated the produced water emission factors based upon the Production
Module of the 2017 Oil and GasTool (EPA 2017d). EPA also updated the commercial and industrial meters
methodologies to develop leak emission factors based upon data from two GTI studies (GTI 2009 and GTI 2019).
Activity Data
Table 3.6-7 shows the activity data for all sources in Natural Gas Systems, for all time series years. Additional
detail on the basis for activity data used across the time series is provided in Table 3.6-8, and below.
For a few sources, recent direct activity data were not available. For these sources, either 2018 data were used
as proxy for 2019 data or a set of industry activity data drivers was developed and was used to update activity data. Key
drivers include statistics on gas production, number of wells, system throughput, miles of various kinds of pipe, and
other statistics that characterize the changes in the U.S. natural gas system infrastructure and operations.
Methodology for well counts and events
EPA used Drillinglnfo and Prism datasets from Enverus (Enverus 2021), covering U.S. oil and natural gas wells to
populate time series activity data for active gas wells, gas wells drilled, and gas well completions and workovers with
hydraulic fracturing (for 1990 to 2010). EPA queried the Enverus datasets for relevant data on an individual well basis-
including location, natural gas and liquids (i.e., oil and condensate) production by year, drill type (e.g., horizontal or
vertical), and date of completion or first production. Non-associated gas wells were classified as any well that had non-
70 Stakeholder materials including EPA memoranda for the Inventory are available at
.
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zero gas production in a given year, and with a gas-to-oil ratio (GOR) of greater than 100 mcf/bbl in that year. Oil wells
were classified as any well that had non-zero liquids production in a given year, and with a GOR of less than or equal to
100 mcf/bbl in that year. Gas wells with hydraulic fracturing were assumed to be the subset of the non-associated gas
wells that had fracking fluid data within Enverus or were horizontally drilled and/or located in an unconventional
formation (i.e., shale, tight sands, or coalbed). Unconventional formations were identified based on well basin, reservoir,
and field data reported in the Enverus datasets referenced against a formation type crosswalk developed by EIA (EIA
2012a).
For 1990 through 2010, gas well completions with hydraulic fracturing were identified as a subset of the gas
wells with hydraulic fracturing that had a date of completion or first production in the specified year. To calculate
workovers for all time series years, EPA applied a refracture rate of 1 percent (i.e., 1 percent of all wells with hydraulic
fracturing are assumed to be refractured in a given year) to the total counts of wells with hydraulic fracturing from the
Enverus datasets. For 2011 forward, EPA used GHGRP data for the total number of well completions. The GHGRP data
represents a subset of the national completions, due to the reporting threshold, and therefore using this data without
scaling it up to national level results in an underestimate. However, because EPA's GHGRP counts of completions were
higher than national counts of completions (estimated using the Enverus datasets), EPA directly used the GHGRP data to
estimate national activity for years 2011 forward.
EPA calculated the percentage of gas well completions and workovers with hydraulic fracturing in each of the
four control categories using year-specific GHGRP data (applying year 2011 factors to earlier years). EPA assumed no REC
use from 1990 through 2000, used a REC use percentage calculated from GHGRP data for 2011 forward, and then used
linear interpolation between the 2000 and 2011 percentages. For flaring, EPA used an assumption of 10 percent (the
average of the percent of completions and workovers that were flared in 2011 through 2013 GHGRP data) flaring from
1990 through 2010 to recognize that some flaring has occurred over that time period. For 2011 forward, EPA used a
flaring percentage calculated from GHGRP data.
1990-2019 Inventory updates to activity data
Summary information for activity data for sources with revisions in this year's Inventory is below. The details
are presented in memoranda,71 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for Natural
Gas Customer Meter Emissions (EPA 2021a) and Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019:
Update for Produced Water Emissions (EPA 2021b), as well as the "Recalculations Discussion" section of the main body
text.
In the production segment, EPA updated the approach for produced water emissions to use produced water
volumes from all gas wells. The customer meters update in the distribution segment did not warrant a new activity data
approach; commercial and industrial meter counts are already available in the Inventory.
Reductions Data
As described under "Emission Factors" above, some sources in Natural Gas Systems rely on CH4 emission
factors developed from the 1996 EPA/GRI study. Application of these emission factors across the time series represents
potential emissions and does not take into account any use of technologies or practices that reduce emissions. To take
into account use of such technologies for emission sources that use potential factors, data were collected on relevant
voluntary and regulatory reductions.
Voluntary and regulatory emission reductions by segment, for all time series years, are included in Table 3.6-1.
Reductions by emission source, for all time series years, are shown in Table 3.6-9.
Voluntary reductions
Voluntary reductions included in the Inventory were those reported to Gas STAR for activities such as replacing
gas engines with electric compressor drivers and installing automated air-to-fuel ratio controls for engines.
Most Gas STAR reductions in the production segment are not classified as applicable to specific emission
sources. As many sources in production are now calculated with net factor approaches, to address potential double-
71 Stakeholder materials including EPA memoranda for the current (i.e., 1990 to 2017) Inventory are available at
.
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counting of reductions, a scaling factor was applied to the "other voluntary reductions" to reduce this reported amount
based on an estimate of the fraction of those reductions that occur in the sources that are now calculated using net
emissions approaches. This fraction was developed by dividing the net emissions from sources with net approaches, by
the total production segment emissions (without deducting the Gas STAR reductions). The result for 2019, is an adjusted
reductions estimate of 6.1 MMT C02 Eq.
Federal regulations
Regulatory actions reducing emissions in the current Inventory include the New Source Performance Standards
(NSPS) and National Emission Standards for Hazardous Air Pollutants (NESHAP) regulations for dehydrator vents in the
production segment.
The Inventory reflects the New Source Performance Standards (NSPS) for oil and gas through the use of a net
factor approach that captures shifts to lower emitting technologies required by the regulation. Examples include
separating gas well completions and workovers with hydraulic fracturing into four categories and developing control
technology-specific methane emission factors and year-specific activity data for each category; establishing control
category-specific emission factors and associated year-specific activity data for condensate tanks; calculating year-
specific activity data for pneumatic controller bleed categories; and estimating year-specific activity data for wet versus
dry seal centrifugal compressors.
In regards to the oil and natural gas industry, the NESHAP regulation addresses HAPs from the oil and natural
gas production sectors and the natural gas transmission and storage sectors of the industry. Though the regulation deals
specifically with HAPs reductions, methane emissions are also incidentally reduced.
The NESHAP regulation requires that glycol dehydration unit vents that have HAP emissions and exceed a gas
throughput threshold be connected to a closed loop emission control system that reduces emissions by 95 percent. The
emissions reductions achieved as a result of NESHAP regulations for glycol dehydrators in the production segment were
calculated using data provided in the Federal Register Background Information Document (BID) for this regulation. The
BID provides the levels of control measures in place before the enactment of regulation. The emissions reductions were
estimated by analyzing the portion of the industry without control measures already in place that would be impacted by
the regulation.
NESHAP driven reductions from storage tanks and from dehydrators in the processing segment are estimated
with net emission methodologies that take into account controls implemented due to regulations.
Methane, Carbon Dioxide, and Nitrous Oxide Emissions by Emission Source for Each Year
Annual CH4, C02, and N20 emissions for each source were estimated by multiplying the activity data for each
year by the corresponding emission factor. These annual emissions for each activity were then summed to estimate the
total annual CH4, C02, and N20 emissions, respectively. As a final step for CH4 emissions, any relevant reductions data
from each segment is summed for each year and deducted from the total calculated emissions in that segment to
estimate net CH4 emissions for the Inventory. CH4 potential emissions, reductions, and net emissions at a segment level
are shown in Table 3.6-1. C02 emissions by segment and source are summarized in Table 3.6-10. N20 emissions by
segment and source are summarized in Table 3.6-14.
Refer to the 1990-2019 Inventory section at https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
systems for the following data tables, in spreadsheet format:
•	Table 3.6-1: CH4 Emissions (kt) for Natural Gas Systems, by Segment and Source, for All Years
•	Table 3.6-2: Average CH4 Emission Factors (kg/unit activity) for Natural Gas Systems Sources, for All Years
•	Table 3.6-3: U.S. Production Sector CH4 Content in Natural Gas by NEMS Region (General Sources)
•	Table 3.6-4: U.S. Production Sector CH4 Content in Natural Gas by NEMS Region (Gas Wells Without Hydraulic
Fracturing)
•	Table 3.6-5: U.S. Production Sector CH4 Content in Natural Gas by NEMS Region (Gas Wells With Hydraulic
Fracturing)
•	Table 3.6-6: CH4 Emission Factors for Natural Gas Systems, Data Sources/Methodology
•	Table 3.6-7: Activity Data for Natural Gas Systems Sources, for All Years
•	Table 3.6-8: Activity Data for Natural Gas Systems, Data Sources/Methodology
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•	Table 3.6-9: Voluntary and Regulatory CH4 Reductions for Natural Gas Systems (kt)
•	Table 3.6-10: C02 Emissions (kt) for Natural Gas Systems, by Segment and Source, for All Years
•	Table 3.6-11: Default Gas Content by Segment, for All Years
•	Table 3.6-12: Average C02 Emission Factors (kg/unit activity) for Natural Gas Systems Sources, for All Years
•	Table 3.6-13: C02 Emission Factors for Natural Gas Systems, Data Sources/Methodology
•	Table 3.6-14: N20 Emissions (kt) for Natural Gas Systems, by Segment and Source, for All Years
•	Table 3.6-15: Average N20 Emission Factors (kg/unit activity) for Natural Gas Systems Sources, for All Years
•	Table 3.6-16: N20 Emission Factors for Natural Gas Systems, Data Sources/Methodology
•	Annex 3.6-17: Electronic Tables - References
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References
AL/OGB (2020) Alabama Oil and Gas Board. Production Data. Available online at:
.
AOGCC (2020a) List of wells. Alaska Oil and Gas Conservation Commission (AOGCC). Available online at:
.
AOGCC (2020b) Oil and Gas Production. Alaska Oil and Gas Conservation Commission (AOGCC). Available online at:
.
API/ANGA (2012) Characterizing Pivotal Sources of Methane Emissions from Natural Gas Production - Summary and
Analysis of API andANGA Survey Responses. Final Report. American Petroleum Institute and America's Natural Gas
Alliance. September 21.
BOEM (2005) Year 2005 Gulfwide Emission Inventory Study. Each GEI study is available online:
.
BOEM (2008) Year 2008 Gulfwide Emission Inventory Study. Each GEI study is available online:
.
BOEM (2011) Year 2011 Gulfwide Emission Inventory Study. Each GEI study is available online:
.
BOEM (2014) Year 2014 Gulfwide Emission Inventory Study. Each GEI study is available online:
.
BOEM (2020a) BOEM Platform Structures Online Query. Available online at:
.
BOEM (2020b) BOEM Oil and Gas Operations Reports - Part A (OGOR-A). Production Data for 1990 to 2019. Download
"Production Data" online at: .
BOEM (2020c) BOEM Oil and Gas Operations Reports - Part A (OGOR-A). Production Data for 1996 to 2019. Available
online at: .
BOEM (2020d) BOEM Oil and Gas Operations Reports - Part B (OGOR-B). Flaring volumes for 1996 to 2019. Available
online at: .
CA/DOC (2020) Annual Oil and Gas Reports for 1990-2019. State Oil and Gas Supervisor, California Department of
Conservation. Available online at:
.
Clearstone (2011) Clearstone Engineering, Development of Updated Emission Factors for Residential Meters, May 2011.
DOE (2020) LNG Annual Reports, 2004-2019. U.S. Department of Energy, Washington, DC. Available online at:
.
EIA (1994) "Emissions of Greenhouse Gases in the United States: 1987-1992." Energy Information Administration, U.S.
Department of Energy, Washington, DC.
EIA (1996) "Emissions of Greenhouse Gases in the United States: 1987-1994." Energy Information Administration, U.S.
Department of Energy, Washington, DC.
EIA (2004) U.S. LNG Markets and Uses. Energy Information Administration, U.S. Department of Energy, Washington, DC.
June 2004. Available online at: .
EIA (2011) "Monthly Energy Review" Table 5.2, Crude Oil and Natural Gas Resource Development. Energy Information
Administration, U.S. Department of Energy, Washington, DC. Available online at: <
https://www.eia.gov/totalenergy/data/monthly/previous.php>.
EIA (2012) Formation crosswalk. Energy Information Administration, U.S. Department of Energy, Washington, DC.
Provided July 7.
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-------
EIA (2020a) "Natural Gas Gross Withdrawals and Production: Marketed Production." Energy Information Administration,
U.S. Department of Energy, Washington, DC. Available online at: .
EIA (2020b) Lease Condensate Production, Natural Gas Navigator. Energy Information Administration, U.S. Department
of Energy, Washington, DC. Available online at .
EIA (2020c) "Table 1—Summary of natural gas supply and disposition in the United States 2013-2018." Natural Gas
Monthly, Energy Information Administration, U.S. Department of Energy, Washington, DC. Available online at
.
EIA (2020d) "Table 2—Natural Gas Consumption in the United States 2013-2019." Natural Gas Monthly, Energy
Information Administration, U.S. Department of Energy, Washington, DC. Available online at
.
EIA (2020e) "Natural Gas Annual Respondent Query System. Report 191 Field Level Storage Data (Annual)." Energy
Information Administration, U.S. Department of Energy, Washington, DC. Available online at <
https://www.eia.gov/cfapps/ngqs/ngqs.cfm?f_report=RP7>.
EIA (2020f) "U.S. Natural Gas Imports, 2016-2019." Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at .
EIA (2020g) Number of Natural Gas Consumers. Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at: .
EIA (2020h) "Monthly Energy Review" Table A4, Approximate Heat Content of Natural Gas. Energy Information
Administration, U.S. Department of Energy, Washington, DC. Available online at:
.
Enverus Drillinglnfo (2019) March 2019 Download. Dl Desktop'ad Enverus Drillinglnfo, Inc.
Enverus (2021) March 2021 Download. Enverus, Inc.
EPA (2013) Updating GHG Inventory Estimate for Hydraulically Fractured Gas Well Completions and Workovers. Available
online at: .
EPA (2015a) Inventory of U.S. GHG Emissions and Sinks 1990-2013: Revision to Well Counts Data. Available online at:
.
EPA (2015b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Offshore Oil and Gas Platforms
Emissions Estimate. Available online at: .
EPA (2016a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas and Petroleum
Production Emissions. Available online at: .
EPA (2016b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas Gathering and
Boosting Emissions. Available online at: .
EPA (2016c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas Transmission
and Storage Emissions. Available online at: .
EPA (2016d) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas Distribution
Emissions. Available online at: .
EPA (2017a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015: Revisions to Natural Gas and Petroleum
Production Emissions. Available online at: .
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EPA (2017b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015: Revisions to Natural Gas Processing
Emissions. Available online at: .
EPA (2017c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015: Incorporating an Estimate for the Aliso
Canyon Leak. Available online at: .
EPA (2017d) 2017 Nonpoint Oil and Gas Emission Estimation Tool, Version 1.2. Prepared for U.S. Environmental
Protection Agency by Eastern Research Group, Inc. (ERG). October 2019.
EPA (2018a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Additional Revisions Under
Consideration. Available online at: .
EPA (2018b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to Create Year-Specific
Emissions and Activity Factors. Available online at: .
EPA (2018c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to C02 Emissions Estimation
Methodologies. Available online at: .
EPA (2019a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Updates to Natural Gas Gathering &
Boosting Pipeline Emissions. Available online at: .
EPA (2019b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Updates to Liquefied Natural Gas
Segment. Available online at: .
EPA (2019c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Other Updates Considered for 2019 and
Future GHGIs. Available online at: .
EPA (2020a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990- 2018: Update for Offshore Production
Emissions. Available online at: .
EPA (2020b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2018: Update for Natural Gas Gathering &
Boosting Station Emissions. Available online at: .
EPA (2020c) Greenhouse Gas Reporting Program- Subpart W-Petroleum and Natural Gas Systems. Environmental
Protection Agency. Data reported as of September 26, 2020.
EPA (2021a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for Natural Gas Customer Meter
Emissions (Customer Meters memo). Available at: .
EPA (2021b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for Produced Water Emissions
(Produced Water memo). Available at: .
EPA (2021c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for Natural Gas and Petroleum
Systems C02 Uncertainty Estimates. Available at: .
EPA/GRI (1996) Methane Emissions from the Natural Gas Industry. Prepared by Harrison, M., T. Shires, J. Wessels, and R.
Cowgill, eds., Radian International LLC for National Risk Management Research Laboratory, Air Pollution Prevention
and Control Division, Research Triangle Park, NC. EPA-600/R-96-080a.
FERC (2017) North American LNG Terminals. Federal Energy Regulatory Commission, Washington, DC. Available online at:
.
GTI (2001) Gas Resource Database: Unconventional Natural Gas and Gas Composition Databases. Second Edition. GRI-
01/0136.
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GTI (2009) Gas Technology Institute and Innovative Environmental Solutions, Field Measurement Program to Improve
Uncertainties for Key Greenhouse Gas Emission Factors for Distribution Sources, November 2009. GTI Project
Number 20497. OTD Project Number 7.7.b.
GTI (2019) Gas Technology Institute and US Department of Energy, Classification of Methane Emissions from Industrial
Meters, Vintage vs Modern Plastic Pipe, and Plastic-lined Steel and Cast-Iron Pipe. June 2019. GTI Project Number
22070. DOE project Number ED-FE0029061.
ICF (1997) "Additional Changes to Activity Factors for Portions of the Gas Industry."September 18,1997.
ICF (2008) "Natural Gas Model Activity Factor Basis Change."January 7, 2008.
ICF (2010) "Emissions from Centrifugal Compressors." December, 2010.
Illinois Office of Oil and Gas Resources Management (2020) State-level natural gas production quantities.
INDNR (2020) Indiana Department of Natural Resources State-level natural gas production quantities.
KSDHE (2020) Kansas Department of Health and Environment County-level produced water quantities.
LA/DNR (2020) Production Data. Louisiana Department of Natural Resources. Available online at:
.
Lamb, et al. (2015) Direct Measurements Show Decreasing Methane Emissions from Natural Gas Local Distribution
Systems in the United States. Environmental Science & Technology, Vol. 49 5161-5169.
Marchese, et al. (2015) Methane Emissions from United States Natural Gas Gathering and Processing. Environmental
Science and Technology, Vol. 49 10718-10727.
OGJ (1997-2014) "Worldwide Gas Processing." Oil & Gas Journal, PennWell Corporation, Tulsa, OK. Available online at:
.
OHEPA (2020) Ohio Environmental Protection Agency Well-level produced water quantities.
OKDEQ (2020) Oklahoma Department of Environmental Quality Well-level produced water quantities.
PADEP (2020) Pennsylvania Department of Environmental Protection Well-level produced water quantities.
PHMSA (2020a) "Annual Report Mileage for Natural Gas Transmission and Gathering Systems." Pipeline and Hazardous
Materials Safety Administration, U.S. Department of Transportation, Washington, DC. Available online at:
.
PHMSA (2020b) "Annual Report Mileage for Natural Gas Distribution Systems." Pipeline and Hazardous Materials Safety
Administration, U.S. Department of Transportation, Washington, DC. Available online at:
.
PHMSA (2020c) LNG Annual Data, Pipeline and Hazardous Materials Safety Administration (PHMSA), Washington, DC.
Available online at: .
Radian/API (1992) "Global Emissions of Methane from Petroleum Sources." American Petroleum Institute, Health and
Environmental Affairs Department, Report No. DR140, February 1992.
TRC (2020) Oil & Gas Production Data Query. Texas Railroad Commission. Available online at:
.
WVDEP (2020) West Virginia Department of Environmental Protection State-level natural gas production quantities.
Zimmerle, et al. (2015) "Methane Emissions from the Natural Gas Transmission and Storage System in the United
States." Environmental Science and Technology, Vol. 49 9374-9383.
Zimmerle, et al. (2019) "Characterization of Methane Emissions from Gathering Compressor Stations." Available at
 October 2019.
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3.7. Methodology for Estimating CO2, CH4, and N2O Emissions from the
Incineration of Waste
Emissions of C02 from the incineration of waste include C02 generated by the incineration of plastics, synthetic
rubber and synthetic fibers in municipal solid waste (MSW), and the incineration of tires (which are composed in part of
synthetic rubber and C black) in a variety of other combustion facilities (e.g., cement kilns). Incineration of waste also
results in emissions of CH4 and N20. The emission estimates are calculated for all four sources on a mass-basis based on
the data available. The methodology for calculating emissions from each of these waste incineration sources is described
in this Annex.
CO2 from Plastics Incineration
I n the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports
(EPA 1999 through 2003, 2005 through 2014), Advancing Sustainable Materials Management: Facts and Figures -
Assessing Trends in Material Generation, Recycling and Disposal in the United States (EPA 2015; EPA 2016; EPA 2018; EPA
2019; EPA 2020) the flows of plastics in the U.S. waste stream are reported for seven resin categories. For 2019, the
quantity generated, recovered, and discarded for each resin is shown in Table A-116. The data set for 1990 through 2019
is incomplete, and several assumptions were employed to bridge the data gaps. The EPA reports do not provide
estimates for individual materials landfilled and incinerated, although they do provide such an estimate for the waste
stream as a whole. To estimate the quantity of plastics landfilled and incinerated, total discards were apportioned based
on the proportions of landfilling and incineration for the entire U.S. waste stream for each year in the time series
according to Biocyde's State of Garbage in America (van Haaren et al. 2010), and Shin (2014). For the 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-116: 2019 Plastics in the Municipal Solid Waste Stream by Resin (kt)
Waste Pathway
PET
HDPE
PVC
LDPE/
LLDPE
PP
PS
Other
Total
Generation
4,799
5,715
762
7,793
7,394
2,050
3,856
32,369
Recovery
826
508
0
336
45
18
1,007
2,740
Discard
3,974
5,207
762
7,457
7,348
2,032
2,849
29,629
Landfill
3,672
4,812
704
6,890
6,790
1,878
2,632
27,377
Combustion
302
396
58
567
558
154
216
2,252
Recovery3
17%
9%
0%
4%
1%
1%
26%
8%
Discard3
83%
91%
100%
96%
99%
99%
74%
92%
Landfill3
77%
84%
92%
88%
92%
92%
68%
85%
Combustion3
6%
7%
8%
7%
8%
8%
6%
7%
Note: Totals may not sum due to independent rounding. Abbreviations: PET (polyethylene terephthalate), HDPE (high density
polyethylene), PVC (polyvinyl chloride), LDPE/LLDPE (linear low density polyethylene), PP (polypropylene), PS (polystyrene).
3 As a percent of waste generation.
Fossil fuel-based C02 emissions were calculated as the product of plastic combusted, C content, and fraction
oxidized (see Table A-117). The C content of each of the six types of plastics is listed, with the value for "other plastics"
assumed equal to the weighted average of the six categories. The fraction oxidized was assumed to be 98 percent.
Table A-117: 2019 Plastics Incinerated (kt), Carbon Content (%), Fraction Oxidized (%) and Carbon Incinerated
(kt)	
Factor
PET
HDPE
PVC
LDPE/
LLDPE
PP
PS
Other
Total
Quantity Combusted
302
396
58
567
558
154
216
2,252
Carbon Content of Resin
63%
86%
38%
86%
86%
92%
81%
NA
Fraction Oxidized
98%
98%
98%
98%
98%
98%
98%
NA
Carbon in Resin Combusted
185
332
22
476
469
140
173
1,797
Emissions (MMT C02 Eq.)
0.7
1.2
0.1
1.7
1.7
0.5
0.6
6.6
Note: Totals may not sum due to independent rounding.
NA (Not Applicable)
A-244 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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C02 from Incineration of Synthetic Rubber and Carbon Black in Tires
Calculating emissions from tire incineration require two pieces of information: the amount of tires incinerated
and the C content of the tires. "2017 U.S. Scrap Tire Management Summary" (RMA 2018) reports that 1,566.5 thousand
of the 3,303 thousand tons of scrap tires generated in 2017 (approximately 53 percent of generation) were used for fuel
purposes. Using RMA's estimates of average tire composition and weight, the mass of synthetic rubber and C black in
scrap tires was determined:
•	Synthetic rubber in tires was estimated to be 90 percent C by weight, based on the weighted average C
contents of the major elastomers used in new tire consumption.72 Table A-118 shows consumption and C
content of elastomers used for tires and other products in 2002, the most recent year for which data are
available.
•	C black is 100 percent C (Aslett Rubber Inc. n.d.).
Multiplying the mass of scrap tires incinerated by the total C content of the synthetic rubber, C black portions
of scrap tires, and then by a 98 percent oxidation factor, yields C02 emissions, as shown in Table A-119. The disposal rate
of rubber in tires (0.3 MMT C/year) is smaller than the consumption rate for tires based on summing the elastomers
listed in Table A-118 (1.3 MMT/year); this is due to the fact that much of the rubber is lost through tire wear during the
product's lifetime and may also reflect the lag time between consumption and disposal of tires. Tire production and fuel
use for 1990 through 2019 were taken from RMA 2006; RMA 2009; RMA 2011; RMA 2014a; RMA 2016; RMA 2018; RMA
2020. For years where data were not reported, data were linearly interpolated or, for the ends of time series, set equal
to the closest year with reported data.
In 2009, RMA changed the reporting of scrap tire data from millions of tires to thousands of short tons of scrap
tire. As a result, the average weight and percent of the market of light duty and commercial scrap tires was used to
convert the previous years from millions of tires to thousands of short tons (STMC 1990 through 1997; RMA 2002
through RMA 2006; RMA 2014b; RMA 2016; RMA 2018; RMA 2020).
Table A-118: Elastomers Consumed in 2002 (kt)
Elastomer
Consumed
Carbon Content
Carbon Equivalent
Styrene butadiene rubber solid
768
91%
700
For Tires
660
91%
602
For Other Products3
108
91%
98
Polybutadiene
583
89%
518
For Tires
408
89%
363
For Other Products
175
89%
155
Ethylene Propylene
301
86%
258
For Tires
6
86%
5
For Other Products
295
86%
253
Polychloroprene
54
59%
32
For Tires
0
59%
0
For Other Products
54
59%
32
Nitrile butadiene rubber solid
84
77%
65
For Tires
1
77%
1
For Other Products
83
77%
64
Polyisoprene
58
88%
51
For Tires
48
88%
42
For Other Products
10
88%
9
Others
367
88%
323
For Tires
184
88%
161
For Other Products
184
88%
161
Total
2,215
NA
1,950
For Tires
1,307
NA
1,174
Note: Totals may not sum due to independent rounding.
72The carbon content of tires (1,174 kt C) divided by the mass of rubber in tires (1,307 kt) equals 90 percent.
A-245
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NA (Not Applicable)
a Used to calculate C content of non-tire rubber products in municipal solid waste.
Table A-119: Scrap Tire Constituents and CO2 Emissions from Scrap Tire Incineration in 2019

Weight of Material


Emissions (MMT
Material
(MMT)
Fraction Oxidized
Carbon Content
CO2 Eq.)
Synthetic Rubber
0.3
98%
90%
1.2
Carbon Black
0.4
98%
100%
1.5
Total
0.7
NA
NA
2.6
NA (Not Applicable)
CO2 from Incineration of Synthetic Rubber in Municipal Solid Waste
Similar to the methodology for scrap tires, C02 emissions from synthetic rubber in MSW were estimated by
multiplying the amount of rubber incinerated by an average rubber C content. The amount of rubber discarded in the
MSW stream was estimated from generation and recycling data.73 provided in the Municipal Solid Waste Generation,
Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2014),
Advancing Sustainable Materials Management: Facts and Figures: Assessing Trends in Material Generation, Recycling
and Disposal in the United States (EPA 2015; EPA 2016; EPA 2018; EPA 2019; EPA 2020), and unpublished backup data
(Schneider 2007). The reports divide rubber found in MSW into three product categories: other durables (not including
tires), non-durables (which includes clothing and footwear and other non-durables), and containers and packaging. EPA
(2020) did not report rubber found in the product category "containers and packaging;" however, containers and
packaging from miscellaneous material types were reported for 2009 through 2019. As a result, EPA assumes that rubber
containers and packaging are reported under the "miscellaneous" category; and therefore, the quantity reported for
2009 through 2019 were set equal to the quantity reported for 2008. Since there was negligible recovery for these
product types, all the waste generated is considered to be discarded. Similar to the plastics method, discards were
apportioned into landfilling and incineration based on their relative proportions, for each year, for the entire U.S. waste
stream. The report aggregates rubber and leather in the MSW stream; an assumed synthetic rubber content of 70
percent was assigned to each product type, as shown in Table A-120.74 A C content of 85 percent was assigned to
synthetic rubber for all product types (based on the weighted average C content of rubber consumed for non-tire uses),
and a 98 percent fraction oxidized was assumed.
Table A-120: Rubber and Leather in Municipal Solid Waste in 2019

Incinerated
Synthetic
Carbon Content
Fraction
Emissions
Product Type
(kt)
Rubber(%)
(%)
Oxidized (%)
(MMT C02 Eq.)
Durables (not Tires)
262
70%
85%
98%
0.8
Non-Durables
81
NA
NA
NA
0.3
Clothing and Footwear
62
70%
85%
98%
0.2
Other Non-Durables
19
70%
85%
98%
0.1
Containers and Packaging
2
70%
85%
98%
0.0
Total
345
NA
NA
NA
1.1
NA (Not Applicable)
CO2 from Incineration of Synthetic Fibers
Carbon dioxide emissions from synthetic fibers were estimated as the product of the amount of synthetic fiber
discarded annually and the average C content of synthetic fiber. Fiber in the MSW stream was estimated from data
provided in the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports
(EPA 1999 through 2003, 2005 through 2014) and Advancing Sustainable Materials Management: Facts and Figures -
Assessing Trends in Material Generation, Recycling and Disposal in the United States (EPA 2015; EPA 2016; EPA 2018; EPA
2019; EPA 2020) for textiles. Production data for the synthetic fibers was based on data from the American Chemical
Society (FEB 2009). The amount of synthetic fiber in MSW was estimated by subtracting (a) the amount recovered from
(b) the waste generated (see Table A-121). As with the other materials in the MSW stream, discards were apportioned
based on the annually variable proportions of landfilling and incineration for the entire U.S. waste stream, as found in
73	Discards = Generation minus recycling.
74	As a sustainably harvested biogenic material, the incineration of leather is assumed to have no net C02 emissions.
A-246 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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van Haaren et al. (2010), and Shin (2014). 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). The average C content of 72
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) based on 2019 fiber production (see Table A-122). The equation relating C02
emissions to the amount of textiles combusted is shown below.
C02 Emissions from the Incineration of Synthetic Fibers = Annual Textile Incineration (kt) x
(Percent of Total Fiber that is Synthetic) x (Average C Content of Synthetic Fiber) x
(44 g CO2/I2 g C)
Table A-121: Synthetic Textiles in MSW (kt)
Year
Generation
Recovery
Discards
Incineration
1990
2,884
328
2,557
332
1995
3,674
447
3,227
442
1996
3,832
472
3,361
467
1997
4,090
526
3,564
458
1998
4,269
556
3,713
407
1999
4,498
611
3,887
406
2000
4,706
655
4,051
417
2001
4,870
715
4,155
432
2002
5,123
750
4,373
459
2003
5,297
774
4,522
472
2004
5,451
884
4,567
473
2005
5,714
908
4,805
481
2006
5,893
933
4,959
479
2007
6,041
953
5,088
470
2008
6,305
968
5,337
470
2009
6,424
978
5,446
458
2010
6,563
1,018
5,545
444
2011
6,513
1,003
5,510
419
2012
7,198
1,137
6,061
461
2013
7,605
1,181
6,424
488
2014
7,565
1,122
6,444
490
2015
7,973
1,221
6,751
513
2016
8,380
1,246
7,134
542
2017
8,385
1,276
7,109
540
2018
8,454
1,246
7,208
548
2019
8,454
1,246
7,208
548
fable A-122: Synthetic Fiber Production in 2019
Fiber
Production (MMT)
Carbon Content

Polyester

1.3
63%

Nylon

0.5
64%

Olefin

1.1
86%

Acrylic

+
68%

Total

3.0
72%

+ Does not exceed 0.05 MMT.
CH4 and N20 from Incineration of Waste
Estimates of N20 emissions from the incineration of waste in the United States are based on the methodology
outlined in the EPA's Compilation of Air Pollutant Emission Factors (EPA 1995) and presented in the Municipal Solid
Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005
through 2014), Advancing Sustainable Materials Management: Facts and Figures: Assessing Trends in Material
Generation, Recycling and Disposal in the United States (EPA 2015; EPA 2016; EPA 2018; EPA 2019; EPA2020)and
unpublished backup data (Schneider 2007). According to this methodology, emissions of N20 from waste incineration are
the product of the mass of waste incinerated, an emission factor of N20 emitted per unit mass of waste incinerated, and
A-247
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an N20 emissions control removal efficiency. The mass of waste incinerated was derived from the results of the biannual
national survey of MSW Generation and Disposition in the United States, published in BioCycle (van Haaren et al. 2010),
and Shin (2014). No information was available on the mass of waste incinerated for 2012 through 2019, so these values
were assumed to be equal to the 2011 value. An emission factor of 50 g N20/metric ton MSW based on the 2006 IPCC
Guidelines and an estimated emissions control removal efficiency of zero percent were used (IPCC 2006). It was assumed
that all MSW incinerators in the United States use continuously-fed stoker technology (Bahor 2009; ERC 2009).
Estimates of CH4 emissions from the incineration of waste in the United States are based on the methodology
outlined in IPCC's 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). According to this
methodology, emissions of CH4 from waste incineration are the product of the mass of waste incinerated and an
emission factor of CH4 emitted per unit mass of waste incinerated. Similar to the N20 emissions methodology, the mass
of waste incinerated was derived from the information published in BioCycle (van Haaren et al. 2010) and Shin (2014).
No information was available on the mass of waste incinerated for 2012 through 2019, so these values were assumed to
be equal to the 2011 value. An emission factor of 0.20 kg CH4/kt MSW was used based on the 2006 IPCC Guidelines and
assuming that all MSW incinerators in the United States use continuously-fed stoker technology (Bahor 2009; ERC 2009).
A-248 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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References
ArSova, Ljupka, Rob van Haaren, Nora Goldstein, Scott M. Kaufman, and Nickolas J. Themelis (2008) "16th Annual
BioCycle Nationwide Survey: The State of Garbage in America" Biocycle, JG Press, Emmaus, PA. December.
Bahor, B (2009) Covanta Energy's public review comments re: Draft Inventory of U.S. Greenhouse Gas Emissions and
Sinks: 1990-2007. Submitted via email on April 9, 2009 to Leif Hockstad, U.S. EPA.
De Soete, G.G. (1993) "Nitrous Oxide from Combustion and Industry: Chemistry, Emissions and Control." In A. R. Van
Amstel, (ed) Proc. of the International Workshop Methane and Nitrous Oxide: Methods in National Emission
Inventories and Options for Control, Amersfoort, NL. February 3-5,1993.
DeZan, D. (2000) Personal Communication between Diane DeZan, Fiber Economics Bureau and Joe Casola, ICF
Consulting. 4 August 2000.
Energy Recovery Council (2009) "2007 Directory of Waste-to-Energy Plants in the United States." Accessed September
29, 2009.
EPA (2020) Advancing Sustainable Materials Management: 2018 Data Tables. Office of Land and Emergency
Management, U.S. Environmental Protection Agency. Washington, D.C. Available online at:
.
EPA (2019) Advancing Sustainable Materials Management: 2016 and 2017 Data Tables. Office of Land and Emergency
Management, U.S. Environmental Protection Agency. Washington, D.C. Available online at:
.
EPA (2018) Advancing Sustainable Materials Management: 2015 Data Tables. Office of Land and Emergency
Management, U.S. Environmental Protection Agency. Washington, D.C. Available online at:
.
EPA (2016) Advancing Sustainable Materials Management: 2014 Fact Sheet - Assessing Trends in Material Generation,
Recycling and Disposal in the United States. Office of Land and Emergency Management, U.S. Environmental
Protection Agency. Washington, D.C. Available online at: .
EPA (2015) Advancing Sustainable Materials Management: Facts and Figures 2013 - AssessingTrends in Material
Generation, Recycling and Disposal in the United States. Office of Solid Waste and Emergency Response, U.S.
Environmental Protection Agency. Washington, D.C. Available online at
.
EPA (1999 through 2003, 2005 through 2014Municipal Solid Waste in the United States: Facts and Figures. Office of Solid
Waste and Emergency Response, U.S. Environmental Protection Agency. Washington, D.C. Available online at
.
EPA (2006) Solid Waste Management and Greenhouse Gases: A Life-Cycle Assessment of Emissions and Sinks. Office of
Solid Waste and Emergency Response, U.S. Environmental Protection Agency. Washington, D.C.
EPA (2000) Characterization of Municipal Solid Waste in the United States: Source Data on the 1999 Update. Office of
Solid Waste, U.S. Environmental Protection Agency. Washington, D.C. EPA530-F-00-024.
EPA (1995) AP 42, Fifth Edition Compilation of Air Pollutant Emission Factors. Office of Air Quality Planning and
Standards, Office of Air and Radiation. U.S. Environmental Protection Agency. Washington, D.C. Available online at:
.
FEB (2009) Fiber Economics Bureau, as cited in C&EN (2009) Chemical Output Slipped In Most Regions Chemical &
Engineering News, American Chemical Society, 6 July. Available online at .
Goldstein, N. and C. Madtes (2001) 13th Annual BioCycle Nationwide Survey: The State of Garbage in America. BioCycle,
JG Press, Emmaus, PA. December 2001.
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IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
Kaufman, et al. (2004) "14th Annual BioCycle Nationwide Survey: The State of Garbage in America 2004" Biocycle, JG
Press, Emmaus, PA. January, 2004.RMA (2018) 2017 U.S. Scrap Tire Management Summary. Rubber Manufacturers
Association, Washington, D.C. July 2018.
<://www.ustires.org/system/files/USTMA_scraptire_summ_2017_072018.pdf> September 27, 2018.
RMA (2020) "2019 U.S. Scrap Tire Management Summary". Rubber Manufacturers Association, Washington, D.C.
October 2020. Available online at:
.
RMA (2018) "2017 U.S. Scrap Tire Management Summary". Rubber Manufacturers Association, Washington, D.C. July
2018. Available online at: .
RMA (2016) "2015 U.S. Scrap Tire Management Summary." Rubber Manufacturers Association. August 2016. Available
online at: < https://www.ustires.org/sites/default/files/MAR_028_USTMA.pdf>.
RMA (2014a) "2013 U.S. Scrap Tire Management Summary." Rubber Manufacturers Association. November 2014.
Available online at: . Accessed
17 November 2014.
RMA (2014b) "Scrap Tire Markets: Facts and Figures - Scrap Tire Characteristics." Available online at:
. Accessed 17 November 2014.
RMA (2012) "Rubber FAQs." Rubber Manufacturers Association. Available online at . Accessed 19 November 2014.
RMA (2011) "U.S. Scrap Tire Management Summary 2005-2009." Rubber Manufacturers Association. October 2011.
Available online at: .
RMA (2009) "Scrap Tire Markets in the United States: 9th Biennial Report." Rubber Manufacturers Association.
Washington, D.C. May 2009.
RMA (2002 through 2006) "U.S. Scrap Tire Markets." Rubber Manufacturers Association. Washington, D.C. Available
online at: .
Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah Shapiro of ICF
International, January 10, 2007.
Shin, D. (2014) Generation and Disposition of Municipal Solid Waste (MSW) in the United States-A National Survey.
Thesis. Columbia University, Department of Earth and Environmental Engineering, January 3, 2014.
Simmons, et al. (2006) "15th Nationwide Survey of Municipal Solid Waste Management in the United States: The State of
Garbage in America" BioCycle, JG Press, Emmaus, PA. April 2006.
STMC (1990 through 1997) "Scrap Tire Use/Disposal Study". Rubber Manufacturers Association: Scrap Tire Management
Council. Available online at: .
Themelis and Shin (2014) U.S. Survey of Generation and Disposition of Municipal Solid Waste. Waste Management.
Columbia University. January 2014. .
van Haaren, Rob, Thermelis, N., and Goldstein, N. (2010) "The State of Garbage in America." BioCycle, October 2010.
Volume 51, Number 10, pg. 16-23.
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3.8. Methodology for Estimating Emissions from International Bunker Fuels used
by the U.S. Military
Bunker fuel emissions estimates for the Department of Defense (DoD) were developed using data generated by
the Defense Logistics Agency Energy (DLA Energy) for aviation and naval fuels. DLA Energy prepared a special report
based on data in the Fuels Automated System (FAS) for calendar year 2019fuel sales in the Continental United States
(CONUS).75 The following steps outline the methodology used for estimating emissions from international bunker fuels
used by the U.S. Military.
Step 1: Omit Extra-Territorial Fuel Deliveries
Beginning with the complete FAS data set for each year, the first step in quantifying DoD-related emissions
from international bunker fuels was to identify data that would be representative of international bunker fuel
consumption as defined by decisions of the UNFCCC (i.e., fuel sold to a vessel, aircraft, or installation within the United
States or its territories and used in international maritime or aviation transport). Therefore, fuel data were categorized
by the location of fuel delivery in order to identify and omit all international fuel transactions/deliveries (i.e., sales
abroad).
Step 2: Allocate Jet Fuel between Aviation and Land-based Vehicles
As a result of DoD76 and NATO77 policies on implementing the Single Fuel For the Battlefield concept, DoD
activities have been increasingly replacing diesel fuel with jet fuel in compression ignition and turbine engines of land-
based equipment. Based on this concept and examination of all data describing jet fuel used in land-based vehicles, it
was determined that a portion of jet fuel consumption should be attributed to ground vehicle use. Based on available
Military Service data and expert judgment, a small fraction of jet fuel use (i.e., between 1.78 and 2.7 times the quantity
of diesel fuel used, depending on the Service) was reallocated from the aviation subtotal to a new land-based jet fuel
category for 1997 and subsequent years. As a result of this reallocation, the jet fuel use reported for aviation was
reduced and the fuel use for land-based equipment increased. DoD's total fuel use did not change. DoD has been
undergoing a transition from JP-8 jet fuel to commercial specification Jet A fuel with additives (JAA) for non-naval
aviation and ground assets. To account for this transition jet fuel used for ground-based vehicles was reallocated from
JP8 prior to 2014 and from JAA in 2014 and subsequent years. The transition was completed in 2016.
Table A-123 displays DoD's consumption of transportation fuels, summarized by fuel type, that remain at the
completion of Step 1, and reflects the adjustments for jet fuel used in land-based equipment, as described above.
Step 3: Omit Land-Based Fuels
Navy and Air Force land-based fuels (i.e., fuel not used by ships or aircraft) were omitted for the purpose of
calculating international bunker fuels. The remaining fuels, listed below, were considered potential DoD international
bunker fuels.
•	Aviation: jet fuels (JP8, JP5, JP4, JAA, JA1, and JAB).
•	Marine: naval distillate fuel (F76), marine gas oil (MGO), and intermediate fuel oil (IFO).
75	FAS contains data for 1995 through 2019, but the dataset was not complete for years prior to 1995. Using DLA aviation and
marine fuel procurement data, fuel quantities from 1990 to 1994 were estimated based on a back-calculation of the 1995 data
in the legacy database, the Defense Fuels Automated Management System (DFAMS). The back-calculation was refined in 1999
to better account for the jet fuel conversion from JP4 to JP8 that occurred within DoD between 1992 and 1995.
76	DoD Directive 4140.25-M-V1, Fuel Standardization and Cataloging, 2013; DoD Instruction 4140.25, DoD Management Policy
for Energy Commodities and Related Services, 2015.
77	NATO Standard Agreement NATO STANAG 4362, Fuels for Future Ground Equipment Using Compression Ignition or Turbine
Engines, 2012.
A-251
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Step 4: Omit Fuel Transactions Received by Military Services that are not considered to be International
Bunker Fuels
Only Navy and Air Force were deemed to be users of military international bunker fuels after sorting the data
by Military Service and applying the following assumptions regarding fuel use by Service.
•	Only fuel delivered to a ship, aircraft, or installation in the United States was considered a potential
international bunker fuel. Fuel consumed in international aviation or marine transport was included
in the bunker fuel estimate of the country where the ship or aircraft was fueled. Fuel consumed
entirely within a country's borders was not considered a bunker fuel.
•	Based on previous discussions with the Army staff, only an extremely small percentage of Army
aviation emissions, and none of Army watercraft emissions, qualified as bunker fuel emissions. The
magnitude of these emissions was judged to be insignificant when compared to Air Force and Navy
emissions. Based on this research, Army bunker fuel emissions were assumed to be zero.
•	Marine Corps aircraft operating while embarked consumed fuel that was reported as delivered to the
Navy. Bunker fuel emissions from embarked Marine Corps aircraft were reported in the Navy bunker
fuel estimates. Bunker fuel emissions from other Marine Corps operations and training were assumed
to be zero.
•	Bunker fuel emissions from other DoD and non-DoD activities (i.e., other federal agencies) that
purchased fuel from DLA Energy were assumed to be zero.
Step 5: Determine Bunker Fuel Percentages
It was necessary to determine what percent of the aviation and marine fuels were used as international bunker
fuels. Military aviation bunkers include international operations (i.e., sorties that originate in the United States and end
in a foreign country), operations conducted from naval vessels at sea, and operations conducted from U.S. installations
principally over international water in direct support of military operations at sea (e.g., anti-submarine warfare flights).
Methods for quantifying aviation and marine bunker fuel percentages are described below.
•	Aviation: The Air Force Aviation bunker fuel percentage was determined to be 13.2 percent. A bunker
fuel weighted average was calculated based on flying hours by major command. International flights
were weighted by an adjustment factor to reflect the fact that they typically last longer than domestic
flights. In addition, a fuel use correction factor was used to account for the fact that transport aircraft
burn more fuel per hour of flight than most tactical aircraft. This percentage was multiplied by total
annual Air Force aviation fuel delivered for U.S. activities, producing an estimate for international
bunker fuel consumed by the Air Force.
The Naval Aviation bunker fuel percentage was calculated to be 40.4 percent by using flying hour data
from Chief of Naval Operations Flying Hour Projection System Budget for fiscal year 1998 and
estimates of bunker fuel percent of flights provided by the fleet. This Naval Aviation bunker fuel
percentage was then multiplied by total annual Navy aviation fuel delivered for U.S. activities, yielding
total Navy aviation bunker fuel consumed.
•	Marine: For marine bunkers, fuels consumed while ships were underway were assumed to be bunker
fuels. The Navy maritime bunker fuel percentage was determined to be 79 percent because the Navy
reported that 79 percent of vessel operations were underway, while the remaining 21 percent of
operations occurred in port (i.e., pierside) in the year 2000.78
Table A-124 and Table A-125 display DoD bunker fuel use totals for the Navy and Air Force.
78 Note that 79 percent is used because it is based on Navy data, but the percentage of time underway may vary from year-to-
year depending on vessel operations. For example, for years prior to 2000, the bunker fuel percentage was 87 percent.
A-252 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Step 6: Calculate Emissions from International Bunker Fuels
Bunker fuel totals were multiplied by appropriate emission factors to determine greenhouse gas (GHG)
emissions. C02 emissions from Aviation Bunkers and distillate Marine Bunkers are the total of military aviation and
marine bunker fuels, respectively.
The rows labeled "U.S. Military" and "U.S. Military Naval Fuels" in the tables in the International Bunker Fuels
section of the Energy chapter were based on the totals provided in Table A-124 and Table A-125, below. C02 emissions
from aviation bunkers and distillate marine bunkers are presented in Table A-129, and are based on emissions from fuels
tallied in Table A-124 and Table A-125.
A-253
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Table A-123: Transportation Fuels from Domestic Fuel Deliveries3 (Million Gallons)
Vehicle Type/Fuel
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Aviation
4,598.4
3,099.9
2,664.4
2,338.1
1,663.9
1,405.0
1,449.7
1,336.4
1,679.5
1,663.7
1,558.0
1,537.7
1,482.2
1,487.6
Total Jet Fuels
4,598.4
3,099.9
2,664.4
2,338.0
1,663.7
1,404.8
1,449.5
1,336.2
1,679.2
1,663.5
1,557.7
1,537.5
1,481.9
1,487.4
JP8
285.7
2,182.8
2,122.7
1,838.8
1,100.1
882.8
865.2
718.0
546.6
126.6
(9.5)
(11.4)
1.9
4.7
JPB
1,025.4
691.2
472.1
421.6
399.3
372.3
362.5
316.4
311.0
316.4
320.4
316.3
304.1
314.4
Other Jet Fuels
3,287.3
225.9
69.6
77.6
164.3
149.7
221.8
301.7
821.6
1,220.5
1,246.9
1,232.7
1,175.9
1,168.2
Aviation Gasoline
+
+
+
0.1
0.2
0.2
0.3
0.2
0.3
0.3
0.3
0.2
0.3
0.2
Marine
686.8
438.9
454.4
604.9
578.8
489.9
490.4
390.4
427.9
421.7
412.4
395.2
370.9
365.4
Middle Distillate














(MGO)
0.0
0.0
48.3
54.0
48.4
37.3
52.9
40.9
62.0
56.0
23.1
24.4
19.9
23.2
Naval Distillate (F76)
686.8
438.9
398.0
525.9
513.7
440.0
428.4
345.7
362.7
363.3
389.1
370.8
351.0
342.2
Intermediate Fuel Oil














(IFO)b
0.0
0.0
8.1
25.0
16.7
12.5
9.1
3.8
3.2
2.4
0.1
0.0
0.0
0.0
Other0
717.1
310.9
248.2
205.6
224.0
208.6
193.8
180.6
190.7
181.1
178.3
165.8
170.4
161.4
Diesel
93.0
119.9
126.6
56.8
64.1
60.9
57.9
54.9
57.5
54.8
54.7
50.4
51.8
48.7
Gasoline
624.1
191.1
74.8
24.3
25.5
22.0
19.6
16.9
16.5
16.2
15.9
15.6
14.7
14.9
Jet Fueld
0.0
0.0
46.7
124.4
134.4
125.6
116.2
108.8
116.7
110.1
107.6
99.9
104.0
97.7
Total (Including
Bunkers)
6,002.4
3,849.8
3,367.0
3,148.6
2,466.7
2,103.5
2,133.9
1,907.5
2,298.2
2,266.5
2,148.7
2,098.7
2,023.4
2,014.3
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values. The negative values in this table represent returned products.
+ Indicates value does not exceed 0.05 million gallons.
a Includes fuel distributed in the United States and U.S. Territories.
b Intermediate fuel oil (IFO 180 and IFO 380) is a blend of distillate and residual fuels. IFO is used by the Military Sealift Command.
c Prior to 2001, gasoline and diesel fuel totals were estimated using data provided by the Military Services for 1990 and 1996. The 1991 through 1995 data points were
interpolated from the Service inventory data. The 1997 through 1999 gasoline and diesel fuel data were initially extrapolated from the 1996 inventory data. Growth factors
used for other diesel and gasoline were 5.2 and -21.1 percent, respectively. However, prior diesel fuel estimates from 1997 through 2000 were reduced according to the
estimated consumption of jet fuel that is assumed to have replaced the diesel fuel consumption in land-based vehicles. Datasets for other diesel and gasoline consumed by the
military in 2000 were estimated based on ground fuels consumption trends. This method produced a result that was more consistent with expected consumption for 2000.
Since 2001, other gasoline and diesel fuel totals were generated by DLA Energy.
d The fraction of jet fuel consumed in land-based vehicles was estimated based on DLA Energy data as well as Military Service and expert judgment.
A-254 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Table A-124: Total U.S. Military Aviation Bunker Fuel (Million Gallons)
Fuel Type/Service
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Jet Fuels














JP8
56.7
300.4
307.6
285.6
182.5
143.4
141.2
122.0
88.0
17.2
2.4
2.5
2.9
1.2
Navy
56.7
38.3
53.4
70.9
60.8
47.1
50.4
48.9
31.2
0.8
5.5
6.4
4.8
2.5
Air Force
+
262.2
254.2
214.7
121.7
96.2
90.8
73.0
56.7
16.4
(3.1)
(3.9)
(1.9)
(1.3)
JP5
370.5
249.8
160.3
160.6
152.5
144.9
141.2
124.9
121.9
124.1
126.1
124.7
120.1
123.9
Navy
365.3
246.3
155.6
156.9
149.7
143.0
139.5
123.6
120.2
122.6
124.7
123.4
118.9
122.5
Air Force
5.3
3.5
4.7
3.7
2.8
1.8
1.7
1.3
1.6
1.5
1.4
1.3
1.2
1.4
JP4
420.8
21.5
+
+
0.1
0.0
0.0
+
0.0
0.0
0.0
0.0
0.0
0.0
Navy
+
+
0.0
+
+
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Air Force
420.8
21.5
+
+
0.1
0.0
0.0
+
0.0
0.0
0.0
0.0
0.0
0.0
JAA
13.7
9.2
12.5
15.5
31.4
31.1
38.6
46.5
128.0
199.8
203.7
198.9
191.8
192.5
Navy
8.5
5.7
7.9
11.6
13.7
14.6
14.8
13.4
36.1
71.7
72.9
67.8
68.1
71.2
Air Force
5.3
3.5
4.5
3.9
17.7
16.5
23.8
33.1
91.9
128.1
130.8
131.1
123.7
121.4
JA1
+
+
+
0.5
0.3
(0.5)
(0.3)
0.6
1.1
0.3
0.5
0.2
0.5
0.3
Navy
+
+
+
+
0.1
(0.5)
(0.3)
0.6
0.7
+
0.1
(+)
(+)
(+)
Air Force
+
+
+
0.5
0.1
(0.1)
(+)
+
0.5
0.3
0.5
0.2
0.5
0.3
JAB
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
Navy
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
Air Force
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
Navy Subtotal
430.5
290.2
216.9
239.4
224.4
204.3
204.5
186.5
188.2
195.0
203.2
197.5
191.8
196.1
Air Force Subtotal
431.3
290.7
263.5
222.9
142.4
114.5
116.3
107.4
150.7
146.4
129.5
128.8
123.5
121.8
Total
861.8
580.9
480.4
462.3
366.7
318.8
320.8
293.9
339.0
341.4
332.8
326.3
315.3
317.9
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values. The negative values in this table represent returned products.
+ Does not exceed 0.05 million gallons.
NO (Not Occurring)
A-255
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Table A-125: Total U.S. DoD Maritime Bunker Fuel (Million Gallons)
Marine
Distillates
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Navy - MGO
0.0
0.0
23.8
38.0
32.9
25.5
36.5
32.3
43.3
37.8
5.7
13.2
8.5
10.6
Navy - F76
522.4
333.8
298.6
413.1
402.2
346.6
337.9
273.1
286.2
286.7
307.8
293.3
276.9
270.0
Navy - IFO
0.0
0.0
6.4
19.7
12.9
9.5
6.1
3.0
1.5
1.9
+
0.0
0.0
0.0
Total
522.4
333.8
328.8
470.7
448.0
381.5
380.6
308.5
331.0
326.3
313.6
306.5
285.4
280.6
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 million gallons.
Table A-126: Aviation and Marine Carbon Contents (MMT Carbon/QBtu) and Fraction Oxidized

Carbon Content
Fraction
Mode (Fuel)
Coefficient
Oxidized
Aviation (Jet Fuel)
Variable
1.00
Marine (Distillate)
Variable
1.00
Marine (Residual)
20.48
1.00
Source: EPA (2010) and IPCC (2006).
Table A-127: Annual Variable Carbon Content Coefficient for Jet Fuel (MMT Carbon/QBtu)
Fuel 1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016
2017
2018
2019
Jet Fuel 19.40 , 19.34 19.70 J 19.70 J 19.70 19.70 19.70 19.70 19.70 19.70 19.70
19.70
19.70
19.70
Source: EPA (2010).



Table A-128: Annual Variable Carbon Content Coefficient for Distillate Fuel Oil (MMT Carbon/QBtu)



Fuel 1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016
2017
2018
2019
Distillate Fuel Oil 20.17 20.17 20.39 J 20.37 J 20.24 20.22 20.22 20.23 20.23 20.22 20.21
20.20
20.22
20.22
Source: EPA (2020).
Table A-129: Total U.S. DoD CO2 Emissions from Bunker Fuels (MMT CO2 Eq.)
Mode
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Aviation
8.1
5.5
4.7
4.5
3.6
3.1
3.1
2.9
3.3
3.3
3.3
3.2
3.1
3.1
Marine
5.4
3.4
3.4
4.9
4.6
3.9
3.9
3.2
3.4
3.4
3.2
3.1
2.9
2.9
Total
13.4
9.0
8.1
9.4
8.2
7.0
7.0
6.0
6.7
6.7
6.5
6.3
6.0
6.0
Note: Totals may not sum due to independent rounding.
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References
DLA Energy (2020) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense Energy
Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, D.C.
EPA (2010) Carbon Content Coefficients Developed for EPA's Inventory of Greenhouse Gases and Sinks. Office of Air and
Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
EPA (2020) EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Updated Gasoline and Diesel Fuel C02
Emission Factors - Memo.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
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3.9. Methodology for Estimating HFC and PFC Emissions from Substitution of
Ozone Depleting Substances
Emissions of HFCs and PFCs from the substitution of ozone depleting substances (ODS) are developed using a
country-specific modeling approach. The Vintaging Model79 was developed as a tool for estimating the annual chemical
emissions from industrial sectors that have historically used ODS in their products. Under the terms of the Montreal
Protocol and the United States Clean Air Act Amendments of 1990, the domestic U.S. consumption of ODS—
chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs)—
has been drastically reduced, forcing these industrial sectors to transition to more ozone friendly chemicals. As these
industries have moved toward ODS alternatives such as hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs), the
Vintaging Model has evolved into a tool for estimating the rise in consumption and emissions of these alternatives, and
the decline of ODS consumption and emissions.
The Vintaging Model estimates emissions from five ODS substitute (i.e., HFC-emitting) end-use sectors:
refrigeration and air-conditioning, foams, aerosols, solvents, and fire-extinguishing. Within these sectors, there are 78
independently modeled end-uses. The model requires information on the market growth for each of the end-uses, a
history of the market transition from ODS to alternatives, and the characteristics of each end-use such as market size or
charge sizes and loss rates. As ODS are phased out, a percentage of the market share originally filled by the ODS is
allocated to each of its substitutes.
The model, named for its method of tracking the emissions of annual "vintages" of new equipment that enter
into service, is a "bottom-up" model. It models the consumption of chemicals based on estimates of the quantity of
equipment or products sold, serviced, and retired each year, and the amount of the chemical required to manufacture
and/or maintain the equipment. The Vintaging Model makes use of this market information to build an inventory of the
in-use stocks of the equipment and ODS and ODS substitute in each of the end-uses. The simulation is considered to be a
"business-as-usual" baseline case and does not incorporate measures to reduce or eliminate the emissions of these
gases other than those regulated by U.S. law or otherwise common in the industry. Emissions are estimated by applying
annual leak rates, service emission rates, and disposal emission rates to each population of equipment. By aggregating
the emission and consumption output from the different end-uses, the model produces estimates of total annual use
and emissions of each chemical.
The Vintaging Model synthesizes data from a variety of sources, including data from the ODS Tracking System
maintained by the Stratospheric Protection Division, the Greenhouse Gas Reporting Program maintained by the Climate
Change Division, and information from submissions to EPA under the Significant New Alternatives Policy (SNAP)
program. Published sources include documents prepared by the United Nations Environment Programme (UNEP)
Technical Options Committees, reports from the Alternative Fluorocarbons Environmental Acceptability Study (AFEAS),
and conference proceedings from the International Conferences on Ozone Protection Technologies and Earth
Technologies Forums. EPA also coordinates extensively with numerous trade associations and individual companies. For
example, the Alliance for Responsible Atmospheric Policy; the Air-Conditioning, Heating and Refrigeration Institute; the
Association of Home Appliance Manufacturers; the American Automobile Manufacturers Association; and many of their
member companies have provided valuable information over the years.
In some instances, the unpublished information that the EPA uses in the model is classified as Confidential
Business Information (CBI). The annual emissions inventories of chemicals are aggregated in such a way that CBI cannot
be inferred. Full public disclosure of the inputs to the Vintaging Model would jeopardize the security of the CBI that has
been entrusted to the EPA. In addition, emissions of certain gases (including HFC-152a, HFC-227ea, HFC-245fa, HFC
365mfc, HFC-43-10mee, HCFO-1233zd(E), HFO-1234yf, HFO-1234ze(E), HFO-1336mzz(Z), C4Fi0, and PFC/PFPEs, the latter
being a proxy for a diverse collection of PFCs and perfluoropolyethers (PFPEs) employed for solvent applications) are
marked as confidential because they are produced or imported by a small number of chemical providers and in such
small quantities or for such discrete applications that reporting national data would effectively be reporting the chemical
provider's output, which is considered confidential business information. These gases are modeled individually in the
Vintaging Model, but are aggregated and reported as an unspecified mix of HFCs and PFCs.
79 Vintaging Model version VM 10 file_v5.1_3.17.21 was used for all Inventory estimates.
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The Vintaging Model is regularly updated to incorporate up-to-date market information, including equipment
stock estimates, leak rates, and sector transitions. In addition, comparisons against published emission and consumption
sources are performed when available. Independent peer reviews of the Vintaging Model are periodically performed,
including one conducted in 2017 (EPA, 2018), to confirm Vintaging Model estimates and identify updates.
The following sections discuss the emission equations used in the Vintaging Model for each broad end-use
category. These equations are applied separately for each chemical used within each of the different end-uses. In the
majority of these end-uses, more than one ODS substitute chemical is used.
In general, the modeled emissions are a function of the amount of chemical consumed in each end-use market.
Estimates of the consumption of ODS alternatives can be inferred by determining the transition path of each regulated
ODS used in the early 1990s. Using data gleaned from a variety of sources, assessments are made regarding which
alternatives have been used, and what fraction of the ODS market in each end-use has been captured by a given
alternative. By combining this with estimates of the total end-use market growth, a consumption value can be estimated
for each chemical used within each end-use.
Methodology
The Vintaging Model estimates the use and emissions of ODS alternatives by taking the following steps:
1.	Gather historical data. The Vintaging Model is populated with information on each end-use, taken from
published sources and industry experts.
2.	Simulate the implementation of new, non-ODS technologies. The Vintaging Model uses detailed
characterizations of the existing uses of the ODS, as well as data on how the substitutes are replacing the ODS, to
simulate the implementation of new technologies that enter the market in compliance with ODS phase-out policies. As
part of this simulation, the ODS substitutes are introduced in each of the end-uses over time as seen historically and as
needed to comply with the ODS phase-out and other regulations.
3.	Estimate emissions of the ODS substitutes. The chemical use is estimated from the amount of substitutes that
are required each year for the manufacture, installation, use, or servicing of products. The emissions are estimated from
the emission profile for each vintage of equipment or product in each end-use. By aggregating the emissions from each
vintage, a time profile of emissions from each end-use is developed.
Each set of end-uses is discussed in more detail in the following sections.
Refrigeration and Air-Conditioning
For refrigeration and air conditioning products, emission calculations are split into three categories: emissions
at first-fill, which arise during manufacture or installation, emissions during equipment lifetime, which arise from annual
leakage and service losses, and disposal emissions, which occur at the time of discard. This methodology is consistent to
the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories, where
the total refrigerant emissions from Ref/AC equipment is the sum of first-fill emissions, annual operational and servicing
emissions, and disposal emissions under the Tier 2a emission factor approach (IPCC 2006). Three separate steps are
required to calculate the lifetime emissions from installation, leakage and service, and the emissions resulting from
disposal of the equipment. The model assumes that equipment is serviced annually so that the amount equivalent to
average annual emissions for each product (and hence for the total of what was added to the bank in a previous year in
equipment that has not yet reached end-of-life) is replaced/applied to the starting charge size (or chemical bank). For
any given year, these first-fill emissions (for new equipment), lifetime emissions (for existing equipment), and disposal
emissions (from discarded equipment) are summed to calculate the total emissions from refrigeration and air-
conditioning. As new technologies replace older ones, it is generally assumed that there are improvements in their leak,
service, and disposal emission rates.
At disposal, refrigerant that is recovered from discarded equipment is assumed to be reused to the extent
necessary in the following calendar year. The Vintaging Model does not make any explicit assumption whether recovered
refrigerant is reused as-is (allowed under U.S. regulations if the refrigerant is reused in the same owner's equipment),
recycled (commonly practiced even when re-used directly), or reclaimed (brought to new refrigerant purity standards
and available to be sold on the open market).
A-259
-------
Step 1: Calculate first-fill emissions
The first-fill emission equation assumes that a certain percentage of the chemical charge will be emitted to the
atmosphere when the equipment is charged with refrigerant during manufacture or installation. First-fill emissions are
considered for all Ref/AC equipment that are charged with refrigerant within the United States, including those which
are produced for export, and excluding those that are imported pre-charged. First-fill emissions are thus a function of the
quantity of chemical contained in new equipment and the proportion of equipment that are filled with refrigerant in the
United States:
Efj = Qq x |f x Aj
where:
Ef
Qc
h
Emissions from Equipment First-fill. Emissions in year j from filling new equipment.
Quantity of Chemical in New Equipment. Total amount of a specific chemical used
to charge new equipment in year j, by weight.
First-fill Leak Rate. Average leak rate during installation or manufacture of new
equipment (expressed as a percentage of total chemical charge).
Applicability of First-fill Leak Rate. Percentage of new equipment that are filled with
refrigerant in the United States in year j.
Year of emission.
Step 2: Calculate lifetime emissions
Emissions from any piece of equipment include both the amount of chemical leaked during equipment
operation and the amount emitted during service. Emissions from leakage and servicing can be expressed as follows:
where:
ESj = (la + ls)xl QcH+1 fori= l^k
Es =	Emissions from Equipment Serviced. Emissions in year j from normal leakage and
servicing (including recharging) of equipment.
Ia	=	Annual Leak Rate. Average annual leak rate during normal equipment operation
(expressed as a percentage of total chemical charge).
Is	=	Service Leak Rate. Average leakage during equipment servicing (expressed as a
percentage of total chemical charge).
Qc =	Quantity of Chemical in New Equipment. Total amount of a specific chemical used
to charge new equipment in a given year by weight.
,	=	Counter, runs from 1 to lifetime (k).
j	=	Year of emission.
k	=	Lifetime. The average lifetime of the equipment.
Step 3: Calculate disposal emissions
The disposal emission equations assume that a certain percentage of the chemical charge will be emitted to the
atmosphere when that vintage is discarded, while remaining refrigerant is assumed to be recovered and reused. Disposal
emissions are thus a function of the quantity of chemical contained in the retiring equipment fleet and the proportion of
chemical released at disposal:
Edj = QCj-k-n x [1 - (rm x rc)]
where:
A-260 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Ed =	Emissions from Equipment Disposed. Emissions in year j from the disposal of
equipment.
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 4: Calculate total emissions
Finally, first-fill, lifetime, and disposal emissions are summed to provide an estimate of total emissions.
Ej = Efi + ESj + Edj
where:
E
Ef
Es
Ed
j
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-130, 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.
Total Emissions. Emissions from refrigeration and air conditioning equipment in
year j.
Emissions from first Equipment Fill. Emissions in year j from filling new equipment.
Emissions from Equipment Serviced. Emissions in year j from leakage and servicing
(including recharging) of equipment.
Emissions from Equipment Disposed. Emissions in year j from the disposal of
equipment.
Year of emission.
A-261
-------
Table A-130: Refrigeration and Air-Conditioning Market Transition Assumptions

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full





Penetration in
Maximum


Penetration
Maximum


Penetration
Maximum

Initial Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
Centrifugal Chillers





HCFO-








CFC-11
HCFC-123
1993
1993
45%
1233zd(E)
2016
2016
1%
None



1.6%





R-514A
2017
2017
1%
None









HCFO-













1233zd(E)
2017
2020
49%
None









R-514A
2018
2020
49%
None





HCFC-22
1991
1993
16%
HFC-134a
2000
2010
100%
R-450A
2017
2017
1%










R-513A
2017
2017
1%










R-450A
2018
2024
49%










R-513A
2018
2024
49%


HFC-134a
1992
1993
39%
R-450A
2017
2017
1%
None









R-513A
2017
2017
1%
None









R-450A
2018
2024
49%
None









R-513A
2018
2024
49%
None




CFC-12
HFC-134a
1992
1994
53%
R-450A
2017
2017
1%
None



1.5%





R-513A
2017
2017
1%
None









R-450A
2018
2024
49%
None









R-513A
2018
2024
49%
None





HCFC-22
1991
1994
16%
HFC-134a
2000
2010
100%
R-450A
2017
2017
1%










R-513A
2017
2017
1%










R-450A
2018
2024
49%










R-513A
2018
2024
49%






HCFO-









HCFC-123
1993
1994
31%
1233zd(E)
2016
2016
1%
None









R-514A
2017
2017
1%
None









HCFO-













1233zd(E)
2017
2020
49%
None









R-514A
2018
2020
49%
None




R-500
HFC-134a
1992
1994
53%
R-450A
2017
2017
1%
None



1.5%





R-513A
2017
2017
1%
None









R-450A
2018
2024
49%
None









R-513A
2018
2024
49%
None





HCFC-22
1991
1994
16%
HFC-134a
2000
2010
100%
R-450A
2017
2017
1%










R-513A
2017
2017
1%










R-450A
2018
2024
49%

A-262 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full





Penetration in
Maximum


Penetration
Maximum


Penetration
Maximum

Initial Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb









R-513A
2018
2024
49%






HCFO-









HCFC-123
1993
1994
31%
1233zd(E)
2016
2016
1%
None









R-514A
2017
2017
1%
None









HCFO-













1233zd(E)
2017
2020
49%
None









R-514A
2018
2020
49%
None




CFC-114
HFC-236fa
1993
1996
100%
HFC-134a
1998
2009
100%
None



1.4%
Cold Storage
CFC-12
HCFC-22
1990
1993
65%
R-404A
1996
2010
75%
R-407F
2017
2023
100%
3.1%





R-507
1996
2010
25%
R-407F
2017
2023
100%


R-404A
1994
1996
26%
R-407F
2017
2023
100%
None





R-507
1994
1996
9%
R-407F
2017
2023
100%
None




HCFC-22
HCFC-22
1992
1993
100%
R-404A
1996
2009
8%
R-407F
2017
2023
100%
3.0%





R-507
1996
2009
3%
R-407F
2017
2023
100%






R-404A
2009
2010
68%
R-407F
2017
2023
100%






R-507
2009
2010
23%
R-407F
2017
2023
100%

R-502
HCFC-22
1990
1993
40%
R-404A
1996
2010
38%
R-407F
2017
2023
100%
2.6%





R-507
1996
2010
12%
R-407F
2017
2023
100%






Non-













ODP/GWP
1996
2010
50%
None





R-404A
1993
1996
45%
R-407F
2017
2023
100%
None





R-507
1994
1996
15%
R-407F
2017
2023
100%
None




Commercial Unitary Air Conditioners (Large)
HCFC-22
HCFC-22
1992
1993
100%
R-410A
2001
2005
5%
None



1.6%





R-407C
2006
2009
1%
None









R-410A
2006
2009
9%
None









R-407C
2009
2010
5%
None









R-410A
2009
2010
81%
None




Commercial Unitary Air Conditioners (Small)
HCFC-22
HCFC-22
1992
1993
100%
R-410A
1996
2000
3%
None



1.9%





R-410A
2001
2005
18%
None









R-410A
2006
2009
8%
None









R-410A
2009
2010
71%
None




A-263
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full





Penetration in
Maximum


Penetration
Maximum


Penetration
Maximum

Initial Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
Dehumidifiers
HCFC-22
HFC-134a
1997
1997
89%
None







1.3%

R-410A
2007
2010
11%
None








Ice Makers
CFC-12
HFC-134a
1993
1995
27%
None







2.1%

R-404A
1993
1995
73%
R-410A
2013
2019
32%
None




Industrial Process Refrigeration





HCFO-








CFC-11
HCFC-123
1992
1994
70%
1233zd(E)
2016
2016
2%
None



3.2%





HCFO-













1233zd(E)
2017
2020
98%
None





HFC-134a
1992
1994
15%
None









HCFC-22
1991
1994
15%
HFC-134a
1995
2010
100%
None




CFC-12
HCFC-22
1991
1994
10%
HFC-134a
1995
2010
15%
None



3.1%





R-404A
1995
2010
50%
None









R-410A
1999
2010
20%
None









R-507
1995
2010
15%
None









HCFO-









HCFC-123
1992
1994
35%
1233zd(E)
2016
2016
2%
None









HCFO-













1233zd(E)
2017
2020
98%
None





HFC-134a
1992
1994
50%
None









R-401A
1995
1996
5%
HFC-134a
1997
2000
100%
None




HCFC-22
HFC-134a
1995
2009
2%
None







3.0%

R-404A
1995
2009
5%
None









R-410A
1999
2009
2%
None









R-507
1995
2009
2%
None









HFC-134a
2009
2010
14%
None









R-404A
2009
2010
45%
None









R-410A
2009
2010
18%
None









R-507
2009
2010
14%
None








Mobile Air Conditioners (Passenger Cars)
CFC-12
HFC-134a
1992
1994
100%
HFO-1234yf
2012
2015
1%
None



0.3%





HFO-1234yf
2016
2021
99%
None




Mobile Air Conditioners (Light Duty Trucks)
CFC-12
HFC-134a
1993
1994
100% | HFO-1234yf
2012
2015
1%
None | | |
1.4%
A-264 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full





Penetration in
Maximum


Penetration
Maximum


Penetration
Maximum

Initial Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb





HFO-1234yf
2016
2021
99%
None




Mobile Air Conditioners (Heavy Duty Vehicles)
CFC-12
HFC-134a
1993
1994
100%
None


1


| 0.8%
Mobile Air Conditioners (School and Tour Buses)
CFC-12
HCFC-22
1994
1995
0.5%
HFC-134a
2006
2007
100%
None



0.3%

HFC-134a
1994
1997
99.5%
None








Mobile Air Conditioners (Transit Buses)
HCFC-22
HFC-134a
1995
2009
100%
None


1


| 0.3%
Mobile Air Conditioners (Trains

HCFC-22
HFC-134a
R-407C
2002
2002
2009
2009
50%
50%
None
None







0.3%
Packaged Terminal Air Conditioners and Heat Pumps
HCFC-22
R-410A
R-410A
2006
2009
2009
2010
10%
90%
None
None







3.0%
Positive Displacement Chillers
Reciprocating and Screw)
CFC-12 HCFC-22C
HFC-134a
2000
2009
9%
R-407C
2010
2020
60%
R-450A
R-513A
R-450A
R-513A
2017
2017
2018
2018
2017
2017
2024
2024
1%
1%
49%
49%
2.5%





R-410A
2010
2020
40%
R-450A
R-513A
R-450A
R-513A
2017
2017
2018
2018
2017
2017
2024
2024
1%
1%
49%
49%


R-407C
2000
2009
1%
R-450A
R-513A
R-450A
R-513A
2017
2017
2018
2018
2017
2017
2024
2024
1%
1%
49%
49%
None
None
None
None





HFC-134a
2009
2010
81%
R-407C
2010
2020
60%
R-450A
R-513A
R-450A
R-513A
2017
2017
2018
2018
2017
2017
2024
2024
1%
1%
49%
49%






R-410A
2010
2020
40%
R-450A
R-513A
R-450A
R-513A
2017
2017
2018
2018
2017
2017
2024
2024
1%
1%
49%
49%


R-407C
2009
2010
9%
R-450A
2017
2017
1%
None




A-265
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full





Penetration in
Maximum


Penetration
Maximum


Penetration
Maximum

Initial Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb





R-513A
2017
2017
1%
None









R-450A
2018
2024
49%
None









R-513A
2018
2024
49%
None




HCFC-22
HFC-134a
2000
2009
9%
R-407C
2010
2020
60%
R-450A
2017
2017
1%
2.5%









R-513A
2017
2017
1%










R-450A
2018
2024
49%










R-513A
2018
2024
49%






R-410A
2010
2020
40%
R-450A
2017
2017
1%










R-513A
2017
2017
1%










R-450A
2018
2024
49%










R-513A
2018
2024
49%


R-407C
2000
2009
1%
R-450A
2017
2017
1%
None









R-513A
2017
2017
1%
None









R-450A
2018
2024
49%
None









R-513A
2018
2024
49%
None





HFC-134a
2009
2010
81%
R-407C
2010
2020
60%
R-450A
2017
2017
1%










R-513A
2017
2017
1%










R-450A
2018
2024
49%










R-513A
2018
2024
49%






R-410A
2010
2020
40%
R-450A
2017
2017
1%










R-513A
2017
2017
1%










R-450A
2018
2024
49%










R-513A
2018
2024
49%


R-407C
2009
2010
9%
R-450A
2017
2017
1%
None









R-513A
2017
2017
1%
None









R-450A
2018
2024
49%
None









R-513A
2018
2024
49%
None




Positive Displacement Chillers
Scroll)
HCFC-22
HFC-134a
2000
2009
9%
R-407C
2010
2020
60%
R-452B
2024
2024
100%
2.5%





R-410A
2010
2020
40%
R-452B
2024
2024
100%


R-407C
2000
2009
1%
R-452B
2024
2024
100%
None





HFC-134a
2009
2010
81%
R-407C
2010
2020
60%
R-452B
2024
2024
100%






R-410A
2010
2020
40%
R-452B
2024
2024
100%


R-407C
2009
2010
9%
R-452B
2024
2024
100%
None




Refrigerated Appliances




Non-







CFC-12
HFC-134a
1994
1995
100%
ODP/GWP
2019
2021
86%
None



1.7%
A-266 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full





Penetration in
Maximum


Penetration
Maximum


Penetration
Maximum

Initial Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb





R-450A
R-513A
2021
2021
2021
2021
7%
7%
None
None




Refrigerated Food Processing and Dispensing Equipment
CFC-12
HCFC-22
1990
1994
100%
HFC-134a
1995
1998
70%
None



2.1%





R-404A
1995
1998
30%
R-448A
2021
2021
50%










R-449A
2021
2021
50%

Residential Unitary Air Conditioners
HCFC-22
HCFC-22
2006
2006
70%
R-410A
2007
2010
29%
None



2.6%





R-410A
2010
2010
71%
None





R-410A
2000
2005
5%
R-410A
2006
2006
100%
None





R-410A
2000
2006
5%
None









R-410A
2006
2006
20%
None








Retail Food (Large; Technology Transition)
DXd
DX
2001
2006
67.5%
DX
2006
2015
62%
None



1.7%





DRe
2000
2015
23%
None









SLSf
2000
2015
15%
None





DR
2000
2006
22.5%
None









SLS
2000
2006
10%
None








Retail Food (Large; Refrigerant Transition)
CFC-12
R-404A
1995
2000
17.5%
R-404A
2000
2000
3.3%
R-407A
2017
2017
100%
1.7%
R-5025




R-407A
2011
2015
63.3%
None









R-407A
2017
2017
33.3%
None





R-507
1995
2000
7.5%
R-404A
2006
2010
71%
R-407A
2017
2017
100%






R-407A
2006
2010
30%
None





HCFC-22
1995
2000
75%
R-404A
2006
2010
13.3%
R-407A
2011
2015
100%






R-407A
2001
2005
1.3%
None









R-404A
2001
2005
12%
R-407A
2017
2017
100%






R-507
2001
2005
6.7%
R-407A
2011
2015
100%






R-404A
2006
2010
34%
R-407A
2011
2015
100%






R-404A
2006
2010
7.3%
R-407A
2017
2017
100%






R-407A
2006
2010
25.3%
None




Retail Food (Large Condensing Units)
HCFC-22
R-402A
1995
2005
5%
R-404A
2006
2006
100%
R-407A
2018
2018
100%
1.5%

R-404A
1995
2005
25%
R-407A
2018
2018
100%
None





R-507
1995
2005
10%
R-407A
2018
2018
100%
None





R-404A
2008
2010
45%
R-407A
2018
2018
100%
None




A-267
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full





Penetration in
Maximum


Penetration
Maximum


Penetration
Maximum

Initial Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb

R-507
2008
2010
15%
R-407A
2018
2018
100%
None




Retail Food (Small Condensing Units)
HCFC-22
R-401A
1995
2005
6%
HFC-134a
2006
2006
100%
None



1.6%

R-402A
1995
2005
4%
HFC-134a
2006
2006
100%
None





HFC-134a
1993
2005
30%
None









R-404A
1995
2005
30%
R-407A
2018
2018
100%






R-404A
2008
2010
30%
R-407A
2018
2018
100%





Retail Food (Smal

CFC-12
HCFC-22
1990
1993
91%
HFC-134a
1993
1995
91%
C02
Non-
ODP/GWP
Non-
ODP/GWP
Non-
ODP/GWP
R-450A
R-513A
Non-
2012
2012
2014
2016
2016
2016
2015
2015
2019
2016
2020
2020
1%
3.7%
31%
17.3%
23%
23%
2.2%





HFC-134a
2000
2009
9%
ODP/GWP
R-450A
R-513A
2014
2016
2016
2019
2020
2020
30%
35%
35%






Non-









R-404A
1990
1993
9%
ODP/GWP
R-448A
R-449A
2016
2019
2019
2016
2020
2020
30%
35%
35%
None
None
None




Transport Refrigeration (Road Transport)
CFC-12
HFC-134a
1993
1995
10%
None







5.5%

R-404A
1993
1995
60%
R-452A
R-452A
2017
2021
2021
2030
5%
95%






HCFC-22
1993
1995
30%
R-410A
2000
2003
5%
None









R-404A
2006
2010
95%
R-452A
R-452A
2017
2021
2021
2030
5%
95%

Transport Refrigeration (Intermodal Containers)
CFC-12
HFC-134a
1993
1993
60%
C02
2017
2021
5%
None



7.3%

R-404A
1993
1993
5%
C02
2017
2021
5%
None





HCFC-22
1993
1993
35%
HFC-134a
2000
2010
100%
C02
2017
2021
5%

Transport Refrigeration (Merchant Fishing Transport)
A-268 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full





Penetration in
Maximum


Penetration
Maximum


Penetration
Maximum

Initial Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
HCFC-22
HFC-134a
R-507
R-404A
1993
1994
1993
1995
1995
1995
10%
10%
10%
None
None
None







5.7%

HCFC-22
1993
1995
70%
R-407C
R-507
R-404A
2000
2006
2006
2005
2010
2010
3%
49%
49%
R-410A
None
None
2005
2007
100%

Transport Refrigeration (Reefer Ships)
HCFC-22
HFC-134a
1993
1995
3.3%
None







4.2%

R-507
1994
1995
3.3%
None









R-404A
1993
1995
3.3%
None









HCFC-22
1993
1995
90%
HFC-134a
2006
2010
25%
None









R-507
2006
2010
25%
None









R-404A
2006
2010
25%
None









R-407C
2006
2010
25%
None




Transport Refrigeration (Vintage Rail Transport)
CFC-12
HCFC-22
1993
1995
100%
HFC-134a
1996
2000
100%
None


| -100%
Transport Refrigeration (Modern Rail Transport)
HFC-134a
R-404A
1999
1999
50%
None







0.3%

HFC-134a
2005
2005
50%
None








Vending Machines
CFC-12
HFC-134a
1995
1998
90%
C02
2012
2012
1%
Propane
100%
2019
2019
-0.03%





Propane
2013
2017
39%
None









Propane
2014
2014
1%
None









Propane
2019
2019
49%
None









R-450A
2019
2019
5%
None









R-513A
2019
2019
5%
None





R-404A
1995
1998
10%
R-450A
2019
2019
50%
None









R-513A
2019
2019
50%
None




Water-Source and Ground-Source Heat Pumps
HCFC-22
R-407C
2000
2006
5%
None
R-410A
2000
2006
5%
None
HFC-134a
2000
2009
2%
None
R-407C
2006
2009
2.5%
None
R-410A
2006
2009
4.5%
None
HFC-134a
2009
2010
18%
None
R-407C
2009
2010
22.5%
None
1.3%
A-269
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full





Penetration in
Maximum


Penetration
Maximum


Penetration
Maximum

Initial Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb

R-410A
2009
2010
40.5%
None








Window Units
HCFC-22
R-410A
R-410A
2008
2009
2009
2010
10%
90%
None
None







4.0%
a Transitions between the start year and date of full penetration in new equipment are assumed to be linear so that in total 100 percent of the market is assigned to the original ODS or the
various ODS substitutes.
b Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
c The CFC-12 reciprocating chillers market for new systems transitioned to HCFC-22 overnight in 1993. This transition is not shown in the table in order to provide the HFC transitions in
greater detail.
d DX refers to direct expansion systems where the compressors are mounted together in a rack and share suction and discharge refrigeration lines that run throughout the store, feeding
refrigerant to the display cases in the sales area.
e DR refers to distributed refrigeration systems that consist of multiple smaller units that are located close to the display cases that they serve such as on the roof above the cases, behind a
nearby wall, or on top of or next to the case in the sales area.
f SLS refers to secondary loop systems wherein a secondary fluid such as glycol or carbon dioxide is cooled by the primary refrigerant in the machine room and then pumped throughout the
store to remove heat from the display equipment.
s The CFC-12 large retail food market for new systems transitioned to R-502 from 1988 to 1990, and subsequently transitioned to HCFC-22 from 1990 to 1993. These transitions are not
shown in the table in order to provide the HFC transitions in greater detail.
A-270 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-131 presents the average equipment lifetimes and annual HFC emission rates (for first-fill, servicing,
leaks, and disposal) for each end-use assumed by the Vintaging Model.
Table A-131: Refrigeration and Air-Conditioning Lifetime Assumptions


HFC Emission Rates
HFC Emission Rates
HFC Emission Rates
End-Use
Lifetime
(First-fill)3
(Servicing and Leaks)
(Disposal)b

(Years)
(%)
(%)
(%)
Centrifugal Chillers
20-27
0.2-0.5
2.0-10.9
10
Cold Storage
20-25
1
15.0
10
Commercial Unitary A/C
15
0.5-1
7.9-8.6
18-40
Condensing Units (Medium Retail Food)
10-20
0.5-3
8-15
10-20
Dehumidifiers
11
0.5-1
0.5
50
Ice Makers
8
0.5-2
3.0
49
Industrial Process Refrigeration
25
1
3.6-12.3
10
Large Retail Food
18
2
17-33
10
Mobile Air Conditioners
5-16
LO
0
1
-------
j	=	Year of emission.
Transition Assumptions
Transition assumptions and growth rates for those items that use ODSs or HFCs as propellants, including vital
medical devices and specialty consumer products, are presented in Table A-149.
A-272 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-132: Aerosol Product Transition Assumptions

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full


Initial


Penetration in
Maximum


Penetration in
Maximum


Penetration in
Maximum

Market
Name of
Start
New
Market
Name of
Start
New
Market
Name of
Start
New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
MDIs
CFC Mixc
HFC-134a
Non-
ODP/GWP
1997
1998
1997
2007
6%
7%
None
None







2.1%

CFC Mix3
2000
2000
87%
HFC-134a
Non-
ODP/GWP
2001
2001
2011
2014
28%
67%
Non-
ODP/GWP
None
2012
2018
64%






HFC-227ea
2007
2013
5%
Non-
ODP/GWP
2015
2018
44%

Consumer Aerosols (Non-MDIs)
NAd
HFC-152a
1990
1991
50%
None







4.2%

HFC-134a
1995
1995
50%
HFC-152a
1997
1998
44%
None









HFC-152a
2001
2005
38%
None









HFO-













1234ze(E)
2016
2018
16%
None




Technical Aerosols (Non-MDIs)
CFC-12
HCFC-142b
Non-
ODP/GWP
1994
1994
1994
1994
10%
5%
HFC-152a
HFC-134a
None
2001
2001
2010
2010
90%
10%
None
None
HFO-



4.2%

HCFC-22
1994
1994
50%
HFC-134a
2001
2010
100%
1234ze(E)
2012
2016
10%


HFC-152a
1994
1994
10%
None









HFC-134a
1994
1994
25%
None








a Transitions between the start year and date of full penetration in new products are assumed to be linear so that in total 100% of the market is assigned to the original ODS or
the various ODS substitutes.
b Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
CCFC Mix consists of CFC-11, CFC-12 and CFC-114 and represents the weighted average of several CFCs consumed for essential use in MDIs from 1993 to 2008. It is assumed that
CFC mix was stockpiled in the United States and used in new products through 2013.
d Consumer Aerosols transitioned away from ODS prior to 1985, the year in which the Vintaging Model begins. The portion of the market that is now using HFC propellants is
modeled.
A-273
-------
Solvents
ODSs, HFCs, PFCs and other chemicals are used as solvents to clean items. For example, electronics may need
to be cleaned after production to remove any manufacturing process oils or residues left. Solvents are applied by moving
the item to be cleaned within a bath or stream of the solvent. Generally, most solvents are assumed to remain in the
liquid phase and are not emitted as gas. Thus, emissions are considered "incomplete," and are a fixed percentage of the
amount of solvent consumed in a year. The solvent is assumed to be recycled or continuously reused through a distilling
and cleaning process until it is eventually almost entirely emitted. The remainder of the consumed solvent is assumed to
be entrained in sludge or wastes and disposed of by incineration or other destruction technologies without being
released to the atmosphere (U.S. EPA 2004). The following equation calculates emissions from solvent applications.
where:
Qc
J
Ej = I x Qq
Emissions. Total emissions of a specific chemical in year j from use in solvent
applications, by weight.
Percent Leakage. The percentage of the total chemical that is leaked to the
atmosphere, assumed to be 90 percent.
Quantity of Chemical. Total quantity of a specific chemical sold for use in solvent
applications in the year j, by weight.
Year of emission.
Transition Assumptions
The transition assumptions and growth rates used within the Vintaging Model for electronics cleaning, metals
cleaning, precision cleaning, and adhesives, coatings and inks, are presented in Table A-133.
Table A-133: Solvent Market Transition Assumptions

Primary Substitute
Secondary Substitute




Date of Full



Date of Full


Initial


Penetration
Maximum


Penetration
Maximum

Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
Adhesives
CH3CCI3 || Non-ODP/GWP
1994
1995
100%
None


| 2.0%
Electronics
CFC-113
Semi-Aqueous
1994
1995
52%
None



2.0%

HCFC-225ca/cb
1994
1995
0.2%
Unknown





HFC-43-10mee
1995
1996
0.7%
None





HFE-7100
1994
1995
0.7%
None





nPB
1992
1996
5%
None





Methyl Siloxanes
1992
1996
0.8%
None





No-Clean
1992
20132
40%
None




CH3CCI3
Non-ODP/GWP
1996
1997
99.8%
None



2.0%





Non-





PFC/PFPE
1996
1997
0.2%
ODP/GWP
2000
2003
90%






Non-









ODP/GWP
2005
2009
10%

Metals
CH3CCI3
Non-ODP/GWP
1992
1996
100%
None



2.0%
CFC-113
Non-ODP/GWP
1992
20132
100%
None



2.0%
CCI4
Non-ODP/GWP
1992
1996
100%
None



2.0%
Precision
CH3CCI3
Non-ODP/GWP
1995
1996
99.3%
None



2.0%

HFC-43-10mee
1995
1996
0.6%
None




A-274 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------

Primary Substitute
Secondary Substitute




Date of Full



Date of Full


Initial


Penetration
Maximum


Penetration
Maximum

Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb





Non-





PFC/PFPE
1995
1996
0.1%
ODP/GWP
Non-
ODP/GWP
2000
2005
2003
2009
90%
10%

CFC-113
Non-ODP/GWP
Methyl Siloxanes
HCFC-225ca/cb
HFE-7100
1995
1995
1995
1995
2013c
1996
1996
1996
90%
6%
1%
3%
None
Unknown
None



2.0%
Note: Non-ODP/GWP includes chemicals with zero ODP and low GWP, such as hydrocarbons and ammonia, as well as not-in-
kind alternatives such as "no clean" technologies.
a Transitions between the start year and date of full penetration in new equipment or chemical supply are assumed to be linear
so that in total 100 percent of the market is assigned to the original ODS or the various ODS substitutes.
b Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
transition assumed to be completed in 2013 to mimic CFC-113 stockpile use.
Fire Extinguishing
ODSs, HFCs, PFCs and other chemicals are used as fire-extinguishing agents, in both hand-held "streaming"
applications as well as in built-up "flooding" equipment similar to water sprinkler systems. Although these systems are
generally built to be leak-tight, some leaks do occur and emissions occur when the agent is released. Total emissions
from fire extinguishing are assumed, in aggregate, to equal a percentage of the total quantity of chemical in operation at
a given time. For modeling purposes, it is assumed that fire extinguishing equipment leaks at a constant rate for an
average equipment lifetime, as shown in the equation below. In streaming systems, non-halon emissions are assumed to
be 3.5 percent of all chemical in use in each year, while in flooding systems 2.5 percent of the installed base of chemical
is assumed to leak annually. Halon systems are assumed to leak at higher rates. The equation is applied for a single year,
accounting for all fire protection equipment in operation in that year. The model assumes that equipment is serviced
annually so that the amount equivalent to average annual emissions for each product (and hence for the total of what
was added to the bank in a previous year in equipment that has not yet reached end-of-life) is replaced/applied to the
starting charge size (or chemical bank). Each fire protection agent is modeled separately. In the Vintaging Model,
streaming applications have a 24-year lifetime and flooding applications have a 33-year lifetime. At end-of-life, remaining
agent is recovered from equipment being disposed and is reused.
Ej = r x Z Qcj-m for i=l^k
where:
Qc
Emissions. Total emissions of a specific chemical in year j for fire extinguishing
equipment, by weight.
Percent Released. The percentage of the total chemical in operation that is released
to the atmosphere.
Quantity of Chemical. Total amount of a specific chemical used in new fire
extinguishing equipment in a given year,y-/+l, by weight.
/'	=	Counter, runs from 1 to lifetime (k).
j	=	Year of emission.
k	=	Lifetime. The average lifetime of the equipment.
Transition Assumptions
Transition assumptions and growth rates for these two fire extinguishing types are presented in Table A-134.
A-275
-------
Table A-134: Fire Extinguishing Market Transition Assumptions

Primary Substitute
Secondary Substitute




Date of Full



Date of Full


Initial


Penetration
Maximum


Penetration
Maximum

Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
Flooding Agents
Halon-









1301
Halon-1301c
1994
1994
4%
Unknown



2.2%

HFC-23
1994
1999
0.2%
None





HFC-227ea
1994
1999
50.2%
FK-5-1-12
2003
2020
35%






HFC-125
2001
2012
10%






Non-









ODP/GWP
2005
2020
13%


Non-ODP/GWP
1994
1994
22%
FK-5-1-12
2003
2020
7%


Non-ODP/GWP
1995
2003
7%
None





C02
1998
2006
7%
None





C4F10
1994
1999
0.5%
FK-5-1-12
2003
2003
100%


HFC-125
1997
2006
9.1%
FK-5-1-12
2003
2020
35%






Non-









ODP/GWP
2005
2020
10%






Non-









ODP/GWP
2005
2019
3%

Streaming Agents
Halon-









1211
Halon-1211c
HFC-236fa
Halotron
1992
1997
1994
1992
1999
1995
5%
3%
0.1%
Unknown
None
Unknown
Non-



3.0%

Halotron
1996
2000
5.4%
ODP/GWP
2020
2020
56%


Non-ODP/GWP
1993
1994
56%
None





Non-ODP/GWP
1995
2024
20%
None





Non-ODP/GWP
1999
2018
10%
None




a Transitions between the start year and date of full penetration in new equipment are assumed to be linear so that in total 100
percent of the market is assigned to the original ODS or the various ODS substitutes.
b Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
c Despite the 1994 consumption ban, a small percentage of new halon systems are assumed to continue to be built and filled
with stockpiled or recovered supplies.
Foam Blowing
ODSs, HFCs, and other chemicals are used to produce foams, including such items as the foam insulation panels
around refrigerators, insulation sprayed on buildings, etc. The chemical is used to create pockets of gas within a
substrate, increasing the insulating properties of the item. Foams are given emission profiles depending on the foam
type (open cell or closed cell). Open cell foams are assumed to be 100 percent emissive in the year of manufacture.
Closed cell foams are assumed to emit a portion of their total HFC content upon manufacture, a portion at a constant
rate over the lifetime of the foam, a portion at disposal, and a portion after disposal; these portions vary by end-use.
Step 1: Calculate manufacturing emissions (open-cell and closed-cell foams)
Manufacturing emissions occur in the year of foam manufacture, and are calculated as presented in the
following equation. Manufacturing emissions are considered for all foam equipment that are filled with foam within the
United States, including those which are produced for export, and excluding those that are imported pre-filled.
Enrtj = Im x Qq
where:
A-276 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Errij =	Emissions from manufacturing. Total emissions of a specific chemical in year j due
to manufacturing losses, by weight.
Im =	Loss Rate. Percent of original blowing agent emitted during foam manufacture. For
open-cell foams, Im is 100%.
Qc =	Quantity of Chemical. Total amount of a specific chemical used to manufacture
closed-cell foams in a given year.
j	=	Year of emission.
Step 2: Calculate lifetime emissions (closed-cell foams)
Lifetime emissions occur annually from closed-cell foams throughout the lifetime of the foam, as calculated as
presented in the following equation.
EUj = lux 2. QCj-i+i fori=l^k
where:
EUj =	Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due
to lifetime losses during use, by weight.
lu	=	Leak Rate. Percent of original blowing agent emitted each year during lifetime use.
Qc =	Quantity of Chemical. Total amount of a specific chemical used to manufacture
closed-cell foams in a given year.
/'	=	Counter, runs from 1 to lifetime (k).
j	=	Year of emission.
k	=	Lifetime. The average lifetime of foam product.
Step 3: Calculate disposal emissions (closed-cell foams)
Disposal emissions occur in the year the foam is disposed, and are calculated as presented in the following
equation.
Edj = Id x Qq.k
where:
Edj =	Emissions from disposal. Total emissions of a specific chemical in year j at disposal,
by weight.
Id	=	Loss Rate. Percent of original blowing agent emitted at disposal.
Qc =	Quantity of Chemical. Total amount of a specific chemical used to manufacture
closed-cell foams in a given year.
j	=	Year of emission.
k	=	Lifetime. The average lifetime of foam product.
Step 4: Calculate post-disposal emissions (closed-cell foams)
Post-disposal emissions occur in the years after the foam is disposed; for example, emissions might occur while
the disposed foam is in a landfill. Currently, the only foam type assumed to have post-disposal emissions is polyurethane
foam used as domestic refrigerator and freezer insulation, which is expected to continue to emit for 26 years post-
disposal, calculated as presented in the following equation.
Epj = Ip x Z QCj-m for m=k^k + 26
A-277
-------
where:
Epj =	Emissions from post disposal. Total post-disposal emissions of a specific chemical in
year j, by weight.
Ip	=	Leak Rate. Percent of original blowing agent emitted post disposal.
Qc =	Quantity of Chemical. Total amount of a specific chemical used to manufacture
closed-cell foams in a given year.
k	=	Lifetime. The average lifetime of foam product.
m	=	Counter. Runs from lifetime (k) to (k+26).
j	=	Year of emission.
Step 5: Calculate total emissions (open-cell and closed-cell foams)
To calculate total emissions from foams in any given year, emissions from all foam stages must be summed, as
presented in the following equation.
Ej = Errij + Euj+ Edj + Epj
where:
Ej	=	Total Emissions. Total emissions of a specific chemical in year j, by weight.
Enrtj =	Emissions from manufacturing. Total emissions of a specific chemical in year j due
to manufacturing losses, by weight.
EUj =	Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due
to lifetime losses during use, by weight.
Edj =	Emissions from disposal. Total emissions of a specific chemical in year j at disposal,
by weight.
Epj =	Emissions from post disposal. Total post-disposal emissions of a specific chemical in
year j, by weight.
Assumptions
The Vintaging Model contains thirteen foam types, whose transition assumptions away from ODS and growth
rates are presented in Table A-135. The emission profiles of these thirteen foam types are shown in Table A-136.
A-278 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-135: Foam Blowing Market Transition Assumptions

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full
Maximum


Date of Full


Initial


Penetration
Maximum


Penetration
Market


Penetration
Maximum

Market
Name of
Start
in New
Market
Name of
Start
in New
Penetrati
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
on
Substitute
Date
Equipment3
Penetration
Rateb
Vending Machine Foam
CFC-11
HCFC-141b
1993
1995
100%
HFC-245fa
2001
2004
100%
Non-ODP/GWP
2004
2006
45%
-0.03%









Non-ODP/GWP
2007
2009
5%










Non-ODP/GWP
2007
2009
25%










Non-ODP/GWP
2010
2010
10%










Non-ODP/GWP
2017
2017
2%










Non-ODP/GWP
2017
2017
8%

Stand-alone Equipment Foam
CFC-11
HCFC-141b
1990
1995
40%
HFC-245fa
HFC-134a
Non-
ODP/GWP
2003
2003
2003
2005
2005
2005
80%
40%
40%
HCFO-1233zd(E)
None
None
2019
2020
25%
2.2%

HCFC-22
1990
1995
56%
HFC-134a
Non-
ODP/GWP
2004
2004
2008
2008
46%
54%
Non-ODP/GWP
HCFO-1233zd(E)
None
2010
2019
2018
2020
32%
36%

Ice Machine Foam
CFC-11
HCFC-141b
1989
1996
40%
C02
2002
2003
69%
None



2.1%





HFC-134a
2002
2003
31%
C02
HCFO-1233zd(E)
2017
2017
2020
2020
47%
20%


HCFC-142b
1989
1996
8%
C02
2002
2003
69%
None









HFC-134a
2002
2003
31%
C02
HCFO-1233zd(E)
2017
2017
2020
2020
47%
20%


HCFC-22
1989
1996
52%
C02
2002
2003
69%
None









HFC-134a
2002
2003
31%
C02
HCFO-1233zd(E)
2017
2017
2020
2020
47%
20%

Refrigerated Food Processing and Dispensing Equipment Foam
CFC-11
HCFC-22
1989
1997
100%
HFC-134a
Non-
ODP/GWP
2004
2009
2004
2008
2010
2008
75%
20%
25%
Non-ODP/GWP
HCFO-1233zd(E)
HFO-1234ze
None
2015
2020
2020
2021
2021
2021
30%
3%
3%
2.1%
Small Walk-in Cooler Foam
CFC-11
HCFC-141b
1990
1995
50%
HFC-245fa
2001
2003
100%
None



1.6%

HCFC-22
1990
1995
50%
HFC-134a
2000
2001
10%
None




A-279
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full
Maximum


Date of Full


Initial


Penetration
Maximum


Penetration
Market


Penetration
Maximum

Market
Name of
Start
in New
Market
Name of
Start
in New
Penetrati
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
on
Substitute
Date
Equipment3
Penetration
Rateb





HFC-245fa
HFC-134a
2009
2009
2010
2010
50%
40%
HCFO-1233zd(E)
None
2020
2020
20%

Large Walk-in Cooler Foam
CFC-11
HCFC-141b
1990
1995
50%
HFC-245fa
2001
2003
100%
None



1.5%

HCFC-22
1990
1995
50%
HFC-134a
2000
2001
10%
None









HFC-245fa
2009
2010
50%
HCFO-1233zd(E)
2020
2020
20%






HFC-134a
2009
2010
40%
None




Display Case Foam
CFC-11
HCFC-141b
1991
1992
50%
HFC-245fa
2003
2003
100%
None



1.7%

HCFC-142b
1991
1992
50%
HFC-245fa
2004
2004
100%
None




CFC-12
HCFC-22
1991
1993
100%
HFC-134a
2003
2007
100%
HCFO-1233zd(E)
2015
2020
60%

Road Transport Foam
CFC-11
HCFC-141b
1989
1996
19%
HCFC-22
C02
Non-
ODP/GWP
1999
1999
1999
2001
2001
2001
37%
11%
53%
HFC-245fa
None
None
2005
2007
100%
5.5%

HCFC-22
1989
1996
81%
HFC-134a
2005
2007
37%
None









HFC-245fa
2005
2007
63%
HCFO-1233zd(E)
2020
2020
76%

Intermodal Container Foam
CFC-11
HCFC-141b
1989
1996
19%
HCFC-22
C02
Non-
ODP/GWP
1999
1999
1999
2001
2001
2001
37%
11%
53%
HFC-245fa
None
None
2005
2007
100%
7.3%

HCFC-22
1989
1996
81%
HFC-134a
2005
2007
37%
None









HFC-245fa
2005
2007
63%
HCFO-1233zd(E)
2020
2020
76%

Flexible PU Foam: Integral Skin Foam
HCFC-141bc
HFC-134a
1996
2000
50%
HFC-245fa
2003
2010
96%
HCFO-1233zd(E)
2017
2017
83%
2.0%









Non-ODP/GWP
2017
2017
6%










HFO-1336mzz(Z)
2017
2017
10%






Non-













ODP/GWP
2003
2010
4%
None





C02
1996
2000
50%
None








Flexible PU Foam: Slabstock Foam,
Moulded Foam










Non-












CFC-11
ODP/GWP
1992
1992
100%
None







2.0%
A-280 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Initial
Market
Segment
Primary Substitute
Secondary Substitute
Tertiary Substitute
Growth
Rateb
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment3
Maximum
Market
Penetration
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment3
Maximum
Market
Penetrati
on
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment3
Maximum
Market
Penetration













Phenolic Foam
CFC-11
HCFC-141b
1989
1990
100%
Non-
ODP/GWP
1992
1992
100%
None



2.0%
Polyolefin Foam
CFC-114
HFC-152a
HCFC-142b
1989
1989
1993
1993
10%
90%
Non-
ODP/GWP
Non-
ODP/GWP
2005
1994
2010
1996
100%
100%
None
None



2.0%
PU and PIR Rigid: Boardstock
CFC-11
HCFC-141b
1993
1996
100%
Non-
ODP/GWP
HC/HFC-
245fa Blend
2000
2000
2003
2003
95%
5%
None
Non-ODP/GWP
2017
2017
100%
4.8%
PU Rigid: Domestic Refrigerator and Freezer Insulation
HCFC-141b
1993
1995
100%
HFC-134a
1996
2001
7%
Non-ODP/GWP
2002
2003
100%




HFC-245fa
2001
2003
50%
Non-ODP/GWP
2015
2020
50%








HCFO-1233zd(E)
2015
2020
50%




HFC-245fa
2006
2009
10%
Non-ODP/GWP
2015
2020
50%








HCFO-1233zd(E)
2015
2020
50%




Non-











ODP/GWP
2002
2005
10%
None







Non-











ODP/GWP
2006
2009
3%
None







Non-











ODP/GWP
2009
2014
20%
None



PU Rigid: One Component Foam

HCFC-













142b/22



Non-








CFC-12
Blend
1989
1996
70%
ODP/GWP
2009
2010
80%
None



4.0%





HFC-134a
2009
2010
10%
HFO-1234ze(E)
2018
2020
100%






HFC-152a
2009
2010
10%
None









Non-









HCFC-22
1989
1996
30%
ODP/GWP
2009
2010
80%
None









HFC-134a
2009
2010
10%
HFO-1234ze(E)
2018
2020
100%






HFC-152a
2009
2010
10%
None




A-281
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full
Maximum


Date of Full


Initial


Penetration
Maximum


Penetration
Market


Penetration
Maximum

Market
Name of
Start
in New
Market
Name of
Start
in New
Penetrati
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
on
Substitute
Date
Equipment3
Penetration
Rateb
PU Rigid: Other: Slabstock Foam
CFC-11
HCFC-141b
1989
1996
100%
C02
1999
2003
45%
None



2.0%





Non-













ODP/GWP
2001
2003
45%
None









HCFC-22
2003
2003
10%
Non-ODP/GWP
2009
2010
100%

PU Rigid: Sandwich Panels: Continuous and Discontinuous
HCFC-141bd
HCFC-22
HCFC-



HFC-







22/Water



245fa/C02







Blend
2001
2003
20%
Blend
Non-
ODP/GWP
2009
2009
2010
2010
50%
50%
HCFO-1233zd(E)
None
2015
2020
100%
HFC-











245fa/C02



HCFO-







Blend
2002
2004
20%
1233zd(E)
2015
2020
100%
None



Non-











ODP/GWP
2001
2004
40%
None
Non-







HFC-134a
2002
2004
20%
ODP/GWP
2015
2020
100%
None



HFC-











245fa/C02



HCFO-







Blend
2009
2010
40%
1233zd(E)
2015
2020
100%
None



Non-











ODP/GWP
2009
2010
20%
None







C02
2009
2010
20%
None
Non-







HFC-134a
2009
2010
20%
ODP/GWP
2015
2020
100%
None



PU Rigid: High Pressure Two-Component Spray Foam
CFC-11
HCFC-141b
1989
1996
100%
HFC-245fa
2002
2003
C
HFO-1336mzz(Z)
2016
2020
100%
0.8%





HFC-



HFO-









245fa/C02



1336mzz(Z)/C02









Blend
2002
2003
C
Blend
2016
2020
100%






HFC-













227ea/HFC-













365mfc













Blend
2002
2003
C
HCFO-1233zd(E)
2016
2020
100%

PU Rigid: Low Pressure Two-Component Spray Foam
CFC-12
HCFC-22
1989
1996
100%
HFC-245fa
2002
2003
15% | HCFO-1233zd(E)
2017
2021
100%
0.8%
A-282 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full
Maximum


Date of Full


Initial


Penetration
Maximum


Penetration
Market


Penetration
Maximum

Market
Name of
Start
in New
Market
Name of
Start
in New
Penetrati
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
on
Substitute
Date
Equipment3
Penetration
Rateb





HFC-134a
2002
2003
85%
HFO-1234ze
2017
2021
100%

XPS: Boardstock Foam
HCFC-











142b/22











Blend
1989
1994
10%
HFC-134a
2009
2010
70%
Non-ODP/GWP
2021
2021
100%




HFC-152a
2009
2010
10%
None







C02
2009
2010
10%
None







Non-











ODP/GWP
2009
2010
10%
None



HCFC-142b
1989
1994
90%
HFC-134a
2009
2010
70%
Non-ODP/GWP
2021
2021
100%




HFC-152a
2009
2010
10%
None







C02
2009
2010
10%
None







Non-











ODP/GWP
2009
2010
10%
None



XPS: Sheet Foam
CFC-12
C02
Non-
1989
1994
1%
None







2.0%

ODP/GWP
1989
1994
99%
C02
HFC-152a
1995
1995
1999
1999
9%
10%
None
None




C (Confidential)
a Transitions between the start year and date of full penetration in new equipment are assumed to be linear so that in total 100 percent of the market is assigned to the original ODS or
the various ODS substitutes.
b Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
c CFC-11 was the initial blowing agent used for through 1989. This transition is not shown in the table in order to provide the HFC transitions in greater detail.
d The CFC-11 PU Rigid: Sandwich Panels: Continuous and Discontinuous market for new systems transitioned to 82 percent HCFC-141b and 18 percent HCFC-22 from 1989 to 1996. These
transitions are not shown in the table in order to provide the HFC transitions in greater detail.
A-283
-------
Table A-136: Emission Profile for the Foam End-Uses

Loss at
Annual
Leakage



Manufacturing
Leakage Rate
Lifetime
Loss at
Total3
Foam End-Use
(%)
(%)
(years)
Disposal (%)
(%)
Flexible PU Foam: Slabstock Foam, Moulded





Foam
100
0
1
0
100
Vending Machine Foam
4
0.25
10
93.5
100
Stand-alone Equipment Foam
4
0.25
10
93.5
100
Ice Machine Foam
4
0.25
8
94.0
100
Refrigerated Food Processing and Dispensing




100
Equipment Foam
4
0.25
10
93.5

Small Walk-in Cooler Foam
4
0.25
20
91.0
100
Large Walk-in Cooler Foam
4
0.25
20
91.0
100
CFC-11 Display Case Foam
4
0.25
18
91.5
100
CFC-12 Display Case Foam
4
0.25
18
91.5
100
Road Transport Foam
4
0.25
12
93.0
100
Intermodal Container Foam
4
0.25
15
92.3
100
Rigid PU: High Pressure Two-Component Spray





Foam
15
1.5
50
10.0
100
Rigid PU: Low Pressure Two-Component Spray





Foam
15
1.5
50
10.0
100
Rigid PU: Slabstock and Other
32.5
0.875
15
54.375
100
Phenolic Foam
28
0.875
32
44.0
100
Polyolefin Foam
40
3
20
0
100
Rigid PU: One Component Foam
95
2.5
2
0
100
XPS: Sheet Foam
50
25
2
0
100
XPS: Boardstock Foam
25
0.75
25
56.25
100
Flexible PU Foam: Integral Skin Foam
95
2.5
2
0
100
Rigid PU: Domestic Refrigerator and Freezer





Insulation (HFC-134a)a
6.5
0.5
14
37.2
50.7
Rigid PU: Domestic Refrigerator and Freezer





Insulation (all others)3
3.75
0.25
14
39.9
47.15
PU and PIR Rigid: Boardstock
10
1
25
22.5
57.5
PU Sandwich Panels: Continuous and





Discontinuous
8.5-11.25
0.5
50
63.75-66.5
100
PIR (Polyisocyanurate)
PU (Polyurethane)
XPS (Extruded Polystyrene)
a Total emissions from foam end-uses are assumed to be 100 percent. In the Rigid PU Domestic Refrigerator and Freezer
Insulation and PU and PIR Boardstock end-uses, the source of emission rates and lifetimes did not yield 100 percent emissions;
the remainder is assumed to be emitted at a rate of 2.0 and 1.5 percent/year, respectively, post-disposal.
Sterilization
Sterilants kill microorganisms on medical equipment and devices. The principal ODS used in this sector was a
blend of 12 percent ethylene oxide (EtO) and 88 percent CFC-12, known as "12/88." In that blend, ethylene oxide
sterilizes the equipment and CFC-12 is a dilutent solvent to form a non-flammable blend. The sterilization sector is
modeled as a single end-use. For sterilization applications, all chemicals that are used in the equipment in any given year
are assumed to be emitted in that year, as shown in the following equation.
Ej - Qq
where:
Qc
Emissions. Total emissions of a specific chemical in year j from use in sterilization
equipment, by weight.
Quantity of Chemical. Total quantity of a specific chemical used in sterilization
equipment in year j, by weight.
Year of emission.
A-284 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Assumptions
The Vintaging Model contains one sterilization end-use, whose transition assumptions away from ODS and
growth rates are presented in Table A-137.
A-285
-------
Table A-137: Sterilization Market Transition Assumptions

Primary Substitute
Secondary Substitute
Tertiary Substitute




Date of Full



Date of Full



Date of Full


Initial


Penetration
Maximum


Penetration
Maximum


Penetration
Maximum

Market

Start
in New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Name of Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment
Penetration
Substitute
Date
Equipment
Penetration
Rate
12/88
EtO
1994
1995
95%
None







2.0%

Non-ODP/GWP
1994
1995
0.8%
None









HCFC-124/EtO Blend
1993
1994
1.4%
Non-ODP/GWP
2015
2015
100%
None





HCFC-22/HCFC-124/EtO Blend
1993
1994
3.1%
Non-ODP/GWP
2010
2010
100%
None




a Transitions between the start year and date of full penetration in new equipment are assumed to be linear so that in total 100 percent of the market is assigned to the original
ODS or the various ODS substitutes.
A-286 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Model Output
By repeating these calculations for each year, the Vintaging Model creates annual profiles of use and emissions
for ODS and ODS substitutes. The results can be shown for each year in two ways: 1) on a chemical-by-chemical basis,
summed across the end-uses, or 2) on an end-use or sector basis. Values for use and emissions are calculated both in
metric tons and in million metric tons of C02 equivalent (MMT C02 Eq.). The conversion of metric tons of chemical to
MMT C02 Eq. is accomplished through a linear scaling of tonnage by the global warming potential (GWP) of each
chemical.
Throughout its development, the Vintaging Model has undergone annual modifications. As new or more
accurate information becomes available, the model is adjusted in such a way that both past and future emission
estimates are often altered.
Bank of ODS and ODS Substitutes
The bank of an ODS or an ODS substitute is "the cumulative difference between the chemical that has been
consumed in an application or sub-application and that which has already been released" (IPCC 2006). For any given
year, the bank is equal to the previous year's bank, less the chemical in equipment disposed of during the year, plus
chemical in new equipment entering the market during that year, less the amount emitted but not replaced, plus the
amount added to replace chemical emitted prior to the given year, as shown in the following equation:
Be, = Bq-rQdj+Qpj-Ee+Qr
where:
Bank of Chemical. Total bank of a specific chemical in year j, by weight.
Qdj
Quantity of Chemical in Equipment Disposed. Total quantity of a specific chemical
in equipment disposed of in year j, by weight.
QPi
Quantity of Chemical Penetrating the Market. Total quantity of a specific chemical
that is entering the market in year j, by weight.
Ee
Emissions of Chemical Not Replaced. Total quantity of a specific chemical that is
emitted during year j but is not replaced in that year. The Vintaging Model assumes
all chemical emitted from refrigeration, air conditioning and fire extinguishing
equipment is replaced in the year it is emitted, hence this term is zero for all
sectors except foam blowing.
Qr
Chemical Replacing Previous Year's Emissions. Total quantity of a specific chemical
that is used to replace emissions that occurred prior to year j. The Vintaging Model
assumes all chemical emitted from refrigeration, air conditioning and fire
extinguishing equipment is replaced in the year it is emitted, hence this term is zero
for all sectors.
J
Year of emission.
A-287
-------
Table A-138 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 1990 to 2019.
Table A-138: Banks of OPS and OPS Substitutes, 1990-2019 (MT)
Year
CFC
HCFC
HFC
1990
706,317
183,974
872
1995
752,774
428,014
50,353
2000
609,993
878,120
189,534
2001
579,845
954,126
218,902
2002
553,906
1,013,434
251,613
2003
528,383
1,057,175
293,825
2004
502,424
1,101,483
337,838
2005
471,487
1,147,856
384,560
2006
439,576
1,190,007
436,931
2007
410,229
1,219,977
490,965
2008
386,025
1,235,871
541,647
2009
371,224
1,227,368
597,134
2010
341,955
1,194,954
668,796
2011
313,158
1,154,641
742,885
2012
284,514
1,111,521
819,235
2013
256,700
1,062,669
898,644
2014
228,748
1,013,579
980,182
2015
200,560
965,727
1,057,421
2016
171,901
916,604
1,132,200
2017
141,755
868,095
1,198,386
2018
119,362
810,765
1,261,835
2019
104,468
745,836
1,317,589
A-288 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
References
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
U.S. EPA (2018) EPA's Vintaging Model of ODS Substitutes: A Summary of the 2017 Peer Review. Office of Air and
Radiation. Document Number EPA-400-F-18-001. Available online at:
.
U.S. EPA (2004) The U.S. Solvent Cleaning Industry and the Transition to Non Ozone Depleting Substances. September
2004. Available online at: .
Data are also taken from various government sources, including rulemaking analyses from the U.S. Department of Energy
and from the Motor Vehicle Emission Simulator (MOVES) model from EPA's Office of Transportation and Air Quality.
A-289
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3.10. Methodology for Estimating CH4 Emissions from Enteric Fermentation
The steps outlined in this annex were used to estimate methane emissions from enteric fermentation for the
years 1990 through 2017. As explained in the Enteric Fermentation chapter, a simplified approach was used to estimate
emissions for 2018 and 2019. The methodology used for 2019 relied on 2019 population estimates and 2017 implied
emission factors and is explained in further detail within Chapter 5.1 Enteric Fermentation (CRF Source Category 3A). The
same methods explained in this annex for 2019 estimates were also used for 2018 estimates. Methane emissions from
enteric fermentation were estimated for seven livestock categories: cattle, horses, sheep, swine, goats, American bison,
and the non-horse equines (mules and asses). Emissions from cattle represent the majority of U.S. emissions from
enteric fermentation; consequently, a more detailed IPCC Tier 2 methodology was used to estimate emissions from
cattle. The IPCC Tier 1 methodology was used to estimate emissions for the other types of livestock, including horses,
goats, sheep, swine, American bison, and mules and asses (IPCC 2006).
Estimate Methane Emissions from Cattle
This section describes the process used to estimate CH4 emissions from enteric fermentation from cattle using
the Cattle Enteric Fermentation Model (CEFM). The CEFM was developed based on recommendations provided in the
2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) and uses information on population, energy
requirements, digestible energy, and CH4 conversion rates to estimate CH4 emissions.-80 The emission methodology
consists of the following three steps: (1) characterize the cattle population to account for animal population categories
with different emission profiles; (2) characterize cattle diets to generate information needed to estimate emission
factors; and (3) estimate emissions using these data and the IPCC Tier 2 equations.
Step 1: Characterize U.S. Cattle Population
The CEFM's state-level cattle population estimates are based on data obtained from the U.S. Department of
Agriculture's (USDA) National Agricultural Statistics Service Quick Stats database (USDA 2020). State-level cattle
population estimates are shown by animal type for 2019 in Table A-139. A national-level summary of the annual average
populations upon which all livestock-related emissions are based is provided in Table A-140. Cattle populations used in
the Enteric Fermentation source category for the 1990 to 2017 Inventory were estimated using the cattle transition
matrix in the CEFM, which uses January 1 USDA population estimates and weight data to simulate the population of U.S.
cattle from birth to slaughter, and results in an estimate of the number of animals in a particular cattle grouping while
taking into account the monthly rate of weight gain, the average weight of the animals, and the death and calving rates.
The use of supplemental USDA data and the cattle transition matrix in the CEFM results in cattle population estimates for
this sector differing slightly from the January 1 or July 1 USDA point estimates and the cattle population data obtained
from the Food and Agriculture Organization of the United Nations (FAO). For 2018 and 2019, state populations were
estimated by calculating ratios of 2017 state populations to the 2017 total national population, then applying those
state-specific ratios to the 2018 and 2019 national total population estimates, see the Enteric Fermentation chapter for
more details about this approach.
Table A-139: 2019 Cattle Population Estimates, by Animal Type and State (1,000 head)
State
Dairy
Calves
Dairy
Cows
Dairy
Repl.
Heif.
7-11
Months
Dairy
Repl.
Heif.
12-23
Months
Bulls
Beef
Calves
Beef
Cows
Beef
Repl.
Heif.
7-11
Months
Beef
Repl.
Heif.
12-23
Months
Steer
Stockers
Heifer
Stockers
Feedlot
Alabama
4
7
1
3
4
359
704
26
63
25
20
7
Alaska
0
0
0
0
0
2
5
0
1
0
0
0
Arizona
102
196
35
81
102
95
187
8
20
132
20
305
Arkansas
3
6
1
2
3
474
928
37
90
55
36
13
California
910
1,756
224
529
910
339
665
28
68
298
85
521
Colorado
80
155
30
70
80
417
817
41
99
423
294
1,122
Conn.
10
19
3
7
10
3
5
0
1
1
1
0
' Additional information on the Cattle Enteric Fermentation Model can be found in ICF (2006).
A-290 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Delaware
3
5
1
2
3
1
3

0
0
1
0
0
Florida
63
122
10
24
63
471
922

27
65
15
17
4
Georgia
43
83
9
21
43
258
505

24
57
18
28
6
Hawaii
1
2
0
1
1
38
75

3
7
5
2
1
Idaho
311
600
91
216
311
259
508

26
63
154
107
320
Illinois
48
93
15
36
48
201
393

16
39
119
61
308
Indiana
96
185
24
56
96
109
213

11
26
54
28
133
Iowa
111
215
40
94
111
500
980

40
97
651
305
1,410
Kansas
78
150
30
70
78
814
1,594

67
162
1,019
807
2,747
Kentucky
30
57
12
28
30
530
1,039

32
78
107
65
21
Louisiana
6
12
1
3
6
232
455

18
44
12
11
3
Maine
16
30
4
10
16
6
11

1
2
2
2
1
Maryland
24
47
9
20
24
22
44

2
6
7
3
11
Mass.
6
12
2
5
6
3
7

0
1
1
1
0
Michigan
220
425
50
118
220
62
122

5
13
84
23
180
Minn.
239
460
87
205
239
192
376

20
50
249
93
457
Miss.
5
9
2
4
5
247
483

20
49
21
17
6
Missouri
44
85
13
31
44 1,065
2,086

80
193
229
133
130
Montana
7
14
3
6
7
770
1,509

92
222
114
145
55
Nebraska
31
60
7
17
31
995
1,949

81
196
1,138
779
2,979
Nevada
16
30
3
8
16
114
223

9
21
22
16
3
N.Hamp.
7
14
2
4
7
3
5

0
1
1
1
0
N.Jersey
3
7
1
3
3
4
8

0
1
1
1
0
N.Mexico
169
325
32
77
169
241
472

22
52
60
51
16
NewYork
322
620
105
247
322
57
112

10
24
22
28
23
N.Car.
23
45
6
15
23
192
376

15
36
21
14
5
N.Dakota
8
16
3
6
8
494
969

44
108
127
121
61
Ohio
136
262
35
83
136
149
292

16
39
109
34
183
Oklahoma
18
35
6
14
18 1,086
2,127

94
227
447
262
365
Oregon
64
124
19
45
64
283
554

23
55
77
65
100
Penn
272
525
93
219
272
96
188

14
34
80
34
112
R.Island
0
1
0
0
0
1
1

0
0
0
0
0
S.Car.
8
15
2
5
8
88
173

7
17
4
6
1
S.Dakota
60
116
13
31
60
862
1,689

85
206
368
299
469
Tenn.
21
41
10
24
21
471
923

31
76
67
51
17
Texas
254
490
77
181
254 2,311
4,528

175
423
1,287
762
2,921
Utah
48
92
16
38
48
175
343

18
44
40
34
26
Vermont
67
129
17
39
67
7
14

1
3
2
4
1
Virginia
45
87
11
26
45
333
653

24
59
82
40
24
Wash.
143
275
35
83
143
117
228

12
30
94
66
229
W.Virg.
4
8
1
3
4
107
210

8
20
19
10
5
Wisconsin
664
1,281
210
494
664
150
294

17
42
199
28
325
Wyoming
3
6
1
2
3
370
725

40
97
80
77
89
Table A-140: Cattle Population Estimates from the CEFM Transition Matrix for 1990-2019 (1,000 head)
Livestock Type


1990
1995
2000
2005
2013
2014
2015
2016 2017
2018a
2019a
Dairy













Dairy Calves (0-6












months)


5,369
5,091
4,951
4,628
4,758
4,740
4,771
4,758 4,785
4,800
4,846
Dairy Cows

10,015
9,482
9,183
9,004
9,221
9,208
9,307
9,310 9,346
9,432
9,353
Dairy Replacements 7-











11 months


1,214
1,216
1,196
1,257
1,341
1,377
1,415
1,414 1,419
1,423
1,403
A-291
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Dairy Replacements 12-
23 months
2,915
2,892
2,812
2,905
3,185
3,202
3,310
3,371
3,343
3,353
3,306
Beef











Beef Calves (0-6 months)
16,909
18,177
17,431
16,918
14,859
14,741
15,000
15,563
15,971
16,021
16,175
Bulls
2,160
2,385
2,293
2,214
2,074
2,038
2,109
2,142
2,244
2,252
2,253
Beef Cows
32,455
35,190
33,575
32,674
29,631
29,085
29,302
30,166
31,213
31,466
31,691
Beef Replacements 7-11











months
1,269
1,493
1,313
1,363
1,291
1,385
1,479
1,515
1,484
1,424
1,372
Beef Replacements 12-











23 months
2,967
3,637
3,097
3,171
3,041
3,121
3,424
3,578
3,598
3,454
3,328
Steer Stockers
10,321
11,716
8,724
8,185
7,457
7,374
7,496
8,150
7,957
8,032
8,144
Heifer Stockers
5,946
6,699
5,371
5,015
4,455
4,280
4,385
4,810
4,754
4,937
5,086
Feedlot Cattle
9,549
11,064
13,006
12,652
13,267
13,219
12,883
13,450
14,340
15,475
15,718
a Population estimates for 2018 and 2019 are based on a simplified calculation approach rather than CEFM output, as noted
above and explained in the Enteric Fermentation Chapter 5.1.
The population transition matrix in the CEFM simulates the U.S. cattle population over time and provides an
estimate of the population age and weight structure by cattle type on a monthly basis.81 Since cattle often do not remain
in a single population type for an entire year (e.g., calves become stockers, stockers become feedlot animals), and
emission profiles vary both between and within each cattle type, these monthly age groups are tracked in the enteric
fermentation model to obtain more accurate emission estimates than would be available from annual point estimates of
population (such as available from USDA statistics) and weight for each cattle type.
The transition matrix tracks both dairy and beef populations, and divides the populations into males and
females, and subdivides the population further into specific cattle groupings for calves, replacements, stockers, feedlot,
and mature animals. The matrix is based primarily on two types of data: population statistics and weight statistics
(including target weights, slaughter weights, and weight gain). Using the weight data, the transition matrix simulates the
growth of animals over time by month. The matrix also relies on supplementary data, such as feedlot placement
statistics, slaughter statistics, death rates, and calving rates, described in further detail below.
The basic method for tracking population of animals per category is based on the number of births (or
graduates) into the monthly age group minus those animals that die or are slaughtered and those that graduate to the
next category (such as stockers to feedlot placements).
Each stage in the cattle lifecycle was modeled to simulate the cattle population from birth to slaughter. This
level of detail accounts for the variability in CH4 emissions associated with each life stage. Given that a stage can last less
than one year (e.g., calves are usually weaned between 4 and 6 months of age), each is modeled on a per-month basis.
The type of cattle also influences CH4 emissions (e.g., beef versus dairy). Consequently, there is an independent
transition matrix for each of three separate lifecycle phases, 1) calves, 2) replacements and stockers, and 3) feedlot
animals. In addition, the number of mature cows and bulls are tabulated for both dairy and beef stock. The transition
matrix estimates total monthly populations for all cattle subtypes. These populations are then reallocated to the state
level based on the percent of the cattle type reported in each state in the January 1 USDA data. Each lifecycle is
discussed separately below, and the categories tracked are listed in Table A-141.
Table A-141: Cattle Population Categories Used for Estimating Cm Emissions
Dairy Cattle	Beef Cattle
Calves	Calves
Heifer Replacements	Heifer Replacements
Cows	Heifer and Steer Stockers
Animals in Feedlots (Heifers & Steer)
Cows
Bulls3
a Bulls (beef and dairy) are accounted for in a single category.
81 Mature animal populations are not assumed to have significant monthly fluctuations, and therefore the populations utilized are the
January estimates downloaded from USDA (2016).
A-292 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
The key variables tracked for each of these cattle population categories are as follows:
Calves. Although enteric emissions are only calculated for 4- to 6-month old calves, it is necessary to calculate
populations from birth as emissions from manure management require total calf populations and the estimates of
populations for older cattle rely on the available supply of calves from birth. The number of animals born on a monthly
basis was used to initiate monthly cohorts and to determine population age structure. The number of calves born each
month was obtained by multiplying annual births by the percentage of births per month. Annual birth information for
each year was taken from USDA (2016). For dairy cows, the number of births is assumed to be distributed equally
throughout the year (approximately 8.3 percent per month) while beef births are distributed according to Table A-142,
based on approximations from the National Animal Health Monitoring System (NAHMS) (USDA/APHIS/VS 1998,1994,
1993). To determine whether calves were born to dairy or beef cows, the dairy cow calving rate (USDA/APHIS/VS 2002,
USDA/APHIS/VS 1996) was multiplied by the total dairy cow population to determine the number of births attributable
to dairy cows, with the remainder assumed to be attributable to beef cows. Total annual calf births are obtained from
USDA and distributed into monthly cohorts by cattle type (beef or dairy). Calf growth is modeled by month, based on
estimated monthly weight gain for each cohort (approximately 61 pounds per month). The total calf population is
modified through time to account for veal calf slaughter at 4 months and a calf death loss of 0.35 percent annually
(distributed across age cohorts up to 6 months of age). An example of a transition matrix for calves is shown in Table A-
143. Note that 1- to 6-month old calves in January of each year have been tracked through the model based on births
and death loss from the previous year.
Table A-142: Estimated Beef Cow Births by Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
7%
15%
28%
22%
9%
3%
2%
2%
3%
4%
3%
3%
Table A-143: Example of Monthly Average Populations from Calf Transition Matrix (1,000 head)
Age (month)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
6
1,163
1,154
1,378
1,618
1,552
1,541
2,515
4,711
8,199
6,637
3,089
1,542
5
1,155
1,379
1,619
1,553
1,541
2,516
4,712
8,202
6,640
3,091
1,544
1,151
4
1,426
1,660
1,598
1,580
2,556
4,754
8,243
6,688
3,135
1,588
1,194
1,184
3
1,662
1,599
1,581
2,557
4,755
8,246
6,690
3,136
1,588
1,194
1,185
1,459
2
1,600
1,582
2,558
4,757
8,249
6,693
3,138
1,589
1,195
1,186
1,460
1,698
1
1,584
2,560
4,760
8,253
6,695
3,139
1,590
1,195
1,186
1,461
1,699
1,635
0
2,562
4,763
8,257
6,698
3,140
1,590
1,196
1,187
1,462
1,700
1,636
1,618
Note: The cohort starting at age 0 months on January 1 is tracked in order to illustrate how a single cohort moves through
the transition matrix. Each month, the cohort reflects the decreases in population due to the estimated 0.35 percent
annual death loss, and between months 4 and 5, a more significant loss is seen than in other months due to estimated
veal slaughter.
Replacements and Stockers. At 7 months of age, calves "graduate" and are separated into the applicable cattle
types: replacements (cattle raised to give birth), or stockers (cattle held for conditioning and growing on grass or other
forage diets). First the number of replacements required for beef and dairy cattle are calculated based on estimated
death losses and population changes between beginning and end of year population estimates. Based on the USDA
estimates for "replacement beef heifers" and "replacement dairy heifers," the transition matrix for the replacements is
back-calculated from the known animal totals from USDA, and the number of calves needed to fill that requirement for
each month is subtracted from the known supply of female calves. All female calves remaining after those needed for
beef and dairy replacements are removed and become "stockers" that can be placed in feedlots (along with all male
calves). During the stocker phase, animals are subtracted out of the transition matrix for placement into feedlots based
on feedlot placement statistics from USDA (2016).
The data and calculations that occur for the stocker category include matrices that estimate the population of
backgrounding heifers and steer, as well as a matrix for total combined stockers. The matrices start with the beginning of
year populations in January and model the progression of each cohort. The age structure of the January population is
based on estimated births by month from the previous two years, although in order to balance the population properly,
an adjustment is added that slightly reduces population percentages in the older populations. The populations are
modified through addition of graduating calves (added in month 7, bottom row of Table A-144) and subtraction through
A-293
-------
death loss and animals placed in feedlots. Eventually, an entire cohort population of stockers may reach zero, indicating
that the complete cohort has been transitioned into feedlots. An example of the transition matrix for stockers is shown
in Table A-144.
Table A-144: Example of Monthly Average Populations from Stocker Transition Matrix (1,000 head)
Age (month) Jan
Feb
Mar
Apr May
Jun
Jul
Aug Sep
Oct
Nov
Dec
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
185
320
260
123
63
48
47
58
67
65
64
982
1,814
3,133
2,545
1,200
3,381
180
146
69
35
27
27
33
38
36
36
845
1,602
2,770
2,255
1,062
2,951
104
49
25
19
19
23
27
26
25
599
1,478
2,556
2,056
945
2,502
800
664
794
37
19
14
14
17
20
19
19
452
1,172
2,309
1,858
855
2,241
15
12
11
14
16
16
15
363
977
1,921
1,639
755
1,872
484
482
956
385
335
557
1,160
9
9
11
13
13
13
295
828
1,619
1,378
629
1,512
9
11
10
10
241
709
1,380
1,172
534
1,117
277
189
341
759
1,109
196
610
1,179
1,000
456
862
214
138
184
420
658
1,100
6
6
6
133
472
900
759
348
603
47
46
76
231
372
649
1,876
3
3
68
331
615
514
237
340
47
46
57
89
209
371
1,503
3,666
1
17
218
387
318
149
129
47
46
57
66
81
185
1,292
3,247
6,504
0
181
313
254
120
61
47
46
57
66
63
80
1,135
2,786
5,984
5,243
47
46
57
66
63
63
1,016
2,445
5,299
4,877
2,353
Note: The cohort starting at age 7 months on January 1 is tracked in order to illustrate how a single cohort moves through
the transition matrix. Each month, the cohort reflects the decreases in population due to the estimated 0.35 percent
annual death loss and loss due to placement in feedlots (the latter resulting in the majority of the loss from the matrix).
In order to ensure a balanced population of both stockers and placements, additional data tables are utilized in
the stocker matrix calculations. The tables summarize the placement data by weight class and month, and is based on
the total number of animals within the population that are available to be placed in feedlots and the actual feedlot
placement statistics provided by USDA (2016). In cases where there are discrepancies between the USDA estimated
placements by weight class and the calculated animals available by weight, the model pulls available stockers from one
higher weight category if available. If there are still not enough animals to fulfill requirements the model pulls animals
from one lower weight category. In the current time series, this method was able to ensure that total placement data
matched USDA estimates, and no shortfalls have occurred.
In addition, average weights were tracked for each monthly age group using starting weight and monthly
weight gain estimates. Weight gain (i.e., pounds per month) was estimated based on weight gain needed to reach a set
target weight, divided by the number of months remaining before target weight was achieved. Birth weight was
assumed to be 88 pounds for both beef and dairy animals. Weaning weights were estimated at 515 pounds. Other
reported target weights were available for 12-, 15-, 24-, and 36-month-old animals, depending on the animal type. Beef
cow mature weight was taken from measurements provided by a major British Bos taurus breed (Enns 2008) and
increased during the time series through 2007.82 Bull mature weight was calculated as 1.5 times the beef cow mature
weight (Doren et al. 1989). Beef replacement weight was calculated as 70 percent of mature weight at 15 months and 85
percent of mature weight at 24 months. As dairy weights are not a trait that is typically tracked, mature weight for dairy
cows was estimated at 1,500 pounds for all years, based on a personal communication with Kris Johnson (2010) and an
estimate from Holstein Association USA (2010).83 Dairy replacement weight at 15 months was assumed to be 875 pounds
and 1,300 pounds at 24 months. Live slaughter weights were estimated from dressed slaughter weight (USDA 2019)
divided by 0.63. This ratio represents the dressed weight (i.e., weight of the carcass after removal of the internal organs),
82 Mature beef weight is held constant after 2007 but future inventory submissions will incorporate known trends through 2007 and
extrapolate to future years, as noted in the Planned Improvements section of 5.1 Enteric Fermentation.
ss Mature dairy weight is based solely on Holstein weight, so could be higher than the national average. Future Inventory submissions
will consider other dairy breeds, as noted in the Planned Improvements section of 5.1 Enteric Fermentation.
A-294 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
to the live weight (i.e., weight taken immediately before slaughter). The annual typical animal mass for each livestock
type are presented in Table A-145.
Weight gain for stocker animals was based on monthly gain estimates from Johnson (1999) for 1989, and from
average daily estimates from Lippke et al. (2000), Pinchack et al. (2004), Platter et al. (2003), and Skogerboe et al. (2000)
for 2000. Interim years were calculated linearly, as shown in Table A-146, and weight gain was held constant starting in
2000. Table A-146 provides weight gains that vary by year in the CEFM.
Table A-145: Typical Animal Mass (lbs)84
Year/Cattle

Dairy
Dairy
Beef

Beef
Steer
Heifer
Steer
Heifer
Type
Calves
Cows3
Replacements'1
Cows3
Bulls3
Replacements'1
Stockersb
Stockersb Feedlotb
Feedlotb
1990
269
1,499
899
1,220
1,830
819
691
651
923
845
1991
270
1,499
897
1,224
1,836
821
694
656
933
855
1992
269
1,499
897
1,262
1,893
840
714
673
936
864
1993
270
1,499
898
1,279
1,918
852
721
683
929
863
1994
270
1,499
897
1,279
1,918
853
720
688
943
875
1995
270
1,499
897
1,281
1,921
857
735
700
947
879
1996
269
1,499
898
1,284
1,926
858
739
707
939
878
1997
270
1,499
899
1,285
1,927
860
736
707
938
876
1998
270
1,499
896
1,295
1,942
865
736
709
956
892
1999
270
1,499
899
1,291
1,936
861
730
708
959
894
2000
270
1,499
896
1,271
1,906
849
719
702
960
898
2001
270
1,499
897
1,271
1,906
850
725
707
963
900
2002
270
1,499
896
1,275
1,912
851
725
707
981
915
2003
270
1,499
899
1,307
1,960
871
718
701
972
904
2004
270
1,499
896
1,322
1,983
877
719
702
966
904
2005
270
1,499
894
1,326
1,989
879
717
706
974
917
2006
270
1,499
897
1,340
2,010
889
724
712
983
925
2007
270
1,499
896
1,347
2,020
894
720
706
991
928
2008
270
1,499
897
1,347
2,020
894
720
704
999
938
2009
270
1,499
895
1,347
2,020
894
730
715
1007
947
2010
270
1,499
897
1,347
2,020
896
726
713
996
937
2011
270
1,499
897
1,347
2,020
891
721
712
989
932
2012
270
1,499
899
1,347
2,020
892
714
706
1003
945
2013
270
1,499
898
1,347
2,020
892
718
709
1016
958
2014
270
1,499
895
1,347
2,020
888
722
714
1022
962
2015
270
1,499
896
1,347
2,020
890
717
714
1037
982
2016
269
1,499
899
1,220
1,830
819
691
651
923
845
2017
269
1,499
899
1,220
1,830
819
691
651
923
845
a Input into the model.
b Annual average calculated in model based on age distribution.
84 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through
2020) Inventory submission.
A-295
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Table A-146: Weight Gains that Vary by Year (lbs)

Steer Stockers to 12
Steer Stockers to 24
Heifer Stockers to 12
Heifer Stockers to 24
Year/Cattle Type
months(lbs/day)
months (lbs/day)
months(lbs/day)
months(lbs/day)
1990
1.53
1.23
1.23
1.08
1991
1.56
1.29
1.29
1.15
1992
1.59
1.35
1.35
1.23
1993
1.62
1.41
1.41
1.30
1994
1.65
1.47
1.47
1.38
1995
1.68
1.53
1.53
1.45
1996
1.71
1.59
1.59
1.53
1997
1.74
1.65
1.65
1.60
1998
1.77
1.71
1.71
1.68
1999
1.80
1.77
1.77
1.75
2000-onwards
1.83
1.83
1.83
1.83
Sources: Enns (2008), Johnson (1999), Lippke et al. (2000), NRC (1999), Pinchack et al. (2004), Platter et al.
(2003), Skogerboe et al. (2000).
Feedlot Animals. Feedlot placement statistics from USDA provide data on the placement of animals from the
stocker population into feedlots on a monthly basis by weight class. The model uses these data to shift a sufficient
number of animals from the stocker cohorts into the feedlot populations to match the reported placement data. After
animals are placed in feedlots they progress through two steps. First, animals spend 25 days on a step-up diet to become
acclimated to the new feed type (e.g., more grain than forage, along with new dietary supplements), during this time
weight gain is estimated to be 2.7 to 3 pounds per day (Johnson 1999). Animals are then switched to a finishing diet
(concentrated, high energy) for a period of time before they are slaughtered. Weight gain during finishing diets is
estimated to be 2.9 to 3.3 pounds per day (Johnson 1999). The length of time an animal spends in a feedlot depends on
the start weight (i.e., placement weight), the rate of weight gain during the start-up and finishing phase of diet, and the
target weight (as determined by weights at slaughter). Additionally, animals remaining in feedlots at the end of the year
are tracked for inclusion in the following year's emission and population counts. For 1990 to 1995, only the total
placement data were available, therefore placements for each weight category (categories displayed in Table A-147) 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-147 provides a summary of the reported feedlot
placement statistics for 2017.
Table A-147: Feedlot Placements in the United States for 2017 (Number of animals placed/1,000 Head)85
Weight
Placed When:
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
< 600 lbs
380
315
350
348
400
375
360
360
405
675
610
470
600-700 lbs
445
330
295
255
315
315
235
285
340
590
545
410
700-800 lbs
585
490
630
490
529
430
385
418
490
510
455
445
>800 lbs
571
559
842
755
875
650
635
865
915
618
489
474
Total
1,981
1,694
2,117
1,848
2,119
1,770
1,615
1,928
2,150
2,393
2,099
1,799
Note: Totals may not sum due to independent rounding.
Source: USDA (2018).
Mature Animals. Energy requirements and hence, composition of diets, level of intake, and emissions for
particular animals, are greatly influenced by whether the animal is pregnant or lactating. Information is therefore needed
on the percentage of all mature animals that are pregnant each month, as well as milk production, to estimate CH4
emissions. A weighted average percent of pregnant cows each month was estimated using information on births by
month and average pregnancy term. For beef cattle, a weighted average total milk production per animal per month was
estimated using information on typical lactation cycles and amounts (NRC 1999), and data on births by month. This
85 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through
2020) Inventory submission.
A-296 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
process results in a range of weighted monthly lactation estimates expressed as pounds per animal per month. The
monthly estimates for daily milk production by beef cows are shown in Table A-148. Annual estimates for dairy cows
were taken from USDA milk production statistics. Dairy lactation estimates for 1990 through 2017 are shown in Table A-
149. Beef and dairy cow and bull populations are assumed to remain relatively static throughout the year, as large
fluctuations in population size are assumed to not occur. These estimates are taken from the USDA beginning and end of
year population datasets.
Table A-148: Estimates of Average Monthly Milk Production by Beef Cows (lbs/cow)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Beef Cow Milk Production
(lbs/ head)	3.3 5.1 8.7 12.0 13.6 13.3 11.7 9.3 6.9 4.4 3.0 2.8
Table A-149: Dairy Lactation Rates by State (lbs/ year/cow)86
State/Year
1990
1995
2000
2005
2011
2012
2013
2014
2015
2016
2017
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
12,214
13,300
17,500
11,841
18,456
17,182
15,606
13,667
14,033
12,973
13.604
16,475
14,707
14,590
15,118
12,576
10,947
11.605
14,619
13,461
14,871
15,394
14,127
12,081
13,632
13,542
13,866
16,400
15,100
13,538
18,815
14,658
15,220
12,624
13,767
12,327
16,273
14,726
14,176
17,000
19,735
12,150
19,573
18,687
16,438
14.500
14,698
15,550
13,654
18,147
15,887
15,375
16,124
14,390
12,469
11.908
16,025
14,725
16,000
17,071
15,894
12.909
14,158
15,000
14,797
18,128
16,300
13,913
18,969
16.501
16,314
13,094
15,917
13,611
17,289
16,492
13,920
14,500
21,820
12,436
21,130
21,618
17,778
14,747
15,688
16,284
14,358
20,816
17,450
16,568
18,298
16,923
12,841
12,034
17,128
16,083
17,091
19,017
17,777
15,028
14,662
17,789
16,513
19,000
17,333
15,250
20,944
17,378
16,746
14,292
17,027
14,440
18,222
18,081
14,000
12,273
22.679
13,545
21,404
22,577
19,200
16,622
16,591
17,259
12,889
22,332
18,827
20,295
20,641
20,505
12,896
12,400
18,030
16,099
17,059
21,635
18,091
15,280
16,026
19,579
17,950
21.680
18.875
16,000
21,192
18,639
18,741
14,182
17,567
16,480
18.876
18,722
14,300
13,800
23,473
11,917
23,438
23,430
19,000
18,300
19,067
18,354
14,421
22,926
18,510
20,657
21,191
21,016
14,342
12,889
18,688
18,654
16,923
23,164
18,996
14,571
14,611
20,571
20,579
22,966
20,429
16,875
24,854
21,046
20,089
18,158
19,194
17,415
20,488
19,495
13,000
14,250
23,979
13,300
23,457
24,158
19,889
19,542
19,024
19,138
14,200
23,376
19,061
21,440
22,015
21,683
15,135
13,059
18,576
19,196
18,250
23,976
19,512
14,214
14,979
21,357
21,179
22,931
19,643
18,571
24,694
21,623
20,435
19,278
19,833
17,896
20,431
19,549
13,000
10,667
23,626
11,667
23,178
24,292
20,556
19,521
19,374
19,600
13,409
23,440
19,063
21,761
22,149
21,881
15,070
12,875
19,548
19,440
17,692
24,116
19,694
13,286
14,663
21,286
21,574
22,034
20,923
18,143
24,944
22,070
20,326
18,944
20,178
17,311
20,439
19,797
13,625
11,667
24,368
13,714
23,786
24,951
20,158
20,104
20,390
20,877
13,591
24,127
19,681
21,865
22,449
22,085
15,905
13,600
19,967
19,740
17,923
24,638
19,841
14,462
15,539
21,500
22,130
23,793
20,143
18,143
25,093
22,325
20,891
20,250
20,318
18,150
20,565
20,121
12,625
11,667
24,402
13,000
23,028
25,733
20,842
19,700
20,656
21,651
15,909
24,126
20.149
22,115
22.929
22,210
17,656
13,429
19,800
20,061
18,083
25.150
20,570
15,000
15,511
21,357
22.930
23,069
20,143
18,143
24,245
22,806
20,957
20,750
20,573
18,641
20,408
20,377
13,143
11,667
24,679
13,333
22,968
25,993
21,526
19,100
20,285
21,786
14,542
24,647
20,340
22,571
23,634
22,801
18,052
14,083
21,000
19,938
18,417
25,957
20,967
14,400
14,824
21,071
23,317
22,000
20,500
17,429
24,479
23,834
20,978
21,500
20,936
18,703
20,744
20,454
14,833
9,667
24,680
13,167
22,755
26,181
22,105
18,560
20,129
21,905
16,913
24,378
20,742
22,802
23,725
23,000
18,589
13,333
21,000
19,854
17,583
26,302
21,537
15,222
14,588
22,154
24,067
22,156
21,000
19.833
24,960
23,936
21,156
21,563
21,259
18,667
20,395
20.834
86 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through
2020) Inventory submission.
A-297
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Rhode Island
14,250
14,773
15,667
17,000
17,909
16,636
19,000
19,000
17,667
17,625
16,250
South Carolina
12,771
14,481
16,087
16,000
17,438
17,250
16,500
16,438
17,400
16,667
16,467
South Dakota
12,257
13,398
15,516
17,741
20,582
21,391
21,521
21,753
22,255
22,139
22,376
Tennessee
11,825
13,740
14,789
15,743
16,200
16,100
15,938
16,196
16,489
16,571
17,325
Texas
14,350
15,244
16,503
19,646
22,232
22,009
21,991
22,268
22,248
22,680
23,589
Utah
15,838
16,739
17,573
18,875
22,161
22,863
22,432
22,989
23,125
22,772
23,316
Vermont
14,528
16,210
17,199
18,469
18,940
19,316
19,448
20,197
20,197
20,977
21,147
Virginia
14,213
15,116
15,833
16,990
17,906
17,990
18,337
19,129
19,462
19,144
19,954
Washington
18,532
20,091
22,644
23,270
23,727
23,794
23,820
24,088
23,848
24,094
23,818
West Virginia
11,250
12,667
15,588
14,923
15,700
15,400
15,200
15,556
15,667
14,889
15,875
Wisconsin
13,973
i 15,397
17,306
18,500
20,599
21,436
21,693
21,869
22,697
23,542
23,725
Wyoming
12,337
\ 13,197 ..
13,571
14,878
20,517
20,650
21,367
21,583
22,567
23,300
23,033
Source: USDA(2018).
Step 2: Characterize U.S. Cattle Population Diets
To support development of digestible energy (DE, the percent of gross energy intake digested by the animal)
and CH4 conversion rate (Ym, the fraction of gross energy converted to CH4) values for each of the cattle population
categories, data were collected on diets considered representative of different regions. For both grazing animals and
animals being fed mixed rations, representative regional diets were estimated using information collected from state
livestock specialists, the USDA, expert opinion, and other literature sources. The designated regions for this analysis for
dairy cattle for all years and foraging beef cattle from 1990 through 2006 are shown in Table A-150. For foraging beef
cattle from 2007 onwards, the regional designations were revised based on data available from the NAHMS 2007
through 2008 survey on cow-calf system management practices (USDA:APHIS:VS 2010) and are shown in Table A-151.
The data for each of the diets (e.g., proportions of different feed constituents, such as hay or grains) were used to
determine feed chemical composition for use in estimating DE and Ym for each animal type.
Table A-150: Regions used for Characterizing the Diets of Dairy Cattle (all years) and Foraging Cattle from 1990-
2006
West
California
Northern
Great Plains
Midwestern
Northeast
Southcentral
Southeast
Alaska
California
Colorado
Illinois
Connecticut
Arkansas
Alabama
Arizona

Kansas
Indiana
Delaware
Louisiana
Florida
Hawaii

Montana
Iowa
Maine
Oklahoma
Georgia
Idaho

Nebraska
Michigan
Maryland
Texas
Kentucky
Nevada

North
Minnesota
Massachusetts

Mississippi
New Mexico

Dakota
Missouri
New

North
Oregon

South
Ohio
Hampshire

Carolina
Utah

Dakota
Wisconsin
New Jersey

South
Washington

Wyoming

New York
Pennsylvania
Rhode Island
Vermont
West Virginia

Carolina
Tennessee
Virginia
Source: USDA (1996).
A-298 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-151: Regions used for Characterizing the Diets of Foraging Cattle from 2007-2017
West
Central
Northeast
Southeast
Alaska
Arizona
California
Colorado
Hawaii
Idaho
Montana
Nevada
New Mexico
Oregon
Utah
Washington
Wyoming
Illinois
Indiana
Iowa
Kansas
Michigan
Minnesota
Missouri
Nebraska
North Dakota
Ohio
South Dakota
Wisconsin
Connecticut
Delaware
Maine
Maryland
Massachusetts
New Hampshire
New Jersey
New York
Pennsylvania
Rhode Island
Vermont
West Virginia
Alabama
Arkansas
Florida
Georgia
Kentucky
Louisiana
Mississippi
North Carolina
Oklahoma
South Carolina
Tennessee
Texas
Virginia
Note: States in bold represent a change in region from the 1990 to 2006 assessment.
Source: Based on data from USDA:APHIS:VS (2010).
DE and Ym vary by diet and animal type. The IPCC recommends Ym values of 3.0+1.0 percent for feedlot cattle
and 6.5+1.0 percent for all other cattle (IPCC 2006). Given the availability of detailed diet information for different
regions and animal types in the United States, DE and Ym values unique to the United States were developed for dairy
and beef cattle. Digestible energy and Ym values were estimated across the time series for each cattle population
category based on physiological modeling, published values, and/or expert opinion.
For dairy cows, ruminant digestion models were used to estimate Ym. The three major categories of input
required by the models are animal description (e.g., cattle type, mature weight), animal performance (e.g., initial and
final weight, age at start of period), and feed characteristics (e.g., chemical composition, habitat, grain or forage). Data
used to simulate ruminant digestion is provided for a particular animal that is then used to represent a group of animals
with similar characteristics. The Ym values were estimated for 1990 using the Donovan and Baldwin model (1999), which
represents physiological processes in the ruminant animals, as well as diet characteristics from USDA (1996). The
Donovan and Baldwin model is able to account for differing diets (i.e., grain-based or forage-based), so that Ym values for
the variable feeding characteristics within the U.S. cattle population can be estimated. Subsequently, a literature review
of dairy diets was conducted and nearly 250 diets were analyzed from 1990 through 2009 across 23 states—the review
indicated highly variable diets, both temporally and spatially. Kebreab et al. (2008) conducted an evaluation of models
and found that the COWPOLL model was the best model for estimating Ym for dairy, so COWPOLL was used to determine
the Ym value associated with each of the evaluated diets. The statistical analysis of the resulting Ym estimates showed a
downward trend in predicting Ym, which inventory team experts modeled using the following best-fit non-liner curve:
The team determined that the most comprehensive approach to estimating annual, region-specific Ym values
was to use the 1990 baseline Ym values derived from Donovan and Baldwin and then scale these Ym values for each year
beyond 1990 with a factor based on this function. The scaling factor is the ratio of the Ym value for the year in question to
the 1990 baseline Ym value. The scaling factor for each year was multiplied by the baseline Ym value. The resulting Ym
equation (incorporating both Donovan and Baldwin (1999) and COWPOLL) is shown below (and described in ERG 2016):
DE values for dairy cows were estimated from the literature search based on the annual trends observed in the
data collection effort. The regional variability observed in the literature search was not statistically significant, and
therefore DE was not varied by region, but did vary over time, and was grouped by the following years 1990 through
1993, 1994 through 1998, 1999 through 2003, 2004 through 2006, 2007, and 2008 onwards.
Ym = 4.52e'i'ea:r-198oJ
A-299
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Considerably less data was available for dairy heifers and dairy calves. Therefore, for dairy heifers assumptions
were based on the relationship of the collected data in the literature on dairy heifers to the data on dairy cow diets.
From this relationship, DE was estimated as the mature cow DE minus three percent, and Ym was estimated as that of the
mature dairy cow plus 0.1 percent.
To calculate the DE values for grazing beef cattle, diet composition assumptions were used to estimate
weighted DE values for a combination of forage and supplemental diets. The forage portion makes up an estimated 85 to
95 percent of grazing beef cattle diets, and there is considerable variation of both forage type and quality across the
United States. Currently there is no comprehensive survey of this data, so for this analysis two regional DE values were
developed to account for the generally lower forage quality in the "West" region of the United States versus all other
regions in Table A-150 (California, Northern Great Plains, Midwestern, Northeast, Southcentral, Southeast) and Table A-
151 (Central, Northeast, and Southeast). For all non-western grazing cattle, the forage DE was an average of the
estimated seasonal values for grass pasture diets for a calculated DE of 64.2 percent. For foraging cattle in the west, the
forage DE was calculated as the seasonal average for grass pasture, meadow and range diets, for a calculated DE of 61.3
percent. The assumed specific components of each of the broad forage types, along with their corresponding DE value
and the calculated regional DE values can be found in Table A-152. In addition, beef cattle are assumed to be fed a
supplemental diet, consequently, two sets of supplemental diets were developed, one for 1990 through 2006 (Donovan
1999) and one for 2007 onwards (Preston 2010, Archibeque 2011, USDA:APHIS:VS 2010) as shown in Table A-153 and
Table A-154 along with the percent of each total diet that is assumed to be made up of the supplemental portion. By
weighting the calculated DE values from the forage and supplemental diets, the DE values for the composite diet were
calculated.87 These values are used for steer and heifer stockers and beef replacements. Finally, for mature beef cows
and bulls, the DE value was adjusted downward by two percent to reflect the lower digestibility diets of mature cattle
based on Johnson (2002). Ym values for all grazing beef cattle were set at 6.5 percent based on Johnson (2002). The Ym
values and the resulting final weighted DE values by region for 2007 onwards are shown in Table A-155.
For feedlot animals, DE and Ym are adjusted over time as diet compositions in actual feedlots are adjusted
based on new and improved nutritional information and availability of feed types. Feedlot diets are assumed to not
differ significantly by state, and therefore only a single set of national diet values is utilized for each year. The DE and Ym
values for 1990 were estimated by Dr. Don Johnson (1999). In the CEFM, the DE values for 1991 through 1999 were
linearly extrapolated based on values for 1990 and 2000. DE and Ym values from 2000 through the current year were
estimated using the MOLLY model as described in Kebreab et al. (2008), based on a series of average diet feed
compositions from Galyean and Gleghorn (2001) for 2000 through 2006 and Vasconcelos and Galyean (2007) for 2007
onwards. In addition, feedlot animals are assumed to spend the first 25 days in the feedlot on a "step-up" diet to
become accustomed to the higher quality feedlot diets. The step-up DE and Ym are calculated as the average of all state
forage and feedlot diet DE and Ym values.
For calves aged 4 through 6 months, a gradual weaning from milk is simulated, with calf diets at 4 months
assumed to be 25 percent forage, increasing to 50 percent forage at age 5 months, and 75 percent forage at age 6
months. The portion of the diet allocated to milk results in zero emissions, as recommended by the IPCC (2006). For
calves, the DE for the remainder of the diet is assumed to be similar to that of slightly older replacement heifers (both
beef and dairy are calculated separately). The Ym for beef calves is also assumed to be similar to that of beef replacement
heifers (6.5 percent), as literature does not provide an alternative Ym for use in beef calves. For dairy calves, the Ym is
assumed to be 7.8 percent at 4 months, 8.03 percent at 5 months, and 8.27 percent at 6 months based on estimates
provided by Soliva (2006) for Ym at 4 and 7 months of age and a linear interpolation for 5 and 6 months.
Table A-156 shows the regional DE and Ym for U.S. cattle in each region for 2017.
87 For example, the West has a forage DE of 61.3 which makes up 90 percent of the diet and a supplemented diet DE of 67.4 percent
was used for 10 percent of the diet, for a total weighted DE of 61.9 percent, as shown in Table A-155.
A-300 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-152: Feed Components and Digestible Energy Values Incorporated into Forage Diet Composition
Estimates


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0)
txo
<
0)
txo
0)
txo
E
0)
4-*
Forage Type
LU
Q
(0
O
IS)
(0
O
-------
Forage Type

0)

0)

0)

LU
O
o
3
4-*
VI
to
a
txo
3
4-*
1A
to
a
0)
F
3
4-*
VI
to
a

E
LU
Q
VI
VI
to
V
'u
a

3
0)
txo
£
(0
cc
3
<
0)
txo
£
(0
cc
0)
.Q
E
0)
4-»
a
0)
_sa_
0)
4-»
£
i
a)
txo
£
(0
cc
-§	«
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a>	Jr
s	£
o
"D
IS
ai
Orchardgrass Dactylis glomerata, fresh,
midbloom	60.13
Pearlmillet Pennisetum glaucum, fresh 68.04
Prairie plants, Midwest, hay, sun-cured 55.53
Rape Brassica napus, fresh, early bloom 80.88
Rye Secale cereale, fresh	71.83
Ryegrass, Perennial Lolium perenne,
fresh	73.68
Saltgrass Distichlis spp, fresh, post ripe 58.06
Sorghum, Sudangrass Sorghum bicolor
sudanense, fresh, early vegetative 73.27
Squirreltail Stanion spp, fresh, stem-
cured	62.00
Summercypress, Gray Kochia vestita,
fresh, stem-cured	65.11
Timothy Phleum pratense, fresh, late
vegetative	73.12
Timothy Phleum pratense, fresh,
midbloom	66.87
Trefoil, Birdsfoot Lotus corniculatus,
fresh	69.07
Vetch Vicia spp, hay, sun-cured	59.44
Wheat Triticum aestivum, straw	45.77
Wheatgrass, Crested Agropyron
desertorum, fresh, early vegetative 79.78
Wheatgrass, Crested Agropyron
desertorum, fresh, full bloom	65.89
Wheatgrass, Crested Agropyron
desertorum, fresh, post ripe	52.99
Winterfat, Common Eurotia lanata,
fresh, stem-cured	40.89
Weighted Average DE
72.99
62.45
57.26
67.11
62.70
60.62
58.59
52.07
64.03
55.11
Forage Diet for West
61.3 10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
Forage Diet for All Other Regions
64.2 33.3%
33.3%
33.3%
-
-
-
-
-
-
-
Note that forages marked with an x indicate that the DE from that specific forage type is included in the general forage type for
that column (e.g., grass pasture, range, meadow or meadow by month or season).
Sources: Preston (2010) and Archibeque (2011).
Table A-153: DE Values with Representative Regional Diets for the Supplemental Diet of Grazing Beef Cattle for
1990-2006
Feed
Source of DE
(NRC 1984)
Unweighted
DE (% of GE) California3
Northern
Great
West Plains
Southcentral Northeast Midwest Southeast
Alfalfa Hay Table 8, feed #006	61.79
Barley	85.08
Bermuda Table 8, feed #030	66.29
Bermuda Hay Table 8, feed #031	50.79
Corn	Table 8, feed #089	88.85
Corn Silage Table 8, feed #095	72.88
65% 30%
10% 15%
10% 10%
30%
25%
25%
29%
40%
11%
12%
13%
20%
30%
13%
20%
35%
A-302 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Cotton Seed
Meal	7%
Grass Hay Table 8, feed #126,
170,274	58.37	40%	30%
Orchard Table 8, feed #147	60.13	40%
Soybean Meal
Supplement	77.15	5%	5%	5%
Sorghum Table 8, feed #211	84.23	20%
Soybean Hulls	66.86	7%
Timothy Hay Table 8, feed #244	60.51	50%
Whole Cotton
Seed	75.75	5%	5%
Wheat
Middlings Table 8, feed #257	68.09	15%	13%
Wheat	Table 8, feed #259	87.95	10%
Weighted Supplement DE (%)
70.1
67.4
73.0
62.0
67.6
66.9
68.0
Percent of Diet that is







Supplement
5%
10%
15%
10%
15%
10%
5%
Source of representative regional diets: Donovan (1999).
a Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table
above.
Table A-154: DE Values and Representative Regional Diets for the Supplemental Diet of Grazing Beef Cattle for
2007-201788
Feed
Source of DE
Unweighted




(NRC1984)
DE (%of GE)
West3
Central3
Northeast3
Southeast3
Alfalfa Hay
Table 8,
feed #006
61.79
65%
30%
12%

Bermuda
Table 8,
feed #030
66.29



20%
Bermuda Hay
Table 8,
feed #031
50.79



20%
Corn
Table 8,
feed #089
88.85
10%
15%
13%
10%
Corn Silage
Table 8,
feed #095
72.88

35%
20%

Grass Hay
Table 8,
feed #126, 170, 274
58.37
10%



Orchard
Table 8,
feed #147
60.13



30%
Protein supplement







(West)
Table 8,
feed #082, 134, 225b
81.01
10%



Protein Supplement







(Central and Northeast)
Table 8,
feed #082, 134, 225b
80.76

10%
10%

Protein Supplement







(Southeast)
Table 8,
feed #082,134,101b
77.89



10%
Sorghum
Table 8,
feed #211
84.23

5%

10%
Timothy Hay
Table 8,
feed #244
60.51


45%

Wheat Middlings
Table 8,
feed #257
68.09

5%


Wheat
Table 8,
feed #259
87.95
5%



Weighted Supplement DE



67.4
73.1
68.9
66.6
Percent of Diet that is Supplement

10%
15%
5%
15%
a Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table
above.
b Not in equal proportions.
Sources of representative regional diets: Donovan (1999), Preston (2010), Archibeque (2011), and USDA:APHIS:VS (2010).
88 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020)
Inventory submission.
A-303
-------
Table A-155: Foraging Animal DE (% of GE) and Ym Values for Each Region and Animal Type for 2007-201789
Animal Type
Data
West3
Central
Northeast
Southeast
Beef Repl. Heifers
DEb
61.9
65.6
64.5
64.6

Y c
i m
6.5%
6.5%
6.5%
6.5%
Beef Calves (4-6 mo)
DE
61.9
65.6
64.5
64.6

Ym
6.5%
6.5%
6.5%
6.5%
Steer Stockers
DE
61.9
65.6
64.5
64.6

Ym
6.5%
6.5%
6.5%
6.5%
Heifer Stockers
DE
61.9
65.6
64.5
64.6

Ym
6.5%
6.5%
6.5%
6.5%
Beef Cows
DE
59.9
63.6
62.5
62.6

Ym
6.5%
6.5%
6.5%
6.5%
Bulls
DE
59.9
63.6
62.5
62.6

Ym
6.5%
6.5%
6.5%
6.5%
a Note that emissions are currently calculated on a state-by-state basis, but diets are
applied by the regions shown in the table above. To see the regional designation per
state, please see Table A-151.
b DE is the digestible energy in units of percent of GE (MJ/Day).
c Ym is the methane conversion rate, the fraction of GE in feed converted to methane.
Table A-156: Regional DE (% of GE) and Ym Rates for Dairy and Feedlot Cattle by Animal Type for 201790
Animal Type
Data
California3
West
Northern
Great Plains
Southcentral
Northeast
Midwest
Southeast
Dairy Repl. Heifers
DEb
63.7
63.7
63.7
63.7
63.7
63.7
63.7

Y c
i m
6.0%
6.0%
5.7%
6.5%
6.4%
5.7%
7.0%
Dairy Calves (4-6








mo)
DE
63.7
63.7
63.7
63.7
63.7
63.7
63.7

Ym
6.5%
6.5%
6.5%
6.5%
6.5%
6.5%
6.5%
Dairy Cows
DE
66.7
66.7
66.7
66.7
66.7
66.7
66.7

Ym
5.9%
5.9%
5.6%
6.4%
6.3%
5.6%
6.9%
Steer Feedlot
DE
82.5
82.5
82.5
82.5
82.5
82.5
82.5

Ym
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
Heifer Feedlot
DE
82.5
82.5
82.5
82.5
82.5
82.5
82.5

Ym
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
a Note that emissions are currently calculated on a state-by-state basis, but diets are applied in Table A-150 by the regions
shown in the table above. To see the regional designation for foraging cattle per state, please see Table A-150.
b DE is the digestible energy in units of percent of GE (MJ/Day).
c Ym is the methane conversion rate, the fraction of GE in feed converted to methane.
Step 3: Estimate CH4 Emissions from Cattle
Emissions by state were estimated in three steps: a) determine gross energy (GE) intake using the Tier 2 IPCC
(2006) equations, b) determine an emission factor using the GE values, Ym and a conversion factor, and c) sum the daily
emissions for each animal type. Finally, the state emissions were aggregated to obtain the national emissions estimate.
The necessary data values for each state and animal type include:
•	Body Weight (kg)
•	Weight Gain (kg/day)
89	This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020)
Inventory submission.
90	This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020)
Inventory submission.
A-304 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
•	Net Energy for Activity (Ca, MJ/day).91
•	Standard Reference Weight (kg).92
•	Milk Production (kg/day)
•	Milk Fat (percent of fat in milk = 4)
•	Pregnancy (percent of population that is pregnant)
•	DE (percent of GE intake digestible)
•	Ym (the fraction of GE converted to CH4)
•	Population
Step 3a: Determine Gross Energy, GE
As shown in the following equation, GE is derived based on the net energy estimates and the feed
characteristics. Only variables relevant to each animal category are used (e.g., estimates for feedlot animals do not
require the NEi factor). All net energy equations are provided in IPCC (2006). Calculated GE values for 2015 are shown by
state and animal type in Table A-157.
GE =
NEm+NEa+NEl+NEmk + NEp
REM
(NES
+ {REG
DE%
100
where,
GE
NEm
NEa
NEi
N EWOrk
NEP
REM
NEg
REG
DE
: Gross energy (MJ/day)
: Net energy required by the animal for maintenance (MJ/day)
: Net energy for animal activity (MJ/day)
: Net energy for lactation (MJ/day)
: Net energy for work (MJ/day)
: Net energy required for pregnancy (MJ/day)
= Ratio of net energy available in a diet for maintenance to digestible energy consumed
: Net energy needed for growth (MJ/day)
: Ratio of net energy available for growth in a diet to digestible energy consumed
: Digestible energy expressed as a percent of gross energy (percent)
91	Zero for feedlot conditions, 0.17 for high quality confined pasture conditions, and 0.36 for extensive open range or hilly terrain
grazing conditions. Ca factor for dairy cows is weighted to account for the fraction of the population in the region that grazes during the
year (IPCC 2006).
92	Standard Reference Weight is the mature weight of a female animal of the animal type being estimated, used in the model to
account for breed potential.
A-305
-------
Table A-157: Calculated Annual GE by Animal Type and State, for 2017 (MJ)93
Dairy	Dairy	Beef	Beef
Replace- Replace-	Replace- Replace-
ment	ment	ment	ment

Dairy
Dairy
Heifers 7-11
Heifers 12-

Beef

Heifers 7-11
Heifers 12-
Steer
Heifer

State
Calves
Cows
Months
23 Months
Bulls
Calves
Beef Cows
Months
23 Months
Stockers
Stockers
Feedlot
Alabama
31
851
55
195
4,166
3,179
55,838
1,432
4,017
1,204
978
285
Alaska
1
29
1
5
240
23
404
13
36
13
15
3
Arizona
857
31,012
1,603
5,698
1,779
911
15,827
489
1,365
6,869
1,025
13,100
Arkansas
26
673
41
146
4,999
4,192
73,645
2,064
5,791
2,649
1,739
577
California
7,670
262,323
10,412
37,010
6,226
3,243
56,341
1,673
4,670
15,553
4,391
22,265
Colorado
677
25,460
1,370
4,870
4,892
3,986
69,243
2,446
6,825
22,033
15,223
48,673
Conn.
83
2,810
130
463
42
23
404
24
67
48
27
10
Delaware
22
668
30
107
25
12
202
8
24
46
11
8
Florida
533
17,431
479
1,704
4,999
4,165
73,162
1,491
4,184
722
815
199
Georgia
363
12,451
411
1,461
2,749
2,280
40,045
1,312
3,682
891
1,359
288
Hawaii
10
305
14
49
356
364
6,331
167
467
259
117
46
Idaho
2,622
94,209
4,247
15,096
3,558
2,476
43,008
1,545
4,311
8,036
5,562
13,980
Illinois
406
13,244
712
2,532
2,036
1,729
30,484
872
2,451
5,636
2,861
13,463
Indiana
809
27,876
1,096
3,896
1,385
938
16,542
581
1,634
2,536
1,324
5,696
Iowa
940
33,194
1,849
6,574
5,701
4,312
76,014
2,151
6,045
30,762
14,303
60,064
Kansas
656
22,722
1,370
4,870
7,738
7,016
123,670
3,604
10,129
48,139
37,878
119,093
Kentucky
249
7,791
548
1,948
5,832
4,692
82,428
1,790
5,021
5,178
3,125
932
Louisiana
52
1,355
55
195
2,583
2,055
36,097
1,014
2,845
602
543
149
Maine
131
4,303
205
730
125
51
889
48
134
97
82
23
Maryland
205
6,525
397
1,412
334
198
3,474
132
369
338
164
466
Mass.
50
1,492
96
341
84
30
525
24
67
48
27
10
Michigan
1,857
70,016
2,329
8,278
1,303
536
9,452
291
817
3,969
1,060
7,508
Minn.
2,010
66,977
4,041
14,366
2,851
1,653
29,145
1,105
3,104
11,741
4,370
19,417
Miss.
39
1,108
82
292
3,166
2,183
38,353
1,110
3,113
1,011
842
242
Missouri
371
10,003
616
2,191
9,774
9,183
161,874
4,302
12,090
10,802
6,225
5,696
Montana
61
2,073
123
438
8,894
7,358
127,821
5,470
15,266
5,962
7,494
2,330
Nebraska
262
9,346
342
1,217
8,959
8,580
151,240
4,360
12,253
53,774
36,553
127,896
Nevada
131
4,443
151
536
1,245
1,089
18,924
528
1,473
1,166
849
155
N. Hamp.
59
1,936
82
292
42
23
404
12
34
36
27
8
N. Jersey
28
902
51
180
84
35
606
19
54
51
33
11
N. Mexico
1,420
51,790
1,507
5,357
3,113
2,302
39,998
1,287
3,592
3,111
2,635
696
93 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020) Inventory submission.
A-306 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------



Dairy
Dairy



Beef
Beef






Replace-
Replace-



Replace-
Replace-






ment
ment



ment
ment




Dairy
Dairy
Heifers 7-11
Heifers 12-

Beef

Heifers 7-11
Heifers 12-
Steer
Heifer

State
Calves
Cows
Months
23 Months
Bulls
Calves
Beef Cows
Months
23 Months
Stockers
Stockers
Feedlot
New York
2,710
96,247
4,863
17,287
1,671
506
8,888
539
1,511
1,087
1,363
1,036
N. Car.
197
6,615
301
1,071
2,583
1,697
29,813
823
2,310
1,036
679
225
N. Dakota
70
2,331
123
438
5,294
4,263
75,147
2,395
6,731
5,988
5,695
2,589
Ohio
1,145
37,854
1,644
5,844
2,443
1,287
22,686
872
2,451
5,166
1,589
7,767
Oklahoma
153
4,701
274
974
13,330
9,610
168,803
5,190
14,562
21,674
12,635
16,052
Oregon
542
17,486
890
3,165
3,558
2,703
46,965
1,351
3,772
4,018
3,367
4,401
Penn.
2,294
74,958
4,315
15,339
2,089
851
14,948
778
2,182
3,865
1,635
4,919
R. Island
3
99
7
24
8
6
113
5
13
12
5
2
S. Car.
66
1,922
96
341
1,250
780
13,698
394
1,105
193
272
60
S. Dakota
507
17,281
616
2,191
8,145
7,436
131,075
4,593
12,906
17,377
14,039
19,676
Tenn.
179
5,396
479
1,704
5,415
4,169
73,242
1,730
4,854
3,251
2,445
746
Texas
2,142
75,504
3,562
12,661
28,327
20,457
359,362
9,665
27,115
62,372
36,682
125,824
Utah
402
14,053
753
2,678
2,401
1,674
29,074
1,094
3,053
2,074
1,756
1,036
Vermont
564
18,581
767
2,727
251
64
1,131
66
185
97
177
35
Virginia
380
12,369
521
1,850
3,333
2,949
51,809
1,336
3,749
3,973
1,902
1,036
Wash.
1,202
42,560
1,644
5,844
1,601
1,114
19,354
747
2,083
4,925
3,425
9,838
W. Virg.
35
983
55
195
1,253
953
16,725
455
1,276
942
463
207
Wisconsin
5,594
197,617
9,727
34,575
2,443
1,296
22,844
930
2,614
9,393
1,324
13,980
Wyoming
26
910
41
146
3,558
3,535
61,416
2,381
6,645
4,147
4,011
3,883
A-307
-------
Step 3b: Determine Emission Factor
The daily emission factor (DayEmit) was determined using the GE value and the methane conversion factor (Ym)
for each category. This relationship is shown in the following equation:
GEx Y
DayEmit =	—
55.65
where,
DayEmit = Emission factor (kg CH4/head/day)
GE = Gross energy intake (MJ/head/day)
Ym	= CH4 conversion rate, which is the fraction of GE in feed converted to CH4 (%)
55.65 = A factor for the energy content of methane (MJ/kg CH4)
The daily emission factors were estimated for each animal type and state. Calculated annual national emission
factors are shown by animal type in Table A-158. State-level emission factors are shown by animal type for 2017 in Table
A-159.
Table A-158: Calculated Annual National Emission Factors for Cattle by Animal Type, for 2017 (kg
ChU/head/year)94
Cattle Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
Dairy












Calves
12
12
12
12
12
12
12
12
12
12
12
12
Cows
124
125
132
133
142
142
144
144
145
146
147
147
Replacements 7-11












months
48
46
46
45
46
46
46
46
46
46
46
46
Replacements 12-23












months
73
69
70
67
69
69
69
69
69
69
69
69
Beef












Calves
11
11
11
11
11
11
11
11
11
11
11
11
Bulls
91
94
94
97
98
98
98
98
98
98
98
98
Cows
89
92
91
94
95
95
95
95
95
95
95
95
Replacements 7-11












months
54
57
56
59
60
60
60
60
60
60
60
60
Replacements 12-23












months
63
66
66
68
70
70
70
70
70
70
70
70
Steer Stockers
55
57
58
58
58
58
58
58
58
58
58
58
Heifer Stockers
52
56
60
60
60
60
60
60
60
60
60
60
Feedlot Cattle
38
36
38
38
42
41
42
42
42
43
43
43
Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the number of days in a year).
94 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020)
Inventory submission.
A-308 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-159: Emission Factors for Cattle by Animal Type and State, for 2017 (kg Cm/head/year)95



Dairy
Dairy



Beef
Beef






Replacement
Replacement



Replacement
Replacement




Dairy
Dairy
Heifers 7-11
Heifers 12-23

Beef
Beef
Heifers 7-11
Heifers 12-23
Steer
Heifer

State
Calves
Cows
Months
Months
Bulls
Calves
Cows
Months
Months
Stockers
Stockers
Feedlot
Alabama
12
138
53
80
97
10
94
60
69
58
60
35
Alaska
12
95
46
69
104
11
100
65
74
62
65
35
Arizona
12
154
46
69
104
11
100
65
74
62
65
34
Arkansas
12
118
49
74
97
10
94
60
69
58
60
34
California
12
146
46
69
104
11
100
65
74
62
65
34
Colorado
12
151
43
65
104
11
100
65
74
62
65
35
Conn.
12
153
48
73
98
11
94
60
69
58
60
35
Delaware
12
138
48
73
98
11
94
60
69
58
60
35
Florida
12
162
53
80
97
10
94
60
69
58
60
36
Georgia
12
170
53
80
97
10
94
60
69
58
60
37
Hawaii
12
124
46
69
104
11
100
65
74
62
65
35
Idaho
12
153
46
69
104
11
100
65
74
62
65
35
Illinois
12
131
43
65
95
10
92
58
68
56
59
35
Indiana
12
139
43
65
95
10
92
58
68
56
59
34
Iowa
12
142
43
65
95
10
92
58
68
56
59
34
Kansas
12
140
43
65
95
10
92
58
68
56
59
35
Kentucky
12
155
53
80
97
10
94
60
69
58
60
35
Louisiana
12
118
49
74
97
10
94
60
69
58
60
35
Maine
12
148
48
73
98
11
94
60
69
58
60
36
Maryland
12
143
48
73
98
11
94
60
69
58
60
35
Mass.
12
134
48
73
98
11
94
60
69
58
60
35
Michigan
12
152
43
65
95
10
92
58
68
56
59
33
Minn.
12
134
43
65
95
10
92
58
68
56
59
34
Miss.
12
140
53
80
97
10
94
60
69
58
60
35
Missouri
12
109
43
65
95
10
92
58
68
56
59
35
Montana
12
137
43
65
104
11
100
65
74
62
65
34
Nebraska
12
144
43
65
95
10
92
58
68
56
59
34
Nevada
12
144
46
69
104
11
100
65
74
62
65
37
N. Hamp.
12
148
48
73
98
11
94
60
69
58
60
35
N. Jersey
12
143
48
73
98
11
94
60
69
58
60
36
N. Mexico
12
155
46
69
104
11
100
65
74
62
65
36
New York
12
160
48
73
98
11
94
60
69
58
60
36
95 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020) Inventory submission.
A-309
-------
N. Car.
12
167
53
80
97
10
94
60
69
58
60
36
N. Dakota
12
134
43
65
95
10
92
58
68
56
59
34
Ohio
12
133
43
65
95
10
92
58
68
56
59
34
Oklahoma
12
141
49
74
97
10
94
60
69
58
60
35
Oregon
12
137
46
69
104
11
100
65
74
62
65
35
Penn.
12
147
48
73
98
11
94
60
69
58
60
35
R. Island
12
128
48
73
98
11
94
60
69
58
60
35
S. Car.
12
145
53
80
97
10
94
60
69
58
60
33
S. Dakota
12
137
43
65
95
10
92
58
68
56
59
34
Tenn.
12
149
53
80
97
10
94
60
69
58
60
35
Texas
12
161
49
74
97
10
94
60
69
58
60
35
Utah
12
149
46
69
104
11
100
65
74
62
65
32
Vermont
12
149
48
73
98
11
94
60
69
58
60
36
Virginia
12
161
53
80
97
10
94
60
69
58
60
34
Wash.
12
151
46
69
104
11
100
65
74
62
65
34
W. Virg.
12
127
48
73
98
11
94
60
69
58
60
35
Wisconsin
12
142
43
65
95
10
92
58
68
56
59
34
Wyoming
12
140
43
65
104
11
100
65
74
62
65
35
Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the number of days in a year).
A-310 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
For quality assurance purposes, U.S. emission factors for each animal type were compared to estimates
provided by the other Annex I member countries of the United Nations Framework Convention on Climate Change
(UNFCCC) (the most recently available summarized results for Annex I countries are through 2012 only). Results,
presented in Table A-160, indicate that U.S. emission factors are comparable to those of other Annex I countries. Results
in Table A-160 are presented along with Tier I emission factors provided by IPCC (2006). Throughout the time series, beef
cattle in the United States generally emit more enteric CH4 per head than other Annex I member countries, while dairy
cattle in the United States generally emit comparable enteric CH4 per head.
Table A-160: Annex I Countries' Implied Emission Factors for Cattle by Year (kg Cm/head/year)96,97
Dairy Cattle
Beef Cattle
United Mean of Implied
States Implied Emission Factors for Annex 1
Year Emission Factor countries (excluding U.S.)
United Mean of Implied
States Implied Emission Factors for Annex 1
Emission Factor countries (excluding U.S.)
1990
107
96
71
53
1991
107
97
71
53
1992
107
96
72
54
1993
106
97
72
54
1994
106
98
73
54
1995
106
98
72
54
1996
105
99
73
54
1997
106
100
73
54
1998
107
101
73
55
1999
110
102
72
55
2000
111
103
72
55
2001
110
104
73
55
2002
111
105
73
55
2003
111
106
73
55
2004
109
107
74
55
2005
110
109
74
55
2006
110
110
74
55
2007
114
111
75
55
2008
115
112
75
55
2009
115
112
75
56
2010
115
113
75
55
2011
116
113
75
55
2012
117
112
75
51
2013
117
NA
75
NA
2014
118
NA
74
NA
2015
117
NA
75
NA
2016
118
NA
75
NA
2017
119
NA
74
NA
Tier 1 EFs For North America, from IPCC
(2006)
121
53
NA (Not Applicable)
Step 3c: Estimate Total Emissions
Emissions were summed for each month and for each state population category using the daily emission factor
for a representative animal and the number of animals in the category. The following equation was used:
Emissionsstate = DayEmitstate x Days/Month x SubPopstate
96	Excluding calves.
97	This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020)
Inventory submission.
A-311
-------
where,
Emissionsstate	=	Emissions for state during the month (kg CH4)
DayEmitstate	=	Emission factor for the subcategory and state (kg CH4/head/day)
Days/Month	=	Number of days in the month
SubPopstate	=	Number of animals in the subcategory and state during the month
This process was repeated for each month, and the monthly totals for each state subcategory were summed to
achieve an emission estimate for a state for the entire year and state estimates were summed to obtain the national
total. The estimates for each of the 10 subcategories of cattle are listed in Table A-161. The emissions for each
subcategory were then aggregated to estimate total emissions from beef cattle and dairy cattle for the entire year.
Table A-161: CH4 Emissions from Cattle (kt)
Cattle Type
1990
1995
2000
2005
2013
2014
2015
2016
2017
2018
2019
Dairy
1,574
1,498
1,519
1,503
1,664
1,679
1,706
1,722
1,730
1,744
1,729
Calves (4-6 months)
62
59 |ii
59 jll
54 f
58
58
58
58
58
58
59
Cows
1,242
1,183
1,209
1,197/
1,325
1,337
1,355
1,367
1,377
1,390
1,379
Replacements 7-11











months
58
56
55 L
56
61
63
65
65
65
65
64
Replacements 12-23











months
212
201
196
196
220
221
228
232
230
231
227
Beef
4,763
5,419
5,070
5,007
4,722
4,660
4,722
4,919
5,052
5,125
5,162
Calves (4-6 months)
182
193
186
179/
157
156
158
164
168
169
171
Bulls
196
225
215
214/
203
200
207
210
220
221
221
Cows
2,884
3,222
3,058
3,056
2,806
2,754
2,774
2,856
2,954
2,978
3,000
Replacements 7-11











months
69
85
74
80/
78
83
89
91
90
86
83
Replacements 12-23











months
188
241
204
217
213
218
239
250
251
241
232
Steer Stockers
563
662
509
473/
431
426
433
472
461
465
472
Heifer Stockers
306
375
323/
299
267
256
263
289
286
297
306
Feedlot Cattle
375
416
502
488/
568
567
558
587
621
667
678
Total
6,338
6,917
6,589
6,510
6,386
6,339
6,427
6,641
6,783
6,869
6,891
Note: Totals may not sum due to independent rounding.
Emission Estimates from Other Livestock
"Other livestock" include horses, sheep, swine, goats, American bison, and mules and asses. All livestock
population data, except for American bison for years prior to 2002, were taken from the U.S. Department of Agriculture
(USDA) National Agricultural Statistics Service (NASS) agricultural statistics database (USDA 2019) or the Census of
Agriculture (USDA 1992,1997, 2002, 2007, 2012). The Manure Management Annex discusses the methods for obtaining
annual average populations and disaggregating into state data where needed and provides the resulting population data
for the other livestock that were used for estimating all livestock-related emissions (see Table A-163). For each animal
category, the USDA publishes monthly, annual, or multi-year livestock population and production estimates. American
bison estimates prior to 2002 were estimated using data from the National Bison Association (1999).
Methane emissions from swine, horses, mules and asses were estimated by multiplying national population
estimates by the default IPCC emission factor (IPCC 2006). For sheep and goats, default national emission factors were
updated to reflect revisions made in the 2019 Refinement to the 2006 IPCC Guidelines. For American bison the emission
factor for buffalo (IPCC 2006) was used and adjusted based on the ratio of live weights of 300 kg for buffalo (IPCC 2006)
and 1,130 pounds (513 kg) for American Bison (National Bison Association 2011) to the 0.75 power. This methodology for
determining emission factors is recommended by IPCC (2006) for animals with similar digestive systems. Table A-162
shows the emission factors used for these other livestock. National enteric fermentation emissions from all livestock
types are shown in Table A-163 and Table A-164. Enteric fermentation emissions from most livestock types, broken
down by state, for 2017 are shown in Table A-165 and Table A-166. Because a simplified calculation approach was used
A-312 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
for 2019 emissions, state-level emission estimates were not calculated for 2019. Livestock populations are shown in
Table A-139.
Table A-162: Emission Factors for Other Livestock (kg Cm/head/year)
Livestock Type
Emission Factor
Swine
1.5
Horses
18
Sheep
9
Goats
9
American Bison
82.2
Mules and Asses
10.0
Source: IPCC (2006), IPCC (2019), except
American Bison, as described in text.
Table A-163: CH4 Emissions from Enteric Fermentation (MMT CO2 Eq.)
Livestock
Type
1990
1995
2000
2005
2013
2014
2015
2016
2017
2018
2019
Beef Cattle
119.1
135.5
126.7
125.2
118.0
116.5
118.0
123.0
126.3
128.1
129.1
Dairy Cattle
39.4
37.5
38.0
37.6
41.6
42.0
42.6
43.0
43.3
43.6
43.2
Swine
2.0
2.2
2.2
2.3
2.5
2.4
2.6
2.6
2.7
2.8
2.9
Horses
1.0
1.2
1.5
1.7
1.6
1.5
1.4
1.4
1.3
1.2
1.1
Sheep
2.6
2.0
1.6
1.4
1.2
1.2
1.2
1.2
1.2
1.2
1.2
Goats
0.6
0.5
0.5
0.7
0.6
0.6
0.6
0.6
0.6
0.6
0.6
American











Bison
0.1
0.2
0.4
0.4
0.3
0.4
0.4
0.4
0.4
0.4
0.4
Mules and











Asses
+
+
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Total
164.7
179.1
171.0
169.3
165.9
164.6
166.9
172.2
175.8
178.0
178.6
+ Does not exceed 0.05 MMT C02 Eq.
Note: Totals may not sum due to independent rounding.
Table A-164: CH4 Emissions from Enteric Fermentation (kt)
Livestock
Type
1990
1995
2000
2005
2013
2014
2015
2016
2017
2018
2019
Beef Cattle
4,763
5,419
5,070
5,007
4,722
4,660
4,722
4,919
5,052
5,125
5,162
Dairy Cattle
1,574
1,498
1,519
1,503
1,664
1,679
1,706
1,722
1,730
1,744
1,729
Swine
81
88
88
92
98
96
102
105
108
111
115
Horses
40
47
61
70
62
60
57
54
51
48
46
Sheep
102
81
63
55
48
47
47
48
47
47
47
Goats
23
21
22
26
24
24
24
24
24
24
24
American











Bison
4
9
16
17
14
14
14
15
15
15
16
Mules and











Asses
1
1
1
2
3
3
3
3
3
3
3
Total
6,588
7,165
6,840
6,772
6,635
6,583
6,675
6,890
7,032
7,119
7,142
Note: Totals may not sum due to independent rounding.
A-313
-------
Table A-165: Cm Emissions from Enteric Fermentation from Cattle (metric tons), by State, for 201998



Dairy




Beef
Beef







Replace-
Dairy



Replace
Replace-







ment
Replace-



-ment
ment







Heifers
ment



Heifers
Heifers





Dairy
Dairy
7-11
Heifers 12-

Beef
Beef
7-11
12-23
Steer
Heifer


State
Calves
Cows
Months
23 Months
Bulls
Calves
Cows
Months
Months
Stockers
Stockers
Feedlot
Total
Alabama
44
966
62
222
4,886
3,760
66,217
1,547
4,339
1,439
1,222
281
84,987
Alaska
2
29
1
5
282
28
479
14
39
15
18
3
915
Arizona
1,238
30,255
1,574
5,594
2,086
1,078
18,769
528
1,474
8,212
1,280
13,144
85,234
Arkansas
38
706
43
154
5,863
4,959
87,334
2,230
6,256
3,167
2,173
579
113,502
California
11,087
255,921
10,222
36,337
7,302
3,836
66,814
1,807
5,044
18,592
5,488
22,478
444,929
Colorado
979
23,498
1,274
4,528
5,738
4,715
82,115
2,642
7,372
26,339
19,023
48,367
226,590
Conn.
120
2,903
135
481
49
27
479
26
73
58
34
10
4,394
Delaware
32
690
31
111
29
14
240
9
25
55
14
8
1,258
Florida
771
19,787
546
1,942
5,863
4,927
86,761
1,611
4,520
864
1,019
189
128,800
Georgia
524
14,134
468
1,665
3,225
2,697
47,489
1,418
3,978
1,065
1,698
270
78,630
Hawaii
15
297
13
48
417
431
7,508
181
504
310
146
46
9,917
Idaho
3,790
91,910
4,170
14,822
4,173
2,929
51,003
1,668
4,656
9,606
6,951
13,774
209,451
Illinois
588
12,223
662
2,354
2,388
2,046
36,151
942
2,647
6,737
3,575
13,256
83,569
Indiana
1,169
25,728
1,019
3,622
1,624
1,110
19,617
628
1,765
3,032
1,655
5,749
66,718
Iowa
1,358
30,636
1,720
6,113
6,687
5,101
90,143
2,323
6,530
36,773
17,874
60,806
266,065
Kansas
948
20,971
1,274
4,528
9,076
8,299
146,658
3,893
10,942
57,546
47,334
118,380
429,849
Kentucky
360
8,845
624
2,220
6,840
5,551
97,750
1,933
5,424
6,189
3,905
922
140,563
Louisiana
76
1,420
58
205
3,029
2,431
42,807
1,096
3,074
720
679
145
55,739
Maine
190
4,446
213
759
147
60
1,054
52
145
116
102
22
7,306
Maryland
297
6,742
413
1,467
392
234
4,120
142
399
404
204
466
15,281
Mass.
73
1,541
100
354
98
35
623
26
73
58
34
10
3,024
Michigan
2,685
64,621
2,165
7,697
1,529
634
11,210
314
882
4,744
1,324
7,771
105,576
Minn.
2,906
61,816
3,758
13,357
3,344
1,956
34,563
1,193
3,353
14,036
5,462
19,734
165,477
Miss.
57
1,258
94
333
3,713
2,583
45,483
1,199
3,363
1,209
1,053
241
60,585
Missouri
537
9,233
573
2,038
11,464
10,863
191,963
4,647
13,059
12,913
7,779
5,592
270,661
Montana
88
1,913
115
408
10,432
8,704
151,580
5,909
16,491
7,127
9,365
2,383
214,515
Nebraska
379
8,626
318
1,132
10,509
10,150
179,353
4,710
13,236
64,283
45,679
128,439
466,813
Nevada
190
4,334
148
526
1,460
1,289
22,441
570
1,591
1,394
1,061
145
35,149
N. Hamp.
85
2,001
85
303
49
27
479
13
36
43
34
8
3,165
9SBecause a simplified calculation approach was used for 2019 emissions, state-level emissions for 2019 were estimated by calculating ratios of 2017 state-level emissions to the 2017 total
national emissions, then applying those state-specific ratios to the 2019 national total emissions estimate.
A-314 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
N.Jersey
41
932
53
187
98
N. Mexico
2,053
50,526
1,480
5,259
3,651
New York
3,917
99,440
5,052
17,957
1,960
N. Car.
284
7,509
343
1,221
3,029
N. Dakota
101
2,152
115
408
6,210
Ohio
1,655
34,938
1,529
5,433
2,866
Oklahoma
221
4,927
289
1,026
15,635
Oregon
783
17,059
874
3,108
4,173
Penn.
3,317
77,445
4,482
15,934
2,450
R. Island
5
103
7
25
10
S. Car.
95
2,182
109
388
1,466
S. Dakota
733
15,949
573
2,038
9,553
Tenn.
259
6,126
546
1,942
6,352
Texas
3,095
79,127
3,752
13,337
33,225
Utah
581
13,710
740
2,630
2,817
Vermont
815
19,197
797
2,833
294
Virginia
550
14,041
593
2,109
3,909
Wash.
1,737
41,521
1,614
5,737
1,878
W. Virg.
51
1,015
57
202
1,470
Wisconsin
8,086
182,391
9,044
32,148
2,866
Wyoming
38
840
38
136
4,173
41
719
21
58
61
41
10
2,261
2,724
47,433
1,390
3,880
3,718
3,293
675
126,081
599
10,540
582
1,632
1,299
1,703
994
145,674
2,008
35,354
889
2,495
1,238
849
215
55,435
5,043
89,116
2,587
7,271
7,158
7,117
2,642
129,918
1,522
26,903
942
2,647
6,176
1,986
7,873
94,470
11,368
200,181
5,607
15,730
25,909
15,789
15,741
312,422
3,198
55,695
1,460
4,074
4,803
4,207
4,297
103,732
1,007
17,726
840
2,357
4,620
2,044
4,815
137,038
8
134
5
15
14
7
2
335
922
16,244
425
1,193
230
340
63
23,659
8,796
155,439
4,961
13,942
20,773
17,543
20,254
270,555
4,932
86,857
1,869
5,243
3,886
3,056
740
121,809
24,200
426,161
10,440
29,290
74,560
45,840
125,949
868,977
1,980
34,478
1,182
3,298
2,479
2,195
1,109
67,198
76
1,341
71
199
116
221
33
25,994
3,489
61,440
1,444
4,050
4,750
2,377
1,057
99,807
1,318
22,951
806
2,251
5,888
4,280
9,892
99,873
1,127
19,834
491
1,378
1,126
579
207
27,538
1,533
27,090
1,005
2,824
11,228
1,655
14,035
293,904
4,182
72,832
2,572
7,178
4,958
5,012
3,832
105,791
A-315
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Table A-166: Cm Emissions from Enteric Fermentation from Other Livestock (metric tons), by State, for 2019"





American
Mules and

State
Swine
Horses
Sheep
Goats
Bison
Asses
Total
Alabama
83
796
115
456
11
112
1,573
Alaska
3
24
115
8
124
0
275
Arizona
266
1,277
1,118
527
8
27
3,222
Arkansas
203
715
115
319
15
77
1,445
California
158
1,544
5,096
1,180
114
57
8,150
Colorado
1,200
1,612
3,978
462
926
60
8,238
Connecticut
6
173
73
51
32
9
344
Delaware
10
64
115
13
13
1
217
Florida
20
1,435
115
563
5
110
2,249
Georgia
105
813
115
625
10
106
1,774
Hawaii
14
75
115
151
7
3
366
Idaho
50
778
2,101
271
1,790
30
5,021
Illinois
8,364
672
492
332
58
45
9,963
Indiana
6,410
1,257
510
373
59
46
8,653
Iowa
35,995
823
1,475
773
206
36
39,309
Kansas
3,197
833
599
449
458
43
5,580
Kentucky
446
1,953
519
527
169
124
3,737
Louisiana
9
686
115
169
6
65
1,052
Maine
7
133
73
52
19
4
288
Maryland
30
465
115
129
4
18
762
Massachusetts
13
228
73
67
1
11
392
Michigan
1,876
1,008
715
265
239
40
4,143
Minnesota
13,483
729
1,162
329
236
33
15,973
Mississippi
899
625
115
285
16
84
2,024
Missouri
5,569
1,343
894
639
87
112
8,644
Montana
300
1,271
2,012
135
1,658
36
5,412
Nebraska
5,432
771
715
266
2,400
23
9,607
Nevada
9
211
545
105
0
5
876
New Hampshire
5
112
73
37
25
5
257
New Jersey
13
382
115
102
7
14
633
New Mexico
2
711
858
320
405
24
2,320
New York
72
1,091
760
262
92
31
2,307
North Carolina
14,148
817
241
501
21
111
15,839
North Dakota
227
445
626
63
1,045
10
2,415
Ohio
4,065
1,591
1,064
547
84
79
7,430
Oklahoma
3,439
2,016
483
879
253
166
7,236
Oregon
14
1,064
1,475
427
164
40
3,186
Pennsylvania
2,001
1,392
858
475
102
97
4,926
Rhode Island
3
32
73
9
-
1
118
South Carolina
313
681
115
368
5
61
1,543
South Dakota
2,658
839
2,235
162
2,424
20
8,337
Tennessee
344
1,442
411
886
29
187
3,299
Texas
1,759
5,389
6,705
7,418
673
865
22,808
Utah
918
878
2,459
182
85
18
4,540
Vermont
6
134
73
86
13
3
315
Virginia
539
1,041
671
434
55
79
2,819
Washington
27
854
402
267
81
32
1,663
99 Because a simplified calculation approach was used for 2019 emissions, state-level emissions for 2019 were estimated by calculating
ratios of 2018 state-level emissions to the 2018 total national emissions, then applying those state-specific ratios to the 2019 national
total emissions estimate.
A-316 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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West Virginia
6
388
313
216
8
37
968
Wisconsin
508
1,173
671
968
515
43
3,878
Wyoming
138
888
3,084
136
810
32
5,088
Indicates there are no emissions, as there is no significant population of this animal type.
A-317
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References
Archibeque, S. (2011) Personal Communication. Shawn Archibeque, Colorado State University, Fort Collins, Colorado and
staff at ICF International.
Crutzen, P.J., I. Aselmann, and W. Seiler (1986) Methane Production by Domestic Animals, Wild Ruminants, Other
Herbivores, Fauna, and Humans. Tellus, 38B:271-284.
Donovan, K. (1999) Personal Communication. Kacey Donovan, University of California at Davis and staff at ICF
International.
Doren, P.E., J. F. Baker, C. R. Long and T. C. Cartwright (1989) Estimating Parameters of Growth Curves of Bulls, J Animal
Science 67:1432-1445.
Enns, M. (2008) Personal Communication. Dr. Mark Enns, Colorado State University and staff at ICF International.
ERG (2016) Development of Methane Conversion Rate Scaling Factor and Diet-Related Inputs to the Cattle Enteric
Fermentation Model for Dairy Cows, Dairy Heifers, and Feedlot Animals. ERG, Lexington, MA. December 2016.
Galyean and Gleghorn (2001) Summary of the 2000 Texas Tech University Consulting Nutritionist Survey. Texas Tech
University. Available online at . June
2009.
Holstein Association (2010) History of the Holstein Breed (website). Available online at
. Accessed September 2010.
ICF (2006) Cattle Enteric Fermentation Model: Model Documentation. Prepared by ICF International for the
Environmental Protection Agency. June 2006.
ICF (2003) Uncertainty Analysis of 2001 Inventory Estimates of Methane Emissions from Livestock Enteric Fermentation in
the U.S. Memorandum from ICF International to the Environmental Protection Agency. May 2003.
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis,
K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United Kingdom 996 pp.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2019) 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The
Intergovernmental Panel on Climate Change. Calvo Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M.,
Ngarize, S., Osako, A., Pyrozhenko, Y., Shermanau, P. and Federici, S. (eds). Hayama, Kanagawa, Japan.
Johnson, D. (2002) Personal Communication. Don Johnson, Colorado State University, Fort Collins, and ICF International.
Johnson, D. (1999) Personal Communication. Don Johnson, Colorado State University, Fort Collins, and David Conneely,
ICF International.
Johnson, K. (2010) Personal Communication. Kris Johnson, Washington State University, Pullman, and ICF International.
Kebreab E., K. A. Johnson, S. L. Archibeque, D. Pape, and T. Wirth (2008) Model for estimating enteric methane emissions
from United States dairy and feedlot cattle. J. Anim. Sci. 86: 2738-2748.
Lippke, H., T. D. Forbes, and W. C. Ellis. (2000) Effect of supplements on growth and forage intake by stocker steers
grazing wheat pasture. J. Anim. Sci. 78:1625-1635.
National Bison Association (2011) Handling & Carcass Info (on website). Available online at:
. Accessed August 16, 2011.
National Bison Association (1999) Total Bison Population—1999. Report provided during personal email communication
with Dave Carter, Executive Director, National Bison Association July 19, 2011.
NRC (1999) 1996 Beef NRC: Appendix Table 22. National Research Council.
NRC (1984) Nutrient requirements for beef cattle (6th Ed.). National Academy Press, Washington, D.C.
A-318 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Pinchak, W.E., D. R. Tolleson, M. McCloy, L. J. Hunt, R. J. Gill, R. J. Ansley, and S. J. Bevers (2004) Morbidity effects on
productivity and profitability of stocker cattle grazing in the southern plains. J. Anim. Sci. 82:2773-2779.
Platter, W. J., J. D. Tatum, K. E. Belk, J. A. Scanga, and G. C. Smith (2003) Effects of repetitive use of hormonal implants on
beef carcass quality, tenderness, and consumer ratings of beef palatability. J. Anim. Sci. 81:984-996.
Preston, R.L. (2010) What's The Feed Composition Value of That Cattle Feed? Beef Magazine, March 1, 2010. Available
at: .
Skogerboe, T. L., L. Thompson, J. M. Cunningham, A. C. Brake, V. K. Karle (2000) The effectiveness of a single dose of
doramectin pour-on in the control of gastrointestinal nematodes in yearling stocker cattle. Vet. Parasitology 87:173-
181.
Soliva, C.R. (2006) Report to the attention of IPCC about the data set and calculation method used to estimate methane
formation from enteric fermentation of agricultural livestock population and manure management in Swiss
agriculture. On behalf of the Federal Office for the Environment (FOEN), Berne, Switzerland.
USDA (2019) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. Available online at .
USDA (2012) Census of Agriculture: 2012 Census Report. United States Department of Agriculture. Available online at:
.
USDA (2007) Census of Agriculture: 2007 Census Report. United States Department of Agriculture. Available online at:
.
USDA (2002) Census of Agriculture: 2002 Census Report. United States Department of Agriculture. Available online at:
.
USDA (1997) Census of Agriculture: 1997 Census Report. United States Department of Agriculture. Available online at:
. Accessed July 18, 2011.
USDA (1996) Beef Cow/Calf Health and Productivity Audit (CHAPA): Forage Analyses from Cow/Calf Herds in 18 States.
National Agriculture Statistics Service, U.S. Department of Agriculture. Washington, D.C. Available online at
. March 1996.
USDA (1992) Census of Agriculture: 1992 Census Report. United States Department of Agriculture. Available online at:
. Accessed July 18, 2011.
USDA:APHIS:VS (2010) Beef 2007-08, Part V: Reference of Beef Cow-calf Management Practices in the United States,
2007-08. USDA-APHIS-VS, CEAH. Fort Collins, CO.
USDA:APHIS:VS (2002) Reference of 2002 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort Collins, CO.
Available online at .
USDA:APHIS:VS (1998) Beef'97, Parts l-IV. USDA-APHIS-VS, CEAH. Fort Collins, CO. Available online at
.
USDA:APHIS:VS (1996) Reference of 1996 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort Collins, CO.
Available online at .
USDA:APHIS:VS (1994) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins, CO. Available
online at .
USDA:APHIS:VS (1993) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins, CO. August
1993. Available online at .
Vasconcelos and Galyean (2007) Nutritional recommendations of feedlot consulting nutritionists: The 2007
Texas Tech University Study. J. Anim. Sci. 85:2772-2781.
A-319
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3.11 Methodology for Estimating CH4 and N2O Emissions from Manure
Management100
The following steps were used to estimate methane (CH4) and nitrous oxide (N20) emissions from the
management of livestock manure for the years 1990 through 2018. As explained in the Manure Management section
(Section 5.2 Manure Management (IPCC Source Category 3B)), a simplified approach was used to estimate emissions for
2019.
Step 1: Livestock Population Characterization Data
Annual animal population data for 1990 through 2018 for all livestock types, except American bison, goats,
horses, mules and asses were obtained from the USDA NASS. The population data used in the emissions calculations for
cattle, swine, and sheep were downloaded from the USDA NASS Quick Stats Database (USDA 2019a). Poultry population
data were obtained from USDA NASS reports (USDA 1995a, 1995b, 1998, 1999, 2004a, 2004b, 2009a, 2009b, 2009c,
2009d, 2010a, 2010b, 2011a, 2011b, 2012a, 2012b, 2013a, 2013b, 2014a, 2014b, 2015a 2015b, 2016a, 2016b, 2017a,
2017b, 2018a, 2018b, 2019b, and 2019c). Goat population data for 1992, 1997, 2002, 2007, 2012, and 2017 were
obtained from the Census of Agriculture (USDA 2019d), as were horse, mule and ass population data for 1987,1992,
1997, 2002, 2007, 2012, and 2017 and American bison population for 2002, 2007, 2012, and 2017. American bison
population data for 1990-1999 were obtained from the National Bison Association (1999). Additional data sources used
and adjustments to these data sets are described below.
Cattle: For all cattle groups (cows, heifers, steers, bulls, and calves), the USDA data provide cattle inventories
from January (for each state) and July (as a U.S. total only) of each year. Cattle inventories change over the course of the
year, sometimes significantly, as new calves are born and as cattle are moved into feedlots and subsequently
slaughtered; therefore, to develop the best estimate for the annual animal population, the populations and the
individual characteristics, such as weight and weight gain, pregnancy, and lactation of each animal type were tracked in
the Cattle Enteric Fermentation Model (CEFM—see section 5.1 Enteric Fermentation). For animals that have relatively
static populations throughout the year, such as mature cows and bulls, the January 1 values were used. For animals that
have fluctuating populations throughout the year, such as calves and growing heifers and steer, the populations are
modeled based on a transition matrix that uses annual population data from USDA along with USDA data on animal
births, placement into feedlots, and slaughter statistics.
Swine: The USDA provides quarterly data for each swine subcategory: breeding, market under 50 pounds
(under 23 kg), market 50 to 119 pounds (23 to 54 kg), market 120 to 179 pounds (54 to 81 kg), and market 180 pounds
and over (greater than 82 kg). The average of the quarterly data was used in the emission calculations. For states where
only December inventory is reported, the December data were used directly.
Sheep: The USDA provides total state-level data annually for lambs and sheep. Population distribution data for
lambs and sheep on feed are not available after 1993 (USDA 1994). The number of lambs and sheep on feed for 1994
through 2018 were calculated using the average of the percent of lambs and sheep on feed from 1990 through 1993. In
addition, all of the sheep and lambs "on feed" are not necessarily on "feedlots;" they may be on pasture/crop residue
supplemented by feed. Data for those animals on feed that are in feedlots versus pasture/crop residue were provided
only for lamb in 1993. To calculate the populations of sheep and lambs in feedlots for all years, it was assumed that the
percentage of sheep and lambs on feed that are in feedlots versus pasture/crop residue is the same as that for lambs in
1993 (Anderson 2000).
Goats: Annual goat population data by state were available for 1992,1997, 2002, 2007, 2012, and 2017 (USDA
2019d). The data for 1992 were used for 1990 through 1992. Data for 1993 through 1996, 1998 through 2001, 2003
100 Note that direct N20 emissions from dung and urine spread onto fields either directly as daily spread or after it is removed
from manure management systems (e.g., lagoon, pit, etc.) and from livestock dung and urine deposited on pasture, range, or
paddock lands are accounted for and discussed in the Agricultural Soil Management source category within the Agriculture
sector. Indirect N20 emissions dung and urine spread onto fields after it is removed from manure management systems (e.g.,
lagoon, pit, etc.) and from livestock dung and urine deposited on pasture, range, or paddock lands are also included in the
Agricultural Soil Management source category.
A-320 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
through 2006, 2008 through 2011, and 2013 through 2016 were interpolated based on the 1992, 1997, 2002, 2007,
2012, and 2017 Census data. Data for 2018 were extrapolated based on 2017 Census data.
Horses: Annual horse population data by state were available for 1987,1992,1997, 2002, 2007, 2012, and
2017 (USDA 2019d). Data for 1990 through 1991, 1993 through 1996, 1998 through 2001, 2003 through 2006, 2008
through 2011, and 2013 through 2016 were interpolated based on the 1987, 1992, 1997, 2002, 2007, 2012, and 2017
Census data. Data for 2018 were extrapolated based on 2017 Census data.
Mules and Asses: Annual mule and ass (burro and donkey) population data by state were available for 1987,
1992, 1997, 2002, 2007, 2012, and 2017 (USDA 2019d). Data for 1990 through 1991, 1993 through 1996, 1998 through
2001,	2003 through 2006, 2008 through 2011, and 2013 through 2016 were interpolated based on the 1987, 1992, 1997,
2002,	2007, 2012, and 2017 Census data. Data for 2018 were extrapolated based on 2017 Census data.
American Bison: Annual American bison population data by state were available for 2002, 2007, 2012, and
2017 (USDA 2019d). Data for 1990 through 1999 were obtained from the Bison Association (1999). Data for 2000, 2001,
2003 through 2006, 2008 through 2011, and 2013 through 2016 were interpolated based on the Bison Association and
2002, 2007, 2012, and 2017 Census data. Data for 2018 were extrapolated based on 2017 Census data.
Poultry: The USDA provides population data for hens (one year old or older), pullets (hens younger than one
year old), other chickens, and production (slaughter) data for broilers and turkeys (USDA 1995a, 1995b, 1998,1999,
2004a, 2004b, 2009b, 2009c, 2009d, 2009e, 2010a, 2010b, 2011a, 2011b, 2012a, 2012b, 2013a, 2013b, 2014a, 2014b,
2015a, 2015b, 2016a, 2016b, 2017a, 2017b, 2018a, 2018b, 2019b, and 2019c). All poultry population data were adjusted
to account for states that report non-disclosed populations to USDA NASS. The combined populations of the states
reporting non-disclosed populations are reported as "other" states. State populations for the non-disclosed states were
estimated by equally distributing the population attributed to "other" states to each of the non-disclosed states.
Because only production data are available for broilers and turkeys, population data are calculated by dividing
the number of animals produced by the number of production cycles per year, or the turnover rate. Based on personal
communications with John Lange, an agricultural statistician with USDA NASS, the broiler turnover rate ranges from 3.4
to 5.5 over the course of the inventory (Lange 2000). For turkeys, the turnover rate ranges from 2.4 to 3.0. A summary of
the livestock population characterization data used to calculate CH4 and N20 emissions is presented in Table A-167.
Step 2: Waste Characteristics Data
Methane and N20 emissions calculations are based on the following animal characteristics for each relevant
livestock population:
•	Volatile solids (VS) excretion rate;
•	Maximum methane producing capacity (B0) for U.S. animal waste;
•	Nitrogen excretion rate (Nex); and
•	Typical animal mass (TAM).
Table A-168 presents a summary of the waste characteristics used in the emissions estimates. Published
sources were reviewed for U.S.-specific livestock waste characterization data that would be consistent with the animal
population data discussed in Step 1. The USDA's Agricultural Waste Management Field Handbook (AWMFH; USDA 1996,
2008) is one of the primary sources of waste characteristics for non-cattle animal groups. Data from the 1996 and 2008
USDA AWMFH were used to estimate VS and Nex for most non-cattle animal groups across the time series of the
Inventory, as shown in Table A-169 (ERG 2010b and 2010c). The 1996 AWMFH data were based on measured values
from U.S. farms; the 2008 AWMFH data were developed using the calculation method created by the American Society
of Agricultural and Biological Engineers (ASABE), which is based on U.S. animal dietary intake and performance
measures. Since the values from each of the two AWMFHs result from different estimation methods and reflect changes
in animal genetics and nutrition over time, both data sources were used to create a time series across the Inventory as
neither value would be appropriate to use across the entire span of Inventory years. Expert sources agreed interpolating
the two data sources across the time series would be appropriate as each methodology reflect the best available for that
time period and the more recent data may not appropriately reflect the historic time series (ERG 2010b). Although the
AWMFH values are lower than the IPCC values, these values are more appropriate for U.S. systems because they have
been calculated using U.S.-specific data. Animal-specific notes about VS and Nex are presented below:
A-321
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•	Swine: The VS and Nex data for breeding swine are from a combination of the types of animals that make
up this animal group, namely gestating and farrowing swine and boars. It is assumed that a group of
breeding swine is typically broken out as 80 percent gestating sows, 15 percent farrowing swine, and 5
percent boars (Safley 2000). Differing trends in VS and Nex values are due to the updated Nex calculation
method from 2008 AWMFH. VS calculations did not follow the same procedure and were updated based
on a fixed ratio of VS to total solids and past ASABE standards (ERG 2010b).
•	Poultry: Due to the change in USDA reporting of hens and pullets in 2005, new nitrogen and VS excretion
rates were calculated for the combined population of hens and pullets; a weighted average rate was
calculated based on hen and pullet population data from 1990 to 2004.
•	Goats, Sheep, Horses, Mules and Asses: In cases where data were not available in the USDA documents,
data from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) or the 2006IPCC
Guidelines were used as a supplement.
The method for calculating VS excretion and Nex for cattle (including American bison, beef and dairy cows,
bulls, heifers, and steers) is based on the relationship between animal performance characteristics such as diet, lactation,
and weight gain and energy utilization. The method used is outlined by the 2006 IPCC Guidelines Tier II methodology, and
is modeled using the CEFM as described in the enteric fermentation portion of the inventory (documented in Moffroid
and Pape 2013) in order to take advantage of the detailed diet and animal performance data assembled as part of the
Tier II analysis for cattle. For American bison, VS and Nex were assumed to be the same as beef NOF bulls.
The VS content of manure is the fraction of the diet consumed by cattle that is not digested and thus excreted
as fecal material; fecal material combined with urinary excretions constitutes manure. The CEFM uses the input of
digestible energy (DE) and the energy requirements of cattle to estimate gross energy (GE) intake and enteric CH4
emissions. GE and DE are used to calculate the indigestible energy per animal as gross energy minus digestible energy
plus the amount of gross energy for urinary energy excretion per animal (2 or 4 percent). This value is then converted to
VS production per animal using the typical conversion of dietary gross energy to dry organic matter of 18.45 MJ/kg, after
subtracting out the ash content of manure. The current equation recommended by the 2006 IPCC Guidelines is:
where,
VS production (kg) = [(GE - DE) + (UE x GE)] x 1 ASH
18.45
GE	= Gross energy intake (MJ)
DE	= Digestible energy (MJ)
(UE x GE)	= Urinary energy expressed as fraction of GE, assumed to be 0.04 except for feedlots
which are reduced 0.02 as a result of the high grain content of their diet.
ASH	= Ash content of manure calculated as a fraction of the dry matter feed intake
(assumed to be 0.08).
18.45	= Conversion factor for dietary GE per kg of dry matter (MJ per kg). This value is
relatively constant across a wide range of forage and grain-based feeds commonly
consumed by livestock.
Total nitrogen ingestion in cattle is determined by dietary protein intake. When feed intake of protein exceeds
the nutrient requirements of the animal, the excess nitrogen is excreted, primarily through the urine. To calculate the
nitrogen excreted by each animal type, the CEFM utilizes the energy balance calculations recommended by the 2006
IPCC Guidelines for gross energy and the energy required for growth along with inputs of weight gain, milk production,
and the percent of crude protein in the diets. The total nitrogen excreted is measured in the CEFM as nitrogen consumed
minus nitrogen retained by the animal for growth and in milk. The basic equation for calculating Nex is shown below,
followed by the equations for each of the constituent parts, based on the 10th Corrigenda for the 2006 IPCC Guidelines
(IPCC 2018).
NeX(j~) Nintake *	^retention_fract(T))
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where,
Nex(T)
Njntake(T)
Nretention(T)
The annual N intake per head of animal of species/category T (kg N animal1 yr1)
Fraction of annual N intake that is retained by animal
N intake is estimated as:
11 in fake (T)
GE
18.45
CP%
100
6.25
where,
Nintakefo	= Daily N consumed per animal of category T (kg N animal"1 day1)
GE	= Gross energy intake of the animal based on digestible energy, milk
production, pregnancy, current weight, mature weight, rate of weight gain, and
IPCC constants (MJ animal1 day-1)
18.45	= Conversion factor for dietary GE per kg of dry matter (MJ kg"1)
CP%	= Percent crude protein in diet, input
6.25	= Conversion from kg of dietary protein to kg of dietary N (kg feed protein per kg N)
The portion of consumed N that is retained as product equals the nitrogen in milk plus the nitrogen required for
weight gain. The N content of milk produced is calculated using milk production and percent protein, along with
conversion factors. The nitrogen retained in body weight gain by stockers, replacements, or feedlot animals is calculated
using the net energy for growth (NEg), weight gain (WG), and other conversion factors and constants. The equation
matches the 10th Corrigenda to the 2006 IPCC Guidelines (IPCC 2018), and is as follows:
N,
retention(T)
Milk
w (Milk PR%\
x V ioo )
6.38
WG x
268-
/7.03 x NEq\
{ WG )
1000 x 6.25
where,
Nretentionfo	= Daily N retained per animal of category T (kg N animal"1 day1)
Milk	= Milk production (kg animal1 day1)
268	= Constant from 2006 IPCC Guidelines
7.03	= Constant from 2006 IPCC Guidelines
NEg	= Net energy for growth, calculated in livestock characterization, based on current
weight, mature weight, rate of weight gain, and IPCC constants, (MJ day1)
1,000	= Conversion from grams to kilograms (g kg1)
6.25	= Conversion from kg dietary protein to kg dietary N (kg protein per kg N)
Milk PR%	= Percent of protein in milk (%)
6.38	= Conversion from milk protein to milk N (kg protein per kg N)
WG	= Weight gain, as input into the CEFM transition matrix (kg day1)
The VS and N equations above were used to calculate VS and Nex rates for each state, animal type (heifers and
steer on feed, heifers and steer not on feed, bulls and American bison), and year. Table A-170 presents the state-specific
VS and Nex production rates used for cattle in 2018. As shown in Table A-170, the differences in the VS daily excretion
and Nex rate trends between dairy cattle animal types is due to milk production. Milk production by cow varies from
state to state and is used in calculating net energy for lactating, which is used to calculate VS and Nex for dairy cows.
Milk production is zero for dairy heifers (dairy heifers do not produce milk because they have not yet had a calf). Over
time, the differences in milk production are also a big driver for the higher variability of VS and Nex rates in dairy cows.
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Step 3: Waste Management System Usage Data
Table A-171 and Table A-172 summarize 2018 manure distribution data among waste management systems
(WMS) at beef feedlots, dairies, dairy heifer facilities, and swine, layer, broiler, and turkey operations. Manure from the
remaining animal types (beef cattle not on feed, American bison, goats, horses, mules and asses and sheep) is managed
on pasture, range, or paddocks, on drylot, or with solids storage systems. Note that the Inventory WMS estimates are
based on state or regional WMS usage data and not built upon farm-level WMS estimates. Additional information on the
development of the manure distribution estimates for each animal type is presented below. Definitions of each WMS
type are presented in Table A-173.
Beef Cattle, Dairy Heifers and American Bison-. The beef feedlot and dairy heifer WMS data were developed
using regional information from EPA's Office of Water's engineering cost analyses conducted to support the
development of effluent limitations guidelines for Concentrated Animal Feeding Operations (EPA 2002b). Based on EPA
site visits and state contacts supporting this work and additional personal communication with the national USDA office
to estimate the percent of beef steers and heifers in feedlots (Milton 2000), feedlot manure is almost exclusively
managed in drylots. Therefore, for these animal groups, the percent of manure deposited in drylots is assumed to be 100
percent. In addition, there is a small amount of manure contained in runoff, which may or may not be collected in runoff
ponds. Using EPA and USDA data and expert opinions (documented in ERG 2000a), the runoff from feedlots was
calculated by region in Calculations: Percent Distribution of Manure for Waste Management Systems and was used to
estimate the percentage of manure managed in runoff ponds in addition to drylots; this percentage ranges from 0.4 to
1.3 percent (ERG 2000a). The percentage of manure generating emissions from beef feedlots is therefore greater than
100 percent. The remaining population categories of beef cattle outside of feedlots are managed through pasture, range,
or paddock systems, which are utilized for the majority of the population of beef cattle in the country. American bison
WMS data were assumed to be the same as beef cattle NOF.
Dairy Cows: The WMS data for dairy cows were developed using state and regional data from the Census of
Agriculture, EPA's Office of Water, USDA, and the expert sources noted below. Farm-size distribution data are reported
in the 1992,1997, 2002, 2007, 2012, and 2017 Census of Agriculture (USDA 2019d). It was assumed that the Census data
provided for 1992 were the same as that for 1990 and 1991, and data provided for 2017 were the same as that for 2018.
Data for 1993 through 1996, 1998 through 2001, and 2003 through 2006, 2008 through 2011, and 2013 through 2016
were interpolated using the 1992,1997, 2002, 2007, 2012, and 2017 Census data. The percent of waste by system was
estimated using the USDA data broken out by geographic region and farm size.
For 1990 through 1996 the following methodology and sources were used to estimate dairy WMS:
Based on EPA site visits and the expert opinion of state contacts, manure from dairy cows at medium (200
through 700 head) and large (greater than 700 head) operations are managed using either flush systems or scrape/slurry
systems. In addition, they may have a solids separator in place prior to their storage component. Estimates of the
percent of farms that use each type of system (by geographic region) were developed by EPA's Office of Water and were
used to estimate the percent of waste managed in lagoons (flush systems), liquid/slurry systems (scrape systems), and
solid storage (separated solids) (EPA 2002b).
Manure management system data for small (fewer than 200 head) dairies were obtained at the regional level
from USDA's Animal and Plant Health Inspection Service (APHIS)'s National Animal Health Monitoring System (Ott 2000).
These data are based on a statistical sample of farms in the 20 U.S. states with the most dairy cows. Small operations are
more likely to use liquid/slurry and solid storage management systems than anaerobic lagoon systems. The reported
manure management systems were deep pit, liquid/slurry (includes slurry tank, slurry earth-basin, and aerated lagoon),
anaerobic lagoon, and solid storage (includes manure pack, outside storage, and inside storage).
Data regarding the use of daily spread and pasture, range, or paddock systems for dairy cattle were obtained
from personal communications with personnel from several organizations. These organizations include state NRCS
offices, state extension services, state universities, USDA NASS, and other experts (Deal 2000, Johnson 2000, Miller 2000,
Stettler 2000, Sweeten 2000, and Wright 2000). Contacts at Cornell University provided survey data on dairy manure
management practices in New York (Poe et al. 1999). Census of Agriculture population data for 1992,1997, 2002, 2007,
2012, and 2017 (USDA 2019d) were used in conjunction with the state data obtained from personal communications to
determine regional percentages of total dairy cattle and dairy waste that are managed using these systems. These
percentages were applied to the total annual dairy cow and heifer state population data for 1990 through 2018, which
were obtained from the USDA NASS (USDA 2018a).
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Of the dairies using systems other than daily spread and pasture, range, or paddock systems, some dairies
reported using more than one type of manure management system. Due to limitations in how USDA APHIS collects the
manure management data, the total percent of systems for a region and farm size is greater than 100 percent. However,
manure is typically partitioned to use only one manure management system, rather than transferred between several
different systems. Emissions estimates are only calculated for the final manure management system used for each
portion of manure. To avoid double counting emissions, the reported percentages of systems in use were adjusted to
equal a total of 100 percent using the same distribution of systems. For example, if USDA reported that 65 percent of
dairies use deep pits to manage manure and 55 percent of dairies use anaerobic lagoons to manage manure, it was
assumed that 54 percent (i.e., 65 percent divided by 120 percent) of the manure is managed with deep pits and 46
percent (i.e., 55 percent divided by 120 percent) of the manure is managed with anaerobic lagoons (ERG 2000a).
Starting in 2016, EPA estimate dairy WMS based on 2016 USDA Economic Research Service (ERS) Agricultural
Resource Management Survey (ARMS) data. These data were obtained from surveys of nationally representative dairy
producers. WMS data for 2016 were assumed the same for 2017 and 2018. WMS for 1997 through 2015 were
interpolated between the data sources used for the 1990-1997 dairy WMS (noted above) and the 2016 ARMs data (ERG
2019).
Finally, the percentage of manure managed with anaerobic digestion (AD) systems with methane capture and
combustion was added to the WMS distributions at the state-level. AD system data were obtained from EPA's AgSTAR
Program's project database (EPA 2019). This database includes basic information for AD systems in the United States,
based on publicly available data and data submitted by farm operators, project developers, financiers, and others
involved in the development of farm AD projects.
Swine: The regional distribution of manure managed in each WMS was estimated using data from a 1995 USDA
APHIS survey, EPA's Office of Water site visits, and 2009 USDA ERS ARMS data (Bush 1998, ERG 2000a, ERG 2018). The
USDA APHIS data are based on a statistical sample of farms in the 16 U.S. states with the most hogs. The ERS ARMS data
are based on surveys of nationally representative swine producers. Prior to 2009, operations with less than 200 head
were assumed to use pasture, range, or paddock systems and swine operations with greater than 200 head were
assigned WMS as obtained from USDA APHIS (Bush 1998). WMS data for 2009 were obtained from USDA ERS ARMS;
WMS data for 2010 through 2018 were assumed to be the same as 2009 (ERG 2018). The percent of waste managed in
each system was estimated using the EPA and USDA data broken out by geographic region and farm size. Farm-size
distribution data reported in the 1992,1997, 2002, 2007, 2012, and 2017 Census of Agriculture (USDA 2019d) were used
to determine the percentage of all swine utilizing the various manure management systems. It was assumed that the
swine farm size data provided for 1992 were the same as that for 1990 and 1991. Data for 1993 through 1996,1998
through 2001, 2003 through 2006, and 2008 through 2011, and 2013 through 2016 were interpolated using the 1992,
1997, 2002, 2007, 2012, and 2017 Census data.
Some swine operations reported using more than one management system; therefore, the total percent of
systems reported by USDA for a region and farm size was greater than 100 percent. Typically, this means that a portion
of the manure at a swine operation is handled in one system (e.g., liquid system), and a separate portion of the manure
is handled in another system (e.g., dry system). However, it is unlikely that the same manure is moved from one system
to another, which could result in increased emissions, so reported systems data were normalized to 100 percent for
incorporation into the WMS distribution, using the same method as described above for dairy operations. As with dairy,
AD WMS were added to the state-level WMS distribution based on data from EPA's AgSTAR database (EPA 2019).
Sheep: WMS data for sheep were obtained from USDA NASS sheep report for years 1990 through 1993 (USDA
1994). Data for 2001 are obtained from USDA APHIS's national sheep report (USDA, APHIS 2003). The USDA APHIS data
are based on a statistical sampled of farms in the 22 U.S. states with the most sheep. The data for years 1994-2000 are
calculated assuming a linear progression from 1993 to 2001. Due to lack of additional data, data for years 2002 and
beyond are assumed to be the same as 2001. Based on expert opinion, it was assumed that all sheep manure not
deposited in feedlots was deposited on pasture, range, or paddock lands (Anderson 2000).
Goats, Horses, and Mules and Asses: WMS data for 1990 to 2018 were obtained from Appendix H of Global
Methane Emissions from Livestock and Poultry Manure (EPA 1992). This report presents state WMS usage in percentages
for the major animal types in the United States, based on information obtained from extension service personnel in each
state. It was assumed that all manure not deposited in pasture, range, or paddock lands was managed in dry systems.
For mules and asses, the WMS was assumed to be the same as horses.
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Poultry—Hens (one year old or older), Pullets (hens less than one year old), and Other Chickens: WMS data for
1992 were obtained from Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). These data were
also used to represent 1990 and 1991. The percentage of layer operations using a shallow pit flush house with anaerobic
lagoon or high-rise house without bedding was obtained for 1999 from a United Egg Producers voluntary survey (UEP
1999). These data were augmented for key poultry states (AL, AR, CA, FL, GA, IA, IN, MN, MO, NC, NE, OH, PA, TX, and
WA) with USDA data (USDA, APHIS 2000). It was assumed that the change in system usage between 1990 and 1999 is
proportionally distributed among those years of the inventory. It was also assumed that system usage in 2000 through
2018 was equal to that estimated for 1999. Data collected for EPA's Office of Water, including information collected
during site visits (EPA 2002b), were used to estimate the distribution of waste by management system and animal type.
As with dairy and swine, using information about AD WMS from EPA's AgSTAR database (EPA 2019), AD was added to
the WMS distribution for poultry operations.
Poultry—Broilers and Turkeys: The percentage of turkeys and broilers on pasture was obtained from the Office
of Air and Radiation's Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that one
percent of poultry waste is deposited in pastures, ranges, and paddocks (EPA 1992). The remainder of waste is assumed
to be deposited in operations with bedding management. As with dairy, swine, and other poultry, AD systems were used
to update the WMS distributions based on information from EPA's AgSTAR database (EPA 2019).
Step 4: Emission Factor Calculations
Methane conversion factors (MCFs) and N20 emission factors (EFs) used in the emission calculations were
determined using the methodologies presented below.
Methane Conversion Factors (MCFs)
Climate-based IPCC default MCFs (IPCC 2006) 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. The average county and state temperature data were obtained from the National Climate
Data Center (NOAA 2019) and each state and year in the inventory was assigned a climate classification of cool,
temperate or warm. Although there are some specific locations in the United States that may be included in the warm
climate category, no aggregated state-level annual average temperatures are included in this category. In addition, some
counties in a particular state may be included in the cool climate category, although the aggregated state-level annual
average temperature may be included in the temperate category. Although considering the temperatures at a state level
instead of a county level may be causing some specific locations to be classified into an inappropriate climate category,
using the state level annual average temperature provides an estimate that is appropriate for calculating the national
average.
For anaerobic lagoons and other liquid systems, a climate-based approach based on the van't Hoff-Arrhenius
equation was developed to estimate MCFs that reflects the seasonal changes in temperatures, and also accounts for
long-term retention time. This approach is consistent with the latest guidelines from IPCC (2006). The van't Hoff-
Arrhenius equation, with a base temperature of 30°C, is shown in the following equation (Safley and Westerman 1990):
where,
/
van't Hoff-Arrhenius/factor, the proportion of VS that are biologically available for
conversion to CH4 based on the temperature of the system
303.15K
Ambient temperature (K) for climate zone (in this case, a weighted value for each
state)
Activation energy constant (15,175 cal/mol)
Ideal gas constant (1.987 cal/K mol)
Ti
T2
E
R
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For those animal populations using liquid manure management systems or manure runoff ponds (i.e., dairy
cow, dairy heifer, layers, beef in feedlots, and swine) monthly average state temperatures were based on the counties
where the specific animal population resides (i.e., the temperatures were weighted based on the percent of animals
located in each county). County population data were calculated from state-level population data from NASS and county-
state distribution data from the 1992, 1997, 2002, 2007, 2012, and 2017 Census data (USDA 2019d). County population
distribution data for 1990 and 1991 were assumed to be the same as 1992; county population distribution data for 1993
through 1996 were interpolated based on 1992 and 1997 data; county population distribution data for 1998 through
2001 were interpolated based on 1997 and 2002 data; county population distribution data for 2003 through 2006 were
interpolated based on 2002 and 2007 data; county population distribution data for 2008 through 2011 were interpolated
based on 2007 and 2012 data; county population distribution data for 2013 through 2016 were interpolated based on
2012 and 2017 data; county population distributions for 2018 were assumed to be the same as 2017.
Annual MCFs for liquid systems are calculated as follows for each animal type, state, and year of the inventory:
•	The weighted-average temperature for a state is calculated using the county population estimates and average
monthly temperature in each county. Monthly temperatures are used to calculate a monthly van't Hoff-
Arrhenius/factor, using the equation presented above. A minimum temperature of 5°C is used for uncovered
anaerobic lagoons and 7.5°C is used for liquid/slurry and deep pit systems due to the biological activity in the
lagoon which keeps the temperature above freezing.
•	Monthly production of VS added to the system is estimated based on the animal type, number of animals
present, and the volatile solids excretion rate of the animals.
•	For lagoon systems, the calculation of methane includes a management and design practices (MDP) factor. This
factor, equal to 0.8, was developed based on model comparisons to empirical CH4 measurement data from
anaerobic lagoon systems in the United States (ERG 2001). The MDP factor represents management and design
factors which cause a system to operate at a less than optimal level.
•	For all systems other than anaerobic lagoons, the amount of VS available for conversion to CH4 each month is
assumed to be equal to the amount of VS produced during the month (from Step 3). For anaerobic lagoons, the
amount of VS available also includes VS that may remain in the system from previous months.
•	The amount of VS consumed during the month is equal to the amount available for conversion multiplied by
the/factor.
•	For anaerobic lagoons, the amount of VS carried over from one month to the next is equal to the amount
available for conversion minus the amount consumed. Lagoons are also modeled to have a solids clean-out
once per year, occurring in the month of October.
•	The estimated amount of CH4 generated during the month is equal to the monthly VS consumed multiplied by
B0.
The annual MCF is then calculated as:
MCFal
CH4 generated
VS produced ;illllll
-------
conversion to CH4. It is assumed that liquid-slurry systems have a retention time less than 30 days, so the liquid-slurry
MCF calculation doesn't reflect the VS carry-over.
The liquid system MCFs are presented in Table A-175 by state, WMS, and animal group for 2018.
Nitrous Oxide Emission Factors
Direct N20 EFs for manure management systems (kg N20-N/kg excreted N) were set equal to the most recent
default IPCC factors (IPCC 2006), presented in Table A-176.
Indirect N20 EFs account for two fractions of nitrogen losses: volatilization of ammonia (NH3) and NOx (Fracgas)
and runoff/leaching (Fracrunoff/ieach). IPCC default indirect N20 EFs were used to estimate indirect N20 emissions. These
factors are 0.010 kg N20-N/kg N for volatilization and 0.0075 kg N20/kg N for runoff/leaching.
Country-specific estimates of N losses were developed for Fracgas and Fracrunoff/ieachfor the United States. The
vast majority of volatilization losses are NH3. Although there are also some small losses of NOx, no quantified estimates
were available for use and those losses are believed to be small (about 1 percent) in comparison to the NH3 losses.
Therefore, Fracgas values were based on WMS-specific volatilization values estimated from U.S. EPA's National Emission
Inventory - Ammonia Emissions from Animal Agriculture Operations (EPA 2005). To estimate Fracmnoff/ieach, data from
EPA's Office of Water were used that estimate the amount of runoff from beef, dairy, and heifer operations in five
geographic regions of the country (EPA 2002b). These estimates were used to develop U.S. runoff factors by animal type,
WMS, and region. Nitrogen losses from leaching are believed to be small in comparison to the runoff losses and there
are a lack of data to quantify these losses. Therefore, leaching losses were assumed to be zero and Fracrunoff/ieach was set
equal to the runoff loss factor. Nitrogen losses from volatilization and runoff/leaching are presented in Table A-177.
Step 5: CH4 Emission Calculations
To calculate CH4 emissions for animals other than cattle, first the amount of VS excreted in manure that is
managed in each WMS was estimated:
VS excretedstate,Anllml,WMS = Population State-Amlml x	x VS x WMS x 365.25
where,
VS excreted state, Animai.wMs = Amount of VS excreted in manure managed in each WMS for each animal type
(kg/yr)
Population state. Animal	= Annual average state animal population by animal type (head)
TAM	= Typical animal mass (kg)
VS	= Volatile solids production rate (kg VS/1000 kg animal mass/day)
WMS	= Distribution of manure by WMS for each animal type in a state (percent)
365.25	= Days per year
Using the CEFM VS data for cattle, the amount of VS excreted in manure that is managed in each WMS was
estimated using the following equation:
VS excretedstate, Animal, WMS = Populationstate, Animal x VS x WMS
where,
VS excreted state, Animai.wMs = Amount of VS excreted in manure managed in each WMS for each animal type
(kg/yr)
Population state. Animal	= Annual average state animal population by animal type (head)
VS	= Volatile solids production rate (kg VS/animal/year)
WMS	= Distribution of manure by WMS for each animal type in a state (percent)
For all animals, the estimated amount of VS excreted into a WMS was used to calculate CH4 emissions using the
following equation:
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where,
CH4
VS excreted wms, state
B0
MCF animal, state, WMS
0.662
CH4 = 5] (VS excreted
Stats. Animal. WMS
St a t a. Animal. WMS
;B„ xMCFx0,662)
= CH4 emissions (kg CH4/yr)
= Amount of VS excreted in manure managed in each WMS (kg/yr)
= Maximum CH4 producing capacity (m3 CH4/kg VS)
= MCF for the animal group, state and WMS (percent)
= Density of methane at 25° C (kg CH4/m3 CH4)
A calculation was developed to estimate the amount of CH4 emitted from AD systems utilizing CH4 capture and
combustion technology. First, AD systems were assumed to produce 90 percent of B0 of the manure. This value is applied
for all climate regions and AD system types. However, this is a conservative assumption as the actual amount of CH4
produced by each AD system is very variable and will change based on operational and climate conditions and an
assumption of 90 percent is likely overestimating CH4 production from some systems and underestimating CH4
production in other systems. The CH4 production of AD systems is calculated using the equation below:
TAM
CH4 ProductionADADSystem = ProductionADADSystem x ^qqq x ^ x B0 x 0.662 x 365.25 x 0.90
where,
CH4 Production ADAdsystem = CH4 production from a particular AD system, (kg/yr)
Population AD state	= Number of animals on a particular AD system
VS	= Volatile solids production rate (kg VS/1000 kg animal mass-day)
TAM	= Typical Animal Mass (kg/head)
B0	= Maximum CH4 producing capacity (CH4 m3/kg VS)
0.662	= Density of CH4 at 25° C (kg CH4/m3 CH4)
365.25	= Days/year
0.90	= CH4 production factor for AD systems
The total amount of CH4 produced by AD is calculated only as a means to estimate the emissions from AD; i.e.,
only the estimated amount of CH4 actually entering the atmosphere from AD is reported in the inventory. The emissions
to the atmosphere from AD are a result of leakage from the system (e.g., from the cover, piping, tank, etc.) and
incomplete combustion and are calculated using the collection efficiency (CE) and destruction efficiency (DE) of the AD
system. The three primary types of AD systems in the United States are covered lagoons, complete mix and plug flow
systems. The CE of covered lagoon systems was assumed to be 75 percent, and the CE of complete mix and plug flow AD
systems was assumed to be 99 percent (EPA 2008). The CH4 DE from flaring or burning in an engine was assumed to be
98 percent; therefore, the amount of CH4 that would not be flared or combusted was assumed to be 2 percent (EPA
2008). The amount of CH4 produced by systems with AD was calculated with the following equation:
f [CH 4 Production AD ADsystemx CE ADsystem x (l — DE)J ^
CH 4 Emissions AD =	£
State, Animal, AD Systems
v+ [CH 4 Production AD ADsystemx (l - CE AI)svstan/
where,
CH4 Emissions AD	= CH4 emissions from AD systems, (kg/yr)
CH4 Production ADAdsystem	= CH4 production from a particular AD system, (kg/yr)
CEad system	= Collection efficiency of the AD system, varies by AD system type
DE	= Destruction efficiency of the AD system, 0.98 for all systems
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Step 6: N20 Emission Calculations
Total N20 emissions from manure management systems were calculated by summing direct and indirect N20
emissions. The first step in estimating direct and indirect N20 emissions was calculating the amount of N excreted in
manure and managed in each WMS. For calves and animals other than cattle the following equation was used:
N excreted StateiA^ mis = PopulationState>Animal xWMS
JAM
' 1000
xNexx 365.25
where,
N eXCreted state. Animal, WMS
Population state
WMS
TAM
Nex
365.25
= Amount of N excreted in manure managed in each WMS for each animal type
(kg/yr)
= Annual average state animal population by animal type (head)
= Distribution of manure by waste management system for each animal type in a
state (percent)
= Typical animal mass (kg)
= Nitrogen excretion rate (kg N/1000 kg animal mass/day)
= Days per year
Using the CEFM Nex data for cattle other than calves, the amount of N excreted was calculated using the
following equation:
N excreted state>Anllml>WMS = Population state>Anllml x WMS x Nex
where,
N excreted state, Animai.wMs = Amount of N excreted in manure managed in each WMS for each animal type
(kg/yr)
Population state	= Annual average state animal population by animal type (head)
WMS	= Distribution of manure by waste management system for each animal type in a
state (percent)
Nex	= Nitrogen excretion rate (kg N/animal/year)
For all animals, direct N20 emissions were calculated as follows:
44
where,
Direct N20= £	N exeretedstate>Anlmal
,WMS X EFwMS X
State, Animal,WMS \
Direct N20	= Direct N20 emissions (kg N20/yr)
N excreted state,Animai.wMs = Amount of N excreted in manure managed in each WMS for each animal type
(kg/yr)
EFwms	= Direct N20 emission factor from IPCC guidelines (kg N20-N /kg N)
44/28	= Conversion factor of N20-N to N20
Indirect N20 emissions were calculated for all animals with the following equation:
Indirect N2O = Z
State., Animal. WMS
N excreted StateiAnjmaji m,IS x-
Frac
gas: WMS
100
xEF,
volatiHza tion
44
28
N excreted
State, Animal. WMS
^ ^nmoffleach. WMS t-t-.	44
:	100	x EF—x ^
where,
Indirect N20	= Indirect N20 emissions (kg N20/yr)
N excreted state,Animai.wMs = Amount of N excreted in manure managed in each WMS for each animal type
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(kg/yr)
FraCgas.wMs	= Nitrogen lost through volatilization in each WMS
FraCrunoff/leach ,wms	- Nitrogen lost through runoff and leaching in each WMS (data were not available
for leaching so the value reflects only runoff)
EFvoiatiiization	= Emission factor for volatilization (0.010 kg N20-N/kg N)
EFrunoff/ieach	= Emission factor for runoff/leaching (0.0075 kg N20-N/kg N)
44/28	= Conversion factor of N20-N to N20
Emission estimates of CH4 and N20 by animal type are presented for all years of the inventory in Table A-178
and Table A-179 respectively. Emission estimates for 2018 are presented by animal type and state in Table A-180 and
Table A-181 respectively.
A-331
-------
Table A-167: Livestock Population (1,000 Head)
Animal Type
1990
1995
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Dairy Cattle
19,512
18,681
17,793
18,297
18,442
18,587
18,505
18,527
18,803
18,853
18,893
19,008
18,909
Dairy Cows
10,015
9,482
9,004
9,087
9,156
9,236
9,221
9,208
9,307
9,310
9,346
9,432
9,353
Dairy Heifer
4,129
4,108
4,162
4,545
4,577
4,581
4,525
4,579
4,725
4,785
4,762
4,776
4,709
Dairy Calves
5,369
5,091
4,628
4,666
4,709
4,770
4,758
4,740
4,771
4,758
4,785
4,800
4,846
Swine3
53,941
58,899
61,073
64,723
65,572
66,363
65,437
64,195
68,178
70,065
72,125
73,793
76,907
Market <50 lb.
18,359
19,656
20,228
19,067
19,285
19,472
19,002
18,939
19,843
20,572
20,973
21,494
22,402
Market 50-119 lb.
11,734
12,836
13,519
16,645
16,904
17,140
16,834
16,559
17,577
18,175
18,767
19,133
20,097
Market 120-179













lb.
9,440
10,545
11,336
12,377
12,514
12,714
12,674
12,281
13,225
13,575
13,982
14,365
14,843
Market >180 lb.
7,510
8,937
9,997
10,856
11,078
11,199
11,116
10,525
11,555
11,714
12,282
12,497
13,151
Breeding
6,899
6,926
5,993
5,778
5,791
5,839
5,812
5,892
5,978
6,030
6,122
6,303
6,415
Beef Cattleb
81,576
90,361
82,193
80,484
78,937
76,858
76,075
75,245
76,080
79,374
81,560
83,061
83,766
Feedlot Steers
6,357
7,233
8,116
8,584
8,771
8,586
8,614
8,695
8,570
9,019
9,572
10,329
10,491
Feedlot Heifers
3,192
3,831
4,536
4,620
4,830
4,742
4,653
4,525
4,313
4,431
4,768
5,146
5,226
NOF Bulls
2,160
2,385
2,214
2,190
2,165
2,100
2,074
2,038
2,109
2,142
2,244
2,252
2,253
Beef Calves
16,909
18,177
16,918
16,067
15,817
15,288
14,859
14,741
15,000
15,563
15,971
16,021
16,175
NOF Heifers
10,182
11,829
9,550
9,349
8,874
8,687
8,787
8,787
9,288
9,903
9,835
9,815
9,786
NOF Steers
10,321
11,716
8,185
8,234
7,568
7,173
7,457
7,374
7,496
8,150
7,957
8,032
8,144
NOF Cows
32,455
35,190
32,674
31,440
30,913
30,282
29,631
29,085
29,302
30,166
31,213
31,466
31,691
Sheep
11,358
8,989
6,135
5,620
5,470
5,375
5,360
5,235
5,270
5,295
5,270
5,265
5,230
Sheep On Feed
1,180
1,771
2,971
2,778
2,687
2,666
2,655
2,585
2,584
2,621
2,615
2,619
2,602
Sheep NOF
10,178
7,218
3,164
2,842
2,783
2,709
2,705
2,650
2,686
2,674
2,655
2,646
2,628
Goats
2,516
2,357
2,897
2,829
2,725
2,622
2,637
2,652
2,668
2,683
2,699
2,714
2,696
Poultry0
1,537,074
1,826,977
2,150,410
2,104,335
2,095,951
2,168,697
2,106,502
2,116,333
2,134,445
2,173,216
2,214,462
2,252,265
2,277,413
Hens >1 yr.
273,467
299,071
348,203
341,884
338,944
346,965
361,403
370,637
351,656
377,299
388,006
396,870
403,273
Pullets
73,167
81,369
96,809
105,738
102,233
104,460
106,646
106,490
118,114
112,061
117,173
124,135
122,095
Chickens
6,545
7,637
8,289
7,390
6,922
6,827
6,853
6,403
7,211
6,759
6,859
6,568
7,130
Broilers
1,066,209
1,331,940
1,613,091
1,567,927
1,565,018
1,625,945
1,551,600
1,553,636
1,579,764
1,595,764
1,620,691
1,643,109
1,668,582
Turkeys
117,685
106,960
84,018
81,396
82,833
84,500
80,000
79,167
77,700
81,333
81,733
81,583
76,333
Horses
2,212
2,632
3,875
3,784
3,703
3,621
3,467
3,312
3,157
3,002
2,847
2,692
2,538
Mules and Asses
63
101
212
289
291
293
298
303
308
313
318
323
328
American Bison
47
104
212
177
169
162
166
171
175
179
184
188
189
Note: Totals may not sum due to independent rounding.
a Prior to 2008, the Market <50 lbs category was <60 lbs and the Market 50-119 lbs category was Market 60-119 lbs; USDA updated the categories to be more consistent with
international animal categories.
b NOF - Not on Feed
c Pullets includes laying pullets, pullets younger than 3 months, and pullets older than 3 months.
Source(s): See Step 1: Livestock Population Characterization Data.
A-332 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-168: Waste Characteristics Data





Maximum Methane Generation



Typical Animal Mass, TAM
Total Nitrogen Excreted, Nexa
Potential, B0
Volatile Solids Excreted, VSa





Value




Value



(m3 Cm/kg VS



Animal Group
(kg)
Source
Value
Source
added)
Source
Value
Source
Dairy Cows
680
CEFM
Table A-170
CEFM
0.24
Morris 1976
Table A-170
CEFM
Dairy Heifers
406-408
CEFM
Table A-170
CEFM
0.17
Bryant et al. 1976
Table A-170
CEFM
Feedlot Steers
419-457
CEFM
Table A-170
CEFM
0.33
Hashimoto 1981
Table A-170
CEFM
Feedlot Heifers
384-430
CEFM
Table A-170
CEFM
0.33
Hashimoto 1981
Table A-170
CEFM
NOF Bulls
831-917
CEFM
Table A-170
CEFM
0.17
Hashimoto 1981
Table A-170
CEFM
NOF Calves
118
ERG 2003b
Table A-169
USDA 1996, 2008
0.17
Hashimoto 1981
Table A-169
USDA 1996, 2008
NOF Heifers
296-407
CEFM
Table A-170
CEFM
0.17
Hashimoto 1981
Table A-170
CEFM
NOF Steers
314-335
CEFM
Table A-170
CEFM
0.17
Hashimoto 1981
Table A-170
CEFM
NOF Cows
554-611
CEFM
Table A-170
CEFM
0.17
Hashimoto 1981
Table A-170
CEFM
American Bison
578.5
Meagher 1986
Table A-170
CEFM
0.17
Hashimoto 1981
Table A-170
CEFM
Market Swine <50 lbs.
13
ERG 2010a
Table A-169
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-169
USDA 1996, 2008
Market Swine <60 lbs.
16
Safley 2000
Table A-169
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-169
USDA 1996, 2008
Market Swine 50-119 lbs.
39
ERG 2010a
Table A-169
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-169
USDA 1996, 2008
Market Swine 60-119 lbs.
41
Safley 2000
Table A-169
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-169
USDA 1996, 2008
Market Swine 120-179 lbs.
68
Safley 2000
Table A-169
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-169
USDA 1996, 2008
Market Swine >180 lbs.
91
Safley 2000
Table A-169
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-169
USDA 1996, 2008
Breeding Swine
198
Safley 2000
Table A-169
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-169
USDA 1996, 2008




ASAE 1998, USDA



ASAE 1998, USDA
Feedlot Sheep
25
EPA 1992
Table A-169
2008
0.36
EPA 1992
Table A-169
2008




ASAE 1998, USDA



ASAE 1998, USDA
NOF Sheep
80
EPA 1992
Table A-169
2008
0.19
EPA 1992
Table A-169
2008
Goats
64
ASAE1998
Table A-169
ASAE1998
0.17
EPA 1992
Table A-169
ASAE 1998




ASAE 1998, USDA



ASAE 1998, USDA
Horses
450
ASAE1998
Table A-169
2008
0.33
EPA 1992
Table A-169
2008
Mules and Asses
130
IPCC 2006
Table A-169
IPCC 2006
0.33
EPA 1992
Table A-169
IPCC 2006
Hens >/= 1 yr
1.8
ASAE1998
Table A-169
USDA 1996, 2008
0.39
Hill 1982
Table A-169
USDA 1996, 2008
Pullets
1.8
ASAE1998
Table A-169
USDA 1996, 2008
0.39
Hill 1982
Table A-169
USDA 1996, 2008
Other Chickens
1.8
ASAE1998
Table A-169
USDA 1996, 2008
0.39
Hill 1982
Table A-169
USDA 1996, 2008
Broilers
0.9
ASAE1998
Table A-169
USDA 1996, 2008
0.36
Hill 1984
Table A-169
USDA 1996, 2008
Turkeys
6.8
ASAE1998
Table A-169
USDA 1996, 2008
0.36
Hill 1984
Table A-169
USDA 1996, 2008
a Nex and VS values vary by year; Table A-170 shows state-level values for 2018 only.
A-333
-------
Table A-169: Estimated Volatile Solids
Asses, and Cattle Calves (kg/day/1000
(VS) and Total Nitrogen Excreted (Nex) Production Rates by year for Swine, Poultry, Sheep, Goats, Horses, Mules and
kg animal mass)101
Animal Type
1990
1995
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
VS
Swine, Market
















<50 lbs.
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
Swine, Market
















50-119 lbs.
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
Swine, Market
















120-179 lbs.
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
Swine, Market
















>180 lbs.
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
Swine, Breeding
2.6
2.6
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
NOF Cattle Calves
6.4
6.4
7.4
7.5
7.6
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
Sheep
9.2
9.2
8.6
8.5
8.4
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
Goats
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
Hens >lyr.
10.1
10.1
10.1
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
Pullets
10.1
10.1
10.1
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
Chickens
10.8
10.8
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
Broilers
15.0
15.0
16.5
16.7
16.8
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
Turkeys
9.7
9.7
00
00
8.7
8.6
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
Horses
10.0
10.0
7.3
6.9
6.5
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
Mules and Asses
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
Nex
















Swine, Market
















<50 lbs.
0.60
0.60
0.84
0.87
0.89
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
Swine, Market
















50-119 lbs.
0.42
0.42
0.51
0.52
0.53
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
Swine, Market
















120-179 lbs.
0.42
0.42
0.51
0.52
0.53
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
Swine, Market
















>180 lbs.
0.42
0.42
0.51
0.52
0.53
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
Swine, Breeding
0.24
0.24
0.21
0.21
0.21
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
NOF Cattle Calves
0.30
0.30
0.41
0.43
0.44
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
Sheep
0.42
0.42
0.44
0.44
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
Goats
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
Hens >lyr.
0.70
0.70
0.77
0.77
0.78
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
1 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020) Inventory submission.
A-334 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Animal Type
1990
1995
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Pullets
0.70
0.70
0.77
0.77
0.78
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
Chickens
0.83
0.£
1.03
1.06
1.08
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
Broilers
1.10
1.10
1.00
0.98
0.97
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
Turkeys
0.74
0.74
0.65
0.64
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
Horses
0.30
0.30
0.26
0.26
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
Mules and Asses
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
A-335
-------
Table A-170: Estimated Volatile Solids (VS) and Total Nitrogen Excreted (Nex) Production Rates by State for Cattle (other than Calves) and American Bisona
for 2018 (kg/animal/year)102
Volatile Solids
Nitrogen Excreted



Beef


Beef
Beef
Beef



Beef
Beef
Beef
Beef
Beef
Beef


Dairy
Dairy
NOF
Beef NOF
Beef NOF
OF
OF
NOF
American
Dairy
Dairy
NOF
NOF
NOF
OF
OF
NOF
American
State
Cow
Heifers
Cow
Heifers
Steer
Heifers
Steer
Bull
Bison
Cow
Heifers
Cow
Heifers
Steer
Heifers
Steer
Bull
Bison
Alabama
2,262
1,252
1,664
1,100
975
691
669
1,721
1,721
136
69
73
50
42
56
57
83
83
Alaska
1,821
1,252
1,891
1,252
1,120
691
669
1,956
1,956
115
69
59
41
33
56
57
69
69
Arizona
2,943
1,252
1,891
1,236
1,120
691
670
1,956
1,956
163
69
59
40
33
56
57
69
69
Arkansas
2,087
1,252
1,664
1,096
975
691
670
1,721
1,721
126
69
73
50
42
56
57
83
83
California
2,780
1,252
1,891
1,230
1,120
691
670
1,956
1,956
155
69
59
39
33
56
57
69
69
Colorado
3,055
1,252
1,891
1,205
1,120
691
669
1,956
1,956
168
69
59
38
33
56
57
69
69
Connecticut
2,751
1,252
1,674
1,097
981
691
669
1,731
1,731
155
69
74
51
42
56
57
84
84
Delaware
2,486
1,252
1,674
1,094
981
691
669
1,731
1,731
143
69
74
51
42
56
57
84
84
Florida
2,657
1,252
1,664
1,103
975
691
668
1,721
1,721
153
69
73
51
42
56
57
83
83
Georgia
2,790
1,252
1,664
1,093
975
691
668
1,721
1,721
158
69
73
50
42
55
57
83
83
Hawaii
2,363
1,252
1,891
1,262
1,120
691
669
1,956
1,956
138
69
59
41
33
56
57
69
69
Idaho
2,920
1,252
1,891
1,220
1,120
691
669
1,956
1,956
162
69
59
39
33
56
57
69
69
Illinois
2,649
1,252
1,589
1,013
927
691
669
1,643
1,643
150
69
75
50
43
56
57
85
85
Indiana
2,803
1,252
1,589
1,022
927
691
670
1,643
1,643
157
69
75
50
43
56
57
85
85
Iowa
2,872
1,252
1,589
995
927
691
670
1,643
1,643
160
69
75
48
43
56
57
85
85
Kansas
2,817
1,252
1,589
986
927
691
669
1,643
1,643
158
69
75
48
43
56
57
85
85
Kentucky
2,542
1,252
1,664
1,081
975
691
669
1,721
1,721
148
69
73
49
42
56
57
83
83
Louisiana
2,100
1,252
1,664
1,103
975
691
669
1,721
1,721
127
69
73
51
42
56
57
83
83
Maine
2,668
1,252
1,674
1,088
981
691
669
1,731
1,731
151
69
74
50
42
56
57
84
84
Maryland
2,582
1,252
1,674
1,095
981
691
670
1,731
1,731
147
69
74
51
42
56
57
84
84
Massachusetts
2,413
1,252
1,674
1,097
981
691
669
1,731
1,731
140
69
74
51
42
56
57
84
84
Michigan
3,064
1,252
1,589
1,010
927
691
670
1,643
1,643
168
69
75
49
43
56
57
85
85
Minnesota
2,708
1,252
1,589
1,008
927
691
670
1,643
1,643
153
69
75
49
43
56
57
85
85
Mississippi
2,291
1,252
1,664
1,098
975
691
669
1,721
1,721
137
69
73
50
42
56
57
83
83
Missouri
2,189
1,252
1,589
1,033
927
691
669
1,643
1,643
131
69
75
51
43
56
57
85
85
Montana
2,754
1,252
1,891
1,248
1,120
691
670
1,956
1,956
155
69
59
40
33
56
57
69
69
Nebraska
2,897
1,252
1,589
991
927
691
670
1,643
1,643
161
69
75
48
43
56
57
85
85
Nevada
2,754
1,252
1,891
1,244
1,120
691
668
1,956
1,956
155
69
59
40
33
55
56
69
69
New Hampshire
2,668
1,252
1,674
1,081
981
691
669
1,731
1,731
151
69
74
50
42
56
57
84
84
New Jersey
2,581
1,252
1,674
1,088
981
691
668
1,731
1,731
147
69
74
50
42
56
57
84
84
New Mexico
2,964
1,252
1,891
1,237
1,120
691
669
1,956
1,956
164
69
59
40
33
56
57
69
69
New York
2,887
1,252
1,674
1,078
981
691
668
1,731
1,731
161
69
74
49
42
56
57
84
84
North Carolina
2,734
1,252
1,664
1,097
975
691
668
1,721
1,721
156
69
73
50
42
56
57
83
83
102 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020) Inventory submission.
A-336 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------

Volatile Solids
Nitrogen Excreted



Beef


Beef
Beef
Beef



Beef
Beef
Beef
Beef
Beef
Beef


Dairy
Dairy
NOF
Beef NOF
Beef NOF
OF
OF
NOF
American
Dairy
Dairy
NOF
NOF
NOF
OF
OF
NOF
American
State
Cow
Heifers
Cow
Heifers
Steer Heifers
Steer
Bull
Bison
Cow
Heifers
Cow
Heifers
Steer
Heifers
Steer
Bull
Bison
North Dakota
2,710
1,252
1,589
1,021
927
691
670
1,643
1,643
153
69
75
50
43
56
57
85
85
Ohio
2,687
1,252
1,589
1,027
927
691
670
1,643
1,643
152
69
75
51
43
56
57
85
85
Oklahoma
2,498
1,252
1,664
1,073
975
691
669
1,721
1,721
144
69
73
49
42
56
57
83
83
Oregon
2,623
1,252
1,891
1,231
1,120
691
669
1,956
1,956
149
69
59
40
33
56
57
69
69
Pennsylvania
2,656
1,252
1,674
1,083
981
691
669
1,731
1,731
151
69
74
50
42
56
57
84
84
Rhode Island
2,313
1,252
1,674
1,097
981
691
669
1,731
1,731
136
69
74
51
42
56
57
84
84
South Carolina
2,384
1,252
1,664
1,100
975
691
671
1,721
1,721
141
69
73
50
42
56
58
83
83
South Dakota
2,771
1,252
1,589
1,014
927
691
670
1,643
1,643
156
69
75
50
43
56
57
85
85
Tennessee
2,448
1,252
1,664
1,086
975
691
669
1,721
1,721
144
69
73
50
42
56
57
83
83
Texas
2,866
1,252
1,664
1,061
975
691
670
1,721
1,721
160
69
73
48
42
56
57
83
83
Utah
2,841
1,252
1,891
1,244
1,120
692
671
1,956
1,956
159
69
59
40
33
56
58
69
69
Vermont
2,679
1,252
1,674
1,077
981
691
668
1,731
1,731
152
69
74
49
42
56
57
84
84
Virginia
2,644
1,252
1,664
1,086
975
691
670
1,721
1,721
152
69
73
50
42
56
57
83
83
Washington
2,878
1,252
1,891
1,213
1,120
691
670
1,956
1,956
160
69
59
39
33
56
57
69
69
West Virginia
2,285
1,252
1,674
1,100
981
691
670
1,731
1,731
135
69
74
51
42
56
57
84
84
Wisconsin
2,872
1,252
1,589
1,033
927
691
670
1,643
1,643
160
69
75
51
43
56
57
85
85
Wyoming
2,820
1,252
1,891
1,242
1,120
691
669
1,956
1,956
158
69
59
40
33
56
57
69
69
a Beef NOF Bull values were used for American bison Nex and VS.
Source: CEFM.
Table A-171: 2018 Manure Distribution Among Waste Management Systems by Operation (Percent)103



Beef Not on Feed












Beef Feedlots
Operations

Dairy Cow Farms3




Dairy Heifer Facilities



Liquid/
Pasture, Range,
Pasture, Range,
Daily
Dry
Solid
Liquid/Anaerobic
Deep
Daily
Dry
Liquid/ Pasture, Range,
State
Dry Lotb
Slurryb
Paddock
Paddock
Spread
Lot Storage
Slurry
Lagoon
Pit
Spreadb
Lotb
Slurryb
Paddock'5
Alabama
100
1
100
48
0
0
14
2
22
14
17
38
0
45
Alaska
100
1
100
25
12
0
26
5
9
22
6
90
1
4
Arizona
100

100
10
0
11
42
6
30
2
10
90
0
0
Arkansas
100
1
100
47
0
0
13
3
23
14
15
28
0
57
California
100
1
100
5
0
3
26
3
54
9
11
88
1
1
Colorado
100

100
11
0
11
41
5
30
2
1
98
0
1
Connecticut
100
1
100
15
3
0
16
6
33
28
43
51
0
6
Delaware
100
1
100
14
2
0
18
7
29
31
44
50
0
6
Florida
100
1
100
48
0
0
7
0
40
4
22
61
1
17
103 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020) Inventory submission.
A-337
-------

Beef Feedlots
Beef Not on Feed
Operations
Dairy Cow Farms3
Dairy Heifer Facilities
State
Dry Lotb
Liquid/
Slurryb
Pasture, Range,
Paddock
Pasture, Range,
Paddock
Daily
Spread
Dry Solid
Lot Storage
Liquid/Anaerobic
Slurry Lagoon
Deep
Pit
Daily
Spreadb
Dry
Lotb
Liquid/ Pasture, Range,
Slurryb Paddockb
Georgia
100
1
100
48
0
0
9
1
36
6
18
42
0
40
Hawaii
100
1
100
4
0
4
27
2
54
9
0
99
1
1
Idaho
100

100
5
0
3
26
2
53
10
1
99
0
0
Illinois
100
1
100
24
0
0
23
3
33
18
8
87
0
5
Indiana
100
1
100
21
0
0
21
2
41
16
13
79
0
8
Iowa
100
1
100
20
0
0
21
3
41
16
10
83
0
6
Kansas
100
1
100
14
0
0
16
1
55
13
5
92
0
3
Kentucky
100
1
100
51
0
0
14
2
23
11
14
24
0
61
Louisiana
100
1
100
48
0
0
13
3
23
12
14
26
0
60
Maine
100
1
100
18
4
0
16
5
30
28
45
48
0
7
Maryland
100
1
100
21
4
0
16
6
23
29
44
49
0
7
Massachusetts
100
1
100
25
5
0
17
6
17
30
45
47
0
7
Michigan
100
1
100
11
3
0
22
6
36
22
6
91
0
3
Minnesota
100
1
100
16
6
0
24
6
26
23
10
84
0
6
Mississippi
100
1
100
50
0
0
14
2
23
11
15
28
0
57
Missouri
100
1
100
29
0
0
25
2
26
17
14
77
0
8
Montana
100

100
19
0
0
21
4
38
18
4
93
0
3
Nebraska
100
1
100
15
0
0
18
2
50
15
6
90
0
4
Nevada
100

100
11
0
0
14
2
61
13
0
99
0
0
New Hampshire
100
1
100
21
4
0
17
5
22
31
44
49
0
7
New Jersey
100
1
100
27
5
0
16
6
16
29
45
47
0
8
New Mexico
100

100
10
0
11
42
6
30
2
10
90
0
0
New York
100
1
100
14
3
0
15
5
38
25
45
48
0
7
North Carolina
100
1
100
48
0
0
10
2
31
9
15
31
0
54
North Dakota
100
1
100
18
0
0
19
3
44
16
11
83
0
6
Ohio
100
1
100
24
0
0
23
2
35
17
14
78
0
8
Oklahoma
100

100
11
0
8
41
5
23
12
6
94
0
0
Oregon
100
1
100
9
0
3
24
4
50
11
0
80
1
20
Pennsylvania
100
1
100
27
6
0
16
5
18
29
47
44
0
9
Rhode Island
100
1
100
29
6
0
17
5
14
30
47
44
0
9
South Carolina
100
1
100
45
0
0
10
2
33
11
15
31
0
54
South Dakota
100
1
100
14
0
0
16
2
54
14
8
87
0
5
Tennessee
100
1
100
48
0
0
12
2
26
11
15
26
0
59
Texas
100
0
100
11
0
10
41
5
30
3
8
92
0
0
Utah
100
0
100
12
0
9
40
5
28
7
1
98
0
1
Vermont
100
1
100
14
3
0
16
5
36
26
44
49
0
7
A-338 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------



Beef Not on Feed












Beef Feedlots
Operations

Dairy Cow Farms3




Dairy Heifer Facilities



Liquid/
Pasture, Range,
Pasture, Range,
Daily
Dry
Solid
Liquid/Anaerobic
Deep
Daily
Dry
Liquid/ Pasture, Range,
State
Dry Lotb
Slurryb
Paddock
Paddock
Spread
Lot Storage
Slurry
Lagoon
Pit
Spreadb
Lotb
Slurryb
Paddock'5
Virginia
100
1
100
49
0
0
12
2
26
11
15
28
0
57
Washington
100
1
100
8
0
3
25
3
51
10
0
83
1
17
West Virginia
100
1
100
29
6
0
17
5
13
30
45
48
0
7
Wisconsin
100
1
100
15
5
0
24
6
27
23
12
82
0
7
Wyoming
100
0
100
16
0
0
18
2
49
15
12
81
0
7
a In the methane inventory for manure management, the percent of dairy cows and swine with AD systems is estimated using data from EPA's AgSTAR Program.
b Deep pit systems are their own manure management systems in the U.S. but are included under Liquid Systems in the UNFCCC CRF tables due to lack of a separate allocation for
those systems within the tables. For Dairy Cows, solid storage and dry lot systems calculated separately in Table A-171, but are reported as "NE" in the UNFCCC CRF tables due to
lack of a separate allocation for those systems within the tables.
Source(s): See Step 3: Waste Management System Usage Data.
A-339
-------
Table A-172: 2018 Manure Distribution Among Waste Management Systems by Operation (Percent) Continued104









Broiler and Turkey




Swine Operations3


Layer Operations
Operations


Pasture,






Poultry
Pasture,



Range,
Solid
Liquid/
Anaerobic

Deep Pit (<1
Anaerobic
without
Range,
Poultry with
State
Paddock
Storage
Slurry
Lagoon
Deep Pit
month)
Lagoon
Litter
Paddock

Litter
Alabama
15
0
29
30
12
14
42
58
1

99
Alaska
57
0
3
2
34
4
25
75
1

99
Arizona
19
0
28
29
11
13
60
40
1

99
Arkansas
6
0
60
26
5
2
0
100
1

99
California
15
0
28
29
13
14
12
88
1

99
Colorado
2
0
53
0
23
22
60
40
1

99
Connecticut
66
0
2
2
26
4
5
95
1

99
Delaware
29
0
4
5
56
5
5
95
1

99
Florida
53
0
20
14
9
5
42
58
1

99
Georgia
13
0
56
28
3
1
42
58
1

99
Hawaii
42
0
22
18
11
7
25
75
1

99
Idaho
16
0
16
3
57
8
60
40
1

99
Illinois
2
0
15
7
71
5
2
98
1

99
Indiana
1
0
3
12
78
7
0
100
1

99
Iowa
1
0
10
4
80
5
0
100
1

99
Kansas
1
0
13
35
21
30
2
98
1

99
Kentucky
8
0
19
21
31
21
5
95
1

99
Louisiana
67
0
17
9
6
2
60
40
1

99
Maine
74
0
2
1
20
4
5
95
1

99
Maryland
37
0
10
2
44
6
5
95
1

99
Massachusetts
60
0
2
2
31
4
5
95
1

99
Michigan
3
0
12
6
69
9
2
98
1

99
Minnesota
1
0
3
2
88
5
0
100
1

99
Mississippi
2
0
31
36
13
18
60
40
1

99
Missouri
2
0
16
33
34
15
0
100
1

99
Montana
3
0
21
2
64
9
60
40
1

99
Nebraska
2
0
9
22
49
19
2
98
1

99
Nevada
12
0
29
32
12
15
0
100
1

99
New Hampshire
65
0
2
2
27
4
5
95
1

99
New Jersey
54
0
3
3
36
4
5
95
1

99
New Mexico
67
0
17
9
6
2
60
40
1

99
104 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through 2020) Inventory submission.
A-340 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------









Broiler and Turkey




Swine Operations3


Layer Operations
Operations


Pasture,






Poultry
Pasture,



Range,
Solid
Liquid/
Anaerobic

Deep Pit (<1
Anaerobic
without
Range,
Poultry with
State
Paddock
Storage
Slurry
Lagoon
Deep Pit
month)
Lagoon
Litter
Paddock

Litter
New York
41
0
6
3
44
5
5
95
1

99
North Carolina
1
0
33
49
1
16
42
58
1

99
North Dakota
2
0
21
2
65
9
2
98
1

99
Ohio
1
0
10
9
67
13
0
100
1

99
Oklahoma
1
0
11
53
3
32
60
40
1

99
Oregon
51
0
20
15
9
5
25
75
1

99
Pennsylvania
1
0
8
5
77
9
0
100
1

99
Rhode Island
64
0
2
2
28
4
5
95
1

99
South Carolina
6
0
30
34
13
16
60
40
1

99
South Dakota
1
0
17
11
57
14
2
98
1

99
Tennessee
7
0
30
33
13
16
5
95
1

99
Texas
6
0
31
34
13
17
12
88
1

99
Utah
1
0
22
2
65
9
60
40
1

99
Vermont
69
0
2
1
24
4
5
95
1

99
Virginia
6
0
14
29
15
35
5
95
1

99
Washington
35
0
12
2
45
7
12
88
1

99
West Virginia
82
0
1
0
13
3
5
95
1

99
Wisconsin
15
0
23
1
57
4
2
98
1

99
Wyoming
3
0
21
2
64
9
60
40
1

99
a In the methane inventory for manure management, the percent of dairy cows and swine with AD systems is estimated using data from EPA's AgSTAR Program.
Deep pit systems are their own manure management systems in the U.S. but are included under Liquid Systems in the UNFCCC CRF tables due to lack of a
separate allocation for those systems within the tables.
b Because manure from beef feedlots and dairy heifers may be managed for long periods of time in multiple systems (i.e., both drylot and runoff collection pond),
the percent of manure that generates emissions is greater than 100 percent.
Source(s): See Step 3: Waste Management System Usage Data.
A-341
-------
Table A-173: Manure Management System Descriptions
Manure Management System Description3
Pasture, Range, Paddock
Daily Spread
Solid Storage
Dry Lot
Liquid/ Slurry
Anaerobic Lagoon
The manure from pasture and range grazing animals is allowed to lie as is and is not managed.
Methane emissions are accounted for under Manure Management, but the N20 emissions
from manure deposited on PRP are included under the Agricultural Soil Management
category.
Manure is routinely removed from a confinement facility and is applied to cropland or pasture
within 24 hours of excretion. Methane and indirect N20 emissions are accounted for under
Manure Management. Direct N20 emissions from land application are covered under the
Agricultural Soil Management category.
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.
A paved or unpaved open confinement area without any significant vegetative cover where
accumulating manure may be removed periodically. Dry lots are most typically found in dry
climates but also are used in humid climates.
Manure is stored as excreted or with some minimal addition of water to facilitate handling
and is stored in either tanks or earthen ponds, usually for periods less than one year.
Uncovered anaerobic lagoons are designed and operated to combine waste stabilization and
storage. Lagoon supernatant is usually used to remove manure from the associated
confinement facilities to the lagoon. Anaerobic lagoons are designed with varying lengths of
storage (up to a year or greater), depending on the climate region, the VS loading rate, and
other operational factors. Anaerobic lagoons accumulate sludge over time, diminishing
treatment capacity. Lagoons must be cleaned out once every 5 to 15 years, and the sludge is
typically applied to agricultural lands. The water from the lagoon may be recycled as flush
water or used to irrigate and fertilize fields. Lagoons are sometimes used in combination with
a solids separator, typically for dairy waste. Solids separators help control the buildup of
nondegradable material such as straw or other bedding materials.
Animal excreta with or without straw are collected and anaerobically digested in a large
containment vessel (complete mix or plug flow digester) or covered lagoon. Digesters are
designed and operated for waste stabilization by the microbial reduction of complex organic
compounds to C02 and CH4, which is captured and flared or used as a fuel.
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.
Enclosed poultry houses use bedding derived from wood shavings, rice hulls, chopped straw,
peanut hulls, or other products, depending on availability. The bedding absorbs moisture and
dilutes the manure produced by the birds. Litter is typically cleaned out completely once a
year. These manure systems are typically used for all poultry breeder flocks and for the
production of meat type chickens (broilers) and other fowl.
In high-rise cages or scrape-out/belt systems, manure is excreted onto the floor below with no
bedding to absorb moisture. The ventilation system dries the manure as it is stored. When
designed and operated properly, this high-rise system is a form of passive windrow
composting.
a Manure management system descriptions and the classification of manure as managed or unmanaged are based on the 2006
IPCC Guidelines for National Greenhouse Gas Inventories (Volume 4: Agriculture, Forestry and Other Land Use, Chapter 10:
Emissions from Livestock and Manure Management, Tables 10.18 and 10.21) and the Development Document for the Final
Revisions to the National Pollutant Discharge Elimination System Regulation and the Effluent Guidelines for Concentrated
Animal Feeding Operations (EPA-821-R-03-001, December 2002).
Anaerobic Digester
Deep Pit
Poultry with Litter
Poultry without Litter
A-342 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
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
Anaerobic Digester
0
0
0
Cattle Deep Litter (<1 month)
3
3
30
Cattle Deep Litter (>1 month)
21
44
76
Composting - In Vessel
0.5
0.5
0.5
Composting - Static Pile
0.5
0.5
0.5
Composting-Extensive/ Passive
0.5
1
1.5
Composting-lntensive
0.5
1
1.5
Daily Spread
0.1
0.5
1
Dry Lot
1
1.5
5
Fuel
10
10
10
Pasture
1
1.5
2
Poultry with bedding
1.5
1.5
1.5
Poultry without bedding
1.5
1.5
1.5
Solid Storage
2
4
5
Source: IPCC (2006).
Table A-175: Methane Conversion Factors by State for Liquid Systems for 2018 (Percent)105
State
Dairy
Swine
Beef
Poultry
Anaerobic Liquid/Slurry
Lagoon and Deep Pit
Anaerobic Liquid/Slurry
Lagoon and Pit Storage
Liquid/Slurry
Anaerobic
Lagoon
Alabama
77
42
77
42
44
77
Alaska
49
15
49
15
15
49
Arizona
78
60
76
48
46
75
Arkansas
75
38
76
40
39
75
California
74
33
74
33
45
74
Colorado
66
22
69
25
25
65
Connecticut
71
27
71
27
27
71
Delaware
75
34
75
34
33
75
Florida
79
58
79
56
53
79
Georgia
78
44
77
42
49
77
Hawaii
77
59
77
59
59
77
Idaho
68
24
64
21
22
64
Illinois
73
31
73
31
30
74
Indiana
72
29
72
29
30
72
Iowa
70
27
71
27
27
71
Kansas
74
34
74
33
33
74
Kentucky
75
34
75
35
34
75
Louisiana
78
50
78
49
52
78
Maine
65
22
65
22
21
65
Maryland
74
32
75
33
32
74
Massachusetts
69
25
70
26
26
70
Michigan
69
25
70
26
26
69
Minnesota
68
25
69
25
25
67
Mississippi
77
45
77
44
46
78
Missouri
74
34
74
34
34
74
Montana
59
19
61
20
20
61
Nebraska
71
28
71
28
27
71
105 This table has not been updated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through
2020) Inventory submission.
A-343
-------

Dairy
Swine
Beef
Poultry

Anaerobic
Liquid/Slurry
Anaerobic Liquid/Slurry

Anaerobic
State
Lagoon
and Deep Pit
Lagoon and Pit Storage
Liquid/Slurry
Lagoon
Nevada
71
27
71
27
24
73
New Hampshire
66
23
67
23
22
67
New Jersey
73
30
73
31
29
73
New Mexico
73
33
70
28
31
71
New York
68
24
69
25
25
69
North Carolina
76
36
78
41
36
76
North Dakota
65
23
65
23
23
65
Ohio
72
29
72
29
29
72
Oklahoma
76
40
75
37
37
76
Oregon
65
22
64
21
22
64
Pennsylvania
72
28
72
28
28
73
Rhode Island
71
27
71
27
27
71
South Carolina
77
43
78
44
41
77
South Dakota
69
25
69
26
26
69
Tennessee
75
35
76
38
36
75
Texas
75
42
76
44
41
77
Utah
68
23
67
23
24
68
Vermont
65
22
65
22
22
65
Virginia
73
30
76
35
31
74
Washington
64
21
64
21
23
65
West Virginia
72
29
72
29
29
72
Wisconsin
68
24
69
25
25
69
Wyoming
61
20
62
20
21
62
Note: MCFs developed using Tier 2 methods described in 2006 IPCC Guidelines, Section 10.4.2.
Table A-176: Direct Nitrous Oxide Emission Factors (kg NzO-N/kg N excreted)
Waste Management System	Direct N2Q Emission	
Aerobic Treatment (forced aeration)	0.005
Aerobic Treatment (natural aeration)	0.01
Anaerobic Digester	0
Anaerobic Lagoon	0
Cattle Deep Bed (active mix)	0.07
Cattle Deep Bed (no mix)	0.01
Compostingjn vessel	0.006
Compostingjntensive	0.1
Composting_passive	0.01
Composting_static	0.006
Daily Spread	0
Pit Storage	0.002
Dry Lot	0.02
Fuel	0
Liquid/Slurry	0.005
Pasture	0
Poultry with bedding	0.001
Poultry without bedding	0.001
Solid Storage	0.005
Source: 2006 IPCC Guidelines.
A-344 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-177: Indirect Nitrous Oxide Loss Factors (Percent)
Runoff/Leaching Nitrogen Lossa
Waste Management	Volatilization	Mid-
Animal Type
System
Nitrogen Loss
Central
Pacific
Atlantic
Midwest
South
Beef Cattle
Dry Lot
23
1.1
3.9
3.6
1.9
4.3
Beef Cattle
Liquid/Slurry
26
0
0
0
0
0
Beef Cattle
Pasture
0
0
0
0
0
0
Dairy Cattle
Anaerobic Lagoon
43
0.2
0.8
0.7
0.4
0.9
Dairy Cattle
Daily Spread
10
0
0
0
0
0
Dairy Cattle
Deep Pit
24
0
0
0
0
0
Dairy Cattle
Dry Lot
15
0.6
2
1.8
0.9
2.2
Dairy Cattle
Liquid/Slurry
26
0.2
0.8
0.7
0.4
0.9
Dairy Cattle
Pasture
0
0
0
0
0
0
Dairy Cattle
Solid Storage
27
0.2
0
0
0
0
American Bison
Pasture
0
0
0
0
0
0
Goats
Dry Lot
23
1.1
3.9
3.6
1.9
4.3
Goats
Pasture
0
0
0
0
0
0
Horses
Dry Lot
23
0
0
0
0
0
Horses
Pasture
0
0
0
0
0
0
Mules and Asses
Dry Lot
23
0
0
0
0
0
Mules and Asses
Pasture
0
0
0
0
0
0
Poultry
Anaerobic Lagoon
54
0.2
0.8
0.7
0.4
0.9
Poultry
Liquid/Slurry
26
0.2
0.8
0.7
0.4
0.9
Poultry
Pasture
0
0
0
0
0
0
Poultry
Poultry with bedding
26
0
0
0
0
0
Poultry
Poultry without bedding
34
0
0
0
0
0
Poultry
Solid Storage
8
0
0
0
0
0
Sheep
Dry Lot
23
1.1
3.9
3.6
1.9
4.3
Sheep
Pasture
0
0
0
0
0
0
Swine
Anaerobic Lagoon
58
0.2
0.8
0.7
0.4
0.9
Swine
Deep Pit
34
0
0
0
0
0
Swine
Liquid/Slurry
26
0.2
0.8
0.7
0.4
0.9
Swine
Pasture
0
0
0
0
0
0
Swine
Solid Storage
45
0
0
0
0
0
a Data for nitrogen losses due to leaching were not available, so the values represent only nitrogen losses due to runoff. Source:
EPA (2002b, 2005).
A-345
-------
Table A-178: Total Methane Emissions from Livestock Manure Management (kt)a b
Animal Type
1990
1995
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Dairy Cattle
589
684
970
1,124
1,144
1,188
1,167
1,190
1,233
1,259
1,270
1,292
1,281
Dairy Cows
581
676
962
1,115
1,134
1,177
1,157
1,180
1,222
1,248
1,259
1,281
1,270
Dairy Heifer
7
7
7
8
8
9
8
8
9
9
9
9
9
Dairy Calves
2
2
2
2
2
2
2
2
2
2
2
2
2
Swine
622
763
812
797
791
821
756
719
808
846
840
888
924
Market Swine
483
607
665
657
653
678
623
585
665
699
697
736
770
Market <50 lbs.
102
121
128
95
94
98
88
86
95
101
100
106
110
Market 50-119 lbs.
101
123
131
144
142
149
136
130
145
155
153
161
170
Market 120-179 lbs.
136
170
184
188
185
193
179
169
192
203
200
213
220
Market >180 lbs.
144
193
222
229
231
238
220
201
232
241
244
256
270
Breeding Swine
139
155
147
140
138
143
133
133
143
146
143
152
155
Beef Cattle
126
139
133
132
131
128
122
120
126
132
136
135
136
Feed lot Steers
14
14
15
16
17
16
16
16
16
17
18
20
20
Feed lot Heifers
7
8
9
9
9
9
9
9
9
9
9
10
10
NOF Bulls
5
5
5
5
5
5
5
5
5
5
5
5
5
Beef Calves
6
7
7
7
7
7
6
6
7
7
7
7
7
NOF Heifers
12
15
13
13
12
12
12
12
13
14
14
13
13
NOF Steers
12
14
10
10
10
9
9
9
9
10
10
10
10
NOF Cows
69
76
73
71
71
69
65
63
67
69
71
70
70
Sheep
7
5
3
3
3
3
3
3
3
3
3
3
3
Goats
1
1
1
1
1
1
1
1
1
1
1
1
1
Poultry
131
128
129
129
127
128
129
132
136
136
137
141
142
Hens >1 yr.
73
69
66
64
64
63
65
67
69
69
70
71
b
Total Pullets
25
22
22
24
23
23
24
24
27
26
26
28
b
Chickens
4
4
3
3
3
3
3
3
3
3
3
3
b
Broilers
19
23
31
31
31
32
31
31
32
32
32
33
b
Turkeys
10
9
7
6
6
6
6
6
6
6
6
6
b
Horses
9
11
12
10
10
10
9
8
8
8
7
7
7
Mules and Asses
+
+
+
+
+
+
+
+
+
+
+
+
+
American Bison
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Does not exceed 0.5 kt.
a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.
b Disaggregated poultry values (e.g., Broilers, Turkeys, etc.) have not been estimated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through
2020) Inventory submission.
A-346 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-179: Total (Direct and Indirect) Nitrous Oxide Emissions from Livestock Manure Management (kt)
Animal Type
1990
1995
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Dairy Cattle
17.7
18.2
18.4
19.0
19.3
19.5
19.4
19.6
20.1
20.3
20.4
20.6
20.3
Dairy Cows
10.6
10.7
10.5
10.7
10.9
11.1
11.1
11.2
11.4
11.5
11.6
11.8
11.7
Dairy Heifer
7.1
7.5
7.8
8.3
8.4
8.5
8.3
8.4
8.7
8.8
8.8
8.8
8.7
Dairy Calves
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Swine
4.0
4.5
5.5
5.8
5.9
6.0
6.0
5.8
6.2
6.3
6.6
6.7
7.0
Market Swine
3.0
3.5
4.6
5.1
5.2
5.2
5.2
5.0
5.4
5.6
5.7
5.9
6.1
Market <50 lbs.
0.6
0.5
0.9
0.7
0.8
0.8
0.7
0.7
0.8
0.8
0.8
0.8
0.9
Market 50-119 lbs.
0.6
0.7
0.9
1.1
1.2
1.2
1.2
1.1
1.2
1.2
1.3
1.3
1.4
Market 120-179 lbs.
0.9
1.0
1.3
1.5
1.5
1.5
1.5
1.5
1.6
1.6
1.7
1.7
1.8
Market >180 lbs.
0.9
1.1
1.5
1.7
1.8
1.8
1.8
1.7
1.8
1.9
2.0
2.0
2.1
Breeding Swine
1.0
1.1
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
Beef Cattle
19.8
21.8
24.0
25.3
25.9
25.8
26.0
26.0
25.8
27.2
28.7
31.0
31.5
Feedlot Steers
13.4
14.4
15.5
16.6
16.9
16.7
17.0
17.3
17.3
18.4
19.3
20.8
21.2
Feedlot Heifers
6.4
7.4
8.5
8.7
9.1
9.0
9.0
8.8
8.5
8.8
9.4
10.1
10.3
Sheep
0.4
0.7
1.2
1.1
1.1
1.1
1.1
1.0
1.0
1.0
1.0
1.0
1.0
Goats
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Poultry
4.7
5.1
5.4
5.2
5.2
5.3
5.2
5.2
5.2
5.4
5.5
5.6
5.6
Hens >1 yr.
1.0
1.0
1.3
1.3
1.3
1.3
1.3
1.4
1.3
1.4
1.4
1.5
a
Total Pullets
0.3
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.5
a
Chickens
+
+
+
+
+
+
+
+
+
+
+
+
a
Broilers
2.2
2.7
3.0
2.8
2.8
2.9
2.7
2.7
2.8
2.8
2.9
2.9
a
Turkeys
1.2
1.1
0.8
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
a
Horses
0.3
0.4
0.5
0.4
0.4
0.4
0.4
0.4
0.4
0.3
0.3
0.3
0.3
Mules and Asses
+
+
+
+
+
+
+
+
+
+
+
+
+
American Bison
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Note: American bison are maintained entirely on pasture, range, and paddock. Emissions from manure deposited on pasture are included in the Agricultural Soils Management
sector.
+ Does not exceed 0.05 kt.
NA (Not Applicable)
a Disaggregated poultry values (e.g., Broilers, Turkeys, etc.) have not been estimated for the current (1990 through 2019) Inventory. It will be updated for the next (1990 through
2020) Inventory submission.
A-347
-------
Table A-180: Methane Emissions by State from Livestock Manure Management for 2019 (kt)a b
State
Beef Cattleb
Dairy
Cattleb
Swineb
Poultry11
Sheep
Goats
Horses
Mules and
Asses
American
Bison
Total
Alabama
2.5970
0.6539
0.9431
14.2777
0.0090
0.0190
0.1453
0.0126
0.0004
18.6580
Alaska
0.0190
0.0083
0.0058
0.3928
0.0060
0.0002
0.0030
+
0.0033
0.4385
Arizona
1.8199
22.8236
2.9344
1.2735
0.0875
0.0219
0.2331
0.0031
0.0003
29.1973
Arkansas
3.4941
0.5077
2.1049
5.4465
0.0090
0.0133
0.1306
0.0087
0.0005
11.7153
California
5.0833
329.5456
1.5365
3.8795
0.3991
0.0492
0.2819
0.0065
0.0046
340.7861
Colorado
4.9474
17.0556
6.7462
4.4197
0.2077
0.0128
0.1961
0.0045
0.0248
33.6149
Connecticut
0.0156
2.6040
0.0128
0.1598
0.0038
0.0014
0.0210
0.0007
0.0008
2.8199
Delaware
0.0082
0.7016
0.0623
1.1306
0.0060
0.0004
0.0078
0.0001
0.0003
1.9173
Florida
3.2825
16.7442
0.1560
5.6139
0.0090
0.0235
0.2618
0.0124
0.0002
26.1036
Georgia
1.9183
11.5361
1.1291
22.8182
0.0090
0.0260
0.1484
0.0120
0.0004
37.5974
Hawaii
0.3126
0.4541
0.1380
0.5880
0.0090
0.0063
0.0137
0.0004
0.0003
1.5224
Idaho
2.2426
95.8068
0.2125
1.0905
0.1097
0.0075
0.0947
0.0023
0.0479
99.6146
Illinois
1.6164
11.8024
63.0469
0.5847
0.0257
0.0092
0.0817
0.0033
0.0013
77.1717
Indiana
0.8167
18.5810
50.6376
1.8749
0.0266
0.0104
0.1529
0.0034
0.0013
72.1048
Iowa
5.6496
33.8749
228.1373
2.4088
0.0770
0.0215
0.1002
0.0027
0.0046
270.2766
Kansas
10.3704
31.1596
37.0565
0.1043
0.0313
0.0125
0.1014
0.0032
0.0103
78.8494
Kentucky
2.6600
5.2184
4.4147
1.8824
0.0271
0.0146
0.2377
0.0093
0.0040
14.4682
Louisiana
1.6747
1.1009
0.0546
2.5533
0.0090
0.0070
0.1253
0.0073
0.0002
5.5324
Maine
0.0351
3.3247
0.0120
0.1520
0.0038
0.0014
0.0162
0.0003
0.0005
3.5459
Maryland
0.1417
5.6168
0.1724
1.4430
0.0060
0.0036
0.0566
0.0014
0.0001
7.4416
Massachusetts
0.0198
0.5108
0.0348
0.0388
0.0038
0.0018
0.0277
0.0008
+
0.6384
Michigan
0.7107
64.6416
12.4077
1.3205
0.0373
0.0074
0.1227
0.0030
0.0054
79.2562
Minnesota
2.0272
49.4142
74.2930
1.6461
0.0607
0.0091
0.0887
0.0025
0.0053
127.5468
Mississippi
1.8258
0.7213
10.4619
10.7866
0.0090
0.0119
0.1141
0.0095
0.0006
23.9406
Missouri
5.3067
8.0597
53.0807
2.0319
0.0467
0.0177
0.1635
0.0084
0.0019
68.7172
Montana
4.6164
1.5076
1.4068
0.8241
0.1050
0.0038
0.1547
0.0027
0.0444
8.6655
Nebraska
11.5142
11.3358
46.0411
0.7482
0.0373
0.0074
0.0938
0.0017
0.0540
69.8336
Nevada
0.6500
6.2466
0.0941
0.0643
0.0285
0.0029
0.0257
0.0004
+
7.1125
New Hampshire
0.0143
1.3386
0.0139
0.1540
0.0038
0.0010
0.0136
0.0003
0.0006
1.5402
New Jersey
0.0217
0.6247
0.0508
0.1629
0.0060
0.0028
0.0465
0.0010
0.0002
0.9168
New Mexico
1.4516
38.8152
0.0086
1.2190
0.0448
0.0089
0.0865
0.0018
0.0108
41.6472
New York
0.4138
88.9549
0.3207
0.7945
0.0397
0.0073
0.1327
0.0023
0.0022
90.6682
North Carolina
1.4282
5.7061
175.1429
16.4504
0.0189
0.0209
0.1491
0.0125
0.0007
198.9297
North Dakota
2.5934
2.3633
1.2216
0.0996
0.0327
0.0017
0.0542
0.0007
0.0235
6.3907
Ohio
1.1691
33.2220
31.2699
1.7210
0.0555
0.0152
0.1936
0.0059
0.0019
67.6542
A-348 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------


Dairy





Mules and
American

State
Beef Cattleb
Cattleb
Swineb
Poultry11
Sheep
Goats
Horses
Asses
Bison
Total
Oklahoma
6.5282
3.6825
45.9594
4.3495
0.0378
0.0366
0.3681
0.0187
0.0060
60.9866
Oregon
1.8615
10.1657
0.0623
1.1271
0.0770
0.0119
0.1295
0.0030
0.0044
13.4425
Pennsylvania
0.8386
48.6026
13.5409
1.8015
0.0448
0.0132
0.1693
0.0073
0.0024
65.0207
Rhode Island
0.0040
0.0592
0.0072
0.1602
0.0038
0.0002
0.0039
0.0001
+
0.2387
South Carolina
0.6475
1.8395
4.0787
5.5800
0.0090
0.0153
0.1243
0.0069
0.0002
12.3014
South Dakota
5.3934
20.7760
18.6498
0.2778
0.1167
0.0045
0.1021
0.0015
0.0545
45.3763
Tennessee
3.5044
4.0683
3.8974
0.9312
0.0322
0.0369
0.2632
0.0210
0.0010
12.7556
Texas
26.7301
62.5412
20.1568
7.8725
0.5251
0.3090
0.9836
0.0973
0.0238
119.2393
Utah
1.0865
9.0730
5.0870
4.2658
0.1284
0.0051
0.1068
0.0013
0.0023
19.7560
Vermont
0.0485
12.9769
0.0127
0.0390
0.0038
0.0024
0.0163
0.0002
0.0003
13.1002
Virginia
1.7166
8.6173
5.6098
1.8196
0.0350
0.0121
0.1266
0.0059
0.0013
17.9442
Washington
1.2747
41.8214
0.0988
1.5168
0.0210
0.0074
0.1039
0.0024
0.0022
44.8485
West Virginia
0.5443
0.6030
0.0104
0.5523
0.0163
0.0060
0.0472
0.0028
0.0002
1.7825
Wisconsin
1.4987
136.4337
2.7568
0.6653
0.0350
0.0269
0.1428
0.0032
0.0116
141.5740
Wyoming
2.3194
0.9227
0.5007
1.0652
0.1610
0.0038
0.1081
0.0024
0.0217
5.1050
+ Does not exceed 0.00005 kt.
a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.
b Disaggregated animal type (E.g., Beef on Feedlots or Dairy Heifer animal types) methane emissions were not estimated by state for the current (1990 through 2019) Inventory.
Disaggregated animal types will be estimated for the next (1990 through 2020) Inventory submission.
Table A-181: Total (Direct and Indirect) Nitrous Oxide Emissions by State from Livestock Manure Management for 2019 (kt)
Dairy	Mules and American

Beef Cattle
Dairy Cow
Heifer
Swine3
Poultry3
Sheep
Goats
Horses
Asses
Bison
Total
Alabama
0.0131
0.0042
0.0036
0.0050
0.4390
0.0049
0.0015
0.0050
0.0004
NA
0.4767
Alaska
0.0002
0.0002
0.0002
0.0001
0.0088
0.0016
+
0.0002
+
NA
0.0112
Arizona
0.6041
0.3673
0.2451
0.0150
0.0092
0.0137
0.0017
0.0080
0.0001
NA
1.2642
Arkansas
0.0270
0.0034
0.0020
0.0125
0.5371
0.0042
0.0011
0.0045
0.0003
NA
0.5921
California
1.0464
2.4317
1.5716
0.0098
0.1223
0.0705
0.0039
0.0097
0.0002
NA
5.2661
Colorado
2.2211
0.2955
0.2273
0.0803
0.0289
0.0487
0.0015
0.0101
0.0002
NA
2.9136
Connecticut
0.0005
0.0201
0.0118
0.0001
0.0085
0.0031
0.0002
0.0011
+
NA
0.0453
Delaware
0.0004
0.0051
0.0027
0.0004
0.0941
0.0049
+
0.0004
+
NA
0.1080
Florida
0.0088
0.0738
0.0507
0.0007
0.0599
0.0049
0.0019
0.0090
0.0004
NA
0.2101
Georgia
0.0125
0.0537
0.0300
0.0065
0.5695
0.0049
0.0021
0.0051
0.0004
NA
0.6847
Hawaii
0.0021
0.0030
0.0023
0.0006
0.0088
0.0016
0.0005
0.0005
+
NA
0.0194
Idaho
0.6321
0.8598
0.7097
0.0025
0.0092
0.0257
0.0009
0.0049
0.0001
NA
2.2449
Illinois
0.6103
0.0919
0.1053
0.5020
0.0464
0.0179
0.0011
0.0042
0.0002
NA
1.3793
A-349
-------
Indiana
0.2652
0.1924
0.1469
0.3908
0.2384
Iowa
2.8060
0.2304
0.2621
2.1552
0.3172
Kansas
5.4547
0.1609
0.2134
0.2194
0.0084
Kentucky
0.0428
0.0355
0.0233
0.0277
0.1330
Louisiana
0.0068
0.0067
0.0025
0.0003
0.0332
Maine
0.0010
0.0297
0.0175
0.0001
0.0085
Maryland
0.0216
0.0445
0.0344
0.0013
0.1118
Massachusetts
0.0005
0.0098
0.0081
0.0003
0.0033
Michigan
0.3592
0.5314
0.3579
0.1164
0.1058
Minnesota
0.9110
0.4930
0.5771
0.7380
0.1938
Mississippi
0.0112
0.0052
0.0040
0.0566
0.2868
Missouri
0.2574
0.0697
0.0811
0.3173
0.2178
Montana
0.1097
0.0148
0.0194
0.0180
0.0078
Nebraska
5.9232
0.0659
0.0524
0.3281
0.0585
Nevada
0.0066
0.0320
0.0253
0.0007
0.0084
New Hampshire
0.0004
0.0131
0.0071
0.0001
0.0085
New Jersey
0.0005
0.0056
0.0042
0.0004
0.0085
New Mexico
0.0309
0.6138
0.2303
0.0001
0.0092
New York
0.0460
0.6766
0.4145
0.0030
0.0457
North Carolina
0.0100
0.0296
0.0160
0.9120
0.4671
North Dakota
0.1220
0.0166
0.0173
0.0134
0.0084
Ohio
0.3633
0.2603
0.2196
0.2584
0.2130
Oklahoma
0.7220
0.0539
0.0434
0.2135
0.0854
Oregon
0.1995
0.1547
0.1209
0.0005
0.0329
Pennsylvania
0.2231
0.4610
0.3403
0.1241
0.2096
Rhode Island
0.0001
0.0006
0.0005
0.0001
0.0085
South Carolina
0.0030
0.0092
0.0051
0.0232
0.1039
South Dakota
0.9360
0.1236
0.0914
0.1651
0.0250
Tennessee
0.0344
0.0254
0.0220
0.0224
0.0709
Texas
5.7874
0.8862
0.5554
0.1188
0.3261
Utah
0.0512
0.1580
0.1244
0.0580
0.0269
Vermont
0.0015
0.1335
0.0664
0.0001
0.0034
Virginia
0.0491
0.0567
0.0252
0.0338
0.1555
Washington
0.4604
0.3785
0.2331
0.0011
0.0516
West Virginia
0.0096
0.0062
0.0047
0.0001
0.0434
Wisconsin
0.6472
1.4557
1.3512
0.0299
0.0530
Wyoming
0.1759
0.0064
0.0056
0.0073
0.0092
+ Does not exceed 0.00005 kt.
A-350 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
0.0186
0.0012
0.0079
0.0002
NA
1.2617
0.0538
0.0025
0.0052
0.0001
NA
5.8325
0.0218
0.0015
0.0052
0.0002
NA
6.0856
0.0220
0.0017
0.0122
0.0005
NA
0.2988
0.0042
0.0006
0.0043
0.0003
NA
0.0587
0.0031
0.0002
0.0008
+
NA
0.0610
0.0049
0.0004
0.0029
0.0001
NA
0.2220
0.0031
0.0002
0.0014
+
NA
0.0267
0.0261
0.0009
0.0063
0.0002
NA
1.5041
0.0424
0.0011
0.0046
0.0001
NA
2.9611
0.0049
0.0009
0.0039
0.0003
NA
0.3741
0.0326
0.0021
0.0084
0.0004
NA
0.9868
0.0246
0.0004
0.0080
0.0001
NA
0.2029
0.0261
0.0009
0.0048
0.0001
NA
6.4601
0.0067
0.0003
0.0013
+
NA
0.0814
0.0031
0.0001
0.0007
+
NA
0.0332
0.0049
0.0003
0.0024
0.0001
NA
0.0269
0.0105
0.0011
0.0045
0.0001
NA
0.9004
0.0322
0.0009
0.0068
0.0001
NA
1.2258
0.0102
0.0017
0.0051
0.0004
NA
1.4521
0.0228
0.0002
0.0028
+
NA
0.2036
0.0448
0.0018
0.0100
0.0003
NA
1.3715
0.0176
0.0029
0.0126
0.0007
NA
1.1520
0.0204
0.0014
0.0067
0.0002
NA
0.5371
0.0363
0.0016
0.0087
0.0004
NA
1.4052
0.0031
+
0.0002
+
NA
0.0131
0.0049
0.0012
0.0043
0.0002
NA
0.1551
0.0815
0.0005
0.0053
0.0001
NA
1.4284
0.0174
0.0029
0.0090
0.0007
NA
0.2051
0.0821
0.0244
0.0338
0.0035
NA
7.8178
0.0301
0.0006
0.0055
0.0001
NA
0.4548
0.0031
0.0003
0.0008
+
NA
0.2092
0.0284
0.0014
0.0065
0.0003
NA
0.3569
0.0056
0.0009
0.0054
0.0001
NA
1.1366
0.0133
0.0007
0.0024
0.0001
NA
0.0805
0.0244
0.0032
0.0074
0.0002
NA
3.5722
0.0378
0.0004
0.0056
0.0001
NA
0.2483
-------
References
Anderson, S. (2000) Personal Communication. Steve Anderson, Agricultural Statistician, National Agriculture Statistics
Service, U.S. Department of Agriculture and Lee-Ann Tracy, ERG. Washington, D.C. May 31, 2000.
ASAE (1998) ASAE Standards 1998, 45th Edition. American Society of Agricultural Engineers. St. Joseph, Ml.Bryant, M.P.,
V.H. Varel, R.A. Frobish, and H.R. Isaacson (1976) In H.G. Schlegel (ed.); Seminar on Microbial Energy Conversion. E.
Goltz KG. Gottingen, Germany.
Bush, E. (1998) Personal communication with Eric Bush, Centers for Epidemiology and Animal Health , U.S. Department
of Agriculture regarding National Animal Health Monitoring System's (NAHMS) Swine '95 Study.
Deal, P. (2000) Personal Communication. Peter B. Deal, Rangeland Management Specialist, Florida Natural Resource
Conservation Service and Lee-Ann Tracy, ERG. June 21, 2000.
EPA (2019) AgSTAR Anaerobic Digester Database. Available online at: , accessed July 2019.
EPA (2008) Climate Leaders Greenhouse Gas Inventory Protocol Offset Project Methodology for Project Type Managing
Manure with Biogas Recovery Systems. U.S. Environmental Protection Agency. Available online at:
.
EPA (2005) National Emission Inventory—Ammonia Emissions from Animal Agricultural Operations, Revised Draft Report.
U.S. Environmental Protection Agency. Washington, D.C. April 22, 2005. Available online at:
.
Retrieved August 2007.
EPA (2002a) Development Document for the Final Revisions to the National Pollutant Discharge Elimination System
(NPDES) Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations (CAFOS). U.S.
Environmental Protection Agency. EPA-821-R-03-001. December 2002.
EPA (2002b) Cost Methodology for the Final Revisions to the National Pollutant Discharge Elimination System Regulation
and the Effluent Guidelines for Concentrated Animal Feeding Operations. U.S. Environmental Protection Agency.
EPA-821-R-03-004. December 2002.
EPA (1992) Global Methane Emissions from Livestock and Poultry Manure, Office of Air and Radiation, U.S. Environmental
Protection Agency. February 1992.
ERG (2019) "Incorporation of USDA 2016 ARMS Dairy Data into the Manure Management Greenhouse Gas Inventory."
Memorandum to USDA OCE and EPA from ERG. December 2019.
ERG (2018) "Incorporation of USDA 2009 ARMS Swine Data into the Manure Management Greenhouse Gas Inventory."
Memorandum to USDA OCE and EPA from ERG. November 2018.
ERG (2010a) "Typical Animal Mass Values for Inventory Swine Categories." Memorandum to EPA from ERG. July 19,
2010.
ERG (2010b) Telecon with William Boyd of USDA NRCS and Cortney Itle of ERG Concerning Updated VS and Nex Rates.
August 8, 2010.
ERG (2010c) "Updating Current Inventory Manure Characteristics new USDA Agricultural Waste Management Field
Handbook Values." Memorandum to EPA from ERG. August 13, 2010.
ERG (2008) "Methodology for Improving Methane Emissions Estimates and Emission Reductions from Anaerobic
Digestion System for the 1990-2007 Greenhouse Gas Inventory for Manure Management." Memorandum to EPA
from ERG. August 18, 2008.
ERG (2003a) "Methodology for Estimating Uncertainty for Manure Management Greenhouse Gas Inventory." Contract
No. GS-10F-0036, Task Order 005. Memorandum to EPA from ERG, Lexington, MA. September 26, 2003.
ERG (2003b) "Changes to Beef Calves and Beef Cows Typical Animal Mass in the Manure Management Greenhouse Gas
Inventory." Memorandum to EPA from ERG, October 7, 2003.
A-351
-------
ERG (2001) Summary of development of MDP Factor for methane conversion factor calculations. ERG, Lexington, MA.
September 2001.
ERG (2000a) Calculations: Percent Distribution of Manure for Waste Management Systems. ERG, Lexington, MA. August
2000.
ERG (2000b) Discussion of Methodology for Estimating Animal Waste Characteristics (Summary of B0 Literature Review).
ERG, Lexington, MA. June 2000.
Garrett, W.N. and D.E. Johnson (1983) "Nutritional energetics of ruminants." Journal of Animal Science, 57(suppl.2):478-
497.
Groffman, P.M., R. Brumme, K. Butterbach-Bahl, K.E. Dobbie, A.R. Mosier, D. Ojima, H. Papen, W.J. Parton, K.A. Smith,
and C. Wagner-Riddle (2000) "Evaluating annual nitrous oxide fluxes at the ecosystem scale." Global Biogeochemcial
Cycles, 14(4): 1061-1070.
Hashimoto, A.G. (1984) "Methane from Swine Manure: Effect of Temperature and Influent Substrate Composition on
Kinetic Parameter (k)." Agricultural Wastes, 9:299-308.
Hashimoto, A.G., V.H. Varel, and Y.R. Chen (1981) "Ultimate Methane Yield from Beef Cattle Manure; Effect of
Temperature, Ration Constituents, Antibiotics and Manure Age." Agricultural Wastes, 3:241-256.
Hill, D.T. (1984) "Methane Productivity of the Major Animal Types." Transactions oftheASAE, 27(2):530-540.
Hill, D.T. (1982) "Design of Digestion Systems for Maximum Methane Production." Transactions oftheASAE, 25(1):226-
230.
IPCC (2018) 10th Corrigenda for the 2006IPCC Guidelines. The National Greenhouse Gas Inventories Programme, The
Intergovernmental Panel on Climate Change. Available online at: .
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
Johnson, D. (2000) Personal Communication. Dan Johnson, State Water Management Engineer, California Natural
Resource Conservation Service and Lee-Ann Tracy, ERG. June 23, 2000.
Lange, J. (2000) Personal Communication. John Lange, Agricultural Statistician, U.S. Department of Agriculture, National
Agriculture Statistics Service and Lee-Ann Tracy, ERG. Washington, D.C. May 8, 2000.
Meagher, M. (1986). Bison. Mammalian Species. 266:1-8.
Miller, P. (2000) Personal Communication. Paul Miller, Iowa Natural Resource Conservation Service and Lee-Ann Tracy,
ERG. June 12, 2000.
Milton, B. (2000) Personal Communication. Bob Milton, Chief of Livestock Branch, U.S. Department of Agriculture,
National Agriculture Statistics Service and Lee-Ann Tracy, ERG. May 1, 2000.
Moffroid, K. and D. Pape. (2014) 1990-2013 Volatile Solids and Nitrogen Excretion Rates. Dataset to EPA from ICF
International. August 2014.
Morris, G.R. (1976) Anaerobic Fermentation of Animal Wastes: A Kinetic and Empirical Design Fermentation. M.S. Thesis.
Cornell University.
National Bison Association (1999) Total Bison Population—1999. Report provided during personal email communication
with Dave Carter, Executive Director, National Bison Association July 19, 2011.
NOAA (2019) National Climate Data Center (NCDC). Available online at: 
(for all states except Alaska and Hawaii) and  (for Alaska and
Hawaii). July 2019.
Ott, S.L. (2000) Dairy '96 Study. Stephen L. Ott, Animal and Plant Health Inspection Service, U.S. Department of
Agriculture. June 19, 2000.
A-352 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Poe, G., N. Bills, B. Bellows, P. Crosscombe, R. Koelsch, M. Kreher, and P. Wright (1999) Staff Paper Documenting the
Status of Dairy Manure Management in New York: Current Practices and Willingness to Participate in Voluntary
Programs. Department of Agricultural, Resource, and Managerial Economics; Cornell University, Ithaca, New York,
September.
Safley, L.M., Jr. (2000) Personal Communication. Deb Bartram, ERG and L.M. Safley, President, Agri-Waste Technology.
June and October 2000.
Safley, L.M., Jr. and P.W. Westerman (1990) "Psychrophilic anaerobic digestion of animal manure: proposed design
methodology." Biological Wastes, 34:133-148.
Stettler, D. (2000) Personal Communication. Don Stettler, Environmental Engineer, National Climate Center, Oregon
Natural Resource Conservation Service and Lee-Ann Tracy, ERG. June 27, 2000.
Sweeten, J. (2000) Personal Communication. John Sweeten, Texas A&M University and Indra Mitra, ERG. June 2000.
UEP (1999) Voluntary Survey Results—Estimated Percentage Participation/Activity. Caged Layer Environmental
Management Practices, Industry data submissions for EPA profile development, United Egg Producers and National
Chicken Council. Received from John Thorne, Capitolink. June 2000.
USDA (2019a) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. Available online at: .
USDA (2019b) Chicken and Eggs 2018 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. March 2019. Available online at: .
USDA (2019c) Poultry - Production and Value 2018 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2019. Available online at: < https://www.nass.usda.gov/Publications/>.
USDA (2019d) 1987, 1992, 1997, 2002, 2007, 2012, and 2017 Census of Agriculture. National Agriculture Statistics
Service, U.S. Department of Agriculture. Washington, D.C. Available online at:
. May 2019.
USDA (2018a) Chicken and Eggs 2017Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. February 2018. Available online at: .
USDA (2018b) Poultry - Production and Value 2017Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2018. Available online at: .
USDA (2017a) Chicken and Eggs 2016 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. February 2017. Available online at: .
USDA (2017b) Poultry- Production and Value 2016 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2017. Available online at: < https://www.nass.usda.gov/Publications/ >.
USDA (2016a) Chicken and Eggs 2015 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. July 2016. Available online at: .
USDA (2016b) Poultry- Production and Value 2015 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. July 2016. Available online at: .
USDA (2015a) Chicken and Eggs 2014 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. February 2015. Available online at: .
USDA (2015b) Poultry- Production and Value 2014 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2015. Available online at: .
USDA (2014a) Chicken and Eggs 2013 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. February 2014. Available online at: .
USDA (2014b) Poultry - Production and Value 2013 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2014. Available online at: .
USDA (2013a) Chicken and Eggs 2012 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. February 2013. Available online at: .
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USDA (2013b) Poultry- Production and Value 2012 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2013. Available online at: .
USDA (2012a) Chicken and Eggs 2011 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. February 2012. Available online at: .
USDA (2012b) Poultry- Production and Value 2011 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2012. Available online at: .
USDA (2011a) Chicken and Eggs 2010 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. February 2011. Available online at: .
USDA (2011b) Poultry - Production and Value 2010 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2011. Available online at: .
USDA (2010a) Chicken and Eggs 2009 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. February 2010. Available online at: .
USDA (2010b) Poultry - Production and Value 2009 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2010. Available online at: .
USDA (2009a) Chicken and Eggs 2008 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
Washington, D.C. February 2009. Available online at: .
USDA (2009b) Poultry - Production and Value 2008 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2009. Available online at: .
USDA (2009c) Chicken and Eggs - Final Estimates 2003-2007. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. March 2009. Available online at: .
USDA (2008) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651. Natural
Resources Conservation Service, U.S. Department of Agriculture.
USDA (2004a) Chicken and Eggs—Final Estimates 1998-2003. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. April 2004. Available online at:
.
USDA (2004b) Poultry Production and Value—Final Estimates 1998-2002. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. April 2004. Available online at:
.
USDA (1999) Poultry Production and Value—Final Estimates 1994-97. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. March 1999. Available online at:
.
USDA (1998) Chicken and Eggs—Final Estimates 1994-97. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. December 1998. Available online at:
.
USDA (1996) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651. Natural
Resources Conservation Service, U.S. Department of Agriculture. July 1996.
USDA (1995a) Poultry Production and Value—Final Estimates 1988-1993. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. March 1995. Available online at:
.
USDA (1995b) Chicken and Eggs—Final Estimates 1988-1993. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. December 1995. Available online at:
.
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USDA (1994) Sheep and Goats—Final Estimates 1989-1993. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. January 31,1994. Available online at:
.
USDA, APHIS (2003) Sheep 2001, Part I: Reference of Sheep Management in the United States, 2001 and Part IV:Baseline
Reference of 2001 Sheep Feedlot Health and Management. USDA-APHIS-VS. Fort Collins, CO. #N356.0702. <
https://www.aphis.usda.gov/animal_health/nahms/sheep/downloads/sheep01/Sheep01_dr_Partl.pdf> and <
https://www.aphis.usda.gov/animal_health/nahms/sheep/downloads/sheep01/Sheep01_dr_PartlV.pdf>.
USDA, APHIS (2000) Layers '99—Part II: References of 1999 Table Egg Layer Management in the U.S. USDA-APHIS-VS.
Fort Collins, CO.
.
USDA, APHIS (1996) Swine '95: Grower/Finisher Part II: Reference of 1995 U.S. Grower/Finisher Health & Management
Practices. USDA-APHIS-VS. Fort Collins, CO.
.
Wright, P. (2000) Personal Communication. Lee-Ann Tracy, ERG and Peter Wright, Cornell University, College of
Agriculture and Life Sciences. June 23, 2000.
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3.12. Methodologies for Estimating Soil Organic C Stock Changes, Soil N20
Emissions, and CH4 Emissions and from Agricultural Lands (Cropland and
Grassland)
This annex provides a detailed description of Tier 1, 2, and 3 methods that are used to estimate soil organic C
stock changes for Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland and Land
Converted to Grassland; direct N20 emissions from cropland and grassland soils; indirect N20 emissions associated with
volatilization, leaching, and runoff of N from croplands and grasslands; and CH4 emissions from rice cultivation.
Nitrous oxide (N20) is produced in soils through the microbial processes of nitrification and denitrification.106
Management influences these processes by modifying the availability of mineral nitrogen (N), which is a key control on
the N20 emissions rates (Mosier et al. 1998; Paustian et al. 2016). Emissions can occur directly in the soil where the N is
made available or can be transported to another location following volatilization, leaching, or runoff, and then converted
into N20. Management practices influence soil organic C stocks in agricultural soils by modifying crop and forage
production and microbial decomposition (Paustian et al. 1997, Paustian et al. 2016). CH4 emissions from rice cultivation
occur under flooded conditions through the process of methanogenesis, and is influenced by water management
practices, organic amendments and cultivar choice (Sanchis et al. 2014). This annex provides the underlying
methodologies for these three emission sources because there is considerable overlap in the methods with the majority
of emissions are estimated using the DayCent ecosystem simulation model.
A combination of Tier 1, 2, and 3 approaches are used to estimate soil organic C stock changes, direct and
indirect soil N20 emissions and CH4 emissions from rice cultivation in agricultural croplands and grasslands. The
methodologies used to estimate soil organic C stock changes include:
1)	A Tier 3 method using the DayCent ecosystem model to estimate soil organic C stock changes in mineral soils on
non-federal lands that have less than 35 percent coarse fragments by volume and are used to produce alfalfa
hay, barley, corn, cotton, grass hay, grass-clover hay, oats, peanuts, potatoes, rice, sorghum, soybeans, sugar
beets, sunflowers, tobacco, and wheat, as well as non-federal grasslands and land use change between
grassland and cropland (with the crops listed above and less than 35 percent coarse fragments);
2)	Tier 2 methods with country-specific factors for estimating mineral soil organic C stock changes for mineral soils
that are very gravelly, cobbly, or shaley (greater than 35 percent coarse fragments by volume), are used to
produce crops or have land use changes to cropland and grassland (other than the conversions between
cropland and grassland that are not simulated with DayCent;
3)	Tier 2 methods with country-specific factors for estimating mineral soil organic C stock changes on federal
lands;
4)	Tier 2 methods with country-specific factors for estimating losses of C from organic soils that are drained for
agricultural production; and
5)	Tier 2 methods for estimating additional changes in mineral soil organic C stocks due to additions of biosolids
(i.e., treated sewage sludge) to soils.
The methodologies used to estimate soil N20 emissions include:
1)	A Tier 3 method using the DayCent ecosystem model to estimate direct emissions from mineral soils that have
less than 35 percent coarse fragments by volume and are used to produce alfalfa hay, barley, corn, cotton,
grass hay, grass-clover hay, oats, peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tobacco
and wheat, as well as non-federal grasslands and land use change between grassland and cropland (with the
crops listed above and less than 35 percent coarse fragments);
2)	A combination of the Tier 1 and 3 methods to estimate indirect N20 emissions associated with management of
cropland and grassland simulated with DayCent;
106 Nitrification and denitrification are driven by the activity of microorganisms in soils. Nitrification is the aerobic microbial
oxidation of ammonium (NH4+) to nitrate (N03 ), and denitrification is the anaerobic microbial reduction of nitrate to N2. Nitrous
oxide is a gaseous intermediate product in the reaction sequence of nitrification and denitrification.
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3)	A Tier 1 method to estimate direct and indirect N20 emissions from mineral soils that are not simulated with
DayCent, including very gravelly, cobbly, or shaley soils (greater than 35 percent coarse fragments by volume);
mineral soils with less than 35 percent coarse fragments that are used to produce crops that are not simulated
by DayCent; crops that are rotated with the crops that are not simulated with DayCent; Pasture/Range/Paddock
(PRP) manure N deposited on federal grasslands; and land application of biosolids (i.e., treated sewage sludge)
to soils; and
4)	A Tier 1 method to estimate direct N20 emissions due to partial or complete drainage of organic soils in
croplands and grasslands.
The methodologies used to estimate soil CH4 emissions from rice cultivation include:
1)	A Tier 3 method using the DayCent ecosystem model to estimate CH4 emissions from mineral soils that have less
than 35 percent coarse fragments by volume and rice grown continuously or in rotation with crops that are
simulated with DayCent, including alfalfa hay, barley, corn, cotton, grass hay, grass-clover hay, oats, peanuts,
potatoes, sorghum, soybeans, sugar beets, sunflowers, tobacco, and wheat; and
2)	A Tier 1 method to estimate CH4 emissions from all other soils used to produce rice that are not estimated with
the Tier 3 method, including rice grown on organic soils (i.e., Histosols), mineral soils with very gravelly, cobbly,
or shaley soils (greater than 35 percent coarse fragments by volume), and rice grown in rotation with crops that
are not simulated by DayCent.
As described above, the Inventory uses a Tier 3 approach to estimate C stock changes, direct soil N20 emissions,
and CH4 emissions from rice cultivation for most agricultural lands. This approach has the following advantages over the
IPCC Tier 1 or 2 approaches:
1)	It utilizes actual weather data at sub-county scales enabling quantification of inter-annual variability in N20
emissions and C stock changes at finer spatial scales, as opposed to a single emission factor for the entire
country for soil N20 or a broad climate region classification for soil organic C stock changes;
2)	The model uses a more detailed characterization of spatially-mapped soil properties that influence soil C and N
dynamics, as opposed to the broad soil taxonomic classifications of the IPCC methodology;
3)	The simulation approach provides a more detailed representation of management influences and their
interactions than are represented by a discrete factor-based approach in the Tier 1 and 2 methods;
4)	The legacy effects of past management can be addressed with the Tier 3 approach such as land use change
from decades prior to the inventory time period that can have ongoing effects on soil organic C stocks, and the
ongoing effects of N fertilization that may continue to stimulate N20 emissions in years after the application;
and
5)	Soil N20 and CH4 emissions, and C stock changes are estimated on a more continuous, daily basis as a function
of the interaction of climate, soil, and land management, compared with the linear rate changes that are
estimated with the Tier 1 and 2 methods.
More information is provided about the model structure and evaluation of the Tier 3 method at the end of this
Annex (See section Tier 3 Model Description, Parameterization and Evaluation).
Splicing methods are used to fill gaps in the time series for the emission sources and are not described in this
annex. Specifically, the splicing methods are applied when there are gaps in the activity data at the end of the time series
and the Tier 1, 2 and 3 methods cannot be applied. The splicing methods are described in the main chapters, particularly
Box 6-6 in the Cropland Remaining Cropland section and Box 5-5 in the Agricultural Soil Management section.
Inventory Compilation Steps
There are five steps involved in this inventory to estimate the following sources: a) soil organic C stock changes
for Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland and Land Converted to
Grassland; b) direct N20 emissions from cropland and grassland soils; indirect N20 emissions from volatilization, leaching,
and runoff from croplands and grasslands; and c) CH4 emissions from rice cultivation. First, the activity data are compiled
from a combination of land-use, livestock, crop, and grassland management surveys, as well as expert knowledge. In the
second, third, and fourth steps, soil organic C stock changes, direct and indirect N20 emissions, and CH4 emissions are
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estimated using Tier 1, 2 and 3 methods. In the fifth step, total emissions are calculated by summing all components for
soil organic C stock changes, N20 emissions and CH4 emissions. The remainder of this annex describes the methods
underlying each step.
Step 1: Derive Activity Data
This step describes how the activity data are derived to estimate soil organic C stock changes, direct and
indirect N20 emissions, and CH4 emissions from rice cultivation. The activity data requirements include: (1) land base and
107
history data, (2) crop-specific mineral N fertilizer rates and timing, (3) crop-specific manure amendment N rates and
timing, (4) other N inputs, (5) tillage practices, (6) cover crop management, (7) planting and harvesting dates for crops,
(8) irrigation data, (9) Enhanced Vegetation Index (EVI), (10) daily weather data, and (11) edaphic characteristics.108
Step la: Activity Data for the Agricultural Land Base and Histories
The U.S. Department of Agriculture's 2015 National Resources Inventory (NRI) (USDA-NRCS 2018a) provides the
basis for identifying the U.S. agricultural land base on non-federal lands, and classifying parcels into Cropland Remaining
Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland.109 The NRI
program have data available from 1979 through 2015 (USDA-NRCS 2018a). The time series will be extended as new data
are released by the USDA NRI program.
The NRI has a stratified multi-stage sampling design, where primary sample units are stratified on the basis of
county and township boundaries defined by the U.S. Public Land Survey (Nusser and Goebel 1997). Within a primary
sample unit, typically a 160-acre (64.75 ha) square quarter-section, three sample locations are selected according to a
restricted randomization procedure. Each sample location in the survey is assigned an area weight (expansion factor)
(Nusser and Goebel 1997). In principle, the expansion factors represent the amount of area with the land use and land
use change history that is the same as the survey location. The NRI uses a sampling approach, and therefore there is
some uncertainty associated with scaling the survey location data to a region or the country using the expansion factors.
In general, the uncertainty declines at larger scales because of a larger sample size, such as states compared to smaller
county units. An extensive amount of soils, land-use, and land management data have been collected through the survey
(Nusser et al. 1998).110 Primary sources for data include aerial photography as well as field visits and county office
records.
The NRI survey provides crop data for most years between 1979 and 2015, with the exception of 1983,1988,
and 1993. These years are gap-filled using an automated set of rules so that cropping sequences are filled with the most
likely crop type given the historical cropping pattern at each NRI survey location. Grassland data are reported on 5-year
increments prior to 1998, but it is assumed that the land use is also grassland between the years of data collection (see
Easter et al. 2008 for more information).
NRI survey locations are included in the land base for the agricultural soil organic C and N20 emissions
inventories if they are identified as cropland or grassland111 between 1990 and 2015 (See Section 6.1 Representation of
the U.S. Land Base for more information about areas in each land use and land use change category).112 NRI survey
locations on federal lands are not sampled by the USDA NRI program. The land use at the survey locations in federal
lands is determined from the National Land Cover Dataset (NLCD) (Yang et al. 2018), and included in the agricultural land
base if the land uses are cropland and/or grassland. The NRI data are harmonized with the Forest Inventory and Analysis
Dataset, and in this process, the land use and land use change data are modified to address differences in Forest Land
107	No data are currently available at the national scale to distinguish the type of fertilizer applied or timing of applications rates. It is a
planned improvement to address variation in these practices in future inventories, such as application of enhanced efficiency
fertilizers.
108	Edaphic characteristics include such factors as soil texture and pH.
109	Note that the Inventory does not include estimates of N20 emissions for federal grasslands with the exception of soil N20 from PRP
manure N, i.e., manure deposited directly onto pasture, range or paddock by grazing livestock.
110	In the current Inventory, NRI data only provide land use and management statistics through 2015. More recent data will be
incorporated in the future to extend the time series of activity data.
111	Includes only non-federal lands because federal lands are not classified into land uses as part of the NRI survey (i.e., they are only
designated as federal lands).
112	Land use for 2016 to 2019 is not compiled, but will be updated with a new release of the NRI data.
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Remaining Forest Land, Land Converted to Forest Land and Forest Land converted to other land uses between the two
national surveys (See Section 6.1 for more information on the U.S. land representation). Through this process, 524,991
survey locations in this NRI are designated as agricultural land in the conterminous United States and Hawaii.
For each year, land parcels are subdivided into Cropland Remaining Cropland, Land Converted to Cropland,
Grassland Remaining Grassland, and Land Converted to Grassland. Land parcels under crop management in a specific
year are classified as Cropland Remaining Cropland if the parcel has been used as cropland for at least 20 years.113
Similarly, land parcels under grassland management in a specific year of the inventory are classified as Grassland
Remaining Grassland if they have been designated as grassland for at least 20 years. Otherwise, land parcels are
classified as Land Converted to Cropland or Land Converted to Grassland based on the most recent use in the inventory
time period. Lands are retained in the land-use change categories (i.e., Land Converted to Cropland and Land Converted
to Grassland) for 20 years as recommended by the IPCC (2006). Lands converted into Cropland and Grassland are further
subdivided into the specific land use conversions (e.g., Forest Land Converted to Cropland).
The Tier 3 method using the DayCent model is applied to estimate soil organic C stock changes, CH4, and N20
emissions for 349,464 NRI survey locations that occur on mineral soils. Parcels of land that are not simulated with
DayCent are allocated to the Tier 2 approach for estimating soil organic C stock change, and a Tier 1 method (IPCC 2006)
to estimate soil N20 emissions114 and to estimate CH4 emissions from rice cultivation (Table A-182).
The land base for the Tier 1 and 2 methods includes 175,527 survey locations, and is comprised of (1) land
parcels occurring on organic soils; (2) land parcels that include non-agricultural uses such as forest or settlements in one
or more years of the inventory; (3) land parcels on mineral soils that are very gravelly, cobbly, or shaley (i.e., classified as
soils that have greater than 35 percent of soil volume comprised of gravel, cobbles, or shale); or (4) land parcels that are
used to produce some of the vegetable crops and perennial/horticultural crops, which are either grown continuously or
in rotation with other crops. DayCent has not been fully tested or developed to simulate biogeochemical processes in
soils used to produce some annual (e.g., lettuce), horticultural (e.g., flowers), or perennial (e.g., vineyards, orchards)
crops and agricultural use of organic soils. In addition, DayCent has not been adequately tested for soils with a high
gravel, cobble, or shale content.
Table A-182: Total Cropland and Grassland Area Estimated with Tier 1/2 and 3 Inventory Approaches (Million
Hectares)	

Land Areas (million ha)

Mineral
Organic

Year
Tier 1/2 Tier 3 Total
Tier 1/2
Total115
1990
152.22 307.63 459.85
1.39
461.24
1991
151.49 307.89 459.37
1.38
460.75
1992
150.83 308.07 458.90
1.38
460.28
1993
149.84 308.47 458.31
1.38
459.69
1994
149.04 308.87 457.91
1.38
459.29
1995
147.92 309.28 457.20
1.37
458.57
1996
146.90 309.75 456.65
1.36
458.01
1997
145.69 310.19 455.88
1.35
457.23
1998
144.67 310.63 455.31
1.35
456.65
1999
143.71 311.10 454.81
1.35
456.16
2000
142.98 311.38 454.36
1.35
455.71
2001
142.49 311.82 454.31
1.34
455.66
113	NRI points are classified according to land-use history records starting in 1979 when the NRI survey began, and consequently the
classifications are based on less than 20 years from 1990 to 1998.
114	The Tier 1 method for soil N20 does not require land area data with the exception of emissions from drainage and cultivation
of organic soils, so in practice the Tier 1 method is only dependent on the amount of N input to mineral soils and not the actual
land area.
115	The current Inventory includes estimation of greenhouse gas emissions and removals from all privately-owned and federal
grasslands and croplands in the conterminous United States and Hawaii, but does not include the croplands and grasslands in
Alaska. This leads to a discrepancy between the total area in this table, which is included in the estimation, compared to the
total managed land area in Section 6.1 Representation of the U.S. Land Base. See Planned Improvement sections in Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland and Land Converted to Grassland for more
information about filling these gaps in the future so that emissions and removals will be estimated for all managed land.
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2002
141.78
312.09
453.87
1.35
455.22
2003
141.15
312.00
453.16
1.32
454.48
2004
140.65
311.92
452.57
1.34
453.90
2005
140.12
311.81
451.93
1.34
453.27
2006
139.57
311.77
451.34
1.33
452.68
2007
139.04
311.74
450.78
1.32
452.10
2008
138.71
311.60
450.31
1.32
451.63
2009
138.36
311.54
449.89
1.32
451.21
2010
138.05
311.43
449.48
1.32
450.80
2011
137.65
311.41
449.06
1.32
450.38
2012
137.28
311.33
448.61
1.32
449.93
2013
136.99
311.12
448.10
1.32
449.42
2014
136.75
310.79
447.54
1.31
448.86
2015
136.38
310.66
447.04
1.30
448.34
Note: In the current Inventory, NRI data only provide land use and management statistics through 2015.
Additional data will be incorporated in the future to extend the time series of the land use data.
NRI survey locations on mineral soils are classified into specific crop categories, continuous pasture/rangeland,
and other non-agricultural uses for the Tier 2 inventory analysis for soil organic C (Table A-183). NRI locations are
assigned to IPCC input categories (low, medium, high, and high with organic amendments) according to the classification
provided in IPCC (2006). For croplands on federal lands, information on specific crop systems is not available, so all
croplands are assumed to be medium input. In addition, NRI differentiates between improved and unimproved
grassland, where improvements include irrigation and interseeding of legumes. Grasslands on federal lands (as identified
with the NLCD) are classified according to rangeland condition (nominal, moderately degraded and severely degraded) in
areas where information is available. For lands managed for livestock grazing by the Bureau of Land Management (BLM),
IPCC rangeland condition classes are interpreted at the state-level from the Rangeland Inventory, Monitoring and
Evaluation Report (BLM 2014). In order to estimate uncertainties, probability distribution functions (PDFs) for the NRI
land-use data are based on replicate weights that allow for proper variance estimates that correctly account for the
complex sampling design. In particular, the variance estimates and resulting PDFs correctly account for spatial or
temporal dependencies. For example, dependencies in land use result from the likelihood that current use is correlated
with past use. These dependencies occur because as an area of a land use/management category increases, the area of
another land use/management category must decline.
Table A-183: Total Land Areas by Land-Use and Management System for the Tier 2 Mineral Soil Organic C
Approach (Million Hectares)
Land Areas (million hectares)
Land-Use/Management













System
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Cropland Systems
33.47
33.18
32.87
32.36
31.86
31.39
30.96
30.49
29.69
29.17
28.78
28.44
28.13
Conservation Reserve













Program
2.74
3.15
3.08
2.91
2.67
2.59
2.46
2.45
1.96
2.12
1.86
1.99
1.73
High Input Cropping













Systems, Full Tillage
2.41
2.21
2.20
2.11
2.28
2.26
2.10
1.99
1.94
1.93
1.97
1.78
1.58
High Input Cropping













Systems, Reduced













Tillage
0.57
0.50
0.50
0.50
0.54
0.52
0.50
0.48
0.49
0.49
0.50
0.49
0.45
High Input Cropping













Systems, No Tillage
0.41
0.37
0.37
0.37
0.38
0.36
0.45
0.43
0.44
0.45
0.45
0.52
0.51
High Input Cropping













Systems with Manure,













Full Tillage
0.67
0.64
0.61
0.59
0.55
0.52
0.51
0.49
0.47
0.43
0.40
0.34
0.32
High Input Cropping













Systems with Manure,













Reduced Tillage
0.18
0.17
0.16
0.16
0.16
0.15
0.14
0.14
0.13
0.13
0.12
0.12
0.11
High Input Cropping













Systems with Manure,













No Tillage
0.22
0.20
0.19
0.19
0.19
0.18
0.17
0.16
0.15
0.15
0.14
0.17
0.17
A-360 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Medium Input Cropping













Systems, Full Tillage
7.03
7.02
6.78
6.57
6.49
6.26
6.32
5.97
5.65
5.47
5.54
4.29
4.03
Medium Input Cropping













Systems, Reduced













Tillage
1.71
1.66
1.62
1.58
1.58
1.53
1.53
1.49
1.40
1.37
1.42
1.68
1.69
Medium Input Cropping













Systems, No Tillage
1.85
1.71
1.68
1.63
1.62
1.60
1.58
1.52
1.45
1.41
1.44
2.33
2.35
Low Input Cropping













Systems, Full Tillage
9.46
9.31
9.31
9.34
9.30
9.40
9.14
9.17
9.30
9.13
9.08
8.21
8.25
Low Input Cropping













Systems, Reduced













Tillage
1.06
1.04
1.04
1.05
1.05
1.07
1.08
1.07
1.11
1.05
1.04
1.11
1.11
Low Input Cropping













Systems, No Tillage
0.68
0.73
0.73
0.74
0.73
0.72
0.90
0.90
0.92
0.86
0.89
1.53
1.52
Hay with Legumes or













Irrigation
1.67
1.67
1.69
1.64
1.50
1.44
1.35
1.38
1.31
1.25
1.14
1.04
1.20
Hay with Legumes or













Irrigation and Manure
0.50
0.49
0.50
0.51
0.48
0.45
0.43
0.47
0.46
0.44
0.41
0.42
0.54
Hay, Unimproved
0.01
0.01
0.02
0.02
0.02
0.02
0.00
0.01
0.07
0.05
0.01
0.03
0.04
Pasture with Legumes













or Irrigation in Rotation
0.02
0.01
0.02
0.01
0.01
0.01
0.01
0.01
0.04
0.03
0.01
0.02
0.02
Pasture with Legumes













or Irrigation and













Manure, in Rotation
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Rice
0.04
0.05
0.04
0.04
0.05
0.06
0.05
0.05
0.05
0.05
0.06
0.07
0.08
Perennials
2.24
2.24
2.31
2.36
2.28
2.25
2.24
2.32
2.38
2.37
2.31
2.28
2.42
Grassland Systems
118.68
118.22
117.88
117.40
117.11
116.46
115.87
115.14
114.93
114.47
114.13
113.98
113.57
Pasture with Legumes













or Irrigation
3.62
3.47
3.28
3.25
3.27
3.14
2.83
2.41
2.51
2.46
2.26
2.17
2.08
Pasture with Legumes













or Irrigation and













Manure
0.17
0.16
0.15
0.15
0.15
0.15
0.15
0.14
0.14
0.14
0.12
0.11
0.11
Rangelands and













Unimproved Pasture
82.27
81.87
81.82
81.68
81.42
80.82
79.85
79.64
78.94
78.42
78.83
78.54
79.53
Rangelands and













Unimproved Pasture,













Moderately Degraded
23.62
23.78
23.91
23.79
23.84
23.95
24.43
24.30
25.08
25.11
24.46
24.70
23.63
Rangelands and













Unimproved Pasture,













Severely Degraded
9.01
8.93
8.72
8.53
8.43
8.41
8.60
8.65
8.25
8.34
8.46
8.46
8.22
Total	152.15 151.40 150.75 149.76 148.97 147.85 146.83 145.63 144.61 143.64 142.91 142.42 141.70
Land-
Use/Management
System	2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Cropland Systems	27.88 27.55 27.39 27.16 26.99 26.83 26.62 26.51 26.33 26.29 26.24 26.16 25.96
Conservation Reserve
Program	1.60 1.50 1.52 1.42 1.38 1.30 1.35 1.26 1.89 0.92 1.43 0.90 0.73
High Input Cropping
Systems, Full Tillage 1.59 1.59 1.60 1.37 1.34 1.37 1.42 1.44 1.30 1.24 1.18 1.14 1.06
High Input Cropping
Systems, Reduced
Tillage	0.47 0.47 0.47 0.49 0.49 0.52 0.53 0.53 0.57 0.55 0.52 0.52 0.50
High Input Cropping
Systems, No Tillage	0.48 0.50 0.50 0.59 0.61 0.63 0.65 0.63 0.72 0.73 0.71 0.71 0.67
High Input Cropping
Systems with Manure,
Full Tillage	0.30 0.29 0.29 0.24 0.26 0.27 0.26 0.27 0.25 0.26 0.28 0.27 0.26
A-361
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High Input Cropping













Systems with Manure,













Reduced Tillage
0.11
0.11
0.11
0.13
0.14
0.13
0.14
0.14
0.17
0.18
0.19
0.18
0.18
High Input Cropping













Systems with Manure,













No Tillage
0.18
0.17
0.17
0.17
0.18
0.18
0.18
0.18
0.19
0.19
0.20
0.20
0.20
Medium Input













Cropping Systems, Full













Tillage
3.98
3.99
3.82
3.50
3.58
3.55
3.49
3.49
3.16
3.39
3.19
3.41
3.26
Medium Input













Cropping Systems,













Reduced Tillage
1.72
1.75
1.71
1.83
1.85
1.85
1.78
1.78
1.87
2.04
1.93
2.10
2.07
Medium Input













Cropping Systems, No













Tillage
2.41
2.40
2.39
2.53
2.57
2.58
2.49
2.49
2.39
2.77
2.49
2.83
2.79
Low Input Cropping













Systems, Full Tillage
8.26
8.11
8.13
7.93
7.83
7.78
7.75
7.72
7.46
7.54
7.52
7.46
7.60
Low Input Cropping













Systems, Reduced













Tillage
1.06
1.01
1.01
1.08
1.02
1.00
1.00
1.01
1.00
1.04
1.04
0.97
1.01
Low Input Cropping













Systems, No Tillage
1.45
1.36
1.38
1.67
1.59
1.56
1.54
1.55
1.39
1.45
1.45
1.34
1.42
Hay with Legumes or













Irrigation
1.18
1.16
1.18
1.16
1.14
1.11
1.06
1.02
0.98
0.99
1.02
1.02
1.02
Hay with Legumes or













Irrigation and Manure
0.52
0.54
0.50
0.49
0.48
0.47
0.46
0.45
0.43
0.43
0.47
0.47
0.48
Hay, Unimproved
0.04
0.05
0.04
0.02
0.03
0.01
0.02
0.02
0.03
0.02
0.01
0.00
0.00
Pasture with Legumes













or Irrigation in













Rotation
0.03
0.03
0.03
0.01
0.02
0.02
0.03
0.02
0.01
0.01
0.01
0.00
0.00
Pasture with Legumes













or Irrigation and













Manure, in Rotation
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Rice
0.06
0.06
0.04
0.04
0.04
0.04
0.03
0.04
0.03
0.03
0.03
0.03
0.03
Perennials
2.43
2.46
2.49
2.46
2.44
2.46
2.44
2.47
2.50
2.53
2.55
2.59
2.65
Grassland Systems
113.20
113.04
112.67
112.34
111.96
111.80
111.65
111.45
111.22
110.90
110.66
110.50
110.29
Pasture with Legumes













or Irrigation
2.01
2.05
1.97
1.91
1.86
1.84
1.85
1.80
1.79
1.71
1.61
1.64
1.59
Pasture with Legumes













or Irrigation and













Manure
0.11
0.11
0.11
0.10
0.09
0.08
0.08
0.08
0.07
0.07
0.07
0.07
0.07
Rangelands and













Unimproved Pasture
79.60
78.73
78.47
78.36
78.00
77.90
77.74
77.75
77.73
77.46
77.40
77.04
77.37
Rangelands and













Unimproved Pasture,













Moderately Degraded
23.19
23.22
23.25
23.15
23.25
23.24
23.25
23.17
23.06
22.89
22.80
22.61
22.51
Rangelands and













Unimproved Pasture,













Severely Degraded
8.28
8.93
8.87
8.82
8.76
8.74
8.71
8.65
8.57
8.77
8.79
9.14
8.74
Total
141.08
140.59
140.05
139.50
138.95
138.63
138.27
137.96
137.55
137.19
136.90
136.66
136.25
Note: In the current Inventory, NRI data only provide land use and management statistics through 2015. Additional data will be
incorporated in the future to extend the time series for the land use and management data.
Organic soils are categorized into land-use systems based on drainage (IPCC 2006) (Table A-184). Undrained
soils are treated as having no loss of organic C or soil N20 emissions. Drained soils are subdivided into those used for
cultivated cropland, which are assumed to have high drainage and relatively large losses of C, and those used for
managed pasture, which are assumed to have less drainage with smaller losses of C. N20 emissions are assumed to be
similar for both drained croplands and grasslands.
A-362 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Table A-184: Total Land Areas for Drained Organic Soils by Land Management Category and Climate Region
(Million Hectares)	
IPCC Land-Use Category





Land Areas (million ha)





for Organic Soils
1990
1991
1992
1993
1994
1995 1996 1997
1998
1999
2000
2001
2002
2003
Cold Temperate
Cultivated Cropland












(high drainage)
0.59
0.58
0.59
0.59
0.59
0.59 0.59 0.60
0.60
0.60
0.59
0.59
0.59
0.59
Managed Pasture












(low drainage)
0.34
0.34
0.35
0.35
0.35
0.35 0.34 0.34
0.34
0.34
0.34
0.35
0.35
0.35
Undrained
0.04
0.05
0.04
0.04
0.03
0.03 0.04 0.03
0.03
0.03
0.04
0.03
0.03
0.02
Total
0.97
0.97
0.98
0.98
0.98
0.98 0.97 0.97
0.97
0.97
0.97
0.97
0.96
0.96
Warm Temperate
Cultivated Cropland












(high drainage)
0.15
0.15
0.15
0.15
0.15
0.15 0.15 0.15
0.15
0.15
0.15
0.15
0.15
0.16
Managed Pasture












(low drainage)
0.08
0.08
0.08
0.08
0.08
0.08 0.08 0.08
0.09
0.09
0.09
0.09
0.09
0.09
Undrained
0.02
0.01
0.01
0.01
0.01
0.01 0.01 0.01
0.00
0.01
0.00
0.01
0.00
0.00
Total
0.25
0.25
0.24
0.24
0.24
0.24 0.24 0.24
0.24
0.24
0.25
0.25
0.25
0.25
Sub-Tropical
Cultivated Cropland












(high drainage)
0.24
0.24
0.24
0.25
0.25
0.25 0.26 0.26
0.26
0.17
0.17
0.29
0.28
0.28
Managed Pasture












(low drainage)
0.12
0.12
0.12
0.12
0.12
0.12 0.12 0.12
0.12
0.12
0.11
0.10
0.10
0.09
Undrained
0.00
0.00
0.00
0.00
0.00
0.00 0.00 0.00
0.00
0.10
0.10
0.00
0.01
0.00
Total	0.37 0.37 0.37 0.37 0.37 0.38 0.38 0.38 0.38 0.38 0.38 0.39 0.39 0.37
IPCC Land-Use Category	Land Areas (million ha)
for Organic Soils	2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
	Cold Temperate	
Cultivated Cropland
(high drainage)	0.59 0.59 0.59 0.59 0.59 0.58 0.58 0.58 0.59 0.60 0.60 0.60
Managed Pasture
(low drainage)	0.37 0.37 0.37 0.37 0.37 0.38 0.38 0.38 0.38 0.38 0.38 0.38
Undrained	0.02 0.03 0.03 0.02 0.03 0.03 0.03 0.03 0.02 0.02 0.01 0.01
Total	0.98 0.98 0.98 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.99 1.00
	Warm Temperate	
Cultivated Cropland
(high drainage)	0.16 0.16 0.16 0.16 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17
Managed Pasture
(low drainage)	0.09 0.10 0.09 0.10 0.09 0.09 0.10 0.10 0.10 0.10 0.10 0.10
Undrained	0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00
Total	0.26 0.26 0.26 0.26 0.26 0.26 0.27 0.27 0.27 0.28 0.28 0.28
	Sub-Tropical	
Cultivated Cropland
(high drainage)	0.27 0.27 0.27 0.26 0.26 0.26 0.26 0.26 0.26 0.24 0.26 0.25
Managed Pasture
(low drainage)	0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09
Undrained	0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.01 0.01 0.03 0.01 0.01
Total	0.37 0.37 0.37 0.36 0.36 0.36 0.36 0.36 0.36 0.35 0.36 0.35
Note: In the current Inventory, NRI data only provide land use and management statistics through 2015. Additional data will be
incorporated in the future to extend the time series for the land use and management data.
The harvested area for rice cultivation is estimated from the NRI based on survey locations classified as flooded
rice (Table A-185). Ratoon crops occur in the Southeastern United States with a second season of rice during the year,
including Louisiana (LSU 2015 for years 2000 through 2015) and Texas (TAMU 2015 for years 1993 through 2015),
averaging 32 percent and 48 percent of rice acres planted, respectively. Florida also has a large fraction of area with a
A-363
-------
ratoon crops (45 percent), but ratoon cropping is uncommon in Arkansas occurring on a relatively small fraction of fields
estimated at about 1 percent. No data are available for ratoon crops in Missouri or Mississippi, and so the amount of
ratooning is assumed similar to Arkansas. Ratoon rice crops are not grown in California.
Table A-185: Total Rice Harvested Area Estimated with Tier 1 and 3 Inventory Approaches (Million Hectares)

Land Areas (Million Hectares)
Year
Tier 1
Tier 3
Total
1990
0.21
1.50
1.71
1991
0.21
1.54
1.74
1992
0.22
1.65
1.87
1993
0.22
1.58
1.80
1994
0.23
1.51
1.74
1995
0.21
1.53
1.74
1996
0.22
1.52
1.74
1997
0.20
1.47
1.67
1998
0.25
1.46
1.70
1999
0.38
1.43
1.81
2000
0.42
1.48
1.90
2001
0.24
1.39
1.63
2002
0.23
1.57
1.80
2003
0.21
1.42
1.63
2004
0.21
1.50
1.71
2005
0.21
1.58
1.79
2006
0.17
1.27
1.44
2007
0.18
1.38
1.56
2008
0.15
1.28
1.44
2009
0.21
1.52
1.73
2010
0.20
1.57
1.77
2011
0.17
1.24
1.41
2012
0.22
1.18
1.40
2013
0.16
1.26
1.42
2014
0.24
1.39
1.63
2015
0.17
1.45
1.62
Note: In the current Inventory, NRI data only provide land use and management statistics through 2015.
Additional data will be incorporated in the future to extend the time series of the land use and management data.
Step lb: Obtain Management Activity Data to estimate Soil Organic C Stock Changes, N2O and CH4 Emissions
from Mineral Soils
The USDA-NRCS Conservation Effects and Assessment Project (CEAP) provides data on a variety of cropland
management activities, and is used to inform the inventory analysis about tillage practices, mineral fertilization, manure
amendments, cover cropping management, as well as planting and harvest dates (USDA-NRCS 2018b; USDA-NRCS 2012).
CEAP data are collected at a subset of NRI survey locations, and currently provide management information from
approximately 2002 to 2006. Respondents provide detailed information about management practices at the NRI survey
locations, such as time of planting and harvest; amount, type and time of fertilization; implement type and timing of soil
cultivation events; and type and timing of cover crop planting and termination practices.
These data are combined with other datasets in an imputation analysis that extends the time series from 1980
to 2015. The imputation analysis is comprised of three steps: a) determine the trends in management activity across the
time series by combining information from several datasets (discussed below); b) use an artificial neural network to
determine the likely management practice at a given NRI survey location (Cheng and Titterington 1994); and c) assign
management practices from the CEAP survey to the specific NRI locations using a predictive mean matching method that
is adapted to reflect the trending information (Little 1988, van Buuren 2012). The artificial neural network is a machine
learning method that approximates nonlinear functions of inputs and searches through a large class of models to impute
an initial value for management practices at specific NRI survey locations. The predictive mean matching method
identifies the most similar management activity recorded in the CEAP survey that matches the prediction from the
A-364 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
artificial neural network. The matching ensures that imputed management activities are realistic for each NRI survey
location, and not odd or physically unrealizable results that could be generated by the artificial neural network. The final
imputation product includes six complete imputations of the management activity data in order to adequately capture
the uncertainty. The sections below provide additional information for each of the management practices.
Synthetic and Manure N Fertilizer Applications: Data on synthetic mineral N fertilizer rates are imputed based
on crop-specific fertilizer rates in the USDA-NRCS CEAP product and fertilizer trends based on USDA-Economic Research
Service (ERS) data. The ERS crop management data had been collected as part of Cropping Practices Surveys through
1995	(USDA-ERS 1997), and are now compiled as part of Agricultural Resource Management Surveys (ARMS) starting in
1996	(USDA-ERS 2018). In these surveys, data on inorganic N fertilization rates are collected for crops in the high
production states and for a subset of low production states. Additional data on fertilization practices are compiled from
other sources, particularly the National Agricultural Statistics Service (USDA-NASS 1992,1999, 2004). These data are
used to build a time series of mineral fertilizer application rates for specific crops and states for 1980 to 2015, to the
extent that data are available. These data are then used to inform the imputation product in combination with the USDA
CEAP survey, as described previously. The donor survey data from CEAP contain both mineral fertilizer rates and manure
amendment rates, so that the selection of a donor via predictive mean matching yields the joint imputation of both
mineral and manure amendment rates. This approach captures the relationship between mineral fertilization and
manure amendment practices for US croplands based directly on the observed patterns in the CEAP survey data.
Fertilizer sales data are used to check and adjust synthetic mineral fertilizer amounts that are simulated with
DayCent. The total amount of synthetic fertilizer used on-farms (cropland and grazing land application) has been
estimated by the USGS from 1990 through 2012 on a county scale from fertilizer sales data (Brakebill and Gronberg
2017). For 2013 through 2015, county-level fertilizer used on-farms is adjusted based on annual fluctuations in total U.S.
fertilizer sales (AAPFCO 2013 through 2017).116 The resulting data are used to check the simulated synthetic fertilizer
inputs in the DayCent simulations at the state scale. Specifically, the simulated amounts of mineral fertilizer application
for each state and year are compared to the sales data. If the simulated amounts exceed the sales data in a year, then
the simulated N20 emissions are reduced based on the amount of simulated fertilizer that exceeded the sales data
relative to the total application of fertilizer in the DayCent simulations for the state. For example, if the simulated
amount exceeded the sales data by 3 percent, then the emissions associated with synthetic mineral fertilization117 is
reduced by 3 percent (the same adjustments are also made for leaching and volatilization losses of N that are used to
estimate indirect N20 emissions). This method ensures that the simulated amount of mineral fertilization using bottom-
up data from the ARMS and CEAP surveys are adjusted so that they do not exceed the sales data. The bottom-up data
from CEAP and ARMS will be further investigated in the future to evaluate the discrepancies with the sales data, and
potentially improve these datasets to attain greater consistency.
Similar to synthetic mineral fertilization in DayCent, total amount of manure available for application to soils is
used to check and adjust the simulated amounts of manure application to soils in the DayCent simulations. The available
manure is estimated using methods described in the Manure Management section (Section 5.2) and annex (Annex 3.10),
and it is assumed that all available manure is applied to soils in cropland and grazing lands. If the amount of manure
amendments in DayCent simulations exceeded the available manure for application to soils, the amount of N20
emissions is reduced based on the amount of over-application in the simulations. For example, if the simulated amount
exceeded the available amount by 2 percent, then the emissions associated with manure N inputs are reduced by 2
percent (the same adjustments is also made for leaching and volatilization losses of N that are used to estimate indirect
N20 emissions). This method ensures that the simulated amount of manure amendments using bottom-up data from the
CEAP survey are adjusted so that they do not exceed the amount of manure available for application to soils. The
bottom-up data from CEAP will be further investigated in the future to evaluate the discrepancies with the manure
availability data, and potentially improve these datasets to attain greater consistency.
The resulting amounts of synthetic and manure fertilizer application data are found in Table A-186.
Simulations are also conducted for the time period prior to 1980 in order to initialize the DayCent model (see
Step 2a), and crop-specific regional fertilizer rates prior to 1980 are based largely on extrapolation/interpolation of
116	The fertilizer consumption data in AAPFCO are recorded in "fertilizer year" totals (i.e., July to June), but are converted to
calendar year totals. This is done by assuming that approximately 35 percent of fertilizer usage occurred from July to December
and 65 percent from January to June (TVA 1992b).
117	See Step 2A for the approach that is used to disaggregate N20 emissions from DayCent into the sources of N inputs (e.g.,
mineral fertilizer inputs).
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mineral fertilizer and manure amendment rates from the years with available data. There are little or no data are
available for some states so a geographic regional mean is used to simulate fertilization rates (e.g., no data are available
for Alabama during the 1970s for corn fertilization rates so mean values from the southeastern United States are used to
simulate fertilization to corn fields in this state).
PRP Manure N: Another key source of N for grasslands is PRP manure N (i.e., manure deposited by grazing
livestock on pasture, range or paddock). The total amount of PRP manure N is estimated using methods described in the
Manure Management section (Section 5.2) and annex (Annex 3.10). Nitrogen from PRP animal waste deposited on non-
federal grasslands in a county is generated by multiplying the total PRP N (based on animal type and population data in a
county) by the fraction of non-federal grassland area in the county. PRP manure N input rates for the Tier 3 DayCent
simulations are estimated by dividing the total PRP manure N amount by the land area associated with non-federal
grasslands in the county from the NRI survey data. During the simulations, the PRP N input is subdivided equally between
urine and solid manure (i.e., 50:50 split), and C is also added with the solids using C:N ratios estimated from livestock-
specific data on manure chemical content in the Agricultural Waste Management Field Handbook (USDA-NRCS 1996).
Total PRP manure N added to soils is found in Table A-186.
Residue N Inputs: Crop residue N, fixation by legumes, and N residue inputs from senesced grass litter are
included as sources of N to the soil, and these sources of N are estimated in the DayCent simulations as a function of
vegetation type, weather, and soil properties. That is, the model accounts for the contribution of N from crop residues to
the soil profile based on simulating the growth of the crop and senescence. This includes the total N inputs of above-
and below-ground N and fixed N in residues that are not harvested or burned (DayCent simulations assumed that 3
percent of non-harvested above ground residues for crops are burned),118 and the resulting amounts can be found in
Table A-186.
Other N Inputs: Other N inputs are estimated within the DayCent simulation, and thus input data are not
required, including mineralization from decomposition of soil organic matter and asymbiotic fixation of N from the
atmosphere. Mineralization of soil organic matter will also include the effect of land use change on this process as
recommended by the IPCC (2006). The influence of additional inputs of N are estimated in the simulations so that there
is full estimation of all emissions from managed lands, as recommended by the IPCC (2006). The simulated N input from
soil organic matter mineralization and asymbiotic N fixation are provided in Table A-186.
Tillage Practices: Tillage practices are grouped into three categories: full, reduced, and no-tillage. Full tillage is
defined as multiple tillage operations every year, including significant soil inversion (e.g., plowing, deep disking) and low
surface residue coverage. This definition corresponds to the intensive tillage and "reduced" tillage systems as defined by
CTIC (2004). No-till is defined as not disturbing the soil except through the use of fertilizer and seed drills and where no-
till is applied to all crops in the rotation. The remainder of the cultivated area is classified as reduced tillage, including
mulch tillage and ridge tillage as defined by CTIC and intermittent no-till. The specific tillage implements and applications
used for different crops, rotations, and regions are derived from the 1995 Cropping Practices Survey by the Economic
Research Service (USDA-ERS 1997).
Tillage practices are estimated for each cropping system based on data from the Conservation Technology
Information Center for 1980 through 2004 (CTIC 2004), USDA-NRCS CEAP survey for 2000 through 2005 (USDA-NRCS
2018b), and USDA ARMS surveys for 2002 through 2015 (Claasen et al. 2018). CTIC compiles data on cropland area under
tillage management classes by major crop species and year for each county. The CTIC and ARMS surveys involve
aggregate area, and therefore they do not fully characterize tillage practices as they are applied within a management
sequence (e.g., crop rotation). This is particularly true for area estimates of cropland under no-till. These estimates
include a relatively high proportion of "intermittent" no-till, where no-till in one year may be followed by tillage in a
subsequent year, leading to no-till practices that are not continuous in time. Estimates of the area under continuous no-
till are provided by experts at CTIC to account for intermittent tillage activity and its impact on soil organic C (Towery
2001).
Tillage data are further processed to impute a tillage management system for each NRI survey location over the
time series from 1980 to 2015. First, we impute a tillage management system for every NRI survey location in the "base
block" of 2001-2005 by forming imputation classes consisting of all NRI survey locations within the same CEAP region,
crop group, and soil texture class. Within one imputation class, NRI locations with missing tillage systems are assigned
118 Another improvement is to reconcile the amount of crop residues burned with the Field Burning of Agricultural Residues
source category (Section 5.5).
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the tillage system of a randomly-selected CEAP donor. Once the base block is imputed, tillage systems for remaining
five-year time blocks are imputed forward and backward in time using trending information obtained from CTIC and
ARMS, described above. The trending information from one-time block to the next is reflected in the imputations by first
constructing the 3x3 transition probability matrix, M, between the two blocks. Let a denote the vector of proportions in
the current time block (already imputed) and let b denote the vector of desired proportions in the target time block (to
be imputed) based on the trending information. The rows of M correspond to the tillage type (no-till, reduced till, or
conventional till) in the target time block and the columns of M correspond to the tillage type in the current time block.
The elements of M are constrained so that (a) each column is a probability distribution (all elements between 0 and 1
and column sums to 1); (b) Ma=b; and (c) the diagonal elements of M are as large as possible. The last constraint implies
as much temporal continuity as possible at a location, subject to overall trends. The solution for M is obtained by a
mathematical optimization technique known as linear programming. Once M is obtained, it is used for imputing the
tillage system as follows: determine the column that corresponds to the tillage system (imputed or real) of the current
block, and use the probabilities in that column to randomly select the tillage system for the target block. Repeat the
construction of M and the imputation block by block forward in time and backward in time.
Cover Crops-. Cover crop data are based on USDA CEAP data (USDA-NRCS 2018b) and information from 2011 to
2016 in the USDA Census of Agriculture (USDA-NASS 2012, 2017). It is assumed that cover cropping was minimal prior to
1990 and the rates increased over the decade to the levels of cover crop management derived from the CEAP survey.
Cover crops in the "base block" of 2001-2005 are determined from the imputation for planting date (cover crops are
assigned based on recipients with donor that had a cover crop in the USDA CEAP survey). For 1996-2000, we randomly
remove cover crop from locations so that remaining cover crop area is about one-half of the 2001-2005 cover crop area.
For 1991-1995, we randomly remove a subset of the remaining area each year until no cover crops are remaining in
1990. For the blocks 2006-2010, 2011-2015, and 2016-2020, we add (or possibly delete, if cover crops declined in a
region) cover crops at random to match the trending information from USDA Census of Agriculture (USDA-NASS 2012,
2017).
Irrigation: NRI (USDA-NRCS 2018a) differentiates between irrigated and non-irrigated land, but does not
provide more detailed information on the type and intensity of irrigation. Hence, irrigation is modeled by assuming that
water is applied to the level of field capacity on the day after the soil at an NRI survey location drains to 60 percent of
field capacity in the DayCent model simulation.
Daily Weather Data: Daily maximum/minimum temperature and precipitation data are based on gridded
weather data from the PRISM Climate Group (2018). Computer-generated weather data are used to drive the DayCent
model simulations because weather station data do not exist near all NRI points. The PRISM product uses interpolation
algorithms to derive weather patterns for areas between the existing network of weather stations (Daly et al. 1998).
PRISM weather data are available for the United States from 1981 through 2015 at a 4 km resolution. Each NRI survey
location is assigned the PRISM weather data for the grid cell containing the survey location.
Enhanced Vegetation Index: The Enhanced Vegetation Index (EVI) from the MODIS vegetation products,
(MOD13Q1 and MYD13Q1) is an input to DayCent for estimating net primary production using the NASA-CASA
production algorithm (Potter et al. 1993, 2007). MODIS imagery is collected on a nominal 8 day-time frequency when
combining the two products. A best approximation of the daily time series of EVI data is derived using a smoothing
process based on the Savitzky-Golay Filter (Savitzky and Golay 1964) after pre-screening for outliers and for cloud-free,
high quality data as identified in the MODIS data product quality layer. The NASA-CASA production algorithm is only used
for the following crops: corn, soybeans, sorghum, cotton, wheat, and other close-grown crops such as barley and oats.119
The MODIS EVI products have a 250 m spatial resolution, and some pixels in images have mixed land uses and
crop types at this resolution, which is problematic for estimating NPP associated with a specific crop at an NRI survey
location. Therefore, a threshold of 90 percent purity in an individual pixel is the cutoff for estimating NPP using the EVI
data derived from the imagery (i.e., pixels with less than 90 percent purity for a crop are assumed to generate bias in the
resulting NPP estimates). The USDA-NASS Crop Data Layer (CDL) (Johnson and Mueller 2010) is used to determine the
purity levels of the EVI data. CDL data have a 30 to 58 m spatial resolution, depending on the year. The level of purity for
individual pixels in the MODIS EVI products is determined by aggregating the crop cover data in CDL to the 250 m
resolution of the EVI data. In this step, the percent cover of individual crops is determined for the 250 m EVI pixels. Pixels
that do not meet a 90 percent purity level for any crop are eliminated from the dataset. CDL does not provide full
119 Additional crops and grassland will be used with the NASA-CASA method in the future, as a planned improvement.
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coverage for crops across the conterminous United States until 2009 so it is not possible to evaluate purity for the entire
cropland area prior to 2009. The nearest pixel with at least 90 percent purity for a crop is assigned to the NRI survey
location based on a 10 km buffer surrounding the survey location. EVI data are not assigned to a survey location if there
are no pixels with at least 90 percent purity within the 10 km buffer. In these cases, production is simulated with a single
value for the maximum daily NPP, which is reduced if there is water, temperature or nutrient stress affecting plant
growth.
Water Management for Rice Cultivation: Rice crop production in the United States is mostly managed with
continuous flooding, but does include a minor amount of land with mid-season drainage or alternate wet-dry periods
(Hardke 2015; UCCE 2015; Hollier 1999; Way et al. 2014). However, continuous flooding is applied to all rice cultivation
areas in the inventory because water management data are not available. Winter flooding is another key practice
associated with water management in rice fields. Winter flooding occurs on 34 percent of rice fields in California (Miller
et al. 2010; Fleskes et al. 2005), and approximately 21 percent of the fields in Arkansas (Wilson and Branson 2005 and
2006; Wilson and Runsick 2007 and 2008; Wilson et al. 2009 and 2010; Hardke and Wilson 2013 and 2014; Hardke 2015).
No data are available on winter flooding for Texas, Louisiana, Florida, Missouri, or Mississippi. For these states, the
average amount of flooding is assumed to be similar to Arkansas. In addition, the amount of winter flooding is assumed
to be relatively constant over the Inventory time period.
Organic Amendments for Rice Cultivation: Rice straw is not typically harvested from fields in the United States.
The C input from rice straw is simulated directly within the DayCent model for the Tier 3 method. For the Tier 1 method,
residues are assumed to be left on the field for more than 30 days prior to cultivation and flooding for the next crop,
with the exception of ratoon crops, which are assumed to have residues on the field for less than 30 days prior to the
second crop in the season. To estimate the amount of rice straw, crop yield data (except rice in Florida) are compiled
from USDA NASS QuickStats (USDA 2015). Rice yield data are not collected by USDA for Florida, and so are derived based
on NRI crop areas and average primary and ratoon rice yields from Deren (2002). Relative proportions of ratoon crops
are derived from information in several publications (Schueneman 1997,1999, 2000, 2001; Deren 2002; Kirstein 2003,
2004, 2006; Cantens 2004, 2005; Gonzalez 2007 through 2014). The yields are multiplied by residue: crop product ratios
from Strehler and Stutzle (1987) to estimate rice straw input amounts for the Tier 1 method.
Soil Properties: Soil texture and drainage capacity (i.e., hydric vs. non-hydric soil characterization) are the main
soil variables used as inputs to the DayCent model. Texture is one of the main controls on soil C turnover and
stabilization in the model, which uses particle size fractions of sand (50-2,000 p.m), silt (2-50 p.m), and clay (<2 p.m) as
inputs. Hydric condition in soils are associated with poor drainage, and hence prone to have a high-water table for part
of the year in their native (pre-cultivation) condition. Non-hydric soils are moderately to well-drained.120 Poorly drained
soils can be subject to anaerobic (lack of oxygen) conditions if water inputs (precipitation and irrigation) exceed water
losses from drainage and evapotranspiration. Depending on moisture conditions, hydric soils can range from fully
aerobic to completely anaerobic, varying over the year. Decomposition rates are modified according to a linear function
that varies from 0.3 under completely anaerobic conditions to 1.0 under fully aerobic conditions (default parameters in
DayCent).121 Other soil characteristics needed in the simulation, such as field capacity and wilting-point water contents,
are estimated from soil texture data using a standardized hydraulic properties calculator (Saxton et al. 1986). Soil input
data are derived from Soil Survey Geographic Database (SSURGO) (Soil Survey Staff 2019). The data are based on field
measurements collected as part of soil survey and mapping. Each NRI survey location is assigned the dominant soil
component in the polygon containing the point from the SSURGO data product.
Step lc: Obtain Additional Management Activity Data for the Tier 1 Method to estimate Soil N2O Emissions from
Mineral Soils
Synthetic N Fertilizer: A process-of-elimination approach is used to estimate synthetic N fertilizer additions to
crops in the Tier 1 method. The total amount of synthetic fertilizer used on-farms has been estimated using USGS and
AAPFCO datasets, as discussed in Step lb (Brakebill and Gronberg 2017; AAPFCO 2013 through 2017). The amount of N
applied to crops in the Tier 1 method (i.e., not simulated by DayCent) is assumed to be the remainder of the fertilizer
that is used on farms after subtracting the amount applied to crops and non-federal grasslands simulated by DayCent.
120	Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a management variable.
121	Hydric soils are primarily subject to anaerobic conditions outside the plant growing season, such as late winter or early spring prior
to planting. Soils that are flooded during much of the year are typically classified as organic soils (e.g., peat), which are not simulated
with the DayCent model.
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The differences are aggregated to the national level, and PDFs are derived based on uncertainties in the amount of N
applied to crops and non-federal grasslands for the Tier 3 method. Total fertilizer application to crops in the Tier 1
method is found in Table A-186.
Managed Livestock Manure and Other Organic Fertilizers: Managed manure N that is not applied to crops and
grassland simulated by DayCent is assumed to be applied to other crops that are included in the Tier 1 method. The total
amount of manure available for application to soils has been estimated with methods described in the Manure
Management section (Section 5.2) and annex (Annex 3.10). Managed manure N applied to croplands for the Tier 1
method is calculated using a process of elimination approach. Specifically, the amount of managed manure N that is
amended to soils in the DayCent model simulations is subtracted from total managed manure N available for application
to soils. The difference is assumed to be applied to croplands that are not included in the DayCent model simulations.
The fate of manure available for application to soils is summarized in Table A-186.
Estimates of total national annual N additions from other commercial organic fertilizers are derived from
organic fertilizer statistics (TVA 1991 through 1994; AAPFCO 1995 through 2017).122 Commercial organic fertilizers
include dried blood, tankage, compost, and other organic materials, which are recorded in mass units of fertilizer. These
data are converted to mass units of N by multiplying the consumption values by the average organic fertilizer N content
of commercial organic fertilizers, which range between 2.3 to 4.2 percent across the time series (TVA 1991 through 1994;
AAPFCO 1995 through 2017). There is potential for double-counting N applications to soils for dried manure and
biosolids (i.e., treated sewage sludge) that are included as commercial fertilizers because these N inputs are already
addressed in the manure dataset (See Manure Management Section 5.2 and Annex 3.10) and biosolids (See Biosolids
below) that are estimated for this Inventory. Therefore, the amounts of dried manure and biosolids in other commercial
organic fertilizer, which are provided in the reports123 (TVA 1991 through 1994; AAPFCO 1995 through 2017), are
subtracted from the total commercial organic fertilizer before estimating emissions. The PDFs are derived for the
organic fertilizer applications assuming a default ±50 percent uncertainty. Annual consumption of other organic
fertilizers is presented in Table A-186.
PRP Manure N: Soil N20 emissions from PRP manure N deposited on federal grasslands are estimated with a
Tier 1 method. PRP manure N data are derived using methods described in the Manure Management section (Section
5.2) and Annex 3.10. PRP N deposited on federal grasslands is calculated using a process of elimination approach.
Specifically, the amount of PRP N included in the DayCent model simulations of non-federal grasslands is subtracted
from total PRP N. This difference was assumed to be deposited on federal grasslands. The total PRP manure N added to
soils is found in Table A-186.
Biosolids (i.e., Treated Sewage Sludge) Amendments: Biosolids are generated from the treatment of raw sewage
in public or private wastewater treatment works and are typically used as a soil amendment, or are sent to waste
disposal facilities, such as landfills. In this Inventory, all biosolids that are amended to agricultural soils are assumed to be
applied to grasslands. Estimates of the amounts of biosolids N applied to agricultural lands are derived from national
data on biosolids generation, disposition, and N content. Total biosolids generation data for 1990 through 2004, in dry
mass units, are obtained from AAPFCO (1995 through 2004). Values for 2005 through 2019 are not available so a "least
squares line" statistical extrapolation using the previous 16 years of data to impute an approximate value. The total
sludge generation estimates are then converted to units of N by applying an average N content of 69 percent (AAPFCO
2000), and disaggregated into use and disposal practices using historical data in EPA (1993) and NEBRA (2007). The use
and disposal practices are agricultural land application, other land application, surface disposal, incineration, landfilling,
ocean dumping (ended in 1992), and other disposal methods. The resulting estimates of biosolids N applied to
agricultural land are used to estimate N20 emissions from agricultural soil management; the estimates of biosolids N
applied to other land and surface-disposed are used in estimating N20 fluxes from soils in Settlements Remaining
Settlements (see section 6.9 of the Land Use, Land-Use Change, and Forestry chapter). Biosolids disposal data are
provided in Table A-186.
122	Similar to the data for synthetic fertilizers described above, the organic fertilizer consumption data are recorded in "fertilizer
year" totals, (i.e., July to June), but are converted to calendar year totals. This is done by assuming that approximately 35
percent of fertilizer usage occurred from July to December and 65 percent from January to June (TVA 1992b).
123	The amount of reported dried manure and biosolids in other organic fertilizers must be converted into units of N. While the
amounts of dried manure and biosolids are provided in each report (TVA 1991 through 1994; AAPFCO 1995 through 2017), the
N contents of dried manure and biosolids are only provided in AAPFCO (2000). The values are 0.5 and 6.0 percent, respectively,
for dried manure and biosolids.
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Residue N Inputs'. Soil N20 emissions for residue N inputs from croplands that are not simulated by DayCent are
estimated with a Tier 1 method. Annual crop production statistics for all major commodity and specialty crops are taken
from U.S. Department of Agriculture crop production reports (USDA-NASS 2019). Total production for each crop is
converted to tons of dry matter product using the residue dry matter fractions. Dry matter yield is then converted to
tons of above- and below-ground biomass N. Above-ground biomass is calculated by using linear equations to estimate
above-ground biomass given dry matter crop yields, and below-ground biomass is calculated by multiplying above-
ground biomass by the below-to-above-ground biomass ratio. N inputs are estimated by multiplying above- and below-
ground biomass by respective N concentrations and by the portion of cropland that is not simulated by DayCent. All
ratios and equations used to calculate residue N inputs are from IPCC (2006) and Williams (2006). PDFs are derived
assuming a ±50 percent uncertainty in the yield estimates (USDA-NASS does not provide uncertainty), along with
uncertainties provided by the IPCC (2006) for dry matter fractions, above-ground residue, ratio of below-ground to
above-ground biomass, and residue N fractions. The resulting annual residue N inputs are presented in Table A-186.
Table A-186: Sources of Soil Nitrogen (kt N)
N Source
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
1.
Synthetic Fertilizer N: Cropland
9,892
10,285
10,274
10,110
11,126
10,300
10,871
10,852
10,815
10,970
2.
Synthetic Fertilizer N: Grassland
13
12
24
56
42
12
10
19
78
19
3.
Managed Manure N: Cropland
2,463
2,495
2,505
2,491
2,553
2,587
2,578
2,596
2,616
2,618
4.
Managed Manure N: Grassland
+
1
1
2
1
+
+
2
1
1
5.
Pasture, Range, & Paddock Manure N
4,097
4,104
4,265
4,354
4,427
4,529
4,495
4,393
4,350
4,287
6.
N from Crop Residue Decomposition3
6,875
7,091
6,693
7,047
6,789
7,255
6,977
6,842
6,881
7,739
7.
N from Grass Residue Decomposition3
12,374
12,298
12,623
12,757
12,217
12,937
12,551
12,644
11,960
13,366
8.
Min. SOM / Asymbiotic N-Fixation:











Cropland15
11,344
10,931
10,686
12,089
10,722
11,596
11,000
11,219
12,605
11,296
9.
Min. SOM / Asymbiotic N-Fixation:











Grassland15
16,445
17,261
17,389
17,205
16,020
17,028
16,820
17,824
17,363
16,807
10.
Treated Sewage Sludge N: Grassland
52
55
58
62
65
68
72
75
78
81
11.
Other Organic Amendments: Cropland0
4
8
6
5
8
10
13
14
12
11
Total
63,559
64,541
64,524 66,178
63,970
66,321
65,385
66,479
66,759
67,195

N Source
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
1.
Synthetic Fertilizer N: Cropland
10,792
10,105
10,542
10,602
11,324
10,723
10,454
11,493
10,932
10,215
2.
Synthetic Fertilizer N: Grassland
24
30
27
24
44
18
19
15
22
18
3.
Managed Manure N: Cropland
2,650
2,635
2,668
2,676
2,595
2,630
2,704
2,726
2,698
2,675
4.
Managed Manure N: Grassland
1
2
+
1
+
1
1
+
1
+
5.
Pasture, Range, & Paddock Manure N
4,194
4,191
4,198
4,203
4,156
4,207
4,260
4,150
4,111
4,078
6.
N from Crop Residue Decomposition3
7,428
7,336
7,262
7,504
7,171
7,337
7,375
7,141
7,255
7,442
7.
N from Grass Residue Decomposition3
12,532
12,936
12,677
13,040
12,243
13,092
12,689
13,178
13,034
12,571
8.
Min. SOM / Asymbiotic N-Fixation:











Cropland15
11,414
11,821
11,284
11,433
12,839
11,494
11,346
11,961
12,054
12,484
9.
Min. SOM / Asymbiotic N-Fixation:











Grassland15
15,687
16,599
16,475
16,991
19,099
17,701
16,934
18,549
17,474
18,120
10.
Treated Sewage Sludge N: Grassland
84
86
89
91
94
98
101
104
107
110
11.
Other Organic Amendments: Cropland0
9
7
8
8
9
10
12
15
12
10
Total

64,814
65,748
65,228
66,574
69,574
67,311
65,896
69,334
67,701
67,722

N Source
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
1.
Synthetic Fertilizer N: Cropland
10,784
11,261
11,906
11,905
11,706
11,480
12,758
12,690
12,956
13,011
2.
Synthetic Fertilizer N: Grassland
11
12
13
11
12
14
12
12
12
12
3.
Managed Manure N: Cropland
2,663
2,686
2,710
2,687
2,680
2,718
2,778
2,844
2,924
2,947
4.
Managed Manure N: Grassland
+
1
1
1
+
1
+
+
+
+
5.
Pasture, Range, & Paddock Manure N
4,027
3,929
3,843
3,801
3,755
3,817
3,949
4,019
4,037
4,052
6.
N from Crop Residue Decomposition3
7,887
7,676
7,448
7,359
7,621
7,231
7,436
7,422
7,622
7,692
7.
N from Grass Residue Decomposition3
12,910
12,499
13,091
12,107
12,211
11,769
12,200
12,089
12,232
12,687
8.
Min. SOM / Asymbiotic N-Fixation:











Cropland15
13,366
11,272
10,216
12,694
13,536
14,311
11,604
11,631
11,995
12,121
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9. Min. SOM / Asymbiotic N-Fixation:
Grassland15
10.	Treated Sewage Sludge N: Grassland
11.	Other Organic Amendments: Cropland
18,527 16,127 15,341 18,472 18,501 19,041 16,731 16,579 16,774 17,398
113 116 119 122 124 127 130 133 136 139
10 12 13 13 11 12 13 12 11 10
Total
70,298 65,591 64,701 69,172 70,157 70,522 67,610 67,430 68,698 70,069
Note: For most activity sources data were not available after 2015 and emissions were estimated with a
data splicing method. Additional activity data will be collected and the Tier 1, 2 and 3 methods will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data
splicing methods.
+ Does not exceed 0.5 kt
a Residue N inputs include unharvested fixed N from legumes as well as crop and grass residue N.
b Mineralization of soil organic matter and the asymbiotic fixation of nitrogen gas.
c Includes dried blood, tankage, compost, other. Excludes dried manure and bio-solids (i.e., treated sewage sludge) used as
commercial fertilizer to avoid double counting.
Step Id: Obtain Additional Management Activity Data for Tier 2 Method to estimate Soil Organic C Stock
Changes in Mineral Soils
Biosolids (i.e., Treated Sewage Sludge) Amendments: Biosolids are generated from the treatment of raw sewage
in public or private wastewater treatment facilities and are typically used as soil amendments or are sent for waste
disposal to landfills. In this Inventory, all biosolids that are amended to agricultural soils are assumed to be applied to
grasslands. See section on biosolids in Step lc for more information about the methods used to derive biosolid N
estimates. The total amount of biosolid N is given in Table A-186. Biosolid N is assumed to be applied at the assimilative
capacity provided in Kellogg et al. (2000), which is the amount of nutrients taken up by a crop and removed at harvest
representing the recommended application rate for manure amendments. In this Inventory, all biosolids are applied to
grasslands so these rates may not be fully representative of amendments of a biosolids, but there are no data available
on N amendments that are specific to grasslands (Future Inventories will incorporate new information when it is
available). This capacity varies from year to year, because it is based on specific crop yields during the respective year
(Kellogg et al. 2000). Total biosolid N available for application is divided by the assimilative capacity to estimate the total
land area over which biosolids had been applied. The resulting estimates are used for the estimation of soil organic C
stock changes associated with application of biosolids.
Wetland Reserve: Wetlands enrolled in the Conservation Reserve Program have been restored in the Northern
Prairie Pothole Region through the Partners for Wildlife Program funded by the U.S. Fish and Wildlife Service (USFWS
2010). The area of restored wetlands is estimated from contract agreements (Euliss and Gleason 2002). While the
contracts provide reasonable estimates of the amount of land restored in the region, they do not provide the
information necessary to estimate uncertainty. Consequently, a ±50 percent range is used to construct the PDFs for the
uncertainty analysis.
Step le: Additional Activity Data for Indirect N2O Emissions
A portion of the N that is applied as synthetic fertilizer, livestock manure, biosolids (i.e., treated sewage sludge),
and other organic amendments volatilizes as NH3 and NOx. In turn, the volatilized N is eventually returned to soils
through atmospheric deposition, thereby increasing mineral N availability and enhancing N20 production. Additional N is
lost from soils through leaching as water percolates through a soil profile and through runoff with overland water flow. N
losses from leaching and runoff enter groundwater and waterways, from which a portion is emitted as N20. However, N
leaching is assumed to be an insignificant source of indirect N20 in cropland and grassland systems where the amount of
precipitation plus irrigation does not exceed 80 percent of the potential evapotranspiration. These areas are typically
semi-arid to arid regions in the Western United States, and nitrate leaching to groundwater is a relatively uncommon
event. Moreover IPCC (2006) recommends limiting the amount of nitrate leaching assumed to be a source of indirect
N20 emissions based on precipitation, irrigation and potential evapotranspiration.
The activity data for synthetic fertilizer, livestock manure, other organic amendments, residue N inputs,
biosolids N, and other N inputs are the same as those used in the calculation of direct emissions from agricultural
mineral soils, and may be found in Table A-186.
Using the DayCent model, volatilization and leaching/surface run-off of N from soils is estimated in the
simulations for crops and non-federal grasslands in the Tier 3 method. DayCent simulates the processes leading to these
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losses of N based on environmental conditions (i.e., weather patterns and soil characteristics), management impacts
(e.g., plowing, irrigation, harvest), and soil N availability. Note that the DayCent model accounts for losses of N from all
anthropogenic activity, not just the inputs of N from mineral fertilization and organic amendments124, which are
addressed in the Tier 1 methodology. Similarly, the N available for producing indirect emissions resulting from grassland
management as well as PRP manure is also estimated by DayCent. However, indirect emissions are not estimated for
leaching and runoff of N if precipitation water input does not exceed 80 percent of the potential evapotranspiration
(Note: Irrigated systems are always assumed to have leaching of N even in drier climates). Volatilized losses of N are
summed for each day in the annual cycle to provide an estimate of the amount of N subject to indirect N20 emissions. In
addition, the daily losses of N through leaching and runoff in overland flow are summed for the annual cycle. Uncertainty
in the estimates is derived from the measure of variability in the fertilizer and organic amendment activity data (see Step
la for further information).
The Tier 1 method is used to estimate N losses from mineral soils due to volatilization and leaching/runoff for
crops, applications of biosolids, and PRP manure on federal grasslands, which are not simulated by DayCent. To estimate
volatilized N losses, the amount of synthetic fertilizers, manure, biosolids, and other organic N inputs are multiplied by
the fraction subject to gaseous losses using the respective default values of 0.1 kg N/kg N added as mineral fertilizers and
0.2 kg N/kg N added as manure (IPCC 2006). Uncertainty in the volatilized N ranges from 0.03-0.3 kg NH3-N+NOx-N/kg N
for synthetic fertilizer and 0.05-0.5 kg NH3-N+NOx-N/kg N for organic amendments (IPCC 2006). Leaching/runoff losses of
N are estimated by summing the N additions from synthetic and other organic fertilizers, manure, biosolids, and above-
and below-ground crop residues, and then multiplied by the default fraction subject to leaching/runoff losses of 0.3 kg
N/kg N applied, with an uncertainty from 0.1-0.8 kg N03-N/kg N (IPCC 2006). However, N leaching is assumed to be an
insignificant source of indirect N20 emissions if the amount of precipitation did not exceed 80 percent of the potential
evapotranspiration, consistent with the Tier 3 method (Note: Irrigated systems are always assumed to have leaching of N
even in drier climates). PDFs are derived for each of the N inputs in the same manner as direct N20 emissions, discussed
in Steps la and lc.
Volatilized N is summed for losses from croplands and grasslands. Similarly, the annual amounts of N lost from
soil profiles through leaching and surface runoff are summed to obtain the total losses for this pathway.
Step 2: Estimate GHG Emissions and Stocks Changes for Mineral Soils: Soil Organic C Stock Changes,
Direct N20 Emissions, and CH4 Emissions from Rice Cultivation
In this step, soil organic C stock changes, N20 emissions, and CH4 emissions from rice cultivation are estimated
for cropland and non-federal grasslands. Three methods are used to estimate soil organic C stock changes, direct N20
emissions from mineral soils, and CH4 emissions from rice cultivation. The DayCent process-based model is used for the
croplands and non-federal grasslands included in the Tier 3 method. A Tier 2 method is used to estimate soil organic C
stock changes for crop types, grasslands (i.e., federal grasslands) and soil types that are not simulated by DayCent and
land use change other than conversions between cropland and grassland. A Tier 1 methodology is used to estimate N20
emissions from crops that are not simulated by DayCent, PRP manure N deposition on federal grasslands, and CH4
emissions from rice cultivation.
Step 2a: Estimate Soil Organic C Stock Changes, Soil N2O Emissions, and CH4 emissions for Crops and Non-Federal
Grassland with the Tier 3 DayCent Model
Crops that are simulated with DayCent include alfalfa hay, barley, corn, cotton, grass hay, grass-clover hay,
oats, peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tobacco, and wheat, which combined
represent approximately 85 percent of total cropland in the United States. The DayCent simulations also include all non-
federal grasslands in the United States.
The methodology description is divided into two sub-steps. First, the DayCent model is used to establish the
initial conditions and C stocks for 1979, which is the first year of the NRI survey. In the second sub-step, DayCent is used
to simulate changes in soil organic C stocks, direct soil N20 emissions, leaching and volatilization losses of N contributing
124 The amount of volatilization and leaching are reduced if the simulated amount of synthetic mineral fertilization in DayCent
exceeds the amount mineral fertilizer sales, or the simulated amount of manure application in DayCent exceeds the manure
available for applications to soils. See subsection on Synthetic and Manure N Fertilizer Applications in Step lb for more
information.
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to indirect N20 emissions, and CH4 emissions from rice cultivation based on the land-use and management histories
recorded in the NRI (USDA-NRCS 2018a).
Simulate Initial Conditions (Pre-NRI Conditions): The purpose of the DayCent model initialization is to estimate
the most accurate stock for the pre-NRI history, and the distribution of organic C among the pools represented in the
model (e.g., Structural, Metabolic, Active, Slow, and Passive). Each pool has a different turnover rate (representing the
heterogeneous nature of soil organic matter), and the amount of C in each pool at any point in time influences the
forward trajectory of the total soil organic C storage. There is currently no national set of soil C measurements
subdivided by the pools that can be used for establishing initial conditions in the model. Sensitivity analysis of the soil
organic C algorithms showed that the rate of change of soil organic matter is relatively insensitive to the amount of total
soil organic C but is highly sensitive to the relative distribution of C among different pools (Parton et al. 1987). By
simulating the historical land use prior to the inventory period, initial pool distributions are estimated in an unbiased
way.
The first step involves running the model to a steady-state condition (e.g., equilibrium) under native vegetation,
historical climate data based on the PRISM product (PRISM Climate Group 2018), and the soil characteristics for the NRI
survey locations. Native vegetation is represented at the MLRA level for pre-settlement time periods in the United
States. The model simulates 5,000 years in the pre-settlement era in order to achieve a steady-state condition.
The second step is to simulate the period of time from European settlement and expansion of agriculture to the
beginning of the NRI survey, representing the influence of historic land-use change and management, particularly the
conversion of native vegetation to agricultural uses. This encompasses a varying time period from land conversion
(depending on historical settlement patterns) to 1979. The information on historical cropping practices used for DayCent
simulations has been gathered from a variety of sources, ranging from the historical accounts of farming practices
reported in the literature (e.g., Miner 1998) to national level databases (e.g., NASS 2004). A detailed description of the
data sources and assumptions used in constructing the base history scenarios of agricultural practices can be found in
Williams and Paustian (2005).
NRI History Simulations: After model initialization, DayCent is used to simulate the NRI land use and
management histories from 1979 through 2015. The simulations address the influence of soil management on direct soil
N20 emissions, soil organic C stock changes, and losses of N from the profile through leaching/runoff and volatilization.
The NRI histories identify the land use and land use change histories for the NRI survey locations, as well as cropping
patterns and irrigation history (see Step la for description of the NRI data). The input data for the model simulations also
include the PRISM weather dataset and SSURGO soils data, synthetic N fertilizer rates, managed manure amendments to
cropland and grassland, manure deposition on grasslands (i.e., PRP), tillage histories, cover crop usage, and EVI data (See
Step lb for description of the inputs). There are six DayCent simulations for each NRI survey location based on the
imputation product in order to capture the uncertainty in the management activity data derived by combining data from
CEAP, ARMS, Census of Agriculture and CTIC surveys. See Step lb for more information. The simulation system
incorporates a dedicated MySQL database server and a parallel processing computer cluster. Input/output operations
are managed by a set of run executive programs.
Evaluating uncertainty is an integral part of the analysis and includes three components: (1) uncertainty in the
management activity data inputs (input uncertainty); (2) uncertainty in the model formulation and parameterization
(structural uncertainty); and (3) uncertainty in the land-use and management system areas (scaling uncertainty) (Ogle et
al. 2010; Del Grosso et al. 2010). For the first component, the uncertainty is based on the six imputations underlying the
data product combining CEAP, ARMS, Census of Agriculture and CTIC survey data. See Step lb for discussion about the
imputation product. The second component deals with uncertainty inherent in model formulation and parameterization.
This component is the largest source of uncertainty in the Tier 3 model-based inventory analysis, accounting for more
than 80 percent of the overall uncertainty in the final estimates (Ogle et al. 2010; Del Grosso et al. 2010). An empirically-
based procedure is applied to develop a structural uncertainty estimator from the relationship between modeled results
and field measurements from agricultural experiments (Ogle et al. 2007). For soil organic C, the DayCent model is
evaluated with measurements from 72 long-term experiment sites and 142 NRI soil monitoring network sites (Spencer et
al. 2011) with 948 observations across all of the sites that represent a variety of management conditions (e.g., variation
in crop rotation, tillage, fertilization rates, and manure amendments). There are 41 experimental sites available with over
200 treatment observations to evaluate structural uncertainty in the N20 emission predictions from DayCent (Del Grosso
et al. 2010). There are 17 long-term experiments with data on CH4 emissions from rice cultivation, representing 238
combinations of management treatments. The inputs to the model are essentially known in the simulations for the long-
term experiments, and, therefore, the analysis is designed to evaluate uncertainties associated with the model structure
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(i.e., model algorithms and parameterization). However, additional uncertainty is introduced with the measurements
from the NRI soil monitoring network because the management data are represented by the six imputations. Therefore,
we statistically analyzed the results and quantified uncertainty for each imputation separately for soil organic C.
The empirical relationship between field measurements and modeled soil organic C stocks, soil N20 emissions
and CH4 emissions are statistically analyzed using linear-mixed effect modeling techniques. The modeled stocks and
emissions are treated as a fixed effect in the statistical models. The resulting relationships are used to make an
adjustment to modeled values if there are biases due to significant mismatches between the modeled and measured
values. Several other variables are tested in these models including soil characteristics, geographic location (i.e., state),
and management practices (e.g., tillage practices, fertilizer rates, rice production with and without winter flooding).
Random effects are included in all of these models to capture the dependence in time series and data collected from the
same site, which are needed to estimate appropriate standard deviations for parameter coefficients. See the Tier 3
Model Description, Parameterization and Evaluation Section for more information about model evaluation, including
graphs illustrating the relationships between modeled and measured values.
The third element is the uncertainty associated with scaling the DayCent results for each NRI survey location to
the entire land base, using the expansion factors and replicate weights provided with the NRI dataset. The expansion
factors represent the number of hectares associated with the land-use and management history for a particular survey
location. The scaling uncertainty is due to the complex sampling design that selects the locations for NRI, and this
uncertainty is properly reflected in the replicate weights for the expansion factor. Briefly, each set of replicate weights is
used to compute one weighted estimate. The empirical variation across the weighted estimates from all replicates is an
estimate of the theoretical scaling uncertainty due to the complex sampling design.
A Monte Carlo approach is used to propagate uncertainty from the three components through the analysis with
1000 iterations for each NRI survey location. In each iteration, there is a random selection of management activity data
from the imputation product; a random draw of parameter values for the uncertainty estimator (Ogle et al. 2010); and a
random draw of a set of replicate weights to scale the emissions and stock changes from the individual NRI survey
locations to the entire domain of the inventory analysis. Note that parameter values for the statistical equations (i.e.,
fixed effects) are selected from their joint probability distribution, as well as random error associated with the time
series and data collected from the same site, and the residual/unexplained error. The randomly selected parameter
values for soil organic C, N20 and CH4 emissions and associated management information are then used as input into the
linear mixed-effect model, and adjusted values are computed for each C stock change, N20 and CH4 emissions estimate.
After completing the Monte Carlo stochastic simulation, the median of the final distribution from the 1000 replicates is
used as the estimate of total emissions or soil organic C stock changes, and a 95 percent confidence interval is based on
2.5 and 97.5 percentile values.
In DayCent, the model cannot distinguish among the original sources of N after the mineral N enters the soil
pools, and therefore it is not possible to determine which management activity led to specific N20 emissions. This
means, for example, that N20 emissions from applied synthetic fertilizer cannot be separated from emissions due to
other N inputs, such as crop residues. It is desirable, however, to report emissions associated with specific N inputs.
Thus, for each NRI survey location, the N inputs in a simulation are determined for anthropogenic practices discussed in
IPCC (2006), including synthetic mineral N fertilization, organic amendments, and crop residue N added to soils (including
N-fixing crops). The percentage of N input for anthropogenic practices is divided by the total N input, and this proportion
is used to determine the amount of N20 emissions assigned to each of the practices. For example, if 70 percent of the
mineral N made available in the soil is due to synthetic mineral fertilization, then 70 percent of the N20 emissions are
assigned to this practice.
A portion of soil N20 emissions is reported under "other N inputs," which includes mineralization due to
decomposition of soil organic matter and litter, as well as asymbiotic N fixation from the atmosphere. Mineralization of
soil organic matter is significant source of N, but is typically less than half of the amount of N made available in cropland
soils compared to application of synthetic fertilizers and manure amendments, along with symbiotic fixation.
Mineralization of soil organic matter accounts for the majority of available N in grassland soils. Asymbiotic N fixation by
soil bacteria is a minor source of N, typically not exceeding 10 percent of total N inputs to agroecosystems. Accounting
for the influence of "other N inputs" is necessary because the processes leading to these inputs of N are influenced by
management.
This attribution of N20 emissions to the individual N inputs to the soils is required for reporting emissions based
on UNFCCC reporting guidelines. However, this method is a simplification of reality to allow partitioning of N20
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emissions, as it assumes that all N inputs have an identical chance of being converted to N20. It is important to realize
that sources such as synthetic fertilization may have a larger impact on N20 emissions than would be suggested by the
associated level of N input for this source (Delgado et al. 2009). Further research will be needed to improve upon this
attribution method, however.
For the land base that is simulated with the DayCent model, direct soil N20 emissions are provided in Table A-
190 and Table A-191, soil organic C stock changes are provided in Table A-192, and rice cultivation CH4 emissions in Table
A-194.
Step 2b: Soil N2O Emissions from Agricultural Lands on Mineral Soils Approximated with the Tier 1 Approach
To estimate direct N20 emissions from N additions to crops in the Tier 1 method, the amount of N in applied
synthetic fertilizer, manure, and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) is
added to N inputs from crop residues, and the resulting annual totals are multiplied by the IPCC default emission factor
of 0.01 kg N20-N/kg N (IPCC 2006). The uncertainty is determined based on simple error propagation methods (IPCC
2006). The uncertainty in the default emission factor ranges from 0.3-3.0 kg N20-N/kg N (IPCC 2006). For flooded rice
soils, the IPCC default emission factor is 0.003 kg N20-N/kg N and the uncertainty range is 0.000-0.006 kg N20-N/kg N
125
(IPCC 2006). Uncertainties in the emission factor and fertilizer additions are combined with uncertainty in the
equations used to calculate residue N additions from above- and below-ground biomass dry matter and N concentration
to derive overall uncertainty.
The Tier 1 method is also used to estimate emissions from manure N deposited by livestock on federal lands
(i.e., PRP manure N), and from biosolids (i.e., treated sewage sludge) application to grasslands. These two sources of N
inputs to soils are multiplied by the IPCC (2006) default emission factors (0.01 kg N20-N/kg N for sludge and horse,
sheep, and goat manure, and 0.02 kg N20-N/kg N for cattle, swine, and poultry manure) to estimate N20 emissions. The
uncertainty is determined based on the simple error propagation methods provided by the IPCC (2006) with uncertainty
in the default emission factor ranging from 0.007 to 0.06 kg N20-N/kg N (IPCC 2006).
The results for direct soil N20 emissions using the Tier 1 method are provided in Table A-190 and Table A-191.
Step 2c: Soil CH4 Emissions from Agricultural Lands Approximated with the Tier 1 Approach
To estimate CH4emissions from rice cultivation for the Tier 1 method, an adjusted daily emission factor is
calculated using the default baseline emission factor of 1.30 kg CH4 ha 1 d 1 (ranging 0.8-2.2 kg CH4 ha 1 d"1) multiplied by a
scaling factor for the cultivation water regime, pre-cultivation water regime and a scaling factor for organic amendments
(IPCC 2006). The water regime during cultivation is continuously flooded for rice production in the United States and so
the scaling factor is always 1 (ranging from 0.79 to 1.26). The pre-season water regime varies based on the proportion of
land with winter flooding; land that does not have winter flooding is assigned a value of 0.68 (ranging from 0.58 to 0.80)
and areas with winter flooding are assigned a value of 1 (ranging from 0.88 to 1.14). Organic amendments are estimated
based on the amount of rice straw and multiplied by 1 (ranging 0.97 to 1.04) for straw incorporated greater than 30 days
before cultivation, and by 0.29 (0.2 to 0.4) for straw incorporated greater than 30 days before cultivation. The adjusted
daily emission factor is multiplied by the cultivation period and harvested area to estimate the total CH4 emissions. The
uncertainty is propagated through the calculation using an Approach 2 method with a Monte Carlo analysis (IPCC 2006),
combining uncertainties associated with the adjusted daily emission factor and the harvested areas derived from the
USDA NRI survey data.
The results for rice CH4 emissions using the Tier 1 method are provided in Table A-194.
Step 2d: Soil Organic C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2
Approach
Mineral soil organic C stock values are derived for crop rotations that were not simulated by DayCent and land
converted from non-agricultural land uses to cropland or grassland from 1990 through 2015, based on the land-use and
management activity data in conjunction with appropriate reference C stocks, land-use change, management, input, and
125 Due to lack of data, uncertainties are not addressed for managed manure N production, PRP manure N production, other
commercial organic fertilizer amendments, indirect losses of N in the DayCent simulations, and biosolids (i.e., treated sewage sludge),
but these sources of uncertainty will be included in future Inventories.
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wetland restoration factors. Each quantity in the inventory calculations has uncertainty that is quantified in PDFs,
including the land use and management activity data based on the six imputations in the data product combining CEAP,
ARMS, Census of Agriculture, and CTIC data (See Step lb for more information); reference C stocks and stock change
factors; and the replicated weights form the NRI survey. A Monte Carlo Analysis is used to quantify uncertainty in soil
organic C stock changes for the inventory period based on random selection of values from each of these sources of
uncertainty. Input values are randomly selected from PDFs in an iterative process to estimate soil organic C change for
1,000 iterations in the analysis.
Derive Mineral Soil Organic C Stock Change Factors: Stock change factors representative of U.S. conditions are
estimated from published studies (Ogle et al. 2003; Ogle et al. 2006). The numerical factors quantify the impact of
changing land use and management on soil organic C storage in mineral soils, including tillage practices, cropping
rotation or intensification, and land conversions between cultivated and native conditions (including set-asides in the
Conservation Reserve Program). Studies from the United States and Canada are used in this analysis under the
assumption that they would best represent management impacts for the Inventory.
The IPCC inventory methodology for agricultural soils divides climate into eight distinct zones based upon
average annual temperature, average annual precipitation, and the length of the dry season (IPCC 2006). Seven of these
climate zones occur in the conterminous United States and Hawaii (Eve et al. 2001). Climate zones are classified using
mean annual precipitation and temperature (1950-2000) data from the WorldClim data set (Hijmans et al. 2005) and
potential evapotranspiration data from the Consortium for Spatial Information (CGIAR-CSI) (Zomer et al. 2008; Zomer et
al. 2007).
Soils are classified into one of seven classes based upon texture, morphology, and ability to store organic
matter (IPCC 2006). Six of the categories are mineral types and one is organic (i.e., Histosol). Reference C stocks,
representing estimates from conventionally managed cropland, are computed for each of the mineral soil types across
the various climate zones, based on pedon (i.e., soil) data from the National Soil Survey Characterization Database (NRCS
1997) (Table A-187). These stocks are used in conjunction with management factors to estimate the change in soil
organic C stocks that result from management and land-use activity. PDFs, which represent the variability in the stock
estimates, are constructed as normal densities based on the mean and variance from the pedon data. Pedon locations
are clumped in various parts of the country, which reduces the statistical independence of individual pedon estimates.
To account for this lack of independence, samples from each climate by soil zone are tested for spatial autocorrelation
using the Moran's I test, and variance terms are inflated by 10 percent for all zones with significant p-values.
Table A-187: U.S. Soil Groupings Based on the IPCC Categories and Dominant Taxonomic Soil, and Reference
Carbon Stocks (Metric Tons C/ha)
Reference Carbon Stock in Climate Regions


Cold
Cold
Warm

Warm


IPCC Inventory Soil USDA Taxonomic Soil
Temperate,
Temperate,
Temperate,
Temperate, Sub-Tropical, Sub-Tropical,
Categories
Orders
Dry
Moist
Dry

Moist
Dry
Moist
High Clay Activity
Vertisols, Mollisols,







Mineral Soils
Inceptisols, Aridisols, and








high base status Alfisols
42 (n = 133)
65 (n = 526)
37 (n = 203)
51 (n
= 424)
42 (n = 26)
57 (n = 12)
Low Clay Activity
Ultisols, Oxisols, acidic







Mineral Soils
Alfisols, and many Entisols
45 (n = 37)
52 (n = 113)
25 (n = 86)
40 (n
= 300)
39 (n = 13)
47 (n = 7)
Sandy Soils
Any soils with greater than
70 percent sand and less
than 8 percent clay (often








Entisols)
24 (n = 5)
40 (n = 43)
16 (n = 19)
30 (n
= 102)
33 (n = 186)
50 (n = 18)
Volcanic Soils
Andisols
124(n = 12)
114 (n = 2)
124 (n = 12)
124(i
n = 12)
124(n = 12)
128 (n = 9)
Spodic Soils
Spodosols
86 (n=20)
74 (n = 13)
86 (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 Soils3
Histosols
NA
NA
NA

NA
NA
NA
Notes: C stocks are for the top 30 cm of the soil profile, and are estimated from pedon data available in the National Soil Survey
Characterization database (NRCS 1997); sample size provided in parentheses (i.e., 'n' values refer to sample size).
a C stocks are not needed for organic soils.
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To estimate the stock change factors for land use, management and input, studies had to report soil organic C
stocks (or information to compute stocks), depth of sampling, and the number of years since a management change to
be included in the analysis. The data are analyzed using linear mixed-effect models, accounting for both fixed and
random effects. Fixed effects included depth, number of years since a management change, climate, and the type of
management change (e.g., reduced tillage vs. no-till). For depth increments, the data are not aggregated for the C stock
measurements; each depth increment (e.g., 0-5 cm, 5-10 cm, and 10-30 cm) is included as a separate observation in the
dataset. Similarly, time-series data are not aggregated in these datasets. Linear regression models assume that the
underlying data are independent observations, but this is not the case with data from the same experimental site, or plot
in a time series. These data are more related to each other than data from other sites (i.e., not independent).
Consequently, random effects are needed to account for the dependence in time-series data and the dependence
among data points representing different depth increments from the same study. Factors are estimated for the effect of
management practices at 20 years for the top 30 cm of the soil (Table A-188). Variance is calculated for each of the
country-specific factor values, and used to construct PDFs with a normal density. In the IPCC method, factor values are
given for improved grassland, high input cropland with organic amendments, and for wetland rice, each of which
influences C stock changes in soils. Specifically, higher stocks are associated with increased productivity and C inputs
(relative to native grassland) on improved grassland with both medium and high input.126 Organic amendments in annual
cropping systems also increase soil organic C stocks due to greater C inputs, while high soil organic C stocks in rice
cultivation are associated with reduced decomposition due to periodic flooding. There are insufficient field studies to
derive factor values for these systems from the published literature, and, thus, the factor values from IPCC (2006) are
used under the assumption that they would best approximate the impacts, given the lack of data to derive country-
specific factors. A measure of uncertainty is provided for these factors in IPCC (2006), which is used to construct PDFs.
Table A-188: Soil Organic Carbon Stock Change Factors for the United States and the IPCC Default Values
Associated with Management Impacts on Mineral Soils	
U.S. Factor
IPCC Warm Moist	Warm Dry	Cool Moist	Cool Dry
default	Climate	Climate	Climate	Climate
Land-Use Change Factors
Cultivated3	1
General Unculta'b (n=251)	1.4
Set-Asidea (n=142)	1.25
Improved Grassland Factors
Medium Input	1.1
High Input	NA
Wetland Rice Production Factorb	1.1
Tillage Factors
Conv. Till	1
Red. Till (n=93)	1.05
No-till (n=212)	1.1
Cropland Input Factors
Low (n=85)	0.9
Medium	1
High (n=22)	1.1
High with amendment15	1.2
Note: The "n" values refer to sample size.
a Factors in the IPCC documentation (IPCC 2006) are converted to represent changes in soil organic C storage from a cultivated
condition rather than a native condition.
b U.S.-specific factors are not estimated for land improvements, rice production, or high input with amendment because of few
studies addressing the impact of legume mixtures, irrigation, or manure applications for crop and grassland in the United
States, or the impact of wetland rice production in the US. Factors provided in IPCC (2006) are used as the best estimates of
these impacts.
1
1.42±0.06
1.31±0.06
1.14±0.06
1.11±0.04
1.1
1
1.08±0.03
1.13±0.02
0.94±0.01
1
1.07±0.02
1.38±0.06
1
1.37±0.05
1.26±0.04
1.14±0.06
1.11±0.04
1.1
1
1.01±0.03
1.05±0.03
0.94±0.01
1
1.07±0.02
1.34±0.08
1
1.24±0.06
1.14±0.06
1.14±0.06
1.11±0.04
1.1
1
1.08±0.03
1.13±0.02
0.94±0.01
1
1.07±0.02
1.38±0.06
1
1.20±0.06
1.10±0.05
1.14±0.06
1.11±0.04
1.1
1
1.01±0.03
1.05±0.03
0.94±0.01
1
1.07±0.02
1.34±0.08
126 Improved grasslands are identified in the NRI as grasslands that are irrigated or seeded with legumes, in addition to those
reclassified as improved with manure amendments.
A-377
-------
Wetland restoration management also influences soil organic C storage in mineral soils, because restoration
leads to higher water tables and inundation of the soil for at least part of the year. A stock change factor is estimated
assessing the difference in soil organic C storage between restored and unrestored wetlands enrolled in the
Conservation Reserve Program (Euliss and Gleason 2002), which represents an initial increase of C in the restored soils
over the first 10 years (Table A-189). A PDF with a normal density is constructed from these data based on results from a
linear regression model. Following the initial increase of C, natural erosion and deposition leads to additional accretion of
C in these wetlands. The mass accumulation rate of organic C is estimated using annual sedimentation rates (cm/yr) in
combination with percent organic C, and soil bulk density (g/cm3) (Euliss and Gleason 2002). Procedures for calculation
of mass accumulation rate are described in Dean and Gorham (1998); the resulting rate and standard deviation are used
to construct a PDF with a normal density (Table A-189).
Table A-189: Rate and standard deviation for the Initial Increase and Subsequent Annual Mass Accumulation
Rate (Mg C/ha-yr) in Soil Organic C Following Wetland Restoration of Conservation Reserve Program
Variable
Value
Factor (Initial Increase—First 10 Years)
1.22±0.18
Mass Accumulation (After Initial 10 Years)
0.79±0.05
Note: Mass accumulation rate represents additional gains in C for mineral soils after the first 10 years (Euliss and Gleason
2002).
Estimate Annual Changes in Mineral Soil Organic C Stocks: In accordance with IPCC methodology, annual
changes in mineral soil organic C are calculated by subtracting the beginning stock from the ending stock and then
dividing by 20.127 For this analysis, stocks are estimated for each year and difference between years is the stock change.
From the final distribution of 1,000 values, the median is used as the estimate of soil organic C stock change and a 95
percent confidence interval is generated based on the simulated values at the 2.5 and 97.5 percentiles in the
distribution.
Soil organic C stock changes using the Tier 2 method are provided in Table A-192 and Table A-194.
Step 2e: Estimate Additional Changes in Soil Organic C Stocks Due to Biosolids (i.e., Treated Sewage Sludge)
Amendments
There are two additional land use and management activities occurring on mineral soils of U.S. agricultural
lands that are not estimated in Steps 2a and 2b. The first activity involves the application of biosolids to agricultural
lands. Minimal data exist on where and how much biosolids are applied to U.S. agricultural soils. However, national
estimates of mineral soil land area receiving biosolids can be approximated based on biosolids N production data, and
the assumption that amendments are applied at a rate equivalent to the assimilative capacity from Kellogg et al. (2000).
In this Inventory, it is assumed that biosolids for agricultural land application to soils is only used as an amendment in
grassland. The impact of organic amendments on soil organic C is calculated as 0.38 metric tonnes C/ha-yr. This rate is
based on the IPCC default method and country-specific factors, by calculating the effect of converting nominal, medium-
input grassland to high input improved grassland. The assumptions for this estimation are as follows: a) the reference C
stock is 50 metric tonnes C/ha, which represents a mid-range value of reference C stocks for the cropland soils in the
United States,128 b) the land use factor for grassland of 1.4 and 1.11 for high input improved grassland are representative
of typical conditions, and c) the change in stocks are occurring over a 20 year (default value) time period (i.e., [50 x 1.4 x
1.11 - 50 x 1.4] / 20 = 0.38). A ±50 percent uncertainty is attached to these estimates due to limited information on
application and the rate of change in soil organic C stock change with amendments of biosolids.
The influence of biosolids (i.e., treated sewage sludge) on soil organic C stocks is provided in Table A-194.
Table A-190: Direct Soil N2O Emissions from Mineral Soils in Cropland (MMT CO2 Eq.)
Land Use Change Category	1990 1991 1992 1993 1994 1995 1996 1997	1998	1999
Total Cropland Mineral Soil Emission 182.1 173.6 169.8 187.5 182.2 180.0 187.7	178.9	176.6	178.6
Tier 3 Cropland 165.0 157.5 152.8 170.7 163.9 161.5 169.0	160.2	158.6	160.7
Inorganic N Fertilizer Application 58.5 57.4 57.1 59.2 63.2 56.7 63.3	59.7	56.8	58.7
Managed Manure Additions 5.3 5.2 5.1 5.3 5.2 4.8 5.3	4.9	4.6	4.6
127	The difference in C stocks is divided by 20 because the stock change factors represent change over a 20-year time period.
128	Reference C stocks are based on cropland soils for the Tier 2 method applied in this Inventory.
A-378 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Crop Residue N
34.2
33.9
31.2
35.4
33.1
35.0
35.3
32.7
30.8
36.4
Min. SOM / Asymbiotic N-Fixationa
67.1
61.0
59.4
70.8
62.4
65.1
65.1
62.9
66.4
61.0
Tier 1 Cropland
17.1
16.2
17.0
16.8
18.4
18.5
18.7
18.7
18.0
17.9
Inorganic N Fertilizer Application
4.6
3.9
4.3
4.6
5.3
5.4
5.9
5.7
4.8
4.8
Managed Manure Additions
7.4
7.4
7.5
7.5
7.9
8.2
8.0
8.2
8.3
8.3
Other Organic Amendments'5
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.1
Crop Residue N
5.1
4.8
5.1
4.7
5.1
4.7
4.8
4.8
4.9
4.7
Implied Emission Factor for Croplandsc(kt
N2Q-N/kt N)	0.012 0.011 0.011 0.011 0.012 0.011 0.012 0.011 0.011 0.011
Land Use Change Category	2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Total Cropland Mineral Soil Emission
173.2
182.6
182.8
184.0
183.6
180.1
175.6
181.4
179.2
180.6
Tier 3 Cropland
155.3
164.7
164.2
164.4
163.2
161.0
156.4
162.1
160.0
163.2
Inorganic N Fertilizer Application
56.8
57.2
59.2
58.2
57.5
58.0
55.8
60.6
57.2
55.5
Managed Manure Additions
4.5
4.5
4.7
4.5
4.5
4.5
4.5
4.6
4.5
4.7
Crop Residue N
33.8
36.0
36.0
37.0
32.8
35.1
34.7
33.0
33.6
35.1
Min. SOM / Asymbiotic N-Fixationa
60.1
66.9
64.2
64.6
68.5
63.3
61.3
63.9
64.6
67.9
Tier 1 Cropland
18.0
17.9
18.7
19.6
20.4
19.1
19.2
19.3
19.2
17.4
Inorganic N Fertilizer Application
4.7
4.7
5.6
6.2
7.2
5.9
5.8
5.8
5.8
4.2
Managed Manure Additions
8.5
8.7
8.7
8.9
8.4
8.6
00
00
00
00
00
00
8.6
Other Organic Amendments'5
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.0
Crop Residue N
4.7
4.5
4.3
4.5
4.8
4.6
4.5
4.6
4.6
4.6
Implied Emission Factor for Croplandsc(kt
N2Q-N/kt N)	0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011
Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Cropland Mineral Soil Emission
182.4
180.4
172.8
193.9
203.6
196.2
187.5
187.1
191.7
193.0
Tier 3 Cropland
163.5
161.4
154.5
174.2
184.2
171.9
164.5
164.9
170.1
171.9
Inorganic N Fertilizer Application
54.8
58.8
61.1
62.9
64.2
54.7
60.4
60.5
62.4
63.1
Managed Manure Additions
4.5
5.0
5.0
5.3
5.2
4.0
5.0
5.0
5.2
5.2
Crop Residue N
35.5
36.6
34.5
35.5
37.7
34.3
35.2
35.3
36.4
36.8
Min. SOM / Asymbiotic N-Fixationa
68.7
61.0
53.9
70.5
77.1
78.9
64.0
64.1
66.1
66.8
Tier 1 Cropland
18.9
19.1
18.3
19.8
19.4
24.3
22.9
22.1
21.6
21.2
Inorganic N Fertilizer Application
5.8
6.4
5.6
7.0
6.3
10.1
8.5
8.0
7.7
7.4
Managed Manure Additions
8.5
8.4
8.4
8.2
8.4
9.4
9.5
9.3
9.1
9.0
Other Organic Amendments'5
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.0
Crop Residue N
4.6
4.3
4.3
4.5
4.7
4.7
4.9
4.8
4.8
4.8
Implied Emission Factor for Croplandsc(kt
N20-N/kt N)
0.010
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
0.011
Note: For most activity sources data were not available after 2015 and emissions were estimated with a
data splicing method. Additional activity data will be collected and the Tier 1, 2 and 3 methods will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data
splicing methods.
a Mineralization of soil organic matter and the asymbiotic fixation of nitrogen gas.
b Includes dried blood, tankage, compost, other. Excludes dried manure and bio-solids (i.e., treated sewage sludge) used as
commercial fertilizer to avoid double counting.
c Annual Implied Emission Factor (kt N20-N/kt N) is calculated by dividing total estimated emissions by total activity data for N
applied.
Table A-191: Direct Soil N2O Emissions from Mineral Soils in Grassland (MMT CO2 Eq.)
Land Use Change Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Grassland Mineral Soil Emission
84.1
84.2
83.3
84.5
80.5
83.4
85.5
86.3
87.4
82.1
Tier 3 Grassland
77.1
77.4
76.3
77.6
73.6
76.7
79.1
80.2
81.2
76.2
Inorganic N Fertilizer Application
0.0
0.0
0.1
0.1
0.1
0.0
0.0
0.0
0.2
0.0
Managed Manure Additions
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pasture, Range, & Paddock N Deposition
7.8
7.7
7.8
8.2
8.5
8.5
8.9
8.4
8.9
8.0
A-379
-------
Grass Residue N
29.7
29.0
28.8
29.5
28.1
29.5
30.0
29.8
29.4
30.2
Min. SOM / Asymbiotic N-Fixationa
39.5
40.7
39.6
39.8
36.9
38.8
40.1
41.9
42.7
37.9
Tier 1 Grassland
7.0
6.8
7.0
6.9
6.9
6.7
6.5
6.2
6.2
5.9
Pasture, Range, & Paddock N Deposition
6.8
6.5
6.7
6.6
6.6
6.4
6.1
5.8
5.8
5.5
Treated Sewage Sludge Additions
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.4
Implied Emission Factor for Grassland15 (kt
N2Q-N/kt N)	0.006 0.006 0.005 0.005 0.005 0.005 0.006 0.005 0.006 0.005
Land Use Change Category	2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Total Grassland Mineral Soil Emission
76.8
83.1
83.7
83.2
91.6
86.4
84.9
87.1
84.5
88.2
Tier 3 Grassland
71.0
77.4
78.1
77.7
86.0
80.8
79.4
81.8
79.2
83.0
Inorganic N Fertilizer Application
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.1
0.0
Managed Manure Additions
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pasture, Range, & Paddock N Deposition
7.9
8.4
8.6
8.3
8.6
8.3
8.5
8.2
8.1
8.5
Grass Residue N
28.0
30.2
30.2
30.1
30.2
30.8
30.3
30.6
30.3
30.5
Min. SOM / Asymbiotic N-Fixationa
35.0
38.7
39.3
39.2
47.1
41.7
40.5
43.0
40.7
43.9
Tier 1 Grassland
5.8
5.7
5.6
5.6
5.6
5.6
5.5
5.3
5.3
5.2
Pasture, Range, & Paddock N Deposition
5.5
5.3
5.2
5.1
5.1
5.1
5.1
4.8
4.8
4.7
Treated Sewage Sludge Additions
0.4
0.4
0.4
0.4
0.4
0.5
0.5
0.5
0.5
0.5
Implied Emission Factor for Grassland15 (kt
N2Q-N/kt N)	0.005 0.005 0.006 0.005 0.006 0.005 0.006 0.005 0.005 0.006
Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Grassland Mineral Soil Emission
90.4
80.9
76.2
92.0
93.3
93.0
88.1
87.4
88.4
91.5
Tier 3 Grassland
85.2
75.7
71.1
87.0
88.2
88.0
83.1
82.4
83.3
86.4
Inorganic N Fertilizer Application
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Managed Manure Additions
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pasture, Range, & Paddock N Deposition
8.4
8.0
7.3
8.1
8.2
8.4
8.4
8.3
8.4
8.7
Grass Residue N
31.5
29.6
29.4
31.2
31.8
30.4
31.5
31.2
31.6
32.8
Min. SOM / Asymbiotic N-Fixationa
45.2
38.1
34.4
47.6
48.2
49.2
43.2
42.8
43.3
44.9
Tier 1 Grassland
5.2
5.1
5.1
5.1
5.0
5.0
5.0
5.0
5.1
5.1
Pasture, Range, & Paddock N Deposition
4.7
4.6
4.6
4.5
4.5
4.4
4.4
4.4
4.4
4.4
Treated Sewage Sludge Additions
0.5
0.5
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.7
Implied Emission Factor for Grassland15 (kt
N20-N/kt N)
0.006
0.005
0.005
0.006
0.006
0.006
0.006
0.006
0.006
0.006
Note: For most activity sources data were not available after 2015 and emissions were estimated with a
data splicing method. Additional activity data will be collected and the Tier 1, 2 and 3 methods will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data
splicing methods.
a Mineralization of soil organic matter and the asymbiotic fixation of nitrogen gas.
b Annual Implied Emission Factor (kt N20-N/kt N) is calculated by dividing total estimated emissions by total activity data for N
applied.
Table A-192: Annual Change in Soil Organic Carbon Stocks in Croplands (MMT CO2 Eq./yr)
Land Use Change Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Cropland Soil Organic C Stock Change
-55.8
-60.3
-56.4
-43.4
-51.0
-39.6
-55.9
-44.5
-38.6
-40.9
Cropland Remaining Cropland (CRC)
-58.2
-63.3
-60.0
-45.8
-53.5
-46.1
-61.4
-53.1
-43.5
-46.0
Tier 2
-0.6
-1.5
-1.6
-1.4
-0.4
-0.6
-0.5
-1.8
-0.7
-1.9
Tier 3
-57.6
-61.7
-58.4
-44.4
-53.1
-45.5
-60.8
-51.3
-42.9
-44.1
Grassland Converted to Cropland (GCC)
4.1
4.9
5.8
4.7
4.8
8.9
8.0
11.3
7.6
7.9
Tier 2
3.9
4.2
4.0
4.0
4.3
4.7
5.0
5.0
5.1
5.0
Tier 3
0.2
0.7
1.8
0.7
0.6
4.2
2.9
6.3
2.5
2.9
Forest Converted to Cropland (FCC) (Tier 2










Only)
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.3
0.3
0.3
Other Lands Converted to Cropland (OCC)










(Tier 2 Only)
-2.3
-2.4
-2.5
-2.7
-2.9
-2.9
-3.0
-3.1
-3.1
-3.2
A-380 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Settlements Converted to Cropland (SCC)
(Tier 2 Only) -0.1 -0.1 -0.1 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2
Wetlands Converted to Cropland (WCC) (Tier
2 Only)	0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3
Land Use Change Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland Soil Organic C Stock Change
-47.0
-56.6
-63.6
-55.8
-58.6
-61.1
-58.3
-61.3
-52.7
-43.0
Cropland Remaining Cropland (CRC)
-51.6
-60.7
-65.4
-57.8
-59.9
-62.4
-58.5
-61.8
-55.4
-46.2
Tier 2
-0.9
-3.9
-5.6
-5.1
-4.9
-5.0
-4.5
-4.9
-4.7
-5.1
Tier 3
-50.7
-56.8
-59.8
-52.7
-55.0
-57.4
-53.9
-56.9
-50.7
-41.1
Grassland Converted to Cropland (GCC)
7.8
7.4
4.9
4.8
4.0
4.0
2.8
2.9
5.0
5.3
Tier 2
5.2
5.2
5.0
4.6
4.8
4.8
4.7
4.7
4.5
4.5
Tier 3
2.6
2.2
-0.1
0.2
-0.7
-0.8
-1.9
-1.8
0.4
0.8
Forest Converted to Cropland (FCC) (Tier 2










Only)
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
Other Lands Converted to Cropland (OCC)










(Tier 2 Only)
-3.6
-3.6
-3.4
-3.2
-3.1
-2.9
-2.9
-2.7
-2.5
-2.4
Settlements Converted to Cropland (SCC)










(Tier 2 Only)
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.1
Wetlands Converted to Cropland (WCC) (Tier










2 Only)
0.4
0.3
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.2

Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Cropland Soil Organic C Stock Change
-46.5
-62.7
-56.2
-43.5
-40.3
-39.9
-51.0
-51.7
-46.3
-44.4
Cropland Remaining Cropland (CRC)
-51.0
-64.1
-58.7
-46.6
-44.7
-44.9
-54.3
-55.1
-49.4
-47.4
Tier 2
-4.6
-5.2
-3.6
-5.6
-5.5
-6.2
-5.7
-5.4
-5.9
-5.9
Tier 3
-46.4
-58.9
-55.1
-41.0
-39.2
-38.8
-48.6
-49.6
-43.5
-41.5
Grassland Converted to Cropland (GCC)
6.7
3.7
4.5
5.2
6.2
6.9
5.2
5.4
5.1
5.1
Tier 2
4.5
4.6
4.7
4.4
4.3
4.2
4.2
4.3
4.3
4.3
Tier 3
2.2
-0.9
-0.1
0.8
1.9
2.7
1.0
1.1
0.9
0.8
Forest Converted to Cropland (FCC) (Tier 2










Only)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Other Lands Converted to Cropland (OCC)










(Tier 2 Only)
-2.4
-2.4
-2.3
-2.3
-2.0
-2.0
-2.1
-2.2
-2.2
-2.3
Settlements Converted to Cropland (SCC)










(Tier 2 Only)
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
Wetlands Converted to Cropland (WCC) (Tier










2 Only)
0.2
0.2
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Table A-193: Annual Change in Soil Organic Carbon Stocks in Grasslands (MMT CO2 Eq./yr)
Land Use Change Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Grassland Soil Organic C Stock Change
-25.6
-25.4
-23.5
-23.9
-42.7
-37.0
-41.7
-39.4
-52.0
-52.0
Grassland Remaining Grassland (GRG)
-2.2
-2.1
-0.5
2.3
-10.7
-2.5
-3.5
0.5
-5.5
-1.3
Tier 2
-0.2
-0.5
-1.1
-1.4
-1.5
-1.4
-0.7
-0.7
-1.5
-1.3
Tier 3
-1.4
-0.9
1.3
4.4
-8.5
-0.4
-2.0
2.1
-3.1
0.9
Treated Sewage Sludge Additions
-0.6
-0.6
-0.7
-0.7
-0.7
-0.8
-0.8
-0.9
-0.9
-0.9
Cropland Converted to Grassland (CCG)
-18.9
-18.7
-18.3
-18.5
-19.8
-19.8
-20.5
-20.1
-24.0
-24.7
Tier 2
-4.0
-3.9
-3.9
-4.3
-4.9
-4.8
-4.8
-4.8
-5.6
-5.9
Tier 3
-15.0
-14.8
-14.4
-14.2
-15.0
-14.9
-15.7
-15.3
-18.3
-18.8
Forest Converted to Grassland (FCG) (Tier 2










Only)
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
Other Lands Converted to Grassland (OCG)










(Tier 2 Only)
-4.2
-4.3
-4.5
-7.2
-11.4
-14.0
-16.7
-18.8
-21.4
-24.7
Settlements Converted to Grassland (SCG)










(Tier 2 Only)
-0.2
-0.2
-0.2
-0.3
-0.5
-0.7
-0.8
-0.9
-1.0
-1.2
Wetlands Converted to Grassland (WCG)










(Tier 2 Only)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
A-381
-------
Land Use Change Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Grassland Soil Organic C Stock Change
-69.9
-61.9
-63.7
-64.8
-58.7
-57.4
-71.2
-55.9
-59.9
-58.6
Grassland Remaining Grassland (GRG)
-13.9
-2.5
-4.0
-5.7
0.0
0.8
-12.0
2.2
-5.0
-3.9
Tier 2
-1.4
-1.5
-2.6
-2.6
-0.9
-1.1
-1.3
-1.4
-1.4
-1.6
Tier 3
-11.5
0.0
-0.4
-2.0
1.9
3.0
-9.6
4.8
-2.3
-1.0
Treated Sewage Sludge Additions
-1.0
-1.0
-1.0
-1.0
-1.1
-1.1
-1.2
-1.2
-1.2
-1.3
Cropland Converted to Grassland (CCG)
-26.4
-26.4
-26.8
-26.1
-25.7
-25.0
-26.0
-24.9
-21.7
-21.5
Tier 2
-6.1
-6.3
-6.2
-5.9
-5.8
-5.6
-5.4
-5.2
-5.0
-4.7
Tier 3
-20.3
-20.2
-20.6
-20.1
-19.9
-19.4
-20.6
-19.8
-16.7
-16.8
Forest Converted to Grassland (FCG) (Tier 2










Only)
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
Other Lands Converted to Grassland (OCG)










(Tier 2 Only)
-28.3
-31.4
-31.4
-31.6
-31.5
-31.7
-31.6
-31.7
-31.7
-31.8
Settlements Converted to Grassland (SCG)










(Tier 2 Only)
-1.3
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
Wetlands Converted to Grassland (WCG)










(Tier 2 Only)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Grassland Soil Organic C Stock Change
-43.0
-45.0
-58.1
-41.8
-32.5
-36.8
-40.0
-38.9
-38.2
-34.3
Grassland Remaining Grassland (GRG)
10.6
7.9
-6.3
6.4
10.0
4.0
0.7
2.2
2.7
5.5
Tier 2
-1.6
-1.5
-0.6
-0.2
1.1
0.1
-0.2
-0.1
0.0
0.0
Tier 3
13.5
10.8
-4.3
8.0
10.3
5.4
2.3
2.5
2.9
5.7
Treated Sewage Sludge Additions
-1.3
-1.3
-1.4
-1.4
-1.4
-1.5
-1.5
-0.2
-0.2
-0.2
Cropland Converted to Grassland (CCG)
-20.3
-19.4
-18.3
-17.5
-15.9
-16.9
-21.3
-21.6
-21.6
-21.1
Tier 2
-4.6
-4.6
-4.5
-4.1
-3.5
-3.4
-5.7
-5.8
-6.0
-6.1
Tier 3
-15.7
-14.8
-13.8
-13.3
-12.4
-13.4
-15.6
-15.8
-15.6
-15.0
Forest Converted to Grassland (FCG) (Tier 2










Only)
-0.1
-0.1
-0.1
-0.1
0.0
-0.1
-0.2
-0.2
-0.2
-0.2
Other Lands Converted to Grassland (OCG)










(Tier 2 Only)
-31.8
-32.1
-32.0
-29.5
-25.6
-22.9
-18.4
-18.4
-18.2
-17.7
Settlements Converted to Grassland (SCG)










(Tier 2 Only)
-1.4
-1.4
-1.4
-1.3
-1.1
-1.0
-0.9
-0.9
-0.9
-0.8
Wetlands Converted to Grassland (WCG)










(Tier 2 Only)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Table A-194: Methane Emissions from Rice Cultivation (MMT CO2 Eg.)
Approach
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Rice Methane Emission
Tier 1
Tier 3
16.0
2.2
13.8
16.1
2.3
13.9
16.1
2.4
13.8
17.1
2.4
14.7
15.7
2.5
13.2
16.5
2.3
14.2
16.7
2.4
14.3
15.4
2.3
13.1
17.1
2.7
14.4
17.7
4.2
13.5

Approach
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Rice Methane Emission
Tier 1
Tier 3
19.0
4.4
14.6
15.4
2.8
12.6
17.7
2.5
15.2
14.7
2.4
12.3
15.6
2.4
13.2
18.0
2.2
15.8
14.7
1.9
12.8
15.9
2.2
13.8
14.1
1.8
12.2
16.2
2.5
13.7

Approach
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Rice Methane Emission
Tier 1
Tier 3
18.9
2.4
16.5
15.3
2.1
13.2
15.2
2.8
12.4
13.8
2.1
11.7
15.4
3.4
12.0
16.2
2.4
13.8
15.8
2.4
13.4
14.9
2.5
12.4
15.6
2.5
13.1
15.1
2.5
12.5

A-382 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Note: Estimates after 2015 are based on a data splicing method (See Rice Cultivation section for more information). The Tier 1
and 3 methods will be applied in a future inventory to recalculate the part of the time series that is estimated with data
splicing.
Step 3: Estimate Soil Organic C Stock Changes and Direct N20 Emissions from Organic Soils
In this step, soil organic C losses and N20 emissions are estimated for organic soils that are drained for
agricultural production.
Step 3a: Direct N2O Emissions Due to Drainage of Organic Soils in Cropland and Grassland
To estimate annual N20 emissions from drainage of organic soils in cropland and grassland, the area of drained
organic soils in croplands and grasslands for temperate regions is multiplied by the IPCC (2006) default emission factor
for temperate soils and the corresponding area in sub-tropical regions is multiplied by the average (12 kg N20-N/ha
cultivated) of IPCC (2006) default emission factors for temperate (8 kg N20-N/ha cultivated) and tropical (16 kg N20-N/ha
cultivated) organic soils. The uncertainty is determined based on simple error propagation methods (IPCC 2006),
including uncertainty in the default emission factor ranging from 2-24 kg N20-N/ha (IPCC 2006). Table A-195 lists the
direct N20 emissions associated with drainage of organic soils in cropland and grassland.
Table A-195: Direct Soil N2O Emissions from Drainage of Organic Soils (MMT CO2 Eq.)
Land Use
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Organic Soil Emissions
6.3
6.2
6.2
6.3
6.3
6.3
6.3
6.2
6.2
6.2
Cropland
3.8
3.8
3.7
3.7
3.7
3.8
3.8
3.7
3.7
3.7
Grassland
2.5
2.5
2.5
2.5
2.6
2.5
2.5
2.5
2.5
2.5
Land Use
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Organic Soil Emission
6.2
6.2
6.2
6.1
6.1
6.1
6.1
6.0
6.0
6.0
Cropland
3.7
3.8
3.8
3.7
3.7
3.7
3.7
3.6
3.6
3.5
Grassland
2.5
2.4
2.4
2.3
2.4
2.4
2.4
2.4
2.4
2.5
Land Use
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Organic Soil Emission
6.0
6.0
6.0
5.9
5.9
5.9
5.9
5.9
5.9
5.9
Cropland
3.5
3.5
3.5
3.5
3.4
3.4
3.4
3.4
3.4
3.4
Grassland
2.5
2.5
2.5
2.5
2.5
2.5
2.5
2.5
2.5
2.5
Step 3b: Soil Organic C Stock Changes Due to Drainage of Organic Soils in Cropland and Grassland
Change in soil organic C stocks due to drainage of organic soils in cropland and grassland are estimated annually
from 1990 through 2015, based on the land-use and management activity data in conjunction with appropriate emission
factors. The activity data are based on annual data from 1990 through 2015 from the NRI. Organic soil emission factors
that are representative of U.S. conditions have been estimated from published studies (Ogle et al. 2003), based on
subsidence studies in the United States and Canada (Table A-196). PDFs are constructed as normal densities based on
the mean C loss rates and associated variances. Input values are randomly selected from PDFs in a Monte Carlo analysis
to estimate soil organic C change for 1,000 iterations and produce a 95 percent confidence interval for the inventory
results. Losses of soil organic C from drainage of cropland and grassland soils are provided in Table A-197 for croplands
and Table A-198 for grasslands.
Table A-196: Carbon Loss Rates for Organic Soils Under Agricultural Management in the United States, and IPCC
Default Rates (Metric Ton C/ha-yr)	


Cropland

Grassland
Region
IPCC
U.S. Revised
IPCC
U.S. Revised
Cold Temperate, Dry & Cold Temperate, Moist
1
11.2±2.5
0.25
2.8±0.5a
Warm Temperate, Dry & Warm Temperate, Moist
10
14.0±2.5
2.5
3.5±0.8a
Sub-Tropical, Dry & Sub-Tropical, Moist
1
14.3±2.5
0.25
2.8±0.5a
a There are not enough data available to estimate a U.S. value for C losses from grassland. Consequently, estimates are 25
percent of the values for cropland, which is an assumption that is used for the IPCC default organic soil C losses on grassland
A-383
-------
Table A-197: Soil Organic Carbon Stock Changes due to Drainage of Organic Soils in Cropland (MMT CO2 Eq.)
Land Use Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Cropland Soil Organic C Stock Change
38.6
38.0
38.1
38.3
38.5
38.6
38.5
38.5
38.5
32.9
Cropland Remaining Cropland (CRC)
35.0
34.2
34.5
34.2
34.2
34.1
33.9
34.0
33.6
28.0
Grassland Converted to Cropland (GCC)
2.7
2.8
2.8
3.1
3.2
3.5
3.5
3.4
3.8
3.8
Forest Converted to Cropland (FCC)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Other Lands Converted to Cropland (OCC)
0.2
0.2
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Settlements Converted to Cropland (SCC)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wetlands Converted to Cropland (WCC)
0.6
0.6
0.6
0.7
0.8
0.9
0.9
0.9
0.9
0.9
Land Use Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland Soil Organic C Stock Change
32.5
39.0
38.8
38.6
38.1
37.7
37.5
36.7
36.4
36.0
Cropland Remaining Cropland (CRC)
27.9
33.5
33.5
33.7
33.8
33.4
33.2
32.6
32.4
32.2
Grassland Converted to Cropland (GCC)
3.6
4.5
4.5
4.1
3.6
3.5
3.5
3.3
3.4
3.1
Forest Converted to Cropland (FCC)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
Other Lands Converted to Cropland (OCC)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Settlements Converted to Cropland (SCC)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wetlands Converted to Cropland (WCC)
0.7
0.7
0.6
0.5
0.6
0.6
0.6
0.6
0.6
0.5
Land Use Category	2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Total Cropland Soil Organic C Stock Change
36.1
36.1
36.2
35.3
36.3
35.8
35.2
36.5
36.5
36.6
Cropland Remaining Cropland (CRC)
32.3
32.4
32.3
31.3
32.5
32.1
31.6
32.8
32.8
32.9
Grassland Converted to Cropland (GCC)
3.1
3.1
3.4
3.5
3.4
3.3
3.3
3.3
3.3
3.3
Forest Converted to Cropland (FCC)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Other Lands Converted to Cropland (OCC)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Settlements Converted to Cropland (SCC)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wetlands Converted to Cropland (WCC)
0.6
0.6
0.5
0.5
0.3
0.3
0.3
0.3
0.4
0.4
Table A-198: Soil Organic Carbon Stock Changes due to Drainage of Organic Soils in Grasslands (MMT CO2 Eq)
Land Use Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Grassland Soil Organic C Stock Change
7.1
7.0
7.1
7.1
7.2
7.1
7.0
7.0
7.0
7.1
Grassland Remaining Grassland (GRG)
6.3
6.2
6.2
6.1
6.1
6.0
6.0
5.9
5.7
5.7
Cropland Converted to Grassland (CCG)
0.6
0.6
0.7
0.8
0.9
0.9
0.8
0.8
1.0
1.0
Forest Converted to Grassland (FCG)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
Other Lands Converted to Grassland (OCG)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Settlements Converted to Grassland (SCG)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wetlands Converted to Grassland (WCG)
0.1
0.1
0.1
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Land Use Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Grassland Soil Organic C Stock Change
7.1
7.0
7.1
6.9
7.1
7.1
7.1
7.1
7.1
7.3
Grassland Remaining Grassland (GRG)
5.6
5.3
5.3
5.2
5.2
5.2
5.2
5.2
5.3
5.3
Cropland Converted to Grassland (CCG)
1.1
1.2
1.4
1.3
1.5
1.5
1.4
1.4
1.3
1.5
Forest Converted to Grassland (FCG)
0.1
0.1
0.1
0.1
0.2
0.2
0.2
0.2
0.2
0.2
Other Lands Converted to Grassland (OCG)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Settlements Converted to Grassland (SCG)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wetlands Converted to Grassland (WCG)
0.3
0.3
0.2
0.2
0.2
0.2
0.3
0.3
0.3
0.3
Land Use Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Grassland Soil Organic C Stock Change
7.3
7.3
7.3
7.3
7.3
7.3
7.3
7.3
7.2
7.2
Grassland Remaining Grassland (GRG)
5.3
5.3
5.3
5.3
5.5
5.4
5.4
5.4
5.4
5.4
Cropland Converted to Grassland (CCG)
1.5
1.4
1.4
1.4
1.3
1.4
1.4
1.4
1.3
1.3
Forest Converted to Grassland (FCG)
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
A-384 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Other Lands Converted to Grassland (OCG) 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Settlements Converted to Grassland (SCG) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Wetlands Converted to Grassland (WCG)	0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.2 0.2 0.2
Step 4: Estimate Indirect Soil N20 Emissions for Croplands and Grasslands
In this step, soil N20 emissions are estimated for the two indirect emission pathways (N20 emissions due to
volatilization, and N20 emissions due to leaching and runoff of N), which are summed to yield total indirect N20
emissions from croplands and grasslands.
Step 4a: Indirect Soil N2O Emissions Due to Volatilization
Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and PRP
manure, are calculated according to the amount of mineral N that is volatilized from the soil profile and later emitted as
soil N20 following atmospheric deposition. See Step le for additional information about the methods used to compute N
losses due to volatilization. The estimated N volatilized is multiplied by the IPCC default emission factor of 0.01 kg N20-
N/kg N (IPCC 2006) to estimate total indirect soil N20 emissions from volatilization. The uncertainty is estimated using
simple error propagation methods (IPCC 2006), by combining uncertainties in the amount of N volatilized, with
uncertainty in the default emission factor ranging from 0.002-0.05 kg N20-N/kg N (IPCC 2006). The estimates and
implied emission factors are provided in Table A-190 for cropland and in Table A-191 for grassland.
Step 4b: Indirect Soil N2O Emissions Due to Leaching and Runoff
The amounts of mineral N from synthetic fertilizers, commercial organic fertilizers, PRP manure, crop residue, N
mineralization, asymbiotic fixation that is transported from the soil profile in water flows are used to calculate indirect
emissions from leaching of mineral N from soils and losses in runoff associated with overland flow. See Step le for
additional information about the methods used to compute N losses from soils due to leaching and runoff in overland
water flows. The total amount of N transported from soil profiles through leaching and surface runoff is multiplied by the
IPCC default emission factor of 0.0075 kg N20-N/kg N (IPCC 2006) to estimate emissions for this source. The uncertainty
is estimated based on simple error propagation methods (IPCC 2006), including uncertainty in the default emission factor
ranging from 0.0005 to 0.025 kg N20-N/kg N (IPCC 2006). The emission estimates are provided in Table A-199 and Table
A-200 including the implied Tier 3 emission factors.
Table A-199: Indirect Soil N2O Emissions for Cropland from Volatilization and Atmospheric Deposition, and from
Leaching and Runoff (MMT CO2 Eq.)
Source
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Cropland Indirect Emissions
Volatilization & Atmospheric Deposition
Leaching & Runoff
34.2
6.5
27.7
31.5
6.3
25.3
33.7
6.1
27.7
37.9
6.4
31.5
29.3
6.6
22.7
34.1
6.7
27.4
33.7
6.7
27.0
32.2
6.7
25.5
36.3
6.9
29.3
32.7
6.9
25.8
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075

Source
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland Indirect Emissions
Volatilization & Atmospheric Deposition
Leaching & Runoff
30.1
7.0
23.1
35.0
7.0
28.0
32.1
7.2
24.8
33.3
7.2
26.1
36.6
7.4
29.2
31.6
7.3
24.4
33.1
7.2
25.8
35.0
7.2
27.8
36.6
7.2
29.3
35.8
7.1
28.7
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075

Source
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Cropland Indirect Emissions
Volatilization & Atmospheric Deposition
Leaching & Runoff
36.1
7.6
28.5
35.2
7.3
28.0
28.6
6.9
21.6
37.9
7.7
30.2
37.6
8.0
29.6
42.7
8.5
34.2
38.8
8.1
30.7
37.4
7.9
29.5
42.3
8.0
34.4
43.8
7.9
35.9
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075
A-385
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Note: Estimates after 2015 are based on a data splicing method (See the Agricultural Soil Management section for more information).
The Tier 1 and 3 methods will be applied in a future inventory to recalculate the part of the time series that is estimated with the data
splicing methods.
Table A-200: Indirect Soil N2O Emissions for Grassland from Volatilization and Atmospheric Deposition, and
from Leaching and Runoff (MMT CO2 Eq.)	
Source
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Grassland Indirect Emissions
9.2
9.1
9.4
9.7
9.0
9.3
9.1
9.5
10.5
9.3
Volatilization & Atmospheric Deposition
3.6
3.5
3.6
3.5
3.5
3.5
3.6
3.6
3.7
3.5
Leaching & Runoff
5.6
5.5
5.8
6.3
5.6
5.8
5.5
5.9
6.8
5.8
Volatilization Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
Leaching & Runoff Implied Emission Factor
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075

Source
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Grassland Indirect Emissions
8.2
9.8
9.5
9.0
10.5
9.2
9.1
10.0
9.9
10.2
Volatilization & Atmospheric Deposition
3.2
3.4
3.5
3.5
3.8
3.6
3.6
3.6
3.5
3.5
Leaching & Runoff
5.0
6.4
6.0
5.5
6.7
5.6
5.5
6.5
6.4
6.7
Volatilization Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
Leaching & Runoff Implied Emission Factor
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075

Source
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Grassland Indirect Emissions
9.8
9.5
8.8
10.2
9.3
10.8
9.9
9.8
10.0
10.4
Volatilization & Atmospheric Deposition
3.6
3.2
3.3
3.7
3.7
3.7
3.5
3.5
3.5
3.6
Leaching & Runoff
6.2
6.2
5.6
6.5
5.6
7.2
6.4
6.3
6.4
6.8
Volatilization Implied Emission Factor	0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
Leaching & Runoff Implied Emission Factor	0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075
Note: Estimates after 2015 are based on a data splicing method (See the Agricultural Soil Management section for more
information). The Tier 1 and 3 methods will be applied in a future inventory to recalculate the part of the time series that is
estimated with the data splicing methods.
Step 5: Estimate Total Emissions for U.S. Agricultural Soils
Total N20 emissions are estimated by summing total direct and indirect emissions for croplands and grasslands
(both organic and mineral soils). Total soil organic C stock changes for cropland (Cropland Remaining Cropland and Land
Converted to Cropland) and grassland (Grassland Remaining Grassland and Land Converted to Grassland) are summed to
determine the total change in soil organic C stocks (both organic and mineral soils). Total rice CH4 emissions are
estimated by summing results from the Tier 1 and 3 methods. The results are provided in Figure A-7. In general, N20
emissions from agricultural soil management have been increasing slightly from 1990 to 2019, while CH4 emissions from
rice cultivation have been relatively stable. Agricultural soil organic C stocks have increased for most years in croplands
and grasslands leading to sequestration of C in soils, with larger increases in grassland soils.
A-386 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Figure A-7: GHG Emissions and Removals for Cropland & Grassland
300
250 -
200 -
150 -
100 -
50 -
0 -
-50
-100 H
Cropland SOC
Grassland SOC
Cropland Soil N20
Rice Cultivation CH,
X —¦
•		 /

/		
1990
1995
2000
2005
Years
2010
2015
Direct and indirect simulated emissions of soil N20 vary regionally in croplands and grasslands as a function of N
input, other management practices, weather, and soil type. The highest total N20 emissions for 2Q15129 occur in Iowa,
Illinois, Kansas, Minnesota, Missouri, Montana, Nebraska, South Dakota, and Texas (Table A-201). These areas are used
to grow corn or have extensive areas of grazing with large amounts of PRP manure N inputs. Note that there are other
management practices, such as fertilizer formulation (Halvorson et al, 2013), that influence emissions but are not
represented in the model simulations. The states with largest increases in soil organic C stocks in 2015 include Illinois,
Iowa, Missouri, Nebraska, North Dakota (Table A-201). These states tend to have larger amounts of land conversion to
grassland and/or more conservation practices such as enrollment in Conservation Reserve Program or adoption of
conservation tillage. For rice cultivation, the states with highest CH4 emissions are Arkansas, California, Louisiana and
Texas (Table A-201). These states also have the largest areas of rice cultivation, and Louisiana and Texas have a relatively
large proportion of fields with a second ratoon crop each year. Ratoon crops extend the period of flooding, and with the
residues left from the initial rice crop, there are additional CH4 emissions compared to non-ratoon rice management
systems.
129 The emissions data at the state scale is available for 1990 to 2015, but data splicing methods have been applied at national
scales to estimate emissions for most emission sub-source categories for 2016 to 2019. Therefore, the final year of emissions
data at the state scale is 2015.
A-387
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Table A-201: Total Soil N2O Emissions (Direct and Indirect), Soil Organic C Stock Changes and Rice CH4 Emissions
from Agricultural Lands by State in 2015 (MMT CO2 Eq.)	

N?0 Emissions3
Soil Organic C Stock Change
Rice
Total
State
Croplands
Grasslands
Croplands
Grasslands
ch4
Emissions
AL
1.34
1.15
-0.39
-1.00
0.00
1.10
AR
5.30
1.37
-0.65
-0.72
6.39
11.69
AZ
0.24
3.82
0.16
-0.27
0.00
3.95
CA
1.08
2.07
0.45
-3.57
4.14
4.17
CO
3.38
4.37
0.06
-2.24
0.00
5.57
CT
0.06
0.02
-0.05
-0.05
0.00
-0.02
DE
0.17
0.02
-0.04
-0.03
0.00
0.12
FL
0.25
1.68
11.88
0.16
0.00
13.97
GA
1.83
0.82
0.35
-0.55
0.00
2.45
Hlb
NE
NE
0.29
0.53
0.00
0.82
IA
21.23
2.14
-3.83
-1.15
0.00
18.39
ID
2.04
1.01
-0.25
-2.05
0.00
0.76
IL
18.43
0.93
-6.23
-0.65
0.00
12.48
IN
9.02
0.61
0.51
-0.52
0.00
9.63
KS
16.28
4.98
-0.77
-1.30
0.00
19.19
KY
3.66
2.28
-0.30
-0.76
0.00
4.88
LA
3.32
0.92
-0.85
-0.55
2.57
5.41
MA
0.08
0.03
0.21
-0.02
0.00
0.30
MD
0.73
0.16
-0.04
-0.11
0.00
0.74
ME
0.16
0.07
-0.12
0.02
0.00
0.13
Ml
3.73
0.70
2.50
-0.25
0.00
6.68
MN
13.26
1.39
5.75
1.18
0.01
21.60
MO
10.71
3.48
-2.93
-0.85
0.00
10.41
MS
3.50
0.84
-1.04
-0.73
1.00
3.57
MT
6.43
6.74
-1.52
1.27
0.00
12.91
NC
2.09
0.60
1.95
-0.63
0.00
4.01
ND
7.80
2.04
-3.12
-1.70
0.00
5.02
NE
13.18
4.94
-2.87
-1.15
0.00
14.10
NH
0.06
0.03
-0.04
0.01
0.00
0.05
NJ
0.14
0.04
-0.01
-0.07
0.00
0.11
NM
0.55
6.63
0.02
2.95
0.00
10.16
NV
0.20
1.10
-0.03
-1.37
0.00
-0.10
NY
2.27
1.04
-0.91
-0.13
0.00
2.28
OH
7.25
0.72
-1.79
-0.84
0.00
5.34
OK
4.56
5.26
0.55
-1.39
0.00
8.98
OR
0.96
1.11
-0.07
-1.65
0.00
0.35
PA
2.70
0.67
-1.33
-0.77
0.00
1.27
Rl
0.01
0.01
0.02
-0.01
0.00
0.03
SC
1.09
0.37
-0.18
-0.37
0.00
0.90
SD
10.84
4.66
-1.99
-0.89
0.00
12.62
TN
2.60
1.67
-0.63
-0.60
0.00
3.04
TX
13.66
16.72
2.10
-1.11
1.43
32.80
UT
0.60
1.26
0.22
-3.72
0.00
-1.65
VA
1.43
1.26
-0.73
-0.42
0.00
1.54
VT
0.35
0.16
-0.11
0.01
0.00
0.42
WA
1.69
0.70
-0.03
0.01
0.00
2.37
Wl
5.98
1.18
2.18
0.24
0.00
9.58
WV
0.24
0.48
-0.30
-0.29
0.00
0.12
WY
0.77
3.79
-0.22
0.03
0.00
4.38
a This table only includes N20 emissions estimated by DayCent using the Tier 3 method.
b N20 emissions are not reported for Hawaii except from cropland organic soils, which
are estimated with the Tier 1 method and therefore not included in this table.
A-388 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Tier 3 Model Description. Parameterization and Evaluation
The DayCent ecosystem model (Parton et al. 1998; Del Grosso et al. 2001, 2011) simulates biogeochemical C
and N fluxes between the atmosphere, vegetation, and soil. The model provides a more complete estimation of soil
organic C stock changes, CH4 and N20 emissions than IPCC Tier 1 or 2 methods by accounting for a broader suite of
environmental drivers that influence emissions and C stock changes. These drivers include soil characteristics, weather
patterns, crop and forage characteristics, and management practices. The DayCent model utilizes the soil C modeling
framework developed in the Century model (Parton et al. 1987,1988,1994; Metherell et al. 1993), but has been refined
to simulate dynamics at a daily time-step. Carbon and N dynamics are linked in plant-soil systems through
biogeochemical processes of microbial decomposition and plant production (McGill and Cole 1981). Coupling the three
source categories (i.e., agricultural soil organic C, rice CH4 and soil N20) in a single inventory analysis ensures that there is
a consistent treatment of the processes and interactions between C and N cycling in soils, and ensuring conservation of
mass. For example, plant growth is controlled by nutrient availability, water, and temperature stress. Plant growth, along
with residue management, determines C inputs to soils and influences C stock changes. Removal of soil mineral N by
microbial organisms influences the amount of production and C inputs, while plant uptake of N influence availability of N
for microbial processes of nitrification and denitrification that generate N20 emissions. Nutrient supply is a function of
external nutrient additions as well as litter and soil organic matter (SOM) decomposition rates, and increasing
decomposition can lead to a reduction in soil organic C stocks due to microbial respiration, and greater N20 emissions by
enhancing mineral N availability in soils.
The DayCent process-based simulation model (daily time-step version of the Century model) has been selected
for the Tier 3 approach based on the following criteria:
1)	The model has been developed in the United States and extensively tested for U.S. conditions (e.g., Parton et al.
1987,1993). In addition, the model has been widely used by researchers and agencies in many other parts of
the world for simulating soil C dynamics at local, regional and national scales (e.g., Brazil, Canada, India, Jordan,
Kenya, Mexico), soil N20 emissions (e.g., Canada, China, Ireland, New Zealand) (Abdalla et al. 2010; Li et al.
2005; Smith et al. 2008; Stehfest and Muller 2004; Cheng et al. 2014), and CH4 emissions (Cheng et al. 2013).
2)	The model is designed to simulate management practices that influence soil C dynamics, CH4 emissions and
direct N20 emissions, with the exception of cultivated organic soils; cobbly, gravelly, or shaley soils; and crops
that have not been parameterized for DayCent simulations (e.g., some vegetables, perennial/horticultural
crops, and crops that are rotated with these crops). For these latter cases, an IPCC Tier 2 method has been used
to estimate soil organic C stock changes, and IPCC Tier 1 method is used to estimate CH4 and N20 emissions.
The model can also be used to estimate the amount of nitrate leaching and runoff, as well as volatilization of
ammonia and nitrogen oxides, which are subject to indirect N20 emissions.
3)	Much of the data needed for the model is available from existing national databases. The exceptions are
management of federal grasslands and amendments of biosolids (i.e., treated sewage sludge) to soils, which
are not known at a sufficient resolution or detail to use the Tier 3 model. Soil N20 emissions and C stock
changes associated with these practices are addressed with Tier 1 and 2 methods, respectively.
DayCent Model Description
Key processes simulated by DayCent include (1) plant growth; (2) organic matter formation and decomposition;
(3) soil water and temperature regimes by layer; (4) nitrification and denitrification processes; and (5) methanogenesis
(Figure A-8). Each submodel is described below.
1) The plant-growth submodel simulates C assimilation through photosynthesis; N uptake; dry matter production;
partitioning of C within the crop or forage; senescence; and mortality. The primary function of the growth
submodel is to estimate the amount, type, and timing of organic matter inputs to soil, and to represent the
influence of the plant on soil water, temperature, and N balance. Yield and removal of harvested biomass are
also simulated. Separate submodels are designed to simulate herbaceous plants (i.e., agricultural crops and
grasses) and woody vegetation (i.e., trees and scrub). Maximum daily net primary production (NPP) is
estimated using the NASA-CASA production algorithm (Potter et al.1993, 2007) and MODIS Enhanced
Vegetation Index (EVI) products, MOD13Q1 and MYD13Q1. The NASA-CASA production algorithm is only used
for the following major crops: corn, soybeans, sorghum, cotton, wheat, and other close-grown crops such as
A-389
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barley arid oats.130 Other regions and crops are simulated with a single value for the maximum daily NPP,
instead of the more dynamic NASA-CASA algorithm. The maximum daily NPP rate is modified by air
temperature and available water to capture temperature and moisture stress. If the NASA-CASA algorithm is
not used in the simulation, then production is further subject to nutrient limitations (i.e., nitrogen). Model
evaluation has shown that the NASA-CASA algorithm improves the precision of NPP estimates by using the EVI
products to inform the production model. The r2 is 83 percent for the NASA-CASA algorithm and 64 percent for
the single parameter value approach. See Figure A-9.
Figure A-8: DayCent Model Flow Diagram
2)
3)
Dissolved Organic C, Dissolved Organic N, Mineral N
Dynamics of soil organic C and N (Figure A-8) are simulated for the surface and belowground litter pools and
soil organic matter in the top 30 cm of the soil profile; mineral N dynamics are simulated through the whole soil
profile. Organic C and N stocks are represented by two plant litter pools (metabolic and structural) and three
soil organic matter (SOM) pools (active, slow, and passive). The metabolic litter pool represents the easily
decomposable constituents of plant residues, while the structural litter pool is composed of more recalcitrant,
ligno-cellulose plant materials. The three SOM pools represent a gradient in decomposability, from active SOM
(representing microbial biomass and associated metabolites) having a rapid turnover (months to years), to
passive SOM (representing highly processed, humified, condensed decomposition products), which is highly
recalcitrant, with mean residence times on the order of several hundred years. The slow pool represents
decomposition products of intermediate stability, having a mean residence time on the order of decades and is
the fraction that tends to be influenced the most by land use and management activity. Soil texture influences
turnover rates of the slow and passive pools. The clay and silt-sized mineral fraction of the soil provides physical
protection from microbial decomposition, leading to enhanced SOM stabilization in finely textured soils. Soil
temperature and moisture, tillage disturbance, aeration, and other factors influence decomposition and loss of
C from the soil organic matter pools.
The soil-water submodel simulates water flows and changes in soil water availability, which influences both
plant growth, decomposition and nutrient cycling. Soil moisture content is simulated through a multi-layer
130 It is a planned improvement to estimate NPP for additional crops and grass forage with the NASA-CASA method in the
future.
A-390 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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profile based on precipitation, snow accumulation and melting, interception, soil and canopy evaporation,
transpiration, soil water movement, runoff, and drainage.
Figure A-9: Modeled versus measured net primary production
a*
sua •

700 |


- OKVJ
oOC 1

HOO 1

JOC -

300 I
*

* V
200 -1
U'? 1


* • I
•ivr^
«w>
'fWv
o . * i
J 10P 200 iuo 400 MH> 601! /d!
Yield Carbon from Published Data (g m*2)


«
§_
feCG
1 * • # i


i • %
»•

• ? *¦
• «
4 DC
. *# *"¦
«
1CU
i* * m
+ • • •

.Vo
" *.K ">

ioo

• •
o -

"J iro 200 303 4,1,1	fO'J 'Of. ,->lY
Yield Carbon from Published Data (g m"J)
Part a) presents results of the NASA-CASA algorithm (r2 = 83°4 and part b) presents the results of a single parameter
value for maximum net primary production (r2 = 64°XJ.
4)	Soil mineral N dynamics are modeled based on N inputs from fertilizer inputs (synthetic and organic), residue N
inputs, soil organic matter mineralization in addition to symbiotic and asymbiotic N fixation. Mineral N is
available for plant and microbial uptake and is largely controlled by the specified stoichiometric limits for these
organisms (i.e., C:N ratios). Mineral and organic N losses are simulated with leaching and runoff, and nitrogen
can be volatilized and lost from the soil through ammonia volatilization, nitrification and denitrification. Soil
N20 emissions occur through nitrification and denitrification. Denitrification is a function of soil N03"
concentration, water filled pore space (WFPS), heterotrophic (i.e., microbial) respiration, and texture.
Nitrification is controlled by soil ammonium (NH4+) concentration, water filled pore space, temperature, and pH
(See Box A-2 for more information).
5)	Methanogenesis is modeled under anaerobic conditions and is controlled by carbon substrate availability,
temperature, and redox potential (Cheng et al. 2013). Carbon substrate supply is determined by decomposition
of residues and soil organic matter, in addition to root exudation. The transport of CH4 to the atmosphere
occurs through the rice plant and via ebullition (i.e., bubbles). CH4 can be oxidized (methanotrophy) as it moves
through a flooded soil and the oxidation rates are higher as the plants mature and in soils with more clay (Sass
et al. 1994).
The model allows for a variety of management options to be simulated, including different crop types, crop
sequences (e.g., rotation), cover crops, tillage practices, fertilization, organic matter addition (e.g., manure
amendments), harvest events (with variable residue removal), drainage, flooding, irrigation, burning, and grazing
intensity. An input "schedule" file is used to simulate the timing of management activities and temporal trends;
schedules can be organized into discrete time blocks to define a repeated sequence of events (e.g., a crop rotation or a
frequency of disturbance such as a burning cycle for perennial grassland). Management options can be specified for any
A-391
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day of a year within a scheduling block, where management codes point to operation-specific parameter files (referred
to as *.100 files), which contain the information used to simulate management effects. 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. (1987,1988,1994,1998), Del Grosso et al. (2001, 2011), Cheng et al. (2013) and
Metherell et al. (1993), and archived copies of the model source code are available.
Box A-2: DayCent Model Simulation of N Gas losses and Nitrate Leaching
The DayCent model simulates the two biogeochemical processes, nitrification and denitrification, that result
in N20 and NOx emissions from soils (Del Grosso et al. 2000, Parton et al. 2001). Nitrification is calculated for the top
15 cm of soil (where nitrification mostly occurs) while denitrification is calculated for the entire soil profile (accounting
for denitrification near the surface and subsurface as nitrate leaches through the profile). The equations and key
parameters controlling N20 emissions from nitrification and denitrification are described below.
Nitrification is controlled by soil ammonium (NH4+) concentration, temperature (t), Water Filled Pore Space
(WFPS) and pH according to the following equation:
Nit = NH4+ x Kmax x F(t) x F(WFPS) x F(pH)
where,

Nit
the
NH4+
the
Kmax —
the
F(t)
the
F(WFPS) =
the
F(pH)
the
= 0.10/day)
The current parameterization used in the model assumes that 1.2 percent of nitrified N is converted to N20.
The model assumes that denitrification rates are controlled by the availability of soil N03" (electron
acceptor), labile C compounds (electron donor) and oxygen (competing electron acceptor). Heterotrophic soil
respiration is used as a proxy for labile C availability, while oxygen availability is a function of soil physical properties
that influence gas diffusivity, soil WFPS, and oxygen demand. The model selects the minimum of the N03" and C02
functions to establish a maximum potential denitrification rate. These rates vary for particular levels of electron
acceptor and C substrate, and account for limitations of oxygen availability to estimate daily denitrification rates
according to the following equation:
where,
Den
F(NOs)
F(C02)
F(WFPS)
Den = min[F(C02), F(N03)] x F(WFPS)
the soil denitrification rate (|_ig N/g soil/day)
a function relating N gas flux to nitrate levels Figure A-lla)
a function relating N gas flux to soil respiration (Figure A-llb)
a dimensionless multiplier (Figure A-llc)
The x inflection point of F(WFPS) is a function of respiration and soil gas diffusivity at field capacity (DFc)
x inflection = 0.90 - M(C02)
where,
M
a multiplier that is a function of DFc- In technical terms, the inflection point is the domain
where either F(WFPS) is not differentiable or its derivative is 0. In this case, the inflection
point can be interpreted as the WFPS value at which denitrification reaches half of its
maximum rate.
A-392 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Respiration has a much stronger effect on the water curve in clay soils with low DFc than in loam or sandy
soils with high DFc (Figure A-lOb). The model assumes that microsites in fine-textured soils can become anaerobic at
relatively low water contents when oxygen demand is high. After calculating total N gas flux, the ratio of N2/N20 is
estimated so that total N gas emissions can be partitioned between N20 and N2:
Rn2/n2o= Fr(N0s/C02) x Fr(WFPS).
where,
Rn2/n2o	= the ratio of N2/N20
Fr(N03/C02) = a function estimating the impact of the availability of electron donor relative to substrate
Fr(WFPS) = a multiplier to account for the effect of soil water on N2:N20.
For Fr(N03/C02), as the ratio of electron donor to substrate increases, a higher portion of N gas is assumed to
be in the form of N20. For Fr(WFPS), as WFPS increases, a higher portion of N gas is assumed to be in the form of N2.
After calculating and summing N20 emissions from nitrification and dentification, NOx emissions are
calculated using a N0x/N20 ratio function based on soil gas diffusivity. The N0x/N20 ratio is high (maximum of about
17) when soil gas diffusivity is high and decreases to a minimum of approximately 0.28 as diffusivity decreases.
Ammonia volatilization is simulated less mechanistically than the other N gas losses. A soil texture specific
portion of N excreted from animals ranging from 15-30 percent is assumed to be volatilized with more volatilization as
soil texture becomes coarser. In addition, a plant specific portion ranging from 2-15 % of harvested or senesced
biomass N is assumed to be volatilized.
A portion of the nitrate is assumed to be dissolved and flows with water between soil layers during saturated
and unsaturated water movement. The portion of nitrate that flows from the upper layer to the lower layer increases
with increasing sand content and with water flow volume so most movement occurs during saturated flow events
triggered by precipitation or irrigation. The amount of nitrate leaching for estimating indirect N20 emissions is based
on the nitrate that flows through the entire profile in the model simulation. In addition to sand content, leaching rates
are influenced by soil depth, plant N demand, precipitation event size, and other factors.
A-393
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Figure A-10: Effect of Soil Temperature (a), Water-Filled Pore Space (b), and pH (c) on Nitrification Rates
Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates
Soil Temperature
WFPS
A-394 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Figure A-ll: Effect of Soil Nitrite Concentration (a), Heterotrophic Respiration Rates (b), and Water-Filled Pore
Space (c) on Denitrification Rates
Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates
NQ ng N/g soil
CQ-ng C/g soil/day
WFPS%
Hot moments, or pulses, of N20 emissions can occur during freeze-thaw events in soils of cold climates, and
these events can contribute a substantial portion of annual emissions in northern temperate and boreal regions
(Butterbach-Bahl et al. 2017). A recent analysis suggests that not accounting for these events could lead to under-
estimation of global agricultural N20 emissions by 17-28 percent (Wagner-Riddle et al. 2017). The mechanisms
responsible for this phenomenon are not entirely understood but the general hypotheses include accumulation of
substrates while the soil is frozen that drives denitrification as the soil thaws; impacts on soil gas diffusivity and 02
A-395
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availability in pores during freeze-thaw events that influence denitrification rates; and differing temperature sensitives of
the enzymatic processes that control the amounts of N2 and N20 gases released during denitrification (Congreves et al.
2018). The denitrification routine in DayCent was amended so that periods of thawing of frozen soils in the 2-5 cm layer
during the late winter/spring will trigger a hot moment or pulse of N20 emissions. Specifically, the soil water content and
microbial respiration controls on denitrification are relaxed for approximately 3 days upon melting and N20 from
denitrification is amplified by an amount proportional to cumulative freezing degree days during the winter season.
DayCent was evaluated using annual high frequency N20 data collected at research sites in eastern and western Canada
(Wagner-Riddle et al. 2017). The results showed less bias with a better match to observed patterns of late winter/spring
emissions than the previous version of the DayCent model (Del Grosso et al. 2020).
DayCent Model Parameterization and Evaluation
DayCent has been widely applied and calibrated over the years through manual parameterization (e.g., Parton
et al. 1998; Del Grosso et al. 2001). However, manual approaches to parametrization do not necessarily provide the best
calibration for a process-based model, and so there is an effort underway to re-parameterize DayCent with Bayesian
calibration methods. There are three steps to this calibration method: a) conduct a sensitivity analysis to identify the
most influential parameters, b) conduct the Bayesian calibration with the most sensitive parameters, and 3) evaluate the
results with independent data. First, the framework uses a global sensitivity analysis to evaluate the importance of
parameters given their full parameter space and potential interactions with other parameters (Saltelli et al. 2008). This
approach is considered more robust for ranking parameter importance rather than a local sensitivity analysis that
focuses on the effect of varying one parameter, generally within a small area of the overall parameter space. The Sobol
method is used to conduct the global sensitivity analysis (Sobol 2001), which is appropriate for the complexity in the
DayCent model (Saltelli 2002). Second, the model is calibrated using Bayesian logic with the Sampling Importance
Resampling (SIR) method (Rubin 1987, Rubin 1988). A set of prior parameter distributions are developed based on the
knowledge of the inventory compilers and information in the published literature. The model is then applied in a Monte
Carlo analysis by randomly selecting values from the prior parameter distributions using a Latin Hypercube Sampling
(LHS) approach. The LHS approach for selecting parameters allows for values that are used in the simulations to be
distributed throughout the entire domain of the prior parameter distributions. The posterior distribution is
approximated from the results generated by the Monte Carlo analysis using a likelihood function and weighting
parameters based on the level of mismatch between modeled and measured emissions or soil organic C stock changes. If
the data are informative, the likelihood will update the prior parameter distribution based on the weighting and lead to
more resolved joint posterior parameter distribution. Third, the model is applied to simulate experimental sites that are
not used in the Bayesian calibration, and the results are evaluated relative to the model application with the prior
parameter distributions. If the model has been improved through the calibration process, then the results should have
less bias and/or variance than the model application with the prior parameter distributions.
This Bayesian calibration model framework has initially been applied to calibrate DayCent for modeling soil
organic C stock changes to a 30 cm depth (Gurung et al. 2020). The analysis reduced uncertainty in model predictions by
a factor of 6.6. See Gurung et al. (2020) for more detail about this application. We anticipate expanding the calibration to
other model processes in the near future, and eventually using the joint posterior parameter distribution to quantify
uncertainty in model predictions. In this Inventory, the maximum a posterior value for each parameter from the
posterior distribution has been used to simulate soil organic C stock changes.
DayCent has been applied to sites that are independent from model calibration to evaluate the effectiveness of
the model for estimating greenhouse gas emissions and soil organic C stock changes in the United States inventory.
Moreover, these analyses are used to quantify uncertainty with an empirical approach as discussed in Step 2a of this
annex (Ogle et al. 2007). Comparison of model results and plot level data show that DayCent simulates soil organic
matter levels with reasonable accuracy. The model was tested and shown to capture the general trends in C storage
across 948 observations from 72 long-term experiment sites and 142 NRI soil monitoring network sites (Spencer et al.
2011) (Figure A-12). Some bias and imprecision occur in predictions of soil organic C, which is reflected in the uncertainty
associated with DayCent model results. Regardless, the Tier 3 approach has considerably less uncertainty than Tier 1 and
2 methods (Del Grosso et al. 2010; Figure A-13).
A-396 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Figure A-12: Comparisons of Results from DayCent Model and Measurements of Soil Organic C Stocks
10
Cropland
y = x
O
O
CO
o
O c	—'
ro
a)
9 -
y = x
Grassland
* su
• • i •.•iL* a I
10
Ln Modeled SOC Stock
<9 C m 2)
A-397
-------
Figure A-13: Comparison of Estimated Soil Organic C Stock Changes and Uncertainties using Tier 1 (IPCC 2006),
Tier 2 (Ogle et al. 2003, 2006) and Tier 3 Methods
Tier 1
Tier 2
Tier 3
Similarly, DayCent model results have been compared to trace gas N20 fluxes for a number of native and
managed systems from 41 experimental sites with over 200 treatment observations (Del Grosso et al. 2001, 2005, 2010)
(Figure A-14). In general, the model simulates accurate emissions, but some bias and imprecision does occur in
predictions, which is reflected in the uncertainty associated with DayCent model results. Comparisons with measured
data showed that DayCent estimated N20 emissions more accurately and precisely than the IPCC Tier 1 methodology
(IPCC 2006) with higher r2 values and a fitted line closer to a perfect 1:1 relationship between measured and modeled
N20 emissions (Del Grosso et al. 2005, 2008). This is not surprising, since DayCent includes site-specific factors (climate,
soil properties, and previous management) that influence N20 emissions. Furthermore, DayCent also simulated N03"
leaching (root mean square error = 20 percent) more accurately than IPCC Tier 1 methodology (root mean square error =
69 percent) (Del Grosso et al. 2005). Volatilization of N gases that contribute to indirect soil N20 emissions is the only
component that has not been thoroughly tested, which is due to a lack of measurement data.
DayCent predictions of soil CH4 emissions have also been compared to experimental measurements from sites
in California, Texas, Arkansas, and Louisiana (Figure A-15). There are 17 long-term experiments with data on CH4
emissions from rice cultivation, representing 238 treatment observations. In general, the model estimates CH4emissions
with no apparent bias, but there is a lack of precision, which is addressed in the uncertainty analysis.
A-398 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Figure A-14: Comparisons of Results from DayCent Model and Measurements of Soil Nitrous Oxide Emissions
"O 2
o 1
(N
2
QJD 0
Corn without Freeze-Thaw Effect * •• y = x
•s .rf ** V /
'¦
/ • * • • • .
• - •
t" <•
Corn with Freeze-Thaw Effect y = x
• ••
• •.*
• '''iPr
• V • •
VT* .
' •
, •" ¦
. • * •
• *
# -
•
Other Crops without Freeze-Thaw Effect v ~ * . *
• •
•
Other Crops with Freeze-Thaw Effect y = *
. * *• •
. A.V.
•
J"* ...
• . • •
••
•" •

#
y =x •"
Grassland • •
•
•
• •"
•
-2 -1 0 1 2 3 4 5
Ln Modeled N2O Emissions
(g N2O-N ha"1 day"1)
. • •

•



0	12	3
Ln Modeled N2O Emissions
(g N2O-N ha"1 day"1)
A-399
-------
Figure A-15: Comparisons of Results from DayCent Model and Measurements of Soil Methane Emissions
(mg CH. m"2d"1)
A-400 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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References
AAPFCO (2008 through 2017) Commercial Fertilizers: 2008-2015. Association of American Plant Food Control Officials.
University of Missouri. Columbia, MO.
AAPFCO (1995 through 2000a, 2002 through 2007) Commercial Fertilizers: 1995-2007. Association of American Plant
Food Control Officials. University of Kentucky. Lexington, KY.
Abdalla, M., Jones, J. Yeluripati, P. Smith, J. Burke and D M. Williams (2010) Testing DayCent and DNDC model
simulations of N20 fluxes and assessing the impacts of climate change on the gas flux and biomass production from
a humid pasture. Atmos. Environ. 44: 2961-2970.
BLM (2014) Rangeland Inventory, Monitoring, and Evaluation Reports. Bureau of Land Management. U.S. Department of
the Interior. Available online at:
.
BOEM (2014) Year 2011 Gulfwide Emissions Inventory Study (BOEM 2014-666) Bureau of Ocean Energy Management,
U.S. Department of the Interior (November 2014) .
Brakebill, J.W. and Gronberg, J.M. (2017) County-Level Estimates of Nitrogen and Phosphorus from Commercial Fertilizer
for the Conterminous United States, 1987-2012: U.S. Geological Survey data release,
.
Butterbach-Bahl, K. & Wolf, B. (2017) Greenhouse gases: Warming from freezing soils. Nature Geosci 10(4): 248-249.
Cantens, G. (2004 through 2005) Personal Communication. Janet Lewis, Assistant to Gaston Cantens, Vice President of
Corporate Relations, Florida Crystals Company and ICF International.
Cheng, K., S.M. Ogle, W.J. Parton, G. Pan (2014) "Simulating greenhouse gas mitigation potentials for Chinese croplands
using the DAYCENT ecosystem model." Global Change Biology 20:948-962.
Cheng, K., S.M. Ogle, W.J. Parton and G. Pan (2013) Predicting methanogenesis from rice paddies using the DAYCENT
ecosystem model. Ecological Modelling 261-262:19-31.
Cheng, B., and D.M. Titterington (1994) "Neural networks: A review from a statistical perspective." Statistical science 9:
2-30.
Cibrowski, P. (1996) Personal Communication. Peter Cibrowski, Minnesota Pollution Control Agency and Heike
Mainhardt, ICF Incorporated. July 29,1996.
Claassen, R., M. Bowman, J. McFadden, D. Smith, and S. Wallander (2018) Tillage intensity and conservation cropping in
the United States, EIB 197. United States Department of Agriculture, Economic Research Service, Washington, D.C.
Congreves, K.A., Wagner-Riddle, C., Si, B.C. and Clough, T.J. (2018) "Nitrous oxide emissions and biogeochemical
responses to soil freezing-thawing and drying-wetting." Soil Biology and Biochemistry 117:5-15.
Coulston, J.W., Woodall, C.W., Domke, G.M., and Walters, B.F. (in preparation). Refined Delineation between Woodlands
and Forests with Implications for U.S. National Greenhouse Gas Inventory of Forests. Climatic Change.
CTIC (2004) 2004 Crop Residue Management Survey. Conservation Technology Information Center. Available online at
.
Daly, C., G.H. Taylor, W.P. Gibson, T. Parzybok, G.L. Johnson, and P.A. Pasteris (1998) "Development of high-quality
spatial datasets for the United States." Proc., 1st International Conference on Geospatial Information in Agriculture
and Forestry, Lake Buena Vista, FL, 1-512-1-519. June 1-3, 1998.
Daly, C., R.P. Neilson, and D.L. Phillips (1994) "A statistical-topographic model for mapping climatological precipitation
over mountainous terrain." Journal of Applied Meteorology, 33:140-158.
Dean, W. E., and E. Gorham (1998) Magnitude and significance of carbon burial in lakes, reservoirs, and peatlands.
Geology 26:535-538.
Del Grosso, S.J., S.M. Ogle, W.J. Parton, E. Marx, R. Gurung, K. Killian, and C. Nevison (2020) Freeze-thaw events
accelerate soil N20 emissions from U.S. Agricultural Soils. In review.
A-401
-------
Del Grosso, S.J., S.M. Ogle, W.J. Parton. (2011) Soil Organic Matter Cycling and Greenhouse Gas Accounting
Methodologies, Chapter 1, pp 3-13 DOI: 10.1021/bk-2011-1072.ch001. In: L. Guo, A. Gunasekara, L. McConnell (Eds.)
Understanding Greenhouse Gas Emissions from Agricultural Management, American Chemical Society, Washington,
D.C.
Del Grosso, S.J., W.J. Parton, C.A. Keough, and M. Reyes-Fox. (2011) Special features of the DayCent modeling package
and additional procedures for parameterization, calibration, validation, and applications, in Methods of Introducing
System Models into Agricultural Research, L.R. Ahuja and Liwang Ma, editors, p. 155-176, American Society of
Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, Wl. USA.
Del Grosso, S.J., S.M. Ogle, W.J. Parton, and F.J. Breidt (2010) "Estimating Uncertainty in N20 Emissions from U.S.
Cropland Soils." Global Biogeochemical Cycles, 24, GB1009, doi:10.1029/2009GB003544.
Del Grosso, S.J., T. Wirth, S.M. Ogle, W.J. Parton (2008) Estimating agricultural nitrous oxide emissions. EOS 89, 529-530.
Del Grosso, S.J., A.R. Mosier, W.J. Parton, and D.S. Ojima (2005) "DAYCENT Model Analysis of Past and Contemporary Soil
N20 and Net Greenhouse Gas Flux for Major Crops in the USA." Soil Tillage and Research, 83: 9-24. doi:
10.1016/j.still.2005.02.007.
Del Grosso, S.J., W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S. Ojima, and D.S. Schimel (2001) "Simulated
Interaction of Carbon Dynamics and Nitrogen Trace Gas Fluxes Using the DAYCENT Model." In Schaffer, M., L. Ma, S.
Hansen, (eds.); Modeling Carbon and Nitrogen Dynamics for Soil Management. CRC Press. Boca Raton, Florida. 303-
332.
Del Grosso, S.J., W.J. Parton, A.R. Mosier, D.S. Ojima, A.E. Kulmala and S. Phongpan (2000) General model for N20 and N2
gas emissions from soils due to denitrification. Global Biogeochem. Cycles, 14:1045-1060.
Delgado, J.A., S.J. Del Grosso, and S.M. Ogle (2009) "15N isotopic crop residue cycling studies and modeling suggest that
IPCC methodologies to assess residue contributions to N20-N emissions should be reevaluated." Nutrient Cycling in
Agroecosystems, DOI 10.1007/sl0705-009-9300-9.
Deren, C. (2002) Personal Communication and Dr. Chris Deren, Everglades Research and Education Centre at the
University of Florida and Caren Mintz, ICF International. August 15, 2002.
Domke, G.M., Woodall, C.W., Smith, J.E., Westfall, J.A., McRoberts, R.E. (2012) Consequences of alternative tree-level
biomass estimation procedures on U.S. forest carbon stock estimates. Forest Ecology and Management. 270:108-
116.
Domke, G.M., Smith, J.E., and Woodall, C.W. (2011) Accounting for density reduction and structural loss in standing dead
trees: Implications for forest biomass and carbon stock estimates in the United States. Carbon Balance and
Management. 6:14.
Domke, G.M., Woodall, C.W., Walters, B.F., McRoberts, R.E., Hatfield, M.A. (In Review) Strategies to compensate for the
effects of nonresponse on forest carbon baseline estimates from the national forest inventory of the United States.
Forest Ecology and Management.
Domke, G.M., Woodall, C.W., Walters, B.F., Smith, J.E. (2013) From models to measurements: comparing down dead
wood carbon stock estimates in the U.S. forest inventory. PLoS ONE 8(3): e59949.
Domke, G.M., Perry, C.H., Walters, B.F., Woodall, C.W., and Smith, J.E. (in preparation). Estimation of forest floor carbon
using the national forest inventory of the United States. Intended outlet: Geoderma.
Easter, M., S. Williams, and S. Ogle. (2008) Gap-filling NRI data for the Soil C Inventory. Natural Resource Ecology
Laboratory, Colorado State University, Fort Collins, CO. Report provided to the U.S. Environmental Protection
Agency, Tom Wirth.
Edmonds, L., N. Gollehon, R.L. Kellogg, B. Kintzer, L. Knight, C. Lander, J. Lemunyon, D. Meyer, D.C. Moffitt, and J.
Schaeffer (2003) "Costs Associated with Development and Implementation of Comprehensive Nutrient
Management Plans." Part 1. Nutrient Management, Land Treatment, Manure and Wastewater Handling and
Storage, and Recordkeeping. Natural Resource Conservation Service, U.S. Department of Agriculture.
EIA (2007) Voluntary Greenhouse Gas Reports for EIA Form 1605B (Reporting Year 2006). Available online at
.
A-402 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Euliss, N., and R. Gleason (2002) Personal communication regarding wetland restoration factor estimates and restoration
activity data. Ned Euliss and Robert Gleason of the U.S. Geological Survey, Jamestown, ND, to Stephen Ogle of the
National Resource Ecology Laboratory, Fort Collins, CO. August 2002.
Fleskes, J.P., Perry, W.M., Petrik, K.L., Spell, R., and Reid, F. (2005) Change in area of winter-flood and dry rice in the
northern Central Valley of California determined by satellite imagery. California Fish and Game, 91: 207-215.
Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J. (2011) Completion of the
2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864.
Gonzalez, R. (2007 through 2014) Email correspondence. Rene Gonzalez, Plant Manager, Sem-Chi Rice Company and ICF
International.
Gurung, R. B., Ogle, S.M., Breidt, F.J., Williams, S.A., Parton, W.J. (2020) Bayesian calibration of the DayCent ecosystem
model to simulate soil organic carbon dynamics and reduce model uncertainty. Geoderma 376:114529.
Halvorson, A.D., C.S. Snyder, A.D. Blaylock, and S.J. Del Grosso (2013) Enhanced Efficiency Nitrogen Fertilizers: Potential
Role in Nitrous Oxide Emission Mitigation. Agronomy Journal, doi:10.2134/agronj2013.0081
Hardke, J.T. (2015) Trends in Arkansas rice production, 2014. B.R. Wells Arkansas Rice Research Studies 2014. Norman,
R.J., and Moldenhauer, K.A.K., (Eds.). Research Series 626, Arkansas Agricultural Experiment Station, University of
Arkansas.
Hardke, J.T., and Wilson, C.E. Jr. (2013) Trends in Arkansas rice production. B.R. Wells Arkansas Rice Research Studies
2012. Norman, R.J., and Moldenhauer, K.A.K., (Eds.). Research Series 609, Arkansas Agricultural Experiment Station,
University of Arkansas.
Hardke, J.T., and Wilson, C.E. Jr. (2014) Trends in Arkansas rice production, 2013. B.R. Wells Arkansas Rice Research
Studies 2013. Norman, R.J., and Moldenhauer, K.A.K., (Eds.). Research Series 617, Arkansas Agricultural Experiment
Station, University of Arkansas.
Harmon, M.E., C.W. Woodall, B. Fasth, J. Sexton, M. Yatkov. (2011) Differences between standing and downed dead tree
wood density reduction factors: A comparison across decay classes and tree species. Res. Paper. NRS-15. Newtown
Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 40 p.
Hollier, C. A. (ed) (1999) Louisiana rice production handbook. Louisiana State University Agricultural Center. LCES
Publication Number 2321.116 pp.
Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis (2005) Very high resolution interpolated climate surfaces
for global land areas. International Journal of Climatology 25:1965-1978.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental Panel on
Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al., eds. August 13, 2004. Available
online at .
Johnson, D.M., and R. Mueller (2010) The 2009 Cropland Data Layer. Photogrammetric engineering and remote sensing
76:1201-1205.
Kirstein, A. (2003 through 2004, 2006) Personal Communication. Arthur Kirstein, Coordinator, Agricultural Economic
Development Program, Palm Beach County Cooperative Extension Service, FL and ICF International.
Kraft, D.L. and H.C. Orender (1993) "Considerations for Using Sludge as a Fuel." Tappi Journal, 76(3): 175-183.
Li, Y., D. Chen, Y. Zhang, R. Edis and H. Ding (2005) Comparison of three modeling approaches for simulating
denitrification and nitrous oxide emissions from loam-textured arable soils. Global Biogeochemical Cycles, 19,
GB3002.
Little, R. (1988) "Missing-data adjustments in large surveys." Journal of Business and Economic Statistics 6: 287-296.
LSU (2015) Louisiana ratoon crop and conservation: Ratoon & Conservation Tillage Estimates. Louisiana State University,
College of Agriculture AgCenter. Available online at: .
A-403
-------
McGill, W.B., and C.V. Cole (1981) Comparative aspects of cycling of organic C, N, S and P through soil organic matter.
Geoderma 26:267-286.
Metherell, A.K., L.A. Harding, C.V. Cole, and W.J. Parton (1993) "CENTURY Soil Organic Matter Model Environment."
Agroecosystem version 4.0. Technical documentation, GPSR Tech. Report No. 4, USDA/ARS, Ft. Collins, CO.
Miller, M.R., Garr, J.D., and Coates, P.S. (2010) Changes in the status of harvested rice fields in the Sacramento Valley,
California: Implications for wintering waterfowl. Wetlands, 30: 939-947.
Miner, C. (1998) Harvesting the High Plains: John Kriss and the business of wheat farming, 1920-1950. University Press of
Kansas, Lawrence, KS.
Miner, R. (2008) "Calculations documenting the greenhouse gas emissions from the pulp and paper industry."
Memorandum from Reid Minor, National Council for Air and Stream Improvement, Inc. (NCASI) to Becky Nicholson,
RTI International, May 21, 2008.
Mosier, A.R., Duxbury, J.M., Freney, J.R., Heinemeyer, O., and Minami, K. (1998) Assessing and mitigating N20 emissions
from agricultural soils. Climatic Change 40:7-38.
Nair, P.K.R. and V.D. Nair. (2003) Carbon storage in North American Agroforestry systems. In Kimble J., Heath L.S.,
Birdsey R.A., Lai R., editors. The potential of U.S. forest soils to sequester carbon and mitigate the greenhouse
effect. CRC Press. Boca Raton, FL, 333-346.
NASS (2004) Agricultural Chemical Usage: 2003 Field Crops Summary. Report AgChl(04)a, National Agricultural Statistics
Service, U.S. Department of Agriculture. Available online at
.
NASS (1999) Agricultural Chemical Usage: 1998 Field Crops Summary. Report AgChl(99). National Agricultural Statistics
Service, U.S. Department of Agriculture. Available online at
.
NASS (1992) Agricultural Chemical Usage: 1991 Field Crops Summary. Report AgChl(92). National Agricultural Statistics
Service, U.S. Department of Agriculture. Available online at
.
NRAES (1992) On-Farm Composting Handbook (NRAES-54). Natural Resource, Agriculture, and Engineering Service.
Available online at .
NRCS (1997) "National Soil Survey Laboratory Characterization Data," Digital Data, Natural Resources Conservation
Service, U.S. Department of Agriculture. Lincoln, NE.
NRCS (1981) Land Resource Regions and Major Land Resource Areas of the United States, USDA Agriculture Handbook
296, United States Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Cente.,
Lincoln, NE, pp. 156.
NRIAI (2003) Regional Budget and Cost Information. U.S. Department of Agriculture, Natural Resources Conservation
Service, Natural Resources Inventory and Analysis Institute. Available online at
.
Nusser, S.M., F.J. Breidt, and. W.A. Fuller (1998) "Design and Estimation for Investigating the Dynamics of Natural
Resources, Ecological Applications, 8:234-245.
Nusser, S.M., J.J. Goebel (1997) The national resources inventory: a long term monitoring programme. Environmental
and Ecological Statistics, 4,181-204.
Ogle, S.M., Woodall, C.W., Swan, A., Smith, J., and Wirth. T. (in preparation). Determining the Managed Land Base for
Delineating Carbon Sources and Sinks in the United States. Environmental Science and Policy.
Ogle, S.M., F.J. Breidt, M. Easter, S. Williams, K. Killian, and K. Paustian (2010) "Scale and uncertainty in modeled soil
organic carbon stock changes for U.S. croplands using a process-based model." Global Change Biology 16:810-822.
Ogle, S.M., F.J. Breidt, M. Easter, S. Williams and K. Paustian. (2007) "Empirically-Based Uncertainty Associated with
Modeling Carbon Sequestration Rates in Soils." Ecological Modeling 205:453-463.
A-404 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Ogle, S.M., F.J. Breidt, and K. Paustian. (2006) "Bias and variance in model results due to spatial scaling of measurements
for parameterization in regional assessments." Global Change Biology 12:516-523.
Ogle, S.M., M.D. Eve, F.J. Breidt, and K. Paustian (2003) "Uncertainty in estimating land use and management impacts on
soil organic carbon storage for U.S. agroecosystems between 1982 and 1997." Global Change Biology 9:1521-1542.
Parton, W.J., D.S. Schimel, C.V. Cole, D.S. Ojima (1987) "Analysis of factors controlling soil organic matter levels in Great
Plains grasslands." Soil Science Society of America Journal 51:1173-1179.
Parton, W. J., J. M. O. Scurlock, D. S. Ojima, T. G. Gilmanov, R. J. Scholes, D. S. Schimel, T. Kirchner, J.-C. Menaut, T.
Seastedt, E. G. Moya, A. Kamnalrut, and J. I. Kinyamario (1993) Observations and modeling of biomass and soil
organic matter dynamics for grassland biomes worldwide. Global Biogeochemical Cycles 7:785-809.
Parton, W.J., D.S. Ojima, C.V. Cole, and D.S. Schimel (1994) "A General Model for Soil Organic Matter Dynamics:
Sensitivity to litter chemistry, texture and management," in Quantitative Modeling of Soil Forming Processes.
Special Publication 39, Soil Science Society of America, Madison, Wl, 147-167.
Parton, W.J., M.D. Hartman, D.S. Ojima, and D.S. Schimel (1998) "DAYCENT: Its Land Surface Submodel: Description and
Testing". Glob. Planet. Chang. 19: 35-48.
Parton, W.J., E.A. Holland, S.J. Del Grosso, M.D. Hartman, R.E. Martin, A.R. Mosier, D.S. Ojima, and D.S. Schimel (2001)
Generalized model for NOx and N20 emissions from soils. Journal of Geophysical Research. 106 (D15):17403-17420.
Paustian, K., Collins, H. P. & Paul, E. A. (1997) Management controls on soil carbon. In: Soil organic matter in temperate
agroecosystems: long-term experiments in North America, ed. E. T. E. Paul E.A., K. Paustian, and C.V. Cole, pp. 15-49.
Boca Raton: CRC Press.
Paustian, K., Lehmann, J., Ogle, S., Reay, D., Robertson, G. P. & Smith, P. (2016) Climate-smart soils. Nature 532(7597):
49-57.
Peer, R., S. Thorneloe, and D. Epperson (1993) "A Comparison of Methods for Estimating Global Methane Emissions from
Landfills." Chemosphere, 26(l-4):387-400.
Potter, C. S., J.T. Randerson, C.B. Fields, P.A. Matson, P.M. Vitousek, H.A. Mooney, and S.A. Klooster. (1993) "Terrestrial
ecosystem production: a process model based on global satellite and surface data." Global Biogeochemical Cycles
7:811-841.
Potter, C., S. Klooster, A. Huete, and V. Genovese (2007) Terrestrial carbon sinks for the United States predicted from
MODIS satellite data and ecosystem modeling. Earth Interactions 11, Article No. 13, DOI 10.1175/EI228.1.
PRISM Climate Group (2018) PRISM Climate Data, Oregon State University, , downloaded
18 July 2018.
Rubin, D.B. (1988) Using the SIR Algorithm to Simulate Posterior Distributions, in: Bernardo, J.M., Degroot, M.H., Lindley,
D. V, Smith, A.F.M. (Eds.), Bayesian Statistics. Oxford University Press, Cambridge, Massachusetts, pp. 395^-02.
Rubin, D.B. (1987) The Calculation of Posterior Distributions by Data Augmentation: Comment: A Noniterative
Sampling/Importance Resampling Alternative to the Data Augmentation Algorithm for Creating a Few Imputations
When Fractions of Missing Information Are Modest: The SIR. J. Am. Stat. Assoc. 82, 543.
.
Quam, V.C., J. Gardner, J.R. Brandle, and T.K. Boes (1992) Windbreaks in Sustainable Agricultural Systems. EC-91-1772.
University of Nebraska Extension. Lincoln, NE.
Saghafi, Abouna (2013) Estimation of fugitive emissions from open cut coal mining and measurable gas content, 13th
Coal Operators' Conference, University of Wollongong, The Australian Institute of Mining and Metallurgy & Mine
Managers Association of Australia, 2013, 306-313.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008) Global
Sensitivity Analysis. The Primer, Global Sensitivity Analysis. The Primer. John Wiley & Sons, Ltd, Chichester, UK.
.
Saltelli, A. (2002) Sensitivity analysis for importance assessment. Risk Anal. 22, 579-590. .
A-405
-------
Sanchis, E., Ferrer, M., Torres, A. G., Cambra-Lopez, M. & Calvet, S. (2012) Effect of Water and Straw Management
Practices on Methane Emissions from Rice Fields: A Review Through a Meta-Analysis. Environmental Engineering
Science 29(12): 1053-1062.
Sass, R.L., F.M. Fisher, S.T. Lewis, M.F. Jund, and F.T. Turner (1994) "Methane emissions from rice fields: effect of soil
texture." Global Biogeochemical Cycles 8:135-140.
Savitzky, A., and M. J. E. Golay (1964) Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Analytical Chemistry 36:1627-1639.
Saxton, K.E., W.J. Rawls, J.S. Romberger, and R.I. Papendick (1986) "Estimating Generalized Soil-Water Characteristics
From Texture." Soil Sci. Soc. Am. J. 50:1031-1036.
Schueneman, T. (1997,1999 through 2001) Personal Communication. Tom Schueneman, Agricultural Extension Agent,
Palm Beach County, FL and ICF International.
Smith, J. (2008) E-mail correspondence between Jean Kim, ICF, and Jim Smith, U.S. Forest Service, December 3, 2008.
Sobol, I.M. (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math.
Comput. Simul. 55, 271-280. .
Soil Survey Staff (2019) Gridded Soil Survey Geographic (gSSURGO) Database for the Conterminous United States. United
States Department of Agriculture, Natural Resources Conservation Service. Available online at
.
Spencer, S., S.M. Ogle, F.J. Breidt, J. Goebel, and K. Paustian (2011) Designing a national soil carbon monitoring network
to support climate change policy: a case example for U.S. agricultural lands. Greenhouse Gas Management &
Measurement 1:167-178.
Stehfest, E., and C. Muller (2004), Simulation of N20 emissions from a urine-affected pasture in New Zealand with the
ecosystem model DayCent, J. Geophys. Res., 109, D03109, doi:10.1029/2003JD004261.
Strehler, A., and W. Stutzle (1987) "Biomass Residues." In Hall, D.O. and Overend, R.P. (eds.). Biomass. John Wiley and
Sons, Ltd. Chichester, UK.
TAMU (2015) Texas Rice Crop Survey. Texas A&M AgriLIFE Research Center at Beaumont. Online at:
.
Towery, D. (2001) Personal Communication. Dan Towery regarding adjustments to the CTIC (1998) tillage data to reflect
long-term trends, Conservation Technology Information Center, West Lafayette, IN, and Marlen Eve, National
Resource Ecology Laboratory, Fort Collins, CO. February 2001.
TVA (1992b) Fertilizer Summary Data 1992. Tennessee Valley Authority, Muscle Shoals, AL.
TVA (1991 through 1992a, 1993 through 1994) Commercial Fertilizers. Tennessee Valley Authority, Muscle Shoals, AL.
UCCE, 2015. Rice Production Manual. Revised 2015. University of California Cooperative Extension, Davis, in
collaboration with the California Rice Research Board.
USDA (2010a) Crop Production 2009 Summary, National Agricultural Statistics Service, Agricultural Statistics Board, U.S.
Department of Agriculture, Washington, DC. Available online at .
USDA (2015) Quick Stats: U.S. & All States Data - Crops. National Agricultural Statistics Service, U.S. Department of
Agriculture. Washington, DC. U.S. Department of Agriculture, National Agricultural Statistics Service. Washington,
D.C., Available online at .
USDA (2003, 2005 through 2006, 2008 through 2009) Crop Production Summary, National Agricultural Statistics Service,
Agricultural Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online at
.
USDA (1998) Field Crops Final Estimates 1992-1997. Statistical Bulletin Number 947a. National Agricultural Statistics
Service, U.S. Department of Agriculture. Washington, DC. Available online at .
Accessed July 2001.
A-406 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
USDA (1996) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651. Natural
Resources Conservation Service, U.S. Department of Agriculture. July 1996.
USDA (1994) Field Crops: Final Estimates, 1987-1992. Statistical Bulletin Number 896, National Agriculture Statistics
Service, U.S. Department of Agriculture. Washington, DC. Available online at .
USDA (1991) State Soil Geographic (STATSGO) Data Base Data use information. Miscellaneous Publication Number 1492,
National Soil Survey Center, Natural Resources Conservation Service, U.S. Department of Agriculture, Fort Worth, TX.
USDA-ERS (2018) Agricultural Resource Management Survey (ARMS) Farm Financial and Crop Production Practices:
Tailored Reports. Available online at: .
USDA-ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, United States Department of
Agriculture. Available online at .
USDA-FSA (2014) Conservation Reserve Program Monthly Summary - September 2014. U.S. Department of Agriculture,
Farm Service Agency, Washington, DC, Available online at .
USDA-NASS (2019) Quick Stats. National Agricultural Statistics Service, United States Department of Agriculture,
Washington, D.C. .
USDA-NRCS (2012) Assessment of the Effects of Conservation Practices on Cultivated Cropland in the Upper Mississippi
River Basin. US Department of Agriculture, Natural Resources Conservation Service,
.
USDA-NRCS (2018a) Summary Report: 2015 National Resources Inventory. Natural Resources Conservation Service,
Washington, D.C., and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa.
.
USDA-NRCS (2018b) CEAP Cropland Farmer Surveys. USDA Natural Resources Conservation Service.

USDA-NRCS (1996) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651.
Natural Resources Conservation Service, U.S. Department of Agriculture. July 1996
USFWS (2010) Strategic Plan: The Partners for Fish and Wildlife Program, Stewardship of Fish and Wildlife Through
Voluntary Conservation. U.S. Fish and Wildlife Service, Washington, DC, USA.
.
Van Buuren, S. (2012) "Flexible imputation of missing data." Chapman & Hall/CRC, Boca Raton, FL.
Vogelman, J.E., S.M. Howard, L. Yang, C. R. Larson, B. K. Wylie, and J. N. Van Driel (2001) "Completion of the 1990's
National Land Cover Data Set for the conterminous United States." Photogrammetric Engineering and Remote
Sensing, 67:650-662.
Wagner-Riddle, C., Congreves, K.A., Abalos, D., Berg, A.A., Brown, S.E., Ambadan, J.T., Gao, X. and Tenuta, M. (2017)
"Globally important nitrous oxide emissions from croplands induced by freeze-thaw cycles." Nature Geoscience
10(4): 279-283.
Way, M.O., McCauley, G.M., Zhou, X.G., Wilson, L.T., and Morace, B. (Eds.). (2014) 2014 Texas Rice Production
Guidelines. Texas A&M AgriLIFE Research Center at Beaumont.
Williams, S.A. (2006) Data compiled for the Consortium for Agricultural Soils Mitigation of Greenhouse Gases (CASMGS)
from an unpublished manuscript. Natural Resource Ecology Laboratory, Colorado State University.
Williams, S. and K. Paustian (2005) Developing Regional Cropping Histories for Century Model U.S.-level Simulations.
Colorado State University, Natural Resources Ecology Laboratory, Fort Collins, CO.
Wilson, C.E. Jr., and Branson, J.W. (2006) Trends in Arkansas rice production. B.R. Wells Arkansas Rice Research Studies
2005. Norman, R.J., Meullenet, J.-F., and Moldenhauer, K.A.K., (Eds.). Research Series 540, Arkansas Agricultural
Experiment Station, University of Arkansas.
A-407
-------
Wilson, C.E. Jr., and Branson, J.W. (2005) Trends in Arkansas rice production. B.R. Wells Arkansas Rice Research Studies
2004. Norman, R.J., Meullenet, J.-F., and Moldenhauer, K.A.K., (Eds.). Research Series 529, Arkansas Agricultural
Experiment Station, University of Arkansas.
Wilson, C.E. Jr., and Runsick, S.K. (2008) Trends in Arkansas rice production. B.R. Wells Arkansas Rice Research Studies
2007. Norman, R.J., Meullenet, J.-F., and Moldenhauer, K.A.K., (Eds.). Research Series 560, Arkansas Agricultural
Experiment Station, University of Arkansas.
Wilson, C.E. Jr., and Runsick, S.K. (2007) Trends in Arkansas rice production. B.R. Wells Arkansas Rice Research Studies
2006. Norman, R.J., Meullenet, J.-F., and Moldenhauer, K.A.K., (Eds.). Research Series 550, Arkansas Agricultural
Experiment Station, University of Arkansas.
Wilson, C.E. Jr., Runsick, S.K., Mazzanti, R. (2009) Trends in Arkansas rice production. B.R. Wells Arkansas Rice Research
Studies 2008. Norman, R.J., Meullenet, J.-F., and Moldenhauer, K.A.K., (Eds.). Research Series 571, Arkansas
Agricultural Experiment Station, University of Arkansas.
Wilson, C.E. Jr., Runsick, S.K., and Mazzanti, R. (2010) Trends in Arkansas rice production. B.R. Wells Arkansas Rice
Research Studies 2009. Norman, R.J., and Moldenhauer, K.A.K., (Eds.). Research Series 581, Arkansas Agricultural
Experiment Station, University of Arkansas.
Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S. M., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M.,
Granneman, B., Liknes, G. C., Rigge, M. & Xian, G. (2018) "A new generation of the United States National Land
Cover Database: Requirements, research priorities, design, and implementation strategies." ISPRS Journal of
Photogrammetry and Remote Sensing 146:108-123.
Zomer RJ, Trabucco A, Bossio DA, van Straaten O, Verchot LV (2008) Climate Change Mitigation: A Spatial Analysis of
Global Land Suitability for Clean Development Mechanism Afforestation and Reforestation. Agric. Ecosystems and
Envir. 126: 67-80.
Zomer RJ, Bossio DA, Trabucco A, Yuanjie L, Gupta DC & Singh VP (2007) Trees and Water: Smallholder Agroforestry on
Irrigated Lands in Northern India. Colombo, Sri Lanka: International Water Management Institute, pp 45. (IWMI
Research Report 122).
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3,13. Methodology for Estimating Net Carbon Stock Changes in Forest
Ecosystems and Harvested Wood Products for Forest Land Remaining
Forest Land and Land Converted to Forest Land as well as Non-C02
Emissions from Forest Fires
This sub-annex expands on the methodology used to estimate net changes in carbon (C) stocks in forest
ecosystems and harvested wood products for Forest Land Remaining Forest Land and Land Converted to Forest Land as
well as non-C02 emissions from forest fires. Full details of the C conversion factors and procedures may be found in the
cited references. For details on the methods used to estimate changes in mineral soil C stocks in the Land Converted to
Forest Land section please refer to Annex 3.12.
Carbon stocks and net stock change in forest ecosystems
The inventory-based methodologies for estimating forest C stocks are based on a combination of approaches
(Woodall et al 2015a) and are consistent with the IPCC (2003, 2006) stock-difference (used for the conterminous United
States (U.S.)) and gain-loss (used for Alaska) methods. Estimates of ecosystem C are based on data from the network of
annual national forest inventory (NFI) plots established and measured by the Forest Inventory and Analysis (FIA) program
within the USDA Forest Service; either direct measurements or variables from the NFI are the basis for estimating metric
tons of C per hectare in forest ecosystem C pools (i.e., above- and belowground biomass, dead wood, litter, and soil
carbon). For the conterminous U.S., plot-level estimates are used to inform land area (by use) and stand age transition
matrices across time which can be summed annually for an estimate of forest C stock change for Forest Land Remaining
Forest Land and Land Converted to Forest Land. A general description of the land use and stand age transition matrices
that are informed by the annual NFI of the U.S. and were used in the estimation framework to compile estimates for the
conterminous U.S. in this Inventory are described in Coulston et al. (2015). The annual NFI data in the conterminous U.S.
allows for empirical estimation of net change in forest ecosystem carbon stocks within the estimation framework. In
contrast, Wyoming and West Oklahoma have limited remeasurement data so theoretical age transition matrices were
developed (Figure A-16). The incorporation of all managed forest land in Alaska was facilitated by an analysis to
determine the managed land base in Alaska (Ogle et al. 2018), the expansion of the NFI into interior Alaska beginning in
2014, and a myriad of publicly available data products that provided information necessary for prediction of C stocks and
fluxes on plots that have yet to be measured as part of the NFI.
The following subsections of this annex describe the estimation system used this year (Figure A-16) including
the methods for estimating individual pools of forest ecosystem C in addition to the approaches to informing land use
and stand age transitions.
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Figure A-16: Flowchart of the inputs necessary in the estimation framework, including the methods for
estimating individual pools of forest C in the conterminous United States
Conterminous US	West Oklahoma and Wyoming
Note: An empirical age class transition matrix was used in every state in the conterminous United States with the exception of
west Oklahoma and Wyoming where a theoretical age class transition matrix was used due to a lack of remeasurements in the
annual NFI.
Forest Land Definition
The definition of forest land within the United States and used for this inventory is defined in Oswalt et al.
(2019) as "Land at least 120 feet (37 meters) wide and at least 1 acre (0.4 hectare) in size with at least 10 percent cover
(or equivalent stocking) by live trees including land that formerly had such tree cover and that will be naturally or
artificially regenerated. Trees are woody plants having a more or less erect perennial stem(s) capable of achieving at
least 3 inches (7.6 cm) in diameter at breast height, or 5 inches (12.7 cm) diameter at root collar, and a height of 16.4
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feet (5 meters) at maturity in situ. The definition here includes all areas recently having such conditions and currently
regenerating or capable of attaining such condition in the near future. Forest land also includes transition zones, such as
areas between forest and non-forest lands that have at least 10 percent cover (or equivalent stocking) with live trees and
forest areas adjacent to urban and built-up lands. Unimproved roads and trails, streams, and clearings in forest areas are
classified as forest if they are less than 120 feet (36.6 meters) wide or an acre (0.4 hectare) in size. Forest land does not
include land that is predominantly under agricultural or urban land use." Timberland is productive forest land, which is
on unreserved land and is producing or capable of producing crops of industrial wood. This is an important subclass of
forest land because timberland is the primary source of C incorporated into harvested wood products. Productivity for
timberland is at a minimum rate of 20 cubic feet per acre (1.4 cubic meters per hectare) per year of industrial wood
(Woudenberg and Farrenkopf 1995). There are about 205 million hectares of timberland in the conterminous United
States, which represents 80 percent of all forest lands over the same area (Oswalt et al. 2014).
Forest Inventory Data
The estimates of forest C stocks are based on data from the annual NFI. NFI data were obtained from the USDA
Forest Service, FIA Program (Frayer and Furnival 1999; USDA Forest Service 2020a; USDA Forest Service 2020b). NFI data
include remote sensing information and a collection of measurements in the field at sample locations called plots. Tree
measurements include diameter at breast height, tree height, species, and variables describing tree form and condition.
On a subset of plots, additional measurements or samples are taken on downed dead wood, litter, and soil variables. The
technical advances needed to estimate C stocks from these data are ongoing (Woodall et al. 2015a) with the latest
research incorporated on an annual basis (see Domke et al. 2016, Domke et. al. 2017). The field protocols are thoroughly
documented and available for download from the USDA Forest Service (2020c). Bechtold and Patterson (2005) provide
the estimation procedures for standard NFI results. The data are freely available for download at USDA Forest Service
(2011b) as the FIA Database (FIADB) Version 8.0 (USDA Forest Service 2020b; USDA Forest Service 2020c); these are the
primary sources of NFI data used to estimate forest C stocks. In addition to the field sampling component, fine-scale
remotely sensed imagery (National Agriculture Imagery Program; NAIP 2015; Woodall et al. 2015b) is used to assign the
land use at each sample location which has a nominal spatial resolution (raster cell size) of 1 m2. Prior to field
measurement of each year's collection of annual plots due for measurement (i.e., panel), each sample location in the
panel (i.e., systematic distribution of plots within each state each year) is photo-interpreted manually to classify the land
use. Annual NFI data are available for the temperate oceanic ecoregion of Alaska (southeast and south central) from
2004 to present as well as for interior Alaska from a pilot inventory in 2014 which became operational in 2016.
Agroforestry systems are not currently accounted for in the U.S. Inventory, since they are not explicitly inventoried by
either of the two primary national natural resource inventory programs: the FIA program of the USDA Forest Service and
the National Resources Inventory (NRI) of the USDA Natural Resources Conservation Service (Perry et al. 2005). The
majority of these tree-based practices do not meet the size and definitions for forests within each of these resource
inventories.
A national plot design and annualized sampling (USDA Forest Service 2020a) were introduced by FIA with most
new annual NFIs beginning after 1998. These are the only NFIs used in the compilation of estimates for this Inventory.
These NFIs involve the sampling of all forest land including reserved and lower productivity lands. All states with the
exception of Hawaii have annualized NFI data available with substantial remeasurement (with the exception of Wyoming
and West Oklahoma) in the conterminous U.S. (Figure A-17). Annualized sampling means that a spatially representative
portion of plots throughout the state is sampled each year, with the goal of measuring all plots once every 5 to 10 years,
depending on the region of the U.S. The full unique set of data with all measured plots, such that each plot has been
measured one time, is called a cycle. Sampling is designed such that partial inventory cycles provide usable, unbiased
samples of forest inventory within the state, but with higher sampling uncertainty than the full cycle. After all plots have
been measured once, the sequence continues with remeasurement of the first year's plots, starting the next new cycle.
Most eastern states have completed three or four cycles of the annualized NFI, and most western states are on their
second annual cycle. Annually updated estimates of forest C stocks are affected by the redundancy in the data used to
generate the annual updates of C stock. For example, a typical annual inventory update for an eastern state will include
new data from remeasurement on 20 percent of plots; data from the remaining 80 percent of plots is identical to that
included in the previous year's annual update. The interpretation and use of the annual inventory data can affect trend
estimates of C stocks and stock changes (e.g., estimates based on 60 percent of an inventory cycle will be different than
estimates with a complete (100 percent) cycle). In general, the C stock and stock change estimates use annual NFI
summaries (updates) with unique sets of plot-level data (that is, without redundant sets); the most-recent annual update
(i.e., 2019) is the exception because it is included in stock change calculations in order to include the most recent
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available data for each state. The specific inventories used in this report are listed in Table A-202 and this list can be
compared with the full set of summaries available for download (USDA Forest Service 2020b),
Figure A-17: Annual FIA plots (remeasured and not remeasured) across the United States including coastal
Alaska through the 2015 field season
Note: Due to the vast number of plots (where land use is measured even if no forest is present) they appear as spatially
contiguous when displayed at the scale and resolution presented in this figure.
It should be noted that as the FIA program explores expansion of its vegetation inventory beyond the forest
land use to other land uses (e.g., woodlands and urban areas) this will require that subsequent inventory observations
will need to be delineated between forest and other land uses as opposed to a strict forest land use inventory. The forest
C estimates provided here represent C stocks and stock change on managed forest lands (IPCC 2006, see Section 6.1
Representation of the U.S. Land Base), which is how all forest lands are classified. In some cases there are NFI plots that
do not meet the height component of the definition of forest land (Coulston et al. 2016). These plots are identified as
"woodlands" (i.e., not forest land use) and were removed from the forest estimates and classified as grassland.131 Note
that minor differences (approximately 2% less forest land area in the CONUS) in identifying and classifying woodland as
"forest" versus "woodland" exist between the current Resources Planning Act Assessment (RPA) data (Oswalt et al.
2014) and the FIADB (USDA Forest Service 2015b) due to a refined modelling approach developed specifically for
Inventory reporting (Coulston et al. 2016). Plots in the coastal region of the conterminous U.S. were also evaluated using
the National Land Cover Database and the Coastal Change Analysis Program data products to ensure that land areas
were completely accounted for in this region and also that they were not included in both the Wetlands category and the
Forest Land category. This resulted in several NFI plots or subplots being removed from the Forest Land compilation.
131 See the Grassland Remaining Grassland and Land Converted to Grassland sections for details.
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Table A-202: Specific annual forest inventories by state used in development of forest C stock and stock change
estimate

Remeasured Annual Plots

Split Annual Cycle Plots

State
Time 1 Year Range
Time 2 Year Range
State
Time 1 Year Range
Time 2 Year Range
Alabama
2006 - 2012
2013 - 2019
Oklahoma (West)
2010 - 2012
2013 - 2018
.Arizona
2001 - 2008
2009 - 2018
Wyoming
2000
2011 - 2019
Arkansas
2007 - 2014
2014 - 2019



California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma (East)
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas (East)
Texas (West)
Utah
Vermont
Virginia
Washington
West Virginia
2001-
2002-
2008-
2008-
2002-
2005-
2004-
2007-
2008-
2008-
2008-
2005-
2001-
2010-
2008-
2008-
2009
2008
2013
2013
2012
2013
2008
2013
2013
2013
2013
2012
2011
2014
2013
2013
2008-
2003-
2008-
2004-
2008-
2009-
2005-
2008-
2003-
2008-
2008-
2008-
2001-
2008-
2008-
2007-
2008-
2005-
2009-
2004-
2000-
2008-
2002-
2013
2008
2013
2008
2013
2014
2008
2013
2013
2013
2013
2013
2009
2013
2013
2014
2013
2012
2014
2012
2008
2013
2014
2009
2011-
2009-
2013-
2013-
2012-
2014-
2009-
2012-
2013-
2013-
2013-
2012-
2009-
2015-
2013-
2013-
2013-
2015-
2014-
2013-
2009-
2013-
2009-
2013-
2015-
2009-
2013-
2011-
2013-
2013-
2014-
2011-
2013-
2013-
2014-
2013-
2012-
2015-
2014-
2009-
2019
2018
2019
2019
2017
2018
2018
2019
2019
2019
2019
2017
2017
2019
2019
2019
2019
2019
2019
2019
2018
2019
2018
2019
2019
2018
2019
2019
2019
2019
2018
2019
2019
2019
2018
2019
2017
2019
2017
2018
Alaska (Coastal)1
Alaska (Interior)1
2004 - 2017
2014, 2016 - 2017
2014 - 2018
2012 - 2019
A-413
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Wisconsin
2008 - 2013
2013 - 2019
1Plots in Alaska have not been split but are included in this column to conserve space in the Table.
Note: Remeasured annual plots represent a complete inventory cycle between measurements of the same plots while spilt
annual cycle plots represent a single inventory cycle of plots that are split where remeasurements have yet to occur.
Estimating Forest Inventory Plot-Level C-Density
For each inventory plot in each state, field data from the FIA program are used alone or in combination with
auxiliary information (e.g., climate, surficial geology, elevation) to predict C density for each forest ecosystem C pool (i.e.,
aboveground and belowground biomass, dead wood, litter, SOC). In the past, most of the conversion factors and models
used for inventory-based forest C estimates (Smith et al. 2010; Heath et al. 2011) were initially developed as an
extension of the forest C simulation model FORCARB (Heath et al. 2010). The conversion factors and model coefficients
were usually categorized by region and forest type. Thus, region and type are specifically defined for each set of
estimates. More recently, the coarse approaches of the past have been updated with empirical information regarding C
variables for individual forest C pools such as dead wood and litter (e.g., Domke et al. 2013 and Domke et al. 2016).
Factors are applied to the forest inventory data at the scale of NFI plots which are a systematic sample of all forest
attributes and land uses within each state. The results are estimates of C density (T per hectare) for each forest
ecosystem C pool. Carbon density for live trees, standing dead trees, understory vegetation, downed dead wood, litter,
and soil organic matter are estimated. All non-soil C pools except litter and downed dead wood can be separated into
aboveground and belowground components. The live tree and understory C pools are combined into the aboveground
and belowground biomass pools in this Inventory. Similarly, standing dead trees and downed dead wood are pooled as
dead wood in this Inventory. C stocks and fluxes for Forest Land Remaining Forest Land and Land Converted to Forest
Land are reported in forest ecosystem C pools following IPCC (2006).
Live tree C pools
Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at
diameter breast height (d.b.h.) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates are made for
above- and below-ground biomass components. If inventory plots include data on individual trees, tree C is based on
Woodall et al. (2011), which is also known as the component ratio method (CRM), and is a function of volume, species,
diameter, and, in some regions, tree height and site quality. The estimated sound volume (i.e., after rotten/missing
deductions) provided in the tree table of the FIADB is the principal input to the CRM biomass calculation for each tree
(Woodall et al. 2011). The estimated volumes of wood and bark are converted to biomass based on the density of each.
Additional components of the trees such as tops, branches, and coarse roots, are estimated according to adjusted
component estimates from Jenkins et al. (2003). Live trees with d.b.h of less than 12.7 cm do not have estimates of
sound volume in the FIADB, and CRM biomass estimates follow a separate process (see Woodall et al. 2011 for details).
An additional component of foliage, which was not explicitly included in Woodall et al. (2011), was added to each tree
following the same CRM method. Carbon is estimated by multiplying the estimated oven-dry biomass by a C fraction of
0.5 because biomass is 50 percent of dry weight (USDA Forest Service 2020d). Further discussion and example
calculations are provided in Woodall et al. (2011) and Domke et al. (2012).
Understory vegetation
Understory vegetation is a minor component of total forest ecosystem biomass. Understory vegetation is
defined as all biomass of undergrowth plants in a forest, including woody shrubs and trees less than 2.54 cm d.b.h. In this
Inventory, it is assumed that 10 percent of understory C mass is belowground. This general root-to-shoot ratio (0.11) is
near the lower range of temperate forest values provided in IPCC (2006) and was selected based on two general
assumptions: ratios are likely to be lower for light-limited understory vegetation as compared with larger trees, and a
greater proportion of all root mass will be less than 2 mm diameter.
Estimates of C density are based on information in Birdsey (1996), which was applied to FIA permanent plots.
These were fit to the model:
Ratio = e'A_Bxln'livetreeCdensity"	(1)
In this model, the ratio is the ratio of understory C density (T C/ha) to live tree C density (above- and below-
ground) according to Jenkins et al. (2003) and expressed in T C/ha. An additional coefficient is provided as a maximum
ratio; that is, any estimate predicted from the model that is greater than the maximum ratio is set equal to the maximum
A-414 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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ratio. A full set of coefficients are in Table A-203. Regions and forest types are the same classifications described in Smith
et al. (2003). As an example, the basic calculation for understory C in aspen-birch forests in the Northeast is:
Understory (T C/ha) = (live tree C density) x e(°-S55-1-03x ln
-------

Douglas-fir
2.342
1.360
4.731

Fir-Spruce
2.129
1.315
4.749

Hardwoods
1.860
1.110
4.745
RMN
Lodgepole Pine
2.571
1.500
4.773
Other Conifer
2.614
1.518
4.821

Pinyon-Juniper
2.708
2.708
4.820

Ponderosa Pine
2.099
1.344
4.776

Nonstocked
4.430
4.430
4.773

Douglas-fir
5.145
2.232
4.829

Fir-Spruce
2.861
1.568
4.822

Hardwoods
1.858
1.110
4.745
RMS
Lodgepole Pine
3.305
1.737
4.797
Other Conifer
2.134
1.382
4.821

Pinyon-Juniper
2.757
2.757
4.820

Ponderosa Pine
3.214
1.732
4.820

Nonstocked
4.243
4.243
4.797

Bottomland Hardwood
0.917
1.109
1.842

Misc. Conifer
1.601
1.129
4.191

Natural Pine
2.166
1.260
4.161
SC
Oak-Pine
1.903
1.190
4.173

Planted Pine
1.489
1.037
4.124

Upland Hardwood
2.089
1.235
4.170

Nonstocked
4.044
4.044
4.170

Bottomland Hardwood
0.834
1.089
1.842

Misc. Conifer
1.601
1.129
4.191

Natural Pine
1.752
1.155
4.178
SE
Oak-Pine
1.642
1.117
4.195

Planted Pine
1.470
1.036
4.141

Upland Hardwood
1.903
1.191
4.182

Nonstocked
4.033
4.033
4.182
a Prediction of ratio of understory C to live tree C is based on the model: Ratio=exp(A - B x ln(tree_carbon_tph)), where "ratio"
is the ratio of understory C density to live tree (above-and below- ground) C density, and "tree_carbon_density" is live tree
(above-and below- ground) C density in T C/ha. Note that this ratio is multiplied by tree C density on each plot to produce
understory vegetation.
b Regions and types as defined in Smith et al. (2003).
c Maximum ratio: any estimate predicted from the model that is greater than the maximum ratio is set equal to the maximum
ratio.
Dead Wood
The standing dead tree estimates are primarily based on plot-level measurements (Domke et al. 2011; Woodall
et al. 2011). This C pool includes aboveground and belowground (coarse root) mass and includes trees of at least 12.7 cm
d.b.h. Calculations follow the basic CRM method applied to live trees (Woodall et al. 2011) with additional modifications
to account for decay and structural loss. In addition to the lack of foliage, two characteristics of standing dead trees that
can substantially affect C mass are decay, which affects density and thus specific C fraction (Domke et al. 2011; Harmon
et al. 2011), and structural loss such as branches and bark (Domke et al. 2011). A C fraction of 0.5 is used for standing
dead trees (USDA forest Service 2020d).
Downed dead wood, inclusive of logging residue, are sampled on a subset of NFI plots. Despite a reduced
sample intensity, a single down woody material population estimate (Woodall et al. 2010; Domke et al. 2013; Woodall et
al. 2013) per state is now incorporated into these empirical downed dead wood estimates. Downed dead wood is
defined as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are not attached to live or
standing dead trees. It also includes stumps and roots of harvested trees. Ratio estimates of downed dead wood to live
tree biomass were developed using FORCARB2 simulations and applied at the plot level (Smith et al. 2004). Estimates for
downed dead wood correspond to the region and forest type classifications described in Smith et al. (2003). A full set of
ratios is provided in Table A-221. An additional component of downed dead wood is a regional average estimate of
logging residue based on Smith et al. (2006) applied at the plot level. These are based on a regional average C density at
age zero and first order decay; initial densities and decay coefficients are provided in Table A-222. These amounts are
A-416 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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added to explicitly account for downed dead wood following harvest. The sum of these two components are then
adjusted by the ratio of population totals; that is, the ratio of plot-based to modeled estimates (Domke et al. 2013). An
example of this 3-part calculation for downed dead wood in a 25-year-old naturally regenerated loblolly pine forest with
82.99 T C/ha in live trees (Jenkins et al. 2003) in Louisiana is as follows:
First, an initial estimate from live tree C density and Table A-204 (SC, Natural Pine)
C density = 82.99 x 0.068 = 5.67 (T C/ha)
Second, an average logging residue from age and Table A-204 (SC, softwood)
C density = 5.5 x e(-25/17.9) = 1.37 (T C/ha)
Third, adjust the sum by the downed dead wood ratio plot-to-model for Louisiana, which was 27.6/31.1 = 0.886
C density = (5.67 + 1.37) x 0.886 = 6.24 (T C/ha)
Table A-204: Ratio for Estimating Downed Dead Wood by Region and Forest Type
Region3
Forest type3
Ratiob

Aspen-Birch
0.078

MBB/Other Hardwood
0.071

Oak-Hickory
0.068
NE
Oak-Pine
0.061
Other Pine
0.065

Spruce-Fir
0.092

White-Red-Jack Pine
0.055

Nonstocked
0.019

Aspen-Birch
0.081

Lowland Hardwood
0.061

Maple-Beech-Birch
0.076
NLS
Oak-Hickory
0.077

Pine
0.072

Spruce-Fir
0.087

Nonstocked
0.027

Conifer
0.073

Lowland Hardwood
0.069
NPS
Maple-Beech-Birch
0.063
Oak-Hickory
0.068

Oak-Pine
0.069

Nonstocked
0.026

Douglas-fir
0.091

Fir-Spruce
0.109

Hardwoods
0.042
PSW
Other Conifer
0.100

Pinyon-Juniper
0.031

Redwood
0.108

Nonstocked
0.022

Douglas-fir
0.103

Fir-Spruce
0.106

Hardwoods
0.027
PWE
Lodgepole Pine
0.093

Pinyon-Juniper
0.032

Ponderosa Pine
0.103

Nonstocked
0.024

Douglas-fir
0.100

Fir-Spruce
0.090
PWW
Other Conifer
0.073
Other Hardwoods
0.062

Red Alder
0.095
Western Hemlock	0.099
A-417
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Nonstocked
0.020

Douglas-fir
0.062

Fir-Spruce
0.100

Hardwoods
0.112
RMN
Lodgepole Pine
0.058
Other Conifer
0.060

Pinyon-Juniper
0.030

Ponderosa Pine
0.087

Nonstocked
0.018

Douglas-fir
0.077

Fir-Spruce
0.079

Hardwoods
0.064
RMS
Lodgepole Pine
0.098
Other Conifer
0.060

Pinyon-Juniper
0.030

Ponderosa Pine
0.082

Nonstocked
0.020

Bottomland Hardwood
0.063

Misc. Conifer
0.068

Natural Pine
0.068
SC
Oak-Pine
0.072

Planted Pine
0.077

Upland Hardwood
0.067

Nonstocked
0.013

Bottomland Hardwood
0.064

Misc. Conifer
0.081

Natural Pine
0.081
SE
Oak-Pine
0.063

Planted Pine
0.075

Upland Hardwood
0.059

Nonstocked
0.012
a Regions and types as defined in Smith et al. (2003).
b The ratio is multiplied by the live tree C density on a plot to produce downed dead wood C density (T C/ha).
Table A-205: Coefficients for Estimating Logging Residue Component of Downed Dead Wood

Forest Type



Groupb (softwood/
Initial C

Region3
hardwood)
Density (T/ha)
Decay Coefficient
Alaska
hardwood
6.9
12.1
Alaska
softwood
8.6
32.3
NE
hardwood
13.9
12.1
NE
softwood
12.1
17.9
NLS
hardwood
9.1
12.1
NLS
softwood
7.2
17.9
NPS
hardwood
9.6
12.1
NPS
softwood
6.4
17.9
PSW
hardwood
9.8
12.1
PSW
softwood
17.5
32.3
PWE
hardwood
3.3
12.1
PWE
softwood
9.5
32.3
PWW
hardwood
18.1
12.1
PWW
softwood
23.6
32.3
RMN
hardwood
7.2
43.5
RMN
softwood
9.0
18.1
RMS
hardwood
5.1
43.5
RMS
softwood
3.7
18.1
SC
hardwood
4.2
8.9
SC
softwood
5.5
17.9
A-418 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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SE
SE
hardwood
softwood
6.4
7.3
8.9
17.9
a Regions are defined in Smith et al. (2003) with the addition of coastal Alaska.
b Forest types are according to majority hardwood or softwood species.
Litter carbon
Carbon in the litter layer is currently sampled on a subset of the NFI plots. Litter C is the pool of organic C
(including material known as duff, humus, and fine woody debris) above the mineral soil and includes woody fragments
with diameters of up to 7.5 cm. Because litter attributes are only collected on a subset of NFI plots, a model (3) was
developed to predict C density based on plot/site variables for plots that lacked litter information (Domke et al. 2016):
P(FFCFull) =f(lat, lort, elev, fortypgrp, above, ppt, tmax, gmi) + u (3)
Where lat = latitude, lort = longitude, elev = elevation, fortypgrp = forest type group, above = aboveground live
tree C (trees > 2.54 cm dbh), ppt = mean annual precipitation, tmax = average maximum temperature, gmi = the ratio of
precipitation to potential evapotranspiration, u = the uncertainty in the prediction resulting from the sample-based
estimates of the model parameters and observed residual variability around this prediction.
Due to data limitations in certain regions and inventory periods a series of reduced non-parametric models,
which did not include climate variables, were used rather than replacing missing variables with imputation techniques.
Database records used to compile estimates for this report were grouped by variable availability and the approaches
described herein were applied. Litter C predictions are expressed as density (T ha-1).
Soil organic carbon
This section provides a summary of the methodology used to predict SOC for this report. A complete
description of the approach is in Domke et al. (2017). The data used to develop the modeling framework to predict SOC
on forest land came from the NFI and the International Soil Carbon Network. Since 2001, the FIA program has collected
soil samples on every 16th base intensity plot (approximately every 2,428 ha) distributed approximately every 38,848 ha,
where at least one forested condition exists (Woodall et al. 2010). On fully forested plots, mineral and organic soils were
sampled adjacent to subplots 2 by taking a single core at each location from two layers: 0 to 10.16 cm and 10.16 to 20.32
cm. The texture of each soil layer was estimated in the field, and physical and chemical properties were determined in
the laboratory (U.S. Forest Service 2011). For this analysis, estimates of SOC from the NFI were calculated following
O'Neill et al. (2005):
YSOC	= C: ¦ BD: ¦ ti ¦ ucf	(4)
t—t	FIA_TOTAL i	i i J
Where y cqc	= tota' mass (Mg C ha-1) of the mineral and organic soil C over all /th layers, C = percent
Lu	FIAJTOTAL	1
organic C in the /'th layer, BDj = bulk density calculated as weight per unit volume of soil (g-cm-3) at the /'th soil layer, t =
thickness (cm) of the /'th soil layer (either 0 to 10.16 cm or 10.16 to 20.32 cm), and ucf= unit conversion factor (100).
The SOCFM_TOwestimates from each plot were assigned by forest condition on each plot, resulting in 3,667
profiles with SOC layer observations at 0 to 10.16 and 10.16 to 20.32 cm depths. Since the United States has historically
reported SOC estimates to a depth of 100 cm (Heath et al. 2011, USEPA 2015), International Soil Carbon Monitoring
Network (ISCN) data from forests in the United States were harmonized with the FIA soil layer observations to develop
model functions of SOC by soil order to a depth of 100 cm. All observations used from the ISCN were contributed by the
Natural Resources Conservation Service. A total of 16,504 soil layers from 2,037 profiles were used from ISCN land uses
defined as deciduous, evergreen, or mixed forest. The FIA-ISCN harmonized dataset used for model selection and
prediction included a total of 5,704 profiles with 23,838 layer observations at depths ranging from 0 to 1,148 cm.
The modeling framework developed to predict SOC for this report was built around strategic-level forest and
soil inventory information and auxiliary variables available for all FIA plots in the United States. The first phase of the
new estimation approach involved fitting models using the midpoint of each soil layer from the harmonized dataset and
SOC estimates at those midpoints. Several linear and nonlinear models were evaluated, and a log-log model provided the
optimal fit to the harmonized data:
A-419
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log j 0 SOC = I + log j 0 Depth
(5)
Where log 10 SOCt = SOC density (Mg C ha-1 cm depth-1) at the midpoint depth, I = intercept,
log 10 Depth = profile midpoint depth (cm).
The model was validated by partitioning the complete harmonized dataset multiple times into training and
testing groups and then repeating this step for each soil order to evaluate model performance by soil order. Extra sum of
squares F tests were used to evaluate whether there were statistically significant differences between the model
coefficients from the model fit to the complete harmonized dataset and models fit to subsets of the data by soil order.
Model coefficients for each soil order were used to predict SOC for the 20.32 to 100 cm layer for all FIA plots with soil
profile observations. Next, the SOC layer observations from the FIA and predictions over the 100 cm profile for each FIA
plot were summed:
SOCx oo = SOCFIA TOTAL + SOC20 ! oo	(6)
Where SOC100 = total estimated SOC density from 0-100 cm for each forest condition with a soil sample in the
FIA database, SOCFIA TOTALas previously defined in model (4), SOC2o-ioo= predicted SOC from 20.32 to 100 cm from
model (5).
In the second phase of the modeling framework, SOC10o estimates for FIA plots were used to predict SOC for
plots lacking SOC10o estimates using a non-parametric model, this particular machine learning tool used bootstrap
aggregating (i.e., bagging) to develop models to improve prediction (Breimen 2001). It also relies on random variable
selection to develop a forest of uncorrelated regression trees. These trees recognize the relationship between a
dependent variable, in this case SOCl00, and a set of predictor variables. All relevant predictor variables—those that may
influence the formation, accumulation, and loss of SOC—from annual inventories collected on all base intensity plots and
auxiliary climate, soil, and topographic variables obtained from the PRISM climate group (Northwest Alliance 2015),
Natural Resources Conservation Service (NRCS 2015), and U.S. Geological Survey (Danielson and Gesch 2011),
respectively, were included in the analysis. Due to regional differences in sampling protocols, many of the predictor
variables included in the variable selection process were not available for all base intensity plots. To avoid problems with
data limitations, pruning was used to reduce the models to the minimum number of relevant predictors (including both
continuous and categorical variables) without substantial loss in explanatory power or increase in root mean squared
error (RMSE). The general form of the full non-parametric models were:
P(SOC) = f (lat, Ion, elev, fortypgrp, ppt, t max, gmi, order, surfgeo)	(7)
Where lat = latitude, lofl = longitude, elev= elevation, fortypgrp = forest type group, ppt = mean
annual precipitation, t lllciX = average maximum temperature, gmi = the ratio of precipitation to potential
evapotranspiration, Order = soil order, surfgeo = surficial geological description.
Compilation of population estimates using NFI plot data
Methods for the conterminous United States
The estimation framework is fundamentally driven by the annual NFI. Unfortunately, the annual NFI does not
extend to 1990 and the periodic data from the NFI are not consistent (e.g., different plot design) with the annual NFI
necessitating the adoption of a system to predict the annual C parameters back to 1990. To facilitate the C prediction
parameters, the estimation framework is comprised of a forest dynamics module (age transition matrices) and a land use
dynamics module (land area transition matrices). The forest dynamics module assesses forest uptake, forest aging, and
disturbance effects (i.e., disturbances such as wind, fire, and floods identified by foresters on inventory plots). The land
use dynamics module assesses C stock transfers associated with afforestation and deforestation (e.g., Woodall et al.
2015b). Both modules are developed from land use area statistics and C stock change or C stock transfer by age class.
The required inputs are estimated from more than 625,000 forest and nonforest observations in the NFI database (U.S.
A-420 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Forest Service 2020a-c). Model predictions for before or after the annual NFI period are constructed from the estimation
framework using only the annual observations. This modeling framework includes opportunities for user-defined
scenarios to evaluate the impacts of land use change and disturbance rates on future C stocks and stock changes. As
annual NFIs have largely completed at least one cycle and been remeasured, age and area transition matrices can be
empirically informed. In contrast, as annual inventories in west Oklahoma and Wyoming are still undergoing their first
complete cycle they are still in the process of being remeasured, and as a result theoretical transition matrices need to
be developed.
Wear and Coulston (2015) and Coulston et al. (2015) provide the framework for the model. The overall
objective is to estimate unmeasured historical changes and future changes in forest C parameters consistent with annual
NFI estimates. For most regions, forest conditions are observed at time t0 and at a subsequent time ti=t0+s, where s is
the time step (time measured in years) and is indexed by discrete (5 year) forest age classes. The inventory from t0 is
then predicted back to the year 1990 and projected from ti to 2019. This prediction approach requires simulating
changes in the age-class distribution resulting from forest aging and disturbance events and then applying C density
estimates for each age class. For all states in the conterminous U.S. (except for Wyoming and west Oklahoma) age class
transition matrices are estimated from observed changes in age classes between t0 and ti. In west Oklahoma and
Wyoming only one inventory was available (t0) so transition matrices were obtained from theory but informed by the
condition of the observed inventory to predict from t0 to 1990 and predict from t0 to 2019.
Theoretical Age Transition Matrices
Without any mortality-inducing disturbance, a projection of forest conditions would proceed by increasing all
forest ages by the length of the time step until all forest resided in a terminal age class where the forest is retained
indefinitely (this is by assumption, where forest C per unit area reaches a stable maximum). For the most basic case,
disturbances (e.g., wildfire or timber harvesting) can reset some of the forest to the first age class. Disturbance can also
alter the age class in more subtle ways. If a portion of trees in a multiple-age forest dies, the trees comprising the
average age calculation change, thereby shifting the average age higher or lower (generally by one age class).
With n age classes, the age transition matrix (T) is an n x n matrix, and each element (Tqr) defines the
proportion of forest area in class q transitioning to class r during the time step (s). The values of the elements of T
depend on a number of factors, including forest disturbances such as harvests, fire, storms, and the value of s, especially
relative to the span of the age classes. For example, holding area fixed, allowing for no mortality, defining the time step s
equivalent to the span of age classes, and defining five age classes results in:
/°
0
0
0

1
0
0
0
0
0
1
0
0
0
0
0
1
0
0
\o
0
0
1
1)
where all forest area progresses to the next age class and forests within the terminal age class are retained
forever. With this version of T, after five time steps all forests would be in the terminal age class. Relaxing these
assumptions changes the structure of T. If all disturbances, including harvesting and fire, that result in stand
regeneration are accounted for and stochastic elements in forest aging are allowed, T defines a traditional Lefkovitch
matrix population model (e.g., Caswell 2001) and becomes:
T =
t1 d±
d-2
d-3
d4
h
1 — ^2 — d2
0
0
0
*2
1 — £3 — ^3
0
0
0
£3
1 - t4 - d.
0
0
0
t4
0
0
0
1 - dj
(9)
Where tq is the proportion of forest of age class q transitioning to age class q+1, dq is the proportion of age
class q that experiences a stand-replacing disturbance, and (1 — tq — dq) is the proportion retained within age class q
(Tqr).
A-421
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Projections and Backcast for West Oklahoma and Wyoming
Projections of forest C in west Oklahoma and Wyoming are based on a life stage model:
ACt = Ct+m — Ct = (FtT — Ft) ¦ Den + Lt ¦ Den	(10)
In this framework T is an age transition matrix that shifts the age distribution of the forest F. The difference in
forest area by age class between time t and t+s is FtT-Ft. This quantity is multiplied by C density by age class (Den) to
estimate C stock change of forest remaining forest between t and t+s. Land use change is accounted for by the addition
of Lt-Den, where Lt identifies the age distribution of net land shifts into or out of forests. A query of the forest inventory
databases provides estimates of F and Den, while inventory observations and modeling assumptions are used to
estimate T. By expanding Den to a matrix of C contained in all the constituent pools of forest carbon, projections for all
pools are generated.
Land use change is incorporated as a 1 x n vector L, with positive entries indicating increased forest area and
negative entries indicating loss of forest area, which provides insights of net change only. Implementing a forest area
change requires some information and assumptions about the distribution of the change across age classes (the n
dimension of L). In the eastern states, projections are based on the projection of observed gross area changes by age
class. In western states, total forest area changes are applied using rules. When net gains are positive, the area is added
to the youngest forest age class; when negative, area is subtracted from all age classes in proportion to the area in each
age class category.
Backcasting forest C inventories generally involve the same concepts as forecasting. An initial age class
distribution is shifted at regular time steps backwards through time, using a transition matrix (B):
Ft-S =Ft B	(11)
B is constructed based on similar logic used for creating T. The matrix cannot simply be derived as the inverse of
T (Ft_s = FtT'1) because of the accumulating final age class (i.e., T does not contain enough information to determine
the proportion of the final age class derived from the n-1 age class and the proportion that is retained in age class n from
the previous time step).132 However, B can be constructed using observed changes from the inventory and assumptions
about transition/accumulation including nonstationary elements of the transition model:
(*-I
B =
> d«
b2
0
0
0
t-'q




di
l-b2
b3
0
0
dz
0
1 -b3
b4
0
d-3
0
0
1- b4
b r
d4
0
0
0
1 — b.
(12)
Forest area changes need to be accounted for in the backcasts as well:
Ft_s = FtB - Lt	(13)
Where Lt is the forest area change between ti and t0 as previously defined.
In west Oklahoma and Wyoming the theoretical life-stage models described by matrices (9) and (10) were
applied. The disturbance factors (d) in both T and B are obtained from the current NFI by assuming that the area of
forest in age class 1 resulted from disturbance in the previous period, the area in age class 2 resulted from disturbance in
the period before that, and so on. The source of disturbed forest was assumed to be proportional to the area of forest in
each age class. For projections (T), the average of implied disturbance for the previous two periods was applied. For the
132 Simulation experiments show that a population that evolves as a function of T can be precisely predicted using T1. However,
applying the inverse to a population that is not consistent with the long-run outcomes of the transition model can result in
predictions of negative areas within some stage age classes.
A-422 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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backcast (B), the disturbance frequencies implied by the age class distribution for each time step are moved. For areas
with empirical transition matrices, change in forest area (Lt) was backcasted/projected using the change in forest area
observed for the period t0 to ti.
Projections and Backcast for CONUS (excluding west Oklahoma and Wyoming)
For all states in the conterminous United States (with the exception of west Oklahoma and Wyoming)
remeasured plots were available. When remeasured data are available, the previously described approach is extended to
estimate change more directly; in this case ACt=Ft-6C, where AC is net stock change by pool within the analysis area, F is
as previously defined, and 6C is an n x cp matrix of per unit area forest C stock change per year by pool (cp) arrayed by
forest age class. Inter-period forest C dynamics are previously described, and the age transition matrix (T) is estimated
from the observed data directly. Forest C change at the end of the next period is defined as: ACt+s = Ft-T-6C. Land use
change and disturbances such as cutting, fire, weather, insects, and diseases were incorporated by generalizing to
account for the change vectors and undisturbed forest remaining as undisturbed forest:
Where Atd = area by age class of each mutually exclusive land category in L which includes d disturbances at time t.
L = (FF, NFF, FNF, Fcut, Ffire, Fweather, Fid) where FF=undisturbed forest remaining as undisturbed forest,
NFF=nonforest to forest conversion, FNF=forest to nonforest conversion, Fcut=cut forest remaining as forest, Ffire=forest
remaining as forest disturbed by fire, Fweather=forest remaining as forest disturbed by weather, and Fid=forest
remaining as forest disturbed by insects and diseases. In the case of land transfers (FNF and NFF), Td is an n x n identity
matrix and 5Cd is a C stock transfer rate by age. Paired measurements for all plots in the inventory provide direct
estimates of all elements of SC,Td, and Atd matrices.
Predictions are developed by specifying either Ft+s or At+sd for either a future or a past state. To move the
system forward, T is specified so that the age transition probabilities are set up as the probability between a time 0 and a
time 1 transition. To move the system backward, T is replaced by B so that the age transition probabilities are for
transitions from time 1 to time 0. Forecasts were developed by assuming the observed land use transitions and
disturbance rates would continue for the next 5 years. Prediction moving back in time were developed using a Markov
Chain process for land use transitions, observed disturbance rates for fire, weather, and insects. Historical forest cutting
was incorporated by using the relationship between the area of forest cutting estimated from the inventory plots and
the volume of roundwood production from the Timber Products Output program (U.S. Forest Service 2020d). This
relationship allowed for the modification of Fcut such that it followed trends described by Oswalt et al. (2014).
Methods for Alaska
Inventory and sampling
The NFI has been measuring plots in southeast and southcentral coastal Alaska as part of the annual NFI since
2004. In 2014, a pilot inventory was established in the Tanana Valley State Forest and Tetlin National Wildlife Refuge in
Interior Alaska. This pilot inventory was a collaboration between the USDA Forest Service, FIA program, the National
Aeronautical and Space Administration, and many other federal, state, and local partners. This effort resulted in the
establishment of 98 field plots which were measured during the summer of 2014 and integrated with NASA's Goddard
LiDAR/Hyperspectral/Thermal (G-LiHT) imaging system. Given the remote nature of Interior Alaska forest, the NFI plots
in the pilot campaign were sampled at a lower intensity than base NFI plots (1 plot per 2403 ha) in the CONUS and
coastal Alaska. Several plot-level protocols were also adapted to accommodate the unique conditions of forests in this
region (see Pattison et al. 2018 for details on plot design and sampling protocols). The pilot field campaign became
operational in 2016 and plots measured on a 1/5 intensity (1 plot per 12013 ha) from 2014, 2016, and 2017 from the
Interior Alaska NFI were used (n = 446) with base-intensity annual NFI plots from coastal AK (n = 2748) in this analysis.
A spatially balanced sampling design was used to identify field sample locations across all of Alaska following
standard FIA procedures with a tessellation of hexagons and one sample plot selected per hexagon -1/5 intensity in
interior Alaska and base-intensity in coastal Alaska (Bechtold and Patterson 2005). The sampling locations were classified
as forest or non-forest using the NLCD from 2001 and 2011. It is important to note that this is different from how NFI
plots are classified into land cover and land use categories in the CONUS where high resolution areal imagery is used.
(14)
A-423
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Since the fine-scale remotely sensed imagery (National Agriculture Imagery Program; NAIP 2015) used in the
conterminous U.S. were not available for AK and given that the NLCD has been used to classify land use categories in
Alaska in the Representation of the U.S. Land Base in this Inventory, the NLCD was the most consistent and credible
option for classification. Next, the forest land was further classified as managed or unmanaged following the definition in
the Representation of the U.S. Land Base and using similar procedures (see Ogle et al. 2018 for details on the managed
land layer for the U.S.).
While only a subset of the total NFI sample was available at the time of this Inventory, all NFI plot locations
within the sampling frame were used in this analysis. Auxiliary climate, soil, structural, disturbance, and topographic
variables were harmonized with each plot location and year of occurrence (if relevant and available) over the entire time
series (1990 to 2019).
Prediction
The harmonized data were used to predict plot-level parameters using non-parametric random forests (RF) for
regression, a machine learning tool that uses bootstrap aggregating (i.e., bagging) to develop models to improve
prediction (Breiman 2001). Random forests also relies on random variable selection to develop a forest of uncorrelated
regression trees. These trees uncover the relationship between a dependent variable (e.g., live aboveground biomass
carbon) and a set of predictor variables. The RF analysis included predictor variables (n > 100) that may influence carbon
stocks within each forest ecosystem pool at each plot location over the entire time series. To avoid problems with data
limitations over the time series, variable pruning was used to reduce the RF models to the minimum number of relevant
predictors without substantial loss in explanatory power or increase in root mean squared error (RMSE; see Domke et al.
2017, Domke et al. In prep for more information). The harmonized dataset used to develop the RF models for each plot-
level parameter were partitioned 10 times into training (70 percent) and testing (30 percent) groups and the results were
evaluated graphically and with a variety of statistical metrics including Spearman's rank correlation, equivalence tests
(Wellek 2003), as well as RMSE. All analyses were conducted using R statistical software (R Core Team 2018).
The RF predictions of carbon stocks for the year 2016 were used as a baseline for plots that have not yet been
measured. Next, simple linear regression was used to predict average annual gains/losses by forest ecosystem carbon
pool using the chronosequence of plot measurements available at the time of this Inventory. These predicted
gains/losses were applied over the time series from the year of measurement or the 2016 base year in the case of plots
that have not yet been measured. Since the RF predictions of carbon stocks and the predicted gains/losses were
obtained from empirical measurements on NFI plots that may have been disturbed at some point over the time series,
the predictions inherently incorporate gains/losses associated with natural disturbance and harvesting. That said, there
was no evidence of fire disturbance on the plots that have been measured to date. To account for carbon losses
associated with fire, carbon stock predictions for plots that have not been measured but were within a fire perimeter
during the Inventory period were adjusted to account for area burned (see Table A-216) and the IPCC (Table 2.6, IPCC
2006) default combustion factor for boreal forests was applied to all live, dead, and litter biomass carbon stocks in the
year of the disturbance. The plot-level predictions in each year were then multiplied by the area they represent within
the sampling frame to compile population estimates over the time series for this Inventory.
Forest Land Remaining Forest Land Area Estimates
Forest land area estimates in section 6.2 Forest Land Remaining Forest Land (CRF Category 4A1) of this
Inventory are compiled using NFI data. Forest Land area estimates obtained from these data are also used as part of
section 6.1 Representation of the U.S. Land Base (CRF Category 4.1). The Forest Land area estimates in section 6.2 do not
include Hawaii as insufficient data is available from the NFI to compile area estimates over the entire time series. The
National Land Cover Dataset is used in addition to NFI estimates in section 6.2 Representation of the U.S. Land Base and
Forest Land in Hawaii are included in that section. This results in small differences in the managed Forest Land area in
sections 6.1 and 6.2 of this Inventory (Table A-214). There are also other factors contributing to the small differences
such as harmonization of aspatial and spatial data across all land use categories in section 6.1 over the entire Inventory
time series.
Carbon in Harvested Wood Products
Estimates of the Harvested Wood Product (HWP) contribution to forest C sinks and emissions (hereafter called
"HWP Contribution") are based on methods described in Skog (2008) using the WOODCARB II model and the U.S. forest
products module (Ince et al. 2011). These methods are based on IPCC (2006) guidance for estimating HWP C. The 2006
IPCC Guidelines provide methods that allow Parties to report HWP Contribution using one of several different accounting
A-424 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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approaches: production, stock change, and atmospheric flow, as well as a default method. The various approaches are
described below. The approaches differ in how HWP Contribution is allocated based on production or consumption as
well as what processes (atmospheric fluxes or stock changes) are emphasized.
•	Production approach: Accounts for the net changes in C stocks in forests and in the wood products pool, but
attributes both to the producing country.
•	Stock-change approach: Accounts for changes in the product pool within the boundaries of the consuming
country.
•	Atmospheric-flow approach: Accounts for net emissions or removals of C to and from the atmosphere within
national boundaries. Carbon removal due to forest growth is accounted for in the producing country while C
emissions to the atmosphere from oxidation of wood products are accounted for in the consuming country.
•	Default approach: Assumes no change in C stocks in HWP. IPCC (2006) requests that such an assumption be
justified if this is how a Party is choosing to report.
The United States uses the production accounting approach (as in previous years) to report HWP Contribution
(Table A-206) but estimates for all three approaches are provides in 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 five HWP variables that can be used to calculate HWP contribution for the stock change and
atmospheric flow approaches for imports and exports are provided in Table A-208. The HWP variables estimated are:
(IA)	annual change of C in wood and paper products in use in the United States,
(IB)	annual change of C in wood and paper products in SWDS in the United States,
(2A) annual change of C in wood and paper products in use in the United States and other countries where the
wood came from trees harvested in the United States,
(2B) annual change of C in wood and paper products in SWDS in the United States and other countries where
the wood came from trees harvested in the United States,
(3)	Carbon in imports of wood, pulp, and paper to the United States,
(4)	Carbon in exports of wood, pulp and paper from the United States, and
(5)	Carbon in annual harvest of wood from forests in the United States. The sum of these variables yield
estimates for HWP contribution under the production accounting approach.
Table A-206: Harvested Wood Products from Wood Harvested in the United States—Annual Additions of C to
Stocks and Total Stocks under the Production Approach
Year
Net C additions per year
MMTC per year)
Total C stocks (MMT C)
Total
Products in use
Products in SWDS

Total
Total
Total
Products in use
Products in SWDS
1990
(33.8)
(14.9)
(18.8)
1895
1249
646
1991
(33.8)
(16.3)
(17.4)
1929
1264
665
1992
(32.9)
(15.0)
(17.9)
1963
1280
683
1993
(33.4)
(15.9)
(17.5)
1996
1295
701
1994
(32.3)
(15.1)
(17.2)
2029
1311
718
1995
(30.6)
(14.1)
(16.5)
2061
1326
735
1996
(32.0)
(14.7)
(17.3)
2092
1340
752
1997
(31.1)
(13.4)
(17.7)
2124
1355
769
1998
(32.5)
(14.1)
(18.4)
2155
1368
787
1999
(30.8)
(12.8)
(18.0)
2188
1382
805
2000
(25.5)
(8.7)
(16.8)
2218
1395
823
2001
(26.8)
(9.6)
(17.2)
2244
1404
840
2002
(25.6)
(9.4)
(16.2)
2271
1413
857
2003
(28.4)
(12.1)
(16.3)
2296
1423
873
2004
(28.7)
(12.4)
(16.4)
2325
1435
890
2005
(28.9)
(11.6)
(17.3)
2353
1447
906
A-425
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2006
(27.3)
(10.0)
(17.4)
2382
1459
923
2007
(20.8)
(3.7)
(17.1)
2410
1469
941
2008
(14.9)
1.8
(16.7)
2430
1473
958
2009
(16.6)
(0.0)
(16.6)
2445
1471
974
2010
(18.8)
(2.0)
(16.8)
2462
1471
991
2011
(19.4)
(2.4)
(17.0)
2481
1473
1008
2012
(20.9)
(3.7)
(17.1)
2500
1475
1025
2013
(22.5)
(5.3)
(17.3)
2521
1479
1042
2014
(23.4)
(6.1)
(17.4)
2543
1484
1059
2015
(24.2)
(6.7)
(17.5)
2567
1490
1076
2016
(25.2)
(7.6)
(17.6)
2591
1497
1094
2017
(26.1)
(8.3)
(17.9)
2616
1505
1112
2018
(26.9)
(8.6)
(18.3)
2642
1513
1129
2019
(29.6)
(10.7)
(18.9)
2669
1521
1148
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
Table A-207: Comparison of Net Annual Change in Harvested Wood Products C Stocks Using Alternative
Accounting Approaches (kt CO2 Eq./year)
HWP Contribution to LULUCF Emissions/ Removals (MMT C02 Eq.)
Inventory Year
Stock-Change
Approach
Atmospheric Flow
Approach
Production
Approach
1990
(116.6)
(131.4)
(123.8)
1991
(120.2)
(131.6)
(123.8)
1992
(127.1)
(127.8)
(120.7)
1993
(130.3)
(129.9)
(122.5)
1994
(126.0)
(128.0)
(118.4)
1995
(122.3)
(122.5)
(112.2)
1996
(131.3)
(127.4)
(117.3)
1997
(137.2)
(122.8)
(114.2)
1998
(147.1)
(127.2)
(119.0)
1999
(141.2)
(120.2)
(112.9)
2000
(125.0)
(100.3)
(93.4)
2001
(130.7)
(103.3)
(98.2)
2002
(125.8)
(98.5)
(93.7)
2003
(143.2)
(107.9)
(104.1)
2004
(142.1)
(109.7)
(105.4)
2005
(136.6)
(112.0)
(106.0)
2006
(113.6)
(109.8)
(100.3)
2007
(72.6)
(88.1)
(76.1)
2008
(41.8)
(70.0)
(54.5)
2009
(48.2)
(79.8)
(60.8)
2010
(51.4)
(92.2)
(69.1)
2011
(59.0)
(95.2)
(71.0)
2012
(72.4)
(102.9)
(76.5)
2013
(85.7)
(109.4)
(82.6)
2014
(92.8)
(113.2)
(86.0)
2015
(99.4)
(116.2)
(88.7)
2016
(103.2)
(120.1)
(92.4)
2017
(132.1)
(119.9)
(95.7)
2018
(135.0)
(125.5)
(98.8)
2019
(152.7)
(129.8)
(108.5)
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
A-426 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Table A-208: Harvested Wood Products Sectoral Background Data for LULUCF—United States

1A
IB
2A
2B
3
4
5
6
7
8
Inventory year
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
HWP

Change in
Change in
Change in
Change in
Imports of
Exports of
Domestic
release of C to
release of C to
Contribution to

stock of
stock of HWP
stock of
stock of
wood, and
wood, and
Harvest
the
the
AFOLU C02

HWP in use
in SWDS from
HWP in use
HWP in
paper
paper

atmosphere
atmosphere
emissions/

from
consumption
produced
SWDS
products plus
products plus

from HWP
from HWP
removals

consumption

from
produced
wood fuel.
wood fuel.

consumption
(including




domestic
from
pulp.
pulp.

(from
firewood)




harvest
domestic
recovered
recovered

fuelwood and
where wood





harvest
paper.
paper.

products in
came from






roundwood/
roundwood/

use and
domestic






chips
chips

products in
harvest (from









SWDS)
products in










use and










products in










SWDS)


ACHWP IU
ACHWP
AC HWP IU
ACHWP
PIM
PEX
H
•fCHWP DC
•fCHWP DH


DC
SWDS DC
DH
SWDS DH















MMTC/yr
MMT C02/yr
1990
13.2
18.6
14.9
18.8
11.6
15.6
144.4
108.6
110.7
(123.8)
1995
17.0
16.3
14.1
16.5
16.7
16.7
134.5
101.1
103.9
(112.2)
2000
16.5
17.6
8.7
16.8
22.1
15.3
127.9
100.5
102.4
(93.4)
2005
18.7
18.6
11.6
17.3
25.5
18.8
120.1
89.6
91.2
(106.0)
2010
(2.1)
16.1
2.0
16.8
13.9
25.0
102.7
77.5
83.9
(69.1)
2015
10.2
16.9
6.7
17.5
18.5
23.1
119.1
87.4
94.9
(88.7)
2016
11.1
17.0
7.6
17.6
18.5
23.1
122.1
89.3
96.9
(92.4)
2017
18.1
17.9
8.3
17.9
22.6
19.3
108.1
75.4
82.0
(95.7)
2018
17.9
18.9
8.6
18.3
21.6
19.0
110.1
75.9
83.2
(98.8)
2019
21.6
20.0
10.7
18.9
24.7
18.5
105.8
70.4
76.2
(108.5)
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
A-427
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Annual estimates of variables 1A, IB, 2A and 2B were calculated by tracking the additions to and removals from
the pool of products held in end uses (e.g., products in uses such as housing or publications) and the pool of products
held in SWDS. In the case of variables 2A and 2B, the pools include products exported and held in other countries and
the pools in the United States exclude products made from wood harvested in other countries. Solidwood products
added to pools include lumber and panels. End-use categories for solidwood include single and multifamily housing,
alteration and repair of housing, and other end uses. There is one product category and one end-use category for paper.
Additions to and removals from pools are tracked beginning in 1900, with the exception that additions of softwood
lumber to housing begins in 1800. Solidwood and paper product production and trade data are from USDA Forest Service
and other sources (Hair and Ulrich 1963; Hair 1958; USDC Bureau of Census 1976; Ulrich, 1985, 1989; Steer 1948; AF&PA
2006a, 2006b; Howard 2003, 2007, Howard and Jones 2016, Howard and Liang 2019).
The rate of removals from products in use and the rate of decay of products in SWDS are specified by first order
(exponential) decay curves with given half-lives (time at which half of amount placed in use will have been discarded
from use). Half-lives for products in use, determined after calibration of the model to meet two criteria, are shown in
Table A-209. The first criterion is that the WOODCARB II model estimate of C in houses standing in 2001 needed to
match an independent estimate of C in housing based on U.S. Census and USDA Forest Service survey data. The second
criterion is that the WOODCARB II model estimate of wood and paper being discarded to SWDS needed to match EPA
estimates of discards over the period 1990 to 2000. This calibration strongly influences the estimate of variable 1A, and
to a lesser extent variable 2A. The calibration also determines the amounts going to SWDS. In addition, WOODCARB II
landfill decay rates have been validated by making sure that estimates of methane emissions from landfills based on EPA
data are reasonable in comparison to methane estimates based on WOODCARB II landfill decay rates.
Decay parameters for products in SWDS are shown in Table A-210. Estimates of IB and 2B also reflect the
change over time in the fraction of products discarded to SWDS (versus burning or recycling) and the fraction of SWDS
that are sanitary landfills versus dumps.
Variables 2A and 2B are used to estimate HWP contribution under the production accounting approach. A key
assumption for estimating these variables is that products exported from the United States and held in pools in other
countries have the same half-lives for products in use, the same percentage of discarded products going to SWDS, and
the same decay rates in SWDS. Summaries of net fluxes and stocks for harvested wood in products and SWDS are in
Table A-206. The decline in net additions to HWP C stocks continued through 2009 from the recent high point in 2006.
This is due to sharp declines in U.S. production of solidwood and paper products in 2009 primarily due to the decline in
housing construction. The low level of gross additions to solidwood and paper products in use in 2009 was exceeded by
discards from uses. The result is a net reduction in the amount of HWP C that is held in products in use during 2009. For
2009 additions to landfills still exceeded emissions from landfills and the net additions to landfills have remained
relatively stable. Overall, there were net C additions to HWP in use and in landfills combined.
A key assumption for estimating these variables is that products exported from the United States and held in
pools in other countries have the same half-lives for products in use, the same percentage of discarded products going to
SWDS, and the same decay rates in SWDS. Summaries of net fluxes and stocks for harvested wood in products and SWDS
are in Land Converted to Forest Land-Soil C Methods.
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-life to single family half life
0.61

Ratio of repair and alterations half-life to single


family half-life
0.30

Half-life for other solidwood product in end uses
38.0
Years
Half-life of paper in end uses
2.54
Years
Source: Skog, K.E. (2008) "Sequestration of C in harvested wood products for the U.S." Forest Products Journal 58:56-72.
A-428 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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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
Percent
Percentage of wood in landfills that is subject to decay
23
Percent
Percentage of paper in landfills that is subject to decay
56
Percent
Half-life of wood in landfills / dumps (portion subject to


decay)
29
Years
Half-life of paper in landfills/ dumps (portion subject to


decay)
14.5
Years
Source: Skog, K.E. (2008) "Sequestration of C in harvested wood products for the U.S." Forest Products Journal 58:56-72.
Table A-211: Net CO2 Flux from Forest Pools in Forest Land Remaining Forest Land and Harvested Wood Pools
(MMT CO2 Eq.)
Carbon Pool
1990
1995
2000
2005
2010
2015
2016
2017
2018
2019
Forest	(663.8)	(647.1)	(624.5)	(555.5)	(611.8)	(582.7)	(629.5)	(564.0)	(599.8)	(583.3)
Aboveground Biomass	(456.4)	(442.3)	(426.0)	(401.3)	(415.4)	(414.2)	(421.3)	(395.1)	(402.4)	(394.0)
Belowground Biomass	(103.7)	(100.7)	(97.3)	(92.0)	(94.2)	(92.6)	(95.0)	(89.2)	(90.9)	(89.2)
Dead Wood	(97.3)	(97.9)	(98.1)	(93.5)	(99.9)	(98.7)	(105.1)	(97.1)	(101.7)	(99.3)
Litter	(8.1)	(7.2)	(3.1)	32.2	0.7	30.5	(3.2)	0.2	(2.3)	(0.5)
Soil (Mineral)	1.5	0.7	(0.5)	(1.5)	(3.0)	(7.3)	(6.8)	14.3	(4.5)	(2.4)
Soil (Organic)	(0.6)	(0.5)	(0.4)	(0.2)	(0.9)	(1.1)	1.2	2.1	1.2	1.2
Drained Organic Soil3	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8
Harvested Wood	(123.8)	(112.2)	(93.4)	(106.0)	(69.1)	(88.7)	(92.4)	(95.7)	(98.8)	(108.5)
Products in Use	(54.8)	(51.7)	(31.9)	(42.6)	(7.4)	(24.6)	(27.8)	(30.3)	(31.5)	(39.2)
SWDS	(69.0)	(60.5)	(61.5)	(63.4)	(61.7)	(64.1)	(64.6)	(65.5)	(67.2)	(69.3)
Total Net Flux
(787.6)
(759.3) (717.9) (661.5) (680.9) (671.4) (721.9) (659.7) (698.6) (691.8)
Note: Parentheses indicate
a These estimates include C
Converted to Forest Land.
negative values.
stock changes from drained organic soils from both Forest Land Remaining Forest Land and Land
Table A-212: Net C Flux from Forest Pools in Forest Land Remaining Forest Land and Harvested Wood Pools
(MMT C)
Carbon Pool
1990
1995
2000
2005
2010
2015
2016
2017
2018
2019
Forest	(181.0)	(176.5) (170.3)	(151.5)	(166.9)	(158.9) (171.7)	(153.8)	(163.6)	(159.1)
Aboveground Biomass	(124.5)	(120.6) (116.2)	(109.5)	(113.3)	(113.0) (114.9)	(107.7)	(109.7)	(107.4)
Belowground Biomass	(28.3)	(27.5) (26.5)	(25.1)	(25.7)	(25.3) (25.9)	(24.3)	(24.8)	(24.3)
Dead Wood	(26.5)	(26.7) (26.7)	(25.5)	(27.2)	(26.9) (28.7)	(26.5)	(27.7)	(27.1)
Litter	(2.2)	(2.0)	(0.8)	8.8	0.2	8.3	(0.9)	0.1	(0.6)	(0.1)
Soil (Mineral)	0.4	0.2	(0.1)	(0.4)	(0.8)	(2.0)	(1.9)	3.9	(1.2)	(0.7)
Soil (Organic)	(0.2)	(0.1)	(0.1)	(0.1)	(0.3)	(0.3)	0.3	0.6	0.3	0.3
Drained Organic Soil3	0.2	0.2	0.2	0.2	0.2	0.2	0.2	0.2	0.2	0.2
Harvested Wood	(33.8)	(30.6) (25.5)	(28.9)	(18.8)	(24.2) (25.2)	(26.1)	(26.9)	(29.6)
Products in Use	(14.9)	(14.1)	(8.7)	(11.6)	(2.0)	(6.7)	(7.6)	(8.3)	(8.6)	(10.7)
SWDS	(18.8)	(16.5) (16.8)	(17.3)	(16.8)	(17.5) (17.6)	(17.9)	(18.3)	(18.9)
Total Net Flux
(214.8) (207.1) (195.8) (180.4) (185.7) (183.1) (196.9) (179.9) (190.5) (188.7)
Note: Parentheses indicate negative values.
+ Absolute value does not exceed 0.05 MMT C.
a These estimates include C stock changes from drained organic soils from both Forest Land Remaining Forest Land and Land
Converted to Forest Land.
A-429
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Table A-213: Forest area (1,000 ha) and C Stocks in Forest Land Remaining Forest Land and Harvested Wood Pools (MMT C)

1990
1995
2000
2005
2010
2015
2016
2017
2018
2019
2020
Forest Area (1000 ha)
279,661
279,755
279,795
279,491
279,537
279,545
279,533
279,511
279,483
279,386
279,289
Carbon Pools











Forest
50,913
51,808
52,681
53,489
54,302
55,125
55,284
55,456
55,610
55,774
55,933
Aboveground Biomass
11,810
12,424
13,019
13,584
14,144
14,707
14,820
14,935
15,043
15,152
15,260
Belowground Biomass
2,319
2,459
2,594
2,723
2,851
2,979
3,004
3,030
3,054
3,079
3,103
Dead Wood
2,049
2,182
2,316
2,446
2,580
2,716
2,743
2,771
2,798
2,825
2,852
Litter
3,656
3,665
3,673
3,655
3,645
3,644
3,636
3,637
3,637
3,638
3,638
Soil (Mineral)
25,145
25,144
25,143
25,145
25,147
25,145
25,147
25,149
25,145
25,146
25,147
Soil (Organic)
5,934
5,934
5,935
5,936
5,936
5,934
5,935
5,934
5,934
5,933
5,933
Harvested Wood
1,895
2,061
2,218
2,353
2,462
2,567
2,591
2,616
2,642
2,669
2,699
Products in Use
1,249
1,326
1,395
1,447
1,471
1,490
1,497
1,505
1,513
1,521
1,532
SWDS
646
735
823
906
991
1,076
1,094
1,112
1,129
1,148
1,167
Total Stock
52,808
53,870
54,899
55,842
56,764
57,692
57,875
58,072
58,252
58,443
58,632
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Table A-214: Forest Land Area Estimates and Differences Between Estimates in 6.1 Representation of the U.S.
Land Base (CRF Category 4.1) and 6.2 Forest Land Remaining Forest Land (CRF Category 4A1) (kha)
Year
Forest Land (managed) - 6.1
Representation of the U.S.
Land Base
Forest Land (managed) -
6.2 Forest Land Remaining
Forest Land
Difference between Forest
Land Areas (managed) - 6.1
and Forest Land Remaining
Forest Land - 6.2 area
estimates
1990
280,393
279,661
733
1995
280,414
279,755
659
2000
280,518
279,795
723
2005
280,207
279,491
716
2010
280,369
279,537
833
2015
280,528
279,545
983
2016
280,529
279,533
996
2017
280,380
279,511
869
2018
280,274
279,483
791
2019
280,274
279,386
888
Table A-215: State-level Net C Flux from all Forest Pools in Forest Land Remaining Forest Land (MMT C) with
Uncertainty Range Relative to Flux Estimate, 2019	
Stock	Lower Lower	Upper	Upper
State
Change
Bound
Bound (%)
Bound
Bound (%)
Alabama
(12.6)
(14.4)
-15%
(10.8)
15%
Alaska
(4.6)
(6.0)
-31%
(3.1)
31%
Arizona
0.6
0.2
-65%
1.0
65%
Arkansas
(8.2)
(9.7)
-19%
(6.6)
19%
California
(7.3)
(15.3)
-109%
0.7
109%
Colorado
3.2
(5.3)
-268%
11.7
268%
Connecticut
(0.9)
(1.2)
-32%
(0.6)
32%
Delaware
(0.1)
(0.1)
-47%
(0.0)
47%
Florida
(5.4)
(6.0)
-12%
(4.7)
12%
Georgia
(8.2)
(8.7)
-6%
(7.7)
6%
Idaho
1.3
(2.3)
-279%
4.8
279%
Illinois
(1.2)
(2.2)
-82%
(0.2)
82%
Indiana
(1.4)
(3.0)
-112%
0.2
112%
Iowa
(0.7)
(1.0)
-44%
(0.4)
44%
Kansas
(0.6)
(1.0)
-68%
(0.2)
68%
Kentucky
(4.8)
(6.3)
-33%
(3.2)
33%
Louisiana
(6.1)
(6.5)
-8%
(5.6)
8%
Maine
(2.6)
(5.6)
-116%
0.4
116%
Maryland
(1.1)
(1.7)
-46%
(0.6)
46%
Massachusetts
(1.3)
(1.6)
-30%
(0.9)
30%
Michigan
(3.9)
(7.5)
-91%
(0.3)
91%
Minnesota
(3.7)
(6.0)
-63%
(1.4)
63%
Mississippi
(15.9)
(18.8)
-18%
(13.0)
18%
Missouri
(2.9)
(5.5)
-88%
(0.4)
88%
Montana
2.8
(5.1)
-281%
10.8
281%
Nebraska
(0.2)
(0.2)
-33%
(0.1)
33%
Nevada
0.0
(0.2)
-780%
0.3
780%
New Hampshire
(1.4)
(2.0)
-42%
(0.8)
42%
New Jersey
(0.6)
(0.7)
-16%
(0.5)
16%
New Mexico
1.0
(0.9)
-183%
3.0
183%
A-431
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New York
(6.5)
(8.8)
-35%
(4.2)
35%
North Carolina
(8.3)
(9.5)
-15%
(7.0)
15%
North Dakota
(0.0)
(0.1)
-449%
0.1
449%
Ohio
(1.7)
(3.8)
-122%
0.4
122%
Oklahoma
(1.4)
(2.1)
-44%
(0.8)
44%
Oregon
(9.9)
(12.0)
-21%
(7.8)
21%
Pennsylvania
(5.3)
(9.8)
-84%
(0.9)
84%
Rhode Island
(0.1)
(0.2)
-169%
0.1
169%
South Carolina
(3.4)
(3.9)
-16%
(2.9)
16%
South Dakota
0.1
(0.2)
-280%
0.5
280%
Tennessee
(6.4)
(7.9)
-24%
(4.9)
24%
Texas
(5.5)
(6.0)
-9%
(5.0)
9%
Utah
1.0
(0.4)
-135%
2.4
135%
Vermont
(1.6)
(2.3)
-45%
(0.9)
45%
Virginia
(11.5)
(14.2)
-24%
(8.7)
24%
Washington
(4.7)
(9.4)
-98%
(0.1)
98%
West Virginia
(4.0)
(5.8)
-43%
(2.3)
43%
Wisconsin
(4.4)
(4.9)
-10%
(4.0)
10%
Wyoming
0.8
0.1
-82%
1.4
82%
49 States
(159.3)
(178.0)
-12%
(140.6)
12%
Note: Parentheses indicate negative values.
+ Absolute value does not exceed 0.05 MMT C.
Land Converted to Forest Land
The following section includes a description of the methodology used to estimate stock changes in all forest C
pools for Land Converted to Forest Land. Forest Inventory and Analysis data and IPCC (2006) defaults for reference C
stocks were used to compile separate estimates for the five C storage pools within an age class transition matrix for the
20-year conversion period (where possible). The 2015 USDA National Resources Inventory (NRI) land-use survey points
were classified according to land-use history records starting in 1982 when the NRI survey began. Consequently, the
classifications from 1990 to 2001 were based on less than 20 years. Furthermore, the FIA data used to compile estimates
of carbon sequestration in the age class transition matrix are based on 5- to 10-yr remeasurements so the exact
conversion period was limited to the remeasured data over the time series. Estimates for aboveground and belowground
biomass, dead wood and litter were based on data collected from the extensive array of permanent, annual forest
inventory plots and associated models (e.g., live tree belowground biomass) in the United States (USDA Forest Service
2020b, 2020c). Carbon conversion factors were applied at the disaggregated level of each inventory plot and then
appropriately expanded to population estimates. To ensure consistency in the Land Converted to Forest Land category
where C stock transfers occur between land-use categories, all soil estimates are based on methods from Ogle et al.
(2003, 2006) and IPCC (2006).
Live tree C pools
Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at
diameter breast height (d.b.h.) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates are made for
above- and below-ground biomass components. If inventory plots include data on individual trees, tree C is based on
Woodall et al. (2011), which is also known as the component ratio method (CRM), and is a function of volume, species,
diameter, and, in some regions, tree height and site quality. The estimated sound volume (i.e., after rotten/missing
deductions) provided in the tree table of the FIADB is the principal input to the CRM biomass calculation for each tree
(Woodall et al. 2011). The estimated volumes of wood and bark are converted to biomass based on the density of each.
Additional components of the trees such as tops, branches, and coarse roots, are estimated according to adjusted
component estimates from Jenkins et al. (2003). Live trees with d.b.h of less than 12.7 cm do not have estimates of
sound volume in the FIADB, and CRM biomass estimates follow a separate process (see Woodall et al. 2011 for details).
An additional component of foliage, which was not explicitly included in Woodall et al. (2011), was added to each tree
following the same CRM method. Carbon is estimated by multiplying the estimated oven-dry biomass by a C constant of
A-432 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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0.5 because biomass is 50 percent of dry weight (USDA Forest Service 2020d). Further discussion and example
calculations are provided in Woodall et al. 2011 and Domke et al. 2012.
Understory vegetation
Understory vegetation is a minor component of total forest ecosystem biomass. Understory vegetation is
defined as all biomass of undergrowth plants in a forest, including woody shrubs and trees less than one-inch d.b.h. In
this Inventory, it is assumed that 10 percent of understory C mass is belowground. This general root-to-shoot ratio (0.11)
is near the lower range of temperate forest values provided in IPCC (2006) and was selected based on two general
assumptions: ratios are likely to be lower for light-limited understory vegetation as compared with larger trees, and a
greater proportion of all root mass will be less than 2 mm diameter.
Estimates of C density are based on information in Birdsey (1996), which was applied to FIA permanent plots.
See model (1) in the Forest Land Remaining Forest Land section of the Annex.
In this model, the ratio is the ratio of understory C density (T C/ha) to live tree C density (above- and below-
ground) according to Jenkins et al. (2003) and expressed in T C/ha. An additional coefficient is provided as a maximum
ratio; that is, any estimate predicted from the model that is greater than the maximum ratio is set equal to the maximum
ratio. A full set of coefficients are in Table A-203. Regions and forest types are the same classifications described in Smith
et al. (2003). An example calculation for understory C in aspen-birch forests in the Northeast is provided in the Forest
Land Remaining Forest Land section of the Annex.
This calculation is followed by three possible modifications. First, the maximum value for the ratio is set to 2.02
(see value in column "maximum ratio"); this also applies to stands with zero tree C, which is undefined in the above
model. Second, the minimum ratio is set to 0.005 (Birdsey 1996). Third, nonstocked (i.e., currently lacking tree cover but
still in the forest land use) and pinyon/juniper forest types (see Table A-203) are set to coefficient A, which is a C density
(T C/ha) for these types only.
Dead wood
The standing dead tree estimates are primarily based on plot-level measurements (Domke et al. 2011; Woodall
et al. 2011). This C pool includes aboveground and belowground (coarse root) mass and includes trees of at least 12.7 cm
d.b.h. Calculations follow the basic CRM method applied to live trees (Woodall et al. 2011) with additional modifications
to account for decay and structural loss. In addition to the lack of foliage, two characteristics of standing dead trees that
can significantly affect C mass are decay, which affects density and thus specific C content (Domke et al. 2011; Harmon et
al. 2011), and structural loss such as branches and bark (Domke et al. 2011). Dry weight to C mass conversion is by
multiplying by 0.5 (USDA Forest Service 2020d).
Downed dead wood, inclusive of logging residue, are sampled on a subset of FIA plots. Despite a reduced
sample intensity, a single down woody material population estimate (Woodall et al. 2010; Domke et al. 2013; Woodall et
al. 2013) per state is now incorporated into these empirical downed dead wood estimates. Downed dead wood is
defined as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are not attached to live or
standing dead trees. It also includes stumps and roots of harvested trees. Ratio estimates of downed dead wood to live
tree biomass were developed using FORCARB2 simulations and applied at the plot level (Smith et al. 2004). Estimates for
downed dead wood correspond to the region and forest type classifications described in Smith et al. (2003). A full set of
ratios is provided in Table A-204. An additional component of downed dead wood is a regional average estimate of
logging residue based on Smith et al. (2006) applied at the plot level. These are based on a regional average C density at
age zero and first order decay; initial densities and decay coefficients are provided in Table A-205. These amounts are
added to explicitly account for downed dead wood following harvest. The sum of these two components are then
adjusted by the ratio of population totals; that is, the ratio of plot-based to modeled estimates (Domke et al. 2013).
Litter carbon
Carbon in the litter layer is currently sampled on a subset of the FIA plots. Litter C is the pool of organic C
(including material known as duff, humus, and fine woody debris) above the mineral soil and includes woody fragments
with diameters of up to 7.5 cm. Because litter attributes are only collected on a subset of FIA plots, a model was
developed to predict C density based on plot/site attributes for plots that lacked litter information (Domke et al. 2016).
As the litter, or forest floor, estimates are an entirely new model this year, a more detailed overview of the
methods is provided here. The first step in model development was to evaluate all relevant variables—those that may
A-433
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influence the formation, accumulation, and decay of forest floor organic matter—from annual inventories collected on
FIADB plots (P2) using all available estimates of forest floor C (n = 4,530) from the P3 plots (hereafter referred to as the
research dataset) compiled from 2000 through 2014 (Domke et al. 2016).
Random forest, a machine learning tool (Domke et al. 2016), was used to evaluate the importance of all
relevant forest floor C predictors available from P2 plots in the research dataset. Given many of the variables were not
available due to regional differences in sampling protocols during periodic inventories, the objective was to reduce the
random forest regression model to the minimum number of relevant predictors without substantial loss in explanatory
power. The model (3) and parameters are described in the Forest Land Remaining Forest Land section of the Annex.
Due to data limitation in certain regions and inventory periods a series of reduced random forest regression
models were used rather than replacing missing variables with imputation techniques in random forest. Database
records used to compile estimates for this report were grouped by variable availability and the approaches described
herein were applied to replace forest floor model predictions from Smith and Heath (2002). Forest floor C predictions are
expressed in T*ha-1.
Mineral Soil
A Tier 2 method is applied to estimate soil C stock changes for Land Converted to Forest Land (Ogle et al. 2003,
2006; IPCC 2006). For this method, land is stratified by climate, soil types, land-use, and land management activity, and
then assigned reference C levels and factors for the forest land and the previous land use. The difference between the
stocks is reported as the stock change under the assumption that the change occurs over 20 years. Reference C stocks
have been estimated from data in the National Soil Survey Characterization Database (USDA-NRCS 1997), and U.S.-
specific stock change factors have been derived from published literature (Ogle et al. 2003; Ogle et al. 2006). Land use
and land use change patterns are determined from a combination of the Forest Inventory and Analysis Dataset (FIA), the
2015 National Resources Inventory (NRI) (USDA-NRCS 2018), and National Land Cover Dataset (NLCD) (Yang et al. 2018).
See Annex 3.12 for more information about this method (Methodology for Estimating N20 Emissions, CH4 Emissions and
Soil Organic C Stock Changes from Agricultural Soil Management).
Table A-216 summarizes the annual change in mineral soil C stocks from U.S. soils that were estimated using a
Tier 2 method (MMT C/year). The range is a 95 percent confidence interval estimated from the standard deviation of the
NRI sampling error and uncertainty associated with the 1000 Monte Carlo simulations (See Annex 3.12). Table A-217
summarizes the total land areas by land use/land use change subcategory that were used to estimate soil C stock
changes for mineral soils between 1990 and 2015.
Land Converted to Forest land area estimates
Forest land area estimates in section 6.3 Land Converted to Forest Land (CRF Category 4A2) of this Inventory are
compiled using NFI data. Forest Land area estimates obtained from these data are also used as part of section 6.1
Representation of the U.S. Land Base (CRF Category 4.1). The Forest Land area estimates in section 6.3 do not include
Hawaii as insufficient data is available from the NFI to compile area estimates over the entire time series. The National
Land Cover Dataset is used in addition to NFI estimates in section 6.1 Representation of the U.S. Land Base and Forest
Land in Hawaii is included in that section. This results in small differences in the managed Forest Land area in sections 6.1
and 6.3 of this Inventory (Table A-218). There are also other factors contributing to the small differences in area such as
harmonization of aspatial and spatial data across all land use categories in section 6.1 over the entire Inventory time
series.
A-434 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Table A-216: Annual change in Mineral Soil C stocks from U.S. agricultural soils that were estimated using a Tier 2 method (MMT C/year)
Category
1990
1995
2000
2005
2010
2015
2016
2017
2018
2019
Cropland Converted to











Forest Land
0.08
0.07
0.07
0.07
0.06
0.05
0.06

0.06
0.06
0.06

(0.03 to
(0.03 to
(0.02 to
(0.02 to
(0.01 to
(0.01 to
(-0.02 to

(-0.02 to
(-0.02 to
(-0.02 to

0.13)
0.12)
0.12)
0.13)
0.11)
0.1)
0.13)

0.13)
0.13)
0.13)
Grassland Converted to











Forest Land
-0.05
-0.05
-0.07
-0.08
-0.08
-0.08
-0.08

-0.08
-0.07
-0.07

(-0.08 to -
(-0.1 to-
(-0.12 to-
(-0.14 to -
(-0.15 to-
(-0.15 to-
(-0.18 to

(-0.17 to
(-0.17 to
(-0.17 to

0.01)
0.01)
0.01)
0.02)
0.02)
0.02)
0.02)

0.02)
0.02)
0.03)
Other Lands Converted to











Forest Land
0.17
0.22
0.24
0.30
0.32
0.31
0.31

0.31
0.31
0.31

(0.13 to
(0.14 to
(0.17 to
(0.22 to
(0.22 to
(0.17 to
(0.13 to

(0.12 to
(0.12 to
(0.11 to

0.21)
0.25)
0.29)
0.36)
0.38)
0.43)
0.5)

0.5)
0.51)
0.51)
Settlements Converted to











Forest Land
0.01
0.01
0.01
0.01
0.01
0.02
0.02

0.02
0.02
0.02


(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.02 to
(0.01 to

(0.01 to
(0.01 to
(0.01 to

(0 to 0.02)
0.01)
0.01)
0.01)
0.01)
0.02)
0.02)

0.02)
0.02)
0.02)
Wetlands Converted to











Forest Land
0.00
0.00
0.00
0.00
0.00
0.00
0.00

0.00
0.00
0.00

(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)

(0 to 0)
(0 to 0)
(0 to 0)
Total Lands Converted to











Forest Lands
0.22
0.25
0.26
0.30
0.31
0.30
0.31

0.31
0.31
0.31
Note: The range is a 95 percent confidence interval from 50,000 simulations (Ogle et al. 2003, 2006).
Table A-217: Total land areas (hectares) by land use/land use change subcategory for mineral soils between 1990 to 2015	
Conversion Land Areas (Hectares xlO6)	1990	1995	2000	2005 2007	2008	2009	2010	2011	2012	2013	2014	2015
Cropland Converted to Forest Land	0.17	0.16	0.17	0.16 0.16	0.15	0.15	0.15	0.15	0.14	0.14	0.14	0.14
Grassland Converted to Forest Land	0.75	0.81	0.80	0.81	0.82	0.84	0.84	0.84	0.83	0.84	0.84	0.83	0.80
Other Lands Converted to Forest Land	0.05	0.06	0.07	0.08 0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09
Settlements Converted to Forest Land	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.02	0.02
Wetlands Converted to Forest Land	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01
Total Lands Converted to Forest Lands
0.99 1.06 1.05 1.08 1.09 1.11 1.11 1.10 1.10 1.10 1.10 1.09 1.06
Note: Estimated with a Tier 2 approach and based on analysis of USDA National Resources Inventory data (USDA-NRCS 2018).
A-435
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Table A-218: Land Converted to Forest Land area estimates and differences between estimates in the Representation of the U.S. Land Base (CRF Category
4.1) and Land Converted to Forest Land (CRF Category 4A1) (kha)	
Year
Cropland
Converted
to Forest
Land - 6.1
Represen-
tation of
the U.S.
Land Base
(CRF
Category
4.1)
Cropland
Converted
to Forest
Land - 6.3
Land
Converted
to Forest
Land (CRF
Category
4A2)
Difference
between
estimates
Grassland
Converted
to Forest
Land - 6.1
Represen-
tation of
the U.S.
Land Base
(CRF
Category
4.1)
Grassland
Converted
to Forest
Land - 6.3
Land
Converted
to Forest
Land (CRF
Category
4A2)
Difference
between
estimates
Other
Lands
Converted
to Forest
Land - 6.1
Represen-
tation of
the U.S.
Land Base
(CRF
Category
4.1)
Other
Lands
Converted
to Forest
Land - 6.3
Land
Converted
to Forest
Land (CRF
Category
4A2)
Difference
between
estimates
Settleme-
nts
Converted
to Forest
Land - 6.1
Represen-
tation of
the U.S.
Land Base
(CRF
Category
4.1)
Settleme-
nts
Converted
to Forest
Land - 6.3
Land
Converted
to Forest
Land (CRF
Category
4A2)
Difference
between
estimates
Wetlands
Converted
to Forest
Land - 6.1
Represen-
tation of
the U.S.
Land Base
(CRF
Category
4.1)
Wetlands
Converted
to Forest
Land - 6.3
Land
Converted
to Forest
Land (CRF
Category
4A2)
Difference
between
estimates
Total
1990

169

321
(151)
919

476
443

50

22
28

12

174
(162)

77

33
44
202
1995

170

325
(155)
1,077

487
590

66

22
44

20

178
(157)

28

33
(5)
316
2000

176

332
(157)
1,129

495
634

74

23
51

23

178
(155)

27

34
(7)
366
2005

167

317
(150)
1,162

495
667

93

22
71

24

172
(148)

28

33
(5)
435
2010

152

331
(179)
1,195

517
678

100

23
76

24

179
(156)

28

34
(5)
414
2015

139

325
(186)
1,125

515
610

100

24
76

27

179
(153)

25

32
(7)
340
2016

134

322
(188)
989

508
481

93

26
68

26

179
(153)

25

31
(6)
203
2017

135

324
(189)
992

491
501

93

27
66

26

178
(152)

25

31
(6)
220
2018

135

326
(191)
992

465
527

93

29
64

26

182
(156)

25

29
(5)
240
2019

135

326
(191)
992

465
527

93

29
64

26

182
(156)

25

29
(5)
240
A-436 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Uncertainty Analysis
The uncertainty analyses for total net flux of forest C (see Table 6-14 in the FLRFL section) are consistent with
the IPCC-recommended Tier 1 methodology (IPCC 2006). Specifically, they are considered approach 1 (propagation of
error [Section 3.2.3.1]) (IPCC 2006). To better understand the effects of covariance, the contributions of sampling error
and modeling error were parsed out. In addition, separate analyses were produced for forest ecosystem and HWP flux.
Estimates of forest C stocks in the United States are based on C estimates assigned to each of several thousand
inventory plots from a regular grid. Uncertainty in these estimates and uncertainty associated with change estimates
arise from many sources including sampling error and modeling error. Here we focus on these two types of error but
acknowledge several other sources of error are present in the overall stock and stock change estimates. In terms of
sampling based uncertainty, design based estimators described by Bechtold and Patterson (2005) were used to quantify
the variance of C stock estimates. In this section we denote the estimate of C stock at time t as Ct and the variances of
the estimate of C stock for time t as Var(Ct). These calculations follow Bechtold and Patterson (2005). The variance of
stock change is then:
Var(Ct2-Ctl)=Var(Ct2)+Var(Ctl)-2-Cov(Ct2,Ctl)	(15)
The uncertainty of a stock estimate associated with sampling error is U(Ct)s= Var(Ct)0.5. The uncertainty of a stock
changes estimate associated with sampling error is U(AC)s=Var(Ct2-Ctl)0.5.
Model-based uncertainty is important because the pool-level C models have error. The total modeling mean-squared
error (MSEm) is approximately 1,622 (Mg/ha)2. The percent modeling error at time t is
%U(Ct)m =100-MSEm/dt	(16)
Where dt is the total C stock density at time t calculated as Ct/At where At is the forest area at time t.
The uncertainty of Ct from modeling error is
The sampling and model based uncertainty are combined for an estimate of total uncertainty. We considered these
sources of uncertainty independent and combined as follow for stock change for stock change (AC):
The mean square error (MSE) of pool models was (MSE, [Mg C/ha]2): soil C (1143.0), litter (78.0), live tree (259.6), dead
trees (101.5), understory (0.9), down dead wood (38.9), total MSE (1,621.9).
Numerous assumptions were adopted for creation of the forest ecosystem uncertainty estimates. Potential
pool error correlations were ignored. Given the magnitude of the MSE for soil, including correlation among pool error
would not appreciably change the modeling error contribution. Modeling error correlation between time 1 and time 2
was assumed to be 1. Because the MSE was fixed over time we assumed a linear relationship dependent on either the
measurements at two points in time or an interpolation of measurements to arrive at annual flux estimates. Error
associated with interpolation to arrive at annual flux is not included.
Uncertainty about net C flux in HWP is based on Skog et al. (2004) and Skog (2008). Latin hypercube sampling is
the basis for the HWP Monte Carlo simulation. Estimates of the HWP variables and HWP Contribution under the
production approach are subject to many sources of uncertainty. An estimate of uncertainty is provided that evaluated
the effect of uncertainty in 13 sources, including production and trade data and parameters used to make the estimate.
Uncertain data and parameters include data on production and trade and factors to convert them to C, the census-based
estimate of C in housing in 2001, the EPA estimate of wood and paper discarded to SWDS for 1990 to 2000, the limits on
decay of wood and paper in SWDS, the decay rate (half-life) of wood and paper in SWDS, the proportion of products
produced in the United States made with wood harvested in the United States, and the rate of storage of wood and
paper C in other countries that came from U.S. harvest, compared to storage in the United States.
The uncertainty about HWP and forest ecosystem net C flux were combined and assumed to be additive.
Typically when propagating error from two estimates the variances of the estimates are additive. However, the
uncertainty around the HWP flux was approximated using a Monte Carlo approach which resulted in the lack of a
U(Ct)m=Ct-%U(Ct)m/100
The model-based uncertainty with respect to stock change is then
U(AC)m=( U(Ctl)m + U(Ct2)m - 2-Cov(U(Ctlm,Ct2m)))0.5
(17)
(18)
U(AC)=( U(AC)m2+ U(AC)s2)0.5 and the 95 percent confidence bounds was +- 2- U(AC)
(19)
A-437
-------
variance estimate for HWP C flux. Therefore, we considered the uncertainty additive between the HWP sequestration
and the Forest Land Remaining Forest Land sequestration. Further, we assumed there was no covariance between the
two estimates which is plausible as the observations used to construct each estimate are independent.
Emissions from Forest Fires
CO2 Emissions from Forest Fires
As stated in other sections, the forest inventory approach implicitly accounts for C02 emissions due to
disturbances. Net C stock change is estimated from successive C stock estimates. A disturbance, such as a forest fire,
removes C from the forest. The inventory data, on which net C stock estimates are based, already reflects the C loss from
such disturbances because only C remaining in the forest is estimated. Estimating the C02 emissions from a disturbance
such as fire and adding those emissions to the net C02 change in forests would result in double-counting the loss from
fire because the inventory data already reflect the loss. There is interest, however, in the size of the C02, CH4, and N20
emissions from disturbances such as fire. These estimated emissions from forest fires are based on IPCC (2006)
methodology, which includes a combination of U.S.-specific data on forest area burned, potential fuel available, and
individual fire severity along with IPCC default emission factors and some combustion factors.
Emissions were calculated following IPCC (2006) methodology, according to equation 2.27 of IPCC (2006,
Volume 4, Chapter 2), which in general terms is:
Emissions = Area burned x Fuel available x Combustion factor x Emission Factor x 10"3
Where the estimate for emissions is in units of metric tons (MT), which is generally summarized as million
metric tons (MMT) per year. Area burned is the annual total area of forest fire in hectares. Fuel available is the mass of
fuel available for combustion in metric tons dry weight per hectare. Combustion factor is the proportion of fuel
consumed by fire and is unitless. The emission factor is gram of emission (in this case C02) per kilogram dry matter burnt,
and the '10-3' balances units. The first three factors are based on datasets specific to U.S. forests, whereas the emissions
factor and in some cases an emission factor employ IPCC (2006) default values. Area burned is based on annual area of
forest coincident with fires according to Monitoring Trends in Burn Severity (MTBS) (MTBS Data Summaries 2018;
Eidenshink et al. 2007) dataset summaries, which include fire data for all 49 states that are a part of these estimates.
That is, the MTBS data used here include the 48 conterminous states as well as Alaska, including interior Alaska; but note
that the fire data used are also reduced to only include managed land (Ogle et al. 2018). Summary information includes
fire identity, origin, dates, location, spatial perimeter of the area burned, and a spatial raster mosaic reflecting variability
of the estimated fire severity within the perimeter. In addition to forest fires, the MTBS data include all wildland and
prescribed fires on other ecosystems such as grasslands and rangelands; the 'forest fire' distinction is not explicitly
included as a part of identifying information for each fire.
Area of forest within the MTBS fire perimeters was determined according to one of the National Land Cover
(NLCD) 2016 datasets (Homer et al. 2015; Yang et al. 2018), which include land cover maps for seven of the years over
the 2001-2016 interval. Alternate estimates of forest land would provide different estimates; for example Ruefenacht et
al. (2008) and the FIADB (USDA Forest Service 2017) provide slightly different estimates and differences vary with
location. Some of these differences can be incorporated into the estimates of uncertainty. The choice of NLCD cover for
these estimates is because it readily facilitates incorporating the MTBS per-fire severity estimates. The Alaska forest area
was allocated to managed and unmanaged areas according to Ogle et al. (2018). The use of the NLCD land cover images
to identify forest land within each MTBS-delineated fire identified forest on 15,837 of the 19,558 fires on the 48
conterminous states for 1990-2019 (data for 2019 were unavailable when these estimates were summarized; therefore
2018, the most recent available estimate, is applied to 2019). Similarly, there were 828 of the 1,044 fires in Alaska for
1990-2019 (data for 2019 were unavailable when these estimates were summarized; therefore 2018, the most recent
available estimate, is applied to 2019) that included some forest land and are considered managed lands.
The area of forest burned as calculated on some of the individual MTBS-delineated fires are different than the
forest areas calculated for the previous inventory; these corrections potentially apply to fires between 1990 and 2016. A
minor source of change in calculated forest area is the addition of NLCD land cover images. The NLCD 2016 data (Yang et
al. 2018) includes years 2001, 2003, 2006, 2008, 2011, 2013, and 2016, which provide greater temporal resolution
relative to the 2001, 2006, and 2011 years used in the previous inventory. This is likely to only have a minor effect on
estimated forest area burned. Most of the differences in annual forest area burned (and thus associated emissions) as
seen in Table A-219 relative to the same table in the previous inventory are due to improperly adjusting the proportion
of forest land within a fire to account for no-data values in an MTBS raster image rather than a similar modified NLCD
A-438 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
raster image that conformed to the spatial extent of the fire. This calculation error only affected some fires; specifically
those where the Landsat images included masked areas (such as for cloud cover). The greater the masked area, the
greater the error in estimated forest land within the fire bounds.
Estimates of fuel availability are based on plot level forest inventory data, which are summarized by ecological
province (see description of the data field 'ecosubcd' in the FIADB, USDA Forest Service 2015). These data are applied to
estimates for fires located within the respective regions. Plot level C stocks (Smith et al. 2013; USDA Forest Service 2019)
are grouped according to live aboveground biomass (live trees and understory), large dead wood (standing dead and
down dead wood), and litter. We assume that while changes in forests have occurred over the years since the 1990 start
of the reporting interval, the current general range of plot level C densities as determined by forest types and stand
structures can be used as a representation of the potential fuel availability over forest lands. The current forest inventory
data and the distribution of metric tons dry matter per hectare are used as the inputs for fuel availability.
Each MTBS defined fire perimeter has an associated burn severity mosaic that includes spatial information on
burn severity, which generally varies across the burned area. Combustion is set to similarly vary. Probabilistic definitions
are assigned for combustion factors as uniform sampling distributions for each the live, dead wood, and litter fuels.
Currently, the uniform distributions for live biomass combustion are defined as 0-0.3, 0.2-0.8, and 0.7-1.0, for burn
severity classes 2, 3, and 4 respectively. Similarly, for dead wood combustion, distributions are defined as 0-0.05, 0.05-
0.5, 0.3-0.9 and 0.8-1.0, for burn severity classes 1, 2, 3, and 4 respectively. Finally, litter combustion distributions are
defined as 0-0.05, 0.-0.1, 0.1-0.7, 0.7-1.0, and 1.0, for burn severity classes 'increased greenness', 1, 2, 3, and 4
respectively (see MTBS documentation for additional information on classifications). Specific classifications not noted
above as well as unburned forest within the perimeter are assumed to have zero fire-based emissions. The combustion
factors used here for temperate forests are interim probabilistic ranges generally based on MTBS related publications
and are subject to change with ongoing improvements (see Planned Improvements in the LULUCF chapter).
The burned area perimeter dataset also was used to identify Alaska fires that were co-located with the area of
permanent inventory plots of the USDA Forest Service's (2017) forest inventory along the southern coastal portion of the
state. The majority of the MTBS-identified burned forest areas in Alaska that coincide with the Forest Service's
permanent plot inventoried area were on the northern (or Cook Inlet) side of the Kenai Peninsula, which is generally
identified as boreal forest. The few fires that were located in the coastal maritime ecoregion (about 1% of Alaska fires)
were assigned fuel and combustion factors as described above. Fuel estimates were not available for the balance of the
Alaska fires (on boreal forest) so they were calculated according to default values for boreal forests (see Table 2.4
Volume 4, Chapter 2 of IPCC 2006). Note that the values used for Alaska (Table 2.4 of IPCC 2006) represent the product
of fuel available and the combustion factor.
The emission factor is an IPCC (2006) default, which for C02 is 1,569 g C02 per kg dry matter of fuel (see Table
2.5 Volume 4, Chapter 2 of IPCC 2006). Table A-219 provides summary values of annual area of forest burned and
emissions calculated as described above following equation 2.27 of IPCC (2006, in Volume 4, Chapter 2). The emission
factor for C02 from Table 2.5 Volume 4, Chapter 2 of IPCC (2006) is provided in Table A-237. Separate calculations were
made for each wild and prescribed fire in each state for each year. The results as MT C02 were summed to the MMT C02
per year values represented in Table A-219, and C emitted per year was based on multiplying by the conversion factor
12/44 (IPCC 2006).
A-439
-------
Table A-219: Areas (Hectares) from Wildfire Statistics and Corresponding Estimates of C and CO2 (MMT/year) Emissions for Wildfires and Prescribed Firesa


1990
1995
2000
2005
2010
2015
2016
2017
2018
2019b

Forest area burned (1000










Conterminous 48
ha)
83.5
101.2
508.3
402.1
115.8
954.1
501.5
1128.1
953.8
953.8
States - Wildfires
C emitted (MMT/yr)
1.7
1.1
5.0
5.6
2.1
31.7
9.2
37.4
31.5
31.5

C02 emitted (MMT/yr)
6.2
3.9
18.3
20.5
7.8
116.4
33.7
137.2
115.5
115.5

Forest area burned (1000










Alaska-Wildfires
ha)
82.5
1.4
59.6
686.7
103.9
631.3
26.8
23.8
45.5
45.5










C emitted (MMT/yr)
1.4
0.0
1.0
12.0
1.8
11.1
0.5
0.4
0.8
0.8

C02 emitted (MMT/yr)
5.3
0.1
3.8
44.1
6.7
40.7
1.7
1.5
2.9
2.9

Forest area burned (1000










Prescribed Fires (all
ha)
5.0
10.6
15.4
43.5
496.3
150.8
249.9
222.7
206.6
206.6
49 states)
C emitted (MMT/yr)
0.1
0.1
0.2
0.4
6.2
1.7
2.7
2.3
2.2
2.2

C02 emitted (MMT/yr)
0.2
0.3
0.8
1.5
22.9
6.1
9.7
8.4
8.0
8.0

CH4 emitted (kt/yr)
34.3
12.0
66.2
193.5
43.6
471.0
106.3
415.1
354.5
354.5
Wildfires (all 49
N20 emitted (kt/yr)
1.9
0.7
3.7
10.7
2.4
26.0
5.9
23.0
19.6
19.6
states)
CO emitted (kt/yr)
782.9
272.6
1506.6
4405.9
989.7
10718.5
2415.4
9462.2
8078.6
8078.6

NOx emitted (kt/yr)
21.9
7.7
42.3
123.5
27.7
300.2
67.9
265.3
226.7
226.7

CH4 emitted (kt/yr)
0.7
0.8
2.5
4.6
68.6
18.3
29.2
25.2
24.0
24.0
Prescribed Fires (all
N20 emitted (kt/yr)
0.0
0.0
0.1
0.3
3.8
1.0
1.6
1.4
1.3
1.3
49 states)
CO emitted (kt/yr)
16.9
18.0
58.0
105.1
1562.1
417.5
664.5
573.7
547.6
547.6

NOx emitted (kt/yr)
0.5
0.5
1.6
2.9
43.8
11.7
18.6
16.1
15.4
15.4
a These emissions have already been accounted for in the estimates of net annual changes in C stocks, which accounts for the amount sequestered minus any
emissions, including the assumption that combusted wood may continue to decay through time.
b The data for 2019 were unavailable when these estimates were summarized; therefore 2018, the most recent available estimate, is applied to 2019.
A-440 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Table A-220: Emission Factors for Extra Tropical Forest Burning and 100-year GWP (AR4), or Equivalence Ratios,
of CH4 and N2O to CO2
Emission Factor (g per kg dry
matter burned)3
Equivalence Ratios'1
CH4 4.70
CH4 to C02
25
N20 0.26
N20 to C02
298
C02 1,569
CO2 to CO2
1
a Source: IPCC(2006)
b Source: IPCC(2007)
Non-C02 Emissions from Forest Fires
Emissions of non-C02 gases-specifically, methane (CH4) and nitrous oxide (N20)-from forest fires are estimated
using the same methodology described above (i.e., equation 2.27 of IPCC 2006, Volume 4, Chapter 2). The only
difference in calculations is the gas-specific emission factors, which are listed in Table A-220. The summed annual
estimates are provided in Table A-219. Conversion of the CH4 and N20 estimates to C02 equivalents (as provided in
Chapter 6-2) is based on global warming potentials (GWPs) provided in the IPCC Fourth Assessment Report (AR4) (IPCC
2007), which are the equivalence ratios listed in Table A-220.
Uncertainty about the non-C02 estimates is based on assigning a probability distribution to represent the
estimated precision of each factor in equation 2.27 of the 2006 IPCC Guidelines (IPCC 2006). These probability
distributions are randomly sampled with each calculation, and this is repeated a large number of times to produce a
histogram, or frequency distribution of values for the calculated emissions. That is, a simple Monte Carlo ("Approach 2")
method was employed to propagate uncertainty in the equation (IPCC 2006). The probabilities used for the factors in
equation 2.27 are considered marginal distributions. The distribution for forest area burned is a uniform distribution
based on the difference in local estimates of forest area - NLCD versus FIA inventory estimates. Fuel availability is the
standard error for the inventory plots within each eco-province. Combustion factor uncertainty is defined above, and
emission factors are normal distributions with mean and standard deviations as defined in the tables IPCC (2006) Tables
2.4, 2.5, and 2.6. These were sampled independently by year and truncated to positive values where necessary. The
equivalence ratios (Table A-220) to represent estimates as C02 equivalent were not considered uncertain values for
these results.
A-441
-------
References
AF&PA. (2006a and earlier). Statistical roundup. (Monthly). Washington, DC: American Forest & Paper Association.
AF&PA. (2006b and earlier). Statistics of paper, paperboard and wood pulp. Washington, DC: American Forest & Paper
Association.
Amichev, B. Y. and J. M. Galbraith (2004) "A Revised Methodology for Estimation of Forest Soil Carbon from Spatial Soils
and Forest Inventory Data Sets." Environmental Management 33(Suppl. 1): S74-S86.
Bechtold, W.A.; Patterson, P.L. (2005) The enhanced forest inventory and analysis program—national sampling design
and estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, NC: U.S. Department of Agriculture Forest Service,
Southern Research Station. 85 p.
Birdsey, R. (1996) "Carbon Storage for Major Forest Types and Regions in the Conterminous United States." In R.N.
Sampson and D. Hair, (eds); Forest and Global Change, Volume 2: Forest Management Opportunities for Mitigating
Carbon Emissions. American Forests. Washington, DC, 1-26 and 261-379 (appendices 262 and 263).
Bodner, T.E. (2008) What improves with increased missing data imputations? Structural Equation Modeling. 15: 651-675.
Breiman L. (2001) Random forests. Machine Learning. 45(l):5-32.
Caswell, H. (2001) Matrix population models. Sunderland, MA: Sinauer Associates, Inc. 722 p.
Coulston, J.W., Wear, D.N., and Vose, J.M. (2015) Complex forest dynamics indicate potential for slowing carbon
accumulation in the southeastern United States. Scientific Reports. 5: 8002.
Coulston, J.W. (In preparation). Tier 1 approaches to approximate the carbon implications of disturbances. On file with
J.W. Coulston (jcoulston@fs.fed.us).
Coulston, J.W., Woodall, C.W., Domke, G.M., and Walters, B.F. (in preparation). Refined Delineation between Woodlands
and Forests with Implications for U.S. National Greenhouse Gas Inventory of Forests.
Danielson J.J.; Gesch D.B. (2011) Global multi-resolution terrain elevation data 2010 (GMTED2010). Open-file report
2011-1073. Reston, VA: U.S. Department of the Interior, Geological Survey. 26 p.
De Vos, B.; Cools, N.; Ilvesniemi, H.; Vesterdal, L.; Vanguelova, E.; Carnicelli, S. (2015) Benchmark values for forest soil
carbon stocks in Europe: results from a large scale forest soil survey. Geoderma. 251: 33-46.
Domke, G.M., Woodall, C.W., Smith, J.E., Westfall, J.A., McRoberts, R.E. (2012) Consequences of alternative tree-level
biomass estimation procedures on U.S. forest carbon stock estimates. Forest Ecology and Management. 270:108-
116.
Domke, G.M., Smith, J.E., and Woodall, C.W. (2011) Accounting for density reduction and structural loss in standing dead
trees: Implications for forest biomass and carbon stock estimates in the United States. Carbon Balance and
Management. 6:14.
Domke, G.M., Woodall, C.W., Walters, B.F., Smith, J.E. (2013) From models to measurements: comparing down dead
wood carbon stock estimates in the U.S. forest inventory. PLoS ONE 8(3): e59949.
Domke, G.M., Perry, C.H., Walters, B.F., Woodall, C.W., and Smith, J.E. (2016). A framework for estimating litter carbon
stocks in forests of the United States. Science oftheTotal Environment. 557-558, 469^-78.
Domke, G.M., Perry, C.H., Walters, B.F., Woodall, C.W., Nave, L., Swanston, C. (2017) Toward inventory-based estimates
of soil organic carbon in forests of the United States. Ecological Applications. 27(4), 1223-1235.Eidenshink, J., B.
Schwind, K. Brewer, Z. Zhu, B. Quayle, and S. Howard. (2007) A project for monitoring trends in burn severity. Fire
Ecology 3(1): 3-21.
Domke, G.M., Walters, B.F., Gray, A., Mueller, B. (In prep). Estimating greenhouse gas emissions and removals on
managed forest land in Alaska, USA. Intended outlet: Proceedings of the National Academy of Science.
Frayer, W.E., and G.M. Furnival (1999) "Forest Survey Sampling Designs: A History." Journal of Forestry 97(12): 4-10.
Freed, R. (2004) Open-dump and Landfill timeline spreadsheet (unpublished). ICF International. Washington, D.C.
A-442 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Hair, D. and A.H. Ulrich (1963) The Demand and price situation for forest products - 1963. U.S. Department of
Agriculture Forest Service, Misc Publication No. 953. Washington, DC.
Hair, D. (1958) "Historical forestry statistics of the United States." Statistical Bull. 228. U.S. Department of Agriculture
Forest Service, Washington, DC.
Hao, W.M. and N.K. Larkin. (2014) Wildland fire emissions, carbon, and climate: Wildland fire detection and burned area
in the United States. Forest Ecology and Management 317: 20-25.
Harmon, M.E., C.W. Woodall, B. Fasth, J. Sexton, M. Yatkov. (2011) Differences between standing and downed dead tree
wood density reduction factors: A comparison across decay classes and tree species. Res. Paper. NRS-15. Newtown
Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 40 p.
Heath, L.S., M.C. Nichols, J.E. Smith, and J.R. Mills. (2010) FORCARB2: An updated version of the U.S. Forest Carbon
Budget Model. Gen. Tech. Rep. NRS-67.USDA Forest Service, Northern Research Station, Newtown Square, PA. 52 p.
[CD-ROM],
Heath, L.S., J.E. Smith, K.E. Skog, D.J. Nowak, and C.W. Woodall. (2011) Managed forest carbon estimates for the U.S.
Greenhouse Gas Inventory, 1990-2008. Journal of Forestry 109(3):167-173.
Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown.
(2015) Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a
decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345-354.
Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N., McKerrow, A., VanDriel, J.N., and Wickham, J.
(2007) Completion of the 2001 National Land Cover Database for the Conterminous United States. Photogrammetric
Engineering and Remote Sensing, Vol. 73, No. 4, pp 337-341.
Howard, James L. (2003) U.S. timber production, trade, consumption, and price statistics 1965 to 2002. Res. Pap. FPL-RP-
615. Madison, Wl: USDA, Forest Service, Forest Products Laboratory. Available online at
.
Howard, J. L. and Liang, S. (2019). U.S. timber production, trade, consumption, and price statistics 1965 to 2017. Res.
Pap. FPL-RP-701. Madison, Wl: USDA, Forest Service, Forest Products Laboratory.
Howard, J. L. and Jones, K.C. (2016) U.S. timber production, trade, consumption, and price statistics 1965 to 2013. Res.
Pap. FPL-RP-679. Madison, Wl: USDA, Forest Service, Forest Products Laboratory.
Howard, J. L. (2007) U.S. timber production, trade, consumption, and price statistics 1965 to 2005. Res. Pap. FPL-RP-637.
Madison, Wl: USDA, Forest Service, Forest Products Laboratory.
Ince, P.J., Kramp, A.D., Skog, K.E., Spelter, H.N. and Wear, D.N. (2011) U.S. Forest Products Module: a technical document
supporting the forest service 2010 RPA assessment. Research Paper-Forest Products Laboratory, USDA Forest
Service, (FPL-RP-662).
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis,
K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 996 pp.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental Panel on
Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al., eds. August 13, 2004. Available
online at .
ISCN. (2015) International Soil Carbon Monitoring Network (http://iscn.fluxdata.org/) database.
Jandl, R., Rodeghiero, M., Martinez, C., Cotrufo, M. F., Bampa, F., van Wesemael, B., Harrison, R.B., Guerrini, I.A., deB
Richter Jr., D., Rustad, L., Lorenz, K., Chabbi, A., Miglietta, F. 2014. Current status, uncertainty and future needs in
soil organic carbon monitoring. Science of the Total Environment, 468, 376-383.
A-443
-------
Jenkins, J,C., D.C. Chojnacky, L.S. Heath, and R.A. Birdsey (2003) "National-scale biomass estimators for United States
tree species." Forest Science 49(1): 12-35.
Jobbagy, E.G.; Jackson, R.B. (2000) The vertical distribution of soil organic carbon and its relation to climate and
vegetation. Ecological Applications. 10: 423-436.
Lai, R. (2005) Forest soils and carbon sequestration. Forest Ecology and Management. 220(1): 242-258.
MTBS Data Summaries. 2018. MTBS Data Access: Fire Level Geospatial Data. (2018, August - last revised). MTBS Project
(USDA Forest Service/U.S. Geological Survey). Available online: .
NAIP. (2015) National Agriculture Imagery Program. U.S. Department of Agriculture, Washington, DC.
.
Natural Resources Conservation Service [NRCS], (2015) Soil geography: Description of STATSG02 database.
 (accessed October
6, 2015).
Northwest Alliance for Computational Science and Engineering. (2015) PRISM Climate Data. Available at
 (accessed October 6, 2015).
Ogle, S.M., M.D. Eve, F.J. Breidt, and K. Paustian (2003) "Uncertainty in estimating land use and management impacts on
soil organic carbon storage for U.S. agroecosystems between 1982 and 1997." Global Change Biology 9:1521-1542.
Ogle, S.M., F.J. Breidt, and K. Paustian. (2006) "Bias and variance in model results due to spatial scaling of measurements
for parameterization in regional assessments." Global Change Biology 12:516-523.
Ogle, S. M., G. M. Domke, W. A. Kurz, M. T. Rocha, T. Huffman, A. Swan, J. E. Smith, C. W. Woodall, and T. Krug. 2018.
Delineating managed land for reporting national greenhouse gas emissions and removals to the United Nations
framework convention on climate change. Carbon Balance and Management 13:9.
O'Neill, K.P., Amacher, M.C., Perry, C.H. (2005) Soils as an indicator of forest health: a guide to the collection, analysis,
and interpretation of soil indicator data in the Forest Inventory and Analysis program. Gen. Tech. Rep. NC-258. St.
Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station. 53 p.
Oswalt, S.N.; Smith, W.B; Miles, P.D.; Pugh, S.A. (2014) Forest Resources of the United States, 2012: a technical
document supporting the Forest Service 2015 update of the RPA Assessment. Gen. Tech. Rep. WO-91. Washington,
DC: U.S. Department of Agriculture, Forest Service, Washington Office. 218 p.
Perry, C.H., C.W. Woodall, and M. Schoeneberger (2005) Inventorying trees in agricultural landscapes: towards an
accounting of "working trees". In: "Moving Agroforestry into the Mainstream." Proc. 9th N. Am. Agroforestry Conf.,
Brooks, K.N. and Ffolliott, P.F. (eds). 12-15 June 2005, Rochester, MN [CD-ROM], Dept. of Forest Resources, Univ.
Minnesota, St. Paul, MN, 12 p. Available online at  (verified 23 Sept 2006).
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing,
Vienna, Austria. URL .
Ruefenacht, B., M.V. Finco, M.D. Nelson, R. Czaplewski, E.H. Helmer, J.A. Blackard, G.R. Holden, A.J. Lister, D. Salajanu, D.
Weyermann, K. Winterberger. 2008. Conterminous U.S. and Alaska Forest Type Mapping Using Forest Inventory and
Analysis. USDA Forest Service - Forest Inventory and Analysis Program & Remote Sensing Applications Center.
Available online at . Accessed 8 September 2015.
Skog, K.E., K. Pingoud, and J.E. Smith (2004) "A method countries can use to estimate changes in carbon stored in
harvested wood products and the uncertainty of such estimates." Environmental Management 33(Suppl. 1):S65-
S73.
Skog, K.E. (2008) "Sequestration of Carbon in harvested wood products for the United States." Forest Products Journal,
58(6): 56-72.
Smith, J. E., L. S. Heath, and C. M. Hoover. 2013. Carbon factors and models for forest carbon estimates for the 2005-
2011 National Greenhouse Gas Inventories of the United States. For. Ecology and Management 307:7-19.
A-444 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Smith, J.E., L.S. Heath, and M.C. Nichols (2010). U.S. Forest Carbon Calculation Tool User's Guide: Forestland Carbon
Stocks and Net Annual Stock Change. General Technical Report NRS-13 revised, U.S. Department of Agriculture
Forest Service, Northern Research Station.
Smith, J.E., L.S. Heath, K.E. Skog, R.A. Birdsey. (2006) Methods for calculating forest ecosystem and harvested carbon
with standard estimates for forest types of the United States. Gen. Tech. Rep. NE-343. U.S. Department of
Agriculture, Forest Service, Northeastern Research Station. Newtown Square, PA.
Smith, J.E., L. S. Heath, and P. B. Woodbury (2004) "How to estimate forest carbon for large areas from inventory data."
Journal of Forestry 102:25-31.
Smith, J.E., L. S. Heath, and J. C. Jenkins (2003) Forest Volume-to-Biomass Models and Estimates of Mass for Live and
Standing Dead Trees of U.S. Forests. General Technical Report NE-298, USDA Forest Service, Northeastern Research
Station, Newtown Square, PA.
Smith, J.E., and L.S. Heath (2002) "A model of forest floor carbon mass for United States forest types." Res. Paper NE-
722. USDA Forest Service, Northeastern Research Station, Newtown Square, PA.
Steer, Henry B. (1948) Lumber production in the United States. Misc. Pub. 669, U.S. Department of Agriculture Forest
Service. Washington, DC.
Sun, O.J.; Campbell, J.; Law, B.E.; Wolf, V. (2004) Dynamics of carbon stocks in soils and detritus across chronosequences
of different forest types in the Pacific Northwest, USA. Global Change Biology. 10(9): 1470-1481.
Tan, Z.X.; Lai, R.; Smeck, N.E.; Calhoun, F.G. (2004) Relationships between surface soil organic carbon pool and site
variables. Geoderma. 121(3): 187-195.
Thompson, J.A.; Kolka, R.K. (2005) Soil carbon storage estimation in a forested watershed using quantitative soil-
landscape modeling. Soil Science Society of America Journal. 69(4): 1086-1093.
Ulrich, A.H. (1989) U.S. Timber Production, Trade, Consumption, and Price Statistics, 1950-1987. USDA Miscellaneous
Publication No. 1471, U.S. Department of Agriculture Forest Service. Washington, DC, 77.
Ulrich, A.H. (1985) U.S. Timber Production, Trade, Consumption, and Price Statistics 1950-1985. Misc. Pub. 1453, U.S.
Department of Agriculture Forest Service. Washington, DC.
United Nations Framework Convention on Climate Change. (2013) Report on the individual review of the inventory
submission of the United States of America submitted in 2012. FCCC/ARR/2012/USA. 42 p.
USDC Bureau of Census (1976) Historical Statistics of the United States, Colonial Times to 1970, Vol. 1. Washington, DC.
USDA Forest Service (2020a) Forest Inventory and Analysis National Program: Program Features. U.S. Department of
Agriculture Forest Service. Washington, D.C. Available online at: . Accessed
10 October 2020.
USDA Forest Service. (2020b) Forest Inventory and Analysis National Program: FIA Data Mart. U.S. Department of
Agriculture Forest Service. Washington, D.C. Available online at: <
https://apps.fs.usda.gov/fia/datamart/datamart.html>. Accessed on 10 October 2020.
USDA Forest Service. (2020c) Forest Inventory and Analysis National Program, FIA library: Field Guides, Methods and
Procedures. U.S. Department of Agriculture Forest Service. Washington, D.C. Available online at:
. Accessed on 10 October 2020.
USDA Forest Service (2020d) Forest Inventory and Analysis National Program, FIA library: Database Documentation. U.S.
Department of Agriculture, Forest Service, Washington Office. Available online at:
. Accessed on 10 October 2020.
USDA-NRCS (1997) "National Soil Survey Laboratory Characterization Data," Digital Data, Natural Resources Conservation
Service, U.S. Department of Agriculture. Lincoln, NE.
USDA-NRCS (2013) Summary Report: 2010 National Resources Inventory, Natural Resources Conservation Service,
Washington, D.C., and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa.
.
A-445
-------
U.S. EPA. (2015) Annex 3.13 Methodology for estimating net carbon stock changes in forest lands remaining forest lands,
in Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013. U.S. Environmental Protection Agency. EPA
430-R-15-004.
Wear, D.N., Coulston, J.W. (2015) From sink to source: Regional variation in U.S. forest carbon futures. Scientific Reports.
5: 16518.
Wellek, S. (2003) Testing statistical hypotheses of equivalence. London, England: Chapman & Hall.
Woldeselassie, M.; Van Miegroet, H.; Gruselle, M.C.; Hambly, N. (2012) Storage and stability of soil organic carbon in
aspen and conifer forest soils of northern Utah. Soil Science Society of America Journal. 76(6): 2230-2240.
Woodall, C.W., L.S. Heath, G.M. Domke, and M.C. Nichols. (2011) Methods and equations for estimating aboveground
volume, biomass, and carbon for trees in the U.S. forest inventory, 2010. Gen. Tech. Rep. NRS-88. Newtown Square,
PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 30 p.
Woodall, C.W., B.L. Conkling, M.C. Amacher, J.W. Coulston, S. Jovan, C.H. Perry, B. Schulz, G.C. Smith, S. Will Wolf.
(2010). The Forest Inventory and Analysis Database Version 4.0: Database Description and Users Manual for Phase
3. Gen. Tech. Rep. NRS-61. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research
Station. 180 p.
Woodall, C.W., Coulston, J.W., Domke, G.M., Walters, B.F., Wear, D.N., Smith, J.E., Anderson, H.-E., Clough, B.J., Cohen,
W.B., Griffith, D.M., Hagan, S.C., Hanou, I.S.; Nichols, M.C., Perry, C.H., Russell, M.B., Westfall, J.A., Wilson, B.T.
(2015a) The U.S. Forest Carbon Accounting Framework: Stocks and Stock change 1990-2016. Gen. Tech. Rep. NRS-
154. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 49 pp.
Woodall, C.W., Walters, B.F., Coulston, J.W., D'Amato, A.W., Domke, G.M., Russell, M.B., Sowers, P.A. (2015b)
Monitoring network confirms land use change is a substantial component of the forest carbon sink in the eastern
United States. Scientific Reports. 5:17028.
Woodall, C.W., Domke, G.M., MacFarlane, D.W., Oswalt, C.M. (2012) Comparing Field- and Model-Based Standing Dead
Tree Carbon Stock Estimates Across Forests of the United States. Forestry 85(1): 125-133.
Woodall, C.W., Walters, B.F., Oswalt, S.N., Domke, G.M., Toney, C., Gray, A.N. (2013) Biomass and carbon attributes of
downed woody materials in forests of the United States. Forest Ecology and Management 305:48-59.
Woodall, C.W., Domke, G.M., MacFarlane, D.W., Oswalt, C.M. (2012) Comparing field- and model-based standing dead
tree carbon stock estimates across forests of the United States. Forestry. 85:125-133.
Woudenberg, S.W. and T.O. Farrenkopf (1995) The Westwide forest inventory data base: user's manual. General
Technical Report INT-GTR-317. U.S. Department of Agriculture Forest Service, Intermountain Research Station.
Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B.,
Rigge, M. and G. Xian. 2018. A New Generation of the United States National Land Cover Database: Requirements,
Research Priorities, Design, and Implementation Strategies, ISPRS Journal of Photogrammetry and Remote Sensing,
146, pp.108-123.
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3.14. Methodology for Estimating CH4 Emissions from Landfills
A combination of Tier 2 and 3 approaches are used to calculate emissions from for MSW Landfills. A Tier 2
approach is used to calculate emissions for industrial waste landfills.
Landfill gas is a mixture of substances generated when bacteria decompose the organic materials contained in
133
solid waste. By volume, landfill gas is about half CH4 and half C02. The amount and rate of CH4 generation depends
upon the quantity and composition of the landfilled material, as well as the surrounding landfill environment. Not all CH4
generated within a landfill is emitted to the atmosphere. The CH4 can be extracted and either flared or utilized for
energy, thus oxidizing the CH4 to C02 during combustion. Of the remaining CH4, a portion oxidizes to C02 as it travels
through the top layer of the landfill cover. In general, landfill-related C02 emissions are of biogenic origin and primarily
result from the decomposition, either aerobic or anaerobic, of organic matter such as food or yard wastes.
Methane emissions from landfills are estimated using two primary methods. The first method uses the first
order decay (FOD) model as described by the 2006IPCC Guidelines to estimate CH4 generation. The amount of CH4
recovered and combusted from MSW landfills is subtracted from the CH4 generation and is then adjusted with an
oxidation factor. The second method used to calculate CH4 emissions from landfills, also called the back-calculation
method, is based off directly measured amounts of recovered CH4 from the landfill gas and is expressed by Equation HH-
8 in CFR Part 98.343 of the EPA's Greenhouse Gas Reporting Program (GHGRP).
The current Inventory methodology uses both methods to estimate CH4 emissions across the time series. The
1990 to 2015 Inventory was the first Inventory to incorporate directly reported GHGRP net CH4 emissions data for
landfills. In previous Inventories, only the first order decay method was used. EPA's GHGRP requires landfills meeting or
exceeding a threshold of 25,000 metric tons (MT) of CH4 generation per year to report a variety of facility-specific
information, including historical and current waste disposal quantities by year, CH4 generation, gas collection system
details, CH4 recovery, and CH4 emissions. EPA's GHGRP provides a consistent methodology, a broader range of values for
the oxidation factor, and allows for facility-specific annual waste disposal data to be used, thus these data are considered
Tier 3 (highest quality data) under the 2006 IPCC Guidelines. Using EPA's GHGRP data was a significant methodological
change and required a merging of the GHGRP methodology with the Inventory methodology used in previous years to
ensure time-series consistency.
Figure A-18 presents the CH4 emissions process—from waste generation to emissions—in graphical format. A
detailed discussion of the steps taken to compile the 1990 to 2019 Inventory are presented in the remainder of this
Annex.
133 Typically, landfill gas also contains small amounts of nitrogen, oxygen, and hydrogen, less than 1 percent nonmethane volatile
organic compounds (NMVOCs), and trace amounts of inorganic compounds.
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Figure A-18: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste
a MSW waste generation is not calculated because annual quantities of waste landfilled are available through secondary
sources as described in figure note b.
b Quantities of MSW landfilled for 1940 through 1988 are based on EPA 1988 and EPA 1993; 1989 through 2004 are based on
BioCycle 2010; 2005 through 2019 are incorporated through the directly reported emissions from MSW landfills to the
Greenhouse Gas Reporting Program. Quantities of industrial waste landfilled are estimated using a disposal factor and
industrial production data sourced from Lockwood Post's Directory and the USDA.
c The 2006 IPCC Guidelines - First Order Decay (FOD) Model is used for industrial waste landfills.
d Two different methodologies are used in the time series for MSW landfills. For 1990 to 2004, the 2006 IPCC Guidelines - FOD
Model is used. For 2005 to 2019, directly reported net CH4 emissions from the GHGRP for 2010 to the current Inventory year
are used with the addition of a scale-up factor applied to each year's emissions. The scale-up factor accounts for emissions
from landfills that do not report to the GHGRP. A scale-up factor of 9 percent is applied to 2005-2016 and a scale-up factor of
11 percent is applied to 2017-2019. The GHGRP emissions from 2010 to the current Inventory year are also used to back-cast
emissions for 2005 to 2009 to merge the FOD methodology with the GHGRP methodology for time series consistency.
Additional details on how the scale-up factor was developed and the back-casting approach are included in Step 4 of this
Annex chapter.
e Methane recovery from industrial waste landfills is not incorporated into the Inventory because it does not appear to be a
common practice according to the GHGRP dataset.
f Methane recovery data are pulled from three recovery databases: EIA 2007, flare vendor database, and EPA (GHGRP) 2015(a).
These databases have not been updated past 2015 because the Inventory strictly uses net emissions from the GHGRP data
which already accounts for CH4 recovery.
g For years 1990 to 2004, the total CH4 generated from MSW landfills and industrial waste landfills are summed. For years 2005
to 2019, only the industrial waste landfills are considered because the directly reported GHGRP emissions are used for MSW
landfills.
h An oxidation factor of 10 percent is applied to all CH4 generated in years 1990 to 2004 (2006 IPCC Guidelines; Mancinelli and
McKay 1985; Czepiel et al 1996). For years 2005 to 2019, directly reported CH4 emissions from the GHGRP are used for MSW
landfills. Various oxidation factor percentages are included in the GHGRP dataset (0,10, 25, and 35) with an average across the
dataset of approximately 20 percent.
Step 1: Estimate Annual Quantities of Solid Waste Placed in MSW Landfills for 1940 to 2004
Total national annual waste generation and disposal data back to 1940 are directly used to estimate CH4
emissions for the 1990 to 2004 Inventory time series. The waste generation and disposal estimates are also made for the
rest of the Inventory time series (i.e., 2005 to the current Inventory year) for informational purposes; these data
however do not inform the annual CH4 emission estimates for this portion of the time series. The specific steps are
described below (in sections la and lb), followed by a summary of a comparative analysis of datasets that contain or are
used to estimate annual waste disposal (in Box A-3).
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la. Historical Estimates: 1940 to 1988
Historical waste data, preferably from 50 years prior to the first year of the inventory time series (i.e. since
1940 because the time series begins in 1990), are required for the FOD model to estimate CH4 generation for the
Inventory time series (IPCC 2006). States and local municipalities across the United States do not consistently track and
report quantities of MSW generated or collected for management, nor do they report end-of-life disposal methods to a
centralized system. Therefore, national MSW landfill waste generation and disposal data are obtained from secondary
data sources or estimated via proxy data.
Estimates of the annual quantity of waste landfilled for 1960 through 1988 were obtained from EPA's
Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress (EPA 1993) and an
extensive landfill survey by the EPA's Office of Solid Waste in 1986 (EPA 1988). Although waste placed in landfills in the
1940s and 1950s contributes very little to current CH4 generation, estimates for those years were included in the FOD
model for completeness in accounting for CH4 generation rates and are based on the population in those years and the
per capita rate for land disposal for the 1960s.
lb. Inventory Time Series Estimates: 1990 to the Current Inventory Year
For 1989 to 2008, estimates of the annual quantity of MSW generated were developed from a survey of state
agencies as reported in the State of Garbage (SOG) in America surveys (BioCycle 2001, 2004, 2006, 2010), adjusted to
include U.S. Territories.134 The SOG surveys collected data from state agencies and then applied the principles of mass
balance where all MSW generated is equal to the amount of MSW landfilled, combusted in waste-to-energy plants,
composted, and/or recycled (BioCycle 2006; Shin 2014). This approach assumes that all waste management methods are
tracked and reported to state agencies. Survey respondents were asked to provide a breakdown of MSW generated and
managed by landfilling, recycling, composting, and combustion (in waste-to-energy facilities) in actual tonnages as
opposed to reporting a percent generated under each waste disposal option. The data reported through the surveys
have typically been adjusted to exclude non-MSW materials (e.g., industrial and agricultural wastes, construction and
demolition debris, automobile scrap, and sludge from wastewater treatment plants) that may be included in survey
responses. While non-municipal solid wastes may have been disposed of in MSW landfills, they were not the primary
type of waste material disposed and are typically inert. In last survey (BioCycle 2010), state agencies were asked to
provide MSW-only data. Where this was not possible, they were asked to provide comments to better understand the
data being reported. Methodological changes have occurred over the time frame the SOG surveys have been published,
which directly impacted the fluctuating trends observed in the waste disposal data and emission estimates from 1990 to
2004 (RTI 2013).
The SOG survey is voluntary and not all states provided data in each survey year. To estimate waste generation
for states that did not provide data in any given reporting year, one of the following methods was used (RTI 2013):
•	For years when a state-specific waste generation rate was available from the previous SOG reporting year
submission, the state-specific waste generation rate for that state was used.
- or -
•	For years where a state-specific waste generation rate was not available from the previous SOG reporting year
submission, the waste amount is generated using the national average waste generation rate. In other words,
Waste Generated = Reporting Year U.S. Population x the National Average Waste Generation Rate
o The National Average Waste Generation Rate is determined by dividing the total reported waste
generated across the reporting states by the total population for reporting states.
o This waste generation rate may be above or below the waste generation rate for the non-reporting
states and contributes to the overall uncertainty of the annual total waste generation amounts used in
the model.
Use of these methods to estimate solid waste generated by states is a key aspect of how the SOG data was
manipulated and why the results differ for total solid waste generated as presented in the SOG reports and in the
Inventory. In the early years (2002 data in particular), SOG made no attempt to fill gaps for non-survey responses. For
134 Since the SOG survey does not include U.S. Territories, waste landfilled in U.S. Territories was estimated using population data for
the U.S. Territories (U.S. Census Bureau 2020) and the per capita rate for waste landfilled from BioCycle (2010).
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the 2004 data, the SOG team used proxy data (mainly from the Waste Business Journal [WBJ]) to fill gaps for non-
reporting states and survey responses.
Although some fluctuation in waste generation data reported by states to the SOG survey is expected, for some
states, the year-to-year fluctuations are quite significant (>20 percent increase or decrease in some case) (RTI 2013). The
SOG survey reports for these years do not provide additional explanation for these fluctuations and the source data are
not available for further assessment. Although exact reasons for the large fluctuations are difficult to obtain without
direct communication with states, staff from the SOG team that were contacted speculated that significant fluctuations
are present because the particular state could not gather complete information for waste generation (i.e., they are
missing part of recycled and composted waste data) during a given reporting year. In addition, SOG team staff
speculated that some states may have included C&D and industrial wastes in their previous MSW generation
submissions but made efforts to exclude that (and other non-MSW categories) in more recent reports (RTI 2013).
The SOG surveys provide state-specific landfill waste generation data used in the Inventory for select years -
1989 to 2000, 2002, 2004, 2006, and 2008. In-between year waste generation is interpolated using the prior and next
SOG report data. For example, waste generated in 2003 = (waste generation in 2002 + waste generation in 2004)/2.
For the Inventory year 2010 and later, EREF's 2016 report entitled, MSW Management in the United States, is
used as the primary data source because BioCycle ceased preparing the SOG surveys. EREF (2016) includes state-specific
landfill MSW generation and disposal data for 2010 and 2013 using a similar methodology as the SOG surveys. Waste
generation data were interpolated for 2009, the year in-between the 2008 SOG survey data and the 2010 EREF data.
Waste generation data were also extrapolated for 2011 and 2012 using the EREF data for 2010 and 2013. Waste
generation data for 2014 and the current year were extrapolated based on the EREF 2013 data and population increases
from the U.S. Census (U.S. Census Bureau 2020). No data source on annual waste generation by state or nationally
(similar to an SOG or EREF report) has been published since EREF (2016).
For each year in the time series, estimates of the quantity of waste landfilled are determined by applying a
waste disposal factor to the total amount of waste generated. A waste disposal factor was determined for each year a
SOG survey was published and is the ratio of the total amount of waste landfilled to the total amount of waste
generated. The waste disposal factor is interpolated for the years in between the SOG surveys and EREF report and
extrapolated for years after the last year of EREF data (i.e., 2013). The waste disposal factor has ranged from
approximately 77 percent in 1990 to 65.3 percent from 2015 to 2019.
Table A-221 shows estimates of MSW generated and landfilled, and industrial waste landfilled. A description of
the data sources used to estimate industrial waste landfilled is included in Step 7. Estimates for MSW generated and
landfilled are presented for various years after 2004 for informational purposes only. As described in Step 4, after 2004,
the Inventory methodology relies on the GHGRP net reported CH4 emissions data, replacing the need for the now
discontinued SOG surveys and intermittent EREF estimates.
Table A-221: Solid Waste in MSW and Industrial Waste Landfills Contributing to ChU Emissions (MMT unless
otherwise noted)	

1990
2005
2015
2016
2017
2018
2019
Total MSW Generated3
270
368
323
325
328
329
330
Percent of MSW Landfilled
77%
64%
65%
65%
65%
65%
65%
Total MSW Landfilled
205
234
209
210
212
213
213
MSW last 30 years'5
4,876
5,992
6,457
6,480
6,502
6,521
6,538
MSW since 1940c
6,808
9,925
12,087
12,297
12,509
12,722
12,935
Total Industrial Waste







Production Data
196
221
209
209
209
209
209
Pulp and Paper Sectord
129
139
122
121
121
121
119
Food and Beverage Sector6
67
82
87
87
88
88
90
Percent Total Industrial Waste







Landfilled
5%
5%
5%
5%
5%
5%
5%
Total Industrial Waste







Landfilled
9.7
10.9
10.3
10.3
10.3
10.3
10.3
Pulp and Paper Sectord
6.5
6.9
6.1
6.1
6.1
6.0
6.0
Food and Beverage Sectore
3.3
4.0
4.2
4.2
4.3
4.3
4.4
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a This estimate represents the waste that has been in place for 30 years or less, which contributes about 90
percent of the CH4 generation. Values are based on EPA (1993) for years 1940 to years 1988 (not
presented in table), BioCycle 2001, 2004, 2006, and 2010 for years 1989 to 2009 (1981 to 2004, and 2006
to 2011 are not presented in table). Values for years 2010 to 2019 are based on EREF (2016) and annual
population data from the U.S. Census Bureau (2020).
b This estimate is the cumulative amount of waste that has been placed in landfills for the 30 years prior to
the year indicated and is the sum of the annual disposal rates used in the first order decay model. Values
are based on EPA 1993; BioCycle 2001, 2004, 2006, and 2010; EREF 2016; and extrapolated data based on
annual population increases (U.S. Census Bureau 2020).
c This estimate represents the cumulative amount of waste that has been placed in landfills since 1940 to
the year indicated and is the sum of the annual disposal rates used in the first order decay model. Values
are based on EPA 1993; BioCycle 2001, 2004, 2006, and 2010; EREF 2016; and extrapolated data based on
annual population increases (U.S. Census Bureau 2020).
d A disposal factor of 0.050 MT/MT of product is applied to total pulp and paper production data to
estimate the annual amount landfilled. See Step 7 for the references and rationale for this method. The
same disposal factor is applied to every year of the time series. Production data from 1990 and 2001 are
from Lockwood-Post's Directory, 2002. Production data from 2002 to 2019 are from the FAOStat database
available at: http://faostat3.fao.org/home/index.htmlttDOWNLOAD. Accessed on May 20, 2020.
e A disposal factor of 0.046 MT/MT of product is applied to total food production data to estimate the
annual amount landfilled. See Step 7 for the references and rationale for this method. The same disposal
factor is applied to every year of the time series. Food production values for 1990 to 2019 are from ERG
2020. 2020 USDA-NASS Ag QuickStats available at http://quickstats.nass.usda.gov (FAO 2019).
Box A-3: Comparison of Annual Waste Disposal Estimates Across Available Data Sources
In 2020, EPA compared the available data on estimates of total waste generated and landfilled as presented
in Table A-221 for the years 2017 and 2018 and found inconsistencies between the estimates of MSW landfilled
between the data sources. Data sources directly compared include the EREF-extrapolated estimate for 2017 and 2018
to the Advancing Sustainable Materials Management: Facts and Figures report (EPA (2020) Advancing Sustainable
Materials Management: Facts and Figures 2018. November 2020). These inconsistencies are expected, as the data
sources use two different methodologies to estimate MSW landfilled. While there are differences in the methods used
between these data sources, the uncertainty factors for MSW Landfills are intended to account for these variabilities in
the emission estimates for 1990 to 2004.
The EREF-extrapolated estimate of total MSW landfilled for 2017 and 2018 is based on a bottom-up approach
using information at the facility-level to estimate MSW for the sector as a whole, while the Facts and Figures report
uses a top-down (materials flow mass balance) approach to estimate the same quantity. The materials flow
methodology is generally based on production data for each material at the state- (recycling, composting) or national-
(waste generation) level. Discarded or landfilled material is Subtitle D waste only and assumed to be the calculated
difference between generation and recovery through recycling and composting, other food management (e.g.,
anaerobic digestion), and combustion (EPA 2020). Subtitle D wastes do not include construction and demolition waste,
for example, which many GHGRP-reporting facilities accept and include in their greenhouse gas reports.
As a quality check, EPA also compared the MSW landfilled estimates from the EREF-extrapolated data, the
Facts and Figures report, and the estimated waste disposed by facilities reporting to EPA's GHGRP under Subpart HH
(MSW Landfills) for 2017 and 2018 plus an 11 percent scale-up factor to account for landfills that do not report to
Subpart HH.
On average, the EREF-extrapolated value was 39 percent less than GHGRP-based estimated waste disposal
amount for the year 2017 and 41 percent less than GHGRP-based estimated waste disposal amount for the year 2018
(including a scale-up factor of 11 percent for 2017 and 2018).
The difference between the EREF-extrapolated and GHGRP-based estimates are largely assumed to be due to
the difference in estimated number of facilities included in the respective sources, and because the EREF 2013 waste
landfilled estimate was extrapolated to 2018 based on population growth. In 2013, EREF estimated 1,540 landfills (data
collected from state agencies, individual facilities for Hawaii and Florida, and estimated using population-based
estimates for Alaska, Idaho and Wyoming). In 2018, the GHGRP-based estimate includes 2,111 total facilities, including
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1,136 facilities reporting to the GHGRP, and 975 assumed or confirmed operational MSW landfills identified through
WBJ 2016 and LMOP 2020 that do not report to the GHGRP.
Estimates of MSW landfilled from the Facts and Figures report for the year 2017 and 2018 were, on average,
61 percent less than the GHGRP + scale-up factor waste quantity (including a scale-up factor of 11 percent and
subtracting 23 percent estimate of construction and demolition waste for both years).
While this 61 percent difference is large, it is not unexpected given the Facts and Figures top-down
methodology and focus on MSW (i.e., non-MSW streams are purposely excluded). The GHGRP uses a facility-specific,
bottom-up approach to estimating emissions while the Facts and Figures report uses a top-down approach which
incorporates many assumptions regarding production, import and export values, and estimated product life are built
into the MSW generation and landfill disposal estimate at the national level. The Facts and Figures report also
specifically omits certain types of waste that are explicitly included in the GHGRP reports, such as construction and
demolition waste, industrial waste, biosolids (sludges), agricultural waste, and other inert wastes (EPA 2020).
Construction and demolition waste that was reported under the GHGRP were excluded to the extent possible, but
because the GHGRP facilities typically report a default waste composition, some construction and demolition waste
may still be included in what is assumed to be the MSW quantity. Additionally, the amount of biosolids (sludges) and
other non-MSW streams could not reliably be estimated and excluded from the GHGRP data and may also be
contributing to the percent difference.
Step 2: Estimate CH4 Generation at MSW Landfills for 1990 to 2004
The FOD method is exclusively used for 1990 to 2004. For the FOD method, methane generation is based on
nationwide MSW generation data, to which a national average disposal factor is applied; it is not landfill-specific.
The FOD method is presented below and is similar to Equation HH-6 in CFR Part 98.343 for MSW landfills, and
Equation TT-6 in CFR Part 98.463 for industrial waste landfills.
CH4 .Solid Waste = [GcH4,MSW — R] — Ox
where,
CH4,soiid waste =	Net CH4 emissions from solid waste
Gch4,msw =	CH4 generation from MSW landfills
R	=	CH4 recovered and combusted
Ox	=	CH4 oxidized from MSW and industrial waste landfills before release to the atmosphere
The input parameters needed for the FOD model equations are the mass of waste disposed each year
(discussed under Step 1), degradable organic carbon (DOC) as a function of methane generation potential (Lo), and the
decay rate constant (k). The equation below provides additional detail on the activity data and emission factors used in
the CH4,mswequation presented above.
CH4,msw = [Wx XioX^X (e-KT-x-i) _
where,
CH4,msw	=	Total CH4 generated from MSW landfills
T	=	Reporting year for which emissions are calculated
x	=	Year in which waste was disposed
S	=	Start year of calculation
Wx	=	Quantity of waste disposed of in the landfill in a given year
L0	=	Methane generation potential (100 m3 CH4/Mg waste; EPA 1998, 2008)
16/12	=	conversion factor from CH4 to C
k	=	Decay rate constant (yr1, see Table A-222)
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The DOC is determined from the CH4 generation potential (L0 in m3 CH4/Mg waste) as shown in the following
equation:
DOC = [Lo x 6.74 x 10 4] -f [Fx 16/12 x DOCf x MCF]
where,
DOC	= degradable organic carbon (fraction, kt C/kt waste),
L0	= CH4 generation potential (100 m3 CH4/Mg waste; EPA 1998, 2008),
6.74 x 10-4 = CH4 density (Mg/m3),
F	= fraction of CH4 by volume in generated landfill gas (equal to 0.5)
16/12 = molecular weight ratio CH4/C,
DOCf	= fraction of DOC that can decompose in the anaerobic conditions in the landfill (fraction equal
to 0.5 for MSW), and
MCF	= methane correction factor for year of disposal (fraction equal to 1 for anaerobic managed
sites).
DOC values can be derived for individual landfills if a good understanding of the waste composition over time is
known. A default DOC value is used in the Inventory because waste composition data are not regularly collected for all
landfills nationwide. When estimating CH4 generation for the years 1990 to 2004, a default DOC value is used. This DOC
value is calculated from a national CH4 generation potential135 of 100 m3 CH4/Mg waste (EPA 2008) as described below.
The DOC value used in the CH4 generation estimates from MSW landfills for 1990 to 2004 is 0.2028, which is
based on the CH4 generation potential of 100 m3 CH4/Mg waste (EPA 1998; EPA 2008). After EPA developed the L0 value,
RTI analyzed data from a set of 52 representative landfills across the United States in different precipitation ranges to
evaluate L0, and ultimately the national DOC value. The 2004 Chartwell Municipal Solid Waste Facility Directory
confirmed that each of the 52 landfills chosen accepted or accepts both MSW and construction and demolition (C&D)
waste (Chartwell 2004; RTI 2009). The values for L0 were evaluated from landfill gas recovery data for this set of 52
landfills, which resulted in a best fit value for L0 of 99 m3/Mg of waste (RTI 2004). This value compares favorably with a
range of 50 to 162 (midrange of 106) m3/Mg presented by Peer, Thorneloe, and Epperson (1993); a range of 87 to 91
m3/Mg from a detailed analysis of 18 landfills sponsored by the Solid Waste Association of North America (SWANA
1998); and a value of 100 m3/Mg recommended in EPA's compilation of emission factors (EPA 1998; EPA 2008; based on
data from 21 landfills). Based on the results from these studies, a value of 100 m3/Mg appears to be a reasonable best
estimate to use in the FOD model for the national inventory for years 1990 through 2004, and is the value used to derive
the DOC value of 0.2028.
In 2004, the FOD model was also applied to the gas recovery data for the 52 landfills to calculate a decay rate
constant (k) directly for L0 = 100 m3/Mg. The decay rate constant was found to increase with annual average
precipitation; consequently, average values of k were developed for three precipitation ranges, shown in Table A-222
and recommended in EPA's compilation of emission factors (EPA 2008).
Table A-222: Average Values for Rate Constant (k) by Precipitation Range (yr1)
Precipitation range (inches/year)
k (yr1)
<20
0.020
20-40
0.038
>40
0.057
These values for k show reasonable agreement with the results of other studies. For example, EPA's
compilation of emission factors (EPA 1998; EPA 2008) recommends a value of 0.02 yr1 for arid areas (less than 25
inches/year of precipitation) and 0.04 yr^for non-arid areas. The SWANA (1998) study of 18 landfills reported a range in
values of k from 0.03 to 0.06 yr1 based on CH4 recovery data collected generally in the time frame of 1986 to 1995.
135 Methane generation potential (L0) varies with the amount of organic content of the waste material. A higher L0
occurs with a higher content of organic waste.
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Using data collected primarily for the year 2000, the distribution of waste-in-place versus precipitation was
developed from over 400 landfills (RTI 2004). A distribution was also developed for population versus precipitation for
comparison. The two distributions were very similar and indicated that population in areas or regions with a given
precipitation range was a reasonable proxy for waste landfilled in regions with the same range of precipitation. Using
U.S. Census data and rainfall data, the distributions of population versus rainfall were developed for each Census decade
from 1950 through 2010. The distributions showed that the U.S. population has shifted to more arid areas over the past
several decades. Consequently, the population distribution was used to apportion the waste landfilled in each decade
according to the precipitation ranges developed for k, as shown in Table A-223.
Table A-223: Percent of U.S. Population within Precipitation Ranges by Decade (%)
Precipitation Range (inches/year)
1950
1960
1970
1980
1990
2000
<20
10
13
14
16
19
20
20-40
40
39
37
36
34
33
>40
50
48
48
48
48
48
Note: The precipitation range data are no longer used in the IPCC waste model (i.e., the FOD method) for 2010 and later years.
Totals may not add to 100% due to rounding.
Source: Years 1950 through 2000 are from RTI (2004) using population data from the U.S. Census Bureau and precipitation data
from the National Climatic Data Center's National Oceanic and Atmospheric Administration.
The 2006 IPCC Guidelines also require annual proportions of waste disposed of in managed landfills versus
unmanaged and uncategorized sites prior to 1980. Based on the historical data presented by Mintz et al. (2003), a
timeline was developed for the transition from the use of unmanaged and uncategorized sites for solid waste disposed
to the use of managed landfills. Based on this timeline, it was estimated that 6 percent of the waste that was land
disposed in 1940 was disposed of in managed landfills and 94 percent was managed in uncategorized sites. The
uncategorized sites represent those sites where not enough information was available to assign a percentage to
unmanaged shallow versus unmanaged deep solid waste disposal sites. Between 1940 and 1980, the fraction of waste
that was land disposed transitioned towards managed landfills until 100 percent of the waste was disposed of in
managed landfills in 1980. For wastes disposed of in the uncategorized sites, a methane correction factor (MCF) of 0.6
was used based on the recommended IPCC default value for uncharacterized land disposal (IPCC 2006). The
recommended IPCC default value for the MCF for managed landfills of 1 (IPCC 2006) has been used for the managed
landfills for the years where the first order decay methodology was used (i.e., 1990 to 2004).
Step 3: Estimate CH4 Emissions Avoided from MSW Landfills for 1990 to 2004
The estimated landfill gas recovered per year (R) at MSW landfills is based on a combination of four databases
that include recovery from flares and/or landfill gas-to-energy projects:
•	a database developed by the Energy Information Administration (EIA) for the voluntary reporting of
greenhouse gases (EIA 2007),
•	a database of LFGE projects that is primarily based on information compiled by EPA LMOP (EPA 2016),
•	the flare vendor database (contains updated sales data collected from vendors of flaring equipment), and
the
•	EPA's GHGRP MSW landfills database (EPA 2015a).136
The EPA's GHGRP MSW landfills database was first introduced as a source for recovery data for the 1990 to
2013 Inventory. The GHGRP MSW landfills database contains facility-reported data that undergoes rigorous verification
and is considered to contain the least uncertain data of the four databases. However, this database only contains a
portion of the landfills in the United States (although, presumably the highest emitters since only those landfills that
meet the methane generation threshold must report) and only contains data from 2010 and later. For landfills in this
136 The 2015 GHGRP dataset is used in the GHGRP MSW landfills dataset described in Step 3a. This database is no
longer updated because the methodology has changed such that the directly reported net methane emissions are used. The
GHGRP dataset is available through Envirofacts .
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database, methane recovery data reported data for 2010 and later were linearly back-casted to 1990, or the date the
landfill gas collection system at a facility began operation, whichever is earliest.
A destruction efficiency of 99 percent was applied to amounts of CH4 recovered to estimate CH4 emissions
avoided for all recovery databases. This value for destruction efficiency was selected based on the range of efficiencies
(86 to 99+ percent) recommended for flares in EPA's AP-42 Compilation of Air Pollutant Emission Factors, Draft Chapter
2.4, Table 2.4-3 (EPA 2008). A typical value of 97.7 percent was presented for the non-methane components (i.e., volatile
organic compounds and non-methane organic compounds) in test results (EPA 2008). An arithmetic average of 98.3
percent and a median value of 99 percent are derived from the test results presented in EPA 2008. Thus, a value of 99
percent for the destruction efficiency of flares has been used in Inventory methodology. Other data sources supporting a
99 percent destruction efficiency include those used to establish New Source Performance Standards (NSPS) for landfills
and in recommendations for closed flares used in the EPA's LMOP.
Step 3a: Estimate CH4 Emissions Avoided Through Landfill Gas-to-Energy (LFGE) and Flaring Projects for
1990 to 2004
The quantity of CH4 avoided due to LFGE systems was estimated based on information from three sources: (1) a
database developed by the EIA for the voluntary reporting of greenhouse gases (EIA 2007); (2) a database compiled by
LMOP and referred to as the LFGE database for the purposes of this inventory (EPA 2016); and (3) the GHGRP MSW
landfills dataset (EPA 2015a). The EIA database included location information for landfills with LFGE projects, estimates
of CH4 reductions, descriptions of the projects, and information on the methodology used to determine the CH4
reductions. In general, the CH4 reductions for each reporting year were based on the measured amount of landfill gas
collected and the percent CH4 in the gas. For the LFGE database, data on landfill gas flow and energy generation (i.e.,
MW capacity) were used to estimate the total direct CH4 emissions avoided due to the LFGE project. The GHGRP MSW
landfills database contains the most detailed data on landfills that reported under EPA's GHGRP for years 2010 through
2015, however the amount of CH4 recovered is not specifically allocated to a flare versus a LFGE project. The allocation
into flares or LFGE was performed by matching landfills to the EIA and LMOP databases for LFGE projects and to the flare
database for flares. Detailed information on the landfill name, owner or operator, city, and state are available for both
the EIA and LFGE databases; consequently, it was straightforward to identify landfills that were in both databases against
those in EPA's GHGRP MSW landfills database.
The same landfill may be included one or more times across these four databases. To avoid double- or triple-
counting CH4 recovery, the landfills across each database were compared and duplicates identified. A hierarchy of
recovery data is used based on the certainty of the data in each database. In summary, the GHGRP > EIA > LFGE > flare
vendor database.
If a landfill in the GHGRP MSW landfills database was also in the EIA, LFGE, and/or flare vendor database, the
avoided emissions were only based on EPA's GHGRP MSW landfills database to avoid counting the recovery amounts
multiple times across the different databases. In other words, the CH4 recovery from the same landfill was not included
in the total recovery from the EIA, LFGE, or flare vendor databases. While the GHGRP contains facility-reported
information on MSW Landfills starting in the year 2010, EPA has back-casted GHGRP emissions to the year 2005 in order
to merge the two methodologies (more information provided in Steps 4a and 4b). Prior to 2005, if a landfill in EPA's
GHGRP was also in the LFGE or EIA databases, the landfill gas project information, specifically the project start year, from
either the LFGE or EIA databases was used as the cutoff year for the estimated CH4 recovery in the GHGRP database. For
example, if a landfill reporting under EPA's GHGRP was also included in the LFGE database under a project that started in
2002 that is still operational, the CH4 recovery data in the GHGRP database for that facility was back-casted to the year
2002 only.
If a landfill in the EIA database was also in the LFGE and/or the flare vendor database, the CH4 recovery was
based on the EIA data because landfill owners or operators directly reported the amount of CH4 recovered using gas flow
concentration and measurements, and because the reporting accounted for changes over time. The EIA database only
includes facility-reported data through 2006; the amount of CH4 recovered in this database for years 2007 and later were
assumed to be the same as in 2006. Nearly all (93 percent) of landfills in the EIA database also report to EPA's GHGRP.
If both the flare data and LFGE recovery data were available for any of the remaining landfills (i.e., not in the
EIA or EPA's GHGRP databases), then the CH4 recovered were based on the LFGE data, which provides reported landfill-
specific data on gas flow for direct use projects and project capacity (i.e., megawatts) for electricity projects. The LFGE
database is based on the most recent EPA LMOP database (published annually). The remaining portion of avoided
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emissions is calculated by the flare vendor database, which estimates CH4 combusted by flares using the midpoint of a
flare's reported capacity. New flare vendor sales data have not been collected since 2015 because these data are not
used for estimates beyond 2005 in the time series. Given that each LFGE project is likely to also have a flare, double
counting reductions from flares and LFGE projects in the LFGE database was avoided by subtracting emission reductions
associated with LFGE projects for which a flare had not been identified from the emission reductions associated with
flares (referred to as the flare correction factor).
Step 3b: Estimate CH4 Emissions Avoided Through Flaring for the Flare Database for 1990 to 2004
To avoid double counting, flares associated with landfills in EPA's GHGRP, EIA and LFGE databases were not
included in the total quantity of CH4 recovery from the flare vendor database. As with the LFGE projects, reductions from
flaring landfill gas in the EIA database were based on measuring the volume of gas collected and the percent of CH4 in
the gas. The information provided by the flare vendors included information on the number of flares, flare design flow
rates or flare dimensions, year of installation, and generally the city and state location of the landfill. When a range of
design flare flow rates was provided by the flare vendor, the median landfill gas flow rate was used to estimate CH4
recovered from each remaining flare (i.e., for each flare not associated with a landfill in the EIA, EPA's GHGRP, or LFGE
databases). Several vendors have provided information on the size of the flare rather than the flare design gas flow rate
for most years of the Inventory. Flares sales data has not been obtained since the 1990 to 2015 Inventory year, when the
net CH4 emission directly reported to EPA's GHGRP began to be used to estimate emission from MSW landfills.
To estimate a median flare gas flow rate for flares associated with these vendors, the size of the flare was
matched with the size and corresponding flow rates provided by other vendors. Some flare vendors reported the
maximum capacity of the flare. An analysis of flare capacity versus measured CH4 flow rates from the EIA database
showed that the flares operated at 51 percent of capacity when averaged over the time series and at 72 percent of
capacity for the highest flow rate for a given year. For those cases when the flare vendor supplied maximum capacity,
the actual flow was estimated as 50 percent of capacity. Total CH4 avoided through flaring from the flare vendor
database was estimated by summing the estimates of CH4 recovered by each flare for each year.
Step 3c: Reduce CH4 Emissions Avoided Through Flaring for 1990 to 2004
If comprehensive data on flares were available, each LFGE project in EPA's GHGRP, EIA, and LFGE databases
would have an identified flare because it is assumed that most LFGE projects have flares. However, given that the flare
vendor database only covers approximately 50 to 75 percent of the flare population, an associated flare was not
identified for all LFGE projects. These LFGE projects likely have flares, yet flares were unable to be identified for one of
two reasons: 1) inadequate identifier information in the flare vendor data, or 2) a lack of the flare in the flare vendor
database. For those projects for which a flare was not identified due to inadequate information, CH4 avoided would be
overestimated, as both the CH4 avoided from flaring and the LFGE project would be counted. To avoid overestimating
emissions avoided from flaring, the CH4 avoided from LFGE projects with no identified flares was determined and the
flaring estimate from the flare vendor database was reduced by this quantity (referred to as a flare correction factor) on
a state-by-state basis. This step likely underestimates CH4 avoided due to flaring but was applied to be conservative in
the estimates of CH4emissions avoided.
Additional effort was undertaken to improve the methodology behind the flare correction factor for the 1990
to 2009 and 1990 to 2014 inventory years to reduce the total number of flares in the flare vendor database that were
not matched to landfills and/or LFGE projects in the EIA and LFGE databases. Each flare in the flare vendor database not
associated with a LFGE project in the EIA, LFGE, or EPA's GHGRP databases was investigated to determine if it could be
matched. For some unmatched flares, the location information was missing or incorrectly transferred to the flare vendor
database and was corrected during the review. In other instances, the landfill names were slightly different between
what the flare vendor provided, and the actual landfill name as listed in the EIA, LFGE and EPA's GHGRP databases. The
remaining flares did not have adequate information through the name, location, or owner to identify it to a landfill in any
of the recovery databases or through an Internet search; it is these flares that are included in the flare correction factor
for the current inventory year.
A large majority of the unmatched flares are associated with landfills in the LFGE database that are currently
flaring but are also considering LFGE. These landfills projects considering a LFGE project are labeled as candidate,
potential, or construction in the LFGE database. The flare vendor database was improved in the 1990 to 2009 inventory
year to match flares with operational, shutdown as well as candidate, potential, and construction LFGE projects, thereby
reducing the total number of unidentified flares in the flare vendor database, all of which are used in the flare correction
A-456 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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factor. The results of this effort significantly decreased the number of flares used in the flare correction factor, and
consequently, increased recovered flare emissions, and decreased net emissions from landfills for the 1990 through 2009
Inventory. The revised state-by-state flare correction factors were applied to the entire Inventory time series (RTI 2010).
Step 4: Estimate CH4 Emissions from MSW Landfills for 2005 to 2009
During preparation of the 1990 to 2015 Inventory, EPA engaged with stakeholders both within and outside of
the landfill industry on the methodology used in the Inventory, the data submitted by facilities under EPA's GHGRP
Subpart HH for MSW Landfills, and the application of this information as direct inputs to the MSW landfill methane
emissions estimates in the 1990 to 2015 Inventory. Based on discussions with stakeholders, EPA developed several
options for improving the Inventory through methodological changes and moved forward with using the directly
reported net GHGRP methane emissions from 2010 to 2015 for MSW landfills in the 1990 to 2015 Inventory.
The Inventory methodology now uses directly reported net CH4 emissions for the 2010 to 2019 reporting years
from EPA's GHGRP to back-cast emissions for 2005 to 2009. The emissions for 2005 to 2009 are recalculated each year
the Inventory is published to account for the additional year of reported data and any revisions that facilities make to
past GHGRP reports. When EPA verifies the greenhouse gas reports, comparisons are made with data submitted in
earlier reporting years and errors may be identified in these earlier year reports. Facility representatives may submit
revised reports for any reporting year in order to correct these errors. Facilities reporting to EPA's GHGRP that do not
have landfill gas collection and control systems use the FOD method. Facilities with landfill gas collection and control
must use both the FOD method and a back-calculation approach. The back-calculation approach starts with the amount
of CH4 recovered and works back through the system to account for gas not collected by the landfill gas collection and
control system (i.e., the collection efficiency).
Including the GHGRP net emissions data was a significant methodological change from the FOD method
previously described in Steps 1 to 3 and only covered a portion of the Inventory time series. Therefore, EPA needed to
merge the previous method with the new (GHGRP) dataset to create a continuous time series and avoid any gaps or
jumps in. estimated emissions in the year the GHGRP net emissions are first included (i.e., 2010).
To accomplish this, EPA back-casted GHGRP net emissions to 2005 to 2009 and added a scale-up factor to
account for emissions from landfills that do not report to the GHGRP. A description of how the scale-up factor was
determined and why the GHGRP emissions were back-casted are included below as Step 4a and Step 4b, respectively.
The methodology described in this section was determined based on the good practice guidance in Volume 1: Chapter 5
Time Series Consistency of the 2006IPCC Guidelines. Additional details including other options considered are included
in RTI 2017a and RTI 2018.
Step 4a: Developing and Applying the Scale-up Factor for MSW Landfills for 2005 to 2009
Landfills that do not meet the reporting threshold are not required to report to the GHGRP. As a result, the
GHGRP dataset is only partially complete when considering the universe of MSW landfills. In theory, national emissions
from MSW landfills equals the emissions from landfills that report to the GHGRP plus emissions from landfills that do not
report to the GHGRP. Therefore, for completeness, a scale-up factor had to be developed to estimate the amount of
emissions from the landfills that do not report to the GHGRP.
To develop the scale-up factor, EPA completed four main steps:
1.	EPA determined the number of landfills that do not report to the GHGRP (hereafter referred to as the non-
reporting landfills). Source databases included the LMOP database 2017 (EPA 2017) and the WBJ Directory
2016 (WBJ 2016). This step identified 1,544 landfills that accepted MSW between 1940 and 2016 and had never
reported to the GHGRP. These landfills and the data collected were compiled into the 2016 Non-Reporting
Landfills Database.
2.	EPA estimated annual waste disposed and the total waste-in-place (WIP) at each non-reporting landfill as of
2016. Both databases include critical details about individual landfills to estimate annual methane emissions,
including the year waste was first accepted, the year the landfill closed (as applicable), and the estimated
amount of waste disposed. But not all details are included for all landfills. A total of 969 of the 1,544 landfills
(63 percent) contained the critical information necessary to estimate WIP.
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a.	For 234 non-reporting landfills, there was not enough information in the source databases to
estimate WIP.
b.	For 341 of the non-reporting landfills, WIP could be estimated with assumptions that either (i)
"forced" the year that waste was first accepted as 30 years prior to the landfill closure year (if a
closure date was included); or (ii) "forced" a closure year of 2016 if the landfill was known to be
closed and a closure year was not included in the source database.
c.	The database was reviewed by industry and staff from LMOP at this stage to help fill data gaps and
rectify discrepancies between individual landfills across the source databases, which improved the
WIP estimates by landfill and overall.
3.	EPA summed the total WIP for the non-reporting landfills. Using the assumptions mentioned above, the total
WIP in 2016 across the non-reporting landfills was approximately 0.922 billion MT.
4.	EPA calculated the scale-up factor (9%) by dividing the non-reporting landfills WIP (0.92 billion MT) by the sum
of the GHGRP WIP and the non-reporting landfills WIP (10.0 billion MT).
Table A-224: Revised Waste-in-Place (WIP) for GHGRP Reporting and Non-reporting Landfills in 2016
Estimated WIP
	Category	(billion metric tons)	Percentage	
Non-reporting	^	9 percent
facilities	(the applied scale-up factor)
GHGRP facilities	9.1	91 percent
Total	10.0	100 percent
Note: The same 9 percent scale-up factor is applied in each year the GHGRP reported emissions are used in the Inventory.
Step 4b: Back-casting GHGRP Emissions for MSW Landfills for 2005 to 2009 to Ensure Time Series
Consistency
Regarding the time series and as stated in 2006IPCC Guidelines Volume 1: Chapter 5 Time Series Consistency
(IPCC 2006), "the time series is a central component of the greenhouse gas inventory because it provides information on
historical emissions trends and tracks the effects of strategies to reduce emissions at the national level. All emissions in a
time series should be estimated consistently, which means that as far as possible, the time series should be calculated
using the same method and data sources in all years" (IPCC 2006). Chapter 5 however, does not recommend back-
casting emissions to 1990 with a limited set of data and instead provides guidance on techniques to splice, or join
methodologies together. One of those techniques is referred to as the overlap technique. The overlap technique is
recommended when new data becomes available for multiple years, which was the case with the GHGRP data, where
directly reported net CH4 emissions data became available for more than 1,200 MSW landfills beginning in 2010. The
GHGRP emissions data had to be merged with emissions from the FOD method to avoid a drastic change in emissions in
2010, when the datasets were combined. EPA also had to consider that according to IPCC's good practice, efforts should
be made to reduce uncertainty in Inventory calculations and that, when compared to the GHGRP data, the FOD method
presents greater uncertainty.
In evaluating the best way to combine the two datasets, EPA considered either using (1) the FOD method from
1990 to 2009, or (2) using the FOD method for a portion of that time series and back-casting the GHGRP emissions data
to a year where emissions from the two methodologies aligned. Plotting the back-casted GHGRP emissions against the
emissions estimates from the FOD method showed an alignment of the data in 2004 and later years which facilitated the
use of the overlap technique while also reducing uncertainty. Therefore, EPA decided to back-cast the GHGRP emissions
from 2009 to 2005 only, to merge the datasets and adhere to the IPCC good practice guidance.
EPA used the Excel Forecast function to back-cast net methane emissions using the GHGRP data. The forecast
function is used to predict a future value by using existing values, but EPA has applied it to predict previous values.
Although it is not ideal, it allowed for expeditious implementation. In the forecast function, the known values are existing
x-values and y-values (i.e., the years and data for the GHGRP, 2010 to 2015). The unknown y-values are the years to be
estimated (i.e., all years prior to 2009). The following Excel formula was used: =FORECAST(year to back-cast, GHGRP data
for 2010 to 2015, years 2010 to 2015). The forecast function is a linear regression; thus, it will not account for annual
fluctuations in CH4 emissions when used for multiple years.
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The years to back-cast the GHGRP data were first determined for the 1990 to 2015 Inventory when a 12.5
percent scale-up factor was used. EPA plotted the net CH4 emissions from the adjusted 1990 to 2014 methodology
against the back-casted GHGRP emissions for 1990 to 2009 and directly reported CH4 emissions for 2010 to 2015 with a
scale-up factor of 12.5 percent applied to all years the GHGRP data are used, 2005 to 2014) as presented in Figure A-19.
The results for the two methods are nearly identical for the years 2005 to 2010, which provides a basis for back-casting
the GHGRP emissions data to 2005 only. However, after applying the 12.5 percent scale-up factor across the time series,
the GHGRP emissions data were now larger than the revised Inventory estimates for the years 2010 to 2015. This
difference was addressed through revisions to the scale-up factor after a more detailed review of the non-reporting
landfills, resulting in a revised scale-up factor of 9 percent (described above in Step 4a), which more closely aligns
emissions estimates between the two methodologies as presented in Figure A-20. EPA therefore decided to maintain
back-casting of the GHGRP emissions from 2005 to 2009 only.
Figure A-19: Comparison of the revised 1990-2014 Inventory methodology against the GHGRP emissions (back-
casted from 2009 to 1990) and directly reported emissions for 2010 to 2014 with a 12.5% scale-up factor
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Figure A-20: Comparison of the revised 1990-2014 Inventory methodology against the GHGRP emissions (back-
casted from 2009 to 1990) and directly reported emissions for 2010 to 2014 with a 9% scale-up factor

¦ Adjusted 1990-2014 Method
Modified GHGRP
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An important factor in this approach is that the back-casted emissions for 2005 to 2009 are subject to change
with each Inventory because the GHGRP dataset may change as facilities revise their annual reports. The revisions are
generally minor considering the entire GHGRP dataset and EPA has not determined any revisions to the back-casting
approach or scale-up factor are necessary to date.
Step 5: Estimate CH4 Emissions from MSW Landfills for 2010 to 2016
CH4 emissions directly reported to EPA's GHGRP are used for 2010 to 2019. Inherent in these direct emissions
are the use of various GHGRP default emission factors such as the gas collection and control system collection
efficiencies (where applicable), decay rate (k), and degradable organic carbon (DOC).
Facilities reporting to Subpart HH of the GHGRP can report their k and DOC values under one of three waste
type options: (1) Bulk waste option, where all waste is accounted for within one bulk k and DOC value; (2) Modified bulk
waste option, where waste disposed of at the landfill can be binned into bulk MSW excluding inerts and construction and
demolition waste, construction and demolition waste, and inerts; and (3) Waste Composition option, where waste
disposed of can be delineated into specific waste streams (i.e., food waste, garden waste, textiles, etc.) OR where
facilities report a known quantity of inert waste and consider the remaining waste as bulk MSW (using the same k and
DOC value for MSW as the bulk waste option).
The GHGRP requires facilities with a gas collection and control system to report their emissions using both a
forward-estimating (i.e., using a first order decay approach, accounting for soil oxidation) and a back-calculating (i.e.,
using methane recovery and collection efficiency data, accounting for soil oxidation) method as described in Chapter 7 of
this Inventory. To determine collection efficiency, facilities are required to report the amount of waste-in-place (surface
area and soil depth) at their landfill as categorized by one of five area types (see Table A-225).
Table A-225: Table HH-3 to Subpart HH of the EPA's Greenhouse Gas Reporting Program, Area Types Applicable
to the Calculation of Gas Collection Efficiency	
	Description	Landfill Gas Collection Efficiency	
Al: Area with no waste-in-place	Not applicable, do not use this area in the calculation
A2: Area without active gas collection, regardless of cover
CE2: O/o
type
A3: Area with daily soil cover and active gas collection	CE3: 60%
A4: Area with an intermediate soil cover, or a final soil cover
not meeting the criteria for A5 below, and active gas	CE4: 75%
collection
A5: Area with a final soil cover of 3 feet or thicker of clay or
final cover (as approved by the relevant agency) and/or	CE5: 95%
geomembrane cover system and active gas collection
Weighted average collection efficiency for landfills:
. . , .	„	CEavel = (A2*CE2) + A3*CE3 + A4*CE4 + A5*CE5) / (A2 + A3
Area weighted average collection efficiency for landfills	+ A4 + A5)
If facilities are unable to bin their waste into these area types, they are instructed to use 0.75, or 75 percent as
a default value. In the EPA's original rulemaking for the GHGRP, the EPA proposed this default collection efficiency of 75
percent because it was determined to be a reasonable central-tendency default considering the availability of data such
as surface monitoring under the EPA's New Source Performance Standards for MSW Landfills (40 CFR Part 60 Subpart
WWW), which suggested that gas collection efficiencies generally range from 60 to 95 percent. This 75 percent default
gas collection efficiency value only applies to areas at the landfill that are under gas collection and control; for areas of
the landfill that are not under gas collection and control, a gas collection efficiency of 0 percent is applied.
The 9 percent scale-up factor is applied to the net annual emissions reported to the GHGRP for 2010 to 2016 as
is done for 2005 to 2009 because the GHGRP does not capture emissions from all landfills in the United States.
Step 6: Estimate CH4 Emissions from MSW Landfills for 2017 to 2019
The scale-up factor was initially developed to use the GHGRP reported data and account for the remaining
subset of landfills that are not required to report to the GHGRP. The EPA acknowledges there are uncertainties
associated with the 9 percent scale-up factor and underlying landfill-specific data used to develop the Non-Reporting
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Landfills database. Specifically, the GHGRP allows facilities to off-ramp (i.e., stop reporting to the GHGRP) after meeting
certain criteria; therefore, the number of facilities and WIP reported under the GHGRP will vary year to year. Nearly 200
facilities have off-ramped from the GHGRP to date, which means there is now more WIP for non-reporting landfills than
there was in the 2016 scale-up factor analysis. Reassessment of the scale-up factor at regular intervals to account for
changes in the GHGRP dataset and LMOP database is considered good practice and was therefore included in the
Planned Improvements section for the previous (1990 to 2018) Inventory.
The methodology used to revise the scale-up factor largely followed that to develop the 2016 Non-Reporting
Landfills Database, as summarized below:
1.	EPA streamlined the layout of the 2016 Non-Reporting Landfills Data to remove extraneous columns, clearly
present the landfill-specific data from the main sources (i.e., the 2017 LMOP Database [EPA 2017] and the
WBJ Directory 2016 [WBJ, 2016]), and the calculation columns that yield the start year, closure year, and WIP
data used to estimate the total WIP at all non-reporting landfills. The database is hereafter referred to as the
2018 Non-Reporting Landfills Database.
2.	EPA added in new or updated data for existing non-reporting landfills and added in entries for new non-
reporting landfills.
a.	We added the 194 landfills that have off-ramped from the GHGRP as of 2019 (EPA 2020a) into the
Non-Reporting Landfills Database.
b.	We cross-referenced and updated the 2017 LMOP Database (EPA 2017) information with the 2020
LMOP Database (EPA 2020b) information. New cases from the 2020 LMOP Database were added,
LMOP IDs were updated for some landfills, and 27 landfills from the 2017 LMOP Database were
removed because they were determined not to be MSW landfills.
c.	These revisions increased the count of non-reporting landfills from 1,544 landfills to 1,672 landfills, a
net increase of 128 landfills from the 2016 Non-Reporting Landfills Database.
3.	EPA conducted additional quality control checks on calculations in the 2016 Non-Reporting Landfills Database
and rectified identified errors, which resulted in an increase of 38,498,070 MT of waste from the 2016 Non-
Reporting Landfills Database.
a.	We identified a formula error that under-estimated the WIP for landfills with a permitted end year
after 2016, especially for those landfills that had reported closure dates in 2030 or later. For
example, if the start year was 1980 and the permitted closure year was 2040, the formula was
estimating 50 years when, for the purposes of this exercise, the number of years should have been
36 years. Dividing the WIP by 60 years results in a lower annual waste disposal value than dividing
the WIP by 36 years (2016-1980). The methodology calculates an annual disposal rate for each
landfill and then applies the annual disposal rate to 2016 minus the start year.
b.	We did not pull in the year the WIP data were from in the 2017 LMOP Database and assumed the
WIP data were from 2016 unless otherwise noted. The WIP year is now included in the 2018 Non-
Reporting Landfills Database. The WBJ Directory does not present the year the WIP data are from,
thus we assumed each data point was from 2016. These assumptions underestimate the amount of
WIP for a large majority of the landfills where the WIP data year is not reported.
4.	EPA estimated annual waste disposed and the total WIP at each non-reporting landfill as of 2018. Where
available, the databases include details about individual landfills to estimate annual methane emissions,
including the year waste was first accepted, the year the landfill closed (as applicable), and the estimated
amount of waste disposed. When enough data were available, EPA estimated WIP by calculating an annual
waste disposal rate and multiplied that by the number of operating years up to the closure year, or 2018 (if
the landfill was known or assumed to be open). EPA used a tiered methodology when a landfill with critical
information was included in more than one database:
Tier 1: If the landfill has off-ramped from the GHGRP, use the Subpart HH WIP value
Tier 2. If the landfill is in the 2020 LMOP Database, use the 2020 LMOP WIP value
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Tier 3. Otherwise, EPA used the average of the estimated WIP value that was forced or
provided from the 2016 Non-reporting Landfill Database industry and LMOP reviewers.
a.	A total of 1,027 of the 1,672 landfills (61 percent) contained enough critical information necessary to
estimate the 2018 WIP (i.e., first year of operation, either total WIP or annual waste disposal data,
and either an indication the landfill was still operating or the closure date). It is important to note
that the WIP estimates are estimates. The quality of the source data for WIP and annual waste
disposed have not been individually verified by the EPA team. In the case of the GHGRP data, the
annual waste disposal quantities are either estimates using defined methodologies or actual waste
disposed from tipping receipts. In general, most landfills have relied on tipping receipts for the past
decade, meaning that annual waste disposed several decades ago are estimates.
b.	For 325 of the 1,672 landfills (19 percent), WIP could be estimated with assumptions that either (i)
"forced" the year that waste was first accepted as 30 years prior to the landfill closure year (if a
closure year was included); or (ii) forced a closure year of 2018 if the landfill was known or thought
to be open and a closure year was not included in the source database. These are the same general
assumptions applied in the 2016 Non-Reporting Landfills Database.
c.	For 320 of the 1,672 landfills (19 percent), there was not enough information in the source databases
to estimate WIP, thus no WIP data was calculated for these facilities, which underestimates the total
WIP for the non-reporting landfills.
5.	EPA summed the total WIP for the non-reporting landfills, yielding 1.2 billion MT of WIP, which is an increase
of approximately 0.27 billion MT of WIP (30 percent increase) from the 2016 Non-Reporting Landfills
Database where the total WIP for non-reporting landfills was estimated at 0.92 billion MT.
6.	EPA calculated the scale-up factor (11 percent) by dividing the non-reporting landfills WIP (1.2 billion MT) by
the sum of the GHGRP WIP and the non-reporting landfills WIP (10.8 billion MT).
Table A-226: Revised Waste-in-Place (WIP) for GHGRP Reporting and Non-reporting Landfills in 2018

Estimated WIP

Category
(billion metric tons)
Percentage
Non-reporting facilities
1.2
11 percent
(the applied scale-up factor)
GHGRP facilities
9.6
89 percent
Total
10.8
100 percent
An 11 percent scale-up factor is applied annually for 2017 to 2019 as is done for 2010 to 2016 because the
GHGRP does not capture emissions from all landfills in the United States. In future inventories, the scale-up factor will be
reassessed annually to include additional facilities that off-ramp from the GHGRP and revisions to the LMOP Database.
Step 7: Estimate CH4 Generation at Industrial Waste Landfills for 1990 to the Current
Inventory Year
A Tier 2 approach (IPCC 2006) is used to estimate annual emissions from industrial waste landfills. A tailored
IPCC waste model, based on the FOD method and country-specific defaults, is exclusively used for 1990 to 2019. For the
FOD method, methane generation is based on nationwide industrial production data from two major sectors - pulp and
paper, and food and beverage manufacturing - to which a national average disposal factor is applied, separately for each
sector. The same disposal factor and emission factors are applied to every year in the time series. The methodology is
not Tier 3 (i.e., it is not landfill-specific) because data for individual landfills are limited. Table A-221 presents the amount
of industrial production data and estimated amount of industrial waste landfilled for select years.
The FOD method is presented below and is similar to Equation HH-6 in CFR Part 98.343 for MSW landfills, and
Equation TT-6 in CFR Part 98.463 for industrial waste landfills.
CH4 .Solid Waste = [GcH4,MSW — R] — Ox
where,
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R
Ox
CH4iSolid Waste —
GcH4,MSW =
Net CH4 emissions from solid waste
CH4 generation from MSW landfills
CH4 recovered and combusted
CH4 oxidized from MSW and industrial waste landfills before release to the atmosphere
Industrial waste landfills receive waste from factories, processing plants, and other manufacturing activities. In
national inventories prior to the 1990 through 2005 inventory, CH4 generation at industrial landfills was estimated as
seven percent of the total CH4 generation from MSW landfills, based on a study conducted by EPA (1993). In 2005, the
methodology was updated and improved by using activity factors (industrial production levels) to estimate the amount
of industrial waste landfilled each year, and by applying the FOD model to estimate CH4 generation. A nationwide survey
of industrial waste landfills found that most of the organic waste placed in industrial waste landfills originated from two
sectors: food processing (meat, vegetables, fruits) and pulp and paper (EPA 1993). Data for annual nationwide
production for the food and beverage processing and pulp and paper sectors were taken from industry and government
sources for recent years and estimates were developed for production for the earlier years for which data were not
For the pulp and paper sector, production data published by the Lockwood-Post's Directory were used for years
1990 to 2001 and production data published by the Food and Agriculture Organization were used for years 2002 through
2019. An extrapolation based on U.S. real gross domestic product was used for years 1940 through 1964.
For the food and beverage processing sector, production data were obtained from the U.S. Department of
Agriculture for the years 1990 through 2019 (ERG 2020). An extrapolation based on U.S. population was used for the
years 1940 through 1989.
In addition to production data for the pulp and paper and food processing sectors, the following inputs are
needed to use the FOD model for estimating CH4 generation from industrial waste landfills: 1) quantity of waste that is
disposed in industrial waste landfills (as a function of production), 2) CH4 generation potential (L0) from which a DOC
value can be calculated, and 3) the decay rate constant (k).
Research into waste generation and disposal in landfills for the pulp and paper sector indicated that the
quantity of waste landfilled was about 0.050 MT/MT of product compared to 0.046 MT/MT product for the food
processing sector (RTI 2006). These emission factors were applied to estimates of annual production to estimate annual
waste disposal in industrial waste landfills (see Table A-221 for select years). Estimates for DOC were derived from
available data (EPA, 2015b; Heath et al., 2010; NCASI, 2005; Kraft and Orender, 1993; NCASI 2008; Flores et al. 1999 as
documented in RTI 2015a). The DOC value for industrial pulp and paper waste is estimated at 0.15 (L0 of 49 m3/MT); the
DOC value for industrial food waste is estimated as 0.26 (L0 of 128 m3/MT) (RTI 2015; RTI 2014). Estimates for k were
taken from the default values in the 2006IPCC Guidelines; the value of k given for food waste with disposal in a wet
temperate climate is 0.19 yr1, and the value given for paper waste is 0.06 yr1.
A literature review was conducted for the 1990 to 2010 and 1990 to 2014 inventory years with the intent of
updating values for L0 (specifically DOC) and k in the pulp and paper sector (RTI 2014). Where pulp and paper mill
wastewater treatment residuals or sludge are the primary constituents of pulp and paper waste landfilled, values for k
137
available in the literature range from 0.01/yr to 0.1/yr, while values for L0 range from 50 m3/Mt to 200 m3/Mt. Values
for these factors are highly variable and are dependent on the soil moisture content, which is generally related to rainfall
amounts. At this time, sufficient data were available through EPA's GHGRP to warrant a change to the L0 (DOC) from 99
to 49 m3/MT, but sufficient data were not obtained to warrant a change to k. EPA will consider an update to the k values
for the pulp and paper sector as new data arises and will work with stakeholders to gather data and other feedback on
potential changes to these values.
As with MSW landfills, a similar trend in disposal practices from unmanaged landfills, or open dumps to
managed landfills was expected for industrial waste landfills; therefore, the same timeline that was developed for MSW
landfills was applied to the industrial landfills to estimate the average MCF. That is, between 1940 and 1980, the fraction
of waste that was land disposed transitioned from 6 percent managed landfills in 1940 and 94 percent open dumps to
100 percent managed landfills in 1980 and on. For wastes disposed of in unmanaged sites, an MCF of 0.6 was used and
137 Sources reviewed included Heath et al. 2010; Miner 2008; Skog 2008; Upton et al. 2008; Barlaz 2006; Sonne 2006; NCASI
2005; Barlaz 1998; and Skog and Nicholson 2000.
available.
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for wastes disposed of in managed landfills, an MCF of 1 was used, based on the recommended IPCC default values (IPCC
2006).
The parameters discussed above were used in the integrated form of the FOD model to estimate CH4
generation from industrial waste landfills.
Step 8: Estimate CH4 Oxidation from MSW and Industrial Waste Landfills
A portion of the CH4 escaping from a landfill oxidizes to C02 in the top layer of the soil. The amount of oxidation
depends upon the characteristics of the soil and the environment. For purposes of this analysis, it was assumed that of
the CH4 generated, minus the amount of gas recovered for flaring or LFGE projects, 10 percent was oxidized in the soil
(Jensen and Pipatti 2002; Mancinelli and McKay 1985; Czepiel et al 1996). The literature was reviewed in 2011 (RTI 2011)
and 2017 (RTI 2017b) to provide recommendations for the most appropriate oxidation rate assumptions. It was found
that oxidation values are highly variable and range from zero to over 100 percent (i.e., the landfill is considered to be an
atmospheric sink by virtue of the landfill gas extraction system pulling atmospheric methane down through the cover).
There is considerable uncertainty and variability surrounding estimates of the rate of oxidation because oxidation is
difficult to measure and varies considerably with the presence of a gas collection system, thickness and type of the cover
material, size and area of the landfill, climate, and the presence of cracks and/or fissures in the cover material through
which methane can escape. IPCC (2006) notes that test results from field and laboratory studies may lead to over-
estimations of oxidation in landfill cover soils because they largely determine oxidation using uniform and homogeneous
soil layers. In addition, several studies note that gas escapes more readily through the side slopes of a landfill as
compared to moving through the cover thus complicating the correlation between oxidation and cover type or gas
recovery.
An oxidation factor of 0.10 (IPCC 2006) is applied for industrial waste landfills for the entire time series. A
variety of oxidation factors (0.0, 0.10, 0.25, or 0.35) are applied for MSW landfills as described below. The oxidation
factors applied for MSW landfills are based on IPCC 2006 (0.10) and scientific literature reviewed for the development of
the GHGRP regulations (40 CFR Part 98).
MSW landfills with landfill gas collection systems are generally designed and managed better to improve gas
recovery. More recent research (2006 to 2012) on landfill cover methane oxidation has relied on stable isotope
techniques that may provide a more reliable measure of oxidation. Results from this recent research consistently point
to higher cover soil methane oxidation rates than the IPCC (2006) default of 10 percent. A continued effort will be made
to review the peer-reviewed literature to better understand how climate, cover type, and gas recovery influence the rate
of oxidation at active and closed landfills. At this time, the IPCC recommended oxidation factor of 10 percent will
continue to be used for all landfills for the years 1990 to 2004 and for industrial waste landfills for the full time series.
For years 2005 to 2018, net CH4 emissions from MSW landfills as directly reported to EPA's GHGRP, which
include the adjustment for oxidation, are used. Subpart HH of the GHGRP includes default values for oxidation which are
dependent on the mass flow rate of CH4 per unit at the bottom of the surface soil prior to any oxidation, also known as
methane flux rate. The oxidation factors included in the GHGRP (0, 0.10, 0.25, 0.35) are based on published, peer-
reviewed literature and facility data provided through external stakeholder engagement. The EPA concluded, during
review of both the literature and facility-reported emissions data, that simply revising the IPCC's Tier 1 oxidation default
of 10 percent to a new singular default oxidation value would not take into account the key variable - methane flux rate -
entering the surface soil layer. More information regarding analysis of methane oxidation fractions can be found in the
memorandums entitled "Review of Oxidation Studies and Associated Cover Depth in the Peer Reviewed Literature", June
17, 2015 (RTI 2015b). More information about the landfill specific conditions required to use higher oxidation factors can
be found in Table HH-4 of 40 CFR Part 98, Subpart HH, as shown below.
Table A-227: Table HH-4 to Subpart HH of Part 98—Landfill Methane Oxidation Fractions
Use this landfill
methane oxidation
	Under these conditions:	fraction:	
I.	For all reporting years prior to the 2013 reporting year
CI: For all landfills regardless of cover type or methane flux	0.10
II.	For the 2013 reporting year and all subsequent years
A-464 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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C2: For landfills that have a geomembrane (synthetic) cover or other non-soil barrier	0.10
meeting the definition of final cover with less than 12 inches of cover soil for greater
than 50% of the landfill area containing waste
C3: For landfills that do not meet the conditions in C2 above and for which you elect not to	0.10
determine methane flux
C4: For landfills that do not meet the conditions in C2 or C3 above and that do not have	0.10
final cover, or intermediate or interim cover3 for greater than 50% of the landfill area
containing waste
C5: For landfills that do not meet the conditions in C2 or C3 above and that have final	0.35
cover, or intermediate or interim cover3 for greater than 50% of the landfill area
containing waste and for which the methane flux rateb is less than 10 grams per square
meter per day (g/m2/d)
C6: For landfills that do not meet the conditions in C2 or C3 above and that have final cover	0.25
or intermediate or interim cover3 for greater than 50% of the landfill area containing
waste and for which the methane flux rateb is 10 to 70 g/m2/d
C7: For landfills that do not meet the conditions in C2 or C3 above and that have final cover	0.10
or intermediate or interim cover3 for greater than 50% of the landfill area containing
waste and for which the methane flux rateb is greater than 70 g/m2/d
3 Where a landfill is in a state that does not have an intermediate or interim cover requirement, the landfill must have
soil cover of 12 inches or greater in order to use an oxidation fraction of 0.25 or 0.35.
b Methane flux rate (in grams per square meter per day; g/m2/d) is the mass flow rate of methane per unit area at the
bottom of the surface soil prior to any oxidation and is calculated as follows:
l-.-.-f | v|LijEk:f! III! 5 (•.! 11'si-' ^uhri.in, ,-r Mr I ilii.iTlMi! ! 1 h ...ibp.lll £ 1 -is'ih'S p:'i 1,
M¥ = KxC!lm /SArea
For Kifoaiictft HH-fc of Urn subpart,
Mt-'=Kx (n..:. i»,|i
For Eipaliprts HH-7 wf ibis subpart,
" R
¦Area
MF = Kx) _"_V
CI ff
For Equation Htt-8 of this subpart.
| /SAtea
11 i
MF=HrJE
I V I.:- j ^.1
1 Ifcs,?-
¦ K i j
A - I	j!
SArci
The EPA's GHGRP also requires landfills to report the type of cover material used at their landfill as: organic
cover, clay cover, sand cover, and/or other soil mixtures.
Step 9: Estimate Total CH4 Emissions for the Inventory
For 1990 to 2004, total CH4 emissions were calculated by adding emissions from MSW and industrial landfills,
and subtracting CH4 recovered and oxidized, as shown in Table A-228. A different methodology is applied for 2005 to
2019 where directly reported net CH4 emissions to EPA's GHGRP plus a scale-up factor to account for landfills that do not
report to the GHGRP was applied. For 2005 to 2009, the directly reported GHGRP net emissions from 2010 to 2018 were
used to back-cast emissions for 2005 to 2009. Note that the emissions values for 2005 to 2009 are re-calculated for each
Inventory and are subject to change if facilities reporting to the GHGRP revise their annual greenhouse gas reports for
any year. A 9 percent scale-up factor was applied annually to the net CH4 reported to the GHGRP for 2005 to 2016, and
an 11 percent scale-up factor was applied to the net CH4 reported to the GHGRP for 2016 to 2019.
A-465
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Table A-228: ChU Emissions from Landfills (kt)

1990
1995
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
MSW CH4 Generation
8,214
9,140
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Industrial CH4

















Generation
484
538
638
641
645
650
655
658
659
660
663
664
665
666
667
668
669
MSW CH4 Recovered
(851)
(2,047) ...............
MSW CH4 Oxidized
(736)
(709)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Industrial CH4

















Oxidized
(48)
(54)
(64)
(64)
(64)
(65)
(66)
(66)
(66)
(66)
(66)
(66)
(66)
(67)
(67)
(67)
(67)
MSW Net CH4

















Emissions
6,627
6,384
4,681
4,593
4,506
4,419
4,331
4,372
4,023
4,070
3,924
3,907
3,858
3,722
3,775
3,881
3,978
Industrial Net CH4

















Emissions
436
485
575
577
580
585
590
592
593
594
596
597
598
599
600
601
602
Net Emissions3
7,063
6,868
5,255
5,171
5,087
5,004
4,921
4,964
4,616
4,664
4,520
4,504
4,456
4,321
4,375
4,482
4,580
Notes: MSW and Industrial CH4 generation in Table A-228 represents emissions before oxidation. Totals may not sum exactly to the last significant figure due to
rounding. Parentheses denote negative values.
Not applicable due to methodology change.
a MSW Net CH4 emissions for years 2010 to 2019 are directly reported CH4 emissions to the EPA's GHGRP for MSW landfills and are back-casted to estimate
emissions for 2005 to 2009. A scale-up factor of 9 percent of each year's emissions from 2005 to 2016, and a scale-up factor of 11 percent of each year's emissions
from 2016 to 2019 is applied to account for landfills that do not report annual methane emissions to the GHGRP. Emissions for years 1990 to 2004 are calculated
by the FOD methodology.
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References
Barlaz, M.A. (2006) "Forest Products Decomposition in Municipal Solid Waste Landfills." Waste Management, 26(4): 321-
333.
Barlaz, M.A. (1998) "Carbon Storage During Biodegradation of Municipal Solid Waste Components in Laboratory-scale
Landfills." Global Biogeochemical Cycles, 12(2): 373-380, June 1998.
BioCycle (2010) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and N.
Themelis. BioCycle. December 2010. Available online at .
BioCycle (2006) "The State of Garbage in America" By N. Goldstein, S. Kaufman, N. Themelis, and J. Thompson Jr.
BioCycle. April 2006. Available online at: .
BioCycle (2004) "The State of Garbage in America" By S. Kaufman, N. Goldstein, K. Millrath, and N. Themelis. January
2004. Available online at: .
BioCycle (2001) "The State of Garbage in America" By S. Kaufman, N. Goldstein, and N. Themelis. December 2001.
Chartwell (2004) Municipal Solid Waste Directory. The Envirobiz Group.
Czepiel, P., B. Mosher, P. Crill, and R. Harriss (1996) "Quantifying the Effect of Oxidation on Landfill Methane Emissions."
Journal of Geophysical Research, 101(D11):16721-16730.
EIA (2007) Voluntary Greenhouse Gas Reports for EIA Form 1605B (Reporting Year 2006). Available online at
.
EPA (2020a) Greenhouse Gas Reporting Program (GHGRP). 2019 Amazon S3 Data. Subpart HH: Municipal Solid Waste
Landfills.
EPA (2020b) Landfill Methane Outreach Program (LMOP). 2019 Landfill and Project Level Data. September 2019.
Available online at: .
EPA (2019a) Methodology for MSW Characterization Numbers. Available online at: <
https://www.epa.gov/sites/production/files/2015-09/documents/06numbers.pdf >.
EPA (2019b) Advancing Sustainable Materials Management: 2017 Fact Sheet. November 2019. Available online at:
.
EPA (2017) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. June 2017.
EPA (2016) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. August 2015.
EPA (2015a) Greenhouse Gas Reporting Program (GHGRP). 2015 Envirofacts. Subpart HH: Municipal Solid Waste
Landfills. Available online at: .
EPA (2015b) Greenhouse Gas Reporting Program (GHGRP). 2015 Envirofacts. Subpart TT: Industrial Waste Landfills.
Available online at: .
EPA (2008) Compilation of Air Pollution Emission Factors, Publication AP-42, Draft Section 2.4 Municipal Solid Waste
Landfills. October 2008.
EPA (1998) Compilation of Air Pollution Emission Factors, Publication AP-42, Section 2.4 Municipal Solid Waste Landfills.
November 1998.
EPA (1993) Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress, U.S.
Environmental Protection Agency, Office of Air and Radiation. Washington, D.C. EPA/430-R-93-003. April 1993.
EPA (1988) National Survey of Solid Waste (Municipal) Landfill Facilities, U.S. Environmental Protection Agency.
Washington, D.C. EPA/530-SW-88-011. September 1988.
EREF (The Environmental Research & Education Foundation) (2016). Municipal Solid Waste Management in the United
States: 2010 & 2013.
A-467
-------
ERG (2020) Draft Production Data Supplied by ERG for 1990-2018 for Pulp and Paper, Fruits and Vegetables, and Meat.
August 2019.
FAO (2019). FAOStat database 2019. Available at .
Flores, R.A., C.W. Shanklin, M. Loza-Garay, S.H. Wie (1999) "Quantification and Characterization of Food Processing
Wastes/Residues." Compost Science & Utilization, 7(1): 63-71.
Heath, L.S. et al. 2010. Greenhouse Gas and Carbon Profile of the U.S. Forest Products Industry Value Chain.
Environmental Science and Technology 44(2010) 3999-4005.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
Jensen, J.E.F., and R. Pipatti (2002) "CH4 Emissions from Solid Waste Disposal." Background paper for the Good Practice
Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
Kraft, D.L. and H.C. Orender (1993) "Considerations for Using Sludge as a Fuel." Tappi Journal, 76(3): 175-183.
Lockwood-Post Directory of Pulp and Paper Mills (2002). Available for purchase at
.
Mancinelli, R. and C. McKay (1985) "Methane-Oxidizing Bacteria in Sanitary Landfills." Proc. First Symposium on
Biotechnical Advances in Processing Municipal Wastes for Fuels and Chemicals, Minneapolis, MN, 437-450. August.
Miner, R. (2008). "Calculations documenting the greenhouse gas emissions from the pulp and paper industry."
Memorandum from Reid Minor, National Council for Air and Stream Improvement, Inc. (NCASI) to Becky Nicholson,
RTI International, May 21, 2008.
Mintz C., R. Freed, and M. Walsh (2003) "Timeline of Anaerobic Land Disposal of Solid Waste." Memorandum toT. Wirth
(EPA) and K. Skog (USDA), December 31, 2003.
National Council for Air and Stream Improvement, Inc. (NCASI) (2008) "Calculations Documenting the Greenhouse Gas
Emissions from the Pulp and Paper Industry." Memorandum to R. Nicholson (RTI).
National Council for Air and Stream Improvement, Inc. (NCASI) (2005) "Calculation Tools for Estimating Greenhouse Gas
Emissions from Pulp and Paper Mills, Version 1.1." July 8, 2005.
Peer, R., S. Thorneloe, and D. Epperson (1993) "A Comparison of Methods for Estimating Global Methane Emissions from
Landfills." Chemosphere, 26(l-4):387-400.
RTI (2018) Methodological changes to the scale-up factor used to estimate emissions from municipal solid waste landfills
in the Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA). In progress.
RTI (2017a) Methodological changes to the methane emissions from municipal solid waste landfills as reflected in the
public review draft of the 1990-2015 Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R.
Schmeltz (EPA). March 31, 2017.
RTI (2017b) Options for revising the oxidation factor for non-reporting landfills for years 1990-2004 in the Inventory time
series. Memorandum prepared by K. Bronstein, M, McGrath, and K. Weitz for R. Schmeltz (EPA). August 13, 2017.
RTI (2015a) Investigate the potential to update DOC and k values for the Pulp and Paper industry in the US Solid Waste
Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA), December 4, 2015.
RTI (2015b) Review of Oxidation Studies and Associated Cover Depth in the Peer-Reviewed Literature. Memorandum
prepared by K. Bronstein, M. McGrath, and J. Coburn (RTI) for R. Schmeltz (EPA). June 17, 2015.
RTI (2014) Analysis of DOC Values for Industrial Solid Waste for the Pulp and Paper Industry and the Food Industry.
Memorandum prepared by J. Coburn for R. Schmeltz (EPA), October 28, 2014.
RTI (2013) Review of State of Garbage data used in the U.S. Non-C02 Greenhouse Gas Inventory for Landfills.
Memorandum prepared by K. Weitz and K. Bronstein (RTI) for R. Schmeltz (EPA). November 25, 2013.
A-468 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
RTI (2011) Updated Research on Methane Oxidation in Landfills. Memorandum prepared by K. Weitz (RTI) for R.
Schmeltz (EPA), January 14, 2011.
RTI (2010) Revision of the flare correction factor to be used in the EPA Greenhouse Gas Inventory. Memorandum
prepared by K. Bronstein, K. Weitz, and J. Coburn for R. Schmeltz (EPA), January 8, 2010.
RTI (2009) GHG Inventory Improvement - Construction & Demolition Waste DOC and Lo Value. Memorandum prepared
by J. Coburn and K. Bronstein (RTI) for R. Schmeltz, April 15, 2010.
RTI (2006) Methane Emissions for Industrial Landfills. Memorandum prepared by K. Weitz and M. Bahner for M. Weitz
(EPA), September 5, 2006.
RTI (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from Landfills.
Memorandum prepared by M. Branscome and J. Coburn (RTI) to E. Scheehle (EPA), August 26, 2004.
Shin, D. (2014) Generation and Disposition of Municipal Solid Waste (MSW) in the United States - A National Survey.
Master of Science thesis submitted to the Department of Earth and Environmental Engineering Fu Foundation
School of Engineering and Applied Science, Columbia University. January 3, 2014. Available online at:
.
Skog, K.E. (2008) "Sequestration of Carbon in harvested wood products for the United States." Forest Products Journal,
58(6): 56-72.
Skog, K. and G.A. Nicholson (2000) "Carbon Sequestration in Wood and Paper Products." USDA Forest Service Gen. Tech.
Rep. RMRS-GTR-59.
Solid Waste Association of North America (SWANA) (1998) Comparison of Models for Predicting Landfill Methane
Recovery. Publication No. GR-LG 0075. March 1998.
Sonne, E. (2006) "Greenhouse Gas Emissions from Forestry Operations: A Life Cycle Assessment." J. Environ. Qual.
35:1439-1450.
Upton, B., R. Miner, M. Spinney, L.S. Heath (2008) "The Greenhouse Gas and Energy Impacts of Using Wood Instead of
Alternatives in Residential Construction in the United States." Biomass and Bioenergy, 32:1-10.
U.S. Census Bureau (2020) Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2019. Available online at
.
Waste Business Journal (WBJ) (2016) Directory of Waste Processing & Disposal Sites 2016.
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ANNEX 4 IPCC Reference Approach for
Estimating C02 Emissions from Fossil Fuel
Combustion
It is possible to estimate carbon dioxide (C02) emissions from fossil fuel consumption using alternative
methodologies and different data sources than those described in Annex 2.1 Methodology for Estimating Emissions of
C02 from Fossil Fuel Combustion. For example, the United Nations Framework Convention on Climate Change (UNFCCC)
reporting guidelines request that countries, in addition to their "bottom-up" sectoral methodology, complete a "top-
down" Reference Approach for estimating C02 emissions from fossil fuel combustion. Volume 2: Energy, Chapter 6:
Reference Approach of the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse
Gas Inventories (IPCC 2006) states, "comparability between the sectoral and reference approaches continues to allow a
country to produce a second independent estimate of C02 emissions from fuel combustion with limited additional effort
and data requirements." This reference method estimates fossil fuel consumption by adjusting national aggregate fuel
production data for imports, exports, and stock changes rather than relying on end-user consumption surveys. The basic
principle is that once carbon (C)-based fuels are brought into a national economy, they are either saved in some way
(e.g., stored in products, kept in fuel stocks, or left unoxidized in ash) or combusted, and therefore the C in them is
oxidized and released into the atmosphere. Accounting for actual consumption of fuels at the sectoral or sub-national
level is not required. The following discussion provides the detailed calculations for estimating C02 emissions from fossil
fuel combustion from the United States using the IPCC-recommended Reference Approach.
Step 1: Collect and Assemble Data in Proper Format
To ensure the comparability of national inventories, the IPCC has recommended that countries report energy
data using the International Energy Agency (IEA) reporting convention. National energy statistics were collected in
physical units from several Energy Information Administration (EIA) documents in order to obtain the necessary data on
production, imports, exports, and stock changes.
It was necessary to modify these data to generate more accurate apparent consumption estimates of these
fuels. The first modification adjusts for consumption of fossil fuel feedstocks accounted for in the Industrial Processes
and Product Use chapter, which include the following: unspecified coal for coal coke used in iron and steel production;
natural gas, distillate fuel, and coal used in iron and steel production; natural gas used for ammonia production;
petroleum coke used in the production of aluminum, ferroalloys, titanium dioxide, ammonia, and silicon carbide; and
other oil and residual fuel oil used in the manufacture of C black. The second modification adjusts for the inclusion of
ethanol in motor gasoline statistics. Ethanol is a biofuel, and net carbon fluxes from changes in biogenic carbon
reservoirs in croplands are accounted for in the estimates for Land Use, Land-Use Change, and Forestry (see Chapter 6).
The third modification adjusts for consumption of bunker fuels, which refer to quantities of fuels used for international
transportation estimated separately from U.S. totals. The fourth modification consists of the addition of U.S. Territories
data that are typically excluded from the national aggregate energy statistics. The territories include Puerto Rico, U.S.
Virgin Islands, Guam, American Samoa, Wake Island, and U.S. Pacific Islands. These data, as well as the production,
import, export, and stock change statistics, are presented in Table A-229.
The C content of fuel varies with the fuel's heat content. Therefore, for an accurate estimation of C02
emissions, fuel statistics were provided on an energy content basis (e.g., Btu or joules). Because detailed fuel production
statistics are typically provided in physical units (as in Table A-229 for 2019), they were converted to units of energy
before C02 emissions were calculated. Fuel statistics were converted to their energy equivalents by using conversion
factors provided by EIA. These factors and their data sources are displayed in Table A-230. The resulting fuel type-specific
energy data for 2019 are provided in Table A-231.
Step 2: Estimate Apparent Fuel Consumption
The next step of the IPCC Reference Approach is to estimate "apparent consumption" of fuels within the
country. This requires a balance of primary fuels produced, plus imports, minus exports, and adjusting for stock changes.
A-470 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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In this way, C enters an economy through energy production and imports (and decreases in fuel stocks) and is
transferred out of the country through exports (and increases in fuel stocks). Thus, apparent consumption of primary
fuels (including crude oil, natural gas liquids, anthracite, bituminous, subbituminous and lignite coal, and natural gas) can
be calculated as follows:
Apparent Consumption = Production + Imports - Exports - Stock Change
Flows of secondary fuels (e.g., gasoline, residual fuel, coke) should be added to primary apparent consumption.
The production of secondary fuels, however, should be ignored in the calculations of apparent consumption since the C
contained in these fuels is already accounted for in the supply of primary fuels from which they were derived (e.g., the
estimate for apparent consumption of crude oil already contains the C from which gasoline would be refined). Flows of
secondary fuels should therefore be calculated as follows:
Secondary Consumption = Imports - Exports - Stock Change
Note that this calculation can result in negative numbers for apparent consumption of secondary fuels. This
result is perfectly acceptable since it merely indicates a net export or stock increase in the country of that fuel when
domestic production is not considered.
Next, the apparent consumption and secondary consumption need to be adjusted for feedstock uses of fuels
accounted for in the Industrial Processes and Product Use chapter, international bunker fuels, and U.S. territory fuel
consumption. Bunker fuels and feedstocks accounted for in the Industrial Processes and Product Use chapter are
subtracted from these estimates, while fuel consumption in U.S. Territories is added.
The IPCC Reference Approach calls for estimating apparent fuel consumption before converting to a common
energy unit. However, certain primary fuels in the United States (e.g., natural gas and steam coal) have separate
conversion factors for production, imports, exports, and stock changes. In these cases, it is not appropriate to multiply
apparent consumption by a single conversion factor since each of its components has different heat contents. Therefore,
United States fuel statistics were converted to their heat equivalents before estimating apparent consumption. Results
are provided in Table A-230.
Step 3: Estimate Carbon Emissions
Once apparent consumption is estimated, the remaining calculations are similar to those for the "bottom-up"
Sectoral Approach (see Annex 2.1 Methodology for Estimating Emissions of C02 from Fossil Fuel Combustion). Potential
C02 emissions were estimated using fuel-specific C coefficients (see Table A-231).138 The C in products from non-energy
uses of fossil fuels (e.g., plastics or asphalt) that is stored was then estimated and subtracted (see Table A-232). This step
differs from the Sectoral Approach in that emissions from both fuel combustion and non-energy uses are accounted for
directly in the Reference Approach. As a result, the Reference Approach emission estimates are comparable to those of
the Sectoral Approach, with the exception that the NEU source category emissions are included in the Reference
Approach and reported separately in the Sectoral Approach.139 Finally, to obtain actual C02 emissions, net emissions
were adjusted for any C that remained unoxidized as a result of incomplete combustion (e.g., C contained in ash or soot).
The fraction oxidized was assumed to be 100 percent for petroleum, coal, and natural gas based on guidance in IPCC
(2006) (see Annex 2.1 Methodology for Estimating Emissions of C02 from Fossil Fuel Combustion).
Step 4: Convert to C02 Emissions
Because the 2006 IPCC Guidelines recommend that countries report greenhouse gas emissions on a full
molecular weight basis, the final step in estimating C02 emissions from fossil fuel consumption was converting from units
138	Carbon coefficients from EIA were used wherever possible. Because EIA did not provide coefficients for coal, the IPCC-
recommended emission factors were used in the top-down calculations for these fuels. See notes in Table A-232 for more
specific source information.
139	The emission scope of the reference and the sectoral approaches is the same since C emissions from NEU (i.e. C not
excluded) are included in both approaches, the energy consumption covered by the sectoral approach includes both fuel
consumption and NEU, which is reported under category 1.A.5 other, hence the scope of energy consumption under the
sectoral approach is comparable with that under the reference approach without excluding NEU. To the extent it is indicated
that NEU emissions are subtracted under the sectoral approach, it means that they are reported separately, not that they are
not covered by the sectoral approach.
A-471
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of C to units of C02. Actual C emissions were multiplied by the molecular-to-atomic weight ratio of C02 to C (44/12) to
obtain total C02 emitted from fossil fuel combustion in million metric tons (MMT). The results are contained in Table A-
232.
Comparison Between Sectoral and Reference Approaches
These two alternative approaches can both produce reliable estimates that are comparable within a few
percent. Note that the reference approach includes emissions from non-energy uses. Therefore, these totals should be
compared to the aggregation of fuel use and emission totals from Annex 2.1 Methodology for Estimating Emissions of
C02 from Fossil Fuel Combustion and Annex 2.3 Methodology for Estimating Carbon Emitted from Non-Energy Uses of
Fossil Fuels. These two sections together are henceforth referred to as the Sectoral Approach. Other than this
distinction, the major difference between methodologies employed by each approach lies in the energy data used to
derive C emissions (i.e., the actual surveyed consumption for the Sectoral Approach versus apparent consumption
derived for the Reference Approach). In theory, both approaches should yield identical results. In practice, however,
slight discrepancies occur. An examination of past Common Reporting Format (CRF) table submissions during UNFCCC
reviews has highlighted the need to further investigate these discrepancies. The investigation found that the most recent
(two to three) inventory years tend to have larger differences in consumption and emissions estimates occurring earlier
in the time series. This is a result of annual energy consumption data revisions in the EIA energy statistics, and the
revisions have the greatest impact on the most recent few years of inventory estimates. As a result, the differences
between the Sectoral and Reference Approach decrease and are resolved over time. For the United States, these
differences are discussed below.
Differences in Total Amount of Energy Consumed
Table A-235 summarizes the differences between the Reference and Sectoral Approaches in estimating total
energy consumption in the United States. Although theoretically the two methods should arrive at the same estimate for
U.S. energy consumption, the Reference Approach provides an energy consumption total that is 1.2 percent lower than
the Sectoral Approach for 2019. The greatest differences lie in lower estimates for petroleum and coal consumption for
the Reference Approach (2.5 percent and 1.7 percent, respectively) and higher estimates for natural gas consumption for
the Reference Approach (0.5 percent).
There are several potential sources for the discrepancies in consumption estimates:
•	Product Definitions. The fuel categories in the Reference Approach are different from those used in the
Sectoral Approach, particularly for petroleum.140 For example, the Reference Approach estimates apparent
consumption for crude oil. Crude oil is not typically consumed directly but refined into other products. As a
result, the United States does not focus on estimating the energy content of the various grades of crude
oil, but rather estimating the energy content of the various products resulting from crude oil refining. The
United States does not believe that estimating apparent consumption for crude oil, and the resulting
energy content of the crude oil, is the most reliable method for the United States to estimate its energy
consumption. Other differences in product definitions include using sector-specific coal statistics in the
Sectoral Approach (i.e., residential, commercial, industrial coking, industrial other, and transportation
coal), while the Reference Approach characterizes coal by rank (e.g., anthracite, bituminous).
•	Heat Equivalents. It can be difficult to obtain heat equivalents for certain fuel types, particularly for
categories such as "crude oil" where the key statistics are derived from thousands of producers in the
United States and abroad. Furthermore, Hydrocarbon Gas Liquids (HGL) is a blend of multiple paraffinic
hydrocarbons: ethane, propane, isobutane, and normal butane, and their associated olefins: ethylene,
propylene, isobutylene, and butylene, each with their own heat content. HGL also includes pentanes plus.
The heat content for HGL varies annually depending upon the components of the blend.
•	Possible Inconsistencies in U.S. Energy Data. The United States has not focused its energy data collection
efforts on obtaining the type of aggregated information used in the Reference Approach. Rather, the
140 For the 1990 to 2019 Inventory, the United States revised the Sectoral Approach and Reference Approach to report
consumption of all HGL components (i.e., ethane, propane, isobutane, normal butane, ethylene, propylene, isobutylene,
butylene, and pentanes plus). Pentanes plus is accounted for separately from other HGL components in the Sectoral Approach
but is included in HGL in the Reference Approach. Carbon contents and heat contents of HGL were also updated accordingly for
each approach.
A-472 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
United States believes that its emphasis on collection of detailed energy consumption data is a more
accurate methodology for the United States to obtain reliable energy data. Therefore, top-down statistics
used in the Reference Approach may not be as accurately collected as bottom-up statistics applied to the
Sectoral Approach.
•	Balancing Item. The Reference Approach uses apparent consumption estimates while the Sectoral
Approach uses reported consumption estimates. While these numbers should be equal, there always
seems to be a slight difference that is often accounted for in energy statistics as a "balancing item."
Differences in Estimated CO2 Emissions
Given these differences in energy consumption data, the next step for each methodology involved estimating
emissions of C02. Table A-236 summarizes the differences between the two methods in estimated C emissions.
As mentioned above, for 2019, the Reference Approach resulted in a 1.2 percent lower estimate of energy
consumption in the United States than the Sectoral Approach. The resulting emissions estimate for the Reference
Approach was 0.1 percent lower. Estimates of natural gas and petroleum emissions from the Reference Approach are
higher (0.6 percent each), and coal emission estimates are lower (2.0 percent) than the Sectoral Approach. Potential
reasons for these differences may include:
•	Product Definitions. Coal data are aggregated differently in each methodology, as noted above. The format
used for the Sectoral Approach likely results in more accurate estimates than in the Reference Approach.
Also, the Reference Approach relies on a "crude oil" category for determining petroleum-related
emissions. Given the many sources of crude oil in the United States, it is not an easy matter to track
potential differences in C content between many different sources of crude; particularly since information
on the C content of crude oil is not regularly collected.
•	Carbon Coefficients. The Reference Approach relies on several default C coefficients by rank provided by
IPCC (2006), while the Sectoral Approach uses annually updated category-specific coefficients by sector
that are likely to be more accurate. Also, as noted above, the C coefficient for crude oil is more uncertain
than that for specific secondary petroleum products, given the many sources and grades of crude oil
consumed in the United States.
Although the two approaches produce similar results, the United States believes that the "bottom-up" Sectoral
Approach provides a more accurate assessment of C02 emissions at the fuel level. This improvement in accuracy is
largely a result of the data collection techniques used in the United States, where there has been more emphasis on
obtaining the detailed products-based information used in the Sectoral Approach than obtaining the aggregated energy
flow data used in the Reference Approach. The United States believes that it is valuable to understand both methods.
A-473
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Table A-229: 2019 U.S. Energy Statistics (Physical Units)
Stock	U.S.
Fuel Category (Units)
Fuel Type
Production
Imports
Exports
Change
Adjustment
Bunkers
Territories
Solid Fuels (Thousand Short Tons)
Anthracite Coal
2,611
[1]
[1]
[1]




Bituminous Coal
338,955
[1]
[1]
[1]




Sub-bituminous Coal
311,552
[1]
[1]
[1]
367



Lignite
53,192
[1]
[1]
[1]
1,539



Coke

116
967
440




Unspecified Coal

6,697
93,765
35,542
20,280

1,217
Gas Fuels (Million Cubic Feet)
Natural Gas
33,551,098
2,741,717
4,656,324
557,803
399,049

46,433
Liquid Fuels (Thousand Barrels)
Crude Oil
4,470,528
2,482,332
1,088,345
(23,901)




HGL
1,760,945
75,438
667,957
27,835


1,658

Other Liquids
0
506,447
148,647
9,113




Motor Gasoline
(24,781)
34,148
297,306
360
238,678

20,779

Aviation Gasoline

347
0
196




Kerosene

703
1,735
205


80

Jet Fuel

59,900
80,401
(1,118)

198,850
5,425

Distillate Fuel

73,886
476,657
(79)
55
16,710
13,793

Residual Fuel

54,299
83,539
2,206
9,000
53,480
7,341

Naphtha for petrochemical feedstocks

5,117
0
(195)




Petroleum Coke

2,312
198,555
1,160
10,182



Other Oil for petrochemical feedstocks

1,011
0
(55)
1,240



Special Naphthas

6,453
0
(234)




Lubricants

16,336
37,321
(1,269)


172

Waxes

1,822
1,486
50




Asphalt/Road Oil

16,000
9,459
(3,963)




Still Gasa

0
0
0




Misc. Products

12
660
9


1,658
Note: Parentheses indicate negative values.
[1] Included in Unspecified Coal
Sources: Solid and Gas Fuels: EIA (2020a and 2020b); Liquid Fuels: EIA (2020c).
a Still gas is reported as petroleum product (liquid fuel) in this report. However, still gas physically exists as a gas, consisting primary of methane and ethane, and some hydrogen
and other trace gases (EIA 2020d).
A-474 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Table A-230: Conversion Factors to Energy Units (Heat Equivalents)





Stock


U.S.
Fuel Category (Units)
Fuel Type
Production
Imports
Exports
Change
Adjustment
Bunkers
Territories
Solid Fuels (Million Btu/Short Ton) Anthracite Coal
22.57







Bituminous Coal
23.89







Sub-bituminous Coal
17.14



25.83



Lignite
12.87



12.87



Coke

20.56
24.58
20.56




Unspecified

25.00
25.97
20.86
21.72

25.14
Natural Gas (BTU/Cubic Foot)

1,038
1,025
1,009
1,038
1,038

1,038
Liquid Fuels (Million Btu/Barrel)
Crude Oil
5.70
6.06
5.71
5.71

5.71
5.71

HGL
4.21
4.21
4.21
4.21

4.21
4.21

Other Liquids
5.83
5.83
5.83
5.83

5.83
5.83

Motor Gasoline
5.05
5.05
5.05
5.05
5.05
5.05
5.05

Aviation Gasoline

5.05
5.05
5.05

5.05
5.05

Kerosene

5.67
5.67
5.67

5.67
5.67

Jet Fuela

5.67
5.67
5.67

5.77
5.67

Distillate Fuel

5.76
5.76
5.76
5.76
5.76
5.76

Residual Oil

6.29
6.29
6.29
6.29
6.29
6.29

Naphtha for petrochemical feedstocks

5.25
5.25
5.25

5.25
5.25

Petroleum Coke

6.02
6.02
6.02
6.02
6.02
6.02

Other Oil for petrochemical feedstocks

5.83
5.83
5.83
5.83
5.83
5.83

Special Naphthas

5.25
5.25
5.25

5.25
5.25

Lubricants

6.07
6.07
6.07

6.07
6.07

Waxes

5.54
5.54
5.54

5.54
5.54

Asphalt/Road Oil

6.64
6.64
6.64

6.64
6.64

Still Gasb

6.00
6.00
6.00

6.00
6.00

Misc. Products

5.80
5.80
5.80

5.80
5.80
Sources: Coal and lignite production: EIA (1992); Coke, Natural Gas Crude Oil, HGL, and Motor Gasoline: EIA (2020b); Unspecified Solid Fuels: EIA (2011).
a Jet fuel used in bunkers has a different heating value based on data specific to that source. When physical values are converted based on a combined heating value across all
sources of jet fuel (as shown in Table l.A(b) of CRF) it will not necessarily match jet fuel bunker data (as shown in Table l.D of CRF). The energy value for bunker fuel in Table
l.D is based on bunkers only and the values in Table l.A(b) are based on apparent consumption including imports, exports, etc. and average heating value.
b Still gas is reported as petroleum product (liquid fuel) in this report. However, still gas physically exists as a gas, consisting primary of methane and ethane, and some hydrogen
and other trace gases (EIA 2020d).
A-475
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Table A-231: 2019 Apparent Consumption of Fossil Fuels (TBtu)





Stock


U.S.
Apparent
Fuel Category
Fuel Type
Production
Imports
Exports
Change
Adjustment
Bunkers
Territories
Consumption
Solid Fuels
Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
58.9
8,097.6
5,340.0
684.4
2.4
23.8
9.0
9.5
19.8


58.9
8,097.6
5,330.5
664.6
(30.4)

Unspecified

167.4
2,435.3
741.4
440.5

30.6
(3,419.1)
Gas Fuels
Natural Gas
34,826.0
2,810.3
4,698.2
579.0
414.2

48.2
31,993.1
Liquid Fuels
Crude Oil
25,473.1
15,045.4
6,212.3
(136.4)



34,442.6

HGL
7,421.4
317.9
2,815.1
117.3


7.0
4,813.9

Other Liquids

2,950.1
865.9
53.1



2,031.1

Motor Gasoline
(125.2)
172.6
1,502.6
1.8


105.0
(1,352.0)

Aviation Gasoline

1.8

1.0



0.8

Kerosene

4.0
9.8
1.2


0.5
(6.6)

Jet Fuel

339.6
455.9
(6.3)

1,147.1
30.8
(1,226.3)

Distillate Fuel

425.8
2,746.7
(0.5)
0.3
96.3
79.5
(2,337.6)

Residual Oil

341.4
525.2
13.9
56.6
336.2
46.2
(544.4)

Naphtha for petrochemical feedstocks

26.9

(1.0)



27.9

Petroleum Coke

13.9
1,196.1
7.0
61.3


(1,250.5)

Other Oil for petrochemical feedstocks

5.9

(0.3)
7.2


(1.0)

Special Naphthas

33.9

(1.2)



35.1

Lubricants

99.1
226.4
(7.7)


1.0
(118.5)

Waxes

10.1
8.2
0.3



1.6

Asphalt/Road Oil

106.2
62.8
(26.3)



69.7

Still Gasa









Misc. Products

0.1
3.8
0.1


9.6
5.8
Total

81,776.2
22,874.5
23,787.9
1,345.2
1,009.4
1,579.6
358.3
77,286.8
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
a Still gas is reported as petroleum product (liquid fuel) in this report. However, still gas physically exists as a gas, consisting primary of methane and ethane, and some hydrogen
and other trace gases (EIA 2020d).
A-476 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Table A-232: 2019 Potential CO2 Emissions


Apparent Consumption
Carbon Coefficients
Potential Emissions
Fuel Category
Fuel Type
(QBtu)
(MMT Carbon/QBtu)
(MMT CO? Eq.)
Solid Fuels
Anthracite Coal
0.06
28.28
6.1

Bituminous Coal
8.10
25.41
754.5

Sub-bituminous Coal
5.33
26.49
517.7

Lignite
0.66
26.75
65.2

Coke
(0.03)
31.00
(3.5)

Unspecified
(3.42)
25.34
(317.6)
Gas Fuels
Natural Gas
31.99
14.43
1,692.2
Liquid Fuels
Crude Oil
34.44
20.31
2,564.4

HGL
4.81
18.58
328.0

Other Liquids
2.03
20.31
151.2

Motor Gasoline
(1.35)
19.46
(96.5)

Aviation Gasoline
+
18.86
0.1

Kerosene
(0.01)
19.96
(0.5)

Jet Fuel
(1.23)
19.70
(88.6)

Distillate Fuel
(2.34)
20.22
(173.3)

Residual Oil
(0.54)
20.48
(40.9)

Naphtha for petrochemical feedstocks
0.03
18.55
1.9

Petroleum Coke
(1.25)
27.85
(127.7)

Other Oil for petrochemical feedstocks
(+)
20.17
(0.1)

Special Naphthas
0.04
19.74
2.5

Lubricants
(0.12)
20.20
(8.8)

Waxes
+
19.80
0.1

Asphalt/Road Oil
0.07
20.55
5.3

Still Gasa
0.00
18.20
0.0

Misc. Products
0.01
0.00
0.0
Total



5,231.8
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
+ Does not exceed 0.005 QBtu or 0.05 MMT C02 Eq.
Sources: C content coefficients by coal rank from USGS (1998), PSU (2010), Gunderson (2019), IGS (2019), ISGS (2019), and EIA (2020a); natural gas C content coefficients from
EPA (2010) and EIA (2020b); unspecified solid fuel and liquid fuel C content coefficients from EPA (2010) and ICF (2020).
a Still gas is reported as petroleum product (liquid fuel) in this report. However, still gas physically exists as a gas, consisting primary of methane and ethane, and some hydrogen
and other trace gases (EIA 2020d).
A-477
-------
Table A-233: 2019 Non-Energy Carbon Stored in Products

Consumption
Carbon
Carbon



for Non-
Coefficients
Content

Carbon

Energy Use
(MMT
(MMT
Fraction Stored (MMT
Fuel Type
(TBtu)
Carbon/QBtu)
Carbon)
Sequestered
CO2 Eq.)
Coal
132.8
31.00
4.12
0.10
2.1
Natural Gas
299.1
14.43
4.31
0.67
10.7
Asphalt & Road Oil
843.9
20.55
17.34
1.00
63.3
HGL
2,523.7
16.85
42.53
0.67
105.1
Lubricants
250.7
20.20
5.06
0.09
1.7
Pentanes Plus
153.5
18.24
2.80
0.67
6.9
Petrochemical Feedstocks
[1]
[1]
[1]
[1]
34.1
Petroleum Coke
0.0
27.85
0.00
0.30
0.0
Special Naphtha
88.7
19.74
1.75
0.67
4.3
Waxes/Misc.
[1]
[1]
[1]
[1]
0.7
Misc. U.S. Territories Petroleum
[1]
[1]
[1]
[1]
0.1
Total	229.0
Note: Totals may not sum due to independent rounding.
[1] Values for Misc. U.S. Territories Petroleum, Petrochemical Feedstocks, and Waxes/Misc. are not shown because
these categories are aggregates of numerous smaller components.
Table A-234: 2019 Reference Approach CO2 Emissions from Fossil Fuel Consumption (MMT CO2 Eq.)

Potential
Carbon
Net
Fraction
Total
Fuel Category
Emissions
Sequestered
Emissions
Oxidized
Emissions
Coal
1,022.4
2.1
1,020.3
100.0%
1,020.3
Petroleum
2,517.2
216.2
2,301.0
100.0%
2,301.0
Natural Gas
1,692.2
10.7
1,681.5
100.0%
1,681.5
Total
5,231.8
229.0
5,002.8

5,002.8
Note: Totals may not sum due to independent rounding.
Table A-235: Fuel Consumption in the United States by Estimating Approach (TBtu)a
Approach
1990
1995
2000
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Sectoral
69,634
74,688
82,554
83,937
82,689
83,921
81,084
76,258
78,689
77,201
75,387
77,324
78,034
77,120
76,286
75,752
79,028
78,196
Coal
18,098
19,210
21,772
22,215
21,864
22,106
21,792
19,271
20,307
19,110
16,866
17,488
17,407
15,079
13,812
13,404
12,798
10,882
Natural Gas
19,173
22,173
23,395
22,283
21,961
23,371
23,594
23,193
24,313
24,679
25,832
26,562
27,145
27,932
28,153
27,742
30,801
31,824
Petroleum
32,363
33,305
37,386
39,439
38,864
38,444
35,699
33,794
34,069
33,412
32,688
33,274
33,483
34,109
34,322
34,607
35,430
35,490
Reference


















(Apparent)
68,869
74,148
82,003
83,963
82,484
84,406
80,749
76,802
78,171
76,804
75,835
76,453
77,215
76,373
75,493
75,362
78,388
77,287
A-478 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
Coal
Natural Gas
Petroleum
17,598
19,280
31,990
18,591
22,277
33,280
20,982
23,487
37,534
22,015
22,350
39,599
21,565
22,030
38,889
21,616
23,442
39,348
21,430
23,666
35,653
19,283
23,277
34,242
19,662
24,409
34,100
18,796
24,778
33,230
16,682
25,924
33,229
17,134
26,639
32,681
17,248
27,228
32,738
14,836
28,011
33,527
13,576
28,236
33,681
13,136
27,862
34,363
12,562
30,931
34,895
10,702
31,993
34,592
Difference
-1.1%
-0.7%
-0.7%
0.0%
-0.2%
0.6%
-0.4%
0.7%
-0.7%
-0.5%
0.6%
-1.1%
-1.1%
-1.0%
-1.0%
-0.5%
-0.8%
-1.2%
Coal
-2.8%
-3.2%
-3.6%
-0.9%
-1.4%
-2.2%
-1.7%
0.1%
-3.2%
-1.6%
-1.1%
-2.0%
-0.9%
-1.6%
-1.7%
-2.0%
-1.8%
-1.7%
Natural Gas
0.6%
0.5%
0.4%
0.3%
0.3%
0.3%
0.3%
0.4%
0.4%
0.4%
0.4%
0.3%
0.3%
0.3%
0.3%
0.4%
0.4%
0.5%
Petroleum
-1.2%
-0.1%
0.4%
0.4%
0.1%
2.4%
-0.1%
1.3%
0.1%
-0.5%
1.7%
-1.8%
-2.2%
-1.7%
-1.9%
-0.7%
-1.5%
-2.5%
Note: Totals may not sum due to independent rounding.
a Includes U.S. Territories. Does not include international bunker fuels.
Table A-236: CO2 Emissions from Fossil Fuel Combustion by Estimating Approach (MMT CO2 Eq.)a
Approach
1990
1995
2000
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Sectoral
4,844
5,144
5,740
5,884
5,795
5,871
5,669
5,279
5,444
5,302
5,099
5,227
5,277
5,119
5,015
4,970
5,134
4,998
Coal
1,720
1,825
2,073
2,123
2,085
2,108
2,079
1,839
1,938
1,824
1,611
1,670
1,661
1,441
1,320
1,282
1,224
1,041
Natural Gas
1,007
1,164
1,228
1,172
1,157
1,231
1,243
1,222
1,279
1,299
1,359
1,397
1,426
1,466
1,477
1,456
1,618
1,671
Petroleum
2,117
2,155
2,440
2,589
2,553
2,531
2,347
2,218
2,227
2,179
2,128
2,160
2,189
2,212
2,219
2,232
2,292
2,287
Reference


















(Apparent)
4,812
5,149
5,721
5,931
5,821
5,932
5,688
5,370
5,444
5,320
5,186
5,216
5,263
5,126
5,023
5,002
5,150
5,003
Coal
1,656
1,757
1,990
2,089
2,051
2,056
2,039
1,834
1,870
1,791
1,589
1,629
1,641
1,413
1,290
1,244
1,196
1,020
Natural Gas
1,014
1,171
1,234
1,176
1,160
1,235
1,247
1,227
1,285
1,305
1,365
1,402
1,431
1,470
1,482
1,463
1,626
1,682
Petroleum
2,142
2,221
2,498
2,665
2,610
2,641
2,401
2,308
2,289
2,224
2,232
2,185
2,191
2,242
2,251
2,296
2,328
2,301
Difference
-0.7%
0.1%
-0.3%
0.8%
0.5%
1.1%
0.3%
1.7%
0.0%
0.3%
1.7%
-0.2%
-0.3%
0.1%
0.2%
0.7%
0.3%
0.1%
Coal
-3.8%
-3.7%
-4.0%
-1.6%
-1.6%
-2.5%
-1.9%
-0.2%
-3.5%
-1.8%
-1.4%
-2.5%
-1.2%
-1.9%
-2.3%
-3.0%
-2.3%
-2.0%
Natural Gas
0.7%
0.6%
0.5%
0.3%
0.3%
0.3%
0.3%
0.4%
0.5%
0.5%
0.4%
0.3%
0.3%
0.3%
0.4%
0.5%
0.5%
0.6%
Petroleum
1.2%
3.0%
2.4%
3.0%
2.2%
4.4%
2.3%
4.0%
2.8%
2.1%
4.9%
1.2%
0.1%
1.4%
1.5%
2.8%
1.6%
0.6%
Note: Totals may not sum due to independent rounding
a Includes U.S. Territories. Does not include international bunker fuels.
A-479
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References
EIA (2020a). Annual Coal Report 2019, Energy Information Administration, U.S. Department of Energy. Washington, D.C.
DOE/EIA-0584(2019).
EIA (2020b). Monthly Energy Review, November 2020, Energy Information Administration, U.S. Department of Energy.
Washington, D.C. DOE/EIA-0035(2020/11).
EIA (2020c). Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, D.C.,
Volume I. DOE/EIA-0340.
EIA (2020d). Still Gas, Glossary. Energy Information Administration, U.S. Department of Energy, Washington, D.C.
Available online at: .
EIA (2011). Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
DOE/EIA-0384(2011).
EIA (1992). Coal and lignite production. EIA State Energy Data Report 1992, Energy Information Administration, U.S.
Department of Energy, Washington, DC.
EPA (2010). Carbon Content Coefficients Developed for EPA's Mandatory Reporting Rule. Office of Air and Radiation,
Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
Gunderson, J. (2019) Montana Coal Sample Database. Data received 28 February 2019 from Jay Gunderson, Montana
Bureau of Mines & Geology.
ICF (2020) Potential Improvements to Energy Sector Hydrocarbon Gas Liquid Carbon Content Coefficients. Memorandum
from ICF to Vincent Camobreco, U.S. Environmental Protection Agency. December 7, 2020.
Illinois State Geological Survey (ISGS) (2019) Illinois Coal Quality Database, Illinois State Geological Survey.
Indiana Geological Survey (IGS) (2019) Indiana Coal Quality Database 2018, Indiana Geological Survey.
IPCC (2006). 2006IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas
Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T., and Tanabe K. (eds.). Published: IGES, Japan.
Pennsylvania State University (PSU) (2010) Coal Sample Bank and Database. Data received by SAIC 18 February 2010
from Gareth Mitchell, The Energy Institute, Pennsylvania State University.
USGS (1998). CoalQual Database Version 2.0, U.S. Geological Survey.
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ANNEX 5 Assessment of the Sources and Sinks of
Greenhouse Gas Emissions Not Included
Although this report is intended to be a comprehensive assessment of anthropogenic141 sources and sinks of
greenhouse gas emissions for the United States, certain sources have been identified but not included in the estimates
presented for various reasons. Before discussing these sources and sinks, it is important to note that processes or
activities that are not anthropogenic in origin or do not result in a net source or sink of greenhouse gas emissions are
intentionally excluded from a national inventory of anthropogenic greenhouse gas emissions, in line with guidance from
the IPCC in their guidelines for national inventories.
The anthropogenic source and sink category of greenhouse gas emissions described in this annex are not
included in the U.S. national inventory estimates. The reasons for not including that source in the national greenhouse
gas Inventory include one or more of the following:
•	Emissions are not likely to occur within the United States.
•	A methodology for estimating emissions from a source does not currently exist.
•	Though an estimating method has been developed, adequate data are not currently available to estimate
emissions.
•	Emissions are determined to be insignificant in terms of overall national emissions, as defined per UNFCCC
reporting guidelines, based on available data or a preliminary assessment of significance. Further, data
collection to estimate emissions would require disproportionate amount of effort (e.g., dependent on
additional resources and impacting improvements to key categories, etc.).
In general, data availability remains the primary constraint for estimating and including the emissions and
removals from source and sink categories that do occur within the United States and are not estimated, as discussed
further below. Methods to estimate emissions and removals from these categories are available in the 2006 IPCC
Guidelines. Many of these categories are insignificant in terms of overall national emissions based on available proxy
information, qualitative information on activity levels per national circumstances, and/or expert judgment, and not
including them introduces a very minor bias.
Reporting of inventories to the UNFCCC under Decision 24/CP.19 states that "Where methodological or data
gaps in inventories exist, information on these gaps should be presented in a transparent manner." Furthermore, these
reporting guidelines allow a country to indicate if a disproportionate amount of effort would be required to collect data
for a gas from a specific category that would be insignificant in terms of the overall level and trend in national
emissions.142 Specifically, where the notation key "NE," meaning not estimated, is used in the Common Reporting Format
(CRF)143 tables that accompany this Inventory report submission to the UNFCCC, countries are required to further
describe why such emissions or removals have not been estimated (UNFCCC 2013).
Based on the latest UNFCCC reporting guidance, the United States is providing more information on the
significance of these excluded categories below and aims to update information on the significance to the extent feasible
during each annual compilation cycle. Data availability may impact the feasibility of undertaking a quantitative
significance assessment. The United States is continually working to improve the understanding of such sources or sinks
and seeking to find the data required to estimate related emissions, prioritizing efforts and resources for significant
categories. As such improvements are implemented, new emission and removal categories will be quantified and
included in the Inventory to enhance completeness of the Inventory.
141	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 (2006 IPCC Guidelines for
National Greenhouse Gas Inventories).
142	Paragraph 37(b) of Decision 24/CP.19 "Revision of the UNFCCC reporting guidelines on annual inventories for Parties
included in Annex I to the Convention." See .
143	See .
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The full list of sources and sink categories not estimated, along with explanations for their exclusion, is provided
in Table 9 of the CRF submission. Information on coverage of activities within the United States, the District of Columbia,
and U.S. Territories is provided in the introductions to the sectoral chapters with details in the category-specific estimate
discussions as relevant. U.S. Territories include American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Commonwealth
of Northern Mariana Islands, and other Pacific Islands which have no permanent population and are inhabited by military
and/or scientific purposes.
Source and Sink Categories Not Estimated
This section provides additional information on the reasons each category was not estimated, arranged by
sector and source or sink category. A summary of these exclusions, including the estimated level of emissions where
feasible, is included in Table A-237. Per 37(b) of the UNFCCC Reporting Guidelines Decision 24/CP.19, considering overall
level and trend of U.S. emissions, the threshold for significance for estimating emissions from a specific category is 500 kt
C02 Eq. Collectively, per paragraph 37(b) of the UNFCCC Reporting Guidelines noted above, these exclusions should not
exceed 0.1 percent of gross emissions, or 6.6 MMT C02 Eq. (6,558 kt C02 Eq.). While it is not possible to proxy all
categories due to the availability of data and the disproportionate efforts to collect data necessary to estimate
emissions, categories for which proxies have been estimated total 2,305 kt C02 Eq.
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Table A-237: Summary of Sources and Sinks Not Included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
CRF Category
Number
Source/Sink Category
Gas(es)
Reason for Exclusion
Estimated 2019
Emissions
(kt C02 Eq.)
Energy
l.A Fossil Fuel Combustion
1.A.3 Transport
l.A.3.a
Domestic Aviation-Biomass
N20
Prior to 2011, no biobased jet fuel was assumed to be used for domestic aviation.
Between 2011 and 2015, 22 airlines performed over 2,500 commercial passenger
flights with blends of up to 50 percent biojet fuel. Furthermore, several airlines
have concluded long-term offtake agreements with biofuel suppliers.144 An
analysis was conducted based on the total annual volumes of fuels specified in the
long-term agreements. Emissions of N20 were estimated based on the factors for
jet fuel combustion. As for jet fuel use in commercial aircraft, contributions of
methane (CH4) emissions are reported as zero.
16
l.A.3.b.iv
Motorcycles-Biomass
CH4and N20
Emissions from ethanol mixed with gasoline in low blends are included in the on-
road gasoline emissions for motorcycles. If there is any use of high blend ethanol
fuel in motorcycles, it is considered insignificant. The percent of VMT from high
ethanol blends in light duty gas vehicles (flex fuel vehicles) is less than 1 percent. If
the same percentage is applied to motorcycle VMT with assumed flex fuel CH4 and
N20 emission factors, it results in estimated emissions of 0.0015 kt C02 Eq.
0.0015
1.A.3.C
Railways-Biomass
CH4 and N20
There are no readily available data sources to estimate the use of biofuel in
railways. Railways represent about 6 percent of all diesel fuel use. An assumption
can be made that railways consume that same percentage of biofuels (6 percent of
all biodiesel). Based on that assumption for biofuel use and applying fossil fuel CH4
and N20 factors results in estimated emissions of 12.9 kt C02 Eq. per year.
12.9
l.A.3.d
Domestic Navigation-Biomass
CH4 and N20
There are no readily available data sources to estimate the use of biofuel in
domestic navigation. Domestic navigation represents about 3 percent of all diesel
fuel use and about 1 percent of all gasoline fuel use. An assumption can be made
that domestic navigation consumes that same percentage of biofuels (3 percent of
all biodiesel and 1 percent of all ethanol use). Based on that assumption for biofuel
use and applying fossil fuel CH4 and N20 factors results in estimated emissions of
39.0 kt C02 Eq. per year.
39.0
l.A.3.d
Domestic Navigation-
Gaseous Fuels
C02
Emissions from gaseous fuel use in domestic navigation are not currently
estimated. Gaseous fuels are used in liquid natural gas (LNG) tankers and are being
demonstrated in a small number of other ships. Data are not available to
NE
144 See .
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characterize these uses currently.
l.A.3.e Other Transportation	
Use of liquid fuels to power pipeline pumps is uncommon, but has occurred. Data	342.6
for fuel used in various activities including pipelines are based on survey data
conducted by the U.S. Energy Information Association (EIA). From January 1983
through December 2009, EIA Survey data included information on liquid fuel used
to power pipelines reported in terms of crude oil product supplied. Reporting of
crude oil used for this purpose was discontinued after December 2009. Beginning
with data for January 2010, product supplied for pipeline fuel is assumed to equal
zero. 1997 was the last year of data reported on pipeline fuel. Taking the data
reported for 1997 of 797,000 barrels of crude oil and using conversion factors of
5.8 MMBtu/bbl and 20.21 MMT C/Qbtu results in estimated emissions of 342.6 kt
C02.
C02 emissions from gaseous fuels used as pipeline transport fuel are estimated in	179.6
the Inventory, however CH4 and N20 emissions from gaseous pipeline fuel use
have not been estimated. The C02 / non-C02 emissions split for other natural gas
combustion can be used to estimate emissions. Based on that analysis, non-C02
emissions represent approximately 0.43 percent of C02 emissions from all natural
gas combustion. If that percentage is applied to C02 emissions from natural gas
use as pipeline fuel, it results in an emissions estimate of 179.6 kt C02 Eq. in 2017.
l.A.3.e.ii Non-Transportation Mobile- CH4andN20 There are no readily available data sources to estimate the use of biofuel in non-	256.4
Biomass	transportation mobile sources. These sources represent about 21 percent of all
diesel fuel use and about 4 percent of all gasoline fuel use. An assumption can be
made that these sources consume that same percentage of biofuels (21 percent of
all biodiesel and 4 percent of all ethanol use). Based on that assumption for biofuel
use and applying fossil fuel CH4 and N20 factors results in estimated emissions of
256.4 kt C02 Eq. per year.
l.A.5.a Other Stationary	
l.A.5.a	Incineration of Waste: Medical C02	The category l.A.5.a Other Stationary sources not specified elsewhere includes	333
Waste Incineration	emissions from waste incineration of the municipal waste stream and waste tires.
The category also includes emissions from non-energy uses of fuels which includes
an energy recovery component that includes emissions from waste gas, waste oils,
tars, and related materials from the industrial sector. While this is not a
comprehensive inclusion of hazardous industrial waste, it does capture a subset.
A portion of hazardous industrial waste not captured is from medical waste.
However, a conservative analysis was conducted based on a study of
	hospital/medical/infectious waste incinerator (HMIWI) facilities in the United	
l.A.3.e.i Pipeline Transport—Liquid	C02, CH4and
Fuels	N20
l.A.3.e.i Pipeline Transport—Gaseous C02, CH4and
Fuels	N20
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States145 showing that medical waste incineration emissions could be considered
insignificant. The analysis was based on assuming the total amount of annual
waste throughput was of fossil origin and an assumption of 68.9 percent carbon
composition of the waste. It was determined that annual greenhouse gas
emissions for medical waste incineration are approximately 333 kt C02 Eq. per
	year.	
l.A.5.a	Stationary Fuel Combustion: CH4andN20 Data are not available to estimate emissions from biomass in U.S. Territories.	74.8
Biomass in U.S. Territories	However, biomass consumption is likely small in comparison with other fuel types.
An estimate of non-C02 emissions from biomass fuels used in Territories can be
made based on assuming the same ratio of domestic biomass non-C02 emissions
to fossil fuel C02 emissions. Non-Territories data indicate that biomass non-C02
emissions represents 0.2 percent of fossil fuel combustion C02 emissions. Applying
this same percentage to proxy U.S. Territories fossil fuel combustion C02 emissions
results in estimated emissions of 74.8 kt C02 Eq. from biomass in U.S. Territories.
l.B Fugitive Emissions from Fuels
l.B.l-Solid Fuels	
l.B.l.a.l.i, Fugitive Emissions from	C02	A preliminary analysis by EPA determined that C02 emissions for active	177
l.B.l.a.l.ii Underground Coal Mining	underground coal mining activities are negligible. The analysis was based on gas
Activities and Post-Mining	composition data from three active underground mines in three different states.146
Activities	An average ratio of C02 to CH4 composition in mine gas was derived for active
underground mines. This ratio was applied as a percentage (0.4 percent) to CH4
emission estimates to derive an estimate of C02 emissions for active underground
mines (including post-mining activities). Applying a C02 emission rate as a
percentage of CH4 emissions for active coal mines results in a national emission
estimate of 177 kt C02 Eq. per year. EPA anticipates including estimates for fugitive
C02 emissions from active underground coal mining in the next Inventory based on
methods in the IPCC2019 Refinement (see Planned Improvements in Chapter 3).
l.B.l.a.l.ii! Fugitive Emissions from	C02	A preliminary analysis by EPA determined that C02 emissions for abandoned	93
Abandoned Underground Coal	underground coal mining activities are negligible. The analysis was based on gas
Mines	composition data from two abandoned underground mines in two different
states.147 An average ratio of C02 to CH4 composition in mine gas was derived for
abandoned mines. This ratio was applied as a percentage (1.5 percent) to CH4
emission estimates to derive an estimate of C02 emissions for abandoned mines.
Applying a C02 emission rate as a percentage of CH4 emissions for abandoned coal
mines results in a national emission estimate below 93 kt C02 Eq. per year. Future
inventories may quantify these emissions, if it is deemed it will not require a
145	RTI 2009. Updated Hospital/Medical/lnfectious Waste Incinerator (HMIWI) Inventory Database.
146	Ruby Canyon Engineering 2008. "Accounting for Carbon Dioxide Emissions in the Coal Emissions Inventory". Memorandum from Ruby Canyon Engineering to EPA.
147	Ibid.
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disproportionate amount of effort.
l.B.l.a.2 Fugitive Emissions from	C02	A preliminary analysis by EPA determined that C02 emissions for active surface	34
Surface Coal Mining Activities	coal mining activities are negligible. The analysis was based on gas composition
and Post-Mining Activities	data from three active underground mines in three different states.148 An average
ratio of C02 to CH4 composition in mine gas was derived for surface mines
(including post-mining activities). This estimate for C02 is considered conservative,
as surface mining fugitive emissions of CH4 are significantly lower than those from
underground coal mines. This ratio was applied as a percentage (0.4 percent) to
CH4 emission estimates to derive an estimate of C02 emissions for surface mines
(including post-mining activities). Applying a C02 emission rate as a percentage of
CH4 emissions for surface coal mines results in a national emission estimate of 34
kt C02 Eq. per year. EPA anticipates including estimates for fugitive C02 emissions
from active surface coal mining in the next inventory based on methods in the IPCC
2019 Refinement (see Planned Improvements in section 3).
1.B.2 - Oil and Natural Gas and Other Emissions from Energy Production	
1.B.2.a.5	Oil: Distribution of Oil Products C02 and CH4 Emissions from the distribution of oil products are not currently estimated due to	NE
lack of available emission factors.
Industrial Processes and Product Use
2.A Mineral Industry	
2.A.4.a	Other Process Uses of	C02	Data are not currently available to estimate emissions from this source. During the	NE
Carbonates: Ceramics Expert Review process for compilation of the current Inventory, EPA sought expert
solicitation on data for carbonate consumption in the ceramics industry but has
yet to identify data sources to apply IPCC methods to proxy emissions and assess
significance.
2.A.4.C	Other Process Uses of	C02	Data are not currently available to estimate emissions from this source. During the	NE
Carbonates: Non-metallurgical	Expert Review process for compilation of the current Inventory, EPA sought expert
Magnesium Production	solicitation on data for non-metallurgical magnesium production but has yet to
identify data sources to apply IPCC methods to proxy emissions and assess
significance.
2.B. Chemical Industry	
2.B.4.b	Glyoxal Production	C02 and N20 Glyoxal production data are not readily available to apply Tier 1 methods and	NE
estimate emissions from this source. EPA continues to conduct basic outreach to
relevant trade associations and review potential databases that can be purchased
and contain the necessary data. Outreach this year did not identify potential data
sources. Any further progress on outreach will be included in next (i.e., 1990
148 Ibid.
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through 2020) Inventory report.
2.B.4.C	Glyoxylic Acid Production	C02 and N20 Data on national glyoxylic acid production data are currently not available to	NE
estimate emissions from this source using IPCC methods and then assess
significance. EPA is conducting basic outreach to relevant trade associations
reviewing potential databases that can be purchased and contain the necessary
data. Outreach this year did not identify potential data sources. Any further
progress on outreach will be included in next (i.e., 1990 through 2020) Inventory
report.
2.B.5.b	Calcium Carbide	CH4	Data are not currently available to estimate CH4 emissions from this source. It is	NE
difficult to obtain production data from trade associations and trade publications.
This information is not collected by USGS, the agency that collects information on
silicon carbide. EPA has initiated some research to obtain data from the limited
production facilities in the United States (less than 5). In addition, during the
Expert Review process for compilation of the current Inventory, EPA sought expert
solicitation on production data for this source, but has yet to identify data sources
to apply Tier 1 methods to proxy emissions and assess significance. Carbon dioxide
emissions from calcium carbide are implicitly accounted for in the storage factor
calculation for the non-energy use of petroleum coke in the Energy chapter.
2.B.8.d	Petrochemical and Carbon Black C02 recovery EPA's GHGRP has data starting in reporting year 2010 on the amount of C02	NE
Production	captured, including at petrochemical facilities and ethylene oxide processes. Due
to schedule and resource constraints, data on C02 sequestration have not been
compiled and need to be reviewed to better understand available data to estimate
the fate of these captured emissions. Any C02 potentially captured from
petrochemical facilities is currently assumed to be released.
2.C. Metal Industry
2.C.1.C Iron and Steel Production:
Direct Reduced Iron (DRI)
Production
ch4
Data on fuel consumption used in the production of DRI are not readily available to
apply the IPCC default Tier 1 CH4emission factor. However, an assumed emission
factor can be developed based on the default energy consumption of 12.5 GJ
natural gas per metric ton of DRI produced. Based on this assumption and annual
DRI production in metric tons results in CH4 emissions of 0.74 kt C02. Eq.
0.74
2.E Electronics Industry
2.E.2 Fluorinated Gas Emissions
from Electronics Industry: TFT
Flat Panel Displays
HFCs, PFCs,
SF6, and NF3
In addition to requiring reporting of emissions from semiconductor manufacturing,
micro-electro-mechanical systems (MEMs), and photovoltaic cells, EPA's GHGRP
requires the reporting of emissions from the manufacture of flat panel displays.
However, no flat panel displays manufacturing facilities have ever reported to
EPA's GHGRP, indicating that there are no facilities in the United States that have
exceeded the GHGRP's applicability threshold for display manufacturers since
2010. The available information on this sector indicates that these emissions are
NE
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well below the significance threshold.149 Per this published literature, the United

States has never been significant display manufacturer aside from a small amount

of manufacturing in the 1990s, but not mass production.
2.G Other
2.G.2	Other Product Manufacture SF6	Emissions of SF6 occur from particle accelerators and military applications, and	700
and Use: SF6 and PFCs from	emissions of PFCs and other F-GHGs occur from military applications such as use of
Other Product Use	fluorinated heat transfer fluids (HTFs). Emissions from some particle accelerators
and from military applications are reported by the U.S. government to the Federal
Energy Management Program along with emissions of other fluorinated
greenhouse gases (e.g., HFCs from mobile and stationary air conditioning) under
the categories "Fugitive Fluorinated Gases and Other Fugitive Emissions" and
"Industrial Process Emissions." Analysis of the underlying data for 2018 indicated
"fugitive" emissions of SF6 of approximately 600 kt C02 Eq. from the U.S.
government as a whole, and "process" emissions of SF6 of approximately 100 kt
C02 Eq. (Emissions of SF6 that are known to be accounted for elsewhere, such as
under Electrical Transmission and Distribution, have been excluded from these
totals.) The sources of the "fugitive" emissions of SF6 were not identified, but the
source of the vast majority of "process" emissions of SF6 was particle accelerators.
Note, fugitive emissions of approximately 200 kt C02 Eq. of compounds that are
commonly used as fluorinated HTFs (HFEs and fully fluorinated compounds, likely
perfluoroamines, perfluoromorpholines, and/or PFPMIEs) were also reported. Per
paragraph 33 of the UN reporting guidelines, such "additional GHGs" should be
reported separately from national totals so are not considered in estimate of 2019
emissions. EPA plans to contact reporting agencies to better understand the
sources of the emissions and the estimation methods used by reporters, which
may equate emissions to consumption and therefore over- or underestimate some
emissions, depending on the circumstances. This step will help EPA improve its
assessment of significance and prioritize incorporating estimates in future
Inventory submissions.
Agriculture
3.A Livestock
3.A.4
Enteric Fermentation: Camels
ch4
Enteric fermentation emissions from camels are not estimated because there is no
2.8



significant population of camels in the United States. Due to limited data




availability (no population data are available from the USDA Agricultural Census),




the estimates are based on use of IPCC defaults and population data from Baum,

149 The Display Industry: Fast to Grow, Slow to Change Article in Information Display 28(5):18-21 ¦ May 2012 with 4. DOI: 10.1002/j.2637-496X.2012.tb00504.x The Display
Industry: Fast to Grow, Slow to Change. Available online at: .
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Doug (2010).150 Based on this source, a Tier 1 estimate of enteric fermentation CH4
emissions from camels results in a value of approximately 2.8 kt C02 Eq. per year
from 1990 to 2019. See Chapter 5.1 for more information.
3.A.4 Enteric Fermentation: Poultry
ch4
No IPCC method has been developed for determining enteric fermentation CH4
emissions from poultry. See Chapter 5.1.
No method
provided in IPCC
Guidelines
3.B.1.4, Manure Management: Camels
3.B.2
CH4 and N20
Manure management emissions from camels are not estimated because there is
no significant population of camels in the United States.151 Due to limited data
availability and disproportionate effort to collect time-series data (i.e., no
population data is available from the Agricultural Census), this estimate is based on
population data from Baum, Doug (2010).152 Based on this source, a Tier 1
estimate of manure management CH4 and N20 emissions from camels results in a
value of approximately 0.14 kt C02 Eq. per year from 1990 to 2019. See Chapter
5.2 for more information.
0.14
3.F Field Burning of Agricultural Residues
3.F.1.4, Sugarcane
3.F.4
CH4 and N20
Currently available data did not allow for identification of burning of sugarcane.
Based on prior analysis, EPA estimates that sugarcane emissions may range from
less than 10.4 to 61.2 kt C02 Eq. (0.42 kt CH4 to 2.45 kt CH4), and less than 11.4 kt
C02 Eq. (0.04 kt N20), across the 1990 to 2016 time series. The estimate for 2016
(37.8 kt C02 Eq.) is the most recent estimate available and can be used as a proxy
for 2019. See the Planned Improvements section in Chapter 5.7 Field Burning of
Agricultural Residues for more information.
37.8
Land Use, Land-Use Change, and Forestry
4.A Forest Land
4.A(II) Emissions and Removals from
Rewetting of Organic and
Mineral Soils
C02and CH4
Emissions from this source may be estimated in future Inventories when data
necessary for classifying the area of rewetted organic and mineral soils become
available.
NE
4.A.1 Forest Land Remaining Forest Land
4.A.1 N mineralization/
immobilization
N20
Direct N20 emissions from N mineralization/immobilization associated with loss or
gain of soil organic matter resulting from change of land use or management of
mineral soils will be estimated in a future Inventory. They are not estimated
currently because resources have limited EPA's ability to use the available data on
soil carbon stock changes on forest lands to estimate these emissions.
NE
150	The status of the camel in the United States of America. Available online at: .
151	Paragraph 37(b) of Decision 24/CP.19 "Revision of the UNFCCC reporting guidelines on annual inventories for Parties included in Annex I to the Convention." See
.
152	The status of the camel in the United States of America. Available online at: .
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4.B Cropland
4. B( 11)
Emissions and Removals from
Rewetting of Organic and
Mineral Soils
C02 and CH4
Emissions of C02 and CH4 from rewetting on mineral or organic cropland soils are
not currently estimated due to lack of activity data on rewetting, except for CH4
emissions from drainage and rewetting for rice cultivation.
NE
4.B.1 Cropland Remaining Cropland
A.B.I
Carbon Stock Change in Living
Biomass and Dead Organic
Matter
C02
Carbon stock change in living biomass and dead organic matter are not estimated,
other than for forest land converted to cropland, because data are currently not
available. The impact of management on perennial biomass C is currently under
investigation for agroforestry management and will be included in a future
Inventory if stock changes are significant and activity data can be compiled for this
source.
NE
4.B.1(V)
Biomass Burning—Controlled
Burning
C02
Emissions of C02 from biomass burning on Croplands Remaining Cropland are only
relevant for perennial biomass and as noted under 4.B.1 above. EPA does not
currently include carbon stock change for perennial biomass on Cropland
Remaining Cropland. The C02 emissions from controlled burning of crop biomass
are not estimated for annual crops as they are part of the annual cycle of C and not
considered net emissions. Methane and N20 emissions are included under 3.F
Field Burning of Agricultural Residues.
NE
4.B.1(V)
Biomass Burning—Wildfires
C02, CH4, and
N20
Emissions from wildfires are not estimated because the activity data on fire area
and fuel load, particularly for perennial vegetation, are not available to apply IPCC
methods.
NE
4.B.2 Land Converted to Cropland
4.B.2
Carbon Stock Change in
Perennial Living Biomass and
Dead Organic Matter
C02
Carbon stock change in living biomass and dead organic matter are not estimated,
other than for forest land converted to cropland, because data are currently not
available. The impact of management on perennial biomass C is currently under
investigation for agroforestry management and will be included in a future
Inventory if stock changes are significant and activity data can be compiled for this
source.
NE
4.B.2(V)
Biomass Burning—Wildfires
and Controlled Burning
C02
Emissions of C02 from biomass burning on Land Converted to Cropland are only
relevant for perennial biomass and as noted under 4.B.2 above EPA does not
currently include carbon stock change for perennial biomass on Land Converted to
Cropland. Emissions from wildfires are not estimated because the activity data on
fire area and fuel load, particularly for perennial vegetation, are not available.
NE
4.C Grassland
4.C(II)
Emissions and Removals from
Rewetting of Organic and
Mineral Soils
C02and CH4
Emissions of CH4 from drainage and C02 and CH4 from rewetting on mineral or
organic Grassland soils are not currently estimated due to lack of activity data.
NE
4.C.2 Land Converted to Grassland
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4.C.2
Carbon Stock Change in Living
Biomass and Dead Organic
Matter
C02


Carbon stock change in living biomass and dead organic matter are not estimated,
other than for forest land converted to grassland, because data are currently not
available. The impact of management on perennial biomass C is currently under
investigation for agroforestry management and will be included in a future
Inventory if stock changes are significant and activity data can be compiled for this
source.
NE
4.D Wetlands
4.D(II)
Flooded Lands and Peat
Extraction Lands: Emissions
and Removals from Drainage
and Rewetting and Other
Management of Organic and
Mineral Soils
C02,
N20
ch4,
and
Data are currently not available to apply IPCC methods and estimate emissions
from rewetting of peat extraction lands and flooded lands.
NE
4.D.1 Wetlands Remaining Wetlands
4.D.1(V)
Biomass Burning: Controlled
Burning, Wildfires
C02,
N20
ch4,
and
Data are not currently available to apply IPCC methods to estimate emissions from
biomass burning in Wetlands.
NE
4.D.1.2
Carbon Stock Change in
Flooded Land Remaining
Flooded Land
C02


Carbon stock changes in flooded land remaining flooded land are not estimated
due to lack of activity data, other than for peatlands and coastal wetlands. See the
Wetlands chapter in the Inventory report to apply IPCC methods.
NE
4.D.2 Land Converted to Wetlands
4.D.2(V)
Biomass Burning: Controlled
Burning, Wildfires
co2,
n2o
ch4,
and
Data are not currently available to apply IPCC methods to estimate emissions from
biomass burning in Wetlands.
NE
4.E Settlements
4. E( V)
Biomass Burning in
Settlements
co2,
n2o
ch4,
and
Data are currently not available to apply IPCC methods to estimate emissions from
biomass burning in Settlements.
NE
4.E.1 Settlements Remaining Settlements
4.E.1
Settlements Remaining
Settlements
ch4


Data are not currently available to apply IPCC methods to estimate CH4 emissions
in Settlements.
NE
4.E.1
Direct N20 Emissions from N
Mineralization/Immobilization
(Mineral Soils)
n2o


Activity data are not available on N20 emissions from nitrogen
mineralization/immobilization in Settlements Remaining Settlements and Land
Converted to Settlements as a result of soil organic carbon stock losses from land
use conversion and management.
NE
4.E.2 Land Converted to Settlements
4.E.2
Direct N20 Emissions from N
Mineralization/Immobilization
n2o


Activity data are not available on N20 emissions from nitrogen
mineralization/immobilization in Settlements Remaining Settlements and Land
Converted to Settlements as a result of soil organic carbon stock losses from land
use conversion and management.
NE
4.F Other Land
A-491
-------
4.F(V)	Biomass Burning	C02, CH4, and Data are not currently available to estimate emissions from biomass burning in	NE
	N2Q	Other Lands.	
Waste	
5.A.1 Solid Waste Disposal	
5.A.l.a	Managed Waste Disposal Sites- CH4andN20 The amount of CH4 flared and the amount of CH4 for energy recovery is not	NE
Anaerobic estimated for the years 2005 through 2019 in the time series. The amount of CH4
flared and recovered for 2005 and each subsequent Inventory year, i.e., through
2019, is included in the net CH4 emissions estimates. A methodological change
was made for 2005 to the current Inventory year to use the directly reported net
CH4 emissions from the EPA's GHGRP versus estimate CH4 generation and
recovery. See the Methodology explanation in Section 7.1.
5.B Biological Treatment of Solid Waste	
5.B.l.a Composting-Municipal Solid Recovered CH4 CH4 and N20 emissions from combustion of the recovered gas at composting sites	NE
Waste	and N20	are very small "so good practice in the Waste Sector does not require their
estimation." (IPCC 2006, Volume 5, Chapter 4, pp. 4.5). EPA will periodically assess
trends and based on significance consider reflecting as over time as data become
available and prioritize with other improvements to make best use of available
resources. Estimating at this time given the likely significance would require a
disproportionate amount of effort, so this will be considered for future Inventories
based on trends and available data.
5.B.2.a, Anaerobic Digestion at Biogas N20	N20 emissions from anaerobic digestion of municipal solid waste at biogas facilities	NE
5.B.2.b Facilities—Municipal Solid	are not estimated because N20 emissions are assumed to be negligible. Further,
Waste and Other Waste	measured data are scare and no default EFs are provided in the guidelines to
facilitate a proxy of these emission). See IPCC (2006) Volume 5, Chapter 4, p. 4.6,
Table 4.1.
5.D Wastewater Treatment
5.D.2	Industrial Wastewater	CH4	Emissions associated with sludge generated from the treatment of industrial	5
wastewater is not included because the likely level of emissions is insignificant and
because quantitative activity data on who operates anaerobic sludge digesters is
unavailable. It would require a disproportionate amount of effort to collect this
data, and more recent methodological work also suggests this is the case (i.e.,
Table 6.3 (Updated) in the IPCC 2019 Refinement only identifies CH4 emissions
from anaerobic digestion of sludge as a source of emissions to be reported in the
Wastewater sector [note that N20 is noted as "not significant" in Table 6.8A]).
Methane emissions from the wastewater treatment category are not considered a
key source category (see Annex 1, Table A-l). In addition, the United States
continues to review the six industries included in the wastewater sector to
determine if activity data are sufficient to include methane emissions from
	anaerobic digestion of sludge. The United States has worked first with the pulp	
A-492 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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and paper industry to confirm that virtually no pulp and paper mills operate
anaerobic sludge digesters and will continue to identify stakeholders in the
remaining five industries to confirm sludge management techniques. The United
States notes that methane emissions associated with anaerobic digestion of
ethanol waste (a combination of process wastewater and solids) is already
included in the Inventory and is not considered sludge management.
The United States believes the likely level of emissions associated with anaerobic
digestion of industrial wastewater sludge is less than 5 kt C02 Eq., which is
considered insignificant for the purposes of inventory reporting under the
UNFCCC.
NE (Not Estimated), indicating also it is not possible to derive a likely level of emissions and/or removals or quantified estimate due to lack of approximated activity data and/or
in some cases also default emission factors but a method is available in the 2006 IPCC Guidelines.
A-493
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ANNEX 6 Additional Information
6.1. Global Warming Potential Values
Global warming potential (GWP) is intended as a quantified measure of the globally averaged relative radiative
forcing impacts of a particular greenhouse gas. It is defined as the cumulative radiative forcing-both direct and indirect
effects-integrated over a specific period of time from the emission of a unit mass of gas relative to some reference gas
(IPCC 2007). Carbon dioxide (C02) was chosen as this reference gas. Direct effects occur when the gas itself is a
greenhouse gas. Indirect radiative forcing occurs when chemical transformations involving the original gas produce a gas
or gases that are greenhouse gases, or when a gas influences other radiatively important processes such as the
atmospheric lifetimes of other gases. The relationship between kilotons (kt) of a gas and million metric tons of C02
equivalents (MMT C02 Eq.) can be expressed as follows:
^ MMT
vl,000 kt
MMT C02 Eq. = (kt of gas) x (GWP) x
where,
MMTC02Eq.	=	Million metric tons of C02 equivalent
kt	=	kilotons (equivalent to a thousand metric tons)
GWP	=	Global warming potential
MMT	=	Million metric tons
GWP values allow policy makers to compare the impacts of emissions and reductions of different gases.
According to the IPCC, GWP values typically have an uncertainty of +35 percent, though some GWP values have larger
uncertainty than others, especially those in which lifetimes have not yet been ascertained. In the following decision, the
countries who are Parties to the United Nations Framework Convention on Climate Change (UNFCCC) have agreed to use
consistent GWP values from the IPCC Fourth Assessment Report (AR4), based upon a 100-year time horizon, although
other time horizon values are available (see Table A-238). While this Inventory uses agreed-upon GWP values according
to the specific reporting requirements of the UNFCCC, described below, unweighted gas emissions and sinks in kilotons
(kt) are provided in the Trends chapter of this report (Table 2-2) and users of the Inventory can apply different metrics
and different time horizons to compare the impacts of different greenhouse gases.
...the global warming potential values used by Parties included in Annex I to the Convention (Annex /
Parties) to calculate the carbon dioxide equivalence of anthropogenic emissions by sources and removals by
sinks of greenhouse gases shall be those listed in the column entitled "Global warming potential for given time
horizon" in table 2.14 of the errata to the contribution of Working Group I to the Fourth Assessment Report of
the Intergovernmental Panel on Climate Change, based on the effects of greenhouse gases over a 100-year time
horizon...153
Greenhouse gases with relatively long atmospheric lifetimes (e.g., C02, CH4, N20, HFCs, PFCs, SF6, and NF3) tend
to be evenly distributed throughout the atmosphere, and consequently global average concentrations can be
determined. However, short-lived gases such as water vapor, carbon monoxide, tropospheric ozone, other indirect
greenhouse gases (e.g., NOx and NMVOCs), and tropospheric aerosols (e.g., S02 products and black carbon) vary
spatially, and consequently it is difficult to quantify their global radiative forcing impacts. GWP values are generally not
attributed to these gases that are short-lived and spatially inhomogeneous in the atmosphere.
153 United Nations Framework Convention on Climate Change; ;
31 January 2014; Report of the Conference of the Parties at its nineteenth session; held in Warsaw from 11 to 23 November
2013; Addendum; Part two: Action taken by the Conference of the Parties at its nineteenth session; Decision 24/CP.19; Revision
of the UNFCCC reporting guidelines on annual inventories for Parties included in Annex I to the Convention; p. 2. (UNFCCC
2014).
A-494 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Table A-238: IPCC AR4 Global Warming Potentials (GWP) and Atmospheric Lifetimes (Years) of Gases Used in
this Report	
Gas
Atmospheric Lifetime
100-year GWPa
20-year GWP
500-year GWP
Carbon dioxide (C02)
See footnote15
1
1
1
Methane (CH4)C
12d
25
72
7.6
Nitrous oxide (N20)
114d
298
289
153
HFC-23
270
14,800
12,000
12,200
HFC-32
4.9
675
2,330
205
HFC-41
2.4
92
323
28
HFC-125
29
3,500
6,350
1,100
HFC-134a
14
1,430
3,830
435
HFC-143a
52
4,470
5,890
1,590
HFC-152a
1.4
124
437
38
HFC-227ea
34.2
3,220
5,310
1,040
HFC-236fa
240
9,810
8,100
7,660
HFC-43-10mee
15.9
1,640
4,140
500
HFC-245fa
7.6
1,030
3,380
314
HFC-365mfc
8.6
794
2,520
241
cf4
50,000d
7,390
5,210
11,200
c2f6
10,000
12,200
8,630
18,200
C3Fs
2,600
8,830
6,310
12,500
C4F6e
1.1
0.003
NA
NA
c-C5Fse
31
1.97
7.0
NA
C4F10
2,600
8,860
6,330
12,500
c-C4Fs
3,200
10,300
7,310
14,700
C5Fi2
4,100
9,160
6,510
13,300
C6F14
3,200
9,300
6,600
13,300
sf6
3,200
22,800
16,300
32,600
NFs
740
17,200
12,300
20,700
NA (Not Available)
a GWP values used in this report are calculated over 100-year time horizon.
b For a given amount of C02 emitted, some fraction of the atmospheric increase in concentration is quickly absorbed by the
oceans and terrestrial vegetation, some fraction of the atmospheric increase will only slowly decrease over a number of
years, and a small portion of the increase will remain for many centuries or more.
c The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and
stratospheric water vapor. The indirect effect due to the production of C02 is not included.
d Methane and N20 have chemical feedback systems that can alter the length of the atmospheric response, in these cases,
global mean atmospheric lifetime (LT) is given first, followed by perturbation time (PT), but only the perturbation time is
listed here and not the atmospheric residence time.
e See Table A-l of FR 40 CFR Part 98.
Source: IPCC (2007)
Table A-239 presents direct GWP values 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; direct GWP values are shown, but AR4 does provide a range of net GWP values
for ozone depleting substances. The IPCC Guidelines and the UNFCCC do not include reporting instructions for estimating
emissions of ODSs because their use is being phased out under the Montreal Protocol (see note below Table A-239). The
effects of these compounds on radiative forcing are not addressed in this report.
Table A-239:	100-year Direct Global Warming Potentials for Select Ozone Depleting Substances
Gas	Direct GWP
CFC-11	4,750
CFC-12	10,900
CFC-113	6,130
HCFC-22	1,810
A-495
-------
HCFC-123
77
HCFC-124
609
HCFC-141b
725
HCFC-142b
2,310
CH3CCI3
146
CCU
1,400
CHsBr
5
Halon-1211
1,890
Halon-1301
7,140
Note: Because these compounds have been shown to deplete stratospheric ozone, they are typically referred to as ODSs.
However, they are also potent greenhouse gases. Recognizing the harmful effects of these compounds on the ozone layer, in
1987 many governments signed the Montreal Protocol on Substances that Deplete the Ozone Layer to limit the production and
importation of a number of CFCs and other halogenated compounds. The United States furthered its commitment to phase-
out ODSs by signing and ratifying the Copenhagen Amendments to the Montreal Protocol in 1992. Under these amendments,
the United States committed to ending the production and importation of halons by 1994, and CFCs by 1996, and HCFCs by
2030.
Source: IPCC(2007).
The IPCC published its Fifth Assessment Report (AR5) in 2013, providing the most current and comprehensive
scientific assessment of climate change (IPCC 2013). Within this report, the GWP values were revised relative to the
IPCC's Fourth Assessment Report (AR4) (IPCC 2007). Although the AR4 GWP values are used throughout this Inventory
report in line with UNFCCC inventory reporting guidelines, it is informative to review the changes to the 100-year GWP
values and the impact they have on the total GWP-weighted emissions of the United States. All GWP values use C02 as a
reference gas; a change in the radiative efficiency of C02 thus impacts the GWP of all other greenhouse gases. Since the
Second Assessment Report (SAR) and Third Assessment Report (TAR), the IPCC has applied an improved calculation of C02
radiative forcing and an improved C02 response function. The GWP values are drawn from IPCC (2007), with updates for
those cases where new laboratory or radiative transfer results have been published. Additionally, the atmospheric
lifetimes of some gases have been recalculated, and updated background concentrations were used. Table A-240 shows
how the GWP values of the other gases relative to C02 tend to be larger in AR4 and AR5 because the revised radiative
forcing of C02 is lower than in earlier assessments, taking into account revisions in lifetimes. Comparisons of GWP values
are based on the 100-year time horizon required for UNFCCC inventory reporting. However, there were some instances
in which other variables, such as the radiative efficiency or the chemical lifetime, were altered that resulted in further
increases or decreases in particular GWP values in AR5, including addressing inconsistencies with incorporating climate
carbon feedbacks. In addition, the values for radiative forcing and lifetimes have been calculated for a variety of
halocarbons. Updates in some well-mixed HFC compounds (including HFC-23, HFC-32, HFC-134a, and HFC-227ea) for AR4
result from investigation into radiative efficiencies in these compounds, with some GWP values changing by up to 40
percent; with this change, the uncertainties associated with these well-mixed HFCs are thought to be approximately 12
percent.
It should be noted that the use of IPCC AR4 GWP values for the current Inventory applies across the entire time
series of the Inventory (i.e., from 1990 to 2019). As such, GWP comparisons throughout this chapter are presented
relative to AR4 GWPs.
A-496 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Table A-240: Comparison of GWP values and Lifetimes Used in the SAR, AR4, and AR5

Lifetime (years)


GWP (100 year)


Difference in GWP (Relative to AR4)














AR5 with







AR5 with




AR5 with
feedbacks'1
Gas
SAR
AR4
AR5
SAR
AR4
AR5a
feedbacks'1
SAR
SAR (%)
AR5a
AR5 (%)
feedbacks'1
(%)
Carbon dioxide (C02)
C
d
d
1
1
1
1
NC
NC
NC
NC
NC
NC
Methane (CH4)0
12±3
8.7/12'
12.4
21
25
28
34
(4)
-16%
3
12%
9
36%
Nitrous oxide (N20)
120
120/114'
121
310
298
265
298
12
4%
(33)
-11%
0
0%
Hydrofluorocarbons













HFC-23
264
270
222
11,700
14,800
12,400
13,856
(3,100)
-21%
(2,400)
-16%
(944)
-6%
HFC-32
5.6
4.9
5.2
650
675
677
817
(25)
-4%
2
+%
142
21%
HFC-41
3.7
NA
2.8
150
NA
116
141
NA
NA
NA
NA
NA
NA
HFC-125
32.6
29
28.2
2,800
3,500
3,170
3,691
(700)
-20%
(330)
-9%
191
5%
HFC-134a
14.6
14
13.4
1,300
1,430
1,300
1,549
(130)
-9%
(130)
-9%
119
8%
HFC-143a
48.3
52
47.1
3,800
4,470
4,800
5,508
(670)
-15%
330
7%
1,038
23%
HFC-152a
1.5
1.4
1.5
140
124
138
167
16
13%
14
11%
43
35%
HFC-227ea
36.5
34.2
38.9
2,900
3,220
3,350
3,860
(320)
-10%
130
4%
640
20%
HFC-236fa
209
240
242
6,300
9,810
8,060
8,998
(3,510)
-36%
(1,750)
-18%
(812)
-8%
HFC-245fa
NA
7.6
7.7
950
1,030
858
1,032
(80)
-8%
(172)
-17%
2
+%
HFC-365mfc
NA
8.6
8.7
860
794
804
966
66
8%
10
1%
172
22%
HFC-43-10mee
17.1
15.9
16.1
1,300
1,640
1,650
1,952
(340)
-21%
10
1%
312
19%
Fully Fluorinated Species













sf6
3,200
3,200
3,200
23,900
22,800
23,500
26,087
1,100
5%
700
3%
3,287
14%
cf4
50,000
50,000
50,000
6,500
7,390
6,630
7,349
(890)
-12%
(760)
-10%
(41)
-1%
c2f6
10,000
10,000
10,000
9,200
12,200
11,100
12,340
(3,000)
-25%
(1,100)
-9%
140
1%
CsFs
2,600
2,600
2,600
7,000
8,830
8,900
9,878
(1,830)
-21%
70
1%
1,048
12%
C4F10
2,600
2,600
2,600
7,000
8,860
9,200
10,213
(1,860)
-21%
340
4%
1,353
15%
c-C4Fs
3,200
3,200
3,200
8,700
10,300
9,540
10,592
(1,600)
-16%
(760)
-7%
292
3%
c-CsFs
NA
NA
31
NA
NA
2.0
NA
NA
NA
NA
NA
NA
NA
C5F12
4,100
4,100
4,100
7,500
9,160
8,550
9,484
(1,660)
-18%
(610)
-7%
324
4%
C6F14
3,200
3,200
3,100
7,400
9,300
7,910
8,780
(1,900)
-20%
(1,390)
-15%
(520)
-6%
NFs
NA
740
500
NA
17,200
16,100
17,885
NA
NA
(1,100)
-6%
685
4%
Note: Parentheses indicate negative values.
+ Does not exceed 0.5 percent.
NC (No Change)
NA (Not Applicable)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report. See footnote e for more information on GWPs for methane of fossil
origin.
b The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02 gases in order to be consistent with the approach used in calculating the
C02 lifetime.
A-497
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c For a given amount of C02 emitted, some fraction of the atmospheric increase in concentration is quickly absorbed by the oceans and terrestrial vegetation, some fraction of the
atmospheric increase will only slowly decrease over a number of years, and a small portion of the increase will remain for many centuries or more. See footnote e for more
information on GWPs for methane of fossil origin.
d No single lifetime can be determined for C02 (see IPCC 2007).
e The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and stratospheric water vapor. Additionally, the AR5 reported
separate values for fossil versus biospheric methane in order to account for the C02 oxidation product. The GWP associated with methane of fossil origin is not shown in this table.
Per AR5, the GWP for methane of fossil origin is 30 versus 28 using methodology most consistent with AR4. If using methodology to include climate carbon feedbacks, per the AR5
report, the value is higher by 2 for GWP for methane of fossil origin, so would be 36 versus 34.
f Methane and N20 have chemical feedback systems that can alter the length of the atmospheric response, in these cases, global mean residence time is given first, followed by
perturbation time.
Source: IPCC (2013), IPCC (2007), IPCC (1996).
A-498 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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The choice of GWP values between the SAR, AR4, and AR5 with or without climate-carbon feedbacks has an
impact on both the overall emissions estimated by the Inventory, as well as the trend in emissions over time. To
summarize, Table A-241 shows the overall trend in U.S. greenhouse gas emissions, by gas, from 1990 through 2019 using
the four GWP sets. The table also presents the impact of SAR and AR5 GWP values with or without feedbacks on the
total emissions for 1990 and for 2019.
Table A-241: Effects on U.S. Greenhouse Gas Emissions Using SAR, AR4, and AR5 GWP values (MMT CO2 Eq.)

Difference in Emissions Between 1990






Gas
and 2019 (Relative to 1990)

Revisions to Annual Emission Estimates (Relative to AR4)





SAR
AR5a
AR5b
SAR
AR5a
AR5b

SAR
AR4
AR5a
AR5b
1990
2019
C02
142.4
142.4
142.4
142.4
NC
NC
NC
NC
NC
NC
ch4
(98.4)
(117.2)
(131.2) (159.3)
(124.3)
93.2
279.7
(105.6)
79.2
237.5
n2o
4.7
4.5
4.0
4.5
18.2
(50.1)
NC
18.4
(50.6)
NC
HFCs, PFCs, SF6,










and NFs
73.4
86.0
84.3
103.7
(11.9)
(9.0)
1.2
(24.5)
(10.8)
18.9
Total
122.0
115.7
99.4
91.2
(118.0)
34.1
280.9
(111.7)
17.8
256.4
Percent Change
1.9%
1.8%
1.5%
1.4%
-1.8%
0.5%
4.4%
-1.7%
0.3%
3.9%
Note: Totals may not sum due to independent rounding. Excludes sinks. Parentheses indicate negative values.
NC (No Change)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report.
b The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02 gases in order to be
consistent with the approach used in calculating the C02 lifetime. Additionally, for methane the AR5 reported separate values
for fossil versus biogenic methane in order to account for the C02 oxidation product and that is not shown on this table. See
footnotes to Table A-238.
When the GWP values from the SAR are applied to the emission estimates presented in this report, total
emissions for the year 2019 are 6,446.7 MMT C02 Eq., as compared to the official emission estimate of 6,558.3 MMT C02
Eq. using AR4 GWP values (i.e., the use of SAR GWPs results in a 1.7 percent decrease relative to emissions estimated
using AR4 GWPs).
Further, Table A-242 and Table A-243 show the comparison of emission estimates using AR5 GWP values
relative to AR4 GWP values without climate-carbon feedbacks for the non-C02 gases, on an emissions and percent
change basis. Table A-244 and Table A-245 show the comparison of emission estimates using AR5 GWP values with
climate-carbon feedbacks. The use of AR5 GWP values without climate-carbon feedbacks.154 results in an increase in
emissions of CH4 and SF6 relative to AR4 GWP values, but a decrease in emissions of other gases. The use of AR5 GWP
values with climate-carbon feedbacks does not impact C02 and N20 emissions; however, it results in an increase in
emissions of CH4, SF6, and NF3 relative to AR4 GWP values, and has mixed impacts on emissions of other gases. Overall,
these comparisons of AR4 and AR5 GWP values do not have a significant effect on calculated U.S. emissions, resulting in
an increase in emissions of less than 1 percent using AR5 GWP values, or approximately 4 percent when using AR5 GWP
values with climate-carbon feedbacks. As with the comparison of SAR and AR4 GWP values presented above, the percent
change in emissions is equal to the percent change in the GWP for each gas; however, in cases where multiple gases are
emitted in varying amounts the percent change is variable over the years, such as with Substitution of Ozone Depleting
Substances.
154 The IPCC AR5 report provides additional information on emission metrics. See .
A-499
-------
Table A-242: Change in U.S. Greenhouse Gas Emissions Using AR5a without Climate-Carbon Feedbacks Relative
to AR4 GWP Values (MMT C02 Eq.)	
Gas
1990
2005
2015
2016
2017
2018
2019
C02
NC
NC
NC
NC
NC
NC
NC
ch4
93.2
82.
78.2
77.1
77.8
78.7
79.2
n2o
(50.1)
(50.5)
(51.9)
(49.9)
(49.4)
(50.9)
(50.6)
HFCs
(7.5)
(11.0)
(10.0)
(9.8)
(10.2)
(10.1)
(10.4)
PFCs
(2.4)
(0.6)
(0.5)
(0.4)
(0.4)
(0.5)
(0.4)
sf6
0.9
0.4
0.2
0.2
0.2
0.2
0.2
nf3
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Unspecified Mix of HFCs,







PFCs, SF6, andNFs
NA
NA
NA
NA
NA
NA
NA
Total
34.1
20.6
16.0
17.1
17.9
17.5
17.8
Notes: Total emissions presented without LULUCF. Totals may not sum due to independent rounding. Parentheses indicate
negative values.
+ Absolute value does not exceed 0.05 MMT C02 Eq.
NC (No Change)
NA (Not Applicable)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report. The AR5 report
has also calculated GWP values (shown in Table A-240) where climate-carbon feedbacks have been included for the non-C02
gases in order to be consistent with the approach used in calculating the C02 lifetime. Additionally, for methane the AR5
reported separate values for fossil versus biogenic methane in order to account for the C02 oxidation product and that is not
shown on this table. See footnotes to Table A-238.
Table A-243: Change in U.S. Greenhouse Gas Emissions Using AR5a without Climate-Carbon Feedbacks Relative
to AR4 GWP Values (Percent)	
Gas/Source
1990
2005
2015
2016
2017
2018
2019
C02
NC
NC
NC
NC
NC
NC
NC
ch4
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
n2o
(11.1%)
(11.1%)
(11.1%)
(11.1%)
(11.1%)
(11.1%)
(11.1%)
sf6
3.1%
3.1%
3.1%
3.1%
3.1%
3.1%
3.1%
nf3
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
HFCs
(16.1%)
(8.6%)
(5.9%)
(5.8%)
(6.0%)
(5.9%)
(6.0%)
Substitution of Ozone







Depleting Substances
11.3%
(7.1%)
(5.7%)
(5.6%)
(5.6%)
(5.7%)
(5.7%)
HCFC-22 Production15
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.2%)
Electronics Industry0
(16.2%)
(16.4%)
(16.4%)
(16.8%)
(16.6%)
(16.7%)
(20.7%)
Magnesium Production and







Processing11
0.0%
0.0%
(9.1%)
(9.1%)
(9.1%)
(9.1%)
(9.1%)
PFCs
(10.0%)
(9.6%)
(9.5%)
(9.5%)
(9.5%)
(9.6%)
(9.7%)
Electronics Industry0
(9.4%)
(9.1%)
(9.2%)
(9.3%)
(9.4%)
(9.4%)
(9.4%)
Aluminum Production6
(10.1%)
(10.1%)
(10.0%)
(9.9%)
(9.9%)
(10.0%)
(10.0%)
Substitution of Ozone







Depleting Substances4'
0.0%
(10.3%)
(10.3%)
(10.3%)
(10.3%)
(10.3%)
(10.3%)
Unspecified Mix of HFCs,







PFCs, SF6, and NF3
NA
NA
NA
NA
NA
NA
NA
Electronics Industry
NA
NA
NA
NA
NA
NA
NA
Total
0.5%
0.3%
0.2%
0.3%
0.3%
0.3%
0.3%
Note: Total emissions presented without LULUCF. Parentheses indicate negative values. Totals may not sum due to
independent rounding.
NC (No Change)
NA (Not Applicable)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report. The AR5 report
has also calculated GWP values (shown in Table A-240) where climate-carbon feedbacks have been included for the non-C02
A-500 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
gases in order to be consistent with the approach used in calculating the C02 lifetime. Additionally, the AR5 reported separate
values for fossil versus biogenic methane in order to account for the C02 oxidation product.
b HFC-23 emitted.
c Emissions from HFC-23, CF4, C2F6, C3Fs, C4FS, SF6, as well as other HFCs and PFCs used as heat transfer fluids.
d Zero change in beginning of time series since emissions were zero.
e PFC emissions from CF4 and C2F6.
f PFC emissions from CF4.
Table A-244: Change in U.S. Greenhouse Gas Emissions Using AR5 with Climate-Carbon Feedbacks3 Relative to
AR4 GWP Values (MMT C02 Eq.)	
Gas
1990
2005
2015
2016
2017
2018
2019
C02
NC
NC
NC
NC
NC
NC
NC
ch4
279.7
247.0
234.6
231.3
233.4
236.1
237.5
n2o
NC
NC
NC
NC
NC
NC
NC
HFCs
(2.9)
9.4
17.7
17.9
17.6
17.7
18.1
PFCs
(+)
+
+
+
+
+
+
sf6
4.2
1.7
0.8
0.9
0.8
0.8
0.9
nf3
+
+
+
+
+
+
+
Unspecified Mix of HFCs,







PFCs, SF6, andNFs
NA
NA
NA
NA
NA
NA
NA
Total
280.9
258.1
253.0
250.0
251.9
254.7
256.5
Notes: Total emissions presented without LULUCF. Totals may not sum due to independent rounding. Parentheses indicate
negative values.
+ Absolute value does not exceed 0.05 MMT C02 Eq.
NC (No Change)
NA (Not Applicable)
a The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02 gases in order to be
consistent with the approach used in calculating the C02 lifetime. Additionally, for methane the AR5 reported separate values
for fossil versus biogenic methane in order to account for the C02 oxidation product and that is not shown on this table. See
footnotes to Table A-238.
Table A-245: Change in U.S. Greenhouse Gas Emissions Using AR5 with Climate-Carbon Feedbacks3 Relative to
AR4 GWP Values (Percent)
Gas/Source
1990
2005
2015
2016
2017
2018
2019
C02
NC
NC
NC
NC
NC
NC
NC
ch4
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
n2o
NC
NC
NC
NC
NC
NC
NC
sf6
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
nf3
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
HFCs
(6.2%)
7.4%
10.5%
10.6%
10.3%
10.5%
10.4%
Substitution of Ozone







Depleting Substances
34.6%
10.0%
11.0%
11.0%
10.9%
10.8%
10.8%
HCFC-22 Production15
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
Electronics Industry0
(6.4%)
(6.6%)
(6.6%)
(7.0%)
(6.8%)
(6.9%)
(11.4%)
Magnesium Production







and Processing11
0.0%
0.0%
8.3%
8.3%
8.3%
8.3%
8.3%
PFCs
(0.2%)
0.3%
0.4%
0.5%
0.4%
0.3%
0.2%
Electronics Industry0
0.6%
0.9%
0.8%
0.7%
0.6%
0.5%
0.5%
Aluminum Production6
(0.3%)
(0.3%)
(0.1%)
(0.0%)
0.0%
(0.1%)
(0.2%)
Substitution of Ozone







Depleting Substances4'
0.0%
(0.6%)
(0.6%)
(0.6%)
(0.6%)
(0.6%)
(0.6%)
Unspecified Mix of HFCs,







PFCs, SF6, and NF3
NA
NA
NA
NA
NA
NA
NA
Electronics Industry
NA
NA
NA
NA
NA
NA
NA
Total	4.4%	3.5%	3.8% 3.8% 3.9% 3.8% 3.9%
A-501
-------
Notes: Total emissions presented without LULUCF. Parentheses indicate negative values. Excludes Sinks.
NC (No Change)
NA (Not Applicable)
+ Does not exceed 0.05 percent.
a The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02 gases in order to be
consistent with the approach used in calculating the C02 lifetime. Additionally, for methane the AR5 reported separate values
for fossil versus biogenic methane in order to account for the C02 oxidation product and that is not shown on this table. See
footnotes to Table A-238.
b HFC-23 emitted.
c Emissions from HFC-23, CF4, C2F6, C3Fs, C4FS, SF6, as well as other HFCs and PFCs used as heat transfer fluids.
d Zero change in beginning of time series since emissions were zero.
e PFC emissions from CF4 and C2F6.
f PFC emissions from CF4.
A-502 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
6.2. Ozone Depleting Substance Emissions
Ozone is present in both the stratosphere,.155 where it shields the earth from harmful levels of ultraviolet
radiation, and at lower concentrations in the troposphere, .156 where it is the main component of anthropogenic
photochemical "smog." Chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl chloroform, and
hydrochlorofluorocarbons (HCFCs), along with certain other chlorine and bromine containing compounds, have been
found to deplete the ozone levels in the stratosphere. These compounds are commonly referred to as ozone depleting
substances (ODSs). If left unchecked, stratospheric ozone depletion could result in a dangerous increase of ultraviolet
radiation reaching the earth's surface. In 1987, nations around the world signed the Montreal Protocol on Substances
that Deplete the Ozone Layer. This landmark agreement created an international framework for limiting, and ultimately
eliminating, the production of most ozone depleting substances. ODSs have historically been used in a variety of
industrial applications, including refrigeration and air conditioning, foam blowing, fire extinguishing, sterilization, solvent
cleaning, and as an aerosol propellant.
In the United States, the Clean Air Act Amendments of 1990 provide the legal instrument for implementation of
the Montreal Protocol controls. The Clean Air Act classifies ozone depleting substances as either Class I or Class II,
depending upon the ozone depletion potential (ODP) of the compound.157 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,.158 and stockpiles of the ODSs, as well as material recovered
from equipment being decommissioned, are used for maintaining the existing equipment. As a result, emissions of Class I
compounds will continue, albeit generally in decreasing amounts, for many more years. Class II designated substances,
all of which are HCFCs, have been, or are being, phased out at later dates than Class I compounds because they have
lower ODPs. These compounds served as interim replacements for Class I compounds in many industrial applications.
The use and emissions of HCFCs in the United States is anticipated to continue for several decades as equipment that use
Class II substances are retired from use. Under current Montreal Protocol controls, however, the production for
domestic use of all HCFCs as an ODS substitute in the United States must end by the year 2030.
In addition to contributing to ozone depletion, CFCs, halons, carbon tetrachloride, methyl chloroform, and
HCFCs are also potent greenhouse gases. However, the depletion of the ozone layer has a cooling effect on the climate
that counteracts the direct warming from tropospheric emissions of ODSs. Stratospheric ozone influences the earth's
radiative balance by absorption and emission of longwave radiation from the troposphere as well as absorption of
shortwave radiation from the sun; overall, stratospheric ozone has a warming effect.
The IPCC has prepared both direct GWP values and net (combined direct warming and indirect cooling) GWP
ranges for some of the most common ozone depleting substances (IPCC 2007). See Annex 6.1 Global Warming Potential
Values, for a listing of the direct GWP values for ODS.
Although the IPCC emission inventory guidelines do not require the reporting of emissions of ozone depleting
substances, the United States believes that the inventory presents a more complete picture of climate impacts when we
include these compounds. Emission estimates for several ozone depleting substances are provided in Table A-246.
155	The stratosphere is the layer from the top of the troposphere up to about 50 kilometers. Approximately 90 percent of
atmospheric ozone is within the stratosphere. The greatest concentration of ozone occurs in the middle of the stratosphere, in
a region commonly called the ozone layer.
156	The troposphere is the layer from the ground up to about 11 kilometers near the poles and 16 kilometers in equatorial
regions (i.e., the lowest layer of the atmosphere, where humans live). It contains roughly 80 percent of the mass of all gases in
the atmosphere and is the site for weather processes including most of the water vapor and clouds.
157	Substances with an ozone depletion potential of 0.2 or greater are designated as Class I. All other designated substances
that deplete stratospheric ozone but which have an ODP of less than 0.2 are Class II.
158	Older refrigeration and air-conditioning equipment, fire extinguishing systems, and foam products blown with CFCs/HCFCs
may still contain Class I ODS.
A-503
-------
Table A-246: Emissions of Ozone Depleting Substances (kt)
Compound
1990
2005
2015
2016
2017
2018
2019
Class 1







CFC-11
31
12
11
11
12
11
9
CFC-12
136
23
4
3
2
1
1
CFC-113
59
17
0
0
0
0
0
CFC-114
4
1
0
0
0
0
0
CFC-115
8
2
+
+
+
+
+
Carbon







Tetrachloride
4
0
0
0
0
0
0
Methyl Chloroform
223
0
0
0
0
0
0
Halon-1211
2
2
1
1
1
1
1
Halon-1301
2
+
+
+
+
+
+
Class II







HCFC-22
31
74
58
54
51
47
43
HCFC-123
0
1
1
1
1
1
1
HCFC-124
0
2
+
+
+
+
+
HCFC-141b
1
4
10
9
8
9
10
HCFC-142b
1
4
2
3
3
4
5
HCFC-225ca/cb
-
3
12
13
14
15
17
+ Does not exceed 0.5 kt.
Methodology and Data Sources
Emissions of ozone depleting substances were estimated using the EPA's Vintaging Model. The model, named
for its method of tracking the emissions of annual "vintages" of new equipment that enter into service, is a "bottom-up"
model. It models the consumption of chemicals based on estimates of the quantity of equipment or products sold,
serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain the
equipment. The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of the
equipment in each of the end-uses. Emissions are estimated by applying annual leak rates, service emission rates, and
disposal emission rates to each population of equipment. By aggregating the emission and consumption output from the
different end-uses, the model produces estimates of total annual use and emissions of each chemical. Please see Annex
3.9, Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances, of this
Inventory for a more detailed discussion of the Vintaging Model.
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 Substitution of Ozone
Depleting Substances section of this report for a more detailed description of the uncertainties that exist in the Vintaging
Model.
A-504 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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6.3. Sulfur Dioxide Emissions
Sulfur dioxide (S02), emitted into the atmosphere through natural and anthropogenic processes, affects the
Earth's radiative budget through photochemical transformation into sulfate aerosols that can (1) scatter sunlight back to
space, thereby reducing the radiation reaching the Earth's surface; (2) affect cloud formation; and (3) affect atmospheric
chemical composition (e.g., stratospheric ozone, by providing surfaces for heterogeneous chemical reactions). The
overall effect of S02-derived aerosols on radiative forcing is believed to be negative (IPCC 2007). However, because S02 is
short-lived and unevenly distributed through the atmosphere, its radiative forcing impacts are highly uncertain. Sulfur
dioxide emissions have been provided below in Table A-247.
The major source of S02 emissions in the United States is the burning of sulfur containing fuels, mainly coal.
Metal smelting and other industrial processes also release significant quantities of S02. The largest contributor to U.S.
emissions of S02 is electricity generation, accounting for 46.9 percent of total S02 emissions in 2019 (see Table A-248);
coal combustion accounted for approximately 92.0 percent of that total. The second largest source was industrial fuel
combustion, which produced 17.4 percent of 2019 S02 emissions (see Table A-247). Overall, S02 emissions in the United
States decreased by 90.6 percent from 1990 to 2019. 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 S02 can cause significant
increases in acute and chronic respiratory diseases. In addition, once S02 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, cause the acidification of lakes and streams, and damage trees. As
a result of these harmful effects, the United States has regulated the emissions of S02 under the Clean Air Act. The EPA
has also developed a strategy to control these emissions via four programs: (1) the National Ambient Air Quality
Standards program,159 (2) New Source Performance Standards,160 (3) the New Source Review/Prevention of Significant
Deterioration Program,161 and (4) the Sulfur Dioxide Allowance Program.162
Table A-247: SO2 Emissions (kt)
Sector/Source	1990	20051
Energy	19,628	12,364
Stationary Sources	18,407	11,541
Oil and Gas Activities	390	1801
Mobile Sources	793	6191
Waste Combustion	38	251
Industrial Processes and
Product Use	1,307	8311
Miscellaneous3	11	1141
Other Industrial Processes	362	3271
Chemical and Allied Product
Manufacturing	269	2281
Metals Processing	659	1581
Storage and Transport	1	2|
Solvent Use	0	+|
Degreasing	0	0|
Graphic Arts	0	o|
Dry Cleaning	NA	o|
Surface Coating	0	0|
Other Industrial	0	+|
Nonindustrial	NA	NA!
| 2015
2016
2017
2018
2019
i 3,096
2,439
1,803
1,724
1,457
| 2,901
2,269
1,638
1,569
1,304
| 92
89
86
86
86
1 78
57
58
47
45
1 26
24
22
22
22
I 482
466
509
509
509
| 137
139
198
198
198
| 145
139
132
132
132
I 108
104
101
101
101
| 89
83
77
77
77
1 2
2
1
1
1
i +
+
+
+
+
1 0
0
0
0
0
! 0
0
0
0
0
I 0
0
0
0
0
1 0
0
0
0
0
1 +
+
+
+
+
! NA
NA
NA
NA
NA
159	[42 U.S.C § 7409, CAA § 109]
160	[42 U.S.C § 7411, CAA § 111]
161	[42 U.S.C § 7473, CAA § 163]
162	[42 U.S.C § 76S1, CAA § 401]
A-505
-------
Agriculture
NA
NA
NA
NA
NA
NA
NA
Agricultural Burning
NA
NA
NA
NA
NA
NA
NA
Waste
+
1
1
1
1
1
1
Landfills
+ IIIII
1 lliiii
1
1
1
1
1
Wastewater Treatment
+ 111
0
0
0
0
0
0
Miscellaneous3

0
0
0
0
0
0
Total
20,935
13,196
3,578
2,906
2,313
2,233
1,966
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.5 kt
NA (Not Applicable)
a Miscellaneous includes other combustion and fugitive dust categories.
Source: Data taken from EPA (2019) and disaggregated based on EPA (2003).
Table A-248: SO2 Emissions from Electricity Generation (kt)
Fuel Type
1990
2005
2015
2016
2017
2018
2019
Coal
13,808
8,680
2,189
1,673
1,156
1,092
849
Oil
580
458
115
88
61
58
45
Gas
11ll
174
44
33
23
22
17
Internal Combustion
4.
57
14
11
8
7
6
Other
NA
7
18
14
9
9
7
Total
14,433
9,439
2,381
1,819
1,257
1,188
923
Note: Totals may not sum due to independent rounding.
NA (Not Applicable)
Source: Data taken from EPA (2019) and disaggregated based on EPA (2003).
A-506 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
6.4. Complete List of Source and Sink Categories
Chapter/Source/Sink
Gas(es)
Energy
Fossil Fuel Combustion
Non-Energy Use of Fossil Fuels
Stationary Combustion (excluding C02)
Mobile Combustion (excluding C02)
Coal Mining
Abandoned Underground Coal Mines
Petroleum Systems
Natural Gas Systems
Abandoned Oil and Gas Wells
Incineration of Waste
Industrial Processes and Product Use
Cement Production
Lime Production
Glass Production
Other Process Uses of Carbonates
Ammonia Production
Urea Consumption for Non-Agricultural Purposes
Nitric Acid Production
AdipicAcid Production
Caprolactam, Glyoxal, and Glyoxylic Production
Carbide Production and Consumption
Titanium Dioxide Production
Soda Ash Production
Petrochemical Production
HCFC-22 Production
Carbon Dioxide Consumption
Phosphoric Acid Production
Iron and Steel Production & Metallurgical Coke Production
Ferroalloy Production
Aluminum Production
Magnesium Production and Processing
Lead Production
Zinc Production
Electronics Industry
Substitution of Ozone Depleting Substances
Electrical Transmission and Distributing
N20 from Product Uses
Agriculture
Enteric Fermentation
Manure Management
Rice Cultivation
Liming
Urea Fertilization
Field Burning of Agricultural Residues
Agricultural Soil Management
Land Use, Land-Use Change, and Forestryc
Forest Land Remaining Forest Land
Land Converted to Forest Land
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
C02
C02
CH4,
ch4,
ch4
ch4
C02,
C02,
C02,
co2.
N20, CO, NOx, NMVOC
N20, CO, NOx, NMVOC
CH4,N20
ch4,n2o
ch4
CH4.N20, NOx, CO, NMVOC
C02
C02
C02
C02
C02
co2
n2o
n2o
n2o
co2, ch4
co2
co2
co2, ch4
HFC-23
C02
C02
co2, ch4
co2, ch4
co2, cf4, c2f6
C02, HFCs, SF6
C02
co2
N20, HFCs, PFCs,a SF6, NF3
HFCs, PFCsb
SF6
n2o
ch4
ch4, n2o
ch4
C02
co2
CH4, N20, NOx, CO
N20
C02, CH4, N20, NOx, CO
C02
C02
C02
C02, CH4, N20, NOx, CO
A-507
-------
Land Converted to Grassland	C02
Wetlands Remaining Wetlands	C02, CH4, N20
Land Converted to Wetlands	C02, CH4
Settlements Remaining Settlements	C02, N20
Land Converted to Settlements	C02
Waste
Landfills	CH4, NOx, CO, NMVOC
Wastewater Treatment	CH4, N20, NOx, CO, NMVOC
Composting	CH4, N20
Anaerobic Digestion at Biogas Facilities	CH
4
a Includes HFC-23, CF4, C2F6, as well as a mix other HFCs and PFCs used as heat transfer fluids.
b Includes HFC-23, HFC-32, HFC-125, HFC-134a, HFC-143a, HFC-236fa, CF4, HFC-152a, HFC-227ea, HFC-245fa, HFC-
4310mee, and PFC/PFPEs.
cThe LULUCF Sector includes CH4 and N20 emissions to the atmosphere and net carbon stock changes. The term
"flux" is used to describe the exchange of C02 to and from the atmosphere, with net flux being either positive or
negative depending on the overall balance. Removal and long-term storage of C02 from the atmosphere is also
referred to as "carbon sequestration."
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-249 provides a guide for determining the magnitude of
metric units.
Table A-249: Guide to Metric Unit Prefixes
Prefix/Symbol
Factor
atto (a)
CO
o
T—1
femto (f)
10"15
pico (p)
10-12
nano (n)
10"9
micro (|a)
10"6
milli (m)
10"3
centi (c)
10"2
deci (d)
10"1
deca (da)
10
hecto (h)
102
kilo (k)
103
mega (M)
106
giga (G)
109
tera (T)
1012
peta (P)
1015
exa (E)
101S
Unit Conversions
1 kilogram	=
1 pound	=
1 short ton	=
1 metric ton =
2.205 pounds
0.454 kilograms
2,000 pounds =
1,000 kilograms =
0.9072 metric tons
1.1023 short tons
1 cubic meter = 35.315 cubic feet
1 cubic foot = 0.02832 cubic meters
1 U.S. gallon = 3.785412 liters
A-508 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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1 barrel (bbl) =
1 barrel (bbl) =
1 liter
0.159 cubic meters
42 U.S. gallons
0.001 cubic meters
1 foot
1 meter
1 mile
1 kilometer
0.3048 meters
3.28 feet
1.609 kilometers
0.621 miles
1 acre	= 43,560 square feet = 0.4047 hectares
1 square mile = 2.589988 square kilometers
4,047 square meters
Degrees Celsius= (Degrees Fahrenheit - 32)*5/9
Degrees Kelvin = Degrees Celsius + 273.15
Density Conversions.163
Methane
Carbon dioxide
1 cubic meter
1 cubic meter
0.67606 kilograms
1.85387 kilograms
Natural gas liquids
1
metric
ton =
11.6 barrels =
1,844.2 liters
Unfinished oils
1
metric
ton =
7.46 barrels =
1,186.04 liters
Alcohol
1
metric
ton =
7.94 barrels =
1,262.36 liters
Liquefied petroleum gas
1
metric
ton =
11.6 barrels =
1,844.2 liters
Aviation gasoline
1
metric
ton =
8.9 barrels =
1,415.0 liters
Naphtha jet fuel
1
metric
ton =
8.27 barrels =
1,314.82 liters
Kerosene jet fuel
1
metric
ton =
7.93 barrels =
1,260.72 liters
Motor gasoline
1
metric
ton =
8.53 barrels =
1,356.16 liters
Kerosene
1
metric
ton =
7.73 barrels =
1,228.97 liters
Naphtha
1
metric
ton =
8.22 barrels =
1,306.87 liters
Distillate
1
metric
ton =
7.46 barrels =
1,186.04 liters
Residual oil
1
metric
ton =
6.66 barrels =
1,058.85 liters
Lubricants
1
metric
ton =
7.06 barrels =
1,122.45 liters
Bitumen
1
metric
ton =
6.06 barrels =
963.46 liters
Waxes
1
metric
ton =
7.87 barrels =
1,251.23 liters
Petroleum coke
1
metric
ton =
5.51 barrels =
876.02 liters
Petrochemical feedstocks
1
metric
ton =
7.46 barrels =
1,186.04 liters
Special naphtha
1
metric
ton =
8.53 barrels =
1,356.16 liters
Miscellaneous products
1
metric
ton =
8.00 barrels =
1,271.90 liters
Energy Conversions
Converting Various Energy Units to Joules
The common energy unit used in international reports of greenhouse gas emissions is the joule. A joule is the
energy required to push with a force of one Newton for one meter. A terajoule (TJ) is one trillion (1012) joules. A British
thermal unit (Btu, the customary U.S. energy unit) is the quantity of heat required to raise the temperature of one pound
of water one degree Fahrenheit at or near 39.2 degrees Fahrenheit.
1 TJ =	2.388xlOn calories
163 Reference: EIA (2007)
A-509
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23.88 metric tons of crude oil equivalent
947.8 million Btus
277,800 kilowatt-hours
Converting Various Physical Units to Energy Units
Data on the production and consumption of fuels are first gathered in physical units. These units must be
converted to their energy equivalents. The conversion factors in Table A-250 can be used as default factors, if local data
are not available. See Appendix A of ElA's Monthly Energy Review, November 2020 (EIA 2020) for more detailed
information on the energy content of various fuels.
Table A-250: Conversion Factors to Energy Units (Heat Equivalents)
Fuel Type (Units)	Factor
Solid Fuels (Million Btu/Short ton)
Anthracite coal	22.57
Bituminous coal	23.89
Sub-bituminous coal	17.14
Lignite	12.87
Coal Coke	24.80
Natural Gas (Btu/Cubic foot)	1,038
Liquid Fuels (Million Btu/Barrel)
Motor gasoline	5.052
Aviation gasoline	5.048
Kerosene	5.670
Jet fuel, kerosene-type	5.670
Distillate fuel	5.825
Residual oil	6.287
Naphtha for petrochemicals	5.248
Petroleum coke	6.024
Other oil for petrochemicals	5.825
Special naphthas	5.248
Lubricants	6.065
Waxes	5.537
Asphalt	6.636
Still gas	6.287
Misc. products	5.796
Note: For petroleum and natural gas, Monthly Energy
Review, November 2020 (EIA 2020). For coal ranks, State
Energy Data Report 1992 (EIA 1993). All values are given in
higher heating values (gross calorific values).
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6.6. Abbreviations
ABS
Acrylonitrile butadiene styrene
AC
Air conditioner
ACC
American Chemistry Council
AEDT
FAA Aviation Environmental Design Tool
AEO
Annual Energy Outlook
AER
All-electric range
AF&PA
American Forest and Paper Association
AFEAS
Alternative Fluorocarbon Environmental Acceptability Study
AFOLU
Agriculture, Forestry, and Other Land Use
AFV
Alternative fuel vehicle
AGA
American Gas Association
AGR
Acid gas removal
AHEF
Atmospheric and Health Effect Framework
AHRI
Air-Conditioning, Heating, and Refrigeration Institute
AISI
American Iron and Steel Institute
ALU
Agriculture and Land Use
ANGA
American Natural Gas Alliance
ANL
Argonne National Laboratory
APC
American Plastics Council
API
American Petroleum Institute
APTA
American Public Transportation Association
AR4
IPCC Fourth Assessment Report
AR5
IPCC Fifth Assessment Report
ARI
Advanced Resources International
ARMA
Autoregressive moving-average
ARMS
Agricultural Resource Management Surveys
ASAE
American Society of Agricultural Engineers
ASLRRA
American Short-line and Regional Railroad Association
ASR
Annual Statistical Report
ASTM
American Society for Testing and Materials
AZR
American Zinc Recycling
BCEF
Biomass conversion and expansion factors
BEA
Bureau of Economic Analysis, U.S. Department of Commerce
BIER
Beverage Industry Environmental Roundtable
BLM
Bureau of Land Management
BoC
Bureau of Census
BOD
Biological oxygen demand
BOD5
Biochemical oxygen demand over a 5-day period
BOEM
Bureau of Ocean Energy Management
BOEMRE
Bureau of Ocean Energy Management, Regulation and Enforcement
BOF
Basic oxygen furnace
BRS
Biennial Reporting System
BSEE
Bureau of Safety and Environmental Enforcement
BTS
Bureau of Transportation Statistics, U.S. Department of Transportation
Btu
British thermal unit
C
Carbon
C&D
Construction and demolition waste
C&EN
Chemical and Engineering News
CAAA
Clean Air Act Amendments of 1990
CAFOS
Concentrated Animal Feeding Operations
CaO
Calcium oxide
CAPP
Canadian Association of Petroleum Producers
CARB
California Air Resources Board
A-511
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CBI
Confidential business information
C-CAP
Coastal Change Analysis Program
CDAT
Chemical Data Access Tool
CEAP
USDA-NRCS Conservation Effects Assessment Program
CEFM
Cattle Enteric Fermentation Model
CEMS
Continuous emission monitoring system
CFC
Chlorofluorocarbon
CFR
Code of Federal Regulations
CGA
Compressed Gas Association
ch4
Methane
CHAPA
Calfornia Health and Productivity Audit
CHP
Combined heat and power
CI
Confidence interval
CIGRE
International Council on Large Electric Systems
CKD
Cement kiln dust
CLE
Crown Light Exposure
CMA
Chemical Manufacturer's Association
CMM
Coal mine methane
CMOP
Coalbed Methane Outreach Program
CMR
Chemical Market Reporter
CNG
Compressed natural gas
CO
Carbon monoxide
C02
Carbon dioxide
COD
Chemical oxygen demand
COGCC
Colorado Oil and Gas Conservation Commission
CONUS
Continental United States
CRF
Common Reporting Format
CRM
Component ratio method
CRP
Conservation Reserve Program
CSRA
Carbon Sequestration Rural Appraisals
CTIC
Conservation Technology Information Center
CVD
Chemical vapor deposition
CWNS
Clean Watershed Needs Survey
d.b.h
Diameter breast height
DE
Digestible energy
DESC
Defense Energy Support Center-DoD's Defense Logistics Agency
DFAMS
Defense Fuels Automated Management System
DGGS
Division of Geological & Geophysical Surveys
DHS
Department of Homeland Security
DLA
DoD's Defense Logistics Agency
DM
Dry matter
DOC
Degradable organic carbon
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
DOM
Dead organic matter
DOT
U.S. Department of Transportation
DRE
Destruction or removal efficiencies
DRI
Direct Reduced Iron
EAF
Electric arc furnace
EDB
Aircraft Engine Emissions Databank
EDF
Environmental Defense Fund
EER
Energy economy ratio
EF
Emission factor
EFMA
European Fertilizer Manufacturers Association
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EJ
Exajoule
EGR
Exhaust gas recirculation
EGU
Electric generating unit
EIA
Energy Information Administration, U.S. Department of Energy
El IP
Emissions Inventory Improvement Program
EOR
Enhanced oil recovery
EPA
U.S. Environmental Protection Agency
EREF
Environment Research & Education Foundation
ERS
Economic Research Service
ETMS
Enhanced Traffic Management System
EV
Electric vehicle
EVI
Enhanced Vegetation Index
FAA
Federal Aviation Administration
FAO
Food and Agricultural Organization
FAOSTAT
Food and Agricultural Organization database
FAS
Fuels Automated System
FCCC
Framework Convention on Climate Change
FEB
Fiber Economics Bureau
FERC
Federal Energy Regulatory Commission
FGD
Flue gas desulfurization
FHWA
Federal Highway Administration
FIA
Forest Inventory and Analysis
FIADB
Forest Inventory and Analysis Database
FIPR
Florida Institute of Phosphate Research
FOD
First order decay
FOEN
Federal Office for the Environment
FQSV
First-quarter of silicon volume
FSA
Farm Service Agency
FTP
Federal Test Procedure
g
Gram
G&B
Gathering and boosting
GaAs
Gallium arsenide
GCV
Gross calorific value
GDP
Gross domestic product
GEI
Gulfwide Emissions Inventory
GHG
Greenhouse gas
GHGRP
EPA's Greenhouse Gas Reporting Program
GIS
Geographic Information Systems
GJ
Gigajoule
GOADS
Gulf Offshore Activity Data System
GOM
Gulf of Mexico
GPG
Good Practice Guidance
GRI
Gas Research Institute
GSAM
Gas Systems Analysis Model
GTI
Gas Technology Institute
GWP
Global warming potential
ha
Hectare
HBFC
Hydrobromofluorocarbon
HC
Hydrocarbon
HCFC
Hydrochlorofluorocarbon
HCFO
Hydrochlorofluoroolefin
HDDV
Heavy duty diesel vehicle
HDGV
Heavy duty gas vehicle
HDPE
High density polyethylene
HF
Hydraulically fractured
HFC
Hydrofluorocarbon
A-513
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HFO
Hydrofluoroolefin
HFE
Hydrofluoroether
HHV
Higher Heating Value
HMA
Hot Mix Asphalt
HMIWI
Hospital/medical/infectious waste incinerator
HTF
Heat Transfer Fluid
HTS
Harmonized Tariff Schedule
HWP
Harvested wood product
IBF
International bunker fuels
IC
Integrated Circuit
ICAO
International Civil Aviation Organization
ICBA
International Carbon Black Association
ICE
Internal combustion engine
ICR
Information Collection Request
IEA
International Energy Agency
IFO
Intermediate Fuel Oil
IGES
Institute of Global Environmental Strategies
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
IPPU
Industrial Processes and Product Use
ITC
U.S. International Trade Commission
ITRS
International Technology Roadmap for Semiconductors
JWR
Jim Walters Resources
KCA
Key category analysis
kg
Kilogram
kt
Kiloton
kWh
Kilowatt hour
LDPE
Low density polyethylene
LDT
Light-duty truck
LDV
Light-duty vehicle
LEV
Low emission vehicles
LFG
Landfill gas
LFGTE
Landfill gas-to-energy
LHV
Lower Heating Value
LKD
Lime kiln dust
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
M&R
Metering and regulating
MARPOL
International Convention for the Prevention of Pollution from Ships
MC
Motorcycle
MCF
Methane conversion factor
MCL
Maximum Contaminant Levels
MCFD
Thousand cubic feet per day
MDI
Metered dose inhalers
MDP
Management and design practices
MECS
EIA Manufacturer's Energy Consumption Survey
MEMS
Micro-electromechanical systems
MER
Monthly Energy Review
MGO
Marine gas oil
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MgO
Magnesium oxide
MJ
Megajoule
MLRA
Major Land Resource Area
mm
Millimeter
MMBtu
Million British thermal units
MMCF
Million cubic feet
MMCFD
Million cubic feet per day
MMS
Minerals Management Service
MMT
Million metric tons
MMTCE
Million metric tons carbon equivalent
MMTCO2 Eq.
Million metric tons carbon dioxide equivalent
MODIS
Moderate Resolution Imaging Spectroradiometer
MoU
Memorandum of Understanding
MOVES
U.S. EPA's Motor Vehicle Emission Simulator model
MPG
Miles per gallon
MRLC
Multi-Resolution Land Characteristics Consortium
MRV
Monitoring, reporting, and verification
MSHA
Mine Safety and Health Administration
MSW
Municipal solid waste
MT
Metric ton
MTBE
Methyl Tertiary Butyl Ether
MTBS
Monitoring Trends in Burn Severity
MVAC
Motor vehicle air conditioning
MY
Model year
N20
Nitrous oxide
NA
Not applicable; Not available
NACWA
National Association of Clean Water Agencies
NAHMS
National Animal Health Monitoring System
NAICS
North American Industry Classification System
NAPAP
National Acid Precipitation and Assessment Program
NARR
North American Regional Reanalysis Product
NAS
National Academies of Sciences, Engineering, and Medicine
NASA
National Aeronautics and Space Administration
NASF
National Association of State Foresters
NASS
USDA's National Agriculture Statistics Service
NC
No change
NCASI
National Council of Air and Stream Improvement
NCV
Net calorific value
ND
No data
NE
Not estimated
NEH
National Engineering Handbook
NEI
National Emissions Inventory
NEMA
National Electrical Manufacturers Association
NEMS
National Energy Modeling System
NESHAP
National Emission Standards for Hazardous Air Pollutants
NEU
Non-Energy Use
NEV
Neighborhood Electric Vehicle
NFs
Nitrogen trifluoride
NFI
National forest inventory
NGL
Natural gas liquids
NIR
National Inventory Report
NLA
National Lime Association
NLCD
National Land Cover Dataset
NMOC
Non-methane organic compounds
NMVOC
Non-methane volatile organic compound
NMOG
Non-methane organic gas
A-515
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NO
Nitric oxide
NO
Not occurring
no2
Nitrogen dioxide
NOx
Nitrogen oxides
NOAA
National Oceanic and Atmospheric Administration
NOF
Not on feed
NPDES
National Pollutant Discharge Elimination System
NPP
Net primary productivity
NPRA
National Petroleum and Refiners Association
NRBP
Northeast Regional Biomass Program
NRC
National Research Council
NRCS
Natural Resources Conservation Service
NREL
National Renewable Energy Laboratory
NRI
National Resources Inventory
NSCEP
National Service Center for Environmental Publications
NSCR
Non-selective catalytic reduction
NSPS
New source performance standards
NWS
National Weather Service
OAG
Official Airline Guide
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
OEM
Original equipment manufacturers
OGJ
Oil & Gas Journal
OGOR
Oil and Gas Operations Reports
OH
Hydroxyl radical
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
OVS
Offset verification statement
PADUS
Protected Areas Database of the United States
PAH
Polycyclic aromatic hydrocarbons
PCA
Portland Cement Association
PCC
Precipitate calcium carbonate
PDF
Probability Density Function
PECVD
Plasma enhanced chemical vapor deposition
PET
Polyethylene terephthalate
PET
Potential evapotranspiration
PEVM
PFC Emissions Vintage Model
PFC
Perfluorocarbon
PFPE
Perfluoropolyether
PHEV
Plug-in hybrid vehicles
PHMSA
Pipeline and Hazardous Materials Safety Administration
PI
Productivity index
PLS
Pregnant liquor solution
POTW
Publicly Owned Treatment Works
ppbv
Parts per billion (109) by volume
PPm
Parts per million
ppmv
Parts per million (106) by volume
pptv
Parts per trillion (1012) by volume
PRCI
Pipeline Research Council International
PRP
Pasture/Range/Paddock
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PS
Polystyrene
PSU
Primary Sample Unit
PU
Polyurethane
PVC
Polyvinyl chloride
PV
Photovoltaic
QA/QC
Quality Assurance and Quality Control
QBtu
Quadrillion Btu
R&D
Research and Development
RECs
Reduced Emissions Completions
RCRA
Resource Conservation and Recovery Act
RFA
Renewable Fuels Association
RFS
Renewable Fuel Standard
RMA
Rubber Manufacturers' Association
RPA
Resources Planning Act
RTO
Regression-through-the-origin
SAE
Society of Automotive Engineers
SAGE
System for assessing Aviation's Global Emissions
SAIC
Science Applications International Corporation
SAN
Styrene Acrylonitrile
SAR
IPCC Second Assessment Report
SCR
Selective catalytic reduction
SCSE
South central and southeastern coastal
SDR
Steel dust recycling
SEC
Securities and Exchange Commission
SEMI
Semiconductor Equipment and Materials Industry
sf6
Sulfur hexafluoride
SIA
Semiconductor Industry Association
SiC
Silicon carbide
SICAS
Semiconductor International Capacity Statistics
SNAP
Significant New Alternative Policy Program
SNG
Synthetic natural gas
S02
Sulfur dioxide
SOC
Soil Organic Carbon
SOG
State of Garbage survey
SOHIO
Standard Oil Company of Ohio
SSURGO
Soil Survey Geographic Database
STMC
Scrap Tire Management Council
SULEV
Super Ultra Low Emissions Vehicle
SWANA
Solid Waste Association of North America
SWDS
Solid waste disposal sites
SWICS
Solid Waste Industry for Climate Solutions
TA
Treated anaerobically (wastewater)
TAM
Typical animal mass
TAME
Tertiary amyl methyl ether
TAR
IPCC Third Assessment Report
TBtu
Trillion Btu
TDN
Total digestible nutrients
TEDB
Transportation Energy Data Book
TFI
The Fertilizer Institute
TIGER
Topological^ Integrated Geographic Encoding and Referencing survey
TJ
Terajoule
TLEV
Traditional low emissions vehicle
TMLA
Total Manufactured Layer Area
TOW
Total organics in wastewater
TPO
Timber Product Output
TRI
Toxic Release Inventory
A-517
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TSDF
Hazardous waste treatment, storage, and disposal facility
TTB
Tax and Trade Bureau
TVA
Tennessee Valley Authority
UAN
Urea ammonium nitrate
UDI
Utility Data Institute
UFORE
U.S. Forest Service's Urban Forest Effects model
UG
Underground (coal mining)
U.S.
United States
U.S. ITC
United States International Trade Commission
UEP
United Egg Producers
ULEV
Ultra low emission vehicle
UNEP
United Nations Environmental Programme
UNFCCC
United Nations Framework Convention on Climate Change
USAA
U.S. Aluminum Association
USAF
United States Air Force
USDA
United States Department of Agriculture
USFS
United States Forest Service
USGS
United States Geological Survey
USITC
U.S. International Trade Commission
VAIP
EPA's Voluntary Aluminum Industrial Partnership
VAM
Ventilation air methane
VKT
Vehicle kilometers traveled
VMT
Vehicle miles traveled
VOCs
Volatile organic compounds
VS
Volatile solids
WBJ
Waste Business Journal
WEF
Water Environment Federation
WERF
Water Environment Research Federation
WFF
World Fab Forecast (previously WFW, World Fab Watch)
WGC
World Gas Conference
WIP
Waste-in-place
WMO
World Meteorological Organization
WMS
Waste management systems
WRRF
Water resource recovery facilities
WTE
Waste-to-energy
WW
Wastewater
WWTP
Wastewater treatment plant
ZEVs
Zero emissions vehicles
6.7. Chemical Formulas
Table A-251: Guide to Chemical Formulas
Symbol
Name
Al
Aluminum
AI2O3
Aluminum oxide
Br
Bromine
C
Carbon
ch4
Methane
c2h6
Ethane
C3Hs
Propane
cf4
Perfluoromethane
c2f6
Perfluoroethane, hexafluoroethane
C-C3F6
Perfluorocyclopropane
CsFs
Perfluoropropane
C4F6
Hexafluoro-l,3-butadiene
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c-C4Fs
Perfluorocyclobutane
C4F10
Perfluorobutane
c-CsFs
Perfluorocyclopentene
C5F12
Perfluoropentane
C6F14
Perfluorohexane
CF3I
Trifluoroiodomethane
CFCI3
Trichlorofluoromethane (CFC-11)
CF2CI2
Dichlorodifluoromethane (CFC-12)
CF3CI
Chlorotrifluoromethane (CFC-13)
C2F3CI3
Trichlorotrifluoroethane (CFC-113)*
CCI3CF3
CFC-113a*
C2F4CI2
Dichlorotetrafluoroethane (CFC-114)
C2F5CI
Chloropentafluoroethane (CFC-115)
CHCI2F
HCFC-21
CHF2CI
Chlorodifluoromethane (HCFC-22)
C2F3HCI2
HCFC-123
C2F4HCI
HCFC-124
C2FH3CI2
HCFC-141b
C2H3F2CI
HCFC-142b
CF3CF2CHCI2
HCFC-225ca
CCIF2CF2CHCIF
HCFC-225cb
CCI4
Carbon tetrachloride
CHCICCI2
Trichloroethylene
CCI2CCI2
Perchloroethylene, tetrachloroethene
CH3CI
Methylchloride
CH3CCI3
Methylchloroform
CH2CI2
Methylenechloride
CHCI3
Chloroform, trichloromethane
CHF3
HFC-23
CH2F2
HFC-32
CH3F
HFC-41
C2HF5
HFC-125
C2H2F4
HFC-134
CH2FCF3
HFC-134a
C2H3F3
HFC-143*
C2H3F3
HFC-143a*
CH2FCH2F
HFC-152*
C2H4F2
HFC-152a*
CH3CH2F
HFC-161
C3HF7
HFC-227ea
CF3CF2CH2F
HFC-236cb
CF3CHFCHF2
HFC-236ea
C3H2F6
HFC-236fa
C3H3F5
HFC-245ca
CHF2CH2CF3
HFC-245fa
CF3CH2CF2CH3
HFC-365mfc
c5H2F10
HFC-43-10mee
CF30CHF2
HFE-125
CF2HOCF2H
HFE-134
CH30CF3
HFE-143a
CF3CHFOCF3
HFE-227ea
CF3CHCIOCHF2
HCFE-235da2
CF3CHFOCHF2
HFE-236ea2
CF3CH20CF3
HFE-236fa
CF3CF20CH3
HFE-245cb2
CHF2CH20CF3
HFE-245fal
A-519
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CF3CH20CHF2
HFE-245fa2
CHF2CF20CH3
HFE-254cb2
CF3CH20CH3
HFE-263fb2
CF3CF20CF2CHF2
HFE-329mcc2
CF3CF20CH2CF3
HFE-338mcf2
CF3CF2CF20CH3
HFE-347mcc3
CF3CF20CH2CHF2
HFE-347mcf2
CF3CHFCF20CH3
HFE-356mec3
CHF2CF2CF20CH3
HFE-356pcc3
CHF2CF20CH2CHF2
HFE-356pcf2
CHF2CF2CH20CHF2
HFE-356pcf3
CF3CF2CH20CH3
HFE-365mcf3
CHF2CF20CH2CH3
HFE-374pcf2
C4F90CH3
HFE-7100
C4F90C2H5
HFE-7200
CH2CFCF3
HFO-1234yf
CHFCHCFs
HFO-1234ze(E)
CF3CHCHCF3
HFO-1336mzz(Z)
C3H2CIF3
HCFO-1233zd(E)
CHF20CF20C2F40CHF2
H-Galden 1040x
CHF20CF20CHF2
HG-10
CHF20CF2CF20CHF2
HG-01
CH30CH3
Dimethyl ether
CH2Br2
Dibromomethane
CH2BrCI
Dibromochloromethane
CHBr3
Tribromomethane
CHBrF2
Bromodifluoromethane
CH3Br
Methylbromide
CF2BrCI
Bromodichloromethane (Halon 1211)
CF3Br(CBrF3)
Bromotrifluoromethane (Halon 1301)
CF3I
FIC-1311
CO
Carbon monoxide
C02
Carbon dioxide
CaC03
Calcium carbonate, Limestone
CaMg(C03)2
Dolomite
CaO
Calcium oxide, Lime
CI
atomic Chlorine
F
Fluorine
Fe
Iron
Fe203
Ferric oxide
FeSi
Ferrosilicon
GaAs
Gallium arsenide
H, H2
atomic Hydrogen, molecular Hydrogen
h2o
Water
H2O2
Hydrogen peroxide
OH
Hydroxyl
N, N2
atomic Nitrogen, molecular Nitrogen
nh3
Ammonia
nh4+
Ammonium ion
HNO3
Nitric acid
MgO
Magnesium oxide
nf3
Nitrogen trifluoride
n2o
Nitrous oxide
NO
Nitric oxide
N02
Nitrogen dioxide
NO3
Nitrate radical
A-520 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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NOx
Nitrogen oxides
Na
Sodium
Na2C03
Sodium carbonate, soda ash
Na3AIF6
Synthetic cryolite
0, 02
atomic Oxygen, molecular Oxygen
03
Ozone
S
atomic Sulfur
H2SO4
Sulfuric acid
sf6
Sulfur hexafluoride
SF5CF3
Trifluoromethylsulphur pentafluoride
S02
Sulfur dioxide
Si
Silicon
SiC
Silicon carbide
Si02
Quartz
* Distinct isomers.
A-521
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References
EIA (2020) Monthly Energy Review, November 2020. Energy Information Administration, U.S. Department of Energy,
Washington, DC. DOE/EIA-0035(2019/11). November 2020.
EIA (2007) Emissions of Greenhouse Gases in the United States 2006, Draft Report. Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE-EIA-0573 (2006).
EPA (2003) E-mail correspondence. Air pollutant data. Office of Air Pollution to the Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency (EPA). December 22, 2003.
EIA (1993) State Energy Data Report 1992, DOE/EIA-O214(93), Energy Information Administration, U.S. Department of
Energy. Washington, DC. December.
EPA (2020) "1970-2019 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory (NEI)
Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Last Modified April 2020. Available
online at: .
EPA (2003) E-mail correspondence. Air pollutant data. Office of Air Pollution to the Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency (EPA). December 22, 2003.
IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change. [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen,
J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA, 1535 pp.
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis,
K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United Kingdom 996 pp.
IPCC (1996) Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change,
J.T.Houghton, L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell (eds.). Cambridge University
Press. Cambridge, United Kingdom.
A-522 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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ANNEX 7 Uncertainty
The annual U.S. Inventory presents the best effort to produce emission estimates for greenhouse gas source and
sink categories in the United States. These estimates were generated according to the UNFCCC reporting guidelines,
following the recommendations set forth in the 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC
2006). This Annex provides an overview of the overall uncertainty analysis conducted to support the U.S. Inventory,
including the sources of uncertainty characterized throughout the Inventory associated with various source categories
(including emissions and sinks), and the methods used to collect, quantify, and present this uncertainty information. An
Addendum to Annex 7 is provided separately which includes additional information related to the uncertainty
characteristics of input variables used in the development of the overall uncertainty estimates reported in Section 1.7 of
the Inventory report.
7.1. Overview
The uncertainty analysis conducted in support of the Inventory (1) determines the quantitative uncertainty
associated with the emission source and sink estimates presented in the main body of this report, (2) evaluates the
relative contribution of the input parameters to the uncertainty associated with each source or sink category estimate
and in the overall inventory and (3) estimates the uncertainty in the overall emissions for the latest year, the base year
and in the emissions trend. Note, overall uncertainty estimates in the Inventory capture quantifiable uncertainties in
the input activity and emission factors data, but do not account for the potential of additional sources of uncertainty
such as modeling uncertainties, measurement errors, and misreporting or misclassification. Thus, the U.S. Inventory
uncertainty analysis helps inform and prioritize improvements for source and sink categories estimation process which
are discussed in the "Planned Improvements" sections of each source or sink category's discussion within the main body
of the report. For each source or sink category, the uncertainty analysis highlights opportunities for changes to data
measurement, data collection, and calculation methodologies to reduce uncertainties.
For some category estimates, such as C02 emissions from energy-related combustion activities, the impact of
uncertainties on overall emission estimates is relatively small. For some other limited categories of emissions,
uncertainties could have a larger impact on the estimates presented (i.e., storage factors of non-energy uses of fossil
fuels). In all source and sink category chapters, the inventory emission estimates include "Uncertainty and Time-Series
Consistency" sections that consider both quantitative and qualitative assessments of uncertainty, considering factors
consistent with good practices noted in Volume 1, Chapter 3 of the 2006 IPCC Guidelines (e.g., completeness of data,
representativeness of data and models, sampling errors, measurement errors). The two major types of uncertainty
associated with these emission estimates are (1) model uncertainty, which arises when the emission and/or removal
estimation models used in developing the Inventory estimates do not fully and accurately characterize the respective
emission and/or removal processes (due to a lack of technical details or other resources), and (2) parameter uncertainty,
which arises due to potential bias or a lack of accurate, complete, representative, or precise input data such as emission
factors and activity data and inherent variability.
The uncertainty associated with emission (or removal) estimation models can be partially analyzed by
comparing the model emission (or removal) results with those of other models developed to characterize the same
emission (or removal) process, after taking into account differences in their conceptual framework, capabilities, data,
and underlying assumptions. However, in many cases it would be very difficult—if not impossible—to use this approach
to quantify the model uncertainty associated with the emission estimates in this report, primarily because most
categories only have a single model that has been developed to estimate emissions. Therefore, model uncertainty was
not quantified in this report. Nonetheless, it has been discussed qualitatively, where appropriate, along with the
individual source or sink category description and inventory estimation methodology.
Parameter uncertainty encompasses several causes such as lack of completeness, lack of data or representative
data, sampling error, random or systematic measurement error, or misreporting or misclassification. Uncertainties
associated with input emission parameters have been quantified for all of the emission sources and sinks included in the
U.S. Inventory totals. Given the very low emissions for these source categories, uncertainty estimates were not derived.
A-523
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7.2. Methodology and Results
The United States has developed both a quality assurance and quality control (QA/QC) and uncertainty
management plan (EPA 2002). Like the QA/QC plan, the uncertainty management plan is part of a continually evolving
process. The uncertainty management plan provides for a quantitative assessment of the Inventory analysis itself,
thereby contributing to continuing efforts to understand both what causes uncertainty and how to improve Inventory
and accuracy Although the plan provides both general and specific guidelines for implementing a quantitative
uncertainty analysis, its components are intended to evolve over time, consistent with the inventory estimation process.
The U.S. plan includes procedures and guidelines, and forms and templates, for developing quantitative assessments of
uncertainty in the national Inventory estimates (EPA 2002). For the 1990 through 2019 Inventory, EPA has used the
uncertainty management plan as well as the methodology presented in the 2006IPCC Guidelines.
The 2006 IPCC Guidelines recommends two methods—Approach 1 and Approach 2—for developing
quantitative estimates of uncertainty associated with individual categories and the overall Inventory estimates. The
United States is continuing efforts to develop quantitative estimates of uncertainty for all source categories using
Approach 2. In following the UNFCCC requirement under Article 4.1, emissions from International Bunker Fuels, Wood
Biomass and Biofuel Consumption, and Indirect Greenhouse Gas Emissions are not included in the total emissions
estimated for the U.S. Inventory; therefore, no quantitative uncertainty estimates have been developed for these
categories.164 C02 Emissions from Biomass and Biofuel Consumption are accounted for implicitly in the Land Use, Land-
Use Change and Forestry (LULUCF) chapter through the calculation of changes in carbon stocks. The Energy sector
provides an estimate of C02 emissions from Biomass and Biofuel Consumption as a memo item for informational
purposes, consistent with the UNFCCC reporting requirements.
Approach 1 and Approach 2 Methods
The Approach 1 method for estimating uncertainty is based on the propagation of errors, as shown in Eq. 3.1 of
the 2006 IPCC Guidelines. This equation combines the random component of uncertainty associated with the activity
data and the emission (or the other) factors. Inherent in employing the Approach 1 method are the assumptions that, for
each source and sink category, (i) both the uncertainties in the activity data and the emission factor values are
approximately normally distributed, (ii) the coefficient of variation (i.e., the ratio of the standard deviation to the mean)
associated with each input variable is less than 30 percent, and (iii) the input variables within and across sub- source
categories are not correlated (i.e., value of each variable is independent of the values of other variables).
The Approach 2 method is preferred if (i) the uncertainty associated with the input variables is large (i.e., >30
percent), (ii) the distributions of uncertainties in the underlying the input variables are not normal (e.g., non-gaussian),
(iii) the estimates of uncertainty associated with the input variables are correlated, and/or if (iv) a complex estimation
methodology and/or several input variables are used to characterize the emission (or removal) process. Due to the input
parameters and estimation methodologies used in the Inventory, the uncertainties are assessed using the Approach 2
method for all categories where sufficient and reliable data are available to characterize the uncertainty of the input
variables.
The Approach 2 method employs the Monte Carlo Stochastic Simulation technique (also referred to as the
Monte Carlo method). Under this method, emission (or removal) estimates for a particular source (or sink) category are
estimated by randomly selecting values of emission factors, activity data, and other estimation parameters according to
their individual Probability Density Functions (PDFs). This process is repeated many times using computer software, in
order to build up the probability density function, which is then used to estimate the final uncertainty values of the
overall emission (or removal) estimates for that source (or sink). For most categories, the Monte Carlo approach is
implemented using commercially available simulation software such as Palisade's @RISK Microsoft Excel add-in.
Characterization of Uncertainty in Input Variables
Both Approach 1 and Approach 2 uncertainty analyses require that all the input variables have defined PDFs. In
the absence of sufficient data measurements, data samples, or expert judgments that determined otherwise, the PDFs
164 However, because the input variables that determine the emissions from the Fossil Fuel Combustion and the International
Bunker Fuels source categories are correlated, uncertainty associated with the activity variables in the International Bunker
Fuels was taken into account in estimating the uncertainty associated with the Fossil Fuel Combustion.
A-524 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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incorporated in the current source or sink category uncertainty analyses were limited to normal, lognormal, uniform,
triangular, pert, and beta distributions. The choice among these six PDFs depended largely on the observed or measured
data and expert judgment. If no additional uncertainty information is available than the previous year's Inventory
uncertainty data is used. Input variables with asymmetrical PDFs shift the overall output which can lead to asymmetrical
bounds for a source (or sink) category and in turn, for the overall Inventory uncertainty analysis.
Individual Source and Sink Category Inventory Uncertainty Estimates
The body of this report provides an overview of the input parameters and sources of uncertainty for each
source and sink category. Table A-252 summarizes results based on assessments of source and sink category-level
uncertainty. The table presents base year (1990) and current year (2019) emissions for each source and sink category.
The combined uncertainty (at the 95 percent confidence interval) for each source and category is expressed as the
percentage above and below the total 2019 emissions estimated for each source and sink category. Uncertainty in the
trend of each source and sink category is described subsequently in this Appendix.
Table A-252: Summary Results of Source and Sink Category Uncertainty Analyses
Base Year
Source or Sink Category
Emissions3
2019 Emissions'1
2019 Uncertainty11

MMT CO? Eq.
MMT CO? Eq.
Lower
Bound
Upper
Bound
co2
5,113.5
5,255.8
-2%
4%
Fossil Fuel Combustion
4,731.5
4,856.7
-2%
4%
Non-Energy Use of Fuels
112.8
128.8
-37%
45%
Iron and Steel Production & Metallurgical Coke
104.7
41.3
-19%
19%
Cement Production
33.5
40.9
-6%
6%
Petroleum Systems
9.7
47.3
-27%
41%
Natural Gas Systems
32.0
37.2
-16%
19%
Petrochemical Production
21.6
30.8
-6%
6%
Ammonia Production
13.0
12.3
-11%
11%
Lime Production
11.7
12.1
-2%
2%
Incineration of Waste
8.1
11.5
-25%
27%
Other Process Uses of Carbonates
6.3
7.5
-12%
15%
Urea Fertilization
2.4
5.3
-43%
3%
Carbon Dioxide Consumption
1.5
4.9
-5%
5%
Urea Consumption for Non-Agricultural Purposes
3.8
6.2
-13%
14%
Liming
4.7
2.4
-111%
88%
Ferroalloy Production
2.2
1.6
-12%
12%
Soda Ash Production
1.4
1.8
-9%
8%
Titanium Dioxide Production
1.2
1.5
-12%
13%
Aluminum Production
6.8
1.9
-2%
2%
Glass Production
1.5
1.3
-4%
4%
Zinc Production
0.6
1.0
-19%
21%
Phosphoric Acid Production
1.5
0.9
-19%
21%
Lead Production
0.5
0.5
-14%
16%
Carbide Production and Consumption
0.4
0.2
-9%
9%
Abandoned Oil and Gas Wells
+
+
-83%
219%
Magnesium Production and Processing
+
+
-4%
4%
Wood Biomass, Ethanol, and Biodiesel Consumptionc
219.4
316.2
NE
NE
International Bunker Fuels'1
103.5
116.1
NE
NE
ch4
776.9
659.7
-8%
11%
Enteric Fermentation
164.7
178.6
-11%
18%
Natural Gas Systems
186.9
157.6
-15%
14%
Landfills
176.6
114.5
-22%
22%
Manure Management
37.1
62.4
-18%
20%
Coal Mining
96.5
47.4
-9%
20%
Petroleum Systems
48.9
39.1
-24%
29%
Wastewater T reatment
20.2
18.4
-28%
38%
Rice Cultivation
16.0
15.1
-75%
149%
A-525
-------
Stationary Combustion
8.6
8.7
-36%
133%
Abandoned Oil and Gas Wells
6.8
6.6
-83%
219%
Abandoned Underground Coal Mines
7.2
5.9
-22%
19%
Mobile Combustion
6.4
2.4
-2%
46%
Composting
0.4
2.3
-50%
50%
Field Burning of Agricultural Residues
0.4
0.4
-18%
18%
Petrochemical Production
0.2
0.3
-57%
47%
Ferroalloy Production
+
+
-12%
12%
Carbide Production and Consumption
+
+
-9%
9%
Iron and Steel Production & Metallurgical Coke
+
+
-19%
19%
Incineration of Waste
+
+
NE
NE
Anaerobic Digestion at Biogas Facilities
+
0.2
-50%
50%
International Bunker Fuels'1
0.2
0.1
NE
NE
n2o
452.7
457.1
-20%
31%
Agricultural Soil Management
315.9
344.6
-27%
26%
Stationary Combustion
25.1
24.9
-25%
51%
Manure Management
14.0
19.6
-16%
24%
Mobile Combustion
44.7
18.0
-9%
19%
AdipicAcid Production
15.2
5.3
-5%
5%
Nitric Acid Production
12.1
10.0
-5%
5%
Wastewater T reatment
18.7
26.4
-37%
209%
N20 from Product Uses
4.2
4.2
-24%
24%
Composting
0.3
2.0
-50%
50%
Caprolactam, Glyoxal, and Glyoxylic Acid Production
1.7
1.4
-31%
32%
Incineration of Waste
0.5
0.3
-50%
325%
Electronics Industry
+
0.2
-9%
9%
Field Burning of Agricultural Residues
0.2
0.2
-17%
17%
Petroleum Systems
+
+
-27%
41%
Natural Gas Systems
+
+
-16%
19%
International Bunker Fuels'1
0.9
1.0
NE
NE
HFCs, PFCs, SF6 and NF3
99.7
185.6
-3%
12%
Substitution of Ozone Depleting Substances
0.2
170.6
-4%
13%
Electronics lndustrye
3.6
4.3
-6%
6%
Electrical Transmission and Distribution
23.2
4.2
-16%
18%
HCFC-22 Production
46.1
3.7
-7%
10%
Aluminum Production
21.5
1.8
-7%
7%
Magnesium Production and Processing
5.2
1.0
-8%
8%
Total Emissions'
6,442.7
6,558.3
-2%
4%
LULUCF Emissions5
7.9
23.5
-14%
19%
LULUCF Carbon Stock Change Fluxh
(908.7)
(812.7)
34%
-18%
LULUCF Sector Net Total1
(900.8)
(789.2)
35%
-19%
Net Emissions (Sources and Sinks)'
5,541.9
5,769.0
-5%
5%
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding
emissions for which uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with
LULUCF.
+ Does not exceed 0.05 MMT C02 Eq. or 0.5 percent.
NE (Not Estimated)
a Base Year is 1990 for all sources.
bThe uncertainty estimates correspond to a 95 percent confidence interval, with the lower bound corresponding to 2.5th
percentile and the upper bound corresponding to 97.5th percentile.
c Emissions from Wood Biomass and Biofuel Consumption are not included in the energy sector totals.
d Emissions from International Bunker Fuels are not included in the totals.
e This source category's estimate for 2019 excludes 0.029 MMT C02 Eq. of HTF emissions included in electronics totals from
photovoltaic manufacturing, as uncertainties associated with those sources were not assessed. Hence, for this source
category, the emissions reported in this table do not match the emission estimates presented in the Industrial Processes and
Product Use chapter of the Inventory,
f Totals exclude emissions for which uncertainty was not quantified.
A-526 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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g LULUCF emissions include the CH4 and N20 emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained
Organic Soils, Grassland Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to
Coastal Wetlands; and N20 emissions from Forest Soils and Settlement Soils.
h LULUCF Carbon Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land,
Land Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland,
Land Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining
Settlements, and Land Converted to Settlements. Since the resulting flux is negative the signs of the resulting lower and upper
bounds are reversed.
'The LULUCF Sector Net Total is the net sum of all CH4 and N20 emissions to the atmosphere plus net carbon stock changes.
Overall (Aggregate) Inventory Level Uncertainty Estimates
The overall level uncertainty estimate for the U.S. Inventory was developed using the IPCC Approach 2
uncertainty estimation methodology for 1990 and 2019. The overall Inventory uncertainty estimates were estimated by
combing the Monte Carlo simulation output data for each emission source or sink category (as described above) across
all sources and categories as a function of gas. If such detailed output data were not available for a particular source or
sink category, individual PDFs were assigned based on the most detailed data available from the category-specific
quantitative uncertainty analysis. The overall Inventory uncertainty was then derived through the resulting PDF of the
combined emissions data.
For select categories such as composting, several LULUCF source categories, and parts of Agricultural Soil
Management source categories, Approach 1 uncertainty results were used in the overall uncertainty analysis. However,
for all other emission sources Approach 2 uncertainty results were used in the overall uncertainty estimation.
The overall uncertainty model results indicate that the 1990 U.S. greenhouse gas emissions are estimated to be
within the range of approximately 6,311.7 to 6,748.8 MMT C02 Eq., reflecting a relative 95 percent confidence interval
uncertainty range of-2 percent to 5 percent with respect to the total U.S. greenhouse gas emission estimate of
approximately 6,442.7 MMT C02 Eq. The uncertainty interval associated with total C02 emissions, ranges from -2 percent
to 5 percent of total C02 emissions estimated. The results indicate that the uncertainty associated with the inventory
estimate of the total CH4 emissions ranges from -9 percent to 11 percent, uncertainty associated with the total inventory
N20 emission estimate ranges from -19 percent to 28 percent, and uncertainty associated with fluorinated greenhouse
gas (F-GHG) emissions ranges from -9 percent to +13 percent. When the LULUCF sector is included in the analysis, the
uncertainty is estimated to be -6 to +6 percent of Net Emissions (sources and sinks) in 1990. The uncertainties presented
are quantifiable uncertainties in the input activity and emission factors data, not uncertainties in the models, data
representativeness, measurement errors, or misreporting or misclassification of data.
Table A-253: Quantitative Uncertainty Assessment of Overall National Inventory Emissions for 1990 (MMT CO2
Eq. and Percent)	
1990
Emission
Estimate Uncertainty Range Relative to Emission Estimate3
Gas	(MMTC02
Eq.)	(MMTC02Eq.)	(%)


Lower
Upper
Lower
Upper




Boundc
Boundc
Bound
Bound


co2
5,113.5
5,008.6
5,349.7
-2%
5%
5,177.3
88.5
CH4d
776.9
710.7
863.0
-9%
11%
785.8
38.7
N2Od
452.7
368.3
581.4
-19%
28%
461.0
54.8
PFCs, HFCs, SF6, and NF3d
99.7
90.2
112.2
-9%
13%
100.3
5.6
Total Emissions
6,442.7
6,311.7
6,748.8
-2%
5%
6,524.4
111.0
LULUCF Emissions6
7.9
6.0
10.0
-24%
26%
8.0
1.0
LULUCF Carbon Stock Change Fluxf
(908.7)
(1,221.6)
(741.6)
34%
-18%
(982.4)
122.3
LULUCF Sector Net Totals
(900.8)
(1,213.8)
(733.6)
35%
-19%
(974.4)
122.3
Net Emissions (Sources and Sinks)
5,541.9
5,232.4
5,877.3
-6%
6%
5,550.0
164.7
Standard
Meanb Deviation'5
(MMT C02 Eq.)
A-527
-------
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding
emissions for which uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with
LULUCF.
+ Does not exceed 0.5 percent.
a The lower and upper bounds for emission estimates correspond to a 95 percent confidence interval, with the lower bound
corresponding to 2.5th percentile and the upper bound corresponding to 97.5th percentile.
b Mean value indicates the arithmetic average of the simulated emission estimates; standard deviation indicates the extent of
deviation of the simulated values from the mean.
c The lower and upper bound emission estimates for the sub-source categories do not sum to total emissions because the low
and high estimates for total emissions were calculated separately through simulations.
d The overall uncertainty estimates did not take into account the uncertainty in the GWP values for CH4, N20, and high GWP
gases used in the inventory emission calculations for 1990.
e LULUCF emissions include the CH4 and N20 emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained
Organic Soils, Grassland Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to
Coastal Wetlands; and N20 emissions from Forest Soils and Settlement Soils.
f LULUCF Carbon Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land,
Land Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland,
Land Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining
Settlements, and Land Converted to Settlements. Since the resulting flux is negative the signs of the resulting lower and upper
bounds are reversed.
g The LULUCF Sector Net Total is the net sum of all CH4 and N20 emissions to the atmosphere plus net carbon stock changes.
The overall uncertainty model results indicate that the 2019 U.S. greenhouse gas emissions are estimated to be
within the range of approximately 6,417.7 to 6,845.6 MMT C02 Eq., reflecting a relative 95 percent confidence interval
uncertainty range of-5 percent to 5 percent with respect to the total U.S. greenhouse gas emission estimate of
approximately 6,558.3 MMT C02 Eq. The uncertainty interval associated with total C02 emissions, which constitute about
80 percent of the total U.S. greenhouse gas emissions in 2019, ranges from -2 percent to 4 percent of total C02 emissions
estimated. The results indicate that the uncertainty associated with the inventory estimate of the total CH4 emissions
ranges from -8 percent to 11 percent, uncertainty associated with the total inventory N20 emission estimate ranges from
-20 percent to 31 percent, and uncertainty associated with fluorinated greenhouse gas (F-GHG) emissions ranges from -3
percent to 12 percent. When the LULUCF sector is included in the analysis, the uncertainty is estimated to be -5 to +5
percent of Net Emissions (sources and sinks) in 2019.
A summary of the overall quantitative uncertainty estimates is shown below.
Table A-254: Quantitative Uncertainty Assessment of Overall National Inventory Emissions for 2019 (MMT CO2
Eq. and Percent)	
2019
Emission	Standard
Estimate Uncertainty Range Relative to Emission Estimate3 Mean'5 Deviation'5
Gas	(MMTC02
Eq.)	(MMTC02Eq.)	(%)	(MMT C02 Eq.)


Lower
Upper
Lower
Upper




Boundc
Boundc
Bound
Bound


co2
5,255.8
5,129.9
5,461.8
-2%
4%
5,295.1
85.4
CH4d
659.7
608.3
732.5
-8%
11%
670.1
31.6
N2Od
457.1
367.5
598.2
-20%
31%
468.7
59.1
PFC, HFC, SFe, and NF3d
185.6
179.4
208.1
-3%
12%
193.1
7.4
Total Emissions
6,558.3
6,417.7
6,845.6
-2%
4%
6,627.0
108.9
LULUCF Emissions6
23.5
20.1
27.9
-14%
19%
23.9
2.0
LULUCF Carbon Stock Change Fluxf
(812.7)
(1,089.1)
(664.0)
34%
-18%
(878.2)
108.4
LULUCF Sector Net Totals
(789.2)
(1,064.9)
(640.0)
35%
-19%
(854.3)
108.4
Net Emissions (Sources and Sinks)
5,255.8
5,129.9
5,461.8
-2%
4%
5,295.1
85.4
A-528 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding
emissions for which uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with
LULUCF.
+ Does not exceed 0.5 percent.
a The lower and upper bounds for emission estimates correspond to a 95 percent confidence interval, with the lower bound
corresponding to 2.5th percentile and the upper bound corresponding to 97.5th percentile.
b Mean value indicates the arithmetic average of the simulated emission estimates; standard deviation indicates the extent of
deviation of the simulated values from the mean.
c The lower and upper bound emission estimates for the sub-source categories do not sum to total emissions because the low
and high estimates for total emissions were calculated separately through simulations.
d The overall uncertainty estimates did not take into account the uncertainty in the GWP values for CH4, N20, and high GWP
gases used in the inventory emission calculations for 2019.
e LULUCF emissions include the CH4 and N20 emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained
Organic Soils, Grassland Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to
Coastal Wetlands; and N20 emissions from Forest Soils and Settlement Soils.
f LULUCF Carbon Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land,
Land Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland,
Land Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining
Settlements, and Land Converted to Settlements. Since the resulting flux is negative the signs of the resulting lower and upper
bounds are reversed.
g The LULUCF Sector Net Total is the net sum of all CH4 and N20 emissions to the atmosphere plus net carbon stock changes.
Trend Uncertainty
In addition to the estimates of uncertainty associated with the current and base year emission estimates, this
Annex also presents the estimates of trend uncertainty. The 2006IPCC Guidelines define trend as the difference in
emissions between the base year (i.e., 1990) and the current year (i.e., 2019) Inventory estimates. However, for
purposes of understanding the concept of trend uncertainty, the emission trend is defined in this Inventory as the
percentage change in the emissions (or removal) estimated for the current year, relative to the emission (or removal)
estimated for the base year. The uncertainty associated with this emission trend is referred to as trend uncertainty.
Under the Approach 1 method, there are two types of uncertainty to consider when estimating the trend
uncertainty in an individual source or sink category. As described in the 2006 IPCC Guidelines, correlated (Type A)
uncertainties are estimated by comparing the change in emissions trend given a 1 percent change in both base (i.e.,
1990) and current emissions (i.e., 2019), while uncorrelated or random errors in the emissions trend (Type B) are
estimated by comparing the change in emissions trend given a 1 percent change in only the current year emissions.
When combined, both types of uncertainty capture the sensitivity in trend emission estimates to sources of uncertainty
that are correlated between the base and current year (Type A), as well as the random component of uncertainty in the
emission estimates (Type B).
Under the Approach 2 method, the trend uncertainty is estimated using the Monte Carlo Stochastic Simulation
technique. As described in the 2006 IPCC Guidelines, this Approach follows four steps. First, the PDFs for emission
factors, activity data, and other input estimation parameters are determined for both the current and base year. For
purposes of this Inventory, due to data limitations, for some categories where uncertainty assessments for 1990 are
undergoing updates for future reports but were not ready to incorporate for this submission, a simple approach has
been adopted, under which the base year source or sink category emissions are assumed to exhibit the same uncertainty
characteristics as the current year emissions (or removals). Source and sink category-specific PDFs for base year
estimates were developed using current year (i.e., 2019) uncertainty output data. These were adjusted to account for
differences in magnitude between the two years' inventory estimates. The second and third steps follow the Monte
Carlo approach described previously to calculate repeated emission estimates for each source and sink category in the
base and current years according to the input data PDFs. The overall Inventory trend uncertainty estimate was
developed by combining all source and sink category-specific trend uncertainty estimates. These trend uncertainty
estimates represent the 95 percent confidence interval of the estimated percent change in emissions between 1990 and
2019 and are shown in Table A-255.
A-529
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Table A-255: Quantitative Assessment of Trend Uncertainty (MMT CO2 Eq. and Percent)
Gas/Source
Base Year
Emissions3
2019
Emissions
Emissions
Trend
Trend Rangeb

(MMT CO
2 Eq).)
(%)
(%)





Lower
Bound
Upper
Bound
CO?
5,113.5
5,255.8
3%
-2%
7%
Fossil Fuel Combustion
4,731.5
4,856.7
3%
-2%
7%
Non-Energy Use of Fuels
112.8
128.8
14%
-34%
87%
Iron and Steel Production & Metallurgical Coke
104.7
41.3
-61%


Production



-70%
-50%
Cement Production
33.5
40.9
22%
8%
40%
Petroleum Systems
9.7
47.3
387%
125%
929%
Natural Gas Systems
32.0
37.2
16%
-20%
69%
Petrochemical Production
21.6
30.8
42%
31%
55%
Ammonia Production
13.0
12.3
-6%
-21%
14%
Lime Production
11.7
12.1
4%
1%
7%
Incineration of Waste
8.1
11.5
42%
-3%
108%
Other Process Uses of Carbonates
6.3
7.5
18%
0%
45%
Urea Fertilization
2.4
5.3
121%
27%
282%
Carbon Dioxide Consumption
1.5
4.9
231%
191%
279%
Urea Consumption for Non-Agricultural
3.8
6.2
64%


Purposes



34%
104%
Liming
4.7
2.4
-48%
-634%
473%
Ferroalloy Production
2.2
1.6
-26%
-37%
-13%
Soda Ash Production
1.4
1.8
25%
11%
41%
Titanium Dioxide Production
1.2
1.5
23%
3%
47%
Aluminum Production
6.8
1.9
-72%
-74%
-71%
Glass Production
1.5
1.3
-17%
-22%
-11%
Zinc Production
0.6
1.0
62%
23%
112%
Phosphoric Acid Production
1.5
0.9
-42%
-57%
-21%
Lead Production
0.5
0.5
5%
-15%
28%
Carbide Production and Consumption
0.4
0.2
-53%
-58%
-47%
Abandoned Oil and Gas Wells
+
+
3%
-1354%
1205%
Magnesium Production and Processing
+
+
-1%
-34%
101%
Wood Biomass and Biofuel Consumptionc
219.4
316.2
44%
NE
NE
International Bunker Fuels'1
103.5
116.1
12%
NE
NE
ch4
776.9
659.7
-15%
-26%
-3%
Enteric Fermentation
164.7
178.6
8%
-20%
47%
Natural Gas Systems
186.9
157.6
-16%
-30%
1%
Landfills
176.6
114.5
-35%
-54%
-7%
Manure Management
37.1
62.4
68%
6%
162%
Coal Mining
96.5
47.4
-51%
-63%
-34%
Petroleum Systems
48.9
39.1
-20%
-55%
43%
Wastewater T reatment
20.2
18.4
-9%
-42%
40%
Rice Cultivation
16.0
15.1
-6%
-944%
1051%
Stationary Combustion
8.6
8.7
1%
-65%
197%
Abandoned Oil and Gas Wells
6.8
6.6
-3%
-88%
644%
Abandoned Underground Coal Mines
7.2
5.9
-18%
-50%
36%
Mobile Combustion
6.4
2.4
-63%
-65%
-47%
Composting
0.4
2.3
495%
166%
1288%
Field Burning of Agricultural Residues
0.4
0.4
14%
-22%
67%
Petrochemical Production
0.2
0.3
51%
-92%
-72%
Ferroalloy Production
+
+
-34%
-44%
-22%
Carbide Production and Consumption
+
+
-67%
-71%
-62%
Iron and Steel Production & Metallurgical Coke
+
+
-64%


Production



-73%
-52%
A-530 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Incineration of Waste
+
+
-32%
NE
NE
Anerobic Digestion at Biogas Facilities
+
0.2
1126%
NE
NE
International Bunker Fuels'1
0.2
0.1
-46%
NE
NE
n2o
452.7
457.1
1%
-27%
43%
Agricultural Soil Management
315.9
344.6
9%
-32%
75%
Stationary Combustion
25.1
24.9
-1%
-45%
82%
Manure Management
14.0
19.6
40%
-7%
114%
Mobile Combustion
44.7
18.0
-60%
-68%
-40%
AdipicAcid Production
15.2
5.3
-65%
-67%
-63%
Nitric Acid Production
12.1
10.0
-18%
-23%
-12%
Wastewater T reatment
18.7
26.4
41%
-57%
307%
N20 from Product Uses
4.2
4.2
+%
-25%
27%
Composting
0.3
2.0
495%
170%
1229%
Caprolactam, Glyoxal, and Glyoxylic Acid
1.7
1.4
-18%


Production



-49%
33%
Incineration of Waste
0.5
0.3
-32%
-86%
224%
Electronics Industry
+
0.2
528%
452%
616%
Field Burning of Agricultural Residues
0.2
0.2
16%
-20%
68%
Petroleum Systems
+
+
179%
32%
498%
Natural Gas Systems
+
+
123%
54%
222%
International Bunker Fuels'1
0.9
1.0
21%
NE
NE
HFCs, PFCs, SF6, and NF3
99.7
185.6
86%
68%
119%
Substitution of Ozone Depleting Substances
0.2
170.6
74,989%
27,837%
783,953%
Electronics Industry
3.6
4.3
22%
12%
33%
Electrical Transmission and Distribution
23.2
4.2
-82%
-88%
-72%
HCFC-22 Production
46.1
3.7
-92%
-93%
-90%
Aluminum Production
21.5
1.8
-92%
-92%
-91%
Magnesium Production and Processing
5.2
1.0
-81%
-86%
-78%
Total Emissions'
6,442.7
6,558.3
2%
-3%
7%
LULUCF Emissions5
7.9
23.5
196%
124%
306%
LULUCF Carbon Stock Change Fluxh
(908.7)
(812.7)
-11%
-37%
27%
LULUCF Sector Net Total1
(900.8)
(789.2)
-12%
-38%
25%
Net Emissions (Sources and Sinks)'
5,541.9
5,769.0
4%
-3%
13%
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding
emissions for which uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with LULUCF.
+ Does not exceed 0.05 MMT C02 Eq. or 0.5 percent.
NE (Not Estimated)
a Base Year is 1990 for all sources.
bThe trend range represents a 95 percent confidence interval for the emission trend, with the lower bound corresponding to
2.5th percentile value and the upper bound corresponding to 97.5th percentile value.
c Emissions from Wood Biomass and Biofuel Consumption are not included specifically in the energy sector totals.
d Emissions from International Bunker Fuels are not included in the totals.
eThis source category's estimate for 2019 excludes 0.029 MMT C02 Eq. of HTF emissions, as uncertainties associated with those
sources were not assessed. Hence, for this source category, the emissions reported in this table do not match the emission
estimates presented in the Industrial Processes and Product Use chapter of the Inventory.
'Totals exclude emissions for which uncertainty was not quantified.
s LULUCF emissions include the CH4 and N20 emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained
Organic Soils, Grassland Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to
Coastal Wetlands; and N20 emissions from Forest Soils and Settlement Soils.
h LULUCF Carbon Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land,
Land Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland,
Land Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining Settlements,
and Land Converted to Settlements.
' The LULUCF Sector Net Total is the net sum of all CH4 and N20 emissions to the atmosphere plus net carbon stock changes.
A-531
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7.3.
Information on Uncertainty Analyses by Source and Sink Category
The quantitative uncertainty estimates associated with each emission and removal category are reported
within sectoral chapters of this Inventory following the discussions of inventory estimates and their estimation
methodology. To better understand the uncertainty analysis details, refer to the respective chapters and Uncertainty and
Time-Series Consistency sections in the body of this report. EPA provides additional documentation on uncertainty
information consistent with the guidance presented in Table 3.3 in Vol. 1, Chapter 3 of the 2006IPCC Guidelines for
National Greenhouse Gas Inventories (IPCC 2006) in an Uncertainty Addendum. Due to the number of detailed tables it is
not published with the Inventory but is available upon request. EPA plans to publish this in a more easily accessible
format with future reports (e.g., the 2022 or 2023 Inventory reports). All uncertainty estimates are reported relative to
the current Inventory estimates for the 95 percent confidence interval, unless otherwise specified.
7.4. Reducing Uncertainty and Planned Improvements
The U.S. has implemented many improvements over the last several years that have reduced uncertainties
across the source and sink categories. These improvements largely result from new data sources that provide more
accurate data and/or increased data coverage, as well as methodological improvements, as described below.
BoxA-4: Reducing Uncertainty
The 2006 IPCC Guidelines provides the following guidance for ways to reduce Inventory uncertainty and improve the
quality of an Inventory and its uncertainty estimates.
•	Improving conceptualization. Improving the inclusiveness of the structural assumptions chosen can reduce
uncertainties. An example is better treatment of seasonality effects that leads to more accurate annual
estimates of emissions or removals for the Agriculture, Land Use, Land Use Change and Forestry sector.
•	Improving models. Improving the model structure and parameterization can lead to better understanding
and characterization of the systematic and random errors, as well as reductions in these causes of
uncertainty.
•	Improving representativeness. This may involve stratification or other sampling strategies. For example,
continuous emissions monitoring systems (CEMS) can be used to reduce uncertainty for some sources and
gases as long as the representativeness is guaranteed. CEMS produces representative data at the facilities
where it is used, but in order to be representative of an entire source category, CEMS data must be available
for a random sample or an entire set of individual facilities that comprise the category. When using CEMS
both concentration and flow will vary, requiring simultaneous sampling of both attributes.
•	Incorporating excluded emission sources. Quantitative estimates for some of the sources and sinks of
greenhouse gas emissions, such as from some land-use activities, industrial processes, and parts of mobile
sources, could not be developed at this time either because data are incomplete or because methodologies
do not exist for estimating emissions from these source categories. See Annex 5 of this report for a
discussion of the sources of greenhouse gas emissions and sinks excluded from this report. Consistent with
IPCC good practice principles, EPA continues efforts to estimate emissions and sinks from excluded emission
and removal sources occurring in U.S. and developing uncertainty estimates for all source and sink
categories for which emissions and removals are estimated.
•	Collecting more measured data. Uncertainty associated with bias and random sampling error can be
reducing by increasing the sample size and filling in data gaps. This applies to both measurements and
surveys.
•	Using more precise measurement methods. Measurement error can be reduced by using more precise
measurement methods, avoiding simplifying assumption, and ensuring that measurement technologies are
appropriately used and calibrated.
•	Eliminating known risk of bias. This is achieved by ensuring instrumentation is properly positioned and
calibrated, models or other estimation procedures are appropriate and representative, and by applying
A-532 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
-------
expert judgements in a systematic way.
• Improving state of knowledge. Improve the understanding of categories and processes leading to emissions
and removals, which can help to discover and correct for problems in incompleteness. It is Good Practice to
continuously improve emissions and removal estimates based on new knowledge.
The following sections describe the ongoing and planned Inventory and Uncertainty analysis improvements in
the context of these specific areas.
Recent and Ongoing Improvements
To collect more measured data, improve representativeness, and use more precise measurement methods,
several source categories in the Inventory now use the U.S. EPA's Greenhouse Gas Reporting Program (GHGRP) data,
which improves Inventory emission (or sink) estimation methods by allowing the incorporation of country-specific data
rather than using default IPCC estimates. EPA's GHGRP relies on facility-level data reported from large facilities emitting
over 25,000 metric tons of C02 equivalent each year. The reported GHGRP data undergo a multi-step verification
process, including automated data checks to ensure consistency, comparison against expected ranged for similar
facilities and industries, and statistical analysis. See Annex 9 for more information on use of GHGRP data in the
Inventory.
In addition to improving Inventory input data and methodologies, the use of EPA's GHGRP data also reduces
uncertainty in select Inventory emission categories. For example, replacing highly uncertain emission factor estimates
with GHGRP data for the Coal Mining category reduced the 95 percent uncertainty bounds for methane emissions from
this category from -15 percent to 18 percent in the 1990 to 2011 inventory down to -9 percent to 20 percent in the
current (1990 to 2019) Inventory. Methane emission estimates from MSW landfills were also revised with GHGRP data,
which resulted in methodological and data quality improvements that also reduced the 95 percent uncertainty bounds
for this category compared to the prior use of default emission factors with larger assumed uncertainties.
Additional ongoing improvements to the U.S. Inventory uncertainty analyses for select categories will help to
eliminate known risk of bias, improve models, and advance the state of knowledge, which may lead to further Inventory
and uncertainty analysis improvements in other areas including improved conceptualization and data representativeness.
For example, the C02 uncertainty analysis for Natural Gas and Petroleum Systems was updated in the current (1990 to
2019) Inventory to account for uncertainties in sources of C02, rather than previous uncertainty estimates that were
based on methane emission uncertainties from these same source categories (see associated Memo165 on updating
uncertainty of C02 emissions from Natural Gas and Petroleum Systems). Finally, ongoing improvements include review of
documentation of source-specific input data and references, PDF distributions, and Monte Carlo analysis results through
the implementation of standardized source-specific uncertainty reporting and documentation templates. Ongoing
improvements to the overall Inventory Uncertainty Analysis documentation will additionally ensure consistency with
IPCC Good Practice and increase the transparency of the overall analysis.
Planned Improvements
EPA continuously seeks new knowledge to improve the Inventory emissions and removal estimates. With
available resources, planned future improvements to the Inventory and Uncertainty Analysis are prioritized by focusing
improvements on categories identified in the Key Category Analysis (Chapter 1.5), or by quantitatively comparing the
relative contributions of uncertainties from various input parameters (e.g., activity data and emission factors) to the total
uncertainty levels within a source or sink category. Quantifying the sensitivity of the overall Inventory uncertainty
bounds to the uncertainty within each source or sink category can also prioritize future Inventory updates.
As described in Chapter 1.5, Key Categories in the current (1990 to 2019) Inventory include (but are not limited
to) categories that fall under Fossil Fuel Combustion (Chapter 3.1), Petroleum and Natural Gas Systems (Chapter 3.6 and
3.7), Industrial Processes and Product Use (Chapter 3), and Agriculture (Chapter 4). Planned improvements for these key
categories largely include the incorporation of more accurate and/or representative input parameters. For example, as
described in Chapter 3.1, planned inventory improvements for emissions from fossil fuel combustion categories include
165 See: 
A-533
-------
efforts to assess the incorporation of more measured input activity data (e.g., GHGRP data, domestic marine activity,
U.S. Territory data) and other input parameters (e.g., updated gasoline carbon factors, emission factors for non-road
equipment, etc.). Similarly, Chapters 3.6 and 3.7 discuss plans to continue stakeholder engagement to assess the
potential for incorporating new input data (e.g., from peer-reviewed publications, industry studies, etc.), updating
methods for select sources (e.g., Offshore Production, unassigned high-emitters), or including new sources (e.g.,
anomalous leak events) within the Petroleum and Natural Gas System categories. Categories within the IPPU sector
(Chapter 4) also discuss plans to assess the future incorporation of additional facility-level GHGRP data, improve
emission models (e.g., for ozone depleting substance substitutes) and the methodological descriptions in the Inventory
report. Similar to other categories, planned improvements to Agricultural emissions from Manure Management and
Enteric Fermentation include the incorporation of new, more accurate and representative data, updates to emission
models and conceptualization (including moving to Tier 2 methods for all sources), as well as revised uncertainty
estimates to the account for recent updates. Details describing the planned improvements for these and nearly all other
individual source and sink categories are included in the category-specific Chapters of this report.
Implementation of these planned improvements will occur on an ongoing basis as new information becomes
available. Improvements are prioritized to make best use of available resources, including efforts to improve the
accuracy of emission factors, collect more detailed and representative activity data, as well as provide better estimates
of input parameter uncertainty. For example, further research is needed in some cases to improve the accuracy of
emission factors, including those currently applied to CH4 and N20 emissions from manure management. Lastly, for many
individual source categories, further research is also needed to characterize the PDFs of their input parameters more
accurately (e.g., emission factors and activity data). This might involve using measured or published statistics or
implementing a rigorous protocol to elicit expert judgment, if published or measured data are not available. Continued
efforts in these areas will reduce Inventory uncertainty and increase the completeness, accuracy, and transparency of
the category-specific and overall Inventory estimates.
Additional planned improvements for the overall Inventory uncertainty analysis include improving the
presentation of uncertainties in a format consistent with suggested tables in Volume 1, Chapter 3 of the 2006IPCC
Guidelines. As resources permit, in particular for key categories, improvements include reviewing and updating the
existing uncertainty models for the base year. This process would improve the base year and Trend uncertainty analyses
but may not eliminate every simplifying assumptions described above due to limited data availability in the base year.
A-534 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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ANNEX 8 QA/QC Procedures
8.1.	Background
The purpose of this annex is to describe the Quality Assurance/Quality Control (QA/QC) procedures and
information quality considerations that are used throughout the process of creating and compiling the Inventory of U.S.
Greenhouse Gas Emissions and Sinks. This includes the evaluation of the quality and relevance of data and models used
as inputs into the Inventory; proper management, incorporation, and aggregation of data; and review of the numbers
and estimates to ensure that they are as accurate and transparent as possible. Quality control—in the form of both good
practices (such as documentation procedures) and checks on whether good practices and procedures are being
followed—is applied at every stage of inventory development and document preparation. In addition, quality assurance
occurs at two stages—an expert review and a public review. While both phases can significantly contribute to the quality
of the Inventory, the public review phase is also essential for promoting the openness ofthe Inventory development
process and the transparency of the inventory data and methods. As described in respective source category text,
comments received from these reviews may also result in updates or changes to continue to improve inventory quality.
8.2.	Purpose
The Quality Assurance/Quality Control and Uncertainty Management Plan for the U.S. Greenhouse Gas
Inventory (QA/QC Management Plan) guides the process of ensuring the quality ofthe Inventory. The QA/QC
Management Plan describes data and methodology checks, develops processes governing peer review and public
comments, and provides guidance on conducting an analysis of the uncertainty surrounding the emission estimates. The
QA/QC Management Plan procedures also stress continual improvement, providing for corrective actions that are
designed to improve the inventory estimates over time.
Key attributes ofthe QA/QC Management Plan are summarized in Figure A-21. These attributes include:
•	Procedures and Forms: detailed and specific systems that serve to standardize the process of documenting and
archiving information, as well as to guide the implementation of QA/QC and the analysis of uncertainty.
•	Implementation of Procedures: application of QA/QC procedures throughout the whole Inventory development
process from initial data collection, through preparation ofthe emission estimates, to publication ofthe
Inventory.
•	Quality Assurance: expert and public reviews for both the Inventory estimates and the report (which is the
primary vehicle for disseminating the results ofthe Inventory development process). The expert technical
review conducted by the UNFCCC supplements these QA processes, consistent with the QA good practice and
the 2006 IPCC Guidelines (IPCC 2006).
•	Quality Control: application of General (Tier 1) and Category-specific (Tier 2) quality controls and checks, as
recommended by 2006 IPCC Guidelines (IPCC 2006), along with consideration of secondary data and category-
specific checks (additional Tier 2 QC) in parallel, and coordination with the uncertainty assessment; the
development of protocols and templates, which provide for more structured communication and integration
with the suppliers of secondary information.
•	Record Keeping: provisions to track which procedures have been followed, the results of the QA/QC process,
uncertainty analysis, and feedback mechanisms for corrective action based on the results of the investigations,
which provide for continual data quality improvement and guided research efforts.
•	Multi-Year Implementation: a schedule for coordinating the application of QA/QC procedures across multiple
years, especially for category-specific QC, focusing on key categories.
•	Interaction and Coordination: promoting communication within the EPA, across Federal agencies and
departments, state government programs, and research institutions and consulting firms involved in supplying
data or preparing estimates for the Inventory. The QA/QC Management Plan itself is intended to be revised to
reflect new information that becomes available as the program develops, methods are improved, or additional
A-535
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supporting documents become necessary. Further information on verification will be included in future
submissions.
In addition, based on the national QA/QC Management Plan for the Inventory, source and sink-specific QA/QC
plans have been developed for a number of sources and sinks. These plans follow the procedures outlined in the national
QA/QC plan, tailoring the procedures to the specific text and spreadsheets of the individual sources. For each
greenhouse gas emissions source or sink included in this Inventory, minimum general QA/QC analysis consistent with
Vol. 1, Chapter 6 of the 2006IPCC Guidelines has been undertaken. Where QA/QC activities for a particular source or sink
category go beyond the general level, and include category-specific checks, further explanation is provided within the
respective category text. Similarly, responses or updates based on comments from the expert, public and the
international technical expert reviews (e.g., UNFCCC) are also addressed within the respective source or sink category
text. For transparency, responses to public and expert review comments are also posted on the EPA website with the
final report.
A-536 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Figure A-21: U.S. QA/QC Plan Summary
(D
C
<
o
¦M
c
03
>
Data
Gathering
Obta i data in
electronic
¦xr-nat (if
possible)
Review
spreadsheet
construction
o Avoid
hardwiring
o Use data
validation
o Protect cells
Develop
automatic
checkers for:
? Out ers,
"egative
values,, or
•nissing data
o Variable
types match
values
o Tiire series
consistency
Maintain
tracking tab for
status of
gathering
efforts
Data
Documentation
Contact reports
for non-electronic
conrruiiCBt oris
Prov de cell
r»;e"er-cei for
pr-iary aaca
elements
Obta'n copies of
all data sources
L>st and location
of any
working/external
spreadsheets
Docv.rne,'"t
assu-npt ons
Comoiete QA/QC
checK sts
CRF and summary
tab links
» Check citations in
methodoloe
rh,ar.oes
Calculating
Emissions
Clearly label
parameters, units,
and conversion
factors
« Review spreadsheet
integrity
q Equations
q Units
c Inputs and
outputs
» Develop automated
checkers lor:
o Input ranges
o Calculations
o Emission
aggregation
o Trend and IEF
checks
Cross-Cutting
Coordination
» Common starting
versions for each
"ventcry year
*	Ut1 :e
jU tenable
summary and
CRF tab for each
source
spreadsheet for
linking to a
master summary
spreadsheet
*	Follow strict
version control
procedures
*	Docurrent
QA/QC
procedures
8.3. Assessment Factors
The Inventory of U.S. Greenhouse Gas Emissions and Sinks development process follows guidance outlined in
EPA's Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated
by the Environmental Protection Agency 166 and A Summary of General Assessment Factors for Evaluating the Quality of
166 epa report #260R-02-008, October 2002, Available online at .
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Scientific and Technical Information.167 This includes evaluating the data and models used as inputs into the Inventory
against the five general assessment factors: soundness, applicability and utility, clarity and completeness, uncertainty
and variability, evaluation and review. Table A-256 defines each factor and explains how it was considered during the
process of creating the current Inventory.
Table A-256: Assessment Factors and Definitions
General Assessment
Factor
Definition
How the Factor was Considered
Soundness (AF1)
The extent to which the
scientific and technical
procedures, measures,
methods or models employed
to generate the information are
reasonable for, and consistent
with their intended application.
The underlying data, methodologies, and models used to
generate the Inventory of U.S. Greenhouse Gas Emissions and
Sinks are reasonable for and consistent with their intended
application, to provide information regarding all sources and
sinks of greenhouse gases in the United States for the
Inventory year, as required per UNFCCC Annex I country
reporting requirements.
The U.S. emissions calculations follow the 2006IPCC
Guidelines developed specifically for UNFCCC inventory
reporting. They are based on the best available, peer-reviewed
scientific information, and have been used by the international
community for over 25 years. When possible, Tier 2 and Tier 3
methodologies from the 2006 IPCC Guidelines are applied to
calculate U.S. emissions more accurately.
Applicability and Utility
(AF2)
The extent to which the
information is relevant for the
Agency's intended use.
The Inventory's underlying data, methodology, and models are
relevant for their intended application because they generate
the sector-specific greenhouse gas emissions trends necessary
for assessing and understanding all sources and sinks of
greenhouse gases in the United States for the Inventory year.
They are relevant for communicating U.S. emissions
information to domestic audiences, and they are consistent
with the 2006 IPCC Guidelines developed specifically for
UNFCCC reporting purposes of international greenhouse gas
inventories.
Clarity and
Completeness (AF3)
The degree of clarity and
completeness with which the
data, assumptions, methods,
quality assurance, sponsoring
organizations and analyzes
employed to generate the
information are documented.
The methodological and calculation approaches applied to
generate the Inventory of U.S. Greenhouse Gas Emissions and
Sinks are extensively documented in the 2006 IPCC Guidelines.
The Inventory report describes its adherence to the 2006 IPCC
Guidelines, and the U.S. Government agencies provide data to
implement the 2006 IPCC Guidelines approaches. Any changes
made to calculations, due to updated data and methods, are
explained and documented in the report consistent with
UNFCCC reporting guidelines.
Uncertainty and
Variability (AF4)
The extent to which the
variability and uncertainty
The evaluation of uncertainties for underlying data is
documented in the Annex 7 Uncertainty to the Inventory of
167 epa report #100/B-03/001, June 2003, Available online at , and Addendum to: A Summary of General Assessment Factors for
Evaluating the Quality of Scientific and Technical Information, December 2012, Available online at
.
A-538 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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(quantitative and qualitative) in
the information or in the
procedures, measures,
methods or models are
evaluated and characterized.
U.S. Greenhouse Gas Emissions and Sinks. In accordance with
the 2006 IPCC Guidelines, the uncertainty associated with the
Inventory's underlying input data was evaluated by running a
Monte Carlo uncertainty analysis on most source and/or
category emissions data to produce a 95 percent confidence
interval for the annual greenhouse gas emissions for that
source and/or sink. The error propagation approach is used to
quantify uncertainties for some categories that are not
significant contributors to emissions across the time series. To
develop overall uncertainty estimates, the Monte Carlo
simulation output data for each emission source and/or sink
category uncertainty analysis were combined by type of gas,
and the probability distributions were fitted to the combined
simulation output data where such simulated output data
were available.
Evaluation and Review
(AF5)
The extent of independent
verification, validation and peer
review of the information or of
the procedures, measures,
methods or models.
The majority of the underlying methodology, calculations, and
models used to generate the Inventory of U.S. Greenhouse Gas
Emissions and Sinks have been independently verified and
peer reviewed as part of their publication in the 2006 IPCC
Guidelines and the 2019 Refinement. In cases where the
methodology differs slightly from the 2006 IPCC Guidelines,
these were independently verified and validated by technical
experts during the annual expert review phase of the
Inventory development process.
For the data used in calculating greenhouse gas emissions for
each source, multiple levels of evaluation and review occur.
Data are compared to results from previous years, and
calculations and equations are continually evaluated and
updated as appropriate. Throughout the process, inventory
data and methodological improvements are planned and
incorporated.
The Inventory undergoes annual cycles of expert and public
review before publication. This process ensures that both
experts and the general public can review each category of
emissions and sinks and have an extended opportunity to
provide feedback on the methodologies used, calculations,
data sources, and presentation of information.
8.4. Responses to Review Processes
EPA is continually working to improve transparency, accuracy, completeness, comparability, and consistency of
emission estimates in the Inventory in response to the feedback received during the Expert, Public, and UNFCCC Review
periods, as well as supplemental stakeholder outreach efforts. For instance, as mentioned in the Planned Improvements
section of the Petroleum and Natural Gas Systems source categories (Section 3.6 and 3.7), EPA has engaged in
stakeholder outreach to increase the transparency in the Inventory methodology and to identify supplemental data
sources that can lead to methodological improvements. During the annual preparation of the Inventory of U.S.
Greenhouse Gas Emissions and Sinks, in considering and prioritizing improvements, EPA reviews the significance of the
A-539
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source and sink category (i.e., key categories), along with QC, OA, and uncertainty assessments. Identified planned
improvements to methods (including data, emissions factors, and other key parameters), along with QA/QC and
uncertainty assessments are documented within each source and sink category to complement the Recalculations and
Improvements chapter. Additionally, the Executive Summary also highlights key changes in methodologies from previous
Inventory reports.
As noted in the previous section, for transparency, responses to comments received while developing the
annual estimates from Public Review and Expert Review are posted on the EPA website with the final Inventory.168
As noted above in section 8.2, the expert technical review conducted by the UNFCCC supplements these QA
processes. This review by an international expert review team (ERT) occurs after submission of the final report to the
UNFCCC and assesses consistency with UNFCCC reporting guidelines. More information on the UNFCCC reporting
guidelines and the review process can be found here:
•	UNFCCC Reporting Guidelines for annual national greenhouse gas inventories169
•	UNFCCC Review Process and Guidelines for annual national greenhouse gas inventories170
•	Inventory Review reports of annual submissions (latest reviews).171
The final annual review report with findings from the UNFCCC expert review of the April 2020 Inventory submission
conducted on November 2-7, 2020 was not received at the time of publication of this report. EPA was unable to provide
accurate responses on how the latest ERT recommendations have been reflected in this Inventory (i.e., to be submitted
to UNFCCC in April 2021) as the draft annual review report was only received for review on April 7, 2021 as this report
was being finalized for submission. Following receipt of the final review report from the UNFCCC ERT, this Annex will be
updated to include a table indicating status areas of improvement identified through UN review to facilitate future
reviews. The updated Annex will be posted on EPA's website.172
168	See .
169	Available online at: .
170	Available online at: .
171	Available online at: .
172	See 
A-540 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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ANNEX 9 Use of EPA Greenhouse Gas Reporting
Program in Inventory
This Annex provides background information on the Greenhouse Gas Reporting Program (GHGRP) and its
relationship to this Inventory. The U.S. Environmental Protection Agency (EPA) tracks U.S. greenhouse gas emissions
through two complementary programs: the Inventory (estimates in this report), and the GHGRP. The Inventory provides
a comprehensive accounting of all emissions from source categories identified in the 2006IPCC Guidelines needed to
understand the United States' total net greenhouse gas emissions in line with the UNFCCC reporting guidelines, while the
GHGRP provides bottom-up detailed information that helps improve understanding of the sources and types of
greenhouse gas emissions at individual facilities and suppliers. The GHGRP provides facility-level greenhouse gas data
from major industrial sources across the United States; it does not provide full coverage of total annual U.S. greenhouse
gas emissions (e.g., the GHGRP excludes emissions from the agricultural, land use, and forestry sectors).
On October 30, 2009, the EPA published a regulation requiring annual reporting of greenhouse gas data from
large facilities173 in the United States. The program implementing the regulation, codified at 40 CFR Part 98, is referred to
as EPA's Greenhouse Gas Reporting Program (GHGRP). The GHGRP covers sources or suppliers in 41 industrial categories
("Subparts"174), including direct greenhouse gas emitters,175 fossil fuel suppliers, industrial gas suppliers, and facilities
that inject carbon dioxide (C02) underground for sequestration or other reasons.176 In general, the threshold for
reporting is 25,000 metric tons or more of C02 Eq. per year.177
Facilities in most source categories subject to the GHGRP began collecting data in 2010 while additional types of
industrial operations began collecting data in 2011. Currently, more than 8,000 facilities and suppliers are required to
report their data annually. Facilities calculate their emissions using methodologies that are specified at 40 CFR Part 98,
and they report their data to EPA using the electronic Greenhouse Gas Reporting Tool (e-GGRT). Annual reports covering
emissions from the prior calendar year are due by March 31st of each year. EPA verifies reported data through a multi-
step process to identify potential errors and ensure that data submitted to EPA are accurate, complete, and consistent.
All reports submitted to EPA are evaluated by electronic validation and verification checks, including industry-specific
checks. If potential errors are identified, EPA will notify the reporter, who can resolve the issue either by providing an
acceptable response describing why the flagged issue is not an error or by correcting the flagged issue and resubmitting
their annual greenhouse gas report.178
The reported data are made available to the public each fall. EPA presents the data collected by its GHGRP in a
number of ways, such as through a data publication tool known as the Facility Level Information on GHGs Tool (FLIGHT).
FLIGHT allows data to be viewed in several formats including maps, tables, charts and graphs for individual facilities or
groups of facilities.179 More information on EPA's GHGRP can be found at https://www.epa.gov/ghgreporting.
173	Annual reporting is at the facility level, except for certain suppliers of fossil fuels and industrial greenhouse gases (i.e.,
reporting at the corporate level).
174	See .
175	Data reporting by affected facilities includes the reporting of emissions from fuel combustion at that affected facility.
176	See  and .
177	For some industrial categories ("Subparts") under the GHGRP, facilities must report if their combined emissions from
stationary fuel combustion and all applicable source categories are above a given threshold (e.g., 25,000 metric tons C02 Eq. or
more per year or another industry-specific threshold). For other source categories, new facilities must report regardless of their
quantity of annual emissions. These categories include, for example, cement production (Subpart H) and aluminum production
(Subpart F). However, any facility regardless of threshold can cease reporting if its emissions fall below 25,000 metric tons C02
Eq. for five years or below 15,000 metric tons C02 Eq for three years, and it informs EPA of its intention to cease reporting and
the reason(s) for any reduction in emissions. See 40 CFR 98.2(a), 98.2(i), and Tables A-3, A-4, and A-4 for more information.
178	See GHGRP Verification Fact Sheet .
179	See .
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The GHGRP dataset is an important resource for the Inventory. EPA uses GHGRP data in a number of categories
to improve the national estimates, consistent with IPCC guidance, as summarized in Table A-257 below. Methodologies
used in the GHGRP are consistent with methods in 2006 IPCC Guidelines, in particular "higher tier" methods which
include collecting facility or plant-specific measurements. The GHGRP provides not only annual emissions information
for reporting facilities and suppliers, but also other annual information, such as activity data and emission factors that
can be used to improve and refine national emission estimates and trends over time. GHGRP data also allow EPA to
disaggregate national inventory estimates in new ways that can highlight differences across regions and sub-categories
of emissions, along with enhancing application of QA/QC procedures and assessment of uncertainties. Consistent with
considerations outlined in the Technical Bulletin 1 on Use of Facility-Specific Data in National Greenhouse Gas Inventories
from the IPCC Task Force on National Greenhouse Gas Inventories (IPCC 2011),180 EPA has paid particular attention both
to ensuring completeness in national coverage of emission estimates over time and to ensuring time-series consistency
by recalculating emissions for 1990 to 2010/2011 when incorporating GHGRP data into source category estimates.181
These issues are discussed further in the chapters where source category emissions estimates use GHGRP data. Source
category definitions are also considered in order to ensure completeness when using GHGRP data. For certain source
categories in the Industrial Processes and Product Use chapter, EPA has relied on data values that have been calculated
by aggregating GHGRP data that are considered confidential business information (CBI) at the facility level. EPA, with
industry engagement, has put forth criteria to confirm that a given data aggregation shields underlying CBI from public
disclosure. EPA is only publishing data values that meet these aggregation criteria.182 Specific uses of aggregated facility-
level data that are CBI are described in the respective methodological sections in Chapter 4 of the Inventory. Beyond the
current uses, EPA continues to analyze the GHGRP data on an annual basis to identify other source categories where it
could be further integrated in future editions of this report (see the Planned Improvement sections of those specific
source categories for details).
180	IPCC Task Force on National Greenhouse Gas Inventories (TFI) (2011). Technical Bulletin 1: Use of Facility-Specific Data
National Greenhouse Gas Inventories. Available at https://www.ipcc-nggip.iges.or.jp/public/tb/TFI_Technical_Bulletin_l.pdf.
181	See .
182U.S. EPA Greenhouse Gas Reporting Program. Confidential Business Information GHG Reporting. See
.
A-542 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019
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Table A-257: Summary of EPA GHGRP Data Use in U.S. Inventory
Inventory Category
GHGRP Industry
Subpart
Initial Calendar
Year of Reporting
under GHGRP
Reporting
Threshold.
183
Type of GHGRP Data Use
National
Inventory
Report (NIR)
Section with
details on
data use
Emissions
or Quantity
Supplied
Emission
Factor (EF)
Activity
Data (AD)
QA/QC.
184
Energy Sector
Fossil Fuel Combustion:
Industrial Sector
C - General Stationary
Fuel Combustion
Sources
2010
Y
•



Section 3.1
and Box 3-4
Coal Mining:
Underground Mines
FF - Underground Coal
Mines
2011
Y
•


•
3.4
Petroleum Systems
W- Petroleum and
Natural Gas Systems;
Y- Petroleum
Refineries
2010, 2011
Y, N
•
•
•
•
3.6
Natural Gas Systems
W- Petroleum and
Natural Gas Systems
2011
Y

•
•
•
3.7
Waste Incineration
C - General Stationary
Fuel Combustion
Sources
2010
Y


•

3.3
Industrial Processes and Product Use Sector
Adipic Acid Production
E-Adipic Acid
Production
2010
N
•



4.8
Aluminum Production
F-Aluminum
Production
2010
N
•



4.19
Urea Consumption
from Non-Agricultural
Use
G - Ammonia
Manufacturing
2010
N


•

4.6
183	Y=25, 000 MTC02 Eq., or industry-specific threshold other than 25, 000 MTC02 Eq.; N = all facilities in industry category must report regardless of annual emissions. Information on
industry-specific threshold and implications of the reporting threshold or lack of threshold in estimating national greenhouse gas emissions is discussed in the respective source category
methodology sections.
184	Consistent with IPCC good practices, QA/QC using GHGRP may not be appropriate if this is the primary data source for estimating emissions. Depending on use, other data sets may be
more appropriate for QA/QC of Inventory estimates.
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Inventory Category
GHGRP Industry
Subpart
Initial Calendar
Year of Reporting
under GHGRP
Reporting
Threshold.
183
Type of GHGRP Data Use
National
Inventory
Report (NIR)
Section with
details on
data use
Emissions
or Quantity
Supplied
Emission
Factor (EF)
Activity
Data (AD)
QA/QC
184
Carbon Dioxide
Consumption
PP-Suppliers of
Carbon Dioxide
2010
Y
•



4.15
Cement Production
H - Cement Production
2010
N


•
•
4.1
Electrical Transmission
and Distribution
DD - Use of Electric
Transmission and
Distribution Equipment;
SS - Manufacture of
Electric Transmission
and Distribution
Equipment
2011
Y
•
•
•

4.25
HCFC-22 Production
0 - HCFC-22 Production
and HFC-23 Destruction
2010
Y
•



4.14
Lead Production
R - Lead Production
2010
Y



•
4.21
Lime Production
S - Lime Production
2010
N
•



4.2
Magnesium Production
and Processing
T- Magnesium
Production
2011
Y
•



4.20
Nitric Acid Production
V-Nitric Acid
Production
2010
N
•
•
•

4.7
Petrochemical
Production
X- Petrochemical
Production
2010
N
•
•
•

4.13
Electronics Industry
I - Electronics
Manufacturing
2011
Y
•



4.23
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Inventory Category
GHGRP Industry
Subpart
Initial Calendar
Year of Reporting
under GHGRP
Reporting
Threshold.
183
Type of GHGRP Data Use
National
Inventory
Report (NIR)
Section with
details on
data use
Emissions
or Quantity
Supplied
Emission
Factor (EF)
Activity
Data (AD)
QA/QC.
184
Substitution of ODS
00 - Suppliers of
Industrial Gases;
QQ- Imports and
Exports of Equipment
Pre-charged with
Fluorinated GHGs or
Containing Fluorinated
GHGs in Closed-cell
Foams
2010, 2011
N
(producers)
Y (all others)



•
4.24
Waste Sector
MSW Landfills
HH - Municipal Solid
Waste Landfills
2010
Y
•
•

•
7.1
Industrial Landfills
TT - Industrial Waste
Landfills
2011
Y



•
7.1
Industrial Wastewater
II - Industrial
Wastewater Treatment
2011
Y



•
7.2
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