1
2
3
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5
6
7
8
9
10
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13
14
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16
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20
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22
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32
33
34
35
36
37
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39
40
41
42
43
44
45
46
47
48
49
50
51
52
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.
ANNEX 1 Key Category Analysis 3
ANNEX 2 Methodology and Data for Estimating C02 Emissions from Fossil Fuel Combustion 51
2.1. Methodology for Estimating Emissions of C02 from Fossil Fuel Combustion 51
2.2. Methodology for Estimating the Carbon Content of Fossil Fuels 95
2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil Fuels 133
ANNEX 3 Methodological Descriptions for Additional Source or Sink Categories 162
3.1. Methodology for Estimating Emissions of CH4, N20, and Indirect Greenhouse Gases from Stationary
Combustion 162
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
170
3.3. Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel Consumption 219
3.4. Methodology for Estimating CH4 Emissions from Coal Mining 226
3.5. Methodology for Estimating CH4, C02, and N20 Emissions from Petroleum Systems 236
3.6. Methodology for Estimating CH4, C02, and N20 Emissions from Natural Gas Systems 241
3.7. Methodology for Estimating C02, CH4, and N20 Emissions from the Incineration of Waste 249
3.8. Methodology for Estimating Emissions from International Bunker Fuels used by the U.S. Military 256
3.9. Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances 263
3.10. Methodology for Estimating CH4 Emissions from Enteric Fermentation 292
3.11. Methodology for Estimating CH4 and N20 Emissions from Manure Management 319
3.12. Methodologies for Estimating Soil Organic C Stock Changes, Soil N20 Emissions, and CH4 Emissions and from
Agricultural Lands (Cropland and Grassland) 354
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 403
3.14. Methodology for Estimating CH4 Emissions from Landfills 443
ANNEX 4 IPCC Reference Approach for Estimating C02 Emissions from Fossil Fuel Combustion 464
ANNEX 5 Assessment of the Sources and Sinks of Greenhouse Gas Emissions Not Included 476
ANNEX 6 Additional Information 489
6.1. Global Warming Potential Values 489
6.2. Ozone Depleting Substance Emissions 502
6.3. Sulfur Dioxide Emissions 504
6.4. Complete List of Source Categories 506
6.5. Constants, Units, and Conversions 508
6.6. Abbreviations 511
6.7. Chemical Formulas 518
ANNEX 7 Uncertainty 522
A-l
-------
1
2
3
4
5
6
7
8
9
10
11
12
522
523
529
530
531
532
532
532
533
535
537
7.1. Overview
7.2. Methodology and Results
7.3. Reducing Uncertainty
7.4. Planned Improvements
7.5. Summary Information on Uncertainty Analyses by Source and Sink Category
ANNEX 8 QA/QC Procedures
8.1. Background
8.2. Purpose
8.3. Assessment Factors
8.4. Responses During the Review Process
ANNEX 9 Use of EPA Greenhouse Gas Reporting Program in Inventory
A-2 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
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. A qualitative review of key categories, along with non-key categories has not identified additional
categories to consider as key.
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
to identifying key categories; provides level and trend assessment equations; and provides a brief statistical evaluation of
IPCC's quantitative methodologies for defining key categories. The United States key category analysis generally follows
the IPCC suggested aggregation level of analysis, but in some cases does differ by avoiding disaggregating into many smaller
categories (i.e., separating pools and subcategories within LULUCF source categories). The UNFCCC common reporting
format 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 of the 2006 IPCC Guidelines.
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 global warming potential (GWP)-
weighted emissions in 2018. 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
-------
Table A-l: Key Source Categories for the United States (1990 and 2018)
Approach 1
Approach 2
Level
Trend Level
Trend
Level
Trend Level
Trend
CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2018 Emissions
Categories
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
Quala
(MMT C02 Eq.)
Energy
l.A.3.b C02 Emissions
from Mobile
C02
•
• •
•
•
• •
•
1,499.8
Combustion: Road
l.A.l C02 Emissions
from Stationary
Combustion - Coal -
co2
•
• •
•
•
• •
•
1,152.9
Electricity Generation
l.A.l C02 Emissions
from Stationary
co2
577.4
Combustion - Gas -
Electricity Generation
1.A.2 C02 Emissions
from Stationary
Combustion - Gas -
co2
•
• •
•
•
• •
•
514.8
Industrial
1.A.2 C02 Emissions
from Stationary
Combustion - Oil -
co2
•
• •
•
•
• •
•
282.1
Industrial
l.A.4.b C02 Emissions
from Stationary
Combustion - Gas -
co2
•
• •
•
•
• •
273.7
Residential
l.A.4.a C02 Emissions
from Stationary
Combustion - Gas -
co2
•
• •
•
•
• •
•
192.6
Commercial
l.A.3.a C02 Emissions
from Mobile
co2
•
• •
•
•
•
173.9
Combustion: Aviation
1.A.5 C02 Emissions
from Non-Energy Use of
co2
•
• •
•
•
• •
•
134.5
Fuels
A-4 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
l.A.4.a C02 Emissions
from Stationary
Combustion - Oil -
Commercial
C02
• • • •
63.9
l.A.4.b C02 Emissions
from Stationary
Combustion - Oil -
Residential
C02
• • • •
• • •
62.2
1.A.2 C02 Emissions
from Stationary
Combustion - Coal -
Industrial
C02
• • • •
• • • •
49.8
l.A.3.e C02 Emissions
from Mobile
Combustion: Other
C02
• • • •
49.2
1.B.2 C02 Emissions
from Petroleum
Systems
C02
• • • •
• • • •
39.4
1.A.3.C C02 Emissions
from Mobile
Combustion: Railways
C02
• •
38.9
l.A.3.d C02 Emissions
from Mobile
Combustion: Marine
C02
• • • •
36.5
1.B.2 C02 Emissions
from Natural Gas
Systems
C02
• •
•
34.9
1.A.5 C02 Emissions
from Stationary
Combustion - Oil - U.S.
Territories
C02
• • • •
34.3
l.A.l C02 Emissions
from Stationary
Combustion - Oil -
Electricity Generation
C02
• • • •
• • •
22.2
1.A.5 C02 Emissions
from Stationary
Combustion - Gas - U.S.
Territories
C02
•
3.0
A-5
-------
l.A.4.a C02 Emissions
from Stationary
Combustion - Coal -
Commercial
C02
• •
1.8
l.A.4.b C02 Emissions
from Stationary
Combustion - Coal -
Residential
C02
• •
0.0
1.B.2 CH4 Emissions
from Natural Gas
Systems
ch4
• • • •
• • • •
139.7
l.B.l Fugitive Emissions
from Coal Mining
ch4
• • • •
• • • •
52.7
1.B.2 CH4 Emissions
from Petroleum
Systems
ch4
• • • •
• • • •
36.6
1.B.2 CH4 Emissions
from Abandoned Oil and
Gas Wells
ch4
• •
7.0
l.A.4.b CH4 Emissions
from Stationary
Combustion -
Residential
ch4
• • • •
4.5
l.A.3.e CH4 Emissions
from Mobile
Combustion: Other
ch4
• •
1.7
l.A.l N20 Emissions
from Stationary
Combustion - Coal -
Electricity Generation
n2o
•
20.3
l.A.3.b N20 Emissions
from Mobile
Combustion: Road
n2o
• • • •
• •
10.4
1.A.2 N20 Emissions
from Stationary
Combustion - Industrial
n2o
•
2.7
Industrial Processes and Product Use
2.C.1 C02 Emissions
from Iron and Steel
C02
•
•
•
•
•
•
•
•
42.7
Production &
A-6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Metallurgical Coke
Production
2.A.1 C02 Emissions
from Cement
Production
C02
• •
40.3
2.B.8 C02 Emissions
from Petrochemical
Production
C02
• • • •
29.4
2.G SF6 Emissions from
Electrical Transmission
and Distribution
sf6
• • • •
• •
4.1
2.B.9 HFC-23 Emissions
from HCFC-22
Production
HFCs
• • • •
• •
3.3
2.C.3 PFC Emissions
from Aluminum
Production
PFCs
• • •
1.6
2.F.1 Emissions from
Substitutes for Ozone
Depleting Substances:
Refrigeration and Air
Conditioning
HFCs, PFCs
• • • •
• • • •
128.9
2.F.4 Emissions from
Substitutes for Ozone
Depleting Substances:
Aerosols
HFCs, PFCs
• •
• •
19.2
2.F.2 Emissions from
Substitutes for Ozone
Depleting Substances:
Foam Blowing Agents
HFCs, PFCs
• •
11.8
2.F.3 Emissions from
Substitutes for Ozone
Depleting Substances:
Fire Protection
HFCs, PFCs
•
2.6
2.F.5 Emissions from
Substitutes for Ozone
Depleting Substances:
Solvents
HFCs, PFCs
•
2.0
Agriculture
A-7
-------
3.G C02 Emissions from
Liming
C02
•
3.1
3.A.1 CH4 Emissions
from Enteric
Fermentation: Cattle
ch4
• • • •
• •
171.7
3.B.1 CH4 Emissions
from Manure
Management: Cattle
ch4
• • • •
• • •
35.7
3.D.1 Direct N20
Emissions from
Agricultural Soil
Management
n2o
• •
• •
285.7
3.D.2 Indirect N20
Emissions from Applied
Nitrogen
n2o
• • • •
• • • •
52.5
3.B.4 CH4 Emissions
from Manure
Management: Other
Livestock
ch4
• •
26.0
3.C CH4 Emissions from
Rice Cultivation
ch4
• •
13.3
Waste
5.A CH4 Emissions from
Landfills
ch4
• • • •
• • • •
110.6
Land Use, Land Use Change, and Forestry
Net C02 Emissions from
Land Converted to
Settlements
C02
• •
• •
79.3
Net C02 Emissions from
Land Converted to
Cropland
C02
•
•
55.3
Net C02 Emissions from
Grassland Remaining
Grassland
C02
• •
11.2
Net C02 Emissions from
Cropland Remaining
Cropland
C02
• •
• •
(16.6)
A-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Net C02 Emissions from
Land Converted to
Grassland
C02
• •
• •
(24.6)
Net C02 Emissions from
Land Converted to
Forest Land
C02
•
•
(110.6)
Net C02 Emissions from
Settlements Remaining
Settlements
C02
• •
• •
(126.2)
Net C02 Emissions from
Forest Land Remaining
Forest Land
C02
• •
• •
(663.2)
CH4 Emissions from
Forest Fires
ch4
•
11.3
N20 Emissions from
Forest Fires
n2o
•
7.5
Subtotal Without LULUCF
6,497.7
Total Emissions Without
LULUCF
6,677.8
Percent of Total Without
LULUCF
97%
Subtotal With LULUCF
5,674.1
Total Emissions With
LULUCF
5,904.1
Percent of Total With
LULUCF
96%
3 Qualitative criteria.
A-9
-------
1
2
3
4
5
6
7
8
9
10
11
12
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 2018) 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.
In addition to conducting Approach 1 and 2 level and trend assessments, a qualitative assessment of the source
categories, as described in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006), 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 update its qualitative assessment on
an annual basis.
Table A-2: U.S. Greenhouse Gas Inventory Source Categories without LULUCF
Direct
Greenhouse
2018 Emissions
Key
ID
Level in which
CRF Source Categories
Gas
(MMT C02 Eq.)
Category?
Criteria3
year(s)?b
Energy
l.A.3.b C02 Emissions from Mobile
C02
1,499.8
•
Li Ti L2T2
1990, 2018
Combustion: Road
l.A.l C02 Emissions from Stationary
co2
1,152.9
•
Li Ti L2T2
1990, 2018
Combustion - Coal - Electricity Generation
l.A.l C02 Emissions from Stationary
co2
577.4
•
Li Ti L2 T2
1990, 2018
Combustion - Gas - Electricity Generation
1.A.2 C02 Emissions from Stationary
co2
514.8
•
Li Ti L2 T2
1990, 2018
Combustion - Gas - Industrial
1.A.2 C02 Emissions from Stationary
co2
282.1
•
Li Ti L2 T2
1990, 2018
Combustion - Oil - Industrial
l.A.4.b C02 Emissions from Stationary
co2
273.7
•
Li Ti L2 T2
1990, 2018
Combustion - Gas - Residential
l.A.4.a C02 Emissions from Stationary
co2
192.6
•
Li Ti L2 T2
1990, 2018
Combustion - Gas - Commercial
l.A.3.a C02 Emissions from Mobile
co2
173.9
•
Li Ti L2
1990, 2018
Combustion: Aviation
1.A.5 C02 Emissions from Non-Energy Use of
co2
134.5
•
Li Ti L2 T2
1990, 2018
Fuels
l.A.4.a C02 Emissions from Stationary
co2
63.9
•
Li Ti
1990,, 2018i
Combustion - Oil - Commercial
l.A.4.b C02 Emissions from Stationary
co2
62.2
•
Li Ti L2 T2
1990, 2018i
Combustion - Oil - Residential
1.A.2 C02 Emissions from Stationary
co2
49.8
•
Li Ti L2 T2
1990, 2018
Combustion - Coal - Industrial
l.A.3.e C02 Emissions from Mobile
co2
49.2
•
Li Ti
1990,, 2018i
Combustion: Other
1.B.2 C02 Emissions from Petroleum Systems
co2
39.4
•
Li Ti L2 T2
2018
1.A.3.C C02 Emissions from Mobile
co2
38.9
•
Li
1990,, 2018i
Combustion: Railways
l.A.3.d C02 Emissions from Mobile
co2
36.5
•
Li Ti
1990,, 2018i
Combustion: Marine
1.B.2 C02 Emissions from Natural Gas Systems
co2
34.9
•
Li L2
1990, 2018
1.A.5 C02 Emissions from Stationary
co2
34.3
•
Li Ti
1990,, 2018i
Combustion - Oil - U.S. Territories
l.A.l C02 Emissions from Stationary
co2
22.2
•
Li Ti L2 T2
1990
Combustion - Oil - Electricity Generation
5.C.1 C02 Emissions from Incineration of
co2
11.1
Waste
A-10 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1.A.5 C02 Emissions from Stationary
Combustion - Coal - U.S. Territories
1.A.5 C02 Emissions from Stationary
Combustion - Gas - 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
1.B.2 CH4 Emissions from Natural Gas Systems
l.B.l Fugitive Emissions from Coal Mining
1.B.2 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
l.A.3.e CH4 Emissions from Mobile
Combustion: Other
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
l.A.4.a CH4 Emissions from Stationary
Combustion - Commercial
l.A.3.b CH4 Emissions from Mobile
Combustion: Road
l.A.l CH4 Emissions from Stationary
Combustion - Gas - Electricity Generation
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
l.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity Generation
l.A.l CH4 Emissions from Stationary
Combustion - Wood - Electricity Generation
5.C.1 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.l N20 Emissions from Stationary
Combustion - Gas - Electricity Generation
1.A.2 N20 Emissions from Stationary
Combustion - Industrial
C02 4.0
C02 3.0 • T2
C02 1.8 • T-,
C02 0.4
C02 +
C02 0.0 • T2
CH4 139.7 • UTtLjTj 1990,2018
CH4 52.7 • UTtLzTz 1990,2018
CH4 36.6 • UTtLzTz 1990,2018
CH4 7.0 • L2 19902, 20182
CH4 6.2
CH4 4.5 • L2T2 19902, 2OI82
CH4 1.7 • T2
CH4 1.6
CH4 1.3
CH4 1.0
CH4 1.0
CH4 0.3
CH4 0.2
CH4 0.1
CH4 0.1
CH4 +
CH4 +
CH4 +
ch4 +
N20 20.3 • L2 19902, 20182
N20 10.4 • UTtTz 1990i
N20 4.1
N20 2.7 • L2 19902
A-ll
-------
l.A.3.e N20 Emissions from Mobile
Combustion: Other
l.A.3.a N20 Emissions from Mobile
Combustion: Aviation
l.A.4.b N20 Emissions from Stationary
Combustion - Residential
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
l.A.4.a N20 Emissions from Stationary
Combustion - Commercial
5.C.1 N20 Emissions from Incineration of
Waste
1.A.3.C N20 Emissions from Mobile
Combustion: Railways
1.A.5 N20 Emissions from Stationary
Combustion - U.S. Territories
1.B.2 N20 Emissions from Petroleum Systems
l.A.l N20 Emissions from Stationary
Combustion - Wood - Electricity Generation
1.B.2 N20 Emissions from Natural Gas Systems
l.A.l N20 Emissions from Stationary
Combustion - Oil - Electricity Generation
l.D.l International Bunker Fuelsc
N20
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
Several
2.5
1.6
0.9
0.5
0.4
0.3
0.3
0.1
0.1
+
+
+
123.3
Industrial Processes and Product Use
2.C.1 C02 Emissions from Iron and Steel C02
Production & Metallurgical Coke Production
2.A.1 C02 Emissions from Cement Production C02
2.B.8 C02 Emissions from Petrochemical C02
Production
2.A.2 C02 Emissions from Lime Production C02
2.B.1 C02 Emissions from Ammonia Production C02
2.A.4 C02 Emissions from Other Process Uses C02
of Carbonates
2.B.10 C02 Emissions from Carbon Dioxide C02
Consumption
2.B.10 C02 Emissions from Urea Consumption C02
for Non-Ag Purposes
2.C.2 C02 Emissions from Ferroalloy C02
Production
2.B.7 C02 Emissions from Soda Ash Production C02
2.B.6 C02 Emissions from Titanium Dioxide C02
Production
2.C.3 C02 Emissions from Aluminum C02
Production
2.A.3 C02 Emissions from Glass Production C02
2.C.6 C02 Emissions from Zinc Production C02
2.B.10 C02 Emissions from Phosphoric Acid C02
Production
2.C.5 C02 Emissions from Lead Production C02
2.B.5 C02 Emissions from Silicon Carbide C02
Production and Consumption
2.C.4 C02 Emissions from Magnesium C02
Production and Processing
2.B.8 CH4 Emissions from Petrochemical CH4
Production
42.7
40.3
29.4
13.9
13.5
9.4
4.5
3.6
2.1
1.7
1.6
1.5
1.3
1.0
0.9
0.6
0.2
0.3
Li Ti L2T2
Li
Li Ti
1990, 2018
1990i, 2018i
1990i, 2018i
A-12 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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.3 N20 Emissions from Adipic Acid
Production
2.B.2 N20 Emissions from Nitric Acid
Production
2.G 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
2.F.3 Emissions from Substitutes for Ozone
Depleting Substances: Fire Protection
2.F.5 Emissions from Substitutes for Ozone
Depleting Substances: Solvents
2.E PFC, HFC, SF6, and NF3 Emissions from
Electronics Industry
2.G SF6 Emissions from Electrical Transmission
and Distribution
2.B.9 HFC-23 Emissions from HCFC-22
Production
2.C.3 PFC Emissions from Aluminum
Production
2.C.4 SF6 Emissions from Magnesium
Production and Processing
2.C.4 HFC-134a Emissions from Magnesium
Production and Processing
CH4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
HFCs, PFCs
HFCs, PFCs
HFCs, PFCs
HFCs, PFCs
HFCs, PFCs
HiGWP
SF6
HFCs
PFC
SF6
HFCs
10.3
9.3
4.2
1.4
0.3
128.9
19.2
11.8
2.6
2.0
4.8
4.1
3.3
1.6
1.1
0.1
LiTi L2T2
T1T2
Ti
T2
t2
LiTiT2
LiTiT2
Li Ti
2018
1990i
1990i
1990i
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
C02
C02
CH4
ch4
ch4
ch4
ch4
ch4
n2o
n2o
4.6
3.1
171.7
35.7
26.0
13.3
5.8
0.4
285.7
52.5
T2
Li Ti L2
Li Ti L2 T2
Li
l2t2
1990, 2018
2018
2018i
19902, 20182
Li L2
Li Ti L2 T2
1990, 2018
1990, 2018
A-13
-------
1
2
3
4
5
6
7
8
9
10
11
3.B. 1 N20 Emissions from Manure N20 15.4
Management: Cattle
3.B.4 N20 Emissions from Manure N20 4.1
Management: Other Livestock
3.F N20 Emissions from Field Burning of N20 0.2
Agricultural Residues
Waste
5.A CH4 Emissions from Landfills
ch4
110.6
•
L1T1 l2t2
1990, 2018
5.D CH4 Emissions from Wastewater
ch4
14.2
Treatment
5.B CH4 Emissions from Composting
ch4
2.5
5.D N20 Emissions from Wastewater
n2o
5.0
Treatment
5.B N20 Emissions from Composting
n2o
2.2
+ Does not exceed 0.05 MMT CO2 Eq.
3 Forthe ID criteria, Q refers to "Qualitative", L refers to a key category identified through a level assessment; T refers to a key category identified
through a trend assessment and the subscripted number refers to either an Approach 1 or Approach 2 assessment (e.g., L2 designates a source is a
key category for an Approach 2 level assessment).
b If the source is a key category for both Li and 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 forthe Approach 2 assessment only in 1990).
c Emissions from these sources not included in emission totals.
Note: LULUCF sources and sinks are not included in this analysis.
Table A-3: U.S. Greenhouse Gas Inventory Source Categories with LULUCF
Direct
Level in
Greenhouse
2018 Emissions
Key
ID
which
CRF Source/Sink Categories
Gas
(MMT C02 Eq.)
Category?
Criteria3
year(s)?b
Energy
l.A.3.b C02 Emissions from Mobile
C02
1,499.8
•
Li T, L2 T2
1990, 2018
Combustion: Road
l.A.l C02 Emissions from Stationary
C02
1,152.9
•
Li T, L2 T2
1990, 2018
Combustion - Coal - Electricity Generation
l.A.l C02 Emissions from Stationary
C02
577.4
•
Li T, L2 T2
1990,, 2018
Combustion - Gas - Electricity Generation
1.A.2 C02 Emissions from Stationary
C02
514.8
•
Li T, L2 T2
1990, 2018
Combustion - Gas - Industrial
1.A.2 C02 Emissions from Stationary
C02
282.1
•
Li T, L2 T2
1990, 2018
Combustion - Oil - Industrial
l.A.4.b C02 Emissions from Stationary
C02
273.7
•
Li T, L2
1990, 2018
Combustion - Gas - Residential
l.A.4.a C02 Emissions from Stationary
C02
192.6
•
Li T, L2 T2
1990, 2018
Combustion - Gas - Commercial
l.A.3.a C02 Emissions from Mobile
C02
173.9
•
Li T, L2
1990, 2018
Combustion: Aviation
1.A.5 C02 Emissions from Non-Energy Use of
C02
134.5
•
Li T, L2 T2
1990, 2018
Fuels
l.A.4.a C02 Emissions from Stationary
C02
63.9
•
Li Ti
1990,, 2018i
Combustion - Oil - Commercial
l.A.4.b C02 Emissions from Stationary
C02
62.2
•
Li Ti T2
1990,, 2018i
Combustion - Oil - Residential
1.A.2 C02 Emissions from Stationary
C02
49.8
•
Li T, L2 T2
1990, 2018i
Combustion - Coal - Industrial
l.A.3.e C02 Emissions from Mobile
C02
49.2
•
Li Ti
1990,, 2018i
Combustion: Other
A-14 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1.B.2 C02 Emissions from Petroleum
Systems
1.A.3.C C02 Emissions from Mobile
Combustion: Railways
l.A.3.d C02 Emissions from Mobile
Combustion: Marine
1.B.2 C02 Emissions from Natural Gas
Systems
1.A.5 C02 Emissions from Stationary
Combustion - Oil - U.S. Territories
l.A.l C02 Emissions from Stationary
Combustion - Oil - Electricity Generation
5.C.1 C02 Emissions from Incineration of
Waste
1.A.5 C02 Emissions from Stationary
Combustion - Coal - U.S. Territories
1.A.5 C02 Emissions from Stationary
Combustion - Gas - 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
1.B.2 CH4 Emissions from Natural Gas
Systems
l.B.l Fugitive Emissions from Coal Mining
1.B.2 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
l.A.3.e CH4 Emissions from Mobile
Combustion: Other
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
l.A.4.a CH4 Emissions from Stationary
Combustion - Commercial
l.A.3.b CH4 Emissions from Mobile
Combustion: Road
l.A.l CH4 Emissions from Stationary
Combustion - Gas - Electricity Generation
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
C02 39.4 • UTtLzTz 2018
C02 38.9 • L, 1990,, 2018i
C02 36.5 • L,T, 1990i, 2018i
C02 34.9 • L, 1990,, 2018i
C02 34.3 • L,T, 1990i, 2018i
C02 22.2 • UTtTz 1990,, 2018,
C02 11.1
C02 4.0
C02 3.0
C02 1.8 • T,
C02 0.4
C02 +
C02 0.0 • T2
CH4 139.7 • UTtUTz 1990,2018
CH4 52.7 • UTtLzTz 1990,2018,
CH4 36.6 • UTtLzTz 1990,2018
CH4 7.0 • L2 1 9902, 20182
CH4 6.2
CH4 4.5 • L2Tz 19902
CH4 1.7 • T2
CH4 1.6
CH4 1.3
CH4 1.0
CH4 1.0
CH4 0.3
CH4 0.2
CH4 0.1
CH4 0.1
A-15
-------
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
l.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity Generation
l.A.l CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
5.C.1 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.l N20 Emissions from Stationary
Combustion - Gas - Electricity Generation
1.A.2 N20 Emissions from Stationary
Combustion - Industrial
l.A.3.e N20 Emissions from Mobile
Combustion: Other
l.A.3.a N20 Emissions from Mobile
Combustion: Aviation
l.A.4.b N20 Emissions from Stationary
Combustion - Residential
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
l.A.4.a N20 Emissions from Stationary
Combustion - Commercial
5.C.1 N20 Emissions from Incineration of
Waste
1.A.3.C N20 Emissions from Mobile
Combustion: Railways
1.A.5 N20 Emissions from Stationary
Combustion - U.S. Territories
1.B.2 N20 Emissions from Petroleum
Systems
l.A.l N20 Emissions from Stationary
Combustion - Wood - Electricity
Generation
1.B.2 N20 Emissions from Natural Gas
Systems
l.A.l N20 Emissions from Stationary
Combustion - Oil - Electricity Generation
l.D.l International Bunker Fuelsc
CH4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
Several
20.3
10.4
4.1
2.7
2.5
1.6
0.9
0.5
0.4
0.3
0.3
0.1
0.1
123.3
L1T1T2
1990i
Industrial Processes
2.C.1 C02 Emissions from Iron and Steel C02
Production & Metallurgical Coke
Production
2.A.1 C02 Emissions from Cement C02
Production
2.B.8 C02 Emissions from Petrochemical C02
Production
2.A.2 C02 Emissions from Lime Production C02
2.B.1 C02 Emissions from Ammonia C02
Production
42.7
40.3
29.4
13.9
13.5
L, T-, L2 T2 1990, 2018i
L, 1990,, 2018i
U T-, 1990,, 2018i
A-16 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
2.A.4 C02 Emissions from Other Process
Uses of Carbonates
2.B.10 C02 Emissions from Carbon Dioxide
Consumption
2.B.10 C02 Emissions from Urea
Consumption for Non-Ag Purposes
2.C.2 C02 Emissions from Ferroalloy
Production
2.B.7 C02 Emissions from Soda Ash
Production
2.B.6 C02 Emissions from Titanium Dioxide
Production
2.C.3 C02 Emissions from Aluminum
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.3 N20 Emissions from Adipic Acid
Production
2.B.2 N20 Emissions from Nitric Acid
Production
2.G 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
2.F.3 Emissions from Substitutes for Ozone
Depleting Substances: Fire Protection
2.F.5 Emissions from Substitutes for Ozone
Depleting Substances: Solvents
2.E PFC, HFC, SF6, and NF3 Emissions from
Electronics Industry
C02 9.4
C02 4.5
C02 3.6
C02 2.1
C02 1.7
C02 1.6
C02 1.5
C02 1.3
C02 1.0
C02 0.9
C02 0.6
C02 0.2
C02 +
CH4 0.3
CH4 +
CH4 +
CH4 +
N20 10.3
N20 9.3
N20 4.2
N20 1.4
N20 0.3
HFCs, PFCs 128.9 • L, T, L2 T2 2018
HFCs, PFCs 19.2 • TMz
HFCs, PFCs 11.8 • T-,
HFCs, PFCs 2.6
HFCs, PFCs 2.0
HiGWP 4.8
A-17
-------
2.G SF6 Emissions from Electrical
sf6
4.1
•
LiTiT2
1990i
Transmission and Distribution
2.B.9 HFC-23 Emissions from HCFC-22
HFCs
3.3
•
LiTiT2
1990i
Production
2.C.3 PFC Emissions from Aluminum
PFC
1.6
•
T1
Production
2.C.4 SF6 Emissions from Magnesium
sf6
1.1
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
4.6
3.G C02 Emissions from Liming
C02
3.1
3.A.1 CH4 Emissions from Enteric
ch4
171.7
•
Li Ti L2
1990, 2018
Fermentation: Cattle
3.B.1 CH4 Emissions from Manure
ch4
35.7
•
LiTiT2
2018i
Management: Cattle
3.B.4 CH4 Emissions from Manure
ch4
26.0
•
Li
2018i
Management: Other Livestock
3.C CH4 Emissions from Rice Cultivation
ch4
13.3
3.A.4 CH4 Emissions from Enteric
ch4
5.8
Fermentation: Other Livestock
3.F CH4 Emissions from Field Burning of
ch4
0.4
Agricultural Residues
3.D.1 Direct N20 Emissions from Agricultural
n2o
285.7
•
Li L2
1990, 2018
Soil Management
3.D.2 Indirect N20 Emissions from Applied
n2o
52.5
•
Li Ti L2 T2
1990, 2018
Nitrogen
3.B.1 N20 Emissions from Manure
n2o
15.4
Management: Cattle
3.B.4 N20 Emissions from Manure
n2o
4.1
Management: Other Livestock
3.F N20 Emissions from Field Burning of
n2o
0.2
Agricultural Residues
Waste
5.A CH4 Emissions from Landfills
ch4
110.6
•
Li Ti L2 T2
1990, 2018
5.D CH4 Emissions from Wastewater
ch4
14.2
Treatment
5.B CH4 Emissions from Composting
ch4
2.5
5.D N20 Emissions from Wastewater
n2o
5.0
Treatment
5.B N20 Emissions from Composting
n2o
2.2
Land Use, Land Use Change, and Forestry
4.E.2 Net C02 Emissions from Land
co2
79.3
•
Li Ti L2 T2
1990, 2018
Converted to Settlements
4.B.2 Net C02 Emissions from Land
co2
55.3
•
Li L2
1990, 2018
Converted to Cropland
4.C.1 Net C02 Emissions from Grassland
co2
11.2
•
l2t2
19902, 20182
Remaining Grassland
4.D.2 Net C02 Emissions from Land
co2
(+)
Converted to Wetlands
4.D.1 Net C02 Emissions from Coastal
co2
(+)
Wetlands Remaining Coastal Wetlands
4.B.1 Net C02 Emissions from Cropland
co2
(+)
•
Li Ti L2 T2
1990, 20182
Remaining Cropland
A-18 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
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
CH4
ch4
ch4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
n2o
n2o
(+)
(+)
(+)
(+)
11.3
3.6
0.3
+
Li T, L2 T2
Li L2
Li Ti L2 T2
Li Ti L2 T2
Ti
2018
1990, 2018
1990, 2018
1990, 2018
7.5
2.4
0.5
0.3
0.1
0.1
Ti
+ Does not exceed 0.05 MMT CO2 Eq.
3 Forthe ID criteria, Q refers to "Qualitative," L refers to a key category identified through a level assessment; T refers to a key category identified
through a trend assessment and the subscripted number refers to either an Approach 1 or Approach 2 assessment (e.g., L2 designates a source is a
key category for an Approach 2 level assessment).
b If the source is a key category for both Li and 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 forthat assessment in onlythat year (e.g., 19902designates a source is
a key category for the Approach 2 assessment only in 1990).
c Emissions from these sources not included in emission totals.
d This source category was excluded from the analysis.
Note: Parentheses indicate negative values (or sequestration).
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 2006 IPCC 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.
Including the Approach 2 provides additional insight into why certain source 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. While C02 emissions from geothermal energy are included in the overall emissions estimate,
they are not an official IPCC source category. As a result, there are no guidelines to associate uncertainty with the emissions
estimate; therefore, an uncertainty analysis was not conducted. The uncertainty associated with C02 from mobile
combustion is applied to each mode's emission estimate. No uncertainty was 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. When source and
A-19
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
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.
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 categories, while still maintaining the category definitions that constitute how the emissions estimates were calculated
for this report. As such, some of the category names used in the key category analysis may differ from the names used in
the main body of the report. Additionally, the United States accounts for some source categories, including fossil fuel
feedstocks, international bunkers, and emissions from U.S. Territories, that are derived from unique data sources using
country-specific methodologies.
Table A-4 through Table A-7 contain the 1990 and 2018 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 decreasing order, so that the source or sink categories with the highest
trend assessments appear first. The trend assessments are summed until the threshold of 95 percent is reached; all
categories that fall within that cumulative 95 percent are considered key categories.
For 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 2018 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.
Table A-4:1990 Key Source Category Approach 1 and Approach 2 Analysis—Level Assessment, without LULUCF
Direct Approach 1 Approach 2
Greenhouse 1990 Estimate Level Cumulativ Level
CRF Source Categories Gas (MMT C02 Eq.) Assessment e 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.bC02 Emissions from Mobile C02 1,163.9 0.18 0.42 6% 0.011
Combustion: Road
1.A.2 C02 Emissions from Stationary C02 408.5 0.06 0.48 7% 0.005
Combustion - Gas - Industrial
A-20 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1.A.2 C02 Emissions from Stationary
C02
293.3
0.05
0.53
21%
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
1.B.2 CH4 Emissions from Natural Gas
ch4
183.2
0.03
0.67
17%
0.005
Systems
5.A CH4 Emissions from Landfills
ch4
179.6
0.03
0.69
40%
0.011
l.A.l C02 Emissions from Stationary
C02
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
0.75
18%
0.004
Fermentation: Cattle
1.A.2 C02 Emissions from Stationary
C02
155.2
0.02
0.77
16%
0.004
Combustion - Coal - Industrial
l.A.4.a C02 Emissions from Stationary
C02
142.0
0.02
0.79
7%
0.002
Combustion - Gas - Commercial
1.A.5 C02 Emissions from Non-Energy Use
C02
119.5
0.02
0.81
38%
0.007
of Fuels
2.C.1 C02 Emissions from Iron and Steel
C02
104.7
0.02
0.83
18%
0.003
Production & Metallurgical Coke
Production
l.A.l C02 Emissions from Stationary
C02
97.5
0.02
0.84
8%
0.001
Combustion - Oil - Electricity
Generation
l.A.4.b C02 Emissions from Stationary
C02
97.4
0.02
0.86
6%
0.001
Combustion - Oil - Residential
l.B.l Fugitive Emissions from Coal Mining
ch4
96.5
0.01
0.87
17%
0.002
l.A.4.a C02 Emissions from Stationary
C02
74.2
0.01
0.88
6%
0.001
Combustion - Oil - Commercial
l.A.3.d C02 Emissions from Mobile
C02
46.3
0.01
0.89
6%
<0.001
Combustion: Marine
1.B.2 CH4 Emissions from Petroleum
ch4
46.2
0.01
0.90
38%
0.003
Systems
2.B.9 HFC-23 Emissions from HCFC-22
HFCs
46.1
0.01
0.91
10%
0.001
Production
3.D.2 Indirect N20 Emissions from
N20
43.4
0.01
0.91
151%
0.010
Applied Nitrogen
l.A.3.b N20 Emissions from Mobile
n2o
37.7
0.01
0.92
14%
0.001
Combustion: Road
l.A.3.e C02 Emissions from Mobile
C02
36.0
0.01
0.92
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
1.B.2 C02 Emissions from Natural Gas
C02
32.2
<0.01
0.94
17%
0.001
Systems
1.A.5 C02 Emissions from Stationary
C02
26.9
<0.01
0.94
11%
<0.001
Combustion - Oil - U.S. Territories
2.G SF6 Emissions from Electrical
SF6
23.2
<0.01
0.95
15%
0.001
Transmission and Distribution
2.B.8 C02 Emissions from Petrochemical
C02
21.6
<0.01
0.95
5%
<0.001
Production
A-21
-------
2.C.3 PFC Emissions from Aluminum
PFCs
21.5
<0.01
0.95
7%
<0.001
Production
l.A.l N20 Emissions from Stationary
N20
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
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
62%
0.002
5.D CH4 Emissions from Wastewater
ch4
15.3
<0.01
0.97
28%
0.001
Treatment
2.B.3 N20 Emissions from Adipic Acid
n2o
15.2
<0.01
0.97
5%
<0.001
Production
2.B.1 C02 Emissions from Ammonia
C02
13.0
<0.01
0.97
5%
<0.001
Production
2.B.2 N20 Emissions from Nitric Acid
N20
12.1
<0.01
0.97
5%
<0.001
Production
l.A.4.a C02 Emissions from Stationary
C02
12.0
<0.01
0.98
15%
<0.001
Combustion - Coal - Commercial
2.A.2 C02 Emissions from Lime
C02
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
1.B.2 C02 Emissions from Petroleum
C02
9.6
<0.01
0.98
38%
0.001
Systems
5.C.1 C02 Emissions from Incineration of
C02
8.0
<0.01
0.98
28%
<0.001
Waste
l.B.l Fugitive Emissions from Abandoned
ch4
7.2
<0.01
0.98
20%
<0.001
Underground Coal Mines
l.A.3.e CH4 Emissions from Mobile
ch4
7.0
<0.01
0.98
52%
0.001
Combustion: Other
2.C.3 C02 Emissions from Aluminum
C02
6.8
<0.01
0.99
2%
<0.001
Production
1.B.2 CH4 Emissions from Abandoned Oil
ch4
6.6
<0.01
0.99
220%
0.002
and Gas Wells
2.A.4 C02 Emissions from Other Process
C02
6.3
<0.01
0.99
15%
<0.001
Uses of Carbonates
3.A.4 CH4 Emissions from Enteric
ch4
5.7
<0.01
0.99
18%
<0.001
Fermentation: Other Livestock
l.A.4.b CH4 Emissions from Stationary
ch4
5.2
<0.01
0.99
230%
0.002
Combustion - Residential
l.A.3.b CH4 Emissions from Mobile
ch4
5.2
<0.01
0.99
26%
<0.001
Combustion: Road
2.C.4 SF6 Emissions from Magnesium
sf6
5.2
<0.01
0.99
7%
<0.001
Production and Processing
3.G C02 Emissions from Liming
co2
4.7
<0.01
0.99
111%
0.001
2.G N20 Emissions from Product Uses
N20
4.2
<0.01
0.99
24%
<0.001
2.B.10 C02 Emissions from Urea
co2
3.8
<0.01
0.99
12%
<0.001
Consumption for Non-Ag Purposes
2.E PFC, HFC, SF6, and NF3 Emissions from
HiGWP
3.6
<0.01
0.99
6%
<0.001
Electronics Industry
5.D N20 Emissions from Wastewater
N20
3.4
<0.01
0.99
109%
0.001
Treatment
1.A.2 N20 Emissions from Stationary
n2o
3.1
<0.01
0.99
199%
0.001
Combustion - Industrial
A-22 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1.A.4.b C02 Emissions from Stationary
Combustion - Coal - Residential
3.B.4 N20 Emissions from Manure
Management: Other Livestock
2.C.2 C02 Emissions from Ferroalloy
Production
3.H C02 Emissions from Urea Fertilization
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
l.A.3.e N20 Emissions from Mobile
Combustion: Other
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.5 C02 Emissions from Stationary
Combustion - Coal - U.S. Territories
2.C.6 C02 Emissions from Zinc Production
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
l.A.3.d CH4 Emissions from Mobile
Combustion: Marine
1.A.l C02 Emissions from Stationary
Combustion - Geothermal Energy
2.C.5 C02 Emissions from Lead
Production
5.C.1 N20 Emissions from Incineration of
Waste
1.A.4.a N20 Emissions from Stationary
Combustion - Commercial
5.B CH4 Emissions from Composting
2.B.5 C02 Emissions from Silicon Carbide
Production and Consumption
3.F CH4 Emissions from Field Burning of
Agricultural Residues
5.B 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
C02
N20
C02
C02
CH4
n2o
n2o
n2o
C02
C02
C02
C02
C02
CH4
n2o
C02
C02
N20
ch4
C02
C02
N20
n2o
ch4
C02
CH4
n2o
n2o
ch4
3.0
2.8
2.2
2.0
1.8
1.8
1.7
1.7
1.5
1.5
1.5
1.4
1.2
1.1
1.0
0.6
0.6
0.6
0.6
0.5
0.5
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.3
<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
<0.01
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
1.00
1.00
NE
24%
12%
35%
47%
61%
66%
32%
5%
21%
5%
9%
13%
139%
217%
19%
16%
44%
85%
NA
15%
334%
175%
50%
9%
16%
50%
48%
9%
<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
<0.001
A-23
-------
1.A.3.C N20 Emissions from Mobile
Combustion: Railways
2.B.8 CH4 Emissions from Petrochemical
Production
2.F.4 Emissions from Substitutes for
Ozone Depleting Substances: Aerosols
3.F N20 Emissions from Field Burning of
Agricultural Residues
l.A.l CH4 Emissions from Stationary
Combustion - Gas - Electricity
Generation
l.A.l N20 Emissions from Stationary
Combustion - Oil - Electricity
Generation
1.A.5 N20 Emissions from Stationary
Combustion - U.S. Territories
1.A.3.C CH4 Emissions from Mobile
Combustion: Railways
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
1.A.5 CH4 Emissions from Stationary
Combustion - U.S. Territories
2.E N20 Emissions from Electronics
Industry
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.C.2 CH4 Emissions from Ferroalloy
Production
1.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity
Generation
2.F.1 Emissions from Substitutes for
Ozone Depleting Substances:
Refrigeration and Air Conditioning
1.B.2 N20 Emissions from Petroleum
Systems
1.B.2 C02 Emissions from Abandoned Oil
and Gas Wells
2.F.2 Emissions from Substitutes for
Ozone Depleting Substances: Foam
Blowing Agents
1.B.2 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.l CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
5.C.1 CH4 Emissions from Incineration of
Waste
N20 0.3 <0.01
CH4 0.2 <0.01
HFCs, PFCs 0.2 <0.01
N20 0.2 <0.01
CH4 0.1 <0.01
N20 0.1 <0.01
N20 0.1 <0.01
CH4 0.1 <0.01
CH4 0.1 <0.01
CH4 + <0.01
N20 + <0.01
CH4 + <0.01
CH4 + <0.01
CH4 + <0.01
CH4 + <0.01
HFCs, PFCs + <0.01
N20 + <0.01
C02 + <0.01
HFCs, PFCs + <0.01
N20 + <0.01
N20 + <0.01
C02 + <0.01
CH4 + <0.01
CH4 + <0.01
1.00 71% <0.001
1.00 57% <0.001
1.00 13% <0.001
1.00 19% <0.001
1.00 2% <0.001
1.00 10% <0.001
1.00 198% <0.001
1.00 26% <0.001
1.00 88% <0.001
1.00 55% <0.001
1.00 0% <0.001
1.00 8% <0.001
1.00 19% <0.001
1.00 12% <0.001
1.00 10% <0.001
1.00 13% <0.001
1.00 38% <0.001
1.00 220% <0.001
1.00 11% <0.001
1.00 17% <0.001
1.00 2% <0.001
1.00 3% <0.001
1.00 2% <0.001
1.00 NE <0.001
A-24 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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
1 + Does not exceed 0.05 MMT CO2 Eq.
2 NE (Not Estimated)
3 NA (Not Available)
4 3 Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
5 Note: LULUCF sources and sinks are not included in this analysis.
6
7 Table A-5:1990 Key Source Category Approach 1 and Approach 2 Analysis—Level Assessment, with LULUCF
Direct
Approach 1
Approach 2
Greenhouse
1990 Estimate
Level
Cumulative
Level
CRF Source/Sink Categories
Gas
(MMT CO2 Eq.)
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
1,163.9
0.15
0.36
6%
0.010
Combustion: Road
4.A.1 Net C02 Emissions from Forest Land
C02
733.9
0.10
0.46
28%
0.027
Remaining Forest Land
1.A.2 C02 Emissions from Stationary
C02
408.5
0.05
0.51
7%
0.004
Combustion - Gas - Industrial
1.A.2 C02 Emissions from Stationary
C02
293.3
0.04
0.55
21%
0.008
Combustion - Oil - Industrial
3.D.1 Direct N20 Emissions from Agricultural
N20
272.5
0.04
0.58
31%
0.011
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
1.B.2 CH4 Emissions from Natural Gas
ch4
183.2
0.02
0.67
17%
0.004
Systems
5.A CH4 Emissions from Landfills
ch4
179.6
0.02
0.69
40%
0.010
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
155.2
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 Use of
co2
119.5
0.02
0.79
38%
0.006
Fuels
4.E.1 Net C02 Emissions from Settlements
co2
109.6
0.01
0.80
94%
0.014
Remaining Settlements
4.A.2 Net C02 Emissions from Land
co2
109.4
0.01
0.82
10%
0.001
Converted to Forest Land
2.C.1 C02 Emissions from Iron and Steel
co2
104.7
0.01
0.83
18%
0.002
Production & Metallurgical Coke
Production
l.A.l C02 Emissions from Stationary
co2
97.5
0.01
0.84
8%
0.001
Combustion - Oil - Electricity Generation
C02 0.0
HFCs, PFCs 0.0
HFCs, PFCs 0.0
HFCs 0.0
<0.01 1.00
<0.01 1.00
<0.01 1.00
<0.01 1.00
17% <0.001
18% <0.001
22% <0.001
21% <0.001
A-25
-------
l.A.4.b C02 Emissions from Stationary
C02
97.4
0.01
0.86
6%
0.001
Combustion - Oil - Residential
l.B.l Fugitive Emissions from Coal Mining
ch4
96.5
0.01
0.87
17%
0.002
l.A.4.a C02 Emissions from Stationary
C02
74.2
0.01
0.88
6%
0.001
Combustion - Oil - Commercial
4.E.2 Net C02 Emissions from Land Converted
C02
62.9
0.01
0.89
33%
0.003
to Settlements
4.B.2 Net C02 Emissions from Land
C02
54.1
0.01
0.89
98%
0.007
Converted to Cropland
l.A.3.d C02 Emissions from Mobile
C02
46.3
0.01
0.90
6%
<0.001
Combustion: Marine
1.B.2 CH4 Emissions from Petroleum Systems
ch4
46.2
0.01
0.91
38%
0.002
2.B.9 HFC-23 Emissions from HCFC-22
HFCs
46.1
0.01
0.91
10%
0.001
Production
3.D.2 Indirect N20 Emissions from Applied
N20
43.4
0.01
0.92
151%
0.009
Nitrogen
l.A.3.b N20 Emissions from Mobile
n2o
37.7
<0.01
0.92
14%
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 Production
C02
33.5
<0.01
0.94
6%
<0.001
1.B.2 C02 Emissions from Natural Gas
C02
32.2
<0.01
0.94
17%
0.001
Systems
1.A.5 C02 Emissions from Stationary
C02
26.9
<0.01
0.95
11%
<0.001
Combustion - Oil - U.S. Territories
4.B.1 Net C02 Emissions from Cropland
C02
23.2
<0.01
0.95
497%
0.015
Remaining Cropland
2.G SF6 Emissions from Electrical
SF6
23.2
<0.01
0.95
15%
<0.001
Transmission and Distribution
2.B.8 C02 Emissions from Petrochemical
C02
21.6
<0.01
0.95
5%
<0.001
Production
2.C.3 PFC Emissions from Aluminum
PFCs
21.5
<0.01
0.96
7%
<0.001
Production
l.A.l N20 Emissions from Stationary
N20
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
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 Cultivation
ch4
16.0
<0.01
0.97
62%
0.001
5.D CH4 Emissions from Wastewater
ch4
15.3
<0.01
0.97
28%
0.001
Treatment
2.B.3 N20 Emissions from Adipic Acid
n2o
15.2
<0.01
0.97
5%
<0.001
Production
2.B.1 C02 Emissions from Ammonia
C02
13.0
<0.01
0.97
5%
<0.001
Production
2.B.2 N20 Emissions from Nitric Acid
N20
12.1
<0.01
0.97
5%
<0.001
Production
l.A.4.a C02 Emissions from Stationary
C02
12.0
<0.01
0.98
15%
<0.001
Combustion - Coal - Commercial
2.A.2 C02 Emissions from Lime Production
C02
11.7
<0.01
0.98
2%
<0.001
3.B.1 N20 Emissions from Manure
n2o
11.2
<0.01
0.98
24%
<0.001
Management: Cattle
1.B.2 C02 Emissions from Petroleum Systems
co2
9.6
<0.01
0.98
38%
<0.001
A-26 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
4.C.1 Net C02 Emissions from Grassland
C02
9.1
<0.01
0.98
1296%
0.016
Remaining Grassland
5.C.1 C02 Emissions from Incineration of
C02
8.0
<0.01
0.98
28%
<0.001
Waste
l.B.l Fugitive Emissions from Abandoned
ch4
7.2
<0.01
0.98
20%
<0.001
Underground Coal Mines
l.A.3.e CH4 Emissions from Mobile
ch4
7.0
<0.01
0.98
52%
<0.001
Combustion: Other
2.C.3 C02 Emissions from Aluminum
C02
6.8
<0.01
0.99
2%
<0.001
Production
4.C.2 Net C02 Emissions from Land
C02
6.7
<0.01
0.99
138%
0.001
Converted to Grassland
1.B.2 CH4 Emissions from Abandoned Oil and
ch4
6.6
<0.01
0.99
220%
0.002
Gas Wells
2.A.4 C02 Emissions from Other Process Uses
C02
6.3
<0.01
0.99
15%
<0.001
of Carbonates
3.A.4 CH4 Emissions from Enteric
ch4
5.7
<0.01
0.99
18%
<0.001
Fermentation: Other Livestock
l.A.4.b CH4 Emissions from Stationary
ch4
5.2
<0.01
0.99
230%
0.002
Combustion - Residential
l.A.3.b CH4 Emissions from Mobile
ch4
5.2
<0.01
0.99
26%
<0.001
Combustion: Road
2.C.4 SF6 Emissions from Magnesium
sf6
5.2
<0.01
0.99
7%
<0.001
Production and Processing
3.G C02 Emissions from Liming
C02
4.7
<0.01
0.99
111%
0.001
2.G N20 Emissions from Product Uses
N20
4.2
<0.01
0.99
24%
<0.001
4.D.1 Net C02 Emissions from Coastal
C02
4.0
<0.01
0.99
77%
<0.001
Wetlands Remaining Coastal Wetlands
2.B.10 C02 Emissions from Urea
C02
3.8
<0.01
0.99
12%
<0.001
Consumption for Non-Ag Purposes
2.E PFC, HFC, SF6, and NF3 Emissions from
HiGWP
3.6
<0.01
0.99
6%
<0.001
Electronics Industry
4.D.1 CH4 Emissions from Coastal Wetlands
ch4
3.4
<0.01
0.99
30%
<0.001
Remaining Coastal Wetlands
5.D N20 Emissions from Wastewater
n2o
3.4
<0.01
0.99
109%
<0.001
Treatment
1.A.2 N20 Emissions from Stationary
n2o
3.1
<0.01
0.99
199%
0.001
Combustion - Industrial
l.A.4.b C02 Emissions from Stationary
C02
3.0
<0.01
1.00
NE
<0.001
Combustion - Coal - Residential
3.B.4 N20 Emissions from Manure
N20
2.8
<0.01
1.00
24%
<0.001
Management: Other Livestock
2.C.2 C02 Emissions from Ferroalloy
C02
2.2
<0.01
1.00
12%
<0.001
Production
3.H C02 Emissions from Urea Fertilization
C02
2.0
<0.01
1.00
35%
<0.001
4.E.1 N20 Emissions from Settlement Soils
N20
2.0
<0.01
1.00
54%
<0.001
1.A.2 CH4 Emissions from Stationary
ch4
1.8
<0.01
1.00
47%
<0.001
Combustion - Industrial
l.A.3.e N20 Emissions from Mobile
n2o
1.8
<0.01
1.00
61%
<0.001
Combustion: Other
l.A.3.a N20 Emissions from Mobile
n2o
1.7
<0.01
1.00
66%
<0.001
Combustion: Aviation
2.B.4 N20 Emissions from Caprolactam,
n2o
1.7
<0.01
1.00
32%
<0.001
Glyoxal, and Glyoxylic Acid Production
2.A.3 C02 Emissions from Glass Production
co2
1.5
<0.01
1.00
5%
<0.001
2.B.10 C02 Emissions from Phosphoric Acid
co2
1.5
<0.01
1.00
21%
<0.001
Production
A-27
-------
2.B.10 C02 Emissions from Carbon Dioxide
C02
1.5
<0.01
1.00
5%
<0.001
Consumption
2.B.7 C02 Emissions from Soda Ash
C02
1.4
<0.01
1.00
9%
<0.001
Production
2.B.6 C02 Emissions from Titanium Dioxide
C02
1.2
<0.01
1.00
13%
<0.001
Production
l.A.4.a CH4 Emissions from Stationary
ch4
1.1
<0.01
1.00
139%
<0.001
Combustion - Commercial
l.A.4.b N20 Emissions from Stationary
n2o
1.0
<0.01
1.00
217%
<0.001
Combustion - Residential
4.A.1 CH4 Emissions from Forest Fires
ch4
0.9
<0.01
1.00
15%
<0.001
1.A.5 C02 Emissions from Stationary
C02
0.6
<0.01
1.00
19%
<0.001
Combustion - Coal - U.S. Territories
2.C.6 C02 Emissions from Zinc Production
C02
0.6
<0.01
1.00
16%
<0.001
4.A.1 N20 Emissions from Forest Fires
N20
0.6
<0.01
1.00
12%
<0.001
l.A.3.d N20 Emissions from Mobile
n2o
0.6
<0.01
1.00
44%
<0.001
Combustion: Marine
l.A.3.d CH4 Emissions from Mobile
ch4
0.6
<0.01
1.00
85%
<0.001
Combustion: Marine
l.A.l C02 Emissions from Stationary
C02
0.5
<0.01
1.00
NA
<0.001
Combustion - Geothermal Energy
2.C.5 C02 Emissions from Lead Production
C02
0.5
<0.01
1.00
15%
<0.001
5.C.1 N20 Emissions from Incineration of
N20
0.5
<0.01
1.00
334%
<0.001
Waste
l.A.4.a N20 Emissions from Stationary
n2o
0.4
<0.01
1.00
175%
<0.001
Combustion - Commercial
5.B CH4 Emissions from Composting
ch4
0.4
<0.01
1.00
50%
<0.001
2.B.5 C02 Emissions from Silicon Carbide
C02
0.4
<0.01
1.00
9%
<0.001
Production and Consumption
3.F CH4 Emissions from Field Burning of
ch4
0.3
<0.01
1.00
16%
<0.001
Agricultural Residues
5.B N20 Emissions from Composting
n2o
0.3
<0.01
1.00
50%
<0.001
l.A.l N20 Emissions from Stationary
n2o
0.3
<0.01
1.00
48%
<0.001
Combustion - Gas - Electricity Generation
l.A.l CH4 Emissions from Stationary
ch4
0.3
<0.01
1.00
9%
<0.001
Combustion - Coal - Electricity Generation
1.A.3.C N20 Emissions from Mobile
n2o
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 Ozone
HFCs,
0.2
<0.01
1.00
13%
<0.001
Depleting Substances: Aerosols
PFCs
3.F N20 Emissions from Field Burning of
N20
0.2
<0.01
1.00
19%
<0.001
Agricultural Residues
4.D.1 N20 Emissions from Coastal Wetlands
n2o
0.1
<0.01
1.00
116%
<0.001
Remaining Coastal Wetlands
4.A.4 N20 Emissions from Drained Organic
n2o
0.1
<0.01
1.00
128%
<0.001
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
10%
<0.001
Combustion - Oil - Electricity Generation
4.F.4 CH4 Emissions from Grass Fires
ch4
0.1
<0.01
1.00
146%
<0.001
A-28 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1.A.5 N20 Emissions from Stationary
Combustion - U.S. Territories
1.A.3.C CH4 Emissions from Mobile
Combustion: Railways
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
1.A.5 CH4 Emissions from Stationary
Combustion - U.S. Territories
4.D.2 Net C02 Emissions from Land
Converted to Wetlands
2.E N20 Emissions from Electronics Industry
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.C.2 CH4 Emissions from Ferroalloy
Production
1.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity Generation
4.D.2 CH4 Emissions from Land Converted to
Coastal Wetlands
2.F.1 Emissions from Substitutes for Ozone
Depleting Substances: Refrigeration and Air
Conditioning
4.A.4 CH4 Emissions from Drained Organic
Soils
1.B.2 N20 Emissions from Petroleum Systems
1.B.2 C02 Emissions from Abandoned Oil and
Gas Wells
4.D.1 CH4 Emissions from Peatlands
Remaining Peatlands
2.F.2 Emissions from Substitutes for Ozone
Depleting Substances: Foam Blowing
Agents
1.B.2 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
4.D.1 N20 Emissions from Peatlands
Remaining Peatlands
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
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
|\|20 0.1 <0.01
CH4 0.1 <0.01
CH4 0.1 <0.01
CH4 + <0.01
C02 + <0.01
|\|20 + <0.01
CH4 + <0.01
CH4 + <0.01
ch4 + <0.01
ch4 + <0.01
ch4 + <0.01
HFCs, + <0.01
PFCs
CH4 + <0.01
|\|20 + <0.01
C02 + <0.01
CH4 + <0.01
HFCs, + <0.01
PFCs
|\|20 + <0.01
|\|20 + <0.01
C02 + <0.01
|\|20 + <0.01
CH4 + <0.01
ch4 + <0.01
co2 0.0 <0.01
HFCs, 0.0 <0.01
PFCs
HFCs, 0.0 <0.01
PFCs
HFCs 0.0 <0.01
1.00 198% <0.001
1.00 26% <0.001
1.00 88% <0.001
1.00 55% <0.001
1.00 34% <0.001
1.00 0% <0.001
1.00 8% <0.001
1.00 19% <0.001
1.00 12% <0.001
1.00 10% <0.001
1.00 30% <0.001
1.00 13% <0.001
1.00 80% <0.001
1.00 38% <0.001
1.00 220% <0.001
1.00 88% <0.001
1.00 11% <0.001
1.00 17% <0.001
1.00 2% <0.001
1.00 3% <0.001
1.00 62% <0.001
1.00 2% <0.001
1.00 NE <0.001
1.00 17% <0.001
1.00 18% <0.001
1.00 22% <0.001
1.00 21% <0.001
A-29
-------
1 NA (Not Available)
2 3 Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
3
4 Table A-6: 2018 Key Source Category Approach 1 and Approach 2 Analysis—Level Assessment, without LULUCF
Direct
Approach 1
Approach 2
Greenhouse
2018 Estimate
Level
Cumulative
Level
CRF Source Categories
Gas
(MMT C02 Eq.)
Assessment
Total
Uncertainty3
Assessment
l.A.3.b C02 Emissions from Mobile
C02
1,499.8
0.22
0.22
6%
0.014
Combustion: Road
l.A.l C02 Emissions from Stationary
co2
1,152.9
0.17
0.40
10%
0.017
Combustion - Coal - Electricity
Generation
l.A.l C02 Emissions from Stationary
co2
577.4
0.09
0.48
5%
0.004
Combustion - Gas - Electricity Generation
1.A.2 C02 Emissions from Stationary
co2
514.8
0.08
0.56
7%
0.005
Combustion - Gas - Industrial
3.D.1 Direct N20 Emissions from
n2o
285.7
0.04
0.60
31%
0.013
Agricultural Soil Management
1.A.2 C02 Emissions from Stationary
co2
282.1
0.04
0.65
21%
0.009
Combustion - Oil - Industrial
l.A.4.b C02 Emissions from Stationary
co2
273.7
0.04
0.69
7%
0.003
Combustion - Gas - Residential
l.A.4.a C02 Emissions from Stationary
co2
192.6
0.03
0.72
7%
0.002
Combustion - Gas - Commercial
l.A.3.a C02 Emissions from Mobile
co2
173.9
0.03
0.74
6%
0.002
Combustion: Aviation
3.A.1 CH4 Emissions from Enteric
ch4
171.7
0.03
0.77
18%
0.005
Fermentation: Cattle
1.B.2 CH4 Emissions from Natural Gas
ch4
139.7
0.02
0.79
17%
0.004
Systems
1.A.5 C02 Emissions from Non-Energy Use
co2
134.5
0.02
0.81
38%
0.008
of Fuels
2.F.1 Emissions from Substitutes for Ozone
HFCs,
128.9
0.02
0.83
13%
0.002
Depleting Substances: Refrigeration and
PFCs
Air Conditioning
5.A CH4 Emissions from Landfills
ch4
110.6
0.01656
0.84
40%
0.007
l.A.4.a C02 Emissions from Stationary
C02
63.9
0.01
0.85
6%
0.001
Combustion - Oil - Commercial
l.A.4.b C02 Emissions from Stationary
C02
62.2
0.01
0.86
6%
0.001
Combustion - Oil - Residential
l.B.l Fugitive Emissions from Coal Mining
ch4
52.7
0.01
0.87
17%
0.001
3.D.2 Indirect N20 Emissions from Applied
n2o
52.5
0.01
0.88
151%
0.012
Nitrogen
1.A.2 C02 Emissions from Stationary
C02
49.8
0.01
0.89
16%
0.001
Combustion - Coal - Industrial
l.A.3.e C02 Emissions from Mobile
co2
49.2
0.01
0.89
6%
<0.001
Combustion: Other
2.C.1 C02 Emissions from Iron and Steel
co2
42.7
0.01
0.90
18%
0.001
Production & Metallurgical Coke
Production
2.A.1 C02 Emissions from Cement
co2
40.3
0.01
0.91
6%
<0.001
Production
1.B.2 C02 Emissions from Petroleum
co2
39.4
0.01
0.91
38%
0.002
Systems
1.A.3.C C02 Emissions from Mobile
co2
38.9
0.01
0.92
6%
<0.001
Combustion: Railways
A-30 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1.B.2 CH4 Emissions from Petroleum
ch4
36.6
0.01
0.92
38%
0.002
Systems
l.A.3.d C02 Emissions from Mobile
C02
36.5
0.01
0.93
6%
<0.001
Combustion: Marine
3.B.1 CH4 Emissions from Manure
ch4
35.7
0.01
0.93
20%
0.001
Management: Cattle
1.B.2 C02 Emissions from Natural Gas
C02
34.9
0.01
0.94
17%
0.001
Systems
1.A.5 C02 Emissions from Stationary
C02
34.3
0.01
0.94
11%
0.001
Combustion - Oil - U.S. Territories
2.B.8 C02 Emissions from Petrochemical
C02
29.4
<0.01
0.95
5%
<0.001
Production
3.B.4 CH4 Emissions from Manure
ch4
26.0
<0.01
0.95
20%
0.001
Management: Other Livestock
l.A.l C02 Emissions from Stationary
C02
22.2
<0.01
0.96
8%
<0.001
Combustion - Oil - Electricity Generation
l.A.l N20 Emissions from Stationary
N20
20.3
<0.01
0.96
48%
0.001
Combustion - Coal - Electricity
Generation
2.F.4 Emissions from Substitutes for Ozone
HFCs,
19.2
<0.01
0.96
13%
<0.001
Depleting Substances: Aerosols
PFCs
3.B.1 N20 Emissions from Manure
N20
15.4
<0.01
0.96
24%
0.001
Management: Cattle
5.D CH4 Emissions from Wastewater
ch4
14.2
<0.01
0.97
28%
0.001
Treatment
2.A.2 C02 Emissions from Lime Production
C02
13.9
<0.01
0.97
2%
<0.001
2.B.1 C02 Emissions from Ammonia
C02
13.5
<0.01
0.97
5%
<0.001
Production
3.C CH4 Emissions from Rice Cultivation
ch4
13.3
<0.01
0.97
62%
0.001
2.F.2 Emissions from Substitutes for Ozone
HFCs,
11.8
<0.01
0.97
11%
<0.001
Depleting Substances: Foam Blowing
PFCs
Agents
5.C.1 C02 Emissions from Incineration of
C02
11.1
<0.01
0.98
28%
<0.001
Waste
l.A.3.b N20 Emissions from Mobile
N20
10.4
<0.01
0.98
14%
<0.001
Combustion: Road
2.B.3 N20 Emissions from Adipic Acid
n2o
10.3
<0.01
0.98
5%
<0.001
Production
2.A.4 C02 Emissions from Other Process
C02
9.4
<0.01
0.98
15%
<0.001
Uses of Carbonates
2.B.2 N20 Emissions from Nitric Acid
N20
9.3
<0.01
0.98
5%
<0.001
Production
1.B.2 CH4 Emissions from Abandoned Oil
ch4
7.0
<0.01
0.98
220%
0.002
and Gas Wells
l.B.l Fugitive Emissions from Abandoned
ch4
6.2
<0.01
0.98
20%
<0.001
Underground Coal Mines
3.A.4 CH4 Emissions from Enteric
ch4
5.8
<0.01
0.98
18%
<0.001
Fermentation: Other Livestock
5.D N20 Emissions from Wastewater
n2o
5.0
<0.01
0.99
109%
0.001
Treatment
2.E PFC, HFC, SF6, and NF3 Emissions from
HiGWP
4.8
<0.01
0.99
6%
<0.001
Electronics Industry
3.H C02 Emissions from Urea Fertilization
C02
4.6
<0.01
0.99
35%
<0.001
l.A.4.b CH4 Emissions from Stationary
ch4
4.5
<0.01
0.99
230%
0.002
Combustion - Residential
2.B.10 C02 Emissions from Carbon Dioxide
C02
4.5
<0.01
0.99
5%
<0.001
Consumption
A-31
-------
2.G N20 Emissions from Product Uses
3.B.4 N20 Emissions from Manure
Management: Other Livestock
1.A.l N20 Emissions from Stationary
Combustion - Gas - Electricity Generation
2.G SF6 Emissions from Electrical
Transmission and Distribution
1.A.5 C02 Emissions from Stationary
Combustion - Coal - U.S. Territories
2.B.10 C02 Emissions from Urea
Consumption for Non-Ag Purposes
2.B.9 HFC-23 Emissions from HCFC-22
Production
3.G C02 Emissions from Liming
1.A.5 C02 Emissions from Stationary
Combustion - Gas - U.S. Territories
1.A.2 N20 Emissions from Stationary
Combustion - Industrial
2.F.3 Emissions from Substitutes for Ozone
Depleting Substances: Fire Protection
1.A.3.e N20 Emissions from Mobile
Combustion: Other
5.B CH4 Emissions from Composting
5.B N20 Emissions from Composting
2.C.2 C02 Emissions from Ferroalloy
Production
2.F.5 Emissions from Substitutes for Ozone
Depleting Substances: Solvents
1.A.4.a C02 Emissions from Stationary
Combustion - Coal - Commercial
2.B.7 C02 Emissions from Soda Ash
Production
l.A.3.e CH4 Emissions from Mobile
Combustion: Other
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
2.B.6 C02 Emissions from Titanium Dioxide
Production
1.A.3.a N20 Emissions from Mobile
Combustion: Aviation
2.C.3 PFC Emissions from Aluminum
Production
2.C.3 C02 Emissions from Aluminum
Production
2.B.4 N20 Emissions from Caprolactam,
Glyoxal, and Glyoxylic Acid Production
1.A.4.a CH4 Emissions from Stationary
Combustion - Commercial
2.A.3 C02 Emissions from Glass Production
2.C.4 SF6 Emissions from Magnesium
Production and Processing
2.C.6 C02 Emissions from Zinc Production
l.A.3.b CH4 Emissions from Mobile
Combustion: Road
l.A.l CH4 Emissions from Stationary
Combustion - Gas - Electricity Generation
|\|20 4.2 <0.01
|\|20 4.1 <0.01
|\|20 4.1 <0.01
SF6 4.1 <0.01
C02 4.0 <0.01
C02 3.6 <0.01
HFCs 3.3 <0.01
C02 3.1 <0.01
C02 3.0 <0.01
|\|20 2.7 <0.01
HFCs, 2.6 <0.01
PFCs
|\|20 2.5 <0.01
CH4 2.5 <0.01
|\|20 2.2 <0.01
C02 2.1 <0.01
HFCs, 2.0 <0.01
PFCs
C02 1.8 <0.01
C02 1.7 <0.01
CH4 1.7 <0.01
CH4 1.6 <0.01
C02 1.6 <0.01
|\|20 1.6 <0.01
PFCs 1.6 <0.01
C02 1.5 <0.01
|\|20 1.4 <0.01
CH4 1.3 <0.01
C02 1.3 <0.01
SF6 1.1 <0.01
C02 1.0 <0.01
CH4 1.0 <0.01
CH4 1.0 <0.01
0.99 24% <0.001
0.99 24% <0.001
0.99 48% <0.001
0.99 15% <0.001
0.99 19% <0.001
0.99 12% <0.001
0.99 10% <0.001
0.99 111% 0.001
0.99 17% <0.001
0.99 199% 0.001
0.99 18% <0.001
0.99 61% <0.001
0.99 50% <0.001
1.00 50% <0.001
1.00 12% <0.001
1.00 22% <0.001
1.00 15% <0.001
1.00 9% <0.001
1.00 52% <0.001
1.00 47% <0.001
1.00 13% <0.001
1.00 66% <0.001
1.00 7% <0.001
1.00 2% <0.001
1.00 32% <0.001
1.00 139% <0.001
1.00 5% <0.001
1.00 7% <0.001
1.00 16% <0.001
1.00 26% <0.001
1.00 2% <0.001
A-32 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
2.B.10 C02 Emissions from Phosphoric Acid
Production
1.A.4.b N20 Emissions from Stationary
Combustion - Residential
2.C.5 C02 Emissions from Lead Production
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
l.A.l C02 Emissions from Stationary
Combustion - Geothermal Energy
3.F CH4 Emissions from Field Burning of
Agricultural Residues
l.A.4.a N20 Emissions from Stationary
Combustion - Commercial
5.C.1 N20 Emissions from Incineration of
Waste
1.A.3.d CH4 Emissions from Mobile
Combustion: Marine
2.B.8 CH4 Emissions from Petrochemical
Production
1.A.3.C N20 Emissions from Mobile
Combustion: Railways
2.E N20 Emissions from Electronics
Industry
1.A.l CH4 Emissions from Stationary
Combustion - Coal - Electricity
Generation
2.B.5 C02 Emissions from Silicon Carbide
Production and Consumption
3.F N20 Emissions from Field Burning of
Agricultural Residues
1.A.5 N20 Emissions from Stationary
Combustion - U.S. Territories
2.C.4 HFC-134a Emissions from Magnesium
Production and Processing
1.A.3.C CH4 Emissions from Mobile
Combustion: Railways
1.B.2 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
1.A.l N20 Emissions from Stationary
Combustion - Wood - Electricity
Generation
2.C.2 CH4 Emissions from Ferroalloy
Production
2.B.5 CH4 Emissions from Silicon Carbide
Production and Consumption
1.B.2 N20 Emissions from Natural Gas
Systems
1.A.l N20 Emissions from Stationary
Combustion - Oil - Electricity Generation
2.C.1 CH4 Emissions from Iron and Steel
Production & Metallurgical Coke
Production
C02
N20
C02
N20
C02
CH4
n2o
n2o
ch4
ch4
n2o
n2o
ch4
C02
N20
n2o
HFCs
CH4
n2o
ch4
ch4
n2o
ch4
ch4
n2o
n2o
ch4
0.9
0.9
0.6
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.2
0.2
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
<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
21%
217%
15%
44%
NA
16%
175%
334%
85%
57%
71%
0%
9%
9%
19%
198%
21%
26%
38%
55%
88%
2%
12%
8%
17%
10%
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
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-33
-------
1.B.2 C02 Emissions from Abandoned Oil
C02
+
<0.01
1.00
220%
<0.001
and Gas Wells
l.A.l CH4 Emissions from Stationary
ch4
+
<0.01
1.00
10%
<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
C02
+
<0.01
1.00
3%
<0.001
Production and Processing
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
C02
0.0
<0.01
1.00
NE
<0.001
Combustion - Coal - Residential
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
NA (Not Available)
3 Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
Note: LULUCF sources and sinks are not included in this analysis.
Table A-7: 2018 Key Source Category Approach 1 and Approach 2 Analysis—Level Assessment with LULUCF
2018
Direct
Estimate
Approach 1
Approach 2
Greenhouse
(MMT C02
Level
Cumulative
Level
CRF Source/Sink Categories
Gas
Eq.)
Assessment
Total
Uncertainty3
Assessment
l.A.3.b C02 Emissions from Mobile
C02
1,499.8
0.19
0.19
6%
0.012
Combustion: Road
l.A.l C02 Emissions from Stationary
C02
1,152.9
0.15
0.34
10%
0.014
Combustion - Coal - Electricity
Generation
4.A.1 Net C02 Emissions from Forest Land
C02
663.2
0.09
0.43
28%
0.023
Remaining Forest Land
l.A.l C02 Emissions from Stationary
C02
577.4
0.07
0.50
5%
0.004
Combustion - Gas - Electricity Generation
1.A.2 C02 Emissions from Stationary
C02
514.8
0.07
0.57
7%
0.005
Combustion - Gas - Industrial
3.D.1 Direct N20 Emissions from
N20
285.7
0.04
0.60
31%
0.011
Agricultural Soil Management
1.A.2 C02 Emissions from Stationary
C02
282.1
0.04
0.64
21%
0.008
Combustion - Oil - Industrial
l.A.4.b C02 Emissions from Stationary
C02
273.7
0.04
0.67
7%
0.002
Combustion - Gas - Residential
l.A.4.a C02 Emissions from Stationary
C02
192.6
0.02
0.70
7%
0.002
Combustion - Gas - Commercial
l.A.3.a C02 Emissions from Mobile
C02
173.9
0.02
0.72
6%
0.001
Combustion: Aviation
3.A.1 CH4 Emissions from Enteric
ch4
171.7
0.02
0.74
18%
0.004
Fermentation: Cattle
1.B.2 CH4 Emissions from Natural Gas
ch4
139.7
0.02
0.76
17%
0.003
Systems
1.A.5 C02 Emissions from Non-Energy Use
co2
134.5
0.02
0.78
38%
0.007
of Fuels
2.F.1 Emissions from Substitutes for Ozone
HFCs,
128.9
0.02
0.79
13%
0.002
Depleting Substances: Refrigeration and
PFCs
Air Conditioning
4.E.1 Net C02 Emissions from Settlements
C02
126.2
0.02
0.81
94%
0.015
Remaining Settlements
A-34 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
4.A.2 Net C02 Emissions from Land
C02
110.6
0.01
0.82
10%
0.001
Converted to Forest Land
5.A CH4 Emissions from Landfills
ch4
110.6
0.01
0.84
40%
0.006
4.E.2 Net C02 Emissions from Land
C02
79.3
0.01
0.85
33%
0.003
Converted to Settlements
l.A.4.a C02 Emissions from Stationary
C02
63.9
0.01
0.86
6%
<0.001
Combustion - Oil - Commercial
l.A.4.b C02 Emissions from Stationary
C02
62.2
0.01
0.87
6%
<0.001
Combustion - Oil - Residential
4.B.2 Net C02 Emissions from Land
C02
55.3
0.01
0.87
98%
0.007
Converted to Cropland
l.B.l Fugitive Emissions from Coal Mining
ch4
52.7
0.01
0.88
17%
0.001
3.D.2 Indirect N20 Emissions from Applied
n2o
52.5
0.01
0.89
151%
0.010
Nitrogen
1.A.2 C02 Emissions from Stationary
C02
49.8
0.01
0.89
16%
0.001
Combustion - Coal - Industrial
l.A.3.e C02 Emissions from Mobile
C02
49.2
0.01
0.90
6%
<0.001
Combustion: Other
2.C.1 C02 Emissions from Iron and Steel
C02
42.7
0.01
0.90
18%
0.001
Production & Metallurgical Coke
Production
2.A.1 C02 Emissions from Cement
C02
40.3
0.01
0.91
6%
<0.001
Production
1.B.2 C02 Emissions from Petroleum
C02
39.4
0.01
0.91
38%
0.002
Systems
1.A.3.C C02 Emissions from Mobile
C02
38.9
<0.01
0.92
6%
<0.001
Combustion: Railways
1.B.2 CH4 Emissions from Petroleum
ch4
36.6
<0.01
0.92
38%
0.002
Systems
l.A.3.d C02 Emissions from Mobile
C02
36.5
<0.01
0.93
6%
<0.001
Combustion: Marine
3.B.1 CH4 Emissions from Manure
ch4
35.7
<0.01
0.93
20%
0.001
Management: Cattle
1.B.2 C02 Emissions from Natural Gas
C02
34.9
<0.01
0.94
17%
0.001
Systems
1.A.5 C02 Emissions from Stationary
C02
34.3
<0.01
0.94
11%
<0.001
Combustion - Oil - U.S. Territories
2.B.8 C02 Emissions from Petrochemical
C02
29.4
<0.01
0.95
5%
<0.001
Production
3.B.4 CH4 Emissions from Manure
ch4
26.0
<0.01
0.95
20%
0.001
Management: Other Livestock
4.C.2 Net C02 Emissions from Land
C02
24.6
<0.01
0.95
138%
0.004
Converted to Grassland
l.A.l C02 Emissions from Stationary
C02
22.2
<0.01
0.95
8%
<0.001
Combustion - Oil - Electricity Generation
l.A.l N20 Emissions from Stationary
N20
20.3
<0.01
0.96
48%
0.001
Combustion - Coal - Electricity
Generation
2.F.4 Emissions from Substitutes for Ozone
HFCs,
19.2
<0.01
0.96
13%
<0.001
Depleting Substances: Aerosols
PFCs
4.B.1 Net C02 Emissions from Cropland
C02
16.6
<0.01
0.96
497%
0.011
Remaining Cropland
3.B.1 N20 Emissions from Manure
N20
15.4
<0.01
0.96
24%
<0.001
Management: Cattle
5.D CH4 Emissions from Wastewater
ch4
14.2
<0.01
0.97
28%
0.001
Treatment
2.A.2 C02 Emissions from Lime Production
C02
13.9
<0.01
0.97
2%
<0.001
A-35
-------
2.B.1 C02 Emissions from Ammonia
C02
13.5
<0.01
0.97
5%
<0.001
Production
3.C CH4 Emissions from Rice Cultivation
ch4
13.3
<0.01
0.97
62%
0.001
2.F.2 Emissions from Substitutes for Ozone
HFCs,
11.8
<0.01
0.97
11%
<0.001
Depleting Substances: Foam Blowing
PFCs
Agents
4.A.1 CH4 Emissions from Forest Fires
ch4
11.3
<0.01
0.97
15%
<0.001
4.C.1 Net C02 Emissions from Grassland
C02
11.2
<0.01
0.98
1296%
0.019
Remaining Grassland
5.C.1 C02 Emissions from Incineration of
C02
11.1
<0.01
0.98
28%
<0.001
Waste
l.A.3.b N20 Emissions from Mobile
N20
10.4
<0.01
0.98
14%
<0.001
Combustion: Road
2.B.3 N20 Emissions from Adipic Acid
n2o
10.3
<0.01
0.98
5%
<0.001
Production
2.A.4 C02 Emissions from Other Process
C02
9.4
<0.01
0.98
15%
<0.001
Uses of Carbonates
2.B.2 N20 Emissions from Nitric Acid
N20
9.3
<0.01
0.98
5%
<0.001
Production
4.A.1 N20 Emissions from Forest Fires
n2o
7.5
<0.01
0.98
12%
<0.001
1.B.2 CH4 Emissions from Abandoned Oil
ch4
7.0
<0.01
0.98
220%
0.002
and Gas Wells
l.B.l Fugitive Emissions from Abandoned
ch4
6.2
<0.01
0.98
20%
<0.001
Underground Coal Mines
3.A.4 CH4 Emissions from Enteric
ch4
5.8
<0.01
0.99
18%
<0.001
Fermentation: Other Livestock
5.D N20 Emissions from Wastewater
n2o
5.0
<0.01
0.99
109%
0.001
Treatment
2.E PFC, HFC, SF6, and NF3 Emissions from
HiGWP
4.8
<0.01
0.99
6%
<0.001
Electronics Industry
3.H C02 Emissions from Urea Fertilization
C02
4.6
<0.01
0.99
35%
<0.001
l.A.4.b CH4 Emissions from Stationary
ch4
4.5
<0.01
0.99
230%
0.001
Combustion - Residential
2.B.10 C02 Emissions from Carbon Dioxide
C02
4.5
<0.01
0.99
5%
<0.001
Consumption
4.D.1 Net C02 Emissions from Coastal
C02
4.4
<0.01
0.99
77%
<0.001
Wetlands Remaining Coastal Wetlands
2.G N20 Emissions from Product Uses
N20
4.2
<0.01
0.99
24%
<0.001
3.B.4 N20 Emissions from Manure
n2o
4.1
<0.01
0.99
24%
<0.001
Management: Other Livestock
l.A.l N20 Emissions from Stationary
n2o
4.1
<0.01
0.99
48%
<0.001
Combustion - Gas - Electricity Generation
2.G SF6 Emissions from Electrical
sf6
4.1
<0.01
0.99
15%
<0.001
Transmission and Distribution
1.A.5 C02 Emissions from Stationary
co2
4.0
<0.01
0.99
19%
<0.001
Combustion - Coal - U.S. Territories
2.B.10 C02 Emissions from Urea
co2
3.6
<0.01
0.99
12%
<0.001
Consumption for Non-Ag Purposes
4.D.1 CH4 Emissions from Coastal Wetlands
ch4
3.6
<0.01
0.99
30%
<0.001
Remaining Coastal Wetlands
2.B.9 HFC-23 Emissions from HCFC-22
HFCs
3.3
<0.01
0.99
10%
<0.001
Production
3.G C02 Emissions from Liming
C02
3.1
<0.01
0.99
111%
<0.001
1.A.5 C02 Emissions from Stationary
C02
3.0
<0.01
0.99
17%
<0.001
Combustion - Gas - U.S. Territories
1.A.2 N20 Emissions from Stationary
N20
2.7
<0.01
0.99
199%
0.001
Combustion - Industrial
A-36 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
2.F.3 Emissions from Substitutes for Ozone
Depleting Substances: Fire Protection
1.A.3.e N20 Emissions from Mobile
Combustion: Other
5.B CH4 Emissions from Composting
4.E.1 N20 Emissions from Settlement Soils
5.B N20 Emissions from Composting
2.C.2 C02 Emissions from Ferroalloy
Production
2.F.5 Emissions from Substitutes for Ozone
Depleting Substances: Solvents
1.A.4.a C02 Emissions from Stationary
Combustion - Coal - Commercial
2.B.7 C02 Emissions from Soda Ash
Production
l.A.3.e CH4 Emissions from Mobile
Combustion: Other
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
2.B.6 C02 Emissions from Titanium Dioxide
Production
1.A.3.a N20 Emissions from Mobile
Combustion: Aviation
2.C.3 PFC Emissions from Aluminum
Production
2.C.3 C02 Emissions from Aluminum
Production
2.B.4 N20 Emissions from Caprolactam,
Glyoxal, and Glyoxylic Acid Production
1.A.4.a CH4 Emissions from Stationary
Combustion - Commercial
2.A.3 C02 Emissions from Glass Production
2.C.4 SF6 Emissions from Magnesium
Production and Processing
2.C.6 C02 Emissions from Zinc Production
l.A.3.b CH4 Emissions from Mobile
Combustion: Road
1.A.l CH4 Emissions from Stationary
Combustion - Gas - Electricity Generation
2.B.10 C02 Emissions from Phosphoric Acid
Production
1.A.4.b N20 Emissions from Stationary
Combustion - Residential
2.C.5 C02 Emissions from Lead Production
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
4.A.1 N20 Emissions from Forest Soils
l.A.l C02 Emissions from Stationary
Combustion - Geothermal Energy
3.F CH4 Emissions from Field Burning of
Agricultural Residues
l.A.4.a N20 Emissions from Stationary
Combustion - Commercial
4.C.1 N20 Emissions from Grass Fires
5.C.1 N20 Emissions from Incineration of
Waste
HFCs, 2.6 <0.01
PFCs
|\|20 2.5 <0.01
CH4 2.5 <0.01
|\|20 2.4 <0.01
|\|20 2.2 <0.01
C02 2.1 <0.01
HFCs, 2.0 <0.01
PFCs
C02 1.8 <0.01
C02 1.7 <0.01
CH4 1.7 <0.01
CH4 1.6 <0.01
C02 1.6 <0.01
|\|20 1.6 <0.01
PFCs 1.6 <0.01
C02 1.5 <0.01
|\|20 1.4 <0.01
CH4 1.3 <0.01
C02 1.3 <0.01
SF6 1.1 <0.01
C02 1.0 <0.01
CH4 1.0 <0.01
CH4 1.0 <0.01
C02 0.9 <0.01
|\|20 0.9 <0.01
C02 0.6 <0.01
|\|20 0.5 <0.01
|\|20 0.5 <0.01
C02 0.4 <0.01
CH4 0.4 <0.01
|\|20 0.4 <0.01
|\|20 0.3 <0.01
|\|20 0.3 <0.01
0.99 18% <0.001
0.99 61% <0.001
1.00 50% <0.001
1.00 54% <0.001
1.00 50% <0.001
1.00 12% <0.001
1.00 22% <0.001
1.00 15% <0.001
1.00 9% <0.001
1.00 52% <0.001
1.00 47% <0.001
1.00 13% <0.001
1.00 66% <0.001
1.00 7% <0.001
1.00 2% <0.001
1.00 32% <0.001
1.00 139% <0.001
1.00 5% <0.001
1.00 7% <0.001
1.00 16% <0.001
1.00 26% <0.001
1.00 2% <0.001
1.00 21% <0.001
1.00 217% <0.001
1.00 15% <0.001
1.00 44% <0.001
1.00 318% <0.001
1.00 NA <0.001
1.00 16% <0.001
1.00 175% <0.001
1.00 146% <0.001
1.00 334% <0.001
A-37
-------
1.A.3.d CH4 Emissions from Mobile
Combustion: Marine
2.B.8 CH4 Emissions from Petrochemical
Production
1.A.3.C N20 Emissions from Mobile
Combustion: Railways
4.F.4 CH4 Emissions from Grass Fires
2.E N20 Emissions from Electronics
Industry
1.A.l CH4 Emissions from Stationary
Combustion - Coal - Electricity
Generation
2.B.5 C02 Emissions from Silicon Carbide
Production and Consumption
3.F N20 Emissions from Field Burning of
Agricultural Residues
4.D.1 N20 Emissions from Coastal
Wetlands Remaining Coastal Wetlands
1.A.5 N20 Emissions from Stationary
Combustion - U.S. Territories
4.A.4 N20 Emissions from Drained Organic
Soils
2.C.4 HFC-134a Emissions from Magnesium
Production and Processing
1.A.3.C CH4 Emissions from Mobile
Combustion: Railways
1.B.2 N20 Emissions from Petroleum
Systems
1.A.5 CH4 Emissions from Stationary
Combustion - U.S. Territories
4.D.2 Net C02 Emissions from Land
Converted to Wetlands
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
1.A.l N20 Emissions from Stationary
Combustion - Wood - Electricity
Generation
2.C.2 CH4 Emissions from Ferroalloy
Production
4.D.2 CH4 Emissions from Land Converted
to Coastal Wetlands
4.A.4 CH4 Emissions from Drained Organic
Soils
2.B.5 CH4 Emissions from Silicon Carbide
Production and Consumption
1.B.2 N20 Emissions from Natural Gas
Systems
1.A.l N20 Emissions from Stationary
Combustion - Oil - Electricity Generation
2.C.1 CH4 Emissions from Iron and Steel
Production & Metallurgical Coke
Production
1.B.2 C02 Emissions from Abandoned Oil
and Gas Wells
4.D.1 CH4 Emissions from Peatlands
Remaining Peatlands
CH4
ch4
n2o
ch4
n2o
ch4
C02
N20
n2o
n2o
n2o
HFCs
CH4
n2o
ch4
C02
CH4
n2o
ch4
ch4
ch4
ch4
n2o
n2o
ch4
co2
ch4
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.1
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
<0.01
<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
85%
57%
71%
146%
0%
9%
9%
19%
116%
198%
128%
21%
26%
38%
55%
34%
88%
2%
12%
30%
80%
8%
17%
10%
19%
220%
88%
<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-38 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
l.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity Generation
1.A.l CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
2.C.4 C02 Emissions from Magnesium
Production and Processing
4.D.1 N20 Emissions from Peatlands
Remaining Peatlands
5.C.1 CH4 Emissions from Incineration of
Waste
l.A.4.b C02 Emissions from Stationary
Combustion - Coal - Residential
1 + Does not exceed 0.05 MMT CO2 Eq.
2 NE (Not Estimated)
3 NA (Not Available)
4 3 Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
5
6 Table A-8:1990-2018 Key Source Category Approach 1 and 2 Analysis—Trend Assessment, without LULUCF
Direct 1990 Estimate 2018 Estimate Approach 1 Approach 2 %
Greenhouse (MMT C02 (MMT C02 Trend Trend Contribution Cumulative
CRF Source Categories Gas Eq.) Eq.) Assessment Assessment to Trend Total
ch4 + <0.01
ch4 + <0.01
co2 + <0.01
|\|20 + <0.01
ch4 + <0.01
co2 0.0 <0.01
1.00 10% <0.001
1.00 2% <0.001
1.00 3% <0.001
1.00 62% <0.001
1.00 NE <0.001
1.00 NE <0.001
l.A.l C02 Emissions from
C02
1,546.5
1,152.9
0.07
0.006
19.3
19
Stationary Combustion - Coal -
Electricity Generation
l.A.l C02 Emissions from
C02
175.4
577.4
0.06
0.003
16.9
36
Stationary Combustion - Gas -
Electricity Generation
l.A.3.b C02 Emissions from
C02
1,163.9
1,499.8
0.05
0.003
12.5
49
Mobile Combustion: Road
2.F.1 Emissions from Substitutes
HFCs,
+
128.9
0.02
0.002
5.5
54
for Ozone Depleting Substances:
PFCs
Refrigeration and Air
Conditioning
1.A.2 C02 Emissions from
C02
155.2
49.8
0.02
0.003
4.8
59
Stationary Combustion - Coal -
Industrial
1.A.2 C02 Emissions from
C02
408.5
514.8
0.01
0.001
3.9
63
Stationary Combustion - Gas -
Industrial
l.A.l C02 Emissions from
C02
97.5
22.2
0.01
0.001
3.4
66
Stationary Combustion - Oil -
Electricity Generation
5.A CH4 Emissions from Landfills
ch4
179.6
110.6
0.01
0.005
3.2
70
2.C.1 C02 Emissions from Iron and
C02
104.7
42.7
0.01
0.002
2.8
72
Steel Production & Metallurgical
Coke Production
1.B.2 CH4 Emissions from Natural
ch4
183.2
139.7
0.01
0.001
2.2
75
Gas Systems
l.B.l Fugitive Emissions from Coal
ch4
96.5
52.7
0.01
0.001
2.0
77
Mining
l.A.4.a C02 Emissions from
C02
142.0
192.6
0.01
<0.001
1.9
78
Stationary Combustion - Gas -
Commercial
2.B.9 HFC-23 Emissions from
HFCs
46.1
3.3
0.01
0.001
1.9
80
HCFC-22 Production
A-39
-------
l.A.4.b C02 Emissions from
C02
97.4
62.2
0.01
<0.001
1.7
82
Stationary Combustion - Oil -
Residential
1.B.2 C02 Emissions from
C02
9.6
39.4
<0.01
0.002
1.3
83
Petroleum Systems
l.A.3.b N20 Emissions from
N20
37.7
10.4
<0.01
0.001
1.2
85
Mobile Combustion: Road
l.A.4.b C02 Emissions from
C02
237.8
273.7
<0.01
<0.001
1.2
86
Stationary Combustion - Gas -
Residential
1.A.2 C02 Emissions from
C02
293.3
282.1
<0.01
0.001
0.9
87
Stationary Combustion - Oil -
Industrial
2.C.3 PFC Emissions from
PFCs
21.5
1.6
<0.01
<0.001
0.9
88
Aluminum Production
l.A.3.a C02 Emissions from Mobile
C02
187.4
173.9
<0.01
<0.001
0.9
88
Combustion: Aviation
2.G SF6 Emissions from Electrical
SF6
23.2
4.1
<0.01
<0.001
0.9
89
Transmission and Distribution
2.F.4 Emissions from Substitutes
HFCs,
0.2
19.2
<0.01
<0.001
0.8
90
for Ozone Depleting Substances:
PFCs
Aerosols
3.B.1 CH4 Emissions from Manure
ch4
17.9
35.7
<0.01
0.001
0.7
91
Management: Cattle
l.A.4.a C02 Emissions from
C02
74.2
63.9
<0.01
<0.001
0.6
91
Stationary Combustion - Oil -
Commercial
l.A.3.e C02 Emissions from Mobile
C02
36.0
49.2
<0.01
<0.001
0.5
92
Combustion: Other
2.F.2 Emissions from Substitutes
HFCs,
+
11.8
<0.01
<0.001
0.5
92
for Ozone Depleting Substances:
PFCs
Foam Blowing Agents
l.A.3.d C02 Emissions from
C02
46.3
36.5
<0.01
<0.001
0.5
93
Mobile Combustion: Marine
1.B.2 CH4 Emissions from
ch4
46.2
36.6
<0.01
0.001
0.5
93
Petroleum Systems
l.A.4.a C02 Emissions from
C02
12.0
1.8
<0.01
<0.001
0.5
94
Stationary Combustion - Coal -
Commercial
1.A.5 C02 Emissions from Non-
C02
119.5
134.5
<0.01
0.001
0.4
94
Energy Use of Fuels
3.D.2 Indirect N20 Emissions from
N20
43.4
52.5
<0.01
0.002
0.3
95
Applied Nitrogen
3.A.1 CH4 Emissions from Enteric
ch4
158.4
171.7
<0.01
<0.001
0.3
95
Fermentation: Cattle
2.B.8 C02 Emissions from
C02
21.6
29.4
<0.01
<0.001
0.3
95
Petrochemical Production
1.A.5 C02 Emissions from
C02
26.9
34.3
<0.01
<0.001
0.3
95
Stationary Combustion - Oil -
U.S. Territories
3.B.4 CH4 Emissions from Manure
ch4
19.3
26.0
<0.01
<0.001
0.3
96
Management: Other Livestock
2.C.3 C02 Emissions from
C02
6.8
1.5
<0.01
<0.001
0.2
96
Aluminum Production
2.A.1 C02 Emissions from Cement
C02
33.5
40.3
<0.01
<0.001
0.2
96
Production
A-40 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
l.A.3.e CH4 Emissions from Mobile
ch4
7.0
1.7
<0.01
<0.001
0.2
96
Combustion: Other
2.B.3 N20 Emissions from Adipic
n2o
15.2
10.3
<0.01
<0.001
0.2
97
Acid Production
l.A.3.b CH4 Emissions from Mobile
ch4
5.2
1.0
<0.01
<0.001
0.2
97
Combustion: Road
2.C.4 SF6 Emissions from
sf6
5.2
1.1
<0.01
<0.001
0.2
97
Magnesium Production and
Processing
3.B.1 N20 Emissions from Manure
n2o
11.2
15.4
<0.01
<0.001
0.2
97
Management: Cattle
l.A.l N20 Emissions from
n2o
0.3
4.1
<0.01
<0.001
0.2
97
Stationary Combustion - Gas -
Electricity Generation
1.A.5 C02 Emissions from
C02
0.6
4.0
<0.01
<0.001
0.1
97
Stationary Combustion - Coal -
U.S. Territories
3.C CH4 Emissions from Rice
ch4
16.0
13.3
<0.01
<0.001
0.1
98
Cultivation
2.B.2 N20 Emissions from Nitric
n2o
12.1
9.3
<0.01
<0.001
0.1
98
Acid Production
l.A.4.b C02 Emissions from
C02
3.0
0.0
<0.01
<0.001
0.1
98
Stationary Combustion - Coal -
Residential
1.A.5 C02 Emissions from
C02
0.0
3.0
<0.01
<0.001
0.1
98
Stationary Combustion - Gas -
U.S. Territories
3.D.1 Direct N20 Emissions from
N20
272.5
285.7
<0.01
<0.001
0.1
98
Agricultural Soil Management
2.B.10 C02 Emissions from Carbon
C02
1.5
4.5
<0.01
<0.001
0.1
98
Dioxide Consumption
2.A.4 C02 Emissions from Other
C02
6.3
9.4
<0.01
<0.001
0.1
98
Process Uses of Carbonates
5.C.1 C02 Emissions from
C02
8.0
11.1
<0.01
<0.001
0.1
99
Incineration of Waste
2.F.3 Emissions from Substitutes
HFCs,
0.0
2.6
<0.01
<0.001
0.1
99
for Ozone Depleting Substances:
PFCs
Fire Protection
3.H C02 Emissions from Urea
C02
2.0
4.6
<0.01
<0.001
0.1
99
Fertilization
1.A.3.C C02 Emissions from Mobile
C02
35.5
38.9
<0.01
<0.001
0.1
99
Combustion: Railways
5.B CH4 Emissions from
ch4
0.4
2.5
<0.01
<0.001
0.1
99
Composting
2.F.5 Emissions from Substitutes
HFCs,
0.0
2.0
<0.01
<0.001
0.1
99
for Ozone Depleting Substances:
PFCs
Solvents
5.B N20 Emissions from
N20
0.3
2.2
<0.01
<0.001
0.1
99
Composting
2.A.2 C02 Emissions from Lime
C02
11.7
13.9
<0.01
<0.001
0.1
99
Production
5.D CH4 Emissions from
ch4
15.3
14.2
<0.01
<0.001
0.1
99
Wastewater T reatment
3.G C02 Emissions from Liming
C02
4.7
3.1
<0.01
<0.001
0.1
99
1.B.2 C02 Emissions from Natural
C02
32.2
34.9
<0.01
<0.001
0.1
99
Gas Systems
A-41
-------
5.D N20 Emissions from
N20
3.4
5.0
<0.01
<0.001
0.1
99
Wastewater T reatment
l.B.l Fugitive Emissions from
ch4
7.2
6.2
<0.01
<0.001
0.1
100
Abandoned Underground Coal
Mines
3.B.4 N20 Emissions from Manure
n2o
2.8
4.1
<0.01
<0.001
<0.1
100
Management: Other Livestock
2.EPFC, HFC, SF6, and NF3
HiGWP
3.6
4.8
<0.01
<0.001
<0.1
100
Emissions from Electronics
Industry
l.A.4.b CH4 Emissions from
ch4
5.2
4.5
<0.01
<0.001
<0.1
100
Stationary Combustion -
Residential
l.A.l CH4 Emissions from
ch4
0.1
1.0
<0.01
<0.001
<0.1
100
Stationary Combustion - Gas -
Electricity Generation
2.B.10 C02 Emissions from
C02
1.5
0.9
<0.01
<0.001
<0.1
100
Phosphoric Acid Production
l.A.3.e N20 Emissions from
N20
1.8
2.5
<0.01
<0.001
<0.1
100
Mobile Combustion: Other
l.A.l N20 Emissions from
n2o
20.1
20.3
<0.01
<0.001
<0.1
100
Stationary Combustion - Coal -
Electricity Generation
1.A.2 N20 Emissions from
n2o
3.1
2.7
<0.01
<0.001
<0.1
100
Stationary Combustion -
Industrial
2.B.6 C02 Emissions from Titanium
C02
1.2
1.6
<0.01
<0.001
<0.1
100
Dioxide Production
2.C.6 C02 Emissions from Zinc
C02
0.6
1.0
<0.01
<0.001
<0.1
100
Production
2.A.3 C02 Emissions from Glass
C02
1.5
1.3
<0.01
<0.001
<0.1
100
Production
2.B.4 N20 Emissions from
n2o
1.7
1.4
<0.01
<0.001
<0.1
100
Caprolactam, Glyoxal, and
Glyoxylic Acid Production
2.B.10 C02 Emissions from Urea
co2
3.8
3.6
<0.01
<0.001
<0.1
100
Consumption for Non-Ag
Purposes
l.A.3.d CH4 Emissions from Mobile
ch4
0.6
0.3
<0.01
<0.001
<0.1
100
Combustion: Marine
1.A.2 CH4 Emissions from
ch4
1.8
1.6
<0.01
<0.001
<0.1
100
Stationary Combustion -
Industrial
2.B.7 C02 Emissions from Soda
co2
1.4
1.7
<0.01
<0.001
<0.1
100
Ash Production
2.E N20 Emissions from
n2o
+
0.3
<0.01
<0.001
<0.1
100
Electronics Industry
l.A.3.a N20 Emissions from
n2o
1.7
1.6
<0.01
<0.001
<0.1
100
Mobile Combustion: Aviation
2.B.5 C02 Emissions from Silicon
co2
0.4
0.2
<0.01
<0.001
<0.1
100
Carbide Production and
Consumption
1.B.2 CH4 Emissions from
ch4
6.6
7.0
<0.01
<0.001
<0.1
100
Abandoned Oil and Gas Wells
l.A.4.b N20 Emissions from N20 1.0 0.9 <0.01 <0.001 <0.1 100
Stationary Combustion -
Residential
A-42 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
2.G N20 Emissions from Product
Uses
2.C.2 C02 Emissions from
Ferroalloy Production
5.C.1 N20 Emissions from
Incineration of Waste
l.A.l C02 Emissions from
Stationary Combustion -
Geothermal Energy
l.A.4.a CH4 Emissions from
Stationary Combustion -
Commercial
l.A.l CH4 Emissions from
Stationary Combustion - Coal -
Electricity Generation
1.A.3.d N20 Emissions from
Mobile Combustion: Marine
3.A.4 CH4 Emissions from Enteric
Fermentation: Other Livestock
2.C.4 HFC-134a Emissions from
Magnesium Production and
Processing
2.B.8 CH4 Emissions from
Petrochemical Production
l.A.l N20 Emissions from
Stationary Combustion - Oil -
Electricity Generation
1.B.2 N20 Emissions from
Petroleum Systems
2.C.5 C02 Emissions from Lead
Production
3.F CH4 Emissions from Field
Burning of Agricultural Residues
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
l.A.4.a N20 Emissions from
Stationary Combustion -
Commercial
1.A.5 N20 Emissions from
Stationary Combustion - U.S.
Territories
1.A.3.C N20 Emissions from
Mobile Combustion: Railways
2.B.5 CH4 Emissions from Silicon
Carbide Production and
Consumption
l.A.l N20 Emissions from
Stationary Combustion - Wood -
Electricity Generation
3.F N20 Emissions from Field
Burning of Agricultural Residues
1.A.l CH4 Emissions from
Stationary Combustion - Oil -
Electricity Generation
2.C.1 CH4 Emissions from Iron and
Steel Production & Metallurgical
Coke Production
N20 4.2 4.2 <0.01 <0.001 <0.1 100
C02 2.2 2.1 <0.01 <0.001 <0.1 100
N20 0.5 0.3 <0.01 <0.001 <0.1 100
C02 0.5 0.4 <0.01 <0.001 <0.1 100
CH4 1.1 1.3 <0.01 <0.001 <0.1 100
CH4 0.3 0.2 <0.01 <0.001 <0.1 100
N20 0.6 0.5 <0.01 <0.001 <0.1 100
CH4 5.7 5.8 <0.01 <0.001 <0.1 100
HFCs 0.0 0.1 <0.01 <0.001 <0.1 100
CH4 0.2 0.3 <0.01 <0.001 <0.1 100
n2o 0.1 + <0.01 <0.001 <0.1 100
n2o + 0.1 <0.01 <0.001 <0.1 100
C02 0.5 0.6 <0.01 <0.001 <0.1 100
CH4 0.3 0.4 <0.01 <0.001 <0.1 100
ch4 0.1 + <0.01 <0.001 <0.1 100
N20 0.4 0.4 <0.01 <0.001 <0.1 100
n2o 0.1 0.1 <0.01 <0.001 <0.1 100
N20 0.3 0.3 <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
n2o + + <0.01 <0.001 <0.1 100
N20 0.2 0.2 <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
A-43
-------
1.A.5 CH4 Emissions from
Stationary Combustion - U.S.
Territories
1.A.3.C CH4 Emissions from Mobile
Combustion: Railways
1.B.2 N20 Emissions from Natural
Gas Systems
2.C.2 CH4 Emissions from
Ferroalloy Production
2.B.1 C02 Emissions from
Ammonia Production
l.A.l CH4 Emissions from
Stationary Combustion - Wood -
Electricity Generation
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
1 + Does not exceed 0.05 MMT CO2 Eq.
2 Note: LULUCF sources and sinks are not included in this analysis.
3
4 Table A-9:1990-2018 Key Source Category Approach 1 and 2 Analysis—Trend Assessment, with LULUCF
Direct 1990 Estimate 2018 Estimate Approach 1 Approach 2 %
Greenhouse (MMTCO2 (MMT C02 Trend Trend Contribution Cumulative
CRF Source/Sink Categories Gas Eq.) Eq.) Assessment Assessment to Trend Total
l.A.l C02 Emissions from
C02
1,546.5
1,152.9
0.06
0.006
17.7
18
Stationary Combustion - Coal -
Electricity Generation
l.A.l C02 Emissions from
C02
175.4
577.4
0.05
0.003
15.8
33
Stationary Combustion - Gas -
Electricity Generation
l.A.3.b C02 Emissions from Mobile
C02
1,163.9
1,499.8
0.04
0.002
11.9
45
Combustion: Road
2.F.1 Emissions from Substitutes for
HFCs,
+
128.9
0.02
0.002
5.1
51
Ozone Depleting Substances:
PFCs
Refrigeration and Air Conditioning
1.A.2 C02 Emissions from
C02
155.2
49.8
0.01
0.002
4.4
55
Stationary Combustion - Coal -
Industrial
4.A.1 Net C02 Emissions from
C02
733.9
663.2
0.01
0.003
3.7
59
Forest Land Remaining Forest
Land
1.A.2 C02 Emissions from
C02
408.5
514.8
0.01
0.001
3.7
62
Stationary Combustion - Gas -
Industrial
l.A.l C02 Emissions from
C02
97.5
22.2
0.01
0.001
3.1
66
Stationary Combustion - Oil -
Electricity Generation
5.A CH4 Emissions from Landfills
ch4
179.6
110.6
0.01
0.004
3.0
69
2.C.1 C02 Emissions from Iron and
C02
104.7
42.7
0.01
0.002
2.6
71
Steel Production & Metallurgical
Coke Production
1.B.2 CH4 Emissions from Natural
ch4
183.2
139.7
0.01
0.001
2.0
73
Gas Systems
ch4 + 0.1 <0.01 <0.001 <0.1 100
ch4 0.1 0.1 <0.01 <0.001 <0.1 100
|\|20 + + <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
C02 13.0 13.5 <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
co2 + + <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
co2 + + <0.01 <0.001 <0.1 100
A-44 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
l.B.l Fugitive Emissions from Coal
ch4
96.5
52.7
0.01
0.001
1.9
75
Mining
l.A.4.a C02 Emissions from
C02
142.0
192.6
0.01
<0.001
1.8
77
Stationary Combustion - Gas -
Commercial
2.B.9 HFC-23 Emissions from HCFC-
HFCs
46.1
3.3
0.01
0.001
1.8
79
22 Production
l.A.4.b C02 Emissions from
C02
97.4
62.2
0.01
<0.001
1.5
80
Stationary Combustion - Oil -
Residential
1.B.2 C02 Emissions from
C02
9.6
39.4
<0.01
0.001
1.2
81
Petroleum Systems
l.A.3.b N20 Emissions from Mobile
N20
37.7
10.4
<0.01
0.001
1.1
82
Combustion: Road
l.A.4.b C02 Emissions from
C02
237.8
273.7
<0.01
<0.001
1.1
84
Stationary Combustion - Gas -
Residential
2.C.3 PFC Emissions from Aluminum
PFCs
21.5
1.6
<0.01
<0.001
0.8
84
Production
1.A.2 C02 Emissions from
C02
293.3
282.1
<0.01
0.001
0.8
85
Stationary Combustion - Oil -
Industrial
2.G SF6 Emissions from Electrical
SF6
23.2
4.1
<0.01
<0.001
0.8
86
Transmission and Distribution
l.A.3.a C02 Emissions from Mobile
C02
187.4
173.9
<0.01
<0.001
0.8
87
Combustion: Aviation
2.F.4 Emissions from Substitutes for
HFCs,
0.2
19.2
<0.01
<0.001
0.8
88
Ozone Depleting Substances:
PFCs
Aerosols
4.C.2 Net C02 Emissions from Land
C02
6.7
24.6
<0.01
0.003
0.7
88
Converted to Grassland
3.B.1 CH4 Emissions from Manure
ch4
17.9
35.7
<0.01
<0.001
0.7
89
Management: Cattle
4.E.2 Net C02 Emissions from Land
C02
62.9
79.3
<0.01
0.001
0.6
90
Converted to Settlements
4.E.1 Net C02 Emissions from
C02
109.6
126.2
<0.01
0.002
0.5
90
Settlements Remaining
Settlements
l.A.4.a C02 Emissions from
C02
74.2
63.9
<0.01
<0.001
0.5
91
Stationary Combustion - Oil -
Commercial
l.A.3.e C02 Emissions from Mobile
C02
36.0
49.2
<0.01
<0.001
0.5
91
Combustion: Other
2.F.2 Emissions from Substitutes for
HFCs,
+
11.8
<0.01
<0.001
0.5
92
Ozone Depleting Substances:
PFCs
Foam Blowing Agents
l.A.3.d C02 Emissions from Mobile
C02
46.3
36.5
<0.01
<0.001
0.4
92
Combustion: Marine
1.A.5 C02 Emissions from Non-
C02
119.5
134.5
<0.01
0.001
0.4
92
Energy Use of Fuels
1.B.2 CH4 Emissions from
ch4
46.2
36.6
<0.01
0.001
0.4
93
Petroleum Systems
l.A.4.a C02 Emissions from
C02
12.0
1.8
<0.01
<0.001
0.4
93
Stationary Combustion - Coal -
Commercial
4.A.1 CH4 Emissions from Forest
ch4
0.9
11.3
<0.01
<0.001
0.4
94
Fires
A-45
-------
3.A.1 CH4 Emissions from Enteric
ch4
158.4
171.7
<0.01
<0.001
0.3
94
Fermentation: Cattle
3.D.2 Indirect N20 Emissions from
n2o
43.4
52.5
<0.01
0.002
0.3
94
Applied Nitrogen
4.B.1 Net C02 Emissions from
C02
23.2
16.6
<0.01
0.005
0.3
95
Cropland Remaining Cropland
2.B.8 C02 Emissions from
C02
21.6
29.4
<0.01
<0.001
0.3
95
Petrochemical Production
4.A.1 N20 Emissions from Forest
N20
0.6
7.5
<0.01
<0.001
0.3
95
Fires
1.A.5 C02 Emissions from
C02
26.9
34.3
<0.01
<0.001
0.3
95
Stationary Combustion - Oil - U.S.
Territories
3.B.4 CH4 Emissions from Manure
ch4
19.3
26.0
<0.01
<0.001
0.2
96
Management: Other Livestock
2.A.1 C02 Emissions from Cement
C02
33.5
40.3
<0.01
<0.001
0.2
96
Production
2.C.3 C02 Emissions from Aluminum
C02
6.8
1.5
<0.01
<0.001
0.2
96
Production
l.A.3.e CH4 Emissions from Mobile
ch4
7.0
1.7
<0.01
<0.001
0.2
96
Combustion: Other
2.B.3 N20 Emissions from Adipic
n2o
15.2
10.3
<0.01
<0.001
0.2
97
Acid Production
3.D.1 Direct N20 Emissions from
n2o
272.5
285.7
<0.01
<0.001
0.2
97
Agricultural Soil Management
l.A.3.b CH4 Emissions from Mobile
ch4
5.2
1.0
<0.01
<0.001
0.2
97
Combustion: Road
2.C.4 SF6 Emissions from
sf6
5.2
1.1
<0.01
<0.001
0.2
97
Magnesium Production and
Processing
3.B.1 N20 Emissions from Manure
n2o
11.2
15.4
<0.01
<0.001
0.2
97
Management: Cattle
l.A.l N20 Emissions from
n2o
0.3
4.1
<0.01
<0.001
0.1
97
Stationary Combustion - Gas -
Electricity Generation
1.A.5 C02 Emissions from
C02
0.6
4.0
<0.01
<0.001
0.1
98
Stationary Combustion - Coal -
U.S. Territories
2.B.2 N20 Emissions from Nitric
N20
12.1
9.3
<0.01
<0.001
0.1
98
Acid Production
3.C CH4 Emissions from Rice
ch4
16.0
13.3
<0.01
<0.001
0.1
98
Cultivation
l.A.4.b C02 Emissions from
C02
3.0
0.0
<0.01
<0.001
0.1
98
Stationary Combustion - Coal -
Residential
1.A.5 C02 Emissions from
C02
0.0
3.0
<0.01
<0.001
0.1
98
Stationary Combustion - Gas -
U.S. Territories
2.B.10 C02 Emissions from Carbon
C02
1.5
4.5
<0.01
<0.001
0.1
98
Dioxide Consumption
2.A.4 C02 Emissions from Other
C02
6.3
9.4
<0.01
<0.001
0.1
98
Process Uses of Carbonates
5.C.1 C02 Emissions from
C02
8.0
11.1
<0.01
<0.001
0.1
98
Incineration of Waste
2.F.3 Emissions from Substitutes for
HFCs,
0.0
2.6
<0.01
<0.001
0.1
98
Ozone Depleting Substances: Fire PFCs
Protection
A-46 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
3.H C02 Emissions from Urea
C02
2.0
4.6
<0.01
<0.001
0.1
99
Fertilization
4.A.2 Net C02 Emissions from Land
C02
109.4
110.6
<0.01
<0.001
0.1
99
Converted to Forest Land
1.A.3.C C02 Emissions from Mobile
C02
35.5
38.9
<0.01
<0.001
0.1
99
Combustion: Railways
5.B CH4 Emissions from Composting
ch4
0.4
2.5
<0.01
<0.001
0.1
99
2.F.5 Emissions from Substitutes for
HFCs,
0.0
2.0
<0.01
<0.001
0.1
99
Ozone Depleting Substances:
PFCs
Solvents
2.A.2 C02 Emissions from Lime
C02
11.7
13.9
<0.01
<0.001
0.1
99
Production
5.B N20 Emissions from
N20
0.3
2.2
<0.01
<0.001
0.1
99
Composting
4.C.1 Net C02 Emissions from
C02
9.1
11.2
<0.01
0.003
0.1
99
Grassland Remaining Grassland
1.B.2 C02 Emissions from Natural
C02
32.2
34.9
<0.01
<0.001
0.1
99
Gas Systems
3.G C02 Emissions from Liming
C02
4.7
3.1
<0.01
<0.001
0.1
99
5.D CH4 Emissions from
ch4
15.3
14.2
<0.01
<0.001
0.1
99
Wastewater Treatment
5.D N20 Emissions from
n2o
3.4
5.0
<0.01
<0.001
0.1
99
Wastewater Treatment
l.B.l Fugitive Emissions from
ch4
7.2
6.2
<0.01
<0.001
<0.1
99
Abandoned Underground Coal
Mines
3.B.4 N20 Emissions from Manure
n2o
2.8
4.1
<0.01
<0.001
<0.1
100
Management: Other Livestock
2.E PFC, HFC, SF6, and NF3
HiGWP
3.6
4.8
<0.01
<0.001
<0.1
100
Emissions from Electronics
Industry
l.A.l CH4 Emissions from Stationary
ch4
0.1
1.0
<0.01
<0.001
<0.1
100
Combustion - Gas - Electricity
Generation
l.A.4.b CH4 Emissions from
ch4
5.2
4.5
<0.01
<0.001
<0.1
100
Stationary Combustion -
Residential
2.B.10 C02 Emissions from
C02
1.5
0.9
<0.01
<0.001
<0.1
100
Phosphoric Acid Production
l.A.3.e N20 Emissions from Mobile
N20
1.8
2.5
<0.01
<0.001
<0.1
100
Combustion: Other
1.A.2 N20 Emissions from
n2o
3.1
2.7
<0.01
<0.001
<0.1
100
Stationary Combustion - Industrial
4.B.2 Net C02 Emissions from Land
C02
54.1
55.3
<0.01
<0.001
<0.1
100
Converted to Cropland
l.A.l N20 Emissions from
N20
20.1
20.3
<0.01
<0.001
<0.1
100
Stationary Combustion - Coal -
Electricity Generation
2.B.6 C02 Emissions from Titanium
C02
1.2
1.6
<0.01
<0.001
<0.1
100
Dioxide Production
4.A.1 N20 Emissions from Forest
N20
0.1
0.5
<0.01
<0.001
<0.1
100
Soils
2.C.6 C02 Emissions from Zinc
C02
0.6
1.0
<0.01
<0.001
<0.1
100
Production
4.E.1 N20 Emissions from
N20
2.0
2.4
<0.01
<0.001
<0.1
100
Settlement Soils
A-47
-------
2.A.3 C02 Emissions from Glass
Production
2.B.4 N20 Emissions from
Caprolactam, Glyoxal, and
Glyoxylic Acid Production
2.B.10 C02 Emissions from Urea
Consumption for Non-Ag
Purposes
4.D.1 Net C02 Emissions from
Coastal Wetlands Remaining
Coastal Wetlands
l.A.3.d CH4 Emissions from Mobile
Combustion: Marine
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
1.B.2 CH4 Emissions from
Abandoned Oil and Gas Wells
2.B.7 C02 Emissions from Soda Ash
Production
4.C.1 N20 Emissions from Grass
Fires
2.E N20 Emissions from Electronics
Industry
4.F.4 CH4 Emissions from Grass
Fires
1.A.3.a N20 Emissions from Mobile
Combustion: Aviation
2.B.5 C02 Emissions from Silicon
Carbide Production and
Consumption
1.A.4.b N20 Emissions from
Stationary Combustion -
Residential
5.C.1 N20 Emissions from
Incineration of Waste
2.C.2 C02 Emissions from Ferroalloy
Production
l.A.4.a CH4 Emissions from
Stationary Combustion -
Commercial
1.A.l C02 Emissions from
Stationary Combustion -
Geothermal Energy
2.G N20 Emissions from Product
Uses
l.A.l CH4 Emissions from Stationary
Combustion - Coal - Electricity
Generation
1.A.3.d N20 Emissions from Mobile
Combustion: Marine
2.C.4 HFC-134a Emissions from
Magnesium Production and
Processing
4.D.1 CH4 Emissions from Coastal
Wetlands Remaining Coastal
Wetlands
C02 1.5 1.3 <0.01 <0.001 <0.1 100
N20 1.7 1.4 <0.01 <0.001 <0.1 100
C02 3.8 3.6 <0.01 <0.001 <0.1 100
C02 4.0 4.4 <0.01 <0.001 <0.1 100
CH4 0.6 0.3 <0.01 <0.001 <0.1 100
CH4 1.8 1.6 <0.01 <0.001 <0.1 100
CH4 6.6 7.0 <0.01 <0.001 <0.1 100
C02 1.4 1.7 <0.01 <0.001 <0.1 100
N20 0.1 0.3 <0.01 <0.001 <0.1 100
N20 + 0.3 <0.01 <0.001 <0.1 100
CH4 0.1 0.3 <0.01 <0.001 <0.1 100
N20 1.7 1.6 <0.01 <0.001 <0.1 100
C02 0.4 0.2 <0.01 <0.001 <0.1 100
N20 1.0 0.9 <0.01 <0.001 <0.1 100
N20 0.5 0.3 <0.01 <0.001 <0.1 100
C02 2.2 2.1 <0.01 <0.001 <0.1 100
CH4 1.1 1.3 <0.01 <0.001 <0.1 100
C02 0.5 0.4 <0.01 <0.001 <0.1 100
N20 4.2 4.2 <0.01 <0.001 <0.1 100
CH4 0.3 0.2 <0.01 <0.001 <0.1 100
N20 0.6 0.5 <0.01 <0.001 <0.1 100
HFCs 0.0 0.1 <0.01 <0.001 <0.1 100
CH4 3.4 3.6 <0.01 <0.001 <0.1 100
A-48 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
2.B.8 CH4 Emissions from
Petrochemical Production
2.B.1 C02 Emissions from Ammonia
Production
l.A.l N20 Emissions from
Stationary Combustion - Oil -
Electricity Generation
3.A.4 CH4 Emissions from Enteric
Fermentation: Other Livestock
1.B.2 N20 Emissions from
Petroleum Systems
2.C.5 C02 Emissions from Lead
Production
3.F CH4 Emissions from Field
Burning of Agricultural Residues
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
1.A.5 N20 Emissions from
Stationary Combustion - U.S.
Territories
l.A.4.a N20 Emissions from
Stationary Combustion -
Commercial
1.A.3.C N20 Emissions from Mobile
Combustion: Railways
2.B.5 CH4 Emissions from Silicon
Carbide Production and
Consumption
l.A.l N20 Emissions from
Stationary Combustion - Wood -
Electricity Generation
3.F N20 Emissions from Field
Burning of Agricultural Residues
1.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity
Generation
2.C.1 CH4 Emissions from Iron and
Steel Production & Metallurgical
Coke Production
1.A.5 CH4 Emissions from Stationary
Combustion - U.S. Territories
1.A.3.C CH4 Emissions from Mobile
Combustion: Railways
1.B.2 N20 Emissions from Natural
Gas Systems
2.C.2 CH4 Emissions from Ferroalloy
Production
4.A.4 N20 Emissions from Drained
Organic Soils
4.D.1 CH4 Emissions from Peatlands
Remaining Peatlands
l.A.l CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
4.D.2 Net C02 Emissions from Land
Converted to Wetlands
CH4 0.2 0.3 <0.01 <0.001 <0.1 100
C02 13.0 13.5 <0.01 <0.001 <0.1 100
N20 0.1 + <0.01 <0.001 <0.1 100
CH4 5.7 5.8 <0.01 <0.001 <0.1 100
N20 + 0.1 <0.01 <0.001 <0.1 100
C02 0.5 0.6 <0.01 <0.001 <0.1 100
CH4 0.3 0.4 <0.01 <0.001 <0.1 100
ch4 0.1 + <0.01 <0.001 <0.1 100
n2o 0.1 0.1 <0.01 <0.001 <0.1 100
N20 0.4 0.4 <0.01 <0.001 <0.1 100
N20 0.3 0.3 <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
n2o + + <0.01 <0.001 <0.1 100
N20 0.2 0.2 <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
ch4 + 0.1 <0.01 <0.001 <0.1 100
ch4 0.1 0.1 <0.01 <0.001 <0.1 100
n2o + + <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
n2o 0.1 0.1 <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
co2 + + <0.01 <0.001 <0.1 100
A-49
-------
4.D.1 N20 Emissions from Coastal
Wetlands Remaining Coastal
Wetlands
1.B.2 C02 Emissions from
Abandoned Oil and Gas Wells
4.D.2 CH4 Emissions from Land
Converted to Coastal Wetlands
4.A.4 CH4 Emissions from Drained
Organic Soils
4.D.1 N20 Emissions from Peatlands
Remaining Peatlands
5.C.1 CH4 Emissions from
Incineration of Waste
2.C.4 C02 Emissions from
Magnesium Production and
Processing
1 + Does not exceed 0.05 MMT CO2 Eq.
2
3
4 References
5 IPCC (2006) Volume 1, Chapter 4: Methodological Choice and Identification of Key Categories, 2006 IPCC Guidelines for
6 National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories Programme, The Intergovernmental
7 Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Negara, and K. Tanabe (eds.). Hayman, Kanagawa,
8 Japan.
9
10
N20
0.1
0.1
<0.01
<0.001
<0.1
100
C02
CH4
ch4
n2o
ch4
C02
<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
100
100
100
100
100
100
A-50 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
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 are presented in Columns 2 through 8 of Table A-ll through Table A-39 with totals
by fuel type in Column 8 and totals by end-use sector in the last rows. Fuel consumption data for the bottom-up approach
were obtained directly from the Energy Information Administration (EIA) of the U.S. Department of Energy. These data
were first gathered in physical units, and then converted to their energy equivalents (see 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 2018 total adjusted energy consumption across all sectors, including U.S. Territories, and
energy types was 73,721.2 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 2019). 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-ll
through Table A-39 and those recommended in the IPCC (2006) emission inventory methodology.
1
First, consumption data in the U.S. Inventory are presented using higher heating values (HHV) rather than the
lower heating values (LHV)2 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
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
'Also referred to as Gross Calorific Values (GCV).
2 Also referred to as Net Calorific Values (NCV).
A-51
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
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. Territories3 were added to domestic consumption of fossil
fuels. Energy use data from U.S. Territories are presented in Column 7 of Table A-ll through Table A-39. 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 non-energy
related activities in IPPU 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
4
as a raw material rather than as a fuel; as such, the total non-energy 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, the reported amount
of coking coal used in these processes is greater than the amount of coking coal reported as use by the 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.
3 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.
4 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 U.S.
A-52 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
In 2018, 18,337 thousand tons of coking coal were consumed,5 resulting in an Energy sector adjustment of 401
TBtu. Natural gas, fuel oil, and coal are fossil fuels 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 2018, the iron and steel industry consumed 2,569 tons of coal (bituminous),
51,035 million ft3 of natural gas, and 3,365 thousand gallons of distillate fuel as fuel. This resulted in Energy chapter
adjustments of roughly 61 TBtu for coal, 53 TBtu for natural gas, and less than 1 TBtu for distillate fuel. In addition, an
additional 124 TBtu is adjusted to account for coking coal consumed for industrial processes other than iron and steel,
lead, and zinc production in 2018.
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. The synthetic natural gas enters the natural gas distribution
system. Since October 2000, a portion of the C02 produced by the coal gasification plant has been exported to Canada by
pipeline. The remainder of the C02 byproduct from the plant is released to the atmosphere. The energy in this synthetic
natural gas enters the natural gas distribution stream, and is accounted for in EIA natural gas combustion statistics. Because
this energy of the synthetic natural gas is already accounted for under natural gas combustion, this amount of energy is
deducted from the industrial coal consumption statistics to avoid double counting. 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 was 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 Emitted in
5 Coking coal includes non-imported coke consumption from the iron and steel, lead, and zinc industries.
A-53
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
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-40, 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-41).
Emissions from international bunker fuels have been estimated separately and not included in national totals.6
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-ll through Table A-39) by fuel-specific C content coefficients (see Table A-42 and Table A-43) that
reflect the amount of C per unit of energy in each fuel. The C content coefficients used in the Inventory were derived by
EIA from detailed fuel information and are similar to the C content coefficients contained in the IPCC's default methodology
(IPCC 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-46) by technology-specific C content coefficients (see Table A-42).
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-44). 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 3.2.
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.
For EPA's GHGRP 2010 through 2017 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
6 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.
A-54 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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.7
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.8 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 2017 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.
7 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: .
8 See .
A-55
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Table A-10: 2018 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
NE
18.7
655.0
NE
12,053.0
43.8
12,770.5
134.2
12,636.2
Residential Coal
NE
NE
NE
Commercial Coal
18.7
18.7
18.7
Industrial Other Coal
531.1
531.1
10.3
520.8
Transportation Coal
NE
NE
NE
Electric Power Coal
12,053.0
12,053.0
12,053.0
U.S. Territory Coal (bit)
43.8
43.8
43.8
Natural Gas
5,173.0
3,639.5
10,060.5
948.0
10,910.8
57.0
30,788.8
331.5
30,457.3
Total Petroleum
921.7
907.0
8,697.3
26,070.3
260.4
549.0
37,405.7
1,698.8
4,918.7 137.8 77.3
30,573.2
Asphalt & Road Oil
792.8
792.8
792.8
Aviation Gasoline
22.4
22.4
22.4
Distillate Fuel Oil
425.7
317.7
1,186.3
6,461.4
80.6
108.3
8,580.0
134.4
5.8
8,439.7
Jet Fuel
3,532.8
NA
45.6
3,578.4
1,146.8
2,431.6
Kerosene
8.8
1.2
1.2
2.3
13.5
13.5
LPG
487.2
176.1
2,813.8
7.9
15.4
3,500.4
2,672.7
827.7
Lubricants
121.2
137.8
1.0
260.0
121.2 137.8 1.0
Motor Gasoline
408.0
298.9
15,305.7
173.2
16,185.8
16,185.8
Residual Fuel
3.5
602.4
78.3
127.0
811.2
417.6
393.6
Other Petroleum
AvGas Blend
Components
(1.6)
(1.6)
(1.6)
Crude Oil
MoGas Blend
Components
Misc. Products
198.0
76.2
274.2
198.0 76.2
Naphtha (<401 deg. F)
447.1
447.1
447.1
Other Oil (>401 deg. F)
239.1
239.1
239.1
Pentanes Plus
224.6
224.6
111.8
112.7
Petroleum Coke
0.4
628.6
101.5
730.5
58.9
671.6
Still Gas
1,612.2
1,612.2
166.9
1,445.3
Special Naphtha
92.0
92.0
92.0
Unfinished Oils
30.9
30.9
30.9
Waxes
12.4
12.4
12.4
Geothermal
54.5
54.5
54.5
Total (All Fuels)
6,094.8
4,565.2
19,412.9
27,018.3
23,278.6
649.7
81,019.5
1,698.8
5,384.4 137.8 77.3
73,721.2
NE (Not Estimated); NA (Not Available)
A-56 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-ll: 2018 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NE
18.7
520.8
NE
12,053.0
43.8
12,636.2
NE
1.8
49.8
NE
1,152.9
4.0
1,208.5
Residential Coal
NE
NE
NE
NE
Commercial Coal
18.7
18.7
1.8
1.8
Industrial Other Coal
520.8
520.8
49.8
49.8
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
12,053.0
12,053.0
1,152.9
1,152.9
U.S. Territory Coal (bit)
43.8
43.8
4.0
4.0
Natural Gas
5,173.0
3,639.5
9,729.0
948.0
10,910.8
57.0
30,457.3
273.7
192.6
514.8
50.2
577.4
3.0
1,611.7
Total Petroleum
921.7
907.0
3,778.7
24,233.8
260.4
471.7
30,573.2
62.2
63.9
282.1
1,748.1
22.2
34.3
2,212.7
Asphalt & Road Oil
Aviation Gasoline
22.4
22.4
1.5
1.5
Distillate Fuel Oil
425.7
317.7
1,180.4
6,327.0
80.6
108.3
8,439.7
31.5
23.5
87.3
467.9
6.0
8.0
624.2
Jet Fuel
2,386.0
NA
45.6
2,431.6
172.3
3.3
175.6
Kerosene
8.8
1.2
1.2
2.3
13.5
0.6
0.1
0.1
0.2
1.0
LPG
487.2
176.1
141.1
7.9
15.4
827.7
30.1
10.9
8.7
0.5
0.9
51.1
Lubricants
Motor Gasoline
408.0
298.9
15,305.7
173.2
16,185.8
29.1
21.3
1,091.9
12.4
1,154.7
Residual Fuel
3.5
184.8
78.3
127.0
393.6
0.3
13.9
5.9
9.5
29.6
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
7.9
7.9
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
A-57
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Total (All Fuels)
6,094.8 4,565.2 14,028.4 25,181.8 23,278.6 572.5 73,721.2
335.9 258.3 846.7 1,798.2 1,752.8 41.4 5,033.3
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-12: 2017 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NE
20.7
569.8
NE
12,622.2
43.8
13,256.5
NE
2.0
54.4
NE
1,207.1
4.0
1,267.5
Residential Coal
NE
NE
NE
NE
Commercial Coal
20.7
20.7
2.0
2.0
Industrial Other Coal
569.8
569.8
54.4
54.4
Transportation Coal
NE
NE
NE
Electric Power Coal
12,622.2
12,622.2
1,207.1
1,207.1
U.S. Territory Coal (bit)
43.8
43.8
4.0
4.0
Natural Gas
4,563.5
3,272.9
9,181.2
798.6
9,555.2
57.0
27,428.3
241.5
173.2
485.8
42.3
505.6
3.0
1,451.4
Total Petroleum
783.6
820.8
3,565.2
24,193.8
217.7
471.7
30,052.7
52.7
57.8
265.7
1,745.2
18.9
34.3
2,174.5
Asphalt & Road Oil
Aviation Gasoline
20.9
20.9
1.4
1.4
Distillate Fuel Oil
344.5
257.1
954.0
6,263.5
54.7
108.3
7,982.2
25.5
19.0
70.6
463.2
4.0
8.0
590.3
Jet Fuel
2,378.1
NA
45.6
2,423.7
171.8
3.3
175.0
Kerosene
8.4
1.2
1.1
2.3
13.0
0.6
0.1
0.1
0.2
1.0
LPG
430.7
155.7
180.0
7.0
15.4
788.7
26.6
9.6
11.1
0.4
0.9
48.7
Lubricants
Motor Gasoline
402.5
294.8
15,304.9
173.1
16,175.4
28.7
21.0
00
T—1
cn
(D
T—1
12.4
1,153.9
Residual Fuel
3.8
219.3
65.8
127.0
415.8
0.3
16.5
4.9
9.5
31.2
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
6.1
6.1
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
A-58 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Special Naphtha
Unfinished Oils
Waxes
Geothermal
76.4
54.3
76.4
54.3
5.7
0.4
5.7
0.4
Total (All Fuels)
5,347.1 4,114.3 13,316.2 24,992.3 22,449.5
572.4 70,791.8
294.2
232.9
806.0 1,787.4 1,732.0
41.4
4,893.9
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-13: 2016 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NE
23.7
620.2
NE
12,996.4
43.8
13,684.1
NE
2.3
59.2
NE
1,242.0
4.0
1,307.5
Residential Coal
NE
NE
NE
NE
Commercial Coal
23.7
23.7
2.3
2.3
Industrial Other Coal
620.2
620.2
59.2
59.2
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
12,996.4
12,996.4
1,242.0
1,242.0
U.S. Territory Coal (bit)
43.8
43.8
4.0
4.0
Natural Gas
4,505.8
3,223.5
8,974.7
757.2
10,301.3
57.0
27,819.6
238.4
170.5
474.8
40.1
545.0
3.0
1,471.8
Total Petroleum
813.0
844.8
3,585.3
23,925.0
243.9
471.7
29,883.7
54.9
59.6
267.7
1,725.2
21.4
34.3
2,163.1
Asphalt & Road Oil
Aviation Gasoline
20.5
20.5
1.4
1.4
Distillate Fuel Oil
369.5
277.2
976.7
6,073.4
54.9
108.3
7,860.0
27.3
20.5
72.2
449.2
4.1
8.0
581.3
Jet Fuel
2,298.8
NA
45.6
2,344.4
166.0
3.3
169.3
Kerosene
13.7
2.1
2.3
2.3
20.3
1.0
0.2
0.2
0.2
1.5
LPG
429.9
150.0
224.1
7.0
15.4
826.3
26.5
9.3
13.8
0.4
0.9
51.0
Lubricants
Motor Gasoline
410.8
287.2
15,352.9
173.2
16,224.1
29.3
20.5
1,095.3
12.4
1,157.4
Residual Fuel
4.4
172.4
70.7
127.0
374.5
0.3
12.9
5.3
9.5
28.1
Other Petroleum
AvGas Blend Components
(0.3)
(0.3)
(+)
(+)
Crude Oil
MoGas Blend
Components
Misc. Products
Naphtha (<401 deg. F)
A-59
-------
Other Oil (>401 deg. F)
Pentanes Plus
56.5
56.5
4.0
4.0
Petroleum Coke
0.3
591.6
118.3
710.2
+
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)
5,318.9 4,092.1
13,180.2 24,682.1 23,595.6
572.5 71,441.5
293.2
232.4
801.7 1,765.3
1,808.9
41.4
4,942.9
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-14: 2015 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NE
31.1
695.6
NE
14,138.3
43.8
14,908.8
NE
3.0
66.3
NE
1,351.4
4.0
1,424.7
Residential Coal
NE
NE
NE
NE
Commercial Coal
31.1
31.1
3.0
3.0
Industrial Other Coal
695.6
695.6
66.3
66.3
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
14,138.3
14,138.3
1,351.4
1,351.4
U.S. Territory Coal (bit)
43.8
43.8
4.0
4.0
Natural Gas
4,776.9
3,315.6
8,778.7
744.8
9,926.5
57.0
27,599.6
252.7
175.4
464.4
39.4
525.2
3.0
1,460.2
Total Petroleum
958.1
950.5
3,625.8
23,379.0
276.0
471.8
29,661.2
65.4
67.3
271.2
1,685.9
23.7
34.3
2,147.7
Asphalt & Road Oil
Aviation Gasoline
21.1
21.1
1.5
1.5
Distillate Fuel Oil
502.3
328.0
1,058.7
6,114.4
70.4
108.3
8,182.1
37.2
24.3
78.3
452.2
5.2
8.0
605.1
Jet Fuel
2,181.9
NA
45.6
2,227.5
157.6
3.3
160.9
Kerosene
10.1
1.4
1.7
2.3
15.5
0.7
0.1
0.1
0.2
1.1
LPG
445.7
148.0
247.6
6.5
15.4
863.2
27.5
9.1
15.3
0.4
0.9
53.3
Lubricants
Motor Gasoline
468.6
321.4
14,998.5
173.3
15,961.7
33.4
22.9
1,070.0
12.4
1,138.7
Residual Fuel
4.0
56.6
93.9
127.0
281.4
0.3
4.2
7.0
9.5
21.1
Other Petroleum
AvGas Blend Components
(0.3)
(0.3)
(+)
(+)
Crude Oil
A-60 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
80.9
80.9
5.7
5.7
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)
5,735.1
4,297.2
13,100.2 24,123.8
24,395.0
572.6 72,223.8
318.1 245.6
802.0 1,725.3
1,900.6
41.4
5,033.0
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
A-61
-------
Table A-15: 2014 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NE
40.2
799.0
NE
16,427.4
43.8
17,310.4
NE
3.8
76.0
NE
1,568.6
4.0
1,652.4
Residential Coal
NE
NE
NE
NE
Commercial Coal
40.2
40.2
3.8
3.8
Industrial Other Coal
799.0
799.0
76.0
76.0
Transportation Coal
NE
NE
NE
NE
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
8,818.1
759.7
8,361.7
56.8
26,810.6
277.7
189.2
467.1
40.2
442.9
3.0
1,420.0
Total Petroleum
1,019.7
569.1
3,603.7
23,212.0
295.5
471.9
29,171.9
69.4
40.0
270.5
1,673.5
25.3
34.3
2,113.0
Asphalt & Road Oil
Aviation Gasoline
21.7
21.7
1.5
1.5
Distillate Fuel Oil
516.4
345.4
1,315.5
5,948.6
82.2
108.3
8,316.3
38.2
25.5
97.3
439.9
6.1
8.0
615.0
Jet Fuel
2,054.3
NA
45.6
2,099.9
148.4
3.3
151.7
Kerosene
13.7
2.0
2.8
2.3
20.9
1.0
0.1
0.2
0.2
1.5
LPG
489.5
160.5
171.5
7.1
15.4
843.9
30.2
9.9
10.6
0.4
0.9
52.1
Lubricants
Motor Gasoline
52.7
205.6
15,103.0
173.3
15,534.7
3.8
14.7
1,077.4
12.4
1,108.2
Residual Fuel
7.9
77.4
95.1
127.0
307.4
0.6
5.8
7.1
9.5
23.1
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
3.1
3.1
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)
6,262.2
4,181.3
13,220.8
23,971.7
25,138.7
572.4
73,347.1
347.1
233.0
813.6
1,713.7
2,037.1
41.4
5,185.9
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
A-62 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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.
Note: Parentheses indicate negative values.
Table A-16: 2013 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NE
41.4
800.0
NE
16,450.6
30.8
17,322.8
NE
3.9
76.0
NE
1,571.3
2.8
1,654.1
Residential Coal
NE
NE
NE
NE
Commercial Coal
41.4
41.4
3.9
3.9
Industrial Other Coal
800.0
800.0
76.0
76.0
Transportation Coal
NE
NE
NE
NE
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
8,525.6
887.3
8,376.3
56.6
26,248.5
266.4
179.2
452.1
47.0
444.2
3.0
1,391.9
Total Petroleum
932.5
591.6
4,122.8
22,562.2
255.2
503.6
28,967.9
63.3
41.7
306.5
1,627.1
22.4
36.6
2,097.5
Asphalt & Road Oil
Aviation Gasoline
22.4
22.4
1.5
1.5
Distillate Fuel Oil
460.8
322.1
1,179.2
5,752.7
55.4
115.5
7,885.8
34.1
23.8
87.2
425.4
4.1
8.5
583.2
Jet Fuel
2,036.9
NA
48.7
2,085.6
147.1
3.5
150.6
Kerosene
8.3
1.0
1.5
2.5
13.2
0.6
0.1
0.1
0.2
1.0
LPG
463.5
151.6
300.7
6.9
16.4
939.1
28.6
9.4
18.6
0.4
1.0
58.0
Lubricants
Motor Gasoline
92.1
606.2
14,542.0
185.0
15,425.3
6.6
43.2
1,037.4
13.2
1,100.4
Residual Fuel
24.4
201.4
77.2
135.5
438.5
1.8
15.1
5.8
10.2
32.9
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
3.3
3.3
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)
5,955.5
4,012.8
13,448.4
23,449.5
25,135.8
590.9
72,592.9
329.6
224.8
834.6
1,674.1
2,038.3
42.5
5,143.9
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
A-63
-------
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-17: 2012 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind. Trans.
Elec.
Terr.
Total
Total Coal
NE
43.6
782.3
NE
15,821.2
36.9
16,684.0
NE
4.1
74.4 NE
1,511.7
3.4
1,593.6
Residential Coal
NE
NE
NE
NE
Commercial Coal
43.6
43.6
4.1
4.1
Industrial Other Coal
782.3
782.3
74.4
74.4
Transportation Coal
NE
NE
NE
NE
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
8,204.2
779.8
9,286.8
49.2
25,521.5
225.1
157.0
435.3 41.4
492.8
2.6
1,354.3
Total Petroleum
842.8
558.2
3,939.5
22,461.7
214.2
517.0
28,533.5
57.5
39.5
295.3 1,619.8
18.3
37.5
2,067.9
Asphalt & Road Oil
Aviation Gasoline
25.1
25.1
1.7
1.7
Distillate Fuel Oil
439.5
323.6
1,150.1
5,710.0
52.4
99.1
7,774.7
32.5
23.9
85.1 422.3
3.9
7.3
575.0
Jet Fuel
1,985.2
NA
57.4
2,042.5
143.4
4.1
147.5
Kerosene
7.7
1.2
2.0
2.3
13.3
0.6
0.1
0.1
0.2
1.0
LPG
395.6
135.5
283.3
7.1
18.5
840.0
24.4
8.4
17.5 0.4
1.1
51.8
Lubricants
Motor Gasoline
66.1
432.2
14,523.3
207.4
15,229.1
4.7
30.8 1,036.1
14.8
1,086.4
Residual Fuel
31.4
211.1
76.7
132.3
451.5
2.4
15.8
5.8
9.9
33.9
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
3.0
3.0
Petroleum Coke
0.4
649.1
85.1
734.6
+
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
A-64 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Waxes
Geothermal
53.1
53.1
0.4
0.4
Total (All Fuels)
5,084.9 3,561.3 12,926.0 23,241.5 25,375.3
603.1 70,792.1
282.6
200.7
805.1 1,661.1 2,023.2
43.5
5,016.2
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-18: 2011 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NE
61.7
866.1
NE
18,035.2
36.9
18,999.9
NE
5.8
82.2
NE
1,722.4
3.4
1,813.8
Residential Coal
NE
NE
NE
NE
Commercial Coal
61.7
61.7
5.8
5.8
Industrial Other Coal
866.1
866.1
82.2
82.2
Transportation Coal
NE
NE
NE
NE
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
7,875.5
733.5
7,712.2
27.1
24,369.0
255.1
170.7
418.1
38.9
409.4
1.4
1,293.7
Total Petroleum
1,048.1
680.7
3,933.0
22,624.1
295.0
496.7
29,077.6
71.5
48.5
295.0
1,631.6
25.8
36.0
2,108.4
Asphalt & Road Oil
Aviation Gasoline
27.1
27.1
1.9
1.9
Distillate Fuel Oil
536.9
402.1
1,260.9
5,726.4
63.7
97.2
8,087.2
39.7
29.7
93.2
423.5
4.7
7.2
598.1
Jet Fuel
2,029.9
NA
51.4
2,081.3
146.6
3.7
150.3
Kerosene
18.5
3.2
3.6
1.2
26.5
1.4
0.2
0.3
0.1
1.9
LPG
492.6
142.5
158.3
7.3
18.8
819.5
30.4
8.8
9.8
0.4
1.2
50.6
Lubricants
Motor Gasoline
79.0
455.9
14,575.5
203.2
15,313.6
5.6
32.5
1,039.8
14.5
1,092.5
Residual Fuel
53.7
46.9
258.0
93.1
124.9
576.6
4.0
3.5
19.4
7.0
9.4
43.3
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
1.9
1.9
A-65
-------
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)
5,852.6 3,958.5
12,674.6 23,357.6
26,094.7
560.7 72,498.8
326.5
225.0
795.3
1,670.5 2,158.1
40.9
5,216.3
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Table A-19: 2010 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NE
69.7
951.6
NE
19,133.5
36.9
20,191.6
NE
6.6
90.2
NE
1,827.2
3.4
1,927.5
Residential Coal
NE
NE
NE
NE
Commercial Coal
69.7
69.7
6.6
6.6
Industrial Other Coal
951.6
951.6
90.2
90.2
Transportation Coal
NE
NE
NE
NE
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
7,685.0
719.0
7,527.6
27.8
24,002.2
258.9
168.0
407.9
38.2
399.5
1.5
1,273.9
Total Petroleum
1,116.6
707.1
3,948.0
22,984.8
370.3
515.5
29,642.4
76.1
50.4
296.7
1,657.3
31.4
37.5
2,149.4
Asphalt & Road Oil
Aviation Gasoline
27.0
27.0
1.9
1.9
Distillate Fuel Oil
557.7
388.5
1,135.8
5,681.9
79.7
87.7
7,931.1
41.2
28.7
84.0
420.2
5.9
6.5
586.6
Jet Fuel
2,097.5
NA
60.3
2,157.7
151.5
4.4
155.8
Kerosene
29.1
4.8
7.3
7.4
48.7
2.1
0.4
0.5
0.5
3.6
LPG
529.8
140.0
149.9
7.5
16.0
843.2
32.7
8.6
9.2
0.5
1.0
52.0
Lubricants
Motor Gasoline
111.8
559.7
14,898.8
176.0
15,746.3
8.0
39.9
1,062.9
12.6
1,123.3
Residual Fuel
61.7
32.2
272.2
154.1
168.1
688.2
4.6
2.4
20.4
11.6
12.6
51.7
Other Petroleum
AvGas Blend Components
(0.2)
(0.2)
(+)
(+)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
A-66 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Other Oil (>401 deg. F)
Pentanes Plus
78.4
78.4
5.5
5.5
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)
5,994.7 3,941.5
12,584.6 23,703.7
27,083.3 580.1
73,888.1
335.0 224.9
794.8 1,695.5
2,258.6
42.4 5,351.2
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-20: 2009 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NE
73.4
877.3
NE
18,225.3
36.9
19,212.8
NE
6.9
83.2
NE
1,740.2
3.4
1,833.7
Residential Coal
NE
NE
NE
NE
Commercial Coal
73.4
73.4
6.9
6.9
Industrial Other Coal
877.3
877.3
83.2
83.2
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
18,225.3
18,225.3
1,740.2
1,740.2
U.S. Territory Coal (bit)
36.9
36.9
3.4
3.4
Natural Gas
4,883.1
3,186.6
7,126.0
714.9
7,022.4
27.4
22,960.3
259.1
169.1
378.0
37.9
372.5
1.5
1,218.1
Total Petroleum
1,139.8
736.4
3,773.7
22,857.3
382.4
525.3
29,414.9
77.6
52.5
283.8
1,647.1
32.2
38.2
2,131.4
Asphalt & Road Oil
Aviation Gasoline
26.6
26.6
1.8
1.8
Distillate Fuel Oil
564.3
383.0
1,019.7
5,452.4
69.6
80.6
7,569.7
41.7
28.3
75.4
403.2
5.1
6.0
559.8
Jet Fuel
2,134.2
NA
61.1
2,195.3
154.1
4.4
158.5
Kerosene
27.7
4.2
4.4
7.9
44.2
2.0
0.3
0.3
0.6
3.2
LPG
547.8
139.0
116.0
28.1
14.9
845.8
33.8
8.6
7.2
1.7
0.9
52.2
Lubricants
Motor Gasoline
138.6
635.5
15,030.4
196.3
16,000.9
9.9
45.3
1,072.3
14.0
1,141.5
Residual Fuel
71.3
67.3
185.7
181.0
164.4
669.7
5.4
5.1
13.9
13.6
12.3
50.3
Other Petroleum
AvGas Blend Components
(0.8)
(0.8)
(0.1)
(0.1)
Crude Oil
A-67
-------
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
64.3
64.3
4.5
4.5
Petroleum Coke
0.2
624.0
131.8
756.1
+
63.7
13.5
77.2
Still Gas
1,321.1
1,321.1
88.1
88.1
Special Naphtha
Unfinished Oils
(77.8)
(77.8)
(5.8)
(5.8)
Waxes
Geothermal
51.2
51.2
0.4
0.4
Total (All Fuels)
6,022.9 3,996.4
11,776.9 23,572.2
25,681.3
589.5 71,639.3
336.6
228.5
745.0
1,685.1 2,145.3
43.1 5,183.6
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-21: 2008 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NE
80.8
1,081.5
NE
20,513.0
36.9
21,712.0
NE
7.6
102.4
NE
1,958.4
3.4
2,071.8
Residential Coal
NE
NE
NE
NE
Commercial Coal
80.8
80.8
7.6
7.6
Industrial Other Coal
1,081.5
1,081.5
102.4
102.4
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
20,513.0
20,513.0
1,958.4
1,958.4
U.S. Territory Coal (bit)
36.9
36.9
3.4
3.4
Natural Gas
5,010.1
3,228.4
7,571.8
692.1
6,828.9
29.3
23,360.6
265.7
171.2
401.5
36.7
362.1
1.6
1,238.7
Total Petroleum
1,202.2
693.5
4,240.2
23,899.9
459.3
487.5
30,982.6
82.1
49.1
317.4
1,723.5
38.4
35.7
2,246.2
Asphalt & Road Oil
Aviation Gasoline
28.3
28.3
2.0
2.0
Distillate Fuel Oil
627.5
321.3
1,103.1
6,058.8
72.5
107.9
8,291.1
46.4
23.8
81.6
448.1
5.4
8.0
613.2
Jet Fuel
2,396.1
NA
34.4
2,430.4
173.0
2.5
175.5
Kerosene
21.3
4.4
3.8
5.8
35.3
1.6
0.3
0.3
0.4
2.6
LPG
553.4
158.2
153.8
40.2
15.7
921.3
34.2
9.8
9.5
2.5
1.0
56.9
Lubricants
Motor Gasoline
138.3
755.9
15,105.3
133.2
16,132.6
9.9
53.9
1,077.6
9.5
1,150.9
Residual Fuel
71.0
131.5
271.3
240.4
190.6
904.8
5.3
9.9
20.4
18.1
14.3
67.9
A-68 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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
77.1
77.1
5.4
5.4
Petroleum Coke
0.3
645.7
146.4
792.3
+
65.9
14.9
80.9
Still Gas
1,423.0
1,423.0
94.9
94.9
Special Naphtha
Unfinished Oils
(53.7)
(53.7)
(4.0)
(4.0)
Waxes
Geothermal
50.6
50.6
0.4
0.4
Total (All Fuels)
6,212.3 4,002.7
12,893.4 24,592.0
27,851.8
553.6 76,105.9
347.8
227.9
821.3 1,760.2
2,359.3
40.6 5,557.1
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-22: 2007 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy
Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
7.8
70.0
1,130.8
NE
20,807.7
36.9
22,053.2
0.7
6.7
107.0
NE
1,986.2
3.4
2,104.1
Residential Coal
7.8
7.8
0.7
0.7
Commercial Coal
70.0
70.0
6.7
6.7
Industrial Other Coal
1,130.8
1,130.8
107.0
107.0
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
20,807.7
20,807.7
1,986.2
1,986.2
U.S. Territory Coal (bit)
36.9
36.9
3.4
3.4
Natural Gas
4,835.4
3,085.1
7,521.8
663.5
7,005.2
26.7
23,137.6
256.4
163.6
398.9
35.2
371.5
1.4
1,226.9
Total Petroleum
1,220.6
745.0
4,832.1
25,142.0
647.8
576.5
33,164.1
84.3
53.3
360.8
1,819.7
52.9
42.2
2,413.2
Asphalt & Road Oil
Aviation Gasoline
31.6
31.6
2.2
2.2
Distillate Fuel Oil
692.4
366.1
1,177.0
6,393.6
88.7
144.5
8,862.2
51.2
27.1
87.0
472.9
6.6
10.7
655.4
Jet Fuel
2,485.0
NA
73.9
2,558.9
179.5
5.3
184.8
Kerosene
43.9
9.2
13.4
5.6
72.1
3.2
0.7
1.0
0.4
5.3
LPG
484.3
121.6
300.2
22.0
11.7
939.6
29.9
7.5
18.5
1.4
0.7
58.0
A-69
-------
Lubricants
Motor Gasoline
172.4
862.8
15,823.8
155.3
17,014.3
12.4
61.9
1,134.8
11.1
1,220.2
Residual Fuel
75.4
130.4
386.1
396.6
185.5
1,174.0
5.7
9.8
29.0
29.8
13.9
88.2
Other Petroleum
AvGas Blend Components
1.8
1.8
0.1
0.1
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
90.4
90.4
6.3
6.3
Petroleum Coke
0.4
708.4
162.6
871.3
+
72.3
16.6
89.0
Still Gas
1,482.6
1,482.6
98.9
98.9
Special Naphtha
Unfinished Oils
65.2
65.2
4.8
4.8
Waxes
Geothermal
49.9
49.9
0.5
0.5
Total (All Fuels)
6,063.8 3,900.1
13,484.7
25,805.5
28,510.7
640.1
78,404.9
341.4
223.6
866.6
1,854.9
2,411.1
47.0
5,744.7
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Table A-23: 2006 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy
Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
6.4
64.8
1,188.8
NE
20,461.9
36.9
21,758.7
0.6
6.2
112.6
NE
1,952.7
3.4
2,075.5
Residential Coal
6.4
6.4
0.6
0.6
Commercial Coal
64.8
64.8
6.2
6.2
Industrial Other Coal
1,188.8
1,188.8
112.6
112.6
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
20,461.9
20,461.9
1,952.7
1,952.7
U.S. Territory Coal (bit)
36.9
36.9
3.4
3.4
Natural Gas
4,475.9
2,901.7
7,323.2
625.0
6,375.1
26.14
21,727.0
237.3
153.8
388.2
33.1
338.0
1.4
1,151.8
Total Petroleum
1,202.8
723.4
4,988.6
25,232.7
637.0
615.4
33,399.8
83.4
51.8
372.4
1,819.6
53.2
45.1
2,425.5
Asphalt & Road Oil
Aviation Gasoline
33.4
33.4
2.3
2.3
Distillate Fuel Oil
690.4
389.0
1,194.7
6,334.2
73.4
87.4
8,769.1
51.1
28.8
88.4
468.5
5.4
6.5
648.5
Jet Fuel
2,523.8
NA
75.8
2,599.6
182.3
5.5
187.8
A-70 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Kerosene
66.4
15.2
29.6
4.3
115.4
4.9
1.1
2.2
0.3
8.5
LPG
446.1
123.3
295.7
27.5
6.6
899.2
27.5
7.6
18.2
1.7
0.4
55.5
Lubricants
Motor Gasoline
120.3
930.3
16,007.4
186.7
17,244.8
8.6
66.4
1,141.9
13.3
1,230.1
Residual Fuel
75.3
176.4
306.3
360.5
254.4
1,172.9
5.7
13.2
23.0
27.1
19.1
88.1
Other Petroleum
AvGas Blend Components
0.6
0.6
+
+
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
70.6
70.6
4.9
4.9
Petroleum Coke
0.3
724.3
203.0
927.6
+
74.0
20.7
94.7
Still Gas
1,496.1
1,496.1
99.8
99.8
Special Naphtha
Unfinished Oils
70.3
70.3
5.2
5.2
Waxes
Geothermal
49.7
49.7
0.5
0.5
Total (All Fuels)
5,685.2
3,689.9
13,500.5
25,857.6
27,523.7
678.4
76,935.3
321.4
211.8
873.2
1,852.7
2,344.4
49.9
5,653.4
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Table A-24: 2005 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 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
1,219.1
NE
20,737.2
32.7
22,094.5
0.8
9.3
115.3
NE
1,982.8
3.0
2,111.2
Residential Coal
8.4
8.4
0.8
0.8
Commercial Coal
97.0
97.0
9.3
9.3
Industrial Other Coal
1,219.1
1,219.1
115.3
115.3
Transportation Coal
NE
NE
NE
NE
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
7,329.7
623.9
6,014.5
24.3
22,012.0
262.2
162.9
388.6
33.1
318.9
1.3
1,167.0
Total Petroleum
1,369.0
763.0
4,629.0
25,363.6
1,222.1
619.6
33,966.3
94.9
54.7
346.2
1,823.0
97.9
45.4
2,462.1
Asphalt & Road Oil
Aviation Gasoline
35.4
35.4
2.4
2.4
A-71
-------
Distillate Fuel Oil
771.6
404.2
1,127.5
6,186.2
114.5
115.3
8,719.4
57.1
29.9
83.4
457.5
8.5
8.5
644.9
Jet Fuel
2,621.7
NA
68.5
2,690.2
189.3
5.0
194.3
Kerosene
83.8
21.6
39.1
5.6
150.1
6.1
1.6
2.9
0.4
11.0
LPG
513.5
131.6
281.9
28.2
0.7
955.9
31.7
8.1
17.4
1.7
0.0
59.0
Lubricants
Motor Gasoline
89.6
697.1
16,235.7
190.8
17,213.2
6.4
49.5
1,152.7
13.5
1,222.1
Residual Fuel
115.8
237.4
256.4
876.5
238.6
1,724.7
8.7
17.8
19.3
65.8
17.9
129.5
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
6.9
6.9
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)
6,323.7
3,933.3
13,177.8
25,987.5
28,024.0
676.6
78,122.9
357.9
226.9
850.1
1,856.1
2,400.0
49.7
5,740.7
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Table A-25: 2004 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans. Elec.
Terr.
Total
Total Coal
11.4
102.9
1,262.0
NE
20,305.0
32.0
21,713.4
1.1
9.8
118.2
NE 1,942.0
2.9
2,074.0
Residential Coal
11.4
11.4
1.1
1.1
Commercial Coal
102.9
102.9
9.8
9.8
Industrial Other Coal
1,262.0
1,262.0
118.2
118.2
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
20,305.0
20,305.0
1,942.0
1,942.0
U.S. Territory Coal (bit)
32.0
32.0
2.9
2.9
Natural Gas
4,980.8
3,201.0
7,913.5
602.0
5,594.9
24.6
22,316.9
264.2
169.8
419.7
31.9 296.8
1.3
1,183.7
Total Petroleum
1,468.5
808.0
4,487.7
25,111.5
1,201.0
651.5
33,728.1
102.3
57.8
335.8
1,805.2 95.8
47.8
2,444.6
A-72 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Asphalt & Road Oil
Aviation Gasoline
31.2
31.2
2.2
2.2
Distillate Fuel Oil
871.3
443.6
1,130.6
5,910.4
111.2
131.7
8,598.8
64.4
32.8
83.6
437.1
8.2
9.7
635.9
Jet Fuel
2,584.8
NA
68.5
2,653.4
186.7
5.0
191.6
Kerosene
84.8
20.5
28.2
6.0
139.5
6.2
1.5
2.1
0.4
10.2
LPG
512.4
152.2
300.3
19.1
0.7
984.8
31.6
9.4
18.5
1.2
+
60.8
Lubricants
Motor Gasoline
69.0
574.3
16,379.5
198.1
17,220.9
4.9
40.8
1,164.1
14.1
1,223.9
Residual Fuel
122.5
204.7
186.4
879.0
246.4
1,639.0
9.2
15.4
14.0
66.0
18.5
123.1
Other Petroleum
AvGas Blend Components
10.6
10.6
0.7
0.7
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
112.1
112.1
7.8
7.8
Petroleum Coke
0.3
719.1
210.8
930.1
+
73.4
21.5
95.0
Still Gas
1,483.3
1,483.3
99.0
99.0
Special Naphtha
Unfinished Oils
(75.6)
(75.6)
(5.6)
(5.6)
Waxes
Geothermal
50.5
50.5
0.5
0.5
Total (All Fuels)
6,460.7
4,111.9
13,663.2
25,713.4
27,151.5
708.1
77,808.8
367.5
237.4
873.7
1,837.2
2,335.0
52.0
5,702.8
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-26: 2003 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Fuel Type
Res.
Comm.
Adjusted Consumption (TBtu)a
Ind. Trans. Elec.
Terr. Total
Res.
Emissions'1 (MMT C02 Eq.) from Energy Use
Comm. Ind. Trans. Elec. Terr.
Total
Total Coal
12.2
82.0
1,248.8
NE 20,184.7
33.9 21,561.7
1.2
7.8
116.9 NE 1,930.0
3.1
2,059.0
Residential Coal
12.2
12.2
1.2
1.2
Commercial Coal
82.0
82.0
7.8
7.8
Industrial Other Coal
1,248.8
1,248.8
116.9
116.9
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
20,184.7
20,184.7
1,930.0
1,930.0
A-73
-------
U.S. Territory Coal (bit)
33.9
33.9
3.1
3.1
Natural Gas
5,209.4
3,260.9
7,845.1
627.4
5,246.2
26.9
22,216.0
276.2
172.9
415.9
33.3
278.1
1.4
1,177.8
Total Petroleum
1,465.9
825.6
4,228.6
24,499.7
1,204.8
617.7
32,842.4
101.7
59.0
316.9
1,759.3
95.0
45.0
2,377.0
Asphalt & Road Oil
Aviation Gasoline
30.2
30.2
2.1
2.1
Distillate Fuel Oil
850.4
452.6
1,054.4
5,704.9
160.8
118.1
8,341.2
62.9
33.5
78.0
421.9
11.9
8.7
616.9
Jet Fuel
2,482.5
NA
76.0
2,558.5
179.3
5.5
184.8
Kerosene
70.3
18.6
24.1
10.7
123.7
5.1
1.4
1.8
0.8
9.1
LPG
545.2
157.1
261.9
17.9
10.5
992.5
33.7
9.7
16.2
1.1
0.7
61.3
Lubricants
Motor Gasoline
85.9
464.3
16,165.1
207.7
16,923.0
6.1
33.0
1,147.4
14.7
1,201.2
Residual Fuel
111.1
176.4
99.1
869.4
194.7
1,450.8
8.3
13.2
7.4
65.3
14.6
108.9
Other Petroleum
AvGas Blend Components
7.5
7.5
0.5
0.5
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
111.3
111.3
7.8
7.8
Petroleum Coke
0.3
701.9
174.7
876.8
+
71.7
17.8
89.5
Still Gas
1,477.3
1,477.3
98.6
98.6
Special Naphtha
Unfinished Oils
(50.4)
(50.4)
(3.7)
(3.7)
Waxes
Geothermal
49.2
49.2
0.5
0.5
Total (All Fuels)
6,687.6
4,168.5
13,322.5
25,127.2
26,685.0
678.5
76,669.2
379.1
239.7
849.8
1,792.5
2,303.6
49.6
5,614.2
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-27: 2002 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Fuel Type
Res.
Comm.
Adjusted Consumption (TBtu)a
Ind. Trans. Elec.
Terr. Total
Res.
Emissions'1 (MMT C02 Eq.) from Energy Use
Comm. Ind. Trans. Elec. Terr.
Total
Total Coal
12.2
89.8
1,243.7 NE 19,782.8
10.8 21,139.3
1.2
8.6 116.6 NE 1,888.9 1.0
2,016.2
Residential Coal
12.2
12.2
1.2
1.2
Commercial Coal
89.8
89.8
8.6
8.6
A-74 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Industrial Other Coal
1,243.7
1,243.7
116.6
116.6
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
19,782.8
19,782.8
1,888.9
1,888.9
U.S. Territory Coal (bit)
10.8
10.8
1.0
1.0
Natural Gas
4,995.0
3,212.5
8,086.3
698.9
5,766.8
22.8
22,782.3
265.1
170.5
429.1
37.1
306.0
1.2
1,208.9
Total Petroleum
1,358.8
696.5
4,065.3
24,528.1
961.2
551.6
32,161.5
93.9
49.7
304.5
1,762.8
76.8
40.2
2,327.9
Asphalt & Road Oil
Aviation Gasoline
33.7
33.7
2.3
2.3
Distillate Fuel Oil
761.0
393.0
1,047.3
5,590.0
127.3
91.3
8,009.9
56.3
29.1
77.5
413.4
9.4
6.8
592.4
Jet Fuel
2,565.5
NA
61.7
2,627.2
185.3
4.5
189.7
Kerosene
59.9
15.9
13.8
8.0
97.7
4.4
1.2
1.0
0.6
7.2
LPG
537.8
141.0
322.2
14.3
11.1
1,026.4
33.2
8.7
19.9
0.9
0.7
63.4
Lubricants
Motor Gasoline
66.5
455.5
16,096.7
187.2
16,806.0
4.7
32.4
1,143.7
13.3
1,194.1
Residual Fuel
79.8
146.1
227.9
658.7
192.2
1,304.7
6.0
11.0
17.1
49.5
14.4
98.0
Other Petroleum
AvGas Blend Components
7.5
7.5
0.5
0.5
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
112.8
112.8
7.9
7.9
Petroleum Coke
0.2
696.3
175.2
871.7
+
71.1
17.9
89.0
Still Gas
1,399.4
1,399.4
93.4
93.4
Special Naphtha
Unfinished Oils
(135.7)
(135.7)
(10.1)
(10.1)
Waxes
Geothermal
49.4
49.4
0.5
0.5
Total (All Fuels)
6,366.0
3,998.8
13,395.2
25,227.0
26,560.2
585.2
76,132.5
360.1
228.7
850.2
1,799.9
2,272.1
42.4
5,553.4
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
A-75
-------
Table A-28: 2001 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
12.0
96.9
1,358.4
NE
19,613.7
3.8
21,084.8
1.1
9.2
127.8
NE
1,868.8
0.4
2,007.3
Residential Coal
12.0
12.0
1.1
1.1
Commercial Coal
96.9
96.9
9.2
9.2
Industrial Other Coal
1,358.4
1,358.4
127.8
127.8
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
19,613.7
19,613.7
1,868.8
1,868.8
U.S. Territory Coal (bit)
3.8
3.8
0.4
0.4
Natural Gas
4,889.0
3,097.3
7,949.0
658.0
5,458.1
22.9
22,074.3
259.2
164.2
421.4
34.9
289.4
1.2
1,170.3
Total Petroleum
1,462.8
763.7
4,201.4
24,016.6
1,276.4
628.2
32,349.0
101.7
54.7
314.4
1,723.5
98.4
45.9
2,338.7
Asphalt & Road Oil
Aviation Gasoline
34.9
34.9
2.4
2.4
Distillate Fuel Oil
841.4
470.9
1,180.5
5,411.3
170.3
106.8
8,181.2
62.2
34.8
87.3
400.2
12.6
7.9
605.1
Jet Fuel
2,626.3
NA
98.2
2,724.5
189.7
7.1
196.8
Kerosene
95.1
31.4
23.2
0.8
150.5
7.0
2.3
1.7
0.1
11.0
LPG
526.4
142.9
305.1
13.7
7.0
995.1
32.6
8.8
18.9
0.8
0.4
61.5
Lubricants
Motor Gasoline
48.5
382.0
15,770.8
186.0
16,387.2
3.4
27.1
1,118.4
13.2
1,162.1
Residual Fuel
69.9
146.7
159.5
1,002.8
229.4
1,608.2
5.2
11.0
12.0
75.3
17.2
120.8
Other Petroleum
AvGas Blend Components
6.1
6.1
0.4
0.4
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
132.6
132.6
9.3
9.3
Petroleum Coke
0.2
683.3
103.2
786.7
+
69.8
10.5
80.3
Still Gas
1,417.3
1,417.3
94.6
94.6
Special Naphtha
Unfinished Oils
(75.4)
(75.4)
(5.6)
(5.6)
Waxes
Geothermal
46.9
46.9
0.4
0.4
Total (All Fuels)
6,363.8
3,957.8
13,508.8
24,674.6
26,395.0
654.9
75,555.0
362.1
228.1
863.6
1,758.4
2,257.1
47.5
5,516.7
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
A-76 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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.
Note: Parentheses indicate negative values.
Table A-29: 2000 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 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
1,348.8
NE
20,220.2
10.3
21,682.4
1.1
8.8
127.3
NE
1,926.4
0.9
2,064.4
Residential Coal
11.4
11.4
1.1
1.1
Commercial Coal
91.9
91.9
8.8
8.8
Industrial Other Coal
1,348.8
1,348.8
127.3
127.3
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
20,220.2
20,220.2
1,926.4
1,926.4
U.S. Territory Coal (bit)
10.3
10.3
0.9
0.9
Natural Gas
5,104.6
3,251.5
8,656.0
672.0
5,293.4
12.7
22,990.2
270.8
172.5
459.2
35.7
280.8
0.7
1,219.7
Total Petroleum
1,422.5
766.0
3,746.7
24,291.6
1,144.1
467.2
31,838.2
98.5
54.7
280.3
1,743.7
88.4
33.9
2,299.5
Asphalt & Road Oil
Aviation Gasoline
36.3
36.3
2.5
2.5
Distillate Fuel Oil
772.3
419.1
996.2
5,436.7
174.7
68.5
7,867.4
57.1
31.0
73.7
402.1
12.9
5.1
581.8
Jet Fuel
2,700.3
NA
73.9
2,774.2
195.0
5.3
200.4
Kerosene
94.6
29.7
15.6
2.3
142.1
6.9
2.2
1.1
0.2
10.4
LPG
555.6
150.6
393.8
11.9
8.0
1,119.9
34.4
9.3
24.4
0.7
0.5
69.4
Lubricants
Motor Gasoline
74.9
252.7
15,663.0
183.1
16,173.7
5.3
17.9
1,110.1
13.0
1,146.3
Residual Fuel
91.6
184.1
443.5
870.8
131.3
1,721.3
6.9
13.8
33.3
65.4
9.9
129.3
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
12.1
12.1
Petroleum Coke
0.2
697.6
98.6
796.4
+
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)
6,538.5
4,109.4
13,751.5
24,963.6
26,705.8
490.1
76,558.9
370.3
236.0
866.8
1,779.4
2,296.0
35.5
5,584.0
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
A-77
-------
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-30: 1999 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec. Terr.
Total
Total Coal
14.0
102.5
1,372.8
NE
19,279.5
10.2
20,778.9
1.3
9.8
129.8
NE
1,835.4 0.9
1,977.3
Residential Coal
14.0
14.0
1.3
1.3
Commercial Coal
102.5
102.5
9.8
9.8
Industrial Other Coal
1,372.8
1,372.8
129.8
129.8
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
19,279.5
19,279.5
1,835.4
1,835.4
U.S. Territory Coal (bit)
10.2
10.2
0.9
0.9
Natural Gas
4,834.9
3,115.0
8,424.6
675.3
4,902.1
21,952.0
256.4
165.2
446.7
35.8
259.9
1,164.0
Total Petroleum
1,342.1
641.9
3,657.7
23,850.0
1,211.2
454.5
31,157.4
92.8
45.8
275.5
1,710.2
93.8 33.0
2,251.2
Asphalt & Road Oil
Aviation Gasoline
39.2
39.2
2.7
2.7
Distillate Fuel Oil
704.3
373.0
982.4
5,245.8
140.0
93.2
7,538.6
52.1
27.6
72.7
388.0
10.4 6.9
557.5
Jet Fuel
2,664.8
NA
62.8
2,727.6
192.5
4.5
197.0
Kerosene
111.2
26.9
12.8
3.5
154.5
8.1
2.0
0.9
0.3
11.3
LPG
526.7
140.3
325.0
14.3
9.2
1,015.5
32.6
8.7
20.1
0.9
0.6
62.8
Lubricants
Motor Gasoline
28.2
150.3
15,710.2
161.7
16,050.4
2.0
10.7
1,113.0
11.5
1,137.1
Residual Fuel
73.3
150.9
175.7
958.7
124.2
1,482.9
5.5
11.3
13.2
72.0 9.3
111.4
Other Petroleum
AvGas Blend Components
6.4
6.4
0.4
0.4
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
183.9
183.9
12.9
12.9
Petroleum Coke
0.1
719.8
112.5
832.4
+
73.5
11.5
85.0
Still Gas
1,414.1
1,414.1
94.3
94.3
Special Naphtha
Unfinished Oils
(287.9)
(287.9)
(21.3)
(21.3)
Waxes
Geothermal
50.6
50.6
0.5
0.5
A-78 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Total (All Fuels)
6,191.0 3,859.5 13,455.1 24,525.3 25,443.4 464.7 73,939.0 350.5 220.7 852.1 1,746.0 2,189.7
34.0 5,393.0
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-31: 1998 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
11.5
93.4
1,470.8
NE
19,215.7
10.5
20,802.0
1.1
8.9
139.1
NE
1,827.1
1.0
1,977.2
Residential Coal
11.5
11.5
1.1
1.1
Commercial Coal
93.4
93.4
8.9
8.9
Industrial Other Coal
1,470.8
1,470.8
139.1
139.1
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
19,215.7
19,215.7
1,827.1
1,827.1
U.S. Territory Coal (bit)
10.5
10.5
1.0
1.0
Natural Gas
4,646.1
3,083.0
8,826.0
666.1
4,674.9
21,896.1
246.1
163.3
467.6
35.3
247.7
1,160.0
Total Petroleum
1,207.2
667.5
3,708.7
22,890.7
1,306.1
442.4
30,222.6
84.0
47.8
279.9
1,643.3
101.3
32.2
2,188.5
Asphalt & Road Oil
Aviation Gasoline
35.5
35.5
2.5
2.5
Distillate Fuel Oil
674.4
374.7
1,026.8
4,949.9
135.6
70.6
7,232.0
49.9
27.7
75.9
366.1
10.0
5.2
534.9
Jet Fuel
2,608.0
NA
58.8
2,666.8
188.4
4.2
192.6
Kerosene
108.3
31.2
22.1
6.0
167.5
7.9
2.3
1.6
0.4
12.3
LPG
424.4
117.7
209.5
17.7
5.9
775.3
26.2
7.3
12.9
1.1
0.4
47.8
Lubricants
Motor Gasoline
58.6
300.1
15,200.7
161.0
15,720.4
4.2
21.3
1,079.4
11.4
1,116.3
Residual Fuel
85.2
173.3
78.9
1,047.0
140.1
1,524.4
6.4
13.0
5.9
78.6
10.5
114.5
Other Petroleum
AvGas Blend Components
4.0
4.0
0.3
0.3
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
148.2
148.2
10.4
10.4
Petroleum Coke
0.1
707.7
123.6
831.4
+
72.3
12.6
84.9
Still Gas
1,431.0
1,431.0
95.5
95.5
Special Naphtha
A-79
-------
Unfinished Oils
Waxes
Geothermal
(313.9)
50.4
(313.9)
50.4
(23.3)
0.5
(23.3)
0.5
Total (All Fuels)
5,864.8 3,843.9 14,005.5 23,556.8
25,247.1
453.0 72,971.1
331.2
220.1
886.5
1,678.6 2,176.6
33.2 5,326.2
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-32: 1997 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
16.0
129.4
1,457.6
NE
18,904.5
10.4
20,518.0
1.5
12.3
137.6
NE
1,796.0
1.0
1,948.4
Residential Coal
16.0
16.0
1.5
1.5
Commercial Coal
129.4
129.4
12.3
12.3
Industrial Other Coal
1,457.6
1,457.6
137.6
137.6
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
18,904.5
18,904.5
1,796.0
1,796.0
U.S. Territory Coal (bit)
10.4
10.4
1.0
1.0
Natural Gas
5,092.9
3,285.3
9,032.5
780.3
4,125.5
22,316.6
270.1
174.3
479.1
41.4
218.8
1,183.7
Total Petroleum
1,333.5
713.6
4,122.3
22,319.8
926.7
439.9
29,855.7
93.0
51.2
306.7
1,601.9
72.2
32.0
2,157.1
Asphalt & Road Oil
Aviation Gasoline
39.7
39.7
2.7
2.7
Distillate Fuel Oil
785.2
398.5
1,056.3
4,797.9
110.5
79.1
7,227.6
58.1
29.5
78.1
354.8
8.2
5.9
534.5
Jet Fuel
2,553.8
NA
61.3
2,615.1
184.4
4.4
188.9
Kerosene
92.9
24.6
18.8
3.9
140.3
6.8
1.8
1.4
0.3
10.3
LPG
455.4
120.4
365.0
14.2
6.5
961.6
28.1
7.4
22.5
0.9
0.4
59.4
Lubricants
Motor Gasoline
58.6
290.6
14,777.7
158.7
15,285.6
4.2
20.6
1,048.8
11.3
1,084.8
Residual Fuel
111.2
240.1
136.5
714.6
130.2
1,332.7
8.4
18.0
10.3
53.7
9.8
100.1
Other Petroleum
AvGas Blend Components
9.1
9.1
0.6
0.6
Crude Oil
4.6
4.6
0.3
0.3
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
165.7
165.7
11.6
11.6
A-80 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Petroleum Coke
0.1
639.9
101.6
741.6
+
65.3
10.4
75.7
Still Gas
1,435.0
1,435.0
95.7
95.7
Special Naphtha
Unfinished Oils
(102.9)
(102.9)
(7.6)
(7.6)
Waxes
Geothermal
50.2
50.2
0.5
0.5
Total (All Fuels)
6,442.4 4,128.3
14,612.5 23,100.1
24,007.0
450.3 72,740.6
364.6
237.8
923.4 1,643.3
2,087.5
33.0
5,289.6
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-33: 1996 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
16.6
121.6
1,454.9
NE
18,429.0
10.3
20,032.4
1.6
11.6
137.3
NE
1,751.5
1.0
1,903.0
Residential Coal
16.6
16.6
1.6
1.6
Commercial Coal
121.6
121.6
11.6
11.6
Industrial Other Coal
1,454.9
1,454.9
137.3
137.3
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
18,429.0
18,429.0
1,751.5
1,751.5
U.S. Territory Coal (bit)
10.3
10.3
1.0
1.0
Natural Gas
5,354.4
3,226.3
9,020.3
736.9
3,862.4
22,200.4
284.0
171.1
478.4
39.1
204.9
1,177.5
Total Petroleum
1,392.1
758.3
4,160.9
22,123.6
817.3
428.1
29,680.2
97.1
54.6
309.8
1,588.1
63.4
31.1
2,144.1
Asphalt & Road Oil
Aviation Gasoline
37.4
37.4
2.6
2.6
Distillate Fuel Oil
834.0
434.9
1,042.6
4,594.9
109.3
73.4
7,089.1
61.7
32.2
77.1
339.8
8.1
5.4
524.3
Jet Fuel
2,556.0
NA
77.2
2,633.2
184.6
5.6
190.2
Kerosene
88.8
21.0
18.3
2.9
131.0
6.5
1.5
1.3
0.2
9.6
LPG
469.3
122.6
335.1
15.7
7.5
950.1
28.9
7.6
20.7
1.0
0.5
58.6
Lubricants
Motor Gasoline
42.5
320.0
14,604.8
150.0
15,117.2
3.0
22.7
1,036.5
10.6
1,072.8
Residual Fuel
137.2
284.7
314.9
628.4
117.1
1,482.3
10.3
21.4
23.6
47.2
8.8
111.3
Other Petroleum
AvGas Blend Components
7.0
7.0
0.5
0.5
Crude Oil
13.7
13.7
1.0
1.0
MoGas Blend Components
Misc. Products
A-81
-------
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
178.9
178.9
12.5
12.5
Petroleum Coke
0.1
638.6
79.6
718.3
+
65.2
8.1
73.3
Still Gas
1,434.9
1,434.9
95.7
95.7
Special Naphtha
Unfinished Oils
(112.8)
(112.8)
(8.4)
(8.4)
Waxes
Geothermal
48.9
48.9
0.5
0.5
Total (All Fuels)
6,763.1 4,106.2
14,636.1 22,860.5
23,157.6
438.4 71,961.9
382.7
237.3
925.5 1,627.2
2,020.2
32.1 5,225.0
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-34: 1995 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 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
1,526.9
NE
17,466.3
10.2
19,137.7
1.7
11.2
144.4
NE
1,659.9
0.9
1,818.0
Residential Coal
17.5
17.5
1.7
1.7
Commercial Coal
116.8
116.8
11.2
11.2
Industrial Other Coal
1,526.9
1,526.9
144.4
144.4
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
17,466.3
17,466.3
1,659.9
1,659.9
U.S. Territory Coal (bit)
10.2
10.2
0.9
0.9
Natural Gas
4,954.2
3,096.0
8,722.5
724.0
4,302.0
21,798.6
262.8
164.2
462.6
38.4
228.2
1,156.2
Total Petroleum
1,260.7
725.1
3,823.9
21,525.9
754.5
458.8
28,548.9
88.3
52.3
284.1
1,542.1
58.7
33.3
2,058.9
Asphalt & Road Oil
Aviation Gasoline
39.6
39.6
2.7
2.7
Distillate Fuel Oil
791.1
418.7
967.1
4,379.4
108.0
86.8
6,751.1
58.5
31.0
71.5
323.9
8.0
6.4
499.3
Jet Fuel
2,428.8
NA
76.0
2,504.8
172.2
5.4
177.6
Kerosene
74.3
22.1
15.4
3.5
115.4
5.4
1.6
1.1
0.3
8.4
LPG
395.3
108.9
342.6
17.8
5.6
870.2
24.4
6.7
21.1
1.1
0.3
53.7
Lubricants
Motor Gasoline
33.8
373.2
14,273.1
146.9
14,827.0
2.4
26.5
1,013.1
10.4
1,052.4
Residual Fuel
141.5
286.2
387.3
566.0
139.8
1,520.8
10.6
21.5
29.1
42.5
10.5
114.2
Other Petroleum
AvGas Blend Components
5.3
5.3
0.4
0.4
A-82 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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
11.9
11.9
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)
6,232.4 3,937.9
14,073.4 22,249.9
22,568.4
469.0 69,530.8
352.8
227.7
891.1 1,580.5
1,947.2
34.3 5,033.5
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-35: 1994 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
20.8
118.1
1,594.9
NE
17,260.9
10.0
19,004.7
2.0
11.3
150.6
NE
1,637.9
0.9
1,802.7
Residential Coal
20.8
20.8
2.0
2.0
Commercial Coal
118.1
118.1
11.3
11.3
Industrial Other Coal
1,594.9
1,594.9
150.6
150.6
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
17,260.9
17,260.9
1,637.9
1,637.9
U.S. Territory Coal (bit)
10.0
10.0
0.9
0.9
Natural Gas
4,959.8
2,962.0
8,290.3
708.5
3,977.3
20,897.9
262.9
157.0
439.5
37.6
210.9
1,107.9
Total Petroleum
1,305.1
778.2
w
00
00
00
W
21,167.1
1,058.7
504.9
28,702.3
91.8
56.3
288.6
1,516.2
81.2
36.8
2,071.0
Asphalt & Road Oil
Aviation Gasoline
38.1
38.1
2.6
2.6
Distillate Fuel Oil
856.0
446.8
974.9
4,183.3
120.0
117.0
6,697.9
63.3
33.0
72.1
309.4
8.9
8.7
495.4
Jet Fuel
2,473.8
NA
65.9
2,539.6
175.5
4.7
180.2
Kerosene
64.9
19.5
16.9
2.9
104.2
4.8
1.4
1.2
0.2
7.6
LPG
384.2
107.4
365.4
34.0
7.2
898.3
23.7
6.6
22.6
2.1
0.4
55.5
Lubricants
Motor Gasoline
32.4
247.6
14,079.8
146.6
14,506.4
2.3
17.6
999.7
10.4
1,030.0
A-83
-------
Residual Fuel
171.9
368.4
358.1
869.0
165.3 1,932.8
12.9
27.7
26.9
65.3
12.4 145.1
Other Petroleum
AvGas Blend Components
6.1
6.1
0.4
0.4
Crude Oil
18.7
18.7
1.4
1.4
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
170.7
170.7
12.0
12.0
Petroleum Coke
0.1
594.9
69.7
664.7
+
60.7
7.1
67.9
Still Gas
1,404.0
1,404.0
93.7
93.7
Special Naphtha
Unfinished Oils
(279.2)
(279.2)
(20.7)
(20.7)
Waxes
Geothermal
53.0
53.0
0.5
0.5
Total (All Fuels)
6,285.8 3,858.3
13,773.5
21,875.6
22,349.9
514.9 68,658.0
356.7
224.7
878.8
1,553.8
1,930.5
37.7 4,982.1
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-36: 1993 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy
Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
25.7
117.3
1,585.0
NE
17,195.9
9.6
18,933.5
2.5
11.3
149.7
NE
1,631.6
0.9
1,795.9
Residential Coal
25.7
25.7
2.5
2.5
Commercial Coal
117.3
117.3
11.3
11.3
Industrial Other Coal
1,585.0
1,585.0
149.7
149.7
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
17,195.9
17,195.9
1,631.6
1,631.6
U.S. Territory Coal (bit)
9.6
9.6
0.9
0.9
Natural Gas
5,058.4
2,920.4
8,263.7
644.1
3,537.5
20,424.1
268.2
154.9
438.2
34.2
187.6
1,083.0
Total Petroleum
1,349.1
784.8
3,780.3
20,534.2
1,123.8
456.1
28,028.2
94.9
56.8
281.6
1,474.6
86.4
33.3
2,027.6
Asphalt & Road Oil
Aviation Gasoline
38.4
38.4
2.7
2.7
Distillate Fuel Oil
883.4
447.3
989.7
3,889.4
86.5
103.0
6,399.2
65.3
33.1
73.2
287.6
6.4
7.6
473.3
Jet Fuel
2,368.4
NA
61.3
2,429.7
168.2
4.4
172.6
Kerosene
75.6
14.0
13.1
3.7
106.4
5.5
1.0
1.0
0.3
7.8
A-84 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
LPG
390.1
109.4
352.8
20.3
4.9
877.5
24.1
6.8
21.8
1.3
0.3
54.2
Lubricants
Motor Gasoline
41.2
249.9
13,850.2
126.9
14,268.3
2.9
17.8
987.2
9.0
1,017.0
Residual Fuel
172.7
382.9
367.5
958.6
156.2
2,038.0
13.0
28.8
27.6
72.0
11.7
153.0
Other Petroleum
AvGas Blend Components
0.1
0.1
+
+
Crude Oil
21.2
21.2
1.6
1.6
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
167.4
167.4
11.7
11.7
Petroleum Coke
0.2
614.6
78.6
693.4
+
62.8
8.0
70.8
Still Gas
1,384.6
1,384.6
92.4
92.4
Special Naphtha
Unfinished Oils
(396.0)
(396.0)
(29.3)
(29.3)
Waxes
Geothermal
57.3
57.3
0.6
0.6
Total (All Fuels)
6,433.2
3,822.5
13,629.0
21,178.3
21,914.5
465.6
67,443.1
365.6
222.9
869.5
1,508.7
1,906.2
34.2
4,907.1
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-37: 1992 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
25.6
116.6
1,554.6
NE
16,465.6
8.8
18,171.1
2.5
11.3
147.4
NE
1,568.5
0.8
1,730.5
Residential Coal
25.6
25.6
2.5
2.5
Commercial Coal
116.6
116.6
11.3
11.3
Industrial Other Coal
1,554.6
1,554.6
147.4
147.4
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
16,465.6
16,465.6
1,568.5
1,568.5
U.S. Territory Coal (bit)
8.8
8.8
0.8
0.8
Natural Gas
4,804.6
2,871.2
8,125.3
608.1
3,511.5
19,920.7
254.6
152.1
430.6
32.2
186.1
1,055.6
Total Petroleum
1,362.8
888.5
3,945.0
20,094.1
990.7
443.0
27,724.2
96.2
64.4
293.4
1,445.5
75.5
32.3
2,007.3
Asphalt & Road Oil
Aviation Gasoline
41.1
41.1
2.8
2.8
A-85
-------
Distillate Fuel Oil
927.9
479.9
1,023.7
3,665.7
73.5
89.6
6,260.3
68.6
35.5
75.7
271.1
5.4
6.6
463.0
Jet Fuel
2,343.8
NA
60.7
2,404.5
166.6
4.3
170.9
Kerosene
65.0
11.1
9.8
3.1
89.1
4.8
0.8
0.7
0.2
6.5
LPG
369.9
107.0
383.9
19.4
11.8
892.1
22.9
6.6
23.7
1.2
0.7
55.1
Lubricants
Motor Gasoline
101.3
247.3
13,624.0
121.5
14,094.0
7.2
17.7
973.7
8.7
1,007.3
Residual Fuel
189.1
323.9
400.1
872.2
156.4
1,941.7
14.2
24.3
30.0
65.5
11.7
145.8
Other Petroleum
AvGas Blend Components
0.2
0.2
+
+
Crude Oil
27.4
27.4
2.0
2.0
MoGas Blend Components
75.7
75.7
5.4
5.4
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
162.5
162.5
11.4
11.4
Petroleum Coke
0.1
627.2
45.0
672.2
+
64.0
4.6
68.6
Still Gas
1,418.4
1,418.4
94.6
94.6
Special Naphtha
Unfinished Oils
(354.8)
(354.8)
(26.3)
(26.3)
Waxes
Geothermal
55.1
55.1
0.5
0.5
Total (All Fuels)
6,193.0
3,876.3
13,624.9
20,702.2
21,022.9
451.9
65,871.1
353.3
227.8
871.3
1,477.8
1,830.7
33.1
4,794.0
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
A-86 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Table A-38: 1991 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
25.4
115.5
1,602.7
NE
16,249.7
7.7
18,001.0
2.4
11.1
152.0
NE
1,547.2
0.7
1,713.5
Residential Coal
25.4
25.4
2.4
2.4
Commercial Coal
115.5
115.5
11.1
11.1
Industrial Other Coal
1,602.7
1,602.7
152.0
152.0
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
16,249.7
16,249.7
1,547.2
1,547.2
U.S. Territory Coal (bit)
7.7
7.7
0.7
0.7
Natural Gas
4,667.2
2,795.4
7,827.8
620.3
3,377.4
19,288.1
247.3
148.1
414.8
32.9
179.0
1,022.1
Total Petroleum
1,382.1
1,011.6
3,668.8
19,363.1
1,198.3
422.4
27,046.3
97.5
73.3
273.2
1,389.6
90.7
30.7
1,954.9
Asphalt & Road Oil
Aviation Gasoline
41.7
41.7
2.9
2.9
Distillate Fuel Oil
931.1
517.8
1,050.6
3,449.7
83.6
69.6
6,102.4
68.9
38.3
77.7
255.1
6.2
5.1
451.3
Jet Fuel
2,373.6
NA
76.8
2,450.4
168.8
5.5
174.3
Kerosene
72.3
12.1
11.4
2.7
98.5
5.3
0.9
0.8
0.2
7.2
LPG
378.6
108.4
284.7
21.2
13.8
806.6
23.4
6.7
17.6
1.3
0.8
49.8
Lubricants
Motor Gasoline
161.5
366.9
13,252.6
123.6
13,904.5
11.5
26.2
944.6
8.8
991.1
Residual Fuel
211.9
270.9
224.4
1,085.3
135.9
1,928.5
15.9
20.3
16.9
81.5
10.2
144.8
Other Petroleum
AvGas Blend Components
(0.1)
(0.1)
(+)
(+)
Crude Oil
38.9
38.9
2.9
2.9
MoGas Blend Components
(25.9)
(25.9)
(1.8)
(1.8)
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
148.2
148.2
10.4
10.4
Petroleum Coke
587.6
29.3
616.9
60.0
3.0
63.0
Still Gas
1,385.9
1,385.9
92.5
92.5
Special Naphtha
Unfinished Oils
(450.2)
(450.2)
(33.3)
(33.3)
Waxes
Geothermal
54.5
54.5
0.5
0.5
Total (All Fuels)
6,074.7
3,922.5
13,099.4
19,983.4
20,879.8
430.1
64,389.9
347.3
232.5
840.0
1,422.5
1,817.4
31.4
4,691.0
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
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-40), and international bunker
fuel consumption (see Table A-41).
A-87
-------
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.
Note: Parentheses indicate negative values.
Table A-39: 1990 Energy Consumption Data and C02 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
Emissions'1 (MMT C02 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
1,640.4
NE
16,261.0
7.0
18,064.0
3.0
12.0
155.2
NE
1,546.5
0.6
1,717.3
Residential Coal
31.1
31.1
3.0
3.0
Commercial Coal
124.5
124.5
12.0
12.0
Industrial Other Coal
1,640.4
1,640.4
155.2
155.2
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
16,261.0
16,261.0
1,546.5
1,546.5
U.S. Territory Coal (bit)
7.0
7.0
0.6
0.6
Natural Gas
4,486.6
2,679.6
7,708.2
679.2
3,308.5
18,862.1
237.8
142.0
408.5
36.0
175.4
999.7
Total Petroleum
1,375.8
1,022.6
3,947.0
19,977.2
1,289.4
370.3
27,982.3
97.4
74.2
293.3
1,433.1
97.5
26.9
2,022.4
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
70.8
6,305.2
70.9
38.9
81.2
262.9
7.1
5.2
466.3
Jet Fuel
2,590.1
NA
59.6
2,649.7
184.2
4.2
188.5
Kerosene
63.9
11.8
12.3
2.5
90.5
4.7
0.9
0.9
0.2
6.6
LPG
352.6
102.4
327.9
22.9
14.5
820.3
21.8
6.3
20.3
1.4
0.9
50.7
Lubricants
Motor Gasoline
153.0
254.8
13,464.1
100.7
13,972.5
10.9
18.1
958.9
7.2
995.1
Residual Fuel
229.8
364.1
300.3
1,162.6
122.2
2,179.0
17.3
27.3
22.6
87.3
9.2
163.6
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
8.8
8.8
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
13,295.6
20,656.4
20,911.6
377.4
64,961.0
338.2
228.2
857.0
1,469.1
1,820.0
27.6
4,740.0
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NE (Not Estimated)
A-88 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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-40), and international bunker
fuel consumption (see Table A-41).
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.
Note: Parentheses indicate negative values.
Table A-40: Unadjusted Non-Energy Fuel Consumption (TBtu)
Sector/Fuel Type
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Industry
4,544.0
5,089.7
5,576.5
5,483.3
4,769.2
4,509.3
4,737.6 4,648.5
4,593.9
4,810.1
4,731.1
4,921.6
4,904.0
5,082.9
5,384.4
Industrial Coking Coal
0.0
37.8
53.5
80.4
29.2
6.4
64.8
60.8
132.5
119.3
48.8
121.8
89.3
111.9
123.9
Industrial Other Coal
8.2
11.3
12.4
11.9
11.9
11.9
10.3
10.3
10.3
10.3
10.3
10.3
10.3
10.3
10.3
Natural Gas to
Chemical Plants,
Other Uses
305.9
371.0
401.7
270.4
233.1
232.7
310.0
309.7
310.6
311.5
330.6
332.2
332.5
331.5
331.5
Asphalt & Road Oil
1,170.2
1,178.2
1,275.7
1,323.2
1,012.0
873.1
877.8
859.5
826.7
783.3
792.6
831.7
853.4
849.2
792.8
LPG
1,201.4
1,586.9
1,759.3
1,659.5
1,596.6
1,748.0
1,901.6
1,943.4
1,986.5
2,149.0
2,148.7
2,215.1
2,254.0
2,333.6
2,672.7
Lubricants
186.3
177.8
189.9
160.2
149.6
134.5
135.9
127.4
118.3
125.1
130.7
142.1
135.1
124.9
121.2
Pentanes Plus
125.2
169.0
171.6
98.1
76.5
63.8
77.7
27.3
42.2
47.1
44.2
80.2
56.1
86.4
111.8
Naphtha (<401 deg. F)
347.8
373.0
613.5
698.7
477.2
471.9
490.6
487.3
453.9
517.8
442.6
428.1
420.0
436.2
447.1
Other Oil (>401 deg. F)
753.9
801.0
722.2
708.0
647.8
424.8
452.5
388.5
287.2
223.9
247.2
229.0
222.5
262.9
239.1
Still Gas
36.7
47.9
17.0
67.7
47.3
133.9
147.8
163.6
160.6
166.7
164.5
162.2
166.1
163.8
166.9
Petroleum Coke
123.1
120.6
98.5
186.9
224.5
180.7
61.0
62.4
67.6
62.4
61.4
62.5
61.1
57.0
58.9
Special Naphtha
107.1
70.8
97.4
62.5
84.9
46.2
26.1
22.6
14.7
100.0
106.1
99.3
93.6
100.3
92.0
Other (Wax/Misc.)
Distillate Fuel Oil
7.0
6.8
11.7
11.7
17.5
17.5
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
19.1
12.2
17.1
15.1
15.3
16.5
14.8
12.4
12.8
10.2
12.4
Miscellaneous
Products
137.8
97.1
119.2
112.8
142.0
151.8
158.7
164.7
161.6
171.2
182.7
188.9
191.3
198.8
198.0
Transportation
176.0
167.9
179.4
151.3
141.3
127.1
154.8
148.4
135.4
143.4
149.4
162.8
154.4
142.0
137.8
Lubricants
176.0
167.9
179.4
151.3
141.3
127.1
154.8
148.4
135.4
143.4
149.4
162.8
154.4
142.0
137.8
U.S. Territories
85.6
90.8
152.4
123.2
132.3
60.4
60.1
75.6
72.0
82.4
77.3
77.3
77.3
77.3
77.3
Lubricants
0.7
2.0
3.1
4.6
2.7
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Other Petroleum
(Misc. Prod.)
84.9
88.8
149.3
118.6
129.6
59.3
59.0
74.6
71.0
81.4
76.2
76.2
76.2
76.2
76.2
Total
4,805.6
5,348.4
5,908.2
5,757.9
5,042.8
4,696.7
4,952.4 4,872.5
4,801.4
5,035.8
4,957.7
5,161.7
5,135.6
5,302.1
5,599.5
Note: These values are unadjusted non-energy fuel use provided by EIA.
for in the Industrial Processes and Product Use chapter.
They have not yet been adjusted to remove petroleum feedstock exports and processes accounted
A-89
-------
Table A-41: International Bunker Fuel Consumption (TBtu)
Fuel Type
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Aviation Jet Fuel
539.4
703.4
880.1
853.1
796.8
749.1
865.4
919.9
916.3
931.6
987.8
1,022.3
1,051.1
1,103.2
1,146.8
Marine Residual Fuel
Oil
715.7
523.2
444.1
581.0
654.6
604.8
619.8
518.4
459.5
379.8
369.2
406.8
450.7
445.3
417.6
Marine Distillate Fuel
Oil
158.0
125.7
85.9
126.9
122.2
111.0
128.2
107.4
91.7
75.4
82.0
113.5
117.5
121.3
134.4
Total
1,413.1
1,352.3
1,410.0
1,561.0
1,573.6
1,464.9
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
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-90 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Table A-42: Key Assumptions for Estimating CP2 Emissions
C Content Coefficient
Fuel Type
(MMT C/QBtu)
Coal
Residential Coal
(See footnote b)
Commercial Coal
(See footnote b)
Industrial Coking Coal
31.00
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
20.17
Distillate Fuel Oil No. 4
20.47
Jet Fuel
(See footnote b)
Kerosene
19.96
LPG (energy use)
(See footnote b)
LPG (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
19.10
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
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-43).
Sources: C coefficients from EIA (2009), EPA (2010), and EPA (2018).
A-91
-------
Table A-43: Annually Variable C Content Coefficients by Year (MMT C/QBtu)
Fuel Type
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Residential Coal3
26.19
26.13
26.00
26.04
25.71
25.73
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
Commercial Coal
26.19
26.13
26.00
26.04
25.71
25.73
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
Industrial Other Coal
25.81
25.79
25.74
25.79
25.81
25.86
25.86
25.88
25.94
25.93
25.95
26.00
26.03
26.06
26.08
Electric Power Coal
25.94
25.92
25.98
26.08
26.04
26.04
26.05
26.05
26.06
26.05
26.04
26.07
26.06
26.08
26.09
Pipeline Natural Gas
14.46
14.47
14.47
14.46
14.46
14.47
14.48
14.48
14.47
14.46
14.45
14.43
14.43
14.43
14.43
LPG (energy use)
16.86
16.82
16.89
16.84
16.83
16.83
16.83
16.83
16.83
16.83
16.83
16.83
16.83
16.83
16.83
LPG (non-energy use)
17.06
17.09
17.09
17.06
17.06
17.06
17.06
17.06
17.06
17.06
17.06
17.06
17.06
17.06
17.06
Motor Gasoline
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
19.46
Jet Fuel
19.40
19.34
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
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
19.46
Misc. Products
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
20.31
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
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
20.31
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 (1990), PSU (2010), Gunderson (2019), IGS (2019), ISGS (2019), and EIA (2001 through 2018); pipeline natural
gas C content coefficients calculated from EIA (2019) and EPA (2010); petroleum carbon contents from EPA (2010). See Annex 2.2 for information on how these C content
coefficients are calculated.
Table A-44: Electricity Consumption by End-Use Sector (Billion Kilowatt-Hours)
End-Use Sector 1990 1995 2000 2005 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Residential 924 1,043 1,192 1,359 1,381 1,365 1,446 1,423 1,374 1,394 1,407 1,403 1,410 1,377 1,462
Commercial 838 953 1,159 1,275 1,336 1,307 1,330 1,328 1,327 1,337 1,352 1,361 1,367 1,353 1,376
Industrial 1,070 1,163 1,235 1,169 1,142 1,044 1,103 1,124 1,123 1,129 1,136 1,128 1,117 1,125 1,097
Transportation3 5 5 5 8 8 8 8 8 8 8 9 9 9 10 11
Total 2,837 3,164 3,592 3,811 3,866 3,724 3,887 3,883 3,832 3,868 3,903 3,900 3,902 3,864 3,945
a Includes electricity used for electric vehicle charging in the residential and commercial sectors.
Note: Does not include the U.S. Territories.
Source: Retail sales of electricity to end-users obtained from EIA (2019). Industrial electricity consumption includes direct use.
A-92 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Table A-45: Electric Power Generation by Fuel Type (Percent)
Fuel Type
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Coal
54.1%
52.7%
53.3%
51.1%
49.5%
45.7%
46.0%
43.5%
38.6%
40.2%
39.9%
34.2%
31.4%
30.9%
28.4%
Natural Gas
10.7%
13.1%
14.2%
17.5%
20.2%
22.1%
22.7%
23.5%
29.1%
26.4%
26.3%
31.6%
32.7%
30.9%
34.1%
Nuclear
19.9%
21.1%
20.7%
20.0%
20.3%
21.0%
20.3%
20.0%
19.8%
20.2%
20.3%
20.4%
20.6%
20.8%
20.1%
Renewables
11.3%
10.9%
8.8%
8.3%
8.8%
10.2%
10.0%
12.2%
11.9%
12.5%
12.8%
13.0%
14.7%
16.8%
16.7%
Petroleum
4.1%
2.1%
2.9%
3.0%
1.1%
0.9%
0.9%
0.7%
0.5%
0.6%
0.7%
0.7%
0.6%
0.5%
0.6%
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%
0.1%
Net Electricity
Generation (Billion
kWh)b
2,905
3,197 «
,> 3,643 =
> 3,902
3,974
3,808
3,971
3,947
3,888
3,901
3,936
3,917
3,917
3,877
4,009
+ 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-44 (i.e., end-use consumption of electricity) due to electricity transmitted across
U.S. borders, as well as transmission and distribution losses.
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(2019).
Table A-46: Geothermal Net Generation by Geotype (Billion Kilowatt-Hours)
Geotype
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Binary
0.08
0.28
0.24
0.68
1.49
1.92
2.41
2.16
2.43
2.75
3.12
3.36
3.62
3.56
3.84
Flash Steam
6.15
7.63
7.43
7.93
7.33
7.14
6.83
7.17
7.02
7.03
6.92
7.00
6.65
6.69
6.39
Dry Steam
9.21
5.47
6.43
6.09
6.02
5.95
5.98
5.98
6.11
6.00
5.84
5.56
5.55
5.67
5.73
Total
15.43
13.38
14.09
14.69
14.84
15.01
15.22
15.32
15.56
15.77
15.88
15.92
15.83
15.93
15.97
Source: EIA (1990 through 2019).
A-93
-------
1 References
2 Browning, L. (2018). Updated Methodology for Estimating Electricity Use from Highway Plug-In Electric Vehicles. Technical
3 Memo, October 2018.
4 EIA (2019) Monthly Energy Review. November 2019, Energy Information Administration, U.S. Department of Energy,
5 Washington, D.C. DOE/EIA-0035(2019/11).
6 EIA (1990 through 2019) Form EIA-923 detailed data with previous form data (EIA-906/920), Energy Information
7 Administration, U.S. Department of Energy. Washington, D.C. DOE/EIA.
8 EIA (2001 through 2018) Annual Coal Distribution Report, Energy Information Administration, U.S. Department of Energy.
9 Washington, D.C. DOE/EIA.
10 EIA (2009) Manufacturing Consumption of Energy 2006. Energy Information Administration, U.S. Department of Energy.
11 Washington, D.C. Released July 2009.
12 EPA (2018) The Emissions & Generation Resource Integrated Database (eGRID) 2016 Technical Support Document. Clean
13 Air Markets Division, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
14 EPA (2010) Carbon Content Coefficients Developed for EPA's Mandatory Reporting Rule. Office of Air and Radiation, Office
15 of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
16 Gunderson, J. (2019) Montana Coal Sample Database. Data received 28 February 2019 from Jay Gunderson, Montana
17 Bureau of Mines & Geology.
18 Illinois State Geological Survey (ISGS) (2019) Illinois Coal Quality Database, Illinois State Geological Survey.
19 Indiana Geological Survey (IGS) (2019) Indiana Coal Quality Database 2018, Indiana Geological Survey.
20 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
21 Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
22 Tanabe (eds.). Hayama, Kanagawa, Japan.
23 Pennsylvania State University (PSU) (2010) Coal Sample Bank and Database. Data received by SAIC 18 February 2010 from
24 Gareth Mitchell, The Energy Institute, Pennsylvania State University.
25 UNFCCC (2014) Report of the Conference of the Parties on its nineteenth session, held in Warsaw from 11 to 23
26 November 2013. (FCCC/CP/2013/10/Add.3). January 31, 2014. Available online at:
27 .
28 USGS (1998) CoalQual Database Version 2.0, U.S. Geological Survey.
29
A-94 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
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). 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-47.
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-95
-------
Table A-47: Carbon Content Coefficients Used in this Report (MMT Carbon/QBtu)
Fuel Type
1990
1995
2000
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Coal
Residential Coala'b
26.19
! 26.13
26.00
26.04
26.26
25.91
25.71
25.73
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
Commercial Coal3
26.19
. 26.13
26.00
26.04
26.26
25.91
25.71
25.73
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
Industrial Coking Coal3
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
Industrial Other Coal3
25.81
25.79
25.74
25.79
25.83
25.81
25.81
25.86
25.86
25.88
25.94
25.93
25.95
26.00
26.03
26.06
26.08
Utility Coal3'c
25.94
25.92
25.98
26.08
26.03
26.03
26.04
26.04
26.05
26.05
26.06
26.05
26.04
26.07
26.06
26.08
26.09
Pipeline Natural Gasd
14.46
14.47
14.47
14.46
14.46
14.46
14.46
14.47
14.48
14.48
14.47
14.46
14.45
14.43
14.43
14.43
14.43
Petroleum
Asphalt and Road Oil
Aviation Gasoline
Distillate Fuel Oil No. 1
Distillate Fuel Oil No. 2
Distillate Fuel Oil No. 4
Jet Fuel3
Kerosene
LPG (energy use)d
LPG (non-energy use)d
Lubricants
Motor Gasolined
Residual Fuel No. 5
Residual Fuel No. 6
20.55
18.86
19.98
20.17
20.47
19.40
19.96
16.86
17.06
20.20
19.42
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.34
19.96
16.82
17.09
20.20
19.36
19.89
20.48
20.55
18.86
20.17
19.98
20.17
20.47
19.70
19.96
16.89
17.09
20.20
19.33
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.84
17.06
20.20
19.36
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.45
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.82
17.05
20.20
19.56
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
Other Petroleum
Av. Gas Blend Comp. 18.87 18.87 18.87
Mo. Gas Blend Comp' 19.42 19.36 19.33
Crude Oild 20.15 20.21 20.22
Misc. Productsd 20.15 20.21 20.22
Misc. Products (Terr.) 20.00 20.00 20.22
Naphtha (<401 deg. F) 18.55 18.55 18.55
Other oil (>401 deg. F) 20.17 20.17 20.17
Pentanes Plus 19.10 19.10 19.10
Petroleum Coke 27.85 27.85 27.85
Still Gas 18.20 18.20 18.20
Special Naphtha 19.74 19.74 19.74
Unfinished Oilsd 20.15 20.21 20.22
Waxes 19.80 19.80 19.80
Other Wax and Misc. 19.80 19.80 19.80
18.87
19.36
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.45
20.28
20.28
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.28
19.80
19.80
18.87
19.56
20.28
20.28
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.28
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
18.87
19.46
20.31
20.31
20.00
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
A-96 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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4
5
6
7
8
9
10
11
12
13
14
Fuel Type
1990
1995
2000
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Geothermal8
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
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
3.22
3.22
3.22
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 C contents based on geotype (i.e., flash steam and dry steam) were obtained from EPA's Emissions & Generation Resource Integrated Database (eGRID) 2016 Technical Support
Document (EPA 2018). C contents were obtained in pounds C02/megawatt hour and were applied to net generation by geotype (in megawatt hours) from EIA (2019). 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.
A-97
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i Coal
2 Although the IPCC (2006) guidelines provide C contents for coal according to rank, it was necessary to develop C
3 content coefficients by consuming sector to match the format in which coal consumption is reported by EIA. Because the
4 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
5 consuming sector vary by year, the weighted average C content for coal combusted in each consuming sector also varies
6 over time. A time series of C contents by coal rank and consuming sector appears in Table A-48.9
7 Methodology
8 The methodology for developing C contents for coal by consuming sector consists of four steps. An additional
9 step has been taken to calculate C contents by coal rank to facilitate comparison with IPCC default values.
10 Step 1: Determine Carbon Contents by Rank and by State of Origin
11 Carbon contents by rank and state of origin are estimated on the basis of 8,672 coal samples, 6,588 of which
12 were collected by the U.S. Geological Survey (USGS) (1998), 504 samples that come from the Pennsylvania State University
13 database (PSU 2010), and the remainder from individual State Geological Surveys. Samples obtained directly from
14 individual State Geological Surveys include 908 samples from the Montana Bureau of Mines & Geology (Gunderson 2019),
15 745 samples from the Indiana Geological Survey Coal Quality Database (IGS 2019), and 460 samples from the Illinois State
16 Geological Survey (ISGS 2019). Because the data obtained directly from the State Geological Surveys for these three states
17 included both samples collected by the USGS and additional samples, these data were used to determine C content
18 coefficients for these states instead of the USGS and Pennsylvania State University data.
19 These coal samples are classified according to rank and state of origin. For each rank in each state, the average
20 heat content and C content of the coal samples are calculated based on the proximate (heat) and ultimate (percent carbon)
21 analyses of the samples. Dividing the C content (reported in pounds of C02) by the heat content (reported in million Btu
22 or MMBtu) yields an average C content coefficient. This coefficient is then converted into units of MMT C/QBtu.
23 Step 2: Determine Weighted Average Carbon Content by State
24 Carbon contents by rank and origin calculated in Step 1 are then weighted by the annual share of state
25 production that was each rank. State production by rank is obtained from the EIA. This step yields a single carbon
26 content per state that varies annually based on production by coal type. However, most coal-producing states
27 produce only one rank of coal. For these states the weighted factor equals the carbon content calculated in Step 1
28 and is constant across the time series.
29 Step 3: Allocate Sectoral Consumption by State of Origin
30 U.S. energy statistics10 through 2018 provide data on the origin of coal used in four areas: 1) the electric
31 power industry, 2) industrial coking, 3) all other industrial uses, and 4) the residential and commercial end-use
32 sectors.11 Because U.S. energy statistics do not provide the distribution of coal rank consumed by each consuming
33 sector, it is assumed that each sector consumes a representative mixture of coal ranks from a particular state that
34 matches the mixture of all coal produced in that state during the year. Thus, the weighted state-level factor
35 developed in Step 2 is applied.
9 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.
10U.S. Energy Information Administration (EIA). Annual Coal Distribution Report (2001-2019b); Coal Industry Annual (1990-2001).
11 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-47 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.
A-98 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
Step 4: Weight Sectoral Carbon Contents to Reflect the Rank and State of Origin of Coal Consumed
2 Sectoral C contents are calculated by multiplying the share of coal purchased from each state by the state's
3 weighted C content estimated in Step 2. The resulting partial C contents are then totaled across all states to generate a
4 national sectoral C content.
5 Csector — Sstatel*Cstatel Sstate2*Cstate2 +.... + Sstate50*Cstate50
6 where,
7 Csector = The C content by consuming sector;
8 Sstate = The portion of consuming sector coal consumption attributed to production from a
9 given state;
10 Cstate = The estimated weighted C content of all ranks produced in a given state.
A-99
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1 Table A-48: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank (MMT C/QBtu) (1990-2018)
Consuming Sector
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Electric Power
25.94
25.92
25.98
26.08
26.04
26.04
26.05
26.05
26.06
26.05
26.04
26.07
26.06
26.08
26.09
Industrial Coking
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
31.00
Other Industrial
25.81
25.79
25.74
25.79
25.81
25.86
25.86
25.88
25.94
25.93
25.95
26.00
26.03
26.06
26.08
Residential/
Commercial3
26.19
26.13
26.00
26.04
25.71
25.73
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
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
28.28
Bituminous
25.38
25.42
25.45
25.45
25.44
25.42
25.42
25.42
25.41
25.41
25.41
25.40
25.40
25.40
25.41
Sub-bituminous
26.46
26.47
26.46
26.48
26.48
26.47
26.47
26.49
26.49
26.49
26.49
26.49
26.49
26.20
26.49
Lignite
26.58
26.59
26.61
26.62
26.64
26.67
26.63
26.61
26.61
26.62
26.63
26.66
26.64
26.67
26.76
2 a In 2008, EIA began collecting consumption data for commercial and institutional consumption rather than commercial and residential consumption.
3 b Emission factors for coal rank are weighted based on production in each state.
4 Sources: C content coefficients calculated from USGS (1998), PSU (2010), Gunderson (2019), IGS (2019), ISGS (2019), EIA (1990 through 2001; 2001 through 2019a; 2001 through 2019b).
A-100 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
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 2019a) is the source for state coal production by rank from
2001 through 2018. 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 2018 was obtained from the ElA's Annual Coal
Distribution Report (EIA 2001 through 2019b). 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 2018, 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 85.59
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 Western Kentucky (91.36 kg C02 per MMBtu). In 2018, Arkansas ceased production of
bituminous coal, and Western Kentucky production accounted for just 6.4 percent of overall bituminous production. More
than 50 percent of bituminous coal was produced in three states in2018: 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-49).
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 2018). Thus, the C content
coefficient for Wyoming (97.22 kg C02 per MMBtu), based on 455 samples, dominates the national average.
A-101
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1 The interquartile range of C content coefficients among samples of sub-bituminous coal in Wyoming was +1.5
2 percent from the mean. Similarly, this range among samples of bituminous coal from West Virginia, Kentucky, and
3 Pennsylvania was +1.2 percent or less for each state. The large number of samples and the low variability within the sample
4 set of the states that represent the predominant source of supply of U.S. coal suggest that the uncertainty in this factor is
5 very low, on the order of +1.0 percent.
6 For comparison, J. Quick (2010) completed an analysis similar in methodology to that used here, in order to
7 generate national average C emission factors as well as county-level factors. This study's rank-based national average
8 factors have a maximum deviation from the factors developed in this Inventory report of 0.78 percent, which is for sub-
9 bituminous (range: -0.32 to +0.78 percent). This corroboration further supports the assertion of minimal uncertainty in the
10 application of the rank-based factors derived for the purposes of this Inventory.
11 Table A-49: Variability in Carbon Content Coefficients by Rank Across States (Kilograms CP2 Per MMBtu)
State
Number of
Samples
Bituminous
Sub-
bituminous
Anthracite
Lignite
Alabama
951
92.84
-
-
99.10
Alaska
91
98.33
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
95.01
-
-
-
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
92.88
-
-
-
Mississippi
8
-
-
-
98.19
Missouri
111
91.71
-
-
-
Montana
908
96.01
96.61
-
98.34
Nebraska
6
103.60
-
-
-
Nevada
2
94.41
-
-
99.86
New Mexico
185
94.29
94.88
103.92
-
North Dakota
202
-
93.97
-
99.48
Ohio
674
91.84
-
-
-
Oklahoma
63
92.33
-
-
-
Pennsylvania
849
93.33
-
103.68
-
Tennessee
61
92.82
-
-
-
Texas
64
85.59
94.19
-
94.47
Utah
169
95.75
91.29
-
-
Virginia
465
93.51
-
98.54
-
Washington
18
94.53
97.36
102.53
106.55
West Virginia
612
93.84
-
-
-
Wyoming
503
94.80
97.22
-
-
U.S. Average
8,672
93.46
96.01
102.15
98.95
12 Note: Indicates no sample data available. Average is weighted by number of samples.
13 Sources: Calculated from USGS(1998) and PSU (2010), Gunderson (2019), IGS (2019), and ISGS (2019).
14
A-102 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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43
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 gasoline12 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.
12 A term used in the gas processing industry to refer to a mixture of liquid hydrocarbons (mostly pentanes and heavier hydrocarbons)
extracted from natural gas.
A-103
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34
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-50.
Table A-50: 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-51.
Table A-51: Carbon Content of Pipeline-Quality Natural Gas by CP2 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 values13 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
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:
13 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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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 2018 based
on the ElA's national average pipeline-quality gas heat content for each year (EIA 2019). The table of average C contents
for each year is shown below in Table A-52.
Table A-52: Carbon Content Coefficients for Natural Gas (MMT Carbon/QBtu)
Fuel Type 1990
1995
2000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Natural Gas 14.46
14.47
14.47
14.46 14.46 14.46 14.46 14.47 14.48 14.48 14.47 14.46 14.45 14.43 14.43 14.43 14.43
Source: Calculated from EPA (2010) and EIA (2019).
Figure A-l: Carbon Content for Samples of Pipeline-Quality Natural Gas Included in the Gas Technology Institute
Database
1u.O
15.5
^ m
¦S2 c
;y ^
8 I
c O
tL" i_
5 a
c I
o td
Si oc
S l -)
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4
5
6
7
8
9
10
11
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13
14
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32
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36
37
38
39
40
41
42
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 14.46 MMT C/QBtu (see Table A-52), 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.
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.
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 gravity14 (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 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
14 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.
Petroleum
(Dfuel* SfUel) / EfUel
where,
The C content coefficient of the fuel
The density of the fuel
The share of the fuel that is C
The heat content of the fuel
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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.
Figure A-2: Estimated and Actual Relationships Between Petroleum Carbon Content Coefficients and Hydrocarbon
Density
24 -i
22
o S
o ®
¦E 0
-p
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39
crude oils, and can form an even higher percentage of heavy fuel oils. Some crude oils and fuel oils also contain appreciable
quantities of oxygen and nitrogen, typically in the form of asphaltenes or various acids. The nitrogen and oxygen content
of crude oils can range from near zero to a few percent by weight. Lighter petroleum products have much lower levels of
impurities, because the refining process tends to concentrate all of the non-hydrocarbons in the residual oil fraction. Light
products usually contain less than 0.5 percent non-hydrocarbons by mass. Thus, the C content of heavy fuel oils can often
be several percent lower than that of lighter fuels, due entirely to the presence of non-hydrocarbons.
Variations in Hydrocarbon Classes
Hydrocarbons can be divided into five general categories, each with a distinctive relationship between density
and C content and physical properties. Refiners tend to control the mix of hydrocarbon types in particular products in
order to give petroleum products distinct properties. The main classes of hydrocarbons are described below.
Paraffins. Paraffins are the most common constituent of crude oil, usually comprising 60 percent by mass.
Paraffins are straight-chain hydrocarbons with the general formula CnH2n+2. Paraffins include ethane (C2H6), propane (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
95
1 Faraffire
t Cycle panaffine
~ Arc rnatics
I 90 J
HI
>¦
_G
C
0
85
Ele nzene ~
To Iuene ~
Xy le ne
$
o
c
m
9
o 80
Cyclo pentane
. ¦
n- pe ntane ¦
"B uta ne
¦ P no pare
1 Et ha ne
75 -
Me t ha ne
Gasoline Jet Fuel
IPG Naphtha Kerosene Diesel
Lute Oil Fuel Oil
T
10
T
T
nr
25
15 20
Number of Carbo n A to ms in Mo leoule
30
35
Source: J.M. Hunt, Ps^disLCT) G-mOTe.rn,fef^.,'ana' Geoiogy (Sari Francisco, CA, W.H. Freeman and Company, 1S7S), 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. El A 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-53. A
description of the methods and data sources for estimating the key parameters for each individual petroleum product
appears below.
A-109
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1 Table A-53: 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
19.46
(See a)
(See a)
(See a)
LPG (total)
16.81
(See b)
(See b)
(See b)
LPG (energy use)
16.83
(See b)
(See b)
(See b)
LPG (non-energy use)
17.06
(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
20.17
5.825
35.8
87.30
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 Oils (>400 deg. F)c
20.17
5.825
35.8
87.30
Aviation Gas
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
20.31
5.796
31.2
85.49
Pentanes Plus
19.10
4.620
81.3
83.63
2 a Calculation of the carbon content coefficient for motor gasoline in 2008 uses separate higher heating values for conventional and
3 reformulated gasoline of 5.253 and 5.150, respectively (EIA 2008a). Densities and carbon shares (percent carbon) are annually variable
4 and separated by both fuel formulation and grade, see Motor Gasoline and Blending Components, below, for details.
5 b LPG is a blend of multiple paraffinic hydrocarbons: ethane, propane, isobutane, and normal butane, each with their own heat content,
6 density and C content, see Table A-56.
7 c Petrochemical feedstocks have been split into naphthas and other oils for this Inventory report. Parameters presented are for naphthas
8 with a boiling temperature less than 400 degrees Fahrenheit. Other oils are petrochemical feedstocks with higher boiling points. They
9 are assumed to have the same characteristics as distillate fuel oil no. 2.
10 Note: Indicates no sample data available.
11 Sources: EIA (1994); EIA (2009a); EPA (2009b); and EPA (2010).
12 Motor Gasoline and Motor Gasoline Blending Components
13 Motor gasoline is a complex mixture of relatively volatile hydrocarbons with or without small quantities of
14 additives, blended to form a fuel suitable for use in spark-ignition engines.15 "Motor Gasoline" includes conventional
15 gasoline; all types of oxygenated gasoline, including gasohol; and reformulated gasoline; but excludes aviation gasoline.
16 Gasoline is the most widely used petroleum product in the United States, and its combustion accounts for nearly
17 22 percent of all U.S. C02 emissions. EIA collects consumption data (i.e., "petroleum products supplied" to end-users) for
18 several types of finished gasoline over the 1990 through 2018 time period: regular, mid-grade, and premium conventional
"Motor gasoline, as defined in ASTM Specification D 4814 or Federal Specification VV-G-1690C, is characterized as having a boiling range
of 122 degrees to 158 degrees Fahrenheit at the 10-percent recovery point to 365 degrees to 374 degrees Fahrenheit at the 90-percent
recovery point.
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1 gasoline (all years) and regular, mid-grade, and premium reformulated gasoline (November 1994 to 2018). Leaded and
2 oxygenated gasoline are not separately included in the data used for this report.16
3 The American Society for Testing and Materials (ASTM) standards permit a broad range of densities for gasoline,
4 ranging from 50 to 70 degrees API gravity, or 111.52 to 112.65 kilograms per barrel (EIA 1994), which implies a range of
5 possible C and energy contents per barrel. Table A-54 reflects changes in the density of gasoline over time and across
6 grades and formulations of gasoline through 2018.
7 Table A-54: Motor Gasoline Density, 1990 - 2018 (Degrees API)
Fuel Grade
1990 1995
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
2017
2018
Conventional -
Winter Grade
Low Octane
High Octane
62.0 , 59.8 ,
59.0 58.0
61.7
59.1
61.6
59.0
61.8
59.9
62.4
60.7
62.6
60.9
62.7
60.0
63.1
60.3
63.0
60.9
63.0
60.9
63.0
60.9
63.0
60.9
63.0
60.9
63.0
60.9
63.0
60.9
63.0
60.9
63.0
60.9
63.0
60.9
63.0
60.9
Conventional -
Summer Grade
Low Octane
High Octane
58.2 , 56.1 ,
55.5 55.1
57.2
55.5
56.5
55.7
56.8
56.0
57.4
57.0
57.9
57.0
57.8
57.4
57.5
56.9
58.6
58.0
58.6
58.0
58.6
58.0
58.6
58.0
58.6
58.0
58.6
58.0
58.6
58.0
58.6
58.0
58.6
58.0
58.6
58.0
58.6
58.0
Reformulated
- Winter Grade
Low Octane
High Octane
IMA , 61.9 ,
NA 59.9
62.6
61.0
61.9
61.8
62.1
61.9
62.7
61.8
62.8
61.8
62.3
61.7
62.1
62.1
62.4
62.5
62.4
62.5
62.4
62.5
62.4
62.5
62.4
62.5
62.4
62.5
62.4
62.5
62.4
62.5
62.4
62.5
62.4
62.5
62.4
62.5
Reformulated
- Summer Grade
Low Octane
High Octane
NA 58.5
NA 56.7
58.8
58.2
58.2
58.0
59.1
58.7
58.1
58.9
58.4
58.1
58.7
59.0
58.5
59.3
59.1
59.8
59.1
59.8
59.1
59.8
59.1
59.8
59.1
59.8
59.1
59.8
59.1
59.8
59.1
59.8
59.1
59.8
59.1
59.8
59.1
59.8
8 Notes: NA (Not Applicable), fuel type was not analyzed. Values in 2008 were used as a proxy for 2009 through 2018.
9 Source: National Institute of Petroleum and Energy Research (1990 through 2009).
10
11 The density of motor gasoline increased across all grades through 1994, partly as a result of the leaded gasoline
12 phase-out. In order to maintain the "anti-knock" quality and octane ratings of gasoline in the absence of lead, the portion
13 of aromatic hydrocarbons blended into gasoline through the refining process was increased. As discussed above, aromatic
14 hydrocarbons have a lower ratio of hydrogen to C than other hydrocarbons typically found in gasoline, and therefore
15 increase fuel density.
16 The trend in gasoline density was reversed beginning in 1996 with the development of fuel additives that raised
17 oxygen content. In 1995, a requirement for reformulated gasoline in non-attainment areas implemented under the Clean
18 Air Act Amendments further changed the composition of gasoline consumed in the United States. Through 2005, methyl
19 tertiary butyl ether (MTBE), ethanol, ethyl tertiary butyl ether (ETBE), and tertiary amyl methyl ether (TAME) were added
20 to reformulated and sometimes to conventional gasoline to boost its oxygen content, reduce its toxics impacts and
21 increase its octane. The increased oxygen reduced the emissions of carbon monoxide and unburned hydrocarbons. These
22 oxygen-rich blending components are also much lower in C than standard gasoline. The average gallon of reformulated
23 gasoline consumed in 2005 contained over 10 percent MTBE and 0.6 percent TAME (by volume). The characteristics of
24 reformulated fuel additives appear in Table A-55.
25
26
16 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.
A-lll
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1 Table A-55: Characteristics of Major Reformulated Fuel Additives
Density (Degrees
Additive 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
2 Source: EPA (2009b).
3
4 Since 2005, due to concerns about the potential environmental consequences of the use of MTBE in fuels, there
5 has been a shift away from the addition of MTBE, TAME, ETBE, and DIPE and towards the use of ethanol as a fuel
6 oxygenate.17 Ethanol, also called ethyl alcohol, is an anhydrous alcohol with molecular formula C2H5OH. Ethanol has a
7 lower C share than other oxygenates, approximately 52 percent compared to about 70 percent for MTBE and TAME. The
8 density of ethanol was calculated by fitting density data at 10-degree intervals to a polynomial of order two and then using
9 the fit to interpolate the value of the density at 15 degrees Celsius. A common fuel mixture of 10 percent denatured
10 ethanol (denatured by 2 percent hydrocarbons) and 90 percent gasoline, known as E10, is widely used in the United States
11 and does not require any modification to vehicle engines or fuel systems. The federal Renewable Fuel Standard (RFS)
12 program requires a certain volume of renewable fuel, including ethanol, be blended into the national fuel supply.18 Ethanol
13 blends up to E85 (85 percent ethanol, 15 percent gasoline) are in use in the United States but can only be used in specially
14 designed vehicles called flexible fuel vehicles (FFVs). Most ethanol fuel in the United States is produced using corn as
15 feedstock,19 although production pathways utilizing agricultural waste, woody biomass and other resources are in
16 development.
17 Methodology
18 Step 1. Disaggregate U.S. gasoline consumption by grade and type
19 Separate monthly data for U.S. sales to end users of finished gasoline by product grade and season for both
20 standard gasoline and reformulated gasoline were obtained from the EIA.
21 Step 2. Develop carbon content coefficients for each grade and type
22 Annual C content coefficients for each gasoline grade, type, and season are derived from four parameters for
23 each constituent of the finished gasoline blend: the volumetric share of each constituent,20 the density of the constituent,
24 share of the constituent21 that is C; and the energy content of a gallon of the relevant formulation of gasoline. The percent
25 by mass of each constituent of each gasoline type was calculated using percent by volume data from the National Institute
26 for Petroleum and Energy Research (NIPER) and the density of each constituent. The ether additives listed in Table A-55
27 are accounted for in both reformulated fuels and conventional fuels, to the extent that they were present in the fuel. From
28 2006 onward, reformulated fuel mass percentages are calculated from their constituents, net of the share provided by
29 ethanol. C content coefficients were then derived from the calculated percent by mass values by weighting the C share of
30 each constituent by its contribution to the total mass of the finished motor gasoline product.
31 Step 3. Weight overall gasoline carbon content coefficient for consumption of each grade and type
"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.
18 https://www.epa.gov/renewable-fuel-standard-program
19https://www.epa.gov/fuels-registration-reporting-and-compliance-help/public-data-renewable-fuel-standard
20 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.
"Saturates are assumed to be octane and aromatics are assumed to be toluene.
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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.
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).
Standard heat contents for motor gasoline of 5.222 MMBtu per barrel conventional gasoline and 5.150 MMBtu
per barrel reformulated gasoline22 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 of the 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 are obtained from NIPER. 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 of the 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).
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
22 The reformulated gasoline heat content is applied to both reformulated blends containing ethers and those containing ethanol.
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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 2018.
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 J P-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 2018, 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 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 2018.
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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 each distillate 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, 5.809 and
6.135 MMBtu per barrel, respectively for distillates No. 1, No. 2, and No. 4, and densities of 35.3, 35.8, and 23.2 degrees
API to calculate C coefficients for each distillate type.
Data Sources
Densities for distillate No. 1 and No. 2 were derived from Alliance of Automobile Manufacturers, Diesel Survey -
Winter 2008 (AAM 2009). Densities are based on four, and 144 samples, respectively. 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 EPA (2009b). And carbon shares for each distillate 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
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
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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), which are also consumed in the United
States, 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. 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
A-116 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Data on the C share and density of residual fuel oil No. 6 were obtained from Green & Perry, ed. (2008). Data on
2 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.
3 (1995). Heat contents for both No. 5 and No. 6 fuel oil are adopted from EPA (2009b).
4 Uncertainty
5 Beyond the application of a C factor based upon No. 6 oil to all residual oil consumption, the largest source of
6 uncertainty in estimating the C content of residual fuel centers on the estimates of density. Fuel oils are likely to differ
7 depending on the application of the fuel (i.e., power generation or as a marine vessel fuel). Slight differences between the
8 density of residual fuel used by utilities and that used in mobile applications are likely attributable to non-sulfur impurities,
9 which reduce the energy content of the fuel, but do not greatly affect the density of the product. Impurities of several
10 percent are commonly observed in residual oil. The extent of the presence of impurities has a greater effect on the
11 uncertainty of C share estimation than it does on density. This is because these impurities do provide some Btu content to
12 the fuel, but they are absent of carbon. Fuel oils with significant sulfur, nitrogen and heavy metals contents would have a
13 different total carbon share than a fuel oil that is closer to pure hydrocarbon. This contributes to the uncertainty of the
14 estimation of an average C share and C coefficient for these varied fuels.
15 The 12 samples of residual oil (EIA 1994) cover a density range from 4.3 percent below to 8.2 percent above the
16 mean density. The observed range of C share in these samples is -2.5 to +1.8 percent of the mean. Overall, the uncertainty
17 associated with the C content of residual fuel is probably +1 percent.
18 Liquefied Petroleum Gases (LPG)
19 EIA identifies four categories of paraffinic hydrocarbons as LPG: ethane, propane, isobutane, and n-butane.
20 Because each of these compounds is a pure paraffinic hydrocarbon, their C shares are easily derived by taking into account
21 the atomic weight of C (12.01) and the atomic weight of hydrogen (1.01). Thus, for example, the C share of propane, CbHs,
22 is 81.71 percent. The densities and heat contents of the compounds are also well known, allowing C content coefficients
23 to be calculated directly. Table A-56 summarizes the physical characteristic of LPG.
24 Table A-56: Physical Characteristics of Liquefied Petroleum Gases
Compound
Chemical
Formula
Density (Barrels
Per Metric Ton)
Carbon Content
(Percent)
Energy Content
(MMBtu/Barrel)
Carbon Content
Coefficient (MMT
C/QBtu)
Ethane
c2h6
11.55
79.89
3.082
17.16
Propane
CsHs
12.76
81.71
3.836
16.76
Isobutane
C4H10
11.42
82.66
3.974
17.77
n-butane
C4H10
10.98
82.66
4.326
17.75
25 Source: Densities - CRC Handbook of Chemistry and Physics (2008/09); Carbon Contents - derived from the atomic weights of the
26 elements; Energy Contents - EPA (2009b). All values are for the compound in liquid form. The density and energy content of
27 ethane are for refrigerated ethane (-89 degrees C). Values for n-butane are for pressurized butane (-25 degrees C).
28
29 Methodology
30 Step 1. Assign carbon content coefficients to each pure paraffinic compound
31 Based on their known physical characteristics, a C content coefficient is assigned to each compound contained in
32 the U.S. energy statistics category, Liquefied Petroleum Gases.
33 Step 2. Weight individual LPG coefficients for share of fuel use consumption
34 AC content coefficient for LPG used as fuel is developed based on the consumption mix of the individual
35 compounds reported in U.S. energy statistics.
36 Step 3. Weight individual LPG coefficients for share of non-fuel use consumption
37 The mix of LPG consumed for non-fuel use differs significantly from the mix of LPG that is combusted. While the
38 majority of LPG consumed for fuel use is propane, ethane is the largest component of LPG used for non-fuel applications.
39 AC content coefficient for LPG used for non-fuel applications is developed based on the consumption mix of the individual
40 compounds reported in U.S. energy statistics.
A-117
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1 Step 4. Weight the carbon content coefficients for fuel use and non-fuel use by their respective shares of
2 consumption
3 The changing shares of LPG fuel use and non-fuel use consumption appear below in Table A-57.
4 Data Sources
5 Data on C share was derived via calculations based on atomic weights of each element of the four individual
6 compounds densities are from the CRC Handbook of Chemistry and Physics, 89th Education. The energy content of each
7 LPG is from the EPA (2009b). LPG consumption was based on data obtained from API (1990 through 2008) and EIA (2009b).
8 Non-fuel use of LPG was obtained from API (1990 through 2008).
9 Uncertainty
10 Because LPG consists of pure paraffinic compounds whose density, heat content and C share are physical
11 constants, there is limited uncertainty associated with the C content coefficient for this petroleum product. Any
12 uncertainty is associated with the collection of data tabulating fuel- and non-fuel consumption in U.S. energy statistics.
13 This uncertainty is likely less than +3 percent.
14 Table A-57: Consumption and Carbon Content Coefficients of Liquefied Petroleum Gases, 1990-2018
1990
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Energy Consumption (QBtu)
Fuel Use
0.88
1.31
1.19
1.20
1.13
1.13
1.16
1.16
1.16
1.16
1.16
1.16
1.16
1.16
1.16
Ethane
0.04
0.10
0.06
0.07
0.06
0.07
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
Propane
0.77
1.07
1.07
1.09
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
Butane
0.06
0.07
0.05
0.05
0.05
0.03
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
Isobutane
0.01
0.06
0.01
0.00
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
Non-Fuel
Use
1.35
1.90
1.74
1.78
1.67
1.80
1.96
1.96
1.96
1.96
1.96
1.96
1.96
1.96
1.96
Ethane
0.71
1.04
0.98
1.03
0.95
1.12
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
1.22
Propane
0.51
0.65
0.63
0.64
0.60
0.60
0.60
0.60
0.60
0.60
0.60
0.60
0.60
0.60
0.60
Butane
0.11
0.11
0.12
0.11
0.12
0.08
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
Isobutane
0.02
0.09
0.02
0.01
0.00
0.01
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
Carbon Content (MMT C/QBtu)
Fuel Use
Non-Fuel
Use
16.86
17.06
16.89
17.09
16.83
17.06
16.82
17.05
16.83
17.06
16.83
17.06
16.83
17.06
16.83
17.06
16.83
17.06
16.83
17.06
16.83
17.06
16.83
17.06
16.83
17.06
16.83
17.06
16.83
17.06
15 Sources: Fuel use of LPG based on data from EIA (2009b) and API (1990 through 2008). Non-fuel use of LPG from API (1990 through
16 2008). Volumes converted using the energy contents provided in Table A-B6. C contents from EPA (2010).
17 Aviation Gasoline
18 Aviation gasoline is used in piston-powered airplane engines. It is a complex mixture of relatively volatile
19 hydrocarbons with or without small quantities of additives, blended to form a fuel suitable for use in aviation reciprocating
20 engines. Fuel specifications are provided in ASTM Specification D910 and Military Specification MIL-G-5572. Aviation gas
21 is a relatively minor contributor to greenhouse gas emissions compared to other petroleum products, representing
22 approximately 0.1 percent of all consumption.
23 The ASTM standards for boiling and freezing points in aviation gasoline effectively limit the aromatics content to
24 a maximum of 25 percent (ASTM D910). Because weight is critical in the operation of an airplane, aviation gas must have
25 as many Btu per pound (implying a lower density) as possible, given other requirements of piston engines such as high
26 anti-knock quality.
27 Methodology
28 AC content coefficient for aviation gasoline was calculated on the basis of the EIA standard heat content of 5.048
29 MMBtu per barrel. This implies a density of approximately 69 degrees API gravity or 5.884 pounds per gallon, based on the
30 relationship between heat content and density of petroleum liquids, as described in Thermal Properties of Petroleum
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1 Products (DOC 1929). To estimate the share of C in the fuel, it was assumed that aviation gasoline is 87.5 percent iso-
2 octane, 9.0 percent toluene, and 3.5 percent xylene. The maximum allowable sulfur content in aviation gasoline is 0.05
3 percent, and the maximum allowable lead content is 0.1 percent. These amounts were judged negligible and excluded for
4 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.
5 Data Sources
6 Data sources include ASTM (1985). A standard heat content for aviation gas was adopted from EIA (2009a).
7 Uncertainty
8 The relationship used to calculate density from heat content has an accuracy of five percent at 1 atm. The
9 uncertainty associated with the C content coefficient for aviation gasoline is larger than that for other liquid petroleum
10 products examined because no ultimate analyses of samples are available. Given the requirements for safe operation of
11 piston-powered aircraft the composition of aviation gas is well bounded, and the uncertainty of the C content coefficient
12 is likely to be+5 percent.
13 Still Gas
14 Still gas, or refinery gas, is composed of light hydrocarbon gases that are released as petroleum is processed in a
15 refinery. The composition of still gas is highly variable, depending primarily on the nature of the refining process and
16 secondarily on the composition of the product being processed. Petroleum refineries produce still gas from many different
17 processes. Still gas can be used as a fuel or feedstock within the refinery, sold as a petrochemical feedstock, or purified
18 and sold as pipeline-quality natural gas. For the purposes of this Inventory, the coefficient derived here is only applied to
19 still gas that is consumed as a fuel. In general, still gas tends to include large amounts of free hydrogen and methane, as
20 well as smaller amounts of heavier hydrocarbons. Because different refinery operations result in different gaseous by-
21 products, it is difficult to determine what represents typical still gas.
22 Methodology
23 The properties of still gas used to calculate the carbon content are taken from the literature. The carbon share
24 of still gas was calculated from its net calorific value and carbon content from IPCC (2006). This calculation yields a carbon
25 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
26 (EIA 2008a) and the relationship between heat content and density that is described by the U.S. Department of Commerce,
27 Bureau of Standards (DOC 1929).
28 Data Sources
29 The carbon share of still gas is calculated from data provided by IPCC (2006). Density is estimated at 0.1405 metric
30 tons per barrel, approximately 28.3 degrees API, based on the heat content of 6.00 MMbtu/barrel of still gas from EIA
31 (2009a).
32 Uncertainty
33 The EIA obtained data on four samples of still gas. Table A-58 below shows the composition of those samples.
34 Table A-58: 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
35 Sources: EIA (2008b).
36 Because the composition of still gas is highly heterogeneous, the C content coefficient for this product is highly
37 uncertain. Gas streams with a large, free-hydrogen content are likely to be used as refinery or chemical feedstocks.
38 Therefore, the sample cited above with the very high H content of 72 percent (and the lowest calculated C content) is less
39 likely to be representative of the still gas streams to which the calculated coefficient is applied. The C content coefficient
40 used for this report is probably at the high end of the plausible range given that it is higher than the greatest sample-based
41 C content in Table A-58.
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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 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.
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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
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
A-121
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1 The average density and C share of kerosene are assumed to be the same as those for distillate No. 1 since the
2 physical characteristics of the products are very similar. Thus, a density of 35.3 degrees API and average C share of 86.40
3 percent were applied to a standard heat content for distillate No. 1 of 5.825 MMBtu per barrel to yield a C content
4 coefficient of 19.96 MMT C/QBtu.
5 Data Sources
6 A standard heat content for distillate No. 1 was adopted from EIA (2009a).
7 Uncertainty
8 Uncertainty in the estimated C content for kerosene is driven by the selection of distillate No. 1 as a proxy for
9 kerosene. If kerosene is more like kerosene-based jet fuel, the true C content coefficient is likely to be some 1.3 percent
10 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
11 percent higher. While kerosene is a light petroleum distillate, like distillate No. 1, the two oil classes have some variation
12 in their properties. For example, the boiling range of kerosene is 250 to 550 degrees Fahrenheit, whereas No. 1 oils typically
13 boil over a range from 350 to 615 degrees Fahrenheit. The properties of individual kerosenes will vary with their use and
14 particular crude origin, as well. Both kerosene and fuel oil No. 1 are primarily composed of hydrocarbons having 9 to 16
15 carbon atoms per molecule. However, kerosene is a straight-run No. 1 fuel oil, additional cracking processes and additives
16 contribute to the range of possible fuels that make up the broader distillate No. 1 oil category.
17 Petroleum Coke
18 Petroleum coke is the solid residue by-product of the extensive processing of crude oil. It is a coal-like solid,
19 usually has a C content greater than 90 percent, and is used as a boiler fuel and industrial raw material.
20 Methodology
21 Ultimate analyses of two samples of petroleum coke showed an average C share of 92.28 percent. The ASTM
22 standard density of 9.543 pounds per gallon was adopted and the EIA standard energy content of 6.024 MMBtu per barrel
23 assumed. Together, these factors produced an estimated C content coefficient of 27.85 MMT C/QBtu.
24 Data Sources
25 C content was derived from two samples from Martin, S.W. (1960). The density of petroleum coke was taken
26 from the ASTM (1985). A standard heat content for petroleum coke was adopted from EIA (2009a).
27 Uncertainty
28 The uncertainty associated with the estimated C content coefficient of petroleum coke can be traced to two
29 factors: the use of only two samples to establish C contents and a standard heat content which may be too low. Together,
30 these uncertainties are likely to bias the C content coefficient upwards by as much as 6 percent.
31 Special Naphtha
32 Special naphtha is defined as a light petroleum product to be used for solvent applications, including commercial
33 hexane and four classes of solvent: (1) Stoddard solvent, used in dry cleaning; (2) high flash point solvent, used as an
34 industrial paint because of its slow evaporative characteristics; (3) odorless solvent, most often used for residential paints;
35 and (4) high solvency mineral spirits, used for architectural finishes. These products differ in both density and C percentage,
36 requiring the development of multiple coefficients.
37 Methodology
38 The method for estimating the C content coefficient of special naphtha includes three steps.
39 Step 1. Estimate the carbon content coefficient for hexane
40 Hexane is a pure paraffin containing 6 C atoms and 14 hydrogen atoms; thus, it is 83.63 percent C. Its density is
41 83.7 degrees API or 5.477 pounds per gallon and its derived C content coefficient is 21.40 MMT C/QBtu.
42 Step 2. Estimate the carbon contents of non-hexane special naphthas
43 The hydrocarbon compounds in special naphthas are assumed to be either paraffinic or aromatic (see discussion
44 above). The portion of aromatics in odorless solvents is estimated at less than 1 percent, Stoddard and high flash point
A-122 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 solvents contain 15 percent aromatics and high solvency mineral spirits contain 30 percent aromatics (Boldt and Hall 1977).
2 These assumptions, when combined with the relevant densities, yield the C content factors contained in Table A-59, below.
3 Table A-59: 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
4 Sources: EIA (2008b) and Boldt and Hall (1977).
5
6 Step 3. Develop weighted carbon content coefficient based on consumption of each special naphtha
7 EIA reports only a single consumption figure for special naphtha. The C contents of the five special naphthas are
8 weighted according to the following formula: approximately 10 percent of all special naphtha consumed is hexane; the
9 remaining 90 percent is assumed to be distributed evenly among the four other solvents. The resulting emissions
10 coefficient for special naphthas is 19.74 MMT C/QBtu.
11 Data Sources
12 A standard heat content for special naphtha was adopted from EIA (2009a). Density and aromatic contents were
13 adopted from Boldt and Hall (1977).
14 Uncertainty
15 The principal uncertainty associated with the estimated C content coefficient for special naphtha is the allocation
16 of overall consumption across individual solvents. The overall uncertainty is bounded on the low end by the C content of
17 odorless solvent and on the upper end by the C content of hexane. This implies an uncertainty band of -1.7 percent to
18 +8.4 percent.
19 Petroleum Waxes
20 The ASTM standards define petroleum wax as a product separated from petroleum that is solid or semi-solid at
21 77 degrees Fahrenheit (25 degrees Celsius). The two classes of petroleum wax are paraffin waxes and microcrystalline
22 waxes. They differ in the number of C atoms and the type of hydrocarbon compounds. Microcrystalline waxes have longer
23 C chains and more variation in their chemical bonds than paraffin waxes.
24 Methodology
25 The method for estimating the C content coefficient for petroleum waxes includes three steps.
26 Step 1. Estimate the carbon content of paraffin waxes
11 For the purposes of this analysis, paraffin waxes are assumed to be composed of 100 percent paraffinic
28 compounds with a chain of 25 C atoms. The resulting C share for paraffinic wax is 85.23 percent and the density is estimated
29 at 45 degrees API or 6.684 pounds per gallon.
30 Step 2. Estimate the carbon content of microcrystalline waxes
31 Microcrystalline waxes are assumed to consist of 50 percent paraffinic and 50 percent cycloparaffinic compounds
32 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
33 degrees API, based on a sample of 10 microcrystalline waxes found in the Petroleum Products Handbook (Martin, S.W.
34 1960).
35 Step 3. Develop a carbon content coefficient for petroleum waxes by weighting the density and carbon content of
36 paraffinic and microcrystalline waxes
37 A weighted average density and C content was calculated for petroleum waxes, assuming that wax consumption
38 is 80 percent paraffin wax and 20 percent microcrystalline wax. The weighted average C content is 85.30 percent, and the
39 weighted average density is 6.75 pounds per gallon. ElA's standard heat content for waxes is 5.537 MMBtu per barrel.
40 These inputs yield a C content coefficient for petroleum waxes of 19.80 MMT C/QBtu.
A-123
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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 these products have the same C content as crude oil.
Methodology
EIA reports on the average density and sulfur content of U.S. crude oil purchased by refineries. To develop a
method of estimating C content based on this information, 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." 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.
23 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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1 Chronology and Explanation of Changes in Individual Carbon Content Coefficients of Fossil Fuels
2 The following section describes changes to carbon content coefficients of fossil fuels, organized by the calendar
3 year in which the update was implemented. Additional information on which Inventory year these changes appear is
4 provided within each section.
5 Coal
6 Original 1994 Analysis
7 A set of 5,426 coal samples from the EIA coal analysis file were used to develop C content estimates. The results
8 from that sample set appear below in Table A-60. The EIA Coal Analysis File was originally developed by the U.S. Bureau of
9 Mines and contained over 60,000 coal samples obtained through numerous coal seams throughout the United States.
10 Many of the samples were collected starting in the 1940s and 1950s through the 1980s and analyzed in U.S. government
11 laboratories. The coefficients developed in 1994 were in use through the 1990 through 2000 Inventory) and are provided
12 in Table A-60.
13 Table A-60: 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
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
14 Sources: Emission factors by consuming sector from B.D. Hong and E.R. Slatnick, "Carbon Dioxide Emission Factors for Coal, "U.S. EIA,
15 Quarterly Coal Report, January-March 1994 (Washington, DC, 1994); and emission factors by rank from Science Applications International
16 Corporation, Analysis of the Relationship Between Heat and Carbon Content of U.S. Fuels: Final Task Report, Prepared for the U.S. EIA,
17 Office of Coal, Nuclear, Electric and Alternative Fuels (Washington, DC 1992).
18
19 Subsequent Updates
20 In 2002 a database compiled by the U.S. Geological Survey (USGS), CoalQual 2.0 (1998), was adopted to update
21 the analysis. The updated sample set included 6,588 coal samples collected by the USGS and its state affiliates between
22 1973 and 1989. The decision to switch to the sample data contained in the USGS CoalQual database from the EIA database
23 was made because the samples contained in the USGS database were collected and analyzed more recently than those
24 obtained by EIA from the Bureau of Mines. The updated methodology first appeared in the 1990-2004 Inventory. The
25 methodology employed for these estimates has remained unchanged since 2002,24 however, the underlying coal data
26 sample set has been updated over the years to integrate new data sets as they became available.
27 In 2010 sample data from the Energy Institute at Pennsylvania State University (504 samples) were added to the
28 6,588 USGS samples to create a new database of 7,092 samples. The new coefficients developed in the 2010 update were
29 first implemented for the 1990 through 2008 Inventory.
30 In 2019 sample data from the Montana Bureau of Mines & Geology (908 samples), the Illinois State Geological
31 Survey Coal Quality Database (460 samples), and the Indiana Geological Survey Coal Quality Database (745 samples) were
32 used to calculate updated carbon contents by rank for Montana, Illinois, and Indiana. Combining revised carbon contents
33 for these three states with the carbon contents for all other states calculated from the USGS and Pennsylvania State
24 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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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. See Table A-61
above for the carbon content coefficients values used in this Inventory.
Natural Gas
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. 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.
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 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-61 below for comparison.
Table A-61: 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-62 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,
A-126 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 the category Misc. Products (U.S. Territories), which is based upon the coefficients calculated for crude oil, was allowed to
2 vary annually with the crude oil coefficient. The petrochemical feedstock category was eliminated because the constituent
3 products—naphthas and other oils—are estimated independently. Further, although the level of aggregation of U.S.
4 energy statistics currently limits the application of coefficients for residual and distillate fuels to these two generic
5 classifications, individual coefficients for the five major types of fuel oil (Nos. 1, 2, 4, 5 and 6) were estimated and are
6 presented in Table A-53 above. Each of the C coefficients applied in previous Inventories are provided below for
7 comparison (Table A-62).
8
9 Table A-62: Carbon Content Coefficients and Underlying Data for Petroleum Products
Carbon
Content
Gross Heat of
(MMT
Combustion
Density
Fuel
C/QBtu)
(MMBtu/Barrel)
(API Gravity)
Percent Carbon
Motor Gasoline
19.33
5.219
59.1
86.60
LPG (total)3
16.99
(See b)
(See b)
(See b)
LPG (energy use)
17.18
(See b)
(See b)
(See b)
LPG (non-energy use)
16.76
(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
20.33
5.796
30.5
85.49
Pentanes Plus
18.24
4.620
81.7
83.70
Natural Gasoline
18.24
4.620
81.7
83.70
10 a LPG is a blend of multiple paraffinic hydrocarbons: ethane, propane, isobutane, and normal butane, each with their own heat content,
11 density and C content, see Table A-B6.
12 b Heat, density, and percent carbon values are provided separately for ethane, propane and isobutene.
13 c Parameters presented are for naphthas with a boiling temperature less than 400 degrees Fahrenheit. Petrochemical feedstocks with
14 higher boiling points are assumed to have the same characteristics as distillate fuel.
15 Note: Indicates no sample data available.
16 Sources: EIA (1994); EIA (2008a); SAIC (2007).
17
18 Additional revisions to the Inventory's C coefficients since 1990 are detailed below.
19 Jet Fuel
20 1995 Update
21 Between 1994 and 1995, the C content coefficient for kerosene-based jet fuel was revised downward from 19.71
22 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
23 collected between 1959 and 1972 and reported on by Martel and Angello in 1977 to one collected by Boeing in 1989 and
24 published by Hadaller and Momenthy in 1990. The downward revision was a result of a decrease in density, as well as
25 slightly lower C shares than in the earlier samples. However, the assumed heat content is unchanged because it is based
A-127
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1 on an EIA standard and probably yields a downward bias in the revised C content coefficient. The coefficient revised in
2 1995 was first implemented in the 1990 through 2007 Inventory.
3 2010 Update
4 The coefficient was revised again for the 1990 through 2008 Inventory, returning to Martel and Angello and NIPER
5 as the source of the carbon share and density data, respectively, for kerosene-based fuels. This change was made in order
6 to align the coefficients used for this report with the values used in EPA's Mandatory Reporting of Greenhouse Gases Rule
7 (EPA 2009b). The return to the use of the Martel and Angello and NIPER coefficients was deemed more appropriate for
8 the Rule as it was considered a more conservative coefficient given the uncertainty and variability in coefficients across
9 the types of jet fuel in use in the United States.
10 Liquefied Petroleum Gases (LPG)
11 The C content coefficient of LPG is updated annually to reflect changes in the consumption mix of the underlying compounds:
12 ethane; propane; isobutane; and normal butane. In 1994, EIA included pentanes plus—assumed to have the characteristics of
13 hexane—in the mix of compounds broadly described as LPG. In 1995, EIA removed pentanes plus from this fuel category.
14 Because pentanes plus is relatively rich in C per unit of energy, its removal from the consumption mix lowered the C content
15 coefficient for LPG from 17.26 MMT C/QBtu to 16.99 MMT C/QBtu. In 1998, EIA began separating LPG consumption into two
16 categories: energy use and non-fuel use and providing individual coefficients for each. Because LPG for fuel use typically
17 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
18 higher than the coefficient for non-fuel use in previous inventories (see
19 Table A-62).
20 However, for the current update of the LPG coefficients, the assumptions that underlie the selection of density
21 and heat content data for each pure LPG compound have been updated, leading to a significant revision of the assumed
22 properties of ethane. In 2010, the physical characteristics of ethane, which constitutes over 90 percent of LPG consumption
23 for non-fuel uses, were updated to reflect ethane that is in (refrigerated) liquid form. Previously, the share of ethane was
24 included using the density and energy content of gaseous ethane. Table A-63, below, compares the values applied for each
25 of the compounds under the two sets of coefficient calculations, those used in the 1990 through 2007 Inventory and those
26 used in the 1990 through 2008 Inventory and on. The C share of each pure compound was also updated by using more
27 precise values for each compound's molecular weight.
28 Due in large part to the revised assumptions for ethane, the weighted C content for non-fuel use is now higher
29 than that of the weighted coefficient for fuel use, which is dominated by the consumption of more dense propane. Under
30 the revised assumptions, each annual weighted coefficient for non-fuel LPG consumption is 1.2 to 1.7 percent higher each
31 year than is that for LPGs consumed for fuel (energy) uses.
32 Table A-63: Physical Characteristics of Liquefied Petroleum Gases
1990-2007
Updated
1990-2007
Updated
1990-2007
Updated
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
33 Sources: Updated: Densities -CRC Handbook of Chemistry and Physics, 89th Ed. (2008/09); Energy Contents - EPA (2009b). All values are
34 for the compound in liquid form. The density and energy content of ethane are for refrigerated ethane (-89 degrees C). Values for n-
35 butane are for pressurized butane (-25 degrees C). Values in previous editions of this Inventory: Gurthrie (1960).
36 Motor Gasoline
37 The C content coefficient for motor gasoline varies annually based on the density of and proportion of additives
38 in a representative sample of motor gasoline examined each year. However, in 1997 EIA began incorporating the effects
39 of the introduction of reformulated gasoline into its estimate of C content coefficients for motor gasoline. This change
40 resulted in a downward step function in C content coefficients for gasoline of approximately 0.3 percent beginning in the
41 1990 through 1995 Inventory. In 2005 through 2006 reformulated fuels containing ethers began to be phased out
42 nationally. Ethanol was added to gasoline blends as a replacement oxygenate, leading to another shift in gasoline density
A-128 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 (see Table A-54), in the list and proportion of constituents that form the blend and in the blended C share based on those
2 constituents.
3
A-129
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Table A-64: 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.
A-130 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 References
2 AAM (2009) Diesel Survey. Alliance of Automobile Manufacturers, Winter 2008.
3 API (1990 through 2008) Sales of Natural Gas Liquids and Liquefied Refinery Gases, American Petroleum Institute.
4 ASTM (1985) ASTM and Other Specifications for Petroleum Products and Lubricants, American Society for Testing and
5 Materials. Philadelphia, PA.
6 Boldt, K. and B.R. Hall (1977) Significance of Tests for Petroleum Products, Philadelphia, PA, American Society for Testing
7 and Materials, p. 30.
8 Chemical Rubber Company (CRC) (2008/2009), Handbook of Chemistry and Physics, 89th Ed., editor D. Lide, Cleveland, OH:
9 CRC Press.
10 DOC (1929) Thermal Properties of Petroleum Products, U.S. Department of Commerce, National Bureau of Standards.
11 Washington, D.C. pp. 16-21.
12 EIA (2019) Monthly Energy Review, November 2019, Energy Information Administration, U.S. Department of Energy,
13 Washington, D.C. DOE/EIA-0035(2019/11).
14 EIA (2001 through 2019a) Annual Coal Report, U.S. Department of Energy, Energy Information Administration. Washington,
15 D.C. DOE/EIA 0584.
16 EIA (2001 through 2019b) Annual Coal Distribution Report, U.S. Department of Energy, Energy Information Administration.
17 Washington, D.C. DOE/EIA.
18 EIA (2008a) Monthly Energy Review, September 2006 and Published Supplemental Tables on Petroleum Product detail.
19 Energy Information Administration, U.S. Department of Energy, Washington, D.C. DOE/EIA-0035(2007/9).
20 EIA (2008b) Documentation for Emissions of Greenhouse Gases in the United States 2006. DOE/EIA-0638(2006). October
21 2008.
22 EIA (2009a) Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
23 DOE/EIA-0384(2008).
24 EIA (2009b) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, D.C.EIA
25 (2001) Cost and Quality of Fuels for Electric Utility Plants 2000, Energy Information Administration. Washington, D.C.
26 August 2001. Available online at .
27 EIA (1990 through 2001) Coal Industry Annual, U.S. Department of Energy, Energy Information Administration.
28 Washington, D.C. DOE/EIA 0584.
29 EIA (1994) Emissions of Greenhouse Gases in the United States 1987-1992, Energy Information Administration, U.S.
30 Department of Energy. Washington, D.C. November, 1994. DOE/EIA 0573.
31 EIA (1993) Btu Tax on Finished Petroleum Products, Energy Information Administration, Petroleum Supply Division
32 (unpublished manuscript, April 1993).
33 EPA (2018) The Emissions & Generation Resource Integrated Database (eGRID) 2016 Technical Support Document. Clean
34 Air Markets Division, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
35 EPA (2010) Carbon Content Coefficients Developed for EPA's Inventory of Greenhouse Gases and Sinks. Office of Air and
36 Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
37 EPA (2009a) "Industry Overview and Current Reporting Requirements for Petroleum Refining and Petroleum Imports,"
38 Petroleum Product Suppliers Technical Support Document for the Proposed Mandatory Reporting Rule. Office of Air
39 and Radiation. 30 January, 2009.
40 EPA (2009b) Mandatory Reporting of Greenhouse Gases Rule. Federal Register Docket ID EPA-HQ-OAR-2008-0508-2278,
41 30 September, 2009.
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17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
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).
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) 2006 IPCC 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.
SAIC (2007) Analysis prepared by Science Applications International Corporation for EPA, Office of Air and Radiation,
Market Policies Branch.
U.S. National Research Council (1927) International Critical Tables of Numerical Data, Physics, Chemistry, and Technology,
New York, NY, McGraw-Hill.
Unzelman, G.H. (1992) "A Sticky Point for Refiners: FCC Gasoline and the Complex Model," Fuel Reformulation, July/August
1992, p. 29.
USGS (1998) CoalQual Database Version 2.0, U.S. Geological Survey.
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-65. 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,
liquefied petroleum gases (LPG), pentanes plus, naphthas, other oils, still gas, special naphtha), (2) asphalt and road oil,
(3) lubricants, (4) waxes, and (5) miscellaneous products. The storage factors25 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-65: Fuel Types and Percent of C Stored for Non-Energy Uses
Sector/Fuel Type Storage Factor (%)
Industry
Industrial Coking Coal3 10%
Industrial Other Coalb 65%
Natural Gas to Chemical Plants'5 65%
Asphalt & Road Oil 100%
LPGb 65%
Lubricants 9%
Pentanes Plusb 65%
Naphtha (<401 deg. F)b 65%
Other Oil (>401 deg. F)b 65%
Still Gasb 65%
Petroleum Cokec 30%
Special Naphthab 65%
Distillate Fuel Oil 50%
Waxes 58%
Miscellaneous Products 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 2018. 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.
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, miscellaneous, and other products follow.
"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, LPG, pentanes plus, naphthas,
other oils, still gas, special naphtha—are used in the manufacture of a wide variety of man-made chemicals and products.
Plastics, rubber, synthetic fibers, solvents, paints, fertilizers, pharmaceuticals, and food additives are just a few of the
derivatives of these fuel types. Chemically speaking, these fuels are diverse, ranging from simple natural gas (i.e.,
predominantly CH4) to heavier, more complex naphthas and other oils.26
After adjustments for (1) use in industrial processes and (2) net exports, these eight fuel categories constituted
approximately 241.4 MMT C02 Eq., or 68 percent, of the 356.2 MMT C02 Eq. of non-energy fuel consumption in 2018. For
2018, the storage factor for the eight fuel categories was 65 percent. In other words, of the net consumption, 65 percent
was destined for long-term storage in products—including products subsequently combusted for waste disposal—while
the remaining 35 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 butanes, butylenes, ethane, ethylene, propane, propylene, LPG, and naphthas (i.e.,
most of the large volume primary chemicals produced by petroleum refineries). These imports and exports are already
factored into the U.S. fuel consumption statistics. However, EIA does not track imports and exports of chemical
intermediates and products produced by the chemical industry (e.g., xylenes, vinyl chloride), which are derived from the
primary chemicals produced by the refineries. These products represent very large flows of C derived from fossil fuels (i.e.,
fossil C), so estimates of net flows not already considered in ElA's dataset were developed for the entire time series from
1990 to 2018.
The approach to estimate imports and exports involves three steps, listed here and then described in more detail
below:
Step 1. Identify commodities derived from petrochemical feedstocks, and calculate net import/export for each.
Step 2. Estimate the C content for each commodity.
Step 3. Sum the net C imports/exports across all commodities.
Step 1 relies heavily on information provided by the National Petrochemical and Refiners Association (NPRA) and
U.S. Bureau of the Census (BoC) trade statistics published by the U.S. International Trade Commission (USITC). NPRA
provided a spreadsheet of the ten-digit BoC Harmonized Tariff Schedule (HTS) Commodity Codes used to compile import-
export data for periodic reports issued to NPRA's membership on trade issues. Additional feedstock commodities were
identified by HTS code in the BoC data system and included in the net import/export analysis.
One of the difficulties in analyzing trade data is that a large portion of the outputs from the refining industry are
fuels and fuel components, and it was difficult to segregate these from the outputs used for non-energy uses. The NPRA-
supplied codes identify fuels and fuel components, thus providing a sound basis for isolating net imports/exports of
26 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.
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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 available27 and cover a complete time series from 1990 to 2018. 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 calculatingthe 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-66. As shown in the table, the United States has been a net exporter of chemical intermediates and products
throughout the 1990 to 2018 period.
Table A-66: Net Exports of Petrochemical Feedstocks, 1990 - 2018 (MMT CP2 Eq.)
1990
2005
2010
2014
2015
2016
2017
2018
Net Exports
12.0
6.5
7.3
3.8
5.5
12.7
13.9
17.1
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), 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-66) to yield net domestic
fuel consumption for non-energy. The balance was apportioned to either stored C or emissive C, based on a storage factor.
The overall storage factor for the feedstocks was determined by developing a mass balance on the C in
feedstocks, and characterizing products, uses, and environmental releases as resulting in either storage or emissions. The
total C in the system was estimated by multiplying net domestic consumption for non-energy by the C content of each of
the feedstocks (i.e., industrial other coal, natural gas for non-fertilizer uses, LPG, pentanes plus, naphthas, other oils, still
gas, special naphtha). 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
27See the U.S. International Trade Commission (USITC) Trade Dataweb at .
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1 black, detergents and personal cleansers, industrial non-methane volatile organic compound (NMVOC) emissions,
2 hazardous waste incineration, industrial toxic chemical (i.e., TRI) releases, pesticides, food additives, antifreeze and deicers
3 (glycols), and silicones.28
4 The C in each product or waste produced was categorized as either stored or emitted. The aggregate storage
5 factor is the C-weighted average of storage across fuel types. As discussed later in the section on uncertainty, the sum of
6 stored C and emitted C (i.e., the outputs of the system) exceeded total C consumption (i.e., the inputs to the system) for
7 some years in the time series. To address this mass imbalance, the storage factor was calculated as C storage divided by
8 total C outputs (rather than C storage divided by C inputs).
9 Note that the system boundaries for the storage factor do not encompass the entire life-cycle of fossil-based C
10 consumed in the United States insofar as emissions of C02from waste combustion are accounted for separately in the
11 Inventory and are discussed in the Incineration of Waste section of the Energy chapter.
12 The following sections provide details on the calculation steps, assumptions, and data sources employed in
13 estimating and classifying the C in each product and waste shown in Table A-67. Summing the C stored and dividing it by
14 total C outputs yields the overall storage factor, as shown in the following equation for 2018:
15 Overall Storage Factor = C Stored / (C Stored + C Emitted + C Unaccounted for) =
16 157.8 MMT C02 Eq. / (157.8 + 65.2 + 18.4) MMT C02 Eq. = 65%
17 Table A-67: C Stored and Emitted by Products from Feedstocks in 2018 (MMT C02 Eq.)
C Stored
C Emitted
Product/Waste Type
(MMT C02 Eq.)
(MMT C02 Eq.)
Industrial Releases
0.1
6.5
TRI Releases
0.1
1.0
Industrial VOCs
NA
4.0
Non-combustion CO
NA
0.6
Flazardous Waste Incineration
NA
0.9
Energy Recovery
NA
45.6
Products
157.7
13.1
Plastics
134.6
NA
Synthetic Rubber
14.6
NA
Antifreeze and Deicers
NA
1.1
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
5.7
Total 157.8 65.2
18 NA (Not Applicable)
19 Note: Totals may not sum due to independent rounding.
20
21 The C unaccounted for is the difference between the C accounted for (discussed below) and the total C in the
22 Total U.S. Petrochemical consumption, which are the potential carbon emissions from all energy consumption in Non-
23 Energy Use.
24 The three categories of C accounted for in the table are industrial releases, energy recovery, and products. Each
25 is discussed below.
28 For the most part, the releases covered by the U.S. Toxic Release Inventory (TRI) represent air emissions or water discharges associated
with production facilities. Similarly, VOC emissions are generally associated with production facilities. These emissions could have been
accounted for as part of the Waste chapter, but because they are not necessarily associated with waste management, they were included
here. Toxic releases are not a "product" category, but they are referred to as such for ease of discussion.
A-136 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Industrial Releases
2 Industrial releases include toxic chemicals reported through the Toxics Release Inventory (TRI), industrial
3 emissions of volatile organic compounds (VOCs), CO emissions (other than those related to fuel combustion), and
4 emissions from hazardous waste incineration.
5 TRI Releases
6 Fossil-derived C is found in many toxic substances released by industrial facilities. The TRI, maintained by EPA,
7 tracks these releases by chemical and environmental release medium (i.e., land, air, or water) on a biennial basis (EPA
8 2000b). By examining the C contents and receiving media for the top 35 toxic chemicals released, which account for 90
9 percent of the total mass of chemicals, the quantity of C stored and emitted in the form of toxic releases can be estimated.
10 The TRI specifies releases by chemical, so C contents were assigned to each chemical based on molecular formula.
11 The TRI also classifies releases by disposal location as either off-site or on-site. The on-site releases are further subdivided
12 into air emissions, surface water discharges, underground injection, and releases to land; the latter is further broken down
13 to disposal in a RCRA Subtitle C (i.e., hazardous waste) landfill or to "Other On-Site Land Disposal."29The C released in each
14 disposal location is provided in Table A-68.
15 Each on-site classification was assigned a storage factor. A 100 percent storage factor was applied to disposition
16 of C to underground injection and to disposal to RCRA-permitted landfills, while the other disposition categories were
17 assumed to result in an ultimate fate of emission as C02 (i.e., a storage factor of zero was applied to these categories). The
18 release allocation is not reported for off-site releases; therefore, the approach was to develop a C-weighted average
19 storage factor for the on-site C and apply it to the off-site releases.
20 For the remaining 10 percent of the TRI releases, the weights of all chemicals were added and an average C
21 content value, based upon the top 35 chemicals' C contents, was applied. The storage and emission allocation for the
22 remaining 10 percent of the TRI releases was carried out in the same fashion as for the 35 major chemicals.
23 Data on TRI releases for the full 1990 through 2018 time series were not readily available. Since this category is
24 small (less than 1 MMT C emitted and stored), the 1998 value was applied for the entire time series.
25 Table A-68:1998 TRI Releases by Disposal Location (kt CP2 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
26 NA (Not Applicable)
27 Note: Totals may not sum due to independent rounding.
28 Volatile Organic Compound Emissions from Industrial Processes and Solvent Evaporation Emissions
29 Data on annual non-methane volatile organic compound (NMVOC) emissions were obtained (EPA 2019) and
30 disaggregated based on EPA (2003), which has been published on the National Emission Inventory (NEI) Air Pollutant
31 Emission Trends web site. The 1990 through 2018 Trends data include information on NMVOC emissions by end-use
32 category; some of these fall into the heading of "industrial releases" in Table A-67 above, and others are related to
33 "product use;" for ease of discussion, both are covered here. The end-use categories that represent "Industrial NMVOC
29 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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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-69 for 1990 through
2018. Industrial NMVOC emissions in 2018 were 4.0 MMT C02 Eq. and solvent evaporation emissions in 2018 were 5.7
MMTC02 Eq.
Table A-69: Industrial and Solvent NMVOC Emissions
1990 1995 2000 2005 2014 2015 2016 2017 2018
Industrial NMVOCsa
NMVOCs ('000 Short Tons) 1,279 1,358 802 825 1,421 1,421 1,421 1,421 1,421
Carbon Content (%) 85% 85% 85% 85% 85% 85% 85% 85% 85%
Carbon Emitted (MMT C02
Eq.) 3.6 3.8 2.3 2.3 4.0 4.0 4.0 4.0 4.0
Solvent Evaporation11
Solvents ('000 Short Tons) 5,750 6,183 4,832
Carbon Content (%) 56% 56% 56%
4,245 3,052 3,052 3,052 3,052 3,052
56% 56% 56% 56% 56% 56%
Carbon Emitted (MMT C02
Eq.) 10.8 11.6 9.0 7.9 7 5.7 5.7 5.7 5.7 5.7
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 2018b) 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-Use Change and
A-138 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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16
17
18
19
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22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
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-70 lists the CO emissions that remain after taking into account the
exclusions listed above.
Table A-70: Non-Combustion Carbon Monoxide Emissions
1990
1995
2000
2005
2014
2015
2016
2017
2018
CO Emissions ('000 Short Tons)
489
481
623
461
420
420
420
420
420
Carbon Emitted (MMT C02 Eq.)
0.7
0.7 A
il 0.9
0.7 A
0.6
0.6
0.6
0.6
0.6
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).30 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; 2018a). 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, and 2017 (EPA
2000a; 2009; 2013a; 2015a; 2016a; 2018a). 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-71). 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 2018 were 0.9 MMT C02Eq. Table A-72 lists the C02
emissions from hazardous waste incineration.
30 [42 U.S.C. §6924, SDWA §3004]
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Table A-71: 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-72: CP2 Emitted from Hazardous Waste Incineration (MMT CQ2 Eq.)
1990
1995
2000
2005
2014
2015
2016
2017
2018
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, and 2014 (EIA 1994; 1997;
2001; 2005; 2010; 2013b; 2017). 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-73). 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, and 45.6 MMT C02 Eq. in 2014. 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, and between 2011 and 2013 have been estimated by linear
interpolation. The value for 1990 is assumed to be the same as the value for 1991, and the values for 2015, 2016, 2017
and 2018 are assumed to be the same as the value for 2014 (Table A-74).
A-140 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Table A-73: Summary of 2014 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
4
1,329
191
106
Chemicals
325
364
6
0
310
128
Plastics and Rubber Products
326
0
0
0
0
0
Nonmetallic Mineral Products
327
0
7
0
0
16
Primary Metals
331
4
0
0
10
15
Fabricated Metal Products
332
0
0
0
0
1
Machinery
333
0
0
0
0
2
Computer and Electronic Products
334
0
0
0
0
0
Electrical Equip., Appliances,
Components
335
0
0
0
0
2
Transportation Equipment
336
4
0
0
1
4
Furniture and Related Products
337
0
0
0
0
2
Miscellaneous
339
0
0
0
0
0
Total (Trillion Btu)
372
17
1,329
511
276
Average C Content (MMT/QBtu)
18.14
20.62
17.51
0
19.37
Fraction Oxidized
1
1
1
0
1
Total C (MMT)
6.75
0.35
23.27
0.00
5.35
Total C (MMT) (ex. still gas from
refining)
6.75
0.35
0.00
0.00
5.35
2 NA (Not Applicable)
3 a C content: Waste Gas is assumed to be same as naphtha <401 deg. F.
4 b C content: Waste Oils/Tars is assumed to be same as asphalt/road oil.
5 c Refinery "still gas" fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate.
6 d Net steam fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate.
7 e C content: "Other" is assumed to be the same as petrochemical feedstocks.
8
9 Table A-74: Carbon Emitted from Fuels Burned for Energy Recovery (MMT CP2 Eq.)
1990
1995
2000
2005
2014
2015
2016
2017
2018
C Emissions
42.5
40.8
J 64.3
73.7
45.6
45.6
45.6
45.6
45.6
10 Products
11 More C is found in products than in industrial releases or energy recovery. The principal types of products are
12 plastics; synthetic rubber; synthetic fiber; C black; pesticides; soaps, detergents, and cleansers; food additives; antifreeze
13 and deicers (glycols); silicones; and solvents. Solvent evaporation was discussed previously along with industrial releases
14 of NMVOCs; the other product types are discussed below.
15 Plastics
16 Data on annual production of plastics through 2005 were taken from the American Plastics Council (APC), as
17 published in Chemical & Engineering News and on the APC and Society of Plastics Industry (SPI) websites, and through
18 direct communication with the APC (APC 2000, 2001, 2003 through 2006; SPI 2000; Eldredge-Roebuck 2000). Data for 2006
19 through 2018 were taken directly or derived from the American Chemistry Council (ACC 2007 through 2019b
20 supplemented by Vallianos 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019). In 2009, the American Chemistry
21 Council consolidated the resin categories for which it reports plastics production. Production numbers in the original
22 categories were provided via personal correspondence for 2009, 2011, 2012, 2013, 2014, 2015, 2016, 2017, and 2018
23 (Vallianos 2011; 2012; 2013; 2014; 2015; 2016; 2017; 2018; 2019). Production figures for the consolidated resin categories
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1 in 2010 were linearly interpolated from 2009 and 2011 data. Production was organized by resin type (see Table A-75) and
2 by year.
3 Several of the resin categories included production from Canada and/or Mexico, in addition to the U.S. values for
4 part of the time series. The production data for the affected resins and years were corrected using an economic adjustment
5 factor, based on the percent of North American production value in this industry sector accounted for by the United States.
6 AC content was then assigned for each resin. These C contents were based on molecular formulae and are listed in Table
7 A-76 and Table A-77. In cases where the resin type is generic, referring to a group of chemicals and not a single polymer
8 (e.g., phenolic resins, other styrenic resins), a representative compound was chosen. For other resins, a weighted C content
9 of 69 percent was assumed (i.e., it was assumed that these resins had the same content as those for which a representative
10 compound could be assigned).
11 There were no emissive uses of plastics identified, so 100 percent of the C was considered stored in products. As
12 noted in the chapter, an estimate of emissions related to the combustion of these plastics in the municipal solid waste
13 stream can be found in the Incineration of Waste section of the Energy chapter; those emissions are not incorporated in
14 the mass balance for feedstocks (described in this annex) to avoid double-counting.
15 Table A-75: 2018 Plastic Resin Production (MMT dry weight) and C Stored (MMT CP2 Eq.)
2018 Production3
Carbon Stored
Resin Type
(MMT dry weight)
(MMT CO? Eq.)
Epoxy
0.2
0.7
Urea
1.1
1.4
Melamine
0.1
0.1
Phenolic
1.5
4.3
Low-Density Polyethylene (LDPE)
3.2
10.1
Linear Low-Density Polyethylene (LLDPE)
7.7
24.1
High Density Polyethylene (HDPE)
8.8
27.8
Polypropylene (PP)
6.6
20.9
Acrylonitrile-butadiene-styrene (ABS)
0.5
1.5
Other Styrenicsb
0.5
1.7
Polystyrene (PS)
1.7
5.8
Nylon
0.5
1.3
Polyvinyl chloride (PVC)C
6.8
9.6
Thermoplastic Polyester
3.2
7.3
All Other (including Polyester (unsaturated))
6.6
16.6
Total 49.1 133.1
16 a Production estimates provided by the American Chemistry Council include Canadian production for Urea,
17 Melamine, Phenolic, LDPE, LLDPE, HDPE, PP, ABS, SAN, Other Styrenics, PS, Nylon, PVC, and Thermoplastic
18 Polyester, and Mexican production for PP, ABS, SAN, Other Styrenics, Nylon, and Thermoplastic Polyester.
19 Values have been adjusted to account just for U.S. production.
20 b Includes Styrene-acrylonitrile (SAN).
21 c Includes copolymers.
22 Note: Totals may not sum due to independent rounding.
23
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Table A-76: 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-77)
Polyvinyl Chloride (PVC)
38%
Polyvinyl chloride
Thermoplastic Polyester
63%
Polyethylene terephthalate
All Other
69%
Weighted average of other resin production
3 Does not include alcoholic hydrogens.
Table A-77: 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, 2016c, 2018b) 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 2018).
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-78). The 1998 consumption data were obtained from he International
Institute of Synthetic Rubber Producers (IISRP) press release Synthetic Rubber Use Growth to Continue Through 2004
Says IISRP and RMA (IISRP 2000).
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1 The 2001 and 2002 consumption data were obtained from the IISRP press release, IISRP Forecasts Moderate Growth in
2 North America to 2007 (IISRP 2003).
3 The rubber in tires that is abraded during use (the difference between new tire and scrap tire rubber weight) was
4 considered to be 100 percent emitted. Other than abraded rubber, there were no emissive uses of scrap tire and non-tire
5 rubber identified, so 100 percent of the non-abraded amount was assumed stored. Emissions related to the combustion
6 of rubber in scrap tires and consumer goods can be found in the Incineration of Waste section of the Energy chapter.
7
8 Table A-78: 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
9 NA (Not Applicable)
10 a Includes consumption in Canada.
11 Note: Totals may not sum due to independent rounding.
12 Synthetic Fibers
13 Annual synthetic fiber production data were obtained from the ACC, as published in the Guide to the Business of
14 Chemistry (ACC 2019a), and the Fiber Economics Bureau, as published in Chemical & Engineering News (FEB 2001, 2003,
15 2005, 2007, 2009, 2010, 2011, 2012, 2013). For acrylic fiber, the most recent data available were for 2012, so it was
16 assumed that the 2013, 2014, 2015, 2016, 2017, and 2018 consumption was equal to that of 2012. For polyester, nylon,
17 and olefin, the most recent data were for 2018. These data are organized by year and fiber type. For each fiber, a C content
18 was assigned based on molecular formula (see Table A-79). For polyester, the C content for poly (ethylene terephthalate)
19 (PET) was used as a representative compound. For nylon, the average C content of nylon 6 and nylon 6.6 was used, since
20 these are the most widely produced nylon fibers. Cellulosic fibers, such as acetate and rayon, have been omitted from the
21 synthetic fibers' C accounting displayed here because much of their C is of biogenic origin and carbon fluxes from biogenic
22 compounds are accounted for in the Land Use, Land-Use Change and Forestry chapter. These fibers account for only 4
23 percent of overall fiber production by weight.
24 There were no emissive uses of fibers identified, so 100 percent of the C was considered stored. Note that
25 emissions related to the combustion of textiles in municipal solid waste are accounted for under the Incineration of Waste
26 section of the Energy chapter.
27 Table A-79: 2018 Fiber Production (MMT), C Content (%), and C Stored (MMT CP2 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
28 + Does not exceed 0.05 MMT.
29 NA (Not Applicable)
30 Note: Totals may not sum due to independent rounding.
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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 2018 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-80: Active Ingredient Consumption in Pesticides (Million lbs.) and C Emitted and Stored (MMT C02 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
+ Does not exceed 0.05 MMT C02 Eq.
a 2012 estimates (EPA 2017).
Note: Totals may not sum due to independent rounding.
Soaps, Shampoos, and Detergents
Cleansers—soaps, shampoos, and detergents—are among the major consumer products that may contain fossil
C. All of the C in cleansers was assumed to be fossil-derived, and, as cleansers eventually biodegrade, all of the C was
assumed to be emitted. The first step in estimating C flows was to characterize the "ingredients" in a sample of cleansers.
For this analysis, cleansers were limited to the following personal household cleaning products: bar soap, shampoo,
laundry detergent (liquid and granular), dishwasher detergent, and dishwashing liquid. Data on the annual consumption
of household personal cleansers were obtained from the U.S. Census Bureau 1992, 1997, 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 2018 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
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1 extrapolated (based on the Census Bureau estimate of total value) to estimate the total quantity of dry and liquid31 cleanser
2 categories, respectively.
3 Next, the total tonnage of cleansers was calculated (wet and dry combined) for 1997. Multiplying the mean C
4 content (21.9 percent) by this value yielded an estimate of 4.6 MMT C02 Eq. in cleansers for 1997. For all subsequent years,
5 it was assumed that the ratio of value of shipments to total carbon content remained constant. For 1998 through 2018,
6 value of shipments was adjusted to 1997 dollars using the producer price index for soap and other detergent
7 manufacturing (Bureau of Labor Statistics 2019). The ratio of value of shipments to carbon content was then applied to
8 arrive at total carbon content of cleansers. Estimates are shown in Table A-81.
9 Table A-81: C Emitted from Utilization of Soaps, Shampoos, and Detergents (MMT CP2 Eq.)
1990
1995
2000
2005
2014
2015
2016
2017
2018
C Emissions
3.6
4.2
4.5
6.7
4.8
4.8
4.7
4.7
4.7
10 Antifreeze and Deicers
11 Glycol compounds, including ethylene glycol, propylene glycol, diethylene glycol, and triethylene glycol, are used
12 as antifreeze in motor vehicles, deicing fluids for commercial aircraft, and other similar uses. These glycol compounds are
13 assumed to ultimately enter wastewater treatment plants where they are degraded by the wastewater treatment process
14 to C02 or to otherwise biodegrade to C02. Glycols are water soluble and degrade rapidly in the environment (Howard
15 1993).
16 Annual production data for each glycol compound used as antifreeze and deicers were obtained from the Guide
17 to the Business of Chemistry (ACC 2019a) and the EPA Chemical Data Access Tool (CDAT) (EPA 2014). Import and export
18 data were used to adjust annual production data to annual consumption data. The percentage of the annual consumption
19 of each glycol compound used for antifreeze and deicing applications was estimated from Chemical Profiles data published
20 on The Innovation Group website32 and from similar data published in the Chemical Market Reporter, which became ICIS
21 Chemical Business in 2005.33 Production data for propylene glycol, diethylene glycol, and triethylene glycol are no longer
22 reported in the Guide to the Business of Chemistry, so data from ICIS Chemical Business on total demand was used with
23 import and export data to estimate production of these chemicals. ICIS last reported total demand for propylene glycol
24 and diethylene glycol in 2006, and triethylene glycol demand in 2007. EPA reported total U.S. production of propylene
25 glycol, diethylene glycol, and triethylene glycol in 2012 in the CDAT (EPA 2014). Total demand for these compounds for
26 2012 was calculated from the 2012 production data using import and export data. Demand for propylene glycol and
27 diethylene glycol was interpolated for years between 2006 and 2012, and demand for triethylene glycol was interpolated
28 for years between 2007 and 2012, using the calculated 2012 total demand values for each compound and the most recently
29 reported total demand data from ICIS. Values for 2014, 2015, 2016, 2017, and 2018 for these compounds were assumed
30 to be the same as the 2012 values.
31 The glycol compounds consumed in antifreeze and deicing applications is assumed to be 100 percent emitted as
32 C02. Emissions of C02 from utilization of antifreeze and deicers are summarized in Table A-82.
33 Table A-82: C Emitted from Utilization of Antifreeze and Deicers (MMT CP2 Eq.)
1990 1995 2000 2005 2014 2015 2016 2017 2018
C Emissions 1.2 1.4 1.5 1.2 0.9 1.0 1.0 1.0 1.1
34 Food Additives
35 Petrochemical feedstocks are used to manufacture synthetic food additives, including preservatives, flavoring
36 agents, and processing agents. These compounds include glycerin, propylene glycol, benzoic acid, and other compounds.
37 These compounds are incorporated into food products, and are assumed to ultimately enter wastewater treatment plants
38 where they are degraded by the wastewater treatment processes to C02 or to otherwise biodegrade to C02. Certain food
31A density of 1.05 g/mL—slightly denser than water—was assumed for liquid cleansers.
32 See .
33 See .
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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 2019a). 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 was estimated from
Chemical Profiles data published on The Innovation Group website34 and from similar data published in the Chemical
Market Reporter, which became ICIS Chemical Business in 2005.35 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 and 2018 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-83.
Table A-83: C Emitted from Utilization of Food Additives (MMT CP2 Eq.)
1990
1995
2000
2005
2014
2015
2016
2017
2018
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 2005.36 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 and 2018 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-84.
34 See .
35 See .
36 See .
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29
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31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
Table A-84: C Stored in Silicone Products (MMT CP2 Eg.)
1990
1995
2000
2005
2014
2015
2016
2017
2018
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 2018. The
Tier 2 analysis was performed to allow the specification of probability density functions for key variables, within a
computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of
uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty estimates
were determined using assumptions based on source category knowledge. Uncertainty estimates for production data (the
majority of the variables) were assumed to exhibit a normal distribution with a relative error of ±20 percent in the
underlying EIA estimates, plus an additional ±15 percent to account for uncertainty in the assignment of imports and
exports. An additional 10 percent (for a total of ±45 percent) was applied to the production of other oils (>401 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 6 percent and the
95 percent confidence interval of 53 percent and 72 percent. This compares to the calculated Inventory estimate of 65
percent. The analysis produced a C emission distribution with a standard deviation of 24.2 MMT C02 Eq. and 95 percent
confidence limits of 53.2 and 139.4 MMT C02 Eq. This compares with a calculated Inventory estimate of 83.7 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, LPG, pentanes plus, naphthas, other oils, still gas, special naphtha) is based on assuming that the
ultimate fates of all of these fuel types—in terms of storage and emissions—are similar. In addition, there are uncertainties
associated with the simplifying assumptions made for each end use category C estimate. Generally, the estimate for a
product is subject to one or 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
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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.
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, and 2014 (EIA 1994, 1997,
2001, 2005, 2010, 2013b, 2017). 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.,
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adhesives and coatings) where the resin may be released to the atmosphere; however, these end-uses are likely to be
small relative to use in plastics.
The quantity of C stored in synthetic rubber only accounts for the C stored in scrap tire synthetic rubber. The
value does not take into account the rubber stored in other durable goods, clothing, footwear, and other non-durable
goods, or containers and packaging. This adds uncertainty to the total mass balance of C stored. There are also
uncertainties as to the assignment of C content values; however, they are much smaller than in the case of plastics. There
are probably fewer variations in rubber formulations than in plastics, and the range of potential C content values is much
narrower. Lastly, assuming that all of the C contained in rubber is stored ignores the possibility of volatilization or
degradation during 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.
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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.37 The next step was
to estimate the C content of the organic emissions. This calculation was based on the C content of CO and phenol, and an
assumption of 85 percent C content for PAHs and HAPs. The C content of asphalt paving is a function of (1) the proportion
of asphalt cement in asphalt paving, assumed to be 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
(EllP 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 2018. 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
"The 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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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 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 (2019), the C content from U.S. production of lubricants in 2018 was approximately 5.3 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 2018 were
about 4.8 MMT C, or 17.5 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.38 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-85 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 (ElIP 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—
38For example, the U.S. EPA "RCRA (Resource Conservation and Recovery Act) On-line" website () has
over 50 entries on used oil regulation and policy for 1994 through 2000.
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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 ofthe 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.
Table A-85: 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 Oila
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-86 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 ofthe 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 ofthe C in landfilled and dumped grease, respectively, would be stored. The
overall storage factor is 82 percent for grease.
Table A-86: 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%
NA (Not Applicable)
Having derived separate storage factors for oil and grease, the last step was to estimate the weighted average
for lubricants as a whole. No data were found apportioning the mass of lubricants into these two categories, but the U.S.
Census Bureau 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 ofthe 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 2018. 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 ofthe Inventory estimate. Statistical analyses or
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1 expert judgments of uncertainty were not available directly from the information sources for the activity variables; thus,
2 uncertainty estimates were determined using assumptions based on source category knowledge. Uncertainty estimates
3 for oil and grease variables were assumed to have a moderate variance, in triangular or uniform distribution. Uncertainty
4 estimates for lubricants production were assumed to be rather high (±20 percent). A narrow uniform distribution, with 6
5 percent uncertainty (± 6 percent) around the mean, was applied to the lubricant C content coefficient.
6 The Monte Carlo analysis produced a storage factor distribution with the 95 percent confidence interval of 4
7 percent and 18 percent. This compares to the calculated Inventory estimate of 9.2 percent. The analysis produced a C
8 emission distribution approximating a normal curve with a standard deviation of 1.5 and 95 percent confidence limits of
9 14.4 MMT C02 Eq. and 20.3 MMT C02 Eq. This compares to an inventory-calculated estimate of 17.5 MMT C02 Eq.
10 The principal sources of uncertainty for the disposition of lubricants are the estimates of the commercial use,
11 post-use, and environmental fate of lubricants, which, as noted above, are largely based on assumptions and judgment.
12 There is no comprehensive system to track used oil and greases, which makes it difficult to develop a verifiable estimate
13 of the commercial fates of oil and grease. The environmental fate estimates for percent of C stored are less uncertain, but
14 also introduce uncertainty in the estimate.
15 The assumption that the mass of oil and grease can be divided according to their value also introduces
16 uncertainty. Given the large difference between the storage factors for oil and grease, changes in their share of total
17 lubricant production have a large effect on the weighted storage factor.
18 Future improvements to the analysis of uncertainty surrounding the lubricants C storage factor and C stored
19 include further refinement of the uncertainty estimates for the individual activity variables.
20 Waxes
21 Waxes are organic substances that are solid at ambient temperature, but whose viscosity decreases as
22 temperature increases. Most commercial waxes are produced from petroleum refining, though "mineral" waxes derived
23 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
24 these uses, suggests that about 42 percent of C in waxes is emitted, and 58 percent is stored.
25 Methodology and Data Sources
26 The National Petroleum Refiners Association (NPRA) considers the exact amount of wax consumed each year by
27 end use to be proprietary (Maguire 2004). In general, about thirty percent of the wax consumed each year is used in
28 packaging materials, though this percentage has declined in recent years. The next highest wax end use, and fastest
29 growing end use, is candles, followed by construction materials and firelogs. Table A-87 categorizes some of the wax end
30 uses, which the NPRA generally classifies into cosmetics, plastics, tires and rubber, hot melt (adhesives), chemically
31 modified wax substances, and other miscellaneous wax uses (NPRA 2002).
32 Table A-87: 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%
33 + Does not exceed 0.5 percent.
34
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1 AC storage factor for each wax end use was estimated and then summed across all end uses to provide an overall
2 C storage factor for wax. Because no specific data on C contents of wax used in each end use were available, all wax
3 products are assumed to have the same C content. Table A-88 categorizes wax end uses identified by the NPRA and lists
4 the estimated C storage factor of each end use.
5 Table A-88: 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%
6 + Does not exceed 0.5 percent.
7 NA (Not Applicable)
8 Notes: Totals may not sum due to independent rounding. Estimates of percent stored are based on ICF professional judgment.
9 Source mass percentages: NPRA (2002).
10
11 Emissive wax end-uses include candles, firelogs (synthetic fireplace logs), hotmelts (adhesives), matches, and
12 explosives. At about 20 percent, candles consume the greatest portion of wax among emissive end uses. As candles
13 combust during use, they release emissions to the atmosphere. For the purposes of the Inventory, it is assumed that 90
14 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
15 combusted during product use, likely resulting in the emission of nearly all C contained in the product. Similarly, C
16 contained in hotmelts is assumed to be emitted as C02 as heat is applied to these products during use. It is estimated that
17 50 percent of the C contained in hot melts is stored. Together, candles, firelogs, and hotmelts constitute approximately 30
18 percent of annual wax production (NPRA 2002).
19 All of the wax utilized in the production of packaging, cosmetics, plastics, tires and rubber, and other products is
20 assumed to remain in the product (i.e., it is assumed that there are no emissions of C02 from wax during the production
21 of the product). Wax is used in many different packaging materials including wrappers, cartons, papers, paperboard, and
22 corrugated products (NPRA 2002). Davie (1993) and Davie et al. (1995) suggest that wax coatings in packaging products
23 degrade rapidly in an aerobic environment, producing C02; however, because packaging products ultimately enter landfills
24 typically having an anaerobic environment, most of the C from this end use is assumed to be stored in the landfill.
25 In construction materials, petroleum wax is used as a water repellent on wood-based composite boards, such as
26 particle board (IGI 2002). Wax used for this end-use should follow the life-cycle of the harvested wood used in product,
27 which is classified into one of 21 categories, evaluated by life-cycle, and ultimately assumed to either be disposed of in
28 landfills or be combusted (EPA 2003).
29 The fate of wax used for packaging, in construction materials, and for most remaining end uses is ultimately to
30 enter the municipal solid waste (MSW) stream, where it is either combusted or sent to landfill for disposal. Most of the C
31 contained in these wax products will be stored. It is assumed that approximately 21 percent of the C contained in these
32 products will be emitted through combustion or at landfill. With the exception of tires and rubber, these end-uses are
33 assigned a C storage factor of 79 percent.
34 Waxes used in tires and rubber follow the life cycle of the tire and rubber products. Used tires are ultimately
35 recycled, landfilled, or combusted. The life-cycle of tires is addressed elsewhere in this annex as part of the discussion of
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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 2018. A Tier 2 analysis
was performed to allow the specification of probability density functions for key variables, within a computational
structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of uncertainty
were not available directly from the information sources for the activity variables; thus, uncertainty estimates were
determined using assumptions based on source category knowledge. Uncertainty estimates for wax variables were
assumed to have a moderate variance, in normal, uniform, or triangular distribution; uniform distributions were applied
to total consumption of waxes and the C content coefficients.
The Monte Carlo analysis produced a storage factor distribution, whose 95 percent confidence interval values fell
within the range of 48 percent and 68 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 0.3 MMT C02 Eq. and 0.7
MMT C02 Eq. This compares with a calculated Inventory estimate of 0.4 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
Data are not available concerning the distribution of each of the above-listed subcategories within the
"miscellaneous products" category. However, based on the anticipated disposition of the products in each subcategory, it
is assumed that all of the C content of miscellaneous products is emitted rather than stored. Petrolatum and specialty oils
(which include greases) are likely to end up in solid waste or wastewater streams rather than in durable products, and
would be emitted through waste treatment. Absorption oil is used in natural gas processing and is not a feedstock for
manufacture of durable products. Jet fuel and rocket fuel are assumed to be combusted in use, and synthetic natural gas
feedstocks are assumed to be converted to synthetic natural gas that is also combusted in use. Lube refining by-products
could potentially be used as feedstocks for manufacture of durable goods, but such by-products are more likely to be used
in emissive uses. Lube refining by-products and absorption oils are liquids and are precluded from disposal in landfills.
Because no sequestering end uses of any of the miscellaneous products subcategories have been identified, a zero percent
storage factor is assigned to miscellaneous products. The C content for 2018 was proxied to the 2008 value, which,
according to EIA (2009), was approximately 20.3 MMT C/QBtu. One hundred percent of the C content is assumed to be
emitted to the atmosphere, where it is oxidized to C02.
Uncertainty
A separate uncertainty analysis was not conducted for miscellaneous products, though this category was included
in the uncertainty analysis of other non-energy uses discussed in the following section.
Other Non-Energy Uses
The remaining fuel types use storage factors that are not based on U.S.-specific analysis. For industrial coking
coal and distillate fuel oil, storage factors were taken from IPCC (2006), which in turn draws from Marland and Rotty (1984).
These factors are 0.1 and 0.5, respectively.
IPCC does not provide guidance on storage factors for the remaining fuel types (petroleum coke, miscellaneous
products, and other petroleum), and assumptions were made based on the potential fate of C in the respective NEUs.
Specifically, the storage factor for petroleum coke is 0.3, based on information from Huurman (2006) indicating that
petroleum coke is used in the Netherlands for production of pigments, with 30 percent being stored long-term. Carbon
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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 2018. A Tier 2 analysis was performed to allow the specification of probability density functions for key
variables, within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or
expert judgments of uncertainty were not available directly from the information sources for some of the activity variables;
thus, uncertainty estimates were determined using assumptions based on source category knowledge. A uniform
distribution was applied to coking coal consumption, while the remaining consumption inputs were assumed to be
normally distributed. The C content coefficients were assumed to have a uniform distribution; the greatest uncertainty
range of 20 percent (± 20 percent) around the Inventory value, was applied to coking coal and miscellaneous products. 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 6 percent
and 43 percent. This compares to the Inventory calculation of weighted average (across the various fuels) storage factor
of about 6.3 percent. The analysis produced an emission distribution, with the 95 percent confidence limit of 18.8 MMT
C02 Eq. and 35.6 MMT C02 Eq. This compares with the Inventory estimate of 32.7 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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27 Producers, Inc. New Release; available online at: .
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43 September 2004.
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2 1950-1982, Tellus 36b:232-261.
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19 June 2000.
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34 American Chemistry Council, December 20, 2015.
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36 American Chemistry Council, November 13, 2014.
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38 American Chemistry Council, November 8, 2013.
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40 American Chemistry Council, September 14, 2012.
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ANNEX 3 Methodological Descriptions for
Additional Source or Sink Categories
3.1. Methodology for Estimating Emissions of CH4, IVhO, and Indirect Greenhouse
Gases from Stationary Combustion
Estimates of CH4 and N20 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-90 through Table A-95.
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 (ElA) Monthly Energy
Review, November 2019 (EIA 2019). 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).39 Fuel consumption for the industrial sector was adjusted to subtract out construction and
agricultural use, which is reported under mobile sources.40 Construction and agricultural fuel use was obtained from EPA
(2018) and the Federal Highway Administration (FHWA) (1996 through 2018). 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-90 provides
annual energy consumption data for the years 1990 through 2018.
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 2019a). Total fuel consumption in the electric power sector from EIA (2019) was apportioned to
each combustion technology type and fuel combination using a ratio of fuel consumption by technology type derived from
EPA (2019a) data. The combustion technology and fuel use data by facility obtained from EPA (2019a) were only available
from 1996 to 2018, so the consumption estimates from 1990 to 1995 were estimated by applying the 1996 consumption
ratio by combustion technology type from EPA (2019a) to the total EIA (2019) 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-91 provides emission factors used for each sector and fuel type. For the
39 U.S. Territories data also include combustion from mobile activities because data to allocate U.S. Territories' energy use were
unavailable. Forthis reason, CH4 and N20 emissions from combustion by U.S. Territories are only included in the stationary combustion
totals.
40 Though emissions from construction and farm use occur due to both stationary and mobile sources, detailed data was not available
to determine the magnitude from each. Currently, these emissions are assumed to be predominantly from mobile sources.
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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-92. Emission factors were taken from U.S. EPA publications on
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 2006 IPCC 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 2019b) 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 (2019b) 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-90: Fuel Consumption by Stationary Combustion for Calculating CH4 and N2Q Emissions (TBtu)
Fuel/End-Use
Sector
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Coal
19,610
20,888
23,080
22,939
22,219
19,664
20,692
19,495
16,901
17,791
17,772
15,416
14,235
13,744
13,123
Residential
31
17
11
8
0
0
0
0
0
0
0
0
0
0
0
Commercial
124
117
92
97
81
73
70
62
44
41
40
31
24
21
19
Industrial
1,640
1,527
1,349
1,219
1,081
877
952
866
782
800
799
696
620
570
521
Electric Power
17,807
19,216
21,618
21,582
21,020
18,677
19,633
18,531
16,038
16,919
16,889
14,645
13,547
13,110
12,540
U.S. Territories3
7
10
10
33
37
37
37
37
37
31
44
44
44
44
44
Petroleum
6,266
5,834
6,375
6,683
5,544
5,053
5,204
4,949
4,660
4,839
4,388
4,870
4,538
4,313
4,733
Residential
1,376
1,261
1,423
1,369
1,202
1,140
1,117
1,048
843
933
1,020
958
813
784
922
Commercial
1,023
725
766
763
693
736
707
681
558
592
569
950
845
821
907
Industrial
2,700
2,530
2,450
2,928
2,673
2,269
2,452
2,450
2,454
2,626
2,171
2,317
2,249
2,166
2,340
Electric Power
797
860
1,269
1,003
488
383
412
273
288
185
157
173
159
71
93
U.S. Territories3
370
459
467
620
487
525
515
497
517
504
472
472
472
472
472
Natural Gas
17,250
19,337
20,919
20,936
22,284
21,951
22,912
23,319
24,613
25,141
25,920
26,636
26,764
26,455
29,345
Residential
4,487
4,954
5,105
4,946
5,010
4,883
4,878
4,805
4,242
5,023
5,242
4,777
4,506
4,563
5,173
Commercial
2,680
3,096
3,252
3,073
3,228
3,187
3,165
3,216
2,960
3,380
3,572
3,316
3,224
3,273
3,640
Industrial
7,708
8,723
8,656
7,330
7,572
7,126
7,685
7,876
8,204
8,526
8,818
8,779
8,975
9,181
9,729
Electric Power
2,376
2,564
3,894
5,562
6,445
6,728
7,157
7,396
9,158
8,156
8,231
9,707
10,003
9,380
10,747
U.S. Territories3
0
0
13
24
29
27
28
27
49
57
57
57
57
57
57
Wood
2,095
2,252
2,138
1,963
1,908
1,778
2,046
2,055
1,989
2,160
2,209
2,127
2,062
2,119
2,204
Residential
580
520
420
430
470
504
541
524
438
572
579
513
448
433
517
Commercial
66
72
71
70
73
73
72
69
61
70
76
79
84
84
84
Industrial
1,442
1,652
1,636
1,452
1,339
1,178
1,409
1,438
1,462
1,489
1,495
1,476
1,474
1,539
1,537
Electric Power
7
8
11
11
27
23
25
24
28
30
60
59
57
62
66
U.S. Territories
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
1 NE (Not Estimated)
2 Note: Totals may not sum due to independent rounding.
3 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.
4
A-164 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Table A-91: CH4 and N2Q Emission Factors by Fuel Type and Sector (g/GJ)a
Fuel/End-Use Sector CH4 N2Q
Coal
Residential 300 1.5
Commercial 10 1.5
Industrial 10 1.5
U.S. Territories 1 1.5
Petroleum
Residential 10 0.6
Commercial 10 0.6
Industrial 3 0.6
U.S. Territories 5 0.6
Natural Gas
Residential 5 0.1
Commercial 5 0.1
Industrial 1 0.1
U.S. Territories 1 0.1
Wood
Residential 300 4.0
Commercial 300 4.0
Industrial 30 4.0
U.S. Territories NA NA_
NA (Not Applicable)
aGJ (Gigajoule) = 109 joules. One joule = 9.486x10 4 Btu.
Table A-92: CH4 and N2Q Emission Factors by Technology Type and Fuel Type for the Electric Power Sector (g/GJ)a
Technology
Configuration
CH4
n2o
Liquid Fuels
Residual Fuel Oil/Shale Oil Boilers
Gas/Diesel Oil Boilers
Large Diesel Oil Engines >600 hp (447kW)
Solid Fuels
Pulverized Bituminous Combination
Boilers
Bituminous Spreader Stoker Boilers
Bituminous Fluidized Bed Combustor
Bituminous Cyclone Furnace
Lignite Atmospheric Fluidized Bed
Natural Gas
Boilers
Gas-Fired Gas Turbines >3MW
Large Dual-Fuel Engines
Combined Cycle
Peat
Peat Fluidized Bed Combustion
Biomass
Wood/Wood Waste Boilers
Wood Recovery Boilers
Normal Firing
Tangential Firing
Normal Firing
Tangential Firing
Dry Bottom, wall fired
Dry Bottom, tangentially fired
Wet bottom
With and without re-injection
Circulating Bed
Bubbling Bed
Circulating Bed
Bubbling Bed
0.8
0.8
0.9
0.9
4.0
0.7
0.7
0.9
1.0
1.0
1.0
0.2
NA
1.0
3.7
258.0
3.7
3.0
3.0
11.0
1.0
0.3
0.3
0.4
0.4
NA
5.8
1.4
1.4
0.7
61
61
0.6
71
0.3
1.3
NA
1.3
7.0
3.0
7.0
1.0
NA (Not Applicable)
3 Ibid.
A-165
-------
1 Table A-93: NOx Emissions from Stationary Combustion (kt)
Sector/Fuel Type
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Electric Power
6,045
5,792
4,829
3,434
2,847
2,552
2,226
1,893
1,779
1,666
1,603
1,327
1,166
1,047
1,009
Coal
5,119
5,061
4,130
2,926
2,426
2,175
1,896
1,613
1,516
1,419
1,366
1,130
994
892
859
Fuel Oil
200
87
147
114
95
85
74
63
59
55
53
44
39
35
34
Natural gas
513
510
376
250
207
186
162
138
129
121
117
97
85
76
73
Wood
NA
NA
36
29
24
21
19
16
15
14
13
11
10
9
8
Other Fuels3
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Internal Combustion
213
134
140
115
95
86
75
63
60
56
54
44
39
35
34
Industrial
2,559
2,650
2,278
1,515
1,165
1,126
1,087
1,048
1,016
984
952
952
952
952
952
Coal
530
541
484
342
263
254
245
237
229
222
215
215
215
215
215
Fuel Oil
240
224
166
101
78
75
73
70
68
66
64
64
64
64
64
Natural gas
877
999
710
469
361
348
336
324
314
305
295
295
295
295
295
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
119
111
109
76
59
57
55
53
51
50
48
48
48
48
48
Internal Combustion
792
774
809
527
405
391
378
364
353
342
331
331
331
331
331
Commercial
671
607
507
490
433
445
456
548
535
521
448
448
448
448
448
Coal
36
35
21
19
15
15
15
15
14
14
14
14
14
14
14
Fuel Oil
88
94
52
49
39
39
38
37
37
37
36
36
36
36
36
Natural gas
181
210
161
155
124
122
120
118
117
116
115
115
115
115
115
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
366
269
273
267
254
269
284
378
366
354
283
283
283
283
283
Residential
749
813
439
418
335
329
324
318
315
312
310
310
310
310
310
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oilb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
42
44
21
20
16
16
16
16
15
15
15
15
15
15
15
Other Fuels3
707
769
417
398
318
313
308
302
300
297
295
295
295
295
295
Total
10,023
9,862
8,053
5,858
4,780
4,452
4,092
3,807
3,645
3,483
3,313
3,036
2,876
2,757
2,719
2 NA (Not Applicable)
3 3 Other Fuels include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2019b).
4 b Residential coal, fuel oil, and natural gas emissions are included in the Other Fuels category (EPA 2019b).
5 Note: Totals may not sum due to independent rounding.
6
7 Table A-94: CO Emissions from Stationary Combustion (kt)
Sector/Fuel Type
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Electric Power
329
337
439
582
660
676
693
710
694
678
661
661
661
661
661
Coal
213
227
221
292
330
339
347
356
348
340
331
331
331
331
331
Fuel Oil
18
9
27
37
42
43
44
45
44
43
42
42
42
42
42
Natural gas
46
49
96
122
138
142
145
149
146
142
139
139
139
139
139
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
A-166 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Other Fuels3
NA
NA
31
43
48
50
51
52
51
50
48
48
48
48
48
Internal Combustion
52
52
63
89
101
103
106
108
106
104
101
101
101
101
101
Industrial
797
958
1,106
1,045
815
834
853
872
861
851
840
840
840
840
840
Coal
95
88
118
115
90
92
94
96
95
94
93
93
93
93
93
Fuel Oil
67
64
48
42
32
33
34
35
34
34
33
33
33
33
33
Natural gas
205
313
355
336
262
268
274
281
277
274
270
270
270
270
270
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
253
270
300
295
230
236
241
247
244
241
238
238
238
238
238
Internal Combustion
177
222
285
257
200
205
209
214
212
209
206
206
206
206
206
Commercial
205
211
151
166
137
138
140
142
134
127
120
120
120
120
120
Coal
13
14
14
14
12
12
12
12
12
11
10
10
10
10
10
Fuel Oil
16
17
17
19
15
16
16
16
15
14
13
13
13
13
13
Natural gas
40
49
83
91
75
76
77
78
74
70
66
66
66
66
66
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
136
132
36
41
34
35
35
35
34
32
30
30
30
30
30
Residential
3,668
3,877
2,644
2,856
2,357
2,387
2,416
2,446
2,319
2,192
2,065
2,065
2,065
2,065
2,065
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oilb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
3,430
3,629
2,416
2,615
2,158
2,185
2,212
2,239
2,123
2,007
1,890
1,890
1,890
1,890
1,890
Other Fuels3
238
248
228
241
199
202
204
207
196
185
174
174
174
174
174
Total
5,000
5,383
4,340
4,648
3,969
4,036
4,103
4,170
4,009
3,847
3,686
3,686
3,686
3,686
3,686
1 NA (Not Applicable)
2 3 Other Fuels include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2019b).
3 b Residential coal, fuel oil, and natural gas emissions are included in the Other Fuels category (EPA 2019b).
4 Note: Totals may not sum due to independent rounding.
5
6 Table A-95: NMVOC Emissions from Stationary Combustion (kt)
Sector/Fuel Type
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Electric Power
43
40
56
44
40
39
38
37
36
35
34
34
34
34
34
Coal
24
26
27
21
19
18
18
18
17
17
16
16
16
16
16
Fuel Oil
5
2
4
3
3
3
3
3
3
3
3
3
3
3
3
Natural Gas
2
2
12
10
9
9
8
8
8
8
8
8
8
8
8
Wood
NA
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
1
Internal Combustion
11
9
11
8
8
7
7
7
7
7
7
7
7
7
7
Industrial
165
187
157
120
97
99
100
101
101
100
99
99
99
99
99
Coal
7
5
9
8
6
6
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
5
Natural Gas
52
66
53
41
33
33
34
34
34
34
34
34
34
34
34
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
A-167
-------
Other Fuels3
46
45
27
22
18
18
18
19
19
18
18
18
18
18
18
Internal Combustion
49
60
58
43
35
35
36
36
36
36
35
35
35
35
35
Commercial
18
21
28
33
36
38
40
42
40
39
35
35
35
35
35
Coal
1
1
1
1
+
+
+
+
+
+
+
+
+
+
+
Fuel Oil
3
3
4
2
2
2
2
2
2
2
1
1
1
1
1
Natural Gas
7
10
14
9
6
7
7
7
7
6
6
6
6
6
6
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
8
8
9
22
28
29
31
32
31
31
28
28
28
28
28
Residential
686
725
837
518
358
378
399
419
389
358
327
327
327
327
327
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oilb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
651
688
809
502
346
366
386
406
376
346
317
317
317
317
317
Other Fuels3
35
37
27
17
12
12
13
14
13
12
11
11
11
11
11
Total
912
973
1,077
716
531
553
576
599
566
532
497
497
497
497
497
1 + Does not exceed 0.5 kt.
2 NA (Not Applicable)
3 3 "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2019b).
4 b Residential coal, fuel oil, and natural gas emissions are included in the "Other Fuels" category (EPA 2019b).
5 Note: Totals may not sum due to independent rounding.
6
7
A-168 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
References
EIA (2019) Monthly Energy Review, November 2019, Energy Information Administration, U.S. Department of Energy,
Washington, D.C. DOE/EIA-0035(2019/11).
EIA (2017) International Energy Statistics 1980-2016. Energy Information Administration, U.S. Department of Energy.
Washington, D.C. Available online at: .
EPA (2019a) Acid Rain Program Dataset 1996-2018. Office of Air and Radiation, Office of Atmospheric Programs, U.S.
Environmental Protection Agency, Washington, D.C.
EPA (2019b) "Criteria pollutants National Tier 1 for 1970 - 2018." National Emissions Inventory (NEI) Air Pollutant
Emissions Trends Data. Office of Air Quality Planning and Standards, May 2019. Available online at <
https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data>.
EPA (2018) 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 2018) Highway Statistics. Federal Highway Administration, U.S. Department of Transportation,
Washington, D.C. Report FHWA-PL-96-023-annual. 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.
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-169
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
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29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
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 2019a and EIA
2018d) 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 2019), 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 2018),41 and are based on federal
and state fuel tax records. Table VM-1 fuel consumption data for 2018 has not yet been published, therefore 2018 fuel
consumption data is estimated using percent change in VMT from 2017 to 2018. 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 2017) 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 2017). 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 forthe 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 2018a), 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
41 In 2011 FHWA changed its methods for estimating vehicle miles traveled (VMT) and related data. These methodological changes included how
vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. These changes were first incorporated for
the 1990 through 2008 Inventory and apply to the 2007 to 2018 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-170 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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18
19
20
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27
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30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
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 2018) for Class I railroads, the American Public Transportation Association (APTA 2007 through 2017 and APTA
2006) and Gaffney (2007) for commuter rail, the Upper Great Plains Transportation Institute (Benson 2002 through 2004)
and Whorton (2006 through 2014) and Railinc (2014 through 2018) for Class II and III railroads, and U.S. Department of
Energy's Transportation Energy Data Book (DOE 1993 through 2017) 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 from ships and boats were taken from ElA's Fuel Oil and Kerosene Sales (1991 through 2017).
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 (2019) for domestic and international commercial
aircraft, and DLA Energy (2019) 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 2019), 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 2019a).
Table A-96 displays estimated fuel consumption by fuel and vehicle type. Table A-97 displays estimated energy
consumption by fuel and vehicle type. The values in both of these 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 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 2016 and biodiesel fuel volumes were removed from diesel fuel consumption volumes for years 2001
through 2016, 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 refinery and blender net volume inputs
of renewable diesel fuel sourced from EIA Petroleum Supply Annual (EIA 2018f) Table 18 - Refinery Net Input of Crude Oil
and Petroleum Products and Table 20 - Blender Net Inputs of Petroleum Products 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 fuel ethanol blended into motor gasoline, ethanol energy consumption data sourced from MER Table 10.2b -
Renewable Energy Consumption: Industrial and Transportation Sectors (EIA 2019a) 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-98.42
42 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-98. 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-96 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-171
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1 Table A-96: Fuel Consumption by Fuel and Vehicle Type (million gallons unless otherwise specified)
Fuel/Vehicle Type
1990
1995
2000
2008a
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Motor Gasolineb
-------
Passenger Cars
+
+
+
+
+
4
14
31
68
113
151
190
238
341
Light-Duty Trucks
+
+
+
+
+
+
0
1
1
2
3
17
32
52
Buses
+
+
+
+
+
2
2
1
1
1
1
2
5
8
Rail
4,751
4,975
5,382
7,653
7,768
7,745
7,770
7,531
8,080
8,517
8,725
9,034
9,624
10,661
1 + Does not exceed 0.05 trillion cubic feet
2 a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2018 time period. These methodological changes include how on-road vehicles are
3 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.
4 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-
5 Use Change and Forestry chapter. This table is calculated with the heat content for gasoline without ethanol (from Table A.2 in the EIA Annual Energy Review) rather than the annually
6 variable quantity-weighted heat content for gasoline with ethanol, which varies by year.
7 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 2018). Table VM-1
8 fuel consumption data for 2018 has not been published yet, therefore 2018 fuel consumption data is estimated using percent change in VMT from 2017 to 2018. Data from Table VM-1 is
9 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
10 TEDB Annex Tables A. 1 through A.6 (DOE 1993 through 2017). TEDB data for 2017 and 2018 has not been published yet, therefore 2016 data are used as a proxy.
11 d Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-upvalues with EIA total gasoline estimates.
12 e Class II and Class III diesel consumption data for 2014-2018 is estimated by applying the historical average fuel usage per carload factor to the annual number of carloads.
13 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
14 consumption are based on data from EIA (2019a). In previous Inventory years, data from DOE TEDB was used to estimate each vehicle class's share of the total natural gas and LPG
15 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
16 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
17 1990 through 2018 time period.
18 8 Fluctuations in reported fuel consumption may reflect data collection problems.
19 h Million kilowatt-hours
20 ' 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
21 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
22 changes were first incorporated in the 2017 Inventory and applied to the 2010 through 2018 time period.
23
24 Table A-97: Energy Consumption by Fuel and Vehicle Type (TBtu)
Fuel/Vehicle Type
1990
1995
2000
2008a
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Motor Gasolineb
-------
Buses
108
118
138
198
183
182
192
206
206
224
229
224
238
249
Medium- and Heavy-
Duty Trucks
2,576
3,220
4,181
4,917
4,426
4,614
4,586
4,601
4,641
4,796
4,901
4,937
5,105
5,147
Recreational Boats
37
37
37
37
37
36
35
35
34
34
35
36
37
38
Ships and Non-
Recreational Boats
91
161
190
114
115
111
148
114
116
99
176
146
135
122
Raile
480
535
569
580
483
520
538
528
532
556
532
485
502
526
Jet Fuelf
2,590
2,429
2,700
2,396
2,134
2,097
2,030
1,985
2,037
2,054
2,182
2,299
2,378
2,386
Commercial Aircraft
1,562
1,638
1,981
1,809
1,699
1,611
1,629
1,611
1,624
1,638
1,692
1,711
1,819
1,843
General Aviation Aircraft
545
454
427
362
241
314
256
224
274
241
319
430
403
393
Military Aircraft
484
337
293
225
194
173
145
150
138
175
171
158
156
150
Aviation Gasoline'
45
40
36
28
27
27
27
25
22
22
21
20
21
22
General Aviation Aircraft
45
40
36
28
27
27
27
25
22
22
21
20
21
22
Residual Fuel Oilf-B
300
387
443
271
186
272
258
211
201
77
57
172
219
185
Ships and Boats
300
387
443
271
186
272
258
211
201
77
57
172
219
185
Natural Gasf
679
724
672
692
715
719
734
780
887
760
745
757
799
948
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
0.3
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
0.3
0.4
0.4
0.5
0.6
0.7
0.7
0.8
Buses
+
+
3
14
15
15
15
15
15
15
17
16
18
18
Pipelines
679
724
668
677
699
703
718
765
872
744
727
740
780
929
LPGf
23
18
12
40
28
7
7
7
7
7
7
7
7
8
Passenger Cars
0.1
0.1
0.1
0.5
0.4
+
+
+
+
0.1
1
0
0
+
Light-Duty Trucks
3
2
2
7
7
2
1
1
1
2
1
1
1
1
Medium- and Heavy-
Duty Trucks
18
14
9
24
16
4
5
5
5
5
4
5
5
5
Buses
1
1
0.8
8
5
1
1
1
1
1
1
1
1
1
Electricity11
3
3
3
5
4
5
4
4
4
4
4
4
4
5
Passenger Cars
+
+
+
+
+
+
+
0.1
0.2
0.4
0.5
0.6
0.8
1.2
Light-Duty Trucks
+
+
+
+
+
+
+
+
+
+
+
0.1
0.1
0.2
Buses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Rail
3
3
3
5
4
4
4
4
4
4
4
3
3
3
Total
20,659
22,253
24,967
24,597
23,577
23,708
23,362
23,245
23,454
23,976
24,128
24,686
24,997
25,187
1 + Does not exceed 0.05 TBtu
2 a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2018 time period. These methodological changes include how on-road vehicles are classified,
3 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.
4 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
5 Change and Forestry chapter.
A-174 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 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 2017). Table VM-1
2 fuel consumption data for 2018 has not been published yet, therefore 2018 fuel consumption data is estimated using percent change in VMT from 2017 to 2018. Data from Table VM-1 is
3 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
4 TEDB Annex Tables A. 1 through A.6 (DOE 1993 through 2017). TEDB data for 2017 and 2018 has not been published yet, therefore 2016 data are used as a proxy.
5 d Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
6 e Class II and Class III diesel consumption data for 2014-2017 is estimated by applying the historical average fuel usage per carload factor to the annual number of carloads.
7 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
8 data from EIA (2019a). 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
9 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
10 class's share of the total natural gas and LPG consumption. These changes were first incorporated in the 2016 Inventory and apply to the 1990-2018 time period.
11s Fluctuations in reported fuel consumption may reflect data collection problems. Residual fuel oil for ships and boats data is based on ElA's October 2019 Monthly Energy Review data.
12 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
13 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
14 were first incorporated in the 2017 Inventory and apply to the 2010 through 2018 time period.
15 Table A-98: Transportation Sector Biofuel Consumption by Fuel Type (million gallons)
Fuel Type
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Ethanol
699
1,290
1,556
| 8,791
10,074
11,836
11,975
11,997
12,157
12,761
12,793
13,261
13,403
13,366
Biodiesel
NA
NA
NA
304
322
260
886
899
1,429
1,417
1,494
2,085
1,985
1,904
16 NA (Not Available)
17 Note: According to the MER, there was no biodiesel consumption prior to 2001.
A-175
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17
18
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22
23
24
25
26
27
28
29
30
31
32
33
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,43 buses, and
motorcycles) were obtained from the FHWA's Highway Statistics (FHWA 1996 through 2018).44 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 2018) 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 and A.6 (DOE 1993 through 2017). These fuel shares are drawn from various sources,
including the Vehicle Inventory and Use Survey, the National Vehicle Population Profile, and the American Public
Transportation Association. 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.45
The resulting national VMT estimates for gasoline and diesel on-road vehicles are presented in Table A-99 and Table A-
100, 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 2018 in Table A-101. 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 2018a).46 Age-specific vehicle
mileage accumulations were also obtained from EPA's MOVES2014b model (EPA 2018a).47
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-107 through Table A-110. 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
43 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.
44 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 2018 time period. This resulted in large changes in VMT data by vehicle class, thus
leading to a shift in emissions among on-road vehicle classes. For example, the category "Passenger Cars" has been replaced by "Light-duty
Vehicles-Short Wheelbase" and "Other 2 axle-4 Tire Vehicles" has been replaced by "Light-duty Vehicles, Long Wheelbase." This change in vehicle
classification has moved some smaller trucks and sport utility vehicles from the light truck category to the passenger vehicle category in this
emission inventory. These changes are reflected in a large drop in light-truck emissions between 2006 and 2007.
45 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.
46 Age distributions were held constant for the period 1990 to 1998, and reflect a 25-year vehicle age span. EPA (2019b) provides a variable age
distribution and 31-year vehicle age span beginning in year 1999.
47 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-176 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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43
44
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. EPATier 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 1996 IPCC
Guidelines, roughly correspond to the introduction of EPA Tier 0 and EPA Tier 1 regulations (EPA 1998).48 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.
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 2018 were
determined using confidential engine family sales data submitted to EPA (EPA 2019c). Vehicle classes and emission
standard tiers to which each engine family was certified were taken from annual certification test results and data (EPA
2018d). 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
CH4 and N20 emission factors 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 Canada laboratory test results of
different vehicle and control technology types. The EPA, CARB and Environment Canada tests were designed following the
Federal Test Procedure (FTP), which covers three separate driving segments, since vehicles emit varying amounts of GHGs
depending on the driving segment. These driving segments are: (1) a transient driving cycle that includes cold start and
running emissions, (2) a cycle that represents running emissions only, and (3) a transient driving cycle that includes hot
start and running emissions. For each test run, a bag was affixed to the tailpipe of the vehicle and the exhaust was collected;
the content of this bag was later analyzed to determine quantities of gases present. The emission characteristics of
Segment 2 was used to define running emissions, and subtracted from the total FTP emissions to determine start
emissions. These were then recombined based upon MOBILE6.2's ratio of start to running emissions for each vehicle class
to approximate average driving characteristics.
Step 4: Determine the Amount of CH4 and 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.49 Except for electric vehicles and plug-in hybrid vehicles, the alternative fuel vehicle VMT were
calculated using the Energy Information Administration (EIA) Alternative Fuel Vehicle Data. The EIA data provides vehicle
48 Forfurther description, see "Definitions of Emission Control Technologies and Standards" section of this annex below.
49 Fuel types used in combination depend on the vehicle class. For light-duty vehicles, gasoline is generally blended with ethanol and diesel is
blended with biodiesel; dual-fuel vehicles can run on gasoline or an alternative fuel - either natural gas or LPG - but not at the same time, while
flex-fuel vehicles are designed to run on E85 (85 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.
A-177
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5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
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, and 2018, 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). EVs were divided into cars and
trucks using confidential engine family sales data submitted to EPA (EPA 2019c). 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 GREET2018 model (ANL 2018). 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-101, while more detailed estimates of VMT by control technology are
shown in Table A-102.
Step 2: Determine CH4 and N20 Emission Factors by Vehicle and Alternative Fuel Type
Methane and N20 emission factors for alternative fuel vehicles (AFVs) are calculated using Argonne National
Laboratory's GREET model (ANL 2018) and are reported in Browning (2018). These emission factors are shown in Table A-
112 and Table A-113.
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-106. Consumption data for ships and boats (i.e., vessel bunkering) were obtained from DHS
(2008) and EIA (1991 through 2018) for distillate fuel, and DHS (2008) and EIA (2019a) 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.50 Gasoline consumption by recreational boats was obtained from the
NONROAD component of EPA's MOVES2014b model (EPA 2018a). Annual diesel consumption for Class I rail was obtained
from the Association of American Railroads (AAR 2008 through 2018), diesel consumption from commuter rail was
obtained from APTA (2007 through 2017) and Gaffney (2007), and consumption by Class II and III rail was provided by
Benson (2002 through 2004) and Whorton (2006 through 2014).51 It is estimated that anaverage 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 2018) and fuel consumption data provided by Whorton, D. (2006 through 2014). Class II and Class III diesel
consumption for 2014-2018 is estimated by multiplying this average historical fuel usage per carload factor by the number
50 See International Bunker Fuels section of the Energy chapter.
51 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.
A-178 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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8
9
10
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12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
of shortline carloads originated each year (Raillnc 2014 through 2017). Diesel consumption by commuter and intercity rail
was obtained from DOE (1993 through 2017). Data on the consumption of jet fuel and aviation gasoline in aircraft were
obtained from EIA (2019a) and FAA (2019), 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 2019 and FAA 2019).
Pipeline fuel consumption was obtained from EIA (2007 through 2018) (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 2018a) for gasoline and diesel powered equipment, and from
FHWA (1996 through 2018) for gasoline consumption by off-road trucks used in the agriculture, industrial, commercial,
and construction sectors.52Specifically, 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 EPA's MOVES2014b model. CH4 emission factors were calculated directly from MOVES. N20 emission factors
were calculated using NONROAD activity and emission factors by fuel type from the European Environment Agency (EEA
2009). Equipment using liquefied petroleum gas (LPG) and compressed natural gas (CNG) were included (see Table A-114
and Table A-115).
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 2016g). 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-116 through Table A-118
provides complete emission estimates for 1990 through 2018.
Table A-99: 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
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
52 "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-179
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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
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
34.6
20.1
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 2018 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.
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 2018 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 2018). Table
VM-1 fuel consumption data for 2018 has not been published yet, therefore 2018 fuel consumption data is estimated using the
percent change in VMT from 2017 to 2018. These mileage consumption estimates are combined with estimates of fuel shares by
vehicle type from DOE's TEDB Annex Tables A.1 through A.6 (DOE 1993 through 2017). TEDB data for 2017 and 2018 has not been
published yet, therefore 2016 data are used as a proxy.
Source: Derived from FHWA (1996 through 2018), DOE (1990 through 2017), Browning (2018a), and Browning (2017).
Table A-100: 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
2012
10.2
23.5
247.5
2013
10.1
23.2
249.9
2014
10.1
24.6
254.3
A-180 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
2015 10.4 24.3 254.6
2016 10.4 24.9 258.3
2017 10.6 24.9 268.2
2018 10.6 25.3 276.1
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 2017 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.
Note: Gasoline and diesel highway vehicle mileage are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through
2018). Table VM-1 fuel consumption data for 2018 has not been published yet, therefore 2018 fuel consumption data is estimated
using the percent change in VMT from 2017 to 2018. These mileage consumption estimates are combined with estimates of fuel
shares by vehicle type from DOE's TEDB Annex Tables A. 1 through A.6 (DOE 1993 through 2017). TEDB data for 2017 and 2018 has not
been published yet, therefore 2016 data are used as a proxy.
Source: Derived from FHWA (1996 through 2018), DOE (1993 through 2017), and Browning (2017), Browning (2018a).
Table A-101: 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
a Heavy Duty-Vehicles includes medium-duty trucks, heavy-duty trucks, and buses.
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 2018 time
period.
Source: Derived from Browning (2017), Browning (2018a), and EIA (2019h).
A-181
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1 Table A-102: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (106 Miles)
Vehicle Type/Year
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Light-Duty Cars
3.7
60.2
105.9
217.1
227.6
232.6
524.5
911.3
1,801.3
2,727.2
3,735.7
4,929.8
6,209.6
9,056.5
Methanol-Flex Fuel ICE
+
45.9
14.3
+
+
+
+
+
+
+
+
+
+
+
Ethanol-Flex Fuel ICE
+
0.3
19.6
79.0
90.3
114.8
111.2
139.8
163.0
127.2
110.4
124.5
87.0
85.6
CNG ICE
+
0.1
5.2
11.7
10.8
10.1
10.8
11.1
12.1
11.6
11.7
13.7
12.9
14.0
CNG Bi-fuel
+
0.2
16.9
12.1
9.3
7.4
6.6
4.1
3.2
2.3
1.7
1.4
1.5
1.7
LPG ICE
1.1
1.1
1.1
1.5
1.5
+
0.1
0.1
0.3
3.1
15.8
6.1
1.8
0.9
LPG Bi-fuel
2.7
2.8
2.8
1.5
1.7
1.2
0.3
0.2
0.2
0.1
0.1
0.1
+
+
Biodiesel (BD100)
+
+
1.2
35.6
41.5
34.3
127.1
156.6
274.1
298.0
337.5
501.3
512.1
521.2
NEVs
+
9.4
43.6
73.7
71.2
61.5
102.9
98.9
103.8
113.2
124.3
83.8
89.9
86.4
Electric Vehicle
+
0.3
1.3
1.9
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
SI PHEV- Electricity
+
+
+
+
+
2.0
51.7
237.0
476.0
732.7
933.7
1,276.5
1,692.0
2,247.5
Fuel Cell Hydrogen
+
+
+
+
+
+
0.1
0.1
0.1
0.1
0.1
1.1
1.4
2.0
Light-Duty Trucks
72.7
87.7
168.2
352.7
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
Ethanol-Flex Fuel ICE
+
0.3
21.9
84.2
96.4
121.7
135.4
180.1
213.6
208.8
218.2
279.1
418.4
411.9
CNG ICE
+
0.1
5.3
9.6
9.1
8.0
8.6
8.9
8.7
7.6
6.6
5.8
8.9
6.5
CNG Bi-fuel
+
0.4
44.3
24.5
20.4
19.0
18.2
14.8
16.1
19.3
20.3
26.3
24.3
28.9
LPG ICE
21.0
24.9
25.9
10.5
12.1
9.7
9.6
5.9
6.3
7.3
7.5
7.3
7.9
8.4
LPG Bi-fuel
51.7
61.2
63.6
23.5
26.8
23.8
12.4
4.9
5.9
21.8
8.7
6.5
7.9
9.0
LNG
+
+
0.1
0.3
0.2
+
+
+
+
+
+
+
0.1
0.1
Biodiesel (BD100)
+
+
3.3
195.1
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
Electric Vehicle
+
0.8
3.8
4.9
4.6
4.3
3.1
6.2
18.4
32.5
35.0
271.8
533.4
847.9
SI PHEV - Electricity
+
+
+
+
+
+
+
+
+
0.4
8.2
45.7
104.4
213.4
Fuel Cell Hydrogen
+
+
+
+
+
+
0.3
0.2
0.2
0.3
0.3
+
+
+
Medium Duty Trucks
255.4
249.9
244.6
602.5
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
CNG ICE
+
+
0.8
6.4
5.7
5.6
7.5
8.9
9.3
10.4
11.7
12.5
13.9
14.9
CNG Bi-fuel
+
0.1
7.8
7.8
6.6
6.3
6.1
6.8
7.1
9.5
10.2
11.3
12.3
13.9
LPG ICE
215.6
210.8
192.5
36.9
33.0
29.0
27.1
25.6
23.6
22.7
17.9
16.0
14.8
12.1
LPG Bi-fuel
39.9
39.0
35.6
12.6
6.4
7.8
7.0
9.4
10.0
12.7
9.5
11.5
12.5
12.9
LNG
+
+
+
+
+
+
+
+
0.1
+
0.1
0.1
0.2
0.3
Biodiesel (BD100)
+
+
7.8
538.8
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
Heavy-Duty Trucks
104.4
102.0
115.4
1,270.4
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
Neat Ethanol ICE
+
+
+
3.6
5.7
9.1
12.6
15.0
20.2
23.9
11.1
7.3
CNG ICE
+
+
0.9
2.6
3.2
3.4
3.4
3.9
4.7
5.2
7.3
9.4
8.5
10.5
LPG ICE
98.1
95.9
87.5
45.2
39.9
33.0
34.7
22.5
22.2
18.0
16.8
15.4
13.6
11.5
LPG Bi-fuel
6.3
6.2
5.6
3.6
4.1
4.3
6.3
4.9
5.2
2.2
2.1
2.1
2.1
2.0
LNG
+
+
+
1.1
1.2
1.5
1.6
1.6
1.4
1.9
2.0
1.6
1.6
1.4
Biodiesel (BD100)
+
+
21.4
1,215.5
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
A-182 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Buses
20.0
38.7
144.3
633.3
664.5
673.1
761.9
754.5
823.8
834.7
921.8
922.8
988.9
996.2
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
CNG ICE
+
1.1
100.2
525.9
560.7
584.2
614.6
606.6
627.9
627.6
705.2
654.5
723.5
734.8
LPG ICE
13.2
12.7
11.5
10.7
7.2
6.5
3.9
3.8
4.0
4.4
3.2
4.4
5.2
4.9
LNG
0.4
8.5
22.3
38.3
34.7
35.5
38.1
39.7
28.4
36.9
36.3
17.5
10.7
6.8
Biodiesel (BD100)
+
+
4.9
51.8
57.5
42.5
100.4
101.0
160.0
159.3
168.8
236.7
227.1
218.9
Electric
+
1.1
5.2
6.5
4.4
4.5
4.5
3.0
3.1
3.6
3.9
7.2
19.2
27.8
Fuel Cell Hydrogen
+
+
+
+
+
+
0.3
0.3
0.3
0.3
0.8
1.1
2.2
2.5
Total VMT
456.3
538.6
778.0
3,076.1
3,242.8
2,847.7
7,333.5
7,863.3
12,345.7
13,283.5
14,938.1
20,465.3
21,704.1
24,485.8
1 + Does not exceed 0.05 million vehicle miles traveled
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
3 different range of fuel types.
4 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
5 incorporated into this year's Inventory and apply to the 2005 to 2018 time period. Source: Derived from Browning (2017), Browning (2018a), and EIA (2019h).
6
A-183
-------
1 Table A-103: Age Distribution by Vehicle/Fuel Type for On-Road Vehicles,3 2018
Vehicle
Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MC
0
7.1%
8.0%
6.6%
10.6%
8.3%
6.2%
7.1%
1
7.2%
8.0%
6.4%
10.7%
8.3%
6.0%
7.2%
2
7.1%
7.9%
6.4%
10.6%
8.3%
5.9%
7.0%
3
6.9%
7.6%
6.1%
10.4%
7.9%
5.7%
6.4%
4
6.8%
7.2%
5.6%
10.1%
7.5%
5.2%
5.9%
5
6.5%
6.7%
5.0%
9.6%
6.9%
4.6%
5.2%
6
6.2%
6.2%
4.5%
9.2%
6.5%
4.3%
4.7%
7
3.8%
4.0%
2.5%
5.6%
4.3%
2.6%
3.7%
8
4.2%
3.4%
1.7%
5.4%
2.4%
1.7%
3.4%
9
3.7%
2.5%
1.5%
3.5%
2.1%
2.1%
3.5%
10
4.6%
4.1%
2.8%
0.3%
4.9%
3.1%
6.2%
11
4.9%
4.1%
2.6%
0.2%
4.2%
6.0%
5.5%
12
4.5%
4.0%
3.6%
3.9%
5.2%
5.1%
5.2%
13
4.2%
3.9%
2.8%
2.5%
4.3%
4.6%
4.6%
14
3.5%
3.7%
3.4%
1.4%
3.6%
3.2%
3.9%
15
3.2%
3.2%
3.0%
1.6%
3.1%
2.8%
3.3%
16
2.8%
2.9%
2.9%
1.5%
2.5%
2.2%
2.9%
17
2.3%
2.4%
2.4%
0.9%
2.7%
2.9%
2.5%
18
2.1%
2.1%
4.6%
0.7%
1.4%
4.5%
2.0%
19
1.6%
1.7%
4.4%
0.4%
1.9%
3.5%
1.5%
20
1.2%
1.3%
1.8%
0.3%
0.7%
2.3%
1.3%
21
1.1%
1.1%
3.3%
0.1%
0.8%
2.2%
1.2%
22
0.8%
0.8%
2.0%
0.1%
0.6%
2.0%
1.1%
23
0.8%
0.7%
2.7%
0.1%
0.4%
2.4%
0.8%
24
0.6%
0.6%
2.1%
0.0%
0.3%
1.8%
0.9%
25
0.5%
0.4%
1.7%
0.0%
0.3%
1.3%
0.8%
26
0.4%
0.3%
1.3%
0.1%
0.2%
0.9%
0.6%
27
0.4%
0.3%
1.0%
0.1%
0.1%
0.9%
0.5%
28
0.3%
0.2%
1.4%
0.0%
0.1%
1.1%
0.4%
29
0.2%
0.2%
1.6%
0.0%
0.1%
1.0%
0.3%
30
0.3%
0.2%
2.3%
0.0%
0.1%
1.7%
0.3%
Total
100.0%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
aThe 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).
Note: This year's Inventory includes updated vehicle population data based on the MOVES 2014b Model.
Source: EPA (2018a).
Table A-104: Annual Average Vehicle Mileage Accumulation per Vehiclea (miles)
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MCb
0
13,472
15,227
18,016
13,472
15,227
41,829
7,674
1
13,217
14,941
18,020
13,216
14,941
41,312
4,098
2
12,940
14,618
18,019
12,940
14,618
41,269
3,100
3
12,645
14,265
18,020
12,645
14,265
41,294
2,563
4
12,333
13,884
17,060
12,333
13,884
38,929
2,218
5
12,007
13,479
16,098
12,007
13,479
36,677
1,972
6
11,668
13,054
15,137
11,668
13,054
35,323
1,788
7
11,318
12,613
13,200
11,318
12,613
39,722
1,642
8
10,961
12,159
11,312
10,961
12,159
38,764
1,519
9
10,596
11,698
10,763
10,596
11,698
39,671
1,420
10
10,228
11,230
11,023
10,228
11,230
27,025
1,335
11
9,858
10,763
9,104
9,858
10,763
35,890
1,258
A-184 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
12
9,488
10,298
8,983
9,488
10,298
29,535
1,197
13
9,119
9,841
7,466
9,119
9,841
27,406
1,136
14
8,756
9,395
7,032
8,756
9,395
21,970
1,082
15
8,398
8,963
6,011
8,398
8,963
20,632
1,036
16
8,049
8,550
5,199
8,049
8,550
16,976
998
17
7,710
8,159
4,776
7,710
8,159
15,723
959
18
7,383
7,795
5,245
7,383
7,795
15,380
921
19
7,071
7,461
4,925
7,071
7,461
13,941
890
20
6,775
7,161
4,518
6,775
7,161
13,410
859
21
6,500
6,899
4,042
6,500
6,899
9,821
836
22
6,244
6,679
3,801
6,244
6,679
10,258
813
23
6,011
6,505
3,761
6,011
6,505
8,437
767
24
5,804
6,380
3,344
5,804
6,380
7,162
721
25
5,623
6,307
3,338
5,623
6,307
6,644
675
26
5,472
6,293
2,649
5,472
6,293
5,957
622
27
5,352
6,293
2,638
5,352
6,293
5,343
576
28
5,266
6,293
2,419
5,265
6,293
4,347
545
29
5,214
6,293
2,267
5,214
6,293
3,360
506
30
5,214
6,293
2,153
5,214
6,293
3,235
468
1 aThe following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV
2 (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles),
3 and MC (motorcycles).
4 b Because of a lack of data, all motorcycles over 12 years old are considered to have the same emissions and travel characteristics, and
5 therefore are presented in aggregate.
6 Source: EPA (2018a).
7
8 Table A-105: VMT Distribution by Vehicle Age and Vehicle/Fuel Type,3 2018
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MC
0
8.88%
10.07%
11.11%
12.02%
10.36%
9.16%
24.91%
1
8.83%
9.88%
10.86%
11.95%
10.16%
8.76%
13.52%
2
8.59%
9.62%
10.76%
11.63%
9.88%
8.67%
9.95%
3
8.18%
9.00%
10.33%
11.07%
9.23%
8.33%
7.48%
4
7.81%
8.24%
9.02%
10.57%
8.45%
7.22%
5.94%
5
7.22%
7.43%
7.60%
9.78%
7.61%
6.05%
4.70%
6
6.70%
6.69%
6.46%
9.10%
6.87%
5.37%
3.81%
7
3.97%
4.19%
3.16%
5.39%
4.41%
3.74%
2.81%
8
4.26%
3.47%
1.77%
4.96%
2.40%
2.38%
2.34%
9
3.68%
2.41%
1.49%
3.11%
2.00%
2.96%
2.27%
10
4.41%
3.82%
2.93%
0.27%
4.53%
2.98%
3.78%
11
4.54%
3.67%
2.20%
0.18%
3.73%
7.69%
3.17%
12
3.96%
3.40%
3.04%
3.12%
4.34%
5.41%
2.86%
13
3.57%
3.21%
1.96%
1.95%
3.41%
4.53%
2.37%
14
2.86%
2.92%
2.27%
1.01%
2.77%
2.51%
1.92%
15
2.51%
2.38%
1.68%
1.12%
2.27%
2.08%
1.57%
16
2.08%
2.04%
1.43%
1.02%
1.75%
1.36%
1.33%
17
1.65%
1.62%
1.07%
0.56%
1.79%
1.65%
1.09%
18
1.46%
1.36%
2.28%
0.43%
0.86%
2.45%
0.83%
19
1.04%
1.07%
2.03%
0.22%
1.13%
1.75%
0.61%
20
0.77%
0.78%
0.77%
0.19%
0.39%
1.12%
0.50%
21
0.65%
0.64%
1.26%
0.07%
0.46%
0.77%
0.47%
22
0.49%
0.44%
0.70%
0.07%
0.33%
0.73%
0.40%
23
0.47%
0.40%
0.95%
0.05%
0.23%
0.72%
0.28%
24
0.34%
0.33%
0.66%
0.01%
0.13%
0.46%
0.31%
25
0.28%
0.23%
0.52%
0.02%
0.14%
0.31%
0.23%
26
0.22%
0.17%
0.32%
0.03%
0.12%
0.19%
0.18%
A-185
-------
27 0.18% 0.14% 0.25% 0.06% 0.07% 0.16% 0.13%
28 0.15% 0.13% 0.32% 0.02% 0.05% 0.17% 0.10%
29 0.12% 0.13% 0.35% 0.01% 0.05% 0.12% 0.07%
3 0 0.16% 0.11% 0.46% 0.00% 0.04% 0.19% 0.07%
Total 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%
1 aThe following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV
2 (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles),
3 and MC (motorcycles).
4 Note: Estimated by weighting data in by data in Table A-104. This year's Inventory includes updated vehicle population data based on
5 the MOVES 2014b. Model that affects this distribution.
A-186 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Table A-106: Fuel Consumption for Off-Road Sources by Fuel Type (million gallons unless otherwise noted)
Vehicle Type/Year
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Aircraft3
19,560
18,320
20,304
17,984
16,030
15,762
15,262
14,914
15,274
15,397
16,338
17,198
17,790
17,860
Aviation Gasoline
374
329
302
235
221
225
225
209
186
181
176
170
174
186
Jet Fuel
19,186
17,991
20,002
17,749
15,809
15,537
15,036
14,705
15,088
15,217
16,162
17,028
17,616
17,674
Commercial Aviationb
11,569
12,136
14,672
13,400
12,588
11,931
12,067
11,932
12,031
12,131
12,534
12,674
13,475
13,650
Ships and Boats
4,826
5,932
6,544
4,778
4,201
4,693
4,833
4,239
4,175
3,191
3,652
4,235
4,469
4,152
Diesel
1,156
1,661
1,882
1,384
1,395
1,361
1,641
1,389
1,414
1,284
1,881
1,680
1,593
1,498
Gasoline
1,611
1,626
1,636
1,514
1,498
1,446
1,401
1,372
1,349
1,323
1,325
1,335
1,344
1,352
Residual
2,060
2,646
3,027
1,880
1,308
1,886
1,791
1,477
1,413
584
445
1,219
1,532
1,302
Construction/Mining Equipment11
Diesel
4,317
4,718
5,181
6,175
5,885
5,727
5,650
5,533
5,447
5,313
5,200
5,483
5,978
6,262
Gasoline
472
437
357
610
583
678
634
651
1,100
710
367
375
375
375
CNG (million cubic feet)
5,082
5,463
6,032
6,708
6,378
6,219
6,121
5,957
5,802
5,598
5,430
5,629
6,018
6,204
LPG
22
24
27
28
27
26
25
24
24
23
22
23
25
26
Agricultural Equipment11
Diesel
3,514
3,400
3,278
4,111
3,938
3,942
3,876
3,932
3,900
3,925
3,862
3,760
3,728
3,732
Gasoline
813
927
652
634
676
692
799
875
655
644
159
168
168
168
CNG (million cubic feet)
1,758
1,712
1,678
1,796
1,677
1,647
1,600
1,611
1,588
1,590
1,561
1,517
1,503
1,502
LPG
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Rail
3,461
3,864
4,106
4,216
3,535
3,807
3,999
3,921
4,025
4,201
4,020
3,715
3,833
3,997
Diesel
3,461
3,864
4,106
4,216
3,535
3,807
3,999
3,921
4,025
4,201
4,020
3,715
3,833
3,997
Othere
Diesel
2,095
2,071
2,047
2,478
2,375
2,450
2,523
2,639
2,725
2,811
2,832
2,851
2,919
3,027
Gasoline
4,371
4,482
4,673
5,455
5,291
5,525
5,344
5,189
5,201
5,281
5,083
5,137
5,178
5,200
CNG (million cubic feet)
20,894
22,584
25,035
29,028
28,163
29,891
32,035
35,085
37,436
39,705
38,069
37,709
38,674
40,390
LPG
1,412
1,809
2,191
2,286
2,130
2,165
2,168
2,181
2,213
2,248
2,279
2,316
2,408
2,526
Total (gallons)
44,863
45,984
49,361
48,755
44,671
45,467
45,113
44,099
44,740
43,745
43,815
45,261
46,871
47,326
Total (million cubic feet)
27,735
29,759
32,745
37,532
36,218
37,757
39,755
42,653
44,826
46,893
45,060
44,854
46,194
48,097
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.
Note: In 2015, EPA incorporated the NONROAD2008 model into MOVES2014a. This year's Inventory uses the NONROAD component of MOVES2014bfor years 1999 through 2018.
Sources: AAR (2008 through 2018), APTA (2007 through 2017), BEA (1991 through 2017), Benson (2002 through 2004), DHS (2008), DOC (1991 through 2019), DESC (2018), DOE (1993 through
2017), DOT (1991 through 2018), EIA (2002), EIA (2007b), EIA (2019a, EIA (2007 through 2018), EIA (1991 through 2018), EPA (2019b), FAA (2019), Gaffney (2007), and Whorton (2006 through
2014).
A-187
-------
1
2
3
4
5
6
7
8
9
10
11
Table A-107: 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%
-
42% 52%
- Not Applicable.
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.
Sources: EPA (1998), EPA (2018d), and EPA (2019c).
Table A-108: 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%
-
1979-1980
20%
80%
.
1981
-
95%
5% - - - - - -
A-188 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
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%
201 8 - - - - - 7% - 38% 55%
- 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.
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.
Sources: EPA (1998), EPA (2018d), and EPA (2019c).
Table A-109: 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 LEV b 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%
-
1988-1989
60%
25%
15%
-
1990-1995
45%
30%
25%
-
1996
-
25%
10%
65%
1997
-
10%
5%
85%
1998
-
-
-
100%
A-189
-------
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%
201 8 - - - - - - 13% - 35% 52%
1 - Not Applicable.
2 a Detailed descriptions of emissions control technologies are provided in the following section of this Annex.
3 b The proportion of LEVs as a whole has decreased since 2000, as carmakers have been able to achieve greater emission reductions
4 with certain types of LEVs, such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a manufacturer can reduce the total
5 number of LEVs they need to build to meet a specified emission average for all of their vehicles in a given model year.
6 Note: In 2016, historical confidential vehicle sales data was re-evaluated to determine the engine technology assignments. First several
7 light-duty trucks were re-characterized as heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales
8 data. Second, which emission standards each vehicle type was assumed to have met were re-examined using confidential sales data.
9 Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and therefore
10 were not included in the engine technology breakouts. For this Inventory, HEVs are now classified as gasoline vehicles across the entire
11 time series.
12 Sources: EPA (1998), EPA (2018d), and EPA (2019c).
13
14 Table A-110: 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 2007-2018
Diesel Medium- and Heavy-Duty Trucks and Buses
Uncontrolled 1960-1989
Moderate control 1990-2003
Advanced control 2004-2006
Aftertreatment 2007-2018
Motorcycles
Uncontrolled 1960-1995
Non-catalyst controls 1996-2018
15 Note: Detailed descriptions of emissions control technologies are provided in the following section of this Annex.
16 Source: EPA (1998) and Browning (2005).
17
18 Table A-lll: Emission Factors for CH4 and N2Q for On-Road Vehicles
Vehicle Type/Control N20 CH4
Technology (g/mi) (g/mi)
Gasoline Passenger Cars
EPA Tier 3 0.0015 0.0055
A-190 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
ARB LEV III 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 LEV III 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 LEV III 0.0136 0.0411
EPA Tier 2 0.0015 0.0297
ARB LEV II 0.0015 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 0.0015 0.0010
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
Non-Catalyst Control 0.0069 0.0672
Uncontrolled 0.0087 0.0899
1 aThe categories "EPA Tier 0" and "EPA Tier 1" were substituted for the early three-way catalyst and advanced three-way catalyst
2 categories, respectively, as defined in the 2006IPCC Guidelines. Detailed descriptions of emissions control technologies are provided
3 at the end of this Annex.
4 Source: ICF (2006b and 2017a).
A-191
-------
Table A-112: Emission Factors for N2Q for Alternative Fuel Vehicles (g/mi)
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Light-Duty Cars
Methanol-Flex Fuel ICE
0.04
0.035
0.034
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
Ethanol-Flex Fuel ICE
0.04
0.035
0.034
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
CNG ICE
0.02
0.021
0.027
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
CNG Bi-fuel
0.02
0.021
0.027
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
LPG ICE
0.02
0.021
0.027
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
LPG Bi-fuel
0.02
0.021
0.027
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.007
0.006
Biodiesel (BD100)
0.00
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
Light-Duty Trucks
Ethanol-Flex Fuel ICE
0.068
0.069
0.072
0.031
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.012
0.011
CNG ICE
0.041
0.041
0.058
0.031
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.012
0.011
CNG Bi-fuel
0.041
0.041
0.058
0.031
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.012
0.011
LPG ICE
0.041
0.041
0.058
0.031
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.015
0.014
0.013
LPG Bi-fuel
0.041
0.041
0.058
0.031
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.015
0.014
0.013
LNG
0.041
0.041
0.058
0.031
0.024
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.012
0.011
Biodiesel (BD100)
0.001
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
Medium Duty Trucks
CNG ICE
0.002
0.002
0.003
0.003
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
CNG Bi-fuel
0.002
0.002
0.003
0.052
0.043
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
LPG ICE
0.055
0.055
0.069
0.052
0.043
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
0.003
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
LNG
0.002
0.002
0.003
0.003
0.003
0.003
0.011
0.019
0.027
0.035
0.043
0.043
0.043
0.043
Biodiesel (BD100)
0.002
0.002
0.003
0.003
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
Heavy-Duty T rucks
0.040
Neat Methanol ICE
0.040
0.040
0.049
0.041
0.034
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
0.041
0.034
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
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
LPG ICE
1.229
0.045
0.049
0.039
0.032
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
0.039
0.032
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
0.002
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
0.002
0.010
0.018
0.027
0.035
0.043
0.043
0.043
0.043
Buses
Neat Methanol ICE
0.045
0.045
0.058
0.048
0.040
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
0.048
0.040
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
0.002
0.001
0.001
0.001
0.001
0.001
0.001
LPG ICE
0.051
0.051
0.058
0.046
0.038
0.030
0.028
0.025
0.022
0.020
0.017
0.017
0.017
0.017
A-192 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
LNG
Biodiesel (BD100)
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.011
0.002
0.019
0.001
0.027
0.001
0.035
0.001
0.043
0.001
0.043
0.001
0.043
0.001
0.043
Source: Developed by ICF (Browning 2017) using ANL (2018)
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.
Table A-113: Emission Factors for CH4 for Alternative Fuel Vehicles (g/mi)
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Light-Duty Cars
Methanol-Flex Fuel ICE
0.034
0.034
0.019
0.014
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
0.008
0.008
Ethanol-Flex Fuel ICE
0.034
0.034
0.019
0.014
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
0.008
0.008
CNG ICE
0.489
0.489
0.249
0.154
0.153
0.153
0.139
0.126
0.113
0.100
0.086
0.085
0.083
0.082
CNG Bi-fuel
0.489
0.489
0.249
0.154
0.153
0.153
0.139
0.126
0.113
0.100
0.086
0.085
0.083
0.082
LPG ICE
0.049
0.049
0.025
0.015
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
0.008
0.008
LPG Bi-fuel
0.049
0.049
0.025
0.015
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
0.008
0.008
Biodiesel (BD100)
0.002
0.002
0.002
0.001
0.001
0.001
0.007
0.013
0.018
0.024
0.030
0.019
0.030
0.030
Light-Duty Trucks
Ethanol-Flex Fuel ICE
0.051
0.051
0.053
0.033
0.033
0.033
0.029
0.025
0.021
0.017
0.013
0.013
0.013
0.012
CNG ICE
0.728
0.725
0.709
0.366
0.349
0.332
0.292
0.251
0.210
0.170
0.129
0.127
0.125
0.123
CNG Bi-fuel
0.728
0.725
0.709
0.366
0.349
0.332
0.292
0.251
0.210
0.170
0.129
0.127
0.125
0.123
LPG ICE
0.073
0.072
0.071
0.037
0.035
0.033
0.029
0.025
0.021
0.017
0.013
0.013
0.013
0.012
LPG Bi-fuel
0.073
0.072
0.071
0.037
0.035
0.033
0.029
0.025
0.021
0.017
0.013
0.013
0.013
0.012
LNG
0.728
0.725
0.709
0.366
0.349
0.332
0.292
0.251
0.210
0.170
0.129
0.127
0.125
0.123
Biodiesel (BD100)
0.005
0.005
0.005
0.002
0.002
0.001
0.007
0.012
0.018
0.023
0.029
0.029
0.029
0.029
Medium Duty Trucks
CNG ICE
6.800
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
CNG Bi-fuel
6.800
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
LPG ICE
0.262
0.262
0.248
0.024
0.023
0.021
0.020
0.018
0.017
0.016
0.014
0.014
0.014
0.014
LPG Bi-fuel
0.262
0.262
0.248
0.024
0.023
0.021
0.020
0.018
0.017
0.016
0.014
0.014
0.014
0.014
LNG
6.800
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
Biodiesel (BD100)
0.004
0.004
0.004
0.002
0.002
0.002
0.004
0.005
0.006
0.008
0.009
0.009
0.009
0.009
Heavy-Duty T rucks
Neat Methanol ICE
0.296
0.296
0.095
0.121
0.136
0.151
0.136
0.120
0.105
0.090
0.075
0.075
0.075
0.075
Neat Ethanol ICE
0.296
0.296
0.095
0.121
0.136
0.151
0.136
0.120
0.105
0.090
0.075
0.075
0.075
0.075
CNG ICE
4.100
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
A-193
-------
LPG ICE
0.158
0.158
0.149
0.015
0.014
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
0.015
0.014
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
4.100
4.020
3.940
3.860
3.780
3.700
3.700
3.700
3.700
Biodiesel (BD100)
0.012
0.012
0.005
0.005
0.005
0.005
0.006
0.007
0.007
0.008
0.009
0.009
0.009
0.009
tuses
Neat Methanol ICE
0.086
0.086
0.067
0.062
0.068
0.075
0.067
0.060
0.052
0.045
0.037
0.032
0.027
0.022
Neat Ethanol ICE
0.086
0.086
0.067
0.062
0.068
0.075
0.067
0.060
0.052
0.045
0.037
0.032
0.027
0.022
18.80
18.800
18.800
18.800
18.800
18.800
17.040
15.280
13.520
11.760
10.000
10.000
10.00
10.000
CNG ICE
0
0
LPG ICE
0.725
0.725
0.686
0.068
0.063
0.058
0.053
0.048
0.044
0.039
0.034
0.034
0.034
0.034
18.80
18.800
18.800
18.800
18.800
18.800
17.040
15.280
13.520
11.760
10.000
10.000
10.00
10.000
LNG
0
0
Biodiesel (BD100)
0.004
0.004
0.003
0.003
0.002
0.002
0.004
0.005
0.006
0.008
0.009
0.009
0.009
0.009
1 Source: Developed by ICF (Browning 2017) using ANL (2018).
2 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
3 electric vehicles (PHEVs) are not included in this table.
4
5
6 Table A-114: Emission Factors for N2Q Emissions from Non-Road Mobile Combustion (g/kg fuel)
1990 1995 2000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Ships and Boats
Residual Fuel Oil 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155
Gasoline
2 Stroke 0.018 0.018 0.018 0.020 0.020 0.020 0.021 0.021 0.021 0.022 0.022 0.022 0.023 0.023
4 Stroke 0.075 0.075 0.076 0.078 0.079 0.079 0.080 0.080 0.081 0.081 0.082 0.082 0.083 0.083
Distillate Fuel Oil 0.156 0.156 0.156 0.156 0.156 0.156 0.156 0.156 0.156 0.156 0.156 0.156 0.156 0.156
Rail
Diesel 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080
Aircraft
Jet Fuel 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100
Aviation Gasoline 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040
Agricultural Equipment3
Gasoline-Equipment
2 Stroke 0.012 0.013 0.014 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020
4 Stroke 0.064 0.065 0.066 0.073 0.073 0.074 0.074 0.075 0.075 0.076 0.076 0.076 0.077 0.077
Gasoline-Off-road Trucks 0.064 0.065 0.066 0.073 0.073 0.074 0.074 0.075 0.075 0.076 0.076 0.076 0.077 0.077
A-194 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Diesel-Equipment
0.152
0.152
0.152
0.152
0.152
0.152
0.152
0.152
0.152
0.152
0.152
0.152
0.152
0.152
Diesel-Off-Road Trucks
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
CNG
0.162
0.162
0.162
0.187
0.191
0.195
0.198
0.199
0.200
0.201
0.202
0.202
0.202
0.202
LPG
0.162
0.162
0.162
0.178
0.180
0.182
0.184
0.185
0.186
0.187
0.188
0.189
0.189
0.190
Construction/Mining Equipment'5
Gasoline-Equipment
2 Stroke
0.017
0.018
0.018
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
0.026
4 Stroke
0.054
0.057
0.060
0.068
0.069
0.069
0.069
0.070
0.070
0.070
0.070
0.070
0.070
0.070
Gasoline-Off-road Trucks
0.054
0.057
0.060
0.068
0.069
0.069
0.069
0.070
0.070
0.070
0.070
0.070
0.070
0.070
Diesel-Equipment
0.148
0.148
0.148
0.147
0.147
0.147
0.147
0.148
0.148
0.148
0.148
0.148
0.148
0.148
Diesel-Off-Road Trucks
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
CNG
0.162
0.162
0.162
0.171
0.171
0.173
0.175
0.176
0.178
0.179
0.181
0.184
0.188
0.191
LPG
0.162
0.162
0.162
0.179
0.181
0.184
0.186
0.188
0.190
0.192
0.193
0.195
0.197
0.198
Lawn and Garden Equipment
Gasoline-Residential
2 Stroke
0.012
0.012
0.013
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.019
0.019
4 Stroke
0.047
0.050
0.053
0.062
0.062
0.062
0.063
0.063
0.063
0.063
0.063
0.063
0.063
0.063
Gasoline-Commercial
2 Stroke
0.014
0.015
0.016
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
0.022
4 Stroke
0.050
0.055
0.059
0.065
0.065
0.065
0.066
0.066
0.066
0.066
0.066
0.066
0.066
0.066
Diesel-Residential
Diesel-Commercial
0.146
0.146
0.146
0.146
0.146
0.146
0.146
0.146
0.146
0.146
0.146
0.146
0.146
0.146
LPG
0.162
0.162
0.162
0.185
0.189
0.193
0.196
0.198
0.200
0.201
0.201
0.202
0.202
0.202
Airport Equipment
Gasoline
4 Stroke
0.071
0.073
0.075
0.086
0.087
0.088
0.089
0.089
0.089
0.090
0.090
0.090
0.090
0.090
Diesel
0.154
0.154
0.154
0.154
0.154
0.154
0.154
0.154
0.154
0.154
0.154
0.154
0.154
0.154
LPG
0.162
0.162
0.162
0.188
0.191
0.194
0.197
0.199
0.200
0.201
0.202
0.202
0.202
0.202
Industrial/Commercial Equipment
Gasoline
2 Stroke
0.012
0.013
0.014
0.020
0.020
0.020
0.020
0.020
0.020
0.020
0.020
0.020
0.020
0.020
4 Stroke
0.054
0.057
0.060
0.068
0.069
0.069
0.070
0.070
0.070
0.070
0.070
0.071
0.071
0.071
Diesel
0.146
0.145
0.145
0.146
0.146
0.146
0.146
0.147
0.147
0.147
0.147
0.147
0.147
0.147
CNG
0.162
0.162
0.162
0.190
0.192
0.195
0.197
0.199
0.200
0.200
0.201
0.201
0.201
0.201
LPG
0.162
0.162
0.162
0.183
0.185
0.189
0.193
0.197
0.198
0.199
0.200
0.201
0.201
0.202
A-195
-------
Logging Equipment
Gasoline
2 Stroke
0.018
0.018
0.019
0.027
0.027
0.027
0.027
0.027
0.027
0.027
0.027
0.027
0.027
0.027
4 Stroke
0.053
0.054
0.055
0.061
0.061
0.062
0.062
0.063
0.064
0.065
0.065
0.066
0.066
0.066
Diesel
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
0.155
Railroad Equipment
Gasoline
4 Stroke
0.052
0.055
0.057
0.066
0.066
0.066
0.067
0.067
0.067
0.067
0.067
0.067
0.067
0.067
Diesel
0.131
0.131
0.131
0.131
0.131
0.131
0.131
0.131
0.131
0.131
0.131
0.131
0.131
0.131
LPG
0.162
0.162
0.162
0.177
0.178
0.179
0.184
0.186
0.189
0.191
0.193
0.194
0.197
0.198
Recreational Equipment
Gasoline
2 Stroke
0.012
0.012
0.012
0.013
0.013
0.013
0.013
0.013
0.014
0.014
0.014
0.014
0.014
0.012
4 Stroke
0.075
0.076
0.078
0.082
0.082
0.083
0.083
0.083
0.083
0.083
0.083
0.083
0.083
0.068
Diesel
0.127
0.127
0.127
0.127
0.127
0.127
0.127
0.127
0.127
0.127
0.127
0.127
0.127
0.127
LPG
0.162
0.162
0.162
0.169
0.171
0.172
0.174
0.175
0.176
0.178
0.179
0.181
0.182
0.184
1 a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
2 b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
3 Source: IPCC (2006) and Browning, L (2018b), EPA (2018a).
4
5
6
7
Table A-115: Emission Factors for CH4 Emissions from Non-Road Mobile Combustion (g/kg fuel)
1990 1995 2000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Ships and Boats
Residual Fuel Oil 0.026 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155 0.155
Gasoline
2 Stroke 5.355 5.259 5.097 4.100 3.948 3.847 3.771 3.676 3.615 3.558 3.509 3.467 3.436 3.419
4 Stroke 3.46S 3.334 3.202 2.739 2.626 2.523 2.464 2.335 2.250 2.169 2.059 1.947 1.844 1.749
Distillate Fuel Oil 0.007 0.007 0.007 0.027 0.035 0.039 0.045 0.051 0.058 0.064 0.074 0.083 0.090 0.097
Rail
Diesel 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250
A-196 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Aircraft
Jet Fuelc
0.000
0.000
0.000
0.000
0.000
0.000
Aviation Gasoline
2.640
2.640
2.640
2.640
2.640
2.640
Agricultural
Equipment3
Gasoline-Equipment
2 Stroke
9.981
9.308
8.669
4.859
4.751
4.681
4 Stroke
7.579
6.957
6.289
4.372
4.160
3.857
Gasoline-Off-road
4.372
4.160
3.857
Trucks
7.579
6.957
6.289
Diesel-Equipment
0.046
0.042
0.039
0.086
0.088
0.092
Diesel-Off-Road
0.067
0.072
0.078
Trucks
0.021
0.022
0.025
109.948
94.762
73.107
CNG
190.689
190.694
190.543
LPG
2.635
2.635
2.633
1.908
1.830
1.685
Construction/Mining
Equipment'5
Gasoline-Equipment
2 Stroke
9.502
8.575
7.813
4.680
4.534
4.484
4 Stroke
11.453
9.310
7.341
4.763
4.253
3.882
Gasoline-Off-road
11.453
9.310
7.341
4.763
4.253
3.882
Trucks
Diesel-Equipment
0.033
0.035
0.039
0.102
0.106
0.111
Diesel-Off-Road
0.021
0.022
0.025
0.067
0.072
0.078
Trucks
187.218
187.298
163.056
162.937
158.345
CNG
186.731
LPG
2.630
2.631
2.622
1.921
1.794
1.621
Lawn and Garden Equipment
Gasoline-Residential
2 Stroke
10.178
9.601
8.926
6.392
6.143
6.027
4 Stroke
10.653
9.628
8.431
6.052
5.563
5.091
Gasoline-Commercial
2 Stroke
9.951
9.088
8.356
5.771
5.671
5.611
4 Stroke
9.SS3
8.724
7.649
5.462
4.784
4.222
Diesel-Commercial
0.037
0.038
0.039
0.085
0.091
0.098
LPG
2.645
2.645
2.639
1.595
1.351
1.094
0.000 0.000
2.640 2.640
4.680 4.649
3.682 3.362
3.682 3.362
0.094 0.095
0.077 0.075
57.129 43.001
1.578 1.446
4.479 4.453
3.458 2.902
3.458 2.902
0.109 0.108
0.077 0.075
151.900 146.586
1.444 1.279
5.983 5.926
4.681 4.081
5.623 5.579
3.901 3.295
0.102 0.106
0.841 0.650
0.000 0.000
2.640 2.640
4.654 4.653
3.198 3.018
3.198 3.018
0.095 0.094
0.074 0.070
31.016 23.342
1.348 1.257
4.452 4.453
2.588 2.366
2.588 2.366
0.104 0.099
0.074 0.070
140.610 135.182
1.138 1.018
5.918 5.916
3.628 3.272
5.574 5.580
2.775 2.430
0.108 0.108
0.494 0.362
0.000 0.000
2.640 2.640
4.661 4.674
2.896 2.813
2.896 2.813
0.093 0.093
0.065 0.057
18.978 15.995
1.206 1.171
4.452 4.445
2.221 2.106
2.221 2.106
0.095 0.084
0.065 0.057
128.314 113.324
0.895 0.753
5.913 5.911
2.943 2.641
5.582 5.580
2.256 2.159
0.108 0.107
0.286 0.233
0.000 0.000
2.640 2.640
4.654 4.644
2.707 2.594
2.707 2.594
0.090 0.087
0.049 0.040
13.841 12.660
1.120 1.066
4.445 4.451
2.036 2.001
2.036 2.001
0.071 0.062
0.049 0.040
94.767 80.043
0.612 0.512
5.910 5.909
2.408 2.278
5.579 5.579
2.114 2.093
0.105 0.102
0.195 0.169
A-197
-------
Airport Equipment
Gasoline
4 Stroke
9.068
7.664
6.531
3.054
2.772
2.535
2.250
1.368
1.222
1.077
1.005
0.958
0.938
0.926
Diesel
0.034
0.032
0.031
0.085
0.089
0.093
0.092
0.092
0.091
0.087
0.080
0.070
0.061
0.053
LPG
2.631
2.632
2.628
1.386
1.200
1.024
0.819
0.651
0.488
0.345
0.262
0.210
0.181
0.163
Industrial/Commercial
Equipment
Gasoline
2 Stroke
10.429
9.648
9.019
5.583
5.538
5.492
5.492
5.447
5.440
5.435
5.432
5.429
5.425
5.424
4 Stroke
11.661
9.547
7.613
4.739
4.170
3.737
3.410
2.838
2.495
2.278
2.141
2.051
1.999
1.964
Diesel
0.037
0.038
0.041
0.116
0.118
0.120
0.115
0.110
0.105
0.098
0.092
0.086
0.078
0.071
CNG
191.224
190.378
189.512
78.830
68.724
55.882
44.440
33.735
27.918
23.310
20.658
18.843
17.220
15.851
LPG
2.584
2.590
2.597
1.675
1.534
1.283
1.034
0.775
0.612
0.474
0.358
0.297
0.248
0.212
Logging Equipment
Gasoline
2 Stroke
9.493
8.567
7.825
4.391
4.357
4.335
4.335
4.309
4.309
4.309
4.309
4.309
4.309
4.309
4 Stroke
8.155
7.486
6.756
4.902
4.752
4.609
4.433
3.982
3.565
3.136
2.791
2.620
2.503
2.404
Diesel
0.021
0.028
0.035
0.121
0.131
0.126
0.106
0.092
0.084
0.077
0.068
0.055
0.039
0.030
Railroad Equipment
Gasoline
4 Stroke
10.361
8.503
6.756
4.222
3.908
3.579
3.258
2.891
2.594
2.361
2.208
2.152
2.101
2.070
Diesel
0.056
0.057
0.059
0.144
0.147
0.149
0.145
0.146
0.145
0.147
0.147
0.147
0.143
0.139
LPG
2.473
2.513
2.563
1.956
1.930
1.849
1.547
1.393
1.210
1.115
0.990
0.893
0.702
0.586
Recreational
Equipment
Gasoline
2 Stroke
4.6S2
4.634
4.592
4.183
4.025
3.886
3.762
3.608
3.474
3.338
3.199
3.060
2.925
2.798
4 Stroke
8.646
7.628
6.781
4.825
4.567
4.331
3.898
3.634
3.483
3.373
3.254
3.167
3.093
3.027
Diesel
0.079
0.077
0.076
0.123
0.128
0.133
0.133
0.134
0.135
0.134
0.134
0.132
0.130
0.128
LPG
2.592
2.593
2.595
2.281
2.203
2.122
2.044
1.962
1.880
1.798
1.713
1.626
1.540
1.452
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 Emissions of CH4 from jet fuels have been zeroed out across the time series. Recent research indicates that modern aircraft jet engines are typically net consumers of methane
(Santoni et al. 2011). Methane is emitted at low power and idle operation, but at higher power modes aircraft engines consumer methane. Over the range of engine operating modes,
aircraft engines are net consumers of methane on average. Based on this data, CH4 emissions factors for jet aircraft were changed to zero in this year's Inventory to reflect the latest
emissions testing data.
Source: IPCC (2006) and Browning, L (2018b), EPA (2018a).
A-198 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
Table A-116: NOx Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Gasoline On-Road
5,746
4,560
3,812
3,317
2,966
2,724
2,805
2,647
2,489
2,332
2,124
1,954
1,766
1,577
Passenger Cars
3,847
2,752
2,084
1,810
1,618
1,486
1,530
1,444
1,358
1,272
1,159
1,066
963
860
Light-Duty Trucks
1,364
1,325
1,303
1,147
1,026
942
970
915
861
806
735
676
611
545
Medium- and Heavy-
Duty Trucks and Buses
515
469
411
348
311
286
294
278
261
245
223
205
185
165
Motorcycles
20
14
13
12
11
10
10
10
9
9
8
7
6
6
Diesel On-Road
2,956
3,493
3,803
2,980
2,665
2,448
2,520
2,379
2,237
2,095
1,908
1,756
1,586
1,417
Passenger Cars
39
19
7
5
5
4
4
4
4
4
3
3
3
2
Light-Duty Trucks
20
12
6
5
4
4
4
4
4
3
3
3
3
2
Medium- and Heavy-
Duty Trucks and Buses
2,897
3,462
3,791
2,970
2,656
2,439
2,512
2,370
2,229
2,088
1,902
1,750
1,581
1,412
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
2,226
2,166
2,118
1,968
1,883
1,797
1,712
1,707
1,703
1,699
1,695
Ships and Boats
402
488
506
460
448
438
407
389
372
354
353
352
351
351
Rail
338
433
451
411
400
391
363
348
332
316
315
314
314
313
Aircraft15
25
31
40
33
32
32
29
28
27
26
26
25
25
25
Agricultural Equipment0
437
478
484
402
392
383
356
340
325
309
309
308
307
306
Construction/Mining
Equipment
641
697
697
578
563
550
511
489
467
445
444
442
441
440
Othere
318
357
407
341
332
324
301
288
275
262
261
261
260
259
Total
10,862
10,536
10,199
8,523
7,797
7,290
7,294
6,909
6,523
6,138
5,740
5,413
5,051
4,689
IE (Included Elsewhere)
a NOx emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial
equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
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.
Table A-117: CO Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Gasoline On-Road 98,328 74,673
Passenger Cars 60,757 42,065
Light-Duty Trucks 29,237 27,048
60,657 29,626 24,515 25,235 24,442 23,573 22,704 21,834 20,871 18,532 16,881 15,230
32,867 16,506 13,659 14,060 13,618 13,134 12,649 12,165 11,628 10,325 9,405 8,485
24,532 11,792 9,758 10,044 9,729 9,383 9,037 8,690 8,307 7,376 6,719 6,062
A-199
-------
1
2
3
4
5
6
7
8
9
10
11
12
Medium- and Heavy-
Duty Trucks and Buses
8,093
5,404
3,104
1,259
1,042
1,073
1,039
1,002
965
928
887
788
718
647
Motorcycles
240
155
154
69
57
58
57
55
53
51
48
43
39
35
Diesel On-Road
1,696
1,424
1,088
454
376
387
375
361
348
335
320
284
259
233
Passenger Cars
35
18
7
3
3
3
3
2
2
2
2
2
2
2
Light-Duty Trucks
22
16
6
3
2
2
2
2
2
2
2
2
2
1
Medium- and Heavy-
Duty Trucks and Buses
1,639
1,391
1,075
448
371
382
370
357
343
330
316
280
255
230
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
16,137
14,365
13,853
13,488
12,981
12,474
11,966
11,968
11,970
11,972
11,974
Ships and Boats
1,559
1,781
1,825
1,327
1,182
1,140
1,109
1,068
1,026
984
984
985
985
985
Rail
85
93
90
65
58
56
54
52
50
48
48
48
48
48
Aircraft15
217
224
245
169
151
145
141
136
131
125
126
126
126
126
Agricultural Equipment0
581
628
626
450
401
386
376
362
348
334
334
334
334
334
Construction/Mining
Equipment
1,090
1,132
1,047
755
672
648
631
607
583
560
560
560
560
560
Other0
15,805
17,676
17,981
13,371
11,903
11,479
11,176
10,756
10,335
9,915
9,916
9,918
9,920
9,922
Total
119,360
97,630
83,559
46,217
39,256
39,475
38,305
36,915
35,525
34,135
33,159
30,786
29,112
27,438
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.
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.
Table A-118: NMVOCs Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Gasoline On-Road
8,110
5,819
4,615
2,641
2,384
2,393
2,485
2,342
2,200
2,058
1,930
1,725
1,558
1,392
Passenger Cars
5,120
3,394
2,610
1,475
1,332
1,336
1,388
1,308
1,229
1,149
1,078
963
870
111
Light-Duty Trucks
2,374
2,019
1,750
1,025
926
929
965
910
854
799
750
670
605
541
Medium- and Heavy-Duty
Trucks and Buses
575
382
232
127
115
115
120
113
106
99
93
83
75
67
Motorcycles
42
24
23
14
12
12
13
12
11
11
10
9
8
7
Diesel On-Road
406
304
216
128
115
116
120
113
106
100
93
83
75
67
Passenger Cars
16
8
3
2
2
2
2
2
2
1
1
1
1
1
A-200 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Light-Duty Trucks
14
9
4
2
2
2
2
2
2
2
2
1
1
1
Medium- and Heavy-Duty
Trucks and Buses
377
286
209
124
112
112
116
110
103
96
90
81
73
65
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
2,310
2,150
2,082
1,957
1,837
1,717
1,597
1,565
1,534
1,503
1,471
Ships and Boats
608
739
744
709
660
639
600
564
527
490
480
471
461
451
Rail
33
36
35
34
32
31
29
27
26
24
23
23
22
22
Aircraft15
28
28
24
19
17
17
16
15
14
13
13
12
12
12
Agricultural Equipment0
85
86
76
70
65
63
60
56
52
49
48
47
46
45
Construction/Mining
Equipment
149
152
130
121
113
109
103
96
90
84
82
81
79
77
Othere
1,512
1,580
1,390
1,356
1,263
1,223
1,149
1,079
1,008
938
919
901
882
864
Total
10,932
8,745
7,230
5,078
4,650
4,591
4,562
4,293
4,023
3,754
3,589
3,342
3,137
2,931
1 IE (Included Elsewhere)
2 a NMVOC emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
3 b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
4 c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
5 d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
6 e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial
7 equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
8 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
9 rounding.
A-201
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
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-107 through Table A-110 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, and was the most
common technology in gasoline passenger cars and light-duty gasoline trucks made from 1975 to 1980 (cars) and 1975 to
1985 (trucks). This technology was also used in some heavy-duty gasoline vehicles between 1982 and 1997. The two-way
catalytic converter oxidizes HC and CO, significantly reducing emissions over 80 percent beyond non-catalyst-system
capacity. One reason unleaded gasoline was introduced in 1975 was due to the fact that oxidation catalysts cannot function
properly with leaded gasoline.
EPA Tier 0
This emission standard from the Clean Air Act was met through the implementation of early "three-way"
catalysts, therefore this technology was used in gasoline passenger cars and light-duty gasoline trucks sold beginning in
the early 1980s, and remained common until 1994. This more sophisticated emission control system improves the
efficiency of the catalyst by converting CO and HC to 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, and applied to light-duty gasoline vehicles beginning in 1994. These advanced
emission control systems included advanced three-way catalysts, electronically controlled fuel injection and ignition
timing, EGR, and air injection.
A-202 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
EPA Tier 2
This emission standard was specified in the 1990 amendments to the Clean Air Act, limiting passenger car NOx
emissions to 0.07 g/mi on average and aligning emissions standards for passenger cars and light-duty trucks.
Manufacturers can meet this average emission level by producing vehicles in 11 emission "Bins," the three highest of which
expire in 2006. These new emission levels represent a 77 to 95 percent reduction in emissions from the EPA Tier 1 standard
set in 1994. Emission reductions were met through the use of more advanced emission control systems and lower sulfur
fuels and are applied to vehicles beginning in 2004. These advanced emission control systems include improved
combustion, advanced three-way catalysts, electronically controlled fuel injection and ignition timing, EGR, and air
injection.
EPA Tier 3
These standards begin in 2017 and are fully phased in by 2025, although some initial 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 LEVI Is 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 initial 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.
A-203
-------
1 Advanced control
2 EGR and modern electronic control of the fuel injection system are designated as advanced control technologies.
3 These technologies provide diesel vehicles with the level of emission control necessary to comply with standards in place
4 from 1996 through 2006.
5 Aftertreatment
6 Use of diesel particulate filters (DPFs), oxidation catalysts and NOx absorbers or selective catalytic reduction (SCR)
7 systems are designated as aftertreatment control. These technologies provide diesel vehicles with a level of emission
8 control necessary to comply with standards in place from 2007 on.
9 Supplemental Information on GHG Emissions from Transportation and Other Mobile Sources
10 This section of this Annex includes supplemental information on the contribution of transportation and other
11 mobile sources to U.S. greenhouse gas emissions. In the main body of the Inventory report, emission estimates are
12 generally presented by greenhouse gas, with separate discussions of the methodologies used to estimate C02, N20, CH4,
13 and HFC emissions. Although the Inventory is not required to provide detail beyond what is contained in the body of this
14 report, the IPCC allows presentation of additional data and detail on emission sources. The purpose of this sub-annex,
15 within the Annex that details the calculation methods and data used for non-C02 calculations, is to provide all
16 transportation estimates presented throughout the report in one place.
17 This section of this Annex reports total greenhouse gas emissions from transportation and other (non-
18 transportation) mobile sources in C02 equivalents, with information on the contribution by greenhouse gas and by mode,
19 vehicle type, and fuel type. In order to calculate these figures, additional analyses were conducted to develop estimates
20 ofC02from non-transportation mobile sources (e.g., agricultural equipment, construction/mining equipment, recreational
21 vehicles), and to provide more detailed breakdowns of emissions by source.
22 Estimation of CO2 from Non-Transportation Mobile Sources
23 The estimates of N20 and CH4 from fuel combustion presented in the Energy chapter of the Inventory include
24 both transportation sources and other mobile sources. Other mobile sources include construction/mining equipment,
25 agricultural equipment, vehicles used off-road, and other sources that have utility associated with their movement but do
26 not have a primary purpose of transporting people or goods (e.g., snowmobiles, riding lawnmowers, etc.). Estimates of
27 C02 from non-transportation mobile sources, based on EIA fuel consumption estimates, are included in the industrial and
28 commercial sectors. In order to provide comparable information on transportation and mobile sources, Table A-119
29 provides estimates of C02 from these other mobile sources, developed from EPA's NONROAD components of the
30 MOVES2014b model and FHWA's Highway Statistics. These other mobile source estimates were developed using the same
31 fuel consumption data utilized in developing the N20 and CH4 estimates (see Table A-106). Note that the method used to
32 estimate fuel consumption volumes for C02 emissions from non-transportation mobile sources for the supplemental
33 information presented in Table A-119, Table A-121, and Table A-122 differs from the method used to estimate fuel
34 consumption volumes for C02 in the industrial and commercial sectors in this Inventory, which include C02 emissions from
35 all non-transportation mobile sources (see Section 3.1 for a discussion of that methodology).
Table A-119: CP2 Emissions from Non-Transportation Mobile Sources (MMT CP2 Eq.)
Fuel Type/
Vehicle Type
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Agricultural
Equipment3
43.4
43.1
39.4
47.4
46.0
46.2
46.4
47.6
45.4
45.5
40.7
39.7
39.4
39.5
Construction
/Mining
Equipment15
48.9
52.6
56.7
68.7
65.4
64.6
63.4
62.3
65.3
60.5
56.4
59.4
64.4
67.4
Other
Sources0
69.2
71.6
75.1
86.2
82.6
85.4
84.5
84.7
86.0
87.8
86.4
87.1
88.6
91.8
Total
161.5
167.3
171.2
202.3
194.0
196.2
194.3
194.6
196.6
193.8
183.6
186.3
192.5
198.6
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.
A-204 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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.
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). In 2015, EPA incorporated the
NONROAD2008 model into MOVES2014a. The current Inventory uses the NONROAD component of MOVES2014bfor years 1999 through
2018.
1 Estimation of HFC Emissions from Transportation Sources
2 In addition to C02, N20 and CH4 emissions, transportation sources also result in emissions of HFCs. HFCs are
3 emitted to the atmosphere during equipment manufacture and operation (as a result of component failure, leaks, and
4 purges), as well as at servicing and disposal events. There are three categories of transportation-related HFC emissions;
5 Mobile air-conditioning represents the emissions from air conditioning units in passenger cars, light-duty trucks, and
6 heavy-duty vehicles; Comfort Cooling represents the emissions from air conditioning units in passenger trains and buses;
7 and Refrigerated Transport represents the emissions from units used to cool freight during transportation.
8 Table A-120 below presents these HFC emissions. Table A-121 presents all transportation and mobile source
9 greenhouse gas emissions, including HFC emissions.
A-205
-------
1 Table A-120: HFC Emissions from Transportation Sources (MMT CQ2Eq.)
Vehicle Type
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Mobile AC
+
19.4
55.2
69.2
68.2
64.7
58.6
52.7
46.7
43.4
40.5
36.9
33.3
31.0
Passenger Cars
+
11.2
28.0
31.2
29.9
27.5
23.9
20.6
17.2
15.8
14.7
13.2
11.4
10.4
Light-Duty Trucks
+
7.8
25.6
35.1
35.2
34.1
31.6
29.2
26.5
24.7
23.0
21.1
19.2
18.1
Heavy-Duty Vehicles
+
0.5
1.6
2.9
3.0
3.1
3.0
2.9
2.9
2.9
2.8
2.7
2.6
2.6
Comfort Cooling for Trains and Buses
+
+
0.1
0.4
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
Rail
+
+
+
+
+
+
+
+
+
+
+
+
+
0.0
Refrigerated Transport
+
0.2
0.8
2.2
2.4
2.9
3.4
3.9
4.4
4.9
5.4
5.9
6.4
6.9
Medium- and Heavy-Duty Trucks
+
0.1
0.4
1.3
1.4
1.6
1.8
2.1
2.3
2.5
2.7
2.9
3.1
3.3
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
0.8
0.9
1.2
1.5
1.7
2.0
2.3
2.6
2.9
3.3
3.6
Total
+
19.6
56.2
71.9
71.1
68.1
62.4
57.1
51.6
48.8
46.3
43.3
40.1
38.5
2 + Does not exceed 0.05 MMT C02 Eq.
3 Note: Totals may not sum due to independent rounding.
A-206 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Mode/Vehicle
Type/Fuel Type
Table A-121 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 2,067.7 MMT C02 Eq. in 2018,
an increase of 22 percent from 1990.53 Transportation sources account for 1,865.0 MMT C02 Eq. while non-transportation
mobile sources account for 202.8 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-119 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 2019 and DLA 2019). 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.54 Recent research indicates that modern aircraft jet engines are typically net consumers of methane (Santoni
et al. 2011). Methane is emitted at low power and idle operation, but at higher power modes aircraft engines 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 2017). Carbon dioxide
emissions from non-transportation mobile sources are calculated using data from EPA's NONROAD component of
MOVES2014b (EPA 2018a). 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 C02
from lubricants (such as motor oil) used in transportation. Together, transportation sources were responsible for 1,865.0
MMTC02 Eq. in 2018.
On-road vehicles were responsible for about 75 percent of all transportation and non-transportation mobile
greenhouse gas emissions in 2018. 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 2018, greenhouse gas emissions from passenger cars increased by 19 percent, while emissions from
light-duty trucks increased by less than one percent. Meanwhile, greenhouse gas emissions from medium- and heavy-duty
53 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 ).
54 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-207
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
trucks increased 88 percent between 1990 and 2018, reflecting the increased volume of total freight movement and an
increasing share transported by trucks.
Greenhouse gas emissions from aircraft decreased seven percent between 1990 and 2018. Emissions from
military aircraft decreased 66 percent between 1990 and 2018. Commercial aircraft emissions rose 27 percent between
1990 and 2007 then dropped 7 percent from 2007 to 2018, a change of approximately 18 percent between 1990 and 2018.
Non-transportation mobile sources, such as construction/mining equipment, agricultural equipment, and
industrial/commercial equipment, emitted approximately 202.8 MMT C02 Eq. in 2018. Together, these sources emitted
more greenhouse gases than ships and boats, and rail combined. Emissions from non-transportation mobile sources
increased, growing approximately 19 percent between 1990 and 2018. 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-122 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 2018). Emissions of C02 from transportation and mobile sources increased by 365.1
MMT C02 Eq. between 1990 and 2018. In contrast, the combined emissions of CH4 and N20 decreased by 36.59 MMT C02
Eq. over the same period, due largely to the introduction of control technologies designed to reduce criteria pollutant
emissions.55 Meanwhile, HFC emissions from mobile air-conditioners and refrigerated transport increased from virtually
no emissions in 1990 to 38.5 MMT C02 Eq. in 2018 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-123 and Table A-124 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-123.
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-124. 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-121. In addition, estimates of
fuel consumption from DOE (1993 through 2017) were used to allocate rail emissions between passenger and freight
categories.
In 2018, passenger transportation modes emitted 1,276.5 MMT C02 Eq., while freight transportation modes
emitted 552.7 MMT C02 Eq. Between 1990 and 2018, the percentage growth of greenhouse gas emissions from freight
sources was 58 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.
55 The decline in CFC emissions is not captured in the official transportation estimates.
A-208 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Table A-121: Total U.S. Greenhouse Gas Emissions from Transportation and Mobile Sources (MMT CP2 Eg.)
Percent
Mode / Vehicle Type / Fuel Change
Type
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
1990-2018
Transportation Total3
1,530.2
1,670.5
1,904.8
1,876.4
1,797.6
1,805.7
1,773.1
1,754.5
1,760.8
1,796.2
1,804.6
1,839.9
1,856.9
1,865.0
22%
On-Road Vehicles
1,206.8
1,341.7
1,545.7
1,560.2
1,514.5
1,514.4
1,483.8
1,474.4
1,471.0
1,520.4
1,517.2
1,540.5
1,546.1
1,547.3
28%
Passenger Cars
639.6
629.9
681.2
782.5
774.5
765.1
756.1
750.0
745.6
760.3
760.2
770.6
767.4
763.8
19%
Gasolineb
631.7
610.8
649.5
747.5
741.0
733.9
728.1
725.3
724.2
739.9
740.7
752.5
750.8
748.0
18%
Dieselb
7.9
7.8
3.6
3.7
3.6
3.7
4.0
4.1
4.0
4.1
4.3
4.3
4.3
4.3
-46%
AFVsc
+
+
+
+
+
+
0.1
0.1
0.2
0.4
0.6
0.7
0.8
1.2
18443%
HFCs from Mobile AC
+
11.2
28.0
31.2
29.9
27.5
23.9
20.6
17.2
15.8
14.7
13.2
11.4
10.4
NA
Light-Duty Trucks
326.7
425.2
503.3
339.8
343.6
340.4
323.5
317.4
314.4
334.7
323.7
332.8
326.9
326.6
0%
Gasolineb
315.1
402.4
457.5
292.1
295.9
293.7
278.9
275.3
275.1
296.2
286.8
297.6
293.4
294.2
-7%
Dieselb
11.5
14.9
20.1
12.1
12.0
12.5
12.9
12.8
12.7
13.7
13.8
14.0
14.1
14.0
22%
AFVsc
0.2
0.2
0.1
0.5
0.4
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.3
43%
HFCs from Mobile AC
+
7.8
25.6
35.1
35.2
34.1
31.6
29.2
26.5
24.7
23.0
21.1
19.2
18.1
NA
Medium- and Heavy-Duty
230.3
275.7
348.3
416.4
376.2
389.4
384.1
385.3
389.5
402.5
410.0
414.2
427.7
431.8
88%
Trucks
Gasolineb
38.5
35.8
36.2
46.1
42.4
42.1
38.6
38.3
39.1
40.3
39.8
40.8
41.6
42.2
10%
Dieselb
190.7
238.4
309.5
364.6
328.3
342.4
340.3
341.6
344.8
356.5
364.4
367.3
379.9
383.3
101%
AFVsc
1.1
0.9
0.6
1.5
1.0
0.3
0.3
0.4
0.4
0.4
0.4
0.5
0.5
0.5
-55%
HFCs from Refrigerated
+
0.6
2.0
4.2
4.4
4.7
4.8
5.0
5.2
5.3
5.5
5.5
5.7
5.9
NA
Transport and Mobile ACe
Buses
8.5
9.2
11.0
17.3
16.1
15.8
16.5
17.6
17.6
19.0
19.5
19.0
20.3
21.2
151%
Gasolineb
0.3
0.4
0.4
0.7
0.7
0.7
0.7
0.8
0.8
0.9
0.9
0.9
0.9
1.0
181%
Dieselb
8.0
8.7
10.2
14.7
13.5
13.5
14.3
15.3
15.3
16.6
17.0
16.6
17.7
18.6
132%
AFVsc
0.1
0.1
0.3
1.5
1.4
1.2
1.1
1.1
1.1
1.1
1.1
1.1
1.2
1.2
1231%
HFCs from Comfort Cooling
+
+
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
1.8
4.3
4.1
3.6
3.5
4.0
3.9
3.8
3.7
3.9
3.8
3.8
118%
Gasolineb
1.7
1.8
1.8
4.3
4.1
3.6
3.5
4.0
3.9
3.8
3.7
3.9
3.8
3.8
118%
Aircraft
189.2
176.7
199.4
176.7
157.4
154.8
149.9
146.5
150.1
151.3
160.5
169.0
174.8
175.5
-7%
General Aviation Aircraft
42.9
35.8
35.9
30.5
21.2
26.7
22.5
19.9
23.6
20.9
26.8
35.1
33.3
32.8
-24%
Jet Fuel'
39.8
33.0
33.4
28.5
19.4
24.8
20.6
18.2
22.0
19.4
25.3
33.7
31.8
31.2
-22%
Aviation Gasoline
3.2
2.8
2.6
2.0
1.9
1.9
1.9
1.8
1.6
1.5
1.5
1.5
1.5
1.6
-50%
Commercial Aircraft
110.9
116.3
140.6
128.4
120.6
114.4
115.7
114.3
115.4
116.3
120.1
121.5
129.2
130.8
18%
Jet Fuel'
110.9
116.3
140.6
128.4
120.6
114.4
115.7
114.3
115.4
116.3
120.1
121.5
129.2
130.8
18%
Military Aircraft
35.3
24.5
22.9
17.7
15.5
13.7
11.7
12.2
11.1
14.1
13.6
12.4
12.3
11.9
-66%
Jet Fuel'
35.3
24.5
22.9
17.7
15.5
13.7
11.7
12.2
11.1
14.1
13.6
12.4
12.3
11.9
-66%
Ships and Boatsd
47.4
59.3
66.0
45.9
39.2
45.1
46.6
40.5
39.9
29.2
33.8
40.9
44.0
40.9
-14%
A-209
-------
Gasoline
14.9
14.8
14.8
13.0
12.7
12.1
11.6
11.4
11.2
10.9
10.9
11.0
11.1
11.1
-25%
Distillate Fuel
9.7
14.9
17.1
11.4
11.4
11.1
13.8
11.2
11.3
10.0
15.9
13.8
13.0
12.1
25%
Residual Fuele
22.9
29.6
33.8
20.7
14.2
20.8
19.7
16.1
15.4
5.9
4.3
13.2
16.7
14.1
-38%
HFCs from Refrigerated
+
+
0.3
0.8
0.9
1.2
1.5
1.7
2.0
2.3
2.6
2.9
3.3
3.6
NA
Transport6
Rail
39.0
43.1
46.1
48.2
40.7
43.6
44.7
43.5
44.0
45.9
43.7
39.9
41.1
42.9
10%
Distillate Fuel'
35.8
40.0
42.5
43.3
36.0
38.8
40.2
39.4
39.7
41.6
39.7
36.2
37.5
39.3
10%
Electricity
3.1
3.1
3.5
4.7
4.5
4.5
4.3
3.9
4.1
4.1
3.8
3.5
3.4
3.4
12%
Other Emissions from Rail
0.1
0.1
+
+
+
+
+
+
+
+
+
+
0.1
0.1
-6%
Electricity Use5
HFCs from Comfort Cooling
+
+
+
+
+
+
+
+
+
+
+
+
+
+
NA
HFCs from Refrigerated
+
+
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
Transport6
Pipelines'1
36.0
38.4
35.5
35.9
37.1
37.3
38.1
40.6
46.2
39.4
38.5
39.2
41.3
49.2
37%
Natural Gas
36.0
38.4
35.5
35.9
37.1
37.3
38.1
40.6
46.2
39.4
38.5
39.2
41.3
49.2
37%
Other Transportation
11.8
11.3
12.1
9.5
8.5
10.4
10.0
9.1
9.6
10.0
11.0
10.4
9.6
9.3
-22%
Lubricants
11.8
11.3
12.1
9.5
8.5
10.4
10.0
9.1
9.6
10.0
11.0
10.4
9.6
9.3
-22%
Non-Transportation Mobile'
170.5
176.4
180.3
210.0
201.2
203.1
201.0
200.8
202.6
199.5
188.8
191.5
197.8
202.8
19%
Total
Agricultural Equipment1''
44.6
44.3
40.4
48.4
47.0
47.1
47.3
48.5
46.2
46.3
41.3
40.4
40.0
40.1
-10%
Gasoline
7.7
8.7
6.1
5.7
6.1
6.2
7.1
7.8
5.8
5.7
1.4
1.5
1.5
1.5
-81%
Diesel
36.6
35.3
34.1
42.5
40.7
40.7
40.0
40.6
40.2
40.5
39.8
38.8
38.5
38.5
5%
CNG
0.3
0.3
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
-66%
LPG
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-10%
Construction/Mining
50.4
54.2
58.3
70.4
67.1
66.2
65.0
63.8
66.8
61.9
57.7
60.6
65.7
68.6
36%
Equipment'^
Gasoline
4.6
4.2
3.3
5.5
5.2
6.1
5.7
5.8
9.7
6.3
3.2
3.3
3.3
3.3
-28%
Diesel
44.9
49.0
53.8
63.8
60.8
59.1
58.3
57.1
56.2
54.8
53.6
56.5
61.6
64.6
44%
CNG
0.8
0.9
1.0
1.0
0.9
0.9
0.9
0.8
0.8
0.7
0.7
0.7
0.7
0.6
-23%
LPG
0.1
0.1
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2
15%
Other Equipment1'1
75.5
77.9
81.7
91.2
87.2
89.8
88.8
88.5
89.6
91.3
89.7
90.5
92.0
94.1
25%
Gasoline
42.1
42.2
43.5
49.5
47.6
49.4
47.6
46.1
46.0
46.6
44.8
45.2
45.5
45.7
9%
Diesel
21.8
21.5
21.3
25.6
24.5
25.3
26.0
27.2
28.1
29.0
29.2
29.4
30.1
31.2
43%
CNG
3.4
3.6
4.0
2.9
2.6
2.6
2.6
2.6
2.7
2.7
2.6
2.5
2.5
2.6
-22%
LPG
8.3
10.6
12.9
13.3
12.4
12.6
12.6
12.6
12.8
13.0
13.1
13.4
13.9
14.6
76%
Transportation and Non-
1,700.7
1,846.9
2,085.1
2,086.4
1,998.8
2,008.8
1,974.1
1,955.3
1,963.3
1,995.7
1,993.4
2,031.4
2,054.7
2,067.7
22%
Transportation Mobile Total1
+ 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.
A-210 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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 2018). Table VM-1 fuel consumption data for 2018 has not been published yet, therefore 2018 fuel consumption data is estimated using the percent change in VMT from 2017
to 2018. 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 2018). These fuel consumption and mileage estimates are combined with estimates of
fuel shares by vehicle type from DOE's TEDB Annex Tables A. 1 through A.6 (DOE 1993 through 2017). TEDB data for 2017 and 2018 has not been published yet, therefore 2016 data are used as a
proxy.
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 2018 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 2018 is not available. Diesel consumption data for 2014-2018 is estimated by applying the historical average fuel usage per carload factor to
the annual number of carloads.
8 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),
i 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.
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. In 2015, EPA incorporated the NONROAD2008 model into MOVES2014a. This year's Inventory uses the NONROAD component of MOVES2014b for years 1999 through
2018. 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.
Table A-122: Transportation and Mobile Source Emissions by Gas (MMT CP2 Eq.)
Percent
Change
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
1990-2018
C02a
1,645.7
1,762.5
1,966.6
1,977.4
1,892.9
1,907.3
1,880.1
1,869.7
1,885.5
1,923.0
1,924.9
1,967.1
1,994.9
2,010.8
22%
n2o
42.0
52.3
51.4
29.9
28.2
27.1
25.8
23.4
21.6
19.7
18.3
17.4
16.3
15.2
-64%
ch4
12.9
12.4
10.8
7.2
6.5
6.1
5.6
5.0
4.6
4.1
3.6
3.4
3.3
3.1
-76%
HFC
+
19.6
56.2
71.9
71.1
68.1
62.4
57.1
51.6
48.8
46.3
43.3
40.1
38.5
NA
Totalb
1,700.6
1,846.8
2,085.0
2,086.3
1,998.7
2,008.7
1,974.0
1,955.2
1,963.2
1,995.6
1,993.2
2,031.3
2,054.6
2,067.6
22%
+ 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-211
-------
aThe 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).
bTotal excludes other emissions from electricity generation and CH4 and N20 emissions from electric rail.
Note: 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 2017). Table VM-1 fuel consumption data for 2017 has not been published yet, therefore 2017 fuel consumption data is estimated using the percent change in VMT
from 2016 to 2017. 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 2018). These fuel consumption and mileage estimates are combined
with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables A.1 through A.6 (DOE 1993 through 2017). TEDB data for 2017 and 2018 has not been published yet, therefore 2016
data are used as a proxy.
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, 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-212 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2018 (MMT C02 Eq.)
¦ Mode/ Vehicle Type ¦ Passenger Cars/Motorcycles
¦ Light-Duty Trucks ¦ Medium- and Heavy-Duty Trucks and Buses
¦ Aircraft ¦ Boats/Ships, Rail, and Pipelines
¦ Mobile AC, Refrig. Transport, Lubricants ¦Non-Transportation Mobile Sources
4,000
3,500
3,000
cr
LU
0~ 2,500
u
I—
I 2,000
1,500
1,000
500
o
i
(N
CO
^r
LO
tO
00
cn
o
T—1
(N
CO
^r
LO
tO
00
cn
o
T—1
CM
ro
^r
LO
tO
00
cn
CD
CD
CD
cn
CD
CD
CD
CD
CD
o
o
O
O
o
o
O
o
o
o
T—1
T—1
T—1
T—1
T—1
T—1
1
i
1
CD
CD
CD
CD
CD
CD
CD
CD
CD
CD
o
o
O
O
o
o
O
o
o
o
o
o
o
o
o
o
o
o
o
i
1
1
1
i
1
1
T—1
1
T—1
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
CM
3
4
5
6
7
8
1 Figure A-4: Domestic
4,500
A-213
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Table A-123: Greenhouse Gas Emissions from Passenger Transportation (MMT CP2 Eq.)
Vehicle Type
1990
1995
2000
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Percent
Change
1990-2018
On-Road
976.5
1,066.0
1,197.4
1,143.9
1,138.3
1,125.0
1,099.7
1,089.0
1,081.5
1,117.9
1,107.1
1,126.4
1,118.4
1,115.4
14%
Vehiclesa'b
Passenger Cars
639.6
629.9
681.2
782.5
774.5
765.1
756.1
750.0
745.6
760.3
760.2
770.6
767.4
763.8
19%
Light-Duty Trucks
326.7
425.2
503.3
339.8
343.6
340.4
323.5
317.4
314.4
334.7
323.7
332.8
326.9
326.6
0%
Buses
8.5
9.2
11.0
17.3
16.1
15.8
16.5
17.6
17.6
19.0
19.5
19.0
20.3
21.2
151%
Motorcycles
1.7
1.8
1.8
4.3
4.1
3.6
3.5
4.0
3.9
3.8
3.7
3.9
3.8
3.8
118%
Aircraft
134.6
132.0
152.2
140.9
125.2
124.8
122.1
118.5
123.1
120.9
130.5
139.8
144.1
144.9
8%
General Aviation
42.9
35.8
35.9
30.5
21.2
26.7
22.5
19.9
23.6
20.9
26.8
35.1
33.3
32.8
-24%
Commercial
91.7
96.2
116.3
110.4
103.9
98.0
99.6
98.6
99.5
100.0
103.6
104.7
110.7
112.1
22%
Aircraft
Recreational
17.6
17.5
17.6
15.7
15.4
14.7
14.2
13.9
13.8
13.6
10.9
11.0
11.1
11.1
-37%
Boats
Passenger Rail
4.4
4.5
5.2
6.3
6.2
6.2
5.9
5.5
5.7
5.7
5.4
5.2
5.1
5.1
16%
Total
1,133.1
1,220.1
1,372.4
1,306.7
1,285.1
1,270.7
1,242.0
1,227.1
1,224.1
1,258.2
1,253.9
1,282.4
1,278.6
1,276.5
13%
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 2018). Table VM-lfuel consumption data for 2018 has not been published yet, therefore 2018 fuel consumption data is estimated using the percent
change in VMT from 2017 to 2018. 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 2018). 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 2017). TEDB data for 2017 and 2018 has
not been published yet, therefore 2016 data are used as a proxy.
Notes: Data from DOE (1993 through 2017) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates. In 2015, EPA
incorporated the NONROAD2008 model into MOVES2014a. This year's Inventory uses the NONROAD component of MOVES2014bfor years 1999 through 2018. 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 2018 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.
Table A-124: Greenhouse Gas Emissions from Domestic Freight Transportation (MMT CQ2 Eq.)
By Mode
1990
1995
2000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Percent
Change
1990-
2018 2018
Trucking3-
230.3
275.2
346.7
413.5 373.1 386.4 381.1 382.4 386.6 399.7 407.2 411.5 425.1 429.2
86%
A-214 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Freight Rail
Ships and Non-Recreational Boats
Pipelines0
Commercial Aircraft
34.5 38.6 40.9 41.9 34.5 37.4 38.7 37.9 38.2 40.1 38.2 34.7 36.0 37.7 9%
29.8 41.8 48.4 30.1 23.8 30.4 32.3 26.5 30.7 19.3 7.2 16.4 20.2 17.9 -40%
36.0 38.4 35.5 35.9 37.1 37.3 38.1 40.6 46.2 39.4 38.5 39.2 41.3 49.2 37%
19.2 20.1 24.3 18.0 16.7 16.3 16.0 15.8 15.9 16.2 16.5 16.8 18.4 18.7 -3%
Total
349.9 414.1 495.8 539.4 485.3 507.8 506.4 503.2 517.6 514.6 507.6 518.6 541.0 552.7 58%
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 (FFIWA 1996 through 2018). Table VM-lfuel consumption data for 2018 has not been published yet, therefore 2018 fuel consumption data is estimated using the percent
change in VMT from 2017 to 2018. 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 FFIWA Flighway Statistics Table VM-1 (FFIWA 1996 through 2018). 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 2017). TEDB data for 2017 and 2018 has
not been published yet, therefore 2016 data are as a proxy.
c Pipelines reflect C02 emissions from natural gas powered pipelines transporting natural gas.
Notes: Data from DOE (1993 through 2017) were used to disaggregate emissions from rail and buses. Emissions from FIFCs have been included in these estimates. In 201S, EPA
incorporated the NONROAD2008 model into MOVES2014a. This year's Inventory uses the NONROAD component of MOVES2014bfor years 1999 through 2018. 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 200S to 2018 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 (FIEVs) were considered alternative fueled vehicles and therefore not included in the engine
technology breakouts. For this Inventory, FIEVs are classified as gasoline vehicles across the entire time series.
A-215
-------
1 References
2 AAR (2008 through 2018) Railroad Facts. Policy and Economics Department, Association of American Railroads,
3 Washington, D.C. Obtained from Clyde Crimmel at AAR.
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8 APTA (2006) Commuter Rail National Totals. American Public Transportation Association, Washington, D.C. Available
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10 BEA (2018) Table 1.1.6. Real Gross Domestic Product, Chained 2009 Dollars. Bureau of Economic Analysis (BEA), U.S.
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12 .
14 Benson, D. (2002 through 2004) Unpublished data. Upper Great Plains Transportation Institute, North Dakota State
15 University and American Short Line & Regional Railroad Association.
16 Browning (2019) Updated On-highway CH4 and N20 Emission Factors for GHG Inventory. Memorandum from ICF to Sarah
17 Roberts and Justine Geidosch, Office of Transportation and Air Quality, U.S. Environmental Protection Agency.
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19 Browning, L. (2018a). Updated Methodology for Estimating Electricity Use from Highway Plug-In Electric Vehicles.
20 Technical Memo, October 2018.
21 Browning, L. (2018b). Updated Non-Highway CH4 and N20 Emission Factors for U.S. GHG Inventory. Technical Memo,
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23 Browning, L. (2017) "Updated Methodology for Estimating CH4 and N20 Emissions from Highway Vehicle Alternative Fuel
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25 Browning, L. (2005) Personal communication with Lou Browning, Emission control technologies for diesel highway
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29 DLA Energy (2019) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense Energy
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43 EIA (2019f) Natural Gas Annual 2018. Energy Information Administration, U.S. Department of Energy. Washington, D.C.
44 DOE/EIA-0131(06).
A-216 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
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EPA (2019c) Confidential Engine Family Sales Data Submitted to EPA By Manufacturers. Office of Transportation and Air
Quality, U.S. Environmental Protection Agency.
EPA (2018d) Annual Certification Test Results Report. Office of Transportation and Air Quality, U.S. Environmental
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EPA (1998) Emissions of Nitrous Oxide from Highway Mobile Sources: Comments on the Draft Inventory of U.S.
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.
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.
Gaffney, J. (2007) Email Communication. John Gaffney, American PublicTransportation Association and Joe Aamidor, ICF
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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 2018) Raillnc Short line and Regional Traffic Index. Carloads Originated Year-to-Date. December
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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
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A-217
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Whorton, D. (2006 through 2014) Personal communication, Class II and III Rail energy consumption, American Short Line
and Regional Railroad Association.
A-218 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
3.3. Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel
Consumption
IPCC Tier 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 2018 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 2018 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 Tier 3B 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 and 2018 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 Tier 3B
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 2018 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-219
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8
9
10
11
12
13
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15
16
17
18
19
20
21
22
23
24
The 1990 commercial aircraft C02 emissions inventory is approximately 15.2 percent lower than the 2018 C02 emissions
inventory. It is important to note that the distance flown increased by 58 percent over this 28-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.56
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
bean emission from aircraft gas turbine engines burning Jet A at higher power settings and is, in fact, consumed in net at
these higher powers."57 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 U.S. 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 less fuel burn for the international U.S. 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.
56 Additional information on the AEDT modeling process is available at:
http://www.faa.gov/about/office_org/headquarters_offices/apl/research/models/
57 Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan,
Turbojet and Turboprop Engines, EPA-420-R-09-901, May 27, 2009, http://www.epa.gov/otaq/aviation.htm
A-220 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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2
3 Note: Hollow markers are estimates from data generated by prior tools and methods. 1990 is estimated using non-radar method.
4 Table A-125: Commercial Aviation Fuel Burn for the United States and Territories
Year
Region
Distance
Flown (nmi)
Fuel
Burn
(M
Gallon)
Fuel
Burn
(TBtu)
Fuel Burn (Kg)
co2
(MMT)
1990
Domestic U.S. 50 States and U.S.
Territories
4,057,195,988
11,568
1,562
34,820,800,463
109.9
International U.S. 50 States and U.S.
Territories
599,486,893
3,155
426
9,497,397,919
30.0
Domestic U.S. 50 States
3,984,482,217
11,287
1,524
33,972,832,399
107.2
International U.S. 50 States
617,671,849
3,228
436
9,714,974,766
30.7
1995a
Domestic U.S. 50 States and U.S.
Territories
NA
12,136
1,638
36,528,990,675
115.2
1996a
Domestic U.S. 50 States and U.S.
Territories
NA
12,492
1,686
37,600,624,534
118.6
1997a
Domestic U.S. 50 States and U.S.
Territories
NA
12,937
1,747
38,940,896,854
122.9
1998a
Domestic U.S. 50 States and U.S.
Territories
NA
12,601
1,701
37,930,582,643
119.7
1999a
Domestic U.S. 50 States and U.S.
Territories
NA
13,726
1,853
41,314,843,250
130.3
2000
Domestic U.S. 50 States and U.S.
Territories
5,994,679,944
14,672
1,981
44,161,841,348
139.3
Figure A-5: Commercial Aviation Fuel Burn for the United States and Territories
0Q
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Commercial Aviation Fuel Burn
for the United States and Territories
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Note: Hollow markers are estimates from data generated by prior tools and methods.
1990 is estimated using non-radar methods.
O States+Territories (Domestic) ~ States+Territories (International) A States (Domestic) O States (International)
„|DDr,
A-221
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International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2001a Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2002a Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2003a Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2004a Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2005 Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2006a Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2007a Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2008a Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2009a Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
1,309,565,963 6,040
5,891,481,028 14,349
1,331,784,289 6,117
815 18,181,535,058 57.4
1,937 43,191,000,202 136.3
826 18,412,169,613 58.1
5,360,977,447 13,121 1,771 39,493,457,147 124.6
1,171,130,679 5,402
5,268,687,772 12,832
1,191,000,288 5,470
729 16,259,550,186 51.3
1,732 38,625,244,409 121.9
739 16,465,804,174 51.9
5,219,345,344 12,774 1,725 38,450,076,259 121.3
1,140,190,481 5,259
5,129,493,877 12,493
1,159,535,153 5,326
710 15,829,987,794 49.9
1,687 37,604,800,905 118.6
719 16,030,792,741 50.6
5,288,138,079 12,942 1,747 38,956,861,262 122.9
1,155,218,577 5,328
5,197,102,340 12,658
1,174,818,219 5,396
719 16,038,632,384 50.6
1,709 38,100,444,893 120.2
728 16,242,084,008 51.2
5,371,498,689 13,146 1,775 39,570,965,441 124.8
1,173,429,093 5,412
5,279,027,890 12,857
1,193,337,698 5,481
731 16,291,460,535 51.4
1,736 38,701,048,784 122.1
740 16,498,119,309 52.1
6,476,007,697 13,976 1,887 42,067,562,737 132.7
1,373,543,928 5,858
6,370,544,998 13,654
1,397,051,323 5,936
791 17,633,508,081 55.6
1,843 41,098,359,387 129.7
801 17,868,972,965 56.4
5,894,323,482 14,426 1,948 43,422,531,461 137.0
1,287,642,623 5,939
5,792,852,211 14,109
1,309,488,994 6,015
802 17,877,159,421 56.4
1,905 42,467,943,091 134.0
812 18,103,932,940 57.1
6,009,247,818 14,707 1,986 44,269,160,525 139.7
1,312,748,383 6,055
5,905,798,114 14,384
1,335,020,703 6,132
817 18,225,718,619 57.5
1,942 43,295,960,105 136.6
828 18,456,913,646 58.2
5,475,092,456 13,400 1,809 40,334,124,033 127.3
1,196,059,638 5,517
5,380,838,282 13,105
1,216,352,196 5,587
745 16,605,654,741 52.4
1,769 39,447,430,318 124.5
754 16,816,299,099 53.1
5,143,268,671 12,588 1,699 37,889,631,668 119.5
1,123,571,175 5,182
5,054,726,871 12,311
700 15,599,251,424 49.2
1,662 37,056,676,966 116.9
A-222 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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International U.S. 50 States
2010 Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2011 Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2012 Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2013 Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2014 Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2015 Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S 50 States
International U.S. 50 States
2016 Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S 50 States
International U.S. 50 States
2017 Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
2018 Domestic U.S. 50 States and U.S.
Territories
International U.S. 50 States and U.S.
Territories
Domestic U.S. 50 States
International U.S. 50 States
1,142,633,881 5,248
5,652,264,576 11,931
1,474,839,733 6,044
5,554,043,585 11,667
1,497,606,695 6,113
709 15,797,129,457 49.8
1,611 35,912,723,830 113.3
816 18,192,953,916 57.4
1,575 35,116,863,245 110.8
825 18,398,996,825 58.0
5,767,378,664 12,067 1,629 36,321,170,730 114.6
1,576,982,962 6,496
5,673,689,481 11,823
1,596,797,398 6,554
877 19,551,631,939
1,596 35,588,754,827
885 19,727,043,614
5,735,605,432 11,932 1,611 35,915,745,616
1,619,012,587 6,464
5,636,910,529 11,672
1,637,917,110 6,507
5,808,034,123 12,031
1,641,151,400 6,611
5,708,807,315 11,780
1,661,167,498 6,657
873 19,457,378,739
1,576 35,132,961,140
879 19,587,140,347
1,624 36,212,974,471
892 19,898,871,458
1,590 35,458,690,595
899 20,036,865,038
5,825,999,388 12,131 1,638 36,514,970,659
1,724,559,209 6,980
5,725,819,482 11,882
1,745,315,059 7,027
5,900,440,363 12,534
1,757,724,661 7,227
5,801,594,806 12,291
1,793,787,700 7,310
942 21,008,818,741
1,604 35,764,791,774
949 21,152,418,387
1,692 37,727,860,796
976 21,752,301,359
1,659 36,997,658,406
987 22,002,733,062
5,929,429,373 12,674 1,711 38,148,578,811
1,817,739,570 7,453
5,827,141,640 12,422
1,839,651,091 7,504
6,264,650,997 13,475
1,944,104,275 7,841
6,214,083,068 13,358
1,912,096,739 7,755
6,408,870,104 13,650
2,037,055,865 8,178
6,318,774,158 13,425
2,066,756,708 8,254
1006 22,434,619,940
1,677 37,391,339,601
1013 22,588,366,704
1,819 40,560,206,261
1,059 23,602,935,694
1,803 40,207,759,885
1,047 23,343,627,689
1,843 41,085,494,597
1,104 24,616,382,063
1,812 40,410,478,534
1,114 24,843,232,462
61.7
112.3
62.2
113.3
61.4
110.8
61.8
114.3
62.8
111.9
63.2
115.2
66.3
112.8
66.7
119.0
68.6
116.7
69.4
120.4
70.8
118.0
71.3
128.0
74.5
126.9
73.6
129.6
77.7
127.5
78.4
NA (Not Applicable)
A-223
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3 Estimates for these years were derived from previously reported tools and methods.
1
A-224 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 References
2 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
3 Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K.
4 Tanabe (eds.)]. Hayama, Kanagawa, Japan.
5 IPCC (1999) Aviation and the Global Atmosphere. Intergovernmental Panel on Climate Change. [J.E. Penner, et al. (eds.)].
6 Cambridge University Press. Cambridge, United Kingdom.
7 Santoni, G., B. Lee, E. Wood, S. Herndon, R. Miake-Lye, S Wofsy, J. McManus, D. Nelson, M. Zahniser (2011) Aircraft
8 emissions of methane and nitrous oxide during the alternative aviation fuel experiment. Environ Sci Technol. 2011 Aug
9 15; 45(16):7075-82.
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25
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29
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33
34
35
36
37
3.4. Methodology for Estimating CH4 Emissions from Coal Mining
EPA uses an IPCCTier 3 method for estimating CH4 emissions from underground mining and an IPCCTier 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 2019).58 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 2018, the average daily CH4 emissions were multiplied by the number of days corresponding to the number of
quarters the mine vent was operating. For example, if the mine vent was operational in one out of the four quarters, the
average daily CH4 emissions were multiplied by 92 days. Total ventilation emissions for a particular year were estimated
by summing emissions from individual mines.
Since 2011, the nation's "gassiest" underground coal mines—those that liberate more than 36,500,000 actual
cubic feet of CH4 per year (about 17,525 MT C02 Eq.)—have been required to report to EPA's GHGRP (EPA 2019).59 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 United States with detectable CH4 concentrations.
Since 2013, ventilation emission estimates have been calculated based on both EPA's GHGRP60 data submitted
by underground mines, and on quarterly measurement data obtained directly from MSHA for the remaining mines. The
quarterly measurements are used to determine the average daily emission rate for the reporting year quarter. The CH4
58 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.
59 Underground coal mines report to EPA under subpart FF of EPA's GHGRP (40 CFR part 98). In 2018,76 underground coal mines reported
to the program.
60 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-226 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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20
liberated from ventilation systems was estimated by summing the emissions from the mines reporting to EPA's GHGRP
and emissions based on MSHA quarterly measurements for the remaining mines not reporting to EPA's GHGRP.
Table A-126: 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 Emissions Factors Used Instead of Mine-Specific Data
1992 1990 Emissions Factors Used Instead of Mine-Specific Data
1993 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3
1994 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3
1995 All Mines Emitting at Least 0.5 MMCFD (Assumed to Account for 94.1% of Total)3
1996 All Mines Emitting at Least 0.5 MMCFD (Assumed to Account for 94.1% of Total)3
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)3
1999 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3
2000 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3
2001 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3
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)3
2004 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3
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)
3 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).
Eighteen mines employed degasification systems in 2018, and the CH4 liberated through these systems was
reported to the EPA's GHGRP (EPA 2019). Eleven of the 18 mines with degasification systems had operational CH4 recovery
and use projects, and the other seven 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
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exclusively to estimate CH4 liberated from degasification systems at 14 of the 18 mines that used degasification systems
in 2018.
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.61 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 four mines with degasification systems that include pre-mining wells that were
mined through in 2018, 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. 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. However, corrections or revisions were not needed
for 2018 GHGRP data.
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 2018. Eleven of these projects
involved degasification systems, one did not use any degasification system, 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 twelve 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 2018. 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). However, corrections or revisions were not needed
for 2018 GHGRP data
• 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 2019). State sales data were used to estimate CH4 recovered and
used from the remaining four mines that deployed degasification systems in 2018 (DMME 2019; GSA
2019). These four mines intersected pre-mining wells in 2018. 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 2018 data came from state gas production
databases (DMME 2019; GSA 2019), as well as mine-specific information on the timing of mined-through,
pre-mining wells (JWR 2010; El Paso 2009, ERG 2019). For pre-mining wells, the cumulative CH4 production
61A well is "mined through" when coal mining development or the working face intersects the borehole or well.
A-228 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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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.
Step 2: Estimate CH4 Emitted from Surface Mines and Post-Mining Activities
Mine-specific data were not available for estimating CH4 emissions from surface coal mines or for post-mining
activities. For surface mines, basin-specific coal production obtained from the Energy Information Administration's Annual
Coal Report was multiplied by basin-specific gas contents and a 150 percent emission factor (to account for CH4from over-
and under-burden) to estimate CH4 emissions (King 1994; Saghafi 2013). For post-mining activities, basin-specific coal
production was 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 emissions 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 was to define the
geographic resolution of the analysis and to collect coal production data at that level of resolution. The analysis was
conducted by coal basin as defined in Table A-127, which presents coal basin definitions by basin and by state.
The Energy Information Administration's Annual Coal Report (EIA 2019) includes state- and county-specific
underground and surface coal production by year. To calculate production by basin, the state level data were grouped into
coal basins using the basin definitions listed in Table A-127. For two states—West Virginia and Kentucky—county-level
production data were used for the basin assignments because coal production occurred in geologically distinct coal basins
within these states. Table A-128 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-129 presents the average in situ content for each basin, along with
the resulting emission factor estimates.
Step 2.3: Estimate CH4 Emitted
The total amount of CH4 emitted from surface mines and post-mining activities was calculated by multiplying the
coal production in each basin by the appropriate emission factors.
Table A-127 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-128 shows annual underground, surface, and total
coal production (in short tons) for each coal basin. Table A-129 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-130 presents annual estimates of CH4 emissions for ventilation and degasification systems, and CH4
recovered and used. Table A-131 presents annual estimates of total CH4 emissions from underground, post-underground,
surface, and post-surface activities.
Table A-127: 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
A-229
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Warrior Basin
Illinois Basin
South West and Rockies Basin
North Great Plains Basin
West Interior Basin
Northwest Basin
Alabama, Mississippi
Illinois, Indiana, Kentucky West
Arizona, California, Colorado, New Mexico, Utah
Montana, North Dakota, Wyoming
Arkansas, Iowa, Kansas, Louisiana, Missouri, Oklahoma, Texas
Alaska, Washington
State
Basin
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Illinois
Indiana
Iowa
Kansas
Kentucky (east)
Kentucky (west)
Louisiana
Maryland
Mississippi
Missouri
Montana
New Mexico
North Dakota
Ohio
Oklahoma
Pennsylvania
Tennessee
Texas
Utah
Virginia
Washington
West Virginia South
West Virginia North
Wyoming
Warrior Basin
Northwest Basin
South West and Rockies Basin
West Interior Basin
South West and Rockies Basin
South West and Rockies Basin
Illinois Basin
Illinois Basin
West Interior Basin
West Interior Basin
Central Appalachian Basin
Illinois Basin
West Interior Basin
Northern Appalachian Basin
Warrior Basin
West Interior Basin
North Great Plains Basin
South West and Rockies Basin
North Great Plains Basin
Northern Appalachian Basin
West Interior Basin
Northern Appalachian Basin
Central Appalachian Basin
West Interior Basin
South West and Rockies Basin
Central Appalachian Basin
Northwest Basin
Central Appalachian Basin
Northern Appalachian Basin
North Great Plains Basin
A-230 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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Figure A-6: Locations of U.S. Coal Basins
Coalbed Methane Fields, Lower 48 States
North Central
Coal. Region
Powder River
WTBasin
Wind'River Basi
Wyoming >1
Michigan
I Basin
Northern
Appalachian
Greater Green^
Riyer Basic ~
¦.' f.. Uinta Basin
innah-Carbon Basin
„Park Basin
Forest City
j Basin
VDenver
¦•-Basin
Ctrerokeelpiatform
San Juah
Basin
Black Warrior
JK Basin
Gulf Coast
Coal Regior
Coaibed Methane Fields
Coal Basins, Regions & Fields
Source: Energy Information Administration based on data from USGS and various published studies
Updated: April 8, 2009
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1 Table A-128: Annual Coal Production (Thousand Short Tons)
Basin
1990
2005
2014
2015
2016
2017
2018
Underground
Coal Production
423,556
368,611
354,705
306,820
252,106
273,130
275,360
N. Appalachia
103,865
111,151
116,700
103,578
94,685
97,742
97,070
Cent. Appalachia
198,412
123,083
64,219
53,230
39,800
46,052
45,306
Warrior
17,531
13,295
12,516
9,897
7,434
10,491
12,199
Illinois
69,167
59,180
105,211
96,361
76,577
80,855
85,416
S. West/Rockies
32,754
60,865
44,302
33,762
26,413
30,047
25,387
N. Great Plains
1,722
572
11,272
9,510
6,776
7,600
9,776
West Interior
105
465
485
482
421
343
206
Northwest
0
0
0
0
0
0
0
Surface Coal
Production
602,753
762,191
643,721
588,736
475,410
500,783
480,080
N. Appalachia
60,761
28,873
17,300
13,201
8,739
9,396
9,218
Cent. Appalachia
94,343
112,222
52,399
37,530
26,759
31,796
33,799
Warrior
11,413
11,599
7,584
6,437
5,079
4,974
5,524
Illinois
72,000
33,702
31,969
27,360
21,707
22,427
21,405
S. West/Rockies
43,863
42,756
27,654
26,020
18,951
19,390
19,599
N. Great Plains
249,356
474,056
458,112
436,928
350,899
372,875
362,664
West Interior
64,310
52,263
47,201
40,083
42,344
38,966
26,969
Northwest
6,707
6,720
1,502
1,177
932
959
902
Total Coal
Production 1,026,309
1,130,802
998,426
895,556
727,516
773,913
755,440
N. Appalachia
164,626
140,024
134,000
116,799
103,424
107,138
106,288
Cent. Appalachia
292,755
235,305
116,618
90,760
66,559
77,848
79,105
Warrior
28,944
24,894
20,100
16,334
12,513
15,465
17,723
Illinois
141,167
92,882
137,180
123,721
98,284
103,282
106,821
S. West/Rockies
76,617
103,621
71,956
59,782
45,364
49,437
44,986
N. Great Plains
251,078
474,628
469,384
446,438
357,675
380,475
372,440
West Interior
64,415
52,728
47,686
40,565
42,765
39,309
27,175
Northwest
6,707
6,720
1,502
1,177
932
959
902
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table A-129: Coal Underground, Surface, and Post-Mining CH4 Emission Factors (ft3 per Short Ton)
Surface
Average
Underground
Surface
Post-Mining
Post-Mining
In Situ
Average
Mine
Surface
Undergroun
Basin
Content
In Situ Content
Factors
Factors
d 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
A-232 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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West Interior (Arkoma Basin)
West Interior (Gulf Coast Basin)
Northwest (AK)
Northwest (WA)
74.5
11.0
16.0
16.0
331.2
127.9
160.0
47.3
111.8
16.5
24.0
24.0
24.2
3.6
5.2
5.2
107.6
41.6
52.0
15.4
1 Sources: 1986 USBM Circular 9067, Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins; U.S. DOE Report
2 DOE/METC/83-76, Methane Recovery from Coalbeds: A Potential Energy Source; 1986-1988 Gas Research Institute Topical Report, A
3 Geologic Assessment of Natural Gas from Coal Seams; 2005 U.S. EPA Draft Report, Surface Mines Emissions Assessment.
9
10
Table A-130: Underground Coal Mining CH4 Emissions (Billion Cubic Feet)
Activity
1990
2005
2014
2015
2016
2017
2018
Ventilation Output
112
75
89
84
76
78
73
Adjustment Factor for Mine
Data
98%
98%
100%
100%
100%
100%
100%
Adjusted Ventilation Output
114
77
89
84
76
78
73
Degasification System Liberated
54
48
42
43
42
41
47
Total Underground Liberated
168
124
131
127
119
120
120
Recovered & Used
(14)
(37)
(35)
(34)
(34)
(35)
(39)
Total
154
87
96
93
85
84
81
Table A-131: Total Coal Mining CH4 Emissions (Billion Cubic Feet)
Activity
1990
2005
2014 2015
2016
2017 2018
Underground Mining
154
87
96
93
85
84
81
Surface Mining
22
25
20
18
14
15
15
Post-Mining
(Underground)
19
16
14
12
10
11
11
Post-Mining (Surface)
5
5
4
4
3
3
3
Total
200
133
134
127
112
114
110
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table A-132: Total Coal Mining CH4 Emissions by State (Million Cubic Feet)
State
1990
2005
2014
2015
2016
2017
2018
Alabama
32,097
15,789
16,301
12,675
10,752
11,044
12,119
Alaska
50
42
44
34
27
28
26
Arizona
151
161
107
91
72
83
87
Arkansas
5
+
176
559
247
770
71
California
1
0
0
0
0
0
0
Colorado
10,187
13,441
4,038
3,248
2,272
1,940
1,616
Illinois
10,180
6,488
9,217
10,547
11,034
8,513
6,530
Indiana
2,232
3,303
7,159
6,891
6,713
6,036
6,729
Iowa
24
0
0
0
0
0
0
Kansas
45
11
4
12
2
0
0
Kentucky
10,018
6,898
8,219
6,378
4,880
4,636
4,636
Louisiana
64
84
52
69
56
42
129
Maryland
474
361
169
171
131
152
113
Mississippi
0
199
209
176
161
146
165
Missouri
166
37
23
9
15
15
16
Montana
1,373
1,468
1,379
1,353
1,004
1,102
1,172
New Mexico
363
2,926
2,219
2,648
1,954
1,728
1,360
North
Dakota
299
306
298
294
287
294
303
Ohio
4,406
3,120
3,267
2,718
1,998
1,473
1,342
Oklahoma
226
825
112
735
867
2,407
2,317
Pennsylvania
21,864
18605
19,803
19,554
17,932
19,662
20,695
Tennessee
276
115
22
40
27
14
23
Texas
1,119
922
876
721
783
730
498
A-233
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Virginia
46,041
8,649
6,980
6,396
6,692
7,663
7,051
Washington
146
154
0
0
0
0
0
West
Virginia
48,335
29,745
37,498
36,460
32,309
33,122
28,686
Wyoming
6,671
14,745
14,339
13,624
10,812
11,497
13,201
Total
200,399
133,182
134,118
127,139
111,816
113,777
109,515
1 + Does not exceed 0.5 million cubic feet.
2 Note: The emission estimates provided above are inclusive of emissions from underground mines, surface mines and post-mining
3 activities. The following states have neither underground nor surface mining and thus report no emissions as a result of coal mining:
4 Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Maine, Massachusetts, Michigan, Minnesota, Nebraska, Nevada, New
5 Hampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, South Carolina, South Dakota, Vermont, and Wisconsin.
A-234 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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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 (2019) DGO Data Information System. Department of Mines, Minerals and Energy of Virginia. Available online at
.
EIA (2019) Annual Coal Report 2018. Table 1. Energy Information Administration, U.S. Department of Energy.
El Paso (2009) Shoal Creek Mine Plan, El Paso Exploration & Production.
EPA (2019) Greenhouse Gas Reporting Program (GHGRP) 2018 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). Correspondence between ERG and Buchanan Mine.
Geological Survey of Alabama State Oil and Gas Board (GSA) (2019) 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 (2019) Marshall County VAM Abatement Project Offset Verification Statement submitted to California Air
Resources Board, July 2019.
MSHA (2019) 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) (2019) Oil & Gas Production Data. Available online at
.
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37
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44
45
46
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3.5. Methodology for Estimating Cm, CO2, and N2O Emissions from Petroleum
Systems
For details on the emission factors, activity data, data sources and methodologies, and for emissions, emission
factors, and activity data, for each year from 1990-2018 please see spreadsheet file annexes for the current (i.e., 1990 to
2018) Inventory, available at https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-svstems. Summary
information is provided below.
TO BE UPDATED FOR FINAL INVENTORY REPORT
As described in the main body text on Petroleum Systems, the Inventory methodology involves the calculation of
CH4, C02, and N20 emissions for 73 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.
In recent Inventories, EPA has revised the emission estimation methodology for many sources in Petroleum
Systems. New data from studies and EPA's GHGRP (EPA 2018d,e) 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, the controlled and uncontrolled emission factors were
developed using data analyzed for the 2015 NSPS OOOOa proposal (EPA 2015a). 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 currently
applied for associated gas venting and flaring and miscellaneous production flaring.
For the refining segment, EPA has directly used the GHGRP data for all emission sources for recent years (2010
forward) (EPA 2018e) and developed source level throughput-based emission factors from GHGRP data to estimate
emissions in earlier time series years (1990-2009). For some sources, 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 for all major activities
since 2010. The national totals of these emissions for each activity were used for the 2010 to 2017 emissions. The national
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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 2015c).
Offshore emissions from shallow water and deep water oil platforms are taken from analysis of the 2011 Gulf-
wide Emission Inventory Study (EPA 2015b; BOEM 2014). The emission factors were assumed to be representative of
emissions from each source type over the period 1990 through 2016, and are used for each year throughout this period.
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
CH4 emissions 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. EPA newly calculated N20 emissions for the 1990 to2017 Inventory, as noted below.
1990-2017 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,62 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Other Updates
Considered for 2019 and Future GHGIs (EPA 2019), as well as the "Recalculations Discussion" section of the main body text.
In the exploration segment, EPA developed new estimates for oil well completions with hydraulic fracturing using
GHGRP emissions and activity data.
In the production segment, EPA developed new estimates for oil well workovers with hydraulic fracturing using
GHGRP emissions and activity data.
In the crude oil transportation segment, EPA newly calculated C02 estimates by multiplying the CH4 emission
factors for each source by a conversion factor, which is the ratio of C02 content and CH4 content in whole crude post-
separator.
EPA newly calculated N20 emissions for the 1990 to2017 Inventory using reported GHGRP data. This update was
applied for flaring sources in the exploration, production, and refining segments.
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, 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, the number of platforms in shallow water and the number of platforms in deep water
are used as activity data and are taken from Bureau of Ocean Energy Management (BOEM) (formerly Bureau of Ocean
Energy Management, Regulation, and Enforcement (BOEMRE)) datasets (BOEM 2011a,b,c). The activity data for the total
crude transported in the transportation segment is not available, therefore the activity data for the refining sector (i.e.,
refinery feed in 1000 bbl/year) was used also for the transportation sector, applying an assumption that all crude
62 Stakeholder materials including EPA memoranda for the current (i.e., 1990 to 2017) Inventory are available at <
https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems>.
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transported is received at refineries. In the few cases where no data were located, oil industry data based on expert
judgment was 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 2017) are not yet available, year 2016
or prior data has been used as proxy.
Methodology for well counts and events
EPA used Dl Desktop, a production database maintained by Drillinglnfo, Inc. (Drillinglnfo 2018), 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 and
workovers with hydraulic fracturing. For more information on the Drillinglnfo data processing, please see Annex 3.6
Methodology for Estimating CH4, C02, and N20 from Natural Gas Systems.
1990-2017 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,63 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Other Updates
Considered for 2019 and Future GHGIs (EPA 2019), as well as the "Recalculations Discussion" section of the main body text.
In the exploration segment, EPA updated the methodology for estimating the number of oil well completions
with hydraulic fracturing and oil wells drilled to use Drillinglnfo data (Drillinglnfo 2018).
In the production segment, EPA updated the methodology for estimating the number of oil well workovers with
hydraulic fracturing to use Drillinglnfo data (Drillinglnfo 2018). EPA also updated the EIA dataset that is used for national
oil production data; the new dataset allows EPA to exclude federal offshore production and focus explicitly on onshore
production data.
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. Emissions at a segment level are shown in Table 3.5-2, Table 3.5-7, and
Table 3.5-10.
Refer to the 1990-2017 Inventory section at https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
svstems 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
63 Stakeholder materials including EPA memoranda for the current (i.e., 1990 to 2017) Inventory are available at <
https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems>.
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1 References
2 API (1989) Aboveground Storage Tank Survey report prepared by Entropy Limited for American Petroleum Institute, April
3 1989.
4 API (1995) API 4615: Emission Factors For Oil and Gas Production Operations. American Petroleum Institute.
5 Washington, DC.
6 API (1996) API 4638: Calculation Workbook For Oil And Gas Production Equipment Fugitive Emissions. American
7 Petroleum Institute. Washington, DC.
8 API (2000) API 4697: Production Tank Emissions Model - A Program For Estimating Emissions From Hydrocarbon
9 Production Tanks - E&P Tank Version 2.0. American Petroleum Institute. Washington, DC.
10 API (2003) Basic Petroleum Data Book, 1990-2003. American Petroleum Institute. Washington, DC.
11 BOEM (2014) Year 2011 Gulfwide Emission Inventory Study. Bureau of Ocean Energy Management, U.S. Department of
12 Interior. OCS Study BOEM 2014-666. Available online at:
13
14 BOEMRE (2011a) Gulf of Mexico Region Offshore Information. Bureau of Ocean Energy Management, Regulation and
15 Enforcement, U.S. Department of Interior.
16 BOEMRE (2011b) Pacific OCS Region Offshore Information. Bureau of Ocean Energy Management, Regulation and
17 Enforcement, U.S. Department of Interior.
18 BOEMRE (2011c) GOM and Pacific OCS Platform Activity. Bureau of Ocean Energy Management, Regulation and
19 Enforcement, U.S. Department of Interior.
20 CAPP (1992) Canadian Association of Petroleum Producers (CAPP), A Detailed Inventory of CH4 and VOC Emissions from
21 Upstream Oil & Gas Operations in Alberta. March 1992.
22 Drillinglnfo (2018) July 2018 Download. Dl Desktop® Drillinglnfo, Inc.
23 EIA (2018a) Monthly Energy Review, 1995-2018 editions. Energy Information Administration, U.S. Department of Energy.
24 Washington, DC. Available online at: < http://www.eia.gov/totalenergy/data/monthly/index.cfm >.
25 EIA (2018b) Petroleum Supply Annual, 2001-2018 editions. U.S Department of Energy Washington, DC. Available online
26 at: .
27 EIA (2018c) Refinery Capacity Report, 2005-2018 editions. Energy Information Administration, U.S. Department of
28 Energy. Washington, DC. Available online at: .
29 EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards, U.S.
30 Environmental Protection Agency. Research Triangle Park, NC. October 1997.
31 EPA (2015a) Background Technical Support Document for the Proposed New Source Performance Standards 40 CFR Part
32 60, subpart OOOOa. Available online at: https://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2010-
33 0505-5021
34 EPA (2015b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Offshore Oil and Gas Platforms
35 Emissions Estimate. Available online at: .
37 EPA (2015c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Refineries Emissions Estimate.
38 Available online at: .
40 EPA (2015d) Inventory of U.S. GHG Emissions and Sinks 1990-2013: Revision to Well Counts Data. Available online at:
41 .
43 EPA (2016a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas and Petroleum
44 Production Emissions. Available online at: < https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
45 systems-ghg-inventory-additional-information-1990-2014-ghg >.
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EPA (2017a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015: Revisions to Natural Gas and Petroleum
Production Emissions. Available online at: < https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
systems-ghg-inventory-additional-information-1990-2015-ghg >.
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 (2018d) Greenhouse Gas Reporting Program - Subpart W - Petroleum and Natural Gas Systems. Environmental
Protection Agency. Data reported as of August 5, 2017.
EPA (2018e) Greenhouse Gas Reporting Program - Subpart Y - Petroleum Refineries. Environmental Protection Agency.
Data reported as of August 5, 2017.
EPA (2019) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Other Updates Considered for 2019 and
Future GHGIs. Available online at: .
EPA/GRI (1996) Methane Emissions from the Natural Gas Industry. Prepared by Radian. U.S. Environmental Protection
Agency. April 1996.
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.
OGJ (2018) Special Report: Pipeline Economics, 2005-2018 Editions. Oil & Gas Journal, PennWell Corporation, Tulsa, OK.
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.
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:
.
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3.6. Methodology for Estimating CH4, CO2, and N2O Emissions from Natural Gas
Systems
For details on the emission factors, activity data, data sources and methodologies, and for emissions, emission
factors, and activity data, for each year from 1990-2018 please see spreadsheet file annexes for the current (i.e., 1990 to
2018) Inventory, available at https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-svstems. Summary
information is provided below.
TO BE UPDATED FOR FINAL INVENTORY REPORT
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/GRI 1996), the Greenhouse Gas Reporting
Program (GHGRP) (EPA 2018d), 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.
In recent Inventories, EPA has revised the CH4 and C02 emission estimation methodology for many sources in
Natural Gas Systems. New data from studies and EPA's GHGRP (EPA 2018d) 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 example: 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; for gas well completions
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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; and 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 existing source-specific methodology was applied to calculate N20 emission
factors. EPA newly calculated N20 emissions for the 1990-2017 Inventory, as noted below.
1990-2017 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,64 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Updates for Natural Gas
Gathering & Boosting Emissions (EPA 2019a), Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Updates
for Liquefied Natural Gas Segment Emissions (EPA 2019b), and Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-
2017: Other Updates Considered for 2019 and Future GHGIs (EPA 2019c), as well as the "Recalculations Discussion" section
of the main body text.
In the production segment, EPA updated the methodology for gathering and boosting pipeline emissions to use
emission factors calculated from reported GHGRP data. In the transmission and storage segment, EPA updated the
methodology for transmission pipeline blowdowns and liquefied natural gas (LNG) terminals and storage facilities to use
emission factors calculated from reported GHGRP data. EPA newly calculated N20 emissions in the 1990-2017 Inventory
using reported GHGRP data. EPA developed estimates for N20 emissions from flaring sources in the exploration,
production, processing, and transmission and storage segments.
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 2016 data were used
as proxy for 2017 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 ofwells, 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 Dl Desktop, a production database maintained by Drillinglnfo, Inc. (Drillinglnfo 2018), 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 Dl Desktop for relevant data on an individual well
basis—including location, natural gas and liquids (i.e., oil and condensate) production by year, drill type (e.g., horizontal
or vertical), and date of completion or first production. Non-associated gas wells were classified as any well within Dl
Desktop that had non-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-
64 Stakeholder materials including EPA memoranda for the current (i.e., 1990 to 2017) Inventory are available at
.
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associated gas wells that were horizontally drilled and/or located in an unconventional formation (i.e., shale, tight sands,
or coalbed). Unconventional formations were identified based on well basin, reservoir, and field data reported in Dl
Desktop referenced against a formation type crosswalk developed by EIA (EIA 2012a).
For 1990 through 2010, gas well completions with hydraulic fracturing were identified as a subset of the gas wells
with hydraulic fracturing that had a date of completion or first production in the specified year. To calculate workovers for
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 Drillinglnfo data.
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 Dl Desktop data), 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.
1990-2017 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,65 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Updates for Natural Gas
Gathering & Boosting Emissions (EPA 2019a), Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Updates
for Liquefied Natural Gas Segment Emissions (EPA 2019b), and Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-
2017: Other Updates Considered for 2019 and Future GHGIs (EPA 2019c), as well as the "Recalculations Discussion" section
of the main body text.
In the exploration segment, EPA updated the data source for well drilling activity. In the production segment, EPA
updated the methodology forgathering and boosting pipeline emissions to use pipeline mileage as-reported in the GHGRP
data for years 2016 forward. In the transmission and storage segment, EPA updated the methodology for LNG terminals
and storage facilities to use activity factors calculated from reported GHGRP data.
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-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
65 Stakeholder materials including EPA memoranda for the current (i.e., 1990 to 2017) Inventory are available at
.
A-243
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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 2017, is that around 80 percent
of the reductions were estimated to occur in sources for which net emissions are now calculated, which yields an adjusted
"other reductions" estimate of 3 MMT C02 Eq.
Federal regulations
Regulatory actions reducing emissions in the current Inventory include National Emission Standards for
Hazardous Air Pollutants (NESHAP) regulations for dehydrator vents in the production segment. 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.
Previous Inventories also took into account NESHAP driven reductions from storage tanks and from dehydrators
in the processing segment; these sources are now estimated with net emission methodologies that take into account
controls implemented due to regulations. In addition to the NESHAP applicable to natural gas, the Inventory reflects the
2012 New Source Performance Standards (NSPS) subpart OOOO 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.
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 emissions 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 1990-2017 Inventory section at https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
svstems 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
• Table3.6-8: Activity Data for Natural Gas Systems, Data Sources/Methodology
• 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
A-244 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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• 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 Electronic Tables - References
A-245
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1 References
2 Alabama (2013) Alabama State Oil and Gas Board. Available online at .
3 API/ANGA (2012) Characterizing Pivotal Sources of Methane Emissions from Natural Gas Production - Summary and
4 Analysis of API andANGA Survey Responses. Final Report. American Petroleum Institute and America's Natural Gas
5 Alliance. September 21.
6 BOEM (2014) Year 2011 Gulfwide Emission Inventory Study. OCS Study BOEM 2014-666. Available online at <
7 https://www.boem.gOv/ESPIS/5/5440.pdf>
8 BOEMRE (2008) Personal communication. Bureau of Ocean Energy Management, Regulation and Enforcement, U.S.
9 Department of Interior.
10 BOEMRE (2011a) Gulf of Mexico Region Offshore Information. Bureau of Ocean Energy Management, Regulation and
11 Enforcement, U.S. Department of Interior.
12 BOEMRE (2011b) Pacific OCS Region Offshore Information. Bureau of Ocean Energy Management, Regulation and
13 Enforcement, U.S. Department of Interior.
14 BOEMRE (2011c) GOM and Pacific OCS Platform Activity. Bureau of Ocean Energy Management, Regulation and
15 Enforcement, U.S. Department of Interior.
16 Clearstone (2011) Clearstone Engineering, Development of Updated Emission Factors for Residential Meters, May 2011.
17 DOE (2018) LNG Annual Reports, 2004 -2017. U.S. Department of Energy, Washington, DC. Available online at:
18
19 Drillinglnfo (2018) Dl Desktop' July 2018 Download. Drillinglnfo, Inc.
20 EIA (1994) "Emissions of Greenhouse Gases in the United States: 1987-1992." Energy Information Administration, U.S.
21 Department of Energy, Washington, DC.
22 EIA (1996) "Emissions of Greenhouse Gases in the United States: 1987-1994." Energy Information Administration, U.S.
23 Department of Energy, Washington, DC.
24 EIA (2004) U.S. LNG Markets and Uses. Energy Information Administration, U.S. Department of Energy, Washington, DC.
25 June 2004. Available online at: .
27 EIA (2011) "Monthly Energy Review" Table 5.2, Crude Oil and Natural Gas Resource Development. Energy Information
28 Administration, U.S. Department of Energy, Washington, DC. Available online at:
29 .
30 EIA (2012) Formation crosswalk. Energy Information Administration, U.S. Department of Energy, Washington, DC.
31 Provided July 7.
32 EIA (2018a) "Natural Gas Gross Withdrawals and Production: Marketed Production." Energy Information Administration,
33 U.S. Department of Energy, Washington, DC. Available online at: .
34 EIA (2018b) Lease Condensate Production, 1989-2011, Natural Gas Navigator. Energy Information Administration, U.S.
35 Department of Energy, Washington, DC. Available online at .
36 EIA (2018c) "Table 1—Summary of natural gas supply and disposition in the United States 2011-2017." Natural Gas
37 Monthly, Energy Information Administration, U.S. Department of Energy, Washington, DC. Available online at
38 .
39 EIA (2018d) "Table 2—Natural Gas Consumption in the United States 2011-2016." Natural Gas Monthly, Energy
40 Information Administration, U.S. Department of Energy, Washington, DC. Available online at
41 .
42 EIA (2018e) "Natural Gas Annual Respondent Query System. Report 191 Field Level Storage Data (Annual)." Energy
43 Information Administration, U.S. Department of Energy, Washington, DC. Available online at <
44 https://www.eia.gov/cfapps/ngqs/ngqs.cfm?f_report=RP7>.
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EIA (2018f) "U.S. Natural Gas Imports, 2015-2017." Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at .
EIA (2018g) Number of Natural Gas Consumers. Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at: .
EIA (2018h) "Monthly Energy Review" Table A4, Approximate Heat Content of Natural Gas. Energy Information
Administration, U.S. Department of Energy, Washington, DC. Available online at:
.
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: .
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 (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: .
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44
EPA (2018d) Greenhouse Gas Reporting Program- Subpart W - Petroleum and Natural Gas Systems. Environmental
Protection Agency. Data reported as of August 19, 2018.
EPA (2019a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Updates for Natural Gas Gathering &
Boosting Emissions. Available online at: .
EPA (2019b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Updates for Liquefied Natural Gas
Segment Emissions. 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: .
FERC (2017) North American LNG Terminals. Federal Energy Regulatory Commission, Washington, DC.
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.
GTI (2001) Gas Resource Database: Unconventional Natural Gas and Gas Composition Databases. Second Edition. GRI-
01/0136.
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.
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.
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:
.
PHMSA (2018a) "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 (2018b) "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 (2018c) 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.
Wyoming (2013) Wyoming Oil and Gas Conservation Commission. Available online at:
.
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.
A-248 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 3.7. Methodology for Estimating CO2, CH4, and N2O Emissions from the Incineration
2 of Waste
3 Emissions of C02 from the incineration of waste include C02 generated by the incineration of plastics, synthetic
4 rubber and synthetic fibers in municipal solid waste (MSW), and incineration of tires (which are composed in part of
5 synthetic rubber and C black) in a variety of other combustion facilities (e.g., cement kilns). Incineration of waste also
6 results in emissions of CH4 and N20. The emission estimates are calculated for all four sources on a mass-basis based on
7 the data available. The methodology for calculating emissions from each of these waste incineration sources is described
8 in this Annex.
9 C02 from Plastics Incineration
10 In the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports
11 (EPA 1999 through 2003, 2005 through 2014), Advancing Sustainable Materials Management: Facts and Figures -
12 Assessing Trends in Material Generation, Recycling and Disposal in the United States (EPA 2015; EPA 2016; EPA 2018; EPA
13 2019) the flows of plastics in the U.S. waste stream are reported for seven resin categories. For 2018, the quantity
14 generated, recovered, and discarded for each resin is shown in Table A-133. The data set for 1990 through 2018 is
15 incomplete, and several assumptions were employed to bridge the data gaps. The EPA reports do not provide estimates
16 for individual materials landfilled and incinerated, although they do provide such an estimate for the waste stream as a
17 whole. To estimate the quantity of plastics landfilled and incinerated, total discards were apportioned based on the
18 proportions of landfilling and incineration for the entire U.S. waste stream for each year in the time series according to
19 Biocycle's State of Garbage in America (van Haaren et al. 2010), and Shin (2014). For those years when distribution by resin
20 category was not reported (1990 through 1994), total values were apportioned according to 1995 (the closest year)
21 distribution ratios. Generation and recovery figures for 2002 and 2004 were linearly interpolated between surrounding
22 years' data.
23 Table A-133: 2018 Plastics in the Municipal Solid Waste Stream by Resin (kt)
Waste Pathway
PET
HDPE
PVC
LDPE/
LLDPE
PP
PS
Other
Total
Generation
4,545
5,79
871
7,330
7,258
2,132
4,373
32,088
Recovery
826
526
0
308
45
9
971
2,685
Discard
3,720
5,053
871
7,022
7,212
2,123
3,402
29,402
Landfill
3,437
4,669
805
6,488
6,664
1,962
3,143
27,168
Combustion
283
384
66
534
548
161
259
2,235
Recovery3
18%
9%
0%
4%
1%
0%
22%
8%
Discard3
82%
91%
100%
96%
99%
100%
78%
92%
Landfill3
76%
84%
92%
89%
92%
92%
72%
85%
Combustion3
6%
7%
8%
7%
8%
8%
6%
7%
24 a As a percent of waste generation.
25 Note: Totals may not sum due to independent rounding. Abbreviations: PET (polyethylene terephthalate), HDPE (high density
26 polyethylene), PVC (polyvinyl chloride), LDPE/LLDPE (linear low density polyethylene), PP (polypropylene), PS (polystyrene).
27
28 Fossil fuel-based C02 emissions were calculated as the product of plastic combusted, C content, and fraction
29 oxidized (see Table A-134). The C content of each of the six types of plastics is listed, with the value for "other plastics"
30 assumed equal to the weighted average of the six categories. The fraction oxidized was assumed to be 98 percent.
31 Table A-134: 2018 Plastics Incinerated (kt), Carbon Content (%), Fraction Oxidized (%) and Carbon Incinerated (kt)
Factor
PET
HDPE
PVC
LDPE/
LLDPE
PP
PS
Other
Total
Quantity Combusted
283
384
66
534
548
161
259
2,235
Carbon Content of Resin
63%
86%
38%
86%
86%
92%
66%
NA
Fraction Oxidized
98%
98%
98%
98%
98%
98%
98%
NA
Carbon in Resin Combusted
173
323
25
448
460
146
167
1,742
Emissions (MMT C02 Eq.)
0.6
1.2
0.1
1.6
1.7
0.5
0.6
6.4
32 NA (Not Applicable)
33 Note: Totals may not sum due to independent rounding.
34
A-249
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C02 from Incineration of Synthetic Rubber and Carbon Black in Tires
Emissions from tire incineration require two pieces of information: the amount of tires incinerated and the C
content of the tires. "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.66 Table A-135 shows consumption and C
content of elastomers used for tires and other products in 2002, the most recent year for which data are
available.
• C black is 100 percent C (Aslett Rubber Inc. n.d.).
Multiplying the mass of scrap tires incinerated by the total C content of the synthetic rubber, C black portions of
scrap tires, and then by a 98 percent oxidation factor, yielded C02 emissions, as shown in Table A-136. 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-135 (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 2018 were taken from RMA 2006; RMA 2009; RMA 2011; RMA 2014a; RMA 2016; RMA 2018, where data were
not reported, they were linearly interpolated between bracketing years' data or, for the ends of time series, set equal to
the closest year with reported data.
In 2009, RMA changed the reporting of scrap tire data from millions of tires to thousands of short tons of scrap
tire. As a result, the average weight and percent of the market of light duty and commercial scrap tires was used to convert
the previous years from millions of tires to thousands of short tons (STMC 1990 through 1997; RMA 2002 through RMA
2006; RMA 2014b; RMA 2016; RMA 2018).
Table A-135: 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
NA (Not Applicable)
a Used to calculate C content of non-tire rubber products in municipal solid waste.
66The carbon content of tires (1,174 kt C) divided by the mass of rubber in tires (1,307 kt) equals 90 percent.
A-250 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Note: Totals may not sum due to independent rounding.
2
3 Table A-136: Scrap Tire Constituents and CP2 Emissions from Scrap Tire Incineration in 2018
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%
1.4
Total
0.7
NA
NA
2.6
4 NA (Not Applicable)
5 CO2 from Incineration of Synthetic Rubber in Municipal Solid Waste
6 Similar to the methodology for scrap tires, C02 emissions from synthetic rubber in MSW were estimated by
7 multiplying the amount of rubber incinerated by an average rubber C content. The amount of rubber discarded in the
8 MSW stream was estimated from generation and recycling data67 provided in the Municipal Solid Waste Generation,
9 Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2014),
10 Advancing Sustainable Materials Management: Facts and Figures: Assessing Trends in Material Generation, Recycling and
11 Disposal in the United States (EPA 2015; EPA 2016; EPA 2018; EPA 2019), and unpublished backup data (Schneider 2007).
12 The reports divide rubber found in MSW into three product categories: other durables (not including tires), non-durables
13 (which includes clothing and footwear and other non-durables), and containers and packaging. EPA (2018) did not report
14 rubber found in the product category "containers and packaging;" however, containers and packaging from miscellaneous
15 material types were reported for 2009 through 2018. As a result, EPA assumes that rubber containers and packaging are
16 reported under the "miscellaneous" category; and therefore, the quantity reported for 2009 through 2018 were set equal
17 to the quantity reported for 2008. Since there was negligible recovery for these product types, all the waste generated is
18 considered to be discarded. Similar to the plastics method, discards were apportioned into landfilling and incineration
19 based on their relative proportions, for each year, for the entire U.S. waste stream. The report aggregates rubber and
20 leather in the MSW stream; an assumed synthetic rubber content of 70 percent was assigned to each product type, as
21 shown in Table A-137.68 A C content of 85 percent was assigned to synthetic rubber for all product types (based on the
22 weighted average C content of rubber consumed for non-tire uses), and a 98 percent fraction oxidized was assumed.
23 Table A-137: Rubber and Leather in Municipal Solid Waste in 2018
Incinerated
Synthetic
Carbon Content
Fraction
Emissions
Product Type
(kt)
Rubber(%)
(%)
Oxidized (%)
(MMT C02 Eq.)
Durables (not Tires)
259
70%
85%
98%
0.8
Non-Durables
81
NA
NA
NA
0.3
Clothing and Footwear
61
70%
85%
98%
0.2
Other Non-Durables
19
70%
85%
98%
0.1
Containers and Packaging
2
70%
85%
98%
0.0
Total
341
NA
NA
NA
1.1
24 NA (Not Applicable)
25
26 CO2 from Incineration of Synthetic Fibers
27 Carbon dioxide emissions from synthetic fibers were estimated as the product of the amount of synthetic fiber
28 discarded annually and the average C content of synthetic fiber. Fiber in the MSW stream was estimated from data
29 provided in the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports
30 (EPA 1999 through 2003, 2005 through 2014) and Advancing Sustainable Materials Management: Facts and Figures -
31 Assessing Trends in Material Generation, Recycling and Disposal in the United States (EPA 2015; EPA 2016; EPA 2018; EPA
32 2019) for textiles. Production data for the synthetic fibers was based on data from the American Chemical Society (FEB
33 2009). The amount of synthetic fiber in MSW was estimated by subtracting (a) the amount recovered from (b) the waste
34 generated (see Table A-138). As with the other materials in the MSW stream, discards were apportioned based on the
35 annually variable proportions of landfilling and incineration for the entire U.S. waste stream, as found in van Haaren et al.
36 (2010), and Shin (2014). It was assumed that approximately 55 percent of the fiber was synthetic in origin, based on
67 Discards = Generation minus recycling.
68 As a sustainably harvested biogenic material, the incineration of leather is assumed to have no net C02 emissions.
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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 2018 fiber production (see Table A-139). 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-138: 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
8,385
1,276
7,109
540
fable A-139: Synthetic Fiber Production in 2018
Fiber
Production (MMT)
Carbon Content
Polyester
1.3
63%
Nylon
0.5
64%
Olefin
1.1
86%
Acrylic
+
68%
Total
3.0
72%
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) 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 an N20 emissions control
removal efficiency. The mass of waste incinerated was derived from the results of the biannual national survey of
A-252 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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25
26
Municipal Solid Waste (MSW) Generation and Disposition in the U.S., published in BioCycle (van Haaren et al. 2010), and
Shin (2014). For waste incineration in the United States, 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) for 1990 through 2008. Data
for 2011 were derived from information in Shin (2014). For waste incineration in the United States, 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). No information was available on the mass of waste
incinerated for 2012 through 2018, so these values were assumed to be equal to the 2011 value.
Despite the differences in methodology and data sources, the two series of references (EPA 2014; van Haaren, Rob,
Themelis, N., and Goldstein, N. 2010) provide estimates of total solid waste incinerated that are relatively consistent (see
Table A-140).
Table A-140: U.S. Municipal Solid Waste Incinerated, as Reported by EPA and BioCycle (Metric Tons)
Year
EPA
BioCycle
1990
28,939,680
30,632,057
1995
32,241,888
29,639,040
2000
30,599,856
25,974,978
2001
30,481,920
25,942,036a
2002
30,255,120
25,802,917
2003
30,028,320
25,930,542b
2004
28,585,872
26,037,823
2005
28,685,664
25,973,520c
2006
28,985,040
25,853,401
2007
29,003,184
24,788,539d
2008
28,622,160
23,674,017
2009
26,317,872
22,714,122e
2010
26,544,672
21,741,734e
2011
26,544,672
20,756,870
2012
26,544,672
20,756,870'
2013
29,629,152
20,756,870'
2014
30,136,361
20,756,870'
2015
30,561,950
20,756,870'
2016
31,111,134
20,756,870'
2017
31,224,236
20,756,870'
2018
31,224,236s
20,756,870'
a Interpolated between 2000 and 2002
values.
b Interpolated between 2002 and 2004
values.
c Interpolated between 2004 and 2006
values.
d Interpolated between 2006 and 2008
values.
e Interpolated between 2011 and 2008
values.
' Set equal to the 2011 value.
8 Set equal to the 2017 value.
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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 (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 <
https://www.epa.gov/facts-and-figures-about-materials-waste-and-recycling/advancing-sustainable-materials-
management >.
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.
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.
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1 Kaufman, et al. (2004) "14th Annual BioCycle Nationwide Survey: The State of Garbage in America 2004" Biocycle, JG
2 Press, Emmaus, PA. January, 2004.RMA (2018) 2017 U.S. Scrap Tire Management Summary. Rubber Manufacturers
3 Association, Washington, D.C. July 2018.
4 https://www.ustires.org/system/files/USTMA_scraptire_summ_2017_072018.pdf. September 27, 2018
5 RMA (2018) "2017 U.S. Scrap Tire Management Summary". Rubber Manufacturers Association, Washington, D.C. July
6 2018. Available online at: .
7 RMA (2016) "2015 U.S. Scrap Tire Management Summary." Rubber Manufacturers Association. August 2016. Available
8 online at: < https://www.ustires.org/sites/default/files/MAR_028_USTMA.pdf>.
9 RMA (2014a) "2013 U.S. Scrap Tire Management Summary." Rubber Manufacturers Association. November 2014.
10 Available online at: . Accessed
11 17 November 2014.
12 RMA (2014b) "Scrap Tire Markets: Facts and Figures - Scrap Tire Characteristics." Available online at:
13 . Accessed 17 November 2014.
14 RMA (2012) "Rubber FAQs." Rubber Manufacturers Association. Available online at . Accessed 19 November 2014.
16 RMA (2011) "U.S. Scrap Tire Management Summary 2005-2009." Rubber Manufacturers Association. October 2011.
17 Available online at: .
18 RMA (2009) "Scrap Tire Markets in the United States: 9th Biennial Report." Rubber Manufacturers Association.
19 Washington, D.C. May 2009.
20 RMA (2002 through 2006) "U.S. Scrap Tire Markets." Rubber Manufacturers Association. Washington, D.C. Available
21 online at: .
22 Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah Shapiro of ICF
23 International, January 10, 2007.
24 Shin, D. (2014) Generation and Disposition of Municipal Solid Waste (MSW) in the United States-A National Survey.
25 Thesis. Columbia University, Department of Earth and Environmental Engineering, January 3, 2014.
26 Simmons, et al. (2006) "15th Nationwide Survey of Municipal Solid Waste Management in the United States: The State of
27 Garbage in America" BioCycle, JG Press, Emmaus, PA. April 2006.
28 STMC (1990 through 1997) "Scrap Tire Use/Disposal Study". Rubber Manufacturers Association: Scrap Tire Management
29 Council. Available online at: .
30 Themelis and Shin (2014) U.S. Survey of Generation and Disposition of Municipal Solid Waste. Waste Management.
31 Columbia University. January 2014. .
32 van Haaren, Rob, Thermelis, N., and Goldstein, N. (2010) "The State of Garbage in America." BioCycle, October 2010.
33 Volume 51, Number 10, pg. 16-23.
34
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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 2018 fuel sales in the Continental United States (CONUS).69
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 JP-8 between Aviation and Land-based Vehicles
As a result of DoD70 and NATO71 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-141 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).
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.
69 FAS contains data for 1995 through 2018, 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.
70 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.
71 NATO Standard Agreement NATO STANAG 4362, Fuels for Future Ground Equipment Using Compression Ignition orTurbine Engines, 2012.
A-256 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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• 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 2000.72
Table A-142 and Table A-143 display DoD bunker fuel use totals for the Navy and Air Force.
Step 6: Calculate Emissions from International Bunker Fuels
Bunker fuel totals were multiplied by appropriate emission factors to determine 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-142 and Table A-143, below. C02 emissions
72 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-257
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from aviation bunkers and distillate marine bunkers are presented in Table A-146, and are based on emissions from fuels
tallied in Table A-142 and Table A-143.
A-258 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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Table A-141: Transportation Fuels from Domestic Fuel Deliveries3 (Million Gallons)
Vehicle Type/Fuel
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Aviation
4,598.4
3,099.9
2,664.4
2,338.1
2,067.8
1,814.5
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
Total Jet Fuels
4,598.4
3,099.9
2,664.4
2,338.0
2,067.7
1,814.3
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
JP8
285.7
2,182.8
2,122.7
1,838.8
1,616.2
1,358.2
1,100.1
882.8
865.2
718.0
546.6
126.6
(9.52)
(11.38)
1.92
JP5
1,025.4
691.2
472.1
421.6
362.2
361.2
399.3
372.3
362.5
316.4
311.0
316.4
320.4
316.3
304.1
Other Jet Fuels
3,287.3
225.9
69.6
77.6
89.2
94.8
164.3
149.7
221.8
301.7
821.6
1,220.5
1,246.9
1,232.7
1,175.9
Aviation Gasoline
+
+
+
0.1
0.1
0.2
0.2
0.2
0.3
0.2
0.3
0.3
0.3
0.2
0.3
Marine
686.8
438.9
454.4
604.9
563.4
485.8
578.8
489.9
490.4
390.4
427.9
421.7
412.4
395.2
370.9
Middle Distillate (MGO)
0.0
0.0
48.3
54.0
55.2
56.8
48.4
37.3
52.9
40.9
62.0
56.0
23.1
24.4
19.9
Naval Distillate (F76)
686.8
438.9
398.0
525.9
483.4
399.0
513.7
440.0
428.4
345.7
362.7
363.3
389.1
370.8
351.0
Intermediate Fuel Oil
(IFO)b
0.0
0.0
8.1
25.0
24.9
30.0
16.7
12.5
9.1
3.8
3.2
2.4
0.1
0.0
0.0
Other0
717.1
310.9
248.2
205.6
173.6
206.8
224.0
208.6
193.8
180.6
190.7
181.1
178.3
165.8
170.4
Diesel
93.0
119.9
126.6
56.8
49.1
58.3
64.1
60.9
57.9
54.9
57.5
54.8
54.7
50.4
51.8
Gasoline
624.1
191.1
74.8
24.3
19.7
25.2
25.5
22.0
19.6
16.9
16.5
16.2
15.9
15.6
14.7
Jet Fueld
0.0
0.0
46.7
124.4
104.8
123.3
134.4
125.6
116.2
108.8
116.7
110.1
107.6
99.9
104.0
Total (Including
Bunkers)
6,002.4
3,849.8
3,367.0
3,148.6
2,804.9
2,507.1
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
1+ Indicates value does not exceed 0.05 million gallons.
2a Includes fuel distributed in the United States and U.S. Territories.
3b Intermediate fuel oil (IFO 180 and IFO 380) is a blend of distillate and residual fuels. IFO is used by the Military Sealift Command.
4C 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
5Service 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
6and-21.1 percent, respectively. However, prior diesel fuel estimates from 1997through 2000 were reduced according to the estimated consumption of jet fuel that is assumed to have replaced
7the 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
8method 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.
9d 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.
lONotes: Totals may not sum due to independent rounding. Parentheses indicate negative values. The negative values in this table represent returned products.
11
12
A-259
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1 Table A-142: Total U.S. Military Aviation Bunker Fuel (Million Gallons)
Fuel Type/Service 1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Jet Fuels
JP8
56.7
300.4
307.6
285.6
229.4
211.4
182.5
143.4
141.2
122.0
88.0
17.2
2.4
2.5
2.9
Navy
56.7
38.3
53.4
70.9
59.2
55.4
60.8
47.1
50.4
48.9
31.2
0.8
5.5
6.4
4.8
Air Force
+
262.2
254.2
214.7
170.3
156.0
121.7
96.2
90.8
73.0
56.7
16.4
(3.14)
(3.85)
(1.92)
JP5
370.5
249.8
160.3
160.6
139.2
137.0
152.5
144.9
141.2
124.9
121.9
124.1
126.1
124.7
120.1
Navy
365.3
246.3
155.6
156.9
136.5
133.5
149.7
143.0
139.5
123.6
120.2
122.6
124.7
123.4
118.9
Air Force
5.3
3.5
4.7
3.7
2.6
3.5
2.8
1.8
1.7
1.3
1.6
1.5
1.4
1.3
1.2
JP4
420.8
21.5
+
+
+
+
0.1
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
JAA
13.7
9.2
12.5
15.5
16.8
18.1
31.4
31.1
38.6
46.5
128.0
199.8
203.7
198.9
191.8
Navy
8.5
5.7
7.9
11.6
12.5
12.3
13.7
14.6
14.8
13.4
36.1
71.7
72.9
67.8
68.1
Air Force
5.3
3.5
4.5
3.9
4.3
5.9
17.7
16.5
23.8
33.1
91.9
128.1
130.8
131.1
123.7
JA1
+
+
+
0.5
1.0
0.6
0.3
(0.5)
(0.3)
0.6
1.1
0.3
0.5
0.2
0.5
Navy
+
+
+
+
0.1
0.1
0.1
(0.5)
(0.3)
0.6
0.7
+
0.1
(+)
+
Air Force
+
+
+
0.5
0.8
0.5
0.1
(0.1)
(+)
+
0.5
0.3
0.5
0.2
0.5
JAB
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Navy
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Air Force
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Navy Subtotal 430.5
Air Force Subtotal 431.3
290.2
290.7
216.9
263.5
239.4
222.9
208.3
178.1
201.3
165.9
224.4
142.4
204.3
114.5
204.5
116.3
186.5
107.4
188.2
150.7
195.0
146.4
203.2
129.5
197.5
128.8
191.8
123.5
Total
861.8
580.9
480.4
462.3
386.3
367.2
366.7
318.8
320.8
293.9
339.0
341.4
332.8
326.3
315.3
2 + Does not exceed 0.05 million gallons.
3 Notes: Totals may not sum dueto independent rounding. Parentheses indicate negative values. The negative values in this table represent returned products.
4
5
A-260 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Table A-143: Total U.S. DoD Maritime Bunker Fuel (Million Gallons)
Marine
Distillates
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Navy - MGO
0.0
0.0
23.8
38.0
40.9
39.9
32.9
25.5
36.5
32.3
43.3
37.8
5.7
13.2
8.5
Navy - F76
522.4
333.8
298.6
413.1
376.9
311.4
402.2
346.6
337.9
273.1
286.2
286.7
307.8
293.3
276.9
Navy - IFO
0.0
0.0
6.4
19.7
19.0
23.1
12.9
9.5
6.1
3.0
1.5
1.9
+
0.0
0.0
Total
522.4
333.8
328.8
470.7
436.7
374.4
448.0
381.5
380.6
308.5
331.0
326.3
313.6
306.5
285.4
2 + Does not exceed 0.05 million gallons.
3 Note: Totals may not sum due to independent rounding.
4
5 Table A-144: 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)
20.17
1.00
Marine (Residual)
20.48
1.00
6 Source: EPA (2010) and IPCC (2006).
7
8 Table A-145: Annual Variable Carbon Content Coefficient for Jet Fuel (MMT Carbon/QBtu)
Fuel 1990
1995
2000
2005
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Jet Fuel 19.40
19.34
19.70
19.70
19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70
9 Source: EPA (2010).
10
11 Table A-146: Total U.S. DoD CP2 Emissions from Bunker Fuels (MMT CP2 Eq.)
Mode 1990
1995
2000
2005
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Aviation 8.1
Marine 5.4
5.5
3.4
4.7
3.4
4.5
4.8
3.8 3.6 3.6 3.1 3.1 2.9 3.3 3.3 3.3 3.2 3.1
4.5 3.8 4.6 3.9 3.9 3.2 3.4 3.3 3.2 3.1 2.9
Total 13.4
9.0
8.0
9.3
8.2 7.4 8.2 7.0 7.0 6.0 6.7 6.7 6.5 6.3 6.0
12 Note: Totals may not sum due to independent rounding.
13
A-261
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1 References
2 DLA Energy (2019) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense Energy
3 Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, D.C.
4 EPA (2010) Carbon Content Coefficients Developed for EPA's Inventory of Greenhouse Gases and Sinks. Office of Air and
5 Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
6 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
7 Programme, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K.
8 Tanabe (eds.). Hayama, Kanagawa, Japan.
9
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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 Model was developed as a tool for estimating the annual chemical
emissions from industrial sectors that have historically used ODS in their products. Under the terms of the Montreal
Protocol and the United States Clean Air Act Amendments of 1990, the domestic U.S. consumption of ODS—
chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs)—has
been drastically reduced, forcing these industrial sectors to transition to more ozone friendly chemicals. As these industries
have moved toward ODS alternatives such as hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs), the Vintaging Model
has evolved into a tool for estimating the rise in consumption and emissions of these alternatives, and the decline of ODS
consumption and emissions.
The Vintaging Model estimates emissions from five ODS substitute (i.e., HFC-emitting) end-use sectors:
refrigeration and air-conditioning, foams, aerosols, solvents, and fire-extinguishing. Within these sectors, there are 68
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-43-
lOmee, 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.
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
A-263
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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 overtime 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-264 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Step 1: Calculate first-fill emissions
2 The first-fill emission equation assumes that a certain percentage of the chemical charge will be emitted to the
3 atmosphere when the equipment is charged with refrigerant during manufacture or installation. First-fill emissions are
4 considered for all Ref/AC equipment that are charged with refrigerant within the United States, including those which are
5 produced for export, and excluding those that are imported pre-charged. First-fill emissions are thus a function of the
6 quantity of chemical contained in new equipment and the proportion of equipment that are filled with refrigerant in the
7 United States:
8 Efj = Qq x |f x Aj
9 where:
10 Ef = Emissions from Equipment First-fill. Emissions in yeary from filling new equipment.
11 Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to
12 charge new equipment in year j, by weight.
13 If = First-fill Leak Rate. Average leak rate during installation or manufacture of new
14 equipment (expressed as a percentage of total chemical charge).
15 A = Applicability of First-fill Leak Rate. Percentage of new equipment that are filled with
16 refrigerant in the United States in year j.
17 j = Year of emission.
18 Step 2: Calculate lifetime emissions
19 Emissions from any piece of equipment include both the amount of chemical leaked during equipment operation
20 and the amount emitted during service. Emissions from leakage and servicing can be expressed as follows:
21 ESj = (la + ls)xl QCj-n-t fori = l^k
22 where:
23 Es = Emissions from Equipment Serviced. Emissions in year j from normal leakage and
24 servicing (including recharging) of equipment.
25 la = Annual Leak Rate. Average annual leak rate during normal equipment operation
26 (expressed as a percentage of total chemical charge).
27 ls = Service Leak Rate. Average leakage during equipment servicing (expressed as a
28 percentage of total chemical charge).
29 Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to
30 charge new equipment in a given year by weight.
31 , = Counter, runs from 1 to lifetime (k).
32 j = Year of emission.
33 k = Lifetime. The average lifetime of the equipment.
34 Step 3: Calculate disposal emissions
35 The disposal emission equations assume that a certain percentage of the chemical charge will be emitted to the
36 atmosphere when that vintage is discarded, while remaining refrigerant is assumed to be recovered and reused. Disposal
37 emissions are thus a function of the quantity of chemical contained in the retiring equipment fleet and the proportion of
38 chemical released at disposal:
39 Edj = QCj-k+i x [1 - (rm x rc)]
40 where:
A-265
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25
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27
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-147, 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-266 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Table A-147: Refrigeration and Air-Conditioning 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
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Equipment1
Penetration
Rate7
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%
A-267
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Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Equipment1
Penetration
Rate7
R-450A
2018
2024
49%
R-513A
2018
2024
49%
HCFO-
HCFC-123
1993
1994
31%
1233zd(E)
R-514A
HCFO-
1233zd(E)
R-514A
2016
2017
2017
2018
2016
2017
2020
2020
1%
1%
49%
49%
None
None
None
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
1.3%
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 1 1
1.3%
A-268 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Equipment1
Penetration
Rate7
R-410A
2001
2005
18%
None
R-410A
2006
2009
8%
None
R-410A
2009
2010
71%
None
Dehumidifiers
HCFC-22
HFC-134a
1997
1997
89%
None
1.3%
R-410A
2007
2010
11%
None
Ice Makers
CFC-12
HFC-134a
1993
1995
25%
None
2.1%
R-404A
1993
1995
75%
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%
A-269
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Equipment1
Penetration
Rate7
HFO-1234yf
2016
2021
99%
None
Mobile Air Conditioners (Light Duty Trucks)
CFC-12
HFC-134a
1993
1994
100%
HFO-1234yf
HFO-1234yf
2012
2016
2015
2021
1%
99%
None
None
1.4%
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-222
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%
A-270 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Initial
Market
Segment
HCFC-22
Primary Substitute
Secondary Substitute
Tertiary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Name of
Substitute
Start Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
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
HFC-134a
2000
2009
9%
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
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
A-271
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Equipment1
Penetration
Rate7
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%
R-450A
2021
2021
7%
None
R-513A
2021
2021
7%
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
R-410A
2007
2010
2010
2010
29%
71%
None
None
1.3%
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)
DX3
DX
2001
2006
67.5%
DX
2006
2015
62%
None
1.7%
DR4
2000
2015
23%
None
SLS5
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-5026
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%
A-272 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Equipment1
Penetration
Rate7
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
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 (Small)
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%
A-273
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Equipment1
Penetration
Rate7
HCFC-22
1993
1995
30%
R-410A
2000
2003
5%
None
R-404A
2006
2010
95%
R-452A
2017
2021
5%
R-452A
2021
2030
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)
HCFC-22
HFC-134a
1993
1995
10%
None
5.7%
R-507
1994
1995
10%
None
R-404A
1993
1995
10%
None
HCFC-22
1993
1995
70%
R-407C
2000
2005
3%
R-410A
2005
2007
100%
R-507
2006
2010
49%
None
R-404A
2006
2010
49%
None
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
A-274 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Equipment1
Penetration
Rate7
R-513A
2019
2019
50%
None
Water-Source and Ground-Source Heat Pumps
HCFC-22
R-407C
2000
2006
5%
None
1.3%
R-410A
2000
2006
5%
None
HFC-134a
2000
2009
2%
None
R-407C
2006
2009
2.5%
None
R-410A
2006
2009
4.5%
None
HFC-134a
2009
2010
18%
None
R-407C
2009
2010
22.5%
None
R-410A
2009
2010
40.5%
None
Window Units
HCFC-22
R-410A
2008
2009
10%
None
4.0%
R-410A
2009
2010
90%
None
1 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.
2 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.
3 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.
4 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.
5 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.
6 The CFC-12 large retail food market for new systems transitioned to R-B02 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.
7 Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
1
A-275
-------
1 Table A-148 presents the average equipment lifetimes and annual HFC emission rates (for first-fill, servicing,
2 leaks, and disposal) for each end-use assumed by the Vintaging Model.
3 Table A-148: Refrigeration and Air-Conditioning Lifetime Assumptions
End-Use
Lifetime
(Years)
HFC Emission Rates
(First-fill)3
(%)
HFC Emission Rates
(Servicing and Leaks)
(%)
HFC Emission Rates
(Disposal)b
(%)
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
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
Mobile Air Conditioners
5-16
0.2-0.5
2.3-18.0
43-50
Positive Displacement Chillers
20
0.2-0.5
0.5-1.5
10
PTAC/PTHP
12
1
3.9
40
Retail Food
10-20
0.5-3
1.0-25
10-35
Refrigerated Appliances
14
0.6
0.6
42
Residential Unitary A/C
15
0.2-1
5.3-10.6
20-40
Transport Refrigeration
9-40
0.2-1
19.4-36.4
10-65
Water & Ground Source Heat Pumps
20
1
3.9
43
Window Units
12
0.5-1
0.6
50
a For some equipment, first-fill emissions are adjusted to account for equipment that are produced in the United States, including those
which are produced for export, and excluding those that are imported pre-charged estimate.
b Disposal emissions rates are developed based on consideration of the original charge size, the percentage of refrigerant likely to remain
in equipment at the time of disposal, and recovery practices assumed to vary by gas type. Because equipment lifetime emissions are
annualized, equipment is assumed to reach the end of its lifetime with a full charge. Therefore, recovery rate is equal to 100 percent -
Disposal Loss Rate (%).
4 Aerosols
5 ODSs, HFCs, and many other chemicals are used as propellant aerosols. Pressurized within a container, a nozzle
6 releases the chemical, which allows the product within the can to also be released. Three types of aerosol products are
7 modeled: metered dose inhalers (MDI), consumer aerosols, and technical aerosols. In the United States, the use of CFCs
8 in consumer aerosols was banned in 1978, and many products transitioned to hydrocarbons or "not-in-kind" technologies,
9 such as solid deodorants and finger-pump hair sprays. However, MDIs and certain technical aerosols continued to use CFCs
10 and HCFCs as propellants because their use was deemed essential. Essential use exemptions granted to the United States
11 under the Montreal Protocol for CFC use in MDIs were limited to the treatment of asthma and chronic obstructive
12 pulmonary disease. Under the Clean Air Act, the use of CFCs and HCFCs was also exempted in technical aerosols for several
13 applications, including industrial cleaners, pesticides, mold release agents, certain dusters, and lubricants.
14 All HFCs used in aerosols are assumed to be emitted in the year of manufacture. Since there is currently no
15 aerosol recycling, it is assumed that all of the annual production of aerosol propellants is released to the atmosphere. The
16 following equation describes the emissions from the aerosols sector.
17 E, = Qq
18 where:
19 E = Emissions. Total emissions of a specific chemical in year j from use in aerosol
20 products, by weight.
21 Qc = Quantity of Chemical. Total quantity of a specific chemical contained in aerosol
22 products sold in year j, by weight.
23 j = Year of emission.
A-276 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 Transition Assumptions
2 Transition assumptions and growth rates for those items that use ODSs or HFCs as propellants, including vital
3 medical devices and specialty consumer products, are presented in Table A-149.
4
A-277
-------
1 Table A-149: 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
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Rate4
MDIs
HFC-134a
1997
1997
6%
None
Non-
ODP/GWP
1998
2007
7%
None
CFC Mix3
2000
2000
87%
HFC-134a
2002
2002
34%
None
HFC-134a
2003
2009
47%
None
HFC-227ea
2006
2009
5%
None
HFC-134a
2010
2011
6%
None
HFC-227ea
2010
2011
1%
None
HFC-134a
2011
2012
3%
None
HFC-227ea
2011
2012
0.3%
None
HFC-134a
2014
2014
3%
None
HFC-227ea
2014
2014
0.3%
None
Consumer Aerosols (Non-MDIs)
NA3
HFC-152a
1990
1991
50%
None
4.2%
HFC-134a
1995
1995
50%
HFC-152a
HFC-152a
HFO-
1234ze(E)
1997
2001
2016
1998
2005
2018
44%
38%
16%
None
None
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
2 1 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
3 ODS substitutes.
4 2 CFC Mix consists of CFC-11, CFC-12 and CFC-114 and represents the weighted average of several CFCs consumed for essential use in MDIs from 1993 to 2008. It is assumed that CFC mix
5 was stockpiled in the United States and used in new products through 2013.
6 3 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.
7 4 Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
A-278 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
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-150.
Table A-150: 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
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Rate3
| Non-ODP/GWP
1994
1995
100% None
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%
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%
CH3CCI3
Non-ODP/GWP
1995
1996
99.3%
None
2.0%
HFC-43-10mee
1995
1996
0.6%
None
PFC/PFPE
1995
1996
0.1%
Non-
2000
2003
90%
A-279
-------
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
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Rate3
ODP/GWP
Non-
ODP/GWP
2005
2009
10%
CFC-113
Non-ODP/GWP
1995
20132
90%
None
2.0%
Methyl Siloxanes
1995
1996
6%
HCFC-225ca/cb
1995
1996
1%
Unknown
HFE-7100
1995
1996
3%
None
1 1 Transitions between the start year and date of full penetration in new equipment or chemical supply are assumed to be linear so that
2 in total 100 percent of the market is assigned to the original ODS or the various ODS substitutes.
3 Note: Non-ODP/GWP includes chemicals with zero ODP and low GWP, such as hydrocarbons and ammonia, as well as not-in-kind
4 alternatives such as "no clean" technologies.
5 2 Transition assumed to be completed in 2013 to mimic CFC-113 stockpile use.
6 3 Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
7 Fire Extinguishing
8 ODSs, HFCs, PFCs and other chemicals are used as fire-extinguishing agents, in both hand-held "streaming"
9 applications as well as in built-up "flooding" equipment similar to water sprinkler systems. Although these systems are
10 generally built to be leak-tight, some leaks do occur and emissions occur when the agent is released. Total emissions from
11 fire extinguishing are assumed, in aggregate, to equal a percentage of the total quantity of chemical in operation at a given
12 time. For modeling purposes, it is assumed that fire extinguishing equipment leaks at a constant rate for an average
13 equipment lifetime, as shown in the equation below. In streaming systems, non-halon emissions are assumed to be 3.5
14 percent of all chemical in use in each year, while in flooding systems 2.5 percent of the installed base of chemical is
15 assumed to leak annually. Halon systems are assumed to leak at higher rates. The equation is applied for a single year,
16 accounting for all fire protection equipment in operation in that year. The model assumes that equipment is serviced
17 annually so that the amount equivalent to average annual emissions for each product (and hence for the total of what was
18 added to the bank in a previous year in equipment that has not yet reached end-of-life) is replaced/applied to the starting
19 charge size (or chemical bank). Each fire protection agent is modeled separately. In the Vintaging Model, streaming
20 applications have a 24-year lifetime and flooding applications have a 33-year lifetime. At end-of-life, remaining agent is
21 recovered from equipment being disposed and is reused.
22 Ej = r x Z Qcj-m for i=l^k
23 where:
24 E = Emissions. Total emissions of a specific chemical in year j for fire extinguishing
25 equipment, by weight.
26 r = Percent Released. The percentage of the total chemical in operation that is released
27 to the atmosphere.
28 Qc = Quantity of Chemical. Total amount of a specific chemical used in new fire
29 extinguishing equipment in a given year,y-/+l, by weight.
30 /' = Counter, runs from 1 to lifetime (k).
31 j = Year of emission.
32 k = Lifetime. The average lifetime of the equipment.
33 Transition Assumptions
34 Transition assumptions and growth rates for these two fire extinguishing types are presented in Table A-151.
A-280 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 Table A-151: 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
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Rate3
Flooding Agents
Halon-
1301
Halon-13012
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-12112
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
2 1 Transitions between the start year and date of full penetration in new equipment are assumed to be linear so that in total 100 percent
3 of the market is assigned to the original ODS or the various ODS substitutes.
4 2 Despite the 1994 consumption ban, a small percentage of new halon systems are assumed to continue to be built and filled with
5 stockpiled or recovered supplies.
6 3 Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
7 Foam Blowing
8 ODSs, HFCs, and other chemicals are used to produce foams, including such items as the foam insulation panels
9 around refrigerators, insulation sprayed on buildings, etc. The chemical is used to create pockets of gas within a substrate,
10 increasing the insulating properties of the item. Foams are given emission profiles depending on the foam type (open cell
11 or closed cell). Open cell foams are assumed to be 100 percent emissive in the year of manufacture. Closed cell foams are
12 assumed to emit a portion of their total HFC content upon manufacture, a portion at a constant rate over the lifetime of
13 the foam, a portion at disposal, and a portion after disposal; these portions vary by end-use.
14 Step 1: Calculate manufacturing emissions (open-cell and closed-cell foams)
15 Manufacturing emissions occur in the year of foam manufacture, and are calculated as presented in the following
16 equation.
17 Enrtj = Im x Qq
18 where:
19 Enrtj = Emissions from manufacturing. Total emissions of a specific chemical in yeary due to
20 manufacturing losses, by weight.
A-281
-------
1 Im = Loss Rate. Percent of original blowing agent emitted during foam manufacture. For
2 open-cell foams, Im is 100%.
3 Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture
4 closed-cell foams in a given year.
5 j = Year of emission.
6 Step 2: Calculate lifetime emissions (closed-cell foams)
7 Lifetime emissions occur annually from closed-cell foams throughout the lifetime of the foam, as calculated as
8 presented in the following equation.
9 EUj = lu x Z Qcj-m for i=l^k
10 where:
11 EUj = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to
12 lifetime losses during use, by weight.
13 lu = Leak Rate. Percent of original blowing agent emitted each year during lifetime use.
14 Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture
15 closed-cell foams in a given year.
16 /' = Counter, runs from 1 to lifetime (k).
17 j = Year of emission.
18 k = Lifetime. The average lifetime of foam product.
19 Step 3: Calculate disposal emissions (closed-cell foams)
20 Disposal emissions occur in the year the foam is disposed, and are calculated as presented in the following
21 equation.
22 Edj = Id x Qcj-k
23 where:
24 Edj = Emissions from disposal. Total emissions of a specific chemical in year j at disposal,
25 by weight.
26 Id = Loss Rate. Percent of original blowing agent emitted at disposal.
27 Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture
28 closed-cell foams in a given year.
29 j = Year of emission.
30 k = Lifetime. The average lifetime of foam product.
31 Step 4: Calculate post-disposal emissions (closed-cell foams)
32 Post-disposal emissions occur in the years after the foam is disposed; for example, emissions might occur while
33 the disposed foam is in a landfill. Currently, the only foam type assumed to have post-disposal emissions is polyurethane
34 foam used as domestic refrigerator and freezer insulation, which is expected to continue to emit for 26 years post-disposal,
35 calculated as presented in the following equation.
36 Epj = Ip x Z QCj-m for m=k^k + 26
A-282 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
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-152. The emission profiles of these thirteen foam types are shown in Table A-153.
A-283
-------
Table A-152: Foam Blowing 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
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Rate3
Commercial Refrigeration Foam
HCFO-
CFC-11
HCFC-141b
1989
1996
40%
HFC-245fa
Non-
ODP/GWP
Non-
2002
2002
2003
2003
80%
20%
1233zd(E)
Non-ODP/GWP
None
2015
2015
2020
2020
70%
30%
6.0%
HCFC-142b
1989
1996
8%
ODP/GWP
2009
2010
80%
None
HCFO-
HFC-245fa
2009
2010
20%
1233zd(E)
Non-ODP/GWP
2015
2015
2020
2020
70%
30%
Non-
HCFC-22
1989
1996
52%
ODP/GWP
2009
2010
80%
None
HCFO-
HFC-245fa
2009
2010
20%
1233zd(E)
Non-ODP/GWP
2015
2015
2020
2020
70%
30%
Flexible PU Foam: Integral Skin Foam
HFC-134a
1996
2000
50%
HFC-245fa
2003
2010
96%
HCFO-
2017
2017
83%
2.0%
HCFC-141b4
1233zd(E)
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%
Phenolic Foam
Non-
CFC-11
HCFC-141b
1989
1990
100%
ODP/GWP
1992
1992
100%
None
2.0%
Polyolefin Foam
A-284 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Initial
Market
Segment
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Growth
Rate3
CFC-114
HFC-152a
1989
1993
10%
Non-
ODP/GWP
Non-
ODP/GWP
2005
2010
100%
None
2.0%
HCFC-142b
1989
1993
90%
1994
1996
100%
None
PU and PIR Rigid: Boardstock
Non-
CFC-11
HCFC-141b
1993
1996
100%
ODP/GWP
HC/HFC-245fa
2000
2003
95%
None
6.0%
Blend
2000
2003
5%
Non-ODP/GWP
2017
2017
100%
PU Rigid: Domestic Refrigerator and Freezer Insulation
CFC-11
HCFC-141b
1993
1995
100%
HFC-134a
1996
2001
7%
Non-ODP/GWP
2002
2003
100%
0.8%
HFC-245fa
2001
2003
50%
Non-ODP/GWP
HCFO-
1233zd(E)
2015
2015
2020
2020
50%
50%
HFC-245fa
2006
2009
10%
Non-ODP/GWP
HCFO-
1233zd(E)
2015
2015
2020
2020
50%
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
HFC-134a
HFC-152a
Non-
2009
2009
2009
2010
2010
2010
80%
10%
10%
None
HFO-1234ze(E)
None
2018
2020
100%
4.0%
HCFC-22
1989
1996
30%
ODP/GWP
HFC-134a
HFC-152a
2009
2009
2009
2010
2010
2010
80%
10%
10%
None
HFO-1234ze(E)
None
2018
2020
100%
PU Rigid: Other: Slabstock Foam
CFC-11 | HCFC-141b | 19891 19961 100% | C02 | 19991 20031 45% | None | | | [ 2.0%
A-285
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Rate3
Non-
ODP/GWP
HCFC-22
2001
2003
2003
2003
45%
10%
None
Non-ODP/GWP
2009
2010
100%
PU Rigid: Sandwich Panels: Continuous and Discontinuous
HCFC-141b2
HCFC-22
HCFC-
22/Water
HFC-245fa/C02
HCFO-
Blend
2001
2003
20%
Blend
Non-
ODP/GWP
2009
2009
2010
2010
50%
50%
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
6.0%
PU Rigid: Spray Foam
HCFO-
CFC-11
HCFC-141b
1989
1996
100%
HFC-245fa
2002
2003
30%
1233zd(E)
2016
2020
100%
6.0%
HFC-245fa/C02
Blend
2002
2003
60%
None
Non-
ODP/GWP
2001
2003
10%
None
XPS: Boardstock Foam
HCFC-142b/22
CFC-12
Blend
1989
1994
10%
HFC-134a
2009
2010
70%
Non-ODP/GWP
2021
2021
100%
2.5%
HFC-152a
2009
2010
10%
None
C02
2009
2010
10%
None
A-286 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Rate3
Non-
ODP/GWP
2009
2010
10%
None
HCFC-142b
1989
1994
90%
HFC-134a
HFC-152a
C02
Non-
ODP/GWP
2009
2009
2009
2009
2010
2010
2010
2010
70%
10%
10%
10%
Non-ODP/GWP
None
None
None
2021
2021
100%
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
1 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.
2 The CFC-11PU Rigid: Sandwich Panels: Continuous and Discontinuous market for new systems transitioned to 82 percent HCFC-141b and 18 percent HCFC-22from 1989 to 1996. These transitions
are not shown in the table in order to provide the HFC transitions in greater detail.
3 Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
4 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.
A-287
-------
1 Table A-153: 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
Commercial Refrigeration
4
0.25
15
92.25
100
Rigid PU: 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
6
1
25
69.0
100
PU Sandwich Panels: Continuous and
Discontinuous
8.5-11.25
0.5
50
63.75-66.5
100
2 PIR (Polyisocyanurate)
3 PU (Polyurethane)
4 XPS (Extruded Polystyrene)
5 a In general, total emissions from foam end-uses are assumed to be 100 percent. In the Rigid PU Domestic Refrigerator and Freezer
6 Insulation end-use, the source of emission rates and lifetimes did not yield 100 percent emission; the remainder is anticipated to be
7 emitted at a rate of 2.0 percent/year post-disposal.
8 Sterilization
9 Sterilants kill microorganisms on medical equipment and devices. The principal ODS used in this sector was a
10 blend of 12 percent ethylene oxide (EtO) and 88 percent CFC-12, known as "12/88." In that blend, ethylene oxide sterilizes
11 the equipment and CFC-12 is a dilutent solvent to form a non-flammable blend. The sterilization sector is modeled as a
12 single end-use. For sterilization applications, all chemicals that are used in the equipment in any given year are assumed
13 to be emitted in that year, as shown in the following equation.
14 E, = Qq
15 where:
16 E = Emissions. Total emissions of a specific chemical in year j from use in sterilization
17 equipment, by weight.
18 Qc = Quantity of Chemical. Total quantity of a specific chemical used in sterilization
19 equipment in yeary, by weight.
20 j = Year of emission.
21 Assumptions
22 The Vintaging Model contains one sterilization end-use, whose transition assumptions away from ODS and
23 growth rates are presented in Table A-154.
A-288 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 Table A-154: 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
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment1
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
1993
1994
1.4%
Non-ODP/GWP
2015
2015
100%
None
Blend
HCFC-22/HCFC-
1993
1994
3.1%
Non-ODP/GWP
2010
2010
100%
None
124/EtO Blend
2 1 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
3 various ODS substitutes.
A-289
-------
1 Model Output
2 By repeating these calculations for each year, the Vintaging Model creates annual profiles of use and emissions
3 for ODS and ODS substitutes. The results can be shown for each year in two ways: 1) on a chemical-by-chemical basis,
4 summed across the end-uses, or 2) on an end-use or sector basis. Values for use and emissions are calculated both in
5 metric tons and in million metric tons of C02 equivalent (MMT C02 Eq.). The conversion of metric tons of chemical to MMT
6 C02 Eq. is accomplished through a linear scaling of tonnage by the global warming potential (GWP) of each chemical.
7 Throughout its development, the Vintaging Model has undergone annual modifications. As new or more accurate
8 information becomes available, the model is adjusted in such a way that both past and future emission estimates are often
9 altered.
10 Bank of ODS and ODS Substitutes
11 The bank of an ODS or an ODS substitute is "the cumulative difference between the chemical that has been
12 consumed in an application or sub-application and that which has already been released" (IPCC 2006). For any given year,
13 the bank is equal to the previous year's bank, less the chemical in equipment disposed of during the year, plus chemical in
14 new equipment entering the market during that year, less the amount emitted but not replaced, plus the amount added
15 to replace chemical emitted prior to the given year, as shown in the following equation:
16 Bq = Bq.rQdj+Qpj-Ee+Qr
17 where:
18 Bq
19 Qdj
20
21 Qpj
22
23 Ee
24
25
26
27
28 Qr
29
30
31
32
33 j
34
35
36 Table A-155 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 1990 to 2018.
37
38 Table A-155: Banks of ODS and ODS Substitutes, 1990-2018 (MT)
Year
CFC
HCFC
HFC
1990
695,056
182,823
872
1995
768,574
421,456
50,353
2000
638,658
872,079
189,537
2001
610,089
947,445
218,644
2002
585,608
1,007,213
247,469
2003
561,341
1,050,545
281,848
2004
536,594
1,094,766
317,702
Bank of Chemical. Total bank of a specific chemical in year j, by weight.
Quantity of Chemical in Equipment Disposed. Total quantity of a specific chemical in
equipment disposed of in year j, by weight.
Quantity of Chemical Penetrating the Market. Total quantity of a specific chemical
that is entering the market in year j, by weight.
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.
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.
Year of emission.
A-290 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
2005
506,767
1,141,564
355,645
2006
476,460
1,184,381
398,696
2007
448,847
1,215,280
442,749
2008
426,406
1,232,231
483,279
2009
413,431
1,224,559
528,250
2010
376,199
1,192,755
588,437
2011
339,448
1,153,001
650,941
2012
302,837
1,110,695
715,568
2013
267,100
1,062,937
783,140
2014
231,330
1,014,922
852,764
2015
195,498
968,218
917,846
2016
159,713
920,181
981,260
2017
123,043
872,759
1,036,888
2018
95,641
814,899
1,090,601
1
2 References
3 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
4 Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
5 Tanabe (eds.). Hayama, Kanagawa, Japan.
6 U.S. EPA (2018) EPA's Vintaging Model of ODS Substitutes: A Summary of the 2017 Peer Review. Office of Air and
7 Radiation. Document Number EPA-400-F-18-001. Available online at:
8 .
10 U.S. EPA (2004) The U.S. Solvent Cleaning Industry and the Transition to Non Ozone Depleting Substances. September
11 2004. Available online at: .
13 Data are also taken from various government sources, including rulemaking analyses from the U.S. Department of Energy
14 and from the Motor Vehicle Emission Simulator (MOVES) model from EPA's Office of Transportation and Air Quality.
A-291
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
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. The methodology used for 2018 relied on 2018 population estimates and 2017 implied emission factors
and is explained in further detail within Chapter 5.1 Enteric Fermentation (CRF Source Category 3A). 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 IPCCTier2 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.73 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 2019). State-level cattle population
estimates are shown by animal type for 2018 in Table A-156. A national-level summary of the annual average populations
upon which all livestock-related emissions are based is provided in Table A-157. 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, 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
national total population estimate, see the Enteric Fermentation chapter for more details about this approach.
Table A-156: 2018 Cattle Population Estimates, by Animal Type and State (1,000 head)
Dairy
Dairy
Beef
Beef
Repl.
Repl.
Repl.
Repl.
Heif.
Heif.
Heif.
Heif.
Dairy
Dairy
7-11
12-23
Beef
Beef
7-11
12-23
Steer
Heifer
State
Calves
Cows
Months
Months
Bulls
Calves
Cows
Months
Months
Stockers
Stockers
Feedlot
Alabama
4
7
1
3
50
356
699
27
65
25
20
6
Alaska
0
0
0
0
3
2
5
0
1
0
0
0
Arizona
101
198
35
83
20
94
185
8
21
130
19
300
Arkansas
3
6
1
2
60
469
921
39
94
54
35
13
California
901
1771
227
536
70
336
660
29
71
294
82
513
Colorado
80
156
30
71
55
413
812
42
103
417
285
1105
Conn.
10
19
3
7
1
3
5
0
1
1
1
0
Delaware
3
5
1
2
0
1
3
0
0
1
0
0
Florida
63
123
10
25
60
466
915
28
68
15
16
4
Georgia
43
84
9
21
33
255
501
25
60
18
27
6
Hawaii
1
2
0
1
4
38
74
3
7
5
2
1
Additional information on the Cattle Enteric Fermentation Model can be found in ICF (2006).
A-292 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Idaho
308
606
93
219
40
257
504
27
65
152
104
315
Illinois
48
94
16
37
25
199
390
17
41
118
59
303
Indiana
95
187
24
56
17
108
212
11
27
53
27
131
Iowa
110
217
40
95
70
495
973
41
100
642
296
1388
Kansas
77
151
30
71
95
806
1583
69
168
1005
784
2704
Kentucky
29
58
12
28
70
525
1031
34
81
105
63
21
Louisiana
6
12
1
3
31
230
452
19
46
12
11
3
Maine
15
30
4
11
2
6
11
1
2
2
2
1
Maryland
24
47
9
20
4
22
43
2
6
7
3
11
Mass.
6
12
2
5
1
3
7
0
1
1
1
0
Michigan
218
429
51
120
16
62
121
6
14
83
22
177
Minn.
236
464
88
208
35
190
373
21
52
245
90
450
Miss.
5
9
2
4
38
244
480
21
50
21
17
6
Missouri
44
86
13
32
120
1055
2072
83
201
226
129
128
Montana
7
14
3
6
100
763
1498
95
231
113
140
54
Nebraska
31
61
7
18
110
985
1936
84
203
1123
756
2933
Nevada
15
30
3
8
14
113
222
9
22
22
16
3
N.Hamp.
7
14
2
4
1
3
5
0
1
1
1
0
N.Jersey
3
7
1
3
1
4
8
0
1
1
1
0
N.Mexico
167
328
33
78
35
239
469
22
54
59
49
15
NewYork
318
626
106
250
20
56
111
10
24
22
27
23
N.Car.
23
45
7
16
31
190
373
15
37
21
14
5
N.Dakota
8
16
3
6
65
490
962
46
112
125
118
60
Ohio
135
264
36
85
30
148
290
17
41
108
33
180
Oklahoma
18
35
6
14
161
1075
2112
97
236
441
255
360
Oregon
64
125
19
46
40
280
550
23
57
76
63
98
Penn
270
530
94
222
25
95
186
15
35
78
33
110
R.Island
0
1
0
0
0
1
1
0
0
0
0
0
S.Car.
8
15
2
5
15
87
171
7
18
4
5
1
S.Dakota
60
117
13
32
100
854
1677
88
214
363
290
462
Tenn.
21
41
10
25
65
467
916
32
79
66
49
17
Texas
252
495
78
183
341
2289
4496
181
439
1270
740
2876
Utah
47
93
16
39
27
173
341
19
46
39
33
25
Vermont
66
130
17
39
3
7
14
1
3
2
4
1
Virginia
45
88
11
27
40
330
648
25
61
81
38
24
Wash.
141
278
36
85
18
115
227
13
31
93
64
226
W.Virg.
4
8
1
3
15
106
209
8
21
19
9
5
Wisconsin
657
1292
213
501
30
149
292
18
43
196
27
320
Wyoming
3
6
1
2
40
366
720
41
100
78
75
88
1
2 Table A-157: Cattle Population Estimates from the CEFM Transition Matrix for 1990-2018 (1,000 head)
Livestock Type
1990
1995
2000
2005
2012 2013 2014 2015 2016 2017 2018
Dairy
Dairy Calves (0-6 months)
Dairy Cows
Dairy Replacements 7-11
months
Dairy Replacements 12-23
months
Beef
Beef Calves (0-6 months)
Bulls
Beef Cows
Beef Replacements 7-11
months
Beef Replacements 12-23
months
Steer Stockers
Heifer Stockers
5,369
10,015
1,214
2,915
5,091
9,482
1,216
2,892
4,951
9,183
1,196
2,812
16,909
2,160
32,455 / 35,190/ 33,5/5
18,177 17,431
2,385/ 2,293
4,628/ 4,770 4,758 4,740 4,771 4,758 4,785 4,800
9,004/ 9,236 9,221 9,208 9,307 9,310 9,346 9,432
1,25/ 1,348 1,341 1,377 1,415 1,414 1,419 1,423
2,905 3,233 3,185 3,202 3,310 3,371 3,343 3,353
16,918 15,288 14,859 14,741 15,000 15,563 15,971 16,021
2,214/ 2,100 2,074 2,038 2,109 2,142 2,244 2,252
32,6/4 30,282 29,631 29,085 29,302 30,166 31,213 31,466
1,269 1,493 1,313 1,363/ 1,263 1,291 1,385 1,479 1,515 1,484 1,424
2,967/ 3,637
10,321 11,716
5,946 6,699
3,097
8,724
5,371
3,1/1
8,185
5,015
2,968 3,041 3,121 3,424 3,578
7,173 7,457 7,374 7,496 8,150 7,957
4,456 4,455 4,280 4,385 4,810 4,754
3,598 3,454
8,032
4,937
A-293
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5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Feedlot Cattle
9,549 * 11,064 * 13,006 * 12,652 * 13,328 13,267 13,219 12,883 13,450 14,340 15,475
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.74 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
overtime 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-158.
Table A-158: Cattle Population Categories Used for Estimating CH4 Emissions
Dairy Cattle Beef Cattle
Calves Calves
Heifer Replacements Heifer Replacements
Cows Heifer and Steer Stockers
Animals in Feedlots (Heifers & Steer)
Cows
Bulls3
a Bulls (beef and dairy) are accounted for in a single category.
The key variables tracked for each of these cattle population categories are as follows:
Calves. Although 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-159, 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
74
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-294 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 and distributed into monthly cohorts by cattle type (beef or dairy). Calf growth is modeled by month, based on estimated
2 monthly weight gain for each cohort (approximately 61 pounds per month). The total calf population is modified through
3 time to account for veal calf slaughter at 4 months and a calf death loss of 0.35 percent annually (distributed across age
4 cohorts up to 6 months of age). An example of a transition matrix for calves is shown in Table A-160. Note that 1- to 6-
5 month old calves in January of each year have been tracked through the model based on births and death loss from the
6 previous year.
7 Table A-159: 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%
8
9 Table A-160: 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.
10 Replacements and Stockers. At 7 months of age, calves "graduate" and are separated into the applicable cattle
11 types: replacements (cattle raised to give birth), or stockers (cattle held for conditioning and growing on grass or other
12 forage diets). First the number of replacements required for beef and dairy cattle are calculated based on estimated death
13 losses and population changes between beginning and end of year population estimates. Based on the USDA estimates for
14 "replacement beef heifers" and "replacement dairy heifers," the transition matrix for the replacements is back-calculated
15 from the known animal totals from USDA, and the number of calves needed to fill that requirement for each month is
16 subtracted from the known supply of female calves. All female calves remaining after those needed for beef and dairy
17 replacements are removed and become "stockers" that can be placed in feedlots (along with all male calves). During the
18 stocker phase, animals are subtracted out of the transition matrix for placement into feedlots based on feedlot placement
19 statistics from USDA (2016).
20 The data and calculations that occur for the stocker category include matrices that estimate the population of
21 backgrounding heifers and steer, as well as a matrix for total combined stockers. The matrices start with the beginning of
22 year populations in January and model the progression of each cohort. The age structure of the January population is
23 based on estimated births by month from the previous two years, although in order to balance the population properly,
24 an adjustment is added that slightly reduces population percentages in the older populations. The populations are
25 modified through addition of graduating calves (added in month 7, bottom row of Table A-161) and subtraction through
26 death loss and animals placed in feedlots. Eventually, an entire cohort population of stockers may reach zero, indicating
27 that the complete cohort has been transitioned into feedlots. An example of the transition matrix for stockers is shown in
28 Table A-161.
A-295
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Table A-161: 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
185
180
104
37
15
9
8
8
6
3
1
0
22
320
146
49
19
12
9
9
9
6
3
17
181
21
260
69
25
14
11
11
11
8
6
68
218
313
20
123
35
19
14
14
13
10
8
133
331
387
254
19
63
27
19
17
16
13
10
196
472
615
318
120
18
48
27
23
20
16
13
241
610
900
514
149
61
17
47
33
27
19
15
295
709
1,179
759
237
129
47
16
58
38
26
19
363
828
1,380
1,000
348
340
47
46
15
67
36
25
452
977
1,619
1,172
456
603
47
46
57
14
65
36
599
1,172
1,921
1,378
534
862
47
46
57
66
13
64
845
1,478
2,309
1,639
629
1,117
47
46
57
66
63
12
982
1,602
2,556
1,858
755
1,512
214
46
57
66
63
63
11
1,814
2,770
2,056
855
1,872
277
138
76
89
81
80
1,016
10
3,133
2,255
945
2,241
385
189
184
231
209
185
1,135
2,445
9
2,545
1,062
2,502
484
335
341
420
372
371
1,292
2,786
5,299
8
1,200
2,951
664
482
557
759
658
649
1,503
3,247
5,984
4,877
7
3,381
800
794
956
1,160
1,109
1,100
1,876
3,666
6,504
5,243
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
feed lots (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 2007.75 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).76 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), 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-162.
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)
75 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.
76 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-296 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 for 2000. Interim years were calculated linearly, as shown in Table A-163, and weight gain was held constant starting in
2 2000. Table A-163 provides weight gains that vary by year in the CEFM.
3 Table A-162: Typical Animal Mass (lbs)77
Year/Cattle Type
Calves
Dairy Dairy
Cowsa Replacements'1
Beef
Cowsa
Beef
Bullsa Replacements'
Steer
3 Stockersb
Heifer
Stockersb
Steer
Feedlotb
Heifer
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
4 a Input into the model.
5 b Annual average calculated in model based on age distribution.
6
77 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through
2019) Inventory submission.
A-297
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
Table A-163: 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-164) 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-164 provides a summary of the reported feedlot placement statistics for 2017.
Table A-164: Feedlot Placements in the United States for 2017 (Number of animals placed/1,000 Head)78
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 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-165. Annual estimates for dairy cows were taken
from USDA milk production statistics. Dairy lactation estimates for 1990 through 2017 are shown in Table A-166. Beef and
dairy cow and bull populations are assumed to remain relatively static throughout the year, as large fluctuations in
78 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through
2019) Inventory submission.
A-298 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 population size are assumed to not occur. These estimates are taken from the USDA beginning and end of year population
2 datasets.
3 Table A-165: 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
4
5 Table A-166: Dairy Lactation Rates by State (lbs/year/cow)79
State/Year
1990
1995
2000
2005
2011
2012
2013
2014
2015
2016
2017
Alabama
12,214
14,176
13,920
14,000
14,300
13,000
13,000
13,625
12,625
13,143
14,833
Alaska
13,300
17,000
14,500
12,273
13,800
14,250
10,667
11,667
11,667
11,667
9,667
Arizona
17,500
19,735
21,820
22,679
23,473
23,979
23,626
24,368
24,402
24,679
24,680
Arkansas
11,841
12,150
12,436
13,545
11,917
13,300
11,667
13,714
13,000
13,333
13,167
California
18,456
19,573
21,130
21,404
23,438
23,457
23,178
23,786
23,028
22,968
22,755
Colorado
17,182
18,687
21,618
22,577
23,430
24,158
24,292
24,951
25,733
25,993
26,181
Connecticut
15,606
16,438
17,778
19,200
19,000
19,889
20,556
20,158
20,842
21,526
22,105
Delaware
13,667
14,500
14,747
16,622
18,300
19,542
19,521
20,104
19,700
19,100
18,560
Florida
14,033
14,698
15,688
16,591
19,067
19,024
19,374
20,390
20,656
20,285
20,129
Georgia
12,973
15,550
16,284
17,259
18,354
19,138
19,600
20,877
21,651
21,786
21,905
Hawaii
13,604
13,654
14,358
12,889
14,421
14,200
13,409
13,591
15,909
14,542
16,913
Idaho
16,475
18,147
20,816
22,332
22,926
23,376
23,440
24,127
24,126
24,647
24,378
Illinois
14,707
15,887
17,450
18,827
18,510
19,061
19,063
19,681
20,149
20,340
20,742
Indiana
14,590
15,375
16,568
20,295
20,657
21,440
21,761
21,865
22,115
22,571
22,802
Iowa
15,118
16,124
18,298
20,641
21,191
22,015
22,149
22,449
22,929
23,634
23,725
Kansas
12,576
14,390
16,923
20,505
21,016
21,683
21,881
22,085
22,210
22,801
23,000
Kentucky
10,947
12,469
12,841
12,896
14,342
15,135
15,070
15,905
17,656
18,052
18,589
Louisiana
11,605
11,908
12,034
12,400
12,889
13,059
12,875
13,600
13,429
14,083
13,333
Maine
14,619
16,025
17,128
18,030
18,688
18,576
19,548
19,967
19,800
21,000
21,000
Maryland
13,461
14,725
16,083
16,099
18,654
19,196
19,440
19,740
20,061
19,938
19,854
Massachusetts
14,871
16,000
17,091
17,059
16,923
18,250
17,692
17,923
18,083
18,417
17,583
Michigan
15,394
17,071
19,017
21,635
23,164
23,976
24,116
24,638
25,150
25,957
26,302
Minnesota
14,127
15,894
17,777
18,091
18,996
19,512
19,694
19,841
20,570
20,967
21,537
Mississippi
12,081
12,909
15,028
15,280
14,571
14,214
13,286
14,462
15,000
14,400
15,222
Missouri
13,632
14,158
14,662
16,026
14,611
14,979
14,663
15,539
15,511
14,824
14,588
Montana
13,542
15,000
17,789
19,579
20,571
21,357
21,286
21,500
21,357
21,071
22,154
Nebraska
13,866
14,797
16,513
17,950
20,579
21,179
21,574
22,130
22,930
23,317
24,067
Nevada
16,400
18,128
19,000
21,680
22,966
22,931
22,034
23,793
23,069
22,000
22,156
New Hampshire
15,100
16,300
17,333
18,875
20,429
19,643
20,923
20,143
20,143
20,500
21,000
New Jersey
13,538
13,913
15,250
16,000
16,875
18,571
18,143
18,143
18,143
17,429
19,833
New Mexico
18,815
18,969
20,944
21,192
24,854
24,694
24,944
25,093
24,245
24,479
24,960
New York
14,658
16,501
17,378
18,639
21,046
21,623
22,070
22,325
22,806
23,834
23,936
North Carolina
15,220
16,314
16,746
18,741
20,089
20,435
20,326
20,891
20,957
20,978
21,156
North Dakota
12,624
13,094
14,292
14,182
18,158
19,278
18,944
20,250
20,750
21,500
21,563
Ohio
13,767
15,917
17,027
17,567
19,194
19,833
20,178
20,318
20,573
20,936
21,259
Oklahoma
12,327
13,611
14,440
16,480
17,415
17,896
17,311
18,150
18,641
18,703
18,667
Oregon
16,273
17,289
18,222
18,876
20,488
20,431
20,439
20,565
20,408
20,744
20,395
Pennsylvania
14,726
16,492
18,081
18,722
19,495
19,549
19,797
20,121
20,377
20,454
20,834
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
79 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through
2019) Inventory submission.
A-299
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
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
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-167. 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 and Table A-168. 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-167: Regions used for Characterizing the Diets of Dairy Cattle (all years) and Foraging Cattle from 1990-2006
West California
Northern
Midwestern Northeast
Southcentral
Southeast
Great Plains
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 Dakota
Minnesota Massachusetts
Mississippi
New Mexico
South Dakota
Missouri New
North Carolina
Oregon
Wyoming
Ohio Hampshire
South Carolina
Utah
Wisconsin New Jersey
Tennessee
Washington
New York
Virginia
Pennsylvania
Rhode Island
Vermont
West Virginia
Source: USDA (1996).
Table A-168: Regions used for Characterizing the Diets of Foraging Cattle from 2007-2017
West
Central
Northeast
Southeast
Alaska
Illinois
Connecticut
Alabama
Arizona
Indiana
Delaware
Arkansas
California
Iowa
Maine
Florida
Colorado
Kansas
Maryland
Georgia
Hawaii
Michigan
Massachusetts
Kentucky
Idaho
Minnesota
New Hampshire
Louisiana
Montana
Missouri
New Jersey
Mississippi
Nevada
Nebraska
New York
North Carolina
New Mexico
North Dakota
Pennsylvania
Oklahoma
Oregon
Ohio
Rhode Island
South Carolina
Utah
South Dakota
Vermont
Tennessee
Washington
Wisconsin
West Virginia
Texas
Wyoming
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).
A-300 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
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.
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-167 (California, Northern Great Plains, Midwestern, Northeast, Southcentral, Southeast) and Table A-168 (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-169. 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-170 and Table A-171 along
with the percent of each total diet that is assumed to be made up of the supplemental portion. By weighting the calculated
Ym = 4.52e\Year-1980)
A-301
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1 DE values from the forage and supplemental diets, the DE values for the composite diet were calculated.80 These values
2 are used for steer and heifer stockers and beef replacements. Finally, for mature beef cows and bulls, the DE value was
3 adjusted downward by two percent to reflect the lower digestibility diets of mature cattle based on Johnson (2002). Ym
4 values for all grazing beef cattle were set at 6.5 percent based on Johnson (2002). The Ym values and the resulting final
5 weighted DE values by region for 2007 onwards are shown in Table A-172.
6 For feedlot animals, DE and Ym are adjusted over time as diet compositions in actual feedlots are adjusted based
7 on new and improved nutritional information and availability of feed types. Feedlot diets are assumed to not differ
8 significantly by state, and therefore only a single set of national diet values is utilized for each year. The DE and Ym values
9 for 1990 were estimated by Dr. Don Johnson (1999). In the CEFM, the DE values for 1991 through 1999 were linearly
10 extrapolated based on values for 1990 and 2000. DE and Ym values from 2000 through the current year were estimated
11 using the MOLLY model as described in Kebreab et al. (2008), based on a series of average diet feed compositions from
12 Galyean and Gleghorn (2001) for 2000 through 2006 and Vasconcelos and Galyean (2007) for 2007 onwards. In addition,
13 feedlot animals are assumed to spend the first 25 days in the feedlot on a "step-up" diet to become accustomed to the
14 higher quality feedlot diets. The step-up DE and Ym are calculated as the average of all state forage and feedlot diet DE and
15 Ym values.
16 For calves aged 4 through 6 months, a gradual weaning from milk is simulated, with calf diets at 4 months
17 assumed to be 25 percent forage, increasing to 50 percent forage at age 5 months, and 75 percent forage at age 6 months.
18 The portion of the diet allocated to milk results in zero emissions, as recommended by the IPCC (2006). For calves, the DE
19 for the remainder of the diet is assumed to be similar to that of slightly older replacement heifers (both beef and dairy are
20 calculated separately). The Ym for beef calves is also assumed to be similar to that of beef replacement heifers (6.5 percent),
21 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
22 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
23 at 4 and 7 months of age and a linear interpolation for 5 and 6 months.
24 Table A-173 shows the regional DE and Ym for U.S. cattle in each region for 2017.
25 Table A-169: Feed Components and Digestible Energy Values Incorporated into Forage Diet Composition Estimates
Bremudagrass, Coastal Cynodon dactylon,
fresh
Bluegrass, Canada Poa compressa, fresh, early
vegetative
Bluegrass, Kentucky Poa pratensis, fresh, early
vegetative
Bluegrass, Kentucky Poa pratensis, fresh,
mature
Bluestem Andropagon spp, fresh, early
Brome Bromus spp, fresh, early vegetative 78.57 x
Brome, Smooth Bromus inermis, fresh, early
Clover, Alsike Trifolium hybridum, fresh, early
80 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-172.
A-302 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
W4-» 4-» 4-» S >« QJO fli E i
tA IA IA £ — 3 8 SC >
m- ro re ^ re 3 3 ^ 5 5 >
o a a a> a ->-»«* £ o o
vo IA M IA £ IA v v v Vai G> -n BjO "O
£v ui c ft E ft tuo tuo (UOtUO^StUO^Cfo
... n 'Z n c res c c c c & c gi 'Z m
Forspp Tvnp _ ^ ^ 3 ^ n re re re rem re p & c
i-orage iype q u t/f u ^ U3iS eg a eg eg eg S ifr S
Clover, LadinoTrifolium repens, fresh, early
vegetative 73.22 x
Clover, Red Trifolium pratense, fresh, early
bloom 71.27 x
Clover, Red Trifolium pratense, fresh, full
bloom 67.44 x x
Corn, Dent Yellow Zea mays indentata, aerial
part without ears, without husks, sun-cured,
(stover)fstraw) 55.28 x
Dropseed, Sand Sporobolus cryptandrus,
fresh, stem cured 64.69 x x x x
Fescue Festuca spp, hay, sun-cured, early
vegetative 67.39 x
Fescue Festuca spp, hay, sun-cured, early
bloom 53.57 x
Grama Bouteloua spp, fresh, early vegetative 67.02 x
Grama Bouteloua spp, fresh, mature 63.38 x x x
Millet, Foxtail Setaria italica, fresh 68.20 x x
Napiergrass Pennisetum purpureum, fresh,
late bloom 57.24 x x
Needleandthread Stipa comata, fresh, stem
cured 60.36 x x x
Orchardgrass Dactylis glomerata, fresh, early
vegetative 75.54 x
Orchardgrass Dactylis glomerata, fresh,
midbloom 60.13 x
Pearlmillet Pennisetum glaucum, fresh 68.04 x
Prairie plants, Midwest, hay, sun-cured 55.53 x x
Rape Brassica napus, fresh, early bloom 80.88 x
Rye Secale cereale, fresh 71.83 x
Ryegrass, Perennial Lolium perenne, fresh 73.68 x
Saltgrass Distichlis spp, fresh, post ripe 58.06 x x
Sorghum, Sudangrass Sorghum bicolor
sudanense, fresh, early vegetative 73.27 x
Squirreltail Stanion spp, fresh, stem-cured 62.00 x x
Summercypress, Gray Kochia vestita, fresh,
stem-cured 65.11 x x x
Timothy Phleum pratense, fresh, late
vegetative 73.12 x
Timothy Phleum pratense, fresh, midbloom 66.87 x
Trefoil, Birdsfoot Lotus corniculatus, fresh 69.07 x
Vetch Vicia spp, hay, sun-cured 59.44 x
Wheat Triticum aestivum, straw 45.77 x
Wheatgrass, Crested Agropyron desertorum,
fresh, early vegetative 79.78 x
Wheatgrass, Crested Agropyron desertorum,
fresh, full bloom 65.89 x x
Wheatgrass, Crested Agropyron desertorum,
fresh, post ripe 52.99 x x x
Winterfat, Common Eurotia lanata, fresh,
stem-cured 40.89 x
Weighted Average DE 72.99 62.45 57.26 67.11 62.70 60.62 58.59 52.07 64.03 55.11
A-303
-------
W+-* +-• +-• S>tUO S C i 1
tA IA IA £ — 3 8 SC >
m- ro re ^ re 3 3 ^ 5 5 >
o a a a> a ->-»«* £ o o
vo IA M IA £ IA v v v Vai G> -n BjO "O
£\ ui c $ ? $ tuo tuo (UOtUO^StUO^Cfo
... n 'Z n c res c c c c & c gi 'Z m
Forspp Tvnp lil ^ ^ 3 ^ n R n n rem ™ "
i-orage iype q u ^ ^ U3iS eg a eg a: w
Forage Diet for West
61.3 10%
10%
10% 10% 10% 10% 10% 10% 10% 10%
Forage Diet for All Other Regions
64.2 33.3%
33.3%
33.3% -------
Sources: Preston (2010) and Archibeque (2011).
Note that forages marked with an x indicate that the DE from that 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).
1 Table A-170: DE Values with Representative Regional Diets for the Supplemental Diet of Grazing Beef Cattle for 1990-
2 2006
Northern
Source of DE
Unweighted
Great
Feed
(NRC 1984)
DE (% of GE)
California9
West
Plains
Southcentral
Northeast
Midwest
Southeast
Alfalfa Hay
Table 1
3, feed #006
61.79
65%
30%
30%
29%
12%
30%
Barley
85.08
10%
15%
Bermuda
Table i
3, feed #030
66.29
35%
Bermuda Hay
Table i
3, feed #031
50.79
40%
Corn
Table i
3, feed #089
88.85
10%
10%
25%
11%
13%
13%
Corn Silage
Table i
3, feed #095
72.88
25%
20%
20%
Cotton Seed
Meal
7%
Grass Hay
Table i
3, feed #126,
170, 274
58.37
40%
30%
Orchard
Table 1
3, feed #147
60.13
40%
Soybean Meal
Supplement
77.15
5%
5%
5%
Sorghum
Table i
3, feed #211
84.23
20%
Soybean Hulls
66.86
7%
Timothy Hay
Table i
3, feed #244
60.51
50%
Whole Cotton
Seed
75.75
5%
5%
Wheat
Middlings
Table i
3, feed #257
68.09
15%
13%
Wheat
Table i
3, 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).
3 Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above.
A-304 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Table A-171: DE Values and Representative Regional Diets for the Supplemental Diet of Grazing Beef Cattle for 2007-
201781
Feed
Source of DE
Unweighted
(NRC1984)
DE (% of GE)
Westa
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%
3 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).
Table A-172: Foraging Animal DE (% of GE) and Ym Values for Each Region and Animal Type for 2007-201782
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%
3 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 seethe
regional designation per state, please see Table A-168.
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.
81 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through 2019)
Inventory submission.
82 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through 2019)
Inventory submission.
A-305
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1
2
3 Table A-173: Regional DE (% of GE) and Ym Rates for Dairy and Feedlot Cattle by Animal Type for 201783
Northern
Animal Type
Data
California9
West
Great Plains
Southcentral
Northeast
Midwest
Southeast
Dairy Repl. Heifers
DEb
63.7
63.7
63.7
63.7
63.7
63.7
63.7
Ymc
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%
4 3 Note that emissions are currently calculated on a state-by-state basis, but diets are applied in Table A-167 by the regions shown in the table
5 above. To seethe regional designation for foraging cattle per state, please see Table A-167.
6 b DE is the digestible energy in units of percent of GE (MJ/Day).
7 c Ym is the methane conversion rate, the fraction of GE in feed converted to methane.
8 Step 3: Estimate CH4 Emissions from Cattle
9 Emissions by state were estimated in three steps: a) determine gross energy (GE) intake using the Tier 2 IPCC
10 (2006) equations, b) determine an emission factor using the GE values, Ym and a conversion factor, and c) sum the daily
11 emissions for each animal type. Finally, the state emissions were aggregated to obtain the national emissions estimate.
12 The necessary data values for each state and animal type include:
13 • Body Weight (kg)
14 • Weight Gain (kg/day)
15 • Net Energy for Activity (Ca, MJ/day)84
16 • Standard Reference Weight (kg)85
17 • Milk Production (kg/day)
18 • Milk Fat (percent of fat in milk = 4)
19 • Pregnancy (percent of population that is pregnant)
20 • DE (percent of GE intake digestible)
21 • Ym (the fraction of GE converted to CH4)
22 • Population
23 Step 3a: Determine Gross Energy, GE
24 As shown in the following equation, GE is derived based on the net energy estimates and the feed characteristics.
25 Only variables relevant to each animal category are used (e.g., estimates for feedlot animals do not require the NEi factor).
26 All net energy equations are provided in IPCC (2006). Calculated GE values for 2015 are shown by state and animal type in
27 Table A-174.
28
83 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through 2019)
Inventory submission.
84 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).
85 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-306 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
285
3
1,100
577
!,265
1,673
10
8
199
288
46
1,980
1,463
1,696
>,064
1,093
932
149
23
466
10
',508
1,417
242
1,696
!,330
',896
155
NEm + NEa + NE, + NEwork + NEp \ | f JVEg
REM + REG
100
where,
GE = Gross energy (MJ/day)
NEm = Net energy required by the animal for maintenance (MJ/day)
NEa = Net energy for animal activity (MJ/day)
NEi = Net energy for lactation (MJ/day)
NEwork = Net energy for work (MJ/day)
NEP = Net energy required for pregnancy (MJ/day)
REM = Ratio of net energy available in a diet for maintenance to digestible energy consumed
NEg = Net energy needed for growth (MJ/day)
REG = Ratio of net energy available for growth in a diet to digestible energy consumed
DE = Digestible energy expressed as a percent of gross energy (percent)
Table A-174: Calculated Annual GE by Animal Type and State, for 2017 (MJ/1,000 head)86
Dairy
Dairy
Beef
Beef
Replace-
Replace-
Replace-
Replace-
ment
ment
ment
ment
Heifers
Heifers
Heifers
Heifers
Dairy
Dairy
7-11
12-23
Beef
Beef
7-11
12-23
Steer
Heifer
State
Calves
Cows
Months
Months
Bulls
Calves
Cows
Months
Months
Stockers
Stockers
Alabama
31
851
55
195
4,166
3,179
55,838
1,432
4,017
1,204
978
Alaska
1
29
1
5
240
23
404
13
36
13
15
Arizona
857
31,012
1,603
5,698
1,779
911
15,827
489
1,365
6,869
1,025
Arkansas
26
673
41
146
4,999
4,192
73,645
2,064
5,791
2,649
1,739
California
7,670
262,323
10,412
37,010
6,226
3,243
56,341
1,673
4,670
15,553
4,391
Colorado
677
25,460
1,370
4,870
4,892
3,986
69,243
2,446
6,825
22,033
15,223
Conn.
83
2,810
130
463
42
23
404
24
67
48
27
Delaware
22
668
30
107
25
12
202
8
24
46
11
Florida
533
17,431
479
1,704
4,999
4,165
73,162
1,491
4,184
722
815
Georgia
363
12,451
411
1,461
2,749
2,280
40,045
1,312
3,682
891
1,359
Hawaii
10
305
14
49
356
364
6,331
167
467
259
117
Idaho
2,622
94,209
4,247
15,096
3,558
2,476
43,008
1,545
4,311
8,036
5,562
Illinois
406
13,244
712
2,532
2,036
1,729
30,484
872
2,451
5,636
2,861
Indiana
809
27,876
1,096
3,896
1,385
938
16,542
581
1,634
2,536
1,324
Iowa
940
33,194
1,849
6,574
5,701
4,312
76,014
2,151
6,045
30,762
14,303
Kansas
656
22,722
1,370
4,870
7,738
7,016
123,670
3,604
10,129
48,139
37,878
Kentucky
249
7,791
548
1,948
5,832
4,692
82,428
1,790
5,021
5,178
3,125
Louisiana
52
1,355
55
195
2,583
2,055
36,097
1,014
2,845
602
543
Maine
131
4,303
205
730
125
51
889
48
134
97
82
Maryland
205
6,525
397
1,412
334
198
3,474
132
369
338
164
Mass.
50
1,492
96
341
84
30
525
24
67
48
27
Michigan
1,857
70,016
2,329
8,278
1,303
536
9,452
291
817
3,969
1,060
Minn.
2,010
66,977
4,041
14,366
2,851
1,653
29,145
1,105
3,104
11,741
4,370
Miss.
39
1,108
82
292
3,166
2,183
38,353
1,110
3,113
1,011
842
Missouri
371
10,003
616
2,191
9,774
9,183
161,874
4,302
12,090
10,802
6,225
Montana
61
2,073
123
438
8,894
7,358
127,821
5,470
15,266
5,962
7,494
Nebraska
262
9,346
342
1,217
8,959
8,580
151,240
4,360
12,253
53,774
36,553
Nevada
131
4,443
151
536
1,245
1,089
18,924
528
1,473
1,166
849
86 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through 2019)
Inventory submission.
A-307
-------
Dairy
Dairy
Beef
Beef
Replace-
Replace-
Replace-
Replace-
ment
ment
ment
ment
Heifers
Heifers
Heifers
Heifers
Dairy
Dairy
7-11
12-23
Beef
Beef
7-11
12-23
Steer
Heifer
State
Calves
Cows
Months
Months
Bulls
Calves
Cows
Months
Months
Stockers
Stockers
Feedlot
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
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
1
2 Step 3b: Determine Emission Factor
3 The daily emission factor (DayEmit) was determined using the GE value and the methane conversion factor (Ym)
4 for each category. This relationship is shown in the following equation:
GEx Y
DayEmit = —
5 55.65
6 where,
7 DayEmit = Emission factor (kg CH4/head/day)
8 GE = Gross energy intake (MJ/head/day)
9 Ym = CH4 conversion rate, which is the fraction of GE in feed converted to CH4 (%)
10 55.65 = A factor for the energy content of methane (MJ/kg CH4)
11
12 The daily emission factors were estimated for each animal type and state. Calculated annual national emission
13 factors are shown by animal type in Table A-175. State-level emission factors are shown by animal type for 2017 in Table
14 A-176.
15
A-308 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 Table A-175: Calculated Annual National Emission Factors for Cattle by Animal Type, for 2017 (kg ChU/head/year)87
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).
Table A-176: Emission Factors for Cattle by Animal Type and State, for 2017 (kg ChU/head/year)88
Dairy
Dairy
Beef
Beef
Replace-
Replace-
Replace-
Replace-
ment
ment
ment
ment
Heifers
Heifers
Heifers
Heifers
Dairy
Dairy
7-11
12-23
Beef
Beef
7-11
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
87 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through 2019)
Inventory submission.
88 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through 2019)
Inventory submission.
A-309
-------
1
2
3
4
5
6
7
8
9
10
11
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
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).
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-
177, indicate that U.S. emission factors are comparable to those of other Annex I countries. Results in Table A-177 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-177: Annex I Countries' Implied Emission Factors for Cattle by Year (kg ChU/head/year)89'90
Dairy Cattle
Beef Cattle
Mean of Implied Emission Factors
Mean of Implied Emission Factors
United States Implied for Annex 1 countries (excluding
United States Implied for Annex 1 countries (excluding
Year
Emission Factor
U.S.)
Emission Factor
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
89
Excluding calves.
90
This table has not been updated for the current (1990 through 2016) Inventory. It will be updated for the next (1990 through 2017)
Inventory submission.
A-310 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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)
1 Step 3c: Estimate Total Emissions
2 Emissions were summed for each month and for each state population category using the daily emission factor
3 for a representative animal and the number of animals in the category. The following equation was used:
4 Emissionsstate = DayEmitstate x Days/Month x SubPopstate
5 where,
6 Emissionsstate
7 DayEmitstate
8 Days/Month
9 SubPopstate
10
11 This process was repeated for each month, and the monthly totals for each state subcategory were summed to
12 achieve an emission estimate for a state for the entire year and state estimates were summed to obtain the national total.
13 The estimates for each of the 10 subcategories of cattle are listed in Table A-178. The emissions for each subcategory were
14 then aggregated to estimate total emissions from beef cattle and dairy cattle for the entire year.
= Emissions for state during the month (kg CH4)
= Emission factor for the subcategory and state (kg CH4/head/day)
= Number of days in the month
= Number of animals in the subcategory and state during the month
A-311
-------
1 Table A-178: CH4 Emissions from Cattle (kt)
Cattle Type
1990
1995
2000
2005
2012
2013
2014
2015
2016
2017
2018
Dairy
1,574
1,498
1,519
1,503
1,670
1,664
1,679
1,706
1,722
1,730
1,744
Calves (4-6 months)
62
59
59
54 J
58
58
58
58
58
58
58
Cows
1,242
1,183
1,209
1,19/
1,326
1,325
1,337
1,355
1,367
1,377
1,390
Replacements 7-11
months
58
56
55
56
62
61
63
65
65
65
65
Replacements 12-23
months
212
201
196
196 7
224
220
221
228
232
230
231
Beef
4,763
5,419
5,070
5,007
4,763
4,722
4,660
4,722
4,919
5,052
5,125
Calves (4-6 months)
182
193
186
179 V
161
157
156
158
164
168
169
Bulls
196
225
215
214/,
206
203
200
207
210
220
221
Cows
2,884
3,222
3,058
3,056/
2,868
2,806
2,754
2,774
2,856
2,954
2,978
Replacements 7-11
months
69
85
74 •'***_,.
80
76
78
83
89
91
90
86
Replacements 12-23
months
188
241
204
217 /
208
213
218
239
250
251
241
Steer Stockers
S63
662
509
473 V
413
431
426
433
472
461
465
Heifer Stockers
306
375
323
299/,
266
267
256
263
289
286
297
Feedlot Cattle
37S
416
502/
488 /
565
568
567
558
587
621
667
Total
6,338
6,917
6,589
6,510
6,433
6,386
6,339
6,427
6,641
6,783
6,869
2 Note: 2018 estimates are based on estimated 2018 population values. Totals may not sum due to independent rounding.
3 Emission Estimates from Other Livestock
4 "Other livestock" include horses, sheep, swine, goats, American bison, and mules and asses. All livestock
5 population data, except for American bison for years prior to 2002, were taken from the U.S. Department of Agriculture
6 (USDA) National Agricultural Statistics Service (NASS) agricultural statistics database (USDA 2019) or the Census of
7 Agriculture (USDA 1992, 1997, 2002, 2007, 2012). The Manure Management Annex discusses the methods for obtaining
8 annual average populations and disaggregating into state data where needed and provides the resulting population data
9 for the other livestock that were used for estimating all livestock-related emissions (see Table A-180). For each animal
10 category, the USDA publishes monthly, annual, or multi-year livestock population and production estimates. American
11 bison estimates prior to 2002 were estimated using data from the National Bison Association (1999).
12 Methane emissions from sheep, goats, swine, horses, mules and asses were estimated by multiplying national
13 population estimates by the default IPCC emission factor (IPCC 2006). For American bison the emission factor for buffalo
14 (IPCC 2006) was used and adjusted based on the ratio of live weights of 300 kg for buffalo (IPCC 2006) and 1,130 pounds
15 (513 kg) for American Bison (National Bison Association 2011) to the 0.75 power. This methodology for determining
16 emission factors is recommended by IPCC (2006) for animals with similar digestive systems. Table A-179 shows the
17 emission factors used for these other livestock. National enteric fermentation emissions from all livestock types are shown
18 in Table A-180 and Table A-181. Enteric fermentation emissions from most livestock types, broken down by state, for 2017
19 are shown in Table A-182 and Table A-183. Because a simplified calculation approach was used for 2018 emissions, state-
20 level emission estimates were not calculated for 2018. Livestock populations are shown in Table A-184.
21 Table A-179: Emission Factors for Other Livestock (kg CFU/head/year)
Livestock Type Emission Factor
Swine 1.5
Horses 18
Sheep 8
Goats 5
American Bison 82.2
Mules and Asses 10.0
22 Source: IPCC (2006), except American Bison, as described in text.
23
A-312 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 Table A-180: CH4 Emissions from Enteric Fermentation (MMT CP2 Eg.)
Livestock Type
1990
1995
2000
2005
2012
2013
2014
2015
2016
2017
2018
Beef Cattle
119.1
135.5
126.7
125.2
119.1
118.0
116.5
118.0
123.0
126.3
128.1
Dairy Cattle
39.4
37.5
38.0
37.6
41.7
41.6
42.0
42.6
43.0
43.3
43.6
Swine
2.0
2.2
2.2
2.3
2.5
2.5
2.4
2.6
2.6
2.7
2.8
Horses
1.0
1.2
1.5
1.7
1.6
1.6
1.5
1.4
1.4
1.3
1.2
Sheep
2.3
1.8
1.4
1.2
1.1
1.1
1.0
1.1
1.1
1.1
1.1
Goats
0.3
0.3
0.3
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.3
American Bison
0.1
0.2
0.4
0.4
0.3
0.3
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
164.2
178.7
170.6
168.9
166.7
165.5
164.2
166.5
171.8
175.4
177.6
2 + Does not exceed 0.05 MMT CO2 Eq.
3 Note: Totals may not sum due to independent rounding.
4
5 Table A-181: CH4 Emissions from Enteric Fermentation (kt)
Livestock
Type
1990
1995
2000
2005
2012
2013
2014
2015
2016
2017
2018
Beef Cattle
4,763
5,419
5,070
5,007
4,763
4,722
4,660
4,722
4,919
5,052
5,125
Dairy Cattle
1,574
1,498
1,519
1,503
1,670
1,664
1,679
1,706
1,722
1,730
1,744
Swine
81
88
88
92
100
98
96
102
105
108
111
Horses
40
47
61
70
65
62
60
57
54
51
48
Sheep
91
72
56
49
43
43
42
42
42
42
42
Goats
13
12
12
14
13
13
13
13
13
13
14
American
Bison
4
9
16
17
13
14
14
14
15
15
15
Mules and
Asses
1
1
1
2
3
3
3
3
3
3
3
Total
6,566
7,146
6,824
6,755
6,670
6,619
6,567
6,660
6,874
7,016
7,103
6 Note: Totals may not sum due to independent rounding.
A-313
-------
1 Table A-182: CH4 Emissions from Enteric Fermentation from Cattle (metric tons), by State, for 201791
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
63
224
4,866
3,713
65,220
1,672
4,692
1,406
1,143
257
84,265
Alaska
2
29
1
5
280
27
472
15
42
15
17
3
909
Arizona
1,223
30,231
1,591
5,656
2,078
1,064
18,486
571
1,594
8,023
1,197
12,054
83,770
Arkansas
37
705
44
156
5,839
4,897
86,018
2,411
6,764
3,094
2,031
531
112,527
California
10,947
255,718
10,337
36,743
7,272
3,788
65,807
1,954
5,454
18,166
5,129
20,614
441,929
Colorado
967
23,480
1,288
4,578
5,714
4,655
80,877
2,856
7,972
25,735
17,780
44,357
220,260
Conn.
119
2,901
137
486
49
27
472
28
78
56
32
9
4,393
Delaware
31
690
32
113
29
13
236
10
27
54
13
7
1,254
Florida
761
19,772
552
1,964
5,839
4,865
85,454
1,742
4,887
844
952
174
127,806
Georgia
518
14,123
474
1,683
3,211
2,663
46,774
1,533
4,301
1,041
1,587
247
78,154
Hawaii
15
297
14
48
416
426
7,394
195
545
303
137
42
9,832
Idaho
3,742
91,837
4,216
14,987
4,155
2,892
50,234
1,804
5,035
9,386
6,497
12,632
207,418
Illinois
580
12,214
670
2,381
2,378
2,020
35,606
1,019
2,862
6,583
3,341
12,157
81,810
Indiana
1,154
25,708
1,030
3,663
1,617
1,096
19,321
679
1,908
2,962
1,547
5,272
65,958
Iowa
1,341
30,612
1,739
6,181
6,659
5,037
88,785
2,512
7,060
35,930
16,707
55,763
258,326
Kansas
936
20,954
1,288
4,578
9,038
8,195
144,448
4,210
11,831
56,227
44,241
108,565
414,511
Kentucky
356
8,838
631
2,244
6,812
5,481
96,277
2,090
5,865
6,047
3,650
845
139,136
Louisiana
75
1,419
58
207
3,017
2,400
42,162
1,185
3,323
703
635
133
55,317
Maine
187
4,443
216
767
146
59
1,038
56
157
113
96
21
7,298
Maryland
293
6,737
417
1,483
390
231
4,058
154
431
395
191
427
15,208
Mass.
72
1,540
101
358
98
35
613
28
78
56
32
9
3,020
Michigan
2,651
64,570
2,190
7,783
1,522
626
11,041
340
954
4,635
1,238
7,126
104,675
Minn.
2,869
61,767
3,800
13,506
3,330
1,931
34,042
1,290
3,626
13,714
5,105
18,097
163,076
Miss.
56
1,257
95
337
3,698
2,550
44,797
1,296
3,636
1,181
984
221
60,109
Missouri
530
9,225
580
2,060
11,416
10,726
189,071
5,025
14,121
12,617
7,270
5,129
267,770
Montana
87
1,912
116
412
10,389
8,594
149,296
6,389
17,831
6,964
8,753
2,185
212,929
Nebraska
374
8,619
322
1,145
10,465
10,021
176,650
5,093
14,312
62,809
42,695
117,788
450,292
Nevada
187
4,331
150
532
1,454
1,272
22,103
616
1,720
1,362
992
133
34,853
N. Hamp.
84
1,999
86
307
49
27
472
14
39
42
32
7
3,158
91 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through 2019) Inventory submission.
A-314 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
N. Jersey
41
931
53
189
98
40
708
22
63
59
38
10
2,252
N. Mexico
2,027
50,486
1,496
5,318
3,636
2,689
46,718
1,503
4,196
3,633
3,077
619
125,399
New York
3,867
99,361
5,108
18,157
1,952
591
10,381
629
1,765
1,270
1,592
912
145,586
N. Car.
281
7,503
347
1,234
3,017
1,982
34,821
962
2,698
1,209
793
197
55,046
N. Dakota
100
2,150
116
412
6,184
4,979
87,773
2,797
7,862
6,994
6,652
2,423
128,442
Ohio
1,634
34,910
1,546
5,494
2,854
1,503
26,498
1,019
2,862
6,034
1,856
7,220
93,430
Oklahoma
218
4,923
292
1,037
15,570
11,224
197,165
6,062
17,008
25,315
14,758
14,436
308,008
Oregon
773
17,045
884
3,142
4,155
3,158
54,856
1,579
4,405
4,693
3,932
3,941
102,564
Penn.
3,275
77,384
4,533
16,111
2,440
994
17,459
909
2,549
4,514
1,910
4,416
136,494
R. Island
5
103
7
26
10
8
132
6
16
14
6
2
334
S. Car.
94
2,181
110
393
1,460
911
15,999
460
1,290
225
317
58
23,498
S. Dakota
724
15,937
580
2,060
9,513
8,685
153,097
5,364
15,075
20,296
16,397
18,574
266,302
Tenn.
256
6,121
552
1,964
6,325
4,870
85,548
2,021
5,669
3,797
2,856
679
120,659
Texas
3,056
79,064
3,794
13,486
33,086
23,895
419,740
11,288
31,671
72,851
42,845
115,505
850,281
Utah
574
13,699
748
2,659
2,805
1,955
33,958
1,278
3,566
2,422
2,052
1,017
66,733
Vermont
805
19,182
806
2,864
293
75
1,321
77
216
113
207
31
25,989
Virginia
543
14,030
600
2,132
3,892
3,445
60,514
1,561
4,379
4,641
2,222
969
98,928
Wash.
1,715
41,488
1,632
5,801
1,870
1,301
22,605
872
2,433
5,753
4,001
9,072
98,544
W. Virg.
50
1,015
58
205
1,464
1,113
19,535
531
1,490
1,100
541
190
27,292
Wisconsin
7,984
182,246
9,145
32,507
2,854
1,514
26,682
1,086
3,053
10,971
1,547
12,871
292,460
Wyoming
37
839
39
137
4,155
4,129
71,735
2,781
7,762
4,844
4,684
3,514
104,657
A-315
-------
1 Table A-183: CH4 Emissions from Enteric Fermentation from Other Livestock (metric tons), by State, for 201792
American Mules and
State
Swine
Horses
Sheep
Goats
Bison
Asses
Total
Alabama
86
725
99
125
21
120
1,175
Alaska
2
16
99
4
131
1
252
Arizona
240
2,089
1,040
506
6
41
3,921
Arkansas
197
778
99
163
27
87
1,350
California
143
1,879
4,800
746
120
62
7,751
Colorado
1,099
1,830
3,360
103
882
68
7,342
Connecticut
4
420
57
21
10
12
524
Delaware
9
150
99
2
8
1
269
Florida
23
2,186
99
232
32
110
2,681
Georgia
120
1,134
99
297
23
88
1,761
Hawaii
8
66
99
84
8
5
269
Idaho
38
879
2,000
92
292
40
3,341
Illinois
7,969
827
440
147
57
32
9,471
Indiana
6,056
2,045
416
151
108
58
8,835
Iowa
33,375
944
1,400
283
151
44
36,196
Kansas
3,011
1,077
544
176
546
34
5,387
Kentucky
615
1,947
384
150
116
135
3,347
Louisiana
9
1,063
99
80
7
84
1,341
Maine
7
213
57
35
22
4
337
Maryland
39
478
99
23
36
12
688
Massachusetts
11
362
57
45
8
3
486
Michigan
1,725
1,347
680
131
156
40
4,080
Minnesota
12,675
767
1,040
153
254
26
14,916
Mississippi
855
937
99
92
4
96
2,083
Missouri
4,969
1,538
720
554
168
86
8,035
Montana
269
1,631
1,840
42
1,206
49
5,036
Nebraska
5,156
1,135
664
85
1,903
43
8,986
Nevada
1
478
504
154
7
7
1,150
New Hampshire
5
149
57
29
25
1
266
New Jersey
19
453
99
29
16
8
624
New Mexico
2
861
776
131
424
18
2,213
New York
72
1,716
640
165
82
41
2,715
North Carolina
13,650
997
240
172
26
96
15,180
North Dakota
221
824
528
26
786
14
2,399
Ohio
4,181
1,963
936
168
70
72
7,391
Oklahoma
3,218
2,741
384
264
796
137
7,540
Oregon
14
926
1,360
142
115
27
2,583
Pennsylvania
1,800
2,222
744
206
108
94
5,173
Rhode Island
3
24
57
5
-
1
91
South Carolina
278
1,107
99
169
11
63
1,726
South Dakota
2,265
1,217
2,000
112
2,765
14
8,373
Tennessee
353
919
368
262
28
126
2,057
Texas
1,459
6,350
5,680
3,089
360
642
17,581
Utah
979
1,047
2,200
61
93
37
4,417
Vermont
6
181
57
73
9
14
340
Virginia
360
1,500
640
193
85
71
2,849
Washington
38
711
384
106
79
34
1,352
West Virginia
8
274
272
49
4
30
635
92 This table has not been updated for the current (1990 through 2018) Inventory. It will be updated for the next (1990 through 2019)
Inventory submission.
A-316 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Wisconsin 458 1,565 608 331 349 58 3,368
Wyoming 135 1,160 2,880 50 787 29 5,041
Indicates there are no emissions, as there is no significant population of this animal type.
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.
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.
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1 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
2 productivity and profitability of stocker cattle grazing in the southern plains. J. Anim. Sci. 82:2773-2779.
3 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
4 beef carcass quality, tenderness, and consumer ratings of beef palatability. J. Anim. Sci. 81:984-996.
5 Preston, R.L. (2010) What's The Feed Composition Value of That Cattle Feed? Beef Magazine, March 1, 2010. Available
6 at: .
7 Skogerboe, T. L., L. Thompson, J. M. Cunningham, A. C. Brake, V. K. Karle (2000) The effectiveness of a single dose of
8 doramectin pour-on in the control of gastrointestinal nematodes in yearling stocker cattle. Vet. Parasitology 87:173-
9 181.
10 Soliva, C.R. (2006) Report to the attention of IPCC about the data set and calculation method used to estimate methane
11 formation from enteric fermentation of agricultural livestock population and manure management in Swiss
12 agriculture. On behalf of the Federal Office for the Environment (FOEN), Berne, Switzerland.
13 USDA (2019) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S. Department of
14 Agriculture. Washington, D.C. Available online at .
15 USDA (2012) Census of Agriculture: 2012 Census Report. United States Department of Agriculture. Available online at:
16 .
17 USDA (2007) Census of Agriculture: 2007 Census Report. United States Department of Agriculture. Available online at:
18 .
19 USDA (2002) Census of Agriculture: 2002 Census Report. United States Department of Agriculture. Available online at:
20 .
21 USDA (1997) Census of Agriculture: 1997 Census Report. United States Department of Agriculture. Available online at:
22 . Accessed July 18, 2011.
23 USDA (1996) Beef Cow/Calf Health and Productivity Audit (CHAPA): Forage Analyses from Cow/Calf Herds in 18 States.
24 National Agriculture Statistics Service, U.S. Department of Agriculture. Washington, D.C. Available online at
25 . March 1996.
26 USDA (1992) Census of Agriculture: 1992 Census Report. United States Department of Agriculture. Available online at:
27 . Accessed July 18, 2011.
28 USDA:APHIS:VS (2010) Beef 2007-08, Part V: Reference of Beef Cow-calf Management Practices in the United States,
29 2007-08. USDA-APHIS-VS, CEAH. Fort Collins, CO.
30 USDA:APHIS:VS (2002) Reference of 2002 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort Collins, CO.
31 Available online at .
32 USDA:APHIS:VS (1998) Beef'97, Parts l-IV. USDA-APHIS-VS, CEAH. Fort Collins, CO. Available online at
33 .
34 USDA:APHIS:VS (1996) Reference of 1996 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort Collins, CO.
35 Available online at .
36 USDA:APHIS:VS (1994) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins, CO. Available
37 online at .
38 USDA:APHIS:VS (1993) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins, CO. August
39 1993. Available online at .
40 Vasconcelos and Galyean (2007) Nutritional recommendations of feedlot consulting nutritionists: The 2007 Texas Tech
41 University Study. J. Anim. Sci. 85:2772-2781.
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3.11. Methodology for Estimating CH4 and N2O Emissions from Manure
Management93
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.
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 2015 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
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.
93 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.
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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 boilers and turkeys, population data are calculated by dividing the
number of animals produced by the number of production cycles per year, or the turnover rate. Based on personal
communications with John Lange, an agricultural statistician with USDA NASS, the broiler turnover rate ranges from 3.4 to
5.5 over the course of the inventory (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-184.
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-185 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-186 (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 overtime,
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:
• 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
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(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 2006 IPCC
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:
VS production (kg) = [(GE - DE) + (UE x GE)] x 1 ASH
18.45
where,
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).94
NeX(j~) Nintake * ^retention_fract(T))
where,
NeX(j) = Annual N excretion rates (kg N animal1 yr1)
Njntake(T) = The annual N intake per head of animal of species/category T (kg N animal"1 yr1)
94 Note that although this equation was updated since the previous Inventory submission, the equations are functionally the same
and do not impact Inventory emissions estimates. The updated equation clarifies the relationship between intake of N and milk
and growth (i.e., the fraction of N retained).
A-321
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Nretentionfo = Traction of annual N intake that is retained by animal
N intake is estimated as:
( CP% s
V. GE T 100
mtaks(T) ][g_45 g_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, and is as follows:
where,
Nretention(T)
Milk
268
7.03
NEg
1,000
6.25
Milk PR%
6.38
WG
N,
retention(T)
Milk x
(Milk PR%\
V 100 )
6.38
WG x
268-
{7.03 x NEg\
[ WG )
1000 x 6.25
= Daily N retained per animal of category T (kg N animal1 day1)
= Milk production (kg animal1 day1)
= Constant from 2006 IPCC Guidelines
= Constant from 2006 IPCC Guidelines
= Net energy for growth, calculated in livestock characterization, based on current
weight, mature weight, rate of weight gain, and IPCC constants, (MJ day1)
= Conversion from grams to kilograms (g kg1)
= Conversion from kg dietary protein to kg dietary N (kg protein per kg N)
= Percent of protein in milk (%)
= Conversion from milk protein to milk N (kg protein per kg N)
= 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-187 presents the state-specific
VS and Nex production rates used for cattle in 2018. As shown in Table A-187, 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.
Step 3: Waste Management System Usage Data
Table A-188 and Table A-189 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
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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-190.
Beef Cattle, Dairy Heifers and American Bison-. The beeffeedlot 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).
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,
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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 1998 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.
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 (UEP1999). These
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1 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
2 data (USDA, APHIS 2000). It was assumed that the change in system usage between 1990 and 1999 is proportionally
3 distributed among those years of the inventory. It was also assumed that system usage in 2000 through 2018 was equal
4 to that estimated for 1999. Data collected for EPA's Office of Water, including information collected during site visits (EPA
5 2002b), were used to estimate the distribution of waste by management system and animal type. As with dairy and swine,
6 using information about AD WMS from EPA's AgSTAR database (EPA 2019), AD was added to the WMS distribution for
7 poultry operations.
8 Poultry—Broilers and Turkeys: The percentage of turkeys and broilers on pasture was obtained from the Office
9 of Air and Radiation's Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that one
10 percent of poultry waste is deposited in pastures, ranges, and paddocks (EPA 1992). The remainder of waste is assumed
11 to be deposited in operations with bedding management. As with dairy, swine, and other poultry, AD systems were used
12 to update the WMS distributions based on information from EPA's AgSTAR database (EPA 2019).
13 Step 4: Emission Factor Calculations
14 Methane conversion factors (MCFs) and N20 emission factors (EFs) used in the emission calculations were
15 determined using the methodologies presented below.
16 Methane Conversion Factors (MCFs)
17 Climate-based IPCC default MCFs (IPCC 2006) were used for all dry systems; these factors are presented in Table
18 A-191. A U.S.-specific methodology was used to develop MCFs for all lagoon and liquid systems.
19 For animal waste managed in dry systems, the appropriate IPCC default MCF was applied based on annual
20 average temperature data. The average county and state temperature data were obtained from the National Climate Data
21 Center (NOAA 2019) and each state and year in the inventory was assigned a climate classification of cool, temperate or
22 warm. Although there are some specific locations in the United States that may be included in the warm climate category,
23 no aggregated state-level annual average temperatures are included in this category. In addition, some counties in a
24 particular state may be included in the cool climate category, although the aggregated state-level annual average
25 temperature may be included in the temperate category. Although considering the temperatures at a state level instead
26 of a county level may be causing some specific locations to be classified into an inappropriate climate category, using the
27 state level annual average temperature provides an estimate that is appropriate for calculating the national average.
28 For anaerobic lagoons and other liquid systems, a climate-based approach based on the van't Hoff-Arrhenius
29 equation was developed to estimate MCFs that reflects the seasonal changes in temperatures, and also accounts for long-
30 term retention time. This approach is consistent with the latest guidelines from IPCC (2006). The van't Hoff-Arrhenius
31 equation, with a base temperature of 30°C, is shown in the following equation (Safley and Westerman 1990):
32
33 where,
34 /
35
36 Ti
37 T2
38
39 E
40 R
41
42 For those animal populations using liquid manure management systems or manure runoff ponds (i.e., dairy cow,
43 dairy heifer, layers, beef in feedlots, and swine) monthly average state temperatures were based on the counties where
44 the specific animal population resides (i.e., the temperatures were weighted based on the percent of animals located in
45 each county). County population data were calculated from state-level population data from NASS and county-state
46 distribution data from the 1992, 1997, 2002, 2007, 2012, and 2017 Census data (USDA 2019d). County population
47 distribution data for 1990 and 1991 were assumed to be the same as 1992; county population distribution data for 1993
f = exp
E(T2-T0
RT{T2
¦ 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)
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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:
MCF CH4 generated ^
-™1 VS produced ai]niialxBo
where,
MCF annual = Methane conversion factor
VS produced annual = Volatile solids excreted annually
B0 = Maximum CH4 producing potential of the waste
In order to account for the carry-over of VS from one year to the next, it is assumed that a portion of the VS from
the previous year are available in the lagoon system in the next year. For example, the VS from October, November, and
December of 2005 are available in the lagoon system starting January of 2006 in the MCF calculation for lagoons in 2006.
Following this procedure, the resulting MCF for lagoons accounts for temperature variation throughout the year, residual
VS in a system (carry-over), and management and design practices that may reduce the VS available for conversion to CH4.
It is assumed that liquid-slurry systems have a retention time less than 30 days, so the liquid-slurry MCF calculation doesn't
reflect the VS carry-over.
The liquid system MCFs are presented in Table A-192 by state, WMS, and animal group for 2018.
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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-193.
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 Fracmnoff/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 Fracrunoff/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-194.
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:
TAM
VS excreted stat(,Aninial>WMS = Population^ ^ x —xVSx 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:
CH4 = £(vs excreted ltate.i^WMS xB, xMCFxO.662)
Stats. Animal WMS
where,
CH4 = CH4 emissions (kg CH4/yr)
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VS excreted wms,state = Amount of VS excreted in manure managed in each WMS (kg/yr)
B0 = Maximum CH4 producing capacity (m3 CH4/kg VS)
MCF animal,state, wms = MCF for the animal group, state and WMS (percent)
0.662 = Density of methane at 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 CH4production
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:
([cH4 Production AD Al)svslcnl xCE Al)svslcrT1 x(l-DE)J <
CH4 Emissions AD = Z r . ( \1
State, Animal, ADSystems + [CH ^ Production AD | )svs^cn1 7 (1 - CE \| )svs^cm jj
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:
TAM
N excreted statei,Wal mis = Population,^ aa*! x WMS x —— x Nex x 365,25
where,
N eXCreted state. Animal, WMS
Population state
WMS
TAM
Nex
365.25
1000
= 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 Stats, Ammal>WMS = Population Stats, Ammal x WMS x Nex
where,
N excreted state,Animal, wms = Amount of N excreted in manure managed in each WMS for each animal type
(kg/yr)
Population state = Annual average state animal population by animal type (head)
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:
Direct N2O = £ N excreted
State, Animal, WMS
44
State, Animal, WMS x X —
where,
Direct N20 = Direct N20 emissions (kg N20/yr)
N excreted state,Animal, 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 = 2
State. Animal WMS
N excreted Statf Animal, wms x "
Frac
'gas, WMS
100
xEF,
volatlza tbn
44
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N excreted state> Anknai, wms
. ^1 ^nmofPleach, WMS nl:- 44
: — X ^nnmoffifeach x ^
where,
Indirect N20
: Indirect N20 emissions (kg N20/yr)
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N excreted
State, Animal, WMS
Fracgas,wMs
Fr3Crunoff/leach,WMS
EF volatilization
EF runoff/leach
44/28
= Amount of N excreted in manure managed in each WMS for each animal type
(kg/yr)
= Nitrogen lost through volatilization in each WMS
= Nitrogen lost through runoff and leaching in each WMS (data were not available
for leaching so the value reflects only runoff)
= Emission factor for volatilization (0.010 kg N20-N/kg N)
= Emission factor for runoff/leaching (0.0075 kg N20-N/kg N)
= 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-195
and Table A-196 respectively. Emission estimates for 2018 are presented by animal type and state in Table A-197 and
Table A-198 respectively.
A-330 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Table A-184: Livestock Population (1,000 Head)
Animal Type
1990
1995
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Dairy Cattle
19,512
18,681
17,793
18,078
18,190
18,422
18,560
18,297
18,442
18,587
18,505
18,527
18,803
18,853
18,893
19,008
Dairy Cows
10,015
9,482
9,004
9,104
9,145
9,257
9,333
9,087
9,156
9,236
9,221
9,208
9,307
9,310
9,346
9,432
Dairy Heifer
4,129
4,108
4,162
4,294
4,343
4,401
4,437
4,545
4,577
4,581
4,525
4,579
4,725
4,785
4,762
4,776
Dairy Calves
5,369
5,091
4,628
4,680
4,703
4,765
4,791
4,666
4,709
4,770
4,758
4,740
4,771
4,758
4,785
4,800
Swine3
53,941
58,899
61,073
61,887
65,417
67,183
65,842
64,723
65,572
66,363
65,437
64,195
68,178
70,065
72,125
73,793
Market <50 lb.
18,359
19,656
20,228
20,514
21,812
19,933
19,411
19,067
19,285
19,472
19,002
18,939
19,843
20,572
20,973
21,494
Market 50-119
lb.
11,734
12,836
13,519
13,727
14,557
17,163
16,942
16,645
16,904
17,140
16,834
16,559
17,577
18,175
18,767
19,133
Market 120-179
lb.
9,440
10,545
11,336
11,443
12,185
12,825
12,517
12,377
12,514
12,714
12,674
12,281
13,225
13,575
13,982
14,365
Market >180 lb.
7,510
8,937
9,997
10,113
10,673
11,161
11,067
10,856
11,078
11,199
11,116
10,525
11,555
11,714
12,282
12,497
Breeding
6,899
6,926
5,993
6,090
6,190
6,102
5,905
5,778
5,791
5,839
5,812
5,892
5,978
6,030
6,122
6,303
Beef Cattle11
81,576
90,361
82,193
83,263
82,801
81,532
80,993
80,484
78,937
76,858
76,075
75,245
76,080
79,374
81,560
83,061
Feedlot Steers
6,357
7,233
8,116
8,724
8,674
8,474
8,434
8,584
8,771
8,586
8,614
8,695
8,570
9,019
9,572
10,329
Feedlot Heifers
3,192
3,831
4,536
4,801
4,730
4,585
4,493
4,620
4,830
4,742
4,653
4,525
4,313
4,431
4,768
5,146
NOF Bulls
2,160
2,385
2,214
2,258
2,214
2,207
2,188
2,190
2,165
2,100
2,074
2,038
2,109
2,142
2,244
2,252
Beef Calves
16,909
18,177
16,918
16,814
16,644
16,231
16,051
16,067
15,817
15,288
14,859
14,741
15,000
15,563
15,971
16,021
NOF Heifers
10,182
11,829
9,550
9,716
9,592
9,356
9,473
9,349
8,874
8,687
8,787
8,787
9,288
9,903
9,835
9,815
NOF Steers
10,321
11,716
8,185
8,248
8,302
8,244
8,560
8,234
7,568
7,173
7,457
7,374
7,496
8,150
7,957
8,032
NOF Cows
32,455
35,190
32,674
32,703
32,644
32,435
31,794
31,440
30,913
30,282
29,631
29,085
29,302
30,166
31,213
31,466
Sheep
11,358
8,989
6,135
6,200
6,120
5,950
5,747
5,620
5,470
5,375
5,360
5,235
5,270
5,295
5,270
5,265
Sheep On Feed
1,180
1,771
2,971
3,026
3,000
2,911
2,806
2,778
2,687
2,666
2,655
2,585
2,584
2,621
2,615
2,619
Sheep NOF
10,178
7,218
3,164
3,174
3,120
3,039
2,941
2,842
2,783
2,709
2,705
2,650
2,686
2,674
2,655
2,646
Goats
2,516
2,357
2,897
3,019
3,141
3,037
2,933
2,829
2,725
2,622
2,637
2,652
2,668
2,683
2,699
2,714
Poultryc
1,537,074
1,826,977
2,150,410
2,154,236
2,166,936
2,175,990
2,088,828
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
Hens >1 yr.
273,467
299,071
348,203
349,888
346,613
339,859
341,005
341,884
338,944
346,965
361,403
370,637
351,656
377,299
388,006
396,870
Pullets
73,167
81,369
96,809
96,596
103,816
99,458
102,301
105,738
102,233
104,460
106,646
106,490
118,114
112,061
117,173
124,135
Chickens
6,545
7,637
8,289
7,938
8,164
7,589
8,487
7,390
6,922
6,827
6,853
6,403
7,211
6,759
6,859
6,568
Broilers
1,066,209
1,331,940
1,613,091
1,612,327
1,619,400
1,638,055
1,554,582
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
Turkeys
117,685
106,960
84,018
87,487
88,943
91,029
82,453
81,396
82,833
84,500
80,000
79,167
77,700
81,333
81,733
81,583
Horses
2,212
2,632
3,875
3,952
4,029
3,947
3,866
3,784
3,703
3,621
3,467
3,312
3,157
3,002
2,847
2,692
Mules and Asses
63
101
212
248
284
286
287
289
291
293
298
303
308
313
318
323
American Bison
47
104
212
205
198
191
184
177
169
162
166
171
175
179
184
188
2 3 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
3 categories.
4 b NOF - Not on Feed
5 c Pullets includes laying pullets, pullets youngerthan 3 months, and pullets olderthan 3 months.
6 Note: Totals may not sum due to independent rounding.
7 Source(s): See Step 1: Livestock Population Characterization Data.
8
9
A-331
-------
1 Table A-185: Waste Characteristics Data
Maximum Methane Generation
Typical Animal Mass, TAM
Total Nitrogen Excreted, Nexa
Potential
Bo
Volatile Solids Excreted, VSa
Value
Value
(m3 CH4/kg VS
Animal Group
(kg)
Source
Value
Source
added)
Source
Value
Source
Dairy Cows
680
CEFM
Table A-187
CEFM
0.24
Morris 1976
Table A-187
CEFM
Dairy Heifers
406-408
CEFM
Table A-187
CEFM
0.17
Bryant et al. 1976
Table A-187
CEFM
Feedlot Steers
419-457
CEFM
Table A-187
CEFM
0.33
Hashimoto 1981
Table A-187
CEFM
Feedlot Heifers
384-430
CEFM
Table A-187
CEFM
0.33
Hashimoto 1981
Table A-187
CEFM
NOF Bulls
831-917
CEFM
Table A-187
CEFM
0.17
Hashimoto 1981
Table A-187
CEFM
NOF Calves
118
ERG 2003b
Table A-186
USDA 1996, 2008
0.17
Hashimoto 1981
Table A-186
USDA 1996, 2008
NOF Heifers
296-407
CEFM
Table A-187
CEFM
0.17
Hashimoto 1981
Table A-187
CEFM
NOF Steers
314-335
CEFM
Table A-187
CEFM
0.17
Hashimoto 1981
Table A-187
CEFM
NOF Cows
554-611
CEFM
Table A-187
CEFM
0.17
Hashimoto 1981
Table A-187
CEFM
American Bison
578.5
Meagher 1986
Table A-187
CEFM
0.17
Hashimoto 1981
Table A-187
CEFM
Market Swine <50 lbs.
13
ERG 2010a
Table A-186
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-186
USDA 1996, 2008
Market Swine <60 lbs.
16
Safley 2000
Table A-186
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-186
USDA 1996, 2008
Market Swine 50-119
Table A-186
Table A-186
lbs.
39
ERG 2010a
USDA 1996, 2008
0.48
Hashimoto 1984
USDA 1996, 2008
Market Swine 60-119
Table A-186
Table A-186
lbs.
41
Safley 2000
USDA 1996, 2008
0.48
Hashimoto 1984
USDA 1996, 2008
Market Swine 120-179
Table A-186
Table A-186
lbs.
68
Safley 2000
USDA 1996, 2008
0.48
Hashimoto 1984
USDA 1996, 2008
Market Swine >180 lbs.
91
Safley 2000
Table A-186
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-186
USDA 1996, 2008
Breeding Swine
198
Safley 2000
Table A-186
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-186
USDA 1996, 2008
Table A-186
Table A-186
ASAE 1998, USDA
Feedlot Sheep
25
EPA 1992
ASAE 1998, USDA 2008
0.36
EPA 1992
2008
Table A-186
Table A-186
ASAE 1998, USDA
NOF Sheep
80
EPA 1992
ASAE 1998, USDA 2008
0.19
EPA 1992
2008
Goats
64
ASAE1998
Table A-186
ASAE 1998
0.17
EPA 1992
Table A-186
ASAE1998
Table A-186
Table A-186
ASAE 1998, USDA
Horses
450
ASAE 1998
ASAE 1998, USDA 2008
0.33
EPA 1992
2008
Mules and Asses
130
IPCC 2006
Table A-186
IPCC 2006
0.33
EPA 1992
Table A-186
IPCC 2006
Hens >/= 1 yr
1.8
ASAE1998
Table A-186
USDA 1996, 2008
0.39
Hill 1982
Table A-186
USDA 1996, 2008
Pullets
1.8
ASAE 1998
Table A-186
USDA 1996, 2008
0.39
Hill 1982
Table A-186
USDA 1996, 2008
Other Chickens
1.8
ASAE1998
Table A-186
USDA 1996, 2008
0.39
Hill 1982
Table A-186
USDA 1996, 2008
Broilers
0.9
ASAE 1998
Table A-186
USDA 1996, 2008
0.36
Hill 1984
Table A-186
USDA 1996, 2008
Turkeys
6.8
ASAE1998
Table A-186
USDA 1996, 2008
0.36
Hill 1984
Table A-186
USDA 1996, 2008
a Nex and VS values vary by year; Table A-187 shows state-level values for 2018 only.
A-332 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Tible A-186: Estimated Volatile Solids (VS) and Total Nitrogen Excreted (Nex) Production Rates by year for Swine, Poultry, Sheep, Goats, Horses, Mules and Asses, and Cattle
Qlves (kg/day/1000 kg animal mass)
Animal Type
1990
1995
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
VS
Swine, Market
<50 lbs.
Swine, Market
50-119 lbs.
Swine, Market
120-179 lbs.
Swine, Market
>180 lbs.
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
5.4
5.4
5.4
8.8
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
8.8
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.
Swine, Market
50-119 lbs.
Swine, Market
120-179 lbs.
Swine, Market
>180 lbs.
0.60
0.42
0.42
0.42
0.60
0.42
0.42
0.42
0.84
0.51
0.51
0.51
0.87
0.52
0.52
0.52
0.89
0.53
0.53
0.53
0.92
0.54
0.54
0.54
0.92
0.54
0.54
0.54
0.92
0.54
0.54
0.54
0.92
0.54
0.54
0.54
0.92
0.54
0.54
0.54
0.92
0.54
0.54
0.54
0.92
0.54
0.54
0.54
0.92
0.54
0.54
0.54
0.92
0.54
0.54
0.54
0.92
0.54
0.54
0.54
0.92
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
A-333
-------
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
0.83
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-334 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 Table A-187: Estimated Volatile Solids (VS) and Total Nitrogen Excreted (Nex) Production Rates by State for Cattle (other than Calves) and American Bisona for 2018
2 (kg/animal/year)
Volatile Solids
Nitrogen Excreted
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Dairy
Dairy
NOF
Beef NOF
Beef NOF
Beef OF
Beef 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
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
A-335
-------
Volatile Solids
Nitrogen Excreted
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Dairy
Dairy
NOF
Beef NOF
Beef NOF
Beef OF
Beef 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
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
1 3 Beef NOF Bull values were used for American bison Nex and VS.
2 Source: CEFM.
3
4 Table A-188: 2018 Manure Distribution Among Waste Management Systems by Operation (Percent)
Beef Not
on Feed
Beef Feedlots
Operations
Dairy Cow Farms9
Dairy Heifer Facilities
Pasture,
Pasture,
Pasture,
Liquid/
Range,
Range,
Daily
Dry
Solid Liquid/Anaerobic
Deep
Daily
Dry Liquid/
Range,
State
Dry Lotb Slurryb
Paddock
Paddock Spread
Lot
Storage
Slurry
Lagoon
Pit
Spreadb
Lotb Slurry11
Paddockb
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 0
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 0
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
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 0
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
A-336 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Beef Feedlots
Beef Not
on Feed
Operations
Dairy Cow Farms9
Dairy Heifer Facilities
State
Liquid/
Dry Lotb Slurryb
Pasture,
Range,
Paddock
Pasture,
Range, Daily
Paddock Spread
Dry Solid Liquid/ Anaerobic
Lot Storage Slurry Lagoon
Deep
Pit
Daily
Spreadb
Pasture,
Dry Liquid/ Range,
Lotb Slurry11 Paddockb
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 0
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 0
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 0
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 0
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
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
3 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.
-------
1
2
3
4
5
6
7
8
9
10
11
b Because manure from beef feed lots 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.
Table A-189: 2018 Manure Distribution Among Waste Management Systems by Operation (Percent) Continued
Swine Operations9
Layer Operations
Broiler and Turkey
Operations
State
Pasture,
Range, Solid Liquid/ Anaerobic
Paddock Storage Slurry Lagoon
Deep Pit
Deep (<1
Pit month)
Anaerobic
Lagoon
Poultry
without
Litter
Pasture,
Range,
Paddock
Poultry
with
Litter
Alabama
IB
0
29
30
12
14
42
58
99
Alaska
57
0
3
2
34
4
25
75
99
Arizona
19
0
28
29
11
13
60
40
99
Arkansas
6
0
60
26
5
2
0
100
99
California
IB
0
28
29
13
14
12
88
99
Colorado
2
0
53
0
23
22
60
40
99
Connecticut
66
0
2
2
26
4
5
95
99
Delaware
29
0
4
5
56
5
5
95
99
Florida
53
0
20
14
9
5
42
58
99
Georgia
13
0
56
28
3
1
42
58
99
Hawaii
42
0
22
18
11
7
25
75
99
Idaho
16
0
16
3
57
8
60
40
99
Illinois
2
0
15
7
71
5
2
98
99
Indiana
1
0
3
12
78
7
0
100
99
Iowa
1
0
10
4
80
5
0
100
99
Kansas
1
0
13
35
21
30
2
98
99
Kentucky
8
0
19
21
31
21
5
95
99
Louisiana
67
0
17
9
6
2
60
40
99
Maine
74
0
2
1
20
4
5
95
99
Maryland
37
0
10
2
44
6
5
95
99
Massachusetts
60
0
2
2
31
4
5
95
99
Michigan
3
0
12
6
69
9
2
98
99
Minnesota
1
0
3
2
88
5
0
100
99
Mississippi
2
0
31
36
13
18
60
40
99
Missouri
2
0
16
33
34
15
0
100
99
A-338 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Swine Operations9
Layer Operations
Broiler and Turkey
Operations
State
Pasture,
Range, Solid Liquid/ Anaerobic
Paddock Storage Slurry Lagoon
Deep Pit
Deep (<1
Pit month)
Anaerobic
Lagoon
Poultry
without
Litter
Pasture,
Range,
Paddock
Poultry
with
Litter
Montana
3
0
21
2
64
9
60
40
99
Nebraska
2
0
9
22
49
19
2
98
99
Nevada
12
0
29
32
12
15
0
100
99
New Hampshire
65
0
2
2
27
4
5
95
99
New Jersey
54
0
3
3
36
4
5
95
99
New Mexico
67
0
17
9
6
2
60
40
99
New York
41
0
6
3
44
5
5
95
99
North Carolina
1
0
33
49
1
16
42
58
99
North Dakota
2
0
21
2
65
9
2
98
99
Ohio
1
0
10
9
67
13
0
100
99
Oklahoma
1
0
11
53
3
32
60
40
99
Oregon
51
0
20
15
9
5
25
75
99
Pennsylvania
1
0
8
5
77
9
0
100
99
Rhode Island
64
0
2
2
28
4
5
95
99
South Carolina
6
0
30
34
13
16
60
40
99
South Dakota
1
0
17
11
57
14
2
98
99
Tennessee
7
0
30
33
13
16
5
95
99
Texas
6
0
31
34
13
17
12
88
99
Utah
1
0
22
2
65
9
60
40
99
Vermont
69
0
2
1
24
4
5
95
99
Virginia
6
0
14
29
15
35
5
95
99
Washington
35
0
12
2
45
7
12
88
99
West Virginia
82
0
1
0
13
3
5
95
99
Wisconsin
15
0
23
1
57
4
2
98
99
Wyoming
3
0
21
2
64
9
60
40
99
1 3 In the methane inventory for manure management, the percent of dairy cows and swine with AD systems is estimated
2 using data from EPA's AgSTAR Program.
3 b Because manure from beef feedlots and dairy heifers may be managed for long periods of time in multiple systems
4 (i.e., both drylot and runoff collection pond), the percent of manure that generates emissions is greater than 100
5 percent.
6 Source(s): See Step 3: Waste Management System Usage Data.
A-339
-------
Table A-190: Manure Management System Descriptions
Manure Management System Description9
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 IB 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.
Poultry without Litter In high-rise cages or scrape-out/belt systems, manure is excreted onto the floor below with no
bedding to absorb moisture. The ventilation system dries the manure as it is stored. When designed
and operated properly, this high-rise system is a form of passive windrow composting.
Anaerobic Digester
Deep Pit
Poultry with Litter
3 Manure management system descriptions and the classification of manure as managed or unmanaged are based on the 2006IPCC Guidelines
for National Greenhouse Gas Inventories (Volume 4: Agriculture, Forestry and Other Land Use, Chapter 10: Emissions from Livestock and Manure
Management, Tables 10.18 and 10.21) and the Development Document for the Final Revisions to the National Pollutant Discharge Elimination
System Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations (EPA-821-R-03-001, December 2002).
Table A-191: Methane Conversion Factors (percent) for Dry Systems
Waste Management System
Cool Climate MCF Temperate Climate MCF
Warm Climate MCF
Aerobic Treatment
Anaerobic Digester
Cattle Deep Litter (<1 month)
Cattle Deep Litter (>1 month)
0
0
3
21
0
0
3
44
0
0
30
76
A-340 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Waste Management System
Cool Climate MCF Temperate Climate MCF
Warm Climate MCF
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
Com posting-Intensive
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).
1
2 Table A-192: Methane Conversion Factors by State for Liquid Systems for 2018 (Percent)
Dairy
Swine
Beef
Poultry
Anaerobic
Liquid/Slurry
Anaerobic
Liquid/Slurry
Anaerobic
State
Lagoon
and Deep Pit
Lagoon
and Pit Storage
Liquid/Slurry
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
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
A-341
-------
Dairy
Swine
Beef
Poultry
Anaerobic
Liquid/Slurry
Anaerobic
Liquid/Slurry
Anaerobic
State
Lagoon
and Deep Pit
Lagoon
and Pit Storage
Liquid/Slurry
Lagoon
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 2006IPCC Guidelines, Section 10.4.2.
1
Table A-193: Direct Nitrous Oxide Emission Factors (kg N20-N/kg N excreted)
Waste Management System
Direct N20 Emission Factor
Aerobic Treatment (forced aeration)
0.005
Aerobic Treatment (natural aeration)
0.01
Anaerobic Digester
0
Anaerobic Lagoon
0
Cattle Deep Bed (active mix)
0.07
Cattle Deep Bed (no mix)
0.01
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.
3
4 Table A-194: Indirect Nitrous Oxide Loss Factors (Percent)
Animal Type
Waste Management
System
Volatilization
Nitrogen Loss
Central
Runoff/Leaching Nitrogen Lossa
Pacific Mid-Atlantic Midwest
South
Beef Cattle
Dry Lot
23
1.1
3.9
3.6
1.9
4.3
Beef Cattle
Liquid/Slurry
26
0
0
0
0
0
Beef Cattle
Pasture
0
0
0
0
0
0
Dairy Cattle
Anaerobic Lagoon
43
0.2
0.8
0.7
0.4
0.9
Dairy Cattle
Daily Spread
10
0
0
0
0
0
Dairy Cattle
Deep Pit
24
0
0
0
0
0
Dairy Cattle
Dry Lot
15
0.6
2
1.8
0.9
2.2
Dairy Cattle
Liquid/Slurry
26
0.2
0.8
0.7
0.4
0.9
Dairy Cattle
Pasture
0
0
0
0
0
0
Dairy Cattle
Solid Storage
27
0.2
0
0
0
0
A-342 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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
1 3 Data for nitrogen losses due to leaching were not available, so the values represent only nitrogen losses due to runoff. Source: EPA (2002b,
2 2005).
A-343
-------
1 Table A-195: Total Methane Emissions from Livestock Manure Management (kt)a
Animal Type
1990
1995
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Dairy Cattle
589
634
970
990
1,105
1,106
1,112
1,124
1,144
1,188
1,167
1,190
1,233
1,259
1,270
1,292
Dairy Cows
581
676
962
981
1,095
1,096
1,102
1,115
1,134
1,177
1,157
1,180
1,222
1,248
1,259
1,281
Dairy Heifer
7
7
7
7
8
8
8
8
8
9
8
8
9
9
9
9
Dairy Calves
2
.'
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Swine
622
763
812
789
851
786
740
797
791
821
756
719
808
846
840
888
Market Swine
483
607
665
643
698
645
608
657
653
678
623
585
665
699
697
736
Market <50 lbs.
102
121
128
125
136
94
88
95
94
98
88
86
95
101
100
106
Market 50-119 lbs.
101
123
131
127
138
143
134
144
142
149
136
130
145
155
153
161
Market 120-179 lbs.
136
170
184
177
193
185
173
188
185
193
179
169
192
203
200
213
Market >180 lbs.
144
193
222
214
232
223
214
229
231
238
220
201
232
241
244
256
Breeding Swine
139
155
147
146
152
140
132
140
138
143
133
133
143
146
143
152
Beef Cattle
126
139
133
137
134
130
130
132
131
128
122
120
126
132
136
135
Feedlot Steers
14
14
15
16
16
16
16
16
17
16
16
16
16
17
18
20
Feedlot Heifers
7
8
9
9
9
9
9
9
9
9
9
9
9
9
9
10
NOF Bulls
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Beef Calves
6
7
7
7
7
7
7
7
7
7
6
6
7
7
7
7
NOF Heifers
12
15
13
13
13
13
13
13
12
12
12
12
13
14
14
13
NOF Steers
12
14
10
11
10
10
11
10
10
9
9
9
9
10
10
10
NOF Cows
69
76
73
75
73
70
70
71
71
69
65
63
67
69
71
70
Sheep
7
5
3
3
3
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
1
1
1
Poultry
131
128
129
131
134
129
128
129
127
128
129
132
136
136
137
141
Hens >1 yr.
73
69
66
66
67
64
64
64
64
63
65
67
69
69
70
71
Total Pullets
25
22
23
25
23
23
24
23
23
24
24
27
26
26
28
Chickens
4
4
3
3
3
3
4
3
3
3
3
3
3
3
3
3
Broilers
19
23
31
32
32
33
31
31
31
32
31
31
32
32
32
33
Turkeys
10
9
7
7
7
7
6
6
6
6
6
6
6
6
6
6
Horses
9
11
12
12
11
10
10
10
10
10
9
8
8
8
7
7
Mules and Asses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
American Bison
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
2
3
4
5
+ Does not exceed 0.5 kt.
3 Accounts for Cm reductions due to capture and destruction of Cm at facilities using anaerobic digesters.
A-344 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 Table A-196: Total (Direct and Indirect) Nitrous Oxide Emissions from Livestock Manure Management (kt)
Animal Type
1990
1995
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Dairy Cattle
17.7
18.2
18.4
19.0
19.0
18.7
19.0
19.0
19.3
19.5
19.4
19.6
20.1
20.3
20.4
20.6
Dairy Cows
10.6
10.7
10.5
10.8
10.8
10.7
10.8
10.7
10.9
11.1
11.1
11.2
11.4
11.5
11.6
11.8
Dairy Heifer
7.1
7.5
7.8
8.2
8.2
8.0
8.1
8.3
8.4
8.5
8.3
8.4
8.7
8.8
8.8
8.8
Dairy Calves
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Swine
4.0
4.5
5.5
5.6
6.0
6.1
5.9
5.8
5.9
6.0
6.0
5.8
6.2
6.3
6.6
6.7
Market Swine
3.0
3.5
4.6
4.8
5.2
5.3
5.2
5.1
5.2
5.2
5.2
5.0
5.4
5.6
5.7
5.9
Market <50 lbs.
0.6
0.6
0.9
0.9
1.0
0.8
0.8
0.7
0.8
0.8
0.7
0.7
0.8
0.8
0.8
0.8
Market 50-119 lbs.
0.6
0.7
0.9
0.9
1.0
1.2
1.2
1.1
1.2
1.2
1.2
1.1
1.2
1.2
1.3
1.3
Market 120-179 lbs.
0.9
1.0
1.3
1.3
1.4
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.6
1.6
1.7
1.7
Market >180 lbs.
0.9
1.1
1.5
1.6
1.7
1.8
1.8
1.7
1.8
1.8
1.8
1.7
1.8
1.9
2.0
2.0
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
0.8
0.8
0.8
Beef Cattle
19.8
21.8
24.0
25.7
25.6
25.1
25.1
25.3
25.9
25.8
26.0
26.0
25.8
27.2
28.7
31.0
Feedlot Steers
13.4
14.4
15.5
16.7
16.7
16.5
16.5
16.6
16.9
16.7
17.0
17.3
17.3
18.4
19.3
20.8
Feedlot Heifers
6.4
7.4
8.5
9.0
8.9
8.7
8.6
8.7
9.1
9.0
9.0
8.8
8.5
8.8
9.4
10.1
Sheep
0.4
0.7
1.2
1.2
1.2
1.2
1.1
1.1
1.1
1.1
1.1
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
0.1
0.1
0.1
Poultry
4.7
5.1
5.4
5.4
5.4
5.4
5.2
5.2
5.2
5.3
5.2
5.2
5.2
5.4
5.5
5.6
Hens >1 yr.
1.0
1.0
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.4
1.3
1.4
1.4
1.5
Total Pullets
0.3
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.5
Chickens
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Broilers
2.2
2.7
3.0
2.9
2.9
2.9
2.7
2.8
2.8
2.9
2.7
2.7
2.8
2.8
2.9
2.9
Turkeys
1.2
1.1
0.8
0.8
0.8
0.8
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Horses
0.3
0.4
0.5
0.5
0.5
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.3
0.3
0.3
Mules and Asses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
American Bison
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
2 + Does not exceed 0.05 kt.
3 NA (Not Applicable)
4 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.
A-345
-------
1 Table A-197: Methane Emissions by State from Livestock Manure Management for 2018 (kt)a
State
Beef on
Feed lots
Beef Not
on Feedb
Dairy
Cow
Dairy
Heifer
Swine-
Market
Swine—
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Mules American
Horses and Asses Bison
Total
Alabama
0.0196
2.5576
0.6444
0.0109
0.6128
0.2921
10.0158
4.0815
0.0228
0.0091
0.0191
0.1542
0.0124
0.0004
18.4526
Alaska
0.0001
0.0188
0.0081
0.0002
0.0041
0.0015
0.3658
+
0.0227
0.0061
0.0002
0.0031
+
0.0033
0.4341
Arizona
0.7467
1.1143
22.6091
0.2781
2.3077
0.5078
1.2367
+
0.0228
0.0881
0.0221
0.2473
0.0030
0.0003
29.1841
Arkansas
0.0392
3.4272
0.5007
0.0080
0.7011
1.3186
0.6322
3.9664
0.7878
0.0091
0.0134
0.1385
0.0085
0.0005
11.5512
California
1.5783
3.9672
328.8087
1.9650
1.2727
0.2016
3.3533
0.2082
0.2751
0.4017
0.0495
0.2991
0.0064
0.0046
342.3915
Colorado
1.7935
3.1421
16.9646
0.1510
4.0937
2.3794
4.3482
+
0.0227
0.2091
0.0129
0.2081
0.0045
0.0246
33.3542
Connecticut
0.0004
0.0187
2.5977
0.0163
0.0090
0.0032
0.1353
+
0.0227
0.0038
0.0014
0.0223
0.0007
0.0008
2.8325
Delaware
0.0003
0.0087
0.7004
0.0040
0.0267
0.0330
0.1412
0.9543
0.0227
0.0061
0.0004
0.0083
0.0001
0.0003
1.9064
Florida
0.0140
3.2761
16.6941
0.1031
0.0933
0.0564
5.2916
0.2375
0.0228
0.0091
0.0236
0.2778
0.0122
0.0002
26.1118
Georgia
0.0195
1.9065
11.4906
0.0823
0.5867
0.4967
17.5986
4.9449
0.0228
0.0091
0.0262
0.1574
0.0118
0.0004
37.3534
Hawaii
0.0035
0.3072
0.4528
0.0030
0.0613
0.0711
0.5588
+
0.0228
0.0091
0.0063
0.0146
0.0004
0.0003
1.5111
Idaho
0.5057
1.8331
95.7254
0.4702
0.1322
0.0717
1.0558
+
0.0227
0.1104
0.0076
0.1005
0.0023
0.0477
100.0852
Illinois
0.5290
1.0916
11.7615
0.0851
48.5808
11.9133
0.3480
0.2075
0.0227
0.0258
0.0093
0.0867
0.0033
0.0013
74.6660
Indiana
0.2287
0.6166
18.5135
0.1298
43.0970
5.4902
1.1482
0.2075
0.4985
0.0268
0.0104
0.1623
0.0034
0.0013
70.1341
Iowa
2.3875
3.2558
33.7951
0.2167
200.2792
18.6204
1.8780
0.2075
0.2966
0.0775
0.0216
0.1063
0.0027
0.0046
261.1497
Kansas
4.7944
5.5176
31.1294
0.1653
30.6612
4.8948
0.0804
+
0.0227
0.0315
0.0126
0.1076
0.0032
0.0103
77.4308
Kentucky
0.0417
2.6069
5.1627
0.0722
3.2804
0.9555
0.7409
1.0980
0.0227
0.0273
0.0147
0.2522
0.0091
0.0040
14.2883
Louisiana
0.0107
1.6534
1.0919
0.0113
0.0273
0.0251
2.2941
0.2082
0.0228
0.0091
0.0071
0.1329
0.0072
0.0002
5.5013
Maine
0.0009
0.0396
3.3115
0.0249
0.0073
0.0042
0.1276
+
0.0227
0.0038
0.0014
0.0172
0.0003
0.0005
3.5620
Maryland
0.0208
0.1287
5.5858
0.0515
0.1167
0.0487
0.3567
1.0477
0.0227
0.0061
0.0036
0.0601
0.0014
0.0001
7.4505
Massachusetts
0.0004
0.0214
0.4993
0.0119
0.0185
0.0149
0.0156
+
0.0227
0.0038
0.0019
0.0294
0.0008
+
0.6408
Michigan
0.3026
0.4836
64.6288
0.2712
9.8014
2.1039
0.9662
0.2075
0.1321
0.0376
0.0074
0.1302
0.0029
0.0053
79.0809
Minnesota
0.7665
1.3319
49.1167
0.4691
61.8333
9.4514
0.3671
0.2139
1.0469
0.0611
0.0092
0.0941
0.0024
0.0053
124.7690
Mississippi
0.0171
1.7959
0.7059
0.0166
8.2978
1.7405
7.9230
2.7217
0.0228
0.0091
0.0119
0.1210
0.0093
0.0006
23.3933
Missouri
0.2271
5.0513
8.0111
0.0746
37.7611
13.1703
0.4748
1.0611
0.4736
0.0470
0.0179
0.1734
0.0082
0.0019
66.5535
Montana
0.0868
4.4934
1.4994
0.0135
0.9287
0.4212
0.7923
+
0.0227
0.1057
0.0038
0.1641
0.0027
0.0441
8.5784
Nebraska
5.0500
6.3788
11.3436
0.0403
34.2176
9.9592
0.5097
0.2075
0.0227
0.0376
0.0074
0.0995
0.0017
0.0537
67.9295
Nevada
0.0054
0.6449
6.2569
0.0168
0.0831
0.0072
0.0409
+
0.0227
0.0287
0.0029
0.0273
0.0004
+
7.1371
New Hampshire
0.0003
0.0164
1.3330
0.0100
0.0098
0.0035
0.1296
+
0.0227
0.0038
0.0010
0.0145
0.0003
0.0006
1.5457
New Jersey
0.0005
0.0223
0.6203
0.0065
0.0389
0.0099
0.1384
+
0.0227
0.0061
0.0028
0.0493
0.0010
0.0002
0.9189
New Mexico
0.0256
1.4761
38.7860
0.1710
0.0030
0.0052
1.1829
+
0.0227
0.0451
0.0089
0.0918
0.0018
0.0108
41.8308
New York
0.0419
0.4874
88.7044
0.5995
0.2457
0.0621
0.5555
0.2075
0.0227
0.0399
0.0073
0.1408
0.0023
0.0022
91.1191
North Carolina
0.0135
1.4156
5.6674
0.0557
135.4980
32.5530
12.2828
3.1731
0.8128
0.0190
0.0210
0.1582
0.0123
0.0007
191.6834
North Dakota
0.1015
2.4732
2.3584
0.0142
0.6622
0.5100
0.0758
+
0.0227
0.0329
0.0018
0.0575
0.0007
0.0234
6.3342
Ohio
0.3124
0.8972
33.1522
0.1943
26.0507
3.9530
1.1444
0.3906
0.1670
0.0559
0.0153
0.2054
0.0058
0.0019
66.5461
Oklahoma
0.6109
5.8725
3.6459
0.0459
28.0251
16.0733
3.5639
0.7148
0.0228
0.0381
0.0368
0.3905
0.0184
0.0059
59.0647
Oregon
0.1866
1.6830
10.0832
0.1121
0.0436
0.0162
0.8844
0.2075
0.0227
0.0775
0.0119
0.1374
0.0030
0.0044
13.4738
Pennsylvania
0.2086
0.7237
48.2122
0.5453
10.8677
2.1249
0.8827
0.7244
0.1745
0.0451
0.0133
0.1797
0.0072
0.0024
64.7116
A-346 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Beef on
Beef Not
Dairy
Dairy
Swine-
Swine—
Mules
American
State
Feed lots
on Feedb
Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
and Asses
Bison
Total
Rhode Island
0.0001
0.0041
0.0585
0.0009
0.0048
0.0021
0.1357
+
0.0227
0.0038
0.0002
0.0042
0.0001
+
0.2372
South Carolina
0.0043
0.6420
1.8258
0.0191
3.5344
0.3791
4.6319
0.8637
0.0228
0.0091
0.0154
0.1319
0.0068
0.0002
12.0865
South Dakota
0.7887
4.5816
20.7912
0.0718
13.2394
4.6553
0.1688
+
0.1059
0.1175
0.0045
0.1083
0.0015
0.0542
44.6887
Tennessee
0.0464
3.4364
4.0185
0.0633
3.0448
0.6947
0.2542
0.6440
0.0228
0.0324
0.0372
0.2793
0.0207
0.0010
12.5957
Texas
7.0735
19.5728
62.1310
0.5981
15.0207
4.3199
5.3891
2.3736
0.0228
0.5286
0.3110
1.0436
0.0958
0.0236
118.5042
Utah
0.0410
1.0540
9.0198
0.0833
3.6981
1.1829
4.1960
+
0.0227
0.1292
0.0051
0.1133
0.0013
0.0023
19.5489
Vermont
0.0014
0.0715
12.9275
0.0930
0.0071
0.0050
0.0159
+
0.0227
0.0038
0.0024
0.0173
0.0002
0.0003
13.1682
Virginia
0.0466
1.6723
8.5790
0.0669
5.2131
0.1696
0.3711
1.0096
0.4187
0.0352
0.0121
0.1343
0.0058
0.0013
17.7358
Washington
0.4326
0.8841
41.7824
0.2062
0.0663
0.0285
1.2698
0.2075
0.0227
0.0211
0.0075
0.1102
0.0024
0.0022
45.0435
West Virginia
0.0090
0.5323
0.5977
0.0070
0.0062
0.0038
0.1674
0.3016
0.0773
0.0164
0.0060
0.0501
0.0027
0.0002
1.7776
Wisconsin
0.5445
1.1872
135.7794
1.1262
2.0359
0.6093
0.4333
0.2020
0.0227
0.0352
0.0271
0.1515
0.0032
0.0115
142.1690
Wyoming
0.1405
2.1605
0.9219
0.0045
0.1464
0.3340
1.0308
+
0.0227
0.1621
0.0038
0.1147
0.0024
0.0216
5.0659
1 + Does not exceed 0.00005 kt.
2 3 Accounts for CFU reductions due to capture and destruction of CFU at facilities using anaerobic digesters.
3 b Beef Not on Feed includes calves.
4
5 Table A-198: Total (Direct and Indirect) Nitrous Oxide Emissions by State from Livestock Manure Management for 2018(kt)
Beef Beef
Feed lot-
Feedlot-
Dairy
Swine-
Swine-
Mules
American
Heifer
Steers
Dairy Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
and Asses
Bison
Total
Alabama
0.0042
0.0087
0.0043
0.0037
0.0036
0.0012
0.0704
0.3611
0.0026
0.0049
0.0015
0.0053
0.0004
NA
0.4720
Alaska
+
0.0001
0.0002
0.0002
0.0001
+
0.0060
+
0.0026
0.0016
+
0.0002
+
NA
0.0110
Arizona
0.1962
0.4029
0.3704
0.2486
0.0123
0.0020
0.0065
+
0.0026
0.0138
0.0017
0.0085
0.0001
NA
1.2656
Arkansas
0.0088
0.0180
0.0034
0.0020
0.0051
0.0069
0.0889
0.3509
0.0913
0.0043
0.0011
0.0048
0.0003
NA
0.5858
California
0.3397
0.6979
2.4521
1.5939
0.0084
0.0010
0.0707
0.0184
0.0319
0.0710
0.0039
0.0103
0.0002
NA
5.2994
Colorado
0.7215
1.4810
0.2980
0.2305
0.0540
0.0230
0.0259
+
0.0026
0.0491
0.0015
0.0107
0.0002
NA
2.8981
Connecticut
0.0001
0.0003
0.0203
0.0119
0.0001
+
0.0057
+
0.0026
0.0031
0.0002
0.0011
+
NA
0.0455
Delaware
0.0001
0.0002
0.0051
0.0027
0.0002
0.0002
0.0057
0.0847
0.0026
0.0049
+
0.0004
+
NA
0.1070
Florida
0.0029
0.0058
0.0744
0.0515
0.0005
0.0002
0.0356
0.0210
0.0026
0.0049
0.0019
0.0095
0.0004
NA
0.2113
Georgia
0.0041
0.0083
0.0541
0.0304
0.0039
0.0024
0.1231
0.4375
0.0026
0.0049
0.0021
0.0054
0.0004
NA
0.6792
Flawaii
0.0007
0.0014
0.0030
0.0023
0.0003
0.0003
0.0060
+
0.0026
0.0016
0.0005
0.0005
+
NA
0.0193
Idaho
0.2054
0.4214
0.8671
0.7198
0.0017
0.0007
0.0065
+
0.0026
0.0259
0.0009
0.0052
0.0001
NA
2.2572
Illinois
0.1983
0.4069
0.0927
0.1068
0.4081
0.0736
0.0248
0.0184
0.0026
0.0180
0.0011
0.0045
0.0002
NA
1.3560
Indiana
0.0861
0.1769
0.1940
0.1490
0.3428
0.0322
0.1594
0.0184
0.0580
0.0187
0.0012
0.0084
0.0002
NA
1.2454
Iowa
0.9109
1.8716
0.2324
0.2658
1.9354
0.1326
0.2608
0.0184
0.0345
0.0541
0.0026
0.0055
0.0001
NA
5.7245
Kansas
1.7718
3.6370
0.1623
0.2164
0.1883
0.0222
0.0057
+
0.0026
0.0220
0.0015
0.0055
0.0002
NA
6.0356
Kentucky
0.0139
0.0285
0.0358
0.0237
0.0219
0.0047
0.0315
0.0975
0.0026
0.0221
0.0018
0.0130
0.0005
NA
0.2974
Louisiana
0.0022
0.0045
0.0067
0.0025
0.0002
0.0001
0.0117
0.0184
0.0026
0.0043
0.0006
0.0046
0.0003
NA
0.0587
Maine
0.0003
0.0007
0.0299
0.0178
0.0001
+
0.0057
+
0.0026
0.0031
0.0002
0.0009
+
NA
0.0614
A-347
-------
Maryland
0.0070
0.0144
0.0449
0.0349
0.0010
0.0003
0.0149
0.0930
0.0026
0.0049
0.0004
0.0031
0.0001
NA
0.2216
Massachusetts
0.0001
0.0003
0.0099
0.0082
0.0002
0.0001
0.0007
+
0.0026
0.0031
0.0002
0.0015
+
NA
0.0269
Michigan
0.1165
0.2397
0.5359
0.3630
0.0964
0.0152
0.0708
0.0184
0.0154
0.0262
0.0009
0.0067
0.0002
NA
1.5053
Minnesota
0.2957
0.6076
0.4971
0.5853
0.6366
0.0716
0.0510
0.0190
0.1217
0.0426
0.0011
0.0049
0.0001
NA
2.9343
Mississippi
0.0036
0.0075
0.0053
0.0041
0.0471
0.0072
0.0407
0.2403
0.0026
0.0049
0.0010
0.0042
0.0003
NA
0.3689
Missouri
0.0836
0.1716
0.0702
0.0823
0.2425
0.0619
0.0661
0.0942
0.0551
0.0328
0.0021
0.0089
0.0004
NA
0.9719
Montana
0.0356
0.0732
0.0150
0.0197
0.0130
0.0043
0.0051
+
0.0026
0.0248
0.0004
0.0085
0.0001
NA
0.2022
Nebraska
1.9233
3.9501
0.0665
0.0532
0.2593
0.0555
0.0368
0.0184
0.0026
0.0262
0.0009
0.0051
0.0001
NA
6.3981
Nevada
0.0022
0.0044
0.0323
0.0256
0.0006
+
0.0057
+
0.0026
0.0067
0.0003
0.0014
+
NA
0.0819
New Hampshire
0.0001
0.0003
0.0132
0.0073
0.0001
+
0.0057
+
0.0026
0.0031
0.0001
0.0007
+
NA
0.0333
New Jersey
0.0002
0.0003
0.0057
0.0043
0.0003
0.0001
0.0057
+
0.0026
0.0049
0.0003
0.0025
0.0001
NA
0.0271
New Mexico
0.0100
0.0206
0.6190
0.2336
+
+
0.0065
+
0.0026
0.0106
0.0011
0.0047
0.0001
NA
0.9088
New York
0.0150
0.0306
0.6822
0.4204
0.0025
0.0005
0.0241
0.0184
0.0026
0.0324
0.0009
0.0073
0.0001
NA
1.2370
North Carolina
0.0032
0.0066
0.0299
0.0162
0.7439
0.1312
0.0870
0.2807
0.0942
0.0103
0.0017
0.0054
0.0004
NA
1.4107
North Dakota
0.0396
0.0814
0.0168
0.0176
0.0082
0.0046
0.0057
+
0.0026
0.0230
0.0002
0.0030
+
NA
0.2026
Ohio
0.1179
0.2423
0.2625
0.2227
0.2230
0.0249
0.1566
0.0347
0.0194
0.0451
0.0018
0.0106
0.0003
NA
1.3619
Oklahoma
0.2347
0.4813
0.0544
0.0440
0.1442
0.0606
0.0186
0.0632
0.0026
0.0177
0.0029
0.0134
0.0007
NA
1.1383
Oregon
0.0648
0.1330
0.1560
0.1226
0.0004
0.0001
0.0114
0.0184
0.0026
0.0205
0.0014
0.0071
0.0002
NA
0.5386
Pennsylvania
0.0725
0.1487
0.4649
0.3451
0.1041
0.0150
0.1227
0.0643
0.0203
0.0366
0.0016
0.0093
0.0004
NA
1.4055
Rhode Island
+
0.0001
0.0006
0.0005
+
+
0.0057
+
0.0026
0.0031
+
0.0002
+
NA
0.0130
South Carolina
0.0010
0.0020
0.0093
0.0052
0.0206
0.0016
0.0237
0.0764
0.0026
0.0049
0.0012
0.0045
0.0002
NA
0.1534
South Dakota
0.3036
0.6244
0.1247
0.0927
0.1259
0.0325
0.0124
+
0.0123
0.0820
0.0005
0.0056
0.0001
NA
1.4168
Tennessee
0.0112
0.0229
0.0256
0.0223
0.0184
0.0031
0.0105
0.0570
0.0026
0.0175
0.0030
0.0096
0.0007
NA
0.2043
Texas
1.8794
3.8593
0.8937
0.5633
0.0941
0.0199
0.1099
0.2100
0.0026
0.0827
0.0246
0.0359
0.0034
NA
7.7788
Utah
0.0166
0.0342
0.1593
0.1262
0.0450
0.0106
0.0239
+
0.0026
0.0303
0.0006
0.0058
0.0001
NA
0.4553
Vermont
0.0005
0.0010
0.1346
0.0674
0.0001
+
0.0007
+
0.0026
0.0031
0.0003
0.0009
+
NA
0.2112
Virginia
0.0159
0.0328
0.0571
0.0255
0.0317
0.0008
0.0154
0.0896
0.0487
0.0286
0.0014
0.0069
0.0003
NA
0.3549
Washington
0.1495
0.3070
0.3817
0.2364
0.0008
0.0003
0.0300
0.0184
0.0026
0.0056
0.0009
0.0057
0.0001
NA
1.1389
West Virginia
0.0031
0.0064
0.0062
0.0047
0.0001
+
0.0072
0.0268
0.0090
0.0133
0.0007
0.0026
0.0001
NA
0.0803
Wisconsin
0.2102
0.4316
1.4680
1.3704
0.0235
0.0052
0.0319
0.0179
0.0026
0.0246
0.0032
0.0078
0.0002
NA
3.5971
Wyoming
0.0571
0.1172
0.0065
0.0057
0.0027
0.0043
0.0065
+
0.0026
0.0380
0.0004
0.0059
0.0001
NA
0.2472
+ Does not exceed 0.00005 kt.
NA (Not Applicable)
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.
A-348 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
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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.
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1 ERG (2001) Summary of development of MDP Factor for methane conversion factor calculations. ERG, Lexington, MA.
2 September 2001.
3 ERG (2000a) Calculations: Percent Distribution of Manure for Waste Management Systems. ERG, Lexington, MA. August
4 2000.
5 ERG (2000b) Discussion of Methodology for Estimating Animal Waste Characteristics (Summary of B0 Literature Review).
6 ERG, Lexington, MA. June 2000.
7 Garrett, W.N. and D.E. Johnson (1983) "Nutritional energetics of ruminants." Journal of Animal Science, 57(suppl.2):478-
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9 Groffman, P.M., R. Brumme, K. Butterbach-Bahl, K.E. Dobbie, A.R. Mosier, D. Ojima, H. Papen, W.J. Parton, K.A. Smith,
10 and C. Wagner-Riddle (2000) "Evaluating annual nitrous oxide fluxes at the ecosystem scale." Global Biogeochemcial
11 Cycles, 14(4): 1061-1070.
12 Hashimoto, A.G. (1984) "Methane from Swine Manure: Effect of Temperature and Influent Substrate Composition on
13 Kinetic Parameter (k)." Agricultural Wastes, 9:299-308.
14 Hashimoto, A.G., V.H. Varel, and Y.R. Chen (1981) "Ultimate Methane Yield from Beef Cattle Manure; Effect of
15 Temperature, Ration Constituents, Antibiotics and Manure Age." Agricultural Wastes, 3:241-256.
16 Hill, D.T. (1984) "Methane Productivity of the Major Animal Types." Transactions oftheASAE, 27(2):530-540.
17 Hill, D.T. (1982) "Design of Digestion Systems for Maximum Methane Production." Transactions oftheASAE, 25(1):226-
18 230.
19 IPCC (2018) 10th Corrigenda for the 2006IPCC Guidelines. The National Greenhouse Gas Inventories Programme, The
20 Intergovernmental Panel on Climate Change. Available online at: .
22 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
23 Programme, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K.
24 Tanabe (eds.). Hayama, Kanagawa, Japan.
25 Johnson, D. (2000) Personal Communication. Dan Johnson, State Water Management Engineer, California Natural
26 Resource Conservation Service and Lee-Ann Tracy, ERG. June 23, 2000.
27 Lange, J. (2000) Personal Communication. John Lange, Agricultural Statistician, U.S. Department of Agriculture, National
28 Agriculture Statistics Service and Lee-Ann Tracy, ERG. Washington, D.C. May 8, 2000.
29 Meagher, M. (1986). Bison. Mammalian Species. 266:1-8.
30 Miller, P. (2000) Personal Communication. Paul Miller, Iowa Natural Resource Conservation Service and Lee-Ann Tracy,
31 ERG. June 12, 2000.
32 Milton, B. (2000) Personal Communication. Bob Milton, Chief of Livestock Branch, U.S. Department of Agriculture,
33 National Agriculture Statistics Service and Lee-Ann Tracy, ERG. May 1, 2000.
34 Moffroid, K. and D. Pape. (2014) 1990-2013 Volatile Solids and Nitrogen Excretion Rates. Dataset to EPA from ICF
35 International. August 2014.
36 Morris, G.R. (1976) Anaerobic Fermentation of Animal Wastes: A Kinetic and Empirical Design Fermentation. M.S. Thesis.
37 Cornell University.
38 National Bison Association (1999) Total Bison Population—1999. Report provided during personal email communication
39 with Dave Carter, Executive Director, National Bison Association July 19, 2011.
40 NOAA (2019) National Climate Data Center (NCDC). Available online at:
41 (for all states except Alaska and Hawaii) and (for Alaska and
42 Hawaii). July 2019.
43 Ott, S.L. (2000) Dairy '96 Study. Stephen L. Ott, Animal and Plant Health Inspection Service, U.S. Department of
44 Agriculture. June 19, 2000.
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1 Poe, G., N. Bills, B. Bellows, P. Crosscombe, R. Koelsch, M. Kreher, and P. Wright (1999) Staff Paper Documenting the
2 Status of Dairy Manure Management in New York: Current Practices and Willingness to Participate in Voluntary
3 Programs. Department of Agricultural, Resource, and Managerial Economics; Cornell University, Ithaca, New York,
4 September.
5 Safley, L.M., Jr. (2000) Personal Communication. Deb Bartram, ERG and L.M. Safley, President, Agri-Waste Technology.
6 June and October 2000.
7 Safley, L.M., Jr. and P.W. Westerman (1990) "Psychrophilic anaerobic digestion of animal manure: proposed design
8 methodology." Biological Wastes, 34:133-148.
9 Stettler, D. (2000) Personal Communication. Don Stettler, Environmental Engineer, National Climate Center, Oregon
10 Natural Resource Conservation Service and Lee-Ann Tracy, ERG. June 27, 2000.
11 Sweeten, J. (2000) Personal Communication. John Sweeten, Texas A&M University and Indra Mitra, ERG. June 2000.
12 UEP (1999) Voluntary Survey Results—Estimated Percentage Participation/Activity. Caged Layer Environmental
13 Management Practices, Industry data submissions for EPA profile development, United Egg Producers and National
14 Chicken Council. Received from John Thorne, Capitolink. June 2000.
15 USDA (2019a) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S. Department of
16 Agriculture. Washington, D.C. Available online at: .
17 USDA (2019b) Chicken and Eggs 2018 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
18 Washington, D.C. March 2019. Available online at: < https://www.nass.usda.gov/Publications/>.
19 USDA (2019c) Poultry - Production and Value 2018 Summary. National Agriculture Statistics Service, U.S. Department of
20 Agriculture. Washington, D.C. April 2019. Available online at: < https://www.nass.usda.gov/Publications/>.
21 USDA (2019d) 1987, 1992, 1997, 2002, 2007, 2012, and 2017 Census of Agriculture. National Agriculture Statistics
22 Service, U.S. Department of Agriculture. Washington, D.C. Available online at: <
23 https://www.nass.usda.gov/AgCensus/index.php>. May 2019.
24 USDA (2018a) Chicken and Eggs 2017Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
25 Washington, D.C. February 2018. Available online at: < https://www.nass.usda.gov/Publications/>.
26 USDA (2018b) Poultry - Production and Value 2017Summary. National Agriculture Statistics Service, U.S. Department of
27 Agriculture. Washington, D.C. April 2018. Available online at: .
28 USDA (2017a) Chicken and Eggs 2016 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
29 Washington, D.C. February 2017. Available online at: .
30 USDA (2017b) Poultry- Production and Value 2016 Summary. National Agriculture Statistics Service, U.S. Department of
31 Agriculture. Washington, D.C. April 2017. Available online at: < https://www.nass.usda.gov/Publications/ >.
32 USDA (2016a) Chicken and Eggs 2015 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
33 Washington, D.C. July 2016. Available online at: .
34 USDA (2016b) Poultry- Production and Value 2015 Summary. National Agriculture Statistics Service, U.S. Department of
35 Agriculture. Washington, D.C. July 2016. Available online at: .
36 USDA (2015a) Chicken and Eggs 2014 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
37 Washington, D.C. February 2015. Available online at: .
38 USDA (2015b) Poultry- Production and Value 2014 Summary. National Agriculture Statistics Service, U.S. Department of
39 Agriculture. Washington, D.C. April 2015. Available online at: .
40 USDA (2014a) Chicken and Eggs 2013 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
41 Washington, D.C. February 2014. Available online at: .
42 USDA (2014b) Poultry- Production and Value 2013 Summary. National Agriculture Statistics Service, U.S. Department of
43 Agriculture. Washington, D.C. April 2014. Available online at: .
44 USDA (2013a) Chicken and Eggs 2012 Summary. National Agriculture Statistics Service, U.S. Department of Agriculture.
45 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: < https://downloads.usda.library.cornell.edu/usda-
esmis/files/8623hx75j/x633f384z/5m60qv97x/chikneggest_Chickens-and-Eggs-Final-Estimates-2003-07.pdf
USDA (2009d) Poultry Production and Value—Final Estimates 2003-2007. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. May 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 ChU 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.95 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 the natural processes of
photosynthesis (i.e., 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 estimated using the DayCent ecosystem simulation model.
A combination of Tier 1, 2, and 3 approaches are used to estimate soil 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 simulation 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 stock change 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 stock change factors for estimating mineral soil organic C stock changes on
federal lands;
4) Tier 2 methods with country-specific emission 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 C stocks due to biosolids (i.e., sewage sludge)
additions to soils.
The methodologies used to estimate soil N20 emissions include:
1) A Tier 3 method using the DayCent ecosystem simulation 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);
95 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 denitrification, which leaks from microbial cells into the soil
and then into the atmosphere. Nitrous oxide is also produced during nitrification, although by a less well-understood
mechanism (Nevison 2000).
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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;
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., 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 simulation 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 broad climate region classification for soil 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 titled Tier 3 Method Description and Model Evaluation).
Splicing methods are used to fill gaps at the end of the time series for these emission sources and are not described in this
annex. 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 estimating 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 from volatilization, leaching, and runoff from croplands and grasslands; and CH4
emissions from rice cultivation. First, the activity data are compiled from a combination of land-use, livestock, crop, and
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1 grassland management surveys, as well as expert knowledge. In the second, third, and fourth steps, soil organic C stock
2 changes, direct and indirect N20 emissions, and CH4 emissions are estimated using Tier 1, 2 and 3 methods. In the fifth
3 step, total emissions are calculated by summing all components for soil organic C stock changes, N20 emissions and CH4
4 emissions. The remainder of this annex describes the methods underlying each step.
5 Step 1: Derive Activity Data
6 This step describes how the activity data are derived to estimate soil organic C stock changes, direct and indirect N20
7 emissions, and CH4 emissions from rice cultivation. The activity data requirements include: (1) land base and history data,
8 (2) crop-specific mineral N fertilizer rates and timing,96 (3) crop-specific manure amendment N rates and timing, (4) other
9 N inputs, (5) tillage practices, (6) cover crop management, (7) planting and harvesting dates for crops, (8) irrigation data,
97
10 (9) Enhanced Vegetation Index (EVI), (10) daily weather data, and (1) edaphic characteristics.
11 Step la: Activity Data for the Agricultural Land Base and Histories
12 The U.S. Department of Agriculture's 2015 National Resources Inventory (NRI) (USDA-NRCS 2018a) provides the basis for
13 identifying the U.S. agricultural land base on non-federal lands, and classifying parcels into Cropland Remaining Cropland,
14 Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland.98 In 1998, the NRI program
15 began collecting annual data, and data are currently available through 2015 (USDA-NRCS 2018a). The time series will be
16 extended as new data are released by the USDA NRI program.
17 The NRI has a stratified multi-stage sampling design, where primary sample units are stratified on the basis of county and
18 township boundaries defined by the U.S. Public Land Survey (Nusser and Goebel 1997). Within a primary sample unit,
19 typically a 160-acre (64.75 ha) square quarter-section, three sample locations are selected according to a restricted
20 randomization procedure. Each sample location in the survey is assigned an area weight (expansion factor) based on other
21 known areas and land-use information (Nusser and Goebel 1997). In principle, the expansion factors represent the amount
22 of area with the land use and land use change history that is the same as the survey location. The NRI uses a sampling
23 approach, and therefore there is some uncertainty associated with scaling the survey location data to a region or the
24 country using the expansion factors. In general, those uncertainties decline at larger scales because of a larger sample size,
25 such as states compared to smaller county units. An extensive amount of soils, land-use, and land management data have
99
26 been collected through the survey (Nusser et al. 1998). Primary sources for data include aerial photography as well as
27 field visits and county office records.
28 The NRI survey provides crop data for most years between 1979 and 2015, with the exception of 1983, 1988, and 1993.
29 These years are gap-filled using an automated set of rules so that cropping sequences are filled with the most likely crop
30 type given the historical cropping pattern at each NRI survey location. Grassland data are reported on 5-year increments
31 prior to 1998, but it is assumed that the land use is also grassland between the years of data collection (see Easter et al.
32 2008 for more information).
33 NRI survey locations are included in the land base for the agricultural soil C and N20 emissions inventories if they are
34 identified as cropland or grassland100 between 1990 and 2015 (See Section 7.1, Land Representation for more information
101
35 about areas in each land use and land use change category). NRI survey locations on federal lands are not sampled by
36 the USDA NRI program. The land use at the survey locations in federal lands is determined from the National Land Cover
37 Dataset (NLCD) (Yang et al. 2018), and included in the agricultural land base if the land uses are cropland and/or grassland.
96 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.
97 Edaphic characteristics include such factors as soil texture and pH.
98 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.
99 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.
100 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).
101 Land use for 2016 to 2018 is not compiled, but will be updated with a new release of the NRI data (i.e., USDA-NRCS 2015).
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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 account for differences in Forest Land 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
102
classified as Cropland Remaining Cropland if the parcel has been used as cropland for at least 20 years. Similarly, land
parcels under grassland management in a specific year of the inventory are classified as Grassland Remaining Grassland if
they 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 2006 IPCC Guidelines. 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 C stock changes, CH4 and N20 emissions for 349,464
NRI survey locations that occur on mineral soils. The actual crop and grassland histories are simulated with the DayCent
model when applying the Tier 3 methods. 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 emissions103 and
CH4 emissions from rice cultivation (Table A-199).
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-199: 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
Total104
1990
1S2.22
307.63
4S9.8S
1.39
461.24
1991
IS 1.49
307.89
4S9.37
1.38
460.75
1992
1S0.83
308.07
4S8.90
1.38
460.28
1993
149.84
308.47
4S8.31
1.38
459.69
1994
149.04
308.87
4S7.91
1.38
459.29
199S
147.92
309.28
4S7.20
1.37
458.57
1996
146.90
309.75
4S6.6S
1.36
458.01
1997
14S.69
310.19
4SS.88
1.3S
457.23
1998
144.67
310.63
4SS.31
1.3S
456.65
1999
143.71
311.10
4S4.81
1.3S
456.16
102 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.
103 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.
104 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.
A-357
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
2000
142.98
311.38
454.36
1.35
455.71
2001
142.49
311.82
454.31
1.34
455.66
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
137.28
311.33
448.61
1.32
449.93
2014
137.28
311.33
448.61
1.32
449.93
2015
137.28
311.33
448.61
1.32
449.93
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 C (Table A-200). 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 are taken into account resulting from the likelihood that current use is correlated with past use. These dependencies
occur because as an area of some land use/management categories increase, the area of other land use/management
categories will decline.
Table A-200: 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
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
A-358 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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
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
A-359
-------
1
2
3
4
5
6
7
8
9
10
11
12
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
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
Irrigation
Pasture with Legumes or
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
Irrigation and Manure
Rangelands and Unimproved
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
Pasture
Rangelands and Unimproved
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
Pasture, Moderately Degraded
Rangelands and Unimproved
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
Pasture, Severely Degraded
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-201). 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.
Table A-201: 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 Land Areas (million ha)
Category 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
A-360 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
2
3
4
5
6
7
8
9
10
11
12
13
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 2012. 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-202). Ratoon crops occur in the Southeast with a second season of rice during the year. Ratoon cropping also occurs in
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 ratoon crops (45
percent), but ratoon cropping is uncommon in Arkansas occurring on relatively small fraction of fields estimated at about
1 percent. No data are available on 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-202: 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.
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Step lb: Obtain Management Activity Data to estimate Soil 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 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 in management
activity. 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 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).105 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-NASS1992,1999, 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).106 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. 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). For example, if the simulated amount exceeded the sales data
by 3 percent, then the emissions associated with synthetic mineral fertilization is reduced by 3 percent (the same
105 Available online: .
106 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).
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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-203.
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 mineral fertilizer and
manure amendment rates from the years with available data. For crops in some states, little or no data are available, and,
therefore, a geographic regional mean is used to simulate fertilization rates (e.g., no data are available for the State of
Alabama during the 1970s for corn fertilization rates; therefore, 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. The total PRP manure N added to soils is found in Table A-203.
Residue N Inputs: Crop residue N, fixation by legumes, and N residue inputs from senesced grass litter are included as
sources of N to the soil, and are estimated in the DayCent simulations as a function of vegetation type, weather, and soil
properties. That is, while the model accounts for the contribution of N from crop residues to the soil profile and subsequent
N20 emissions, this source of mineral soil N is not "activity data" as it is not a model input. The simulated total N inputs of
above- and below-ground residue N and fixed N, which are not harvested or burned (the DayCent simulations assumed
107
that 3 percent of non-harvested above ground residues for crops are burned), are provided in Table A-203.
Other N Inputs: Other N inputs are estimated within the DayCent simulation, and thus input data are not required,
including mineralization from decomposition of soil organic matter and asymbiotic fixation of N from the atmosphere.
Mineralization of soil organic matter will also include the effect of land use change on this process as recommended by
the IPCC (2006). The influence of additional inputs of N are estimated in the simulations so that there is full accounting of
all emissions from managed lands, as recommended by the IPCC (2006). The simulated N input from residues, soil organic
matter mineralization and asymbiotic N fixation are provided in Table A-203.
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. Reduced tillage made up the remainder of the cultivated area, including mulch tillage
107 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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and ridge tillage as defined by CTIC and intermittent no-till. The specific tillage implements and applications used for
different crops, rotations, and regions to represent the three tillage classes are derived from the 1995 Cropping Practices
Survey by the Economic Research Service (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 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 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—from the trending information—in the target time
block (to be imputed). 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). Going back in time, 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 half the remaining area. For 1990 and before, we remove all cover crops. Going
forward in time, 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 respect 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 with intervals between irrigation events occurring each time that soils drain to 60 percent of
field capacity.
Daily Weather Data: Daily maximum/minimum temperature and precipitation data are based on gridded weather data
from the PRISM Climate Group (2018). It is necessary to use computer-generated weather data because weather station
data do not exist near all NRI points. The PRISM product uses this information with interpolation algorithms to derive
weather patterns for areas between these 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
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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.108
The MODIS EVI products have a 250 m spatial resolution, and some pixels in images have mixed land uses and crop types
at this resolution, which is problematic for estimating NPP associated with a specific crop at a NRI 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 CDLto the 250m resolution of the EVI data.
In this step, the percent cover of individual crops is determined for the 250m EVI pixels. Pixels that do not meet a 90
percent purity level for any crop are eliminated from the dataset. CDL does not provide full coverage for crop maps 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 the state of 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 DayCent model, which uses particle size fractions of sand (50-2,000 |j.m), silt (2-50 |j.m), and clay (<2 |j.m) as inputs.
Hydric condition are poorly-drained, 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.109 Poorly drained soils can be subject to anaerobic
(lack of oxygen) conditions if water inputs (precipitation and irrigation) exceed water losses from drainage and
evapotranspiration. Depending on moisture conditions, hydric soils can range from being fully aerobic to completely
anaerobic, varying over the year. Decomposition rates are modified according to a linear function that varies from 0.3
108 Additional crops and grassland will be used with the NASA-CASA method in thefuture, as a planned improvement.
109 Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a management variable.
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under completely anaerobic conditions to 1.0 under fully aerobic conditions (default parameters in DayCent).110 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. 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-
203.
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-203.
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).111 Commercial organic fertilizers include dried blood,
tankage, compost, and other organic materials, which are recorded in mass units of fertilizer, and had to be converted to
mass units of N by multiplying the consumption values by the average organic fertilizer N content of 0.5 percent (AAPFCO
2000). Dried manure and biosolids (i.e., sewage sludge) that are used as commercial fertilizer are subtracted from totals
to avoid double counting because dried manure is counted with the manure available for application to soils, and biosolids
are assumed to be applied to grasslands. 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-203.
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 generated by 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-203.
Biosolids (i.e., 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 2018 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
110 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.
111 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).
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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-203.
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-203.
Table A-203: Sources of Soil Nitrogen (kt N)
N Source
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
1.
Synthetic Fertilizer N: Cropland
9892
10285
10274
10110
11126
10300
10871
10852
10815
10970
2.
Synthetic Fertilizer N: Grassland
13
12
24
56
42
12
10
19
78
19
3.
Managed Manure N: Cropland
2463
2495
2505
2491
2553
2587
2578
2605
2635
2644
4.
Managed Manure N: Grassland
0
1
1
2
1
0
0
2
1
1
5.
Pasture, Range, & Paddock Manure N
4097
4104
4265
4354
4427
4529
4495
4384
4331
4259
6.
N from Crop Residue Decomposition3
6875
7091
6693
7047
6789
7255
6977
6842
6881
7739
7.
N from Grass Residue Decomposition3
12374
12298
12623
12757
12217
12937
12551
12644
11960
13366
8.
Min. SOM / Asymbiotic N-Fixation: Cropland11
11344
10931
10686
12089
10722
11596
11000
11219
12605
11296
9.
Min. SOM / Asymbiotic N-Fixation: Grassland11
16445
17261
17389
17205
16020
17028
16820
17824
17363
16807
10.
Sewage Sludge / Bio-solids 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
63559
64541
64524
66178
63970
66321
65385
66479
66758
67193
N Source
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
1.
Synthetic Fertilizer N: Cropland
10792
10105
10542
10602
11324
10723
10454
11493
10932
10215
2.
Synthetic Fertilizer N: Grassland
24
30
27
24
44
18
19
15
22
18
3.
Managed Manure N: Cropland
2685
2679
2720
2737
2660
2703
2786
2815
2792
2777
4.
Managed Manure N: Grassland
1
2
0
1
0
1
1
0
1
0
5.
Pasture, Range, & Paddock Manure N
4155
4142
4140
4138
4087
4131
4175
4059
4015
3975
6.
N from Crop Residue Decomposition3
7428
7336
7262
7504
7171
7337
7375
7141
7255
7442
7.
N from Grass Residue Decomposition3
12532
12936
12677
13040
12243
13092
12689
13178
13034
12571
8.
Min. SOM / Asymbiotic N-Fixation: Cropland11
11414
11821
11284
11433
12839
11494
11346
11961
12054
12484
9.
Min. SOM / Asymbiotic N-Fixation: Grassland11
15687
16599
16475
16991
19099
17701
16934
18549
17474
18120
10.
Sewage Sludge / Bio-solids 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 64810 65744 65223 66569 69570 67307 65892 69330 67699 67721
N Source
2010
2011
2012
2013
2014
2015
2016
2017
2018
1.
Synthetic Fertilizer N: Cropland
10784
11261
11906
11905
11706
11480
11306
11234
11454
2.
Synthetic Fertilizer N: Grassland
11
12
13
11
12
14
13
13
13
3.
Managed Manure N: Cropland
2771
2802
2836
2820
2822
2870
2876
2856
2858
4.
Managed Manure N: Grassland
0
1
1
1
0
0
0
0
0
5.
Pasture, Range, & Paddock Manure N
3920
3815
3720
3676
3627
3683
3558
3530
3569
6.
N from Crop Residue Decomposition3
7887
7676
7448
7359
7621
7231
7004
6989
7176
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7. N from Grass Residue Decomposition3 12910 12499 13091 12107 12211 11769 11092 10991 11120
8. Min.SOM/AsymbioticN-Fixation: Cropland11 13366 11272 10216 12694 13536 14311 13705 13737 14168
9. Min.SOM/AsymbioticN-Fixation: Grassland11 18527 16127 15341 18472 18501 19041 17947 17785 17994
10. Sewage Sludge/Bio-solids N: Grassland 113 116 119 122 124 127 130 133 136
11. Other Organic Amendments: Cropland1 10 12 13 13 11 12 13 12 11
Total 70299 65592 64704 69179 70171 70538 67643 67279 68498
1 NE (Not Estimated)
2 Note: For most activity sources data were not available after 2015 and emissions were estimated with a data splicing
3 method. Additional activity data will be collected and the Tier 1, 2 and 3 methods will be applied in a future inventory
4 to recalculate the part of the time series that is estimated with the data splicing methods.
5 3 Residue N inputs include unharvested fixed N from legumes as well as crop and grass residue N.
6 b Mineralization of soil organic matter and the asymbiotic fixation of nitrogen gas.
7 includes dried blood, tankage, compost, other. Excludes dried manure and bio-solids (i.e., sewage sludge) used as commercial fertilizer to avoid
8 double counting.
9
10 Step Id: Obtain Additional Management Activity Data for Tier 2 Method to estimate Soil C Stock Changes in
11 Mineral Soils
12 Biosolids (i.e., Sewage Sludge) Amendments: Biosolids are generated from the treatment of raw sewage in public or private
13 wastewater treatment facilities and are typically used as a soil amendment or is sent for waste disposal to landfills. In this
14 Inventory, all biosolids that are amended to agricultural soils are assumed to be applied to grasslands. See section on
15 biosolids in Step lc for more information about the methods used to derive biosolid N estimates. The total amount of
16 biosolid N is given in Table A-203. Biosolid N is assumed to be applied at the assimilative capacity provided in Kellogg et al.
17 (2000), which is the amount of nutrients taken up by a crop and removed at harvest representing the recommended
18 application rate for manure amendments. In this Inventory, all biosolids are applied to grasslands so these rates may not
19 be fully representative of amendments of a biosolids, but there are no data available on N amendments that are specific
20 to grasslands (Future Inventories will incorporate new information when it is available). This capacity varies from year to
21 year, because it is based on specific crop yields during the respective year (Kellogg et al. 2000). Total biosolid N available
22 for application is divided by the assimilative capacity to estimate the total land area over which biosolids had been applied.
23 The resulting estimates are used for the estimation of soil C stock change.
24 Wetland Reserve: Wetlands enrolled in the Conservation Reserve Program have been restored in the Northern Prairie
25 Pothole Region through the Partners for Wildlife Program funded by the U.S. Fish and Wildlife Service (USFWS 2010). The
26 area of restored wetlands is estimated from contract agreements (Euliss and Gleason 2002). While the contracts provide
27 reasonable estimates of the amount of land restored in the region, they do not provide the information necessary to
28 estimate uncertainty. Consequently, a ±50 percent range is used to construct the PDFs for the uncertainty analysis.
29 Step le: Additional Activity Data for Indirect N2O Emissions
30 A portion of the N that is applied as synthetic fertilizer, livestock manure, biosolids (i.e., sewage sludge), and other organic
31 amendments volatilizes as NH3 and NOx. In turn, the volatilized N is eventually returned to soils through atmospheric
32 deposition, thereby increasing mineral N availability and enhancing N20 production. Additional N is lost from soils through
33 leaching as water percolates through a soil profile and through runoff with overland water flow. N losses from leaching
34 and runoff enter groundwater and waterways, from which a portion is emitted as N20. However, N leaching is assumed to
35 be an insignificant source of indirect N20 in cropland and grassland systems where the amount of precipitation plus
36 irrigation does not exceed 80 percent of the potential evapotranspiration. These areas are typically semi-arid to arid
37 regions in the Western United States, and nitrate leaching to groundwater is a relatively uncommon event. Moreover IPCC
38 (2006) recommends limiting the amount of nitrate leaching assumed to be a source of indirect N20 emissions based on
39 precipitation, irrigation and potential evapotranspiration.
40 The activity data for synthetic fertilizer, livestock manure, other organic amendments, residue N inputs, biosolids N, and
41 other N inputs are the same as those used in the calculation of direct emissions from agricultural mineral soils, and may
42 be found in Table A-203.
43 Using the DayCent model, volatilization and leaching/surface run-off of N from soils is estimated in the simulations for
44 crops and non-federal grasslands in the Tier 3 method. DayCent simulates the processes leading to these losses of N based
45 on environmental conditions (i.e., weather patterns and soil characteristics), management impacts (e.g., plowing,
46 irrigation, harvest), and soil N availability. Note that the DayCent model accounts for losses of N from all anthropogenic
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1 activity, not just the inputs of N from mineral fertilization and organic amendments112, which are addressed in the Tier 1
2 methodology. Similarly, the N available for producing indirect emissions resulting from grassland management as well as
3 PRP manure is also estimated by DayCent. However, indirect emissions are not estimated for leaching and runoff of N if
4 precipitation plus irrigation does not exceed 80 percent of the potential evapotranspiration. Volatilized losses of N are
5 summed for each day in the annual cycle to provide an estimate of the amount of N subject to indirect N20 emissions. In
6 addition, the daily losses of N through leaching and runoff in overland flow are summed for the annual cycle. Uncertainty
7 in the estimates is derived from the measure of variability in the fertilizer and organic amendment activity data (see Step
8 la for further information).
9 The Tier 1 method is used to estimate N losses from mineral soils due to volatilization and leaching/runoff for crops,
10 biosolids applications, and PRP manure on federal grasslands, which are not simulated by DayCent. To estimate volatilized
11 N losses, the amount of synthetic fertilizers, manure, biosolids, and other organic N inputs are multiplied by the fraction
12 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
13 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
14 fertilizer and 0.05-0.5 kg NH3-N+NOx-N/kg N for organic amendments (IPCC 2006). Leaching/runoff losses of N are
15 estimated by summing the N additions from synthetic and other organic fertilizers, manure, biosolids, and above- and
16 below-ground crop residues, and then multiplying by the default fraction subject to leaching/runoff losses of 0.3 kg N/kg
17 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
18 insignificant source of indirect N20 emissions if the amount of precipitation plus irrigation did not exceed 80 percent of
19 the potential evapotranspiration, consistent with the Tier 3 method. PDFs are derived for each of the N inputs in the same
20 manner as direct N20 emissions, discussed in Steps la and lc.
21 Volatilized N is summed for losses from croplands and grasslands. Similarly, the annual amounts of N lost from soil profiles
22 through leaching and surface runoff are summed to obtain the total losses for this pathway.
23 Step 2: Estimate GHG Emissions and Stocks Changes for Mineral Soils: Soil Organic C Stock Changes,
24 Direct N20 Emissions, and CH4 Emissions from Rice Cultivation
25 In this step, soil organic C stock changes, N20 emissions, and CH4 emissions from rice cultivation are estimated for cropland
26 and non-federal grasslands. Three methods are used to estimate soil organic C stock changes, direct N20 emissions from
27 mineral soils, and CH4 emissions from rice cultivation. The DayCent process-based model is used for the croplands and
28 non-federal grasslands included in the Tier 3 method. A Tier 2 method is used to estimate soil organic C stock changes for
29 crop types, grasslands (i.e., federal grasslands) and soil types that are not simulated by DayCent and land use change other
30 than conversions between cropland and grassland. A Tier 1 methodology is used to estimate N20 emissions from crops
31 that are not simulated by DayCent, PRP manure N deposition on federal grasslands, and CH4 emissions from rice cultivation.
32 Step 2a: Estimate Soil Organic C Stock Changes, Soil N2O Emissions, and CH4 emissions for Crops and Non-Federal
33 Grassland with the Tier 3 DayCent Model
34 Crops that are simulated with DayCent include alfalfa hay, barley, corn, cotton, grass hay, grass-clover hay, oats, peanuts,
35 potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tobacco and wheat, which combined represent approximately
36 85 percent of total cropland in the United States. The DayCent simulations also include all non-federal grasslands in the
37 United States.
38 The methodology description is divided into two sub-steps. First, the DayCent model is used to establish the initial
39 conditions and C stocks for 1979, which is the first year of the NRI survey. In the second sub-step, DayCent is used to
40 simulate changes in soil organic C stocks, direct soil N20 emissions, leaching and volatilization losses of N contributing to
41 indirect N20 emissions, and CH4 emissions from rice cultivation based on the land-use and management histories recorded
42 in the NRI (USDA-NRCS 2018a).
43 Simulate Initial Conditions (Pre-NRI Conditions): The purpose of the DayCent model initialization is to estimate the most
44 accurate stock for the pre-NRI history, and the distribution of organic C among the pools represented in the model (e.g.,
112 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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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 (1981 through 2010), 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
(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
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treated as a fixed effect in the statistical model. The resulting relationship is 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 Section, Tier 3 Method Description and Model
Evaluation, 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 point. 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 equation (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 value for soil organic C, N20
and CH4 emissions and associated management information is 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 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 point, the
N inputs in a simulation are determined for anthropogenic practices discussed in IPCC (2006), including synthetic mineral
N fertilization, organic amendments, and crop residue N added to soils (including N-fixing crops). The percentage of N
input for anthropogenic practices is divided by the total N input, and this proportion is used to determine the amount of
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 need for reporting emissions in a manner
consistent with UNFCCC reporting guidelines. However, this method is a simplification of reality to allow partitioning of
N20 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 Table A-207 and Table
A-208, soil organic C stock changes are provided in Table A-209, and rice cultivation CH4 emissions in Table A-211.
A-371
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1 Step 2b: Soil N2O Emissions from Agricultural Lands on Mineral Soils Approximated with the Tier 1 Approach
2 To estimate direct N20 emissions from N additions to crops in the Tier 1 method, the amount of N in applied synthetic
3 fertilizer, manure and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) is added to N
4 inputs from crop residues, and the resulting annual totals are multiplied by the IPCC default emission factor of 0.01 kg N20-
5 N/kg N (IPCC 2006). The uncertainty is determined based on simple error propagation methods (IPCC 2006). The
6 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
113
7 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 (IPCC 2006).
8 Uncertainties in the emission factor and fertilizer additions are combined with uncertainty in the equations used to
9 calculate residue N additions from above- and below-ground biomass dry matter and N concentration to derive overall
10 uncertainty.
11 The Tier 1 method is also used to estimate emissions from manure N deposited by livestock on federal lands (i.e., PRP
12 manure N), and from biosolids (i.e., sewage sludge) application to grasslands. These two sources of N inputs to soils are
13 multiplied by the IPCC (2006) default emission factors (0.01 kg N20-N/kg N for sludge and horse, sheep, and goat manure,
14 and 0.02 kg N20-N/kg N for cattle, swine, and poultry manure) to estimate N20 emissions. The uncertainty is determined
15 based on the Tier 1 error propagation methods provided by the IPCC (2006) with uncertainty in the default emission factor
16 ranging from 0.007 to 0.06 kg N20-N/kg N (IPCC 2006).
17 The results for direct soil N20 emissions using the Tier 1 method are provided in Table A-207 and Table A-208.
18 Step 2c: Soil CH4 Emissions from Agricultural Lands Approximated with the Tier 1 Approach
19 To estimate CH4emissions from rice cultivation for the Tier 1 method, an adjusted daily emission factor is calculated using
20 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
21 the cultivation water regime, pre-cultivation water regime and a scaling factor for organic amendments (IPCC 2006). The
22 water regime during cultivation is continuously flooded for rice production in the United States and so the scaling factor is
23 always 1 (ranging from 0.79 to 1.26). The pre-season water regime varies based on the proportion of land with winter
24 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
25 winter flooding are assigned a value of 1 (ranging from 0.88 to 1.14). Organic amendments are estimated based on the
26 amount of rice straw and multiplied by 1 (ranging 0.97 to 1.04) for straw incorporated greater than 30 days before
27 cultivation, and by 0.29 (0.2 to 0.4) for straw incorporated greater than 30 days before cultivation. The adjusted daily
28 emission factor is multiplied by the cultivation period and harvested area to estimate the total CH4 emissions. The
29 uncertainty is propagated through the calculation using an Approach 2 method with a Monte Carlo analysis (IPCC 2006),
30 combining uncertainties associated with the adjusted daily emission factor and the harvested areas derived from the USDA
31 NRI survey data.
32 The results for rice CH4 emissions using the Tier 1 method are provided in Table A-211.
33 Step 2d: Soil Organic CStock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2 Approach
34 Mineral soil organic C stock values are derived for crop rotations that were not simulated by DayCent and land converted
35 from non-agricultural land uses to cropland or grassland from 1990 through 2015, based on the land-use and management
36 activity data in conjunction with appropriate reference C stocks, land-use change, management, input, and wetland
37 restoration factors. Each quantity in the inventory calculations has uncertainty that is quantified in PDFs, including the land
38 use and management activity data based on the six imputations in the data product combining CEAP, ARMS, Census of
39 Agriculture, and CTIC data (See Step lb for more information); reference C stocks and stock change factors; and the
40 replicated weights form the NRI survey. A Monte Carlo Analysis is used to quantify uncertainty in soil organic C stock
41 changes for the inventory period based on random selection of values from each of these sources of uncertainty. Input
42 values are randomly selected from PDFs in an iterative process to estimate SOC change for 1,000 times.
43 Derive Mineral Soil Organic C Stock Change Factors: Stock change factors representative of U.S. conditions are estimated
44 from published studies (Ogle et al. 2003; Ogle et al. 2006). The numerical factors quantify the impact of changing land use
113 Due to lack of data, uncertainties in managed manure N production, PRP manure N production, other commercial organic fertilizer
amendments, indirect losses of N in the DayCent simulations, and biosolids (i.e., sewage sludge) amendments to soils are currently
treated as certain; these sources of uncertainty will be included in future Inventories.
A-372 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
and management on SOC storage in mineral soils, including tillage practices, cropping rotation or intensification, and land
conversions between cultivated and native conditions (including set-asides in the Conservation Reserve Program). 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-204). These
stocks are used in conjunction with management factors to estimate the change in SOC stocks that result from
management and land-use activity. PDFs, which represent the variability in the stock estimates, are constructed as normal
densities based on the mean and variance from the pedon data. Pedon locations are clumped in various parts of the
country, which reduces the statistical independence of individual pedon estimates. To account for this lack of
independence, samples from each climate by soil zone are tested for spatial autocorrelation using the Moran's I test, and
variance terms are inflated by 10 percent for all zones with significant p-values.
Table A-204: 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
Temperate,
Temperate,
Temperate,
Temperate, Sub-Tropical, Sub-Tropical,
Categories
USDA Taxonomic Soil 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 (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
3 C stocks are not needed for organic soils.
Notes: C stocks are for the top 30 cm of the soil profile, and are estimated from pedon data available in the National Soil Survey Characterization
database (NRCS 1997); sample size provided in parentheses (i.e., 'n' values referto sample size).
To estimate the stock change factors for land use, management and input, studies had to report SOC stocks (or information
to compute stocks), depth of sampling, and the number of years since a management change to be included in the analysis.
The data are analyzed using linear mixed-effect 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 point in the dataset. Similarly, time-series data are not
aggregated in these datasets. Linear regression models assume that the underlying data are independent observations,
but this is not the case with data from the same experimental site, or plot in a time series. These data are more related to
each other than data from other sites (i.e., not independent). Consequently, random effects are needed to account for the
dependence in time-series data and the dependence among data points representing different depth increments from the
same study. Factors are estimated for the effect of management practices at 20 years for the top 30 cm of the soil (Table
A-205). Variance is calculated for each of the U.S. factor values, and used to construct PDFs with a normal density. In the
IPCC method, specific factor values are given for improved grassland, high input cropland with organic amendments, and
A-373
-------
1 for wetland rice, each of which influences C stock changes in soils. Specifically, higher stocks are associated with increased
114
2 productivity and C inputs (relative to native grassland) on improved grassland with both medium and high input. Organic
3 amendments in annual cropping systems also increase SOC stocks due to greater C inputs, while high SOC stocks in rice
4 cultivation are associated with reduced decomposition due to periodic flooding. There are insufficient field studies to
5 derive factor values for these systems from the published literature, and, thus, estimates from IPCC (2006) are used under
6 the assumption that they would best approximate the impacts, given the lack of sufficient data to derive U.S.-specific
7 factors. A measure of uncertainty is provided for these factors in IPCC (2006), which is used to construct PDFs.
8 Table A-205: Soil Organic Carbon Stock Change Factors for the United States and the IPCC Default Values Associated
9 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
1
1
1
1
General Unculta b (n=251)
1.4
1.42±0.06
1.37±0.05
1.24±0.06
1.20±0.06
Set-Asidea (n=142)
1.25
1.31±0.06
1.26±0.04
1.14±0.06
1.10±0.05
Improved Grassland Factors
Medium Input
1.1
1.14±0.06
1.14±0.06
1.14±0.06
1.14±0.06
High Input
NA
1.11±0.04
1.11±0.04
1.11±0.04
1.11±0.04
Wetland Rice Production Factorb
1.1
1.1
1.1
1.1
1.1
Tillage Factors
Conv. Till
1
1
1
1
1
Red. Till (n=93)
1.05
1.08 ±0.03
1.01±0.03
1.08 ±0.03
1.01±0.03
No-till (n=212)
1.1
1.13±0.02
1.05±0.03
1.13±0.02
1.05±0.03
Cropland Input Factors
Low (n=85)
0.9
0.94±0.01
0.94±0.01
0.94±0.01
0.94±0.01
Medium
1
1
1
1
1
High (n=22)
1.1
1.07±0.02
1.07±0.02
1.07±0.02
1.07±0.02
High with amendment11
1.2
1.38±0.06
1.34±0.08
1.38±0.06
1.34±0.08
10 3 Factors in the IPCC documentation (IPCC 2006) are converted to represent changes in SOC storage from a cultivated condition rather than a
11 native condition.
12 b U.S.-specific factors are not estimated for land improvements, rice production, or high input with amendment because of few studies
13 addressing the impact of legume mixtures, irrigation, or manure applications for crop and grassland in the United States, or the impact of
14 wetland rice production in the US. Factors provided in IPCC (2006) are used as the best estimates of these impacts.
15 Note: The "n" values refer to sample size.
16
17 Wetland restoration management also influences SOC storage in mineral soils, because restoration leads to higher water
18 tables and inundation of the soil for at least part of the year. A stock change factor is estimated assessing the difference in
19 SOC storage between restored and unrestored wetlands enrolled in the Conservation Reserve Program (Euliss and Gleason
20 2002), which represents an initial increase of C in the restored soils over the first 10 years (Table A-206). A PDF with a
21 normal density is constructed from these data based on results from a linear regression model. Following the initial
22 increase of C, natural erosion and deposition leads to additional accretion of C in these wetlands. The mass accumulation
23 rate of organic C is estimated using annual sedimentation rates (cm/yr) in combination with percent organic C, and soil
24 bulk density (g/cm3) (Euliss and Gleason 2002). Procedures for calculation of mass accumulation rate are described in Dean
25 and Gorham (1998); the resulting rate and standard deviation are used to construct a PDF with a normal density (Table A-
26 206).
27
28 Table A-206: Rate and standard deviation for the Initial Increase and Subsequent Annual Mass Accumulation Rate (Mg
29 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
30 Note: Mass accumulation rate represents additional gains in C for mineral soils after the first 10 years (Euliss and Gleason 2002).
114 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-374 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
2 Estimate Annual Changes in Mineral Soil Organic C Stocks: In accordance with IPCC methodology, annual changes in
115
3 mineral soil C are calculated by subtracting the beginning stock from the ending stock and then dividing by 20. For this
4 analysis, stocks are estimated for each year and difference between years is the stock change. From the final distribution
5 of 1,000 values, the median is used as the estimate of soil organic C stock change and a 95 percent confidence interval is
6 generated based on the simulated values at the 2.5 and 97.5 percentiles in the distribution.
7
8 Soil organic C stock changes using the Tier 2 method are provided in Table A-209 and Table A-211.
9 Step 2e: Estimate Additional Changes in Soil Organic C Stocks Due to Biosolids (i.e., Sewage Sludge) Amendments
10 There are two additional land use and management activities occurring on mineral soils of U.S. agricultural lands that are
11 not estimated in Steps 2a and 2b. The first activity involves the application of biosolids to agricultural lands. Minimal data
12 exist on where and how much biosolids are applied to U.S. agricultural soils, but national estimates of mineral soil land
13 area receiving biosolids can be approximated based on biosolids N production data, and the assumption that amendments
14 are applied at a rate equivalent to the assimilative capacity from Kellogg et al. (2000). In this Inventory, it is assumed that
15 biosolids for agricultural land application to soils is only used as an amendment in grassland. The impact of organic
16 amendments on SOC is calculated as 0.38 metric tonnes C/ha-yr. This rate is based on the IPCC default method and country-
17 specific factors, by calculating the effect of converting nominal, medium-input grassland to high input improved grassland.
18 The assumptions are that the reference C stock is 50 metric tonnes C/ha, which represents a mid-range value of reference
19 C stocks for the cropland soils in the United States,116 that the land use factor for grassland of 1.4 and 1.11 for high input
20 improved grassland are representative of typical conditions, and that the change in stocks are occurring over a 20 year
21 (default value) time period (i.e., [50 x 1.4 x l.ll - 50 x 1.4] / 20 = 0.38). A ±50 percent uncertainty is attached to these
22 estimates due to limited information on application and the rate of change in soil C stock change with biosolids
23 amendments.
24 The influence of biosolids (i.e., sewage sludge) on soil organic C stocks is provided in Table A-211.
25 Table A-207: Direct Soil N2Q Emissions from Mineral Soils in Cropland (MMT CP2 Eq.)
Land Use Change Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Cropland Mineral Soil Emission
182.1
173.5
169.7
187.5
182.1
179.9
187.6
178.8
176.5
178.6
Tier 3 Cropland
165.0
157.4
152.7
170.7
163.8
161.5
168.9
160.2
158.5
160.6
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
5.2
5.1
5.0
5.2
5.1
4.7
5.2
4.8
4.5
4.6
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
16.1
16.9
16.8
18.3
18.4
18.7
18.7
18.0
18.0
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
7.9
8.2
8.3
8.4
Other Organic Amendments'1
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 Factorfor Croplandse(kt N2Q-N/kt N) 0.013 0.012 0.012 0.013 0.012 0.012 0.013 0.012 0.011 0.012
26
Land Use Change Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland Mineral Soil Emission
173.3
182.7
183.0
184.1
183.8
180.3
175.8
181.7
179.6
181.0
Tier 3 Cropland
155.2
164.6
164.1
164.3
163.2
160.9
156.4
162.1
159.9
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
4.4
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
18.1
18.1
18.9
19.9
20.7
19.4
19.5
19.6
19.6
17.8
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
8.6
00
00
8.9
9.1
8.6
8.9
9.1
9.1
9.2
9.0
Other Organic Amendments'1
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.0
115 The difference in C stocks is divided by 20 because the stock change factors represent change over a 20-year time period.
116 Reference C stocks are based on cropland soils for the Tier 2 method applied in this Inventory.
A-375
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
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 Factorfor Croplandsc(kt N2Q-N/kt N) 0.011 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012
Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
Total Cropland Mineral Soil Emission
182.8
180.9
173.3
194.4
204.2
196.8
188.2
187.9
192.6
Tier 3 Cropland
163.4
161.3
154.5
174.1
184.1
171.8
164.5
164.9
170.1
Inorganic N Fertilizer Application
54.8
58.8
61.1
62.9
64.2
54.7
52.4
52.5
54.1
Managed Manure Additions
4.5
5.0
5.0
5.3
5.2
4.0
3.8
3.8
3.9
Crop Residue N
35.5
36.6
34.5
35.5
37.7
34.3
32.8
32.9
33.9
Min. SOM / Asymbiotic N-Fixationa
68.7
61.0
53.9
70.5
77.1
78.9
75.5
75.7
78.1
Tier 1 Cropland
19.3
19.6
18.8
20.3
20.0
25.0
23.6
23.0
22.5
Inorganic N Fertilizer Application
5.8
6.4
5.6
7.0
6.3
10.1
8.5
8.0
7.7
Managed Manure Additions
8.9
00
00
8.9
8.7
9.0
10.1
10.2
10.1
10.0
Other Organic Amendments'1
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Crop Residue N
4.6
4.3
4.3
4.5
4.7
4.7
4.9
4.8
4.8
Implied Emission Factorfor Croplandsc(kt N20-N/kt N)
0.011
0.012
0.011
0.012
0.012
0.012
NE
NE
NE
NE - Not Estimated
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., sewage sludge) used as commercial fertilizer to
avoid double counting.
cThe Annual Implied Emission Factor (kt N20-N/kt N) is calculated by dividing total estimated emissions by total activity data for N applied; The
Implied Emission Factor is not calculated for 2016-2018 due to lack of activity data for most sources.
Table A-208: Direct Soil N2Q Emissions from Mineral Soils in Grassland (MMT CP2 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
86.2
87.2
81.8
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
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
6.1
6.0
5.7
Pasture, Range, & Paddock N Deposition
6.8
6.5
6.7
6.6
6.6
6.4
6.1
5.7
5.6
5.3
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 Factorfor Grassland11 (kt N20-N/kt N)
0.005
0.005
0.005
0.005
0.005
0.005
0.005
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
76.5
82.6
83.2
82.7
91.0
85.7
84.1
86.2
83.6
87.2
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
5.5
5.3
5.1
5.0
4.9
4.9
4.8
4.4
4.4
4.3
Pasture, Range, & Paddock N Deposition
5.1
4.9
4.7
4.5
4.5
4.4
4.3
4.0
3.9
3.8
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 Factorfor Grassland11 (kt N20-N/kt N)
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
Total Grassland Mineral Soil Emission
89.4
79.8
75.1
90.9
92.1
91.8
86.9
86.2
87.2
Tier 3 Grassland
85.2
75.7
71.1
87.0
88.2
88.0
83.1
82.4
83.3
Inorganic N Fertilizer Application
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
Pasture, Range, & Paddock N Deposition
8.4
8.0
7.3
8.1
8.2
8.4
8.1
8.0
8.1
A-376 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Grass Residue N
31.5
29.6
29.4
31.2
31.8
30.4
28.6
28.4
28.7
Min. SOM / Asymbiotic N-Fixationa
45.2
38.1
34.4
47.6
48.2
49.2
46.3
45.9
46.5
Tier 1 Grassland
4.3
4.1
4.0
4.0
3.9
3.8
3.8
3.8
3.9
Pasture, Range, & Paddock N Deposition
3.7
3.6
3.5
3.4
3.3
3.2
3.2
3.2
3.2
Sewage Sludge Additions
0.5
0.5
0.6
0.6
0.6
0.6
0.6
0.6
0.6
Implied Emission Factor for Grassland11 (kt N20-N/kt N)
0.005
0.005
0.005
0.006
0.006
0.006
NE
NE
NE
1 NE - Not Estimated
2 Note: For most activity sources data were not available after 2015 and emissions were estimated with a data splicing
3 method. Additional activity data will be collected and the Tier 1, 2 and 3 methods will be applied in a future inventory
4 to recalculate the part of the time series that is estimated with the data splicing methods.
5 a Mineralization of soil organic matter and the asymbiotic fixation of nitrogen gas.
6 bThe annual Implied Emission Factor (kt N20-N/kt N) is calculated by dividing total estimated emissions by total activity data for N applied; The
7 Implied Emission Factor is not calculated for 2016-2018 due to lack of activity data for most sources.
8
9 Table A-209: Annual Change in Soil Organic Carbon Stocks in Croplands (MMT CP2 Eq./yr)
Land Use Change Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Cropland SOC 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
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
10
Land Use Change Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland SOC 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
11
Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
Total Cropland SOC Stock Change
-46.5
-62.7
-56.2
-43.5
-40.3
-39.9
-51.0
-51.7
-46.3
Cropland Remaining Cropland (CRC)
-51.0
-64.1
-58.7
-46.6
-44.7
-44.9
-54.3
-55.1
-49.4
Tier 2
-4.6
-5.2
-3.6
-5.6
-5.5
-6.2
-5.7
-5.4
-5.9
Tier 3
-46.4
-58.9
-55.1
-41.0
-39.2
-38.8
-48.6
-49.6
-43.5
Grassland Converted to Cropland (GCC)
6.7
3.7
4.5
5.2
6.2
6.9
5.2
5.4
5.1
Tier 2
4.5
4.6
4.7
4.4
4.3
4.2
4.2
4.3
4.3
Tier 3
2.2
-0.9
-0.1
0.8
1.9
2.7
1.0
1.1
0.9
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
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
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
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
12
13
14 Table A-210: Annual Change in Soil Organic Carbon Stocks in Grasslands (MMT CP2 Eq./yr)
Land Use Change Category 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Total Grassland SOC Stock Change -25.6 -25.4 -23.5 -23.9 -42.7 -37.0 -41.7 -39.4 -52.0 -52.0
A-377
-------
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
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
Land Use Change Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Grassland SOC 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
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
2
Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
Total Grassland SOC Stock Change
-43.0
-45.0
-58.1
-41.8
-32.5
-36.8
-42.3
-41.1
-40.4
Grassland Remaining Grassland (GRG)
10.6
7.9
-6.3
6.4
10.0
4.0
0.1
1.5
1.8
Tier 2
-1.6
-1.5
-0.6
-0.2
1.1
0.1
-0.8
-0.9
-0.9
Tier 3
13.5
10.8
-4.3
8.0
10.3
5.4
2.3
2.5
2.9
Sewage Sludge Additions
-1.3
-1.3
-1.4
-1.4
-1.4
-1.5
-1.5
-0.2
-0.2
Cropland Converted to Grassland (CCG)
-20.3
-19.4
-18.3
-17.5
-15.9
-16.9
-19.1
-19.4
-19.3
Tier 2
-4.6
-4.6
-4.5
-4.1
-3.5
-3.4
-3.5
-3.6
-3.7
Tier 3
-15.7
-14.8
-13.8
-13.3
-12.4
-13.4
-15.6
-15.8
-15.6
Forest Converted to Grassland (FCG) (Tier 2 Only)
-0.1
-0.1
-0.1
-0.1
0.0
-0.1
-0.1
0.0
0.0
Other Lands Converted to Grassland (OCG) (Tier 2 Only)
-31.8
-32.1
-32.0
-29.5
-25.6
-22.9
-22.3
-22.2
-21.9
Settlements Converted to Grassland (SCG) (Tier 2 Only)
-1.4
-1.4
-1.4
-1.3
-1.1
-1.0
-0.9
-1.0
-0.9
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
3
4 Table A-211: Methane Emissions from Rice Cultivation (MMT CQ2 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
6
Approach
2010
2011
2012
2013
2014
2015
2016
2017
2018
Total Rice Methane Emission
18.9
15.3
15.2
13.8
15.4
16.2
13.5
12.8
13.3
Tier 1
2.4
2.1
2.8
2.1
3.4
2.4
2.4
2.5
2.5
Tier 3
16.5
13.2
12.4
11.7
12.0
13.8
11.1
10.3
10.8
A-378 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1 Note: Estimates after 2015 are based on a data splicing method (See the Rice Cultivation section for more information). The Tier 1 and 3 methods
2 will be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
3
4
5 Step 3: Estimate Soil Organic C Stock Changes and Direct N20 Emissions from Organic Soils
6 In this step, soil organic C losses and N20 emissions are estimated for organic soils that are drained for agricultural
7 production.
8 Step 3a: Direct N2O Emissions Due to Drainage of Organic Soils in Cropland and Grassland
9 To estimate annual N20 emissions from drainage of organic soils in cropland and grassland, the area of drained organic
10 soils in croplands and grasslands for temperate regions is multiplied by the IPCC (2006) default emission factor for
11 temperate soils and the corresponding area in sub-tropical regions is multiplied by the average (12 kg N20-N/ha cultivated)
12 of IPCC (2006) default emission factors for temperate (8 kg N20-N/ha cultivated) and tropical (16 kg N20-N/ha cultivated)
13 organic soils. The uncertainty is determined based on simple error propagation methods (IPCC 2006), including uncertainty
14 in the default emission factor ranging from 2-24 kg N20-N/ha (IPCC 2006). Table A-212 lists the direct N20 emissions
15 associated with drainage of organic soils in cropland and grassland.
16
17 Table A-212: Direct Soil N2Q Emissions from Drainage of Organic Soils (MMT CQ2 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
19
Land Use
2010
2011
2012
2013
2014
2015
2016
2017
2018
Total Organic Soil Emission
6.0
6.0
6.0
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
Grassland
2.5
2.5
2.5
2.5
2.5
2.5
2.5
2.5
2.5
20 Step 3b: Soil Organic C Stock Changes Due to Drainage of Organic Soils in Cropland and Grassland
21 Change in soil organic C stocks due to drainage of cropland and grassland soils are estimated annually from 1990 through
22 2015, based on the land-use and management activity data in conjunction with appropriate emission factors. The activity
23 data are based on annual data from 1990 through 2015 from the NRI. Organic Soil emission factors representative of U.S.
24 conditions have been estimated from published studies (Ogle et al. 2003), based on subsidence studies in the United States
25 and Canada (Table A-213). PDFs are constructed as normal densities based on the mean C loss rates and associated
26 variances. Input values are randomly selected from PDFs in a Monte Carlo analysis to estimate SOC change for 1,000 times
27 and produce a 95 percent confidence interval for the inventory results. Losses of soil organic C from drainage of cropland
28 and grassland soils are provided in Table A-214 for croplands and Table A-215 for grasslands.
29 Table A-213: Carbon Loss Rates for Organic Soils Under Agricultural Management in the United States, and IPCC Default
30 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+0.53
Warm Temperate, Dry & Warm Temperate, Moist
10
14.0+2.5
2.5
3.5+0.83
Sub-Tropical, Dry & Sub-Tropical, Moist
1
14.3+2.5
0.25
2.8+0.53
31 3 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
32 for cropland, which is an assumption that is used for the IPCC default organic soil C losses on grassland.
A-379
-------
1
2 Table A-214: Soil Organic Carbon Stock Changes due to Drainage of Organic Soils in Cropland (MMT CP2 Eq)
Land Use Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Cropland SOC 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
3
Land Use Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland SOC 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
4
Land Use Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
Total Cropland SOC Stock Change
36.1
36.1
36.2
35.3
36.3
35.8
35.2
36.5
36.5
Cropland Remaining Cropland (CRC)
32.3
32.4
32.3
31.3
32.5
32.1
31.6
32.8
32.8
Grassland Converted to Cropland (GCC)
3.1
3.1
3.4
3.5
3.4
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
Other Lands Converted to Cropland (OCC)
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
Wetlands Converted to Cropland (WCC)
0.6
0.6
0.5
0.5
0.3
0.3
0.3
0.3
0.4
Table A-215: Soil Organic Carbon Stock Changes due to Drainage of Organic Soils in Grasslands (MMT C02 Eq)
Land Use Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Grassland SOC 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 SOC 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
Total Grassland SOC Stock Change
7.3
7.3
7.3
7.3
7.3
7.3
7.3
7.3
7.2
Grassland Remaining Grassland (GRG)
5.3
5.3
5.3
5.3
5.5
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
Forest Converted to Grassland (FCG)
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Other Lands Converted to Grassland (OCG)
0.0
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
A-380 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Wetlands Converted to Grassland (WCG) 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.2 0.2
1
2
3 Step 4: Estimate Indirect Soil N20 Emissions for Croplands and Grasslands
4 In this step, soil N20 emissions are estimated for the two indirect emission pathways (N20 emissions due to volatilization,
5 and N20 emissions due to leaching and runoff of N), which are summed to yield total indirect N20 emissions from croplands
6 and grasslands.
7 Step 4a: Indirect Soil N2O Emissions Due to Volatilization
8 Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and PRP manure, are
9 calculated according to the amount of mineral N that is volatilized from the soil profile and later emitted as soil N20
10 following atmospheric deposition. See Step le for additional information about the methods used to compute N losses
11 due to volatilization. The estimated N volatilized is multiplied by the IPCC default emission factor of 0.01 kg N20-N/kg N
12 (IPCC 2006) to estimate total indirect soil N20 emissions from volatilization. The uncertainty is estimated using simple error
13 propagation methods (IPCC 2006), by combining uncertainties in the amount of N volatilized, with uncertainty in the
14 default emission factor ranging from 0.002-0.05 kg N20-N/kg N (IPCC 2006). The estimates and implied emission factors
15 are provided in Table A-207 for cropland and in Table A-208 for grassland.
16 Step 4b: Indirect Soil N2O Emissions Due to Leaching and Runoff
17 The amounts of mineral N from synthetic fertilizers, commercial organic fertilizers, PRP manure, crop residue, N
18 mineralization, asymbiotic fixation that is transported from the soil profile in water flows are used to calculate indirect
19 emissions from leaching of mineral N from soils and losses in runoff associated with overland flow. See Step le for
20 additional information about the methods used to compute N losses from soils due to leaching and runoff in overland
21 water flows. The total amount of N transported from soil profiles through leaching and surface runoff is multiplied by the
22 IPCC default emission factor of 0.0075 kg N20-N/kg N (IPCC 2006) to estimate emissions for this source. The uncertainty is
23 estimated based on simple error propagation methods (IPCC 2006), including uncertainty in the default emission factor
24 ranging from 0.0005 to 0.025 kg N20-N/kg N (IPCC 2006).The emission estimates are provided in Table A-216 and Table A-
25 217 including the implied Tier 3 emission factors.
26 Table A-216: Indirect Soil N20 Emissions for Cropland from Volatilization and Atmospheric Deposition, and from
27 Leaching and Runoff (MMT CP2 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
29.4
32.7
6.9
25.9
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100
0.007B
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
Source
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland Indirect Emissions
Volatilization & Atmospheric Deposition
Leaching & Runoff
30.2
7.1
23.1
35.1
7.1
28.0
32.2
7.3
24.9
33.4
7.3
26.2
36.8
7.5
29.3
31.8
7.3
24.4
33.2
7.3
25.9
35.2
7.3
27.9
36.7
7.3
29.4
36.0
7.2
28.8
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
0.0100
0.0075
29
Source
2010
2011
2012
2013
2014
2015
2016
2017
2018
Total Cropland Indirect Emissions
36.3
35.5
28.8
38.1
37.9
43.0
39.2
37.8
42.8
Volatilization & Atmospheric Deposition
7.7
7.4
7.0
7.8
8.2
8.6
8.3
8.1
8.2
Leaching & Runoff
28.6
28.1
21.8
30.3
29.7
34.4
30.9
29.7
34.6
Volatilization Implied Emission Factor
0.0100
0.0100
0.0100
NE
NE
NE
NE
NE
Leaching & Runoff Implied Emission Factor
0.0075
0.0075
0.0075
NE
NE
NE
NE
NE
A-381
-------
1 NE (Not Estimated)
2 Note: Estimates after 2015 are based on a data splicing method (See the Agricultural Soil Management section for more information).
3 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
4 splicing methods.
5
6 Table A-217: Indirect Soil N20 Emissions for Grassland from Volatilization and Atmospheric Deposition, and from
7 Leaching and Runoff (MMT CP2 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
9.4
10.4
9.2
Volatilization & Atmospheric Deposition
3.6
3.5
3.6
3.5
3.5
3.5
3.6
3.6
3.6
3.4
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
8.1
9.7
9.4
8.9
10.3
9.1
9.0
9.9
9.7
10.0
Volatilization & Atmospheric Deposition
3.1
3.4
3.5
3.4
3.7
3.6
3.5
3.5
3.4
3.4
Leaching & Runoff
5.0
6.4
5.9
5.4
6.6
5.5
5.5
6.4
6.3
6.6
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
9
Source
2010
2011
2012
2013
2014
2015
2016
2017
2018
Total Grassland Indirect Emissions
9.6
9.3
8.6
10.0
9.1
10.6
9.6
9.6
9.7
Volatilization & Atmospheric Deposition
3.5
3.1
3.1
3.6
3.6
3.5
3.4
3.4
3.4
Leaching & Runoff
6.1
6.1
5.5
6.4
5.5
7.1
6.3
6.2
6.3
Volatilization Implied Emission Factor
0.0100
0.0100
0.0100
NE
NE
NE
NE
NE
Leaching & Runoff Implied Emission Factor
0.0075
0.0075
0.0075
NE
NE
NE
NE
NE
10 NE (Not Estimated)
11 Note: Estimates after 2015 are based on a data splicing method (Seethe Agricultural Soil Management section for more information). The Tier 1
12 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.
13
14 Step 5: Estimate Total Emissions for U.S. Agricultural Soils
15 Total N20 emissions are estimated by summing total direct and indirect emissions for croplands and grasslands (both
16 organic and mineral soils). Total soil organic C stock changes for cropland (Cropland Remaining Cropland and Land
17 Converted to Cropland) and grassland (Grassland Remaining Grassland and Land Converted to Grassland) are summed to
18 determine the total change in soil organic C stocks (both organic and mineral soils). Total rice CH4 emissions are estimated
19 by summing results from the Tier 1 and 3 methods. The results are provided in Figure A-7. In general, N20 emissions from
20 agricultural soil management have been increasing slightly from 1990 to 2018, while CH4 emissions from rice cultivation
21 have been relatively stable. Agricultural soil organic C stocks have increased for most years in croplands and grasslands
22 leading to sequestration of C in soils, with larger increases in grassland soils.
A-382 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Figure A-7: GHG Emissions and Removals for Cropland & Grassland (MMT C02 Eq.)
300 -
250 -
200 -
150 -
100 -
50 -
0 -
-50 -
-100 -
Cropland SOC
Grassland SOC
Cropland Soil N20
Rice Cultivation CH.
N-"s /
/ •
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 2015117 occur in Iowa, Illinois,
Kansas, Minnesota, Missouri, Montana, Nebraska, South Dakota, and Texas (Table A-218). 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-218). 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-218). 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.
117 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 2018. Therefore, the final year of emissions
data at the state scale is 2015.
A-383
-------
1 Table A-218: Total Soil N20 Emissions (Direct and Indirect), Soil Organic C Stock Changes and Rice CH4 Emissions from
2 Agricultural Lands by State in 2015 (MMT CP2 Eq.)
N20 Emissions9
Soil 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
3 This table only includes N2O emissions estimated by DayCent using the Tier 3 method.
3 b N2O emissions are not reported for Hawaii except from cropland organic soils, which are estimated
4 with the Tier 1 method and therefore not included in this table.
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Tier 3 Method Description and Model 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 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 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, tobacco,
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 N leaching and runoff, as well as
volatilization of N, which is 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 biosolids (i.e., sewage sludge) amendments to soils, which are not
known at a sufficient resolution to use the Tier 3 model. Soil N20 emissions and C stock changes associated with
these practices are addressed with a Tier 1 and 2 method, 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
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for the following major crops: corn, soybeans, sorghum, cotton and wheat,118 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
Plant
Production
Submodel
f(TEMP)
f(WFPS)
f(SOLAR)
EVI/PRDX
Biomass
SOM HH
Submodel Brefifw
C02,Nmii^flHS5j|
f(TEXT)
f(MOIST)
f(TEMP)
f(Kp)
f(Lignin:]V>
co2,^
. Nmin
Active
SOM
co2,
Slow
SOM
C02v\min
f(STORM£,
BASED
Passive
SOM
"c02,Nmin
o
Dissolved Organic C, Dissolved Organic N, Mineral N
2) 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.
3) The soil-water submodel simulates water flows and changes in soil water availability, which influences both
plant growth, decomposition and nutrient cycling. The moisture content of soils are simulated through a multi-
118 It is a planned improvement to estimate NPP for additional crops and grass forage with the NASA-CASA method in the future.
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layer 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 (g C rrr2)
Yield Carbon from Published Data (g m"*)
Yield Carbon from Published Data (g m'*)
Part a) presents results of the NASA-CASA algorithm (Is = 83°/Q and part b) presents the results of a single parameter
value for maximum net primary production (r2 = 64°/^.
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 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
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1 used to simulate management effects. User-specified management activities can be defined by adding to or editing the
2 contents of the *.100 files. Additional details of the model formulation are given in Parton et al. (1987,1988,1994,1998),
3 Del Grosso et al. (2001, 2011), Cheng et al. (2013) and Metherell et al. (1993), and archived copies of the model source
4 code are available.
Box A-2 DayCent Model Simulation of Nitrification and Denitrification
The DayCent model simulates the two biogeochemical processes, nitrification and denitrification, that result in N20
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 differentiate or its derivative is 0. In this case, the
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inflection point can be interpreted as the WFPS value at which denitrification reaches half
of its maximum rate.
Respiration has a much stronger effect on the water curve in clay soils with low DFc than in loam or sandy soils with high
DFc (Figure A-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.
A-389
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Figure A-10: Effect of Soil Temperature (a), Water-Filled Pore Space (b), and pH (c) ori Nitrification Rates
Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates
Soil Temperature
1 i i r i i I
3456789 10
pH
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1 Figure A-ll: Effect of Soil Nitrite Concentration (a), Heterotrophic Respiration Rates (b), and Water-Filled Pore Space
2 (c) on Denitrification Rates
Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates
35 -i
o
z
o
z
+
•£
200
NQ ng N/g soil
CQpg C/g soil/day
—r
20
loam-high respt/ A / clay low resp
loam-low resp
100
Hot moments, or pulses, of N2O 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 N2O 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 O2
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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 N2O 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 N2O emissions.
Specifically, the soil water content and microbial respiration controls on denitrification are relaxed for
approximately 3 days upon melting and N2O from denitrification is amplified by an amount proportional to
cumulative freezing degree days during the winter season. DayCent was evaluated using annual high frequency
N2O 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 Evaluation
Comparison of model results and plot level data show that DayCent simulates soil organic matter levels with reasonable
accuracy (Ogle et al. 2007). 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).
Figure A-12: Comparisons of Results from DayCent Model and Measurements of Soil Organic C Stocks
Cropland
y - *
o
o
35
o
9
8
7
10
y = x
3 O)
& *—
Grassland
7
8
9
10
Ln Modeled SOC Stock
(9 C m2)
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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 Cm 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 Cm emissions with no
apparent bias, but there is a lack of precision, which is addressed in the uncertainty analysis.
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Figure A-14: Comparisons of Results from DayCent Model and Measurements of Soil Nitrous Oxide Emissions
¦p z
o
CsJ
Z
QD 0
Corn without Freeze-Thaw Effect « •• y-x •'
' *•* .**
** *\jL /
— • f V
«.
§ " «•
Corn with Freeze-Thaw Effect y = x
• • *
* •
f .#' . /¦
i
• *
* *" *
, ¦ * ¦
•' •
# -
Other Crops without Freeze-Thaw Effect ^ * ."
• •
Other Crops with Freeze-Thaw Effect V = x . •"
• * ** •*' •
• ••• ajk •
. S
• • • % • .
••• 1 '
#
•
4.
• ** **t • •* ^
•,r •
• •
m • *.
• • • •
•" *
•
y = x •'
Grassland
• V
• .•
¦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"-*- day"-*-)
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Figure A-15: Comparisons of Results from DayCent Model and Measurements of Soil Methane Emissions
x v
O -a
-a 1
.
BOEM (2014) Year 2011 Gulfwide Emissions Inventory Study (BOEM 2014-666) Bureau of Ocean Energy Management, U.S.
Department of the Interior (November 2014) .
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for the Conterminous United States, 1987-2012: U.S. Geological Survey data release,
https://doi.org/10.5066/F7H41PKX.
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.
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to soil freezing-thawing and drying-wetting." Soil Biology and Biochemistry 117:5-15.
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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 no 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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1 Figure A-16: Flowchart of the inputs necessary in the estimation framework, including the methods for estimating
2 individual pools of forest C in the conterminous United States
4 Note: An empirical age class transition matrix was used in every state in the conterminous United States with the exception of west Oklahoma
5 and Wyoming where a theoretical age class transition matrix was used due to a lack of remeasurements in the annual NFI,
6
7 Forest Land Definition
8 The definition of forest land within the United States and used for this Inventory is defined in Oswalt et al. (2014)
9 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
10 equivalent stocking) by live trees including land that formerly had such tree cover and that will be naturally or artificially
11 regenerated. Trees are woody plants having a more or less erect perennial stem(s) capable of achieving at least 3 inches
12 (7.6 cm) in diameter at breast height, or 5 inches (12.7 cm) diameter at root collar, and a height of 16.4 feet (5 meters) at
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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 2018a; USDA Forest Service 2018b). 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 (2018c). 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 2018b; USDA Forest Service 2018c); 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 2015a) 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., 2018) is the exception because it is included in stock change calculations in order to include the most recent available
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data for each state. The specific inventories used in this report are listed in Table A-219 and this list can be compared with
the full set of summaries available for download (USDA Forest Service 2018b).
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.119 Note that minor differences
(approximately 2 percent 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.
119 See the Grassland Remaining Grassland and Land Converted to Grassland sections for details.
A-406 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Table A-219: 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
2001-2012
2011-2018
Oklahoma (West)
2010-2012
2013 - 2016
Arizona
2001 - 2007
2011-2017
Wyoming
2000
2011-2017
Arkansas
2006 - 2013
2013 - 2018
California
2001 - 2006
2011-2016
Alaska (Coastal)1
2004 - 2017
Colorado
2002 - 2007
2012 - 2017
Alaska (Interior)1
2014, 2016-2017
Connecticut
2006-2011
2011-2017
Delaware
2006-2011
2011-2017
Florida
2002 - 2011
2012 - 2016
Georgia
2005 - 2012
2013 - 2017
Idaho
2004 - 2007
2014-2017
Indiana
2007 - 2012
2012 - 2018
Iowa
2007 - 2012
2012 - 2018
Kansas
2006-2011
2011-2017
Kentucky
2005 - 2011
2011-2016
Louisiana
2001 - 2010
2009 - 2016
Maine
2008 - 2012
2013 - 2017
Maryland
2006-2011
2011-2017
Massachusetts
2006-2011
2011-2017
Michigan
2007 - 2012
2012 - 2018
Minnesota
2009 - 2013
2014 - 2018
Mississippi
2006 - 2012
2009 - 2017
Missouri
2007 - 2012
2012 - 2018
Montana
2003 - 2007
2013 - 2017
Nebraska
2007 - 2012
2012 - 2018
Nevada
2004 - 2007
2014-2017
New Hampshire
2005 - 2011
2011-2017
New Jersey
2007 - 2012
2012 - 2017
New Mexico
2005 - 2007
2015 - 2017
New York
2005 - 2011
2011-2017
North Carolina
2003 - 2013
2009 - 2018
North Dakota
2007 - 2012
2012 - 2018
Ohio
2005 - 2011
2011-2017
Oklahoma (East)
2008 - 2012
2012 - 2016
Oregon
2001 - 2006
2011-2016
Pennsylvania
2006-2011
2011-2017
Rhode Island
2007-2011
2011-2017
South Carolina
2007 - 2014
2013 - 2017
South Dakota
2007 - 2012
2012 - 2018
Tennessee
2005 - 2011
2010-2015
Texas (East)
2004 - 2012
2009 - 2017
Texas (West)
2004 - 2007
2014-2015
Utah
2000 - 2007
2010-2017
Vermont
2006-2011
2011-2017
Virginia
2008 - 2014
2013 - 2017
Washington
2002 - 2006
2012 - 2016
West Virginia
2006-2011
2011-2017
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Wisconsin
2007 - 2012
2012 - 2018
'Plots 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 2018d). 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-408 DRAFT Inventory of U.S. Greenhouse lias Emissions ana Sinks: iyyu-zui»
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1 ratio. A full set of coefficients are in Table A-220. Regions and forest types are the same classifications described in Smith
2 et al. (2003). As an example, the basic calculation for understory C in aspen-birch forests in the Northeast is:
3 Understory (T C/ha) = (live tree C density) x el0-855-1-03* ln
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Hardwoods
1.860
1.110
4.745
Lodgepole Pine
2.571
1.500
4.773
Other Conifer
2.614
1.518
4.821
Pinyon-Juniper
2.708
2.708
4.820
Ponderosa Pine
2.099
1.344
4.776
Nonstocked
4.430
4.430
4.773
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
3 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 greaterthan 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 2018d).
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 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:
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First, an initial estimate from live tree C density and Table A-221 (SC, Natural Pine)
2 C density = 82.99 x 0.068 = 5.67 (T C/ha)
3 Second, an average logging residue from age and Table A-221 (SC, softwood)
4 C density = 5.5 x e(-25/17.9) = 1.37 (T C/ha)
5 Third, adjust the sum by the downed dead wood ratio plot-to-model for Louisiana, which was 27.6/31.1 = 0.886
6 C density = (5.67 + 1.37) x 0.886 = 6.24 (T C/ha)
7 Table A-221: Ratio for Estimating Downed Dead Wood by Region and Forest Type
Region9
Forest type3
Ratiob
Aspen-Birch
0.078
MBB/Other Hardwood
0.071
Oak-Hickory
0.068
NE
Oak-Pine
0.061
Other Pine
0.06B
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
Other Conifer
0.073
PWW
Other Hardwoods
0.062
Red Alder
0.09B
Western Hemlock
0.099
Nonstocked
0.020
Douglas-fir
0.062
Fir-Spruce
0.100
RMN
Hardwoods
0.112
Lodgepole Pine
0.0B8
Other Conifer
0.060
Pinyon-Juniper
0.030
A-411
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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
0.07B
Upland Hardwood
0.0B9
Nonstocked
0.012
1 3 Regions and types as defined in Smith et al. (2003).
2 b The ratio is multiplied by the live tree C density on a plot to produce downed dead wood C density (T C/ha).
3
4 Table A-222: Coefficients for Estimating Logging Residue Component of Downed Dead Wood
Forest Type Groupb
(softwood/ Initial C Density
Region9
hardwood)
(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
SE
hardwood
6.4
8.9
SE
softwood
7.3
17.9
5 3 Regions are defined in Smith et al. (2003) with the addition of coastal Alaska.
6 b Forest types are according to majority hardwood or softwood species.
7
8 Litter carbon
9 Carbon in the litter layer is currently sampled on a subset of the NFI plots. Litter C is the pool of organic C
10 (including material known as duff, humus, and fine woody debris) above the mineral soil and includes woody fragments
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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 2428 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):
\SOC = C ¦ BD ¦ t. ¦ uct (4)
t—t FIA _ TOTAL i i i J
Where \'soc = tota' mass (Mg C ha-1) of the mineral and organic soil C over all /th layers, C = percent
Lu HA _TOTAL '
organic C in the /'th layer, BDt = 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).
TheSOCFM_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:
log10 SOCj = I + log10 Depth (5)
Where log10 SOC1, = SOC density (Mg C ha-1 cm depth-1) at the midpoint depth, 1 = intercept,
log10 Depth = profile midpoint depth (cm).
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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:
SOClQ0 = SOCFIATOTAL + SOC20_l00 (6)
Where SOC100 = total estimated SOC density from 0-100 cm for each forest condition with a soil sample in the
FIA database, SOCFIA TOTAL as 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 SOCjoo 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 SOCm, 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 ULclX = average maximum temperature, gtni= 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. Forest
Service 2018a-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.
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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 =
1 — t1 — d1
ti
0
0
0
1 -t2-d
^2
0
0
0
1 — £3 — ^3
£3
0
0
0
1 £4 d4
<^5 \
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).
Projections and Backcastfor 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
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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).120 However, B can be constructed using observed changes from the inventory and assumptions
about transition/accumulation including nonstationary elements of the transition model:
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
br
d4
0
0
0
1 — b.
\
(12)
Forest area changes need to be accounted for in the backcasts as well:
F t-s = F fB — 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 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.
120 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.
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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 2018d). 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. 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
(14)
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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 2018).
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-233) 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
Lands in Hawaii are included in that section. This results in small differences in the managed Forest Land area between
sections 6.1 and 6.2 of this Inventory (Table A-231). 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
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.
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1 • Production approach: Accounts for the net changes in C stocks in forests and in the wood products pool,
2 but attributes both to the producing country.
3 • Stock-change approach: Accounts for changes in the product pool within the boundaries of the consuming
4 country.
5 • Atmospheric-flow approach: Accounts for net emissions or removals of C to and from the atmosphere
6 within national boundaries. Carbon removal due to forest growth is accounted for in the producing country
7 while C emissions to the atmosphere from oxidation of wood products are accounted for in the consuming
8 country.
9 • Default approach: Assumes no change in C stocks in HWP. IPCC (2006) requests that such an assumption be
10 justified if this is how a Party is choosing to report.
11 The United States uses the production accounting approach (as in previous years) to report HWP Contribution
12 (Table A-223). Annual estimates of change are calculated by tracking the additions to and removals from the pool of
13 products held in end uses (i.e., products in use such as housing or publications) and the pool of products held in solid waste
14 disposal sites (SWDS).
15 Estimates of five HWP variables that can be used to calculate HWP contribution for the stock change and
16 atmospheric flow approaches for imports and exports are provided in Table A-221. The HWP variables estimated are:
17 (1A) annual change of C in wood and paper products in use in the United States,
18 (IB) annual change of C in wood and paper products in SWDS in the United States,
19 (2A) annual change of C in wood and paper products in use in the United States and other countries where the
20 wood came from trees harvested in the United States,
21 (2B) annual change of C in wood and paper products in SWDS in the United States and other countries where
22 the wood came from trees harvested in the United States,
23 (3) Carbon in imports of wood, pulp, and paper to the United States,
24 (4) Carbon in exports of wood, pulp and paper from the United States, and
25 (5) Carbon in annual harvest of wood from forests in the United States. The sum of these variables yield
26 estimates for HWP contribution under the production accounting approach.
27
28 Table A-223: Harvested Wood Products from Wood Harvested in the United States—Annual Additions of C to Stocks
29 and Total Stocks under the Production Approach
Year
Net C additions per year
MMT C 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
2006
(27.3)
(10.0)
(17.4)
2382
1459
923
A-419
-------
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
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
1
2 Table A-224: Comparison of Net Annual Change in Harvested Wood Products C Stocks Using Alternative Accounting
3 Approaches (kt CP2 Eq./year)
HWP Contribution to LULUCF Emissions/ Removals (MMT CP2 Eq.)
Stock-Change
Atmospheric Flow
Production
Inventory Year
Approach
Approach
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)
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
A-420 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
Table A-225: Harvested Wood Products Sectoral Background Data for LULUCF—United States
1A
IB
2A
2B
3
4
5
6
7
8
Inventory
Annual
Annual
Annual Change
Annual
Annual
Annual
Annual
Annual
Annual release
HWP Contribution to
year
Change in
Change in
in stock of
Change in
Imports of
Exports of
Domestic
release of C to
of C to the
AFOLU C02 emissions/
stock of HWP
stock of HWP
HWP in use
stock of
wood, and
wood, and
Harvest
the
atmosphere
removals
in use from
in SWDS from
produced from
HWP in
paper
paper
atmosphere
from HWP
consumption
consumption
domestic
SWDS
products
products plus
from HWP
(including
harvest
produced
plus wood
wood fuel.
consumption
firewood)
from
fuel, pulp.
pulp.
(from
where wood
domestic
recovered
recovered
fuelwood and
came from
harvest
paper.
paper.
products in
domestic
roundwood/
roundwood/
use and
harvest (from
chips
chips
products in
products in use
SWDS)
and products
in SWDS)
ACHWP IU DC
ACHWP SWDS
AC HWP IU DH
ACHWP
PIM
PEX
H
¦fCHWP DC
¦fCHWP DH
DC
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)
1991
15.8
17.0
16.3
17.4
12.9
16.0
139.4
103.5
105.6
(123.8)
1992
17.0
17.6
15.0
17.9
14.5
14.7
134.6
99.7
101.6
(120.7)
1993
18.3
17.2
15.9
17.5
15.7
15.6
134.8
99.3
101.3
(122.5)
1994
17.3
17.1
15.1
17.2
16.7
17.3
137.0
102.1
104.7
(118.4)
1995
17.0
16.3
14.1
16.5
16.7
16.7
134.5
101.1
103.9
(112.2)
1996
18.7
17.1
14.7
17.3
18.0
16.9
135.4
100.7
103.4
(117.3)
1997
19.7
17.8
13.4
17.7
19.0
15.1
134.2
100.7
103.1
(114.2)
1998
21.4
18.7
14.1
18.4
20.7
15.2
134.2
99.5
101.7
(119.0)
1999
20.0
18.5
12.8
18.0
21.9
16.2
133.7
100.9
102.9
(112.9)
2000
16.5
17.6
8.7
16.8
22.1
15.3
127.9
100.5
102.4
(93.4)
2001
17.4
18.2
9.6
17.2
23.2
15.7
126.9
98.7
100.1
(98.2)
A-421
-------
2002 17.0 17.3 9.4 16.2 23.8
2003 21.4 17.6 12.1 16.3 26.6
2004 21.0 17.8 12.4 16.4 26.9
2005 18.7 18.6 11.6 17.3 25.5
2006 12.7 18.3 10.0 17.4 21.7
2007 2.3 17.5 3.7 17.1 17.0
2008 (5.2) 16.6 (1.8) 16.7 13.0
2009 (3.1) 16.3 0.0 16.6 14.1
2010 (2.1) 16.1 2.0 16.8 13.9
2011 (0.0) 16.1 2.4 17.0 14.0
2012 3.5 16.3 3.7 17.1 15.3
2013 6.9 16.5 5.3 17.3 17.1
2014 8.7 16.7 6.1 17.4 17.7
2015 10.2 16.9 6.7 17.5 18.5
2016 11.1 17.0 7.6 17.6 18.5
2017 18.1 17.9 8.3 17.9 22.6
2018 17.9 18.9 8.6 18.3 21.6
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
A-422 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
16.3
17.0
18.1
18.8
20.6
21.2
20.7
22.7
25.0
23.8
23.6
23.5
23.3
23.1
23.1
19.3
19.0
126.5
121.8
123.5
120.1
117.6
104.4
94.5
97.6
102.7
106.7
111.2
115.0
117.0
119.1
122.1
108.1
110.1
99.6
92.4
93.6
89.6
87.6
80.4
75.4
75.9
77.5
80.8
83.2
85.2
86.1
87.4
89.3
75.4
75.9
100.9
93.5
94.8
91.2
90.2
83.7
79.6
81.0
83.9
87.4
90.4
92.5
93.6
94.9
96.9
82.0
83.2
(93.7)
(104.1)
(105.4)
(106.0)
(100.3)
(76.1)
(54.5)
(60.8)
(69.1)
(71.0)
(76.5)
(82.6)
(86.0)
(88.7)
(92.4)
(95.7)
(98.8)
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
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-
226. The first criterion is that the WOODCARB II model estimate of C in houses standing in 2001 needed to match an
independent estimate of C in housing based on U.S. Census and USDA Forest Service survey data. The second criterion is
that the WOODCARB II model estimate of wood and paper being discarded to SWDS needed to match EPA estimates of
discards over the period 1990 to 2000. This calibration strongly influences the estimate of variable 1A, and to a lesser
extent variable 2A. The calibration also determines the amounts going to SWDS. In addition, WOODCARB II landfill decay
rates have been validated by making sure that estimates of methane emissions from landfills based on EPA data are
reasonable in comparison to methane estimates based on WOODCARB II landfill decay rates.
Decay parameters for products in SWDS are shown in Table A-227. Estimates of 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-
223 and Table A-224. 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-226: Half-life of Solidwood and Paper Products in End-Uses
Parameter
Value
Units
Half-life of wood in single family housing 1920 and before
78.0
Years
Half-life of wood in single family housing 1920-1939
78.0
Years
Half-life of wood in single family housing 1940-1959
80.0
Years
Half-life of wood in single family housing 1960-1979
81.9
Years
Half-life of wood in single family housing 1980 +
83.9
Years
Ratio of multifamily half-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-423
-------
Table A-227: Parameters Determining Decay of Wood and Paper in SWDS
Parameter
Value
Units
Percentage of wood and paper in dumps that is subject to decay
100
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.
A-424 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Table A-228: Net C02 Flux from Forest Pools in Forest Land Remaining Forest Land and Harvested Wood Pools (MMT C02 Eq.)
Carbon Pool
1990
1995
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Forest
(610.1)
(598.7)
(543.3)
(560.0)
(464.0)
(513.6)
(572.6)
(570.2)
(576.3)
(548.2)
(577.4)
(556.2)
(583.2)
(568.5)
(573.8)
(532.8)
(587.4)
(565.5)
(552.0)
(564.5)
Aboveground
Biomass
(425.1)
(416.1)
(383.4)
(387.2)
(369.4)
(378.3)
(391.3)
(392.0)
(394.0)
(391.4)
(398.1)
(391.3)
(405.3)
(394.3)
(403.1)
(390.8)
(404.6)
(397.0)
(381.2)
(385.2)
Belowground
Biomass
(98.6)
(96.6)
(89.7)
(90.2)
(86.5)
(88.4)
(90.8)
(90.9)
(91.3)
(90.0)
(91.8)
(90.3)
(92.1)
(92.8)
(92.5)
(88.9)
(92.9)
(91.1)
(87.6)
(88.6)
Dead Wood
(81.9)
(82.8)
(80.3)
(82.2)
(73.2)
(78.2)
(84.1)
(83.9)
(84.7)
(81.5)
(84.8)
(83.4)
(87.1)
(83.7)
(84.5)
(80.3)
(88.4)
(87.6)
(83.1)
(86.4)
Litter
(5.0)
(3.5)
10.7
0.4
66.0
32.4
(5.2)
(3.0)
(5.1)
16.4
0.3
(1.4)
(3.8)
5.2
(0.5)
30.2
(3.1)
(0.9)
(3.5)
(3.1)
Soil (Mineral)
0.3
(0.1)
(1.1)
(1.3)
(1.4)
(1.6)
(1.8)
(1.1)
(2.0)
(2.4)
(2.9)
4.6
3.7
(4.4)
5.7
(2.7)
(0.6)
8.2
1.4
(3.3)
Soil (Organic)
(0.6)
(0.5)
(0.3)
(0.3)
(0.2)
(0.2)
(0.1)
(0.1)
+
+
(0.8)
4.9
0.6
0.6
0.3
(1.0)
1.4
2.3
1.4
1.4
Drained Organic
Soil3
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
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)
(98.2)
(93.7)
(104.1)
(105.4)
(106.0)
(100.3)
(76.1)
(54.5)
(60.8)
(69.1)
(71.0)
(76.5)
(82.6)
(86.0)
(88.7)
(92.4)
(95.7)
(98.8)
Products in Use
(54.8)
(51.7)
(35.1)
(34.5)
(44.4)
(45.4)
(42.6)
(36.6)
(13.5)
6.6
(0.0)
(7.4)
(8.7)
(13.6)
(19.3)
(22.3)
(24.6)
(27.8)
(30.3)
(31.5)
SWDS
(69.0)
(60.5)
(63.1)
(59.3)
(59.6)
(60.0)
(63.4)
(63.7)
(62.6)
(61.1)
(60.8)
(61.7)
(62.3)
(62.8)
(63.3)
(63.7)
(64.1)
(64.6)
(65.5)
(67.2)
Total Net Flux
(733.9)
(710.9)
(641.5)
(653.7)
(568.1)
(619.0)
(678.6)
(670.5)
(652.4)
(602.7)
(638.2)
(625.3)
(654.2)
(645.0)
(656.4)
(618.8)
(676.1)
(657.9)
(647.7)
(663.2)
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
3 These estimates include C stock changes from drained organic soils from both Forest Land Remaining Forest Land and Land Converted to Forest Land.
Note: Parentheses indicate negative values.
Table A-229: Net C Flux from Forest Pools in Forest Land Remaining Forest Land and Harvested Wood Pools (MMT C)
Carbon Pool
1990
1995
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Forest
(166.4)
(163.3)
(148.2)
(152.7)
(126.5)
(140.1)
(156.2)
(155.5)
(157.2)
(149.5)
(157.5)
(151.7)
(159.1)
(155.0)
(156.5)
(145.3)
(160.2)
(154.2)
(150.5)
(153.9)
Aboveground
Biomass
(115.9)
(113.5)
(104.6)
(105.6)
(100.7)
(103.2)
(106.7)
(106.9)
(107.4)
(106.8)
(108.6)
(106.7)
(110.5)
(107.5)
(109.9)
(106.6)
(110.4)
(108.3)
(104.0)
(105.1)
Belowground
Biomass
(26.9)
(26.3)
(24.5)
(24.6)
(23.6)
(24.1)
(24.8)
(24.8)
(24.9)
(24.6)
(25.0)
(24.6)
(25.1)
(25.3)
(25.2)
(24.2)
(25.3)
(24.9)
(23.9)
(24.2)
Dead Wood
(22.3)
(22.6)
(21.9)
(22.4)
(20.0)
(21.3)
(22.9)
(22.9)
(23.1)
(22.2)
(23.1)
(22.7)
(23.8)
(22.8)
(23.1)
(21.9)
(24.1)
(23.9)
(22.7)
(23.6)
Litter
(1.4)
(1.0)
2.9
0.1
18.0
8.8
(1.4)
(0.8)
(1.4)
4.5
0.1
(0.4)
(1.0)
1.4
(0.1)
8.2
(0.8)
(0.3)
(1.0)
(0.8)
Soil (Mineral)
0.1
(0.0)
(0.3)
(0.3)
(0.4)
(0.4)
(0.5)
(0.3)
(0.5)
(0.7)
(0.8)
1.3
1.0
(1.2)
1.6
(0.7)
(0.2)
2.2
0.4
(0.9)
Soil (Organic)
(0.2)
(0.1)
(0.1)
(0.1)
(0.1)
(0.0)
(0.0)
(0.0)
(0.0)
0.0
(0.2)
1.3
0.2
0.2
0.1
(0.3)
0.4
0.6
0.4
0.4
Drained Organic
Soil3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Harvested
Wood
(33.8)
(30.6)
(26.8)
(25.6)
(28.4)
(28.7)
(28.9)
(27.3)
(20.8)
(14.9)
(16.6)
(18.8)
(19.4)
(20.9)
(22.5)
(23.4)
(24.2)
(25.2)
(26.1)
(26.9)
Products in Use
(14.9)
(14.1)
(9.6)
(9.4)
(12.1)
(12.4)
(11.6)
(10.0)
(3.7)
1.8
(0.0)
(2.0)
(2.4)
(3.7)
(5.3)
(6.1)
(6.7)
(7.6)
(8.3)
(8.6)
SWDS
(18.8)
(16.5)
(17.2)
(16.2)
(16.3)
(16.4)
(17.3)
(17.4)
(17.1)
(16.7)
(16.6)
(16.8)
(17.0)
(17.1)
(17.3)
(17.4)
(17.5)
(17.6)
(17.9)
(18.3)
Total Net Flux
(200.2)
(193.9)
(174.9)
(178.3)
(154.9)
(168.8)
(185.1)
(182.9)
(177.9)
(164.4)
(174.1)
(170.5)
(178.4)
(175.9)
(179.0)
(168.8)
(184.4)
(179.4)
(176.7)
(180.9)
A-425
-------
+ Absolute value does not exceed 0.05 MMT C.
3 These estimates include C stock changes from drained organic soils from both Forest Land Remaining Forest Land and Land Converted to Forest Land.
Note: Parentheses indicate negative values.
Table A-230: Forest area (1,000 ha) and C Stocks in Forest Land Remaining Forest Land and Harvested Wood Pools (MMT C)
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Forest Area
(1000 ha)
279,748
279,840
280,025
279,762
279,783
279,814
279,861
279,918
279,931
279,957
279,960
279,977
280,041
280,041
279,893
279,787
279,682
Carbon Pools
Forest
51,527
52,358
53,161
54,042
54,198
54,355
54,505
54,663
54,815
54,974
55,129
55,286
55,431
55,592
55,746
55,897
56,051
Aboveground
Biomass
11,833
12,408
12,962
13,590
13,697
13,805
13,911
14,020
14,127
14,237
14,345
14,455
14,561
14,672
14,780
14,884
14,989
Belowground
Biomass
2,350
2,483
2,612
2,759
2,783
2,808
2,833
2,858
2,883
2,908
2,933
2,958
2,982
3,008
3,033
3,056
3,081
Dead Wood
2,120
2,233
2,346
2,477
2,500
2,523
2,545
2,568
2,591
2,615
2,638
2,661
2,683
2,707
2,731
2,753
2,777
Litter
3,662
3,670
3,676
3,649
3,649
3,651
3,646
3,646
3,647
3,648
3,646
3,646
3,638
3,639
3,639
3,640
3,641
Soil (Mineral)
25,636
25,636
25,637
25,639
25,639
25,640
25,641
25,641
25,640
25,639
25,640
25,639
25,640
25,640
25,637
25,637
25,638
Soil (Organic)
5,927
5,928
5,928
5,929
5,929
5,929
5,929
5,929
5,927
5,927
5,927
5,927
5,927
5,927
5,926
5,926
5,926
Harvested
Wood
1,895
2,061
2,218
2,382
2,410
2,430
2,445
2,462
2,481
2,500
2,521
2,543
2,567
2,591
2,616
2,642
2,669
Products in
Use
1,249
1,326
1,395
1,459
1,469
1,473
1,471
1,471
1,473
1,475
1,479
1,484
1,490
1,497
1,505
1,513
1,521
SWDS
646
735
823
923
941
958
974
991
1,008
1,025
1,042
1,059
1,076
1,094
1,112
1,129
1,148
Total Stock
53,423
54,419
55,380
56,424
56,607
56,786
56,950
57,124
57,295
57,474
57,650
57,829
57,998
58,183
58,362
58,539
58,720
A-426 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Table A-231: 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)
Difference between Forest
Forest Land (managed) - 6.1
Forest Land (managed) - 6.2
Land Areas (managed) - 6.1
Year
Representation of the U.S.
Forest Land Remaining
and Forest Land Remaining
Land Base
Forest Land
Forest Land - 6.2 area
estimates
1990
280,393
279,748
645
1991
280,412
279,768
644
1992
280,407
279,764
642
1993
280,449
279,818
631
1994
280,421
279,814
606
1995
280,414
279,840
574
1996
280,437
279,870
566
1997
280,442
279,894
548
1998
280,436
279,919
518
1999
280,501
279,992
509
2000
280,518
280,025
493
2001
280,113
279,631
481
2002
280,157
279,675
482
2003
280,180
279,720
460
2004
280,224
279,767
457
2005
280,207
279,749
458
2006
280,216
279,762
454
2007
280,236
279,783
453
2008
280,266
279,814
452
2009
280,313
279,861
452
2010
280,369
279,918
452
2011
280,384
279,931
453
2012
280,386
279,957
429
2013
280,394
279,960
435
2014
280,438
279,977
461
2015
280,528
280,041
487
2016
280,529
280,041
487
2017
280,380
279,893
487
2018
280,274
279,787
487
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
2018b, 2018c). 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).
A-427
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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
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 0.5 because biomass is 50 percent of dry
weight (USDA Forest Service 2018d). 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-220. 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-220) 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 2018d).
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
A-428 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
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 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
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
2010 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-231 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-232
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
A-429
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6.3 of this Inventory (Table A-234). 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-430 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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Table A-231: 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
2011
2012
2013
2014
2015
2016
2017
2018
Cropland Converted
to Forest Land
0.08
0.07
0.07
0.07
0.06
0.06
0.06
0.06
0.05
0.05
0.06
0.06
0.06
(0.03 to
(0.03 to
(0.02 to
(0.02 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(-0.02 to
(-0.02 to
(-0.02 to
0.13)
0.12)
0.12)
0.13)
0.11)
0.11)
0.11)
0.1)
0.1)
0.1)
0.13)
0.13)
0.13)
Grassland Converted
to Forest Land
-0.05
-0.05
-0.07
-0.08
-0.08
-0.07
-0.08
-0.08
-0.09
-0.08
-0.08
-0.08
-0.07
(-0.08 to
(-0.1 to-
(-0.12 to
(-0.14 to
(-0.15 to
(-0.13 to
(-0.14 to
(-0.15 to
(-0.16 to
(-0.15 to
(-0.18 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)
-0.02)
0.02)
0.02)
0.02)
Other Lands
Converted to Forest
Land
0.17
0.22
0.24
0.30
0.32
0.31
0.31
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
(0.21 to
(0.21 to
(0.19 to
(0.19 to
(0.17 to
(0.13 to
(0.12 to
(0.12 to
0.21)
0.25)
0.29)
0.36)
0.38)
0.38)
0.38)
0.39)
0.41)
0.43)
0.5)
0.5)
0.51)
Settlements
Converted to Forest
Land
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.02
(0 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.02 to
(0.01 to
(0.01 to
(0.01 to
0.02)
0.01)
0.01)
0.01)
0.01)
0.02)
0.02)
0.02)
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
0.00
0.00
0.00
(OtoO)
(OtoO)
(OtoO)
(OtoO)
(OtoO)
(OtoO)
(OtoO)
(OtoO)
(OtoO)
(OtoO)
(OtoO)
(OtoO)
(OtoO)
Total Lands
Converted to Forest
Lands
0.22
0.25
0.26
0.30
0.31
0.31
0.30
0.30
0.29
0.30
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-232: Total land areas (hectares) by land use/land use change subcategory for mineral soils between 1990 to 2015
~~7 ! . 77 7TT 777 7777 7777 7777 7777 2007 2008 2009 2010 2011 2012 2013
Conversion Land Areas (Hectares xlO6) 1990 1995
2000 2005
2014 2015
Cropland Converted to Forest Land
Grassland Converted to Forest Land
Other Lands Converted to Forest Land
Settlements Converted to Forest Land
Wetlands Converted to Forest Land
0.17
0.75
0.05
0.01
0.01
0.16
0.81
0.06
0.01
0.01
0.17 0.16
0.80 0.81
0.07 0.08
0.01 0.01
0.01 0.01
0.16
0.82
0.09
0.01
0.01
0.15
0.84
0.09
0.01
0.01
0.15
0.84
0.09
0.01
0.01
0.15
0.84
0.09
0.01
0.01
0.15
0.83
0.09
0.01
0.01
0.14
0.84
0.09
0.01
0.01
0.14
0.84
0.09
0.01
0.01
0.14
0.83
0.09
0.02
0.01
0.14
0.80
0.09
0.02
0.01
Total Lands Converted to Forest Lands9
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-431
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1 Table A-233: Forest Land Area Estimates and Differences Between Estimates in 6.1 Representation of the U.S. Land Base and 6.3 Land Converted to Forest Land (kha).
2
Area (Thousand Hectares)
Forest
Difference
Land (managed) -
Forest
between Forest
6.1
Land
Land (managed) -
Representation of
(managed) - 6.3
6.1 Areas and Land
the U.S. Land
Land Converted
Converted to Forest
Year
Base
to Forest Land
Land - 6.3 Areas
1990
1228
1120
108
1991
1230
1119
110
1992
1272
1154
118
1993
1268
1139
129
1994
1328
1172
156
1995
1362
1175
187
1996
1367
1171
197
1997
1388
1178
210
1998
1428
1187
241
1999
1399
1151
248
2000
1428
1163
265
2001
1442
1162
280
2002
1440
1162
278
2003
1443
1161
282
2004
1435
1153
282
2005
1474
1191
283
2006
1485
1200
285
2007
1487
1202
285
2008
1512
1221
291
2009
1512
1222
291
2010
1498
1207
291
2011
1494
1207
287
2012
1517
1207
310
2013
1513
1207
306
2014
1465
1189
276
2015
1416
1168
249
2016
1267
1102
165
2017
1272
1107
164
2018
1272
1107
164
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Uncertainty Analysis
The uncertainty analyses for total net flux of forest C (see Table 6-11 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 variance estimate for HWP
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)
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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
1990-2017 (data for 2018 were unavailable when these estimates were summarized; therefore 2017, the most recent
available estimate, is applied to 2018). Similarly, there were 828 of the 1,044 fires in Alaska for 1990-2017 (data for 2018
were unavailable when these estimates were summarized; therefore 2017, the most recent available estimate, is applied
to 2018 ) 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
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forest area burned. Most of the differences in annual forest area burned (and thus associated emissions) as seen in Table
A-235 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 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-235 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-234. 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-235, and C emitted per year was based on multiplying by the conversion factor 12/44 (IPCC 2006).
A-435
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1 Table A-234: Areas (Hectares) from Wildfire Statistics and Corresponding Estimates of C and C02 (MMT/year) Emissions for Wildfires and Prescribed Firesa
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018"
Forest area
bu rned
Conterminous
(1000 ha)
83.4
103.0
508.1
402.1
115.9
716.1
1244.3
279.9
521.1
954.4
507.4
1156.4
1156.4
48 States -
C emitted
Wildfires
(MMT/yr)
CO2 emitted
1.7
1.1
5.0
5.6
2.1
6.6
30.0
3.5
16.5
31.6
9.3
38.5
38.5
(MMT/yr)
6.2
4.1
18.5
20.5
7.8
24.1
109.9
12.9
60.3
115.8
34.0
141.1
141.1
Forest area
bu rned
Alaska -
Wildfires
(1000 ha)
C emitted
(MMT/yr)
CO2 emitted
82.5
1.4
1.4
0.0
59.6
1.0
686.7
12.0
103.9
1.8
28.0
0.5
14.9
0.3
185.3
3.3
53.7
0.9
638.4
11.2
26.8
0.5
23.8
0.4
23.8
0.4
(MMT/yr)
5.3
0.1
3.8
44.1
6.7
1.8
1.0
11.9
3.5
41.2
1.7
1.5
1.5
Forest area
bu rned
Prescribed Fires
(all 49 states)
(1000 ha)
C emitted
(MMT/yr)
CO2 emitted
5.0
0.1
10.6
0.1
15.4
0.2
43.5
0.4
496.3
6.2
166.7
1.7
71.1
0.8
232.2
2.6
237.0
2.8
150.8
1.7
250.4
2.6
227.2
2.3
227.2
2.3
(MMT/yr)
0.2
0.3
0.8
1.5
22.9
6.3
2.9
9.6
10.4
6.1
9.7
8.6
8.6
CH4 emitted
(kt/yr)
34.4
12.6
66.8
193.7
43.4
77.5
332.0
74.3
191.1
470.2
106.8
426.8
426.8
N2O emitted
Wildfires (all 49
(kt/yr)
1.9
0.7
3.7
10.7
2.4
4.3
18.4
4.1
10.6
26.0
5.9
23.6
23.6
states)
CO emitted
(kt/yr)
783.8
286.2
1520.5
4401.9
988.2
1764.4
7,559.8
1,694.1
4,345.2
10,707.6
2,432.1
9,729.9
9,729.9
NOx emitted
(kt/yr)
21.9
8.0
42.6
123.6
27.7
49.5
212.0
47.4
122.1
300.2
68.3
272.5
272.5
CH4 emitted
(kt/yr)
0.7
0.8
2.6
4.6
68.6
18.7
8.6
28.9
31.2
18.3
29.0
25.6
25.6
N2O emitted
Prescribed Fires
(kt/yr)
0.0
0.0
0.1
0.3
3.8
1.0
0.5
1.6
1.7
1.0
1.6
1.4
1.4
(all 49 states)
CO emitted
(kt/yr)
16.9
18.0
58.1
105.1
1561.2
426.4
197.2
657.1
709.8
417.5
659.7
583.7
583.7
NOx emitted
(kt/yr)
0.5
0.5
1.6
2.9
43.8
12.0
5.5
18.4
19.9
11.7
18.5
16.4
16.4
2 3 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
3 combusted wood may continue to decay through time.
4 b The data for 2018 were unavailable when these estimates were summarized; therefore 2017, the most recent available estimate, is applied to 2018.
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Table A-235: Emission Fators for Extra Tropical Forest Burning and 100-year GWP (AR4), or Equivalence Ratios, of CH4
and N2Oto C02
Emission Factor (g per kg dry
matter burned)a
Equivalence Ratios'1
CH4 4.70
CH4 to C02
25
N20 0.26
N20 to C02
298
C02 1,569
C02 to C02
1
3 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-236. The summed annual estimates are
provided in Table A-235. 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-236.
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-236) to represent estimates as C02 equivalent were not considered uncertain values for these results.
A-437
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3.14. Methodology for Estimating CH4 Emissions from Landfills
Landfill gas is a mixture of substances generated when bacteria decompose the organic materials contained in
121
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 2006 IPCC 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
1990-2015 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 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 2018 Inventory are presented in the remainder of this Annex.
Figure A-18: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste
a MSW waste generation is not calculated because annual quantities of waste disposal are available through EPA 2018; annual production
data used for industrial waste (Lockwood Post's Directory and the USDA).
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 2018 are incorporated through the directly reported emissions from MSW landfills to the Greenhouse Gas
121 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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Reporting Program (EPA 2018). 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. Two different methodologies are used
in the time series for MSW landfills.
d For 1990 to 2004, the 2006 IPCC Guidelines - FOD Model is used. For 2005 to 2018, 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 equal to 9 percent of each year's emissions. The
scale-up factor accounts for emissions from landfills that do not report to the GHGRP. 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.
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 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.
8 For years 1990 to 2004, the total CH4 generated from MSW landfills and industrial waste landfills are summed. For years 2005 to 2018,
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 for MSW landfills and in all years of the time
series for industrial waste landfills (2006 IPCC Guidelines; Mancinelli and McKay 1985; Czepiel et al 1996). For years 2005 to 2018,
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
To estimate the amount of CH4 generated in a landfill in a given year, information is needed on the quantity and
composition of the waste in the landfill for multiple decades, as well as the landfill characteristics (e.g., size, aridity, waste
density). Estimates and/or directly measured amounts of waste placed in municipal solid waste (MSW) and industrial waste
landfills are available through various studies, surveys, and regulatory reporting programs (i.e., EPA's GHGRP). The
composition of the amount of waste placed in these landfills is not readily available for most years the landfills were in
operation. Consequently, and for the purposes of estimating CH4 generation, the Inventory methodology assumes that all
waste placed in MSW landfills is bulk MSW (waste that is composed of both organic and inorganic materials), and that all
waste placed in industrial waste landfills is from either pulp and paper manufacturing facilities or food and beverage
facilities.
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, specifically the
SOG surveys, published approximately every two years, with the most recent publication date of 2014. The SOG survey
was the only continually updated nationwide survey of waste disposed in landfills in the United States and was the primary
data source with which to estimate nationwide CH4 generation from MSW landfills. Currently, EPA's GHGRP waste disposal
data, EPA's Advancing Sustainable Materials Management: Facts and Figures report waste disposal data, and MSW
management data published by the Environmental Research and Education Foundation (EREF) are available.
The SOG surveys collected data from the 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 survey 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 these wastes may have been
disposed of in MSW landfills, they were not the primary type of waste material disposed and were typically inert. In the
most recent survey, state agencies were asked to provide already filtered, MSW-only data. Where this was not possible,
they were asked to provide comments to better understand the data being reported. All state disposal data were adjusted
for imports and exports across state lines where imported waste was included in a state's total while exported waste was
not. Methodological changes occurred over the time frame the SOG survey has been published, and this affected the
fluctuating trends observed in the data (RTI 2013).
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State-specific landfill MSW generation data and a national average disposal factor for 1989 through 2004 were
obtained from the SOG survey every two years (i.e., 2002, 2004 as published in BioCycle 2006). The landfill inventory
calculations start with hard numbers (where available) as presented in the SOG documentation for the report years 2002
and 2004. 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. In 2006, BioCycle published their 15th
Nationwide Survey which also contained estimations of landfilled quantities generated for the years 1990 through 2000.
In-between year waste generation is again interpolated using the prior and next SOG report data in order to determine an
approximate quantity for waste generation in the year 2001. The quantities of waste generated across all states are
summed and that value is then used as the nationwide quantity of waste generated in each year of the time series. The
SOG survey is voluntary and not all states provide 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 particular 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 estimated by SOG and in the Inventory. In the
early years (2002 data in particular), SOG made no attempt to fill gaps for non-survey responses. For the 2004 data, the
SOG team used proxy data (mainly from the WBJ) to fill gaps for non-reporting states and survey responses.
Another key aspect of the SOG survey is that it focuses on MSW-only data. The data states collect for solid waste
typically are representative of total solid waste and not the MSW-only fraction. In the early years of the SOG survey, most
states reported total solid waste rather than MSW-only waste. The SOG team, in response, "filtered" the state-reported
data to reflect the MSW-only portion.
This data source also contains the waste generation data reported by states to the SOG survey, which fluctuates
from year to year. Although some fluctuation 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 speculate 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).
Recently, the EREF published a report, MSW Management in the United States, which includes state-specific
landfill MSW generation and disposal data for 2010 and 2013 using a similar methodology as the SOG surveys (EREF 2016).
Because of this similar methodology, EREF data were used to populate data for years where BioCycle data was not available
when possible. State-specific landfill waste generation data for the years in between the SOG surveys and EREF report
(e.g., 2001, 2003, etc.) were either interpolated or extrapolated based on the SOG or EREF data and the U.S. Census
population data (U.S. Census Bureau 2019).
Historical waste data, preferably since 1940, are required for the FOD model to estimate CH4 generation for the
Inventory time series, as the 2006 IPCC Guidelines recommend at least 50 years of waste disposal data to estimate CH4
emissions. 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
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1 extensive landfill survey by the EPA's Office of Solid Waste in 1986 (EPA 1988). Although waste placed in landfills in the
2 1940s and 1950s contributes very little to current CH4 generation, estimates for those years were included in the FOD
3 model for completeness in accounting for CH4 generation rates and are based on the population in those years and the
4 per capita rate for land disposal for the 1960s. For calculations in the current Inventory, wastes landfilled prior to 1980
5 were broken into two groups: wastes disposed in landfills (MCF of 1) and those disposed in uncategorized site as (MCF of
6 0.6). All calculations after 1980 assume waste is disposed in managed, modern landfills.
7 For 1989 to 2004, estimates of the annual quantity of waste placed in MSW landfills were developed from a
8 survey of State agencies as reported in the State of Garbage (SOG) in America surveys (BioCycle 2001, 2004, 2006, 2010)
9 and recent data from the Environmental Research & Education Foundation (EREF 2016), adjusted to include U.S.
10 Territories.122 The SOG surveys and EREF (2016) provide state-specific landfill waste generation data, collectively back to
11 1989. The SOG survey is no longer updated, but is available every two years for the years 2002 and 2004 (as published in
12 BioCycle 2006). A linear interpolation was used to estimate the amount of waste generated in 2001, 2003.
13 Estimates of the quantity of waste landfilled are determined by applying a waste disposal factor to the total
14 amount of waste generated. A national average waste disposal factor is determined for each year a SOG survey and EREF
15 report is published and is the ratio of the total amount of waste landfilled to the total amount of waste generated. The
16 waste disposal factor is interpolated for the years in-between the SOG surveys and EREF data, and extrapolated for years
17 after the last year of data. Methodological changes have occurred over the time that the SOG survey has been published,
18 and this has resulted in fluctuating trends in the data.
19 Table A-236 shows estimates of waste quantities contributing to CH4 emissions. The table shows SOG (Biocycle
20 2010) and EREF (EREF 2016) estimates of total waste generated and total waste landfilled (adjusted for U.S. Territories)
21 for various years over the 1990 to 2017 timeframe even though the Inventory methodology does not use the data for 2005
22 onward.
23 Table A-236: Solid Waste in MSW and Industrial Waste Landfills Contributing to CH4 Emissions (MMT unless otherwise
24 noted)
1990
2005
2012
2013
2014
2015
2016
2017
2018
Total MSW Generated3
270
368
319
319
320
322
324
326
328
Percent of MSW Landfilled
77%
64%
63%
64%
64%
65%
65%
65%
65%
Total MSW Landfilled
205
234
200
201
202
208
209
211
212
MSW last 30 years
4,876
5,992
6,388
6,411
6,432
6,455
6,476
6,497
6,515
MSW since 1940b
6,808
9,925
11,474
11,675
11,878
12,085
12,294
12,505
12,716
Total Industrial Waste Landfilled
9.7
10.9
10.5
10.3
10.4
10.3
10.3
10.3
10.1
Food and Beverage Sector0
6.4
6.9
6.2
6.0
6.2
6.1
6.1
6.0
5.8
Pulp and Paper Sectord
3.3
4.0
4.2
4.2
4.2
4.2
4.2
4.2
4.3
25 3 This estimate represents the waste that has been in place for 30 years or less, which contributes about 90 percent of the Cm generation. Values
26 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 2014
27 (1981 to 2004, and 2006 to 2011 are not presented in table). Values for years 2010 to 2018 are based on EREF (2016) and annual population
28 data from the U.S. Census Bureau (2019).
29 bThis 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
30 annual disposal rates used in the first order decay model. Values are based on EPA 1993; BioCycle 2001, 2004, 2006, and 2010; and EREF 2016.
31 c Food production values for 1990 to 2018 are from ERG. 2019 USDA-NASS Ag QuickStats available at http://quickstats.nass.usda.gov (FAO
32 2019).
33 d Production data from 1990 and 2001 are from Lockwood-Post's Directory, 2002. Production data from 2002 to 2018 are from the FAOStat
34 database available at: http://faostat3.fao.Org/home/index.html#DOWNLOAD. Accessed on May 20, 2019.
35
36 EPA compared the SOG and EREF estimates of total waste generated and landfilled presented in Table A-235 to
37 the recently published Advancing Sustainable Materials Management: Facts and Figures report (EPA 2019b, Table 2, latest
38 year of data is 2017) and found inconsistencies between the estimates of MSW landfilled between the two data sources.
39 These inconsistencies are expected, as the data sources use two different methodologies to estimate MSW landfilled. Both
40 the SOG and EREF estimates of total MSW landfilled are derived via a bottom-up approach using information at the facility-
41 level to estimate MSW for the sector as a whole, while the Advancing Sustainable Materials Management: Facts and
122 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 2019) and the per capita rate for waste landfilled from BioCycle (2010).
A-446 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Figures report uses a top-down (materials flow mass balance) approach to estimate the same quantity. The materials flow
2 methodology is generally based on production data for each material at the state- (recycling, composting) or national-
3 (waste generation) level. Discarded or landfilled material is Subtitle D waste only and assumed to be the calculated
4 difference between generation and recovery through recycling and composting (EPA 2019a). Subtitle D wastes do not
5 include construction and demolition waste, for example, which many GHGRP-reporting facilities accept and include in their
6 GHG reports.
7 As a quality check, EPA compared the MSW landfilled estimates from the SOG, EREF, and Advancing Sustainable
8 Materials Management: Facts and Figures reports with MSW landfilled amounts for the 2017 year as reported to the EPA's
9 GHGRP under subpart HH (MSW Landfills). On average, the SOG and EREF estimations were 36 percent less than GHGRP
10 reported waste quantities (including a scale-up factor of 9 percent to account for operational facilities that do not report
11 to the GHGRP) for the year 2017. Estimates of MSW landfilled from the Advancing Sustainable Materials Management:
12 Facts and Figures report for the year 2017 were, on average, 60 percent less than the GHGRP waste quantities used in the
13 Inventory. While this percent difference is large, it is not unexpected. The GHGRP uses a facility-specific, bottom-up
14 approach to estimating emissions while the Advancing Sustainable Materials Management: Facts and Figures report uses
15 a top-down approach which incorporates many assumptions about disposal and recycling at a national level. The
16 Advancing Sustainable Materials Management: Facts and Figures report also specifically omits certain types of waste that
17 are explicitly included in the GHGRP reports, such as construction and demolition waste, biosolids (sludges), and other
18 inert wastes (EPA 2019a). The exclusion of these waste categories likely accounts for much of the discrepancies between
19 these two data sets.
20 EPA is now using facility-reported data from subpart HH of the GHGRP to calculate emissions from the Landfills
21 sector for the Inventory years 2005-present, replacing the need for now discontinued SOG surveys and intermittent EREF
22 estimates of MSW landfilled for this timeframe. To maintain a more consistent methodology across the entire Landfill
23 sector time series, EPA has kept the SOG and EREF estimates of MSW landfilled as a basis for emissions calculations for
24 Inventory years 1990-2004 since these methodologies use a bottom-up approach like the GHGRP methodology used in the
25 latter portion of the time series. While there remain some differences in the methods used between these data sources,
26 the uncertainty factors (Table 7-5) for MSW Landfills are intended to account for these variabilities in the waste disposal
27 estimates.
28 Step 2: Estimate CH4 Generation at MSW Landfills for 1990 to 2004
29 The FOD method is exclusively used for 1990 to 2004. For the FOD method, methane generation is based on
30 nationwide MSW generation data, to which a national average disposal factor is applied; it is not landfill-specific.
31 The FOD method is presented below and is similar to Equation HH-5 in CFR Part 98.343 for MSW landfills, and
32 Equation TT-6 in CFR Part 98.463 for industrial waste landfills.
33 CH 4,Solid Waste - [CH4,MSW + CH4,|nd - R] - Ox
34
where,
35
CH4iSolid Waste —
Net CH4 emissions from solid waste
36
CH4,msw =
CH4 generation from MSW landfills
37
CH4i|nd —
CH4 generation from industrial landfills
38
R
CH4 recovered and combusted (only for MSW landfills)
39
Ox
CH4 oxidized from MSW and industrial waste landfills before release to the atmosphere
40
41 The input parameters needed for the FOD model equations are the mass of waste disposed each year (discussed
42 under Step 1), degradable organic carbon (DOC) as a function of methane generation potential (Lo), and the decay rate
43 constant (k). The equation below provides additional detail on the activity data and emission factors used in the CH4,Msw
44 equation presented above.
45
46 CH4,msw = [ZI=| [wxxLoX^X Ce-feC7--^-i) _ e-fcP"-*))}]
47
48 where,
49
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CH 4,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-237)
The DOC is determined from the CH4 generation potential (L0 in m3 CH4/Mg waste) as shown in the following
equation:
DOC = [L0 x 6.74 x 10"4] -r [F x 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 potential123 of 100 m3 CH4/Mg waste (EPA 2008) as described below.
The DOC value used in the CH4 generation estimates from MSW landfills for 1990-2004 is 0.2028, and 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-237 and recommended
in EPA's compilation of emission factors (EPA 2008).
Table A-237: Average Values for Rate Constant (k) by Precipitation Range (yr1)
Precipitation range (inches/year) k (yr1)
<20 0.020
123 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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>40
0.038
0.0B7
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.
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-238.
Table A-238: Percent of U.S. Population within Precipitation Ranges (%)
Precipitation Range (inches/year)
1950
1960
1970
1980
1990
2000
2010
<20
10
13
14
16
19
19
18
20-40
40
39
37
36
34
33
44
>40
50
48
48
48
48
48
38
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. Year 2010 is based on the methodology from RTI (2004) and the U.S.
Bureau of Census and precipitation data from the National Climatic Data Center's National Oceanic and Atmospheric Administration where
available.
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)124,
• the flare vendor database (contains updated sales data collected from vendors of flaring equipment), and
the
• EPA's GHGRP MSW landfills database (EPA 2015a)
The EPA's GHGRP MSW landfills database was first introduced as a source for recovery data for the 1990 to 2013
Inventory (2 years before the full GHGRP data set started being used for net CH4 emissions for the Inventory). The GHGRP
MSW landfills database contains facility-reported data that undergoes rigorous verification and is considered to contain
124 The LFGE database was not updated for the 1990 to 2018 Inventory because the methodology does not use this database for
years 2005 and later, thus the LMOP 2016 database is the most recent year reflected in the LFGE database for the Inventory.
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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 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.
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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 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 1990-2015 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 CH4 emissions
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.
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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 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 1990-2015 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 1990-2015 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 1990-2015 Inventory.
The Inventory methodology now uses directly reported net CH4 emissions for the 2010 to 2018 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 2006 IPCC 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. We 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 Waste Business Journal (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.
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2. We 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.
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 waste
used if the landfill was known to be closed and a closure year was not included in the
source database.
3. We 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 million
metric tons.
4. We calculated the scale-up factor (9%) by dividing the non-reporting landfills WIP (0.92 million
metric tons) by the sum of the GHGRP WIP and the non-reporting landfills WIP (10.0 million
metric tons).
Table A- 239. Revised Waste-in-Place (WIP) for GHGRP Reporting and Non-reporting Landfills in 2016
Estimated WIP
Category (million metric tons) Percentage
. r ... . 9 percent
Non-reporting facilities 0.92 , , ,
(the applied scale-up factor)
GHGRP facilities 9.08 91 percent
Total 10.0 100 percent
The same 9% 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 2006 IPCC 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
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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 we have 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.
The years to back-cast the GHGRP data were first determined for the 1990-2015 Inventory when a 12.5% scale-
up factor was used. EPA plotted the net CH4 emissions from the adjusted 1990-2014 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%
applied to all years the GHGRP data are used, 2005 to 2014) as presented in Figure A-19. Only data up until 2014 are
presented in Figure A-19 and Figure A-20 below, as they directly compare to the 1990-2014 revised Inventory. 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% 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% (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 2014with 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 2014with a 9% scale-up factor
8
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Adjusted 1990-2014 Method — — Modified GHGRP
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. EPA will continue to evaluate the data submitted to the GHGRP each
year to determine if any changes are needed to the back-casting approach or the scale-up factor.
Step 5: Estimate CH4 Emissions from MSW Landfills for 2010 to the Current Inventory Year
CH4 emissions directly reported to EPA's GHGRP are used for 2010 to 2018. 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-240).
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Table A-240: 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 type
CE2: 0%.
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 AB below, and active gas collection
CE4: 75%.
AB: Area with a final soil cover of 3 feet or thicker of clay or final cover (as
approved by the relevant agency) and/or geomembrane cover system and
active gas collection
CEB: 95%.
Weighted average collection efficiency for landfills:
Area weighted average collection efficiency for landfills
CEavel = (A2*CE2 + A3*CE3 +
A4*CE4 + A5*CE5)/(A2 + A3 + A4
+ AB).
3
4 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
5 value. In the EPA's original rulemaking for the GHGRP, the EPA proposed this default collection efficiency of 75 percent
6 because it was determined to be a reasonable central-tendency default considering the availability of data such as surface
7 monitoring under the EPA's New Source Performance Standards for MSW Landfills (40 CFR Part 60 Subpart WWW), which
8 suggested that gas collection efficiencies generally range from 60 to 95 percent. This 75 percent default gas collection
9 efficiency value only applies to areas at the landfill that are under gas collection and control; for areas of the landfill that
10 are not under gas collection and control, a gas collection efficiency of 0 percent is applied.
11 The 9 percent scale-up factor is applied to the net annual emissions reported to the GHGRP for 2010 to 2018 as
12 is done for 2005 to 2009 because the GHGRP does not capture emissions from all landfills in the United States.
13 Step 6; Estimate CH4 Generation at Industrial Waste Landfills for 1990 to the Current Inventory Year
14 Industrial waste landfills receive waste from factories, processing plants, and other manufacturing activities. In
15 national inventories prior to the 1990 through 2005 inventory, CH4 generation at industrial landfills was estimated as seven
16 percent of the total CH4 generation from MSW landfills, based on a study conducted by EPA (1993). In 2005, the
17 methodology was updated and improved by using activity factors (industrial production levels) to estimate the amount of
18 industrial waste landfilled each year, and by applying the FOD model to estimate CH4 generation. A nationwide survey of
19 industrial waste landfills found that most of the organic waste placed in industrial landfills originated from two sectors:
20 food processing (meat, vegetables, fruits) and pulp and paper (EPA 1993). Data for annual nationwide production for the
21 food processing and pulp and paper sectors were taken from industry and government sources for recent years; estimates
22 were developed for production for the earlier years for which data were not available. For the pulp and paper sector,
23 production data published by the Lockwood-Post's Directory were used for years 1990 to 2001 and production data
24 published by the Food and Agriculture Organization were used for years 2002 through 2017. An extrapolation based on
25 U.S. real gross domestic product was used for years 1940 through 1964. For the food processing sector, production levels
26 were obtained or developed from the U.S. Department of Agriculture for the years 1990 through 2017 (ERG 2019). An
27 extrapolation based on U.S. population was used for the years 1940 through 1989.
28 In addition to production data for the pulp and paper and food processing sectors, the following inputs are
29 needed to use the FOD model for estimating CH4 generation from industrial waste landfills: 1) quantity of waste that is
30 disposed in industrial waste landfills (as a function of production), 2) CH4 generation potential (L0) from which a DOC value
31 can be calculated, and 3) the decay rate constant (k).
32 Research into waste generation and disposal in landfills for the pulp and paper sector indicated that the quantity
33 of waste landfilled was about 0.050 MT/MT of product compared to 0.046 MT/MT product for the food processing sector
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(RTI 2006). These factors were applied to estimates of annual production to estimate annual waste disposal in industrial
waste landfills. 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 2015a; 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 yn1, 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
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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 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 7: 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, a number of
studies note that gas escapes more readily through the side slopes of a landfill as compared to moving through the cover
thus complicating the correlation between oxidation and cover type or gas recovery.
Sites with landfill gas collection systems are generally designed and managed better to improve gas recovery.
More recent research (2006 to 2012) on landfill cover methane oxidation has relied on stable isotope techniques that may
provide a more reliable measure of oxidation. Results from this recent research consistently point to higher cover soil
methane oxidation rates than the IPCC (2006) default of 10 percent. A continued effort will be made to review the 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
125 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.
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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
titled "Review of Methane Flux and Soil Oxidation Data", December 7, 2012 (RTI 2012), and "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- 241. 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
C2: For landfills that have a geomembrane (synthetic) cover or other non-soil barrier meeting the definition of final 0.0
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 determine methane 0.10
flux
C4: For landfills that do not meet the conditions in C2 or C3 above and that do not have final cover, or 0.10
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 cover, or intermediate or 0.35
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 or intermediate or 0.25
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 or intermediate or 0.10
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 located 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 priorto any oxidation and is calculated as follows:
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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 8: 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-242. A different methodology is applied for 2005 to 2018
where directly reported net CH4 emissions to EPA's GHGRP plus the 9 percent scale-up factor were 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. The 9 percent scale-up factor was also
applied annually for 2005 to 2009.
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1 Table A-242: CH4 Emissions from Landfills (kt)
1990
1995
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
MSW CH4 Generation
Industrial CH4
Generation
8,214
484
9,140
537
10,270
618
10,477
625
10,669
629
636
639
643
648
653
656
657
659
661
662
663
664
665
666
MSW CH4 Recovered
(718)
(1,935)
(4,894)
(4,995)
(5,304)
MSW CH4 Oxidized
Industrial CH4Oxidized
(750)
(48)
(720)
(54)
(538)
(62)
(548)
(63)
(537)
(63)
(64)
(64)
(64)
(65)
(65)
(66)
(66)
(66)
(66)
(66)
(66)
(66)
(67)
(67)
MSW Net CH4 Emissions
6,746
6,484
5,394
5,496
5,395
4,681
4,593
4,506
4,419
4,331
4,372
4,023
4,070
3,924
3,907
3,855
3,724
3,709
3,823
Industrial Net CH4
Emissions
436
483
556
563
566
572
575
578
583
588
590
591
593
595
596
597
598
599
599
Net Emissions9
7,182
6,967
5,394
5,496
5,395
5,253
5,168
5,084
5,002
4,919
4,963
4,614
4,662
4,519
4,503
4,452
4,322
4,308
4,422
2 Not applicable due to methodology change.
3 Notes: MSW and Industrial Cm generation in Table A-242 represents emissions before oxidation. Totals may not sum exactly to the last significant figure due to rounding. Parentheses denote
4 negative values.
5 3 MSW Net Cm emissions for years 2010 to 2018 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
6 factor of 9 percent of each year's emissions from 2005 to 2018 is applied to account for landfills that do not report annual methane emissions to the GHGRP. Emissions for years 1990 to 2004 are
7 calculated by the FOD methodology.
8
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21 Emissions from the Pulp and Paper Industry." Memorandum to R. Nicholson (RTI).
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25 Landfills." Chemosphere, 26(l-4):387-400.
26 RTI (2018) Methodological changes to the scale-up factor used to estimate emissions from municipal solid waste landfills in the
27 Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA). In progress.
28 RTI (2017a) Methodological changes to the methane emissions from municipal solid waste landfills as reflected in the public
29 review draft of the 1990-2015 Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA).
30 March 31, 2017.
31 RTI (2017b) Options for revising the oxidation factor for non-reporting landfills for years 1990-2004 in the Inventory time series.
32 Memorandum prepared by K. Bronstein, M, McGrath, and K. Weitz for R. Schmeltz (EPA). August 13, 2017.
33 RTI (2015a) Investigate the potential to update DOC and k values for the Pulp and Paper industry in the US Solid Waste
34 Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA), December 4, 2015.
35 RTI (2015b) Review of Oxidation Studies and Associated Cover Depth in the Peer-Reviewed Literature. Memorandum prepared
36 by K. Bronstein, M. McGrath, and J. Coburn (RTI) for R. Schmeltz (EPA). June 17, 2015.
37 RTI (2013) Review of State of Garbage data used in the U.S. Non-C02 Greenhouse Gas Inventory for Landfills.
38 Memorandum prepared by K. Weitz and K. Bronstein (RTI) for R. Schmeltz (EPA). November 25, 2013.
39 RTI (2014) Analysis of DOC Values for Industrial Solid Waste for the Pulp and Paper Industry and the Food Industry.
40 Memorandum prepared by J. Coburn for R. Schmeltz (EPA), October 28, 2014.
41 RTI (2012) Review of Methane Flux and Soil Oxidation Data. Memorandum prepared by J. Coburn and K. Bronstein for R.
42 Schmeltz (EPA), December 12, 2012
43 RTI (2011) Updated Research on Methane Oxidation in Landfills. Memorandum prepared by K. Weitz (RTI) for R. Schmeltz (EPA),
44 January 14, 2011.
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1 RTI (2010) Revision of the flare correction factor to be used in the EPA Greenhouse Gas Inventory. Memorandum prepared by
2 K. Bronstein, K. Weitz, and J. Coburn for R. Schmeltz (EPA), January 8, 2010.
3 RTI (2009) GHG Inventory Improvement - Construction & Demolition Waste DOC and L0 Value. Memorandum prepared by J.
4 Coburn and K. Bronstein (RTI) for R. Schmeltz, April 15, 2010.
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6 September 5, 2006.
7 RTI (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from Landfills.
8 Memorandum prepared by M. Branscome and J. Coburn (RTI) to E. Scheehle (EPA), August 26, 2004.
9 Shin, D. (2014) Generation and Disposition of Municipal Solid Waste (MSW) in the United States - A National Survey.
10 Master of Science thesis submitted to the Department of Earth and Environmental Engineering Fu Foundation School of
11 Engineering and Applied Science, Columbia University. January 3, 2014. Available online at:
12 .
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14 56-72.
15 Skog, K. and G.A. Nicholson (2000) "Carbon Sequestration in Wood and Paper Products." USDA Forest Service Gen. Tech. Rep.
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18 Publication No. GR-LG 0075. March 1998.
19 Sonne, E. (2006) "Greenhouse Gas Emissions from Forestry Operations: A Life Cycle Assessment." J. Environ. Qua!. 35:1439-
20 1450.
21 Upton, B., R. Miner, M. Spinney, L.S. Heath (2008) "The Greenhouse Gas and Energy Impacts of Using Wood Instead of
22 Alternatives in Residential Construction in the United States." Biomass and Bioenergy, 32:1-10.
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24 .
26 Waste Business Journal (WBJ) (2016) Directory of Waste Processing & Disposal Sites 2016.
27
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ANNEX 4 IPCC Reference Approach for
Estimating CO2 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 fact that EIA energy statistics include
synthetic natural gas in coal and natural gas data. The third 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 fourth modification
adjusts for consumption of bunker fuels, which refer to quantities of fuels used for international transportation estimated
separately from U.S. totals. The fifth modification consists of the addition of U.S. Territories data that are typically excluded
from the national aggregate energy statistics. The territories include Puerto Rico, U.S. Virgin Islands, Guam, American
Samoa, Wake Island, and U.S. Pacific Islands. These data, as well as the production, import, export, and stock change
statistics, are presented in Table A-243.
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-243 for 2018), 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-244. The resulting fuel type-specific energy data for
2018 are provided in Table A-245.
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Step 2: Estimate Apparent Fuel Consumption
The next step of the IPCC Reference Approach is to estimate "apparent consumption" of fuels within the country.
This requires a balance of primary fuels produced, plus imports, minus exports, and adjusting for stock changes. In this
way, C enters an economy through energy production and imports (and decreases in fuel stocks) and is transferred out of
the country through exports (and increases in fuel stocks). Thus, apparent consumption of primary fuels (including crude
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-244.
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-245).126 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-247). This step
differs from the Sectoral Approach in that emissions from both fuel combustion and non-energy uses are accounted for in
the Reference Approach. 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 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-246.
126 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-246 for more specific source information.
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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-249 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 1.7 percent lower than
the Sectoral Approach for 2018. The greatest differences lie in lower estimates for petroleum and coal consumption for
the Reference Approach (3.4 percent and 1.7 percent, respectively) and higher estimates for natural gas consumption for
the Reference Approach (0.4 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. For example, the Reference Approach estimates apparent
consumption for crude oil. Crude oil is not typically consumed directly but refined into other products. As a
result, the United States does not focus on estimating the energy content of the various grades of crude oil,
but rather estimating the energy content of the various products resulting from crude oil refining. The United
States does not believe that estimating apparent consumption for crude oil, and the resulting energy content
of the crude oil, is the most reliable method for the United States to estimate its energy consumption. Other
differences in product definitions include using sector-specific coal statistics in the Sectoral Approach (i.e.,
residential, commercial, industrial coking, industrial other, and transportation coal), while the Reference
Approach characterizes coal by rank (i.e., anthracite, bituminous, etc.). Also, the liquefied petroleum gas
(LPG) statistics used in the bottom-up calculations are a composite category composed of natural gas liquids
(NGL) and LPG.
• Heat Equivalents. It can be difficult to obtain heat equivalents for certain fuel types, particularly for
categories such as "crude oil" where the key statistics are derived from thousands of producers in the United
States and abroad.
• Possible inconsistencies in U.S. Energy Data. The United States has not focused its energy data collection
efforts on obtaining the type of aggregated information used in the Reference Approach. Rather, the United
States believes that its emphasis on collection of detailed energy consumption data is a more accurate
methodology for the United States to obtain reliable energy data. Therefore, top-down statistics used in the
Reference Approach may not be as accurately collected as bottom-up statistics applied to the Sectoral
Approach.
• Balancing Item. The Reference Approach uses apparent consumption estimates while the Sectoral Approach
uses reported consumption estimates. While these numbers should be equal, there always seems to be a
slight difference that is often accounted for in energy statistics as a "balancing item."
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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-250 summarizes the differences between the two methods in estimated C emissions.
As mentioned above, for 2018, the Reference Approach resulted in a 1.7 percent lower estimate of energy
consumption in the United States than the Sectoral Approach. The resulting emissions estimate for the Reference
Approach was 1.3 percent lower. Estimates of natural gas emissions from the Reference Approach are higher (0.5 percent),
and coal and petroleum emission estimates are lower (2.3 percent and 2.1 percent, respectively) 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.
References
EIA (2019a). Annual Coal Report 2018, Energy Information Administration, U.S. Department of Energy. Washington, D.C.
DOE/EIA-0584(2018).
EIA (2019b). Monthly Energy Review, November 2019, Energy Information Administration, U.S. Department of Energy.
Washington, D.C. DOE/EIA-0035(2019/11).
EIA (2019c). Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, D.C.,
Volume I. DOE/EIA-0340.
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.
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). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas
Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T., and Tanabe K. (eds.). Published: IGES, Japan.
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.
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1 USGS (1998). CoalQual Database Version 2.0, U.S. Geological Survey.
A-468 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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Table A-243: 2018 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
1,896
[1]
[1]
[1]
Bituminous Coal
357,226
[1]
[1]
[1]
Sub-bituminous Coal
340,007
[1]
[1]
[1]
367
Lignite
57,038
[1]
[1]
[1]
4,854
Coke
117
1,151
(204)
Unspecified Coal
5,954
115,632
(36,910)
3,606
1,963
Gas Fuels (Million Cubic Feet)
Natural Gas
30,481,655
2,888,847
3,607,418
(312,251)
349,027
55,000
Liquid Fuels (Thousand Barrels)
Crude Oil
4,011,521
2,835,491
747,540
7,163
Nat Gas Liquids and Liquefied Refinery
Gases
1,594,813
71,953
584,596
(980)
4,005
Other Liquids
0
469,808
186,963
9,889
Motor Gasoline
36,772
16,343
320,755
1,246
236,769
34,263
Aviation Gasoline
72
0
(85)
Kerosene
616
1,560
475
411
Jet Fuel
45,352
81,343
281
198,850
8,044
Distillate Fuel
63,769
470,334
(5,476)
80
16,286
18,586
Residual Fuel
77,166
117,265
(1,063)
9,000
66,417
20,195
Naphtha for petrochemical feedstocks
6,445
0
545
Petroleum Coke
4,175
214,443
(620)
12,563
Other Oil for petrochemical feedstocks
1,410
0
(24)
1,240
Special Naphthas
4,688
0
269
Lubricants
15,838
38,504
1,934
172
Waxes
1,943
1,554
(179)
Asphalt/Road Oil
13,876
9,238
5,321
Still Gas
0
0
0
Misc. Products
97
356
(11)
13,144
[1] Included in Unspecified Coal
Note: Parentheses indicate negative values.
Sources: Solid and Gas Fuels: EIA (2019a and 2019b); Liquid Fuels: EIA (2019c).
A-469
-------
Table A-244: 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
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
22.57
23.89
17.14
12.87
20.42
24.29
20.42
28.16
12.87
Unspecified
25.00
25.97
20.86
128.14
25.14
Natural Gas (BTU/Cubic Foot)
1,036
1,025
1,009
1,036
1,036
1,036
Liquid Fuels (Million Btu/Barrel)
Crude Oil
Nat Gas Liquids and Liquefied Refinery
5.71
3.59
6.06
5.72
5.72
5.72
5.72
Gases
3.59
3.59
3.59
3.59
3.59
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 Fuel
5.67
5.67
5.67
5.77
5.67
Distillate Fuel
5.83
5.83
5.83
5.83
5.83
5.83
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 Gas
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, NGL and Motor Gasoline: EIA (2019b); Unspecified Solid Fuels: EIA (2011).
A-470 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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Table A-245: 2018 Apparent Consumption of Fossil Fuels (TBtu)
U.S.
Apparent
Fuel Category
Fuel Type
Production
Imports
Exports Stock Change
Adjustment
Bunkers
Territories
Consumption
Solid Fuels
Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
42.8
8,534.1
5,827.7
733.8
2.4
28.0
(4.2)
10.3
62.4
42.8
8,534.1
5,817.4
671.4
(21.4)
Unspecified
148.9
3,003.2
(770.0)
462.0
49.3
(2,497.1)
Gas Fuels
Natural Gas
31,579.0
2,961.1
3,639.9
(323.5)
361.6
57.0
30,919.1
Liquid Fuels
Crude Oil
22,889.7
17,191.6
4,276.7
41.0
35,763.7
Nat Gas Liquids and Liquefied Refinery Gases
5,727.0
258.4
2,099.3
(3.5)
14.4
3,904.0
Other Liquids
2,736.6
1,089.1
57.6
1,590.0
Motor Gasoline
185.8
82.6
1,621.1
6.3
173.2
(1,185.8)
Aviation Gasoline
0.4
(0.4)
(0.4)
1.2
Kerosene
3.5
8.8
2.7
2.3
(5.7)
Jet Fuel
257.1
461.2
1.6
1,146.8
45.6
(1,306.8)
Distillate Fuel
371.5
2,739.7
(31.9)
0.5
94.9
108.3
(2,323.4)
Residual Oil
485.1
737.2
(6.7)
56.6
417.6
127.0
(592.6)
Naphtha for petrochemical feedstocks
33.8
2.9
31.0
Petroleum Coke
25.2
1,291.8
(3.7)
75.7
(1,338.6)
Other Oil for petrochemical feedstocks
8.2
(0.1)
7.2
1.1
Special Naphthas
24.6
1.4
23.2
Lubricants
96.1
233.5
11.7
1.0
(148.2)
Waxes
10.8
8.6
(1.0)
3.1
Asphalt/Road Oil
92.1
61.3
35.3
(4.5)
Still Gas
Misc. Products
0.6
2.1
(0.1)
76.2
74.7
Total
75,520.1
24,790.4
21,301.0
(984.6)
1,036.4
1,659.2
654.3
77,952.7
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
A-471
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Table A-246: 2018 Potential C02 Emissions
Carbon Coefficients
Potential Emissions
Fuel Category Fuel Type
Apparent Consumption (QBtu)
(MMT Carbon/QBtu)
(MMTCO2 Eq.)
Solid Fuels Anthracite Coal
0.04
28.28
4.4
Bituminous Coal
8.53
25.41
795.0
Sub-bituminous Coal
5.82
26.49
565.1
Lignite
0.67
26.76
65.9
Coke
(0.02)
31.00
(2.4)
Unspecified
(2.50)
25.34
(232.0)
Gas Fuels Natural Gas
30.92
14.43
1,636.1
Liquid Fuels Crude Oil
35.76
20.31
2,662.7
Nat Gas Liquids and LRGs
3.90
16.81
240.6
Other Liquids
1.59
20.31
118.4
Motor Gasoline
(1.19)
19.46
(84.6)
Aviation Gasoline
+
18.86
0.1
Kerosene
(0.01)
19.96
(0.4)
Jet Fuel
(1.31)
19.70
(94.4)
Distillate Fuel
(2.32)
20.17
(171.8)
Residual Oil
(0.59)
20.48
(44.5)
Naphtha for petrochemical feedstocks
0.03
18.55
2.1
Petroleum Coke
(1.34)
27.85
(136.7)
Other Oil for petrochemical feedstocks
+
20.17
0.1
Special Naphthas
0.02
19.74
1.7
Lubricants
(0.15)
20.20
(11.0)
Waxes
+
19.80
0.2
Asphalt/Road Oil
H
20.55
(0.3)
Still Gas
0.00
18.20
0.0
Misc. Products
0.07
20.31
5.6
Total
5,319.9
+ Does not exceed 0.005 QBtu or 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Sources: C content coefficients by coal rank from USGS (1998), PSU (2010), Gunderson (2019), IGS (2019), ISGS (2019), and EIA (2019a); natural gas C content coefficients
from EPA (2010) and EIA (2019b); unspecified solid fuel and liquid fuel C content coefficients from EPA (2010).
Table A-247: 2018 Non-Energy Carbon Stored in Products
Carbon
Carbon
Consumption
Coefficients
Content
Carbon Stored
for Non-Energy
(MMT
(MMT
Fraction
(MMTCO2
Fuel Type
Use (TBtu)
Carbon/QBtu)
Carbon)
Sequestered
Eq.)
Coal
123.9
31.00
3.84
0.10
2.1
Natural Gas
304.1
14.43
4.39
0.65
10.5
A-472 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Asphalt & Road Oil
792.8
20.55
16.29
1.00
59.5
LPG
2,485.5
17.06
42.40
0.65
101.6
Lubricants
260.0
20.20
5.25
0.09
1.8
Pentanes Plus
104.8
19.10
2.00
0.65
4.8
Petrochemical Feedstocks
[1]
[1]
[1]
[1]
36.1
Petroleum Coke
0.0
27.85
0.00
0.30
0.0
Special Naphtha
86.5
19.74
1.71
0.65
4.1
Waxes/M isc.
[1]
[1]
[1]
[1]
0.7
Misc. U.S. Territories Petroleum
[1]
[1]
[1]
[1]
0.6
Total
221.7
[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.
Note: Totals may not sum due to independent rounding.
Table A-248: 2018 Reference Approach CP2 Emissions from Fossil Fuel Consumption (MMT CP2 Eq. unless otherwise noted)
Potential
Carbon
Net
Fraction
Total
Fuel Category
Emissions
Sequestered
Emissions
Oxidized
Emissions
Coal
1,196.0
2.1
1,193.9
100.0%
1,193.9
Petroleum
2,487.8
209.1
2,278.6
100.0%
2,278.6
Natural Gas
1,636.1
10.5
1,625.6
100.0%
1,625.6
Total
5,319.9
221.7
5,098.2
5,098.2
Note: Totals may not sum due to independent rounding.
Table A-249: 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
Sectoral
69,714
74,834
82,419
83,831
82,636
83,804
81,098
76,285
78,789
77,319
75,540
77,575
78,251
77,331
76,523
76,040
79,266
Coal
18,072
19,187
21,748
22,187
21,833
22,067
21,753
19,231
20,267
19,071
16,827
17,452
17,370
15,041
13,784
13,379
12,770
Natural Gas
19,168
22,170
23,392
22,282
21,960
23,371
23,594
23,193
24,312
24,679
25,832
26,560
27,141
27,932
28,152
27,760
30,789
Petroleum
32,474
33,477
37,279
39,361
38,843
38,366
35,751
33,861
34,210
33,569
32,881
33,563
33,740
34,359
34,587
34,901
35,707
Reference
(Apparent)
68,686
73,965
81,452
83,430
81,987
83,816
80,327
76,371
77,784
76,372
75,481
76,172
76,889
76,063
75,171
75,005
77,953
Coal
17,573
18,567
20,957
21,986
21,534
21,577
21,391
19,243
19,620
18,756
16,642
17,097
17,210
14,796
13,548
13,112
12,547
Natural Gas
19,276
22,274
23,484
22,349
22,029
23,441
23,666
23,277
24,409
24,778
25,924
26,637
27,225
28,011
28,236
27,880
30,919
Petroleum
31,837
33,124
37,010
39,095
38,424
38,799
35,270
33,851
33,755
32,838
32,915
32,438
32,454
33,255
33,388
34,013
34,486
Difference
-1.5%
-1.2%
-1.2%
-0.5%
-0.8%
+%
-1.0%
0.1%
-1.3%
-1.2%
-0.1%
-1.8%
-1.7%
-1.6%
-1.8%
-1.4%
-1.7%
Coal
-2.8%
-3.2%
-3.6%
-0.9%
-1.4%
-2.2%
-1.7%
0.1%
-3.2%
-1.7%
-1.1%
-2.0%
-0.9%
-1.6%
-1.7%
-2.0%
-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%
Petroleum
-2.0%
< -1.1%
-0.7%
-0.7%
-1.1%
1.1%
-1.3%
+%
-1.3%
-2.2%
0.1%
-3.4%
-3.8%
-3.2%
-3.5%
-2.5%
-3.4%
+ Does not exceed 0.05%.
3 Includes U.S. Territories. Does not include international bunkerfuels.
Note: Totals may not sum due to independent rounding.
A-473
-------
Table A-250: CP2 Emissions from Fossil Fuel Combustion by Estimating Approach (MMT CP2 Eq.)a
Approach
1990
1995
2000
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Sectoral
4,859
5,161
5,724
5,880
5,794
5,868
5,683
5,290
5,465
5,325
5,125
5,267
5,306
5,160
5,056
5,017
5,167
Coal
1,718
1,822
2,070
2,120
2,082
2,105
2,075
1,835
1,934
1,820
1,607
1,667
1,658
1,438
1,317
1,279
1,222
Natural Gas
1,006
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
1,457
1,617
Petroleum
2,135
2,175
2,426
2,588
2,555
2,532
2,364
2,233
2,251
2,206
2,158
2,203
2,222
2,256
2,262
2,281
2,329
Reference
(Apparent)
4,791
5,128
5,678
5,887
5,778
5,883
5,648
5,327
5,404
5,277
5,144
5,180
5,224
5,086
4,980
4,957
5,098
Coal
1,653
1,755
1,988
2,087
2,048
2,052
2,035
1,830
1,866
1,787
1,585
1,625
1,637
1,409
1,287
1,241
1,194
Natural Gas
1,013
1,171
1,233
1,176
1,160
1,235
1,247
1,227
1,285
1,305
1,365
1,402
1,431
1,471
1,482
1,464
1,626
Petroleum
2,125
2,203
2,457
2,624
2,569
2,596
2,366
2,270
2,253
2,185
2,194
2,153
2,156
2,206
2,211
2,252
2,279
Difference
-1.4%
-0.6%
-0.8%
0.1%
-0.3%
0.3%
-0.6%
0.7%
-1.1%
-0.9%
0.4%
-1.6%
-1.5%
-1.4%
-1.5%
-1.2%
-1.3%
Coal
-3.8%
-3.7%
-4.0%
-1.6%
-1.7%
-2.5%
-1.9%
-0.2%
-3.5%
-1.8%
-1.4%
-2.5%
-1.2%
-2.0%
-2.3%
-3.0%
-2.3%
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%
Petroleum
-0.5%
1.3%
1.3%
1.4%
0.6%
2.5%
0.1%
1.6%
0.1%
-1.0%
1.7%
-2.3%
-2.9%
-2.2%
-2.3%
-1.3%
-2.1%
3 Includes U.S. Territories. Does not include international bunkerfuels.
Note: Totals may not sum due to independent rounding.
A-474 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
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 anthropogenic127 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 United States 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 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 main constraint for estimating and includingthe 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.128
Specifically, where the notation key "NE," meaning not estimated, is used in the Common Reporting Format (CRF)129 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.
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 and its territories is
127The 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).
128 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 .
129 See .
A-475
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33
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36
37
38
39
40
41
42
43
provided within the sectoral chapters and category-specific estimate discussions and will be updated further in this Annex
in the next Inventory and future submissions as part of ongoing improvement efforts.
Source and Sink Categories Not Estimated
The following section is arranged by sector and source or sink category, providing additional information on the
reasons the category was not estimated. 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. Estimates for the insignificant sources have not been provided in prior inventory submissions.
Energy
CRF Category 1.A.3: CH4 and N20 Emissions from Transport and Mobile Fuel Combustion—Biomass
N20 emissions from biomass fuel use in domestic aviation (l.A.3.a) and N20 and CH4 emissions from biomass fuel
use in motorcycles (l.A.3.b.iv), railways (l.A.3.c), domestic navigation (l.A.3.d) and other transportation - non-
transportation mobile (l.A.3.e.ii) sources are not currently estimated.
Prior to 2011, no biobased jet fuel was assumed to be used for domestic aviation. Between 2011 and 2015, 22
airlines have 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.130 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, and as for jet fuel use in commercial aircraft, contributions of methane (CH4) emissions
are reported as zero. It was determined that annual non-C02 greenhouse gas emissions from the volume of fuel used
would be 16.4 kt C02 Eq. per year, so considered insignificant for the purposes of inventory reporting under the UNFCCC.
There are no readily available data sources to estimate the use of biofuel in rail, navigation and non-
transportation mobile sources. These sources represent about 30 percent of all diesel fuel use and about 5 percent of all
gasoline fuel use. An assumption can be made that these sources consume that same percentage of biofuels (30 percent
of all biodiesel and 5 percent of all ethanol use). Based on that assumption for biofuel use and applying the fossil fuel N20
and CH4 factors results in 287 kt C02 Eq. emissions per year, so considered insignificant for the purposes of inventory
reporting under the UNFCCC.
CRF Category l.A.3.d: C02 Emissions from Domestic Navigation—Gaseous Fuels
Emissions from gaseous fuels 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
characterize these uses currently.
CRF Category l.A.3.e.i: C02, CH4, and N20 Emissions from Liquid Fuels and CH4 and N20 Emissions from Gaseous
Fuels in Other Transportation—Pipeline Transport
Use of liquid fuels to power pipeline pumps is uncommon, but has occurred. Data 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 including information on liquid fuel used to power pipelines, it was 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 emissions of 342.6 kt C02.
C02 emissions from gaseous fuels used as pipeline transport fuel are estimated in 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 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.
130 See .
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CRF Category l.A.5.a: C02 Emissions from Medical Waste Incineration
Waste incineration of the municipal waste stream and hazardous waste incineration of fossil fuel-derived
materials are reported in two sections of the Energy chapter of the Inventory, specifically in the section on C02 emissions
from waste incineration, and in the calculation of emissions and storage from non-energy uses of fossil fuels.
In the calculation of emissions and storage from non-energy uses of fossil fuels, there is 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.
Furthermore, a conservative analysis was conducted based on a study of hospital/medical/infectious waste
incinerator (HMIWI) facilities in the United States131 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, so considered insignificant for the
purposes of inventory reporting under the UNFCCC.132
CRF Category l.A.5.a: CH4 and N20 Emissions from Stationary Fuel Combustion—Biomass in U.S. Territories
Data are not available to estimate emissions from 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.
Appling this same percentage to U.S. Territories fossil fuel combustion C02 emissions results in 74.8 kt C02 Eq. emissions
from biomass in U.S. Territories.
CRF Category l.B.l.a.l.i and l.B.l.a.l.ii: C02 from Fugitive Emissions from Underground Coal Mining Activities
and Post-Mining Activities
A preliminary analysis by EPA determined that C02 emissions for active underground coal mining activities are
negligible. The analysis was based on gas composition data from three active underground mines in three different
states.133 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, which is considered insignificant for
the purposes of inventory reporting under the UNFCCC. Future inventories may quantify these emissions, if it is deemed it
will not require a disproportionate amount of effort.
CRF Category l.B.l.a.l.iii: C02 from Fugitive Emissions from Abandoned Underground Coal Mines
A preliminary analysis by EPA determined that C02 emissions for abandoned underground coal mining activities
are negligible. The analysis was based on gas composition data from two abandoned underground mines in two different
states.134 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, which is considered insignificant for the purposes of inventory reporting under the
UNFCCC. Future inventories may quantify these emissions, if it is deemed it will not require a disproportionate amount of
effort.
131 RTI 2009. Updated Hospital/Medical/lnfectious Waste Incinerator (HMIWI) Inventory Database.
132 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 .
133 Ruby Canyon Engineering 2008. "Accounting for Carbon Dioxide Emissions in the Coal Emissions Inventory". Memorandum
from Ruby Canyon Engineering to EPA.
134 Ibid.
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CRF Category l.B.l.a.2.i and l.B.l.a.2.ii: C02 from Fugitive Emissions from Surface Coal Mining Activities and
Post-Mining Activities
A preliminary analysis by EPA determined that C02 emissions for active surface coal mining activities are
negligible. The analysis was based on gas composition data from three active underground mines in three different
states.135 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, which is considered insignificant for the purposes of inventory reporting under the UNFCCC. Future
inventories may quantify these emissions, if it is deemed it will not require a disproportionate amount of effort.
CRF Category l.B.2.a.5: C02 and CH4 from Fugitive Emissions from the Distribution of Oil
Emissions from the distribution of oil products are not currently estimated due to lack of available emission
factors.
Industrial Processes and Product Use
CRF Category 2.A.4.a: C02 Emissions from Process Uses of Carbonates-Ceramics
Data are not currently available to estimate emissions from this source. During the Expert Review process, EPA
sought expert solicitation on data for carbonate consumption in the ceramics industry but has yet to identify data sources
to apply Tier 1 methods to proxy emissions and assess significance.
CRF Category 2.A.4.C: C02 Emissions from Process Uses of Carbonates-Non-metallurgical Magnesium
Production
Data are not currently available to estimate emissions from this source. During the Expert Review process, EPA
sought expert solicitation on data for non-metallurgical magnesium production but has yet to identify data sources to
apply Tier 1 methods to proxy emissions and assess significance.
CRF Category 2.B.4.b: C02 and N20 Emissions from Glyoxal Production
Glyoxal production data are not readily available to estimate emissions from this source to apply Tier 1 methods.
EPA continues to conduct basic outreach to relevant trade associations and reviewing potential databases that can be
purchased and contain the necessary data. Progress on outreach will be included in next Inventory (i.e., 1990 through 2018
report).
CRF Category 2.B.4.C: C02 and N20 Emissions from Glyoxylic Acid Production
Data on national glyoxylic acid production data are currently not available to estimate emissions from this source
using Tier 1 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. Progress on outreach will be included
in next Inventory (i.e., 1990 through 2018 report).
CRF Category 2.B.5.b CH4 Emissions from Calcium Carbide
Data are not currently available to estimate CH4 emissions from this source. It is 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 US
(less than 5). In addition, during the 30-day Expert Review period, 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
135 Ibid.
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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.
CRF Category 2.B.8.d: C02 recovered from Petrochemical and Carbon Black Production
GHGRP has preliminary data for reporting years 2010 through 2016 on the amount of C02 recovery occurring at
petrochemical facilities from EO processes. Due to schedule and resource constraints, data has not been compiled, and
needs to be compiled and reviewed to better understand available data to estimate these recovered emissions.
CRF Category 2.C.l.c: CH4 Emissions from Direct Reduced Iron (DRI) Production
Data on fuel consumption used in the production of DRI are not readily available to apply the IPCC default Tier 1
CH4 emission factor or develop any proxy analysis. The emissions are assumed to be insignificant but this analysis will be
updated in future Inventory submissions to quantitatively justify emissions reporting as "not estimated." Neither the
emissions nor underlying activity data are reported to EPA through the facility-level mandatory Greenhouse Gas Reporting
Program (GHGRP).
CRF Category 2.E.2: Fluorinated Gas Emissions from Electronics Industry—TFT Flat Panel Displays
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 indicate these emissions are well below the significance
threshold.
CRF Category 2.G: SF6 and PFC Emissions from Other Product Use
Emissions of SF6 occur from particle accelerators and military applications, and emissions of PFCs and other F-
GHGs occur from military applications such as use of 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. Fugitive emissions of approximately 200 kt C02 Eq. of compounds
that are commonly used as fluorinated HTFs (HFEs and fully fluorinated compounds) were also reported. 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
CRF Category 3.A.4: CH4 Emissions from Enteric Fermentation—Camels
Enteric fermentation emissions from camels are not estimated because there is no significant population of
camels in the United States. Due to limited data availability (no population data are available from the Agricultural Census),
the estimates are based on use of IPCC defaults and population data from Baum, Doug (2010).136 Based on this paper, 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 2018. Given insignificance of these emissions in terms of the overall level and trend in national emissions,
there are no immediate improvement plans to include this emission category.
136 The status of the camel in the United States and America. Available online at:
.
A-479
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CRF Category 3.A.4: CH4 Emissions from Enteric Fermentation—Poultry
2 No IPCC method has been developed for determining enteric fermentation CH4 emissions from poultry. Based on
3 expert input, developing of a country-specific method would require a disproportionate amount of resources given the
4 magnitude of this source category.
5 CRF Category 3.B.1.4 and 3.B.2: CH4 and N20 Emissions from Manure Management—Camels
6 Manure management emissions from camels are not estimated because there is no significant population of
7 camels in the United States.137 Due to limited data availability and disproportionate effort to collect [time-series] data
8 (i.e., no population data is available from the Agricultural Census), this estimate is based on population data from Baum,
9 Doug (2010).138 Based on this paper, a Tier 1 estimate of manure management CH4 and N20 emissions from camels results
10 in values between approximately 0.14 kt C02 Eq. per year from 1990 to 2016. Given insignificance of these emissions in
11 terms of the overall level and trend in national emissions, there are no immediate improvement plans to include this
12 emission category.
13 CRF Category 3.F.1.4 and 3.F.4: CH4 and N20 Emissions from Field Burning of Agricultural Residues—Rye and
14 Sugarcane
15 Remote sensing data were used in combination with a resource survey to estimate non-C02 emissions from
16 agricultural residue burning. These data did not allow identification of burning of sugarcane or rye. This potential gap in
17 the activity data will be re-evaluated in a future inventory using other datasets.
is Land Use, Land-Use Change, and Forestry
19 CRF Category 4.A(II): Emissions from Rewetted Organic and Mineral Soils in Forest Land
20 Emissions from this source will be estimated in future Inventories when data necessary for classifying the area of
21 rewetted organic and mineral soils become available. Work is underway to assemble these data in collaboration with the
22 U.S. Geological Survey, which has developed a surface water layer remote sensing product spanning the inventory time
23 series and that may be useful in identifying areas where organic and mineral soils have been drained and then rewetted.
24 CRF Category 4.A(III): Direct N20 Emissions from N mineralization/immobilization in Forest Land Remaining
25 Forest Land
26 Direct N20 emissions from N mineralization/immobilization associated with loss or gain of soil organic matter
27 resulting from change of land use or management of mineral soils will be estimated in a future Inventory. They are not
28 estimated currently because resources have limited EPA's ability to use the available data on soil carbon stock changes on
29 forest lands to estimate these emissions.
30 CRF Category 4.B(II): C02, CH4, and N20 Emissions and Removals from Drainage and Rewetting and Other
31 Management of Organic and Mineral Soils in Cropland
32 Emissions of C02 and CH4 from rewetting on mineral or organic cropland soils are not currently estimated due to
33 lack of activity data on rewetting, except for CH4 emissions from drainage and rewetting for rice cultivation. Work is
34 underway to assemble these data in collaboration with the U.S. Geological Survey, which has developed a surface water
35 layer remote sensing product spanning the inventory time series that may be useful in identifying areas where organic and
36 mineral soils have been drained and then rewetted.
137 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 .
138 The status of the camel in the United States and America. Available online at:
.
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CRF Category 4.B.1: Carbon Stock Change in Living Biomass in Cropland Remaining Cropland
Carbon stock change in living biomass is not estimated because data are currently not available. The impact of
management on 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.
CRF Category 4.B.1(V): C02 Emissions from Biomass Burning in Cropland Remaining Cropland— Wildfires and
Controlled Burning
The C02 emissions for from controlled burning of crop biomass are not estimated 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. Emissions from wildfires are not estimated because the activity data on fire area and fuel load,
particularly for perennial vegetation, are not available.
CRF Category 4.B.2: Carbon Stock Change in Living Biomass in Grassland Converted to Cropland
Carbon stock change in living biomass is not estimated because data are currently not available. Similar to CRF
Category 4.B.l, the changes in biomass C are under investigation for agroforestry and will be included in a future Inventory
if the necessary activity data can be compiled.
CRF Category 4.B.2(V): C02, CH4, and N20 Emissions from Biomass Burning in Land Converted to Cropland—
Wildfires and Controlled Burning
Methane and N20 emissions from controlled burning of crop biomass on land converted to cropland are included
under 3.F Field Burning of Agricultural Residues. Carbon dioxide emissions from the burning of crop biomass are not
estimated as they are part of the annual cycle of C and not considered net emissions. Emissions from wildfires are not
estimated because the activity data on fire area and fuel, particularly for perennial vegetation, are not available.
CRF Category 4.C(II): C02, CH4, and N20 Emissions and Removals from Drainage and Rewetting and Other
Management of Organic and Mineral Soils in Grassland
Emissions of C02 and CH4 from rewetting on mineral or organic Grassland soils are not currently estimated due
to lack of activity data on rewetting. Work is underway to assemble these data in collaboration with the U.S. Geological
Survey, which has developed a surface water layer remote sensing product spanning the inventory time series that may
be useful in identifying areas where organic and mineral soils have been drained and then rewetted.
CRF Category 4.C.1: Carbon Stock Change in Living Biomass in Grassland Remaining Grassland
Carbon stock change in living biomass is not estimated because data are currently not available. Woodlands occur
in grasslands because these woodland areas do not meet the definition of forest lands. A method is under development
to estimate the C stock changes for these areas, particularly in the Western United States, and will be include in a future
Inventory (see Planned Improvements of Section 6.6 of Grassland Remaining Grassland and Box 6-6).
CRF Category 4.C.l(V) and 4.C.2(V): C02,CH4, and N20 Emissions from Biomass Burning in Grassland Remaining
Grassland and Land Converted to Grassland—Wildfires and Controlled Burning
Due to lack of data on perennial biomass on grasslands, carbon dioxide emissions from biomass burning in
Grassland Remaining Grassland are not estimated with the Tier 1 method since only annual, not perennial, vegetation is
included in the estimate. Methane and N20 emissions from wildfires are reported under 4.C.1 4(V) Grassland Remaining
Grassland Biomass Burning.
CRF Category 4.D(II): C02, CH4, and N20 Emissions and Removals from Drainage and Rewetting and Other
Management of Organic and Mineral Soils in Wetlands—Flooded Lands and Peat Extraction Lands
Data are currently not available to estimate emissions.
A-481
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1 CRF Category 4.D.l(V) and 4.D.2(V): C02, CH4, and N20 Emissions from Biomass Burning in Wetlands Remaining
2 Wetlands and Land Converted to Wetlands —Wildfires and Controlled Burning
3 Data are not currently available to estimate emissions.
4 CRF Category 4.D.1.2: Carbon Stock Change in Flooded Land Remaining Flooded Land
5 Carbon stock changes in flooded land remaining flooded land are not estimated due to lack of activity data, other
6 than for peatlands and coastal wetlands. See the Wetlands Chapter in the Inventory Report.
7 CRF Category 4.E: C02, CH4, and N20 Emissions from Biomass Burning in Settlements
8 Data are currently not available to estimate emissions.
9 CRF Category 4.E.1: Direct N20 Emissions from Nitrogen Mineralization/Immobilization in Settlements
10 Remaining Settlements
11 Activity data not available on N20 emissions from nitrogen mineralization/immobilization in settlements as a
12 result of soil organic carbon stock losses from land use conversion and management.
13 CRF Category 4.E.2: Direct N20 Emissions from Nitrogen Mineralization/Immobilization in Land Converted to
14 Settlements
15 Data are not available on N20 emissions from nitrogen mineralization/immobilization in Land Converted to
16 Settlements as a result of soil organic carbon stock losses from land use conversion and management.
17 CRF Category 4.F: C02, CH4, and N20 Emissions from Biomass Burning in Other Land
18 Data are currently not available to estimate emissions.
19 Waste
20 CRF Category 5.A.l.a: CH4 and N20 Emissions from Solid Waste Disposal/Managed Waste Disposal Sites-
21 Anaerobic
22 The amount of CH4 flared and the amount of CH4 for energy recovery is not estimated for the years 2005
23 through 2018 in the time series. The amount of CH4 flared and recovered for 2005 and each subsequent Inventory year,
24 i.e., through 2018, is included in the net CH4 emissions estimates. A methodological change was made for 2005 to the
25 current Inventory year to use the directly reported net CH4 emissions from the EPA's GHGRP versus estimate CH4
26 generation and recovery. See the Methodology explanation in Section 7.1.
27 CRF Category 5.B.l.a: CH4 and N20 Emissions from Biological Treatment of Solid Waste/Composting -
28 Municipal Solid Waste
29 The amount of CH4 flared at composting sites is not estimated due to a lack of activity data.
30 CRF Category 5.B.2.a: CH4 and N20 Emissions from Biological Treatment of Solid Waste - Anaerobic Digestion
31 at Biogas Facilities - Municipal Solid Waste and Other
32 Methane and N20 emissions from anaerobic digestion of municipal solid waste at biogas facilities are not
33 currently estimated. Basic research was initiated that indicate some activity for this category is occurring in the United
34 States, but EPA needs to conduct further research on available multi-year activity data to create a time series. Initial
35 data for 2015 indicates emissions of 7.8 kt of CH4. Pending additional resources, EPA will continue researching
36 availability of activity data and feasibility to report these emissions and report on progress in future Inventory
37 submissions.
38 CRF Category 5.D.2: N20 Emissions from Wastewater Treatment and Discharge—Industrial Wastewater
39 Nitrous oxide emissions from stand-alone industrial wastewater treatment are not currently estimated. Per
40 section 6.3.4 of 2006IPCC Guidelines'. "The methodology does not include N20 emissions from industrial sources, except
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1 for industrial wastewater that is co-discharged with domestic wastewater into the sewer system. The N20 emissions
2 from industrial sources are believed to be insignificant compared to emissions from domestic wastewater." EPA may
3 undertake voluntary efforts to review the 2019 Refinement to the 2006 IPCC Guidelines which contain a methodology for
4 estimating N20 emissions from Industrial Wastewater for incorporation in a future submissions. This improvement will
5 be prioritized with other improvements to make best use of available data and resources.
6
7 Assessment of Aggregated Not Estimated Emission Sources and Sinks
8 A summary of these exclusions, including the estimated level of emissions where feasible, is included in Table A-251.
9 Collectively, per paragraph 37(b) of the UNFCCC Reporting Guidelines noted above, it is likely that these exclusions
10 should not exceed 0.1 percent of gross emissions, or 6,677.8 MMT C02 Eq. (667,779 kt C02 Eq).
A-483
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Table A-251: Summary of Sources and Sinks Not Included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
CRF Category
Source/Sink Category
Sub-Category
Gas(es)
Estimated
Reason for Exclusion
Number
2018
Emissions
(kt C02 Eq.)
Energy
l.A Fossil Fuel Combustion
l.A.3.a
Transport
Domestic Aviation-Biomass
N20
16.4
Data availabil
ty
l.A.3.b.iv
Transport
Motorcycles-Biomass
CH4 and N20
NQ
Data availabil
ty
1.A.3.C
Transport
Railways-Biomass
CH4 and N20
NQ
Data availabil
ty
l.A.3.d
Transport
Domestic Navigation-Biomass
CH4 and N20
NQ
Data availabil
ty
l.A.3.d
Transport
Domestic Navigation—Gaseous Fuels
C02
NQ
Data availabil
ty
l.A.3.e.i
Other Transportation
Pipeline Transport—Liquid Fuels
CO2, CH4 and N20
343
Data availabil
ty
l.A.3.e.i
Other Transportation
Pipeline Transport—Gaseous Fuels
CO2, CH4 and N20
180
Data availabil
ty
l.A.3.e.ii
Other Transportation
Non-Transportation Mobile-Biomass
CH4 and N20
NQ
Data availabil
ty
l.A.5.a
Incineration of Waste
Medical Waste Incineration
C02
333
Data availabil
ty
l.A.5.a
Stationary Fuel
Combustion
Biomass in U.S. Territories
CH4 and N20
75
Data availabil
ty
l.B Fugitive Emissions from Fuels
l.B.l.a.l.i,
Underground Mines
Fugitive Emissions from Underground Coal
C02
177
Emissions negligible
l.B.l.a.l.ii
Mining Activities and Post-Mining Activities
l.B.l.a.l.ii
Abandoned Underground
Fugitive Emissions from Abandoned
C02
94
Emissions negligible
i
Coal Mines
Underground Coal Mines
l.B.l.a.2
Surface Mines
Fugitive Emissions from Surface Coal Mining
C02
34
Emissions negligible
Activities and Post-Mining Activities
l.B.2.a.5
Oil
Distribution of Oil Products
C02and CH4
NQ
Lack of emission
factor data
Industrial Processes and Product Use
2.A Mineral Industry
2.A.4.a
Other Process Uses of
Ceramics
C02
NQA
Data availability
Carbonates
2.A.4.C
Other Process Uses of
Non-metallurgical Magnesium Production
C02
NQ
Data availability
Carbonates
2.B. Chemical Industry
2.B.4.b
Glyoxal Production
C02 and N20
NQ
Data availability
2.B.4.C
Glyoxylic Acid Production
C02 and N20
NQ
Data availability
2.C. Metal Industry
2.C.1.C
Iron and Steel Production
Direct Reduced Iron (DRI) Production
ch4
NQ
Data availability
2.E Electronics Industry
A-484 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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2.E.2 Fluorinated Gas Emissions TFT Flat Panel Displays HFCs, PFCs, SF6, NA Data availability
from Electronics Industry and NF3
2.G Other
2.G.2
Other Product
Manufacture and Use
SF6 and PFCs from Other Product Use
SF6
900
Data availability
Agriculture
3.A Livestock
3.A.4 Enteric Fermentation
3.A.4
Enteric Fermentation
3.B.1.4, Manure Management
3.B.2
3.F Field Burning of Agricultural Residues
3.F.1.4, Field Burning of
3.F.4 Agricultural Residues
Camels
Poultry
Camels
Rye, Sugarcane
CH4
ch4
CH4 and N20
CH4 and N20
No method
+(0.14)
NA
No significant camel
population in U.S.
2006 IPCC Guidelines
do not provide a
method.
No significant camel
population in U.S.
Insignificant burning
of rye along with data
availability issues for
both rye and
sugarcane
Land Use, Land-Use Change, and Forestry
4.A Forest Land
4.A(II)
4.A.1
4.B Cropland
4. B(l I)
4.B.1
4.B.1(V)
Forest Land
Forest Land Remaining
Forest Land
Cropland
Cropland Remaining
Cropland
Cropland Remaining
Cropland
Emissions and Removals from Drainage and
Rewetting and Other Management of
Organic and Mineral Soils
N mineralization/immobilization
Emissions and Removals from Drainage and
Rewetting and Other Management of
Organic and Mineral Soils
Carbon Stock Change in Living Biomass
Biomass Burning—Controlled Burning
C02, CH4, and N20 NQ Data availability
N20
NQ Data availability
C02, CH4, and N20 NQ Data availability
C02
C02
NQ Data availability
NQ Data availability
A-485
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4.B.1(V)
4.B.2
4.B.2(V)
4.C Grassland
4.C(II)
4.C.1
4.C.1(V)
4.C.2(V)
4.D Wetlands
4.D(II)
4.D.1(V)
4.D.1.2
4.D.2(V)
4.E Settlements
4. E (V)
4.E.1
4.E.1
4.E.2
4.F Other Land
4-F(V)
Cropland Remaining
Cropland
Grassland Converted to
Cropland
Land Converted to
Cropland
Grassland
Grassland Remaining
Grassland
Grassland Remaining
Grassland
Land Converted to
Grassland
Wetlands—Flooded Lands
and Peat Extraction Lands
Wetlands Remaining
Wetlands
Flooded Land Remaining
Flooded Land
Land Converted to
Wetlands
Settlements
Settlements
Settlements Remaining
Settlements
Land Converted to
Settlements
Biomass Burning
Biomass Burning—Wildfires
Carbon Stock Change in Living Biomass
Biomass Burning—Wildfires and Controlled
Burning
Emissions and Removals from Drainage and
Rewetting and Other Management of
Organic and Mineral Soils
Carbon Stock Change in Living Biomass
Biomass Burning: Controlled Burning,
Wildfires
Biomass Burning: Controlled Burning,
Wildfires
Emissions and Removals from Drainage and
Rewetting and Other Management of
Organic and Mineral Soils
Biomass Burning: Controlled Burning,
Wildfires
Carbon Stock Change
Biomass Burning: Controlled Burning,
Wildfires
Biomass Burning Settlements
Settlements Remaining Settlements
Direct N20 Emissions from N
Mineralization/Immobilization (Mineral Soils)
Direct N20 Emissions from N
Mineralization/Immobilization
Other Land
C02, CH4, and N20
C02 NQ
C02, CH4, and N20 NQ
C02, CH4, and N20 NQ
C02 NQ
C02 NQ
C02 NQ
C02, CH4, and N20 NQ
C02, CH4, and N20 NQ
C02 NQ
C02, CH4, and N20 NQ
C02, CH4, and N20 NAQ
CH4 NQ
N20 NQ
N20 NQ
C02, CH4, and N2Q NQ
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Data availability
Waste
5.D Wastewater Treatment
5.B.2.a Biological Treatment of
and b Solid Waste
Anaerobic Digestion at Biogas Facilities-
Municipal Solid Waste and Other
CH4 and N20
Data availability
A-486 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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N20 No method 2006IPCC Guidelines
do not provide a
method.
1 NQ (Quantified estimate not available due to insufficient data)
2 - Not Applicable
3 + Less than 0.5 kt C02 Eq.
5.D.2 Wastewater Treatment Industrial Wastewater
and Discharge
A-487
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25
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29
30
31
32
33
34
35
36
37
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:
MMTC02 Eq. =(ktofgas)x(GWP)xf MMT
where,
l,000kt
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 1335 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 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-252). 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
, . 139
horizon...
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
139 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-488 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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24
it is difficult to quantify their global radiative forcing impacts. GWP values are generally not attributed to these gases that
are short-lived and spatially inhomogeneous in the atmosphere.
Table A-252: IPCCAR4 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-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
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
NA
0.003
NA
NA
c-C5Fse
NA
1.97
NA
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
CHsF
3.7
150
490
45
CH2FCF3
14
1,430
3,400
420
c2h2f4
10.6
1,000
2,900
310
sf6
3,200
22,800
16,300
32,600
nf3
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.
cThe 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.
Source: IPCC (2007)
e See Table A-l of 40 (CFR 98).
Table A-253 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-253). The effects of
these compounds on radiative forcing are not addressed in this report.
Table A-253: 100-year Direct Global Warming Potentials for Select Ozone Depleting Substances
Gas Direct GWP
CFC-11 4,750
CFC-12 10,900
A-489
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3
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5
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7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
CFC-113
6,130
HCFC-22
1,810
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.
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-254 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. 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 2018). As such, GWP comparisons throughout this chapter are presented relative
to AR4 GWPs.
A-490 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 Table A-254: 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
feedbacks
feedbacks'1
Gas
SAR
AR4
AR5
SAR
AR4
AR5a
b
SAR
SAR (%)
AR5a
AR5 (%)
b
(%)
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-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
NA
1,030
858
1,032
NA
NA
(172)
-17%
2
+%
HFC-365mfc
NA
6.6
8.7
NA
794
804
966
NA
NA
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%
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%
2 + Does not exceed 0.05 percent.
3 NC (No Change)
4 NA (Not Applicable)
5 a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report.
6 b The GWP values presented here from the ARB report include climate-carbon feedbacks for the non-C02 gases in order to be consistent with the approach used in calculating the C02 lifetime.
7 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
8 increase will only slowly decrease over a number of years, and a small portion of the increase will remain for many centuries or more.
9 d No single lifetime can be determined for C02 (see IPCC 2007).
A-491
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1 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 ARB reported separate
2 values for fossil versus biogenic methane in order to account for the C02 oxidation product.
3 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
4 time.
5 Note: Parentheses indicate negative values. Source: IPCC (2013), IPCC (2007), IPCC (1996).
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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 overtime. To summarize,
Table A-255 shows the overall trend in U.S. greenhouse gas emissions, by gas, from 1990 through 2018 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 2018.
Table A-255: Effects on U.S. Greenhouse Gas Emissions Using SAR, AR4, and AR5 GWP values (MMT CP2 Eq.)
Difference in Emissions Between 1990
Gas
and 2018 (Relative to 1990)
Revisions to Annual Emission Estimates (Relative to AR4)
SAR
AR5a
AR5b
SAR
AR5a
AR5b
SAR
AR4
AR5a
AR5b
1990
2018
C02
300.9
300.9
300.9
300.9
NC
NC
NC
NC
NC
NC
ch4
(117.5)
(139.9)
(156.7)
(190.2)
(123.9)
92.9
278.8
(101.5)
76.2
228.5
n2o
(0.0)
(0.0)
(0.0)
(0.0)
17.5
(48.1)
NC
17.5
(48.1)
NC
HFCs, PFCs, SF6,
and NFs
68.1
79.7
78.4
96.8
(11.9)
(9.0)
1.2
(23.6)
(10.4)
18.3
Total
251.5
240.7
222.6
207.5
(118.3)
35.8
280.1
(107.6)
17.7
246.8
Percent Change
4.0%
3.7%
3.4%
3.1%
-1.8%
0.6%
4.4%
-1.6%
0.3%
3.7%
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 ARB 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, the ARB reported separate values for fossil versus biogenic methane
in order to account for the C02 oxidation product.
Notes: Totals may not sum due to independent rounding. Excludes sinks. Parentheses indicate negative values.
When the GWP values from the SAR are applied to the emission estimates presented in this report, total
emissions for the year 2018 are 6,570.2 MMT C02 Eq., as compared to the official emission estimate of 6,677.8 MMT C02
Eq. using AR4 GWP values (i.e., the use of SAR GWPs results in a 1.6 percent decrease relative to emissions estimated using
AR4 GWPs). Table A-256 provides a detailed summary of U.S. greenhouse gas emissions and sinks for 1990 through 2018,
using the GWP values from the SAR. The percent change in emissions for a given gas resulting from using different GWPs
is equal to the percent change in the GWP; however, in cases where emissions of multiple gases are combined, as with
HFCs or PFCs, the percent change will be a function of the relative quantity of the individual gases. Table A-257 summarizes
the resulting change in emissions from using SAR GWP values relative to emissions using AR4 values for 1990 through
2018, including the percent change for 2018.
Table A-256: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks using the SAR GWP values (MMT CP2 Eq.)
Gas/Source
1990
2005
2014
2015
2016
2017
2018
co2
5,128.3
6,131.9
5,562.9
5,413.7
5,293.5
5,256.0
5,429.2
Fossil Fuel Combustion
4,740.0
5,740.7
5,185.9
5,033.0
4,942.9
4,893.9
5,033.3
Transportation
1,469.1
1,856.1
1,713.7
1,725.3
1,765.3
1,787.4
1,798.2
Electric Power Sector
1,820.0
2,400.0
2,037.1
1,900.6
1,808.9
1,732.0
1,752.8
Industrial
857.0
850.1
813.6
802.0
801.7
806.0
846.7
Residential
338.2
357.9
347.1
318.1
293.2
294.2
335.9
Commercial
228.2
226.9
233.0
245.6
232.4
232.9
258.3
U.S. Territories
27.S
49.7
41.4
41.4
41.4
41.4
41.4
Non-Energy Use of Fuels
119.5
139.7
120.0
127.0
113.7
123.1
134.5
Iron and Steel Production &
Metallurgical Coke Production
104.7
70.1
58.2
47.9
43.6
40.8
42.7
Cement Production
33.5
46.2
39.4
39.9
39.4
40.3
40.3
Petroleum Systems
9.6
12.2
30.5
32.6
23.0
24.5
39.4
Natural Gas Systems
32.2
25.3
29.6
29.3
29.9
30.4
34.9
Petrochemical Production
21.6
27.4
26.3
28.1
28.3
28.9
29.4
Lime Production
11.7
14.6
14.2
13.3
12.9
13.1
13.9
Ammonia Production
13.0
9.2
9.4
10.6
10.8
13.2
13.5
Incineration of Waste
8.0
12.5
10.4
10.8
10.9
11.1
11.1
A-493
-------
Other Process Uses of Carbonates
6.3
7.6
13.0
12.2
11.0
10.1
9.4
Urea Fertilization
2.0
3.1
3.9
4.1
4.0
4.5
4.6
Carbon Dioxide Consumption
1.5
1.4
4.5
4.5
4.5
4.5
4.5
Urea Consumption for Non-
Agricultural Purposes
3.8
3.7
1.8
4.6
5.1
3.8
3.6
Liming
4.7
4.3
3.6
3.7
3.1
3.1
3.1
Ferroalloy Production
2.2
1.4
1.9
2.0
1.8
2.0
2.1
Soda Ash Production
1.4
1.7
1.7
1.7
1.7
1.8
1.7
Titanium Dioxide Production
1.2
1.8
1.7
1.6
1.7
1.7
1.6
Aluminum Production
6.8
4.1
2.8
2.8
1.3
1.2
1.5
Glass Production
1.5
1.9
1.3
1.3
1.2
1.3
1.3
Zinc Production
0.6
1.0
1.0
0.9
0.9
1.0
1.0
Phosphoric Acid Production
1.5
1.3
1.0
1.0
1.0
1.0
0.9
Lead Production
0.5
0.6
0.5
0.5
0.4
0.5
0.6
Carbide Production and
Consumption
0.4
0.2
0.2
0.2
0.2
0.2
0.2
Abandoned Oil and Gas Wells
+
+
+
+
+
+
+
Magnesium Production and
Processing
+
+
+
+
+
+
+
Wood Biomass, Ethanol, and
Biodiesel Consumptiona
219.4
230.7
323.2
317.7
317.2
322.2
328.9
International Bunker Fuelsb
103.5
113.1
103.4
110.9
116.6
120.1
122.1
CH4c
650.5
570.9
536.8
536.3
527.7
529.4
533.1
Enteric Fermentation
137.9
141.8
137.9
139.9
144.3
147.3
149.2
Natural Gas Systems
153.9
132.8
118.5
119.2
117.5
116.8
117.3
Landfills
150.8
110.3
94.6
93.5
90.8
90.5
92.9
Manure Management
31.2
43.3
45.6
48.6
50.1
50.3
51.8
Coal Mining
81.1
53.9
54.2
51.4
45.2
46.0
44.3
Petroleum Systems
38.8
32.6
36.5
34.1
32.7
32.6
30.7
Wastewater Treatment
12.9
13.0
12.0
12.2
12.1
11.9
11.9
Rice Cultivation
13.4
15.1
12.9
13.6
11.3
10.7
11.2
Stationary Combustion
7.2
6.6
7.5
7.1
6.7
6.6
7.3
Abandoned Oil and Gas Wells
5.5
5.8
6.0
6.0
6.1
5.9
5.9
Abandoned Underground Coal
Mines
6.0
5.5
5.3
5.4
5.6
5.4
5.2
Mobile Combustion
10.9
8.0
3.5
3.1
2.9
2.8
2.6
Composting
0.3
1.6
1.8
1.8
1.9
2.1
2.1
Field Burning of Agricultural
Residues
0.3
0.3
0.3
0.3
0.3
0.3
0.3
Petrochemical Production
0.2
0.1
0.1
0.2
0.2
0.2
0.3
Ferroalloy Production
+
+
+
+
+
+
+
Carbide Production and
Consumption
+
+
+
+
+
+
+
Iron and Steel Production &
Metallurgical Coke Production
+
+
+
+
+
+
+
Incineration of Waste
+
+
+
+
+
+
+
International Bunker Fuelsb
0.1
0.1
0.1
0.1
0.1
0.1
0.1
N2Oc
452.1
450.1
467.4
461.9
443.5
438.2
452.1
Agricultural Soil Management
328.6
325.7
363.3
362.1
343.1
340.6
351.8
Stationary Combustion
26.1
35.7
34.3
31.8
31.3
29.8
29.6
Manure Management
14.6
17.0
18.0
18.2
18.8
19.4
20.2
Mobile Combustion
43.7
38.8
20.5
19.1
18.1
16.9
15.8
Adipic Acid Production
15.8
7.4
5.7
4.4
7.3
7.7
10.7
Nitric Acid Production
12.6
11.8
11.4
12.0
10.5
9.7
9.7
Wastewater Treatment
3.5
4.6
5.0
5.0
5.1
5.2
5.2
A-494 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
N20 from Product Uses
4.4
4.4
4.4
4.4
4.4
4.4
4.4
Composting
0.4
1.7
1.9
2.0
2.1
2.3
2.3
Caprolactam, Glyoxal, and Glyoxylic
Acid Production
1.7
2.2
2.1
2.1
2.1
1.5
1.5
Incineration of Waste
0.5
0.4
0.3
0.3
0.3
0.3
0.3
Electronics Industry
+
0.1
0.2
0.2
0.2
0.3
0.3
Field Burning of Agricultural
Residues
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Petroleum Systems
+
+
+
+
+
+
0.1
Natural Gas Systems
+
+
+
+
+
+
+
International Bunker Fuelsb
0.9
1.0
1.0
1.0
1.1
1.1
1.1
HFCs
36.8
111.3
141.5
144.8
144.7
146.3
145.8
Substitution of Ozone Depleting
Substancesd
0.3
95.3
137.2
141.1
142.1
141.8
142.8
HCFC-22 Production
36.4
15.8
4.0
3.4
2.2
4.1
2.6
Electronics Industry
0.2
0.2
0.2
0.3
0.3
0.3
0.3
Magnesium Production and
Processing
0.0
0.0
0.1
0.1
0.1
0.1
0.1
PFCs
20.6
5.6
4.7
4.2
3.6
3.3
3.9
Electronics Industry
2.2
2.6
2.5
2.5
2.4
2.4
2.5
Aluminum Production
18.4
3.0
2.1
1.7
1.1
0.9
1.3
Substitution of Ozone Depleting
Substancesd
0.0
+
+
+
+
+
+
sf6
30.2
12.4
6.8
5.7
6.3
6.2
6.2
Electrical Transmission and
Distribution
24.3
8.8
5.0
3.9
4.3
4.3
4.3
Magnesium Production and
Processing
5.4
2.9
1.0
1.0
1.2
1.1
1.2
Electronics Industry
0.5
0.7
0.8
0.8
0.9
0.8
0.8
nf3
NA
NA
NA
NA
NA
NA
NA
Electronics Industry
NA
NA
NA
NA
NA
NA
NA
Unspecified Mix of HFCs, NF3, PFCs,
and SF6
NA
NA
NA
NA
NA
NA
NA
Electronics Industry
NA
NA
NA
NA
NA
NA
NA
Total
6,318.7
7,282.0
6,720.0
6,566.7
6,419.4
6,379.4
6,570.2
LULUCF Emissionsc
6.8
15.2
15.3
25.3
11.8
24.1
24.1
LULUCF CH4 Emissions
3.7
7.4
8.0
13.6
6.1
12.8
12.8
LULUCF N20 Emissions
3.1
7.8
7.3
11.7
5.7
11.3
11.3
LULUCF Carbon Stock Change8
(860.7)
(831.0)
(739.6)
(802.9)
(801.7)
(789.9)
(799.9)
LULUCF Sector Net Total'
(854.0)
(815.8)
(724.3)
(777.7)
(789.9)
(765.9)
(775.7)
Net Emissions (Sources and Sinks)
5,464.8
6,466.2
5,995.8
5,789.0
5,629.5
5,613.5
5,794.5
Notes: Total emissions presented without LULUCF. Net emissions presented with LULUCF. Totals may not sum due to independent
rounding. Parentheses indicate net sequestration.
+ Does not exceed 0.05 MMT C02 Eq.
NA (Not Applicable)
a Emissions from Wood Biomass and Biofuel Consumption 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.
b Emissions from International Bunker Fuels are not included in totals.
c LULUCF emissions of CH4 and N20 are reported separately from gross emissions totals. 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.
d Small amounts of PFC emissions also result from this source.
e 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
A-495
-------
1 to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining Settlements, and Land Converted to
2 Settlements.
3 f The LULUCF Sector Net Total is the net sum of all CH4 and N20 emissions to the atmosphere plus net carbon stock changes.
4
5
6 Table A-257: Change in U.S. Greenhouse Gas Emissions Using SAR GWP values relative to AR4 GWP values (MMT C02
7 EqJ
Gas/Source
1990
2005
2014
2015
2016
2017
2018
Percent
Change
in 2018
co2
NC
NC
NC
NC
NC
NC
NC
NA
ch4
(123.9)
(108.7)
(102.2)
(102.2)
(100.5)
(100.8)
(101.5)
(16%)
Enteric Fermentation
(26.3)
(27.0)
(26.3)
(26.6)
(27.5)
(28.1)
(28.4)
(16%)
Natural Gas Systems
(29.3)
(25.3)
(22.6)
(22.7)
(22.4)
(22.2)
(22.3)
(16%)
Landfills
(28.7)
(21.0)
(18.0)
(17.8)
(17.3)
(17.2)
(17.7)
(16%)
Manure Management
(5.9)
(8.2)
(8.7)
(9.3)
(9.5)
(9.6)
(9.9)
(16%)
Coal Mining
(15.4)
(10.3)
(10.3)
(9.8)
(8.6)
(8.8)
(8.4)
(16%)
Petroleum Systems
(7.4)
(6.2)
(7.0)
(6.5)
(6.2)
(6.2)
(5.9)
(16%)
Wastewater Treatment
(2.5)
(2.5)
(2.3)
(2.3)
(2.3)
(2.3)
(2.3)
(16%)
Rice Cultivation
(2.6)
(2.9)
(2.5)
(2.6)
(2.2)
(2.0)
(2.1)
(16%)
Stationary Combustion
(1.4)
(1.3)
(1.4)
(1.4)
(1.3)
(1.2)
(1.4)
(16%)
Abandoned Oil and Gas Wells
(1.1)
(1.1)
(1.1)
(1.1)
(1.2)
(1.1)
(1.1)
(16%)
Abandoned Underground Coal
Mines
(1.2)
(1.1)
(1.0)
(1.0)
(1.1)
(1.0)
(1.0)
(16%)
Mobile Combustion
(2.1)
(1.5)
(0.7)
(0.6)
(0.6)
(0.5)
(0.5)
(16%)
Composting
(0.1)
(0.3)
(0.3)
(0.3)
(0.4)
(0.4)
(0.4)
(16%)
Field Burning of Agricultural
Residues
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(16%)
Petrochemical Production
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(16%)
Ferroalloy Production
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(16%)
Carbide Production and
Consumption
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(16%)
Iron and Steel Production &
Metallurgical Coke Production
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(16%)
Incineration of Waste
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(16%)
International Bunker Fuelsa
M
M
M
M
M
M
M
(16%)
n2o
17.5
17.4
18.1
17.9
17.2
17.0
17.5
4%
Agricultural Soil Management
12.7
12.6
14.1
14.0
13.3
13.2
13.6
4%
Stationary Combustion
1.0
1.4
1.3
1.2
1.2
1.2
1.1
4%
Manure Management
0.6
0.7
0.7
0.7
0.7
0.8
0.8
4%
Mobile Combustion
1.7
1.5
0.8
0.7
0.7
0.7
0.6
4%
Nitric Acid Production
0.5
0.5
0.4
0.5
0.4
0.4
0.4
4%
Adipic Acid Production
0.6
0.3
0.2
0.2
0.3
0.3
0.4
4%
Wastewater Treatment
0.1
0.2
0.2
0.2
0.2
0.2
0.2
4%
N20 from Product Uses
0.2
0.2
0.2
0.2
0.2
0.2
0.2
4%
Composting
+
0.1
0.1
0.1
0.1
0.1
0.1
4%
Caprolactam, Glyoxal, and
Glyoxylic Acid Production
0.1
0.1
0.1
0.1
0.1
0.1
0.1
4%
Incineration of Waste
+
+
+
+
+
+
+
4%
Electronics Industry
+
+
+
+
+
+
+
4%
Field Burning of Agricultural
Residues
+
+
+
+
+
+
+
4%
Petroleum Systems
+
+
+
+
+
+
+
4%
Natural Gas Systems
+
+
+
+
+
+
+
4%
International Bunker Fuelsa
+
+
+
+
+
+
+
4%
A-496 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
HFCs
(9.7)
(15.4)
(20.9)
(21.5)
(21.7)
(22.4)
(22.4)
(13%)
Substitution of Ozone Depleting
Substances'5
+
(11.2)
(19.8)
(20.5)
(21.0)
(21.3)
(21.7)
(13%)
HCFC-22 Production
(9.7)
(4.2)
(1.1)
(0.9)
(0.6)
(1.1)
(0.7)
(21%)
Electronics Industry
(+)
(+)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(21%)
Magnesium Production and
Processing
0.0
0.0
(+)
(+)
(+)
(+)
(+)
(9%)
PFCs
(3.6)
(1.1)
(0.9)
(0.9)
(0.7)
(0.7)
(0.8)
(16%)
Electronics Industry
(0.6)
(0.7)
(0.6)
(0.5)
(0.5)
(0.5)
(0.5)
(17%)
Aluminum Production
(3.0)
(0.5)
(0.4)
(0.3)
(0.2)
(0.2)
(0.2)
(15%)
Substitution of Ozone Depleting
Substances
0.0
(+)
(+)
(+)
(+)
(+)
(+)
(12%)
sf6
1.4
0.6
0.3
0.3
0.3
0.3
0.3
5%
Electrical Transmission and
Distribution
1.1
0.4
0.2
0.2
0.2
0.2
0.2
5%
Magnesium Production and
Processing
0.3
0.1
+
+
0.1
0.1
0.1
5%
Electronics Industry
+
+
+
+
+
+
+
5%
nf3
NA
NA
NA
NA
NA
NA
NA
NA
Electronics Industry
NA
NA
NA
NA
NA
NA
NA
NA
Unspecified Mix of HFCs, NF3, PFCs,
and SF6
NA
NA
NA
NA
NA
NA
NA
NA
Electronics Industry
NA
NA
NA
NA
NA
NA
NA
NA
Total
(118.3)
(107.8)
(106.3)
(107.0)
(106.1)
(107.3)
(107.6)
(2%)
+ Absolute value does not exceed 0.05 MMT C02 Eq.
NC (No Change)
NA (Not Applicable)
a Emissions from International Bunker Fuels are not included in totals.
b Small amounts of PFC emissions also result from this source.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table A-258 below shows a comparison of total emissions estimates by sector using both the IPCC SAR and AR4
GWP values. For most sectors, the change in emissions that result from using SAR relative to AR4 GWP values was minimal.
The effect on emissions from waste was by far the greatest (14.9 percent decrease in 2018 using SAR GWP values, relative
to emissions using AR4 GWP values), due the predominance of CH4 emissions in this sector. Emissions from all other sectors
were comprised of mainly C02 or a mix of gases, which moderated the effect of the changes.
Table A-258: Comparison of Emissions by Sector using IPCC AR4 and SAR GWP Values (MMT CP2 Eq.)
Sector 1990 2005 2014 2015 2016 2017 2018
Energy
AR4GWP, Used In
Inventory
SAR GWP, Updated
Difference (%)
Industrial Processes and
Product Use
AR4GWP, Used In
Inventory
SAR GWP, Updated
Difference (%)
Agriculture
AR4GWP, Used In
Inventory
SAR GWP, Updated
Difference (%)
5,338.2
5,283.1
(1.0%)
6,294.4
6,250.6
(0.7%)
5.705.2
5.663.3
(0.7%)
5,551.3
5,510.2
(0.7%)
5,426.1
5,386.8
(0.7%)
5,385.4
5,346.0
(0.7%)
5.551.3
5.512.4
(0.7%)
345.6
334.9
(3.1%)
364.8
349.3
(4.3%)
376.9
355.7
(5.6%)
373.1
351.3
(5.9%)
367.3
345.4
(5.9%)
367.7
345.1
(6.1%)
373.6
351.0
(6.0%)
554.4
532.8
(3.9%)
575.9
551.0
(4.3%)
608.6
585.8
(3.7%)
614.6
590.7
(3.9%)
600.5
575.3
(4.2%)
602.3
576.5
(4.3%)
618.5
592.4
(4.2%)
A-497
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
LULUCF
AR4GWP, Used In
Inventory
(853.4)
(814.7)
(723.0)
(775.5)
(788.9)
(763.9)
(773.7)
SAR GWP, Updated
(854.0)
(815.8)
(724.3)
(777.7)
(789.9)
(765.9)
(775.7)
Difference (%)
0.1%
0.1%
0.2%
0.3%
0.1%
0.3%
0.3%
Waste
AR4GWP, Used In
Inventory
199.0
154.7
135.6
134.7
131.6
131.4
134.4
SAR GWP, Updated
167.9
131.1
115.3
114.4
111.9
111.8
114.4
Difference (%)
(15.6%)
(15.2%)
(15.0%)
(15.0%)
(14.9%)
(14.9%)
(14.9%)
Net Emissions
AR4 GWP, Used In
Inventory
5,583.7
6,575.1
6,103.3
5,898.2
5,736.6
5,722.9
5,904.1
SAR GWP, Updated
5,464.8
6,466.2
5,995.8
5,789.0
5,629.5
5,613.5
5,794.5
Difference (%)
(2.1%)
(1.7%)
(1.8%)
(1.9%)
(1.9%)
(1.9%)
(1.9%)
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Further, Table A-259 and Table A-260 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-261 and Table A-262 show the comparison of emission estimates using AR5 GWP values with climate-carbon
feedbacks. The use of AR5 GWP values without climate-carbon feedbacks™ 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.
Table A-259: Change in U.S. Greenhouse Gas Emissions Using AR5a without Climate-Carbon Feedbacks Relative to AR4
GWP Values (MMT CP2 Eq.)
Gas
1990
2005
2014
2015
2016
2017
2018
C02
NC
NC
NC
NC
NC
NC
NC
ch4
92.9
81.6
76.7
76.6
75.4
75.6
76.2
n2o
(48.1)
(47.9)
(49.8)
(49.2)
(47.2)
(46.6)
(48.1)
HFCs
(7.5)
(10.9)
(9.8)
(10.0)
(9.8)
(10.2)
(10.0)
PFCs
(2.4)
(0.6)
(0.5)
(0.5)
(0.4)
(0.4)
(0.4)
sf6
0.9
0.4
0.2
0.2
0.2
0.2
0.2
nf3
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Unspecified Mix of HFCs, NF3,
PFCs, and SF6
NA
NA
NA
NA
NA
NA
NA
Total
35.8
22.4
16.7
17.1
18.1
18.5
17.7
+ Absolute value does not exceed 0.05 MMT C02 Eq.
NC (No Change)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report. The ARB report has also
calculated GWP values (shown in Table A-261) 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, the ARB reported separate values for fossil versus
biogenic methane in order to account for the C02 oxidation product.
Notes: Total emissions presented without LULUCF. Totals may not sum due to independent rounding. Parentheses indicate negative
values.
140 The IPCC AR5 report provides additional information on emission metrics. See .
A-498 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Table A-260: Change in U.S. Greenhouse Gas Emissions Using AR5a without Climate-Carbon Feedbacks Relative to AR4
GWP Values (Percent)
Gas/Source
1990
2005
2014
2015
2016
2017
2018
co2
NC
NC
NC
NC
NC
NC
NC
ch4
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
n2o
(11%)
(11%)
(11%)
(11%)
(11%)
(11%)
(11%)
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%)
(6.0%)
(6.0%)
(5.9%)
(6.0%)
(6.0%)
Substitution of Ozone
Depleting Substances
11.3%
(7.2%)
(5.7%)
(5.7%)
(5.7%)
(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%)
(16.7%)
(16.8%)
(16.4%)
(16.7%)
(16.6%)
(16.2%)
Magnesium Production and
Processing11
0.0%
0.0%
(9.1%)
(9.1%)
(9.1%)
(9.1%)
(9.1%)
PFCs
(10.0%)
(9.7%)
(9.5%)
(9.5%)
(9.5%)
(9.5%)
(9.6%)
Electronics Industry0
(9.4%)
(9.3%)
(9.2%)
(9.2%)
(9.3%)
(9.4%)
(9.4%)
Aluminum Production6
(10.1%)
(10.1%)
(10.0%)
(10.0%)
(9.9%)
(9.8%)
(9.9%)
Substitution of Ozone
Depleting Substances4'
0.0%
(10.3%)
(10.3%)
(10.3%)
(10.3%)
(10.3%)
(10.3%)
Unspecified Mix of HFCs, NF3,
PFCs, and SF6
NA
NA
NA
NA
NA
NA
NA
Electronics Industry
NA
NA
NA
NA
NA
NA
NA
Total
0.6%
0.3%
0.2%
0.3%
0.3%
0.3%
0.3%
NC (No Change)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report. The ARB report has also
calculated GWP values (shown in Table A-262) 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, the ARB 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, C3F8, C4F8, 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.
Note: Total emissions presented without LULUCF. Parentheses indicate negative values.
Table A-261: Change in U.S. Greenhouse Gas Emissions Using AR5 with Climate-Carbon Feedbacks3 Relative to AR4 GWP
Values (MMT CP2 Eq.)
Gas
1990
2005
2014
2015
2016
2017
2018
C02
NC
NC
NC
NC
NC
NC
NC
ch4
278.8
244.7
230.1
229.9
226.2
226.9
228.5
n2o
NC
NC
NC
NC
NC
NC
NC
HFCs
(2.9)
9.2
16.8
17.3
17.6
17.3
17.5
PFCs
(+)
+
+
+
+
+
+
sf6
4.2
1.7
0.9
0.8
0.9
0.9
0.9
nf3
+
+
+
+
+
+
+
Unspecified Mix of HFCs,
NF3, PFCs, and SF6
NA
NA
NA
NA
NA
NA
NA
Total
280.1
255.6
247.8
248.0
244.6
245.1
246.8
+ Absolute value does not exceed 0.0B MMT C02 Eq.
NC (No Change)
a The GWP values presented here from the ARB 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, the ARB reported separate values for fossil versus biogenic methane
in order to account for the C02 oxidation product.
A-499
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Notes: Total emissions presented without LULUCF. Totals may not sum due to independent rounding. Parentheses indicate negative
values.
Table A-262: Change in U.S. Greenhouse Gas Emissions Using AR5 with Climate-Carbon Feedbacks3 Relative to AR4 GWP
Values (Percent)
Gas/Source
1990
2005
2014
2015
2016
2017
2018
co2
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%)
7.3%
10.3%
10.4%
10.6%
10.3%
10.4%
Substitution of Ozone
Depleting Substances
34.6%
9.9%
10.9%
10.9%
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%)
(6.9%)
(7.1%)
(6.6%)
(6.9%)
(6.8%)
(6.3%)
Magnesium Production
and Processing11
0.0%
0.0%
8.3%
8.3%
8.3%
8.3%
8.3%
PFCs
(0.2%)
0.2%
0.4%
0.4%
0.5%
0.4%
0.3%
Electronics Industry0
0.6%
0.8%
0.8%
0.8%
0.7%
0.6%
0.5%
Aluminum Production6
(0.3%)
(0.3%)
(0.1%)
(0.1%)
0.0%
0.0%
(0.1%)
Substitution of Ozone
Depleting Substances4'
0.0%
(0.6%)
(0.6%)
(0.6%)
(0.6%)
(0.6%)
(0.6%)
Unspecified Mix of HFCs,
NF3, PFCs, and SF6
NA
NA
NA
NA
NA
NA
NA
Electronics Industry
NA
NA
NA
NA
NA
NA
NA
Total
4.4%
3.5%
3.6%
3.7%
3.8%
3.8%
3.7%
+ Does not exceed 0.05 percent.
NC (No Change)
a The GWP values presented here from the ARB 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, the ARB 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, C3F8, C4F8, 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.
Notes: Total emissions presented without LULUCF. Parentheses indicate negative values. Excludes Sinks.
A-500 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
6.2. Ozone Depleting Substance Emissions
Ozone is present in both the stratosphere,141 where it shields the earth from harmful levels of ultraviolet
radiation, and at lower concentrations in the troposphere,142 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.143 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,144 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, and in some cases continue to serve, as interim replacements for Class I compounds in
many industrial applications. The use and emissions of HCFCs in the United States is anticipated to 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 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-263.
Table A-263: Emissions of Ozone Depleting Substances (kt)
Compound 1990 2005 2014 2015 2016 2017 2018
Class I
CFC-11 29 12 24 25 25 25 20
141 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.
142 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.
143 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.
144 Older refrigeration and air-conditioning equipment, fire extinguishing systems, and foam products blown with CFCs/HCFCs
may still contain Class I ODS.
A-501
-------
CFC-12
CFC-113
CFC-114
CFC-115
Carbon
Tetrachloride
Methyl Chloroform
Halon-1211
Halon-1301
Class II
HCFC-22
HCFC-123
HCFC-124
HCFC-141b
HCFC-142b
HCFC-225ca/cb
1 + Does not exceed 0.5 kt.
2
3 Methodology and Data Sources
4 Emissions of ozone depleting substances were estimated using the EPA's Vintaging Model. The model, named for
5 its method of tracking the emissions of annual "vintages" of new equipment that enter into service, is a "bottom-up"
6 model. It models the consumption of chemicals based on estimates of the quantity of equipment or products sold,
7 serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain the equipment.
8 The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of the equipment in
9 each of the end-uses. Emissions are estimated by applying annual leak rates, service emission rates, and disposal emission
10 rates to each population of equipment. By aggregating the emission and consumption output from the different end-uses,
11 the model produces estimates of total annual use and emissions of each chemical. Please see Annex 3.9, Methodology for
12 Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances, of this Inventory for a more detailed
13 discussion of the Vintaging Model.
14 Uncertainties
15 Uncertainties exist with regard to the levels of chemical production, equipment sales, equipment characteristics,
16 and end-use emissions profiles that are used by these models. Please see the Substitution of Ozone Depleting Substances
17 section of this report for a more detailed description of the uncertainties that exist in the Vintaging Model.
18
135
22
4
4
3
2
1
59
17
0
0
0
0
0
4
1
0
0
0
0
0
8
2
+
+
+
+
+
4
0
0
0
0
0
0
223
0
0
0
0
0
0
2
2
1
1
1
1
1
2
+
+
+
+
+
+
30
74
60
56
53
50
46
0
1
1
1
1
1
1
0
2
1
+
+
+
+
1
4
10
9
9
8
12
1
4
2
2
3
4
4
+
3
11
12
13
14
15
A-502 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
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-264.
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 47.8 percent of total S02 emissions in 2018 (see Table A-265); coal
combustion accounted for approximately 92.0 percent of that total. The second largest source was industrial fuel
combustion, which produced 20.0 percent of 2018 S02 emissions (see Table A-264). Overall, S02 emissions in the United
States decreased by 88.1 percent from 1990 to 2018. 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,145 (2) New Source Performance Standards,146 (3) the New Source Review/Prevention of Significant Deterioration
Program,147 and (4) the Sulfur Dioxide Allowance Program.148
Table A-264: SP2 Emissions (kt)
Sector/Source
1990
2005
2014
2015
2016
2017
2018
Energy
19,628
12,364
3,742
2,844
2,187
2,050
1,983
Stationary Sources
18,407
11,541
3,532
2,635
1,978
1,841
1,774
Oil and Gas Activities
391
180
94
94
94
94
94
Mobile Sources
793
619
88
87
87
87
87
Waste Combustion
38
25
27
27
27
27
27
Industrial Processes and
Product Use 1,307 8311
Other Industrial Processes 362 3271
Miscellaneous3 1 1141
Chemical and Allied Product
Manufacturing 26! 2281
Metals Processing 659 1581
Storage and Transport I 2|
Solvent Use 0 +
Degreasing 0 01
Graphic Arts 0 o|
Dry Cleaning NA o|
Surface Coating 0 0|
Other Industrial 0 +|
497
497
497
497
497
151
151
151
151
151
136
136
136
136
136
112
112
112
112
112
95
95
95
95
95
3
3
3
3
3
+
+
+
+
+
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
+
+
+
+
+
145 [42 U.S.C § 7409, CAA § 109]
146 [42 U.S.C § 7411, CAA § 111]
147 [42 U.S.C § 7473, CAA § 163]
148 [42 U.S.C § 7651, CAA § 401]
A-503
-------
1
2
3
4
5
6
7
8
9
10
11
Nonindustrial
NA
NA
NA
NA
NA
NA
NA
Agriculture
NA
NA
NA
NA
NA
NA
NA
Agricultural Burning
NA
NA
NA
NA
NA
NA
NA
Waste
+llll
1
1
1
1
1
1
Landfills
+ 1111
1 lljl
1
1
1
1
1
Wastewater Treatment
+ 11111
0
0
0
0
0
0
Miscellaneous3
0
0
0
0
0
0
Total
20,935
13,196
4,240
3,342
2,685
2,548
2,481
+ Does not exceed 0.5 kt
NA (Not Applicable)
a Miscellaneous includes other combustion and fugitive dust categories.
Note: Totals may not sum due to independent rounding.
Source: Data taken from EPA (2019) and disaggregated based on EPA (2003).
Table A-265: SP2 Emissions from Electricity Generation (kt)
Fuel Type
Coal
Oil
Gas
Internal Combustion
Other
Total
14,433
( 2005
2014
2015
2016
2017
2018
| 8,680
2,706
1,881
1,277
1,151
1,090
| 458
143
99
67
61
57
1 174
54
38
26
23
22
1 57
18
12
8
8
7
j 71
22
15
10
9
9
| 9,439
2,943
2,046
1,388
1,252
1,185
NA (Not Applicable)
Note: Totals may not sum due to independent rounding.
Source: Data taken from EPA (2019) and disaggregated based on EPA (2003).
A-504 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
i 6.4. Complete List of Source Categories
Chapter/Source
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
C02
C02
CH4,
ch4,
ch4
ch4
ch4,
ch4,
co2,
co2.
N20, CO, NOx, NMVOC
N20, CO, NOx, NMVOC
N20
n2o
ch4
CH4.N20, NOx, CO, NMVOC
C02
C02
C02
C02
C02
C02
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,aSFe, NF3
HFCs, PFCsb
SF6
n2o
ch4
ch4, n2o
ch4
C02
co2
CH4, N20, NOx, CO
N20
C02, CH4, N20, NOx, CO
C02
C02
A-505
-------
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Wetlands Remaining Wetlands
Land Converted to Wetlands
Settlements Remaining Settlements
Land Converted to Settlements
C02
C02, CH4, N20, NOx, CO
C02
C02, CH4, n2o
C02, CH4
C02, N20
C02
Waste
Landfills
Wastewater T reatment
Composting
CH4, NOx, CO, NMVOC
CH4, N20, NOx, CO, NMVOC
CH4, n2o
1 a Includes HFC-23, CF4, C2F6, as well as a mix other HFCs and PFCs used as heat transfer fluids.
2 b Includes HFC-23, HFC-32, HFC-12B, HFC-134a, HFC-143a, HFC-236fa, CF4, HFC-152a, HFC-227ea, HFC-24Bfa, HFC-4310mee,
3 and PFC/PFPEs.
4 cThe LULUCF Sector includes CH4 and N20 emissions to the atmosphere and net carbon stock changes. The term "flux" is
5 used to describe the net emissions of greenhouse gases accounting for both the emissions of C02 to and the removals of
6 C02 from the atmosphere. Removal of C02 from the atmosphere is also referred to as "carbon sequestration."
7
A-506 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
6.5. Constants, Units, and Conversions
2 Metric Prefixes
3 Although most activity data for the United States is gathered in customary U.S. units, these units are converted
4 into metric units per international reporting guidelines. Table A-266 provides a guide for determining the magnitude of
5 metric units.
6 Table A-266: 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 (p)
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
7
8 Unit Conversions
9
1 kilogram =
1 pound =
1 short ton =
1 metric ton =
10
1 cubic meter =
1 cubic foot =
1 U.S. gallon =
1 barrel (bbl) =
1 barrel (bbl) =
1 liter
11
1 foot =
1 meter =
1 mile =
1 kilometer =
12
1 acre =
1 square mile =
13
Degrees Celsius =
Degrees Kelvin =
A-507
2.205 pounds
0.454 kilograms
2,000 pounds = 0.9072 metric tons
1,000 kilograms = 1.1023 short tons
35.315 cubic feet
0.02832 cubic meters
3.785412 liters
0.159 cubic meters
42 U.S. gallons
0.001 cubic meters
0.3048 meters
3.28 feet
1.609 kilometers
0.621 miles
43,560 square feet = 0.4047 hectares = 4,047 square meters
2.589988 square kilometers
(Degrees Fahrenheit - 32)*5/9
Degrees Celsius + 273.15
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1
2
3
4
5 Density Conversions149
6
Methane
1 cubic meter =
= 0.67606 kilograms
Carbon dioxide
1 cubic meter =
= 1.85387 kilograms
Natural gas liquids
1 metric ton =
11.6 barrels =
1,844.2 liters
Unfinished oils
1 metric ton =
7.46 barrels =
1,186.04 liters
Alcohol
1 metric ton =
7.94 barrels =
1,262.36 liters
Liquefied petroleum gas
1 metric ton =
11.6 barrels =
1,844.2 liters
Aviation gasoline
1 metric ton =
8.9 barrels =
1,415.0 liters
Naphtha jet fuel
1 metric ton =
8.27 barrels =
1,314.82 liters
Kerosene jet fuel
1 metric ton =
7.93 barrels =
1,260.72 liters
Motor gasoline
1 metric ton =
8.53 barrels =
1,356.16 liters
Kerosene
1 metric ton =
7.73 barrels =
1,228.97 liters
Naphtha
1 metric ton =
8.22 barrels =
1,306.87 liters
Distillate
1 metric ton =
7.46 barrels =
1,186.04 liters
Residual oil
1 metric ton =
6.66 barrels =
1,058.85 liters
Lubricants
1 metric ton =
7.06 barrels =
1,122.45 liters
Bitumen
1 metric ton =
6.06 barrels =
963.46 liters
Waxes
1 metric ton =
7.87 barrels =
1,251.23 liters
Petroleum coke
1 metric ton =
5.51 barrels =
876.02 liters
Petrochemical feedstocks
1 metric ton =
7.46 barrels =
1,186.04 liters
Special naphtha
1 metric ton =
8.53 barrels =
1,356.16 liters
Miscellaneous products
1 metric ton =
8.00 barrels =
1,271.90 liters
8
9 Energy Conversions
10 Converting Various Energy Units to Joules
11 The common energy unit used in international reports of greenhouse gas emissions is the joule. A joule is the
12 energy required to push with a force of one Newton for one meter. A terajoule (TJ) is one trillion (1012) joules. A British
13 thermal unit (Btu, the customary U.S. energy unit) is the quantity of heat required to raise the temperature of one pound
14 of water one degree Fahrenheit at or near 39.2 degrees Fahrenheit.
2.3881011 calories
23.88 metric tons of crude oil equivalent
947.8 million Btus
277,800 kilowatt-hours
15 Converting Various Physical Units to Energy Units
16 Data on the production and consumption of fuels are first gathered in physical units. These units must be
17 converted to their energy equivalents. The conversion factors in Table A-267 can be used as default factors, if local data
18 are not available. See Appendix A of ElA's Monthly Energy Review, November 2019 (EIA 2019) for more detailed
19 information on the energy content of various fuels.
20
149 Reference: EIA (2007)
A-508 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
Table A-267: 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
Coke 21.49
Natural Gas (Btu/Cubic foot) 1,036
Liquid Fuels (Million Btu/Barrel)
Motor gasoline 5.054
Aviation gasoline 5.048
Kerosene 5.670
Jet fuel, kerosene-type 5.670
Distillate fuel 5.825
Residual oil 6.287
Naphtha for petrochemicals 5.248
Petroleum coke 6.024
Other oil for petrochemicals 5.825
Special naphthas 5.248
Lubricants 6.065
Waxes 5.537
Asphalt 6.636
Still gas 6.000
Misc. products 5.796
Note: For petroleum and natural gas, Monthly Energy
Review, November 2019 (EIA 2019). For coal ranks, State
Energy Data Report 1992 (EIA 1993). All values are given in
higher heating values (gross calorific values).
-------
1
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
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
CaO
Calcium oxide
CAPP
Canadian Association of Petroleum Producers
CARB
California Air Resources Board
CBI
Confidential business information
A-510 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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
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
A-511
-------
EJ
Exajoule
EGR
Exhaust gas recirculation
EGU
Electric generating unit
EIA
Energy Information Administration, U.S. Department of Energy
El 1P
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
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
GHG
Greenhouse gas
GHGRP
EPA's Greenhouse Gas Reporting Program
GIS
Geographic Information Systems
GJ
Gigajoule
GOADS
Gulf Offshore Activity Data System
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
HFO
Hydrofluoroolefin
HFE
Hydrofluoroethers
A-512 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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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
A-513
-------
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
NE
Not estimated
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
NO
Nitric oxide
A-514 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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
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
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
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
PS
Polystyrene
PSU
Primary Sample Unit
A-515
-------
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
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
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
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
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
TRI
Toxic Release Inventory
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)
A-516 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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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
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
WTE
Waste-to-energy
WW
Wastewater
WWTP
Wastewater treatment plant
ZEVs
Zero emissions vehicles
6.7.
Chemical Formulas
Table A-268: 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
c-C4Fs
Perfluorocyclobutane
C4F10
Perfluorobutane
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
CF3CH20CHF2
HFE-245fa2
CHF2CF20CH3
HFE-254cb2
CF3CH20CH3
HFE-263fb2
CF3CF20CF2CHF2
HFE-329mcc2
CF3CF20CH2CF3
HFE-338mcf2
CF3CF2CF20CH3
HFE-347mcc3
CF3CF20CH2CHF2
HFE-347mcf2
CF3CHFCF20CH3
HFE-356mec3
A-518 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
-------
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
NOx
Nitrogen oxides
Na
Sodium
Na2C03
Sodium carbonate, soda ash
Na3AIF6
Synthetic cryolite
0, 02
atomic Oxygen, molecular Oxygen
O3
Ozone
A-519
-------
so2
Si
Sic
Si02
s
h2so4
sf6
SF5CF3
atomic Sulfur
Sulfuric acid
Sulfur hexafluoride
Trifluoromethylsulphur pentafluoride
Sulfur dioxide
Silicon
Silicon carbide
Quartz
1 * Distinct isomers.
2
3 References
4 EIA (2019) Monthly Energy Review, November 2019. Energy Information Administration, U.S. Department of Energy,
5 Washington, DC. DOE/EIA-0035(2019/11). November 2019.
6 EIA (2007) Emissions of Greenhouse Gases in the United States 2006, Draft Report. Office of Integrated Analysis and
7 Forecasting, Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE-EIA-0573 (2006).
8 EIA (1993) State Energy Data Report 1992, DOE/EIA-O214(93), Energy Information Administration, U.S. Department of
9 Energy. Washington, DC. December.
10 EPA (2019) "1970-2018 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory (NEI)
11 Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Last Modified March 2018. Available
12 online at: .
13 EPA (2003) E-mail correspondence. Air pollutant data. Office of Air Pollution to the Office of Air Quality Planning and
14 Standards, U.S. Environmental Protection Agency (EPA). December 22, 2003.
15 IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment
16 Report of the Intergovernmental Panel on Climate Change. [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen,
17 J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United
18 Kingdom and New York, NY, USA, 1535 pp.
19 IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
20 Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B.
21 Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United Kingdom 996 pp.
22 IPCC (1996) Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change,
23 J.T.Houghton, L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell (eds.). Cambridge University
24 Press. Cambridge, United Kingdom
A-520 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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38
39
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41
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47
ANNEX 7 Uncertainty
The annual U.S. Inventory presents the best effort to produce estimates for greenhouse gas source and sink categories
in the United States. These estimates were generated according to the UNFCCC reporting guidelines, following the
recommendations set forth in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). This Annex
provides an overview of the uncertainty analysis conducted to support the U.S. Inventory, describes the sources of
uncertainty characterized throughout the Inventory associated with various source categories (including emissions and
sinks), and describes the methods through which uncertainty information was collected, quantified, and presented. An
Addendum to Annex 7 is provided separately which includes additional information related to the characteristics of input
variables used in the development of the uncertainty estimates reported in the Inventory.
7.1. Overview
The primary purpose of the uncertainty analysis conducted in support of the U.S. Inventory is (1) to determine
the quantitative uncertainty associated with the emission (and removal) estimates presented in the main body of this
report based on the uncertainty associated with the input parameters used in the emission (and removal) estimation
methodologies and (2) to evaluate the relative importance of the input parameters in contributing to uncertainty in the
associated source or sink category inventory estimate and in the overall inventory estimate. Thus, the U.S. Inventory
uncertainty analysis provides a strong foundation for developing future improvements to the inventory estimation process.
For each source or sink category, the analysis highlights opportunities for changes to data measurement, data collection,
and calculation methodologies. These are presented in the "Planned Improvements" sections of each source or sink
category's discussion in the main body of the report.
For some of the current estimates, such as C02 emissions from energy-related combustion activities, the impact
of uncertainties on overall emission estimates is believed to be 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). As noted, for all source categories, 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), resulting in the use of incorrect
or incomplete estimation methodologies, and (2) parameter uncertainty, which arises due to a lack of precise input data
such as emission factors and activity data.
The model uncertainty can be partially analyzed by comparing the model results with those of other models
developed to characterize the same emission (or removal) process, after taking into account the differences in their
conceptual framework, capabilities, data, and assumptions. However, it would be very difficult—if not impossible—to
quantify the model uncertainty associated with the emission estimates (primarily because, in most cases, only a single
model has been developed to estimate emissions from any one source). Therefore, model uncertainty was not quantified
in this report. Nonetheless, it has been discussed qualitatively, where appropriate, along with the individual source 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, misreporting or misclassification, or missing data.
Parameter uncertainty is, therefore, the principal type and source of uncertainty associated with the national Inventory
emission estimates and is the main focus of the quantitative uncertainty analyses in this report. Parameter uncertainty has
been quantified for all of the emission sources and sinks included in the U.S. Inventory totals, with the exception of a few
very small emission source categories (i.e., CH4 emissions from Incineration of Waste, and certain F-GHGs, photovoltaics
(PV), micro-electro-mechanical systems (MEMS) devices, and Heat Transfer Fluids (HTFs) from the Electronics Industry).
Given the very low emissions for these source categories, uncertainty estimates were not derived. Uncertainty associated
with three other source categories (International Bunker Fuels, Energy Sources of Indirect Greenhouse Gas Emissions, and
A-521
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
C02 emissions from Wood Biomass and Biofuel Consumption) whose emissions are not included in the Inventory totals is
discussed qualitatively in their respective sections in the main body of the report.
7.2. Methodology and Results
The United States has developed 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 quality. Although the plan
provides both general and specific guidelines for implementing quantitative uncertainty analysis, its components are
intended to evolve over time, consistent with the inventory estimation process. The U.S. plan includes procedures and
guidelines, and forms and templates, for developing quantitative assessments of uncertainty in the national Inventory
estimates (EPA 2002). For the 1990 through 2018 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 in the inventory estimate of individual source categories and the overall Inventory. Of these, the
Approach 2 method is both more flexible and reliable than Approach 1; both approaches are described in the next section.
The United States is in the process of implementing a multi-year strategy to develop quantitative estimates of uncertainty
for all source categories using the 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 source categories.150 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 does provide an estimate of C02 emissions from Biomass and Biofuel Consumption provided 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 error propagation equation. This equation
combines the uncertainty associated with the activity data and the uncertainty associated with the emission (or the other)
factors. The Approach 1 method is applicable where emissions (or removals) are usually estimated as the product of an
activity value and an emission factor or as the sum of individual sub-source or sink category values. Inherent in employing
the Approach 1 method are the assumptions that, for each source and sink category, (i) both 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 (i) if the uncertainty associated with the input variables is significantly large,
(ii) if the distributions underlying the input variables are not normal, (iii) if the estimates of uncertainty associated with the
input variables are correlated, and/or (iv) if a sophisticated estimation methodology and/or several input variables are
used to characterize the emission (or removal) process correctly. In practice, the Approach 2 is the preferred method of
uncertainty analysis for all source categories where sufficient and reliable data are available to characterize the uncertainty
of the input variables.
The Approach 2 method employs the Monte Carlo Stochastic Simulation technique (also referred to as the Monte
Carlo method). Under this method, estimates of emissions (or removals) for a particular source or sink category are
generated many times (equal to the number of simulations specified) using an uncertainty model, which is an emission (or
removal) estimation equation that imitates or is the same as the inventory estimation model for a particular source or sink
category. These estimates are generated using the respective, randomly-selected values for the constituent input variables
using commercially available simulation software such as @RISK.
150 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-522 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1
Characterization of Uncertainty in Input Variables
2 Both Approach 1 and Approach 2 uncertainty analyses require that all the input variables are well-characterized
3 in terms of their Probability Density Functions (PDFs). In the absence of particularly convincing data measurements,
4 sufficient data samples, or expert judgments that determined otherwise, the PDFs incorporated in the current source or
5 sink category uncertainty analyses were limited to normal, lognormal, uniform, triangular, and beta distributions. The
6 choice among these five PDFs depended largely on the observed or measured data and expert judgment.
7 Source and Sink Category Inventory Uncertainty Estimates
8 Discussion surrounding the input parameters and sources of uncertainty for each source and sink category
9 appears in the body of this report. Table A-269 summarizes results based on assessments of source and sink category-level
10 uncertainty. The table presents base year (1990 or 1995) and current year (2018) emissions for each source and sink
11 category. The combined uncertainty (at the 95 percent confidence interval) for each source and category is expressed as
12 the percentage deviation above and below the total 2018 emissions estimated for that source and category. Source or sink
13 category trend uncertainty is described subsequently in this Appendix.
14 Table A-269: Summary Results of Source and Sink Category Uncertainty Analyses- TO BE UPDATED FOR FINAL
15 INVENTORY REPORT
Source or Sink Category
Base Year Emissions-'
2017 Emissions1'
2017 Uncertainty1'
MMT CO-Eq.
MMT CO-Eq.
Low
High
co2
5,121.2
5,270.7
-2%
4%
Fossil Fuel Combustion
4,738.8
4,912.0
-2%
5%
Non-Energy Use of Fuels
119.6
123.2
-23%
37%
Iron and Steel Production & Metallurgical Coke Production
101.6
41.8
-18%
18%
Cement Production
33.5
40.3
-6%
6%
Petrochemical Production
21.2
28.2
-5%
5%
Natural Gas Systems
30.0
26.3
-16%
17%
Petroleum Systems
9.0
23.3
-30%
34%
Ammonia Production
13.0
13.2
-5%
5%
Lime Production
11.7
13.1
-2%
2%
Incineration of Waste
8.0
10.8
-11%
15%
Other Process Uses of Carbonates
6.3
10.1
-12%
15%
Urea Fertilization
2.4
5.1
-43%
3%
Urea Consumption for Non-Agricultural Purposes
3.8
5.0
-12%
12%
Carbon Dioxide Consumption
1.5
4.5
-5%
5%
Liming
4.7
3.2
-111%
89%
Ferroalloy Production
2.2
2.0
-12%
12%
Soda Ash Production
1.4
1.8
-9%
8%
Titanium Dioxide Production
1.2
1.7
-13%
13%
Glass Production
1.5
1.3
-4%
5%
Aluminum Production
6.8
1.2
-3%
3%
Phosphoric Acid Production
1.5
1.0
-19%
21%
Zinc Production
0.6
1.0
-16%
16%
Lead Production
0.5
0.5
-15%
15%
Silicon Carbide Production and Consumption
0.4
0.2
-9%
9%
Abandoned Oil and Gas Wells
+
+
-83%
215%
Magnesium Production and Processing
+
+
-8%
8%
Wood Biomass, Ethanol, and Biodiesel Consumptionc
219.4
116.6
NE
NE
International Bunker Fuels"
103.5
120.1
NE
NE
CH,
779.8
656.3
-9%
14%
Enteric Fermentation
164.2
175.4
-11%
18%
Natural Gas Systems
193.1
165.6
-16%
17%
Landfills
179.6
107.7
-11%
40%
Manure Management
37.1
61.7
-18%
20%
Coal Mining
96.5
55.7
-9%
19%
A-523
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3
4
5
6
7
8
9
10
11
12
13
14
Petroleum Systems
42.1
37.7
-30%
34%
Wastewater T reatrnent
15.3
14.2
-28%
22%
Rice Cultivation
16.0
11.3
-25%
49%
Stationary Combustion
8.6
7.8
-33%
124
Abandoned Oil and Gas Wells
6.6
6.9
-83%
215
Abandoned Underground Coal Mines
7.2
6.4
-21%
19%
Mobile Combustion
12.9
3.2
-8%
27%
Composting
0.4
2.2
-50%
50%
Petrochemical Production
0.2
0.3
-57%
45%
Field Burning of Agricultural Residues
0.1
0.2
-51%
49%
Ferroalloy Production
+
+
-12%
12%
Silicon Carbide Production and Consumption
+
+
-8%
8%
Iron and Steel Production & Metallurgical Coke Production
+
+
-19%
19%
Incineration of Waste
+
+
NE
NE
International Bunker Fuels'1
0.2
0.1
NE
NE
NO
370.3
360.5
-12%
21%
Agricultural Soil Management
251.7
266.4
-17%
26%
Direct
212.7
-17%
19%
Indirect
39.0
38.8
-59%
144%
Stationary Combustion
25.1
28.6
-28%
52%
Manure Management
14.0
18.7
-16%
24%
Mobile Combustion
42.0
16.9
-8%
14%
Nitric Acid Production
12.1
9.3
-5%
5%
Adipic Acid Production
15.2
7.4
-5%
5%
Wastewater T reatrnent
3.4
5.0
-75%
108%
N20 from Product Uses
4.2
4.2
-24%
24%
Composting
0.3
1.9
-50%
50%
Caprolactam, Glyoxal, and Glyoxylic Acid Production
1.7
1.4
-31%
32%
Incineration of Waste
0.5
0.3
-47%
301%
Semiconductor Manufacture
+
0.2
-12%
12%
Field Burning of Agricultural Residues
+
0.1
-47%
46%
Petroleum Systems
+
+
-30%
34%
Natural Gas Systems
+
+
-16%
17%
International Bunker Fuelsd
0.9
1.0
NE
NE
HFCs, PFCs, SFr. and NF .
130.8
169.1
-+%
11%
Substitution of Ozone Depleting Substances
31.4
152.7
-+%
12%
HCFC-22 Production
46.1
5.2
-7%
10%
Semiconductor Manufacture6
3.6
4.7
-6%
6%
Electrical Transmission and Distribution
23.1
4.3
-14%
17%
Magnesium Production and Processing
5.2
1.2
-7%
7%
Aluminum Production
21.5
1.1
-9%
9%
Total Emissions
6,371.0
6,456.7
-2%
4%
LULUCF Emissions'
7.8
15.5
-17%
20%
LULUCF Carbon Stock Change
(814.8)
(729.6)
50%
-33%
LULUCF Sector Net Total
(807.0)
(714.1)
51%
-34%
Net Emissions (Sources and Sinks)1
5,564.0
5,742.6
-6%
7%
+ Does not exceed 0.05 MMT CO, Eq. or 0.5 percent.
NE (Not Estimated)
¦' Base Year is 1990 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen 1995.
''The uncertainty estimates correspond to a 95 percent confidence interval, with the lower bound corresponding to 2.5"' percentile and the
upper bound corresponding to 97.5"' percentile.
' Emissions from Wood Biomass and Biofuel Consumption are not included in summing energy sector totals.
11 Emissions from International Bunker Fuels are not included in the totals.
' This source category's estimate for 2017 excludes 0.023 MMT CO, Eq. of HTF emissions, as uncertainties associated with those sources were
not assessed. Hence, forth is 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,
i Totals exclude emissions for which uncertainty was not quantified.
B LULUCF emissions include the CH.i and N,0 emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained Organic Soils,
Grassland Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH.i emissions from Land Converted to Coastal Wetlands; and N,0
emissions from Forest Soils and Settlement Soils.
A-524 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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1 h LULUCF Carbon Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land, Land Converted to
2 Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to Grassland,
3 Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining Settlements, and Land Converted to Settlements.
4 'The LULUCF Sector Net Total is the net sum of all Cm and N2O emissions to the atmosphere plus net carbon stock changes.
5 Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding emissions for which
6 uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with LULUCF.
7 Overall (Aggregate) Inventory Level Uncertainty Estimates
8 The overall level uncertainty estimate for the U.S. Inventory was developed using the IPCC Approach 2
9 uncertainty estimation methodology. The uncertainty models of all the emission source categories could not be directly
10 integrated to develop the overall uncertainty estimates due to software constraints in integrating multiple, large
11 uncertainty models. Therefore, an alternative approach was adopted to develop the overall uncertainty estimates. The
12 Monte Carlo simulation output data for each emission source or sink category uncertainty analysis were combined by type
13 of gas and the probability distributions were fitted to the combined simulation output data, where such simulated output
14 data were available. If such detailed output data were not available for particular emissions sources, individual probability
15 distributions were assigned to those sources or sink category emission estimates based on the most detailed data available
16 from the quantitative uncertainty analysis performed.
17 Approach 1 uncertainty results were used in the overall uncertainty analysis estimation for Composting, several
18 LULUCF source categories, and parts of Agricultural Soil Management source categories. However, for all other emission
19 sources (excluding international bunker fuels, C02 from biomass and biofuel combustion, CH4 from incineration of waste,
20 and certain F-GHGs, photovoltaics (PV), micro-electro-mechanical systems (MEMS) devices, and Heat Transfer Fluids
21 (HTFs) from the Electronics Industry)), Approach 2 uncertainty results were used in the overall uncertainty estimation.
22 The overall uncertainty model results indicate that the 2017 U.S. greenhouse gas emissions are estimated to be
23 within the range of approximately 6,350.6 to 6,742.9 MMT C02 Eq., reflecting a relative 95 percent confidence interval
24 uncertainty range of -2 percent to 4 percent with respect to the total U.S. greenhouse gas emission estimate of
25 approximately 6,456.7 MMT C02 Eq. The uncertainty interval associated with total C02 emissions, which constitute about
26 82 percent of the total U.S. greenhouse gas emissions in 2017, ranges from -2 percent to 4 percent of total C02 emissions
27 estimated. The results indicate that the uncertainty associated with the inventory estimate of the total CH4 emissions
28 ranges from -9 percent to 14 percent, uncertainty associated with the total inventory N20 emission estimate ranges from
29 -12 percent to 21 percent, and uncertainty associated with fluorinated greenhouse gas (F-GHG) emissions ranges from -
30 0.1 percent to 11 percent.
31 A summary of the overall quantitative uncertainty estimates is shown below.
32 Table A-270: Quantitative Uncertainty Assessment of Overall National Inventory Emissions (MMT C02 Eq. and Percent)
33 - TO BE UPDATED FOR FINAL INVENTORY REPORT
2017 Emission
Standard
Estimate
Uncertainty Range Relative to Emission Estimate9
Meanb
Deviation6
Gas
(MMT COz Eq.)
(MMT COz Eq.)
(%)
(MMT COz Eq.)
Lower
Upper
Lower
Upper
Bound0
Bound0
Bound
Bound
C02
5,270.7
5,154.8
5,499.8
-2%
4%
5,326.0
88.7
CH4d
656.3
596.0
747.6
-9%
14%
670.5
38.7
N2Od
360.5
316.2
434.7
-12%
21%
368.7
30.4
PFC, HFC, SF6, and NF3d
169.1
168.9
188.2
-+%
11%
178.4
5.0
Total Emissions
6,456.7
6,350.6
6,742.9
-2%
4%
6,543.6
101.0
LULUCF Emissionse
15.5
12.9
18.6
-17%
20%
15.7
1.5
LULUCF Carbon Stock Change Fluxf
(729.6)
(1,094.4)
(488.5)
50%
-33%
(793.4)
154.0
LULUCF Sector Net Totals
(714.1)
(1,078.2)
(472.8)
51%
-34%
(777.7)
154.0
Net Emissions (Sources and Sinks)
5,742.6
5,408.2
6,130.0
-6%
7%
5,765.9
183.6
34 + Does not exceed 0.5 percent.
35 3 The lower and upper bounds for emission estimates correspond to a 95 percent confidence interval, with the lower bound corresponding to
36 2.5th percentile and the upper bound corresponding to 97.5th percentile.
A-525
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1 b Mean value indicates the arithmetic average of the simulated emission estimates; standard deviation indicates the extent of deviation of the
2 simulated values from the mean.
3 c The lower and upper bound emission estimates for the sub-source categories do not sum to total emissions because the low and high
4 estimates for total emissions were calculated separately through simulations.
5 d The overall uncertainty estimates did not take into account the uncertainty in the GWP values for CFU, N2O, and high GWP gases used in the
6 inventory emission calculations for 2017.
7 0 LULUCF emissions include the CH4 and N2O emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained Organic Soils,
8 Grassland Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to Coastal Wetlands; and N2O
9 emissions from Forest Soils and Settlement Soils.
10 f LULUCF Carbon Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land, Land Converted to
11 Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to Grassland,
12 Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining Settlements, and Land Converted to Settlements.
13 8 The LULUCF Sector Net Total is the net sum of all CH4 and N2O emissions to the atmosphere plus net carbon stock changes.
14 Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding emissions for which
15 uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with LULUCF.
16 Trend Uncertainty
17 In addition to the estimates of uncertainty associated with the current year's emission estimates, this Annex also
18 presents the estimates of trend uncertainty. The 2006IPCC Guidelines defines trend as the difference in emissions between
19 the base year (i.e., 1990) and the current year (i.e., 2018) Inventory estimates. However, for purposes of understanding
20 the concept of trend uncertainty, the emission trend is defined in this Inventory as the percentage change in the emissions
21 (or removal) estimated for the current year, relative to the emission (or removal) estimated for the base year. The
22 uncertainty associated with this emission trend is referred to as trend uncertainty.
23 Under the Approach 1 method, the trend uncertainty for a source and sink category is estimated using the
24 sensitivity of the calculated difference between the base year and the current year (i.e., 2018) emissions to an incremental
25 (i.e., 1 percent) increase in one or both of these values for that source and sink category. The two sensitivities are expressed
26 as percentages: Type A sensitivity highlights the effect on the difference between the base and the current year emissions
27 caused by a 1 percent change in both, while Type B sensitivity highlights the effect caused by a change to only the current
28 year's emissions. Both sensitivities are simplifications introduced in order to analyze the correlation between the base and
29 the current year estimates. Once calculated, the two sensitivities are combined using the error propagation equation to
30 estimate the overall trend uncertainty.
31 Under the Approach 2 method, the trend uncertainty is estimated using the Monte Carlo Stochastic Simulation
32 technique. The trend uncertainty analysis takes into account the fact that the base and the current year estimates often
33 share input variables. For purposes of the current Inventory, a simple approach has been adopted, under which the base
34 year source or sink category emissions are assumed to exhibit the same uncertainty characteristics as the current year
35 emissions (or removals). Source and sink category-specific PDFs for base year estimates were developed using current year
36 (i.e., 2018) uncertainty output data. These were adjusted to account for differences in magnitude between the two years'
37 inventory estimates. Then, for each source and sink category, a trend uncertainty estimate was developed using the Monte
38 Carlo method. The overall inventory trend uncertainty estimate was developed by combining all source and sink category-
39 specific trend uncertainty estimates. These trend uncertainty estimates present the range of likely change from base year
40 to 2018 and are shown in Table A-271.
41 Table A-271: Quantitative Assessment of Trend Uncertainty (MMT C02 Eq. and Percent) - TO BE UPDATED FOR FINAL
42 INVENTORY REPORT
Base Year
2017
Emissions
Gas/Source
Emissions9
Emissions
Trend
Trend Range1
3
(MMTCO2 Eq.)
(%)
(%)
Lower
Upper
Bound
Bound
C02
5,121.2
5,270.7
3%
-2%
8%
Fossil Fuel Combustion
4,738.8
4,912.0
4%
-1%
9%
Non-Energy Use of Fuels
119.6
123.2
3%
-34%
60%
Natural Gas Systems
30.0
26.3
-12%
-39%
25%
Cement Production
33.5
40.3
20%
10%
31%
Lime Production
11.7
13.1
12%
9%
16%
Other Process Uses of Carbonates
6.3
10.1
61%
33%
95%
A-526 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2018
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Soda Ash Production
1.4
1.8
22%
8%
39%
Carbon Dioxide Consumption
1.5
4.5
204%
183%
226%
Incineration of Waste
8.0
10.8
36%
13%
62%
Titanium Dioxide Production
1.2
1.7
41%
17%
69%
Aluminum Production
6.8
1.2
-82%
-83%
-82%
Iron and Steel Production & Metallurgical Coke Production
101.6
41.8
-59%
-68%
-47%
Ferroalloy Production
2.2
2.0
-8%
-23%
9%
Glass Production
1.5
1.3
-14%
-20%
-9%
Ammonia Production
13.0
13.2
1%
-5%
8%
Urea Consumption for Non-Agricultural Purposes
3.8
5.0
31%
11%
54%
Phosphoric Acid Production
1.5
1.0
-33%
-50%
-10%
Petrochemical Production
21.2
28.2
33%
23%
44%
Silicon Carbide Production and Consumption
0.4
0.2
-50%
-56%
-43%
Lead Production
0.5
0.5
-12%
-29%
9%
Zinc Production
0.6
1.0
60%
27%
101%
Liming
4.7
3.2
-32%
-786%
763%
Urea Fertilization
2.4
5.1
109%
19%
263%
Petroleum Systems
9.0
23.3
161%
26%
436%
Abandoned Oil and Gas Wells
+
+
12%
-1,368%
1,554%
Magnesium Production and Processing
+
+
123%
98%
152%
Wood Biomass and Biofuel Consumptionc
219.4
116.6
-47%
NE
NE
International Bunker Fuelc'1
103.5
120.1
16%
NE
NE
ch4
779.8
656.3
-16%
-29%
(+)%
Stationary Combustion
8.6
7.8
-9%
-64%
126%
Mobile Combustion
12.9
3.2
-75%
-80%
-69%
Coal Mining
96.5
55.7
-42%
-57%
-23%
Abandoned Underground Coal Mines
7.2
6.4
-11%
-45%
47%
Natural Gas Systems
193.1
165.6
-14%
-40%
22%
Petroleum Systems
42.1
37.7
-10%
-57%
87%
Abandoned Oil and Gas Wells
6.6
6.9
6%
-1,361%
1,356%
Petrochemical Production
0.2
0.3
14%
-54%
174%
Silicon Carbide Production and Consumption
+
+
-67%
-70%
-63%
Iron and Steel Production & Metallurgical Coke Production
+
+
-66%
-74%
-54%
Ferroalloy Production
+
+
-18%
-31%
-3%
Enteric Fermentation
164.2
175.4
7%
-21%
44%
Manure Management
37.1
61.7
66%
6%
159%
Rice Cultivation
16.0
11.3
-29%
-68%
58%
Field Burning of Agricultural Residues
0.1
0.2
82%
-53%
656%
Landfills
179.6
107.7
-40%
-64%
1%
Wastewater T reatment
15.3
14.2
-7%
-36%
33%
Composting
0.4
2.2
464%
149%
1,216%
Incineration of Waste
+
+
-32%
NE
NE
International Bunker Fuelsd
0.2
0.1
-44%
NE
NE
n2o
370.3
360.5
-3%
-20%
22%
Stationary Combustion
25.1
28.6
14%
-35%
101%
Mobile Combustion
42.0
16.9
-60%
-65%
-53%
Natural Gas Systems
+
+
438%
327%
578%
Petroleum Systems
+
+
77%
11%
178%
Adipic Acid Production
15.2
7.4
-51%
-55%
-48%
Nitric Acid Production
12.1
9.3
-23%
-28%
-18%
Manure Management
14.0
18.7
34%
-12%
105%
Agricultural Soil Management
251.7
266.4
6%
-21%
44%
Field Burning of Agricultural Residues
+
0.1
72%
-50%
488%
Wastewater T reatment
3.4
5.0
46%
-68%
556%
N20 from Product Uses
4.2
4.2
+%
-30%
42%
Caprolactam, Glyoxal, and Glyoxylic Acid Production
1.7
1.4
-16%
-47%
34%
Incineration of Waste
0.5
0.3
-32%
-84%
192%
Settlement Soils
1.4
2.5
72%
-10%
222%
Composting
0.3
1.9
464%
152%
1,149%
Semiconductor Manufacture
+
0.2
597%
490%
722%
A-527
-------
International Bunker Fuelsd
0.9
1.0
19%
NE
NE
HFCs, PFCs, SF6, and NF3
130.8
169.1
29%
24%
45%
Substitution of Ozone Depleting Substances
31.4
152.7
386%
347%
429%
HCFC-22 Production
46.1
5.2
-89%
-91%
-87%
Semiconductor Manufacture6
3.6
4.7
31%
21%
42%
Aluminum Production
21.5
1.1
-95%
-95%
-94%
Electrical Transmission and Distribution
23.1
4.3
-81%
-85%
-77%
Magnesium Production and Processing
5.2
1.2
-78%
-82%
-77%
Total Emissions'
6,402.1
6,456.7
1%
-3%
5%
LULUCF Emissions^
7.8
15.5
99%
60%
169%
LULUCF Carbon Stock Changeh
(814.8)
(729.6)
-10%
-50%
62%
LULUCF Sector Net Total1
(807.0)
(714.1)
-12%
-51%
62%
Net Emissions (Sources and Sinks)'
5,595.1
5,742.6
3%
-7%
13%
1 + Does not exceed 0.05 MMT CO2 Eq. or 0.5 percent.
2 NE (Not Estimated)
3 3 Base Year is 1990 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen 1995.
4 bThe trend range represents a 95 percent confidence interval for the emission trend, with the lower bound corresponding to 2.5th percentile
5 value and the upper bound corresponding to 97.5th percentile value.
6 c Emissions from Wood Biomass and Biofuel Consumption are not included specifically in summing energy sector totals.
7 d Emissions from International Bunker Fuels are not included in the totals.
8 eThis source category's estimate for 2017 excludes 0.023 MMT CO2 Eq. of HTF emissions, as uncertainties associated with those sources were
9 not assessed. Hence, forth is source category, the emissions reported in this table do not match the emission estimates presented in the
10 Industrial Processes and Product Use chapter of the Inventory.
11 'Totals exclude emissions for which uncertainty was not quantified.
12 8 LULUCF emissions include the CH4 and N2O emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained Organic Soils, Grassland
13 Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to Coastal Wetlands; and N2O emissions from
14 Forest Soils and Settlement Soils.
15 h LULUCF Carbon Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land, Land Converted to
16 Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to Grassland,
17 Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining Settlements, and Land Converted to Settlements.
18 1 The LULUCF Sector Net Total is the net sum of all CH4 and N2O emissions to the atmosphere plus net carbon stock changes.
19 Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding emissions for
20 which uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with LULUCF.
21
22 7.3. Reducing Uncertainty
23 There have been many improvements in reducing uncertainties across source and sink categories over the last
24 several years. These improvements are result of new data sources that provide more accurate data or more coverage, as
25 well as methodological improvements. Several source categories now use the U.S. EPA's GHGRP reported data, which is
26 an improvement over prior methods using default emission factors and provides more country-specific data for Inventory
27 calculations. EPA's GHGRP relies on facility-level data which undergoes a multi-step verification process, including
28 automated data checks to ensure consistency, comparison against expected ranges for similar facilities and industries, and
29 statistical analysis.
30 For example, the use of EPA's GHGRP reported data to estimate CH4 emissions from Coal Mining resulted in the
31 uncertainty bounds of -9 to 19 percent in the 1990 to 2017 Inventory, which was an improvement over the uncertainty
32 bounds in the 1990 to 2011 Inventory of -15 to 18 percent. Prior to 2012, Coal Mining emissions were estimated using an
33 array of emission factor estimations with higher assumed uncertainty. Estimates of CH4 emissions from MSW landfills were
34 also revised with the availability of GHGRP reported data resulting in methodological and data quality improvements that
35 reduced uncertainty. Previously, MSW landfill emissions estimates were calculated using a model and default factors with
36 higher assumed uncertainty.
37 Due to the availability of GHGRP reported data, Semiconductor Manufacturing emissions methodology as well as
38 the uncertainty model was revised for the 1990 to 2012 Inventory. The revised model to estimate uncertainty relies on
39 analysis conducted during the development of the EPA's GHGRP Subpart I rulemaking to estimate uncertainty associated
40 with facility-reported emissions. These results were applied to the GHGRP-reported data as well as to the non-reported
41 emissions. An improved methodology to estimate non-reported emissions along with improved methodology to estimate
42 uncertainty of these non-reported emissions led to a reduced overall uncertainty of -6 to 6 percent in the 1990 to 2017
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44
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46
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Inventory compared against a range of-8 to 9 percent in the 1990 to 2011 Inventory for the emissions of F-GHGs from the
Semiconductor Manufacturing source category.
7.4. Planned Improvements
Identifying the sources of uncertainty in the emission and removal estimates of the Inventory and quantifying
the magnitude of the associated uncertainty is the crucial first step towards improving those estimates. Quantitative
assessment of the parameter uncertainty may also provide information about the relative importance of input parameters
(such as activity data and emission factors), based on their relative contribution to the uncertainty within the source or
sink category estimates. Such information can be used to prioritize resources with a goal of reducing uncertainty over time
within or among inventory source categories and their input parameters. In the current Inventory, potential sources of
model uncertainty have been identified for some emission source categories, and uncertainty estimates based on their
parameters' uncertainty have been developed for all the emission source categories, with the exception of CH4 from
Incineration of Waste, and the International Bunker Fuels, C02 from Wood Biomass and Biofuel Consumption, and Indirect
Greenhouse Gas Emissions source categories, which are not included in the energy sector totals. C02 Emissions from Wood
Biofuel and Ethanol Consumption, however, 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 does include an estimate of C02
emissions from Wood Biomass and Biofuel Consumption in total emissions estimates, but rather it is provided as a memo
item for informational purposes.
Specific areas that require further research to reduce uncertainties and improve the quality of uncertainty
estimates include:
• 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, Forestry and Other Land Use (AFOLU) Sector.
• Incorporating excluded emission sources. Quantitative estimates for some of the sources and sinks of
greenhouse gas emissions, such as from some land-use activities, industrial processes, and parts of mobile
sources, could not be developed at this time either because data are incomplete or because methodologies do
not exist for estimating emissions from these source categories. See Annex 5 of this report for a discussion of
the sources of greenhouse gas emissions and sinks excluded from this report. In the future, consistent with
IPCC good practice principles, efforts will focus on estimating 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.
• Improving the accuracy of emission factors. Further research is needed in some cases to improve the accuracy
of emission factors used to calculate emissions from a variety of sources. For example, the accuracy of current
emission factors applied to CH4 and N20 emissions from stationary and mobile combustion are highly uncertain,
and research is underway to improve these emission factors.
• Collecting detailed activity data. Although methodologies exist for estimating emissions for some sources,
problems arise in obtaining activity data at a level of detail in which aggregate emission factors can be applied.
• Improving models. Improving 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.
• Collecting more measured data and using more precise measurement methods. Uncertainty associated with
bias and random sampling error can be reducing by increasing the sample size and filling in data gaps.
Measurement error can be reduced by using more precise measurement methods, avoiding simplifying
assumption, and ensuring that measurement technologies are appropriately used and calibrated.
• Refine source and sink category and overall uncertainty estimates. For many individual source categories,
further research is needed to more accurately characterize PDFs that surround emissions modeling input
variables. This might involve using measured or published statistics or implementing rigorous elicitation
protocol to elicit expert judgments, if published or measured data are not available. For example, activity data
provided by EPA's GHGRP are used to develop estimates for several source categories—including but not
A-529
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22
23
24
25
26
27
28
limited to Magnesium Production and Processing, Semiconductor Manufacturing, and Electrical Transmission
and Distribution—and could potentially be implemented for additional source categories to improve
uncertainty results, where appropriate.
• Improve characterization of trend uncertainty associated with base year Inventory estimates. The
characterization of base year uncertainty estimates could be improved, by developing explicit uncertainty
models for the base year. This would then improve the analysis of trend uncertainty. However, not all of the
simplifying assumptions described in the "Trend Uncertainty" section above may be eliminated through this
process due to a lack of availability of more appropriate data.
• Improving state of knowledge and eliminating known risk of bias. Use expert judgment to improve the
understanding of categories and processes leading to emissions and removals. Ensure methodologies, models,
and estimation procedures are used appropriately and as advised by 2006IPCC Guidelines.
7.5. Summary 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, as needed. EPA provides additional documentation on uncertainty
information consistent with the guidance presented in Table 3.3 in Vol. 1, Chapter 3 of the 2006 IPCC 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. All uncertainty estimates are reported relative to the
current Inventory estimates for the 95 percent confidence interval, unless otherwise specified.
References
EPA (2002) Quality Assurance/Quality Control and Uncertainty Management Plan for the U.S. Greenhouse Gas Inventory:
Procedures Manual for Quality Assurance/Quality Control and Uncertainty Analysis, U.S. Greenhouse Gas Inventory
Program, U.S. Environmental Protection Agency, Office of Atmospheric Programs, EPA 430-R-02-007B, June 2002.
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.
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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 promotingthe openness of the 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 of the 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 of the 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 of the emission estimates, to publication of the
Inventory.
• Quality Assurance: expert and public reviews for both the Inventory estimates and the report (which is the
primary vehicle for disseminating the results of the 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
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reflect new information that becomes available as the program develops, methods are improved, or additional
supporting documents become necessary.
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 2006 IPCC Guidelines has been undertaken. Where QA/QC activities for a particular source go beyond the general
level, and include category-specific checks, further explanation is provided within the respective source 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.
Figure A-21: U.S. QA/QC Plan Summary
Obtain data in electronic
format (if possible)
Review spreadsheet
construction
Avoid hardwiring
• Use data va I i dation
Protect cells
Develop automatic
checkers for:
• Outliers, negative
values, or missing
data
Variable types
match values
Time series
consistency
Maintain trackingtab for
status of gathering
efforts
Check input data for
transcription errors
Inspect automatic
checkers
Identify spreadsheet
modifications that could
provide additional
QA/QC checks
Contact reports for non-
electronic communications
Provide cell references for
primary data elements
Obtain copies of all data
sources
~stand location of any
working/external
spreadsheets
Document assumptions
Check citations in
spreadsheetandtextfor
accuracy and style
Check reference docket for
new citations
Review documentation for
any data / methodology
changes
Clearly label parameters,
units, and conversion
factors
Review spreadsheet
integrity
• Equations
¦ Units
• Inputs and outputs
Develop automated
checkers for:
• Input ranges
• Calculations
• Emission aggregation
Reproduce calculations
Review time series
consistency
Review changes in
data/consistency with IPCC
methodology
Common starting
versions for each
inventoryyear
Utilize unalterable
summarytab foreach
source spreadsheet for
linkingto a master
summary spreadsheet
Follow strictversion
control procedures
Document QA/QC
procedures
Data Gathering
Data Documentation CalculatingEmissions
Cross-Cutting
Coordination
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
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1 by the Environmental Protection Agency151 and A Summary of General Assessment Factors for Evaluating the Quality of
2 Scientific and Technical Information.152 This includes evaluating the data and models used as inputs into the Inventory
3 against the five general assessment factors: soundness, applicability and utility, clarity and completeness, uncertainty
4 and variability, evaluation and review. Table A-272 defines each factor and explains how it was considered during the
5 process of creating the current Inventory.
6 Table A-272: Assessment Factors and Definitions153
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 1 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 20 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
151 EPA report #260R-02-008, October 2002, Available online at .
152 EPA report #100/B-03/001, June 2003, Available online at , and Addendum to: A Summary of General Assessment Factors for
153 Evaluating the Quality of Scientific and Technical Information, December 2012, Available online at
.
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explained and documented in the report consistent with
UNFCCC reporting guidelines.
Uncertainty and
Variability (AF4)
The extent to which the
variability and uncertainty
(quantitative and qualitative) in
the information or in the
procedures, measures,
methods or models are
evaluated and characterized.
The evaluation of uncertainties for underlying data is
documented in the Uncertainty section of the Annex to the
Inventory of U.S. Greenhouse Gas Emissions and Sinks. In
accordance with the 2006 IPCC Guidelines, the uncertainty
associated with the Inventory's underlying data, methodology,
and models was evaluated by running a Monte Carlo
uncertainty analysis on source category emissions data to
produce a 95 percent confidence interval for the annual
greenhouse gas emissions for that source. To develop overall
uncertainty estimates, the Monte Carlo simulation output data
for each emission source 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. 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.
1
2 8.4. Responses During the Review Process
3 EPA is continually working to improve transparency, accuracy, completeness, comparability, and consistency of
4 emission estimates in the Inventory in response to the feedback received during the Expert, Public, and UNFCCC Review
5 periods, as well as stakeholder outreach. For instance, as mentioned in the Planned Improvements section of the
6 Petroleum and Natural Gas Systems source categories (Section 3.6 and 3.7), EPA has engaged in stakeholder outreach to
7 increase the transparency in the Inventory methodology and to identify supplemental data sources that can lead to
8 methodological improvements. During the annual preparation of the Inventory of U.S. Greenhouse Gas Emissions and
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Sinks, In considering and prioritizing improvements, EPA reviews the significance of the 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.154
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 inventories155
• UNFCCC Review Process and Guidelines for annual national greenhouse gas inventories156
• Inventory Review reports of annual submissions (latest reviews).157
The findings from the UNFCCC expert review of the April 2019 Inventory submission completed October 7-12, 2019 were
not available to EPA at the time of publication of this draft Inventory (i.e., February 2020) to enable EPA to provide accurate
responses on how ERT recommendations have been reflected in this latest draft Inventory (i.e. to finalize for submission
in 2020). Following receipt of the final review report from the UNFCCC ERT, this Annex will include an update of Table A-
287 which was included in Annex 8 of the previous Inventory (i.e., 2018 submission). The update will include responses to
the latest recommendations to facilitate future reviews.
154 See .
155 Available online at: .
156 Available online at: .
157 Available online at: .
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34
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 2006 IPCC 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. GHG 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 facilities158 in the United States. The program implementing the rule, 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"159), including direct greenhouse gas emitters,160 fossil fuel suppliers, industrial gas suppliers, and facilities that
inject carbon dioxide (C02) underground for sequestration or other reasons.161 In general, the threshold for reporting is
25,000 metric tons or more of C02 Eq. per year.162
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.163
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.164 More information on the GHGRP can be found at https://www.epa.gov/ghgreporting.
158 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).
159 See .
160 Data reporting by affected facilities includes the reporting of emissions from fuel combustion at that affected facility.
161 See and .
162 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.
163 See GHGRP Verification Fact Sheet https://www.epa.gov/sites/production/files/2015-
07/documents/ghgrp_verification_factsheet.pdf.
164 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-273 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),165 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 categories estimates.166 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 Process 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.167 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).
165 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.
166 See .
167U.S. EPA Greenhouse Gas Reporting Program. Confidential Business Information GHG Reporting. See
.
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1 Table A-273: Summary of EPA GHGRP Data Use in U.S. GHG Inventory
GHG Inventory
Category
GHGRP Industry
Subpart
Initial Calendar
Year of Reporting
under GHGRP
Reporting
Threshold
168
Type of GHGRP Data Use
National GHG
Inventory
Report (NIR)
Section with
details on
data use
Emissions
or Quantity
Supplied
Emission
Factor (EF)
Activity
Data (AD)
QA/QC
169
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
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
Carbon Dioxide
Consumption
PP-Suppliers of
Carbon Dioxide
2010
Y
•
4.15
168 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 GHG emissions is discussed in the respective source category
methodology sections.
169 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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GHG Inventory
Category
GHGRP Industry
Subpart
Initial Calendar
Year of Reporting
under GHGRP
Reporting
Threshold
168
Type of GHGRP Data Use
National GHG
Inventory
Report (NIR)
Section with
details on
data use
Emissions
or Quantity
Supplied
Emission
Factor (EF)
Activity
Data (AD)
QA/QC
169
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
1 - Electronics
Manufacturing
2011
Y
•
4.23
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GHG Inventory
Category
GHGRP Industry
Subpart
Initial Calendar
Year of Reporting
under GHGRP
Reporting
Threshold
168
Type of GHGRP Data Use
National GHG
Inventory
Report (NIR)
Section with
details on
data use
Emissions
or Quantity
Supplied
Emission
Factor (EF)
Activity
Data (AD)
QA/QC
169
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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