Annexes to the Inventoiy of U.S. GHG Emissions
and Sinks
The following eight 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 2013, 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 CO2 emissions from fossil fuel combustion, the
carbon content of fossil fuels, and the amount of carbon stored in products from non-energy uses of fossil fuels. Annex 3
discusses the methodologies used for a number of individual source categories in greater detail than was presented in the
main body of the report and includes explicit activity data and emission factor tables. Annex 4 presents the IPCC reference
approach for estimating CO2 emissions from fossil fuel combustion. Annex 5 addresses the criteria for the inclusion of an
emission source 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. Finally, Annex 8 provides information on the QA/QC
methods and procedures used in the development of the Inventory.
Annexes to the Inventory of U.S. GHG Emissions and Sinks 1
ANNEX 1 Key Category Analysis 2
ANNEX 2 Methodology and Data for Estimating CO2 Emissions from Fossil Fuel Combustion 32
2.1. Methodology for Estimating Emissions of CO2 from Fossil Fuel Combustion 32
2.2. Methodology for Estimating the Carbon Content of Fossil Fuels 68
2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil Fuels 105
ANNEX 3 Methodological Descriptions for Additional Source or Sink Categories 133
3.1. Methodology for Estimating Emissions of CH4, N2O, and Indirect Greenhouse Gases from Stationary Combustion 133
3.2. Methodology for Estimating Emissions of CH4, N2O, and Indirect Greenhouse Gases from Mobile Combustion and
Methodology for and Supplemental Information on Transportation-Related GHG Emissions 141
3.3. Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel Consumption 184
3.4. Methodology for Estimating CH: Emissions from Coal Mining 189
3.5. Methodology for Estimating CH: and CO2 Emissions from Petroleum Systems 197
3.6. Methodology for Estimating CH: and CO2 Emissions from Natural Gas Systems 202
3.7. Methodology for Estimating CO2, CH4, and N2O Emissions from the Incineration of Waste 209
3.8. Methodology for Estimating Emissions from International Bunker Fuels used by the U.S. Military 216
3.9. Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances 222
3.10. Methodology for Estimating CH: Emissions from Enteric Fermentation 246
3.11. Methodology for Estimating CH: and N2O Emissions from Manure Management 271
3.12. Methodology for Estimating N2O Emissions, CH4 Emissions and Soil Organic C Stock Changes from Agricultural Lands
(Cropland and Grassland) 303
3.13. Methodology for Estimating Net Carbon Stock Changes in Forest Land Remaining Forest Land and Land Converted to
Forest Land 368
3.14. Methodology for Estimating CH: Emissions from Landfills 403
ANNEX 4 IPCC Reference Approach for Estimating CO2 Emissions from Fossil Fuel Combustion 416
ANNEX 5 Assessment of the Sources and Sinks of Greenhouse Gas Emissions Not Included 426
ANNEX 6 Additional Information 435
6.1. Global Warming Potential Values 435
6.2. Ozone Depleting Substance Emissions 446
6.3. Sulfur Dioxide Emissions 448
ANNEX 7 Uncertainty 465
7.1. Overview 465
7.2. Methodology and Results 465
7.3. Reducing Uncertainty 471
7.4. Planned Improvements 472
7.5. Summary Information on Uncertainty Analyses by Source and Sink Category 473
ANNEX 8 QA/QC Procedures 487
8.1. Background 487
8.2. Purpose 487
8.3. Assessment Factors 488
8.4. Responses During the Review Process 490
A-1
-------
ANNEX 1 Key Category Analysis
The United States has identified national key categories based on the estimates presented in this report. The 2006
Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories (IPCC 2006)
describes a key category as a "[category] that is prioritized within the national inventory system because its estimate has a
significant influence on a country's total inventory of greenhouse gases in terms of the absolute level, the trend, or the
uncertainty in emissions and removals." By definition, key categories are sources or sinks that have the greatest contribution
to the absolute overall level of national emissions in any of the years covered by the time series. In addition, when an entire
time series of emission estimates is prepared, a determination of key categories must also account for the influence of the
trends of individual categories. Therefore, a trend assessment is conducted to identify source and sink categories for which
significant uncertainty in the estimate would have considerable effects on overall emission trends. Finally, a qualitative
evaluation of key categories should be performed, in order to capture any key categories that were not identified in either of
the quantitative analyses, but can be considered key because of the unique country-specific estimation methods.
The methodology for conducting a key category analysis, as defined by 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. 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 2016. The table also indicates the
criteria used in identifying these categories (i.e., level, trend, Approach 1, Approach 2, and/or qualitative assessments).
A-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-1: Key Source Categories forthe United States 11990-2016)
IPCC Source/Sink Categories
Greenhouse
Gas
Approach 1
Approach 2
Quala
2016
Emissions
(MMT C02
Eq.)
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
Energy
CO2 Emissions from Mobile
Combustion: Road
C02
.
1,496.0
CO2 Emissions from Stationary
Combustion - Coal - Electricity
Generation
C02
.
1,241.4
CO2 Emissions from Stationary
Combustion - Gas - Electricity
Generation
C02
.
546.0
CO2 Emissions from Stationary
Combustion - Gas - Industrial
C02
.
477.9
CO2 Emissions from Stationary
Combustion - Oil - Industrial
C02
.
272.5
CO2 Emissions from Stationary
Combustion - Gas - Residential
C02
238.3
CO2 Emissions from Stationary
Combustion - Gas - Commercial
C02
.
170.3
CO2 Emissions from Mobile
Combustion: Aviation
C02
167.4
CO2 Emissions from Non-Energy Use
of Fuels
C02
.
112.2
CO2 Emissions from Mobile
Combustion: Other
C02
80.2
CO2 Emissions from Stationary
Combustion - Oil - Commercial
C02
58.7
CO2 Emissions from Stationary
Combustion - Coal - Industrial
C02
.
58.7
CO2 Emissions from Stationary
Combustion - Oil - Residential
C02
•
54.2
CO2 Emissions from Mobile
Combustion: Marine
C02
39.0
CO2 Emissions from Stationary
Combustion - Oil - U.S. Territories
C02
34.3
CO2 Emissions from Natural Gas
Systems
C02
25.5
CO2 Emissions from Petroleum
Systems
C02
.
.
22.8
A-3
-------
CO2 Emissions from Stationary
Combustion - Oil - Electricity
Generation
C02
.
.
21.4
CO2 Emissions from Stationary
Combustion - Gas - U.S. Territories
C02
•
3.0
CO2 Emissions from Stationary
Combustion - Coal - Commercial
C02
•
2.2
CO2 Emissions from Stationary
Combustion - Coal - Residential
C02
•
0.0
CH4 Emissions from Natural Gas
Systems
CH4
.
.
163.5
Fugitive Emissions from Coal Mining
ch4
.
.
53.8
CH4 Emissions from Petroleum
Systems
ch4
•
•
38.6
CH4 Emissions from Abandoned Oil
and Gas Wells
ch4
•
7.1
Non-C02 Emissions from Stationary
Combustion - Residential
cm
.
3.4
CH4 Emissions from Mobile
Combustion: Other
cm
•
2.1
Non-C02 Emissions from Stationary
Combustion - Electricity Generation
n2o
•
•
14.9
N2O Emissions from Mobile
Combustion: Road
n2o
.
•
13.2
Non-C02 Emissions from Stationary
Combustion - Industrial
n2o
•
2.5
International Bunker Fuelsb
Several
•
117.7
Industrial Processes and Product Use
CO2 Emissions from Iron and Steel
Production & Metallurgical Coke
Production
C02
.
.
42.3
CO2 Emissions from Cement
Production
C02
•
39.4
CO2 Emissions from Petrochemical
Production
C02
.
28.1
N2O Emissions from Adipic Acid
Production
N20
7.0
Emissions from Substitutes for Ozone
Depleting Substances
HiGWP
.
.
159.1
SFe Emissions from Electrical
Transmission and Distribution
HiGWP
•
4.3
HFC-23 Emissions from HCFC-22
Production
HiGWP
.
•
2.8
A-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
PFC Emissions from Aluminum
Production
HiGWP
•
•
1.4
Agriculture
Cm Emissions from Enteric
Fermentation
CH4
170.1
CH4 Emissions from Manure
Management
ch4
.
.
67.7
CH4 Emissions from Rice Cultivation
ch4
•
13.7
Direct N2O Emissions from Agricultural
Soil Management
N2O
.
.
237.6
Indirect N2O Emissions from Applied
Nitrogen
N2O
.
.
45.9
Waste
CH4 Emissions from Landfills
CH4
.
.
107.7
Land Use, Land Use Change, and Forestry
Net CO2 Emissions from Land
Converted to Settlements
CO2
68.0
Net CO2 Emissions from Land
Converted to Cropland
CO2
23.8
CO2 Emissions from Land Converted
to Grassland
CO2
22.0
Net CO2 Emissions from Grassland
Remaining Grassland
CO2
(1.6)
Net CO2 Emissions from Cropland
Remaining Cropland
CO2
•
(9.9)
Net CO2 Emissions from Land
Converted to Forest Land
CO2
•
(75.0)
Net CO2 Emissions from Settlements
Remaining Settlements
CO2
•
•
(103.7)
Net CO2 Emissions from Forest Land
Remaining Forest Land
CO2
•
•
(670.5)
CH4 Emissions from Forest Fires
CH4
•
18.5
N2O Emissions from Forest Fires
N2O
•
12.2
Subtotal Without LULUCF
6,348.5
Total Emissions Without LULUCF
6,511.3
Percent of Total Without LULUCF
97%
Subtotal With LULUCF
5,610.8
Total Emissions With LULUCF
5,794.5
Percent of Total With LULUCF
97%
'Qualitative criteria.
b Emissions from this source not included in totals.
Note: Parentheses indicate negative values (or sequestration).
A-5
-------
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 2016)
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
2016 Emissions
Key
ID
Level in which
IPCC Source Categories
Gas
(MMT CO2 Eq.)
Category?
Criteria3
year(s)?b
Energy
CO2 Emissions from Mobile Combustion: Road
C02
1,496.0
Li T1 L2T2
1990, 2016
CO2 Emissions from Stationary Combustion - Coal -
Electricity Generation
C02
1,241.4
Li T1 L2T2
1990, 2016
CO2 Emissions from Stationary Combustion - Gas -
Electricity Generation
C02
546.0
Li T1 L2T2
1990, 2016
CO2 Emissions from Stationary Combustion - Gas -
Industrial
C02
477.9
Li T1 L2T2
1990, 2016
CO2 Emissions from Stationary Combustion - Oil -
Industrial
C02
272.5
Li T1 L2T2
1990, 2016
CO2 Emissions from Stationary Combustion - Gas -
Residential
C02
238.3
Li L2
1990, 2016
CO2 Emissions from Stationary Combustion - Gas -
Commercial
C02
170.3
Li T1 L2T2
1990, 2016
CO2 Emissions from Mobile Combustion: Aviation
C02
167.4
Li T1 L2
1990, 2016
CO2 Emissions from Non-Energy Use of Fuels
C02
112.2
Li T1 L2T2
1990, 2016
CO2 Emissions from Mobile Combustion: Other
C02
80.2
Li
1990i, 2016i
CO2 Emissions from Stationary Combustion - Oil -
Commercial
C02
58.7
Li T1
1990i, 2016i
CO2 Emissions from Stationary Combustion - Coal -
Industrial
C02
58.7
Li T1 L2T2
1990, 2016
CO2 Emissions from Stationary Combustion - Oil -
Residential
C02
54.2
Li T1 L2T2
1990,20161
CO2 Emissions from Mobile Combustion: Marine
C02
39.0
Li T1
1990i, 2016i
CO2 Emissions from Stationary Combustion - Oil - U.S.
Territories
C02
34.3
Li T1
1990i, 2016i
CO2 Emissions from Natural Gas Systems
C02
25.5
Li
1990i
CO2 Emissions from Petroleum Systems
C02
22.8
T1 L2 T2
20162
CO2 Emissions from Stationary Combustion - Oil -
Electricity Generation
C02
21.4
Li T1 L2T2
1990
CO2 Emissions from Incineration of Waste
C02
10.7
CO2 Emissions from Stationary Combustion - Coal -
C02
4.0
U.S. Territories
CO2 Emissions from Stationary Combustion - Gas -
C02
3.0
T2
U.S. Territories
CO2 Emissions from Stationary Combustion - Coal -
C02
2.2
T1
Commercial
CO2 Emissions from Stationary Combustion -
C02
0.4
Geothermal Energy
CO2 Emissions from Abandoned Oil and Gas Wells
C02
+
CO2 Emissions from Stationary Combustion - Coal -
C02
0.0
T2
Residential
CH4 Emissions from Natural Gas Systems
ch4
163.5
•
Li T1 L2T2
1990, 2016
A-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Fugitive Emissions from Coal Mining
CH4
53.8
Li T1 L2T2
1990, 2016
Cm Emissions from Petroleum Systems
ch4
38.6
Li L2
1990, 2016
CH4 Emissions from Abandoned Oil and Gas Wells
ch4
7.1
L2
19902,20162
Fugitive Emissions from Abandoned Underground Coal
Mines
ch4
6.7
Non-C02 Emissions from Stationary Combustion -
Residential
ch4
3.4
L2 T2
19902,20162
CH4 Emissions from Mobile Combustion: Other
ch4
2.1
T2
Non-C02 Emissions from Stationary Combustion -
Industrial
ch4
1.6
Non-C02 Emissions from Stationary Combustion -
ch4
1.2
Commercial
Non-C02 Emissions from Stationary Combustion -
ch4
1.1
Electricity Generation
CH4 Emissions from Mobile Combustion: Road
ch4
1.1
CH4 Emissions from Mobile Combustion: Marine
ch4
0.3
Non-C02 Emissions from Stationary Combustion - U.S.
ch4
0.1
Territories
CH4 Emissions from Mobile Combustion: Aviation
ch4
+
CH4 Emissions from Incineration of Waste
ch4
+
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
N2O
14.9
T1 L2 T2
20162
N2O Emissions from Mobile Combustion: Road
N2O
13.2
Li T1 L2T2
1990
N2O Emissions from Mobile Combustion: Other
N2O
3.2
Non-C02 Emissions from Stationary Combustion -
Industrial
N2O
2.5
L2
19902
N2O Emissions from Mobile Combustion: Aviation
N2O
1.5
Non-C02 Emissions from Stationary Combustion -
N2O
0.7
Residential
N2O Emissions from Mobile Combustion: Marine
N2O
0.5
Non-C02 Emissions from Stationary Combustion -
N2O
0.3
Commercial
N2O Emissions from Incineration of Waste
N2O
0.3
Non-C02 Emissions from Stationary Combustion - U.S.
N2O
0.1
Territories
International Bunker Fuelsc
Several
117.7
•
Industrial Processes and Product Use
CO2 Emissions from Iron and Steel Production &
C02
42.3
Li T1 L2T2
1990, 2016
Metallurgical Coke Production
CO2 Emissions from Cement Production
C02
39.4
Li
1990i, 2016i
CO2 Emissions from Petrochemical Production
C02
28.1
Li T1
2016i
CO2 Emissions from Lime Production
C02
12.9
CO2 Emissions from Ammonia Production
C02
12.2
CO2 Emissions from Other Process Uses of
Carbonates
C02
11.0
CO2 Emissions from Carbon Dioxide Consumption
C02
4.5
CO2 Emissions from Urea Consumption for Non-Ag
Purposes
C02
4.0
CO2 Emissions from Ferroalloy Production
C02
1.8
CO2 Emissions from Soda Ash Production
C02
1.7
CO2 Emissions from Titanium Dioxide Production
C02
1.6
CO2 Emissions from Aluminum Production
C02
1.3
CO2 Emissions from Glass Production
C02
1.2
CO2 Emissions from Phosphoric Acid Production
C02
1.0
CO2 Emissions from Zinc Production
C02
0.9
CO2 Emissions from Lead Production
C02
0.5
CO2 Emissions from Silicon Carbide Production and
C02
0.2
Consumption
A-7
-------
CO2 Emissions from Magnesium Production and
CO2
Processing
CH4 Emissions from Petrochemical Production
CH4
0.2
CH4 Emissions from Ferroalloy Production
ch4
+
CH4 Emissions from Silicon Carbide Production and
ch4
Consumption
+
CH4 Emissions from Iron and Steel Production &
ch4
Metallurgical Coke Production
+
N2O Emissions from Nitric Acid Production
N2O
10.2
N2O Emissions from Adipic Acid Production
N2O
7.0
T1
N2O Emissions from Product Uses
N2O
4.2
N2O Emissions from Caprolactam, Glyoxal, and
N2O
2.0
Glyoxylic Acid Production
N2O Emissions from Semiconductor Manufacture
N2O
0.2
Emissions from Substitutes for Ozone Depleting
Substances
HiGWP
159.1
Li T1 L2T2
2016
PFC, HFC, SFe, and NF3 Emissions from
HiGWP
4.7
Semiconductor Manufacture
SF6 Emissions from Electrical Transmission and
HiGWP
4.3
T1T2
Distribution
HFC-23 Emissions from HCFC-22 Production
HiGWP
2.8
L1T1T2
1990i
PFC Emissions from Aluminum Production
HiGWP
1.4
T1T2
SF6 Emissions from Magnesium Production and
HiGWP
1.0
Processing
HFC-134a Emissions from Magnesium Production and
HiGWP
0.1
Processing
Agriculture
CO2 Emissions from Urea Fertilization
CO2
5.1
CO2 Emissions from Liming
CO2
3.9
CH4 Emissions from Enteric Fermentation
CH4
170.1
Li L2
1990, 2016
CH4 Emissions from Manure Management
ch4
67.7
Li T1 L2T2
1990, 2016
CH4 Emissions from Rice Cultivation
ch4
13.7
L2 T2
19902,20162
CH4 Emissions from Field Burning of Agricultural
ch4
0.3
Residues
Direct N2O Emissions from Agricultural Soil
N2O
237.6
Li T1 L2T2
1990, 2016
Management
Indirect N2O Emissions from Applied Nitrogen
N2O
45.9
Li T1 L2T2
1990, 2016
N2O Emissions from Manure Management
N2O
18.1
N2O Emissions from Field Burning of Agricultural
Residues
N2O
0.1
Waste
CH4 Emissions from Landfills
CH4
107.7
Li T1 L2T2
1990, 2016
CH4 Emissions from Wastewater Treatment
ch4
14.8
CH4 Emissions from Composting
ch4
2.1
N2O Emissions from Wastewater Treatment
N2O
5.0
N2O Emissions from Composting
N2O
1.9
+ Does not exceed 0.05 MMT CO2 Eq.
a For the 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 for the Approach 2
assessment only in 1990).
'Emissions from these sources not included in totals.
Note: LULUCF sources and sinks are not included in this analysis.
A-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-3: U.S. Greenhouse Gas Inventory Source Categories with LULUCF
Direct
Greenhouse
2016 Emissions
Key
ID
Level in which
IPCC Source/Sink Categories
Gas
(MMT CO2 Eq.)
Category?
Criteria3
year(s)?b
Energy
CO2 Emissions from Mobile Combustion: Road
C02
1,496.0
Li T1 L2T2
1990,2016
CO2 Emissions from Stationary Combustion - Coal -
Electricity Generation
C02
1,241.4
Li T1 L2T2
1990,2016
CO2 Emissions from Stationary Combustion - Gas -
Electricity Generation
C02
546.0
Li T1 L2T2
19901,2016
CO2 Emissions from Stationary Combustion - Gas -
Industrial
C02
477.9
Li T1 L2T2
1990,2016
CO2 Emissions from Stationary Combustion - Oil -
Industrial
C02
272.5
Li T1 L2T2
1990,2016
CO2 Emissions from Stationary Combustion - Gas -
Residential
C02
238.3
Li L2
1990,2016
CO2 Emissions from Stationary Combustion - Gas -
Commercial
C02
170.3
Li T1 L2
19901,2016
CO2 Emissions from Mobile Combustion: Aviation
C02
167.4
Li T1 L2
1990, 2016i
CO2 Emissions from Non-Energy Use of Fuels
C02
112.2
Li T1 L2
1990,2016
CO2 Emissions from Mobile Combustion: Other
C02
80.2
Li
1990i, 2016i
CO2 Emissions from Stationary Combustion - Oil -
Commercial
C02
58.7
Li T1
1990i, 2016i
CO2 Emissions from Stationary Combustion - Coal -
Industrial
C02
58.7
Li T1 L2T2
1990, 2016i
CO2 Emissions from Stationary Combustion - Oil -
Residential
C02
54.2
Li T1
1990i, 2016i
CO2 Emissions from Mobile Combustion: Marine
C02
39.0
Li T1
1990i, 2016i
CO2 Emissions from Stationary Combustion - Oil -
U.S. Territories
C02
34.3
Li T1
1990i, 2016i
CO2 Emissions from Natural Gas Systems
C02
25.5
Li
1990i, 2016i
CO2 Emissions from Petroleum Systems
C02
22.8
Li T1 T2
2016i
CO2 Emissions from Stationary Combustion - Oil -
Electricity Generation
C02
21.4
Li T1 T2
1990i
CO2 Emissions from Incineration of Waste
C02
10.7
CO2 Emissions from Stationary Combustion - Coal -
C02
4.0
U.S. Territories
CO2 Emissions from Stationary Combustion - Gas -
C02
3.0
U.S. Territories
CO2 Emissions from Stationary Combustion - Coal -
Commercial
C02
2.2
•
T1
CO2 Emissions from Stationary Combustion -
C02
0.4
Geothermal Energy
CO2 Emissions from Abandoned Oil and Gas Wells
C02
+
CO2 Emissions from Stationary Combustion - Coal -
Residential
C02
0.0
CH4 Emissions from Natural Gas Systems
CH4
163.5
•
Li T1 L2T2
1990,2016
Fugitive Emissions from Coal Mining
ch4
53.8
•
Li T1 L2T2
1990, 2016i
CH4 Emissions from Petroleum Systems
ch4
38.6
•
Li L2
1990,2016
CH4 Emissions from Abandoned Oil and Gas Wells
ch4
7.1
•
L2
19902, 20162
Fugitive Emissions from Abandoned Underground
Coal Mines
cm
6.7
Non-C02 Emissions from Stationary Combustion -
Residential
cm
3.4
•
l2 t2
19902
CH4 Emissions from Mobile Combustion: Other
cm
2.1
Non-C02 Emissions from Stationary Combustion -
Industrial
cm
1.6
Non-C02 Emissions from Stationary Combustion -
cm
1.2
Commercial
A-9
-------
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
Cm Emissions from Mobile Combustion: Road
CH4 Emissions from Mobile Combustion: Marine
Non-C02 Emissions from Stationary Combustion -
U.S. Territories
CH4 Emissions from Mobile Combustion: Aviation
CH4 Emissions from Incineration of Waste
Non-C02 Emissions from Stationary Combustion - ... > T
Electricity Generation 2 1
N2O Emissions from Mobile Combustion: Road N2O 13.2 • L1T1 1990i
N2O Emissions from Mobile Combustion: Other
Non-C02 Emissions from Stationary Combustion -
Industrial
N2O Emissions from Mobile Combustion: Aviation
Non-C02 Emissions from Stationary Combustion -
Residential
N2O Emissions from Mobile Combustion: Marine
Non-C02 Emissions from Stationary Combustion -
Commercial
N2O Emissions from Incineration of Waste
Non-C02 Emissions from Stationary Combustion -
U.S. Territories
International Bunker Fuelsc
Industrial Processes and Product Use
L1T1L2T2 1990,20161
Li 1990i, 2016i
• Li T1 2016i
CH4
1.1
ch4
1.1
ch4
0.3
ch4
0.1
ch4
+
ch4
+
n20
14.9
N20
13.2
N20
3.2
N20
2.5
N20
1.5
N20
0.7
N20
0.5
N20
0.3
N20
0.3
N20
0.1
Several
117.7
CO2 Emissions from Iron and Steel Production &
CO2
42.3
Metallurgical Coke Production
CO2 Emissions from Cement Production
CO2
39.4
CO2 Emissions from Petrochemical Production
CO2
28.1
CO2 Emissions from Lime Production
CO2
12.9
CO2 Emissions from Ammonia Production
CO2
12.2
CO2 Emissions from Other Process Uses of
Carbonates
CO2
11.0
CO2 Emissions from Carbon Dioxide Consumption
CO2
4.5
CO2 Emissions from Urea Consumption for Non-Ag
Purposes
CO2
4.0
CO2 Emissions from Ferroalloy Production
CO2
1.8
CO2 Emissions from Soda Ash Production
CO2
1.7
CO2 Emissions from Titanium Dioxide Production
CO2
1.6
CO2 Emissions from Aluminum Production
CO2
1.3
CO2 Emissions from Glass Production
CO2
1.2
CO2 Emissions from Phosphoric Acid Production
CO2
1.0
CO2 Emissions from Zinc Production
CO2
0.9
CO2 Emissions from Lead Production
CO2
0.5
CO2 Emissions from Silicon Carbide Production and
CO2
0.2
Consumption
CO2 Emissions from Magnesium Production and
CO2
Processing
CH4 Emissions from Petrochemical Production
CH4
0.2
CH4 Emissions from Ferroalloy Production
ch4
+
CH4 Emissions from Silicon Carbide Production and
ch4
Consumption
+
CH4 Emissions from Iron and Steel Production &
ch4
Metallurgical Coke Production
+
N2O Emissions from Nitric Acid Production
N2O
10.2
N2O Emissions from Adipic Acid Production
N2O
7.0
N2O Emissions from Product Uses
N2O
4.2
T1
A-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
N2O Emissions from Caprolactam, Glyoxal, and
Glyoxylic Acid Production
N2O Emissions from Semiconductor Manufacture
N20
N20
2.0
0.2
Emissions from Substitutes for Ozone Depleting
Substances
HiGWP
159.1
Li T1 L2T2
2016
PFC, HFC, SFe, and NF3 Emissions from
HiGWP
4.7
Semiconductor Manufacture
SF6 Emissions from Electrical Transmission and
Distribution
HiGWP
4.3
T1
HFC-23 Emissions from HCFC-22 Production
HiGWP
2.8
L1T1T2
1990i
PFC Emissions from Aluminum Production
HiGWP
1.4
T1
SF6 Emissions from Magnesium Production and
Processing
HFC-134a Emissions from Magnesium Production
and Processing
HiGWP
HiGWP
1.0
0.1
Agriculture
CO2 Emissions from Urea Fertilization
CO2
5.1
CO2 Emissions from Liming
CO2
3.9
CH4 Emissions from Enteric Fermentation
CH4
170.1
Li L2
1990,2016
CH4 Emissions from Manure Management
ch4
67.7
Li T1 L2T2
19901,2016
CH4 Emissions from Rice Cultivation
ch4
13.7
CH4 Emissions from Field Burning of Agricultural
Residues
ch4
0.3
Direct N2O Emissions from Agricultural Soil
Management
Indirect N2O Emissions from Applied Nitrogen
N2O
N2O
237.6
45.9
L1T1L2
Li T1 L2T2
1990,2016
1990,2016
N2O Emissions from Manure Management
N2O
18.1
N2O Emissions from Field Burning of Agricultural
Residues
N2O
0.1
Waste
CH4 Emissions from Landfills
CH4
107.7
Li T1 L2T2
1990,2016
CH4 Emissions from Wastewater Treatment
ch4
14.8
CH4 Emissions from Composting
ch4
2.1
N2O Emissions from Wastewater Treatment
N2O
5.0
N2O Emissions from Composting
N2O
1.9
Land Use, Land Use Change, and Forestry
Net CO2 Emissions from Land Converted to
Settlements
CO2
68.0
Li T1 L2T2
19901,2016
Net CO2 Emissions from Land Converted to
Cropland
Net CO2 Emissions from Land Converted to
Grassland
O O
P P
23.8
22.0
Li T1 L2T2
L2 T2
1990,2016
19902, 20162
Net CO2 Emissions from Land Converted to
CO2
Wetlands
1 + 1
Net CO2 Emissions from Grassland Remaining
Grassland
CO2
(+)
L2 T2
19902, 20162
Net CO2 Emissions from Coastal Wetlands
OOO
pOp
Remaining Coastal Wetlands
Net CO2 Emissions from Cropland Remaining
Cropland
Net CO2 Emissions from Land Converted to Forest
Land
(+)
(+)
Li T1 L2T2
Li T1
1990, 20162
1990i, 2016i
Net CO2 Emissions from Settlements Remaining
Settlements
CO2
(+)
Li T1 L2T2
1990,2016
Net CO2 Emissions from Forest Land Remaining
Forest Land
CO2
(+)
Li T1 L2T2
1990,2016
CH4 Emissions from Forest Fires
ch4
18.5
T1 L2 T2
20162
CH4 Emissions from Coastal Wetlands Remaining
ch4
3.6
Coastal Wetlands
A-11
-------
CH4 Emissions from Grass Fires
CH4 Emissions from Drained Organic Soils
CH4 Emissions from Land Converted to Coastal
Wetlands
CH4 Emissions from Peatlands Remaining
Peatlands
N2O Emissions from Forest Fires
N2O Emissions from Settlement Soils
N2O Emissions from Forest Soils
N2O Emissions from Grass Fires
N2O Emissions from Coastal Wetlands Remaining
Coastal Wetlands
N2O Emissions from Drained Organic Soils
CH4
CH4
ch4
ch4
N20
N20
N20
N20
N20
N20
12.2
2.5
0.5
0.3
0.1
0.1
0.3
+
+
+
T1 L2 T2
20162
N2O Emissions from Peatlands Remaining .. _
Peatlands ^ !
+ Does not exceed 0.05 MMT CO2 Eq.
a For the 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 for that assessment in only that year (e.g., 19902 designates a source is a key category for the Approach
2 assessment only in 1990).
'Emissions from these sources not included in totals.
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 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) was designed to establish a
general level where the key category analysis covers approximately 75 to 92 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 CO2 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 CO2 from mobile combustion is
applied to each mode's emission estimate. No uncertainty was associated with CH4 emissions from waste incineration
because emissions are less than 0.05 kt CH4 and an uncertainty analysis was not conducted. When source and sink categories
are sorted in decreasing order of this calculation, those that fall at the top of the list and cumulatively account for 90 percent
of emissions are considered key categories. The key categories identified by the Approach 2 level assessment may differ
from those identified by the Approach 1 assessment. The final set of key categories includes all source and sink categories
identified as key by either the Approach 1 or the Approach 2 assessment, keeping in mind that the two assessments are not
mutually exclusive.
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.
A-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-4 through Table A-7 contain the 1990 and 2016 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 2016 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 fl-4:1990 Key Source Category Approach land Approach 2 Analysis—Level Assessment, without LULUCF
Direct
Approach 1
Approach 2
Greenhouse
1990 Estimate
Level
Cumulative
Level
IPCC Source Categories
Gas
(MMT CO2 Eq.)
Assessment
Total
Uncertainty3
Assessment
CO2 Emissions from Stationary Combustion - Coal
- Electricity Generation
C02
1,547.6
0.24
0.24
10%
0.023
CO2 Emissions from Mobile Combustion: Road
C02
1,162.7
0.18
0.43
6%
0.012
CO2 Emissions from Stationary Combustion - Gas -
Industrial
C02
408.9
0.06
0.49
7%
0.005
CO2 Emissions from Stationary Combustion - Oil -
Industrial
C02
294.7
0.05
0.54
21%
0.010
CO2 Emissions from Stationary Combustion - Gas -
Residential
C02
238.0
0.04
0.57
7%
0.003
Direct N2O Emissions from Agricultural Soil
Management
N20
212.0
0.03
0.61
16%
0.005
CH4 Emissions from Natural Gas Systems
ch4
195.2
0.03
0.64
17%
0.005
CO2 Emissions from Mobile Combustion: Aviation
co2
187.4
0.03
0.67
6%
0.002
CH4 Emissions from Landfills
ch4
179.6
0.03
0.70
23%
0.007
CO2 Emissions from Stationary Combustion - Gas -
Electricity Generation
co2
175.3
0.03
0.72
5%
0.001
CH4 Emissions from Enteric Fermentation
ch4
164.2
0.03
0.75
18%
0.005
CO2 Emissions from Stationary Combustion - Coal
- Industrial
co2
155.3
0.02
0.77
16%
0.004
CO2 Emissions from Stationary Combustion - Gas -
Commercial
co2
142.1
0.02
0.80
7%
0.002
CO2 Emissions from Non-Energy Use of Fuels
co2
119.5
0.02
0.82
39%
0.007
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
co2
101.6
0.02
0.83
17%
0.003
CO2 Emissions from Stationary Combustion - Oil -
Electricity Generation
co2
97.5
0.02
0.85
9%
0.001
CO2 Emissions from Stationary Combustion - Oil -
Residential
co2
97.4
0.02
0.86
6%
0.001
A-13
-------
Fugitive Emissions from Coal Mining
CH4
96.5
0.02
0.88
14%
0.002
CO2 Emissions from Mobile Combustion: Other
C02
73.2
0.01
0.89
6%
0.001
CO2 Emissions from Stationary Combustion - Oil -
Commercial
C02
73.1
0.01
0.90
6%
0.001
HFC-23 Emissions from HCFC-22 Production
HFCs
46.1
0.01
0.91
10%
0.001
CO2 Emissions from Mobile Combustion: Marine
CO2
44.3
0.01
0.91
6%
<0.001
CH4 Emissions from Petroleum Systems
CH4
39.8
0.01
0.92
34%
0.002
Indirect N2O Emissions from Applied Nitrogen
N2O
38.5
0.01
0.93
154%
0.009
N2O Emissions from Mobile Combustion: Road
N2O
37.6
0.01
0.93
14%
0.001
CH4 Emissions from Manure Management
CH4
37.2
0.01
0.94
20%
0.001
CO2 Emissions from Cement Production
CO2
33.5
0.01
0.94
6%
<0.001
CO2 Emissions from Natural Gas Systems
CO2
29.8
<0.01
0.95
17%
0.001
CO2 Emissions from Stationary Combustion - Oil -
U.S. Territories
CO2
26.9
<0.01
0.95
11%
<0.001
SFe Emissions from Electrical Transmission and
Distribution
SFe
23.1
<0.01
0.96
14%
0.001
PFC Emissions from Aluminum Production
PFCs
21.5
<0.01
0.96
8%
<0.001
CO2 Emissions from Petrochemical Production
CO2
21.2
<0.01
0.96
5%
<0.001
CH4 Emissions from Rice Cultivation
CH4
16.0
<0.01
0.97
64%
0.002
CH4 Emissions from Wastewater Treatment
ch4
15.7
<0.01
0.97
27%
0.001
N2O Emissions from Adipic Acid Production
N2O
15.2
<0.01
0.97
5%
<0.001
N2O Emissions from Manure Management
N2O
14.0
<0.01
0.97
24%
0.001
CO2 Emissions from Ammonia Production
CO2
13.0
<0.01
0.97
7%
<0.001
N2O Emissions from Nitric Acid Production
N2O
12.1
<0.01
0.98
5%
<0.001
CO2 Emissions from Stationary Combustion - Coal
- Commercial
CO2
12.0
<0.01
0.98
15%
<0.001
CO2 Emissions from Lime Production
CO2
11.7
<0.01
0.98
2%
<0.001
CO2 Emissions from Incineration of Waste
CO2
8.0
<0.01
0.98
26%
<0.001
CO2 Emissions from Petroleum Systems
CO2
7.7
<0.01
0.98
34%
<0.001
Fugitive Emissions from Abandoned Underground
Coal Mines
CH4
7.2
<0.01
0.98
22%
<0.001
CH4 Emissions from Mobile Combustion: Other
ch4
7.0
<0.01
0.99
50%
0.001
CO2 Emissions from Aluminum Production
CO2
6.8
<0.01
0.99
3%
<0.001
CH4 Emissions from Abandoned Oil and Gas Wells
CH4
6.5
<0.01
0.99
215%
0.002
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
N2O
6.5
<0.01
0.99
43%
<0.001
CO2 Emissions from Other Process Uses of
Carbonates
CO2
6.3
<0.01
0.99
15%
<0.001
Non-C02 Emissions from Stationary Combustion -
Residential
CH4
5.2
<0.01
0.99
227%
0.002
CH4 Emissions from Mobile Combustion: Road
ch4
5.2
<0.01
0.99
26%
<0.001
SFe Emissions from Magnesium Production and
Processing
CO2 Emissions from Liming
SFe
CO2
5.2
4.7
<0.01
<0.01
0.99
0.99
6%
111%
<0.001
0.001
N2O Emissions from Product Uses
N2O
4.2
<0.01
0.99
24%
<0.001
CO2 Emissions from Urea Consumption for Non-Ag
Purposes
PFC, HFC, SFe, and NF3 Emissions from
Semiconductor Manufacture
CO2
Several
OO CO
CO CO
<0.01
<0.01
0.99
0.99
12%
6%
<0.001
<0.001
N2O Emissions from Wastewater Treatment
N20
3.4
<0.01
0.99
112%
0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
N20
3.1
<0.01
1.00
206%
0.001
CO2 Emissions from Stationary Combustion - Coal
- Residential
C02
3.0
<0.01
1.00
NE
<0.001
CO2 Emissions from Urea Fertilization
C02
2.4
<0.01
1.00
43%
<0.001
CO2 Emissions from Ferroalloy Production
C02
2.2
<0.01
1.00
12%
<0.001
A-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Non-C02 Emissions from Stationary Combustion -
Industrial
CH4
1.8
<0.01
1.00
50%
<0.001
N2O Emissions from Mobile Combustion: Other
N20
1.8
<0.01
1.00
59%
<0.001
N2O Emissions from Mobile Combustion: Aviation
N20
1.7
<0.01
1.00
67%
<0.001
N2O Emissions from Caprolactam, Glyoxal, and
Glyoxylic Acid Production
N20
1.7
<0.01
1.00
31%
<0.001
CO2 Emissions from Glass Production
C02
1.5
<0.01
1.00
4%
<0.001
CO2 Emissions from Phosphoric Acid Production
C02
1.5
<0.01
1.00
21%
<0.001
CO2 Emissions from Carbon Dioxide Consumption
C02
1.5
<0.01
1.00
5%
<0.001
CO2 Emissions from Soda Ash Production
C02
1.4
<0.01
1.00
9%
<0.001
CO2 Emissions from Titanium Dioxide Production
C02
1.2
<0.01
1.00
13%
<0.001
Non-C02 Emissions from Stationary Combustion -
Commercial
CH4
1.1
<0.01
1.00
145%
<0.001
Non-C02 Emissions from Stationary Combustion -
Residential
N20
1.0
<0.01
1.00
212%
<0.001
CO2 Emissions from Stationary Combustion - Coal
- U.S. Territories
C02
0.6
<0.01
1.00
19%
<0.001
CO2 Emissions from Zinc Production
C02
0.6
<0.01
1.00
16%
<0.001
N2O Emissions from Mobile Combustion: Marine
N20
0.6
<0.01
1.00
46%
<0.001
CO2 Emissions from Lead Production
C02
0.5
<0.01
1.00
15%
<0.001
CH4 Emissions from Mobile Combustion: Marine
CH4
0.5
<0.01
1.00
85%
<0.001
N2O Emissions from Incineration of Waste
N20
0.5
<0.01
1.00
327%
<0.001
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
CH4
0.4
<0.01
1.00
4%
<0.001
CO2 Emissions from Stationary Combustion -
C02
0.4
<0.01
1.00
NA
<0.001
Geothermal Energy
Non-C02 Emissions from Stationary Combustion -
N20
0.4
<0.01
1.00
176%
<0.001
Commercial
CH4 Emissions from Composting
CH4
0.4
<0.01
1.00
50%
<0.001
CO2 Emissions from Silicon Carbide Production
CO2
0.4
<0.01
1.00
9%
<0.001
and Consumption
N2O Emissions from Composting
N2O
0.3
<0.01
1.00
50%
<0.001
Emissions from Substitutes for Ozone Depleting
Several
0.3
<0.01
1.00
12%
<0.001
Substances
CH4 Emissions from Field Burning of Agricultural
CH4
0.2
<0.01
1.00
14%
<0.001
Residues
CH4 Emissions from Petrochemical Production
ch4
0.2
<0.01
1.00
57%
<0.001
N2O Emissions from Field Burning of Agricultural
Residues
N20
0.1
<0.01
1.00
14%
<0.001
Non-C02 Emissions from Stationary Combustion -
U.S. Territories
N20
0.1
<0.01
1.00
198%
<0.001
CH4 Emissions from Mobile Combustion: Aviation
CH4
0.1
<0.01
1.00
88%
<0.001
Non-C02 Emissions from Stationary Combustion -
U.S. Territories
ch4
+
<0.01
1.00
55%
<0.001
N2O Emissions from Semiconductor Manufacture
N20
+
<0.01
1.00
12%
<0.001
CH4 Emissions from Silicon Carbide Production
CH4
<0.01
1.00
10%
<0.001
and Consumption
CH4 Emissions from Iron and Steel Production &
ch4
<0.01
1.00
20%
<0.001
Metallurgical Coke Production
+
CH4 Emissions from Ferroalloy Production
ch4
+
<0.01
1.00
12%
<0.001
CO2 Emissions from Abandoned Oil and Gas Wells
CO2
+
<0.01
1.00
215%
<0.001
CO2 Emissions from Magnesium Production and
CO2
+
<0.01
1.00
2%
<0.001
Processing
CH4 Emissions from Incineration of Waste
CH4
+
<0.01
1.00
NE
<0.001
HFC-134a Emissions from Magnesium Production
HFCs
0.0
<0.01
1.00
4%
<0.001
and Processing
CO2 Emissions from Stationary Combustion - Gas -
U.S. Territories
CO2
0.0
<0.01
1.00
17%
<0.001
A-15
-------
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
NA (Not Available)
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
Note: LULUCF sources and sinks are not included in this analysis.
Table fl-5:1990 Key Source Category Approach land Approach 2 Analysis—Level Assessment, with LULUCF
Direct
Approach 1
Approach 2
Greenhouse
1990 Estimate
Level
Cumulative
Level
IPCC Source/Sink Categories
Gas
(MMT CO2 Eq.)
Assessment
Total
Uncertainty3
Assessment
CO2 Emissions from Stationary Combustion - Coal -
Electricity Generation
C02
1,547.6
0.21
0.21
10%
0.020
CO2 Emissions from Mobile Combustion: Road
C02
1,162.7
0.16
0.37
6%
0.010
Net CO2 Emissions from Forest Land Remaining Forest
Land
C02
697.7
0.09
0.46
78%
0.073
CO2 Emissions from Stationary Combustion - Gas -
Industrial
C02
408.9
0.06
0.52
7%
0.004
CO2 Emissions from Stationary Combustion - Oil -
Industrial
C02
294.7
0.04
0.56
21%
0.008
CO2 Emissions from Stationary Combustion - Gas -
Residential
C02
238.0
0.03
0.59
7%
0.002
Direct N2O Emissions from Agricultural Soil
Management
N20
212.0
0.03
0.62
16%
0.005
CH4 Emissions from Natural Gas Systems
CH4
195.2
0.03
0.64
17%
0.004
CO2 Emissions from Mobile Combustion: Aviation
CO2
187.4
0.03
0.67
6%
0.002
CH4 Emissions from Landfills
ch4
179.6
0.02
0.69
23%
0.006
CO2 Emissions from Stationary Combustion - Gas -
Electricity Generation
co2
175.3
0.02
0.72
5%
0.001
CH4 Emissions from Enteric Fermentation
ch4
164.2
0.02
0.74
18%
0.004
CO2 Emissions from Stationary Combustion - Coal -
Industrial
co2
155.3
0.02
0.76
16%
0.003
CO2 Emissions from Stationary Combustion - Gas -
Commercial
co2
142.1
0.02
0.78
7%
0.001
CO2 Emissions from Non-Energy Use of Fuels
co2
119.5
0.02
0.80
39%
0.006
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
co2
101.6
0.01
0.81
17%
0.002
CO2 Emissions from Stationary Combustion - Oil -
Electricity Generation
co2
97.5
0.01
0.82
9%
0.001
CO2 Emissions from Stationary Combustion - Oil -
Residential
co2
97.4
0.01
0.84
6%
0.001
Fugitive Emissions from Coal Mining
ch4
96.5
0.01
0.85
14%
0.002
Net CO2 Emissions from Land Converted to Forest Land
co2
92.0
0.01
0.86
11%
0.001
Net CO2 Emissions from Settlements Remaining
Settlements
co2
86.2
0.01
0.87
85%
0.010
CO2 Emissions from Mobile Combustion: Other
co2
73.2
0.01
0.88
6%
0.001
CO2 Emissions from Stationary Combustion - Oil -
Commercial
co2
73.1
0.01
0.89
6%
0.001
HFC-23 Emissions from HCFC-22 Production
HFCs
46.1
0.01
0.90
10%
0.001
CO2 Emissions from Mobile Combustion: Marine
CO2
44.3
0.01
0.90
6%
<0.001
Net CO2 Emissions from Land Converted to Cropland
CO2
43.3
0.01
0.91
77%
0.005
Net CO2 Emissions from Cropland Remaining Cropland
CO2
40.9
0.01
0.92
452%
0.025
CH4 Emissions from Petroleum Systems
CH4
39.8
0.01
0.92
34%
0.002
Indirect N2O Emissions from Applied Nitrogen
N2O
38.5
0.01
0.93
154%
0.008
N2O Emissions from Mobile Combustion: Road
N2O
37.6
0.01
0.93
14%
0.001
Net CO2 Emissions from Land Converted to Settlements
CO2
37.2
0.01
0.94
29%
0.001
CH4 Emissions from Manure Management
CH4
37.2
0.01
0.94
20%
0.001
CO2 Emissions from Cement Production
CO2
33.5
<0.01
0.95
6%
<0.001
CO2 Emissions from Natural Gas Systems
CO2
29.8
<0.01
0.95
17%
0.001
A-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
CO2 Emissions from Stationary Combustion - Oil - U.S.
Territories
co2
26.9
<0.01
0.95
11%
<0.001
SFe Emissions from Electrical Transmission and
Distribution
SFe
23.1
<0.01
0.96
14%
<0.001
PFC Emissions from Aluminum Production
PFCs
21.5
<0.01
0.96
8%
<0.001
CO2 Emissions from Petrochemical Production
CO2
21.2
<0.01
0.96
5%
<0.001
Net CO2 Emissions from Land Converted to Grassland
CO2
17.9
<0.01
0.97
134%
0.003
CH4 Emissions from Rice Cultivation
CH4
16.0
<0.01
0.97
64%
0.001
CH4 Emissions from Wastewater Treatment
ch4
15.7
<0.01
0.97
27%
0.001
N2O Emissions from Adipic Acid Production
N2O
15.2
<0.01
0.97
5%
<0.001
N2O Emissions from Manure Management
N2O
14.0
<0.01
0.97
24%
<0.001
CO2 Emissions from Ammonia Production
CO2
13.0
<0.01
0.98
7%
<0.001
N2O Emissions from Nitric Acid Production
N2O
12.1
<0.01
0.98
5%
<0.001
CO2 Emissions from Stationary Combustion - Coal -
Commercial
CO2
12.0
<0.01
0.98
15%
<0.001
CO2 Emissions from Lime Production
CO2
11.7
<0.01
0.98
2%
<0.001
CO2 Emissions from Incineration of Waste
CO2
8.0
<0.01
0.98
26%
<0.001
CO2 Emissions from Petroleum Systems
CO2
7.7
<0.01
0.98
34%
<0.001
Net CO2 Emissions from Coastal Wetlands Remaining
Coastal Wetlands
CO2
7.6
<0.01
0.98
59%
0.001
Fugitive Emissions from Abandoned Underground Coal
Mines
CH4
7.2
<0.01
0.98
22%
<0.001
CH4 Emissions from Mobile Combustion: Other
ch4
7.0
<0.01
0.99
50%
<0.001
CO2 Emissions from Aluminum Production
CO2
6.8
<0.01
0.99
3%
<0.001
CH4 Emissions from Abandoned Oil and Gas Wells
CH4
6.5
<0.01
0.99
215%
0.002
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
N2O
6.5
<0.01
0.99
43%
<0.001
CO2 Emissions from Other Process Uses of Carbonates
CO2
6.3
<0.01
0.99
15%
<0.001
Non-C02 Emissions from Stationary Combustion -
Residential
CH4
5.2
<0.01
0.99
227%
0.002
CH4 Emissions from Mobile Combustion: Road
ch4
5.2
<0.01
0.99
26%
<0.001
SFe Emissions from Magnesium Production and
Processing
CO2 Emissions from Liming
SFe
CO2
5.2
4.7
<0.01
<0.01
0.99
0.99
6%
111%
<0.001
0.001
N2O Emissions from Product Uses
N2O
4.2
<0.01
0.99
24%
<0.001
Net CO2 Emissions from Grassland Remaining
Grassland
CO2
4.2
<0.01
0.99
2503%
0.014
CO2 Emissions from Urea Consumption for Non-Ag
Purposes
PFC, HFC, SFe, and NF3 Emissions from Semiconductor
Manufacture
CO2
Several
OO CO
CO CO
<0.01
<0.01
0.99
0.99
12%
6%
<0.001
<0.001
CH4 Emissions from Coastal Wetlands Remaining
Coastal Wetlands
CH4
3.4
<0.01
0.99
30%
<0.001
N2O Emissions from Wastewater Treatment
N20
3.4
<0.01
0.99
112%
0.001
CH4 Emissions from Forest Fires
CH4
3.2
<0.01
0.99
127%
0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
N20
3.1
<0.01
1.00
206%
0.001
CO2 Emissions from Stationary Combustion - Coal -
Residential
C02
3.0
<0.01
1.00
NE
<0.001
CO2 Emissions from Urea Fertilization
C02
2.4
<0.01
1.00
43%
<0.001
CO2 Emissions from Ferroalloy Production
C02
2.2
<0.01
1.00
12%
<0.001
N2O Emissions from Forest Fires
N20
2.1
<0.01
1.00
120%
<0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
CH4
1.8
<0.01
1.00
50%
<0.001
N2O Emissions from Mobile Combustion: Other
N20
1.8
<0.01
1.00
59%
<0.001
N2O Emissions from Mobile Combustion: Aviation
N20
1.7
<0.01
1.00
67%
<0.001
N2O Emissions from Caprolactam, Glyoxal, and
f5l\/rw\/lir Arirl Prnrlnrtion
N20
1.7
<0.01
1.00
31%
<0.001
Glyoxylic Acid Production
A-17
-------
CO2 Emissions from Glass Production
C02
1.5
<0.01
1.00
4%
<0.001
CO2 Emissions from Phosphoric Acid Production
C02
1.5
<0.01
1.00
21%
<0.001
CO2 Emissions from Carbon Dioxide Consumption
C02
1.5
<0.01
1.00
5%
<0.001
CO2 Emissions from Soda Ash Production
C02
1.4
<0.01
1.00
9%
<0.001
N2O Emissions from Settlement Soils
N20
1.4
<0.01
1.00
45%
<0.001
CO2 Emissions from Titanium Dioxide Production
C02
1.2
<0.01
1.00
13%
<0.001
Non-C02 Emissions from Stationary Combustion -
Commercial
CH4
1.1
<0.01
1.00
145%
<0.001
Non-C02 Emissions from Stationary Combustion -
Residential
N20
1.0
<0.01
1.00
212%
<0.001
CO2 Emissions from Stationary Combustion - Coal -
U.S. Territories
C02
0.6
<0.01
1.00
19%
<0.001
CO2 Emissions from Zinc Production
C02
0.6
<0.01
1.00
16%
<0.001
N2O Emissions from Mobile Combustion: Marine
N20
0.6
<0.01
1.00
46%
<0.001
CO2 Emissions from Lead Production
C02
0.5
<0.01
1.00
15%
<0.001
CH4 Emissions from Mobile Combustion: Marine
CH4
0.5
<0.01
1.00
85%
<0.001
N2O Emissions from Incineration of Waste
N20
0.5
<0.01
1.00
327%
<0.001
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
CH4
0.4
<0.01
1.00
4%
<0.001
CO2 Emissions from Stationary Combustion -
Geothermal Energy
C02
0.4
<0.01
1.00
NA
<0.001
Non-C02 Emissions from Stationary Combustion -
Commercial
N20
0.4
<0.01
1.00
176%
<0.001
CH4 Emissions from Composting
CH4
0.4
<0.01
1.00
50%
<0.001
CO2 Emissions from Silicon Carbide Production and
C02
0.4
<0.01
1.00
9%
<0.001
Consumption
N2O Emissions from Composting
N20
0.3
<0.01
1.00
50%
<0.001
Emissions from Substitutes for Ozone Depleting
Substances
Several
0.3
<0.01
1.00
12%
<0.001
CH4 Emissions from Field Burning of Agricultural
Residues
CH4
0.2
<0.01
1.00
14%
<0.001
CH4 Emissions from Petrochemical Production
ch4
0.2
<0.01
1.00
57%
<0.001
N2O Emissions from Coastal Wetlands Remaining
Coastal Wetlands
N20
0.1
<0.01
1.00
116%
<0.001
N2O Emissions from Drained Organic Soils
N20
0.1
<0.01
1.00
124%
<0.001
N2O Emissions from Forest Soils
N20
0.1
<0.01
1.00
318%
<0.001
N2O Emissions from Grass Fires
N20
0.1
<0.01
1.00
144%
<0.001
N2O Emissions from Field Burning of Agricultural
Residues
N20
0.1
<0.01
1.00
14%
<0.001
CH4 Emissions from Grass Fires
CH4
0.1
<0.01
1.00
145%
<0.001
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
N20
0.1
<0.01
1.00
198%
<0.001
CH4 Emissions from Mobile Combustion: Aviation
CH4
0.1
<0.01
1.00
88%
<0.001
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
ch4
+
<0.01
1.00
55%
<0.001
N2O Emissions from Semiconductor Manufacture
N20
+
<0.01
1.00
12%
<0.001
CH4 Emissions from Silicon Carbide Production and
Consumption
CH4 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CH4
ch4
+
+
<0.01
<0.01
1.00
1.00
10%
20%
<0.001
<0.001
Net CO2 Emissions from Land Converted to Wetlands
CO2
+
<0.01
1.00
30%
<0.001
CH4 Emissions from Ferroalloy Production
CH4
+
<0.01
1.00
12%
<0.001
CH4 Emissions from Land Converted to Coastal
Wetlands
ch4
+
<0.01
1.00
30%
<0.001
CH4 Emissions from Drained Organic Soils
ch4
+
<0.01
1.00
76%
<0.001
CO2 Emissions from Abandoned Oil and Gas Wells
CO2
+
<0.01
1.00
215%
<0.001
CH4 Emissions from Peatlands Remaining Peatlands
CH4
+
<0.01
1.00
78%
<0.001
A-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
C02 Emissions from Magnesium Production and
Processing
N2O Emissions from Peatlands Remaining Peatlands
CH4 Emissions from Incineration of Waste
CO2 Emissions from Stationary Combustion - Gas - U.S.
Territories
HFC-134a Emissions from Magnesium Production and
Processing
C02
+
<0.01
1.00
2%
<0.001
N20
+
<0.01
1.00
53%
<0.001
ch4
+
<0.01
1.00
NE
<0.001
co2
0.0
<0.01
1.00
17%
<0.001
HFCs
0.0
<0.01
1.00
4%
<0.001
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
NA (Not Available)
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
Tablefl-6:2016 Key Source Category Approach 1 and Approach 2 Analysis—Level Assessment, without LULUCF
Direct Approach 1
Greenhouse 2016 Estimate Level
Gas (MMT CO2 Eq.) Assessment
IPCC Source Categories
Cumulative
Total Uncertainty3
Approach 2
Level
Assessment
CO2 Emissions from Mobile Combustion: Road
CO2
1,496.0
0.23
0.23
6%
0.015
CO2 Emissions from Stationary Combustion - Coal -
Electricity Generation
CO2
1,241.4
0.19
0.42
10%
0.018
CO2 Emissions from Stationary Combustion - Gas -
Electricity Generation
CO2
546.0
0.08
0.50
5%
0.004
CO2 Emissions from Stationary Combustion - Gas -
Industrial
CO2
477.9
0.07
0.58
7%
0.005
CO2 Emissions from Stationary Combustion - Oil -
Industrial
CO2
272.5
0.04
0.62
21%
0.009
CO2 Emissions from Stationary Combustion - Gas -
Residential
CO2
238.3
0.04
0.66
7%
0.003
Direct N2O Emissions from Agricultural Soil
Management
CO2 Emissions from Stationary Combustion - Gas -
Commercial
N2O
CO2
237.6
170.3
0.04
0.03
0.69
0.72
16%
7%
0.006
0.002
CH4 Emissions from Enteric Fermentation
CH4
170.1
0.03
0.74
18%
0.005
CO2 Emissions from Mobile Combustion: Aviation
CO2
167.4
0.03
0.77
6%
0.002
CH4 Emissions from Natural Gas Systems
CH4
163.5
0.03
0.80
17%
0.004
Emissions from Substitutes for Ozone Depleting
Substances
Several
159.1
0.02
0.82
12%
0.003
CO2 Emissions from Non-Energy Use of Fuels
CO2
112.2
0.02
0.84
39%
0.007
CH4 Emissions from Landfills
CH4
107.7
0.02
0.85
23%
0.004
CO2 Emissions from Mobile Combustion: Other
CO2
80.2
0.01
0.87
6%
0.001
CH4 Emissions from Manure Management
CH4
67.7
0.01
0.88
20%
0.002
CO2 Emissions from Stationary Combustion - Oil -
Commercial
CO2
58.7
0.01
0.89
6%
0.001
CO2 Emissions from Stationary Combustion - Coal -
Industrial
CO2
58.7
0.01
0.89
16%
0.001
CO2 Emissions from Stationary Combustion - Oil -
Residential
CO2
54.2
0.01
0.90
6%
<0.001
Fugitive Emissions from Coal Mining
CH4
53.8
0.01
0.91
14%
0.001
Indirect N2O Emissions from Applied Nitrogen
N2O
45.9
0.01
0.92
154%
0.011
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CO2
42.3
0.01
0.92
17%
0.001
CO2 Emissions from Cement Production
CO2
39.4
0.01
0.93
6%
<0.001
CO2 Emissions from Mobile Combustion: Marine
CO2
39.0
0.01
0.94
6%
<0.001
CH4 Emissions from Petroleum Systems
CH4
38.6
0.01
0.94
34%
0.002
CO2 Emissions from Stationary Combustion - Oil - U.S.
Territories
CO2
34.3
0.01
0.95
11%
0.001
CO2 Emissions from Petrochemical Production
CO2
28.1
<0.01
0.95
5%
<0.001
CO2 Emissions from Natural Gas Systems
CO2
25.5
<0.01
0.96
17%
0.001
CO2 Emissions from Petroleum Systems
CO2
22.8
<0.01
0.96
34%
0.001
A-19
-------
CO2 Emissions from Stationary Combustion - Oil -
Electricity Generation
C02
21.4
<0.01
0.96
9%
<0.001
N2O Emissions from Manure Management
N20
18.1
<0.01
0.97
24%
0.001
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
N20
14.9
<0.01
0.97
43%
0.001
CH4 Emissions from Wastewater Treatment
CH4
14.8
<0.01
0.97
27%
0.001
CH4 Emissions from Rice Cultivation
ch4
13.7
<0.01
0.97
64%
0.001
N2O Emissions from Mobile Combustion: Road
N20
13.2
<0.01
0.97
14%
<0.001
CO2 Emissions from Lime Production
C02
12.9
<0.01
0.98
2%
<0.001
CO2 Emissions from Ammonia Production
C02
12.2
<0.01
0.98
7%
<0.001
CO2 Emissions from Other Process Uses of
Carbonates
C02
11.0
<0.01
0.98
15%
<0.001
CO2 Emissions from Incineration of Waste
C02
10.7
<0.01
0.98
26%
<0.001
N2O Emissions from Nitric Acid Production
N20
10.2
<0.01
0.98
5%
<0.001
CH4 Emissions from Abandoned Oil and Gas Wells
CH4
7.1
<0.01
0.98
215%
0.002
N2O Emissions from Adipic Acid Production
N20
7.0
<0.01
0.99
5%
<0.001
Fugitive Emissions from Abandoned Underground Coal
Mines
CH4
6.7
<0.01
0.99
22%
<0.001
CO2 Emissions from Urea Fertilization
C02
5.1
<0.01
0.99
43%
<0.001
N2O Emissions from Wastewater Treatment
N20
5.0
<0.01
0.99
112%
0.001
PFC, HFC, SFe, and NF3 Emissions from
Semiconductor Manufacture
Several
4.7
<0.01
0.99
6%
<0.001
CO2 Emissions from Carbon Dioxide Consumption
C02
4.5
<0.01
0.99
5%
<0.001
SF6 Emissions from Electrical Transmission and
Distribution
SFe
4.3
<0.01
0.99
14%
<0.001
N2O Emissions from Product Uses
N2O
4.2
<0.01
0.99
24%
<0.001
CO2 Emissions from Stationary Combustion - Coal -
U.S. Territories
CO2
4.0
<0.01
0.99
19%
<0.001
CO2 Emissions from Urea Consumption for Non-Ag
Purposes
CO2
4.0
<0.01
0.99
12%
<0.001
CO2 Emissions from Liming
CO2
3.9
<0.01
0.99
111%
0.001
Non-C02 Emissions from Stationary Combustion -
Residential
CH4
3.4
<0.01
0.99
227%
0.001
N2O Emissions from Mobile Combustion: Other
N2O
3.2
<0.01
0.99
59%
<0.001
CO2 Emissions from Stationary Combustion - Gas -
U.S. Territories
CO2
3.0
<0.01
0.99
17%
<0.001
HFC-23 Emissions from HCFC-22 Production
HFCs
2.8
<0.01
0.99
10%
<0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
N2O
2.5
<0.01
0.99
206%
0.001
CO2 Emissions from Stationary Combustion - Coal -
Commercial
CO2
2.2
<0.01
1.00
15%
<0.001
CH4 Emissions from Mobile Combustion: Other
CH4
2.1
<0.01
1.00
50%
<0.001
CH4 Emissions from Composting
ch4
2.1
<0.01
1.00
50%
<0.001
N2O Emissions from Caprolactam, Glyoxal, and
Glyoxylic Acid Production
N2O
2.0
<0.01
1.00
31%
<0.001
N2O Emissions from Composting
N2O
1.9
<0.01
1.00
50%
<0.001
CO2 Emissions from Ferroalloy Production
CO2
1.8
<0.01
1.00
12%
<0.001
CO2 Emissions from Soda Ash Production
CO2
1.7
<0.01
1.00
9%
<0.001
CO2 Emissions from Titanium Dioxide Production
CO2
1.6
<0.01
1.00
13%
<0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
CH4
1.6
<0.01
1.00
50%
<0.001
N2O Emissions from Mobile Combustion: Aviation
N2O
1.5
<0.01
1.00
67%
<0.001
PFC Emissions from Aluminum Production
PFCs
1.4
<0.01
1.00
8%
<0.001
CO2 Emissions from Aluminum Production
CO2
1.3
<0.01
1.00
3%
<0.001
CO2 Emissions from Glass Production
CO2
1.2
<0.01
1.00
4%
<0.001
Non-C02 Emissions from Stationary Combustion -
Commercial
CH4
1.2
<0.01
1.00
145%
<0.001
A-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
Cm Emissions from Mobile Combustion: Road
SF6 Emissions from Magnesium Production and
Processing
CO2 Emissions from Phosphoric Acid Production
CO2 Emissions from Zinc Production
Non-C02 Emissions from Stationary Combustion -
Residential
N2O Emissions from Mobile Combustion: Marine
CO2 Emissions from Lead Production
CO2 Emissions from Stationary Combustion -
Geothermal Energy
CH4 Emissions from Mobile Combustion: Marine
Non-C02 Emissions from Stationary Combustion -
Commercial
N2O Emissions from Incineration of Waste
CH4 Emissions from Field Burning of Agricultural
Residues
CH4 Emissions from Petrochemical Production
N2O Emissions from Semiconductor Manufacture
CO2 Emissions from Silicon Carbide Production and
Consumption
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
HFC-134a Emissions from Magnesium Production and
Processing
N2O Emissions from Field Burning of Agricultural
Residues
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
CH4 Emissions from Mobile Combustion: Aviation
CH4 Emissions from Ferroalloy Production
CH4 Emissions from Silicon Carbide Production and
Consumption
CH4 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CO2 Emissions from Abandoned Oil and Gas Wells
CO2 Emissions from Magnesium Production and
Processing
CH4 Emissions from Incineration of Waste
CO2 Emissions from Stationary Combustion - Coal -
Residential
ch4
1.1
<0.01
1.00
4%
<0.001
ch4
1.1
<0.01
1.00
26%
<0.001
sf6
1.0
<0.01
1.00
6%
<0.001
co2
1.0
<0.01
1.00
21%
<0.001
co2
0.9
<0.01
1.00
16%
<0.001
N20
0.7
<0.01
1.00
212%
<0.001
N20
0.5
<0.01
1.00
46%
<0.001
co2
0.5
<0.01
1.00
15%
<0.001
co2
0.4
<0.01
1.00
NA
<0.001
ch4
0.3
<0.01
1.00
85%
<0.001
n20
0.3
<0.01
1.00
176%
<0.001
N20
0.3
<0.01
1.00
327%
<0.001
ch4
0.3
<0.01
1.00
14%
<0.001
ch4
0.2
<0.01
1.00
57%
<0.001
n20
0.2
<0.01
1.00
12%
<0.001
co2
0.2
<0.01
1.00
9%
<0.001
N20
0.1
<0.01
1.00
198%
<0.001
HFCs
0.1
<0.01
1.00
4%
<0.001
N2O
0.1
<0.01
1.00
14%
<0.001
CH4
0.1
<0.01
1.00
55%
<0.001
ch4
+
<0.01
1.00
88%
<0.001
ch4
+
<0.01
1.00
12%
<0.001
ch4
+
<0.01
1.00
10%
<0.001
ch4
+
<0.01
1.00
20%
<0.001
co2
+
<0.01
1.00
215%
<0.001
co2
+
<0.01
1.00
2%
<0.001
ch4
+
<0.01
1.00
NE
<0.001
co2
0.0
<0.01
1.00
NE
<0.001
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
NA (Not Available)
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
Note: LULUCF sources and sinks are not included in this analysis.
Table fl-7:2016 Key Source Category Approach 1 and Approach 2 Analysis—Level Assessment with LULUCF
IPCC Source/Sink Categories
Direct Approach 1
Greenhouse 2016 Estimate Level
Gas (MMT CO2 Eq.) Assessment
Cumulative
Total
Uncertainty3
Approach 2
Level
Assessment
CO2 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Coal -
Electricity Generation
Net CO2 Emissions from Forest Land Remaining Forest
Land
OOO
pOp
1,496.0
1,241.4
670.5
0.20
0.16
0.09
0.20
0.36
0.45
6%
10%
78%
0.013
0.016
0.069
A-21
-------
CO2 Emissions from Stationary Combustion - Gas -
Electricity Generation
C02
546.0
0.07
0.52
5%
0.004
CO2 Emissions from Stationary Combustion - Gas -
Industrial
C02
477.9
0.06
0.59
7%
0.005
CO2 Emissions from Stationary Combustion - Oil -
Industrial
C02
272.5
0.04
0.62
21%
0.007
CO2 Emissions from Stationary Combustion - Gas -
Residential
C02
238.3
0.03
0.66
7%
0.002
Direct N2O Emissions from Agricultural Soil Management
N20
237.6
0.03
0.69
16%
0.005
CO2 Emissions from Stationary Combustion - Gas -
Commercial
C02
170.3
0.02
0.71
7%
0.002
CH4 Emissions from Enteric Fermentation
CH4
170.1
0.02
0.73
18%
0.004
CO2 Emissions from Mobile Combustion: Aviation
C02
167.4
0.02
0.76
6%
0.001
CH4 Emissions from Natural Gas Systems
CH4
163.5
0.02
0.78
17%
0.004
Emissions from Substitutes for Ozone Depleting
Substances
Several
159.1
0.02
0.80
12%
0.002
Net CO2 Emissions from Non-Energy Use of Fuels
C02
112.2
0.01
0.81
39%
0.006
CH4 Emissions from Landfills
CH4
107.7
0.01
0.83
23%
0.003
Net CO2 Emissions from Settlements Remaining
Settlements
C02
103.7
0.01
0.84
85%
0.012
CO2 Emissions from Mobile Combustion: Other
C02
80.2
0.01
0.85
6%
0.001
Net CO2 Emissions from Land Converted to Forest Land
C02
75.0
0.01
0.86
11%
0.001
Net CO2 Emissions from Land Converted to Settlements
C02
68.0
0.01
0.87
29%
0.003
CH4 Emissions from Manure Management
CH4
67.7
0.01
0.88
20%
0.002
CO2 Emissions from Stationary Combustion - Oil -
Commercial
C02
58.7
0.01
0.89
6%
<0.001
CO2 Emissions from Stationary Combustion - Coal -
Industrial
C02
58.7
0.01
0.90
16%
0.001
CO2 Emissions from Stationary Combustion - Oil -
Residential
C02
54.2
0.01
0.90
6%
<0.001
Fugitive Emissions from Coal Mining
CH4
53.8
0.01
0.91
14%
0.001
Indirect N2O Emissions from Applied Nitrogen
N20
45.9
0.01
0.92
154%
0.009
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
C02
42.3
0.01
0.92
17%
0.001
CO2 Emissions from Cement Production
C02
39.4
0.01
0.93
6%
<0.001
CO2 Emissions from Mobile Combustion: Marine
C02
39.0
0.01
0.93
6%
<0.001
CH4 Emissions from Petroleum Systems
CH4
38.6
0.01
0.94
34%
0.002
CO2 Emissions from Stationary Combustion - Oil - U.S.
Territories
C02
34.3
<0.01
0.94
11%
0.001
CO2 Emissions from Petrochemical Production
C02
28.1
<0.01
0.95
5%
<0.001
CO2 Emissions from Natural Gas Systems
C02
25.5
<0.01
0.95
17%
0.001
Net CO2 Emissions from Land Converted to Cropland
C02
23.8
<0.01
0.95
77%
0.002
CO2 Emissions from Petroleum Systems
C02
22.8
<0.01
0.95
34%
0.001
Net CO2 Emissions from Land Converted to Grassland
C02
22.0
<0.01
0.96
134%
0.004
CO2 Emissions from Stationary Combustion - Oil -
Electricity Generation
C02
21.4
<0.01
0.96
9%
<0.001
CH4 Emissions from Forest Fires
CH4
18.5
<0.01
0.96
127%
0.003
N2O Emissions from Manure Management
N20
18.1
<0.01
0.97
24%
0.001
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
N20
14.9
<0.01
0.97
43%
0.001
CH4 Emissions from Wastewater Treatment
CH4
14.8
<0.01
0.97
27%
0.001
CH4 Emissions from Rice Cultivation
ch4
13.7
<0.01
0.97
64%
0.001
N2O Emissions from Mobile Combustion: Road
N2O
13.2
<0.01
0.97
14%
<0.001
CO2 Emissions from Lime Production
CO2
12.9
<0.01
0.97
2%
<0.001
CO2 Emissions from Ammonia Production
CO2
12.2
<0.01
0.98
7%
<0.001
N2O Emissions from Forest Fires
N2O
12.2
<0.01
0.98
120%
0.002
CO2 Emissions from Other Process Uses of Carbonates
CO2
11.0
<0.01
0.98
15%
<0.001
A-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
CO2 Emissions from Incineration of Waste
CO2
10.7
<0.01
0.98
26%
<0.001
N2O Emissions from Nitric Acid Production
N2O
10.2
<0.01
0.98
5%
<0.001
Net CO2 Emissions from Cropland Remaining Cropland
CO2
9.9
<0.01
0.98
452%
0.006
Net CO2 Emissions from Coastal Wetlands Remaining
Coastal Wetlands
CO2
7.9
<0.01
0.98
59%
0.001
CH4 Emissions from Abandoned Oil and Gas Wells
CH4
7.1
<0.01
0.99
215%
0.002
N2O Emissions from Adipic Acid Production
N2O
7.0
<0.01
0.99
5%
<0.001
Fugitive Emissions from Abandoned Underground Coal
Mines
CH4
6.7
<0.01
0.99
22%
<0.001
CO2 Emissions from Urea Fertilization
CO2
5.1
<0.01
0.99
43%
<0.001
N2O Emissions from Wastewater Treatment
N2O
5.0
<0.01
0.99
112%
0.001
PFC, HFC, SF6, and NF3 Emissions from
Semiconductor Manufacture
Several
4.7
<0.01
0.99
6%
<0.001
CO2 Emissions from Carbon Dioxide Consumption
CO2
4.5
<0.01
0.99
5%
<0.001
SF6 Emissions from Electrical Transmission and
Distribution
SF6
4.3
<0.01
0.99
14%
<0.001
N2O Emissions from Product Uses
N2O
4.2
<0.01
0.99
24%
<0.001
CO2 Emissions from Stationary Combustion - Coal - U.S.
Territories
CO2
4.0
<0.01
0.99
19%
<0.001
CO2 Emissions from Urea Consumption for Non-Ag
Purposes
CO2
4.0
<0.01
0.99
12%
<0.001
CO2 Emissions from Liming
CO2
3.9
<0.01
0.99
111%
0.001
CH4 Emissions from Coastal Wetlands Remaining
Coastal Wetlands
CH4
3.6
<0.01
0.99
30%
<0.001
Non-C02 Emissions from Stationary Combustion -
Residential
ch4
3.4
<0.01
0.99
227%
0.001
N2O Emissions from Mobile Combustion: Other
N20
3.2
<0.01
0.99
59%
<0.001
CO2 Emissions from Stationary Combustion - Gas - U.S.
Territories
co2
3.0
<0.01
0.99
17%
<0.001
HFC-23 Emissions from HCFC-22 Production
HFCs
2.8
<0.01
0.99
10%
<0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
N2O
2.5
<0.01
0.99
206%
0.001
N2O Emissions from Settlement Soils
N2O
2.5
<0.01
1.00
45%
<0.001
CO2 Emissions from Stationary Combustion - Coal -
Commercial
CO2
2.2
<0.01
1.00
15%
<0.001
CH4 Emissions from Mobile Combustion: Other
cm
2.1
<0.01
1.00
50%
<0.001
CH4 Emissions from Composting
cm
2.1
<0.01
1.00
50%
<0.001
N2O Emissions from Caprolactam, Glyoxal, and Glyoxylic
Acid Production
n2o
2.0
<0.01
1.00
31%
<0.001
N2O Emissions from Composting
n2o
1.9
<0.01
1.00
50%
<0.001
CO2 Emissions from Ferroalloy Production
co2
1.8
<0.01
1.00
12%
<0.001
CO2 Emissions from Soda Ash Production
co2
1.7
<0.01
1.00
9%
<0.001
Net CO2 Emissions from Grassland Remaining
Grassland
CO2 1.6 <0.01 1.00 2503% 0.005
CO2 Emissions from Titanium Dioxide Production
CO2
1.6
<0.01
1.00
13%
<0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
cm
1.6
<0.01
1.00
50%
<0.001
N2O Emissions from Mobile Combustion: Aviation
N2O
1.5
<0.01
1.00
67%
<0.001
PFC Emissions from Aluminum Production
PFCs
1.4
<0.01
1.00
8%
<0.001
CO2 Emissions from Aluminum Production
CO2
1.3
<0.01
1.00
3%
<0.001
CO2 Emissions from Glass Production
CO2
1.2
<0.01
1.00
4%
<0.001
Non-C02 Emissions from Stationary Combustion -
Commercial
cm
1.2
<0.01
1.00
145%
<0.001
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
cm
1.1
<0.01
1.00
4%
<0.001
Cm Emissions from Mobile Combustion: Road
cm
1.1
<0.01
1.00
26%
<0.001
SF6 Emissions from Magnesium Production and
sf6
1.0
<0.01
1.00
6%
<0.001
Processing
A-23
-------
CO2 Emissions from Phosphoric Acid Production
C02
1.0
<0.01
1.00
21%
<0.001
CO2 Emissions from Zinc Production
C02
0.9
<0.01
1.00
16%
<0.001
Non-C02 Emissions from Stationary Combustion -
Residential
N20
0.7
<0.01
1.00
212%
<0.001
N2O Emissions from Mobile Combustion: Marine
N20
0.5
<0.01
1.00
46%
<0.001
CO2 Emissions from Lead Production
C02
0.5
<0.01
1.00
15%
<0.001
N2O Emissions from Forest Soils
N20
0.5
<0.01
1.00
318%
<0.001
CO2 Emissions from Stationary Combustion -
Geothermal Energy
C02
0.4
<0.01
1.00
NA
<0.001
CH4 Emissions from Mobile Combustion: Marine
CH4
0.3
<0.01
1.00
85%
<0.001
Non-C02 Emissions from Stationary Combustion -
Commercial
N20
0.3
<0.01
1.00
176%
<0.001
N2O Emissions from Grass Fires
N20
0.3
<0.01
1.00
144%
<0.001
N2O Emissions from Incineration of Waste
N20
0.3
<0.01
1.00
327%
<0.001
CH4 Emissions from Grass Fires
CH4
0.3
<0.01
1.00
145%
<0.001
CH4 Emissions from Field Burning of Agricultural
Residues
ch4
0.3
<0.01
1.00
14%
<0.001
CH4 Emissions from Petrochemical Production
ch4
0.2
<0.01
1.00
57%
<0.001
N2O Emissions from Semiconductor Manufacture
N20
0.2
<0.01
1.00
12%
<0.001
CO2 Emissions from Silicon Carbide Production and
Consumption
C02
0.2
<0.01
1.00
9%
<0.001
N2O Emissions from Coastal Wetlands Remaining
Coastal Wetlands
N20
0.1
<0.01
1.00
116%
<0.001
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
N20
0.1
<0.01
1.00
198%
<0.001
HFC-134a Emissions from Magnesium Production and
Processing
HFCs
0.1
<0.01
1.00
4%
<0.001
N2O Emissions from Drained Organic Soils
N2O
0.1
<0.01
1.00
124%
<0.001
N2O Emissions from Field Burning of Agricultural
Residues
N2O
0.1
<0.01
1.00
14%
<0.001
Non-C02 Emissions from Stationary Combustion - U.S.
Territories
CH4
0.1
<0.01
1.00
55%
<0.001
CH4 Emissions from Mobile Combustion: Aviation
ch4
+
<0.01
1.00
88%
<0.001
Net CO2 Emissions from Land Converted to Wetlands
CO2
+
<0.01
1.00
30%
<0.001
CH4 Emissions from Drained Organic Soils
CH4
+
<0.01
1.00
76%
<0.001
CH4 Emissions from Ferroalloy Production
ch4
+
<0.01
1.00
12%
<0.001
CH4 Emissions from Land Converted to Coastal
Wetlands
ch4
+
<0.01
1.00
30%
<0.001
CH4 Emissions from Silicon Carbide Production and
ch4
<0.01
1.00
10%
<0.001
Consumption
CH4 Emissions from Iron and Steel Production &
Metallurgical Coke Production
ch4
+
<0.01
1.00
20%
<0.001
CO2 Emissions from Abandoned Oil and Gas Wells
CO2
+
<0.01
1.00
215%
<0.001
CH4 Emissions from Peatlands Remaining Peatlands
CH4
+
<0.01
1.00
78%
<0.001
CO2 Emissions from Magnesium Production and
Processing
CO2
+
<0.01
1.00
2%
<0.001
N2O Emissions from Peatlands Remaining Peatlands
N2O
+
<0.01
1.00
53%
<0.001
CH4 Emissions from Incineration of Waste
CH4
+
<0.01
1.00
NE
<0.001
CO2 Emissions from Stationary Combustion - Coal -
Residential
CO2
0.0
<0.01
1.00
NE
<0.001
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
NA (Not Available)
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
A-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-8:1990-2016 Key Source Category flpproachi and 2 Analysis—Trend Assessment, without LULUCF
Direct Approach 1 Approach 2 %
Greenhouse 1990 Estimate 2016 Estimate Trend Trend Contribution Cumulative
IPCC Source Categories
Gas
(MMT CO2 Eq.)
(MMT CO2 Eq.) Assessment Assessment
to Trend
Total
C02
175.3
546.0
0.06
0.003
17.4
17
C02
1,547.6
1,241.4
0.05
0.005
16.3
34
C02
1,162.7
1,496.0
0.05
0.003
14.5
48
Several
0.3
159.1
0.02
0.003
7.5
56
C02
155.3
58.7
0.02
0.002
4.8
61
C02
97.5
21.4
0.01
0.001
3.7
64
CH4
179.6
107.7
0.01
0.003
3.6
68
C02
101.6
42.3
0.01
0.002
2.9
71
C02
408.9
477.9
0.01
0.001
2.8
74
C02
97.4
54.2
0.01
<0.001
2.2
76
CH4
96.5
53.8
0.01
0.001
2.1
78
HFCs
46.1
2.8
0.01
0.001
2.1
80
ch4
195.2
163.5
0.01
0.001
1.7
82
cm
37.2
67.7
<0.01
0.001
1.4
83
co2
294.7
272.5
<0.01
0.001
1.4
85
n2o
37.6
13.2
<0.01
0.001
1.2
86
co2
142.1
170.3
<0.01
<0.001
1.2
87
co2
187.4
167.4
<0.01
<0.001
1.2
88
PFCs
21.5
1.4
<0.01
<0.001
1.0
89
N2O
212.0
237.6
<0.01
0.001
1.0
90
SFe
23.1
4.3
<0.01
<0.001
0.9
91
CO2
73.1
58.7
<0.01
<0.001
0.8
92
CO2
7.7
22.8
<0.01
0.001
0.7
92
CO2
119.5
112.2
<0.01
0.001
0.5
93
CO2
12.0
2.2
<0.01
<0.001
0.5
93
N2O
15.2
7.0
<0.01
<0.001
0.4
94
N2O
6.5
14.9
<0.01
0.001
0.4
94
CO2
26.9
34.3
<0.01
<0.001
0.3
95
N2O
38.5
45.9
<0.01
0.002
0.3
95
CO2
44.3
39.0
<0.01
<0.001
0.3
95
CO2
21.2
28.1
<0.01
<0.001
0.3
95
CO2
6.8
1.3
<0.01
<0.001
0.3
96
CO2
238.0
238.3
<0.01
<0.001
0.3
96
CO2
73.2
80.2
<0.01
<0.001
0.2
96
CO2
33.5
39.4
<0.01
<0.001
0.2
96
CO2
29.8
25.5
<0.01
<0.001
0.2
97
CO2 Emissions from Stationary Combustion -
Gas - Electricity Generation
CO2 Emissions from Stationary Combustion -
Coal - Electricity Generation
CO2 Emissions from Mobile Combustion: Road
Emissions from Substitutes for Ozone
Depleting Substances
CO2 Emissions from Stationary Combustion -
Coal - Industrial
CO2 Emissions from Stationary Combustion -
Oil - Electricity Generation
CH4 Emissions from Landfills
CO2 Emissions from Iron and Steel Production
& Metallurgical Coke Production
CO2 Emissions from Stationary Combustion -
Gas - Industrial
CO2 Emissions from Stationary Combustion -
Oil - Residential
Fugitive Emissions from Coal Mining
HFC-23 Emissions from HCFC-22 Production
CH4 Emissions from Natural Gas Systems
CH4 Emissions from Manure Management
CO2 Emissions from Stationary Combustion -
Oil - Industrial
N2O Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion -
Gas - Commercial
CO2 Emissions from Mobile Combustion:
Aviation
PFC Emissions from Aluminum Production
Direct N2O Emissions from Agricultural Soil
Management
SF6 Emissions from Electrical Transmission
and Distribution
CO2 Emissions from Stationary Combustion -
Oil - Commercial
CO2 Emissions from Petroleum Systems
CO2 Emissions from Non-Energy Use of Fuels
CO2 Emissions from Stationary Combustion -
Coal - Commercial
N2O Emissions from Adipic Acid Production
Non-C02 Emissions from Stationary
Combustion - Electricity Generation
CO2 Emissions from Stationary Combustion -
Oil - U.S. Territories
Indirect N2O Emissions from Applied Nitrogen
CO2 Emissions from Mobile Combustion:
Marine
CO2 Emissions from Petrochemical Production
CO2 Emissions from Aluminum Production
CO2 Emissions from Stationary Combustion -
Gas - Residential
CO2 Emissions from Mobile Combustion: Other
CO2 Emissions from Cement Production
CO2 Emissions from Natural Gas Systems
A-25
-------
Cm Emissions from Mobile Combustion: Other
cm
7.0
2.1
<0.01
<0.001
0.2
97
CO2 Emissions from Other Process Uses of
Carbonates
CO2
6.3
11.0
<0.01
<0.001
0.2
97
SF6 Emissions from Magnesium Production
and Processing
SFe
5.2
1.0
<0.01
<0.001
0.2
97
Cm Emissions from Mobile Combustion: Road
cm
5.2
1.1
<0.01
<0.001
0.2
98
N2O Emissions from Manure Management
N2O
14.0
18.1
<0.01
<0.001
0.2
98
CO2 Emissions from Stationary Combustion -
Coal - U.S. Territories
CO2
0.6
4.0
<0.01
<0.001
0.2
98
CO2 Emissions from Stationary Combustion -
Coal - Residential
CO2
3.0
0.0
<0.01
<0.001
0.1
98
CO2 Emissions from Stationary Combustion -
Gas-U.S. Territories
CO2
0.0
3.0
<0.01
<0.001
0.1
98
CO2 Emissions from Carbon Dioxide
Consumption
CO2
1.5
4.5
<0.01
<0.001
0.1
98
Cm Emissions from Rice Cultivation
cm
16.0
13.7
<0.01
<0.001
0.1
98
CO2 Emissions from Urea Fertilization
co2
2.4
5.1
<0.01
<0.001
0.1
99
CO2 Emissions from Incineration of Waste
co2
8.0
10.7
<0.01
<0.001
0.1
99
N2O Emissions from Nitric Acid Production
N20
12.1
10.2
<0.01
<0.001
0.1
99
Cm Emissions from Petroleum Systems
cm
39.8
38.6
<0.01
<0.001
0.1
99
Non-C02 Emissions from Stationary
Combustion - Residential
cm
5.2
3.4
<0.01
0.001
0.1
99
CH4 Emissions from Enteric Fermentation
CH4 Emissions from Composting
N2O Emissions from Composting
N2O Emissions from Wastewater Treatment
N2O Emissions from Mobile Combustion: Other
CH4 Emissions from Wastewater Treatment
CO2 Emissions from Ammonia Production
PFC, HFC, SFe, and NF3 Emissions from
Semiconductor Manufacture
CO2 Emissions from Lime Production
CO2 Emissions from Liming
Non-C02 Emissions from Stationary
Combustion - Electricity Generation
Fugitive Emissions from Abandoned
Underground Coal Mines
Non-C02 Emissions from Stationary
Combustion - Industrial
CO2 Emissions from Phosphoric Acid
Production
CH4 Emissions from Abandoned Oil and Gas
Wells
CO2 Emissions from Ferroalloy Production
CO2 Emissions from Titanium Dioxide
Production
Non-C02 Emissions from Stationary
Combustion - Residential
CO2 Emissions from Glass Production
Non-C02 Emissions from Stationary
Combustion - Industrial
N2O Emissions from Caprolactam, Glyoxal,
and Glyoxylic Acid Production
CO2 Emissions from Zinc Production
CO2 Emissions from Soda Ash Production
N2O Emissions from Mobile Combustion:
Aviation
CO2 Emissions from Silicon Carbide
Production and Consumption
CH4
CH4
N20
N20
N20
CH4
C02
Several
C02
C02
CH4
cm
N20
C02
cm
co2
co2
N20
co2
cm
n2o
co2
co2
n2o
co2
164.2
0.4
0.3
3.4
I.8
15.7
13.0
3.6
II.7
4.7
0.4
7.2
3.1
1.5
6.5
2.2
1.2
1.0
1.5
1.8
1.7
0.6
1.4
1.7
0.4
170.1
2.1
1.9
5.0
3.2
14.8
12.2
4.7
12.9
3.9
1.1
6.7
2.5
1.0
7.1
1.8
1.6
0.7
1.2
1.6
2.0
0.9
1.7
1.5
0.2
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001
<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
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
99
99
99
99
99
99
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
A-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
N2O Emissions from Semiconductor
Manufacture
N2O Emissions from Incineration of Waste
CH4 Emissions from Mobile Combustion:
Marine
N2O Emissions from Product Uses
HFC-134a Emissions from Magnesium
Production and Processing
Non-C02 Emissions from Stationary
Combustion - Commercial
CO2 Emissions from Urea Consumption for
Non-Ag Purposes
N2O Emissions from Mobile Combustion:
Marine
Non-C02 Emissions from Stationary
Combustion - Commercial
CO2 Emissions from Lead Production
CH4 Emissions from Field Burning of
Agricultural Residues
CH4 Emissions from Mobile Combustion:
Aviation
Non-C02 Emissions from Stationary
Combustion - U.S. Territories
CH4 Emissions from Petrochemical Production
CH4 Emissions from Silicon Carbide
Production and Consumption
CH4 Emissions from Iron and Steel Production
& Metallurgical Coke Production
N2O Emissions from Field Burning of
Agricultural Residues
Non-C02 Emissions from Stationary
Combustion - U.S. Territories
CH4 Emissions from Ferroalloy Production
CO2 Emissions from Magnesium Production
and Processing
CO2 Emissions from Abandoned Oil and Gas
Wells
CO2 Emissions from Stationary Combustion -
Geothermal Energy
CH4 Emissions from Incineration of Waste
N20
+
0.2
<0.01
<0.001
<0.1
100
N20
0.5
0.3
<0.01
<0.001
<0.1
100
ch4
0.5
0.3
<0.01
<0.001
<0.1
100
n20
4.2
4.2
<0.01
<0.001
<0.1
100
HFCs
0.0
0.1
<0.01
<0.001
<0.1
100
CH4
1.1
1.2
<0.01
<0.001
<0.1
100
CO2
3.8
4.0
<0.01
<0.001
<0.1
100
N2O
0.6
0.5
<0.01
<0.001
<0.1
100
N2O
0.4
0.3
<0.01
<0.001
<0.1
100
CO2
0.5
0.5
<0.01
<0.001
<0.1
100
CH4
0.2
0.3
<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
CH4
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
N2O
0.1
0.1
<0.01
<0.001
<0.1
100
CH4
+
0.1
<0.01
<0.001
<0.1
100
ch4
+
+
<0.01
<0.001
<0.1
100
CO2
+
+
<0.01
<0.001
<0.1
100
CO2
+
+
<0.01
<0.001
<0.1
100
CO2
0.4
0.4
<0.01
<0.001
<0.1
100
CH4
+
+
<0.01
<0.001
<0.1
100
+ Does not exceed 0.05 MMT CO2 Eq.
Note: LULUCF sources and sinks are not included in this analysis.
Table fl-9:1990-2016 Key Source Category Approach land 2 Analysis—Trend Assessment, with LULUCF
Direct Approach 1 Approach 2 %
Greenhouse 1990 Estimate 2016 Estimate Trend Trend Contribution Cumulative
IPCC Source Categories Gas (MMT CO2 Eq.) (MMT CO2 Eq.) Assessment Assessment to Trend Total
CO2 Emissions from Stationary Combustion
- Gas - Electricity Generation
CO2
175.3
546.0
0.05
0.003
16.0
16
CO2 Emissions from Stationary Combustion
- Coal - Electricity Generation
CO2
1,547.6
1,241.4
0.05
0.004
14.6
31
CO2 Emissions from Mobile Combustion:
Road
CO2
1,162.7
1,496.0
0.04
0.003
13.6
44
Emissions from Substitutes for Ozone
Depleting Substances
Several
0.3
159.1
0.02
0.003
6.9
51
CO2 Emissions from Stationary Combustion
- Coal - Industrial
CO2
155.3
58.7
0.01
0.002
4.3
56
CO2 Emissions from Stationary Combustion
- Oil - Electricity Generation
CO2
97.5
21.4
0.01
0.001
3.4
59
CH4 Emissions from Landfills
CH4
179.6
107.7
0.01
0.002
3.3
62
A-27
-------
C02 Emissions from Stationary Combustion
- Gas - Industrial
CO2 Emissions from Iron and Steel
Production & Metallurgical Coke
Production
CO2 Emissions from Stationary Combustion
- Oil - Residential
Fugitive Emissions from Coal Mining
HFC-23 Emissions from HCFC-22
Production
Net CO2 Emissions from Forest Land
Remaining Forest Land
CH4 Emissions from Natural Gas Systems
Net CO2 Emissions from Cropland
Remaining Cropland
Net CO2 Emissions from Land Converted to
Settlements
CH4 Emissions from Manure Management
CO2 Emissions from Stationary Combustion
- Oil - Industrial
CO2 Emissions from Stationary Combustion
- Gas - Commercial
N2O Emissions from Mobile Combustion:
Road
CO2 Emissions from Mobile Combustion:
Aviation
Direct N2O Emissions from Agricultural Soil
Management
PFC Emissions from Aluminum Production
Net CO2 Emissions from Land Converted to
Cropland
SF6 Emissions from Electrical Transmission
and Distribution
Net CO2 Emissions from Land Converted to
Forest Land
Net CO2 Emissions from Settlements
Remaining Settlements
CO2 Emissions from Stationary Combustion
- Oil - Commercial
CH4 Emissions from Forest Fires
CO2 Emissions from Petroleum Systems
N2O Emissions from Forest Fires
CO2 Emissions from Stationary Combustion
- Coal - Commercial
CO2 Emissions from Non-Energy Use of
Fuels
N2O Emissions from Adipic Acid Production
Non-C02 Emissions from Stationary
Combustion - Electricity Generation
CO2 Emissions from Stationary Combustion
- Oil - U.S. Territories
Indirect N2O Emissions from Applied
Nitrogen
CO2 Emissions from Petrochemical
Production
CO2 Emissions from Mobile Combustion:
Marine
C02
408.9
477.9
0.01
0.001
2.7
65
co2
101.6
42.3
0.01
0.001
2.7
68
co2
97.4
54.2
0.01
<0.001
2.0
70
ch4
96.5
53.8
0.01
0.001
1.9
72
HFCs
46.1
2.8
0.01
0.001
1.9
73
CO2
697.7
670.5
0.01
0.004
1.8
75
ch4
195.2
163.5
<0.01
0.001
1.5
77
co2
40.9
9.9
<0.01
0.019
1.4
78
co2
37.2
68.0
<0.01
0.001
1.3
79
ch4
37.2
67.7
<0.01
0.001
1.3
81
co2
294.7
272.5
<0.01
0.001
1.2
82
co2
142.1
170.3
<0.01
<0.001
1.1
83
n2o
37.6
13.2
<0.01
<0.001
1.1
84
co2
187.4
167.4
<0.01
<0.001
1.0
85
n2o
212.0
237.6
<0.01
<0.001
0.9
86
PFCs
21.5
1.4
<0.01
<0.001
0.9
87
CO2
43.3
23.8
<0.01
0.002
0.9
88
SFe
23.1
4.3
<0.01
<0.001
0.8
89
CO2
92.0
75.0
<0.01
<0.001
0.8
90
CO2
86.2
103.7
<0.01
0.002
0.7
90
CO2
73.1
58.7
<0.01
<0.001
0.7
91
cm
3.2
18.5
<0.01
0.003
0.7
92
co2
7.7
22.8
<0.01
0.001
0.7
92
n2o
2.1
12.2
<0.01
0.002
0.4
93
co2
12.0
2.2
<0.01
<0.001
0.4
93
co2
119.5
112.2
<0.01
0.001
0.4
94
n2o
15.2
7.0
<0.01
<0.001
0.4
94
n2o
6.5
14.9
<0.01
<0.001
0.4
94
co2
26.9
34.3
<0.01
<0.001
0.3
95
n2o
38.5
45.9
<0.01
0.001
0.3
95
co2
21.2
28.1
<0.01
<0.001
0.3
95
co2
44.3
39.0
<0.01
<0.001
0.3
95
CO2 Emissions from Aluminum Production
CO2 Emissions from Mobile Combustion:
Other
CO2 Emissions from Cement Production
C02 6.8 1.3
CO2 73.2 80.2
CO2 33.5 39.4
<0.01
<0.001
0.2
96
<0.01
<0.001
0.2
96
<0.01
<0.001
0.2
96
A-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
CH4 Emissions from Mobile Combustion:
Other
CH4
7.0
CO2 Emissions from Natural Gas Systems
CO2
29.8
CO2 Emissions from Other Process Uses of
Carbonates
CO2
6.3
SF6 Emissions from Magnesium Production
SFe
5.2
and Processing
CH4 Emissions from Mobile Combustion:
Road
CH4
5.2
CO2 Emissions from Stationary Combustion
CO2
238.0
- Gas - Residential
N2O Emissions from Manure Management
N2O
14.0
2.1
<0.01
<0.001
0.2
96
25.5
<0.01
<0.001
0.2
97
11.0
<0.01
<0.001
0.2
97
1.0
<0.01
<0.001
0.2
97
1.1
<0.01
<0.001
0.2
97
238.3
<0.01
<0.001
0.2
97
18.1
<0.01
<0.001
0.2
98
Net CO2 Emissions from Land Converted to
Grassland
CO2 17.9 22.0 <0.01 0.001 0.2 98
CO2 Emissions from Stationary Combustion
-Coal - U.S. Territories
CO2 Emissions from Stationary Combustion
- Coal - Residential
CO2 Emissions from Stationary Combustion
-Gas-U.S. Territories
CO2 Emissions from Carbon Dioxide
Consumption
CH4 Emissions from Enteric Fermentation
CO2
CO2
CO2
CO2
CH4
0.6
3.0
0.0
1.5
164.2
4.0
0.0
3.0
4.5
170.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001
<0.001
<0.001
<0.001
<0.001
0.1
0.1
0.1
0.1
0.1
98
98
98
98
98
Net CO2 Emissions from Grassland
Remaining Grassland
CO2
4.2
1.6
<0.01
0.009
Non-C02 Emissions from Stationary
Combustion - Residential
CH4
5.2
3.4
<0.01
0.001
0.1
0.1
98
CO2 Emissions from Urea Fertilization CO2 2.4 5.1 <0.01 <0.001 0.1 99
CH4 Emissions from Rice Cultivation CH4 16.0 13.7 <0.01 <0.001 0.1 99
CO2 Emissions from Incineration of Waste CO2 8.0 10.7 <0.01 <0.001 0.1 99
N2O Emissions from Nitric Acid Production N2O 12.1 10.2 <0.01 <0.001 0.1 99
99
CH4 Emissions from Petroleum Systems
CH4 Emissions from Composting
N2O Emissions from Composting
N2O Emissions from Wastewater Treatment
N2O Emissions from Mobile Combustion:
Other
CH4 Emissions from Wastewater Treatment
PFC, HFC, SFe, and NF3 Emissions from
Semiconductor Manufacture
CO2 Emissions from Ammonia Production
N2O Emissions from Settlement Soils
CO2 Emissions from Lime Production
CO2 Emissions from Liming
Non-C02 Emissions from Stationary
Combustion - Electricity Generation
Non-C02 Emissions from Stationary
Combustion - Industrial
Fugitive Emissions from Abandoned
Underground Coal Mines
CO2 Emissions from Phosphoric Acid
Production
CH4 Emissions from Abandoned Oil and Gas
Wells
CO2 Emissions from Ferroalloy Production
CO2 Emissions from Titanium Dioxide
Production
N2O Emissions from Forest Soils
CH4
ch4
N2O
N2O
N2O
CH4
Several
CO2
N2O
CO2
CO2
CH4
N2O
CH4
CO2
CH4
CO2
CO2
N2O
39.8
0.4
0.3
3.4
1.8
15.7
3.6
13.0
1.4
11.7
4.7
0.4
3.1
7.2
1.5
6.5
2.2
1.2
0.1
38.6
2.1
1.9
5.0
3.2
14.8
4.7
12.2
2.5
12.9
3.9
1.1
2.5
6.7
1.0
7.1
1.8
1.6
0.5
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001
<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
0.1
0.1
0.1
0.1
0.1
0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
<0.1
99
99
99
99
99
99
99
100
100
100
100
100
100
100
100
100
100
100
100
A-29
-------
Non-C02 Emissions from Stationary
Combustion - Residential
CO2 Emissions from Glass Production
Non-C02 Emissions from Stationary
Combustion - Industrial
N2O Emissions from Caprolactam, Glyoxal,
and Glyoxylic Acid Production
CO2 Emissions from Zinc Production
CO2 Emissions from Soda Ash Production
N2O Emissions from Mobile Combustion:
Aviation
N2O Emissions from Grass Fires
CH4 Emissions from Grass Fires
CO2 Emissions from Silicon Carbide
Production and Consumption
N2O Emissions from Semiconductor
Manufacture
Net CO2 Emissions from Coastal Wetlands
Remaining Coastal Wetlands
N2O Emissions from Incineration of Waste
CH4 Emissions from Mobile Combustion:
Marine
CO2 Emissions from Urea Consumption for
Non-Ag Purposes
HFC-134a Emissions from Magnesium
Production and Processing
N2O Emissions from Product Uses
Non-C02 Emissions from Stationary
Combustion - Commercial
CH4 Emissions from Coastal Wetlands
Remaining Coastal Wetlands
N2O Emissions from Mobile Combustion:
Marine
Non-C02 Emissions from Stationary
Combustion - Commercial
CO2 Emissions from Lead Production
CH4 Emissions from Field Burning of
Agricultural Residues
CH4 Emissions from Mobile Combustion:
Aviation
Non-C02 Emissions from Stationary
Combustion - U.S. Territories
CH4 Emissions from Petrochemical
Production
CH4 Emissions from Silicon Carbide
Production and Consumption
CH4 Emissions from Iron and Steel
Production & Metallurgical Coke
Production
N2O Emissions from Field Burning of
Agricultural Residues
Non-C02 Emissions from Stationary
Combustion - U.S. Territories
N2O Emissions from Coastal Wetlands
Remaining Coastal Wetlands
Net CO2 Emissions from Land Converted to
Wetlands
CH4 Emissions from Ferroalloy Production
CO2 Emissions from Stationary Combustion
- Geothermal Energy
N20 1.0 0.7
C02 1.5 1.2
CH4 1.8 1.6
N2O 1.7 2.0
CO2 0.6 0.9
CO2 1.4 1.7
N2O 1.7 1.5
N2O 0.1 0.3
CH4 0.1 0.3
CO2 0.4 0.2
N2O + 0.2
CO2 7.6 7.9
N2O 0.5 0.3
CH4 0.5 0.3
CO2 3.8 4.0
HFCs 0.0 0.1
N2O 4.2 4.2
CH4 1.1 1.2
CH4 3.4 3.6
N2O 0.6 0.5
N2O 0.4 0.3
CO2 0.5 0.5
CH4 0.2 0.3
CH4 0.1 +
N2O 0.1 0.1
CH4 0.2 0.2
CH4 + +
CH4 + +
N2O 0.1 0.1
CH4 + 0.1
N2O 0.1 0.1
CO2 + +
ch4 + +
CO2 0.4 0.4
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
<0.01
<0.001
<0.1
100
A-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
CH4 Emissions from Land Converted to
Coastal Wetlands
CH4
+
+
<0.01
<0.001
<0.1
100
N2O Emissions from Drained Organic Soils
N2O
0.1
0.1
<0.01
<0.001
<0.1
100
CH4 Emissions from Peatlands Remaining
Peatlands
ch4
+
+
<0.01
<0.001
<0.1
100
CO2 Emissions from Magnesium Production
and Processing
CO2 Emissions from Abandoned Oil and
Gas Wells
O O
P P
+
+
+
+
<0.01
<0.01
<0.001
<0.001
<0.1
<0.1
100
100
CH4 Emissions from Drained Organic Soils
ch4
+
+
<0.01
<0.001
<0.1
100
N2O Emissions from Peatlands Remaining
Peatlands
n2o
+
+
<0.01
<0.001
<0.1
100
CH4 Emissions from Incineration of Waste
ch4
+
+
<0.01
<0.001
<0.1
100
¦ Does not exceed 0.05 MMT CO2 Eq.
References
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Negara, and K.
Tanabe (eds.). Hayman, Kanagawa, Japan.
A-31
-------
ANNEX 2 Methodology and Data for Estimating CO2
Emissions from Fossil Fuel Combustion
2.1. Methodology for Estimating Emissions of CO2 from Fossil Fuel Combustion
Carbon dioxide (CO2) emissions from fossil fuel combustion were estimated using a "bottom-up" methodology
characterized by 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 CO2 emissions from fossil fuel combustion
is conceptually similar to the approach recommended by the Intergovernmental Panel on Climate Change (IPCC) for
countries that intend to develop detailed, 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-l 1 through Table A-36 with totals
by fuel type in Column 8 and totals by end-use sector in the last rows. Fuel consumption data for the bottom-up approach
were obtained directly from the Energy Information Administration (EIA) of the U.S. Department of Energy. These data
were first gathered in physical units, and then converted to their energy equivalents (see the Constants, Units, and
Conversions Annex). The EIA data were collected through a variety of consumption surveys at the point of delivery or use
and qualified with survey data on fuel production, imports, exports, and stock changes. Individual data elements were
supplied by a variety of sources within EIA. Most information was taken from published reports, although some data were
drawn from unpublished energy studies and databases maintained by EIA.
Energy 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 2016 total adjusted energy consumption across all sectors, including U.S. Territories, and energy types
was 71,761.5 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 EIA's distribution of electricity retail sales to
ultimate customers (i.e., residential, commercial, industrial, and other). Because the "other" fuel use includes sales to both
the commercial and transportation sectors, EIA's limited transportation electricity use data were subtracted from "other"
electricity use and also reported separately. This total was consequently combined with the commercial electricity data.
Further information on these electricity end uses is described in EIA's Monthly Energy Review (EIA 2018).
There are also three basic differences between the consumption data presented in Table A-10 and Table A-ll
through Table A-37 and those recommended in the IPCC (2006) emission inventory methodology.
First, consumption data in the U.S. Inventory are presented using higher heating values (HHV)1 rather than the
lower heating values (LHV) 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 International
Energy Agency for converting from HHV to LHV is to multiply the energy content by 0.95 for petroleum and coal and by
0.9 for natural gas.
Second, while EIA's energy use data for the United States includes only the 50 U.S. states and the District of
Columbia, the data reported to the 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
1 Also referred to as Gross Calorific Values (GCV).
2
Also referred to as Net Calorific Values (NCV).
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.
A-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
fuels. Energy use data from U.S. Territories are presented in Column 7 of Table A-l 1 through Table A-36. 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 as a raw
material rather than as a fuel;4 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.
In 2016, 16,485 Thousand Tons of coking coal were consumed,5 resulting in an Energy sector adjustment of 383
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 2016, the iron and steel industry consumed 44,388 Million ft3 of natural gas,
6,124 Thousand gallons of distillate fuel, and 1,935 Tons of coal (bituminous) as fuel. This resulted in Energy chapter
adjustments of roughly 46 TBtu for each natural gas and coal, and 1 TBtu for distillate fuel. In addition, an additional 88.8
TBtu is adjusted to account for unaccounted for coking coal within the iron and steel production sector in 2016.
Step 3: Adjust for Conversion of Fossil Fuels and Exports
First, a portion of industrial "other" coal that is accounted for in EIA coal combustion statistics is actually used to
make "synthetic natural gas" via coal gasification at the Dakota Gasification Plant, a synthetic natural gas plant. The plant
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.
5 Coking coal includes non-imported coke consumption from the iron and steel, lead, and zinc industries.
A-33
-------
produces synthetic natural gas and byproduct CO2. The synthetic natural gas enters the natural gas distribution system. Since
October 2000, a portion of the CO2 produced by the coal gasification plant has been exported to Canada by pipeline. The
remainder of the CO2 byproduct from the plant is released to the atmosphere. The energy in this synthetic natural gas enters
the natural gas distribution stream, and is accounted for in EIA natural gas combustion statistics. Because this energy of the
synthetic natural gas is already accounted for as natural gas combustion, this amount of energy is deducted from the industrial
coal consumption statistics to avoid double counting. The exported CO2 is not emitted to the atmosphere in the United States,
and therefore the energy associated with the amount of CO2 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 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-38, 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-39).
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-l 1 through Table A-37) by fuel-specific C content coefficients (see Table A-40 and Table A-41) 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.
Step 8: Estimate CO2 Emissions
Actual CO2 emissions in the United States were summarized by major fuel (i.e., coal, petroleum, natural gas,
geothermal) and consuming sector (i.e., residential, commercial, industrial, transportation, electric power, and U.S.
Territories). Emission estimates are expressed in million metric tons of carbon dioxide equivalents (MMT CO2 Eq.). To
convert from C content to CO2 emissions, the fraction of C that is oxidized was applied. This fraction was 100 percent based
on guidance in IPCC (2006).
To determine total emissions by final end-use sector, emissions from electric power were distributed to each end-
use sector according to its share of aggregate electricity use (see Table A-42). This pro-rated approach to allocating emissions
6 Refer to the International Bunker Fuels section of the Energy chapter and Annex 0 for a description of the methodology for distinguishing
between international and domestic fuel consumption.
A-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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 1. 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 EIA's industrial sector fuel consumption
data from select industries.
For EPA's GHGRP 2010 through 2016 reporting years, facility-level fossil fuel combustion emissions reported
through EPA's GHGRP were categorized and distributed to specific industry types by utilizing facility-reported NAICS
codes (as published by the U.S. Census Bureau). As noted previously in this report, the definitions and provisions for
reporting fuel types in EPA's GHGRP include some differences from the Inventory's use of EIA national fuel statistics to
meet the UNFCCC reporting guidelines. The IPCC has provided guidance on aligning facility-level reported fuels and fuel
types published in national energy statistics, which guided this exercise.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 EIA'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 2016 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.
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-35
-------
Table A-10:2016 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
Unadjusted NEU Consumption
Total Adjusted
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Bunker Fuel
Ind.
Trans. Terr.
Consumption
Total Coal
NE
23.7
719.4
NE
12,996.4
43.8
13,783.3
99.2
13,684.1
Residential Coal
NE
NE
NE
Commercial Coal
23.7
23.7
23.7
Industrial Coking Coal
Industrial Other Coal
88.8
630.6
88.8
630.6
88.8
10.3
620.2
Transportation Coal
Electric Power Coal
NE
12,996.4
NE
12,996.4
NE
12,996.4
U.S. Territory Coal (bit)
Natural Gas
4,495.6
3,213.1
9,328.2
766.7
10,301.3
43.8
57.0
43.8
28,161.9
311.8
43.8
27,850.1
Total Petroleum
803.7
831.8
8,150.8
25,916.7
243.9
549.2
36,496.1
1,619.3
4,485.6
140.6 77.3
30,173.3
Asphalt & Road Oil
Aviation Gasoline
853.4
20.5
853.4
20.5
853.4
20.5
Distillate Fuel Oil
365.1
273.9
970.9
6,374.6
54.9
108.3
8,147.8
117.5
5.8
8,024.4
Jet Fuel
3,349.9
NA
45.6
3,395.5
1,051.1
2,344.4
Kerosene
13.7
2.1
2.3
2.3
20.3
20.3
LPG
424.9
141.2
2,570.6
40.0
15.4
3,192.1
2,254.0
938.1
Lubricants
148.9
140.6
1.0
290.5
148.9
140.6 1.0
Motor Gasoline
409.9
286.6
15,368.0
173.4
16,237.9
16,237.9
Residual Fuel
4.4
623.1
70.7
127.0
825.2
450.7
374.5
Other Petroleum
AvGas Blend Components
Crude Oil
(0.3)
(0.3)
(0.3)
MoGas Blend
Components
Misc. Products
191.3
76.2
267.6
191.3
76.2
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
420.0
222.5
112.2
420.0
222.5
112.2
420.0
222.5
56.1
56.1
Petroleum Coke
0.3
652.7
118.3
771.3
61.1
710.2
Still Gas
1,604.7
1,604.7
166.1
1,438.6
Special Naphtha
Unfinished Oils
93.6
8.6
93.6
8.6
93.6
8.6
Waxes
12.9
12.9
12.9
Geothermal
54.0
54.0
54.0
Total (All Fuels)
5,299.3
4,068.7
18,198.4
26,683.4
23,595.6
650.0
78,495.3
1,619.3
4,896.6
140.6 77.3
71,761.5
NE (Not Estimated); NA (Not Available); Note: Parentheses indicate negative values.
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-11:2016 Energy Consumption I
lata and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
2.2
58.7
NE
1,241.4
4.0
1,306.4
Residential Coal
NE
NE
NE
NE
Commercial Coal
23.7
23.7
2.2
2.2
Industrial Other Coal
620.2
620.2
58.7
58.7
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
12,996.4
12,996.4
1,241.4
1,241.4
U.S. Territory Coal (bit)
43.8
43.8
4.0
4.0
Natural Gas
4,495.6
3,213.1
9,016.4
766.7
10,301.3
57.0
27,850.1
238.3
170.3
477.9
40.6
546.0
3.0
1,476.1
Total Petroleum
803.7
831.8
3,665.2
24,156.8
243.9
471.9
30,173.3
54.2
58.7
272.5
1,741.9
21.4
34.3
2,183.1
Asphalt & Road Oil
Aviation Gasoline
20.5
20.5
1.4
1.4
Distillate Fuel Oil
365.1
273.9
965.1
6,257.1
54.9
108.3
8,024.4
27.0
20.3
71.4
462.8
4.1
8.0
593.5
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
424.9
141.2
316.6
40.0
15.4
938.1
26.2
8.7
19.5
2.5
0.9
57.9
Lubricants
Motor Gasoline
409.9
286.6
15,368.0
173.4
16,237.9
29.2
20.4
1,096.3
12.4
1,158.4
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)
(0.0)
(0.0)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
56.1
56.1
3.9
3.9
Petroleum Coke
0.3
591.6
118.3
710.2
0.0
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)
5,299.3
4,068.7
13,301.8
24,923.5
23,595.6
572.7
71,761.5
292.5
231.3
809.1
1,782.6
1,809.3
41.4
4,966.0
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-37
-------
Table A-12:2015 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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 2.9
65.9
NE
1,350.5
4.0
1,423.3
Residential Coal
NE
NE
NE
NE
Commercial Coal
31.1
31.1
2.9
2.9
Industrial Other Coal
695.6
695.6
65.9
65.9
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
14,138.3
14,138.3
1,350.5
1,350.5
U.S. Territory Coal (bit)
43.8
43.8
4.0
4.0
Natural Gas
4,776.9
3,315.6
8,799.1
744.8
9,926.5
57.0
27,619.9
253.2 175.7
466.4
39.5
526.1
3.0
1,463.9
Total Petroleum
930.3
942.1
3,725.3
23,521.6
276.0
471.9
29,867.2
63.6 66.7
277.3
1,696.0
23.7
34.3
2,161.6
Asphalt & Road Oil
Aviation Gasoline
21.1
21.1
1.5
1.5
Distillate Fuel Oil
498.7
325.7
1,051.1
6,217.1
70.4
108.3
8,271.2
36.9 24.1
77.7
459.8
5.2
8.0
611.7
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
O
O
0.1
0.2
1.1
LPG
421.5
140.0
354.0
39.7
15.4
970.6
26.0 8.6
21.8
2.5
0.9
59.9
Lubricants
Motor Gasoline
470.6
322.8
15,005.1
173.4
15,971.9
33.6
23.0
1,070.5
12.4
1,139.4
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
(0.4)
(0.4)
(0.0)
(0.0)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
80.2
80.2
5.6
5.6
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)
5,707.2
4,288.8
13,220.1
24,266.4
24,395.0
572.7
72,450.2
316.8 245.4
809.5
1,735.5
1,900.7
41.4
5,049.3
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-13:2014 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
75.6
NE
1,569.1
4.0
1,652.6
Residential Coal
NE
NE
NE
NE
Commercial Coal
40.2
40.2
3.8
3.8
Industrial Other Coal
799.0
799.0
75.6
75.6
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
16,427.4
16,427.4
1,569.1
1,569.1
U.S. Territory Coal (bit)
43.8
43.8
4.0
4.0
Natural Gas
5,242.5
3,571.9
8,836.8
759.7
8,361.7
56.8
26,829.4
277.9 189.3
468.4
40.3
443.2
3.0
1,422.0
Total Petroleum
988.2
574.0
3,763.5
23,261.7
295.5
471.9
29,354.8
67.4 40.4
280.9
1,676.9
25.3
34.3
2,125.3
Asphalt & Road Oil
Aviation Gasoline
21.7
21.7
1.5
1.5
Distillate Fuel Oil
513.6
343.6
1,308.3
6,034.2
82.2
108.3
8,390.2
38.0 25.4
96.8
446.3
6.1
8.0
620.5
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
460.8
151.1
275.7
47.1
15.4
950.1
28.4 9.3
17.0
2.9
0.9
58.6
Lubricants
Motor Gasoline
68.9
268.7
15,027.1
173.4
15,538.0
4.9
19.2
1,072.0
12.4
1,108.5
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)
(0.0)
(0.0)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
44.2
44.2
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)
6,230.7
4,186.2
13,399.4
24,021.4
25,138.7
572.4
73,548.8
345.3 233.6
824.9
1,717.1
2,038.0
41.4
5,200.3
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-39
-------
Table A-14:2013 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
75.7
NE
1,571.3
2.8
1,653.8
Residential Coal
NE
NE
NE
NE
Commercial Coal
41.4
41.4
3.9
3.9
Industrial Other Coal
800.0
800.0
75.7
75.7
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
8,525.3
887.3
8,376.3
56.6
26,248.2
266.2 179.1
451.9
47.0
444.0
3.0
1,391.2
Total Petroleum
937.0
605.9
4,259.3
22,612.2
255.2
503.6
29,173.2
63.5 42.7
315.7
1,630.6
22.4
36.6
2,111.5
Asphalt & Road Oil
Aviation Gasoline
22.4
22.4
1.5
1.5
Distillate Fuel Oil
457.2
319.6
1,169.8
5,866.3
55.4
115.5
7,983.8
33.8 23.6
86.5
433.9
4.1
8.5
590.5
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
471.6
154.3
353.5
44.5
16.4
1,040.2
29.1 9.5
21.8
2.7
1.0
64.2
Lubricants
Motor Gasoline
106.3
699.7
14,440.7
185.1
15,431.8
7.6
49.9
1,030.2
13.2
1,100.9
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)
(0.0)
(0.0)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
47.1
47.1
3.3
3.3
Petroleum Coke
0.4
600.9
122.5
723.7
0.0
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)
5,959.9
4,027.1
13,584.6
23,499.5
25,135.8
591.0
72,797.9
329.7 225.7
843.3
1,677.6
2,038.1
42.5
5,156.9
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-15:2012 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
74.1
NE
1,511.2
3.4
1,592.8
Residential Coal
NE
NE
NE
NE
Commercial Coal
43.6
43.6
4.1
4.1
Industrial Other Coal
782.3
782.3
74.1
74.1
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
15,821.2
15,821.2
1,511.2
1,511.2
U.S. Territory Coal (bit)
36.9
36.9
3.4
3.4
Natural Gas
4,242.1
2,959.5
8,203.0
779.8
9,286.8
49.2
25,520.3
224.8 156.9
434.8
41.3
492.2
2.6
1,352.6
Total Petroleum
846.1
570.6
4,070.7
22,474.4
214.2
517.1
28,693.1
57.7 40.4
304.1
1,620.6
18.3
37.5
2,078.5
Asphalt & Road Oil
Aviation Gasoline
25.1
25.1
1.7
1.7
Distillate Fuel Oil
437.5
322.1
1,144.7
5,780.1
52.4
99.1
7,836.0
32.4 23.8
84.7
427.5
3.9
7.3
579.5
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
400.9
137.4
341.5
37.1
18.5
935.4
24.7 8.5
21.1
2.3
1.1
57.7
Lubricants
Motor Gasoline
78.1
510.9
14,435.8
207.5
15,232.3
5.6
36.4
1,029.8
14.8
1,086.7
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
(0.0)
(0.0)
(0.0)
(0.0)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
42.2
42.2
3.0
3.0
Petroleum Coke
0.4
649.1
85.1
734.6
0.0
66.3
8.7
75.0
Still Gas
1,320.2
1,320.2
88.1
88.1
Special Naphtha
Unfinished Oils
60.1
60.1
4.5
4.5
Waxes
Geothermal
53.1
53.1
0.4
0.4
Total (All Fuels)
5,088.2
3,573.7
13,056.0
23,254.2
25,375.3
603.1
70,950.5
282.5 201.3
812.9
1,661.9
2,022.2
43.5
5,024.4
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-41
-------
Table A-16:2011 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
82.0
NE
1,722.7
3.4
1,813.9
Residential Coal
NE
NE
NE
NE
Commercial Coal
61.7
61.7
5.8
5.8
Industrial Other Coal
866.1
866.1
82.0
82.0
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
18,035.2
18,035.2
1,722.7
1,722.7
U.S. Territory Coal (bit)
36.9
36.9
3.4
3.4
Natural Gas
4,804.6
3,216.1
7,873.4
733.5
7,712.2
27.1
24,366.9
254.7 170.5
417.3
38.9
408.8
1.4
1,291.5
Total Petroleum
1,040.1
690.7
4,085.2
22,664.6
295.0
496.9
29,272.6
70.9 49.2
305.1
1,634.5
25.8
36.0
2,121.5
Asphalt & Road Oil
Aviation Gasoline
27.1
27.1
1.9
1.9
Distillate Fuel Oil
534.8
400.5
1,255.8
5,814.5
63.7
97.2
8,166.4
39.6 29.6
92.9
430.0
4.7
7.2
604.0
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
486.8
140.8
238.8
34.0
18.8
919.2
30.0 8.7
14.7
2.1
1.2
56.7
Lubricants
Motor Gasoline
92.3
532.9
14,501.1
203.4
15,329.8
6.6
38.0
1,034.5
14.5
1,093.6
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
0.0
0.0
0.0
0.0
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
27.3
27.3
1.9
1.9
Petroleum Coke
0.2
600.3
138.3
738.8
0.0
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)
5,844.7
3,968.5
12,824.7
23,398.1
26,094.7
560.9
72,691.6
325.6 225.5
804.4
1,673.3
2,157.7
40.9
5,227.4
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-17:2010 Energy Consumption Da
ta 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 CO2 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
90.1
NE
1,827.6
3.4
1,927.7
Residential Coal
NE
NE
NE
NE
Commercial Coal
69.7
69.7
6.6
6.6
Industrial Other Coal
951.6
951.6
90.1
90.1
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
19,133.5
19,133.5
1,827.6
1,827.6
U.S. Territory Coal (bit)
36.9
36.9
3.4
3.4
Natural Gas
4,878.1
3,164.7
7,683.2
719.0
7,527.6
27.8
24,000.4
258.6
167.7
407.2
38.1
399.0
1.5
1,272.1
Total Petroleum
1,116.2
722.2
4,092.9
22,974.2
370.3
515.9
29,791.7
76.0
51.4
306.3
1,656.4
31.4
37.6
2,159.2
Asphalt & Road Oil
Aviation Gasoline
27.0
27.0
1.9
1.9
Distillate Fuel Oil
557.0
388.0
1,134.4
5,706.1
79.7
87.7
7,952.8
41.2
28.7
83.9
422.0
5.9
6.5
588.2
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
530.1
140.1
219.3
29.5
16.0
935.0
32.7
8.6
13.5
1.8
1.0
57.7
Lubricants
Motor Gasoline
127.2
637.2
14,841.9
176.4
15,782.8
9.1
45.5
1,058.8
12.6
1,125.9
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)
(0.0)
(0.0)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
77.8
77.8
5.4
5.4
Petroleum Coke
0.3
633.0
136.6
770.0
0.0
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)
5,994.3
3,956.5
12,727.7
23,693.2
27,083.3
580.5
74,035.6
334.6
225.7
803.6
1,694.5
2,258.4
42.4
5,359.3
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-43
-------
Table A-18:2009 Energy Consumption D
ata 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 CO2 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
83.0
NE
1,740.9
3.4
1,834.2
Residential Coal
NE
NE
NE
NE
Commercial Coal
73.4
73.4
6.9
6.9
Industrial Other Coal
877.3
877.3
83.0
83.0
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
18,225.3
18,225.3
1,740.9
1,740.9
U.S. Territory Coal (bit)
36.9
36.9
3.4
3.4
Natural Gas
4,883.1
3,186.6
7,125.1
714.9
7,022.4
27.4
22,959.4
258.8
168.9
377.6
37.9
372.2
1.5
1,216.9
Total Petroleum
1,138.2
752.0
3,932.5
22,837.7
382.4
525.7
29,568.4
77.5
53.6
294.3
1,645.8
32.2
38.3
2,141.6
Asphalt & Road Oil
Aviation Gasoline
26.6
26.6
1.8
1.8
Distillate Fuel Oil
563.4
382.4
1,018.1
5,488.2
69.6
80.6
7,602.3
41.7
28.3
75.3
405.9
5.1
6.0
562.2
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
547.1
138.9
201.7
28.0
14.9
930.6
33.8
8.6
12.4
1.7
0.9
57.4
Lubricants
Motor Gasoline
155.0
710.7
14,974.9
196.7
16,037.4
11.1
50.7
1,068.3
14.0
1,144.1
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
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
63.8
63.8
4.5
4.5
Petroleum Coke
0.2
624.0
131.8
756.1
0.0
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)
6,021.3
4,012.0
11,934.8
23,552.5
25,681.3
589.9
71,791.9
336.3
229.4
755.0
1,683.7
2,145.7
43.1
5,193.2
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-19:2008 Energy Consumption D
ata 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 CO2 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
1,959.4
3.4
2,072.8
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
1,959.4
1,959.4
U.S. Territory Coal (bit)
36.9
36.9
3.4
3.4
Natural Gas
5,010.1
3,228.4
7,571.4
692.1
6,828.9
29.3
23,360.2
265.5
171.1
401.3
36.7
361.9
1.6
1,238.1
Total Petroleum
1,201.5
706.0
4,381.9
23,883.6
459.3
488.0
31,120.3
82.1
50.0
326.8
1,722.4
38.4
35.7
2,255.3
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
552.7
158.0
226.7
40.1
15.7
993.3
34.1
9.7
14.0
2.5
1.0
61.3
Lubricants
Motor Gasoline
151.0
825.2
15,089.0
133.7
16,198.9
10.8
58.9
1,076.4
9.5
1,155.6
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
Other Petroleum
AvGas Blend Components
0.1
0.1
0.0
0.0
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
76.5
76.5
5.4
5.4
Petroleum Coke
0.3
645.7
146.4
792.3
0.0
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)
6,211.6
4,015.2
13,034.7
24,575.7
27,851.8
554.1
76,243.2
347.6
228.7
830.5
1,759.1
2,360.1
40.7
5,566.6
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-45
-------
Table A-20:2007 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
1,987.3
3.4
2,105.1
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
1,987.3
1,987.3
U.S. Territory Coal (bit)
36.9
36.9
3.4
3.4
Natural Gas
4,835.4
3,085.1
7,521.3
663.5
7,005.2
26.7
23,137.2
256.3 163.5
398.6
35.2
371.3
1.4
1,226.3
Total Petroleum
1,219.9
755.0
4,961.5
25,124.8
647.8
576.9
33,285.9
84.3 54.0
369.2
1,818.5
52.9
42.3
2,421.2
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
483.7
121.4
379.1
21.9
11.7
1,017.8
29.8 7.5
23.4
1.4
0.7
62.8
Lubricants
Motor Gasoline
182.6
913.9
15,806.5
155.7
17,058.8
13.1
65.5
1,133.6
11.2
1,223.4
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
89.7
89.7
6.3
6.3
Petroleum Coke
0.4
708.4
162.6
871.3
0.0
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
0.4
0.4
Total (All Fuels)
6,063.2
3,910.1
13,613.6
25,788.2
28,510.7
640.5
78,526.3
341.3 224.2
874.9
1,853.6
2,411.9
47.1
5,753.0
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-21:2006 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
1,953.7
3.4
2,076.6
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
1,953.7
1,953.7
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
1,202.3
728.8
5,105.1
25,204.4
637.0
615.6
33,493.0
83.4 52.1
380.0
1,817.6
53.2
45.1
2,431.5
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
Kerosene
66.4
15.2
29.6
4.3
115.4
4.9 1.1
2.2
0.3
8.5
LPG
445.5
123.2
369.7
27.5
6.6
972.5
27.5 7.6
22.8
1.7
0.4
60.0
Lubricants
Motor Gasoline
125.9
973.2
15,979.2
186.9
17,265.2
9.0
69.4
1,139.9
13.3
1,231.6
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
0.0
0.0
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
70.0
70.0
4.9
4.9
Petroleum Coke
0.3
724.3
203.0
927.6
0.0
74.0
20.7
94.7
Still Gas
1,496.2
1,496.2
99.8
99.8
Special Naphtha
Unfinished Oils
70.3
70.3
5.2
5.2
Waxes
Geothermal
49.7
49.7
0.4
0.4
Total (All Fuels)
5,684.6
3,695.3
13,617.0
25,829.4
27,523.7
678.6
77,028.5
321.3 212.2
880.8
1,850.7
2,345.3
49.9
5,660.3
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-47
-------
Table A-22:2005 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9 10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res. Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
8.4
97.0
1,219.1
NE
20,737.2
32.7
22,094.5
0.8 9.3
115.3
NE
1,983.8
3.0
2,112.3
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
1,983.8
1,983.8
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
388.5
33.1
318.8
1.3
1,166.7
Total Petroleum
1,368.3
765.7
4,718.2
25,358.6
1,222.1
619.9
34,052.7
94.9 54.9
351.9
1,822.7
97.9
45.4
2,467.6
Asphalt & Road Oil
Aviation Gasoline
35.4
35.4
2.4
2.4
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
512.9
131.4
349.6
28.2
0.7
1,022.8
31.7 8.1
21.6
1.7
0.0
63.1
Lubricants
Motor Gasoline
92.4
719.3
16,230.7
191.1
17,233.6
6.6
51.1
1,152.4
13.6
1,223.6
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
98.1
98.1
6.9
6.9
Petroleum Coke
0.3
706.6
231.1
938.0
0.0
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
0.4
0.4
Total (All Fuels)
6,323.1
3,935.9
13,267.0
25,982.5
28,024.0
676.9
78,209.4
357.8 227.0
855.7
1,855.8
2,400.9
49.7
5,746.9
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
A-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-23:2004 Energy Consumption D
ata 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 CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
11.4
102.9
1,262.0
NE
20,305.0
32.0
21,713.3
1.1
9.8
118.3
NE
1,943.1
2.9
2,075.1
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
118.3
118.3
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
20,305.0
20,305.0
1,943.1
1,943.1
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
264.1
169.7
419.6
31.9
296.7
1.3
1,183.4
Total Petroleum
1,467.8
810.8
4,584.8
25,099.5
1,201.0
651.7
33,815.6
102.2
58.0
342.0
1,804.4
95.8
47.8
2,450.2
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
511.7
152.0
372.7
19.1
0.7
1,056.3
31.6
9.4
23.0
1.2
0.0
65.2
Lubricants
Motor Gasoline
72.0
599.9
16,367.5
198.3
17,237.7
5.1
42.6
1,163.2
14.1
1,225.1
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
111.2
111.2
7.8
7.8
Petroleum Coke
0.3
719.1
210.8
930.1
0.0
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
0.4
0.4
Total (All Fuels)
6,460.1
4,114.7
13,760.4
25,701.4
27,151.5
708.3
77,896.4
367.4
237.5
879.9
1,836.3
2,335.9
52.0
5,709.1
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-49
-------
Table fl-24:2003 Energy Consumption D
ata 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 CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
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
117.0
NE
1,931.0
3.1
2,060.1
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
117.0
117.0
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
20,184.7
20,184.7
1,931.0
1,931.0
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
275.9
172.7
415.4
33.2
277.8
1.4
1,176.4
Total Petroleum
1,468.3
831.2
4,319.5
24,492.0
1,204.8
617.9
32,933.8
101.9
59.4
322.8
1,758.7
95.0
45.0
2,382.9
Asphalt & Road Oil
Aviation Gasoline
30.2
30.2
2.1
2.1
Distillate Fuel Oil
853.5
454.2
1,058.2
5,704.9
160.8
118.1
8,349.6
63.1
33.6
78.3
421.9
11.9
8.7
617.5
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
544.5
156.9
326.8
17.9
10.5
1,056.6
33.7
9.7
20.2
1.1
0.7
65.3
Lubricants
Motor Gasoline
90.1
487.3
16,157.4
207.9
16,942.8
6.4
34.6
1,146.9
14.8
1,202.6
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
110.4
110.4
7.7
7.7
Petroleum Coke
0.3
701.9
174.7
876.8
0.0
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
0.4
0.4
Total (All Fuels)
6,689.9
4,174.1
13,413.4
25,119.4
26,685.0
678.7
76,760.6
378.9
239.9
855.2
1,791.9
2,304.2
49.6
5,619.8
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-25:2002 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9 10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res. 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
1,889.9
1.0
2,017.2
Residential Coal
12.2
12.2
1.2
1.2
Commercial Coal
89.8
89.8
8.6
8.6
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
1,889.9
1,889.9
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
264.7 170.3
428.6
37.0
305.7
1.2
1,207.5
Total Petroleum
1,360.9
701.0
4,161.3
24,541.8
961.2
552.0
32,278.1
94.1 50.0
310.7
1,763.7
76.8
40.2
2,335.5
Asphalt & Road Oil
Aviation Gasoline
33.7
33.7
2.3
2.3
Distillate Fuel Oil
763.8
394.5
1,051.2
5,590.0
127.3
91.3
8,018.1
56.5 29.2
77.7
413.4
9.4
6.8
593.0
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
537.1
140.8
393.3
14.3
11.1
1,096.6
33.2 8.7
24.3
0.9
0.7
67.8
Lubricants
Motor Gasoline
69.7
477.4
16,110.4
187.7
16,845.2
5.0
33.9
1,144.7
13.3
1,196.9
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
111.9
111.9
7.8
7.8
Petroleum Coke
0.2
696.3
175.2
871.7
0.0
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
0.4
0.4
Total (All Fuels)
6,368.1
4,003.3
13,491.2
25,240.7
26,560.2
585.6
76,249.1
360.0 228.8
855.9
1,800.8
2,272.7
42.5
5,560.6
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-51
-------
Table A-26:2001 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
1,869.8
0.4
2,008.4
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
1,869.8
1,869.8
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
259.1
164.2
421.3
34.9
289.3
1.2
1,170.0
Total Petroleum
1,464.9
766.9
4,286.3
24,040.5
1,276.4
628.7
32,463.7
101.9
54.9
319.8
1,725.2
98.4
45.9
2,346.3
Asphalt & Road Oil
Aviation Gasoline
34.9
34.9
2.4
2.4
Distillate Fuel Oil
844.1
472.4
1,184.5
5,411.3
170.3
106.8
8,189.5
62.4
34.9
87.6
400.2
12.6
7.9
605.7
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
525.7
142.7
372.1
13.7
7.0
1,061.2
32.5
00
CO
23.0
0.8
0.4
65.6
Lubricants
Motor Gasoline
50.4
397.0
15,794.7
186.4
16,428.5
3.6
28.2
1,120.1
13.2
1,165.1
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
131.6
131.6
9.2
9.2
Petroleum Coke
0.2
683.3
103.2
786.7
0.0
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)
6,365.9
3,961.1
13,593.8
24,698.5
26,395.0
655.3
75,669.6
362.2
228.3
869.0
1,760.1
2,257.9
47.5
5,525.0
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-27:2000 Energy Consumption D
ata 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 CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
11.4
91.9
1,348.8
NE
20,220.2
10.3
21,682.4
1.1
8.8
127.3
NE
1,927.4
0.9
2,065.5
Residential Coal
11.4
11.4
1.1
1.1
Commercial Coal
91.9
91.9
00
CO
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
1,927.4
1,927.4
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
270.7
172.5
459.1
35.6
280.8
0.7
1,219.4
Total Petroleum
1,429.5
774.8
3,846.9
24,310.7
1,144.1
467.7
31,973.7
99.0
55.3
286.8
1,745.1
88.4
33.9
2,308.5
Asphalt & Road Oil
Aviation Gasoline
36.3
36.3
2.5
2.5
Distillate Fuel Oil
780.0
423.3
1,006.4
5,436.7
174.7
68.5
7,889.5
57.7
31.3
74.4
402.1
12.9
5.1
583.5
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
554.9
150.4
468.7
11.9
8.0
1,193.9
34.4
9.3
29.0
0.7
0.5
74.0
Lubricants
Motor Gasoline
79.8
269.0
15,682.1
183.6
16,214.5
5.7
19.1
1,111.5
13.0
1,149.2
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
171.6
171.6
12.0
12.0
Petroleum Coke
0.2
697.6
98.6
796.4
0.0
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
0.4
0.4
Total (All Fuels)
6,545.4
4,118.2
13,851.7
24,982.7
26,705.8
490.6
76,694.4
370.8
236.6
873.2
1,780.7
2,296.9
35.6
5,593.7
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-53
-------
Table A-28:1999 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
20,779.0
1.3
9.8
129.9
NE
1,836.4
0.9
1,978.3
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
129.9
129.9
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
19,279.5
19,279.5
1,836.4
1,836.4
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
256.3
165.1
446.6
35.8
259.9
1,163.8
Total Petroleum
1,344.0
645.3
3,742.8
23,869.7
1,211.2
454.9
31,267.9
92.9
46.0
280.9
1,711.6
93.8
33.1
2,258.4
Asphalt & Road Oil
Aviation Gasoline
39.2
39.2
2.7
2.7
Distillate Fuel Oil
706.9
374.4
986.1
5,245.8
140.0
93.2
7,546.3
52.3
27.7
72.9
388.0
10.4
6.9
558.1
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
526.0
140.2
395.9
14.3
9.2
1,085.5
32.5
8.7
24.5
0.9
0.6
67.1
Lubricants
Motor Gasoline
30.4
162.2
15,729.9
162.0
16,084.6
2.2
11.5
1,114.4
11.5
1,139.5
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
182.5
182.5
12.8
12.8
Petroleum Coke
0.1
719.8
112.5
832.4
0.0
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
0.4
0.4
Total (All Fuels)
6,192.9
3,862.9
13,540.2
24,545.0
25,443.4
465.1
74,049.5
350.6
220.9
857.4
1,747.4
2,190.5
34.0
5,400.8
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-29:1998 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res. Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
11.5
93.4
1,470.8
NE
19,215.7
10.5
20,802.0
1.1 8.9
139.1
NE
1,828.2
1.0
1,978.3
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
1,828.2
1,828.2
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
246.0 163.3
467.4
35.3
247.6
1,159.5
Total Petroleum
1,209.0
671.4
3,787.1
22,910.4
1,306.1
442.8
30,326.8
84.1 48.1
284.9
1,644.7
101.3
32.3
2,195.4
Asphalt & Road Oil
Aviation Gasoline
35.5
35.5
2.5
2.5
Distillate Fuel Oil
676.8
376.0
1,030.5
4,949.9
135.6
70.6
7,239.5
50.1 27.8
76.2
366.1
10.0
5.2
535.4
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
423.9
117.6
271.6
17.6
5.9
836.7
26.1 7.2
16.7
1.1
0.4
51.6
Lubricants
Motor Gasoline
61.3
313.8
15,220.5
161.3
15,756.9
4.4
22.3
1,080.8
11.5
1,118.9
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
147.0
147.0
10.3
10.3
Petroleum Coke
0.1
707.7
123.6
831.4
0.0
72.3
12.6
84.9
Still Gas
1,431.0
1,431.0
95.5
95.5
Special Naphtha
Unfinished Oils
(313.9)
(313.9)
(23.3)
(23.3)
Waxes
Geothermal
50.4
50.4
0.4
0.4
Total (All Fuels)
5,866.6
3,847.8
14,083.9
23,576.5
25,247.1
453.4
73,075.3
331.2 220.3
891.4
1,680.0
2,177.4
33.2
5,333.5
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-55
-------
Table A-30:1997 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
1,797.0
1.0
1,949.5
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
1,797.0
1,797.0
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
270.1 174.2
479.0
41.4
218.8
1,183.4
Total Petroleum
1,335.0
717.3
4,202.8
22,332.2
926.7
440.2
29,954.2
93.1 51.5
311.8
1,602.8
72.2
32.0
2,163.5
Asphalt & Road Oil
Aviation Gasoline
39.7
39.7
2.7
2.7
Distillate Fuel Oil
787.3
399.6
1,059.3
4,797.9
110.5
79.1
7,233.8
58.2 29.6
78.3
354.8
8.2
5.9
535.0
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
CO
CO
CO
1.4
0.3
10.3
LPG
454.8
120.2
429.9
14.2
6.5
1,025.7
28.1 7.4
26.5
0.9
0.4
63.3
Lubricants
Motor Gasoline
61.4
304.6
14,790.1
159.0
15,315.2
4.4
21.6
1,049.6
11.3
1,086.9
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
164.5
164.5
11.5
11.5
Petroleum Coke
0.1
639.9
101.6
741.6
0.0
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
0.4
0.4
Total (All Fuels)
6,443.9
4,132.0
14,693.0
23,112.5
24,007.0
450.6
72,839.1
364.7 238.0
928.4
1,644.2
2,088.4
33.0
5,296.7
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-38), and international bunker fuel consumption (see Table
A-39).
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-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-31:1996 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
137.4
NE
1,752.4
1.0
1,903.9
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
137.4
137.4
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
18,429.0
18,429.0
1,752.4
1,752.4
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
283.9 171.1
478.3
39.1
204.8
1,177.2
Total Petroleum
1,398.0
763.6
4,249.7
22,126.5
817.3
428.3
29,783.3
97.6 55.0
315.5
1,588.3
63.4
31.1
2,150.8
Asphalt & Road Oil
Aviation Gasoline
37.4
37.4
2.6
2.6
Distillate Fuel Oil
840.5
438.3
1,050.9
4,594.9
109.3
73.4
7,107.4
62.2 32.4
77.7
339.8
8.1
5.4
525.6
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
468.7
122.4
401.7
15.6
7.5
1,015.9
28.9 7.5
24.8
1.0
0.5
62.6
Lubricants
Motor Gasoline
44.5
335.3
14,607.7
150.2
15,137.6
3.2
23.8
1,036.7
10.7
1,074.3
Residual Fuel
137.2
284.7
314.9
628.4
117.1
1,482.3
10.3
21.4
23.6
47.2
00
CO
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
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
177.5
177.5
12.4
12.4
Petroleum Coke
0.1
638.6
79.6
718.3
0.0
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
0.4
0.4
Total Coal
6,769.0
4,111.5
14,724.9
22,863.3
23,157.6
438.6
72,065.0
383.1 237.7
931.2
1,627.3
2,021.0
32.1
5,232.4
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-57
-------
Table fl-32:1995 Energy Consumption D
ata and CG2 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 CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
17.5
116.8
1,526.9
NE
17,466.3
10.2
19,137.7
1.7
11.2
144.4
NE
1,660.7
0.9
1,819.0
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
1,660.7
1,660.7
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
262.7
164.2
462.5
38.4
228.1
1,155.9
Total Petroleum
1,262.3
727.7
3,904.3
21,545.6
754.5
459.1
28,653.6
88.5
52.5
289.2
1,543.5
58.7
33.4
2,065.8
Asphalt & Road Oil
Aviation Gasoline
39.6
39.6
2.7
2.7
Distillate Fuel Oil
793.2
419.8
969.7
4,379.4
108.0
86.8
6,756.8
58.7
31.0
71.7
323.9
8.0
6.4
499.7
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
394.8
108.7
403.4
17.7
5.6
930.3
24.4
6.7
24.9
1.1
0.3
57.4
Lubricants
Motor Gasoline
35.5
391.6
14,292.8
147.3
14,867.2
2.5
27.8
1,014.5
10.5
1,055.2
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
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
169.0
169.0
11.8
11.8
Petroleum Coke
0.1
600.7
80.6
681.4
0.0
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
0.3
0.3
Total Coal
6,233.9
3,940.5
14,153.8
22,269.6
22,568.4
469.4
69,635.5
352.8
227.9
896.2
1,581.9
1,947.9
34.3
5,041.0
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-33:1994 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
150.7
NE
1,638.8
0.9
1,803.7
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
150.7
150.7
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
17,260.9
17,260.9
1,638.8
1,638.8
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
439.4
37.6
210.8
1,107.6
Total Petroleum
1,306.7
781.4
3,964.6
21,194.4
1,058.7
505.4
28,811.2
91.9 56.6
293.5
1,518.1
81.2
36.8
2,078.2
Asphalt & Road Oil
Aviation Gasoline
38.1
38.1
2.6
2.6
Distillate Fuel Oil
858.1
447.9
977.4
4,183.3
120.0
117.0
6,703.6
63.5 33.1
72.3
309.4
8.9
8.7
495.8
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
383.7
107.3
423.1
34.0
7.2
955.2
23.7 6.6
26.1
2.1
0.4
59.0
Lubricants
Motor Gasoline
34.7
265.1
14,107.1
147.1
14,554.1
2.5
18.8
1,001.6
10.4
1,033.3
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
169.4
169.4
11.9
11.9
Petroleum Coke
0.1
594.9
69.7
664.7
0.0
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
0.4
0.4
Total Coal
6,287.4
3,861.5
13,849.7
21,903.0
22,349.9
515.4
68,766.9
356.8 224.9
883.6
1,555.7
1,931.2
37.8
4,990.0
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-38), and international bunker fuel consumption (see Table
A-39).
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-59
-------
Table A-34:1993 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
149.8
NE
1,632.5
0.9
1,796.9
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
149.8
149.8
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
17,195.9
17,195.9
1,632.5
1,632.5
U.S. Territory Coal (bit)
9.6
9.6
0.9
0.9
Natural Gas
5,063.3
2,923.3
8,272.5
644.7
3,537.5
20,441.3
268.4 155.0
438.6
34.2
187.5
1,083.7
Total Petroleum
1,348.5
787.5
3,856.3
20,554.5
1,123.8
456.4
28,126.9
94.9 57.0
286.4
1,476.0
86.4
33.3
2,034.1
Asphalt & Road Oil
Aviation Gasoline
38.4
38.4
2.7
2.7
Distillate Fuel Oil
883.3
447.2
989.9
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
LPG
389.6
109.2
412.2
20.2
4.9
936.2
24.0 6.7
25.4
1.2
0.3
57.8
Lubricants
Motor Gasoline
44.1
267.6
13,870.6
127.3
14,309.6
3.1
19.1
988.7
9.1
1,019.9
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
0.2
0.2
0.0
0.0
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
166.1
166.1
11.6
11.6
Petroleum Coke
0.2
614.6
78.6
693.4
0.0
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
0.4
0.4
Total Coal
6,437.5
3,828.0
13,713.7
21,199.3
21,914.5
466.0
67,559.1
365.8 223.2
874.8
1,510.2
1,906.9
34.2
4,915.1
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-35:1992 Energy Consumption D
ata and CG2 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 CO2 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
1,569.6
0.8
1,731.6
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
1,569.6
1,569.6
U.S. Territory Coal (bit)
00
CO
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
254.5
152.1
430.5
32.2
186.0
1,055.4
Total Petroleum
1,365.8
872.4
3,962.9
20,074.1
990.7
443.0
27,708.9
96.5
63.2
294.1
1,444.1
75.5
32.3
2,005.7
Asphalt & Road Oil
Aviation Gasoline
41.1
41.1
2.8
2.8
Distillate Fuel Oil
931.4
481.7
1,028.5
3,665.7
73.5
89.6
6,270.4
68.9
35.6
76.1
271.1
5.4
6.6
463.7
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
369.4
106.9
441.8
19.4
11.8
949.4
22.8
6.6
27.3
1.2
0.7
58.7
Lubricants
Motor Gasoline
83.5
203.7
13,604.0
121.5
14,012.6
6.0
14.6
972.3
8.7
1,001.5
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
0.0
0.0
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
161.3
161.3
11.3
11.3
Petroleum Coke
0.1
627.2
45.0
672.2
0.0
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
0.4
0.4
Total Coal
6,196.0
3,860.1
13,642.8
20,682.2
21,022.9
451.9
65,855.8
353.5
226.6
872.0
1,476.3
1,831.5
33.1
4,793.1
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-36:1991 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 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
152.1
NE
1,548.2
0.7
1,714.6
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
152.1
152.1
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
16,249.7
16,249.7
1,548.2
1,548.2
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
414.7
32.9
178.9
1,021.8
Total Petroleum
1,381.5
996.2
3,690.9
19,341.4
1,198.3
422.4
27,030.5
97.5 72.2
274.2
1,388.1
90.7
30.7
1,953.3
Asphalt & Road Oil
Aviation Gasoline
41.7
41.7
2.9
2.9
Distillate Fuel Oil
931.0
517.7
1,050.8
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
378.1
108.2
342.2
21.1
13.8
863.5
23.3 6.7
21.1
1.3
0.8
53.3
Lubricants
Motor Gasoline
146.2
332.3
13,230.9
123.6
13,833.0
10.4
23.7
943.0
00
CO
986.0
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)
(0.0)
(0.0)
Crude Oil
39.0
39.0
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
147.0
147.0
10.3
10.3
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
0.4
0.4
Total Coal
6,074.0
3,907.1
13,121.4
19,961.7
20,879.8
430.1
64,374.1
347.2 231.4
841.0
1,420.9
1,818.2
31.4
4,690.1
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-37:1990 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res. Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
31.1
124.5
1,640.5
NE
16,261.0
7.0
18,064.0
3.0 12.0
155.3
NE
1,547.6
0.6
1,718.4
Residential Coal
31.1
31.1
3.0
3.0
Commercial Coal
124.5
124.5
12.0
12.0
Industrial Other Coal
1,640.5
1,640.5
155.3
155.3
Transportation Coal
NE
NE
NE
NE
Electric Power Coal
16,261.0
16,261.0
1,547.6
1,547.6
U.S. Territory Coal (bit)
7.0
7.0
0.6
0.6
Natural Gas
4,490.9
2,682.2
7,716.4
679.9
3,308.5
18,877.9
238.0 142.1
408.9
36.0
175.3
1,000.3
Total Petroleum
1,375.2
1,007.4
3,973.6
19,955.3
1,289.4
370.3
27,971.3
97.4 73.1
294.7
1,431.5
97.5
26.9
2,021.2
Asphalt & Road Oil
Aviation Gasoline
45.0
45.0
3.1
3.1
Distillate Fuel Oil
959.2
525.4
1,098.5
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
O
CO
0.9
0.2
6.6
LPG
352.1
102.3
380.2
22.9
14.5
872.0
21.8 6.3
23.5
1.4
0.9
53.9
Lubricants
Motor Gasoline
138.0
229.8
13,442.3
100.7
13,910.8
9.8
16.4
957.3
7.2
990.7
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
0.0
0.0
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
125.2
125.2
8.8
00
CO
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
0.4
0.4
Total Coal
5,897.2
3,814.0
13,330.4
20,635.2
20,911.6
377.4
64,965.8
338.3 227.2
858.8
1,467.6
1,820.8
27.6
4,740.3
NE (Not Estimated)
NA (Not Available)
Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
A-39).
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-63
-------
Table fl-38: Unadjusted Non-Energy Fuel Consumption UBtul
Sector/Fuel Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Industry
4,544.0
5,089.7
5,576.5
5,263.7
5,425.7
5,342.9
5,847.2
5,483.3
5,470.0
5,225.2
4,769.7
4,510.2
4,753.0
4,665.0
4,611.3
4,823.4
4,725.6
4,915.9
4,896.6
Industrial Coking Coal
0.0
< 37.8 /
53.5
24.8
40.3
51.9
167.8
80.4
62.9
2.3
29.2
6.4
64.8
60.8
132.5
119.3
48.8
121.8
88.8
Industrial Other Coal
8.2
"1 113
12.4
11.3
12.0
11.9
11.9
11.9
11.9
11.9
11.9
11.9
10.3
10.3
10.3
10.3
10.3
10.3
10.3
Natural Gas to Chemical
Plants, Other Uses
305.9
s 371.0
401.7
391.8
380.7
345.3
306.6
270.4
233.4
233.6
233.6
233.6
311.8
311.8
311.8
311.8
311.8
311.8
311.8
Asphalt & Road Oil
1,170.2
1,178.2
1,275.7
1,256.9
1,240.0
1,219.5
1,303.8
1,323.2
1,261.2
1,197.0
1,012.0
873.1
877.8
859.5
826.7
783.3
792.6
831.7
853.4
LPG
1,201.4
1,586.9
1,759.3
1,642.3
1,766.3
1,701.6
1,768.5
1,659.5
1,734.6
1,726.7
1,596.6
1,748.0
1,901.6
1,943.4
1,990.5
2,149.0
2,148.7
2,215.1
2,254.0
Lubricants
186.3
177.8
189.9
174.0
171.9
159.0
161.0
160.2
156.1
161.2
149.6
134.5
149.5
141.8
130.5
138.1
144.0
156.8
148.9
Pentanes Plus
125.2
* 169.0
171.6
131.6
111.9
110.4
111.2
98.1
70.1
89.7
76.5
63.8
77.7
27.3
42.2
47.1
44.2
80.2
56.1
Naphtha (<401 deg. F)
347.8
373.0
613.5
493.7
582.6
613.0
749.4
698.7
628.9
562.5
477.2
471.9
490.6
487.3
453.9
517.8
442.6
428.1
420.0
Other Oil (>401 deg. F)
753.9
: 801.0
722.2
662.5
632.1
699.4
779.5
708.0
790.6
744.1
647.8
424.8
452.5
388.5
287.2
223.9
247.2
229.0
222.5
Still Gas
36.7
47.9
17.0
49.3
61.7
59.0
62.9
67.7
57.2
44.2
47.3
133.9
147.8
163.6
160.6
166.7
164.5
162.2
166.1
Petroleum Coke
123.1
120.6
98.5
174.3
145.8
122.8
217.7
186.9
213.6
201.2
224.5
180.7
61.0
62.4
67.6
62.4
61.4
62.5
61.1
Special Naphtha
107.1
70.8
97.4
78.5
102.4
80.5
51.0
62.5
70.1
78.0
84.9
46.2
26.1
22.6
14.7
100.0
106.1
99.3
93.6
Other (Wax/Misc.)
Distillate Fuel Oil
7.0
6.8
11.7
11.7
11.7
11.7
11.7
11.7
17.5
17.5
17.5
17.5
5.8
5.8
5.8
5.8
5.8
5.8
5.8
Waxes
33.3
' 40.6
33.1
36.3
32.2
31.1
30.8
31.4
26.2
21.9
19.1
12.2
17.1
15.1
15.3
16.5
14.8
12.4
12.9
Miscellaneous Products
137.8
97.1
119.2
124.9
134.2
126.0
113.4
112.8
136.0
133.5
142.0
151.8
158.7
164.7
161.6
171.2
182.7
188.9
191.3
Transportation
176.0
167.9
179.4
164.3
162.4
150.1
152.1
151.3
147.4
152.2
141.3
127.1
141.2
133.9
123.2
130.4
136.0
148.1
140.6
Lubricants
176.0
167.9
179.4
164.3
162.4
150.1
152.1
151.3
147.4
152.2
141.3
127.1
141.2
133.9
123.2
130.4
136.0
148.1
140.6
U.S. Territories
85.6
90.8
152.4
83.2
140.8
123.5
111.0
123.2
133.5
71.8
132.3
60.4
60.1
75.6
72.0
82.4
77.3
77.3
77.3
Lubricants
0.7
2.0
3.1
2.5
3.0
4.9
5.1
4.6
6.2
5.9
2.7
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
80.7
137.8
118.6
105.9
118.6
127.3
65.9
129.6
59.3
59.0
74.6
71.0
81.4
76.2
76.2
76.2
Total
4,805.6
5,348.4
5,908.2
5,511.3
5,728.9
5,616.5
6,110.3
5,757.9
5,750.9
5,449.3
5,043.3
4,697.6
4,954.2
4,874.6
4,806.6
5,036.2
4,938.9
5,141.3
5,114.5
Note: These values are unadjusted non-energy fuel use provided by EIA. They have not yet been adjusted to remove petroleum feedstock exports and processes accounted for in the Industrial Processes
and Product Use chapter.
Table fl-39: International Bunker Fuel Consumption UBtul
Fuel Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Marine Residual Fuel Oil
715.7
523.2
444.1
426.0
448.9
471.8
553.1
581.0
599.4
607.5
654.6
604.8
619.8
518.4
459.5
379.8
369.2
406.8
450.7
Marine Distillate Fuel Oil &
Other
158.0
125.7
85.9
72.4
82.6
103.9
143.6
126.9
119.3
111.3
122.2
111.0
128.2
107.4
91.7
75.4
82.0
113.5
117.5
Aviation Jet Fuel
539.4
703.4
880.1
799.7
774.8
783.0
797.7
853.1
855.6
872.7
796.8
749.1
865.4
919.9
916.3
931.6
987.8
1,022.3
1,051.1
Total
1,413.1
1,352.3
1,410.0
1,298.1
1,306.3
1,358.7
1,494.4
1,561.0
1,574.2
1,591.5
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
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-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-40: Keyflssumptions for Estimating CO; Emissions
C Content Coefficient
Fuel Type
(MMT C/QBtu)
Coal
Residential Coal
(See c)
Commercial Coal
(See c)
Industrial Coking Coal
(See c)
Industrial Other Coal
(See c)
Electric Power Coal
(See c)
U.S. Territory Coal (bit)
25.14
Pipeline Natural Gas
Flare Gas3
14.92
Petroleum
Asphalt & Road Oil
20.55
Aviation Gasoline
18.86
Distillate Fuel Oil No. 1
19.98
Distillate Fuel Oil No. 2b
20.17
Distillate Fuel Oil No. 4
20.47
Jet Fuel
(See c)
Kerosene
19.96
LPG (energy use)
(See c)
LPG (non-energy use)
(See c)
Lubricants
20.20
Motor Gasoline
(See c)
Residual Fuel Oil No. 5
19.89
Residual Fuel Oil No. 6b
20.48
Other Petroleum
AvGas Blend Components
18.87
Crude Oil
(See c)
MoGas Blend Components
(See c)
Misc. Products
(See c)
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 c)
Waxes
19.80
Geothermal
2.05
a Flare gas is not used in the CO2 from fossil fuel combustion calculations and is presented for informational purposes only.
b Distillate fuel oil No.2 and residual fuel oil No. 6 are used in the CO2 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.
c These coefficients vary annually due to fluctuations in fuel quality (see Table A-41).
Sources: C coefficients from EIA (2009b) and EPA (2010a).
A-65
-------
Table fl-41: flnnuallyVariahle C Content Coefficients by Year [MBIT C/QBtul
Fuel Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Residential Coal
26.20
26.13
26.01
26.00
25.98
26.04
25.91
26.09
26.29
25.94
25.7 a
25.7 a
25.7 a
25.7 a
25.71a
25.71a
25.71a
25.71a
25.71a
Commercial Coal
26.00
26.13
26.01
26.00
25.98
26.04
25.91
26.09
26.29
25.94
25.71
25.71
25.71
25.71
25.71
25.71
25.71
25.71
25.71
Industrial Coking Coal
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
31.00
31.00
Industrial Other Coal
25.82
25.80
25.74
25.66
25.57
25.55
25.56
25.80
25.84
25.82
25.82
25.82
25.82
25.82
25.82
25.82
25.82
25.82
25.82
Electric Power Coal
25.96
25.93
26.00
26.00
26.05
26.09
26.10
26.09
26.04
26.05
26.05
26.05
26.05
26.05
26.05
26.05
26.05
26.05
26.05
Pipeline Natural Gas
14.45
14.46
14.47
14.46
14.46
14.44
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
LPG (energy use)
16.86
16.82
16.89
16.87
16.85
16.86
16.84
16.84
16.83
16.82
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
17.10
17.09
17.09
17.07
17.06
17.06
17.05
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
19.34
19.38
19.36
19.38
19.36
19.45
19.56
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
19.70
19.70
19.70
19.70
MoGas Blend
Components
19.42
19.36
19.33
19.34
19.38
19.36
19.38
19.36
19.45
19.56
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
20.27
20.28
20.25
20.31
20.31
20.28
20.28
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
20.27
20.28
20.25
20.31
20.31
20.28
20.28
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
20.27
20.28
20.25
20.31
20.31
20.28
20.28
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: EPA (2010a).
Table fl-42: Electricity Consumption by End-Use Sector [Billion Kilowatt-Hours]
End-Use Sector
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Residential
924
1,043
1,192
1,202
1,265
1,276
1,292
1,359
1,352
1,392
1,381
1,365
1,446
1,423
1,375
1,395
1,407
1,404
1,411
Commercial
838
953
1,159
1,191
1,205
1,199
1,230
1,275
1,300
1,336
1,336
1,307
1,330
1,328
1,327
1,337
1,352
1,361
1,367
Industrial
1,070
1,163
1,235
1,159
1,156
1,181
1,186
1,169
1,158
1,154
1,142
1,044
1,103
1,124
1,123
1,129
1,136
1,128
1,117
Transportation
5
5
5
6
6
7
7
8
7
8
8
8
8
8
7
8
8
8
7
Total
2,837
3,164
3,592
3,557
3,632
3,662
3,716
3,811
3,817
3,890
3,866
3,724
3,887
3,883
3,832
3,868
3,903
3,900
3,902
Note: Does not include the U.S. Territories.
Source: EIA (2018).
A-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
References
EIA (2018) Monthly Energy Review. February 2018, Energy Information Administration, U.S. Department of Energy,
Washington, DC. DOE/EIA-0035(2018/02)
EIA (2013) EIA Manufacturing Consumption of Energy (MECS) 2010. U.S. Department of Energy, Energy Information
Administration, Washington, D.C.
EIA (2009b) Manufacturing Consumption of Energy 2006. Energy Information Administration, U.S. Department of Energy.
Washington, DC. Released July, 2009.
EPA (2010a) 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.
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.
UNFCCC (2014) Decision 24/CP 19, Revision of the UNFCCC reporting guidelines on annual inventories for Parties
included in Annex I to the Convention. United Nations Framework Convention on Climate Change (UNFCCC)
Conference of the Parties Nineteenth session, Warsaw, Poland. November 23, 2013. Available online at:
.
A-67
-------
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 (CO2) 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-43.
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 because 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-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-43: Carbon Content Coefficients Used in this Report (MMT Carbon/QBtu)
Fuel Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Coal
Residential Coal3
26.20
26.13
26.01
26.00
25.98
26.04
25.91
26.09
26.29
25.94
25.71"
25.71"
25.71"
25.71"
25.71"
25.71"
25.71"
25.71"
25.71"
Commercial Coal3
26.20
26.13
26.01
26.00
25.98
26.04
25.91
26.09
26.29
25.94
25.71
25.71
25.71
25.71
25.71
25.71
25.71
25.71
25.71
Industrial Coking
Coal3
25.53
25.57
25.63
25.63
25.65
25.63
25.63
25.60
25.60
25.61
25.61
25.61
25.61
25.61
25.61
25.61
25.61
25.61
25.61
Industrial Other
Coal3
25.82
25.80
25.74
25.66
25.57
25.55
25.56
25.80
25.84
25.82
25.82
25.82
25.82
25.82
25.82
25.82
25.82
25.82
25.82
Utility Coal3'0
25.96
25.93
26.00
26.00
26.05
26.09
26.10
26.09
26.04
26.05
26.05
26.05
26.05
26.05
26.05
26.05
26.05
26.05
26.05
Pipeline Natural
Gasd
14.45
14.46
14.47
14.46
14.46
14.44
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
Flare Gas
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
15.31
Petroleum
Asphalt and Road
Oil
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
Aviation Gasoline
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
Distillate Fuel Oil
No. 1
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
19.98
Distillate Fuel Oil
No. 2
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
Distillate Fuel Oil
No. 4
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
20.47
Jet Fuel3
19.40
19.34
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
Kerosene
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
LPG (energy use)d
16.86
16.82
16.89
16.87
16.85
16.86
16.84
16.84
16.83
16.82
16.83
16.83
16.83
16.83
16.83
16.83
16.83
16.83
16.83
LPG (non-energy
use)d
17.06
17.09
17.09
17.10
17.09
17.09
17.07
17.06
17.06
17.05
17.06
17.06
17.06
17.06
17.06
17.06
17.06
17.06
17.06
Lubricants
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
Motor Gasolined
19.42
19.36
19.33
19.34
19.38
19.36
19.38
19.36
19.45
19.56
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
Residual Fuel No.
5
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
19.89
Residual Fuel No.
6
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
Other Petroleum
Av. Gas Blend
Comp.
Mo. Gas Blend
Compc
Crude Oild
18.87 18.87
18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87
19.42
20.15
19.36
20.21
19.33 19.34 19.38 19.36 19.38 19.36 19.45 19.56 19.46
20.22 20.27 20.28 20.25 20.31 20.31 20.28 20.28 20.31
19.46 19.46 19.46 19.46 19.46 19.46 19.46 19.46
20.31 20.31 20.31 20.31 20.31 20.31 20.31 20.31
A-69
-------
Misc. Products'1
20.15
20.21
20.22
20.27
20.28
20.25
20.31
20.31
20.28
20.28
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
Misc. Products
(Terr.)
20.15
20.21
20.22
20.27
20.28
20.25
20.31
20.31
20.28
20.28
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
Naphtha (<401
deg. F)
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
Other oil (>401
deg. F)
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
Pentanes Plus
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
19.10
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
27.85
27.85
27.85
27.85
27.85
Still Gas
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
Special Naphtha
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
Unfinished Oilsd
20.15
20.21
20.22
20.27
20.28
20.25
20.31
20.31
20.28
20.28
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
Waxes
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
Other Wax and
Misc.
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
Geothermal
2.05
2.05
2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05
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, the number cited here is developed
from commercial/institutional consumption.
c 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 CO2
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.
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Coal
Although the IPCC (2006) guidelines provide C contents for coal according to rank, it was necessary to develop C
content coefficients by consuming sector to match the format in which coal consumption is reported by EIA. Because the C
content of coal varies by the state in which it was mined and by coal rank, and because the sources of coal for each consuming
sector vary by year, the weighted average C content for coal combusted in each consuming sector also varies over time. A
time series of C contents by coal rank and consuming sector appears in Table A-44.9
Methodology
The methodology for developing C contents for coal by consuming sector consists of four steps. An additional step
has been taken to calculate C contents by coal rank to facilitate comparison with IPCC default values.
Step 1: Determine Carbon Contents by Rank and by State of Origin
Carbon contents by rank and state of origin are estimated on the basis of 7,092 coal samples, 6,588 of which were
collected by the U.S. Geological Survey (USGS 1998) and 504 samples that come from the Pennsylvania State University
database (PSU 2010). These coal samples are classified according to rank and state of origin. For each rank in each state,
the average heat content and C content of the coal samples are calculated based on the proximate (heat) and ultimate (percent
carbon) analyses of the samples. Dividing the C content (reported in pounds CO2) by the heat content (reported in million
Btu or MMBtu) yields an average C content coefficient. This coefficient is then converted into units of MMT C/QBtu.
Step 2: Determine Weighted Average Carbon Content by State
Carbon contents by rank and origin calculated in Step 1 are then weighted by the annual share of state
production that was each rank. State production by rank is obtained from the EIA. This step yields a single carbon
content per state that varies annually based on production. However, most coal-producing states produce only one
rank of coal. For these states the weighted factor equals the carbon content calculated in Step 1 and is constant across
the time series.
Step 3: Allocate Sectoral Consumption by State of Origin
U.S. energy statistics10 through 2007 provide data on the origin of coal used in four areas: 1) the electric
power industry, 2) industrial coking, 3) all other industrial uses, and 4) the residential and commercial end-use
sectors." Because U.S. energy statistics do not provide the distribution of coal rank consumed by each consuming
sector, it is assumed that each sector consumes a representative mixture of coal ranks from a particular state that
matches the mixture of all coal produced in that state during the year. Thus, the weighted state-level factor developed
in Step 2 is applied.
Step 4: Weight Sectoral Carbon Contents to Reflect the Rank and State of Origin of Coal Consumed
Sectoral C contents are calculated by multiplying the share of coal purchased from each state by the state's
weighted C content estimated in Step 2. The resulting partial C contents are then totaled across all states to generate a
national sectoral C content.
Csector = Sstatel XCstatel + Sstate2 XCstate2 +.... + Sstate50XCstate50
where,
CSector = The C content by consuming sector;
Sstate = The portion of consuming sector coal consumption attributed to production from a
given state;
Cstate = The estimated weighted C content of all ranks produced in a given state.
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.
10 U.S. Energy Information Administration (EIA). Coal Distribution - Annual (2001-2009b); and Coal Industry Annual (1990-2001).
Beginning in 2008, the EIA collects and reports data on commercial and institutional coal consumption, rather than residential and
commercial consumption. Thus, the residential/commercial coal coefficient reported in Table A-43 for 2009 represents the mix of coal
consumed by commercial and institutional users. Currently, only an extremely small amount of coal is consumed in the U.S. residential
sector.
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Table fl-44: Carbon Content Coefficients for Coal by Consuming Sector and Coal Bank [MBIT C/QBtu) 11990-20161
Consuming Sector
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Electric Power
25.96
25.93
26.00
26.00
26.05
26.09
26.10
26.09
26.04
26.05
26.05
26.05
26.05
26.05
26.05
26.05
26.05
26.05
26.05
Industrial Coking
25.53
25.57
25.63
25.63
25.65
25.63
25.63
25.60
25.60
25.61
25.61
25.61
25.61
25.61
25.61
25.61
25.61
25.61
25.61
Other Industrial
25.82
25.80
25.74
25.66
25.57
25.55
25.56
25.80
25.84
25.82
25.82
25.82
25.82
25.82
25.82
25.82
25.82
25.82
25.82
Residential/
Commercial3
26.20
26.13
26.01
26.00
25.98
26.04
25.91
26.09
26.29
25.94
25.71
25.71
25.71
25.71
25.71
25.71
25.71
25.71
25.71
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
28.28
28.28
28.28
28.28
Bituminous
25.38
25.42
25.45
25.46
25.46
25.45
25.45
25.45
25.45
25.45
25.44
25.44
25.44
25.44
25.44
25.44
25.44
25.44
25.44
Sub-bituminous
26.50
26.50
26.49
26.50
26.50
26.50
26.50
26.50
26.50
26.50
26.50
26.50
26.50
26.50
26.50
26.50
26.50
26.50
26.50
Lignite
26.58
26.59 -
26.61
26.62
26.63
26.62
26.62
26.62
26.62
26.64
26.65
26.65
26.65
26.65
26.65
26.65
26.65
26.65
26.65
a In 2008, the EIA began collecting consumption data for commercial and institutional consumption rather than commercial and residential consumption.
b Emission factors for coal rank are weighted based on production in each state.
Sources: C content coefficients calculated from USGS (1998) and PSU (2010); data presented in EPA (2010).
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Step 5: Develop National-Level Carbon Contents by Rank for Comparison to IPCC Defaults
Although not used to calculate emissions, national-level C contents by rank are more easily compared to C contents
of other countries than are sectoral C contents. This step requires weighting the state-level C contents by rank developed
under Step 1 by overall coal production by state and rank. Each state-level C content by rank is multiplied by the share of
national production of that rank that each state represents. The resulting partial C contents are then summed across all states
to generate an overall C content for each rank.
Nrank = Prankl X Crankl "1" Prank2 X Crank2 "1" ..."1- PranknX 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 the 7,092 coal samples, 6,588 of which are from USGS (1998)
and 504 that come from the Pennsylvania State University Coal Database (PSU 2010). 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. Only the subset of PSU samples that are whole-seam channel samples are included in the
development of carbon factors in order to increase data accuracy.
Data on coal consumption by sector and state of origin, as well as coal production by state and rank, were obtained
from EIA. The EIA's Annual Coal Report (EIA 2001 through 2009a) is the source for state coal production by rank from
2001 through 2008. In prior years, the 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 2008 was obtained from the EIA's Coal
Distribution -Annual (EIA 2001 through 2009b). 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 47.3 percent and 46.1 percent of total U.S. supply in 2008, respectively. State average C content coefficients for
bituminous coal vary from a low of 85.59 kg CO2 per MMBtu in Texas to a high of 105.21 kg CO2 per MMBtu in Montana.
However, Texas bituminous coal is considered anomalous,12 has not been produced since 2004 and production since 1990
peaked at just 446,000 short tons in 1996. The next lowest average emission factor for bituminous coal is found in Western
Kentucky (91.36 kg CO2 per MMBtu). In 2000, Montana produced no bituminous coal and Western Kentucky production
accounted for just 4.5 percent of overall bituminous production. In 2008, more than 60 percent of bituminous coal was
produced in three states: West Virginia, Kentucky (predominantly from the Eastern production region), 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-45).
Similarly, the C content coefficients for sub-bituminous coal range from 91.29 kg CO2 per MMBtu in Utah to
98.10 kg CO2 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 87 percent of total
sub-bituminous coal production in the United States in each year since 1990. Thus, the C content coefficient for Wyoming
(97.22 kg CO2 per MMBtu), based on 455 samples, dominates the national average.
The interquartile range of C content coefficients among samples of sub-bituminous coal in Wyoming was +1.5
percent from the mean. Similarly, this range among samples of bituminous coal from West Virginia, Kentucky, and
Pennsylvania was +1.2 percent or less for each state. The large number of samples and the low variability within the sample
set of the states that represent the predominant source of supply of U. S. coal suggest that the uncertainty in this factor is very
low, on the order of +1.0 percent.
12 See, for example: San Filipo, 1999. USGS. (U.S. Geological Survey Open-File Report 99-301), Ch. 4.
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For comparison, J. Quick (2010) completed an analysis similar in methodology to that used here, in order to
generate national average C emission factors as well as county-level factors. This study's rank-based national average factors
have a maximum deviation from the factors developed in this Inventory report of -0.55 percent, which is for lignite (range:
-0.55 to +0.1 percent). This corroboration further supports the assertion of minimal uncertainty in the application of the rank-
based factors derived for the purposes of this Inventory.
Table fl-45: Variability in Carbon Content Coefficients by Bank Across States [Kilograms CO; Per MMBtul
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
57
92.33
-
-
-
Indiana
146
92.65
-
-
-
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
309
105.21
97.73
-
99.40
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
7,071
93.13
96.94
104.29
98.63
Note:Indicates no sample data available. Average is weighted by number of samples.
Sources: Calculated from USGS (1998) and PSU (2010); data presented in EPA (2010).
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 (C3H8), butane (C4H10), and, to a lesser extent, pentane (C5H12)
and hexane (CsHm). 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
A-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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feedstock, propane and butane have diverse uses, and natural gasoline13 contributes to the gasoline/naphtha "octane pool,"
used primarily to make motor gasoline.
Raw natural gas can also contain varying amounts of non-hydrocarbon gases, such as CO2, nitrogen, helium and
other noble gases, and hydrogen sulfide. The share of non-hydrocarbon gases is usually less than 5 percent of the total, but
there are individual natural gas reservoirs where the share can be much larger. The treatment of non-hydrocarbon gases in
raw gas varies. Hydrogen sulfide is always removed. Inert gases are removed if their presence is substantial enough to reduce
the energy content of the gas below pipeline specifications (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 CO2, it will typically have
a higher overall C content than gas that has been processed and moved to end-use customers via transmission and distribution
pipelines.
Methodology
The methodology for estimating the C contents of 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 CO2 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 CO2). 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.
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 1,037
Btu per cubic foot, and has varied by less than 2 percent (1,022 to 1,037 Btu per cubic foot) over the past 5 years. Meanwhile,
the average heat content of the 6,743 samples was 1,027 Btu per cubic foot, and the median heat content was 1,031 Btu per
cubic foot. Thus, the average heat content of the sample set falls well within the typical range of natural gas consumed in
13 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-75
-------
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-46.
Table fl-46: 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 CO2 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 CO2 only
(n=6,522) and those with less than 1.0 or 1.5 percent CO2 and less than 1,050 Btu/cf (n=4,888 and 6,166, respectively).
These stratifications were chosen to exclude samples with CO2 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-47.
Table fl-47: Carbon Content of Pipeline-Quality Natural Gas by CO; and Heat Content (MMT C/QBtu)
Sample Average Carbon Content
Full Sample 1448
<1.0%CO2 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. The regression used carbon content as the dependent variable and heat content as the independent
variable. The resulting R-squared values" for each of the sub-samples ranged from 0.79 for samples with less than 1.5
percent CO2 and under 1,050 Btu/cf to 0.91 for samples containing less than 1.0 percent CO2 only. However, the sub-sample
with less than 1.5 percent CO2 and 1,050 Btu/cf was chosen as the representative sample for two reasons. First, it most
accurately reflects the range of CO2 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:
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 2010 based on
the EIA's national average pipeline-quality gas heat content for each year. The table of average C contents for each year is
shown below in Table A-48.
Table fl-48: Carbon Content Coefficients for Natural Gas [MBIT Carbon/QBtu)
Fuel Type
1990
1995
2000
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Natural Gas
14.45
14.46
, 14.47
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
Source: EPA (2010).
Step 5. Apply carbon content coefficients developed in Step 3 to flare gas
Selecting a C content coefficient for flare gas was much more difficult than for pipeline natural gas, because of the
uncertainty of its composition and of the combustion efficiency of the flare. Because EIA estimates the heat content of flare
14
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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gas at 1,130 Btu per cubic foot, the average C content for samples with more than 1,100 Btu per cubic foot (n=18) was
chosen as the relevant sub-sample from which to calculate a flare gas carbon content. The sample dataset did not include
any samples with more than 1,130 Btu per cubic foot.
Hexane was not removed from flare gas samples since it is assumed that natural gas liquids are present in samples
with higher heat contents. Carbon contents were calculated for each sample with a heat content of more than 1,100 Btu per
cubic foot. The simple average C content for the sample sub-set representing flare gas is shown below in Table A-49.
Table fl-49: Carbon Content of Flare Gas [MBIT C/QBtu)
Relevant Sub-Sample
Average Carbon Content
>1,100 Btu/cf
15.31
Source: EPA (2010).
Data Sources
Natural gas samples were obtained from the Gas Technology Institute (1992). Average heat content data for natural
gas consumed in the United States was taken from EIA (2009a).
Uncertainty
The assignment of C content coefficients for natural gas, and particularly for flare gas, requires more subjective
judgment than the methodology used for coal. This subjective judgment may introduce additional uncertainty.
Figure A-l shows the relationship between the calculated C content for each natural gas sample and its energy
content. This figure illustrates the relatively restricted range of variation in both the energy content (which varies by about
6 percent from average) and the C emission coefficient of natural gas (which varies by about 5 percent). Thus, the knowledge
that gas has been sold via pipeline to an end-use consumer allows its C emission coefficient to be predicted with an accuracy
of + 5.0 percent.
A-77
-------
Figure A-1: Carbon Content for Samples of Pipeline-Quality Natural Gas Included in the Gas Technology Institute
Database
10.0
= National Average
15.5
15.0
14.5
14.0
970 990 1,010 1,030 1,050 1,070
Energy Content (Btu per Cubic Foot)
1,090 1,110
1,130
Source: EIA(1994) Energy Information Administration, Emissions of Greenhouse Gases in the United States 1987-1^2, U.S. Department of
Energy, Washington, DC, November, 1S94, D0HEIAŁi573, Appendix A
Natural gas suppliers may achieve the same overall energy content from a wide variety of methane, higher
hydrocarbon, and non-hydrocarbon gas combinations. Thus, the plot reveals large variations in C content for a single Btu
value. In fact, the variation in C content for a single Btu value may be nearly as great as the variation for the whole sample.
As a result, while energy content has some predictive value, the specific energy content does not substantially improve the
accuracy of an estimated C content coefficient beyond the +5.0 percent offered with the knowledge that it is of pipeline-
quality.
The plot of C content also reveals other interesting anomalies. Samples with the lowest emissions coefficients tend
to have energy contents of about 1,000 Btu per cubic foot. They are composed of almost pure CH4. Samples with a greater
proportion of NGLs (e.g., ethane, propane, and butane) tend to have energy contents greater than 1,000 Btu per cubic foot,
along with higher emissions coefficients. Samples with a greater proportion of inert gases tend to have lower energy content,
but they usually contain CO2 as one of the inert gases and, consequently, also tend to have higher emission coefficients (see
left side of Figure A-1).
For the full sample (n=6,743), the average C content of a cubic foot of gas was 14.48 MMT C/QBtu (see Table A-
48). 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). Flowever, 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 14.47 MMT C/QBtu, 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.
Petroleum
There are four critical determinants of the C content coefficient for a petroleum-based fuel:
A-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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• 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 have been updated for this edition of
the 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 gravity15 (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
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.
15 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.
Cfuel
(Din,IX 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
A-79
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Figure A-2: Estimated and Actual Relationships Between Petroleum Carbon Content Coefficients and Hydrocarbon Density
24
22 ~
!i
s t
o Ł
~E O
"D i_
-------
Paraffins. Paraffins are the most common constituent of crude oil, usually comprising 60 percent by mass. Paraffins
are straight-chain hydrocarbons with the general formula CnH2n+2- Paraffins include ethane (C2H6), propane (C3H8), butane
(C4H10), and octane (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 (CeHe), toluene (C7H8), and xylene (CsHio). 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 (CioFk and 93.71 percent C by mass) and anthracene (CmFIio 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. Flydrocarbon 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.
A-81
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Figure A-3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number
100
95
90 -
Ł
TO
"¦
J2
C
OS
Q
"E
ai
2
I 80
75 -
70
¦ Paraffins
t Cyclo paraffins
~ Am matics
Benzene W
Toluene ~
Xy lene
Cyclo pentene ' TTTTTTTT*TTTTTTT
n- pe ntane ¦ ¦
"Butane
1 Propane
" Ethane
Methane
Gasoline Jet Fuel
LPG Naphtha Kerosene Diesel
Like Gil Fuel Oil
10 15 20 25
Number of Carbo n Atoms in Molecule
30
35
Source: J.M. Hunt, Ptefrafeitn (jSochemBf/yaref Geohiv i r Francisco, CA, W.H, Freeman ar>d Company, 1S7S), pp. 31-57.
If nothing is known about the composition of a particular petroleum product, assuming that it is 85.7 percent C by
mass is not an unreasonable first approximation. Since denser products have higher C numbers, this guess would be most
likely to be correct for crude oils and fuel oils. The C content of lighter products is more affected by the shares of paraffins
and aromatics in the blend.
Energy Content of Petroleum Products
The exact energy content (gross heat of combustion) of petroleum products is not generally known. EIA estimates
energy consumption in Btu on the basis of a set of industry-standard conversion factors. These conversion factors are
generally accurate to within 3 to 5 percent.
Individual Petroleum Products
The United States maintains data on the consumption of more than twenty separate petroleum products and product
categories. The C contents, heat contents, and density for each product are provided below in Table A-50. A description of
the methods and data sources for estimating the key parameters for each individual petroleum product appears below.
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Table fl-50: Carbon Content Coefficients and Underlying Data for Petroleum Products
2008 Carbon Content
Gross Heat of Combustion
Density
Percent
Fuel
(MMT C/QBtu)
(MMBtu/Barrel)
(API Gravity)
Carbon
Motor Gasoline
19.46
(See a)
(See a)
(See a)
LPG(total)
16.97
(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
5.809
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
6.317
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
5.825
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
a Calculation of the carbon content coefficient for motor gasoline in 2008 uses separate higher heating values for conventional and reformulated gasoline
of 5.253 and 5.150, respectively (EIA 2008a). Densities and carbon shares (percent carbon) are annually variable and separated by both fuel formulation
and grade, see Motor Gasoline and Blending Components, below, for details.
b LPG is a blend of multiple paraffinic hydrocarbons: ethane, propane, isobutane, and normal butane, each with their own heat content, density and C
content, see Table A-53.
c Petrochemical feedstocks have been split into naphthas and other oils for this Inventory report. Parameters presented are for naphthas with a boiling
temperature less than 400 degrees Fahrenheit. Other oils are petrochemical feedstocks with higher boiling points. They are assumed to have the same
characteristics as distillate fuel oil no. 2.
Note:Indicates no sample data available.
Sources: EIA (1994); EIA (2009a); EPA (2009b); and EPA (2010).
Motor Gasoline and Motor Gasoline Blending Components
Motor gasoline is a complex mixture of relatively volatile hydrocarbons with or without small quantities of
additives, blended to form a fuel suitable for use in spark-ignition engines.16 "Motor Gasoline" includes conventional
gasoline; all types of oxygenated gasoline, including gasohol; and reformulated gasoline; but excludes aviation gasoline.
Gasoline is the most widely used petroleum product in the United States, and its combustion accounts for nearly
20 percent of all U.S. CO2 emissions. EIA collects consumption data (i.e., "petroleum products supplied" to end-users) for
several types of finished gasoline over the 1990 through 2016 time period: regular, mid-grade and premium conventional
gasoline (all years) and regular, mid-grade and premium reformulated gasoline (November 1994 to 2016). Leaded and
oxygenated gasoline are not separately included in the data used for this report."
16 Motor gasoline, as defined in ASTM Specification D 4814 or Federal Specification W-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.
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-83
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The American Society for Testing and Materials (ASTM) standards permit a broad range of densities for gasoline,
ranging from 50 to 70 degrees API gravity, or 111.52 to 112.65 kilograms per barrel (EIA 1994), which implies a range of
possible C and energy contents per barrel. Table A-51 reflects changes in the density of gasoline over time and across grades
and formulations of gasoline through 2016.
Table fl-51: Motor Gasoline Density,1990- 2016 [Degrees API)
Fuel Grade
1990 1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Conventional
-Winter Grade
Low Octane
High Octane
62.0 59.8
59.0 58.0
61.6
59.7
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
Conventional
- Summer Grade
Low Octane
High Octane
58.2 56.1
55.5 J 55.1
56.8
55.8
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
Reformulated
-Winter Grade
Low Octane
High Octane
NA 61.9
NA.J 59.9
62.7
61.1
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
Reformulated
- Summer Grade
Low Octane
High Octane
NA , 58.5
NA . 56 7
58.4
58.3
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
Notes: NA (Not Applicable), fuel type was not analyzed.
Source: National Institute of Petroleum and Energy Research (1990 through 2009).
The density of motor gasoline increased across all grades through 1994, partly as a result of the leaded gasoline
phase-out. In order to maintain the "anti-knock" quality and octane ratings of gasoline in the absence of lead, the portion of
aromatic hydrocarbons blended into gasoline through the refining process was increased. As discussed above, aromatic
hydrocarbons have a lower ratio of hydrogen to C than other hydrocarbons typically found in gasoline, and therefore increase
fuel density.
The trend in gasoline density was reversed beginning in 1996 with the development of fuel additives that raised
oxygen content. In 1995, a requirement for reformulated gasoline in non-attainment areas implemented under the Clean Air
Act Amendments further changed the composition of gasoline consumed in the United States. Through 2005, methyl tertiary
butyl ether (MTBE), ethanol, ethyl tertiary butyl ether (ETBE), and tertiary amyl methyl ether (TAME) were added to
reformulated and sometimes to conventional gasoline to boost its oxygen content, reduce its toxics impacts and increase its
octane. The increased oxygen reduced the emissions of carbon monoxide and unburned hydrocarbons. These oxygen-rich
blending components are also much lower in C than standard gasoline. The average gallon of reformulated gasoline
consumed in 2005 contained over 10 percent MTBE and 0.6 percent TAME (by volume). The characteristics of reformulated
fuel additives appear in Table A-52.
Table fl-52: Characteristics of Major Reformulated Fuel Additives
Additive
Density (Degrees API)
Carbon Share (Percent)
MTBE
58.6
68.13
ETBE
58.5
70.53
TAME
51.2
70.53
DIPE
62.7
70.53
Ethanol (100%)
45.8
52.14
Source: EPA (2009b).
Since 2005, due to concerns about the potential environmental consequences of the use of MTBE in fuels, there
has been a shift away from the addition of MTBE, TAME, ETBE, and DIPE and towards the use of ethanol as a fuel
oxygenate.18 Ethanol, also called ethyl alcohol, is an anhydrous alcohol with molecular formula C2H5OH. Ethanol has a
lower C share than other oxygenates, approximately 52 percent compared to about 70 percent for MTBE and TAME. The
density of ethanol was calculated by fitting density data at 10 degree intervals to a polynomial of order two and then using
the fit to interpolate the value of the density at 15 degrees Celsius. A common fuel mixture of 10 percent denatured ethanol
18 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.
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(denatured by 2 percent hydrocarbons) and 90 percent gasoline, known as E10, is widely used in the United States and does
not require any modification to vehicle engines or fuel systems. The average gallon of reformulated alcohol blend gasoline
in 2008 contained 8.6 percent ethanol (by volume). As of 2010, ten states require the use of ethanol-blended fuel, while the
federal Renewable Fuel Standard (RFS) program requires a certain volume of renewable fuel, including ethanol, be blended
into the national fuel supply.19 Ethanol blends up to E85 (85 percent ethanol, 15 percent gasoline) are in use in the United
States but can only be used in specially designed vehicles called flexible fuel vehicles (FFVs). Most ethanol fuel in the
United States is produced using corn as feedstock,20 although production pathways utilizing agricultural waste, woody
biomass and other resources are in development.
Methodology
Step 1. Disaggregate U.S. gasoline consumption by grade and type
Separate monthly data for U.S. sales to end users of finished gasoline by product grade and season for both standard
gasoline and reformulated gasoline were obtained from the EIA.
Step 2. Develop carbon content coefficients for each grade and type
Annual C content coefficients for each gasoline grade, type, and season are derived from four parameters for each
constituent of the finished gasoline blend: the volumetric share of each constituent,21 the density of the constituent, share of
the constituent that is C; and the energy content of a gallon of the relevant formulation of gasoline. The percent by mass of
each constituent of each gasoline type was calculated using percent by volume data from the National Institute for Petroleum
and Energy Research (NIPER) and the density of each constituent. The ether additives listed in Table A-52 are accounted
for in both reformulated fuels and conventional fuels, to the extent that they were present in the fuel. From 2006 onward,
reformulated fuel mass percentages are calculated from their constituents, net of the share provided by ethanol. C content
coefficients were then derived from the calculated percent by mass values by weighting the C share of each constituent by
its contribution to the total mass of the finished motor gasoline product.
Step 3. Weight overall gasoline carbon content coefficient for consumption of each grade and type
The C content for each grade, type, and season of fuel is multiplied by the share of annual consumption represented
by the grade and fuel type during the relevant time period. Individual coefficients are then summed and totaled to yield an
overall C content coefficient for each year.
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 5.253 MMBtu per barrel conventional gasoline and 5.150 MMBtu
per barrel reformulated gasoline23 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
19
Ethanol.org. Available at . Retrieved February 19, 2010.
20
"Ethanol Market Penetration." Alternative Fuels and Advanced Vehicles Data Center, U.S. DOE. Available at
. Retrieved February 19, 2010.
21
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.
22
Saturates are assumed to be octane and aromatics are assumed to be toluene.
23
The reformulated gasoline heat content is applied to both reformulated blends containing ethers and those containing ethanol.
A-85
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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
ethane is 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 2016.
The third primary contributor to uncertainty is the assumed heat content. The heat contents are industry standards
established many years ago. The heat contents are standard conversion factors used by EIA to convert volumetric energy
data to energy units. Because the heat contents of fuels change over time, without necessarily and directly altering their
volume, the conversion of known volumetric data to energy units may introduce bias. Thus, a more precise approach to
estimating emissions factors would be to calculate C content per unit of volume, rather than per unit of energy. Adopting
this approach, however, makes it difficult to compare U.S. C content coefficients with those of other nations.
The changes in density of motor gasoline over the last decade suggest that the heat content of the fuels is also
changing. However, that change within any season grade has been less than 1 percent over the decade. Of greater concern
is the use of a standardized heat content across grades that show a variation in density of +1.5 percent from the mean for
conventional gasoline and +1.0 percent for reformulated fuels.
Jet Fuel
Jet fuel is a refined petroleum product used in jet aircraft engines. There are two classes of jet fuel used in the
United States: "naphtha-based" jet fuels and "kerosene-based" jet fuels. In 1989, 13 percent of U.S. consumption was
naphtha-based fuel, with the remainder kerosene-based jet fuel. In 1993, the U. S. Department of Defense began a conversion
from naphtha-based JP-4 jet fuel to kerosene-based jet fuel, because of the possibility of increased demand for reformulated
motor gasoline limiting refinery production of naphtha-based jet fuel. By 1996, naphtha-based jet fuel represented less than
one-half of one percent of all jet fuel consumption. The C content coefficient for jet fuel used in this report prior to 1996
A-86 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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represents a consumption-weighted combination of the naphtha-based and kerosene-based coefficients. From 1996 to 2016,
only the kerosene-based portion of total consumption is considered significant.
Methodology
Step 1. Estimate the carbon content for naphtha-basedjet 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 EIA's standard heat content of 5.670 MMBtu per barrel was adopted for kerosene-based jet fuel.
Step 3. Weight the overalljet fuel carbon content coefficient for consumption of each type offuel (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 2016.
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.
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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 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
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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
Data on the C share and density of residual fuel oil No. 6 were obtained from Green & Perry, ed. (2008). Data on
the C share of fuel oil No. 5 was adopted from EIA (1994), and the density of No. 5 was obtained from Wauquier, J.-P., ed.
(1995). Heat contents for both No. 5 and No. 6 fuel oil are adopted from EPA (2009b).
Uncertainty
Beyond the application of a C factor based upon No. 6 oil to all residual oil consumption, the largest source of
uncertainty in estimating the C content of residual fuel centers on the estimates of density. Fuel oils are likely to differ
depending on the application of the fuel (i.e., power generation or as a marine vessel fuel). Slight differences between the
density of residual fuel used by utilities and that used in mobile applications are likely attributable to non-sulfur impurities,
which reduce the energy content of the fuel, but do not greatly affect the density of the product. Impurities of several percent
are commonly observed in residual oil. The extent of the presence of impurities has a greater effect on the uncertainty of C
share estimation than it does on density. This is because these impurities do provide some Btu content to the fuel, but they
are absent of carbon. Fuel oils with significant sulfur, nitrogen and heavy metals contents would have a different total carbon
share than a fuel oil that is closer to pure hydrocarbon. This contributes to the uncertainty of the estimation of an average C
share and C coefficient for these varied fuels.
The 12 samples of residual oil (EIA 1994) cover a density range from 4.3 percent below to 8.2 percent above the
mean density. The observed range of C share in these samples is -2.5 to +1.8 percent of the mean. Overall, the uncertainty
associated with the C content of residual fuel is probably +1 percent.
Liquefied Petroleum Gases (LPG)
EIA identifies four categories of paraffinic hydrocarbons as LPG: ethane, propane, isobutane, and n-butane.
Because each of these compounds is a pure paraffinic hydrocarbon, their C shares are easily derived by taking into account
the atomic weight of C (12.01) and the atomic weight of hydrogen (1.01). Thus, for example, the C share of propane, C3H8,
is 81.71 percent. The densities and heat contents of the compounds are also well known, allowing C content coefficients to
be calculated directly. Table A-53 summarizes the physical characteristic of LPG.
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Table fl-53: Physical Characteristics of Liquefied Petroleum Gases
Carbon Content
Chemical
Density (Barrels
Carbon Content
Energy Content
Coefficient (MMT
Compound
Formula
Per Metric Ton)
(Percent)
(MMBtu/Barrel)
C/QBtu)
Ethane
C2H6
11.55
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
Source: Densities - CRC Handbook of Chemistry and Physics (2008/09); Carbon Contents - derived from the atomic weights of the elements;
Energy Contents - EPA (2009b). All values are for the compound in liquid form. The density and energy content of ethane are for refrigerated
ethane (-89 degrees C). Values for n-butane are for pressurized butane (-25 degrees C).
Methodology
Step 1. Assign carbon content coefficients to each pure paraffinic compound
Based on their known physical characteristics, a C content coefficient is assigned to each compound contained in
the U.S. energy statistics category, Liquefied Petroleum Gases.
Step 2. Weight individual LPG coefficients for share of fuel use consumption
A C content coefficient for LPG used as fuel is developed based on the consumption mix of the individual
compounds reported in U.S. energy statistics.
Step 3. Weight individual LPG coefficients for share of non-fuel use consumption
The mix of LPG consumed for non-fuel use differs significantly from the mix of LPG that is combusted. While
the majority of LPG consumed for fuel use is propane, ethane is the largest component of LPG used for non-fuel applications.
A C content coefficient for LPG used for non-fuel applications is developed based on the consumption mix of the individual
compounds reported in U.S. energy statistics.
Step 4. Weight the carbon content coefficients for fuel use and non-fuel use by their respective shares of
consumption
The changing shares of LPG fuel use and non-fuel use consumption appear below in Table A-54.
Data Sources
Data on C share was derived via calculations based on atomic weights of each element of the four individual
compounds densities are from the CRC Handbook of Chemistry and Physics, 89th Education. The energy content of each
LPG is from the EPA (2009b). LPG consumption was based on data obtained from API (1990 through 2008) and EIA
(2009b). Non-fuel use of LPG was obtained from API (1990 through 2008).
Uncertainty
Because LPG consists of pure paraffinic compounds whose density, heat content and C share are physical
constants, there is limited uncertainty associated with the C content coefficient for this petroleum product. Any uncertainty
is associated with the collection of data tabulating fuel- and non-fuel consumption in U. S. energy statistics. This uncertainty
is likely less than +3 percent.
Tahle A-54: Consumption and Carhon Content Coefficients of LiquefieJ Petroleum Gases,1990-2016
1990
2000
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Energy Consumption (QBtu)
Fuel Use
0.88
1.31
1.21
1.19
1.20
1.13
1.13
1.16
1.16
1.16
1.16
1.16
1.16
1.16
Ethane
0.04
0.10
0.06
0.06
0.07
0.06
0.07
0.08
0.08
0.08
0.08
0.08
0.08
0.08
Propane
0.77
1.07
1.08
1.07
1.09
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
0.05
0.03
0.05
0.05
0.05
0.05
0.05
0.05
0.05
Isobutane
0.01
0.06
0.01
0.01
0.00
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
Non-Fuel Use
1.35
1.90
1.70
1.74
1.78
1.67
1.80
1.96
1.96
1.96
1.96
1.96
1.96
1.96
Ethane
0.71
1.04
0.91
0.98
1.03
0.95
1.12
1.22
1.22
1.22
1.22
1.22
1.22
1.22
Propane
0.51
0.65
0.63
0.63
0.64
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
0.12
0.11
0.12
0.08
0.12
0.12
0.12
0.12
0.12
0.12
0.12
Isobutane
0.02
0.09
0.03
0.02
0.01
0.00
0.01
0.03
0.03
0.03
0.03
0.03
0.03
0.03
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Carbon Content (MMT C/QBtu)
Fuel Use 16.86 , 16.89
16.84
16.83
16.82
16.83
16.83
16.83
16.83
16.83
16.83
16.83
16.83
16.83
Non-Fuel Use 17.06 1 17.09
17.06
17.06
17.05
17.06
17.06
17.06
17.06
17.06
17.06
17.06
17.06
17.06
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 2008). Volumes
converted using the energy contents provided in Table A-53. C contents from EPA (2010).
Aviation Gasoline
Aviation gasoline is used in piston-powered airplane engines. It is a complex mixture of relatively volatile
hydrocarbons with or without small quantities of additives, blended to form a fuel suitable for use in aviation reciprocating
engines. Fuel specifications are provided in ASTM Specification D910 and Military Specification MIL-G-5572. Aviation
gas is a relatively minor contributor to greenhouse gas emissions compared to other petroleum products, representing
approximately 0.1 percent of all consumption.
The ASTM standards for boiling and freezing points in aviation gasoline effectively limit the aromatics content to
a maximum of 25 percent (ASTM D910). Because weight is critical in the operation of an airplane, aviation gas must have
as many Btu per pound (implying a lower density) as possible, given other requirements of piston engines such as high anti-
knock quality.
Methodology
A C content coefficient for aviation gasoline was calculated on the basis of the EIA standard heat content of 5.048
MMBtu per barrel. This implies a density of approximately 69 degrees API gravity or 5.884 pounds per gallon, based on the
relationship between heat content and density of petroleum liquids, as described in Thermal Properties of Petroleum
Products (DOC 1929). To estimate the share of C in the fuel, it was assumed that aviation gasoline is 87.5 percent iso-
octane, 9.0 percent toluene, and 3.5 percent xylene. The maximum allowable sulfur content in aviation gasoline is 0.05
percent, and the maximum allowable lead content is 0.1 percent. These amounts were judged negligible and excluded for
the purposes of this analysis. This yielded a C share of 85.00 percent and a C content coefficient of 18.86 MMT C/QBtu.
Data Sources
Data sources include ASTM (1985). A standard heat content for aviation gas was adopted from EIA (2009a).
Uncertainty
The relationship used to calculate density from heat content has an accuracy of five percent at 1 atm. The
uncertainty associated with the C content coefficient for aviation gasoline is larger than that for other liquid petroleum
products examined because no ultimate analyses of samples are available. Given the requirements for safe operation of
piston-powered aircraft the composition of aviation gas is well bounded and the uncertainty of the C content coefficient is
likely to be +5 percent.
Still Gas
Still gas, or refinery gas, is composed of light hydrocarbon gases that are released as petroleum is processed in a
refinery. The composition of still gas is highly variable, depending primarily on the nature of the refining process and
secondarily on the composition of the product being processed. Petroleum refineries produce still gas from many different
processes. Still gas can be used as a fuel or feedstock within the refinery, sold as a petrochemical feedstock, or purified and
sold as pipeline-quality natural gas. For the purposes of this Inventory, the coefficient derived here is only applied to still
gas that is consumed as a fuel. In general, still gas tends to include large amounts of free hydrogen and methane, as well as
smaller amounts of heavier hydrocarbons. Because different refinery operations result in different gaseous by-products, it is
difficult to determine what represents typical still gas.
Methodology
The properties of still gas used to calculate the carbon content are taken from the literature. The carbon share of
still gas was calculated from its net calorific value and carbon content from IPCC (2006). This calculation yields a carbon
share of 77.7 percent. The density of still gas was estimated to be 0.1405 metric tons per barrel based on its heat content
(EIA 2008a) and the relationship between heat content and density that is described by the U.S. Department of Commerce,
Bureau of Standards (DOC 1929).
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Data Sources
The carbon share of still gas is calculated from data provided by IPCC (2006). Density is estimated at 0.1405
metric tons per barrel, approximately 28.3 degrees API, based on the heat content of 6.00 MMbtu/barrel of still gas from
EIA (2009a).
Uncertainty
The EIA obtained data on four samples of still gas. Table A-55 below shows the composition of those samples.
Table fl-55: Composition, Energy Content, and Carbon Content Coefficient for Four Samples of Still Gas
Sample
Hydrogen
Methane
Ethane
Propane
Btu Per Cubic
Carbon Content
(%)
(%)
(%)
(%)
Foot
(MMT C/QBtu)
One
12.7
28.1
17.1
11.9
1,388
17.51
Two
34.7
20.5
20.5
6.7
1,143
14.33
Three
72.0
12.8
10.3
3.8
672
10.23
Four
17.0
31.0
16.2
2.4
1,100
15.99
Sources: EIA (2008b).
Because the composition of still gas is highly heterogeneous, the C content coefficient for this product is highly
uncertain. Gas streams with a large, free-hydrogen content are likely to be used as refinery or chemical feedstocks. Therefore,
the sample cited above with the very high H content of 72 percent (and the lowest calculated C content) is less likely to be
representative of the still gas streams to which the calculated coefficient is applied. The C content coefficient used for this
report is probably at the high end of the plausible range given that it is higher than the greatest sample-based C content in
Table A-55.
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 (2009a). 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
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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 (2009a). The carbon content of lubricants is adopted from
ultimate analysis of one sample of motor oil (EPA 2009a). The density of lubricating oils was determined by ASTM (1985).
Uncertainty
Uncertainty in the estimated C content coefficient for lubricants is driven by the large range of product
compositions and end uses in this category combined with an inability to establish the shares of the various products captured
under this category in U.S. energy statistics. Because lubricants may be produced from either the distillate or residual
fractions during refineries, the possible C content coefficients range from 19.89 MMT C/QBtu to 21.48 MMT C/QBtu or an
uncertainty band from -1.5 percent to +1.4 percent of the estimated value.
Petrochemical Feedstocks
U.S. energy statistics distinguish between two different kinds of petrochemical feedstocks: those with a boiling
temperature below 400 degrees Fahrenheit, generally called "naphtha," and those with a boiling temperature 401 degrees
Fahrenheit and above, referred to as "other oils" for the purposes of this Inventory.
Methodology
The C content of these petrochemical feedstocks are estimated independently according to the following steps.
Step 1. Estimate the carbon content coefficient for naphtha
Because reformed naphtha is used to make motor gasoline (hydrogen is released to raise aromatics content and
octane rating), "straight-run" naphtha is assumed to be used as a petrochemical feedstock. Ultimate analyses of five samples
of naphtha were examined and showed an average C share of 84.11 percent. A density of 62.4 degrees API gravity was taken
from the Handbook of Petroleum Refining Processes, 3rd ed. (Meyers 2004). The standard EIA heat content of 5.248 MMBtu
per barrel is used to estimate a C content coefficient of 18.55 MMT C/QBtu.
Step 2. Estimate the carbon content coefficient for petrochemical feedstocks with a boiling temperature 400
degrees Fahrenheit and above ("other oils ")
The boiling temperature of this product places it into the "middle distillate" fraction in the refining process, and
EIA estimates that these petrochemical feedstocks have the same heat content as distillate fuel No. 2. Thus, the C content
coefficient of 20.17 MMT C/QBtu used for distillate fuel No. 2 is also adopted for this portion of the petrochemical
feedstocks category.
Data Sources
Naphthas: Data on the C content was taken from Unzelman (1992). Density is from Meyers (2004). A standard
heat content for naphthas was adopted from EIA (2009a). Other oils: See Distillate Fuel, Distillate No.2.
Uncertainty
Petrochemical feedstocks are not so much distinguished on the basis of chemical composition as on the identity of
the purchaser, who are presumed to be a chemical company or a petrochemical unit co-located on the refinery grounds.
Naphthas are defined, for the purposes of U.S. energy statistics, as those naphtha products destined for use as a petrochemical
feedstock. Because naphthas are also commonly used to produce motor gasoline, there exists a considerable degree of
uncertainty about the exact composition of petrochemical feedstocks.
Different naphthas are distinguished by their density and by the share of paraffins, isoparaffins, olefins, naphthenes
and aromatics contained in the oil. Naphtha from the same crude oil fraction may have vastly different 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
A-93
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of the uncertainty that underlies the average carbon share calculation, which is based on a limited number of samples. Two
additional samples cited by the EIA (1994) have a range of 83.80 to 84.42 percent C.
The uncertainty of the C content for other oils is based upon the assumption of distillate oil No. 2 as a product
representative of the ill-defined classification of "other oils," and from the calculation of the C content of No. 2 itself (see
"Distillate Fuels," above). While No. 2 distillate is used as a proxy for "other oils" for the purposes of this Inventory's carbon
coefficient, important differences exist between these two petroleum products, contributing some uncertainty to the cross-
application. Other oils are defined herein as those "oils with a boiling range equal to or greater than 401 degrees F that are
generally intended for use as a petrochemical feedstock and are not defined elsewhere." For comparison, various material
safety data sheets (MSDSs) published by producers of distillate No. 2 indicate a boiling range for this product of 320 to 700
degrees Fahrenheit. The relatively open definition of the classification "other oils" leaves room for potentially significant
variation in the heating value, density and carbon share properties of each feedstock oil having a boiling point above 400
degrees Fahrenheit, creating a large band of uncertainty beyond that associated with the C factor for distillate No. 2.
Kerosene
A light petroleum distillate that is used in space heaters, cook stoves, and water heaters and is suitable for use as a
light source when burned in wick-fed lamps, kerosene is drawn from the same petroleum fraction as jet fuel. Kerosene is
generally comparable to No. 1 distillate oil.
Methodology
The average density and C share of kerosene are assumed to be the same as those for distillate No. 1 since the
physical characteristics of the products are very similar. Thus, a density of 35.3 degrees API and average C share of 86.40
percent were applied to a standard heat content for distillate No. 1 of 5.825 MMBtu per barrel to yield a C content coefficient
of 19.96 MMT C/QBtu.
Data Sources
A standard heat content for distillate No. 1 was adopted from EIA (2009a).
Uncertainty
Uncertainty in the estimated C content for kerosene is driven by the selection of distillate No. 1 as a proxy for
kerosene. If kerosene is more like kerosene-based jet fuel, the true C content coefficient is likely to be some 1.3 percent
lower. If kerosene is more aptly compared to No. 2 distillate oil, then the true C content coefficient is likely to be about 1.1
percent higher. While kerosene is a light petroleum distillate, like distillate No. 1, the two oil classes are do have some
variation in their properties. For example, the boiling range of kerosene is 250 to 550 degrees Fahrenheit, whereas No. 1 oils
typically boil over a range from 350 to 615 degrees Fahrenheit. The properties of individual kerosenes will vary with their
use and particular crude origin, as well. Both kerosene and fuel oil No. 1 are primarily composed of hydrocarbons having 9
to 16 carbon atoms per molecule. However, kerosene is a straight-run No. 1 fuel oil, additional cracking processes and
additives contribute to the range of possible fuels that make up the broader distillate No. 1 oil category.
Petroleum Coke
Petroleum coke is the solid residue by-product of the extensive processing of crude oil. It is a coal-like solid,
usually has a C content greater than 90 percent, and is used as a boiler fuel and industrial raw material.
Methodology
Ultimate analyses of two samples of petroleum coke showed an average C share of 92.28 percent. The ASTM
standard density of 9.543 pounds per gallon was adopted and the EIA standard energy content of 6.024 MMBtu per barrel
assumed. Together, these factors produced an estimated C content coefficient of 27.85 MMT C/QBtu.
Data Sources
C content was derived from two samples from Martin, S.W. (1960). The density of petroleum coke was taken from
the ASTM (1985). A standard heat content for petroleum coke was adopted from EIA (2009a).
A-94 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Uncertainty
The uncertainty associated with the estimated C content coefficient of petroleum coke can be traced to two factors:
the use of only two samples to establish C contents and a standard heat content which may be too low. Together, these
uncertainties are likely to bias the C content coefficient upwards by as much as 6 percent.
Special Naphtha
Special naphtha is defined as a light petroleum product to be used for solvent applications, including commercial
hexane and four classes of solvent: (1) Stoddard solvent, used in dry cleaning; (2) high flash point solvent, used as an
industrial paint because of its slow evaporative characteristics; (3) odorless solvent, most often used for residential paints;
and (4) high solvency mineral spirits, used for architectural finishes. These products differ in both density and C percentage,
requiring the development of multiple coefficients.
Methodology
The method for estimating the C content coefficient of special naphtha includes three steps.
Step 1. Estimate the carbon content coefficient for hexane
Hexane is a pure paraffin containing 6 C atoms and 14 hydrogen atoms; thus, it is 83.63 percent C. Its density is
83.7 degrees API or 5.477 pounds per gallon and its derived C content coefficient is 21.40 MMT C/QBtu.
Step 2. Estimate the carbon contents ofnon-hexane special naphthas
The hydrocarbon compounds in special naphthas are assumed to be either paraffinic or aromatic (see discussion
above). The portion of aromatics in odorless solvents is estimated at less than 1 percent, Stoddard and high flash point
solvents contain 15 percent aromatics and high solvency mineral spirits contain 30 percent aromatics (Boldt and Hall 1977).
These assumptions, when combined with the relevant densities, yield the C content factors contained in Table A-56, below.
Table fl-56: Characteristics of Non-hexane Special Naphthas
Aromatic Content
Density
Carbon Share
Carbon Content
Special Naphtha
(Percent)
(Degrees API)
(Percent Mass)
(MMT C/QBtu)
Odorless Solvent
1
55.0
84.51
19.41
Stoddard Solvent
15
47.9
84.44
20.11
High Flash Point
15
47.6
84.70
20.17
Mineral Spirits
30
43.6
85.83
20.99
Sources: EIA (2008b) and Boldt and Hall (1977).
Step 3. Develop weighted carbon content coefficient based on consumption of each special naphtha
EIA reports only a single consumption figure for special naphtha. The C contents of the five special naphthas are
weighted according to the following formula: approximately 10 percent of all special naphtha consumed is hexane; the
remaining 90 percent is assumed to be distributed evenly among the four other solvents. The resulting emissions coefficient
for special naphthas is 19.74 MMT C/QBtu.
Data Sources
A standard heat content for special naphtha was adopted from EIA (2009a). Density and aromatic contents were
adopted from Boldt and Hall (1977).
Uncertainty
The principal uncertainty associated with the estimated C content coefficient for special naphtha is the allocation
of overall consumption across individual solvents. The overall uncertainty is bounded on the low end by the C content of
odorless solvent and on the upper end by the C content of hexane. This implies an uncertainty band of-1.7 percent to +8.4
percent.
Petroleum Waxes
The ASTM standards define petroleum wax as a product separated from petroleum that is solid or semi-solid at 77
degrees Fahrenheit (25 degrees Celsius). The two classes of petroleum wax are paraffin waxes and microcrystalline waxes.
They differ in the number of C atoms and the type of hydrocarbon compounds. Microcrystalline waxes have longer C chains
and more variation in their chemical bonds than paraffin waxes.
A-95
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Methodology
The method for estimating the C content coefficient for petroleum waxes includes three steps.
Step 1. Estimate the carbon content of paraffin waxes
For the purposes of this analysis, paraffin waxes are assumed to be composed of 100 percent paraffinic compounds
with a chain of 25 C atoms. The resulting C share for paraffinic wax is 85.23 percent and the density is estimated at 45
degrees API or 6.684 pounds per gallon.
Step 2. Estimate the carbon content of microcrystalline waxes
Microcrystalline waxes are assumed to consist of 50 percent paraffinic and 50 percent cycloparaffinic compounds
with a chain of 40 C atoms, yielding a C share of 85.56 percent. The density of microcrystalline waxes is estimated at 36.7
degrees API, based on a sample of 10 microcrystalline waxes found in the Petroleum Products Handbook (Martin, S.W.
1960).
Step 3. Develop a carbon content coefficient for petroleum waxes by weighting the density and carbon content of
paraffinic and microcrystalline waxes
A weighted average density and C content was calculated for petroleum waxes, assuming that wax consumption
is 80 percent paraffin wax and 20 percent microcrystalline wax. The weighted average C content is 85.30 percent, and the
weighted average density is 6.75 pounds per gallon. EIA's standard heat content for waxes is 5.537 MMBtu per barrel.
These inputs yield a C content coefficient for petroleum waxes of 19.80 MMT C/QBtu.
Data Sources
Density of paraffin wax was taken from ASTM (1985). Density of microcrystalline waxes was derived from 10
samples found in Guthrie (1960). A standard heat content for petroleum waxes was adopted from EIA (2009a).
Uncertainty
Although there is considerable qualitative uncertainty associated with the allocation of petroleum waxes and
microcrystalline waxes, the quantitative variation in the C contents for all waxes is limited to + 1 percent because of the
nearly uniform relationship between C and other elements in petroleum waxes broadly defined.
Crude Oil, Unfinished Oils, and Miscellaneous Products
U.S. energy statistics include several categories of petroleum products designed to ensure that reported refinery
accounts "balance" and cover any "loopholes" in the taxonomy of petroleum products. These categories include crude oil,
unfinished oils, and miscellaneous products. Crude oil is rarely consumed directly, miscellaneous products account for less
than one percent of oil consumption, and unfinished oils are a balancing item that may show negative consumption. For C
accounting purposes, it was assumed that all 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 0.35.24 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 2016 crude oil
quality data (30.21 degrees API and 1.47 percent sulfur) produces an estimated C content of 84.79 percent. Applying the
24 R-squared represents the percentage of variation in the dependent variable (in this case carbon content) explained by variation in the
independent variables.
A-96 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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density and C content to the EIA standard energy content for crude oil of 5.800 MMBtu per barrel produced an emissions
coefficient of 20.31 MMT C/QBtu.
Data Sources
Carbon content was derived from 182 crude oil samples, including 150 samples from U.S. National Research
Council (1927). A standard heat content for crude oil was adopted from EIA (2009a).
Uncertainty
The uncertainty of the estimated C content for crude oil centers on the 35 percent of variation that cannot be
explained by density and sulfur content. This variation is likely to alter the C content coefficient by +3 percent. Since
unfinished oils and miscellaneous products are impossible to define, the uncertainty of applying a crude oil C content is
likely to be bounded by the range of petroleum products described in this chapter at +10 percent.
Chronology and Explanation of Changes in Individual Carbon Content Coefficients of Fossil Fuels
Coal
Original 1994 Analysis
A set of 5,426 coal samples from the EIA coal analysis file were used to develop C content estimates. The results
from that sample set appear below in Table A-57. The EIA Coal Analysis File was originally developed by the U.S. Bureau
of Mines and contained over 60,000 coal samples obtained through numerous coal seams throughout the United States.
Many of the samples were collected starting in the 1940s and 1950s through the 1980s and analyzed in U.S. government
laboratories.
Table A-57: Carbon Content Coefficients for Coal by Consuming Sector and Coal Bank, 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
Sources: Emission factors by consuming sector from B.D. Hong and E.R. Slatnick, "Carbon Dioxide Emission Factors for Coal," U.S. Energy Information
Administration, Quarterly Coal Report, January-March 1994 (Washington, DC, 1994); and emission factors by rank from Science Applications International
Corporation, Analysis of the Relationship Between Heat and Carbon Content of U.S. Fuels: Final Task Report, Prepared for the U.S. Energy Information
Administration, Office of Coal, Nuclear, Electric and Alternative Fuels (Washington, DC 1992).
2002 Update
The methodology employed for these estimates was unchanged from previous years; however, the underlying coal
data sample set was updated. A new database, CoalQual 2.0 (1998), compiled by the U.S. Geological Survey (USGS) was
adopted for the updated analysis. The updated sample set included 6,588 coal samples collected by the USGS and its state
affiliates between 1973 and 1989. The decision to switch to the sample data contained in the USGS CoalQual database from
the EIA database was made because the samples contained in the USGS database were collected and analyzed more recently
than those obtained by EIA from the Bureau of Mines. The new coefficients developed in the 2002 revision were in use
through the 1990 through 2007 Inventory and are provided in Table A-59.
A-97
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Table fl-58: Carbon Content Coefficients for Coal by Consuming Sector and Coal Bank, 1990 - 2000 [MBIT C/QBtu)
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Consuming Sector
Electric Power
25.68
25.69
25.69
25.71
25.72
25.74
25.74
25.76
25.76
25.76
25.76
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
25.60
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
oal Rank
Anthracite
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
Bituminous
25.43
25.45
25.44
25.45
25.46
25.47
25.47
25.48
25.47
25.48
25.49
Sub-bituminous
26.50
26.49
26.49
26.48
26.49
26.49
26.49
26.49
26.49
26.49
26.48
Lignite
26.19
26.21
26.22
26.21
26.24
26.22
26.17
26.20
26.23
26.26
26.30
Sources: Data from USGS, U.S. Coal Quality Database Version 2.0 (1998) and analysis prepared by SAIC (2007).
2007 Update
The analysis of the USGS Coal Qual data was updated in 2007 to make a technical correction that affected the value for lignite and those sectors which consume
lignite Table A-59 contains the annual coefficients that resulted from the 2007 analysis.
Table fl-59: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank, 1990-2007 [MBIT C/QBtu]
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Consuming Sector
Electric Power
25.68
: 25.74
25.74
25.76
25.76
25.76
25.76
25.76
25.76
25.76
25.76
25.76
25.76
25.76
Industrial Coking
25.51
•i 25.53
25.55
25.56
25.56
25.56
25.56
25.56
25.56
25.56
25.56
25.56
25.56
25.56
Other Industrial
25.58
! 25.63
25.61
25.63
25.63
25.63
25.63
25.63
25.63
25.63
25.63
25.63
25.63
25.63
Residential/Commercial
25.92
' 26.00
25.92
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
Coal Rank
Anthracite
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
28.26
Bituminous
25.43
? 25.47
25.47
25.48
25.47
25.48
25.49
25.49
25.49
25.49
25.49
25.49
25.49
25.49
Sub-bituminous
26.50
26.49
26.49
26.49
26.49
26.49
26.48
26.48
26.48
26.48
26.48
26.48
26.48
26.48
Lignite
26.19
26.22
26.17
26.20
26.23
26.26
26.30
26.30
26.30
26.30
26.30
26.30
26.30
26.57
Sources: Data from USGS, U.S. Coal Quality Database Version 2.0 (1998) and analysis prepared by (SAIC 2007).
A-98 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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2010 Update
The estimated annual C content coefficients for coal by rank and sector of consumption were updated again in
2010. Sample data from the Energy Institute at Pennsylvania State University (504 samples) were added to the 6,588 USGS
samples to create a new database of 7,092 samples. The same analytical method used in the 2002 update was applied using
these additional samples to calculate revised state-level carbon contents for each coal rank and then for national average
consumption by end-use sector and by rank.
Natural Gas
A revised analytical methodology underlies the natural gas coefficients used in this report. Prior to the current
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. In the previous Inventory, 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 current Inventory is revised to make use of the same set of samples, but utilizes 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 calculates an average C content from all samples with less than 1.5 percent CO2 and less than
1,050 Btu/cf (samples most closely approximating the makeup of pipeline quality natural gas). The result was identical to
the previous weighted national average of 14.47 MMT C/QBtu. The average C contents from the 1994 calculations are
presented in Table A-60 below for comparison.
Table fl-60: Carbon Content of Pipeline-Quality Natural Gas by Energy Content (MMT C/QBtu)
Sample Average Carbon Content
GRI Full Sample 1451
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
All of the petroleum product C coefficients except that for Aviation Gasoline Blending Components have been
updated for the current Inventory. EPA is updating 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 the previous
report are provided in Table A-61 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 Rule (EPA 2009b). In some cases, the
heat content applied to the conversion to a carbon-per-unit-energy basis has also been updated. Additionally, the category
Misc. Products (U.S. Territories), which is based upon the coefficients calculated for crude oil, has been allowed to vary
annually with the crude oil coefficient. The petrochemical feedstock category has been eliminated for this report because
the constituent products—naphthas and other oils—are estimated independently. Further, although the level of aggregation
of U.S. energy statistics currently limits the application of coefficients for residual and distillate fuels to these two generic
classifications, individual coefficients for the five major types of fuel oil (Nos. 1, 2, 4, 5 and 6) have been estimated for the
current report and are presented in Table A-50 above. Each of the C coefficients applied in the previous Inventory is provided
below for comparison (Table A-61).
A-99
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Table fl-61: Carbon Content Coefficients and Underlying Data for Petroleum Products
2007 Carbon
Gross Heat of
Content
Combustion
Density
Fuel
(MMT 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
r--
CO
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
a LPG is a blend of multiple paraffinic hydrocarbons: ethane, propane, isobutane, and normal butane, each with their own heat content, density and C
content, see Table A-53.
b Heat, density, and percent carbon values are provided separately for ethane, propane and isobutene.
c Parameters presented are for naphthas with a boiling temperature less than 400 degrees Fahrenheit. Petrochemical feedstocks with higher boiling points
are assumed to have the same characteristics as distillate fuel.
Note:Indicates no sample data available.
Sources: EIA (1994); EIA (2008a); SAIC (2007).
Additional revisions to the Inventory's C coefficients since 1990 are detailed below.
Jet Fuel
1995 Update
Between 1994 and 1995, the C content coefficient for kerosene-based jet fuel was revised downward from 19.71
MMT C/QBtu to 19.33 MMT C/QBtu. This downward revision was the result of a shift in the sample set used from one
collected between 1959 and 1972 and reported on by Martel and Angello in 1977 to one collected by Boeing in 1989 and
published by Hadaller and Momenthy in 1990. The downward revision was a result of a decrease in density, as well as
slightly lower C shares than in the earlier samples. However, the assumed heat content is unchanged because it is based on
an EIA standard and probably yields a downward bias in the revised C content coefficient.
1990 through 2008 Inventory Update
The coefficient was revised again for the 1990 through 2008 Inventory, returning to Martel and Angello and NIPER
as the source of the carbon share and density data, respectively, for kerosene-based fuels. This change was made in order to
align the coefficients used for this report with the values used in EPA's Mandatory Reporting of Greenhouse Gases Rule
(EPA 2009b). The return to the use of the Martel and Angello and NIPER coefficients was deemed more appropriate for the
Rule as it was considered a more conservative coefficient given the uncertainty and variability in coefficients across the
types of jet fuel in use in the United States. The factor will be revisited in future Inventories in light of data received from
reporting entities in response to the Rule.
A-100 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Liquefied Petroleum Gases (LPG)
The C content coefficient of LPG is updated annually to reflect changes in the consumption mix of the underlying
compounds: ethane; propane; isobutane; and normal butane. In 1994, EIA included pentanes plus—assumed to have the
characteristics of hexane—in the mix of compounds broadly described as LPG. In 1995, EIA removed pentanes plus from
this fuel category. Because pentanes plus is relatively rich in C per unit of energy, its removal from the consumption mix
lowered the C content coefficient for LPG from 17.26 MMT C/QBtu to 16.99 MMT C/QBtu. In 1998, EIA began separating
LPG consumption into two categories: energy use and non-fuel use and providing individual coefficients for each. Because
LPG for fuel use typically contains higher proportions of propane than LPG for non-fuel use, the C content coefficient for
fuel use was 1.8 to 2.5 percent higher than the coefficient for non-fuel use in previous inventories (see Table A-61).
However, for the current update of the LPG coefficients, the assumptions that underlie the selection of density and
heat content data for each pure LPG compound have been updated, leading to a significant revision of the assumed properties
of ethane. For this report, the physical characteristics of ethane, which constitutes over 90 percent of LPG consumption for
non-fuel uses, have been updated to reflect ethane that is in (refrigerated) liquid form. Previously, the share of ethane was
included using the density and energy content of gaseous ethane. Table A-62, below, compares the values applied for each
of the compounds under the two sets of coefficient calculations. The C share of each pure compound was also updated by
using more precise values for each compound's molecular weight.
Due in large part to the revised assumptions for ethane, the weighted C content for non-fuel use is now higher than
that of the weighted coefficient for fuel use, which is dominated by the consumption of more dense propane. Under the
revised assumptions, each annual weighted coefficient for non-fuel LPG consumption is 1.2 to 1.7 percent higher each year
than is that for LPGs consumed for fuel (energy) uses.
Table A-62: 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
CsHs
12.44
12.76
3.824
3.836
17.20
16.76
Isobutane
C4H10
11.20
11.42
4.162
3.974
17.75
17.77
n-butane
C4H10
10.79
10.98
4.328
4.326
17.72
17.75
Sources: Updated: Densities - CRC Handbook of Chemistry and Physics, 89th Ed. (2008/09); Energy Contents - EPA (2009b). All values are for the
compound in liquid form. The density and energy content of ethane are for refrigerated ethane (-89 degrees C). Values for n-butane are for pressurized
butane (-25 degrees C). Values in previous editions of this Inventory: Gurthrie (1960).
Motor Gasoline
The C content coefficient for motor gasoline varies annually based on the density of and proportion of additives in
a representative sample of motor gasoline examined each year. However, in 1997 EIA began incorporating the effects of the
introduction of reformulated gasoline into its estimate of C content coefficients for motor gasoline. This change resulted in
a downward step function in C content coefficients for gasoline of approximately 0.3 percent beginning in 1995. In 2005
through 2006 reformulated fuels containing ethers began to be phased out nationally. Ethanol was added to gasoline blends
as a replacement oxygenate, leading to another shift in gasoline density (see Table A-51), in the list and proportion of
constituents that form the blend and in the blended C share based on those constituents.
A-101
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Table fl-63: Carbon Content Coefficients for Petroleum Products, 1990-2007 (MMTG/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)3
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)3
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
Av Gas Blend Comp.
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
Mo Gas Blend Comp3
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-102 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
References
AAM (2009) Diesel Survey. Alliance of Automobile Manufacturers, Winter 2008.
API (1990 through 2008) Sales of Natural Gas Liquids and Liquefied Refinery Gases, American Petroleum Institute.
ASTM (1985) ASTM and Other Specifications for Petroleum Products and Lubricants, American Society for Testing and
Materials. Philadelphia, PA.
Boldt, K. andB.R. Hall (1977) Significance of Tests for Petroleum Products, Philadelphia, PA, American Society for Testing
and Materials, p. 30.
Chemical Rubber Company (CRC) (2008/2009), Handbook of Chemistry and Physics, 89th Ed., editor D. Lide, Cleveland,
OH: CRC Press.
DOC (1929) Thermal Properties of Petroleum Products, U.S. Department of Commerce, National Bureau of Standards.
Washington, DC. pp. 16-21.
EIA (2001 through 2009b) Coal Distribution - Annual, U.S. Department of Energy, Energy Information Administration.
Washington, DC. DOE/EIA.
EIA (2008a) Monthly Energy Review, September 2006 and Published Supplemental Tables on Petroleum Product detail.
Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2007/9).
EIA (2008b) Documentation for Emissions of Greenhouse Gases in the United States 2006. DOE/EIA-0638(2006). October
2008.
EIA (2009a) Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, DC.
DOE/EIA-0384(2008).
EIA (2009b) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, DC.
Available online at
.
EIA (2001 through 2009a) Annual Coal Report, U.S. Department of Energy, Energy Information Administration.
Washington, DC. DOE/EIA 0584.
EIA (2001) Cost and Quality of Fuels for Electric Utility Plants 2000, Energy Information Administration. Washington,
DC. August 2001. Available online at .
EIA (1990 through 2001) Coal Industry Annual, U.S. Department of Energy, Energy Information Administration.
Washington, DC. DOE/EIA 0584.
EIA (1994) Emissions of Greenhouse Gases in the United States 1987-1992, Energy Information Administration, U.S.
Department of Energy. Washington, DC. November, 1994. DOE/EIA 0573.
EIA (1993) Btu Tax on Finished Petroleum Products, Energy Information Administration, Petroleum Supply Division
(unpublished manuscript, April 1993).
EPA (2010) Carbon Content Coefficients Developedfor EPA's Inventory of Greenhouse Gases and Sinks. Office of Air and
Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
EPA (2009a) "Industry Overview and Current Reporting Requirements for Petroleum Refining and Petroleum Imports,"
Petroleum Product Suppliers Technical Support Document for the Proposed Mandatory Reporting Rule. Office of Air
and Radiation. 30 January, 2009.
EPA (2009b) Mandatory Reporting of Greenhouse Gases Rule. Federal Register Docket ID EPA-HQ-OAR-2008-0508-
2278, 30 September, 2009.
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.
Guthrie, V.B., ed. (1960) Characteristics of Compounds, Petroleum Products Handbook, p.3-3. New York, NY, McGraw-
Hill.
A-103
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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).
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 (1990 through 2009) Motor Gasolines, Summer andMotor 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) CoalQualDatabase Version 2.0, U.S. Geological Survey.
Wauquier, J., ed. (1995) Petroleum Refining, Crude Oil, Petroleum Products and Process Flowsheets (Editions Technip -
Pans, 1995) pg.225, Table 5.16.
A-104 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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-64. The remaining
C—i.e., that which is not stored—is emitted. This sub-annex explains the methods and data sources employed in developing
the storage factors for petrochemical feedstocks (industrial other coal, natural gas for non-fertilizer uses, liquefied petroleum
gases (LPG), pentanes plus, naphthas, other oils, still gas, special naphtha), asphalt and road oil, lubricants, waxes, and
miscellaneous products. The storage factors25 for the remaining non-energy fuel uses are either based on values
recommended for use by IPCC (2006), or when these were not available, assumptions based on the potential fate of C in the
respective non-energy use (NEU) products.
Table A-64: Fuel Types and Percent of C Stored for Non-Energy Uses
Sector/Fuel Type
Storage Factor (%)
Industry
Industrial Coking Coal3
10%
Industrial Other Coalb
70%
Natural Gas to Chemical Plantsb
70%
Asphalt & Road Oil
100%
LPGb
70%
Lubricants
9%
Pentanes Plusb
70%
Naphtha (<401 deg. F)b
70%
Other Oil (>401 deg. F)b
70%
Still Gasb
70%
Petroleum Cokec
30%
Special Naphthab
70%
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 2016 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, and miscellaneous products follow.
25
Throughout this section, references to "storage factors" represent the proportion of carbon stored.
A-105
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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 211.6 MMT CO2 Eq., or 64 percent, of the 328.8 MMT CO2 Eq. of non-energy fuel consumption in 2016.
For 2016, the storage factor for the eight fuel categories was 70 percent. In other words, of the net consumption, 70 percent
was destined for long-term storage in products—including products subsequently combusted for waste disposal—while the
remaining 30 percent was emitted to the atmosphere directly as CO2 (e.g., through combustion of industrial by-products) or
indirectly as CO2 precursors (e.g., through evaporative product use). The indirect emissions include a variety of organic
gases such as volatile organic compounds (VOCs) and carbon monoxide (CO), which eventually oxidize into CO2 in the
atmosphere. The derivation of the storage factor is described in the following sections.
Methodology and Data Sources
The petrochemical feedstocks storage factor is equal to the ratio of C stored in the final products to total C content
for the non-energy fossil fuel feedstocks used in industrial processes, after adjusting for net exports of feedstocks. One
aggregate storage factor was calculated to represent all eight fuel feedstock types. The feedstocks were grouped because of
the overlap of their derivative products. Due to the many reaction pathways involved in producing petrochemical products
(or wastes), it becomes extraordinarily complex to link individual products (or wastes) to their parent fuel feedstocks.
Import and export data for feedstocks were obtained from the Energy Information Administration (EIA) for the
major categories of petrochemical feedstocks. EIA's Petroleum Supply Annual publication tracks imports and exports of
petrochemical feedstocks, including butanes, butylenes, ethane, ethylene, propane, propylene, LPG, and naphthas (i.e., most
of the large volume primary chemicals produced by petroleum refineries). These imports and exports are already factored
into the U.S. fuel consumption statistics. However, EIA does not track imports and exports of chemical intermediates and
products produced by the chemical industry (e.g., xylenes, vinyl chloride), which are derived from the primary chemicals
produced by the refineries. These products represent very large flows of C derived from fossil fuels (i.e., fossil C), so
estimates of net flows not already considered in EIA's dataset were developed for the entire time series from 1990 to 2016.
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
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.
26Naphthas 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.
A-106 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 2016. These statistics
include information on imports and exports of thousands of commodities. After collecting information on annual flows of
the more than 100 commodities identified by NPRA, Step 2 involves calculating the C content for each commodity from its
chemical formula. In cases where the imports and exports were expressed in units of volume, rather than mass, they were
converted to mass based on the commodities' densities.
Step 3 involves summing the net C imports/exports across all commodities. The results of this step are shown in
Table A-65. As shown in the table, the United States has been a net exporter of chemical intermediates and products
throughout the 1990 to 2016 period.
Table fl-65: Net Exports of Petrochemical Feedstocks,1990 - 2016 [MBIT CO; Eq.)
1990
2005
2012
2013
2014
2015
2016
Net Exports
12.0
, 6.5
10.1
8.4
3.8
5.5
12.7
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-65) 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
black, detergents and personal cleansers, industrial non-methane volatile organic compound (NMVOC) emissions,
hazardous waste incineration, industrial toxic chemical (i.e., TRI) releases, pesticides, food additives, antifreeze and deicers
(glycols), and silicones.28
The C in each product or waste produced was categorized as either stored or emitted. The aggregate storage factor
is the C-weighted average of storage across fuel types. As discussed later in the section on uncertainty, the sum of stored C
and emitted C (i.e., the outputs of the system) exceeded total C consumption (i.e., the inputs to the system) for some years
in the time series. To address this mass imbalance, the storage factor was calculated as C storage divided by total C outputs
(rather than C storage divided by C inputs).
27
See the U.S. International Trade Commission (USITC) Trade Dataweb at .
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-107
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Note that the system boundaries for the storage factor do not encompass the entire life-cycle of fossil-based C
consumed in the United States insofar as emissions of CO2 from waste combustion are accounted for separately in the
Inventory and are discussed in the Incineration of Waste section of the Energy chapter.
The following sections provide details on the calculation steps, assumptions, and data sources employed in
estimating and classifying the C in each product and waste shown in Table A-66. Summing the C stored and dividing it by
total C outputs yields the overall storage factor, as shown in the following equation for 2016:
Overall Storage Factor = C Stored / (C Stored + C Emitted + C Unaccounted for) =
148.7 MMT CO2 Eq. / (148.7 + 63.6 + 0.0) MMT CO2 Eq. = 70%
Table A-66: C Stored and Emitted by Products from Feedstocks in 2016 (MMT CO2 Eq.)
C Stored
C Emitted
Product/Waste Type
(MMT C02 Eq.)
(MMT CO2 Eq.)
Industrial Releases
0.1
6.5
TRI Releases
0.1
1.0
Industrial VOCs
NA
4.1
Non-combustion CO
NA
0.6
Hazardous Waste Incin.
NA
0.8
Energy Recovery
NA
44.1
Products
148.6
12.9
Plastics
126.5
NA
Synthetic Rubber
13.7
NA
Antifreeze and deicers
NA
1.0
Abraded tire rubber
NA
0.3
Food additives
NA
1.1
Silicones
0.5
NA
Synthetic Fiber
7.7
NA
Pesticides
0.2
0.3
Soaps, shampoos, detergents
NA
4.7
Solvent VOCs
NA
5.5
Total 148.7 63.6
NA (Not Applicable)
Note: Totals may not sum due to independent rounding.
The C unaccounted for is the difference between the C accounted for (discussed below) and the total C in the Total
U.S. Petrochemical consumption, which are the potential carbon emissions from all energy consumption in Non-Energy
Use.
The three categories of C accounted for in the table are industrial releases, energy recovery, and products. Each is
discussed below.
Industrial Releases
Industrial releases include toxic chemicals reported through the Toxics Release Inventory (TRI), industrial
emissions of volatile organic compounds (VOCs), CO emissions (other than those related to fuel combustion), and emissions
from hazardous waste incineration.
TRI Releases
Fossil-derived C is found in many toxic substances released by industrial facilities. The TRI, maintained by EPA,
tracks these releases by chemical and environmental release medium (i.e., land, air, or water) on a biennial basis (EPA
2000b). By examining the C contents and receiving media for the top 35 toxic chemicals released, which account for 90
percent of the total mass of chemicals, the quantity of C stored and emitted in the form of toxic releases can be estimated.
The TRI specifies releases by chemical, so C contents were assigned to each chemical based on molecular formula.
The TRI also classifies releases by disposal location as either off-site or on-site. The on-site releases are further subdivided
into air emissions, surface water discharges, underground injection, and releases to land; the latter is further broken down to
A-108 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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disposal in a RCRA Subtitle C (i.e., hazardous waste) landfill or to "Other On-Site Land Disposal."29 The C released in each
disposal location is provided in Table A-67.
Each on-site classification was assigned a storage factor. A 100 percent storage factor was applied to disposition
of C to underground injection and to disposal to RCRA-permitted landfills, while the other disposition categories were
assumed to result in an ultimate fate of emission as CO2 (i.e., a storage factor of zero was applied to these categories). The
release allocation is not reported for off-site releases; therefore, the approach was to develop a C-weighted average storage
factor for the on-site C and apply it to the off-site releases.
For the remaining 10 percent of the TRI releases, the weights of all chemicals were added and an average C content
value, based upon the top 35 chemicals' C contents, was applied. The storage and emission allocation for the remaining 10
percent of the TRI releases was carried out in the same fashion as for the 35 major chemicals.
Data on TRI releases for the full 1990 through 2016 time series were not readily available. Since this category is
small (less than 1 MMT C emitted and stored), the 1998 value was applied for the entire time series.
Table A-67:1998 TBI Releases by Disposal Location [kt CO; Eq.l
Carbon Stored
Carbon Emitted
Disposal Location
(kt C02 Eq.)
(kt C02 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
NA (Not Applicable)
Note: Totals may not sum due to independent rounding.
Volatile Organic Compound Emissions from Industrial Processes and Solvent Evaporation Emissions
Data on annual non-methane volatile organic compound (NMVOC) emissions were obtained (EPA 2016b) and
disaggregated based on EPA (2003), which has been published on the National Emission Inventory (NEI) Air Pollutant
Emission Trends web site. The 1990 through 2016 Trends data include information on NMVOC emissions by end-use
category; some of these fall into the heading of "industrial releases" in Table A-66 above, and others are related to "product
use;" for ease of discussion, both are covered here. The end-use categories that represent "Industrial NMVOC Emissions"
include some chemical and allied products, certain petroleum related industries, and other industrial processes. NMVOC
emissions from solvent utilization (product use) were considered to be a result of non-energy use of petrochemical
feedstocks. These categories were used to distinguish non-energy uses from energy uses; other categories where VOCs could
be emitted due to combustion of fossil fuels were excluded to avoid double counting.
Because solvent evaporation and industrial NMVOC emission data are provided in tons of total NMVOCs,
assumptions were made concerning the average C content of the NMVOCs for each category of emissions. The assumptions
for calculating the C fraction of industrial and solvent utilization emissions were made separately and differ significantly.
For industrial NMVOC emissions, a C content of 85 percent was assumed. This value was chosen to reflect the C content
of an average volatile organic compound based on the list of the most abundant NMVOCs provided in the Trends Report.
The list contains only pure hydrocarbons, including saturated alkanes (C contents ranging from 80 to 85 percent based upon
C number), alkenes (C contents approximately 85 percent), and some aromatics (C contents approximately 90 percent,
depending upon substitution).
An EPA solvent evaporation emissions dataset (Tooly 2001) was used to estimate the C content of solvent
emissions. The dataset identifies solvent emissions by compound or compound category for six different solvent end-use
categories: degreasing, graphic arts, dry cleaning, surface coating, other industrial processes, and non-industrial processes.
The percent C of each compound identified in the dataset was calculated based on the molecular formula of the individual
compound (e.g., the C content of methylene chloride is 14 percent; the C content of toluene is 91 percent). For solvent
emissions that are identified in the EPA dataset only by chemical category (e.g., butanediol derivatives) a single individual
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).
A-109
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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-68 for 1990 through
2016. Industrial NMVOC emissions in 2016 were 4.1 MMT CO2 Eq. and solvent evaporation emissions in 2016 were 5.5
MMT C02 Eq.
Table A-68: Industrial and Solvent NMVOC Emissions
1990
1995
2000
2005
2012
2013
2014
2015
2016
Industrial NMVOCs3
NMVOCs ('000 Short Tons)
1,279
1,358
802
825
1,342
1,396
1,449
1,449
1,449
Carbon Content (%)
85%
85%
85%
85%
85%
85%
85%
85%
85%
Carbon Emitted (MMT CO2 Eq.)
3.6
3.8
2.3
2.3
3.8
3.9
4.1
4.1
4.1
Solvent Evaporation11
Solvents ('000 Short Tons)
5,750
6,183
4,832
4,245
2,855
2,898
2,942
2,942
2,942
Carbon Content (%)
56%
56%
56%
56%
56%
56%
56%
56%
56%
Carbon Emitted (MMT CO2 Eq.)
10.8
11.6 :
9.0
7.9
5.3
5.4
5.5
5.5
5.5
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 2016b), 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 CO2) 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 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-69 lists the CO emissions that remain after taking into account the exclusions listed above.
Table A-69: Non-Combustion Carbon Monoxide Emissions
1990
1995
2000
2005
2012
2013
2014
2015
2016
CO Emissions ('000 Short Tons)
489
481
623
461
376
403
431
431
431
Carbon Emitted (MMT CO2 Eq.)
0.7
0.7
0.9
0.7
0.5
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 the EPA.
Hazardous wastes must be treated prior to disposal according to the federal regulations established under the
authority of RCRA. Combustion is one of the most common techniques for hazardous waste treatment, particularly for those
wastes that are primarily organic in composition or contain primarily organic contaminants. Generally speaking, combustion
devices fall into two categories: incinerators that burn waste solely for the purpose of waste management, and boilers and
industrial furnaces (BIFs) that burn waste in part to recover energy from the waste. More than half of the hazardous waste
combusted in the United States is burned in BIFs; because these processes are included in the energy recovery calculations
described below, they are not included as part of hazardous waste incineration.
30 [42 U.S.C. §6924, SDWA §3004]
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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). 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, and 2015 (EPA 2000a;
2009; 2013a; 2015a; 2016a). 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. The quantity of
combusted hazardous waste in 2016 was proxied to the 2015 value. For each of the waste types, assumptions were developed
on average waste composition (see Table A-70). 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
2016 were 0.8 MMT CC^Eq. Table A-71 lists the CO2 emissions from hazardous waste incineration.
Table fl-70: 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-71: CO2 Emitted from Hazardous Waste Incineration [MBIT CO; EqJ
1990
1995
2000
2005
2012
2013
2014
2015
2016
CO2 Emissions
1.1
1.7
1.4
1.5
0.8
0.9
0.9
0.8
0.8
Energy Recovery
The amount of feedstocks combusted for energy recovery was estimated from data included in EIA's
Manufacturers Energy Consumption Survey (MECS) for 1991, 1994, 1998, 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"
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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-72). The conversion factors listed in Annex 2.1 were used to convert the Btu values for each fuel feedstock to
MMT CO2. Petrochemical feedstocks combusted for energy recovery corresponded to 42.7 MMT C02Eq. in 1991, 34.6
MMT CO2Eq. in 1994, 57.7 MMT C02Eq. in 1998, 68.6 MMT C02 Eq. in 2002, 73.5 MMT C02 Eq. in 2006, 40.6 MMT
C02Eq. in 2010, and 44.1 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 value for 2015 and 2016 are assumed to be the same as the value for 2014 (Table A-73).
Table fl-72: Summary of 2014 MECS Data for Other Fuels Used in Manufacturing/Energy Recovery [Trillion Btu]
Waste
Refinery Still
Other
Subsectorand Industry
NAICS CODE
Waste Gas3
Oils/Tarsb
Gasc
Net 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
0
Primary Metals
331
4
0
0
10
10
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
255
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
4.94
Total C (MMT) (ex. still gas from
refining)
6.75
0.35
0.00
0.00
4.94
NA (Not Applicable)
a C content: Waste Gas is assumed to be same as naphtha <401 deg. F.
b C content: Waste Oils/Tars is assumed to be same as asphalt/road oil.
c Refinery "still gas" fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate.
d Net steam fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate.
e C content: "Other" is assumed to be the same as petrochemical feedstocks.
Table A-73: Carbon Emitted from Fuels Burned for Energy Recovery [MBIT CO; Eg.]
1990
1995
2000
2005
2012
2013
2014
2015
2016
C Emissions
42.7
40.4
63.1
. 72.3
42.4
43.3
44.1
44.1
44.1
Products
More C is found in products than in industrial releases or energy recovery. The principal types of products are
plastics; synthetic rubber; synthetic fiber; C black; pesticides; soaps, detergents, and cleansers; food additives; antifreeze
and deicers (glycols); silicones; and solvents. Solvent evaporation was discussed previously along with industrial releases
of NMVOCs; the other product types are discussed below.
Plastics
Data on annual production of plastics through 2005 were taken from the American Plastics Council (APC), as
published in Chemical & Engineering News and on the APC and Society of Plastics Industry (SPI) websites, and through
direct communication with the APC (APC 2000, 2001, 2003 through 2006; SPI 2000; Eldredge-Roebuck 2000). Data for
2006 through 2016 were taken directly or derived from the American Chemistry Council (ACC 2007 through 2017b
A-112 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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supplemented by Vallianos 2011, 2012, 2013, 2014, 2015, 2016, 2017). In 2009, the American Chemistry Council
consolidated the resin categories for which it reports plastics production. Production numbers in the original categories were
provided via personal correspondence for 2009, 2011, 2012, 2013, 2014,2015, and 2016 (Vallianos 2011; 2012; 2013; 2014;
2015; 2016; 2017). Production figures for the consolidated resin categories in 2010 were linearly interpolated from 2009
and 2011 data. Production was organized by resin type (see Table A-74) and by year.
Several of the resin categories included production from Canada and/or Mexico, in addition to the U.S. values for
part of the time series. The production data for the affected resins and years were corrected using an economic adjustment
factor, based on the percent of North American production value in this industry sector accounted for by the United States.
A C content was then assigned for each resin. These C contents were based on molecular formulae and are listed in Table
A-75 and Table A-76. In cases where the resin type is generic, referring to a group of chemicals and not a single polymer
(e.g., phenolic resins, other styrenic resins), a representative compound was chosen. For other resins, a weighted C content
of 68 percent was assumed (i.e., it was assumed that these resins had the same content as those for which a representative
compound could be assigned).
There were no emissive uses of plastics identified, so 100 percent of the C was considered stored in products. As
noted in the chapter, an estimate of emissions related to the combustion of these plastics in the municipal solid waste stream
can be found in the Incineration of Waste section of the Energy chapter; those emissions are not incorporated in the mass
balance for feedstocks (described in this annex) to avoid double-counting.
Table fl-74:2016 Plastic Resin Production [MMT dry weight] and C Stored [MMT CO; Eg.]
2016 Production3
Carbon Stored
Resin Type
(MMT dry weight)
(MMT CO2 Eq.)
Epoxy
0.2
0.7
Urea
1.1
1.4
Melamine
0.1
0.1
Phenolic
1.6
4.4
Low-Density Polyethylene (LDPE)
3.0
9.4
Linear Low-Density Polyethylene (LLDPE)
6.2
19.4
High Density Polyethylene (HDPE)
8.1
25.5
Polypropylene (PP)
6.8
21.2
Acrylonitrile-butadiene-styrene (ABS)
0.5
1.5
Other Styrenicsb
0.5
1.7
Polystyrene (PS)
1.8
6.2
Nylon
0.6
1.3
Polyvinyl chloride (PVC)c
6.5
9.2
Thermoplastic Polyester
3.4
7.8
All Other (including Polyester (unsaturated))
6.1
15.3
Total
46.3
125.0
a Production estimates provided by the American Chemistry Council include Canadian production for Urea, Melamine, Phenolic, LDPE, LLDPE, HDPE,
PP, ABS, SAN, Other Styrenics, PS, Nylon, PVC, and Thermoplastic Polyester, and Mexican production for PP, ABS, SAN, Other Styrenics, Nylon, and
Thermoplastic Polyester. Values have been adjusted to account just for U.S. production.
b Includes Styrene-acrylonitrile (SAN).
c Includes copolymers.
Note: Totals may not sum due to independent rounding.
A-113
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Table A-75: Assigned G 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-76)
Polyvinyl Chloride (PVC)
38%
Polyvinyl chloride
Thermoplastic Polyester
63%
Polyethylene terephthalate
All Other
69%
Weighted average of other resin production
a Does not include alcoholic hydrogens.
Table fl-76: 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 2015 U.S. Scrap
Tire Management Summary (RMA 2016), 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, 201 la, 2013b, 2014, 2016c) 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 2016). Import and export data were obtained
from the published by the U.S. International Trade Commission (U.S. International Trade Commission 1990 through 2016).
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-77). The 1998 consumption data were obtained from the International Institute of
Synthetic Rubber Producers (IISRP) press release Synthetic Rubber Use Growth to Continue Through 2004, Says IISRP and
RMA (IISRP 2000). The 2001 and 2002 consumption data were obtained from the IISRP press release, IISRP Forecasts
Moderate Growth in North America to 2007 (IISRP 2003).
The rubber in tires that is abraded during use (the difference between new tire and scrap tire rubber weight) was
considered to be 100 percent emitted. Other than abraded rubber, there were no emissive uses of scrap tire and non-tire
rubber identified, so 100 percent of the non-abraded amount was assumed stored. Emissions related to the combustion of
rubber in scrap tires and consumer goods can be found in the Incineration of Waste section of the Energy chapter.
A-114 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table fl-77:2002 Buhher Consumption tktl 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
NA (Not Applicable)
a Includes consumption in Canada.
Note: Totals may not sum due to independent rounding.
Synthetic Fibers
Annual synthetic fiber production data were obtained from the Fiber Economics Bureau, as published in Chemical
& Engineering News (FEB 2001, 2003, 2005, 2007, 2009, 2010, 2011, 2012, 2013). The most recent data available were for
2012, so it was assumed that the 2013, 2014, 2015, and 2016 consumption was equal to that of 2012. One new update from
Chemical & Engineering News (C&EN 2017) was used to update the polyester value for 2016. These data are organized by
year and fiber type. For each fiber, a C content was assigned based on molecular formula (see Table A-78). For polyester,
the C content for poly (ethylene terephthalate) (PET) was used as a representative compound. For nylon, the average C
content of nylon 6 and nylon 6.6 was used, since these are the most widely produced nylon fibers. Cellulosic fibers, such as
acetate and rayon, have been omitted from the synthetic fibers' C accounting displayed here because much of their C is of
biogenic origin and carbon fluxes from biogenic compounds are accounted for in the Land Use, Land-Use Change and
Forestry chapter. These fibers account for only 4 percent of overall fiber production by weight.
There were no emissive uses of fibers identified, so 100 percent of the C was considered stored. Note that emissions
related to the combustion of textiles in municipal solid waste are accounted for under the Incineration of Waste section of
the Energy chapter.
Table A-70:2016 Fiber Production [MBIT], C Content [%], and C Stored [MMTCO2 Eq.l
Production
C Stored
Fiber Type
(MMT)
C Content
(MMT C02 Eq.)
Polyester
1.4
63%
3.1
Nylon
0.6
64%
1.3
Olefin
1.0
86%
3.2
Acrylic
+
68%
0.1
Total
3.0
NA
7.7
+ Does not exceed 0.05 MMT.
NA (Not Applicable)
Note: Totals may not sum due to independent rounding.
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, 201 lb, 2017) reports.
The most recent data available were for 2012, so it was assumed that the 2013 through 2016 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.
A-115
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Table fl-79: Active Ingredient Consumption in Pesticides [Million lbs.) and C Emitted and Stored [MMT CO2 Eq.) in 2012
Active Ingredient
C Emitted
C Stored
Pesticide Use3
(Million lbs.)
(MMT C02 Eq.)
(MMT CO2 Eq.)
Agricultural Uses
606.0
0.2
0.1
Non-Agricultural Uses
58.0
+
+
Home & Garden
37.8
+
+
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 CO2 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 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 2016 was assumed to equal the 2012 value. Cleanser production values were adjusted by import
and export data to develop U.S. consumption estimates.
Chemical formulae were used to determine C contents (as percentages) of the ingredients in the cleansers. Each
product's overall C content was then derived from the composition and contents of its ingredients. From these values the
mean C content for cleansers was calculated to be 21.9 percent.
The Census Bureau presents consumption data in terms of quantity (in units of million gallons or million pounds)
and/or terms of value (thousands of dollars) for eight specific categories, such as "household liquid laundry detergents,
heavy duty" and "household dry alkaline automatic dishwashing detergents." Additionally, the report provides dollar values
for the total consumption of "soaps, detergents, etc.—dry" and "soaps, detergents, etc.—liquid." The categories for which
both quantity and value data are available is a subset of total production. Those categories that presented both quantity and
value data were used to derive pounds per dollar and gallons per dollar conversion rates, and they were extrapolated (based
on the Census Bureau estimate of total value) to estimate the total quantity of dry and liquid" cleanser categories,
respectively.
Next, the total tonnage of cleansers was calculated (wet and dry combined) for 1997. Multiplying the mean C
content (21.9percent) by this value yielded an estimate of 4.6MMT CO2 Eq. in cleansers for 1997. For all subsequent years,
it was assumed that the ratio of value of shipments to total carbon content remained constant. For 1998 through 2016, value
of shipments was adjusted to 1997 dollars using the producer price index for soap and other detergent manufacturing (Bureau
of Labor Statistics 2016). The ratio of value of shipments to carbon content was then applied to arrive at total carbon content
of cleansers. Estimates are shown in Table A-80.
Table fl-80: C Emitted from Utilization of Soaps, Shampoos, and Detergents [MBIT CO; EqJ
1990
1995
2000
2005
2012
2013
2014
2015
2016
C Emissions
3.6 A
4.2
4.5
6.7
4.7
4.7
4.8
4.8
4.7
Antifreeze and Deicers
Glycol compounds, including ethylene glycol, propylene glycol, diethylene glycol, and triethylene glycol, are used
as antifreeze in motor vehicles, deicing fluids for commercial aircraft, and other similar uses. These glycol compounds are
assumed to ultimately enter wastewater treatment plants where they are degraded by the wastewater treatment process to
CO2 or to otherwise biodegrade to CO2. Glycols are water soluble and degrade rapidly in the environment (Floward 1993).
31
A density of 1.05 g/mL—slightly denser than water—was assumed for liquid cleansers.
A-116 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Annual production data for each glycol compound used as antifreeze and deicers were obtained from the Guide to
the Business of Chemistry (ACC 2017a) and the EPA Chemical Data Access Tool (CDAT) (EPA 2014). Import and export
data were used to adjust annual production data to annual consumption data. The percentage of the annual consumption of
each glycol compound used for antifreeze and deicing applications was estimated from Chemical Profiles data published on
The Innovation Group website32 and from similar data published in the Chemical Market Reporter, which became ICIS
Chemical Business in 2005.33 Production data for propylene glycol, diethylene glycol, and triethylene glycol are no longer
reported in the Guide to the Business of Chemistry, so data from ICIS Chemical Business on total demand was used with
import and export data to estimate production of these chemicals. ICIS last reported total demand for propylene glycol and
diethylene glycol in 2006, and triethylene glycol demand in 2007. EPA reported total U.S. production of propylene glycol,
diethylene glycol, and triethylene glycol in 2012 in the CDAT (EPA 2014). Total demand for these compounds for 2012
was calculated from the 2012 production data using import and export data. Demand for propylene glycol and diethylene
glycol was interpolated for years between 2006 and 2012, and demand for triethylene glycol was interpolated for years
between 2007 and 2012, using the calculated 2012 total demand values for each compound and the most recently reported
total demand data from ICIS. Values for 2013, 2014, 2015, and 2016 for these compounds were assumed to be the same as
the 2012 values. .
The glycol compounds consumed in antifreeze and deicing applications is assumed to be 100 percent emitted as
CO2. Emissions of CO2 from utilization of antifreeze and deicers are summarized in Table A-81.
Table fl-81: C Emitted from Utilization of Antifreeze and Deicers [MBIT CO; EqJ
1990
1995
2000
2005
2012
2013
2014
2015
2016
C Emissions
1.2
1.4
1.5
1.2
0.9
0.8
0.9
1.0
1.0
Food Additives
Petrochemical feedstocks are used to manufacture synthetic food additives, including preservatives, flavoring
agents, and processing agents. These compounds include glycerin, propylene glycol, benzoic acid, and other compounds.
These compounds are incorporated into food products, and are assumed to ultimately enter wastewater treatment plants
where they are degraded by the wastewater treatment processes to CO2 or to otherwise biodegrade to CO2. Certain food
additives, e.g., glycerin, are manufactured both from petrochemical feedstocks and from biogenic feedstocks. Food additives
that are derived from biogenic feedstocks are 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 2017a). Historical values for adipic acid were adjusted according to the most recent data in the 2017 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 acetic acid and maleic anhydride were last reported by ICIS in 2005, and dipropylene glycol demand 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 2013,
2014, 2015, and 2016 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 CO2. Emissions of CO2 from
utilization of synthetic food additives are summarized in Table A-82.
32
See .
33
See .
34
See .
35
See .
A-117
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Table fl-82: C Emitted from Utilization of Food Additives [MMT CO; EqJ
1990
1995
2000
2005
2012
2013
2014
2015
2016
C Emissions
0.6
0.7 ,,
0.7
0.8 ,
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 CO2 in the
manufacturing process. It is also assumed that the C contained in the silicone products is stored, and not emitted as CO2.
Annual production data for each silicone manufacturing compound were obtained from the Guide to the Business
of Chemistry (ACC 2015b). 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 2005.16 ICIS last reported production of methyl chloride in 2007. EPA reported total
U.S. production of methyl chloride in 2012 in the CD AT (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 2013, 2014, 2015 and 2016 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
CO2. Storage of silicone manufacturing compounds is summarized in Table A-83.
Table fl-83: C Stored in Silicone Products [MBIT CO; EqJ
1990
1995
2000
2005
2012
2013
2014
2015
2016
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 2016. 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 mean of 68 percent, a standard deviation
of 4 percent, and the 95 percent confidence interval of 57 percent and 72 percent. This compares to the calculated Inventory
estimate of 70 percent. The analysis produced a C emission distribution with a mean of 68.6 MMT CO2 Eq., standard
deviation of 16.8 MMT CO2 Eq., and 95 percent confidence limits of 47.9 and 111.3 MMT CO2 Eq. This compares with a
calculated Inventory estimate of 63.4 MMT CO2 Eq.
The apparently tight confidence limits for the storage factor and C storage probably understate uncertainty, as a
result of the way this initial analysis was structured. As discussed above, the storage factor for feedstocks is based on an
analysis of six fates that result in long-term storage (e.g., plastics production), and eleven that result in emissions (e.g.,
volatile organic compound emissions). Rather than modeling the total uncertainty around all 17 of these fate processes, the
current analysis addresses only the storage fates, and assumes that all C that is not stored is emitted. As the production
statistics that drive the storage factors are relatively well-characterized, this approach yields a result that is probably biased
toward understating uncertainty.
As far as specific sources of uncertainty, there are several cross-cutting factors that pervade the characterization of
C flows for feedstocks. The aggregate storage factor for petrochemical feedstocks (industrial other coal, natural gas for non-
36 Ibid.
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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 CO2 may actually take place on a time-scale of several years or decades. By attributing the emissions
to the year in which the C enters the mass balance (i.e., the year in which it leaves refineries as a 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 2016). The complexity of the organic chemical industry, with multiple feedstocks, intermediates, and subtle
differences in nomenclature, makes it difficult to ensure that the adjustments to the EIA data for imports and exports is
accurate and the approach used here may underestimate or overestimate net exports of C.
Oxidation factors have been applied to non-energy uses of petrochemical feedstocks in the same manner as for
energy uses. However, for those fuels where IPCC storage factors are used, this "oxidation factor" may be inherent in the
storage factor applied when calculating emissions from non-energy consumption, which would result in a double-counting
of the unoxidized C. Oxidation factors are small corrections, on the order of 1 percent, and therefore application of oxidation
factors to non-energy uses may result in a slight underestimation of C emissions from non-energy uses.
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 CO2 emissions from solvent evaporation and industrial NMVOC
emissions are in the estimates of (a) total emissions and (b) their C content. Solvent evaporation and industrial NMVOC
emissions reported by EPA are based on a number of data sources and emission factors, and may underestimate or
overestimate emissions. The C content for solvent evaporation emissions is calculated directly from the specific solvent
compounds identified by EPA as being emitted, and is thought to have relatively low uncertainty. The C content for industrial
emissions has more uncertainty, however, as it is calculated from the average C content of an average volatile organic
compound based on the list of the most abundant measured NMVOCs provided in EPA (2002a).
Uncertainty in the hazardous waste combustion analysis is introduced by the assumptions about the composition
of combusted hazardous wastes, including the characterization that hazardous wastes are similar to mixtures of water,
noncombustibles, and fuel equivalent materials. Another limitation is the assumption that all of the C that enters hazardous
waste combustion is emitted—some small fraction is likely to be sequestered in combustion ash—but given that the
destruction and removal efficiency for hazardous organics is required to meet or exceed 99.99 percent, this is a very minor
source of uncertainty. C emission estimates from hazardous waste should be considered central value estimates that are
likely to be accurate to within +50 percent.
The amount of feedstocks combusted for energy recovery was estimated from data included in the Manufacturers
Energy Consumption Surveys (MECS) for 1991, 1994, 1998, 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
A-119
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refinery still gas; waste gas; waste oils, tars, and related materials; petroleum coke, coke oven and blast furnace gases; and
other uncharacterized fuels. Moreover, the industries using these "other" fuels are also identified only in broad categories,
including the petroleum and coal products, chemicals, primary metals, nonmetallic minerals, and other manufacturing
sectors. The "other" fuel consumption data are reported in BTUs (energy units) and there is uncertainty concerning the
selection of a specific conversion factor for each broad "other" fuel category to convert energy units to mass units. Taken as
a whole, the estimate of energy recovery emissions probably introduces more uncertainty than any other element of the non-
energy analysis.
Uncertainty in the C storage estimate for plastics arises primarily from four factors. First, production of some
plastic resins is not tracked directly and must be estimated based on other market data. Second, the raw data on production
for several resins include Canadian and/or Mexican production and may overestimate the amount of plastic produced from
U.S. fuel feedstocks; this analysis includes adjustments to "back out" the Canadian and Mexican values, but these
adjustments are approximate. Third, the assumed C content values are estimates for representative compounds, and thus do
not account for the many formulations of resins available. This uncertainty is greater for resin categories that are generic
(e.g., phenolics, other styrenics, nylon) than for resins with more specific formulations (e.g., polypropylene, polyethylene).
Fourth, the assumption that all of the C contained in plastics is stored ignores certain end uses (e.g., adhesives and coatings)
where the resin may be released to the atmosphere; however, these end-uses are likely to be small relative to use in plastics.
The quantity of C stored in synthetic rubber only accounts for the C stored in scrap tire synthetic rubber. The value
does not take into account the rubber stored in other durable goods, clothing, footwear, and other non-durable goods, or
containers and packaging. This adds uncertainty to the total mass balance of C stored. There are also uncertainties as to the
assignment of C content values; however, they are much smaller than in the case of plastics. There are probably fewer
variations in rubber formulations than in plastics, and the range of potential C content values is much narrower. Lastly,
assuming that all of the C contained in rubber is stored ignores the possibility of volatilization or degradation during product
lifetimes. Flowever, 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 (UMA), 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
A-120 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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asphalt is composed of asphalt cement thinned with a volatile petroleum distillate (e.g., naphtha). Emulsified asphalt contains
only asphalt cement and water. Roofing products are the other significant end use of asphalt cement, accounting for
approximately 14 percent of U.S. production (Kelly 2000). No data were available on the fate of C in asphalt roofing; it was
assumed that it has the same fate as C in asphalt paving applications.
Methodology and Data Sources
A C storage factor was calculated for each type of asphalt paving. The fraction of C emitted by each asphalt type
was multiplied by consumption data for asphalt paving (EPA 2001) to estimate a weighted average C storage factor for
asphalt as a whole.
The fraction of C emitted by HMA was determined by first calculating the organic emissions (volatile organic
compounds [VOCs], carbon monoxide [CO], polycyclic aromatic hydrocarbons [PAHs], hazardous air pollutants [HAPs],
and phenol) from HMA paving, using emission factors reported in EPA (2001) and total HMA production.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
(EIIP 2001). "Asphalt Paving Operation" AP-42 (EPA 2001) provided the emissions source information used in the
calculation of the C storage factor for cut-back asphalt. The storage factor for emulsified asphalt was provided by Alan
James of Akzo Nobel Coatings, Inc. (James 2000).
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the asphalt C storage factor and the quantity of C stored in asphalt in 2016. 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
37
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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percent, while the asphalt property variables were assumed to have narrower distributions. A narrow uniform distribution,
with maximum 5 percent uncertainty (± 5 percent) around the mean, was applied to the C content coefficient.
The Monte Carlo analysis produced a tight distribution of storage factor values, with the 95 percent confidence
interval of 99 percent and 100 percent, with the mean value of 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 mean of 0.3 MMT CO2 Eq., standard
deviation of 0.1 and 95 percent confidence limits of 0.1 MMT CO2 Eq. and 0.6 MMT CO2 Eq. This compares to an Inventory
calculated estimate of 0.3 MMT CC^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 CO2 is uncertain. Long-
term studies may reveal higher lifetime emissions rates associated with degradation.
Many of the values used in the analysis are also uncertain and are based on estimates and professional judgment.
For example, the asphalt cement input for hot mix asphalt was based on expert advice indicating that the range is variable—
from about 3 to 5 percent—with actual content based on climate and geographical factors (Connolly 2000). Over this range,
the effect on the calculated C storage factor is minimal (on the order of 0.1 percent). Similarly, changes in the assumed C
content of asphalt cement would have only a minor effect.
The consumption figures for cut-back and emulsified asphalts are based on information reported for 1994. More
recent trends indicate a decrease in cut-back use due to high VOC emission levels and a related increase in emulsified asphalt
use as a substitute. 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 (2018), the C content from U.S. production of lubricants in 2016 was approximately 5.9 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 2016 were about
5.3 MMT C, or 19.5 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-84 provides an estimated allocation of the fates of lubricant oils (Rinehart 2000), along with an estimate
of the proportion of C stored in each fate. The ultimate fate of the majority of oils (about 84 percent) is combustion, either
during initial use or after collection as used oil. Combustion results in 99 percent oxidation to CO2 (EIIP 1999), with
correspondingly little long-term storage of C in the form of ash. Dumping onto the ground or into storm sewers, primarily
by "do-it-yourselfers" who change their own oil, is another fate that results in conversion to CO2 given that the releases are
generally small and most of the oil is biodegraded (based on the observation that land farming—application to soil—is one
of the most frequently used methods for degrading refinery wastes). In the landfill environment, which tends to be anaerobic
within municipal landfills, it is assumed that 90 percent of the oil persists in an 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.,
38 For example, the U.S. EPA "RCRA (Resource Conservation and Recovery Act) On-line" web site ()
has over 50 entries on used oil regulation and policy for 1994 through 2000.
A-122 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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after re-refining, the oil would have the same probability of combustion, landfilling, or dumping as virgin oil), that is, it was
assumed that about 97 percent of the C in re-refined oil is ultimately oxidized. Because of the dominance of fates that result
in eventual release as CO2, only about 3 percent of the C in oil lubricants goes into long-term storage.
Table fl-84: 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-85 provides analogous estimates for lubricant greases. Unlike oils, grease is generally not combusted
during use, and combustion for energy recovery and re-refining is thought to be negligible. Although little is known about
the fate of waste grease, it was assumed that 90 percent of the non-combusted portion is landfilled, and the remainder is
dumped onto the ground or storm sewers. Because much of the waste grease will be in containers that render it relatively
inaccessible to biodegradation, and because greases contain longer chain paraffins, which are more persistent than oils, it
was assumed that 90 percent and 50 percent of the C in landfilled and dumped grease, respectively, would be stored. The
overall storage factor is 82 percent for grease.
Table A-85: Commercial and Environmental Fate of Grease Lubricants (Percent)
Fate of Grease
Portion of Total
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 of the Census 1999). Assuming
that the mass of lubricants can be allocated according to the proportion of value of production (92 percent oil, 8 percent
grease), applying these weights to the storage factors for oils and greases (3 percent and 82 percent) yields an overall storage
factor of 9.2 percent.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the lubricants weighted average C storage factor and the quantity of C emitted from lubricants
in 2016. The Tier 2 analysis was performed to allow the specification of probability density functions for key variables,
within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert
judgments of uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty
estimates were determined using assumptions based on source category knowledge. Uncertainty estimates for oil and grease
variables were assumed to have a moderate variance, in triangular or uniform distribution. Uncertainty estimates for
lubricants production were assumed to be rather high (±20 percent). A narrow uniform distribution, with 6 percent
uncertainty (± 6 percent) around the mean, was applied to the lubricant C content coefficient.
The Monte Carlo analysis produced a storage factor distribution with the 95 percent confidence interval of 4 percent
and 17 percent around a mean value of 10 percent. This compares to the calculated Inventory estimate of 9.2 percent. The
analysis produced a C emission distribution approximating a normal curve with a mean of 19.3 MMT CO2 Eq., standard
A-123
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deviation of 1.7 and 95 percent confidence limits of 16.2 MMT CO2 Eq. and 22.7 MMT CO2 Eq. This compares to an
inventory-calculated estimate of 19.5 MMT CC^Eq.
The principal sources of uncertainty for the disposition of lubricants are the estimates of the commercial use, post-
use, and environmental fate of lubricants, which, as noted above, are largely based on assumptions and judgment. There is
no comprehensive system to track used oil and greases, which makes it difficult to develop a verifiable estimate of the
commercial fates of oil and grease. The environmental fate estimates for percent of C stored are less uncertain, but also
introduce uncertainty in the estimate.
The assumption that the mass of oil and grease can be divided according to their value also introduces uncertainty.
Given the large difference between the storage factors for oil and grease, changes in their share of total lubricant production
have a large effect on the weighted storage factor.
Future improvements to the analysis of uncertainty surrounding the lubricants C storage factor and C stored include
further refinement of the uncertainty estimates for the individual activity variables.
Waxes
Waxes are organic substances that are solid at ambient temperature, but whose viscosity decreases as temperature
increases. Most commercial waxes are produced from petroleum refining, though "mineral" waxes derived from animals,
plants, and lignite (coal) are also used. An analysis of wax end uses in the United States, and the fate of C in these uses,
suggests that about 42 percent of C in waxes is emitted, and 58 percent is stored.
Methodology and Data Sources
The National Petroleum Refiners Association (NPRA) considers the exact amount of wax consumed each year by
end use to be proprietary (Maguire 2004). In general, about thirty percent of the wax consumed each year is used in packaging
materials, though this percentage has declined in recent years. The next highest wax end use, and fastest growing end use,
is candles, followed by construction materials and firelogs. Table A-86 categorizes some of the wax end uses, which the
NPRA generally classifies into cosmetics, plastics, tires and rubber, hot melt (adhesives), chemically modified wax
substances, and other miscellaneous wax uses (NPRA 2002).
Table fl-86: 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
0%
1%
Other
2%
9%
Total
42%
58%
+ Does not exceed 0.5 percent.
A C storage factor for each wax end use was estimated and then summed across all end uses to provide an overall
C storage factor for wax. Because no specific data on C contents of wax used in each end use were available, all wax products
are assumed to have the same C content. Table A-87 categorizes wax end uses identified by the NPRA, and lists the estimated
C storage factor of each end use.
A-124 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table fl-87: Wax End-Uses by Fate, Percent of Total Mass, Percent C Stored, and Percent of Total G Mass Stored
Use
Percent of Total Percent of C
Wax Mass Stored
Percent of Total 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%
+ Does not exceed 0.5 percent.
Notes: Totals may not sum due to independent rounding. Estimates of percent stored are based on professional judgment, ICF International.
Source mass percentages: NPRA (2002).
Emissive wax end-uses include candles, firelogs (synthetic fireplace logs), hotmelts (adhesives), matches, and
explosives. At about 20 percent, candles consume the greatest portion of wax among emissive end uses. As candles combust
during use, they release emissions to the atmosphere. For the purposes of the Inventory, it is assumed that 90 percent of C
contained in candles is emitted as CO2. In firelogs, petroleum wax is used as a binder and as a fuel, and is combusted during
product use, likely resulting in the emission of nearly all C contained in the product. Similarly, C contained in hotmelts is
assumed to be emitted as CO2 as heat is applied to these products during use. It is estimated that 50 percent of the C contained
in hot melts is stored. Together, candles, firelogs, and hotmelts constitute approximately 30 percent of annual wax production
(NPRA 2002).
All of the wax utilized in the production of packaging, cosmetics, plastics, tires and rubber, and other products is
assumed to remain in the product (i.e., it is assumed that there are no emissions of CO2 from wax during the production of
the product). Wax is used in many different packaging materials including wrappers, cartons, papers, paperboard, and
corrugated products (NPRA 2002). Davie (1993) and Davie et al. (1995) suggest that wax coatings in packaging products
degrade rapidly in an aerobic environment, producing CO2; however, because packaging products ultimately enter landfills
typically having an anaerobic environment, most of the C from this end use is assumed to be stored in the landfill.
In construction materials, petroleum wax is used as a water repellent on wood-based composite boards, such as
particle board (IGI 2002). Wax used for this end-use should follow the life-cycle of the harvested wood used in product,
which is classified into one of 21 categories, evaluated by life-cycle, and ultimately assumed to either be disposed of in
landfills or be combusted (EPA 2003).
The fate of wax used for packaging, in construction materials, and for most remaining end uses is ultimately to
enter the municipal solid waste (MSW) stream, where it is either combusted or sent to landfill for disposal. Most of the C
contained in these wax products will be stored. It is assumed that approximately 21 percent of the C contained in these
products will be emitted through combustion or at landfill. With the exception of tires and rubber, these end-uses are assigned
a C storage factor of 79 percent.
Waxes used in tires and rubber follow the life cycle of the tire and rubber products. Used tires are ultimately
recycled, landfilled, or combusted. The life-cycle of tires is addressed elsewhere in this annex as part of the discussion of
rubber products derived from petrochemical feedstocks. For the purposes of the estimation of the C storage factor for waxes,
wax contained in tires and rubber products is assigned a C storage factor of 47 percent.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the wax C storage factor and the quantity of C emitted from wax in 2016. 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
A-125
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variance, in normal, uniform, or triangular distribution; uniform distributions were applied to total consumption of waxes
and the C content coefficients.
The Monte Carlo analysis produced a storage factor distribution, whose 95 percent confidence interval values fell
within the range of 47 percent and 67 percent, around the mean value of 58 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 CO2 Eq. and 0.7 MMT CC^Eq., with a mean value of 0.5 MMT CC^Eq. This compares with a calculated Inventory
estimate of 0.4 MMT CC^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 2016 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 CO2.
Uncertainty
A separate uncertainty analysis was not conducted for miscellaneous products, though this category was included
in the uncertainty analysis of other non-energy uses discussed in the following section.
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
dioxide emissions from carbide production are implicitly accounted for in the storage factor calculation for the non-energy
use of petroleum coke. EIA (2018) defines "miscellaneous products" as "all finished products not classified elsewhere (e.g.,
petrolatum, lube refining by-products (aromatic extracts and tars), absorption oils, ram-jet fuel, petroleum rocket fuels,
synthetic natural gas feedstocks, and specialty oils)." All of these uses are emissive, and therefore the storage factor for
miscellaneous products is set at zero. 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.
A-126 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 2016. 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, with a mean of 22 percent. This compares to the Inventory calculation of weighted average (across the various
fuels) storage factor of about 6.4 percent. The analysis produced an emission distribution, with the 95 percent confidence
limit of 17.5 MMT CO2 Eq. and 31.2 MMT CO2 Eq., and a mean of 24.4 MMT CO2 Eq. This compares with the Inventory
estimate of 28.6 MMT CO2 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.
A-127
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FEB (2001) Fiber Economics Bureau, as cited in ACS (2001) Production: slow gains in output of chemicals and products
lagged behind U.S. economy as a whole Chemical & Engineering News, American Chemical Society, 25 June.
Available online at: .
Financial Planning Association (2006) Canada/US Cross-Border Tools: US/Canada Exchange Rates. Available online at:
. Accessed August 16, 2006.
Gosselin, Smith, and Hodge (1984) Clinical Toxicology of Commercial Products. Fifth Edition, Williams & Wilkins,
Baltimore.
Huurman, J.W.F. (2006) Recalculation of Dutch Stationary Greenhouse Gas Emissions Based on sectoral Energy
Statistics 1990-2002. Statistics Netherlands, Voorburg, The Netherlands.
A-130 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
IGI (2002) 100 Industry Applications. The International Group Inc. Available online at
.
IISRP (2003) "IISRP Forecasts Moderate Growth in North America to 2007" International Institute of Synthetic Rubber
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IISRP (2000) Synthetic Rubber Use Growth to Continue Through 2004, Says IISRP and RMA. International Institute of
Synthetic Rubber Producers press release.
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de actividad. Available online at:
. Accessed August
15.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds.; Institute for Global Environmental
Strategies (IGES). Hayama, Kanagawa, Japan.
James, A. (2000) Personal communication between Suzanne Bratis of ICF International and Alan James of Akzo Nobel
Coatings, Inc. July 2000. (Tel: 614-294-3361).
Kelly (2000) Personal communication between Tom Smith, ICF Consulting and Peter Kelly, Asphalt Roofing
Manufacturers Association, August 2000.
Maguire (2004) Personal communication with J. Maguire, National Petrochemicals and Refiners Association. August -
September 2004.
Marland, G., and R.M. Rotty (1984) Carbon dioxide emissions from fossil fuels: A procedure for estimation and results for
1950-1982, Tellus 36b:232-261.
NPRA (2002) North American Wax - A Report Card. Available online at:
. Accessed 17 September 2009.
Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah Shapiro of
ICF International, January 10, 2007.
SPI (2000) The Society of the Plastics Industry Website, http://www.plasticsindustry.org/industry/stat3.htm, Accessed 28
June 2000.
U.S. Bureau of the Census (1994, 1999, 2004, 2009, 2014) 1992, 1997, 2002, 2007, 2012 Economic Census. Available
online at
.
U.S. International Trade Commission (1990 through 2016) "Interactive Tariff and Trade DataWeb: Quick Query."
Available online at . Accessed November 2016.
Vallianos, Jean (2016) Personal communication between Drew Stilson of ICF and Jean Vallianos of the American
Chemistry Council, November 17, 2016.
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American Chemistry Council, December 20, 2015.
A-131
-------
Vallianos, Jean (2014) Personal communication between Sarah Biggar of ICF International and Jean Vallianos of the
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Vallianos, Jean (2012) Personal communication between Ben Eskin of ICF International and Jean Vallianos of the
American Chemistry Council, September 14, 2012.
Vallianos, Jean (2011) Personal communication between Joe Indvik of ICF International and Jean Vallianos of the American
Chemistry Council, January 4, 2011.
A-132 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
ANNEX 3 Methodological Descriptions for Additional
Source or Sink Categories
3.1. Methodology for Estimating Emissions of CH4, N2O, and Indirect Greenhouse Gases
from Stationary Combustion
Estimates of CH4 and N2O Emissions
Methane (CH4) and nitrous oxide (N2O) emissions from stationary combustion were estimated using 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-88 through Table A-93.
Step 1: Determine Energy Consumption by Sector and Fuel Type
Energy consumption from stationary combustion activities was grouped by sector: industrial, commercial,
residential, electric power, and U.S. Territories. For CH4 and N2O from industrial, commercial, residential, and U.S.
Territories, estimates were based upon consumption of coal, gas, oil, and wood. Energy consumption and wood consumption
data for the United States were obtained from the Energy Information Administration's (EIA) Monthly Energy Review,
February 2018 (EIA 2018). 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 EIA'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 (2017) and the
Federal Highway Administration (FHWA) (1996 through 2016). 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-88 provides annual energy
consumption data for the years 1990 through 2016.
In this Inventory, the emission estimation methodology for the electric power sector used a Tier 2 methodology as
fuel consumption by technology-type for the electric power sector was obtained from the Acid Rain Program Dataset (EPA
2016a). This combustion technology-and fuel-use data was available by facility from 1996 to 2016. Since there was a
difference between the EPA (2016a) and EIA (2018) total energy consumption estimates, the remainder between total energy
consumption using EPA (2016a) and EIA (2018) was apportioned to each combustion technology type and fuel combination
using a ratio of energy consumption by technology type from 1996 to 2016.
Energy consumption estimates were not available from 1990 to 1995 in the EPA (2016a) dataset, and as a result,
consumption was calculated using total electric power consumption from EIA (2018) and the ratio of combustion technology
and fuel types from EPA (2016a). The consumption estimates from 1990 to 1995 were estimated by applying the 1996
consumption ratio by combustion technology type to the total EIA consumption for each year from 1990 to 1995.
Step 2: Determine the Amount of CH4 and N2O Emitted
Activity data for industrial, commercial, residential, and U.S. Territories and fuel type for each of these sectors
were then multiplied by default Tier 1 emission factors to obtain emission estimates. Emission factors for the residential,
commercial, and industrial sectors were taken from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC 2006). These N2O emission factors by fuel type (consistent across sectors) were also assumed for U.S. Territories.
The CH4 emission factors by fuel type for U. S. Territories were estimated based on the emission factor for the primary sector
in which each fuel was combusted. Table A-89 provides emission factors used for each sector and fuel type. For the electric
power sector, emissions were estimated by multiplying fossil fuel and wood consumption by technology- and fuel-specific
U.S. Territories data also include combustion from mobile activities because data to allocate U.S. Territories' energy use were
unavailable. For this reason, CH4 and N2O emissions from combustion by U.S. Territories are only included in the stationary combustion
totals.
4" Though emissions from construction and farm use occur due to both stationary and mobile sources, detailed data was not available to
determine the magnitude from each. Currently, these emissions are assumed to be predominantly from mobile sources.
A-133
-------
Tier 2 IPCC emission factors shown in Table A-90. Emission factors were taken from U.S. EPA publications on emissions
rates for combustion sources. The EPA factors were in large part used in the 2006 IPCC Guidelines as the factors presented.
Estimates of N0X, 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 2016b), and disaggregated based on EPA (2003).
For indirect greenhouse gases, the major source categories included coal, fuel oil, natural gas, wood, other fuels
(i.e., bagasse, liquefied petroleum gases, coke, coke oven gas, and others), and stationary internal combustion, which
includes emissions from internal combustion engines not used in transportation. EPA periodically estimates emissions of
NOx, CO, and NMVOCs by sector and fuel type using a "bottom-up" estimating procedure. In other words, the emissions
were calculated either for individual sources (e.g., industrial boilers) or for many sources combined, using basic activity data
(e.g., fuel consumption or deliveries, etc.) as indicators of emissions. The national activity data used to calculate the
individual categories were obtained from various sources. Depending upon the category, these activity data may include fuel
consumption or deliveries of fuel, tons of refuse burned, raw material processed, etc. Activity data were used in conjunction
with emission factors that relate the quantity of emissions to the activity.
The basic calculation procedure for most source categories presented in EPA (2003) and EPA (2016b) is
represented by the following equation:
EP)S = As x EFP)S x (l - Cp.s/100)
where,
E
= Emissions
P
= Pollutant
s
= Source category
A
= Activity level
EF
= Emission factor
C
= Percent control efficiency
The EPA currently derives the overall emission control efficiency of a category from a variety of sources, including
published reports, the 1985 National Acid Precipitation and Assessment Program (NAPAP) emissions inventory, and other
EPA databases. The U.S. approach for estimating emissions of NOx, CO, and NMVOCs from stationary combustion as
described above is similar to the methodology recommended by the IPCC.
A-134 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-88: Fuel Consumption hy Stationary Combustion for Calculating CHa and N2O Emissions tTBtul
Fuel/End-Use
Sector
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Coal
19,610
20,888
23,080
22,391
22,343
22,576
22,636
22,949
22,458
22,710
22,225
19,670
20,697
18,989
16,715
17,393
17,366
15,110
13,968
Residential
31
17
11
12
12
12
11
8
6
8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Commercial
124
117
92
97
90
82
103
97
65
70
81
73
70
62
44
41
40
31
24
Industrial
1,640
1,527
1,349
1,358
1,244
1,249
1,262
1,219
1,189
1,131
1,081
877
952
866
782
800
799
696
620
Electric Power
17,807
19,217
21,618
20,920
20,987
21,199
21,228
21,591
21,161
21,465
21,026
18,682
19,639
18,024
15,852
16,521
16,483
14,339
13,280
U.S. Territories
7
10
10
4
11
34
32
33
37
37
37
37
37
37
37
31
44
44
44
Petroleum
6,516
6,035
6,493
7,041
6,426
6,818
6,993
6,924
6,644
6,543
5,698
5,131
5,215
4,895
4,551
4,700
4,181
4,535
4,191
Residential
1,375
1,262
1,429
1,465
1,361
1,468
1,468
1,368
1,202
1,220
1,202
1,138
1,116
1,040
846
937
988
930
804
Commercial
1,007
728
775
767
701
831
811
766
729
755
706
752
722
691
571
606
574
942
832
Industrial
2,966
2,726
2,552
2,902
2,738
2,857
3,056
3,166
3,507
3,373
2,815
2,332
2,448
2,402
2,344
2,473
1,993
2,022
1,928
Electric Power
797
860
1,269
1,279
1,074
1,043
1,007
1,004
590
618
488
383
412
266
273
180
153
169
156
U.S. Territories
370
459
468
629
552
618
652
620
616
577
488
526
516
497
517
504
472
472
472
Natural Gas
17,266
19,337
20,919
20,224
20,908
20,894
21,152
20,938
20,626
22,019
22,286
21,952
22,912
23,115
24,137
24,949
25,741
26,453
26,588
Residential
4,491
4,954
5,105
4,889
4,995
5,209
4,981
4,946
4,476
4,835
5,010
4,883
4,878
4,805
4,242
5,023
5,242
4,777
4,496
Commercial
2,682
3,096
3,252
3,097
3,212
3,261
3,201
3,073
2,902
3,085
3,228
3,187
3,165
3,216
2,960
3,380
3,572
3,316
3,213
Industrial
7,716
8,723
8,656
7,949
8,086
7,845
7,914
7,330
7,323
7,521
7,571
7,125
7,683
7,873
8,203
8,525
8,837
8,799
9,016
Electric Power
2,376
2,564
3,894
4,266
4,591
4,551
5,032
5,565
5,899
6,550
6,447
6,730
7,159
7,194
8,683
7,964
8,033
9,505
9,805
U.S. Territories
0.0
0.0
13
23
23
27
25
24
26
27
29
27
28
27
49
57
57
57
57
Wood
2,216
2,370
2,262
2,006
1,995
2,002
2,121
2,137
2,099
2,089
2,059
1,931
2,116
2,139
2,133
2,347
2,412
2,241
2,153
Residential
580
520
420
370
380
400
410
430
380
420
470
500
440
450
420
580
590
440
373
Commercial
66
72
71
67
69
71
70
70
65
70
73
73
72
69
61
70
75
81
82
Industrial
1,442
1,652
1,636
1,443
1,396
1,363
1,476
1,452
1,472
1,413
1,339
1,178
1,409
1,438
1,462
1,489
1,495
1,476
1,474
Electric Power
129
125
134
126
150
167
165
185
182
186
177
180
196
182
190
207
251
244
224
U.S. Territories
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE (Not Estimated)
Note: Totals may not sum due to independent rounding.
A-135
-------
Table fl-89: CHa and N2O Emission Factors by Fuel Type and Sector (g/GJ)a
Fuel/End-Use Sector
CH4
N2O
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 = 9.486x1 CHBtu.
Table fl-90: CHa and N2O Emission Factors byTecbnologyType and Fuel Type fortbe Electric Power Sector tg/GJla
Technology
Confiquration
CH4
N20
Liquid Fuels
Residual Fuel Oil/Shale Oil Boilers
Normal Firing
0.8
0.3
Tangential Firing
0.8
0.3
Gas/Diesel Oil Boilers
Normal Firing
0.9
0.4
Tangential Firing
0.9
0.4
Large Diesel Oil Engines >600 hp (447kW)
4
NA
Solid Fuels
Pulverized Bituminous Combination Boilers
Dry Bottom, wall fired
0.7
0.5
Dry Bottom, tangentially fired
0.7
1.4
Wet bottom
0.9
1.4
Bituminous Spreader Stoker Boilers
With and without re-injection
1
0.7
Bituminous Fluidized Bed Combustor
Circulating Bed
1
61
Bubbling Bed
1
61
Bituminous Cyclone Furnace
0.2
0.6
Lignite Atmospheric Fluidized Bed
NA
71
Natural Gas
Boilers
1.0
0.3
Gas-Fired Gas Turbines >3MW
3.7
1.3
Large Dual-Fuel Engines
258
NA
Combined Cycle
3.7
1.3
Peat
Peat Fluidized Bed Combustion
Circulating Bed
3
7
Bubbling Bed
3
3
Biomass
Wood/Wood Waste Boilers
11
7
Wood Recovery Boilers
1
1
NA (Not Applicable)
a Ibid.
A-136 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-91: NOk Emissions from Stationary Combustion tktl
Sector/Fuel Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Electric Power
6,045
5,792
4,829
4,454
4,265
3,930
3,595
3,434
3,249
3,064
2,847
2,552
2,226
1,893
1,779
1,666
1,552
1,321
953
Coal
5,119
5,061 A
4,130
3,802
3,634
3,349
3,063
2,926
2,768
2,611
2,426
2,175
1,896
1,613
1,516
1,419
1,323
1,126
812
Fuel Oil
200
87/=?
147
149
142
131
120
114
108
102
95
85
74
63
59
55
52
44
32
Natural gas
513
510gJ
376
325
310
286
262
250
236
223
207
186
162
138
129
121
113
96
69
Wood
NA
. NA
36
37
36
33
30
29
27
26
24
21
19
16
15
14
13
11
8
Other Fuels3
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Internal Combustion
213
134
140
140
143
132
121
115
109
103
95
86
75
63
60
56
52
44
32
Industrial
2,559
2,650
2,278
2,296
1,699
1,641
1,580
1,515
1,400
1,285
1,165
1,126
1,087
1,048
1,028
1,009
990
990
990
Coal
530
541 / „
484
518
384
371
357
342
316
290
263
254
245
237
232
228
223
223
223
Fuel Oil
240
224
166
153
114
110
106
101
94
86
78
75
73
70
69
67
66
66
66
Natural gas
877
999
710
711
526
508
489
469
433
398
361
348
336
324
318
312
306
306
306
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
119
111
109
116
86
83
80
76
70
65
59
57
55
53
52
51
50
50
50
Internal Combustion
792
774/
809
798
591
570
549
527
486
446
405
391
378
364
357
351
344
344
344
Commercial
671
607
507
428
438
408
378
490
471
452
433
445
456
548
534
519
443
443
443
Coal
36
35
21
21
19
19
19
19
18
17
15
15
15
15
14
14
14
14
14
Fuel Oil
88
94
52
52
50
49
49
49
46
43
39
39
38
37
37
36
36
36
36
Natural gas
181
210
161
165
157
156
156
155
145
135
124
122
120
118
116
115
113
113
113
Wood
NA
. NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
366
269
273
189
212
183
154
267
263
258
254
269
284
378
366
353
280
280
280
Residential
749
813
439
446
422
422
420
418
390
363
335
329
324
318
314
310
306
306
306
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oil"
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
- NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
42
44/
21
22
21
21
21
20
19
18
16
16
16
16
15
15
15
15
15
Other Fuels3
707
769
417
424
402
401
400
398
371
345
318
313
308
302
298
295
291
291
291
Total
10,023
9,862
8,053
7,623
6,825
6,401
5,973
5,858
5,511
5,163
4,780
4,452
4,092
3,807
3,655
3,504
3,291
3,061
2,692
NA (Not Applicable)
a Other Fuels include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2016b).
b Residential coal, fuel oil, and natural gas emissions are included in the Other Fuels category (EPA 2016b).
Note: Totals may not sum due to independent rounding.
Table fl-92: CO Emissions from Stationary Combustion tktl
Sector/Fuel Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Electric Power
329
337
439
439
594
591
586
582
609
637
660
676
693
710
690
669
649
649
649
Coal
213
227
221
220
298
296
294
292
305
319
330
339
347
356
346
335
325
325
325
Fuel Oil
18
9
27
28
38
37
37
37
38
40
42
43
44
45
44
42
41
41
41
Natural gas
46
49
96
92
125
124
123
122
128
134
138
142
145
149
145
140
136
136
136
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
NA
NA
31
32
44
43
43
43
45
47
48
50
51
52
51
49
48
48
48
Internal Combustion
52
52
63
67
91
90
90
89
93
97
101
103
106
108
105
102
99
99
99
Industrial
797
958
1,106
1,137
1,150
1,116
1,081
1,045
968
892
815
834
853
872
871
869
868
868
868
A-137
-------
Coal
95
88
118
125
127
123
119
115
107
98
90
92
94
96
96
96
96
96
96
Fuel Oil
67
64
48
45
46
44
43
42
39
35
32
33
34
35
35
35
35
35
35
Natural gas
205
313
355
366
370
359
348
336
312
287
262
268
274
281
280
280
279
279
279
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
253
270
300
321
325
316
306
295
274
252
230
236
241
247
246
246
245
245
245
Internal Combustion
177
222
285
279
282
274
266
257
238
219
200
205
209
214
214
213
213
213
213
Commercial
205
211
151
154
177
173
169
166
156
146
137
138
140
142
135
129
122
122
122
Coal
13
14
14
13
15
15
15
14
14
13
12
12
12
12
12
11
11
11
11
Fuel Oil
16
17
17
17
20
19
19
19
18
16
15
16
16
16
15
14
14
14
14
Natural gas
40
49
83
84
97
95
93
91
86
80
75
76
77
78
74
71
67
67
67
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
136
132
36
38
44
43
42
41
39
37
34
35
35
35
34
32
30
30
30
Residential
3,668
3,877
2,644
2,648
3,044
2,982
2,919
2,856
2,690
2,524
2,357
2,387
2,416
2,446
2,331
2,217
2,103
2,103
2,103
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oil"
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
3,430
3,629
2,416
2,424
2,787
2,730
2,673
2,615
2,463
2,310
2,158
2,185
2,212
2,239
2,134
2,030
1,925
1,925
1,925
Other Fuels3
238
248
228
224
257
252
247
241
227
213
199
202
204
207
197
187
178
178
178
Total
5,000
5,383
4,340
4,377
4,965
4,862
4,756
4,648
4,423
4,198
3,969
4,036
4,103
4,170
4,027
3,884
3,741
3,741
3,741
NA (Not Applicable)
a Other Fuels include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2016b).
b Residential coal, fuel oil, and natural gas emissions are included in the Other Fuels category (EPA 2016b).
Note: Totals may not sum due to independent rounding.
Table fl-93: NMVOC Emissions from Stationary Combustion tktl
Sector/Fuel Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Electric Power
43
40
56
55
45
45
44
44
42
41
40
39
38
37
36
35
34
34
34
Coal
24
26
27
26
21
21
21
21
20
20
19
18
18
18
17
17
16
16
16
Fuel Oil
5
2
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
Natural Gas
2
2
12
12
10
10
10
10
9
9
9
9
8
8
8
8
8
8
8
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
NA
NA
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Internal Combustion
11
9
11
10
9
9
8
8
8
8
8
7
7
7
7
7
6
6
6
Industrial
165
187
157
159
138
132
126
120
113
105
97
99
100
101
101
100
100
100
100
Coal
7
5
9
10
9
9
8
8
7
7
6
6
7
7
7
7
7
7
7
Fuel Oil
11
11
9
9
7
7
7
6
6
6
5
5
5
5
5
5
5
5
5
Natural Gas
52
66
53
54
47
45
43
41
38
36
33
33
34
34
34
34
34
34
34
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
46
45
27
29
25
24
23
22
21
19
18
18
18
19
19
19
18
18
18
Internal Combustion
49
60
58
57
49
47
45
43
40
37
35
35
36
36
36
36
36
36
36
Commercial
18
21
28
29
61
54
48
33
34
35
36
38
40
42
40
39
35
35
35
Coal
1
1
1
1
1
1
1
1
1
+
+
+
+
+
+
+
+
+
+
Fuel Oil
3
3
4
4
6
5
3
2
2
2
2
2
2
2
2
2
1
1
1
Natural Gas
7
10
14
14
23
18
14
9
8
7
6
7
7
7
7
6
6
6
6
A-138 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
8
8
9
10
31
30
30
22
24
26
28
29
31
32
31
30
27
27
27
Residential
686
725
837
836
1,341
1,067
793
518
465
411
358
378
399
419
392
365
338
338
338
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oil"
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
651
688
809
809
1,297
1,032
767
502
450
398
346
366
386
406
380
353
327
327
327
Other Fuels3
35
37
27
27
43
35
26
17
15
13
12
12
13
14
13
12
11
11
11
Total
912
973
1,077
1,080
1,585
1,298
1,011
716
654
593
531
553
576
599
569
539
507
507
507
+ Does not exceed 0.5 kt.
NA (Not Applicable)
a "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2016b).
b Residential coal, fuel oil, and natural gas emissions are included in the "Other Fuels" category (EPA 2016b).
Note: Totals may not sum due to independent rounding.
A-139
-------
References
EIA (2018) Monthly Energy Review, February 2018, Energy Information Administration, U.S. Department of Energy,
Washington, DC. DOE/EIA-0035(2018/02).
EIA (2017) International Energy Statistics 1980-2016. Energy Information Administration, U.S. Department of Energy.
Washington, D.C. Available online at: .
EPA (2017) Motor Vehicle Emissions Simulator (Moves) 2014a. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at: .
EPA (2016a) Acid Rain Program Dataset 1996 - 2016. Office of Air and Radiation, Office of Atmospheric Programs, U.S.
Environmental Protection Agency, Washington, D.C.
EPA (2016b) "1970 - 2015 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards, October 2014. 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.
FHWA (1996 through 2016) 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, DC.
A-140 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
3.2. Methodology for Estimating Emissions of CH4, N2O, and Indirect Greenhouse
Gases from Mobile Combustion and Methodology for and Supplemental
Information on Transportation-Related GHG Emissions
Estimating CO2 Emissions by Transportation Mode
Transportation-related CO2 emissions, as presented in the CO2 Emissions from Fossil Fuel Combustion section of
the Energy chapter, were calculated using the methodology described in Annex 2.1. This section provides additional
information on the data sources and approach used for each transportation fuel type. As noted in Annex 2.1, CO2 emissions
estimates for the transportation sector were calculated directly for on-road diesel fuel and motor gasoline based on data
sources for individual modes of transportation (considered a bottom up approach). For most other fuel and energy types
(aviation gasoline, residual fuel oil, natural gas, LPG, and electricity), CO2 emissions were calculated based on transportation
sector-wide fuel consumption estimates from the Energy Information Administration (EIA 2017a and EIA 2016d) 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 2018), while CO2 emissions from other aircraft j et
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 2017),41 and are based on federal and state
fuel tax records. These fuel consumption estimates were then combined with estimates of fuel shares by vehicle type from
DOE's Transportation Energy Data Book Annex Tables A. 1 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 for the on-road and non-road fuel consumption estimate, as described above, is
considered to be the most representative of the transportation sector's share of the EIA total consumption. Therefore, for
years in which there was a disparity between EIA'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 MOVES2014a model (EPA 2017b), 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 EIA's total
diesel estimate for all sectors, the diesel consumption totals for the residential, commercial, and industrial sectors were
adjusted proportionately.
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 2016 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-141
-------
Estimates of diesel fuel consumption from rail were taken from the Association of American Railroads (AAR 2008
through 2017) 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) for Class II and III railroads, and U.S. Department of Energy's Transportation Energy
Data Book (DOE 1993 through 2017) for passenger rail. Estimates of diesel from ships and boats were taken from EIA's
Fuel Oil and Kerosene Sales (1991 through 2017).
As noted above, for fuels other than motor gasoline and diesel, EIA's transportation sector total was apportioned
to specific transportation sources. For jet fuel, estimates come from: FAA (2018) for domestic and international commercial
aircraft, and DLA Energy (2017) for domestic and international military aircraft. General aviation jet fuel consumption is
calculated as the difference between total jet fuel consumption as reported by EIA and the total consumption from
commercial and military jet fuel consumption. Commercial jet fuel CO2 estimates are obtained directly from the Federal
Aviation Administration (FAA 2018), while CO2 emissions from domestic military and general aviation jet fuel consumption
is determined using a top down approach. Domestic commercial jet fuel CO2 from FAA is subtracted from total domestic
jet fuel CO2 emissions, and this remaining value is apportioned among domestic military and domestic general aviation
based on their relative proportion of energy consumption. Estimates for biofuels, including ethanol and biodiesel, were
discussed separately in Section 3.2 Carbon Emitted from Non-Energy Uses of Fossil Fuels under the methodology for
Estimating CO2 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 2018a).
Table A-94 displays estimated fuel consumption by fuel and vehicle type. Table A-95 displays estimated energy
consumption by fuel and vehicle type. The values in both of these tables correspond to the figures used to calculate CO2
emissions from transportation. Except as noted above, they are estimated based on EIA transportation sector energy estimates
by fuel type, with activity data used to apportion consumption to the various modes of transport. 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 EIA'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 2017f) 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 2018a) 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-96.42
42 Note that the refinery and blender net volume inputs of renewable diesel fuel sourced from EIA's Petroleum Supply Annual (PSA) differs from
the biodiesel volume presented in Table A-96. 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-94 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%, f 00%) blends
of biodiesel. This value is sourced from MER Table f 0.4 and is calculated as biodiesel production plus biodiesel net imports minus biodiesel stock
exchange.
A-142 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-94: Fuel Consumption hy Fuel and Vehicle Type [million gallons unless otherwise specified]
Fuel/Vehicle Type
1990
1995
2000
2006
2007a
2008
2009
2010
2011
2012
2013
2014
2015
2016
Motor Gasolinebc
107,477
114,277
125,385
127,760
127,130
121,360
120,441
119,372
116,631
116,105
116,145
120,861
120,685
123,603
Passenger Cars
67,879
65,702
70,468
68,842
86,114
82,186
81,357
80,632
79,941
79,735
79,693
81,785
82,447
83,948
Light-Duty Trucks
33,762
42,903
49,107
53,289
33,950
32,087
32,452
32,224
30,587
30,235
30,254
32,722
31,926
33,202
Motorcycles
189
194
203
204
459
472
453
398
388
444
423
422
412
430
Buses
38
40
42
40
77
79
81
79
77
89
92
101
102
102
Medium- and Heavy-Duty Trucks
4,232
3,937
3,961
3,851
5,018
5,064
4,652
4,624
4,241
4,214
4,305
4,456
4,428
4,554
Recreational Boatsd
1,377
1,501
1,604
1,535
1,513
1,471
1,446
1,414
1,398
1,388
1,379
1,375
1,370
1,367
Distillate Fuel Oil (Diesel Fuel)b c
25,631
31,604
39,241
45,844
46,427
44,026
39,873
41,477
42,280
42,045
42,672
43,900
45,231
46,695
Passenger Cars
771
765
356
403
403
363
354
367
399
401
399
406
422
427
Light-Duty Trucks
1,119
1,452
1,961
2,611
1,327
1,184
1,180
1,227
1,277
1,271
1,265
1,360
1,368
1,412
Buses
781
851
997
1,034
1,520
1,436
1,335
1,326
1,419
1,515
1,525
1,653
1,681
1,670
Medium- and Heavy-Duty Trucks
18,574
23,240
30,179
36,089
37,517
35,726
32,364
33,683
33,859
33,877
34,426
35,418
36,281
37,031
Recreational Boats
194
232
270
319
327
335
343
351
357
364
368
375
383
1,565
Ships and Non-Recreational Boats
732
1,200
1,372
724
794
767
768
726
993
733
741
605
1,181
977
Raile
3,461
3,863
4,106
4,664
4,538
4,215
3,529
3,798
3,975
3,884
3,948
4,083
3,915
3,615
Jet Fuel'
19,186
17,991
20,002
18,695
18,407
17,749
15,809
15,537
15,036
14,705
15,088
15,217
16,162
17,028
Commercial Aircraft
11,569
12,136
14,672
14,426
14,708
13,400
12,588
11,931
12,067
11,932
12,031
12,131
12,534
12,674
General Aviation Aircraft
4,034
3,361
3,163
2,590
2,043
2,682
1,787
2,322
1,895
1,659
2,033
1,786
2,361
3,184
Military Aircraft
3,583
2,495
2,167
1,679
1,656
1,667
1,434
1,283
1,074
1,114
1,024
1,300
1,267
1,170
Aviation Gasoline'
374
329
302
278
263
235
221
225
225
209
186
181
176
170
General Aviation Aircraft
374
329
302
278
263
235
221
225
225
209
186
181
176
170
Residual Fuel Oil' s
2,006
2,587
2,963
2,046
2,579
1,812
1,241
1,818
1,723
1,410
1,345
517
378
1,152
Ships and Boats
2,006
2,587
2,963
2,046
2,579
1,812
1,241
1,818
1,723
1,410
1,345
517
378
1,152
Natural Gas' (trillion cubic feet)
0.7
0.7
0.7
0.6
0.6
0.7
0.7
0.7
0.7
0.8
0.9
0.7
0.7
0.7
Passenger Cars
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Light-Duty Trucks
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Medium- and Heavy-Duty Trucks
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Buses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Pipelines
0.7
0.7
0.7
0.6
0.6
0.7
0.7
0.7
0.7
0.7
0.8
0.7
0.7
0.7
LPG'
259
200
135
317
253
463
328
344
403
438
522
555
466
475
Passenger Cars
1
0.9
0.6
3
3
5
5
2
1
1
2
10
48
84
Light-Duty Trucks
35
27
18
81
60
84
82
81
77
44
58
119
68
45
Medium- and Heavy-Duty Trucks
206
159
107
193
148
276
185
203
278
339
393
362
300
299
Buses
17
13
9
40
42
97
55
58
47
54
69
65
51
46
Electricity''h
4,751
4,975
5,382
7,358
8,173
7,653
7,768
7,712
7,672
7,320
7,625
7,758
7,637
7,497
Rail
4,751
4,975
5,382
7,358
8,173
7,653
7,768
7,712
7,672
7,320
7,625
7,758
7,637
7,497
+ Does not exceed 0.05 trillion cubic feet
aln 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2016 time period. These methodological changes include howon-road vehicles are classified, moving from a
system based on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class between 2006 and 2007.
b Figures do not include ethanol blended in motor gasoline or biodiesel blended into distillate fuel oil. Net carbon fluxes associated with ethanol are accounted for in the Land Use, Land-Use Change and Forestry
chapter. This table is calculated with the heat content for gasoline without ethanol (from Table A.2 in the EIA Annual Energy Review) rather than the annually variable quantity-weighted heat content for
gasoline with ethanol, which varies by year.
A-143
-------
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). Data from Table VM-1 is used
to estimate the share of consumption between each on-road vehicle class. These fuel consumption estimates are combined with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables A. 1
through A.6 (DOE 1993 through 2017). TEDB data for 2016 has not been published yet, therefore 2015 data are used as a proxy.
d Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
e Class II and Class III diesel consumption data for 2014 to 2016 is not available, therefore 2013 data are used as a proxy.
f Estimated based on EIA transportation sector energy estimates by fuel type, with bottom-up activity data used for apportionment to modes. Transportation sector natural gas and LPG consumption are based
on data from EIA (2017a). In previous Inventory years, data from DOE TEDB was used to estimate each vehicle class's share of the total natural gas and LPG consumption. Since TEDB does not include
estimates for natural gas use by medium and heavy duty trucks or LPG use by passenger cars, EIA Alternative Fuel Vehicle Data (Browning 2017) is now used to determine each vehicle class's share of the
total natural gas and LPG consumption. These changes were first incorporated in this year's Inventory and apply to the 1990 through 2016 time period.
9 Fluctuations in reported fuel consumption may reflect data collection problems.
h Million kilowatt-hours
Table fl-95: Energy Consumption by Fuel and Vehicle Type IThtul
Fuel/Vehicle Type
1990
1995
2000
2006
2007a
2008
2009
2010
2011
2012
2013
2014
2015
2016
Motor Gasolinebc
13,442
14,293
15,682
15,979
15,807
15,089
14,975
14,842
14,501
14,436
14,441
15,027
15,005
15,368
Passenger Cars
8,490
8,218
8,814
8,610
10,707
10,218
10,115
10,025
9,939
9,914
9,909
10,169
10,251
10,438
Light-Duty Trucks
4,223
5,366
6,142
6,665
4,221
3,989
4,035
4,007
3,803
3,759
3,762
4,068
3,969
4,128
Motorcycles
24
24
25
25
57
59
56
50
48
55
53
52
51
53
Buses
5
5
5
5
10
10
10
10
10
11
11
13
13
13
Medium- and Heavy-Duty
529
492
495
482
624
630
578
575
527
524
535
554
551
566
Trucks
Recreational Boatsd
172
188
201
192
188
183
180
176
174
173
171
171
170
170
Distillate Fuel Oil (Diesel
3,555
4,379
5,437
6,334
6,394
6,059
5,488
5,706
5,814
5,780
5,866
6,034
6,217
6,257
Fuel)c
Passenger Cars
107
106
49
56
55
50
49
51
55
55
55
56
58
59
Light-Duty Trucks
155
201
272
361
183
163
162
169
176
175
174
187
188
194
Buses
108
118
138
143
209
198
184
182
195
208
210
227
231
230
Medium- and Heavy-Duty
2,576
3,220
4,181
4,986
5,167
4,917
4,455
4,634
4,656
4,657
4,733
4,868
4,987
5,090
Trucks
Recreational Boats
27
32
37
44
45
46
47
48
49
50
51
52
53
54
Ships and Non-Recreational
102
166
190
100
109
106
106
100
137
101
102
83
162
134
Boats
Raile
480
535
569
644
625
580
486
523
547
534
543
561
538
497
Jet Fuel'
2,590
2,429
2,700
2,524
2,485
2,396
2,134
2,097
2,030
1,985
2,037
2,054
2,182
2,299
Commercial Aircraft
1,562
1,638
1,981
1,948
1,986
1,809
1,699
1,611
1,629
1,611
1,624
1,638
1,692
1,711
General Aviation Aircraft
545
454
427
350
276
362
241
314
256
224
274
241
319
430
Military Aircraft
484
337
293
227
224
225
194
173
145
150
138
175
171
158
Aviation Gasoline'
45
40
36
33
32
28
27
27
27
25
22
22
21
20
General Aviation Aircraft
45
40
36
33
32
28
27
27
27
25
22
22
21
20
Residual Fuel Oil' s
300
387
443
306
386
271
186
272
258
211
201
77
57
172
Ships and Boats
300
387
443
306
386
271
186
272
258
211
201
77
57
172
Natural Gas'
680
724
672
625
663
692
715
719
734
780
887
760
745
767
Passenger Cars
+
+
0.1
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Light-Duty Trucks
+
+
0.4
0.6
0.5
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
A-144 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Medium- and Heavy-Duty
+
+
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.5
0.6
0.7
Trucks
Buses
+
+
3
13
14
14
15
15
15
15
15
15
17
18
Pipelines
680
724
668
611
649
677
699
703
718
765
872
744
727
747
LPGf
23
18
12
27
22
40
28
29
34
37
44
47
40
40
Passenger Cars
0.1
0.1
0.1
0.2
0.2
0.5
0.4
0.2
0.1
0.1
0.2
0.8
4
7
Light-Duty Trucks
3
2
2
7
5
7
7
7
7
4
5
10
6
4
Medium- and Heavy-Duty
18
14
9
17
13
24
16
17
23
29
34
31
26
25
Trucks
Buses
1
1
0.8
3
4
8
5
5
4
5
6
5
4
4
Electricity'
3
3
3
5
5
5
4
4
4
4
4
4
4
3
Rail
3
3
3
5
5
5
4
4
4
4
4
4
4
3
Total
20,638
22,273
24,986
25,834
25,793
24,580
23,557
23,698
23,402
23,258 23,503
24,025
24,270
24,927
+ Does not exceed 0.05 tBtu
aln 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2016 time period. These methodological changes include howon-road vehicles are classified, moving from a system
based on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class between 2006 and 2007.
b Figures do not include ethanol blended in motor gasoline or biodiesel blended into distillate fuel oil. Net carbon fluxes associated with ethanol are accounted for in the Land Use, Land-Use Change and Forestry
chapter.
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). Data from Table VM-1 is used to
estimate the share of consumption between each on-road vehicle class. These fuel consumption estimates are combined with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables A.1 through
A.6 (DOE 1993 through 2017). TEDB data for 2016 has not been published yet, therefore 2015 data are used as a proxy.
d Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
eClass II and Class II diesel consumption data for 2014-2016 is not available, therefore 2013 data are used as a proxy.
f Estimated based on EIA transportation sector energy estimates, with bottom-up data used for apportionment to modes. Transportation sector natural gas and LPG consumption are based on data from EIA (2017a).
In previous Inventory years, data from DOE TEDB was used to estimate each vehicle class's share of the total natural gas and LPG consumption. Since TEDB does not include estimates for natural gas use by
medium and heavy duty trucks or LPG use by passenger cars, EIA Alternative Fuel Vehicle Data (Browning 2017) is now used to determine each vehicle class's share of the total natural gas and LPG consumption.
These changes were first incorporated in this year's Inventory and apply to the 1990-2016 time period.
a Fluctuations in reported fuel consumption may reflect data collection problems. Residual fuel oil for ships and boats data is based on ElA's February 2018 Monthly Energy Review data.
Table fl-96: Transportation Sector Biofuel Consumption by Fuel Type [million gallons]
Fuel Type
1990
1995
2000 -
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Ethanol
712
1,326
1,590
1 5'207
6,563
9,263
10,537
12,282
12,329
12,324
12,646
12,908
13,102
13,493
Biodiesel
NA
NA
i NA
261
354
304
322
260
886
899
1,429
1,417
1,494
2,085
NA (Not Available)
Note: According to the MER, there was no biodiesel consumption prior to 2001.
A-145
-------
Estimates of CH4 and N2O Emissions
Mobile source emissions of greenhouse gases other than CO2 are reported by transport mode (e.g., road, rail,
aviation, and waterborne), vehicle type, and fuel type. Emissions estimates of CH4 and N2O were derived using a
methodology similar to that outlined in the 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 2017).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 2017) 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-97 and Table A-98,
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 2016 in Table A-99. 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 MOVES2014a model for years 2009 forward (EPA 2017b).46 Age-specific vehicle mileage
accumulations were also obtained from EPA's MOVES2014a model (EPA 2017b).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-105 through Table A-108. The categories "EPA Tier 0" and "EPA Tier 1" were used instead of the early three-way catalyst
and advanced three-way catalyst categories, respectively, as defined in the Revised 1996 IPCC Guidelines. EPA Tier 0, EPA
Tier 1, EPA Tier 2, and EPA Tier 3 refer to U.S. emission regulations and California Air Resources Board (CARB) LEV,
CARB LEVII, and CARB LEVII refer to California emissions regulations, rather than control technologies; however, each
does correspond to particular combinations of control technologies and engine design. EPA Tier 2 and Tier 3 and its
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 2016 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 (2017b) 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 MOVES2014a model resulted in a decrease in
emissions due to more miles driven by newer light-duty gasoline vehicles.
A-146 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
predecessors EPA Tier 1 and Tier 0 as well as CARB LEV, LEVII, and LEVIII apply to vehicles equipped with three-way
catalysts. The introduction of "early three-way catalysts," and "advanced three-way catalysts," as described in the Revised
1996IPCC Guidelines, roughly correspond to the introduction of EPA Tier 0 and EPA Tier 1 regulations (EPA 1998b).48
EPA Tier 2 regulations affect vehicles produced starting in 2004 and are responsible for a noticeable decrease in N2O
emissions compared EPA Tier 1 emissions technology (EPA 1999b). EPA Tier 3 regulations affect vehicles produced
starting in 2015 and are fully phased in by 2025. ARB LEVII regulations affect California vehicles produced starting in
2004 while ARB LEVIII 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 (1998b). Assignments for 1998 through 2016 were
determined using confidential engine family sales data submitted to EPA (EPA 2017d). Vehicle classes and emission
standard tiers to which each engine family was certified were taken from annual certification test results and data (EPA
2017c). 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, and CARB LEV, CARB LEVII and EPA Tier 3/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.
Step 3: Determine CH4 and N2O Emission Factors by Vehicle, Fuel, and Control Technology Type
Emission factors for gasoline and diesel on-road vehicles utilizing EPA Tier 2, EPA Tier 3, and CARB LEV,
LEVII, and LEVIII technologies were developed by ICF (2017a). These new emission factors were calculated for N2O based
upon a regression analysis done by EPA and for CH4 based on the ratio of NMOG emission standards. 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 N2O Emitted by Vehicle, Fuel, and Control Technology Type
Emissions of CH4 and N2O were then calculated by multiplying total VMT by vehicle, fuel, and control technology
type by the emission factors developed in Step 3.
Methodology for Alternative Fuel Vehicles (AFVs)
Step 1: Determine Vehicle Miles Traveled by Vehicle and Fuel Type
VMT for alternative fuel and advanced technology vehicles were calculated from "Updated Methodology for
Estimating CH4 and N2O 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
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
48 For further 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. Fleavy-duty vehicles are more
likely to run on diesel fuel, natural gas, or LPG.
A-147
-------
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 2016, 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 (PFlEVs) were
taken from data compiled by the Electric Drive Transportation Association from 2011 to 2016 (EDTA 2017). EVs were
divided into cars and trucks using confidential engine family sales data submitted to EPA (EPA 2017d). Fuel use per vehicle
for personal EVs and PFlEVs 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
GREET2016 model (ANL 2016). 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-99, while more detailed estimates of VMT by control technology are shown in Table
A-100.
Step 2: Determine ChU and N2O Emission Factors by Vehicle and Alternative Fuel Type
Methane and N2O emission factors for alternative fuel vehicles (AFVs) are calculated using Argonne National
Laboratory's GREET model (ANL 2016) and are reported in Browning (2017). These emission factors are shown in Table
A-l 10 and Table A-lll.
Step 3: Determine the Amount of CH4 and N2O Emitted by Vehicle and Fuel Type
Emissions of CH4 and N2O were calculated by multiplying total VMT for each vehicle and fuel type (Step 1) by
the appropriate emission factors (Step 2).
Methodology for Non-Road Mobile Sources
Methane and N2O emissions from non-road mobile sources were estimated by applying emission factors to the
amount of fuel consumed by mode and vehicle type.
Activity data for non-road vehicles include annual fuel consumption statistics by transportation mode and fuel
type, as shown in Table A-104. Consumption data for ships and boats (i.e., vessel bunkering) were obtained from DF1S
(2008) and EIA (1991 through 2017) for distillate fuel, and DHS (2008) and EIA (2017a) for residual fuel; marine transport
fuel consumption data for U.S. Territories (EIA 2015) 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 MOVES2014a model (EPA 2017b). Annual diesel consumption for Class I rail was
obtained from the Association of American Railroads (AAR 2008 through 2017), 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 Diesel consumption by commuter and intercity rail
was obtained from DOE (1993 through 2016). Data on the consumption of jet fuel and aviation gasoline in aircraft were
obtained from EIA (2017a) and FAA (2017), as described in Annex 2.1: Methodology for Estimating Emissions of CO2
from Fossil Fuel Combustion, and were reduced by the amount allocated to international bunker fuels (DLA 2017 and FAA
2018). Pipeline fuel consumption was obtained from EIA (2007 through 2016) (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 MOVES2014a model (EPA 2017b) for gasoline and diesel powered equipment, and
from FHWA (1996 through 2017) for gasoline consumption by off-road trucks used in the agriculture, industrial,
50 See International Bunker Fuels section of the Energy chapter.
51 Diesel consumption from Class II and Class III railroad were unavailable for 2014-2016. Values are proxied from 2013, which is the last year
the data was available.
A-148 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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commercial, and construction sectors.52 Specifically, 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 N2O 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 N2O from non-road mobile sources were calculated using the updated 2006 IPCC Tier 3
guidance and EPA's MOVES2014a model. CH4 emission factors were calculated directly from MOVES. N2O 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-l 12 and Table A-113).
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-l 14 through Table A-l 16
provides complete emission estimates for 1990 through 2016.
Table A-97: Vehicle Miles Traveled for Gasoline On-Road Vehicles (billion miles)
Passenger
Light-Duty
Heavy-Duty
Year
Cars
Trucks
Vehicles'1
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
821.6
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
905.9
23.9
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
2007a
2,093.7
562.8
34.2
21.4
2008
2,014.4
580.9
35.0
20.8
2009
2,005.4
592.5
32.5
20.8
2010
2,015.3
597.4
32.3
18.5
2011
2,035.6
579.6
30.2
18.5
2012
2,051.7
576.8
30.5
21.4
2013
2,062.2
578.7
31.2
20.4
2014
2,058.6
612.4
31.7
20.0
2015
2,133.0
606.1
31.8
19.6
2016
2,175.1
630.7
32.7
20.4
a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2016 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.
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-149
-------
b Heavy-Duty Vehicles includes Medium-DutyTrucks, Heavy-Duty Trucks, and Buses.
Note: 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 2016 time period. This resulted in large reductions in AFV VMT, thus
leading to a shift in VMT to conventional on-road vehicle classes.
Note: Gasoline and diesel highway vehicle mileage are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2017). 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 2016 has not been published yet, therefore 2015 data are used as a proxy
Source: Derived from FHWA (1996 through 2017), DOE (1990 through 2017), and Browning (2017).
Table fl-98: Vehicle Miles Traveled for Diesel On-Road Vehicles (billion miles)
Passenger
Light-Duty
Heavy-Duty
Year
Cars
Trucks
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
140.6
1994
18.3
25.3
150.9
1995
17.3
26.9
159.1
1996
14.7
27.8
164.6
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
199.6
2004
8.5
41.4
202.1
2005
8.5
41.9
203.7
2006
8.4
43.4
203.2
2007"
10.5
23.3
282.8
2008
10.1
24.1
288.3
2009
10.0
24.6
267.5
2010
10.1
24.8
265.7
2011
10.1
23.3
247.8
2012
10.1
23.1
250.3
2013
10.1
22.5
252.5
2014
10.0
23.9
256.9
2015
10.3
23.5
255.5
2016
10.4
23.8
261.7
a Heavy-Duty Vehicles includes Medium-DutyTrucks, Heavy-Duty Trucks, and Buses.
b In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2016 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: 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 2014 Inventory and apply to the 1990 to 2016 time period. This resulted in large reductions in AFV VMT, thus leading to a shift in
VMT to conventional on-road vehicle classes.
Note: Gasoline and diesel highway vehicle mileage are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2017). 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 2016 has not been published yet, therefore 2015 data are used as a proxy
Source: Derived from FHWA (1996 through 2017), DOE (1993 through 2017), and Browning (2017).
A-150 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-99: 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
0.4
1993
0.0
0.1
0.5
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
0.5
1999
0.1
0.1
0.4
2000
0.1
0.2
0.5
2001
0.1
0.2
0.6
2002
0.1
0.3
0.8
2003
0.2
0.3
0.8
2004
0.2
0.3
0.9
2005
0.2
0.3
1.0
2006
0.2
0.5
1.3
2007
0.3
0.6
1.7
2008
0.3
0.5
2.2
2009
0.3
0.5
2.6
2010
0.3
0.5
2.3
2011
0.6
1.2
3.4
2012
1.0
1.4
3.2
2013
2.1
2.1
6.5
2014
3.5
2.1
6.5
2015
4.5
2.2
8.8
2016
6.3
3.4
9.9
a Heavy Duty-Vehicles includes medium-duty trucks, heavy-duty trucks, and buses.
Note: 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 2014 Inventory and apply to the 1990 to 2016 time period. This resulted in large reductions in AFV VMT, thus leading to a shift in
VMT to conventional on-road vehicle classes. In 2016, 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 first incorporated in the current Inventory and apply to the 2005 to
2016 time period.
Source: Derived from Browning (2017), EIA (2017e), and EDTA (2017).
A-151
-------
Table fl-100: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (106 Miles)
Vehicle Type/Year
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Light-Duty Cars
4.0
56.0
78.1
230.5
252.3
260.5
295.9
344.6
559.7
998.1
2,120.9
3,491.1
4,484.0
6,267.5
Methanol-Flex Fuel ICE
+
48.9
15.2
+
+
+
+
+
+
+
+
+
+
+
Ethanol-Flex Fuel ICE
+
0.3
20.9
59.2
72.8
84.2
96.2
122.2
118.5
148.9
173.5
135.4
117.7
82.0
CNG ICE
+
0.1
5.5
14.5
14.1
12.5
11.5
10.8
11.5
11.9
12.9
12.4
12.5
11.8
CNG Bi-fuel
+
0.2
18.0
25.3
19.1
12.8
10.0
7.9
7.0
4.4
3.4
2.5
1.8
1.3
LPG ICE
1.1
1.2
1.2
0.2
1.6
1.7
1.7
+
0.2
0.2
0.4
3.5
17.0
28.8
LPG Bi-fuel
2.8
3.0
3.0
3.8
1.7
1.6
1.8
1.2
0.3
0.3
0.2
0.1
0.1
0.1
Biodiesel (BD100)
+
+
1.0
41.4
50.2
39.1
46.4
39.4
149.5
180.7
311.4
334.8
374.9
563.7
NEVs
+
2.0
11.9
81.7
82.8
87.7
83.7
68.5
97.1
83.5
72.9
63.9
45.4
28.5
Electric Vehicle
+
0.2
1.5
4.5
9.7
20.7
44.1
94.3
169.0
531.3
1,474.8
2,820.7
3,703.8
5,269.1
SI PHEV - Electricity
+
+
+
+
+
+
+
+
6.4
36.8
71.3
117.7
210.8
282.1
Fuel Cell Hydrogen
+
+
+
+
0.3
0.2
0.5
0.2
0.1
0.1
0.1
0.1
0.1
0.1
Light-Duty Trucks
77.3
93.2
180.9
491.3
555.3
458.1
510.6
462.8
1,234.3
1,366.3
2,099.4
2,142.1
2,245.9
3,442.7
Ethanol-Flex Fuel ICE
+
0.3
23.4
62.8
77.0
89.6
102.7
130.9
144.1
191.8
227.2
222.3
232.2
229.0
CNG ICE
+
0.1
5.6
15.0
13.2
10.2
9.7
8.5
9.1
9.4
9.2
8.1
7.0
4.8
CNG Bi-fuel
+
0.4
47.2
68.6
60.9
26.0
21.7
20.3
19.4
15.7
17.1
20.6
21.7
27.2
LPG ICE
22.4
26.5
27.6
28.6
22.8
11.2
12.9
10.3
10.2
6.3
6.7
7.8
8.0
7.1
LPG Bi-fuel
55.0
65.1
67.7
55.0
32.2
25.1
29.2
25.3
13.2
5.2
6.3
23.2
9.3
4.2
LNG
+
+
0.1
0.2
0.2
0.3
0.2
+
+
+
+
+
+
+
Biodiesel (BD100)
+
+
4.1
253.9
341.1
287.9
326.6
260.2
1,033.2
1,133.8
1,815.1
1,825.0
1,934.7
2,679.5
Electric Vehicle
+
0.8
5.3
7.1
7.9
7.7
7.5
7.2
4.8
3.8
17.4
35.0
32.7
459.9
SI PHEV - Electricity
+
+
+
+
+
+
+
+
+
+
+
+
+
30.7
Fuel Cell Hydrogen
+
+
+
+
0.1
0.1
0.2
0.1
0.3
0.2
0.2
0.3
0.3
0.3
Medium Duty Trucks
273.4
267.5
260.6
523.9
626.9
567.6
597.4
448.3
1,406.3
1,466.0
2,325.4
2,351.4
2,501.6
3,530.2
CNG ICE
+
+
0.9
2.3
4.9
6.8
6.1
5.9
8.1
9.5
10.0
11.2
12.6
13.1
CNG Bi-fuel
+
0.1
8.3
10.6
9.6
8.4
7.0
6.7
6.5
7.3
7.6
10.2
11.0
13.7
LPG ICE
230.7
225.6
206.0
69.8
52.1
39.5
35.3
31.1
29.0
27.4
25.2
24.4
19.3
17.9
LPG Bi-fuel
42.7
41.7
38.1
19.2
8.4
13.5
6.8
8.4
7.5
10.0
10.7
13.6
10.2
9.8
LNG
+
+
+
+
+
+
+
+
+
+
0.1
+
0.1
0.2
Biodiesel (BD100)
+
+
7.3
422.0
552.0
499.4
542.3
396.2
1,355.2
1,411.8
2,271.9
2,291.8
2,448.4
3,475.5
Heavy-Duty Trucks
108.3
105.9
117.4
174.3
407.1
1,016.1
1,364.7
1,159.7
1,215.9
1,009.9
3,353.2
3,382.6
5,390.7
5,388.1
Neat Ethanol ICE
+
+
+
1.8
2.2
2.6
3.0
3.7
5.9
9.4
13.0
15.6
21.0
25.5
CNG ICE
+
+
0.9
2.7
2.9
2.7
3.4
3.6
3.6
4.1
5.0
5.5
7.7
9.4
LPG ICE
101.7
99.5
90.9
63.8
54.8
46.8
41.4
34.1
35.9
23.3
23.0
18.6
17.5
14.9
LPG Bi-fuel
6.5
6.4
5.8
3.8
3.7
3.7
4.3
4.5
6.6
5.1
5.4
2.3
2.2
2.0
LNG
+
+
+
0.9
0.9
1.2
1.3
1.5
+
+
+
+
+
+
Biodiesel (BD100)
+
+
19.7
101.2
342.6
959.1
1,311.4
1,112.3
1,164.0
968.0
3,306.7
3,340.6
5,342.5
5,336.3
Buses
20.6
39.8
146.9
624.7
623.3
654.5
684.5
695.4
745.6
720.2
778.9
792.0
925.9
986.4
Neat Methanol ICE
6.5
10.6
+
+
+
+
+
+
+
+
+
+
+
+
Neat Ethanol ICE
+
4.9
0.1
+
+
+
+
+
+
0.1
0.1
2.7
3.7
4.0
CNG ICE
+
1.1
104.1
481.7
509.8
546.2
581.7
605.4
637.1
628.3
650.1
650.3
731.0
792.1
LPG ICE
13.6
13.2
12.0
11.0
10.2
11.1
7.5
6.7
4.0
3.9
4.1
4.5
3.3
2.8
LNG
0.4
8.9 /
23.2
66.8
40.2
39.8
36.0
36.8
39.5
41.1
29.4
38.2
37.6
37.4
A-152 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Biodiesel (BD100)
+
+
0.8
38.9
53.3
46.6
51.7
38.1
56.2
41.3
90.0
90.9
144.4
143.7
Electric
+
1.1
6.8
26.1
9.6
10.6
7.6
8.3
8.4
5.1
4.9
5.1
5.0
5.5
Fuel Cell Hydrogen
+
+
+
0.1
0.1
0.1
0.1
0.2
0.3
0.3
0.3
0.4
0.9
0.9
Total VMT
483.6
562.4
783.9
2,044.7
2,465.0
2,956.8
3,453.2
3,110.8
5,161.9
5,560.5
10,677.8
12,159.2
15,548.2
19,615.0
+ Does not exceed 0.05 million vehicle miles traveled
Note: Throughout the rest of this Inventory, medium-duty trucks are grouped with heavy-duty trucks; they are reported separately here because these two categories may run on a slightly different range of fuel types.
In 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 2014 Inventory and apply to the 1990 to 2016 time period. This resulted in large reductions in AFV VMT, thus leading to a shift in VMT to conventional on-road vehicle classes. In 2016, 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 first incorporated in this year's Inventory and
apply to the 2005 to 2016 time period.
Source: Derived from Browning (2017), EIA (2017e), and EDTA (2017).
A-153
-------
Table fl-101: flge Distribution by Vehicle/Fuel Type for On-Road Vehicles,3 2016
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MC
0
7.3%
8.3%
6.5%
12.7%
8.9%
6.2%
7.3%
1
7.1%
8.0%
6.2%
12.4%
8.5%
6.0%
7.2%
2
7.0%
7.6%
5.7%
12.1%
8.0%
5.5%
6.9%
3
6.7%
7.2%
5.2%
11.6%
7.6%
4.9%
6.1%
4
6.4%
6.8%
4.8%
11.2%
7.2%
4.6%
5.5%
5
4.0%
4.5%
2.7%
6.9%
4.9%
2.9%
4.4%
6
4.4%
3.9%
1.8%
6.6%
2.8%
1.9%
4.0%
7
4.0%
2.9%
1.6%
4.3%
2.5%
2.3%
4.1%
8
5.0%
4.8%
3.0%
0.4%
5.9%
3.4%
7.3%
9
5.4%
4.9%
2.8%
0.3%
5.2%
6.7%
6.5%
10
4.9%
4.8%
3.9%
5.0%
6.4%
5.7%
6.2%
11
4.8%
4.9%
3.1%
3.4%
5.4%
5.2%
5.4%
12
4.4%
4.7%
3.8%
2.0%
4.7%
3.6%
4.6%
13
4.4%
4.2%
3.3%
2.5%
4.2%
3.2%
3.9%
14
3.9%
3.9%
3.3%
2.5%
3.5%
2.6%
3.4%
15
3.4%
3.3%
2.7%
1.5%
3.8%
3.4%
2.9%
16
3.2%
3.0%
5.3%
1.2%
2.0%
5.2%
2.3%
17
2.4%
2.5%
5.1%
0.7%
2.7%
4.1%
1.8%
18
1.9%
1.9%
2.1%
0.6%
1.0%
2.8%
1.5%
19
1.7%
1.6%
3.9%
0.2%
1.2%
2.6%
1.4%
20
1.3%
1.2%
2.3%
0.2%
0.9%
2.3%
1.3%
21
1.3%
1.1%
3.2%
0.2%
0.7%
2.9%
0.9%
22
1.0%
0.9%
2.5%
0.0%
0.4%
2.2%
1.1%
23
0.9%
0.7%
2.0%
0.1%
0.4%
1.6%
0.9%
24
0.7%
0.5%
1.5%
0.1%
0.4%
1.1%
0.7%
25
0.6%
0.4%
1.2%
0.2%
0.2%
1.1%
0.6%
26
0.5%
0.4%
1.7%
0.1%
0.2%
1.3%
0.5%
27
0.4%
0.4%
2.0%
0.1%
0.2%
1.3%
0.4%
28
0.3%
0.3%
1.6%
0.0%
0.1%
1.1%
0.3%
29
0.3%
0.2%
1.5%
0.5%
0.0%
0.9%
0.3%
30
0.3%
0.2%
3.5%
0.5%
0.2%
1.7%
0.3%
Total
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV (heavy-duty
gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC (motorcycles).
Note: This year's Inventory includes updated vehicle population data based on the MOVES 2014a Model.
Source: EPA (2017b).
Table fl-102: Annual Average Vehicle Mileage Accumulation per Vehiclea [miles]
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MCb
0
13,624
15,400
18,821
13,624
15,400
41,865
7,586
1
13,366
15,110
18,820
13,366
15,110
41,876
4,051
2
13,086
14,784
18,824
13,086
14,784
41,610
3,065
3
12,788
14,426
18,827
12,788
14,426
41,385
2,534
4
12,473
14,041
17,824
12,473
14,041
39,984
2,192
5
12,142
13,632
15,660
12,142
13,632
44,727
1,950
6
11,800
13,202
13,494
11,800
13,202
43,638
1,768
7
11,446
12,755
12,969
11,446
12,755
44,901
1,624
8
11,085
12,297
13,472
11,085
12,296
31,398
1,502
9
10,716
11,830
11,226
10,716
11,830
41,575
1,403
10
10,344
11,357
11,288
10,344
11,357
34,672
1,320
11
9,969
10,884
9,516
9,969
10,884
32,618
1,244
12
9,595
10,415
9,207
9,595
10,415
26,639
1,183
13
9,222
9,953
8,086
9,222
9,953
25,494
1,123
14
8,855
9,501
7,270
8,855
9,501
21,531
1,070
15
8,493
9,064
6,109
8,493
9,064
19,092
1,024
16
8,140
8,647
6,087
8,140
8,647
17,199
986
17
7,797
8,251
5,765
7,797
8,251
15,704
948
18
7,467
7,883
5,338
7,467
7,883
15,275
910
A-154 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
19
7,151
7,546
4,801
7,151
7,546
11,347
880
20
6,852
7,242
4,530
6,852
7,242
12,005
850
21
6,573
6,978
4,487
6,573
6,978
9,963
827
22
6,315
6,754
4,029
6,315
6,754
8,689
804
23
6,079
6,579
4,021
6,079
6,579
8,129
759
24
5,869
6,452
3,330
5,869
6,452
7,420
713
25
5,687
6,378
3,296
5,687
6,378
6,747
668
26
5,534
6,365
3,070
5,534
6,365
5,726
615
27
5,413
6,365
2,888
5,413
6,365
4,765
569
28
5,325
6,365
2,584
5,325
6,365
4,257
539
29
5,273
6,365
2,363
5,273
6,365
3,968
501
30
5,273
6,365
2,150
5,273
6,365
3,292
463
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV (heavy-duty
gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC (motorcycles).
b Because of a lack of data, all motorcycles over 12 years old are considered to have the same emissions and travel characteristics, and therefore are
presented in aggregate.
Source: EPA (2017b).
Table fl-103: VMT Distribution by Vehicle flge and Vehicle/Fuel Type,3 2016
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MC
0
9.27%
10.62%
11.79%
14.43%
11.05%
9.13%
25.43%
1
8.86%
9.98%
11.32%
13.79%
10.38%
8.77%
13.27%
2
8.52%
9.27%
10.45%
13.27%
9.63%
8.01%
9.65%
3
7.96%
8.54%
9.41%
12.39%
8.86%
7.15%
7.10%
4
7.47%
7.88%
8.19%
11.65%
8.17%
6.46%
5.49%
5
4.47%
5.05%
4.07%
6.97%
5.36%
4.50%
3.92%
6
4.85%
4.26%
2.32%
6.50%
2.99%
2.89%
3.21%
7
4.24%
3.03%
1.99%
4.13%
2.55%
3.62%
3.05%
8
5.15%
4.90%
3.95%
0.36%
5.92%
3.77%
5.00%
9
5.37%
4.82%
3.03%
0.24%
4.98%
9.73%
4.16%
10
4.74%
4.56%
4.29%
4.31%
5.93%
6.98%
3.71%
11
4.48%
4.39%
2.84%
2.83%
4.77%
5.98%
3.06%
12
3.95%
4.07%
3.40%
1.61%
3.95%
3.40%
2.47%
13
3.76%
3.45%
2.59%
1.94%
3.36%
2.87%
2.00%
14
3.24%
3.08%
2.31%
1.83%
2.69%
1.94%
1.68%
15
2.67%
2.48%
1.60%
1.04%
2.80%
2.27%
1.37%
16
2.42%
2.12%
3.11%
0.82%
1.37%
3.14%
1.05%
17
1.76%
1.69%
2.82%
0.43%
1.82%
2.27%
0.76%
18
1.33%
1.25%
1.09%
0.38%
0.64%
1.48%
0.62%
19
1.13%
1.02%
1.81%
0.13%
0.75%
1.05%
0.58%
20
0.86%
0.70%
1.01%
0.14%
0.55%
0.99%
0.49%
21
0.82%
0.64%
1.39%
0.10%
0.38%
1.00%
0.36%
22
0.60%
0.53%
0.98%
0.01%
0.21%
0.66%
0.40%
23
0.48%
0.36%
0.77%
0.04%
0.22%
0.45%
0.31%
24
0.38%
0.27%
0.50%
0.06%
0.20%
0.29%
0.24%
25
0.31%
0.22%
0.40%
0.11%
0.11%
0.25%
0.18%
26
0.25%
0.20%
0.50%
0.04%
0.09%
0.26%
0.13%
27
0.20%
0.20%
0.56%
0.02%
0.08%
0.21%
0.09%
28
0.15%
0.17%
0.41%
0.01%
0.06%
0.16%
0.07%
29
0.12%
0.13%
0.35%
0.21%
0.03%
0.13%
0.07%
30
0.17%
0.12%
0.74%
0.20%
0.09%
0.20%
0.06%
Total
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks),HDGV (heavy-duty
gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC (motorcycles).
Note: Estimated by weighting data in by data in Table A-102. This year's Inventory includes updated vehicle population data based on the MOVES
2014a. Model that affects this distribution.
A-155
-------
Table fl-104: Fuel Consumption for Off-Road Sources by Fuel Type [million gallons unless otherwise noted]
Vehicle Type/Year
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Aircraft3
19,560
18,320
20,304
18,973
18,670
17,984
16,030
15,762
15,262
14,914
15,274
15,397
16,338
17,198
Aviation Gasoline
374
329
302
278
263
235
221
225
225
209
186
181
176
170
Jet Fuel
19,186
17,991
20,002
18,695
18,407
17,749
15,809
15,537
15,036
14,705
15,088
15,217
16,162
17,028
Commercial Aviation11
11,569
12,136
14,672
14,426
14,708
13,400
12,588
11,931
12,067
11,932
12,031
12,131
12,534
12,674
Ships and Boats
4,599
5,829
6,538
5,120
5,598
4,841
4,271
4,802
4,976
4,402
4,354
3,391
3,845
4,415
Diesel
1,156
1,661
1,882
1,409
1,365
1,384
1,395
1,361
1,641
1,389
1,414
1,284
1,881
1,680
Gasoline
1,383
1,522
1,629
1,597
1,587
1,577
1,568
1,556
1,545
1,535
1,528
1,522
1,519
1,516
Residual
2,060
2,646
3,027
2,114
2,647
1,880
1,308
1,886
1,791
1,477
1,413
584
445
1,219
Construction/Mining Equipment1
Diesel
3,736
4,460
5,181
6,069
6,216
6,363
6,511
6,658
6,806
6,954
7,102
7,250
7,399
7,546
Gasoline
484
438
342
686
569
575
556
655
612
632
1,085
698
367
375
CNG (million cubic ft)
4,566
5,145
5,724
6,212
6,250
6,287
6,324
6,361
6,397
6,434
6,471
6,508
6,545
6,583
LPG
20
23
25
26
26
26
26
26
27
27
27
27
27
28
Agricultural Equipment11
Diesel
2,360
2,818
3,277
3,782
3,865
3,948
4,032
4,115
4,199
4,282
4,366
4,450
4,534
4,617
Gasoline
813
927
652
1,229
1,061
634
676
692
799
875
655
644
159
168
CNG (million cubic ft)
3,364
2,325
1,287
241
155
95
60
37
22
12
6
3
2
2
LPG
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
Rail
3,461
3,864
4,106
4,665
4,539
4,216
3,535
3,807
3,999
3,921
4,025
4,175
4,000
3,693
Diesel
3,461
3,864
4,106
4,665
4,539
4,216
3,535
3,807
3,999
3,921
4,025
4,175
4,000
3,693
Other®
Diesel
1,447
1,749
2,050
2,446
2,512
2,579
2,645
2,711
2,778
2,844
2,910
2,977
3,043
3,108
Gasoline'
4,437
4,543
4,748
5,924
5,717
5,782
5,810
6,093
5,990
5,859
5,890
5,975
5,893
5,986
CNG (million cubic ft)
17,998
20,816
23,629
24,162
24,276
24,435
24,610
24,796
24,999
25,232
25,598
25,988
26,394
26,818
LPG
1,399
1,799
2,200
2,454
2,465
2,477
2,490
2,504
2,520
2,540
2,582
2,628
2,677
2,726
Total gallons
42,316
44,769
49,423
51,374
51,239
49,426
46,581
47,825
47,967
47,251
48,271
47,614
48,283
49,861
Total million cubic ft
25,928
28,287
30,640
30,614
30,680
30,817
30,993
31,194
31,418
31,678
32,075
32,499
32,941
33,403
a For aircraft, this is aviation gasoline. For all other categories, this is motor gasoline.
b Commercial aviation, as modeled in FM'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 MOVES2014a for years 1999 through 2016.
Sources: AAR (2008 through 2017), APTA (2007 through 2016), BEA (1991 through 2017), Benson (2002 through 2004), DHS (2008), DOC (1991 through 2017), DESC (2017), DOE (1993 through 2016), DOT (1991
through 2017), EIA (2002), EIA (2007b), EIA (2017d), EIA (2007 through 2017), EIA (1991 through 2016), EPA (2017b), FAA (2017), Gaffney (2007), and Whorton (2006 through 2014).
A-156 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-105: Control Technology Assignments for Gasoline Passenger Cars [Percent of VMT1
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%
2016
-
-
-
-
-
25%
50%
24%
- 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 (2017c), and EPA (2017d).
A-157
-------
Table fl-106: Control Technology Assignments for Gasoline Light-Duty Trucks [Percent of VMTF
CARB LEV
Model Years Non-catalyst Oxidation EPA Tier 0 EPA Tier 1 CARB LEVb CARB LEV 2 EPA Tier 2 3/EPATier3
1973-1974 100%
1975 30% 70%
1976 20% 80%
1977-1978 25% 75%
1979-1980 20% 80%
1981 - 95% 5%
1982 - 90% 10%
1983 - 80% 20%
1984 - 70% 30%
1985 - 60% 40%
1986 - 50% 50%
1987-1993 - 5% 95%
1994 - - 60% 40%
1995 - - 20% 80%
1996 ... 100%
1997 ... 100%
1998
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%
18%
- 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 (2017c), and EPA (2017d).
Table fl-107: Control Technology Assignments for Gasoline Heavy-Duty Vehicles [Percent of VMT)a
Non- CARB LEV 3/
Model Years Uncontrolled catalyst Oxidation EPA Tier 0 EPA Tier 1 CARB LEV" CARB LEV 2 EPA Tier 2 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%
A-158 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1998 .... 100% -
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%
2009-2015 - - - - - - - 100%
2016 ; ; ; ; ; ; 24% 10% 66%
- Not Applicable.
a Detailed descriptions of emissions control technologies are provided in the following section of this Annex.
b The proportion of LEVs as a whole has decreased since 2000, as carmakers have been able to achieve greater emission reductions with certain types
of LEVs, such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a manufacturer can reduce the total number of LEVs they need to
build to meet a specified emission average for all of their vehicles in a given model year.
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 (2017c), and EPA (2017d).
Table fl-108: Control Technology Assignments for Diesel On-BoaJ 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 1996-2015
Diesel Medium- and Heavy-Duty Trucks and Buses
Uncontrolled 1960-1990
Moderate control 1991-2003
Advanced control 2004-2006
Aftertreatment 2007-2015
Motorcycles
Uncontrolled 1960-1995
Non-catalyst controls 1996-2016
Note: Detailed descriptions of emissions control technologies are provided in the following section of this Annex.
Source: EPA (1998) and Browning (2005).
Table A-109: Emission Factors for CH4 and N2O for On-Road Vehicles
N20
CH4
Vehicle Type/Control Technology
(g/mi)
(g/mi)
Gasoline Passenger Cars
EPA Tier 3 /ARB LEVIN
0.0067
0.0022
EPA Tier 2
0.0082
0.0078
ARB LEV II
0.0082
0.0061
ARB LEV
0.0205
0.0100
EPA Tier 1a
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/ARB LEVIN
0.0067
0.0020
EPA Tier 2
0.0082
0.0080
ARB LEV II
0.0082
0.0056
ARB LEV
0.0223
0.0148
EPA Tier 1a
0.0871
0.0452
A-159
-------
EPA Tier Oa 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/ARB LEV III 0.0160 0.0115
EPA Tier 2 0.0082 0.0085
ARB LEV II 0.0175 0.0212
ARB LEV 0.0466 0.0300
EPA Tier 1a 0.1750 0.0655
EPA Tier 0a 0.2135 0.2630
Oxidation Catalyst 0.1317 0.2356
Non-Catalyst Control 0.0473 0.4181
Uncontrolled 0.0497 0.4604
Diesel Passenger Cars
Advanced 0.0010 0.0005
Moderate 0.0010 0.0005
Uncontrolled 0.0012 0.0006
Diesel Light-Duty Trucks
Advanced 0.0015 0.0010
Moderate 0.0014 0.0009
Uncontrolled 0.0017 0.0011
Diesel Medium- and Heavy-Duty
Trucks and Buses
Aftertreatment 0.0048 0.0051
Advanced 0.0048 0.0051
Moderate 0.0048 0.0051
Uncontrolled 0.0048 0.0051
Motorcycles
Non-Catalyst Control 0.0069 0.0672
Uncontrolled 0.0087 0.0899
a The categories "EPA Tier 0" and "EPA Tier 1" were substituted for the early three-way catalyst and advanced three-way catalyst categories,
respectively as defined in the 2006IPCC Guidelines. Detailed descriptions of emissions control technologies are provided at the end of this Annex.
Source: ICF (2006b and 2017a).
A-160 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-110: Emission Factors for N2O for Alternative Fuel Vehicles tg/mi)
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Light-Duty Cars
Methanol-Flex Fuel ICE
0.035
0.035
0.034
0.017
0.014
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
Ethanol-Flex Fuel ICE
0.035
0.035
0.034
0.017
0.014
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
CNG ICE
0.021
0.021
0.027
0.017
0.014
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
CNG Bi-fuel
0.021
0.021
0.027
0.017
0.014
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
LPG ICE
0.021
0.021
0.027
0.017
0.014
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
LPG Bi-fuel
0.021
0.021
0.027
0.017
0.014
0.012
0.010
0.008
0.008
0.008
0.008
0.008
0.008
0.007
Biodiesel (BD100)
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
Light-Duty Trucks
Ethanol-Flex Fuel ICE
0.068
0.068
0.072
0.046
0.039
0.031
0.024
0.016
0.016
0.016
0.016
0.015
0.015
0.014
CNG ICE
0.041
0.041
0.058
0.046
0.039
0.031
0.024
0.016
0.016
0.016
0.016
0.015
0.015
0.014
CNG Bi-fuel
0.041
0.041
0.058
0.046
0.039
0.031
0.024
0.016
0.016
0.016
0.016
0.015
0.015
0.014
LPG ICE
0.041
0.041
0.058
0.046
0.039
0.031
0.024
0.016
0.016
0.016
0.016
0.015
0.015
0.014
LPG Bi-fuel
0.041
0.041
0.058
0.046
0.039
0.031
0.024
0.016
0.016
0.016
0.016
0.015
0.015
0.014
LNG
0.041
0.041
0.058
0.046
0.039
0.031
0.024
0.016
0.016
0.016
0.016
0.015
0.015
0.014
Biodiesel (BD100)
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
Medium Duty Trucks
CNG ICE
0.002
0.002
0.003
0.003
0.003
0.003
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
CNG Bi-fuel
0.002
0.002
0.003
0.003
0.003
0.003
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
LPG ICE
0.055
0.055
0.069
0.070
0.061
0.052
0.043
0.034
0.034
0.034
0.034
0.034
0.034
0.034
LPG Bi-fuel
0.055
0.055
0.069
0.070
0.061
0.052
0.043
0.034
0.034
0.034
0.034
0.034
0.034
0.034
LNG
0.002
0.002
0.003
0.003
0.003
0.003
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
Biodiesel (BD100)
0.002
0.002
0.003
0.003
0.003
0.003
0.003
0.003
0.003
0.003
0.003
0.003
0.003
0.003
Heavy-Duty Trucks
Neat Methanol ICE
0.040
0.040
0.049
0.055
0.048
0.041
0.034
0.028
0.028
0.028
0.028
0.028
0.028
0.028
Neat Ethanol ICE
0.040
0.040
0.049
0.055
0.048
0.041
0.034
0.028
0.028
0.028
0.028
0.028
0.028
0.028
CNG ICE
0.002
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
LPG ICE
0.045
0.045
0.049
0.052
0.046
0.039
0.032
0.026
0.026
0.026
0.026
0.026
0.026
0.026
LPG Bi-fuel
1.229
0.045
0.049
0.052
0.046
0.039
0.032
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
0.002
0.002
0.001
0.001
0.001
0.001
0.001
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
0.002
Buses
Neat Methanol ICE 0.045 0.045 0.058 0.064 0.056 0.048 0.040 0.032 0.032 0.032 0.032 0.032 0.032 0.032
Neat Ethanol ICE 0.045 0.045 0.058 0.064 0.056 0.048 0.040 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 0.002 0.002 0.001 0.001 0.001 0.001
LPGICE 0.051 0.051 0.058 0.062 0.054 0.046 0.038 0.030 0.028 0.025 0.022 0.020 0.017 0.017
LNG 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.001 0.001 0.001 0.001
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 0.002
Source: Developed by ICF (Browning 2017) using ANL (2016)
A-161
-------
Table fl-111: Emission Factors for CHj for Alternative Fuel Vehicles tg/mi)
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Light-Duty Cars
Methanol-Flex Fuel ICE
0.034
0.034
0.019
0.013
0.014
0.014
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
Ethanol-Flex Fuel ICE
0.034
0.034
0.019
0.013
0.014
0.014
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
CNG ICE
0.489
0.489
0.249
0.156
0.155
0.154
0.153
0.153
0.139
0.126
0.113
0.100
0.086
0.085
CNG Bi-fuel
0.489
0.489
0.249
0.156
0.155
0.154
0.153
0.153
0.139
0.126
0.113
0.100
0.086
0.085
LPG ICE
0.049
0.049
0.025
0.016
0.016
0.015
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
LPG Bi-fuel
0.049
0.049
0.025
0.016
0.016
0.015
0.015
0.015
0.014
0.013
0.011
0.010
0.009
0.008
Biodiesel (BD100)
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.021
0.042
0.063
0.083
0.104
0.132
Light-Duty Trucks
Ethanol-Flex Fuel ICE
0.052
0.051
0.053
0.033
0.033
0.033
0.033
0.033
0.029
0.025
0.021
0.017
0.013
0.013
CNG ICE
0.737
0.731
0.709
0.399
0.381
0.364
0.346
0.329
0.288
0.248
0.208
0.168
0.128
0.126
CNG Bi-fuel
0.737
0.731
0.709
0.399
0.381
0.364
0.346
0.329
0.288
0.248
0.208
0.168
0.128
0.126
LPG ICE
0.074
0.073
0.071
0.040
0.038
0.036
0.035
0.033
0.029
0.025
0.021
0.017
0.013
0.013
LPG Bi-fuel
0.074
0.073
0.071
0.040
0.038
0.036
0.035
0.033
0.029
0.025
0.021
0.017
0.013
0.013
LNG
0.737
0.731
0.709
0.399
0.381
0.364
0.346
0.329
0.288
0.248
0.208
0.168
0.128
0.126
Biodiesel (BD100)
0.004
0.005
0.005
0.002
0.002
0.002
0.002
0.001
0.021
0.041
0.060
0.080
0.100
0.103
Medium Duty Trucks
CNG ICE
6.800
6.800
6.800
6.800
6.800
6.800
6.800
6.800
6.280
5.760
5.240
4.720
4.200
4.200
CNG Bi-fuel
6.800
6.800
6.800
6.800
6.800
6.800
6.800
6.800
6.280
5.760
5.240
4.720
4.200
4.200
LPG ICE
0.262
0.262
0.248
0.028
0.026
0.024
0.023
0.021
0.020
0.018
0.017
0.016
0.014
0.014
LPG Bi-fuel
0.262
0.262
0.248
0.028
0.026
0.024
0.023
0.021
0.020
0.018
0.017
0.016
0.014
0.014
LNG
6.800
6.800
6.800
6.800
6.800
6.800
6.800
6.800
6.280
5.760
5.240
4.720
4.200
4.200
Biodiesel (BD100)
0.004
0.004
0.004
0.003
0.002
0.002
0.002
0.002
0.017
0.031
0.046
0.060
0.074
0.075
Heavy-Duty Trucks
Neat Methanol ICE
0.296
0.296
0.095
0.091
0.106
0.121
0.136
0.151
0.136
0.120
0.105
0.090
0.075
0.075
Neat Ethanol ICE
0.296
0.296
0.095
0.091
0.106
0.121
0.136
0.151
0.136
0.120
0.105
0.090
0.075
0.075
CNG ICE
4.100
4.100
4.100
4.100
4.100
4.100
4.100
4.100
4.020
3.940
3.860
3.780
3.700
3.700
LPG ICE
0.158
0.158
0.149
0.017
0.016
0.015
0.014
0.013
0.013
0.013
0.013
0.013
0.013
0.013
LPG Bi-fuel
0.158
0.158
0.149
0.017
0.016
0.015
0.014
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
4.100
4.100
4.020
3.940
3.860
3.780
3.700
3.700
Biodiesel (BD100)
0.012
0.012
0.005
0.005
0.005
0.005
0.005
0.005
0.074
0.143
0.211
0.280
0.349
0.348
Buses
Neat Methanol ICE
0.086
0.086
0.067
0.049
0.055
0.062
0.068
0.075
0.062
0.049
0.037
0.024
0.011
0.011
Neat Ethanol ICE
0.086
0.086
0.067
0.049
0.055
0.062
0.068
0.075
0.062
0.049
0.037
0.024
0.011
0.011
CNG ICE
18.800
18.800
18.800
18.800
18.800
18.800
18.800
18.800
17.040
15.280
13.520
11.760
10.000
10.000
LPG ICE
0.725
0.725
0.686
0.077
0.072
0.068
0.063
0.058
0.053
0.048
0.044
0.039
0.034
0.034
LNG
18.800
18.800
18.800
18.800
18.800
18.800
18.800
18.800
17.040
15.280
13.520
11.760
10.000
10.000
Biodiesel (BD100)
0.004
0.004
0.003
0.003
0.003
0.003
0.002
0.002
0.013
0.023
0.033
0.043
0.053
0.053
Source: Developed by ICF (Browning 2017) using ANL (2016)
A-162 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-112: Emission Factors for N2O Emissions from Non-BoaJ Mobile Combustion tg/kg fuel]
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
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
0.017
0.018
0.018
0.019
0.020
0.020
0.020
0.021
0.021
0.021
0.022
0.022
0.022
0.022
4 Stroke
0.075
0.075
0.076
0.078
0.078
0.079
0.079
0.080
0.080
0.081
0.081
0.082
0.082
0.083
Distillate Fuel Oil
D^il
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
Kail
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
0.019
0.019
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
0.070
0.071
0.072
0.073
0.073
0.074
0.075
0.075
0.076
0.076
0.077
Gasoline-Off-road Trucks
0.064
0.065
0.066
0.070
0.071
0.072
0.073
0.073
0.074
0.075
0.075
0.076
0.076
0.077
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
0.162
0.162
0.163
0.163
0.163
0.163
0.163
0.162
0.162
0.162
0.162
LPG
0.162
0.162
0.162
0.170
0.173
0.175
0.178
0.180
0.182
0.184
0.185
0.186
0.187
0.188
Construction/Mining Equipment11
Gasoline-Equipment
2 Stroke
0.017
0.018
0.018
0.023
0.025
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
0.067
0.068
0.068
0.069
0.069
0.070
0.070
0.070
0.070
0.070
0.070
Gasoline-Off-road Trucks
0.054
0.057
0.060
0.067
0.068
0.068
0.069
0.069
0.070
0.070
0.070
0.070
0.070
0.070
Diesel-Equipment
0.148
0.148
0.148
0.148
0.148
0.148
0.148
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
0.169
0.171
0.173
0.175
0.178
0.180
0.182
0.184
0.186
0.188
0.190
LPG
0.162
0.162
0.162
0.174
0.178
0.181
0.184
0.187
0.190
0.192
0.194
0.195
0.197
0.198
Lawn and Garden Equipment
Gasoline-Residential
2 Stroke
0.012
0.012
0.013
0.016
0.017
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
0.018
4 Stroke
0.047
0.050
0.053
0.060
0.061
0.062
0.062
0.062
0.063
0.063
0.063
0.063
0.063
0.063
Gasoline-Commercial
2 Stroke
0.014
0.015
0.016
0.020
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
0.064
0.065
0.065
0.065
0.065
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
0.176
0.181
0.185
0.189
0.193
0.197
0.199
0.200
0.201
0.201
0.202
Airport Equipment
Gasoline
A-163
-------
4 Stroke
0.071
0.073
0.075
0.081
0.082
0.084
0.085
0.087
0.088
0.089
0.089
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
0.176
0.181
0.185
0.189
0.193
0.197
0.199
0.200
0.201
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
0.056
0.058
0.060
0.065
0.066
0.066
0.067
0.067
0.067
0.067
0.067
0.067
0.067
0.067
Diesel
0.145
0.145
0.145
0.145
0.145
0.145
0.145
0.145
0.145
0.145
0.145
0.145
0.145
0.145
CNG
0.162
0.162
0.162
0.182
0.185
0.188
0.191
0.193
0.195
0.197
0.198
0.199
0.200
0.200
LPG
0.162
0.162
0.162
0.175
0.179
0.183
0.186
0.190
0.194
0.197
0.198
0.199
0.200
0.201
Logging Equipment
Gasoline
2 Stroke
0.018
0.018
0.019
0.024
0.026
0.027
0.027
0.027
0.027
0.027
0.027
0.027
0.027
0.027
4 Stroke
0.053
0.053
0.055
0.061
0.062
0.062
0.063
0.064
0.065
0.065
0.066
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
0.065
0.065
0.066
0.066
0.066
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
0.170
0.173
0.176
0.178
0.181
0.184
0.186
0.189
0.191
0.194
0.196
Recreational Equipment
Gasoline
2 Stroke
0.013
0.013
0.015
0.020
0.020
0.021
0.021
0.022
0.022
0.023
0.023
0.023
0.023
0.023
4 Stroke
0.076
0.077
0.078
0.086
0.086
0.086
0.087
0.087
0.087
0.087
0.087
0.087
0.088
0.088
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
0.167
0.168
0.170
0.171
0.172
0.174
0.175
0.177
0.178
0.180
0.181
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
Source: IPCC (2006) and ICF (2017b), EPA (2017b)
Table fl-113: Emission Factors for Clh Emissions from Non-Boa J Mobile Combustion tg/kg fuel]
1990 1995 2000 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
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 5.412 5.284 5.098 4.382 4.267 4.061 3.911 3.803 3.723 3.632 3.576 3.524 3.483 3.449
4 Stroke 3.469 3.334 3.203 2.949 2.929 2.704 2.591 2.449 2.356 2.217 2.127 2.037 1.950 1.865
Distillate Fuel Oil 0.007 0.007 0.007 0.017 0.026 0.035 0.044 0.053 0.061 0.069 0.076 0.083 0.089 0.095
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
Aircraft
Jet Fuelc 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 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 2.640 2.640 2.640 2.640 2.640 2.640 2.640 2.640
A-164 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Agricultural
Equipment3
Gasoline-Equipment
2 Stroke 9.969
4 Stroke 7.434
Gasoline-Off-road
Trucks 7.434
Diesel-Equipment 0.045
Diesel-Off-Road Trucks 0.021
CNG 194.782
LPG 2.629
Construction/Mining Equipment11
Gasoline-Equipment
2 Stroke 9.503
4 Stroke 11.477
Gasoline-Off-road
Trucks 11.477
Diesel-Equipment 0.033
Diesel-Off-Road Trucks 0.021
CNG 186.710
LPG 2.625
Lawn and Garden Equipment
Gasoline-Residential
2 Stroke 10.154
4 Stroke 10.670
Gasoline-Commercial
2 Stroke 9.947
4 Stroke 9.967
Diesel-Commercial 0.039
LPG 2.635
Airport Equipment
Gasoline
4 Stroke 9.095
Diesel 0.0322
LPG 2.615
Industrial/Commercial Equipment
Gasoline
2 Stroke 10.431
4 Stroke 11.493
Diesel 0.0363
CNG 190.129
LPG 2.601
9.291
8.658
5.648
5.138
6.786
6.110
5.223
5.038
6.786
6.110
5.223
5.038
0.041
0.037
0.066
0.071
0.022
0.025
0.057
0.065
195.095
196.101
205.654
205.358
2.629
2.629
2.329
2.180
8.576
7.820
5.818
4.950
9.340
7.418
5.855
5.560
9.340
7.418
5.855
5.560
0.035
0.039
0.088
0.096
0.022
0.025
0.057
0.065
186.729
186.776
168.663
159.961
2.625
2.626
2.173
1.953
9.576
8.904
7.193
6.815
9.624
8.408
6.930
6.768
9.073
8.335
6.506
5.999
8.786
7.707
6.632
6.396
0.039
0.039
0.071
0.080
2.635
2.635
2.094
1.823
7.688
6.562
5.550
4.709
0.031
0.031
0.077
0.083
2.616
2.617
2.075
1.808
9.649
9.020
5.712
5.698
9.533
7.723
6.442
6.039
0.038
0.042
0.097
0.109
189.960
189.819
102.017
87.080
2.597
2.593
1.886
1.647
4.831 4.717
4.514 4.177
4.514 4.177
0.075 0.079
0.071 0.078
204.857 204.091
2.025 1.866
4.665 4.528
4.672 4.156
4.672 4.156
0.101 0.106
0.071 0.078
151.161 142.298
1.743 1.534
6.378 6.137
6.039 5.544
5.696 5.592
5.434 4.714
0.087 0.093
1.555 1.287
3.330 2.963
0.087 0.091
1.540 1.271
5.573 5.521
4.910 4.242
0.112 0.114
74.609 62.914
1.406 1.166
4.673 4.674
3.860 3.635
3.860 3.635
0.083 0.086
0.083 0.092
203.843 205.046
1.706 1.574
4.483 4.478
3.810 3.401
3.810 3.401
0.110 0.112
0.083 0.092
133.343 124.331
1.334 1.154
6.025 5.985
5.069 4.657
5.550 5.546
4.199 3.871
0.099 0.104
1.017 0.763
2.632 2.281
0.095 0.097
1.005 0.751
5.484 5.476
3.866 3.625
0.115 0.114
51.956 41.906
0.930 0.711
4.644 4.644
3.341 3.145
3.341 3.145
0.087 0.088
0.100 0.107
205.212 205.710
1.459 1.351
4.451 4.451
2.837 2.528
2.837 2.528
0.111 0.109
0.100 0.107
115.283 106.188
0.990 0.855
5.929 5.924
4.054 3.602
5.511 5.511
3.278 2.772
0.108 0.111
0.592 0.454
1.382 1.240
0.098 0.098
0.580 0.443
5.434 5.427
3.068 2.688
0.112 0.107
35.486 29.881
0.570 0.447
4.645 4.645
2.966 2.799
2.966 2.799
0.089 0.089
0.112 0.110
206.756 206.788
1.263 1.189
4.451 4.451
2.306 2.170
2.306 2.170
0.107 0.105
0.112 0.110
97.065 87.955
0.733 0.625
5.924 5.925
3.246 2.921
5.511 5.511
2.423 2.247
0.113 0.115
0.335 0.266
1.103 1.033
0.096 0.094
0.325 0.258
5.422 5.419
2.424 2.283
0.103 0.099
25.156 22.437
0.345 0.289
4.645
2.659
2.659
0.089
0.107
206.795
1.120
4.451
2.086
2.086
0.102
0.107
78.860
0.545
5.925
2.625
5.511
2.151
0.116
0.219
0.983
0.092
0.213
5.416
2.194
0.096
102.017
1.886
A-165
-------
Logging Equipment
Gasoline
2 Stroke
9.493
8.567
7.825
5.738
4.715
4.391
4.357
4.335
4.335
4.309
4.309
4.309
4.309
4.309
4 Stroke
8.528
7.723
6.816
4.985
4.750
4.225
3.918
3.650
3.441
3.165
2.992
2.841
2.707
2.590
Diesel
0.0207
0.027
0.035
0.102
0.11
0.116
0.121
0.12
0.115
0.107
0.101
0.096
0.091
0.087
Railroad Equipment
Gasoline
4 Stroke
10.832
8.825
6.822
5.327
5.048
4.240
3.764
3.549
3.375
2.970
2.592
2.342
2.220
2.141
Diesel
0.056
0.057
0.059
0.116
0.124
0.129
0.135
0.140
0.142
0.140
0.138
0.136
0.134
0.132
LPG
2.603
2.603
26.04
2.303
2.153
2.000
1.844
1.685
1.525
1.363
1.200
1.038
0.880
0.727
Recreational
Equipment
Gasoline
2 Stroke
4.700
4.679
4.794
5.280
5.159
4.921
4.739
4.585
4.450
4.279
4.106
3.922
3.733
3.542
4 Stroke
8.595
7.599
6.748
5.327
5.179
4.719
4.458
4.229
3.940
3.723
3.602
3.501
3.394
3.309
Diesel
0.0786
0.077
0.075
0.116
0.125
0.129
0.133
0.137
0.138
0.138
0.137
0.137
0.136
0.135
LPG
2.609
2.609
2.609
2.436
2.356
2.276
2.195
2.113
2.030
1.947
1.864
1.780
1.695
1.610
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 ICF (2017b), EPA (2017b)
A-166 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-114: HOk Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Gasoline On-Road
5,746
4,560
3,812
3,819
3,654
3,317
2,966
2,724
2,805
2,614
2,423
2,232
1,976
1,723
Passenger Cars
3,847
2,752
2,084
2,083
1,993
1,810
1,618
1,486
1,530
1,426
1,322
1,217
1,078
940
Light-Duty Trucks
1,364
1,325
1,303
1,321
1,264
1,147
1,026
942
970
904
838
772
683
596
Medium- and Heavy-Duty
Trucks and Buses
515
469
411
401
383
348
311
286
294
274
254
234
207
181
Motorcycles
20
14
13
14
13
12
11
10
10
10
9
8
7
6
Diesel On-Road
2,956
3,493
3,803
3,431
3,283
2,980
2,665
2,448
2,520
2,349
2,177
2,005
1,776
1,548
Passenger Cars
39
19
7
6
6
5
5
4
4
4
4
4
3
3
Light-Duty Trucks
20
12
6
6
5
5
4
4
4
4
4
3
3
3
Medium- and Heavy-Duty
Trucks and Buses
2,897
3,462
3,791
3,420
3,272
2,970
2,656
2,439
2,512
2,341
2,169
1,998
1,769
1,543
Alternative Fuel On-Roada
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
Non-Road
2,160
2,483
2,584
2,490
2,249
2,226
2,166
2,118
1,968
1,908
1,848
1,788
1,665
1,543
Ships and Boats
402
488
506
515
465
460
448
438
407
395
382
370
344
319
Rail
338
433
451
460
415
411
400
391
363
352
341
330
307
285
Aircraft15
25
31
40
37
34
33
32
32
29
29
28
27
25
23
Agricultural Equipment1
437
478
484
450
407
402
392
383
356
345
334
323
301
279
Construction/Mining
Equipment"1
641
697
697
647
584
578
563
550
511
496
480
464
433
401
Oth ere
318
357
407
381
344
341
332
324
301
292
283
274
255
236
Total
10,862
10,536
10,199
9,740
9,186
8,523
7,797
7,290
7,294
6,871
6,448
6,024
5,417
4,814
IE (Included Elsewhere)
a NO* 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 MOVES2014a is a change that affects the emissions time series. Totals may not sum due to independent rounding.
A-167
-------
Table A-115: GO Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Gasoline On-Road
98,328
74,673
60,657
35,781
33,298
29,626
24,515
25,235
24,442
22,805
21,167
19,529
17,739
15,968
Passenger Cars
60,757
42,065
32,867
19,936
18,552
16,506
13,659
14,060
13,618
12,706
11,793
10,881
9,883
8,897
Light-Duty Trucks
29,237
27,048
24,532
14,242
13,253
11,792
9,758
10,044
9,729
9,077
8,425
7,773
7,061
6,356
Medium- and Heavy-Duty
Trucks and Buses
8,093
5,404
3,104
1,521
1,416
1,259
1,042
1,073
1,039
969
900
830
754
679
Motorcycles
240
155
154
83
77
69
57
58
57
53
49
45
41
37
Diesel On-Road
1,696
1,424
1,088
548
510
454
376
387
375
349
324
299
272
245
Passenger Cars
35
18
7
4
3
3
3
3
3
2
2
2
2
2
Light-Duty Trucks
22
16
6
3
3
3
2
2
2
2
2
2
2
1
Medium- and Heavy-Duty
Trucks and Buses
1,639
1,391
1,075
541
504
448
371
382
370
345
320
295
268
242
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
18,382
17,001
16,137
14,365
13,853
13,488
12,999
12,509
12,019
11,870
11,720
Ships and Boats
1,559
1,781
1,825
1,512
1,398
1,327
1,182
1,140
1,109
1,069
1,029
989
976
964
Rail
85
93
90
74
69
65
58
56
54
52
50
48
48
47
Aircraft15
217
224
245
193
178
169
151
145
141
136
131
126
124
123
Agricultural Equipment1
581
628
626
513
474
450
401
386
376
363
349
335
331
327
Construction/Mining
Equipment"1
1,090
1,132
1,047
860
795
755
672
648
631
608
585
562
555
548
Othere
15,805
17,676
17,981
15,231
14,087
13,371
11,903
11,479
11,176
10,770
10,364
9,959
9,835
9,711
Total
119,360
97,630
83,559
54,712
50,809
46,217
39,256
39,475
38,305
36,153
34,000
31,848
29,881
27,934
IE (Included Elsewhere)
aCO 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 MOVES2014a is a change that affects the emissions time series. Totals may not sum due to independent rounding.
A-168 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-116: NMVOGs Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Gasoline On-Road
8,110
5,819
4,615
= 2,997
3,015
2,641
2,384
2,393
2,485
2,292
2,099
1,906
1,716
1,527
Passenger Cars
5,120
3,394
2,610
S 1,674
1,684
1,475
1,332
1,336
1,388
1,280
1,172
1,065
958
853
Light-Duty Trucks
2,374
2,019
1,750
! 1,164
1,171
1,025
926
929
965
890
815
740
666
593
Medium- and Heavy-Duty
83
Trucks and Buses
575
382
232
i 144
145
127
115
115
120
110
101
92
73
Motorcycles
42
24
23
15
15
14
12
12
13
12
11
10
9
8
Diesel On-Road
406
304
216
145
146
128
115
116
120
111
102
92
83
74
Passenger Cars
16
8
3
! 2
2
2
2
2
2
2
1
1
1
1
Light-Duty Trucks
14
9
4
I 3
3
2
2
2
2
2
2
2
1
1
Medium- and Heavy-Duty
80
Trucks and Buses
377
286
209
i 140
141
124
112
112
116
107
98
89
72
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
! 2,491
2,383
2,310
2,150
2,082
1,957
1,840
1,723
1,607
1,519
1,431
Ships and Boats
608
739
744
764
731
709
660
639
600
565
529
493
466
439
Rail
33
36
35
37
35
34
32
31
29
27
26
24
23
21
Aircraft15
28
28
24
! 20
19
19
17
17
16
15
14
13
12
12
Agricultural Equipment0
85
86
76
76
73
70
65
63
60
56
52
49
46
44
Construction/Mining
80
Equipment1
149
152
130
131
125
121
113
109
103
97
91
84
75
Othere
1,512
1,580
1,390
1,463
1,399
1,356
1,263
1,223
1,149
1,081
1,012
944
892
840
Total
10,932
8,745
7,230
5,634
5,544
5,078
4,650
4,591
4,562
4,243
3,924
3,605
3,318
3,032
IE (Included Elsewhere)
a NMVOC emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel
consumption from trucks that are used off-road for commercial/industrial purposes.
Notes: The source of this data is the National Emissions Inventory. Updates to estimates from MOVES2014a is a change that affects the emissions time series. Totals may not sum due to independent rounding.
A-169
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Definitions of Emission Control Technologies and Standards
The N2O and CH4 emission factors used depend on the emission standards in place and the corresponding level of
control technology for each vehicle type. Table A-105 through Table A-108 show the years in which these technologies or
standards were in place and the penetration level for each vehicle type. These categories are defined below and were
compiled from EPA (1993, 1994a, 1994b, 1998, 1999a) and IPCC/UNEP/OECD/IEA (1997).
Uncontrolled
Vehicles manufactured prior to the implementation of pollution control technologies are designated as
uncontrolled. Gasoline passenger cars and light-duty trucks (pre-1973), gasoline heavy-duty vehicles (pre-1984), diesel
vehicles (pre-1983), and motorcycles (pre-1996) are assumed to have no control technologies in place.
Gasoline Emission Controls
Below are the control technologies and emissions standards applicable to gasoline vehicles.
Non-catalyst
These emission controls were common in gasoline passenger cars and light-duty gasoline trucks during model
years (1973 -1974) but phased out thereafter, in heavy-duty gasoline vehicles beginning in the mid-1980s, and in motorcycles
beginning in 1996. This technology reduces hydrocarbon (HC) and carbon monoxide (CO) emissions through adjustments
to ignition timing and air-fuel ratio, air injection into the exhaust manifold, and exhaust gas recirculation (EGR) valves,
which also helps meet vehicle NOx standards.
Oxidation Catalyst
This control technology designation represents the introduction of the catalytic converter, and was the most
common technology in gasoline passenger cars and light-duty gasoline trucks made from 1975 to 1980 (cars) and 1975 to
1985 (trucks). This technology was also used in some heavy-duty gasoline vehicles between 1982 and 1997. The two-way
catalytic converter oxidizes HC and CO, significantly reducing emissions over 80 percent beyond non-catalyst-system
capacity. One reason unleaded gasoline was introduced in 1975 was due to the fact that oxidation catalysts cannot function
properly with leaded gasoline.
EPA TierO
This emission standard from the Clean Air Act was met through the implementation of early "three-way" catalysts,
therefore this technology was used in gasoline passenger cars and light-duty gasoline trucks sold beginning in the early
1980s, and remained common until 1994. This more sophisticated emission control system improves the efficiency of the
catalyst by converting CO and HC to CO2 and H2O, reducing NOx to nitrogen and oxygen, and using an on-board diagnostic
computer and oxygen sensor. In addition, this type of catalyst includes a fuel metering system (carburetor or fuel injection)
with electronic "trim" (also known as a "closed-loop system"). New cars with three-way catalysts met the Clean Air Act's
amended standards (enacted in 1977) of reducing HC to 0.41 g/mile by 1980, CO to 3.4 g/mile by 1981 and NOx to 1.0
g/mile by 1981.
EPA Tier 1
This emission standard created through the 1990 amendments to the Clean Air Act limited passenger car NOx
emissions to 0.4 g/mi, and HC emissions to 0.25 g/mi. These bounds respectively amounted to a 60 and 40 percent reduction
from the EPA Tier 0 standard set in 1981. For light-duty trucks, this standard set emissions at 0.4 to 1.1 g/mi for NOx, and
0.25 to 0.39 g/mi for HCs, depending on the weight of the truck. Emission reductions were met through the use of more
advanced emission control systems, and applied to light-duty gasoline vehicles beginning in 1994. These advanced emission
control systems included advanced three-way catalysts, electronically controlled fuel injection and ignition timing, EGR,
and air injection.
EPA Tier 2
This emission standard was specified in the 1990 amendments to the Clean Air Act, limiting passenger car NOx
emissions to 0.07 g/mi on average and aligning emissions standards for passenger cars and light-duty trucks. Manufacturers
can meet this average emission level by producing vehicles in 11 emission "Bins," the three highest of which expire in 2006.
These new emission levels represent a 77 to 95 percent reduction in emissions from the EPA Tier 1 standard set in 1994.
A-170 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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.
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 N2O emission factor data for LEVs to distinguish among the different types of vehicles. Zero emission
vehicles (ZEVs) are incorporated into the alternative fuel and advanced technology vehicle assessments.
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 LEVIIs are treated the same due to the fact that there are very limited CH4 or N2O
emission factor data for LEVIIs to distinguish among the different types of vehicles. Zero emission vehicles (ZEVs) are
incorporated into the alternative fuel and advanced technology vehicle assessments.
EPA Tier 3/CARBLEVIII
The EPA Tier 3 and ARB LEVIII standards are harmonized and thus treated as one category. These standards
begin in 2017 and are fully phased in by 2025 but some initial vehicles were produced earlier. Tier 3/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 and some heavy-duty vehicles. EPA is also extending the
regulatory useful life period during which the standards apply from 120,000 miles to 150,000 miles. In this analysis, all
categories of Tier 3/LEVIII are treated the same due to the fact that there are very limited CH4 or N2O emission factor data
for these vehicles to distinguish among the different types of vehicles. Zero emission vehicles (ZEVs) are incorporated into
the alternative fuel and advanced technology vehicle assessments.
Diesel Emission Controls
Below are the three levels of emissions control for diesel vehicles.
Moderate control
Improved injection timing technology and combustion system design for light- and heavy-duty diesel vehicles
(generally in place in model years 1983 to 1995) are considered moderate control technologies. These controls were
implemented to meet emission standards for diesel trucks and buses adopted by the EPA in 1985 to be met in 1991 and
1994.
Advanced control
EGR and modern electronic control of the fuel injection system are designated as advanced control technologies.
These technologies provide diesel vehicles with the level of emission control necessary to comply with standards in place
from 1996 through 2006.
Aftertreatment
Use of diesel particulate filters (DPFs), oxidation catalysts and NOx absorbers or selective catalytic reduction
(SCR) systems are designated as aftertreatment control. These technologies provide diesel vehicles with a level of emission
control necessary to comply with standards in place from 2007 on.
Supplemental Information on GHG Emissions from Transportation and Other Mobile Sources
This section of this Annex includes supplemental information on the contribution of transportation and other
mobile sources to U.S. greenhouse gas emissions. In the main body of the Inventory report, emission estimates are generally
presented by greenhouse gas, with separate discussions of the methodologies used to estimate CO2, N2O, CH4, and HFC
A-171
-------
emissions. Although the Inventory is not required to provide detail beyond what is contained in the body of this report, the
IPCC allows presentation of additional data and detail on emission sources. The purpose of this sub-annex, within the Annex
that details the calculation methods and data used for non-CC>2 calculations, is to provide all transportation estimates
presented throughout the report in one place.
This section of this Annex reports total greenhouse gas emissions from transportation and other (non-
transportation) mobile sources in CO2 equivalents, with information on the contribution by greenhouse gas and by mode,
vehicle type, and fuel type. In order to calculate these figures, additional analyses were conducted to develop estimates of
CO2 from non-transportation mobile sources (e.g., agricultural equipment, construction/mining equipment, recreational
vehicles), and to provide more detailed breakdowns of emissions by source.
Estimation of CO2 from Non-Transportation Mobile Sources
The estimates of N2O and CH4 from fuel combustion presented in the Energy chapter of the Inventory include both
transportation sources and other mobile sources. Other mobile sources include construction/mining equipment, agricultural
equipment, vehicles used off-road, and other sources that have utility associated with their movement but do not have a
primary purpose of transporting people or goods (e.g., snowmobiles, riding lawnmowers, etc.). Estimates of CO2 from non-
transportation mobile sources, based on EIA fuel consumption estimates, are included in the industrial and commercial
sectors. In order to provide comparable information on transportation and mobile sources, Table A-l 17 provides estimates
of CO2 from these other mobile sources, developed from EPA's NONROAD components of the MOVES2014a model and
FHWA's Highway Statistics. These other mobile source estimates were developed using the same fuel consumption data
utilized in developing the N2O and CH4 estimates (see Table A-104). Note that the method used to estimate fuel consumption
volumes for CO2 emissions from non-transportation mobile sources for the supplemental information presented in Table A-
117, Table A-l 19, and Table A-120 differs from the method used to estimate fuel consumption volumes for CO2 in the
industrial and commercial sectors in this Inventory, which include CO2 emissions from all non-transportation mobile sources
(see Section 3.1 for a discussion of that methodology).
Table A-117: CO2 Emissions from Non-Transportation Mobile Sources [MBIT CO; Eq.l
Fuel Type/
Vehicle Type
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Agricultural
Equipment3
31.6
37.2
39.4
49.5
48.7
45.7
46.9
47.8
49.6
51.1
50.0
50.8
47.5
48.4
Construction/
Mining
Equipmentb
43.0
50.0
56.5
68.5
68.8
70.3
71.6
73.9
75.0
76.6
82.0
80.2
78.9
80.4
Other
Sources0
62.9
68.8
75.9
91.5
90.3
91.0
91.6
94.5
94.1
93.8
95.0
96.6
96.9
98.5
Total
137.5
156.0
171.8
209.5
207.8
207.0
210.1
216.3
218.7
221.5
227.1
227.6
223.2
227.3
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
c "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment,
commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
Note: The method used to estimate CO2 emissions in this supplementary information table differs from the method used to estimate CO2 in the industrial and
commercial sectors in the Inventory, which include CO2 emissions from all non-transportation mobile sources (see Section 3.1 for the methodology for
estimating CO2 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 MOVES2014a for years 1999 through 2016.
Estimation of HFC Emissions from Transportation Sources
In addition to CO2, N2O and CH4 emissions, transportation sources also result in emissions of HFCs. HFCs are
emitted to the atmosphere during equipment manufacture and operation (as a result of component failure, leaks, and
purges), as well as at servicing and disposal events. There are three categories of transportation-related HFC emissions;
Mobile air-conditioning represents the emissions from air conditioning units in passenger cars, light-duty trucks, and
heavy-duty vehicles; Comfort Cooling represents the emissions from air conditioning units in passenger trains and buses;
and Refrigerated Transport represents the emissions from units used to cool freight during transportation.
Table A-l 18 below presents these HFC emissions. Table A-l 19 presents all transportation and mobile source
greenhouse gas emissions, including HFC emissions.
A-172 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-118: HFC Emissions from Transportation Sources [MBIT CO; Eg.]
Vehicle Type
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Mobile AC
+
19.4
55.2
68.3
68.8
69.2
68.2
64.7
58.7
52.9
46.9
43.7
40.9
37.4
Passenger Cars
+
11.2
28.0
31.7
31.5
31.2
29.9
27.5
23.9
20.6
17.3
15.9
14.8
13.3
Light-Duty Trucks
+
7.8
25.6
33.9
34.5
35.1
35.2
34.2
31.7
29.3
26.7
24.9
23.3
21.4
Heavy-Duty Vehicles
+
0.5
1.6
2.6
2.8
2.9
3.0
3.1
3.0
2.9
2.9
2.9
2.8
2.7
Comfort Cooling for Trains and Buses
+
+
0.1
0.3
0.4
0.4
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
School and Tour Buses
+
+
0.1
0.3
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Transit Buses
+
+
+
+
+
+
+
+
+
+
+
+
0.1
0.1
Rail
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Refrigerated Transport
+
0.2
0.8
2.0
2.3
2.6
2.9
3.5
4.1
4.7
5.3
5.8
6.4
6.9
Medium- and Heavy-Duty Trucks
+
0.1
0.4
1.4
1.6
1.7
1.9
2.2
2.5
2.8
3.1
3.4
3.6
3.9
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
0.5
0.6
0.8
0.9
1.2
1.5
1.7
2.0
2.3
2.6
2.9
Total
+
19.6
56.2
70.6
71.5
72.3
71.6
68.7
63.2
58.1
52.7
50.0
47.7
44.8
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
A-173
-------
Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Mode/Vehicle Type/Fuel
Type
Table A-l 19 presents estimates of greenhouse gas emissions from an expanded analysis including all transportation
and additional mobile sources, as well as emissions from electricity generation by the consuming category, in CO2
equivalents. In total, transportation and non-transportation mobile sources emitted 2,089.9 MMT CO2 Eq. in 2016, an
increase of 25 percent from 1990.53 Transportation sources account for 1,857.6 MMT CO2 Eq. while non-transportation
mobile sources account for 232.2 MMT CO2 Eq. These estimates include HFC emissions for mobile AC, comfort cooling
for trains and buses, and refrigerated transport. These estimates were generated using the estimates of CO2 emissions from
transportation sources reported in Section 3.1 CO2 Emissions from Fossil Fuel Combustion, and CH4 emissions and N2O
emissions reported in the Mobile Combustion section of the Energy chapter; information on FIFCs 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 CO2 emitted from non-transportation mobile sources reported in
Table A-l 17 above.
Although all emissions reported here are based on estimates reported throughout this Inventory, some additional
calculations were performed in order to provide a detailed breakdown of emissions by mode and vehicle category. In the
case of N2O and CH4, additional calculations were performed to develop 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 CO2 emissions. N2O
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 2018 and DLA 2017). 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, N2O and CH4 estimates were developed for medium- and heavy-duty trucks and buses by multiplying
the emission estimates for heavy-duty vehicles for each fuel type (gasoline, diesel) from the Mobile Combustion section in
the Energy chapter, by the portion of fuel used by each vehicle type (from DOE 1993 through 2017). Carbon dioxide
emissions from non-transportation mobile sources are calculated using data from EPA's NONROAD component of
MOVES2014a (EPA 2017b). Otherwise, the table and figure are drawn directly from emission estimates presented elsewhere
in the Inventory, and are dependent on the methodologies presented in Annex 2.1 (for CO2), Chapter 4, and Annex 3.9 (for
HFCs), and earlier in this Annex (for CH4 and N2O).
Transportation sources include on-road vehicles, aircraft, boats and ships, rail, and pipelines (note: pipelines are a
transportation source but are stationary, not mobile sources). In addition, transportation-related greenhouse gas emissions
also include HFC released from mobile air-conditioners and refrigerated transport, and the release of CO2 from lubricants
(such as motor oil) used in transportation. Together, transportation sources were responsible for 1,857.6 MMT CO2 Eq. in
2016.
On-road vehicles were responsible for about 76 percent of all transportation and non-transportation mobile
greenhouse gas emissions in 2016. 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 2016, greenhouse gas emissions from passenger cars increased by 21 percent, while emissions from light-
duty trucks increased by two percent. Meanwhile, greenhouse gas emissions from medium- and heavy-duty trucks increased
85 percent between 1990 and 2016, reflecting the increased volume of total freight movement and an increasing share
transported by trucks.
53 Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan, Turbojet and
Turboprop Engines," EPA-420-R-09-90f, May 27, 2009 (see ).
54 In 20f f FHWA changed how they defined vehicle types for the purposes of reporting VMT for the years 2007 to 20f 0. 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-174 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Greenhouse gas emissions from aircraft decreased 11 percent between 1990 and 2016. Emissions from military
aircraft decreased 65 percent between 1990 and 2016. Commercial aircraft emissions rose 27 percent between 1990 and
2007 then dropped 14 percent from 2007 to 2016, a change of approximately 10 percent between 1990 and 2016.
Non-transportation mobile sources, such as construction/mining equipment, agricultural equipment, and
industrial/commercial equipment, emitted approximately 232.2 MMT CO2 Eq. in 2016. Together, these sources emitted
more greenhouse gases than ships and boats, and rail combined. Emissions from non-transportation mobile sources increased
rapidly, growing approximately 59 percent between 1990 and 2016. Methane and N2O emissions from these sources are
included in the "Mobile Combustion" section and CO2 emissions are included in the relevant economic sectors.
Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Gas
Table A-120 presents estimates of greenhouse gas emissions from transportation and other mobile sources broken
down by greenhouse gas. As this table shows, CO2 accounts for the vast majority of transportation greenhouse gas emissions
(approximately 97 percent in 2016). Emissions of CO2 from transportation and mobile sources increased by 402.9 MMT
CO2 Eq. between 1990 and 2016. In contrast, the combined emissions of CH4 and N2O decreased by 32.37 MMT CO2 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 44.8 MMT CO2 Eq. in 2016 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-121 and Table A-122 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-121. 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-122. 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-119. In addition, estimates of
fuel consumption from DOE (1993 through 2017) were used to allocate rail emissions between passenger and freight
categories.
In 2016, passenger transportation modes emitted 1,287.5 MMT CO2 Eq., while freight transportation modes
emitted 531.6 MMT CO2 Eq. Between 1990 and 2016, the percentage growth of greenhouse gas emissions from freight
sources was 52 percent, while emissions from passenger sources grew by 14 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-175
-------
Table fl-119: Total U.S. Greenhouse Gas Emissions from Transportation and Mobile Sources [MBIT CO; EqJ
Percent
Change
1990-
Mode / Vehicle Type / Fuel Type
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2016
Transportation Total3'
1,528.6
1,671.8
1,906.1
1,972.9
1,973.5
1,876.0
1,797.1
1,804.9
1,776.1
1,755.8
1,764.7
1,800.0
1,815.1
1,857.6
22%
On-Road Vehicles
1,207.3
1,343.9
1,547.1
1,642.5
1,637.9
1,559.4
1,513.3
1,513.5
1,485.9
1,474.6
1,473.4
1,523.0
1,526.3
1,556.0
29%
Passenger Cars
639.9
631.2
682.0
667.3
823.5
782.0
772.3
762.7
752.7
745.9
740.8
756.7
761.0
772.2
21%
Gasolineb<
632.0
612.2
650.3
631.5
787.9
747.0
738.7
731.4
724.6
721.2
719.5
736.6
741.6
754.1
19%
Dieselb>
7.9
7.8
3.7
4.1
4.1
3.7
3.6
3.7
4.1
4.1
4.1
4.1
4.3
4.3
-45%
AFVsc
+
+
+
+
+
+
+
+
+
+
+
0.1
0.3
0.4
6,924%
HFCs from Mobile AC
+
11.2
28.0
31.7
31.5
31.2
29.9
27.5
23.9
20.6
17.3
15.9
14.8
13.3
NA
Light-Duty Trucks
326.9
426.1
503.9
551.5
358.9
339.6
342.8
339.8
322.7
316.2
313.2
334.2
324.8
334.2
2%
Gasolineb
315.2
403.2
458.0
490.4
310.5
292.0
295.0
292.7
277.6
273.7
273.3
294.8
287.2
298.2
-5%
Dieselb
11.5
14.9
20.1
26.7
13.5
12.1
12.0
12.5
13.0
12.9
12.9
13.8
13.9
14.4
25%
AFVsc
0.2
0.1
0.1
0.5
0.4
0.5
0.4
0.4
0.4
0.2
0.3
0.6
0.4
0.3
32%
HFCs from Mobile AC
+
7.8
25.6
33.9
34.5
35.1
35.2
34.2
31.7
29.3
26.7
24.9
23.3
21.4
NA
Medium- and Heavy-Duty
Trucks
230.3
275.7
348.4
409.4
433.6
416.2
378.0
391.4
390.1
390.5
397.4
408.7
417.1
425.9
85%
Gasolineb
38.5
35.9
36.2
35.3
45.9
46.0
42.2
41.9
38.4
38.1
38.8
40.1
39.8
40.8
6%
Dieselb
190.7
238.4
309.5
369.1
382.5
364.0
329.9
343.1
344.7
344.8
350.4
360.4
369.2
376.8
98%
AFVsc
1.1
0.9
0.6
1.1
0.8
1.5
1.0
1.1
1.5
1.8
2.1
2.0
1.7
1.7
46%
HFCs from Refrigerated
Transport and Mobile ACe
+
0.6
2.0
4.0
4.4
4.6
4.9
5.3
5.5
5.8
6.0
6.3
6.5
6.6
NA
Buses
8.5
9.2
11.0
12.4
17.8
17.3
16.2
16.1
16.9
18.0
18.2
19.5
19.8
19.8
134%
Gasolineb
0.3
0.4
0.4
0.4
0.7
0.7
0.7
0.7
0.7
0.8
0.8
0.9
0.9
0.9
166%
Dieselb
8.0
8.7
10.2
10.6
15.5
14.6
13.6
13.5
14.4
15.4
15.5
16.8
17.1
17.0
112%
AFVsc
0.1
0.1
0.3
1.2
1.2
1.6
1.4
1.4
1.3
1.3
1.4
1.3
1.4
1.4
1,433%
HFCs from Comfort Cooling
+
+
0.1
0.3
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
1.9
4.2
4.3
4.1
3.6
3.5
4.0
3.8
3.8
3.7
3.9
125%
Gasolineb
1.7
1.8
1.8
1.9
4.2
4.3
4.1
3.6
3.5
4.0
3.8
3.8
3.7
3.9
125%
Aircraft
189.2
176.7
199.4
186.3
183.4
176.7
157.4
154.8
149.9
146.5
150.1
151.3
160.5
169.0
-11%
General Aviation Aircraft
42.9
35.8
35.9
30.1
24.4
30.5
21.2
26.7
22.5
19.9
23.6
20.9
26.8
35.1
-18%
Jet Fuel'
39.8
33.0
33.4
27.7
22.2
28.5
19.4
24.8
20.6
18.2
22.0
19.4
25.3
33.7
-15%
Aviation Gasoline
3.2
2.8
2.6
2.4
2.2
2.0
1.9
1.9
1.9
1.8
1.6
1.5
1.5
1.5
-54%
Commercial Aircraft
110.9
116.3
140.6
138.3
141.0
128.4
120.6
114.4
115.7
114.3
115.4
116.3
120.1
121.5
10%
Jet Fuel'
110.9
116.3
140.6
138.3
141.0
128.4
120.6
114.4
115.7
114.3
115.4
116.3
120.1
121.5
10%
Military Aircraft
35.3
24.5
22.9
18.0
18.0
17.7
15.5
13.7
11.7
12.2
11.1
14.1
13.6
12.4
-65%
Jet Fuel'
35.3
24.5
22.9
18.0
18.0
17.7
15.5
13.7
11.7
12.2
11.1
14.1
13.6
12.4
-65%
Ships and Boatsd
45.3
58.4
66.0
48.9
55.7
46.4
39.9
46.1
48.0
41.9
41.5
31.0
35.7
42.8
-6%
Gasoline
12.8
13.9
14.8
14.2
14.0
13.5
13.3
13.0
12.8
12.7
12.6
12.6
12.5
12.5
-2%
Distillate Fuel
9.7
14.9
17.1
10.9
11.6
11.4
11.5
11.2
14.0
11.4
11.5
10.2
16.2
14.2
47%
Residual Fuele
22.9
29.6
33.8
23.4
29.5
20.7
14.2
20.8
19.7
16.1
15.4
5.9
4.3
13.2
-42%
HFCs from Refrigerated
Transport6
+
+
0.3
0.5
0.6
0.8
0.9
1.2
1.5
1.7
2.0
2.3
2.6
2.9
NA
A-176 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Rail
38.9
43.1
46.1
52.8
51.9
48.2
41.0
43.7
45.3
43.9
44.8
46.2
44.1
40.8
5%
Distillate Fuel'
35.8
40.0
42.5
48.1
46.7
43.3
36.3
39.0
40.8
39.9
40.5
41.9
40.2
37.1
4%
Electricity
3.1
3.1
3.5
4.5
5.1
4.7
4.5
4.5
4.3
3.9
4.0
4.1
3.8
3.5
15%
Other Emissions from Rail
Electricity Use s
0.1
0.1
+
+
+
+
+
+
+
+
+
+
+
+
-29%
HFCs from Comfort Cooling
+
+
+
+
+
+
+
+
+
+
+
+
+
+
NA
HFCs from Refrigerated
Transport6
+
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
NA
Pipelines'1
36.0
38.4
35.4
32.4
34.4
35.9
37.1
37.3
38.1
40.5
46.2
39.4
38.5
39.6
10%
Natural Gas
36.0
38.4
35.4
32.4
34.4
35.9
37.1
37.3
38.1
40.5
46.2
39.4
38.5
39.6
10%
Other Transportation
11.8
11.3
12.1
9.9
10.2
9.5
8.5
9.5
9.0
8.3
8.8
9.1
10.0
9.5
-20%
Lubricants
11.8
11.3
12.1
9.9
10.2
9.5
8.5
9.5
9.0
8.3
00
CO
9.1
10.0
9.5
-20%
Non-Transportation Mobile'
Tntal
145.8
164.4
180.3
218.0
215.5
214.0
216.7
222.7
224.8
227.2
232.6
232.9
228.2
232.2
59%
Agricultural Equipments
32.8
38.3
40.3
50.6
49.7
46.6
47.8
48.7
50.5
52.0
50.9
51.6
48.2
49.1
50%
Gasoline
7.7
8.7
6.1
11.4
9.8
5.8
6.1
6.2
7.1
7.8
5.8
5.7
1.4
1.5
-81%
Diesel
24.6
29.3
34.1
39.2
39.9
40.8
41.6
42.5
43.3
44.2
45.0
45.9
46.8
47.6
94%
CNG
0.5
0.4
0.2
+
+
+
+
+
+
+
+
+
+
+
-100%
LPG
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-19%
Construction/ Mining11
Equipment''"1
44.4
51.5
58.1
70.3
70.5
72.0
73.2
75.6
76.6
78.3
83.7
81.8
80.4
81.9
84%
Gasoline
4.7
4.2
3.2
6.4
5.3
5.2
5.0
5.9
5.5
5.6
9.6
6.2
3.2
3.3
-30%
Diesel
38.9
46.3
53.8
62.9
64.2
65.7
67.2
68.7
70.2
71.7
73.3
74.8
76.3
77.8
100%
CNG
0.7
0.8
0.9
0.9
0.9
0.9
0.8
0.8
0.8
0.8
0.7
0.7
0.7
0.7
-9%
LPG
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
30%
Other Equipment'1
68.6
74.6
81.9
97.1
95.2
95.5
95.7
98.4
97.7
97.0
98.0
99.4
99.6
101.1
47%
Gasoline
42.7
42.9
44.3
55.0
52.9
52.6
52.4
54.6
53.4
52.0
52.1
52.8
52.0
52.7
23%
Diesel
15.0
18.2
21.3
25.3
26.0
26.6
27.3
28.0
28.7
29.3
30.0
30.7
31.4
32.0
113%
CNG
2.9
3.3
3.8
3.1
2.7
2.5
2.4
2.2
2.1
2.0
1.9
1.9
1.9
1.8
-36%
LPG
7.9
10.2
12.5
13.6
13.7
13.7
13.6
13.7
13.5
13.7
13.9
14.1
14.4
14.5
83%
Transportation and Non-
1,674.4
1,836.2
2,086.3
2,190.9
2,189.0
2,090.0
2,013.8
2,027.6
2,000.9
1,983.0
1,997.4
2,032.8
2,043.3
2,089.8
25%
Transportation Mobile Total1
+ Does not exceed 0.05 MMT CO2 Eq.; NA - Not Applicable, as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.
a Not including emissions from international bunker fuels.
b Gasoline and diesel highway vehicle fuel consumption estimates used to develop CO2 estimates in this Inventory are based on data from FHWA Highway Statistics Table MF-21, MF-27 and VM-1 (FHWA 1996
through 2017). Data from Table VM-1 are used to estimate the share of fuel consumption between each on-road vehicle class. For mobile CH4 and N2O emissions estimates, gasoline and diesel highway vehicle
mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2017). 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 2016 has not been published yet, therefore 2015 data are used as a proxy.
c 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 2014 Inventory and apply to the 1990 to 2016 time period. This resulted in large reductions in AFV VMT, thus leading to a shift in VMT to conventional on-road vehicle classes.
d Fluctuations in emission estimates reflect data collection problems. Note that CH4 and N2O from U.S. Territories are included in this value, but not CO2 emissions from U.S. Territories, which are estimated
A-177
-------
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 2016 is not available, therefore 2013 data are used as a proxy.
9 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 CO2 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 CO2 emissions from non-transportation mobile sources in this supplementary information table differs from the method used to estimate CO2 in the industrial and commercial
sectors in the Inventory, which include CO2 emissions from all non-transportation mobile sources (see Section 3.1 for the methodology for estimating CO2 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.
'"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 N2O emissions from mobile combustion relative to previous Inventories are largely due to updates made to the Motor Vehicle Emissions Simulator (MOVES2014a) model that is used
to estimate on-road gasoline vehicle distribution and mileage across the time series. See Section 3.1 "CH4 and N2O from Mobile Combustion" for more detail. In 2015, EPA incorporated the NONROAD2008 model
into MOVES2014a. This year's Inventory uses the NONROAD component of MOVES2014a for years 1999 through 2016. 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 fl-120: Transportation and Mobile Source Emissions by Gas [MBIT CO; EqJ
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Percent
Change
1990-2016
C02a
1,620.0
1,752.2
1,968.1
2,074.7
2,076.7
1,980.2
1,906.8
1,924.8
1,905.3
1,895.5
1,917.5
1,957.9
1,972.4
2,022.8
25%
N20
41.7
52.2
51.4
36.5
32.7
30.4
28.9
27.9
26.6
24.3
22.5
20.6
19.3
18.4
-56%
ch4
12.7
12.2
10.6
9.0
8.0
7.1
6.5
6.2
5.7
5.1
4.7
4.2
3.8
3.6
-71%
HFC
+
19.6
56.2
70.6
71.5
72.3
71.6
68.7
63.2
58.1
52.7
50.0
47.7
44.8
NA
Total"
1,674.4
1,836.1
2,086.3
2,190.8
2,189.0
2,089.9
2,013.7
2,027.5
2,000.9
1,982.9
1,997.3
2,032.8
2,043.2
2,089.7
25%
+ Does not exceed 0.05 MMT CO2 Eq.; NA - Not Applicable, as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.
aThe method used to estimate CO2 emissions from non-transportation mobile sources in this supplementary information table differs from the method used to estimate CO2 in the industrial and commercial sectors in
the Inventory, which include CO2 emissions from all non-transportation mobile sources (see Section 3.1 for the methodology for estimating CO2 emissions from fossil fuel combustion in this Inventory).
b Total excludes other emissions from electricity generation and CH4 and N2O emissions from electric rail.
Note: Gasoline and diesel highway vehicle fuel consumption estimates used to develop CO2 estimates in this Inventory are based on data from FHWA Highway Statistics Table MF-21, MF-27 and VM-1 (FHWA
1996 through 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 N2O emissions estimates, gasoline and diesel highway
vehicle mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2017). 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 2016 has not been published yet, therefore 2015 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-178 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Figure fl-4:
Domestic Greenhouse Gas Emissions by Mode and Vehicle Type,1990 to 2016 (MMT CO2 Eq.)
2,500
Passenger Cars/Motorcycles
I Medium-and Heavy-Duty Trucks and Buses
Boats/Ships, Rail, and Pipelines
Non-Transportation Mobile Sources
Light-Duty Trucks
I Aircraft
I Mobile AC, Refrig. Transport, Lubricants
2,000
O
u
1,500
1,000
500
0
1
(N
ro
^r
LO
to
00
cn
O
(N
ro
^r
LO
to
OO
cn
0
t-h
(N
cn
CD
CD
CD
cn
CD
CD
cn
CD
cn
0
O
O
0
O
O
O
0
0
0
t-h
1
CD
CD
CD
CD
CD
CD
CD
CD
CD
cn
0
O
O
0
O
O
O
0
0
0
0
0
O
1
T-\
1
1
1
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
(N
-------
Table fl-121: Greenhouse Gas Emissions from PassengerTransportation [MMT CO; Eg.]
Vehicle Type
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Percent
Change
1990-2016
On-Road
976.9
1,068.2
1,198.7
1,233.1
1,204.3
1,143.2
1,135.3
1,122.2
1,095.8
1,084.1
1,076.0
1,114.3
1,109.3
1,130.0
Vehicles3'11
16%
Passenger Cars
639.9
631.2
682.0
667.3
823.5
782.0
772.3
762.7
752.7
745.9
740.8
756.7
761.0
772.2
21%
Light-Duty Trucks
326.9
426.1
503.9
551.5
358.9
339.6
342.8
339.8
322.7
316.2
313.2
334.2
324.8
334.2
2%
Buses
8.5
9.2
11.0
12.4
17.8
17.3
16.2
16.1
16.9
18.0
18.2
19.5
19.8
19.8
134%
Motorcycles
1.7
1.8
1.8
1.9
4.2
4.3
4.1
3.6
3.5
4.0
3.8
3.8
3.7
3.9
125%
Aircraft
134.6
132.0
152.2
146.6
144.9
140.9
125.2
124.8
122.1
118.5
123.1
120.9
130.5
139.8
4%
General Aviation
42.9
35.8
35.9
30.1
24.4
30.5
21.2
26.7
22.5
19.9
23.6
20.9
26.8
35.1
-18%
Commercial Aircraft
91.7
96.2
116.3
116.5
120.4
110.4
103.9
98.0
99.6
98.6
99.5
100.0
103.6
104.7
14%
Recreational Boats
14.8
16.2
17.6
17.5
17.3
16.9
16.7
16.5
16.4
16.4
16.5
16.6
12.5
12.5
-15%
Passenger Rail
4.4
4.5
5.2
6.0
6.6
6.2
6.1
6.1
5.9
5.5
5.7
5.7
5.4
5.2
18%
Total
1,130.7
1,220.9
1,373.6
1,403.1
1,373.0
1,307.3
1,283.3
1,269.6
1,240.3
1,224.6
1,221.4
1,257.5
1,257.6
1,287.5
14%
a The current Inventory includes updated vehicle population data based on the MOVES2014a Model.
b Gasoline and diesel highway vehicle fuel consumption estimates used to develop CO2 estimates in this Inventory are based on data from FHWA Highway Statistics Table MF-21, MF-27 and VM-1 (FHWA1996
through 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 N2O emissions estimates, gasoline and diesel highway vehicle
mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2017). 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 2016 has not been published yet, therefore 2015 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 MOVES2014a for years 1999 through 2016. In 2015, EIA changed its methods for estimating AFVfuel
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 2014 Inventory and apply to the
1990 through 2016 time period. This resulted in large reductions in AFV VMT, thus leading to a shift in VMT to conventional on-road vehicle classes.
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 fl-122: Greenhouse Gas Emissions from Domestic Freight Transportation [MBIT CO; Eg.]
Percent
Change
1990-
By Mode
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2016
Truckingab
230.3
275.3
346.8
406.8
430.8
413.3
375.0
388.3
387.1
387.5
394.5
405.8
414.3
423.2
84%
Freight Rail
34.5
38.6
40.9
46.8
45.3
41.9
34.8
37.5
39.3
38.4
39.0
40.4
38.7
35.6
3%
Ships and Non-Recreational Boats
30.6
42.2
48.4
31.5
38.4
29.5
23.1
29.5
31.5
25.5
29.6
18.4
7.2
16.4
-46%
Pipelines'1
36.0
38.4
35.4
32.4
34.4
35.9
37.1
37.3
38.1
40.5
46.2
39.4
38.5
39.6
10%
Commercial Aircraft
19.2
20.1
24.3
21.8
20.5
18.0
16.7
16.3
16.0
15.8
15.9
16.2
16.5
16.8
-12%
Total
350.7
414.5
495.9
539.2
569.4
538.6
486.6
509.0
512.1
507.7
525.2
520.3
515.2
531.6
52%
a The current Inventory includes updated vehicle population data based on the MOVES2014a Model.
b Gasoline and diesel highway vehicle fuel consumption estimates used to develop CO2 estimates in this Inventory are based on data from FHWA Highway Statistics Table MF-21, MF-27 and VM-1 (FHWA 1996
through 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 N2O emissions estimates, gasoline and diesel highway vehicle
A-180 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2017). 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 2016 has not been published yet, therefore 2015 data are as a proxy.
d Pipelines reflect CO2 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 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 MOVES2014a for years 1999 through 2016. 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 2014 Inventory and apply to the
1990 to 2016 time period. This resulted in large reductions in AFV VMT, thus leading to a shift in VMT to conventional on-road vehicle classes.
In 2016, historical confidential vehicle sales data were re-evaluated to determine the engine technology assignments. First, several light-duty trucks were re-characterized as heavy-duty vehicles based upon
gross vehicle weight rating (GVWR) and confidential sales data. Second, the emission standards each vehicle type was assumed to have met were re-examined using confidential sales data. Also, in previous
Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and therefore not included in the engine technology breakouts. For this Inventory, HEVs are classified as
gasoline vehicles across the entire time series.
A-181
-------
References
AAR (2008 through 2017) Railroad Facts. Policy and Economics Department, Association of American Railroads, Washington,
D.C. Obtained from Clyde Crimmel at AAR.
ANL (2016) The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model (GREET 2016). Argonne
National Laboratory. October 2016. Available at .
APTA (2007 through 2017) Public Transportation Fact Book. American Public Transportation Association, Washington, D.C.
Available online at: .
APTA (2006) Commuter Rail National Totals. American Public Transportation Association, Washington, D.C. Available online
at: .
Benson, D. (2002 through 2004) Unpublished data. Upper Great Plains Transportation Institute, North Dakota State University
and American Short Line & Regional Railroad Association.
Browning, L. (2017) "Updated Methodology for Estimating CELi and N2O Emissions from Highway Vehicle Alternative Fuel
Vehicles". Technical Memo, October 2017.
Browning, L. (2005) Personal communication with Lou Browning, Emission control technologies for diesel highway vehicles
specialist, ICF.
DHS (2008) Email Communication. Elissa Kay, Department of Homeland Security and Joe Aamidor, ICF International. January
11,2008.
DLA Energy (2017) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense Energy
Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, D.C.
DOE (1993 through 2017) Transportation Energy Data Book Edition 36. Office of Transportation Technologies, Center for
Transportation Analysis, Energy Division, Oak Ridge National Laboratory. ORNL-6978.
EDTA (2017) Electric Drive Sales Dashboard. Electric Drive Transportation Association, Washington, D.C. Available at:
.
EEA (2009) EMEP/EAA Air Pollutant Emission Inventory Guidebook. European Environment Agency, Copenhagen, Denmark.
Available online at: EI A (2017a) Monthly Energy Review, October 2017, Energy Information Administration, U.S.
Department of Energy, Washington, DC. DOE/EIA-0035(2017/10).
EIA (2017 d) Natural Gas Annual 2016. Energy Information Administration, U.S. Department of Energy. Washington, D.C.
DOE/EIA-0131 (06).
EIA (2017e) Alternative Fuels Data Tables. Energy Information Administration, U.S. Department of Energy. Washington,
D.C. Available online at: .
EIA (2017 f) Petroleum Supply Annual 2016. Energy Information Administration, U.S. Department ofEnergy. Washington, D.C.
DOE/EIA-810.
EIA (1991 through 20\l)Fuel Oil and Kerosene Sales. Energy Information Administration, U.S. Department ofEnergy.
Washington, D.C. Available online at: .
EPA (2017b). Motor Vehicle Emissions Simulator (MOVES) 2014a. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at: .
EPA (2017c) Annual Certification Test Results Report. Office of Transportation and Air Quality, U.S. Environmental Protection
Agency. Available online at: .
EPA (2017d) Confidential Engine Family Sales Data Submitted to EPA By Manufacturers. Office of Transportation and Air
Quality, U.S. Environmental Protection Agency.
EPA (2016g) "1970 - 2016 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory (NEI)
Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available online at:
.
EPA (2000) Mobile6 Vehicle Emission Modeling Software. Office of Mobile Sources, U.S. Environmental Protection Agency,
Ann Arbor, Michigan.
A-182 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
EPA (1999b) Regulatory Announcement: EPA's Program for Cleaner Vehicles and Cleaner Gasoline. Office of Mobile Sources.
December 1999. EPA420-F-99-051. Available online at:
.
EPA (1998b) Emissions of Nitrous Oxide from Highway Mobile Sources: Comments on the Draft Inventory of U.S. Greenhouse
Gas Emissions and Sinks, 1990-1996. Office of Mobile Sources, Assessment and Modeling Division, U.S. Environmental
Protection Agency. August 1998. EPA420-R-98-009.
FAA (2018) Personal Communication between FAA and John Steller, Mausami Desai, and Vincent Camobreco for aviation
emissions estimates from the Aviation Environmental Design Tool (AEDT). January 2018.
FFIWA (1996 through 2017) Highway Statistics. Federal Highway Administration, U.S. Department of Transportation,
Washington, D.C. Report FE[WA-PL-96-023-annual. Available online at:
.
FHWA (2015) Off-Highway and Public-Use Gasoline Consumption Estimation Models Used in the Federal Highway
Administration, Publication Number FHWA-PL-17-012. Available online at:
Gaffney, J. (2007) Email Communication. John Gaffney, American Public Transportation Association and Joe Aamidor, ICF
International. December 17, 2007.
ICF (2004) Update of Methane and Nitrous Oxide Emission Factors for On-Highway Vehicles. Final Report to U.S.
Environmental Protection Agency. February 2004.
ICF (2017a) Updated On-highway CH4 and N2O Emission Factors for GHG Inventory. Memorandum from ICF to Sarah Roberts
and Justine Geidosch, Office of Transportation and Air Quality, U.S. Environmental Protection Agency. October 2017.
ICF (2017b) Updated Non-Highway CH4 and N2O Emission Factors for U.S. GHG Inventory. Memorandum from ICF to Sarah
Roberts and Justine Geidosch, Office of Transportation and Air Quality, U.S. Environmental Protection Agency. October
2017.
Santoni, G., B. Lee, E. Wood, S. Herndon, R. Miake-Lye, S Wofsy, J. McManus, D. Nelson, M. Zahniser (2011) Aircraft
emissions of methane and nitrous oxide during the alternative aviation fuel experiment. Environ Sci Technol. 2011 Aug 15;
45(16): 7075-82.
Whorton, D. (2006 through 2014) Personal communication, Class II and III Rail energy consumption, American Short Line and
Regional Railroad Association.
A-183
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3.3. Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel
Consumption
IPCC Tier 3B Method: Commercial aircraft jet fuel burn and carbon dioxide (CO2) emissions estimates were
developed by the U.S. Federal Aviation Administration (FAA) using radar-informed data from the FAA Enhanced Traffic
Management System (ETMS) for 2000 through 2016 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 (IPCC 2006).
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 2016 using the same radar-informed data modeled with AEDT.
Since this process builds estimates from flight-specific information, the emissions estimates for commercial aircraft can
include emissions associated with the U.S. Territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake
Island, and other U.S. Pacific Islands). However, to allow for the alignment of emissions estimates for commercial aircraft
with other data that is provided without the U.S. Territories, this annex includes emissions estimates for commercial aircraft
both with and without the U.S. Territories included.
Time Series and Analysis Update: The FAA incrementally improves the consistency, robustness, and fidelity of
the CO2 emissions modeling for commercial aircraft, which is the basis of the Tier3B inventories presented in this report.
While the FAA does not anticipate significant changes to the AEDT model in the future, recommended improvements are
limited by budget and time constraints, as well as data availability. For instance, previous reports included reported annual
CO2 emission estimates for 2000 through 2005 that were modeled using the FAA's System for assessing Aviation's Global
Emissions (SAGE). That tool and its capabilities were significantly improved after it was incorporated and evolved into
AEDT. For this report, the AEDT model was used to generate annual CO2 emission estimates for 2000, 2005, 2010, 2011,
2012, 2013, 2014, 2015 and 2016 only. The reported annual CO2 emissions values for 2001 through 2004 were estimated
from the previously reported SAGE data. Likewise, CO2 emissions values for 2006 through 2009 were estimated by
interpolation to preserve trends from past reports.
Commercial aircraft radar data sets are not available for years prior to 2000. Instead, the FAA applied a Tier3B
methodology by developing Official Airline Guide (OAG) schedule-informed estimates modeled with AEDT and great
circle trajectories for 1990, 2000 and 2010. The ratios between the OAG schedule-informed and the radar-informed
inventories for the years 2000 and 2010 were applied to the 1990 OAG scheduled-informed inventory to generate the best
possible CO2 inventory estimate for commercial aircraft in 1990. The resultant 1990 CO2 inventory served as the reference
for generating the additional 1991 to 1999 emissions estimates, which were established using previously available trends.
Notes on the 1990 CO2 Emissions Inventory for Commercial Aircraft: There are uncertainties associated with
the modeled 1990 data that do not exist for the modeled 2000 to 2016 data. Radar-based data is not available for 1990. The
OAG schedule information generally includes fewer carriers than radar information, and this will result in a different fleet
mix, and in turn, different CO2 emissions than would be quantified using a radar-based data set. For this reason, the FAA
adjusted the 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 CO2 emissions as compared
to data from 2000 forward. Also, the revised 1990 CO2 emissions inventory now reflects only commercial aircraft jet fuel
consumption, while previous reports may have aggregatedjet 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.
The 1990 commercial aircraft CO2 emissions inventory is approximately 8.7 percent lower than the 2016 CO2
emissions inventory. It is important to note that the distance flown increased by more than 45 percent over this 25-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
56 Additional information on the AEDT modeling process is available at:
.
A-184 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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 (Santoni et al. 2011). As a
result, the U.S. Environmental Protection Agency published that " ...methane is no longer considered to be an emission from
aircraft gas turbine engines burning Jet A at higher power settings and is, in fact, consumed in net at these higher powers."-1
In accordance with the following statements in the 2006IPCC 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., NjO 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.
Figure A-5: Commercial Aviation Fuel Burn for the United States and Territories
Commercial Aviation Fuel Burn
for the United States and Territories
5.00E+10
4.50E+10
4.00E+10
_ 3.50E+10
55
^ 3.00E+10
E 2.50E+10
CO
2.00E+10
00 cr> o
O H fN m -sf
cr>cr>cr>(T'>(x>cr>cr>cr>
(XicTicricTtcricricricTicxicr)
~i—1—1—1—1—1 1
.
A-185
-------
Table A-123: Commercial Aviation Fuel Burn forthe United States and Territories
Fuel
Fuel
Distance
Burn (M
Burn
CO2
Year
Region
Flown (nmi)
Gallon)
(Tbtu)
Fuel Burn (Kg)
(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
International U.S. 50 States and U.S. Territories
1,309,565,963
6,040
815
18,181,535,058
57.4
Domestic U.S. 50 States
5,891,481,028
14,349
1,937
43,191,000,202
136.3
International U.S. 50 States
1,331,784,289
6,117
826
18,412,169,613
58.1
2001 a
Domestic U.S. 50 States and U.S. Territories
5,360,977,447
13,121
1,771
39,493,457,147
124.6
International U.S. 50 States and U.S. Territories
1,171,130,679
5,402
729
16,259,550,186
51.3
Domestic U.S. 50 States
5,268,687,772
12,832
1,732
38,625,244,409
121.9
International U.S. 50 States
1,191,000,288
5,470
739
16,465,804,174
51.9
2002a
Domestic U.S. 50 States and U.S. Territories
5,219,345,344
12,774
1,725
38,450,076,259
121.3
International U.S. 50 States and U.S. Territories
1,140,190,481
5,259
710
15,829,987,794
49.9
Domestic U.S. 50 States
5,129,493,877
12,493
1,687
37,604,800,905
118.6
International U.S. 50 States
1,159,535,153
5,326
719
16,030,792,741
50.6
2003a
Domestic U.S. 50 States and U.S. Territories
5,288,138,079
12,942
1,747
38,956,861,262
122.9
International U.S. 50 States and U.S. Territories
1,155,218,577
5,328
719
16,038,632,384
50.6
Domestic U.S. 50 States
5,197,102,340
12,658
1,709
38,100,444,893
120.2
International U.S. 50 States
1,174,818,219
5,396
728
16,242,084,008
51.2
2004a
Domestic U.S. 50 States and U.S. Territories
5,371,498,689
13,146
1,775
39,570,965,441
124.8
International U.S. 50 States and U.S. Territories
1,173,429,093
5,412
731
16,291,460,535
51.4
Domestic U.S. 50 States
5,279,027,890
12,857
1,736
38,701,048,784
122.1
International U.S. 50 States
1,193,337,698
5,481
740
16,498,119,309
52.1
2005
Domestic U.S. 50 States and U.S. Territories
6,476,007,697
13,976
1,887
42,067,562,737
132.7
International U.S. 50 States and U.S. Territories
1,373,543,928
5,858
791
17,633,508,081
55.6
Domestic U.S. 50 States
6,370,544,998
13,654
1,843
41,098,359,387
129.7
International U.S. 50 States
1,397,051,323
5,936
801
17,868,972,965
56.4
2006a
Domestic U.S. 50 States and U.S. Territories
5,894,323,482
14,426
1,948
43,422,531,461
137.0
International U.S. 50 States and U.S. Territories
1,287,642,623
5,939
802
17,877,159,421
56.4
Domestic U.S. 50 States
5,792,852,211
14,109
1,905
42,467,943,091
134.0
International U.S. 50 States
1,309,488,994
6,015
812
18,103,932,940
57.1
2007a
Domestic U.S. 50 States and U.S. Territories
6,009,247,818
14,707
1,986
44,269,160,525
139.7
International U.S. 50 States and U.S. Territories
1,312,748,383
6,055
817
18,225,718,619
57.5
Domestic U.S. 50 States
5,905,798,114
14,384
1,942
43,295,960,105
136.6
International U.S. 50 States
1,335,020,703
6,132
828
18,456,913,646
58.2
2008a
Domestic U.S. 50 States and U.S. Territories
5,475,092,456
13,400
1,809
40,334,124,033
127.3
International U.S. 50 States and U.S. Territories
1,196,059,638
5,517
745
16,605,654,741
52.4
Domestic U.S. 50 States
5,380,838,282
13,105
1,769
39,447,430,318
124.5
International U.S. 50 States
1,216,352,196
5,587
754
16,816,299,099
53.1
2009a
Domestic U.S. 50 States and U.S. Territories
5,143,268,671
12,588
1,699
37,889,631,668
119.5
International U.S. 50 States and U.S. Territories
1,123,571,175
5,182
700
15,599,251,424
49.2
Domestic U.S. 50 States
5,054,726,871
12,311
1,662
37,056,676,966
116.9
International U.S. 50 States
1,142,633,881
5,248
709
15,797,129,457
49.8
2010
Domestic U.S. 50 States and U.S. Territories
5,652,264,576
11,931
1,611
35,912,723,830
113.3
International U.S. 50 States and U.S. Territories
1,474,839,733
6,044
816
18,192,953,916
57.4
Domestic U.S. 50 States
5,554,043,585
11,667
1,575
35,116,863,245
110.8
International U.S. 50 States
1,497,606,695
6,113
825
18,398,996,825
58.0
2011
Domestic U.S. 50 States and U.S. Territories
5,767,378,664
12,067
1,629
36,321,170,730
114.6
International U.S. 50 States and U.S. Territories
1,576,982,962
6,496
877
19,551,631,939
61.7
Domestic U.S. 50 States
5,673,689,481
11,823
1,596
35,588,754,827
112.3
A-186 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
International U.S. 50 States
1,596,797,398
6,554
885
19,727,043,614
62.2
2012
Domestic U.S. 50 States and U.S. Territories
5,735,605,432
11,932
1,611
35,915,745,616
113.3
International U.S. 50 States and U.S. Territories
1,619,012,587
6,464
873
19,457,378,739
61.4
Domestic U.S. 50 States
5,636,910,529
11,672
1,576
35,132,961,140
110.8
International U.S. 50 States
1,637,917,110
6,507
879
19,587,140,347
61.8
2013
Domestic U.S. 50 States and U.S. Territories
5,808,034,123
12,031
1,624
36,212,974,471
114.3
International U.S. 50 States and U.S. Territories
1,641,151,400
6,611
892
19,898,871,458
62.8
Domestic U.S. 50 States
5,708,807,315
11,780
1,590
35,458,690,595
111.9
International U.S. 50 States
1,661,167,498
6,657
899
20,036,865,038
63.2
2014
Domestic U.S. 50 States and U.S. Territories
5,825,999,388
12,131
1,638
36,514,970,659
115.2
International U.S. 50 States and U.S. Territories
1,724,559,209
6,980
942
21,008,818,741
66.3
Domestic U.S. 50 States
5,725,819,482
11,882
1,604
35,764,791,774
112.8
International U.S. 50 States
1,745,315,059
7,027
949
21,152,418,387
66.7
2015
Domestic U.S. 50 States and U.S. Territories
5,900,440,363
12,534
1,692
37,727,860,796
119.0
International U.S. 50 States and U.S. Territories
1,757,724,661
7,227
976
21,752,301,359
68.6
Domestic U.S 50 States
5,801,594,806
12,291
1,659
36,997,658,406
116.7
International U.S. 50 States
1,793,787,700
7,310
987
22,002,733,062
69.4
2016
Domestic U.S. 50 States and U.S. Territories
5,929,429,373
12,674
1,711
38,148,578,811
120.4
International U.S. 50 States and U.S. Territories
1,817,739,570
7,453
1006
22,434,619,940
70.8
Domestic U.S 50 States
5,827,141,640
12,422
1,677
37,391,339,601
118.0
International U.S. 50 States
1,839,651,091
7,504
1013
22,588,366,704
71.3
NA (Not Applicable)
a Estimates for these years were derived from previously reported tools and methods
A-187
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References
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and
K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
IPCC (1999) Aviation and the Global Atmosphere. Intergovernmental Panel on Climate Change. [J.E. Penner, et al. (eds.)].
Cambridge University Press. Cambridge, United Kingdom.
Santoni, G., B. Lee, E. Wood, S. Herndon, R. Miake-Lye, S Wofsy, J. McManus, D. Nelson, M. Zahniser (2011) Aircraft
emissions of methane and nitrous oxide during the alternative aviation fuel experiment. Environ Sci Technol. 2011 Aug
15; 45(16):7075-82.
A-188 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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3.4. Methodology for Estimating CH4 Emissions from Coal Mining
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 from degasification systems. Some mines recover
and use the generated CH4, thereby reducing emissions to the atmosphere. Total CH4 emitted from underground mines equals
the CH4 liberated from ventilation systems, plus the CH4 liberated from degasification systems, minus CH4 recovered and
used.
Step 1.1: Estimate CH4 Liberated from Ventilation Systems
All coal mines with detectable CH4 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 emissions levels at underground mines. MSHA maintains a database of measurement data from all
underground mines with detectable levels of CH4 in their ventilation air (MSHA 2017).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 2015, 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 CO2 Eq.)—have been required to report to EPA's GHGRP (EPA 2016).59 Mines that
report to EPA's GHGRP must report quarterly measurements of CH4 emissions from ventilation systems to EPA; 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.60
Since 2013, ventilation emission estimates have been calculated based on both EPA's GHGRP61 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
liberated from ventilation systems was estimated by summing the emissions from the EPA's GHGRP mines and emissions
based on MSHA quarterly measurements for the remaining mines not reporting to EPA's GHGRP.
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 2016, 90 underground coal mines
reported to the program.
60 MSHA records coal mine CH4 readings with concentrations of greater than 50 ppm (parts per million) CH4. Readings below this
threshold are considered non-detectable.
61 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-189
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Table fl-124: 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)
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).
Twenty-five mines employed degasification systems in 2016, and the CH4 liberated through these systems was
reported to the EPA's GHGRP (EPA 2017). Fifteen of these mines reported CH4 recovery and use projects, and the other
ten 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 to
estimate CH4 liberated from degasification systems at 20 of the 25 mines that used degasification systems in 2016.
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.62 EPA's GHGRP does not require gas production from virgin coal seams
(coalbed methane) to be reported by coal mines under subpart FF. Most pre-mining wells drilled from the surface are
considered coalbed methane wells and are reported under another subpart of the program (subpart W, "Petroleum and Natural
Gas Systems"). As a result, for the five mines with degasification systems that include pre-mining wells that were mined
through in 2016, EPA's GHGRP information was supplemented with historical data from state gas well production databases
(DMME 2017; GSA 2017; WVGES 2017), as well as with mine-specific information regarding the dates on which pre-
62 A well is "mined through" when coal mining development or the working face intersects the borehole or well.
A-190 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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mining wells were mined through (JWR 2010; El Paso 2009). For pre-mining wells, the cumulative CH4 production from
the well is totaled using gas sales data, and considered liberated from the mine's degasification system the year in which the
well is mined through.
EPA's GHGRP reports with CH4 liberated from degasification systems are reviewed for errors in reporting. For
some mines, GF1GRP data are corrected for the Inventory based on expert judgment. Common errors include reporting CFLi
liberated as CH4 destroyed and vice versa. Other errors include reporting CFLi destroyed without reporting any CH4 liberated
by degasification systems. In the rare cases where GHGRP data are inaccurate and gas sales data unavailable, estimates of
CH4 liberated are based on historical CH4 liberation rates. For one mine, due to a lack of mine-provided information used in
prior years and a GHGRP reporting discrepancy, the CH4 liberated was based on an estimate from historical mine-provided
CH4 recovery and use rates and state gas sales records (DMME 2017).
Step 1.3: Estimate CH4 Recovered from Ventilation and Degasification Systems, and Utilized or Destroyed (Emissions
Avoided)
Of the 15 active coal mines with operational CH4 recovery and use projects in 2016, 14 sold the recovered CH4 to
a pipeline, including one mine that used CH4 to fuel a thermal coal dryer and one mine that used CH4 to heat mine ventilation
air.
Ten of the 15 mines deployed degasification systems in 2016; for those mines, estimates of CH4 recovered from
the systems were exclusively based on GHGRP data. 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.
Of the 15 mines with CH4 recovery in 2016, four intersected pre-mining wells in 2016. EPA's GHGRP and
supplemental data were used to estimate CH4 recovered and used at two of these mines, while supplemental data alone were
used at the other two mines that reported as a single entity to EPA's GHGRP. Supplemental information was used for these
four mines because estimating CH4 recovery and use from pre-mining wells requires additional data (not reported under
subpart FF ofEPA's GHGRP; see discussion in step 1.2 above) to account for the emissions avoided. The supplemental data
came from state gas production databases (GSA 2017; WVGES 2016), as well as mine-specific information on the timing
of mined-through pre-mining wells (JWR 2010; El Paso 2009). For pre-mining wells, the cumulative CH4 production from
the wells was totaled using gas sales data, and considered to be CH4 recovered and used from the mine's degasification
system the year in which the well is mined through.
For one mine, due to a lack of mine-provided information used in prior years and a GHGRP reporting discrepancy,
the CH4 recovered and used was based on an estimate from historical mine-provided CH4 recovery and use rates and state
gas sales records (DMME 2017). In 2016, the availability of the Virginia Division of Gas and Oil Data Information System
made it possible to estimate recovered degasification emissions for this mine based on published well production.
EPA's GHGRP reports with CH4 recovered and used from degasification systems 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). In 2016, GHGRP information was not used to estimate CH4 recovered and used at two mines because of a lack
of mine-provided information used in prior years and GHGRP reporting discrepancies.
In 2016, one mine destroyed a portion of its CH4 emissions from ventilation systems using thermal oxidation
technology. The amount of CH4 recovered and destroyed by the project was determined through publicly available emission
reduction project information (ACR 2017).
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 CH4 from over-
and under-burden) to estimate CH4 emissions (see 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).
A-191
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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-125, which presents coal basin definitions by basin and by state.
The Energy Information Administration's Annual Coal Report (EIA 2017) 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-125. 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-126 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 CFU content in the basin. For this analysis, the post-mining emission factor was determined to be 32.5 percent
of the in situ CFLi content in the basin. Table A-127 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 CFU 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-125 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-126 shows annual underground, surface, and total
coal production (in short tons) for each coal basin. Table A-127 shows the surface, post-surface, and post-underground
emission factors used for estimating CFU emissions for each of the categories. Table A-128 presents annual estimates of
CFLi emissions for ventilation and degasification systems, and CFU used and emitted by underground coal mines. Table A-
129 presents annual estimates of total CFU emissions from underground, post-underground, surface, and post-surface
activities. Table A-130 provides the total net CH4 emissions by state.
Table fl-125: Coal Basin Definitions by Basin and by State
Basin
States
Northern Appalachian Basin
Maryland, Ohio, Pennsylvania, West Virginia North
Central Appalachian Basin
Kentucky East, Tennessee, Virginia, West Virginia South
Warrior Basin
Alabama, Mississippi
Illinois Basin
Illinois, Indiana, Kentucky West
South West and Rockies Basin
Arizona, California, Colorado, New Mexico, Utah
North Great Plains Basin
Montana, North Dakota, Wyoming
West Interior Basin
Arkansas, Iowa, Kansas, Louisiana, Missouri, Oklahoma, Texas
Northwest Basin
Alaska, Washington
State
Basin
Alabama
Warrior Basin
Alaska
Northwest Basin
Arizona
South West and Rockies Basin
Arkansas
West Interior Basin
California
South West and Rockies Basin
Colorado
South West and Rockies Basin
Illinois
Illinois Basin
Indiana
Illinois Basin
Iowa
West Interior Basin
Kansas
West Interior Basin
Kentucky (east)
Central Appalachian Basin
Kentucky (west)
Illinois Basin
Louisiana
West Interior Basin
Maryland
Northern Appalachian Basin
Mississippi
Warrior Basin
Missouri
West Interior Basin
Montana
North Great Plains Basin
A-192 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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New Mexico
North Dakota
Ohio
Oklahoma
Pennsylvania
Tennessee
Texas
Utah
Virginia
Washington
West Virginia South
West Virginia North
Wyoming
Figure A-6: Locations of U.S. Goal Basins
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
Coalbed Methane Fields, Lower 48 States
North Central
Coal Region
Powder River
VATBasin
Wind River Basin.
Wyoming /
Michigan
I Basin
Northern
Appalachian
snnah-Carbon Basin
^Park Basin
Illinois
. Basin
Cherokee! Platform
¦SW Colorado
Coal Area
Sari Juan Basinl
Basin
Arkoma.^
Basin,1
Black Warrior
JK, Basin
Southwestern
Coal Region
Gulf Coast
Coal Regior
Terlingua
Coalbed Methane Fields
Source: Energy Information Administration based on data from USGS and various published studies
Updated: April 8, 2009
A-193
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Table A-126: Annual Goal Production (Thousand Short Tons)
Basin
1990
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
Underground
Coal
Production
423,556
368,611
357,074
332,061
337,155
345,607
342,387
341,216
354,705
306,820
251,771
N. Appalachia 103,865 111,151 105,228 99,629 103,109 105,752 103,408 104,198 116,700 103,578 94,679
Cent.
Appalachia 198,412 123,083 114,998 98,689 96,354 94,034 78,067 70,440 64,219 53,230 39,863
Warrior 17,531 13,295 12,281 11,505 12,513 10,879 12,570 13,391 12,516 9,897 6,943
Illinois 69,167 59,180 64,609 67,186 72,178 81,089 92,500 98,331 105,211 96,361 76,572
S. West/Rockies
32,754
60,865
55,781
50,416
44,368
45,139
45,052
41,232
44,302
33,762
26,161
N. Great Plains
1,722
572
3,669
4,248
8,208
8,179
10,345
13,126
11,272
9,510
7,151
West Interior
105
465
508
388
425
535
445
498
485
482
402
Northwest
0
0
0
0
0
0
0
0
0
0
0
Surface Coal
Production
602,753
762,191
813,321
740,175
764,709
754,871
672,748
640,740
643,721
588,774
475,631
N. Appalachia
60,761
28,873
30,413
26,552
26,082
26,382
21,411
19,339
17,300
13,491
8,686
oeni.
Appalachia
94,343
112,222
118,962
97,778
89,788
90,778
69,721
57,173
52,399
37,278
26,974
Warrior
11,413
11,599
11,172
10,731
11,406
10,939
9,705
8,695
7,584
6,437
5,047
Illinois
72,000
33,702
34,266
34,837
32,911
34,943
34,771
33,798
31,969
27,360
21,679
S. West/Rockies
43,863
42,756
34,283
32,167
28,889
31,432
30,475
28,968
27,564
26,020
18,980
N. Great Plains
249,356
474,056
538,387
496,290
507,995
502,734
455,320
444,740
458,112
436,928
350,799
West Interior
64,310
52,263
44,361
39,960
46,136
55,514
49,293
46,477
47,201
40,083
42,534
Northwest
6,707
6,720
1,477
1,860
2,151
2,149
2,052
1,550
1,502
1,177
932
Total Coal
Production
1,026,309
1,130,802
1,170,395
1,072,236
1,101,864
1,100,478
1,015,135
981,956
998,426
895,594
727,402
N. Appalachia
164,626
140,024
135,641
126,181
129,191
132,134
124,819
123,537
134,000
117,069
103,365
oeni.
Appalachia
292,755
235,305
233,960
196,467
186,142
184,812
147,788
127,613
116,618
90,508
66,837
Warrior
28,944
24,894
23,453
22,236
23,919
21,818
22,275
22,086
20,100
16,334
11,990
Illinois
141,167
92,882
98,875
102,023
105,089
116,032
127,271
132,129
137,180
123,721
98,251
S. West/Rockies
76,617
103,621
90,064
82,583
73,257
76,571
75,527
70,200
71,956
59,782
45,141
N. Great Plains
251,078
474,628
542,056
500,538
516,203
510,913
465,665
457,866
469,384
446,438
357,950
West Interior
64,415
52,728
44,869
40,348
46,561
56,049
49,738
46,975
47,686
40,565
42,936
Northwest
6,707
6,720
1,477
1,860
2,151
2,149
2,052
1,550
1,502
1,177
932
Note: Totals may not sum due to independent rounding.
Source for 1990 through 2015 data: EIA (1990 through 2015), Annual Coal Report. Table 1. U.S. Department of Energy.
Source for 2015 data: spreadsheet for the 2015 Annual Coal Report.
Tahle fl-127: Coal Underground, Surface, and Post-Mining CHa Emission Factors [ft3 per Short Ton!
Surface Average
Underground Average
Surface Mine
Post-Mining
Post-Mining
Basin
In Situ Content
In Situ Content
Factors
Surface Factors
Underground
Northern Appalachia
59.5
138.4
89.3
19.3
45.0
Central Appalachia (WV)
24.9
136.8
37.4
8.1
44.5
Central Appalachia (VA)
24.9
399.1
37.4
8.1
129.7
Central Appalachia (E KY)
24.9
61.4
37.4
8.1
20.0
Warrior
30.7
266.7
46.1
10.0
86.7
Illinois
34.3
64.3
51.5
11.1
20.9
Rockies (Piceance Basin)
33.1
196.4
49.7
10.8
63.8
Rockies (Uinta Basin)
16.0
99.4
24.0
5.2
32.3
Rockies (San Juan Basin)
7.3
104.8
11.0
2.4
34.1
Rockies (Green River Basin)
33.1
247.2
49.7
10.8
80.3
Rockies (Raton Basin)
33.1
127.9
49.7
10.8
41.6
N. Great Plains (WY, MT)
20.0
15.8
30.0
6.5
5.1
N. Great Plains (ND)
5.6
15.8
8.4
1.8
5.1
West Interior (Forest City, Cherokee Basins)
34.3
64.3
51.5
11.1
20.9
West Interior (Arkoma Basin)
74.5
331.2
111.8
24.2
107.6
West Interior (Gulf Coast Basin)
11.0
127.9
16.5
3.6
41.6
Northwest (AK)
16.0
160.0
24.0
1.8
52.0
Northwest (WA)
16.0
47.3
24.0
5.2
15.4
Sources: 1986 USBM Circular 9067, Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins] U.S. DOE Report DOE/METC/83-76,
Methane Recovery from Coalbeds: A Potential Energy Source; 1986-1988 Gas Research Institute Topical Report, A Geologic Assessment of Natural Gas from
Coal Seams; 2005 U.S. EPA Draft Report, Surface Mines Emissions Assessment.
A-194 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table fl-128: UnJergrounJ Coal Mining Clh Emissions [Billion Cubic Feet]
Activity
1990
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
Ventilation Output
112
75
100
114
117
97
90
89
89
84
76
Adjustment Factor for Mine Dataa
98%
98%
99%
99%
99%
99%
99%
100%
100%
100%
100%
Adjusted Ventilation Output
114
77
101
115
118
98
91
89
89
84
76
Degasification System Liberated
54
48
49
49
58
48
45
45
43
43
42
Total Underground Liberated
168
124
150
163
177
147
137
134
131
127
119
Recovered & Used
(14)
(37)
(40)
(40)
(49)
(42)
(38)
(38)
(35)
(34)
(34)
Total
154
87
110
123
128
104
98
96
96
93
85
'Refer to Table A-124.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table fl-129: Total Coal Mining CHa Emissions [Billion Cubic Feet]
Activity
1990
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
Underground Mining
154
87
110
123
128
104
98
96
96
93
85
Surface Mining
22
25
27
24
24
24
21
20
20
18
14
Post-Mining
(Underground)
19
16
15
14
14
14
14
14
14
12
10
Post-Mining (Surface)
5
5
6
5
5
5
5
4
4
4
3
Total
200
132
157
166
171
148
138
134
134
127
112
Note: Totals may not sum due to independent rounding.
Table fl-130: Total Coal Mining CHa Emissions by State [Million Cubic Feet]
State
1990
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
Alabama
32,097
15,789
20,992
22,119
21,377
18,530
18,129
17,486
16,301
12,675
10,708
Alaska
50
42
43
54
63
63
60
45
44
34
27
Arizona
151
161
107
100
103
108
100
101
107
91
72
Arkansas
5
+
237
119
130
348
391
214
176
559
245
California
1
0
0
0
0
0
0
0
0
0
0
Colorado
10,187
13,441
12,871
13,999
16,470
11,187
9,305
4,838
4,038
3,248
2,272
Illinois
10,180
6,488
7,568
7,231
8,622
7,579
9,763
8,920
9,217
10,547
11,035
Indiana
2,232
3,303
5,047
5,763
5,938
6,203
7,374
6,427
7,159
6,891
6,713
Iowa
24
0
0
0
0
0
0
0
0
0
0
Kansas
45
11
14
12
8
2
1
1
4
12
2
Kentucky
10,018
6,898
9,986
12,035
12,303
10,592
7,993
8,098
8,219
6,377
4,882
Louisiana
64
84
77
73
79
168
80
56
52
69
56
Maryland
474
361
263
219
238
263
197
166
169
170
127
Mississippi
0
199
159
193
224
154
165
200
209
176
161
Missouri
166
3
15
28
29
29
26
26
23
9
15
Montana
1,373
1,468
1,629
1,417
1,495
1,445
1,160
1,269
1,379
1,353
1,004
New Mexico
363
2,926
3,411
3,836
3,956
4,187
2,148
2,845
2,219
2,648
1,954
North Dakota
299
306
303
306
296
289
281
282
298
294
287
Ohio
4,406
3,120
3,686
4,443
3,614
3,909
3,389
3,182
3,267
2,718
1,999
Oklahoma
226
825
932
624
436
360
499
282
112
735
864
Pennsylvania
21,864
17,904
20,684
22,939
23,372
17,708
17,773
20,953
19,803
19,554
17,930
Tennessee
276
115
86
69
67
60
35
31
22
40
26
Texas
1,119
922
783
704
823
922
887
854
876
721
787
Utah
3,587
4,787
5,524
5,449
5,628
3,651
3,624
2,733
1,605
1,737
781
Virginia
46,041
8,649
9,223
8,042
9,061
8,526
6,516
8,141
6,980
6,396
6,682
Washington
146
154
0
0
0
0
0
0
0
0
0
West Virginia
48,335
29,745
36,421
40,452
40,638
35,709
33,608
32,998
37,498
36,460
32,322
Wyoming
6,671
14,745
16,959
15,627
16,032
15,916
14,507
14,025
14,339
13,624
10,810
Total
200,399
132,481
157,112
165,854
171,000
147,908
138,012
134,173
134,118
127,139
111,763
+ Does not exceed 0.5 million cubic feet.
Note: The emission estimates provided above are inclusive of emissions from underground mines, surface mines and post-mining activities. The following states
have neither underground nor surface mining and thus report no emissions as a result of coal mining: Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho,
Maine, Massachusetts, Michigan, Minnesota, Nebraska, Nevada, New Hampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, South
Carolina, South Dakota, Vermont, and Wisconsin.
A-195
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References
AAPG (1984) Coalbed Methane Resources of the United States. AAPG Studies in Geology Series #17.
ACR (2017) Project Database. American Carbon Registry. Available online at .
Creedy, D.P. (1993) Chemosphere. Vol. 26, pp. 419-440.
DMME (2017) DGO Data Information System. Department of Mines, Minerals and Energy of Virginia. Available online
at .
EIA (2017) Annual Coal Report 2015. Table 1. Energy Information Administration, U.S. Department of Energy.
El Paso (2009) Shoal Creek Mine Plan, El Paso Exploration & Production.
EPA (2017) Greenhouse Gas Reporting Program (GHGRP) 2015 Envirofacts. Subpart FF: Underground Coal Mines.
Available online at .
EPA (2005) Surface Mines Emissions Assessment. E)raft. 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.
Geological Survey of Alabama State Oil and Gas Board (GSA) (2017) Well Records Database. Available online at
.
IEA (2017) Key World Energy Statistics. Coal Production, International Energy Agency.
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.
MSHA (2017) 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) (2017) Oil & Gas Production Data. Available online at
.
A-196 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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3.5. Methodology for Estimating CH4 and CO2 Emissions from Petroleum Systems
As described in the main body text on Petroleum Systems, the Inventory methodology involves the calculation of
emissions for 65 activities that emit CH4 and 35 activities that emit non-combustion CO2 from petroleum systems 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 and Table 3.5-7 show CH4 and CO2 emissions, respectively, for all sources in Petroleum Systems, for
all time series years. Table 3.5-3 and Table 3.5-8 show the CH4 and CO2 effective 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 and Table
3.5-9, and below.
In addition to the Greenhouse Gas Reporting Program (GHGRP), key references for emission factors for CH4 and
non-combustion-related CO2 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 CPU-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 2017a,b) 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 CO2
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 2017b) 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 and CO2 emissions for all major activities since
2010. The national totals of these emissions for each activity were used for the 2010 to 2016 emissions. The national emission
totals for each activity were divided by refinery feed rates for those four Inventory years to develop average activity-specific
emission factors, which were used to estimate national emissions for each refinery activity from 1990 to 2009 based on
national refinery feed rates for each year (EPA 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 CCVspecific emission factor is not available for a source, the CO2 emission factors were derived from
the corresponding source CH4 emission factors. The amount of CO2 in the crude oil stream changes as it passes through
various equipment in petroleum production operations. As a result, four distinct stages/streams with varying CO2 contents
exist. The four streams that are used to estimate the emissions factors are the associated gas stream separated from crude oil,
A-197
-------
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, CO2 emission factors are estimated by multiplying the existing
CH4 emissions factors by a conversion factor, which is the ratio of CO2 content to methane content for the particular stream.
Ratios of CO2 to CH4 volume in emissions are presented in Table 3.5-1.
1990-2016 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 three memoranda, Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Additional Revisions
Under Consideration (2018a),Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to Create Year-
Specific Emissions and Activity Factors (2018b), and Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016:
Revisions to CO2 Emissions Estimation Methodologies (2018c), as well as the "Recalculations Discussion" section of the
main body text.
In the exploration segment, EPA developed new CH4 and CO2 estimates for vented and flared oil well testing
(during non-completion events) using GHGRP emissions and activity data.
In the production segment, EPA developed new CH4 and CO2 estimates for associated gas venting and flaring and
miscellaneous production flaring; for these sources, EPA uses a basin-level aggregation and production-based scaling
approach to calculate emission factors from GHGRP data. EPA developed CO2 emissions estimates for oil tanks using
GHGRP data and a throughput-based approach to calculate emission factors, which is identical to the methodology to
calculate CH4 emissions.
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 201 la,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 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 CO2 emission sources, the activity factors are the same as for CH4 emission sources. In some
instances, where recent time series data (e.g., year 2016) are not yet available, year 2015 or prior data has been used as
proxy.
Methodology for well counts and events
For hydraulically fractured oil well completions, EPA developed activity data specific to each year of the time
series using the date of completion or first reported production available from a data set licensed by Drillinglnfo, Inc. For
more information on the Drillinglnfo data processing, please see Annex 3.6 Methodology for Estimating CH4 and CO2 from
Natural Gas Systems.
1990-2016 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 three memoranda, Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Additional Revisions
Under Consideration (2018a), Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to Create Year-
Specific Emissions and Activity Factors (2018b), and Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016:
Revisions to CO2 Emissions Estimation Methodologies (2018c), as well as the "Recalculations Discussion" section of the
main body text.
In the exploration segment, EPA developed new CH4 and CO2 estimates for vented and flared oil well testing
(during non-completion events) using GHGRP emissions and activity data.
A-198 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
In the production segment, EPA developed new CH4 and CO2 estimates for associated gas venting and flaring and
miscellaneous production flaring; for these sources, EPA uses a basin-level aggregation and production-based scaling
approach to calculate activity data from GHGRP data. EPA developed CO2 emissions estimates for oil tanks using GHGRP
data and a throughput-based approach to calculate activity, which is identical to the methodology to calculate CH4 emissions.
EPA also used a more recent version of the Drillinglnfo data set to update well counts data in the Inventory; though this
does not reflect a methodological revision or major changes to the activity data. Lastly, EPA recalculated activity factors of
equipment per well using the latest GHGRP RY2015 data, which included some resubmissions. This resulted in minor
changes across the time series
Methane and Carbon Dioxide Emissions by Emission Source for Each Year
Annual CH4 emissions and CO2 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 and CO2 emissions, respectively. Emissions at a segment level are shown in Table 3.5-2 and Table 3.5-7.
Refer to the 1990-2016 Inventory section at https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
systems for the following data tables, in Excel format:
Table 3.5-1
Table 3.5-2
Table 3.5-3
Table 3.5-4
Table 3.5-5
Table 3.5-6
Table 3.5-7
Table 3.5-8
Table 3.5-9
Ratios of CO2 to CH4 Volume in Emissions from Petroleum Production Field Operations
CH4 Emissions (kt) for Petroleum Systems, by Segment and Source, for All Years
Effective CH4 Emission Factors (kg/unit activity) for Petroleum Systems Sources, for All Years
CH4 Emission Factors for Petroleum Systems, Data Sources/Methodology
Activity Data for Petroleum Systems Sources, for All Years
Activity Data for Petroleum Systems, Data Sources/Methodology
CO2 Emissions (kt) for Petroleum Systems, by Segment and Source, for All Years
Effective CO2 Emission Factors (kg/unit activity) for Petroleum Systems Sources, for All Years
CO2 Emission Factors for Petroleum Systems, Data Sources/Methodology
A-199
-------
References
API (1989) Aboveground Storage Tank Survey report prepared by Entropy Limited for American Petroleum Institute,
April 1989.
API (1995) API 4615: Emission Factors For Oil and Gas Production Operations. American Petroleum Institute.
Washington, DC.
API (1996) API 4638: Calculation Workbook For Oil And Gas Production Equipment Fugitive Emissions. American
Petroleum Institute. Washington, DC.
API (2000) API 4697: Production Tank Emissions Model - A Program For Estimating Emissions From Hydrocarbon
Production Tanks - E&P Tank Version 2.0. American Petroleum Institute. Washington, DC.
API (2003) Basic Petroleum Data Book, 1990-2003. American Petroleum Institute. Washington, DC.
BOEM (2014) Year 2011 Gulfwide Emission Inventory Study. Bureau of Ocean Energy Management, U.S. Department of
Interior. OCS Study BOEM 2014-666. Available online at:
BOEMRE (201 la) Gulf of Mexico Region Offshore Information. Bureau of Ocean Energy Management, Regulation and
Enforcement, U.S. Department of Interior.
BOEMRE (201 lb) Pacific OCS Region Offshore Information. Bureau of Ocean Energy Management, Regulation and
Enforcement, U.S. Department of Interior.
BOEMRE (201 lc) GOM and Pacific OCS Platform Activity. Bureau of Ocean Energy Management, Regulation and
Enforcement, U.S. Department of Interior.
CAPP (1992) Canadian Association of Petroleum Producers (CAPP), A Detailed Inventory of CH4 and VOC Emissions
from Upstream Oil & Gas Operations in Alberta. March 1992.
Drillinglnfo (2016) April 2016 Download. DI Desktop® Drillinglnfo, Inc.
EIA (2017a) Monthly Energy Review, 1995-2017 editions. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available online at: < http://www.eia.gov/totalenergy/data/monthly/index.cfm >.
EIA (2017b) Petroleum Supply Annual, 2001-2017 editions. U.S Department of Energy Washington, DC. Available
online at: .
EIA (2017c) Refinery Capacity Report, 2005-2017 editions. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available online at: .
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency. Research Triangle Park, NC. October 1997.
EPA (2015a) Background Technical Support Document for the Proposed New Source Performance Standards 40 CFR Part
60, subpart OOOOa. Available online at: https://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2010-
0505-5021
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 (2015c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Refineries Emissions
Estimate. Available online at: .
EPA (2015d) Inventory of U.S. GHG Emissions and Sinks 1990-2013: Revision to Well Counts Data. 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: < https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-
ghg-inventory-additional-information-1990-2014-ghg >.
EPA (2017a) Greenhouse Gas Reporting Program - Subpart W - Petroleum and Natural Gas Systems. Environmental
Protection Agency. Data reported as of August 5, 2017.
A-200 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
EPA (2017b) Greenhouse Gas Reporting Program - Subpart Y - Petroleum Refineries. Environmental Protection Agency.
Data reported as of August 5, 2017.
EPA (2017c) 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 CO2 Emissions Estimation
Methodologies. 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 (2016) Special Report: Pipeline Economics, 2005-2016 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 (2015) Waterborne Commerce of the United States, Part 5: National Summaries, 2000-2015 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 and CO2 Emissions from Natural Gas Systems
As described in the main body text on Natural Gas Systems, the Inventory methodology involves the calculation
of CH4 and CO2 emissions for over 100 emissions sources, and then the summation of emissions for each natural gas sector
stage. 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 and Table 3.6-10 show CH4 and CO2 emissions, respectively, for all sources in Natural Gas Systems,
for all time series years. Table 3.6-2 and Table 3.6-12 show the CFLi and CO2 effective 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 CFLi and CO2 emission factors used across the time series is provided in Table
3.6-6 and Table 3.6-13, and below.
Key references for emission factors for CFLi and non-combustion-related CO2 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), 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 CO2 emission estimation methodology for many sources in
Natural Gas Systems. New data from studies and EPA's GHGRP (EPA 2017a) 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 (1990-1992), 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 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 without hydraulic fracturing, separate emissions estimates were developed for
completions that event and completions that flare. In addition, net emissions are calculated for miscellaneous production
flaring. For liquids unloading, separate emissions estimates were developed for wells with plunger lifts and wells without
plunger lifts. Likewise, for condensate tanks, emissions estimates were developed for large and small tanks with flaring or
VRU control, without control devices, and with upstream malfunctioning separator dump valves. For pneumatic controllers,
separate estimates are developed for low bleed, high bleed, and intermittent controllers. Chemical injection pumps estimates
are calculated with an emission factor developed with GHGRP data, which is based on the previous GRI/EPA factor but
takes into account operating hours. For all sources in the processing and distribution segments, and most sources in the
transmission and storage segment, 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 CC^-specific emission factor is not available for a source, the CO2 emission factors were derived from
the corresponding source CH4 emission factors using default gas composition data. CO2 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
A-202 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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).
1990-2016 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,63 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Additional Revisions
Under Consideration (2018a), Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to Create Year-
Specific Emissions and Activity Factors (2018b), and Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016:
Revisions to CO2 Emissions Estimation Methodologies (2018c), as well as the "Recalculations Discussion" section of the
main body text.
In the exploration segment, EPA developed new CH4 and CO2 estimates for vented and flared oil well testing
(during non-completion events) using GHGRP emissions and activity data. EPA also developed year-specific emission
factors for non-hydraulically fractured gas well completions and hydraulically fractured gas well completions (for years
2011-2016).
For the production segment, EPA developed control category- and year-specific CO2 and CH4 emission factors
from GHGRP data for non-hydraulically fractured gas well workovers, hydraulically fractured gas well workovers, liquids
unloading, and miscellaneous production flaring. Control category-specific CO2 emission factors were developed from
GHGRP data for production storage tanks.
For the processing segment, CO2 emission factors were developed from GHGRP data using the same methodology
as for CH4 emission factors. EPA calculated CO2 emission factors for plant fugitives, compressors, dehydrators, flares,
blowdowns, and acid gas removal vents from GHGRP data for years 2011 to 2016. In order to create time series consistency
for emission factors between earlier years' estimates (1990 to 1992) that generally rely on data fromGRI/EPA 1996 and the
most recent years' estimates (2011 to 2016) that were calculated using data from the GHGRP, linear interpolation between
the data endpoints of 1992 (GRI/EPA) and 2011 (GHGRP) was used for calculations.
For the transmission and storage segment, CO2 and CH4 emission factors were newly developed from GHGRP
data for station flares. For the storage segment, the emission estimate for year 2016 was adjusted upward to account for the
Aliso Canyon leak.
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 2015 data were used as
proxy for 2016 data or a set of industry activity data drivers was developed and was used to update activity data. Key drivers
include statistics on gas production, number of wells, system throughput, miles of various kinds of pipe, and other statistics
that characterize the changes in the U.S. natural gas system infrastructure and operations.
Methodology for well counts and events
EPA used DI Desktop, a production database maintained by Drillinglnfo, Inc. (Drillinglnfo 2017), covering U.S.
oil and natural gas wells to populate activity data for gas wells, oil wells (in petroleum systems) gas well completions and
workovers with hydraulic fracturing for 1990-2010, and oil well completions for all years of the time series. EPA queried
DI Desktop for relevant data on an individual well basis—including location, natural gas and liquids (i.e., oil and condensate)
production by year, drill type (e.g., horizontal or vertical), and date of completion or first production. Non-associated gas
wells were classified as any well within DI 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-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 DI Desktop referenced against a formation type crosswalk developed by EIA (EIA
2012a).
For 1990 through 2010, gas well completions with hydraulic fracturing were identified as a subset of the gas wells
with hydraulic fracturing that had a date of completion or first production in the specified year. To calculate workovers for
1990 through 2016, EPA applied a refracture rate of 1 percent (i.e., 1 percent of all wells with hydraulic fracturing are
63 Draft and final memoranda for the 1990-2016 Inventory are available here < https://www.epa.gov/ghgemissions/natural-gas-
and-petroleum-systems>.
A-203
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assumed to be refractured in a given year) to the total counts of wells with hydraulic fracturing from the Drillinglnfo data.
For 2011 through 2016, 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, obtained using DI Desktop data, EPA directly used the GHGRP data for completions for 2011 through
2016.
EPA calculated the percentage of gas well completions and workovers with hydraulic fracturing in each of the four
control categories using 2011 through 2016 Subpart W data. EPA assumed no REC use from 1990 through 2000, used
GHGRP RECs percentage for 2011 through 2016, 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 through 2016, EPA used the GHGRP data on flaring.
1990-2016 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,64 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Additional Revisions Under
Consideration (2018a), Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to Create Year-
Specific Emissions and Activity Factors (2018b), and Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016:
Revisions to CO2 Emissions Estimation Methodologies (2018c), as well as the "Recalculations Discussion" section of the
main body text.
In the exploration segment, EPA developed new CH4 and CO2 estimates for vented and flared gas well testing
(during non-completion events) using GHGRP emissions and activity data. EPA also developed year-specific activity factors
for non-hydraulically fractured gas well completions and hydraulically fractured gas well completions (for years 2011 -2016).
For the production segment, EPA developed control category- and year-specific CO2 and CH4 activity factors from
GHGRP data for non-hydraulically fractured gas well workovers, hydraulically fractured gas well workovers, liquids
unloading, and miscellaneous production flaring. For miscellaneous production flaring, EPA uses a basin-level aggregation
and production-based scaling approach to calculate activity data from GHGRP data. EPA also used a more recent version
of the Drillinglnfo data set to update well counts data in the Inventory; though this does not reflect a methodological revision
or maj or changes to the activity data. Lastly, EPA recalculated activity factors of equipment per well using the latest GHGRP
RY2015 data, which included some resubmissions. This resulted in minor changes across the time series.
For the transmission and storage segment, the flares emission factors developed from GHGRP data are at a station-
level and the methodology to determine the number of stations did not change from previous Inventories.
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, installing automated air-to-fuel ratio controls for engines, and implementing
gas recovery for pipeline maintenance operations.
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 approaches.
64 Draft and final memoranda for the 1990-2016 Inventory are available here .
A-204 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 2016, 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 CO2 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 and Carbon Dioxide Emissions by Emission Source for Each Year
Annual CH4 emissions and CO2 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 and CO2 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. CO2 emissions by
segment and source are summarized in Table 3.6-10.
Refer to the 1990-2016 Inventory section at https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
svstems for the following data tables, in Excel format:
• Table 3.6-1: CH4 Emissions (kt) for Natural Gas Systems, by Segment and Source, for All Years
• Table 3.6-2: Effective 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: CO2 Emissions (kt) for Natural Gas Systems, by Segment and Source, for All Years
• Table 3.6-11: Default Gas Content by Segment, for All Years
• Table 3.6-12: Effective CO2 Emission Factors (kg/unit activity) for Natural Gas Systems Sources, for All Years
• Table 3.6-13: CO2 Emission Factors for Natural Gas Systems, Data Sources/Methodology
A-205
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References
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EIA (2017g) Number of Natural Gas Consumers - Residential. Energy Information Administration, U.S. Department of
Energy, Washington, DC. Available online at: .
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3.7. Methodology for Estimating CO2, CH4, and N2O Emissions from the Incineration of
Waste
Emissions of CO2 from the incineration of waste include CO2 generated by the incineration of plastics, synthetic
rubber and synthetic fibers in municipal solid waste (MSW), and incineration of tires (which are composed in part of
synthetic rubber and C black) in a variety of other combustion facilities (e.g., cement kilns). Incineration of waste also results
in emissions of CH4 and N2O. The emission estimates are calculated for all four sources on a mass-basis based on the data
available. The methodology for calculating emissions from each of these waste incineration sources is described in this
Annex.
CO2 from Plastics Incineration
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, 2016) the flows of
plastics in the U.S. waste stream are reported for seven resin categories. For 2016, the quantity generated, recovered, and
discarded for each resin is shown in Table A-131. The data set for 1990 through 2016 is incomplete, and several assumptions
were employed to bridge the data gaps. The EPA reports do not provide estimates for individual materials landfilled and
incinerated, although they do provide such an estimate for the waste stream as a whole. To estimate the quantity of plastics
landfilled and incinerated, total discards were apportioned based on the proportions of landfilling and incineration for the
entire U.S. waste stream for each year in the time series according to Biocycle 's State of Garbage in America (van Haaren
et al. 2010), and Shin (2014). For those years when distribution by resin category was not reported (1990 through 1994),
total values were apportioned according to 1995 (the closest year) distribution ratios. Generation and recovery figures for
2002 and 2004 were linearly interpolated between surrounding years' data.
Table fl-131:2016 Plastics in the Municipal Solid Waste Stream by Resin tktl
Waste Pathway
PET
HDPE
PVC
LDPE /
LLDPE
PP
PS
Other
Total
Generation
4,600
5,289
762
6,995
6,450
2,114
3,955
30,164
Recovery
880
553
0
408
54
27
953
2,876
Discard
3,720
4,736
762
6,586
6,396
2,087
3,003
27,289
Landfill
3,437
4,376
704
6,086
5,910
1,928
2,775
25,215
Combustion
283
360
58
501
486
159
228
2,074
Recovery3
19%
10%
0%
6%
1%
1%
24%
10%
Discard3
81%
90%
100%
94%
99%
99%
76%
90%
Landfill3
75%
83%
92%
87%
92%
91%
70%
84%
Combustion3
6%
7%
8%
7%
8%
8%
6%
7%
a As a percent of waste generation.
Note: Totals may not sum due to independent rounding. Abbreviations: PET (polyethylene terephthalate), HDPE (high density polyethylene), PVC
(polyvinyl chloride), LDPE/LLDPE (linear low density polyethylene), PP (polypropylene), PS (polystyrene).
Fossil fuel-based CO2 emissions were calculated as the product of plastic combusted, C content, and fraction
oxidized (see Table A-132). The C content of each of the six types of plastics is listed, with the value for "other plastics"
assumed equal to the weighted average of the six categories. The fraction oxidized was assumed to be 98 percent.
Table A-132:2016 Plastics Incinerated tktl, Carbon Content [%], Fraction Oxidize J [%] and Carbon Incinerated tktl
Factor
PET
HDPE
PVC
LDPE /
LLDPE
PP
PS
Other
Total
Quantity Combusted
283
360
58
501
486
159
228
2,074
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
302
22
420
408
143
147
1,617
Emissions (MMT CO2 Eq.)
0.6
1.1
0.1
1.5
1.5
0.5
0.5
5.9
NA (Not Applicable)
a Weighted average of other plastics produced.
Note: Totals may not sum due to independent rounding
CO2 from Incineration of Synthetic Rubber and Carbon Black in Tires
Emissions from tire incineration require two pieces of information: the amount of tires incinerated and the C
content of the tires. "2015 U.S. Scrap Tire Management Summary" (RMA 2016) reports that 1,923 thousand of the 3,551
A-209
-------
thousand tons of scrap tires generated in 2015 (approximately 54 percent of generation) were used for fuel purposes. The
2015 value was used for 2016. 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.65 Table A-133 shows consumption and C content of
elastomers used for tires and other products in 2002, the most recent year for which data are available.
• C black is 100 percent C (Aslett Rubber Inc. n.d.).
Multiplying the mass of scrap tires incinerated by the total C content of the synthetic rubber, C black portions of
scrap tires, and then by a 98 percent oxidation factor, yielded CO2 emissions, as shown in Table A-134. 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-131 (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 2016 were taken from RMA 2006, RMA2009, RMA 2011; RMA 2014a; RMA2016; where data were not reported,
they were linearly interpolated between bracketing years' data or, for the ends of time series, set equal to the closest year
with reported data.
In 2009, RMA changed the reporting of scrap tire data from millions of tires to thousands of short tons of scrap
tire. As a result, the average weight and percent of the market of light duty and commercial scrap tires was used to convert
the previous years from millions of tires to thousands of short tons (STMC 1990 through 1997; RMA 2002 through 2006,
2014b, 2016).
Table A-133: 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.
Note: Totals may not sum due to independent rounding.
65 The carbon content of tires (1,174 kt C) divided by the mass of rubber in tires (1,307 kt) equals 90 percent.
A-210 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-134: Scrap Tire Constituents and CO; Emissions from Scrap Tire Incineration in 2016
Weight of Material
Emissions (MMT
Material
(MMT)
Fraction Oxidized
Carbon Content
C02 Eq.)
Synthetic Rubber
0.3
98%
90%
1.2
Carbon Black
0.4
98%
100%
1.5
Total
0.8
NA
NA
2.7
NA (Not Applicable)
CO2 from Incineration of Synthetic Rubber in Municipal Solid Waste
Similar to the methodology for scrap tires, CO2 emissions from synthetic rubber in MSW were estimated by
multiplying the amount of rubber incinerated by an average rubber C content. The amount of rubber discarded in the MSW
stream was estimated from generation and recycling data provided in the Municipal Solid Waste Generation, Recycling,
and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2014), Advancing
Sustainable Materials Management: Facts and Figures: Assessing Trends in Material Generation, Recycling and Disposal
in the United States (EPA 2015, 2016), and unpublished backup data (Schneider 2007). The reports divide rubber found in
MSW into three product categories: other durables (not including tires), non-durables (which includes clothing and footwear
and other non-durables), and containers and packaging. EPA (2016) did not report rubber found in the product category
"containers and packaging;" however, containers and packaging from miscellaneous material types were reported for 2009
through 2016. As a result, EPA assumes that rubber containers and packaging are reported under the "miscellaneous"
category; and therefore, the quantity reported for 2009 through 2016 were set equal to the quantity reported for 2008. Since
there was negligible recovery for these product types, all the waste generated is considered to be discarded. Similar to the
plastics method, discards were apportioned into landfilling and incineration based on their relative proportions, for each
year, for the entire U.S. waste stream. The report aggregates rubber and leather in the MSW stream; an assumed synthetic
rubber content of 70 percent was assigned to each product type, as shown in T able A-135.67 AC content of 85 percent was
assigned to synthetic rubber for all product types (based on the weighted average C content of rubber consumed for non-tire
uses), and a 98 percent fraction oxidized was assumed.
Table fl-135: Rubber and Leather in Municipal Solid Waste in 2016
Incinerated
Synthetic
Carbon Content
Fraction Oxidized
Emissions
Product Type
(kt)
Rubber(%)
(%)
(%)
(MMT C02 Eq.)
Durables (not Tires)
259
70%
85%
98%
0.8
Non-Durables
79
NA
NA
NA
0.2
Clothing and Footwear
60
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
NA (Not Applicable)
CO2 from Incineration of Synthetic Fibers
Carbon dioxide emissions from synthetic fibers were estimated as the product of the amount of synthetic fiber
discarded annually and the average C content of synthetic fiber. Fiber in the MSW stream was estimated from data provided
in the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA
1999 through 2003, 2005 through 2014) and Advancing Sustainable Materials Management: Facts and Figures - Assessing
Trends in Material Generation, Recycling and Disposal in the United States (EPA 2015, 2016) for textiles. Production data
for the synthetic fibers was based on data from the American Chemical Society (FEB 2009). The amount of synthetic fiber
in MSW was estimated by subtracting (a) the amount recovered from (b) the waste generated (see Table A-136). As with
the other materials in the MSW stream, discards were apportioned based on the annually variable proportions of landfilling
and incineration for the entire U.S. waste stream, as found in van Flaaren et al. (2010), and Shin (2014). It was assumed that
approximately 55 percent of the fiber was synthetic in origin, based on information received from the Fiber Economics
Bureau (DeZan 2000). The average C content of 71 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 2016 fiber production
(see Table A-137). The equation relating CO2 emissions to the amount of textiles combusted is shown below.
66 Discards = Generation minus recycling.
As a sustainably harvested biogenic material, the incineration of leather is assumed to have no net CO2 emissions.
A-211
-------
CCh 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
(44gC02/12gC)
Table fl-136: Synthetic Textiles in MSW tktl
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
8,052
1,301
6,751
513
2015
8,052
1,301
6,751
513
2016
8,052
1,301
6,751
513
fable A-137: Synthetic Fiber Production in 2016
Fiber
Production (MMT)
Carbon Content
Polyester
1.4
63%
Nylon
0.6
64%
Olefin
1.0
86%
Acrylic
0.0
68%
Total
3.0
71%
CH4 and N2O from Incineration of Waste
Estimates of N2O emissions from the incineration of waste in the United States are based on the methodology
outlined in the EPA's Compilation of Air Pollutant Emission Factors (EPA 1995) and presented in the Municipal Solid
Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (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, 2016) and unpublished backup data (Schneider 2007).
According to this methodology, emissions of N2O from waste incineration are the product of the mass of waste incinerated,
an emission factor of N2O emitted per unit mass of waste incinerated, and an N2O emissions control removal efficiency. The
mass of waste incinerated was derived from the results of the biannual national survey of 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 2006IPCC 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 CFI4 emitted per unit mass of waste incinerated. Similar to the N2O emissions methodology, the mass of waste
incinerated was derived from the information published in BioCycle (van Flaaren et al. 2010) for 1990 through 2008. Data
A-212 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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 2006IPCC 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 2016, 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-13 8).
Table A-138: U.S. Municipal Solid Waste Incinerated, as Reported by EPA and BioCycle (Metric Tons)
Year
EPA
BioCycle
1990
28,939,680
30,632,057
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
25,930,542"
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
30,136,3619
20,756,870'
2016
30,136,361s
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
f Set equal to the 2011 value
a Set equal to the 2014 value.
A-213
-------
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, Ernmaus, 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 (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 Managements, U.S. Environmental
Protection Agency. Washington, D.C. Available online at: .
EPA (2015) Advancing Sustainable Materials Management: Facts and Figures 2013 - Assessing Trends 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 2014) Municipal Solid Waste in the United States: Facts and Figures. Office of
Solid Waste and Emergency Response, U.S. Environmental Protection Agency. Washington, DC. Available online at
.
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, DC.
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, DC. 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. Envrionmental 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, Ernmaus, 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.
Kaufman, et al. (2004) "14th Annual BioCycle Nationwide Survey: The State of Garbage in America 2004" Biocycle, JG
Press, Ernmaus, PA. January, 2004.
RMA (2016) "2015 U.S. Scrap Tire Management Summary." Rubber Manufacturers Association. August 2016. Available
online at: .
RMA (2014a) "2013 U.S. Scrap Tire Management Summary." Rubber Manufacturers Association. November 2014.
Available online at: . Accessed 17
November 2014.
A-214 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
RMA (2012) "Rubber FAQs." Rubber Manufacturers Association. Available online at . Accessed 19 November 2014.
RMA (2011) "U.S. Scrap Tire Management Summary 2005-2009." Rubber Manufacturers Association. October 2011.
Available online at: .
Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah Shapiro of
ICF International, January 10, 2007.
Shin, D. (2014) Generation and Disposition of Municipal Solid Waste (MSW) in the United States-A National Survey.
Thesis. Columbia University, Department of Earth and Environmental Engineering, January 3, 2014.
Simmons, et al. (2006) "15th Nationwide Survey of Municipal Solid Waste Management in the United States: The State of
Garbage in America" BioCycle, JG Press, Emmaus, PA. April 2006.
STMC (1990 through 1997) Scrap Tire Use/Disposal Study. Rubber Manufacturers Association: Scrap Tire Management
Council. Available online at: .
Themelis and Shin (2014) U.S. Survey of Generation and Disposition of Municipal Solid Waste. Waste Management.
Columbia University. January 2014. .
van Flaaren, Rob, Thermelis, N, and Goldstein, N. (2010) "The State of Garbage in America." BioCycle, October 2010.
Volume 51, Number 10, pg. 16-23.
A-215
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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 2016 fuel sales in the Continental United States (CONUS).68
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 DoD69 and NATO™ policies on implementing the Single Fuel For the Battlefield concept, DoD
activities have been increasingly replacing diesel fuel with JP8 (a type of jet fuel) in compression ignition and turbine engines
of land-based equipment. DoD is replacing JP-8 with commercial specification Jet A fuel with additives (JAA) for non-
naval aviation and ground assets. The transition is scheduled to be completed in 2016. Based on this concept and examination
of all data describing jet fuel used in land-based vehicles, it was determined that a portion of JP8 consumption should be
attributed to ground vehicle use. Based on available Military Service data and expert judgment, a small fraction of the total
JP8 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-basedjet fuel category for 1997 and subsequent years. As a result of this reallocation, the
JP8 use reported for aviation was reduced and the total fuel use for land-based equipment increased. DoD's total fuel use
did not change.
Table A-139 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.
• 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.
68 FAS contains data for 1995 through 2016, but the dataset was not complete for years prior to 1995. Using DLA aviation and marine fiiel
procurement data, fiiel 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 fiiel conversion from JP4
to JP8 that occurred within DoD between 1992 and 1995.
69 DoD Directive 4140.25-M-V1, Fuel Standardization and Cataloging, 2013; DoD Directive 4140.25, DoD Management Policy for Energy
Commodities and Related Services, 2004.
70 NATO Standard Agreement NATO STANAG 4362, Fuels for Future Ground Equipment Using Compression Ignition or Turbine Engines, 2012.
A-216 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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• 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 Proj ection 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 2000.71
Table A-140 and Table A-141 display DoD bunker fuel use totals for the Navy and Air Force.
Step 6: Calculate Emissions from International Bunker Fuels
Bunker fuel totals were multiplied by appropriate emission factors to determine greenhouse gas emissions. CO2
emissions from Aviation Bunkers and distillate Marine Bunkers are the total of military aviation and marine bunker fuels,
respectively.
The rows labeled "U.S. Military" and "U.S. Military Naval Fuels" in the tables in the International Bunker Fuels
section of the Energy chapter were based on the totals provided in Table A-140 and Table A-141, below. CO2 emissions
from aviation bunkers and distillate marine bunkers are presented in Table A-144, and are based on emissions from fuels
tallied in Table A-140 and Table A-141.
71 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-217
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Table fl-139: Transportation Fuels from Domestic Fuel Deliveries3 [Million Gallons]
Vehicle
Type/Fuel
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Aviation
4,598.4
3,099.9
2,664.4
2,900.6
2,609.8
2,615.0
2,703.1
2,338.1
2,092.0
2,081.0
2,067.8
1,814.5
1,663.9
1,405.0
1,449.7
1,336.4
1,679.5
1,663.7
1,558.0
Total Jet Fuels
4,598.4
3,099.9
2,664.4
2,900.6
2,609.6
2,614.9
2,703.1
2,338.0
2,091.9
2,080.9
2,067.7
1,814.3
1,663.7
1,404.8
1,449.5
1,336.2
1,679.2
1,663.5
1,557.7
JP8
285.7
2,182.8
2,122.7
2,326.2
2,091.4
2,094.3
2,126.2
1,838.8
1,709.3
1,618.5
1,616.2
1,358.2
1,100.1
882.8
865.2
718.0
546.6
126.6
M
JP5
1,025.4
691.2
472.1
503.2
442.2
409.1
433.7
421.6
325.5
376.1
362.2
361.2
399.3
372.3
362.5
316.4
311.0
316.4
320.4
Other Jet Fuels
3,287.3
225.9
69.6
71.2
76.1
111.4
143.2
77.6
57.0
86.3
89.2
94.8
164.3
149.7
221.8
301.7
821.6
1,220.5
1,246.9
Aviation
Gasoline
+
+
+
+
0.1
0.1
+
0.1
0.1
0.2
0.1
0.2
0.2
0.2
0.3
0.2
0.3
0.3
0.3
Marine
686.8
438.9
454.4
418.4
455.8
609.1
704.5
604.9
531.6
572.8
563.4
485.8
578.8
489.9
490.4
390.4
427.9
421.7
412.4
Middle Distillate
(MGO)
+
+
48.3
33.0
41.2
88.1
71.2
54.0
45.8
45.7
55.2
56.8
48.4
37.3
52.9
40.9
62.0
56.0
23.1
Naval Distillate
(F76)
686.8
438.9
398.0
369.1
395.1
460.9
583.5
525.9
453.6
516.0
483.4
399.0
513.7
440.0
428.4
345.7
362.7
363.3
389.1
Intermediate
Fuel Oil
(IFO)b
+
+
8.1
16.3
19.5
60.2
49.9
25.0
32.2
11.1
24.9
30.0
16.7
12.5
9.1
3.8
3.2
2.4
0.1
Other1
717.1
310.9
248.2
109.8
211.1
221.2
170.9
205.6
107.3
169.0
173.6
206.8
224.0
208.6
193.8
180.6
190.7
181.1
178.3
Diesel
93.0
119.9
126.6
26.6
57.7
60.8
46.4
56.8
30.6
47.3
49.1
58.3
64.1
60.9
57.9
54.9
57.5
54.8
54.7
Gasoline
624.1
191.1
74.8
24.7
27.5
26.5
19.4
24.3
11.7
19.2
19.7
25.2
25.5
22.0
19.6
16.9
16.5
16.2
15.9
Jet Fueld
+
+
46.7
58.4
125.9
133.9
105.1
124.4
65.0
102.6
104.8
123.3
134.4
125.6
116.2
108.8
116.7
110.1
107.6
Total
(Including
Bunkers)
6,002.4
3,849.8
3,367.0
3,428.8
3,276.7
3,445.3
3,578.5
3,148.6
2,730.9
2,822.8
2,804.9
2,507.1
2,466.7
2,103.5
2,133.9
1,907.5
2,298.2
2,266.5
2,148.7
+ Indicates value does not exceed 0.05 million gallons.
a Includes fuel distributed in the United States and U.S. Territories.
b Intermediate fuel oil (IFO 180 and IFO 380) is a blend of distillate and residual fuels. IFO is used by the Military Sealift Command.
c Prior to 2001, gasoline and diesel fuel totals were estimated using data provided by the Military Services for 1990 and 1996. The 1991 through 1995 data points were interpolated from the Service inventory data. The 1997
through 1999 gasoline and diesel fuel data were initially extrapolated from the 1996 inventory data. Growth factors used for other diesel and gasoline were 5.2 and -21.1 percent, respectively. However, prior diesel fuel estimates
from 1997 through 2000 were reduced according to the estimated consumption of jet fuel that is assumed to have replaced the diesel fuel consumption in land-based vehicles. Datasets for other diesel and gasoline consumed
by the military in 2000 were estimated based on ground fuels consumption trends. This method produced a result that was more consistent with expected consumption for 2000. Since 2001, other gasoline and diesel fuel totals
were generated by DLA Energy.
d The fraction of jet fuel consumed in land-based vehicles was estimated based on DLA Energy data as well as Military Service and expert judgment.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values. The negative values in this table represent returned products.
A-218 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-140: Total U.S. Military Aviation Bunker Fuel [Million Gallons]
Fuel Type/Service
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Jet Fuels
JP8
56.7
300.4
307.6
341.2
309.5
305.1
309.8
285.6
262.5
249.1
229.4
211.4
182.5
143.4
141.2
122.0
88.0
17.2
2.4
Navy
56.7
38.3
53.4
73.8
86.6
76.3
79.2
70.9
64.7
62.7
59.2
55.4
60.8
47.1
50.4
48.9
31.2
0.8
5.5
Air Force
+
262.2
254.2
267.4
222.9
228.7
230.6
214.7
197.8
186.5
170.3
156.0
121.7
96.2
90.8
73.0
56.7
16.4
M
JP5
370.5
249.8
160.3
169.7
158.3
146.1
157.9
160.6
125.0
144.5
139.2
137.0
152.5
144.9
141.2
124.9
121.9
124.1
126.1
Navy
365.3
246.3
155.6
163.7
153.0
141.3
153.8
156.9
122.8
141.8
136.5
133.5
149.7
143.0
139.5
123.6
120.2
122.6
124.7
Air Force
5.3
3.5
4.7
6.1
5.3
4.9
4.1
3.7
2.3
2.7
2.6
3.5
2.8
1.8
1.7
1.3
1.6
1.5
1.4
JP4
420.8
21.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
Navy
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Air Force
420.8
21.5
+
+
+
+
+
+
+
+
+
+
0.1
+
+
+
+
+
+
JAA
13.7
9.2
12.5
12.6
13.7
21.7
30.0
15.5
11.7
15.6
16.8
18.1
31.4
31.1
38.6
46.5
128.0
199.8
203.7
Navy
8.5
5.7
7.9
8.0
9.8
15.5
21.5
11.6
9.1
11.7
12.5
12.3
13.7
14.6
14.8
13.4
36.1
71.7
72.9
Air Force
5.3
3.5
4.5
4.6
3.8
6.2
8.6
3.9
2.6
3.9
4.3
5.9
17.7
16.5
23.8
33.1
91.9
128.1
130.8
JA1
+
+
+
0.1
0.6
0.2
0.5
0.5
0.4
1.1
1.0
0.6
0.3
(+)
(+)
0.6
1.1
0.3
0.5
Navy
+
+
+
+
+
+
+
+
+
0.1
0.1
0.1
0.1
M
M
0.6
0.7
+
0.1
Air Force
+
+
+
0.1
0.6
0.2
0.5
0.5
0.4
1.0
0.8
0.5
0.1
M
M
+
0.5
0.3
0.5
JAB
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Navy
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Air Force
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Navy Subtotal
430.5
290.2
216.9
245.5
249.4
233.1
254.4
239.4
196.6
216.3
208.3
201.3
224.4
204.3
204.5
186.5
188.2
195.0
203.2
Air Force Subtotal
431.3
290.7
263.5
278.1
232.7
239.9
243.7
222.9
203.1
194.0
178.1
165.9
142.4
114.5
116.3
107.4
150.7
146.4
129.5
Total
861.8
580.9
480.4
523.6
482.1
473.0
498.1
462.3
399.7
410.3
386.3
367.2
366.7
318.8
320.8
293.9
339.0
341.4
332.8
+ Does not exceed 0.05 million gallons.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values. The negative values in this table represent returned products.
A-219
-------
Table A-141: Total U.S. DoD Maritime Bunker Fuel (Million Gallons)
Marine
Distillates
1990
1995
I 2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Navy - MGO
0.0
0.0
23.8
22.5
27.1
63.7
56.2
38.0
33.0
31.6
40.9
39.9
32.9
25.5
36.5
32.3
43.3
37.8
5.7
Navy - F76
522.4
333.8
I 298.6
282.6
305.6
347.8
434.4
413.1
355.9
404.1
376.9
311.4
402.2
346.6
337.9
273.1
286.2
286.7
307.8
Navy - IFO
+
+
6.4
12.9
15.4
47.5
39.4
19.7
25.4
00
CO
19.0
23.1
12.9
9.5
6.1
3.0
1.5
1.9
+
Total
522.4
333.8
328.8
318.0
348.2
459.0
530.0
470.7
414.3
444.4
436.7
374.4
448.0
381.5
380.6
308.5
331.0
326.3
313.6
+ Does not exceed 0.05 million gallons.
Note: Totals may not sum due to independent rounding.
Table fl-142: Aviation and Marine Carbon Contents 1MMT Carhon/QBtul 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
Source: EPA (2010) and IPCC (2006).
Table fl-143: Annual Variable Carbon Content Coefficient for Jet FueHMMT Carbon/QBtu)
Fuel
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Jet Fuel
19.40
, 19.34
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
Source: EPA (2010)
Table fl-144: Total U.S. DoD CO; Emissions from Bunker Fuels tMMT CO2 Eg.]
Mode
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Aviation
8.1
5.5
4.7
5.1
4.7
4.6
4.8
4.5
3.9
4.0
3.8
3.6
3.6
3.1
3.1
2.9
3.3
3.3
3.3
Marine
5.4
3.4
3.4
3.3
3.6
4.7
5.4
4.8
4.2
4.6
4.5
3.8
4.6
3.9
3.9
3.2
3.4
3.3
3.2
Total
13.4
9.0
8.0
8.3
8.3
9.3
10.3
9.3
8.1
8.5
8.2
7.4
8.2
7.0
7.0
6.0
6.7
6.7
6.5
Note: Totals may not sum due to independent rounding.
A-220 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
References
DLA Energy (2017) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense
Energy Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, D.C.
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.
A-221
-------
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., F1FC-emitting) end-use sectors:
refrigeration and air-conditioning, foams, aerosols, solvents, and fire-extinguishing. Within these sectors, there are 67
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.
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:
A-222 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1. Gather historical data. The Vintaging Model is populated with information on each end-use, taken from
published sources and industry experts.
2. Simulate the implementation of new, non-ODS technologies. The Vintaging Model uses detailed
characterizations of the existing uses of the ODS, as well as data on how the substitutes are replacing the ODS, to simulate
the implementation of new technologies that enter the market in compliance with ODS phase-out policies. As part of this
simulation, the ODS substitutes are introduced in each of the end-uses over time as seen historically and as needed to comply
with the ODS phase-out and other regulations.
3. Estimate emissions of the ODS substitutes. The chemical use is estimated from the amount of substitutes that
are required each year for the manufacture, installation, use, or servicing of products. The emissions are estimated from the
emission profile for each vintage of equipment or product in each end-use. By aggregating the emissions from each vintage,
a time profile of emissions from each end-use is developed.
Each set of end-uses is discussed in more detail in the following sections.
Refrigeration and Air-Conditioning
For refrigeration and air conditioning products, emission calculations are split into two categories: emissions
during equipment lifetime, which arise from annual leakage and service losses, and disposal emissions, which occur at the
time of discard. Two separate steps are required to calculate the lifetime emissions from leakage and service, and the
emissions resulting from disposal of the equipment. 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 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.
Step 1: Calculate lifetime emissions
Emissions from any piece of equipment include both the amount of chemical leaked during equipment operation
and the amount emitted during service. Emissions from leakage and servicing can be expressed as follows:
where:
Esj = (la + h) xY, Qq -i+2 for i = l->k
Es = Emissions from Equipment Serviced. Emissions in year j from normal leakage and
servicing (including recharging) of equipment.
la = Annual Leak Rate. Average annual leak rate during normal equipment operation
(expressed as a percentage of total chemical charge).
ls = Service Leak Rate. Average leakage during equipment servicing (expressed as a
percentage of total chemical charge).
Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to
charge new equipment in a given year by weight.
, = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of the equipment.
Step 2: Calculate disposal emissions
The disposal emission equations assume that a certain percentage of the chemical charge will be emitted to the
atmosphere when that vintage is discarded. Disposal emissions are thus a function of the quantity of chemical contained in
the retiring equipment fleet and the proportion of chemical released at disposal:
Edj = Qq-k+i x [1 - [rm x rc)]
where:
A-223
-------
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 3: Calculate total emissions
Finally, lifetime and disposal emissions are summed to provide an estimate of total emissions.
Ej = Esj + Edj
where:
E = Total Emissions. Emissions from refrigeration and air conditioning equipment in year
j-
Es = Emissions from Equipment Serviced. Emissions in year j from leakage and servicing
(including recharging) of equipment.
Ed = Emissions from Equipment Disposed. Emissions in year j from the disposal of
equipment.
j = Year of emission.
Assumptions
The assumptions used by the Vintaging Model to trace the transition of each type of equipment away from ODS
are presented in Table A-145, below. As new technologies replace older ones, it is generally assumed that there are
improvements in their leak, service, and disposal emission rates. Additionally, the market for each equipment type is
assumed to grow independently, according to annual growth rates.
A-224 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-145: Refrigeration andflir-Conditioning Market Transition Assumptions
Initial
Market
Segment
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Growth
Rate7
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
Centrifugal Chillers
CFC-11
HCFC-123
1993
1993
45%
HCFO-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%
HCFC-123
1993
1994
31%
HCFO-1233zd(E)
2016
2016
1%
None
R-514A
2017
2017
1%
None
HCFO-1233zd(E)
2017
2020
49%
None
R-514A
2018
2020
49%
None
R-500
HFC-134a
1992
1994
53%
R-450A
2017
2017
1%
None
1.5%
R-513A
2017
2017
1%
None
R-450A
2018
2024
49%
None
R-513A
2018
2024
49%
None
HCFC-22
1991
1994
16%
HFC-134a
2000
2010
100%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
HCFC-123
1993
1994
31%
HCFO-1233zd(E)
2016
2016
1%
None
R-514A
2017
2017
1%
None
HCFO-1233zd(E)
2017
2020
49%
None
R-514A
2018
2020
49%
None
CFC-114
HFC-236fa
1993
1996
100%
HFC-134a
1998
2009
100%
None.
1.4%
A-225
-------
Initial
Market
Segment
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Growth
Rate7
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
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
1.3%
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
CFC-11
HCFC-123
1992
1994
70%
LLJ^
Tlf
N
CO
CO
CM
O
u_
O
X
2016
2016
2%
None
3.2%
HCFO-1233zd(E)
2017
2020
98%
None
HFC-134a
1992
1994
15%
None
A-226 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration in
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
New
Market
Growth
Segment
Substitute
Date
Eguipment1
Penetration
Substitute
Date
Eguipment1
Penetration
Substitute
Eguipment1
Penetration
Rate7
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
HCFC-123
1992
1994
35%
HCFO-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
HFO-1234yf
2012
2016
2015
2021
1%
99%
None
None
0.3%
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 | 19931 1994
Mobile Air Conditioners (School and Tour Buse
100%|| None I I I II II I II 0.8%
s)
CFC-12
HCFC-22
HFC-134a
1994
1994
1995
1997
0.5%
99.5%
HFC-134a
None
2006
2007
100%
None
0.3%
Mobile Air Conditioners (Transit Buses)
HCFC-22 || HFC-134a | 19951 20091 100%|| None I I I II II I II 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 1
HCFC-222 | HFC-134a
2000
2009
9% | R-407C
2010
2020
60% || R-450A
2017
2017
1%|| 2.5%
A-227
-------
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration in
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Equipment1
Penetration
Rate7
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
HCFC-22
HFC-134a
2000
2009
9%
R-407C
2010
2020
60%
R-450A
2017
2017
1%
2.5%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
R-410A
2010
2020
40%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
R-407C
2000
2009
1%
R-450A
2017
2017
1%
None
R-513A
2017
2017
1%
None
R-450A
2018
2024
49%
None
R-513A
2018
2024
49%
None
HFC-134a
2009
2010
81%
R-407C
2010
2020
60%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
A-228 Inventory of U
S. Greenhouse Gas
Emissions and Sinks: 1990-2016
-------
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration in
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
New
Market
Growth
Segment
Substitute
Date
Eguipment1
Penetration
Substitute
Date
Eguipment1
Penetration
Substitute
Eguipment1
Penetration
Rate7
R-410A
2010
2020
40%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
R-407C
2009
2010
9%
R-450A
2017
2017
1%
None
R-513A
2017
2017
1%
None
R-450A
2018
2024
49%
None
R-513A
2018
2024
49%
None
Positive Displacement Chillers (Scroll)
HCFC-22
HFC-134a
2000
2009
9%
R-407C
2010
2020
60%
R-452B
2024
2024
100%
2.5%
R-410A
2010
2020
40%
R-452B
2024
2024
100%
R-407C
2000
2009
1%
R-452B
2024
2024
100%
None
HFC-134a
2009
2010
81%
R-407C
2010
2020
60%
R-452B
2024
2024
100%
R-410A
2010
2020
40%
R-452B
2024
2024
100%
R-407C
2009
2010
9%
R-452B
2024
2024
100%
None
Refrigerated Appliances
CFC-12
HFC-134a
1994
1995
100%
Non-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
2007
2010
29%
None
1.3%
R-410A
2010
2010
71%
None
R-410A
2000
2005
5%
R-410A
2006
2006
100%
None
R-410A
2000
2006
5%
None
R-410A
2006
2006
20%
None
Retail Food (Large; Technology Transition)
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
A-229
-------
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration in
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start Date
New
Market
Growth
Segment
Substitute
Date
Eguipment1
Penetration
Substitute
Date
Eguipment1
Penetration
Substitute
Eguipment1
Penetration
Rate7
R-507
1995
2000
7.5%
R-404A
2006
2010
71%
R-407A
2017
2017
100%
R-407A
2006
2010
30%
None
HCFC-22
1995
2000
75%
R-404A
2006
2010
13.3%
R-407A
2011
2015
100%
R-407A
2001
2005
1.3%
None
R-404A
2001
2005
12%
R-407A
2017
2017
100%
R-507
2001
2005
6.7%
R-407A
2011
2015
100%
R-404A
2006
2010
34%
R-407A
2011
2015
100%
R-404A
2006
2010
7.3%
R-407A
2017
2017
100%
R-407A
2006
2010
25.3%
None
Retail Food (Large Condensing Units)
HCFC-22
R-402A
1995
2005
5%
R-404A
2006
2006
100%
R-407A
2018
2018
100%
1.5%
R-404A
1995
2005
25%
R-407A
2018
2018
100%
None
R-507
1995
2005
10%
R-407A
2018
2018
100%
None
R-404A
2008
2010
45%
R-407A
2018
2018
100%
None
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
2012
2015
1%
2.2%
Non-ODP/GWP
2012
2015
3.7%
Non-ODP/GWP
2014
2019
31%
Non-ODP/GWP
2016
2016
17.3%
R-450A
2016
2020
23%
R-513A
2016
2020
23%
HFC-134a
2000
2009
9%
Non-ODP/GWP
2014
2019
30%
R-450A
2016
2020
35%
R-513A
2016
2020
35%
R-404A
1990
1993
9%
Non-ODP/GWP
2016
2016
30%
None
R-448A
2019
2020
35%
None
R-449A
2019
2020
35%
None
Transport Refrigeration (Road Transport)
CFC-12
HFC-134a
1993
1995
10%
None
5.5%
R-404A
1993
1995
60%
R-452A
2017
2021
5%
R-452A
2021
2030
95%
A-230 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Initial
Market
Segment
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Growth
Rate7
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Eguipment1
Maximum
Market
Penetration
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Eguipment1
Maximum
Market
Penetration
Name of
Substitute
Start Date
Date of Full
Penetration in
New
Eguipment1
Maximum
Market
Penetration
HCFC-22
1993
1995
30%
R-410A
R-404A
2000
2006
2003
2010
5%
95%
None
R-452A
R-452A
2017
2021
2021
2030
5%
95%
Transport Refrigeration (Intermodal Containers
CFC-12
HFC-134a
R-404A
HCFC-22
1993
1993
1993
1993
1993
1993
60%
5%
35%
C02
CO2
HFC-134a
2017
2017
2000
2021
2021
2010
5%
5%
100%
None
None
CO2
2017
2021
5%
7.3%
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
sO
0s
0
0
HFC-134a
1996
2000
sO
0s
0
0
None
II -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%
CO2
2012
2012
1%
Propane
100%
2019
2019
-0.03%
Propane
2013
2017
39%
None
Propane
2014
2014
1%
None
Propane
2019
2019
49%
None
R-450A
2019
2019
5%
None
R-513A
2019
2019
5%
None
R-404A
1995
1998
10%
R-450A
2019
2019
50%
None
R-513A
2019
2019
50%
None
Water-Source and Ground-Source Heat Pumps
HCFC-22
R-407C
2000
2006
5%
None
1.3%
R-410A
2000
2006
5%
None
A-231
-------
Initial
Market
Segment
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Growth
Rate7
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
HFC-134a
R-407C
R-410A
HFC-134a
R-407C
R-410A
2000
2006
2006
2009
2009
2009
2009
2009
2009
2010
2010
2010
2%
2.5%
4.5%
18%
22.5%
40.5%
None
None
None
None
None
None
Window Units
HCFC-22
R-410A
R-410A
2008
2009
2009
2010
10%
90%
None
None
4.0%
1 Transitions between the start year and date of full penetration in new equipment are assumed to be linear.
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-502 from 1988 to 1990, and subsequently transitioned to HCFC-22 from 1990 to 1993. These transitions are not shown in the table in order to
provide the HFC transitions in greater detail.
7 Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
A-232 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-146 presents the average equipment lifetimes and annual HFC emission rates (for servicing, leaks, and
disposal) for each end-use assumed by the Vintaging Model.
Table fl-146: Refrigeration and flir-Contlitioning Lifetime Assumptions
HFC Emission Rates
HFC Emission Rates
End-Use
Lifetime
(Servicing and Leaks)
(Disposal)1
(Years)
(%)
(%)
Centrifugal Chillers
20-27
2.0-10.9
10
Cold Storage
20-25
15.0
10
Commercial Unitary A/C
15
7.9-8.6
30-40
Dehumidifiers
11
0.5
50
Ice Makers
8
3.0
49
Industrial Process Refrigeration
25
3.6-12.3
10
Mobile Air Conditioners
5-16
2.3-18.0
43-50
Positive Displacement Chillers
20
0.5-1.5
10
PTAC/PTHP
12
3.9
40
Retail Food
10-20
1.0-25
10-35
Refrigerated Appliances
14
0.6
42
Residential Unitary A/C
15
5.3-10.6
40
Transport Refrigeration
9-40
19.4-36.4
10-65
Water & Ground Source Heat Pumps
20
3.9
43
Window Units
12
0.6
50
1 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% - Disposal Loss Rate (%).
Aerosols
ODSs, HFCs, and many other chemicals are used as propellant aerosols. Pressurized within a container, a nozzle
releases the chemical, which allows the product within the can to also be released. Two types of aerosol products are
modeled: metered dose inhalers (MDI) and consumer aerosols. In the United States, the use of CFCs in consumer aerosols
was banned in 1978, and many products transitioned to hydrocarbons or "not-in-kind" technologies, such as solid deodorants
and finger-pump hair sprays. However, MDIs continued to use CFCs as propellants because their use was deemed essential.
Essential use exemptions granted to the United States under the Montreal Protocol for CFC use in MDIs were limited to the
treatment of asthma and chronic obstructive pulmonary disease.
All HFCs used in aerosols are assumed to be emitted in the year of manufacture. Since there is currently no aerosol
recycling, it is assumed that all of the annual production of aerosol propellants is released to the atmosphere. The following
equation describes the emissions from the aerosols sector.
Ej = Qcj
where:
E
Qc
j
Transition Assumptions
Transition assumptions and growth rates for those items that use ODSs or HFCs as propellants, including vital
medical devices and specialty consumer products, are presented in Table A-147.
Emissions. Total emissions of a specific chemical in year j from use in aerosol
products, by weight.
Quantity of Chemical. Total quantity of a specific chemical contained in aerosol
products sold in year j, by weight.
Year of emission.
A-233
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Table fl-147: Aerosol Product Transition Assumptions
Primary Substitute
Secondary Substitute
Date of Full
Date of Full
Initial
Penetration in
Maximum
Penetration in
Maximum
Market
Name of
Start
New
Market
Name of
Start
New
Market
Growth
Seqment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Rate4
MDIs
CFC Mix2
HFC-134a
1997
1997
6%
None
0.8%
Non-ODP/GWP
1998
2007
7%
None
CFC Mix3
2000
2000
87%
HFC-134a
2002
2002
34%
HFC-134a
2003
2009
47%
HFC-227ea
2006
2009
5%
HFC-134a
2010
2011
6%
HFC-227ea
2010
2011
1%
HFC-134a
2011
2012
3%
HFC-227ea
2011
2012
0.3%
HFC-134a
2014
2014
3%
HFC-227ea
2014
2014
0.3%
Consumer Aerosols (Non-MDIs)
NA3
HFC-152a
1990
1991
50%
None
2.0%
HFC-134a
1995
1995
50%
HFC-152a
1997
1998
44%
HFC-152a
2001
2005
36%
HFO-1234ze(E)
2016
2018
7%
1 Transitions between the start year and date of full penetration in new products are assumed to be linear.
2CFC Mix consists of CFC-11, CFC-12 and CFC-114 and represents the weighted average of several CFCs consumed for essential use in MDIs from
1993 to 2008. It is assumed that CFC mix was stockpiled in the United States and used in new products through 2013.
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.
4 Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
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. The
following equation calculates emissions from solvent applications.
where:
E
I
Qc
j
Transition Assumptions
Ej = 1 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.
The transition assumptions and growth rates used within the Vintaging Model for electronics cleaning, metals
cleaning, precision cleaning, and adhesives, coatings and inks, are presented in Table A-148.
A-234 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table A-148:
Solvent Market Transition Assumptions
Primary Substitute
Secondar
1 Substitute
Date of Full
Date of Full
Penetration in
Maximum
Penetration in
Maximum
Initial Market
Name of
Start
New
Market
Name of
Start
New
Market
Growth Rate3
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Adhesives
CH3CCI3
Non-ODP/GWP
1994
1995
sO
0s
0
0
None
2.0%
Electronics
CFC-113
Semi-Aqueous
1994
1995
52%
None
2.0%
HCFC-225ca/cb
1994
1995
0.2%
Unknown
HFC-43-10mee
1995
1996
0.7%
None
HFE-7100
1994
1995
0.7%
None
nPB
1992
1996
5%
None
Methyl Siloxanes
1992
1996
0.8%
None
No-Clean
1992
20132
40%
None
CH3CCI3
Non-ODP/GWP
1996
1997
99.8%
None
2.0%
PFC/PFPE
1996
1997
0.2%
Non-ODP/GWP
2000
2003
90%
Non-ODP/GWP
2005
2009
10%
Metals
CH3CCI3
Non-ODP/GWP
1992
1996
100%
None
2.0%
CFC-113
Non-ODP/GWP
1992
20132
100%
None
2.0%
ecu
Non-ODP/GWP
1992
1996
100%
None
2.0%
Precision
CH3CCI3
Non-ODP/GWP
1995
1996
99.3%
None
2.0%
HFC-43-10mee
1995
1996
0.6%
None
PFC/PFPE
1995
1996
0.1%
Non-ODP/GWP
2000
2003
90%
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 Transitions between the start year and date of full penetration in new equipment or chemical supply are assumed to be linear.
Note: Non-ODP/GWP includes chemicals with zero ODP and low GWP, such as hydrocarbons and ammonia, as well as not-in-kind alternatives such as
"no clean" technologies.
2 Transition assumed to be completed in 2013 to mimic CFC-113 stockpile use.
3 Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
Fire Extinguishing
ODSs, HFCs, PFCs and other chemicals are used as fire-extinguishing agents, in both hand-held "streaming"
applications as well as in built-up "flooding" equipment similar to water sprinkler systems. Although these systems are
generally built to be leak-tight, some leaks do occur and emissions occur when the agent is released. Total emissions from
fire extinguishing are assumed, in aggregate, to equal a percentage of the total quantity of chemical in operation at a given
time. For modeling purposes, it is assumed that fire extinguishing equipment leaks at a constant rate for an average
equipment lifetime, as shown in the equation below. In streaming systems, non-halon emissions are assumed to be 3.5
percent of all chemical in use in each year, while in flooding systems 2.5 percent of the installed base of chemical is assumed
to leak annually. Halon systems are assumed to leak at higher rates. The equation is applied for a single year, accounting for
all fire protection equipment in operation in that year. The model assumes that equipment is serviced annually so that the
amount equivalent to average annual emissions for each product (and hence for the total of what was added to the bank in a
previous year in equipment that has not yet reached end-of-life) is replaced/applied to the starting charge size (or chemical
bank). Each fire protection agent is modeled separately. In the Vintaging Model, streaming applications have a 12-year
lifetime and flooding applications have a 20-year lifetime.
where:
Ej = r x 2 Qcj-ui for i=l —>k
Emissions. Total emissions of a specific chemical in year j for streaming fire
extinguishing equipment, by weight.
A-235
-------
2c
Percent Released. The percentage of the total chemical in operation that is released to
the atmosphere.
Quantity of Chemical. Total amount of a specific chemical used in new fire
extinguishing equipment in a given year, _/-/+!, by weight.
i = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of the equipment.
Transition Assumptions
Transition assumptions and growth rates for these two fire extinguishing types are presented in Table A-149.
Table fl-149: F re Extinguishing Market Transition Assumptions
Primary
Substitute
Secondary Substitute
Date of Full
Date of Full
Penetration
Maximum
Penetration
Maximum
Initial 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
1992
1992
5%
Unknown
3.0%
HFC-236fa
1997
1999
3%
None
Halotron
1994
1995
0.1%
Unknown
Halotron
1996
2000
5.4%
Non-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
1 Transitions between the start year and date of full penetration in new equipment are assumed to be linear.
2 Despite the 1994 consumption ban, a small percentage of new halon systems are assumed to continue to be built and filled with stockpiled or recovered
supplies.
3 Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
Foam Blowing
ODSs, HFCs, and other chemicals are used to produce foams, including such items as the foam insulation panels
around refrigerators, insulation sprayed on buildings, etc. The chemical is used to create pockets of gas within a substrate,
increasing the insulating properties of the item. Foams are given emission profiles depending on the foam type (open cell or
closed cell). Open cell foams are assumed to be 100 percent emissive in the year of manufacture. Closed cell foams are
assumed to emit a portion of their total HFC content upon manufacture, a portion at a constant rate over the lifetime of the
foam, a portion at disposal, and a portion after disposal; these portions vary by end-use.
Step 1: Calculate manufacturing emissions (open-cell and closed-cell foams)
Manufacturing emissions occur in the year of foam manufacture, and are calculated as presented in the following
equation.
Errij = 1m x Qq
A-236 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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where:
Enij = Emissions from manufacturing. Total emissions of a specific chemical in year j due to
manufacturing losses, by weight.
Im = Loss Rate. Percent of original blowing agent emitted during foam manufacture. For
open-cell foams, Im is 100%.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture
closed-cell foams in a given year.
j = Year of emission.
Step 2: Calculate lifetime emissions (closed-cell foams)
Lifetime emissions occur annually from closed-cell foams throughout the lifetime of the foam, as calculated as
presented in the following equation.
Euj = lux 2 Qcj-ui fori=l-
where:
Euj = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due
to lifetime losses during use, by weight.
lu = Leak Rate. Percent of original blowing agent emitted each year during lifetime use.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture
closed-cell foams in a given year.
i = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of foam product.
Step 3: Calculate disposal emissions (closed-cell foams)
Disposal emissions occur in the year the foam is disposed, and are calculated as presented in the following equation.
Edj = Id x Qcj-k
where:
Edj = Emissions from disposal. Total emissions of a specific chemical in year j at disposal,
by weight.
Id = Loss Rate. Percent of original blowing agent emitted at disposal.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture
closed-cell foams in a given year.
j = Year of emission.
k = Lifetime. The average lifetime of foam product.
Step 4: Calculate post-disposal emissions (closed-cell foams)
Post-disposal emissions occur in the years after the foam is disposed; for example, emissions might occur while
the disposed foam is in a landfill. Currently, the only foam type assumed to have post-disposal emissions is polyurethane
foam used as domestic refrigerator and freezer insulation, which is expected to continue to emit for 26 years post-disposal,
calculated as presented in the following equation.
Epj = Ip x 2 Qcj-m for m=k->k + 26
A-237
-------
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.
where:
Ej = Errij + Euj + Edj + Epj
Ej = Total Emissions. Total emissions of a specific chemical in year j, by weight.
Enij = 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-150. The emission profiles of these thirteen foam types are shown in Table A-151.
A-238 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table fl-150: 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 in
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start
New
Market
Growth
Segment
Substitute
Date
Eguipment1
Penetration
Substitute
Date
Eguipment1
Penetration
Substitute
Date
Eguipment1
Penetration
Rate3
Commercial Refrigeration Foam
HCFC-141b
1989
1996
40%
HFC-245fa
2002
2003
80%
HCFO-1233zd(E)
2015
2020
70%
Non-ODP/GWP
2015
2020
30%
Non-ODP/GWP
2002
2003
20%
None
HCFC-142b
1989
1996
8%
Non-ODP/GWP
2009
2010
80%
None
HFC-245fa
2009
2010
20%
HCFO-1233zd(E)
2015
2020
70%
Non-ODP/GWP
2015
2020
30%
HCFC-22
1989
1996
52%
Non-ODP/GWP
2009
2010
80%
None
HFC-245fa
2009
2010
20%
HCFO-1233zd(E)
2015
2020
70%
Non-ODP/GWP
2015
2020
30%
CFC-11
6.0%
Flexible PU Foam: Integral Skin Foam
CFC-11
HCFC-141b
1989
1990
100%
HFC-134a
1993
1996
25%
CO2
HCFO-1233zd(E)
2015
2015
2017
2017
50%
50%
2.0%
HFC-134a
1994
1996
25%
CO2
HCFO-1233zd(E)
2015
2015
2017
2017
50%
50%
C02
1993
1996
25%
None
CO2
1994
1996
25%
None
Flexible PU Foam: Slabstock Foam, Moulded Foam
CFC-11
Non-ODP/GWP
1992
1992
100%
None
2.0%
Phenolic Foam
CFC-11
HCFC-141b
1989
1990
100%
Non-ODP/GWP
1992
1992
100%|| None
2.0%
Polyolefin Foam
CFC-114
HFC-152a
HCFC-142b
1989
1989
1993
1993
10%
90%
Non-ODP/GWP
Non-ODP/GWP
2005
1994
2010
1996
100%
100%
None
None
2.0%
PU and PIR
Rigid: Boardstock
CFC-11
HCFC-141b
1993
1996
100%
Non-ODP/GWP
HC/HFC-245fa
Blend
2000
2000
2003
2003
95%
5%
None
Non-ODP/GWP
2017
2017
100%
6.0%
PU Rigid: Domestic Refrigerator and Freezer Insulation
CFC-11
HCFC-141b
1993
1995
100%
HFC-134a
HFC-245fa
HFC-245fa
Non-ODP/GWP
Non-ODP/GWP
1996
2001
2006
2002
2006
2001
2003
2009
2005
2009
7%
50%
10%
10%
3%
Non-ODP/GWP
Non-ODP/GWP
HCFO-1233zd(E)
Non-ODP/GWP
HCFO-1233zd(E)
None
None
2002
2015
2015
2015
2015
2003
2020
2020
2020
2020
100%
50%
50%
50%
50%
0.8%
A-239
-------
Primary
Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration in
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start
New
Market
Growth
Segment
Substitute
Date
Eguipment1
Penetration
Substitute
Date
Eguipment1
Penetration
Substitute
Date
Eguipment1
Penetration
Rate3
Non-ODP/GWP
2009
2014
20%
None
PU Rigid: One Component Foam
HCFC-142b/22
CFC-12
Blend
1989
1996
70%
Non-ODP/GWP
2009
2010
80%
None
4.0%
HFC-134a
2009
2010
10%
HFO-1234ze(E)
2018
2020
100%
HFC-152a
2009
2010
10%
None
HCFC-22
1989
1996
30%
Non-ODP/GWP
2009
2010
80%
None
HFC-134a
2009
2010
10%
HFO-1234ze(E)
2018
2020
100%
HFC-152a
2009
2010
10%
None
PU Rigid: Other: Slabstock Foam
CFC-11
HCFC-141b
1989
1996
100%
C02
1999
2003
45%
None
2.0%
Non-ODP/GWP
2001
2003
45%
None
HCFC-22
2003
2003
10%
Non-ODP/GWP
2009
2010
100%
PU Rigid: Sandwich Panels: Continuous and Discontinuous
HCFC-
HCFC-22/Water
HFC-245fa/C02
141b2
Blend
HFC-245fa/C02
2001
2003
20%
Blend
Non-ODP/GWP
2009
2009
2010
2010
50%
50%
HCFO-1233zd(E)
None
2015
2020
100%
6.0%
Blend
2002
2004
20%
HCFO-1233zd(E)
2015
2020
100%
None
Non-ODP/GWP
2001
2004
40%
None
HFC-134a
2002
2004
20%
Non-ODP/GWP
2015
2020
100%
None
HFC-245fa/C02
HCFC-22
Blend
Non-ODP/GWP
CO2
2009
2009
2009
2010
2010
2010
40%
20%
20%
HCFO-1233zd(E)
None
None
2015
2020
100%
None
HFC-134a
2009
2010
20%
Non-ODP/GWP
2015
2020
100%
None
PU Rigid: Spray Foam
CFC-11
HCFC-141b
1989
1996
100%
HFC-245fa
HFC-245fa/C02
Blend
Non-ODP/GWP
2002
2002
2001
2003
2003
2003
30%
60%
10%
HCFO-1233zd(E)
None
None
2016
2020
100%
6.0%
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
CO2
2009
2010
10%
None
Non-ODP/GWP
2009
2010
10%
None
HCFC-142b
1989
1994
90%
HFC-134a
2009
2010
70%
Non-ODP/GWP
2021
2021
100%
A-240 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 in
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start
New
Market
Growth
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Rate3
HFC-152a
2009
2010
10%
None
CO2
2009
2010
10%
None
Non-ODP/GWP
2009
2010
10%
None
XPS: Sheet Foam
CFC-12
C02
1989
1994
1%
None
2.0%
Non-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.
2 The CFC-11 PU Rigid: Sandwich Panels: Continuous and Discontinuous market for new systems transitioned to 82 percent HCFC-141b and 18 percent HCFC-22 from 1989 to 1996. These transitions are not
shown in the table in order to provide the HFC transitions in greater detail.
3 Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
A-241
-------
Table A-151: Emission Profile for the Foam End-Uses
Annual
Leakage
Loss at
Leakage Rate
Lifetime
Loss at
Total3
Foam End-Use
Manufacturing (%)
(%)
(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 Foam3
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
6.5
0.5
14
37.2
50.7
(HFC-134a)a
Rigid PU: Domestic Refrigerator and Freezer
3.75
0.25
14
39.9
47.15
Insulation (all others)3
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
PIR (Polyisocyanurate)
PU (Polyurethane)
XPS (Extruded Polystyrene)
a In general, total emissions from foam end-uses are assumed to be 100 percent. In the Rigid PU Domestic Refrigerator and Freezer Insulation end-use,
the source of emission rates and lifetimes did not yield 100 percent emission; the remainder is anticipated to be emitted at a rate of 2.0 percent/year post-
disposal.
Sterilization
Sterilants kill microorganisms on medical equipment and devices. The principal ODS used in this sector was a
blend of 12 percent ethylene oxide (EtO) and 88 percent CFC-12, known as "12/88." In that blend, ethylene oxide sterilizes
the equipment and CFC-12 is a dilutent solvent to form a non-flammable blend. The sterilization sector is modeled as a
single end-use. For sterilization applications, all chemicals that are used in the equipment in any given year are assumed to
be emitted in that year, as shown in the following equation.
Ej = Qcj
where:
E
Qc
j
Assumptions
The Vintaging Model contains one sterilization end-use, whose transition assumptions away from ODS and growth
rates are presented in Table A-152.
Emissions. Total emissions of a specific chemical in year j from use in sterilization
equipment, by weight.
Quantity of Chemical. Total quantity of a specific chemical used in sterilization
equipment in year j, by weight.
Year of emission.
A-242 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-152: Sterilization Market Transition Assumptions
Primary
Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Date of Full
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Name of
Start
Penetration in
Market
Growth
Segment
Substitute
Date
Equipment
Penetration
Substitute
Date
Equipment
Penetration
Substitute
Date
New 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
1 Transitions between the start year and date of full penetration in new equipment are assumed to be linear.
A-243
-------
Model Output
By repeating these calculations for each year, the Vintaging Model creates annual profiles of use and emissions
for ODS and ODS substitutes. The results can be shown for each year in two ways: 1) on a chemical-by-chemical basis,
summed across the end-uses, or 2) on an end-use or sector basis. Values for use and emissions are calculated both in metric
tons and in million metric tons of CO2 equivalent (MMT CO2 Eq.). The conversion of metric tons of chemical to MMT CO2
Eq. is accomplished through a linear scaling of tonnage by the global warming potential (GWP) of each chemical.
Throughout its development, the Vintaging Model has undergone annual modifications. As new or more accurate
information becomes available, the model is adjusted in such a way that both past and future emission estimates are often
altered.
Bank of ODS and ODS Substitutes
The bank of an ODS or an ODS substitute is "the cumulative difference between the chemical that has been
consumed in an application or sub-application and that which has already been released" (IPCC 2006). For any given year,
the bank is equal to the previous year's bank, less the chemical in equipment disposed of during the year, plus chemical in
new equipment entering the market during that year, less the amount emitted but not replaced, plus the amount added to
replace chemical emitted prior to the given year, as shown in the following equation:
where:
Bq = Bcj-i-Qdj+Qpj+Ee-Qr
Bcj = Bank of Chemical. Total bank of a specific chemical in year j, by weight.
Qdj = Quantity of Chemical in Equipment Disposed. Total quantity of a specific chemical in
equipment disposed of in year j, by weight.
Qpj = Quantity of Chemical Penetrating the Market. Total quantity of a specific chemical
that is entering the market in year j, by weight.
Ee = Emissions of Chemical Not Replaced. Total quantity of a specific chemical that is
emitted during year j but is not replaced in that year. The Vintaging Model assumes
all chemical emitted from refrigeration, air conditioning and fire extinguishing
equipment is replaced in the year it is emitted, hence this term is zero for all sectors
except foam blowing.
Qr = Chemical Replacing Previous Year's Emissions. Total quantity of a specific chemical
that is used to replace emissions that occurred prior to year j. The Vintaging Model
assumes all chemical emitted from refrigeration, air conditioning and fire
extinguishing equipment is replaced in the year it is emitted, hence this term is zero
for all sectors.
j = Year of emission.
Table A-153 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 1990 to 2016.
A-244 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-153: Banks of OPS and OPS Substitutes,1990-2016 (MT)
Year
CFC
HCFC
HFC
1990
695,056
281,709
872
1995
768,574
508,368
50,476
2000
638,658
938,206
188,673
2001
610,089
1,007,715
217,780
2002
585,608
1,061,150
246,831
2003
561,341
1,098,002
281,638
2004
536,594
1,135,039
318,012
2005
506,767
1,176,248
356,687
2006
476,460
1,213,580
401,312
2007
448,847
1,241,851
446,888
2008
426,406
1,259,130
488,956
2009
413,431
1,251,240
535,405
2010
376,199
1,214,125
600,722
2011
339,448
1,166,631
669,511
2012
302,837
1,117,975
739,950
2013
267,100
1,064,448
813,160
2014
231,330
1,009,404
889,352
2015
195,498
955,531
960,318
2016
159,713
905,296
1,026,487
References
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
Data are also taken from various government sources, including rulemaking analyses from the U.S. Department of Energy
and from the Motor Vehicle Emission Simulator (MOVES) model from EPA's Office of Transportation and Air
Quality.
A-245
-------
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 2015. As explained in the Enteric Fermentation chapter, a simplified approach was used to estimate emissions
for 2016. The methodology used for 2016 relied on 2016 population estimates and 2015 implied emission factors and is
explained in further detail within Chapter 5.1 Enteric Fermentation (IPCC 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 IPCC Tier 2 methodology was used to estimate emissions from cattle. The IPCC
Tier 1 methodology was used to estimate emissions for the other types of livestock, including horses, goats, sheep, swine,
American bison, and mules and asses (IPCC 2006).
Estimate Methane Emissions from Cattle
This section describes the process used to estimate CH4 emissions from enteric fermentation from cattle using the
Cattle Enteric Fermentation Model (CEFM). The CEFM was developed based on recommendations provided in the 2006
IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) and uses information on population, energy
72
requirements, digestible energy, and CH4 conversion rates to estimate CH4 emissions. 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 2016). State-level cattle
population estimates are shown by animal type for 2015 in Table A-154. A national-level summary of the annual average
populations upon which all livestock-related emissions are based is provided in Table A-155. Cattle populations used in the
Enteric Fermentation source category were estimated using the cattle transition matrix in the CEFM, which uses January 1
USDA population estimates and weight data to simulate the population of U.S. cattle from birth to slaughter, and results in
an estimate of the number of animals in a particular cattle grouping while taking into account the monthly rate of weight
gain, the average weight of the animals, and the death and calving rates. The use of supplemental USDA data and the cattle
transition matrix in the CEFM results in cattle population estimates for this sector differing slightly from the January 1 or
July 1 USDA point estimates and the cattle population data obtained from the Food and Agriculture Organization of the
United Nations (FAO).
Table fl-154:2015 Cattle Population Estimates from the CEFM Transition Matrix, by Animal Type and State 11,000 head!73
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
8
1
2
45
336
652
27
63
24
16
5
Alaska
0
0
0
0
2
2
4
0
1
0
0
0
Arizona
100
195
20
46
20
90
175
8
19
132
11
254
Arkansas
4
7
1
3
55
445
863
36
84
63
27
11
California
911
1,780
232
541
70
304
590
31
73
265
73
438
Colorado
74
145
30
70
55
374
725
41
96
375
244
927
Conn.
10
19
2
6
1
3
5
1
1
1
0
0
Delaware
3
5
1
2
0
1
3
0
0
1
0
0
Florida
63
124
11
25
60
467
906
31
73
12
16
3
Georgia
41
81
8
19
28
247
479
22
51
22
16
4
Hawaii
1
2
0
1
4
35
69
3
6
4
3
1
Idaho
296
579
96
225
40
238
461
29
67
135
88
241
Illinois
48
94
16
37
25
189
366
15
36
101
51
234
Indiana
93
181
24
56
17
103
199
12
28
56
23
101
72 Additional information on the Cattle Enteric Fermentation Model can be found in ICF (2006).
73 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-246 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Iowa
107
210
39
91
60
464
900
44
101
620
328
1,210
Kansas
73
143
27
63
95
736
1,427
70
163
909
692
2,149
Kentucky
32
63
14
32
65
514
997
34
79
96
55
15
Louisiana
7
14
2
4
30
240
466
18
42
12
12
3
Maine
15
30
5
11
2
6
11
1
3
2
1
0
Maryland
25
49
8
18
4
22
42
2
5
7
5
10
Mass.
6
13
2
5
1
3
6
0
1
1
1
0
Michigan
206
403
50
117
15
55
107
6
13
82
18
156
Minn.
235
460
84
197
35
175
340
22
51
240
83
379
Miss.
6
12
2
4
38
241
468
23
55
27
17
6
Missouri
46
89
18
42
110
955
1,851
83
194
192
117
70
Montana
7
14
2
5
100
772
1,496
105
245
84
103
42
Nebraska
28
54
6
14
95
906
1,756
102
236
1,121
666
2,484
Nevada
14
28
3
6
12
109
212
9
21
21
15
4
N. Hamp.
7
14
2
4
1
2
3
0
1
0
0
0
N. Jersey
4
7
1
3
1
4
8
0
1
1
0
0
N. Mexico
165
323
33
77
35
210
407
21
48
46
36
10
New York
315
615
105
246
15
54
105
10
23
17
23
25
N. Car.
24
47
5
13
29
187
363
17
39
17
14
4
N. Dakota
8
16
2
4
50
461
894
41
95
103
96
42
Ohio
137
268
38
8
8
25
145
282
12
28
94
26
166
Oklahoma
20
40
8
18
140
970
1,880
102
236
418
190
262
Oregon
64
125
18
42
40
271
525
27
62
79
55
82
Penn
271
530
92
214
25
77
150
13
31
70
29
93
R.Island
0
1
0
0
0
1
2
0
0
0
0
0
S. Car.
8
15
2
4
14
88
170
7
17
4
6
1
S. Dakota
51
99
20
46
100
831
1,611
98
228
327
255
379
Tenn.
24
47
8
18
60
450
873
34
79
58
34
9
Texas
241
470
75
176
320
2,131
4,130
181
422
1,212
723
2,474
Utah
49
96
14
34
22
167
324
19
44
38
33
24
Vermont
68
132
17
39
3
6
12
1
2
2
3
1
Virginia
48
93
13
30
40
329
637
27
62
75
22
20
Wash.
142
277
41
96
18
102
198
13
30
84
70
205
W. Virg.
5
9
1
3
13
95
185
8
19
21
10
4
Wisconsin
653
1,275
220
513
35
142
275
18
42
180
23
257
Wyoming
3
6
2
4
40
358
694
47
109
65
74
74
Table A-155: Cattle Population Estimates from the CEFM Transition Matrix for 1990-2015 (1,000 head)74
Livestock Type
1990
1995
2000
2005
2011
2012
2013
2014
2015
Dairy
Dairy Calves (0-6 months)
5,369
5,091
4,951
4,628
4,709
4,770
4,758
4,727
4,764
Dairy Cows
10,015
9,482
9,183
9,004
9,156
9,236
9,221
9,208
9,307
Dairy Replacements 7-11 months
1,214
1,216
1,196
1,257
1,362
1,348
1,341
1,356
1,417
Dairy Replacements 12-23 months
2,915
2,892
2,812
2,905
3,215
3,233
3,185
3,190
3,310
Beef
Beef Calves (0-6 months)
16,909
18,177
17,431
16,918
15,817
15,288
14,859
14,946
15,117
Bulls
2,160
2,385
2,293
2,214
2,165
2,100
2,074
2,038
2,109
Beef Cows
32,455
35,190
33,575
32,674
30,913
30,282
29,631
29,085
29,302
Beef Replacements 7-11 months
1,269
1,493
1,313
1,363
1,232
1,263
1,291
1,342
1,473
Beef Replacements 12-23 months
2,967
3,637
3,097
3,171
2,889
2,968
3,041
3,113
3,422
Steer Stackers
10,321
11,716
8,724
8,185
7,568
7,173
7,457
7,411
7,517
Heifer Stackers
5,946
6,699
5,371
5,015
4,752
4,456
4,455
4,384
4,402
Feedlot Cattle
9,549
11,064
13,006
12,652
13,601
13,328
13,267
13,222
12,883
74 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-247
-------
The population transition matrix in the CEFM simulates the U.S. cattle population over time and provides an
estimate of the population age and weight structure by cattle type on a monthly basis. Since cattle often do not remain in a
single population type for an entire year (e.g., calves become stackers, stackers 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, stackers, feedlot, and mature
animals. The matrix is based primarily on two types of data: population statistics and weight statistics (including target
weights, slaughter weights, and weight gain). Using the weight data, the transition matrix simulates the growth of animals
over time by month. The matrix also relies on supplementary data, such as feedlot placement statistics, slaughter statistics,
death rates, and calving rates, described in further detail below.
The basic method for tracking population of animals per category is based on the number of births (or graduates)
into the monthly age group minus those animals that die or are slaughtered and those that graduate to the next category (such
as stackers 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 stackers, 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-156.
Table fl-156: Cattle Population Categories Used for Estimating Clh Emissions
Dairy Cattle Beef Cattle
Calves Calves
Heifer Replacements Heifer Replacements
Cows Heifer and Steer Stackers
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-157, based on approximations from the
National Animal Health Monitoring System (NAHMS) (USDA/APHIS/VS 1998, 1994, 1993). To determine whether calves
were born to dairy or beef cows, the dairy cow calving rate (USDA/APHIS/VS 2002, USDA/APHIS/VS 1996) was
multiplied by the total dairy cow population to determine the number of births attributable to dairy cows, with the remainder
assumed to be attributable to beef cows. Total annual calf births are obtained from USDA, and distributed into monthly
cohorts by cattle type (beef or dairy). Calf growth is modeled by month, based on estimated monthly weight gain for each
cohort (approximately 61 pounds per month). The total calf population is modified through time to account for veal calf
slaughter at 4 months and a calf death loss of 0.35 percent annually (distributed across age cohorts up to 6 months of age).
An example of a transition matrix for calves is shown in Table A-158. Note that 1- to 6-month old calves in January of each
year have been tracked through the model based on births and death loss from the previous year.
75 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-248 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-157: Estimated Beef Cow Births by Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
7%
15%
28%
22%
9%
3%
2%
2%
3%
4%
3%
3%
Table fl-158: Example of Monthly Average Populations from Calf Transition Matrix 11,000 head]
Age (month)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
6
1,138
1,131
1,389
1,612
1,554
1,538
2,431 |
4,488
7,755
6,298
2,971
1,522
5
1,131
1,389
1,612
1,554
1,538
2,431
4,488
7,755
6,298
2,971
1,522
1,153
4
1,389
1,612
1,554
1,538
2,431
4,488
7,755
6,298
2,971
1,522
1,153
1,144
3
1,612
1,554
1,538
2,431
4,488
7,755
6,298
2,971
1,522
1,153
1,144
1,402
2
1,554
1,538
2,431
4,488
7,755
6,298
2,971
1,522
1,153
1,144
1,402
1,625
1
1,538
2,431
4,488
7,755
6,298
2,971
1,522
1,153
1,144
1,402
1,625
1,565
0 I
2,431
4,488
7,755
6,298
2,971
1,522
1,153
1,144
1,402
1,625
1,565
1,547
Note: The cohort starting at age 0 months on January 1 is tracked in order to illustrate how a single cohort moves through the transition matrix. Each month,
the cohort reflects the decreases in population due to the estimated 0.35 percent annual death loss, and between months 4 and 5, a more significant loss is
seen than in other months due to estimated veal slaughter.
Replacements and Stockers. At 7 months of age, calves "graduate" and are separated into the applicable cattle
types: replacements (cattle raised to give birth), or stockers (cattle held for conditioning and growing on grass or other forage
diets). First the number of replacements required for beef and dairy cattle are calculated based on estimated death losses and
population changes between beginning and end of year population estimates. Based on the USDA estimates for "replacement
beef heifers" and "replacement dairy heifers," the transition matrix for the replacements is back-calculated from the known
animal totals from USDA, and the number of calves needed to fill that requirement for each month is subtracted from the
known supply of female calves. All female calves remaining after those needed for beef and dairy replacements are removed
and become "stockers" that can be placed in feedlots (along with all male calves). During the stacker phase, animals are
subtracted out of the transition matrix for placement into feedlots based on feedlot placement statistics from USDA (2016).
The data and calculations that occur for the stacker category include matrices that estimate the population of
backgrounding heifers and steer, as well as a matrix for total combined stockers. The matrices start with the beginning of
year populations in January and model the progression of each cohort. The age structure of the January population is based
on estimated births by month from the previous two years, although in order to balance the population properly, an
adjustment is added that slightly reduces population percentages in the older populations. The populations are modified
through addition of graduating calves (added in month 7, bottom row of Table A-159) and subtraction through death loss
and animals placed in feedlots. Eventually, an entire cohort population of stockers may reach zero, indicating that the
complete cohort has been transitioned into feedlots. An example of the transition matrix for stockers is shown in Table A-
159.
Table fl-159: Example of Monthly Average Populations from StockerTransition Matrix 11,000 head)
Age (month)
Jan
Feb
Mar
Apr May
Jun
Jul
Aug Sep
Oct
Nov
Dec
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
185
320
260
123
63
48
47
58
67
65
64
982
1,814
3,133
2,545
1,200
180
146
69
35
27
27
33
38
36
36
845
1,602
2,770
2,255
1,062
104
49
25
19
19
23
27
26
25
599
1,478
2,556
2,056
945
37
19
14
14
17
20
19
19
452
1,172
2,309
1,858
855
15
12
11
14
16
16
15
363
977
1,921
1,639
755
9
9
11
13
13
13
295
828
1,619
1,378
629
9
11
10
10
241
709
1,380
1,172
534
196
610
1,179
1,000
456
6
6
6
133
472
900
759
348
3
3
68
331
615
514
237
1
17
218
387
318
149
0
181
313
254
120
3,381
2,951
2,502
2,241
1,872
1,512
1,117
862
603
340
129
61
800
664
794
484
482
956
385
335
557
1,160
277
189
341
759
1,109
214
138
184
420
658
1,100
47
46
76
231
372
649
1,876
47
46
57
89
209
371
1,503
3,666
47
46
57
66
81
185
1,292
3,247
6,504
47
46
57
66
63
80
1,135
2,786
5,984
5,243
47
46
57
66
63
63
1,016
2,445
5,299
4,877
2,353
Note: The cohort starting at age 7 months on January 1 is tracked in order to
cohort reflects the decreases in population due to the estimated 0.35 percent
majority of the loss from the matrix).
illustrate how a single cohort moves through the transition matrix. Each month, the
annual death loss and loss due to placement in feedlots (the latter resulting in the
A-249
-------
In order to ensure a balanced population of both stackers and placements, additional data tables are utilized in the
stacker 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 USD A (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 stackers 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. 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,
77
based on a personal communication with Kris Johnson (2010) and an estimate from Holstein Association USA (2010).
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 2016) 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-160.
Weight gain for stacker animals was based on monthly gain estimates from Johnson (1999) for 1989, and from
average daily estimates from Lippke et al. (2000), Pinchack et al. (2004), Platter et al. (2003), and Skogerboe et al. (2000)
for 2000. Interim years were calculated linearly, as shown in Table A-161, and weight gain was held constant starting in
2000. Table A-161 provides weight gains that vary by year in the CEFM.
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.
77
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-250 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-160: Typical Animal Mass tlhsl78
Year/Cattle
Dairy
Dairy
Beef
Beef
Steer
Heifer
Steer
Heifer
Type
Calves
Cows3
Replacements'1
Cows3
Bulls3
Replacements'1
Stackers'
Stackers'
Feedlot"
Feedlot"
1990
269
1,500
899
1,221
1,832
819
691
651
923
845
1991
270
1,500
897
1,225
1,838
821
694
656
933
855
1992
269
1,500
897
1,263
1,895
840
714
673
936
864
1993
270
1,500
898
1,280
1,920
852
721
683
929
863
1994
270
1,500
897
1,280
1,920
853
720
688
943
875
1995
270
1,500
897
1,282
1,923
857
735
700
947
879
1996
269
1,500
898
1,285
1,928
858
739
707
939
878
1997
270
1,500
899
1,286
1,929
860
736
707
938
876
1998
270
1,500
896
1,296
1,944
865
736
709
956
892
1999
270
1,500
899
1,292
1,938
861
730
708
959
894
2000
270
1,500
896
1,272
1,908
849
719
702
960
898
2001
270
1,500
897
1,272
1,908
850
725
707
963
900
2002
270
1,500
896
1,276
1,914
851
725
707
981
915
2003
270
1,500
899
1,308
1,962
871
718
701
972
904
2004
270
1,500
896
1,323
1,985
877
719
702
966
904
2005
270
1,500
894
1,327
1,991
879
717
706
974
917
2006
270
1,500
897
1,341
2,012
889
724
712
983
925
2007
270
1,500
896
1,348
2,022
894
720
706
991
928
2008
270
1,500
897
1,348
2,022
894
720
704
999
938
2009
270
1,500
895
1,348
2,022
894
730
715
1007
947
2010
270
1,500
897
1,348
2,022
896
726
713
996
937
2011
270
1,500
897
1,348
2,022
891
721
712
989
932
2012
270
1,500
899
1,348
2,022
892
714
706
1003
945
2013
270
1,500
898
1,348
2,022
892
718
709
1016
958
2014
270
1,500
895
1,348
2,022
888
722
714
1022
962
2015
270
1,500
896
1,348
2,022
891
717
713
1037
982
'Input into the model.
b Annual average calculated in model based on age distribution.
Table fl-161: Weight Gains that Vary by Year tlhsl
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
stacker population into feedlots on a monthly basis by weight class. The model uses these data to shift a sufficient number
of animals from the stacker 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
78 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-251
-------
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-162) 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-162 provides a summary of the reported feedlot placement statistics for 2015.
Table fl-162: Fee Jlot Placements in the United States for 2015 [Number of animals place J/1,000 Head!79
Weight
Placed When:
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
< 600 lbs
410
330
365
320
360
350
365
395
405
645
470
375
600-700 lbs
340
265
275
240
260
250
235
215
290
530
387
355
700-800 lbs
474
396
449
348
389
336
327
362
416
431
310
357
> 800 lbs
565
560
720
640
710
545
620
660
830
680
435
440
Total
1,789
1,551
1,809
1,548
1,719
1,481
1,547
1,632
1,941
2,286
1,602
1,527
Note: Totals may not sum due to independent rounding.
Source: USDA (2016).
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-163. Annual estimates for dairy cows were taken from USDA milk
production statistics. Dairy lactation estimates for 1990 through 2015 are shown in Table A-164. Beef and dairy cow and
bull populations are assumed to remain relatively static throughout the year, as large fluctuations in population size are
assumed to not occur. These estimates are taken from the USDA beginning and end of year population datasets.
Table fl-163: Estimates of Average Monthly Milk Production by Beef Cows [lbs/cowl80
Jan
Feb Mar
Apr
May Jun
Jul
Aug
Sep
Oct Nov Dec
Beef Cow Milk Production (lbs/ head)
3.3
5.1 8.7
12.0
13.6 13.3
11.7
9.3
6.9
4.4 3.0 2.8
Table A-164: Dairy Lactation Rates by State (lbs/year/cow)81
State/Year
1990
1995
2000
2005
2011
2012
2013
2014
2015
Alabama
12,214
14,176
13,920
14,000
14,300
13,000
13,000
13,625
12,625
Alaska
13,300
17,000
14,500
12,273
13,800
14,250
10,667
11,667
11,667
Arizona
17,500
19,735
21,820
22,679
23,473
23,979
23,626
24,368
24,477
Arkansas
11,841
12,150
12,436
13,545
11,917
13,300
11,667
13,714
13,000
California
18,456
19,573
21,130
21,404
23,438
23,457
23,178
23,786
23,002
Colorado
17,182
18,687
21,618
22,577
23,430
24,158
24,292
24,951
25,685
Connecticut
15,606
16,438
17,778
19,200
19,000
19,889
20,556
20,158
20,842
Delaware
13,667
14,500
14,747
16,622
18,300
19,542
19,521
20,104
19,700
Florida
14,033
14,698
15,688
16,591
19,067
19,024
19,374
20,390
20,656
Georgia
12,973
15,550
16,284
17,259
18,354
19,138
19,600
20,877
21,651
Hawaii
13,604
13,654
14,358
12,889
14,421
14,200
13,409
13,591
15,909
Idaho
16,475
18,147
20,816
22,332
22,926
23,376
23,440
24,127
24,126
Illinois
14,707
15,887
17,450
18,827
18,510
19,061
19,063
19,681
20,128
Indiana
14,590
15,375
16,568
20,295
20,657
21,440
21,761
21,865
22,143
Iowa
15,118
16,124
18,298
20,641
21,191
22,015
22,149
22,449
22,943
Kansas
12,576
14,390
16,923
20,505
21,016
21,683
21,881
22,085
22,231
Kentucky
10,947
12,469
12,841
12,896
14,342
15,135
15,070
15,905
17,607
79 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.
80 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.
81 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-252 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Louisiana
11,605
11,908
12,034
' 12,400
12,889
13,059
12,875
13,600
13,429
Maine
14,619
16,025
17,128
18,030
18,688
18,576
19,548
19,967
19,800
Maryland
13,461
14,725
16,083
16,099
18,654
19,196
19,440
19,740
20,061
Massachusetts
14,871
16,000
17,091
17,059
16,923
18,250
17,692
17,923
18,083
Michigan
15,394
17,071
19,017
21,635
23,164
23,976
24,116
24,638
25,130
Minnesota
14,127
15,894
17,777
18,091
18,996
19,512
19,694
19,841
20,578
Mississippi
12,081
12,909
15,028
15,280
14,571
14,214
13,286
14,462
15,000
Missouri
13,632
14,158
14,662
16,026
14,611
14,979
14,663
15,539
15,511
Montana
13,542
15,000
17,789
19,579
20,571
21,357
21,286
21,500
21,357
Nebraska
13,866
14,797
16,513
17,950
20,579
21,179
21,574
22,130
22,930
Nevada
16,400
18,128
19,000
21,680
22,966
22,931
22,034
23,793
23,069
New Hampshire
15,100
16,300
17,333
18,875
20,429
19,643
20,923
20,143
20,143
New Jersey
13,538
13,913
15,250
16,000
16,875
18,571
18,143
18,143
18,143
New Mexico
18,815
18,969
20,944
21,192
24,854
24,694
24,944
25,093
24,245
New York
14,658
16,501
17,378
18,639
21,046
21,623
22,070
22,325
22,816
North Carolina
15,220
16,314
16,746
18,741
20,089
20,435
20,326
20,891
20,979
North Dakota
12,624
13,094
14,292
14,182
18,158
19,278
18,944
20,250
20,750
Ohio
13,767
15,917
17,027
17,567
19,194
19,833
20,178
20,318
20,573
Oklahoma
12,327
13,611
14,440
16,480
17,415
17,896
17,311
18,150
18,462
Oregon
16,273
17,289
18,222
18,876
20,488
20,431
20,439
20,565
20,408
Pennsylvania
14,726
16,492
18,081
18,722
19,495
19,549
19,797
20,121
20,387
Rhode Island
14,250
14,773
15,667
17,000
17,909
16,636
19,000
19,000
17,667
South Carolina
12,771
14,481
16,087
16,000
17,438
17,250
16,500
16,438
17,400
South Dakota
12,257
13,398
15,516
17,741
20,582
21,391
21,521
21,753
22,255
Tennessee
11,825
13,740
14,789
15,743
16,200
16,100
15,938
16,196
16,489
Texas
14,350
15,244
16,503
19,646
22,232
22,009
21,991
22,268
22,235
Utah
15,838
16,739
17,573
18,875
22,161
22,863
22,432
22,989
23,146
Vermont
14,528
16,210
17,199
18,469
18,940
19,316
19,448
20,197
20,197
Virginia
14,213
15,116
15,833
16,990
17,906
17,990
18,337
19,129
19,462
Washington
18,532
20,091
22,644
23,270
23,727
23,794
23,820
24,088
23,848
West Virginia
11,250
12,667
15,588
14,923
15,700
15,400
15,200
15,556
15,667
Wisconsin
13,973
15,397
17,306
18,500
20,599
21,436
21,693
21,869
22,697
Wyoming
12,337
13,197
13,571
14,878
20,517
20,650
21,367
21,583
22,567
Source: USDA (2016).
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-165. 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:APF1IS:VS 2010) and are shown in and Table A-166. 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.
A-253
-------
Table fl-165: Regions used for Characterizing the Diets of Dairy Cattle tall years] and Foraging Cattle from 1990-2006
West California Northern Great Midwestern Northeast Southcentral Southeast
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 Hampshire
North Carolina
Oregon
Wyoming
Ohio
New Jersey
South Carolina
Utah
Wisconsin
New York
Tennessee
Washington
Pennsylvania
Virginia
Rhode Island
Vermont
West Virginia
Source: USDA (1996).
Table A-166: Regions used for Characterizing the Diets of Foraging Cattle from 2007-201582
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).
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:
Ym = 4.52eVS'ear-1980j
82 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-254 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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-
165 (California, Northern Great Plains, Midwestern, Northeast, Southcentral, Southeast) and Table A-166 (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-167. 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:APF1IS:VS 2010) as shown in Table A-168 and Table A-169 along with the percent of each
total diet that is assumed to be made up of the supplemental portion. By weighting the calculated DE values from the forage
and supplemental diets, the DE values for the composite diet were calculated. These values are used for steer and heifer
stackers and beef replacements. Finally, for mature beef cows and bulls, the DE value was adjusted downward by two
percent to reflect the lower digestibility diets of mature cattle based on Johnson (2002). Ym values for all grazing beef cattle
were set at 6.5 percent based on Johnson (2002). The Ym values and the resulting final weighted DE values by region for
2007 onwards are shown in Table A-170.
For feedlot animals, DE and Ym are adjusted over time as diet compositions in actual feedlots are adjusted based
on new and improved nutritional information and availability of feed types. Feedlot diets are assumed to not differ
significantly by state, and therefore only a single set of national diet values is utilized for each year. The DE and Ym values
for 1990 were estimated by Dr. Don Johnson (1999). In the CEFM, the DE values for 1991 through 1999 were linearly
extrapolated based on values for 1990 and 2000. DE and Ym values from 2000 through the current year were estimated using
the MOLLY model as described in Kebreab et al. (2008), based on a series of average diet feed compositions from Galyean
and Gleghorn (2001) for 2000 through 2006 and Vasconcelos and Galyean (2007) for 2007 onwards. In addition, feedlot
animals are assumed to spend the first 25 days in the feedlot on a "step-up" diet to become accustomed to the higher quality
feedlot diets. The step-up DE and Ym are calculated as the average of all state forage and feedlot diet DE and Ym values.
For calves aged 4 through 6 months, a gradual weaning from milk is simulated, with calf diets at 4 months assumed
to be 25 percent forage, increasing to 50 percent forage at age 5 months, and 75 percent forage at age 6 months. The portion
of the diet allocated to milk results in zero emissions, as recommended by the IPCC (2006). For calves, the DE for the
remainder of the diet is assumed to be similar to that of slightly older replacement heifers (both beef and dairy are calculated
separately). The Ym for beef calves is also assumed to be similar to that of beef replacement heifers (6.5 percent), as literature
does not provide an alternative Ym for use in beef calves. For dairy calves, the Ym is assumed to be 7.8 percent at 4 months,
8.03 percent at 5 months, and 8.27 percent at 6 months based on estimates provided by Soliva (2006) for Ym at 4 and 7
months of age and a linear interpolation for 5 and 6 months.
83 por 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-170.
=YmQ990)EXP
(Year -1980 )
A-255
-------
Table A-171 shows the regional DE and Ym for U.S. cattle in each region for 2015.
Table fl-167: Feed Components and Digestible Energy Values Incorporated into Forage Diet Composition Estimates
O 5
TO TO TO 3 3 3 ft >
Q. Q. qJ Q. -j —i < -= 9 5
O
o>(aEca a> a> a> a> ^ a> o o> o
_ c wj m rn rn rn rn "D E ^
Forage Type q (5 Ł (5 « (5 Ł I I I I <%
Bahiagrass Paspalum notatum, fresh 61.38
Bermudagrass Cynodon dactylon, fresh 66.29 x
Bremudagrass, Coastal Cynodon dactylon, fresh 65.53 x
Bluegrass, Canada Poa compressa, fresh, early
vegetative 73.99 x
Bluegrass, Kentucky Poa pratensis, fresh, early
vegetative 75.62 x
Bluegrass, Kentucky Poa pratensis, fresh, mature 59.00 x
Bluestem Andropagon spp, fresh, early vegetative 73.17
Bluestem Andropagon spp, fresh, mature 56.82
Brome Bromus spp, fresh, early vegetative 78.57 x
Brome, Smooth Bromus inermis, fresh, early
vegetative 75.71 x
Brome, Smooth Bromus inermis, fresh, mature 57.58 x
Buffalograss, Buchloe dactyloides, fresh 64.02
Clover, Alsike Trifolium hybridum, fresh, early
vegetative 70.62 x
Clover, Ladino Trifolium repens, fresh, early
vegetative 73.22 x
Clover, Red Trifolium pratense, fresh, early bloom 71.27 x
Clover, Red Trifolium pratense, fresh, full bloom 67.44 x
Corn, Dent Yellow Zea mays indentata, aerial part
without ears, without husks, sun-cured,
(stover)(straw) 55.28
Dropseed, Sand Sporobolus cryptandrus, fresh,
stem cured 64.69
Fescue Festuca spp, hay, sun-cured, early
vegetative 67.39 x
Fescue Festuca spp, hay, sun-cured, early bloom 53.57
Grama Bouteloua spp, fresh, early vegetative 67.02 x
Grama Bouteloua spp, fresh, mature 63.38 x
Millet, Foxtail Setaria italica, fresh 68.20 x
Napiergrass Pennisetum purpureum, fresh, late
bloom 57.24 x
Needleandthread Stipa comata, fresh, stem cured 60.36
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
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
Sorghum, Sudangrass Sorghum bicolor
sudanense, fresh, early vegetative 73.27 x
Squirreltail Stanion spp, fresh, stem-cured 62.00 x
Summercypress, Gray Kochia vestita, fresh, stem-
cured 65.11
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
A-256 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
O V) V) V) E ¦>» °> is E .
ro ro ro 3 3= _S > .L
o Q- Q. 5 Q. -3-j< -g jg g 3
.0 f/1 cn f/i E= f/i qj a> qj a; JF a> o o> o
a) o> O) o> .2 a) *o c 35
— E Si = c c c eq. c 5 =
rnnf1Q Tuna LU >- o. >- 3 >- ro ro to to to a> to a> q.
i-orage lype q a m a m o ll qc gz oc cc. tn cc. g w
Wheat Triticum aestivum, straw
45.77
X
Wheatgrass, Crested Agropyron desertorum,
fresh, early vegetative
Wheatgrass, Crested Agropyron desertorum,
79.78
X
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
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).
Table A-168: DEValues with Representative Regional Diets forthe Supplemental Diet of Grazing Deef Cattle for 1990-2006
Northern
Source of DE
Unweighted
Great
Feed
(NRC1984)
DE (% of GE)
California3
West
Plains
Southcentral
Northeast
Midwest
Southeast
Alfalfa Hay
Table 8, feed #006
61.79
65%
30%
30%
29%
12%
30%
Barley
85.08
10%
15%
Bermuda
Table 8, feed #030
66.29
35%
Bermuda Hay
Table 8, feed #031
50.79
40%
Corn
Table 8, feed #089
88.85
10%
10%
25%
11%
13%
13%
Corn Silage
Table 8, feed #095
72.88
25%
20%
20%
Cotton Seed
Meal
7%
Grass Hay
Table 8, feed #126,
170, 274
58.37
40%
30%
Orchard
Table 8, feed #147
60.13
40%
Soybean Meal
Supplement
77.15
5%
5%
5%
Sorghum
Table 8, feed #211
84.23
20%
Soybean Hulls
66.86
7%
Timothy Hay
Table 8, feed #244
60.51
50%
Whole Cotton
Seed
75.75
5%
5%
Wheat Middlings Table 8, feed #257
68.09
15%
13%
Wheat
Table 8, feed #259
87.95
10%
Weighted Supplement DE (%)
70.1
67.4
73.0
62.0
67.6
66.9
68.0
Percent of Diet that is Supplement
5%
10%
15%
10%
15%
10%
5%
Source of representative regional diets: Donovan (1999).
a Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above.
A-257
-------
Table fl-169: DE Values and Representative Regional Diets for the Supplemental Diet of Grazing Beef Cattle for 2007-201584
Feed
Source of DE
(NRC1984)
Unweighted
DE (% of GE)
West3
Central3
Northeast3
Southeast3
Alfalfa Hay
Table 8, feed #006
61.79
65%
30%
12%
Bermuda
Table 8, feed #030
66.29
20%
Bermuda Hay
Table 8, feed #031
50.79
20%
Corn
Table 8, feed #089
88.85
10%
15%
13%
10%
Corn Silage
Table 8, feed #095
72.88
35%
20%
Grass Hay
Table 8, feed #126,170, 274
58.37
10%
Orchard
Table 8, feed #147
60.13
30%
Protein supplement (West)
Table 8, feed #082,134, 225"
81.01
10%
Protein Supplement (Central
and Northeast)
Table 8, feed #082,134, 225"
80.76
10%
10%
Protein Supplement
(Southeast)
Table 8, feed #082,134,101b
77.89
10%
Sorghum
Table 8, feed #211
84.23
5%
10%
Timothy Hay
Table 8, feed #244
60.51
45%
Wheat Middlings
Table 8, feed #257
68.09
5%
Wheat
Table 8, feed #259
87.95
5%
Weighted Supplement DE
67.4
73.1
68.9
66.6
Percent of Diet that is Supplement
10%
15%
5%
15%
a Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above.
b Not in equal proportions.
Sources of representative regional diets: Donovan (1999), Preston (2010), Archibeque (2011), and USDA:APHIS:VS (2010).
Table fl-170: Foraging Animal DE t% of GE1 and Ym Values for Each Region and Animal Type for 2007-201585
Animal Type
Data
West3
Central
Northeast
Southeast
Beef Repl. Heifers
DEb
61.9
65.6
64.5
64.6
Ymc
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 Stackers
DE
61.9
65.6
64.5
64.6
Ym
6.5%
6.5%
6.5%
6.5%
Heifer Stackers
DE
61.9
65.6
64.5
64.6
Ym
6.5%
6.5%
6.5%
6.5%
Beef Cows
DE
59.9
63.6
62.5
62.6
Ym
6.5%
6.5%
6.5%
6.5%
Bulls
DE
59.9
63.6
62.5
62.6
Ym
6.5%
6.5%
6.5%
6.5%
a Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. To see the regional designation
per state, please see Table A-166.
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.
84 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.
85 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-258 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-171: Regional DE t% oTGEl and Ym Bates for Dairy and Fee Jlot Cattle by Animal Type for 2015s6
Animal Type
Data
California3
West
Northern
Great Plains
Southcentral
Northeast
Midwest
Southeast
Dairy Repl. Heifers
DEb
63.7
63.7
63.7
63.7
63.7
63.7
63.7
Ym<
6.0%
6.0%
5.7%
6.5%
6.4%
5.7%
7.0%
Dairy Calves (4-6 mo)
DE
63.7
63.7
63.7
63.7
63.7
63.7
63.7
Ym
6.5%
6.5%
6.5%
6.5%
6.5%
6.5%
6.5%
Dairy Cows
DE
66.7
66.7
66.7
66.7
66.7
66.7
66.7
Ym
5.9%
5.9%
5.6%
6.4%
6.3%
5.6%
6.9%
Steer Feedlot
DE
82.5
82.5
82.5
82.5
82.5
82.5
82.5
Ym
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
Heifer Feedlot
DE
82.5
82.5
82.5
82.5
82.5
82.5
82.5
Ym
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
a Note that emissions are currently calculated on a state-by-state basis, but diets are applied in Table A-165 by the regions shown in the table above. To see the
regional designation for foraging cattle per state, please see Table A-165.
b DE is the digestible energy in units of percent of GE (MJ/Day).
c Ym is the methane conversion rate, the fraction of GE in feed converted to methane.
Step 3: Estimate ChU Emissions from Cattle
Emissions by state were estimated in three steps: a) determine gross energy (GE) intake using the Tier 2 IPCC
(2006) equations, b) determine an emission factor using the GE values, Ym and a conversion factor, and c) sum the daily
emissions for each animal type. Finally, the state emissions were aggregated to obtain the national emissions estimate. The
necessary data values for each state and animal type include:
• Body Weight (kg)
• Weight Gain (kg/day)
• Net Energy for Activity (Ca, MJ/day)87
• Standard Reference Weight (kg)
• Milk Production (kg/day)
• Milk Fat (percent of fat in milk = 4)
• Pregnancy (percent of population that is pregnant)
• DE (percent of GE intake digestible)
• Ym (the fraction of GE converted to CPU)
• Population
Step 3a: Determine Gross Energy, GE
As shown in the following equation, GE is derived based on the net energy estimates and the feed characteristics.
Only variables relevant to each animal category are used (e.g., estimates for feedlot animals do not require the NEi factor).
All net energy equations are provided in IPCC (2006). Calculated GE values for 2015 are shown by state and animal type
in Table A-172.
GE =
REM
NES
REG
DE%
100
where,
^ 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.
^ 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).
^ 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-259
-------
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 fl-172: Calculated Annual GE by Animal Type and State,for 2015 [MJ/1,000 head!89
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
35
902
41
146
3,749
3,017
52,535
1,385
3,732
1,188
801
227
Alaska
1
32
1
5
213
21
370
12
32
8
3
1
Arizona
852
30,695
897
3,164
1,779
874
15,053
453
1,216
7,034
604
11,759
Arkansas
31
781
55
195
4,582
3,993
69,536
1,854
4,998
3,090
1,362
513
California
7,779
267,829
10,630
37,484
6,226
2,947
50,750
1,734
4,648
14,069
4,026
19,832
Colorado
634
23,528
1,381
4,868
4,892
3,621
62,362
2,267
6,078
19,952
13,517
43,396
Conn.
83
2,713
110
389
42
23
404
31
84
48
13
7
Delaware
22
691
35
122
33
12
202
9
23
52
19
8
Florida
542
17,979
483
1,704
4,999
4,192
73,000
1,607
4,331
594
801
156
Georgia
354
12,068
373
1,314
2,333
2,216
38,595
1,113
2,999
1,070
801
212
Hawaii
10
271
14
49
356
344
5,918
147
393
230
144
40
Idaho
2,530
90,326
4,418
15,578
3,558
2,302
39,654
1,600
4,290
7,162
4,889
11,432
Illinois
411
13,154
718
2,531
2,036
1,650
28,830
771
2,081
4,867
2,577
10,732
Indiana
791
26,794
1,104
3,894
1,385
897
15,675
602
1,626
2,688
1,145
4,666
Iowa
918
31,762
1,795
6,328
4,887
4,057
70,894
2,168
5,854
29,897
16,397
56,928
Kansas
625
21,219
1,242
4,381
7,738
6,432
112,406
3,493
9,431
43,803
34,617
101,724
Kentucky
275
8,363
621
2,191
5,415
4,613
80,333
1,731
4,664
4,753
2,803
700
Louisiana
61
1,586
69
243
2,499
2,156
37,548
915
2,466
570
614
133
Maine
131
4,159
221
779
125
51
889
56
150
107
67
20
Maryland
214
6,844
345
1,217
334
195
3,394
112
301
358
241
467
Mass.
55
1,647
97
341
84
26
444
25
67
48
27
8
Michigan
1,761
64,494
2,305
8,130
1,222
482
8,428
277
748
3,940
911
7,466
Minn.
2,010
65,205
3,865
13,631
2,851
1,533
26,782
1,084
2,927
11,588
4,164
17,965
Miss.
52
1,467
83
292
3,166
2,165
37,709
1,199
3,232
1,331
881
252
Missouri
389
10,804
828
2,921
8,959
8,343
145,805
4,156
11,220
9,270
5,856
3,266
Montana
61
2,028
97
341
8,894
7,472
128,681
5,801
15,552
4,476
5,695
1,866
Nebraska
236
8,165
276
974
7,738
7,915
138,321
5,059
13,659
54,000
33,315
118,056
Nevada
122
4,249
124
438
1,067
1,059
18,236
493
1,323
1,126
834
187
N. Hamp.
61
1,960
76
268
42
14
242
12
33
24
13
4
N. Jersey
31
924
52
185
84
35
606
16
43
48
24
8
N. Mexico
1,412
50,542
1,519
5,355
3,113
2,033
35,009
1,134
3,039
2,430
2,013
467
New York
2,688
92,703
4,832
17,038
1,253
487
8,484
508
1,370
834
1,178
1,213
N. Car.
205
6,876
248
876
2,416
1,680
29,249
865
2,332
856
721
178
N. Dakota
70
2,279
83
292
4,072
4,030
70,421
2,036
5,496
4,983
4,815
2,053
Ohio
1,171
37,983
1,726
6,085
2,036
1,271
22,213
602
1,626
4,519
1,301
7,933
Oklahoma
175
5,340
345
1,217
11,664
8,698
151,480
5,192
13,993
20,677
9,745
12,366
Oregon
546
17,633
828
2,921
3,558
2,622
45,159
1,467
3,933
4,221
3,020
3,966
Penn.
2,316
74,719
4,211
14,848
2,089
696
12,120
682
1,838
3,457
1,473
4,200
R. Island
4
117
7
24
8
7
121
6
17
12
5
2
89 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-260 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
S. Car.
66
1,979
69
243
1,166
787
13,698
371
1,000
214
320
59
S. Dakota
433
14,700
897
3,164
8,145
7,262
126,900
4,879
13,171
15,760
12,754
17,965
Tenn.
205
6,028
345
1,217
4,999
4,039
70,341
1,731
4,664
2,852
1,735
524
Texas
2,054
69,866
3,451
12,170
26,661
19,109
332,772
9,272
24,988
59,892
37,111
117,123
Utah
420
14,598
663
2,337
1,957
1,618
27,869
1,040
2,789
1,995
1,841
1,120
Vermont
577
18,509
773
2,726
251
56
970
50
134
95
134
25
Virginia
406
13,038
594
2,093
3,333
2,947
51,326
1,360
3,665
3,684
1,121
933
Wash.
1,211
42,903
1,877
6,621
1,601
989
17,031
720
1,931
4,476
3,883
9,799
W. Virg.
39
1,098
55
195
1,086
859
14,948
409
1,108
1,049
536
187
Wisconsin
5,572
191,584
10,078
35,537
2,851
1,240
21,662
903
2,439
8,691
1,171
12,132
Wyoming
26
898
69
243
3,558
3,466
59,696
2,574
6,900
3,453
4,084
3,500
Step 3b: Determine Emission Factor
The daily emission factor (DayEmit) was determined using the GE value and the methane conversion factor (Ym)
for each category. This relationship is shown in the following equation:
GE x Y
DayEmit = —
55.65
where,
DayEmit = Emission factor (kg CTU/head/day)
GE = Gross energy intake (MJ/head/day)
Ym = CH4 conversion rate, which is the fraction of GE in feed converted to CH4 (%)
55.65 = A factor for the energy content of methane (MJ/kg CH4)
The daily emission factors were estimated for each animal type and state. Calculated annual national emission
factors are shown by animal type in Table A-173. State-level emission factors are shown by animal type for 2015 in Table
A-174.
Table fl-173: Calculated Annual National Emission Factors for Cattle by Animal Type, for 2015 [kg dh/bead/year]90
Cattle Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
Dairy
Calves
12
12
12
12
12
12
12
12
12
12
Cows
124
125
132
133
142
142
144
144
145
146
Replacements 7-11 months
48
46
46
45
46
46
46
46
46
46
Replacements 12-23 months
73
69
70
67
69
69
69
69
69
69
Beef
Calves
11
11
11
11
11
11
11
11
11
11
Bulls
91
94
94
97
98
98
98
98
98
98
Cows
89
92
91
94
95
95
95
95
95
95
Replacements 7-11 months
54
57
56
59
60
60
60
60
60
60
Replacements 12-23 months
63
66
66
68
70
70
70
70
70
70
Steer Stackers
55
57
58
58
58
58
58
58
58
58
Heifer Stackers
52
56
60
60
60
60
60
60
60
60
Feedlot Cattle
39
38
39
39
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).
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-261
-------
Table fl-174: Emission Factors for Cattle by Animal Type and State,for 2015 [kg CHJhead/year)91
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
128
53
80
97
10
94
60
69
58
60
33
Alaska
12
103
46
69
104
11
100
64
74
62
65
33
Arizona
12
154
46
69
104
11
100
64
74
62
65
34
Arkansas
12
117
49
74
97
10
94
60
69
58
60
33
California
12
147
46
69
104
11
100
64
74
62
65
33
Colorado
12
150
43
65
104
11
100
64
74
62
65
34
Conn.
12
148
48
73
98
11
94
60
69
58
60
34
Delaware
12
143
48
73
98
11
94
60
69
58
60
35
Florida
12
165
53
80
97
10
94
60
69
58
60
34
Georgia
12
169
53
80
97
10
94
60
69
58
60
35
Hawaii
12
120
46
69
104
11
100
64
74
62
65
36
Idaho
12
152
46
69
104
11
100
64
74
62
65
35
Illinois
12
129
43
65
95
10
92
58
68
56
58
34
Indiana
12
137
43
65
95
10
92
58
68
56
58
34
Iowa
12
140
43
65
95
10
92
58
68
56
58
34
Kansas
12
137
43
65
95
10
92
58
68
56
58
35
Kentucky
12
151
53
80
97
10
94
60
69
58
60
33
Louisiana
12
119
49
74
97
10
94
60
69
58
60
35
Maine
12
143
48
73
98
11
94
60
69
58
60
34
Maryland
12
144
48
73
98
11
94
60
69
58
60
34
Mass.
12
136
48
73
98
11
94
60
69
58
60
36
Michigan
12
148
43
65
95
10
92
58
68
56
58
35
Minn.
12
131
43
65
95
10
92
58
68
56
58
35
Miss.
12
139
53
80
97
10
94
60
69
58
60
33
Missouri
12
112
43
65
95
10
92
58
68
56
58
34
Montana
12
134
43
65
104
11
100
64
74
62
65
32
Nebraska
12
140
43
65
95
10
92
58
68
56
58
35
Nevada
12
148
46
69
104
11
100
64
74
62
65
34
N. Hamp.
12
145
48
73
98
11
94
60
69
58
60
34
N. Jersey
12
136
48
73
98
11
94
60
69
58
60
34
N. Mexico
12
153
46
69
104
11
100
64
74
62
65
35
New York
12
156
48
73
98
11
94
60
69
58
60
35
N. Car.
12
166
53
80
97
10
94
60
69
58
60
34
N. Dakota
12
132
43
65
95
10
92
58
68
56
58
35
Ohio
12
131
43
65
95
10
92
58
68
56
58
35
Oklahoma
12
140
49
74
97
10
94
60
69
58
60
34
Oregon
12
138
46
69
104
11
100
64
74
62
65
35
Penn.
12
146
48
73
98
11
94
60
69
58
60
33
R. Island
12
135
48
73
98
11
94
60
69
58
60
34
S. Car.
12
150
53
80
97
10
94
60
69
58
60
33
S. Dakota
12
137
43
65
95
10
92
58
68
56
58
35
Tenn.
12
146
53
80
97
10
94
60
69
58
60
40
Texas
12
156
49
74
97
10
94
60
69
58
60
35
Utah
12
149
46
69
104
11
100
64
74
62
65
34
Vermont
12
145
48
73
98
11
94
60
69
58
60
34
Virginia
12
159
53
80
97
10
94
60
69
58
60
34
Wash.
12
151
46
69
104
11
100
64
74
62
65
35
W. Virg.
12
126
48
73
98
11
94
60
69
58
60
33
Wisconsin
12
139
43
65
95
10
92
58
68
56
58
34
Wyoming
12
138
43
65
104
11
100
64
74
62
65
34
Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the number of days in a year).
91 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-262 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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-
175, indicate that U.S. emission factors are comparable to those of other Annex I countries. Results in Table A-175 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 CFU per head.
Table A-175: Annex I Countries' Implied Emission Factors for Cattle by Year [kg CHa/hea J/yearl92 93
Dairy Cattle Beef Cattle
Year
United States Implied Mean of Implied Emission Factors for
Emission Factor Annex I countries (excluding U.S.)
United States Implied
Emission Factor
Mean of Implied Emission Factors
for Annex I countries (excluding
U.S.)
1990
107
96
71
53
1991
107
97
71
53
1992
107
96
72
54
1993
106
97
72
54
1994
106
98
73
54
1995
106
98
72
54
1996
105
99
73
54
1997
106
100
73
54
1998
107
101
73
55
1999
110
102
72
55
2000
111
103
72
55
2001
110
104
73
55
2002
111
105
73
55
2003
111
106
73
55
2004
109
107
74
55
2005
110
109
74
55
2006
110
110
74
55
2007
114
111
75
55
2008
115
112
75
55
2009
115
112
75
56
2010
115
113
75
55
2011
116
113
75
55
2012
117
112
75
51
2013
117
NA
75
NA
2014
118
NA
74
NA
2015
117
NA
75
NA
Tier I EFs For North America, from IPCC
(2006)
121
53
Step 3c: Estimate Total Emissions
Emissions were summed for each month and for each state population category using the daily emission factor for
a representative animal and the number of animals in the category. The following equation was used:
where,
Emissionsstate
DayEmiWe
Days/Month
SubPop state
Emissionsstate = DayEmitstate x Days/Month x SubPopstate
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
92 Excluding calves.
93 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-263
-------
This process was repeated for each month, and the monthly totals for each state subcategory were summed to
achieve an emission estimate for a state for the entire year and state estimates were summed to obtain the national total. The
estimates for each of the 10 subcategories of cattle are listed in Table A-176. The emissions for each subcategory were then
aggregated to estimate total emissions from beef cattle and dairy cattle for the entire year.
Table A-176: Oh Emissions from Cattle (kt)94
Cattle Type
1990
1995
2000
2005
2011
2012
2013
2014
2015
Dairy
1,574
1,498
1,519
1,503
1,645
1,670
1,664
1,679
1,706
Calves (4-6 months)
62
59
59
54
57
58
58
58
58
Cows
1,242
1,183
1,209
1,197
1,302
1,326
1,325
1,337
1,355
Replacements 7-11 months
58
56
55
56
63
62
61
63
65
Replacements 12-23 months
212
201
196
196
223
224
220
221
228
Beef
4,763
5,419
5,070
5,007
4,873
4,763
4,722
4,660
4,724
Calves (4-6 months)
182
193
186
179
166
161
157
156
159
Bulls
196
225
215
214
212
206
203
200
207
Cows
2,884
3,222
3,058
3,056
2,927
2,868
2,806
2,754
2,774
Replacements 7-11 months
69
85
74
80
74
76
78
83
89
Replacements 12-23 months
188
241
204
217
202
208
213
218
239
Steer Stackers
563
662
509
473
436
413
431
426
434
Heifer Stackers
306
375
323
299
283
266
267
256
264
Feedlot Cattle
375
416
502
488
573
565
568
567
558
Total
6,338
6,917
6,589
6,510
6,518
6,433
6,386
6,339
6,430
Note: Totals may not sum due to independent rounding.
Emission Estimates from Other Livestock
"Other livestock" include horses, sheep, swine, goats, American bison, and mules and asses. All livestock
population data, except for American bison for years prior to 2002, were taken from the U.S. Department of Agriculture
(USDA) National Agricultural Statistics Service (NASS) agricultural statistics database (USDA 2016) or earlier census data
(USDA 1992, 1997). The Manure Management Annex discusses the methods for obtaining annual average populations and
disaggregating into state data where needed and provides the resulting population data for the other livestock that were used
for estimating all livestock-related emissions (see Table A-178). For each animal category, the USDA publishes monthly,
annual, or multi-year livestock population and production estimates. American bison estimates prior to 2002 were estimated
using data from the National Bison Association (1999).
Methane emissions from sheep, goats, swine, horses, mules and asses were estimated by multiplying national
population estimates by the default IPCC emission factor (IPCC 2006). For American bison the emission factor for buffalo
(IPCC 2006) was used and adjusted based on the ratio of live weights of 300 kg for buffalo (IPCC 2006) and 1,130 pounds
(513 kg) for American Bison (National Bison Association 2011) to the 0.75 power. This methodology for determining
emission factors is recommended by IPCC (2006) for animals with similar digestive systems. Table A-177 shows the
emission factors used for these other livestock. National enteric fermentation emissions from all livestock types are shown
in Table A-178 and Table A-179. Enteric fermentation emissions from most livestock types, broken down by state, for 2015
are shown in Table A-180 and Table A-185. Livestock populations are shown in Table A-182.
Table fl-177: Emission Factors Tor Other Livestock tkg CHi/head/year)
Livestock Type Emission Factor
Swine
Horses
Sheep
Goats
American Bison
Mules and Asses
1.5
18
8
5
82.2
10.0
Source: IPCC (2006), except American Bison, as described in text.
94 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-264 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-178: CHa Emissions from Enteric Fermentation [MBIT CO2 Eg.]95
Livestock Type
1990
1995
2000
2005
2011
2012
2013
2014
2015
Beef Cattle
119.1
135.5
126.7
125.2
121.8
119.1
118.0
116.5
118.1
Dairy Cattle
39.4
37.5
38.0
37.6
41.1
41.7
41.6
42.0
42.6
Swine
2.0
2.2
2.2
2.3
2.5
2.5
2.5
2.4
2.6
Horses
1.0
1.2
1.5
1.7
1.7
1.6
1.6
1.6
1.5
Sheep
2.3
1.8
1.4
1.2
1.1
1.1
1.1
1.0
1.1
Goats
0.3
0.3
0.3
0.4
0.3
0.3
0.3
0.3
0.3
American Bison
0.1
0.2
0.4
0.4
0.3
0.3
0.3
0.3
0.3
Mules and Asses
+
+
+
0.1
0.1
0.1
0.1
0.1
0.1
Total
164.2
178.7
170.6
168.9
168.9
166.7
165.5
164.2
166.5
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not
sum due to independent rounding.
Table fl-179: CHa
Emissions from Enteric Fermentation Iktl96
Livestock Type
1990
1995
2000
2005
2011
2012
2013
2014
2015
Beef Cattle
4,763
5,419
5,070
5,007
4,873
4,763
4,722
4,660
4,724
Dairy Cattle
1,574
1,498
1,519
1,503
1,645
1,670
1,664
1,679
1,706
Swine
81
88
88
92
98
100
98
96
102
Horses
40
47
61
70
67
65
64
62
61
Sheep
91
72
56
49
44
43
43
42
42
Goats
13
12
12
14
14
13
13
12
12
American Bison
4
9
16
17
14
13
13
12
12
Mules and Asses
1
1
1
2
3
3
3
3
3
Total
6,566
7.146
6,824
6,755
6,757
6,670
6,619
6,572
6,661
Note: Totals may not sum due to independent rounding.
95 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.
96 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-265
-------
Table fl-180: CHa Emissions from Enteric Fermentation from Cattle [metric tons], by State, for 201597
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
Total
Alabama
50
1,025
48
169
4,379
3,523
61,361
1,617
4,359
1,388
936
215
79,069
Alaska
2
31
1
5
249
25
432
14
38
9
3
1
810
Arizona
1,216
29,979
893
3,147
2,078
1,021
17,582
530
1,420
8,216
705
11,002
77,789
Arkansas
44
819
59
208
5,352
4,664
81,219
2,166
5,837
3,609
1,590
496
106,063
California
11,102
261,579
10,573
37,283
7,272
3,442
59,276
2,025
5,429
16,433
4,703
18,975
438,091
Colorado
904
21,739
1,300
4,585
5,714
4,229
72,840
2,648
7,099
23,305
15,788
40,154
200,306
Conn.
119
2,806
116
410
49
27
472
36
98
56
16
7
4,211
Delaware
31
715
36
128
39
14
236
10
27
61
22
7
1,327
Florida
773
20,432
558
1,967
5,839
4,896
85,266
1,877
5,059
694
936
144
128,440
Georgia
505
13,714
430
1,517
2,725
2,589
45,080
1,300
3,502
1,249
936
191
73,738
Hawaii
14
264
14
48
416
401
6,912
171
459
269
168
35
9,172
Idaho
3,611
88,218
4,394
15,494
4,155
2,689
46,316
1,869
5,011
8,366
5,711
10,421
196,256
Illinois
586
12,154
676
2,384
2,378
1,927
33,674
900
2,431
5,685
3,010
10,129
75,935
Indiana
1,129
24,757
1,040
3,668
1,617
1,048
18,309
704
1,899
3,140
1,338
4,377
63,026
Iowa
1,310
29,347
1,690
5,961
5,708
4,738
82,805
2,533
6,837
34,920
19,152
52,417
247,419
Kansas
892
19,606
1,170
4,127
9,038
7,513
131,292
4,080
11,016
51,162
40,433
93,069
373,397
Kentucky
393
9,504
717
2,529
6,325
5,388
93,830
2,022
5,448
5,552
3,274
670
135,652
Louisiana
87
1,664
74
260
2,919
2,518
43,856
1,069
2,880
666
717
122
56,832
Maine
187
4,301
232
820
146
60
1,038
65
176
125
78
18
7,247
Maryland
306
7,079
363
1,281
390
228
3,964
130
351
418
282
429
15,220
Mass.
78
1,703
102
359
98
30
519
29
78
56
31
8
3,090
Michigan
2,514
59,590
2,172
7,658
1,427
563
9,845
324
874
4,602
1,064
6,775
97,405
Minn.
2,869
60,246
3,641
12,839
3,330
1,790
31,282
1,266
3,419
13,535
4,864
16,426
155,507
Miss.
75
1,667
96
337
3,698
2,529
44,044
1,401
3,775
1,555
1,029
241
60,447
Missouri
555
9,983
780
2,751
10,465
9,745
170,302
4,854
13,105
10,828
6,840
3,046
243,254
Montana
87
1,874
91
321
10,389
8,727
150,301
6,776
18,165
5,229
6,651
1,839
210,450
Nebraska
337
7,544
260
917
9,038
9,245
161,561
5,910
15,954
63,073
38,913
107,555
420,306
Nevada
175
4,150
124
436
1,247
1,237
21,299
576
1,545
1,315
974
172
33,249
N. Hamp.
87
2,027
80
282
49
16
283
14
39
28
16
4
2,925
N. Jersey
44
955
55
195
98
41
708
19
51
56
28
8
2,256
N. Mexico
2,015
49,363
1,510
5,326
3,636
2,374
40,891
1,324
3,549
2,838
2,351
429
115,607
New York
3,836
95,883
5,084
17,929
1,464
569
9,909
594
1,600
975
1,376
1,089
140,309
97 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-266 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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N. Car.
293
7,814
287
1,012
2,822
1,962
34,163
1,011
2,724
999
842
167
54,095
N. Dakota
100
2,106
78
275
4,757
4,707
82,253
2,378
6,419
5,820
5,624
1,835
116,351
Ohio
1,672
35,095
1,625
5,732
2,378
1,485
25,946
704
1,899
5,279
1,520
7,204
90,538
Oklahoma
249
5,602
368
1,299
13,624
10,160
176,931
6,065
16,345
24,151
11,382
11,367
277,542
Oregon
780
17,222
824
2,905
4,155
3,063
52,746
1,713
4,593
4,930
3,527
3,558
100,016
Penn.
3,306
77,283
4,431
15,624
2,440
813
14,156
797
2,147
4,038
1,721
4,036
130,789
R. Island
6
121
7
26
10
8
142
7
20
14
6
2
368
S. Car.
94
2,249
80
281
1,362
919
15,999
433
1,167
250
374
57
23,265
S. Dakota
617
13,582
845
2,980
9,513
8,482
148,221
5,698
15,384
18,408
14,896
16,426
255,053
Tenn.
293
6,850
398
1,405
5,839
4,718
82,160
2,022
5,448
3,331
2,027
410
114,901
Texas
2,932
73,299
3,683
12,987
31,140
22,319
388,683
10,830
29,187
69,955
43,346
107,136
795,495
Utah
599
14,257
659
2,324
2,285
1,890
32,552
1,215
3,257
2,330
2,150
1,047
64,566
Vermont
823
19,144
813
2,869
293
65
1,132
58
156
111
156
24
25,645
Virginia
580
14,817
685
2,416
3,892
3,442
59,949
1,588
4,281
4,303
1,310
858
98,123
Wash.
1,728
41,902
1,867
6,585
1,870
1,155
19,893
841
2,255
5,229
4,535
8,876
96,736
W. Virg.
56
1,136
58
205
1,269
1,003
17,459
478
1,288
1,225
626
180
24,983
Wisconsin
7,953
177,016
9,492
33,473
3,330
1,448
25,301
1,055
2,849
10,151
1,368
11,152
284,588
Wyoming
37
830
65
229
4,155
4,048
69,725
3,006
8,059
4,033
4,770
3,217
102,177
A-267
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Table fl-181: CHj Emissions from Enteric Fermentation from Other Livestock [metric tons), by State, for 201598
American Mules and
State
Swine
Horses
Sheep
Goats
Bison
Asses
Total
Alabama
150
894
97
181
15
117
1,454
Alaska
2
21
97
3
149
1
273
Arizona
198
1,919
1,200
447
-
37
3,800
Arkansas
249
907
97
181
7
85
1,525
California
143
2,154
4,800
728
72
64
7,961
Colorado
1,076
1,893
3,360
131
648
65
7,173
Connecticut
4
378
57
21
11
10
481
Delaware
5
135
97
5
8
1
250
Florida
24
2,183
97
243
-
101
2,648
Georgia
240
1,184
97
322
13
88
1,943
Hawaii
14
77
97
76
4
4
271
Idaho
38
970
2,080
92
373
39
3,592
Illinois
7,238
948
456
151
28
34
8,855
Indiana
5,644
1,928
400
168
79
55
8,274
Iowa
31,575
1,014
1,400
282
99
44
34,414
Kansas
2,861
1,185
528
190
377
36
5,178
Kentucky
623
2,190
384
218
102
131
3,648
Louisiana
12
1,068
97
86
2
77
1,341
Maine
7
214
57
34
15
4
331
Maryland
32
493
97
35
19
12
688
Massachusetts
17
363
57
44
6
5
492
Michigan
1,676
1,442
608
133
71
41
3,971
Minnesota
12,075
938
1,040
159
97
29
14,339
Mississippi
773
985
97
104
-
91
2,049
Missouri
4,481
1,767
680
540
84
93
7,645
Montana
263
1,684
1,720
46
1,211
48
4,971
Nebraska
4,838
1,144
648
103
2,164
40
8,936
Nevada
2
448
552
135
3
6
1,147
New Hampshire
6
155
57
27
27
2
274
New Jersey
18
471
97
34
17
9
646
New Mexico
2
882
720
141
441
18
2,204
New York
114
1,679
640
172
40
38
2,682
North Carolina
12,863
1,079
240
236
9
94
14,521
North Dakota
207
821
512
25
474
13
2,052
Ohio
3,611
2,000
968
204
45
71
6,899
Oklahoma
3,289
2,789
424
337
764
136
7,738
Oregon
15
1,063
1,560
151
125
31
2,945
Pennsylvania
1,748
2,197
688
224
39
94
4,989
Rhode Island
2
32
57
5
-
1
98
South Carolina
353
1,042
97
179
10
59
1,739
South Dakota
1,999
1,227
2,040
100
2,515
15
7,896
Tennessee
330
1,247
352
341
0
137
2,407
Texas
1,324
6,660
5,760
3,611
285
636
18,276
Utah
1,058
1,053
2,320
66
80
33
4,610
Vermont
6
193
57
65
6
13
340
Virginia
405
1,525
600
217
80
70
2,898
Washington
38
892
416
118
51
35
1,550
West Virginia
8
355
264
67
-
29
722
Wisconsin
480
1,684
616
321
256
58
3,415
Wyoming
158
1,218
2,760
49
638
28
4,850
Indicates there are no emissions, as there is no significant population of this animal type.
98 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-268 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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A-270 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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3.11. Methodology for Estimating CH4 and N2O Emissions from Manure Management"
The following steps were used to estimate methane (CH4) and nitrous oxide (N2O) emissions from the management
of livestock manure for the years 1990 through 2015. As explained in the Manure Management section (Section 5.2 Manure
Management (IPCC Source Category 3B)), a simplified approach was used to estimate emissions for 2016.
Step 1: Livestock Population Characterization Data
Annual animal population data for 1990 through 2015 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 2016a). Poultry population data
were obtained from USDA NASS reports (USDA 1995a, 1995b, 1998, 1999, 2004a, 2004b, 2009a, 2009b, 2009c, 2009d,
2010a, 2010b, 201 la, 201 lb, 2012a, 2012b, 2013a, 2013b, 2014b, 2014c, 2015a 2015b, 2016b, and 2016c). Goat population
data for 1992, 1997, 2002, 2007, and 2012 were obtained from the Census of Agriculture (USDA 2014a), as were horse,
mule and ass population data for 1987, 1992, 1997, 2002, 2007, and 2012, and American bison population for 2002, 2007,
and 2012. 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
lamb and sheep on feed are not available after 1993 (USDA 1994). The number of lamb and sheep on feed for 1994 through
2015 were calculated using the average of the percent of lamb and sheep on feed from 1990 through 1993. In addition, all
of the sheep and lamb "on feed" are not necessarily on "feedlots;" they may be on pasture/crop residue supplemented by
feed. Data for those animals on feed that are in feedlots versus pasture/crop residue were provided only for lamb in 1993.
To calculate the populations of sheep and lamb in feedlots for all years, it was assumed that the percentage of sheep and
lamb on feed that are in feedlots versus pasture/crop residue is the same as that for lambs in 1993 (Anderson 2000).
Goats: Annual goat population data by state were available for 1992, 1997, 2002, 2007, and 2012 (USDA 2014a).
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 2015 were interpolated and extrapolated based on the 1992, 1997, 2002, 2007, and
2012 Census data.
Horses: Annual horse population data by state were available for 1987, 1992, 1997, 2002, 2007, and 2012 (USDA
2014a). Data for 1990 through 1991, 1993 through 1996, 1998 through 2001, 2003 through 2006, 2008 through 2011, and
2013 through 2015 were interpolated and extrapolated based on the 1987, 1992, 1997, 2002, 2007, and 2012 Census data.
Mules and Asses: Annual mule and ass (burro and donkey) population data by state were available for 1987, 1992,
1997, 2002, 2007, and 2012 (USDA 2014a). Data for 1990 through 1991, 1993 through 1996, 1998 through 2001, 2003
99 Note that direct N2O 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
N2O emissions dung and urine spread onto fields after it is removed from manure management systems (e.g., lagoon, pit, etc.) and
from livestock dung and urine deposited on pasture, range, or paddock lands are also included in the Agricultural Soil Management
source category.
A-271
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through 2006, 2008 through 2011, and 2013 through 2015 were interpolated and extrapolated based on the 1987, 1992,
1997, 2002, 2007, and 2012 Census data.
American Bison: Annual American bison population data by state were available for 2002, 2007, and 2012 (USDA
2014a). 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 2015 were interpolated and extrapolated based on the Bison Association and
2002, 2007, and 2012 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, 2014b, 2014c, 2015a,
2015b, 2016b, and 2016c). 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 N2O emissions is presented in Table A-182.
Step 2: Waste Characteristics Data
Methane and N2O emissions calculations are based on the following animal characteristics for each relevant
livestock population:
• Volatile solids (VS) excretion rate;
• Maximum methane producing capacity (B0) for U.S. animal waste;
• Nitrogen excretion rate (Nex); and
• Typical animal mass (TAM).
Table A-183 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-184 (ERG 2010b and 2010c). The 1996 AWMFH data were based on measured values from U.S. farms;
the 2008 AWMFH data were developed using the calculation method created by the American Society of Agricultural and
Biological Engineers (ASABE), which is based on U.S. animal dietary intake and performance measures. Since the values
from each of the two AWMFHs result from different estimation methods and reflect changes in animal genetics and nutrition
over time, both data sources were used to create a time series across the Inventory as neither value would be appropriate to
use across the entire span of Inventory years. Expert sources agreed interpolating the two data sources across the time series
would be appropriate as each methodology reflect the best available for that time period and the more recent data may not
appropriately reflect the historic time series (ERG 2010b). Although the AWMFH values are lower than the IPCC values,
these values are more appropriate for U.S. systems because they have been calculated using U.S.-specific data. Animal-
specific notes about VS and Nex are presented below:
• Swine: The VS and Nex data for breeding swine are from a combination of the types of animals that make up
this animal group, namely gestating and farrowing swine and boars. It is assumed that a group of breeding
swine is typically broken out as 80 percent gestating sows, 15 percent farrowing swine, and 5 percent boars
(Safley 2000). Differing trends in VS and Nex values are due to the updated Nex calculation method from
2008 AWMFH. VS calculations did not follow the same procedure and were updated based on a fixed ratio
of VS to total solids and past ASABE standards (ERG 2010b).
• Poultry: Due to the change in USDA reporting of hens and pullets in 2005, new nitrogen and VS excretion
rates were calculated for the combined population of hens and pullets; a weighted average rate was calculated
based on hen and pullet population data from 1990 to 2004.
• Goats, Sheep, Horses, Mules and Asses: In cases where data were not available in the USDA documents, data
from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) or the 2006 IPCC
Guidelines were used as a supplement.
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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 2006IPCC 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 l~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 2006IPCC 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.
N
excreted
= N
consumed
-(N
growth ^milk ,
where,
N excreted
N consumed
N growth
N milk
= Daily N excreted per animal, kg per animal per day.
= Daily N intake per animal, kg per animal per day
= Nitrogen retained by the animal for growth, kg per animal per day
= Nitrogen retained in milk, kg per animal per day
The equation for N consumed is based on the 2006IPCC Guidelines, and is estimated as:
f cp% V
ar
N„
umed
18.45
100
6.25
v J
where,
N consumed
GE
18.45
= Daily N intake per animal, kg per animal per day
= Gross energy intake, as calculated in the CEFM, MJ per animal per day
= Conversion factor for dietary GE per kg of dry matter, MJ per kg.
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CP% = Percent crude protein in diet, input into the CEFM
6.25 = Conversion from kg of dietary protein to kg of dietary N, kg feed per kg N
The portion of consumed N that is retained as product equals the nitrogen required for weight gain plus that in
milk. The nitrogen retained in body weight gain by stackers, 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 current 2006
IPCC Guidelines recommendations, and is as follows:
WG*
N,
268-(7-°3,A-Eii
WG
1000
growth 6 25
where,
N growth = Nitrogen retained by the animal for growth, kg per animal per day
WG = Daily weight gain of the animal, as input into the CEFM transition matrix, kg per day
268 = Constant from 2006 IPCC Guidelines
7.03 = Constant from 2006 IPCC Guidelines
NEg = Net energy required for growth, as calculated in the CEFM, MJ per animal per day
1,000 = Conversion from grams to kilograms
6.25 = Conversion from kg of dietary protein to kg of dietary N, kg feed per kg N
The N content of milk produced also currently matches the 2006 IPCC Guidelines, and is calculated using milk
production and percent protein, along with conversion factors. Milk N retained as product is calculated using the following
equation:
™«-«' pr%
N = lit
milk 6.38
where,
N milk = Nitrogen retained in milk, kg per animal per day
milk = Milk production, kg per day
pr% = Percent protein in milk, estimated from the fat content as 1.9 + 0.4 * %Fat
(Fat assumed to be 4%)
100 = Conversion from percent to value (e.g., 4% to 0.04)
6.38 = Conversion from kg Protein to kg N
The VS and N equations above were used to calculate VS and Nex rates for each state, animal type (heifers and
steer on feed, heifers and steer not on feed, bulls and American bison), and year. Table A-185 presents the state-specific VS
and Nex production rates used for cattle in 2015. As shown in Table A-185, 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-186 summarizes 2015 manure distribution data among waste management systems (WMS) at beef
feedlots, dairies, dairy heifer facilities, and swine, layer, broiler, and turkey operations. Manure from the remaining animal
types (beef cattle not on feed, American bison, goats, horses, mules and asses and sheep) is managed on pasture, range, or
paddocks, on drylot, or with solids storage systems. Note that the Inventory WMS estimates are based on state or regional
WMS usage data and not built upon farm-level WMS estimates. Additional information on the development of the manure
distribution estimates for each animal type is presented below. Definitions of each WMS type are presented in Table A-187.
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Beef Cattle, Dairy Heifers and American Bison: The beef feedlot and dairy heifer WMS data were developed
using regional information from EPA's Office of Water's engineering cost analyses conducted to support the development
of effluent limitations guidelines for Concentrated Animal Feeding Operations (EPA 2002b). Based on EPA site visits and
state contacts supporting this work and additional personal communication with the national USDA office to estimate the
percent of beef steers and heifers in feedlots (Milton 2000), feedlot manure is almost exclusively managed in drylots.
Therefore, for these animal groups, the percent of manure deposited in drylots is assumed to be 100 percent. In addition,
there is a small amount of manure contained in runoff, which may or may not be collected in runoff ponds. Using EPA and
USDA data and expert opinions (documented in ERG 2000a), the runoff from feedlots was calculated by region in
Calculations: Percent Distribution of Manure for Waste Management Systems and was used to estimate the percentage of
manure managed in runoff ponds in addition to drylots; this percentage ranges from 0.4 to 1.3 percent (ERG 2000a). The
percentage of manure generating emissions from beef feedlots is therefore greater than 100 percent. The remaining
population categories of beef cattle outside of feedlots are managed through pasture, range, or paddock systems, which are
utilized for the majority of the population of beef cattle in the country. American bison WMS data were assumed to be the
same as beef cattle NOF.
Dairy Cows: The WMS data for dairy cows were developed using state and regional data from the Census of
Agriculture, EPA's Office of Water, USDA, and the expert sources noted below. Farm-size distribution data are reported in
the 1992, 1997, 2002, and 2007 and 2012 Census of Agriculture (USDA 2016d). It was assumed that the Census data
provided for 1992 were the same as that for 1990 and 1991, and data provided for 2012 were the same as that for 2013
through 2015. Data for 1993 through 1996, 1998 through 2001, and 2003 through 2006, and 2008 through 2011 were
interpolated using the 1992, 1997, 2002, 2007, and 2012 data. The percent of waste by system was estimated using the
USDA data broken out by geographic region and farm size.
Based on EPA site visits and the expert opinion of state contacts, manure from dairy cows at medium (200 through
700 head) and large (greater than 700 head) operations are managed using either flush systems or scrape/slurry systems. In
addition, they may have a solids separator in place prior to their storage component. Estimates of the percent of farms that
use each type of system (by geographic region) were developed by EPA's Office of Water, and were used to estimate the
percent of waste managed in lagoons (flush systems), liquid/slurry systems (scrape systems), and solid storage (separated
solids) (EPA 2002b).
Manure management system data for small (fewer than 200 head) dairies were obtained 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, USDANASS, 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, and 2012 (USDA
2016d) 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 2015, which were obtained from the USDA
NASS (USDA 2016a).
Of the dairies using systems other than daily spread and pasture, range, or paddock systems, some dairies reported
using more than one type of manure management system. Due to limitations in how USDA APHIS collects the manure
management data, the total percent of systems for a region and farm size is greater than 100 percent. However, manure is
typically partitioned to use only one manure management system, rather than transferred between several different systems.
Emissions estimates are only calculated for the final manure management system used for each portion of manure. To avoid
double counting emissions, the reported percentages of systems in use were adjusted to equal a total of 100 percent using
the same distribution of systems. For example, if USDA reported that 65 percent of dairies use deep pits to manage manure
and 55 percent of dairies use anaerobic lagoons to manage manure, it was assumed that 54 percent (i.e., 65 percent divided
by 120 percent) of the manure is managed with deep pits and 46 percent (i.e., 55 percent divided by 120 percent) of the
manure is managed with anaerobic lagoons (ERG 2000a).
Finally, the percentage of manure managed with anaerobic digestion (AD) systems with methane capture and
combustion was added to the WMS distributions at the state-level. AD system data were obtained from EPA's AgSTAR
Program's project database (EPA 2016). This database includes basic information for AD systems in the United States,
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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 USDA APHIS
report and EPA's Office of Water site visits (Bush 1998, ERG 2000a). The USDA APHIS data are based on a statistical
sample of farms in the 16 U.S. states with the most hogs. For operations with less than 200 head, manure management
system data were obtained from USDA APHIS (Bush 1998); it was assumed that those operations use pasture, range, or
paddock systems. For swine operations with greater than 200 head, the percent of waste managed in each system was
estimated using the EPA and USDA data broken out by geographic region and farm size. Farm-size distribution data reported
in the 1992, 1997, 2002, 2007, and 2012 Census of Agriculture (USDA 2016d) were used to determine the percentage of all
swine utilizing the various manure management systems. It was assumed that the swine farm size data provided for 1992
were the same as that for 1990 and 1991, and data provided for 2012 were the same as that for 2013 through 2015. Data for
1993 through 1996, 1998 through 2001, 2003 through 2006, and 2008 through 2011 were interpolated using the 1992, 1997,
2002, 2007, and 2012 data. The manure management systems reported in the census were deep pit, liquid/slurry (includes
above- and below-ground slurry), anaerobic lagoon, and solid storage (includes solids separated from liquids).
Some swine operations reported using more than one management system; therefore, the total percent of systems
reported by USDA for a region and farm size was greater than 100 percent. Typically, this means that a portion of the manure
at a swine operation is handled in one system (e.g., liquid system), and a separate portion of the manure is handled in another
system (e.g., dry system). However, it is unlikely that the same manure is moved from one system to another, which could
result in increased emissions, so reported systems data were normalized to 100 percent for incorporation into the WMS
distribution, using the same method as described above for dairy operations. As with dairy, AD WMS were added to the
state-level WMS distribution based on data from EPA's AgSTAR database (EPA 2016).
Sheep: WMS data for sheep were obtained from USDANASS 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 2015 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 (UEP 1999). These
data were augmented for key poultry states (AL, AR, CA, FL, GA, IA, IN, MN, MO, NC, NE, OH, PA, TX, and WA) with
USDA data (USDA, APHIS 2000). It was assumed that the change in system usage between 1990 and 1999 is proportionally
distributed among those years of the inventory. It was also assumed that system usage in 2000 through 2015 was equal to
that estimated for 1999. Data collected for EPA's Office of Water, including information collected during site visits (EPA
2002b), were used to estimate the distribution of waste by management system and animal type. As with dairy and swine,
using information about AD WMS from EPA's AgSTAR database (EPA 2016), AD was added to the WMS distribution for
poultry operations.
Poultry—Broilers and Turkeys: The percentage of turkeys and broilers on pasture was obtained from the Office
of Air and Radiation's Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that one
percent of poultry waste is deposited in pastures, ranges, and paddocks (EPA 1992). The remainder of waste is assumed to
be deposited in operations with bedding management. As with dairy, swine, and other poultry, AD systems were used to
update the WMS distributions based on information from EPA's AgSTAR database (EPA 2016).
Step 4: Emission Factor Calculations
Methane conversion factors (MCFs) and N2O emission factors (EFs) used in the emission calculations were
determined using the methodologies presented below.
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Methane Conversion Factors (MCFs)
Climate-based IPCC default MCFs (IPCC 2006) were used for all dry systems; these factors are presented in Table
A-188. A U.S.-specific methodology was used to develop MCFs for all lagoon and liquid systems.
For animal waste managed in dry systems, the appropriate IPCC default MCF was applied based on annual average
temperature data. The average county and state temperature data were obtained from the National Climate Data Center
(NOAA 2016) and each state and year in the inventory was assigned a climate classification of cool, temperate or warm.
Although there are some specific locations in the United States that may be included in the warm climate category, no
aggregated state-level annual average temperatures are included in this category. In addition, some counties in a particular
state may be included in the cool climate category, although the aggregated state-level annual average temperature may be
included in the temperate category. Although considering the temperatures at a state level instead of a county level may be
causing some specific locations to be classified into an inappropriate climate category, using the state level annual average
temperature provides an estimate that is appropriate for calculating the national average.
For anaerobic lagoons and other liquid systems, a climate-based approach based on the van't Hoff-Arrhenius
equation was developed to estimate MCFs that reflects the seasonal changes in temperatures, and also accounts for long-
term retention time. This approach is consistent with the latest guidelines from IPCC (2006). The van't Hoff-Arrhenius
equation, with a base temperature of 30°C, is shown in the following equation (Safley and Westerman 1990):
/ = ex p
where,
RTxT2
f = van't Hoff-Arrhenius /factor, the proportion of VS that are biologically available for
conversion to CH4 based on the temperature of the system
Ti = 303.15K
T2 = Ambient temperature (K) for climate zone (in this case, a weighted value for each
state)
E = Activation energy constant (15,175 cal/mol)
R = Ideal gas constant (1.987 cal/K mol)
For those animal populations using liquid manure management systems or manure runoff ponds (i.e., dairy cow,
dairy heifer, layers, beef in feedlots, and swine) monthly average state temperatures were based on the counties where the
specific animal population resides (i.e., the temperatures were weighted based on the percent of animals located in each
county). County population data were calculated from state-level population data from NASS and county-state distribution
data from the 1992, 1997, 2002, and 2007 Census data (USDA 2014a). County population distribution data for 1990 and
1991 were assumed to be the same as 1992; county population distribution data for 1993 through 1996 were interpolated
based on 1992 and 1997 data; county population data for 1998 through 2001 were interpolated based on 1997 and 2002 data;
county population data for 2003 through 2006 were interpolated based on 2002 and 2007 data; county population data for
2008 through 2015 were assumed to be the same as 2007.
Annual MCFs for liquid systems are calculated as follows for each animal type, state, and year of the inventory:
• The weighted-average temperature for a state is calculated using the county population estimates and average
monthly temperature in each county. Monthly temperatures are used to calculate a monthly van't Hoff-Arrhenius/
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.
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• 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 the
maximum CH4 potential of the waste (B0).
The annual MCF is then calculated as:
where,
MCF annual
VS produced annual
Bo
MCF,
CH4 generated
VS produced mim
„,xB„
= Methane conversion factor
= Volatile solids excreted annually
= Maximum CFLi 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-189 by state, WMS, and animal group for 2015.
Nitrous Oxide Emission Factors
Direct N2O 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-190.
Indirect N2O EFs account for two fractions of nitrogen losses: volatilization of ammonia (NH3) and NOx (Fracgas)
and runoff/leaching (Fracrlmoffieach). IPCC default indirect N2O EFs were used to estimate indirect N2O 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 FraCmnoffleach for the United States. The vast
majority of volatilization losses are NH3. Although there are also some small losses of NOx, no quantified estimates were
available for use and those losses are believed to be small (about 1 percent) in comparison to the NH3 losses. Therefore,
Fracgas values were based on WMS-specific volatilization values estimated from U.S. EPA's National Emission Inventory -
Ammonia Emissions from Animal Agriculture Operations (EPA 2005). To estimate Fracrlmofflieach, 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 FraCnmofMeach was set equal to the runoff loss factor. Nitrogen
losses from volatilization and runoff/leaching are presented in Table A-191.
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 excretedstate>AmnBl>WMS = Population state>Amlml x x VS x WMS x 365.25
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where,
VS excreted state, Animal, wms = Amount of VS excreted in manure managed in each WMS for each animal type
(kg/yr)
Population state, Animal = Annual average state animal population by animal type (head)
TAM = Typical animal mass (kg)
VS = Volatile solids production rate (kg VS/1000 kg animal mass/day)
WMS = Distribution of manure by WMS for each animal type in a state (percent)
365.25 = Days per year
Using the CEFM VS data for cattle, the amount of VS excreted in manure that is managed in each WMS was
estimated using the following equation:
VS eXCretedstate, Animal, WMS PopilltltiOllsi ilc Animal X VS X WMS
where,
VS excreted state, Animal, wms = Amount of VS excreted in manure managed in each WMS for each animal type
(kg/yr)
Population state, Animal = Annual average state animal population by animal type (head)
VS = Volatile solids production rate (kg VS/animal/year)
WMS = Distribution of manure by WMS for each animal type in a state (percent)
For all animals, the estimated amount of VS excreted into a WMS was used to calculate CH4 emissions using the
following equation:
CH4 = 7, (VS excreted
State, Animal, WMS
-------
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:
[/[CH 4 Production A D AI)svstcm x CE Al)svstcm x (l - DE)] 1
CH4 Emissions AD = Ł
State, Animal, AD Systems
where,
, + [CH 4 Production ADADsystemx (l - CE ADsystem/
CELi Emissions AD = CH4 emissions from AD systems, (kg/yr)
CELi Production ADad system = CH4 production from a particular AD system, (kg/yr)
CEad system = Collection efficiency of the AD system, varies by AD system type
DE = Destruction efficiency of the AD system, 0.98 for all systems
Step 6: N2O Emission Calculations
Total N2O emissions from manure management systems were calculated by summing direct and indirect N2O
emissions. The first step in estimating direct and indirect N2O emissions was calculating the amount of N excreted in manure
and managed in each WMS. For calves and animals other than cattle the following equation was used:
N excreted state> Animl;WMs = Populationstate Aniiml x WMSx x Nex x 365.25
where,
N excreted state, Animal, wms = Amount of N excreted in manure managed in each WMS for each animal type
(kg/yr)
Population state = Annual average state animal population by animal type (head)
WMS = Distribution of manure by waste management system for each animal type in a state
(percent)
TAM = Typical animal mass (kg)
Nex = Total Kjeldahl nitrogen excretion rate (kg N/1000 kg animal mass/day)
365.25 = Days per year
Using the CEFMNex data for cattle other than calves, the amount of N excreted was calculated using the following
equation:
N excreted Stats, Ammal)WMS = Population state>Amiml 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 = Total Kjeldahl N excretion rate (kg N/animal/year)
For all animals, direct N2O emissions were calculated as follows:
44
where,
Direct N20= Ł N excretedstate>Ammal>WMS x EFWMS x —
State, Animal, WMS V
Direct N2O = Direct N2O emissions (kg N20/yr)
N excreted state, Animal, wms = Amount of N excreted in manure managed in each WMS for each animal type
A-280 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
EFwms
44/28
(kg/yr)
= Direct N2O emission factor from IPCC guidelines (kg N2O-N /kg N)
= Conversion factor of N2O-N to N2O
Indirect N2O emissions were calculated for all animals with the following equation:
Indirect N2O = X
St at e, Ani mal, WMS
where,
Indirect N2O
N excreted State, Animal, WMS
Fracgas,wMs
F raCrunoff/leach,WMS
EF volatilization
EFrunofPleach
44/28
N excreted
Frac
gas,WMS
State, Ani mal, WMS
100
:EF.
volatilizteion "
44
28
N excreted
Frac
State, Ani mal, WMS
runofFleach, WMS
loo
: EF.
runnofFleach
44
28
= Indirect N2O emissions (kg N20/yr)
= 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 N2O-N to N2O
Emission estimates of CH4 andN20 by animal type are presented for all years of the inventory in Table A-192
and Table A-193 respectively. Emission estimates for 2015 are presented by animal type and state in Table A-194 and
Table A- 195 respectively.
A-281
-------
Table fl-182: Livestock Population 11,000 Head]100
Animal Type
1990
1995
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Dairy Cattle
19,512
18,681 ,
17,927
17,833
17,919
17,642
17,793
18,078
18,190
18,422
18,560
18,297
18,442
18,587
18,505
18,527
18,798
Dairy Cows
10,015
9,482 t
9,172
9,106
9,142
8,988
9,004
9,104
9,145
9,257
9,333
9,087
9,156
9,236
9,221
9,208
9,307
Dairy Heifer
4,129
4,108
4,045
4,060
4,073
4,033
4,162
4,294
4,343
4,401
4,437
4,545
4,577
4,581
4,525
4,579
4,727
Dairy Calves
5,369
5,091
4,668
4,704
4,621
4,628
4,680
4,703
4,765
4,791
4,666
4,709
4,770
4,758
4,740
4,764
4,764
Swine3
53,941
58,899
58,913
60,028
59,827
60,735
61,073
61,887
65,417
67,183
65,842
64,723
65,572
66,363
65,437
64,325
68,191
Market <50 lb.
18,359
19,656
19,659
19,863
19,929
20,222
20,228
20,514
21,812
19,933
19,411
19,067
19,285
19,472
19,002
18,952
19,836
Market 50-119
lb.
11,734
12,836
12,900
13,284
13,138
13,400
13,519
13,727
14,557
17,163
16,942
16,645
16,904
17,140
16,834
16,576
17,577
Market 120-179
lb.
9,440
10,545
10,708
11,013
11,050
11,227
11,336
11,443
12,185
12,825
12,517
12,377
12,514
12,714
12,674
12,333
13,225
Market >180 lb.
7,510
8,937
9,465
9,738
9,701
9,922
9,997
10,113
10,673
11,161
11,067
10,856
11,078
11,199
11,116
10,572
11,575
Breeding
6,899
6,926
6,181
6,129
6,011
5,963
5,993
6,090
6,190
6,102
5,905
5,778
5,791
5,839
5,812
5,892
5,978
Beef Cattleb
81,576
90,361
84,237
84,260
83,361
81,672
82,193
83,263
82,801
81,532
80,993
80,484
78,937
76,858
76,075
75,245
76,225
Feedlot Steers
6,357
7,233
7,932
8,116
8,416
8,018
8,116
8,724
8,674
8,474
8,434
8,584
8,771
8,586
8,614
8,695
8,562
Feedlot Heifers
3,192
3,831
4,569
4,557
4,676
4,521
4,536
4,801
4,730
4,585
4,493
4,620
4,830
4,742
4,653
4,525
4,321
NOF Bulls
2,160
2,385
2,274
2,244
2,248
2,201
2,214
2,258
2,214
2,207
2,188
2,190
2,165
2,100
2,074
2,038
2,109
Beef Calves
16,909
18,177
17,483
17,126
17,013
16,918
16,814
16,644
16,231
16,051
16,067
15,817
15,288
14,859
14,741
15,117
15,117
NOF Heifers
10,182
11,829
9,832
9,843
9,564
9,321
9,550
9,716
9,592
9,356
9,473
9,349
8,874
8,687
8,787
8,787
9,297
NOF Steers
10,321
11,716
8,724
8,883
8,347
8,067
8,185
8,248
8,302
8,244
8,560
8,234
7,568
7,173
7,457
7,374
7,517
NOF Cows
32,455
35,190
33,398
33,134
32,983
32,531
32,674
32,703
32,644
32,435
31,794
31,440
30,913
30,282
29,631
29,085
29,302
Sheep
11,358
8,989
6,908
6,623
6,321
6,065
6,135
6,200
6,120
5,950
5,747
5,620
5,470
5,375
5,360
5,245
5,280
Sheep On Feed
1,180
1,771
3,256
3,143
3,049
2,923
2,971
3,026
3,000
2,911
2,806
2,778
2,687
2,666
2,655
2,593
2,593
Sheep NOF
10,178
7,218
3,652
3,480
3,272
3,142
3,164
3,174
3,120
3,039
2,941
2,842
2,783
2,709
2,705
2,652
2,652
Goats
2,516
2,357
2,475
2,530
2,652
2,774
2,897
3,019
3,141
3,037
2,933
2,829
2,725
2,622
2,518
2,414
2,310
Poultry1
1,537,074
1,826,977
2,060,398
2,097,691
2,085,268
2,130,877
2,150,410
2,154,236
2,166,936
2,175,990
2,088,828
2,104,335
2,095,951
2,168,697
2,106,502
2,116,333
2,127,997
Hens>1 yr.
273,467
299,071
340,317
340,209
340,979
343,922
348,203
349,888
346,613
339,859
341,005
341,884
338,944
346,965
361,403
370,637
346,343
Pullets
73,167
81,369
95,656
95,289
100,346
101,429
96,809
96,596
103,816
99,458
102,301
105,738
102,233
104,460
106,646
106,490
116,802
Chickens
6,545
7,637
8,126
8,353
8,439
8,248
8,289
7,938
8,164
7,589
8,487
7,390
6,922
6,827
6,853
6,403
7,770
Broilers
1,066,209
1,331,940
1,525,413
1,562,015
1,544,155
1,589,209
1,613,091
1,612,327
1,619,400
1,638,055
1,554,582
1,567,927
1,565,018
1,625,945
1,551,600
1,553,636
1,579,382
Turkeys
117,685
106,960
90,887
91,826
91,349
88,069
84,018
87,487
88,943
91,029
82,453
81,396
82,833
84,500
80,000
79,167
77,700
Horses
2,212
2,632
3,519
3,644
3,721
3,798
3,875
3,952
4,029
3,947
3,866
3,784
3,703
3,621
3,540
3,458
3,377
Mules and Asses
63
101 /
109
105
141
177
212
248
284
286
287
289
291
293
294
296
298
American Bison
47
104
213
232
225
218
212
205
198
191
184
177
169
162
155
148
141
a Prior to 2008, the Market <50 lbs cateqory was <60 lbs and the Market 50-119 lbs cateqory was Market 60-119 lbs; USDA updated the cateqories to be more consistent with international animal cateqories.
b NOF - Not on Feed
c Pullets includes laying pullets, pullets younger than 3 months, and pullets older than 3 months.
Note: Totals may not sum due to independent rounding.
Source(s): See Step 1: Livestock Population Characterization Data
100 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-282 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-183: Waste Characteristics Data
Typical Animal Mass, TAM
Total Kjeldahl Nitrogen Excreted, Nexa
Maximum Methane Generation Potential, Bo
Volatile Solids Excreted, VSa
Value
Value
(m3 CHVkg VS
Animal Group
(kg)
Source
Value
Source
added)
Source
Value
Source
Dairy Cows
680
CEFM
Table A-185
CEFM
0.24
Morris 1976
Table A-185
CEFM
Dairy Heifers
406-408
CEFM
Table A-185
CEFM
0.17
Bryant etal. 1976
Table A-185
CEFM
Feedlot Steers
419-457
CEFM
Table A-185
CEFM
0.33
Hashimoto 1981
Table A-185
CEFM
Feedlot Heifers
384-430
CEFM
Table A-185
CEFM
0.33
Hashimoto 1981
Table A-185
CEFM
NOF Bulls
831-917
CEFM
Table A-185
CEFM
0.17
Hashimoto 1981
Table A-185
CEFM
NOF Calves
118
ERG 2003b
Table A-184
USDA 1996, 2008
0.17
Hashimoto 1981
Table A-184
USDA 1996, 2008
NOF Heifers
296-407
CEFM
Table A-185
CEFM
0.17
Hashimoto 1981
Table A-185
CEFM
NOF Steers
314-335
CEFM
Table A-185
CEFM
0.17
Hashimoto 1981
Table A-185
CEFM
NOF Cows
554-611
CEFM
Table A-185
CEFM
0.17
Hashimoto 1981
Table A-185
CEFM
American Bison
578.5
Meagher 1986
Table A-185
CEFM
0.17
Hashimoto 1981
Table A-185
CEFM
Market Swine <50 lbs.
13
ERG 2010a
Table A-184
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-184
USDA 1996, 2008
Market Swine <60 lbs.
16
Safley 2000
Table A-184
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-184
USDA 1996, 2008
Market Swine 50-119
lbs.
39
ERG 2010a
Table A-184
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-184
USDA 1996, 2008
Market Swine 60-119
lbs.
41
Safley 2000
Table A-184
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-184
USDA 1996, 2008
Market Swine 120-179 lbs.
68
Safley 2000
Table A-184
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-184
USDA 1996, 2008
Market Swine >180 lbs
91
Safley 2000
Table A-184
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-184
USDA 1996, 2008
Breeding Swine
198
Safley 2000
Table A-184
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-184
USDA 1996, 2008
Feedlot Sheep
25
EPA 1992
Table A-184
ASAE 1998, USDA 2008
0.36
EPA 1992
Table A-184
ASAE
1998, USDA 2008
NOF Sheep
80
EPA 1992
Table A-184
ASAE 1998, USDA 2008
0.19
EPA 1992
Table A-184
ASAE
1998, USDA 2008
Goats
64
ASAE 1998
Table A-184
ASAE 1998
0.17
EPA 1992
Table A-184
ASAE 1998
Horses
450
ASAE 1998
Table A-184
ASAE 1998, USDA 2008
0.33
EPA 1992
Table A-184
ASAE
1998, USDA 2008
Mules and Asses
130
IPCC 2006
Table A-184
IPCC 2006
0.33
EPA 1992
Table A-184
IPCC 2006
Hens >/= 1 yr
1.8
ASAE 1998
Table A-184
USDA 1996, 2008
0.39
Hill 1982
Table A-184
USDA 1996, 2008
Pullets
1.8
ASAE 1998
Table A-184
USDA 1996, 2008
0.39
Hill 1982
Table A-184
USDA 1996, 2008
Other Chickens
1.8
ASAE 1998
Table A-184
USDA 1996, 2008
0.39
Hill 1982
Table A-184
USDA 1996, 2008
Broilers
0.9
ASAE 1998
Table A-184
USDA 1996, 2008
0.36
Hill 1984
Table A-184
USDA 1996, 2008
T urkeys
6.8
ASAE 1998
Table A-184
USDA 1996, 2008
0.36
Hill 1984
Table A-184
USDA 1996, 2008
a Nex and VS values vary by year; Table A-185 shows state-level values for 2015 only.
A-283
-------
Table A-184: Estimated Volatile Solids (VS) and Total Kjeldahl Nitrogen Excreted (Nex) Production Rates by year for Swine, Poultry, Sheep, Goats, Horses, Mules and Asses, and
Cattle Calves tkg/day/1000 kg animal mass]101
Animal Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
VS
Swine, Market
<50 lbs.
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
Swine, Market
50-119 lbs.
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
Swine, Market
120-179 lbs.
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
Swine, Market
>180 lbs.
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
Swine, Breeding
2.6
2.6
2.6
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
NOF Cattle Calves
6.4
6.4
6.8
6.9
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
Sheep
9.2
9.2
9.0
8.9
8.8
8.8
8.7
8.6
8.5
8.4
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
Goats
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
Hens >1yr.
10.1
10.1
10.1
10.1
10.1
10.1
10.1
10.1
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
Pullets
10.1
10.1
10.1
10.1
10.1
10.1
10.1
10.1
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
Chickens
10.8
10.8
10.9
10.9
10.9
10.9
10.9
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
Broilers
15.0
15.0
15.7
15.8
16.0
16.2
16.3
16.5
16.7
16.8
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
Turkeys
9.7
9.7
9.3
9.2
9.1
9.0
8.9
8.8
8.7
8.6
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
Horses
10.0
10.0
9.2
8.8
8.4
8.1
7.7
7.3
6.9
6.5
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
Mules and Asses
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
Nex
Swine, Market
<50 lbs.
0.60
0.60
0.71
0.73
0.76
0.79
0.81
0.84
0.87
0.89
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
Swine, Market
50-119 lbs.
0.42
0.42
0.46
0.47
0.48
0.49
0.50
0.51
0.52
0.53
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
Swine, Market
120-179 lbs.
0.42
0.42
0.46
0.47
0.48
0.49
0.50
0.51
0.52
0.53
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
Swine, Market
>180 lbs.
0.42
0.42
0.46
0.47
0.48
0.49
0.50
0.51
0.52
0.53
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
Swine, Breeding
0.24
0.24
0.22
0.22
0.22
0.22
0.21
0.21
0.21
0.21
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
NOF Cattle Calves
0.30
0.30
0.35
0.36
0.38
0.39
0.40
0.41
0.43
0.44
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
Sheep
0.42
0.42
0.43
0.43
0.43
0.44
0.44
0.44
0.44
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
101 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-284 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Animal Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
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
0.45
0.45
Hens >1yr.
0.70
0.70
0.73
0.73
0.74
0.75
0.76
0.77
0.77
0.78
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
Pullets
0.70
0.70
0.73
0.73
0.74
0.75
0.76
0.77
0.77
0.78
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
Chickens
0.83
0.83
0.92
0.94
0.97
0.99
1.01
1.03
1.06
1.08
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
Broilers
1.10
1.10
1.05
1.04
1.03
1.02
1.01
1.00
0.98
0.97
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
Turkeys
0.74
0.74
0.70
0.69
0.68
0.67
0.66
0.65
0.64
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
Horses
0.30
0.30
0.29
0.28
0.28
0.27
0.27
0.26
0.26
0.25
0.25
0.25
0.25
0.25
0.25
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
0.30
0.30
Source: USDAAWMFH (1996, 2008)
A-285
-------
Table A-185: Estimated Volatile Solids (VS) and Total Kjeldahl Nitrogen Excreted (Nex) Production Rates by State for Cattle (other than Calves) and American Bison3 for 2015
tkg/animal/year]1'2
Volatile Solids
Nitrogen Excreted
State
Dairy Cow
Dairy
Heifers
Beef NOF
Cow
Beef NOF
Heifers
Beef NOF
Steer
Beef OF
Heifers
Beef OF
Steer
Beef NOF
Bull
American
Bison
Dairy
Cow
Dairy
Heifers
Beef
NOF
Cow
Beef
NOF
Heifers
Beef
NOF
Steer
Beef OF
Heifers
Beef OF
Steer
Beef
NOF
Bull
American
Bison
Alabama
2,097
1,251
1,664
1,101
972
690
669
1,721
1,721
128
69
73
51
42
56
57
83
83
Alaska
1,971
1,251
1,891
1,273
1,116
690
670
1,956
1,956
121
69
59
42
33
56
57
69
69
Arizona
2,928
1,251
1,891
1,247
1,116
691
669
1,956
1,956
162
69
59
40
33
56
57
69
69
Arkansas
2,075
1,251
1,664
1,097
972
690
669
1,721
1,721
126
69
73
50
42
56
57
83
83
California
2,799
1,251
1,891
1,232
1,116
690
669
1,956
1,956
156
69
59
40
33
56
57
69
69
Colorado
3,018
1,251
1,891
1,204
1,116
691
669
1,956
1,956
166
69
59
38
33
56
57
69
69
Connecticut
2,656
1,251
1,674
1,111
977
691
669
1,731
1,731
151
69
74
52
42
56
57
84
84
Delaware
2,571
1,251
1,674
1,081
977
691
668
1,731
1,731
147
69
74
50
42
56
57
84
84
Florida
2,697
1,251
1,664
1,103
972
691
669
1,721
1,721
154
69
73
51
42
56
57
83
83
Georgia
2,771
1,251
1,664
1,098
972
691
668
1,721
1,721
157
69
73
50
42
56
57
83
83
Hawaii
2,288
1,251
1,891
1,254
1,116
691
668
1,956
1,956
135
69
59
41
33
56
57
69
69
Idaho
2,902
1,251
1,891
1,224
1,116
691
669
1,956
1,956
161
69
59
39
33
56
57
69
69
Illinois
2,603
1,251
1,589
1,011
924
691
669
1,643
1,643
148
69
75
49
43
56
57
85
85
Indiana
2,753
1,251
1,589
1,025
924
691
669
1,643
1,643
155
69
75
50
43
56
57
85
85
Iowa
2,813
1,251
1,589
991
924
691
669
1,643
1,643
157
69
75
48
43
56
57
85
85
Kansas
2,760
1,251
1,589
985
924
691
669
1,643
1,643
155
69
75
48
43
56
57
85
85
Kentucky
2,469
1,251
1,664
1,082
972
690
669
1,721
1,721
144
69
73
49
42
56
57
83
83
Louisiana
2,107
1,251
1,664
1,099
972
691
669
1,721
1,721
127
69
73
50
42
56
57
83
83
Maine
2,578
1,251
1,674
1,095
977
691
669
1,731
1,731
147
69
74
51
42
56
57
84
84
Maryland
2,598
1,251
1,674
1,081
977
691
669
1,731
1,731
148
69
74
50
42
56
57
84
84
Massachusetts
2,450
1,251
1,674
1,097
977
691
668
1,731
1,731
142
69
74
51
42
56
57
84
84
Michigan
2,977
1,251
1,589
1,011
924
691
669
1,643
1,643
164
69
75
49
43
56
57
85
85
Minnesota
2,636
1,251
1,589
1,007
924
691
669
1,643
1,643
150
69
75
49
43
56
57
85
85
Mississippi
2,274
1,251
1,664
1,097
972
690
669
1,721
1,721
136
69
73
50
42
56
57
83
83
Missouri
2,258
1,251
1,589
1,032
924
691
669
1,643
1,643
134
69
75
51
43
56
57
85
85
Montana
2,695
1,251
1,891
1,254
1,116
690
670
1,956
1,956
152
69
59
41
33
56
58
69
69
Nebraska
2,812
1,251
1,589
994
924
691
669
1,643
1,643
157
69
75
48
43
56
57
85
85
Nevada
2,823
1,251
1,891
1,241
1,116
691
669
1,956
1,956
158
69
59
40
33
56
57
69
69
New Hampshire
2,604
1,251
1,674
1,097
977
691
669
1,731
1,731
148
69
74
51
42
56
57
84
84
New Jersey
2,454
1,251
1,674
1,090
977
691
669
1,731
1,731
142
69
74
50
42
56
57
84
84
New Mexico
2,910
1,251
1,891
1,239
1,116
691
669
1,956
1,956
162
69
59
40
33
56
57
69
69
New York
2,804
1,251
1,674
1,079
977
691
668
1,731
1,731
157
69
74
50
42
56
57
84
84
North Carolina
2,721
1,251
1,664
1,095
972
691
669
1,721
1,721
155
69
73
50
42
56
57
83
83
North Dakota
2,649
1,251
1,589
1,020
924
691
668
1,643
1,643
150
69
75
50
43
56
57
85
85
Ohio
2,636
1,251
1,589
1,022
924
691
669
1,643
1,643
150
69
75
50
43
56
57
85
85
Oklahoma
2,483
1,251
1,664
1,078
972
691
669
1,721
1,721
143
69
73
49
42
56
57
83
83
Oregon
2,624
1,251
1,891
1,235
1,116
691
668
1,956
1,956
149
69
59
40
33
56
57
69
69
Pennsylvania
2,622
1,251
1,674
1,081
977
690
669
1,731
1,731
149
69
74
50
42
56
57
84
84
102 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-286 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Volatile Solids
Nitrogen Excreted
Beef
Beef
Beef
Beef
Dairy
Beef NOF
Beef NOF
Beef NOF
Beef OF
Beef OF Beef NOF
American
Dairy
Dairy
NOF
NOF
NOF
Beef OF
Beef OF
NOF
American
State
Dairy Cow
Heifers
Cow
Heifers
Steer
Heifers
Steer
Bull
Bison
Cow
Heifers
Cow
Heifers
Steer
Heifers
Steer
Bull
Bison
Rhode Island
2,419
1,251
1,674
1,101
977
691
669
1,731
1,731
140
69
74
51
42
56
57
84
84
South Carolina
2,454
1,251
1,664
1,095
972
690
669
1,721
1,721
144
69
73
50
42
56
57
83
83
South Dakota
2,762
1,251
1,589
1,017
924
691
669
1,643
1,643
155
69
75
50
43
56
57
85
85
Tennessee
2,385
1,251
1,664
1,092
972
692
666
1,721
1,721
141
69
73
50
42
55
56
83
83
Texas
2,765
1,251
1,664
1,058
972
691
669
1,721
1,721
155
69
73
48
42
56
57
83
83
Utah
2,828
1,251
1,891
1,240
1,116
691
669
1,956
1,956
158
69
59
40
33
56
57
69
69
Vermont
2,608
1,251
1,674
1,075
977
691
669
1,731
1,731
149
69
74
49
42
56
57
84
84
Virginia
2,608
1,251
1,664
1,095
972
691
669
1,721
1,721
150
69
73
50
42
56
57
83
83
Washington
2,881
1,251
1,891
1,207
1,116
691
668
1,956
1,956
160
69
59
38
33
56
57
69
69
West Virginia
2,269
1,251
1,674
1,093
977
690
669
1,731
1,731
134
69
74
51
42
56
57
84
84
Wisconsin
2,795
1,251
1,589
1,034
924
691
669
1,643
1,643
157
69
75
51
43
56
57
85
85
Wyoming
2,785
1,251
1,891
1,242
1,116
691
669
1,956
1,956
156
69
59
40
33
56
57
69
69
a Beef NOF Bull values were used for American bison Nex and VS.
Source: CEFM.
Tablefl-186: 2
115 Manure Distribution Among Waste Management Systems by Operation [Percent]
Beef Not on
Feed
Broiler and Turkey
Beef Feedlots
Operations
Dairy Cow Farms3
Dairy Heifer Facilities
Swine Operations3
Layer Operations
Operations
Pasture,
Pasture,
Pasture,
Pasture,
Poultry
Pasture,
Poultry
Liquid/
Range,
Range,
Daily
Solid Liquid/ Anaerobic
Deep
Daily
Dry Liquid/
Range,
Range,
Solid Liquid/ Anaerobic
Deep
Anaerobic
without
Range,
with
State
DryLotb Slurry"
Paddock
Paddock Spread
Storage
Slurry
Lagoon
Pit
Spread"
Lotb Slurry"
Paddock"
Paddock
Storage
Slurry Lagoon
Pit
Lagoon
Litter
Paddock
Litter
Alabama
100 1
100
51
16
9
9
14
0
17
38
0
45
6
4
7
54
30
42
58
1
99
Alaska
100 1
100
4
7
34
19
25
10
6
90
1
4
66
1
9
7
16
25
75
1
99
Arizona
100 0
100
0
10
9
19
61
0
10
90
0
0
5
4
7
54
31
60
40
1
99
Arkansas
100 1
100
63
14
8
6
8
0
15
28
0
57
5
4
17
36
38
0
100
1
99
California
100 1
100
0
10
9
20
60
0
11
88
1
1
20
3
7
43
27
12
88
1
99
Colorado
100 0
100
0
1
11
22
66
0
1
98
0
1
1
6
26
17
50
60
40
1
99
Connecticut
100 1
100
6
43
15
22
13
2
43
51
0
6
83
1
5
4
8
5
95
1
99
Delaware
100 1
100
6
44
18
19
11
2
44
50
0
6
17
4
22
16
41
5
95
1
99
Florida
100 1
100
12
22
7
15
43
0
22
61
1
17
73
1
7
6
13
42
58
1
99
Georgia
100 1
100
28
20
10
13
29
0
18
42
0
40
8
3
6
53
30
42
58
1
99
Hawaii
100 1
100
1
0
11
21
67
0
0
99
1
1
47
2
15
11
25
25
75
1
99
Idaho
100 0
100
0
0
11
22
66
0
1
99
0
0
9
5
24
16
46
60
40
1
99
Illinois
100 1
100
3
6
35
33
19
4
8
87
0
5
1
5
29
13
53
2
98
1
99
Indiana
100 1
100
6
10
26
30
26
2
13
79
0
8
1
5
29
13
52
0
100
1
99
Iowa
100 1
100
3
5
30
34
25
3
10
83
0
6
0
4
8
56
32
0
100
1
99
Kansas
100 1
100
2
3
15
38
40
1
5
92
0
3
1
5
29
13
53
2
98
1
99
103 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-287
-------
Beef Feedlots
Beef Not on
Feed
Operations
Dairy Cow Farms3
Dairy Heifer Facilities
Swine Operations"
Layer Operations
Broiler and Turkey
Operations
State
Liquid/
DryLotb Slurry"
Pasture,
Range,
Paddock
Pasture,
Range, Daily Solid Liquid/ Anaerobic
Paddock Spread Storage Slurry Lagoon
Deep
Pit
Daily
Spreadb
Dry Liquid/
Lotb Slurry"
Pasture,
Range,
Paddock"
Pasture,
Range, Solid Liquid/ Anaerobic
Paddock Storage Slurry Lagoon
Deep
Pit
Anaerobic
Lagoon
Poultry
without
Litter
Pasture,
Range,
Paddock
Poultry
with
Litter
Kentucky
100
1
100
57
15
15
8
4
1
14
24
0
61
5
4
8
52
31
5
95
1
99
Louisiana
100
1
100
51
16
9
9
14
0
14
26
0
60
89
0
3
2
5
60
40
1
99
Maine
100
1
100
6
44
18
19
12
2
45
48
0
7
75
1
7
5
12
5
95
1
99
Maryland
100
1
100
6
44
20
17
10
3
44
49
0
7
19
4
22
15
40
5
95
1
99
Massachusetts
100
1
100
7
45
22
17
7
2
45
47
0
7
67
1
9
7
15
5
95
1
99
Michigan
100
1
100
2
3
20
39
33
2
6
91
0
3
2
5
26
17
49
2
98
1
99
Minnesota
100
1
100
4
7
35
30
20
4
10
84
0
6
0
5
26
17
50
0
100
1
99
Mississippi
100
1
100
55
15
10
8
11
1
15
28
0
57
1
4
6
59
31
60
40
1
99
Missouri
100
1
100
7
12
39
24
14
4
14
77
0
8
1
5
29
13
53
0
100
1
99
Montana
100
100
3
4
19
27
43
4
4
93
0
3
3
5
26
17
50
60
40
1
99
Nebraska
100
1
100
3
5
21
36
33
2
6
90
0
4
1
5
29
14
52
2
98
1
99
Nevada
100
100
0
0
10
23
66
0
0
99
0
0
100
0
0
0
0
0
100
1
99
New Hampshire
100
1
100
6
44
18
19
10
2
44
49
0
7
100
0
0
0
0
5
95
1
99
New Jersey
100
1
100
8
46
25
13
6
3
45
47
0
8
70
1
8
6
14
5
95
1
99
New Mexico
100
100
0
10
9
19
61
0
10
90
0
0
74
1
7
6
12
60
40
1
99
New York
100
1
100
6
44
16
18
14
2
45
48
0
7
30
4
19
13
35
5
95
1
99
North Carolina
100
1
100
41
18
10
17
13
1
15
31
0
54
0
4
6
59
31
42
58
1
99
North Dakota
100
1
100
5
9
27
31
25
2
11
83
0
6
2
5
26
17
50
2
98
1
99
Ohio
100
1
100
7
11
33
27
19
3
14
78
0
8
2
5
28
13
52
0
100
1
99
Oklahoma
100
100
0
8
17
22
50
3
6
94
0
0
1
4
6
59
31
60
40
1
99
Oregon
100
1
100
12
0
10
22
54
1
0
80
1
20
78
1
6
5
11
25
75
1
99
Pennsylvania
100
1
100
8
46
24
13
7
2
47
44
0
9
3
5
26
18
48
0
100
1
99
Rhode Island
100
1
100
7
45
24
15
6
3
47
44
0
9
77
1
6
5
11
5
95
1
99
South Carolina
100
1
100
44
17
7
12
20
0
15
31
0
54
5
4
7
54
31
60
40
1
99
South Dakota
100
1
100
2
4
17
39
38
1
8
87
0
5
1
5
26
17
50
2
98
1
99
Tennessee
100
1
100
55
15
12
10
5
2
15
26
0
59
11
3
7
50
29
5
95
1
99
Texas
100
100
0
9
11
21
59
1
8
92
0
0
6
4
6
56
30
12
88
1
99
Utah
100
100
1
1
13
24
60
1
1
98
0
1
1
6
26
17
51
60
40
1
99
Vermont
100
1
100
5
43
15
20
15
2
44
49
0
7
81
1
5
4
9
5
95
1
99
Virginia
100
1
100
52
16
12
12
7
2
15
28
0
57
7
3
7
53
30
5
95
1
99
Washington
100
1
100
8
0
10
22
59
1
0
83
1
17
33
3
18
13
33
12
88
1
99
West Virginia
100
1
100
8
46
24
14
5
3
45
48
0
7
93
0
2
1
3
5
95
1
99
Wisconsin
100
1
100
4
6
32
32
22
3
12
82
0
7
12
4
24
17
43
2
98
1
99
Wyoming
100
0
100
4
7
19
21
44
4
12
81
0
7
1
6
26
17
51
60
40
1
99
a In the methane inventory for manure management, the percent of dairy cows and swine with AD systems is estimated using data from EPA's AgSTAR Program.
b Because manure from beef feedlots and dairy heifers may be managed for long periods of time in multiple systems (i.e., both drylot and runoff collection pond), the percent of manure that generates emissions is greater than 100 percent.
Source(s): See Step 3: Waste Management System Usage Data
A-288 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-187: Manure Management System Descriptions
Manure Management System Description3
Pasture, Range, Paddock The manure from pasture and range grazing animals is allowed to lie as is, and is not managed. Methane
emissions are accounted for under Manure Management, but the N2O emissions from manure deposited on PRP
are included under the Agricultural Soil Management category.
Manure is routinely removed from a confinement facility and is applied to cropland or pasture within 24 hours of
excretion. Methane and indirect N2O emissions are accounted for under Manure Management. Direct N2O
emissions from land application are covered under the Agricultural Soil Management category.
The storage of manure, typically for a period of several months, in unconfined piles or stacks. Manure is able to
be stacked due to the presence of a sufficient amount of bedding material or loss of moisture by evaporation.
A paved or unpaved open confinement area without any significant vegetative cover where accumulating manure
may be removed periodically. Dry lots are most typically found in dry climates but also are used in humid climates.
Manure is stored as excreted or with some minimal addition of water to facilitate handling and is stored in either
tanks or earthen ponds, usually for periods less than one year.
Uncovered anaerobic lagoons are designed and operated to combine waste stabilization and storage. Lagoon
supernatant is usually used to remove manure from the associated confinement facilities to the lagoon. Anaerobic
lagoons are designed with varying lengths of storage (up to a year or greater), depending on the climate region,
the VS loading rate, and other operational factors. Anaerobic lagoons accumulate sludge over time, diminishing
treatment capacity. Lagoons must be cleaned out once every 5 to 15 years, and the sludge is typically applied to
agricultural lands. The water from the lagoon may be recycled as flush water or used to irrigate and fertilize fields.
Lagoons are sometimes used in combination with a solids separator, typically for dairy waste. Solids separators
help control the buildup of nondegradable material such as straw or other bedding materials.
Animal excreta with or without straw are collected and anaerobically digested in a large containment vessel
(complete mix or plug flow digester) or covered lagoon. Digesters are designed and operated for waste
stabilization by the microbial reduction of complex organic compounds to CO2 and CH4, which is captured and
flared or used as a fuel.
Collection and storage of manure usually with little or no added water typically below a slatted floor in an enclosed
animal confinement facility. Typical storage periods range from 5 to 12 months, after which manure is removed
from the pit and transferred to a treatment system or applied to land.
Enclosed poultry houses use bedding derived from wood shavings, rice hulls, chopped straw, peanut hulls, or
other products, depending on availability. The bedding absorbs moisture and dilutes the manure produced by the
birds. Litter is typically cleaned out completely once a year. These manure systems are typically used for all
poultry breeder flocks and for the production of meat type chickens (broilers) and other fowl.
In high-rise cages or scrape-out/belt systems, manure is excreted onto the floor below with no bedding to absorb
moisture. The ventilation system dries the manure as it is stored. When designed and operated properly, this high-
rise system is a form of passive windrow composting.
a Manure management system descriptions 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 fl-188: Methane Conversion Factors [percent] for Dry Systems
Waste Management System
Cool Climate MCF
Temperate Climate MCF
Warm Climate MCF
Aerobic Treatment
0
0
0
Anaerobic Digester
0
0
0
Cattle Deep Litter (<1 month)
3
3
30
Cattle Deep Litter (>1 month)
21
44
76
Composting - In Vessel
0.5
0.5
0.5
Composting - Static Pile
0.5
0.5
0.5
Composting-Extensive/ Passive
0.5
1
1.5
Composting-lntensive
0.5
1
1.5
Daily Spread
0.1
0.5
1
Dry Lot
1
1.5
5
Fuel
10
10
10
Pasture
1
1.5
2
Poultry with bedding
1.5
1.5
1.5
Daily Spread
Solid Storage
Dry Lot
Liquid/Slurry
Anaerobic Lagoon
Anaerobic Digester
Deep Pit
Poultry with Litter
Poultry without Litter
A-289
-------
Waste Management System
Cool Climate MCF
Temperate Climate MCF
Warm Climate MCF
Poultry without bedding
1.5
1.5
1.5
Solid Storage
2
4
5
Source: IPCC (2006)
Table A-189: Methane Conversion Factors by State for Liquid Systems for 2015
[Percent]104
Dairy
Swine
Beef
Poultry
Anaerobic Liquid/Slurry and
Anaerobic
Liquid/Slurry
Anaerobic
State
Lagoon
Deep Pit
Lagoon
and Deep Pit
Liquid/Slurry
Lagoon
Alabama
78
42
78
41
43
78
Alaska
49
15
49
15
15
49
Arizona
79
58
78
49
54
75
Arkansas
77
37
78
40
37
77
California
73
34
73
33
43
74
Colorado
66
22
68
24
24
65
Connecticut
71
26
71
26
26
71
Delaware
76
33
76
33
33
76
Florida
82
60
81
58
58
81
Georgia
78
44
78
42
42
77
Hawaii
77
58
77
58
58
77
Idaho
68
25
65
22
22
67
Illinois
73
30
73
29
28
73
Indiana
71
27
72
28
28
72
Iowa
70
26
70
26
26
70
Kansas
76
34
76
33
34
76
Kentucky
75
33
75
33
32
76
Louisiana
80
50
80
50
50
79
Maine
65
21
65
21
21
65
Maryland
75
31
75
32
32
75
Massachusetts
69
24
70
25
25
70
Michigan
68
24
69
24
24
68
Minnesota
68
24
69
24
24
68
Mississippi
79
45
78
43
46
79
Missouri
75
32
74
32
32
75
Montana
60
19
63
21
21
63
Nebraska
72
27
72
28
27
72
Nevada
70
26
71
28
26
70
New Hampshire
66
22
66
23
22
66
New Jersey
74
30
75
31
29
74
New Mexico
74
32
72
29
30
71
New York
67
23
68
24
24
68
North Carolina
76
35
78
41
33
76
North Dakota
67
23
67
23
23
67
Ohio
71
27
72
28
28
72
Oklahoma
78
40
77
37
37
77
Oregon
65
23
65
23
23
65
Pennsylvania
71
27
72
28
28
72
Rhode Island
71
26
71
26
26
71
South Carolina
78
43
79
44
42
78
South Dakota
69
25
70
25
25
70
Tennessee
76
34
76
36
35
76
Texas
78
42
78
45
39
79
Utah
66
22
69
25
24
65
Vermont
64
21
64
21
21
65
Virginia
73
30
76
33
31
74
Washington
65
23
67
24
25
66
West Virginia
72
28
72
28
27
71
Wisconsin
67
23
68
24
24
68
104 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-290 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Dairy
Swine
Beef
Poultry
Anaerobic Liquid/Slurry and
Anaerobic
Liquid/Slurry
Anaerobic
State
Lagoon Deep Pit
Lagoon
and Deep Pit
Liquid/Slurry
Lagoon
Wyoming
62 20
63
21
22
62
Note: MCFs developed using Tier 2 methods described in 2006IPCC Guidelines, Section 10.4.2.
Table fl-190: Direct Nitrous Oxide Emission Factors [kg NzO-N/kg Kjdl N excreted)105
Waste Management System
Direct N2O Emission
Factor
Aerobic Treatment (forced aeration)
0.005
Aerobic Treatment (natural aeration)
0.01
Anaerobic Digester
0
Anaerobic Lagoon
0
Cattle Deep Bed (active mix)
0.07
Cattle Deep Bed (no mix)
0.01
Compostingjn vessel
0.006
Compostingjntensive
0.1
Composting_passive
0.01
Composting_static
0.006
Daily Spread
0
Deep Pit
0.002
Dry Lot
0.02
Fuel
0
Liquid/Slurry
0.005
Pasture
0
Poultry with bedding
0.001
Poultry without bedding
0.001
Solid Storage
0.005
Source: 2006 IPCC Guidelines
Table A-191: Indirect Nitrous Oxide Loss Factors (Percent)
Waste Management Volatilization Runoff/Leaching Nitrogen Loss"
Animal Type
System
Nitrogen Loss
Central
Pacific
Mid-Atlantic
Midwest
South
Beef Cattle
Dry Lot
23
1.1
3.9
3.6
1.9
4.3
Beef Cattle
Liquid/Slurry
26
0
0
0
0
0
Beef Cattle
Pasture
0
0
0
0
0
0
Dairy Cattle
Anaerobic Lagoon
43
0.2
0.8
0.7
0.4
0.9
Dairy Cattle
Daily Spread
10
0
0
0
0
0
Dairy Cattle
Deep Pit
24
0
0
0
0
0
Dairy Cattle
Dry Lot
15
0.6
2
1.8
0.9
2.2
Dairy Cattle
Liquid/Slurry
26
0.2
0.8
0.7
0.4
0.9
Dairy Cattle
Pasture
0
0
0
0
0
0
Dairy Cattle
Solid Storage
27
0.2
0
0
0
0
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
105 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-291
-------
Poultry
Poultry with bedding
26
0
0
0
0
0
Poultry
Poultry without bedding
34
0
0
0
0
0
Poultry
Solid Storage
8
0
0
0
0
0
Sheep
Dry Lot
23
1.1
3.9
3.6
1.9
4.3
Sheep
Pasture
0
0
0
0
0
0
Swine
Anaerobic Lagoon
58
0.2
0.8
0.7
0.4
0.9
Swine
Deep Pit
34
0
0
0
0
0
Swine
Liquid/Slurry
26
0.2
0.8
0.7
0.4
0.9
Swine
Pasture
0
0
0
0
0
0
Swine
Solid Storage
45
0
0
0
0
0
a Data for nitrogen losses due to leaching were not available, so the values represent only nitrogen losses due to runoff.
Source: EPA (2002b, 2005).
A-292 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-192: Total Methane Emissions from Livestock Manure Management [W1"
Animal Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Dairy Cattle
590
685
889
951
985
1,036
988
1,057
1,091
1,212
1,243
1,243
1,256
1,297
1,373
1,338
1,361
1,391
Dairy Cows
581
676
880
942
977
1,027
980
1,049
1,083
1,202
1,233
1,233
1,247
1,288
1,363
1,328
1,350
1,380
Dairy Heifer
7
7
7
7
7
7
6
7
7
8
8
8
8
8
9
8
8
9
Dairy Calves
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Swine
622
763
835
854
877
859
858
916
902
984
938
899
950
949
982
930
890
985
Market Swine
483
608
681
697
719
705
707
755
742
816
780
751
794
795
823
779
739
826
Market <50 lbs.
102
121
131
134
137
135
135
142
141
155
110
104
110
110
114
106
104
113
Market 50-119 lbs.
101
123
136
138
144
140
141
150
148
163
174
168
177
177
184
174
167
185
Market 120-179
lbs.
136
170
189
192
199
196
196
210
206
228
228
219
233
232
241
231
219
244
Market >180 lbs.
144
193
225
232
240
234
235
252
247
270
268
260
274
276
283
268
249
284
Breeding Swine
139
155
155
158
158
154
151
161
160
168
158
149
156
155
159
151
151
160
Beef Cattle
126
139
131
134
131
131
129
133
137
134
130
130
132
131
128
121
120
126
Feedlot Steers
14
14
15
15
15
16
15
15
16
16
16
16
16
17
16
16
16
16
Feedlot Heifers
7
8
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
NOF Bulls
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Beef Calves
6
7
7
7
7
7
7
7
7
7
7
7
7
7
7
6
6
7
NOF Heifers
12
15
13
13
13
13
12
13
13
13
13
13
13
12
12
12
12
13
NOF Steers
12
14
11
11
11
10
10
10
11
10
10
11
10
10
9
9
9
9
NOF Cows
69
76
71
73
71
71
71
73
75
73
70
70
71
71
69
64
63
67
Sheep
7
5
4
4
4
4
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
1
1
Poultry
131
128
127
131
129
130
129
129
131
134
129
128
129
127
128
128
131
135
Hens>1 yr.
73
69
66
70
67
68
66
66
66
67
64
64
64
63
63
65
67
68
Total Pullets
25
22
22
22
22
22
23
22
23
25
23
23
24
23
23
24
24
26
Chickens
4
4
3
3
4
4
3
3
3
3
3
4
3
3
3
3
3
3
Broilers
19
23
28
28
29
29
30
31
32
32
33
31
31
31
32
31
31
32
Turkeys
10
9
7
7
7
7
7
7
7
7
7
6
6
6
6
6
6
6
Horses
9
11
13
13
13
13
12
12
12
11
10
10
10
10
10
9
9
9
Mules and Asses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
American Bison
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Does not exceed 0.5 kt.
a Accounts for CbU reductions due to capture and destruction of CbU at facilities using anaerobic digesters.
106 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-293
-------
Table fl-193: Total [Direct and Indirect] Nitrous Oxide Emissions from Livestock Manure Management Hal107
Animal Type
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Dairy Cattle
17.7
18.2
18.4
18.7
18.9
19.1
18.2
18.7
19.3
19.3
19.0
19.2
19.3
19.5
19.8
19.7
19.8
20.3
Dairy Cows
10.6
10.7
10.8
10.9
11.0
11.1
10.6
10.8
11.1
11.1
10.9
11.1
11.0
11.1
11.3
11.3
11.4
11.6
Dairy Heifer
7.1
7.5
7.6
7.8
7.9
8.0
7.6
7.8
8.2
8.2
8.0
8.1
8.3
8.4
8.5
8.3
8.4
8.7
Dairy Calves
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Swine
4.0
4.5
5.0
5.1
5.3
5.4
5.6
5.7
5.9
6.3
6.4
6.3
6.2
6.3
6.4
6.3
6.2
6.6
Market Swine
3.0
3.5
4.1
4.2
4.4
4.5
4.7
4.9
5.0
5.5
5.6
5.5
5.4
5.5
5.6
5.5
5.4
5.8
Market <50 lbs.
0.6
0.6
0.8
0.8
0.8
0.9
0.9
0.9
1.0
1.1
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
Market 50-119
lbs.
0.6
0.7
0.8
0.8
0.9
0.9
0.9
1.0
1.0
1.1
1.3
1.2
1.2
1.2
1.3
1.2
1.2
1.3
Market 120-179
lbs.
0.9
1.0
1.1
1.2
1.2
1.3
1.3
1.4
1.4
1.5
1.6
1.6
1.6
1.6
1.6
1.6
1.6
1.7
Market >180
lbs.
0.9
1.1
1.3
1.4
1.5
1.5
1.6
1.6
1.6
1.8
1.9
1.9
1.8
1.9
1.9
1.9
1.8
2.0
Breeding Swine
1.0
1.1
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.9
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
Beef Cattle
19.8
21.8
25.0
24.1
24.8
25.0
23.6
24.0
25.7
25.6
25.1
25.1
25.3
25.9
25.8
26.0
26.0
25.8
Feedlot Steers
13.4
14.4
16.1
15.4
16.0
16.3
15.3
15.5
16.7
16.7
16.5
16.5
16.6
16.9
16.7
17.0
17.3
17.3
Feedlot Heifers
6.4
7.4
8.9
8.6
8.7
8.8
8.4
8.5
9.0
8.9
8.7
8.6
8.7
9.1
9.0
9.0
8.8
8.5
Sheep
0.4
0.7
1.1
1.2
1.2
1.2
1.1
1.2
1.2
1.2
1.2
1.1
1.1
1.1
1.1
1.1
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
0.1
0.1
Poultry
4.7
5.1
5.3
5.3
5.4
5.3
5.4
5.4
5.4
5.4
5.4
5.2
5.2
5.2
5.3
5.2
5.2
5.2
Hens>1 yr.
1.0
1.0
1.1
1.2
1.2
1.2
1.2
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.4
1.3
Total Pullets
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Chickens
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Broilers
2.2
2.7
2.9
2.9
3.0
2.9
2.9
3.0
2.9
2.9
2.9
2.7
2.8
2.8
2.9
2.7
2.7
2.8
Turkeys
1.2
1.1
0.9
0.9
0.9
0.9
0.8
0.8
0.8
0.8
0.8
0.7
0.7
0.7
0.7
0.7
0.7
0.7
Horses
0.3
0.4
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Mules and
Asses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
American Bison
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
+ Does not exceed 0.5 kt.
NA (Not Applicable)
Note: American bison are maintained entirely on unmanaged WMS; there are no American bison N2O emissions from managed systems.
107 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-294 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-194: Methane Emissions hy State from Livestock Manure Management for 2015 Ufl)"1"
Beef on
Beef Not
Dairy
Dairy
Swine-
Swine—
Mules and
American
State
Feedlots
on Feedb
Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
Asses
Bison
Total
Alabama
0.0151
2.3891
0.4306
0.0081
1.6671
0.4671
9.3023
3.9333
0.0208
0.0085
0.0135
0.1632
0.0132
0.0005
18.4326
Alaska
+
0.0166
0.0161
0.0002
0.0023
0.0015
0.2705
+
0.0207
0.0057
0.0002
0.0026
+
0.0040
0.3403
Arizona
0.6429
1.0611
54.2481
0.1537
2.0060
0.6661
0.9310
+
0.0208
0.1057
0.0335
0.3502
0.0041
+
60.2232
Arkansas
0.0336
3.2110
0.2329
0.0106
0.9678
1.8692
0.6232
3.4942
0.6877
0.0085
0.0136
0.1655
0.0095
0.0002
11.3275
California
1.3318
3.6631
399.7726
1.9988
1.3906
0.1080
2.8400
0.2036
0.2876
0.4229
0.0546
0.3931
0.0072
0.0029
412.4769
Colorado
1.5020
2.8195
33.5230
0.1506
4.5934
2.6309
3.9822
+
0.0207
0.1973
0.0066
0.2303
0.0049
0.0174
49.6787
Connecticut
0.0003
0.0189
1.1524
0.0137
0.0044
0.0021
0.1012
+
0.0207
0.0034
0.0011
0.0459
0.0008
0.0002
1.3650
Delaware
0.0003
0.0092
0.3293
0.0045
0.0194
0.0124
0.1061
0.8844
0.0207
0.0057
0.0003
0.0164
0.0001
0.0002
1.4088
Florida
0.0111
3.2606
23.4736
0.1038
0.0609
0.0435
7.0412
0.2365
0.0208
0.0085
0.0182
0.3985
0.0113
+
34.6884
Georgia
0.0133
1.7873
10.2297
0.0740
2.2700
0.8846
16.1855
4.8657
0.0208
0.0085
0.0241
0.2160
0.0099
0.0005
36.5899
Hawaii
0.0027
0.2858
0.5184
0.0029
0.0615
0.0411
0.4163
+
0.0208
0.0085
0.0057
0.0140
0.0005
0.0002
1.3784
Idaho
0.3871
1.6927
122.1684
0.4856
0.1477
0.0870
0.8315
+
0.0207
0.1222
0.0046
0.1180
0.0030
0.0100
126.0784
Illinois
0.4035
1.0113
9.3806
0.0844
44.8221
10.5079
0.2696
0.2029
0.0207
0.0268
0.0076
0.1153
0.0026
0.0006
66.8560
Indiana
0.1744
0.5910
15.9269
0.1285
36.1877
5.5922
1.0429
0.2029
0.4811
0.0235
0.0084
0.2346
0.0041
0.0018
60.5999
Iowa
2.0667
3.1056
26.2279
0.2073
315.5249
31.8828
1.3261
0.2029
0.2268
0.0822
0.0141
0.1234
0.0033
0.0022
380.9962
Kansas
3.8121
5.0002
27.4863
0.1485
21.7869
4.1020
0.0607
+
0.0207
0.0310
0.0095
0.1442
0.0027
0.0085
62.6134
Kentucky
0.0302
2.5062
1.6651
0.0802
6.5611
1.4136
0.6808
1.1139
0.0207
0.0226
0.0109
0.2664
0.0099
0.0024
14.3840
Louisiana
0.0089
1.6866
0.8189
0.0141
0.0125
0.0090
2.4196
0.2036
0.0208
0.0085
0.0064
0.1950
0.0086
0.0001
5.4126
Maine
0.0008
0.0399
1.3959
0.0265
0.0092
0.0050
0.0951
+
0.0207
0.0034
0.0017
0.0260
0.0003
0.0004
1.6248
Maryland
0.0193
0.1268
2.6337
0.0442
0.1577
0.0992
0.3375
1.0987
0.0207
0.0057
0.0018
0.0600
0.0009
0.0005
4.6067
Massachusetts
0.0003
0.0194
0.3615
0.0118
0.0355
0.0145
0.0135
+
0.0207
0.0034
0.0022
0.0442
0.0004
0.0001
0.5276
Michigan
0.2642
0.4435
60.3755
0.2640
10.0486
2.0626
0.8292
0.2029
0.1296
0.0357
0.0066
0.1755
0.0031
0.0016
74.8427
Minnesota
0.6416
1.2525
37.3493
0.4428
67.6863
10.8214
0.3365
0.1680
1.0219
0.0611
0.0080
0.1142
0.0021
0.0022
119.9079
Mississippi
0.0173
1.7844
0.4742
0.0166
8.6468
1.8000
8.2770
2.6243
0.0208
0.0085
0.0078
0.1798
0.0102
+
23.8678
Missouri
0.1236
4.5447
6.6046
0.0985
23.2820
8.4510
0.4077
1.0665
0.4736
0.0399
0.0270
0.2150
0.0070
0.0019
45.3430
Montana
0.0679
4.4400
1.7480
0.0105
1.1951
0.3948
0.3877
+
0.0207
0.1010
0.0023
0.2049
0.0036
0.0324
8.6089
Nebraska
4.2658
5.9518
8.3449
0.0321
27.8275
8.3543
0.4730
0.2029
0.0207
0.0381
0.0051
0.1392
0.0030
0.0487
55.7072
Nevada
0.0064
0.6131
6.6164
0.0137
0.0002
0.0002
0.0305
+
0.0207
0.0324
0.0068
0.0545
0.0005
0.0001
7.3955
New Hampshire
0.0002
0.0117
0.6768
0.0092
0.0011
0.0005
0.0961
+
0.0207
0.0034
0.0014
0.0189
0.0001
0.0006
0.8406
New Jersey
0.0003
0.0218
0.2701
0.0067
0.0477
0.0117
0.1047
+
0.0207
0.0057
0.0017
0.0573
0.0006
0.0004
0.5495
New Mexico
0.0164
1.2871
77.4625
0.1702
0.0033
0.0033
0.8774
+
0.0207
0.0423
0.0070
0.1073
0.0014
0.0118
80.0107
New York
0.0459
0.4512
34.6877
0.5859
0.4460
0.1100
0.6165
0.2029
0.0207
0.0376
0.0086
0.2043
0.0029
0.0009
37.4209
North Carolina
0.0076
0.9251
3.4750
0.0453
138.7190
33.7077
13.0670
2.9882
0.7753
0.0211
0.0177
0.1970
0.0106
0.0002
193.9568
North Dakota
0.0711
2.2401
1.6826
0.0094
0.8179
0.5490
0.0571
+
0.0207
0.0301
0.0013
0.0998
0.0010
0.0107
5.5907
Ohio
0.2862
0.8297
22.4935
0.2006
22.0331
3.6934
1.0647
0.2911
0.1296
0.0569
0.0102
0.2433
0.0053
0.0010
51.3385
Oklahoma
0.6394
7.8899
7.8296
0.0572
29.2451
16.8565
3.3746
0.7882
0.0208
0.0374
0.0252
0.5090
0.0153
0.0270
67.3153
Oregon
0.1582
1.6292
20.1686
0.1044
0.0176
0.0096
0.8733
0.2029
0.0207
0.0916
0.0076
0.1293
0.0023
0.0033
23.4187
Pennsylvania
0.1757
0.6268
18.5185
0.5243
11.1041
2.0637
0.8124
0.6893
0.1620
0.0404
0.0112
0.2673
0.0071
0.0009
35.0035
108 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-295
-------
Beef on
Beef Not
Dairy
Dairy
Swine-
Swine—
Mules and
American
State
Feedlots
on Feedb
Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
Asses
Bison
Total
Rhode Island
0.0001
0.0044
0.0325
0.0009
0.0028
0.0022
0.1017
+
0.0207
0.0034
0.0002
0.0039
0.0001
+
0.1728
South Carolina
0.0039
0.6349
1.2822
0.0136
4.4947
0.4184
5.0005
0.8812
0.0208
0.0085
0.0134
0.1901
0.0067
0.0003
12.9693
South Dakota
0.6453
4.3993
15.3640
0.1034
11.0760
3.3747
0.1014
+
0.1072
0.1198
0.0050
0.1493
0.0011
0.0566
35.5031
Tennessee
0.0259
3.2529
1.5998
0.0449
3.6006
0.6394
0.2260
0.6727
0.0208
0.0310
0.0256
0.2276
0.0155
+
10.3826
Texas
6.0470
18.2179
113.7558
0.5747
13.4191
3.4142
4.9055
2.2109
0.0208
0.5075
0.2708
1.2156
0.0715
0.0101
164.6413
Utah
0.0391
0.9999
19.2656
0.0724
5.7015
1.2942
4.0254
+
0.0897
0.1363
0.0033
0.1281
0.0025
0.0021
31.7600
Vermont
0.0010
0.0651
6.6809
0.0927
0.0063
0.0041
0.0102
+
0.0207
0.0034
0.0032
0.0234
0.0010
0.0001
6.9122
Virginia
0.0383
1.6286
3.5346
0.0753
4.8657
0.1645
0.3717
0.9514
0.4237
0.0352
0.0109
0.1856
0.0053
0.0019
12.2925
Washington
0.4006
0.8123
52.7571
0.2355
0.1193
0.0689
1.4902
0.2029
0.0207
0.0244
0.0059
0.1085
0.0026
0.0014
56.2502
West Virginia
0.0078
0.4799
0.2976
0.0069
0.0038
0.0031
0.1820
0.3392
0.0748
0.0155
0.0033
0.0432
0.0022
+
1.4594
Wisconsin
0.4347
1.1380
123.7352
1.1506
2.4610
0.7450
0.3117
0.1951
0.0207
0.0362
0.0160
0.2049
0.0043
0.0058
130.4592
Wyoming
0.1192
2.1020
0.8642
0.0075
0.3552
0.4711
0.7751
+
0.0207
0.1621
0.0024
0.1482
0.0021
0.0171
5.0468
+ Does not exceed 0.00005 kt.
a Accounts for CbU reductions due to capture and destruction of CbU at facilities using anaerobic digesters.
b Beef Not on Feed includes calves.
109
Table fl-195: Total [Direct and Indirect] Nitrous Oxide Emissions by State from Livestock Manure Management for 2015 tktl
Beef Beef
Feedlot-
Feedlot-
Dairy
Swine-
Swine-
Mules and
American
Heifer
Steers
Dairy Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
Asses
Bison
Total
Alabama
0.0033
0.0067
0.0036
0.0027
0.0081
0.0017
0.0650
0.3480
0.0024
0.0046
0.0011
0.0056
0.0005
NA
0.4534
Alaska
+
+
0.0003
0.0002
+
+
0.0045
+
0.0024
0.0015
+
0.0001
+
NA
0.0091
Arizona
0.1684
0.3396
0.2457
0.1379
0.0094
0.0023
0.0048
+
0.0024
0.0165
0.0026
0.0120
0.0001
NA
0.9419
Arkansas
0.0077
0.0155
0.0023
0.0027
0.0059
0.0082
0.0882
0.3091
0.0797
0.0040
0.0011
0.0057
0.0003
NA
0.5304
California
0.2950
0.5935
2.1651
1.6122
0.0078
0.0004
0.0596
0.0180
0.0333
0.0747
0.0043
0.0135
0.0003
NA
4.8778
Colorado
0.6137
1.2390
0.2039
0.2301
0.0485
0.0205
0.0239
+
0.0024
0.0463
0.0008
0.0119
0.0003
NA
2.4411
Connecticut
0.0001
0.0002
0.0174
0.0100
+
+
0.0043
+
0.0024
0.0027
0.0001
0.0024
+
NA
0.0397
Delaware
0.0001
0.0002
0.0045
0.0031
0.0002
0.0001
0.0043
0.0785
0.0024
0.0046
+
0.0008
+
NA
0.0988
Florida
0.0022
0.0045
0.1143
0.0514
0.0003
0.0002
0.0463
0.0209
0.0024
0.0046
0.0015
0.0137
0.0004
NA
0.2627
Georgia
0.0029
0.0060
0.0648
0.0273
0.0110
0.0032
0.1128
0.4305
0.0024
0.0046
0.0019
0.0074
0.0004
NA
0.6752
Hawaii
0.0005
0.0011
0.0025
0.0023
0.0003
0.0002
0.0045
+
0.0024
0.0015
0.0005
0.0005
+
NA
0.0163
Idaho
0.1589
0.3214
0.7943
0.7418
0.0016
0.0007
0.0048
+
0.0024
0.0287
0.0005
0.0061
0.0002
NA
2.0613
Illinois
0.1558
0.3138
0.1370
0.1066
0.4147
0.0709
0.0192
0.0180
0.0024
0.0187
0.0009
0.0059
0.0001
NA
1.2640
Indiana
0.0673
0.1356
0.2458
0.1488
0.3485
0.0395
0.1449
0.0180
0.0559
0.0164
0.0010
0.0121
0.0002
NA
1.2338
Iowa
0.8033
1.6226
0.3207
0.2555
1.8775
0.1390
0.1842
0.0180
0.0264
0.0574
0.0017
0.0064
0.0002
NA
5.3127
Kansas
1.4249
2.8802
0.2062
0.1944
0.1831
0.0255
0.0043
+
0.0024
0.0217
0.0011
0.0074
0.0001
NA
4.9514
Kentucky
0.0104
0.0209
0.0301
0.0266
0.0353
0.0056
0.0277
0.0989
0.0024
0.0183
0.0013
0.0137
0.0005
NA
0.2918
Louisiana
0.0019
0.0038
0.0062
0.0031
0.0001
+
0.0121
0.0180
0.0024
0.0040
0.0005
0.0067
0.0003
NA
0.0591
Maine
0.0003
0.0006
0.0265
0.0189
0.0001
+
0.0043
+
0.0024
0.0027
0.0002
0.0013
+
NA
0.0573
109 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-296 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Maryland
0.0066
0.0134
0.0444
0.0301
0.0013
0.0006
0.0137
0.0975
0.0024
Massachusetts
0.0001
0.0002
0.0106
0.0082
0.0003
0.0001
0.0006
+
0.0024
Michigan
0.1036
0.2096
0.6380
0.3560
0.1016
0.0155
0.0612
0.0180
0.0151
Minnesota
0.2515
0.5083
0.6572
0.5547
0.6848
0.0807
0.0468
0.0149
0.1188
Mississippi
0.0038
0.0076
0.0053
0.0041
0.0415
0.0062
0.0423
0.2322
0.0024
Missouri
0.0468
0.0943
0.1073
0.1095
0.2157
0.0569
0.0568
0.0947
0.0551
Montana
0.0283
0.0569
0.0188
0.0153
0.0140
0.0034
0.0024
+
0.0024
Nebraska
1.6457
3.3280
0.0799
0.0425
0.2698
0.0596
0.0341
0.0180
0.0024
Nevada
0.0026
0.0053
0.0376
0.0209
+
+
0.0042
+
0.0024
New Hampshire
0.0001
0.0001
0.0125
0.0066
+
+
0.0043
+
0.0024
New Jersey
0.0001
0.0002
0.0058
0.0044
0.0004
0.0001
0.0043
+
0.0024
New Mexico
0.0065
0.0132
0.4053
0.2332
+
+
0.0048
+
0.0024
New York
0.0167
0.0339
0.5709
0.4138
0.0045
0.0008
0.0269
0.0180
0.0024
North Carolina
0.0026
0.0052
0.0331
0.0132
0.6712
0.1203
0.0923
0.2644
0.0898
North Dakota
0.0280
0.0567
0.0218
0.0117
0.0088
0.0044
0.0043
+
0.0024
Ohio
0.1102
0.2229
0.3582
0.2316
0.2128
0.0262
0.1466
0.0258
0.0151
Oklahoma
0.1736
0.3507
0.0481
0.0549
0.1469
0.0616
0.0172
0.0697
0.0024
Oregon
0.0548
0.1110
0.1444
0.1130
0.0002
0.0001
0.0111
0.0180
0.0024
Pennsylvania
0.0627
0.1260
0.4561
0.3336
0.1024
0.0142
0.1130
0.0612
0.0188
Rhode Island
+
0.0001
0.0008
0.0005
+
+
0.0043
+
0.0024
South Carolina
0.0009
0.0018
0.0084
0.0037
0.0227
0.0016
0.0252
0.0780
0.0024
South Dakota
0.2515
0.5083
0.1437
0.1336
0.1087
0.0244
0.0074
+
0.0125
Tennessee
0.0062
0.0127
0.0226
0.0159
0.0187
0.0025
0.0093
0.0595
0.0024
Texas
1.6347
3.3045
0.5806
0.5408
0.0713
0.0133
0.0977
0.1956
0.0024
Utah
0.0160
0.0323
0.1305
0.1100
0.0573
0.0107
0.0240
+
0.0104
Vermont
0.0004
0.0007
0.1181
0.0673
0.0001
+
0.0005
+
0.0024
Virginia
0.0132
0.0267
0.0512
0.0288
0.0256
0.0006
0.0154
0.0844
0.0493
Washington
0.1369
0.2771
0.3563
0.2674
0.0012
0.0005
0.0347
0.0180
0.0024
West Virginia
0.0028
0.0056
0.0071
0.0047
+
+
0.0078
0.0301
0.0087
Wisconsin
0.1709
0.3452
1.9134
1.4068
0.0245
0.0055
0.0230
0.0173
0.0024
Wyoming
0.0491
0.0992
0.0077
0.0095
0.0049
0.0047
0.0048
+
0.0024
+ Does not exceed 0.00005 kt.
NA (Not Applicable)
Note: American bison are maintained entirely on unmanaged WMS; there are no American bison N2O emissions from managed systems.
0.0046
0.0002
0.0031
+
NA
0.2178
0.0027
0.0003
0.0023
+
NA
0.0278
0.0249
0.0008
0.0090
0.0002
NA
1.5535
0.0426
0.0009
0.0059
0.0001
NA
2.9672
0.0046
0.0006
0.0062
0.0004
NA
0.3570
0.0279
0.0032
0.0111
0.0004
NA
0.8796
0.0237
0.0003
0.0106
0.0002
NA
0.1762
0.0266
0.0006
0.0072
0.0002
NA
5.5145
0.0076
0.0008
0.0028
+
NA
0.0843
0.0027
0.0002
0.0010
+
NA
0.0299
0.0046
0.0002
0.0030
+
NA
0.0255
0.0099
0.0008
0.0055
0.0001
NA
0.6819
0.0305
0.0010
0.0105
0.0002
NA
1.1301
0.0114
0.0014
0.0068
0.0004
NA
1.3121
0.0210
0.0001
0.0051
0.0001
NA
0.1644
0.0459
0.0012
0.0125
0.0003
NA
1.4093
0.0173
0.0020
0.0175
0.0005
NA
0.9625
0.0243
0.0009
0.0067
0.0001
NA
0.4869
0.0328
0.0013
0.0138
0.0004
NA
1.3363
0.0027
+
0.0002
+
NA
0.0110
0.0046
0.0011
0.0065
0.0002
NA
0.1571
0.0837
0.0006
0.0077
0.0001
NA
1.2821
0.0168
0.0020
0.0078
0.0005
NA
0.1769
0.0794
0.0214
0.0418
0.0025
NA
6.5860
0.0320
0.0004
0.0066
0.0001
NA
0.4303
0.0027
0.0004
0.0012
0.0001
NA
0.1938
0.0286
0.0013
0.0096
0.0003
NA
0.3352
0.0065
0.0007
0.0056
0.0001
NA
1.1075
0.0126
0.0004
0.0022
0.0001
NA
0.0822
0.0253
0.0019
0.0106
0.0002
NA
3.9470
0.0380
0.0003
0.0076
0.0001
NA
0.2285
A-297
-------
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A-302 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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3.12. Methodology for Estimating N2O Emissions, CH4 Emissions and Soil Organic C
Stock Changes 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 N2O emissions from cropland and grassland soils; indirect N2O emissions from volatilization, leaching,
and runoff from croplands and grasslands; and CH4 emissions from rice cultivation. Splicing methods are used to fill gaps
at the end of the time series for these emission sources, which are not described in this annex. The splicing methods are
applied for two reasons. First, the Inventory is currently compiled every two years for many categories in the AFOLU sector
in order to conserve resources that are needed to implement improvements. Second, even in years that the Inventory is
compiled fully with the Tier 1, 2 and 3 methods, there are typically gaps in the activity data at the end of the time series,
which means that these methods cannot be applied. The splicing methods are described in the main chapters, particularly
Box 6-4 in the Cropland Remaining Cropland section and Box 5-3 in the Agricultural Soil Management section.
Nitrous oxide (N2O) is produced in soils through the microbial processes of nitrification and denitrification.110 Management
influences these processes by modifying the availability of mineral nitrogen (N), which is a key control on the N2O emissions
rates (Mosier et al. 1998). Emissions can occur directly in the soil where the N is made available or can be transported to
another location following volatilization, leaching, or runoff, and then converted into N2O. Management 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. CH4 emissions from rice cultivation occur under flooded conditions through the
process of methanogenesis. This sub-annex describes the methodologies used to calculate N2O emissions from agricultural
soil management and annual carbon (C) stock changes from mineral and organic soils classified as Cropland Remaining
Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland , and CH4
emissions from Rice Cultivation. This annex provides the underlying methodologies for these three emission sources because
112
there is considerable overlap in the methods with the majority of emissions estimated using the DayCent biogeochemical
simulation model.
A combination of Tier 1, 2 and 3 approaches are used to estimate direct and indirect N2O emissions and C stock changes in
agricultural soils. More specifically, the methodologies used to estimate soil N2O emissions include:
1) A Tier 3 method using the DayCent biogeochemical 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, dry beans, grass hay, grass-clover hay, oats, onions, peanuts, potatoes, rice, sorghum, soybeans, sugar
beets, sunflowers, tomatoes, and wheat, as well as non-federal grasslands and land use change between grassland
and cropland (with the crops listed above and less than 35 percent coarse fragments);
2) A combination of the Tier 3 and 1 methods to estimate indirect N2O emissions associated with management of
cropland and grassland simulated with DayCent in Item 1;
3) A Tier 1 method to estimate direct and indirect N2O 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; and crops that are rotated with the crops that are not simulated with DayCent Pasture/Range/Paddock
(PRP) manure N deposited on federal grasslands; and
4) A Tier 1 method to estimate direct N2O emissions due to partial or complete drainage of organic soils in
croplands and grasslands.
110 Nitrification and denitrification are driven by the activity of microorganisms in soils. Nitrification is the aerobic microbial
oxidation of ammonium (NH4+) to nitrate (NO3"), 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).
111 Soil C stock change methods for forestland are described in the Forestland Remaining Forestland section.
112 Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical
environment.
A-303
-------
The methodologies used to estimate soil CH4 emissions from rice cultivation include:
1) A Tier 3 method using the DayCent biogeochemical 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 a
crop listed in (1) for soil N2O emissions; 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.
The methodologies used to estimate soil organic C stock changes include:
1) A Tier 3 method using the DayCent biogeochemical simulation model to estimate soil organic C stock changes in
mineral soils as described in Item 1 for N2O emissions;
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 included in Item 1) 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 partly
or completely 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.
As described above, the Inventory uses a Tier 3 approach to estimate direct soil N2O emissions, CH4 emissions from rice
cultivation, and C stock changes for the majority of 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 N2O
emissions and C stock changes at finer spatial scales, as opposed to a single emission factor for the entire country
for soil N2O 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; and
4) Soil N2O 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.
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 N2O 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 capable of simulating cropland, grassland, forest, and savanna ecosystems, and land-use transitions
between these different land uses. It is, thus, well suited to model land-use change effects.
3) The model is designed to simulate management practices that influence soil C dynamics, CH4 emissions and
direct N2O 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
for soil C stock changes and IPCC Tier 1 method for CH4 andN20 emissions. The model can also be used
estimate the amount of N leaching and runoff, as well as volatilization of N, which is subject to indirect N2O
emissions.
4) 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
A-304 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
at a sufficient resolution to use the Tier 3 model. Soil N2O emissions and C stock changes associated with these
practices are addressed with a Tier 1 and 2 method, respectively.
Overall, the Tier 3 approach is used to estimate approximately about 91 percent of direct soil N2O emissions 94 percent of
the rice cultivation, and 88 percent of the land area associated with estimation of soil organic C stock changes under
agricultural management in the United States.
Tier 3 Method Description and Model Evaluation
The DayCent biogeochemical model (Parton et al. 1998; Del Grosso et al. 2001, 2011) simulates biogeochemical C and N
fluxes between the atmosphere, vegetation, and soil. The model provides a more complete estimation of soil C stock changes,
CH4 and N2O emissions than IPCC Tier 1 or 2 methods by accounting for a broader suite of environmental drivers that
influence emissions and 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 N2O) in a single inventory analysis ensures that there is a consistent treatment of the processes and
interactions between C and N cycling in soils. For example, plant growth is controlled by nutrient availability, water, and
temperature stress. Plant growth, along with residue management, determines C inputs to soils and influences C stock
changes. Removal of soil mineral N by plants influences the amount of N that can be converted into N2O. Nutrient supply
is a function of external nutrient additions as well as litter and soil organic matter (SOM) decomposition rates, and increasing
decomposition can lead to a reduction in soil organic C stocks due to microbial respiration, and greater N2O emissions by
enhancing mineral N availability in soils.
Key processes simulated by DayCent include (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-
7). Each of these submodels will be described separately 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 andMYD13Ql, or an approximation of EVI data derived from the MODIS products
(Gurung et al. 2009). The NASA-CASA production algorithm is only used for the following major crops: corn,
soybeans, sorghum, cotton and wheat.113 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-8.
2) Dynamics of soil organic C and N (Figure A-7) are simulated for the surface and belowground litter pools and
soil organic matter in the top 20 cm of the soil profile; mineral N dynamics are simulated through the whole soil
profile. Organic C and N stocks are represented by two plant litter pools (metabolic and structural) and three soil
organic matter (SOM) pools (active, slow, and passive). The metabolic litter pool represents the easily
decomposable constituents of plant residues, while the structural litter pool is composed of more recalcitrant,
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,
113 It is a planned improvement to estimate NPP for additional crops and grass forage with the NASA-CASA method in the future.
A-305
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with mean residence times on the order of several hundred years. The slow pool represents decomposition
products of intermediate stability, having a mean residence time on the order of decades and is the fraction that
tends to change the most in response to changes in land use and management. Soil texture influences turnover
rates of the slow and passive pools. The clay and silt-sized mineral fraction of the soil provides physical
protection from microbial decomposition, leading to enhanced SOM stabilization in finely textured soils. Soil
temperature and moisture, tillage disturbance, aeration, and other factors influence decomposition and loss of C
from the soil organic matter pools.
3) The soil-water submodel simulates water flows and changes in soil water availability, which influences both
plant growth and decomposition/nutrient cycling processes. The moisture content of soils are simulated through a
multi-layer profile based on precipitation, snow accumulation and melting, interception, soil and canopy
evaporation, transpiration, soil water movement, runoff, and drainage.
Figure fl-7: DayCent Model Flew Diagram
Water
Submodel
jj >
Layerj
Layer- ' i
Soil Surfa :e
Layer
Heat &
Water
Layer
CN
Plant EVI/PRDX
A
Production
Submodel
f(TEMP)
f(WFPS) Biomass
f(SOLAR)
1
N Gases
ch4
SOM
Submodel
CO,,Nmii
f(TEXT)
f(MoisT) Active
f(TEMP) S0M
f(Kp)
f(Lignin:]Y*
co2,^
. Nmin
Slow
SOM
C02,Nmin
f(STORMf,
Passive
SOM
CO,,Nmin
Dissolved Organic C, Dissolved Organic N, Mineral N
A-306 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Figure 1-1: Modeled versus measured net primary production (g G m l
a)
800
700
- = 0.834
E
em 600
| 500
re
2 400
• • •%
OJ
> 300
T3
9
"35 200
x>
o
2 100
0
100 200 300 400 500 600 700 800
Yield Carbon from Published Data (g m"^)
b)
rT 700
E
0.644
oo 600
C
o
-O
500
300
TJ
o«
ai 200
-o
o
S 100
•r
100 200 300 400 500 600 700 800
0
Yield Carbon from Published Data (g m"*)
Part a) presents results of the NASA-CASA algorithm (r2 = 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. N2O
emissions occur through nitrification and denitrification. Denitrification is a function of soil NO3" concentration,
water filled pore space (WFPS), heterotrophic (i.e., microbial) respiration, and texture. Nitrification is controlled
by soil ammonium (NH/) concentration, water filled pore space, temperature, and pH (See Box 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 CTLtto 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), 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 used to simulate
management effects. User-specified management activities can be defined by adding to or editing the contents of the *.100
files. Additional details of the model formulation are given in Parton et al. (1987, 1988, 1994, 1998), Del Grosso et al. (2001,
2011), Cheng et al. (2013) and Metherell et al. (1993), and archived copies of the model source code are available.
A-307
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Box 2. DayCent Model Simulation of Nitrification and Denitrification
The DayCent model simulates the two biogeochemical processes, nitrification and denitrification, that result in N2O
emissions from soils (Del Grosso et al. 2000, Parton et al. 2001). Nitrification is calculated for the top 15 cm of soil (where
nitrification mostly occurs) while denitrification is calculated for the entire soil profile (accounting for denitrification near
the surface and subsurface as nitrate leaches through the profile). The equations and key parameters controlling N2O
emissions from nitrification and denitrification are described below.
Nitrification is controlled by soil ammonium (NH/) concentration, temperature (t), Water Filled Pore Space (WFPS) and
pH according to the following equation:
Nit = NH4+ x Km* x F(t) x F(WFPS) x F(pH)
where,
Nit = the soil nitrification rate (g N/m2/day)
NH4+ = the model-derived soil ammonium concentration (g N/m2)
Kmax = the maximum fraction of NH/ nitrified (Kmx = 0.10/day)
F(t) = the effect of soil temperature on nitrification (Figure A-9a)
F(WFPS) = the effect of soil water content and soil texture on nitrification (Figure A-9b)
F(pFl) = the effect of soil pFl on nitrification (Figure A-9c)
The current parameterization used in the model assumes that 1.2 percent of nitrified N is converted to N2O.
The model assumes that denitrification rates are controlled by the availability of soil NO3" (electron acceptor), labile C
compounds (electron donor) and oxygen (competing electron acceptor). Fleterotrophic soil respiration is used as a proxy for
labile C availability, while oxygen availability is a function of soil physical properties that influence gas diffusivity, soil
WFPS, and oxygen demand. The model selects the minimum of the NO3" and CO2 functions to establish a maximum potential
denitrification rate. These rates vary for particular levels of electron acceptor and C substrate, and account for limitations of
oxygen availability to estimate daily denitrification rates according to the following equation:
Den = min[F(C02), F(NQ3)] x F(WFPS)
where,
Den =
F(N03)
F(co2) =
F(WFPS) =
the soil denitrification rate (|_ig N/g soil/day)
a function relating N gas flux to nitrate levels (Figure A-10a)
a function relating N gas flux to soil respiration (Figure A-10b)
a dimensionless multiplier (Figure A-10c)
The x inflection point of F(WFPS) is a function of respiration and soil gas diffusivity at field capacity (Dfc):
x inflection = 0.90 - M(CC>2)
where,
M = a multiplier that is a function of Dfc- In technical terms, the inflection point is the domain where
either F(WFPS) is not differentiable or its derivative is 0. In this case, the inflection point can
be interpreted as the WFPS value at which denitrification reaches half of its maximum rate.
Respiration has a much stronger effect on the water curve in clay soils with low Dfc than in loam or sandy soils with high
Dfc (Figure A-9b). The model assumes that microsites in fine-textured soils can become anaerobic at relatively low water
contents when oxygen demand is high. After calculating total N gas flux, the ratio of N2/N2O is estimated so that total N gas
emissions can be partitioned between N2O and N2:
R-N2/N20 = Fr(N03/C02) x Fr(WFPS).
where,
A-308 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Rn2W20 = the ratio of N2/N2O
FrfNCVCCb) = 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 N?:N20.
For FC(N( >s/C(as the ratio of electron donor to substrate increases, a higher portion of N gas is assumed to be in the form
of N2O. For Fr(WFPS), as WFPS increases, a higher portion of N gas is assumed to be in the form of N2.
Figure A-9: Effect of Soil Temperature (a), Water-Filled Pore Space (b), and! pH (c) on Nitrification Rates
Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates
1.2 n
1
0.8
0.6
0.4
0.2
0
1.2-1
1
0.8-
0.6-
0.4
0.2
0
30 40
Soil Temperature
WFPS
A-309
-------
Figure A-t 0: Effect of Soil Nitrite Concentration (a), Heterotrophic Respiration Rates (b), and Water-Filled Pore Space (c) on
Denitrification Rates
Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates
25-
'c
20 -
z
d
=L
o
z
10-
z
0
100
200
300
400
NQ ng N/g soil
40-i
30-
"o
25-
20-
o
z
10 -
5-
0
20
40
60
80
100
CQng C/g soil/day
c
c
day-high resp
fC
u
ic
'c
'V
Cl
0.4 -
clay-low resp
loam-high resp,,
0.2 -
loam-low resp
0
20
40
60
80
100
WFPS%
Comparison of model results and plot level data show that DayCent reliably simulates soil organic matter levels (Ogle et al.
2007). The model was tested and shown to capture the general trends in C storage across 908 treatment observations from
92 experimental sites (Figure A-l 1). 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-12).
A-310 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Figure fl-11: Comparisons of Results from DayCent Model and Measurements of Soil Organic G Stocks
1501 n 1
Set-Aside y=*, No-till
125 -
0
Fallow in Rotation
Hay/Pasture in Rotation
100
75
50
25
0
0
25
50
75
100
125
150
Sqrt Modeled SOC
(gCm2)
Figure A-12: Comparison of Estimated Soil Organic G Stock Changes and Uncertainties using Tier 1 (IPCC 2000),Tier 2 (Ogle
et al. 2003,2000) and Tier 3 Methods
A-311
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Similarly, DayCent model results have been compared to trace gas N2O fluxes for a number of native and managed systems
(Del Grosso et al. 2001, 2005, 2010) (Figure A-13). 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 N2O emissions more accurately and precisely than the
IPCC Tier 1 methodology (IPCC 2006) (See Agricultural Soil Management, QA/QC and Verification Section). The linear
regression of simulated vs. measured emissions for DayCent had higher r2 values and a fitted line closer to a perfect 1:1
relationship between measured and modeled N2O emissions compared to the IPCC Tier 1 approach (Del Grosso et al. 2005,
2008). This is not surprising, since DayCent includes site-specific factors (climate, soil properties, and previous
management) that influence N2O emissions. Furthermore, DayCent also simulated NO3- leaching (root mean square error =
20 percent) more accurately than IPCC Tier 1 methodology (root mean square error = 69 percent) (Del Grosso et al. 2005).
Volatilization of N gases that contribute to indirect soil N2O emissions is the only component that has not been thoroughly
tested, which is due to a lack of measurement data. Overall, the Tier 3 approach has reduced uncertainties in the agricultural
soil C stock changes andN20 emissions compared to using lower Tier methods.
Figure A-13: Comparisons of Results from DayCent Model and Measurements of Soil Nitrous Oxide Emissions
Com .
* * • * ** k.
/ • .J*.
1* fc* V*C"
. ** * • * 1 *
* 4» «| -r *5* •# . *
% • if- .. •
« a (
% H
t
Other Crops
* * * » m ¦ •
* • „
% mm . • • ,
• * * *
• # , * • »f
• #**'
" • . •
•
Small Grain
Bare Fallow
•
' *
m €• • *
. . *. \tX ..
• # ; * •
*« •«
• - *
• • . % <1
1 • •• • • • •
* * . •
• *
"* ¦
-¦ *
9 *»
Soybeans
•
Grassland /
d* •*
* * • • #
• i* 1
• , 5. « ¦
* .-V mm •
« *
•
•
A.**
0
z1
1
I
5
9.'
Ln Modeled N20 Emissions
(g N2O-N ha"1 day"1)
DayCent predictions of soil CH4 emissions have also been compared to experimental measurements from sites in
California, Texas, Arkansas and Louisiana (Figure A-14). There are 10 experiments and 126 treatment observations.
In general, the model estimates CH4 emissions in most states with no apparent bias, but there is a lack of precision,
which is addressed in the uncertainty analysis. The exception is California where the model tends to over-estimate
low emission rates, and this additional uncertainty is captured in the error propagation associated with the inventory
analysis for California.
A-312 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Figure fl-14: Comparisons of Results from DayCent Model and Measurements of Soil Methane Emissions
70
California
All other states
60
50 -
E 40 -
¦3"
I
u 30 -
CuO
E
20 -
0
10
20
30
40
50
60
0
10
20
30
40
50
60
70
Sqrt Modeled CH4
(mg CH4 rrf2 d"1)
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 N2O emissions from cropland and
grassland soils; indirect N2O emissions from volatilization, leaching, and runoff from croplands and grasslands; and CH4
emissions from rice cultivation. First, the activity data are derived from a combination of land-use, livestock, crop, and
grassland management surveys, as well as expert knowledge. In the second, third, and fourth steps, soil organic C stock
changes, direct and indirect N2O emissions, and CH4 emissions are estimated using DayCent and/or the Tier 1 and 2 methods.
In the fifth step, total emissions are computed by summing all components separately for soil organic C stock changes, N2O
emissions and CH4 emissions. The remainder of this annex describes the methods underlying each step.
Step 1: Derive Activity Data
This step describes how the activity data are derived to estimate soil organic C stock changes, direct and indirect N2O
emissions, and CH4 emissions from rice cultivation. The activity data requirements include: (1) land base and history data,
114 . . . '
(2) crop-specific mineral N fertilizer rates, (3) crop-specific manure amendment N rates and timing, (4) other N inputs,
(5) tillage practices, (6) irrigation data, (7) Enhanced Vegetation Index (EVI), (8) daily weather data, and (9) edaphic
characteristics."5
Step 1a: Activity Data for the Agricultural Land Base and Histories
The U.S. Department of Agriculture's 2012 National Resources Inventory (NRI) (USDA-NRCS 2015) provides the basis
for identifying the U.S. agricultural land base on non-federal lands, and classifying parcels into Cropland Remaining
Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland. In 1998, the
NRI program began collecting annual data, and data are currently available through 2012 (USDA-NRCS 2015). The time
series will be extended as new data are released by the USDA NRI program. Note that the Inventory does not include
estimates of N2O emissions for federal grasslands (with the exception of soil N2O from PRP manure N, i.e., manure deposited
directly onto pasture, range or paddock by grazing livestock) and a minor amount of croplands on federal lands.
The NRI has a stratified multi-stage sampling design, where primary sample units are stratified on the basis of county and
township boundaries defined by the U.S. Public Land Survey (Nusser and Goebel 1997). Within a primary sample unit,
typically a 160-acre (64.75 ha) square quarter-section, three sample points are selected according to a restricted
randomization procedure. Each point in the survey is assigned an area weight (expansion factor) based on other known areas
and land-use information (Nusser and Goebel 1997). In principle, the expansion factors represent the amount of area with
the land use and land use change history that is the same as the point location. It is important to note that the NRI uses a
114 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.
115 Edaphic characteristics include such factors as soil texture and pH.
A-313
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sampling approach, and therefore there is some uncertainty associated with scaling the point data to a region or the country
using the expansion factors. In general, those uncertainties decline at larger scales, such as states compared to smaller county
units, because of a larger sample size. An extensive amount of soils, land-use, and land management data have been collected
through the survey (Nusser et al. 1998). Primary sources for data include aerial photography and remote sensing imagery
as well as field visits and county office records.
The annual NRI data product provides crop data for most years between 1979 and 2012, with the exception of 1983, 1988,
and 1993. These years are gap-filled using an automated set of rules so that cropping sequences are filled with the most
likely crop type given the historical cropping pattern at each NRI point location. Grassland data are reported on 5-year
increments prior to 1998, but it is assumed that the land use is also grassland between the years of data collection (see Easter
et al. 2008 for more information).
NRI points are included in the land base for the agricultural soil C and N2O emissions inventories if they are identified as
cropland or grassland between 1990 and 2012 (Table A-196). NRI does not provide land use data on federal lands,
therefore land use on federal lands are derived from the National Land Cover Database (NLCD) (Fry et al. 2011; Homer et
al. 2007; Homer et al. 2015). Federal NRI points are classified as cropland or grassland according to the NLCD and included
in the agricultural land base. The NRI data are reconciled with the Forest Inventory and Analysis Dataset, and in this process,
the time series for Grassland Remaining Grassland, Land Converted to Grassland, Wetland Remaining Wetland and Land
Converted to Wetlands are modified to account for differences in forest land area between the two national surveys (See
Section 6.1 for more information on the U.S. land representation). Overall, 674,613 NRI survey points are included in the
inventory (USDA-NRCS 2015).
For each year, land parcels are subdivided into Cropland Remaining Cropland, Land Converted to Cropland, Grassland
Remaining Grassland, and Land Converted to Grassland. Land parcels under cropping management in a specific year are
119
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 2006IPCC Guidelines. Lands converted into Cropland and Grassland are further subdivided into the
specific land use conversions (e.g., Forest Land Converted to Cropland).
Table A-196: Total Land Areas for the Agricultural Soil G and N2O Inventory, Subdivided by Land Use Categories (Million
Hectares)
Land Areas (million ha)
Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Mineral Soils
421.74
421.03
420.41
419.74
419.11
418.34
417.45
416.61
415.72
414.93
414.32
413.79
413.29
Croplands
172.92
172.82
172.61
172.13
171.76
171.34
171.02
170.59
168.14
167.71
167.42
166.85
166.54
Cropland Remaining Cropland
160.44
159.99
159.52
157.78
156.20
155.62
155.01
154.46
150.61
149.80
149.71
149.34
149.15
Grassland Converted to Cropland
1179
12.13
12.39
13.62
14.77
14.95
15.22
15.36
16.75
17.13
16.92
16.75
16.66
Forest Converted to Cropland
0.28
0.27
0.26
0.24
0.24
0.23
0.23
0.22
0.21
0.19
0.17
0.14
0.13
Other Lands Converted to Cropland
0.20
0.21
0.22
0.23
0.26
0.26
0.27
0.27
0.27
0.27
0.32
0.31
0.29
Settlements Converted to Croplands
0.08
0.08
0.08
0.09
0.09
0.09
0.09
0.09
0.10
0.10
0.10
0.11
0.11
Wetlands Converted to Croplands
0.14
0.14
0.14
0.16
0.19
0.19
0.20
0.19
0.20
0.20
0.20
0.20
0.20
Grasslands
248.82
248.21
247.80
247.61
247.35
247.00
246.44
246.02
247.58
247.22
246.90
246.94
246.75
Grasslands Remaining Grasslands
238.89
238.06
237.34
235.76
234.27
233.70
232.99
232.40
230.08
229.24
228.31
227.57
226.99
Croplands Converted to Grasslands
8.65
8.77
8.95
10.24
11.38
11.58
11.69
11.84
15.43
15.83
16.29
16.98
17.32
Forest Converted to Grasslands
0.57
0.58
0.61
0.60
0.58
0.58
0.59
0.59
0.80
0.80
0.81
0.80
0.81
Other Lands Converted to Grasslands
0.41
0.43
0.47
0.54
0.63
0.66
0.67
0.71
0.77
0.82
0.95
1.03
1.05
Settlements Converted to Grasslands
0.06
0.07
0.07
0.08
0.09
0.09
0.09
0.09
0.10
0.11
0.11
0.12
0.13
Wetlands Converted to Grasslands
0.24
0.30
0.37
0.39
0.40
0.40
0.40
0.39
0.41
0.42
0.43
0.44
0.45
Organic Soils
1.43
1.42
1.41
1.42
1.43
1.43
1.42
1.42
1.42
1.33
1.32
1.41
1.42
Croplands
0.73
0.72
0.72
0.72
0.72
0.73
0.73
0.73
0.73
0.64
0.63
0.74
0.74
Cropland Remaining Cropland
0.66
0.64
0.65
0.64
0.64
0.64
0.63
0.63
0.62
0.54
0.54
0.62
0.63
116 In the current Inventory, NRI data only provide land use and management statistics through 2012. More recent data will be incorporated
in the future to extend the time series of activity data.
117 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).
118 Land use for 2013 to 2016 is not compiled, but will be updated with newer NRI (i.e., USDA-NRCS 2015).
119 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.
A-314 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Grassland Converted to Cropland
0.06
0.06
0.06
0.06
0.06
0.07
0.07
0.07
0.08
0.08
0.07
0.09
0.09
Forest Converted to Cropland
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Other Lands Converted to Cropland
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Settlements Converted to Croplands
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
Wetlands Converted to Croplands
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
Grasslands
0.70
0.70
0.69
0.70
0.71
0.70
0.69
0.69
0.70
0.69
0.69
0.68
0.68
Grasslands Remaining Grasslands
0.64
0.63
0.63
0.62
0.62
0.61
0.61
0.60
0.59
0.59
0.58
0.55
0.55
Croplands Converted to Grasslands
0.05
0.05
0.05
0.05
0.06
0.06
0.06
0.06
0.08
0.08
0.08
0.09
0.10
Forest Converted to Grasslands
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
Other Lands Converted to Grasslands
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Settlements Converted to Grasslands
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Wetlands Converted to Grasslands
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.03
0.03
0.02
Total 423.18 422.45 421.82 421.16 420.54 419.77 418.88 418.02 417.15 416.26 415.64 415.20 414.71
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 data.
Category
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Mineral Soils
412.44
411.78
410.79
410.09
409.44
408.83
408.29
407.70
407.18
406.59
Croplands
165.89
164.76
164.42
164.02
163.70
163.22
162.81
162.37
162.08
161.86
Cropland Remaining Cropland
149.81
149.73
149.40
149.09
149.32
149.46
149.68
149.28
148.86
148.59
Grassland Converted to Cropland
15.42
14.42
14.43
14.33
13.82
13.25
12.66
12.63
12.75
12.80
Forest Converted to Cropland
0.11
0.10
0.10
0.09
0.09
0.06
0.06
0.06
0.06
0.06
Other Lands Converted to Cropland
0.27
0.26
0.25
0.25
0.24
0.23
0.22
0.22
0.21
0.21
Settlements Converted to Croplands
0.09
0.08
0.09
0.09
0.09
0.08
0.07
0.08
0.08
0.09
Wetlands Converted to Croplands
0.19
0.17
0.17
0.17
0.15
0.13
0.11
0.11
0.12
0.12
Grasslands
246.55
247.01
246.37
246.08
245.74
245.61
245.48
245.33
245.10
244.74
Grasslands Remaining Grasslands
227.28
227.37
226.83
226.48
226.44
226.74
226.93
226.62
226.26
226.03
Croplands Converted to Grasslands
16.89
17.31
17.14
17.21
16.92
16.61
16.36
16.57
16.76
16.72
Forest Converted to Grasslands
0.77
0.73
0.76
0.72
0.68
0.63
0.62
0.60
0.59
0.57
Other Lands Converted to Grasslands
1.05
1.05
1.07
1.09
1.12
1.14
1.13
1.14
1.14
1.12
Settlements Converted to Grasslands
0.12
0.12
0.12
0.13
0.13
0.13
0.12
0.12
0.12
0.13
Wetlands Converted to Grasslands
0.44
0.44
0.44
0.44
0.44
0.37
0.31
0.27
0.23
0.17
Organic Soils
1.41
1.41
1.40
1.38
1.37
1.36
1.37
1.36
1.33
1.33
Croplands
0.74
0.74
0.73
0.73
0.72
0.71
0.72
0.71
0.68
0.69
Cropland Remaining Cropland
0.64
0.64
0.64
0.64
0.64
0.63
0.64
0.64
0.61
0.61
Grassland Converted to Cropland
0.09
0.07
0.07
0.07
0.07
0.07
0.06
0.06
0.06
0.07
Forest Converted to Cropland
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Other Lands Converted to Cropland
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Settlements Converted to Croplands
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Wetlands Converted to Croplands
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
Grasslands
0.66
0.67
0.67
0.65
0.65
0.65
0.65
0.65
0.65
0.64
Grasslands Remaining Grasslands
0.54
0.54
0.53
0.52
0.51
0.51
0.51
0.50
0.50
0.49
Croplands Converted to Grasslands
0.09
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
Forest Converted to Grasslands
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
Other Lands Converted to Grasslands
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Settlements Converted to Grasslands
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Wetlands Converted to Grasslands
0.02
0.02
0.02
0.02
0.02
0.03
0.03
0.03
0.03
0.03
Total
413.85
413.18
412.19
411.48
410.81
410.19
409.66
409.06
408.51
407.92
Notes: The area estimates are not consistent with the land area values shown in the Representation ofthe U.S. Land Base section because the current Inventory
does not estimate emissions and removals for all managed lands. Specifically, grassland and cropland in Alaska are not included in the current Inventory. 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 data.
The Tier 3 method using the DayCent model is applied to estimate soil C stock changes, CH4 and N2O emissions for most
of the NRI points 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 N2O emissions120 and CH4
emissions from rice cultivation (Table A-197).
The land base for the Tier 1 and 2 methods includes (1) land parcels occurring on organic soils; (2) land parcels that include
non-agricultural uses such as forest and federal lands in one or more years of the inventory; (3) land parcels on mineral soils
that are very gravelly, cobbly, or shaley (i.e., classified as soils that have greater than 35 percent of soil volume comprised
120 The Tier 1 method for soil N2O does not require land area data with the exception of emissions from drainage and cultivation
of organic soils, so in practice the Tier 1 method is only dependent on the amount of N input to mineral soils and not the actual
land area.
A-315
-------
of gravel, cobbles, or shale); or (4) land parcels that are used to produce some of the vegetable crops, perennial/horticultural
crops, and tobacco, which are either grown continuously or in rotation with other crops. DayCent has not been fully tested
or developed to simulate biogeochemical processes in soils used to produce some annual (e.g., tobacco), horticultural (e.g.,
flowers), or perennial (e.g., vineyards, orchards) crops and agricultural use of organic soils. In addition, DayCent has not
been adequately tested for soils with a high gravel, cobble, or shale content.
Table fl-197: Total Land Area Estimated with Tier 2 and 3 Inventory Approaches (Million Hectares)
Land Areas (million ha)
Year
Tier 1/2
Mineral
Tier 3
Total
Organic
Tier 1/2
Total
1990
106.49
315.25
421.74
1.43
423.18
1991
105.49
315.54
421.03
1.42
422.45
1992
104.55
315.86
420.41
1.41
421.82
1993
103.40
316.34
419.74
1.42
421.16
1994
102.30
316.81
419.11
1.43
420.54
1995
101.02
317.33
418.34
1.43
419.77
1996
99.68
317.78
417.45
1.42
418.88
1997
98.34
318.26
416.61
1.42
418.02
1998
96.96
318.77
415.72
1.42
417.15
1999
95.63
319.30
414.93
1.33
416.26
2000
94.65
319.66
414.32
1.32
415.64
2001
93.80
320.00
413.79
1.41
415.20
2002
92.97
320.32
413.29
1.42
414.71
2003
92.14
320.30
412.44
1.41
413.85
2004
91.47
320.31
411.78
1.41
413.18
2005
90.53
320.27
410.79
1.40
412.19
2006
89.87
320.23
410.09
1.38
411.48
2007
89.24
320.20
409.44
1.37
410.81
2008
88.83
320.00
408.83
1.36
410.19
2009
88.45
319.84
408.29
1.37
409.66
2010
88.05
319.65
407.70
1.36
409.06
2011
87.60
319.57
407.18
1.33
408.51
2012
87.26
319.34
406.59
1.33
407.92
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 of the land use data.
NRI points on mineral soils are classified into specific crop categories, continuous pasture/rangeland, and other non-
agricultural uses for the soil C Tier 2 inventory analysis (Table A-198). NRI points 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 cropping 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 constructed as multivariate
normal based on the total area estimates for each land-use/management category and associated covariance matrix. Through
this approach, dependencies in land use are taken into account resulting from the likelihood that current use is correlated
with past use. These dependencies occur because as some land use/management categories increase in area, the area of other
land use/management categories will decline. The covariance matrix addresses these relationships.
Table A-198: Total Land Areas by Land-Use and Management System for the Tier 2 Mineral Soil Organic G 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
22.42
22.14
21.80
21.33
20.90
20.48
20.07
19.62
18.83
18.33
17.93
17.67
17.43
Conservation Reserve Program
1.98
2.25
2.30
2.16
1.97
1.90
1.77
1.73
1.32
1.25
1.14
1.12
1.07
High Input Cropping Systems, Full
1.42
1.24
1.12
0.98
0.91
0.88
0.91
0.59
0.57
0.63
0.69
0.66
0.60
Tillage
High Input Cropping Systems,
1.00
1.08
1.15
1.18
1.24
1.27
1.27
1.38
1.32
1.22
1.12
1.09
1.01
Reduced Tillage
High Input Cropping Systems, No
0.10
0.11
0.05
0.07
0.08
0.10
0.10
0.17
0.17
0.18
0.18
0.19
0.18
Tillage
High Input Cropping Systems with
0.10
0.09
0.09
0.08
0.07
0.07
0.08
0.05
0.05
0.05
0.06
0.06
0.05
Manure, Full Tillage
A-316 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
High Input Cropping Systems with
0.08
0.09
0.08
0.09
0.09
0.10
0.10
0.11
0.11
0.09
0.09
0.08
0.07
Manure, Reduced Tillage
High Input Cropping Systems with
0.01
0.01
0.00
0.00
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
Manure, No Tillage
Medium Input Cropping Systems,
5.31
4.95
4.34
3.80
3.47
3.47
3.35
1.80
1.76
1.89
2.01
2.09
2.15
Full Tillage
Medium Input Cropping Systems,
4.32
4.38
4.85
5.10
5.19
4.83
4.80
5.87
5.59
5.29
4.97
4.79
4.64
Reduced Tillage
Medium Input Cropping Systems,
0.34
0.37
0.23
0.31
0.36
0.43
0.42
0.74
0.74
0.76
0.79
0.79
0.79
No Tillage
Low Input Cropping Systems, Full
2.91
2.84
2.76
2.68
2.60
2.63
2.64
2.49
2.35
2.20
2.18
2.08
1.99
Tillage
Low Input Cropping Systems,
0.07
0.05
0.18
0.19
0.25
0.27
0.26
0.32
0.37
0.37
0.38
0.39
0.40
Reduced Tillage
Low Input Cropping Systems, No
0.02
0.02
0.01
0.02
0.03
0.04
0.04
0.06
0.07
0.07
0.08
0.11
0.13
Tillage
Hay with Legumes or Irrigation
1.23
1.18
1.09
1.16
1.12
1.07
0.95
0.88
0.94
0.88
0.76
0.73
0.82
Hay with Legumes or Irrigation and
0.06
0.06
0.06
0.06
0.06
0.06
0.05
0.05
0.05
0.05
0.04
0.04
0.05
Manure
Hay, Unimproved
0.71
0.72
0.76
0.70
0.67
0.62
0.62
0.67
0.62
0.56
0.53
0.49
0.53
Pasture with Legumes or Irrigation
2.42
2.41
2.41
2.43
2.45
2.43
2.41
2.38
2.48
2.51
2.55
2.61
2.60
in Rotation
Pasture with Legumes or Irrigation
0.14
0.14
0.14
0.15
0.15
0.15
0.15
0.15
0.16
0.16
0.16
0.16
0.16
and Manure, in Rotation
Rice
0.17
0.15
0.17
0.16
0.16
0.16
0.16
0.16
0.17
0.14
0.19
0.18
0.18
Grassland Systems
84.07
83.35
82.75
82.07
81.40
80.54
79.60
78.73
78.13
77.30
76.72
76.13
75.54
Pasture with Legumes or Irrigation
5.59
5.39
5.11
5.03
5.01
4.82
4.46
3.98
4.00
3.88
3.64
3.52
3.40
Pasture with Legumes or Irrigation
0.17
0.17
0.15
0.15
0.15
0.15
0.13
0.11
0.11
0.11
0.10
0.09
0.09
and Manure
Rangelands and Unimproved
47.71
47.17
47.00
46.75
46.26
45.56
44.53
44.27
43.47
42.77
43.10
42.64
43.43
Pasture
Rangelands and Unimproved
22.07
22.19
22.26
22.10
22.09
22.16
22.49
22.36
23.01
22.95
22.29
22.34
21.31
Pasture, Moderately Degraded
Rangelands and Unimproved
8.52
8.43
8.23
8.04
7.89
7.85
7.99
8.00
7.54
7.59
7.60
7.54
7.31
Pasture, Severely Degraded
Total 106.49 105.49 104.55 103.40 102.30 101.02 99.68 98.34 96.96 95.63 94.65 93.80 92.97
Land-Use/Management System
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Cropland Systems
17.13
16.76
16.57
16.40
16.22
16.13
16.00
15.90
15.78
15.73
Conservation Reserve Program
0.92
0.68
0.76
0.75
0.73
0.69
0.68
0.66
0.61
0.55
High Input Cropping Systems, Full
0.60
0.59
0.57
0.55
0.53
0.54
0.55
0.53
0.53
0.51
Tillage
High Input Cropping Systems,
1.00
0.96
0.93
0.91
0.88
0.90
0.91
0.88
0.88
0.86
Reduced Tillage
High Input Cropping Systems, No
0.20
0.21
0.21
0.20
0.19
0.19
0.19
0.19
0.19
0.18
Tillage
High Input Cropping Systems with
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
Manure, Full Tillage
High Input Cropping Systems with
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.06
Manure, Reduced Tillage
High Input Cropping Systems with
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
Manure, No Tillage
Medium Input Cropping Systems,
2.08
2.03
2.00
1.98
1.98
1.98
1.98
1.97
1.97
1.97
Full Tillage
Medium Input Cropping Systems,
4.55
4.50
4.42
4.38
4.39
4.39
4.39
4.38
4.38
4.39
Reduced Tillage
Medium Input Cropping Systems,
0.88
0.98
0.96
0.95
0.96
0.96
0.96
0.96
0.96
0.96
No Tillage
Low Input Cropping Systems, Full
1.90
1.77
1.74
1.74
1.69
1.66
1.56
1.55
1.51
1.55
Tillage
Low Input Cropping Systems,
0.42
0.45
0.44
0.43
0.42
0.41
0.38
0.38
0.37
0.39
Reduced Tillage
Low Input Cropping Systems, No
0.20
0.25
0.25
0.25
0.24
0.24
0.22
0.22
0.22
0.22
Tillage
Hay with Legumes or Irrigation
0.77
0.77
0.75
0.73
0.73
0.72
0.68
0.70
0.66
0.66
Hay with Legumes or Irrigation and
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
Manure
Hay, Unimproved
0.53
0.50
0.50
0.49
0.50
0.48
0.47
0.46
0.46
0.46
A-317
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Pasture with Legumes or Irrigation
2.58
2.57
2.56
2.56
2.52
2.51
2.57
2.56
2.58
2.57
in Rotation
Pasture with Legumes or Irrigation
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
and Manure, in Rotation
Rice
0.16
0.17
0.16
0.14
0.13
0.14
0.12
0.14
0.14
0.13
Grassland Systems
75.01
74.71
73.96
73.47
73.02
72.70
72.45
72.16
71.82
71.53
Pasture with Legumes or Irrigation
3.28
3.25
3.17
3.09
2.98
2.90
2.90
2.81
2.76
2.73
Pasture with Legumes or Irrigation
and Manure
0.08
0.08
0.08
0.08
0.07
0.07
0.07
0.07
0.06
0.06
Rangelands and Unimproved
Pasture
43.43
42.65
42.19
41.96
41.66
41.52
41.32
41.29
41.07
40.87
Rangelands and Unimproved
20.86
20.84
20.76
20.64
20.69
20.63
20.62
20.52
20.48
20.43
Pasture, Moderately Degraded
Rangelands and Unimproved
Pasture, Severely Degraded
7.36
7.89
7.77
7.70
7.62
7.58
7.54
7.47
7.45
7.43
Total
92.14
91.47
90.53
89.87
89.24
88.83
88.45
88.05
87.60
87.26
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.
Organic soils are categorized into land-use systems based on drainage (IPCC 2006). Undrained soils are treated as having
no loss of organic C or soil N2O emissions. Drained soils are subdivided into those used for cultivated cropland, which are
assumed to have high drainage and relatively large losses of C, and those used for managed pasture, which are assumed to
have less drainage with smaller losses of C. N2O emissions are assumed to be similar for both drained croplands and
grasslands. Overall, the area of organic soils drained for cropland and grassland has remained relatively stable since 1990
(see Table A-199).
Table A-199: 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)
0.43
0.42
0.42
0.43
0.42
0.42
0.42
0.43
0.43
0.42
0.41
0.41
0.41
0.41
Managed Pasture
(low drainage)
0.47
0.47
0.47
0.47
0.48
0.48
0.47
0.47
0.47
0.46
0.46
0.47
0.47
0.46
Undrained
0.05
0.05
0.05
0.05
0.04
0.04
0.04
0.03
0.03
0.03
0.04
0.03
0.03
0.03
Total
0.95
0.95
0.94
0.94
0.94
0.94
0.93
0.92
0.92
0.91
0.92
0.91
0.90
0.90
Warm Temperate
Cultivated Cropland
(high drainage)
0.10
0.10
0.09
0.09
0.10
0.10
0.09
0.09
0.09
0.09
0.09
0.09
0.10
0.10
Managed Pasture
(low drainage)
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.10
0.10
0.10
0.10
0.10
0.11
Undrained
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.00
Total
0.21
0.20
0.19
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.21
Sub-Tropical
Cultivated Cropland
(high drainage)
0.20
0.20
0.20
0.20
0.21
0.21
0.21
0.21
0.21
0.13
0.13
0.24
0.24
0.23
Managed Pasture
(low drainage)
0.14
0.14
0.13
0.13
0.14
0.13
0.13
0.13
0.13
0.13
0.13
0.11
0.11
0.10
Undrained
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.09
0.09
0.00
0.00
0.00
Total
0.34
0.34
0.34
0.34
0.34
0.34
0.35
0.34
0.35
0.35
0.34
0.35
0.35
0.33
IPCC Land-Use Category
Land Areas (million ha)
for Organic Soils
2004
2005
2006
2007
2008
2009
2010
2011
2012
Cold Temperate
Cultivated Cropland
(high drainage)
0.41
0.41
0.40
0.41
0.40
0.40
0.40
0.40
0.41
Managed Pasture
(low drainage)
0.47
0.47
0.47
0.46
0.46
0.46
0.46
0.45
0.44
Undrained
0.02
0.02
0.03
0.02
0.03
0.03
0.03
0.02
0.02
Total
0.90
0.90
0.90
0.89
0.89
0.88
0.88
0.88
0.87
Warm Temperate
Cultivated Cropland
(high drainage)
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
Managed Pasture
(low drainage)
0.10
0.10
0.09
0.09
0.09
0.09
0.09
0.09
0.09
A-318 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Undrained
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Total
0.21
0.21
0.19
0.19
0.19
0.19
0.19
0.19
0.19
Sub-Tropical
Cultivated Cropland
(high drainage)
0.23
0.22
0.22
0.21
0.21
0.22
0.22
0.18
0.18
Managed Pasture
(low drainage)
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
Undrained
0.01
0.01
0.01
0.01
0.02
0.00
0.00
0.03
0.03
Total
0.33
0.33
0.33
0.33
0.32
0.32
0.32
0.32
0.32
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 points based on survey locations classified as flooded rice
(Table A-200). 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 2013, 2015) and Texas (TAMU 2015 for years 1993 through 2014),
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 fl-2
DO: Total Rice Harvested Area 1
Land Areas (Million Hectares)
Year
Tier 1
Tier 3
Total
1990
0.16
1.54
1.70
1991
0.16
1.60
1.76
1992
0.17
1.67
1.84
1993
0.17
1.63
1.80
1994
0.17
1.53
1.70
1995
0.15
1.56
1.71
1996
0.15
1.56
1.72
1997
0.15
1.52
1.67
1998
0.17
1.43
1.60
1999
0.31
1.49
1.80
2000
0.33
1.51
1.84
2001
0.18
1.44
1.62
2002
0.18
1.60
1.79
2003
0.15
1.47
1.62
2004
0.17
1.53
1.69
2005
0.18
1.65
1.83
2006
0.14
1.33
1.48
2007
0.12
1.45
1.57
2008
0.14
1.27
1.41
2009
0.14
1.57
1.71
2010
0.15
1.61
1.76
2011
0.13
1.32
1.45
2012
0.11
1.18
1.29
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 of the land use and management data.
Step 1b: Obtain Management Activity Data for the Tier 3 Method to estimate Soil C Stock Changes, CH4 and AfeO Emissions from
Mineral Soils
Synthetic N Fertilizer Application: Data on N fertilizer rates are based primarily on the USDA-Economic Research Service
Cropping Practices Survey through 1995 (USDA-ERS 1997), which became the Agricultural Resource Management
Surveys (ARMS) in 1996 (USDA-ERS 2015).121 In these surveys, data on inorganic N fertilization rates are collected for
crops simulated by DayCent (barley, corn, cotton, dry beans, hay, oats, onions, peanuts, potatoes, rice, sorghum, soybeans,
sugar beets, sunflowers, tomatoes, and wheat) in the high production states and for a subset of low production states. These
data are used to build a time series of fertilizer application rates for specific crops and states for two periods, 1990 through
121 Available online: .
A-319
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1999 and 2000 through 2012. If only a single survey is available for a crop, as is the case with sorghum, the rates for the one
survey are used for both time periods.
Mean fertilizer rates and standard deviations for irrigated and rainfed crops are produced for each state. If a state is not
surveyed for a particular crop or if there are not enough data to produce a state-level estimate, then data are aggregated to
USDA Farm Production Regions in order to estimate a mean and standard deviation for fertilization rates (Farm Production
Regions are groups of states in the United States with similar agricultural commodities). If Farm Production Region data are
not available, crop data are aggregated to the entire United States (all major states surveyed) to estimate a mean and standard
deviation. Standard deviations for fertilizer rates are used to construct PDFs with log-normal densities in order to address
uncertainties in application rates (see Step 2a for discussion of uncertainty methods). The survey summaries also present
estimates for fraction of crop acres receiving fertilizer, and these fractions are used to determine if a crop is receiving
fertilizer. Alfalfa hay and grass-clover hay are assumed to not be fertilized, but grass hay is fertilized according to rates from
published farm enterprise budgets (NRIAI 2003). Total fertilizer application data are found in Table A-201.
Simulations are conducted for the period prior to 1990 in order to initialize the DayCent model (see Step 2a), and crop-
specific regional fertilizer rates prior to 1990 are based largely on extrapolation/interpolation of fertilizer rates from the years
with available data. For crops in some states, little or no data are available, and, therefore, a geographic regional mean is
used to simulate N fertilization rates (e.g., no data are available for the State of Alabama during the 1970s and 1980s for
corn fertilization rates; therefore, mean values from the southeastern United States are used to simulate fertilization to corn
fields in this state).
Managed Livestock Manure Amendments:171 County-level manure addition estimates have been derived from manure N
addition rates developed by the USDA Natural Resources Conservation Service (NRCS) (Edmonds et al. 2003). Working
with the farm-level crop and animal data from the 1997 Census of Agriculture, USDA-NRCS has coupled estimates of
manure N produced with estimates of manure N recoverability by animal waste management system to produce county-
level rates of manure N application to cropland and pasture. Edmonds et al. (2003) defined a hierarchy that included 24
crops, permanent pasture, and cropland used as pasture. They estimated the area amended with manure and application rates
in 1997 for both manure-producing farms and manure-receiving farms within a county and for two scenarios—before
implementation of Comprehensive Nutrient Management Plans (baseline) and after implementation (Edmonds et al. 2003).
The goal of nutrient management plans is to apply manure nutrients at a rate meeting plant demand, thus limiting leaching
losses of nutrients to groundwater and waterways.
For DayCent simulations, the rates for manure-producing farms and manure-receiving farms have been area-weighted and
combined to produce a single county-level estimate for the amount of land amended with manure and the manure N
application rate for each crop in each county. The estimates are based on the assumption that Comprehensive Nutrient
Management Plans have not been fully implemented. This is a conservative assumption because it allows for higher leaching
rates due to some over-application of manure to soils. In order to address uncertainty in these data, uniform probability
distributions are constructed based on the proportion of land receiving manure versus the amount not receiving manure for
each crop type and pasture. For example, if 20 percent of land producing corn in a county is amended with manure, randomly
drawing a value equal to or greater than 0 and less than 20 would lead to a simulation with a manure amendment, while
drawing a value greater than or equal to 20 and less than 100 would lead to no amendment in the simulation (see Step 2a for
further discussion of uncertainty methods).
Edmonds et al. (2003) only provides manure application rate data for 1997, but the amount of managed manure available
for soil application changes annually, so the area amended with manure is adjusted relative to 1997 to account for all the
manure available for application in other years. Specifically, the manure N available for application in other years is divided
by the manure N available in 1997. If the ratio is greater than 1, there is more manure N available in that county relative to
the amount in 1997, and so it is assumed a larger area is amended with manure. In contrast, ratios less than one imply less
area is amended with manure because there is a lower amount available in the year compared to 1997. The amendment area
in each county for 1997 is multiplied by the ratio to reflect the impact of manure N availability on the area amended. The
amount of managed manure N available for application to soils is calculated by determining the populations of livestock on
feedlots or otherwise housed, requiring collection and management of the manure. The methods are described in the Manure
Management section (Section 5.2) and annex (Annex 3.10). The total managed manure N applied to soils is found in Table
A-202.
To estimate C inputs (associated with manure N application rates derived from Edmonds et al. (2003), carbon-nitrogen (C :N)
ratios for livestock-specific manure types are adapted from the Agricultural Waste Management Field Handbook (USDA
122 por jjje jnventory, total livestock manure is divided into two general categories: (1) managed manure, and (2) unmanaged manure.
Managed manure includes manure stored in management systems such as drylots, pits and lagoons, as well as manure applied to soils
through daily spread manure operations. Unmanaged manure encompasses all manure deposited on soils by animals on PRP.
A-320 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1996), On-Farm Composting Handbook (NRAES 1992), and recoverability factors provided by Edmonds et al (2003). The
C:N ratios are applied to county-level estimates of manure N excreted by animal type and management system to produce a
weighted county average C:N ratio for manure amendments. The average C:N ratio is used to determine the associated C
input for crop amendments derived from Edmonds et al. (2003).
To account for the common practice of reducing inorganic N fertilizer inputs when manure is added to a cropland soil, crop-
specific reduction factors are derived from mineral fertilization data for land amended with manure versus land not amended
with manure in the ERS 1995 Cropping Practices Survey (USDA-ERS 1997). Mineral N fertilization rates are reduced for
crops receiving manure N based on a fraction of the amount of manure N applied, depending on the crop and whether it is
irrigated or rainfed. The reduction factors are randomly selected from PDFs with normal densities in order to address
uncertainties in the dependence between manure amendments and mineral fertilizer application.
PRP Manure N: Another key source of N for grasslands is PRP manure N deposition (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-202.
Residue N Inputs: Crop residue N, fixation by legumes, and N residue inputs from senesced grass litter are included as
sources of N to the soil, and are estimated in the DayCent simulations as a function of vegetation type, weather, and soil
properties. That is, while the model accounts for the contribution of N from crop residues to the soil profile and subsequent
N2O emissions, this source of mineral soil N is not "activity data" 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 that
123
3 percent of non-harvested above ground residues for crops are burned), are provided in Table A-203.
Other NInputs: 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 3 categories: full, reduced, and no-tillage. Full tillage is defined as
multiple tillage operations every year, including significant soil inversion (e.g., plowing, deep disking) and low surface
residue coverage. This definition corresponds to the intensive tillage and "reduced" tillage systems as defined by CTIC
(2004). No-till is defined as not disturbing the soil except through the use of fertilizer and seed drills and where no-till is
applied to all crops in the rotation. Reduced tillage made up the remainder of the cultivated area, including mulch tillage and
ridge tillage as defined by CTIC and intermittent no-till. The specific tillage implements and applications used for different
crops, rotations, and regions to represent the three tillage classes are derived from the 1995 Cropping Practices Survey by
the Economic Research Service (USDA-ERS 1997).
Tillage practices are estimated for each cropping system based on data from the Conservation Technology Information
124
Center (CTIC 2004). CTIC compiles data on cropland area under five tillage classes by major crop species and year for
each county. Because the surveys involve county-level aggregate area, they do not fully characterize tillage practices as they
are applied within a management sequence (e.g., crop rotation). This is particularly true for area estimates of cropland under
no-till. 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. For example, a common practice in maize-soybean rotations is to use tillage in the
maize crop while no-till is used for soybean, such that no-till practices are not continuous in time. Estimates of the area
under continuous no-till are provided by experts at CTIC to account for intermittent tillage activity and its impact on soil C
(Towery 2001).
Tillage data are further processed to construct PDFs. Transitions between tillage systems are based on observed county-level
changes in the frequency distribution of the area under full, reduced, and no-till from the 1980s through 2004. Generally,
the fraction of full tillage decreased during this time span, with concomitant increases in reduced till and no -till management.
123 Another improvement is to reconcile the amount of crop residues burned with the Field Burning of Agricultural Residues source
category (Section 5.5).
124 National scale tillage data are no longer collected by CTIC, and a new data source will be needed, which is a planned
improvement.
A-321
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Transitions that are modeled and applied to NRI points occurring within a county are full tillage to reduced and no-till, and
reduced tillage to no-till. The remaining amount of cropland is assumed to have no change in tillage (e.g., full tillage
remained in full tillage). Transition matrices are constructed from CTIC data to represent tillage changes for three time
periods, 1980 through 1989, 1990 through 1999, 2000 through 2012. Areas in each of the three tillage classes—full till (FT),
reduced till (RT), no-till (NT)—in 1989 (the first year the CTIC data are available) are used for the first time period, data
from 1997 are used for the second time period, and data from 2004 are used for the last time period. Percentage areas of
cropland in each county are calculated for each possible transition (e.g., FT^FT, FT^RT, FT^NT, RT^RT, RT^NT)
to obtain a probability for each tillage transition at an NRI point. It is assumed that there are no transitions for NT^FT or
NT^NT after accounting for NT systems that have intermittent tillage. Uniform probability distributions are established
for each tillage scenario in the county. For example, a particular crop rotation had 80 percent chance of remaining in full
tillage over the two decades, a 15 percent chance of a transition from full to reduced tillage and a 5 percent chance of a
transition from full to no-till. The uniform distribution is subdivided into three segments with random draws in the Monte
Carlo simulation (discussed in Step 2b) leading to full tillage over the entire time period if the value is greater than or equal
to 0 and less than 80, a transition from full to reduced till if the random draw is equal to or greater than 80 and less than 95,
or a transition from full to no-till if the draw is greater than or equal to 95. See step 2b for additional discussion of the
uncertainty analysis.
Irrigation: NRI (USDA-NRCS 2015) differentiates between irrigated and non-irrigated land, but does not provide more
detailed information on the type and intensity of irrigation. Hence, irrigation is modeled by assuming that applied water to
field capacity with intervals between irrigation events where the soils drain to about 60 percent of field capacity.
Daily Weather Data: Daily maximum/minimum temperature and precipitation data are based on gridded weather data from
the PRISM Climate Group (2015). 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 2012 at a 4 km resolution. Each NRI point is assigned the PRISM weather data for the grid cell containing the
point.
Enhanced Vegetation Index: The Enhanced Vegetation Index (EVI) from the MODIS vegetation products, (MOD13Q1 and
MYD13Q1) is an input to DayCent for estimating net primary production using the NASA-CASA production algorithm
(Potter etal. 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,
125
soybeans, sorghum, cotton, wheat and other close-grown crops such as barley and oats.
The MODIS EVI products have a 250 m spatial resolution, and some pixels in images have mixed land uses and crop types
at this resolution, which is problematic for estimating NPP associated with a specific crop at a NRI point. Therefore, a
threshold of 90 percent purity in an individual pixel is the cutoff for estimating NPP using the EVI data derived from the
imagery (i.e., pixels with less than 90 percent purity for a crop are assumed to generate bias in the resulting NPP estimates).
The USDA-NASS Crop Data Layer (CDL) (Johnson and Mueller 2010) is used to determine the purity levels of the EVI
data. CDL data have a 30 to 58 m spatial resolution, depending on the year. The level of purity for individual pixels in the
MODIS EVI products is determined by aggregating the crop cover data in CDL to the 250m resolution of the EVI data. In
this step, the percent cover of individual crops is determined for the 250m EVI pixels. Pixels that did not meet a 90 percent
purity level for any crop are eliminated from the dataset. CDL 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 point based on a 10 km buffer surrounding the
survey location. EVI data are not assigned to a point if there are no pixels with at least 90 percent purity within the 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 the plants 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
1 9S
Additional crops and grassland will be used with the NASA-CASA method in the future, as a planned improvement.
A-322 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 Sttitzle
(1987), to estimate rice straw input amounts for the Tier 1 method.
Soil Properties: Soil texture and natural drainage capacity (i.e., hydric vs. non-hydric soil characterization) are the main soil
variables used as input to the DayCent model. Texture is one of the main controls on soil C turnover and stabilization in the
DayCent model, which uses particle size fractions of sand (50-2,000 |im), silt (2-50 |im), and clay (<2 |im) 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.126 Poorly drained soils can be subject to anaerobic (lack of
oxygen) conditions if water inputs (precipitation and irrigation) exceed water losses from drainage and evapotranspiration.
Depending on moisture conditions, hydric soils can range from being fully aerobic to completely anaerobic, varying over
the year. Decomposition rates are modified according to a linear function that varies from 0.3 under completely anaerobic
conditions to 1.0 under fully aerobic conditions (default parameters in DayCent).127 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
(S SURGO) (Soil Survey Staff 2015). The data are based on field measurements collected as part of soil survey and mapping.
Each NRI point is assigned the dominant soil component in the polygon containing the point from the S SURGO data product.
Step 1c: Obtain Additional Management Activity Data for the Tier 1 Method to estimate Soil N2O Emissions from Mineral Soils
Synthetic N Fertilizer: A process-of-elimination approach is used to estimate synthetic N fertilizer additions to crops in the
Tier 1 method. The total amount of fertilizer used on-farms has been estimated by the USGS from 1990 through 2001 on a
county scale from fertilizer sales data (Ruddy et al. 2006). For 2002 through 2012, county-level fertilizer used on-farms is
adjusted based on annual fluctuations in total U.S. fertilizer sales (AAPFCO 1995 through 2007; AAPFCO 2008 through
2012). The fertilizer consumption data are recorded in "fertilizer year" totals, (i.e., July to June), but are converted to calendar
year totals. This is done by assuming that approximately 35 percent of fertilizer usage occurred from July to December and
65 percent from January to June (TVA 1992b). Fertilizer application data are available for crops and grasslands simulated
by DayCent (discussed in Step la section for Tier 3). Thus, 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 used on farms after subtracting the amount applied
to crops and non-federal grasslands simulated by DayCent. The differences are aggregated to the state level, and PDFs are
derived based on uncertainties in the amount of N applied to 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-204.
Managed Livestock Manure and Other Organic Amendments: Manure N that is not applied to crops and grassland simulated
by DayCent is assumed to be applied to other crops that are included in the Tier 1 method. Estimates of total national annual
N additions from other commercial organic fertilizers are derived from organic fertilizer statistics (TVA 1991 through 1994;
AAPFCO 1995 through 2016). Commercial organic fertilizers include dried blood, tankage, compost, and other; dried
manure and biosolids (i.e., sewage sludge) that are used as commercial fertilizer are subtracted from totals to avoid double
counting. The dried manure N is counted with the non-commercial manure applications, and biosolids is assumed to be
applied only to grasslands. The organic fertilizer data, which are recorded in mass units of fertilizer, had to be converted to
mass units of N by multiplying the consumption values by the average organic fertilizer N content of 0.5 percent (AAPFCO
2000). 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
1^6 Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a management variable.
127 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.
A-323
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1992b). 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-205. The fate of manure N is summarized in Table A-202.
PRPManure N: Soil N2O 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. The amount of PRP N generated
by DayCent model simulations of non-federal grasslands was subtracted from total PRP N and this difference was assumed
to be applied to federal grasslands. The total PRP manure N added to soils is found in Table A-202.
Biosolids (i.e., Sewage Sludge) Amendments: Biosolids is generated from the treatment of raw sewage in public or private
wastewater treatment works and is typically used as a soil amendment, or is sent to waste disposal facilities, such as landfills.
In this Inventory, all 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 2016 were not available so a "least squares line" statistical extrapolation using the
previous 16 years of data was used to arrive at an approximate value. The total sludge generation estimates are then converted
to units of N by applying an average N content of 69 percent (AAPFCO 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 N2O emissions
from agricultural soil management; the estimates of biosolids N applied to other land and surface-disposed are used in
estimating N2O 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-206.
Residue N Inputs: Soil N2O 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 2015). Total production for each crop is converted to tons
of dry matter product using the residue dry matter fractions shown in Table A-207. Dry matter yield is then converted to
tonnes 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 was 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-208.
Step 1d: Obtain Additional Management Activity Data for the Tier 2 Method to estimate Soil C Stock Changes in Mineral Soils
Tillage Practices: For the Tier 2 method that is used to estimate soil organic C stock changes, PDFs are constructed for the
CTIC tillage data (CTIC 2004) as bivariate normal on a log-ratio scale to reflect negative dependence among tillage classes.
This structure ensured that simulated tillage percentages are non-negative and summed to 100 percent. CTIC data do not
differentiate between continuous and intermittent use of no-tillage, which is important for estimating SOC storage. Thus,
regionally based estimates for continuous no-tillage (defined as 5 or more years of continuous use) are modified based on
consultation with CTIC experts, as discussed in Step la (downward adjustment of total no-tillage area based on the amount
of no-tillage that is rotated with more intensive tillage practices) (Towery 2001).
Managed Livestock Manure Amendments: USDA provides information on the amount of land amended with manure for
1997 based on manure production data and field-scale surveys detailing application rates that had been collected in the
Census of Agriculture (Edmonds et al. 2003). Similar to the DayCent model discussion in Steplb, the amount of land
receiving manure is based on the estimates provided by Edmonds et al. (2003), as a proportion of crop and grassland amended
with manure within individual climate regions. The resulting proportions are used to re-classify a portion of crop and
grassland into a new management category. Specifically, a portion of medium input cropping systems is re-classified as high
input, and a portion of the high input systems is re-classified as high input with amendment. In grassland systems, the
estimated proportions for land amended with manure are used to re-classify a portion of nominally-managed grassland as
improved, and a portion of improved grassland as improved with high input. These classification approaches are consistent
with the IPCC inventory methodology (IPCC 2006). Uncertainties in the amount of land amended with manure are based
on the sample variance at the climate region scale, assuming normal density PDFs (i.e., variance of the climate region
estimates, which are derived from county-scale proportions).
A-324 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Biosolids (i.e., Sewage Sludge) Amendments: Biosolids are generated from the treatment of raw sewage in public or private
wastewater treatment facilities and are typically used as a soil amendment or is sent for waste disposal to landfills. In this
Inventory, all biosolids that are amended to agricultural soils are assumed to be applied to grasslands. See section on
biosolids in Step lc for more information about the methods used to derive biosolids N estimates. The total amount of
biosolids N is given in Table A-206. Biosolids N is assumed to be applied at the assimilative capacity provided in Kellogg
et al. (2000), which is the amount of nutrients taken up by a crop and removed at harvest, representing the recommended
application rate for manure amendments. This capacity varies from year to year, because it is based on specific crop yields
during the respective year (Kellogg et al. 2000). Total biosolids N available for application is divided by the assimilative
capacity to estimate the total land area over which biosolids had been applied. The resulting estimates are used for the
estimation of soil C stock change.
Wetland Reserve: Wetlands enrolled in the Conservation Reserve Program have been restored in the Northern Prairie Pothole
Region through the Partners for Wildlife Program funded by the U.S. Fish and Wildlife Service (USFWS 2010). The area
of restored wetlands is estimated from contract agreements (Euliss and Gleason 2002). While the contracts provide
reasonable estimates of the amount of land restored in the region, they do not provide the information necessary to estimate
uncertainty. Consequently, a ±50 percent range is used to construct the PDFs for the uncertainty analysis.
A-325
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Table fl-201: Synthetic Fertilizer N Added to Tier 3 Crops tkt HI
1990
1991
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Fertilizer N
9,681
9,571
9,750
9,742
9,620
9,343
9,697
9,532
9,546
9,570
9,565
9,689
9,465
10,263
9,850
9,755
9,912
9,935
10,101
Note: The latter part of the time series in this Inventory is estimated with data splicing methods. 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.
Table fl-202: Fate of Livestock Manure Nitrogen tkt N1
Activity
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Managed Manure N Applied to Tier 3
Cropland and Non-federal Grasslands3,b
819
812
834
857
789
739
773
784
794
965
977
953
972
956
917
929
917
979
937
988
1,015
1,015
1,026
Managed Manure N Applied to Tier 1
Cropland1
1,311
1,350
1,341
1,288
1,402
1,484
1,446
1,467
1,429
1,302
1,330
1,335
1,358
1,382
1,327
1,359
1,449
1,420
1,421
1,338
1,298
1,319
1,320
Managed Manure N Applied to Grasslands
404
400
396
411
435
428
424
423
482
463
467
478
480
484
506
502
502
497
497
497
494
490
482
Pasture, Range, & Paddock Manure N
4,097
4,104
4,265
4,354
4,427
4,529
4,493
4,382
4,327
4,255
4,150
4,137
4,134
4,132
4,081
4,124
4,168
4,051
4,036
4,025
3,998
3,924
3,862
Total
6,631
6,666
6,836
6,911
7,054
7,180
7,136
7,055
7,032
6,985
6,924
6,903
6,943
6,954
6,830
6,914
7,036
6,946
6,891
6,849
6,806
6,748
6,690
a Accounts for N volatilized and leached/runoff during treatment, storage and transport before soil application.
b Includes managed manure and daily spread manure amendments
'Totals may not sum exactly due to rounding.
Note: The latter part of the time series in this Inventory is estimated with data splicing methods. 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.
Table fl-203: Crop Residue N and Otber N Inputs to Tier 3 Crops as Simulated by Day Cent tkt HI
Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Residue Na 3,880 4,105 3,722 4,051 3,741 4,183 3,934 3,967 3,891 4,604 4,222 4,199 4,204 4,303 3,954 4,218 4,082 4,171 3,969 4,072 4,484 4,426 4,369
Mineralization &
Asymbiotic Fixation 11,962 11,401 11,469 12,313 11,470 12,122 11,767 11,892 13,247 11,891 12,151 12,752 12,151 12,834 13,909 12,738 12,627 13,111 13,175 13,789 14,334 12,752 11,646
a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
Note: The latter part of the time series in this Inventory is estimated with data splicing methods. 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.
Table fl-204: Syntbetic Fertilizer N Added to Tier 1 Crops tkt HI
Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Fertilizer N 1,291 1,308 1,232 1,137 2,007 1,496 1,865 1,699 1,807 2,042 1,734 1,271 1,438 1,716 1,872 1,489 1,755 1,584 1,453 1,212 1,433 1,815 2,017
Note: The latter part of the time series in this Inventory is estimated with data splicing methods. 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.
Table A-205: Otber Organic Commercial Fertilizer Consumption on Agricultural Lands tkt HI
Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Other Commercial
Organic Fertilizer Na 4 8 6 5 8 10 13 14 12 11 9 7 8 8 9 10 12 15 12 10 10 12 13
a Includes dried blood, tankage, compost, other. Excludes dried manure and biosolids (i.e., sewage sludge) used as commercial fertilizer to avoid double counting.
Note: The latter part of the time series in this Inventory is estimated with data splicing methods. 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-326 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table fl-206: Biosoli Js [i.e., Sewage Sludge] Nitrogen by Disposal Practice tkt HI
Disposal Practice
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014 2015 2016
Applied to Agricultural
Soils
52
55
58
62
65
68
72
75
78
81
84
86
89
91
94
98
101
104
107
110
113
116
119
122
124 127 130
Other Land
Application
25
26
26
27
27
28
29
29
29
30
30
30
30
30
30
31
31
32
32
32
32
33
33
33
33 33 34
Surface Disposal
20
19
19
18
17
16
15
14
13
12
10
9
8
6
5
5
4
4
3
3
3
2
2
2
2 1 1
Total
97
100
104
107
109
111
116
118
121
122
124
125
127
128
130
134
137
140
142
145
148
151
153
156
159 162 165
Note: Totals may not sum due to independent rounding.
A-327
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Table fl-207: Keyflssumptions for Crop Production in the Tier 1 Method
Ratio of Below-
Dry Matter Fraction
Above-ground Residue
ground
Residue N Fraction
of Harvested
Residue to Above-
Crop
Product
Slope
Intercept
ground Biomass
Above-ground
Below-ground
Alfalfa
0.9
0.29
0
0.4
0.027
0.019
Asparagus
0.07
0.5
0
0.2
0.006
0.009
Barley
0.89
0.98
0.59
0.22
0.007
0.014
Beans and Lentils
0.9
0.36
0.68
0.19
0.01
0.01
Broccoli
0.09
0.1
0
0.11
0.006
0.009
Cabbage
0.08
0.1
0
0.11
0.006
0.009
Carrots
0.13
0.46
0.02
0.15
0.019
0.014
Cauliflower
0.08
0.1
0
0.11
0.006
0.009
Celery
0.05
0.23
0
0.11
0.006
0.009
Corn
0.87
1.03
0.61
0.22
0.006
0.007
Corn for silage
0.3
0.3
0
0.22
0.006
0.007
Cotton
0.93
1.49
4.41
0.13
0.012
0.007
Cucumbers
0.04
1.77
0
0.03
0.006
0.009
Flaxseed
0.88
1.09
0.88
0.22
0.006
0.009
Garlic
0.11
0.23
0
0.15
0.019
0.014
Greens
0.08
0.1
0
0.11
0.006
0.009
Hay Grass
0.9
0.18
0
0.54
0.015
0.012
Hay legume
0.9
0.235
0
0.47
0.021
0.0155
Lettuce Head
0.04
0.1
0
0.11
0.006
0.009
Lettuce Leaf
0.04
0.1
0
0.11
0.006
0.009
Melons Cantaloup
0.06
1.77
0
0.04
0.006
0.009
Melons Honeydew
0.06
1.77
0
0.04
0.006
0.009
Melons Watermelon
0.085
1.77
0
0.04
0.006
0.009
Millet
0.88
1.09
0.88
0.22
0.006
0.009
Oats
0.89
0.91
0.89
0.25
0.007
0.008
Onions
0.12
0.23
0
0.14
0.019
0.014
Other Vegetables
0.05
0.59
0.57
0.19
0.006
0.009
Peanuts
0.94
1.07
1.54
0.2
0.016
0.014
Peas
0.91
1.13
0.85
0.05
0.011
0.008
Peppers
0.08
1.4
0
0.14
0.006
0.009
Potatoes
0.22
0.1
1.06
0.2
0.019
0.014
Pumpkins
0.1
1.77
0
0.04
0.006
0.009
Radishes
0.05
1.21
0.46
0.15
0.019
0.014
Rice
0.89
0.95
2.46
0.16
0.007
0.009
Sorghum Grain
0.89
0.88
1.33
0.22
0.007
0.006
Sorghum for silage
0.3
0.3
0
0.22
0.007
0.006
Soybeans
0.91
0.93
1.35
0.19
0.008
0.008
Squash
0.05
1.57
0
0.04
0.006
0.009
Sugar beets
0.22
0.1
1.06
0.2
0.019
0.014
Sugarcane
0.25
0.41
0
0.16
0.007
0.005
Sunflower
0.88
1.09
0.88
0.22
0.006
0.009
Sweet Potatoes
0.35
0.27
1.74
0.15
0.019
0.014
Tobacco
0.87
0.3
0
0.4
0.008
0.018
Tomatoes
0.05
0.59
0.57
0.19
0.006
0.009
Wheat
0.89
1.51
0.52
0.24
0.006
0.009
Table fl-208: Nitrogen in Crop Residues Retained on Soils Producing Crops Not Simulated hy DayCenHkt N)
Crop Type
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Alfalfa
83,273
71,670
62,572
68,216
72,375
72,238
62,135
58,522
72,454
67,821
62,386
Asparagus
7
15
5
5
7
6
5
16
13
8
11
Barley
7,202
6,493
8,095
6,897
5,438
7,046
4,574
5,699
4,060
3,817
3,745
Beans and
Lentils
1,988
2,087
1,905
1,941
2,086
2,157
2,217
2,169
2,383
2,083
1,795
Broccoli
6
3
3
3
3
5
5
5
1
1
36
Cabbage
76
89
73
71
48
59
77
91
70
72
28
Carrots
1,653
1,406
1,354
1,505
1,734
1,767
1,610
1,859
1,322
1,853
1,376
Cauliflower
6
1
0
1
2
3
3
3
1
4
9
A-328 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Celery
164
164
160
171
172
175
169
167
152
150
208
Corn
157,085
140,273
163,074
116,107
152,485
120,853
132,009
129,618
128,842
119,312
117,803
Corn for silage
6,044
6,040
6,065
5,686
5,680
5,169
5,207
5,810
5,152
5,366
5,241
Cotton
44,527
44,892
42,250
45,751
48,029
54,878
55,629
52,605
40,598
44,643
38,834
Cucumbers
108
107
77
132
90
104
89
82
96
41
17
Flaxseed
9,109
10,390
11,706
8,780
10,272
9,141
9,346
9,263
10,024
8,286
8,895
Garlic
260
367
310
265
249
226
259
191
493
617
475
Greens
0
0
0
0
0
0
0
0
0
0
9
Hay Grass
47,058
46,868
47,621
46,389
49,742
47,119
42,954
42,977
41,591
39,399
37,948
Hay legume
49,609
46,763
45,714
47,095
47,296
45,540
39,727
39,074
39,119
35,655
32,621
Lettuce Head
26
26
36
37
34
30
22
17
11
12
11
Lettuce Leaf
25
32
26
22
22
20
17
26
26
33
21
Melons
Cantaloup
498
427
436
397
422
518
472
391
346
461
333
Melons
Honeydew
293
273
278
287
275
204
254
166
170
263
181
Melons
Watermelon
2,100
2,026
1,976
2,082
2,126
2,009
2,093
2,050
2,195
2,492
2,768
Millet
159,271
166,193
168,344
160,264
157,997
157,691
154,741
155,367
145,258
144,807
101,557
Oats
3,804
2,827
2,522
2,831
2,690
2,431
1,911
2,352
1,933
1,183
2,022
Onions
607
708
615
739
661
735
821
650
661
926
608
Other
Vegetables
3,450
3,231
3,284
3,181
2,805
2,637
3,038
2,603
2,295
2,185
2,926
Peanuts
13,828
15,423
14,802
12,379
15,090
12,005
10,851
13,080
11,464
10,669
9,060
Peas
3,066
3,333
3,466
3,705
3,233
4,523
2,825
3,859
3,498
3,244
3,168
Peppers
214
284
257
276
311
291
399
384
440
364
606
Potatoes
4,907
5,921
5,233
4,945
5,392
5,051
5,620
4,266
4,348
4,317
4,045
Pumpkins
238
254
244
265
246
290
293
267
130
95
168
Radishes
0
0
0
0
0
0
0
0
0
0
34
Rice
9,659
9,199
9,170
9,214
10,496
8,712
9,712
9,028
10,687
17,999
20,037
Sorghum Grain
5,348
4,588
5,361
3,883
3,553
2,936
3,462
2,503
2,698
2,311
2,272
Sorghum for
silage
218
236
282
252
179
211
225
187
168
167
121
Soybeans
70,073
67,980
63,414
57,642
66,042
52,916
56,572
51,691
55,228
50,664
50,411
Squash
97
56
56
70
87
103
105
111
92
70
149
Sugar beets
6,277
6,482
7,213
6,166
7,228
6,084
4,504
4,486
3,420
3,934
4,749
Sugarcane
19,061
18,530
17,444
16,158
14,347
12,147
13,280
12,690
12,663
14,968
15,376
Sunflower
654
434
713
649
680
523
465
429
733
1,243
750
Sweet Potatoes
2,432
2,739
3,102
3,085
2,891
2,860
3,410
3,540
1,695
1,459
3,079
Tobacco
3,450
2,546
2,174
2,051
2,332
1,941
1,903
2,216
1,753
1,365
1,257
Tomatoes
2,567
2,623
2,840
2,856
3,010
3,016
3,004
2,127
3,039
4,321
2,982
Wheat
42,145
32,638
40,295
38,640
33,911
31,264
33,943
33,578
31,322
25,797
29,723
Total
762,484
726,638
744,564
681,091
731,766
677,633
669,956
656,211
642,645
624,478
569,854
Crop Type
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Alfalfa
62,642
62,504
57,362
56,258
52,225
53,611
53,721
49,848
49,151
48,486
45,641
Asparagus
14
15
8
23
18
13
20
9
6
7
12
Barley
2,152
2,931
2,119
3,561
1,634
1,928
1,814
2,353
3,387
3,464
3,183
Beans and
Lentils
1,788
2,389
1,967
1,421
2,204
2,234
2,224
2,130
2,245
2,499
3,086
Broccoli
8
1
6
0
3
2
1
10
0
2
8
Cabbage
55
60
40
26
24
38
55
79
36
53
41
Carrots
2,053
3,012
2,011
1,640
654
1,330
450
1,145
919
1,207
2,530
Cauliflower
2
4
4
0
5
0
0
5
1
0
5
Celery
287
445
403
693
632
630
731
718
640
43
47
Corn
114,825
101,695
110,754
125,805
112,545
113,901
129,388
123,378
131,972
118,018
119,829
Corn for silage
4,943
4,644
4,747
4,595
4,016
3,862
4,874
4,136
3,148
3,369
3,682
Cotton
47,280
42,225
46,101
39,763
45,836
44,746
34,876
31,812
29,707
34,049
40,214
Cucumbers
5
36
52
76
30
29
17
15
0
0
23
A-329
-------
Flaxseed
7,615
6,205
6,483
4,995
5,662
4,742
4,537
4,019
5,333
4,880
3,771
Garlic
592
296
338
367
407
497
331
351
338
66
101
Greens
14
0
0
0
0
0
0
0
0
0
0
Hay Grass
35,074
33,468
35,766
35,552
31,241
28,897
30,394
29,387
28,696
27,765
26,698
Hay legume
31,120
29,950
30,343
28,541
26,310
24,668
24,915
24,415
24,724
23,527
22,687
Lettuce Head
7
9
34
16
23
43
68
55
58
206
79
Lettuce Leaf
23
46
8
17
21
4
12
20
3
0
9
Melons
Cantaloup
661
419
442
448
406
263
419
322
281
1,006
616
Melons
Honeydew
87
143
113
141
73
64
50
18
0
6
37
Melons
Watermelon
3,666
3,345
3,642
4,521
3,676
3,733
4,176
4,835
4,479
2,593
4,891
Millet
139,408
79,101
92,480
104,170
109,375
95,290
124,607
122,910
126,667
121,519
108,278
Oats
1,716
1,667
1,658
1,721
2,019
1,540
1,584
1,732
2,073
1,868
1,305
Onions
573
621
711
1,067
771
808
860
985
1,013
1,046
1,579
Other Vegetables
2,552
2,748
2,622
2,767
2,993
2,574
3,052
2,453
2,512
952
1,022
Peanuts
12,198
10,447
11,961
14,464
13,977
10,533
12,173
12,259
10,775
12,284
11,419
Peas
5,793
4,706
4,646
6,401
5,336
4,253
4,981
4,137
5,594
4,779
3,523
Peppers
677
665
688
660
504
569
564
673
665
641
550
Potatoes
3,857
5,357
4,765
4,557
4,874
6,515
4,524
4,918
4,982
4,279
5,589
Pumpkins
131
194
206
259
219
196
200
291
188
974
877
Radishes
89
0
0
0
0
0
0
0
0
0
0
Rice
12,505
12,280
10,362
11,660
11,741
10,078
8,815
9,487
10,804
10,807
9,220
Sorghum Grain
1,810
1,357
1,633
1,727
1,324
946
2,017
1,508
688
1,019
1,032
Sorghum for
silage
76
130
193
133
195
175
205
115
220
173
76
Soybeans
49,766
49,052
43,170
52,790
51,285
50,506
44,114
47,300
52,562
51,685
44,481
Squash
156
132
119
178
159
144
147
120
120
356
165
Sugar beets
3,555
2,955
2,560
2,999
2,560
3,373
1,630
1,887
1,796
3,799
2,223
Sugarcane
15,019
15,472
15,441
17,283
16,033
18,923
16,296
13,743
13,828
11,800
12,669
Sunflower
842
344
451
374
388
718
741
793
753
411
663
Sweet Potatoes
2,346
2,420
1,954
1,417
2,140
1,237
1,630
1,091
1,829
2,608
2,377
Tobacco
923
824
914
1,172
775
1,044
851
1,105
801
817
459
Tomatoes
3,270
3,382
3,206
4,005
2,812
3,513
3,795
3,297
3,445
5,366
3,795
Wheat
23,357
20,961
27,902
26,404
24,732
20,419
22,511
29,263
25,360
26,126
24,705
Total
595,531
508,657
530,385
564,667
541,856
518,590
548,369
539,128
551,800
534,564
513,209
Crop Type
2012
Alfalfa
40,879
Asparagus
6
Barley
3,932
Beans and
Lentils
2,522
Broccoli
4
Cabbage
47
Carrots
1,375
Cauliflower
4
Celery
81
Corn
107,230
Corn for silage
2,642
Cotton
38,179
Cucumbers
92
Flaxseed
3,658
Garlic
92
Greens
0
Hay Grass
24,077
Hay legume
20,211
Lettuce Head
31
A-330 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Lettuce Leaf
5
Melons
Cantaloup
591
Melons
Honeydew
71
Melons
Watermelon
4,332
Millet
74,845
Oats
1,286
Onions
1,513
Other
Vegetables
833
Peanuts
16,557
Peas
4,009
Peppers
864
Potatoes
6,080
Pumpkins
898
Radishes
0
Rice
8,640
Sorghum Grain
824
Sorghum for
silage
147
Soybeans
44,875
Squash
302
Sugar beets
2,658
Sugarcane
13,471
Sunflower
761
Sweet Potatoes
2,743
Tobacco
1,065
Tomatoes
4,305
Wheat
24,343
Total
461,080
Note: The latter part of the time series (i.e., 2013-2016) in this Inventory is estimated with data splicing methods. 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.
Step Ye: Additional Activity Data for Indirect N2O Emissions
A portion of the N that is applied as synthetic fertilizer, livestock manure, biosolids (i.e., sewage sludge), and other organic
amendments volatilizes as NH3 and NOx. In turn, this N is returned to soils through atmospheric deposition, thereby
increasing mineral N availability and enhancing N2O production. Additional N is lost from soils through leaching as water
percolates through a soil profile and through runoff with overland water flow. N losses from leaching and runoff enter
groundwater and waterways, from which a portion is emitted as N2O. However, N leaching is assumed to be an insignificant
source of indirect N2O in cropland and grassland systems where the amount of precipitation plus irrigation does not exceed
80 percent of the potential evapotranspiration. These areas are typically semi-arid to arid, and nitrate leaching to groundwater
is a relatively uncommon event; moreover IPCC (2006) recommends limiting the amount of nitrate leaching assumed to be
a source of indirect N2O emissions based on precipitation, irrigation and potential evapotranspiration.
The activity data for synthetic fertilizer, livestock manure, other organic amendments, residue N inputs, biosolids N, and
other N inputs are the same as those used in the calculation of direct emissions from agricultural mineral soils, and may be
found in Table A-201 through Table A-206, and Table A-208.
Using the DayCent model, volatilization and leaching/surface run-off of N from soils is estimated in the simulations for
crops and non-federal grasslands in the Tier 3 method. DayCent simulates the processes leading to these losses of N based
on environmental conditions (i.e., weather patterns and soil characteristics), management impacts (e.g., plowing, irrigation,
harvest), and soil N availability. Note that the DayCent model accounts for losses of N from all anthropogenic activity, not
just the inputs of N from mineral fertilization and organic amendments, which are addressed in the Tier 1 methodology.
Similarly, the N available for producing indirect emissions resulting from grassland management as well as deposited PRP
manure is also estimated by DayCent. However, indirect emissions are not estimated f the amount of precipitation plus
irrigation does not exceed 80 percent of the potential evapotranspiration. Volatilized losses of N are summed for each day
in the annual cycle to provide an estimate of the amount of N subject to indirect N2O emissions. In addition, the daily losses
A-331
-------
of N through leaching and runoff in overland flow are summed for the annual cycle. Uncertainty in the estimates is derived
from the measure of variability in the fertilizer and organic amendment activity data (see Step la for further information).
The Tier 1 method is used to estimate N losses from mineral soils due to volatilization and leaching/runoff for crops,
biosolids applications, and PRP manure on federal grasslands, which are not simulated by DayCent. To estimate volatilized
losses, synthetic fertilizers, manure, biosolids, and other organic N inputs are multiplied by the fraction subject to gaseous
losses using the respective default values of 0.1 kg N/kg N added as mineral fertilizers and 0.2 kg N/kg N added as manure
(IPCC 2006). Uncertainty in the volatilized N ranges from 0.03-0.3 kg NH3-N+NOx-N/kg N for synthetic fertilizer and 0.05-
0.5 kg NH3-N+NOx-N/kg N for organic amendments (IPCC 2006). Leaching/runoff losses of N are estimated by summing
the N additions from synthetic and other organic fertilizers, manure, biosolids, and above- and below-ground crop residues,
and then multiplying by the default fraction subject to leaching/runoff losses of 0.3 kg N/kg N applied, with an uncertainty
from 0.1-0.8 kg N03-N/kg N (IPCC 2006). However, N leaching is assumed to be an insignificant source of indirect N2O
emissions if the amount of precipitation plus irrigation did not exceed 80 percent of the potential evapotranspiration. PDFs
are derived for each of the N inputs in the same manner as direct N2O emissions, discussed in Steps la and lc.
Volatilized N is summed for losses from croplands and grasslands. Similarly, the annual amounts of N lost from soil profiles
through leaching and surface runoff are summed to obtain the total losses for this pathway.
Step 2: Estimate Soil Organic C Stock Changes, Direct N2O Emissions from Mineral Soils, and CH4 Emissions from Rice
Cultivation
In this step, soil organic C stock changes, N2O emissions, and CH4 emissions from rice cultivation are estimated for cropland
and non-federal grasslands. Three methods are used to estimate soil organic C stock changes, direct N2O emissions from
mineral soils, and CH4 emissions from rice cultivation. The DayCent process-based model is used for the croplands and non-
federal grasslands included in the Tier 3 method. A Tier 2 method is used to estimate soil organic C stock changes for crop
histories that included crops that are not simulated by DayCent and land use change other than conversions between cropland
and grassland. A Tier 1 methodology is used to estimate N2O emissions from crops that are not simulated by DayCent, PRP
manure N deposition on federal grasslands, and CH4 emissions from rice cultivation. Soil organic C stock changes and N2O
emissions are not estimated for federal grasslands (other than the effect of PRP manure N), but are under evaluation as a
planned improvement and may be estimated in future inventories.
Step 2a: Estimate Soil Organic C Stock Changes, N2O Emissions, and CH4 emissions for Crops and Non-Federal Grassland
with the Tier 3 DayCent Model
Crops that are simulated with DayCent include alfalfa hay, barley, corn, cotton, dry beans, grass hay, grass-clover hay, oats,
onions, peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tomatoes, and wheat, which combined represent
approximately 90 percent of total cropland in the United States. The DayCent simulations also includes all non-federal
grasslands in the United States.
The methodology description is divided into two sub-steps. First, the DayCent model is used to establish the initial conditions
andC stocks for 1979, which is the first year of theNRI survey. In the second sub-step, DayCent is used to simulate changes
in soil organic C stocks, direct N2O emissions, and CH4 emissions from rice cultivation based on the land-use and
management histories recorded in the NRI (USDA-NRCS 2015).
Simulate Initial Conditions (Pre-NRI Conditions): DayCent model initialization involves two steps, with the goal of
estimating the most accurate stock for the pre-NRI history, and the distribution of organic C among the pools represented in
the model (e.g., Structural, Metabolic, Active, Slow, and Passive). Each pool has a different turnover rate (representing the
heterogeneous nature of soil organic matter), and the amount of C in each pool at any point in time influences the forward
trajectory of the total soil organic C storage. There is currently no national set of soil C measurements that can be used for
establishing initial conditions in the model. Sensitivity analysis of the soil organic C algorithms showed that the rate of
change of soil organic matter is relatively insensitive to the amount of total soil organic C but is highly sensitive to the
relative distribution of C among different pools (Parton et al. 1987). By simulating the historical land use prior to the
inventory period, initial pool distributions are estimated in an unbiased way.
The first step involves running the model to a steady-state condition (e.g., equilibrium) under native vegetation, historical
climate data based on the PRISM product (1981 through 2010), and the soil physical attributes for the NRI points. Native
vegetation is represented at the MLRA level for pre-settlement time periods in the United States. The model simulates 5,000
years in the pre-settlement era in order to achieve a steady-state condition.
The second step is to simulate the period of time from European settlement and expansion of agriculture to the beginning of
the NRI survey, representing the influence of historic land-use change and management, particularly the conversion of native
vegetation to agricultural uses. This encompasses a varying time period from land conversion (depending on historical
A-332 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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 2012. The simulations address the influence of soil management on direct N2O emissions, soil organic C
stock changes and losses of N from the profile through leaching/runoff and volatilization. The NRI histories identify the
land use and land use change histories for the NRI survey locations, as well as cropping patterns and irrigation history (see
Step 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 and EVI data (See Step lb for description of the inputs). The total
number of DayCent simulations is over 18 million with 100 repeated simulations (i.e., iterations) for each NRI point location
in a Monte Carlo Analysis. The simulation system incorporates a dedicated MySQL database server and a 30-node parallel
processing computer cluster. Input/output operations are managed by a set of run executive programs written in PERL.
The simulations for the NRI history are integrated with the uncertainty analysis. Evaluating uncertainty is an integral part of
the analysis and includes three components: (1) uncertainty in the main activity data inputs affecting soil C and N2O
emissions (input uncertainty); (2) uncertainty in the model formulation and parameterization (structural uncertainty); and
(3) uncertainty in the land-use and management system areas (scaling uncertainty) (Ogle et al. 2010; Del Grosso et al. 2010).
For component 1, input uncertainty is evaluated for fertilization management, manure applications, and tillage, which are
the primary management activity data that are supplemental to the NRI observations and have significant influence on soil
organic C dynamics, soil N2O and CH4 emissions. As described in Step lb, PDFs are derived from surveys at the county
scale for the inputs in most cases. In addition, uncertainty is included for predictions of EVI data that are needed to fill-data
gaps and extend the time series (see Enhanced Vegetation Index in Step lb). To represent uncertainty in all of these inputs,
a Monte-Carlo Analysis is used with 100 iterations for each NRI point; random draws are made from PDFs for fertilizer,
manure application, tillage, and EVI predictions. As described above, an adjustment factor is also selected from PDFs with
normal densities to represent the dependence between manure amendments and N fertilizer application rates.
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 92
long-term field experiments that have over 900 treatment observations, representing a variety of management conditions
(e.g., variation in crop rotation, tillage, fertilization rates, and manure amendments). There are 41 experimental sites
available with over200 treatment observations to evaluate structural uncertainty in the N2O emission predictions from
DayCent (Del Grosso et al. 2010). There are 10 experiments with 126 treatment observations for CH4 emissions from rice
cultivation. The inputs to the model are essentially known in the simulations for the long-term experiments, and, therefore,
the analysis is designed to evaluate uncertainties associated with the model structure (i.e., model algorithms and
parameterization). USDA is developing a national soil monitoring network to evaluate the Inventory in the future (Spencer
et al. 2011).
The relationship between modeled soil organic C stocks and field measurements are statistically analyzed using linear-mixed
effect modeling techniques (Figure A-l 1). Additional fixed effects are included in the mixed effect model if they explained
significant variation in the relationship between modeled and measured stocks (i.e., if they met an alpha level of 0.05 for
significance). Several variables are tested, including land-use class; type of tillage; cropping system; geographic location;
climate; soil texture; time since the management change; original land cover (i.e., forest or grassland); grain harvest as
predicted by the model compared to the experimental values; and variation in fertilizer and residue management. The final
cropland model includes variables for modeled soil organic C inclusion of hay/pasture in cropping rotations, use of no-till,
set-aside lands, organic matter amendments, and inclusion of bare fallow in the rotation, which are significant at an alpha
level of 0.05. The final grassland model only included the model soil organic C. These fixed effects are used to make an
adjustment to modeled values due to biases that are creating significant mismatches between the modeled and measured
stocks. For soil N2O, simulated DayCent emissions are a highly significant predictor of the measurements, with a p-value of
<0.01. Several other variables are considered in the statistical model to evaluate if DayCent exhibits bias under certain
conditions related to climate, soil types, and management practices (Figure A-l 3). Random effects are included in the model
to capture the dependence in time series and data collected from the same site, which are needed to estimate appropriate
standard deviations for parameter coefficients. For rice CH4 emissions, simulated DayCent emissions are a significant
predictor of measured emission, similar to the results for soil N2O emissions. Several other variables are tested including
soil characteristics, geographic location (i.e., state), and management practices (e.g., with and without winter flooding)
(Figure A-14). The only other significant variable is geographic location because the model does not predict emissions as
A-333
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accurately for California as other rice-producing states. Random effects are included to capture the dependence in time series
and the data collected from the same site.
A Monte Carlo approach is used to apply the uncertainty estimator (Ogle et al. 2010). Parameter values for the statistical
equation (i.e., fixed effects) are selected from their joint probability distribution, as well as random error associated with
fine-scale estimates at NRI points, and the residual or unexplained error associated with the linear mixed-effect model. The
estimate and associated management information is then used as input into the equation, and adjusted values are computed
for each C stock, N2O and CH4 emissions estimate. The variance of the adjusted estimates is computed from the 100
simulated values from the Monte Carlo analysis.
The third element is the uncertainty associated with scaling the DayCent results for each NRI point to the entire land base,
using the expansion factors provided with the NRI survey dataset. The expansion factors represent the number of hectares
associated with the land-use and management history for a particular point. This uncertainty is determined by computing the
variances from a set of replicated weights for the expansion factor. For the land base that is simulated with the DayCent
model, soil organic C stock changes are provided in Table A-209, soil N2O emissions are provided in Table A-210, and rice
cultivation CH4 emissions in Table A-211.
Table A-209: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Land Base Simulated with the
Tier 3 BayCent Model-Based Approach
[MMTCO2 Eq.)
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Year
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
1990
(65.75)
(98.30) to (33.20)
20.62
11.32 to 29.93
(10.20)
(47.18) to 26.77
(5.11)
(9.71) to (.51)
1991
(71.64)
(103.66) to (39.63)
21.41
11.46 to 31.37
(12.53)
(50.86) to 25.80
(5.19)
(9.27) to (1.12)
1992
(63.04)
(91.32) to (34.76)
23.61
13.62 to 33.60
(6.81)
(37.06) to 23.44
(4.92)
(10.03) to.18
1993
(43.64)
(73.09) to (14.20)
17.95
7.22 to 28.69
1.66
(33.17) to 36.50
(5.53)
(10.31) to (.74)
1994
(55.49)
(86.59) to (24.40)
14.40
3.88 to 24.92
(24.13)
(58.06) to 9.80
(7.36)
(12.99) to (1.73)
1995
(49.18)
(80.21) to (18.15)
20.04
8.90 to 31.17
(0.96)
(33.43) to 31.50
(6.37)
(12.27) to (.48)
1996
(57.70)
(87.89) to (27.50)
16.93
7.08 to 26.79
(22.31)
(53.52) to 8.90
(7.59)
(14.10) to (1.08)
1997
(55.46)
(89.14) to (21.79)
18.98
8.58 to 29.37
(9.10)
(47.05) to 28.84
(7.46)
(13.49) to (1.43)
1998
(44.19)
(76.62) to (11.76)
12.57
1.18 to 23.95
(16.03)
(53.16) to 21.10
(8.12)
(15.15) to (1.10)
1999
(59.68)
(88.69) to (30.67)
12.78
2.58 to 22.98
(3.96)
(36.93) to 29.02
(8.55)
(15.40) to (1.69)
2000
(65.43)
(100.61) to (30.26)
12.95
1.93 to 23.98
(33.13)
(72.27) to 6.01
(10.51)
(17.58) to (3.44)
2001
(58.29)
(91.06) to (25.51)
11.21
.34 to 22.09
(8.82)
(40.46) to 22.82
(9.81)
(17.37) to (2.26)
2002
(54.71)
(83.13) to (26.29)
11.21
.07 to 22.34
(9.63)
(45.47) to 26.20
(10.51)
(17.31) to (3.70)
2003
(47.63)
(78.33) to (16.94)
13.08
2.53 to 23.63
(6.34)
(39.14) to 26.46
(10.52)
(17.46) to (3.59)
2004
(47.56)
(79.85) to (15.27)
12.63
3.60 to 21.66
0.42
(34.25) to 35.09
(9.91)
(17.96) to (1.86)
2005
(50.81)
(84.26) to (17.36)
12.40
1.10 to 23.71
1.97
(34.50) to 38.43
(10.22)
(17.93) to (2.52)
2006
(47.47)
(76.01) to (18.92)
13.21
3.03 to 23.39
(14.85)
(48.99) to 19.29
(12.24)
(20.62) to (3.86)
2007
(45.56)
(76.20) to (14.92)
11.83
1.45 to 22.21
1.80
(31.07) to 34.67
(10.92)
(19.03) to (2.81)
2008
(34.45)
(67.84) to (1.06)
12.68
2.46 to 22.89
(10.05)
(43.50) to 23.39
(10.84)
(18.52) to (3.17)
2009
(29.33)
(58.63) to (.04)
12.56
3.13 to 21.99
(5.66)
(43.85) to 32.53
(10.64)
(17.89) to (3.38)
2010
(29.43)
(62.67) to 3.80
14.53
5.38 to 23.68
1.34
(30.62) to 33.30
(10.76)
(19.24) to (2.29)
2011
(43.60)
(76.77) to (10.44)
14.27
4.15 to 24.40
(15.97)
(54.46) to 22.52
(10.97)
(18.96) to (2.98)
2012
(46.60)
(83.06) to (10.14)
13.38
2.10 to 24.66
(24.56)
(60.90) to 11.78
(11.21)
(19.48) to (2.94)
Note: Estimates after 2012 are based on a data splicing method (See the Cropland Remaining Cropland section for more information). The Tier 3 method will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Table A-210: Annual N2O Emissions (95% Confidence Interval) for the Land Base Simulated with the Tier 3 OayCent Model-
Based Approach (MBIT CO2 Eg.)
Tier 3 Cropland
Non-Federal Grasslands
Year
Estimate
95% CI
Estimate
95% CI
1990
128.5
121.74to 137.33
51.2
48.03 to 55.26
1991
128.4
121.69 to 137.19
52.4
49.16 to 56.50
1992
128.5
121.81 to 137.26
51.4
48.61 to 55.10
1993
128.5
121.73 to 137.53
53.0
50.04 to 56.74
1994
127.8
121.29 to 136.23
49.4
46.52 to 53.03
1995
129.1
122.46 to 137.81
50.8
47.92 to 54.42
1996
129.6
122.94 to 138.48
53.9
50.62 to 58.21
1997
129.2
122.48 to 138.03
54.0
50.91 to 57.96
1998
136.2
128.94 to 145.71
58.6
55.19 to 62.99
1999
129.6
122.95 to 138.19
49.2
46.60 to 52.59
2000
132.2
125.41 to 141.24
49.7
46.63 to 53.68
A-334 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
2001
134.0
127.0 to 143.26
51.8
48.89 to 55.69
2002
132.5
125.60 to 141.66
53.8
50.52 to 58.06
2003
135.4
128.41 to 144.74
52.3
49.40 to 56.13
2004
142.0
134.79 to 151.32
62.7
58.77 to 67.99
2005
134.7
127.87 to 143.71
53.4
50.55 to 56.97
2006
135.7
128.82 to 144.66
55.9
52.77 to 60.04
2007
140.8
133.4 to 150.41
57.7
54.19 to 62.42
2008
137.3
130.17 to 146.68
54.7
51.81 to 58.49
2009
139.6
132.41 to 148.95
58.2
54.74 to 62.67
2010
144.2
136.70 to 154.10
57.4
54.31 to 61.52
2011
138.0
130.98 to 147.26
50.9
48.40 to 54.24
2012
135.7
128.72 to 144.87
47.7
44.84 to 51.4
Note: Estimates after 2012 are based on a data splicing method (See the Agricultural Soil Management section for more information). The Tier 3 method will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Table A-211: Annual CH4 Emissions from Rice Cultivation (95% Confidence Interval) for Rice Cultivation Simulated with the
Tier 3 DayCent Model-Rased Approach [MBIT CO2 Eq.)
Year
Estimate
95% CI
1990
14.39
10.22 to 18.57
1991
15.18
10.86 to 19.49
1992
15.17
10.58 to 19.76
1993
15.24
11.05 to 19.44
1994
13.10
9.20 to 16.99
1995
14.23
10.22 to 18.23
1996
14.40
10.32 to 18.48
1997
14.22
10.16 to 18.27
1998
14.35
9.96 to 18.73
1999
14.82
10.13 to 19.52
2000
14.98
10.45 to 19.51
2001
13.62
9.32 to 17.93
2002
14.62
10.23 to 19.01
2003
12.58
8.76 to 16.41
2004
12.26
8.40 to 16.12
2005
14.93
10.35 to 19.52
2006
11.38
7.96 to 14.80
2007
12.54
8.82 to 16.27
2008
9.92
6.85 to 12.99
2009
12.76
8.80 to 16.71
2010
14.09
9.85 to 18.33
2011
12.59
8.92 to 16.26
2012
9.96
6.70 to 13.22
Note: Estimates after 2012 are based on a data splicing method (See the Rice Cultivation section for more information). The Tier 3 method will be applied in a
future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
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 N2O emissions. This means, for example,
that N2O emissions from applied synthetic fertilizer cannot be separated from emissions due to other N inputs, such as crop
residues. It is desirable, however, to report emissions associated with specific N inputs. Thus, for each NRI point, the N
inputs in a simulation are determined for anthropogenic practices discussed in IPCC (2006), including synthetic mineral N
fertilization, organic amendments, and crop residue N added to soils (including N-fixing crops). The percentage of N input
for anthropogenic practices is divided by the total N input, and this proportion is used to determine the amount of N2O
emissions assigned to each of the practices. For example, if 70 percent of the mineral N made available in the soil is due
to mineral fertilization, then 70 percent of the N2O emissions are assigned to this practice. The remainder of soil N2O
emissions is reported under "other N inputs," which includes mineralization due to decomposition of soil organic matter and
litter, as well as asymbiotic N fixation from the atmosphere. Asymbiotic N fixation by soil bacteria is a minor source of N,
^ This method is a simplification of reality to allow partitioning of N2O emissions, as it assumes that all N inputs have an identical chance
of being converted to N2O. This is unlikely to be the case, but DAYCENT does not track N2O emissions by source of mineral N so this
approximation is the only approach that can be used currently for partitioning N2O emissions by source of N input. Moreover, this approach
is similar to the IPCC Tier 1 method (IPCC 2006), which uses the same direct emissions factor for most N sources (e.g., PRP). Further
research and model development may allow for other approaches in the future.
A-335
-------
typically not exceeding 10 percent of total N inputs to agroecosystems. Mineralization of soil organic matter is a more
significant source of N, but is still typically less than half of the amount of N made available in the cropland soils compared
to application of synthetic fertilizers and manure amendments, along with symbiotic fixation. Mineralization of soil organic
matter accounts for the majority of available N in grassland soils. Accounting for the influence of "other N inputs" is
necessary because the processes leading to these inputs of N are influenced by management. While this method allows for
attribution of N2O emissions to the individual N inputs to the soils, it is important to realize that sources such as synthetic
fertilization may have a larger impact on N2O emissions than would be suggested by the associated level of N input for this
source (Delgado et al. 2009). Further research will be needed to improve upon this attribution method, however. The results
associated with subdividing the N2O emissions based on N inputs are provided in Table A-212 and Table A-213.
Table fl-212: Direct N2O Emissions from Cropland Soils [MBIT CO2 Eq.l
Activity
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Mineral Soils
144.1
144.2
143.9
143.0
147.1
146.2 148.2
147.0
154.3
148.1
149.2
148.9
148.0
152.4
159.5
150.6
Tier 3
128.5
128.4
128.5
128.5
127.8
129.1 129.6
129.2
136.2
129.6
132.2
134.0
132.5
135.4
142.0
134.7
Synthetic Fertilizer
47.5
47.8
49.3
47.1
48.6
46.2
48.7
48.0
47.9
45.4
47.8
46.8
47.5
47.3
48.3
47.6
Managed Manure
3.9
3.8
4.0
4.0
3.8
3.5
3.7
3.7
3.9
4.6
4.7
4.6
4.7
4.6
4.5
4.4
Residue Na
18.6
20.1
18.3
18.9
18.4
20.2
19.2
19.2
18.9
22.1
20.5
20.2
20.5
20.9
19.6
20.5
Mineralization and
Asymbiotic Fixation
58.4
56.7
56.9
58.5
56.9
59.2
58.1
58.3
65.5
57.5
59.2
62.4
59.8
62.6
69.6
62.2
Tier 1
15.7
15.8
15.5
14.5
19.3
17.1
18.6
17.9
18.1
18.5
17.0
14.9
15.4
17.0
17.6
15.8
Synthetic Fertilizer
6.0
6.1
5.8
5.3
9.4
7.0
8.7
8.0
8.5
9.6
8.1
6.0
6.7
8.0
8.8
7.0
Managed Manure and
Other Organic
Commercial Fertilizer
6.2
6.4
6.3
6.1
6.6
7.0
6.8
6.9
6.7
6.1
6.3
6.3
6.4
6.5
6.3
6.4
Residue Na
3.5
3.3
3.4
3.1
3.3
3.1
3.0
3.0
2.9
2.8
2.6
2.7
2.3
2.4
2.6
2.5
Organic Soils
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.4
3.4
3.3
3.3
3.3
Total3
147.5
147.5
147.2
146.3
150.3
149.5 151.5
150.3
157.5
151.4
152.5
152.3
151.3
155.7
162.9
153.9
1 Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
Activity
2006
2007
2008
2009
2010
2011
2012
Mineral Soils
153.1
157.4
153.3
154.1
159.5
155.1
153.5
Tier 3
135.7
140.8
137.3
139.6
144.2
138.0
135.7
Synthetic Fertilizer
47.9
50.8
48.9
48.0
48.3
49.5
51.0
Managed Manure
4.5
4.7
4.5
4.8
4.9
4.9
5.0
Residue Na
20.3
20.4
19.3
19.6
21.5
21.5
21.4
Mineralization and
Asymbiotic Fixation
63.0
64.9
64.7
67.2
69.6
62.1
58.2
Tier 1
17.4
16.6
16.0
14.5
15.3
17.1
17.8
Synthetic Fertilizer
8.2
7.4
6.8
5.7
6.7
8.5
9.4
Managed Manure and
Other Organic
Commercial Fertilizer
6.8
6.7
6.7
6.3
6.1
6.2
6.2
Residue Na
2.4
2.5
2.4
2.5
2.4
2.3
2.1
Organic Soils
3.3
3.3
3.3
3.2
3.2
3.2
3.2
Total3
156.4
160.7
156.5
157.3
162.7
158.3
156.7
a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
Note: Estimates after 2012 are based on a data splicing method (See the Agricultural Soil Management section for more information). The Tier 1 and 3 methods
will be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
A-336 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table fl-213: Direct N2O Emissions from Grasslands [MMT CO2 Eg.]
Activity
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Mineral Soils
61.3
62.3
61.6
63.2
59.6
61.0
63.9
63.4
67.6
58.0
58.2
60.0
61.8
60.1
70.4
61.1
Tier 3
51.2
52.4
51.4
53.0
49.4
50.8
53.9
54.0
58.6
49.2
49.7
51.8
53.8
52.3
62.7
53.4
Synthetic Fertilizer
0.9
0.9
0.9
0.8
0.9
0.8
0.8
0.8
0.9
0.8
0.8
0.7
0.7
0.7
0.8
0.8
PRP Manure
6.3
6.2
6.3
6.5
6.6
6.6
7.2
6.7
7.5
6.2
6.5
6.6
7.0
6.6
7.5
6.5
Managed Manure
0.9
0.8
0.8
0.9
0.9
0.9
0.9
0.9
1.1
0.9
1.0
1.0
1.1
1.0
1.2
1.1
Residue Na
Mineralization and Asymbiotic
Fixation
14.5
28.5
14.6
29.9
14.7
28.6
15.3
29.5
13.4
27.5
15.0
27.5
14.6
30.3
15.2
30.4
15.1
33.9
15.3
26.0
13.9
27.5
15.0
28.5
14.9
30.0
14.9
29.0
15.8
37.5
15.8
29.2
Tier 1
10.1
9.9
10.2
10.3
10.3
10.2
10.0
9.4
9.1
8.7
8.5
8.2
8.0
7.8
7.7
7.8
PRP Manure
9.9
9.7
9.9
10.0
10.0
9.9
9.6
9.1
8.7
8.4
8.1
7.8
7.6
7.3
7.2
7.3
Biosolids (i.e..Sewage Sludge)
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.5
Organic Soils
3.3
3.2
3.2
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.4
3.5
3.5
3.5
3.5
3.5
Total
64.5
65.6
64.8
66.5
63.0
64.3
67.2
66.7
71.0
61.3
61.5
63.5
65.3
63.6
73.9
64.6
Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
Activity
2006
2007
2008
2009
2010
2011
2012
Mineral Soils
63.7
65.1
62.2
65.8
65.1
58.8
55.7
Tier 3
55.9
57.7
54.7
58.2
57.4
50.9
47.7
Synthetic Fertilizer
0.8
0.8
0.7
0.8
0.8
0.8
0.7
PRP Manure
7.1
6.9
6.6
7.0
6.6
6.3
5.9
Managed Manure
1.1
1.1
1.0
1.1
1.1
1.1
1.1
Residue Na
Mineralization and Asymbiotic
Fixation
15.6
31.3
16.5
32.6
15.5
30.9
15.4
33.8
16.5
32.4
14.8
28.1
14.2
25.8
Tier 1
7.8
7.4
7.5
7.6
7.7
7.8
8.0
PRP Manure
7.3
6.9
7.0
7.1
7.1
7.3
7.4
Biosolids (i.e., Sewage Sludge)
0.5
0.5
0.5
0.5
0.5
0.5
0.6
Organic Soils
3.5
3.4
3.4
3.4
3.3
3.3
3.3
Total
67.2
68.5
65.6
69.1
68.5
62.1
59.0
Note: Estimates after 2012 are based on a data splicing method (See the Agricultural Soil Management section for more information). The Tier 1 and 3 methods
will be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Step 2b: Soil N2O Emissions from Agricultural Lands on Mineral Soils Approximated with the Tier 1 Approach
To estimate direct N2O emissions from N additions to crops in the Tier 1 method, the amount of N in applied synthetic
fertilizer, manure and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) is added to N
inputs from crop residues, and the resulting annual totals are multiplied by the IPCC default emission factor of 0.01 kgN20-
N/kg N (IPCC 2006) (see Table A-212). The uncertainty is determined based on simple error propagation methods (IPCC
2006). The uncertainty in the default emission factor ranges from 0.3-3.0 kg N20-N/kg N (IPCC 2006). For flooded rice
soils, the IPCC default emission factor is 0.003 kg N20-N/kg N and the uncertainty range is 0.000-0.006 kg N20-N/kg N
129
(IPCC 2006). Uncertainties in the emission factor and fertilizer additions are combined with uncertainty in the equations
used to calculate residue N additions from above- and below-ground biomass dry matter and N concentration to derive
overall uncertainty.
The Tier 1 method is also used to estimate emissions from manure N deposited by livestock on federal lands (i.e., PRP
manure N), and from biosolids (i.e., sewage sludge) application to grasslands. These two sources of N inputs to soils are
multiplied by the IPCC (2006) default emission factors (0.01 kg N20-N/kg N for sludge and horse, sheep, and goat manure,
and 0.02 kg N20-N/kg N for cattle, swine, and poultry manure) to estimate N2O emissions (Table A-213). The uncertainty
129 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-337
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is determined based on the Tier 1 error propagation methods provided by the IPCC (2006) with uncertainty in the default
emission factor ranging from 0.007 to 0.06 kg N20-N/kg N (IPCC 2006).
Step 2c: Soil Cfa Emissions from Agricultural Lands Approximated with the Tier 1 Approach
To estimate CH4 emissions from rice cultivation for the Tier 1 method, an adjusted daily emission factor is calculated using
the default baseline emission factor of 1.30 kg CH4 ha"1 d"1 (ranging 0.8-2.2 kg CH4 ha"1 d"1) multiplied by a scaling factor
for the cultivation water regime, pre-cultivation water regime and a scaling factor for organic amendments (IPCC 2006).
The water regime during cultivation is continuously flooded for rice production in the United States and so the scaling factor
is always 1 (ranging from 0.79 to 1.26). The pre-season water regime varies based on the proportion of land with winter
flooding; land that does not have winter flooding is assigned a value of 0.68 (ranging from 0.58 to 0.80) and areas with
winter flooding are assigned a value of 1 (ranging from 0.88 to 1.14). Organic amendments are estimated based on the
amount of rice straw and multiplied by 1 (ranging 0.97 to 1.04) for straw incorporated greater than 30 days before cultivation,
and by 0.29 (0.2 to 0.4) for straw incorporated greater than 30 days before cultivation. The adjusted daily emission factor is
multiplied by the cultivation period and harvested area to estimate the total CH4 emissions. The uncertainty is propagated
through the calculation using an Approach 2 method with a Monte Carlo analysis (IPCC 2006), combining uncertainties
associated with the adjusted daily emission factor and the harvested areas derived from the USDA NRI survey data.
Step 2d: Soil Organic C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2 Approach
Mineral soil organic C stock values are derived for crop rotations that were not simulated by DayCent and land converted
from non-agricultural land uses to cropland or grassland from 1990 through 2012, based on the land-use and management
activity data in conjunction with appropriate reference C stocks, land-use change, management, input, and wetland
restoration factors. Each input to the inventory calculations for the Tier 2 approach has uncertainty that is quantified in PDFs,
including the land-use and management activity data, reference C stocks, and management factors. A Monte Carlo Analysis
is used to quantify uncertainty in soil organic C stock changes for the inventory period based on uncertainty in the inputs.
Input values are randomly selected from PDFs in an iterative process to estimate SOC change for 50,000 times and produce
a 95 percent confidence interval for the inventory results.
Derive Mineral Soil Organic C Stock Change Factors: Stock change factors representative of U.S. conditions are estimated
from published studies (Ogle et al. 2003; Ogle et al. 2006). The numerical factors quantify the impact of changing land use
and management on SOC storage in mineral soils, including tillage practices, cropping rotation or intensification, and land
conversions between cultivated and native conditions (including 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) (Table A-214). Seven of these
climate zones occur in the conterminous United States and Hawaii (Eve et al. 2001).
Table A-214: Characteristics of the IPCC Climate Zones that Occur in the United States
Annual Average
Length of Dry Season
Climate Zone
Temperature fC)
Average Annual Precipitation (mm)
(months)
Cold Temperate, Dry
<10
< Potential Evapotranspiration
NA
Cold Temperate, Moist
<10
> Potential Evapotranspiration
NA
Warm Temperate, Dry
10-20
<600
NA
Warm Temperate, Moist
10-20
> Potential Evapotranspiration
NA
Sub-Tropical, Dry3
>20
<1,000
Usually long
Sub-Tropical, Moist (w/short dry season)3
>20
1,000-2,000
<5
a The climate characteristics listed in the table for these zones are those that correspond to the tropical dry and tropical moist zones of the IPCC.
They have been renamed "sub-tropical" here.
Mean precipitation and temperature (1950-2000) variables 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)
are used to classify climate zones (Figure A-15). IPCC climate zones are assigned to NRI point locations.
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 onpedon (i.e., soil) data from the National Soil Survey Characterization Database (NRCS 1997) (Table A-215). 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
A-338 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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.
Figure A-15: IPGC Climate Zones
JQ
Hawaii
IPCC Climate Zones
¦ Tropical Moist
Tropical Dry
Warm Temperate Moist
Warm Temperate Dry
¦ Cool Temperate Moist
Cool Temperate Dry
Boreal Moist
Boreal Dry
No Data
Table A-215: 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
Vertisols, Mollisols, Inceptisols,
High Clay Activity
Aridisols, and high base status
Mineral Soils
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 Alfisols,
Mineral Soils
and many Entisols
45 (n = 37)
52 (n = 113)
25 (n = 86)
40 (n = 300)
39 (n = 13)
47 (n = 7)
Any soils with greater than 70
percent sand and less than 8
Sandy Soils
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
a C stocks are not needed for organic soils.
Notes: C stocks are for the top 30 cm of the soil profile, and are estimated from pedon data available in the National Soil Survey Characterization database (NRCS
1997); sample size provided in parentheses (i.e., 'n! values refer to sample size).
To estimate the land use, management and input factors, studies had to report SOC stocks (or information to compute stocks),
depth of sampling, and the number of years since a management change to be included in the analysis. The data are analyzed
using linear mixed-effect 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. Tinear
regression models assume that the underlying data are independent observations, but this is not the case with data from the
A-339
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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-216). Variance is calculated for each of the U.S.
factor values, and used to construct PDFs with a normal density. In the IPCC method, specific factor values are given for
improved grassland, high input cropland with organic amendments, and for wetland rice, each of which influences C stock
changes in soils. Specifically, higher stocks are associated with increased productivity and C inputs (relative to native
grassland) on improved grassland with both medium and high input. Organic amendments in annual cropping systems
also increase SOC stocks due to greater C inputs, while high SOC stocks in rice cultivation are associated with reduced
decomposition due to periodic flooding. There are insufficient field studies to derive factor values for these systems from
the published literature, and, thus, estimates from IPCC (2006) are used under the assumption that they would best
approximate the impacts, given the lack of sufficient data to derive U.S.-specific factors. A measure of uncertainty is
provided for these factors in IPCC (2006), which is used to construct PDFs.
Table A-216: Soil Organic Carbon Stock Change Factors for the United States and the IPCC Default Values Associated with
Management Impacts on Mineral Soils
U.S. Factor
IPCC
Warm Moist
Warm Dry
Cool Moist
Cool Dry
default
Climate
Climate
Climate
Climate
Land-Use Change Factors
Cultivated3
1
1
1
1
1
General Uncult.ab (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
a Factors in the IPCC documentation (IPCC 2006) are converted to represent changes in SOC storage from a cultivated condition rather than a native condition.
b U .S.-specific factors are not estimated for land improvements, rice production, or high input with amendment because of few studies addressing the impact of
legume mixtures, irrigation, or manure applications for crop and grassland in the United States, or the impact of wetland rice production in the US. Factors provided
in IPCC (2006) are used as the best estimates of these impacts.
Note: The "n" values refer to sample size.
Wetland restoration management also influences SOC storage in mineral soils, because restoration leads to higher water
tables and inundation of the soil for at least part of the year. A stock change factor is estimated assessing the difference in
SOC storage between restored and unrestored wetlands enrolled in the Conservation Reserve Program (Euliss and Gleason
2002), which represents an initial increase of C in the restored soils over the first 10 years (Table A-217). A PDF with a
normal density is constructed from these data based on results from a linear regression model. Following the initial increase
of C, natural erosion and deposition leads to additional accretion of C in these wetlands. The mass accumulation rate of
organic C is estimated using annual sedimentation rates (cm/yr) in combination with percent organic C, and soil bulk density
(g/cm3) (Euliss and Gleason 2002). Procedures for calculation of mass accumulation rate are described in Dean and Gorham
(1998); the resulting rate and standard deviation are used to construct a PDF with a normal density (Table A-217).
Table A-217: Rate and standard deviation for the Initial Increase and Subsequent Annual Mass Accumulation Rate (Mg
C/ha-yrl in Soil Organic C Following Wetland Restoration of Conservation Reserve Program
Variable
Value
Factor (Initial Increase—First 10 Years)
1.22±0.18
Mass Accumulation (After Initial 10 Years)
0.79±0.05
Note: Mass accumulation rate represents additional gains in C for mineral soils after the first 10 years (Euliss and Gleason 2002).
130 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.
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Estimate Annual Changes in Mineral Soil Organic C Stocks: In accordance with IPCC methodology, annual changes in
mineral soil C are calculated by subtracting the beginning stock from the ending stock and then dividing by 20. For this
analysis, stocks are estimated for each year and difference between years is the stock change. From the final distribution of
50,000 values, a 95 percent confidence interval is generated based on the simulated values at the 2.5 and 97.5 percentiles in
the distribution (Ogle et al. 2003). Soil organic C stock changes are provided in Table A-218 through
Table fl-223
Step 2e: Estimate Additional Changes in Soil Organic C Stocks Due to Biosolids (i.e., Sewage Sludge) Amendments
There are two additional land use and management activities in U.S. agricultural lands that are not estimated in Steps 2a and
2b. The first activity involves the application of biosolids to agricultural lands. Minimal data exist on where and how much
biosolids are applied to U.S. agricultural soils, but national estimates of mineral soil land area receiving biosolids can be
approximated based on biosolids N production data, and the assumption that amendments are applied at a rate equivalent to
the assimilative capacity from Kellogg et al. (2000). In this Inventory, it is assumed that biosolids for agricultural land
application is only applied to grassland. The impact of organic amendments on SOC is calculated as 0.38 metric tonnes
C/ha-yr. This rate is based on the IPCC default method and country-specific factors (see Table A-216), by calculating the
effect of converting nominal, medium-input grassland to high input improved grassland. The assumptions are that reference
C stock are 50 metric tonnes C/ha, which represents a mid-range value of reference C stocks for the cropland soils in the
132
United States, that the land use factor for grassland of 1.4 and 1.11 for high input improved grassland are representative
of typical conditions, and that the change in stocks are occurring over a 20 year (default value) time period (i.e., [50 x 1.4 x
1.11 - 50 x 1.4] / 20 = 0.38). A nominal ±50 percent uncertainty is attached to these estimates due to limited information on
application and the rate of change in soil C stock change with biosolids amendments. The influence of biosolids on soil
organic C stocks are provided in Table A-224.
131 The difference in C stocks is divided by 20 because the stock change factors represent change over a 20-year time period.
132 Reference C stocks are based on cropland soils for the Tier 2 method applied in this Inventory.
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Table A-218: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Non-Federal Cropland Land
Base Estimated with the Tier 2 Analysis using U.S. Factor Values [MBIT CO; Eq./yrl
Non-Federal Cropland Remaining Grassland Converted to Forest Converted to Other Land Converted to
Croplands: Cropland Cropland Cropland Cropland
Year
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
Mineral
Soils
1990
-5.44
(7.97) to (2.99)
1.32
(0.72) to 2.26
0.22
(0.12) to .38
0.16
(0.09) to 0.28
1991
-6.19
(8.94) to (3.53)
1.26
(0.84) to 2.25
0.20
(0.13) to .36
0.15
(0.10) to 0.27
1992
-6.19
(9.77) to (3.63)
1.29
(1.07) to 2.32
0.19
(0.16) to .35
0.16
(0.13) to 0.29
1993
-6.95
(10.16) to (3.79)
1.35
(0.75) to 2.45
0.18
(0.10) to .32
0.17
(0.09) to 0.31
1994
-6.70
(9.93) to (3.52)
1.48
(0.37) to 2.63
0.18
(0.05) to .33
0.19
(0.05) to 0.35
1995
-6.46
(9.52) to (3.48)
1.59
(0.35) to 2.76
0.19
(0.04) to .32
0.21
(0.05) to 0.36
1996
-6.10
(9.09) to (3.14)
1.65
(0.35) to 2.86
0.19
(0.04) to .33
0.22
(0.05) to 0.37
1997
-7.63
(11.37) to (3.99)
1.41
(0.47) to 2.63
0.15
(0.05) to .29
0.19
(0.06) to 0.35
1998
-7.33
(11.0) to (3.79)
1.63
(0.38) to 3.01
0.15
(0.04) to .28
0.20
(0.05) to 0.36
1999
-7.06
(10.56) to (3.71)
1.49
(0.33) to 2.79
0.13
(0.03) to .24
0.19
(0.04) to 0.35
2000
-6.75
(10.09) to (3.56)
1.48
(0.39) to 2.78
0.12
(0.03) to .22
0.22
(0.06) to 0.42
2001
-6.71
(9.94) to (3.62)
1.54
(0.36) to 2.85
0.10
(0.02) to. 18
0.23
(0.05) to 0.42
2002
-6.72
(9.79) to (3.79)
1.45
(0.30) to 2.66
0.09
(0.02) to. 16
0.20
(0.04) to 0.36
2003
-6.05
(8.97) to (3.30)
1.42
(0.24) to 2.59
0.08
(0.01) to.15
0.19
(0.03) to 0.34
2004
-5.42
(8.24) to (2.74)
1.60
(0.18) to 2.79
0.08
(0.01) to.15
0.21
(0.02) to 0.37
2005
-5.39
(7.97) to (2.97)
1.53
(0.18) to 2.70
0.08
(0.01) to.13
0.19
(0.02) to 0.34
2006
-4.36
(6.67) to (2.21)
1.77
(0.19) to 2.89
0.09
(0.01) to.14
0.23
(0.02) to 0.37
2007
-3.96
(6.14) to (1.97)
1.83
(0.13) to 2.92
0.08
(0.01) to.13
0.23
(0.02) to 0.36
2008
-3.37
(5.37) to (1.53)
1.86
(0.06) to 2.98
0.06
0 to 0.10
0.24
(0.01) to 0.38
2009
-3.52
(5.32) to (1.88)
1.70
(0.02) to 2.72
0.06
0 to 0.09
0.22
0 to 0.36
2010
-3.58
(5.45) to (1.91)
1.68
0.02 to 2.68
0.06
0 to 0.09
0.22
0 to 0.35
2011
-3.49
(5.17) to (1.99)
1.70
0.01 to 2.68
0.06
0 to 0.09
0.22
0 to 0.35
2012
-2.88
(4.40) to (1.55)
1.69
(0.01) to 2.64
0.06
0 to 0.10
0.22
0 to 0.34
Note: Estimates after 2012 are based on a data splicing method (See the Cropland Remaining Cropland section for more information). The Tier 2 method will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Non-Federal
Settlements Converted to
Wetlands Converted to
Croplands:
Cropland
Cropland
Year
Estimate 95% CI
Estimate 95% CI
Mineral
Soils
1990 0.06
1991 0.06
1992 0.06
1993 0.06
1994 0.07
1995 0.07
1996 0.07
1997 0.06
1998 0.08
1999 0.07
2000 0.07
2001 0.08
2002 0.07
2003 0.07
2004 0.07
2005 0.07
2006 0.08
2007 0.09
2008 0.08
(0.04) to 0.11
0.11
(0.06) to 0.19
(0.04) to 0.11
0.10
(0.07) to 0.18
(0.05) to 0.11
0.10
(0.08) to 0.18
(0.04) to 0.12
0.12
(0.07) to 0.22
(0.02) to 0.12
0.15
(0.04) to 0.26
(0.02) to 0.13
0.15
(0.03) to 0.27
(0.02) to 0.13
0.16
(0.03) to 0.28
(0.02) to 0.12
0.14
(0.05) to 0.25
(0.02) to 0.14
0.15
(0.03) to 0.27
(0.02) to 0.13
0.14
(0.03) to 0.25
(0.02) to 0.14
0.14
(0.04) to 0.26
(0.02) to 0.14
0.14
(0.03) to 0.26
(0.02) to 0.14
0.13
(0.03) to 0.25
(0.01) to 0.12
0.13
(0.02) to 0.23
(0.01) to 0.12
0.14
(0.02) to 0.25
(0.01) to 0.12
0.13
(0.02) to 0.23
(0.01) to 0.13
0.15
(0.02) to 0.25
(0.01) to 0.14
0.14
(0.01) to 0.23
0 to 0.13
0.14
0 to 0.22
A-342 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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2009 0.07 0 to 0.12 0.11 0 to 0.18
2010 0.08 0 to 0.12 0.11 0 to 0.18
2011 0.09 0 to 0.14 0.12 0 to 0.19
2012 0.09 0 to 0.15 0.12 0 to 0.19
Note: Estimates after 2012 are based on a data splicing method (See the Cropland Remaining Cropland section for more information). The Tier 2 method will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Table A-219: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Federal Cropland Land Base
Estimated with the Tier 2 Analysis using U.S. Factor Values [MBIT CO; Eq./yrl
Federal Croplands:
Cropland Remaining
Cropland
Grassland Converted to
Cropland
Forest Converted to
Cropland
Other Land Converted to
Cropland
Year
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
Estimate
! 95% CI
Mineral Soils
1990
0.00
(0.01) to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1991
0.00
(0.01) to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1992
0.00
(0.02) to 0.02
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1993
0.00
(0.02) to 0.02
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1994
0.00
(0.03) to 0.02
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1995
0.00
(0.03) to 0.03
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1996
0.00
(0.03) to 0.02
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1997
-0.01
(0.05) to 0.02
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1998
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1999
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2000
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2001
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2002
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2003
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2004
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2005
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2006
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2007
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2008
0.00
(0.01) to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2009
0.00
0.0 to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2010
0.00
0.0 to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2011
0.00
0.0 to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
2012
0.00
0.0 to 0.0
0.00
0.0 to 0.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
Note: Estimates after 2012 are based on a data splicing method (See the Cropland Remaining Cropland section for more information). The Tier 2 method will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Federal Croplands:
Settlements Converted to
Wetlands Converted to
Cropland
Cropland
Year
Estimate 95% CI
Estimate
95% CI
Mineral Soils
1990
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1991
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1992
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1993
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1994
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1995
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1996
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1997
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1998
0.00
0.0 to 0.0
0.00
0.0 to 0.01
1999
0.00
0.0 to 0.0
0.00
0.0 to 0.01
2000
0.00
0.0 to 0.0
0.00
0.0 to 0.01
2001
0.00
0.0 to 0.0
0.00
0.0 to 0.01
2002
0.00
0.0 to 0.0
0.00
0.0 to 0.01
2003
0.00
0.0 to 0.0
0.00
0.0 to 0.01
2004
0.00
0.0 to 0.0
0.00
0.0 to 0.01
2005
0.00
0.0 to 0.0
0.00
0.0 to 0.01
2006
0.00
0.0 to 0.0
0.00
0.0 to 0.01
2007
0.00
0.0 to 0.0
0.00
0.0 to 0.01
2008
0.00
0.0 to 0.0
0.00
0.0 to 0.01
A-343
-------
2009 0.00 0.0 to 0.0 0.00 0.0 to 0.01
2010 0.00 0.0 to 0.0 0.00 0.0 to 0.01
2011 0.00 0.0 to 0.0 0.00 0.0 to 0.01
2012 0.00 0.0 to 0.0 0.00 0.0 to 0.01
Note: Estimates after 2012 are based on a data splicing method (See the Cropland Remaining Cropland section for more information). The Tier 2 method will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Table A-220: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Total Cropland Land Base
Estimated with the Tier 2 Analysis using U.S. FactorValues [MBIT CO; Eq./yrl
Total
Cropland Remaining Cropland
Grassland Converted to
Forest Converted to
Other Land Converted to
Croplands:
Cropland
Cropland
Cropland
Year
Estimate 95% CI
Estimate 95% CI
Estimate 95% CI
Estimate 95% CI
Mineral Soils
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Organic Soils
1990
30.25
20.02 to 43.38
2.52
1.46 to 3.95
0.11
.06 to.18
0.10
.0 to .22
1991
29.75
19.76 to 42.59
2.55
1.53 to 3.87
0.11
.06 to.18
0.10
.0 to .24
1992
29.71
19.60 to 42.96
2.58
1.50 to 4.0
0.10
.05 to.17
0.04
.0 to .14
1993
29.54
19.53 to 42.63
2.71
1.60 to 4.16
0.10
.06 to.17
0.10
.0 to .24
1994
29.37
19.32 to 42.42
2.71
1.62 to 4.14
0.10
.05 to.17
0.10
.0 to .23
1995
29.34
19.27 to 42.49
2.93
1.71 to 4.50
0.09
.05 to.16
0.10
.0 to .24
1996
29.27
19.18 to 42.44
3.02
1.76 to 4.66
0.10
.05 to.17
0.10
.0 to .24
1997
29.26
19.19 to 42.51
3.00
1.79 to 4.61
0.10
.05 to.17
0.10
.0 to .24
1998
28.83
18.80 to 42.07
3.51
1.82 to 5.76
0.09
.04 to.17
0.06
.0 to .20
1999
24.45
15.81 to 35.47
3.53
1.82 to 5.80
0.09
.04 to.16
0.06
.0 to .20
2000
24.53
15.85 to 35.55
3.25
1.76 to 5.24
0.09
.04 to.16
0.06
.0 to .20
2001
28.99
18.76 to 42.52
4.18
1.92 to 7.59
0.08
.04 to.15
0.06
.0 to .20
2002
29.32
19.09 to 42.88
4.18
1.91 to 7.52
0.06
.02 to.12
0.06
.0 to .20
2003
29.65
19.33 to 43.45
3.99
1.78 to 7.27
0.08
.03 to.15
0.06
.0 to .20
2004
29.95
19.52 to 43.88
3.39
1.51 to 6.05
0.05
.01 to.10
0.06
.0 to .20
2005
29.66
19.26 to 43.32
3.33
1.47 to 5.93
0.04
.01 to.10
0.06
.0 to .20
2006
29.59
19.24 to 43.36
3.26
1.49 to 5.77
0.04
.01 to .09
0.06
.0 to .20
2007
29.46
19.33 to 42.90
3.23
1.39 to 5.83
0.02
.01 to .05
0.06
.0 to .20
2008
29.35
19.18 to 42.70
3.00
1.25 to 5.54
0.03
.01 to .07
0.06
.0 to .20
2009
29.70
19.31 to 43.44
2.94
1.20 to 5.41
0.03
.01 to .07
0.06
.0 to .20
2010
29.65
19.31 to 43.32
2.87
1.23 to 5.30
0.03
.01 to .07
0.00
.0 to .0
2011
27.95
18.31 to 40.44
2.98
1.09 to 5.61
0.02
.0 to .05
0.00
.0 to .0
2012
28.10
18.47 to 40.58
3.03
1.18 to 5.65
0.02
.0 to .03
0.00
.0 to .0
Note: Estimates after 2012 are based on a data splicing method (See the Cropland Remaining Cropland section for more information). The Tier 2 method will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
-5.44
(7.97) to (3.0)
1.32
(.72) to 2.26
0.22
(.12) to .38
0.16
(.09) to .28
-6.19
(8.94) to (3.53)
1.26
(.84) to 2.25
0.20
(.13) to .36
0.15
(.10) to .27
-6.19
(9.77) to (3.63)
1.29
(1.07) to 2.32
0.19
(.16) to .35
0.16
(.13) to .29
-6.95
(10.17) to (3.79)
1.35
(.75) to 2.45
0.18
(.10) to .32
0.17
(.09) to .31
-6.71
(9.94) to (3.52)
1.48
(.37) to 2.63
0.18
(.05) to .33
0.19
(.05) to .35
-6.46
(9.52) to (3.48)
1.59
(.35) to 2.76
0.19
(.04) to .32
0.21
(.05) to .36
-6.10
(9.10) to (3.15)
1.65
(.35) to 2.86
0.19
(.04) to .33
0.22
(.05) to .37
-7.64
(11.38) to (4.0)
1.41
(.47) to 2.63
0.15
(.05) to .29
0.19
(.06) to .35
-7.33
(11.0) to (3.79)
1.63
(.37) to 3.01
0.15
(.04) to .28
0.20
(.05) to .36
-7.07
(10.57) to (3.71)
1.49
(.33) to 2.79
0.13
(.03) to .24
0.19
(.04) to .35
-6.75
(10.09) to (3.56)
1.48
(.39) to 2.78
0.12
(.03) to .22
0.22
(.06) to .42
-6.71
(9.94) to (3.62)
1.54
(.35) to 2.85
0.10
(.02) to. 18
0.23
(.05) to .42
-6.72
(9.79) to (3.80)
1.45
(.30) to 2.66
0.09
(.02) to. 16
0.20
(.04) to .36
-6.05
(8.97) to (3.30)
1.42
(.24) to 2.59
0.08
(.01) to.15
0.19
(.03) to .34
-5.43
(8.24) to (2.75)
1.60
(.17) to 2.79
0.08
(.01) to.15
0.21
(.02) to .37
-5.40
(7.97) to (2.98)
1.53
(.18) to 2.70
0.08
(.01) to.13
0.19
(.02) to .34
-4.36
(6.67) to (2.22)
1.77
(.19) to 2.89
0.09
(.01) to.14
0.23
(.02) to .37
-3.96
(6.14) to (1.98)
1.83
(.13) to 2.92
0.08
(.01) to.13
0.23
(.02) to .36
-3.37
(5.38) to (1.53)
1.86
(.06) to 2.98
0.06
.0 to .10
0.24
(.01) to .38
-3.52
(5.33) to (1.88)
1.70
(.02) to 2.72
0.06
.0 to .09
0.22
.0 to .36
-3.58
(5.45) to (1.91)
1.68
.02 to 2.68
0.06
.0 to .09
0.22
.0 to .35
-3.49
(5.17) to (1.99)
1.70
.01 to 2.68
0.06
.0 to .09
0.22
.0 to .35
-2.88
(4.41) to (1.55)
1.70
(.01) to 2.64
0.06
.0 to .10
0.22
.0 to .34
A-344 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Total
Settlements Converted to
Wetlands Converted to
Croplands:
Cropland
Cropland
Year
Estimate
95% CI
Estimate
95% CI
Mineral Soils
1990
0.06
(0.04) to 0.11
0.11
(0.06) to 0.19
1991
0.06
(0.04) to 0.11
0.10
(0.07) to 0.18
1992
0.06
(0.05) to 0.11
0.10
(0.08) to 0.18
1993
0.06
(0.04) to 0.12
0.12
(0.07) to 0.22
1994
0.07
(0.02) to 0.12
0.15
(0.04) to 0.26
1995
0.07
(0.02) to 0.13
0.15
(0.03) to 0.27
1996
0.07
(0.02) to 0.13
0.16
(0.03) to 0.28
1997
0.06
(0.02) to 0.12
0.14
(0.05) to 0.25
1998
0.08
(0.02) to 0.14
0.15
(0.03) to 0.28
1999
0.07
(0.02) to 0.13
0.14
(0.03) to 0.26
2000
0.07
(0.02) to 0.14
0.14
(0.04) to 0.26
2001
0.08
(0.02) to 0.14
0.14
(0.03) to 0.26
2002
0.07
(0.02) to 0.14
0.14
(0.03) to 0.25
2003
0.07
(0.01) to 0.12
0.13
(0.02) to 0.24
2004
0.07
(0.01) to 0.12
0.14
(0.01) to 0.25
2005
0.07
(0.01) to 0.12
0.13
(0.01) to 0.23
2006
0.08
(0.01) to 0.13
0.15
(0.01) to 0.25
2007
0.09
(0.01) to 0.14
0.15
(0.01) to 0.23
2008
0.08
0.0 to 0.13
0.14
0.0 to 0.22
2009
0.07
0.0 to 0.12
0.12
0.0 to 0.18
2010
0.08
0.0 to 0.12
0.11
0.0 to 0.18
2011
0.09
0.0 to 0.14
0.12
0.0 to 0.19
2012
0.09
0.0 to 0.15
0.12
0.0 to 0.19
Organic Soils
1990
0.03
.0 to .06
0.62
.30 to 1.07
1991
0.03
.0 to .07
0.63
.29 to 1.10
1992
0.03
.0 to .06
0.63
.34 to 1.03
1993
0.03
.0 to .06
0.81
.48 to 1.23
1994
0.05
.02 to .09
0.96
.56 to 1.48
1995
0.04
.01 to .07
0.99
.61 to 1.49
1996
0.05
.02 to .09
1.01
.59 to 1.55
1997
0.04
.01 to .07
1.00
.58 to 1.55
1998
0.04
.01 to .08
0.95
.55 to 1.49
1999
0.04
.01 to .08
0.95
.54 to 1.50
2000
0.04
.01 to .08
0.86
.48 to 1.36
2001
0.04
.01 to .08
0.83
.44 to 1.33
2002
0.04
.01 to .08
0.81
.44 to 1.29
2003
0.03
.0 to .06
0.69
.36 to 1.13
2004
0.03
.0 to .07
0.72
.40 to 1.14
2005
0.03
.0 to .08
0.71
.40 to 1.14
2006
0.03
.0 to .08
0.71
.40 to 1.14
2007
0.05
.02 to .10
0.69
.36 to 1.16
2008
0.05
.01 to .11
0.55
.31 to .87
2009
0.05
.01 to .10
0.50
.29 to .78
2010
0.05
.01 to .10
0.50
.28 to .80
2011
0.07
.02 to .15
0.53
.30 to .84
2012
0.09
.04 to .17
0.53
.31 to .83
Note: Estimates after 2012 are based on a data splicing method (See the Cropland Remaining Cropland section for more information). The Tier 2 method will be
applied in a future Inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Table A-221: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Non-Federal Grasslands Land
Base Estimated with the Tier 2 Analysis using U.S. Factor Values [MBIT CO; Eq./yrl
Non-Federal
Grassland Remaining
Cropland Converted to
Forest Converted to
Other Land Converted to
Grasslands:
Grassland
Grassland
Grassland
Grassland
Year
Estimate 95% CI
Estimate 95% CI
Estimate 95% CI
Estimate 95% CI
Mineral Soils
1990 -0.43 (1.02) to (.03) -2.90 (4.17) to (1.74) -0.75 (1.09) to (0.45) -0.54 (0.78) to (0.33)
A-345
-------
1991
-0.54
(1.18) to (.08)
-2.90
(4.16) to (1.75)
-0.77
(1.10) to (0.46)
-0.56
(0.81) to
0.34)
1992
-0.54
(1.50) to (.13)
-2.79
(4.01) to (1.68)
-0.75
(1.08) to (0.45)
-0.58
(0.83) to
0.35)
1993
-0.44
(1.05) to (.04)
-2.94
(4.22) to (1.77)
-0.74
(1.07) to (0.45)
-0.67
(0.96) to
0.40)
1994
-0.09
(0.52) to 0.28
-3.10
(4.46) to (1.86)
-0.72
(1.04) to (0.44)
-0.79
(1.14) to
0.47)
1995
-0.09
(0.49) to 0.26
-2.89
(4.16) to (1.73)
-0.70
(1.01) to (0.42)
-0.80
(1.15) to
0.48)
1996
-0.10
(0.49) to 0.23
-2.69
(3.87) to (1.62)
-0.70
(1.0) to (0.42)
-0.79
(1.13) to
0.47)
1997
-0.22
(0.65) to 0.07
-2.59
(3.69) to (1.59)
-0.70
(0.99) to (0.43)
-0.84
(1.20) to
0.51)
1998
-0.09
(0.51) to 0.27
-3.22
(4.61) to (1.94)
-0.70
(1.01) to 0(.42)
-0.92
(1.32) to
0.56)
1999
-0.06
(0.45) to 0.29
-3.11
(4.46) to (1.89)
-0.69
(0.99) to (0.42)
-0.96
(1.37) to
0.58)
2000
-0.13
(0.54) to 0.17
-3.16
(4.52) to (1.91)
-0.70
(1.01) to (0.43)
-1.12
(1.61) to
0.68)
2001
-0.10
(0.48) to 0.21
-3.06
(4.39) to (1.84)
-0.67
(0.96) to (0.40)
-1.16
(1.66) to
0.70)
2002
-0.06
(0.41) to 0.24
-2.78
(4.0) to (1.67)
-0.62
(0.90) to (0.37)
-1.09
(1.57) to
0.65)
2003
-0.01
(0.32) to 0.29
-2.51
(3.62) to (1.49)
-0.55
(0.79) to (0.33)
-1.03
(1.48) to
0.61)
2004
0.06
(0.23) to 0.39
-2.65
(3.83) to (1.58)
-0.53
(0.76) to (0.31)
-1.07
(1.54) to
0.64)
2005
0.05
(0.24) to 0.39
-2.43
(3.51) to (1.44)
-0.47
(0.68) to (0.28)
-1.08
(1.56) to
0.64)
2006
0.05
(0.25) to 0.40
-1.91
(2.82) to (1.07)
-0.35
(0.52) to (0.20)
-0.90
(1.33) to
0.51)
2007
0.10
(0.17) to 0.43
-1.59
(2.37) to (0.88)
-0.29
(0.43) to (0.16)
-0.83
(1.25) to
0.46)
2008
0.16
(0.08) to 0.52
-1.45
(2.15) to (0.80)
-0.25
(0.37) to (0.14)
-0.83
(1.24) to
0.46)
2009
0.26
(0.02) to 0.69
-1.38
(2.06) to (0.77)
-0.24
(0.36) to (0.14)
-0.85
(1.26) to
0.47)
2010
0.31
0.02 to 0.73
-1.31
(1.95) to (0.73)
-0.23
(0.34) to (0.13)
-0.84
(1.25) to
0.47)
2011
0.31
0.02 to 0.76
-1.22
(1.83) to (0.67)
-0.21
(0.31) to (0.12)
-0.81
(1.22) to
0.45)
2012
0.24
(0.01) to 0.65
-1.16
(1.73) to (0.64)
-0.20
(0.29) to (0.11)
-0.80
(1.19) to
0.44)
Note: Estimates after 2012 are based on a data splicing method (See the Grassland Remaining Grassland section for more information). The Tier 2 method will
be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods
Non-Federal
Settlements Converted to
Wetlands Converted to
Grasslands:
Grassland
Grassland
Year
Estimate
95% CI
Estimate
95% CI
Mineral Soils
1990
-0.08
(0.12) to (0.05)
-0.32
(0.46) to (0
19)
1991
-0.09
(0.13) to (0.05)
-0.39
(0.56) to (0
23)
1992
-0.09
(0.12) to (0.05)
-0.46
(0.66) to (0
28)
1993
-0.10
(0.14) to (0.06)
-0.48
(0.69) to (0
29)
1994
-0.11
(0.15) to (0.06)
-0.50
(0.72) to (0
30)
1995
-0.10
(0.15) to (0.06)
-0.48
(0.70) to (0
29)
1996
-0.11
(0.16) to (0.07)
-0.47
(0.67) to (0
28)
1997
-0.11
(0.16) to (0.07)
-0.47
(0.66) to (0
29)
1998
-0.12
(0.18) to (0.07)
-0.49
(0.70) to (0
29)
1999
-0.13
(0.18) to (0.08)
-0.48
(0.69) to (0
29)
2000
-0.13
(0.19) to (0.08)
-0.50
(0.71) to (0
30)
2001
-0.14
(0.20) to (0.08)
-0.49
(0.70) to (0
29)
2002
-0.14
(0.19) to (0.08)
-0.45
(0.65) to (0
27)
2003
-0.12
(0.17) to (0.07)
-0.42
(0.61) to (0
25)
2004
-0.12
(0.18) to (0.07)
-0.44
(0.63) to (0
26)
2005
-0.12
(0.18) to (0.07)
-0.43
(0.62) to (0
26)
2006
-0.11
(0.16) to (0.06)
-0.36
(0.53) to (0
20)
2007
-0.10
(0.15) to (0.05)
-0.32
(0.48) to (0
18)
2008
-0.09
(0.14) to (0.05)
-0.26
(0.39) to (0
15)
2009
-0.09
(0.13) to (0.05)
-0.23
(0.34) to (0
13)
2010
-0.09
(0.14) to (0.05)
-0.19
(0.29) to (0
11)
2011
-0.09
(0.14) to (0.05)
-0.16
(0.23) to (0
09)
2012
-0.09
(0.14) to (0.05)
-0.11
(0.17)to (0
06)
Note: Estimates after 2012 are based on a data splicing method (See the Grassland Remaining Grassland section for more information). The Tier 2 method will
be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods
Table A-222: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Federal Grasslands Land Base
Estimated with the Tier 2 Analysis using U.S. Factor Values (MMT C02 Eq./yr)
Grassland Remaining
Cropland Converted to
Forest Converted to
Other Land Converted to
rcuci di widddiaiius.
Grassland
Grassland
Grassland
Grassland
Year
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
A-346 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Mineral Soils
1990
-0.20
(8.94) to 9.45
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1991
-0.30
(9.28) to 8.76
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1992
-0.30
(10.08) to 8.06
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1993
-1.16
(11.03) to 7.60
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1994
-1.50
(11.79) to 7.18
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1995
-1.52
(12.0) to 7.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1996
-0.90
(10.65) to 7.01
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1997
-0.83
(10.42) to 7.27
0.00
0.0 to 0.0
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1998
-1.62
(13.58) to 7.15
0.00
(0.03) to 0.02
-0.10
(0.75) to 0.52
-0.01
(0.04) to 0.03
1999
-1.44
(13.27) to 7.24
0.00
(0.03) to 0.02
-0.10
(0.74) to 0.52
-0.01
(0.04) to 0.03
2000
-1.70
(12.68) to 6.38
0.00
(0.03) to 0.02
-0.10
(0.74) to 0.51
-0.01
(0.04) to 0.03
2001
-1.71
(12.81) to 6.44
0.00
(0.03) to 0.02
-0.10
(0.73) to 0.51
-0.01
(0.04) to 0.03
2002
-2.72
(14.63) to 7.05
0.00
(0.03) to 0.02
-0.11
(0.70) to 0.45
-0.01
(0.04) to 0.03
2003
-2.73
(14.72) to 7.76
0.00
(0.03) to 0.02
-0.11
(0.70) to 0.45
-0.01
(0.04) to 0.03
2004
-1.28
(11.29) to 8.85
0.00
(0.03) to 0.02
-0.11
(0.70) to 0.46
-0.01
(0.04) to 0.03
2005
-1.37
(11.44) to 8.50
0.00
0.0 to 0.0
-0.07
(0.86) to 0.70
0.00
(0.02) to 0.02
2006
-1.51
(11.82) to 8.56
0.00
0.0 to 0.0
-0.07
(0.86) to 0.70
0.00
(0.02) to 0.02
2007
-1.63
(11.93) to 8.11
0.00
0.0 to 0.0
-0.07
(0.86) to 0.70
0.00
(0.02) to 0.02
2008
-1.67
(12.11) to 8.20
0.00
0.0 to 0.0
-0.07
(0.86) to 0.70
0.00
(0.02) to 0.02
2009
-1.46
(11.57) to 7.14
0.00
0.0 to 0.0
-0.07
(0.85) to 0.70
0.00
(0.02) to 0.02
2010
-1.53
(11.51) to 7.48
0.00
0.0 to 0.0
-0.07
(0.86) to 0.69
0.00
(0.02) to 0.02
2011
-1.15
(10.79) to 8.01
0.00
0.0 to 0.0
-0.07
(0.85) to 0.69
0.00
(0.02) to 0.02
2012
-0.67
(9.89) to 8.69
0.00
0.0 to 0.0
-0.07
(0.85) to 0.69
0.00
(0.02) to 0.02
Federal Grasslands:
Settlements Converted to
Grassland
Wetlands Converted to
Grassland
Year
Estimate
95% CI
Estimate
95% CI
Mineral Soils
1990
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1991
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1992
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1993
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1994
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1995
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1996
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1997
0.00
0.0 to 0.0
0.00
0.0 to 0.0
1998
0.00
0.0 to 0.0
-0.01
(0.05) to 0.03
1999
0.00
0.0 to 0.0
-0.01
(0.05) to 0.03
2000
0.00
0.0 to 0.0
-0.01
(0.05) to 0.03
2001
0.00
0.0 to 0.0
-0.01
(0.05) to 0.03
2002
0.00
0.0 to 0.0
-0.01
(0.04) to 0.03
2003
0.00
0.0 to 0.0
-0.01
(0.04) to 0.03
2004
0.00
0.0 to 0.0
-0.01
(0.04) to 0.03
2005
0.00
0.0 to 0.0
0.00
(0.04) to 0.03
2006
0.00
0.0 to 0.0
0.00
(0.04) to 0.03
2007
0.00
0.0 to 0.0
0.00
(0.04) to 0.03
2008
0.00
0.0 to 0.0
0.00
(0.04) to 0.03
2009
0.00
0.0 to 0.0
0.00
(0.04) to 0.03
2010
0.00
0.0 to 0.0
0.00
(0.04) to 0.03
2011
0.00
0.0 to 0.0
0.00
(0.04) to 0.03
2012
0.00
0.0 to 0.0
0.00
(0.04) to 0.03
Note: Estimates after 2012 are based on a data splicing method (See the Grassland Remaining Grassland section for more information). The Tier 2 method will
be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
A-347
-------
Table A-223: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Total Grassland Land Base
Estimated with the Tier 2 Analysis using U.S. Factor Values [MBIT CO; Eq./yrl
Total Grasslands:
Grassland Remaining
Cropland Converted to
Forest Converted to
Other Land Converted to
Grassland
Grassland
Grassland
Grassland
Year
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
Mineral Soils
1990
-0.62
(9.39) to 9.03
-2.90
(4.17) to (1.74)
-0.75
(1.09) to (0.45)
-0.54
(0.78) to (0.33)
1991
-0.84
(9.85) to 8.23
-2.90
(4.16) to (1.75)
-0.77
(1.10) to (0.46)
-0.56
(0.81) to (0.34)
1992
-0.84
(10.83) to 7.36
-2.79
(4.01) to (1.68)
-0.75
(1.08) to (0.45)
-0.58
(0.83) to (0.35)
1993
-1.60
(11.49) to 7.16
-2.94
(4.22) to (1.77)
-0.74
(1.07) to (0.45)
-0.67
(0.96) to (0.40)
1994
-1.59
(11.89) to 7.09
-3.10
(4.46) to (1.86)
-0.72
(1.04) to (0.44)
-0.79
(1.14) to (0.47)
1995
-1.61
(12.10) to 6.93
-2.89
(4.16) to (1.73)
-0.70
(1.01) to (0.42)
-0.80
(1.15) to (0.48)
1996
-1.00
(10.76) to 6.92
-2.69
(3.87) to (1.62)
-0.70
(1.0) to (0.42)
-0.79
(1.13) to (0.47)
1997
-1.05
(10.65) to 7.06
-2.59
(3.69) to (1.59)
-0.70
(.99) to (0.43)
-0.84
(1.20) to (0.51)
1998
-1.71
(13.68) to 7.07
-3.22
(4.61) to (1.95)
-0.80
(1.52) to (0.12)
-0.92
(1.32) to (0.56)
1999
-1.49
(13.34) to 7.19
-3.12
(4.46) to (1.89)
-0.79
(1.50) to (0.12)
-0.96
(1.38) to (0.58)
2000
-1.84
(12.83) to 6.25
-3.16
(4.52) to (1.91)
-0.80
(1.51) to (0.14)
-1.13
(1.61) to (0.68)
2001
-1.81
(12.91) to 6.35
-3.06
(4.39) to (1.84)
-0.77
(1.46) to (0.10)
-1.17
(1.67) to (0.70)
2002
-2.78
(14.69) to 7.0
-2.78
(4.0) to (1.67)
-0.74
(1.38) to (0.12)
-1.10
(1.57) to (0.66)
2003
-2.74
(14.73) to 7.75
-2.51
(3.62) to (1.49)
-0.66
(1.30) to (0.06)
-1.04
(1.50) to (0.62)
2004
-1.22
(11.23) to 8.91
-2.66
(3.84) to (1.58)
-0.63
(1.27) to (0.03)
-1.08
(1.55) to (0.64)
2005
-1.32
(11.39) to 8.56
-2.43
(3.51) to (1.44)
-0.54
(1.36) to 0.25
-1.08
(1.56) to (0.64)
2006
-1.45
(11.77) to 8.62
-1.91
(2.82) to (1.07)
-0.42
(1.23) to 0.36
-0.90
(1.34) to (0.51)
2007
-1.53
(11.84) to 8.21
-1.59
(2.37) to (.88)
-0.35
(1.16) to 0.42
-0.84
(1.25) to (0.46)
2008
-1.50
(11.95) to 8.37
-1.45
(2.15) to (.80)
-0.31
(1.12) to 0.46
-0.84
(1.24) to (0.47)
2009
-1.20
(11.32) to 7.41
-1.38
(2.06) to (.77)
-0.31
(1.10) to 0.46
-0.85
(1.26) to (0.47)
2010
-1.22
(11.20) to 7.80
-1.31
(1.95) to (.73)
-0.29
(1.09) to 0.47
-0.84
(1.25) to (0.47)
2011
-0.84
(10.48) to 8.34
-1.22
(1.83) to (.67)
-0.28
(1.07) to 0.49
-0.82
(1.23) to (0.45)
2012
-0.43
(9.65) to 8.94
-1.16
(1.73) to (.64)
-0.26
(1.05) to 0.50
-0.80
(1.19) to (0.44)
Organic Soils
1990
7.21
4.07 to 11.35
0.53
.23 to .98
0.01
.0 to .03
0.04
.01 to .09
1991
7.16
4.0 to 11.43
0.53
.23 to .97
0.01
.0 to .03
0.04
.01 to .09
1992
7.08
3.95 to 11.25
0.51
.22 to .94
0.01
.0 to .03
0.04
.01 to .09
1993
7.03
3.90 to 11.26
0.57
.26 to 1.0
0.02
.01 to .04
0.04
.01 to .09
1994
6.99
3.91 to 11.08
0.70
.32 to 1.27
0.02
.01 to .04
0.04
.01 to .09
1995
6.93
3.88 to 11.02
0.70
.31 to 1.27
0.02
.01 to .03
0.04
.01 to .09
1996
6.85
3.82 to 10.90
0.68
.30 to 1.24
0.02
.01 to .03
0.04
.01 to .09
1997
6.77
3.77 to 10.77
0.69
.32 to 1.23
0.02
.01 to .03
0.03
.0 to .07
1998
6.67
3.70 to 10.68
0.86
.43 to 1.49
0.02
.0 to .03
0.03
.0 to .07
1999
6.62
3.67 to 10.58
0.84
.41 to 1.44
0.01
.0 to .03
0.03
.0 to .07
2000
6.50
3.61 to 10.34
0.88
.44 to 1.51
0.05
.01 to .10
0.03
.0 to .07
2001
6.20
3.42 to 9.91
0.99
.50 to 1.67
0.06
.02 to .12
0.03
.0 to .08
2002
6.14
3.39 to 9.79
1.10
.55 to 1.84
0.06
.02 to .12
0.03
.0 to .08
2003
6.05
3.33 to 9.69
1.03
.53 to 1.74
0.07
.03 to .14
0.03
.0 to .08
2004
6.01
3.28 to 9.65
1.13
.57 to 1.91
0.09
.04 to .16
0.04
.01 to .09
2005
5.97
3.27 to 9.58
1.13
.58 to 1.91
0.09
.04 to .16
0.04
.01 to .09
2006
5.76
3.12 to 9.33
1.13
.57 to 1.90
0.09
.04 to .17
0.04
.01 to .09
2007
5.73
3.11 to 9.26
1.11
.57 to 1.87
0.09
.04 to .17
0.04
.01 to .09
2008
5.69
3.08 to 9.18
1.07
.54 to 1.82
0.10
.04 to .19
0.05
.01 to .10
2009
5.68
3.08 to 9.17
1.15
.59 to 1.92
0.10
.04 to .18
0.03
.01 to .07
2010
5.64
3.07 to 9.12
1.15
.59 to 1.92
0.10
.04 to .18
0.03
.01 to .07
2011
5.61
3.05 to 9.08
1.14
.58 to 1.93
0.10
.04 to .19
0.05
.02 to .11
2012
5.53
3.0 to 8.91
1.12
.57 to 1.88
0.10
.04 to .19
0.05
.02 to .11
Total Grasslands:
Settlements Converted to
Grassland
Wetlands Converted to
Grassland
Year
Estimate 95% CI
Estimate 95% CI
Mineral Soils
1990 -0.08 (0.12) to (0.05) -0.32 (0.46) to (0.19)
A-348 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1991
-0.09
(0.13) to (0.05)
-0.39
(0.56) to (0.23)
1992
-0.09
(0.12) to (0.05)
-0.46
(0.66) to (0.28)
1993
-0.10
(0.14) to (0.06)
-0.48
(0.69) to (0.29)
1994
-0.11
(0.15) to (0.06)
-0.50
(0.72) to (0.30)
1995
-0.10
(0.15) to (0.06)
-0.48
(0.70) to (0.29)
1996
-0.11
(0.16) to (0.07)
-0.47
(0.67) to (0.28)
1997
-0.11
(0.16) to (0.07)
-0.47
(0.66) to (0.29)
1998
-0.12
(0.18) to (0.07)
-0.49
(0.71) to (0.30)
1999
-0.13
(0.18) to (0.08)
-0.49
(0.70) to (0.29)
2000
-0.13
(0.19) to (0.08)
-0.50
(0.72) to (0.30)
2001
-0.14
(0.20) to (0.08)
-0.49
(0.71) to (0.30)
2002
-0.14
(0.19) to (0.08)
-0.46
(0.66) to (0.28)
2003
-0.12
(0.17) to (0.07)
-0.43
(0.62) to (0.25)
2004
-0.12
(0.18) to (0.07)
-0.45
(0.65) to (0.26)
2005
-0.12
(0.18) to (0.07)
-0.43
(0.63) to (0.25)
2006
-0.11
(0.16) to (0.06)
-0.36
(0.54) to (0.20)
2007
-0.10
(0.15) to (0.05)
-0.33
(0.49) to (0.18)
2008
-0.09
(0.14) to (0.05)
-0.26
(0.40) to (0.14)
2009
-0.09
(0.13) to (0.05)
-0.23
(0.34) to (0.12)
2010
-0.09
(0.14) to (0.05)
-0.20
(0.30) to (0.10)
2011
-0.09
(0.14) to (0.05)
-0.16
(0.24) to (0.08)
2012
-0.09
(0.14) to (0.05)
-0.11
(0.18) to (0.05)
Organic Soils
1990
0.00
.0 to .0
0.12
.05 to .23
1991
0.00
.0 to .0
0.12
.05 to .22
1992
0.00
.0 to .0
0.12
.02 to .30
1993
0.00
.0 to .01
0.18
.07 to .36
1994
0.01
.0 to .02
0.24
.11 to .42
1995
0.01
.0 to .02
0.24
.12 to .40
1996
0.01
.0 to .02
0.24
.13 to .39
1997
0.01
.0 to .03
0.24
.13 to .40
1998
0.02
.0 to .04
0.25
.13 to .41
1999
0.02
.0 to .04
0.25
.13 to .41
2000
0.02
.0 to .04
0.30
.16 to .48
2001
0.02
.0 to .04
0.30
.16 to .49
2002
0.02
.0 to .04
0.28
.15 to .45
2003
0.02
.0 to .04
0.24
.14 to .38
2004
0.02
.0 to .04
0.24
.13 to .39
2005
0.02
.0 to .04
0.26
.14 to .42
2006
0.02
.0 to .04
0.28
.15 to .44
2007
0.02
.0 to .04
0.28
.15 to .45
2008
0.02
.0 to .04
0.28
.16 to .46
2009
0.02
.0 to .04
0.33
.19 to .52
2010
0.02
.0 to .04
0.34
.19 to .54
2011
0.02
.0 to .04
0.33
.19 to .53
2012
0.02
.0 to .04
0.33
.19 to .52
Note: Estimates after 2012 are based on a data splicing method (See the Grassland Remaining Grassland section for more information). The Tier 2 method will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Step 3: Estimate Soil Organic C Stock Changes and Direct N2O Emissions from Organic Soils
In this step, soil organic C losses and N2O emissions are estimated for organic soils that are drained for agricultural
production.
Step 3a: Direct AfeO Emissions Due to Drainage of Organic Soils in Cropland and Grassland
To estimate annual N2O emissions from drainage of organic soils in cropland and grassland, the area of drained organic soils
in croplands and grasslands for temperate regions is multiplied by the IPCC (2006) default emission factor for temperate
soils and the corresponding area in sub-tropical regions is multiplied by the average (12 kg N20-N/ha cultivated) of IPCC
(2006) default emission factors for temperate (8 kg N20-N/ha cultivated) and tropical (16 kg N20-N/ha cultivated) organic
soils. The uncertainty is determined based on simple error propagation methods (IPCC 2006), including uncertainty in the
default emission factor ranging from 2-24 kg N20-N/ha (IPCC 2006).
A-349
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Step 3b: Soil Organic C Stock Changes Due to Drainage of Organic Soils in Cropland and Grassland
Change in soil organic C stocks due to drainage of cropland and grassland soils are estimated annually from 1990 through
2012, based on the land-use and management activity data in conjunction with appropriate emission factors. The activity
data are based on annual data from 1990 through 2012 from the NRI. Organic Soil emission factors representative of U.S.
conditions have been estimated from published studies (Ogle et al. 2003), based on subsidence studies in the United States
and Canada (Table A-225). PDFs are constructed as normal densities based on the mean C loss rates and associated
variances. Input values are randomly selected from PDFs in a Monte Carlo analysis to estimate SOC change for 50,000
times and produce a 95 percent confidence interval for the inventory results. Losses of soil organic C from drainage of
cropland and grassland soils are provided in Table A-218 and Table A-221.
Step 4: Estimate Indirect N2O Emissions for Croplands and Grasslands
In this step, N2O emissions are estimated for the two indirect emission pathways (N2O emissions due to volatilization, and
N2O emissions due to leaching and runoff of N), which are summed to yield total indirect N2O emissions from croplands
and grasslands.
Step 4a: Indirect Soil N2O Emissions Due to Volatilization
Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and PRP manure, are
calculated according to the amount of mineral N that is transported in gaseous forms from the soil profile and later emitted
as soil N2O following atmospheric deposition. See Step le for additional information about the methods used to compute N
losses due to volatilization. The estimated N volatilized is multiplied by the IPCC default emission factor of 0.01 kg N2O-
N/kg N (IPCC 2006) to estimate total N2O emissions from volatilization. The uncertainty is estimated using simple error
propagation methods (IPCC 2006), by combining uncertainties in the amount of N volatilized, with uncertainty in the default
emission factor ranging from 0.002-0.05 kg N20-N/kg N (IPCC 2006). The estimates are provided in Table A-226 and
implied Tier 3 emission factors are in Table A-229 and Table A-230.
Step 4b: Indirect Soil N2O Emissions Due to Leaching and Runoff
The amount of mineral N from synthetic fertilizers, commercial organic fertilizers, PRP manure, crop residue, N
mineralization, asymbiotic fixation that is transported from the soil profile in aqueous form is used to calculate indirect
emissions from leaching of mineral N from soils and losses in runoff of water associated with overland flow. See Step le
for additional information about the methods used to compute N losses from soils due to leaching and runoff in overland
water flows. The total amount of N transported from soil profiles through leaching and surface runoff is multiplied by the
IPCC default emission factor of 0.0075 kg N20-N/kg N (IPCC 2006) to estimate emissions for this source. The emission
estimates are provided in Table A-227 and implied Tier 3 emission factors are in Table A-229 and Table A-230. The
uncertainty is estimated based on simple error propagation methods (IPCC 2006), including uncertainty in the default
emission factor ranging from 0.0005 to 0.025 kg N20-N/kg N (IPCC 2006).
Step 5: Estimate Total Soil Organic C Stock Changes and N2O Emissions for U.S. Soils
Step 5a: Estimate Total Soil N2O Emissions
Total N2O emissions are estimated by adding total direct emissions (from mineral cropland soils, drainage and cultivation
of organic soils, and grassland management) to indirect emissions. Uncertainties in the final estimate are combined using
simple error propagation methods (IPCC 2006), and expressed as a 95 percent confidence interval. Estimates are provided
in Table A-228.
Direct and indirect simulated emissions of soil N2O vary regionally in croplands as a function of N input amount and timing
of fertilization, tillage intensity, crop rotation sequence, weather, and soil type. Note that there are other management
practices, such as fertilizer formulation (Halvorson et al. 2013), that influence emissions but are not represented in the model
simulations. The highest total N2O emissions occur in Iowa, Illinois, Kansas, Minnesota, Nebraska and Texas (Table A-
232). On a per area unit basis, direct N2O emissions are high in some Northeast, Midwest, and many of the Mississippi River
Basin states where there are high N inputs to hay, corn and soybean crops, and in some western states where irrigated crops
are grown that require high N inputs. Note that although the total crop area in the northeast is relatively low, emissions are
high on a per unit area basis because of freeze/thaw cycles during spring that saturate surface soil layers and enhance
denitrification rates.
Direct emissions from non-federal grasslands are typically lower than the emissions from croplands (Table A-232) because
N inputs tend to be lower, particularly from synthetic fertilizer. Texas, Oklahoma, Kansas, Montana, Missouri, and Kentucky
are the highest emitters for this category due to large land areas used for pastures and rangeland (Table A-232). On a per-
A-350 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
unit of area basis, direct N2O emissions are higher in the some of the Southeastern, Appalachians, and Midwestern states
because these grasslands are more intensively managed (legume seeding, fertilization) while western rangelands receive
few, if any, N inputs. Also, rainfall is limited in most of the western United States, and grasslands are not typically irrigated
so minimal leaching and runoff of N occurs in these grasslands, and therefore there are lower indirect N2O emissions.
Step 5b: Estimate Total Soil Organic Stock Change
The sum of total CO2 emissions and removals from the Tier 3 DayCent Model Approach, Tier 2 IPCC Methods and
additional land-use and management considerations are provided in Table A-233. The states with highest total amounts of
C sequestration are California, Illinois, Iowa, Kentucky, Missouri, North Dakota and Tennessee (Table A-234). For organic
soils, emission rates are highest in the regions that contain the majority of drained organic soils, including California, Florida,
Indiana, Michigan, Minnesota, North Carolina and Wisconsin. On a per unit of area basis, the emission rate patterns are very
similar to the total emissions in each state, with the highest rates in coastal states of the Southeast, states surrounding the
Great Lakes, and California.
Step 5c: Estimate Total CH4 Emissions from Rice Cultivation
The sum of total CH4 emissions from the Tier 3 DayCent Model Approach and Tier 1 IPCC Methods are provided in Table
A-231. The states with highest total emissions are Arkansas, California, Louisiana and Texas (Table A-235). 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 time under flooded conditions, which leads to more CH4
emissions.
A-351
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Table A-224: Assumptions and Calculations to Estimate the Contribution to Soil Organic Carbon Stocks from Application of
Biosolids (i.e.,Sewag
e Sludge) to Mineral Soils
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Biosolids N Applied to
Agricultural Land (Mg N)a
51,848
55,107
58,480
61,971
64,721
67,505
72,081
75,195
78,353
80,932
Assimilative Capacity
(Mg N/ha)b
0.12
0.12
0.12
0.122
0.122
0.122
0.122
0.122
0.122
0.122
Area covered by
Available Biosolids N
(ha)'
432,067
459,226
487,336
507,957
530,503
553,322
590,828
616,357
642,240
663,381
Average Annual Rate of
C storage (Mg C/ha-yr)d
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
Contribution to Soil C
(MMT C02/yr)ef
-0.60
-0.64
-0.68
-0.71
-0.74
-0.77
-0.82
-0.86
-0.89
-0.92
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Biosolids N Applied to
Agricultural Land (Mg N)a
83,523
86,124
88,736
91,358
93,991
98,400
101,314
104,222
107,123
110,018
Assimilative Capacity
(Mg N/ha)b
0.122
0.122
0.122
0.122
0.122
0.122
0.122
0.122
0.122
0.122
Area covered by
Available Biosolids N
(ha)'
684,612
705,932
727,341
748,836
770,418
806,559
830,447
854,276
878,055
901,790
Average Annual Rate of
C storage (Mg C/ha-yr)d
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
Contribution to Soil C
(MMT C02/yr)ef
-0.95
-0.98
-1.01
-1.04
-1.07
-1.12
-1.16
-1.19
-1.22
-1.26
2010
2011
2012
2013
2014
2015
2016
Biosolids N Applied to
Agricultural Land (Mg N)a
112,909
115,797
118,681
121,563
124,443
127,322
130,200
Assimilative Capacity
(Mg N/ha)b
0.122
0.122
0.122
0.122
0.122
0.122
0.122
Area covered by
Available Biosolids N
(ha)'
925,487
949,154
972,796
996,417
1,020,025
1,043,622
1,067,213
Average Annual Rate of
C storage (Mg C/ha-yr)d
0.38
0.38
0.38
0.38
0.38
0.38
0.38
Contribution to Soil C
(MMT C02/yr)ef
-1.29
-1.32
-1.36
-1.39
-1.42
-1.45
-1.49
a N applied to soils described in Step 1d.
b Assimilative Capacity is the national average amount of manure-derived N that can be applied on cropland without buildup of nutrients in the soil (Kellogg et al.,
2000).
c Area covered by biosolids N available for application to soils is the available N applied at the assimilative capacity rate. The 1992 assimilative capacity rate was
applied to 1990 -1992 and the 1997 rate was applied to 1993-2016.
d Annual rate of C storage based on national average increase in C storage for grazing lands that is attributed to organic matter amendments (0.38 Mg/ha-yr)
e Contribution to Soil C is estimated as the product of the area covered by the available biosolids N and the average annual C storage attributed to an organic
matter amendment.
f Some small, undetermined fraction of this applied N is probably not applied to agricultural soils, but instead is applied to forests, home gardens, and other
lands.
Note: Values in parentheses indicate net C storage.
A-352 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table A-225: Carbon Loss Rates for Organic Soils Under Agricultural Management in the United States, and IPGG Default
Bates [Metric Ton C/ba-yrl
Cropland Grassland
Region IPCC U.S. Revised IPCC U.S. Revised
Cold Temperate, Dry & Cold Temperate, Moist 1 11.2±2.5 0.25 2.8±0.5a
Warm Temperate, Dry & Warm Temperate, Moist 10 14.0±2.5 2.5 3.5±0.8a
Sub-Tropical, Dry & Sub-Tropical, Moist 1 14.3±2.5 025 2.8±0.5a
a There are not enough data available to estimate a U.S. value for C losses from grassland. Consequently, estimates are 25 percent of the values for cropland,
which is an assumption that is used for the IPCC default organic soil C losses on grassland.
Table fl-226: Indirect N2O Emissions from Volatilization and Atmospheric Deposition tMMT CO2 Eg.]
Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Croplands 5.9 5.9 5.7 5.8 6.3 6.3 6.3 6.3 6.7 6.6 6.6 6.4 6.4 6.7 6.9 6.6 6.8 6.7 6.6 6.5 7.0 6.8 6.5
Grasslands 4.4 4.4 4.4 4.4 4.4 4.5 4.5 4.5 4.7 4.4 4.2 4.4 4.4 4.4 4.8 4.5 4.5 4.5 4.4 4.5 4.6 4.2 4.2
Total 10.2 10.4 10.1 10.2 10.7 10.8 10.8 10.8 11.3 10.9 10.8 10.8 10.7 11.1 11.6 11.1 11.3 11.2 11.0 11.0 11.6 11.0 10.8
Note: Estimates after 2012 are based on a data splicing method (See the Agricultural Soil Management section for more information). The Tier 1 and 3 methods
will be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Table fl-227: Indirect N2O Emissions from Leaching and Runoff [MMT CO; Eg.)
Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Croplands 25.0 21.3 24.3 31.4 18.0 23.7 23.5 21.4 27.3 22.0 19.4 25.2 21.9 24.2 28.6 21.4 24.9 26.8 29.1 29.2 28.6 29.0 18.9
Grasslands 3.2 3.1 2.9 3.5 2.9 3.0 3.0 3.0 3.7 2.7 2.5 3.2 3.3 2.7 3.5 2.5 2.8 3.2 3.3 3.7 2.9 3.5 2.6
Total 28.2 24.4 27.2 34.9 20.9 26.7 26.5 24.4 31.0 24.7 21.8 28.4 25.2 26.8 32.1 23.9 27.7 30.1 32.4 32.8 31.5 32.6 21.5
Note: Estimates after 2012 are based on a data splicing method (See the Agricultural Soil Management section for more information). The Tier 1 and 3 methods
will be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Table fl-228: Total N2O Emissions from Agricultural Soil Management [MMT CO2 Eg.]
Activity
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Total Direct
212.0
213.0
212.1
212.8
213.3
213.7
218.7
217.1
228.5
212.6
214.0
215.8
216.7
219.3
236.7
218.5
53.6
53.9
55.0
52.4
58.0
53.2
57.4
56.0
56.4
54.9
55.9
52.7
54.2
55.4
57.0
54.6
10.0
10.2
10.3
10.0
10.4
10.5
10.6
10.7
10.6
10.7
11.0
10.9
11.1
11.1
10.8
10.9
22.1
23.4
21.7
22.0
21.7
23.3
22.2
22.2
21.8
25.0
23.1
22.9
22.8
23.3
22.2
22.9
58.4
56.7
56.9
58.5
56.9
59.2
58.1
58.3
65.5
57.5
59.2
62.4
59.8
62.6
69.6
62.2
Direct Emissions from
Mineral Cropland Soils 144.1 144.2 143.9 143.0 147.1 146.2 148.2 147.0 154.3 148.1 149.2 148.9 148.0 152.4 159.5 150.6
Synthetic Fertilizer
Organic Amendment3
Residue Nb
Mineralization and
Asymbiotic Fixation
Direct Emissions from
Drained Organic
Cropland Soils
Direct Emissions from
Mineral Grassland Soils
Synthetic Mineral
Fertilizer
PRP Manure
Managed Manure
Biosolids (i.e., Sewage
Sludge)
Residueb
Mineralization and
Asymbiotic Fixation
Direct Emissions from
Drained Organic
Grassland Soils 3.3 3.2 3.2 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.4 3.5 3.5 3.5 3.5 3.5
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.3
3.4
3.4
3.3
3.3
3.3
61.3
62.3
61.6
63.2
59.6
61.0
63.9
63.4
67.6
58.0
58.2
60.0
61.8
60.1
70.4
61.1
0.9
0.9
0.9
0.8
0.9
0.8
0.8
0.8
0.9
0.8
0.8
0.7
0.7
0.7
0.8
0.8
16.1
15.9
16.2
16.5
16.6
16.5
16.9
15.8
16.2
14.5
14.6
14.4
14.6
14.0
14.7
13.8
0.9
0.8
0.8
0.9
0.9
0.9
0.9
0.9
1.1
0.9
1.0
1.0
1.1
1.0
1.2
1.1
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.5
14.5
14.6
14.7
15.3
13.4
15.0
14.6
15.2
15.1
15.3
13.9
15.0
14.9
14.9
15.8
15.8
28.5
29.9
28.6
29.5
27.5
27.5
30.3
30.4
33.9
26.0
27.5
28.5
30.0
29.0
37.5
29.2
Total Indirect
38.5
34.8
37.4
45.1
31.7
37.5
37.3
35.2
42.4
35.7
32.6
39.1
35.9
37.9
43.7
35.0
Volatilization
10.2
10.4
10.1
10.2
10.7
10.8
10.8
10.8
11.3
10.9
10.8
10.7
10.7
11.1
11.6
11.1
Leaching/Runoff
28.2
24.4
27.2
34.9
20.9
26.7
26.5
24.4
31.0
24.7
21.8
28.4
25.2
26.8
32.1
23.9
Total Emissions 250.5 247.8 249.4 257.9 244.9 251.3 256.0 252.3 270.9 248.3 246.6 254.9 252.6 257.2 280.5 253.5
Activity
2006
2007
2008
2009
2010
2011
2012
Total Direct
223.6
229.2
222.1
226.4
231.1
220.4
215.6
Direct Emissions from
Mineral Cropland Soils 153.1 157.4 153.3 154.1 159.5 155.1 153.5
A-353
-------
Synthetic Fertilizer
56.2
58.2
55.7
53.7
55.0
58.0
60.4
Organic Amendment3
11.3
11.4
11.2
11.1
11.0
11.2
11.3
Residue Nb
22.6
22.8
21.7
22.1
24.0
23.9
23.5
Mineralization and
Asymbiotic Fixation
63.0
64.9
64.7
67.2
69.6
62.1
58.2
Direct Emissions from
Drained Organic Cropland
Soils
3.3
3.3
3.3
3.2
3.2
3.2
3.2
Direct Emissions from
Mineral Grassland Soils
63.7
65.1
62.2
65.8
65.1
58.8
55.7
Synthetic Mineral Fertilizer
0.8
0.8
0.7
0.8
0.8
0.8
0.7
PRP Manure
14.4
13.7
13.5
14.1
13.7
13.6
13.3
Managed Manure
1.1
1.1
1.0
1.1
1.1
1.1
1.1
Biosolids (i.e., Sewage
Sludge)
0.5
0.5
0.5
0.5
0.5
0.5
0.6
Residueb
15.6
16.5
15.5
15.4
16.5
14.8
14.2
Mineralization and
Asymbiotic Fixation
31.3
32.6
30.9
33.8
32.4
28.1
25.8
Direct Emissions from
Drained Organic Grassland
Soils
3.5
3.4
3.4
3.4
3.3
3.3
3.3
Total Indirect
39.0
41.3
43.5
43.8
43.1
43.6
32.3
Volatilization
11.3
11.2
11.0
11.0
11.6
11.0
10.7
Leaching/Runoff
27.7
30.1
32.4
32.8
31.5
32.6
21.5
Total Emissions
262.6
270.5
265.6
270.2 274.3
263.9
247.9
a Organic amendment inputs include managed manure amendments, daily spread manure and other
commercial organic fertilizer (i.e., dried blood, tankage, compost, and other).
b Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
Note: Emissions values are presented in CO2 equivalent mass units using IPCC AR4 GWP values. Estimates after 2012 are based on a data splicing method
(See the Agricultural Soil Management section for more information). The Tier 1 and 3 methods will be applied in a future inventory to recalculate the part of the
time series that is estimated with the data splicing methods.
Table A-229: Implied Tier 3 Cropland Indirect Emission Factors
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Indirect N Inputs
N Inputs Volatilization (N
fertilizer + N manure)
N Inputs Leachnig (N fertilizer
+ N manure + N residue)
Total Indirect Activity
Volatilization
Leaching/Runoff
Implied EF Volotilization
Implied EF Leaching
10,500 10,383 10,665 10,754 10,468 10,174 10,523 10,527 10,414 10,308 10,674 10,486
14,379 14,488 14,387 14,805 14,208 14,357 14,457 14,494 14,305 14,912 14,897 14,685
866.3
6,330.6
0.083
0.440
869.5
5,268.8
0.084
0.364
832.0
6,149.5
0.078
0.427
870.2
8,208.6
0.081
0.554
870.7
4,102.8
0.083
0.289
906.5
5,841.1
0.089
0.407
876.0
5,695.1
0.083
0.394
888.6
5,147.7
0.084
0.355
959.2
6,797.5
0.092
0.475
938.2
5,258.9
0.091
0.353
965.0
4,591.5
0.090
0.308
970.6
6,382.3
0.093
0.435
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Indirect N Inputs
N Inputs Volatilization (N
fertilizer + N manure)
N Inputs Leachnig (N fertilizer
+ N manure + N residue)
Total Indirect Activity
Volatilization
Leaching/Runoff
Implied EF Volotilization
Implied EF Leaching
10,518 10,526 10,482 10,618 10,382 11,242 10,787 10,743 10,926 10,950 11,127
14,721 14,829 14,436 14,836 14,464 15,413 14,755 14,815 15,411 15,376 15,497
945.0
5,393.7
0.090
0.366
973.9
5,949.8
0.093
0.401
1010.6
7,191.6
0.096
0.498
998.3
5,232.5
0.094
0.353
989.5
6,129.7
0.095
0.424
991.1
6,739.7
0.088
0.437
979.6
7,415.4
0.091
0.503
995.5
7,543.1
0.093
0.509
1101.7
7,333.1
0.101
0.476
996.5
7,323.4
0.091
0.476
925.2
4,375.4
0.083
0.282
Note: Estimates after 2012 are based on a data splicing method (See the Agricultural Soil Management section for more information). The Tier 3 method will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
A-354 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-230: Implied Tier 3 Grassland Indirect Emission Factors
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Indirect N Inputs
N Inputs Volatilization (N
fertilizer + N PRP manure + N
managed manure)
3,875
3,892
4,041
4,078
4,242
4,287
4,286
4,258
4,293
4,250
4,172
4,130
4,183
N Inputs Leachnig (N residue)
7,967
7,946
8,192
8,392
7,653
8,588
8,073
8,226
7,540
9,150
8,131
8,490
8,117
N Inputs Leachnig (N fertilizer
+ N PRP manure + N
managed manure + N residue)
11,841
11,838
12,233
12,470
11,896
12,875
12,360
12,483
11,832
13,400
12,304
12,620
12,300
Total Indirect Activity
Volatilization
701.5
714.9
712.8
707.1
703.9
731.8
729.0
737.7
789.6
730.7
695.9
741.0
745.9
Leaching/Runoff
664.2
638.1
575.2
726.1
571.8
599.1
594.9
612.9
834.5
561.4
493.0
709.5
731.2
Implied Fraction of N
Volatilization
0.181
0.184
0.176
0.173
0.166
0.171
0.170
0.173
0.184
0.172
0.167
0.179
0.178
Implied Fraction of N
Leaching/Runoff
0.056
0.054
0.047
0.058
0.048
0.047
0.048
0.049
0.071
0.042
0.040
0.056
0.059
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Indirect N Inputs
N Inputs Volatilization (N
fertilizer + N PRP manure + N
managed manure)
4,221
4,224
4,261
4,318
4,231
4,195
4,194
4,179
4,074
3,992
N Inputs Leachnig (N residue)
8,549
7,746
8,722
8,070
8,757
8,454
8,242
8,903
8,508
9,005
N Inputs Leachnig (N fertilizer
+ N PRP manure + N
managed manure + N residue)
12,770
11,971
12,984
12,389
12,988
12,649
12,436
13,082
12,582
12,997
Total Indirect Activity
Volatilization
768.0
843.0
779.0
776.7
788.2
771.1
782.9
798.3
722.7
716.7
Leaching/Runoff
559.5
792.2
515.6
599.6
731.8
759.1
844.4
612.7
802.0
545.9
Implied Fraction of N
Volatilization
0.182
0.200
0.183
0.180
0.186
0.184
0.187
0.191
0.177
0.180
Implied Fraction of N
Leaching/Runoff
0.044
0.066
0.040
0.048
0.056
0.060
0.068
0.047
0.064
0.042
Note: Estimates after 2012 are based on a data splicing method (See the Agricultural Soil Management section for more information). The Tier 3 method will be
applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Table A-231: Total CH4 Emissions from Cultivation of Rice Estimated with Tier 1 and 3 Inventory Approaches (MMT CO2 Eq.)
Rice Methane (MMT CO2 Eg)
Approach
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Tier 1
1.63
1.64
1.70
1.70
1.75
1.53
1.57
1.58
1.76
3.25
3.29
1.93
1.84
1.72
1.85
1.74
1.48
Tier 3
14.39
15.18
15.17
15.24
13.10
14.23
14.40
14.22
14.35
14.82
14.98
13.62
14.62
12.58
12.26
14.93
11.38
Total
16.02
16.82
16.87
16.94
14.84
15.76
15.97
15.80
16.10
18.08
18.27
15.56
16.46
14.31
14.11
16.68
12.86
Approach
2007
2008
2009
2010
2011
2012
Tier 1
1.40
1.59
1.70
1.79
1.51
1.38
Tier 3
12.54
9.92
12.76
14.09
12.59
9.96
Total
13.94
11.51
14.45
15.88
14.10
11.34
Note: Estimates after 2012 are based on a data splicing method (See the Rice Cultivation section for more information). The Tier 1 and 3 methods will be applied
in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
Table fl-232: Total 2012 N2O Emissions [Direct and Indirect! fromflgricultural Soil Management by State (MMT CO2 Eq.)
State
Croplands3
Grasslands'1
Total
Lower
Bound
Upper
Bound
AL
1.57
1.27
3.00
2.44
4.13
AR
4.84
1.38
6.50
5.18
9.10
AZ
0.55
0.87
1.84
1.45
3.14
CA
4.17
1.12
8.69
5.85
18.38
CO
2.87
2.06
5.13
4.33
6.80
CT
0.11
0.02
0.14
0.10
0.24
A-355
-------
DE
0.16
0.01
0.19
0.13
0.34
FL
1.91
2.98
5.72
4.45
10.09
GA
2.47
0.94
3.68
2.76
5.69
HI'4
0.01
0.13
0.14
0.04
0.27
IA
13.34
1.38
15.12
12.13
20.45
ID
2.74
0.86
3.81
3.04
5.67
IL
12.68
0.71
13.40
10.32
18.72
IN
7.58
0.63
8.19
6.24
11.80
KS
10.22
2.93
13.44
11.16
17.35
KY
3.26
2.33
5.60
4.62
7.31
LA
3.09
0.98
4.51
3.65
6.13
MA
0.14
1.26
0.20
0.15
0.30
MD
0.73
0.12
1.00
0.75
1.57
ME
0.23
0.17
0.38
0.27
0.58
Ml
3.99
0.65
5.08
4.03
7.26
MN
9.62
0.91
11.33
9.22
14.99
MO
7.33
3.08
10.61
8.64
14.08
MS
3.45
0.94
4.44
3.52
6.13
MT
3.29
3.04
6.34
5.33
7.94
NC
2.84
0.68
3.76
2.75
6.03
ND
6.02
1.05
7.02
5.61
9.08
NE
9.49
1.42
11.27
9.13
15.34
NH
0.07
0.02
0.13
0.09
0.20
NJ
0.15
0.11
0.23
0.17
0.36
NM
0.74
2.30
2.95
2.41
4.28
NV
0.25
1.23
0.76
0.61
1.18
NY
2.93
0.73
4.01
3.13
6.12
OH
6.39
0.71
8.32
6.51
12.36
OK
3.05
3.61
6.75
5.68
8.68
OR
1.25
1.02
2.51
2.06
3.63
PA
2.76
0.57
3.68
2.85
5.74
Rl
0.01
0.01
0.02
0.01
0.04
SC
1.13
0.40
1.51
1.08
2.39
SD
5.33
1.83
7.16
5.86
9.23
TN
2.50
1.80
4.35
3.53
5.83
TX
12.07
11.64
24.67
20.69
31.66
UT
0.59
0.76
1.44
1.16
2.16
VA
1.43
1.24
2.71
2.23
3.60
VT
0.45
0.12
0.64
0.48
1.04
WA
2.05
0.63
3.06
2.54
4.35
Wl
5.84
1.01
7.64
6.24
11.05
WV
0.28
0.41
0.70
0.58
0.91
WY
0.95
1.56
2.76
2.33
3.77
a Emissions from non-manure organic N inputs for crops not simulated by DayCent were not estimated (NE) at
the state level.
b Emissions from biosolids (i.e., sewage sludge) applied to grasslands and were not estimated (NE) at the state
level.
c N2O emissions are not reported for Hawaii except from cropland organic soils.
Table A-233: Annual Soil G Stock Change in Cropland Remaining Cropland (CRC), land Converted to Cropland(LCC),
Grassland Remaining Grassland [GRG1, and land Converted to Grassland [LCG1, in U.S. Agricultural Soils [MBIT CO2 EqJ
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Net emissions based on Tier 3 Century-based analysis (Step 2)
CRC (65.7)
(71.6)
(63.0)
(43.6)
(55.5)
(49.2)
(57.7)
(55.5)
(44.2)
(59.7)
(65.4)
(58.3)
(54.7)
(47.6)
(47.6)
(50.8)
GCC 20.6
21.4
23.6
18.0
14.4
20.0
16.9
19.0
12.6
12.8
13.0
11.2
11.2
13.1
12.6
12.4
GRG (10.2)
(12.5)
(6.8)
1.7
(24.1)
(1.0)
(22.3)
(9.1)
(16.0)
(4.0)
(33.1)
(8.8)
(9.6)
(6.3)
0.4
2.0
CCG (5.1)
(5.2)
(4.9)
(5.5)
(7.4)
(6.4)
(7.6)
(7.5)
(8.1)
(8.5)
(10.5)
(9.8)
(10.5)
(10.5)
(9.9)
(10.2)
Net emissions based on the IPCC Tier 2 analysis (Step 3)
Mineral Soils
CRC (5.4)
(6.2)
(6.6)
(6.9)
(6.7)
(6.5)
(6.1)
(7.6)
(7.3)
(7.1)
(6.7)
(6.7)
(6.7)
(6.0)
(5.4)
(5.4)
GCC 1.3
1.3
1.3
1.3
1.5
1.6
1.6
1.4
1.6
1.5
1.5
1.5
1.5
1.4
1.6
1.5
FCC 0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
OCC 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
SCC 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
wee o.i
0.1
0.1
0.1
0.1
0.2
0.2
0.1
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
GRG (0.6)
(0.8)
(1.4)
(1.6)
(1.6)
(1.6)
(1.0)
(1.0)
(1.7)
(1.5)
(1.8)
(1.8)
(2.8)
(2.7)
(1.2)
(1.3)
A-356 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
CCG
(2.9)
(2.9)
(2.8)
(2.9)
(3.1)
(2.9)
(2.7)
(2.6)
(3.2)
(3.1)
(3.2)
(3.1)
(2.8)
(2.5)
(2.7)
(2.4)
FCG
(0.8)
(0.8)
(0.8)
(0.7)
(0.7)
(0.7)
(0.7)
(0.7)
(0.8)
(0.8)
(0.8)
(0.8)
(0.7)
(0.7)
(0.6)
(0.5)
OCG
(0.5)
(0.6)
(0.6)
(0.7)
(0.8)
(0.8)
(0.8)
(0.8)
(0.9)
(1.0)
(1.1)
(1.2)
(1.1)
(1.0)
(1.1)
(1.1)
SCG
(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)
WCG
(0.3)
(0.4)
(0.5)
(0.5)
(0.5)
(0.5)
(0.5)
(0.5)
(0.5)
(0.5)
(0.5)
(0.5)
(0.5)
(0.4)
(0.4)
(0.4)
Organic Soils
CRC
30.3
29.8
29.7
29.5
29.4
29.3
29.3
29.3
28.8
24.4
24.5
29.0
29.3
29.6
29.9
29.7
GCC
2.5
2.5
2.6
2.7
2.7
2.9
3.0
3.0
3.5
3.5
3.3
4.2
4.2
4.0
3.4
3.3
FCC
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
0.0
0.0
OCC
0.1
0.1
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
see
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
wee
0.6
0.6
0.6
0.8
1.0
1.0
1.0
1.0
1.0
1.0
0.9
0.8
0.8
0.7
0.7
0.7
GRG
7.2
7.2
7.1
7.0
7.0
6.9
6.8
6.8
6.7
6.6
6.5
6.2
6.1
6.1
6.0
6.0
CCG
0.5
0.5
0.5
0.6
0.7
0.7
0.7
0.7
0.9
0.8
0.9
1.0
1.1
1.0
1.1
1.1
FCG
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.1
0.1
OCG
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
SCG
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
WCG
0.1
0.1
0.1
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.3
0.3
0.3
0.2
0.2
0.3
Additional changes in net emissions from mineral soils based on application of biosolids (i.e.,sewage sludge) to agricultural land (Step 4)
GRG
(0.6)
(0.6)
(0.7)
(0.7)
(0.7)
(0.8)
(0.8)
(0.9)
(0.9)
(0.9)
(1.0)
(1.0)
(1.0)
(1.0)
(1.1)
(1.1)
Additional changes in net emissions from mineral soils based on additional enrollment of CRP land (Step 4)
CRC __________
Total Stock Changes by Land Use/Land-Use Change Category (Step 5)
CRC
(40.9)
(48.1)
(40.0)
(21.1)
(32.8)
(26.3)
(34.5)
(33.8)
(22.7)
(42.3)
(47.7)
(36.0)
(32.1)
(24.0)
(23.0)
(26.5)
GCC
24.5
25.2
27.5
22.0
18.6
24.6
21.6
23.4
17.7
17.8
17.7
16.9
16.8
18.5
17.6
17.3
FCC
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.1
0.2
0.1
0.1
OCC
0.3
0.2
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.3
0.3
0.3
0.3
0.3
0.3
see
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
wee
0.7
0.7
0.7
0.9
1.1
1.1
1.2
1.1
1.1
1.1
1.0
1.0
0.9
0.8
0.9
0.8
GRG
(4.2)
(6.9)
(1.8)
6.4
(19.5)
3.6
(17.3)
(4.2)
(12.0)
0.2
(29.4)
(5.4)
(7.3)
(4.1)
4.1
5.5
CCG
(7.5)
(7.6)
(7.2)
(7.9)
(9.8)
(8.6)
(9.6)
(9.4)
(10.5)
(10.8)
(12.8)
(11.9)
(12.2)
(12.0)
(11.4)
(11.5)
FCG
(0.7)
(0.8)
(0.7)
(0.7)
(0.7)
(0.7)
(0.7)
(0.7)
(0.8)
(0.8)
(0.8)
(0.7)
(0.7)
(0.6)
(0.5)
(0.4)
OCG
(0.5)
(0.5)
(0.5)
(0.6)
(0.8)
(0.8)
(0.8)
(0.8)
(0.9)
(0.9)
(1.1)
(1.1)
(1.1)
(1.0)
(1.0)
(1.0)
SCG
(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)
WCG
(0.2)
(0.3)
(0.3)
(0.3)
(0.3)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
Total'
(28.3)
(37.5)
(21.9)
(0.8)
(43.5)
(6.7)
(39.7)
(24.1)
(27.7)
(35.5)
(72.8)
(37.0)
(35.3)
(22.2)
(13.3)
(15.8)
Notes: Totals may not sum due to independent rounding. Parentheses indicate net C accumulation.
2006
2007
2008
2009
2010
2011
2012
Net emissions based on Tier 3 Century-based analysis (Step 2)
CRC
(47.5)
(45.6)
(34.4)
(29.3)
(29.4)
(43.6)
(46.6)
GCC
13.2
11.8
12.7
12.6
14.5
14.3
13.4
GRG
(14.8)
1.8
(10.1)
(5.7)
1.3
(16.0)
(24.6)
CCG
(12.2)
(10.9)
(10.8)
(10.6)
(10.8)
(11.0)
(11.2)
Net emissions based on the IPCC Tier 2 analysis (Step 3)
Mineral Soils
CRC
(4.4)
(4.0)
(3.4)
(3.5)
(3.6)
(3.5)
(2.9)
GCC
1.8
1.8
1.9
1.7
1.7
1.7
1.7
FCC
0.1
0.1
0.1
0.1
0.1
0.1
0.1
OCC
0.2
0.2
0.2
0.2
0.2
0.2
0.2
see
0.1
0.1
0.1
0.1
0.1
0.1
0.1
wee
0.2
0.1
0.1
0.1
0.1
0.1
0.1
GRG
(1.5)
(1.5)
(1.5)
(1.2)
(1.2)
(0.8)
(0.4)
CCG
(1.9)
(1.6)
(1.4)
(1.4)
(1.3)
(1.2)
(1.2)
FCG
(0.4)
(0.4)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
OCG
(0.9)
(0.8)
(0.8)
(0.8)
(0.8)
(0.8)
(0.8)
SCG
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
WCG
(0.4)
(0.3)
(0.3)
(0.2)
(0.2)
(0.2)
(0.1)
Organic Soils
CRC
29.6
29.5
29.3
29.7
29.6
27.9
28.1
GCC
3.3
3.2
3.0
2.9
2.9
3.0
3.0
FCC
0.0
0.0
0.0
0.0
0.0
0.0
0.0
OCC
0.1
0.1
0.1
0.1
0.0
0.0
0.0
see
0.0
0.1
0.1
0.0
0.0
0.1
0.1
wee
0.7
0.7
0.5
0.5
0.5
0.5
0.5
GRG
5.8
5.7
5.7
5.7
5.6
5.6
5.5
CCG
1.1
1.1
1.1
1.1
1.1
1.1
1.1
FCG
0.1
0.1
0.1
0.1
0.1
0.1
0.1
OCG
0.0
0.0
0.0
0.0
0.0
0.1
0.1
A-357
-------
SCG 0.0 0.0 0.0 0.0 0.0 0.0 0.0
WCG 0.3 0.3 0.3 0.3 0.3 0.3 0.3
Additional changes in net emissions from mineral soils based on
application of biosolids (i.e., sewage sludge) to agricultural land (Step 4)
GRG (1.2) (1.2) (1.2) (1.3) (1.3) (1.3) (1.4)
Additional changes in net emissions from mineral soils based on
additional enrollment of CRP land (Step 4)
CRC -
Total Stock Changes by Land Use/Land-Use Change Category (Step 5)
CRC
(22.2)
(20.1)
(8.5)
(3.2)
(3.4)
(19.1)
(21.4)
GCC
18.2
16.9
17.5
17.2
19.1
19.0
18.1
FCC
0.1
0.1
0.1
0.1
0.1
0.1
0.1
OCC
0.3
0.3
0.3
0.3
0.2
0.2
0.2
see
0.1
0.1
0.1
0.1
0.1
0.2
0.2
wee
0.9
0.8
0.7
0.6
0.6
0.7
0.7
GRG
(11.7)
4.8
(7.1)
(2.4)
4.5
(12.5)
(20.8)
CCG
(13.0)
(11.4)
(11.2)
(10.9)
(10.9)
(11.0)
(11.3)
FCG
(0.3)
(0.3)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
OCG
(0.9)
(0.8)
(0.8)
(0.8)
(0.8)
(0.8)
(0.7)
SCG
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
WCG
(0.1)
(0.0)
0.0
0.1
0.1
0.2
0.2
Total'
(28.7)
(9.6)
(9.1)
0.9
9.4
(23.5)
(35.0)
Notes: Totals may not sum due to independent rounding. Note: Estimates after
2012 are based on a data splicing method (See the Cropland Remaining
Cropland section for more information). The Tier 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.
Table fl-234: Soil C Stock Change for Mineral and Organic Soils in 2012 by State (MMT CO2 Eq.)
State
Mineral Soil
Organic Soil
Total
AL
(1.03)
0.01
(1.02)
AR
(1.00)
-
(1.00)
AZ
(0.42)
-
(0.42)
CA
(3.72)
1.58
(2.13)
CO
(0.02)
0.00
(0.01)
CT
(0.02)
0.01
(0.02)
DE
(0.04)
-
(0.04)
FL
0.12
12.21
12.32
GA
0.18
-
0.18
HI
(0.08)
0.77
0.69
IA
(9.18)
0.73
(8.45)
ID
(1.25)
0.03
(1.22)
IL
(6.20)
0.52
(5.68)
IN
(1.64)
2.36
0.72
KS
(2.43)
-
(2.43)
KY
(1.39)
-
(1.39)
LA
(0.13)
0.51
0.39
MA
(0.06)
0.28
0.23
MD
(0.19)
0.01
(0.18)
ME
(0.11)
0.01
(0.10)
Ml
(0.02)
3.40
3.37
MN
(4.11)
7.65
3.55
MO
(5.91)
-
(5.91)
MS
(1.05)
0.01
(1.04)
MT
(4.40)
0.15
(4.26)
NC
(0.57)
1.89
1.32
ND
(10.32)
0.01
(10.30)
NE
(5.17)
0.00
(5.16)
NH
(0.03)
0.02
(0.01)
NJ
(0.02)
0.12
0.10
NM
2.64
-
2.64
NV
(1.08)
0.00
(1.08)
NY
(0.33)
0.53
0.20
OH
(1.52)
0.48
(1.04)
OK
(0.62)
-
(0.62)
OR
(0.61)
0.30
(0.31)
PA
(0.43)
0.05
(0.38)
A-358 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Rl
(0.00)
0.02
0.02
SC
(0.56)
0.02
(0.54)
SD
(5.89)
-
(5.89)
TN
(1.51)
-
(1.51)
TX
2.44
-
2.44
UT
0.95
0.08
1.02
VA
(1.29)
0.00
(1.29)
VT
(0.08)
0.06
(0.02)
WA
(0.60)
0.38
(0.23)
Wl
(0.06)
2.90
2.85
WV
(0.53)
-
(0.53)
WY
(2.96)
-
(2.96)
Indicates there are no cropland or grassland organic soils in the state.
Note: Parentheses indicate net C accumulation. Estimates do not include
soil C stock change associated with federal croplands and grasslands,
CRP enrollment after 2012, or biosolids (i.e., sewage sludge) application
to soils, which were only estimated at the national scale. The sum of state
results will not match the national results because state results are
generated in a separate programming package, the biosolids are not
included, and differences arise due to rounding of values in this table. Only
national-scale soil C stock changes are estimated for 2013 to 2016 in this
Inventory using a splicing method, and therefore the state-scale stock
changes are based on inventory data from 2012.
Table fl-235: Total CHa Emissions from Rice Cultivation in 2012 by State (MMT CO2 Eq.)
State
Total
AL
-
AR
3.75
AZ
-
CA
2.04
CO
-
CT
-
DE
-
FL
-
GA
-
HI
-
IA
-
ID
-
IL
IM
-
IN
KS
_
KY
-
LA
3.79
MA
-
MD
-
ME
yi
-
Ivll
MN
0.03
MO
0.29
MS
0.47
MT
-
NC
-
ND
-
NE
-
NH
-
NJ
MU
-
IMIVI
NV
_
NY
-
OH
-
OK
-
OR
-
PA
-
Rl
-
SC
-
SD
-
TN
-
TX
0.85
A-359
-------
UT
VA
VT
WA
Wl
WV
WY -
Note: Only national-scale CbU emissions
from rice cultivation are estimated for 2013
to 2016 in this Inventory using a splicing
method, and therefore the state-scale
emissions are based on inventory data
from 2012.
A-360 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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3.13. Methodology for Estimating Net Carbon Stock Changes in Forest Land Remaining
Forest Land and Land Converted to Forest Land
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-
002 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 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 IPCC (2003, 2006) stock-difference methods. Estimates of ecosystem C are
based on data from the network of annual inventory plots established and measured by the Forest Inventory and Analysis
program within the USDA Forest Service; either direct measurements or attributes of forest inventories are the basis for
estimating metric tons of C per hectare in IPCC pools (i.e., above- and belowground biomass, dead wood, litter, and soil
organic carbon). 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. Recent publications (Coulston et al. 2015; Woodall et al. 2015a) detail the land use and stand age
transition matrices that are informed by the annual forest inventory of the United States and were used in the accounting
framework used in this Inventory. The annual forest inventories in the eastern United States have been remeasured which
allows for empirical estimation of forest C stock net change within the accounting framework. In contrast, as numerous
western states have not yet been remeasured, theoretical age transition matrices have been developed (Figure A-16).
The following subsections of this annex will describe the estimation system used this year (Figure A-16) including
the methods for estimating individual pools of forest C in addition to the eastern versus western approach to informing land
use and stand age transitions.
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Figure A-16: Flowchart of the inputs necessary in the accounting framework, including the methods for estimating
ndividual pools of forest G in the eastern and western conterminous U.S. states and coastal Alaska
Eastern US
Western US
Annual C flux by land use change, forests remaining forest, and total
net sequestration in managed US forests, 1990-2016
Empirical age class transition
matrix (Coulston et al. 2015)
Area by age class
and domain
(Coulston etal.
2015)
Area by age class
and domain
(Wear and
Coulston 2015)
Theoretical age class transition matrix
(Wear and Coulston 2015)
Carbon density by age class and domain at
tt and t2 (Wear and Coulston 2015)
Carbon stock change by age class and domain
between t and t (Coulston et al. 2015)
Post stratified estimator for population estimates by
age class and domain (Bechtold and Patterson 2005)
Current carbon stock change by age class and domain (Wear and Coulston 2015)
Aboveground live (Woodall
etal.2011, EPA 2015)
Belowground live (Woodall
etal. 2011, EPA 2015)
Deadwood (Domke et al.
2011,2012)
Litter (Domke et al. 2016)
SOC (Domke et al. 2017)
Aboveground live (Woodall
etal.2011, EPA2015)
Belowground live (Woodall
etal.2011, EPA2015)
Deadwood (Domke et al.
2011,2012)
Litter (Domke et al. 2016)
SOC (Domke et a I. 2017)
Note: An empirical age class transition matrix was used in the Eastern United States while a theoretical age class transition matrix was used in
the Western United States.
Forest Land Definition
The definition of forest land within the United States and used for this Inventory is defined in Oswalt et al. (2014)
as "Tand at least 120 feet (37 meters) wide and at least 1 acre (0.4 hectare) in size with at least 10 percent cover (or equivalent
stocking) by live trees including land that formerly had such tree cover and that will be naturally or artificially regenerated.
Trees are woody plants having a more or less erect perennial stem(s) capable of achieving at least 3 inches (7.6 cm) in
diameter at breast height, or 5 inches (12.7 cm) diameter at root collar, and a height of 16.4 feet (5 meters) at maturity in
situ. The definition here includes all areas recently having such conditions and currently regenerating or capable of attaining
such condition in the near future. Forest land also includes transition zones, such as areas between forest and non-forest
lands that have at least 10 percent cover (or equivalent stocking) with live trees and forest areas adjacent to urban and built -
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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 forest inventory surveys. Forest inventory data were
obtained from the USDA Forest Service, Forest Inventory and Analysis (FIA) Program (Frayer and Furnival 1999; USDA
Forest Service 2015a; USDA Forest Service 2015b). Forest Inventory and Analysis 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, height, and species. On a subset of plots, additional measurements or samples are taken of downed dead wood, litter,
and soil attributes. 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. In press). The field protocols
are thoroughly documented and available for download from the USDA Forest Service (2015c). Bechtold and Patterson
(2005) provide the estimation procedures for standard forest inventory results. The data are freely available for download at
USDA Forest Service (2011b) as the FIA Database (FIADB) Version 6.0 (USDA Forest Service 2015b; USDA Forest
Service 2015c); these data are the primary sources of forest inventory 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 by a forester to determine land use. As annual forest inventories have only just begun in the U.S. Territories and
in Hawaii, there is an assumption that these areas account for a net C change of zero. Survey data are available for the
temperate oceanic ecoregion of Alaska (southeast and south central). These inventory data are publicly available for 6.2
million hectares of forest land, and these inventoried lands, representing an estimated 12 percent of the total forest land in
Alaska, contribute to the forest C stocks presented here. Agroforestry systems are also not currently accounted for in the
U.S. Inventory, since they are not explicitly inventoried by either of the two primary national natural resource inventory
programs: the FIA program of the USDA Forest Service and the National Resources Inventory (NRI) of the USDA Natural
Resources Conservation Service (Perry et al. 2005). The majority of these tree-based practices do not meet the size and
definitions for forests within each of these resource inventories.
A national plot design and annualized sampling (USDA Forest Service 2015a) were introduced by FIA with most
new annual inventories beginning after 1998. These are the only forest inventories used in the current accounting framework
and subsequently in this submission. These surveys involve the sampling of all forest land including reserved and lower
productivity lands. Almost all states have annualized inventory data available with substantial remeasurement in the eastern
United States (Figure A-17). Annualized sampling means that a portion of plots throughout the state is sampled each year,
with the goal of measuring all plots once every 5 to 10 years, depending on the region of the United States. The full unique
set of data with all measured plots, such that each plot has been measured one time, is called a cycle. Sampling is designed
such that partial inventory cycles provide usable, unbiased samples of forest inventory within the state, but with higher
sampling errors than the full cycle. After all plots have been measured once, the sequence continues with remeasurement of
the first year's plots, starting the next new cycle. Most eastern states have completed one or two cycles of the annualized
inventories, and some western states have begun remeasuring with a 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 calculations use annual inventory summaries (updates) with unique sets of plot-level data (that is, without
redundant sets); the most-recent annual update (i.e., 2016) is the exception because it is included in stock change calculations
in order to include the most recent available data for each state. The specific inventories used in this report are listed in Table
A-236 and this list can be compared with the full set of summaries available for download (USDA Forest Service 2015b).
A-370 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Figure fl-17: Annual FIA plots (remeasured and not remeasured) across the United States including coastal Alaska through
the 2015 field season
Coastal Alaska
v
Remeasured
Not Remeasured
«# ';
m.
r. '* ,
' *
U-
rt' Sw
; ' ¦ s
T'jfC !•
V
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) 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 on the 48 conterminous states. However, Alaska is considered to have
significant areas of both managed and unmanaged forest lands. A new model delineating managed versus unmanaged lands
for the United States (Ogle et al. in preparation), and used in this Inventory, is consistent with the assumption of managed
forest lands on the 48 states. However, the model of Ogle et al. (in preparation) identifies some of the forest land in south
central and southeastern coastal Alaska as unmanaged; this is in contrast to past assumptions of "managed" for these forest
lands included in the FIA program. Therefore, the estimates for coastal Alaska as included here reflect that adjustment,
which effectively reduces the forest area included here by about 5 percent. A second modification to the use of the FIADB-
defined forest land introduced this year is to identify plots that do not meet the height component of the definition of
forestland (Coulston et al. 2016). These plots were identified as "other wooded lands" (i.e., not forest land use) and were
removed from forest estimates and classified as grassland.133 Note that minor differences in identifying and classifying
woodland as "forest" versus "other wooded" exist between the current Resources Planning Act Assessment (RPA) data
(Oswalt et al. 2014) and the FIADB (1JSDA Forest Service 2015b) due to a refined modelling approach developed
specifically for this report (Coulston et al. 2016).
133 See the Grassland Remaining Grassland section for details.
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Tahlefl-236: Specific annual forest inventories by state used in development of forest Cstockand stock change estimates
Remeasured Annual Plots
Split Annual Cycle Plots
Time 2 Year
State
Time 1 Year Range
Time 2 Year Range
State
Time 1 Year Range
Range
Alabama
2001 -2011
2006 -2015
Alaska (Coastal)
2004-2008
2009 -2013
Arkansas
2006-2010
2011 -2015
Arizona
2004-2008
2009 -2013
Connecticut
2005 -2010
2010 -2015
California
2001 - 2005
2006 -2010
Delaware
2005-2010
2010-2015
Colorado
2004-2008
2009 -2013
Florida
2002 -2011
2010 -2014
Idaho
2004-2008
2009 -2013
Georgia
2005-2009
2010-2014
Montana
2004-2008
2009 -2013
Illinois
2005 -2010
2010 -2015
Nevada
2004-2008
2009 -2013
Indiana
2005 -2010
2010 -2015
1 lvvV 1V1 vA 1 vU
1999
2005 -2013
Iowa
2005 -2010
2010 -2015
Oklahoma (West)
2009 -2010
2011 -2013
Kansas
2005 -2010
2010 -2015
Oregon
2001 - 2005
2006 -2010
Kentucky
2000 -2009
2006 -2013
Texas (West)
2004-2007
2008 -2012
Louisiana
2001 -2008
2009 -2014
Utah
2004-2008
2009 -2013
Maine
2006 -2010
2011 -2015
Washington
2002 - 2006
2007 -2011
Maryland
2004-2009
2009 -2014
Wyoming
2000
2011-2013
MdSSdChlJS6ttS
2005 -2010
2010 -2015
Michigan
2005-2010
2010-2015
Minnesota
2006 -2010
2011 -2015
Mississippi
2006
2009-2014
Missouri
2005 -2010
2010 -2015
Nebraska
2005-2010
2010-2015
New Hampshire
2004-2010
2010 -2015
New Jersey
2004-2009
2009-2014
New York
2003 -2009
2009 -2014
North Carolina
2003 -2007
2009 -2015
•
North Dakota
2005 -2010
2010 -2015
Ohio
2003 -2009
2009 -2014
Oklahoma (East)
2008
2010 -2014
Pennsylvania
2005 -2010
2010 -2015
Rhode Island
2005 -2010
2010 -2015
South Carolina
2002 -2011
2009 -2015
South Dakota
2005 -2010
2010 -2015
Tennessee
2000 -2009
2005-2013
Texas (East)
2002 -2008
2005-2012
Vermont
2005-2010
2010-2015
Virginia
2002 -2011
2009 -2014
West Virginia
2004-2009
2009-2014
Wisconsin
2005 -2010
2010 -2015
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 IPCC 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 offshoot 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 attributes of individual forest C pools such
A-372 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 FIA inventory plots which are a systematic sample of all forests attributes and land uses within each state. The
results are estimates of C density (T per hectare) for the various forest pools. Carbon density for live trees, standing dead
trees, understory vegetation, downed dead wood, litter, and soil organic matter are estimated. All non-soil C pools except
litter can be separated into aboveground and belowground components. The live tree and understory C pools are combined
into the biomass pool in this inventory. Similarly, standing dead trees and downed dead wood are pooled as dead wood in
this inventory. C stocks and fluxes for Forest Land Remaining Forest Land are reported in pools following IPCC (2006).
Live tree C pools
Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at
diameter breast height (d.b.h.) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates are made for above-
and below-ground biomass components. If inventory plots include data on individual trees, tree C is based on Woodall et al.
(2011), which is also known as the component ratio method (CRM), and is a function of volume, species, diameter, and, in
some regions, tree height and site quality. The estimated sound volume (i.e., after rotten/missing deductions) provided in the
tree table of the FIADB is the principal input to the CRM biomass calculation for each tree (Woodall et al. 2011). The
estimated volumes of wood and bark are converted to biomass based on the density of each. Additional components of the
trees such as tops, branches, and coarse roots, are estimated according to adjusted component estimates from Jenkins et al.
(2003). Live trees with d.b.h of less than 12.7 cm do not have estimates of sound volume in the FIADB, and CRM biomass
estimates follow a separate process (see Woodall et al. 2011 for details). An additional component of foliage, which was not
explicitly included in Woodall et al. (2011), was added to each tree following the same CRM method. Carbon is estimated
by multiplying the estimated oven-dry biomass by a C constant of 0.5 because biomass is 50 percent of dry weight (IPCC
2006). 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.
These were fit to the model:
Ratio — Q(A ~ ® x Wllve tree C density)) ^ |
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-237. Regions and forest types are the same classifications described in Smith et al.
(2003). As an example, the basic calculation for understory C in aspen-birch forests in the Northeast is:
Understory (T C/ha) = (live tree C density) x e(0 855" 103 x ln(tree c densl'y)) (2)
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 piny on/juniper forest types (see Table A-237) are set to coefficient A, which is a C density (T C/ha)
for these types only.
Table A-237: Coefficients for Estimating the Ratio of G Density of Understory Vegetation (above- and belowground, T G/ha)
by Region and Forest Type3
Maximum
Regionb
Forest Typeb
A
B
ratio0
Aspen-Birch
0.855
1.032
2.023
MBB/Other Hardwood
0.892
1.079
2.076
Oak-Hickory
0.842
1.053
2.057
NE
Oak-Pine
1.960
1.235
4.203
Other Pine
2.149
1.268
4.191
Spruce-Fir
0.825
1.121
2.140
White-Red-Jack Pine
1.000
1.116
2.098
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Nonstocked
2.020
2.020
2.060
Aspen-Birch
0.777
1.018
2.023
Lowland Hardwood
0.650
0.997
2.037
Maple-Beech-Birch
0.863
1.120
2.129
NLS
Oak-Hickory
0.965
1.091
2.072
Pine
0.740
1.014
2.046
Spruce-Fir
1.656
1.318
2.136
Nonstocked
1.928
1.928
2.117
Conifer
1.189
1.190
2.114
Lowland Hardwood
1.370
1.177
2.055
MDQ
Maple-Beech-Birch
1.126
1.201
2.130
Into
Oak-Hickory
1.139
1.138
2.072
Oak-Pine
2.014
1.215
4.185
Nonstocked
2.052
2.052
2.072
Douglas-fir
2.084
1.201
4.626
Fir-Spruce
1.983
1.268
4.806
Hardwoods
1.571
1.038
4.745
psw
Other Conifer
4.032
1.785
4.768
Pinyon-Juniper
4.430
4.430
4.820
Redwood
2.513
1.312
4.698
Nonstocked
4.431
4.431
4.626
Douglas-fir
1.544
1.064
4.626
Fir-Spruce
1.583
1.156
4.806
Hardwoods
1.900
1.133
4.745
PWE
Lodgepole Pine
1.790
1.257
4.823
Pinyon-Juniper
2.708
2.708
4.820
Ponderosa Pine
1.768
1.213
4.768
Nonstocked
4.315
4.315
4.626
Douglas-fir
1.727
1.108
4.609
Fir-Spruce
1.770
1.164
4.807
Other Conifer
2.874
1.534
4.768
PWW
Other Hardwoods
2.157
1.220
4.745
Red Alder
2.094
1.230
4.745
Western Hemlock
2.081
1.218
4.693
Nonstocked
4.401
4.401
4.589
Douglas-fir
2.342
1.360
4.731
Fir-Spruce
2.129
1.315
4.749
Hardwoods
1.860
1.110
4.745
RMN
Lodgepole Pine
2.571
1.500
4.773
Other Conifer
2.614
1.518
4.821
Pinyon-Juniper
2.708
2.708
4.820
Ponderosa Pine
2.099
1.344
4.776
Nonstocked
4.430
4.430
4.773
Douglas-fir
5.145
2.232
4.829
Fir-Spruce
2.861
1.568
4.822
Hardwoods
1.858
1.110
4.745
RM^
Lodgepole Pine
3.305
1.737
4.797
rxlvlo
Other Conifer
2.134
1.382
4.821
Pinyon-Juniper
2.757
2.757
4.820
Ponderosa Pine
3.214
1.732
4.820
Nonstocked
4.243
4.243
4.797
Bottomland Hardwood
0.917
1.109
1.842
Misc. Conifer
1.601
1.129
4.191
Natural Pine
2.166
1.260
4.161
sc
Oak-Pine
1.903
1.190
4.173
Planted Pine
1.489
1.037
4.124
Upland Hardwood
2.089
1.235
4.170
Nonstocked
4.044
4.044
4.170
Bottomland Hardwood
0.834
1.089
1.842
Misc. Conifer
1.601
1.129
4.191
Natural Pine
1.752
1.155
4.178
SE
Oak-Pine
1.642
1.117
4.195
Planted Pine
1.470
1.036
4.141
Upland Hardwood
1.903
1.191
4.182
Nonstocked
4.033
4.033
4.182
A-374 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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a Prediction of ratio of understory C to live tree C is based on the model: Ratio=exp(A -B* ln(tree_carbon_tph)), where "ratio" is the ratio of understory C density
to live tree (above-and below- ground) C density, and "tree_carbon_density" is live tree (above-and below- ground) C density in T C/ha. Note that this ratio is
multiplied by tree C density on each plot to produce understory vegetation.
b Regions and types as defined in Smith et al. (2003).
c Maximum ratio: any estimate predicted from the model that is greater than the maximum ratio is set equal to the maximum ratio.
Dead Wood
The standing dead tree estimates are primarily based on plot-level measurements (Domke et al. 2011; Woodall et
al. 2011). This C pool includes aboveground and belowground (coarse root) mass and includes trees of at least 12.7 cm
d.b.h. Calculations follow the basic CRM method applied to live trees (Woodall et al. 2011) with additional modifications
to account for decay and structural loss. In addition to the lack of foliage, two characteristics of standing dead trees that can
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.
Downed dead wood, inclusive of logging residue, are sampled on a subset of FIA plots. Despite a reduced sample
intensity, a single down woody material population estimate (Woodall et al. 2010; Domke et al. 2013; Woodall et al. 2013)
per state is now incorporated into these empirical downed dead wood estimates. Downed dead wood is defined as pieces of
dead wood greater than 7.5 cm diameter, at transect intersection, that are not attached to live or standing dead trees. It also
includes stumps and roots of harvested trees. Ratio estimates of downed dead wood to live tree biomass were developed
using FORCARB2 simulations and applied at the plot level (Smith et al. 2004). Estimates for downed dead wood correspond
to the region and forest type classifications described in Smith et al. (2003). A full set of ratios is provided in Table A-238.
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-239. 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:
First, an initial estimate from live tree C density and Table A-238 (SC, Natural Pine)
C density = 82.99 x 0.068 = 5.67 (T C/ha)
Second, an average logging residue from age and Table A-238 (SC, softwood)
C density = 5.5 x e(-25/17.9) = 1.37 (T C/ha)
Third, adjust the sum by the downed dead wood ratio plot-to-model for Louisiana, which was 27.6/31.1 = 0.886
C density = (5.67 + 1.37) x 0.886 = 6.24 (T C/ha)
Table A-238: Ratio for Estimating Downed Dead Wood by Region and Forest Type
Region3 Forest type3 Ratiob
NE
Aspen-Birch
MBB/Other Hardwood
Oak-Hickory
Oak-Pine
Other Pine
Spruce-Fir
White-Red-Jack Pine
Nonstocked
0.078
0.071
0.068
0.061
0.065
0.092
0.055
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
Np„ Lowland Hardwood 0.069
Maple-Beech-Birch 0.063
Oak-Hickory 0.068
A-375
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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
0.095
Western Hemlock
0.099
Nonstocked
0.020
Douglas-fir
0.062
Fir-Spruce
0.100
Hardwoods
0.112
RMN
Lodgepole Pine
0.058
Other Conifer
0.060
Pinyon-Juniper
0.030
Ponderosa Pine
0.087
Nonstocked
0.018
Douglas-fir
0.077
Fir-Spruce
0.079
Hardwoods
0.064
RM^
Lodgepole Pine
0.098
rxlvio
Other Conifer
0.060
Pinyon-Juniper
0.030
Ponderosa Pine
0.082
Nonstocked
0.020
Bottomland Hardwood
0.063
Misc. Conifer
0.068
Natural Pine
0.068
sc
Oak-Pine
0.072
Planted Pine
0.077
Upland Hardwood
0.067
Nonstocked
0.013
Bottomland Hardwood
0.064
Misc. Conifer
0.081
Natural Pine
0.081
SE
Oak-Pine
0.063
Planted Pine
0.075
Upland Hardwood
0.059
Nonstocked
0.012
a Regions and types as defined in Smith et al. (2003).
b The ratio is multiplied by the live tree C density on a plot to produce downed dead wood C density (T C/ha).
Table fl-239: Coefficients for Estimating Logging Residue Component of Downed Dead Wood
Forest Type Groupb
(softwood/ Initial C Density
Region3 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
A-376 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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NLS
softwood
7.2
17.9
NPS
hardwood
9.6
12.1
NPS
softwood
6.4
17.9
PSW
hardwood
9.8
12.1
PSW
softwood
17.5
32.3
PWE
hardwood
3.3
12.1
PWE
softwood
9.5
32.3
PWW
hardwood
18.1
12.1
PWW
softwood
23.6
32.3
RMN
hardwood
7.2
43.5
RMN
softwood
9.0
18.1
RMS
hardwood
5.1
43.5
RMS
softwood
3.7
18.1
SC
hardwood
4.2
8.9
SC
softwood
5.5
17.9
SE
hardwood
6.4
8.9
SE
softwood
7.3
17.9
a Regions are defined in Smith et al. (2003) with the addition of coastal Alaska.
b Forest types are according to majority hardwood or softwood species.
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 form of
the full random forest model was:
/'(/•'/•'( ',.u//) = f (lat, Ion, elev, fortypgrp,above,ppt, tmax, gmi) + u (3)
where: lat = latitude, Ion = 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.
For each replacement, u was independently and randomly generated from a N(0,a) distribution with a incorporating
the variability from both sources. This process of randomly selecting and incorporating u may be considered an imputation.
Each model prediction was replaced independently m times and m separate estimates were combined where m = 1,000 in
this analysis.
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 Fleath (2002). Forest floor C predictions are expressed in T•ha-1.
Soil organic carbon
Soil organic carbon (SOC) is the largest terrestrial C sink, and management of this pool is a critical component of
efforts to mitigate atmospheric C concentrations. In the U.S., SOC in forests is monitored by the national forest inventory
conducted by the FIA program (O'Neill et al. 2005). In previous C inventory submissions, SOC predictions were based, in
part, on a model using the State Soil Geographic (STATSGO) database compiled by the Natural Resources Conservation
Service (NRCS) (Amichev and Glabraith 2004), hereafter referred to as the country-specific (CSsoc) model. Estimates of
A-377
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forest SOC found in the STATSGO database may be based on expert opinion and/or lack systematic field observations, but
these country-specific model predictions have been used in past C inventory submissions. The FIA program has been
consistently measuring soil attributes as part of the inventory since 2001 and has amassed an extensive inventory of SOC in
forest land in the conterminous United States and coastal Alaska (O'Neill et al. 2005). More than 5,000 profile observations
of SOC on forest land from FIA and the International Soil Carbon Monitoring Network (ISCN 2015) were used to develop
and implement a modeling framework that includes site-, stand-, and climate-specific variables that yield predictions of SOC
stocks and stock changes specific to forest land in the United States. 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 new modeling framework to predict SOC on forest land came from the FIA program
and the ISCN. Since 2001, the FIA program has collected soil samples on every 16th base intensity plot 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, 3, and 4 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 FIA program were calculated following O'Neill et al. (2005):
V SOC =CrBDrtrucf (4)
L-i FIA TOTAL i i i J V J
Where ^SOC = total mass (Mg C ha-1) of the mineral and organic soil C over all rth layers, q = percent
organic C in the rth layer, BDt = bulk density calculated as weight per unit volume of soil (g-cm-3) at the rth soil layer, f.
= thickness (cm) of the rth soil layer (either 0 to 10.16 cm or 10.16 to 20.32 cm), and ucf= unit conversion factor (100).
The SOCFIA TOTAL estimates 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), 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:
log 10 SOCt =1 + log 10 Depth (5)
Where log 10 SOCt = SOC density (Mg C ha-1 cm depth-1) at the midpoint depth, I = intercept, log 10 Depth
= profile midpoint depth (cm).
The model was validated by partitioning the complete harmonized dataset multiple times into training and testing
groups and then repeating this step for each soil order to evaluate model performance by soil order. Extra sum of squares F
tests were used to evaluate whether there were statistically significant differences between the model coefficients from the
model fit to the complete harmonized dataset and models fit to subsets of the data by soil order. Model coefficients for each
soil order were used to predict SOC for the 20.32 to 100 cm layer for all FIA plots with soil profile observations. Next, the
SOC layer observations from the FIA and predictions over the 100 cm profile for each FIA plot were summed:
SOC1M — SOCFIA TOTAL + SOC2(^l(M (6)
A-378 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Where SOC100= total estimated SOC density from 0-100 cm for each forest condition with a soil sample in the
FIA database, SOCFIA TOTALas previously defined in model (4), SOC20_ 100 = predicted SOC from 20.32 to 100 cm from
model (5).
In the second phase of the modeling framework, SOC100 estimates for FIA plots were used to predict SOC for plots
lacking SOC^00estimates using Random forests , a machine learning tool that uses bootstrap aggregating (i.e., bagging) to
develop models to improve prediction (Breimen 2001). Random forests 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
SOC100, 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 RF
analysis. Due to regional differences in sampling protocols, many of the predictor variables included in the RF variable
selection process were not available for all base intensity plots. To avoid problems with data limitations, pruning was used
to reduce the RF 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
RF models were:
P(SOC) = f (lat, Ion, elev, fortypgrp ppt, t max, gmi, order, surfgeo) (7)
where lat = latitude, Ion = longitude, elev = elevation, fortypgrp = forest type group, ppt= mean annual
precipitation, t max = average maximum temperature, gmi = the ratio of precipitation to potential evapotranspiration,
order = soil order, surfgeo = surficial geological description.
Moving the Annual Forest Inventory Backwards and Forwards in Time: Transition Matrices
The accounting framework used this year is fundamentally driven by the annual forest inventory system conducted
by the FIA program of the U.S. Forest Service (2015a-d). Unfortunately, the annual inventory system does not extend into
the 1990's and the periodic data are not consistent (e.g., different plot design) with the annual inventory necessitating the
adoption of a system to "backcast" the annual C estimates. Likewise, forecasting the annual inventory can enable the
monitoring of U.S. greenhouse gas emission reduction targets, however, that is an activity beyond the scope of this
document. To facilitate the backcasting of the U.S. annual forest inventory C estimates, the accounting 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 sequestration, 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 andnonforest observations in the FIA national database (U.S. Forest Service 2015a-c). Model predictions for
before or after the annual inventory period are constructed from the accounting 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 forest inventories in the eastern United
States 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 western states are still undergoing their first complete cycle they are still in
the process of being remeasured, and as a result theoretical transition matrices need to be developed.
Wear and Coulston (2015) and Coulston et al. (2015) provide the framework for the projection model. The overall
objective is to estimate unmeasured historical changes and future changes in forest C consistent with annual forest inventory
measurements. For most regions, forest conditions are observed at time to and at a subsequent time ti=to+s, where s is the
time step (time measured in years) and is indexed by discrete (5 year) forest age classes. The inventory from to is then
backcasted to the year 1990 (on average about 16 years) and projected from ti to 2017 about 5 years for the next Inventory
report). This backcasting/projection 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 the North, South (except for west
Texas and west Oklahoma), and Rocky Mountains regions of the country, age class transition matrices are estimated from
observed changes in age classes between to and ti. In the remainder of the regions (Pacific Coast including Alaska, west
A-379
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Texas, and west Oklahoma), only one inventory was available (to) so transition matrices were derived from theory but
informed by the condition of the observed inventory to backcast from to to 1990 and project from to to 2017.
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 (5). 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:
T =
/o 0 0 0 0\
1 0 0 0 0
0 10 0 0
0 0 10 0
\0 0 0 1 1/
(8)
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:
t1 d±
d-2
d-3
d4
<^5
h
1 — t2 — d2
0
0
0
0
*2
1 — Ł3 — ^3
0
0
0
0
Ł3
1 Ł4 d4
0
0
0
0
t4
1 — d.
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 Backcast for Pacific Coast, Rocky Mountains, West Texas, and West Oklahoma
Projections of forest C in the Pacific (including Alaska), Rocky Mountains, west Texas and west Oklahoma are
based on a life stage model:
ACt = Ct+m — Ct = (FtT — Ft) ¦ Den + Lt ¦ Den (10)
In this framework T is an age transition matrix that shifts the age distribution of the forest F. The difference in
forest area by age class between time t and t+s is FtT-Ft. This quantity is multiplied by C density by age class (Den) to
estimate C stock change of forest remaining forest between t and t+s. Land use change is accounted for by the addition of
Lt-Den, where Lt identifies the age distribution of net land shifts into or out of forests. A query of the forest inventory
databases provides estimates of F and Den, while inventory observations and modeling assumptions are used to estimate T.
By expanding Den to a matrix of C contained in all the constituent pools of forest carbon, projections for all pools are
generated.
Land use change is incorporated as a 1 x n vector L, with positive entries indicating increased forest area and
negative entries indicating loss of forest area, which provides insights of net change only. Implementing a forest area change
requires some information and assumptions about the distribution of the change across age classes (the n dimension of L).
A-380 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 = FfT'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). 134 However, B can be constructed using observed changes from the inventory and assumptions about
transition/accumulation including nonstationary elements of the transition model:
(12)
f1-!
B =
> d«
b2
0
0
0
t-'q
di
l-b2
b3
0
0
dz
0
1 -b3
b4
0
d-3
0
0
1- b4
b r
d4
0
0
0
1 — b.
\
Forest area changes need to be accounted for in the backcasts as well:
Ft-s = FtB — Lt (13)
Where Lt is the forest area change between ti and to as previously defined.
In the Rocky Mountains, age class transition matrices were empirically derived from observed changes in age
classes between to and ti. The frequency of transitions was constructed between age classes observed at to and ti to define T
and between age classes ti and to to define B. In the Pacific Coast region, including Alaska, west Texas, and west Oklahoma,
the theoretical life-stage models described by matrices (9) and (10) were applied. The disturbance factors (d) in both T and
B are derived from the current inventory 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), we move the disturbance frequencies implied by
the age class distribution for each time step. For areas with empirical transition matrices, change in forest area (Lt) was
backcasted/projected using the change in forest area observed for the period to to ti. In the Pacific, including Alaska, west
Texas, and west Oklahoma, it was assumed that total forest land area remained constant for the time period examined.
Projections and Backcast for North, South, East Texas, and East Oklahoma
For the eastern United States a full set of 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-8C, where AC is net
stock change by pool within the analysis area, F is as previously defined, and SC 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-SC. 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:
ACt+s = ^(Atd-Td-8Cd) (14)
del
134 Simulation experiments show that a population that evolves as a function of T can be precisely backcast using T"1. However,
applying the inverse to a population that is not consistent with the long-run outcomes of the transition model can result in proj ections
of negative areas within some stage age classes.
A-381
-------
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
SCd 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.
Projections 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. Backcasts were developed using a Markov Chain process for land use transitions, observed
disturbance rates for fire, weather, and insects. Flistorical 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 2015d). This relationship allowed for the modification of Fcut such that it
followed trends described by Oswalt et al. (2014).
Carbon in Harvested Wood Products
Estimates of the Flarvested Wood Product (HWP) contribution to forest C sinks and emissions (hereafter called
"F1WP 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.
• Production approach: Accounts for the net changes in C stocks in forests and in the wood products pool,
but attributes both to the producing country.
• Stock-change approach: Accounts for changes in the product pool within the boundaries of the consuming
country.
• Atmospheric-flow approach: Accounts for net emissions or removals of C to and from the atmosphere
within national boundaries. Carbon removal due to forest growth is accounted for in the producing country
while C emissions to the atmosphere from oxidation of wood products are accounted for in the consuming
country.
• Default approach: Assumes no change in C stocks in HWP. IPCC (2006) requests that such an assumption
be justified if this is how a Party is choosing to report.
The United States uses the production accounting approach (as in previous years) to report HWP Contribution
(Table A-240). Annual estimates of change are calculated by tracking the additions to and removals from the pool of products
held in end uses (i.e., products in use such as housing or publications) and the pool of products held in solid waste disposal
sites (SWDS).
Estimates of five HWP variables that can be used to calculate HWP contribution for the stock change and
atmospheric flow approaches for imports and exports are provided in Table A-238. The HWP variables estimated are:
(1 A) annual change of C in wood and paper products in use in the United States,
(IB) annual change of C in wood and paper products in SWDS in the United States,
(2A) annual change of C in wood and paper products in use in the United States and other countries where the
wood came from trees harvested in the United States,
(2B) annual change of C in wood and paper products in SWDS in the United States and other countries where the
wood came from trees harvested in the United States,
(3) Carbon in imports of wood, pulp, and paper to the United States,
(4) Carbon in exports of wood, pulp and paper from the United States, and
A-382 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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(5) Carbon in annual harvest of wood from forests in the United States. The sum of these variables yield
estimates for HWP contribution under the production accounting approach.
Table A-240: Harvested Wood Products from Wood Harvested in the U.S.—Annual Additions of G to Stocks 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)
1,895
1,249
646
1991
(33.8)
(16.3)
(17.4)
1,929
1,264
665
1992
(32.9)
(15.0)
(17.9)
1,963
1,280
683
1993
(33.4)
(15.9)
(17.5)
1,996
1,295
701
1994
(32.3)
(15.1)
(17.2)
2,029
1,311
718
1995
(30.6)
(14.1)
(16.5)
2,061
1,326
735
1996
(32.0)
(14.7)
(17.3)
2,092
1,340
752
1997
(31.1)
(13.4)
(17.7)
2,124
1,355
769
1998
(32.5)
(14.1)
(18.4)
2,155
1,368
787
1999
(30.8)
(12.8)
(18.0)
2,188
1,382
805
2000
(25.5)
(8.7)
(16.8)
2,218
1,395
823
2001
(26.8)
(9.6)
(17.2)
2,244
1,404
840
2002
(25.6)
(9.5)
(16.2)
2,271
1,414
857
2003
(28.6)
(12.3)
(16.3)
2,296
1,423
873
2004
(28.1)
(11.8)
(16.3)
2,325
1,435
890
2005
(29.5)
(12.2)
(17.3)
2,353
1,447
906
2006
(28.1)
(10.7)
(17.4)
2,382
1,459
923
2007
(20.9)
(3.8)
(17.1)
2,411
1,470
941
2008
(14.6)
2.1
(16.7)
2,431
1,474
958
2009
(16.2)
0.4
(16.6)
2,446
1,472
974
2010
(18.3)
(1.6)
(16.8)
2,462
1,471
991
2011
(17.9)
(1.1)
(16.9)
2,481
1,473
1,008
2012
(18.9)
(1.9)
(17.0)
2,498
1,474
1,025
2013
(20.6)
(3.5)
(17.1)
2,517
1,476
1,042
2014
(20.8)
(3.7)
(17.1)
2,538
1,479
1,059
2015
(26.1)
(8.6)
(17.6)
2,559
1,483
1,076
2016
(27.2)
(9.1)
(18.0)
2,585
1,492
1,093
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
Table A-241: Comparison of Het Annual Change in Harvested Wood Products C Stocks Using Alternative Accounting
Approaches tkt CO; Eq./yearl
HWP Contribution to LULUCF Emissions/ removals (MMT CO2 Eg.)
Stock-Change
Atmospheric Flow
Production
Inventory Year
Approach
Approach
Approach
1990
(116,345)
(131,436)
(123,758)
1991
(119,985)
(131,633)
(123,791)
1992
(126,805)
(127,819)
(120,708)
1993
(129,954)
(129,882)
(122,498)
1994
(125,981)
(128,010)
(118,411)
1995
(122,340)
(122,495)
(112,219)
1996
(131,434)
(127,378)
(117,344)
1997
(137,218)
(122,781)
(114,188)
1998
(147,057)
(127,427)
(119,182)
1999
(141,195)
(120,395)
(112,969)
2000
(125,039)
(100,417)
(93,479)
2001
(130,714)
(103,339)
(98,188)
2002
(125,812)
(98,663)
(93,967)
2003
(143,193)
(108,453)
(104,747)
2004
(142,102)
(107,342)
(103,215)
2005
(138,130)
(113,897)
(108,034)
2006
(115,181)
(111,489)
(102,984)
2007
(73,134)
(88,392)
(76,807)
2008
(41,284)
(68,789)
(53,386)
2009
(47,980)
(78,261)
(59,367)
A-383
-------
2010 (50,802) (90,214) (67,279)
2011 (54,008) (89,470) (65,710)
2012 (64,774) (94,413) (69,154)
2013 (80,511) (102,379) (75,552)
2014 (85,209) (102,765) (76,356)
2015 (130,361) (119,057) (95,859)
201 6 (134,510) (119,863) (99,618)
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
A-384 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table fl-242: Harvested Wood Products Sectoral Background Data for LULUCF—United States
1A
1B
2A
2B
3
4
5
6
7
8
Inventory
Annual Change
Annual Change
Annual Change in
Annual
Annual
Annual
Annual
Annual release
Annual release
HWP
year
in stock of HWP
in stock of HWP
stock of HWP in
Change in
Imports of
Exports of
Domestic
of C to the
of C to the
Contribution to
in use from
in SWDS from
use produced
stock of HWP
wood, and
wood, and
Harvest
atmosphere
atmosphere from
AFOLU C02
consumption
consumption
from domestic
in SWDS
paper
paper
from HWP
HWP (including
emissions/
harvest
produced
products plus
products plus
consumption
firewood) where
removals
from
wood fuel,
wood fuel,
(from fuelwood
wood came from
domestic
pulp,
pulp,
and products in
domestic harvest
harvest
recovered
recovered
use and
(from products in
paper,
paper,
products in
use and products
roundwood/
roundwood/
SWDS)
in SWDS)
chips
chips
ACHWPIU DC
ACHWP SWDS
AC HWP IU DH
ACHWP
PIM
PEX
H
fCHWP DC
fCHWP DH
DC
SWDS DH
ktC/yr
kt C02/yr
1990
13,129
18,602
14,940
18,812
11,552
15,667
144,435
108,588
110,682
(123,758)
1991
15,718
17,006
16,334
17,427
12,856
16,032
139,389
103,489
105,627
(123,791)
1992
16,957
17,627
14,971
17,949
14,512
14,788
134,554
99,694
101,633
(120,708)
1993
18,221
17,221
15,930
17,479
15,685
15,665
134,750
99,328
101,342
(122,498)
1994
17,307
17,051
15,065
17,229
16,712
17,266
137,027
102,115
104,733
(118,411)
1995
17,018
16,348
14,092
16,513
16,691
16,733
134,477
101,069
103,872
(112,219)
1996
18,756
17,090
14,740
17,263
17,983
16,877
135,439
100,699
103,436
(117,344)
1997
19,654
17,769
13,404
17,738
18,994
15,057
134,206
100,720
103,064
(114,188)
1998
21,444
18,662
14,146
18,359
20,599
15,245
134,193
99,440
101,689
(119,182)
1999
20,000
18,508
12,840
17,970
21,858
16,185
133,694
100,859
102,884
(112,969)
2000
16,491
17,610
8,713
16,781
22,051
15,336
127,896
100,510
102,402
(93,479)
2001
17,414
18,235
9,566
17,213
23,210
15,744
126,866
98,683
100,087
(98,188)
2002
16,986
17,326
9,453
16,175
23,707
16,303
123,606
96,698
97,978
(93,967)
2003
21,409
17,644
12,273
16,294
26,428
16,953
118,852
89,274
90,284
(104,747)
2004
20,990
17,765
11,826
16,324
26,793
17,312
120,393
91,118
92,244
(103,215)
2005
19,085
18,587
12,158
17,306
25,445
18,836
118,544
87,481
89,080
(108,034)
2006
13,104
18,309
10,661
17,425
21,663
20,657
115,827
85,421
87,740
(102,984)
2007
2,434
17,512
3,825
17,122
16,997
21,159
101,525
77,418
80,577
(76,807)
2008
(5,364)
16,623
(2,098)
16,657
13,115
20,616
90,576
71,815
76,016
(53,386)
2009
(3,191)
16,277
(383)
16,574
14,162
22,420
92,792
71,448
76,601
(59,367)
2010
(2,281)
16,136
1,559
16,790
13,923
24,672
97,134
72,530
78,785
(67,279)
2011
(1,299)
16,028
1,055
16,866
13,580
23,252
99,934
75,533
82,013
(65,710)
2012
1,555
16,110
1,900
16,960
14,700
22,783
103,331
77,582
84,471
(69,154)
2013
5,600
16,358
3,535
17,070
16,881
22,845
118,155
90,233
97,550
(75,552)
2014
6,764
16,475
3,731
17,094
17,478
22,266
108,071
80,044
87,247
(76,356)
2015
17,967
17,587
8,566
17,577
21,686
18,603
110,347
77,877
84,204
(95,859)
2016
18,154
18,530
9,142
18,026
22,649
18,655
112,630
79,940
85,461
(99,618)
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
A-385
-------
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).
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-
243. 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 1 A, 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-244. 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-240 and Table
A-241. 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 fl-243: 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 ofC in harvested wood products for the U.S." Forest Products Journal 58:56-72.
Table A-244: 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 ofC in harvested wood products for the U.S." Forest Products Journal 58:56-72.
A-386 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table fl-245: Net CO2 Flux from Forest Pools in Forest Land Remaining Forest Landz n d Harvested Wood Pools (MMT G02Eq.)
Carbon Pool
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Forest
(574.7)
(577.4)
(523.0)
(518.3)
(521.3)
(524.6)
(526.2)
(557.3)
(563.8)
(572.2)
(579.0)
(584.5)
(602.0)
(605.0)
(598.5)
(596.1)
(593.7)
(571.1)
(571.6)
Aboveground Biomass
(327.9)
(328.8)
(268.6)
(272.9)
(275.0)
(277.0)
(279.2)
(314.4)
(314.5)
(320.3)
(324.7)
(328.0)
(334.4)
(337.2)
(331.5)
(329.6)
(327.7)
(310.0)
(315.3)
Belowground Biomass
(70.0)
(70.2)
(56.4)
(57.4)
(57.8)
(58.2)
(58.6)
(66.6)
(66.4)
(67.5)
(68.4)
(69.0)
(70.3)
(71.0)
(69.7)
(69.2)
(68.7)
(64.6)
(65.7)
Dead Wood
(33.5)
(38.3)
(45.6)
(35.1)
(35.3)
(35.6)
(34.5)
(40.3)
(42.3)
(42.7)
(43.2)
(43.8)
(45.6)
(48.5)
(49.1)
(49.2)
(49.2)
(43.7)
(39.2)
Litter
(17.0)
(16.8)
(12.8)
(13.5)
(13.6)
(13.7)
(13.9)
(14.3)
(14.0)
(14.1)
(14.3)
(14.1)
(16.5)
(16.5)
(16.3)
(16.3)
(16.3)
(15.2)
(16.1)
Soil (Mineral)
(126.1)
(123.3)
(139.6)
(139.3)
(139.6)
(140.0)
(140.1)
(121.7)
(126.6)
(127.6)
(128.4)
(129.6)
(135.3)
(131.9)
(132.0)
(131.9)
(131.9)
(137.6)
(135.4)
Soil (Organic)
(0.1)
(0.1)
M
M
M
M
M
M
+
+
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Harvested Wood
(123.8)
(112.2)
(93.5)
(98.2)
(94.0)
(104.7)
(103.2)
(108.0)
(103.0)
(76.8)
(53.4)
(59.4)
(67.3)
(65.7)
(69.2)
(75.6)
(76.4)
(95.9)
(99.6)
Products in Use
(54.8)
(51.7)
(31.9)
(35.1)
(34.7)
(45.0)
(43.4)
(44.6)
(39.1)
(14.0)
7.7
1.4
(5.7)
(3.9)
(7.0)
(13.0)
(13.7)
(31.4)
(33.5)
SWDS
(69.0)
(60.5)
(61.5)
(63.1)
(59.3)
(59.7)
(59.9)
(63.5)
(63.9)
(62.8)
(61.1)
(60.8)
(61.6)
(61.8)
(62.2)
(62.6)
(62.7)
(64.4)
(66.1)
Total Net Flux
(698.4)
(689.6)
(616.5)
(616.4)
(615.2)
(629.3)
(629.4)
(665.3)
(666.8)
(649.0)
(632.4)
(643.9)
(669.3)
(670.7)
(667.6)
(671.6)
(670.0)
(666.9)
(671.2)
+Absolute value does not exceed 0.05 MMT CO2 Eq.
Note: Parentheses indicate negative values.
Table fl-246: Net C Flux from Forest Pools in ForestLandRemaining Forest LandiM Harvested Wood Pools [MBIT CI
Carbon Pool
1990
1995
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Forest
(156.7)
(157.5)
(142.6)
(141.4)
(142.2)
(143.1)
(143.5)
(152.0)
(153.8)
(156.0)
(157.9)
(159.4)
(164.2)
(165.0)
(163.2)
(162.6)
(161.9)
(155.7)
(155.9)
Aboveground Biomass
(89.4)
(89.7)
(73.3)
(74.4)
(75.0)
(75.6)
(76.2)
(85.7)
(85.8)
(87.3)
(88.5)
(89.4)
(91.2)
(92.0)
(90.4)
(89.9)
(89.4)
(84.6)
(86.0)
Belowground Biomass
(19.1)
(19.2)
(15.4)
(15.7)
(15.8)
(15.9)
(16.0)
(18.2)
(18.1)
(18.4)
(18.7)
(18.8)
(19.2)
(19.4)
(19.0)
(18.9)
(18.7)
(17.6)
(17.9)
Dead Wood
(9.1)
(10.4)
(12.4)
(9.6)
(9.6)
(9.7)
(9.4)
(11.0)
(11.5)
(11.6)
(11.8)
(11.9)
(12.4)
(13.2)
(13.4)
(13.4)
(13.4)
(11.9)
(10.7)
Litter
(4.6)
(4.6)
(3.5)
(3.7)
(3.7)
(3.7)
(3.8)
(3.9)
(3.8)
(3.9)
(3.9)
(3.9)
(4.5)
(4.5)
(4.4)
(4.4)
(4.4)
(4.1)
(4.4)
Soil (Mineral)
(34.4)
(33.6)
(38.1)
(38.0)
(38.1)
(38.2)
(38.2)
(33.2)
(34.5)
(34.8)
(35.0)
(35.3)
(36.9)
(36.0)
(36.0)
(36.0)
(36.0)
(37.5)
(36.9)
Soil (Organic)
M
M
M
M
M
M
M
M
+
+
+
+
+
+
+
+
+
+
+
Harvested Wood
(33.8)
(30.6)
(25.5)
(26.8)
(25.6)
(28.6)
(28.1)
(29.5)
(28.1)
(20.9)
(14.6)
(16.2)
(18.3)
(17.9)
(18.9)
(20.6)
(20.8)
(26.1)
(27.2)
Products in Use
(14.9)
(14.1)
(8.7)
(9.6)
(9.5)
(12.3)
(11.8)
(12.2)
(10.7)
(3.8)
2.1
0.4
(1.6)
(1.1)
(1.9)
(3.5)
(3.7)
(8.6)
(9.1)
SWDS
(18.8)
(16.5)
(16.8)
(17.2)
(16.2)
(16.3)
(16.3)
(17.3)
(17.4)
(17.1)
(16.7)
(16.6)
(16.8)
(16.9)
(17.0)
(17.1)
(17.1)
(17.6)
(18.0)
Total Net Flux
(190.5)
(188.1)
(168.1)
(168.1)
(167.8)
(171.6)
(171.7)
(181.5)
(181.9)
(177.0)
(172.5)
(175.6)
(182.5)
(182.9)
(182.1)
(183.2)
(182.7)
(181.9)
(183.1)
+Absolute value does not exceed 0.05 MMT C.
Note: Parentheses indicate negative values.
Table fl-247: Forest area 11,000 bal and C Stocks in ForestlandRemaining Forest Land and Harvested Wood Pools [MBIT CI
1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Forest Area (1000 ha) 262,119 263,516 265,022 267,479 268,044 268,618 269,163 269,710 270,258 270,654 271,064 271,512 271,812 272,113 272,260 272,260
Carbon Pools
A-387
-------
Forest
46,967
47,753
48,510
49,223
49,375
49,529
49,685
49,843
50,002
50,166
50,331
50,494
50,657
50,819
50,975
51,131
Aboveground Biomass
11,889
12,335
12,748
13,122
13,208
13,294
13,381
13,470
13,559
13,650
13,742
13,833
13,922
14,012
14,096
14,182
Belowground Biomass
2,439
2,534
2,622
2,700
2,718
2,737
2,755
2,774
2,792
2,812
2,831
2,850
2,869
2,888
2,905
2,923
Dead Wood
2,262
2,310
2,373
2,424
2,435
2,446
2,458
2,470
2,482
2,494
2,507
2,521
2,534
2,548
2,560
2,570
Litter
2,568
2,591
2,612
2,630
2,634
2,638
2,642
2,646
2,650
2,654
2,659
2,663
2,668
2,672
2,676
2,680
Soil (Mineral)
27,456
27,630
27,804
27,994
28,027
28,062
28,097
28,132
28,167
28,204
28,240
28,276
28,312
28,348
28,385
28,422
Soil (Organic)
352
352
352
352
352
352
352
352
352
352
352
352
352
352
352
352
Harvested Wood
1,895
2,061
2,218
2,353
2,382
2,411
2,431
2,446
2,462
2,481
2,498
2,517
2,538
2,559
2,585
2,612
Products in Use
1,249
1,326
1,395
1,447
1,459
1,470
1,474
1,472
1,471
1,473
1,474
1,476
1,479
1,483
1,482
1,501
SWDS
646
735
823
906
923
941
958
974
991
1,008
1,025
1,042
1,059
1,076
1,093
1,111
Total Stock
48,862
49,814
50,729
51,576
51,757
51,939
52,116
52,289
52,464
52,647
52,830
53,012
53,195
53,378
53,560
53,743
A-388 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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 2009 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 2015b, 2015c).
Carbon conversion factors were applied at the disaggregated level of each inventory plot and then appropriately expanded
to population estimates. To ensure consistency in the Land Converted to Forest Land category where C stock transfers
occur between land-use categories, all soil estimates are based on methods from Ogle et al. (2003, 2006) and IPCC (2006).
Live tree C pools
Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at
diameter breast height (d.b.h.) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates are made for above-
and below-ground biomass components. If inventory plots include data on individual trees, tree C is based on Woodall et al.
(2011), which is also known as the component ratio method (CRM), and is a function of volume, species, diameter, and, in
some regions, tree height and site quality. The estimated sound volume (i.e., after rotten/missing deductions) provided in the
tree table of the FIADB is the principal input to the CRM biomass calculation for each tree (Woodall et al. 2011). The
estimated volumes of wood and bark are converted to biomass based on the density of each. Additional components of the
trees such as tops, branches, and coarse roots, are estimated according to adjusted component estimates from Jenkins et al.
(2003). Live trees with d.b.h of less than 12.7 cm do not have estimates of sound volume in the FIADB, and CRM biomass
estimates follow a separate process (see Woodall et al. 2011 for details). An additional component of foliage, which was not
explicitly included in Woodall et al. (2011), was added to each tree following the same CRM method. Carbon is estimated
by multiplying the estimated oven-dry biomass by a C constant of 0.5 because biomass is 50 percent of dry weight (IPCC
2006). 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-237. Regions and forest types are the same classifications described in Smith et al.
(2003). An example example calculation for understory C in aspen-birch forests in the Northeast is 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 piny on/juniper forest types (see Table A-237) 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
A-389
-------
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.
Downed dead wood, inclusive of logging residue, are sampled on a subset of FIA plots. Despite a reduced sample
intensity, a single down woody material population estimate (Woodall et al. 2010; Domke et al. 2013; Woodall et al. 2013)
per state is now incorporated into these empirical downed dead wood estimates. Downed dead wood is defined as pieces of
dead wood greater than 7.5 cm diameter, at transect intersection, that are not attached to live or standing dead trees. It also
includes stumps and roots of harvested trees. Ratio estimates of downed dead wood to live tree biomass were developed
using FORCARB2 simulations and applied at the plot level (Smith et al. 2004). Estimates for downed dead wood correspond
to the region and forest type classifications described in Smith et al. (2003). A full set of ratios is provided in Table A-238.
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-239. 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 2013), and National Land Cover Dataset (NLCD) (Homer et al.
2007). See Annex 3.12 for more information about this method (Methodology for Estimating N2O Emissions, CH4
Emissions and Soil Organic C Stock Changes from Agricultural Soil Management).
Table A-248 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 from 50,000 simulations (Ogle et al. 2003,
2006).
Table A-249 summarizes the total land areas by land use/land use change subcategory for mineral soils between
1990 and 2015 estimated with a Tier 2 approach and based on analysis ofUSDA National Resources Inventory data (USDA-
NRCS 2013).
A-390 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-248: 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
2008
2009
2010
2011
2012
2013
2014
2015
2016
Cropland Converted to
Forest Land
0.01
(-0.01 to
0.03)
0.01
(-0.01 to
0.04)
0.03
(-0.01 to
0.08)
0.02
(-0.02 to
0.06)
-0.01
(-0.03 to
0.01)
-0.01
(-0.03 to
0.01)
-0.01
(-0.02 to
0.01)
-0.01
(-0.02 to
0.01)
-0.01
(-0.02 to
0.01)
-0.01
(-0.02 to
0.01)
-0.01
(-0.03 to
0.01)
0.00
(-0.03 to
0.02)
0.00
(-0.03 to
0.02)
Grassland Converted to
Forest Land
0.03
(-0.03 to
0.1)
0.05
(-0.04 to
0.16)
0.09
(-0.04 to
0.24)
0.06
(-0.05 to
0.18)
-0.02
(-0.08 to
0.05)
-0.02
(-0.09 to
0.04)
-0.02
(-0.09 to
0.04)
-0.02
(-0.08 to
0.04)
-0.02
(-0.08 to
0.04)
-0.02
(-0.05 to
0.02)
-0.01
(-0.06 to
0.05)
-0.01
(-0.07 to
0.05)
0.00
(-0.07 to
0.06)
Other Lands Converted to
Forest Land
0.00
(0 to 0.01)
0.01
(-0.01 to
0.02)
0.02
(-0.01 to
0.04)
0.01
(-0.01 to
0.04)
0.00
(-0.01 to
0.01)
0.00
(-0.01 to
0.01)
0.00
(-0.01 to
0.01)
0.00
(-0.01 to
0.01)
0.00
(-0.01 to
0.01)
0.00
(-0.01 to
0.01)
0.00
(-0.01 to
0.01)
0.00
(-0.01 to
0.01)
0.00
(-0.01 to
0.02)
Settlements 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
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
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
(0 to 0)
(0 to 0)
(0 to 0.01)
(0 to 0.01)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
Total Lands Converted
to Forest Lands
0.05
0.08
0.15
0.10
-0.03
-0.04
-0.03
-0.03
-0.04
-0.02
-0.01
-0.01
0.00
Note: The range is a 95 percent confidence interval
torn 50,000 simulations (Ogle et al. 2003, 2006).
Table A-249: Total land areas (hectares) by land use/land use change subcategory for mineral soils between 1990 to 2016
Conversion Land Areas (Hectares x 106)
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
Cropland Converted to Forest Land
0.21
0.20
0.25
0.20
0.18
0.18
0.17
0.17
0.17
0.17
0.17
0.17
0.17
Grassland Converted to Forest Land
0.71
0.79
0.78
0.62
0.60
0.60
0.60
0.60
0.59
0.59
0.59
0.59
0.59
Other Lands Converted to Forest Land
0.09
0.10
0.13
0.13
0.10
0.10
0.10
0.09
0.09
0.09
0.09
0.09
0.09
Settlements Converted to Forest Land
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
Wetlands Converted to Forest Land
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
Total Lands Converted to Forest Lands
1.04
1.13
1.20
0.98
0.91
0.91
0.90
0.90
0.89
0.89
0.89
0.89
0.89
Note: Estimated with a Tier 2 approach and based on analysis of USDA National Resources Inventory data (USDA-NRCS 2013).
A-391
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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
U(Ct)m=Ct-%U(Ct)m/100 (17)
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 (18)
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):
U(AC)=( U(AC)m2+ U(AC)s2)0.5 and the 95 percent confidence bounds was +- 2- U(AC) (19)
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 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.
A-392 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Emissions from Forest Fires
CO2 Emissions from Forest Fires
As stated in other sections, the forest inventory approach implicitly accounts for CO2 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 CO2 emissions from a disturbance
such as fire and adding those emissions to the net CO2 change in forests would result in double-counting the loss from fire
because the inventory data already reflect the loss. There is interest, however, in the size of the CO2, CH4, and N2O
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 area burned and potential fuel available for
combustion along with IPCC default combustion and emission 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 103 (20)
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 CO2) per kilogram dry matter burnt, and the
"10"3" balances units. The first two factors are based on datasets specific to U.S. forests, whereas the last two factors
employ IPCC (2006) default values.
Area burned is based on annual area of forest fires according to Monitoring Trends in Burn Severity (MTBS)
(MTBS Data Summaries 2015; Eidenshink et al. 2007) dataset summaries,135 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. Summary
information includes fire identity, year, location, area burned, fire intensity, and other fire characteristics. 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 included as a part of identifying information for each fire. An additional
spatial dataset - National MTBS Burned Area Boundaries-provides information to locate fires.136 These individual-fire
boundary data were used to partition the area burned in each fire to forest versus non-forest.
The MTBS fire data records include land cover information from the National Land Cover (NLCD) dataset
(Homer et al. 2015), which can be used to distinguish forest fires from other wildland fires within the MTBS data.
However, the forest land cover of the NLCD data, including the 2011 land cover (Homer et al. 2015) provides an estimate
of forest land that is approximately 20 percent lower than forest area identified by the forest inventory of the USDA Forest
Service (USDA Forest Service 2015b, e.g., data as of 2 June 2015) for the conterminous United States. This suggests that
annual area of forest fires identified with the NLCD cover data may underestimate area of forest burned, but the difference
between USDA Forest Service (2015) and Homer et al. (2015) for each individual fire, if any, is dependent on specific
areas where the fires actually occur. As an alternative data source, forest area for conterminous United States and Alaska
are defined by Ruefenacht et al. (2008). The forest area for the conterminous states representative of approximately 2002
is within 2 percent of the forest areas estimated for 1990 through 2016 in U.S. EPA (2016). These data were used to
partition the perimeter data to forest for each fire (that is, area of forest relative to entire area of the fire for each MTBS
fire). We assume that while changes in forests have occurred both before and since the data for Ruefenacht et al. (2008)
were compiled, changes in forest versus non-forest status on lands subject to wildfires are likely minimal enough to make
this dataset appropriate for this use. In addition, the Alaska forest area was allocated to managed and unmanaged areas
according to Ogle et al. (in preparation), as discussed in more detail above.
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 (2015b) forest inventory along the southern coastal portion of the
state. The only MTBS-identified burned forest areas in Alaska that coincide with the Forest Service's permanent plot
135 See .
136 See .
A-393
-------
inventoried area were on the northern (or Cook Met) side of the Kenai Peninsula, which is generally identified as boreal
forest. From this, all MTBS fires of interest identified in Alaska are considered boreal forests.
Estimates of fuel availability are based on plot level forest inventory data, which are summarized by state and
applied to all fires within the respective states. Plot level C stocks are defined by C conversion factors applied to current
USDA Forest Service inventory data (USDA Forest Service 2015b; U.S. EPA 2016; Smith et al. 2010) and summarized by
state. 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 the forest lands of a given state. We use the current forest inventory
data137 and the distribution of metric tons dry matter per hectare as the inputs for fuel availability. Fuel estimated for
wildfires included all aboveground biomass (live trees and understory) as well as standing dead trees, down dead wood,
and forest floor litter; whereas, fuel estimated for prescribed fires was based on the non-living components only.
The combustion factor used here for temperate forests is 0.45 (see Table 2.6 Volume 4, Chapter 2 of IPCC 2006).
Similarly, the emission factor is an IPCC (2006) default, which for CO2 is 1,569 g CO2 per kg dry matter of fuel (see Table
2.5 Volume 4, Chapter 2 of IPCC 2006). With the application of equation 2.27 of IPCC (2006, in Volume 4, Chapter 2)
defaults were used for mass of fuel available for the Alaska estimates because of the very limited coverage of boreal
forests in the available U.S. forest inventories (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.
Table A-250 provides summary values of annual area burned, area identified as forest fire, and emissions
calculated according to equation 2.27 of IPCC (2006, in Volume 4, Chapter 2). The emission factor for CO2 from Table
2.5 Volume 4, Chapter 2 of IPCC (2006) is provided in Table A-251. Separate calculations were made for each wild and
prescribed fire in each state for each year. The results as MT CO2 were summed to the MMT CO2 per year values
represented in Table A-250, and C emitted per year (Table A-250 and Table A-253) was based on multiplying by the
conversion factor 12/44 (IPCC 2006).
137 Retrieved from on June 2, 2015.
A-394 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table fl-250: Areas [Hectares] from Wildfire Statistics and Corresponding Estimates of C and CO; [MMT/yearl Emissions for Wildfires and Prescribed Fires3
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016"
Managed land
burned (1000 ha)
462.8
544.0
2,257.6
1,723.9
1,698.5
1,489.9
579.2
3,187.2
3,421.8
1,093.9
1,994.9
2,308.3
2,308.3
Conterminous 48
Forest area burned
(1000 ha)
184.2
128.7
1,016.2
603.3
724.42
493.5
142.6
1,242.1
1,451.7
640.0
679.0
1,137.9
1,137.9
States - Wildfires
C emitted (MMT/yr)
6.1
2.5
12.0
25.9
10.8
3.5
22.1
37.8
18.5
22.9
44.7
44.7
CO2 emitted
(MMT/yr)
22.5
9.2
97.0
44.1
94.9
39.6
12.9
81.0
138.6
67.9
84.1
164.1
164.1
Managed land
burned (1000 ha)
306.4
11.7
163.7
1,323.9
24.5
695.1
203.8
70.1
56.9
375.4
78.6
1,420.2
1,420.2
Forest area burned
Alaska - Wildfires
(1000 ha)
303.4
10.0
160.9
1,253.8
16.8
682.8
175.0
55.0
41.6
347.1
75.6
1,256.8
1,256.8
C emitted (MMT/yr)
5.4
1 0.2 ;Ł
22.0
0.3
11.8
3.1
1.0
0.7
6.1
1.3
22.0
22.0
CO2 emitted
(MMT/yr)
19.6
0.6
80.6
1.1
43.3
11.3
3.5
2.7
22.3
4.9
80.7
80.7
Managed land
burned (1000 ha)
10.3
16.0
83.1
107.1
319.3
407.9
754.1
993.7
149.2
275.8
299.5
200.7
200.7
Forest area burned
Prescribed Fires
(1000 ha)
6.1
10.9
62.1
251.1
317.8
657.3
242.9
110.4
268.6
281.9
175.1
175.1
(all 49 states)
C emitted (MMT/yr)
0.0
0.1
0.3
1.6
2.1
5.1
1.6
0.8
1.5
1.7
1.0
1.0
CO2 emitted
(MMT/yr)
0.2
1.3
6.0
7.8
18.6
5.9
2.9
5.5
6.1
3.5
3.5
CPU emitted (kt/yr)
126.1
29.8
322.9
372.9
288.2
248.7
72.2
255.5
423.9
270.1
270.7
729.3
729.3
Wildfires (all 49
N2O emitted (kt/yr)
7.0
1.6
20.6
16.1
13.8
4.0
14.1
23.2
15.1
14.8
40.3
40.3
states)
CO emitted (kt/yr)
2,868
676
7.324
8,399
6,439
5,726
1,652
5,744
9,602
6,253
6,180
16,512
16,512
NOx emitted (kt/yr)
80.8
18.9
203.7
236.7
181.9
161.4
46.3
161.0
270.5
174.9
173.7
467.0
467.0
CPU emitted (kt/yr)
0.5
4 0.7
3.8
18.0
23.8
55.6
18.0
8.8
16.3
18.3
10.5
10.5
Prescribed Fires
N2O emitted (kt/yr)
0.0
i 0.0
0.2
1.0
1.3
3.1
1.0
0.5
0.9
1.0
0.6
0.6
(all 49 states)
CO emitted (kt/yr)
11.9
15.9
85.4
405.0
534.7
1,259.5
410.6
202.1
370.9
415.2
240.0
240.0
NO* emitted (kt/yr)
0.3
0.4
2.4
11.3
15.2
35.4
11.5
5.6
10.4
11.7
6.7
6.7
a These emissions have already been accounted for in the estimates of net annual changes in C stocks, which accounts for the amount sequestered minus any emissions, including the assumption that combusted wood may
continue to decay through time.
b The data for 2016 were unavailable when these estimates were summarized; therefore 2015, the most recent available estimate, is applied to 2016.
A-395
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Table A-251: Emission Factors for Extra Tropical Forest Burning and 100-year GWP (AIM), or equivalence ratios, of CH4 and
N2O to CO2
Emission Factor (g per kg dry
matter burned)3
Equivalence Ratios'1
CH4 4.70
CH4 to CO2
25
N20 0.26
N2O to CO2
298
C02 1,569
CO2 to CO2
1
a Source: IPCC (2006)
b Source: IPCC (2007)
The set of fire emissions estimates using MODIS imagery and post-fire observations developed for Alaska by
Veraverbeke et al. (2015a) is used here to provide a comparison with the estimates developed here (i.e., Table A-253). The
spatial Alaskan Fire Emissions Database (AKFED, Veraverbeke et al. 2015b) was partitioned to forest land based on both
Ruefenacht et al. (2008) and Homer et al. (2015) as well as managed/unmanaged (Ogle et al. in preparation). The
estimates of annual C emitted from fire are in Table A-252, which also includes the estimates for managed forest land
(both wildland and prescribed) that underlie the values provided in Table A-250. Note that the values in the six rightmost
columns effectively partition the C emissions estimates provided in Veraverbeke et al. (2015a, see Table 2). That is, Table
A-252, column 2 provides the estimates developed for this Inventory while each of columns 3 -5 and 6-8 sum to the
emissions estimates of Veraverbeke et al. (2015a); the differences between the two sets are how they are partitioned
according to forest land.
Table fl-252: Estimated C emissions IMMT/yr) for fire based on the flKFED, and partitioned to managed forest land in Alaska
Forest land based on Ruefenacht et al. (2008)
Forest land based on Homer et al. (2015)
Year3
Managed .. .
forest land fMan?f ,
(Table A-250)b forestland
Unmanaged
forest land
Non-forest
land
Managed
forest land
Unmanaged
forest land
Non-forest
land
2001
0.7 0.8
0.3
C emitted (MMT/year)
0.0 0.1
0.0
1.1
2002
11.2 12.7
3.3
0.8
1.5
0.4
14.8
2003
2.8 4.0
1.4
0.0
0.6
0.2
4.7
2004
34.4 51.8
16.6
1.0
7.0
2.5
59.9
2005
22.0 29.8
14.1
1.7
4.1
1.9
39.6
2006
1.4 0.7
0.1
0.0
0.1
0.0
0.7
2007
1.5 1.4
1.0
2.9
0.3
0.1
4.9
2008
0.3 0.4
0.4
0.1
0.1
0.0
0.8
2009
12.0 16.3
9.8
0.2
1.5
0.7
24.1
2010
4.7 4.6
1.1
0.3
0.7
0.1
5.1
2011
1.0 1.5
0.3
0.1
0.8
0.2
0.9
2012
0.8 0.8
0.2
0.2
0.4
0.2
0.6
2013
6.1 7.4
2.5
0.3
4.7
1.7
3.7
a The AKFED data include the years 2001-2013 (Veraverbeke et al. 2015b).
b Values include both wildland and prescribed fires in Alaska.
N011-CO2 Emissions from Forest Fires
Emissions of non-CC>2 gases-specifically, methane (CH4) and nitrous oxide (N2(D)-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-251. The summed annual
estimates are provided in Table A-253. Conversion of the CH4 and N2O estimates to CO2 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-251. An example application of these ratios for the current year's
estimate of CH4 emissions is: 7.34 MMT C02 Eq. = 293,836 MT CH4 x (25 kg C02 / 1 kg CH4) x 10"6.
Uncertainty about the non-CC>2 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). In general, probability densities are normal
and also considered marginal distributions.
Estimates of burned forest area from the MTBS data (MTBS Data Summaries 2015; Ruefenacht et al. 2008; Ogle
et al. in preparation) are assigned a normal distribution with relatively low uncertainty with a standard deviation of 4
A-396 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
percent, and these were sampled independently by year (Homer et al. 2015; Hao and Larkin 2014; Eidenshink et al. 2007).
Fuel available is based on the distribution of plot level C densities (as metric tons dry matter per hectare) as defined within
the current USDA Forest Service inventory data (USDA Forest Service 2015; U.S. EPA 2016). We assume that current
data adequately represent the general range of plot level C densities within a state's forest land, given the limitations of the
older inventory data as discussed elsewhere in this report. The plot-level C densities are summarized as dry weight
densities (metric tons per hectare) for each plot with all aboveground dry weight summed as potential fuel for wildfires
and all non-living components of aboveground dry weight assigned as potential fuel for prescribed fires. Frequency
distributions of the plot data indicate that densities are distributed approximately lognormally. Each state's data are fit to a
lognormal distribution, and these were sampled independently by state and year. Note that each state has separate
lognormal distributions for wild versus prescribed fire fuels, yet the same sampling sequence was used (i.e., jointly
distributed within each state by year). Estimates for the Alaska fuel-by-combustion value as well as the combustion factor
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-251) to represent estimates as CO2 equivalent were not considered uncertain values for
these results.
Table fl-253: Estimated C Released and Estimates of Non-CO; Emissions [MMT/yearl for U.S. forests
Year
C Emitted
(MMT/yr)
CH4 Emitted
(MMT/yr)
N20
(MMT/yr)
1990
42,306
127
7
1991
48,467
147
8
1992
21,470
65
4
1993
12,060
36
2
1994
77,059
234
13
1995
10,186
31
2
1996
50,308
150
8
1997
6,582
20
1
1998
27,816
84
5
1999
64,197
191
11
2000
107,993
325
18
2001
56,711
169
9
2002
152,689
462
26
2003
87,512
262
15
2004
152,406
456
25
2005
125,921
377
21
2006
108,000
323
18
2007
160,680
479
27
2008
101,915
306
17
2009
90,771
272
15
2010
42,794
128
7
2011
90,465
273
15
2012
144,192
433
24
2013
95,638
286
16
2014
95,027
289
16
2015
248,237
740
41
2016a
248,237
740
41
a The data for 2016 were unavailable when these estimates were summarized; therefore 2015, the most recent available estimate, is applied to 2016.
A-397
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A-402 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 solid
waste. By volume, landfill gas is about half CH4 and half CO2. The amount and rate of CH4 generation depends upon the
quantity and composition of the landfilled material, as well as the surrounding landfill environment. Not all CH4 generated
within a landfill is emitted to the atmosphere. The CH4 can be extracted and either flared or utilized for energy, thus oxidizing
the CH4 to CO2 during combustion. Of the remaining CH4, a portion oxidizes to CO2 as it travels through the top layer of
the landfill cover. In general, landfill-related CO2 emissions are of biogenic origin and primarily result from the
decomposition, either aerobic or anaerobic, of organic matter such as food or yard wastes.
Methane emissions from landfills are estimated using two primary methods. The first method uses the first order
decay (FOD) model as described by the 2006IPCC Guidelines to estimate CH4 generation. The amount of CH4 recovered
and combusted from MSW landfills is subtracted from the CH4 generation, and is then adjusted with an oxidation factor.
The second method used to calculate CH4 emissions from landfills, also called the back-calculation method, is based off
directly measured amounts of recovered CH4 from the landfill gas and is expressed by Equation HH-8 in CFR Part 98.343
of the EPA's Greenhouse Gas Reporting Program (GHGRP).
The current Inventory methodology uses both methods to estimate CH4 emissions across the time series. The 1990-
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 2016 Inventory are presented in the remainder of this Annex.
Figure fl-18: Methane Emissions Resulting from Landfiiiing Municipal and Industrial Waste
MSW dfjd Industrial
Wavto Onoratiad"s
Incinerated
Composted
Recycled
tgqp to Date
Industrial Wa^tr
Lardfills1
'H lancS'Nferi0
1990-2004
MSW I aniifilU
JQCfc to Ddte
MSW landfills*
Mori let out?! ed
CH„ Gpnri.-'iion1
Non-recovered
CI I,, Gcnp«dtio(i
Recovered CH,
Gerwistion1
¦J Tola! CM,
; Generated11
Oxidised'
I—»
Tula I CH,
I mittpd
Pared
Rdrove: ed tor
Energy
a MSW waste generation is not calculated because annual quantities of waste disposal are available through EPA 2017b; 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 2016
are based on EPA 2017b. 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 Model is used for industrial waste landfills. Two different methodologies are used in the time series for MSW
landfills.
138 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.
A-403
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d For 1990 to 2004, the 2006IPCC Guidelines - First Order Decay Model is used. For 2005 to 2016, directly reported net CH4 emissions from the GFIGRP 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 GFIGRP.
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
GFIGRP dataset.
f Data are pulled from three recovery databases: EIA 2007, flare vendor database (2015), and EPA (GFIGRP) 2016(b). These databases have not been updated
past 2015 because the Inventory strictly uses net emissions from the GFIGRP data.
a For years 1990 to 2004, the total CH4 generated from MSW landfills and industrial waste landfills are summed. For years 2005 to 2016, only the industrial waste
landfills are considered because the directly reported GFIGRP emissions are used for MSW landfills.
h An oxidation factor of 10 percent is applied to all CH4 generated in years 1990 to 2004 (2006 IPCC Guidelines; Mancinelli and McKay 1985; Czepiel et al 1996).
For years 2005 to 2016, directly reported CH4 emissions from the GFIGRP are used for MSW landfills. Various oxidation factor percentages are included in the
GFIGRP 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, and that all waste placed in industrial waste landfills is from either pulp and
paper manufacturing facilities or food and beverage facilities.
Historical waste data, preferably since 1940, are required for the FOD model to estimate CH4 generation for the
Inventory time series. Estimates of waste placed in landfills in the 1940s and 1950s were developed based on U. S. population
for each year and the per capital disposal rates from the 1960s. Estimates of the annual quantity of waste placed in landfills
from 1960 through 1983 were developed from EPA's 1993 Report to Congress (EPA 1993) and a 1986 survey of MSW
landfills (EPA 1988).
For 1989 to 2004, estimates of the annual quantity of waste placed in MSW landfills were developed from a survey
of State agencies as reported in the State of Garbage (SOG) in America surveys (BioCycle 2010) and recent data from the
139
Environmental Research & Education Foundation (EREF), adjusted to include U.S. Territories. The SOG surveys and
EREF (2016) provide state-specific landfill waste generation data and a national average disposal factor back to 1989. The
SOG survey is no longer updated, but is available every two years for the years 2002, 2004, 2006, and 2008 (as published
in BioCycle 2006; 2008, and 2010). EREF published a report in 2016 for data years 2010 and 2013 using a similar
methodology as the SOG surveys (EREF 2016). EREF plans to publish updated reports every three years. A linear
interpolation was used to estimate the amount of waste generated in 2001, 2003, 2005, 2007, 2009, 2011, 2012; data were
extrapolated for 2014 to 2016 based on national population growth because waste generation data are not available for these
years. Upon publication of the next EREF report, the waste generated for 2014 to the current Inventory year will be updated.
Estimates of the quantity of waste landfilled are determined by applying a waste disposal factor to the total amount
of waste generated. A waste disposal factor is determined for each year a SOG survey and EREF report is published and is
the ratio of the total amount of waste landfilled to the total amount of waste generated. The waste disposal factor is
interpolated for the years in-between the SOG surveys and EREF data, and extrapolated for years after the last year of data.
Methodological changes have occurred over the time that the SOG survey has been published, and this has resulted in
fluctuating trends in the data.
Table A-254 shows estimates of waste quantities contributing to CH4 emissions. The table shows SOG and EREF
(EREF 2016) estimates of total waste generated and total waste landfilled (adjusted for U.S. Territories) for various years
over the 1990 to 2016 timeframe even though the Inventory methodology does not use the data for 2005 onward.
Table A-254: Solid Waste in MSW and Industrial Waste Landfills Contributing to CH4 Emissions (MMT unless otherwise
noted)
1990
2005
2012
2013
2014
2015
2016
Total MSW Generated3
270
368
319
319
320
322
324
Percent of MSW Landfilled
77%
64%
63%
64%
64%
64%
64%
Total MSW Landfilled
205
234
200
201
202
203
205
MSW last 30 years
4,876
5,992
6,388
6,411
6,432
6,451
6,468
139 Since the SOG survey does not include U.S. Territories, waste landfilled in U.S. Territories was estimated using population data for
the U.S. Territories (U.S. Census Bureau 2013) and the per capita rate for waste landfilled from BioCycle (2010).
A-404 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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MSW since 1940b
6,808
9,925
11,474
11,675
11,878
12,081
12,286
Total Industrial Waste Landfilled
9.7
10.9
10.5
10.3
10.4
10.3
10.3
Pulp and Paper Sector
6.4
1 6.9
6.2
6.0
6.2
6.1
6.1
Food and Beverage Sector11
3.3
4.0 ,
4.2
4.2
4.2
4.2
4.2
a This estimate represents the waste that has been in place for 30 years or less, which contributes about 90 percent of the CPU generation. Values are based on
EPA (1993) for years 1940 to years 1988 (not presented in table), BioCycle 2001, 2004, 2006, and 2010 for years 1989 to 2014 (1981 to 2004, and 2006 to 2011
are not presented in table). Values for years 2010 to 2016 are based on EREF (2016) and annual population data from the U.S. Census Bureau.
b This estimate represents the cumulative amount of waste that has been placed in landfills since 1940 to the year indicated and is the sum of the annual disposal
rates used in the first order decay model. Values are based on EPA 1993; BioCycle 2001, 2004, 2006, and 2010; and EREF 2016.
c Production data from 1990 and 2001 are from Lockwood-Post's Directory, 2002. Production data from 2002-2016 are from the FAOStat database available at:
http://faostat3.fao.Org/home/index.html#DOWNLOAD. Accessed on September 8, 2017.
d Food production values for 1990 to 2016 are from ERG. USDA-NASS Ag QuickStats available at http://quickstats.nass.usda.gov.
Step 2: Estimate ChU Generation at MSW Landfills for 1990 to 2004
The FOD method is exclusively used for 1990 to 2004. For the FOD method, methane generation is based on
nationwide MSW generation data, to which a national average disposal factor is applied; it was not landfill-specific. Directly
reported CH4 emissions fromEPA's GF1GRP are used for years they are available (i.e., 2010 to 2015), and then back-casted
for years 2005 to 2009. Landfill facilities reporting to EPA's GF1GRP use a combination of the FOD method and the back-
calculation method to develop their CH4 emissions values. Landfills reporting to EPA's GHGRP without gas collection and
control apply the FOD method, while the landfills with gas collection and control may apply either the FOD method or the
back-calculation method, whichever is most appropriate for their site-specific landfill condition. It should be noted that most
landfills with gas collection and control report using the back-calculation method.
The FOD method is presented below, and is similar to Equation HH-5 in CFR Part 98.343 for MSW landfills, and
Equation TT-6 in CFR Part 98.463 for industrial waste landfills.
CH4 .Solid Waste = [CH4.MSW + CH4,Ind — R] - Ox
where,
CLL^soiid waste = Net CH4 emissions from solid waste
CLL^msw = CH4 generation from MSW landfills
CLLynd = CH4 generation from industrial landfills
R = CH4 recovered and combusted (only for MSW landfills)
Ox = CH4 oxidized from MSW and industrial waste landfills before release to the atmosphere
The input parameters needed for the FOD model equations are the mass of waste disposed each year (discussed
under Step 1), degradable organic carbon (DOC), and the decay rate constant (k). The equation below provides additional
detail on the activity data and emission factors used in the CH4,msw equation presented above.
CH4,msw = [Wx X Lo X Ł X (e-fcO--*-!) _
where,
Gch4 = Total amount of CH4 generated
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 CLL/Mg waste; EPA 1998, 2008)
16/12 = conversion factor from CH4 to C
k = Decay rate constant (yr"1, see Table A-277)
The DOC is determined from the CH4 generation potential (Lo in m3 CLLi/Mg waste) as shown in the following
equation:
DOC = [Lo x 6.74 x 10 4] -f [Fx 16/12 x DOCf x MCF]
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where,
DOC
degradable organic carbon (fraction, kt C/kt waste),
Lo =
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 potential140 of 100 m3 CH4/Mg waste (EPA AP-42) as described in the
next few paragraphs.
The DOC value used in the CH4 generation estimates from MSW landfills 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 Lo = 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-255 and recommended
in EPA's compilation of emission factors (EPA 2008).
Table A-255: Average Values for Bate Constant tkl by Precipitation Range (yrl
Precipitation range (inches/year)
k (yr-1)
<20
0.020
20-40
0.038
>40
0.057
These values for k show reasonable agreement with the results of other studies. For example, EPA's compilation
of emission factors (EPA 1998; EPA 2008) recommends a value of 0.02 yr"1 for arid areas (less than 25 inches/year of
precipitation) and 0.04 yr"1 for non-arid areas. The SWANA (1998) study of 18 landfills reported a range in values of k from
0.03 to 0.06 yr"1 based on CH4 recovery data collected generally in the time frame of 1986 to 1995.
Using data collected primarily for the year 2000, the distribution of waste-in-place versus precipitation was
developed from over 400 landfills (RTI 2004). A distribution was also developed for population 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-256.
140 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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Table fl-256: 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
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 ChU Emissions Avoided from MSW Landfills for 1990 to 2004
The estimated landfill gas recovered per year (R) at MSW landfills is based on a combination of four databases
that include recovery from flares and/or landfill gas-to-energy projects:
• a database developed by the Energy Information Administration (EIA) for the voluntary reporting of
greenhouse gases (EIA 2007),
• a database of LFGE projects that is primarily based on information compiled by EPA LMOP (EPA 2016),
• the flare vendor database (contains updated sales data collected from vendors of flaring equipment), and the
• EPA's GHGRP MSW landfills database (EPA 2015) ,M1
The EPA's GHGRP MSW landfills database was first introduced as a data source for the 1990 to 2013 Inventory.
The GHGRP MSW landfills database contains facility-reported data that undergoes rigorous verification and is considered
to contain the least uncertain data of the four databases. However, this database only contains a portion of the landfills in
the United States (although, presumably the highest emitters since only those landfills that meet the methane generation
threshold must report) and only contains data from 2010 and later. For landfills in this 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
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
141 The 2015 GHGRP dataset is used in the GHGRP MSW landfills dataset described in Step 3a. This database is no longer updated
because the methodology has changed such that the directly reported net methane emissions are used. The GHGRP dataset is
available through Envirofacts .
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landfills dataset (EPA 2015). 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.
To avoid double-counting CH4 recovery, a hierarchical approach is applied after matching landfills in one database
to the other databases. If a landfill in the EIA database was also in the LFGE and/or the flare vendor database, the CH4
recovery was based on the EIA data because landfill owners or operators directly reported the amount of CH4 recovered
using gas flow concentration and measurements, and because the reporting accounted for changes over time. The EIA
database only includes facility-reported data through 2006; the amount of CH4 recovered in this database for years 2007 and
later were assumed to be the same as in 2006. Nearly all (93 percent) of landfills in the EIA database also report to EPA's
GHGRP.
If both the flare data and LFGE recovery data were available for any of the remaining landfills (i.e., not in the EIA
or EPA's GHGRP databases), then the CH4 recovered were based on the LFGE data, which provides reported landfill-
specific data on gas flow for direct use projects and project capacity (i.e., megawatts) for electricity projects. The LFGE
database is based on the most recent EPA LMOP database (published annually). The remaining portion of avoided 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 were unable to be obtained for the current Inventory year. 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
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 for the past three Inventory years.
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. Flare sales data were not provided to the EPA for the previous and
current Inventory year.
Step 3c: Reduce CH4 Emissions Avoided Through Flaring
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
A-408 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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.
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.
Step 4: Estimate CH4 Emissions from MSW Landfills for 2005 to 2009
For 2005 to 2009, back-casted GHGRP net emissions plus a scale-up factor to account for emission from landfills
that do not report to the GHGRP are used. The GHGRP data were first incorporated into the methodology in the 1990 to
2015 Inventory. Including the GHGRP net emissions data was a significant methodological change from the FOD method
previously described in Steps 1 to 3; therefore, EPA needed to merge the previous method with the new (GHGRP) dataset.
A summary of how and why the GHGRP emissions were back-casted and how the scale-up factor was estimated are included
here. The methodology described in this section was determined based on the good practice guidance in Volume 1: Chapter
5 Time Series Consistency of the 2006IPCC Guidelines. Additional details including other options considered are included
in RTI2017.
Facilities reporting to the GHGRP without landfill gas collection and control use the FOD method. Facilities
reporting to the GHGRP with landfill gas collection and control must use two methodologies, the FOD method (expressed
by Equation HH-5 in CFR Part 98.343), and the back-calculation methodology, which is based on directly measured amounts
of recovered CH4 from the landfill gas and is expressed by Equation HH-8 in CFR Part 98.343 (also presented below). The
two parts of Equation HH-8 consider the portion of CH4 in the landfill gas that is not collected by the landfill gas collection
system; and the portion that is collected. First, the recovered CH4 is adjusted with the collection efficiency of the gas
collection and control system and the fraction of hours the recovery system operated in the calendar year. This quantity
represents the amount of CH4 in the landfill gas that is not captured by the collection system; it is then adjusted for oxidation.
The second portion of the equation adjusts the portion of CH4 in the collected landfill gas with the efficiency of the
destruction device(s), and the fraction of hours the destruction device(s) operated during the year.
CH4,Solid Waste = [( - r) x{1 - OX) + R x (l - (DE x fDestj)]
\CE X f REC '
Where,
R = Quantity of recovered CH4 from Equation HH-4 of the EPA's GHGRP
CE = Collection efficiency estimated at the landfill, taking into account system coverage, operation,
and cover system materials from Table HH-3 of the EPA's GHGRP. If area by soil cover type
information is not available, the default value of 0.75 should be used, (percent)
fREc = fraction of hours the recovery system was operating (percent) OX = oxidation factor (percent)
DE = destruction efficiency (percent)
foest = fraction of hours the destruction device was operating (fraction)
For completeness, and because the GHGRP only includes a subset of U.S. landfills, a scale-up factor had to be
developed to estimate the amount of emissions from the landfills that do not report to the GHGRP. Landfills that do not
meet the reporting threshold to the GHGRP are not required to report to the GHGRP. Therefore, the GHGRP dataset is only
partially complete when considering the universe of MSW landfills. In theory, national emissions from MSW landfills equals
A-409
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the emissions from the GHGRP plus emissions from landfills that do not report to the GHGRP. The scale-up factor was first
applied in the 1990 to 2015 Inventory (as 12.5 percent) and was revised for the 1990 to 2016 Inventory to 9 percent. The
remainder of this section describes how the steps taken to increase time series consistency after incorporating the GHGRP
data.
Regarding the time series and as stated in 2006IPCC Guidelines Volume 1: Chapter 5 Time Series Consistency
(IPCC 2006), "the time series is a central component of the greenhouse gas inventory because it provides information on
historical emissions trends and tracks the effects of strategies to reduce emissions at the national level. All emissions in a
time series should be estimated consistently, which means that as far as possible, the time series should be calculated using
the same method and data sources in all years" (IPCC 2006). This chapter however, recommends against back-casting
emissions back 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. This was the case with the GHGRP data, where directly
reported 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 the FOD method from 1990
to 2009, or using the FOD method for a portion of that time and back-casting the GHGRP emissions data to a year where
emissions from the two methodologies aligned. Plotting the back-casted GHGRP emissions against the emissions estimates
from the FOD method showed an alignment of the data in 2004 and later years which facilitated the use of the overlap
technique while also reducing uncertainty. Therefore, EPA decided to back-cast the GHGRP emissions from 2009 to 2005
only to merge the datasets and adhere to the IPCC good practice guidance.
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 GHG reports.
For the 1990 to 2016 Inventory, EPA revisited the methodology used to calculate the scale-up factor in the 1990
to 2015 Inventory and, with stakeholder input, decided to base the scale-up factor on the total amount of buried waste
between landfills not reporting to the GHGRP and those reporting to the GHGRP. There are significant uncertainties in the
data quality of the total amount of buried waste at the non-reporting landfills, and for some landfills, no information was
available at all. There is much less uncertainty in these amounts for the landfills reporting to the GHGRP. Additionally, this
variable provides a direct basis for comparing emissions from these two sets of landfills because landfill methane generation
emissions are directly related to the amount of waste disposed in addition to other less static variables (e.g., waste
composition) and is the basis for the FOD method used in the earlier part of the time series (1990 to 2004). Details on how
the 9 percent scale-up factor was derived is included in RTI (2018). Like the 1990 to 2015 Inventory, the scale-up factor is
applied to all years from 2005 to 2016.
Step 5: Estimate CH4 Emissions from MSW Landfills for 2010 to 2016
Directly reported CH4 emissions to EPA's GHGRP are used for 2010 to 2016. The 9 percent scale-up factor is
applied annually as is done for 2005 to 2009 because the GHGRP does not capture emissions from all landfills in the United
States.
Step 6: Estimate CH4 Generation at Industrial Waste Landfills for 1990 to the Current Inventory Year
Industrial waste landfills receive waste from factories, processing plants, and other manufacturing activities. In
national inventories prior to the 1990 through 2005 inventory, CH4 generation at industrial landfills was estimated as seven
percent of the total CH4 generation from MSW landfills, based on a study conducted by EPA (1993). In 2005, the
methodology was updated and improved by using activity factors (industrial production levels) to estimate the amount of
industrial waste landfilled each year, and by applying the FOD model to estimate CH4 generation. A nationwide survey of
industrial waste landfills found that most of the organic waste placed in industrial landfills originated from two sectors: food
processing (meat, vegetables, fruits) and pulp and paper (EPA 1993). Data for annual nationwide production for the food
processing and pulp and paper sectors were taken from industry and government sources for recent years; estimates were
developed for production for the earlier years for which data were not available. For the pulp and paper sector, production
data published by the Lockwood-Post's Directory were used for years 1990 to 2001 and production data published by the
U.S. Department of Agriculture were used for years 2002 through 2016. An extrapolation based on U.S. real gross domestic
product was used for years 1940 through 1964. For the food processing sector, production levels were obtained or developed
A-410 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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from the U.S. Department of Agriculture for the years 1990 through 2016 (ERG 2017). An extrapolation based on U.S.
population was used for the years 1940 through 1989.
In addition to production data for the pulp and paper and food processing sectors, the following inputs are needed
to use the FOD model for estimating CH4 generation from industrial waste landfills: 1) quantity of waste that is disposed in
industrial waste landfills (as a function of production), 2) CH4 generation potential (Lo) from which a DOC value can be
calculated, and 3) the decay rate constant (k).
Research into waste generation and disposal in landfills for the pulp and paper sector indicated that the quantity of
waste landfilled was about 0.050 MT/MT of product compared to 0.046 MT/MT product for the food processing sector (RTI
2006). These 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 2015). The DOC value for industrial pulp and paper
waste is estimated at 0.15 (L0 of 49 m3/MT); the DOC value for industrial food waste is estimated as 0.26 (L0 of 128 m3/MT)
(RTI 2015; RTI 2014). Estimates for k were taken from the default values in the 2006IPCC Guidelines; the value of k given
for food waste with disposal in a wet temperate climate is 0.19 yr"1, and the value given for paper waste is 0.06 yr"1.
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. Where pulp and paper mill wastewater treatment
residuals or sludge are the primary constituents of pulp and paper waste landfilled, values for k available in the literature
142
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 for the current inventory year. 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 time line that was developed for MSW landfills was
applied to the industrial landfills to estimate the average MCF. That is, between 1940 and 1980, the fraction of waste that
was land disposed transitioned from 6 percent managed landfills in 1940 and 94 percent open dumps to 100 percent managed
landfills in 1980 and on. For wastes disposed of in 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 CO2 in the top layer of the soil. The amount of oxidation
depends upon the characteristics of the soil and the environment. For purposes of this analysis, it was assumed that of the
CH4 generated, minus the amount of gas recovered for flaring or LFGE projects, 10 percent was oxidized in the soil (Jensen
and Pipatti 2002; Mancinelli and McKay 1985; Czepiel et al 1996). The literature review was reviewed in 2011 (RTI 2011)
and 2016 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
142 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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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.
For years 2005 to 2016, directly reported CH4 emissions to EPA's GHGRP, which include the adjustment for
oxidation, are used. EPA's GHGRP allows facilities to use a range of oxidation factors: 0.0, 0.10, 0.25, 0.35. The average
oxidation factor across all facilities reporting to the GHGRP for the years data are available is approximately 20 percent,
thus this value is essentially the oxidation factor applied for years 2005 to 2016.
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-257. A different methodology is applied for 2005 to 2016.
Directly reported net CH4 emissions to EPA's GHGRP plus the 9 percent scale-up factor were applied for 2010 to 2016. For
2005 to 2009, the directly-reported GHGRP net emissions from 2010 to 2016 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 GHG reports for any year. The 9 percent scale-up factor was also
applied annually for 2005 to 2009.
A-412 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-257: CHa Emissions from Landfills (kt)
1990
1995
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
MSW CH4 Generation
8,214
9,140
10,270
10,477
10,669
-
-
-
-
-
-
-
-
-
-
-
-
Industrial CH4 Generation
484
537
618
625
629
636
639
643
648
653
656
657
659
661
662
663
664
MSW CH4 Recovered
(718)
(1,935)
(4,894)
(4,995)
(5,304)
-
-
-
-
-
-
-
-
-
-
-
-
MSW CH4 Oxidized
(750)
(720)
(538)
(548)
(537)
-
-
-
-
-
-
-
-
-
-
-
-
Industrial CH4 Oxidized
(48)
(54)
(62)
(63)
(63)
(64)
(64)
(64)
(65)
(65)
(66)
(66)
(66)
(66)
(66)
(66)
(66)
MSW Net CH4 Emissions
(GHGRP)
-
-
-
-
-
4,737
4,645
4,552
4,459
4,366
4,402
4,043
4,087
3,936
3,913
3,870
3,708
Net Emissions3
7,182
6,967
5,394
5,496
5,395
5,310
5,220
5,130
5,042
4,954
4,992
4,634
4,680
4,531
4,509
4,467
4,306
Notes: MSW and Industrial CPU generation in Table A-257 represents emissions before oxidation. Totals may not sum exactly to the last significant figure due to rounding. Parentheses denote negative values.
aMSW Net CbU emissions for years 2010 to 2016 are directly reported CPU emissions to the EPA's GHGRP for MSW landfills and are back-casted to estimate emissions for 2005 to 2009. A scale-up factor of 9 percent of
each year's emissions from 2005 to 2016 is applied to 2005 to 2016 to account for landfills that do not report annual methane emissions to the GHGRP. Emissions for years 1990 to 2004 are calculated by the FOD
methodology.
A-413
-------
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EPA (1993) Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress, U.S.
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Heath, L.S. et al. 2010. Greenhouse Gas and Carbon Profile of the U.S. Forest Products Industry Value Chain. Environmental
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Coburn and K. Bronstein (RTI) for R. Schmeltz, April 15,2010.
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September 5, 2006.
RTI (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from Landfills.
Memorandum prepared by M. Branscome and J. Coburn (RTI) to E. Scheehle (EPA), August 26,2004.
Skog, K.E. (2008) "Sequestration of Carbon in harvested wood products for the United States." Forest Products Journal, 58(6):
56-72.
Skog, K. and G.A. Nicholson (2000) "Carbon Sequestration in Wood and Paper Products." USDA Forest Service Gen. Tech.
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Sonne, E. (2006) "Greenhouse Gas Emissions from Forestry Operations: A Life Cycle Assessment." J. Environ. Qual. 35:1439-
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Upton, B., R. Miner, M. Spinney, L.S. Heath (2008) "The Greenhouse Gas and Energy Impacts of Using Wood Instead of
Alternatives in Residential Construction in the United States." Biomass and Bioenergy, 32: 1 -10.
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Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2016. Available online at:
.
Waste Business Journal (WBJ) (2016) Directory of Waste Processing & Disposal Sites 2016.
A-415
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ANNEX 4IPCC Reference Approach for Estimating CO2
Emissions from Fossil Fuel Combustion
It is possible to estimate carbon dioxide (CO2) emissions from fossil fuel consumption using alternative
methodologies and different data sources than those described in the Estimating Emissions from Fossil Fuel Combustion
Annex. For example, the 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 CO2 emissions from fossil fuel combustion. Volume 2: Energy, Chapter 6: Reference Approach of the 2006
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 CO2 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 CO2 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 Intergovernmental Panel on Climate Change (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 make a number of modifications to these data to generate more accurate apparent consumption
estimates of these fuels. The first modification adjusts for consumption of fossil fuel feedstocks accounted for in the
Industrial Processes 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-258.
The C content of fuel varies with the fuel's heat content. Therefore, for an accurate estimation of CO2 emissions,
fuel statistics were provided on an energy content basis (e.g., Btu or joules). Because detailed fuel production statistics are
typically provided in physical units (as in Table A-258 for 2016), they were converted to units of energy before CO2
emissions were calculated. Fuel statistics were converted to their energy equivalents by using conversion factors provided
by EIA. These factors and their data sources are displayed in Table A-259. The resulting fuel type-specific energy data for
2016 are provided in Table A-260.
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
A-416 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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-259.
Step 3: Estimate Carbon Emissions
Once apparent consumption is estimated, the remaining calculations are similar to those for the "bottom-up"
Sectoral Approach (see Estimating Emissions from Fossil Fuel Combustion Annex). Potential CO2 emissions were estimated
using fuel-specific C coefficients (see Table A-260).143 The C in products from non-energy uses of fossil fuels (e.g., plastics
or asphalt) was then estimated and subtracted (see Table A-262). This step differs from the Sectoral Approach in that
emissions from both fuel combustion and non-energy uses are accounted for in this approach. Finally, to obtain actual CO2
emissions, net emissions were adjusted for any C that remained unoxidized as a result of incomplete combustion (e.g., C
contained in ash or soot). 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 CO2 from Fossil Fuel Combustion).
Step 4: Convert to CO2 Emissions
Because the 2006 IPCC Guidelines recommend that countries report greenhouse gas emissions on a full molecular
weight basis, the final step in estimating CO2 emissions from fossil fuel consumption was converting from units of C to units
of CO2. Actual C emissions were multiplied by the molecular-to-atomic weight ratio of CO2 to C (44/12) to obtain total CO2
emitted from fossil fuel combustion in million metric tons (MMT). The results are contained in Table A-261.
Comparison Between Sectoral and Reference Approaches
These two alternative approaches can both produce reliable estimates that are comparable within a few percent.
Note that the reference approach includes emissions from non-energy uses. Therefore, these totals should be compared to
the aggregation of fuel use and emission totals from Emissions of CO2 from Fossil Fuel Combustion and Carbon Emitted
from Non-Energy Uses of Fossil Fuels Annexes. These two sections together are henceforth referred to as the Sectoral
Approach. Other than this distinction, the major difference between methodologies employed by each approach lies in the
energy data used to derive C emissions (i.e., the actual surveyed consumption for the Sectoral Approach versus apparent
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.
143 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-261 for more specific source information.
A-417
-------
Differences in Total Amount of Energy Consumed
Table A-263 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 1.8 percent lower than the Sectoral
Approach for 2016. The greatest differences lie in lower estimates for coal and petroleum consumption for the Reference
Approach (1.0 percent and 4.0 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 actually a composite category composed of natural gas
liquids (NGL) and LPG.
• Heat Equivalents. It can be difficult to obtain heat equivalents for certain fuel types, particularly for categories
such as "crude oil" where the key statistics are derived from thousands of producers in the United States and
abroad.
• Possible inconsistencies in U.S. Energy Data. The United States has not focused its energy data collection
efforts on obtaining the type of aggregated information used in the Reference Approach. Rather, the United
States believes that its emphasis on collection of detailed energy consumption data is a more accurate
methodology for the United States to obtain reliable energy data. Therefore, top-down statistics used in the
Reference Approach may not be as accurately collected as bottom-up statistics applied to the Sectoral
Approach.
• Balancing Item. The Reference Approach uses apparent consumption estimates while the Sectoral Approach
uses reported consumption estimates. While these numbers should be equal, there always seems to be a slight
difference that is often accounted for in energy statistics as a "balancing item."
Differences in Estimated CO2 Emissions
Given these differences in energy consumption data, the next step for each methodology involved estimating
emissions of CO2. Table A-265 summarizes the differences between the two methods in estimated C emissions.
As mentioned above, for 2016, the Reference Approach resulted in a 1.8 percent lower estimate of energy
consumption in the United States than the Sectoral Approach. The resulting emissions estimate for the Reference Approach
was 1.5 percent lower. Estimates of natural gas emissions from the Reference Approach are higher (0.5 percent), and coal
and petroleum emission estimates are lower (1.4 percent and 2.8 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.
A-418 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Although the two approaches produce similar results, the United States believes that the "bottom-up" Sectoral
Approach provides a more accurate assessment of CO2 emissions at the fuel level. This improvement in accuracy is largely
a result of the data collection techniques used in the United States, where there has been more emphasis on obtaining the
detailed products-based information used in the Sectoral Approach than obtaining the aggregated energy flow data used in
the Reference Approach. The United States believes that it is valuable to understand both methods.
References
EIA (2018) Monthly Energy Review, February 2018. Energy Information Administration, U.S. Department of Energy.
Washington, D.C. DOE/EIA-0035(2018/02).
EIA (1995-2016). Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington,
DC, Volume I. DOE/EIA-0340.
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.
IPCC (2006). 2006IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas
Inventories Programme, EgglestonH.S., BuendiaL., MiwaK.,Ngara T., andTanabe K. (eds.). Published: IGES, Japan.
SAIC (2004). Analysis prepared by Science Applications International Corporation for EPA, Office of Air and Radiation,
Market Policies Branch.
USGS (1998). CoalQualDatabase Version 2.0, U.S. Geological Survey.
A-419
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Table fl-258:2016 U.S. Energy Statistics [Physical Unitsl
Stock
U.S.
Fuel Category (Units)
Fuel Type
Production
Imports
Exports
Change
Adjustment
Bunkers
Territories
Solid Fuels (Thousand Short Tons)
Anthracite Coal
Bituminous Coal
1,193
328,816
[1]
[1]
[1]
[1]
[1]
[1]
Sub-bituminous Coal
345,808
[1]
[1]
[1]
367
Lignite
Coke
52,547
[1]
140
[1]
857
[1]
(90)
4,427
Unspecified Coal
9,850
60,271
(45,441)
2,849
1,963
Gas Fuels (Million Cubic Feet)
Natural Gas
26,477,512
3,006,439
2,335,448
(338,757)
281,893
55,000
Liquid Fuels (Thousand Barrels)
Crude Oil
Nat Gas Liquids and Liquefied Refinery Gases
Other Liquids
3,241,591
1,284,357
0
2,873,208
66,025
486,281
216,274
443,388
178,024
35,365
5,704
2,660
4,005
Motor Gasoline
Aviation Gasoline
(38,487)
21,644
111
232,562
0
(212)
116
234,608
34,263
Kerosene
855
3,295
(446)
411
Jet Fuel
53,677
64,149
2,620
181,199
8,044
Distillate Fuel
53,642
431,475
4,769
146
12,711
18,586
Residual Fuel
74,849
108,979
(673)
14,000
71,685
20,195
Naphtha for petrochemical feedstocks
Petroleum Coke
9,784
3,627
0
209,723
(92)
668
9,491
Other Oil for petrochemical feedstocks
Special Naphthas
Lubricants
1,836
5,073
14,316
0
0
28,223
45
(118)
(1,086)
1,240
172
Waxes
1,983
1,298
98
Asphalt/Road Oil
Still Gas
13,302
0
7,438
0
(1,774)
0
Misc. Products
14
542
(94)
13,144
[1] Included in Unspecified Coal
Note: Parentheses indicate negative values.
Sources: Solid and Gas Fuels: EIA (2018); Liquid Fuels: EIA (1995-2016).
A-420 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-259: Conversion Factors to Energy Units [Heat Equivalents]
Stock
U.S.
Fuel Category (Units)
Fuel Type
Production
Imports
Exports
Change
Adjustment
Bunkers
Territories
Solid Fuels (Million Btu/Short Ton)
Anthracite Coal
22.57
Bituminous Coal
23.89
Sub-bituminous Coal
17.14
28.16
Lignite
12.87
12.87
Coke
22.33
25.66
22.33
Unspecified
25.00
25.97
20.86
150.17
25.14
Natural Gas (BTU/Cubic Foot)
1,037
1,025
1,009
1,037
1,036
1,037
Liquid Fuels (Million Btu/Barrel)
Crude Oil
5.72
6.05
5.72
5.72
5.72
5.72
Nat Gas Liquids and Liquefied Refinery Gases
3.71
3.71
3.71
3.71
3.71
3.71
Other Liquids
5.83
5.83
5.83
5.83
5.83
5.83
Motor Gasoline
5.06
5.06
5.06
5.06
5.06
5.06
5.06
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
5.80
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); Unspecified Solid Fuels, Coke, Natural Gas and Petroleum Products: EIA (1995-2016).
A-421
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Table fl-260:2016 Apparent Consumption of Fossil Fuels UBtul
U.S. Apparent
Fuel Category
Fuel Type
Production
Imports
Exports Stock Change
Adjustment
Bunkers
Territories
Consumption
Solid Fuels
Anthracite Coal
26.9
26.9
Bituminous Coal
7,855.4
7,855.4
Sub-bituminous Coal
5,927.2
10.3
5,916.8
Lignite
676.1
57.0
619.1
Coke
3.1
22.0
(2.0)
(16.9)
Unspecified
246.2
1,565.4
(947.9)
427.9
49.3
(749.7)
Gas Fuels
Natural Gas
27,457.2
3,081.6
2,356.5
(351.3)
292.2
57.0
28,298.5
Liquid Fuels
Crude Oil
18,548.4
17,391.5
1,238.0
202.4
34,499.5
Nat Gas Liquids and Liquefied Refinery Gases
4,770.1
245.2
1,646.7
21.2
14.9
3,362.3
Other Liquids
2,832.6
1,037.0
15.5
1,780.1
Motor Gasoline
(194.7)
109.5
1,176.5
(1.1)
173.3
(1,087.3)
Aviation Gasoline
0.6
0.6
0.6
(0.6)
Kerosene
4.8
18.7
(2.5)
2.3
(9.0)
Jet Fuel
304.3
363.7
14.9
1,051.1
45.6
(1,079.7)
Distillate Fuel
312.5
2,513.3
27.8
0.8
74.0
108.3
(2,195.3)
Residual Oil
470.6
685.2
(4.2)
88.0
450.7
127.0
(622.1)
Naphtha for petrochemical feedstocks
51.3
(0.5)
51.8
Petroleum Coke
21.8
1,263.4
4.0
57.2
(1,302.7)
Other Oil for petrochemical feedstocks
10.7
0.3
7.2
3.2
Special Naphthas
26.6
(0.6)
27.2
Lubricants
86.8
171.2
(6.6)
1.0
(76.7)
Waxes
11.0
7.2
0.5
3.3
Asphalt/Road Oil
88.3
49.4
(11.8)
50.7
Still Gas
Misc. Products
0.1
3.1
(0.5)
76.2
r—
CO
r—
Total
65,066.5
25,299.3
14,117.7
(1,041.9)
940.6
1,575.8
655.0
75,428.5
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
A-422 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Tablefl-261:2016 Potential CO2 Emissions
Carbon Coefficients
Potential Emissions
Fuel Category
Fuel Type
Apparent Consumption (QBtu)
(MMT Carbon/QBtu)
(MMT CO2 Eq.)
Solid Fuels
Anthracite Coal
0.03
28.28
2.8
Bituminous Coal
7.86
25.44
732.8
Sub-bituminous Coal
5.92
26.50
574.9
Lignite
0.62
26.65
60.5
Coke
(0.02)
31.00
(1.9)
Unspecified
(0.75)
25.34
(69.6)
Gas Fuels
Natural Gas
28.30
14.46
1,499.9
Liquid Fuels
Crude Oil
34.50
20.31
2,568.6
Nat Gas Liquids and LRGs
3.36
16.87
208.0
Other Liquids
1.78
20.31
132.5
Motor Gasoline
(1.09)
19.46
(77.6)
Aviation Gasoline
(0.00)
18.86
(0.0)
Kerosene
(0.01)
19.96
(0.7)
Jet Fuel
(1.08)
19.70
(78.0)
Distillate Fuel
(2.20)
20.17
(162.4)
Residual Oil
(0.62)
20.48
(46.7)
Naphtha for petrochemical feedstocks
0.05
18.55
3.5
Petroleum Coke
(1.30)
27.85
(133.0)
Other Oil for petrochemical feedstocks
0.00
20.17
0.2
Special Naphthas
0.03
19.74
2.0
Lubricants
(0.08)
20.20
(5.7)
Waxes
0.00
19.80
0.2
Asphalt/Road Oil
0.05
20.55
3.8
Still Gas
18.20
Misc. Products
0.07
20.31
5.5
Total
5,219.8
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Sources: C content coefficients by coal rank from USGS (1998) and SAIC (2004); Unspecified Solid Fuels, EIA (1995-2016), Natural Gas and Liquid Fuels: EPA (2010).
A-423
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Table fl-262:2016 Non-Energy Carbon Stored in Products
Carbon
Consumption
Coefficients
Carbon
for Non-Energy
(MMT
Content
Fraction
Carbon Stored
Fuel Type
Use (TBtu)
Carbon/QBtu)
(MMT Carbon)
Sequestered
(MMT CO2 Eq.)
Coal
89.9
31.00
2.79
0.10
1.7
Natural Gas
289.5
14.46
4.18
0.66
10.1
Asphalt & Road Oil
853.4
20.55
17.54
1.00
64.0
LPG
2,117.6
17.06
36.13
0.66
87.2
Lubricants
290.5
20.20
5.87
0.09
2.0
Pentanes Plus
53.0
19.10
1.01
0.66
2.4
Petrochemical Feedstocks
[1]
[1]
[1]
[1]
34.7
Petroleum Coke
0.0
27.85
0.00
0.30
0.0
Special Naphtha
88.7
19.74
1.75
0.66
4.2
Waxes/Misc.
[1]
[1]
[1]
[1]
0.8
Misc. U.S. Territories Petroleum
[11
[11
[11
[11
0.6
Total
207.7
[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 fl-263:2016 Reference Approach CO; Emissions from Fossil Fuel Consumption UMMTCO2 Eg. unless otherwise noted]
Potential
Carbon
Net
Fraction
Total
Fuel Category
Emissions
Sequestered
Emissions
Oxidized
Emissions
Coal
1,299.5
1.7
1,297.8
100.0%
1,297.8
Petroleum
2,420.4
195.9
2,224.5
100.0%
2,224.5
Natural Gas
1,499.9
10.1
1,489.8
100.0%
1,489.8
Total
5,219.8
207.7
5,012.1
5,012.1
Note: Totals may not sum due to independent rounding.
Tahle fl-264: Fuel Consumption in the United States by Estimating Approach [TBtuF
Approach
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Sectoral
69,719
74,938
82,555
82,730
83,926
81,236
76,438
78,938
77,514
75,704
77,780
78,434
77,543
76,841
Coal
18,072
19,187
21,748
21,834
22,067
21,753
19,231
20,267
19,071
16,827
17,452
17,370
15,041
13,784
Natural Gas
19,184
22,170
23,392
21,960
23,371
23,594
23,193
24,312
24,679
25,832
26,560
27,141
27,938
28,180
Petroleum
32,463
33,582
37,414
38,936
38,487
35,889
34,014
34,359
33,764
33,045
33,768
33,923
34,565
34,877
Reference (Apparent)
68,725
74,015
81,521
82,055
83,890
80,390
76,450
77,866
76,511
75,388
76,055
76,726
76,091
75,429
Coal
17,573
18,567
20,957
21,534
21,577
21,391
19,243
19,620
18,756
16,483
16,941
17,047
14,822
13,652
Natural Gas
19,276
22,274
23,484
22,029
23,441
23,666
23,277
24,409
24,778
25,924
26,637
27,225
28,017
28,298
Petroleum
31,877
33,174
37,079
38,492
38,872
35,333
33,931
33,836
32,977
32,981
32,477
32,454
33,252
33,478
Difference
-1.4%
-1.2%
-1.3%
-0.8%
0.0%
-1.0%
0.0%
-1.4%
-1.3%
-0.4%
-2.2%
-2.2%
-1.9%
-1.8%
Coal
-2.8%
-3.2%
-3.6%
-1.4%
-2.2%
-1.7%
0.1%
-3.2%
-1.7%
-2.0%
-2.9%
-1.9%
-1.5%
-1.0%
Natural Gas
0.5%
0.5%
0.4%
0.3%
0.3%
0.3%
0.4%
0.4%
0.4%
0.4%
0.3%
0.3%
0.3%
0.4%
Petroleum
-1.8%
-1.2%
-0.9%
-1.1%
1.0%
-1.5%
-0.2%
-1.5%
-2.3%
-0.2%
-3.8%
-4.3%
-3.8%
-4.0%
a Includes U.S. Territories. Does not include international bunker fuels.
Note: Totals may not sum due to independent rounding.
A-424 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table fl-265: CO; Emissions from Fossil Fuel Combustion by Estimating Approach [MMT CO2 Eq.la
Approach
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Sectoral
4,859
5,169
5,733
5,800
5,878
5,692
5,302
5,482
5,346
5,137
5,290
5,328
5,184
5,088
Coal
1,719
1,823
2,071
2,083
2,106
2,076
1,835
1,935
1,820
1,607
1,666
1,658
1,436
1,316
Natural Gas
1,007
1,164
1,227
1,156
1,231
1,242
1,221
1,278
1,297
1,358
1,397
1,428
1,470
1,482
Petroleum
2,134
2,182
2,434
2,560
2,542
2,373
2,246
2,269
2,228
2,173
2,226
2,242
2,278
2,290
Reference (Apparent)
4,794
5,132
5,682
5,782
5,890
5,652
5,336
5,419
5,294
5,140
5,181
5,222
5,101
5,012
Coal
1,654
1,756
1,988
2,049
2,053
2,036
1,832
1,868
1,789
1,573
1,614
1,626
1,414
1,298
Natural Gas
1,013
1,170
1,233
1,160
1,235
1,247
1,226
1,284
1,303
1,364
1,402
1,433
1,475
1,480
Petroleum
2,127
2,206
2,461
2,573
2,603
2,369
2,277
2,267
2,203
2,203
2,165
2,163
2,213
2,224
Difference
-1.4%
-0.7%
-0.9%
-0.3%
0.2%
-0.7%
0.6%
-1.2%
-1.0%
0.1%
-2.1%
-2.0%
-1.6%
-1.5%
Coal
-3.8%
-3.7%
-4.0%
-1.7%
-2.5%
-1.9%
-0.2%
-3.4%
-1.8%
-2.1%
-3.1%
-2.0%
-1.6%
-1.4%
Natural Gas
0.6%
0.6%
0.5%
0.3%
0.3%
0.3%
0.4%
0.5%
0.5%
0.4%
0.3%
0.3%
0.3%
0.5%
Petroleum
-0.3%
1.1%
1.1%
0.5%
2.4%
-0.1%
1.4%
-0.1%
-1.1%
1.4%
-2.8%
-3.5%
-2.8%
-2.8%
a Includes U.S. Territories. Does not include international bunker fuels.
Note: Totals may not sum due to independent rounding.
A-425
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ANNEX 5 Assessment of the Sources and Sinks of
Greenhouse Gas Emissions Not Included
Although this report is intended to be a comprehensive assessment of anthropogenic144 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 not significant 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., pending additional
resources).
In general, data availability remains the main constraint for estimating and including the emissions and removals
from source and sink categories discussed. Methods to estimate emissions and removals from these categories were
introduced with 2006 IPCC Guidelines. Also, many of the categories discussed below are determined to be not significant
in terms of overall national emissions based on qualitative information on activity levels per national circumstances, expert
judgment, and available proxy information, and not including them introduces a very minor bias.
Reporting of inventories to the UNFCCC under Decision 24/CP. 19 requests "Where methodological or data gaps
in inventories exist, information on these gaps should be presented in a transparent manner." Furthermore, these revised
reporting guidelines allow a country to indicate that 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.145
Specifically, where the notation key "NE," meaning not estimated, is used in the Common Reporting Format (CRF)146 tables
that accompany this Inventory report submission to the UNFCCC, countries are required to 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 the annual compilation cycle. Data constraints may impact the feasibility of undertaking a quantitative significance
assessment. The United States is continually working to improve upon 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 provided
within the sectoral chapters and category-specific estimate discussions and will be updated in this Annex in the next
Inventory (i.e., 2019 submission).
144 The term "anthropogenic," in this context, refers to greenhouse gas emissions and removals that are a direct result of human
activities or are the result of natural processes that have been affected by human activities (2006 IPCC Guidelines for National
Greenhouse Gas Inventories).
145 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 .
146 See .
A-426 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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.
Energy
CRF Category 1.A.3: ChU and N2O Emissions from Transport Fuel Combustion—Biomass
Emissions from biomass fuel use in domestic aviation (l.A.3.a), motorcycles (l.A.3.b), railways (l.A.3.c), and
domestic navigation (l.A.3.d) are not currently estimated. EPA has determined that the use of biodiesel in rail and navigation
was likely insignificant, and there are not readily available data sources to estimate biodiesel consumption from these
sources.
Emissions from ethanol mixed with gasoline in low blends are included in the on-road gasoline emissions for
motorcycles. If there is any use of high blend ethanol fuel in motorcycles, it is likely insignificant.
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.147 An analysis was conducted based on
the total annual volumes of fuels specified in the long-term agreements. Emissions of N2O 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-CC>2 greenhouse gas emissions from the volume of fuel used would be
16.4 kt CO2 Eq. per year, well below 500 kt CO2 Eq. per year and considered insignificant for the purposes of inventory
reporting under the UNFCCC.
CRF Category 1.A.3.e.i: CO2 Emissions from Liquid Fuels in Other Transportation—Pipeline Transport
Use of liquid fuels to power pipeline pumps is uncommon, but does occur. Data on use of these fuels are currently
unavailable to characterize this activity. Data for fuel used in various activities including pipelines are based on survey data
conducted by the U.S. Energy Information Association (EIA). In 1981, EIA eliminated the requirement to report crude oil
use in pipelines or burned on leases as either distillate or residual fuel oil. It would require a disproportionate level of effort
to change existing surveys to collect this data given this is not a significant activity.
CRF Category 1.A.3.e.ii: CH4 and N2O Emissions from Biomass in Other Transportation—Non-Transportation Mobile
Biomass based fuels used in non-transportation mobile applications are currently not estimated. The use of biofuels
in non-transportation mobile applications is insignificant and there are no readily available data sources to estimate it.
CRF Category 1.A.5.a: CO2 Emissions from Non-Hazardous Industrial Waste Incineration and 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 CO2 emissions from waste
incineration, and in the calculation of emissions and storage from non-energy uses of fossil fuels.
Two additional categories of waste incineration that are not directly included in our calculations are industrial non-
hazardous waste and medical waste incineration. Data are not readily available for these sources.
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 non-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 States148 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
147 See : .
148 RTI 2009. Updated Hospital/Medical/Infectious Waste Incinerator (HMIWI) Inventory Database.
A-427
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medical waste incineration are ~333 kt CO2 Eq. per year, below 500 kt CO2 Eq. per year and considered insignificant for the
purposes of inventory reporting under the UNFCCC.149
CRF Category 1.A.5.a: CH4 and N2O 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, and therefore CH4 and N2O emissions are considered insignificant.
CRF Category 1.B.1.a.1.i: CO2 from Fugitive Emissions from Underground Coal Mining Activities and Post-Mining
Activities
A preliminary analysis by EPA determined that CO2 emissions for active underground coal mining activities are
negligible. Applying a CO2 emission rate as a percentage of CH4 emissions for active coal mines results in a national
emission estimate below 500 kt CO2 Eq. per year or 0.05 percent of national emissions. Future inventories may quantify
these emissions, if it is deemed it will not require a disproportionate amount of effort.
CRF Category 1.B.1.a.1.iii: CO2 from Fugitive Emissions from Abandoned Underground Coal Mines
A preliminary analysis by EPA determined that CO2 emissions for abandoned underground coal mining activities
are negligible. Applying a CO2 emission rate as a percentage of CH4 emissions for abandoned coal mines results in a national
emission estimate below 500 kt CO2 Eq. per year or 0.05 percent of national emissions. Future inventories may quantify
these emissions, if it is deemed it will not require a disproportionate amount of effort.
CRF Category 1.B.1.a.2.i: CO2 from Fugitive Emissions from Surface Coal Mining Activities
A preliminary analysis by EPA determined that CO2 emissions for active surface coal mining activities are
negligible. Applying a CO2 emission rate as a percentage of CH4 emissions for active coal mines results in a national
emission estimate below 500 kt CO2 Eq. per year or 0.05 percent of national emissions. Future inventories may quantify
these emissions, if it is deemed it will not require a disproportionate amount of effort. While CFLi recovery projects were
operating at surface mines from 2006 to 2010, the avoided emissions were so small that they were not included in the
Inventory estimates.
CRF Category 1.B.2.a.3: CO2 from Fugitive Emissions from the Transport of Oil
Based on a preliminary analysis, EPA determined that CO2 emissions from the transport of oil are negligible.
Assuming the same CO2 content as gas from post-separator whole crude and applying this to the CH4 estimates from
transport of oil results in a national emission estimate of 1.2 kt, significantly less than 0.05 percent of national emissions.
CRF Category 1.B.2.a.5: CO2 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.
CRF Category 1.B.2.C.2: N2O from Fugitive Emissions from Venting and Flaring
Data are currently not available to estimate N2O emissions from venting and flaring from oil production, natural
gas production, and combined oil and natural gas production. EPA is assessing whether data may be available to include
this in future inventories.
Industrial Processes and Product Use
CRF Category 2.A.4.a: CO2 Emissions from Process Uses of Carbonates-Ceramics
Data are not currently available to estimate emissions from this source. During the Expert Review Period of the
current Inventory report, 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.
149 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 .
A-428 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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CRF Category 2.A.4.c: CO2 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 Period of the
current Inventory report, EPA sought expert solicitation on data for non-metallurgical magnesium production but has yet to
identify data sources to apply Tier 1 methods.
CRF Category 2.B.4.b: CO2 and N2O Emissions from Glyoxal Production
Current and historical glyoxal production data are not readily available to estimate emissions from this source.
EPA is conducting 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 2017 report).
CRF Category 2.B.4.c: CO2 and N2O Emissions from Glyoxylic Acid Production
Current and historical glyoxylic adic production data are currently not available to estimate emissions from this
source. 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 2017 report).
CRF Category 2.C.1.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." These emissions
are not reported to EPA through the facility-level mandatory Greenhouse Gas Reporting Program (GHGRP).
CRF Category 2.E.2,2.E.3, and 2.E.4: Fluorinated Gas Emissions from Electronics Industry—TFT Flat Panel Displays,
Photovoltaics, and Heat Transfer Fluid
In addition to requiring reporting of emissions from semiconductor manufacturing, EPA's GHGRP requires the
reporting of emissions from other types of electronics manufacturing, including micro-electro-mechanical systems (MEMs),
flat panel displays, and photovoltaic cells. There currently are seven MEMs manufacturers (most of which report emissions
for semiconductor and MEMs manufacturing separately), one photovoltaic cell manufacturer, and no flat panel displays
manufacturing facilities reporting to EPA's GHGRP. Emissions from MEMs and photovoltaic cell manufacturing could be
included in totals in future Inventory reports - currently they are not represented in inventory emissions totals for electronics
manufacturing. These emissions could be estimated for the full time series (including prior to the GHGRP) and for MEMS
and photovoltaic cell manufacturers that are not reporting to the GHGRP; however, at this time the contribution to total
emissions is not significant enough to warrant the development of the methodologies that would be necessary to backcast
these emissions to 1990 and estimate emissions for non-reporters for 2011 through 2016. The emissions reported by facilities
manufacturing MEMs ranged from 0.0045 to 0.0185 MMT CO2 Eq. from 2011 to 2016; they were equivalent to 0.0001
percent to 0.0003 percent of U.S. total emissions in 2011 to 2016. Similarly, emissions from manufacturing of photovoltaic
cells were equivalent to only 0.0001 percent and 0.0002 percent of U.S. total emissions in 2015 and 2016, respectively.
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. A Tier 1 estimate of enteric fermentation CH4 emissions from camels results in a value of approximately
2.8 kt CO2 Eq. per year from 1990 to 2016. Due to limited data availability (no population data is available from the
Agricultural Census), the estimates are based on use of IPCC defaults and population data from Baum, Doug (2010).150
CRF Category 3.A.4: CH4 Emissions from Enteric Fermentation—Poultry
No IPCC method has been developed for determining enteric fermentation CH4 emissions from poultry. Based on
expert input, developing of a country-specific method would require a disproportionate amount of resources given the
magnitude of this source category.
150 The status of the camel in the United States and America. Available online at:
.
A-429
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CRF Category 3.B.4: ChU and N2O Emissions from Manure Management—Camels
Manure management emissions from camels are not estimated because there is no significant population of camels
in the United States.151 A Tier 1 estimate of manure management CH4 and N2O emissions from camels results in values
between approximately 0.14 kt CO2 Eq. per year from 1990 to 2016. This is significantly less than 0.05 percent of national
emissions. Due to limited data availability (i.e., no population data is available from the Agricultural Census), this estimate
is based on population data from Baum, Doug (2010).152
CRF Category 3.F.1.2: CH4 and N2O Emissions from Field Burning of Agricultural Residues—Barley, Oats, Rye,
Potatoes
There is no significant burning of barley, oats, rye, and potatoes in the United States, based on analysis of remote
sensing data, and therefore emissions from field burning of agricultural residues from these crops are not currently estimated.
Additional analyses will be conducted to quantitatively justify emissions reporting as "not estimated" and considered
insignificant following a new analysis of fire products based on LandSat and MODIS imagery. These analyses are underway
and will be completed for the next Inventory.
Land Use, Land-Use Change, and Forestry
CRF Category 4.A.1: Emissions from Rewetted Organic Soils in Forest Land Remaining Forest Land
Emissions from this source will be estimated in future Inventories when data necessary for classifying the area of
rewetted organic soils become available. 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 can be
combined with soil maps to identify areas where organic soils have been drained and then rewetted.
CRF Category 4.A.1: Direct N2O Emissions from N mineralization/immobilization in Forest Land Remaining Forest
Land
Direct N2O emissions from N mineralization/immobilization will be estimated in a future Inventory. They are not
estimated currently because resources have limited EPA's ability to utilize the available data on soil carbon stock changes
on forest lands to estimate these emissions.
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.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. 1, the impact of biomass C is under investigation for agroforestry and will be included in a future Inventory if
significant and activity data can be compiled.
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 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 6-8).
151 Paragraph 37(b) of Decision 24/CP. 19 "Revision of the UNFCCC reporting guidelines on annual inventories for Parties included
in Annex I to the Convention." See .
152 The status of the camel in the United States and America. Available online at:
.
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Waste
CRF Category 5.D.2: N2O Emissions from Wastewater Treatment and Discharge—Industrial Wastewater
Nitrous oxide emissions from stand-alone industrial wastewater treatment are not currently estimated. Per section
6.3.4 of 2006IPCC Guidelines: "The methodology does not include N2O emissions from industrial sources, except for
industrial wastewater that is co-discharged with domestic wastewater into the sewer system. The N2O emissions from
industrial sources are believed to be insignificant compared to emissions from domestic wastewater."
A summary of these exclusions, including the estimated level of emissions where feasible, is included in Table A-266.
A-431
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Table fl-266: Summary of Sources and Sinks Not Included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
CRF Category
Source/Sink Category
Sub-Category
Gas(es)
Estimated
Reason for Exclusion
Number
2016
Emissions (kt
C02 Eq.)
Energy
1.A Fossil Fuel Combustion
1.A.3.a
Transport
Domestic Aviation-Biomass
ChUand N2O
16.4
Data availabil
ty
1.A.3.b
Transport
Motorcycles-Biomass
ChUand N2O
NA
Data availabil
ty
1.A.3.C
Transport
Railways-Biomass
ChUand N2O
NA
Data availabil
ty
1.A.3.d
Transport
Domestic Navigation-Biomass
ChUand N2O
NA
Data availabil
ty
1.A.3.e.i
Other Transportation
Pipeline Transport
CO2, CH4 and N2O
NA
Data availabil
ty
1.A.3.e.ii
Other Transportation
Non-Transportation Mobile-Biomass
ChUand N2O
NA
Data availabil
ty
1.A.5.a
Incineration of Waste
Non-Hazardous Industrial Waste Incineration and
C02
333
Data availabil
ty
Medical Waste Incineration
1.A.5.a
Stationary Fuel Combustion
Biomass in U.S. Territories
ChUand N2O
NA
Data availability
1.B Fugitive Emissions from Fuels
1.B.1.a.1
Underground Mines
Fugitive Emissions from Underground Coal
C02
<500
Emissions negligible
Mining Activities and Post-Mining Activities
1.B.1.a.1.iii
Abandoned Underground
Fugitive Emissions from Abandoned Underground
C02
<500
Emissions negligible
Coal Mines
Coal Mines
1.B.1.a.2
Surface Mines
Fugitive Emissions from Surface Coal Mining
C02
<500
Emissions negligible
Activities and Post-Mining Activities
1.B.2.a.3
Oil and Natural Gas
Fugitive Emissions from the Transport of Oil
C02
1.2
Emissions negligible
1.B.2.a.5
Oil
Distribution of Oil Products
CH4
NA
Lack of emission factor
1.B.2.C.2
Venting and Flaring
Fugitive Emissions from Venting and Flaring from
N2O
NA
data
Data availability
oil production, natural gas production, and
combined oil and natural gas production
Industrial Processes and Product Use
2.A Mineral Industry
2.A.4.a
Other Process Uses of
Ceramics
C02
NA
Data availability
Carbonates
2.A.4.C
Other Process Uses of
Non-metallurgical Magnesium Production
C02
NA
Data availability
Carbonates
2.B. Chemical Industry
2.B.4.b
Glyoxal Production
CO2 and N2O
NA
Data availability
2.B.4.C
Glyoxylic Acid Production
CO2 and CH4
NA
Data availability
2.C. Metal Industry
2.C.1.C
Iron and Steel Production
Direct Reduced Iron (DRI) Production
CH4
NA
Data availability
2.E Electronics Industry
A-432 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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2.E.2
2.E.3
2.E.5
2.G Other
2.G.2
Fluorinated Gas
from Electronics
Fluorinated Gas
from Electronics
Fluorinated Gas
from Electronics
Emissions
Industry
Emissions
Industry
Emissions
Industry
Other Product Manufacture
and Use
TFT Flat Panel Displays
Photovoltaics
MEMs
SF6 and PFCs from Other Product Use
HFCs, PFCs, SF6,
and NF3
HFCs, PFCs, SF6,
and NF3
HFCs, PFCs, SF6,
and NF3
SF6 and PFCs
NA
7
19
NA
Data availability
Data availability
Data availability
Data availability
Agriculture
3.A Livestock
3.A.4
3.A.4
3.B.4
Enteric Fermentation
Enteric Fermentation
Manure Management
3.F Field Burning of Agricultural Residues
3.F.1.2 Field Burning of Agricultural
Residues
Camels
Poultry
Camels
Barley, Oats, Rye, Potatoes
CH4
ch4
ch4, N2O
CH4, N2O
<2.8 No significant camel
population in U.S.
NA 2006IPCC Guidelines
do not provide a method.
<0.14 No significant camel
population in U.S.
NA Data availability
Land Use, Land-Use Change, and Forestry
4.A Forest Land
4.A.1
4.A.1
4.A.2
4.B Cropland
4.B.1
4.B.2
4.C Grassland
4.C.1
4.C.2
4.D Wetlands
4.D.1
4.D.2
Forest Land Remaining
Forest Land
Forest Land Remaining
Forest Land
Land Converted to Forest
Land
Cropland Remaining
Cropland
Grassland Converted to
Cropland
Grassland Remaining
Grassland
Land Converted to Grassland
Wetlands Remaining
Wetlands
Land Converted to Wetlands
Emissions from Rewetted Organic Soils
N mineralization/immobilization
Carbon Stock Change in Organic Soils
Carbon Stock Change in Living Biomass
Carbon Stock Change in Living Biomass
Biomass Burning: Controlled Burning, Wildfires
Biomass Burning: Controlled Burning, Wildfires
Biomass Burning: Controlled Burning, Wildfires
Biomass Burning: Controlled Burning, Wildfires
CH4
N2O
C02
C02
C02
C02
C02
CO2, CH4, and N2O
CO2, CH4, and N2O
NA Data availability
NA Data availability
NA Data availability
NA Data availability
NA Data availability
NA Emissions not estimated
with the Tier 1 method.
NA Emissions not estimated
with the Tier 1 method.
NA Data availability
NA Data availability
A-433
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4.E Settlements
4.E
Settlements
Biomass Burning Settlements
CO2, CH4, and N2O
NA
Data availability
4.E.1
Settlements
Settlements Remaining Settlements
CH4
NA
Data availability
4.E.1
Settlements Remaining
Direct N2O Emissions from N
N2O
NA
Data availability
Settlements
Mineralization/Immobilization (Mineral Soils)
4.E.2
Land Converted to
Direct N2O Emissions from N
N2O
NA
Data availability
Settlements
Mineralization/Immobilization
4.F Other Land
4.F
Biomass Burning
Other Land
CO2, CH4, and N2O
NA
Data availability
Waste
5.D Wastewater T reatment
5.D.2
Industrial Wastewater
Wastewater Treatment and Discharge
N2O
NA
2006IPCC Guidelines
do not provide a method.
NA (Not Available)
A-434 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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ANNEX 6 Additional Information
6.1. Global Warming Potential Values
Global Warming Potential (GWP) is intended as a quantified measure of the globally averaged relative radiative
forcing impacts of a particular greenhouse gas. It is defined as the cumulative radiative forcing-both direct and indirect
effects—integrated over a specific period of time from the emission of a unit mass of gas relative to some reference gas
(IPCC 2007). Carbon dioxide (CO2) was chosen as this reference gas. Direct effects occur when the gas itself is a greenhouse
gas. Indirect radiative forcing occurs when chemical transformations involving the original gas produce a gas or gases that
are greenhouse gases, or when a gas influences other radiatively important processes such as the atmospheric lifetimes of
other gases. The relationship between kilotons (kt) of a gas and million metric tons of CO2 equivalents (MMT CO2 Eq.) can
be expressed as follows:
MMT C02 Eq. = (kt of gas) x (GWP ) x I MMT
1,000 kt
where,
MMT CO2 Eq. = Million metric tons of CO2 equivalent
kt = kilotons (equivalent to a thousand metric tons)
GWP = Global warming potential
MMT = Million metric tons
GWP values allow policy makers to compare the impacts of emissions and reductions of different gases. According
to the IPCC, GWP values typically have an uncertainty of +35 percent, though some GWP values have larger uncertainty
than others, especially those in which lifetimes have not yet been ascertained. In the following decision, the 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-267). 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 I
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 forgiven time horizon "
in table 2.14 of the errata to the contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, based on the effects of greenhouse gases over a 100-year time
horizon...
Greenhouse gases with relatively long atmospheric lifetimes (e.g., CO2, CH4, N2O, HFCs, PFCs, SFg, 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., SO2 products and black carbon) vary spatially, and consequently it is
difficult to quantify their global radiative forcing impacts. GWP values are generally not attributed to these gases that are
short-lived and spatially inhomogeneous in the atmosphere.
153 United Nations Framework Convention on Climate Change; ; 31
January 2014; Report of the Conference of the Parties at its nineteenth session; held in Warsaw from 11 to 23 November 2013;
Addendum; Part two: Action taken by the Conference of the Parties at its nineteenth session; Decision 24/CP. 19; Revision of the
UNFCCC reporting guidelines on annual inventories for Parties included in Annex I to the Convention; p. 2. (UNFCCC 2014)
A-435
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Table fl-267: IPCC flR4 Global Warming Potentials 1GWP1 and Atmospheric Lifetimes [Years] of Gases Used in this Report
Gas
Atmospheric Lifetime
100-year GWPa
20-year GWP
500-year GWP
Carbon dioxide (CO2)
See footnoteb
1
1
1
Methane (Cm)0
12d
25
72
7.6
Nitrous oxide (N2O)
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
CsFs
2,600
8,830
6,310
12,500
C4F10
2,600
8,860
6,330
12,500
C-C4F8
3,200
10,300
7,310
14,700
C5F12
4,100
9,160
6,510
13,300
C6F14
3,200
9,300
6,600
13,300
SFe
3,200
22,800
16,300
32,600
nf3
740
17,200
12,300
20,700
a GWP values used in this report are calculated over 100 year time horizon.
b For a given amount of CO2 emitted, some fraction of the atmospheric increase in concentration is quickly absorbed by the oceans and terrestrial
vegetation, some fraction of the atmospheric increase will only slowly decrease over a number of years, and a small portion of the increase will remain
for many centuries or more.
c The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and stratospheric water vapor.
The indirect effect due to the production of CO2 is not included.
d Methane and N2O 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)
Table A-268 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-268). The effects of
these compounds on radiative forcing are not addressed in this report.
Tahle A-268:100-year Direct Glohal Warming Potentials for Select Ozone Depleting Substances
Gas Direct GWP
CFC-11
4,750
CFC-12
10,900
CFC-113
6,130
HCFC-22
1,810
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
A-436 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 CO2 as a reference
gas; a change in the radiative efficiency of CO2 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 CO2
radiative forcing and an improved CO2 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-269 shows how the GWP
values of the other gases relative to CO2 tend to be larger in AR4 and AR5 because the revised radiative forcing of CO2 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 2016). As such, GWP comparisons throughout this chapter are presented relative
to AR4 GWPs.
A-437
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Table fl-269: Comparison of GWP values and Lifetimes Used in the SflR,flR4, andflR5
Lifetime (years)
GWP (100 year)
Difference in GWP (Relative to AR4)
AR5 with
AR5 with
AR5 with
Gas
SAR
AR4
AR5
SAR
AR4
AR5a
feedbacks'1
SAR
SAR (%)
AR5a
AR5 (%)
feedbacks'1 feedbacks'1 (%)
Carbon dioxide (CO2)
C
d
d
1
1
1
1
NC
NC
NC
NC
NC
NC
Methane (CH4)e
12±3
8.7/12'
12.4
21
25
28
34
(4)
(16%)
3
12%
9
36%
Nitrous oxide (N2O)
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
1032
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
SFe
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%
C3F8
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-C4F8
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)%
nf3
NA
740
500
NA
17,200
16,100
17,885
NA
NA
(1,100)
(6%)
685
4%
+ Does not exceed 0.05 or 0.05 percent.
NC (No Change)
NA (Not Applicable)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report.
b The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02 gases in order to be consistent with the approach used in calculating the CO2 lifetime. Additionally, the AR5
reported separate values for fossil versus biogenic methane in order to account for the CO2 oxidation product.
c For a given amount of CO2 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.
d No single lifetime can be determined for CO2 (see IPCC 2007).
e The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and stratospheric water vapor. Additionally, the AR5 reported separate values for fossil versus
biogenic methane in order to account for the CO2 oxidation product..
f Methane and N2O have chemical feedback systems that can alter the length of the atmospheric response, in these cases, global mean residence time is given first, followed by perturbation time.
Note: Parentheses indicate negative values. Source: IPCC (2013), IPCC (2007), IPCC (1996).
A-438 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
The choice of GWP values between the SAR, AR4, and AR5 with or without climate-carbon feedbacks has an
impact on both the overall emissions estimated by the Inventory, as well as the trend in emissions over time. To summarize,
Table A-270 shows the overall trend in U.S. greenhouse gas emissions, by gas, from 1990 through 2016 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 2016.
Table A-270: Effects on U.S. Greenhouse Gas Emissions Using SJ
AR.AR4, and AR5 GWP values (MMT CO Eq.l
Difference in Emissions Between 1990 and
Gas
2016 (Relative to 1990)
Revisions to Annual Emission Estimates (Relative to AR4)
SAR
AR5a
AR5b
SAR
AR5a
AR5b
SAR
AR4
AR5a
AR5b
1990
2016
C02
189.6
189.6
189.6
189.6
NC
NC
NC
NC
NC
NC
cm
(102.9)
(122.5)
(137.2)
(166.6)
(124.8)
93.6
280.8
(105.2)
78.9
236.7
n2o
15.4
14.8
13.2
14.8
14.3
(39.3)
NC
14.9
(40.9)
NC
HFCs, PFCs, SFe,
and NF3
62.6
73.8
73.1
90.5
(11.9)
(9.0)
1.3
(23.2)
(9.7)
17.9
Total
164.6
155.7
138.6
128.2
(122.4)
45.3
282.0
(113.5)
28.3
254.6
Percent Change
2.6%
2.4%
2.2%
1.9%
-1.9%
0.7%
4.4%
-1.7%
0.4%
3.9%
NC (No Change)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report.
b The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02 gases in order to be consistent with the
approach used in calculating the CO2 lifetime. Additionally, the AR5 reported separate values for fossil versus biogenic methane in order to account for
the CO2 oxidation product.
Note: 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 2016 are 6,397.8 MMT CO2 Eq., as compared to the official emission estimate of 6,511.3 MMT CO2 Eq. using
AR4 GWP values (i.e., the use of SAR GWPs results in a 1.7 percent decrease relative to emissions estimated using AR4
GWPs). Table A-271 provides a detailed summary of U.S. greenhouse gas emissions and sinks for 1990 through 2016, 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-272 summarizes the
resulting change in emissions from using SAR GWP values relative to emissions using AR4 values for 1990 through 2016,
including the percent change for 2016.
Table A-271: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks using the SflR GWP values [MMT CO; EqJ
Gas/Source
1990
2005
2012
2013
2014
2015
2016
C02
5,121.3
6,132.0
5,366.7
5,519.6
5,568.8
5,420.8
5,310.9
Fossil Fuel Combustion
4,740.3
5,746.9
5,024.4
5,156.9
5,200.3
5,049.3
4,966.0
Electic Power
1,820.8
2,400.9
2,022.2
2,038.1
2,038.0
1,900.7
1,809.3
Transportation
1,467.6
1,855.8
1,661.9
1,677.6
1,717.1
1,735.5
1,782.6
Industrial
858.8
855.7
812.9
843.3
824.9
809.5
809.1
Residential
338.3
357.8
282.5
329.7
345.3
316.8
292.5
Commercial
227.2
227.0
201.3
225.7
233.6
245.4
231.3
U.S. Territories
27.6
49.7
43.5
42.5
41.4
41.4
41.4
Non-Energy Use of Fuels
119.5
138.9
108.0
123.5
118.9
125.6
112.2
Iron and Steel Production &
Metallurgical Coke Production
101.6
68.2
55.6
53.5
58.4
47.8
42.3
Cement Production
33.5
46.2
35.3
36.4
39.4
39.9
39.4
Petrochemical Production
21.2
26.8
26.5
26.4
26.5
28.1
28.1
Natural Gas Systems
29.8
22.5
23.3
24.8
25.3
24.9
25.5
Petroleum Systems
7.7
11.7
19.3
22.6
26.3
28.8
22.8
Lime Production
11.7
14.6
13.8
14.0
14.2
13.3
12.9
Ammonia Production
13.0
9.2
9.4
10.0
9.6
10.9
12.2
Other Process Uses of Carbonates
6.3
7.6
9.1
11.5
13.0
12.3
11.0
Incineration of Waste
8.0
12.5
10.4
10.4
10.6
10.7
10.7
Urea Fertilization
2.4
3.5
4.3
4.4
4.5
4.9
5.1
Carbon Dioxide Consumption
1.5
1.4
4.0
4.2
4.5
4.5
4.5
Urea Consumption for Non-Agricultural
Purposes
3.8
3.7
4.4
4.1
1.5
4.2
4.0
A-439
-------
Liming
4.7
4.3
6.0
3.9
3.6
3.8
3.9
Ferroalloy Production
2.2
1.4
1.9
1.8
1.9
2.0
1.8
Soda Ash Production
1.4
1.7
1.7
1.7
1.7
1.7
1.7
Titanium Dioxide Production
1.2
1.8
1.5
1.7
1.7
1.6
1.6
Aluminum Production
6.8
4.1
3.4
3.3
2.8
2.8
1.3
Glass Production
1.5
1.9
1.2
1.3
1.3
1.3
1.2
Phosphoric Acid Production
1.5
1.3
1.1
1.1
1.0
1.0
1.0
Zinc Production
0.6
1.0
1.5
1.4
1.0
0.9
0.9
Lead Production
0.5
0.6
0.5
0.5
0.5
0.5
0.5
Silicon 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
287.7
316.4
324.3
310.4
309.3
International Bunker Fuelsb
103.5
113.1
105.8
99.8
103.4
110.9
116.6
CH4c
655.2
578.4
556.5
556.5
557.7
558.9
552.2
Enteric Fermentation
137.9
141.8
140.1
139.0
137.9
139.9
142.9
Natural Gas Systems
163.9
142.1
134.1
137.6
138.0
139.7
137.4
Landfills
150.8
111.5
98.3
95.1
94.7
93.8
90.4
Manure Management
31.2
47.3
55.1
53.1
52.8
55.7
56.9
Coal Mining
81.1
53.9
55.8
54.3
54.2
51.4
45.2
Petroleum Systems
33.4
27.0
27.4
30.7
32.4
32.0
32.4
Wastewater Treatment
13.2
13.3
12.7
12.5
12.6
12.7
12.5
Rice Cultivation
13.5
14.0
9.5
9.7
10.7
10.3
11.5
Stationary Combustion
7.2
6.6
6.2
7.4
7.5
6.7
6.2
Abandoned Oil and Gas Wells
5.5
5.8
5.9
5.9
5.9
6.0
6.0
Abandoned Underground Coal Mines
6.0
5.5
5.2
5.2
5.3
5.4
5.6
Mobile Combustion
10.7
7.9
4.3
3.9
3.6
3.2
3.1
Composting
0.3
1.6
1.6
1.7
1.8
1.8
1.8
Field Burning of Agricultural Residues
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Petrochemical Production
0.2
0.1
0.1
0.1
0.1
0.2
0.2
Ferroalloy Production
+
+
+
+
+
+
+
Silicon Carbide Production and
Consumption
+
+
+
+
+
+
+
Iron and Steel Production &
Metallurgical Coke Production
+
+
+
+
+
+
+
Incineration of Waste
+
+
+
+
+
+
+
International Bunker Fuels'1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
N2Oc
369.0
372.2
349.3
377.9
375.7
394.9
384.4
Agricultural Soil Management
260.5
263.7
257.9
287.7
285.0
306.9
295.0
Stationary Combustion
11.5
18.2
17.6
19.5
19.8
18.8
19.3
Mobile Combustion
43.4
40.4
25.2
23.4
21.5
20.1
19.1
Manure Management
14.6
17.2
18.2
18.2
18.2
18.4
18.9
Nitric Acid Production
12.6
11.8
10.9
11.1
11.4
12.0
10.6
Adipic Acid Production
15.8
7.4
5.8
4.1
5.7
4.4
7.3
Wastewater Treatment
3.5
4.6
4.8
4.9
5.0
5.0
5.2
N2O from Product Uses
4.4
4.4
4.4
4.4
4.4
4.4
4.4
Caprolactam, Glyoxal, and Glyoxylic
Acid Production
1.7
2.2
2.1
2.1
2.1
2.1
2.1
Composting
0.4
1.7
1.8
1.9
1.9
2.0
2.0
Incineration of Waste
0.5
0.4
0.3
0.3
0.3
0.3
0.3
Semiconductor Manufacture
+
0.1
0.2
0.2
0.2
0.2
0.2
Field Burning of Agricultural Residues
0.1
0.1
0.1
0.1
0.1
0.1
0.1
International Bunker Fuels'1
0.9
1.0
1.0
0.9
0.9
1.0
1.0
HFCs
36.9
107.8
131.0
131.1
135.6
139.0
140.2
Substitution of Ozone Depleting
Substances'1
0.3
91.8
126.5
127.7
131.3
135.3
137.6
A-440 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
HCFC-22 Production
36.4
15.8
4.3
3.2
4.0
3.4
2.2
Semiconductor Manufacture
0.2
0.2
0.2
0.1
0.2
0.3
0.3
Magnesium Production and Processing
0.0
0.0
+
0.1
0.1
0.1
0.1
PFCs
20.6
5.6
4.9
4.8
4.7
4.2
3.6
Semiconductor Manufacture
2.2
2.6
2.4
2.3
2.5
2.5
2.4
Aluminum Production
18.4
3.0
2.5
2.5
2.1
1.7
1.1
Substitution of Ozone Depleting
Substances
0.0
+
+
+
+
+
+
SFe
30.2
12.3
7.0
6.6
6.7
6.2
6.5
Electrical Transmission and Distribution
24.2
8.7
4.9
4.7
4.9
4.5
4.5
Magnesium Production and Processing
5.4
2.9
1.7
1.5
1.0
0.9
1.1
Semiconductor Manufacture
0.5
0.7
0.4
0.4
0.8
0.8
0.9
NFs
NA
NA
NA
NA
NA
NA
NA
Semiconductor Manufacture
NA
NA
NA
NA
NA
NA
NA
Total
6,233.2
7,208.3
6,415.4
6,596.5
6,649.2
6,524.0
6,397.8
LULUCF Emissions0
9.7
21.3
24.1
17.8
18.2
35.2
35.1
LULUCF CH4 Emissions
5.6
11.1
12.6
9.2
9.4
18.8
18.8
LULUCF N2O Emissions
4.1
10.1
11.5
8.6
00
CO
16.4
16.3
LULUCF Carbon Stock Changee
(830.2)
(754.2)
(779.5)
(755.0)
(760.0)
(733.4)
(754.9)
LULUCF Sector Net Total'
(820.5)
(732.9)
(755.4)
(737.2)
(741.8)
(698.1)
(719.7)
Net Emissions (Sources and Sinks)
5,412.7
6,475.4
5,660.0
5,859.4
5,907.4
5,825.9
5,678.1
Notes: Total emissions presented without LULUCF. Net emissions presented with LULUCF.
+ Does not exceed 0.05 MMT CO2 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 ChUand N2O are reported separately from gross emissions totals. LULUCF emissions include the CH4 and N2O 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 N2O 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 to Grassland, Wetlands Remaining
Wetlands, Land Converted to Wetlands, Settlements Remaining Settlements, and Land Converted to Settlements.
' The LULUCF Sector Net Total is the net sum of all CH4 and N2O emissions to the atmosphere plus net carbon stock changes.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Table fl-272: Change in U.S. Greenhouse Gas Emissions Using SflR GWP values relative to flR4 GWP values [MMT CO; Eg.]
Gas/Source
1990
2005
2012
2013
2014
2015
2016
Percent
Change
in 2016
CO2
NC
NC
NC
NC
NC
NC
NC
NC
CH4
(124.8)
(110.2)
(106.0)
(106.0)
(106.2)
(106.5)
(105.2)
(16%)
Enteric Fermentation
(26.3)
(27.0)
(26.7)
(26.5)
(26.3)
(26.6)
(27.2)
(16%)
Natural Gas Systems
(31.2)
(27.1)
(25.5)
(26.2)
(26.3)
(26.6)
(26.2)
(16%)
Landfills
(28.7)
(21.2)
(18.7)
(18.1)
(18.0)
(17.9)
(17.2)
(16%)
Manure Management
(5.9)
(9.0)
(10.5)
(10.1)
(10.1)
(10.6)
(10.8)
(16%)
Coal Mining
(15.4)
(10.3)
(10.6)
(10.3)
(10.3)
(9.8)
(8.6)
(16%)
Petroleum Systems
(6.4)
(5.1)
(5.2)
(5.9)
(6.2)
(6.1)
(6.2)
(16%)
Wastewater Treatment
(2.5)
(2.5)
(2.4)
(2.4)
(2.4)
(2.4)
(2.4)
(16%)
Rice Cultivation
(2.6)
(2.7)
(1.8)
(1.8)
(2.0)
(2.0)
(2.2)
(16%)
Stationary Combustion
(1.4)
(1.3)
(1.2)
(1.4)
(1.4)
(1.3)
(1.2)
(16%)
Abandoned Oil and Gas Wells
(1.0)
(1.1)
(1.1)
(1.1)
(1.1)
(1.1)
(1.1)
(16%)
Abandoned Underground Coal Mines
(1.2)
(1.1)
(1.0)
(1.0)
(1.0)
(1.0)
(1.1)
(16%)
Mobile Combustion
(2.0)
(1.5)
(0.8)
(0.8)
(0.7)
(0.6)
(0.6)
(16%)
Composting
(0.1)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
(16%)
Field Burning of Agricultural
Residues
M
M
M
M
M
M
M
(16%)
Petrochemical Production
M
M
M
M
M
M
M
(16%)
A-441
-------
Ferroalloy Production
M
M
M
M
M
M
M
(16%)
Silicon Carbide Production and
Consumption
M
M
M
M
M
M
M
(16%)
Iron and Steel Production &
Metallurgical Coke Production
M
M
M
M
M
M
M
(16%)
Incineration of Waste
M
M
M
M
M
M
M
(16%)
International Bunker Fuelsa
M
M
M
M
M
M
M
(16%)
N2O
14.3
14.4
13.5
14.6
14.5
15.3
14.9
4%
Agricultural Soil Management
10.1
10.2
10.0
11.1
11.0
11.9
11.4
4%
Stationary Combustion
0.4
0.7
0.7
0.8
0.8
0.7
0.7
4%
Mobile Combustion
1.7
1.6
1.0
0.9
0.8
0.8
0.7
4%
Manure Management
0.6
0.7
0.7
0.7
0.7
0.7
0.7
4%
Nitric Acid Production
0.5
0.5
0.4
0.4
0.4
0.5
0.4
4%
Adipic Acid Production
0.6
0.3
0.2
0.2
0.2
0.2
0.3
4%
Wastewater Treatment
0.1
0.2
0.2
0.2
0.2
0.2
0.2
4%
N2O from Product Uses
0.2
0.2
0.2
0.2
0.2
0.2
0.2
4%
Caprolactam, Glyoxal, and Glyoxylic
Acid Production
0.1
0.1
0.1
0.1
0.1
0.1
0.1
4%
Composting
+
0.1
0.1
0.1
0.1
0.1
0.1
4%
Incineration of Waste
+
+
+
+
+
+
+
4%
Semiconductor Manufacture
+
+
+
+
+
+
+
4%
Field Burning of Agricultural
Residues
+
+
+
+
+
+
+
International Bunker Fuelsa
+
+
+
+
+
+
+
4%
HFCs
(9.7)
(15.2)
(19.6)
(20.0)
(21.1)
(21.8)
(22.1)
(14%)
Substitution of Ozone Depleting
Substancesb
+
(10.9)
(18.4)
(19.1)
(20.0)
(20.8)
(21.5)
(13%)
HCFC-22 Production
(9.7)
(4.2)
(1.1)
(0.9)
(1.1)
(0.9)
(0.6)
(21%)
Semiconductor Manufacture
M
M
M
M
(0.1)
(0.1)
(0.1)
(21%)
Magnesium Production and
Processing
0.0
0.0
M
M
M
M
M
(9%)
PFCs
(3.6)
(1.1)
(1.0)
(1.0)
(0.9)
(0.9)
(0.7)
(17%)
Semiconductor Manufacture
M
(0.7)
(0.6)
(0.5)
(0.6)
(0.6)
(0.5)
(18%)
Aluminum Production
(3.0)
(0.5)
(0.4)
(0.4)
(0.4)
(0.3)
(0.2)
(16%)
Substitution of Ozone Depleting
Substances
0.0
M
M
M
M
M
M
(12%)
SFe
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
+
+
+
5%
Semiconductor Manufacture
+
+
+
+
+
+
+
5%
NFs
NA
NA
NA
NA
NA
NA
NA
NA
Semiconductor Manufacture
NA
NA
NA
NA
NA
NA
NA
NA
Total
(122.4)
(112.0)
(113.4)
(112.6)
(113.9)
(114.1)
(113.5)
(1.7%)
NC (No Change)
NA (Not Applicable)
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
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-273 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 (15.0 percent decrease in 2016 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 CO2 or a mix of gases, which moderated the effect of the changes.
A-442 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-273: Comparison of Emissions by Sector using IPCC flR4 and SflB GWP Values [MMT CO; Eg.)
Sector
1990
2005
2012
2013
2014
2015
2016
Energy
AR4 GWP, Used In Inventory
5,325.1
6,285.2
5,511.2
5,671.4
5,715.4
5,567.8
5,455.2
SAR GWP
5,268.6
6,240.1
5,467.3
5,626.4
5,670.0
5,522.8
5,411.8
Difference (%)
(1.1%)
(0.7%)
(0.8%)
(0.8%)
(0.8%)
(0.8%)
(0.8%)
Industrial Processes and
Product Use
AR4 GWP, Used In Inventory
342.0
358.6
357.4
357.9
371.4
367.8
362.1
SAR GWP
331.4
343.4
337.5
337.6
350.1
345.8
339.8
Difference (%)
(3.1%)
(4.2%)
(5.6%)
(5.7%)
(5.7%)
(6.0%)
(6.1%)
Agriculture
AR4 GWP, Used In Inventory
489.2
520.0
519.8
543.1
539.8
566.9
562.6
SAR GWP
465.0
492.2
491.4
516.5
513.2
540.2
534.5
Difference (%)
(4.9%)
(5.4%)
(5.5%)
(4.9%)
(4.9%)
(4.7%)
(5.0%)
LULUCF
AR4 GWP, Used In Inventory
(819.6)
(731.1)
(753.5)
(735.8)
(740.4)
(695.2)
(716.8)
SAR GWP
(820.5)
(732.9)
(755.4)
(737.2)
(741.8)
(698.1)
(719.7)
Difference (%)
0.1%
0.2%
0.3%
0.2%
0.2%
0.4%
0.4%
Waste
AR4 GWP, Used In Inventory
199.3
156.4
140.4
136.7
136.5
135.6
131.5
SAR GWP
168.2
132.6
119.2
116.1
116.0
115.3
111.8
Difference (%)
(15.6%)
(15.2%)
(15.1%)
(15.0%)
(15.0%)
(15.0%)
(15.0%)
Net Emissions (Sources and
Sinks)
AR4 GWP, Used In Inventory
5,536.0
6,589.1
5,775.3
5,973.3
6,022.8
5,942.9
5,794.5
SAR GWP
5,412.7
6,475.4
5,660.0
5,859.4
5,907.4
5,825.9
5,678.1
Difference (%)
(2.2%)
(1.7%)
(2.0%)
(1.9%)
(1.9%)
(2.0%)
(2.0%)
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Further, Table A-274 and Table A-275 show the comparison of emission estimates using AR5 GWP values relative
to AR4 GWP values without climate-carbon feedbacks for the non-CC>2 gases, on an emissions and percent change basis.
Table A-276 and Table A-277 show the comparison of emission estimates using AR5 GWP values with climate-carbon
154
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 CO2 and N2O emissions; however, it results in an increase in emissions of CH4, SF6, and
NF3 relative to AR4 GWP values, and has mixed impacts on emissions of other gases. Overall, these comparisons of AR4
and AR5 GWP values do not have a significant effect on calculated U.S. emissions, resulting in an increase in emissions of
less than 1 percent using AR5 GWP values, or approximately 4 percent when using AR5 GWP values with climate-carbon
feedbacks. As with the comparison of SAR and AR4 GWP values presented above, the percent change in emissions is equal
to the percent change in the GWP for each gas; however, in cases where multiple gases are emitted in varying amounts the
percent change is variable over the years, such as with Substitution of Ozone Depleting Substances.
154 The IPCC AR5 report provides additional information on emission metrics. See .
A-443
-------
Table A-274: Change in U.S. Greenhouse Gas Emissions Using AR5a without Climate-Carbon Feedbacks Relative to AR4 GWP
Values [MMTCO Eg.)
Gas
1990
2005
2012
2013
2014
2015
2016
C02
NC
NC
NC
NC
NC
NC
NC
cm
93.6
82.6
79.5
79.5
79.7
79.8
78.9
n2o
(39.3)
(39.6)
(37.2)
(40.2)
(40.0)
(42.0)
(40.9)
HFCs
(7.5)
(10.6)
(9.4)
(9.0)
(9.3)
(9.5)
(9.5)
PFCs
(2.4)
(0.6)
(0.6)
(0.6)
(0.5)
(0.5)
(0.4)
SFe
0.9
0.4
0.2
0.2
0.2
0.2
0.2
nf3
M
M
M
M
M
M
M
Total
45.3
32.1
32.5
29.9
30.0
27.9
28.3
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
NC (No Change)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report. The AR5 report has also calculated GWP
values (shown in Table A-276) where climate-carbon feedbacks have been included for the non-C02 gases in order to be consistent with the approach
used in calculating the CO2 lifetime. Additionally, the AR5 reported separate values for fossil versus biogenic methane in order to account for the CO2
oxidation product.
Notes: Total emissions presented without LULUCF. Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table A-275: Change in U.S. Greenhouse Gas Emissions Using AR5a without Climate-Carbon Feedbacks Relative to AR4 GWP
Values (Percent)
Gas/Source
1990
2005
2012
2013
2014
2015
2016
CO2
NC
NC
NC
NC
NC
NC
NC
CH4
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
N2O
(11.1%)
(11.1%)
(11.1%)
(11.1%)
(11.1%)
(11.1%)
(11.1%)
SFe
3.1%
3.1%
3.1%
3.1%
3.1%
3.1%
3.1%
NFs
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
HFCs
(16.0%)
(8.6%)
(6.2%)
(6.0%)
(6.0%)
(5.9%)
(5.8%)
Substitution of Ozone
Depleting Substances
11.3%
(7.1%)
(5.8%)
(5.7%)
(5.6%)
(5.6%)
(5.6%)
HCFC-22 Production"
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.2%)
Semiconductor Manufacture1
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.1%)
(16.2%)
Magnesium Production and
Processing11
0.0%
0.0%
(9.1%)
(9.1%)
(9.1%)
(9.1%)
(9.1%)
PFCs
(10.0%)
(9.6%)
(9.5%)
(9.6%)
(9.5%)
(9.5%)
(9.4%)
Semiconductor Manufacture1
(9.4%)
(9.2%)
(9.1%)
(9.2%)
(9.2%)
(9.2%)
(9.2%)
Aluminum Production6
(10.1%)
(10.1%)
(10.0%)
(10.0%)
(10.0%)
(10.0%)
(9.9%)
Substitution of Ozone
Depleting Substances'^
0.0%
(10.3%)
(10.3%)
(10.3%)
(10.3%)
(10.3%)
(10.3%)
Total
0.7%
0.4%
0.5%
0.4%
0.4%
0.4%
0.4%
NC (No Change)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report. The AR5 report has also calculated GWP
values (shown in Table A-277) where climate-carbon feedbacks have been included for the non-C02 gases in order to be consistent with the approach
used in calculating the CO2 lifetime. Additionally, the AR5 reported separate values for fossil versus biogenic methane in order to account for the CO2
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.
A-444 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-276: Change in U.S. Greenhouse Gas Emissions Using AR5 with Glimate-Garbon Feedbacks3 Relative to AR4 GWP
Values [MMTCO Eg.)
Gas
1990
2005
2012
2013
2014
2015
2016
C02
NC
NC
NC
NC
NC
NC
NC
cm
280.8
247.9
238.5
238.5
239.0
239.5
236.7
n2o
NC
NC
NC
NC
NC
NC
NC
HFCs
(2.9)
8.9
15.2
15.7
16.1
16.6
17.0
PFCs
M
+
+
+
+
+
+
SFe
4.2
1.7
1.0
0.9
0.9
0.9
0.9
nf3
+
+
+
+
+
+
+
Total
282.0
258.5
254.7
255.1
256.1
257.1
254.6
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
NC (No Change)
a The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02 gases in order to be consistent with the
approach used in calculating the CO2 lifetime. Additionally, the AR5 reported separate values for fossil versus biogenic methane in order to account for
the CO2 oxidation product.
Notes: Total emissions presented without LULUCF. Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table fl-277: Change in U.S. Greenhouse Gas Emissions Using AR5 with Climate-Carbon Feedbacks3 Relative to AR4 GWP
Values (Percent)
Gas/Source
1990
2005
2012
2013
2014
2015
2016
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
SFe
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
NFs
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
HFCs
(6.1%)
7.2%
10.1%
10.4%
10.3%
10.3%
10.5%
Substitution of Ozone
Depleting Substances
34.7%
9.9%
10.8%
10.9%
10.9%
10.8%
10.8%
HCFC-22 Production"
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
Semiconductor
Manufacture0
(6.4%)
(6.3%)
(6.3%)
(6.3%)
(6.3%)
(6.3%)
(6.3%)
Magnesium Production and
Processing"1
0.0%
0.0%
8.3%
8.3%
8.3%
8.3%
8.3%
PFCs
(0.2%)
0.3%
0.4%
0.3%
0.4%
0.4%
0.5%
Semiconductor
Manufacture0
0.6%
0.9%
0.9%
0.9%
0.8%
0.8%
0.7%
Aluminum Production6
(0.3%)
(0.3%)
(0.2%)
(0.2%)
(0.1%)
(0.1%)
+%
Substitution of Ozone
Depleting Substancesd>f
0.0%
(0.6%)
(0.6%)
(0.6%)
(0.6%)
(0.6%)
(0.6%)
Total
4.4%
3.5%
3.9%
3.8%
3.8%
3.9%
3.9%
NC (No Change)
+ Does not exceed 0.05 percent.
a The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02 gases in order to be consistent with the
approach used in calculating the CO2 lifetime. Additionally, the AR5 reported separate values for fossil versus biogenic methane in order to account for
the CO2 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-445
-------
6.2. Ozone Depleting Substance Emissions
Ozone is present in both the stratosphere,155 where it shields the earth from harmful levels of ultraviolet radiation,
and at lower concentrations in the troposphere, 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. 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, 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-278.
Table fl-278: Emissions of Ozone Depleting Substances tktl
Compound
1990
2005
2012
2013
2014
2015
2016
Class I
CFC-11
29
12
24
24
24
25
25
CFC-12
132
22
5
5
4
4
3
CFC-113
59
17
2
0
0
0
0
CFC-114
4
1
+
+
0
0
0
CFC-115
8
2
+
+
+
+
+
155 The stratosphere is the layer from the top of the troposphere up to about 50 kilometers. Approximately 90 percent of atmospheric
ozone is within the stratosphere. The greatest concentration of ozone occurs in the middle of the stratosphere, in a region commonly
called the ozone layer.
156 The troposphere is the layer from the ground up to about 11 kilometers near the poles and 16 kilometers in equatorial regions
(i.e., the lowest layer of the atmosphere, where humans live). It contains roughly 80 percent of the mass of all gases in the
atmosphere and is the site for weather processes including most of the water vapor and clouds.
157 Substances with an ozone depletion potential of 0.2 or greater are designated as Class I. All other designated substances that
deplete stratospheric ozone but which have an ODP of less than 0.2 are Class II.
158 Older refrigeration and air-conditioning equipment, fire extinguishing systems, and foam products blown with CFCs/HCFCs
may still contain Class I ODS.
A-446 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Carbon Tetrachloride
4
0
0
0
0
0
0
Methyl Chloroform
223
0
0
0
0
0
0
Halon-1211
2
1
1
1
1
1
+
Halon-1301
2
+
+
+
+
+
+
Class II
HCFC-22
49
82
70
67
63
59
54
HCFC-123
0
1
1
1
1
1
1
HCFC-124
0
2
1
1
1
+
+
HCFC-141b
1
4
9
10
10
9
9
HCFC-142b
1
4
1
1
2
2
3
HCFC-225ca/cb
0
+
+
+
+
+
+
+ Does not exceed 0.5 kt.
Methodology and Data Sources
Emissions of ozone depleting substances were estimated using the EPA's Vintaging Model. The model, named for
its method of tracking the emissions of annual "vintages" of new equipment that enter into service, is a "bottom-up" model.
It models the consumption of chemicals based on estimates of the quantity of equipment or products sold, serviced, and
retired each year, and the amount of the chemical required to manufacture and/or maintain the equipment. The Vintaging
Model makes use of this market information to build an inventory of the in-use stocks of the equipment in each of the end-
uses. Emissions are estimated by applying annual leak rates, service emission rates, and disposal emission rates to each
population of equipment. By aggregating the emission and consumption output from the different end-uses, the model
produces estimates of total annual use and emissions of each chemical. Please see Annex 3.9, Methodology for Estimating
HFC and PFC Emissions from Substitution of Ozone Depleting Substances, of this Inventory for a more detailed discussion
of the Vintaging Model.
Uncertainties
Uncertainties exist with regard to the levels of chemical production, equipment sales, equipment characteristics,
and end-use emissions profiles that are used by these models. Please see the Substitution of Ozone Depleting Substances
section of this report for a more detailed description of the uncertainties that exist in the Vintaging Model.
A-447
-------
6.3. Sulfur Dioxide Emissions
Sulfur dioxide (SO2), emitted into the atmosphere through natural and anthropogenic processes, affects the Earth's
radiative budget through photochemical transformation into sulfate aerosols that can (1) scatter sunlight back to space,
thereby reducing the radiation reaching the Earth's surface; (2) affect cloud formation; and (3) affect atmospheric chemical
composition (e.g., stratospheric ozone, by providing surfaces for heterogeneous chemical reactions). The overall effect of
SCVderived aerosols on radiative forcing is believed to be negative (IPCC 2007). However, because SO2 is short-lived and
unevenly distributed through the atmosphere, its radiative forcing impacts are highly uncertain. Sulfur dioxide emissions
have been provided below in Table A-279.
The major source of SO2 emissions in the United States is the burning of sulfur containing fuels, mainly coal. Metal
smelting and other industrial processes also release significant quantities of SO2. The largest contributor to U.S. emissions
of SO2 is electricity generation, accounting for 43.8 percent of total SO2 emissions in 2016 (see Table A-280); coal
combustion accounted for approximately 92.0 percent of that total. The second largest source was industrial fuel combustion,
which produced 20.2 percent of 2016 SO2 emissions. Overall, SO2 emissions in the United States decreased by 88.3 percent
from 1990 to 2016. The majority of this decline came from reductions from electricity generation, primarily due to increased
consumption of low sulfur coal from surface mines in western states.
Sulfur dioxide is important for reasons other than its effect on radiative forcing. It is a major contributor to the
formation of urban smog and acid rain. As a contributor to urban smog, high concentrations of SO2 can cause significant
increases in acute and chronic respiratory diseases. In addition, once SO2 is emitted, it is chemically transformed in the
atmosphere and returns to earth as the primary contributor to acid deposition, or acid rain. Acid rain has been found to
accelerate the decay of building materials and paints, cause the acidification of lakes and streams, and damage trees. As a
result of these harmful effects, the United States has regulated the emissions of SO2 under the Clean Air Act. The EPA has
also developed a strategy to control these emissions via four programs: (1) the National Ambient Air Quality Standards
program,159 (2) New Source Performance Standards,160 (3) the New Source Review/Prevention of Significant Deterioration
Program,161 and (4) the Sulfur Dioxide Allowance Program.162
Table A-279: SO2 Emissions (kt)
Sector/Source
19901
20051
2012
2013
2014
2015
2016
Energy 19,628
Stationary Sources 18,4071
Oil and Gas Activities 3901
Mobile Sources 7931
Waste Combustion 381
Industrial Processes and
Product Use 1,3071
Other Industrial Processes 3621
Miscellaneous3 111
Chemical and Allied Product
Manufacturing 2691
Metals Processing 659 (
Storage and Transport 61
Solvent Use o|
Degreasing 0|
Graphic Arts 01
Dry Cleaning NAJ
Surface Coating 0|
Other Industrial 01
Nonindustrial NAj
Agriculture NA|
Agricultural Burning NA|
Waste +1
12,364 J
11,541
180|
6"l 91
251
8311
3271
1141
2281
1581
°|
o{
o|
°l
+ {
naI
MA I
NA|
l!
5,271
5,006
108
142
15
604
171
179
115
131
0
0
0
0
+
NA
NA
NA
+
5,270
5,005
108
142
15
604
171
179
115
131
0
0
0
0
+
NA
NA
NA
+
3,859
3,640
93
95
32
496
156
135
104
98
0
0
0
0
+
NA
NA
NA
1
2,950
2,756
93
70
32
496
156
135
104
98
0
0
0
0
+
NA
NA
NA
1
1,959
1,790
93
44
32
496
156
135
104
98
0
0
0
0
+
NA
NA
NA
1
159 [42 U.S.C § 7409, CAA § 109]
160 [42 U.S.C §7411, CAA § 111]
161 [42 U.S.C § 7473, CAA § 163]
162 [42 U.S.C § 7651, CAA § 401]
A-448 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Landfills
Wastewater Treatment
Miscellaneous3
Total
+ 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 (2016) and disaggregated based on EPA (2003).
Table fl-280: SO; Emissions from Electricity Generation tktl
Fuel Type
1990
2005 111
2012
2013
2014
2015
2016
Coal
13,808
8,680 ill
3,858
3,856
2,690
1,877
989
Oil
580
458 H
203
203
142
99
52
Gas
1
174 Ih
77
77
54
38
20
Internal Combustion
45
57 H
25
25
18
12
7
Other
NA
71 H
31
31
22
15
8
Total
14,433
9.439 Hi
4,195
4,194
2,925
2,041
1,075
NA (Not Applicable)
Note: Totals may not sum due to independent rounding.
Source: Data taken from EPA (2016) and disaggregated based on EPA (2003).
_L_i_
20,935 13,196
1 1
0 0
0 0
5,876 5,874 4,357 3,448
2,457
A-449
-------
6.4. Complete List of Source Categories
Chapter/Source
Gas(es)
Energy
Fossil Fuel Combustion
Non-Energy Use of Fossil Fuels
Stationary Combustion (excluding CO2)
Mobile Combustion (excluding CO2)
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
Adipic Acid Production
Caprolactam, Glyoxal, and Glyoxylic Production
Silicon 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
Semiconductor Manufacture
Substitution of Ozone Depleting Substances
Electrical Transmission and Distributing
N2O 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 Forestry0
Forest Land Remaining Forest Land
Land Converted to Forest Land
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Wetlands Remaining Wetlands
Land Converted to Wetlands
C02
C02
CH4,
CH4,
ch4
ch4
cm
cm
co2,
co2,
N2O, CO, NOx, NMVOC
N2O, CO, NOx, NMVOC
cm
cm, N2O, NOx, CO, NMVOC
co2
co2
co2
co2
co2
co2
N20
N20
N20
co2, cm
co2
co2
co2, cm
HFC-23
CO2
CO2
co2, cm
co2, cm
C02, CF4, C2F6
CO2, HFCs, SFe
co2
co2
N2O, HFCs, PFCs,a SFe, NFs
HFCs, PFCsb
SFe
N2O
cm
cm, n2o
cm
co2
co2
cm, n2o, NOx, co
n2o
co2, cm, n2o, NOx, co
co2
co2
co2
co2,cm, n2o, NOx, co
co2
co2, cm, n2o
co2, cm
A-450 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Settlements Remaining Settlements CO2, N2O
Land Converted to Settlements CO2
Waste
Landfills CH4, N0X, CO, NMVOC
Wastewater Treatment CH4, N2O, NOx, CO, NMVOC
Composting CH4, N2O
a Includes HFC-23, CF4, C2F6, as well as other HFCs and PFCs used as heat transfer fluids.
" Includes HFC-23, HFC-32, HFC-125, HFC-134a, HFC-143a, HFC-236fa, CF4, HFC-152a, HFC-227ea, HFC-245fa, HFC-4310mee, and
PFC/PFPEs.
cThe LULUCF Sector includes CH4 and N2O emissions to the atmosphere and net carbon stock changes. The term "flux" is used to
describe the net emissions of greenhouse gases accounting for both the emissions of CO2 to and the removals of CO2 from the
atmosphere. Removal of CO2 from the atmosphere is also referred to as "carbon sequestration."
A-451
-------
6.5. Constants, Units, and Conversions
Metric Prefixes
Although most activity data for the United States is gathered in customary U.S. units, these units are converted
into metric units per international reporting guidelines. Table A-281 provides a guide for determining the magnitude of
metric units.
Table A-281: Guide to Metric Unit Prefixes
Prefix/Symbol
Factor
atto (a)
10-18
femto (f)
10-15
pico (p)
10-12
nano (n)
10"9
micro (|j)
10-6
milli (m)
10"3
centi (c)
10"2
deci (d)
10-1
deca (da)
10
hecto (h)
102
kilo (k)
103
mega (M)
106
giga (G)
109
tera (T)
1012
peta (P)
1015
exa (E)
1018
Unit Conversions
1 kilogram
1 pound
1 short ton
1 metric ton
2.205 pounds
0.454 kilograms
2,000 pounds
1,000 kilograms
0.9072 metric tons
1.1023 short tons
1 cubic meter =
1 cubic foot =
1 U.S. gallon =
1 barrel (bbl)
1 barrel (bbl)
1 liter
35.315 cubic feet
0.02832 cubic meters
3.785412 liters
0.159 cubic meters
42 U.S. gallons
0.001 cubic meters
1 foot
1 meter
1 mile
1 kilometer
0.3048 meters
3.28 feet
1.609 kilometers
0.622 miles
1 acre = 43,560 square feet = 0.4047 hectares
1 square mile = 2.589988 square kilometers
4,047 square meters
Degrees Celsius
Degrees Kelvin
(Degrees Fahrenheit - 32)*5/9
Degrees Celsius + 273.15
A-452 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Density Conversions163
Methane
1 cubic meter
= 0.67606 kilograms
Carbon dioxide
1 cubic meter
= 1.85387 kilograms
Natural gas liquids
1 metric
ton
= 11.6 barrels =
1,844.2 liters
Unfinished oils
1 metric
ton
= 7.46 barrels =
1,186.04 liters
Alcohol
1 metric
ton
= 7.94 barrels =
1,262.36 liters
Liquefied petroleum gas
1 metric
ton
= 11.6 barrels =
1,844.2 liters
Aviation gasoline
1 metric
ton
= 8.9 barrels =
1,415.0 liters
Naphtha jet fuel
1 metric
ton
= 8.27 barrels =
1,314.82 liters
Kerosene jet fuel
1 metric
ton
= 7.93 barrels =
1,260.72 liters
Motor gasoline
1 metric
ton
= 8.53 barrels =
1,356.16 liters
Kerosene
1 metric
ton
= 7.73 barrels =
1,228.97 liters
Naphtha
1 metric
ton
= 8.22 barrels =
1,306.87 liters
Distillate
1 metric
ton
= 7.46 barrels =
1,186.04 liters
Residual oil
1 metric
ton
= 6.66 barrels =
1,058.85 liters
Lubricants
1 metric
ton
= 7.06 barrels =
1,122.45 liters
Bitumen
1 metric
ton
= 6.06 barrels =
963.46 liters
Waxes
1 metric
ton
= 7.87 barrels =
1,251.23 liters
Petroleum coke
1 metric
ton
= 5.51 barrels =
876.02 liters
Petrochemical feedstocks
1 metric
ton
= 7.46 barrels =
1,186.04 liters
Special naphtha
1 metric
ton
= 8.53 barrels =
1,356.16 liters
Miscellaneous products
1 metric
ton
= 8.00 barrels =
1,271.90 liters
Energy Conversions
Converting Various Energy Units to Joules
The common energy unit used in international reports of greenhouse gas emissions is the joule. A joule is the
energy required to push with a force of one Newton for one meter. A terajoule (TJ) is one trillion (1012) joules. A British
thermal unit (Btu, the customary U.S. energy unit) is the quantity of heat required to raise the temperature of one pound of
water one degree Fahrenheit at or near 39.2 degrees Fahrenheit.
2.388x1011 calories
, T | _ 23.88 metric tons of crude oil equivalent
947.8 million Btus
277,800 kilowatt-hours
Converting Various Physical Units to Energy Units
Data on the production and consumption of fuels are first gathered in physical units. These units must be converted
to their energy equivalents. The conversion factors in Table A-282 can be used as default factors, if local data are not
available. See Appendix A of EIA's Monthly Energy Review, February 2018 (EIA 2018) for more detailed information on
the energy content of various fuels.
163 Reference: EIA (2007)
A-453
-------
Table fl-282: Conversion Factors to Energy Units (Heat Equivalents)
Fuel Type (Units) Factor
Solid Fuels (Million Btu/Short ton)
Anthracite coal 22.573
Bituminous coal 23.89
Sub-bituminous coal 17.14
Lignite 12.866
Coke 23.367
Natural Gas (Btu/Cubic foot) 1,037
Liquid Fuels (Million Btu/Barrel)
Motor gasoline 5.059
Aviation gasoline 5.048
Kerosene 5.670
Jet fuel, kerosene-type 5.670
Distillate fuel 5.773
Residual oil 6.287
Naphtha for petrochemicals 5.248
Petroleum coke 6.104
Other oil for petrochemicals 5.825
Special naphthas 5.248
Lubricants 6.065
Waxes 5.537
Asphalt 6.636
Still gas 6.287
Misc. products 5.796
Note: For petroleum and natural gas, Monthly Energy
Review, February 2018 (EIA 2018). For coal ranks, State
Energy Data Report 1992 (EIA 1993). All values are given in
higher heating values (gross calorific values).
A-454 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
6.6. Abbreviations
ABS
Acrylonitrile butadiene styrene
AC
Air conditioner
ACC
American Chemistry Council
AEDT
FAA Aviation Environmental Design Tool
AEO
Annual Energy Outlook
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
AHEF
Atmospheric and Health Effect Framework
AHRI
Air-Conditioning, Heating, and Refrigeration Institute
AISI
American Iron and Steel Institute
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
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
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
C-CAP
Coastal Change Analysis Program
CDAP
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
cm
Methane
CHP
Combined heat and power
A-455
-------
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
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
DHS
Department of Homeland Security
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
DOT
U.S. Department of Transportation
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
EJ
Exajoule
EGR
Exhaust gas recirculation
EGU
Electric generating unit
EIA
Energy Information Administration, U.S. Department of Energy
EIIP
Emissions Inventory Improvement Program
EOR
Enhanced oil recovery
EPA
U.S. Environmental Protection Agency
ERS
Economic Research Service
EV
Electric vehicle
EVI
Enhanced Vegetation Index
FAA
Federal Aviation Administration
FAO
Food and Agricultural Organization
FAOSTAT
Food and Agricultural Organization database
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
A-456 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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
GaAs
Gallium Arsenide
GCV
Gross calorific value
GDP
Gross domestic product
GHG
Greenhouse gas
GHGRP
Greenhouse Gas Reporting Program
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
Hydrobromofluoro carbon
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
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
ICE
Internal combustion engine
IEA
International Energy Agency
IFO
Intermediate Fuel Oil
IISRP
International Institute of Synthetic Rubber Products
ILENR
Illinois Department of Energy and Natural Resources
IMO
International Maritime Organization
IPAA
Independent Petroleum Association of America
IPCC
Intergovernmental Panel on Climate Change
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
LDDT
Light-duty diesel truck
LDDV
Light-duty diesel vehicle
LDGT
Light-duty gas truck
LDGV
Light-duty gas vehicle
A-457
-------
LDPE
Low density polyethylene
LDT
Light-duty truck
LDV
Light-duty vehicle
LEV
Low emission vehicles
LFG
Landfill gas
LFGE
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
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
MECS
EIA Manufacturer's Energy Consumption Survey
MEM
Micro-electromechanical systems
MER
Monthly Energy Review
MGO
Marine gas oil
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
N2O
Nitrous oxide
NA
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
A-458 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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
NF3
Nitrogen trifluoride
NGHGI
National Greenhouse Gas 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
NO
Not occurring
N02
Nitrogen Dioxide
NOx
Nitrogen oxides
NOAA
National Oceanic and Atmospheric Administration
NPDES
National Pollutant Discharge Elimination System
NPRA
National Petroleum and Refiners Association
NRC
National Research Council
NRCS
Natural Resources Conservation Service
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
PAH
Polycyclic aromatic hydrocarbons
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
PHMSA
Pipeline and Hazardous Materials Safety Administration
PI
Productivity index
PLS
Pregnant liquor solution
A-459
-------
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
PRP
Pasture/Range/Paddock
PS
Polystyrene
PSU
Primary Sample Unit
PU
Polyurethane
PVC
Polyvinyl chloride
PV
Photovoltaic
QA/QC
Quality Assurance and Quality Control
QBtu
Quadrillion Btu
R&D
Research and Development
RECs
Reduced Emissions Completions
RCRA
Resource Conservation and Recovery Act
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
SEC
Securities and Exchange Commission
SEMI
Semiconductor Equipment and Materials Industry
SFe
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
TVA
Tennessee Valley Authority
UAN
Urea ammonium nitrate
UDI
Utility Data Institute
UFORE
U.S. Forest Service's Urban Forest Effects model
A-460 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
UG
Underground (coal mining)
U.S.
United States
U.S. ITC
United States International Trade Commission
UEP
United Egg Producers
ULEV
Ultra low emission vehicle
UNEP
United Nations Environmental Programme
UNFCCC
United Nations Framework Convention on Climate Change
USAA
U.S. Aluminum Association
USAF
United States Air Force
USDA
United States Department of Agriculture
USFS
United States Forest Service
USGS
United States Geological Survey
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
A-461
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6.7. Chemical Formulas
Table A-283: Guide to Chemical Formulas
Symbol Name
Al Aluminum
AI2O3 Aluminum Oxide
Br Bromine
C Carbon
CH4 Methane
C2H6 Ethane
C3H8 Propane
CF4 Perfluoromethane
C2F6 Perfluoroethane, hexafluoroethane
C-C3F6 Perfluorocyclopropane
C3F8 Perfluoropropane
C-C4F8 Perfluorocyclobutane
C4F10 Perfluorobutane
C5F12 Perfluoropentane
C6F14 Perfluorohexane
CF3I Trifluoroiodomethane
CFCb 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)
CHCbF HCFC-21
CHF2CI Chlorodifluoromethane (HCFC-22)
C2F3HCI2 HCFC-123
C2F4HCI HCFC-124
C2FH3CI2 HCFC-141b
C2H3F2CI HCFC-142b
CF3CF2CHCI2 HCFC-225ca
CCIF2CF2CHCIF HCFC-225cb
CCU Carbon tetrachloride
CHCICCb Trichloroethylene
CCI2CCI2 Perchloroethylene, tetrachloroethene
CH3CI Methylchloride
CH3CCI3 Methylchloroform
CH2CI2 Methylenechloride
CHCb Chloroform, trichloromethane
CHFs 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
A-462 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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-245fa1
CF3CH20CHF2
HFE-245fa2
CHF2CF20CH3
HFE-254cb2
CF3CH20CH3
HFE-263fb2
CF3CF20CF2CHF2
HFE-329mcc2
CF3CF20CH2CF3
HFE-338mcf2
CF3CF2CF20CH3
HFE-347mcc3
CF3CF20CH2CHF2
HFE-347mcf2
CF3CHFCF20CH3
HFE-356mec3
CHF2CF2CF20CH3
HFE-356pcc3
CHF2CF20CH2CHF2
HFE-356pcf2
CHF2CF2CH20CHF2
HFE-356pcf3
CF3CF2CH20CH3
HFE-365mcf3
CHF2CF20CH2CH3
HFE-374pcf2
C4F90CH3
HFE-7100
C4F90C2H5
HFE-7200
CH2CFCF3
HFO-1234yf
CHFCHCFs
HFO-1234ze(E)
CF3CHCHCF3
HFO-1336mzz(Z)
C3H2CIF3
HCFO-1233zd(E)
CHF2OCF2OC2F4OCHF2
H-Galden 1040x
CHF2OCF2OCHF2
HG-10
CHF2OCF2CF2OCHF2
HG-01
CH3OCH3
Dimethyl ether
CH2Br2
Dibromomethane
CH2BrCI
Dibromochloromethane
CHBrs
Tribromomethane
CHBrF2
Bromodifluoromethane
CHsBr
Methylbromide
CF2BrCI
Bromodichloromethane (Halon 1211)
CF3Br(CBrF3)
Bromotrifluoromethane (Halon 1301)
CFsl
FIC-1311
CO
Carbon monoxide
CO2
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
A-463
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nh3
Ammonia
nh4+
Ammonium ion
HNOs
Nitric acid
MgO
Magnesium oxide
NF3
Nitrogen trifluoride
n2o
Nitrous oxide
NO
Nitric oxide
N02
Nitrogen dioxide
no3
Nitrate radical
N0x
Nitrogen oxides
Na
Sodium
Na2C03
Sodium carbonate, soda ash
NasAIFe
Synthetic cryolite
0,02
atomic Oxygen, molecular Oxygen
03
Ozone
S
atomic Sulfur
H2SO4
Sulfuric acid
SFe
Sulfur hexafluoride
SF5CF3
T rifluoromethylsulphur pentafluoride
S02
Sulfur dioxide
Si
Silicon
SiC
Silicon carbide
Si02
Quartz
* Distinct isomers.
References
EIA (2018) Monthly Energy Review, February 2018. Energy Information Administration, U.S. Department of Energy,
Washington, DC. DOE/EIA-0035(2018/2). February 2018.
EIA (2007) Emissions of Greenhouse Gases in the United States 2006, Draft Report. Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE-EIA-0573(2006).
EIA (1993) State Energy Data Report 1992, DOE/EIA-0214(93), Energy Information Administration, U.S. Department of
Energy. Washington, DC. December.
EPA (2016) "1970-2016 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory (NEI)
Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Last Modified December 2016.
Available online at: .
EPA (2003) E-mail correspondence. Air pollutant data. Office of Air Pollution to the Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency (EPA). December 22, 2003.
IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change. [Stacker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K.
Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, 1535 pp.
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis,
K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United Kingdom 996 pp.
IPCC (1996) Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change, J.T.
Houghton, L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell (eds.). Cambridge University
Press. Cambridge, United Kingdom.
A-464 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 2006IPCC 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.
The current inventory emission estimates for some source categories, such as for CO2 Emissions from Fossil Fuel
Combustion, have relatively low level of uncertainty associated with them. 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 those noted in Volume 1, Chapter 3 of the 2006
IPCC Guidelines (i.e., completeness of data, representativeness of data and models, sampling errors, measurement errors,
etc.). 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 one very
small emission source category, CH4 emissions from Incineration of Waste, given the very low emissions for CH4 from
Incineration of Waste, no uncertainty estimate was derived. Uncertainty associated with three other source categories
(International Bunker Fuels, Energy Sources of Indirect Greenhouse Gas Emissions, and CO2 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
A-465
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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 2016 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.164 CO2 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 CO2 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.
Characterization of Uncertainty in Input Variables
Both Approach 1 and Approach 2 uncertainty analyses require that all the input variables are well-characterized in
terms of their Probability Density Functions (PDFs). In the absence of particularly convincing data measurements, sufficient
data samples, or expert judgments that determined otherwise, the PDFs incorporated in the current source or sink category
uncertainty analyses were limited to normal, lognormal, uniform, triangular, and beta distributions. The choice among these
five PDFs depended largely on the observed or measured data and expert judgment.
Source and Sink Category Inventory Uncertainty Estimates
Discussion surrounding the input parameters and sources of uncertainty for each source and sink category appears
in the body of this report. Table A-284 summarizes results based on assessments of source and sink category-level
uncertainty. The table presents base year (1990 or 1995) and current year (2016) emissions for each source and sink category.
164 However, because the input variables that determine the emissions from the Fossil Fuel Combustion and the International Bunker
Fuels source categories are correlated, uncertainty associated with the activity variables in the International Bunker Fuels was taken
into account in estimating the uncertainty associated with the Fossil Fuel Combustion.
A-466 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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The combined uncertainty (at the 95 percent confidence interval) for each source and category is expressed as the percentage
deviation above and below the total 2016 emissions estimated for that source and category. Source or sink category trend
uncertainty is described subsequently in this Appendix.
Table fl-284: Summary Results of Source and Sink Category Uncertainty Analyses
Source or Sink Category
Base Year Emissions3
2016 Emissions'1
2016 Uncertainty11
MMT CO2 Eq.
MMT CO2 Eq.
Low
High
C02
5,121.3
5,310.9
-2%
5%
Fossil Fuel Combustion
4,740.3
4,966.0
-2%
5%
Non-Energy Use of Fuels
119.5
112.2
-19%
39%
Iron and Steel Production & Metallurgical Coke Production
101.6
42.3
-17%
17%
Cement Production
33.5
39.4
-6%
6%
Petrochemical Production
21.2
28.1
-5%
5%
Natural Gas Systems
29.8
25.5
-16%
17%
Petroleum Systems
7.7
22.8
-30%
34%
Lime Production
11.7
12.9
-2%
2%
Ammonia Production
13.0
12.2
-7%
7%
Other Process Uses of Carbonates
6.3
11.0
-12%
15%
Incineration of Waste
8.0
10.7
-22%
26%
Urea Fertilization
2.4
5.1
-43%
3%
Carbon Dioxide Consumption
1.5
4.5
-5%
5%
Urea Consumption for Non-Agricultural Purposes
3.8
4.0
-12%
12%
Liming
4.7
3.9
-111%
88%
Ferroalloy Production
2.2
1.8
-12%
12%
Soda Ash Production
1.4
1.7
-9%
8%
Titanium Dioxide Production
1.2
1.6
-12%
13%
Aluminum Production
6.8
1.3
-3%
2%
Glass Production
1.5
1.2
-4%
4%
Phosphoric Acid Production
1.5
1.0
-19%
21%
Zinc Production
0.6
0.9
-16%
16%
Lead Production
0.5
0.5
-14%
15%
Silicon Carbide Production and Consumption
0.4
0.2
-9%
9%
Abandoned Oil and Gas Wells
+
+
-83%
215%
Magnesium Production and Processing
+
+
-2%
2%
Wood Biomass, Ethanol, and Biodiesel Consumptionc
219.4
309.3
NE
NE
International Bunker Fuels?
103.5
116.6
NE
NE
CH4
779.9
657.4
-3%
19%
Enteric Fermentation
164.2
170.1
-11%
18%
Natural Gas Systems
195.2
163.5
-16%
17%
Landfills
179.6
107.7
-23%
23%
Manure Management
37.2
67.7
-18%
20%
Coal Mining
96.5
53.8
-12%
14%
Petroleum Systems
39.8
38.6
-30%
34%
Wastewater T reatment
15.7
14.8
-27%
23%
Rice Cultivation
16.0
13.7
-32%
64%
Stationary Combustion
8.6
7.3
-30%
114%
Abandoned Oil and Gas Wells
6.5
7.1
-83%
215%
Abandoned Underground Coal Mines
7.2
6.7
-18%
22%
Mobile Combustion
12.7
3.64
-7%
26%
Composting
0.4
2.1
-50%
50%
Field Burning of Agricultural Residues
0.2
0.3
-14%
14%
Petrochemical Production
0.2
0.2
-57%
46%
Ferroalloy Production
+
+
-12%
12%
Silicon Carbide Production and Consumption
+
+
-9%
10%
Iron and Steel Production & Metallurgical Coke Production
+
+
-20%
20%
Incineration of Waste
+
+
NE
NE
International Bunker Fuels?
0.2
0.1
NE
NE
N20
354.8
369.5
-13%
22%
A-467
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Agricultural Soil Management
250.5
283.6
-24%
39%
Direct
212.0
237.6
-16%
16%
Indirect
38.5
45.9
-65%
154%
Stationary Combustion
11.1
18.6
-22%
52%
Mobile Combustion
41.7
18.4
-9%
14%
Manure Management
14.0
18.1
-16%
24%
Nitric Acid Production
12.1
10.2
-5%
5%
Adipic Acid Production
15.2
7.0
-5%
5%
Wastewater T reatment
3.4
5.0
-75%
112%
N2O from Product Uses
4.2
4.2
-24%
24%
Caprolactam, Glyoxal, and Glyoxylic Acid Production
1.7
2.0
-31%
31%
Composting
0.3
1.9
-50%
50%
Incineration of Waste
0.5
0.3
-51%
327%
Semiconductor Manufacture
+
0.2
-12%
12%
Field Burning of Agricultural Residues
0.1
0.1
-14%
14%
International Bunker Fuels?
0.9
1.0
NE
NE
HFCs, PFCs, SF6 and NF3
130.8
173.5
-3%
11%
Substitution of Ozone Depleting Substances
31.4
159.1
-3%
12%
Semiconductor Manufacture
3.6
4.7
-6%
6%
Electrical Transmission and Distribution
23.1
4.3
-13%
14%
HCFC-22 Production
46.1
2.8
-7%
10%
Aluminum Production
21.5
1.4
-8%
8%
Magnesium Production and Processing
5.2
1.1
-5%
5%
Total Emissions6
6,355.6
6,511.3
-1%
5%
LULUCF Emissions'
10.6
38.1
-40%
73%
LULUCF Carbon Stock Changes
(830.2)
(754.9)
-21%
30%
LULUCF Sector Net Totalh
(819.6)
(716.8)
-22%
31%
Net Emissions (Sources and Sinks)
5,536.0
5,794.5
-3%
6%
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
a Base Year is 1990 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen 1995.
b The uncertainty estimates correspond to a 95 percent confidence interval, with the lower bound corresponding to 2.5th percentile and the upper bound
corresponding to 97.5th percentile.
c Emissions from Wood Biomass and Biofuel Consumption are not included in summing energy sector totals.
d Emissions from International Bunker Fuels are not included in the totals.
e Totals exclude emissions for which uncertainty was not quantified.
f LULUCF emissions include the CFU and N2O emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained Organic Soils, Grassland Fires,
and Coastal Wetlands Remaining Coastal Wetlands; CFU emissions from Land Converted to Coastal Wetlands; and N2O emissions from Forest Soils and
Settlement Soils.
9 LULUCF Carbon Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land, Land Converted to Forest Land,
Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to Grassland, Wetlands Remaining Wetlands,
Land Converted to Wetlands, Settlements Remaining Settlements, and Land Converted to Settlements.
h The LULUCF Sector Net Total is the net sum of all CFU and N2O emissions to the atmosphere plus net carbon stock changes.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding emissions for which uncertainty was
not quantified) is presented without LULUCF. Net emissions is presented with LULUCF.
Overall (Aggregate) Inventory Level Uncertainty Estimates
The overall level uncertainty estimate for the U. S. Inventory was developed using the IPCC Approach 2 uncertainty
estimation methodology. The uncertainty models of all the emission source categories could not be directly integrated to
develop the overall uncertainty estimates due to software constraints in integrating multiple, large uncertainty models.
Therefore, an alternative approach was adopted to develop the overall uncertainty estimates. The Monte Carlo simulation
output data for each emission source or sink category uncertainty analysis were combined by type of gas and the probability
distributions were fitted to the combined simulation output data, where such simulated output data were available. If such
detailed output data were not available for particular emissions sources, individual probability distributions were assigned
to those source or sink category emission estimates based on the most detailed data available from the quantitative
uncertainty analysis performed.
For Composting and parts of Agricultural Soil Management source categories, Approach 1 uncertainty results were
used in the overall uncertainty analysis estimation. However, for all other emission sources (excluding international bunker
fuels, CO2 from biomass and biofuel combustion, and CH4 from incineration of waste), Approach 2 uncertainty results were
used in the overall uncertainty estimation.
A-468 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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The overall uncertainty model results indicate that the 2016 U.S. greenhouse gas emissions are estimated to be
within the range of approximately 6,439.6 to 6,835.2 MMT CO2 Eq., reflecting a relative 95 percent confidence interval
uncertainty range of-1 percent to 5 percent with respect to the total U.S. greenhouse gas emission estimate of approximately
6,511.3 MMT CO2 Eq. The uncertainty interval associated with total CO2 emissions, which constitute about 82 percent of
the total U.S. greenhouse gas emissions in 2016, ranges from -2 percent to 5 percent of total CO2 emissions estimated. The
results indicate that the uncertainty associated with the inventory estimate of the total CH4 emissions ranges from -3 percent
to 19 percent, uncertainty associated with the total inventory N2O emission estimate ranges from -13 percent to 22 percent,
and uncertainty associated with Fluorinated GHG emissions ranges from -3 percent to 11 percent.
A summary of the overall quantitative uncertainty estimates is shown below.
Table fl-285: Quantitative Uncertainty Assessment of Overall National Inventory Emissions [MMTCO; Eq. and Percent]
2016 Emission
Standard
Estimate
Uncertainty Range Relative to Emission Estimate3
Meanb
Deviation11
Gas
(MMT C02 Eq.)
(MMT C02 Eq.
)
(%)
(MMT CO2 Eq.)
Lower
Upper
Lower
Upper
Boundc
Boundc
Bound
Bound
C02
5,310.9
5,211.4
5,555.2
-2%
5%
5,379.4
88.4
CH4d
657.4
637.0
780.8
-3%
19%
699.0
36.3
N2Od
369.5
321.7
451.8
-13%
22%
375.1
33.4
PFC, HFC, SFe, and NF3d
173.5
168.4
192.1
-3%
11%
180.3
6.1
Total Emissions
6511.3
6,439.6
6,835.2
-1%
5%
6,633.8
101.2
LULUCF Emissions6
38.1
22.8
65.7
-40%
73%
38.4
11.2
LULUCF Carbon Stock Change Flux1,
(754.9)
(979.5)
-598.2
-21%
30%
(790.5)
96.9
LULUCF Sector Net Totaig
(716.8)
(940.3)
-560.5
-22%
31%
(752.0)
97.4
Net Emissions (Sources and Sinks)
5,794.5
5,607.0
6,155.0
-3%
6%
5,881.8
140.9
a The lower and upper bounds for emission estimates correspond to a 95 percent confidence interval, with the lower bound corresponding to 2.5th percentile
and the upper bound corresponding to 97.5th percentile.
b Mean value indicates the arithmetic average of the simulated emission estimates; standard deviation indicates the extent of deviation of the simulated values
from the mean.
c The lower and upper bound emission estimates for the sub-source categories do not sum to total emissions because the low and high estimates for total
emissions were calculated separately through simulations.
d The overall uncertainty estimates did not take into account the uncertainty in the GWP values for CPU, N2O and high GWP gases used in the inventory
emission calculations for 2016.
e LULUCF emissions include the CPU and N2O emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained Organic Soils, Grassland Fires,
and Coastal Wetlands Remaining Coastal Wetlands; CPU emissions from Land Converted to Coastal Wetlands; and N2O emissions from Forest Soils and
Settlement Soils.
f LULUCF Carbon Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land, Land Converted to Forest Land,
Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to Grassland, Wetlands Remaining Wetlands,
Land Converted to Wetlands, Settlements Remaining Settlements, and Land Converted to Settlements.
9 The LULUCF Sector Net Total is the net sum of all CFUandlxhO emissions to the atmosphere plus net carbon stock changes.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding emissions for which uncertainty was
not quantified) is presented without LULUCF. Net emissions is presented with LULUCF.
Trend Uncertainty
In addition to the estimates of uncertainty associated with the current year's emission estimates, this Annex also
presents the estimates of trend uncertainty. The 2006IPCC Guidelines defines trend as the difference in emissions between
the base year (i.e., 1990) and the current year (i.e., 2016) Inventory estimates. However, for purposes of understanding the
concept of trend uncertainty, the emission trend is defined in this Inventory as the percentage change in the emissions (or
removal) estimated for the current year, relative to the emission (or removal) estimated for the base year. The uncertainty
associated with this emission trend is referred to as trend uncertainty.
Under the Approach 1 method, the trend uncertainty for a source and sink category is estimated using the sensitivity
of the calculated difference between the base year and the current year (i.e., 2016) emissions to an incremental (i.e., 1
percent) increase in one or both of these values for that source and sink category. The two sensitivities are expressed as
percentages: Type A sensitivity highlights the effect on the difference between the base and the current year emissions
caused by a 1 percent change in both, while Type B sensitivity highlights the effect caused by a change to only the current
year's emissions. Both sensitivities are simplifications introduced in order to analyze the correlation between the base and
the current year estimates. Once calculated, the two sensitivities are combined using the error propagation equation to
estimate the overall trend uncertainty.
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Under the Approach 2 method, the trend uncertainty is estimated using the Monte Carlo Stochastic Simulation
technique. The trend uncertainty analysis takes into account the fact that the base and the current year estimates often share
input variables. For purposes of the current Inventory, a simple approach has been adopted, under which the base year source
or sink category emissions are assumed to exhibit the same uncertainty characteristics as the current year emissions (or
removals). Source and sink category-specific PDFs for base year estimates were developed using current year (i.e., 2016)
uncertainty output data. These were adjusted to account for differences in magnitude between the two years' inventory
estimates. Then, for each source and sink category, a trend uncertainty estimate was developed using the Monte Carlo
method. The overall inventory trend uncertainty estimate was developed by combining all source and sink category-specific
trend uncertainty estimates. These trend uncertainty estimates present the range of likely change from base year to 2016,
and are shown in Table A-286.
Table fl-286: Quantitative Assessment of Trend Uncertainty [MBIT CO; Eq. and Percent]
Base Year
2016
Emissions
Gas/Source
Emissions"
Emissions
Trend
Trend Rangeb
(MMT CO2 Eq.)
(%)
(%)
Lower
Upper
Bound
Bound
CO2
5,121.3
5,310.9
4%
-1%
9%
Fossil Fuel Combustion
4,740.3
4,966.0
5%
0%
10%
Non-Energy Use of Fuels
119.5
112.2
-6%
-36%
40%
Natural Gas Systems
29.8
25.5
-14%
-40%
21%
Cement Production
33.5
39.4
18%
8%
29%
Lime Production
11.7
12.9
11%
8%
14%
Other Process Uses of Carbonates
6.3
11.0
74%
45%
111%
Soda Ash Production
1.4
1.7
20%
6%
37%
Carbon Dioxide Consumption
1.5
4.5
204%
183%
226%
Incineration of Waste
8.0
10.7
34%
-5%
89%
Titanium Dioxide Production
1.2
1.6
35%
12%
61%
Aluminum Production
6.8
1.3
-80%
-81%
-80%
Iron and Steel Production & Metallurgical Coke Production
101.6
42.3
-58%
-68%
-47%
Ferroalloy Production
2.2
1.8
-17%
-30%
-1%
Glass Production
1.5
1.2
-19%
-24%
-14%
Ammonia Production
13.0
12.2
-7%
-16%
4%
Urea Consumption for Non-Agricultural Purposes
3.8
4.0
5%
-13%
24%
Phosphoric Acid Production
1.5
1.0
-35%
-52%
-13%
Petrochemical Production
21.2
28.1
33%
23%
43%
Silicon Carbide Production and Consumption
0.4
0.2
-54%
-59%
-47%
Lead Production
0.5
0.5
-7%
-25%
15%
Zinc Production
0.6
0.9
46%
16%
84%
Liming
4.7
3.9
-17%
-105%
342%
Urea Fertilization
2.4
5.1
111%
39%
222%
Petroleum Systems
7.7
22.8
196%
28%
577%
Abandoned Oil and Gas Wells
+
+
15%
-110%
521%
Magnesium Production and Processing
+
+
95%
89%
102%
Wood Biomass and Biofuel Consumptionc
219.4
309.3
41%
NE
NE
International Bunker Fuele1
103.5
116.6
13%
NE
NE
CH4
779.9
657.4
-16%
-27%
-4%
Stationary Combustion
8.6
7.3
-15%
-63%
98%
Mobile Combustion
12.7
3.6
-71%
-77%
-64%
Coal Mining
96.5
53.8
-44%
-66%
-48%
Abandoned Underground Coal Mines
7.2
6.7
-7%
-38%
32%
Natural Gas Systems
195.2
163.5
-16%
-41%
19%
Petroleum Systems
39.8
38.6
-3%
-58%
119%
Abandoned Oil and Gas Wells
6.5
7.1
9%
-82%
254%
Petrochemical Production
0.2
0.2
12%
-55%
172%
Silicon Carbide Production and Consumption
+
+
-67%
-71%
-62%
Iron and Steel Production & Metallurgical Coke Production
+
+
-65%
-74%
-54%
Ferroalloy Production
+
+
-26%
-38%
-12%
Enteric Fermentation
164.2
170.1
4%
-15%
28%
Manure Management
37.2
67.7
82%
23%
156%
Rice Cultivation
16.0
13.7
-14%
-67%
119%
Field Burning of Agricultural Residues
0.2
0.3
20%
-36%
121%
Landfills
179.6
107.7
-40%
-57%
-17%
Wastewater T reatment
15.7
14.8
-5%
-35%
38%
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Composting
0.4
2.1
455%
147%
1157%
Incineration of Waste
+
+
-32%
NE
NE
International Bunker Fuels'1
0.2
0.1
-43%
NE
NE
N20
354.8
369.5
4%
-25%
55%
Stationary Combustion
11.1
18.6
68%
3%
177%
Mobile Combustion
41.7
18.4
-56%
-62%
-48%
Adipic Acid Production
15.2
7.0
-54%
-57%
-51%
Nitric Acid Production
12.1
10.2
-16%
-22%
-10%
Manure Management
14.0
18.1
30%
-1%
71%
Agricultural Soil Management
250.5
283.6
13%
-31%
85%
Field Burning of Agricultural Residues
0.1
0.1
21%
-23%
88%
Wastewater T reatment
3.4
5.0
46%
-68%
533%
N20 from Product Uses
4.2
4.2
0%
-33%
46%
Caprolactam, Glyoxal, and Glyoxylic Acid Production
1.7
2.0
21%
-25%
94%
Incineration of Waste
0.5
0.3
-32%
-85%
227%
Settlement Soils
1.4
2.5
75%
-3%
215%
Composting
0.3
1.9
455%
148%
1128%
Semiconductor Manufacture
+
0.2
555%
453%
673%
International Bunker Fuels'1
0.9
1.0
15%
NE
NE
HFCs, PFCs, SF6, and NF3
130.3
184.7
42%
36%
56%
Substitution of Ozone Depleting Substances
31.4
159.1
406%
357%
461%
HCFC-22 Production
46.1
2.8
-94%
-95%
-93%
Semiconductor Manufacture
3.6
4.7
33%
23%
44%
Aluminum Production
21.5
1.4
-94%
-94%
-93%
Electrical Transmission and Distribution
23.1
4.3
-81%
-84%
-78%
Magnesium Production and Processing
5.2
1.1
-78%
-82%
-79%
Total Emissions6
6,386.8
6,511.3
2%
-2%
7%
LULUCF Emissions'
10.6
38.1
258%
92%
684%
LULUCF Carbon Stock Change9
(830.2)
(754.9)
-9%
-36%
28%
LULUCF Sector Net Totalh
(819.6)
(716.8)
-13%
-39%
25%
Net Emissions (Sources and Sinks)
5,567.2
5,794.5
4%
-3%
12%
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
a Base Year is 1990 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen 1995.
b The trend range represents a 95 percent confidence interval for the emission trend, with the lower bound corresponding to 2.5th percentile value and the upper
bound corresponding to 97.5th percentile value.
c Emissions from Wood Biomass and Biofuel Consumption are not included specifically in summing energy sector totals.
d Emissions from International Bunker Fuels are not included in the totals.
e Totals exclude emissions for which uncertainty was not quantified.
f LULUCF emissions include the CPU and N2O emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained Organic Soils, Grassland Fires,
and Coastal Wetlands Remaining Coastal Wetlands; CPU emissions from Land Converted to Coastal Wetlands; and N2O emissions from Forest Soils and
Settlement Soils.
9 LULUCF Carbon Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land, Land Converted to Forest Land,
Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to Grassland, Wetlands Remaining Wetlands,
Land Converted to Wetlands, Settlements Remaining Settlements, and Land Converted to Settlements.
h The LULUCF Sector Net Total is the net sum of all CFU and N2O emissions to the atmosphere plus net carbon stock changes.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding emissions for which uncertainty was
not quantified) is presented without LULUCF. Net emissions is presented with LULUCF.
7.3. Reducing Uncertainty
There have been many improvements in reducing uncertainties across source and sink categories over the last
several years. These improvements are result of new data sources that provide more accurate data or more coverage, as well
as methodological improvements. Several source categories now use the U.S. EPA's GHGRP reported data, which is an
improvement over prior methods using default emission factors and provides more country-specific data for Inventory
calculations. EPA's GHGRP relies on facility-level data which undergoes a multi-step verification process, including
automated data checks to ensure consistency, comparison against expected ranges for similar facilities and industries, and
statistical analysis.
For example, the use of EPA's GHGRP reported data to estimate CH4 emissions from Coal Mining resulted in the
uncertainty bounds of -12 to 14 percent in the 1990 to 2016 Inventory, which was an improvement over the uncertainty
bounds in the 1990 to 2011 Inventory of -15 to 18 percent. Prior to 2012, Coal Mining emissions were estimated using an
array of emission factor estimations with higher assumed uncertainty. Estimates of CH4 emissions from MSW landfills were
also revised with the availability of GHGRP reported data resulting in methodological and data quality improvements that
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reduced uncertainty. Previously, MSW landfill emissions estimates were calculated using a model and default factors with
higher assumed uncertainty.
Due to the availability of GHGRP reported data, Semiconductor Manufacturing emissions methodology as well as
the uncertainty model was revised for the 1990 to 2012 Inventory. The revised model to estimate uncertainty relies on
analysis conducted during the development of the EPA's GHGRP Subpart I rulemaking to estimate uncertainty associated
with facility-reported emissions. These results were applied to the GHGRP-reported data as well as to the non-reported
emissions. An improved methodology to estimate non-reported emissions along with improved methodology to estimate
uncertainty of these non-reported emissions led to a reduced overall uncertainty of -6 to 6 percent in the 1990 to 2016
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, CO2 from Wood Biomass and Biofuel Consumption, and Indirect Greenhouse Gas Emissions source
categories, which are not included in the energy sector totals. CO2 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 provide an estimate of CO2 emissions from Wood Biomass
and Biofuel Consumption 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 from excluded emission sources and developing uncertainty estimates
for all source categories for which emissions are estimated.
• Improving the accuracy of emission factors. Further research is needed in some cases to improve the accuracy of
emission factors used to calculate emissions from a variety of sources. For example, the accuracy of current
emission factors applied to CH4 and N2O emissions from stationary and mobile combustion are highly uncertain,
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
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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 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. This
section provides summary descriptions of the uncertainty analyses performed for some of the source and sink categories,
including the models and methods used to calculate the emission estimates and the potential sources of uncertainty
surrounding them. These source or sink categories are organized below in the same order as the categories in each chapter
of the main section of this Inventory. To avoid repetition, the following uncertainty analysis discussions of individual source
categories do not include descriptions of these source categories. Hence, to better understand the details provided below,
refer to the respective chapters and sections in the main section of this Inventory, as needed. All uncertainty estimates are
reported relative to the current Inventory estimates for the 95 percent confidence interval, unless otherwise specified.
Energy
The uncertainty analysis descriptions in this section correspond to source categories included in the Energy chapter
of the Inventory. For additional information on uncertainty for Energy sources, refer to Section 3.2.
CO2 from Fossil Fuel Combustion
For estimates of CO2 from fossil fuel combustion, the amount of CO2 emitted is directly related to the amount of
fuel consumed, the fraction of the fuel that is oxidized, and the carbon content of the fuel.
Although statistics of total fossil fuel and other energy consumption are relatively accurate, the allocation of this
consumption to individual end-use sectors (i.e., residential, commercial, industrial, and transportation) is less certain. For
this uncertainty estimation, the inventory estimation model for CO2 from fossil fuel combustion was integrated with the
relevant variables from the inventory estimation model for International Bunker Fuels, to realistically characterize the
interaction (or endogenous correlation) between the variables of these two models.
In developing the uncertainty estimation model, uniform distributions were assumed for all activity-related input
variables and emission factors, based on the SAIC/EIA (2001) report.165 Triangular distributions were assigned for the
oxidization factors (or combustion efficiencies). The uncertainty ranges were assigned to the input variables based on the
data reported in SAIC/EIA (2001) and on conversations with various agency personnel.166
The uncertainty ranges for the activity-related input variables were typically asymmetric around their inventory
estimates; the uncertainty ranges for the emissions factors were symmetric. Bias (or systematic uncertainties) associated
165 SAIC/EIA (2001) characterizes the underlying probability density function for the input variables as a combination of
uniform and normal distributions (the former to represent the bias component and the latter to represent the random
component). However, for purposes of the current uncertainty analysis, it was determined that uniform distribution was
more appropriate to characterize the probability density function underlying each of these variables.
166 In the SAIC/EIA (2001) report, the quantitative uncertainty estimates were developed for each of the three major fossil
fuels used within each end-use sector; the variations within the sub-fuel types within each end-use sector were not modeled.
However, for purposes of assigning uncertainty estimates to the sub-fuel type categories within each end-use sector in the
current uncertainty analysis, SAIC/EIA (2001)-reported uncertainty estimates were extrapolated.
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with these variables accounted for much of the uncertainties associated with these variables (SAIC/EIA 2001).167 For
purposes of this uncertainty analysis, each input variable was simulated 10,000 times through Monte Carlo sampling.
CH4 and N2O from Stationary Combustion
The uncertainty estimation model for this source category was developed by integrating the CH4 andN20 stationary
source inventory estimation models with the model for CO2 from fossil fuel combustion to realistically characterize the
interaction (or endogenous correlation) between the variables of these three models. About 55 input variables were simulated
for the uncertainty analysis of this source category (about 20 from the CO2 emissions from fossil fuel combustion inventory
estimation model and about 35 from the stationary source inventory models).
In developing the uncertainty estimation model, uniform distribution was assumed for all activity-related input
variables and N2O emission factors, based on the SAIC/EIA (2001) report. For these variables, the uncertainty ranges were
assigned to the input variables based on the data reported in SAIC/EIA (2001). However, the CH4 emission factors differ
from those used by EIA. These factors and uncertainty ranges are based on IPCC default uncertainty estimates (IPCC 2006).
CH4 and N2O from Mobile Combustion
The uncertainty analysis was performed on 2016 estimates of CH4 and N2O emissions, incorporating probability
distribution functions associated with the major input variables. For the purposes of this analysis, the uncertainty was
modeled for the following four major sets of input variables: (1) VMT data, by on-road vehicle and fuel type and (2) emission
factor data, by on-road vehicle, fuel, and control technology type, (3) fuel consumption, data, by non-road vehicle and
equipment type, and (4) emission factor data, by non-road vehicle and equipment type.
Carbon Emitted from Non-Energy Uses of Fossil Fuels
An uncertainty analysis was conducted to quantify the uncertainty surrounding the estimates of emissions and
storage factors from non-energy uses. This analysis, performed using @RISK software and the IPCC-recommended
Approach 2 methodology (Monte Carlo Stochastic Simulation technique), provides for the specification of probability
density functions for key variables within a computational structure that mirrors the calculation of the inventory estimate.
The results presented below provide the 95 percent confidence interval, the range of values within which emissions are likely
to fall, for this source category.
As noted above, the non-energy use analysis is based on U.S.-specific storage factors for (1) feedstock materials
(natural gas, LPG, pentanes plus, naphthas, other oils, still gas, special naphthas, and other industrial coal), (2) asphalt, (3)
lubricants, and (4) waxes. For the remaining fuel types (the "other" category in Table 3-20 and Table 3-21 in the MR), the
storage factors were taken directly from IPCC (2006), where available, and otherwise assumptions were made based on the
potential fate of carbon in the respective NEU products. To characterize uncertainty, five separate analyses were conducted,
corresponding to each of the five categories. In all cases, statistical analyses or expert judgments of uncertainty were not
available directly from the information sources for all the activity variables; thus, uncertainty estimates were determined
using assumptions based on source category knowledge.
Incineration of Waste
The uncertainties in the waste incineration emission estimates arise from both the assumptions applied to the data
and from the quality of the data. Key factors include MSW incineration rate; fraction oxidized; missing data on waste
composition; average C content of waste components; assumptions on the synthetic/biogenic C ratio; and combustion
conditions affecting N2O emissions. The highest levels of uncertainty surround the variables that are based on assumptions
(e.g., percent of clothing and footwear composed of synthetic rubber); the lowest levels of uncertainty surround variables
that were determined by quantitative measurements (e.g., combustion efficiency, C content of C black).
Coal Mining
A quantitative uncertainty analysis was conducted for the coal mining source category using the IPCC-
recommended Approach 2 uncertainty estimation methodology. Because emission estimates from underground ventilation
systems were based on actual measurement data from EPA's GHGRP or from MSHA, uncertainty is relatively low.
Estimates of CH4 recovered by degasification systems are relatively certain for utilized CH4 because of the
availability of EPA's GHGRP data and gas sales information. Many of the recovery estimates use data on wells within 100
167 Although, in general, random uncertainties are the main focus of statistical uncertainty analysis, when the uncertainty
estimates are elicited from experts, their estimates include both random and systematic uncertainties. Hence, both these types
of uncertainties are represented in this uncertainty analysis.
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feet of a mined area. However, uncertainty exists concerning the radius of influence of each well. The number of wells
counted, and thus the avoided emissions, may vary if the drainage area is found to be larger or smaller than estimated.
In 2015 and 2016, a small level of uncertainty was introduced with using estimated rather than measured values of
recovered methane from two of the mines with degasification systems. An increased level of uncertainty was applied to
these two subsources, but the change had little impact on the overall uncertainty.
Surface mining and post-mining emissions are associated with considerably more uncertainty than underground
mines, because of the difficulty in developing accurate emission factors from field measurements. However, since
underground emissions constitute the majority of total coal mining emissions, the uncertainty associated with underground
emissions is the primary factor that determines overall uncertainty.
Abandoned Underground Coal Mines
A quantitative uncertainty analysis was conducted to estimate the uncertainty surrounding the estimates of
emissions from abandoned underground coal mines. The uncertainty analysis described below provides for the specification
of probability density functions for key variables within a computational structure that mirrors the calculation of the
inventory estimate. The results provide the range within which, with 95 percent certainty, emissions from this source
category are likely to fall.
A quantitative uncertainty analysis was conducted to estimate the uncertainty surrounding the estimates of emissions from
abandoned underground coal mines using probability density functions for key variables within a computational structure
that mirrors the calculation of the inventory estimate. The results provide the range within which, with 95 percent certainty,
emissions from this source category are likely to fall.
As discussed above, the low, mid and high model generated decline curves for each basin were fitted to a hyperbolic
decline curve. The decline curve parameters, Di and b, for the low, mid and high decline curves were then used to define a
triangular distribution and together with the initial rate value of a mine's emissions and time from abandonment, a probability
density function for each mine in the coal basin was generated.
Petroleum Systems
In recent years, EPA has made significant revisions to the Inventory methodology to use updated activity and
emissions data. To update its characterization of uncertainty, EPA has conducted a quantitative uncertainty analysis using
the IPCC Approach 2 methodology (Monte Carlo Simulation technique). For more information, please see the 2018
Uncertainty Memo. EPA used Microsoft Excel's @RISK add-in tool to estimate the 95 percent confidence bound around
methane emissions from petroleum systems for the current Inventory, then applied the calculated bounds to both CH4 and
CO2 emissions estimates. For the analysis, EPA focused on the five highest methane-emitting sources for the year 2016,
which together emitted 78 percent of methane from petroleum systems in 2016, and extrapolated the estimated uncertainty
for the remaining sources. The @RISK add-in provides for the specification of probability density functions (PDFs) for key
variables within a computational structure that mirrors the calculation of the inventory estimate. The IPCC guidance notes
that in using this method, "some uncertainties that are not addressed by statistical means may exist, including those arising
from omissions or double counting, or other conceptual errors, or from incomplete understanding of the processes that may
lead to inaccuracies in estimates developed from models." As a result, the understanding of the uncertainty of emission
estimates for this category evolves and improves as the underlying methodologies and datasets improve. The uncertainty
bounds reported below only reflect those uncertainties that EPA has been able to quantify and do not incorporate
considerations such as modeling uncertainty, data representativeness, measurement errors, misreporting or misclassification.
Natural Gas Systems
In recent years, EPA has made significant revisions to the Inventory methodology to use updated activity and
emissions data. To update its characterization of uncertainty, EPA has conducted a quantitative uncertainty analysis using
the IPCC Approach 2 methodology (Monte Carlo Simulation technique). For more information, please see the 2018
Uncertainty Memo. EPA used Microsoft Excel's @RISK add-in tool to estimate the 95 percent confidence bound around
CH4 emissions from natural gas systems for the current Inventory, then applied the calculated bounds to both CH4 and CO2
emissions estimates. For the analysis, EPA focused on the 16 highest-emitting sources for the year 2016, which together
emitted 78 percent of methane from natural gas systems in 2016, and extrapolated the estimated uncertainty for the remaining
sources. The @RISK add-in provides for the specification of probability density functions (PDFs) for key variables within
a computational structure that mirrors the calculation of the inventory estimate. The IPCC guidance notes that in using this
method, "some uncertainties that are not addressed by statistical means may exist, including those arising from omissions
or double counting, or other conceptual errors, or from incomplete understanding of the processes that may lead to
inaccuracies in estimates developed from models." The uncertainty bounds reported below only reflect those uncertainties
that EPA has been able to quantify and do not incorporate considerations such as modeling uncertainty, data
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representativeness, measurement errors, misreporting or misclassification. The understanding of the uncertainty of emission
estimates for this category evolves and improves as the underlying methodologies and datasets improve.
International Bunker Fuels
Emission estimates related to the consumption of international bunker fuels are subject to the same uncertainties
as those from domestic aviation and marine mobile combustion emissions; however, additional uncertainties result from the
difficulty in collecting accurate fuel consumption activity data for international transport activities separate from domestic
transport activities. Uncertainties exist with regard to the total fuel used by military aircraft and ships, and in the activity
data on military operations and training that were used to estimate percentages of total fuel use reported as bunker fuel
emissions. There are uncertainties in aircraft operations and training activity data. There is uncertainty associated with
ground fuel estimates for 1997 through 2001. Small fuel quantities may have been used in vehicles or equipment other than
that which was assumed for each fuel type. There are also uncertainties in fuel end-uses by fuel type, emissions factors, fuel
densities, diesel fuel sulfur content, aircraft and vessel engine characteristics and fuel efficiencies, and the methodology used
to back-calculate the data set to 1990 using the original set from 1995.
Emission estimates related to the consumption of international bunker fuels are subject to the same uncertainties
as those from domestic aviation and marine mobile combustion emissions; however, additional uncertainties result from the
difficulty in collecting accurate fuel consumption activity data for international transport activities separate from domestic
transport activities. Uncertainties exist with regard to the total fuel used by military aircraft and ships, and in the activity
data on military operations and training that were used to estimate percentages of total fuel use reported as bunker fuel
emissions. There are also uncertainties in fuel end-uses by fuel-type, emissions factors, fuel densities, diesel fuel sulfur
content, aircraft and vessel engine characteristics and fuel efficiencies, and the methodology used to back-calculate the data
set to 1990 using the original set from 1995.
There is also concern regarding the reliability of the existing DOC (2017) data on marine vessel fuel consumption
reported at U.S. customs stations due to the significant degree of inter-annual variation.
Wood Biomass and Biofuel Consumption
It is assumed that the combustion efficiency for woody biomass is 100 percent, which is believed to be an
overestimate of the efficiency of wood combustion processes in the United States. Decreasing the combustion efficiency
would decrease emission estimates for CO2. Additionally, the heat content applied to the consumption of woody biomass in
the residential, commercial, and electric power sectors is unlikely to be a completely accurate representation of the heat
content for all the different types of woody biomass consumed within these sectors. Emission estimates from ethanol and
biodiesel production are more certain than estimates from woody biomass consumption due to better activity data collection
methods and uniform combustion techniques.
Industrial Processes and Product Use
The uncertainty analysis descriptions in this section correspond to source categories included in the Industrial
Processes and Product Use chapter of the Inventory.
Cement Production
The uncertainties contained in these estimates are primarily due to uncertainties in the lime content of clinker and
in the percentage of CKD recycled inside the cement kiln. Uncertainty is also associated with the assumption that all calcium-
containing raw materials are CaC03, when a small percentage likely consists of other carbonate and non-carbonate raw
materials.
Lime Production
The uncertainties contained in these estimates can be attributed to slight differences in the chemical composition
of lime products and CO2 recovery rates for on-site process use over the time series. Although the methodology accounts
for various formulations of lime, it does not account for the trace impurities found in lime, such as iron oxide, alumina, and
silica. In addition, a portion of the CO2 emitted during lime production will actually be reabsorbed when the lime is
consumed, especially at captive lime production facilities. Another uncertainty is the assumption that calcination emissions
for LKD are around 2 percent. Publicly available on LKD generation rates, total quantities not used in cement production,
and types of other byproducts/wastes produced at lime facilities is limited.
Glass Production
The uncertainty levels presented in this section arise in part due to variations in the chemical composition of
limestone used in glass production. In addition to calcium carbonate, limestone may contain smaller amounts of magnesia,
silica, and sulfur, among other minerals (potassium carbonate, strontium carbonate and barium carbonate, and dead burned
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dolomite). Similarly, the quality of the limestone (and mix of carbonates) used for glass manufacturing will depend on the
type of glass being manufactured.
The estimates below also account for uncertainty associated with activity data. Large fluctuations in reported
consumption exist, reflecting year-to-year changes in the number of survey responders. The uncertainty resulting from a
shifting survey population is exacerbated by the gaps in the time series of reports. The accuracy of distribution by end use
is also uncertain because this value is reported by the manufacturer of the input carbonates (limestone, dolomite and soda
ash) and not the end user.
There is a high uncertainty associated with this estimate, as dolomite is a major raw material consumed in glass
production. Additionally, there is significant inherent uncertainty associated with estimating withheld data points for specific
end uses of limestone and dolomite. The uncertainty of the estimates for limestone and dolomite used in glass making is
especially high. Lastly, much of the limestone consumed in the United States is reported as "other unspecified uses;"
therefore, it is difficult to accurately allocate this unspecified quantity to the correct end-uses.
Other Process Uses of Carbonates
The uncertainty levels presented in this section account for uncertainty associated with activity data. Data on
limestone and dolomite consumption are collected by USGS through voluntary national surveys. The uncertainty resulting
from a shifting survey population is exacerbated by the gaps in the time series of reports. The accuracy of distribution by
end use is also uncertain because this value is reported by the producer/mines and not the end user. Additionally, there is
significant inherent uncertainty associated with estimating withheld data points for specific end uses of limestone and
dolomite. Lastly, much of the limestone consumed in the United States is reported as "other unspecified uses;" therefore, it
is difficult to accurately allocate this unspecified quantity to the correct end-uses.
Uncertainty in the estimates also arises in part due to variations in the chemical composition of limestone. In
addition to calcium carbonate, limestone may contain smaller amounts of magnesia, silica, and sulfur, among other minerals.
The exact specifications for limestone or dolomite used as flux stone vary with the pyrometallurgical process and the kind
of ore processed.
For emissions from soda ash consumption, the primary source of uncertainty, results from the fact that these
emissions are dependent upon the type of processing employed by each end-use. Specific emission factors for each end-use
are not available, so a Tier 1 default emission factor is used for all end uses.
Ammonia Production
The uncertainties presented in this section are primarily due to how accurately the emission factor used represents
an average across all ammonia plants using natural gas feedstock. Uncertainties are also associated with ammonia production
estimates and the assumption that all ammonia production and subsequent urea production was from the same process—
conventional catalytic reforming of natural gas feedstock, with the exception of one ammonia production plant located in
Kansas that is manufacturing ammonia from petroleum coke feedstock. Uncertainty is also associated with the
representativeness of the emission factor used for the petroleum coke-based ammonia process. It is also assumed that
ammonia and urea are produced at collocated plants from the same natural gas raw material. The uncertainty of the total
urea production activity data, based on USGS Minerals Yearbook: Nitrogen data, is a function of the reliability of reported
production data and is influenced by the completeness of the survey responses.
Urea Consumption for Non-Agricultural Purposes
The primary uncertainties associated with this source category are associated with the accuracy of these estimates
as well as the fact that each estimate is obtained from a different data source. Because urea production estimates are no
longer available from the USGS, there is additional uncertainty associated with urea produced beginning in 2011. There is
also uncertainty associated with the assumption that all of the carbon in urea is released into the environment as CO2 during
use.
Nitric Acid Production
Uncertainty associated with the parameters used to estimate N2O emissions includes the share of U.S. nitric acid
production attributable to each emission abatement technology over the time series (especially prior to 2010), and the
associated emission factors applied to each abatement technology type.
Uncertainty associated with the parameters used to estimate N2O emissions includes the share of U.S. nitric acid
production attributable to each emission abatement technology over the time series (especially prior to 2010), and the
associated emission factors applied to each abatement technology type. The annual production reported by each nitric acid
facility under EPA's GHGRP and then aggregated to estimate national N2O emissions is assumed to have low uncertainty.
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Adipic Acid Production
Uncertainty associated with N2O emission estimates includes the methods used by companies to monitor and
estimate emissions. While some information has been obtained through outreach with facilities, limited information is
available over the time series on these methods, abatement technology destruction and removal efficiency rates and plant
specific production levels.
Silicon Carbide Production and Consumption
There is uncertainty associated with the emission factors used because they are based on stoichiometry as opposed
to monitoring of actual SiC production plants. For CH4, there is also uncertainty associated with the hydrogen-containing
volatile compounds in the petroleum coke (IPCC 2006). There is also uncertainty associated with the use or destruction of
methane generated from the process in addition to uncertainty associated with levels of production, net imports, consumption
levels, and the percent of total consumption that is attributed to metallurgical and other non-abrasive uses.
Titanium Dioxide Production
Each year, the U.S. Geological Survey (USGS) collects titanium industry data for titanium mineral and pigment
production operations. If TiC>2 pigment plants do not respond, production from the operations is estimated based on prior
year production levels and industry trends. Variability in response rates varies from 67 to 100 percent of TiC>2 pigment plants
over the time series.
Although some TiC>2 may be produced using graphite or other carbon inputs, information and data regarding these
practices were not available. Titanium dioxide produced using graphite inputs, for example, may generate differing amounts
of CC^per unit of TiC>2 produced as compared to that generated using petroleum coke in production. While the most accurate
method to estimate emissions would be to base calculations on the amount of reducing agent used in each process rather
than on the amount of TiC>2 produced, sufficient data were not available to do so.
Soda Ash Production
Emission estimates from soda ash production have relatively low associated uncertainty levels in that reliable and
accurate data sources are available for the emission factor and activity data for trona-based soda ash production. Soda ash
production data was collected by the USGS from voluntary surveys. One source of uncertainty is the purity of the trona ore
used for manufacturing soda ash. The emission factor used for this estimate assumes the ore is 100 percent pure, and likely
overestimates the emissions from soda ash manufacture.
Petrochemical Production
The CH4 and CO2 emission factors used for acrylonitrile and methanol production are based on a limited number
of studies. Using plant-specific factors instead of default or average factors could increase the accuracy of the emission
estimates; however, such data were not available for the current Inventory report. There is some uncertainty in the
applicability of the average emission factors for each petrochemical type across all prior years. While petrochemical
production processes in the United States have not changed significantly since 1990, some operational efficiencies have
been implemented at facilities over the time series.
HCFC-22 Production
The uncertainty analysis presented in this section was based on a plant-level Monte Carlo Stochastic Simulation
for 2006. A normal probability density function was assumed for all measurements and biases except the equipment leak
estimates for one plant; a log-normal probability density function was used for this plant's equipment leak estimates. The
simulation for 2006 yielded a 95-percent confidence interval for U.S. emissions of 6.8 percent below to 9.6 percent above
the reported total.
The relative errors yielded by the Monte Carlo Stochastic Simulation for 2006 were applied to the U.S. emission
estimate for 2016. The resulting estimates of absolute uncertainty are likely to be reasonably accurate because (1) the
methods used by the two remaining plants to estimate their emissions are not believed to have changed significantly since
2006, and (2) although the distribution of emissions among the plants has changed between 2006 and 2016 (because one
plant has closed), the plant that currently accounts for most emissions had a relative uncertainty in its 2006 (as well as 2005)
emissions estimate that was similar to the relative uncertainty for total U.S. emissions. Thus, the closure of one plant is not
likely to have a large impact on the uncertainty of the national emission estimate.
Carbon Dioxide Consumption
There is uncertainty associated with the data reported through EPA's GHGRP. Specifically, there is uncertainty
associated with the amount of CO2 consumed for food and beverage applications given a threshold for reporting under
GHGRP applicable to those reporting under Subpart PP, in addition to the exclusion of the amount of CO2 transferred to all
other end-use categories. Second, uncertainty is associated with the exclusion of imports/exports data for CO2 suppliers.
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Phosphoric Acid Production
Regional production for 2016 was estimated based on regional production data from previous years and multiplied
by regionally-specific emission factors. There is uncertainty associated with the degree to which the estimated 2016 regional
production data represents actual production in those regions. Total U.S. phosphate rock production data are not considered
to be a significant source of uncertainty because all the domestic phosphate rock producers report their annual production to
theUSGS.
An additional source of uncertainty in the calculation of CO2 emissions from phosphoric acid production is the
carbonate composition of phosphate rock; the composition of phosphate rock varies depending upon where the material is
mined, and may also vary over time. Another source of uncertainty is the disposition of the organic carbon content of the
phosphate rock. A third source of uncertainty is the assumption that all domestically-produced phosphate rock is used in
phosphoric acid production and used without first being calcined.
Iron and Steel Production and Metallurgical Coke Production
Uncertainty is associated with the total U.S. coking coal consumption, total U.S. coke production and materials
consumed during this process. Therefore, for the purpose of this analysis, uncertainty parameters are applied to primary data
inputs to the calculation (i.e., coking coal consumption and metallurgical coke production) only.
There is uncertainty associated with the assumption that all coal used for purposes other than coking coal is for
direct injection coal. There is also uncertainty associated with the C contents for pellets, sinter, and natural ore, which are
assumed to equal the C contents of direct reduced iron, when consumed in the blast furnace. There is uncertainty associated
with the consumption of natural ore under current industry practices. For EAF steel production, there is uncertainty
associated with the amount of EAF anode and charge carbon consumed due to inconsistent data throughout the time series.
Also for EAF steel production, there is uncertainty associated with the assumption that 100 percent of the natural gas
attributed to "steelmaking furnaces" by AISI is process-related and nothing is combusted for energy purposes. Uncertainty
is also associated with the use of process gases such as blast furnace gas and coke oven gas.
Ferroalloy Production
Annual ferroalloy production was reported by the USGS in three broad categories until the 2010 publication:
ferroalloys containing 25 to 55 percent silicon (including miscellaneous alloys), ferroalloys containing 56 to 95 percent
silicon, and silicon metal (through 2005 only, 2005 value used as proxy for 2005 through 2010). Starting with the 2011
Minerals Yearbook, USGS started reporting all the ferroalloy production under a single category: total silicon materials
production. The total silicon materials quantity was allocated across the three categories based on the 2010 production shares
for the three categories. Refer to the Methodology section for further details. Additionally, production data for silvery pig
iron (alloys containing less than 25 percent silicon) are not reported by the USGS to avoid disclosing proprietary company
data. Emissions from this production category, therefore, were not estimated.
Also, some ferroalloys may be produced using wood or other biomass as a primary or secondary carbon source
(carbonaceous reductants), however information and data regarding these practices were not available. Emissions from
ferroalloys produced with wood or other biomass would not be counted under this source because wood-based carbon is of
biogenic origin. 168 Even though emissions from ferroalloys produced with coking coal or graphite inputs would be counted
in national trends, they may be generated with varying amounts of CO2 per unit of ferroalloy produced. The most accurate
method for these estimates would be to base calculations on the amount of reducing agent used in the process, rather than
the amount of ferroalloys produced. These data, however, were not available, and are also often considered confidential
business information.
Aluminum Production
Uncertainty was assigned to the CO2, CF4, and C2F6 emission values reported by each individual facility to EPA's
GF1GRP. Uncertainty surrounding the reported CO2, CF4, and C2F6 emission values were determined to have a normal
distribution with uncertainty ranges of ±6, ±16, and ±20 percent, respectively.
Magnesium Production
Uncertainty surrounding the total estimated emissions in 2016 is attributed to the uncertainties around SF6, HFC-
134a, and CO2 emission estimates. To estimate the uncertainty surrounding the estimated 2016 SFe emissions from
magnesium production and processing, the uncertainties associated with three variables were estimated: (1) emissions
reported by magnesium producers and processors for 2016 through EPA's GF1GRP, (2) emissions estimated for magnesium
producers and processors that reported via the Partnership in prior years but did not report 2016 emissions through EPA's
168 Emissions and sinks of biogenic carbon are accounted for in the Land Use, Land-Use Change, and Forestry chapter.
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GHGRP, and (3) emissions estimated for magnesium producers and processors that did not participate in the Partnership or
report through EPA's GHGRP. Additional uncertainties exist in these estimates that are not addressed in this methodology,
such as the basic assumption that SF6 neither reacts nor decomposes during use.
Lead Production
Uncertainty associated with lead production relates to the emission factors and activity data used. The direct
smelting emission factor used in primary production is taken from Sjardin (2003) who averaged the values provided by three
other studies (Dutrizac et al. 2000; Morris et al. 1983; Ullman 1997). For secondary production, Sjardin (2003) added a CO2
emission factor associated with battery treatment. The applicability of these emission factors to plants in the United States
is uncertain. There is also a smaller level of uncertainty associated with the accuracy of primary and secondary production
data provided by the USGS which is collected via voluntary surveys; the uncertainty of the activity data is a function of the
reliability of reported plant-level production data and the completeness of the survey response.
Zinc Production
The uncertainty associated with these estimates is two-fold, relating to activity data and emission factors used.
First, there is uncertainty associated with the amount of EAF dust consumed in the United States to produce secondary zinc
using emission-intensive Waelz kilns. Second, there is uncertainty associated with the emission factors used to estimate CO2
emissions from secondary zinc production processes.
Semiconductor Manufacturing
The equation used to estimate uncertainty is:
Total Emissions (Et) = GF1GRP Reported F-GF1G Emissions (Er,f-ghg) + Non-Reporters' Estimated F-GF1G
Emissions (Enr,f-ghg) + GF1GRP Reported N2O Emissions (Er,n2o) + Non-Reporters' Estimated N2O Emissions (Enr,n2o)
where Er and Enr denote totals for the indicated subcategories of emissions for F-GF1G and N2O, respectively.
The uncertainty in Et presented in Table 4-98 of the NIR results from the convolution of four distributions of
emissions, each reflecting separate estimates of possible values of Er^-ghg, Er^o, Enr,f-ghg, and Enr,n2o- The approach and
methods for estimating each distribution and combining them to arrive at the reported 95 percent confidence interval (CI)
are described in the remainder of this section.
The uncertainty estimate of Er, f-ghg, or GF1GRP-reported F-GF1G emissions, is developed based on gas-specific
uncertainty estimates of emissions for two industry segments, one processing 200 mm wafers and one processing 300 mm
wafers. Uncertainties in emissions for each gas and industry segment were developed during the assessment of emission
estimation methods for the subpart I GF1GRP rulemaking in 2012 (see Technical Support for Modifications to the
Fluorinated Greenhouse Gas Emission Estimation Method Option for Semiconductor Facilities under Subpart I, docket
EPA-HQ-OAR-2011-0028).169 The 2012 analysis did not take into account the use of abatement. For the industry segment
that processed 200 mm wafers, estimates of uncertainties at a 95 percent CI ranged from ±29 percent for C3F8 to ±10 percent
for CF4. For the corresponding 300 mm industry segment, estimates of the 95 percent CI ranged from ±36 percent for C4F8
to ±16 percent for CF4. These gas and wafer-specific uncertainty estimates are applied to the total emissions of the facilities
that did not abate emissions as reported under EPA's GHGRP.
For those facilities reporting abatement of emissions under EPA's GHGRP, estimates of uncertainties for the no
abatement industry segments are modified to reflect the use of full abatement (abatement of all gases from all cleaning and
etching equipment) and partial abatement. These assumptions used to develop uncertainties for the partial and full abatement
facilities are identical for 200 mm and 300 mm wafer processing facilities. For all facilities reporting gas abatement, a
triangular distribution of destruction or removal efficiency is assumed for each gas. The triangular distributions range from
an asymmetric and highly uncertain distribution of zero percent minimum to 90 percent maximum with 70 percent most
likely value for CF4 to a symmetric and less uncertain distribution of 85 percent minimum to 95 percent maximum with 90
169 On November 13, 2013, EPA published a final rule revising subpart I (Electronics Manufacturing) of the GHGRP (78 FR
68162). The revised rule includes updated default emission factors and updated default destruction and removal efficiencies that
are slightly different from those that semiconductor manufacturers were required to use to report their 2012 emissions. The
uncertainty analyses that were performed during the development of the revised rule focused on these updated defaults, but are
expected to be reasonably representative of the uncertainties associated with the older defaults, particularly for estimates at the
country level. (They may somewhat underestimate the uncertainties associated with the older defaults at the facility level.) For
simplicity, the 2012 estimates are assumed to be unbiased although in some cases, the updated (and therefore more representative)
defaults are higher or lower than the older defaults. Multiple models and sensitivity scenarios were run for the subpart I analysis.
The uncertainty analysis presented here made use of the Input gas and wafer size model (Model 1) under the following conditions:
Year = 2010, f= 20, n = SIA3.
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percent most likely value for C4F8, NF3, and SF6. For facilities reporting partial abatement, the distribution of fraction of the
gas fed through the abatement device, for each gas, is assumed to be triangularly distributed as well. It is assumed that no
more than 50 percent of the gases are abated (i.e., the maximum value) and that 50 percent is the most likely value and the
minimum is zero percent. Consideration of abatement then resulted in four additional industry segments, two 200-rnm wafer-
processing segments (one fully and one partially abating each gas) and two 300-mm wafer-processing segment (one fully
and the other partially abating each gas). Gas-specific emission uncertainties were estimated by convolving the distributions
of unabated emissions with the appropriate distribution of abatement efficiency for fully and partially abated facilities using
a Montel Carlo simulation.
The uncertainty in Er^-ghg is obtained by allocating the estimates of uncertainties to the total GHGRP-reported
emissions from each of the six industry segments, and then running a Monte Carlo simulation which results in the 95 percent
CI for emissions from GHGRP reporting facilities (Er^-ghg).
The uncertainty in Erin2o is obtained by assuming that the uncertainty in the emissions reported by each of the
GHGRP reporting facilities results from the uncertainty in quantity of N2O consumed and the N2O emission factor (or
utilization). Similar to analyses completed for subpart I (see Technical Support for Modifications to the Fluorinated
Greenhouse Gas Emission Estimation Method Option for Semiconductor Facilities under Subpart I, docket EPA-HQ-OAR-
2011-0028), the uncertainty of N2O consumed was assumed to be 20 percent. Consumption of N2O for GHGRP reporting
facilities was estimated by back- calculating from emissions reported and assuming no abatement. The quantity of N2O
utilized (the complement of the emission factor) was assumed to have a triangular distribution with a minimum value of
zero percent, mode of 20 percent and maximum value of 84 percent. The minimum was selected based on physical
limitations, the mode was set equivalent to the subpart I default N2O utilization rate for chemical vapor deposition, and the
maximum was set equal to the maximum utilization rate found in ISMI Analysis of Nitrous Oxide Survey Data (ISMI, 2009).
The inputs were used to simulate emissions for each of the GHGRP reporting, N20-emitting facilities. The uncertainty for
the total reported N2O emissions was then estimated by combining the uncertainties of each of the facilities reported
emissions using Monte Carlo simulation.
Substitution of Ozone Depleting Substances
Given that emissions of ODS substitutes occur from thousands of different kinds of equipment and from millions
of point and mobile sources throughout the United States, emission estimates must be made using analytical tools such as
the Vintaging Model or the methods outlined in IPCC (2006). Though the model is more comprehensive than the IPCC
default methodology, significant uncertainties still exist with regard to the levels of equipment sales, equipment
characteristics, and end-use emissions profiles that were used to estimate annual emissions for the various compounds.
The uncertainty analysis quantifies the level of uncertainty associated with the aggregate emissions across the 67
end-uses in the Vintaging Model. In order to calculate uncertainty, functional forms were developed to simplify some of the
complex "vintaging" aspects of some end-use sectors, especially with respect to refrigeration and air-conditioning, and to a
lesser degree, fire extinguishing. These sectors calculate emissions based on the entire lifetime of equipment, not just
equipment put into commission in the current year, thereby necessitating simplifying equations. The functional forms used
variables that included growth rates, emission factors, transition from ODSs, change in charge size as a result of the
transition, disposal quantities, disposal emission rates, and either stock for the current year or original ODS consumption.
Uncertainty was estimated around each variable within the functional forms based on expert judgment, and a Monte Carlo
analysis was performed. The most significant sources of uncertainty for this source category include the emission factors
for residential unitary air-conditioners, as well as the percent of non-MDI aerosol propellant that is HFC-152a.
Electrical Transmission and Distribution
To estimate the uncertainty associated with emissions of SFg from Electrical Transmission and Distribution,
uncertainties associated with four quantities were estimated: (1) emissions from Partners, (2) emissions from GHGRP-Only
Reporters, (3) emissions from Non-Reporters, and (4) emissions from manufacturers of electrical equipment.
Nitrous Oxide from Product Uses
The overall uncertainty associated with the 2016 N2O emission estimate from N2O product usage was calculated
using the 2006 IPCC Guidelines (2006) Approach 2 methodology. Uncertainty associated with the parameters used to
estimate N2O emissions include production data, total market share of each end use, and the emission factors applied to each
end use, respectively.
Agriculture
The uncertainty analysis descriptions in this section correspond to some source categories included in the
Agriculture chapter of the Inventory.
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Enteric Fermentation
A quantitative uncertainty analysis for this source category was performed using the IPCC-recommended
Approach 2 uncertainty estimation methodology based on a Monte Carlo Stochastic Simulation technique as described in
ICF (2003). These uncertainty estimates were developed for the 1990 through 2001 Inventory (i.e., 2003 submission to the
UNFCCC). There have been no significant changes to the methodology since that time; consequently, these uncertainty
estimates were directly applied to the 2016 emission estimates in this Inventory.
A total of 185 primary input variables (177 for cattle and 8 for non-cattle) were identified as key input variables
for the uncertainty analysis. A normal distribution was assumed for almost all activity- and emission factor-related input
variables. Triangular distributions were assigned to three input variables (specifically, cow-birth ratios for the three most
recent years included in the 2001 model run) to ensure only positive values would be simulated.
Manure Management
An analysis (ERG 2003a) was conducted for the manure management emission estimates presented in the 1990
through 2001 Inventory (i.e., 2003 submission to the UNFCCC) to determine the uncertainty associated with estimating CH4
and N2O emissions from livestock manure management. These uncertainty estimates were directly applied to the 2016
emission estimates as there have not been significant changes in the methodology since that time.
Rice Cultivation
Sources of uncertainty in the Tier 3 method include management practices, uncertainties in model structure (i.e.,
algorithms and parameterization), and variance associated with the NRI sample. Sources of uncertainty in the IPCC (2006)
Tier 1 method include the emission factors, management practices, and variance associated with the NRI sample. A Monte
Carlo analysis was used to propagate uncertainties in the Tier 1 and 3 methods. The uncertainties from the Tier 1 and 3
approaches are combined to produce the final CH4 emissions estimate using simple error propagation (IPCC 2006).
Agricultural Soil Management
Uncertainty is estimated for each of the following five components of N2O emissions from agricultural soil
management: (1) direct emissions simulated by DAYCENT; (2) the components of indirect emissions (N volatilized and
leached or runoff) simulated by DAYCENT; (3) direct emissions calculated with the IPCC (2006) Tier 1 method; (4) the
components of indirect emissions (N volatilized and leached or runoff) calculated with the IPCC (2006) Tier 1 method; and
(5) indirect emissions estimated with the IPCC (2006) Tier 1 method.
Liming
Uncertainty regarding the amount of limestone and dolomite applied to soils was estimated at ±15 percent with
normal densities (Tepordei 2003; Willett 2013b). Analysis of the uncertainty associated with the emission factors included
the fraction of lime dissolved by nitric acid versus the fraction that reacts with carbonic acid, and the portion of bicarbonate
that leaches through the soil and is transported to the ocean. The probability distribution functions for the fraction of lime
dissolved by nitric acid and the portion of bicarbonate that leaches through the soil were represented as triangular
distributions between ranges of zero and 100 percent of the estimates.
Urea Fertilization
The largest source of uncertainty was the default emission factor, which assumes that 100 percent of the C in
CO(NH2)2 applied to soils is ultimately emitted into the environment as CO2. In addition, urea consumption data also have
uncertainty that is propagated through the emission calculation using a Monte Carlo simulation approach as described by
the IPCC (2006).
Field Burning of Agricultural Residues
Due to data limitations, uncertainty resulting from the fact that emissions from burning of Kentucky bluegrass and
"other crop" residues are not included in the emissions estimates was not incorporated into the uncertainty analysis.
Land Use, Land-Use Change, and Forestry
The uncertainty analysis descriptions in this section correspond to source categories included in the Land Use,
Land-Use Change, and Forestry chapter of the Inventory.
Forest Land Remaining Forest Land
The uncertainty analysis descriptions in this section correspond to source categories included in the Forest Land
Remaining Forest Land sub-chapter of Land Use, Land-Use Change, and Forestry chapter of the Inventory.
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Changes in Forest Carbon Stocks
A quantitative uncertainty analysis placed bounds on current flux for forest ecosystems through a combination of
sample-based and model-based approaches to uncertainty for forest ecosystem CO2 flux (IPCC Approach 1).
Non-CC>2 Emissions from Forest Fires
In order to quantify the uncertainties for non-CC>2 emissions from wildfires and prescribed burns, a Monte
Carlo (IPCC Approach 2) sampling approach was employed to propagate uncertainty based on the model and data
applied for U.S. forest land. See IPCC (2006) and Annex 3.13 for the quantities and assumptions employed to
define and propagate uncertainty.
N2O Emissions from N Additions to Forest Soils
The amount of N2O emitted from forests depends not only on N inputs and fertilized area, but also on a
large number of variables, including organic C availability, oxygen gas partial pressure, soil moisture content, pH,
temperature, and tree planting/harvesting cycles. The effect of the combined interaction of these variables on N2O
flux is complex and highly uncertain.
Uncertainties exist in the fertilization rates, annual area of forest lands receiving fertilizer, and the
emission factors. The uncertainty ranges around the 2004 activity data and emission factor input variables are
directly applied to the 2016 emission estimates. IPCC (2006) provided estimates for the uncertainty associated
with direct and indirect N2O emission factor for synthetic N fertilizer application to soils.
Drained Organic Soils
Uncertainties are based on the sampling error associated with forest area and the uncertainties provided
in the Chapter 2 (IPCC 2014) emissions factors.
Land Converted to Forest Land
Uncertainty estimates for forest pool C stock changes were developed using the same methodologies as described
in the Forest Land Remaining Forest Land section for aboveground and belowground biomass, dead wood, and litter. The
exception was when IPCC default estimates were used for reference C stocks in certain conversion categories (i.e., Cropland
Converted to Forest Land and Grassland Converted to Forest Land). In those cases, the uncertainties associated with the
IPCC (2006) defaults were included in the uncertainty calculations.
Cropland Remaining Cropland
The uncertainty analysis descriptions in this section correspond to source categories included in the Cropland
Remaining Cropland sub-chapter of Land Use, Land-Use Change, and Forestry chapter of the Inventory.
Mineral and Organic Soil Carbon Stock Change
Uncertainty associated with the Cropland Remaining Cropland land-use category was addressed for
changes in agricultural soil C stocks (including both mineral and organic soils).
Land Converted to Cropland
The uncertainty analysis for biomass, dead wood and litter C losses with Forest Land Converted to Cropland is
conducted in the same way as the uncertainty assessment for forest ecosystem C flux in the Forest Land Remaining Forest
Land category.
Uncertainty estimates are presented in Table 6-33 of the NIR for each subsource (i.e., biomass C stocks, dead wood
C stocks, litter C stocks, mineral soil C stocks and organic soil C stocks) and the method applied in the Inventory analysis
(i.e., Tier 2 and Tier 3).
Grassland Remaining Grassland
The uncertainty analysis descriptions in this section correspond to source categories included in the Grassland
Remaining Grassland sub-chapter of Land Use, Land-Use Change, and Forestry chapter of the Inventory.
Soil Carbon Stock Changes
Uncertainty analysis for mineral soil C stock changes using the Tier 3 and Tier 2 methodologies are based
on a Monte Carlo approach that is described in the Cropland Remaining Cropland section.
Non-C02 Emissions from Grassland Fires
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Uncertainty is associated with lack of reporting of emissions from biomass burning in grassland of
Alaska. There is also uncertainty due to lack of reporting combustion of woody biomass, and this is another planned
improvement.
Land Converted to Grassland
The uncertainty analysis for biomass, dead wood and litter C losses with Forest Land Converted to Grassland is
conducted in the same way as the uncertainty assessment for forest ecosystem C flux in the Forest Land Remaining Forest
Land category.
Wetlands Remaining Wetlands
The uncertainty analysis descriptions in this section correspond to source categories included in the Wetlands
Remaining Wetlands sub-chapter of Land Use, Land-Use Change, and Forestry chapter of the Inventory.
Peatlands Remaining Peatlands
The uncertainty associated with peat production data was estimated to be ± 25 percent (Apodaca 2008)
and assumed to be normally distributed. The uncertainty associated with peat production data stems from the fact
that the USGS receives data from the smaller peat producers but estimates production from some larger peat
distributors. The peat type production percentages were assumed to have the same uncertainty values and
distribution as the peat production data (i.e., ± 25 percent with a normal distribution). The uncertainty associated
with the reported production data for Alaska was assumed to be the same as for the lower 48 states, or ± 25 percent
with a normal distribution. It should be noted that the DGGS estimates that around half of producers do not respond
to their survey with peat production data; therefore, the production numbers reported are likely to underestimate
Alaska peat production (Szumigala 2008). The uncertainty associated with the average bulk density values was
estimated to be ± 25 percent with a normal distribution (Apodaca 2008). IPCC (2006 and 2013) gives uncertainty
values for the emissions factors for the area of peat deposits managed for peat extraction based on the range of
underlying data used to determine the emission factors. The uncertainty associated with the emission factors was
assumed to be triangularly distributed. The uncertainty values surrounding the C fractions were based on IPCC
(2006) and the uncertainty was assumed to be uniformly distributed. The uncertainty values associated with the
fraction of peatland covered by ditches was assumed to be ± 100 percent with a normal distribution based on the
assumption that greater than 10 percent coverage, the upper uncertainty bound, is not typical of drained organic
soils outside of The Netherlands (IPCC 2013).
Coastal Wetlands
Underlying uncertainties in estimates of soil C stock changes and methane emissions include error in
uncertainties associated with Tier 2 literature values of soil C stocks and methane flux and assumptions that
underlie the methodological approaches applied and uncertainties linked to interpretation of remote sensing data.
Uncertainty specific to coastal wetlands include differentiation of palustrine and estuarine community classes
which determines the soil C stock and methane flux applied. Soil C stocks and methane fluxes applied are
determined from vegetation community classes across the coastal zone and identified by NOAA C-CAP.
Community classes are further subcategorized by climate zones and growth form (forest, shrub-scrub, marsh). Soil
C stock data for all subcategories are not available and thus assumptions were applied using expert judgement
about the most appropriate assignment of a soil C stock to a disaggregation of a community class.
Uncertainties in N2O emissions from aquaculture are based on expert judgement for the NOAA Fisheries
of the United States fisheries production data (± 100 percent) multiplied by default uncertainty level for N2O
emissions found in Table 4.15, chapter 4 of the Wetlands Supplement.
Land Converted to Coastal Wetlands
Underlying uncertainties in estimates of soil C removal factors and CH4 include error in uncertainties associated
with Tier 2 literature values of soil C removal estimates and CH4 flux, assumptions that underlie the methodological
approaches applied and uncertainties linked to interpretation of remote sensing data.
Settlements Remaining Settlements
The uncertainty analysis descriptions in this section correspond to source categories included in the Settlements
Remaining Settlements sub-chapter of the Land Use, Land-Use Change, and Forestry chapter of the Inventory.
Soil Carbon Stock Changes
Uncertainty of soil carbon stock changes is a result of soil C losses from drained organic soils in
Settlements Remaining Settlements.
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Changes in Carbon Stocks in Urban Trees
Uncertainty associated with changes in C stocks in urban trees includes the uncertainty associated with urban area,
percent urban tree coverage, and estimates of gross and net C sequestration for each of the 50 states and the District of
Columbia.
Additional uncertainty is associated with the biomass equations, conversion factors, and decomposition
assumptions used to calculate C sequestration and emission estimates (Nowak et al. 2002).
N2O Fluxes from Settlement Soils
The amount of N2O emitted from settlement soils depends not only on N inputs and area of drained organic soils,
but also on a large number of variables that can influence rates of nitrification and denitrification, including organic C
availability; rate, application method, and timing of N input; oxygen gas partial pressure; soil moisture content; pH;
temperature; and irrigation/watering practices. The effect of the combined interaction of these variables on N2O emissions
is complex and highly uncertain. The IPCC default methodology does not explicitly incorporate any of these variables,
except variations in the total amount of fertilizer N and biosolids applications. All settlement soils are treated equivalently
under this methodology.
Uncertainties exist in both the fertilizer N and biosolids application rates in addition to the emission factors.
Uncertainty in fertilizer N application is assigned a default level of ±50 percent. 170 Uncertainty in drained organic soils is
based on the estimated variance from the NRI survey (USDA-NRCS 2015). For 2013 to 2016, there is also additional
uncertainty associated with the surrogate data method. Uncertainty in the amounts of biosolids applied to non-agricultural
lands and used in surface disposal is derived from variability in several factors, including: (1) N content of biosolids; (2)
total sludge applied in 2000; (3) wastewater existing flow in 1996 and 2000; and (4) the biosolids disposal practice
distributions to non-agricultural land application and surface disposal. Uncertainty in the direct and indirect emission factors
is provided by IPCC (2006).
Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills
The uncertainty analysis for landfilled yard trimmings and food scraps includes an evaluation of the effects of
uncertainty for the following data and factors: disposal in landfills per year (tons of C), initial C content, moisture content,
decay rate, and proportion of C stored. The C storage landfill estimates are also a function of the composition of the yard
trimmings (i.e., the proportions of grass, leaves and branches in the yard trimmings mixture). There are respective
uncertainties associated with each of these factors.
Waste
The uncertainty analysis descriptions in this section correspond to source categories included in the Waste chapter
of the Inventory.
Landfills
Several types of uncertainty are associated with the estimates of CH4 emissions from MSW and industrial waste
landfills when the FOD method is applied directly for 1990 to 2004 in the Waste Model and, to some extent, in the GHGRP
methodology. The approach used in the MSW emission estimates assumes that the CH4 generation potential (L0) and the
rate of decay that produces CH4 from MSW, as determined from several studies of CH4 recovery at MSW landfills, are
representative of conditions at U.S. MSW landfills. When this top-down approach is applied at the nationwide level, the
uncertainties are assumed to be less than when applying this approach to individual landfills and then aggregating the results
to the national level. In other words, the FOD method as applied in this Inventory is not facility-specific modeling and while
this approach may over- or under-estimate CH4 generation at some landfills if used at the facility-level, the result is expected
to balance out because it is being applied nationwide. There is also a high degree of uncertainty and variability associated
with the FOD model, particularly when a homogeneous waste composition and hypothetical decomposition rates are applied
to heterogeneous landfills (IPCC 2006). There is less uncertainty in the GHGRP data because this methodology is facility-
specific, uses directly measured CH4 recovery data (when applicable), and allows for a variety of landfill gas collection
efficiencies, destruction efficiencies, and/or oxidation factors to be used.
Uncertainty also exists in the scale-up factor applied for years 2005 to 2009 and in the back-casted emissions
estimates for 2005 to 2009. Limited information is available for landfills that do not report to the GHGRP and assumptions
were made for many landfills in order to estimate the scale-up factor. Additionally, a simple methodology was used to back-
170 No uncertainty is provided with the USGS fertilizer consumption data (Ruddy et al. 2006) so a conservative ±50 percent
is used in the analysis. Biosolids data are also assumed to have an uncertainty of ±50 percent.
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cast emissions for 2005 to 2009 using the GHGRP emissions from 2010 to 2016. This methodology does not factor in annual
landfill to landfill changes in landfill CH4 generation and recovery.
Aside from the uncertainty in estimating landfill CH4 generation, uncertainty also exists in the estimates of the
landfill gas oxidized. Another significant source of uncertainty lies with the estimates of CH4 recovered by flaring and gas-
to-energy projects at MSW landfills that are sourced from the Inventory's CH4 recovery databases (used for years 1990 to
2004).
The lack of landfill-specific information regarding the number and type of industrial waste landfills in the United
States is a primary source of uncertainty with respect to the industrial waste generation and emission estimates.
Wastewater Treatment
The overall uncertainty associated with both the 2016 CH4 and N2O emission estimates from wastewater treatment
and discharge was calculated using the 2006 IPCC Guidelines Approach 2 methodology (IPCC 2006). Uncertainty
associated with the parameters used to estimate CH4 emissions include that of numerous input variables used to model
emissions from domestic wastewater, and wastewater from pulp and paper manufacturing, meat and poultry processing,
fruits and vegetable processing, ethanol production, and petroleum refining.
Uncertainty associated with the parameters used to estimate N2O emissions include that of biosolids disposal, total
U.S. population, average protein consumed per person, fraction of N in protein, non-consumption nitrogen factor, emission
factors per capita and per mass of sewage-N, and for the percentage of total population using centralized wastewater
treatment plants.
Uncertainty associated with constructed wetlands parameters including U.S. population served by constructed
wetlands, and emission and conversion factors are from IPCC (2014), whereas uncertainty associated with POTW flow to
constructed wetlands and influent BOD and nitrogen concentrations were based on expert judgment.
Composting
The estimated uncertainty from the 2006 IPCC Guidelines is ±50 percent for the Approach 1 methodology.
References
Apodaca, L. (2008) E-mail correspondence. Lori Apodaca, Peat Commodity Specialist, USGS and Emily Rowan, ICF
International. October and November.
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.
ERG (2003) "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.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and
K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
IPCC (2013) 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands.
Hiraishi, T., Krug, T., Tanabe, K., Srivastava, N, Baasansuren, J., Fukuda, M. and Troxler, T.G. (eds.). Published:
IPCC, Switzerland.
Mutmansky, Jan M. and Yanbei Wang (2000) Analysis of Potential Errors in Determination of Coal Mine Annual
Methane Emissions. Mineral Resources Engineering, 9(4).
Nowak, D.J., D.E. Crane, J.C. Stevens, and M. Ibarra (2002) Brooklyn's Urban Forest. General Technical Report NE-290.
U.S. Department of Agriculture Forest Service, Newtown Square, PA.
Tepordei, V.V. (2003) Personal communication. Valentin Tepordei, U.S. Geological Survey and ICF Consulting, August
18, 2003.
Willett, J.C. (2013) Personal Communication. Jason Willet, U.S. Geological Survey and ICF International. September 9,
2013.
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ANNEX 8 QA/QC Procedures
8.1. Background
The purpose of this annex is to describe the Quality Assurance/Quality Control (QA/QC) procedures and
information quality considerations that are used throughout the process of creating and compiling the Inventory of U.S.
Greenhouse Gas Emissions and Sinks. This includes the evaluation of the quality and relevance of data and models used as
inputs into the Inventory; proper management, incorporation, and aggregation of data; and review of the numbers and
estimates to ensure that they are as accurate and transparent as possible. Quality control—in the form of both good practices
(such as documentation procedures) and checks on whether good practices and procedures are being followed—is applied
at every stage of inventory development and document preparation. In addition, quality assurance occurs at two stages—an
expert review and a public review. While both phases can significantly contribute to the quality of the Inventory, the public
review phase is also essential for promoting the openness 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 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-19. 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 2006IPCC Guidelines (IPCC
2006).
• Quality Control, consideration of secondary data and category-specific checks (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.
• General (Tier I) and category-specific (Tier 2) Checks: quality controls and checks, as recommended by the
IPCC Good Practice Guidance and 2006 IPCC Guidelines (IPCC 2006).
• Record Keeping: provisions to track which procedures have been followed, the results of the QA/QC process,
uncertainty analysis, and feedback mechanisms for corrective action based on the results of the investigations,
which provide for continual data quality improvement and guided research efforts.
• Multi-Year Implementation : a schedule for coordinating the application of QA/QC procedures across multiple
years, especially for category-specific QC, focusing on key categories.
• Interaction and Coordination: promoting communication within the EPA, across Federal agencies and
departments, state government programs, and research institutions and consulting firms involved in supplying
data or preparing estimates for the Inventory. The QA/QC Management Plan itself is intended to be revised to
reflect new information that becomes available as the program develops, methods are improved, or additional
supporting documents become necessary.
A-487
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In addition, based on the national QA/QC Management Plan for the Inventory, source and sink-specific QA/QC
plans have been developed for a number of sources and sinks. These plans follow the procedures outlined in the national
QA/QC plan, tailoring the procedures to the specific text and spreadsheets of the individual sources. For each greenhouse
gas emissions source or sink included in this Inventory, minimum general QA/QC analysis consistent with Vol. 1, Chapter
6 of the 2006IPCC Guidelines has been undertaken. Where QA/QC activities for a particular source 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-19: U.S.QA/QG Plan Summary
« Obtain data in electronic
* Contact reports for non-
* Clearly label parameters,
format (if possible}
electronic communications
units, and conversion
• Review spreadsheet
• Provide ceil references for
factors
construction
primary data elements
* Review spreadsheet
Avoid hardwiring
• Obtain copies of all data
integrity
* Use data validation
sources
• Equations
Protect cells
¦ Units
* Inputs and outputs
« Develop automatic
* List and location of any
checkers for:
working/external
* Develop automated
* Out! iers. negative
values, or missing
spreadsheets
checkers for;
data
• Document assumptions
* Input ranges
Vanabfe types
* Calculations
match values
* Emission aggregation
Time series
consistency
* Maintain tracking tab for
status of gathering
effct's
*
>.-*¦ ChecfeifiputdaMfor' '¦¦¦
• Check in
* Reproduce calculations
transcription errors
spreadsheet andtext ~or
» Reviewi-me series
• inspect automatic
accuracy ar.d style
z . consistency'. • ;•
checkers
• Check re-erencedocket for
• Review changes in
* Identify spreadsheet
f'QVJ citations
riata/cons*stercy vv'th i?CC
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• Hev?ew documentation for
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provide acfdlttor.a*
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QA/QC checks
changes
Common starting
versions for each
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Utilize unalterable
summarytab for each
source spreadsheet for
linkingto a master
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Follow strict version
control procedures
Document QA/QC
procedures
Data Gathering
Data Documentation Calculating Emissions
Cross-Cutting
Coordination
8.3. Assessment Factors
The Inventory of U.S. Greenhouse Gas Emissions and Sinks development process follows guidance outlined in
EP A's Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity, of Information
Disseminated by the Environmental Protection Agency171 and A Summary of General Assessment Factors for Evaluating
the Quality of Scientific and Technical Information.172 This includes evaluating the data and models used as inputs into the
171 EPA report #260R-02-008, October 2002, Available online at .
172 EPA report #100/B-03/001, June 2003, Available online at , and Addendum to: A Summary of General Assessment Factors for
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Inventory against the five general assessment factors: soundness, applicability and utility, clarity and completeness,
uncertainty and variability, evaluation and review. Table A-287 defines each factor and explains how it was considered
during the process of creating the current Inventory.
Table A-287: Assessment Factors and Definitions173
General Assessment
Factor
Definition
How the Factor was Considered
Soundness (AF1)
The extent to which the scientific
and technical procedures,
measures, methods or models
employed to generate the
information are reasonable for, and
consistent with their intended
application.
The underlying data, methodologies, and models used to generate the
Inventory of U.S. Greenhouse Gas Emissions and Sinks are
reasonable for and consistent with their intended application, to
provide information regarding all sources and sinks of greenhouse
gases in the United States for the Inventory year, as required per
UNFCCC Annex I country reporting requirements.
The U.S. emissions calculations follow the 2006IPCC Guidelines
developed specifically for UNFCCC inventory reporting. They are
based on the best available, peer-reviewed scientific information, and
have been used by the international community for over 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 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,
173 Evaluating the Quality of Scientific and Technical Information, December 2012, Available online at
.
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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 2006IPCC 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.
8.4. Responses During the Review Process
During the annual preparation of the Inventory of U.S. Greenhouse Gas Emissions and Sinks, EPA receives
comments and implements methodological improvements to the U.S. Inventory to improve the transparency, accuracy,
completeness, comparability, and consistency of emission estimates. EPA reviews the significance of the improvement, QC,
and uncertainty assessments when considering improvements to the Inventory. Planned improvements are documented
within each source and sink category's Planned Improvements section, as well as the Recalculations and Improvements
chapter. Additionally, the Executive Summary, also highlights key changes in methodologies from previous Inventory
reports.
EPA is continually working to improve the Inventory in response to the feedback received during the Expert,
Public, and UNFCCC Review periods, as well as stakeholder outreach. For instance, as mentioned in the Planned
Improvements section of the Landfills source category (Section 7.1), EPA has engaged in stakeholder outreach to increase
the transparency in the Inventory methodology and to identify supplemental data sources that can lead to methodological
improvements.
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.174
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 inventories:
https://unfccc.int/resource/docs/2013/copl9/eng/10a03.pdf#page=2
• UNFCCC Review Process and Guidelines for annual national greenhouse gas inventories:
https://unfccc.int/resource/docs/2014/cop20/eng/10a03.pdf#page=3
174 See .
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• Inventory Review reports of annual submissions (latest reviews):
https://unfccc.int/process/transparency-and-reporting/reporting-and-review-under-the-
convention/greenhouse-gas-inventories-annex-i-parties/inventory-review-reports-2016
Table A-288 summarizes the areas of improvement identified through UN review and the response column
provides a status of the findings.
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Table fl-288: Response to UN Review of the 2016 Inventory Submission
No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
1.
(G.1)
General
NA
Improve the completeness of the inventory, in particular for those categories for which
there are methodologies in IPCC guidelines for national greenhouse gas inventories. A
number of categories are reported as "NE" because no data are available (as reported
in Common Reporting Format (CRF) table 9) for which methodologies are available in
the 2006 IPCC Guidelines.
Addressing. See updated explanations
in Annex 5 and CRF Table 9 of the
current Inventory.
2.
(G.2)
General
NA
Ensure time-series consistency when using Greenhouse Gas Reporting Program
(GHGRP) data directly in the national GHG inventory. The United States reported that
EPA will continue to assess GHGRP data to improve the inventory.
Completed. When GHGRP data are
used, respective categories address
time-series consistency in accordance
with IPCC's technical bulletin on use of
facility-specific data in national
greenhouse gas inventories and Vol. 1,
Chapter 5 on Time Series Consistency
from the 2006 IPCC Guidelines.
3.
(G.5)
General
NA - Multiple
categories
Use the plant-specific emissions from GHGRP to improve the disaggregation of
combustion and industrial process emissions. In the section on planned improvements
in the national inventory report (NIR) (1 ,B.2.c Venting and flaring - oil and natural gas
-CO2 and CH4), the United States includes the investigation into the appropriateness
of using associated gas venting and flaring data from GHGRP.
See Introduction to IPPU chapter of
current Inventory (i.e., 2018
submission). The U.S. has integrated
GHGRP or other appropriate data where
feasible to improve disaggregation of
combustion and industrial process
emissions and also indicated under
category-level planned improvement
discussions where further work is being
considered while also avoiding double
counting of emissions. See Response in
Energy, rows 9 and 11.
Energy
4.
(E.1)
Energy
NA
Include information on the progress made in the plan to use GHGRP data to: develop
more accurate national emission factors (EFs) based on plant-specific measurements;
estimate emissions for more detailed categories and subcategories; disaggregate
energy consumption data based on the facility-level reporting, and indicate which data
have been sourced from GHGRP and which from other sources. The United States
stated in the NIR 2016 (pp. 3 and 4) that the "GHGRP dataset and the data presented
in this inventory report are complementary and, as indicated in the respective planned
improvements sections for categories in this chapter, EPA is analyzing how to use
facility-level GHGRP data to improve the national estimates presented in this
inventory."
Completed. For the current Inventory
(i.e., 2018 submission), EPA clarified
how GHGRP data are used as
applicable, for example estimating
emissions from more detailed
categories, and for the next Inventory
(i.e., 2019 submission), EPA will update
the status of its approach. EPA
continues to assess how to use facility-
level GHGRP data to improve the
national estimates.
5.
(E.2)
Energy
1.A. Fuel combustion-
sectoral approach - all
fuels-CO2, CH4 and
Collect the necessary activity data (AD) and EFs to prepare emission estimates for the
combustion of biomass and other fuels for these categories, including those used in
the United States territories, focusing resources, as appropriate, on improvements in
Addressing. The CRF Tables document
accounting for emissions from these
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N20 (29, 2013) (32 and
51,2012)
line with the Revised 1996IPCC Guidelines and the IPCC Good Practice Guidance,
and report the corresponding emissions.
The United States still has subcategories for which estimates have not been prepared,
for example: biomass consumption under the category other (1 ,A.5.a); gaseous fuels
for railways (1 ,A.3.c) and domestic navigation (1 ,A.3.d) under the category transport;
AD for exploration of oil (1 ,B.2.a) and exploration and processing (1 ,B.2.b) under the
category oil, natural gas and other emissions from energy production; and AD of CO2
transport and storage (1 .C).
sources, including if they are Included
Elsewhere (IE) or Not Estimated (NE).
6.
(E.4)
Energy
NA- Multiple
categories
Report emissions from all categories and for the full time series at the most
disaggregated level, in line with the UNFCCC Annex I inventory reporting guidelines, in
particular for manufacturing industries and construction and fugitive emissions.
The Expert Review Team (ERT) noted that the situation has been gradually improving
since the 2013 submission and that individualized emission estimates for petroleum
refining (1 .A.1 .b) and subcategories under manufacturing industries and construction
(1 .A.2) are now reported for all fuels excluding biomass and other fuels. However, the
lack of disaggregation remains in some categories, in particular
agriculture/forestry/fisheries (1 ,A.4.c) under other sectors, venting and flaring under
fugitive emissions (1 ,B.2.c), heavy-duty trucks and buses (1 ,A.3.b.iii) under the
category transport, and commercial and institutional (1 ,A.4.a) under the category other
sectors.
Addressing. Some emissions previously
not estimated (1 ,B.2.a and 1 ,B.2.b,
exploration in oil and gas systems) have
been included in the current Inventory
(i.e., 2018 submission). Emissions are
reported to the disaggregated level
available with the data and the CRF
Tables document accounting for
emissions from all applicable sources,
including if they are Included Elsewhere
(IE) or Not Estimated (NE).
7.
(E.5)
Energy
Fuel combustion -
reference approach -
all fuels-CO2, CH4
and N20 (32,2013)
(41,2012)
Provide a more transparent clarification of how the difference in emissions between the
reference and the sectoral approaches is determined and which fuels are subtracted
as non-energy use (NEU) and feedstocks.
The United States provided a theoretical explanation of the reference approach, and
also indicated in the NIR (p. A-431, Annex 4) that "Bunker fuels and feedstocks
accounted for in the IPPU chapter are subtracted from these estimates, while fuel
consumption in U.S. Territories is added". The ERT notes that transparency is not fully
achieved in the information provided for some categories, especially for NEU of fuels in
the iron and steel category.
Completed. More information on the Iron
and Steel adjustment for NEU of fuels is
included in Annex 2 of the current
Inventory (i.e., 2018 submission).
8.
(E.6)
Energy
International aviation -
liquid fuels-CO2, CH4
and N20 (35,2013)
Harmonize and reconcile the data between the reference and the sectoral approach or
furnish an adequate explanation of these inconsistencies, where appropriate.
The United States indicated in the NIR (p. 3-90) that "the feasibility of including data
from a broader range of domestic and international sources for bunker fuels, including
data from studies such as the Third IMO GHG Study 2014, is being considered".
Addressing. EPA continues to evaluate
the feasibility of using data from other
sources for the reference approach and
will update as appropriate in the next
Inventory (i.e., 2019 submission).
9.
(E.7,
G.6)
Energy
Feedstocks, reductants
and other NEU of fuels
— all fuels — CO2, CH4
Allocate emissions from NEU of fuels reported under the energy sector to the correct
categories in accordance with the UNFCCC Annex I inventory reporting guidelines and
the Revised 1996 IPCC Guidelines.
Completed. The United States has
improved the explanation of its country-
specific approach to the allocation of
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and N20 (38,2013)
(47,2012)
Report only emissions from fuels combusted for the use of energy under fuel
combustion, and reallocate the relevant emissions currently reported under the
subcategory NEU (other) and part of the fuel used under the subcategory United
States territories (other).
In CRF table 1 .A.4, the United States reported aggregated data and emissions from
liquid fuels, solid fuels and gaseous fuels under the subcategory NEU (other). During
the review, the United States explained that it uses a country-specific methodology for
the non-energy use of fuels in line with paragraph 10 of the UNFCCC Annex I
inventory reporting guidelines to most accurately portray emissions from this category
for the United States and reported in line with paragraph 35 of the UNFCCC Annex I
inventory reporting guidelines. However, noting that paragraph 35 refers to the
requirement to report on "how feedstocks and non-energy use of fuels have been
accounted for in the inventory, under the energy or industrial processes sector, in
accordance with the 2006IPCC Guidelines, and noting that the 2006IPCC
Guidelines", and also noting that this indicates that the reporting of emissions from
NEU under the IPPU sector and the emissions of combustion is under the energy
sector, with specific exception, e.g., the coke making, the ERT is of the view that the
issue identified in paragraph 38 of the ARR2014 and paragraph 47 in ARR2012 is not
yet resolved.175
NEU of fuels in the introduction of the
IPPU chapter and Annex 2.
The United States uses a country-
specific methodology for non-energy
use of fuels in line with para. 10,
Decision 24/CP.19 to most accurately
portray U.S. emissions from NEU.
The United States continues to evaluate
ways to update this approach and
provides more clarification as applicable
in the current Inventory (i.e., 2018
submission).
10.
(E.8)
Energy
1.A. Fuel combustion -
sectoral approach -
solid, liquid and
gaseous fuels - CO2,
N20 and CH4 (39,
2013)
Complete the collection of AD for the consumption of biomass and other fuels for the
years 2010 and 2011.
Consumption of biomass in the subcategory industries (1 .A.lc.i) and consumption of
liquid, solid, gaseous and biomass fuels in the subcategory other energy industries
(1 .A.1 ciii) under manufacture of solid fuels and other energy industries are reported as
"IE", and the United States explained that data are not available to estimate fuel
consumption separately from those for the category public electricity and heat
production (1 .A.1 .a).
The United States indicated in the NIR 2016 (p.3-32) that "In examining data from
EPA's GHGRP that would be useful to improve the emission estimates for the CO2
from fossil fuel combustion category, particular attention will also be made to ensure
time-series consistency, as the facility-level reporting data from EPA's GHGRP are not
available for all inventory years as reported in this Inventory". The United States further
explained that in the NIR, "analyses will be conducted to align reported facility-level
fuel types and IPCC fuel types per the national energy statistics. Additional work will
commence to ensure CO2 emissions from biomass are separated in the facility-level
reported data, and maintaining consistency with national energy statistics provided by
the U.S. Energy Information Administration (EIA)".
Addressing. EPA continues to examine
the use of GHGRP data for
disaggregation of emission estimates.
Further clarification is planned for the
next Inventory (i.e., 2019 submission).
175 The UNFCCC ERT also raised a similar comment on emissions from NEU under the General section. To streamline the review, both comments are consolidated here.
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11.
(E.11)
Energy
1.B.2.C Venting and
flaring - oil and natural
gas-C02 and Cm
(44,2013)
Make efforts to use GHGRP data to improve the resolution and disaggregation of
fugitive emissions from flaring and venting.
In the section on planned improvements in the NIR (p.3-66), the United States includes
the investigation into the appropriateness of using associated gas venting and flaring
data from GHGRP.
Completed. In this year's inventory, EPA
has included improved estimates for
associated gas venting and flaring CO2
and CH4 emissions using GHGRP data.
However, based on available U.S. data
and methods, the United States cannot
accurately develop an estimate of
vented versus flaring versus leak
emissions consistently across natural
gas and petroleum systems.
12.
(E.13)
Energy
1.A. Fuel combustion-
sectoral approach - all
fuels-CO2, CH4 and
N20
Previous review reports have noted that the inventory for the energy sector of the
United States is not sufficiently transparent, given that emissions from consumption of
all fuel types for some categories were aggregated and reported under the
subcategory other, under manufacturing industries and construction. During the
review, the United States pointed out that it has reported disaggregated emissions to
the extent possible given the break in data collection by industrial classification with
currently available data. The Party also indicated that some of the emissions under
transport (1 A3), for example emissions from heavy-duty trucks and buses, are
disaggregated in the CRF tables of the Party's 2016 submission.
Referring to the recommendation in previous review reports that the Party estimate
emissions from all categories and for the full time series at the most disaggregated
level, in line with the UNFCCC Annex I inventory reporting guidelines, the ERT
recommends that the Party report disaggregated categories to the level where the EFs
are distinguished (e.g. heavy-duty trucks and buses under road transport and also the
categories and subcategories referred to in E.18 below).
Completed. Emissions are reported to
the disaggregated level available with
the data and the CRF tables document
accounting for emissions from all
applicable sources including if they are
Included Elsewhere (IE) or Not
Estimated (NE).
Annex 5 includes further information on
NE sources.
13.
(E.14)
Energy
1 ,A.3.b Road
transportation - liquid
fuels - CO2
The NIR states that the number of vehicle miles travelled by light-duty motor vehicles
(passenger cars and light-duty trucks) increased by 37 percent from 1990 to 2014 as a
result of a confluence of factors, including population growth, economic growth, urban
sprawl and periods of low fuel prices. However, the CO2 emissions from light-duty
trucks have remained almost the same during this period. One of the reasons provided
by the Party in response to a question raised by the ERT during the review is an
increased share of new vehicles in the respective total stocks, resulting in better fuel
economy of the respective vehicular stock. However, these details are not provided in
the NIR. During the review, the United States also provided additional information on
penetration, sales and fuel efficiency of new road vehicles over the years. The ERT
considered that this helps to clarify the downward trends to a certain extent.
The ERT recommends that the United States reference data provided in Annex 3.2 to
the NIR when discussing trends in CO2 emissions from road transportation by vehicle
mode and provide more information on the national average fuel economy for each
major road transport mode at a disaggregated level where the EFs (e.g. passenger
Completed. For the current Inventory
(i.e., 2018 submission) when discussing
trends in the transportation sector, the
United States references Annex 3.2
data by vehicle mode and provides
transparent information on vehicle miles
travelled and the share of new vehicles
(in vehicle miles travelled) where
possible.
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U.S. Response
cars, light-duty trucks, heavy-duty trucks, buses) are distinguished for each inventory
year.
14.
(E.15)
Energy
1 ,A.3.b Road
transportation - liquid
fuels - Cm and N2O
N2O emissions from road transport are a key category for the United States in 2014.
The ERT noted that the implied emission factors (lEFs) for N2O emissions from
gasoline have consistently declined from 8.78 kg/TJ in 1990 to 2.55 kg/TJ in 2014.
Similarly, the lEFs for CH4 emissions have consistently declined from 14.55 kg/TJ in
1990 to 3.57 kg/TJ in 2014. The reasons for this are not transparently explained in the
NIR. During the review, the Party provided additional information on penetration, sales
and fuel efficiency of new road vehicles over the years of the inventory. The ERT
considered that this helps to clarify the downward trends to a certain extent.
The ERT recommends that, in order to improve the transparency of its reporting, the
Party reference data in Annex 3.2 when discussing trends in CH4 and N2O emissions
from road transportation by vehicle mode and provide information on penetration, sales
and fuel efficiency of new road vehicles over the years of the inventory in its NIR to
demonstrate the decrease in CH4 and N2O emissions is due to an increase in vehicle
miles traveled (VMT) percentage by vehicles with lower emission factors (i.e. Low
emission vehicles (LEV) and EPA Tier 2).
Completed. For the current Inventory
(i.e., 2018 submission), the United
States references and discusses
updates to the CH4 and N2O EFs for
mobile sources and explains impacts on
emission trends.
15.
(E.16,
E.17)
Energy
1 ,A.3.c Railways -
gaseous fuels-CO2,
CH4 and N2O
1 ,A.3.d Domestic
navigation - gaseous
fuels - CO2, CH4 and
N20
In CRF table 9, the United States has used the notation key "NE" with the explanation:
"It is unlikely that gaseous fuels are used by railways [or by shipping], but if small uses
occur this fuel use is reported under the aggregated commercial category". The ERT
noted that, in the absence of any further information, this explanation is not sufficiently
transparent to allow the ERT to consider whether the Party should be using the
notation key "NE" or "IE" (i.e. included in the subcategory commercial/institutional
under other sectors, as reported in CRF table 9).
Completed: This emission source was
changed to Included Elsewhere (IE) in
the previous Inventory (i.e., 2017
submission) and CRF with a discussion
of how that was determined.
The ERT recommends that the Party provide an explanation as to why CO2, CH4 and
N2O emissions from gaseous fuels used in railways and by shipping have not been
estimated in both the NIR and CRF table 9, in accordance with paragraph 37 of the
UNFCCC Annex I inventory reporting guidelines and in a transparent manner. Further,
the ERT recommends that, if the emissions from the small uses of gaseous fuels are
considered to be insignificant, the Party provide in the NIR justification for the
exclusion in terms of the likely level of emissions, in accordance with paragraph 37(b)
of the UNFCCC Annex I inventory reporting guidelines.176
16.
(E.18)
Energy
1 .A.5. Other (not
specified elsewhere) -
liquid, solid and
gaseous fuels - CO2
The United States reported aggregated data and emissions from NEU of liquid fuels,
solid fuels and gaseous fuels under Other (1 .A.5). In the NIR, the Party explains that
the consumption data of fuels have been adjusted to subtract those relating to
industrial processes and product use, which are reported under the IPPU sector, and
NEU which are reported under Other (1 .A.5). The ERT noted that, in a footnote in the
Completed. The United States uses a
country-specific methodology for non-
energy use of fuels in line with para. 10,
Decision 24/CP.19 to most accurately
176 The UNFCCC ERT also raised a similar comment on 1 ,A.3.d Domestic navigation - gaseous fuels. To streamline review, both comments are consolidated here.
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NIR, the Party explained "some degree of double counting may occur between these
estimates of NEU of fuels and process emissions from petrochemical production
presented in the IPPU sector". Further, the Party explained, in the same footnote, "data
integration is not feasible at this time as feedstock data from EIA used to estimate NEU
of fuels are aggregated by fuel type, rather than disaggregated by both fuel type and
particular industries (e.g. petrochemical production), as currently collected through
GHGRP and used for the petrochemical production category".
Noting that, according to the 2006IPCC Guidelines, only emissions from fuels
combusted for the use of their energy should be reported under fuel combustion, the
ERT recommends that the Party reallocate the emissions from NEU of fuels and
process emissions currently reported under the subcategory NEU (other) under the
energy sector to the relevant categories under the energy and IPPU sectors in order to
avoid underestimation or overestimation of emissions.
portray U.S. emissions from NEU. See
row 9.
EPA continues to evaluate ways to
update this approach including use of
GHGRP data and provides more
clarification as applicable in the current
Inventory (i.e., 2018 submission).
17.
(E.19)
Energy
1.B Fugitive emissions
from fuels - CO2
The United States reported CO2 fugitive emissions from coal mining and natural gas
exploration as "NE", and "IE" is reported for oil exploration, in CRF tables 1 .B.1 and
1 .B.2. In CRF table 9, the Party indicated that emissions from these categories are not
estimated because of difficulties in obtaining data, and the inclusion of emissions from
these categories will be investigated for future inventories. During the review, the Party
further informed the ERT that CO2 emissions from exploration is included in production
emissions, and due to overlap in exploration and production data and emissions
sources, these emissions will continue to be reported in production.
The ERT recommends that the Party correct the notation key for CO2 emissions from
the natural gas exploration (from "NE" to "IE") to reflect that those emissions are
included in the CO2 from natural gas production.
Completed. In the current Inventory (i.e.,
2018 submission), the exploration
emissions are reported separately from
production segment emissions.
18.
(E.20)
Energy
1.B.2.C Venting and
flaring-CO2 and CH4
The United States used the notation key "IE" for CO2 and CH4 emissions from venting
and flaring activities under the category venting and flaring (1 ,B.2.c), and included the
emissions under the fugitive subcategories of oil (1 ,B.2.a) and gas (1 ,B.2.b). However,
the ERT noted that, in the NIR, the Party reports that the vented ChU and CO2
emissions account for a large portion of the emissions from production operations. For
example, it is indicated in the NIR that the flare emissions from crude oil refining
accounts for slightly more than 94 percent of the total CO2 emissions in petroleum
systems. NIR tables 3-36 to 3-39 present the values for CO2 and CH4 emissions from
various venting operations in petroleum systems. During the review, the Party
explained that data are unavailable to estimate the split between venting, flaring and
fugitives for these sources.
Noting that the Party indicates that CH4 emissions from petroleum systems is a key
category, the ERT recommends that the United States enhance the transparency in
reporting these emissions in accordance with the UNFCCC Annex I inventory reporting
guidelines.
Completed: See row 11. Based on
available U.S. data and methods, the
United States cannot accurately develop
an estimate of vented versus flaring
versus leak emissions consistently
across natural gas and petroleum
systems.
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19.
(E.21)
Energy
1 .C Carbon dioxide
transport and storage -
C02
In the NIR (p. 3-67), the Party explained that facilities conducting geologic
sequestration of CO2 are required to develop and implement an EPA-approved site-
specific monitoring, reporting and verification plan, and to report the amount of CO2
sequestered using a mass balance approach. The Party further explains that available
GHGRP data relevant for this inventory estimate consists of national-level annual
quantities of CO2 captured and extracted for enhanced oil recovery (EOR) applications
for 2010 to 2014. Table 3-44 in the NIR provide the amount of potential emissions from
CO2 capture and extraction for EOR operations. However, the United States reported
CO2 emissions from CO2 transport, injection and storage as "NE", explaining that
preliminary data were used to develop an estimate of potential emissions from this
category, and that the availability of data to estimate emissions from this category
continues to be evaluated for inclusion in future inventories. During the review, the
United States explained that CO2 emissions are currently included in the sections on
natural gas systems and ammonia production of the NIR.
The ERT recommends that the United States update the notation key from "NE" to "IE"
to address how emissions from CO2 transport injection and storage are estimated.
Completed. The United States
implemented this recommendation in
the current Inventory (i.e., 2018
submission).
Industrial Processes and Product Use
20.
(1.1)
IPPU
2. General (IPPU) -
C02 and CH4 (46,
2013) (62 and 75,
2012)
Improve the completeness of the inventory, in particular for CO2 emissions from
calcium carbide production and CH4 emissions from styrene.
The United States has improved the completeness of IPPU estimates, for example, a
new vending machine end-use of hydrofluorocarbons (HFCs) is included within the
EPA's Vintaging Model. However, several sources in the IPPU sector are reported as
"NE", including CO2 from calcium carbide production. The ERT note that 2006IPCC
Guidelines do not provide a methodology for styrene production.
During the Expert Review phase of the
current Inventory (i.e., 2018
submission), EPA sought expert
solicitation on data for calcium carbide
industry. See Annex 5 of the 2018
submission for more information on
calcium carbide. Reporting of CO2
emissions from calcium carbide has
been changed from "NE" to "IE".
21.
(I.7)
IPPU
2.B.9 Fluorochemical
production - HFC-23
(57,2013)
Ensure that the necessary QA/QC and verification measures are implemented at the
plant level to ensure that continuous monitoring results in more accurate estimates.
The NIR does not describe the QA/QC measures (e.g. QA processes within the
GHGRP reporting system) or verification measures at the plant-specific (or source-
specific) level.
Completed. Discussion on QA/QC and
Verification is included in Chapter 4.13
HCFC-22 Production (IPCC Source
Category 2B9a).
22.
(I.9)
IPPU
2.C.1 Iron and steel
production - CO2 (54,
2013) (69, 2012)
Include a clear explanation of how natural gas used as fuel in coke plants in the iron
and steel production process is reflected in the emission estimates within the inventory
and in the carbon balance for activities related to iron and steel production.
The NIR contains several clarifications of the reporting of natural gas in this category,
including where there are gaps in data yet to be addressed. No carbon balance for iron
and steel production is provided.
Addressing. To improve transparency,
EPA will work to incorporate a carbon
balance to demonstrate how emission
estimates avoid the risks of gaps and
double counting in line with guidance
provided in the reporting 2006 IPCC
Guidelines in Vol. 3, Ch. 4, sections
4.2.2.5 and 4.2.4.2 (Reporting and
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documentation). This improvement is
noted in the Planned Improvements
section of the Iron and Steel Production
chapter of the current Inventory (i.e.,
2018 submission), but implementation
may take additional time pending
available resources.
23.
(1.12)
IPPU
2.F. Product uses as
substitutes for ozone
depleting substances -
HFCs and SF6 (58,
2013)
Provide further information on the EPA Vintaging model, and the assumptions and
factors used in the model to calculate equipment disposal quantities and equipment
disposal emission rates.
The NIR Annex 3.9 provides some insight into the methods used to estimate disposal
emissions. However, the ERT noted that the explanatory text provided to the previous
ERT is not included.
Completed. Emissions at disposal are
calculated as explained in Annex 3.9.
Disposal emission rates and equipment
lifetimes (i.e., the time after placed into
service that equipment is disposed of)
are also shown. A footnote has been
added to explain the calculation. The
number of products and hence the
amount of chemical placed into service
in each year, and hence the emissions
at disposal, rely on confidential business
information that EPA may not publish
under U.S. regulations.
24.
(1.13)
IPPU
2. General (IPPU)-all
gases
The ERT noted that the information provided in the CRF tables and the NIR on
recalculations was inconsistent. Data presented in the NIR (table 9-1) did not match
the data presented within the CRF tables (e.g. table 8.S.1 and 8.S.4) for several IPPU
categories. For example, CRF table 8.S.4 reports 2013 recalculations for HFC
emissions from 2.F.4 aerosols, and recalculations from an unspecified mix of HFCs
and perfluorocarbons (PFCs) from 2.F.6 other applications, but neither of these
recalculations is referenced in NIR table 9-1. The ERT also noted typographical errors
in the recalculations table (table 9.1) in the NIR and also in the completion of CRF
table 2(I).A-Hs1 (interchanging of rows of production data in 2.B). During the review,
the United States indicated that it has experienced multiple problems in importing data
into the new CRF Reporter software. However, the United States did not respond to
questions regarding the errors in the NIR and a request for revised recalculations data.
As a result, the ERT was not provided with a full and transparent description of the
recalculations in the 2016 submission, and hence was unable to review the rationale
and accuracy of recalculations in the IPPU sector.
The ERT recommends that the United States report full and detailed explanations of all
recalculations to IPPU categories in the NIR, and provide information on changes to
methods, assumptions, AD and EFs across all years as well as the rationale for the
recalculations.
Completed. The United States provided
full and detailed explanations of
recalculations to IPPU categories in the
current Inventory (i.e., 2018
submission). For example, the
Recalculations Discussion in Section
4.24 - Substitution of Ozone Depleting
Substances (ODS) includes a
description of updates to assumptions in
EPA's Vintaging Model, which is used to
estimate the actual—versus potential-
emissions of various ODS substitutes.
The Vintaging Model was revised in
response to a peer review conducted on
end uses within the Refrigeration/Air
Conditioning and Fire Protection
sectors.
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25.
(1.14)
IPPU
2. General (IPPU) -
C02
Annex 2 to the NIR (p. A-31) describes the derivation of petroleum coke energy and
NEU allocations; petroleum coke use in the IPPU sector is subtracted from the overall
energy balance, based on reported AD estimates for five IPPU categories. However, in
CRF tables 1 ,A(b) and 1 ,A(d) the "carbon excluded" for petroleum coke is reported as
"NO". This is not consistent with the information in Annex 2 to the NIR and within the
IPPU chapter, which indicate that petroleum coke is used in several emissive non-
energy applications. During the review, the United States provided a time series of the
adjustments made to the energy data for petroleum coke use in the production of
titanium dioxide, silicon carbide, aluminium, ferroalloys and ammonia. The United
States also noted that it had experienced multiple problems importing data into the
new CRF Reporter software.
The ERT recommends that the United States correct the reference approach
calculations for petroleum coke in accordance with the 2006IPCC Guidelines, and
report the relevant information in a consistent way in the energy and IPPU chapters of
the NIR and in the CRF tables. The ERT also recommends that, to improve the
transparency of the data sources and data checks conducted, the United States
include the information provided to the ERT during the review week, including the
adjustments made to the energy data for petroleum coke use in the production of
titanium dioxide, silicon carbide, aluminium, ferroalloys and ammonia, in future
submissions.
Completed. See CRF Tables 1 ,A(b) and
1 ,A(d) of the previous Inventory and
CRF submission (i.e., 2017 submission).
Additional information regarding the
adjustments made to the Energy
chapter were included in the previous
and current Inventories (see Annex 2 of
2018 submission). More information on
adjustments for IPPU categories will be
updated in future inventories consistent
with methodological improvements.
26.
(1.15)
IPPU
2. General (IPPU)-all
gases
The ERT noted that the inventory of the United States is not complete, because there
are categories that are not estimated and the NIR referred to gaps in the inventory.
The ERT also noted that the list of sources "not included" in the inventory for the IPPU
sector presented in Annex 5 to the NIR is inconsistent with the information presented
in CRF table 9. For example, CRF table 9 lists categories that are not mentioned in
Annex 5 to the NIR, in particular: CO2 from iron and steel pellet production; CO2 from
ceramics production; CO2 from non-metallurgical magnesium production; SF6 from
other product use; HFCs and SF6 from photovoltaics and heat transfer fluids; and
PFCs from other product use. Furthermore, the ERT notes that the NIR does not
include the justification required by paragraph 37(b) of the UNFCCC Annex I inventory
reporting guidelines for the following categories that are reported as "NE": CH4 from
direct reduced iron; CO2 from ceramics and non-metallurgical magnesium production;
CO2 from iron and steel pellet production; and N2O from glyoxal and glyoxylic acid
production. The ERT further noted that, in the NIR, the United States indicates the
estimation of F-gases from heat transfer fluids and the GHG emissions from pellet
production as the priorities of the planned improvements.
The ERT recommends that the United States estimate and report emissions from
those categories currently reported as "NE" in the next submission to improve
completeness and consistency of the inventory.
Completed. Within the previous and
current Inventory (i.e., 2017 and 2018
submissions), the United States updated
Annex 5 of the NIR to reflect the IPPU
source categories listed as "NE" within
CRF table 9 and Annex 5. In addition,
Annex 5 has been updated to include
justification for reporting categories as
"NE", consistent with UNFCCC reporting
guidelines.
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27.
(1.17)
IPPU
2.A.4 Other process
uses of carbonates -
C02
The ERT noted that the NIR describes several difficulties in accessing accurate and
complete AD for this key category, primarily from the U.S. Geological Survey (USGS)
statistical publications, including: extensive reporting of "unspecified uses" for crushed
stone (limestone and dolomite); suppression of confidential data on limestone and
dolomite end uses; and no date available for limestone and dolomite use in production
of ceramics and non-metallurgical magnesium. As a result, the ERT notes that: (1)
emissions from ceramics and non-metallurgical magnesium production are reported as
"NE"; and (2) the derivation of complete and accurate AD for other emissive uses of
limestone and dolomite is subject to considerable uncertainty, as evidenced by the
large recalculation of 2013 date. For example, the estimated AD for total limestone and
dolomite use in this category in 2013 reported in the 2016 submission are 220 percent
higher than those in the 2015 submission, and the emissions for this category for 2013
are 235 percent higher in the 2016 submission than in the 2015 submission.
During the review, the United States stated that EPA has assessed data availability but
has not found alternative sources of data for carbonate consumption in the country.
The United States also stated that GHGRP date at the facility level are incomplete and
rarely include carbonate consumption by type, and that EPA will continue its efforts to
work with USGS on opportunities to improve existing surveys and to seek alternative
date sources.
The ERT recommends that the United States conduct further research and
consultation with industry, state-level regulators and/or statistical agencies to access
additional AD and EFs and/or to seek verification of the current method and
assumptions, and report on progress in the NIR.
Addressing. Data on ceramics and non-
metallurgical magnesium has yet to be
identified.
The United States will continue its
efforts to work with USGS to help
resolve/describe uncertainties and
assess reporting possibilities of "other"
emissive uses of limestone and
dolomite. These improvements may
take time given the need to coordinate
with appropriate technical staff at
various agencies and available
resources to implement updates.
28.
(1.18)
IPPU
2.B.1 Ammonia
production - CO2
The ERT noted that in the NIR, the United States indicates that all emissions from
fuels consumed for energy purposes during ammonia production are accounted for in
the energy sector. During the review, the United States explained that it uses a
country-specific approach to estimate the CO2 emissions from ammonia production to
avoid double counting, consistent with paragraphs 10 and 11 of UNFCCC Annex I
inventory reporting guidelines. The ERT also noted that this is not consistent with the
2006IPCC Guidelines, which state, "in the case of ammonia production no distinction
is made between fuel and feedstock emissions with all emissions accounted for in the
IPPU sector" (volume 3, Chapter 3, section 3.2.2). The ERT further noted that the IEF
for ammonia production (0.90 t/t) is one of the lowest of all reporting Parties (range:
0.06-3.27 t/t). The ERT is of the view that it is likely that this category will be identified
as key by a level assessment, if the allocation of emissions is performed in accordance
with the 2006 IPCC Guidelines.
The ERT noted that the NIR indicates planned work to determine which EFs to include
in both fuel and feedstock CO2 emissions, and to improve the accuracy of the emission
estimates based on the enhanced use of the GHGRP data.
Completed. The United States has
addressed this comment within the
Ammonia Production chapter of the
current Inventory (i.e., 2018 submission)
to increase transparency. CO2
emissions from production of synthetic
ammonia from natural gas feedstock are
estimated using a country-specific
approach modified from the 2006 IPCC
Guidelines (IPCC 2006) Tier 1 and 2
methods. In the country-specific
approach, to avoid double counting,
emissions are not based on total fuel
requirement per the 2006 IPCC
Guidelines due to data disaggregation
limitations of energy statistics provided
by the EIA. A country-specific emission
factor is developed and applied to
A-501
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Comment
U.S. Response
The ERT recommends that the United States provide the information, in both IPPU
and energy chapters, on the country-specific approach used to estimate CO2
emissions from ammonia production, justify the reason for its methodological choice
and explain why it is unable to implement the estimates following the 2006IPCC
Guidelines as outlined in paragraph 11 of UNFCCC Annex I inventory reporting
guidelines.
national ammonia production to
estimate emissions from feedstock
consumption, excluding consumption of
fuel for energy purposes to avoid double
counting. The IEF is based on current
IPCC methods and is thus appropriate
for the country-specific method.
29.
(1.19)
IPPU
2.B.1 Ammonia
production - CO2
The ERT noted that, during the review, the United States indicated that it is working
with appropriate energy data (EIA) institutions and GHGRP to obtain the necessary
data to improve the country-specific approach and enhance its consistency with the
2006 IPCC Guidelines. The ERT commends the United States for the planned
improvements and recommends that the United States allocate emissions from all
fossil fuel uses (i.e. fuel and feedstock use) for ammonia production under subcategory
2.B.1 of the IPPU sector in accordance with the 2006 IPCC Guidelines.
Addressing. The United States is
continuing this work of allocating all
fossil fuel uses for ammonia production
to the IPPU chapter. To increase
transparency, additional information has
been included in the Ammonia
Production chapter of the current
Inventory (i.e., 2018 submission). For
the current Inventory, national
circumstances regarding the
aggregation and reporting of national
energy statistics have not allowed EPA
to allocate and report these emissions
within the Ammonia Production category
without double counting of emissions
from fuel use.
30.
(I 20)
IPPU
2.B.4 Caprolactam,
glyoxal and glyoxylic
acid production - CO2
and N2O
The ERT noted that all subcategories under this category are reported as "NE".
However, international statistical data177 indicate that the United States is potentially
one of the largest producing countries for caprolactam. During the review, the United
States indicated that the EPA has reviewed data availability and obtained annual
production data on caprolactam for 2004 to 2015 from the American Chemistry
Council.
The ERT recommends that the United States estimate emissions from caprolactam
production in accordance with the method provided in the 2006 IPCC Guidelines and
with the use of available AD, and report on the emissions from this category in its next
inventory submission.
Partially completed. The United States
has included the emissions estimate for
caprolactam production in the current
Inventory (i.e., 2018 submission). Data
on glyoxal and glyoxylic acid has yet to
be identified. See Annex 5 for additional
information.
31.
(1.21)
IPPU
2.B.5 Carbide
production - CO2 and
cm
The ERT noted that emissions from calcium carbide production are reported as "NE",
although the lack of emission estimates for this category has been the subject of
recommendations in all review reports since 2008. During the review, the ERT
provided information on calcium carbide production plants in the United States based
Addressing. The United States has
begun reporting the CO2 emissions from
carbide production as "IE", as these
emissions are implicitly accounted for in
177 See .
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U.S. Response
on public domain data from the United States Chemical Safety and Hazard
Investigation Board report of February 2013. The United States stated that the existing
statistical and trade publications do not include national time-series data on calcium
carbide production, however, some recent literature references were identified during
the compilation of the 2015 NIR that provide some information on potential calcium
carbide production at specific facilities in the country (including information cited by the
ERT and information on associated facilities that had closed).
The ERT recommends that the United States progress with research and consultation
(e.g. with regulators, plant operators, statistical agencies) to obtain AD (e.g. based on
reported production capacities for the known operating plant) and report emission
estimates based on methods consistent with the 2006IPCC Guidelines across the
time series.
the storage factor calculation for the
non-energy use of petroleum coke in the
Energy chapter. CH4 emissions from
calcium carbide production are reported
as "NA" because the 2006 IPCC
Guidelines only provide information on a
Tier 3 CH4 approach for calcium carbide
production.
32.
(1.22)
IPPU
2.B.8 Petrochemical
and carbon black
production - Cm and
N20
The ERT noted that the NIR 2016 (chapter 4.12) indicates that a subset of facilities
reporting under GHGRP use alternative methods to the carbon balance approach (e.g.
Continuous Emission Monitoring Systems or other engineering approaches) to monitor
CO2 emissions, and that these facilities are required to report CH4 and N2O emissions
as well. However, the ERT noted that CH4 and N2O from combustion and flaring are
currently not included in the national inventory estimates.
During the review, the United States explained that the EPA coordinator for the IPPU
inventory has requested the provision of aggregated and quality-checked data on CH4
and N2O emissions where reported from the GHGRP coordinator, with a view to
integrating these data in future submissions to improve the completeness of national
inventory estimates.
The ERT recommends that the United States progress its plans to analyse GHGRP
data and include emissions from those installations not currently included in the
inventory.
Addressing. The United States would
like to clarify that the subset of GHGRP
facilities using alternative methods are
only required to report CH4 and N2O
emissions from combustion of process
off-gas, rather than complete CH4 and
N2O emissions. This clarification is
included in the current Inventory (i.e.,
2018 submission).
In addition, the United States plans to
begin work with industry experts to
assess GHGRP data to improve
completeness of the petrochemical
production inventory, as noted in the
Planned Improvements section.
33.
(1.24)
IPPU
2.B.8 Petrochemical
and carbon black
production - CO2 and
cm
The ERT noted that the NIR 2016 (chapters 3.2 and 4.12) highlights that the United
States inventory currently may include double counting of emissions between NEU of
fuels in the energy sector and petrochemical production in the IPPU sector. The NIR
(p. 3-40) transparently states that data integration (i.e. between the energy balance,
GHGRP data and the GHG inventory) is not feasible because the EIA data on
feedstock (i.e. NEU data) within the energy balance are presented by commodity only,
with no resolution of data by industry sector (such as petrochemical production),
whereas GHGRP data provide feedstock type for each installation only, and not the AD
that underpin reported emissions. The ERT noted that emissions from fuels and
feedstocks used for energy purposes are accounted for in the energy sector (NIR p. 4-
42), which is not consistent with the 2006 IPCC Guidelines (volume 3, chapter 3,
section 3.9.1, "allocation and reporting"), and therefore that the estimates for
Addressing. The United States is
addressing this comment by providing
additional information within the Energy
and IPPU chapters of the current
Inventory (i.e., 2018 submission) to
improve the explanation and justification
of using a country-specific approach to
estimate CO2 emissions from
petrochemical production. See Annex 2.
A-503
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U.S. Response
petrochemical production emissions are not comparable with those of other reporting
Parties.
The ERT recommends that, in both the IPPU and energy chapters of the NIR, the
United States provide information on the country-specific approach used to estimate
CO2 emissions from petrochemical production, justify the reason for its methodological
choice and explain why it was unable to implement the estimates following the 2006
IPCC Guidelines as outlined in paragraphs 10 and 11 of the UNFCCC Annex I
inventory reporting guidelines.
34.
(I-25)
IPPU
2.B.8 Petrochemical
and carbon black
production - CO2 and
cm
In addition to the recommendation above, the ERT further recommends that the United
States develop a methodology that is consistent with the 2006 IPCC Guidelines as
soon as is practicable, allocating all relevant fuel and feedstock emissions within the
IPPU sector.
Addressing. The United States will work
to address this comment to improve the
comparability of petrochemical
production estimates with other Parties,
consistent with 2006 IPCC Guidelines,
noting that an improvement may take
time to implement.
35.
(I 26)
IPPU
2.B.8 Petrochemical
and carbon black
production - CO2
The ERT noted that the country-specific EF for ethylene production that is derived from
GHGRP data and applied to AD from 1990 to 2009 is among the lowest of all reporting
Parties. The ERT also noted that the lEFs derived from GHGRP data decline from 0.84
t C02/t ethylene in 2010 to 0.741 C02/t ethylene in 2014. During the review, the United
States provided additional information on the category-specific QC, including the
consultation with the industry experts that indicates that there have been no significant
changes to the processes over time and hence the lEFs derived from GHGRP are the
best available for the whole time series, and that the GHGRP reporting provides a
largely complete picture of emissions and production information. The ERT further
notes that the United States' approach in using lEFs derived from a country-specific
method (e.g. GHGRP data for the feedstock component) across the time series
appears to be justified.
The ERT recommends that the United States provide an explanation for its country-
specific approaches using the EFs derived from GHGRP data, including the outcome
of consultation with industry experts, and the results of the quality checks between
GHGRP production estimates and data from trade association membership surveys.
Partially completed. The United States
has added additional explanation to the
current Inventory to improve
transparency of the country-specific
methodology. The United States has
completed an initial comparison of
industry data to data from the EPA
GHGRP but additional time is needed to
conduct further analysis of the most up-
to-date data and report these results.
Additional explanation on the outcome
of consultation with industry experts, the
country-specific quality checks and
uncertainties, and results of these
quality checks will be included in future
Inventory submissions, as additional
time is needed to complete this review.
36.
(I 27)
IPPU
2.C.1 Iron and steel
production - CO2
In addition to the issues noted above, the ERT noted that the NIR (p. 4-60) indicates
that data on natural gas consumption and coke oven gas production at merchant coke
plants are not available and are therefore omitted from the inventory emission
estimates. The ERT considers that, because the United States did not provide a
carbon balance for coke production and iron and steel production within the NIR and
did not respond to the ERT's request for further information during the review, it is not
feasible for the ERT to fully assess the completeness and comparability of the United
Addressing. The United States has
identified this as a planned improvement
within the NIR. The U.S. has initiated
review of the available EPA GHGRP
data for information on consumption and
production from merchant coke plants.
As indicated in the NIR, due to resource
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U.S. Response
States' submission; for example, regarding the allocation of emissions across
categories in the energy sector and the IPPU sector.
The ERT recommends that the United States conduct further research and
consultation with industry, regulators and statistical agencies as necessary in order to
access complete AD on natural gas consumption and coke oven gas production at
merchant coke plants, and obtain EFs and/or emission estimates.
and timing constraints, this improvement
is taking more time to implement.
37.
(1.28)
IPPU
2.C.1 Iron and steel
production - CO2
The ERT noted that the IPPU chapter of the NIR indicates that CO2 emissions from
coke production are allocated in the IPPU sector together with iron and steel
production emissions instead of the energy sector as outlined in the 2006IPCC
Guidelines. The NIR provides a transparent explanation of the country-specific
approach used for the allocation of these emissions. However, the ERT noted that the
NIR is unclear about the fate of other by-product emissions from coke production and
iron and steel production such as secondary gases (notably blast furnace gas) that
may be used to provide process heat or for power generation at integrated iron and
steel facilities.
According to the 2006 IPCC Guidelines (sections 4.2.2.5 and 4.2.4.2), the relationship
between the emissions reported under the energy and IPPU sectors are to be clearly
managed and reported to avoid the risks of gaps and double counting, and "a clear
explanation of the linkage with the source category 1A (Fuel Combustion) estimate for
integrated coke production emissions" has to be provided "to demonstrate that double
counting or missing emissions have not occurred", if the tier 2 method was used.
In order to improve the transparency of the reporting in the NIR and the CRF tables,
the ERT recommends that the United States explain the allocation of the emissions
from coke production and iron and steel production across both the energy and IPPU
sectors, including the amount of carbon stored in the products of iron and steel
production. This could be done, for example, through the provision of a quantitative
summary of the carbon balance that the United States uses to compile and quality
check the inventory estimates.
Completed. The United States
incorporated additional information to
improve the transparency of other by-
product emissions within the Iron and
Steel Production chapter of the current
Inventory (i.e., 2018 submission). See
Annex 2.
As noted in response to comment
above, the United States will work to
incorporate information to explain
allocation of emissions from coke
production and iron and steel across
Energy and IPPU categories, including
carbon stored in iron and steel
production products potentially through
a summary of the carbon balance within
the Iron and Steel chapter, which has
been noted in the Iron and Steel
Production Planned Improvements
section.
38.
(1.29)
IPPU
2.F. Product uses as
substitutes for ozone
depleting substances -
HFCs and PFCs
The ERT noted that the NIR (Annex 3.9) provides a wealth of useful information on the
models used to estimate emissions from this category, including the Refrigeration and
Air-Con model, but that other key information to ensure transparency of the method
and model assumptions is missing. For example, the chemical recovery rates applied
in the calculations for disposal emissions in the Refrigeration and Air-Con model are
not detailed, and although tables A-169 and A-170 provide a lot of detailed data, the
explanation of the estimation methodologies and the application of the tabulated data
within the model calculations is not clear. During the review, the United States
provided many detailed clarifications on the model calculations, references and the
application of data from the tables in the NIR.
Completed. Two footnotes have been
added to the table, one to indicate the
linear substitution between "start" and
"full penetration" dates, and another to
explain Growth Rate.
EPA does not refer to the introduction of
substitutes as "overlapping equipment
technology substitutions." Instead, a
specific portion of each end-use will
have a specific chemical (or blend, e.g.,
in the case of many refrigeration and air-
A-505
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
The ERT recommends that the United States improve the documentation of the
Refrigeration and Air-Con model by including the clarifications on model assumptions,
data sources and calculation methodologies provided to the ERT during the review,
including: the assumed linear substitution trend between "start' and "full penetration"
dates for substitution gases; the information on the annual growth rates cited in the
NIR are the average annual growth rate for individual market sectors from the base
year to 2030 that are applied within the model; the model calculation approach for
overlapping equipment technology substitutions; details of country-specific
circumstances and key references for the annual emission rates for servicing and
leaks applied; and assumed recovery, re-use and recycling of fluids at end of life (e.g.
for fire extinguishers).
conditioning end-uses) and these vary
by year as a substitute is introduced
over time (linearly as stated by the
response above). This is stated under
Step 2 under "Methodology" at the
beginning of Annex 3.9 where it says
"As part of this simulation, the ODS
substitutes are introduced in each of the
end-uses over time."
39.
(I 30)
IPPU
2.F.1 Refrigeration and
air conditioning - HFCs
and PFCs
The ERT noted that there is no methodological information in the NIR to explain the
derivation of emission estimates from the manufacture of new products for sectors
including refrigeration and air conditioning, although emissions are reported in CRF
table 2(II).B-Hs2. During the review, the United States clarified that it considers that
there should not be any emissions from the manufacture of new refrigeration and air-
conditioning equipment, based on the assumption that emissions during equipment
manufacture are essentially negligible. The United States explained that the values in
the CRF table are incorrect owing to a spreadsheet formula error when the foam sector
was disaggregated into closed-cell and open-cell foams in the model that converts
outputs from the EPA's Vintaging Model to the CRF Reporter software. In this case,
the emissions estimated for servicing activities for commercial refrigeration and
domestic refrigeration were attributed to "Actual emissions from manufacturing" rather
than a component of "Actual emissions from stocks". The ERT notes that the
assumption that there are no emissions in the product manufacture stage for
refrigeration and air-conditioning sources is not consistent with the 2006IPCC
Guidelines (volume 2, chapter 7, section 7.5.2.1). Furthermore, the ERT notes that the
United States also highlighted that many cold storage and retail food units in the
United States are large systems with kilometres of piping and hundreds of joints and
component connections that are prone to leakage; therefore, the ERT considers that
initial charging losses are highly likely to occur where new industrial units are charged
in situ.
The ERT recommends that the United States either review and update its assumptions
regarding product manufacture losses or provide information in the NIR to justify the
assumption that all such losses are "negligible" and accurately reflect country-specific
circumstances.
Addressing. EPA is researching and
gathering data so that emissions from
manufacturing / first-fill operations can
be accurately assessed.
The error in converting model results to
the CRF table was addressed.
EPA initiated a peer review of the model
and has incorporated results in the
current Inventory (i.e., 2018
submission).
EPA will continue to incorporate peer
review results, including any related to
the assumption that there are no
emissions in the product manufacture
stage, as we further review the
information provided.
40.
(1.31)
IPPU
2.F.2 Foam blowing
agents - HFCs and
PFCs
The ERT noted that in the NIR (table A-175), the sum of model losses for extruded
polystyrene sheet foam totals 90 percent, whereas for all other foams (with the
exception of insulation that is assumed to be landfilled) 100 percent leakage is
estimated. Further, the ERT noted that the model assumes that no foam products are
collected at the end of their use and the F-gases are either recovered or destroyed to
Completed. Additional information has
been obtained and implemented
regarding extruded polystyrene sheet
foam. Losses now total 100 percent.
A-506 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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U.S. Response
avoid release. During the review, the United States clarified that the reason for the
extruded polystyrene sheet foam total of 90 percent is not known, and confirmed that
the model does not take into account the recovery or destruction of blowing agents at
end of life, because this is not required by federal regulations and because, at end of
life, foam insulation is removed from decommissioned buildings and typically landfilled.
The United States further noted that there are several incentive schemes to promote
the recovery of HFC blowing agents in building insulation foams, and destruction
facilities that recover blowing agents from domestic refrigeration foam, for example
through the EPA's voluntary Responsible Appliance Disposal Program. The model
does not account for these activities as they are not regarded as widespread in the
United States.
The ERT recommends that the United States review the model assumptions and
QA/QC of the model to eliminate the unexplained inconsistencies regarding the fate of
foam blowing agents, and update assumptions to reflect national practices (e.g. to
recover or destroy foam blowing agents). Furthermore, the ERT recommends that the
United States include in the NIR clarifications regarding how the model accounts for
end-of-life practices for foam blowing agents.
Data provided under the Responsible
Appliance Disposal Program were
reviewed and support the simplifying
assumption that HFC foam blowing
agent recovery and destruction is
negligible.
Annex 3.9 indicates how the model
accounts for end-of-life emissions from
the foams sector. See for instance
Steps 3 and 4 in the Foam Blowing
methodology and the information
contained in Table A-151.
41.
(I 32)
IPPU
2.F.5 Solvents - HFCs
and PFCs
The ERT noted that, in the method description for emissions from solvents provided in
Annex 3.9 (p. A-247) to the NIR, the United States applies an assumption that only 90
percent of solvents are emitted. This is not consistent with the 2006IPCC Guidelines
(section 7.2.2, chapter 7), which indicate that emissions from solvent applications are
typically 100 percent emitted within two years of initial use. In order to estimate
emissions in such cases, it is necessary to determine the total amount of each HFC or
PFC chemical sold in solvent. Furthermore, the ERT noted that the use of the notation
key "NA" to report emissions from solvents in the CRF tables is not correct.
The ERT recommends that the United States either review and update its assumptions
regarding solvent emissions or provide country-specific information to justify the
assumption that only 90 percent of solvents are emitted, and revise the reporting of
emissions from solvents within the CRF tables.
Completed. The 90 percent assumption
has been reviewed and confirmed. The
Inventory indicates that the other 10
percent become entrained in waste
products that are then destroyed.
42.
(I.33)
IPPU
2.F.6 Other
applications (product
uses as substitutes for
ozone depleting
substances) - HFCs
and PFCs
The ERT noted that CRF table 2(H) of the 2016 submission reports emissions from an
unspecified mix of HFCs and PFCs in the subcategory other applications (2.F.6) under
the category product uses as substitutes for ODS (2.F.6) for which no details are
provided in the NIR, and that these emissions constitute about 5.8 percent of the total
for the highest-emitting key category in the IPPU sector in 2013. Furthermore, the ERT
noted that the emissions data presented for each of the subcategories under product
uses as substitutes for ODS (2.F) in CRF table 2(l)s2 are not consistent with the
subtotals presented in table 4-96 of the NIR, and that this inconsistency appears to be
caused (at least in part) by the reporting of the "unspecified mix" of gases in the CRF
table. During the review, the United States clarified that the "unspecified mix" of gases
are aggregated and treated as confidential information because they are produced or
Completed. Certain gases within the
unspecified mix of HFCs and PFCs are
only used in one particular sector or
subcategory; and, therefore, publishing
the unspecified mix of HFCs and PFCs
at the subcategory level would reveal
confidential business information.
A-507
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
imported by a small number of chemical providers and in such small quantities or for
such discrete applications that reporting national data would result in disclosure of
confidential information.
The ERT recommends that the United States provide in the NIR detailed information
including the, quality checks for all gases and sources included in the unspecified mix
of HFCs and PFCs in the subcategory other applications under the category product
uses as substitutes for ODS.
43.
(I 34)
IPPU
2.F.6 Other
applications (product
uses as substitutes for
ozone depleting
substances) - HFCs
and PFCs
The ERT recommends that the United States improve the consistency between its NIR
and CRF tables for the reporting of subcategories of product uses as substitutes for
ODS.
See row 42.
Agriculture
44.
(A.2)
Agriculture
3.B Manure
management -Cm
and N2O
Investigate the reasons for the differences between the trends of volatile solid (VS)
daily excretion and nitrogen excretion (Nex) rates per animal type for sheep and swine.
This information was not provided in the 2016 submission. During the review, the
United States explained that the manure management inventory team obtains its data
from the Cattle Enteric Fermentation Model (CEFM), and that the team will work with
the enteric fermentation inventory team to clarify the reasons for the different trends of
VS values and Nex rates for sheep and swine.
Completed. The United States added
additional text to Annex 3.11
(Methodology for Estimating CH4 and
N2O Emissions from Manure
Management) of the previous Inventory
(i.e., 2017 submission) to clarify this
trend.
45.
(A3)
Agriculture
3.B.1 Cattle - CH4 and
N20 (71, 2013)
Include explanations for the trends of VS daily excretion and Nex rates per animal for
dairy cattle.
This information was not provided in the 2016 submission. During the review, the
United States explained that the manure management inventory team obtains its data
from the CEFM, and that the team will work with the enteric fermentation inventory
team to clarify the reasons for the different trends of VS values and Nex rates of dairy
cattle.
Completed. The difference 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 since 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. This trend
explanation has been added to Annex
3.11 for the current Inventory (i.e., 2018
submission).
A-508 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
46.
(A.4)
Agriculture
3.D.a.6 Cultivation of
organic soils (i.e.
histosols) - N2O (74,
2013)
Revise the AD and emission estimates for cultivation of histosols in agricultural soils
and revise the QC process in order to ensure consistency in the inventory, and provide
information on these improvements.
The United States did not provide information on the revision or the recalculation to
address the recommendation, and the ERT noted that an inconsistency in the area of
cultivated organic soil between CRF table 3.D (1,352,082.22 ha) and the NIR (1.21
million ha) (annex p. A-332) still exists in the 2016 submission. During the review, the
United States explained that it has experienced multiple problems importing data from
its country-specific methods into the new CRF Reporter agriculture modules. The
United States indicated that it is investigating options to solve the problems.
Completed. The United States
addressed this issue in the CRF tables
for the current Inventory (i.e., 2018
submission).
47.
(A.8)
Agriculture
3.D.a.3 Urine and dung
deposited by grazing
animals-N2O (77,
2013) (92, 2012)
Resolve the inconsistency in the total N excretion on pasture, range and paddock
between CRF table 4.B(b), N2O emissions from manure management, and CRF table
4.D, agricultural soils.
The total N excretion on pasture, range and paddock reported in CRF table 3.B(b) and
in CRF table 3.D are inconsistent. In addition, the ERT noted that the total N excretion
on pasture, range and paddock was reported as 4,265,716,593.73 kg/year in CRF
table 3.D, while 3,672 kt N was provided in the NIR (annex table A-223).
During the review, the United States explained that it had experienced problems in
importing data from its country-specific methods in to the new CRF Reporter
agriculture modules, and it was investigating options to solve the problems that it
continues to experience with CRF Reporter.
Completed. The discrepancy between
the total N excretion on pasture, range
and paddock reported in CRF tables 3.D
and in the NIR has been resolved.
The United States addressed the
discrepancy between CRF tables 4.B(b)
and 4.D in the CRF tables in the current
Inventory (i.e., 2018 submission).
48.
(A.9)
Agriculture
3.D.a.3 Urine and dung
deposited by grazing
animals-N2O (77,
2013) (92, 2012)
Improve QC procedures to avoid inconsistencies in the total N excretion on pasture,
range and paddock between CRF tables 4.B(b) and 4.D and provide information on
these improvements.
There is some information on QC improvement in the NIR, but inconsistencies in the
total N excretion on pasture, range and paddock between CRF table 3.B(b) and CRF
table 3.D still exist. During the review, the United States explained that it had
experienced problems in importing data from its country-specific methods in to the new
CRF Reporter agriculture modules, and it was investigating options to solve the
problems that it continues to experience with CRF Reporter.
Completed. See response to the
comment above. The United States is
improving QC procedures to resolve
many issues experienced with the CRF
Reporter.
49.
(A.11,
A. 15)
Agriculture
3.A.1 Cattle-CH4
3.B.1 Cattle-CH4
In CRF table 3.As1 of the 2016 submission, the United States chose option C for
reporting CH4 emissions under enteric fermentation. As for enteric fermentation, the
United States chose option C for reporting CH4 emissions from cattle manure
management in CRF tables 3.B(a)s1 and 3.B(a)s2. According to footnote 4 of CRF
table 3.As1,3.B(a)s1, and 3.B(a)s2, option C should be used when Parties want to
report a more disaggregate livestock categorization compared with option A and option
B. However, the United States reported only dairy cattle and non-dairy cattle emissions
under option C, and the cells for all other subcategories of cattle were reported as "IE",
Completed. The United States
addressed this issue in the CRF tables
in the current Inventory (i.e., 2018
submission) to reflect the previous
recommendations made by the ERT.
The United States is reporting more
disaggregated data, where available
and applicable, within the CRF tables.
A-509
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
except for location in warm regions, which was reported as "NO". Further, the ERT
noted that, in CRF table 9 in which emissions reported as "IE" are allocated should be
explained by the United States, information is not complete.
During the review, the United States stated that it can investigate updating the
information provided in the CRF tables. The United States also explained that it had
made attempts to present disaggregated data during the initial CRF input phase of the
2016 submission. However, since it experienced problems in data input, it took the
approach of previous years of inputting those data as "IE".
If the United States does not use more disaggregate livestock categorization in
estimating emissions, the ERT recommends the United States use option A in reporting
data and emissions for cattle.178
50.
(A-12)
Agriculture
3.A.1 Cattle-CH4
The United States applied a tier 2 methodology with regional feed digestibility and Ym
to estimate enteric CH4 from dairy cattle and beef cattle, and in the NIR 2015 (p. A-
255), it stated that daily EFs were estimated for each animal type and state regions.
Information such as cattle population, typical animal mass, weight gain at country level,
dairy lactation rates, feed digestibility and Ym at state level and regional level was
included in the NIR and/or its annexes. However, the ERT considers that the
transparency could be further improved by including the average gross energy intake
and EFs for each animal type, by state. In addition, in the NIR (pp. A-266-A-267) the
United States explained that Ym values were determined for 1990 using the Donovan
and Baldwin model (1999), and the values for 1990 were used as the baseline to
estimate for 1991 and beyond by scaling Ym values for each diets with the COWPOLL
model. The scaling factor is shown as Ym = Ym(1990)EXP[1,22/(YEAR-
1980)]/EXP[1.22/(1990-1980)], but the NIR does not provide information on the
development of the scaling factor equation and related verification. During the review,
the United States stated that it will include in the NIR population, average gross energy
intake and EFs for each animal type, by state, and provide information on Ym, which
will include detailed procedures for and verification of the development of Ym.
The ERT recommends that the United States include in the NIR the values of
population, average gross energy intake and EFs for each animal type, by state, as
well as information on the procedure.
Completed. In Annex 3.10 (beginning on
page A-239) of the previous Inventory
(i.e., 2017 submission), cattle
population, gross energy intake and
emission factors by animal type, by
state were provided. In addition,
additional information on Ym was
included.
51.
(A-13)
Agriculture
3.A.1 Cattle-CH4
In the NIR (p. 5-4), the United States stated that the CEFM was used to estimate CH4
emissions from cattle enteric fermentation. It also indicated that significant scientific
literature exists and, in its emission estimations, the United States incorporated
information and analyses of livestock population, feeding practices and production
characteristics. In Annex 3 to the NIR 2016, the United States explained that the
CEFM was developed based on recommendations provided in the 2006IPCC
Completed. The CEFM uses the
methods in the 2006 IPCC Guidelines to
estimate enteric emissions. The CEFM
then tracks the populations and weights
of these animals more accurately
through a transition matrix, so the
178 The UNFCCC ERT also raised a similar comment on 3.B.1 Cattle - CH4. To streamline review, both comments are consolidated here.
A-510 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
Guidelines. However, the NIR does not provide information that explains how the
CEFM is compatible with the methodologies in the 2006IPCC Guidelines, as required
by paragraph 10 of the UNFCCC Annex I inventory reporting guidelines. During the
review, the United States stated that it will provide information on the compatibility of
the CEFM with the methodologies provided by the 2006 IPCC Guidelines.
The ERT recommends that the United States report in its NIR on the compatibility of
estimates obtained using the CEFM with estimates obtained using methodologies from
the 2006 IPCC Guidelines.
United States can develop more refined
estimates based on the methods in the
2006 IPCC Guidelines. More information
on CEFM is provided in Annex 3.
52.
(A. 14)
Agriculture
3.B Manure
management -Cm
and N2O
The ERT noted that in the NIR (p. 5-11) and its Annex 3.11 (pp. A.286-A.288), the
amount of manure management system (MMS) usage has not been updated for
several years (e.g. the most recent data for cattle are from a publication dated 2000,
and those for swine are dated 2007). In the NIR 2015 (p. 5-15) the United States
stated that the 2012 Agricultural Census data will be incorporated into the inventory
and will be used to update county-level animal population and MMS estimates. During
the review, the United States stated that it plans to update the MMS data in future
inventories, and that EPA is working with the United States Department of Agriculture
to obtain updated data.
The ERT recommends that the United States obtain updated MMS data and estimate
emissions using the updated MMS usage data in its submission. If this is not possible,
the ERT recommends that the United States report on progress in its effort to update
the MMS data.
Completed. The United States updated
the waste management system (WMS)
data within the previous Inventory (i.e.,
2017 submission) with data from the
2012 U.S. Department of Agriculture,
Agricultural Census. These updated
data are noted in the chapter and annex
of the 2017 submission.
53.
(A-17)
Agriculture
3.D.a.3 Urine and dung
deposited by grazing
animals - N2O
The ERT noted an inconsistency between CRF table 3.D and the NIR regarding the N
input from manure applied to soils (table A-223) and N input from sewage sludge
applied to soils (table A-227). During the review, the United States explained that it has
experienced multiple problems importing data derived from its country-specific
methods into the new CRF Reporter agriculture modules. The United States also
indicated that it is investigating options to solve the problems.
The ERT recommends that the United States ensure consistency between the data
provided in CRF table 3.D and the data provided in the NIR regarding the N input from
manure applied to soils and N input from sewage sludge applied to soils.
Completed. The United States has
resolved the issue of consistency
between N input from manure applied to
soils and N input from sewage sludge
applied to soils reported in the NIR and
CRF table 3.D in the previous Inventory
(i.e., 2017 submission).
54.
(A. 18)
Agriculture
3.D.b Indirect N2O
emissions from
managed soils - N2O
The ERT noted that the United States, in response to a previous recommendation to
include weighted national averages for the fractions listed in CRF table 4.D, corrected
the AD and provided a documentation box in CRF table 3.D in the 2016 submission,
explaining in the NIR that "N fixation, volatilized N, and N leached and run-off do not
strictly represent AD because they are calculated by the process-based model
(DAYCENT). Fractions were not used because a process-based model was used to
calculate emissions." During the review, the United States explained that it estimated
the N volatilized and N lost through leaching and run-off using the DAYCENT model,
and it reported these values in the inventory worksheets, which could be made
Addressing. The DAYCENT model is a
Tier 3 approach, consistent with the
2006 IPCC Guidelines and the
NIR/annex provides a very detailed
explanation of how the DAYCENT
model works and cites further literature
that can be reviewed if the ERT would
like more detail. The United States will
continue efforts to improve the
A-511
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
available to the ERT during an in-country review, and that these values can be
included in the next NIR in Annex 3.12 (methodology for estimating N2O emissions). In
addition, the United States stated that indirect soil N2O emissions are estimated using
a tier 1 method for a small percentage of the N inputs, such as fertilization and organic
amendments to vegetable and perennial crops, as well as federal grasslands.
The ERT recommends that the United States provide an explanation of how its
methodology and the use of the DAYCENT model to estimate N volatilized and N loss
is both compatible with the 2006IPCC Guidelines and based on science.
transparency of the NIR in future
submissions, specifically, the Annex will
be revised for the next Inventory (i.e.,
2019 submission) to improve
transparency.
55.
(A. 19)
Agriculture
3. J Other (C02
emissions from liming,
urea application and
other carbon-
containing fertilizers) -
C02
In CRF table 3G-I, the United States reported CO2 emissions from liming and urea
application as "IE", and information on CO2 emissions from liming and urea application
was included under the LULUCF sector in the NIR. During the review, the United
States stated that emissions from liming and urea fertilization will be reported under
the agriculture sector in the 2017 submission.
The ERT recommends that the United States report CO2 emissions from liming and
urea fertilization under the agriculture sector.
Completed. For the previous Inventory
(i.e., 2017 submission), the United
States began reporting emissions from
liming and urea fertilization under the
Agriculture chapter.
Land Use, Land-Use Change, and Forestry
56.
(L.1)
LULUCF
4. General (LULUCF) -
C02, CH4 and N20 (80,
2013) (103,107,109,
2012)
Estimate emissions from the carbon stock changes from mineral soils under forest
land, living biomass under cropland and grassland, dead organic matter (DOM) under
land converted to cropland and land converted to grassland, land converted to
wetlands, soil organic carbon (SOC) under land converted to settlements and land
converted to other land; N2O emissions from disturbance associated with land-use
conversion to cropland; CH4 and N2O emissions from biomass burning (land converted
to forest land, cropland, grassland and wetlands); and CO2 emissions from biomass
burning (excluding forest land remaining forest land).
The United States has newly included, in CRF 2016, estimates for mineral soils under
lands converted to forest land and living biomass for forest land converted to non-
forest land. However, emissions from living biomass have only been estimated for
forest land converted to grassland and cropland. In addition, the following are reported
as "NE": estimates of DOM under land converted to cropland, grassland, wetlands,
settlements and other land; SOC for land converted to settlements and other lands;
and CO2, N2O and CH4 associated with biomass burning in land converted to forest
land, cropland, grassland and wetlands.
Addressing. The United States is
continuing to address these missing
carbon stock changes and non-C02
emissions by accessing additional data
sources and incorporating them into our
methods. Additional refinements will be
provided in the next Inventory (i.e., 2019
submission).
57.
(L.2)
LULUCF
4. General (LULUCF) -
CO2 (81,2013)
Conclude the technical work under way to be able to provide estimates for the carbon
stock changes in the living biomass and DOM pools for each conversion category from
forest land to any other land use for each year based on a reliable Land-Use Change
(LUC) matrix, and report on the achievements made.
The United States has made considerable progress towards a reliable land tracking
system and has provided a complete description of the underlying accounting
See response above.
A-512 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
framework in the NIR (chapter 6.1). However, emissions from DOM have not been
estimated, except for forest land remaining forest land. Further improvements
regarding the implementation of the new accounting framework for land use are
necessary.
58.
(L.3)
LULUCF
4. General (LULUCF) -
C02, CH4 and N20 (82,
2013) (97, 2012)
Include all managed federal lands in the inventory and improve the consistency of the
time series of national areas and report on the achievements made.
Not all managed federal lands are included in the inventory. The ERT notes that in
document FCCC/ARR/2013/USA the United States explained that the inconsistencies
arose as a portion of the managed land not included in the CRF tables, although it was
reported in the NIR. The ERT notes that the total area reported in the CRF tables in
the 2016 submission for all land uses (4.A to 4.E) still fluctuates throughout the period,
and an explanation for this has not been provided in the NIR.
Addressing. The United States is
continuing to improve our ability to
estimate emissions/removals from all
federal lands through collection of
additional data. The major missing
component of federal land is in Alaska
where data are sparse.
The United States will ensure future
submissions have consistent areas
reported in the NIR and CRF, or an
explanation will be provided to explain
why there is a difference.
59.
(L.5)
LULUCF
Land representation -
C02, CH4 and N20 (84,
2013) (97 and 98,
2012)
Check the coherence of reported data on land-use areas reported in the NIR and those
reported in the CRF tables, applying the appropriate QC checks.
The lack of consistency between the NIR (table 6-6) and CRF table 4.E remains in the
2016 submission.
The United States will ensure future
submissions have consistent areas
reported in the NIR and CRF, or an
explanation will be provided to explain
why there is a difference.
60.
(L.10)
LULUCF
4.A.1 Forest land
remaining forest land -
CO2 (90, 2013)
Make every effort to report the carbon stock changes in the mineral soils and organic
soils pools separately.
During the review, the United States stated that this issue has not yet been addressed.
However, the United States expects that organic soil emissions will be minimal in
forest land remaining forest land.
Completed. In the previous Inventory
(i.e., 2017 submission), the United
States reports mineral and organics soil
pools separately for Forest Land
Remaining Forest Land.
61.
(L.14)
LULUCF
4.B.1 Cropland
remaining cropland -
C02 (93,2013) (107,
2012)
Estimate the carbon stock changes in living biomass in perennial crops for all years in
the time series.
Living biomass has not yet been estimated in cropland remaining cropland. During the
review, the United States explained that it plans to include herbaceous and perennial
cropland biomass using the IPCC default carbon stock values and, depending on
resources, it will develop country-specific carbon stock values in the next two to three
years.
Addressing. The United States has
identified this as an improvement but
due to other major ongoing
improvements identified in the Planned
Improvement section, the United States
will not have the resources to implement
it for several years.
62.
(L.15)
LULUCF
4.E. Settlements - CO2
(94,2013)
Eliminate the overlap between the urban forest inventory and the forest inventory.
The United States explained this problem in the improvement plan in the NIR (p.6-84).
Addressing. The United States intends
to utilize Forest Inventory and Analysis
(FIA) plots on Urban lands to resolve the
overlap problem between forest lands
A-513
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
and urban forests. This will take place
over the next 2 to 3 years.
63.
(L.17)
LULUCF
4.H Other (LULUCF) -
C02 (96, 2013) (112,
2012)
Reflect the intersectoral linkages and document the differences in the decay values for
yard trimmings and food scraps.
The United States provided information on decay factors in the NIR and also
introduced correction factors. However, it remains unclear to the ERT how the
correction factors apply to the decay factors and, as such, how consistency with the
waste sector is ensured.
Partially completed. The United States
has provided detailed information on
how the correction factor relates to the
decay factors in the Methodology
section of Landfilled Yard Trimmings
and Food Scraps as well as how the
decay rate relates to the Landfills in the
Waste sector. The United States will
continue to work towards developing
greater consistency with the Waste
sector over the next several years as
resources allow.
64.
(L.21)
LULUCF
4. General (LULUCF) -
CO2, CH4 andlxhO
The ERT noted discrepancies between land-use areas in the time series reported in
the CRF tables. For instance, in CRF table 4.1, the final area at the end of year and
the initial area on the subsequent year are different for all land categories except for
unmanaged forest land. The ERT also noted that in the 2016 submission the United
States introduced a new Forest Carbon Accounting Framework (FCAF) (Woodall et al.,
2015d] for land tracking of areas of land use and land-use change for the entire time
series. Further, the ERT noted that in the NIR (chapter 6.1), the United States stated
that approximately 46,213 kha are considered unmanaged, whereas in CRF table 4.1,
the total unmanaged land (46,213.27 kha) does not match the sum of unmanaged
forest land (9,634.34 kha), grassland (25,782.12 kha) and wetlands ("IE"). During the
review, the United States explained that this problem would be resolved and clarified in
the 2017 submission.
The ERT recommends that the United States resolve the inconsistencies in land-use
areas in the time series reported in the CRF tables and the inconsistences in
information on land-use areas between the NIR and CRF table 4.1 by subcategorizing
the managed lands for which estimates are calculated in order to separate them from
those for which there are currently no methodologies available, noting that the United
States can use the notation keys "NE" or "NA" for the latter subcategory.
The United States is implementing
additional QA/QC checks to ensure
consistency between the NIR and CRF.
Explanations have been updated where
CRF structure is inconsistent with format
of U.S. data. This is reflected in the
current Inventory (i.e., 2018
submission).
65.
(L.22)
LULUCF
Land representation -
CO2, CH4 andlxhO
The United States in the NIR that the total area of forest land remaining forest land in
table 6-12 (271,719 kha) does not correspond with the total area reported in chapter
6.1 (table 6-7) (294,051 kha for 2014) under the land representation for forest land,
explaining that this is due to the fact that a part of the managed land of Alaska (interior
of Alaska) and all of Hawaii's forest lands have not been estimated owing to limited
data on land management in the interior of Alaska and on all of Hawaii's forests. In
CRF table 4.A, the reported area is 271,719 kha. The ERT considers that this
discrepancy could be prevented in the future by including the different territories (49
Completed. The United States corrected
this for the previous Inventory (i.e., 2017
submission) by providing additional
explanation in the NIR and CRF.
A-514 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
states, Hawaii and Alaska) and by using the notation keys "NA" or "NE" for carbon
fluxes for Alaska and for all of Hawaii's forests in CRF table 4.A.
The ERT recommends that the United States augment the transparency of the NIR
and CRF table 4.A by reporting the territories not included separately as "NA" or if it is
not possible, provide the additional documentation to explain why there is a
discrepancy between the areas shown in CRF table 4.A and NIR table 6-12.
66.
(L.23)
LULUCF
Land representation -
CO2, CH4 andN20
The ERT noted that the total national area, as reported in CRF table 4.1 for all land
uses, is not constant in the period 1990-2014, fluctuating between 719,564.15 kha
(1990) and 714,948.55 kha (2010), which is a variation of 5,227.59 kha (7 percent). As
identified in document FCCC/ARR/2011/USA, the United States used several data
sources to construct the land area representation: a National Resources Inventory
(NRI) survey for 1998 data; available data from FIA (years of which are different for the
various states, ranging from 2002 to 2012); and the National Land Cover Dataset
(NLCD), a land cover classification scheme, with data available for 1992,2001 and
2006. The United States explains in the NIR 2016 that the NRI and FIA have different
criteria for classifying forest land in addition to different sampling designs, leading to
discrepancies in the resulting estimates of land area for non-federal land. Similarly,
there are discrepancies between the NLCD and the FIA data for defining and
classifying forest land on federal lands. FIA has the main database for forest statistics,
and data from the NRI and NLCD are adjusted to achieve consistency with FIA
estimates of forest land.
In the NIR 2016 the United States specified that, for harmonization purposes, the non-
forest land-use area had been updated in proportion to the total forest land area from
FIA. However, the ERT noted that the information is not sufficient for it to understand
how the data referring to various years, coverage and resolution, with different
classification systems, have been harmonized and used to classify the territory
according to the IPCC land-use categories. During the review, the United States
explained that cropland areas were based solely on the NRI data for non-federal lands,
on NLCD data for federal lands, and that cropland areas were not adjusted in the
harmonization process.
The ERT recommends that the United States, when providing detailed information in
the NIR on how the different data sources were harmonized, provide explicit
information on how the model ensures consistent integration of the three data sources;
for example, by including a visual flow chart of data processing during the
harmonization process.
Addressing. The United States is in the
process of updating the land
representation analysis to incorporate
new datasets. This work will be
completed in time for the next Inventory
(i.e., 2019 submission). As a part of this
process the United States will provide
additional explanation in the NIR on how
the different databases are combined to
create the U.S. land representation
matrix. This will be incorporated into the
next Inventory (i.e., 2019 submission).
A-515
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Comment
U.S. Response
67.
(L.24)
LULUCF
4. General (LULUCF) -
C02
The United States introduced the new FCAF179 to estimate consistent and reliable
land-use change in the 2016 inventory submission. The United States mentioned in the
annex 8 to the NIR (table A-304) that verification measurements have been
implemented for the majority of the underlying methodology, calculations and models
that are contained in the NIR. During the review, the United States explained that the
FCAF has been previously used for a regional analysis, and provided the reference to
the peer reviewed paper (Coulston et al., 2015180) of that regional analysis.
Furthermore, the United States explained that the model used for the FCAF has not
been compared with similar models used by other countries. During the review, the
ERT did not receive information on the type of verification measures that have been
implemented (e.g. information on peer reviews or sensitivity analysis of the model
implemented on a national scale).
The ERT recommends that the United States include the information on the use of the
model for the regional analysis in the QA/QC and verification section of chapter 6.1 of
the NIR.
Addressing. The United States is
working to implement a new system for
estimating C stock changes on forest
lands that will replace the FCAF (see
Planned Improvements section for
Forest Land Remaining Forest Land).
As part of this effort, the United States
will incorporate available information on
verification into the NIR. The new forest
accounting system will be implemented
in the next Inventory (i.e., 2019
submission).
68.
(L.25)
LULUCF
4. General (LULUCF) -
C02
The ERT noted that, in the NIR (p.6-57), the United States reported the difference
between the stocks reported as the stock change under the assumption that the
change occurred in the year of the conversion, and those areas are also reflected in
CRF tables 4.B and 4.C. However, the area in CRF tables 4.B and 4.C and NIR table 6
should cover the entire area lost from forest land conversion to cropland or forest land
conversion to grassland over a 20-year timespan according to footnote 2 of CRF table
4.B, which indicates that areas for land converted to cropland shall be reported as the
cumulative area (over 20 years) remaining in the category in the reporting year. In
response to a question raised by the ERT, the United States explained that because
the 2016 submission was the first to include forest land conversions, many of the noted
issues were identified at a point where it was not possible to correct them. The United
States indicated that these issues have been addressed and the corrections will be
applied in the 2017 submission.
The ERT recommends that the United States estimate emissions from forest land
converted to another land use over a 20-year timespan by subdividing the conversion
category into area actually converted and area converted during the past 19 years.
The ERT also recommends that the United States ensure consistency in reporting of
land area between the NIR and CRF tables 4.B and 4.C.
The U.S. Inventory does include the
cumulative area of forest conversion to
cropland and forest conversion to
grassland over a 20-year time span.
However, EPA assumes all losses of
biomass and dead organic matter occur
in the first year of the conversion.
However, there is a small difference in
the area between Table 6-7 in the Land
Representation section and CRF. This
difference is because croplands in
Alaska and federally-managed lands are
not included in the inventory, and
grasslands in Alaska are not included in
the inventory. The EPA is working to
compile the activity data and address C
stock changes for these areas as part of
a future submission.
179 Woodall CW, Coulston JW, Domke GM, Walters BF, Wear DN, Smith JE, Andersen H-E, Clough BJ, Cohen WB, Griffith DM, Hagen SC, Hanou IS, Nichols MC, Perry CH,
Russell MB, Westfall J and Wilson BT. 2015. The US Forest Carbon Accounting Frame-work: Stocks and Stock change 1990-2016. Gen. Tech. Rep. NRS-154. Newtown Square,
PA: United States Department of Agriculture, Forest Service, Northern Research Station.
180 Coulston JW, Wear DN and Vose JM. 2015. Complex forest dynamics indicate potential for slowing carbon accumulation in the southeastern United States. Scientific Reports.
5: p. 8002.
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69.
(L.26)
LULUCF
4.A.1 Forest land
remaining forest land -
C02
The United States explained in the NIR that the FCAF is fundamentally driven by the
annual forest inventory system conducted by FIA programme, and the FCAF system
comprises a forest dynamics module and a land-use dynamics module. The forest
dynamics module assesses forest sequestration, forest ageing and disturbance
effects. The land-use dynamics module assesses carbon stock transfers associated
with afforestation and deforestation. The required inputs are estimated from more than
625,000 forest and non-forest observations in the FIA national database. Model
predictions for before or after the annual inventory period are constructed from the
FCAF system using the annual observations. However, since carbon density
estimations (tonnes per hectare) for live trees, by type and by region, are not explicitly
mentioned in the NIR, the ERT was not able to verity the accuracy of the estimations
for carbon stocks and CO2 fluxes. During the review, the United States provided the
ERT with background information on the FIA survey methods, specifically on age
classes, classification, and classification by forest and non-forest for the sample plots.
The ERT recommends that the United States include in the NIR the background
information provided to the ERT on the FIA survey methods, specifically on age
classes, classification, and classification by forest and non-forest for the sample plots,
in order to allow the ERT verify the accuracy of the estimations for carbon stocks and
CO2 fluxes. The ERT also recommends that the United States annex to the NIR
detailed tables on average carbon fluxes by region and type (e.g. the region and forest
type classifications described in Smith et al. (2006)181 and used for estimates for
downed deadwood and understory, which might better reflect the diversity of forest
types and age classes). Furthermore, the ERT recommends that the United States
disaggregate the carbon fluxes by region and type in the CRF tables, which will ensure
transparency and repeatability of methods.
The United States will include in the NIR
the background information provided to
the ERT on the FIA methods,
specifically on age classes,
classification, and classification by forest
and non-forest for the sample plots.
All FIA data used to compile estimates
in the NIR are publicly available and
were specifically referenced in the NIR
(Table A-236). Detailed tables on
average carbon fluxes by region and
forest type, and carbon pool will be
included in forthcoming U.S.
Department of Agriculture (USDA)
Forest Service publications and
referenced in the NIR.
The United States is currently working
on a compilation system that will provide
more spatially and temporally resolved
estimates for the NIR. Once completed
and vetted this system will be used to
produce state-level estimates for
inclusion in the NIR (see Planned
Improvements in the Forest Land
Remaining Forest Land section).
70.
(L.27)
LULUCF
4.A.2 Land converted
to forest land - CO2
The United States has not estimated removals in the biomass pool from regrowth
(reforestation/afforestation) in CRF table 4.A and states in its NIR that research is
under way to include those removals. The United States also clarifies the need to
revise the length of time a land remains in a conversion category after change. The
ERT noted that the calculation of carbon stock change in living biomass in land
converted to forest land is mandatory under the 2006IPCC Guidelines. In the NIR
(p.6-27), the United States explained that the forest dynamics module assesses
carbon stock transfers (removals) associated with afforestation. However, during the
review, the United States clarified that those removals from afforestation have not
been reported in forest land remaining forest land, and in CRF table 4.A, "NA" is
reported under all land converted to forest land.
Completed. The United States has
reported the net change in biomass in
CRF Table 4.A in the previous Inventory
(i.e., 2017 submission).
181 Smith JE, Heath LS, Skog KE and Birdsey RA. 2006. Methods for Calculating Forest Ecosystem and Harvested Carbon with Standard Estimates for Forest Types of the United
States. Gen. Tech. Rep. NE-343. Newtown Square, PA: United States Department of Agriculture, Forest Service, Northeastern Research Station.
A-517
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No.
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Comment
U.S. Response
The ERT recommends that the United States complete the emission estimates of living
biomass for land converted to forest land in accordance with the 2006IPCC
Guidelines.
71.
(L.28)
LULUCF
4.A.2 Land converted
to forest land - CO2
The ERT noted that the United States reported "NA" for deadwood and litter in its
reporting for land converted to forest land. These pools are mandatory under the 2006
IPCC Guidelines. During the review, the United States explained that it elected to
remove the estimates from the submission because of a problem identified shortly
before submission. Emissions and removals for all carbon pools in the category land
converted to forest land will be included in the 2017 submission and will be based on a
20-year default using a conversion matrix.
The ERT recommends that the United States estimate carbon stock change for
deadwood and litter in land converted to forest land in accordance with the 2006 IPCC
Guidelines.
Completed. Emissions and removals for
all carbon pools in the category Land
Converted to Forest Land are included
in the previous Inventory (i.e., 2017
submission).
72.
(L.29)
LULUCF
4.B Cropland-CO2
In the NIR (table 6-23), the United States clarifies in a footnote that estimates after
2010 are based on projections using NRI data for 2010 and therefore may not fully
reflect changes occurring in the latter part of the time series. The United States
explained that more recent information is currently available but data were not
available in time to incorporate them into the 2016 inventory submission.
The ERT recommends that the United States apply the most recent information and
data obtained since 2010 for the emission estimates under this category.
Completed. The previous Inventory (i.e.,
2017 submission) utilized the NRI with
data through 2012. An updated NRI will
be available in Spring of 2018 that will
be used for the 2020 or 2012
submission as resources allow.
73.
(L.30)
LULUCF
4.B.1 Cropland
remaining cropland -
C02
In the NIR (p.6-43), the United States explains that NRI survey locations are classified
according to land-use histories starting in 1979; consequently, the classifications are
based on fewer than 20 years from 1990 to 1998, and this may have led to an
overestimation of the area of cropland remaining cropland. The ERT considers that this
is not in line with the 2006 IPCC Guidelines, which indicate the default land transition
value to be 20 years. Further, the ERT notes that an overestimation of cropland in the
remaining class may underestimate emissions if higher carbon stocks occurred in the
previous land use before 1979. During the review, the United States explained that
additional carbon losses would likely be minimal because cropland area has been
declining over the past three decades owing to the expansion of forests and urban
areas. During the review, the United States further informed the ERT of the on-going
effort to develop a land representation dataset from early generation Landsat imagery
to investigate the possibility of extending the time series for the land use data from
1979 to 1970. The United States also indicated alternative options for extrapolating the
trends in land use back to the 1970s using agricultural and forestry statistics or other
relevant information.
Noting that it is important to avoid potential overestimation or underestimation of
estimates in all IPCC categories, the ERT recommends that the United States
progress its efforts to obtain data of land-use histories starting from 1971 or earlier for
Addressing. The United States intends
to utilize the Landsat imagery to inform
the land use data prior to 1979. This will
be a multi-year process that will be
implemented in a future Inventory
submission.
A-518 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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U.S. Response
input to the land-use change matrices for cropland, and apply those data for the
emission estimates.
74.
(L.31)
LULUCF
4.B.1 Cropland
remaining cropland -
C02
The ERT noted that the areas of mineral and organic soils reported in CRF table 4.B
(616.61 kha and 151,388.48 kha, respectively) have been interchanged for cropland
remaining cropland (a total of 152,005.09 kha) compared with the areas reported in the
NIR (Annex 3.12, table A-217) (151.39 Mha for mineral soils and 0.62 Mhafor organic
soils). In response to a question raised by the ERT, the United States acknowledged
the error and stated that QC measures are in place but had not been completed prior
to the submission of the CRF tables.
The ERT recommends that the United States apply the appropriate QC check to
ensure consistency of the areas of mineral and organic soils reported in CRF table 4.B
and the NIR.
Completed. The United States has
modified the compilation schedule of the
Inventory in order to allow more time to
perform QC measures on the CRF
tables. This has been implemented for
the current Inventory (i.e., 2018
submission).
75.
(L.32)
LULUCF
4.B.2.1 Forest land
converted to cropland -
C02
The ERT noted that in CRF table 4.B the implied carbon stock change factor for 2014
for living biomass for forest land converted to cropland (-65.531 C/ha) is high
compared with other implied carbon stock change factors from neighbouring countries.
For instance, Canada has reported -0.951 C/ha, which is 50 times lower than the
factor reported by the United States. The ERT also noted that, in the NIR (p.6-57), the
United States explained that it calculates the difference between the stocks reported
as the stock change under the assumption that the change occurred in the year of the
conversion.
The ERT recommends that the United States include a transparent explanation of how
the losses (-3,129 kt C in CRF table 4.B for forest land converted to cropland) have
been calculated based on carbon densities in forest land, and amend the information
on biomass carbon stock changes in the NIR (p.6-57).
First, the estimates from Canada may
not be comparable to those in the
United States given markedly different
climates, forest types, compilation
methods, and management systems.
That said, the Canadian estimates seem
unrealistically low given that
conversions for Forest Land to Cropland
(at least in the United States) are
typically on productive sites that would
support crop production and warrant the
cost of conversion from forest land.
These sites typically had or have the
potential to support high tree biomass.
In the United States the implied carbon
stock change factor for 2014 for living
biomass for forest land converted to
cropland was estimated directly from
FIA plots that were Forest land at the
measurement period prior to 2014 and
classified as cropland at the subsequent
measurement period; these are based
on actual field observations.
Additionally, the implied carbon stock
change factor for 2014 for living
biomass for forest land converted to
cropland was -12.901 C/ha not -65.53t
A-519
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U.S. Response
C/ha as suggested in the ERT
description.
76.
(L.33)
LULUCF
4.C.2 Land converted
to grassland - CO2
The ERT noted, in CRF table 4.C, an implied carbon stock change factor for mineral
soils under forest land converted to grasslands of 0.13 tC/ha in 2013. For the
conversion from grasslands to forest land an implied carbon stock change factor for
mineral soils increases annually by 0.101 C/ha. Both conversions would lead to an
increase in carbon stock. In the planned improvements provided in the NIR, the United
States explains that different tier level methods are used for estimating carbon stock
changes in forest land, grassland and cropland. The ERT noted that this could result in
inconsistent implied carbon stock factors for mineral soils for those categories.
Recognizing this, the United States indicates in the NIR that it plans to update and
revise the estimates of emissions and removals from mineral soils in conversions from
forest land to grasslands.
The ERT recommends that the United States revise the estimates for carbon stock
change in mineral soils under forest land converted to grasslands using the updated
data for mineral soils and report the result in the NIR.
The United States does apply a
consistent method to all lands that are
undergoing land use change (using a
Tier 2 method). The differences
between forest land converted to
grassland and grassland converted to
forest land is the management on the
grassland. For example, improved
grassland will have a larger stock than
an unimproved grassland. Regardless,
the United States plans to update and
revise the estimates of emissions and
removals from mineral soils in
conversions from forest land to
grasslands in subsequent NIRs with the
goal of providing more accurate results
with the latest experimental results.
77.
(L.34)
LULUCF
4.D.1 Wetlands
remaining wetlands -
CO2, CH4 andlxhO
The United States reported an area of peatland remaining peatland in CRF table 4.D
for 2014 of 5.31 kha. The ERT notes that the United States reported the data for peat
production in Alaska separately from the data for the other 48 states reporting areas of
peatland in the NIR, due to methodological differences in data collection and
calculation. The areas of peatland are not reported separately in the NIR and CRF
table 4.D, with only the national total being reported. The ERT also noted that in CRF
table 4(H), "NE" is reported for the areas of peat extraction lands, although N2O and
CH4 emissions from drained organic soils are reported, for which the ERT considers
that the same area should be used for on-site CO2 and estimating CO2 emissions
during peat extraction, according to information in the NIR (p.6-76).
The ERT recommends that the United States provide consistent information on the
calculation of the total managed peatland and on how the calculation relates to the
extracted area in the CRF tables and in the NIR. Noting that the United States is aware
of the need for determining the quantity of peat harvested per hectare and the total
area undergoing peat extraction, the ERT recommends that the United States provide
the respective AD and lEFs for on-site CH4 and N2O emission estimates in CRF table
4(H) for organic soils under peat extraction.
Completed. The United States reports
peat production data for Alaska
separately from other states in the NIR
because of a difference in calculation
methodology between Alaska and the
remainder of the United States Alaska
conducts its own mineral survey and
reports peat production by volume,
rather than by weight. Volume
production data are used to calculate
off-site CO2 emissions from Alaska
applying the same methodology but with
volume-specific C fraction conversion
factors from 2006IPCC Guidelines.
The United States reports total U.S.
peat production in the CRF tables,
instead of reporting Alaska separately.
This is also the case for peat area and
emissions. Reporting Alaska separately
from the rest of the United States seems
to be inconsistent, as most sources
A-520 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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U.S. Response
present total U.S. values in the CRF
tables.
CRF table 4(ll) is updated for the current
Inventory (i.e., 2018 submission) to
include CO2 values for areas of peat
extraction lands instead of "NE".
78.
(L.35)
LULUCF
4.D.2.3 Land converted
to wetlands-CO2, CH4
and N2O
The United States has not estimated emissions for wetlands remaining wetlands
separately from land converted to wetlands. The United States explained in the NIR
that it was not able to separate CH4, CO2 and N2O emissions for wetlands remaining
wetlands and land converted to wetlands. The United States also explained in the NIR
that research to track GHG fluxes across wetlands remaining wetlands and land
converted to wetlands is ongoing, and until such time that reliable and comprehensive
estimates of GHG fluxes across these LULUCF categories can be produced, it is not
possible to separate CO2, CH4 and N2O fluxes on land converted to wetlands from
fluxes on wetlands remaining wetlands.
The ERT recommends that the United States use the AD reported in table 6-7 of the
NIR to separate CO2, CH4 and N2O emissions from land converted to wetlands and
wetlands remaining wetlands.
Addressing. In the previous Inventory
(i.e., 2017 submission), the United
States has improved on the reporting of
emissions for wetland remaining
wetlands and land converted to
wetlands and will continue to refine the
estimates for future Inventory
submissions.
79.
(L.38)
LULUCF
4.E.1 Settlements
remaining settlements
-CO2
The United States reported changes in the carbon stocks in landfills relating to yard
trimming and food scraps under settlements remaining settlements in CRF table 4.E.
In the NIR (chapter 6.14, "Other (IPCC Source category 4.H)"), the United States
included details on which methodologies were used for these subcategories, but no
reference is given in chapter 6.10 ("Settlements remaining settlements"). During the
review, the United States explained that, for its next submission, it will report the
information for carbon stocks in landfills relating to yard trimming and food scraps
under the section on settlements in the NIR.
The ERT recommends that the United States check the coherence of reported data,
applying the appropriate QC checks, in order to ensure consistency between the CRF
tables and the NIR.
Completed. The United States began
reporting the carbon stock changes from
Landfilled Yard Trimmings and Food
Scraps in the Settlements sections of
both the NIR and CRF in the previous
Inventory (i.e., 2017 submission).
80.
(L.39)
LULUCF
4.E.2.5 Other land
converted to
settlements - CO2
The United States reports carbon stock changes as "NE" for all pools under land
converted to settlements, and explains in the NIR that, given the lack of available
information, it is not possible to separate CO2 or N2O fluxes on land converted to
settlements from fluxes on settlements remaining settlements at this time. Noting that
CO2 from landfilled yard trimming and food scraps and urban tree soils under
settlements remaining settlements is a key category, the ERT finds that land converted
to settlements might become a key category if the United States were to estimate
these emissions because according to the NIR (p.6-86), land under a number of uses
undergoes urbanization in the United States each year.
Partially completed. In the previous
Inventory (i.e., 2017 submission) the
United States reports carbon stock
changes for Land Converted to
Settlements separately from
Settlements Remaining Settlements.
However, at this time it is not possible to
report the N2O fluxes in this way due to
lack of activity data. The United States
will also apply the notation key IE for
A-521
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
The ERT recommends that the United States estimate carbon stock changes in living
biomass and dead organic matter. If this is not possible, the ERT recommends that the
United States use the notation key "IE" for area under land converted to settlements in
order to be consistent with the information in the NIR stating that other lands converted
to settlements cannot be separated from settlements remaining settlements.
those situations where it is not possible
to separate the emissions into land use
and land use conversion categories.
81.
(L.40)
LULUCF
4(l) Direct N20
emissions from
nitrogen inputs to
managed soils - N2O
In CRF table 4(l), the United States reports, for the entire time series, N2O emissions
from land converted to forest land as "IE" and "NA", from wetlands as "NA", and from
land converted to settlements as "NA". However, the ERT noted that the direct and
indirect N2O emissions from managed soils under land converted to forest land have
been included in forest land remaining forest land (NIR, table 6-19). Similarly, the NIR
states that N2O fluxes for lands converted to settlements are reported under
settlements remaining settlements. Under flooded wetlands, N2O emissions have not
been estimated. The United States provided, during the review, information showing
that it avoids double counting for N in peat that is used as fertilizer in horticulture peat
(applied to agricultural soils).
The ERT recommends that the United States use the notation key "NE" and/or "IE" in
reporting AD and N2O emissions from land converted to forest land, wetlands, and land
converted to settlements, as appropriate, in order to be consistent with the explanation
provided in the NIR, and provide information showing how it avoids double counting for
N, without omitting N input in peat.
Completed. In the previous Inventory
(i.e., 2017 submission) the United
States has modified the use of notation
keys in Table 4(l) to better reflect how
the emissions are reported in the NIR.
82.
(L.41)
LULUCF
4 (III) Direct N20
emissions from N
mineralization/
immobilization - N2O
The ERT noted that in CRF table 4(lll) the United States reported direct N2O emissions
from mineralization/immobilization for all land categories as "NA", but the LULUCF
chapter of the NIR does not include a section that provides information on the use of
the notation key "NA" for reporting the direct N2O emissions resulting from land use or
management of mineral soils
The ERT recommends that the United States include an explanation in the NIR for the
reporting of "NA" for all land categories for direct N2O emissions from
mineralization/immobilization.
Addressing. Direct N2O emissions from
mineralization/immobilization are
reported from croplands and grasslands
in agricultural soil management, and
therefore the notation key should be
"IE". Direct N2O emissions from
mineralization/immobilization are not
reported for other land uses, but will be
investigated and reported in a future
submission based on the
recommendation of the ERT.
83.
(L.42)
LULUCF
4 (V) Biomass burning
-CO2, CH4 and N2O
The ERT noted that the United States has provided CH4 and N2O emissions from
forest fires in forest land remaining forest land only. Emissions from biomass burning
under other land categories are reported as "NE" or "NA", except for N2O emissions
from cropland remaining cropland and from grassland, which are reported as "IE". For
the category forest land remaining forest land, the United States has mentioned in the
improvement plan the use of country-specific combustion factors to calculate
emissions from burning and stated that the information is provided by the Monitoring
Trends in Burn Severity data summaries. Currently those data are unused for the
emission estimates for this category. During the review, the United States stated that it
Partially complete. In the previous
Inventory (i.e., 2017 submission), the
United States began reporting biomass
burning for forestland and grassland. In
both cases, it is not possible to separate
emissions by conversion categories.
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is working on research for country-specific factors and the work will be used as it
matures.
Noting that Cm and N2O emissions from forest fires are key categories, the ERT
recommends that the United States estimate CH4 and N2O emissions from biomass
burning in land converted to forest land, land converted to wetlands, cropland,
grassland, and settlements, and populate CRF table 4(V) to improve completeness.
84.
(L.43)
LULUCF
4.G Harvested wood
products (HWP)-C02
The United States used the production accounting approach to report CO2 emissions
relating to HWP. Under the production approach, carbon in exported wood was
estimated as if it remains in the United States, and carbon in imported wood was not
included in the estimates. A tier 3 approach based on the use of country-specific data
and methods to estimate HWP variables was used for the emission estimates. During
the review, the United States explained that the criteria in the WOODCARB II model
that were used to estimate the HWP contribution to forest carbon sinks and emissions
are fixed and were developed using country-specific data. The United States also
stated that exports represent an estimated 9 percent of total production in the United
States. The ERT noted that the United States has not provided the AD on production,
imports and exports of wood needed to estimate the HWP variables (i.e. HWP in
products in use - domestic consumption (1 .A), HWP in products in use - domestic
harvest (2.A), carbon in annual imports of HWP (PIM), carbon in annual exports of
HWP (Pex) and carbon in annual harvest of roundwood (H)) for 1961 to the present,
which is not in line with the good practice in the 2006IPCC Guidelines, which require
this information to be provided in CRF table 4.Gs2.
The ERT recommends that the United States provide in the NIR information showing
that data on the life cycle of exported HWP for those countries to which most of its
products are exported are comparable with country-specific data, or adjust the data
accordingly.
Completed. These data are available for
the United States and are included in
the CRF tables for the current Inventory
(i.e., 2018 submission).
Waste
85.
(W.2)
Waste
5.A Solid waste
disposal on land - Cm
Report on the trend of total waste generated, provide explanations, and revise the
data, if necessary. Some information is provided in the NIR.
Completed. The methodology is based
on national waste generation from 1990
to 2004 and then switches to directly
reported net emissions for 2005 to 2016,
meaning we are no longer using
national waste generation data as the
basis for emissions. We include details
on the national waste generation data in
the Methodology portion of Section 7.1
in the NIR and explain year-to-year
variations.
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Sector
Source/Sink Category
Comment
U.S. Response
86.
(W.3)
Waste
5.A Solid waste
disposal on land - Cm
Revise the estimates of emissions from solid waste disposal on land by incorporating
the revised degradable organic carbon (DOC) values into the emission estimation. The
United States reports some effort to address the issue in the NIR (e.g. revision of the
DOC value for landfilled pulp and paper on p. 7-11). However, during the review, the
United States confirmed that the constant value is used in the entire time series. The
ERT considers that if a constant value is used, the emission estimation does not
capture the changing waste composition over the time series.
Addressing. The DOC value applied to
industrial waste landfills is constant for
the entire time series. The United States
is investigating facility-specific DOC
data reported under Subpart TT
(Industrial Waste Landfills) of EPA's
GHGRP to determine whether the pulp
and paper and food and beverage waste
composition has changed in recent
years. Industrial waste composition
tends to remain consistent from year to
year when looking at single industries.
The industrial waste composition and
consequently DOC will change when the
input material changes, or when a
process changes.
With regard to municipal solid waste
(MSW) landfills, the United States has
collected all publicly available and online
MSW characterization study data since
1990 and is reviewing them to
determine the impact of a changing
waste composition. The level of detail in
individual waste composition studies
varies significantly. If applicable, EPA
may revise the DOC value from 1990 to
2004. The methodology for 2005 to
2016 uses directly reported methane
emissions to the GHGRP, a regulation
that defines DOC values that can be
applied. Updates to DOC value(s) for
2005 to 2016 must be considered in
context of updates to methods in the
GHGRP.
87.
(W.4)
Waste
5.A Solid waste
disposal on land - Cm
Report the composition of waste landfilled, with the amounts/shares and corresponding
coefficients, including DOC. No relevant information on the composition of waste
landfilled is provided in the NIR. In the NIR (p.7-8), the United States explains that the
information on the amount and composition of waste placed in every MSW and
industrial waste landfill for each year of a landfill's operation is not available. In the NIR
(p.7-9), the United States also reports that it is currently compiling the waste
composition studies and data that have been performed in the past decade and may
Addressing. See comment above; the
United States is investigating waste
characterization studies completed
across the United States since 1990 to
better define the composition over the
time series.
A-524 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
revise the default waste composition applied to MSW landfilled in the first order decay
(FOD) model in future inventory estimates.
The current Inventory includes a
national estimate of waste composition
at the end of Section 7.1 - Landfills. The
United States can include more detail on
the waste composition applied by the
Waste Model in the 1990 through 2017
NIR. The composition of MSW landfilled
is generally not available for many of the
1,500 active MSW landfills in the United
States, which is why the composition is
estimated at a national level. The United
States is investigating variations from
the national composition to landfill-
specific waste composition studies and
will summarize this information in a
future Inventory submissions.
88.
(W.5)
Waste
5.D.2 Industrial
wastewater - ChU
Include information on the non-estimation of ChU emissions from sludge under
industrial wastewater. No information is provided in the NIR. During the review, the
United States explained that continuous efforts are under way to ensure the
completeness of the United States inventory.
Addressing. Efforts are continuing to
ensure completeness of the United
States inventory.
89.
(W.8)
Waste
5.C.1 Waste
incineration-CH4 and
N20
Make efforts to collect the necessary AD for the emission estimation of CH4 and N2O
from non-hazardous industrial waste and medical waste incineration, and to include
these estimates in future inventory submissions, providing all necessary explanations
in the NIR. In the NIR (p.7-32) the United States indicated that data are not readily
available to estimate emissions from incineration of non-hazardous industrial waste,
while annual emissions from medical waste incineration would be below 500 kt CO2
eq. During the review, no justification was provided for the insignificance of emissions
from medical waste.
Completed. See Annex 5.
90.
(W.9)
Waste
5. General (waste) -
CO2, CH4, and N2O
In previous review reports the ERTs recommended that the United States provide
descriptions of the waste management practices used in the country. During the
current review the United States explained that boxes 7-3,7-4 and 7-5 of the NIR with
accompanying tables, graphs and charts describe and depict the waste management
practices in the United States. The ERT commends the United States for its efforts.
The ERT noted that, as described in the NIR (Box 7-3), the United States uses two
sources of data on solid waste management: BioCycle and Earth Engineering Center
of Columbia University's State of Garbage in America surveys and the EPA's Municipal
Solid Waste in the United States Facts and Figures. The United States indicates that
the data on waste management, waste composition and the recovery of degradable
waste presented in the NIR (Box 7-4) are taken from an EPA Facts and Figures report
that is not consistent with the State of Garbage surveys, which the United States
indicates in the NIR is the preferred data source for estimating waste generation and
Addressing. The United States
understands this comment and is
working to rectify the inconsistency
between the two data sources. Since
this comment was made, the United
States has transitioned away from the
BioCycle data and is now using facility-
specific, directly reported information.
The United States is also investigating
facility-specific waste composition
studies and trends and will investigate
differences between facility-specific data
and the MSW Facts and Figures data
A-525
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
disposal amounts in the inventory. The ERT considers that this has created an
inconsistency issue within the NIR. For example, the United States reported in chapter
7 of the NIR that landfilling accounts for 53 percent of total waste management
practices while in Annex 3.14 to the NIR the same information is reported as 63
percent. The reported trend for landfilled waste from 1990 to 2013 is also different. The
ERT recommends that the United States provide background information that is
consistent with the data actually used for the emission estimates, including the waste
management practices, in a clear manner.
(now called Sustainable Materials
Management Report).
91.
(W.10)
Waste
5.A Solid waste
disposal on land - ChU
The United States provided in its NIR some information explaining the trend of total
waste generated. In response to a question from the ERT during the review, the United
States also provided a memorandum, "Review of State of Garbage data used in the U.S.
Non-C02 Greenhouse Gas Inventory for Landfills", which helped the ERT to review the
trend of generated waste. The ERT recommends that the United States include in the
NIR a summary of information on the actual trend of total waste generated as contained
in the memorandum "Review of State of Garbage data used in the U.S. Non-C02
Greenhouse Gas Inventory for Landfills", which was provided to the ERT during the
review.
Completed. The United States has
included this information in the
Methodology section of Section 7.1 in
the current Inventory (i.e., 2018
submission). Please note that the
information included in the
memorandum ("Review of State of
Garbage data used in the U.S. Non-C02
Greenhouse Gas Inventory for
Landfills") only applies to a portion of the
time series (1990 to 2004) due to a
methodological / activity data change for
2005 to 2016.
92.
(W.11)
Waste
5.A.1.a Anaerobic-
cm
The ERT identified that the United States reported total MSW generated and not total
waste landfilled in CRF table 5.A. During the review, the United States explained that
issues with data import to the CRF Reporter software are under investigation in order to
improve the consistency of the CRF tables. The ERT recommends that the United States
strengthen its QA/QC procedures related to consistency checks between information
reported in CRF table 5.A on AD and the NIR, in order to avoid similar errors in future
submissions.
Addressing. The information presented
in the Landfills workbook CRF Table 5A
in the previous NIR was solid waste
generated. This has been changed to
MSW landfilled in the current Inventory
(i.e., 2018 submission). The solid waste
disposed by year is also presented in
the "Inv Tables" worksheet in row 48.
The main Landfills chapter presents
emissions and recovery estimates only,
while the Annex presents the activity
data, including the amount of MSW
landfilled by year.
Note that the data sources have
changed for the time series and the
United States is no longer relying on the
BioCycle State of Garbage reports to
estimate the amount of MSW generated
and landfilled. The United States is still
A-526 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
presenting estimates of MSW generated
and landfilled in the NIR, but the United
States does not directly use this
information to estimate net emissions. In
the next Inventory (i.e., 2019
submission), the United States will
attempt to address this inconsistency so
that the data used for waste disposal
amounts reflects the source used for the
emissions estimates.
93.
(W.12)
Waste
5.A.1.a Anaerobic-
cm
The NIR states that the United States assumes over 99 percent of the organic waste
placed in industrial waste landfills originates from the food processing (meat, vegetables,
fruits) and pulp and paper industries (EPA, 1993), and therefore estimates of industrial
landfill emissions focused on these two industries. The ERT noted that in the section on
planned improvements in the NIR (p.7-12), the United States includes a possible revision
to the waste disposal factor currently used for the pulp and paper industry to use
production data from pulp and paper facilities obtained from GHGRP, and the possible
addition of other industries (e.g. metal foundries, petroleum refineries and chemical
manufacturing facilities). The ERT considers that the share of organic waste placed in
industrial landfills may be different from that assumed in 1993. Therefore, the ERT
recommends that the United States obtain up-to-date data on the type and fractions of
organic waste placed in industrial waste landfills and revise the CH4 estimates from all
major industrial waste landfills.
Addressing. EPA plans to document the
assumptions regarding the percentage
and composition of industrial waste
landfilled and compare this information
to that reported to the EPA's GHGRP in
a technical memorandum. The GHGRP
data contains the most up-to-date and
comprehensive information available
about industrial waste.
94.
(W.14)
Waste
5.B.2 Anaerobic
digestion at biogas
facilities - ChU
The United States reports the notation key "IE" for CH4 emissions from anaerobic
digestion at biogas facilities. During the review, the United States explained that
disaggregated data are not available and it is assumed that CH4 emissions are included
in the aggregated data reported under the category managed waste disposal sites
(5.A.1). The ERT noted that, according to the 2006IPCC Guidelines (volume 5, chapter
5, section 4.1), the emissions from unintentional leakages during anaerobic digestion
should be reported in the waste sector and, also according to the 2006 IPCC Guidelines,
in the absence of further information, it is recommended to use a default value of 5
percent.
The ERT recommends that the United States estimate and report CH4 emissions from
unintentional leakages using the default value of 5 percent provided by the 2006 IPCC
Guidelines.
Addressing. The United States will
investigate the data sources and
practices of anaerobic digestion in more
detail and will assess the addition of a 5
percent factor to account for
unintentional leakages in a future
Inventory (targeting the 1990 through
2017 Inventory).
95.
(W.15)
Waste
5.C.1 Waste
incineration - CO2,
CH4, and N2O
The ERT identified a few inconsistencies within the NIR. For example, the United States
reported in figure 7-2 of the NIR that 13 percent of waste was incinerated in 2013 while
NIR tables 3-26s and A-272 of the NIR both report 7.6 percent for the same year. During
the review, the United States explained that multiple references were utilized to estimate
CO2 emissions from waste incineration (focused on fossil-derived waste) and then for
Addressing. The United States is
investigating additional sources of
information on waste incineration
including the GHGRP and plans to
A-527
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
CH4 and N2O emissions from waste incineration (based on total mass). The United
States stated that steps will be taken to better coordinate waste references across all
categories in the next inventory submission. The ERT recommends that the United
States provide in the NIR consistent information on the data that are used for the
estimation of emissions from waste incineration.
update waste references (targeting the
1990 through 2017 Inventory).
96.
(W.16)
Waste
5.C.1 Waste
incineration - CO2,
CH4, and N2O
In the previous review report the ERT recommended that the United States estimate
emissions from the incineration of non-hazardous industrial waste and medical waste.
In the current NIR, the United States indicated that data are not readily available to
estimate emissions from the incineration of non-hazardous industrial waste and that,
based on a report from RTI, medical waste incineration would be below 500 kt CO2 eq
per year, which the United States considered to be insignificant for the purpose of
inventory reporting. The ERT recommends that the United States provide in Annex 5 to
the NIR a specific reference to the RTI report justifying the insignificance of the
emissions from the incineration of medical waste, in accordance with paragraph 37(b)
of the UNFCCC Annex I inventory reporting guidelines.
Completed. See Annex 5.
97.
(W.17)
Waste
5.D Wastewater
treatment and
discharge - CH4
The United States reports the notation key "IE" for CH4 flared from domestic wastewater
(5.D.1) and other (5.D.3). During the review, the United States explained that aggregated
data were reported under "amount of CH4 for energy recovery". The ERT recommends
that the United States provide information in CRF table 9 to indicate where all emissions
reported as "IE" are included.
Completed. The United States has
clarified that the notation key for CH4
flared from domestic wastewater is IE
because CH4 flared values are not
directly estimated, rather combined with
CH4 energy recovery, due to a lack of
available activity data.
98.
(W.18)
Waste
5.D.1 Domestic
wastewater - N2O
The ERT noted that the equation used to estimate Neffluent explained in the NIR is not
consistent with the method provided in the 2006IPCC Guidelines (volume 5, chapter 5,
box 6.1) for estimating emissions from advanced centralized wastewater treatment
plants. During the review, the United States explained that it uses the equation to
estimate emissions from domestic wastewater effluent (equation 6.7) with the total
annual amount of N in the wastewater effluent estimated using equation 6.8 provided in
the 2006 IPCC Guidelines. To reflect the N2O emissions from domestic wastewater
treated in the centralized treatment plant prior to discharge as effluent, the United States
subtracted the N associated with such plant emissions from the total N20effluent which
was estimated using equation 6.8. During the review, the United States agreed that, in
applying equation 6.8 provided in the 2006 IPCC Guidelines, an adjustment should be
made to the N20effluent equation used to estimate emissions so as to properly back-
calculate and subtract N associated with N2O emissions from centralized treatment
plants, and suggested a revised equation which considers the underestimation of N
treated by biological denitrification. Further, the United States explained that the revised
equation, which adjusts the over-deduction of N treated by biological denitrification, still
does not consider N discharge from the percentage of the population which uses a septic
system because the septic systems in the United States do not discharge to aquatic
environments. The ERT recommends that the United States estimate the N2O emissions
Completed. Beginning with the previous
inventory (i.e., 2017 submission), the
United States implemented revisions to
the calculation of Neffluent that
subtracts N associated with N2O
emissions from centralized treatment
plants.
A-528 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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No.
ID
Sector
Source/Sink Category
Comment
U.S. Response
using the revised equations and report the emissions with the background information
in the next submission.
NA (Not Applicable)
A-529
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