1
2
3
4
5
6
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8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
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25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
DRAFT 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 2003). 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 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.
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 183
3.4. Methodology for Estimating CH: Emissions from Coal Mining 188
3.5. Methodology for Estimating CH: and CO2 Emissions from Petroleum Systems 196
3.6. Methodology for Estimating CH: and CO2 Emissions from Natural Gas Systems 201
3.7. Methodology for Estimating CO2, CH4, and N2O Emissions from the Incineration of Waste - TO BE UPDATED FOR FINAL
INVENTORY REPORT 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 369
3.14. Methodology for Estimating CH: Emissions from Landfills 405
ANNEX 4 IPCC Reference Approach for Estimating CO2 Emissions from Fossil Fuel Combustion 418
ANNEX 5 Assessment of the Sources and Sinks of Greenhouse Gas Emissions Not Included 428
ANNEX 6 Additional Information 433
6.1. Global Warming Potential Values 433
6.2. Ozone Depleting Substance Emissions 444
6.3. Sulfur Dioxide Emissions 446
ANNEX 7 Uncertainty 463
7.1. Overview 463
7.2. Methodology and Results 463
7.3. Reducing Uncertainty 470
7.4. Planned Improvements 470
7.5. Additional Information on Uncertainty Analyses by Source 471
ANNEX 8 QA/QC Procedures 483
8.1. Background 483
8.2. Purpose 483
8.3. Assessment Factors 484
8.4. Responses During the Review Process 486
A-1
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table fl-1: Key Source Categories forthe United States 11990-2016)
Approach 1
Approach 2
2016
Level
Trend
Level
Trend
Level
Trend
Level
Trend
Emissions
Greenhouse
Without
Without
With
With
Without
Without
With
With
(MMT C02
IPCC Source Categories
Gas
LULUCF
LULUCF
LULUCF
LULUCF
LULUCF
LULUCF
LULUCF
LULUCF
Quala
Eq.)
Energy
Emissions from Mobile
CO2 Emissions from Stationary
Combustion - Coal - Electricity
Generation
CO- Emissions from Stationary
Combustion - Gas - Electricity
Generation
CO2 Emissions from Stationary
Combustion - Gas - Industrial
¦¦IB
anary
CO2 Emissions from Stationary
Combustion - Gas - Residential
Emissions from Stationary
bustion - Gas - Commercial
CO2 Emissions from Mobile
Combustion: Aviation
DO2 Emissions from Non-Energy Use
CO2 Emissions from Mobile
Combustion: Other
from Stationary
CO2 Emissions from Stationary
Combustion - Oil - Residential
CO- Emissions from Stationary
Combustion - Oil - Commercial
CO2 Emissions from Mobile
Combustion: Marine
Stationary
CO2 Emissions from Natural Gas
Systems
CO2 Emissions from Petroleum
Systems
C02
C02
C02
C02
C02
C02
C02
C02
¦
C02
C02
C02
C02
C02
A-3
-------
C02 Emissions from Stationary
Combustion - Oil - Electricity
Generation
CO2 Emissions from Stationary
Combustion - Gas - U.S. Territories
CO2 Emissions from Stationary
Combustion - Coal - Commercial
Non-C02 Emissions from Stationary
Combustion - Residential
DO2 Emissions from Stationary
oustion - Electricity Generation
N2O Emissions from Mobile
Combustion: Road
¦ CO Emissions from Stationary
strial
CO2 Emissions from Iron and Steel
Production & Metallurgical Coke
Production
diis from Cement
CO2 Emissions from Petrochemical
Production
CO2 Emissions from Other Process
Uses of Carbonates
d ns from I
PFC Emissions from Aluminum
Production
N2O Emissions from Adipic Acid
Production
; from Substitutes for Ozone
Substances
SF6 Emissions from Electrical
Transmission and Distribution
: Emissions from HCFC-22
C02
C02
C02
C02
C02
C02
C02
CH4
N 0
N20
N 0
Several
N20
HiGWP
HiGWP
HiGWP
HiGWP
CO2 Emissions from Liming
C02
21.2
3.0
2.3
53.8
3.4
14.91
13.1
2.4 !
¦
1
115.5
42.2
39.4
27.4
11.2
7.0
173.9
4.3
111111
1.4
3.9
A-4 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Cm Emissions from Enteric
BMBI
ChU Emissions from Manure
Management
CH4
.
.
67.7
CH4 Emissions from Rice Cultivation
r h
wri4
Direct N2O Emissions from Agricultural
Soil Management
Indirect N2O Emissions from Applied
n2o
n2o
• • • •
• • •
237.6
MRHHI
Waste
CH4 Emissions from Landfills
107.7
Land Use, Land Use Change, and Forestry
Net CO2 Emissions from Land
Converted to Settlements
68.0
Net CO2 Emissions from Land
CO2
23.8
Net CO2 Emissions from Grassland
)
ill 9
liSHIl
(1.6)
WMi
Net CO2 Emissions from Land
Converted to Forest Land
Net CO2 Emissions from Settlements
Remaining Settlements
O O
O O
•
(75.0)
(103.7)
Net CO2 Emissions from Forest Land
Remaining Forest Land
CO2
ch4
•
(670.5)
18.5
N2O Emissions from Forest Fires
n2o
12.2
Subtotal Without LULUCF
6,390.8
Total Emissions Without LULUCF
6,546.2
Percent of Total Without LULUCF
98%
Subtotal With LULUCF
5,651.5
Total Emissions With LULUCF
5,829.3
Percent of Total With LULUCF
97%
1 'Qualitative criteria.
2 b Emissions from this source not included in totals.
3 Note: Parentheses indicate negative values (or sequestration).
A-5
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1
2
3
4
5
6
7
8
9
10
11
12
Table A-2 provides a complete listing of source categories by IPCC sector, along with notations on the criteria
used in identifying key categories, without LULUCF sources and sinks. Similarly, Table A-3 provides a complete listing of
source and sink categories by IPCC sector, along with notations on the criteria used in identifying key categories, including
LULUCF sources and sinks. The notations refer specifically to the year(s) in the Inventory time series (i.e., 1990 to 2016)
in which each source 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
IPCC Source Categories
Direct
Greenhouse
Gas
2016 Emissions
(MMT C02 Eq.)
Key
Category?
ID
Criteria3
Level in which
year(s)?b
Energy
CO2 Emissions from Mobile Combustion: Road
C02
1,504.0
Li T1 L2 T2
1990, 2016
CO2 Emissions from Stationary Combustion - Coal -
Electricity Generation
C02
1,241.3
Li T1 L2 T2
1990, 2016
CO2 Emissions from Stationary Combustion - Gas -
Electricity Generation
C02
545.9
Li T1 L2 T2
1990, 2016
CO2 Emissions from Stationary Combustion - Gas -
Industrial
C02
478.8
Li T1 L2 T2
1990, 2016
CO2 Emissions from Stationary Combustion - Oil -
Industrial
C02
269.7
Li T1 L2 T2
1990, 2016
CO2 Emissions from Stationary Combustion - Gas -
Residential
C02
238.3
L1T1L2
1990, 2016
CO2 Emissions from Stationary Combustion - Gas -
Commercial
C02
170.3
Li T1 L2 T2
1990, 2016
CO2 Emissions from Mobile Combustion: Aviation
C02
169.6
Li T1 L2 T2
1990, 2016
CO2 Emissions from Non-Energy Use of Fuels
C02
121.0
Li L2
1990, 2016
CO2 Emissions from Mobile Combustion: Other
C02
80.1
Li
1990i, 2016i
CO2 Emissions from Stationary Combustion - Coal -
Industrial
C02
59.0
Li T1 L2 T2
1990, 2016
CO2 Emissions from Stationary Combustion - Oil -
Residential
C02
58.0
Li T1 L2 T2
1990,20161
CO2 Emissions from Stationary Combustion - Oil -
Commercial
C02
55.3
L1T1T2
1990i, 2016i
CO2 Emissions from Mobile Combustion: Marine
C02
41.1
Li
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
26.7
Li
1990i
CO2 Emissions from Petroleum Systems
C02
25.5
T1 L2 T2
20162
CO2 Emissions from Stationary Combustion - Oil -
C02
21.2
Li T1 L2 T2
1990
Electricity Generation
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
2.3
T1
Commercial
CO2 Emissions from Stationary Combustion -
C02
0.4
Geothermal Energy
CO2 Emissions from Stationary Combustion - Coal -
C02
0.0
Residential
CH4 Emissions from Natural Gas Systems
CH4
162.1
Li T1 L2 T2
1990, 2016
Fugitive Emissions from Coal Mining
CH4
53.8
Li T1 L2 T2
1990, 2016
A-6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
CH4 Emissions from Petroleum Systems
CH4
39.3
CH4 Emissions from Abandoned Oil and Gas Wells
ch4
7.1
Fugitive Emissions from Abandoned Underground Coal
Mines
ch4
6.7
Non- CO2 Emissions from Stationary Combustion -
Residential
ch4
3.4
CH4 Emissions from Mobile Combustion: Other
ch4
1.5
Non- CO2 Emissions from Stationary Combustion -
Industrial
ch4
1.4
Non- CO2 Emissions from Stationary Combustion -
ch4
1.2
Commercial
Non- CO2 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- CO2 Emissions from Stationary Combustion -
ch4
0.1
U.S. Territories
CH4 Emissions from Mobile Combustion: Aviation
ch4
+
CH4 Emissions from Incineration of Waste
ch4
+
Non- CO2 Emissions from Stationary Combustion -
N2O
14.9
Electricity Generation
N2O Emissions from Mobile Combustion: Road
N2O
13.1
N2O Emissions from Mobile Combustion: Other
N2O
2.7
Non- CO2 Emissions from Stationary Combustion -
N2O
2.4
Industrial
N2O Emissions from Mobile Combustion: Aviation
N2O
1.6
Non- CO2 Emissions from Stationary Combustion -
N2O
0.7
Residential
N2O Emissions from Mobile Combustion: Marine
N2O
0.5
Non- CO2 Emissions from Stationary Combustion -
N2O
0.3
Commercial
N2O Emissions from Incineration of Waste
N2O
0.3
Non- CO2 Emissions from Stationary Combustion -
N2O
0.1
U.S. Territories
International Bunker Fuelsc
Several
115.5
LiL2 1990,2016
L2 19902,20162
Ti L2 20162
L1T1L2T2 1990
L2 19902
Industrial Processes
CO2 Emissions from Lime Production
CO2 Emissions from Other Process Uses of
Carbonates
CO2 Emissions from Ammonia Production
CO2 Emissions from Carbon Dioxide Consumption
CO2 Emissions from Urea Consumption for Non-
Agricultural Purposes
CO2 Emissions from Ferroalloy Production
CO2 Emissions from Soda Ash Production
CO2 Emissions from Titanium Dioxide Production
CO2 Emissions from Aluminum Production
CO2 Emissions from Glass Production
CO2 Emissions from Phosphoric Acid Production
CO2 Emissions from Zinc Production
CO2 Emissions from Lead Production
CO2 Emissions from Silicon Carbide Production and
Consumption
L1T1L2T2 1990,2016
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CO2 Emissions from Cement Production CO2 39.4 • Li 1990i,2016i
CO2 Emissions from Petrochemical Production CO2 27.4 • Li 20161
CO2
42.2
CO2
39.4
CO2
27.4
CO2
13.3
CO2
11.2
CO2
11.2
CO2
4.5
CO2
4.0
CO2
1.8
CO2
1.7
CO2
1.6
CO2
1.3
CO2
1.3
CO2
1.0
CO2
0.9
CO2
0.5
CO2
0.2
T1
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
173.9
Li T1 L2 T2
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
L2
19902
CH4 Emissions from Enteric Fermentation
CH4
170.1
Li L2
1990, 2016
CH4 Emissions from Manure Management
ch4
67.7
Li T1 L2 T2
1990, 2016
CH4 Emissions from Rice Cultivation
ch4
13.7
L2
19902,20162
CH4 Emissions from Field Burning of Agricultural
ch4
0.3
Residues
Direct N2O Emissions from Agricultural Soil
N2O
237.6
Li T1 L2 T2
1990, 2016
Management
Indirect N2O Emissions from Applied Nitrogen
N2O
45.9
L1T1L2
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 L2 T2
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
1 + Does not exceed 0.05 MMT CO2 Eq.
2 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
3 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
4 level assessment).
5 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
6 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
7 assessment only in 1990).
8 c Emissions from these sources not included in totals.
9 Note: LULUCF sources and sinks are not included in this analysis.
10
A-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-3: U.S. Greenhouse Gas Inventory Source Categories with LULUGF
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
1,504.0
Li T1 L2 T2
1990,2016
CO2 Emissions from Stationary Combustion - Coal -
C02
1,241.3
Li T1 L2 T2
1990,2016
Electricity Generation
CO2 Emissions from Stationary Combustion - Gas -
C02
545.9
Li T1 L2 T2
19901,2016
Electricity Generation
CO2 Emissions from Stationary Combustion - Gas -
C02
478.8
Li T1 L2
1990,2016
Industrial
CO2 Emissions from Stationary Combustion - Oil -
C02
269.7
Li T1 L2 T2
1990,2016
Industrial
CO2 Emissions from Stationary Combustion - Gas -
C02
238.3
Li L2
1990,2016
Residential
CO2 Emissions from Stationary Combustion - Gas -
C02
170.3
Li T1 L2
19901,2016
Commercial
CO2 Emissions from Mobile Combustion: Aviation
C02
169.6
Li T1 L2
1990,2016
CO2 Emissions from Non-Energy Use of Fuels
C02
121.0
Li L2
1990,2016
CO2 Emissions from Mobile Combustion: Other
C02
80.1
Li
1990i,20161
CO2 Emissions from Stationary Combustion - Coal -
C02
59.0
Li T1 L2 T2
1990, 2016i
Industrial
CO2 Emissions from Stationary Combustion - Oil -
C02
58.0
Li T1
1990i,20161
Residential
CO2 Emissions from Stationary Combustion - Oil -
C02
55.3
Li T1
1990i,20161
Commercial
CO2 Emissions from Mobile Combustion: Marine
C02
41.1
Li
1990i,20161
CO2 Emissions from Stationary Combustion - Oil -
C02
34.3
Li T1
1990i,20161
U.S. Territories
CO2 Emissions from Natural Gas Systems
C02
26.7
Li
1990i,20161
CO2 Emissions from Petroleum Systems
C02
25.5
Li T1 T2
2016i
CO2 Emissions from Stationary Combustion - Oil -
C02
21.2
Li T1 T2
1990i
Electricity Generation
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 -
C02
2.3
•
T1
Commercial
CO2 Emissions from Stationary Combustion -
C02
0.4
Geothermal Energy
CO2 Emissions from Stationary Combustion - Coal -
C02
0.0
Residential
CH4 Emissions from Natural Gas Systems
CH4
162.1
•
Li T1 L2 T2
1990,2016
Fugitive Emissions from Coal Mining
ch4
53.8
•
Li T1 L2 T2
1990, 2016i
CH4 Emissions from Petroleum Systems
ch4
39.3
•
Li L2
1990,2016
CH4 Emissions from Abandoned Oil and Gas Wells
ch4
7.1
Fugitive Emissions from Abandoned Underground
cm
6.7
Coal Mines
Non-C02 Emissions from Stationary Combustion -
cm
3.4
•
L2
19902
Residential
CH4 Emissions from Mobile Combustion: Other
cm
1.5
Non- CO2 Emissions from Stationary Combustion -
cm
1.4
Industrial
Non- CO2 Emissions from Stationary Combustion -
cm
1.2
Commercial
A-9
-------
Non- CO2 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- CO2 Emissions from Stationary Combustion -
ch4
0.1
U.S. Territories
CH4 Emissions from Mobile Combustion: Aviation
ch4
+
CH4 Emissions from Incineration of Waste
ch4
+
Non-C02 Emissions from Stationary Combustion -
N2O
14.9
T1
Electricity Generation
N2O Emissions from Mobile Combustion: Road
N2O
13.1
r~
-H
i^H
CO
CO
0
N2O Emissions from Mobile Combustion: Other
N2O
2.7
Non-C02 Emissions from Stationary Combustion -
N2O
2.4
Industrial
N2O Emissions from Mobile Combustion: Aviation
N2O
1.6
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 -
N2O
0.1
U.S. Territories
International Bunker Fuelsc
Several
115.5
•
Industrial Processes
CO2 Emissions from Iron and Steel Production &
C02
42.2
L1T1L2T2 1990,20161
Metallurgical Coke Production
CO2 Emissions from Cement Production
C02
39.4
Li 1990i, 20161
CO2 Emissions from Petrochemical Production
C02
27.4
CO
0
CM
_U
CO2 Emissions from Lime Production
C02
13.3
CO2 Emissions from Other Process Uses of
C02
11.2
T1
Carbonates
CO2 Emissions from Ammonia Production
C02
11.2
CO2 Emissions from Carbon Dioxide Consumption
C02
4.5
CO2 Emissions from Urea Consumption for Non-
C02
4.0
Agricultural Purposes
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
1.3
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
CO2 Emissions from Magnesium Production and
C02
+
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
N20
10.2
N2O Emissions from Adipic Acid Production
N20
7.0
T1
N2O Emissions from Product Uses
N20
4.2
A-10 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
N2O Emissions from Caprolactam, Glyoxal, and
N20
2.0
Glyoxylic Acid Production
N2O Emissions from Semiconductor Manufacture
N20
0.2
Emissions from Substitutes for Ozone Depleting
HiGWP
173.9
Li T1 L2 T2
2016
Substances
PFC, HFC, SFe, and NF3 Emissions from
HiGWP
4.7
Semiconductor Manufacture
SF6 Emissions from Electrical Transmission and
HiGWP
4.3
T1
Distribution
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
HiGWP
1.0
Processing
HFC-134a Emissions from Magnesium Production
HiGWP
0.1
and 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 L2 T2
19901,2016
CH4 Emissions from Rice Cultivation
ch4
13.7
CH4 Emissions from Field Burning of Agricultural
ch4
0.3
Residues
Direct N2O Emissions from Agricultural Soil
N2O
237.6
L1T1L2
1990,2016
Management
Indirect N2O Emissions from Applied Nitrogen
N2O
45.9
Li T1 L2 T2
1990,2016
N2O Emissions from Manure Management
N2O
18.1
N2O Emissions from Field Burning of Agricultural
N2O
0.1
Residues
Waste
CH4 Emissions from Landfills
CH4
107.7
Li T1 L2 T2
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
CO2
68.0
Li T1 L2 T2
19901,2016
Settlements
Net CO2 Emissions from Land Converted to
CO2
23.8
Li T1 L2 T2
1990, 20162
Cropland
Net CO2 Emissions from Land Converted to
CO2
22.0
L2 T2
19902, 20162
Grassland
Net CO2 Emissions from Land Converted to
CO2
(+)
Wetlands
Net CO2 Emissions from Grassland Remaining
CO2
(+)
L2 T2
19902, 20162
Grassland
Net CO2 Emissions from Coastal Wetlands
CO2
(+)
Remaining Coastal Wetlands
Net CO2 Emissions from Cropland Remaining
CO2
(+)
Li T1 L2 T2
1990, 20162
Cropland
Net CO2 Emissions from Land Converted to Forest
CO2
(+)
Li T1
1990i,20161
Land
Net CO2 Emissions from Settlements Remaining
CO2
(+)
Li T1 L2 T2
1990,2016
Settlements
Net CO2 Emissions from Forest Land Remaining
CO2
(+)
Li T1 L2 T2
1990,2016
Forest Land
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
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
CH4 Emissions from Grassland Fires
CH4
0.3
CH4 Emissions from Drained Organic Soils
ch4
+
CH4 Emissions from Land Converted to Coastal
ch4
+
Wetlands
CH4 Emissions from Peatlands Remaining
ch4
+
Peatlands
N2O Emissions from Forest Fires
n20
12.2
N2O Emissions from Settlement Soils
N20
2.5
N2O Emissions from Forest Soils
N20
0.5
N2O Emissions from Grassland Fires
N20
0.3
N2O Emissions from Coastal Wetlands Remaining
N20
0.1
Coastal Wetlands
N2O Emissions from Drained Organic Soils
N20
0.1
N2O Emissions from Peatlands Remaining
Peatlands
N20
+
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., 19902designates 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. Uncertainty is not estimated for the following sources: CO2 emissions from stationary combustion -
geothermal energy; CO2 emissions from mobile combustion by mode of transportation; and CH4 from the incineration of
waste. 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 category is split into many subcategories, then the subcategories may have contributions to the
total inventory that are too small for those source categories to be considered key. Similarly, a collection of small, non-key
source categories adding up to less than 5 percent of total emissions could become key source categories if those source
categories were aggregated into a single source category. The United States has attempted to define source and sink
categories by the conventions which would allow comparison with other international key categories, while still maintaining
the category definitions that constitute how the emissions estimates were calculated for this report. As such, some of the
category names used in the key category analysis may differ from the names used in the main body of the report.
Additionally, the United States accounts for some source categories, including fossil fuel feedstocks, international bunkers,
and emissions from U.S. Territories, that are derived from unique data sources using country-specific methodologies.
A-12 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
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
1,162.3
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
310.4
0.05
0.54
21%
0.010
CO2 Emissions from Stationary Combustion - Gas -
Residential
C02
238.0
0.04
0.58
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
193.7
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%
0.006
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
119.6
0.02
0.82
38%
0.007
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
co2
101.5
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
5%
0.001
A-13
-------
Fugitive Emissions from Coal Mining
CH4
96.5
0.02
0.88
15%
0.002
CO2 Emissions from Stationary Combustion - Oil -
Commercial
C02
73.3
0.01
0.89
6%
0.001
CO2 Emissions from Mobile Combustion: Other
C02
73.2
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
42.3
0.01
0.92
36%
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
19%
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
29.7
<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
10%
<0.001
PFC Emissions from Aluminum Production
PFCs
21.5
<0.01
0.96
10%
<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
26%
0.001
N2O Emissions from Adipic Acid Production
N2O
15.2
<0.01
0.97
4%
<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
8%
<0.001
N2O Emissions from Nitric Acid Production
N2O
12.1
<0.01
0.98
6%
<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
3%
<0.001
CO2 Emissions from Petroleum Systems
CO2
9.4
<0.01
0.98
36%
0.001
CO2 Emissions from Incineration of Waste
CO2
8.0
<0.01
0.98
13%
<0.001
Fugitive Emissions from Abandoned Underground
Coal Mines
CH4
7.2
<0.01
0.98
22%
<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
32%
<0.001
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
N2O
6.5
<0.01
0.99
42%
<0.001
Non-C02 Emissions from Stationary Combustion -
Residential
CH4
5.2
<0.01
0.99
220%
0.002
CH4 Emissions from Mobile Combustion: Road
ch4
5.2
<0.01
0.99
27%
<0.001
SFe Emissions from Magnesium Production and
Processing
CO2 Emissions from Other Process Uses of
Carbonates
SFe
CO2
5.2
4.9
<0.01
<0.01
0.99
0.99
6%
16%
<0.001
<0.001
CO2 Emissions from Liming
CO2
4.7
<0.01
0.99
111%
0.001
N2O Emissions from Product Uses
N2O
4.2
<0.01
0.99
24%
<0.001
CH4 Emissions from Mobile Combustion: Other
CH4
4.1
<0.01
0.99
67%
<0.001
CO2 Emissions from Urea Consumption for Non-
Agricultural 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%
5%
<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
3.2
<0.01
1.00
204%
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Non-C02 Emissions from Stationary Combustion -
Industrial
N2O Emissions from Mobile Combustion: Aviation
N2O Emissions from Caprolactam, Glyoxal, and
Glyoxylic Acid Production
CO2 Emissions from Glass Production
CO2 Emissions from Phosphoric Acid Production
N2O Emissions from Mobile Combustion: Other
CO2 Emissions from Carbon Dioxide Consumption
CO2 Emissions from Soda Ash Production
CO2 Emissions from Titanium Dioxide Production
Non-C02 Emissions from Stationary Combustion -
Commercial
Non-C02 Emissions from Stationary Combustion -
Residential
CO2 Emissions from Stationary Combustion - Coal -
U.S. Territories
CO2 Emissions from Zinc Production
N2O Emissions from Mobile Combustion: Marine
CO2 Emissions from Lead Production
CH4 Emissions from Mobile Combustion: Marine
N2O Emissions from Incineration of Waste
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
CO2 Emissions from Stationary Combustion -
Geothermal Energy
Non-C02 Emissions from Stationary Combustion -
Commercial
CH4 Emissions from Composting
CO2 Emissions from Silicon Carbide Production and
Consumption
N2O Emissions from Composting
Emissions from Substitutes for Ozone Depleting
Substances
CH4 Emissions from Field Burning of Agricultural
Residues
CH4 Emissions from Petrochemical Production
N2O Emissions from Field Burning of Agricultural
Residues
Non-C02 Emissions from Stationary Combustion -
U.S. Territories
CH4 Emissions from Mobile Combustion: Aviation
Non-C02 Emissions from Stationary Combustion -
U.S. Territories
N2O Emissions from Semiconductor Manufacture
CH4 Emissions from Silicon Carbide Production and
Consumption
CH4 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CH4 Emissions from Ferroalloy Production
CO2 Emissions from Magnesium Production and
Processing
CH4 Emissions from Incineration of Waste
HFC-134a Emissions from Magnesium Production
and Processing
CO2 Emissions from Stationary Combustion - Gas -
U.S. Territories
ch4
1.9
<0.01
1.00
49%
<0.001
n20
1.7
<0.01
1.00
68%
<0.001
N20
1.7
<0.01
1.00
40%
<0.001
co2
1.5
<0.01
1.00
5%
<0.001
co2
1.5
<0.01
1.00
20%
<0.001
N20
1.5
<0.01
1.00
68%
<0.001
co2
1.5
<0.01
1.00
5%
<0.001
co2
1.4
<0.01
1.00
7%
<0.001
co2
1.2
<0.01
1.00
13%
<0.001
ch4
1.1
<0.01
1.00
144%
<0.001
n20
1.0
<0.01
1.00
209%
<0.001
co2
0.6
<0.01
1.00
19%
<0.001
co2
0.6
<0.01
1.00
21%
<0.001
N20
0.6
<0.01
1.00
44%
<0.001
co2
0.5
<0.01
1.00
16%
<0.001
ch4
0.5
<0.01
1.00
69%
<0.001
n20
0.5
<0.01
1.00
330%
<0.001
ch4
0.4
<0.01
1.00
4%
<0.001
co2
0.4
<0.01
1.00
NA
<0.001
N20
0.4
<0.01
1.00
175%
<0.001
ch4
0.4
<0.01
1.00
50%
<0.001
co2
0.4
<0.01
1.00
9%
<0.001
N20
0.3
<0.01
1.00
50%
<0.001
Several
0.3
<0.01
1.00
12%
<0.001
CH4
0.2
<0.01
1.00
31%
<0.001
ch4
0.2
<0.01
1.00
57%
<0.001
N20
0.1
<0.01
1.00
14%
<0.001
N20
0.1
<0.01
1.00
199%
<0.001
CH4
0.1
<0.01
1.00
88%
<0.001
ch4
+
<0.01
1.00
55%
<0.001
N2O
+
<0.01
1.00
13%
<0.001
CH4
+
<0.01
1.00
10%
<0.001
ch4
+
<0.01
1.00
19%
<0.001
ch4
+
<0.01
1.00
12%
<0.001
CO2
+
<0.01
1.00
2%
<0.001
CH4
+
<0.01
1.00
NE
<0.001
HFCs
0.0
<0.01
1.00
4%
<0.001
CO2
0.0
<0.01
1.00
17%
<0.001
1 + Does not exceed 0.05 MMT CO2 Eq.
A-15
-------
1 NE (Not Estimated)
2 NA (Not Available)
3 a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
4 Note: LULUCF sources and sinks are not included in this analysis.
5
6 Tablefl-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 Categories
Gas
(MMT C02 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
1,162.3
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
310.4
0.04
0.56
21%
0.009
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
193.7
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
119.6
0.02
0.80
38%
0.006
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
co2
101.5
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
5%
0.001
Fugitive Emissions from Coal Mining
ch4
96.5
0.01
0.85
15%
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
35%
0.004
CO2 Emissions from Stationary Combustion - Oil -
Commercial
co2
73.3
0.01
0.88
6%
0.001
CO2 Emissions from Mobile Combustion: Other
co2
73.2
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
CH4 Emissions from Petroleum Systems
CH4
42.3
0.01
0.92
36%
0.002
Net CO2 Emissions from Cropland Remaining Cropland
CO2
40.9
0.01
0.92
452%
0.025
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
19%
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
29.7
<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
A-16 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
SFe Emissions from Electrical Transmission and
SFe
23.1
<0.01
0.96
10%
<0.001
Distribution
PFC Emissions from Aluminum Production
PFCs
21.5
<0.01
0.96
10%
<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
26%
0.001
N2O Emissions from Adipic Acid Production
N2O
15.2
<0.01
0.97
4%
<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
8%
<0.001
N2O Emissions from Nitric Acid Production
N2O
12.1
<0.01
0.98
6%
<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
3%
<0.001
CO2 Emissions from Petroleum Systems
CO2
9.4
<0.01
0.98
36%
<0.001
CO2 Emissions from Incineration of Waste
CO2
8.0
<0.01
0.98
13%
<0.001
Net CO2 Emissions from Coastal Wetlands Remaining
CO2
7.6
<0.01
0.98
59%
0.001
Coastal Wetlands
Fugitive Emissions from Abandoned Underground Coal
Mines
CH4
7.2
<0.01
0.98
22%
<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
32%
<0.001
Non-C02 Emissions from Stationary Combustion -
N2O
6.5
<0.01
0.99
42%
<0.001
Electricity Generation
Non-C02 Emissions from Stationary Combustion -
CH4
5.2
<0.01
0.99
220%
0.002
Residential
CH4 Emissions from Mobile Combustion: Road
ch4
5.2
<0.01
0.99
27%
<0.001
SFe Emissions from Magnesium Production and
SFe
5.2
<0.01
0.99
6%
<0.001
Processing
CO2 Emissions from Other Process Uses of Carbonates
CO2
4.9
<0.01
0.99
16%
<0.001
CO2 Emissions from Liming
CO2
4.7
<0.01
0.99
111%
0.001
N2O Emissions from Product Uses
N2O
4.2
<0.01
0.99
24%
<0.001
Net CO2 Emissions from Grassland Remaining
CO2
4.2
<0.01
0.99
2503%
0.014
Grassland
CH4 Emissions from Mobile Combustion: Other
CH4
4.1
<0.01
0.99
67%
<0.001
CO2 Emissions from Urea Consumption for Non-
CO2
3.8
<0.01
0.99
12%
<0.001
Agricultural Purposes
PFC, HFC, SFe, and NF3 Emissions from Semiconductor
Several
3.6
<0.01
0.99
5%
<0.001
Manufacture
CH4 Emissions from Coastal Wetlands Remaining
CH4
3.4
<0.01
0.99
30%
<0.001
Coastal Wetlands
N2O Emissions from Wastewater Treatment
N20
3.4
<0.01
0.99
112%
0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
N20
3.2
<0.01
0.99
204%
0.001
CH4 Emissions from Forest Fires
CH4
3.2
<0.01
1.00
127%
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
1.9
<0.01
1.00
49%
<0.001
N2O Emissions from Mobile Combustion: Aviation
N20
1.7
<0.01
1.00
68%
<0.001
N2O Emissions from Caprolactam, Glyoxal, and
Glyoxylic Acid Production
N20
1.7
<0.01
1.00
40%
<0.001
CO2 Emissions from Glass Production
C02
1.5
<0.01
1.00
5%
<0.001
CO2 Emissions from Phosphoric Acid Production
C02
1.5
<0.01
1.00
20%
<0.001
N2O Emissions from Mobile Combustion: Other
N20
1.5
<0.01
1.00
68%
<0.001
A-17
-------
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
7%
<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
144%
<0.001
Non-C02 Emissions from Stationary Combustion -
Residential
N20
1.0
<0.01
1.00
209%
<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
21%
<0.001
N2O Emissions from Mobile Combustion: Marine
N20
0.6
<0.01
1.00
44%
<0.001
CO2 Emissions from Lead Production
C02
0.5
<0.01
1.00
16%
<0.001
CH4 Emissions from Mobile Combustion: Marine
CH4
0.5
<0.01
1.00
69%
<0.001
N2O Emissions from Incineration of Waste
N20
0.5
<0.01
1.00
330%
<0.001
Non-C02 Emissions from Stationary Combustion -
CH4
0.4
<0.01
1.00
4%
<0.001
Electricity Generation
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
175%
<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
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
31%
<0.001
Residues
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 Grassland Fires
N20
0.1
<0.01
1.00
167%
<0.001
N2O Emissions from Field Burning of Agricultural
N20
0.1
<0.01
1.00
14%
<0.001
Residues
CH4 Emissions from Grassland Fires
CH4
0.1
<0.01
1.00
133%
<0.001
Non-C02 Emissions from Stationary Combustion - U.S.
N20
0.1
<0.01
1.00
199%
<0.001
Territories
CH4 Emissions from Mobile Combustion: Aviation
CH4
0.1
<0.01
1.00
88%
<0.001
Non-C02 Emissions from Stationary Combustion - U.S.
ch4
+
<0.01
1.00
55%
<0.001
Territories
N2O Emissions from Semiconductor Manufacture
N20
+
<0.01
1.00
13%
<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 &
ch4
<0.01
1.00
19%
<0.001
Metallurgical Coke Production
+
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
ch4
<0.01
1.00
30%
<0.001
Wetlands
+
CH4 Emissions from Drained Organic Soils
ch4
+
<0.01
1.00
76%
<0.001
CH4 Emissions from Peatlands Remaining Peatlands
ch4
+
<0.01
1.00
53%
<0.001
CO2 Emissions from Magnesium Production and
CO2
+
<0.01
1.00
2%
<0.001
Processing
N2O Emissions from Peatlands Remaining Peatlands
N2O
+
<0.01
1.00
78%
<0.001
A-18 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
CH4 Emissions from Incineration of Waste
HFC-134a Emissions from Magnesium Production and
Processing
CO2 Emissions from Stationary Combustion - Gas - U.S
Territories
1 + Does not exceed 0.05 MMT CO2 Eq.
2 NE (Not Estimated)
3 NA (Not Available)
4 a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
5
6 Tablefl-6:2016 Key Source Category Approach 1 and Approach 2 Analysis—Level Assessment, without LULUCF
Direct
Approach 1
Approach 2
Greenhouse
2016 Estimate
Level
Cumulative
Level
IPCC Source Categories
Gas
(MMT CO2 Eq.)
Assessment
Total
Uncertainty3
Assessment
CO2 Emissions from Mobile Combustion: Road
CO2
1,504.0
0.23
0.23
6%
0.015
CO2 Emissions from Stationary Combustion - Coal -
Electricity Generation
CO2
1,241.3
0.19
0.42
10%
0.018
CO2 Emissions from Stationary Combustion - Gas -
Electricity Generation
CO2
545.9
0.08
0.50
5%
0.004
CO2 Emissions from Stationary Combustion - Gas -
Industrial
CO2
478.8
0.07
0.58
7%
0.005
CO2 Emissions from Stationary Combustion - Oil -
Industrial
CO2
269.7
0.04
0.62
21%
0.009
CO2 Emissions from Stationary Combustion - Gas -
Residential
CO2
238.3
0.04
0.65
7%
0.003
Direct N2O Emissions from Agricultural Soil
Management
Emissions from Substitutes for Ozone Depleting
Substances
N2O
Several
237.6
173.9
0.04
0.03
0.69
0.72
16%
12%
0.006
0.003
CO2 Emissions from Stationary Combustion - Gas -
Commercial
CO2
170.3
0.03
0.74
7%
0.002
CH4 Emissions from Enteric Fermentation
CH4
170.1
0.03
0.77
18%
0.005
CO2 Emissions from Mobile Combustion: Aviation
CO2
169.6
0.03
0.79
6%
0.002
CH4 Emissions from Natural Gas Systems
CH4
162.1
0.02
0.82
17%
0.004
CO2 Emissions from Non-Energy Use of Fuels
CO2
121.0
0.02
0.84
38%
0.007
CH4 Emissions from Landfills
CH4
107.7
0.02
0.85
23%
0.004
CO2 Emissions from Mobile Combustion: Other
CO2
80.1
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 - Coal -
Industrial
CO2
59.0
0.01
0.89
16%
0.001
CO2 Emissions from Stationary Combustion - Oil -
Residential
CO2
58.0
0.01
0.89
5%
<0.001
CO2 Emissions from Stationary Combustion - Oil -
Commercial
CO2
55.3
0.01
0.90
6%
<0.001
Fugitive Emissions from Coal Mining
CH4
53.8
0.01
0.91
15%
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 &
CO2
42.2
0.01
0.92
17%
0.001
Metallurgical Coke Production
CO2 Emissions from Mobile Combustion: Marine
CO2
41.1
0.01
0.93
6%
<0.001
CO2 Emissions from Cement Production
CO2
39.4
0.01
0.94
6%
<0.001
CH4 Emissions from Petroleum Systems
CH4
39.3
0.01
0.94
36%
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
27.4
<0.01
0.95
5%
<0.001
CO2 Emissions from Natural Gas Systems
CO2
26.7
<0.01
0.96
17%
0.001
CO2 Emissions from Petroleum Systems
CO2
25.5
<0.01
0.96
36%
0.001
CO2 Emissions from Stationary Combustion - Oil -
Electricity Generation
CO2
21.2
<0.01
0.96
9%
<0.001
N2O Emissions from Manure Management
N2O
18.1
<0.01
0.97
24%
0.001
CH4 + <0.01
HFCs 0.0 <0.01
C02 0.0 <0.01
1.00 NE <0.001
1.00 4% <0.001
1.00 17% <0.001
A-19
-------
l\lon-C02 Emissions from Stationary Combustion -
Electricity Generation
N2O
14.9
<0.01
0.97
42%
0.001
Cm Emissions from Wastewater Treatment
CH4
14.8
<0.01
0.97
26%
0.001
CH4 Emissions from Rice Cultivation
ch4
13.7
<0.01
0.97
64%
0.001
CO2 Emissions from Lime Production
C02
13.3
<0.01
0.97
3%
<0.001
N2O Emissions from Mobile Combustion: Road
N20
13.1
<0.01
0.98
19%
<0.001
CO2 Emissions from Other Process Uses of
Carbonates
C02
11.2
<0.01
0.98
16%
<0.001
CO2 Emissions from Ammonia Production
C02
11.2
<0.01
0.98
8%
<0.001
CO2 Emissions from Incineration of Waste
C02
10.7
<0.01
0.98
13%
<0.001
N2O Emissions from Nitric Acid Production
N20
10.2
<0.01
0.98
6%
<0.001
CH4 Emissions from Abandoned Oil and Gas Wells
CH4
7.1
<0.01
0.98
32%
<0.001
N2O Emissions from Adipic Acid Production
N20
7.0
<0.01
0.99
4%
<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
5%
<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
10%
<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-
Agricultural 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
220%
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
N2O Emissions from Mobile Combustion: Other
N2O
2.7
<0.01
0.99
68%
<0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
N2O
2.4
<0.01
0.99
204%
0.001
CO2 Emissions from Stationary Combustion - Coal -
Commercial
CO2
2.3
<0.01
1.00
15%
<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
40%
<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
7%
<0.001
CO2 Emissions from Titanium Dioxide Production
CO2
1.6
<0.01
1.00
13%
<0.001
N2O Emissions from Mobile Combustion: Aviation
N2O
1.6
<0.01
1.00
68%
<0.001
CH4 Emissions from Mobile Combustion: Other
CH4
1.5
<0.01
1.00
67%
<0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
ch4
1.4
<0.01
1.00
49%
<0.001
PFC Emissions from Aluminum Production
PFCs
1.4
<0.01
1.00
10%
<0.001
CO2 Emissions from Aluminum Production
CO2
1.3
<0.01
1.00
3%
<0.001
CO2 Emissions from Glass Production
CO2
1.3
<0.01
1.00
5%
<0.001
Non-C02 Emissions from Stationary Combustion -
Commercial
CH4
1.2
<0.01
1.00
144%
<0.001
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
ch4
1.1
<0.01
1.00
4%
<0.001
CH4 Emissions from Mobile Combustion: Road
ch4
1.1
<0.01
1.00
27%
<0.001
A-20 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
SFe 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
N2O Emissions from Semiconductor Manufacture
CH4 Emissions from Petrochemical Production
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 Magnesium Production and
Processing
CH4 Emissions from Incineration of Waste
CO2 Emissions from Stationary Combustion - Coal -
Residential
SFe
1.0
<0.01
1.00
6%
<0.001
C02
1.0
<0.01
1.00
20%
<0.001
CO2
0.9
<0.01
1.00
21%
<0.001
N2O
0.7
<0.01
1.00
209%
<0.001
N2O
0.5
<0.01
1.00
44%
<0.001
CO2
0.5
<0.01
1.00
16%
<0.001
CO2
0.4
<0.01
1.00
NA
<0.001
CH4
0.3
<0.01
1.00
69%
<0.001
N2O
0.3
<0.01
1.00
175%
<0.001
N2O
0.3
<0.01
1.00
330%
<0.001
CH4
0.3
<0.01
1.00
31%
<0.001
N2O
0.2
<0.01
1.00
13%
<0.001
CH4
0.2
<0.01
1.00
57%
<0.001
CO2
0.2
<0.01
1.00
9%
<0.001
N2O
0.1
<0.01
1.00
199%
<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
19%
<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 flpproachi and Approach 2 Analysis—Level Assessment with LULUCF
Direct
Approach 1
Approach 2
Greenhouse 2016 Estimate
Level
Cumulative
Level
IPCC Source Categories
Gas
(MMT CO2 Eq.) Assessment
Total
Uncertainty3
Assessment
CO2 Emissions from Mobile Combustion: Road
C02
1,504.0
0.20
0.20
6%
0.013
CO2 Emissions from Stationary Combustion - Coal -
Electricity Generation
CO2
1,241.3
0.16
0.36
10%
0.016
Net CO2 Emissions from Forest Land Remaining Forest
Land
CO2
670.5
0.09
0.45
78%
0.069
CO2 Emissions from Stationary Combustion - Gas -
Electricity Generation
CO2
545.9
0.07
0.52
5%
0.004
CO2 Emissions from Stationary Combustion - Gas -
Industrial
CO2
478.8
0.06
0.59
7%
0.005
A-21
-------
CO2 Emissions from Stationary Combustion - Oil -
Industrial
C02
269.7
0.04
0.62
21%
0.008
CO2 Emissions from Stationary Combustion - Gas -
Residential
C02
238.3
0.03
0.65
7%
0.002
Direct N2O Emissions from Agricultural Soil Management
N20
237.6
0.03
0.69
16%
0.005
Emissions from Substitutes for Ozone Depleting
Substances
Several
173.9
0.02
0.71
12%
0.003
CO2 Emissions from Stationary Combustion - Gas -
Commercial
C02
170.3
0.02
0.73
7%
0.002
CH4 Emissions from Enteric Fermentation
CH4
170.1
0.02
0.75
18%
0.004
CO2 Emissions from Mobile Combustion: Aviation
C02
169.6
0.02
0.78
6%
0.001
CH4 Emissions from Natural Gas Systems
CH4
162.1
0.02
0.80
17%
0.004
CO2 Emissions from Non-Energy Use of Fuels
C02
121.0
0.02
0.81
38%
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
35%
0.005
CO2 Emissions from Mobile Combustion: Other
C02
80.1
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 - Coal -
Industrial
C02
59.0
0.01
0.89
16%
0.001
CO2 Emissions from Stationary Combustion - Oil -
Residential
C02
58.0
0.01
0.90
5%
<0.001
CO2 Emissions from Stationary Combustion - Oil -
Commercial
C02
55.3
0.01
0.90
6%
<0.001
Fugitive Emissions from Coal Mining
CH4
53.8
0.01
0.91
15%
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
42.2
0.01
0.92
17%
0.001
CO2 Emissions from Mobile Combustion: Marine
C02
41.1
0.01
0.93
6%
<0.001
CO2 Emissions from Cement Production
C02
39.4
0.01
0.93
6%
<0.001
CH4 Emissions from Petroleum Systems
CH4
39.3
0.01
0.94
36%
0.002
CO2 Emissions from Stationary Combustion - Oil - U.S.
Territories
CO2
34.3
<0.01
0.94
11%
0.001
CO2 Emissions from Petrochemical Production
CO2
27.4
<0.01
0.95
5%
<0.001
CO2 Emissions from Natural Gas Systems
CO2
26.7
<0.01
0.95
17%
0.001
CO2 Emissions from Petroleum Systems
CO2
25.5
<0.01
0.95
36%
0.001
Net CO2 Emissions from Land Converted to Cropland
CO2
23.8
<0.01
0.96
77%
0.002
Net CO2 Emissions from Land Converted to Grassland
CO2
22.0
<0.01
0.96
134%
0.004
CO2 Emissions from Stationary Combustion - Oil -
Electricity Generation
CO2
21.2
<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
N2O
18.1
<0.01
0.97
24%
0.001
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
N2O
14.9
<0.01
0.97
42%
0.001
CH4 Emissions from Wastewater Treatment
CH4
14.8
<0.01
0.97
26%
0.001
CH4 Emissions from Rice Cultivation
ch4
13.7
<0.01
0.97
64%
0.001
CO2 Emissions from Lime Production
CO2
13.3
<0.01
0.97
3%
<0.001
N2O Emissions from Mobile Combustion: Road
N2O
13.1
<0.01
0.97
19%
<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
11.2
<0.01
0.98
16%
<0.001
CO2 Emissions from Ammonia Production
CO2
11.2
<0.01
0.98
8%
<0.001
CO2 Emissions from Incineration of Waste
CO2
10.7
<0.01
0.98
13%
<0.001
N2O Emissions from Nitric Acid Production
N2O
10.2
<0.01
0.98
6%
<0.001
Net CO2 Emissions from Cropland Remaining Cropland
CO2
9.9
<0.01
0.98
452%
0.006
A-22 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Net CO2 Emissions from Coastal Wetlands Remaining
Coastal Wetlands
C02
7.9
<0.01
0.98
59%
0.001
CH4 Emissions from Abandoned Oil and Gas Wells
CH4
7.1
<0.01
0.99
32%
<0.001
N2O Emissions from Adipic Acid Production
N20
7.0
<0.01
0.99
4%
<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
5%
<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
10%
<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-
Agricultural 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
220%
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
N2O Emissions from Mobile Combustion: Other
N2O
2.7
<0.01
0.99
68%
<0.001
N2O Emissions from Settlement Soils
N2O
2.5
<0.01
0.99
45%
<0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
N2O
2.4
<0.01
1.00
204%
0.001
CO2 Emissions from Stationary Combustion - Coal -
Commercial
CO2
2.3
<0.01
1.00
15%
<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
40%
<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
7%
<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
N2O Emissions from Mobile Combustion: Aviation
N2O
1.6
<0.01
1.00
68%
<0.001
CH4 Emissions from Mobile Combustion: Other
CH4
1.5
<0.01
1.00
67%
<0.001
Non-C02 Emissions from Stationary Combustion -
Industrial
ch4
1.4
<0.01
1.00
49%
<0.001
PFC Emissions from Aluminum Production
PFCs
1.4
<0.01
1.00
10%
<0.001
CO2 Emissions from Aluminum Production
CO2
1.3
<0.01
1.00
3%
<0.001
CO2 Emissions from Glass Production
CO2
1.3
<0.01
1.00
5%
<0.001
Non-C02 Emissions from Stationary Combustion -
Commercial
CH4
1.2
<0.01
1.00
144%
<0.001
Non-C02 Emissions from Stationary Combustion -
Electricity Generation
ch4
1.1
<0.01
1.00
4%
<0.001
CH4 Emissions from Mobile Combustion: Road
ch4
1.1
<0.01
1.00
27%
<0.001
SF6 Emissions from Magnesium Production and
Processing
SFe
1.0
<0.01
1.00
6%
<0.001
CO2 Emissions from Phosphoric Acid Production
CO2
1.0
<0.01
1.00
20%
<0.001
CO2 Emissions from Zinc Production
CO2
0.9
<0.01
1.00
21%
<0.001
Non-C02 Emissions from Stationary Combustion -
Residential
N2O
0.7
<0.01
1.00
209%
<0.001
A-23
-------
N2O Emissions from Mobile Combustion: Marine
N20
0.5
<0.01
1.00
44%
<0.001
CO2 Emissions from Lead Production
C02
0.5
<0.01
1.00
16%
<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
69%
<0.001
Non-C02 Emissions from Stationary Combustion -
Commercial
N20
0.3
<0.01
1.00
175%
<0.001
N2O Emissions from Grassland Fires
N20
0.3
<0.01
1.00
167%
<0.001
N2O Emissions from Incineration of Waste
N20
0.3
<0.01
1.00
330%
<0.001
CH4 Emissions from Grassland Fires
CH4
0.3
<0.01
1.00
133%
<0.001
CH4 Emissions from Field Burning of Agricultural
Residues
ch4
0.3
<0.01
1.00
31%
<0.001
N2O Emissions from Semiconductor Manufacture
N20
0.2
<0.01
1.00
13%
<0.001
CH4 Emissions from Petrochemical Production
CH4
0.2
<0.01
1.00
57%
<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
199%
<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
19%
<0.001
CH4 Emissions from Peatlands Remaining Peatlands
ch4
+
<0.01
1.00
53%
<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
78%
<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
1 + Does not exceed 0.05 MMT CO2 Eq.
2 NE (Not Estimated)
3 NA (Not Available)
4 a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
5
A-24 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
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 - Industrial
Fugitive Emissions from Coal Mining
HFC-23 Emissions from HCFC-22 Production
CO2 Emissions from Stationary Combustion -
Oil - Residential
CH4 Emissions from Natural Gas Systems
CH4 Emissions from Manure Management
N2O Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion -
Gas - Commercial
CO2 Emissions from Mobile Combustion:
Aviation
PFC Emissions from Aluminum Production
CO2 Emissions from Stationary Combustion -
Oil - Commercial
Direct N2O Emissions from Agricultural Soil
Management
SF6 Emissions from Electrical Transmission
and Distribution
CO2 Emissions from Petroleum Systems
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 Stationary Combustion -
Gas - Residential
CO2 Emissions from Other Process Uses of
Carbonates
C02
175.3
545.9
0.06
0.004
17.1
17
co2
1,547.6
1,241.3
0.05
0.001
16.3
33
co2
1,162.3
1,504.0
0.05
0.002
14.5
48
Several
0.3
173.9
0.03
0.002
8.1
56
C02
155.3
59.0
0.02
0.002
4.7
61
C02
97.5
21.2
0.01
0.001
3.7
64
CH4
179.6
107.7
0.01
0.001
3.6
68
C02
101.5
42.2
0.01
0.001
2.9
71
C02
408.9
478.8
0.01
0.001
2.7
74
C02
310.4
269.7
0.01
<0.001
2.3
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
CO2
97.4
58.0
0.01
0.001
2.0
82
ch4
193.7
162.1
0.01
<0.001
1.7
84
cm
37.2
67.7
<0.01
<0.001
1.4
85
n2o
37.6
13.1
<0.01
<0.001
1.2
86
co2
142.1
170.3
<0.01
<0.001
1.1
87
co2
187.4
169.6
<0.01
<0.001
1.1
89
PFCs
21.5
1.4
<0.01
<0.001
1.0
90
CO2
73.3
55.3
<0.01
<0.001
0.9
90
N2O
212.0
237.6
<0.01
<0.001
0.9
91
SFe
23.1
4.3
<0.01
<0.001
0.9
92
CO2
9.4
25.5
<0.01
<0.001
0.7
93
CO2
12.0
2.3
<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
<0.001
0.3
95
CO2
238.0
238.3
<0.01
<0.001
0.3
95
CO2
4.9
11.2
<0.01
<0.001
0.3
95
CO2 Emissions from Aluminum Production
CO2
6.8
1.3
<0.01
<0.001
0.3
96
CO2 Emissions from Petrochemical Production
CO2
21.2
27.4
<0.01
<0.001
0.3
96
CO2 Emissions from Cement Production
CO2
33.5
39.4
<0.01
<0.001
0.2
96
CO2 Emissions from Mobile Combustion: Other
CO2
73.2
80.1
<0.01
<0.001
0.2
96
CO2 Emissions from Mobile Combustion:
Marine
CO2
44.3
41.1
<0.01
<0.001
0.2
97
A-25
-------
SFe Emissions from Magnesium Production
and Processing
Cm Emissions from Mobile Combustion: Road
CH4 Emissions from Petroleum Systems
CO2 Emissions from Natural Gas Systems
N2O Emissions from Manure Management
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 Rice Cultivation
CH4 Emissions from Mobile Combustion: Other
CO2 Emissions from Urea Fertilization
CO2 Emissions from Incineration of Waste
N2O Emissions from Nitric Acid Production
CO2 Emissions from Ammonia Production
Non-C02 Emissions from Stationary
Combustion - Residential
CO2 Emissions from Non-Energy Use of Fuels
CH4 Emissions from Composting
N2O Emissions from Composting
N2O Emissions from Wastewater Treatment
CH4 Emissions from Enteric Fermentation
CO2 Emissions from Lime Production
CH4 Emissions from Wastewater Treatment
N2O Emissions from Mobile Combustion: Other
PFC, HFC, SF6, and NF3 Emissions from
Semiconductor Manufacture
CO2 Emissions from Liming
Non-C02 Emissions from Stationary
Combustion - Industrial
Non-C02 Emissions from Stationary
Combustion - Electricity Generation
Fugitive Emissions from Abandoned
Underground Coal Mines
CO2 Emissions from Phosphoric Acid
Production
Non-C02 Emissions from Stationary
Combustion - Industrial
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
N2O Emissions from Caprolactam, Glyoxal,
and Glyoxylic Acid Production
CO2 Emissions from Glass 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
SFe
5.2
1.0
<0.01
<0.001
0.2
97
CH4
5.2
1.1
<0.01
<0.001
0.2
97
ch4
42.3
39.3
<0.01
<0.001
0.2
97
CO2
29.7
26.7
<0.01
<0.001
0.2
97
N2O
14.0
18.1
<0.01
<0.001
0.2
98
CO2
0.6
4.0
<0.01
<0.001
0.2
98
CO2
3.0
0.0
<0.01
<0.001
0.1
98
CO2
0.0
3.0
<0.01
<0.001
0.1
98
CO2
1.5
4.5
<0.01
<0.001
0.1
98
CH4
16.0
13.7
<0.01
<0.001
0.1
98
ch4
4.1
1.5
<0.01
<0.001
0.1
98
CO2
2.4
5.1
<0.01
<0.001
0.1
99
CO2
8.0
10.7
<0.01
<0.001
0.1
99
N2O
12.1
10.2
<0.01
<0.001
0.1
99
CO2
13.0
11.2
<0.01
<0.001
0.1
99
CH4
5.2
3.4
<0.01
<0.001
0.1
99
CO2
119.6
121.0
<0.01
<0.001
0.1
99
CH4
0.4
2.1
<0.01
<0.001
0.1
99
N2O
0.3
1.9
<0.01
<0.001
0.1
99
N2O
3.4
5.0
<0.01
<0.001
0.1
99
CH4
164.2
170.1
<0.01
<0.001
0.1
99
CO2
11.7
13.3
<0.01
<0.001
0.1
99
CH4
15.7
14.8
<0.01
<0.001
0.1
99
N2O
1.5
2.7
<0.01
<0.001
0.1
100
Several
3.6
4.7
<0.01
<0.001
0.1
100
C02
4.7
3.9
<0.01
<0.001
<0.1
100
N20
3.2
2.4
<0.01
<0.001
<0.1
100
CH4
0.4
1.1
<0.01
<0.001
<0.1
100
ch4
7.2
6.7
<0.01
<0.001
<0.1
100
C02
1.5
1.0
<0.01
<0.001
<0.1
100
CH4
1.9
1.4
<0.01
<0.001
<0.1
100
ch4
6.5
7.1
<0.01
<0.001
<0.1
100
C02
2.2
1.8
<0.01
<0.001
<0.1
100
C02
1.2
1.6
<0.01
<0.001
<0.1
100
N20
1.0
0.7
<0.01
<0.001
<0.1
100
N20
1.7
2.0
<0.01
<0.001
<0.1
100
C02
1.5
1.3
<0.01
<0.001
<0.1
100
C02
0.6
0.9
<0.01
<0.001
<0.1
100
C02
1.4
1.7
<0.01
<0.001
<0.1
100
N20
1.7
1.6
<0.01
<0.001
<0.1
100
C02
0.4
0.2
<0.01
<0.001
<0.1
100
A-26 DRAFT 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
CO2 Emissions from Urea Consumption for
Non-Agricultural Purposes
Non-C02 Emissions from Stationary
Combustion - Commercial
Non-C02 Emissions from Stationary
Combustion - Commercial
CO2 Emissions from Lead Production
CH4 Emissions from Petrochemical Production
CH4 Emissions from Field Burning of
Agricultural Residues
CO2 Emissions from Stationary Combustion -
Geothermal Energy
CH4 Emissions from Mobile Combustion:
Aviation
N2O Emissions from Mobile Combustion:
Marine
Non-C02 Emissions from Stationary
Combustion - U.S. Territories
CH4 Emissions from Silicon Carbide
Production and Consumption
CH4 Emissions from Iron and Steel Production
& Metallurgical Coke Production
Non-C02 Emissions from Stationary
Combustion - U.S. Territories
N2O Emissions from Field Burning of
Agricultural Residues
CH4 Emissions from Ferroalloy Production
CO2 Emissions from Magnesium Production
and Processing
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
CO2
3.8
4.0
<0.01
<0.001
<0.1
100
CH4
1.1
1.2
<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
0.2
<0.01
<0.001
<0.1
100
ch4
0.2
0.3
<0.01
<0.001
<0.1
100
CO2
0.4
0.4
<0.01
<0.001
<0.1
100
CH4
0.1
+
<0.01
<0.001
<0.1
100
N2O
0.6
0.5
<0.01
<0.001
<0.1
100
N2O
0.1
0.1
<0.01
<0.001
<0.1
100
CH4
+
+
<0.01
<0.001
<0.1
100
ch4
+
+
<0.01
<0.001
<0.1
100
ch4
+
0.1
<0.01
<0.001
<0.1
100
N2O
0.1
0.1
<0.01
<0.001
<0.1
100
CH4
+
+
<0.01
<0.001
<0.1
100
CO2
+
+
<0.01
<0.001
<0.1
100
CH4
+
+
<0.01
<0.001
<0.1
100
1 + Does not exceed 0.05 MMT CO2 Eq.
2 Note: LULUCF sources and sinks are not included in this analysis.
3
4 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
545.9
0.05
0.003
15.8
16
CO2 Emissions from Stationary Combustion
- Coal - Electricity Generation
CO2
1,547.6
1,241.3
0.05
0.004
14.6
30
CO2 Emissions from Mobile Combustion:
Road
CO2
1,162.3
1,504.0
0.04
0.003
13.6
44
Emissions from Substitutes for Ozone
Depleting Substances
Several
0.3
173.9
0.02
0.003
7.5
51
CO2 Emissions from Stationary Combustion
- Coal - Industrial
CO2
155.3
59.0
0.01
0.002
4.3
56
CO2 Emissions from Stationary Combustion
- Oil - Electricity Generation
CO2
97.5
21.2
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 Iron and Steel
Production & Metallurgical Coke
Production
CO2 Emissions from Stationary Combustion
- Gas - Industrial
CO2 Emissions from Stationary Combustion
- Oil - Industrial
Fugitive Emissions from Coal Mining
HFC-23 Emissions from HCFC-22
Production
Net CO2 Emissions from Forest Land
Remaining Forest Land
CO2 Emissions from Stationary Combustion
- Oil - Residential
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
N2O Emissions from Mobile Combustion:
Road
CO2 Emissions from Stationary Combustion
- Gas - Commercial
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
CO2 Emissions from Stationary Combustion
- Oil - Commercial
SF6 Emissions from Electrical Transmission
and Distribution
Net CO2 Emissions from Land Converted to
Forest Land
CO2 Emissions from Petroleum Systems
Net CO2 Emissions from Settlements
Remaining Settlements
CH4 Emissions from Forest Fires
N2O Emissions from Forest Fires
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 Other Process Uses of
Carbonates
C02
101.5
42.2
0.01
0.001
2.6
65
co2
408.9
478.8
0.01
0.001
2.6
68
co2
310.4
269.7
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
CO2
97.4
58.0
0.01
<0.001
1.8
77
ch4
193.7
162.1
<0.01
0.001
1.5
79
co2
40.9
9.9
<0.01
0.019
1.4
80
co2
37.2
68.0
<0.01
0.001
1.3
81
ch4
37.2
67.7
<0.01
0.001
1.3
83
n2o
37.6
13.1
<0.01
0.001
1.1
84
co2
142.1
170.3
<0.01
<0.001
1.1
85
co2
187.4
169.6
<0.01
<0.001
0.9
86
n2o
212.0
237.6
<0.01
<0.001
0.9
87
PFCs
21.5
1.4
<0.01
<0.001
0.9
87
CO2
43.3
23.8
<0.01
0.002
0.9
88
CO2
73.3
55.3
<0.01
<0.001
0.8
89
SFe
23.1
4.3
<0.01
<0.001
0.8
90
CO2
92.0
75.0
<0.01
<0.001
0.8
91
CO2
9.4
25.5
<0.01
0.001
0.7
92
CO2
86.2
103.7
<0.01
0.001
0.7
92
cm
3.2
18.5
<0.01
0.003
0.7
93
n2o
2.1
12.2
<0.01
0.002
0.4
93
co2
12.0
2.3
<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
4.9
11.2
<0.01
<0.001
0.3
95
CO2 Emissions from Petrochemical
Production
CO2
21.2
CO2 Emissions from Aluminum Production
CO2
6.8
CO2 Emissions from Mobile Combustion:
Other
CO2
73.2
CO2 Emissions from Cement Production
CO2
33.5
CO2 Emissions from Stationary Combustion
CO2
238.0
- Gas - Residential
27.4
<0.01
<0.001
0.2
96
1.3
<0.01
<0.001
0.2
96
80.1
<0.01
<0.001
0.2
96
39.4
<0.01
<0.001
0.2
96
238.3
<0.01
<0.001
0.2
96
A-28 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
SFe Emissions from Magnesium Production
and Processing
Cm Emissions from Mobile Combustion:
Road
CO2 Emissions from Mobile Combustion:
Marine
CH4 Emissions from Petroleum Systems
N2O Emissions from Manure Management
Net CO2 Emissions from Land Converted to
Grassland
CO2 Emissions from Natural Gas Systems
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 Mobile Combustion:
Other
Net CO2 Emissions from Grassland
Remaining Grassland
CH4 Emissions from Rice Cultivation
CO2 Emissions from Urea Fertilization
CO2 Emissions from Incineration of Waste
CH4 Emissions from Enteric Fermentation
N2O Emissions from Nitric Acid Production
CO2 Emissions from Ammonia Production
Non-C02 Emissions from Stationary
Combustion - Residential
CH4 Emissions from Composting
N2O Emissions from Composting
N2O Emissions from Wastewater Treatment
CO2 Emissions from Lime Production
CH4 Emissions from Wastewater Treatment
CO2 Emissions from Non-Energy Use of
Fuels
N2O Emissions from Mobile Combustion:
Other
PFC, HFC, SF6, and NF3 Emissions from
Semiconductor Manufacture
N2O Emissions from Settlement Soils
CO2 Emissions from Liming
Non-C02 Emissions from Stationary
Combustion - Industrial
Non-C02 Emissions from Stationary
Combustion - Electricity Generation
Fugitive Emissions from Abandoned
Underground Coal Mines
CO2 Emissions from Phosphoric Acid
Production
Non-C02 Emissions from Stationary
Combustion - Industrial
CH4 Emissions from Abandoned Oil and Gas
Wells
CO2 Emissions from Ferroalloy Production
CO2 Emissions from Titanium Dioxide
Production
SFe
5.2
1.0
<0.01
<0.001
0.2
97
CH4
5.2
1.1
<0.01
<0.001
0.2
97
CO2
44.3
41.1
<0.01
<0.001
0.2
97
CH4
42.3
39.3
<0.01
<0.001
0.2
97
N2O
14.0
18.1
<0.01
<0.001
0.2
97
CO2
17.9
22.0
<0.01
0.001
0.2
97
CO2
29.7
26.7
<0.01
<0.001
0.2
98
CO2
0.6
4.0
<0.01
<0.001
0.1
98
CO2
3.0
0.0
<0.01
<0.001
0.1
98
CO2
0.0
3.0
<0.01
<0.001
0.1
98
CO2
1.5
4.5
<0.01
<0.001
0.1
98
CH4
4.1
1.5
<0.01
<0.001
0.1
98
CO2
4.2
1.6
<0.01
0.009
0.1
98
CH4
16.0
13.7
<0.01
<0.001
0.1
98
CO2
2.4
5.1
<0.01
<0.001
0.1
99
CO2
8.0
10.7
<0.01
<0.001
0.1
99
CH4
164.2
170.1
<0.01
<0.001
0.1
99
N2O
12.1
10.2
<0.01
<0.001
0.1
99
CO2
13.0
11.2
<0.01
<0.001
0.1
99
CH4
5.2
3.4
<0.01
0.001
0.1
99
ch4
0.4
2.1
<0.01
<0.001
0.1
99
N2O
0.3
1.9
<0.01
<0.001
0.1
99
N2O
3.4
5.0
<0.01
<0.001
0.1
99
CO2
11.7
13.3
<0.01
<0.001
0.1
99
CH4
15.7
14.8
<0.01
<0.001
0.1
99
CO2
119.6
121.0
<0.01
<0.001
<0.1
99
N2O
1.5
2.7
<0.01
<0.001
<0.1
99
Several
3.6
4.7
<0.01
<0.001
<0.1
100
N20
1.4
2.5
<0.01
<0.001
<0.1
100
C02
4.7
3.9
<0.01
<0.001
<0.1
100
N20
3.2
2.4
<0.01
<0.001
<0.1
100
CH4
0.4
1.1
<0.01
<0.001
<0.1
100
ch4
7.2
6.7
<0.01
<0.001
<0.1
100
C02
1.5
1.0
<0.01
<0.001
<0.1
100
CH4
1.9
1.4
<0.01
<0.001
<0.1
100
ch4
6.5
7.1
<0.01
<0.001
<0.1
100
C02
2.2
1.8
<0.01
<0.001
<0.1
100
C02
1.2
1.6
<0.01
<0.001
<0.1
100
A-29
-------
N2O Emissions from Forest Soils
Non-C02 Emissions from Stationary
Combustion - Residential
N2O Emissions from Caprolactam, Glyoxal,
and Glyoxylic Acid Production
CO2 Emissions from Zinc Production
CO2 Emissions from Glass Production
CO2 Emissions from Soda Ash Production
N2O Emissions from Grassland Fires
N2O Emissions from Mobile Combustion:
Aviation
CH4 Emissions from Grassland Fires
CO2 Emissions from Silicon Carbide
Production and Consumption
N2O Emissions from Semiconductor
Manufacture
N2O Emissions from Incineration of Waste
Net CO2 Emissions from Coastal Wetlands
Remaining Coastal Wetlands
CH4 Emissions from Mobile Combustion:
Marine
N2O Emissions from Product Uses
HFC-134a Emissions from Magnesium
Production and Processing
CO2 Emissions from Urea Consumption for
Non-Agricultural Purposes
Non-C02 Emissions from Stationary
Combustion - Commercial
Non-C02 Emissions from Stationary
Combustion - Commercial
CH4 Emissions from Coastal Wetlands
Remaining Coastal Wetlands
CO2 Emissions from Lead Production
CH4 Emissions from Petrochemical
Production
CO2 Emissions from Stationary Combustion
- Geothermal Energy
CH4 Emissions from Field Burning of
Agricultural Residues
CH4 Emissions from Mobile Combustion:
Aviation
Non-C02 Emissions from Stationary
Combustion - U.S. Territories
N2O Emissions from Mobile Combustion:
Marine
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
CH4 Emissions from Land Converted to
Coastal Wetlands
N20 0.1 0.5
N20 1.0 0.7
N2O 1.7 2.0
CO2 0.6 0.9
CO2 1.5 1.3
CO2 1.4 1.7
N2O 0.1 0.3
N2O 1.7 1.6
CH4 0.1 0.3
CO2 0.4 0.2
N2O + 0.2
N2O 0.5 0.3
CO2 7.6 7.9
CH4 0.5 0.3
N2O 4.2 4.2
HFCs 0.0 0.1
CO2 3.8 4.0
CH4 1.1 1.2
N2O 0.4 0.3
CH4 3.4 3.6
CO2 0.5 0.5
CH4 0.2 0.2
CO2 0.4 0.4
CH4 0.2 0.3
CH4 0.1 +
N2O 0.1 0.1
N2O 0.6 0.5
CH4 + +
CH4 + +
N2O 0.1 0.1
CH4 + 0.1
N2O 0.1 0.1
CO2 + +
ch4 + +
ch4 + +
<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
<0.01
<0.001
<0.1
100
A-30 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
N2O Emissions from Drained Organic Soils
CH4 Emissions from Peatlands Remaining
Peatlands
CO2 Emissions from Magnesium Production
and Processing
CH4 Emissions from Drained Organic Soils
N2O Emissions from Peatlands Remaining
Peatlands
CH4 Emissions from Incineration of Waste
1 + Does not exceed 0.05 MMT CO2 Eq.
2
3
4 References
5 IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
6 Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Negara, and K.
7 Tanabe (eds.). Hayman, Kanagawa, Japan.
8
9
N2O 0.1 0.1 <0.01 <0.001 <0.1 100
ch4 + + <0.01 <0.001 <0.1 100
C02
+
+
<0.01
<0.001
<0.1
100
ch4
+
+
<0.01
<0.001
<0.1
100
n2o
+
+
<0.01
<0.001
<0.1
100
ch4
+
+
<0.01
<0.001
<0.1
100
A-31
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
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 71,934.0 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 2017).
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
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 th 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
w ithin the 1 Inergy chapter to avoid double counting of emissions from consumption of these fuels during non-energy related
activities in IPPIJ 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:1 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. ^ resulting in an Energy sector adjustment of 382
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 IV 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 88.6
TBtu is adjusted to account for unaccounted for coking coal within the iron and steel production sector in 2016.
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
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
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
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).
6 Refer to the International Bunker Fuels section of the Energy chapter and Annex 3.3 for a description of the methodology for
distinguishing between international and domestic fuel consumption.
A-34 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
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 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.
[BEGIN BOX]
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.
[END BOX]
7 See Section 4 "Use of Facility-Level Data in Good Practice National Greenhouse Gas Inventories" of the IPCC meeting report, and
specifically the section on using facility-level data in conjunction with energy data, available at: .
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
24.5
722.2
NE
12,994.9
43.8
13,785.3
98.9
13,686.4
Residential Coal
Commercial Coal
24.5
24.5
24.5
Industrial Coking Coal
Industrial Other Coal
88.6
633.6
88.6
633.6
88.6
10.3
623.3
Transportation Coal
Electric Power Coal
NE
12,994.9
NE
12,994.9
12,994.9
U.S. Territory Coal (bit)
Natural Gas
4,495.6
3,213.1
9,346.3
766.7
10,299.5
43.8
57.0
43.8
28,178.2
311.8
43.8
27,866.4
Total Petroleum
854.2
783.3
8,130.7
26,056.8
240.4
549.2
36,614.7
1,589.3
4,485.6
140.6 77.3
30,321.8
Asphalt & Road Oil
Aviation Gasoline
853.4
20.5
853.4
20.5
853.4
20.5
Distillate Fuel Oil
415.5
271.4
880.9
6,419.3
52.7
108.3
8,148.1
117.5
5.8
8,024.7
Jet Fuel
3,349.9
NA
45.6
3,395.5
1,021.1
2,374.4
Kerosene
13.8
1.9
2.4
2.3
20.3
20.3
LPG
424.9
141.2
2,682.8
40.0
15.4
3,304.2
2,254.0
1,050.2
Lubricants
148.9
140.6
1.0
290.5
148.9
140.6 1.0
Motor Gasoline
362.6
244.4
15,457.6
173.4
16,237.9
16,237.9
Residual Fuel
6.0
628.9
69.4
127.0
831.3
450.7
380.6
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
59.4
59.4
59.4
Total (All Fuels)
5,349.8
4,020.8
18,199.2
26,823.5
23,594.2
650.0
78,637.6
1,589.3
4,896.3
140.6 77.3
71,934.0
2 NE (Not Estimated); NA (Not Available); Note: Parentheses indicate negative values.
3 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
4 A-39).
5 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i Table fl-11:2016 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Adjusted Consumption (TBtu)a
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
24.5
623.3
NE
12,994.9
43.8
13,686.4
2.3
59.0
NE
1,241.3
4.0
1,306.6
Residential Coal
Commercial Coal
24.5
24.5
2.3
2.3
Industrial Other Coal
623.3
623.3
59.0
59.0
Transportation Coal
NE
NE
Electric Power Coal
12,994.9
12,994.9
1,241.3
1,241.3
U.S. Territory Coal (bit)
43.8
43.8
4.0
4.0
Natural Gas
4,495.6
3,213.1
9,034.5
766.7
10,299.5
57.0
27,866.4
238.3 170.3
478.8
40.6
545.9
3.0
1,477.0
Total Petroleum
854.2
783.3
3,645.1
24,326.9
240.4
471.9
30,321.8
58.0 55.3
269.7
1,754.2
21.2
34.3
2,192.7
Asphalt & Road Oil
Aviation Gasoline
20.5
20.5
1.4
1.4
Distillate Fuel Oil
415.5
271.4
875.1
6,301.8
52.7
108.3
8,024.7
30.7 20.1
64.7
466.1
3.9
8.0
593.5
Jet Fuel
2,328.8
NA
45.6
2,374.4
168.2
3.3
171.5
Kerosene
13.8
1.9
2.4
2.3
20.3
O
O
0.2
0.2
1.5
LPG
424.9
141.2
428.7
40.0
15.4
1,050.2
26.2 8.7
26.5
2.5
0.9
64.8
Lubricants
Motor Gasoline
362.6
244.4
15,457.6
173.4
16,237.9
25.9
17.4
1,102.7
12.4
1,158.4
Residual Fuel
6.0
178.2
69.4
127.0
380.6
0.4
13.4
5.2
9.5
28.6
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
59.4
59.4
0.4
0.4
Total (All Fuels)
5,349.8
4,020.8
13,302.9
25,093.6
23,594.2
572.7
71,934.0
296.2 227.9
807.6
1,794.9
1,808.8
41.4
4,976.7
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
A-37
-------
i 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
31.1
695.6
NE
14,138.3
43.8
14,908.8
2.9
65.9
NE
1,350.5
4.0
1,423.3
Residential Coal
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
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
8,805.0
744.8
9,926.5
57.0
27,625.8
253.2 175.7
466.7
39.5
526.1
3.0
1,464.2
Total Petroleum
930.3
945.2
3,879.3
23,522.0
276.0
471.9
30,024.7
63.6 67.0
286.7
1,696.0
23.7
34.3
2,171.3
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
511.6
39.7
15.4
1,128.2
26.0 8.6
31.6
2.5
0.9
69.6
Lubricants
Motor Gasoline
473.7
319.2
15,005.6
173.4
15,971.8
33.8
22.8
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
4,291.9
13,379.9
24,266.9
24,395.0
572.7
72,613.6
316.8 245.6
819.3
1,735.5
1,900.7
41.4
5,059.3
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
A-38 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
40.2
799.0
NE
16,427.4
43.8
17,310.4
3.8
75.6
NE
1,569.1
4.0
1,652.6
Residential Coal
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
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
3,858.1
23,261.7
295.5
471.9
29,449.4
67.4 40.4
286.8
1,676.9
25.3
34.3
2,131.1
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
370.3
47.1
15.4
1,044.7
28.4 9.3
22.9
2.9
0.9
64.5
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
13,494.0
24,021.4
25,138.7
572.4
73,643.4
345.3 233.6
830.8
1,717.1
2,038.0
41.4
5,206.1
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-39
-------
i 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
41.4
800.0
NE
16,450.6
30.8
17,322.8
3.9
75.7
NE
1,571.3
2.8
1,653.8
Residential Coal
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
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
4,347.1
22,612.2
255.2
503.6
29,261.0
63.5 42.7
321.1
1,630.6
22.4
36.6
2,116.9
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
441.3
44.5
16.4
1,128.0
29.1 9.5
27.2
2.7
1.0
69.6
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
13,672.4
23,499.5
25,135.8
591.0
72,885.7
329.7 225.7
848.7
1,677.6
2,038.1
42.5
5,162.3
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values
8
A-40 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
43.6
782.3
NE
15,821.2
36.9
16,684.0
4.1
74.1
NE
1,511.2
3.4
1,592.8
Residential Coal
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
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
4,159.1
22,474.4
214.2
517.1
28,781.5
57.7 40.4
309.6
1,620.6
18.3
37.5
2,084.0
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
429.9
37.1
18.5
1,023.8
24.7 8.5
26.5
2.3
1.1
63.2
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
13,144.4
23,254.2
25,375.3
603.1
71,038.9
282.5 201.3
818.4
1,661.9
2,022.2
43.5
5,029.8
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
A-41
-------
i 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
61.7
866.1
NE
18,035.2
36.9
18,999.9
5.8
82.0
NE
1,722.7
3.4
1,813.9
Residential Coal
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
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
4,134.7
22,664.6
295.0
496.9
29,322.1
70.9 49.2
308.2
1,634.5
25.8
36.0
2,124.6
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
288.3
34.0
18.8
968.7
30.0 8.7
17.8
2.1
1.2
59.8
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
12,874.2
23,398.1
26,094.7
560.9
72,741.1
325.6 225.5
807.4
1,673.3
2,157.7
40.9
5,230.4
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7
8
A-42 DRAFT 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
69.7
951.6
NE
19,133.5
36.9
20,191.6
6.6
90.1
NE
1,827.6
3.4
1,927.7
Residential Coal
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
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
4,248.4
22,974.2
370.3
515.9
29,947.2
76.0
51.4
315.9
1,656.4
31.4
37.6
2,168.8
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
374.8
29.5
16.0
1,090.5
32.7
8.6
23.1
1.8
1.0
67.3
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
12,883.2
23,693.2
27,083.3
580.5
74,191.1
334.6
225.7
813.2
1,694.5
2,258.4
42.4
5,368.9
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
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
73.4
877.3
NE
18,225.3
36.9
19,212.8
6.9
83.0
NE
1,740.9
3.4
1,834.2
Residential Coal
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
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
4,060.0
22,837.7
382.4
525.7
29,696.0
77.5
53.6
302.2
1,645.8
32.2
38.3
2,149.5
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
329.3
28.0
14.9
1,058.2
33.8
8.6
20.3
1.7
0.9
65.3
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
12,062.4
23,552.5
25,681.3
589.9
71,919.4
336.3
229.4
762.9
1,683.7
2,145.7
43.1
5,201.0
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-44 DRAFT 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
80.8
1,081.5
NE
20,513.0
36.9
21,712.0
7.6
102.4
NE
1,959.4
3.4
2,072.8
Residential Coal
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
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
4,534.9
23,883.6
459.3
488.0
31,273.4
82.1
50.0
336.3
1,722.4
38.4
35.7
2,264.8
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
379.8
40.1
15.7
1,146.3
34.1
9.7
23.4
2.5
1.0
70.7
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
13,187.8
24,575.7
27,851.8
554.1
76,396.2
347.6
228.7
839.9
1,759.1
2,360.1
40.7
5,576.1
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-45
-------
i 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
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
5,140.8
25,124.8
647.8
576.9
33,465.3
84.3 54.0
380.3
1,818.5
52.9
42.3
2,432.3
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
558.5
21.9
11.7
1,197.1
29.8 7.5
34.5
1.4
0.7
73.8
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
13,792.9
25,788.2
28,510.7
640.5
78,705.6
341.3 224.2
886.0
1,853.6
2,411.9
47.1
5,764.1
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7
A-46 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
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
5,245.2
25,204.4
637.0
615.6
33,633.1
83.4 52.1
388.6
1,817.6
53.2
45.1
2,440.1
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
509.8
27.5
6.6
1,112.6
27.5 7.6
31.5
1.7
0.4
68.7
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
13,757.1
25,829.4
27,523.7
678.6
77,168.6
321.3 212.2
889.5
1,850.7
2,345.3
49.9
5,668.9
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7
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
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
4,914.4
25,358.6
1,222.1
619.9
34,249.0
94.9 54.9
364.0
1,822.7
97.9
45.4
2,479.7
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
545.8
28.2
0.7
1,219.0
31.7 8.1
33.7
1.7
0.0
75.2
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
13,463.2
25,982.5
28,024.0
676.9
78,405.7
357.8 227.0
867.8
1,855.8
2,400.9
49.7
5,759.1
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7
8
A-48 DRAFT 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
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
4,807.2
25,099.5
1,201.0
651.7
34,038.1
102.2
58.0
355.7
1,804.4
95.8
47.8
2,463.9
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
595.2
19.1
0.7
1,278.7
31.6
9.4
36.7
1.2
0.0
79.0
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
13,982.8
25,701.4
27,151.5
708.3
78,118.8
367.4
237.5
893.6
1,836.3
2,335.9
52.0
5,722.8
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
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
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
4,540.4
24,492.0
1,204.8
617.9
33,154.6
101.9
59.4
336.4
1,758.7
95.0
45.0
2,396.5
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
547.7
17.9
10.5
1,277.4
33.7
9.7
33.8
1.1
0.7
78.9
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
13,634.2
25,119.4
26,685.0
678.7
76,981.5
378.9
239.9
868.8
1,791.9
2,304.2
49.6
5,633.4
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-50 DRAFT 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
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
4,385.1
24,541.8
961.2
552.0
32,502.0
94.1 50.0
324.5
1,763.7
76.8
40.2
2,349.3
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
617.2
14.3
11.1
1,320.5
33.2 8.7
38.1
0.9
0.7
81.6
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
13,715.1
25,240.7
26,560.2
585.6
76,473.0
360.0 228.8
869.7
1,800.8
2,272.7
42.5
5,574.4
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-51
-------
Table A-26:2001 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions'1 (MMT 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
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
4,549.5
24,040.5
1,276.4
628.7
32,726.9
101.9
54.9
336.1
1,725.2
98.4
45.9
2,362.5
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
635.3
13.7
7.0
1,324.4
32.5
00
CO
39.3
0.8
0.4
81.9
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
13,856.9
24,698.5
26,395.0
655.3
75,932.8
362.2
228.3
885.2
1,760.1
2,257.9
47.5
5,541.2
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-52 DRAFT 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
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
4,190.1
24,310.7
1,144.1
467.7
32,316.9
99.0
55.3
308.0
1,745.1
88.4
33.9
2,329.8
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
811.9
11.9
8.0
1,537.1
34.4
9.3
50.3
0.7
0.5
95.2
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
14,194.8
24,982.7
26,705.8
490.6
77,037.6
370.8
236.6
894.4
1,780.7
2,296.9
35.6
5,615.0
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
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
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
4,107.8
23,869.7
1,211.2
454.9
31,632.9
92.9
46.0
303.5
1,711.6
93.8
33.1
2,280.9
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
760.9
14.3
9.2
1,450.5
32.5
8.7
47.1
0.9
0.6
89.7
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
13,905.2
24,545.0
25,443.4
465.1
74,414.4
350.6
220.9
880.0
1,747.4
2,190.5
34.0
5,423.4
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-54 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
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
672.6
4,087.1
22,903.2
1,306.1
442.8
30,620.8
84.1 48.2
303.4
1,644.2
101.3
32.3
2,213.5
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
565.7
17.6
5.9
1,130.7
26.1 7.2
34.9
1.1
0.4
69.7
Lubricants
Motor Gasoline
62.5
319.8
15,213.3
161.3
15,756.9
4.4
22.7
1,080.3
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
3,849.0
14,383.9
23,569.3
25,247.1
453.4
73,369.3
331.2 220.4
909.9
1,679.5
2,177.4
33.2
5,351.6
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-55
-------
i 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
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
719.3
4,541.9
22,320.0
926.7
440.2
30,283.2
93.1 51.7
332.9
1,601.9
72.2
32.0
2,183.8
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
758.8
14.2
6.5
1,354.6
28.1 7.4
46.8
0.9
0.4
83.6
Lubricants
Motor Gasoline
63.5
314.7
14,777.9
159.0
15,315.2
4.5
22.3
1,048.8
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
4,134.1
15,032.1
23,100.4
24,007.0
450.6
73,168.0
364.7 238.2
949.5
1,643.3
2,088.4
33.0
5,317.0
2 NE (Not Estimated)
3 NA (Not Available)
4 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
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-56 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
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
765.9
4,622.0
22,106.9
817.3
428.3
30,138.3
97.6 55.1
338.6
1,586.9
63.4
31.1
2,172.7
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
756.7
15.6
7.5
1,370.9
28.9 7.5
46.7
1.0
0.5
84.5
Lubricants
Motor Gasoline
46.8
352.6
14,588.1
150.2
15,137.6
3.3
25.0
1,035.3
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
4,113.8
15,097.2
22,843.7
23,157.6
438.6
72,420.0
383.1 237.8
954.3
1,626.0
2,021.0
32.1
5,254.2
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
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
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
729.6
4,263.0
21,523.0
754.5
459.1
28,991.5
88.5
52.7
311.5
1,541.9
58.7
33.4
2,086.6
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
741.3
17.7
5.6
1,268.2
24.4
6.7
45.7
1.1
0.3
78.2
Lubricants
Motor Gasoline
37.3
412.3
14,270.2
147.3
14,867.2
2.6
29.3
1,012.9
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
3,942.4
14,512.5
22,247.0
22,568.4
469.4
69,973.5
352.8
228.0
918.5
1,580.3
1,947.9
34.3
5,061.8
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-58 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
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
783.6
4,320.3
21,175.3
1,058.7
505.4
29,150.0
91.9 56.7
315.7
1,516.8
81.2
36.8
2,099.2
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
761.8
34.0
7.2
1,293.9
23.7 6.6
47.1
2.1
0.4
79.9
Lubricants
Motor Gasoline
37.0
282.0
14,088.0
147.1
14,554.1
2.6
20.0
1,000.3
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
3,863.7
14,205.4
21,883.8
22,349.9
515.4
69,105.6
356.8 225.0
905.8
1,554.3
1,931.2
37.8
5,010.9
2 NE (Not Estimated)
3 NA (Not Available)
4 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
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-59
-------
i 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
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
789.7
4,202.0
20,538.9
1,123.8
456.4
28,459.2
94.9 57.1
307.9
1,474.9
86.4
33.3
2,054.6
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
744.5
20.2
4.9
1,268.5
24.0 6.7
46.0
1.2
0.3
78.3
Lubricants
Motor Gasoline
46.3
281.0
13,854.9
127.3
14,309.6
3.3
20.0
987.5
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
3,830.3
14,059.5
21,183.6
21,914.5
466.0
67,891.3
365.8 223.4
896.2
1,509.1
1,906.9
34.2
4,935.6
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-60 DRAFT 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
Electric Power Coal
16,465.6
16,465.6
1,569.6
1,569.6
U.S. Territory Coal (bit)
00
CO
00
CO
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
875.9
4,294.1
20,061.9
990.7
443.0
28,031.5
96.5
63.5
314.6
1,443.2
75.5
32.3
2,025.7
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
764.4
19.4
11.8
1,271.9
22.8
6.6
47.2
1.2
0.7
78.6
Lubricants
Motor Gasoline
87.0
212.4
13,591.8
121.5
14,012.6
6.2
15.2
971.4
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
3,863.7
13,974.0
20,670.0
21,022.9
451.9
66,178.4
353.5
226.9
892.5
1,475.5
1,831.5
33.1
4,813.0
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-61
-------
i 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
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
998.8
3,990.9
19,332.6
1,198.3
422.4
27,324.5
97.5 72.4
292.8
1,387.4
90.7
30.7
1,971.4
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
636.3
21.1
13.8
1,157.5
23.3 6.7
39.3
1.3
0.8
71.4
Lubricants
Motor Gasoline
148.9
338.4
13,222.1
123.6
13,833.0
10.6
24.1
942.4
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
3,909.8
13,421.5
19,953.0
20,879.8
430.1
64,668.2
347.2 231.6
859.6
1,420.3
1,818.2
31.4
4,708.2
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
A-62 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
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
1,009.3
4,227.2
19,950.1
1,289.4
370.3
28,221.6
97.4 73.3
310.4
1,431.2
97.5
26.9
2,036.6
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
630.5
22.9
14.5
1,122.3
21.8 6.3
39.0
1.4
0.9
69.4
Lubricants
Motor Gasoline
139.9
233.1
13,437.1
100.7
13,910.8
10.0
16.6
956.9
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
3,816.0
13,584.0
20,630.0
20,911.6
377.4
65,216.2
338.3 227.4
874.5
1,467.2
1,820.8
27.6
4,755.8
2 NE (Not Estimated)
3 NA (Not Available)
4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-38), and international bunker fuel consumption (see Table
5 A-39).
6 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.
7 Note: Parentheses indicate negative values.
8
9
A-63
-------
i 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
4,670.2
4,607.3
4,829.8
4,719.4
4,918.8
4,896.3
Industrial Coking Coal
0.0
i 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
88.6
Industrial Other Coal
8.2
3 113
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
1 371.0
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
1,948.5
1,986.5
2,155.4
2,142.5
2,218.0
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
4,879.8
4,802.6
5,042.6
4,932.7
5,144.2
5,114.2
2 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
3 and Product Use chapter.
4
5 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
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
1,021.1
Marine Distillate Fuel Oil &
Other
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
Aviation Jet Fuel
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
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
1,589.3
6 Note: Further information on the calculation of international bunker fuel consumption of aviation jet fuel is provided in Annex 3.3 Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel
7 Consumption.
A-64 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1
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
2 a Flare gas is not used in the CO2 from fossil fuel combustion calculations and is presented for informational purposes only.
3 b Distillate fuel oil No.2 and residual fuel oil No. 6 are used in the CO2 from fossil fuel combustion calculations, and other
4 oil types are presented for informational purposes only. An additional discussion on the derivation of these carbon content
5 coefficients is presented in Annex 2.2.
6 c These coefficients vary annually due to fluctuations in fuel quality (see Table A-41).
7 Sources: C coefficients from EIA (2009b) and EPA (2010a).
A-65
-------
i 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
2 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
3 from commercial/institutional consumption.
4 Source: EPA (2010a).
5
6
7 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
1,407
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
1,360
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
1,078
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
3,853
8 Note: Does not include the U.S. Territories.
9 Source: EIA (2017).
A-66 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
References
EIA (2017) Monthly Energy Review. October 2017, Energy Information Administration, U.S. Department of Energy,
Washington, DC. DOE/EIA-0035(2017/10)
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
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
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 DRAFT 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 Coal3C
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
1 a C contents vary annually based on changes in annual mix of production and end-use consumption of coal from each producing state.
2 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
3 from commercial/institutional consumption..
4 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
5 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
6 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
7 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
8 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
9 coefficients are presented in higher heating value because U.S. energy statistics are reported by higher heating value.
10 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.
U.S. Energy Information Administration (EIA). Coal Distribution - Annual (2001-2009b); and Coal Industry Annual (1990-2001).
11 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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i 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/
Commercial
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 Rank
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
2 a In 2008, the EIA began collecting consumption data for commercial and institutional consumption rather than commercial and residential consumption.
3 Sources: C content coefficients calculated from USGS (1998) and PSU (2010); data presented in EPA (2010).
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45
46
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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1 For comparison, J. Quick (2010) completed an analysis similar in methodology to that used here, in order to
2 generate national average C emission factors as well as county-level factors. This study's rank-based national average
3 factors have a maximum deviation from the factors developed in this Inventory report of -0.55 percent, which is for lignite
4 (range: -0.55 to +0.1 percent). This corroboration further supports the assertion of minimal uncertainty in the application of
5 the rank-based factors derived for the purposes of this Inventory.
6 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
98.10
-
98.65
Arizona
15
93.94
97.34
-
-
Arkansas
80
96.36
-
-
94.97
Colorado
318
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
103.60
99.40
Nevada
2
94.41
-
-
99.86
New Mexico
185
94.29
94.89
103.92
-
North Dakota
202
-
93.97
-
99.48
Ohio
674
91.84
-
-
-
Oklahoma
63
92.33
-
-
-
Pennsylvania
861
93.33
-
103.68
-
Tennessee
61
92.82
-
-
-
Texas
64
85.59
94.19
-
94.47
Utah
169
95.75
91.29
-
-
Virginia
470
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
7,092
93.13
96.94
104.29
98.63
7 Note:Indicates no sample data available.
8 Sources: Calculated from USGS (1998), and PSU (2010); data presented in EPA (2010).
9
10 Natural Gas
11 Natural gas is predominantly composed of methane (CH4), which is 75 percent C by weight and contains 14.2
12 MMT C/QBtu (higher heating value), but it may also contain many other compounds that can lower or raise its overall C
13 content. These other compounds may be divided into two classes: (1) natural gas liquids (NGLs) and (2) non-hydrocarbon
14 gases. The most common NGLs are ethane (C2H6), propane (C3H8), butane (C4H10), and, to a lesser extent, pentane (C5H12)
15 and hexane (CsHh). Because the NGLs have more C atoms than CH4 (which has only one), their presence increases the
16 overall C content of natural gas. NGLs have a commercial value greater than that of CH4, and therefore are usually separated
17 from raw natural gas at gas processing plants and sold as separate products. Ethane is typically used as a petrochemical
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44
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
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.
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1 consumed in the United States, suggesting that these samples continue to be representative of natural gas "as consumed" in
2 the United States. The average and median composition of these samples appear in Table A-46.
3 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
4 Source: Gas Technology Institute (1992).
5
6 Carbon contents were calculated for a series of sub-samples based on their CO2 content and heat content. Carbon
7 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
8 (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).
9 These stratifications were chosen to exclude samples with CO2 content and heat contents outside the range of pipeline-
10 quality natural gas. In addition, hexane was removed from the samples since it is usually stripped out of raw natural gas
11 before delivery because it is a valuable natural gas liquid used as a feedstock for gasoline. The average carbon contents for
12 the four separate sub-samples are shown below in Table A-47.
13 Table A-47: Carbon Content of Pipeline-Quality Natural Gas by CO; and Heat Content [MBIT 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
14 Source: EPA (2010).
15 Step 4. Apply carbon content coefficients developed in Step 3 to pipeline natural gas
16 A regression analysis was performed on the sub-samples in to further examine the relationship between carbon (C)
17 content and heat content. The regression used carbon content as the dependent variable and heat content as the independent
18 variable. The resulting R-squared values" for each of the sub-samples ranged from 0.79 for samples with less than 1.5
19 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
20 with less than 1.5 percent CO2 and 1,050 Btu/cf was chosen as the representative sample for two reasons. First, it most
21 accurately reflects the range of CO2 content and heat content of pipeline quality natural gas. Secondly, the R-squared value,
22 although it is the lowest of the sub-groups tested, remains relatively high. This high R-squared indicates a low percentage
23 of variation in C content as related to heat content. The regression for this sub-sample resulted in the following equation:
24 C Content = (0.011 x Heat Content) + 3.5341
25 This equation was used to estimate the annual predicted carbon content of natural gas from 1990 to 2010 based on
26 the EIA's national average pipeline-quality gas heat content for each year. The table of average C contents for each year is
27 shown below in Table A-48.
28 Table A-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
, 1447
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
14.46
29 Source: EPA (2010).
30 Step 5. Apply carbon content coefficients developed in Step 3 to flare gas
31 Selecting a C content coefficient for flare gas was much more difficult than for pipeline natural gas, because of the
32 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
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15
16
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18
19
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21
22
23
24
25
26
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 -
14.5 -
¦V.
14.0
970 990 1,010 1,030 1,050 1,070 1,090 1,110 1,130
Energy Content (Btu per Cubic Foot)
Source: EIA(1994) Energy Information Adminstation, Emssbreof Greenhouse Gases in the United States 19S7-1932, U.S. Department of
Energy, Washirgton, DC, November, 1994, DOEfEIA0573, Apperdix 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). Ffowever, 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:
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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 -
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¦E c
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Paraffins. Paraffins are the most common constituent of crude oil, usually comprising 60 percent by mass.
Paraffins are straight-chain hydrocarbons with the general formula CnH2n+2- Paraffins include ethane (C2H6), propane (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 (CsfL), 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 (CioHs and 93.71 percent C by mass) and anthracene (C14H10 and 97.7 percent C). They are
relatively rare but do appear in heavier petroleum products.
Figure A-3 illustrates the share of C by weight for each class of hydrocarbon. Hydrocarbon molecules containing
2 to 4 C atoms are all natural gas liquids; hydrocarbons with 5 to 10 C atoms are predominantly found in naphtha and
gasoline; and hydrocarbon compounds with 12 to 20 C atoms comprise "middle distillates," which are used to make diesel
fuel, kerosene and jet fuel. Larger molecules which can be vacuum distilled may be used as lubricants, waxes, and residual
fuel oil or cracked and blended into the gasoline or distillate pools.
A-81
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Figure A-3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number
100
95
£
"qj
>¦
_Lj
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_g 85
i_
as
o
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ai
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a) 80
75 -
70
1 Paraffins
t Cycle paraffins
~ Ano rnatics
Be nzene ~
Toluene ~l
Xylene
Cyclo peritane
n- pe ntane ¦ ¦
"Butane
1 Fro pane
1 Ethane
Methane
G as o I i ne Jet F ue I
LPG Naphtha Kerosene Diesel
Liise Oil Fuel Oil
T
"T"
15
r
20
10 15 20 25
Number of Carbo n Atoms in Molecule
30
35
Source: J.M. Hunt, f-TslrakLfn G'soc/tsrn.fef.y.a.nd Geology (San Francisco, CA, W.H. Freeman and Company, 1979), pp. 31-37.
If nothing is known about the composition of a particular petroleum product, assuming that it is 85.7 percent C by
mass is not an unreasonable first approximation. Since denser products have higher C numbers, this guess would be most
likely to be correct for crude oils and fuel oils. The C content of lighter products is more affected by the shares of paraffins
and aromatics in the blend.
Energy Content of Petroleum Products
The exact energy content (gross heat of combustion) of petroleum products is not generally known. EIA estimates
energy consumption in Btu on the basis of a set of industry-standard conversion factors. These conversion factors are
generally accurate to within 3 to 5 percent.
Individual Petroleum Products
The United States maintains data on the consumption of more than twenty separate petroleum products and product
categories. The C contents, heat contents, and density for each product are provided below in Table A-50. A description of
the methods and data sources for estimating the key parameters for each individual petroleum product appears below.
A-82 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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(totel)
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.17
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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1 The American Society for Testing and Materials (ASTM) standards permit a broad range of densities for gasoline,
2 ranging from 50 to 70 degrees API gravity, or 111.52 to 112.65 kilograms per barrel (EIA 1994), which implies a range of
3 possible C and energy contents per barrel. Table A-51 reflects changes in the density of gasoline over time and across grades
4 and formulations of gasoline through 2016.
5 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 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 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
6 Notes: NA (Not Applicable), fuel type was not analyzed.
7 Source: National Institute of Petroleum and Energy Research (1990 through 2016).
8
9 The density of motor gasoline increased across all grades through 1994, partly as a result of the leaded gasoline
10 phase-out. In order to maintain the "anti-knock" quality and octane ratings of gasoline in the absence of lead, the portion of
11 aromatic hydrocarbons blended into gasoline through the refining process was increased. As discussed above, aromatic
12 hydrocarbons have a lower ratio of hydrogen to C than other hydrocarbons typically found in gasoline, and therefore increase
13 fuel density.
14 The trend in gasoline density was reversed beginning in 1996 with the development of fuel additives that raised
15 oxygen content. In 1995, a requirement for reformulated gasoline in non-attainment areas implemented under the Clean Air
16 Act Amendments further changed the composition of gasoline consumed in the United States. Through 2005, methyl tertiary
17 butyl ether (MTBE), ethanol, ethyl tertiary butyl ether (ETBE), and tertiary amyl methyl ether (TAME) were added to
18 reformulated and sometimes to conventional gasoline to boost its oxygen content, reduce its toxics impacts and increase its
19 octane. The increased oxygen reduced the emissions of carbon monoxide and unburned hydrocarbons. These oxygen-rich
20 blending components are also much lower in C than standard gasoline. The average gallon of reformulated gasoline
21 consumed in 2005 contained over 10 percent MTBE and 0.6 percent TAME (by volume). The characteristics of reformulated
22 fuel additives appear in Table A-52.
23 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
24 Source: EPA (2009b).
25
26 Since 2005, due to concerns about the potential environmental consequences of the use of MTBE in fuels, there
27 has been a shift away from the addition of MTBE, TAME, ETBE, and DIPE and towards the use of ethanol as a fuel
28 oxygenate.18 Ethanol, also called ethyl alcohol, is an anhydrous alcohol with molecular formula C2H5OH. Ethanol has a
29 lower C share than other oxygenates, approximately 52 percent compared to about 70 percent for MTBE and TAME. The
30 density of ethanol was calculated by fitting density data at 10 degree intervals to a polynomial of order two and then using
31 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 (NTPER) 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 fromNIPER (1990 through 2016). Data on the characteristics
of reformulated gasoline, including C share, were also taken fromNIPER (1990 through 2016).
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 2016. 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 2-19-2010.
20
"Ethanol Market Penetration." Alternative Fuels and Advanced Vehicles Data Center, U.S. DOE. Available at
. Retrieved 2-19-2010.
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.
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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
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one-half of one percent of all jet fuel consumption. The C content coefficient for jet fuel used in this report prior to 1996
represents a consumption-weighted combination of the naphtha-based and kerosene-based coefficients. From 1996 to 2016,
only the kerosene-based portion of total consumption is considered significant.
Methodology
Step 1. Estimate the carbon content for naphtha-based jet fuels
Because naphtha-based jet fuels are used on a limited basis in the United States, sample data on its characteristics
are limited. The density of naphtha-based jet fuel (49 degrees) was estimated as the central point of the acceptable API
gravity range published by ASTM. The heat content of the fuel was assumed to be 5.355 MMBtu per barrel based on EIA
industry standards. The C fraction was derived from an estimated hydrogen content of 14.1 percent (Martel and Angello
1977), and an estimated content of sulfur and other non-hydrocarbons of 0.1 percent.
Step 2. Estimate the carbon content for kerosene-based jet fuels
The density of kerosene-based jet fuels was estimated at 42 degrees API and the carbon share at 86.3 percent. The
density estimate was based on 38 fuel samples examined by NIPER. Carbon share was estimated on the basis of a hydrogen
content of 13.6 percent found in fuel samples taken in 1959 and reported by Martel and Angello, and on an assumed sulfur
content of 0.1 percent. The EIA's standard heat content of 5.670 MMBtu per barrel was adopted for kerosene-based jet fuel.
Step 3. Weight the overall jet fuel carbon content coefficient for consumption of each type 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 purpo ses
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 forNos. 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 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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i 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
2 Source: Densities - CRC Handbook of Chemistry and Physics (2008/09); Carbon Contents - derived from the atomic weights of the elements;
3 Energy Contents - EPA (2009b). All values are for the compound in liquid form. The density and energy content of ethane are for refrigerated
4 ethane (-89 degrees C). Values for n-butane are for pressurized butane (-25 degrees C).
5
6 Methodology
7 Step 1. Assign carbon content coefficients to each pure paraffinic compound
8 Based on their known physical characteristics, a C content coefficient is assigned to each compound contained in
9 the U.S. energy statistics category, Liquefied Petroleum Gases.
10 Step 2. Weight individual LPG coefficients for share of fuel use consumption
11 AC content coefficient for LPG used as fuel is developed based on the consumption mix of the individual
12 compounds reported in U.S. energy statistics.
13 Step 3. Weight individual LPG coefficients for share of non-fuel use consumption
14 The mix of LPG consumed for non-fuel use differs significantly from the mix of LPG that is combusted. While
15 the maj ority of LPG consumed for fuel use is propane, ethane is the largest component of LPG used for non-fuel applications.
16 AC content coefficient for LPG used for non-fuel applications is developed based on the consumption mix of the individual
17 compounds reported in U.S. energy statistics.
18 Step 4. Weight the carbon content coefficients for fuel use and non-fuel use by their respective shares of
19 consumption
20 The changing shares of LPG fuel use and non-fuel use consumption appear below in Table A-54.
21 Data Sources
22 Data on C share was derived via calculations based on atomic weights of each element of the four individual
23 compounds densities are from the CRC Handbook of Chemistry and Physics, 89th Education. The energy content of each
24 LPG is from the EPA (2009b). LPG consumption was based on data obtained from API (1990 through 2016) and EIA
25 (2009b). Non-fuel use of LPG was obtained from API (1990 through 2016).
26 Uncertainty
27 Because LPG consists of pure paraffinic compounds whose density, heat content and C share are physical
28 constants, there is limited uncertainty associated with the C content coefficient for this petroleum product. Any uncertainty
29 is associated with the collection of data tabulating fuel- and non-fuel consumption in U. S. energy statistics. This uncertainty
30 is likely less than +3 percent.
31 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 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
1 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
2 converted using the energy contents provided in Table A-53. C contents from EPA (2010).
3 Aviation Gasoline
4 Aviation gasoline is used in piston-powered airplane engines. It is a complex mixture of relatively volatile
5 hydrocarbons with or without small quantities of additives, blended to form a fuel suitable for use in aviation reciprocating
6 engines. Fuel specifications are provided in ASTM Specification D910 and Military Specification MIL-G-5572. Aviation
7 gas is a relatively minor contributor to greenhouse gas emissions compared to other petroleum products, representing
8 approximately 0.1 percent of all consumption.
9 The ASTM standards for boiling and freezing points in aviation gasoline effectively limit the aromatics content to
10 a maximum of 25 percent (ASTM D910). Because weight is critical in the operation of an airplane, aviation gas must have
11 as many Btu per pound (implying a lower density) as possible, given other requirements of piston engines such as high anti-
12 knock quality.
13 Methodology
14 AC content coefficient for aviation gasoline was calculated on the basis of the EIA standard heat content of 5.048
15 MMBtu per barrel. This implies a density of approximately 69 degrees API gravity or 5.884 pounds per gallon, based on
16 the relationship between heat content and density of petroleum liquids, as described in Thermal Properties of Petroleum
17 Products (DOC 1929). To estimate the share of C in the fuel, it was assumed that aviation gasoline is 87.5 percent iso-
18 octane, 9.0 percent toluene, and 3.5 percent xylene. The maximum allowable sulfur content in aviation gasoline is 0.05
19 percent, and the maximum allowable lead content is 0.1 percent. These amounts were judged negligible and excluded for
20 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.
21 Data Sources
22 Data sources include ASTM (1985). A standard heat content for aviation gas was adopted from EIA (2009a).
23 Uncertainty
24 The relationship used to calculate density from heat content has an accuracy of five percent at 1 atm. The
25 uncertainty associated with the C content coefficient for aviation gasoline is larger than that for other liquid petroleum
26 products examined because no ultimate analyses of samples are available. Given the requirements for safe operation of
27 piston-powered aircraft the composition of aviation gas is well bounded and the uncertainty of the C content coefficient is
28 likely to be +5 percent.
29 Still Gas
30 Still gas, or refinery gas, is composed of light hydrocarbon gases that are released as petroleum is processed in a
31 refinery. The composition of still gas is highly variable, depending primarily on the nature of the refining process and
32 secondarily on the composition of the product being processed. Petroleum refineries produce still gas from many different
33 processes. Still gas can be used as a fuel or feedstock within the refinery, sold as a petrochemical feedstock, or purified and
34 sold as pipeline-quality natural gas. For the purposes of this Inventory, the coefficient derived here is only applied to still
35 gas that is consumed as a fuel. In general, still gas tends to include large amounts of free hydrogen and methane, as well as
36 smaller amounts of heavier hydrocarbons. Because different refinery operations result in different gaseous by-products, it
37 is difficult to determine what represents typical still gas.
38 Methodology
39 The properties of still gas used to calculate the carbon content are taken from the literature. The carbon share of
40 still gas was calculated from its net calorific value and carbon content from IPCC (2006). This calculation yields a carbon
41 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
42 (EIA 2008a) and the relationship between heat content and density that is described by the U.S. Department of Commerce,
43 Bureau of Standards (DOC 1929).
A-91
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1 Data Sources
2 The carbon share of still gas is calculated from data provided by IPCC (2006). Density is estimated at 0.1405
3 metric tons per barrel, approximately 28.3 degrees API, based on the heat content of 6.00 MMbtu/barrel of still gas from
4 EIA (2009a).
5 Uncertainty
6 The EIA obtained data on four samples of still gas. Table A-55 below shows the composition of those samples.
7 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
8 Sources: EIA (2008b).
9 Because the composition of still gas is highly heterogeneous, the C content coefficient for this product is highly
10 uncertain. Gas streams with a large, free-hydrogen content are likely to be used as refinery or chemical feedstocks.
11 Therefore, the sample cited above with the very high H content of 72 percent (and the lowest calculated C content) is less
12 likely to be representative of the still gas streams to which the calculated coefficient is applied. The C content coefficient
13 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
14 content in Table A-55.
15 Asphalt
16 Asphalt is used to pave roads. Because most of its C is retained in those roads, it is a small source of carbon
17 dioxide emissions. It is derived from a class of hydrocarbons called "asphaltenes," which are abundant in some crude oils
18 but not in others. Asphaltenes have oxygen and nitrogen atoms bound into their molecular structure, so that they tend to
19 have lower C contents than do other hydrocarbons.
20 Methodology
21 Ultimate analyses of twelve samples of asphalts showed an average C content of 83.47 percent. The EIA standard
22 Btu content for asphalt of 6.636 MMBtu per barrel was assumed. The ASTM petroleum measurement tables show a density
23 of 5.6 degrees API or 8.605 pounds per gallon for asphalt. Together, these variables generate C content coefficient of 20.55
24 MMT C/QBtu.
25 Data Sources
26 A standard heat content for asphalt was adopted from EIA (2009a). The density of asphalt was determined by the
27 ASTM (1985). C share is adopted from analyses in EIA (2008b).
28 Uncertainty
29 The share of C in asphalt ranges from 79 to 88 percent by weight. Also present in the mixture are hydrogen and
30 sulfur, with shares by weight ranging from seven to 13 percent for hydrogen, and from trace levels to eight percent for sulfur.
31 Because C share and total heat content in asphalts do vary systematically, the overall C content coefficient is likely to be
32 accurate to +5 percent.
33 Lubricants
34 Lubricants are substances used to reduce friction between bearing surfaces, or incorporated into processing
35 materials used in the manufacture of other products, or used as carriers of other materials. Petroleum lubricants may be
36 produced either from distillates or residues. Lubricants include all grades of lubricating oils, from spindle oil to cylinder oil
37 to those used in greases. Lubricant consumption is dominated by motor oil for automobiles, but there is a large range of
38 product compositions and end uses within this category.
39 Methodology
40 The ASTM Petroleum Measurement tables give the density of lubricants at 25.6 degrees API, or 0.1428 metric
41 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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2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
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. 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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5
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7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 Uncertainty
2 The uncertainty associated with the estimated C content coefficient of petroleum coke can be traced to two factors:
3 the use of only two samples to establish C contents and a standard heat content which may be too low. Together, these
4 uncertainties are likely to bias the C content coefficient upwards by as much as 6 percent.
5 Special Naphtha
6 Special naphtha is defined as a light petroleum product to be used for solvent applications, including commercial
7 hexane and four classes of solvent: (1) Stoddard solvent, used in dry cleaning; (2) high flash point solvent, used as an
8 industrial paint because of its slow evaporative characteristics; (3) odorless solvent, most often used for residential paints;
9 and (4) high solvency mineral spirits, used for architectural finishes. These products differ in both density and C percentage,
10 requiring the development of multiple coefficients.
11 Methodology
12 The method for estimating the C content coefficient of special naphtha includes three steps.
13 Step 1. Estimate the carbon content coefficient for hexane
14 Hexane is a pure paraffin containing 6 C atoms and 14 hydrogen atoms; thus, it is 83.63 percent C. Its density is
15 83.7 degrees API or 5.477 pounds per gallon and its derived C content coefficient is 21.40 MMT C/QBtu.
16 Step 2. Estimate the carbon contents of non-hexane special naphthas
17 The hydrocarbon compounds in special naphthas are assumed to be either paraffinic or aromatic (see discussion
18 above). The portion of aromatics in odorless solvents is estimated at less than 1 percent, Stoddard and high flash point
19 solvents contain 15 percent aromatics and high solvency mineral spirits contain 30 percent aromatics (Boldt and Hall 1977).
20 These assumptions, when combined with the relevant densities, yield the C content factors contained in Table A-56, below.
21 Table A-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
22 Sources: EIA (2008b) and Boldt and Hall (1977).
23
24 Step 3. Develop weighted carbon content coefficient based on consumption of each special naphtha
25 EIA reports only a single consumption figure for special naphtha. The C contents of the five special naphthas are
26 weighted according to the following formula: approximately 10 percent of all special naphtha consumed is hexane; the
27 remaining 90 percent is assumed to be distributed evenly among the four other solvents. The resulting emissions coefficient
28 for special naphthas is 19.74 MMT C/QBtu.
29 Data Sources
30 A standard heat content for special naphtha was adopted from EIA (2009a). Density and aromatic contents were
31 adopted from Boldt and Hall (1977).
32 Uncertainty
33 The principal uncertainty associated with the estimated C content coefficient for special naphtha is the allocation
34 of overall consumption across individual solvents. The overall uncertainty is bounded on the low end by the C content of
35 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
36 percent.
37 Petroleum Waxes
38 The ASTM standards define petroleum wax as a product separated from petroleum that is solid or semi-solid at 77
39 degrees Fahrenheit (25 degrees Celsius). The two classes of petroleum wax are paraffin waxes and microcrystalline waxes.
40 They differ in the number of C atoms and the type of hydrocarbon compounds. Microcrystalline waxes have longer C chains
41 and more variation in their chemical bonds than paraffin waxes.
A-95
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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
Methodology
The method for estimating the C content coefficient for petroleum waxes includes three steps.
Step 1. Estimate the carbon content of paraffin waxes
For the purposes of this analysis, paraffin waxes are assumed to be composed of 100 percent paraffinic compounds
with a chain of 25 C atoms. The resulting C share for paraffinic wax is 85.23 percent and the density is estimated at 45
degrees API or 6.684 pounds per gallon.
Step 2. Estimate the carbon content of microcrystalline waxes
Microcrystalline waxes are assumed to consist of 50 percent paraffinic and 50 percent cycloparaffinic compounds
with a chain of 40 C atoms, yielding a C share of 85.56 percent. The density of microcrystalline waxes is estimated at 36.7
degrees API, based on a sample of 10 microcrystalline waxes found in the Petroleum Products Handbook.
Step 3. Develop a carbon content coefficient for petroleum waxes by weighting the density and carbon content of
paraffinic and microcrystalline waxes
A weighted average density and C content was calculated for petroleum waxes, assuming that wax consumption
is 80 percent paraffin wax and 20 percent microcrystalline wax. The weighted average C content is 85.30 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
^ 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 density and C content to the EIA standard energy content for crude oil of 5.800 MMBtu per barrel produced an emissions
2 coefficient of 20.31 MMT C/QBtu.
3 Data Sources
4 Carbon content was derived from 182 crude oil samples, including 150 samples from U.S. National Research
5 Council (1927). A standard heat content for crude oil was adopted from EIA (2009a).
6 Uncertainty
7 The uncertainty of the estimated C content for crude oil centers on the 35 percent of variation that cannot be
8 explained by density and sulfur content. This variation is likely to alter the C content coefficient by +3 percent. Since
9 unfinished oils and miscellaneous products are impossible to define, the uncertainty of applying a crude oil C content is
10 likely to be bounded by the range of petroleum products described in this chapter at +10 percent.
11 Chronology and Explanation of Changes in Individual Carbon Content Coefficients of Fossil Fuels
12 Coal
13 Original 1994 Analysis
14 A set of 5,426 coal samples from the EIA coal analysis file were used to develop C content estimates. The results
15 from that sample set appear below in Table A-57. The EIA Coal Analysis File was originally developed by the U.S. Bureau
16 of Mines and contained over 60,000 coal samples obtained through numerous coal seams throughout the United States.
17 Many of the samples were collected starting in the 1940s and 1950s through the 1980s and analyzed in U.S. government
18 laboratories.
19 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
20 Sources: Emission factors by consuming sector from B.D. Hong and E.R. Slatnick, "Carbon Dioxide Emission Factors for Coal," U.S. Energy Information
21 Administration, Quarterly Coal Report, January-March 1994 (Washington, DC, 1994); and emission factors by rank from Science Applications International
22 Corporation, Analysis of the Relationship Between Heat and Carbon Content of U.S. Fuels: Final Task Report, Prepared for the U.S. Energy Information
23 Administration, Office of Coal, Nuclear, Electric and Alternative Fuels (Washington, DC 1992).
24
25 2002 Update
26 The methodology employed for these estimates was unchanged from previous years; however, the underlying coal
27 data sample set was updated. A new database, CoalQual 2.0 (1998), compiled by the U.S. Geological Survey (USGS) was
28 adopted for the updated analysis. The updated sample set included 6,588 coal samples collected by the USGS and its state
29 affiliates between 1973 and 1989. The decision to switch to the sample data contained in the USGS CoalQual database from
30 the EIA database was made because the samples contained in the USGS database were collected and analyzed more recently
31 than those obtained by EIA from the Bureau of Mines. The new coefficients developed in the 2002 revision were in use
32 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 [MMT 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
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
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
2 Sources: Data from USGS, U.S. Coal Quality Database Version 2.0 (1998) and analysis prepared by SAIC (2007).
3
4 2007 Update
5 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
6 lignite Table A-59 contains the annual coefficients that resulted from the 2007 analysis.
Table A-59: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank, 1990-2007 [MMT 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
: 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
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
-------
i 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
2 a LPG is a blend of multiple paraffinic hydrocarbons: ethane, propane, isobutane, and normal butane, each with their own heat content, density and C
3 content, see Table A-53.
4 b Heat, density, and percent carbon values are provided separately for ethane, propane and isobutene.
5 c Parameters presented are for naphthas with a boiling temperature less than 400 degrees Fahrenheit. Petrochemical feedstocks with higher boiling
6 points are assumed to have the same characteristics as distillate fuel.
7 Note:Indicates no sample data available.
8 Sources: EIA (1994); EIA (2008a); SAIC (2007).
9
10 Additional revisions to the Inventory's C coefficients since 1990 are detailed below.
11 Jet Fuel
12 1995 Update
13 Between 1994 and 1995, the C content coefficient for kerosene-based jet fuel was revised downward from 19.71
14 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
15 collected between 1959 and 1972 and reported on by Martel and Angello in 1977 to one collected by Boeing in 1989 and
16 published by Hadaller and Momenthy in 1990. The downward revision was a result of a decrease in density, as well as
17 slightly lower C shares than in the earlier samples. However, the assumed heat content is unchanged because it is based on
18 an EIA standard and probably yields a downward bias in the revised C content coefficient.
19 1990 through 2008 Inventory Update
20 The coefficient was revised again for the 1990 through 2008 Inventory, returning to Martel and Angello and NIPER
21 as the source of the carbon share and density data, respectively, for kerosene-based fuels. This change was made in order to
22 align the coefficients used for this report with the values used in EPA's Mandatory Reporting of Greenhouse Gases Rule
23 (EPA 2009b). The return to the use of the Martel and Angello and NIPER coefficients was deemed more appropriate for
24 the Rule as it was considered a more conservative coefficient given the uncertainty and variability in coefficients across the
25 types of jet fuel in use in the United States. The factor will be revisited in future Inventories in light of data received from
26 reporting entities in response to the Rule.
A-100 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 Liquefied Petroleum Gases (LPG)
2 The C content coefficient of LPG is updated annually to reflect changes in the consumption mix of the underlying
3 compounds: ethane; propane; isobutane; and normal butane. In 1994, EIA included pentanes plus—assumed to have the
4 characteristics of hexane—in the mix of compounds broadly described as LPG. In 1995, EIA removed pentanes plus from
5 this fuel category. Because pentanes plus is relatively rich in C per unit of energy, its removal from the consumption mix
6 lowered the C content coefficient for LPG from 17.26 MMT C/QBtu to 16.99 MMT C/QBtu. In 1998, EIA began separating
7 LPG consumption into two categories: energy use and non-fuel use and providing individual coefficients for each. Because
8 LPG for fuel use typically contains higher proportions of propane than LPG for non-fuel use, the C content coefficient for
9 fuel use was 1.8 to 2.5 percent higher than the coefficient for non-fuel use in previous inventories (see Table A-61).
10 However, for the current update of the LPG coefficients, the assumptions that underlie the selection of density and
11 heat content data for each pure LPG compound have been updated, leading to a significant revision of the assumed properties
12 of ethane. For this report, the physical characteristics of ethane, which constitutes over 90 percent of LPG consumption for
13 non-fuel uses, have been updated to reflect ethane that is in (refrigerated) liquid form. Previously, the share of ethane was
14 included using the density and energy content of gaseous ethane. Table A-62, below, compares the values applied for each
15 of the compounds under the two sets of coefficient calculations. The C share of each pure compound was also updated by
16 using more precise values for each compound's molecular weight.
17 Due in large part to the revised assumptions for ethane, the weighted C content for non-fuel use is now higher than
18 that of the weighted coefficient for fuel use, which is dominated by the consumption of more dense propane. Under the
19 revised assumptions, each annual weighted coefficient for non-fuel LPG consumption is 1.2 to 1.7 percent higher each year
20 than is that for LPGs consumed for fuel (energy) uses.
21 Table fl-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
22 Sources: Updated: Densities - CRC Handbook of Chemistry and Physics, 89th Ed. (2008/09); Energy Contents - EPA (2009b). All values are for the
23 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
24 butane (-25 degrees C). Values in previous editions of this Inventory: Gurthrie (1960).
25 Motor Gasoline
26 The C content coefficient for motor gasoline varies annually based on the density of and proportion of additives in
27 a representative sample of motor gasoline examined each year. However, in 1997 EIA began incorporating the effects of
28 the introduction of reformulated gasoline into its estimate of C content coefficients for motor gasoline. This change resulted
29 in a downward step function in C content coefficients for gasoline of approximately 0.3 percent beginning in 1995. In 2005
30 through 2006 reformulated fuels containing ethers began to be phased out nationally. Ethanol was added to gasoline blends
31 as a replacement oxygenate, leading to another shift in gasoline density (see Table A-51), in the list and proportion of
32 constituents that form the blend and in the blended C share based on those constituents.
A-101
-------
Table fl-63: Carbon Content Coefficients for Petroleum Products, 1990-2007 IMMTC/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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 References
2 AAM (2009) Diesel Survey. Alliance of Automobile Manufacturers, Winter 2008.
3 API (1990 through 2008) Sales of Natural Gas Liquids and Liquefied Refinery Gases, American Petroleum Institute.
4 ASTM (1985) ASTM and Other Specifications for Petroleum Products and Lubricants, American Society for Testing and
5 Materials. Philadelphia, PA.
6 Boldt, K. andB.R. Hall (1977) Significance of Tests for Petroleum Products, Philadelphia, PA, American Society for Testing
7 and Materials, p. 30.
8 Chemical Rubber Company (CRC), (2008/2009), Handbook of Chemistry and Physics, 89th Ed., editor D. Lide, Cleveland,
9 OH: CRC Press.
10 DOC (1929) Thermal Properties of Petroleum Products, U.S. Department of Commerce, National Bureau of Standards.
11 Washington, DC. pp. 16-21.
12 EIA (2001-2009b) Coal Distribution - Annual, U.S. Department of Energy, Energy Information Administration.
13 Washington, DC. DOE/EIA.
14 EIA (2008a) Monthly Energy Review, September 2006 and Published Supplemental Tables on Petroleum Product detail.
15 Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2007/9).
16 EIA (2008b) Documentation for Emissions of Greenhouse Gases in the United States 2006. DOE/EIA-0638(2006). October
17 2008.
18 EIA (2009a) Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, DC.
19 DOE/EIA-0384(2008).
20 EIA (2009b) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, DC.
21 Available online at
22 .
24 EIA (2001-2009a) Annual Coal Report, U.S. Department of Energy, Energy Information Administration. Washington, DC.
25 DOE/EIA 0584.
26 EIA (2001) Cost and Quality of Fuels for Electric Utility Plants 2000, Energy Information Administration. Washington,
27 DC. August 2001. Available online at .
28 EIA (1990-2001) Coal Industry Annual, U. S. Department of Energy, Energy Information Administration. Washington, DC.
29 DOE/EIA 0584.
30 EIA (1994) Emissions of Greenhouse Gases in the United States 1987-1992, Energy Information Administration, U.S.
31 Department of Energy. Washington, DC. November, 1994. DOE/EIA 0573.
32 EIA (1993) Btu Tax on Finished Petroleum Products, Energy Information Administration, Petroleum Supply Division
33 (unpublished manuscript, April 1993).
34 EPA (2010) Carbon Content Coefficients Developedfor EPA's Inventory of Greenhouse Gases and Sinks. Office of Air and
35 Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
36 EPA (2009a) "Industry Overview and Current Reporting Requirements for Petroleum Refining and Petroleum Imports,"
37 Petroleum Product Suppliers Technical Support Document for the Proposed Mandatory Reporting Rule. Office of Air
38 and Radiation. 30 January, 2009.
39 EPA (2009b) Mandatory Reporting of Greenhouse Gases Rule. Federal Register Docket ID EPA-HQ-OAR-2008-0508-
40 2278, 30 September, 2009.
41 Gas Technology Institute (1992) Database as documented in W.E. Liss, W.H. Thrasher, G.F. Steinmetz, P. Chowdiah, and
42 A. Atari, Variability of Natural Gas Composition in Select Major Metropolitan Areas of the United States. GRI-
43 92/0123. March 1992.
44 Green & Perry (ed.) (2008). Perry's Chemical Engineers 'Handbook, 8th Ed. New York, NY, McGraw-Hill.
45 Guthrie, V.B. (ed.) (1960) Characteristics of Compounds, Petroleum Products Handbook, p.3-3. New York, NY, McGraw-
46 Hill.
A-103
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2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
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.
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 2015) Motor Gasolines, Summer saAMotor 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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
66%
Natural Gas to Chemical Plantsb
66%
Asphalt & Road Oil
100%
LPGb
66%
Lubricants
9%
Pentanes Plusb
66%
Naphtha (<401 deg. F)b
66%
Other Oil (>401 deg. F)b
66%
Still Gas"
66%
Petroleum Cokec
30%
Special Naphthab
66%
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.
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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 328.7 MMT CO2 Eq. of non-energy fuel consumption in 2016.
For 2016, the storage factor for the eight fuel categories was 66 percent. In other words, of the net consumption, 66 percent
was destined for long-term storage in products—including products subsequently combusted for waste disposal—while the
remaining 34 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.
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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 .
For the most part, the releases covered by the U.S. Toxic Release Inventory (TRI) represent air emissions or water discharges associated
with production facilities. Similarly, VOC emissions are generally associated with production facilities. These emissions could have been
accounted for as part of the Waste chapter, but because they are not necessarily associated with waste management, they were included
here. Toxic releases are not a "product" category, but they are referred to as such for ease of discussion.
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1 Note that the system boundaries for the storage factor do not encompass the entire life-cycle of fossil-based C
2 consumed in the United States insofar as emissions of CO2 from waste combustion are accounted for separately in the
3 Inventory and are discussed in the Incineration of Waste section of the Energy chapter.
4 The following sections provide details on the calculation steps, assumptions, and data sources employed in
5 estimating and classifying the C in each product and waste shown in Table A-66. Summing the C stored and dividing it by
6 total C outputs yields the overall storage factor, as shown in the following equation for 2016:
7 Overall Storage Factor = C Stored / (C Stored + C Emitted + C Unaccounted for) =
8 139.4 MMT CO2 Eq. / (139.4 + 63.6 + 8.7) MMT CO2 Eq. = 66%
9 Table fl-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
139.3
12.9
Plastics
126.5
NA
Synthetic Rubber
4.4
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 139.4 63.6
10 NA (Not Applicable)
11 Note: Totals may not sum due to independent rounding.
12
13 The C unaccounted for is the difference between the C accounted for (discussed below) and the total C in the Total
14 US Petrochemical consumption, which are the potential carbon emissions from all energy consumption in Non-Energy Use.
15 The three categories of C accounted for in the table are industrial releases, energy recovery, and products. Each is
16 discussed below.
17 Industrial Releases
18 Industrial releases include toxic chemicals reported through the Toxics Release Inventory (TRI), industrial
19 emissions of volatile organic compounds (VOCs), CO emissions (other than those related to fuel combustion), and emissions
20 from hazardous waste incineration.
21 TRI Releases
22 Fossil-derived C is found in many toxic substances released by industrial facilities. The TRI, maintained by EPA,
23 tracks these releases by chemical and environmental release medium (i.e., land, air, or water) on a biennial basis (EPA
24 2000b). By examining the C contents and receiving media for the top 35 toxic chemicals released, which account for 90
25 percent of the total mass of chemicals, the quantity of C stored and emitted in the form of toxic releases can be estimated.
26 The TRI specifies releases by chemical, so C contents were assigned to each chemical based on molecular formula.
27 The TRI also classifies releases by disposal location as either off-site or on-site. The on-site releases are further subdivided
28 into air emissions, surface water discharges, underground injection, and releases to land; the latter is further broken down to
A-108 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 disposal in a RCRA Subtitle C (i.e., hazardous waste) landfill or to "Other On-Site Land Disposal."29 The C released in each
2 disposal location is provided in Table A-67.
3 Each on-site classification was assigned a storage factor. A 100 percent storage factor was applied to disposition
4 of C to underground injection and to disposal to RCRA-permitted landfills, while the other disposition categories were
5 assumed to result in an ultimate fate of emission as CO2 (i.e., a storage factor of zero was applied to these categories). The
6 release allocation is not reported for off-site releases; therefore, the approach was to develop a C-weighted average storage
7 factor for the on-site C and apply it to the off-site releases.
8 For the remaining 10 percent of the TRI releases, the weights of all chemicals were added and an average C content
9 value, based upon the top 35 chemicals' C contents, was applied. The storage and emission allocation for the remaining 10
10 percent of the TRI releases was carried out in the same fashion as for the 35 major chemicals.
11 Data on TRI releases for the full 1990 through 2016 time series were not readily available. Since this category is
12 small (less than 1 MMT C emitted and stored), the 1998 value was applied for the entire time series.
13 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
924.0
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
36.0
Total
97.2
982.6
14 NA (Not Applicable)
15 Note: Totals may not sum due to independent rounding.
16 Volatile Organic Compound Emissions from Industrial Processes and Solvent Evaporation Emissions
17 Data on annual non-methane volatile organic compound (NMVOC) emissions were obtained (EPA 2016b) and
18 disaggregated based on EPA (2003), which has been published on the National Emission Inventory (NEI) Air Pollutant
19 Emission Trends web site. The 1990 through 2016 Trends data include information on NMVOC emissions by end-use
20 category; some of these fall into the heading of "industrial releases" in Table A-66 above, and others are related to "product
21 use;" for ease of discussion, both are covered here. The end-use categories that represent "Industrial NMVOC Emissions"
22 include some chemical and allied products, certain petroleum related industries, and other industrial processes. NMVOC
23 emissions from solvent utilization (product use) were considered to be a result of non-energy use of petrochemical
24 feedstocks. These categories were used to distinguish non-energy uses from energy uses; other categories where VOCs
25 could be emitted due to combustion of fossil fuels were excluded to avoid double counting.
26 Because solvent evaporation and industrial NMVOC emission data are provided in tons of total NMVOCs,
27 assumptions were made concerning the average C content of the NMVOCs for each category of emissions. The assumptions
28 for calculating the C fraction of industrial and solvent utilization emissions were made separately and differ significantly.
29 For industrial NMVOC emissions, a C content of 85 percent was assumed. This value was chosen to reflect the C content
30 of an average volatile organic compound based on the list of the most abundant NMVOCs provided in the Trends Report.
31 The list contains only pure hydrocarbons, including saturated alkanes (C contents ranging from 80 to 85 percent based upon
32 C number), alkenes (C contents approximately 85 percent), and some aromatics (C contents approximately 90 percent,
33 depending upon substitution).
34 An EPA solvent evaporation emissions dataset (Tooly 2001) was used to estimate the C content of solvent
35 emissions. The dataset identifies solvent emissions by compound or compound category for six different solvent end-use
36 categories: degreasing, graphic arts, dry cleaning, surface coating, other industrial processes, and non-industrial processes.
37 The percent C of each compound identified in the dataset was calculated based on the molecular formula of the individual
38 compound (e.g., the C content of methylene chloride is 14 percent; the C content of toluene is 91 percent). For solvent
39 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).
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1 compound was selected to represent each category, and the C content of the category was estimated based on the C content
2 of the representative compound. The overall C content of the solvent evaporation emissions for 1998, estimated to be 56
3 percent, is assumed to be constant across the entire time series.
4 The results of the industrial and solvent NMVOC emissions analysis are provided in Table A-68 for 1990 through
5 2016. Industrial NMVOC emissions in 2016 were 4.1 MMT CO2 Eq. and solvent evaporation emissions in 2016 were 5.5
6 MMT C02 Eq.
7 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
8 a Includes emissions from chemical and allied products, petroleum and related industries, and other industrial processes categories.
9 b Includes solvent usage and solvent evaporation emissions from degreasing, graphic arts, dry cleaning, surface coating, other industrial processes, and
10 non-industrial processes.
11 Non-Combustion Carbon Monoxide Emissions
12 Carbon monoxide (CO) emissions data were also obtained from the NEI data (EPA 2016b), and disaggregated
13 based on EPA (2003). There are three categories of CO emissions in the report that are classified as process-related
14 emissions not related to fuel combustion. These include chemical and allied products manufacturing, metals processing,
15 and other industrial processes. Some of these CO emissions are accounted for in the Industrial Processes and Product Use
16 section of this report, and are therefore not accounted for in this section. These include total C emissions from the primary
17 aluminum, titanium dioxide, iron and steel, and ferroalloys production processes. The total C (CO and CO2) emissions from
18 oil and gas production, petroleum refining, and asphalt manufacturing are also accounted for elsewhere in this Inventory.
19 Biogenic emissions (e.g., pulp and paper process emissions) are accounted for in the Land Use, Land-Use Change and
20 Forestry chapter and excluded from calculation of CO emissions in this section. Those CO emissions that are not accounted
21 for elsewhere are considered to be by-products of non-fuel use of feedstocks, and are thus included in the calculation of the
22 petrochemical feedstocks storage factor. Table A-69 lists the CO emissions that remain after taking into account the
23 exclusions listed above.
24 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
25 Note: Includes emissions from chemical and allied products, petroleum and related industries, metals processing, and other industrial processes
26 categories.
27 Hazardous Waste Incineration
28 Hazardous wastes are defined by the EPA under the Resource Conservation and Recovery Act (RCRA).30
29 Industrial wastes, such as rejected products, spent reagents, reaction by-products, and sludges from wastewater or air
30 pollution control, are federally regulated as hazardous wastes if they are found to be ignitable, corrosive, reactive, or toxic
31 according to standardized tests or studies conducted by the EPA.
32 Hazardous wastes must be treated prior to disposal according to the federal regulations established under the
33 authority of RCRA. Combustion is one of the most common techniques for hazardous waste treatment, particularly for
34 those wastes that are primarily organic in composition or contain primarily organic contaminants. Generally speaking,
35 combustion devices fall into two categories: incinerators that burn waste solely for the purpose of waste management, and
36 boilers and industrial furnaces (BIFs) that burn waste in part to recover energy from the waste. More than half of the
30 [42 U.S.C. §6924, SDWA §3004]
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hazardous waste combusted in the United States is burned in BIFs; because these processes are included in the energy
recovery calculations described below, they are not included as part of hazardous waste incineration.
EPA's Office of Solid Waste requires biennial reporting of hazardous waste management activities, and these
reports provide estimates of the amount of hazardous waste burned for incineration or energy recovery. EPA stores this
information in its Resource Conservation and Recovery Act (RCRA) Information system (EPA 2013a), formerly reported
in its Biennial Reporting System (BRS) database (EPA 2000a, 2009, 2015a, 2016a). 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, 2017a). 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
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is accounted for in the Waste Incineration chapter; data from the Rubber Manufacturers Association (RMA 2009a) were
used to subtract out energy recovery from scrap tires in these industries. Consumption of net steam, assumed to be generated
from fossil fuel combustion, is also included separately in the fuel use data from EIA. Therefore, these categories of "other"
fuels are addressed elsewhere in the Inventory and not considered as part of the petrochemical feedstocks energy recovery
analysis. Liquor or black liquor and woodchips and bark are assumed to be biogenic fuels, in accordance with IPCC (2006),
and therefore are not included in the Inventory. The remaining categories of fuels, including waste gas; waste oils, tars, and
related materials; and other uncharacterized fuels are assumed to be petrochemical feedstocks burned for energy recovery
(see Table A-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 CC^Eq. 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
CO2 Eq. in 2010, and 44.1 MMT CO2 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.
A-112 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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7
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17
18
19
20
21
22
23
24
25
26
27
28
29
30
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 2017
supplemented by Vallianos 2011, 2012, 2013, 2014, 2015, 2016). 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 1% by weight)
Resin Type
C Content
Source of C Content Assumption
Epoxy
76%
Typical epoxy resin made from epichlorhydrin and bisphenol A
Polyester (Unsaturated)
63%
Poly (ethylene terephthalate) (PET)
Urea
34%
50%carbamal, 50% N-(hydroxymethyl) urea *
Melamine
29%
Trimethylol 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
2 a Does not include alcoholic hydrogens.
3
4 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%
5 Synthetic Rubber
6 Data on synthetic rubber in tires were derived from data on the scrap tire market and the composition of scrap tires
7 from the Rubber Manufacturers' Association (RMA). The market information is presented in the report 2015 U.S. Scrap
8 Tire Management Summary (RMA 2016), while the tire composition information is from the "Scrap Tires, Facts and
9 Figures" section of the organization's website (RMA 2009). Data on synthetic rubber in other products (durable goods,
10 nondurable goods, and containers and packaging) were obtained from EPA's Municipal Solid Waste in the United States
11 reports (1996 through 2003a, 2005, 2007b, 2008, 2009a, 201 la, 2013b; 2014, 2016c) and detailed unpublished backup data
12 for some years not shown in the Characterization of Municipal Solid Waste in the United States reports (Schneider 2007).
13 The abraded rubber from scrap passenger tires was assumed to be 2.5 pounds per scrap tire, while the abraded rubber from
14 scrap commercial tires was assumed to be 10 pounds per scrap tire. Data on abraded rubber weight were obtained by
15 calculating the average weight difference between new and scrap tires (RMA 2016). Import and export data were obtained
16 from the published by the U.S. International Trade Commission (U.S. International Trade Commission 2017).
17 AC content for synthetic rubber (90 percent for tire synthetic rubber and 85 percent for non-tire synthetic rubber)
18 was assigned based on the weighted average of C contents (based on molecular formula) by elastomer type consumed in
19 1998, 2001, and 2002 (see Table A-77). The 1998 consumption data were obtained from the International Institute of
20 Synthetic Rubber Producers (IISRP) press release Synthetic Rubber Use Growth to Continue Through 2004, Says IISRP and
21 RMA (IISRP 2000). The 2001 and 2002 consumption data were obtained from the IISRP press release, IISRP Forecasts
22 Moderate Growth in North America to 2007 (IISRP 2003).
23 The rubber in tires that is abraded during use (the difference between new tire and scrap tire rubber weight) was
24 considered to be 100 percent emitted. Other than abraded rubber, there were no emissive uses of scrap tire and non-tire
25 rubber identified, so 100 percent of the non-abraded amount was assumed stored. Emissions related to the combustion of
26 rubber in scrap tires and consumer goods can be found in the Incineration of Waste section of the Energy chapter.
A-114 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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i 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
2 NA (Not Applicable)
3 a Includes consumption in Canada.
4 Note: Totals may not sum due to independent rounding.
5 Synthetic Fibers
6 Annual synthetic fiber production data were obtained from the Fiber Economics Bureau, as published in Chemical
1 & Engineering News (FEB 2001, 2003, 2005, 2007, 2009, 2010, 2011, 2012, 2013). The most recent data available were
8 for 2012, so it was assumed that the 2013, 2014, 2015, and 2016 consumption was equal to that of 2012. One new update
9 from Chemical & Engineering News (C&EN 2017) was used to update the polyester value for 2016. These data are organized
10 by year and fiber type. For each fiber, a C content was assigned based on molecular formula (see Table A-78). For polyester,
11 the C content for poly (ethylene terephthalate) (PET) was used as a representative compound. For nylon, the average C
12 content of nylon 6 and nylon 6.6 was used, since these are the most widely produced nylon fibers. Cellulosic fibers, such
13 as acetate and rayon, have been omitted from the synthetic fibers' C accounting displayed here because much of their C is
14 of biogenic origin and carbon fluxes from biogenic compounds are accounted for in the Land Use, Land-Use Change and
15 Forestry chapter. These fibers account for only 4 percent of overall fiber production by weight.
16 There were no emissive uses of fibers identified, so 100 percent of the C was considered stored. Note that emissions
17 related to the combustion of textiles in municipal solid waste are accounted for under the Incineration of Waste section of
18 the Energy chapter.
19 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
20 + Does not exceed 0.05 MMT.
21 NA (Not Applicable)
22 Note: Totals may not sum due to independent rounding.
23 Pesticides
24 Pesticide consumption data were obtained from the 1994/1995, 1996/1997, 1998/1999, 2000/2001, 2006/2007,
25 and 2008-2012 Pesticides Industry Sales and Usage Market Estimates (EPA 1998, 1999, 2002, 2004, 201 lb, 2017) reports.
26 The most recent data available were for 2012, so it was assumed that the 2013 through 2016 consumption was equal to that
27 of 2012. Active ingredient compound names and consumption weights were available for the top 25 agriculturally-used
28 pesticides and top 10 pesticides used in the home and garden and the industry/commercial/government categories. The
29 report provides a range of consumption for each active ingredient; the midpoint was used to represent actual consumption.
30 Each of these compounds was assigned a C content value based on molecular formula. If the compound contained aromatic
31 rings substituted with chlorine or other halogens, then the compound was considered persistent and the C in the compound
32 was assumed to be stored. All other pesticides were assumed to release their C to the atmosphere. Over one-third of 2012
33 total pesticide active ingredient consumption was not specified by chemical type in the Sales and Usage report (EPA 2017).
34 This unspecified portion of the active ingredient consumption was treated as a single chemical and assigned a C content and
35 a storage factor based on the weighted average of the known chemicals' values.
A-115
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i Table fl-79: Active Ingredient Consumption in Pesticides [Million lbs.) and C Emitted and Stored [MMT CO; EqJ 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
2 + Does not exceed 0.05 MMT CO2 Eq.
3 a 2012 estimates (EPA 2017).
4 Note: Totals may not sum due to independent rounding.
5 Soaps, Shampoos, and Detergents
6 Cleansers—soaps, shampoos, and detergents—are among the major consumer products that may contain fossil C.
7 All of the C in cleansers was assumed to be fossil-derived, and, as cleansers eventually biodegrade, all of the C was assumed
8 to be emitted. The first step in estimating C flows was to characterize the "ingredients" in a sample of cleansers. For this
9 analysis, cleansers were limited to the following personal household cleaning products: bar soap, shampoo, laundry
10 detergent (liquid and granular), dishwasher detergent, and dishwashing liquid. Data on the annual consumption of household
11 personal cleansers were obtained from the U.S. Census Bureau 1992, 1997, 2002, 2007, 2012 Economic Census (U.S.
12 Bureau of the Census 1994, 1999, 2004, 2009, 2014). Production values for 1990 and 1991 were assumed to be the same
13 as the 1992 value; consumption was interpolated between 1992 and 1997, 1997 and 2002, 2002 and 2007, and 2007 and
14 2012; production for 2013 through 2016 was assumed to equal the 2012 value. Cleanser production values were adjusted
15 by import and export data to develop U.S. consumption estimates.
16 Chemical formulae were used to determine C contents (as percentages) of the ingredients in the cleansers. Each
17 product's overall C content was then derived from the composition and contents of its ingredients. From these values the
18 mean C content for cleansers was calculated to be 21.9 percent.
19 The Census Bureau presents consumption data in terms of quantity (in units of million gallons or million pounds)
20 and/or terms of value (thousands of dollars) for eight specific categories, such as "household liquid laundry detergents,
21 heavy duty" and "household dry alkaline automatic dishwashing detergents." Additionally, the report provides dollar values
22 for the total consumption of "soaps, detergents, etc.—dry" and "soaps, detergents, etc.—liquid." The categories for which
23 both quantity and value data are available is a subset of total production. Those categories that presented both quantity and
24 value data were used to derive pounds per dollar and gallons per dollar conversion rates, and they were extrapolated (based
25 on the Census Bureau estimate of total value) to estimate the total quantity of dry and liquid" cleanser categories,
26 respectively.
27 Next, the total tonnage of cleansers was calculated (wet and dry combined) for 1997. Multiplying the mean C
28 content (21.9 percent) by this value yielded an estimate of 4.6 MMT CO2 Eq. in cleansers for 1997. For all subsequent
29 years, it was assumed that the ratio of value of shipments to total carbon content remained constant. For 1998 through 2016,
30 value of shipments was adjusted to 1997 dollars using the producer price index for soap and other detergent manufacturing
31 (Bureau of Labor Statistics 2016). The ratio of value of shipments to carbon content was then applied to arrive at total
32 carbon content of cleansers. Estimates are shown in Table A-80.
33 Table A-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 /
4.2
4.5
6.7
4.7
4.7
4.8
4.8
4.7
3 4 Antifreeze and Deicers
35 Glycol compounds, including ethylene glycol, propylene glycol, diethylene glycol, and triethylene glycol, are used
36 as antifreeze in motor vehicles, deicing fluids for commercial aircraft, and other similar uses. These glycol compounds are
37 assumed to ultimately enter wastewater treatment plants where they are degraded by the wastewater treatment process to
38 CO2 or to otherwise biodegrade to CO2. Glycols are water soluble and degrade rapidly in the environment (Howard 1993).
31
A density of 1.05 g/mL—slightly denser than water—was assumed for liquid cleansers.
A-116 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Annual production data for each glycol compound used as antifreeze and deicers were obtained from the Guide to
the Business of Chemistry (ACC 2016) 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. Production data for ethylene glycol in 2016 was proxied to the 2015 value.
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 2016). Historical values for adipic acid were adjusted according to the most recent data in the 2016 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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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
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 2005.36 ICIS last reported production of methyl chloride in 2007. EPA reported total
U.S. production of methyl chloride in 2012 in the CDAT (EPA 2014). Total consumption of methyl chloride for 2012 was
calculated from the 2012 production data using import and export data. Production of methyl chloride was interpolated for
years between 2007 and 2012, using the calculated 2012 total production value for methyl chloride and the most recently
reported total production data from ICIS. The production values for 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 64 percent, a standard deviation
of 4.7 percent, and the 95 percent confidence interval of 53 percent and 69 percent. This compares to the calculated Inventory
estimate of 65.9 percent. The analysis produced a C emission distribution with a mean of 76.2 MMT CO2 Eq., standard
deviation of 19.2 MMT CO2 Eq., and 95 percent confidence limits of 51.0 and 121.3 MMT CO2 Eq. This compares with a
calculated Inventory estimate of 72.3 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, 2017a). 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
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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. However, the proportion of the total C that is released to the atmosphere during use is probably negligible.
A small degree of uncertainty arises from the assignment of C content values in textiles; however, the magnitude
of this uncertainty is less than that for plastics or rubber. Although there is considerable variation in final textile products,
the stock fiber formulations are standardized and proscribed explicitly by the Federal Trade Commission.
For pesticides, the largest source of uncertainty involves the assumption that an active ingredient's C is either zero
percent stored or 100 percent stored. This split is a generalization of chemical behavior, based upon active-ingredient
molecular structure, and not on compound-specific environmental data. The mechanism by which a compound is bound or
released from soils is very complicated and can be affected by many variables, including the type of crop, temperature,
application method, and harvesting practice. Another smaller source of uncertainty arises from the C content values applied
to the unaccounted for portion of active ingredient. C contents vary widely among pesticides, from 7 to 77 percent, and the
remaining pesticides may have a chemical make-up that is very different from the 49 pesticides that have been examined.
Additionally, pesticide consumption data were only available for 1987, 1993, 1995, 1997, 1999, 2001, 2007, 2009, and
2012; the majority of the time series data were interpolated or held constant at the latest (2012) value. Another source of
uncertainty is that only the "active" ingredients of pesticides are considered in the calculations; the "inactive" ingredients
may also be derived from petrochemical feedstocks.
It is important to note that development of this uncertainty analysis is a multi-year process. The current feedstocks
analysis examines NEU fuels that end in storage fates. Thus, only C stored in pesticides, plastics, synthetic fibers, synthetic
rubbers, silicones, and TRI releases to underground injection and Subtitle C landfills is accounted for in the uncertainty
estimate above. In the future this analysis will be expanded to include the uncertainty surrounding emitted fates in addition
to the storage fates. Estimates of variable uncertainty will also be refined where possible to include fewer assumptions.
With these major changes in future Inventories, the uncertainty estimate is expected to change, and likely increase. An
increase in the uncertainty estimate in the coming years will not indicate that the Inventory calculations have become less
certain, but rather that the methods for estimating uncertainty have become more comprehensive; thus, potential future
changes in the results of this analysis will reflect a change in the uncertainty analysis, not a change in the Inventory quality.
Asphalt and Road Oil
Asphalt is one of the principal non-energy uses of fossil fuels. The term "asphalt" generally refers to a mixture of
asphalt cement and a rock material aggregate, a volatile petroleum distillate, or water. For the purposes of this analysis,
"asphalt" is used interchangeably with asphalt cement, a residue of crude oil. Though minor amounts of C are emitted
during production, asphalt has an overall C storage factor of almost 100 percent, as discussed below.
Paving is the primary application of asphalt cement, comprising 86 percent of production. The three types of
asphalt paving produced in the United States are hot mix asphalt (HMA), cut-backs, and emulsified asphalt. UMA, which
makes up 90 percent of total asphalt paving (EPA 2001), contains asphalt cement mixed with an aggregate of rock materials.
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Cut-back asphalt is composed of asphalt cement thinned with a volatile petroleum distillate (e.g., naphtha). Emulsified
asphalt contains only asphalt cement and water. Roofing products are the other significant end use of asphalt cement,
accounting for approximately 14 percent of U.S. production (Kelly 2000). No data were available on the fate of C in asphalt
roofing; it was assumed that it has the same fate as C in asphalt paving applications.
Methodology and Data Sources
A C storage factor was calculated for each type of asphalt paving. The fraction of C emitted by each asphalt type
was multiplied by consumption data for asphalt paving (EPA 2001) to estimate a weighted average C storage factor for
asphalt as a whole.
The fraction of C emitted by HMA was determined by first calculating the organic emissions (volatile organic
compounds [VOCs], carbon monoxide [CO], polycyclic aromatic hydrocarbons [PAHs], hazardous air pollutants [HAPs],
and phenol) from HMA paving, using emission factors reported in EPA (2001) and total HMA production.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' sAP-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 99.5 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 CC^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 (2017b), 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.
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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 A-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)
Portion of Total
Fate of Grease
Grease
C Stored
Combusted During Use
5%
0.1%
Not Combusted During Use
95%
81.7%
Landfilled
90%
77.0%
Dumped on the ground or in storm sewers
10%
4.8%
Weighted Average
NA
81.8%
NA (Not Applicable)
Having derived separate storage factors for oil and grease, the last step was to estimate the weighted average for
lubricants as a whole. No data were found apportioning the mass of lubricants into these two categories, but the U.S. Census
Bureau does maintain records of the value of production of lubricating oils and lubricating greases. These were retrieved
from the relevant industry series summaries from the 1997 Economic Census (U.S. Bureau 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
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1 deviation of 1.7 and 95 percent confidence limits of 16.1 MMT CO2 Eq. and 22.8 MMT CO2 Eq. This compares to an
2 inventory-calculated estimate of 19.5 MMT CC^Eq.
3 The principal sources of uncertainty for the disposition of lubricants are the estimates of the commercial use, post-
4 use, and environmental fate of lubricants, which, as noted above, are largely based on assumptions and judgment. There is
5 no comprehensive system to track used oil and greases, which makes it difficult to develop a verifiable estimate of the
6 commercial fates of oil and grease. The environmental fate estimates for percent of C stored are less uncertain, but also
7 introduce uncertainty in the estimate.
8 The assumption that the mass of oil and grease can be divided according to their value also introduces uncertainty.
9 Given the large difference between the storage factors for oil and grease, changes in their share of total lubricant production
10 have a large effect on the weighted storage factor.
11 Future improvements to the analysis of uncertainty surrounding the lubricants C storage factor and C stored include
12 further refinement of the uncertainty estimates for the individual activity variables.
13 Waxes
14 Waxes are organic substances that are solid at ambient temperature, but whose viscosity decreases as temperature
15 increases. Most commercial waxes are produced from petroleum refining, though "mineral" waxes derived from animals,
16 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,
17 suggests that about 42 percent of C in waxes is emitted, and 58 percent is stored.
18 Methodology and Data Sources
19 The National Petroleum Refiners Association (NPRA) considers the exact amount of wax consumed each year by
20 end use to be proprietary (Maguire 2004). In general, about thirty percent of the wax consumed each year is used in
21 packaging materials, though this percentage has declined in recent years. The next highest wax end use, and fastest growing
22 end use, is candles, followed by construction materials and firelogs. Table A-86 categorizes some of the wax end uses,
23 which the NPRA generally classifies into cosmetics, plastics, tires and rubber, hot melt (adhesives), chemically modified
24 wax substances, and other miscellaneous wax uses (NPRA 2002).
25 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%
26 + Does not exceed 0.5 percent.
27
28 AC storage factor for each wax end use was estimated and then summed across all end uses to provide an overall
29 C storage factor for wax. Because no specific data on C contents of wax used in each end use were available, all wax
30 products are assumed to have the same C content. Table A-87 categorizes wax end uses identified by the NPRA, and lists
31 the estimated C storage factor of each end use.
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i Table fl-87: Wax End-Uses by Fate, Percent of Total Mass, Percent C Stored, and Percent of Total C 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%
2 + Does not exceed 0.5 percent.
3 Notes: Totals may not sum due to independent rounding. Estimates of percent stored are based on professional judgment, ICF International.
4 Source mass percentages: NPRA (2002).
5
6 Emissive wax end-uses include candles, firelogs (synthetic fireplace logs), hotmelts (adhesives), matches, and
7 explosives. At about 20 percent, candles consume the greatest portion of wax among emissive end uses. As candles combust
8 during use, they release emissions to the atmosphere. For the purposes of the Inventory, it is assumed that 90 percent of C
9 contained in candles is emitted as CO2. In firelogs, petroleum wax is used as a binder and as a fuel, and is combusted during
10 product use, likely resulting in the emission of nearly all C contained in the product. Similarly, C contained in hotmelts is
11 assumed to be emitted as CO2 as heat is applied to these products during use. It is estimated that 50 percent of the C
12 contained in hot melts is stored. Together, candles, firelogs, and hotmelts constitute approximately 30 percent of annual
13 wax production (NPRA 2002).
14 All of the wax utilized in the production of packaging, cosmetics, plastics, tires and rubber, and other products is
15 assumed to remain in the product (i.e., it is assumed that there are no emissions of CO2 from wax during the production of
16 the product). Wax is used in many different packaging materials including wrappers, cartons, papers, paperboard, and
17 corrugated products (NPRA 2002). Davie (1993) and Davie et al. (1995) suggest that wax coatings in packaging products
18 degrade rapidly in an aerobic environment, producing CO2; however, because packaging products ultimately enter landfills
19 typically having an anaerobic environment, most of the C from this end use is assumed to be stored in the landfill.
20 In construction materials, petroleum wax is used as a water repellent on wood-based composite boards, such as
21 particle board (IGI 2002). Wax used for this end-use should follow the life-cycle of the harvested wood used in product,
22 which is classified into one of 21 categories, evaluated by life-cycle, and ultimately assumed to either be disposed of in
23 landfills or be combusted (EPA 2003).
24 The fate of wax used for packaging, in construction materials, and for most remaining end uses is ultimately to
25 enter the municipal solid waste (MSW) stream, where it is either combusted or sent to landfill for disposal. Most of the C
26 contained in these wax products will be stored. It is assumed that approximately 21 percent of the C contained in these
27 products will be emitted through combustion or at landfill. With the exception of tires and rubber, these end-uses are
28 assigned a C storage factor of 79 percent.
29 Waxes used in tires and rubber follow the life cycle of the tire and rubber products. Used tires are ultimately
30 recycled, landfilled, or combusted. The life-cycle of tires is addressed elsewhere in this annex as part of the discussion of
31 rubber products derived from petrochemical feedstocks. For the purposes of the estimation of the C storage factor for waxes,
32 wax contained in tires and rubber products is assigned a C storage factor of 47 percent.
33 Uncertainty
34 A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
35 surrounding the estimates of the wax C storage factor and the quantity of C emitted from wax in 2016. A Tier 2 analysis
36 was performed to allow the specification of probability density functions for key variables, within a computational structure
37 that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of uncertainty were not
38 available directly from the information sources for the activity variables; thus, uncertainty estimates were determined using
3 9 assumptions based on source category knowledge. Uncertainty estimates for wax variables were assumed to have a moderate
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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 48 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. and0.7MMT CO2 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. EIA (2014)
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.
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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 18.3 to 22.3 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 17.3 MMT CO2 Eq. and 31.1 MMT CO2 Eq., and a mean of 24.4 MMT CO2 Eq. This compares with the Inventory
estimate of 28.5 MMT CC^Eq., which falls closer to the upper boundary of the 95 percent confidence limit. The uncertainty
analysis results are driven primarily by the very broad uncertainty inputs for the storage factors.
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References
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Davie, I.N. (1993) "Compostability of Petroleum Wax-based Coatings." Technical Association for Pulp and Paper
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American Plastics Council, 11 July 2000.
EIIP (2001) "Area Sources" Asphalt Paving, Emissions Inventory Improvement Program: State and Territorial Air
Pollution Program Administrators/Association of Local Air Pollution Control Officials and U.S. EPA, EIIP
Document Series Vol. Ill, Ch. 17. (STAPPA/ALAPCO/EPA), Washington DC, January 2001. Available online at
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EIIP (1999) Methods for Estimating Greenhouse Gas Emissions from Combustion of Fossil Fuels. Emissions Inventory
Improvement Program: State and Territorial Air Pollution Program Administrators/Association of Local Air Pollution
Control Officials and U.S. Environmental Protection Agency, EIIP Document Series Volume VIII, Chapter 1,
STAPPA/ALAPCO/EPA, Washington, DC. August 2000. Available online at
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EPA (2016c) Advancing Sustainable Materials Management: 2014 Facts and Figures Fact Sheet. Office of Solid Waste
and Emergency Response, U.S. Environmental Protection Agency, Washington, D.C. Available online at:
. Accessed January 2015.
EPA (1996-2003a, 2005, 2007b, 2008, 2009a, 201 la, 2013b; 2014) Municipal Solid Waste in the United States: Facts and
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1 EPA (201 lb) EPA's Pesticides Industry Sales and Usage, 2006 and 2007 Market Estimates. Available online at
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3 EPA (2009) Biennial Reporting System (BRS) Database. U.S. Environmental Protection Agency, Envirofacts Warehouse.
4 Washington, DC. Available online at .
5 EPA (2006) Air Emissions Trends - Continued Progress Through 2005. U.S. Environmental Protection Agency,
6 Washington DC. December 19, 2006. Available online at: .
7 EPA (2004) EPA's Pesticides Industry Sales and Usage, 2000 and 2001 Market Estimates. Available online at
8 . Accessed September 2006.
9 EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and the
10 Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
11 EPA (2002) EPA's Pesticides Industry Sales and Usage, 1998 and 1999 Market Estimates, table 3.6. Available online at
12 .
15 EPA (2000a) Biennial Reporting System (BRS). U.S. Environmental Protection Agency, Envirofacts Warehouse.
16 Washington, DC. Available online at .
17 EPA (2000b) Toxics Release Inventory, 1998. U.S. Environmental Protection Agency, Office of Environmental
18 Information, Office of Information Analysis and Access, Washington, DC. Available online at
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20 EPA (1999) EPA's Pesticides Industry Sales and Usage, 1996-1997 Market Estimates and Available online at:
21 .
27 FEB (2012) Fiber Economics Bureau, as cited in C&EN (2012) Too Quiet After the Storm: After a rebound in 2010,
28 chemical production hardly grew in 2011. Chemical &Engineering News, American Chemical Society, 2 July.
29 Available online at: .
30 FEB (2011) Fiber Economics Bureau, as cited in C&EN (2011) Output Ramps up in all Regions. Chemical & Engineering
31 News, American Chemical Society, 4 July. Available online at: .
32 FEB (2010) Fiber Economics Bureau, as cited in C&EN (2010) Output Declines in U.S., Europe. Chemical &
33 Engineering News, American Chemical Society, 6 July. Available online at: .
34 FEB (2009) Fiber Economics Bureau, as cited in C&EN (2009) Chemical Output Slipped In Most Regions. Chemical &
35 Engineering News, American Chemical Society, 6 July. Available online at: .
36 FEB (2007) Fiber Economics Bureau, as cited in C&EN (2007) Gains in Chemical Output Continue. Chemical &
37 Engineering News, American Chemical Society. July 2, 2007. Available online at: .
38 FEB (2005) Fiber Economics Bureau, as cited in C&EN (2005) Production: Growth in Most Regions. Chemical &
39 Engineering News, American Chemical Society, 11 July. Available online at: .
40 FEB (2003) Fiber Economics Bureau, as cited in C&EN (2003) Production Inches Up in Most Countries. Chemical &
41 Engineering News, American Chemical Society, 7 July. Available online at: .
42 FEB (2001) Fiber Economics Bureau, as cited in ACS (2001) Production: slow gains in output of chemicals and products
43 lagged behind U.S. economy as a whole Chemical & Engineering News, American Chemical Society, 25 June.
44 Available online at: .
45 Financial Planning Association (2006) Canada/US Cross-Border Tools: US/Canada Exchange Rates. Available online at:
46 . Accessed August 16,2006.
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1 Gosselin, Smith, and Hodge (1984) Clinical Toxicology of Commercial Products. Fifth Edition, Williams & Wilkins,
2 Baltimore.
3 Huurman, J.W.F. (2006) Recalculation of Dutch Stationary Greenhouse Gas Emissions Based on sectoral Energy
4 Statistics 1990-2002. Statistics Netherlands, Voorburg, The Netherlands.
5 IGI (2002) 100 Industry Applications. The International Group Inc. Available online at
6 .
7 IISRP (2003) "IISRP Forecasts Moderate Growth in North America to 2007" International Institute of Synthetic Rubber
8 Producers, Inc. New Release; available online at: .
10 IISRP (2000) Synthetic Rubber Use Growth to Continue Through 2004, Says IISRP andRMA. International Institute of
11 Synthetic Rubber Producers press release.
12 INEGI (2006) Produccion bruta total de las unidades economicas manufactureras por Subsector, Rama, Subrama y Clase
13 de actividad. Available online at:
14 . Accessed August
15 15.
16 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
17 Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds.; Institute for Global Environmental
18 Strategies (IGES). Hayama, Kanagawa, Japan.
19 James, A. (2000) Personal communication between Suzanne Bratis of ICF International and Alan James of Akzo Nobel
20 Coatings, Inc. July 2000. (Tel: 614-294-3361).
21 Kelly (2000) Personal communication between Tom Smith, ICF Consulting and Peter Kelly, Asphalt Roofing
22 Manufacturers Association, August 2000.
23 Maguire (2004) Personal communication with J. Maguire, National Petrochemicals and Refiners Association. August -
24 September 2004.
25 Marland, G., and R.M. Rotty (1984) Carbon dioxide emissions from fossil fuels: A procedure for estimation and results for
26 1950-1982, Tellus 36b:232-261.
27 NPRA (2002) North American Wax - A Report Card. Available online at:
28 . Accessed 17 September 2009.
39 Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah Shapiro of
40 ICF International, January 10, 2007.
41 SPI (2000) The Society of the Plastics Industry Website, http://www.plasticsindustry.org/industry/stat3.htm, Accessed 28
42 June 2000.
43 U.S. Bureau of the Census (1994, 1999, 2004, 2009, 2014) 1992, 1997, 2002, 2007, 2012 Economic Census. Available
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45 .
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A-131
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1 Vallianos, Jean (2016) Personal communication between Drew Stilson of ICF and Jean Vallianos of the American
2 Chemistry Council, November 17, 2016.
3 Vallianos, Jean (2015) Personal communication between Tyler Fitch of ICF International and Jean Vallianos of the
4 American Chemistry Council, December 20, 2015.
5 Vallianos, Jean (2014) Personal communication between Sarah Biggar of ICF International and Jean Vallianos of the
6 American Chemistry Council, November 13, 2014.
7 Vallianos, Jean (2013) Personal communication between Sarah Biggar of ICF International and Jean Vallianos of the
8 American Chemistry Council, November 8, 2013.
9 Vallianos, Jean (2012) Personal communication between Ben Eskin of ICF International and Jean Vallianos of the
10 American Chemistry Council, September 14, 2012.
11 Vallianos, Jean (2011) Personal communication between Joe Indvik of ICF International and Jean Vallianos of the American
12 Chemistry Council, January 4, 2011.
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ANNEX 3 Methodological Descriptions for Additional
Source or Sink Categories
3.1. Methodology for Estimating Emissions of ChU, 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,
October 2017 (EIA 2017a). 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 2017b) 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 (2017a) total energy consumption estimates, the remainder between total
energy consumption using EPA (2016a) and EIA (2017a) 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 (2017a) 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.
Though emissions from construction and farm use occur due to both stationary and mobile sources, detailed data was not available to
determine the magnitude from each. Currently, these emissions are assumed to be predominantly from mobile sources.
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1 Tier 2 IPCC emission factors shown in Table A-90. Emission factors were taken from U.S. EPA publications on emissions
2 rates for combustion sources. The EPA factors were in large part used in the 2006 IPCC Guidelines as the factors presented.
3 Estimates of N0X, CO, and NMVOC Emissions
4 Emissions estimates for NOx, CO, and NMVOCs were obtained from data published on the National Emission
5 Inventory (NEI) Air Pollutant Emission Trends web site (EPA 2016b), and disaggregated based on EPA (2003).
6 For indirect greenhouse gases, the major source categories included coal, fuel oil, natural gas, wood, other fuels
7 (i.e., bagasse, liquefied petroleum gases, coke, coke oven gas, and others), and stationary internal combustion, which
8 includes emissions from internal combustion engines not used in transportation. EPA periodically estimates emissions of
9 NOx, CO, and NMVOCs by sector and fuel type using a "bottom-up" estimating procedure. In other words, the emissions
10 were calculated either for individual sources (e.g., industrial boilers) or for many sources combined, using basic activity data
11 (e.g., fuel consumption or deliveries, etc.) as indicators of emissions. The national activity data used to calculate the
12 individual categories were obtained from various sources. Depending upon the category, these activity data may include fuel
13 consumption or deliveries of fuel, tons of refuse burned, raw material processed, etc. Activity data were used in conjunction
14 with emission factors that relate the quantity of emissions to the activity.
15 The basic calculation procedure for most source categories presented in EPA (2003) and EPA (2016b) is
16 represented by the following equation:
17 Ep,s = As x EFP,S x (l - Cp.s/100)
18 where,
19
E
= Emissions
20
P
= Pollutant
21
s
= Source category
22
A
= Activity level
23
EF
= Emission factor
24
C
= Percent control efficiency
25
26 The EPA currently derives the overall emission control efficiency of a category from a variety of sources, including
27 published reports, the 1985 National Acid Precipitation and Assessment Program (NAPAP) emissions inventory, and other
28 EPA databases. The U.S. approach for estimating emissions of NOx, CO, and NMVOCs from stationary combustion as
29 described above is similar to the methodology recommended by the IPCC (IPCC 2006).
30
31
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i Table fl-88: Fuel Consumption by Stationary Combustion for Calculating Clh and N2O Emissions ITBtul
Fuel/End-Use
Sector
1990
1995
2000
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Coal
19,610
20.888
23,080
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
13,972
Residential
31
17
11
12
12
11
8
6
8
0
0
0
0
0
0
0
0
0
Commercial
124
117
92
90
82
103
97
65
70
81
73
70
62
44
41
40
31
24
Industrial
1,640
1,527
1,349
1,244
1,249
1,262
1,219
1,189
1,131
1,081
877
952
866
782
800
799
696
623
Electric Power
17,807
19,217
21,618
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
11
34
32
33
37
37
37
37
37
37
37
31
44
44
44
Petroleum
6,834
6.471
6,923
6,742
7,133
7,313
7,220
6,886
6,827
5,958
5,367
5,482
5,059
4,755
4,907
4,396
4,816
4,301
Residential
1,375
1,262
1,429
1,361
1,468
1,468
1,368
1,202
1,220
1,202
1,138
1,116
1,040
846
937
988
930
854
Commercial
1,009
730
775
701
831
811
766
729
755
706
752
722
691
571
606
574
945
783
Industrial
3,282
3,160
2,982
3,054
3,173
3,376
3,462
3,749
3,657
3,075
2,568
2,715
2,565
2,549
2,680
2,209
2,299
2,036
Electric Power
797
860
1,269
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
552
618
652
620
616
577
488
526
516
497
517
504
472
472
472
Natural Gas
17,266
19.337
20,919
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
26,459
26,606
Residential
4,491
4,954
5,105
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
3,212
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
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
8,820
9,035
Electric Power
2,376
2,564
3,894
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
13
23
27
25
24
26
27
29
27
28
27
49
57
57
57
57
Wood
2,216
2.370
2,262
1,995
2,002
2,121
2,137
2,099
2,089
2,059
1,931
1,981
2,010
2,010
2,170
2,242
2,071
1,959
Residential
580
520
420
380
400
410
430
380
420
470
500
440
450
420
580
590
440
373
Commercial
66
72
71
69
71
70
70
65
70
73
73
72
69
61
70
75
81
82
Industrial
1,442
1,652
1,636
1,396
1,363
1,476
1,452
1,472
1,413
1,339
1,178
1,273
1,309
1,339
1,312
1,325
1,306
1,283
Electric Power
129
125
134
150
167
165
185
182
186
177
180
196
182
190
207
251
244
222
U.S. Territories
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
2 NE (Not Estimated)
3 Note: Totals may not sum due to independent rounding.
4
A-135
-------
i Table fl-89: CHa and N2O Emission Factors hy 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
2 NA (Not Applicable)
3 aGJ (Gigajoule) = 109 joules. One joule = 9.486x1 CHBtu.
4
5 Table fl-90: CH4 and N2O Emission Factors hy Technology Type and Fuel Type for the Electric Power Sector [g/GJF
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
6 NA (Not Applicable)
7 a Ibid.
8
A-136 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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 H
147
149
142
131
120
114
108
102
95
85
74
63
59
55
52
44
32
Natural gas
513
51055""*
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
1344
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
2 NA (Not Applicable)
3 a Other Fuels include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2016b).
4 b Residential coal, fuel oil, and natural gas emissions are included in the Other Fuels category (EPA 2016b).
5 Note: Totals may not sum due to independent rounding.
6
7 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
1 NA (Not Applicable)
2 a Other Fuels include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2016b).
3 b Residential coal, fuel oil, and natural gas emissions are included in the Other Fuels category (EPA 2016b).
4 Note: Totals may not sum due to independent rounding.
5
6 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 DRAFT 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
1 + Does not exceed 0.5 kt.
2 NA (Not Applicable)
3 a "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2016b).
4 b Residential coal, fuel oil, and natural gas emissions are included in the "Other Fuels" category (EPA 2016b).
5 Note: Totals may not sum due to independent rounding.
6
A-139
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References
EIA (2017a) Monthly Energy Review, October 2017, Energy Information Administration, U.S. Department of Energy,
Washington, DC. DOE/EIA-0035(2017/10).
EIA (2017b) 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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17
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20
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24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
3.2. Methodology for Estimating Emissions of ChU, 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 2017), 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.42 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 2016) 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
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23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
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 2016 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 2016) for passenger rail. Estimates of diesel from ships and boats were taken from EIA's
Fuel Oil and Kerosene Sales (1991 through 2016).
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 (2017) 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 2017), while CO2 emissions from domestic military and general aviation j et 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 2017a).
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 20171) 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 2017a) 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-96.43
43 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%, 100%) blends
of biodiesel. This value is sourced from MER Table 10.4 and is calculated as biodiesel production plus biodiesel net imports minus biodiesel stock
exchange.
A-142 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i Table fl-94: Fuel Consumption by 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
107,435
114,096
125,385
127,760
127,130
121,360
120,441
119,372
116,631
116,105
116,145
120,861
120,689124,324
Passenger Cars
67,852
65,597
70,468
68,842
86,114
82,186
81,357
80,632
79,941
79,735
79,693
81,785
82,448
84,961
Light-Duty Trucks
33,748
42,834
49,107
53,289
33,950
32,087
32,452
32,224
30,587
30,235
30,254
32,722
31,930
32,903
Motorcycles
189
193
203
204
459
472
453
398
388
444
423
422
412
425
Buses
38
40
42
40
77
79
81
79
77
89
92
101
101
104
Medium- and Heavy-Duty Trucks
4,231
3,931
3,961
3,851
5,018
5,064
4,652
4,624
4,241
4,214
4,305
4,456
4,428
4,563
Recreational Boatsd
1,377
1,500
1,604
1,535
1,513
1,471
1,446
1,414
1,398
1,388
1,379
1,375
1,370
1,368
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
47,021
Passenger Cars
771
765
356
403
403
363
354
367
399
401
399
406
422
432
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
1,367
1,400
Buses
781
851
997
1,034
1,520
1,436
1,335
1,326
1,419
1,515
1,525
1,653
1,699
1,740
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
36,263
37,124
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
1,145
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
3,914
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
17,250
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
12,534
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
3,546
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
1,191
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
1,191
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
7,499
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
7,499
2 + Does not exceed 0.05 million cubic feet
3 a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2015 time period. These methodological changes include howon-road vehicles are classified, moving from a
4 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.
5 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
6 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
7 gasoline with ethanol, which varies by year.
A-143
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1 c Gasoline and diesel highway vehicle fuel consumption estimates are based on data from FHWA Highway Statistics Table MF-21, MF-27, and VM-1 (FHWA 1996 through 2017). Table VM-1 data for 2016 has
2 not been published yet, therefore 2016 mileage data is estimated using the 1.7% increase in FHWA Traffic Volume Trends from 2015 to 2016. Data from Table VM-1 is used to estimate the share of
3 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
4 through 2016). TEDB data for 2015 and 2016 has not been published yet, therefore 2014 data is used as a proxy.
5 d Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
6 e Class II and Class III diesel consumption data for 2014-2016 is not available yet, therefore 2013 data is used as a proxy.
7 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
8 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
9 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
10 total natural gas and LPG consumption. These changes were first incorporated in this year's Inventory and apply to the 1990-2016 time period.
11 9 Fluctuations in reported fuel consumption may reflect data collection problems.
12 h Million kilowatt-hours
13
14
15 Table fl-95: Energy Consumption by Fuel and Vehicle Type [Tbtu]
Fuel/Vehicle Type
1990
1995
2000
2006
2007a
2008
2009
2010
2011
2012
2013
2014
2015
2016
Motor Gasolinebc
13,437
14,270
15,682
15,979
15,807
15,089
14,975
14,842
14,501
14,436
14,441
15,027
15,006
15,458
Passenger Cars
8,486
8,204
8,814
8,610
10,707
10,218
10,115
10,025
9,939
9,914
9,909
10,169
10,251
10,563
Light-Duty Trucks
4,221
5,357
6,142
6,665
4,221
3,989
4,035
4,007
3,803
3,759
3,762
4,068
3,970
4,091
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
12
13
Medium- and Heavy-Duty
529
492
495
482
624
630
578
575
527
524
535
554
551
567
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
6,302
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
192
Buses
108
118
138
143
209
198
184
182
195
208
210
227
234
239
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
4,985
5,103
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
157
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
2,329
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
1,692
General Aviation Aircraft
545
454
427
350
276
362
241
314
256
224
274
241
319
479
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
178
Ships and Boats
300
387
443
306
386
271
186
272
258
211
201
77
57
178
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
A-144 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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
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
4
Rail
3
3
3
5
5
5
4
4
4
4
4
4
4
4
Total
20,633
22,250
24,986
25,834
25,793
24,580
23,557
23,698
23,402
23,258
23,503
24,025
24,271
25,097
1 + Does not exceed 0.05 tBtu
2 a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2015 time period. These methodological changes include howon-road vehicles are classified, moving from a system
3 based on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class between 2006 and 2007.
4 b Figures do not include ethanol blended in motor gasoline or biodiesel blended into distillate fuel oil. Net carbon fluxes associated with ethanol are accounted for in the Land Use, Land-Use Change and Forestry
5 chapter.
6 c Gasoline and diesel highway vehicle fuel consumption estimates are based on data from FHWA Highway Statistics Table MF-21, MF-27, and VM-1 (FHWA 1996 through 2017). Table VM-1 data for 2016 has not
7 been published yet, therefore 2016 mileage data is estimated using the 1.7% increase in FHWA Traffic Volume Trends from 2015 to 2016.Data from Table VM-1 is used to estimate the share of consumption
8 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 2016).
9 TEDB data for 2015 has not been published yet, therefore 2014 data is used as a proxy.
10 d Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
11 eClass II and Class II diesel consumption data for 2014-2016 is not available yet, therefore 2013 data is used as a proxy.
12 '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).
13 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
14 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.
15 These changes were first incorporated in this year's Inventory and apply to the 1990-2016 time period.
16 a Fluctuations in reported fuel consumption may reflect data collection problems. Residual fuel oil for ships and boats data is based on ElA's October 2017 Monthly Energy Review data.
17
18 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
13,096
13,480
Biodiesel
NA
NA
NA
261
354
304
322
260
886
899
1,429
1,417
1,494
2,085
19 NA (Not Available)
20 Note: According to the MER, there was no biodiesel consumption prior to 2001.
A-145
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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,44 buses, and
motorcycles) were obtained from the FHWA's Highway Statistics (FHWA 1996 through 2017).45 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 2016). 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.46
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).47 Age-specific vehicle mileage
accumulations were also obtained from EPA's MOVES2014a model (EPA 2017b).48
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
44 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.
45 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.
46 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.
47 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.
48 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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44
45
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).49
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 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.50 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
49 For further description, see "Definitions of Emission Control Technologies and Standards" section of this annex below.
50 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
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44
45
46
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 5 years of data.
For the current Inventory, counts of electric vehicles (EVs) and plug-in hybrid-electric vehicles (PHEVs) were
taken from data compiled by the 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 PHEVs were assumed to be the same as those for the public fleet vehicles surveyed by EIA and
provided in their data tables.
Because AFVs run on different fuel types, their fuel use characteristics are not directly comparable. Accordingly,
fuel economy for each vehicle type is expressed in gasoline equivalent terms, i.e., how much gasoline contains the equivalent
amount of energy as the alternative fuel. Energy economy ratios (the ratio of the gasoline equivalent fuel economy of a given
technology to that of conventional gasoline or diesel vehicles) were taken from the Argonne National Laboratory's
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
Laboratories 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 2016) 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.51 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 2016) and Gaffney (2007), and consumption by Class II and III rail was provided
by Benson (2002 through 2004) and Whorton (2006 through 2014).52 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
2017). 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,
51 See International Bunker Fuels section of the Energy chapter.
52 Diesel consumption from Class II and Class III railroad were unavailable for 2014 and 2015. Values are proxied from 2013, which is the last
year the data was available.
A-148 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 commercial, and construction sectors.53 Specifically, this Inventory uses FHWA's Agriculture, Construction, and
2 Commercial/Industrial MF-24 fuel volumes along with the MOVES NONROAD model gasoline volumes to estimate non-
3 road mobile source CH4 andN20 emissions for these categories. For agriculture, the MF-24 gasoline volume is used directly
4 because it includes both off-road trucks and equipment. For construction and commercial/industrial gasoline estimates, the
5 2014 and older MF-24 volumes represented off-road trucks only; therefore, the MOVES NONROAD gasoline volumes for
6 construction and commercial/industrial are added to the respective categories in the Inventory. Beginning in 2015, this
7 addition is no longer necessary since the FHWA updated its method for estimating on-road and non-road gasoline
8 consumption. Among the method updates, FHWA now incorporates MOVES NONROAD equipment gasoline volumes in
9 the construction and commercial/industrial categories.
10 Emissions of CH4 and N2O from non-road mobile sources were calculated using the updated 2006 IPCC Tier 3
11 guidance and EPA's MOVES2014a model. CH4 emission factors were calculated directly from MOVES. N2O emission
12 factors were calculated using NONROAD activity and emission factors by fuel type from the European Environment Agency
13 (EEA 2009). Equipment using liquefied petroleum gas (LPG) and compressed natural gas (CNG) were included (see Table
14 A-l 12 and Table A-l 13).
15 Estimates of NOx, CO, and NMVOC Emissions
16 The emission estimates of NOx, CO, and NMVOCs from mobile combustion (transportation) were obtained from
17 EPA's National Emission Inventory (NEI) Air Pollutant Emission Trends web site (EPA 2016g). This EPA report provides
18 emission estimates for these gases by fuel type using a procedure whereby emissions were calculated using basic activity
19 data, such as amount of fuel delivered or miles traveled, as indicators of emissions. Table A-l 14 through Table A-l 16
20 provides complete emission estimates for 1990 through 2016.
21 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
2,167.2
615.8
32.3
19.9
22 a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2015 time period. These methodological changes
23 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
24 changes in VMT data by vehicle class between 2006 and 2007.
53 "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.
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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). Table
VM-1 data for 2016 has not been published yet, therefore 2016 mileage data is estimated using the 1.7% increase in FHWA Traffic Volume Trends
from 2015 to 2016. 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 2016). TEDB data for 2015 and 2016 has not been published yet, therefore 2014 data are used as a
proxy
Source: Derived from FHWA (1996 through 2017), DOE (1990 through 2016), 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
10.3
23.2
258.8
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 2015 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). VM-1
data for 2016 has not been published yet, therefore 2016 mileage data is estimated using the 1.7% increase in FHWA Traffic Volume Trends from 2015
to 2016. 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 2016). TEDB data for 2015 and 2016 has not been published yet, therefore 2014 data is used as a proxy
Source: Derived from FHWA (1996 through 2017), DOE (1993 through 2016), and Browning (2017).
A-150 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1
2
3
4
5
6
7
8
9
10
11
12
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 1990 through 2014 Inventory and apply to the 1990 to 2015 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
-------
i Table fl-100: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles 110s 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 DRAFT 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
2 + Does not exceed 0.05 million vehicle miles traveled
3 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.
4 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
5 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
6 incorporated in the 2014 Inventory and apply to the 1990 to 2015 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
7 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
8 apply to the 2005 to 2016 time period.
9 Source: Derived from Browning (2017), EIA (2017e), and EDTA (2017).
10
A-153
-------
i 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%
2 a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV (heavy-duty
3 gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC (motorcycles).
4 Note: This year's Inventory includes updated vehicle population data based on the MOVES 2014a Model.
5 Source: EPA (2017b).
6
7 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 DRAFT 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
1 a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV (heavy-duty
2 gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC (motorcycles).
3 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
4 presented in aggregate.
5 Source: EPA (2017b).
6
7 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%
8 a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks),HDGV (heavy-duty
9 gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC (motorcycles).
10 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
11 2014a. Model that affects this distribution.
A-155
-------
Table fl-104: Fuel Consumption for Off-Road Sources by Fuel Type [million gallons]
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
17,420
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
17,250
Commercial
Aviationb
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
12,534
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
4,626
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
1,852
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
1,258
Construction/Mining
Equipments
3,766
4,355
4,893
6,017
6,029
6,165
6,274
6,503
6,590
6,741
7,323
7,067
6,866
6,995
Diesel
3,282
3,918
4,551
5,331
5,460
5,589
5,719
5,848
5,978
6,108
6,238
6,369
6,499
6,628
Gasoline'
484
438
342
686
569
575
556
655
612
632
1,085
698
367
367
Agricultural
Equipment11
3,173
3,745
3,929
5,011
4,926
4,582
4,708
4,807
4,998
5,157
5,021
5,094
4,693
4,776
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
159
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®
5,885
6,292
6,798
8,370
8,229
8,360
8,455
8,804
8,768
8,703
8,800
8,952
8,937
9,061
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
5,953
Total
40,443
42,405
46,568
48,155
47,991
46,148
43,273
44,485
44,593
43,838
44,798
44,077
44,678
46,572
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1
2
3
4
5
6
7
8
9
10
Table A-105: Control Technology Assignments for Gasoline Passenger Gars (Percent of VMT)
Model Years
Non-catalyst
Oxidation
EPA Tier 0
EPA Tier 1
CARB LEV
CARB LEV 2
EPA Tier 2
CARB LEV 3/
EPA Tier 3
1973-1974
100%
-
-
-
-
-
-
-
1975
20%
80%
-
-
-
-
-
-
1976-1977
15%
85%
-
-
-
-
-
-
1978-1979
10%
90%
-
-
-
-
-
-
1980
5%
88%
7%
-
-
-
-
-
1981
-
15%
85%
-
-
-
-
-
1982
-
14%
86%
-
-
-
-
-
1983
-
12%
88%
-
-
-
-
-
1984-1993
-
-
100%
-
-
-
-
-
1994
-
-
80%
20%
-
-
-
-
1995
-
-
60%
40%
-
-
-
-
1996
-
-
40%
54%
6%
-
-
-
1997
-
-
20%
68%
12%
-
-
-
1998
-
-
<1%
82%
18%
-
-
-
1999
-
-
<1%
67%
33%
-
-
-
2000
-
-
-
44%
56%
-
-
-
2001
-
-
-
3%
97%
-
-
-
2002
-
-
-
1%
99%
-
-
-
2003
-
-
-
<1%
85%
2%
12%
-
2004
-
-
-
<1%
24%
16%
60%
-
2005
-
-
-
-
13%
27%
60%
-
2006
-
-
-
-
18%
35%
47%
-
2007
-
-
-
-
4%
43%
53%
-
2008
-
-
-
-
2%
42%
56%
-
2009
-
-
-
-
<1%
43%
57%
-
2010
-
-
-
-
-
44%
56%
-
2011
-
-
-
-
-
42%
58%
-
2012
-
-
-
-
-
41%
59%
-
2013
-
-
-
-
-
40%
60%
-
2014
-
-
-
-
-
37%
62%
1%
2015
-
-
-
-
-
33%
56%
11%
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
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
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).
Tahle fl-107: Control Technology Assignments for Gasoline Heavy-Duty Vehicles [Percent of VMTF
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
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 DRAFT 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)
-------
2 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
3 Source: Developed by ICF (Browning 2017) using ANL (2016)
A-162 DRAFT 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
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
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
Airport Equipment
Gasoline
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
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
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
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
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
D^il
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
Kail
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 Fuel0
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
Agricultural Equipment3
Gasoline-Equipment
2 Stroke
9.969
9.291
8.658
5.648
5.138
4.831
4.717
4.673
4.674
4.644
4.644
4.645
4.645
4.645
4 Stroke
7.434
6.786
6.110
5.223
5.038
4.514
4.177
3.860
3.635
3.341
3.145
2.966
2.799
2.659
Gasoline-Off-road Trucks
7.434
6.786
6.110
5.223
5.038
4.514
4.177
3.860
3.635
3.341
3.145
2.966
2.799
2.659
Diesel-Equipment
0.045
0.041
0.037
0.066
0.071
0.075
0.079
0.083
0.086
0.087
0.088
0.089
0.089
0.089
Diesel-Off-Road Trucks
0.021
0.022
0.025
0.057
0.065
0.071
0.078
0.083
0.092
0.100
0.107
0.112
0.110
0.107
Construction/Mining Equipment11
Gasoline-Equipment
2 Stroke
9.503
8.576
7.820
5.818
4.950
4.665
4.528
4.483
4.478
4.451
4.451
4.451
4.451
4.451
4 Stroke
11.477
9.340
7.418
5.855
5.560
4.672
4.156
3.810
3.401
2.837
2.528
2.306
2.170
2.086
A-164 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Gasoline-Off-road Trucks 11.477 9.340 7.418 5.855 5.560 4.672 4.156 3.810 3.401 2.837 2.528 2.306 2.170 2.086
Diesel-Equipment 0.033 0.035 0.039 0.088 0.096 0.101 0.106 0.110 0.112 0.111 0.109 0.107 0.105 0.102
Diesel-Off-Road Trucks 0.021 0.022 0.025 0.057 0.065 0.071 0.078 0.083 0.092 0.100 0.107 0.112 0.110 0.107
Lawn and Garden Equipment
Gasoline-Residential
2 Stroke 10.154 9.576 8.904 7.193 6.815 6.378 6.137 6.025 5.985 5.929 5.924 5.924 5.925 5.925
4 Stroke 10.670 9.624 8.408 6.930 6.768 6.039 5.544 5.069 4.657 4.054 3.602 3.246 2.921 2.625
Gasoline-Commercial
2 Stroke 9.947 9.073 8.335 6.506 5.999 5.696 5.592 5.550 5.546 5.511 5.511 5.511 5.511 5.511
4 Stroke 9.967 8.786 7.707 6.632 6.396 5.434 4.714 4.199 3.871 3.278 2.772 2.423 2.247 2.151
Diesel-Commercial 0.039 0.039 0.039 0.071 0.080 0.087 0.093 0.099 0.104 0.108 0.111 0.113 0.115 0.116
Airport Equipment
Gasoline
4 Stroke 9.095 7.688 6.562 5.550 4.709 3.330 2.963 2.632 2.281 1.382 1.240 1.103 1.033 0.983
Diesel 0.0322 0.031 0.031 0.077 0.083 0.087 0.091 0.095 0.097 0.098 0.098 0.096 0.094 0.092
Industrial/Commercial Equipment
Gasoline
2 Stroke 10.431 9.649 9.020 5.712 5.698 5.573 5.521 5.484 5.476 5.434 5.427 5.422 5.419 5.416
4 Stroke 11.493 9.533 7.723 6.442 6.039 4.910 4.242 3.866 3.625 3.068 2.688 2.424 2.283 2.194
Diesel 0.0363 0.038 0.042 0.097 0.109 0.112 0.114 0.115 0.114 0.112 0.107 0.103 0.099 0.096
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
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
1 a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
2 b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
3 c Emissions of Cl-U 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
4 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.
5 Based on this data, Cl-U emissions factors for jet aircraft were changed to zero in this year's Inventory to reflect the latest emissions testing data.
6 Source: IPCC (2006) and ICF (2017b), EPA (2017b)
A-165
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Table A-114: NOx 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
181
Trucks and Buses
515
469
411
401
383
348
311
286
294
274
254
234
207
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
1,543
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
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
Aircraftb
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
Othere
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.
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
679
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
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
A-166 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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
1 IE (Included Elsewhere)
2 aCO emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
3 b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
4 c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
5 d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
6 e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel
7 consumption from trucks that are used off-road for commercial/industrial purposes.
8 Notes: The source of this data is the National Emissions Inventory. Updates to estimates from MOVES2014a is a change that affects the emissions time series. Totals may not sum due to independent rounding.
9
10
A-167
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1
2 Table A-116: NMVOCs 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
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
73
Trucks and Buses
575
382
232
144
145
127
115
115
120
110
101
92
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
3
3
2
2
2
2
2
2
2
1
1
Medium- and Heavy-Duty
80
72
Trucks and Buses
377
286
209
140
141
124
112
112
116
107
98
89
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
75
Equipment1
149
152
130
131
125
121
113
109
103
97
91
84
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
3 IE (Included Elsewhere)
4 a NMVOC emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
5 b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
6 c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
7 d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
8 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
9 consumption from trucks that are used off-road for commercial/industrial purposes.
10 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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3
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7
8
9
10
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13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
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.
A-169
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25
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33
34
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36
37
38
39
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42
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46
47
48
These new emission levels represent a 77 to 95 percent reduction in emissions from the EPA Tier 1 standard set in 1994.
Emission reductions were met through the use of more advanced emission control systems and lower sulfur fuels and are
applied to vehicles beginning in 2004. These advanced emission control systems include improved combustion, advanced
three-way catalysts, electronically controlled fuel injection and ignition timing, EGR, and air injection.
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-170 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 emissions. Although the Inventory is not required to provide detail beyond what is contained in the body of this report, the
2 IPCC allows presentation of additional data and detail on emission sources. The purpose of this sub-annex, within the Annex
3 that details the calculation methods and data used for non-CC>2 calculations, is to provide all transportation estimates
4 presented throughout the report in one place.
5 This section of this Annex reports total greenhouse gas emissions from transportation and other (non-
6 transportation) mobile sources in CO2 equivalents, with information on the contribution by greenhouse gas and by mode,
7 vehicle type, and fuel type. In order to calculate these figures, additional analyses were conducted to develop estimates of
8 CO2 from non-transportation mobile sources (e.g., agricultural equipment, construction/mining equipment, recreational
9 vehicles), and to provide more detailed breakdowns of emissions by source.
10 Estimation of CO2 from Non-Transportation Mobile Sources
11 The estimates of N2O and CH4 from fuel combustion presented in the Energy chapter of the Inventory include both
12 transportation sources and other mobile sources. Other mobile sources include construction/mining equipment, agricultural
13 equipment, vehicles used off-road, and other sources that have utility associated with their movement but do not have a
14 primary purpose of transporting people or goods (e.g., snowmobiles, riding lawnmowers, etc.). Estimates of CO2 from non-
15 transportation mobile sources, based on EIA fuel consumption estimates, are included in the industrial and commercial
16 sectors. In order to provide comparable information on transportation and mobile sources, Table A-l 17 provides estimates
17 of CO2 from these other mobile sources, developed from EPA's NONROAD components of the MOVES2014a model and
18 FHWA's Highway Statistics. These other mobile source estimates were developed using the same fuel consumption data
19 utilized in developing the N2O andCTU estimates (see Table A-104). Note that the method used to estimate fuel consumption
20 volumes for CO2 emissions from non-transportation mobile sources for the supplemental information presented in Table A-
21 117, Table A-l 19, and Table A-120 differs from the method used to estimate fuel consumption volumes for CO2 in the
22 industrial and commercial sectors in this Inventory, which include CO2 emissions from all non-transportation mobile sources
23 (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
Equipment^0
Construction/
Mining
Equipments
Other
Sourcesbc
31.4
38.0
54.3
37.1
44.0
57.8
49.5 48.7 45.7 46.9 47.I
49.6 51.1 50.0 50.8 47.5 48.3
60.5 60.6 61.9 63.0 65.1 66.1 67.5 72.8 70.7 69.2 70.5
77.1 75.7 76.4 77.1 80.0 79.6 79.1 80.1 81.4 81.4 82.6
Total
123.7
138.9
151.6
187.1 185.1 184.0 187.0 192.9 195.3 197.7 202.8 203.0 198.1 201.4
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.
24 Estimation of HFC Emissions from Transportation Sources
25 In addition to CO2, N2O and CH4 emissions, transportation sources also result in emissions of HFCs. HFCs are
26 emitted to the atmosphere during equipment manufacture and operation (as a result of component failure, leaks, and
27 purges), as well as at servicing and disposal events. There are three categories of transportation-related HFC emissions;
28 Mobile air-conditioning represents the emissions from air conditioning units in passenger cars and light-duty trucks;
29 Comfort Cooling represents the emissions from air conditioning units in passenger trains and buses; and Refrigerated
30 Transport represents the emissions from units used to cool freight during transportation.
31 Table A-l 18 below presents these HFC emissions. Table A-l 19 presents all transportation and mobile source
32 greenhouse gas emissions, including HFC emissions.
A-171
-------
i 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
+
18.9
53.5
65.6
66.0
66.3
65.2
61.7
55.7
49.9
44.0
40.9
38.3
34.9
Passenger Cars
+
11.2
28.1
31.7
31.5
31.2
29.9
27.5
23.9
20.6
17.3
16.0
14.9
13.4
Light-Duty Trucks
+
7.8
25.4
33.9
34.5
35.1
35.2
34.2
31.7
29.3
26.7
25.0
23.4
21.5
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
0.1
Rail
+
+
+
+
+
+
+
+
+
+
+
+
+
0.0
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
3.7
Rail
+
0.0
0.3
0.5
0.6
0.8
0.9
1.2
1.5
1.7
2.0
2.3
2.6
0.3
Ships and Boats
+
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
2.9
Total
+
19.1
54.5
67.9
68.7
69.3
68.5
65.6
60.2
55.1
49.8
47.2
45.1
42.4
2 + Does not exceed 0.05 MMT CO2 Eq.
3 Note: Totals may not sum due to independent rounding.
A-172 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Mode/Vehicle Type/Fuel
Type
Table A-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 2,072.5 MMT CO2 Eq. in 2016, an
increase of 25 percent from 1990.54 Transportation sources account for 1,867.4 MMT CO2 Eq. while non-transportation
mobile sources account for 205.1 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 HFCs from mobile air
conditioners, comfort cooling for trains and buses, and refrigerated transportation from the Substitution of Ozone Depleting
Substances section of the IPPU chapter; and estimates of 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 2017 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.55 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 2016). 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 1,867.4 MMT CO2 Eq. in
2016.
On-road vehicles were responsible for about 75 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 22 percent, while emissions from light-
duty trucks increased by one percent.56 Meanwhile, greenhouse gas emissions from medium- and heavy-duty trucks
54 In 2016, FHWA changed its methods for estimating the share of gasoline used in on-road and non-road applications, which created a time-
series inconsistency between 2015 and previous years in this Inventory. The method updates are discussed in the Planned Improvements sections
of Section 3.1 under C02 from Fossil Fuel Combustion and CH4 and NJO from Mobile Combustion.
55 Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan, Turbojet and Turboprop
Engines," EPA-420-R-09-901, May 27, 2009 (see ).
56 In 2011 FHWA changed how they defined vehicle types for the purposes of reporting VMT for the years 2007 to 2010. The old approach to
vehicle classification was based on body type and split passenger vehicles into "Passenger Cars" and "Other 2 Axle 4-Tire Vehicles." The new
approach is a vehicle classification system based on wheelbase. Vehicles with a wheelbase less than or equal to 121 inches are counted as "Light-
duty Vehicles -Short Wheelbase." Passenger vehicles with a wheelbase greater than 121 inches are counted as "Light-duty Vehicles - Long
Wheelbase." This change in vehicle classification has moved some smaller trucks and sport utility vehicles from the light truck category to the
passenger vehicle category in this Inventory. These changes are reflected in a large drop in light-truck emissions between 2006 and 2007.
A-173
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
increased 84 percent between 1990 and 2016, reflecting the increased volume of total freight movement and an increasing
share transported by trucks.
Greenhouse gas emissions from aircraft decreased 9 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 15 percent from 2007 to 2016, a change of approximately 8 percent between 1990 and 2016.
Non-transportation mobile sources, such as construction/mining equipment, agricultural equipment, and
industrial/commercial equipment, emitted approximately 205.1 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 403.5 MMT
CO2 Eq. between 1990 and 2016. In contrast, the combined emissions of CH4 and N2O decreased by 30.51 MMT CO2 Eq.
over the same period, due largely to the introduction of control technologies designed to reduce criteria pollutant emissions.57
Meanwhile, HFC emissions from mobile air-conditioners and refrigerated transport increased from virtually no emissions
in 1990 to 42.4 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 2016) were used to allocate rail emissions between passenger and freight
categories.
In 2016, passenger transportation modes emitted 1,297.2 MMT CO2 Eq., while freight transportation modes
emitted 532.8 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 15 percent. This difference in growth is due largely
to the rapid increase in emissions associated with medium- and heavy-duty trucks.
The decline in CFC emissions is not captured in the official transportation estimates.
A-174 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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'
1,528.2
1,669.7
1,904.4
1,970.2
1,970.7
1,873.1
1,794.1
1,801.8
1,773.1
1,752.8
1,761.9
1,797.2
1,812.5
1,867.4
22%
On-Road Vehicles
1,206.9
1,341.9
1,545.3
1,639.7
1,635.0
1,556.3
1,510.1
1,510.3
1,482.8
1,471.5
1,470.4
1,520.0
1,523.5
1,561.1
29%
Passenger Cars
639.6
630.2
682.1
667.3
823.5
782.0
772.3
762.7
752.7
745.9
740.9
756.8
761.1
781.3
22%
Gasolineb<
631.7
611.2
650.3
631.5
787.9
747.0
738.7
731.4
724.6
721.2
719.5
736.6
741.6
763.0
21%
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
4.4
-44%
AFVsc
+
+
+
+
+
+
+
+
+
+
+
0.1
0.3
0.4
6,924%
HFCs from Mobile AC
+
11.2
28.1
31.7
31.5
31.2
29.9
27.5
23.9
20.6
17.3
16.0
14.9
13.4
NA
Light-Duty Trucks
326.8
425.4
503.7
551.5
358.9
339.6
342.8
339.8
322.7
316.2
313.2
334.3
324.9
331.4
1%
Gasolineb
315.1
402.6
458.0
490.4
310.5
292.0
295.0
292.7
277.6
273.7
273.3
294.8
287.2
295.4
-6%
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
14.2
24%
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
25.4
33.9
34.5
35.1
35.2
34.2
31.7
29.3
26.7
25.0
23.4
21.5
NA
Medium- and Heavy-Duty
Trucks
230.3
275.2
346.7
406.7
430.7
413.1
374.8
388.2
387.0
387.3
394.3
405.6
413.9
424.0
84%
Gasolineb
38.5
35.8
36.2
35.3
45.9
46.0
42.2
41.9
38.4
38.1
38.8
40.1
39.8
40.9
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
369.0
377.8
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
Transport6
+
0.1
0.4
1.3
1.4
1.6
1.7
2.0
2.3
2.6
2.9
3.2
3.4
3.7
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
20.0
20.5
142%
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
170%
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
17.3
17.7
121%
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
3.8
123%
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
3.8
123%
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
171.2
-9%
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
38.8
-10%
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
37.3
-6%
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
120.1
8%
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
120.1
8%
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
12.3
-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
12.3
-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
44.9
-1%
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
15.9
65%
A-175
-------
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
13.6
-41%
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
Rail
38.9
43.1
46.2
52.9
52.1
48.3
41.1
43.9
45.5
44.1
45.0
46.4
44.3
41.1
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
3.6
16%
Other Emissions from Rail
Electricity Use s
0.1
0.1
+
+
+
+
+
+
+
+
+
+
+
+
-29%
HFCs from Comfort Cooling
+
+
+
+
+
+
+
+
+
+
+
+
+
+
NA
HFCs from Refrigerated
Transport6
+
+
0.2
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
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
8.8
9.1
10.0
9.5
-20%
Non-Transportation Mobile'
Total
128.9
143.9
156.2
192.5
190.2
188.7
191.5
197.5
199.7
201.9
207.0
206.9
201.8
205.1
59%
Agricultural Equipment' '
32.3
38.0
40.1
50.6
49.7
46.5
47.7
48.7
50.5
52.0
50.9
51.6
48.2
49.0
52%
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
1.4
-82%
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%
Construction/ Mining11
Equipment''"1
38.9
44.9
50.5
61.6
61.7
62.9
64.1
66.2
67.1
68.6
73.9
71.8
70.3
71.6
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
3.2
-31%
Diesel
34.1
40.7
47.3
55.3
56.4
57.7
59.1
60.4
61.7
63.0
64.4
65.7
67.0
68.4
100%
Other Equipment'1
57.8
61.1
65.6
80.4
78.8
79.2
79.7
82.5
82.1
81.3
82.2
83.5
83.3
84.5
46%
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
52.4
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%
Transportation and Non-
Transportation Mobile Total1
1,657.1
1,813.6
2,060.6
2,162.8
2,161.0
2,061.8
1,985.6
1,999.3
1,972.8
1,954.8
1,968.8
2,004.1
2,014.3
2,072.5
25%
1 + 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.
2 a Not including emissions from international bunker fuels.
3 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
4 through 2017). VM-1 data for 2016 has not been published yet, therefore 2016 mileage data is estimated using the 1.7 percent increase in FHWA Traffic Volume Trends from 2015 to 2016. Data from Table VM-1
5 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
6 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
7 A.1 through A.6 (DOE 1993 through 2016). TEDB data for 2015 and 2016 has not been published yet, therefore 2014 data are used as a proxy.
8 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
9 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
10 first incorporated in the 1990 through 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.
11 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
12 separately in the section on U.S. Territories.
13 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.
14 f Class II and Class III diesel consumption data for 2014 to 2016 is not available yet, therefore 2013 data are used as a proxy.
A-176 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 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
2 distribution, and a portion of Other Process Uses of Carbonates (from pollution control equipment installed in electricity generation plants).
3 h Includes only CO2 from natural gas used to power natural gas pipelines; does not include emissions from electricity use or non-C02 gases.
4 ' 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
5 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).
6 i Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
7 k Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
8 1 "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial equipment, and industrial equipment, as well as
9 fuel consumption from trucks that are used off-road for commercial/industrial purposes.
10 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
11 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
12 into MOVES2014a. This year's Inventory uses the NONROAD component of MOVES2014a for years 1999 through 2016. In 2016, historical confidential vehicle sales data was re-evaluated to determine the
13 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
14 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
15 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.
16
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
1,605.8
1,733.5
1,947.8
2,052.2
2,054.0
1,957.3
1,883.8
1,901.5
1,881.9
1,871.8
1,893.2
1,933.3
1,947.3
2,009.3
25%
N20
41.5
51.9
51.1
36.2
32.3
29.9
28.4
27.4
26.2
23.8
22.0
20.2
18.8
17.8
-57%
ch4
9.8
9.0
7.2
6.4
5.9
5.2
4.8
4.7
4.4
4.0
3.7
3.4
3.1
3.0
-70%
HFC
+
19.1
54.5
67.9
68.7
69.3
68.5
65.6
60.2
55.1
49.8
47.2
45.1
42.4
NA
Total"
1,657.0
1,813.6
2,060.6
2,162.7
2,160.9
2,061.7
1,985.6
1,999.2
1,972.8
1,954.7
1,968.7
2,004.1
2,014.3
2,072.4
25%
17 + 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.
18 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
19 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).
20 b Total excludes other emissions from electricity generation and CH4 and N2O emissions from electric rail.
21 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
22 1996 through 2017). VM-1 data for 2016 has not been published yet, therefore 2016 mileage data is estimated using the 1.7 percent increase in FHWA Traffic Volume Trends from 2015 to 2016. Data from Table
23 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
24 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
25 Tables A.1 through A.6 (DOE 1993 through 2016). TEDB data for 2015 and 2016 has not been published yet, therefore 2014 data are used as a proxy.
26 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
27 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
28 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
29 as gasoline vehicles across the entire time series.
A-177
-------
i Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2016 (MMT CO: Eq.)
2,000
1,500
o
u
1,000
500
c m
a $
Ships, Rail, and Pipelines
Aircraft
Non-Transportation Mobile Sources
Medium- and Heavy-Duty Trucks and Buses
Light-Duty Trucks
Passenger Cars/Motorcycles
Mobile AC, Refrig. Transport, Lubricants
—i ro
. _ B S _ _
CM OJ fNi fN CM fM
LO
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Table fl-121: Greenhouse Gas Emissions from Passenger Transportation [MBIT CO; Eg.]
Vehicle Type
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Percent
Change
1990-2016
On-Road
976.6
1,066.7
1,198.6
1,233.1
1,204.3
1,143.2
1,135.3
1,122.2
1,095.8
1,084.1
1,076.1
1,114.4
1,109.6
1,137.1
16%
Vehicles3'11
Passenger Cars
639.6
630.2
682.1
667.3
823.5
782.0
772.3
762.7
752.7
745.9
740.9
756.8
761.1
781.3
22%
Light-Duty Trucks
326.8
425.4
503.7
551.5
358.9
339.6
342.8
339.8
322.7
316.2
313.2
334.3
324.9
331.4
1%
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
20.0
20.5
142%
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
3.8
123%
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
142.4
6%
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
38.8
-10%
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
103.6
13%
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
19%
Total
1,130.4
1,219.4
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
1,257.6
1,258.0
1,297.2
15%
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). VM-1 data for 2016 has not been published yet, therefore 2016 mileage data is estimated using the 1.7% increase in FHWA Traffic Volume Trends from 2015 to 2016. 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 2016). TEDB data for 2015 and 2016 has not been published yet, therefore 2014 data are used as a proxy.
Notes: Data from DOE (1993 through 2016) 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 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.
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
275.2
346.7
406.7
430.7
413.1
374.8
388.2
387.0
387.3
394.3
405.6
413.9
424.0
84%
Freight Rail
34.5
38.6
40.9
46.9
45.4
42.1
34.9
37.7
39.5
38.6
39.2
40.6
38.9
35.8
4%
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
20.1
7.2
16.8
-45%
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
16.5
-14%
Total
350.6
414.5
495.9
539.2
569.4
538.6
486.6
509.0
512.1
507.7
525.2
522.0
515.0
532.8
52%
a The current Inventory includes updated vehicle population data based on the MOVES2014a Model.
A-179
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
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 2016). VM-1 data for 2016 has not been published yet, therefore 2016 mileage data is estimated using the 1.7% increase in FHWA Traffic Volume Trends from 2015 to 2016. 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 2016). 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 2016). TEDB data for 2015 has not been published yet, therefore 2014 data is as a proxy.
d Pipelines reflect CO2 emissions from natural gas powered pipelines transporting natural gas.
Notes: Data from DOE (1993 through 2015) 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 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.
A-180 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 References
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A-181
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A-182 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
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
A-183
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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.58
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."59
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.
58 Additional information on the AEDT modeling process is available at:
.
59 Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan, Turbojet
and Turboprop Engines, EPA-420-R-09-901, May 27, 2009. See .
A-184 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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igure fl-5: Commercial Aviation Fuel Burn for the United States and Territories
Commercial Aviation Fuel Burn
for the United States and Territories
bO
D
CD
~aj
3
5.00E+10
4.50E+10
4.00E+10
3.50E+10
3.00E+10
2.50E+10
2.00E+10
1.50E+10
1.00E+10
5.00E+09
0.00E+00
O
o
o ° o
nil*
11
~
O
1 1 1 1 1 1 r
rMPO^rLD^DI^OOCTtO
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HHHHHHHHHHf\|fMfM(N(NfMfNfNfNfN(N(MfM(N(N(Nrs|
Note: Hollow markers are estimates from data generated by prior tools and methods.
1990 is estimated using non-radar methods.
O States+Territories (Domestic) ~ States+Territories (International) A States (Domestic) OStates (International)
Note: Hollow markers are estimates from data generated by prior tools and methods. 1990 is estimated using non-radar methods.
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
1997 =
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
A-185
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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
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-186 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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i References
2 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
3 Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and
4 K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
5 IPCC (1999) Aviation and the Global Atmosphere. Intergovernmental Panel on Climate Change. [J.E. Penner, et al. (eds.)].
6 Cambridge University Press. Cambridge, United Kingdom.
7 Santoni, G., B. Lee, E. Wood, S. Herndon, R. Miake-Lye, S Wofsy, J. McManus, D. Nelson, M. Zahniser (2011) Aircraft
8 emissions of methane and nitrous oxide during the alternative aviation fuel experiment. Environ Sci Technol. 2011 Aug
9 15; 45(16):7075-82.
10
A-187
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3.4. Methodology for Estimating ChU 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).60 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).61 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.62
Since 2013, ventilation emission estimates have been calculated based on both EPA's GHGRP63 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.
60 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.
61 Underground coal mines report to EPA under Subpart FF of EPA's GHGRP. In 2016, 90 underground coal mines reported to the
program.
62 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.
63 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-188 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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.64 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; GSA2017; WVGES 2017), as well as with mine-specific information regarding the dates on which
pre-mining wells were mined through (JWR 2010; El Paso 2009). For pre-mining wells, the cumulative CH4 production
64 A well is "mined through" when coal mining development or the working face intersects the borehole or well.
A-189
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
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, GHGRP data are corrected for the Inventory based on expert judgment. Common errors include reporting CH4
liberated as CH4 destroyed and vice versa. Other errors include reporting CH4 destroyed without reporting any CH4 liberated
by degasification systems. In the rare cases where GHGRP data are inaccurate and gas sales data 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-190 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 Step 2.1: Define the Geographic Resolution of the Analysis and Collect Coal Production Data
2 The first step in estimating CH4 emissions from surface mining and post-mining activities was to define the
3 geographic resolution of the analysis and to collect coal production data at that level of resolution. The analysis was
4 conducted by coal basin as defined in Table A-125, which presents coal basin definitions by basin and by state.
5 The Energy Information Administration's Annual Coal Report (EIA 2017) includes state- and county-specific
6 underground and surface coal production by year. To calculate production by basin, the state level data were grouped into
7 coal basins using the basin definitions listed in Table A-125. For two states—West Virginia and Kentucky-county-level
8 production data were used for the basin assignments because coal production occurred in geologically distinct coal basins
9 within these states. Table A-126 presents the coal production data aggregated by basin.
10 Step 2.2: Estimate Emission Factors for Each Emissions Type
11 Emission factors for surface-mined coal were developed from the in situ CH4 content of the surface coal in each
12 basin. Based on analyses conducted in Canada and Australia on coals similar to those present in the United States (King
13 1994; Saghafi 2013), the surface mining emission factor used was conservatively estimated to be 150 percent of the in situ
14 CH4 content of the basin. Furthermore, the post-mining emission factors used were estimated to be 25 to 40 percent of the
15 average in situ CFU content in the basin. For this analysis, the post-mining emission factor was determined to be 32.5 percent
16 of the in situ CFLi content in the basin. Table A-127 presents the average in situ content for each basin, along with the
17 resulting emission factor estimates.
18 Step 2.3: Estimate CH4 Emitted
19 The total amount of CFU emitted from surface mines and post-mining activities was calculated by multiplying the
20 coal production in each basin by the appropriate emission factors.
21 Table A-125 lists each of the major coal mine basins in the United States and the states in which they are located.
22 As shown in Figure A-6, several coal basins span several states. Table A-126 shows annual underground, surface, and total
23 coal production (in short tons) for each coal basin. Table A-127 shows the surface, post-surface, and post-underground
24 emission factors used for estimating CFU emissions for each of the categories. Table A-128 presents annual estimates of
25 CFLi emissions for ventilation and degasification systems, and GT4 used and emitted by underground coal mines. Table A-
26 129 presents annual estimates of total CFU emissions from underground, post-underground, surface, and post-surface
27 activities. Table A-130 provides the total net CFU emissions by state.
28 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
New Mexico
South West and Rockies Basin
A-191
-------
North Dakota
Ohio
Oklahoma
Pennsylvania.
Tennessee
Texas
Utah
Virginia
Washington
West Virginia South
West Virginia North
Wyoming
2 Figure A-6: Locations of U.S.
North Great Plains Basin
Northern Appalachian Basin
West Interior Basin
Northern Appalachian Basin
Centra! 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
Basins
3
Coalbed Methane Fields, Lower 48 States
North Central
Coal Region
/d^r River
Basin
Big HoirT
\Mncf River Basin.. Basin
Wyoming j
Michigan
Basin
mnah-Carbon Basin
-Park Basin
Forest City
i Basin
Illinois
Basin
¦SW Colorado
Coal Area
irowits
astrr—
San Juah
Basin
Miles
Black Warrior
^ Basin
Southwestern
Coal Region
Gulf Coast
Coal Regior
Source: Energy Information Administration based on data from USGS and various published studies
Updated: April 8, 2009
• Coalbed Methane Fields
Coal Basins, Regions & Fields
A-192 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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
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
Cent.
Appalachia
Warrior
Illinois
S. West/Rockies
N. Great Plains
West Interior
Northwest
60,761
94,343
11,413
72,000
43,863
249,356
64,310
6,707
28,873
112,222
11,599
33,702
42,756
474,056
52,263
6,720
30,413 26,552 26,082 26,382
118,962
11,172
34,266
34,283
538,387
44,361
1,477
97,778
10,731
34,837
32,167
496,290
39,960
1,860
89,788
11,406
32,911
28,889
507,995
46,136
2,151
90,778
10,939
34,943
31,432
502,734
55,514
2,149
21,411
69,721
9,705
34,771
30,475
455,320
49,293
2,052
19,339 17,300 13,491
57,173
8,695
33,798
28,968
444,740
46,477
1,550
52,399
7,584
31,969
27,564
458,112
47,201
1,502
37,278
6,437
27,360
26,020
436,928
40,083
1,177
8,686
26,974
5,047
21,679
18,980
350,799
42,534
932
Total Coal
Production
N. Appalachia
Cent.
Appalachia
Warrior
Illinois
S. West/Rockies
N. Great Plains
West Interior
Northwest
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
164,626
292,755
28,944
141,167
76,617
251,078
64,415
6,707
140,024
235,305
24,894
92,882
103,621
474,628
52,728
6,720
135,641 126,181 129,191 132,134 124,819 123,537 134,000 117,069
233,960
23,453
98,875
90,064
542,056
44,869
1,477
196,467
22,236
102,023
82,583
500,538
40,348
1,860
186,142
23,919
105,089
73,257
516,203
46,561
2,151
184,812
21,818
116,032
76,571
510,913
56,049
2,149
147,788
22,275
127,271
75,527
465,665
49,738
2,052
127,613
22,086
132,129
70,200
457,866
46,975
1,550
116,618
20,100
137,180
71,956
469,384
47,686
1,502
90,508
16,334
123,721
59,782
446,438
40,565
1,177
103,365
66,837
11,990
98,251
45,141
357,950
42,936
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 ofU.
Methane Recovery from Coalbeds: A Potential Energy Source; 1986-1988 Gas Research Institute Topical
Coal Seams; 2005 U.S. EPA Draft Report, Surface Mines Emissions Assessment.
S. Coal Basins] U.S. DOE Report DOE/METC/83-76,
Report, A Geologic Assessment of Natural Gas from
A-193
-------
i 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.
2
3 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.
4 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
5 + Does not exceed 0.5 million cubic feet.
6 Note: The emission estimates provided above are inclusive of emissions from underground mines, surface mines and post-mining activities. The following states
7 have neither underground nor surface mining and thus report no emissions as a result of coal mining: Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho,
8 Maine, Massachusetts, Michigan, Minnesota, Nebraska, Nevada, New Hampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, South
9 Carolina, South Dakota, Vermont, and Wisconsin.
10 References
11 AAPG (1984) Coalbed Methane Resources of the United States. AAPG Studies in Geology Series #17.
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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
.
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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 estimates of CH4 emissions from petroleum systems
are largely based on RY2010 through RY2016 GHGRP data (EPA 2017), Dnllmglnfo, EPA/GRI 1996, and EPA 1999.
Petroleum systems includes emission estimates for activities occurring in petroleum systems from the oil wellhead through
crude oil refining, including activities for crude oil production field operations, crude oil transportation activities, and
refining operations. Emissions are estimated for each activity by multiplying emission factors (e.g., emission rate per
equipment or per activity) by corresponding activity data (e.g., equipment count or frequency of activity).
Tables referenced in Annex 3.5 are available at https://www.epa.gov/ghgemissions/stakeholder-process-natural-
gas-and-petroleum-svstems-1990-2016-inventorv.
[Gray highlighted text to be updated in the Final 2018 GHG Inventory.]
Most of the activities are part of crude oil production Field operations, which account For 97.9 percent oF total oil
industry CI 11 emissions. Crude transportation and reFining accounted For the remaining CI 11 emissions oF approximately 0.5
and 1.6 percent, respectively. Non-combustion C(); emissions w ere analyzed For production operations and asphalt blow ing.
Flaring, and process vents in reFining operations. Non-combustion C(): emissions From transportation operations are not
included because they are negligible. The Following steps were taken to estimate CI 11 and C(); emissions From petroleum
systems.
Methane Emission Factors
In addition to the C ireenhouse C ias Reporting Program ((il I( iRP), key relerenees For emission Factors For CI 11 and
non-eombuslion-relaled CO: emissions From the U.S. petroleum industry include a 1999 EPA/Radian report Methane
Emissions from the I \S. Petroleum Industry (EPA/Radian 1999), which contained the most recent and comprehensive
determination 0FCII1 emission Factors For CI I i-emittiiig activities in the oil industry at that time, a 1999 ICPA/ICT' dralt
report Estimates of Methane Emissions from the l.S. Oil Industry (EPA/ICE 1999) which is largely based 011 the 1999
EPA/Radian report, and a detailed study by the (ias Research Institute and EPA Methane Emissions from the Xatural (ias
Industry (ICPA/GRI 1996). These studies still represent best available data in many cases, in particular For early years oFtlie
time series.
In recent Inventories, EPA has revised the emission estimation methodology For many sources in Petroleum
Systems. New data From studies and ICPA's GIIC iRP (EPA 2016a,b) allows For emission Factors to be calculated that account
For adoption oF control technologies and emission reduction practices, l or 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 relied control adoption over time), l'or the reFining segment, EPA has directly used the
C il I( iRP data For all emission sources For recent years (201 1 Forward) and developed source level throughput-based emission
Factors From (il IGRP data to estimate emissions in earlier time series years (1990 through 2010). For some sources, EPA
continues to apply the historical emission Factors For all time series years.
Many key sources in Petroleum Systems currently use emission Factors that account For control technologies and
emission reduction practices when paired with related activity data (see below section "Activity Data"). This approach
allows For net emissions to be calculated directly. For associated gas. separate emission estimates are developed From
(il I( iRP data For venting and fiaring. For oil tanks, emissions estimates were developed For large and small tanks with Flaring
or VR1J control, w itlioul control devices, and w itli upstream malFunctioning separator dump valves. For oil well completions
w itli hydraulic Fracturing, the controlled and uncontrolled emission Factors w ere developed using data analyzed For the 2015
NSPS ()()( X )a proposal (I CPA 2015a). 1'or pneumatic controllers, separate estimates are developed For low bleed, high bleed,
and intermittent controllers, l 'or chemical injection pumps, the estimate is calculated with an emission Factor developed with
(il IGRP data, which is based 011 the previous (iRI/1 CPA Factor but takes into account operating hours.
l or petroleum reFining activities, as described above. 2010 to 2015 emissions were directly obtained From I CPA's
(il IGRP (1 CPA 2016b). All refineries have been required to report CI 11 and C(): emissions For all major activities since 2010.
The national totals oF these emissions For each activity were used For the 2010 to 2015 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 rales For each year (I CPA 2015c).
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()ffshore emissions from shallow water and deep water oil platforms are taken from analysis of the 2D 11 (uilf-wicle
luiiission /inviiiory Study (1 CPA 2015b; BC )1 CM 2014). The emission factors w ere assumed to he representative of emissions
from each source type over the period 1990 through 2014. and are used for each year throughout this period.
In general, the C( Remission factors were derived from the corresponding source CI 11emission factors. The amount
of C(); 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 C()? contents exist. The four streams that are used to estimate the emissions
factors are the associated gas stream separated from crude oil. hydrocarbons Hashed out from crude oil (such as in storage
tanks), whole crude oil itself when it leaks downstream, and gas emissions from offshore oil platforms. The standard
approach used to estimate CC): emission factors was to use the CI 11 emissions factors and multiply them by a conversion
factor, which is the ratio of CO: content to methane content for the particular stream. Ratios of CO: to CI 11 volume in
emissions are presented in Table 3.5-1. The exceptions are the emissions factor for storage tanks, which are estimated using
API I C&P Tank Calc simulation runs of tank emissions for crude oil of different gravities less than 45 API degrees; emission
factors for shallow water and deep water platforms, which are estimated from analysis of the 2011 (hi [/-wide Emission
liiwnlorv Smdv (BOICM 2014); and the emissions estimates for refineries, which are estimated using the data from U.S.
1 CPA's Cil ICiRP.
Table 3.5-2 below shows CI 11 emissions for all sources in Petroleum Systems, for all time series years. Table 3.5-
3 show s the average emission factors for all sources in Petroleum Systems, for all time series years. These average emission
factors are calculated by dividing net emissions by activity.
Additional detail on the basis for emission factors used across the time series is provided in Table 3.5-4.
1990-2015 Inventory updates to emission factors
Summary information for emission factors for sources w ith revisions in this year's Inventory is below. The details
are presented in an April 2017 memorandum addressing the natural gas and petroleum production segment (see "Revisions
to Natural (ias and Petroleum Production I emissions,") (I CPA 2017a). as well as the "Recalculations Discussion" section of
the main body text.
I-'or the production segment, oil tank control category-specific emission factors based on Cil ICiRP data and
associated gas venting and Hating emission factors based on C il ICiRP data are used for the full time series.
Activity Data
Table 3.5-5 show s the activity data for all sources in Petroleum Systems, for all time series years, l-'or 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 I CPA
1996. and/or Cil ICiRP data, l-'or major equipment, pneumatic controllers, and chemical injection pumps, C il IC iRP 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 Cil ICiRP-based estimates with existing
estimates in years 1990 to 1995. In other cases, the activity data w ere held constant from 1990 through 2014 based on I CPA
(1999). I .astly, the previous year's data w ere used when data for the current year w ere unavailable, l-'or 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 ICnergy Management (BO] CM) (formerly Bureau of Ocean ICnergy Management. Regulation, and
I enforcement (BOICMRIC)) datasets (BOICM 201 la,b.c).
Additional detail on the basis for activity data used across the time series is provided in Table 3.5-6.
Methodology for well counts and events
I CPA used 1)1 Desktop, a production database maintained by Drillinglnfo, Inc. (Drillinglnfo 2016), covering 1 J.S.
oil and natural gas wells to populate activity data for oil wells with and without hydraulic fracturing, and oil well completions
with hydraulic fracturing. I CPA 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 dale of completion
or first production. Oil wells were classified as any well that had non-zero liquids production in a given year, and with a
CiC )R of less than or equal to 100 mcf/bbl in that year. C )il wells with hydraulic fracturing were assumed to be the subset of
the oil wells that were horizontally drilled and/or located in an unconventional formation (i.e., shale, tight sands, or coalbed).
I Jneonventional formations were identified based on well basin, reservoir, and field data reported in DI Desktop referenced
against a formation type crosswalk developed by I CIA (I CIA 2012a).
l-'or hydraulically fractured oil well completions. I CPA 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. l-'or
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more information on llie 1 )rillinglnlb data processing, please see Annex 3.6 Methodology for I estimating CI 11 and C( ); from
Natural C ias Systems.
Methodology for transportation segment
The activity data for the total crude transported in the transportation segment is mil available. In this case, all the
crude oil that was transported was assumed to go to refineries. Therefore, the activity data for the refining sector (i.e., refinery
feed in 1000 bbl/year) was used also for the transportation sector. In the few cases where no data were located, oil industry
data based on expert judgment was used. In the case of non-combustion CO; emission sources, the activity factors are the
same as for CI 11 emission sources. In some instances, where 2014 data are not yet available 2013 or prior data has been used
as proxy.
1990-2015 Inventory updates to activity data
Summary information for activity data for sources w ith revisions in this year's Inventory is below. The details are
presented in an April 2017 memorandum addressing the natural gas and petroleum production segment (see "Revisions to
Natural ( ias and Petroleum Production I emissions.") (I CPA 2017a), as well as the "Recalculations Discussion" section of the
main body text.
For the production segment, oil tank throughput for years 2011 to 2015 was allocated to tank control categories
based on 2015 (il I( iRPdata. For year 1990. throughput was allocated between large and small tanks according to the fraction
observed in GIKiRP data for year 2015, and the previous Inventory assumption of 0 percent control on tanks was applied
for 1990. Linear interpolation from the throughput allocations in year 1990 was used to assign activity data (throughput) by
category for years 1991 through 2010.
For associated gas venting and llaring. the fraction of wells that either vent or Hare associated gas each year was
developed from 2015 CilKiRP data. Then, that fraction of wells is split into wells that vent and wells that Hare based on
year-specific Cil IC >RP data for 201 1 to 2015; the 201 1 split between venting and llaring wells is applied to all prior years.
Additionally in the production segment. Cil KiRP-based activity factors (i.e.. counts per oil well) were calculated
from 2015 CilKiRP data and applied for the years 201 1 to 2015 for pneumatic controllers, chemical injection pumps,
separators, heater-treaters, and headers. The year-specific bleed type split (i.e., continuous high bleed, continuous low bleed,
and intermittent bleed) was also developed for pneumatic controllers from 201 1 to 2015 C il KiRP data.
Methane and Carbon Dioxide Emissions by Emission Source for Each Year
Annual CI 11 emissions and C(); 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 CI 11 and CO; emissions, respectively. As various updates to emission factors have resulted in calculation of net
emissions (already taking into account any reduced emissions) for sources in petroleum production, 1 CPA no longer takes
into account (ias STAR reductions in emission calculations, as had been dime for previous Inventories. Net emissions at a
segment level are shown in Table 3.5-2.
The same procedure for estimating CI 11 emissions applies for estimating non-energy related C(); emissions. C();
emissions by segment and source are summarized in Table 3.5-7 below. In this year's Inventory. I CPA has held constant the
C(); \allies from the previous GI IC i Inventory as it assesses improvements to the C(); estimates. See Planned Improvements
in the main body text on Natural (ias Systems.
Refer to https://ww w.cpa.uov/i»hucmissions/natural-uas-and-pclrolcum-svslcms-iihi»-invcnlorv'-additional-
information- 1990-2015-i»hi» for the following data tables, in ICxcel format:
• Table 3.5-1: Ratios of C(); to CI I j Volume in I emissions from Petroleum Production Field ()perations
• Table 3.5-2: CI 11 I emissions (kt) for Petroleum Systems, by Segment and Source, for All Years
• Table 3.5-3: ] effective CI 11 I emission Factors (kg/unit activity) for Petroleum Systems Sources, for All Years
• Table 3.5-4: CI 11 I emission Factors for Petroleum Systems. Data Sources/Methodology
• Table 3.5-5: Activity Data for Petroleum Systems Sources, for All Years
• Table 3.5-6: Activity Data for Petroleum Systems. Data Sources/Methodology
• Table 3.5-7: C(); I emissions (kt) for Petroleum Systems, by Segment and Source, for All Years
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1 References
2 API (2003) Basic Petroleum Data Book. 1990-2003. American Petroleum Institute. Washington. DC.
3 B( )KM (201 lb) Platform Information and Data. Bureau of ()cean 1 energy Management, IJ.S. Department of Interior.
4 Available online at: .
5 B( )HM (201 le) Pacific ()CS Region. Bureau of Ocean I energy Management. I J.S. Department of Interior. Available (inline
6 at: .
7 BOICM (2014) Year 2011 (iulfwide Emission Inventory Study. Bureau of Ocean Knergy Management, IJ.S. Department of
8 Interior. C )CS Study B( )KM 2014-666. Available (inline at:
9 .
10 BOICMR1: (201 la) (iulf of Mexico Region Offshore Information. Bureau of Ocean I Cnergy Management. Regulation and
11 I enforcement, U.S. I department of Interior.
12 B( )I:MRI: (201 lb) Pacific ()CS Region ()ffshore Information. Bureau of ()cean I energy Management, Regulation and
13 I enforcement, U.S. I department of Interior.
14 B( )1:MRI: (201 lc) C >( )M and Pacific ()CS Platform Activity. Bureau of ()cean I energy Management. Regulation and
15 Enforcement, I J.S. Department of Interior.
16 BC )KM (201 Id) ()CS Platform Activity. Bureau of ()eean I energy Management, U.S. Department of Interior. Available
17 (inline at:
18 .
20 C APP (1992) Canadian Association of Petroleum Producers (C API5), A I detailed Inventory of CI 11 and VOC I Emissions
21 from I Jpstream Oil & C ias ()perations in Alberta. March 1992.
22 Idrillinglnfo (2016) April 2016 Download. Idl Desktop''R Drillinglnfo. Inc.
23 1 CIA (2016a) Monthly I Cnergy Review. 1995-2016 editions. I Cnergy Information Administration. U.S. Department of
24 ICnergy. Washington, DC. Available online at: .
25 MIA (2016b) Petroleum Supply Annual. 2001-2016 editions. IJ.S Department of ICnergy Washington, DC. Available
26 online at: .
27 1:1 A (2016c) Refinery Capacity Report. 2005-2016 editions. ICnergy Information Administration, U.S. Department of
28 ICnergy. Washington. DC. Available online at: .
29 I CPA (1997) Compilation of Air Pollutant I Emission f actors. AP-42. ()ffice of Air Quality Planning and Standards. I J.S.
30 I environmental Protection Agency. Research Triangle Park. NC. ()ctober 1997.
31 l ePA (2015a) Background Technical Support Document for the Proposed New Source Performance Standards 40 CT'R Part
32 60. subpart ( X )()()a. Available online at: .
34 I CPA (2015b) hnentoiy of I J.S. C ireenhouse C ias I emissions and Sinks 1990-2013: I Jpdate to ()flshore ()il and C ias Platforms
35 I emissions 1 estimate. A\ailable online at: .
37 l ePA (2015c) Inventory of I J.S. C ireenhouse (ias I emissions and Sinks 1990-2013: I Jpdate to Refineries I emissions I estimate.
38 Available online at: .
42 I CPA (2016a) (ireenhouse (ias Reporting Program - Subpart II - Petroleum and Xatural (ias Systems. 1 environmental
43 Protection Agency. I data reported as of August 13. 2016.
44 I CPA (2016b) (ireenhouse (ias Reporting Program - Subpart Y - Petroleum Refineries. 1 environmental Protection Agency.
45 I data reported as of August 13. 2016.
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1 CPA (2016c) Inventory ol'U.S. Greenhouse Gas I emissions and Sinks 1990-2014: Revisions to Natural Gas and Petroleum
Production I emissions. Available online at:
I CPA (2017a) Inventory ol'IJ.S. Greenhouse Gas 1 emissions and Sinks 1990-2015: Revisions to Natural Gas and Petroleum
Production I emissions. Available online at: .
I CPA/Radian (1999) Methane I emissions from the IJ.S. Petroleum Industry. Prepared by Radian International. IJ.S.
I environmental Protection Agency. February 1999.
I CPA/ICT (1999) 1 estimates of Methane I emissions from the U.S. Oil Industry (Draft Report). Prepared by ICF
International. Office of Air and Radiation, U.S. 1 environmental Protection Agency. October 1999.
1CPA/C iRI (1996) Methane I emissions from the Natural Gas Industry. Prepared by Radian. I J.S. I environmental Protection
Agency. April 1996.
OG.I (2016) Special Report: Pipeline I economics, 2005-2016 I editions. Oil .
Radian/API (1992) "(ilobal 1 emissions of Methane from Petroleum Sources." American Petroleum Institute. I Iealth and
I environmental Affairs Department, Report No. DR140. February 1992.
WCIJS (2015) Waterborne Commerce of the United States, Part 5: National Summaries. 2000-2015 I editions. United
States Army Corps of I engineers. Washington. DC, July 20. 2015. Latest edition available (inline at:
.
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3.6. Methodology for Estimating ChU 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 most 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.
Tables referenced in Annex 3.6 are available at https://www.epa.gov/ghgemissions/stakeholder-process-natural-
gas-and-petroleum-svstems-1990-2016-inventorv.
[Gray highlighted text to be updated in the Final 2018 GHG Inventory.]
Methane Emission Factors
Table 3.6-2 show s the average emissions per unit lor all sources in Natural (ias Systems, lor all time series years.
Key references for emission factors for CI 11 and non-combiistion-relaled C(); emissions from the I J.S. natural gas industry
include the 1996 (ias Research Institute (GRI) and 1 CPA study (ICPA/GRI 1996). the 1 CPA's Greenhouse Gas Reporting
Program (( >1 K iRP). and others.
The ICPA/GRI study developed over 80 CI I j 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. I CPA has revised the emission estimation methodology for many sources in Natural (ias
Systems. New data from studies and 1 CPA's GIIGRP (ICPA 2016a) allows for the use of emission factors that account for
adoption of control technologies and emission reduction practices. For some sources. I CPA 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 relied control adoption over lime). In other cases. I CPA retains emission factors from the ICPA/GRI
study for early time series years (1990 through 1992). applies updated emission factors in recent years (e.g.. 201 1 forward),
and uses interpolation to calculate emission factors for intermediate years. For some sources, I CPA continues to apply the
1 CPA/( iRI emission factors for all time series years, and accounts for emission reductions through data reported to (ias STAR
or estimated based on regulations (see below section "Reductions Data").
Many key sources in the production segment currently use emission factors that account for control technologies
and emission reduction practices when paired with related activity data (see below section "Activity Data"). For gas well
completions and workovers with hydraulic fracturing, separate emissions estimates were developed for hydraulically
fractured completions and workovers that vent. Ilared hydraulic fracturing completions and workovers, hydraulic fracturing
completions and workovers with reduced emissions completions (RICCs), and hydraulic fracturing completions and
workovers with RICCs that Hare. For liquids unloading, separate emissions estimates were developed for wells with plunger
lifts and w ells w ithout plunger lifts. I .ikew ise. for condensate tanks, emissions estimates w ere developed for large and small
tanks with flaring or VRIJ 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 GIIGRP data, which is based on the
previous GRI/1CPA factor but takes into account operating hours. For most sources in the processing, transmission and
storage, and distribution segments, net emission factors have been developed for application in recent years of the time
series, while the existing emission factors are applied in early time series years.
Table 3.6-1 below show s CI I j emissions for all sources in Natural (ias Systems, for all time series years. Table
3.6-2 below shows the effective emission factors for all sources in Natural (ias 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
relied 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 (jRI/ICPA study, the values take
into account methane compositions from (il l 2001 adjusted year to year using gross production for National ICnergy
Modeling System (NICMS) oil and gas supply module regions from the I CIA. These adjusted region-specific annual CI 11
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 emission factors used across the lime series is provided in Table 3.6-6.
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1990-2015 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 addressing the production, processing, and storage segments (see "Revisions to Natural
Cias and Petroleum Production Emissions." "Revisions to Natural Cias Processing Emissions," and "Incorporating an
1 Cstimate for the Aliso Canyon Leak.") (I CPA 2017a, b. and c). as well as the "Recalculations Discussion" section of the main
body text.
For the production segment, condensate storage tanks control category-specific emission factors and liquids
unloading category-specific emission factors based on C il1( iRP data are used for the full time series. C lathering and boosting
episodic event emissions are represented w ith a new emission factor based on Marchese el al.. (2015) for the full time series.
l'or the processing segment, emission factors were developed from C il K iRP data for plant fugitives, compressors,
dehydrators. Hares, and blowdowns: and used for 201 1 to 2015. In order to create time-series consistency for emission
factors between earlier years" estimates (1990 to 1992) that generally rely on data from C iRI/1CPA 1996 and the most recent
years' estimates (201 1 to 2015) that were calculated using data from ]CPA's Cil KiRP. linear interpolation between the data
endpoints of 1992 (C iRI/l CPA) and 201 1 (C il I(iRP) was used for calculations.
l-'or the storage segment, the emission estimate for vear 2015 incorporated emissions data for the Aliso Canvon
leak.
Acli\il\
Table 3.6-7 shows the activity data for all sources in Natural Cias Systems, for all time series years. For a few
sources, recent direct activity data w ere not available. I-'or these sources, either 2014 data w ere used as proxy for 2015 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 I J.S. natural gas system infrastructure and operations.
Additional detail on the basis for activity data used across the time series is provided in Table 3.6-8.
Methodology for well counts and events
1 CPA used 1)1 Desktop, a production database maintained by Drillinglnfo. Inc. (Drillinglnfo 2016), covering I J.S.
oil and natural gas wells to populate activity data for non-associated gas wells with and without hydraulic fracturing, and
completions with hydraulic fracturing. I CPA 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-lo-oil ratio (CiC)R) of greater than 100 mcf/bbl in that year. Cias 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 w ell basin, reservoir, and field data reported in 1 )11 )esktop referenced against a formation tvpe erossw alk developed
by I CI A (I CI A 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 2010, ] CPA applied a re fracture rate of 1 percent (i.e., 1 percent of all wells with hydraulic fracturing are
assumed to be relractured in a given year) to the total counts of wells with hydraulic fracturing from the Drillinglnfo data.
For 201 1 through 2015.1 CPA used C iIIC iRP data for the total number of well completions and workovers. The C il ICiRP data
represent a subset of total national completions and workovers due to the reporting threshold and therefore using this data
without scaling it up to national level could result in an underestimate. However, because ]CPA's Cil ICiRP counts of
completions and workovers were higher than national counts of completions and workovers. obtained using DI Desktop
data, I CPA directly used the C il IC iRP data for completions and w orkovers for 2011 through 2015.
1 CPA calculated the percentage of gas well completions and workovers with hydraulic fracturing in the each of the
four control categories using 2011 through 2015 Subpart W data. I CPA assumed no R1CC use from 1990 through 2000. used
C il IC iRP R1 CCs percentage for 2011 through 2015. and then used linear interpolation betw een the 2000 and 2011 percentages.
For Hating. 1 CPA used an assumption of 10 percent (the average of the percent of completions and workovers that were llared
in 201 1 through 2013 Cil IC iRP data) flaring from 1990 through 2010 to recognize that some llaring has occurred over that
time period. For 201 1 through 2015.1 CPA used the C il IC iRP data on llaring.
1990-2015 Inventory updates to activity data
A-202 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Summary information for activity data for sources w ith revisions in this year's Inventory is below. The details are
presented in two memoranda addressing the production and processing segments (see "Revisions to Natural Cias and
Petroleum Production I emissions" and "Revisions to Natural C ias Processing 1 emissions") (FPA 2017a and b), as w ell as the
"Recalculations Discussion" section of the main body text.
For the production segment, condensate storage tank throughput was allocated to tank control categories based on
2015 Cil KiRP data for years 2011 forward. For year 1990. throughput was allocated between large and small tanks according
to the fraction observed in (il I( iRP data for year 2015. and the existing Inventory assumption of 50 percent control on large
tanks was applied for 1990, assuming that any controlled tanks had Hares (as opposed to VRI J): small tanks were assumed
to be uncontrolled in 1990. Linear interpolation from the throughput allocations in year 1990 was used to assign activity
data (throughput) by category for years 1991 through 2010.
l or liquids unloading, activity data (fraction of wells conducting liquids unloading, and split between plunger and
non-plunger lilt) were developed from Cil KiRP data for recent time series years. The existing activity basis (API/AN(iA
(2012)) was used to estimate the fraction of wells conducting liquids unloading. KPA interpolated between 1990 (assuming
all wells in 1990 that conducted liquids unloading vented without plunger lifts) to the activity category allocations in based
tin subpart W in year 201 1 to populate activity data for years 1991 through 2010.
Additionally, in the production segment, (il KiRP-based activity factors (i.e.. counts per gas well) were calculated
from 2015 Cil KiRP data and applied for the years 201 1 to 2015 for in-line heaters, separators, dehydrators, compressors,
meters/piping, pneumatic pumps, and pneumatic controllers. The year-specific bleed type split (i.e.. continuous high bleed,
continuous low bleed, and intermittent bleed w as also developed for pneumatic controllers from 201 1 to 2015 C il IC iRP data).
Activity data for gathering and boosting episodic event emissions is the count of gathering and boosting stations
by year.
l-'orthe processing segment, the new emission factors developed from C il IC iRP data for plant fugitives, dehydrators.
Hares, and blow downs are applied at the plant-level, while the compressor factors are applied at the compressor-level. The
data source for national plant counts (C )il and C ias Journal) remains unchanged from previous Inventories.
Reductions Data
As described under "Fmission Factors'" above, some sources in Natural Cias Systems rely on emission factors
developed from the 1996 FPA/CiRI 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 fable 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 Cias STAR for activities such as replacing
gas engines with electric compressor drivers, installing automated air-lo-luel ratio controls for engines, and implementing
gas recovery for pipeline pigging operations.
There are significant Cias S'l'AR reductions in the production segment that are not classified as applicable to
specific emission sources. As many sources in production are now calculated w ith net factor approaches, to address potential
double-counting of reductions, a scaling factor w as applied to the "other voluntary reductions" to reduce this reported amount
based an estimate of the fraction of those reductions that occur in the sources that are now calculated using net emissions
approaches. This fraction was developed by dividing the net emissions from sources with net approaches, by the total
production segment emissions (w ithoul deducting the C ias S'l'AR reductions). The result for 2015, is that around 70 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 5 MM'f CC): I ]q.
Federal regulations
Regulatory actions reducing emissions in the current Inventory include National 1 emission Standards for I Ia/.ardous
Air Pollutants (NFSIIAP) regulations and the 2012 New Source Performance Standards (NSPS) subpart ( X X X ) for oil and
gas . In regards to the oil and natural gas industry, the NFSIIAP regulation addresses I IAPs 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 I IAPs reductions, methane emissions are also incidentally reduced.
A-203
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The NKSIIAP regulation requires that glycol dehydration unit vents that have IIAP emissions and exceed a gas
throughput threshold he connected to a closed loop emission control system that reduces emissions by 95 percent. The
emissions reductions achieved as a result of NKSIIAP 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 w ithout control measures already in place that would be impacted by the
regulation. Previous Inventories also look into account NI1SIIAP 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.
The Inventory rellects the NSPS subpart ()()()() 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 Emissions by Emission Source for Each Year
Annual CI I j 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 CI 11
emissions. As a final step, any relevant reductions data from each segment is summed for each year and deducted from the
total emissions to estimate net CI 11 emissions for the Inventory. Potential emissions, reductions, and net emissions at a
segment level are shown in Table 3.6-1.
CO; Mmissimis
The same procedure for estimating CI 11 emissions applies for estimating non-energy related C(); emissions, except
the emission estimates are not adjusted for reductions due to the Natural (ias STAR program or regulations. C(): emissions
by segment and source are summarized in Table 3.6-10 below. In this year's Inventory. KPA has held constant the CO;
values from the previous CilKi Inventory (developed using the methodology as described in this section) as it assesses
improvements to the C(); estimates. See Planned Improvements in the main body text on Natural C ias Systems.
Produced natural gas contains, in some cases, as much as 8 percent C( );. The same vented and fugitive natural gas
that led to CI I j emissions also contains a certain volume of CO:. Accordingly, the CO; emissions for each sector can be
estimated using the same activity data for these vented and fugitive sources. The emission factors used to estimate CI 11 were
also used to calculate non-combustion C(); emissions. The C ias Technology Institute's (C iff formerly C iRI) I Jneonvenlional
Natural (ias and (ias Composition I )atabases ((i f I 2001) w ere used to adapt the CI 11 emission factors into non-combustion
related CO; emission factors, for the CO; content used to develop CO; emission factors from CI 11 potential factors, see
Table 3.6-1 1.
In the processing sector, the C(); content of the natural gas remains the same as the C(); content in the production
sector for the equipment upstream of the acid gas removal unit because produced natural gas is usually only minimally
treated after being produced and then transported to natural gas processing plants via gathering pipelines. The C(); content
in gas for the remaining equipment that is downstream of the acid gas removal is the same as in pipeline quality gas. The
KPA/GRI study estimates the average CI I j content of natural gas in the processing sector to be 87 percent CI I j. The
processing sector C(); emission factors were developed using CI 11 emission factors proportioned to reflect the C(); content
of either produced natural gas or pipeline quality gas using the same methodology as the production sector.
for the transmission sector. CO; content in natural gas transmission pipelines was estimated for the top 20
transmission pipeline companies in the United States (separate analyses identified the top 20 companies based on gas
throughput and total pipeline miles). The weighted average CO; content in the transmission pipeline quality gas in both
cases—total gas throughput and total miles of pipeline—was estimated to be about 1 percent. To estimate the C(); emissions
for the transmission sector, the CI 11 emission factors were proportioned from the 93.4 percent CI 11 reported in KPA/GRI
(1996) to relied the 1 percent CO; content found in transmission quality natural gas.
The natural gas in the distribution sector of the system has the same characteristics as the natural gas in the
transmission sector. The CI I j content (93.4 percent) and CO; content (1 percent) are identical to transmission segment
contents due to the absence of any further treatment between sector boundaries. Thus, the CI I j emissions factors were
converted to C(); emission factors using the same methodology as discussed for the transmission sector.
A-204 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Three exceptions to this methodology are C( >: emissions from Hares. C()? from acid gas removal units, and C()?
from condensate tanks. In the case of Hare emissions, a direct C( >: emission factor from I HA (1996) was used. This emission
factor was applied to the portion of offshore gas that is not vented and all of the gas reported as vented and llared onshore
by IT A. including associated gas. The amount of C( h emissions from an acid gas unit in a processing plant is equal to the
difference in CO; concentrations between produced natural gas and pipeline quality gas applied to the throughput of the
plant. This methodology was applied to the national gas throughput using national average C( >: concentrations in produced
gas (3.45 percent) and transmission quality gas (1 percent). Data were unavailable to use annual values for CO:
concentration, for condensate tanks, a series of I l&P Tank (1 H'A 1999) simulations provide the total C(): vented per barrel
of condensate throughput from fixed roof lank Hash gas lor condensate gravities of API 45 degree and higher. The ratios of
emissions to throughput were used to estimate the C(); emission factor for condensate passing through I'ixed roof tanks.
Refer to htlns://www.ena.ao\/»hizemissions/natural-izas-and-netroleum-s\stems-»hiZ-in\entorv-additional-
information- 1990-2015-uhu for the following data tables, in Kxcel formal:
• Table 3.6-1: CI 11 Emissions (kl) for Natural C ias Systems, by Segment and Source, for All Years
• Table 3.6-2: Average CI 11 I Emission factors (kg/unit activity) for Natural C ias Systems Sources, for All Years
• Table 3.6-3: I J.S. Production Sector CI 11 Content in Natural (ias by NKMS Region ((ieneral Sources)
• Table 3.6-4: I J.S. Production Sector CI 11 Content in Natural C ias by NHMS Region (( ias Wells Without
I Iydraulic fracturing)
• Table 3.6-5: I J.S. Production Sector CI 11 Content in Natural (ias by NKMS Region ((ias Wells With I Iydraulic
fracturing)
• Table 3.6-6: CI 11 I emission factors for Natural C ias Systems. Data Sources/Methodology
• Table 3.6-7: Activity Data for Natural (ias Systems Sources, for All Years
• Table 3.6-8: Activity Data for Natural (ias Systems, Data Sources/Methodology
• Table 3.6-9: Voluntary and Regulatory CI 11 Reductions for Natural C ias Systems (kl)
• Table 3.6-10: C(): Emissions (kl) for Natural (ias Systems, by Segment and Source, for All Years
• Table 3.6-1 1: I J.S. Production Sector C(): Content in Natural (ias by N11MS Region and formation Type for all
\ ears
A-205
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i References
2 Alabama (2016) Alabama Slate ()il and C ias Hoard. Available online at .
3 API/ANC iA (2012) (haraclerizing I'ivolal Sources of\ leiliune Emissions from Xaliiral (ias Production Summary and
4 . Ina/ysis q/'AI'l and. I.Yf i. I Survey Responses. Final Report. American Petroleum Institute and America's Natural (ias
5 Alliance. September 2 1.
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7 htlps://\\\\\\ . boem.gov/1 CSPIS/5/5440.pdf>.
8 BOICMRIC (201 la) (iulf of Mexico Region ()ffshore Information. Bureau of Ocean ICnergy Management. Regulation and
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10 B( )lCMRI v (201 lb) Pacific ()CS Region ()ffshore Information. Bureau of ()cean ICnergy Management, ReguUilion and
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12 B( )IEMRIC (201 lc) C >( )M and Pacific C )CS Platform Activity. Bureau of Ocean ICnergy Management. ReguUilion and
13 I Enforcement. IJ.S. Department of Interior.
14 B( )l 'MRl•! (201 Id) Pacific ()CS Region. Bureau of ()cean I Energy Management. Regulation and I Enforcement. I J.S.
15 I )epartment of Interior.
16 Clearstone (201 1) Clearstone I Engineering. Development of Updated Emission factors for Residential Meters, May 201 1.
17 Drillinglnlb (2016) DI Desktop* April 2016 Download. Drillinglnfo, Inc.
18 ] CIA (2004) I \S. I.\(i larkets and I ses. I Energy Information Administration. U.S. 1 )epartment of ICnergy. Washington.
19 DC. June 2004. Available online at: .
21 I CI A (201 1) "Monthly I Cnergy Review" Table 5.2. Crude Oil and Natural (ias Resource Development. ICnergy Information
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23 .
24 ICIA (2012a) Formation crosswalk. ICnergy Information Administration. U.S. Department of ICnergy. Washington, DC.
25 Provided July 7.
26 ICIA (2016a) "Natural (ias Gross Withdrawals and Production: Marketed Production." ICnergy Information Administration.
27 IJ.S.l Oeparlmenl of I Cnergy. Washington, 1 )C. Available online at: .
28 I CIA (2016b) I.ease Condensate Production. 1989-2011. Natural (ias Navigator. ICnergy Information Administration. U.S.
29 Department of ICnergy. Washington, DC. Available online at .
30 ICIA (2016c) "Table 1—Summary of natural gas supply and disposition in the United States 201 1-2016." Natural (ias
31 Monthly. ICnergy Information Administration, U.S. Department of I Cnergy, Washington. DC. Available online at
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33 ICIA (2016d) "Table 2—Natural (ias Consumption in the United States 201 1-2016." Natural (ias Monthly. ICnergy
34 Information Administration. U.S. Department of I Cnergy. Washington. DC. Available online at
35 .
36 ICIA (2016e) "Natural (ias Annual Respondent Query System. Report 191 Field Level Storage Data (Annual)." ICnergy
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38 https://w ww .eia.gov/efapps/ngqs/ngqs.efm'.'f_reporl=RP7>.
39 1 CIA (20160 "U.S. Natural (ias Imports. 2014-2016." ICnergy Information Administration. U.S. Department of ICnergy.
40 Washington. DC. Available online at .
41 ICIA (2015a) Number of Natural (ias Consumers - Residential. ICnergy Information Administration. U.S. Department of
42 ICnergy, Washington. DC. Available online at: .
44 I CIA (2015b) Number of Natural (ias Consumers - Commercial. ICnergy Information Administration. U.S. Department of
45 ICnergy. Washington. DC. Available online at: .
A-206 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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38
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42
43
44
45
46
47
48
1 CIA (2015c) Number of Natural Gas Consumers - Industrial, ICnergy Information Administration, IJ.S. Department of
ICnergy. Washington, DC. Available online at: < http://\\ww.eia.gov/dnav/ng/
ng_eons_num_a_ICP( i0_VN7_Count_a.htm>.lCIA (2014a) I J.S. Imports by Country. ICnergy Information
Administration, IJ.S. Department of ICnergy. Washington, DC. Available online at: .
1CPA (2013) I Jpdating (iIIC i Inventory 1 Estimate for I Ivdraulieally Fractured C ias Well Completions and Workovers.
Available (inline at: .
1 CPA (2015a) Inventory of IJ.S. Cil ICi I emissions and Sinks 1990-2013: Revision to Well Counts Data. Available online at:
.
I CPA (2015b) Inventory of I J.S. (ireenhouse (ias I emissions and Sinks 1990-2013: I Jpdate to (MTshore ()il and (ias
Platforms I emissions I Cstimate. Available online at: .
I CPA (2016a) (ireenhouse (ias Reporting Program- Subpart 11' Petroleum ami Xati/ral (ias Systems. I environmental
Protection Agency. I )ata reported as of August 13. 2016.
I CPA (2016b) Inventory of I J.S. Greenhouse Gas 1 emissions and Sinks 1990-2014: Revisions to Natural (ias and Petroleum
Production I emissions. Available online at: .
I CPA (2016c) Inventory of I J.S. (ireenhouse (ias I emissions and Sinks 1990-2014: Revisions to Natural (ias (lathering and
Boosting I emissions. Available online at: .
I CPA (2016d) Inventory of I J.S. Greenhouse (ias 1 emissions and Sinks 1990-2014: Revisions to Natural (ias Transmission
and Storage I emissions. Available online at: .
I CPA (2016e) Inventory of I J.S. (ireenhouse (ias I emissions and Sinks 1990-2014: Revisions to Natural (ias Distribution
I emissions. Available (inline at: .
I CPA (2017a) Inventory of IJ.S. (ireenhouse (ias I emissions and Sinks 1990-2015: Revisions to Natural (ias and Petroleum
Production I emissions. Available online at: .
I CPA (2017b) Inventory of IJ.S. (ireenhouse (ias I emissions and Sinks 1990-2015: Revisions to Natural (ias Processing
I emissions. Available online at: .
I CPA (2017c) Inventory of I J.S. Greenhouse ( ias I emissions and Sinks 1990-2015: Incorporating an I Cstimate for the Aliso
Canyon I .eak. Available online at: .
ICPA/GRI (1996) Methane Emissions from the Xatural (ias Industry. Prepared by I Iarrison. M.. T. Shires, .1. Wessels. and
R. Cowgill. eds.. Radian International I.I.C for National Risk Management Research Laboratory. Air Pollution
Prevention and Control Division. Research Triangle Park. NC. ICPA-600/R-96-080a.
f'lCRC (2016) Xorth. Imerican I.XC 'terminals, federal ICnergy Regulatory Commission. Washington. DC.
(i l l (2001) (ias Resource Database: I Jneonventional Natural (ias and (ias Composition Databases. Second I edition. GRI-
01/0136.
(iTI (2009) (ias Technology Institute and Innovative I environmental Solutions, field Measurement Program to Improve
I Jncertainties for Key Greenhouse (ias 1 emission factors for Distribution Sources. November 2009. (i l l Project
Number 20497. ()TD Project Number 7.7.b.
ICf (1997) ICf Memo - Sept. 18, 1997 - "Additional Changes to Activity factors for Portions of the (ias Industry."
ICP (2004) ICf memo - Methane I emissions from Condensate Tanks in the ()il and Natural (ias Industry. 02-24-2004
ICf (2008) ICf Memo - Jan. 07. 2008 - "Natural (ias Model Activity factor Basis Change."
ICf (2010) ICf Memo - December. 2010 - "Lmissions from Centrifugal Compressors."
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19
I.amb, cl al. (2015) "Direct Measurements Show Decreasing Methane Emissions from Natural Cias Local Distribution
Systems in the United Slates." Environmental Science A Technology, I'ol. 49 5161-5169.
Marehese, el al. (2015) "Methane Emissions from United Slates Natural Cias Gathering and Processing." Lnvironmental
Science and Technology. Vol. 49 10718-10727.
Radian/API (1992) "(ilobal 1 emissions of Methane from Petroleum Sources."" American Petroleum Institute. I lealth and
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Materials Safety Administration. I J.S. Department of Transportation. Washington. DC. Available (inline at:
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/.immerle. el al. (2015) "Methane Emissions from the Natural Cias Transmission and Storage System in the United States."
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1 3.7. Methodology for Estimating CO2, CH4, and N2O Emissions from the Incineration of
2 Waste - TO BE UPDATED FOR FINAL INVENTORY REPORT
3 Emissions of CO2 from the incineration of waste include CO2 generated by the incineration of plastics, synthetic
4 rubber and synthetic fibers in municipal solid waste (MSW), and incineration of tires (which are composed in part of
5 synthetic rubber and C black) in a variety of other combustion facilities (e.g., cement kilns). Incineration of waste also
6 results in emissions of CH4 and N2O. The emission estimates are calculated for all four sources on a mass-basis based on
7 the data available. The methodology for calculating emissions from each of these waste incineration sources is described in
8 this Annex.
9 CO2 from Plastics Incineration
10 In the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports
11 (EPA 1999 through 2003, 2005 through 2014), Advancing Sustainable Materials Management: Facts and Figures -
12 Assessing Trends in Material Generation, Recycling and Disposal in the United States (EPA 2015, 2016) the flows of
13 plastics in the U.S. waste stream are reported for seven resin categories. For 2015, the quantity generated, recovered, and
14 discarded for each resin is shown in Table A-131. The data set for 1990 through 2015 is incomplete, and several assumptions
15 were employed to bridge the data gaps. The EPA reports do not provide estimates for individual materials landfilled and
16 incinerated, although they do provide such an estimate for the waste stream as a whole. To estimate the quantity of plastics
17 landfilled and incinerated, total discards were apportioned based on the proportions of landfilling and incineration for the
18 entire U.S. waste stream for each year in the time series according to Biocycle 's State of Garbage in America (van Haaren
19 et al. 2010), and Shin (2014). For those years when distribution by resin category was not reported (1990 through 1994),
20 total values were apportioned according to 1995 (the closest year) distribution ratios. Generation and recovery figures for
21 2002 and 2004 were linearly interpolated between surrounding years' data.
22 Table A-131:2015 Plastics in the Municipal Solid Waste Stream by Resin tktl
LDPE/
Waste Pathway
PET
HDPE
PVC
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%
23 a As a percent of waste generation.
24 Note: Totals may not sum due to independent rounding. Abbreviations: PET (polyethylene terephthalate), HDPE (high density polyethylene), PVC
25 (polyvinyl chloride), LDPE/LLDPE (linear low density polyethylene), PP (polypropylene), PS (polystyrene).
26
27 Fossil fuel-based CO2 emissions were calculated as the product of plastic combusted, C content, and fraction
28 oxidized (see Table A-132). The C content of each of the six types of plastics is listed, with the value for "other plastics"
29 assumed equal to the weighted average of the six categories. The fraction oxidized was assumed to be 98 percent.
30 Table fl-132:2015 Plastics Incinerated tktl, Carbon Content [%), Fraction OxiJizeJ [%] and Carbon Incinerated tktl
LDPE /
Factor
PET
HDPE
PVC
LLDPE
PP
PS
Other
Total
Quantity Combusted
Carbon Content of Resin
Fraction Oxidized
Carbon in Resin Combusted
283
63%
98%
173
360
86%
98%
302
58
38%
98%
22
501
86%
98%
420
486
86%
98%
408
159
92%
98%
143
228
66%
98%
147
2,074
1,617
Emissions (MMT CO2 Eq.)
0.6
1.1
0.1
1.5
1.5
0.5
0.5
5.9
31 a Weighted average of other plastics produced.
32 Note: Totals may not sum due to independent rounding
33
34 CO2 from Incineration of Synthetic Rubber and Carbon Black in Tires
35 Emissions from tire incineration require two pieces of information: the amount of tires incinerated and the C
36 content of the tires. "2014 U.S. Scrap Tire Management Summary" (RMA 2016) reports that 1,923 thousand of the 3,551
37 thousand tons of scrap tires generated in 2015 (approximately 54 percent of generation) were used for fuel purposes. Using
A-209
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1 RMA's estimates of average lire composition and weight, the mass of synthetic rubber and C black in scrap tires was
2 determined:
3 • Synthetic rubber in tires w as estimated to be 90 percent C by w eight, based on the w eighted average C contents
4 of the major elastomers used in new lire consumption/0 Table A-133 shows consumption and C content of
5 elastomers used for tires and other products in 2002, the most recent year for which data are available.
6 • C black is 100 percent C (Aslett Rubber Inc. n.d.).
7 Multiplying the mass of scrap tires incinerated by the total C content of the synthetic rubber, C black portions of
8 scrap tires, and then by a 98 percent oxidation factor, yielded C( h emissions, as shown in Table A-134. The disposal rate
9 of rubber in tires (0.3 MMT C/year) is smaller than the consumption rate for tires based on summing the elastomers listed
10 in Table A-13 1 (1.3 MM'I'/year): this is due to the fact that much of the rubber is lost through lire wear during the product's
11 lifetime and may also relied the lag time between consumption and disposal of tires. Tire production and fuel use for 1990
12 through 2015 were taken from RMA 2006. RMA 2009, RMA 201 1: RMA 2014a: RMA2016: where data were not reported.
13 they were linearly interpolated between bracketing years' data or. for the ends of time series, set equal to the closest year
14 w ith reported data.
15 In 2009. RMA changed the reporting of scrap lire data from millions of tires to thousands of short tons of scrap
16 lire. As a result, the average weight and percent of the market of light duty and commercial scrap tires was used to convert
17 the previous vears from millions of tires to thousands of short tons (S'l'MC 1990 through 1997: RMA 2002 through 2006.
18 2014b. 2016).
19 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
20 NA (Not Applicable)
21 a Used to calculate C content of non-tire rubber products in municipal solid waste.
22 Note: Totals may not sum due to independent rounding.
23
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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i Table A-134: Scrap Tire Constituents and GO? Emissions from Scrap Tire Incineration in 2015
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%
1.4
Total
0.8
NA
NA
2.7
2 NA (Not Applicable)
3 CO2 from Incineration of Synthetic Rubber in Municipal Solid Waste
4 Similar to the methodology for scrap tires, CO? emissions from synthetic rubber in MSW were estimated by
5 multiplying the amount of rubber incinerated by an average rubber C content. The amount of rubber discarded in the MSW
6 stream was estimated from generation and recycling data'' provided in the Municipal Solid Haste (feneration. Recycling.
1 and Disposal in the I'nited States: I-'acts and l-'igures reports (KPA 1999 through 2003. 2005 through 2014). Advancing
8 Sustainable late rials lanagenient: I''acts and l-'igures:. Issessing Trends in \ late rial (generation. Recycling and Disposal
9 in the I nited States (KPA 2015, 2016), and unpublished backup data (Schneider 2007). The reports divide rubber found in
10 MSW into three product categories: other durables (mil including tires), non-durables (which includes clothing and footwear
11 and other non-durables), and containers and packaging. KPA (2016) did not report rubber found in the product category
12 "containers and packaging:" however, containers and packaging from miscellaneous material types were reported for 2009
13 through 2015. As a result. KPA assumes that rubber containers and packaging are reported under the "miscellaneous"
14 category: and therefore, the quantity reported for 2009 through 2015 were set equal to the quantity reported for 2008. Since
15 there was negligible recovery for these product types, all the waste generated is considered to be discarded. Similar to the
16 plastics method, discards were apportioned into landfilling and incineration based on their relative proportions, for each
17 year, for the entire 1 J.S. waste stream. The report aggregates rubber and leather in the MSW stream: an assumed synthetic
18 rubber content of 70 percent was assigned to each product type, as shown in Table A-135.67 A C content of 85 percent was
19 assigned to synthetic rubber for all product types (based on the weighted average C content of rubber consumed for non-lire
20 uses), and a 98 percent fraction oxidized was assumed.
21 TahleA-135: Rubber and Leather in Municipal Solid Waste in 2014
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
-
-
-
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
-
-
-
1.1
22 -Not Applicable
23
24 CO2 from Incineration of Synthetic Fibers
25 Carbon dioxide emissions from synthetic fibers were estimated as the product of the amount of synthetic fiber
26 discarded annually and the average C content of synthetic fiber, fiber in the MSW stream w as estimated from data provided
27 in the Municipal Solid H aste (feneration, Recycling, and Disposal in the I'nited States: /-'acts and Figures reports (KPA
28 1999 through 2003. 2005 through 2014) and. Idvancing Sustainable laterials lanagenient: I-'acts and l-'igures . Issessing
29 Trends in laterial (feneration. Recycling and Disposal in the I 'nited States (KPA 2015. 2016) for textiles. Production data
30 for the synthetic fibers was based 011 data from the American Chemical Society (I-'KB 2009). The amount of synthetic fiber
31 in MSW was estimated by subtracting (a) the amount recovered from (b) the waste generated (see Table A-136). As with
32 the other materials in the MSW stream, discards were apportioned based on the annually variable proportions of landfilling
33 and incineration for the entire I J.S. waste stream, as found in van I Iaaren el al. (2010), and Shin (2014). It was assumed that
34 approximately 55 percent of the fiber was synthetic in origin, based 011 information received from the fiber Keonomies
35 Bureau (l)e/.an 2000). The average C content t>f 71 percent was assigned to synthetic fiber using the production-weighted
36 average of the C contents of the four major fiber types (polyester, nylon, olefin, and acrylic) based 011 2015 fiber production
37 (see Table A-137). The equation relating C(): emissions to the amount of textiles combusted is shown below.
38 CO2 Emissions from the Incineration of Synthetic Fibers = Annual Textile Incineration (kt) x
39 (Percent of Total Fiber that is Synthetic) x (Average C Content of Synthetic Fiber) x
66 Discards = Generation minus recycling.
®7 As a sustainably harvested biogenic material, the incineration of leather is assumed to have no net CO2 emissions.
A-211
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i i i uco ,/i^nCi
Table A-136: Synthetic Textiles in MSW (kt)
Year
Generation
Recovery
Discards
Incineration
1990
2,884
328
2,557
332
1995
3,674
447
3,227
442
1996
3,832
472
3,361
467
1997
4,090
526
3,564
458
1998
4,269
556
3,713
407
1999
4,498
611
3,887
406
2000
4,706
655
4,051
417
2001
4,870
715
4,155
432
2002
5,123
750
4,373
459
2003
5,297
774
4,522
472
2004
5,451
884
4,567
473
2005
5,714
913
4,800
480
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
6,508
998
5,510
441
2011
6,513
1,003
5,510
419
2012
7,114
1,117
5,997
456
2013
7,496
894
6,602
502
2014
8,052
1,301
6,751
513
2015
8,052
1,301
6,751
513
'able A-137: Synthetic Fiher Production in 2015
Fiber
Production (MMT)
Carbon Content
Polyester
1.2
63%
Nylon
0.5
64%
Olefin
1.0
86%
Acrylic
-
68%
Total
2.8
71%
CH4 and N2O from Incineration of Waste
Kstimates of N?() emissions from the incineration of waste in the United States are based 011 the methodology
outlined in the FPA's Compilation of Air Pollutant I {mission Factors (I {PA 1995) and presented in the Municipal Solid
Waste (feneration, Recycling, and Disposal in the I nited Stales: l-'acls and Figures reports (FPA 1999 through 2003. 2005
through 2014). Advancing Sustainable Materials Management: I-'acts and l-'igures: Assessing Trends in Material
(feneration. Recycling and Disposal in the I 'nitedStates (I {PA 2015. 2016) and unpublished backup data (Schneider 2007).
According to this methodology, emissions ol'NM ) from waste incineration are the product of the mass of waste incinerated,
an emission factor of NM) emitted per unit mass of waste incinerated, and an N:() emissions control removal efficiency.
The mass of waste incinerated was derived from the results of the biannual national survey of Municipal Solid Waste (MSW)
(feneration and Disposition in the U.S.. published in HioCycIe (van 1 laaren el al. 2010), and Shin (2014). For waste
incineration in the I Jnited Stales, an emission factor of 50 gN;( )/melric ton MSW based 011 the 2006 ll'("(" (iuidelines and
an estimated emissions control removal efficiency of zero percent were used (IPCC 2006). It was assumed that all MSW
incinerators in the I Jnited Slates use continuously-fed stoker technology (Bailor 2009; ] {RC 2009).
Fstimates of CI 11 emissions from the incineration of waste in the United States are based 011 the methodology
outlined in IPCC's 2006 U'CC (iuidelines for Xational (ireenhouse (las Inventories (IPCC 2006). According to this
methodology, emissions of CI 11 from waste incineration are the product of the mass of waste incinerated and an emission
factor of CI 11 emitted per unit mass of waste incinerated. Similar to the N:() emissions methodology, the mass of waste
incinerated was derived from the information published in BioCyde (van Ilaaren el al. 2010) for 1990 through 2008. Data
for 2011 were derived from information in Shin (2014). For waste incineration in the I Jnited Slates, an emission factor of
0.20 kg CI I |/kt MSW was used based on the 2006 fl'("(" (iuidelines and assuming that all MSW incinerators in the I Jnited
States use continuously-led stoker technology (Bailor 2009; ]{RC 2009). No information was available 011 the mass of waste
incinerated for 2012 through 2015. so these values were assumed to be equal to the 201 1 value.
A-212 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Despite the differences in methodology and data sources, the two series of references (KPA 2014; van Ilaaren.
Rob. Themelis. N.. and Goldstein. N. 2010) provide estimates of total solid waste incinerated that are relatively consistent
(see Table A-138).
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
25,942,036^
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
22,714,122°
2010
26,544,672
21,741,734°
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'
a Interpolated between 2000 and 2002 values.
b Interpolated between 2002 and 2004 values.
c Interpolated between 2004 and 2006 values.
d Interpolated between 2006 and 2008 values
e Interpolated between 2011 and 2008 values
'Set equal to the 2011 value
g Set equal to the 2014 value.
A-213
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References
ArSova. I.jupka, Rob van I Iaaren. Nina Goldstein, Scotl M. Kaufman, and Niekolas .1. Themelis (2008) "16th Annual
BioCycle Nationwide Survey: The State of Garbage in America" Biocycle. .Ki Press, ICmmaus, PA. December.
Bailor. B (2009) Covanta Energy's public review comments re: Draft Inventory of I \S. (ireenlioii.se (las Emissions and
Sinks: 1990-2007. Submitted via email on April 9, 2009 to I.eif I lockstad. I J.S. I CPA.
I)e Soete. C i.G. (1993) "Nitrous ()xide from Combustion and Industry: Chemistry. Emissions and Control." In A. R. Van
Amstel, (ed) Proe. of the International Workshop Methane and Nitrous Oxide: Methods in National Emission
Inventories and ()ptions for Control. Amersfoort, NI.. February 3-5. 1993.
De/.an. 1). (2000) Personal Communication between Diane De/an, l'iber I economics Bureau and Joe Casola. ICF
Consulting. 4 August 2000.
I Cnergy Reco\ cry Council (2009) "2007 1 )irectory of Waste-to-1 Cnergy Plants in the I Jnited States." Accessed September
29.2009.
I 'PA (2016) Advancing Sustainable Materials Management: 2014 Pact Sheet - Assessing Trends in Material (feneration.
Recycling and Disposal in the I Jnited States. ( M'fiee of I.and and Emergency Managements. I J.S. I environmental
Protection Agency. Washington. D.C. Available online at: .
] CPA (2015) Advancing Sustainable Materials Management: Pacts and figures 2013 - Assessing Trends in Material
C feneration. Recycling and 1 )isposal in the I Jnited States. (M'fiee of Solid Waste and 1 emergency Response. I J.S.
¦ environmental Protection Agency. Washington. D.C. Available online at
.
I CPA (1999 through 2003. 2005 through 2014) Municipal Solid Waste in the I Jnited States: Pacts and figures. (M'fiee of
Solid Waste and I emergency Response, I J.S. I environmental Protection Agency. Washington. DC. Available online at
.
1 CPA (2006) Solid Waste Management and C freenhouse C fases: A I ,ife-Cyele Assessment of 1 emissions and Sinks. ( M'fiee
of Solid Waste and 1 emergency Response. I J.S. 1 environmental Protection Agency. Washington, 1 )C.
I CPA (2000) Characterization of Municipal Solid Waste in the I Jnited States: Source 1 )ata on the 1999 I Jpdate. ( M'fiee of
Solid Waste. IJ.S. ] environmental Protection Agency. Washington. DC. 1CPA530-F-00-024.
1 CPA (1995) AP 42, fifth 1 edition Compilation of Air Pollutant I emission Factors. (M'fiee of Air Quality Planning and
Standards. (M'fiee of Air and Radiation. I J.S. 1 Cnvrionmental Protection Agency. Washington. 1 ).C. Available online
at: .
1'ICB (2009) fiber I economics Bureau, as cited in C&1CN (2009) Chemical Output Slipped In Most Regions Chemical &
I engineering News. American Chemical Society. 6 July. Available (inline at .
(ioldslein. N. andC. Madles (2001) 13th Annual BioCycle Nationwide Survey: The State of Garbage in America.
I BioCycle. JG Press. ICmmaus. 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, I I.S. ICggleston, I.. Buendia. K. Miwa. T Ngara. and K.
Tanabe (eds.). Ilayama. Kanagawa. Japan.
Kaufman, el al. (2004) "14th Annual BioCycle Nationwide Survey: The Slate of Garbage in America 2004" Biocycle. JG
Press. Hmmaus. PA. January, 2004.
RMA (2016) "2015 IJ.S. Scrap Tire Management Summary." Rubber Manufacturers Association. August 2016. Available
online at: .
RMA (2014a) "2013 I J.S. Scrap Tire Management Summary." Rubber Manufacturers Association. November 2014.
Available (inline at: . Accessed 17
November 2014.
RMA (2014b) "Scrap Tire Markets: facts and figures - Scrap Tire Characteristics." Available (inline at
. Accessed 17
November 2014.
A-214 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 RMA (2012) "Rubber FAQs." Rubber Manufacturers Association. Available online at . Accessed 19 November 2014.
3 RMA (2011) "I J.S. Scrap Tire Management Summary 2005-2009/' Rubber Manufacturers Association. ()ctober 2011.
4 Available (inline at: .
5 RMA (2009) Scrap Tire Markets in the United Slates: 9th Biennial Report. Rubber Manufacturers Association.
6 Washington. DC. May 2009.
7 RMA (2002 through 2006) I J.S. Scrap Tire Markets. Rubber Manufacturers Association. Washington, DC. Available
8 (inline at: .
9 Schneider, S. (2007) 1 i-mail betw een Shelly Schneider of Franklin Associates (a division of I IRC >) and Sarah Shapiro of
10 ICT International, January 10,2007.
11 Shin, D. (2014) C ieneration and Disposition of Municipal Solid Waste (MSW) in the I Jnited States-A National Survey.
12 Thesis. Columbia I Jniversily. 1 )epartment of 1 Carth and 1 Invironmental 1 Ingineering. January 3. 2014.
13 Simmons, el al. (2006) "15th Nationwide Survey of Municipal Solid Waste Management in the United States: The State of
14 (iarbage in America" BioCyele, JCi Press, fnimans, PA. April 2006.
15 STMC (1990 through 1997) Scrap Tire I Jse/Disposal Study. Rubber Manufacturers Association: Scrap Tire Management
16 Council. Available online at: .
17 Themelis and Shin (2014) I J.S. Survey of (feneration and Disposition of Municipal Solid Waste. Waste Management.
18 Columbia University. January 2014. .
19 van 1 laaren. Rob, Thermelis. N.. and C ioldstein. N. (2010) "The State of (iarbage in America." BioCyele. ()etober 2010.
20 Volume 5 1. Number 10. pg. 16-23.
21
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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-based jet 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
68 FAS contains data for 1995 through 2016, but the dataset was not complete for years prior to 1995. Using DLA aviation and marine fuel
procurement data, fuel quantities from 1990 to 1994 were estimated based on a back-calculation of the 1995 data in the legacy database, the Defense
Fuels Automated Management System (DFAMS). The back-calculation was refined in 1999 to better account for the jet fuel conversion from JP4
to JP8 that occurred within DoD between 1992 and 1995.
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 Equipments Using Compression Ignition or Turbine Engines,
2012.
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17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
the bunker fuel estimate of the country where the ship or aircraft was fueled. Fuel consumed entirely
within a country's borders was not considered a bunker fuel.
• Based on previous discussions with the Army staff, only an extremely small percentage of Army
aviation emissions, and none of Army watercraft emissions, qualified as bunker fuel emissions. The
magnitude of these emissions was judged to be insignificant when compared to Air Force and Navy
emissions. Based on this research, Army bunker fuel emissions were assumed to be zero.
• Marine Corps aircraft operating while embarked consumed fuel that was reported as delivered to the
Navy. Bunker fuel emissions from embarked Marine Corps aircraft were reported in the Navy bunker
fuel estimates. Bunker fuel emissions from other Marine Corps operations and training were assumed
to be zero.
• Bunker fuel emissions from other DoD and non-DoD activities (i.e., other federal agencies) that
purchased fuel from DLA Energy were assumed to be zero.
Step 5: Determine Bunker Fuel Percentages
It was necessary to determine what percent of the aviation and marine fuels were used as international bunker fuels.
Military aviation bunkers include international operations (i.e., sorties that originate in the United States and end in a foreign
country), operations conducted from naval vessels at sea, and operations conducted from U.S. installations principally over
international water in direct support of military operations at sea (e.g., anti-submarine warfare flights). Methods for
quantifying aviation and marine bunker fuel percentages are described below.
• Aviation: The Air Force Aviation bunker fuel percentage was determined to be 13.2 percent. A bunker
fuel weighted average was calculated based on flying hours by major command. International flights
were weighted by an adjustment factor to reflect the fact that they typically last longer than domestic
flights. In addition, a fuel use correction factor was used to account for the fact that transport aircraft
burn more fuel per hour of flight than most tactical aircraft. This percentage was multiplied by total
annual Air Force aviation fuel delivered for U. S. activities, producing an estimate for international bunker
fuel consumed by the Air Force.
The Naval Aviation bunker fuel percentage was calculated to be 40.4 percent by using flying hour data
from Chief of Naval Operations Flying Hour 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."
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
-------
Table A-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
(+)
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
+
+
+
+
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
Gasoline
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
+
+
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
(MGO)
Naval Distillate
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
(F76)
Intermediate
+
+
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
Fuel Oil
o t.
u_ a>
—- _c
O
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
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
Bunkers)
1 + Indicates value does not exceed 0.05 million gallons.
2 a Includes fuel distributed in the United States and U.S. Territories.
3 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.
4 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.
5 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,
6 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
7 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.
8 Since 2001, other gasoline and diesel fuel totals were generated by DLA Energy.
9 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.
10 Note: Totals may not sum due to independent rounding.
11
A-218 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
M
M
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
2 + Does not exceed 0.05 million gallons.
3 Note: Totals may not sum due to independent rounding. The negative values in this table represent returned products.
4
5
A-219
-------
i Table fl-141: Total U.S.DoD Maritime Bunker FueUMillion 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
0.0
0.0
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
2 + Does not exceed 0.05 million gallons.
3 Note: Totals may not sum due to independent rounding.
4
5 Table fl-142: Aviation and Marine Carbon Contents [MMTCarbon/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
6 Source: EPA (2010) and IPCC (2006).
7
8 Table fl-143: Annual Variable Carbon Content Coefficient for Jet Fuel [MMTCarbon/QBtul
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
9 Source: EPA (2010)
10
11 Table fl-144: Total U.S. DoD CO; Emissions from Bunker Fuels [MBIT CO; 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
12 Note: Totals may not sum due to independent rounding.
13
A-220 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i References
2 DLA Energy (2017) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense
3 Energy Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, D.C.
4 IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
5 Programme, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and
6 K. Tanabe (eds.). Hayama, Kanagawa, Japan.
A-221
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
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 end-use sectors: refrigeration and air-
conditioning, foams, aerosols, solvents, and fire-extinguishing. Within these sectors, there are 66 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:
1. Gather historical data. The Vintaging Model is populated with information on each end-use, taken from
published sources and industry experts.
A-222 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 2. Simulate the implementation of new, non-ODS technologies. The Vintaging Model uses detailed
2 characterizations of the existing uses of the ODS, as well as data on how the substitutes are replacing the ODS, to simulate
3 the implementation of new technologies that enter the market in compliance with ODS phase-out policies. As part of this
4 simulation, the ODS substitutes are introduced in each of the end-uses over time as seen historically and as needed to comply
5 with the ODS phase-out and other regulations.
6 3. Estimate emissions of the ODS substitutes. The chemical use is estimated from the amount of substitutes that
7 are required each year for the manufacture, installation, use, or servicing of products. The emissions are estimated from the
8 emission profile for each vintage of equipment or product in each end-use. By aggregating the emissions from each vintage,
9 a time profile of emissions from each end-use is developed.
10 Each set of end-uses is discussed in more detail in the following sections.
11 Refrigeration and Air-Conditioning
12 For refrigeration and air conditioning products, emission calculations are split into two categories: emissions
13 during equipment lifetime, which arise from annual leakage and service losses, and disposal emissions, which occur at the
14 time of discard. Two separate steps are required to calculate the lifetime emissions from leakage and service, and the
15 emissions resulting from disposal of the equipment. The model assumes that equipment is serviced annually so that the
16 amount equivalent to average annual emissions for each product (and hence for the total of what was added to the bank in a
17 previous year in equipment that has not yet reached end-of-life) is replaced/applied to the starting charge size (or chemical
18 bank). For any given year, these lifetime emissions (for existing equipment) and disposal emissions (from discarded
19 equipment) are summed to calculate the total emissions from refrigeration and air-conditioning. As new technologies replace
20 older ones, it is generally assumed that there are improvements in their leak, service, and disposal emission rates.
21 Step 1: Calculate lifetime emissions
22 Emissions from any piece of equipment include both the amount of chemical leaked during equipment operation
23 and the amount emitted during service. Emissions from leakage and servicing can be expressed as follows:
24 Esj = (la + h) xYi Qq-i+1 fori = l->k
25 where:
26 Es = Emissions from Equipment Serviced. Emissions in year j from normal leakage and
27 servicing (including recharging) of equipment.
28 la = Annual Leak Rate. Average annual leak rate during normal equipment operation
29 (expressed as a percentage of total chemical charge).
30 ls = Service Leak Rate. Average leakage during equipment servicing (expressed as a
31 percentage of total chemical charge).
32 Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to
33 charge new equipment in a given year by weight.
34 i = Counter, runs from 1 to lifetime (k).
35 j = Year of emission.
36 k = Lifetime. The average lifetime of the equipment.
37 Step 2: Calculate disposal emissions
38 The disposal emission equations assume that a certain percentage of the chemical charge will be emitted to the
39 atmosphere when that vintage is discarded. Disposal emissions are thus a function of the quantity of chemical contained in
40 the retiring equipment fleet and the proportion of chemical released at disposal:
41 Edj = Qq-k+i x [1 - [rm x rc)]
42 where:
43 Ed = Emissions from Equipment Disposed. Emissions in year j from the disposal of
44 equipment.
A-223
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
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.
J
Assumptions
E
Es
Ed
Total Emissions. Emissions from refrigeration and air conditioning equipment in year
j-
Emissions from Equipment Serviced. Emissions in year j from leakage and servicing
(including recharging) of equipment.
Emissions from Equipment Disposed. Emissions in year j from the disposal of
equipment.
Year of emission.
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i Table fl-145: Refrigeration andflir-Conditioning Market Transition Assumptions
Initial
Market
Segment
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Growth
Rate
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%
R-450A
2017
2017
1%
1.4%
A-225
-------
Initial
Market
Segment
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Growth
Rate
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Name of
Substitute
Start Date
Date of Full
Penetration in
New
Equipment1
Maximum
Market
Penetration
R-513A
R-450A
R-513A
2017
2018
2018
2017
2024
2024
1%
49%
49%
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%
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 DRAFT 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
Rate
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 (Passeng
er Cars)
CFC-12
HFC-134a
1992
1994
100%
HFO-1234yf
2012
2015
1%
None
0.3%
HFO-1234yf
2016
2021
99%
None
Mobile Air Conditioners (Light Duty Trucks)
CFC-12
HFC-134a
1993
1994
100%
HFO-1234yf
2012
2015
1%
None
1.4%
HFO-1234yf
2016
2021
99%
None
Mobile Air Conditioners (School and Tour Buses)
CFC-12
HCFC-22
1994
1995
0.5%
HFC-134a
2006
2007
100%
None
0.3%
HFC-134a
1194
1997
99.5%
None
Mobile Air Conditioners (Transit Buses)
HCFC-22
HFC-134a
1995
2009
100%
None
0.3%
Mobile Air Conditioners (Trains)
HCFC-22
HFC-134a
2002
2009
50%
None
0.3%
R-407C
2002
2009
50%
None
Packaged Terminal Air Conditioners and Heat Pumps
HCFC-22
R-410A
2006
2009
10%
None
3.0%
R-410A
2009
2010
90%
None
Positive Displacement Chillers (Reciprocating and Screw)
CFC-12
HCFC-222
HFC-134a
2000
2009
9%
R-407C
2010
2020
60%
R-450A
2017
2017
1%
2.5%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
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
Rate
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%
R-410A
2010
2020
40%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
A-228 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Initial
Market
Segment
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Growth
Rate
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Name of
Substitute
Start Date
Date of Full
Penetration in
New
Equipment1
Maximum
Market
Penetration
R-407C
2009
2010
9%
R-450A
R-513A
R-450A
R-513A
2017
2017
2018
2018
2017
2017
2024
2024
1%
1%
49%
49%
R-450A
R-513A
None
None
None
None
2018
2018
2024
2024
49%
49%
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
R-410A
2007
2010
2010
2010
29%
71%
None
None
1.3%
R-410A
2000
2005
5%
R-410A
2006
2006
100%
None
R-410A
2000
2006
5%
None
R-410A
2006
2006
20%
None
Retail Food (Large; Technology Transition)
DX3
DX
2000
2006
67.5%
DX
2006
2015
35%
None
1.7%
DR4
2000
2015
23%
None
SLS5
2000
2015
15%
None
DR
2001
2006
22.5%
None
SLS
2001
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-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%
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
Rate
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
Non-ODP/GWP
CO2
Non-ODP/GWP
CO2
Non-ODP/GWP
R-450A
R-513A
2012
2012
2016
2016
2014
2014
2016
2016
2015
2015
2016
2016
2019
2019
2020
2020
1%
3.7%
11%
17.3%
7%
13%
23%
23%
2.2%
HFC-134a
2000
2009
9%
CO2
Non-ODP/GWP
R-450A
R-513A
2014
2014
2016
2016
2019
2019
2020
2020
10%
20%
35%
35%
R-404A
1990
1993
9%
Non-ODP/GWP
R-448A
R-449A
2016
2019
2019
2016
2020
2020
30%
35%
35%
None
None
None
Transport Refrigeration (Road Transport)
CFC-12
HFC-134a
1993
1995
10%
None
5.5%
R-404A
1993
1995
60%
None
HCFC-22
1993
1995
30%
R-410A
2000
2003
5%
CO2
2017
2021
5%
A-230 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Initial
Market
Segment
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Growth
Rate
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
R-404A
2006
2010
95%
C02
2017
2021
5%
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-404
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 | | |
-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%
2018
2018
-0.03%
CO2
2013
2017
39%
Propane
100%
2018
2018
Propane
2014
2014
1%
None
Propane
2015
2015
49%
None
R-450A
2019
2019
5%
None
R-513A
2019
2019
5%
None
R-404A
1995
1998
10%
R-450A
2019
2019
50%
None
R-513A
2019
2019
50%
None
Water-Source and Ground-Source Heat Pumps
HCFC-22
R-407C
2000
2006
5%
None
R-410A
2000
2006
5%
None
HFC-134a
2000
2009
2%
None
R-407C
2006
2009
2.5%
None
1.3%
A-231
-------
Initial
Market
Segment
Primary Substitute
Secondar\
Substitute
Tertiary Substitute
Growth
Rate
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment1
Maximum
Market
Penetration
Name of
Substitute
Start Date
Date of Full
Penetration in
New
Equipment1
Maximum
Market
Penetration
R-410A
HFC-134a
R-407C
R-410A
2006
2009
2009
2009
2009
2010
2010
2010
4.5%
18%
22.5%
40.5%
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.
A-232 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 Table A-146 presents the average equipment lifetimes and annual HFC emission rates (for servicing, leaks, and
2 disposal) for each end-use assumed by the Vintaging Model.
3 Table fl-146: Refrigeration and flir-Contlitioning Lifetime Assumptions
HFC Emission Rates
HFC Emission Rates
End-Use
Lifetime
(Servicing and Leaks)
(Disposal)
(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
11.8
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
4
5 Aerosols
6 ODSs, HFCs, and many other chemicals are used as propellant aerosols. Pressurized within a container, a nozzle
7 releases the chemical, which allows the product within the can to also be released. Two types of aerosol products are
8 modeled: metered dose inhalers (MDI) and consumer aerosols. In the United States, the use of CFCs in consumer aerosols
9 was banned in 1978, and many products transitioned to hydrocarbons or "not-in-kind" technologies, such as solid deodorants
10 and finger-pump hair sprays. Flowever, MDIs continued to use CFCs as propellants because their use was deemed essential.
11 Essential use exemptions granted to the United States under the Montreal Protocol for CFC use in MDIs were limited to the
12 treatment of asthma and chronic obstructive pulmonary disease.
13 All HFCs and PFCs used in aerosols are assumed to be emitted in the year of manufacture. Since there is currently
14 no aerosol recycling, it is assumed that all of the annual production of aerosol propellants is released to the atmosphere. The
15 following equation describes the emissions from the aerosols sector.
16 Ej=Qc]
17 where:
18 E = Emissions. Total emissions of a specific chemical in year j from use in aerosol
19 products, by weight.
20 Qc = Quantity of Chemical. Total quantity of a specific chemical contained in aerosol
21 products sold in year j, by weight.
22 j = Year of emission.
23 Transition Assumptions
24 Transition assumptions and growth rates for those items that use ODSs or HFCs as propellants, including vital
25 medical devices and specialty consumer products, are presented in Table A-147.
26
A-233
-------
i 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
Seqment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Growth Rate
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%
2 1 Transitions between the start year and date of full penetration in new equipment are assumed to be linear.
3 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
4 1993 to 2008.
5 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
6 HFC propellants is modeled.
7
8 Solvents
9 ODSs, HFCs, PFCs and other chemicals are used as solvents to clean items. For example, electronics may need to
10 be cleaned after production to remove any manufacturing process oils or residues left. Solvents are applied by moving the
11 item to be cleaned within a bath or stream of the solvent. Generally, most solvents are assumed to remain in the liquid phase
12 and are not emitted as gas. Thus, emissions are considered "incomplete," and are a fixed percentage of the amount of solvent
13 consumed in a year. The solvent is assumed to be recycled or continuously reused through a distilling and cleaning process
14 until it is eventually almost entirely emitted. The remainder of the consumed solvent is assumed to be entrained in sludge or
15 wastes and disposed of by incineration or other destruction technologies without being released to the atmosphere. The
16 following equation calculates emissions from solvent applications.
17 Ej = lxQq
18 where:
19 E = Emissions. Total emissions of a specific chemical in year j from use in solvent
20 applications, by weight.
21 I = Percent Leakage. The percentage of the total chemical that is leaked to the atmosphere,
22 assumed to be 90 percent.
23 Qc = Quantity of Chemical. Total quantity of a specific chemical sold for use in solvent
24 applications in the year j, by weight.
25 j = Year of emission.
26 Transition Assumptions
27 The transition assumptions and growth rates used within the Vintaging Model for electronics cleaning, metals
28 cleaning, precision cleaning, and adhesives, coatings and inks, are presented in Table A-148.
A-234 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
Table A-148:
Solvent Market Transition Assumptions
Primary Substitute
Secondary 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
Segment
Substitute
Date
Equipment1
Penetration
Substitute
Date
Equipment1
Penetration
Rate
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
1996
40%
None
CH3CCI3
Non-ODP/GWP
1996
1997
99.8%
None
2.0%
PFC/PFPE
1996
1997
0.2%
Non-ODP/GWP
2000
2003
90%
Non-ODP/GWP
2005
2009
10%
Metals
CH3CCI3
Non-ODP/GWP
1992
1996
100%
None
2.0%
CFC-113
Non-ODP/GWP
1992
1996
100%
None
2.0%
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
1996
96%
None
2.0%
HCFC-225ca/cb
1995
1996
1%
Unknown
HFE-7100
1995
1996
3%
None
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
1 Transitions between the start year and date of full
Note: Non-ODP/GWP includes chemicals with zero
"no clean" technologies.
penetration in new equipment are assumed to be linear.
ODP and low GWP, such as hydrocarbons and ammonia, as well as not-in-kind alternatives such as
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. TTalon 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.
Ej = r x 2 Qcj-ui for i=l —>k
where:
Emissions. Total emissions of a specific chemical in year j for streaming fire
extinguishing equipment, by weight.
Percent Released. The percentage of the total chemical in operation that is released to
the atmosphere.
A-235
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
2c
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
Rate
Flooding Agents
Halon-1301
Halon-13012
1994
1994
4%
Unknown
2.2%
HFC-23
1994
1999
0.2%
None
HFC-227ea
1994
1999
18%
FK-5-1-12
2003
2010
10%
HFC-125
2001
2008
10%
Non-ODP/GWP
1994
1994
46%
FK-5-1-12
2003
2010
7%
Non-ODP/GWP
1995
2034
10%
None
Non-ODP/GWP
1998
2027
10%
None
C4F10
1994
1999
1%
FK-5-1-12
2003
2003
100%
HFC-125
1997
2006
11%
None
Streaming Agents
Halon-1211
Halon-1211*
1992
1992
5%
Unknown
3.0%
HFC-236fa
1997
1999
3%
None
Halotron
1994
1995
0.1%
None
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
2 Despite the 1994 consumption ban:
supplies.
and date of full penetration in new equipment are assumed to be linear.
a small percentage of new halon systems are assumed to continue to be built and filled with stockpiled or recovered
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.
where:
Enii
Im
Errij = Imx Qq
Emissions from manufacturing. Total emissions of a specific chemical in year j due to
manufacturing losses, by weight.
Loss Rate. Percent of original blowing agent emitted during foam manufacture. For
open-cell foams, Im is 100%.
A-236 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture
2 closed-cell foams in a given year.
3 j = Year of emission.
4 Step 2: Calculate lifetime emissions (closed-cell foams)
5 Lifetime emissions occur annually from closed-cell foams throughout the lifetime of the foam, as calculated as
6 presented in the following equation.
7 Euj = lux 2 Qcj-ui fori=l->k
8 where:
9 Euj = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due
10 to lifetime losses during use, by weight.
11 lu = Leak Rate. Percent of original blowing agent emitted each year during lifetime use.
12 Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture
13 closed-cell foams in a given year.
14 i = Counter, runs from 1 to lifetime (k).
15 j = Year of emission.
16 k = Lifetime. The average lifetime of foam product.
17 Step 3: Calculate disposal emissions (closed-cell foams)
18 Disposal emissions occur in the year the foam is disposed, and are calculated as presented in the following equation.
19 Edj= Id x Qq-k
20 where:
21 Edj = Emissions from disposal. Total emissions of a specific chemical in year j at disposal,
22 by weight.
23 Id = Loss Rate. Percent of original blowing agent emitted at disposal.
24 Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture
25 closed-cell foams in a given year.
26 j = Year of emission.
27 k = Lifetime. The average lifetime of foam product.
28 Step 4: Calculate post-disposal emissions (closed-cell foams)
29 Post-disposal emissions occur in the years after the foam is disposed; for example, emissions might occur while
30 the disposed foam is in a landfill. Currently, the only foam type assumed to have post-disposal emissions is polyurethane
31 foam used as domestic refrigerator and freezer insulation, which is expected to continue to emit for 26 years post-disposal,
32 calculated as presented in the following equation.
33 Epj = Ip x 2 Qcj-m for m=k->k + 26
34 where:
35 Epj = Emissions from post disposal. Total post-disposal emissions of a specific chemical in
36 year j, by weight.
37 Ip = Leak Rate. Percent of original blowing agent emitted post disposal.
38 Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture
39 closed-cell foams in a given year.
40 k = Lifetime. The average lifetime of foam product.
A-237
-------
1
2
Counter. Runs from lifetime (k) to (k+26).
Year of emission.
3 Step 5: Calculate total emissions (open-cell and closed-cell foams)
4 To calculate total emissions from foams in any given year, emissions from all foam stages must be summed, as
5 presented in the following equation.
6 Ej = Errij + Euj + Edj + Epj
7 where:
8 Ej
9 Em =
10
11 Euj =
12
13 Edj
14
15 EPj
16
17 Assumptions
18 The Vintaging Model contains thirteen foam types, whose transition assumptions away from ODS and growth rates
19 are presented in Table A-150. The emission profiles of these thirteen foam types are shown in Table A-151.
Total Emissions. Total emissions of a specific chemical in year j, by weight.
Emissions from manufacturing. Total emissions of a specific chemical in year j due to
manufacturing losses, by weight.
Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due
to lifetime losses during use, by weight.
Emissions from disposal. Total emissions of a specific chemical in year j at disposal,
by weight.
Emissions from post disposal. Total post-disposal emissions of a specific chemical in
year j, by weight.
A-238 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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
Rate
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
Rate
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
2002
2002
2003
2003
30%
60%
HCFO-1233zd(E)
None
None
2016
2020
100%
6.0%
Non-ODP/GWP
2001
2003
10%
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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
Rate
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 1 Transitions between the start year and date of full penetration in new equipment are assumed to be linear.
2 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
3 shown in the table in order to provide the HFC transitions in greater detail.
A-241
-------
i Table A-151: Emission Profile forthe 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 (CFC-11, HCFC-141b, HFC-245fa, HCFO-
1233zd(E), No GWP/ODP)3
3.75
0.25
14
39.9
47.15
Rigid PU: Domestic Refrigerator and Freezer
Insulation (HFC-134a)a
6.5
0.5
14
37.2
47.15
PU and PIR Rigid: Boardstock
6
1
25
69.0
100
PU Sandwich Panels: Continuous and Discontinuous
8.5-11.25
0.5
50
63.75-66.5
100
2 PIR (Polyisocyanurate)
3 PU (Polyurethane)
4 XPS (Extruded Polystyrene)
5 a In general, total emissions from foam end-uses are assumed to be 100 percent. In the Rigid PU Domestic Refrigerator and Freezer Insulation end-use,
6 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-
7 disposal.
8
9 Sterilization
10 Sterilants kill microorganisms on medical equipment and devices. The principal ODS used in this sector was a
11 blend of 12 percent ethylene oxide (EtO) and 88 percent CFC-12, known as "12/88." In that blend, ethylene oxide sterilizes
12 the equipment and CFC-12 is a dilutent solvent to form a non-flammable blend. The sterilization sector is modeled as a
13 single end-use. For sterilization applications, all chemicals that are used in the equipment in any given year are assumed to
14 be emitted in that year, as shown in the following equation.
15 Ej = Qq
16 where:
17 E
18
19 Qc
20
21 j
22 Assumptions
23 The Vintaging Model contains one sterilization end-use, whose transition assumptions away from ODS and growth
24 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
1%
None
HCFC/EtO Blends
1993
1994
4%
Non-ODP/GWP
2010
2010
100%
None
2 1 Transitions between the start year and date of full penetration in new equipment are assumed to be linear.
A-243
-------
1 Model Output
2 By repeating these calculations for each year, the Vintaging Model creates annual profiles of use and emissions
3 for ODS and ODS substitutes. The results can be shown for each year in two ways: 1) on a chemical-by-chemical basis,
4 summed across the end-uses, or 2) on an end-use or sector basis. Values for use and emissions are calculated both in metric
5 tons and in million metric tons of CO2 equivalent (MMT CO2 Eq.). The conversion of metric tons of chemical to MMT CO2
6 Eq. is accomplished through a linear scaling of tonnage by the global warming potential (GWP) of each chemical.
7 Throughout its development, the Vintaging Model has undergone annual modifications. As new or more accurate
8 information becomes available, the model is adjusted in such a way that both past and future emission estimates are often
9 altered.
10 Bank of ODS and ODS Substitutes
11 The bank of an ODS or an ODS substitute is "the cumulative difference between the chemical that has been
12 consumed in an application or sub-application and that which has already been released" (IPCC 2006). For any given year,
13 the bank is equal to the previous year's bank, less the chemical in equipment disposed of during the year, plus chemical in
14 new equipment entering the market during that year, less the amount emitted but not replaced, plus the amount added to
15 replace chemical emitted prior to the given year, as shown in the following equation:
16 Bq = Bcj-i-Qdj+Qpj+Ee-Qr
17 where:
18 Bcj = Bank of Chemical. Total bank of a specific chemical in year j, by weight.
19 Qdj = Quantity of Chemical in Equipment Disposed. Total quantity of a specific chemical in
20 equipment disposed of in year j, by weight.
21 Qpj = Quantity of Chemical Penetrating the Market. Total quantity of a specific chemical
22 that is entering the market in year j, by weight.
23 Ee = Emissions of Chemical Not Replaced. Total quantity of a specific chemical that is
24 emitted during year j but is not replaced in that year. The Vintaging Model assumes
25 all chemical emitted from refrigeration, air conditioning and fire extinguishing
26 equipment is replaced in the year it is emitted, hence this term is zero for all sectors
27 except foam blowing.
28 Qr = Chemical Replacing Previous Year's Emissions. Total quantity of a specific chemical
29 that is used to replace emissions that occurred prior to year j. The Vintaging Model
30 assumes all chemical emitted from refrigeration, air conditioning and fire
31 extinguishing equipment is replaced in the year it is emitted, hence this term is zero
32 for all sectors.
33 j = Year of emission.
34 Table A-153 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 1990 to 2016.
A-244 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i Table fl-153: Banks of OPS and OPS Substitutes, 1990-2016 [MT1
Year CFC HCFC HFC
1990
683,812
281,709
872
1995
761,171
508,368
49,295
2000
635,849
938,342
182,398
2001
607,607
1,007,873
210,388
2002
583,435
1,061,219
238,332
2003
559,499
1,097,975
272,030
2004
535,143
1,135,575
307,244
2005
505,684
1,176,784
344,659
2006
475,707
1,213,959
387,933
2007
448,358
1,242,230
432,429
2008
426,222
1,259,372
473,437
2009
413,431
1,251,425
519,154
2010
376,199
1,214,311
584,001
2011
339,448
1,166,817
647,961
2012
302,837
1,118,161
718,801
2013
267,100
1,064,634
791,307
2014
231,330
1,009,540
863,770
2015
195,498
955,581
932,018
2016
159,713
905,314
993,504
2
3 References
4 IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
5 Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
6 Tanabe (eds.). Hayama, Kanagawa, Japan.
7
A-245
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
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
1780
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 DRAFT 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
3
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.
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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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 A-158: Example of Monthly Average Populations from Calf Transition Matrix (1,000 head)
Age (month)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
6
1,138
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
o 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 Stocker Transition Matrix [1,000 head)
Age (month) Jan Feb Mar Apr May Jun Jul Aug Sep Oct
Nov
Dec
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
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
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26
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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
alnput 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
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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: Feedlot Placements in the United States for 2015 [Number of animals placed/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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
12
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 2007-2008 survey on cow-calf system
management practices (USDA:APF[IS: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
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1
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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
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 1990-2006 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:
( 1-22 1
Ym = 4.52eWr-i98oJ
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
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):
Ym = Y (1990) EXpi? — ,1 !EXp{- — ,1
{(Year- 1980)J (l 990 -1980))
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:APF[IS: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.
^ For example, the West has a forage DE of 61.3 which makes up 90 percent of the diet and a supplemented diet DE of 67.4 percent was
used for 10 percent of the diet, for a total weighted DE of 61.9 percent, as shown in Table A-170.
A-255
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1 Table A-171 shows the regional DE and Ym for U. S. cattle in each region for 2015.
2 Table fl-167: Feed Components and Digestible Energy Values Incorporated into Forage Diet Composition Estimates
Forage Type
O 00 00 00 c .£» o> £ .E
ii_ ra w w 3 3 3 o >
OQ-Q-aJQ- -9-9< "E > 5 5
ca ca E ca o> o> a> a> 00)0
o> a) o) a) .g o> *o c "a
m l. m L m — C C C C Q, C IV k.
LU i- o. i- 3 *- ro ra to ra ra a> ra o. o
q CD o m o ll. ai cl ai oc S
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
A-256 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
CD "co "co "co c ^ o)
o
< ^ 5 5 ¥
Forage Type
jjj d) OCT o
JB O) "O E
O- c ro ~
_ CD nj QJ CL
C3 w C3 w id LL. ai ei o; eg co ai g a
Cm
-------
i 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 #082,134,101"
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%
2 3 Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above.
3 b Not in equal proportions.
4 Sources of representative regional diets: Donovan (1999), Preston (2010), Archibeque (2011), and USDA:APHIS:VS (2010).
5
6 Table fl-170: Foraging Animal DE [% 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%
7 3 Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. To see the regional
8 designation per state, please see Table A-166.
9 b DE is the digestible energy in units of percent of GE (MJ/Day).
10 c Ym is the methane conversion rate, the fraction of GE in feed converted to methane.
11
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Table A-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 CFU)
• 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 =
'NEm+NEa+NEl+NEwork + NEf
REM
NE^_
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
-------
1
2
3
4
5
6
7
8
9
10
11
12
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 DRAFT 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
2 Step 3b: Determine Emission Factor
3 The daily emission factor (DayEmit) was determined using the GE value and the methane conversion factor (Ym)
4 for each category. This relationship is shown in the following equation:
G£xY
DayEmit = —
5 55.65
6 where,
7 DayEmit = Emission factor (kg CTU/head/day)
8 GE = Gross energy intake (MJ/head/day)
9 Ym = CH4 conversion rate, which is the fraction of GE in feed converted to CH4 (%)
10 55.65 = A factor for the energy content of methane (MJ/kg CH4)
11
12 The daily emission factors were estimated for each animal type and state. Calculated annual national emission
13 factors are shown by animal type in Table A-173. State-level emission factors are shown by animal type for 2015 in Table
14 A-174.
15 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
16 Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the number of days in a year).
17
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
-------
i 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
2 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 For quality assurance purposes, U.S. emission factors for each animal type were compared to estimates provided
2 by the other Annex I member countries of the United Nations Framework Convention on Climate Change (UNFCCC) (the
3 most recently available summarized results for Annex I countries are through 2012 only). Results, presented in Table A-
4 175, indicate that U.S. emission factors are comparable to those of other Annex I countries. Results in Table A-175 are
5 presented along with Tier I emission factors provided by IPCC (2006). Throughout the time series, beef cattle in the United
6 States generally emit more enteric CH4 per head than other Annex I member countries, while dairy cattle in the United States
7 generally emit comparable enteric CFU per head.
8
9 Table fl-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
10
Step 3c: Estimate Total Emissions
11
12
13
14
15
16
17
18
19
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 = DayEmiWe x Days/Month x SubPopstate
Emissions for state during the month (kg CFU)
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
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
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: CH4 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
6 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
-------
i 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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
-------
i 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
2 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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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15 USDA (2002) Census of Agriculture: 2002 Census Report. United States Department of Agriculture. Available online at:
16 .
17 USDA (1997) Census of Agriculture: 1997 Census Report. United States Department of Agriculture. Available online at:
18 . Accessed July 18, 2011.
19 USDA (1996) Beef Cow/Calf Health and Productivity Audit (CHAPA): Forage Analyses from Cow/Calf Herds in 18
20 States. National Agriculture Statistics Service, U.S. Department of Agriculture. Washington, D.C. Available online
21 at . March 1996.
22 USDA (1992) Census of Agriculture: 1992 Census Report. United States Department of Agriculture. Available online at:
23 . Accessed July 18, 2011.
24 USDA:APF[IS:VS (2010) Beef 2007-08, Part V: Reference of Beef Cow-calf Management Practices in the United States,
25 2007-08. USDA-APHIS-VS, CEAH. Fort Collins, CO.
26 USDA:APHIS: VS (2002) Reference of2002 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort Collins, CO.
27 Available online at .
28 USDA:APHIS: VS (1998) Beef '97, Parts I-IV. USDA-APHIS-VS, CEAH. Fort Collins, CO. Available online at
29 .
30 USDA: APHIS: VS (1996) Reference of1996 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort Collins, CO.
31 Available online at .
32 USDA: APHIS: VS (1994) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins, CO.
33 Available online at .
34 USDA: APHIS: VS (1993) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins, CO.
35 August 1993. Available online at .
36 Vasconcelos and Galyean (2007) Nutritional recommendations of feedlot consulting nutritionists: The 2007 Texas Tech
37 University Study. J. Anim. Sci. 85:2772-2781.
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3.11. Methodology for Estimating ChU 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 chapter (Chapter 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.
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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:
( cp% Y
aF
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„
' (7.03'Mg)'
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:
N = L™.
milk ^
O.Jo
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.
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.
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
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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,
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
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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.
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
A-276 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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(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 (IPCC 2006). The van't Hoff-Arrhenius
equation, with a base temperature of 30°C, is shown in the following equation (Safley and Westerman 1990):
where,
/
= van't Hoff-Arrhenius/factor, the proportion of VS that are biologically available for
conversion to CFU based on the temperature of the system
Ti
= 303.15K
t2
= Ambient temperature (EC) 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.
• 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.
A-277
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• 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:
MCF
CH4 generated mnnal
VS produced mnualxB0
where,
MCF annual
VS produced annual
Bo
= 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 FraCmnotmeach 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 excreted state>Alllmal,WMS = PopulalionSM(, Animal x x VS x WMSx 365.25
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
A-278 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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 = ^(VSexcreted StlltlAl«a,™sxB0 xMCFx0.662)
State, Animal, WMS
where,
CFLi = CFU emissions (kg CHVyr)
VS excreted wms, state = Amount of VS excreted in manure managed in each WMS (kg/yr)
B0 = Maximum CFU producing capacity (m3 CFLi/kg VS)
MCF animal, state, wms = MCF for the animal group, state and WMS (percent)
0.662 = Density of methane at 25° C (kg CFLi/m3 CFU)
A calculation was developed to estimate the amount of CH4 emitted from AD systems utilizing CFU capture and
combustion technology. First, AD systems were assumed to produce 90 percent of the maximum CH4 producing capacity
(Bo) of the manure. This value is applied for all climate regions and AD system types. Flowever, this is a conservative
assumption as the actual amount of CFU produced by each AD system is very variable and will change based on operational
and climate conditions and an assumption of 90 percent is likely overestimating CFU production from some systems and
underestimating CFU production in other systems. The CFU production of AD systems is calculated using the equation below:
TAM
CH4 ProductionADADSystem = ProductionADADSystem x ^qqq x ^ x B0 x 0.662 x 365.25 x 0.90
where,
CFLi Production ADad system = CFU production from a particular AD system, (kg/yr)
Population AD state = Number of animals on a particular AD system
VS = Volatile solids production rate (kg VS/1000 kg animal mass-day)
TAM = Typical Animal Mass (kg/head)
B0 = Maximum CFLi producing capacity (CFU m3/kg VS)
0.662 = Density of CH4 at 25° C (kg CH4/m3 CH4)
365.25 = Days/year
0.90 = CFU production factor for AD systems
The total amount of CFU produced by AD is calculated only as a means to estimate the emissions from AD; i.e.,
only the estimated amount of CFU actually entering the atmosphere from AD is reported in the inventory. The emissions to
the atmosphere from AD are a result of leakage from the system (e.g., from the cover, piping, tank, etc.) and incomplete
combustion and are calculated using the collection efficiency (CE) and destruction efficiency (DE) of the AD system. The
three primary types of AD systems in the United States are covered lagoons, complete mix and plug flow systems. The CE
of covered lagoon systems was assumed to be 75 percent, and the CE of complete mix and plug flow AD systems was
assumed to be 99 percent (EPA 2008). The CH4 DE from flaring or burning in an engine was assumed to be 98 percent;
therefore, the amount of CH4 that would not be flared or combusted was assumed to be 2 percent (EPA 2008). The amount
of CFU produced by systems with AD was calculated with the following equation:
A-279
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CH, Emissions AD =
State, Animal, AD Systems
[CH4 Production AD
+ [cH4 Production AD Ar)svslcmx (l -CE
AD system X AD system X
(1-DE)]
AD system
where,
CH4 Emissions AD = CH4 emissions from AD systems, (kg/yr)
CH4 Production ADad system = CH4 production from a particular AD system, (kg/yr)
CEad system = Collection efficiency of the AD system, varies by AD system type
DE = Destruction efficiency of the AD system, 0.98 for all systems
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 stat» WMS = Populationstat x WMSx
TAM
1000
xNex 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 state)Animal)WM3 = Populationstate)Animal x WMSx 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
Direct N2O = £ | N excreted
State, Animal, WMS
State, Animal, WMS A ^ WMS
EF,i
44
28
where,
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
(kg/yr)
EFwms = Direct N2O emission factor from IPCC guidelines (kg N2O-N /kg N)
44/28 = Conversion factor of N2O-N to N2O
Indirect N2O emissions were calculated for all animals with the following equation:
A-280 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Indirect N2O = X
State, Animal, WMS
N excreted
N excreted
Frac
gas, WMS
State, Animal, WMS
State, Animal, WMS
100
:EF,
volatiliziion
44
28
Frac
runoff/leach, WMS
loo
:EF,
runnoff/leach
44
28
where,
Indirect N2O
N excreted state, Animal, WMS
F raCgas,wMs
F raCrUn0f61each,WMS
EF volatilization
EF runofPleach
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
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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
18,142
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
Dairy Cows
10,015
9,482 ?
9,183
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
Dairy Heifer
4,129
4,108
4,008
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
Dairy Calves
5,369
5,091
17,431
17,508
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
Swine3
53,941
58,899
58,864
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
Market <50 lb.
18,359
19,656
19,574
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
Market 50-119
lb.
11,734
12,836
12,926
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
Market 120-179
lb.
9,440
10,545
10,748
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
Market >180 lb.
7,510
8,937
9,385
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
Breeding
6,899
6,926
6,231
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
Beef Cattleb
81,576
90,361
84,810
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
Feedlot Steers
6,357
7,233
8,304
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
Feedlot Heifers
3,192
3,831
4,702
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
NOF Bulls
2,160
2,385
2,293
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
Beef Calves
16,909
18,177
4,951
4,710
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
NOF Heifers
10,182
11,829
9,781
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
NOF Steers
10,321
11,716
8,724
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
NOF Cows
32,455
35,190
33,575
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
Sheep
11,358
8,989
7,036
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
Sheep On Feed
1,180
1,771
2,963
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
Sheep NOF
10,178
7,218
4,073
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
Goats
2,516
2,357
2,419
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
Poultry1
1,537,074
1,826,977
2,033,123
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
Hens>1 yr.
273,467
299,071
333,593
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
Pullets
73,167
81,369
95,159
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
Chickens
6,545
7,637
8,088
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
Broilers
1,066,209
1,331,940
1,506,127
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
Turkeys
117,685
106,960
90,155
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
Horses
2,212
2,632
3,395
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
Mules and Asses
63
101
112
109
105
141
177
212
248
284
286
287
289
291
293
294
296
American Bison
47
104
194
213
232
225
218
212
205
198
191
184
177
169
162
155
148
1 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.
2 b NOF - Not on Feed
3 c Pullets includes laying pullets, pullets younger than 3 months, and pullets older than 3 months.
4 Note: Totals may not sum due to independent rounding.
5 Source(s): See Step 1: Livestock Population Characterization Data
6
7
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 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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 fl-184: Estimated Volatile Solids IVS) 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 DRAFT 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 DRAFT 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
1 a Beef NOF Bull values were used for American bison Nex and VS.
2 Source: CEFM.
3
Table fl-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 Feediots
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
1 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.
2 b Because manure from beef feediots 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
3 percent.
4 Source(s): See Step 3: Waste Management System Usage Data
A-288 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 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.
2 a Manure management system descriptions are based on the 2006IPCC Guidelines for National Greenhouse Gas Inventories (Volume 4: Agriculture, Forestry and
3 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
4 to the National Pollutant Discharge Elimination System Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations (EPA-821 -R-03-001,
5 December 2002).
6
7 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)
1
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 DRAFT 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.
1
Table A-190: Direct Nitrous Oxide Emission Factors (kg N20-N/kg Kjdl N)105
Direct N2O Emission
Waste Management System
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
3
4 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
1 a Data for nitrogen losses due to leaching were not available, so the values represent only nitrogen losses due to runoff.
2 Source: EPA (2002b, 2005).
A-292 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i Table fl-192: Total Methane Emissions from Livestock Manure Management [kt)3"16
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
0
0
0
0
0
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
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
2 + Does not exceed 0.5 kt.
3 a Accounts for CH4 reductions due to capture and destruction of CbU at facilities using anaerobic digesters.
4
5 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
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.
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-293
-------
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
1 + Does not exceed 0.5 kt.
2 NA (Not Applicable)
3 Note: American bison are maintained entirely on unmanaged WMS; there are no American bison N2O emissions from managed systems.
A-294 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i Table fl-194: Methane Emissions by 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
1 + Does not exceed 0.00005 kt.
2 a Accounts for CbU reductions due to capture and destruction of CbU at facilities using anaerobic digesters.
3 b Beef Not on Feed includes calves.
4
109
5 Table fl-195: 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 DRAFT 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
0.0046
0.0002
0.0031
+
NA
0.2178
Massachusetts
0.0001
0.0002
0.0106
0.0082
0.0003
0.0001
0.0006
+
0.0024
0.0027
0.0003
0.0023
+
NA
0.0278
Michigan
0.1036
0.2096
0.6380
0.3560
0.1016
0.0155
0.0612
0.0180
0.0151
0.0249
0.0008
0.0090
0.0002
NA
1.5535
Minnesota
0.2515
0.5083
0.6572
0.5547
0.6848
0.0807
0.0468
0.0149
0.1188
0.0426
0.0009
0.0059
0.0001
NA
2.9672
Mississippi
0.0038
0.0076
0.0053
0.0041
0.0415
0.0062
0.0423
0.2322
0.0024
0.0046
0.0006
0.0062
0.0004
NA
0.3570
Missouri
0.0468
0.0943
0.1073
0.1095
0.2157
0.0569
0.0568
0.0947
0.0551
0.0279
0.0032
0.0111
0.0004
NA
0.8796
Montana
0.0283
0.0569
0.0188
0.0153
0.0140
0.0034
0.0024
+
0.0024
0.0237
0.0003
0.0106
0.0002
NA
0.1762
Nebraska
1.6457
3.3280
0.0799
0.0425
0.2698
0.0596
0.0341
0.0180
0.0024
0.0266
0.0006
0.0072
0.0002
NA
5.5145
Nevada
0.0026
0.0053
0.0376
0.0209
+
+
0.0042
+
0.0024
0.0076
0.0008
0.0028
+
NA
0.0843
New Hampshire
0.0001
0.0001
0.0125
0.0066
+
+
0.0043
+
0.0024
0.0027
0.0002
0.0010
+
NA
0.0299
New Jersey
0.0001
0.0002
0.0058
0.0044
0.0004
0.0001
0.0043
+
0.0024
0.0046
0.0002
0.0030
+
NA
0.0255
New Mexico
0.0065
0.0132
0.4053
0.2332
+
+
0.0048
+
0.0024
0.0099
0.0008
0.0055
0.0001
NA
0.6819
New York
0.0167
0.0339
0.5709
0.4138
0.0045
0.0008
0.0269
0.0180
0.0024
0.0305
0.0010
0.0105
0.0002
NA
1.1301
North Carolina
0.0026
0.0052
0.0331
0.0132
0.6712
0.1203
0.0923
0.2644
0.0898
0.0114
0.0014
0.0068
0.0004
NA
1.3121
North Dakota
0.0280
0.0567
0.0218
0.0117
0.0088
0.0044
0.0043
+
0.0024
0.0210
0.0001
0.0051
0.0001
NA
0.1644
Ohio
0.1102
0.2229
0.3582
0.2316
0.2128
0.0262
0.1466
0.0258
0.0151
0.0459
0.0012
0.0125
0.0003
NA
1.4093
Oklahoma
0.1736
0.3507
0.0481
0.0549
0.1469
0.0616
0.0172
0.0697
0.0024
0.0173
0.0020
0.0175
0.0005
NA
0.9625
Oregon
0.0548
0.1110
0.1444
0.1130
0.0002
0.0001
0.0111
0.0180
0.0024
0.0243
0.0009
0.0067
0.0001
NA
0.4869
Pennsylvania
0.0627
0.1260
0.4561
0.3336
0.1024
0.0142
0.1130
0.0612
0.0188
0.0328
0.0013
0.0138
0.0004
NA
1.3363
Rhode Island
+
0.0001
0.0008
0.0005
+
+
0.0043
+
0.0024
0.0027
+
0.0002
+
NA
0.0110
South Carolina
0.0009
0.0018
0.0084
0.0037
0.0227
0.0016
0.0252
0.0780
0.0024
0.0046
0.0011
0.0065
0.0002
NA
0.1571
South Dakota
0.2515
0.5083
0.1437
0.1336
0.1087
0.0244
0.0074
+
0.0125
0.0837
0.0006
0.0077
0.0001
NA
1.2821
Tennessee
0.0062
0.0127
0.0226
0.0159
0.0187
0.0025
0.0093
0.0595
0.0024
0.0168
0.0020
0.0078
0.0005
NA
0.1769
Texas
1.6347
3.3045
0.5806
0.5408
0.0713
0.0133
0.0977
0.1956
0.0024
0.0794
0.0214
0.0418
0.0025
NA
6.5860
Utah
0.0160
0.0323
0.1305
0.1100
0.0573
0.0107
0.0240
+
0.0104
0.0320
0.0004
0.0066
0.0001
NA
0.4303
Vermont
0.0004
0.0007
0.1181
0.0673
0.0001
+
0.0005
+
0.0024
0.0027
0.0004
0.0012
0.0001
NA
0.1938
Virginia
0.0132
0.0267
0.0512
0.0288
0.0256
0.0006
0.0154
0.0844
0.0493
0.0286
0.0013
0.0096
0.0003
NA
0.3352
Washington
0.1369
0.2771
0.3563
0.2674
0.0012
0.0005
0.0347
0.0180
0.0024
0.0065
0.0007
0.0056
0.0001
NA
1.1075
West Virginia
0.0028
0.0056
0.0071
0.0047
+
+
0.0078
0.0301
0.0087
0.0126
0.0004
0.0022
0.0001
NA
0.0822
Wisconsin
0.1709
0.3452
1.9134
1.4068
0.0245
0.0055
0.0230
0.0173
0.0024
0.0253
0.0019
0.0106
0.0002
NA
3.9470
Wyoming
0.0491
0.0992
0.0077
0.0095
0.0049
0.0047
0.0048
+
0.0024
0.0380
0.0003
0.0076
0.0001
NA
0.2285
+ 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.
A-297
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23
24
25
26
27
28
29
30
31
32
33
34
35
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38
39
40
41
42
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44
45
46
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Natural Resource Conservation Service and Lee-Ann Tracy, ERG. June 27, 2000.
Sweeten, J. (2000) Personal Communication. John Sweeten, Texas A&M University and Indra Mitra, ERG. June 2000.
UEP (1999) Voluntary Survey Results—Estimated Percentage Participation/Activity. Caged Layer Environmental
Management Practices, Industry data submissions for EPA profile development, United Egg Producers and National
Chicken Council. Received from John Thorne, Capitolink. June 2000.
USDA (2016a) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. Available online at: .
USDA (2016b) Chicken and Eggs 2015 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. July 2016. Available online at: .
USDA (2016c) Poultry - Production and Value 2015 Summary. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. July 2016. Available online at: .
USDA(2016d) 1987, 1992, 1997, 2002, 2007, and 2012 Census of Agriculture. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, D.C. Available online at: . July
2016.
USDA (2015a) Chicken and Eggs 2014 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2015. Available online at:
.
USDA (2015b) Poultry - Production and Value 2014 Summary. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. April 2015. Available online at:
.
USDA (2014a) 1987, 1992, 1997, 2002, 2007, and 2012 Census of Agriculture. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, D.C. Available online at: . May
2014.
USDA (2014b) Chicken and Eggs 2013 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2014. Available online at:
.
USDA (2014c) Poultry - Production and Value 2013 Summary. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. April 2014. Available online at:
.
USDA (2013a) Chicken and Eggs 2012 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2013. Available online at:
.
USDA (2013b) Poultry - Production and Value 2012 Summary. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. April 2013. Available online at:
.
USDA (2012a) Chicken and Eggs 2011 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2012. Available online at:
.
USDA (2012b) Poultry - Production and Value 2011 Summary. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. April 2012. Available online at:
.
USDA (201 la) Chicken and Eggs 2010 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2011. Available online at:
.
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USDA (201 lb) Poultry - Production and Value 2010 Summary. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. April 2011. Available online at:
.
USDA (2010a) Chicken and Eggs 2009 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2010. Available online at:
.
USDA (2010b) Poultry - Production and Value 2009 Summary. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. April 2010. Available online at:
.
USDA (2009a) Chicken and Eggs 2008 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. February 2009. Available online at:
.
USDA (2009b) Poultry - Production and Value 2008 Summary. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. April 2009. Available online at:
.
USDA (2009c) Chicken and Eggs - Final Estimates 2003-2007. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. March 2009. Available online at:
.
USDA (2008) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651.
Natural Resources Conservation Service, U.S. Department of Agriculture.
USDA (2004a) Chicken and Eggs—Final Estimates 1998-2003. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. April 2004. Available online at:
.
USDA (2004b) Poultry Production and Value—Final Estimates 1998-2002. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. April 2004. Available online at:
.
USDA (1999) Poultry Production and Value—Final Estimates 1994-97. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. March 1999. Available online at:
.
USDA (1998) Chicken and Eggs—Final Estimates 1994-97. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. December 1998. Available online at:
.
USDA (1996) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651.
Natural Resources Conservation Service, U.S. Department of Agriculture. July 1996.
USDA (1995a) Poultry Production and Value—Final Estimates 1988-1993. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, D.C. March 1995. Available online at:
.
USDA (1995b) Chicken and Eggs—Final Estimates 1988-1993. National Agriculture Statistics Service, U.S. Department
of Agriculture. Washington, D.C. December 1995. Available online at:
.
USDA (1994) Sheep and Goats—Final Estimates 1989-1993. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, D.C. January 31, 1994. Available online at:
.
USDA, APFUS (2003) Sheep 2001, Parti: Reference of Sheep Management in the United States, 2001 and Part
IV:Baseline Reference of2001 Sheep Feedlot Health and Management. USDA-APFHS-VS. Fort Collins, CO.
#N356.0702. .
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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.
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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
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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.
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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 1-7: DayGent Model Flow Diagram
tTN
Plant EVI/PRDX A
Production V
Submodel
f(TEMP)
f(WFPS)
f(SOLAR) ILS
Biomass
k
\7
SOM
Submodel
f(TEXT)
f(MoisT> Active
f(TEMP) SQM
f(Kp)
f(Lignin:N^
c°^
. IN min
Slow
SOM
CO„Nmin
X(STORMf,
Passive
SOM
CO,,Nmin
xz
Dissolved Organic C, Dissolved Organic N, Mineral N
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Figure ft-8: Medeled versus measured net primary production (g G m l
Yield Carbon from Published Data (g m"2)
Yield Carbon from Published Data (g m"2)
Part a) presents results of the NASA-CASA algorithm (r3 = 83°/§ and part b) presents the results of a single parameter
value for maximum net primary production (r2 = 64°/Q.
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 1XII: ) 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 CH4 to the atmosphere occurs
through the rice plant and via ebullition (i.e., bubbles). CI 11 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 Grossq et al. (2001,
2011), Cheng et al. (2013) and Metherell et al. (1993), and archived copies of the model source code are available.
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[BEGIN TEXT BOX]
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 Kmx 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 NH4+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(pF[) = the effect of soil pH 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). Heterotrophic soil respiration is used as a proxy for
labile C availability, while oxygen availability is a function of soil physical properties that influence gas diffusivity, soil
WFPS, and oxygen demand. The model selects the minimum of the NO3" and CO2 functions to establish a maximum potential
denitrification rate. 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
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12
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,
Rn2/n2o = the ratio of N2/N2O
FrfTSICVCCh) = a function estimating the impact of the availability of electron donor relative to substrate
Fr(WFPS) = a multiplier to account for the effect of soil water on N2:N2(D.
For FrfTSICVCCh), as the ratio of electron donor to substrate increases, a higher portion of N gas is assumed to be in the form
of N2O. For Fr(WFPS), as WFPS increases, a higher portion of N gas is assumed to be in the form of N2.
[END TEXT BOX]
A-309
-------
i 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
2
WFPS
1.2-
1 -
0.8-
0.6 -
0.4-
0.2-
0
0
30 40
Soil Temperature
A-310 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 Figure A-t 0: Effect of Soil Nitrite Concentration la]. Heterotrophic Respiration Rates lb), and Water-Filled Pore Space (c) on
2 Denitrification Rates
Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates
35-
30-
>x
p
25-
o
OI
20 -
O)
=5.
15-
O
7
+
10"
5-
0
o
z
200
NQ pg N/g soil
CQng C/g soil/day
loam-high resp/ M / clay-low resp
loam-low resp
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-311
-------
Sqrt Modeled SOC
(gCm2)
2
3
4 Figure A-12: Comparison of Estimated Soil Organic G Stock Changes and Uncertainties using Tier 1CIPGC 20061, Tier 2 (Ogle
5 etal. 2003,2006) and Tier 3 Methods
T_ 80
>*
CM
O
o
O) 60
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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
Ln Modeled N20 Emissions
(g N2O-N ha^dsf1)
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-313
-------
1
Figure fl-14: Comparisons of Results from DayCent Model and Measurements of Soil Methane Emissions
Sqrt Modeled CH4
^ (mg CH4 m 2 d"1)
3 Inventory Compilation Steps
4 There are five steps involved in estimating soil organic C stock changes for Cropland Remaining Cropland, Land Converted
5 to Cropland, Grassland Remaining Grassland and Land Converted to Grassland; direct N2O emissions from cropland and
6 grassland soils; indirect N2O emissions from volatilization, leaching, and runoff from croplands and grasslands; and CH4
7 emissions from rice cultivation. First, the activity data are derived from a combination of land-use, livestock, crop, and
8 grassland management surveys, as well as expert knowledge. In the second, third, and fourth steps, soil organic C stock
9 changes, direct and indirect N2O emissions, and CH4 emissions are estimated using DayCent and/or the Tier 1 and 2 methods.
10 In the fifth step, total emissions are computed by summing all components separately for soil organic C stock changes, N2O
11 emissions and CH4 emissions. The remainder of this annex describes the methods underlying each step.
12 Step 1: Derive Activity Data
13 This step describes how the activity data are derived to estimate soil organic C stock changes, direct and indirect N2O
14 emissions, and CH4 emissions from rice cultivation. The activity data requirements include: (1) land base and history data,
114 . . . "
15 (2) crop-specific mineral N fertilizer rates, (3) crop-specific manure amendment N rates and timing, (4) other N inputs,
16 (5) tillage practices, (6) irrigation data, (7) Enhanced Vegetation Index (EVI), (8) daily weather data, and (9) edaphic
1 ¦ ¦ 115 '
17 characteristics.
18 Step 1a: Activity Data for the Agricultural Land Base and Histories
19 The U.S. Department of Agriculture's 2012 National Resources Inventory (NRI) (USDA-NRCS 2015) provides the basis
20 for identifying the U.S. agricultural land base on non-federal lands, and classifying parcels into Cropland Remaining
21 Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland. In 1998, the
22 NRI program began collecting annual data, and data are currently available through 2012 (USDA-NRCS 2015). The time
23 series will be extended as new data are released by the USDA NRI program. Note that the Inventory does not include
24 estimates of N2O emissions for federal grasslands (with the exception of soil N2O fromPRP manure N, i.e., manure deposited
25 directly onto pasture, range or paddock by grazing livestock) and a minor amount of croplands on federal lands.
26 The NRI has a stratified multi-stage sampling design, where primary sample units are stratified on the basis of county and
27 township boundaries defined by the U.S. Public Land Survey (Nusser and Goebel 1997). Within a primary sample unit,
28 typically a 160-acre (64.75 ha) square quarter-section, three sample points are selected according to a restricted
29 randomization procedure. Each point in the survey is assigned an area weight (expansion factor) based on other known areas
30 and land-use information (Nusser and Goebel 1997). In principle, the expansion factors represent the amount of area with
31 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-314 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 sampling approach, and therefore there is some uncertainty associated with scaling the point data to a region or the country
2 using the expansion factors. In general, those uncertainties decline at larger scales, such as states compared to smaller county
3 units, because of a larger sample size. An extensive amount of soils, land-use, and land management data have been collected
4 through the survey (Nusser et al. 1998). Primary sources for data include aerial photography and remote sensing imagery
5 as well as field visits and county office records.
6 The annual NRI data product provides crop data for most years between 1979 and 2012, with the exception of 1983, 1988,
7 and 1993. These years are gap-filled using an automated set of rules so that cropping sequences are filled with the most
8 likely crop type given the historical cropping pattern at each NRI point location. Grassland data are reported on 5-year
9 increments prior to 1998, but it is assumed that the land use is also grassland between the years of data collection (see Easter
10 et al. 2008 for more information).
11 NRI points are included in the land base for the agricultural soil C and N2O emissions inventories if they are identified as
12 cropland or grassland between 1990 and 2012 (Table A-196). NRI does not provide land use data on federal lands,
13 therefore land use on federal lands are derived from the National Land Cover Database (NLCD) (Fry et al. 2011; Homer et
14 al. 2007; Homer et al. 2015). Federal NRI points are classified as cropland or grassland according to the NLCD and included
15 in the agricultural land base. The NRI data are reconciled with the Forest Inventory and Analysis Dataset, and in this process,
16 the time series for Grassland Remaining Grassland, Land Converted to Grassland, Wetland Remaining Wetland and Land
17 Converted to Wetlands are modified to account for differences in forest land area between the two national surveys (See
18 Section 6.1 for more information on the U.S. land representation). Overall, 674,613 NRI survey points are included in the
19 inventory (USDA-NRCS 2015).
20 For each year, land parcels are subdivided into Cropland Remaining Cropland, Land Converted to Cropland, Grassland
21 Remaining Grassland, and Land Converted to Grassland. Land parcels under cropping management in a specific year are
119
22 classified as Cropland Remaining Cropland if the parcel has been used as cropland for at least 20 years. Similarly land
23 parcels under grassland management in a specific year of the inventory are classified as Grassland Remaining Grassland if
24 they have been designated as grassland for at least 20 years. Otherwise, land parcels are classified as Land Converted to
25 Cropland or Land Converted to Grassland based on the most recent use in the inventory time period. Lands are retained in
26 the land-use change categories (i.e., Land Converted to Cropland and Land Converted to Grassland) for 20 years as
27 recommended by the 2006IPCC Guidelines. Lands converted into Cropland and Grassland are further subdivided into the
28 specific land use conversions (e.g., Forest Land Converted to Cropland).
29 Table A-196: Total Land Areas for the Agricultural Soil G and N2O Inventory, Subdivided by Land Use Categories (Million
30 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
11.79
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-315
-------
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
1 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
2 time series for the land use data.
3
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
4 Notes: The area estimates are not consistent with the land area values shown in the Representation of the U.S. Land Base chapter because the current Inventory
5 does not estimate emissions and removals for all managed lands. Specifically, grassland and cropland in Alaska are not included in the current Inventory. Note:
6 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
7 for the land use data.
8
9 The Tier 3 method using the DayCent model is applied to estimate soil C stock changes, CH4 and N2O emissions for most
10 of the NRI points that occur on mineral soils. The actual crop and grassland histories are simulated with the DayCent model
11 when applying the Tier 3 methods. Parcels of land that are not simulated with DayCent are allocated to the Tier 2 approach
12 for estimating soil organic C stock change, and a Tier 1 method (IPCC 2006) to estimate soil N2O emissions120 and CH4
13 emissions from rice cultivation (Table A-197).
14 The land base for the Tier 1 and 2 methods includes (1) land parcels occurring on organic soils; (2) land parcels that include
15 non-agricultural uses such as forest and federal lands in one or more years of the inventory; (3) land parcels on mineral soils
16 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-316 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
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 A-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-317
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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
1
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
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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
0.08
0.08
0.08
0.08
0.07
0.07
0.07
0.07
0.06
0.06
and Manure
Rangelands and Unimproved
43.43
42.65
42.19
41.96
41.66
41.52
41.32
41.29
41.07
40.87
Pasture
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
7.36
7.89
7.77
7.70
7.62
7.58
7.54
7.47
7.45
7.43
Pasture, Severely Degraded
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
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21
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 N2O 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: .
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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
For the Inventory, 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.
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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
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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
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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-322 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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,
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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
Additional crops and grassland will be used with the NASA-CASA method in the future, as a planned improvement.
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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.
^ Hydric soils are primarily subject to anaerobic conditions outside the plant growing season, such as late winter or early spring prior to
planting. Soils that are flooded during much of the year are typically classified as organic soils (e.g., peat), which are not simulated with
the DayCent model.
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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,
andN 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).
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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-326 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
2 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
3 inventory to recalculate the part of the time series that is estimated with the data splicing methods.
4
5 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
6 a Accounts for N volatilized and leached/runoff during treatment, storage and transport before soil application.
7 b Includes managed manure and daily spread manure amendments
8 'Totals may not sum exactly due to rounding.
9 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
10 part of the time series that is estimated with the data splicing methods.
11 Table fl-203: Crop Residue N and Other 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
12 a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
13 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
14 part of the time series that is estimated with the data splicing methods.
15 Tahle fl-204: Synthetic 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
16 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
17 inventory to recalculate the part of the time series that is estimated with the data splicing methods.
18 Tahle fl-205: Other 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
19 a Includes dried blood, tankage, compost, other. Excludes dried manure and biosolids (i.e., sewage sludge) used as commercial fertilizer to avoid double counting.
20 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
21 Inventory to recalculate the part of the time series that is estimated with the data splicing methods.
22
A-327
-------
1 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
2 Note: Totals may not sum due to independent rounding.
3
A-328 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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 A-208: Nitrogen in Crop Residues Retained on Soils Producing Crops not Simulated by DayCent (kt 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-329
-------
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
1
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-330 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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
1
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-331
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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
1 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
2 methods will be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
3 Step Ye: Additional Activity Data for Indirect N2O Emissions
4 A portion of the N that is applied as synthetic fertilizer, livestock manure, biosolids (i.e., sewage sludge), and other organic
5 amendments volatilizes as NH3 and NOx. In turn, this N is returned to soils through atmospheric deposition, thereby
6 increasing mineral N availability and enhancing N2O production. Additional N is lost from soils through leaching as water
7 percolates through a soil profile and through runoff with overland water flow. N losses from leaching and runoff enter
8 groundwater and waterways, from which a portion is emitted as N2O. However, N leaching is assumed to be an insignificant
9 source of indirect N2O in cropland and grassland systems where the amount of precipitation plus irrigation does not exceed
10 80 percent of the potential evapotranspiration. These areas are typically semi-arid to arid, and nitrate leaching to groundwater
11 is a relatively uncommon event; moreover IPCC (2006) recommends limiting the amount of nitrate leaching assumed to be
12 a source of indirect N2O emissions based on precipitation, irrigation and potential evapotranspiration.
13 The activity data for synthetic fertilizer, livestock manure, other organic amendments, residue N inputs, biosolids N, and
14 other N inputs are the same as those used in the calculation of direct emissions from agricultural mineral soils, and may be
15 found in Table A-201 through Table A-206, and Table A-208.
16 Using the DayCent model, volatilization and leaching/surface run-off of N from soils is estimated in the simulations for
17 crops and non-federal grasslands in the Tier 3 method. DayCent simulates the processes leading to these losses of N based
18 on environmental conditions (i.e., weather patterns and soil characteristics), management impacts (e.g., plowing, irrigation,
19 harvest), and soil N availability. Note that the DayCent model accounts for losses of N from all anthropogenic activity, not
20 just the inputs of N from mineral fertilization and organic amendments, which are addressed in the Tier 1 methodology.
21 Similarly, the N available for producing indirect emissions resulting from grassland management as well as deposited PRP
22 manure is also estimated by DayCent. However, indirect emissions are not estimated f the amount of precipitation plus
23 irrigation does not exceed 80 percent of the potential evapotranspiration. Volatilized losses of N are summed for each day
24 in the annual cycle to provide an estimate of the amount of N subject to indirect N2O emissions. In addition, the daily losses
A-332 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 of N through leaching and runoff in overland flow are summed for the annual cycle. Uncertainty in the estimates is derived
2 from the measure of variability in the fertilizer and organic amendment activity data (see Step la for further information).
3 The Tier 1 method is used to estimate N losses from mineral soils due to volatilization and leaching/runoff for crops,
4 biosolids applications, and PRP manure on federal grasslands, which are not simulated by DayCent. To estimate volatilized
5 losses, synthetic fertilizers, manure, biosolids, and other organic N inputs are multiplied by the fraction subject to gaseous
6 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
7 (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-
8 0.5 kg NH3-N+NOx-N/kg N for organic amendments (IPCC 2006). Leaching/runoff losses of N are estimated by summing
9 the N additions from synthetic and other organic fertilizers, manure, biosolids, and above- and below-ground crop residues,
10 and then multiplying by the default fraction subject to leaching/runoff losses of 0.3 kg N/kg N applied, with an uncertainty
11 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
12 emissions if the amount of precipitation plus irrigation did not exceed 80 percent of the potential evapotranspiration. PDFs
13 are derived for each of the N inputs in the same manner as direct N2O emissions, discussed in Steps la and lc.
14 Volatilized N is summed for losses from croplands and grasslands. Similarly, the annual amounts of N lost from soil profiles
15 through leaching and surface runoff are summed to obtain the total losses for this pathway.
16 Step 2: Estimate Soil Organic C Stock Changes, Direct N2O Emissions from Mineral Soils, and CH4 Emissions from Rice
17 Cultivation
18 In this step, soil organic C stock changes, N2O emissions, and CH4 emissions from rice cultivation are estimated for cropland
19 and non-federal grasslands. Three methods are used to estimate soil organic C stock changes, direct N2O emissions from
20 mineral soils, and CH4 emissions from rice cultivation. The DayCent process-based model is used for the croplands and non-
21 federal grasslands included in the Tier 3 method. A Tier 2 method is used to estimate soil organic C stock changes for crop
22 histories that included crops that are not simulated by DayCent and land use change other than conversions between cropland
23 and grassland. A Tier 1 methodology is used to estimate N2O emissions from crops that are not simulated by DayCent, PRP
24 manure N deposition on federal grasslands, and CH4 emissions from rice cultivation. Soil organic C stock changes and N2O
25 emissions are not estimated for federal grasslands (other than the effect of PRP manure N), but are under evaluation as a
26 planned improvement and may be estimated in future inventories.
27 Step 2a: Estimate Soil Organic C Stock Changes, N2O Emissions, and CH4 emissions for Crops and Non-Federal Grassland
28 with the Tier 3 DayCent Model
29 Crops that are simulated with DayCent include alfalfa hay, barley, corn, cotton, dry beans, grass hay, grass-clover hay, oats,
30 onions, peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tomatoes, and wheat, which combined represent
31 approximately 90 percent of total cropland in the United States. The DayCent simulations also includes all non-federal
32 grasslands in the United States.
3 3 The methodology description is divided into two sub-steps. First, the DayCent model is used to establish the initial conditions
34 andC stocks for 1979, which is the first year of theNRI survey. In the second sub-step, DayCent is used to simulate changes
35 in soil organic C stocks, direct N2O emissions, and CH4 emissions from rice cultivation based on the land-use and
36 management histories recorded in the NRI (USDA-NRCS 2015).
37 Simulate Initial Conditions (Pre-NRI Conditions): DayCent model initialization involves two steps, with the goal of
38 estimating the most accurate stock for the pre-NRI history, and the distribution of organic C among the pools represented in
39 the model (e.g., Structural, Metabolic, Active, Slow, and Passive). Each pool has a different turnover rate (representing the
40 heterogeneous nature of soil organic matter), and the amount of C in each pool at any point in time influences the forward
41 trajectory of the total soil organic C storage. There is currently no national set of soil C measurements that can be used for
42 establishing initial conditions in the model. Sensitivity analysis of the soil organic C algorithms showed that the rate of
43 change of soil organic matter is relatively insensitive to the amount of total soil organic C but is highly sensitive to the
44 relative distribution of C among different pools (Parton et al. 1987). By simulating the historical land use prior to the
45 inventory period, initial pool distributions are estimated in an unbiased way.
46 The first step involves running the model to a steady-state condition (e.g., equilibrium) under native vegetation, historical
47 climate data based on the PRISM product (1981 through 2010), and the soil physical attributes for the NRI points. Native
48 vegetation is represented at the MLRA level for pre-settlement time periods in the United States. The model simulates 5,000
49 years in the pre-settlement era in order to achieve a steady-state condition.
50 The second step is to simulate the period of time from European settlement and expansion of agriculture to the beginning of
51 the NRI survey, representing the influence of historic land-use change and management, particularly the conversion of native
52 vegetation to agricultural uses. This encompasses a varying time period from land conversion (depending on historical
A-333
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5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
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52
53
54
55
56
57
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 a 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)
A-334 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 (Figure A-14). The only other significant variable is geographic location because the model does not predict emissions as
2 accurately for California as other rice-producing states. Random effects are included to capture the dependence in time series
3 and the data collected from the same site.
4 A Monte Carlo approach is used to apply the uncertainty estimator (Ogle et al. 2010). Parameter values for the statistical
5 equation (i.e., fixed effects) are selected from their joint probability distribution, as well as random error associated with
6 fine-scale estimates at NRI points, and the residual or unexplained error associated with the linear mixed-effect model. The
7 estimate and associated management information is then used as input into the equation, and adjusted values are computed
8 for each C stock, N2O and CH4 emissions estimate. The variance of the adjusted estimates is computed from the 100
9 simulated values from the Monte Carlo analysis.
10 The third element is the uncertainty associated with scaling the DayCent results for each NRI point to the entire land base,
11 using the expansion factors provided with the NRI survey dataset. The expansion factors represent the number of hectares
12 associated with the land-use and management history for a particular point. This uncertainty is determined by computing the
13 variances from a set of replicated weights for the expansion factor. For the land base that is simulated with the DayCent
14 model, soil organic C stock changes are provided in Table A-209, soil N2O emissions are provided in Table A-210, and rice
15 cultivation CH4 emissions in Table A-211.
16 Table A-209: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Land Base Simulated with the
Tier 3 OayCent Model-Based Approach
[MMT CO2 Eq.)
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Year
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
Estimate
95% CI
1990
(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)
18 Note: Estimates after 2012 are based on a data splicing method (Seethe Cropland Remaining Cropland section for more information). The Tier 3 method will be
19 applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
20
21 Table A-210: Annual N2O Emissions (95% Confidence Interval) for the Land Base Simulated with the Tier 3 OayCent Model-
22 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
A-335
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2000
132.2
125.41 to 141.24
49.7
46.63 to 53.68
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
1 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
2 applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
3
4 Table A-211: Annual CH4 Emissions from Rice Cultivation (95% Confidence Interval) for Rice Cultivation Simulated with the
5 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
6 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
7 future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
8
9 In DayCent, the model cannot distinguish among the original sources of N after the mineral N enters the soil pools, and
10 therefore it is not possible to determine which management activity led to specific N2O emissions. This means, for example,
11 that N2O emissions from applied synthetic fertilizer cannot be separated from emissions due to other N inputs, such as crop
12 residues. It is desirable, however, to report emissions associated with specific N inputs. Thus, for each NRI point, the N
13 inputs in a simulation are determined for anthropogenic practices discussed in IPCC (2006), including synthetic mineral N
14 fertilization, organic amendments, and crop residue N added to soils (including N-fixing crops). The percentage of N input
15 for anthropogenic practices is divided by the total N input, and this proportion is used to determine the amount of N2O
16 emissions assigned to each of the practices. For example, if 70 percent of the mineral N made available in the soil is due
17 to mineral fertilization, then 70 percent of the N2O emissions are assigned to this practice. The remainder of soil N2O
18 emissions is reported under "other N inputs," which includes mineralization due to decomposition of soil organic matter and
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-336 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 litter, as well as asymbiotic N fixation from the atmosphere. Asymbiotic N fixation by soil bacteria is a minor source of N,
2 typically not exceeding 10 percent of total N inputs to agroecosystems. Mineralization of soil organic matter is a more
3 significant source of N, but is still typically less than half of the amount of N made available in the cropland soils compared
4 to application of synthetic fertilizers and manure amendments, along with symbiotic fixation. Mineralization of soil organic
5 matter accounts for the majority of available N in grassland soils. Accounting for the influence of "other N inputs" is
6 necessary because the processes leading to these inputs of N are influenced by management. While this method allows for
7 attribution of N2O emissions to the individual N inputs to the soils, it is important to realize that sources such as synthetic
8 fertilization may have a larger impact on N2O emissions than would be suggested by the associated level of N input for this
9 source (Delgado et al. 2009). Further research will be needed to improve upon this attribution method, however. The results
10 associated with subdividing the N2O emissions based on N inputs are provided in Table A-212 and Table A-213.
11 Table A-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
12 a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
13
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
14 a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
15 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
16 will be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
17
18
19
A-337
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1
2 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
6 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
7 will be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
8 Step 2b: Soil N2O Emissions from Agricultural Lands on Mineral Soils Approximated with the Tier 1 Approach
9 To estimate direct N2O emissions from N additions to crops in the Tier 1 method, the amount of N in applied synthetic
10 fertilizer, manure and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) is added to N
11 inputs from crop residues, and the resulting annual totals are multiplied by the IPCC default emission factor of 0.01 kg N2O-
12 N/kg N (IPCC 2006) (see Table A-212). The uncertainty is determined based on simple error propagation methods (IPCC
13 2006). The uncertainty in the default emission factor ranges from 0.3-3.0 kg N20-N/kg N (IPCC 2006). For flooded rice
14 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
15 (IPCC 2006). Uncertainties in the emission factor and fertilizer additions are combined with uncertainty in the equations
16 used to calculate residue N additions from above- and below-ground biomass dry matter and N concentration to derive
17 overall uncertainty.
18 The Tier 1 method is also used to estimate emissions from manure N deposited by livestock on federal lands (i.e., PRP
19 manure N), and from biosolids (i.e., sewage sludge) application to grasslands. These two sources of N inputs to soils are
20 multiplied by the IPCC (2006) default emission factors (0.01 kg N20-N/kg N for sludge and horse, sheep, and goat manure,
21 and 0.02 kg N20-N/kg N for cattle, swine, and poultry manure) to estimate N2O emissions (Table A-213). The uncertainty
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-338 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 is determined based on the Tier 1 error propagation methods provided by the IPCC (2006) with uncertainty in the default
2 emission factor ranging from 0.007 to 0.06 kg N20-N/kg N (IPCC 2006).
3 Step 2c: Soil Cfa Emissions from Agricultural Lands Approximated with the Tier 1 Approach
4 To estimate CH4 emissions from rice cultivation for the Tier 1 method, an adjusted daily emission factor is calculated using
5 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
6 for the cultivation water regime, pre-cultivation water regime and a scaling factor for organic amendments (IPCC 2006).
7 The water regime during cultivation is continuously flooded for rice production in the United States and so the scaling factor
8 is always 1 (ranging from 0.79 to 1.26). The pre-season water regime varies based on the proportion of land with winter
9 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
10 winter flooding are assigned a value of 1 (ranging from 0.88 to 1.14). Organic amendments are estimated based on the
11 amount of rice straw and multiplied by 1 (ranging 0.97 to 1.04) for straw incorporated greater than 30 days before cultivation,
12 and by 0.29 (0.2 to 0.4) for straw incorporated greater than 30 days before cultivation. The adjusted daily emission factor is
13 multiplied by the cultivation period and harvested area to estimate the total CH4 emissions. The uncertainty is propagated
14 through the calculation using an Approach 2 method with a Monte Carlo analysis (IPCC 2006), combining uncertainties
15 associated with the adjusted daily emission factor and the harvested areas derived from the USDA NRI survey data.
16 Step 2d: Soil Organic C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2 Approach
17 Mineral soil organic C stock values are derived for crop rotations that were not simulated by DayCent and land converted
18 from non-agricultural land uses to cropland or grassland from 1990-2012, based on the land-use and management activity
19 data in conjunction with appropriate reference C stocks, land-use change, management, input, and wetland restoration
20 factors. Each input to the inventory calculations for the Tier 2 approach has uncertainty that is quantified in PDFs, including
21 the land-use and management activity data, reference C stocks, and management factors. A Monte Carlo Analysis is used to
22 quantify uncertainty in soil organic C stock changes for the inventory period based on uncertainty in the inputs. Input values
23 are randomly selected from PDFs in an iterative process to estimate SOC change for 50,000 times and produce a 95 percent
24 confidence interval for the inventory results.
25 Derive Mineral Soil Organic C Stock Change Factors: Stock change factors representative of U.S. conditions are estimated
26 from published studies (Ogle et al. 2003; Ogle et al. 2006). The numerical factors quantify the impact of changing land use
27 and management on SOC storage in mineral soils, including tillage practices, cropping rotation or intensification, and land
28 conversions between cultivated and native conditions (including set-asides in the Conservation Reserve Program). Studies
29 from the United States and Canada are used in this analysis under the assumption that they would best represent management
30 impacts for the Inventory.
31 The IPCC inventory methodology for agricultural soils divides climate into eight distinct zones based upon average annual
32 temperature, average annual precipitation, and the length of the dry season (IPCC 2006) (Table A-214). Seven of these
33 climate zones occur in the conterminous United States and Hawaii (Eve et al. 2001).
34 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.
35
36 Mean precipitation and temperature (1950-2000) variables from the WorldClim data set (Hijmans et al. 2005)) and potential
37 evapotranspiration data from the Consortium for Spatial Information (CGIAR-CSI) (Zomer et al. 2008; Zomer et al. 2007)
38 are used to classify climate zones (Figure A-15). IPCC climate zones are assigned to NRI point locations.
39 Soils are classified into one of seven classes based upon texture, morphology, and ability to store organic matter (IPCC
40 2006). Six of the categories are mineral types and one is organic (i.e., Histosol). Reference C stocks, representing estimates
41 from conventionally managed cropland, are computed for each of the mineral soil types across the various climate zones,
42 based onpedon (i.e., soil) data from the National Soil Survey Characterization Database (NRCS 1997) (Table A-215). These
43 stocks are used in conjunction with management factors to estimate the change in SOC stocks that result from management
44 and land-use activity. PDFs, which represent the variability in the stock estimates, are constructed as normal densities based
A-339
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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: IPCC Climate Zones
Hawaii
IPCC Climate Zones
I Tropical Moist
Tropical Dry
Warm Temperate Moist
Warm Temperate Dry
I Cool Temperate Moist
Cool Temperate Dry
Boreal Moist
Boreal Dry
No Data
Table A-215: U.S. Soil Groupings Based on the IPGG 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
Any soils with greater than 70
percent sand and less than 8
45 (n = 37)
52 (n = 113)
25 (n = 86)
40 (n = 300)
39 (n = 13)
47 (n = 7)
Sandy Soils
percent clay (often Entisols)
24 (n s 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. Linear
regression models assume that the underlying data are independent observations, but this is not the case with data from the
A-340 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
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.
A-341
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
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 A-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.
A-342 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 Table A-218: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Non-Federal Cropland Land
2 Base Estimated with the Tier 2 Analysis using U.S. Factor Values [MMT 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
3 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
4 applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
5
Non-Federal 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 0.27
1999
0.07
(0.02) to 0.13
0.14
(0.03) to 0.25
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
0.13
(0.03) to 0.25
2003
0.07
(0.01) to 0.12
0.13
(0.02) to 0.23
2004
0.07
(0.01) to 0.12
0.14
(0.02) to 0.25
2005
0.07
(0.01) to 0.12
0.13
(0.02) to 0.23
2006
0.08
(0.01) to 0.13
0.15
(0.02) to 0.25
2007
0.09
(0.01) to 0.14
0.14
(0.01) to 0.23
2008
0.08
0 to 0.13
0.14
0 to 0.22
A-343
-------
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
1 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
2 applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
3
4 Table A-219: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Federal Cropland Land Base
5 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
6 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
7 applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
8
9
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
A-344 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
2008
0.00
0.0 to 0.0
0.00
0.0 to 0.01
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
1 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
2 applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
3
4 Table A-220: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Total Cropland Land Base
5 Estimated with the Tier 2 Analysis using U.S. Factor Values [MBIT CO; Eq./yrl
Total
Grassland Converted to
Forest Converted to
Other Land Converted to
Croplands:
oropiana Remaining uopiana
Cropland
Cropland
Cropland
Year
Estimate
95% CI
Estimate
95% CI
Estimate 95% CI
Estimate
95% CI
Mineral Soils
1990
-5.44
(7.97) to (3.0)
1.32
(.72) to 2.26
0.22
(.12) to .38
0.16
(.09) to .28
1991
-6.19
(8.94) to (3.53)
1.26
(.84) to 2.25
0.20
(.13) to .36
0.15
(.10) to .27
1992
-6.19
(9.77) to (3.63)
1.29
(1.07) to 2.32
0.19
(.16) to .35
0.16
(.13) to .29
1993
-6.95
(10.17) to (3.79)
1.35
(.75) to 2.45
0.18
(.10) to .32
0.17
(.09) to .31
1994
-6.71
(9.94) to (3.52)
1.48
(.37) to 2.63
0.18
(.05) to .33
0.19
(.05) to .35
1995
-6.46
(9.52) to (3.48)
1.59
(.35) to 2.76
0.19
(.04) to .32
0.21
(.05) to .36
1996
-6.10
(9.10) to (3.15)
1.65
(.35) to 2.86
0.19
(.04) to .33
0.22
(.05) to .37
1997
-7.64
(11.38) to (4.0)
1.41
(.47) to 2.63
0.15
(.05) to .29
0.19
(.06) to .35
1998
-7.33
(11.0) to (3.79)
1.63
(.37) to 3.01
0.15
(.04) to .28
0.20
(.05) to .36
1999
-7.07
(10.57) to (3.71)
1.49
(.33) to 2.79
0.13
(.03) to .24
0.19
(.04) to .35
2000
-6.75
(10.09) to (3.56)
1.48
(.39) to 2.78
0.12
(.03) to .22
0.22
(.06) to .42
2001
-6.71
(9.94) to (3.62)
1.54
(.35) to 2.85
0.10
(.02) to. 18
0.23
(.05) to .42
2002
-6.72
(9.79) to (3.80)
1.45
(.30) to 2.66
0.09
(.02) to. 16
0.20
(.04) to .36
2003
-6.05
(8.97) to (3.30)
1.42
(.24) to 2.59
0.08
(.01) to.15
0.19
(.03) to .34
2004
-5.43
(8.24) to (2.75)
1.60
(.17) to 2.79
0.08
(.01) to.15
0.21
(.02) to .37
2005
-5.40
(7.97) to (2.98)
1.53
(.18) to 2.70
0.08
(.01) to.13
0.19
(.02) to .34
2006
-4.36
(6.67) to (2.22)
1.77
(.19) to 2.89
0.09
(.01) to.14
0.23
(.02) to .37
2007
-3.96
(6.14) to (1.98)
1.83
(.13) to 2.92
0.08
(.01) to.13
0.23
(.02) to .36
2008
-3.37
(5.38) to (1.53)
1.86
(.06) to 2.98
0.06
.0 to .10
0.24
(.01) to .38
2009
-3.52
(5.33) to (1.88)
1.70
(.02) to 2.72
0.06
.0 to .09
0.22
.0 to .36
2010
-3.58
(5.45) to (1.91)
1.68
.02 to 2.68
0.06
.0 to .09
0.22
.0 to .35
2011
-3.49
(5.17) to (1.99)
1.70
.01 to 2.68
0.06
.0 to .09
0.22
.0 to .35
2012
-2.88
(4.41) to (1.55)
1.70
(.01) to 2.64
0.06
.0 to .10
0.22
.0 to .34
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
6 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
7 applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
8
A-345
-------
1
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
2 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
3 applied in a future Inventory to recalculate the part of the time series that is estimated with the data splicing methods.
4
5 Table A-221: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Non-Federal Grasslands Land
6 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
A-346 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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)
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
be applied in a future
2012 are based on a data splicing method
inventory to recalculate the part of the time
(See the Grassland Remaining Grassland section for more information). The Tier 2 method will
series that is estimated with the data splicing methods.
Non-Federal
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)
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)
5 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
6 be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
7
A-347
-------
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 [MBIT CO; Eq./yrl
Federal 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
-0.20
(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
5 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
6 be applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
A-348 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1
2
3 Table A-223: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Total Grassland Land Base
4 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
5
Total Grasslands:
Settlements Converted to
Grassland
Wetlands Converted to
Grassland
Year
Estimate
95% CI
Estimate
95% CI
Mineral Soils
1990
1991
-0.08
-0.09
(0.12) to (0.05)
(0.13) to (0.05)
-0.32
-0.39
(0.46) to (0.19)
(0.56) to (0.23)
A-349
-------
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
1 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
2 applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
3
4 Step 3: Estimate Soil Organic C Stock Changes and Direct N2O Emissions from Organic Soils
5 In this step, soil organic C losses and N2O emissions are estimated for organic soils that are drained for agricultural
6 production.
7 Step 3a: Direct AfeO Emissions Due to Drainage of Organic Soils in Cropland and Grassland
8 To estimate annual N2O emissions from drainage of organic soils in cropland and grassland, the area of drained organic soils
9 in croplands and grasslands for temperate regions is multiplied by the IPCC (2006) default emission factor for temperate
10 soils and the corresponding area in sub-tropical regions is multiplied by the average (12 kg N20-N/ha cultivated) of IPCC
11 (2006) default emission factors for temperate (8 kg N20-N/ha cultivated) and tropical (16 kg N20-N/ha cultivated) organic
12 soils. The uncertainty is determined based on simple error propagation methods (IPCC 2006), including uncertainty in the
13 default emission factor ranging from 2-24 kg N20-N/ha (IPCC 2006).
A-350 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 Step 3b: Soil Organic C Stock Changes Due to Drainage of Organic Soils in Cropland and Grassland
2 Change in soil organic C stocks due to drainage of cropland and grassland soils are estimated annually from 1990 through
3 2012, based on the land-use and management activity data in conjunction with appropriate emission factors. The activity
4 data are based on annual data from 1990 through 2012 from the NRI. Organic Soil emission factors representative of U.S.
5 conditions have been estimated from published studies (Ogle et al. 2003), based on subsidence studies in the United States
6 and Canada (Table A-225). PDFs are constructed as normal densities based on the mean C loss rates and associated
7 variances. Input values are randomly selected from PDFs in a Monte Carlo analysis to estimate SOC change for 50,000
8 times and produce a 95 percent confidence interval for the inventory results. Losses of soil organic C from drainage of
9 cropland and grassland soils are provided in Table A-218 and Table A-221.
10 Step 4: Estimate Indirect N2O Emissions for Croplands and Grasslands
11 In this step, N2O emissions are estimated for the two indirect emission pathways (N2O emissions due to volatilization, and
12 N2O emissions due to leaching and runoff of N), which are summed to yield total indirect N2O emissions from croplands
13 and grasslands.
14 Step 4a: Indirect Soil N2O Emissions Due to Volatilization
15 Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and PRP manure, are
16 calculated according to the amount of mineral N that is transported in gaseous forms from the soil profile and later emitted
17 as soil N2O following atmospheric deposition. See Step le for additional information about the methods used to compute N
18 losses due to volatilization. The estimated N volatilized is multiplied by the IPCC default emission factor of 0.01 kg N2O-
19 N/kg N (IPCC 2006) to estimate total N2O emissions from volatilization. The uncertainty is estimated using simple error
20 propagation methods (IPCC 2006), by combining uncertainties in the amount of N volatilized, with uncertainty in the default
21 emission factor ranging from 0.002-0.05 kg N20-N/kg N (IPCC 2006). The estimates are provided in Table A-226 and
22 implied Tier 3 emission factors are in Table A-229 and Table A-230.
23 Step 4b: Indirect Soil N2O Emissions Due to Leaching and Runoff
24 The amount of mineral N from synthetic fertilizers, commercial organic fertilizers, PRP manure, crop residue, N
25 mineralization, asymbiotic fixation that is transported from the soil profile in aqueous form is used to calculate indirect
26 emissions from leaching of mineral N from soils and losses in runoff of water associated with overland flow. See Step le
27 for additional information about the methods used to compute N losses from soils due to leaching and runoff in overland
28 water flows. The total amount of N transported from soil profiles through leaching and surface runoff is multiplied by the
29 IPCC default emission factor of 0.0075 kg N20-N/kg N (IPCC 2006) to estimate emissions for this source. The emission
30 estimates are provided in Table A-227 and implied Tier 3 emission factors are in Table A-229 and Table A-230. The
31 uncertainty is estimated based on simple error propagation methods (IPCC 2006), including uncertainty in the default
32 emission factor ranging from 0.0005 to 0.025 kg N20-N/kg N (IPCC 2006).
33 Step 5: Estimate Total Soil Organic C Stock Changes and N2O Emissions for U.S. Soils
3 4 Step 5a: Estimate Total Soil N2O Emissions
35 Total N2O emissions are estimated by adding total direct emissions (from mineral cropland soils, drainage and cultivation
36 of organic soils, and grassland management) to indirect emissions. Uncertainties in the final estimate are combined using
37 simple error propagation methods (IPCC 2006), and expressed as a 95 percent confidence interval. Estimates are provided
38 in Table A-228.
39 Direct and indirect simulated emissions of soil N2O vary regionally in croplands as a function of N input amount and timing
40 of fertilization, tillage intensity, crop rotation sequence, weather, and soil type. Note that there are other management
41 practices, such as fertilizer formulation (Halvorson et al. 2013), that influence emissions but are not represented in the model
42 simulations. The highest total N2O emissions occur in Iowa, Illinois, Kansas, Minnesota, Nebraska and Texas (Table A-
43 232). On a per area unit basis, direct N2O emissions are high in some Northeast, Midwest, and many of the Mississippi River
44 Basin states where there are high N inputs to hay, corn and soybean crops, and in some western states where irrigated crops
45 are grown that require high N inputs. Note that although the total crop area in the northeast is relatively low, emissions are
46 high on a per unit area basis because of freeze/thaw cycles during spring that saturate surface soil layers and enhance
47 denitrification rates.
48 Direct emissions from non-federal grasslands are typically lower than the emissions from croplands (Table A-232) because
49 N inputs tend to be lower, particularly from synthetic fertilizer. Texas, Oklahoma, Kansas, Montana, Missouri, and Kentucky
50 are the highest emitters for this category due to large land areas used for pastures and rangeland (Table A-232). On a per-
A-351
-------
1 unit of area basis, direct N2O emissions are higher in the some of the Southeastern, Appalachians, and Midwestern states
2 because these grasslands are more intensively managed (legume seeding, fertilization) while western rangelands receive
3 few, if any, N inputs. Also, rainfall is limited in most of the western United States, and grasslands are not typically irrigated
4 so minimal leaching and runoff of N occurs in these grasslands, and therefore there are lower indirect N2O emissions.
5 Step 5b: Estimate Total Soil Organic Stock Change
6 The sum of total CO2 emissions and removals from the Tier 3 DayCent Model Approach, Tier 2 IPCC Methods and
7 additional land-use and management considerations are provided in Table A-233. The states with highest total amounts of
8 C sequestration are California, Illinois, Iowa, Kentucky, Missouri, North Dakota and Tennessee (Table A-234). For organic
9 soils, emission rates are highest in the regions that contain the majority of drained organic soils, including California, Florida,
10 Indiana, Michigan, Minnesota, North Carolina and Wisconsin. On a per unit of area basis, the emission rate patterns are very
11 similar to the total emissions in each state, with the highest rates in coastal states of the Southeast, states surrounding the
12 Great Lakes, and California.
13 Step 5c: Estimate Total CH4 Emissions from Rice Cultivation
14 The sum of total CH4 emissions from the Tier 3 DayCent Model Approach and Tier 1 IPCC Methods are provided in Table
15 A-231. The states with highest total emissions are Arkansas, California, Louisiana and Texas (Table A-235). These states
16 also have the largest areas of rice cultivation, and Louisiana and Texas have a relatively large proportion of fields with a
17 second ratoon crop each year. Ratoon crops extend the period of time under flooded conditions, which leads to more CH4
18 emissions.
A-352 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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>e'f
-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>e'f
-1.29
-1.32
-1.36
-1.39
-1.42
-1.45
-1.49
5 a N applied to soils described in Step 1d.
6 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.,
7 2000).
8 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
9 applied to 1990 -1992 and the 1997 rate was applied to 1993-2016.
10 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)
11 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
12 matter amendment.
13 '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
14 lands.
15 Note: Values in parentheses indicate net C storage.
A-353
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
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
'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
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
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
Organic Amendment3
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
Residue Nb
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
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
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-354 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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
9
10
11
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-355
-------
i 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
3 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
4 applied in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
5 Table A-231: Total CHa 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
7 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
8 in a future inventory to recalculate the part of the time series that is estimated with the data splicing methods.
9
10 Table fl-232: Total 2012* N2O Emissions [Direct and Indirect] from Agricultural 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
A-356 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
CT
0.11
0.02
0.14
0.10
0.24
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.
1 b Emissions from biosolids (i.e., sewage sludge) applied to grasslands and were not estimated (NE) at the state
2 level.
3 c N2O emissions are not reported for Hawaii except from cropland organic soils.
4 * The estimates are under review and will be updated for the public review version of the Inventory. These
5 results are independent from the national estimates provided in the Agricultural Soil Management Section, which
6 are final in the expert review version.
7
8 Table A-233: Annual Soil G Stock Change in Cropland Remaining Cropland (CRC), land Converted to Cropland(LCC),
9 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
A-357
-------
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
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
0.1
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)
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
A-358 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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
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)
1 Notes: Totals may not sum due to independent rounding. Note: Estimates after
2 2012 are based on a data splicing method (See the Cropland Remaining
3 Cropland section for more information). The Tier 2 and 3 methods will be
4 applied in a future inventory to recalculate the part of the time series that is
5 estimated with the data splicing methods.
6
7 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
A-359
-------
2
3
4
5
6
7
8
9
10
11
12
13
14
OH
(1.52)
0.48
(1.CH)
OK
(0.62)
-
(0.62)
OR
(0.61)
0.30
(0.31)
PA
(0.43)
0.05
(0.38)
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
-
A-360 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
sc
SD
TN
TX 0.85
UT
VA
VT
WA
Wl
WV
WY -
1 Note: Only national-scale CbU emissions
2 from rice cultivation are estimated for 2013
3 to 2016 in this Inventory using a splicing
4 method, and therefore the state-scale
5 emissions are based on inventory data
6 from 2012.
7
8
A-361
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Farm Service Agency, Washington, DC, Available online at https://www.fsa.usda.gov/Assets/USDA-FSA-
Public/usdafiles/Conservation/PDF/summarysept2014.pdf.
USDA-NASS (2015) Quick Stats: U.S. & All States Data; Crops; Production and Area Harvested; 1990 - 2014. National
Agricultural Statistics Service, U.S. Department of Agriculture. Washington, D.C. U.S. Department of Agriculture,
National Agricultural Statistics Service. Washington, D.C., Available online at: .
USDA-NRCS (2015) Summary Report: 2012 National Resources Inventory, Natural Resources Conservation Service,
Washington, D.C., and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa.
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Wilson, C.E. Jr., and Runsick, S.K. (2007) Trends in Arkansas rice production. B.R. Wells Arkansas Rice Research Studies
2006. Norman, R.J., Meullenet, J.-F., and Moldenhauer, K.A.K., (Eds.). Research Series 550, Arkansas Agricultural
Experiment Station, University of Arkansas.
Wilson, C.E. Jr., Runsick, S.K., Mazzanti, R. (2009) Trends in Arkansas rice production. B.R. Wells Arkansas Rice Research
Studies 2008. Norman, R.J., Meullenet, J.-F., and Moldenhauer, K.A.K., (Eds.). Research Series 571, Arkansas
Agricultural Experiment Station, University of Arkansas.
Wilson, C.E. Jr., Runsick, S.K., and Mazzanti, R. (2010) Trends in Arkansas rice production. B.R. Wells Arkansas Rice
Research Studies 2009. Norman, R.J., and Moldenhauer, K.A.K., (Eds.). Research Series 581, Arkansas Agricultural
Experiment Station, University of Arkansas.
Zomer RJ, Trabucco A, Bossio DA, van Straaten O, Verchot LV (2008) Climate Change Mitigation: A Spatial Analysis of
Global Land Suitability for Clean Development Mechanism Afforestation and Reforestation. Agric. Ecosystems and
Envir. 126: 67-80.
Zomer RJ, Bossio DA, Trabucco A, Yuanjie L, Gupta DC & Singh VP (2007) Trees and Water: Smallholder Agroforestry
on Irrigated Lands in Northern India. Colombo, Sri Lanka: International Water Management Institute, pp 45. (IWMI
Research Report 122).
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1 3.13. Methodology for Estimating Net Carbon Stock Changes in Forest Land Remaining
2 Forest Land and Land Converted to Forest Land
3 This sub-annex expands on the methodology used to estimate net changes in carbon (C) stocks in forest ecosystems
4 and harvested wood products for Forest Land Remaining Forest Land and Land Converted to Forest Land as well as non-
5 CO2 emissions from forest fires. Full details of the C conversion factors and procedures may be found in the cited references.
6 For details on the methods used to estimate changes in soil C stocks in the Land Converted to Forest Land section please
7 refer to Annex 3.12.
8 Carbon stocks and net stock change in forest ecosystems
9 The inventory-based methodologies for estimating forest C stocks are based on a combination of approaches
10 (Woodall et al 2015a) and are consistent with IPCC (2003, 2006) stock-difference methods. Estimates of ecosystem C are
11 based on data from the network of annual inventory plots established and measured by the Forest Inventory and Analysis
12 program within the USDA Forest Service; either direct measurements or attributes of forest inventories are the basis for
13 estimating metric tons of C per hectare in IPCC pools (i.e., above- and belowground biomass, dead wood, litter, and soil
14 organic carbon). Plot-level estimates are used to inform land area (by use) and stand age transition matrices across time
15 which can be summed annually for an estimate of forest C stock change for Forest Land Remaining Forest Land and Land
16 Converted to Forest Land. Recent publications (Coulston et al. 2015; Woodall et al. 2015a) detail the land use and stand age
17 transition matrices that are informed by the annual forest inventory of the United States and were used in the accounting
18 framework used in this Inventory. The annual forest inventories in the eastern United States have been remeasured which
19 allows for empirical estimation of forest C stock net change within the accounting framework. In contrast, as numerous
20 western states have not yet been remeasured, theoretical age transition matrices have been developed (Figure A-16).
21 The following subsections of this annex will describe the estimation system used this year (Figure A-16) including
22 the methods for estimating individual pools of forest C in addition to the eastern versus western approach to informing land
23 use and stand age transitions.
24
A-369
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i Figure 1-18: Flowchart of the inputs necessary in the accounting framework, including the methods for estimating
4 Note: An empirical age class transition matrix was used in the Eastern United States while a theoretical age class transition matrix was used in
5 the Western United States.
6
7 Forest Land Definition
8 The definition of forest land within the United States and used for this Inventory is defined in Oswalt et al. (2014)
9 as "Land at least 120 feet (37 meters) wide and at least 1 acre (0.4 hectare) in size with at least 10 percent cover (or equivalent
10 stocking) by live trees including land that formerly had such tree cover and that will be naturally or artificially regenerated.
11 Trees are woody plants having a more or less erect perennial stem(s) capable of achieving at least 3 inches (7.6 cm) in
12 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
13 situ. The definition here includes all areas recently having such conditions and currently regenerating or capable of attaining
14 such condition in the near future. Forest land also includes transition zones, such as areas between forest and non-forest
15 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).
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Figure ft-17: Annual FIA plots (remeasured and not remeasured) across the United States including coastal Alaska through
the 2015 field season
m
•r-»
. m.p<" '
*?-¦
*
Remeasured
Not Remeasured
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 G 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 (USDA 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.
A-372 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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i Tahlefl-236: Specific annual forest inventories by state used in development of forest C stock and 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
,#At!zsja
2004 - 2008
/P8S-|§}3
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
New Mexico
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
2 Note: Remeasured annual plots represent a complete inventory cycle between measurements of the same plots while spilt annual cycle plots represent a single
3 inventory cycle of plots that are split where remeasurements have yet to occur.
4
5 Estimating Forest Inventory Plot-Level C-Density
6 For each inventory plot in each state, field data from the FIA program are used alone or in combination with
7 auxiliary information (e.g., climate, surficial geology, elevation) to predict C density for each IPCC pool (i.e., aboveground
8 and belowground biomass, dead wood, litter, SOC). In the past, most of the conversion factors and models used for
9 inventory-based forest C estimates (Smith et al. 2010; Heath et al. 2011) were initially developed as an offshoot of the forest
10 C simulation model FORCARB (Heath et al. 2010). The conversion factors and model coefficients were usually categorized
11 by region and forest type. Thus, region and type are specifically defined for each set of estimates. More recently, the coarse
12 approaches of the past have been updated with empirical information regarding C attributes of individual forest C pools such
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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 — e^A ~ ® x W^lve tree C density)) ^ j
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(a855" 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
A-374 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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
RMM
Lodgepole Pine
2.571
1.500
4.773
rxlvMN
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
Ixlvio
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
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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 type" Ratiob
Aspen-Birch
MBB/Other Hardwood
0.078
0.071
NE
Oak-Hickory
Oak-Pine
0.068
0.061
Other Pine
0.065
Spruce-Fir
White-Red-Jack Pine
0.092
0.055
Nonstocked
0.019
Aspen-Birch
Lowland Hardwood
0.081
0.061
NLS
Maple-Beech-Birch
Oak-Hickory
Pine
0.076
0.077
0.072
Spruce-Fir
Nonstocked
0.087
0.027
Conifer
0.073
NPS
Lowland Hardwood
0.069
Maple-Beech-Birch
0.063
Oak-Hickory
0.068
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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
RMS
Lodgepole Pine
0.098
Other Conifer
0.060
Pinyon-Juniper
0.030
Ponderosa Pine
0.082
Nonstocked
0.020
Bottomland Hardwood
0.063
Misc. Conifer
0.068
Natural Pine
0.068
SC
Oak-Pine
0.072
Planted Pine
0.077
Upland Hardwood
0.067
Nonstocked
0.013
Bottomland Hardwood
0.064
Misc. Conifer
0.081
Natural Pine
0.081
SE
Oak-Pine
0.063
Planted Pine
0.075
Upland Hardwood
0.059
Nonstocked
0.012
1 a Regions and types as defined in Smith et al. (2003).
2 b The ratio is multiplied by the live tree C density on a plot to produce downed dead wood C density (T C/ha).
3
4 Table 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
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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:
]'(] '] '( '/ ((//) = f {lot, Ion, elev,fortypgrp, above, ppt,t max, 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
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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 = C, • BD ¦ t ¦ ucf (4)
L-t FIA_TOTAL i i i J v '
Where VSOC = total mass (Mg C ha-1) of the mineral and organic soil C over all rth layers, r = percent
FIAJOIAL
organic C in the rth layer, BDj = bulk density calculated as weight per unit volume of soil (g-cm-3) at the rth soil layer, t
= 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 j 0 SOCt=I + log, 0 Depth (5)
Where log 10 SOCt= SOC density (Mg C ha-1 cm depth-1) at the midpoint depth, /= intercept,
log10 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:
SOC100 = SOCFm T(yrAL + SOC20_100 (6)
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Where SOCl00= total estimated SOC density from 0-100 cm for each forest condition with a soil sample in the
FIA database, SOCFIA TOTAL as previously defined in model (4), SOC20_100 = predicted SOC from 20.32 to 100 cm from
model (5).
In the second phase of the modeling framework, SOCW0 estimates for FIA plots were used to predict SOC for plots
lacking SOCW0estimates 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
SOCl00, and a set of predictor variables. All relevant predictor variables—those that may influence the formation,
accumulation, and loss of SOC—from annual inventories collected on all base intensity plots and auxiliary climate, soil, and
topographic variables obtained from the PRISM climate group (Northwest Alliance 2015), Natural Resources Conservation
Service (NRCS 2015), and U.S. Geological Survey (Danielson and Gesch 2011), respectively, were included in the 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) = fijat,Ion, e/ev, forlypgrp, 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
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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 =
'0 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 — ^2 — d2
0
0
0
0
*2
1 — £3 — ^3
0
0
0
0
£3
1 £4 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).
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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 = FtB (11)
B is constructed based on similar logic used for creating T. The matrix cannot simply be derived as the inverse of
T (Ft_s = FtT_1) because of the accumulating final age class (i.e., T does not contain enough information to determine the
proportion of the final age class derived from the n-1 age class and the proportion that is retained in age class n from the
previous time step). 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.
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48
Where Am = 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. Historical forest cutting was incorporated by using the relationship between
the area of forest cutting estimated from the inventory plots and the volume of roundwood production from the Timber
Products Output program (U.S. Forest Service 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 Harvested Wood Product (HWP) contribution to forest C sinks and emissions (hereafter called
"HWP Contribution") are based on methods described in Skog (2008) using the WOODCARB II model and the U.S. forest
products module (Ince et al. 2011). These methods are based on IPCC (2006) guidance for estimating HWP C. The 2006
IPCC Guidelines provide methods that allow Parties to report HWP Contribution using one of several different accounting
approaches: production, stock change, and atmospheric flow, as well as a default method. The various approaches are
described below. The approaches differ in how HWP Contribution is allocated based on production or consumption as well
as what processes (atmospheric fluxes or stock changes) are emphasized.
• 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-383
-------
1 (5) Carbon in annual harvest of wood from forests in the United States. The sum of these variables yield
2 estimates for HWP contribution under the production accounting approach.
A-384 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1 Table A-240: Harvested Wood Products from Wood Harvested in the U.S.—Annual Additions of G to Stocks and Total Stocks
2 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).
3
4 Table A-241: Comparison of Het Annual Change in Harvested Wood Products C Stocks Using Alternative Accounting
5 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)
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)
A-385
-------
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-386 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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).
2
A-387
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
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-388 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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)
1 + Absolute value does not exceed 0.05 MMT CO2 Eq.
2 Note: Parentheses indicate negative values.
3
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)
(0.0)
(0.0)
M
M
M
0.0
0.0
M
+
0.0
+
+
+
+
+
+
+
+
0.026
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)
4 + Absolute value does not exceed 0.05 MMT C
5 Note: Parentheses indicate negative values.
6
Table fl-247: Forest area 11,000 ha) 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-389
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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
1,492
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
1
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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
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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-392 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Table A-240: Annual change in Mineral Soil G stocks from U.S. agricu
tural soils t
hat were estimated us
ng a Tier 2 method (MM
T G/year)
Category
1990
1995
2000
2005
2008
2009
2010
2011
2012
2013
2014
2015
2016
Cropland Converted to
Forest Land
(-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 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
Wetlands Converted to
Forest Land
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0.01)
0.00
(0 to 0.01)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(0 to 0)
0.00
(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
2 Note: The range is a 95 percent confidence interval from 50,000 simulations (Ogle et al. 2003, 2006).
3 Table fl-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
4 Note: Estimated with a Tier 2 approach and based on analysis of USDA National Resources Inventory data (USDA-NRCS 2013).
A-393
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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.
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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 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 .
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inventoried area were on the northern (or Cook Inlet) 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.
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Table fl-250: Areas [Hectares] from Wildfire Statistics and Corresponding Estimates of C an J 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
Forest area burned
£.a n n
Conterminous 48
(1000 ha)
184.2
128.7
1,016.2
603.3
724.42
493.5
142.6
1,242.1
1,451.7
04U.U
679.0
1,137.9
1,137.9
States - Wildfires
C emitted (MMT/yr)
6.1
2.5
26.5
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
0.2
2.8
22.0
0.3
11.8
3.1
1.0
0.7
6.1
1.3
22.0
22.0
CO2 emitted
A Q
(MMT/yr)
19.6
0.6
10.4
80.6
1.1
43.3
11.3
3.5
2.7
22.3
4.y
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
22.7
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
0.2
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
0.2
0.6
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
17.7
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
0.7
1.8
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
0.0
0.1
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
40.3
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
1.1
2.4
11.3
15.2
35.4
11.5
5.6
10.4
11.7
6.7
6.7
2 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
3 continue to decay through time.
4 b The data for 2016 were unavailable when these estimates were summarized; therefore 2015, the most recent available estimate, is applied to 2016.
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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 (
Table A-250)b
Managed
Unmanaged
Non-forest
Managed
Unmanaged
Non-for
forest land
forest land
land
forest land
forest land
land
C emitted (MMT/year)
2001
0.7
0.8
0.3
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
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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
2016=
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-399
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3.14. Methodology for Estimating ChU 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 (TOD) 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 A-18: Methane Emissions Resulting from Landfiiiing Municipal and Industrial Waste
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 1940 through 1988 based on EPA 1988 and EPA 1993; 1989 through 2004 based on BioCycle 2010; 2005 through 2016 based on EPA 2017b.
c Two different methodologies are used in the time series for MSW landfills. For 1990 to 2004, the IPCC 2006 Guidelines - First Order Decay Model is used. For
2005 to 2016, directly reported net CPU emissions from the GHGRP are used with the addition of a scale-up factor equal to 9 percent of each year's emissions.
The scale-up factor accounts for emissions from landfills that do not report to the GHGRP.
d The IPCC 2006 Guidelines - First Order Decay Model is used for industrial waste landfills.
1"'®® 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-405
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1 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
2 GHGRP dataset.
3 f Data are pulled from three recovery databases: EIA 2007, flare vendor database (2015), and EPA (GHGRP) 2016(b). These databases have not been updated
4 past 2015 because the Inventory strictly uses net emissions from the GHGRP data.
5 a Data are pulled from three recovery databases: EIA 2007, the LFGE database (EPA 2015a), and EPA (GHGRP) 2016(b). These databases have not been
6 updated past 2015 because the Inventory strictly uses net emissions from the GHGRP data.
7 h 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
8 landfills are considered because the directly reported GHGRP emissions are used for MSW landfills.
9 'An oxidation factor of 10 percent is applied to all CH4 generated in years 1990 to 2004 (IPCC2006 Guidelines; Mancinelli and McKay 1985; Czepiel et al 1996).
10 For years 2005 to 2016, directly reported CH4 emissions from the GHGRP are used for MSW landfills. Various oxidation factor percentagesare included in the
11 GHGRP dataset (0,10, 25, and 35) with an average across the dataset of approximately 20 percent.
12
13 Step 1: Estimate Annual Quantities of Solid Waste Placed in MSW Landfills for 1940 to 2004
14 To estimate the amount of CH4 generated in a landfill in a given year, information is needed on the quantity and
15 composition of the waste in the landfill for multiple decades, as well as the landfill characteristics (e.g., size, aridity, waste
16 density). Estimates and/or directly measured amounts of waste placed in municipal solid waste (MSW) and industrial waste
17 landfills are available through various studies, surveys, and regulatory reporting programs (i.e., EPA's GHGRP). The
18 composition of the amount of waste placed in these landfills is not readily available for most years the landfills were in
19 operation. Consequently, and for the purposes of estimating CH4 generation, the Inventory methodology assumes that all
20 waste placed in MSW landfills is bulk MSW, and that all waste placed in industrial waste landfills is from either pulp and
21 paper manufacturing facilities or food and beverage facilities.
22 Historical waste data, preferably since 1940, are required for the FOD model to estimate CH4 generation for the
23 Inventory time series. Estimates of waste placed in landfills in the 1940s and 1950s were developed based on U. S. population
24 for each year and the per capital disposal rates from the 1960s. Estimates of the annual quantity of waste placed in landfills
25 from 1960 through 1983 were developed from EPA's 1993 Report to Congress (EPA 1993) and a 1986 survey of MSW
26 landfills (EPA 1988).
27 For 1989 to 2004, estimates of the annual quantity of waste placed in MSW landfills were developed from a survey
28 of State agencies as reported in the State of Garbage (SOG) in America surveys (BioCycle 2010) and recent data from the
139
29 Environmental Research & Education Foundation (EREF), adjusted to include U.S. Territories. The SOG surveys and
30 EREF (2016) provide state-specific landfill waste generation data and a national average disposal factor back to 1989. The
31 SOG survey is no longer updated, but is available every two years for the years 2002, 2004, 2006, and 2008 (as published
32 in BioCycle 2006; 2008, and 2010). EREF published a report in 2016 for data years 2010 and 2013 using a similar
33 methodology as the SOG surveys (EREF 2016). EREF plans to publish updated reports every three years. A linear
34 interpolation was used to estimate the amount of waste generated in 2001, 2003, 2005, 2007, 2009, 2011, 2012; data were
35 extrapolated for 2014 to 2016 based on national population growth because waste generation data are not available for these
36 years. Upon publication of the next EREF report, the waste generated for 2014 to the current Inventory year will be updated.
37 Estimates of the quantity of waste landfilled are determined by applying a waste disposal factor to the total amount
38 of waste generated. A waste disposal factor is determined for each year a SOG survey and EREF report is published and is
39 the ratio of the total amount of waste landfilled to the total amount of waste generated. The waste disposal factor is
40 interpolated for the years in-between the SOG surveys and EREF data, and extrapolated for years after the last year of data.
41 Methodological changes have occurred over the time that the SOG survey has been published, and this has resulted in
42 fluctuating trends in the data.
43 Table A-254 shows estimates of waste quantities contributing to CH4 emissions. The table shows SOG and EREF
44 (EREF 2016) estimates of total waste generated and total waste landfilled (adjusted for U.S. Territories) for various years
45 over the 1990 to 2016 timeframe even though the Inventory methodology does not use the data for 2005 onward.
46 Table A-254: Solid Waste in MSW and Industrial Waste Landfills Contributing to CHa Emissions (MMT unless otherwise
47 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-406 DRAFT 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
Food and Beverage Sector
6.4
4 6.9
6.2
6.0
6.2
6.1
6.1
Pulp and Paper Sector1
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 Food production values for 1990 to 2016 are from ERG. USDA-NASS Ag QuickStats available at http://quickstats.nass.usda.gov.
d 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.
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 CFU emissions fromEPA's GHGRP 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 GHGRP use a combination of the FOD method and the back-
calculation method to develop their CFU 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 ^ X (e-fcC-*-i) _ e-*C-*))}]
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-276)
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 lyr 11
Precipitation range (inches/year)
k (yr1)
<20
0.020
20-40
0.038
>40
0.057
These values for k show reasonable agreement with the results of other studies. For example, EPA's compilation
of emission factors (EPA 1998; EPA 2008) recommends a value of 0.02 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.
A-408 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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
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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 2006IPCC Guidelines also require annual proportions of waste disposed of in managed landfills versus open
dumps 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 open dumps for solid waste disposed to the use of managed landfills. Based on this timeline, it was estimated
that 6 percent of the waste that was land disposed in 1940 was disposed of in managed landfills and 94 percent was managed
in open dumps. Between 1940 and 1980, the fraction of waste 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 dumps, a methane
correction factor (MCF) of 0.6 was used based on the recommended IPCC default value for uncharacterized land disposal
(IPCC 2006); this MCF is equivalent to assuming 50 percent of the open dumps are deep and 50 percent are shallow. The
recommended IPCC default value for the MCF for managed landfills of 1 (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
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
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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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 CFU 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
vendor database was reduced by this quantity (referred to as a flare correction factor) on a state-by-state basis. This step
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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(l - OX) + R x (1 - (DE x fDest))\
\CExfREC / v J
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
the emissions from the GHGRP plus emissions from landfills that do not report to the GHGRP. The scale-up factor was first
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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 (memorandum in progress). 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
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.
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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 MTVMT 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 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 dumps, an MCF of 0.6 was used and for wastes disposed of in managed landfills, an MCF
of 1 was used, based on the recommended IPCC default values (IPCC 2006).
The parameters discussed above were used in the integrated form of the FOD model to estimate CH4 generation
from industrial 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
literature to better understand how climate, cover type, and gas recovery influence the rate of oxidation at active and closed
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.
A-413
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1 landfills. At this time, the IPCC recommended oxidation factor of 10 percent will continue to be used for all landfills for the
2 years 1990 to 2004.
3 For years 2005 to 2016, directly reported CH4 emissions to EPA's GHGRP, which include the adjustment for
4 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
5 oxidation factor across all facilities reporting to the GHGRP for the years data are available is approximately 20 percent,
6 thus this value is essentially the oxidation factor applied for years 2005 to 2016.
7 Step 8: Estimate Total CH4 Emissions for the Inventory
8 For 1990 to 2004, total CH4 emissions were calculated by adding emissions from MSW and industrial landfills,
9 and subtracting CH4 recovered and oxidized, as shown in Table A-257. A different methodology is applied for 2005 to 2016.
10 Directly reported net CH4 emissions to EPA's GHGRP plus the 9 percent scale-up factor were applied for 2010 to 2016. For
11 2005 to 2009, the directly-reported GHGRP net emissions from 2010 to 2016 were used to back-cast emissions for 2005 to
12 2009. Note that the emissions values for 2005 to 2009 are re-calculated for each Inventory and are subject to change if
13 facilities reporting to the GHGRP revise their annual GHG reports for any year. The 9 percent scale-up factor was also
14 applied annually for 2005 to 2009.
15
A-414 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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i Table fl-257: Clh 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 Emissions"
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
2 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.
3 aMSW Net CbU emissions for years 2010 to 2016 are directly reported CPU emissions to the EPA'sGHGRP for MSW landfills and are back-casted to estimate emissions for 2005 to 2009. A scale-up factor of 9 percent of
4 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
5 methodology.
6
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Environmental Protection Agency, Office of Air and Radiation. Washington, D.C. EPA/430-R-93-003. April 1993.
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ERG (2017) Draft Production Data Supplied by ERG for 1990-2016 for Pulp and Paper, Fruits and Vegetables, and Meat.
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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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Emissions from the Pulp and Paper Industry." Memorandum to R. Nicholson (RTI).
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Emissions from Pulp and Paper Mills, Version 1.1." July 8,2005.
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Landfills." Chemosphere, 26(l-4):387-400.
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Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA). In progress.
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review draft of the 1990-2015 Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA).
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Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA), December 4,2015.
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Memorandum prepared by J. Coburn for R. Schmeltz (EPA), October 28,2014.
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(EPA), January 14,2011.
RTI (2009) GHG Inventory Improvement - Construction & Demolition Waste DOC and L0 Value. Memorandum prepared by J.
Coburn and K. Bronstein (RTI) for R. Schmeltz, April 15,2010.
RTI (2006) Methane Emissions for Industrial Landfills. Memorandum prepared by K. Weitz and M. Bahner for M. Weitz (EPA),
September 5, 2006.
RTI (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from Landfills.
Memorandum prepared by M. Branscome and J. Coburn (RTI) to E. Scheehle (EPA), August 26, 2004.
Skog, K.E. (2008) "Sequestration of Carbon in harvested wood products for the United States." Forest Products Journal, 58(6):
56-72.
Skog, K. and G.A. Nicholson (2000) "Carbon Sequestration in Wood and Paper Products." USDA Forest Service Gen. Tech.
Rep. RMRS-GTR-59.
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Publication No. GR-LG 0075. March 1998.
Sonne, E. (2006) "Greenhouse Gas Emissions from Forestry Operations: A Life Cycle Assessment." J. Environ. Qual. 35:1439-
1450.
Upton, B., R. Miner, M. Spinney, L.S. Heath (2008) "The Greenhouse Gas and Energy Impacts of Using Wood Instead of
Alternatives in Residential Construction in the United States." Biomass and Bioenergy, 32: 1 -10.
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Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2016. Available online at:
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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
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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-419
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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 2.3 percent lower than the Sectoral
Approach for 2016. The greatest differences lie in lower estimates for coal and petroleum consumption for the Reference
Approach (2.5 percent and 4.3 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 2.3 percent lower estimate of energy
consumption in the United States than the Sectoral Approach. The resulting emissions estimate for the Reference Approach
was 2.0 percent lower. Estimates of natural gas emissions from the Reference Approach are higher (0.5 percent), and coal
and petroleum emission estimates are lower (2.9 percent and 3.2 percent, respectively) than the Sectoral Approach. Potential
reasons for these differences may include:
• Product Definitions. Coal data is aggregated differently in each methodology, as noted above. The format
used for the Sectoral Approach likely results in more accurate estimates than in the Reference Approach. Also,
the Reference Approach relies on a "crude oil" category for determining petroleum-related emissions. Given
the many sources of crude oil in the United States, it is not an easy matter to track potential differences in C
content between many different sources of crude; particularly since information on the C content of crude oil
is not regularly collected.
• Carbon Coefficients. The Reference Approach relies on several default C coefficients by rank provided by
IPCC (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-420 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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 (2017) Monthly Energy Review, October 2017. Energy Information Administration, U.S. Department of Energy.
Washington, D.C. DOE/EIA-0035(2017/10).
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-421
-------
i Tablefl-258:2016 U.S. EnergyStatistics [Physical Units!
Stock
U.S.
Fuel Category (Units)
Fuel Type
Production
Imports
Exports
Change
Adjustment
Bunkers
Territories
Solid Fuels (Thousand Short Tons)
Anthracite Coal
1,193
[1]
[1]
[1]
Bituminous Coal
328,756
[1]
[1]
[1]
Sub-bituminous Coal
345,745
[1]
[1]
[1]
367
Lignite
52,537
[1]
[1]
[1]
4,427
Coke
140
857
(90)
Unspecified Coal
9,850
60,271
(35,115)
2,849
1,963
Gas Fuels (Million Cubic Feet)
Natural Gas
26,475,768
3,006,439
2,335,448
(338,757)
281,893
55,000
Liquid Fuels (Thousand Barrels)
Crude Oil
3,241,591
2,873,208
216,274
35,365
Nat Gas Liquids and Liquefied Refinery Gases
1,284,357
66,025
443,388
5,704
4,005
Other Liquids
0
486,281
178,024
2,660
Motor Gasoline
(38,487)
21,644
232,562
(212)
234,608
34,263
Aviation Gasoline
111
0
116
Kerosene
855
3,295
(446)
411
Jet Fuel
53,677
64,149
2,620
175,795
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
9,784
0
(92)
Petroleum Coke
3,627
209,723
668
9,491
Other Oil for petrochemical feedstocks
1,836
0
45
1,240
Special Naphthas
5,073
0
(118)
Lubricants
14,316
28,223
(1,086)
172
Waxes
1,983
1,298
98
Asphalt/Road Oil
13,302
7,438
(1,774)
Still Gas
0
0
0
Misc. Products
14
542
(94)
13,144
2 [1] Included in Unspecified Coal
3 Note: Parentheses indicate negative values.
4 Sources: Solid and Gas Fuels: EIA (2017); Liquid Fuels: EIA (1995-2016).
5
A-422 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
22.63
25.05
22.63
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
5.81
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
2 Sources: Coal and lignite production: EIA (1992); Unspecified Solid Fuels, Coke, Natural Gas and Petroleum Products: EIA (1995-2016).
3
A-423
-------
i 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
7,854.0
7,854.0
Sub-bituminous Coal
5,926.1
10.3
5,915.7
Lignite
675.9
57.0
619.0
Coke
3.2
21.5
(2.0)
(16.3)
Unspecified
246.2
1,565.4
(732.5)
427.9
49.3
(965.1)
Gas Fuels
Natural Gas
27,455.4
3,081.6
2,356.5
(351.3)
292.2
57.0
28,296.7
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
1,021.1
45.6
(1,049.7)
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
65,062.1
25,299.3
14,117.2
(826.5)
940.6
1,545.8
655.0
75,239.2
2 Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
3
4
A-424 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
i 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
7.85
25.44
732.7
Sub-bituminous Coal
5.92
26.50
574.8
Lignite
0.62
26.65
60.5
Coke
(0.02)
31.00
(1.8)
Unspecified
(0.97)
25.34
(89.7)
Gas Fuels
Natural Gas
28.30
14.46
1,499.8
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
(1.05)
19.70
(75.8)
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
5,201.6
2 Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
3 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).
4
A-425
-------
i 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
88.6
31.00
2.75
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
[1]
[1]
[1]
[1]
0.6
Total
207.7
2 [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.
3 Note: Totals may not sum due to independent rounding.
4
5 Table fl-263:2016 Reference Approach CO; Emissions from Fossil Fuel Consumption [MMTCO2 Eg. unless otherwise noted]
Potential
Carbon
Net
Fraction
Total
Fuel Category
Emissions
Sequestered
Emissions
Oxidized
Emissions
Coal
1,279.3
1.7
1,277.6
100.0%
1,277.6
Petroleum
2,422.6
195.9
2,226.7
100.0%
2,226.7
Natural Gas
1,499.8
10.1
1,489.7
100.0%
1,489.7
Total
5,201.6
207.7
4,993.9
4,993.9
6 Note: Totals may not sum due to independent rounding.
7
A-426 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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i Table fl-264: Fuel Consumption in the United States by Estimating Approach ITBtul3
Approach
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Sectoral
69,969
75,276
82,898
82,870
84,105
81,389
76,566
79,093
77,569
75,788
77,874
78,522
77,704
76,989
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
13,785
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
28,178
Petroleum
32,713
33,920
37,758
39,076
38,667
36,042
34,142
34,515
33,819
33,129
33,862
34,011
34,725
35,025
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
75,239
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
13,434
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
28,297
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
33,508
Difference
-1.8%
-1.7%
-1.7%
-1.0%
-0.3%
-1.2%
-0.2%
-1.6%
-1.4%
-0.5%
-2.3%
-2.3%
-2.1%
-2.3%
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%
-2.5%
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
-2.6%
-2.2%
-1.8%
-1.5%
0.5%
-2.0%
-0.6%
-2.0%
-2.5%
-0.4%
-4.1%
-4.6%
-4.2%
-4.3%
a Includes U.S. Territories. Does not include international bunker fuels
Note: Totals may not sum due to independent rounding
Table A-265: CO2 Emissions from Fossil Fuel Combustion by Estimating Approach (MMT CO2 Eq.)
Approach
1990
1995
2000
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Sectoral
4,875
5,190
5,754
5,809
5,889
5,701
5,310
5,492
5,349
5,143
5,295
5,334
5,194
5,097
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
2,149
2,203 /
2,456
2,569
2,553
2,382
2,253
2,279
2,231
2,178
2,231
2,247
2,288
2,299
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
4,994
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
1,278
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
1,490
Petroleum
2,127
2,206 /
2,461
2,573
2,603
2,369
2,277
2,267
2,202
2,203
2,164
2,164
2,213
2,227
Difference
-1.7%
-1.1%
-1.3%
-0.5%
0.0%
-0.9%
0.5%
-1.3%
-1.0%
0.0%
-2.2%
-2.1%
-1.8%
-2.0%
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%
-2.9%
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
-1.0%
0.1%
0.2%
0.2%
1.9%
-0.5%
1.1%
-0.5%
-1.3%
1.1%
-3.0%
-3.7%
-3.3%
-3.2%
6 a Includes U.S. Territories. Does not include international bunker fuels.
7 Note: Totals may not sum due to independent rounding
A-427
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
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, 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 emission sources
discussed. Methods to estimate emissions from these sources were introduced with 2006 IPCC Guidelines. Also, many of
the sources discussed below are determined to be not significant in terms of overall national emissions, 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 aims to provide transparent information on the
significance of these excluded categories in future Inventory reports to the extent feasible. 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 and seeking to find the data required to estimate related emissions. As such improvements
are implemented, new emission sources are quantified and included in the Inventory to enhance completeness of the
Inventory.
The full list of sources not estimated, along with explanations for their exclusion, are provided in Table 9 of the
CRF submission. Additional information for some specific source categories is provided below. Note the numerical
references shown for categories below (e.g., 1.A.3) are consistent with CRF category numbers and may vary slightly from
the references in the 2006 IPCC Guidelines.
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-428 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 Source Categories Not Estimated
2 The following section is arranged by sector and source or sink category, providing additional information on the
3 reasons the source was not estimated.
4 Energy
5 CRF Category 1.A.3: ChU and N2O Emissions from Transport Fuel Combustion—Biomass
6 Emissions from biomass fuel use in domestic aviation (l.A.3.a), motorcycles (l.A.3.b), railways (l.A.3.c), and
7 domestic navigation (1 A.3.d) are not currently estimated. EPA has determined that the use ofbiodiesel in rail and navigation
8 was likely insignificant, and there are not readily available data sources to estimate biodiesel consumption from these
9 sources.
10 Emissions from ethanol mixed with gasoline in low blends are included in the on-road gasoline emissions for
11 motorcycles. If there is any use of high blend ethanol fuel in motorcycles, it is likely insignificant.
12 Prior to 2011, no biobased jet fuel was assumed to be used for domestic aviation. Between 2011 and 2015, 22
13 airlines have performed over 2,500 commercial passenger flights with blends of up to 50 percent biojet fuel. Furthermore,
14 several airlines have concluded long-term offtake agreements withbiofuel suppliers.147 An analysis was conducted based on
15 the total annual volumes of fuels specified in the long-term agreements and it was determined that annual non-CCh
16 greenhouse gas emissions from the volume of fuel used would be below 500 kt CO2 Eq. per year and considered insignificant
17 for the purposes of inventory reporting under the UNFCCC.
18 CRF Category 1.A.3.e.i: CO2 Emissions from Liquid Fuels in Other Transportation—Pipeline Transport
19 Use of liquid fuels to power pipeline pumps is uncommon, but does occur. Data are currently unavailable to
20 characterize this activity.
21 CRF Category 1.A.3.e.ii: CH4 and N2O Emissions from Biomass and Gaseous Fuels in Other Transportation—Non-
22 Transportation Mobile
23 Biomass based fuels used in non-transportation mobile applications are currently not estimated. The use of biofuels
24 in non-transportation mobile applications is insignificant and there are no readily available data sources to estimate it.
25 LPG/CNG non-road equipment represent a relatively small emission source category, for which the EPA is currently
26 compiling emission factor data sources for inclusion in a future Inventory.
27 CRF Category 1.A.5.a: CO2 Emissions from Non-Hazardous Industrial Waste Incineration and Medical Waste
28 Incineration
29 Waste incineration of the municipal waste stream and hazardous waste incineration of fossil fuel-derived materials
30 are reported in two sections of the Energy chapter of the Inventory, specifically in the section on CO2 emissions from waste
31 incineration, and in the calculation of emissions and storage from non-energy uses of fossil fuels.
32 Two additional categories of waste incineration that are not directly included in our calculations are industrial non-
33 hazardous waste and medical waste incineration. Data are not readily available for these sources.
34 In the calculation of emissions and storage from non-energy uses of fossil fuels, there is an energy recovery
35 component that includes emissions from waste gas; waste oils, tars, and related materials from the industrial sector. While
36 this is not a comprehensive inclusion of non-hazardous industrial waste, it does capture a subset.
37 Furthermore, an analysis was conducted based on a study of hospital/ medical/ infectious waste incinerator
3 8 (HMIWI) facilities in the United States showing that medical waste incineration emissions could be considered insignificant.
39 Based on that study's information of waste throughput and an analysis of fossil-based composition of the waste, it was
40 determined that annual greenhouse gas emissions for medical waste incineration would be below 500 kt CO2 Eq. per year
41 and considered insignificant for the purposes of inventory reporting under the UNFCCC.148
147 See : .
148 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-429
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1 CRF Category 1.A.5.a: ChU and N2O Emissions from Stationary Fuel Combustion—Biomass in U.S. Territories
2 Data are not available to estimate emissions from biomass in U.S. Territories. However, biomass consumption is
3 likely small in comparison with other fuel types, and therefore CH4 and N2O emissions are considered insignificant.
4 CRF Category 1.B.1.a.1.i: CO2 and CH4 from Fugitive Emissions from Underground Coal Mining Activities
5 A preliminary analysis by EPA determined that CO2 emissions for active underground coal mining activities are
6 negligible. Applying a CO2 emission rate as a percentage of CH4 emissions for active coal mines results in a national
7 emission estimate below 500 kt CO2 Eq. per year. Future inventories may quantify these emissions, if it is deemed it will
8 not require a disproportionate amount of effort.
9 CRF Category 1.B.1.a.1.iii: CO2 from Fugitive Emissions from Abandoned Underground Coal Mines
10 A preliminary analysis by EPA determined that CO2 emissions for abandoned underground coal mining activities
11 are negligible. Applying a CO2 emission rate as a percentage of CH4 emissions for abandoned coal mines results in a national
12 emission estimate below 500 kt CO2 Eq. per year. Future inventories may quantify these emissions, if it is deemed it will
13 not require a disproportionate amount of effort.
14 CRF Category 1.B.1.a.2.i: CO2 and CH4 from Fugitive Emissions from Surface Coal Mining Activities
15 A preliminary analysis by EPA determined that CO2 emissions for active surface coal mining activities are
16 negligible. Applying a CO2 emission rate as a percentage of CH4 emissions for active coal mines results in a national
17 emission estimate below 500 kt CO2 Eq. per year. Future inventories may quantify these emissions, if it is deemed it will
18 not require a disproportionate amount of effort. While CFLi recovery projects were operating at surface mines from 2006 to
19 2010, the avoided emissions were so small that they were not included in the Inventory estimates.
20 CRF Category 1.B.2.a.3: CO2 from Fugitive Emissions from the Transport of Oil
21 Based on a preliminary analysis, EPA determined that CO2 emissions from the transport of oil are negligible.
22 Assuming the same CO2 content as gas from post-separator whole crude and applying this to the CH4 estimates from
23 transport of oil results in a national emission estimate of 1.2 kt, significantly less than 0.05 percent of national emissions.
24 CRF Category 1.B.2.C.2: N2O Emissions from Fugitive Emissions from Venting and Flaring
25 Data are currently not available to estimate N2O emissions from venting and flaring from oil production, natural
26 gas production, and combined oil and natural gas production.
27 Industrial Processes and Product Use
28 CRF Category 2.A.4.a: CO2 Emissions from Process Uses of Carbonates-Ceramics
29 Data are not currently available to estimate emissions from this source. During the Expert Review phase of the
30 current Inventory report, EPA sought expert solicitation on data for carbonate consumption in the ceramics industry but has
31 yet to identify data sources.
32 CRF Category 2.A.4.c: CO2 Emissions from Process Uses of Carbonates-Non-metallurgical Magnesium Production
33 Data are not currently available to estimate emissions from this source. During the Expert Review phase of the
34 current Inventory report, EPA sought expert solicitation on data for non-metallurgical magnesium production but has yet to
35 identify data sources.
36 CRF Category 2.B.4.b: CO2 and N2O Emissions from Glyoxal Production
37 Data are currently not available to estimate emissions from this source.
38 CRF Category 2.B.4.c: CO2 and N2O Emissions from Glyoxylic Acid Production
39 Data are currently not available to estimate emissions from this source.
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32
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39
40
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43
CRF Category 2.B.5.b: CO2 and CH4 Emissions from Calcium Carbide Production
Carbon dioxide is formed by the oxidation of petroleum coke in the production of calcium carbide. These CO2
emissions are implicitly accounted for in the storage factor calculation for the non-energy use of petroleum coke in the
Energy chapter. CH4 may also be emitted from the production of calcium carbide because the petroleum coke used in the
process contains volatile organic compounds, which form CH4 during thermal decomposition. During the Expert Review
phase of the current Inventory report, EPA sought expert solicitation on data for calcium carbide industry but has yet to
identify enough data to complete an analysis. Through previous review processes, EPA has identified literature indicating
that one facility is operating at over 100,000 tons of calcium carbide production capacity in the United States. Pending
review of this information, research on historical production and resources, EPA plans to integrate emission estimates to
improve completeness in the next Inventory 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. 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, 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.
Agriculture
CRF Category 3.A.4: CH4 Emissions from Enteric Fermentation—Camels and Llamas
Enteric fermentation emissions from camels and llamas are not estimated because there is no significant population
of camels and llamas in the United States. Additional analyses will be conducted to quantitatively justify emissions reporting
as "not estimated" and considered insignificant.
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.
CRF Category 3.A.4: CH4 and N2O Emissions from Manure Management—Camels and Llamas
Manure management emissions from camels and llamas are not estimated because there is no significant population
of camels and llamas in the United States. Additional analyses will be conducted to quantitatively justify emissions reporting
as "not estimated" and considered insignificant.149
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, 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.
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-431
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19
20
21
22
23
24
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 become available.
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 are not estimated because data are currently not
available.
CRF Category 4.A.2: Carbon Stock Change in Organic Soils in Land Converted to Forest Land
Carbon stock change in organic soils are not currently estimated. Emissions from this source will be estimated in
future Inventories when data become available.
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.
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.
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.
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 due to a
lack of emission factor data. 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."
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37
38
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:
MMTC02 Eq. =(ktofgas)x(GWP)x' MMT
where,
1,000 kt
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
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-266). 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
j 150
horizon...
Greenhouse gases with relatively long atmospheric lifetimes (e.g., CO2, CH4, N2O, HFCs, PFCs, SF6, and NF3)
tend to be evenly distributed throughout the atmosphere, and consequently global average concentrations can be determined.
However, short-lived gases such as water vapor, carbon monoxide, tropospheric ozone, other indirect greenhouse gases (e.g.,
NOx and NMVOCs), and tropospheric aerosols (e.g., 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.
Table A-266: IPCC flR4 Global Warming Potentials [GWP] and Atmospheric Lifetimes [Years] of Gases Used in this Report
Gas Atmospheric Lifetime 100-year GWPa 20-year GWP 500-year GWP
Carbon dioxide (CO2) See footnoteb 1 1 1
Methane (CH4)c 12d 25 72 7.6
Nitrous oxide (N20) 114d 298 289 153
HFC-23 270 14,800 12,000 12,200
150 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-433
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17
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19
20
21
22
23
24
25
26
27
28
29
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-267 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-267). The effects of
these compounds on radiative forcing are not addressed in this report.
Table A-267:100-year Direct Global Warming Potentials for Select Ozone Depleting Substances
Gas Direct GWP
CFC-11
4,750
CFC-12
10,900
CFC-113
6,130
HCFC-22
1,810
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 ozone depleting substances (ODSs).
However, they are also potent greenhouse gases. Recognizing the harmful effects of these compounds on the ozone layer, in 1987 many governments
signed the Montreal Protocol on Substances that Deplete the Ozone Layer to limit the production and importation of a number of CFCs and other
halogenated compounds. The United States furthered its commitment to 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
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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-268 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-435
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i Table fl-268: 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%
2 + Does not exceed 0.05 or 0.05 percent.
3 NC (No Change)
4 NA (Not Applicable)
5 a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report.
6 b The GWP values presented here from the 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
7 reported separate values for fossil versus biogenic methane in order to account for the CO2 oxidation product.
8 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
9 slowly decrease over a number of years, and a small portion of the increase will remain for many centuries or more.
10 d No single lifetime can be determined for CO2 (see IPCC 2007).
11 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
12 biogenic methane in order to account for the CO2 oxidation product..
13 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.
14 Note: Parentheses indicate negative values. Source: IPCC (2013), IPCC (2007), IPCC (1996).
A-436 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
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-269 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-269: Effects on U.S. Greenhouse Gas Emissions Using S
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
196.5
196.5
196.5
196.5
NC
NC
NC
NC
NC
NC
cm
(102.8)
(122.3)
(137.0)
(166.4)
(124.5)
93.4
280.1
(104.9)
78.7
236.1
n2o
14.8
14.2
12.6
14.2
14.3
(39.3)
NC
14.9
(40.8)
NC
HFCs, PFCs, SFe,
and NF3
74.5
88.6
86.6
106.3
(11.9)
(9.0)
1.3
(26.0)
(11.0)
19.0
Total
183.0
177.0
158.8
150.7
(122.1)
45.1
281.4
(116.1)
26.9
255.1
Percent Change
2.9%
2.8%
2.5%
2.3%
(1.9%)
0.7%
4.4%
(1.8%)
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 6,430.1 MMT CO2 Eq., as compared to the official emission estimate of 6,546.2 MMT CO2 Eq. using
AR4 GWP values (i.e., the use of SAR GWPs results in a 1.8 percent decrease relative to emissions estimated using AR4
GWPs). Table A-270 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-271 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-270: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks using the SflR GWP values [MMT CO; Eg.]
Gas/Source
1990
2005
2012
2013
2014
2015
2016
C02
5,136.8
6,150.8
5,383.7
5,541.7
5,590.5
5,449.5
5,333.4
Fossil Fuel Combustion
4,755.8
5,759.1
5,029.8
5,162.3
5,206.1
5,059.3
4,976.7
Electricity Generation
1,820.8
2,400.9
2,022.2
2,038.1
2,038.0
1,900.7
1,808.8
Transportation
1,467.2
1,855.8
1,661.9
1,677.6
1,717.1
1,735.5
1,794.9
Industrial
874.5
867.8
818.4
848.7
830.8
819.3
807.6
Residential
338.3
357.8
282.5
329.7
345.3
316.8
296.2
Commercial
227.4
227.0
201.3
225.7
233.6
245.6
227.9
U.S. Territories
27.6
49.7
43.5
42.5
41.4
41.4
41.4
Non-Energy Use of Fuels
119.6
141.7
113.3
133.2
127.8
135.1
121.0
Iron and Steel Production &
Metallurgical Coke Production
101.5
68.0
55.4
53.3
58.2
47.7
42.2
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
27.4
Natural Gas Systems
29.7
22.5
24.4
26.0
27.0
26.3
26.7
Petroleum Systems
9.4
17.0
25.6
29.7
32.9
38.0
25.5
Lime Production
11.7
14.6
13.8
14.0
14.2
13.3
13.3
Other Process Uses of Carbonates
4.9
6.3
8.0
10.4
11.8
11.2
11.2
Ammonia Production
13.0
9.2
9.4
10.0
9.6
10.6
11.2
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-437
-------
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
1.3
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
Magnesium Production and Processing
+
+
+
+
+
+
+
Wood Biomass, Ethanol, and Biodiesel
Consumptiona
219.4
230.7
276.2
299.8
308.3
294.5
291.1
International Bunker Fuelsb
103.5
113.1
105.8
99.8
103.4
110.9
114.4
CH4C
653.6
570.6
555.5
554.0
558.9
557.8
550.9
Enteric Fermentation
137.9
141.8
140.1
139.0
137.9
139.9
142.9
Natural Gas Systems
162.7
134.4
131.7
134.1
137.9
138.1
136.1
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
35.6
29.1
29.7
32.6
34.4
33.1
33.0
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
7.3
6.6
6.1
7.3
7.4
6.6
6.1
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
8.2
5.5
3.4
3.1
2.9
2.6
2.5
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 Fuelsb
0.1
0.1
0.1
0.1
0.1
0.1
0.1
N2Oc
368.9
371.8
348.7
377.2
375.0
394.2
383.6
Agricultural Soil Management
260.5
263.7
257.9
287.7
285.0
306.9
295.0
Stationary Combustion
11.6
18.2
17.5
19.3
19.7
18.7
19.1
Manure Management
14.6
17.2
18.2
18.2
18.2
18.4
18.9
Mobile Combustion
43.1
40.0
24.8
22.9
21.0
19.6
18.5
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 Fuelsb
0.9
1.0
1.0
0.9
0.9
1.0
1.0
HFCs
36.9
105.1
135.2
137.4
143.7
149.0
152.1
Substitution of Ozone Depleting
Substances'1
0.3
89.1
130.7
134.0
139.4
145.3
149.6
HCFC-22 Production
36.4
15.8
4.3
3.2
4.0
3.4
2.2
A-438 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
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
6.9
6.6
6.6
6.2
6.5
Electrical Transmission and Distribution
24.2
8.7
4.9
4.7
4.8
4.4
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
6,247.1
7,216.2
6,434.9
6,621.8
6,679.4
6,560.9
6,430.1
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)
(719.8)
Net Emissions (Sources and Sinks)
5,426.5
6,483.3
5,679.5
5,884.6
5,937.6
5,862.8
5,710.3
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-271: Change in U.S. Greenhouse Gas Emissions Using SflR GWP values relative to flR4 GWP values [MMT CO; EqJ
Percent
Change
Gas/Source
1990
2005
2012
2013
2014
2015
2016
in 2016
CO2
NC
NC
NC
NC
NC
NC
NC
NC
CH4
(124.5)
(108.7)
(105.8)
(105.5)
(106.5)
(106.2)
(104.9)
(16%)
Enteric Fermentation
(26.3)
(27.0)
(26.7)
(26.5)
(26.3)
(26.6)
(27.2)
(16%)
Natural Gas Systems
(31.0)
(25.6)
(25.1)
(25.5)
(26.3)
(26.3)
(25.9)
(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
(6.8)
(5.5)
(5.7)
(6.2)
(6.6)
(6.3)
(6.3)
(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
(1.6)
(1.1)
(0.6)
(0.6)
(0.5)
(0.5)
(0.5)
(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%)
Ferroalloy Production
M
M
M
M
M
M
M
(16%)
A-439
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
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%)
N20
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
0.7
0.8
0.7
0.7
4%
Manure Management
0.6
0.7
0.7
0.7
0.7
0.7
0.7
4%
Mobile Combustion
1.7
1.5
1.0
0.9
0.8
0.8
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%
N20 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)
(14.9)
(20.8)
(21.7)
(23.1)
(24.2)
(25.0)
(14%)
Substitution of Ozone Depleting
Substancesb
+
(10.7)
(19.6)
(20.8)
(22.0)
(23.3)
(24.3)
(14%)
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 Emissions
(122.1)
(110.2)
(114.4)
(113.8)
(116.2)
(116.4)
(116.1)
(1.8%)
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-272 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.
Table A-272: Comparison of Emissions by Sector using IPCC flR4 and SflR GWP Values [MMT CO; Eg.)
Sector 1990 2005 2012 2013 2014 2015 2016
A-440 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Energy
AR4 GWP, Used In Inventory
5,340.2
6,295.7
5,527.6
5,691.1
5,739.1
5,596.0
5,476.4
SAR GWP
5,284.0
6,252.1
5,483.9
5,646.6
5,693.5
5,551.2
5,433.2
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
340.5
354.2
361.6
364.7
380.2
378.8
375.7
SAR GWP
329.9
339.2
340.4
342.6
356.8
354.3
350.7
Difference (%)
(3.1%)
(4.2%)
(5.9%)
(6.0%)
(6.2%)
(6.5%)
(6.7%)
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)
(719.8)
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
AR4 GWP, Used In Inventory
5,549.6
6,595.3
5,795.9
5,999.9
6,055.2
5,982.1
5,829.3
SAR GWP
5,426.5
6,483.3
5,679.5
5,884.6
5,937.6
5,862.8
5,710.3
Difference (%)
(2.2%)
(1.7%)
(2.0%)
(1.9%)
(1.9%)
(2.0%)
(2.0%)
+ Does not exceed 0.05 percent.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Further, Table A-273 and Table A-274 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-275 and Table A-276 show the comparison of emission estimates using AR5 GWP values with climate-carbon
feedbacks. The use of AR5 GWP values without climate-carbon feedbacks results in an increase in emissions of CH4 and
SF6 relative to AR4 GWP values, but a decrease in emissions of other gases. The use of AR5 GWP values with climate-
carbon feedbacks does not impact 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.
Table A-273: 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
93.4
81.5
79.4
79.1
79.8
79.7
78.7
n2o
(39.3)
(39.6)
(37.1)
(40.2)
(39.9)
(42.0)
(40.8)
HFCs
(7.5)
(10.4)
(9.9)
(9.7)
(10.2)
(10.6)
(10.7)
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
45.1
31.2
32.0
28.9
29.3
26.8
26.9
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
NC (No Change)
151 The IPCC AR5 report provides additional information on emission metrics. See .
A-441
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22
23
24
25
26
27
28
29
30
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-275) 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-274: 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%)
(8.7%)
(6.3%)
(6.1%)
(6.1%)
(6.1%)
(6.0%)
Substitution of Ozone
Depleting Substances
11.3%
(7.1%)
(6.0%)
(5.8%)
(5.8%)
(5.8%)
(5.9%)
HCFC-22 Production"
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.2%)
(16.2%)
Semiconductor Manufacture0
(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 Manufacture0
(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-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.
b HFC-23 emitted
c Emissions from HFC-23, CF4, C2F6, C3F8, C4F8, SF6, as well as other HFCs and PFCs used as heat transfer fluids.
d Zero change in beginning of time series since emissions were zero.
e PFC emissions from CF4 and C2F6.
f PFC emissions from CF4.
Note: Total emissions presented without LULUCF. Parentheses indicate negative values.
Table A-275: Change in U.S. Greenhouse Gas Emissions Using AR5 with Climate-Carbon Feedbacks3 Relative to AR4 GWP
Values [MMTCO Eg.)
Gas
1990
2005
2012
2013
2014
2015
2016
CO2
NC
NC
NC
NC
NC
NC
NC
CH4
280.1
244.5
238.1
237.4
239.5
239.0
236.1
N2O
NC
NC
NC
NC
NC
NC
NC
HFCs
(2.9)
8.6
15.5
16.2
16.8
17.5
18.1
PFCs
M
+
+
+
+
+
+
SFe
4.2
1.7
1.0
0.9
0.9
0.8
0.9
nf3
+
+
+
+
+
+
+
Total
281.4
254.9
254.6
254.6
257.3
257.4
255.1
+ 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 A-276: 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
A-442 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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5
6
7
8
9
10
11
12
ch4
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
n20
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%
10.0%
10.2%
10.1%
10.1%
10.2%
Substitution of Ozone
Depleting Substances
34.7%
9.9%
10.6%
10.6%
10.6%
10.6%
10.5%
HCFC-22 Production"
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
Semiconductor Manufacture1
(6.4%)
(6.3%)
(6.3%)
(6.3%)
(6.3%)
(6.3%)
(6.3%)
Magnesium Production and
Processing11
0.0%
0.0%
8.3%
8.3%
8.3%
8.3%
8.3%
PFCs
(0.2%)
0.3%
0.4%
0.3%
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 Substances'^
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-443
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35
6.2.
Ozone Depleting Substance Emissions
152
Ozone is present in both the stratosphere, where it shields the earth from harmful levels of ultraviolet radiation,
153
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,
154
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
ozone depletion potentials. 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 net GWP values for ODS.
Although the IPCC emission inventory guidelines do not require the reporting of emissions of ozone depleting
substances, the United States believes that 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-277.
Table fl-277: 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
128
22
5
5
4
4
3
CFC-113
59
0
0
0
0
0
0
CFC-114
4
1
+
+
0
0
0
CFC-115
8
2
+
+
+
+
+
152 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.
153 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.
154 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.
155 Older refrigeration and air-conditioning equipment, fire extinguishing systems, meter-dose inhalers, and foam products blown
with CFCs/HCFCs may still contain ODS.
A-444 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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
76
73
69
65
60
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
+
+
+
+
+
+
1 + Does not exceed 0.5 kt.
2
3 Methodology and Data Sources
4 Emissions of ozone depleting substances were estimated using the EPA's Vintaging Model. The model, named for
5 its method of tracking the emissions of annual "vintages" of new equipment that enter into service, is a "bottom-up" model.
6 It models the consumption of chemicals based on estimates of the quantity of equipment or products sold, serviced, and
7 retired each year, and the amount of the chemical required to manufacture and/or maintain the equipment. The Vintaging
8 Model makes use of this market information to build an inventory of the in-use stocks of the equipment in each of the end-
9 uses. Emissions are estimated by applying annual leak rates, service emission rates, and disposal emission rates to each
10 population of equipment. By aggregating the emission and consumption output from the different end-uses, the model
11 produces estimates of total annual use and emissions of each chemical. Please see Annex 3.9, Methodology for Estimating
12 HFC and PFC Emissions from Substitution of Ozone Depleting Substances, of this Inventory for a more detailed discussion
13 of the Vintaging Model.
14 Uncertainties
15 Uncertainties exist with regard to the levels of chemical production, equipment sales, equipment characteristics,
16 and end-use emissions profiles that are used by these models. Please see the Substitution of Ozone Depleting Substances
17 section of this report for a more detailed description of the uncertainties that exist in the Vintaging Model.
18
A-445
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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
S02-derived 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-278.
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-279); 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,156 (2) New Source Performance Standards,157 (3) the New Source Review/Prevention of Significant Deterioration
Program,158 and (4) the Sulfur Dioxide Allowance Program.159
Table A-278: SO2 Emissions (kt)
Sector/Source
19901
20051
2012
2013
2014
2015
2016
Energy 19,628 J
Stationary Sources 18,4071
Oil and Gas Activities 390 j
Mobile Sources 7931
Waste Combustion 381
Industrial Processes and
Product Use 1,3071
Other Industrial Processes 3621
Miscellaneous3 111
Chemical and Allied Product
Manufacturing 269
Metals Processing 6591
Storage and Transport 61
Solvent Use o|
Degreasing 0|
Graphic Arts 0|
Dry Cleaning NA|
Surface Coating 0|
Other Industrial 0|
Nonindustrial NA|
Agriculture NA|
Agricultural Burning NA|
Waste +1
12,364 J
11,5411
180|
6191
251
8311
3271
114|
2281
1581
2|
+1
o|
o(
o|
of
+ l
na1
na|
na|
1!
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
3
+
0
0
0
0
+
NA
NA
NA
1
2,950
2,756
93
70
32
496
156
135
104
98
3
+
0
0
0
0
+
NA
NA
NA
1
1,959
1,790
93
44
32
496
156
135
104
98
3
+
0
0
0
0
+
NA
NA
NA
1
156 [42 U.S.C § 7409, CAA § 109]
157 [42 U.S.C §7411, CAA § 111]
158 [42 U.S.C § 7473, CAA § 163]
159 [42 U.S.C § 7651, CAA § 401]
A-446 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1
2
3
4
5
6
7
8
9
10
11
Landfills
Wastewater Treatment
Miscellaneous3
Total
20,9351
13,196
5,876 5,874 4,357 3,448 2,457
+ 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-279: SO; Emissions from Electricity Generation tktl
Fuel Type
1990
20051111
2012
2013
2014
2015
2016
Coal
13,808
8,680 111
3,858
3,856
2,690
1,877
989
Oil
580
4581111
203
203
142
99
52
Gas
1
1741|(|J
77
77
54
38
20
Misc. Internal Combustion
45
57 111
25
25
18
12
7
Other
NA
31
31
22
15
8
Total
14,433 9,4391
4,195 4,194 2,925 2,041
NA (Not Applicable)
Note: Totals may not sum due to independent rounding.
Source: Data taken from EPA (2016) and disaggregated based on EPA (2003).
1,075
A-447
-------
i 6.4. Complete List of Source Categories
Chapter/Source
Gas(es)
Energy
Fossil Fuel Combustion
Non-Energy Use of Fossil Fuels
Stationary Combustion (excluding 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, N2O, CO, NOx, NMVOC
CH4, N2O, CO, NOx, NMVOC
ch4
ch4
cm
cm
cm
C02,Cm, N20, NOx, CO, NMVOC
co2
co2
co2
co2
co2
co2
n2o
n2o
n2o
co2, cm
co2
co2
co2, cm
HFC-23
CO2
CO2
co2, cm
co2, cm
C02, CF4, C2F6
CO2, HFCs, SFe
CO2
CO2
N2O, HFCs, PFCsb, SFe, NFs
HFCs, PFCsa
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-448 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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Settlements Remaining Settlements
Land Converted to Settlements
C02, N20
C02
Waste
Landfills
Wastewater Treatment
Composting
CH4, NOx, CO, NMVOC
CH4, N2O, NOx, CO, NMVOC
CH4, N2O
1 a Includes HFC-23, HFC-32, HFC-125, HFC-134a, HFC-143a, HFC-236fa, CF4, HFC-152a, HFC-227ea, HFC-245fa, HFC-4310mee, and
2 PFC/PFPEs.
3 b Includes HFC-23, CF4, C2F6, as well as other HFCs and PFCs used as heat transfer fluids.
4 c The LULUCF Sector includes CH4 and N2O emissions to the atmosphere and net carbon stock changes. The term "flux" is used to
5 describe the net emissions of greenhouse gases accounting for both the emissions of CO2 to and the removals of CO2 from the
6 atmosphere. Removal of CO2 from the atmosphere is also referred to as "carbon sequestration."
7
A-449
-------
1
6.5. Constants, Units, and Conversions
2 Metric Prefixes
3 Although most activity data for the United States is gathered in customary U.S. units, these units are converted
4 into metric units per international reporting guidelines. Table A-280 provides a guide for determining the magnitude of
5 metric units.
6 Table A-280: 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
7
8 Unit Conversions
9
1 kilogram =
1 pound =
1 short ton =
1 metric ton =
10
1 cubic meter =
1 cubic foot =
1 U.S. gallon =
1 barrel (bbl)
1 barrel (bbl)
1 liter
11
1 foot =
1 meter =
1 mile =
1 kilometer =
12
1 acre =
1 square mile =
13
Degrees Celsius =
Degrees Kelvin =
14
15
16
17
A-450 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
2.205 pounds
0.454 kilograms
2,000 pounds = 0.9072 metric tons
1,000 kilograms = 1.1023 short tons
35.315 cubic feet
0.02832 cubic meters
3.785412 liters
0.159 cubic meters
42 U.S. gallons
0.001 cubic meters
0.3048 meters
3.28 feet
1.609 kilometers
0.622 miles
43,560 square feet = 0.4047 hectares = 4,047 square meters
2.589988 square kilometers
(Degrees Fahrenheit - 32)*5/9
Degrees Celsius + 273.15
-------
Density Conversions160
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
4
5 Energy Conversions
6 Converting Various Energy Units to Joules
7 The common energy unit used in international reports of greenhouse gas emissions is the joule. A joule is the
8 energy required to push with a force of one Newton for one meter. A terajoule (TJ) is one trillion (1012) joules. A British
9 thermal unit (Btu, the customary U.S. energy unit) is the quantity of heat required to raise the temperature of one pound of
10 water one degree Fahrenheit at or near 39.2 degrees Fahrenheit.
2.388x1011 calories
, _. _ 23.88 metric tons of crude oil equivalent
947.8 million Btus
277,800 kilowatt-hours
11 Converting Various Physical Units to Energy Units
12 Data on the production and consumption of fuels are first gathered in physical units. These units must be converted
13 to their energy equivalents. The conversion factors in Table A-281 can be used as default factors, if local data are not
14 available. See Appendix A of EIA's Monthly Energy Review October 2017 (EIA 2017) for more detailed information on the
15 energy content of various fuels.
16
160 Reference: EIA (2007)
A-451
-------
i Table fl-281: 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
2 Note: For petroleum and natural gas, Monthly Energy
3 Review October 2017 (E\l\ 2017). For coal ranks, State
4 Energy Data Report 1992 (EIA 1993). All values are given in
5 higher heating values (gross calorific values).
6
7
A-452 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
6.6. Abbreviations
AAPFCO American Association of Plant Food Control Officials
ABS Acrylonitrile butadiene styrene
AC Air conditioner
ACC American Chemistry Council
AEDT FAA Aviation Environmental Design Tool
AEO Annual Energy Outlook
AFEAS Alternative Fluorocarbon Environmental Acceptability Study
AFV Alternative fuel vehicle
AGA American Gas Association
AHEF Atmospheric and Health Effect Framework
AISI American Iron and Steel Institute
ALU Agriculture and Land Use National Greenhouse Gas Inventory
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
ARMS Agricultural Resource Management Surveys
ASAE American Society of Agricultural Engineers
ASTM American Society for Testing and Materials
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&EN Chemical and Engineering News
CAAA Clean Air Act Amendments of 1990
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
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
ETMS
Enhanced Traffic Management System
EV
Electric vehicle
EVI
Enhanced Vegetation Index
FAA
Federal Aviation Administration
FAO
Food and Agricultural Organization
FAOSTAT
Food and Agricultural Organization database
FCCC
Framework Convention on Climate Change
FEB
Fiber Economics Bureau
FERC
Federal Energy Regulatory Commission
FGD
Flue gas desulfurization
FHWA
Federal Highway Administration
FIA
Forest Inventory and Analysis
FIADB
Forest Inventory and Analysis Database
FIPR
Florida Institute of Phosphate Research
FQSV
First-quarter of silicon volume
FSA
Farm Service Agency
FTP
Federal Test Procedure
A-454 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
g
Gram
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
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
IDB
Integrated Database
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
LDPE
Low density polyethylene
LDT
Light-duty truck
LDV
Light-duty vehicle
LEV
Low emission vehicles
LFG
Landfill gas
A-455
-------
LFGTE Landfill gas-to-energy
LHV Lower Heating Value
LKD Lime kiln dust
LLDPE Linear low density polyethylene
LMOP EPA's Landfill Methane Outreach Program
LNG Liquefied natural gas
LPG Liquefied petroleum gas(es)
LTO Landing and take-off
LULUCF Land use, land-use change, and forestry
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
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
MMT CO2 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
NASA National Aeronautics and Space Administration
NASF National Association of State Foresters
NASS USDA's National Agriculture Statistics Service
NC No change
NCASI National Council of Air and Stream Improvement
NCV Net calorific value
NE Not estimated
NEI National Emissions Inventory
NEMA National Electrical Manufacturers Association
A-456 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
-------
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
NO
Nitric oxide
NO
Not occurring
N02
Nitrogen Dioxide
NOx
Nitrogen oxides
NOAA
National Oceanic and Atmospheric Administration
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
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
A-457
-------
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
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 amy I 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
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
A-458 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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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
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
1
2
3
A-459
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1 6.7. Chemical Formulas
2 Table fl-282: 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
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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
H, H2
atomic Hydrogen, molecular Hydrogen
H20
Water
H2O2
Hydrogen peroxide
OH
Hydroxyl
N, N2
atomic Nitrogen, molecular Nitrogen
A-461
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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
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.
2
3 References
4 EIA (2017) Monthly Energy Review, October 2017. Energy Information Administration, U.S. Department of Energy,
5 Washington, DC. DOE/EIA-0035(2017/10). October 2017.
6 EIA (2007) Emissions of Greenhouse Gases in the United States 2006, Draft Report. Office of Integrated Analysis and
7 Forecasting, Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE-EIA-0573(2006).
8 EIA (1998) Emissions of Greenhouse Gases in the United States, DOE/EIA-0573(97), Energy Information Administration,
9 U.S. Department of Energy. Washington, DC. October.
10 EIA (1993) State Energy Data Report 1992, DOE/EIA-0214(93), Energy Information Administration, U.S. Department of
11 Energy. Washington, DC. December.
12 EPA (2016) "1970-2016 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory (NEI)
13 Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Last Modified December 2016.
14 Available online at: .
15 EPA (2003) E-mail correspondence. Air pollutant data. Office of Air Pollution to the Office of Air Quality Planning and
16 Standards, U.S. Environmental Protection Agency (EPA). December 22, 2003.
17 IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment
18 Report of the Intergovernmental Panel on Climate Change. [Stacker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K.
19 Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge,
20 United Kingdom and New York, NY, USA, 1535 pp.
21 IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
22 Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis,
23 K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United Kingdom 996 pp.
24 IPCC (1996) Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change, J.T.
25 Houghton, L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell (eds.). Cambridge University
26 Press. Cambridge, United Kingdom.
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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.
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
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 category, the
analysis highlights opportunities for changes to data measurement, data collection, and calculation methodologies. These
are presented in the "Planned Improvements" sections of each source category's discussion in the main body of the report.
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 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
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
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46
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.161 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 category values. Inherent in employing the
Approach 1 method are the assumptions that, for each source category, (i) both the activity data and the emission factor
values are approximately normally distributed, (ii) the coefficient of variation (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 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 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 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 Category Inventory Uncertainty Estimates
Discussion surrounding the input parameters and sources of uncertainty for each source category appears in the
body of this report. Table A-283 summarizes results based on assessments of source category-level uncertainty. The table
presents base year (1990 or 1995) and current year (2016) emissions for each source category. The combined uncertainty
(at the 95 percent confidence interval) for each source category is expressed as the percentage deviation above and below
161 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-464 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 the total 2016 emissions estimated for that source category. Source category trend uncertainty is described subsequently in
2 this Appendix.
3 Table A-283: Summary Results ofSource Category Uncertainty Analyses-TO BE UPDATED FOB FINAL INVENTORY REPORT
Source Category
Base Year Emissions11-1
2015 Emissions-1
2015 Uncertainty11
MMT CO2 Eq.
MMT CO2 Eq.
Low
High
C02
5.122.6
5.411.0
-2%
4%
Fossil Fuel Combustion'
4739.9
5,049.4
-2%
5%
Non-Energy Use of Fuels
117.6
125.5
-25%
37%
Iron and Steel Production & Metallurgical Coke Production
101.5
48.9
-17%
17%
Natural Gas Systems
37.7
42.4
-19%
30%
Cement Production
33.5
39.9
-6%
6%
Petrochemical Production
21.3
28.1
-5%
5%
Lime Production
11.7
13.3
-3%
3%
Other Process Uses of Carbonates
4.9
11.2
-13%
16%
Ammonia Production
13.0
10.8
-8%
8%
Incineration of Waste
8.0
10.7
-10%
13%
Urea Fertilization
2.4
5.0
-43%
3%
Carbon Dioxide Consumption
1.5
4.3
-5%
5%
Liming
4.7
3.8
-111%
88%
Petroleum Systems
3.6
3.6
-24%
149%
Soda Ash Production and Consumption
2.8
2.8
-7%
6%
Aluminum Production
6.8
2.8
-2%
2%
Ferroalloy Production
2.2
2.0
-12%
12%
Titanium Dioxide Production
1.2
1.6
-12%
13%
Glass Production
1.5
1.3
-4%
5%
Urea Consumption for Non-Agricultural Purposes
3.8
1.1
-12%
12%
Phosphoric Acid Production
1.5
1.0
-19%
20%
Zinc Production
0.6
0.9
-19%
21%
Lead Production
0.5
0.5
-15%
16%
Silicon Carbide Production and Consumption
0.4
0.2
-9%
9%
Magnesium Production and Processing
+
+
-3%
4%
Wood Biomass and Biofuel Consumptiond
219.4
291.7
NE
NE
International Bunker Fuelse
103.5
110.8
NE
NE
CHj
780.8
655.7
-9%
19%
Enteric Fermentation
164.2
166.5
-11%
18%
Natural Gas Systems
194.1
162.4
-19%
30%
Landfills
179.6
115.7
-9%
9%
Manure Management
37.2
66.3
-18%
20%
Coal Mining
96.5
60.9
-12%
16%
Petroleum Systems
55.5
39.9
-24%
149%
Wastewater T reatment
15.7
14.8
-26%
22%
Rice Cultivation
16.0
11.2
-28%
28%
Stationary Combustion
8.5
7.0
-36%
136%
Abandoned Underground Coal Mines
7.2
6.4
-18%
24%
Composting
0.4
2.1
-50%
50%
Mobile Combustion
5.6
2.0
-18%
27%
Field Burning of Agricultural Residues
0.2
0.3
-40%
41%
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
+
+
-19%
19%
Incineration of Waste
+
+
NE
NE
International Bunker Fuels6
0.2
0.1
NE
NE
N2O
359.5
334.8
-10%
27%
Agricultural Soil Management
256.6
251.3
-18%
47%
Direct
212.0
213.3
-16%
26%
Indirect
44.6
38.0
-46%
155%
A-465
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Stationary Combustion
11.9
23.1
-22%
50%
Manure Management
14.0
17.7
-16%
24%
Mobile Combustion
41
15.1
-13%
19%
Nitric Acid Production
12.1
11.6
-5%
6%
Wastewater T reatment
3.4
5.0
-75%
107%
Adipic Acid Production
15.2
4.3
-4%
4%
N2O from Product Uses
4.2
4.2
-24%
24%
Composting
0.3
1.9
-50%
50%
Incineration of Waste
0.5
0.3
-51%
330%
Semiconductor Manufacture
+
0.2
-13%
13%
Field Burning of Agricultural Residues
0.1
0.1
-29%
30%
International Bunker Fuels6
0.9
0.9
NE
NE
HFCs. PFCs. SF». and NFj
130.3
184.7
-1%
11%
Substitution of Ozone Depleting Substances'
30.9
168.5
-1%
12%
Semiconductor Manufacture
3.6
4.8
-5%
5%
HCFC-22 Production
46.1
4.3
-7%
10%
Electrical Transmission and Distribution
23.1
4.2
-10%
11%
Aluminum Production
21.5
2.0
-6%
6%
Magnesium Production and Processing
5.2
1.0
-6%
6%
Total Emissions
6.393.3
6.586.2
-1%
5%
LULUCF Emissions
10.6
19.7
-26%
94%
LULUCF Carbon Stock Change
(830.2)
(778.7)
28%
-20%
LULUCF Sector Net Total
(819.6)
(758.9)
28%
-21%
Net Emissions (Sources and Sinks)
5.573.7
5.827.3
-3%
7%
Notes: Total emissions (excluding emissions for which uncertainty was not quantified) is presented without LULUCF. Net emissions is presented with LULUCF.
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
a Emission estimates reported in this table correspond to emissions from only those source categories for which quantitative uncertainty was performed for the
current Inventory. Thus the totals reported for 2015 in this table exclude approximately 0.4 MMT CO2 Eq. of emissions for which quantitative uncertainty was
not assessed. Hence, these emission estimates do not match the final total U.S. greenhouse gas emission estimates presented in this Inventory. All
uncertainty estimates correspond only to the totals reported in this table.
b The uncertainty estimates correspond to a S5 percent confidence interval, with the lower bound corresponding to 2.5th percentile and the upper bound
corresponding to S7.5lh percentile.
c This source category's Inventory estimates exclude CO2 emissions from geothermal sources, as quantitative uncertainty analysis was not performed for that
sub-source category. Flence. for this source category, the emissions reported in this table do not match the emission estimates presented in the Energy chapter
of the Inventory.
d Emissions from Wood Biomass and Biofuel Consumption are not included in summing energy sector totals.
e Emissions from International Bunker Fuels are not included in the totals.
f This source category's estimate for 2015 excludes 0.003 MMT of CO2 Eq. from several very small emission sources, as uncertainty associated with those
sources was not assessed. Flence. for this source category, the emissions reported in this table do not match the emission estimates presented in the Industrial
Processes and Product Use chapter of the Inventory,
s Totals exclude emissions for which uncertainty was not quantified.
hBase Year is 1SS0 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen 1SS5.
' 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.
i 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.
k The LULUCF Sector Net Total is the net sum of all CPU and N2O emissions to the atmosphere plus net carbon stock changes.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Overall (Aggregate) Inventory Level Uncertainty Estimates
The overall level uncertainty estimate lor the I J.S. Inventory was developed using the II'CC Approaeh 2 uncertainty
estimation methodology. The uneertainty models of all the emission souree eategories eould not be directly integrated to
develop the overall uneertainty estimates due to software eonstraints in integrating multiple, large uneertainty models.
Therefore, an alternative approaeh was adopted to develop the overall uneertainty estimates. The Monte Carlo simulation
output data for eaeli emission souree category uneertainty analysis were eombined by type of gas and the probability
distributions were fitted to the eombined simulation output data, where sueh simulated output data were available. If such
detailed output data were not available for particular emissions sources, individual probability distributions were assigned
to those source category emission estimates based on the most detailed data available from the quantitative uncertainty
analysis performed.
A-466 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016
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1 For Composting and parts of Agricultural Soil Management source categories. Approach 1 uncertainty results were
2 used in the overall uncertainty analysis estimation. 1 low ever, for all other emission sources (excluding international hunker
3 fuels. C(): from biomass and bio fuel combustion, and CI 11 from incineration of waste). Approach 2 uncertainly results were
4 used in the overall uncertainty estimation.
5 The overall uncertainty model results indicate that the 2015 IJ.S. greenhouse gas emissions are estimated to be
6 within the range of approximately 6,505.0 to 6,919.9 MMT CO; Kq.. rellecling a relative 95 percent confidence interval
7 uncertainty range of -1 percent to 5 percent w ith respect to the total I J.S. greenhouse gas emission estimate of approximately
8 6,586.2 MMT C(); Kq. The uncertainty interval associated with total C()2 emissions, which constitute about 82 percent of
9 the total I J.S. greenhouse gas emissions in 2015. ranges from -2 percent to 4 percent of total C(); emissions estimated. The
10 results indicate that the uncertainty associated with the inventory estimate of the total CI 11 emissions ranges from -9 percent
11 to 19 percent, uncertainty associated w ith the total inventory N;() emission estimate ranges from -10 percent to 27 percent.
12 and uncertainty associated w ith lTuorinated C >1IC i emissions ranges from -1 percent to 11 percent.
13 A summary of the overall quantitative uncertainly estimates is shown below.
14 Table A-284: Quantitative Uncertainty Assessment of Overall National Inventory Emissions (MMT GO? Eq. and Percent)
2015 Emission
Standard
Estimate-1
Uncertainty Range Relative to Emission Estimate11
Meanc
Deviation11
Gas
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
(MMT CO2 Eq.)
Lower
Upper
Lower
Upper
Boundd
Boundc
Bound
Bound
C02
5.411.0
5.305.4
5.652.4
-2%
4%
5.474.3
90.2
CH4e
655.7
599.9
779.2
-9%
19%
681.8
45.3
N2O
334.8
302.5
424.6
-10%
27%
357.0
30.7
PFC, HFC, SFe. and NF3e
184.7
183.1
204.4
-1%
11%
193.4
5.5
Total Emissions
6.586.2
6.505.0
6.919.9
-1%
5%
6.706.6
106.0
LULUCF Emissions
19.7
14.6
38.2
-26%
94%
23.3
6.3
LULUCF Carbon Stock Change Flux
(778.7)
(993.1)
(620.7)
-20%
28%
(808.4)
94.7
LULUCF Sector Net Total
(758.9)
(969.7)
(597.9)
-21%
28%
(785.1)
94.8
Net Emissions (Sources and Sinks)
5.827.3
5.643.8
6.207.4
-3%
7%
5.921.5
142.8
15 Notes: Total emissions (excluding emissions for which uncertainty was not quantified) is presented without LULUCF. Net emissions is presented with LULUCF
16 a Emission estimates reported in this table correspond to emissions from only those source categories for which quantitative uncertainty was performed this
17 year. Thus the totals reported in this table exclude approximately 0.4 MMT CO2 Eq. of emissions for which quantitative uncertainty was not assessed. Hence.
18 these emission estimates do not match the final total U.S. greenhouse gas emission estimates presented in this Inventory.
19 b The lower and upper bounds for emission estimates correspond to a S5 percent confidence interval. with the lower bound corresponding to 2.5th percentile
20 and the upper bound corresponding to S7.5th percentile.
21 c Mean value indicates the arithmetic average of the simulated emission estimates: standard deviation indicates the extent of deviation of the simulated values
22 from the mean.
23 d 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
24 emissions were calculated separately through simulations.
25 e The overall uncertainty estimates did not take into account the uncertainty in the GWP values for CFU. N2O and high GWP gases used in the inventory
26 emission calculations for 2015.
27 f LULUCF emissions include the CFU and N2O emissions reported for Peatlands Remaining Peatlands. Forest Fires. Drained Organic Soils. Grassland Fires.
28 and Coastal Wetlands Remaining Coastal Wetlands'. CFU emissions from Land Converted to Coastal Wetlands', and N2O emissions from Forest Soils and
29 Settlement Soils.
30 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.
31 Cropland Remaining Cropland. Land Converted to Cropland. Grassland Remaining Grassland. Land Converted to Grassland. Wetlands Remaining Wetlands.
32 Land Converted to Wetlands. Settlements Remaining Settlements, and Land Converted to Settlements.
33 h The LULUCF Sector Net Total is the net sum of all CFU and N2O emissions to the atmosphere plus net carbon stock changes.
34 Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
35
36 Trend Uncertainty
37 In addition to the estimates of uncertainly associated with the current year's emission estimates, this Annex also
38 presents the estimates of trend uncertainty. The 2006 ll'("(" (jiikk'liiu's defines trend as the difference in emissions betw ecu
39 the base year (i.e.. 1990) and the current year (i.e.. 2015) Inventory estimates. I Iowever. for purposes of understanding the
40 concept of trend uncertainty, the emission trend is defined in this Inventory as the percentage change in the emissions (or
41 removal) estimated for the current year, relative to the emission (or removal) estimated for the base year. The uncertainty
42 associated with this emission trend is referred to as trend uncertainly.
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I Jnder the Approach 1 method, the trend uncertainty lor a source category is estimated using the sensitivity of the
calculated difference between the base year and the current year (i.e.. 2015) emissions to an incremental (i.e.. 1 percent)
increase in one or both of these values for that source category. The two sensitivities are expressed as percentages: Type A
sensitivity highlights the effect on the difference between the base and the current year emissions caused by a 1 percent
change in both, while Type B sensitivity highlights the effect caused by a change to only the current year's emissions. Both
sensitivities are simplifications introduced in order to analyze the correlation betw een 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.
I Jnder 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 category emissions (or removals) are assumed to exhibit the same uncertainty characteristics as the current year
emissions (or removals). Source category-specific PDl's for base year estimates were developed using current year (i.e..
2015) uncertainty output data. These were adjusted to account for differences in magnitude between the two years" inventory
estimates. Then. Ibr each source category, a trend uncertainty estimate was developed using the Monte Carlo method. The
overall inventory trend uncertainty estimate was developed by combining all source category-specific trend uncertainly
estimates. These trend uncertainty estimates present the range of likely change from base year to 2015, and are shown in
Table A-285.
Tahle fl-285: Quantitative Assessment of Trend Uncertainty [MMT CO? Eq. and Percent]
Base Year 2015 Emissions
Gas/Source
Emissions'"1
Emissions-1
Trend-1
Trend Range l b
(MMT CO2 Eq.)
(%)
(%)
Lower
Bound
Upper
Bound
C02
5.122.6
5.411.0
6%
1%
11%
Fossil Fuel Combustion1
4.739.9
5.049.4
7%
2%
12%
Non-Energy Use of Fuels
117.6
125.5
7%
-34%
71%
Iron and Steel Production & Metallurgical Coke Production
101.5
48.9
-52%
-62%
-39%
Natural Gas Systems
37.7
42.4
12%
-20%
59%
Cement Production
33.5
39.9
19%
9%
30%
Petrochemical Production
21.3
28.1
32%
23%
41%
Lime Production
11.7
13.3
14%
10%
18%
Other Process Uses of Carbonates
4.9
11.2
129%
88%
181%
Ammonia Production
13.0
10.8
-17%
-26%
-7%
Incineration of Waste
8.0
10.7
34%
14%
58%
Urea Fertilization
2.4
5.0
108%
36%
222%
Carbon Dioxide Consumption
1.5
4.3
192%
172%
213%
Liming
4.7
3.8
-18%
-99%
338%
Petroleum Systems
3.6
3.6
0%
-56%
133%
Soda Ash Production and Consumption
2.8
2.8
-1%
-10%
9%
Aluminum Production
6.8
2.8
-59%
-61%
-58%
Ferroalloy Production
2.2
2.0
-9%
-24%
9%
Titanium Dioxide Production
1.2
1.6
37%
14%
64%
Glass Production
1.5
1.3
-15%
-21%
-10%
Urea Consumption for Non-Agricultural Purposes
3.8
1.1
-70%
-75%
-65%
Phosphoric Acid Production
1.5
1.0
QCO/
"OD /o
-51%
-13%
Zinc Production
0.6
0.9
48%
11%
97%
Lead Production
0.5
0.5
-8%
-27%
14%
Silicon Carbide Production and Consumption
0.4
0.2
-52%
-58%
-45%
Magnesium Production and Processing
+
+
90%
80%
99%
Wood Biomass and Biofuel Consumptione
219.4
291.7
33%
NE
NE
International Bunker Fuels'
103.5
110.8
7%
NE
NE
CHi
780.8
655.7
-16%
-32%
1%
Enteric Fermentation
164.2
166.5
1%
-17%
25%
Natural Gas Systems
194.1
162.4
-16%
-41%
19%
Landfills
179.6
115.7
-36%
-74%
58%
Manure Management
37.2
66.3
78%
21%
147%
Coal Mining
96.5
60.9
-37%
-56%
-33%
Petroleum Systems
55.5
39.9
-28%
-68%
66%
Wastewater T reatment
15.7
14.8
-6%
-34%
33%
Rice Cultivation
16.0
11.2
Qfi 0/
"OU /O
-73%
89%
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Stationary Combustion
8.5
7.0
-17%
-69%
129%
Abandoned Underground Coal Mines
7.2
6.4
-11%
-43%
28%
Composting
0.4
2.1
452%
142%
1139%
Mobile Combustion
5.6
2.0
-64%
-74%
-51%
Field Burning of Agricultural Residues
0.2
0.3
25%
-32%
132%
Petrochemical Production
0.2
0.2
-18%
-66%
95%
Ferroalloy Production
+
+
-19%
-32%
-4%
Silicon Carbide Production and Consumption
+
+
-67%
-71%
-62%
Iron and Steel Production & Metallurgical Coke Production
+
+
-60%
-70%
-46%
Incineration of Waste
+
+
-32%
NE
NE
International Bunker Fuels'
0.2
0.1
-48%
NE
NE
N2O
359.5
334.8
-7%
-34%
35%
Agricultural Soil Management
256.6
251.3
-2%
-41%
58%
Stationary Combustion
11.9
23.1
94%
22%
212%
Manure Management
14.0
17.7
27%
-4%
66%
Mobile Combustion
41.2
15.1
-63%
-70%
-55%
Nitric Acid Production
12.1
11.6
-5%
-12%
3%
Wastewater T reatment
3.4
5.0
47%
-66%
553%
Adipic Acid Production
15.2
4.3
-72%
-73%
-70%
N2O from Product Uses
4.2
4.2
0%
-32%
47%
Settlement Soils
1.4
2.5
77%
-96%
7073%
Composting
0.3
1.9
452%
147%
1143%
Incineration of Waste
0.5
0.3
-32%
-85%
197%
Semiconductor Manufacture
+
0.2
579%
471%
702%
Field Burning of Agricultural Residues
0.1
0.1
23%
-21%
93%
International Bunker Fuels'
0.9
0.9
10%
NE
NE
HFCs. PFCs. SF». and NFj
130.3
184.7
42%
36%
56%
Substitution of Ozone Depleting Substances9
30.9
168.5
445%
399%
494%
Semiconductor Manufacture
3.6
4.8
34%
24%
45%
HCFC-22 Production
46.1
4.3
-91%
-92%
-90%
Electrical Transmission and Distribution
23.1
4.2
-82%
-85%
-79%
Aluminum Production
21.5
2.0
-91%
-91%
-90%
Magnesium Production and Processing
5.2
1.0
-80%
-84%
-80%
Total Emissions
6.393.3
6.586.2
3%
-2%
8%
LULUCF Emissions
10.6
19.7
85%
-2%
285%
LULUCF Carbon Stock Change
(830.2)
(778.7)
-6%
-33%
30%
LULUCF Sector Net Total
(819.6)
(758.9)
-7%
-34%
29%
Net Emissions (Sources and Sinks)
5.573.7
5.827.3
5%
-3%
12%
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
a Emission estimates reported in this table correspond to emissions from only those source categories for which quantitative uncertainty was performed for the
current Inventory. Thus the totals reported for 2015 in this table exclude approximately 0.4 MMT CO2 Eq. of emissions for which quantitative uncertainty was not
assessed. Hence, these emission estimates do not match the final total U.S. greenhouse gas emission estimates presented in this Inventory. All uncertainty
estimates correspond only to the totals reported in this table.
b The trend range represents a S5 percent confidence interval for the emission trend, with the lower bound corresponding to 2.5th percentile value and the upper
bound corresponding to S7.5th percentile value.
c This source category's inventory estimates exclude CO2 emissions from geothermal sources, as quantitative uncertainty analysis was not performed for that
sub-source category. Hence, for this source category, the emissions reported in this table do not match the emission estimates presented in the Energy chapter
of the Inventory.
d Sinks are only included in Net Emissions.
J Emissions from Wood Biomass and Biofuel Consumption are not included specifically in summing energy sector totals.
'Emissions from International Bunker Fuels are not included in the totals.
9 This source category's estimate for 2015 excludes 0.003 MMT of CO2 Eq. from several very small emission sources, as uncertainty associated with those
sources was not assessed. Hence, for this source category, the emissions reported in this table do not match the emission estimates presented in the Industrial
Processes and Product Use chapter of the Inventory.
h Totals exclude emissions for which uncertainty was not quantified.
'Base Year is 1SS0 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen 1SS5.
' LULUCF emissions include the CH4and 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.
i 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.
k The LULUCF Sector Net Total is the net sum of all CH4and N2O emissions to the atmosphere plus net carbon stock changes.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Notes: Total emissions (excluding emissions for which uncertainty was not quantified) is presented without LULUCF. Net emissions is presented with LULUCF.
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7.3. Reducing Uncertainty
There have been many improvements in reducing uncertainties across source 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 IJ.S. 1 CPA's CilKiRP reported data, which is an
improvement over prior methods using default emission factors and provides more country-specific data for Inventory
calculations. I CPA's CilKiRP 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 I CPA's C il IC iRP reported data to estimate CI 11 emissions from Coal Mining resulted in the
uncertainty hounds of-12 to 16 percent in the 1990 to 2015 Inventory, which was an improvement over the uncertainty
bounds in the 1990 to 2011 Inventory of -15 18 percent. Prior to 2012. Coal Mining emissions were estimated using an
array of emission factor estimations w ith higher assumed uncertainty. 1 estimates of CI 11 emissions from MSW landfills were
also revised with the availability t>f C il ICiRP reported data resulting in methodological and data quality improvements that
reduced uncertainty. Previously. MSW landfill emissions estimates were calculated using a model and default factors with
higher assumed uncertainty.
Due to the availability t>f CiIIC iRP 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 1 CPA's CilKiRP Subpart I rulemaking to estimate uncertainly associated
with facility-reported emissions. These results were applied to the C il KiRP-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 -5 to 5 percent in the 1990 to 2015
Inventory compared against a range of -8 to 9 percent in the 1990 to 201 1 Inventory for the emissions of F-C il IC is from the
Semiconductor Manufacturing source category.
7.4. Planned Improvements
Identifying the sources of uncertainty in the emission and sink estimates of the Inventory and quantifying the
magnitude of the associated uncertainty is the crucial first step towards improving those estimates. Quantitative assessment
of the parameter uncertainty may also provide information about the relative importance of input parameters (such as activity
data and emission factors), based on their relative contribution to the uncertainty within the source category estimates. Such
information can be used to prioritize resources with a goal of reducing uncertainty over lime within or aiming 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 uncertainly estimates based on their parameters' uncertainty have been
developed lor all the emission source categories, with the exception of CI 11 from Incineration of Waste, and the International
Hunker Fuels. CO: from Wood Biomass and Biofuel Consumption, and Indirect (ireenhouse Cias I emissions source
categories, wliieh are not included in the energy sector totals. C(): 1 emissions from Wood 1 iioluel and I Clhanol Consumption
however are accounted for implicitly in the Land Use, I.and-Use Change and Forestry (I.UI.IJCF) chapter through the
calculation of changes in carbon stocks. The I Cnergy sector does provide an estimate of CO: emissions from Wood Biomass
and Biofuel Consumption provided as a menu) item for informational purposes.
Specific areas that require further research to reduce uncertainties and improve the quality of uncertainly estimates
include:
• Improving conceptualization. Improving the inelusiveness 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 ()ther I.and I Jse (AFX )I.l J) 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
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emission factors applied to CI 11 and N;() 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 emirs, as well as reductions in these causes of uncertainly.
• ("ollecting more measured data and using more precise measurement methods. I Jncertainty associated w ith bias
and random sampling emir can be reducing by increasing the sample size and filling in data gaps. Measurement
emir can be reduced by using more precise measurement methods, avoiding simplifying assumption, and
ensuring that measurement technologies are appropriately used and calibrated.
• Refine Source ('ategory and Overall I ncertainty Kstimates. For many individual source categories, further
research is needed to more accurately characterize PDl 's that surround emissions modeling input variables. This
might involve using measured or published statistics or implementing rigorous elicitation protocol to elicit expert
judgments, if published or measured data are not available, l-'or example, activity data provided by FPA's
C.il IC iRP are used to develop estimates for several source categories—including but mil 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 uncertainly estimates could be improved, by developing explicit uncertainty models
for the base year. This would then improve the analysis of trend uncertainty. I low ever, not all of the simplifying
assumptions described in the "Trend I Jncertainty" 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. I Jse expert judgment to improve the
understanding of categories and processes leading to emissions and removals. I insure methodologies, models,
and estimation procedures are used appropriately and as advised by 2006 II'C(" (luidelines.
7.5. Additional Information on Uncertainty Analyses by Source
The quantitative uncertainty estimates associated with each emission and sink source category are reported in each
chapter of this Inventory following the discussions of inventory estimates and their estimation methodology. This section
provides additional descriptions of the uncertainty analyses performed for some of the sources, including the models and
methods used to calculate the emission estimates and the potential sources of uncertainly surrounding them. These sources
are organized below in the same order as the sources in each chapter of the main section of this Inventory. To avoid repetition,
the following uncertainty analysis discussions of individual source categories do not include descriptions of these source
categories. I Ience. 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 1 energy chapter
of the Inventory.
CC>2from Fossil Fuel Combustion
For estimates of C( h from fossil fuel combustion, there are uncertainties in the consumption data, carbon content
of fuels and products, and carbon oxidation efficiencies.
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 CO: 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.
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In developing the uncertainly estimation model, uniform distributions were assumed for all activity-related input
variables and emission factors, based on the SAIC/1:1 A (2001) report.1"2 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/1:IA (2001) and on conversations w ith various agency personnel.
The uncertainly 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
with these variables accounted for much of the uncertainties associated with these variables (SAIC/FIA 2001).1"1 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 uncertainly estimation model for this source category w as developed by integrating the CI 11 and N?() stationary
source inventory estimation models with the model for CO? 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 C( >: emissions from fossil fuel combustion inventory
estimation model and about 35 from the stationary source inventory models).
In developing the uncertainly estimation model, uniform distribution was assumed for all activity-related input
variables and N:() emission factors, based 011 the SAIC/1 :IA (2001) report.1""' For these variables, the uncertainty ranges
were assigned to the input variables based on the data reported in SAIC/1:1A (2001).I low ever, the CI 11 emission factors
differ from those used by 1:1 A. Since these factors were obtained from IPCC/I JNI:P/( )I:CI)/I1:A (1997). uncertainty ranges
were assigned based 011 IPCC default uncertainty estimates (IPCC 2006).
CH4 and N2O from Mobile Combustion
The uncertainty analysis w as performed 011 2015 estimates of CI 11 and NM ) emissions, incorporating probability
distribution functions associated with the major input variables. For the purposes of this analysis, the uncertainly was
modeled for the follow ing four major sets of input variables: (1) VMT data, by 011-road vehicle and fuel type. (2) emission
factor data, by 011-road vehicle, fuel, and control technology type. (3) fuel consumption, data, by 11011-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 uncertainly surrounding the estimates of emissions and
storage factors from non-energy uses.
The non-energy use analysis is based 011 U.S.-specific storage factors for (1) feedstock materials (natural gas, I.PC i.
pentanes plus, naphthas, other oils, still gas, special naphthas, and other industrial coal). (2) asphalt. (3) lubricants, and (4)
waxes. To characterize uncertainty, five separate analyses were conducted, corresponding to each of the live categories. In
all cases, statistical analyses or expert judgments of uncertainly were not available directly from the information sources for
162 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.
163 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.
164 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.
165 SAIC/EIA (2001) characterizes the underlying probability density function for the input variables as a combination of uniform
and normal distributions (the former distribution 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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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; traction oxidized: missing data on waste
composition: average C content of waste components: assumptions on the synlhelie/biogenic C ratio: and combustion
conditions affecting NM ) 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
The uncertainty associated with emission estimates from underground ventilation systems can be attributed to the
fact that the actual measurement data from MSI 1A or KPA's Greenhouse Gas Reporting Program (GIIGRP) used were not
continuous but rather an average of quarterly instantaneous readings. Additionally, the measurement equipment used can be
expected to have resulted in an average of 10 percent overestimation of annual CI 11 emissions (Mutmansky & Wang 2000).
C il IGRP data was used for a number of the mines beginning in 2013. however, the equipment uncertaintv is applied to both
MSI IA and GIIGRP data.
Mstimates of CI 11 recovered by degasification systems are relatively certain for utilized CI 11 because of the
availability of gas sales information. Many of the recovery estimates use data on wells within 100 feet of a mined area.
I Iowever. 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 a small level of uncertainty w as introduced w ith 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
subsourees. but the change had little impact on the overall uncertainly.
Abandoned Underground Coal Mines
A quantitative uncertainly 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.
The parameters for which values must be estimated for each mine in order to predict its decline curve are: 1) the
coal's adsorption isotherm: 2) CI 11 How capacity as expressed by permeability: and 3) pressure at abandonment. Because
these parameters are not available for each mine, a methodological approach to estimating emissions w as used that generates
a probability distribution of potential outcomes based on the most likely value and the probable range of values for each
parameter. The range of values is not meant to capture the extreme values, but rather values that represent the highest and
lowest quartile of the cumulative probability density function of each parameter. Once the low. mid. and high values are
selected, they are applied to a probability density function.
Petroleum Systems
The uncertainly analysis conducted for the 1990 through 2009 Inventory has not been updated for the 1990 through
2015 Inventory: instead, the uncertainty percentage ranges calculated previously were applied to 2015 emission estimates.
Natural Gas Systems
The uncertainty analysis conducted lor the 1990 through 2009 Inventory has not been updated for the 1990 through
2015 Inventory: instead, the uncertainty percentage ranges calculated previously were applied to 2015 emission estimates.
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 (in 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.
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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 I Jnited States. Decreasing the combustion efficiency
would decrease emission estimates. Additionally, the heal 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 andbiodiesel
consumption 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 I Jse 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 CK1) recycled inside the cement kiln. Uncertainty is also associated with the assumption that all
calcium-containing raw materials are CaC()-,, when a small percentage likely consists of other carbonate (e.g.. magnesium
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 C(); 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 CO: 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 I,KD are around 2 percent. Publicly available data on I.KD generation rates, total quantities mil 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. The uncertainty estimates also account for uncertainty associated with activity data.
Fluctuations in reported consumption exist, rellecting year-to-year changes in the number of survey responders. The
accuracy of distribution by end use is also uncertain because this value is reported by the manufacturer of the input carbonates
(limestone, dolomite & soda ash) 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.
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 IJSCiS through voluntary national surveys. Fluctuations in reported
consumption exist, rellecting year-to-year changes in the number of survey responders. The accuracy of distribution by end-
use is also uncertain because this value is reported by the producer/mines and mil the end-user. Additionally, there is
significant inherent uncertainly associated with estimating withheld data points lor specific end-uses of limestone and
dolomite. 1 .astly. much of the limestone consumed in the I Jnited States is reported as "other unspecified uses;"' therefore, it
is difficult to accurately allocate this unspecified quantity to the correct end-uses. I Jncertainty in the estimates also arises in
part due to variations in the chemical composition of limestone.
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. I Jneertainlies are also associated w ith ammonia production
estimates and the assumption that all ammonia production and subsequent urea production was from the same process.
I Jncertainty is also associated w ith 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 total urea production activity data, collected through voluntary surveys, is a function of the reliability of
reported production data and is influenced by the completeness of the survey responses.
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Urea Consumption for Non-Agricultural Purposes
The primary uncertainties associated w ith this source category are associated w ilh the accuracy of the estimates of
urea production, urea imports, urea exports, and the amount of urea used as fertilizer 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 I JSCiS, 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 C()? during use.
Nitric Acid Production
Uncertainty associated with the parameters used to estimate N:() 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 I CPA's Cil KiRP and then aggregated to estimate national N:() emissions is assumed to have low uncertainty.
Adipic Acid Production
Uncertainty associated with N:() emission estimates includes the methods used by companies to monitor and
estimate emissions.
Silicon Carbide Production and Consumption
There is uncertainty associated with the emission factors used because they are based on sloichiomelry as opposed
to monitoring of actual SiC production plants, l-'or CI 1i, there is also uncertainty associated with the hydrogen-containing
volatile compounds in the petroleum coke (IPCC 2006). There is also uncertainly associated with the use or destruction of
methane generated from the process in addition to uncertainty associated w ith 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
I Jncertainty of activity data is also a function of the reliability of reported production data and is inllueneed by the
completeness of the IJSCiS survey responses; variability in response rates varies over the time series. As of 2004, the last
remaining sulfate-process plant in the I Jnited States closed. Since annual Ti( >: production was not reported by I JSCiS by the
type of production process used (chloride or sulfate) prior to 2004 and only the percentage of total production capacity by
process was reported, the percent of total Ti(); production capacity that was attributed to the chloride process was multiplied
by total Ti( h production to estimate the amount of Tit): produced using the chloride process, Finally. the emission factor
was applied uniformly to all chloride-process production, and no data were available to account for differences in production
efficiency among chloride-process plants.
Soda Ash Production and Consumption
I imission 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. Soda ash production data was collected by the
USC IS from voluntary surveys. ()ne source of uncertainly is the purity of the trona ore used for manufacturing soda ash. The
primary source of uncertainty, however, results from the fact that emissions from soda ash consumption are dependent upon
the type of processing employed by each end-use. Additional uncertainty comes from the reported consumption and
allocation of consumption within sectors that is collected on a quarterly basis by the USC >S.
Petrochemical Production
Sources of uncertainty on the CIli and CO: emission factors used for acrylonitrile and methanol production are
derived from the use of default or average factors from a limited number of studies. 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 I J.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 2015. The resulting estimates of absolute uncertainly are likely to be reasonably accurate because (1) the
methods used by the three plants to estimate their emissions are not believed to have changed significantly since 2006. and
(2) although the distribution of emissions among the plants may have changed between 2006 and 2015, the two plants that
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contribute significantly to omissions were estimated to have similar relative uncertainties in their 2006 (as well as 2005)
emission estimates.
Carbon Dioxide Consumption
I Jncertainty is associated with the data reported through ]CPA's Cil ICiRP, specifically the amount of C(); consumed
for food and beverage applications given a threshold for reporting under I CPA's C ilIC iRP applicable to those reporting under
Subpart PP. in addition to the exclusion of the amount of C(); transferred to all other end-use categories. I Jncertainty is also
associated with the exclusion of imports/exports data for C(): suppliers.
Phosphoric Acid Production
Regional production for 2015 was estimated based on regional production data from previous years and multiplied
by regionally-specific emission factors. There is uncertainty associated w ith the degree to whicli the estimated 2015 regional
production data represents actual production in those regions, 'filial I J.S. phosphate nick production data and data for imports
and exports of phosphate nick are not considered to be a significant source of uncertainty.
An additional source of uncertainly is the carbonate composition of phosphate rock; the composition of phosphate
nick 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 nick. A third source of uncertainty is the assumption that all
domestically-produced phosphate nick 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 IJ.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 pellet production, direct reduced iron and sinter
consumption are equal to production. There is uncertainly associated with the assumption that all coal used for purposes
other than coking coal is for direct injection coal: some of this coal may be used lor electricity generation. There is also
uncertainty associated w ith the C contents for pellets, sinter, and natural ore. For electric arc furnace (I CAP) steel production,
there is uncertainty associated with the amount of I CAP anode and charge C consumed due to inconsistent data throughout
the time series. Also for I CAP steel production, there is uncertainty associated with the assumption that 100 percent of the
natural gas attributed to "steelmaking furnaces" by A1S1 is process-related and nothing is combusted for energy purposes.
I Jncertainty is also associated with the use of process gases such as blast furnace gas and coke oven gas.
Ferroalloy Production
I Jncertainty for this source is associated with the type and availability of annual ferroalloy production data, which
have varied over the time series. Such production data may or may not include details such as ferroalloy content, production
practices (e.g.. biomass used as primary or secondary carbon source), amount of reducing agent used, and furnace specifics
(e.g.. type, operation technique, control technology).
Aluminum Production
l Jncertainty was assigned to the C( K CT'i. and C;P„ emission values reported by each individual facility to I CPA's
Cil ICiRP. Uncertainty surrounding the reported CO:. CT'i. and C':l'„ emission values were determined to have a normal
distribution with uncertainty ranges of ±6. ±16. and ±20 percent, respectively.
Magnesium Production
I Jncertainty surrounding the total estimated emissions in 2015 is attributed to the uncertainties around SP„. 1 Il'C-
134a and CO: emission estimates. To estimate the uncertainty surrounding the estimated 2015 Sl'„ emissions from
magnesium production and processing, the uncertainties associated with three variables were estimated: (1) emissions
reported by magnesium producers and processors for 2015 through I CPA's C il KiRP. (2) emissions estimated for magnesium
producers and processors that reported via the Partnership in prior years but did mil report 2015 emissions through I CPA's
Cil IC iRP, and (3) emissions estimated for magnesium producers and processors that did mil participate in the Partnership or
report through I CPA's Cil IC iRP. Additional uncertainties exist in these estimates that are not addressed in this methodology,
such as the basic assumption that SP„ neither reacts nor decomposes during use.
Lead Production
Uncertainty associated with lead production relates to the applicability of emission factors and the accuracy of
primary and secondary production data provided by the USC iS 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.
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Zinc Production
There is uncertainly associated w ith the amount of 1i AF dust consumed in the I Jnited Slates to produce secondary
zinc using emission-intensive Waelz kilns.
There are also uncertainties associated with the accuracy of the emission factors used to estimate CO; emissions
from secondary zinc production processes.
Semiconductor Manufacturing
The equation used to estimate uncertainty is:
Total Emissions (Er) = GHGRP Reported F-GHG Emissions (Er.i-ciic) + Non-Reporters' Estimated F-GHG
Emissions (E\r.r;iii;) (II IGI\I' l\i. porLi-d N2O Emissions (Er.v) + Non-Reporters' Estimated N2O Emissions
(E\R.\2il)
where Fr and Par denote totals for the indicated subcategories of emissions for F-(iI K i and N?(). respectively.
The uncertainty estimate of Mr. r-uiin- or (>1 KiRP reported F-(iI Ki emissions, is developed based 011 gas-specific
uncertainly estimates of emissions for two industry segments, one processing 200 mm wafers and one processing 300 mm
wafers. These gas and wafer-specific uncertainly estimates are applied to the total emissions of the facilities that did not
abate emissions as reported under 1 CPA's C >11(iRP.
l or those facilities reporting abatement of emissions under 1 CPA's (il IC iRP, estimates of uncertainties for the 110
abatement industry segments are modified to reflect the use of full and partial abatement. For all facilities reporting gas
abatement, a triangular distribution of destruction or removal efficiency is assumed for each gas. 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, (ias-speeific 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 ICrj-uMu is obtained by allocating the estimates of uncertainties to the total (>I I( iRP-reported
emissions from each of the six industry segments. The uncertainty in Fr.\:o is obtained by assuming that the uncertainly in
the emissions reported by each of the C il KiRP reporting facilities results from the uncertainty in quantity of N?() consumed
and the N;() emission factor (or utilization). The quantity of N?() utilized (the complement of the emission factor) was
assumed to have a triangular distribution with a minimum value of 0 percent, mode of 20 percent and maximum value of 84
percent. The uncertainty for the total reported N;() emissions was then estimated by combining the uncertainties of each of
the facilities reported emissions using Monte Carlo simulation. The estimate of uncertainty in I arj-uik, and F\r.\:o entailed
developing estimates of uncertainties for the emissions factors for each non-reporting sub-category and the corresponding
estimates off MI .A.
The uncertainty in TMI.A depends 011 the uncertainty of two variables - an estimate of the uncertainty in the
average annual capacity utilization for each level of production of labs (e.g.. full scale or R&l) production) and a
corresponding estimate of the uncertainly in the number of layers manufactured. For both variables, the distributions of
capacity utilizations and number of manufactured layers are assumed triangular for all categories of non-reporting labs. For
production labs and for facilities that manufacture discrete devices, the most probable utilization is assumed to be 82 percent,
with the highest and lowest utilization assumed to be 89 percent, and 70 percent, respectively. The most probable values
for utilization for R&l) facilities are assumed to be 20 percent, with the highest utilization at 30 percent, and the lowest
utilization at 10 percent. For the triangular distributions that govern the number of possible layers manufactured, it is
assumed the most probable value is one layer less than reported in the I'l'RS: the smallest number varied by technology
generation between one and two layers less than given in the I'l'RS and largest number of layers corresponded to the figure
given in the I'l'RS.
The uncertainty bounds for the average capacity utilization and the number of layers manufactured are used as
inputs in a separate Monte Carlo simulation to estimate the uncertainly around the IMF A of both individual facilities as
well as the total non-reporting TMI.A of each sub-population. The uncertainty around the emission factors for each non-
reporting category of facilities is dependent 011 the uncertainty of the total emissions (MMT C(); I Cq.) and the TMI.A of each
reporting facility in that category. For simplicity, the results of the Monte Carlo simulations 011 the bounds of the gas- and
wafer size-specific emissions as well as the TMI.A and emission factors are assumed to be normally distributed and the
uncertainty bounds are assigned at 1.96 standard deviations around the estimated mean. The departures from normality w ere
observed to be small. The final step in estimating the uncertainty in emissions of non-reporting facilities is convolving the
distribution of emission factors with the distribution of TMI.A using Monte Carlo simulation.
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Substitution of Ozone Depleting Substances
Given that omissions of (JDS substitutes occur from thousands of different kinds of equipment and from millions
of point and mobile sources throughout the United States, significant uncertainties 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 uncertainly analysis quantifies the level of uncertainty associated with the aggregate emissions across the 65
end-uses in I CPA's 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. The functional forms used variables that included. Uncertainty was estimated around
each variable within the functional forms (e.g., growth rates, emission factors, transition from (JDSs. change in charge size
as a result of the transition, disposal quantities, disposal emission rates, and either stock for the current year or original ()1 )S
consumption) based on expert judgment. The most significant sources of uncertainty for this source category include the
emission factors for residential unitary AC. as well as the percent of non-MDI aerosol propellant that is 1 Il'C-152a.
Electrical Transmission and Distribution
To estimate the uncertainty associated with emissions of Sl '„ from I electrical Transmission and Distribution,
uncertainties associated with four quantities were estimated: (1) emissions from Partners. (2) emissions from (il I( iRP-( Jnly
Reporters. (3) emissions from Non-Reporters, and (4) emissions from manufacturers of electrical equipment. I Jncertainties
were also estimated regarding (1) the quantity of Sl'„ supplied with equipment by equipment manufacturers, which is
projected from Partner provided nameplate capacity data and industry Sl'„ nameplate capacity estimates, and (2) the
manufacturers" Sl '„ emissions rate.
Nitrous Oxide from Product Uses
The overall uncertainty associated with the 2015 N?() emission estimate from N:() product usage was calculated
using the 2006 JI'CC (iiikk'/iiics (IPCC 2006) Approach 2 methodology. Uncertainty associated with the parameters used
to estimate N:() 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.
Enteric Fermentation
Uncertainty estimates were developed for the 1990 through 2001 Inventory report (i.e., 2003 submission to the
UNl-'CCC). There have been no significant changes to the methodology since that time; consequently, these uncertainty
estimates were directly applied to the 2015 emission estimates in the current Inventory report.
A total of 185 primary input variables 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 to ensure only positive values would be simulated.
Manure Management
An analysis (ICR(i 2003) was conducted for the manure management emission estimates presented in the 1990
through 2001 Inventory report (i.e.. 2003 submission to the UNl-'CCC) to determine the uncertainty associated with
estimating CI 11 and N:() emissions from livestock manure management. These uncertainty estimates were directly applied
to the 2015 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, and the uncertainties from each approach
are combined to produce the final CI 11 emissions estimate using simple error propagation (IPCC 2006).
Agricultural Soil Management
Uncertainty was estimated for each of the following live components of N?( J emissions from agricultural soil
management: (1) direct emissions simulated by DAYCICNTi (2) the components of indirect emissions (N volatilized and
leached or runoff) simulated by DAYCICNTi (3) direct emissions approximated with the IPCC (2006) Approach 1 method:
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(4) the components of indirect emissions (N volatilized and leached or runoff) approximated with the IPCC (2006) Approach
1 method: and (5) indirect emissions estimated with the IPCC (2006) Approach 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 2013). 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 smoothed triangular
distributions between ranges of zero and 100 percent of the estimates.
Urea Fertilization
The largest source of uncertainly was the default emission factor, which assumes that 100 percent of the C in
C( )(NI 1:): applied to soils is ultimately emitted into the environment as C( K 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.
I.and-lJse 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 I'ore.si Land
Remaining I •'ore.si Land sub-chapter of Land I Jse. I.and-l Jse Change, and forestry chapter of the Inventory.
Changes in Forest Carbon Stocks
A quantitative uncertainty analysis placed bounds on current flux for forest ecosystems as well as C in
harvested wood products through Monte Carlo Stochastic Simulation of the Methods and probabilistic sampling
of C conversion factors and inventory data.
Non-CC>2 Emissions from Forest Fires
In order to quantify the uncertainties lor emissions from forest fires calculated as described above, a
Monte Carlo (IPCC Approach 2) sampling approach was employed to propagate uncertainty in the equation as it
was applied for I J.S. forest land. See IPCC (2006) and Annex 3.13 lor the quantities and assumptions employed to
define and propagate uncertainty.
N2O Emissions from N Additions to Forest Soils
The amount of N;() emitted from forests depends mil only 011 N inputs and fertilized area, but also 011 a
large number of variables, including organic C availability, oxygen gas partial pressure, soil moisture content, pi I.
temperature, and tree planting/harvesting cycles. The effect of the combined interaction of these variables 011 N;()
flux is complex and highly uncertain.
I Jncertainties exist in the fertilization rates, annual area of forest lands receiving fertilizer, and the
emission factors. The uncertainty ranges around the 2005 activity data and emission factor input variables were
directly applied to the 2015 emissions estimates. IPCC (2006) provided estimates for the uncertainty associated
with direct and indirect NM ) emission factor lor synthetic N fertilizer application to soils.
Drained Organic Soils
I Jncertainties are based 011 the sampling error associated with forest area and the uncertainties provided
in the Chapter 2 (IPCC 2013) emissions factors.
Land Converted to Forest Land
I Jncertainty estimates for forest pool C stock changes w ere developed using the same methodologies as described
in the I-'ore.si Land Remaining L'ore.si Land section for aboveground and biomass ground biomass. dead wood, and litter.
The exception was when IPCC default estimates were used for reference C stocks in certain conversion categories (i.e..
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1 Cropland Convened lo I-'ore.si Land and (irassland Converted to I-'ore.si Land). In those cases, the uncertainties associated
2 with the IPCC (2006) defaults were included in the uncertainty calculations.
3 Cropland Remaining Cropland
4 The uncertainty analysis descriptions in this section correspond to source categories included in the Cropland
5 Remaining ('ropland sub-chapter of I .and I Jse. I ,and-l Jse Change, and Forestry chapter of the Inventory.
6 Mineral and Organic Soil Carbon Stock Change
7 Uncertainty associated with the Cropland Remaining Cropland land-use category was addressed lor
8 changes in agricultural soil C stocks (including both mineral and organic soils).
9 Land Converted to Cropland
10 I Jncerlainly analysis for mineral soil C slock changes using the Tier 3 and Tier 2 methodologies are based on the
11 same method described for ('ropland Remaining ('ropland.
12 Uncertainty was estimated for each subsource (i.e.. biomass C stocks, mineral soil C stocks and organic soil C
13 slocks) and method that was used in the Inventory analysis (i.e.. Tier 2 and Tier 3).
14 Grassland Remaining Grassland
15 The uncertainty analysis descriptions in this section correspond to source categories included in the (Grassland
16 Remaining (irassland sub-chapter of I.and I Jse. I.and-! Jse Change, and Forestry chapter of the Inventory.
17 Soil Carbon Stock Changes
18 I Jncerlainly was estimated for each subsource (i.e.. mineral soil C slocks and organic soil C slocks) and
19 disaggregated to the level of the inventory methodology employed (i.e.. Tier 2 and Tier 3).
20 Non-CC>2 Emissions from Grassland Fires
21 Uncertainly is associated with lack of reporting of emissions from biomass burning in grassland of
22 Alaska.
23 Land Converted to Grassland
24 Uncertainly was estimated for each subsource (i.e.. biomass C slocks, mineral soil C slocks and organic soil C
25 stocks) and disaggregated lo the level of the inventory methodology employed (i.e.. Tier 2 and Tier 3).
26 Wetlands Remaining Wetlands
27 The uncertainty analysis descriptions in this section correspond lo source categories included in the Wetlands
28 Remaining Wetlands sub-chapter of I .and I Jse. I .and-! Jse Change, and Forestry chapter of the Inventory.
29 Peatlands Remaining Peatlands
30 The uncertainly associated with peal production data was estimated to be ± 25 percent (Apodaca 2008).
31 assumed to be normally distributed, and is attributed lo the I JSCS receives data from the smaller peat producers
32 but estimates production from some larger peal distributors. The uncertainly associated with the reported
33 production data for Alaska was assumed lo be the same as for the lower 48 slates, or ± 25 percent with a normal
34 distribution. The uncertainly associated with the average bulk density values was estimated lo be ± 25 percent
35 with a normal distribution (Apodaca 2008). The uncertainly associated with the emission factors was assumed lo
36 be triangularly distributed. The uncertainly values surrounding the C fractions were based on IPCC (2006) and
37 the uncertainly was assumed lo be uniformly distributed. The uncertainly values associated with the fraction of
38 pealland covered by ditches was assumed to be ± 100 percent with a normal distribution based on the assumption
39 that greater than 10 percent coverage, the upper uncertainly bound, is not typical of drained organic soils outside
40 off he Netherlands (IPCC 2013).
41 Coastal Wetlands
42 Underlying uncertainties in estimates of soil C stock changes and CI 11 include error in uncertainties
43 associated with Tier 2 literature values t>f st>il C slocks and CI 11 llu\. assumptions thai underlie the methodological
44 approaches applied and uncertainties linked to interpretation of remote sensing data. I Jncerlainly specific lo coastal
45 wetlands include differentiation of paluslrine and esluarine community classes which determines the soil C slock
46 and methane flux applied. Soil C stocks and CI 11 fluxes applied are determined from vegetation community classes
47 across the coastal /one and identified by NC )AA C-CAP. Community classes are further subcategori/ed by climate
48 /ones and growth form (forest, shrub-scrub, marsh). Uncertainties for soil C slock data for all subcategories are
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not available and thus assumptions were applied using expert judgemenl about the most appropriate assignment of
a soil C slock to a disaggregation of a community class.
Additionally, uncertainties in N:() emissions from aquaculture are based on expert judgement for the
N( )AA l-'isheries of the I nited Stales fisheries production data and stem from an overestimate of fisheries
production from coastal wetland areas due to the inclusion of fish production in non-coastal wetland areas.
Land Converted to Coastal Wetlands
I Jnderlying uncertainties in estimates of soil C removal factors and CI 11 include error in uncertainties associated
with Tier 2 literature values of soil C removal estimates and CI 11 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 I.and I Jse. I.and-lJse 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.
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 el al. 2002).
N2O Fluxes from Settlement Soils
The amount of N?() emitted from settlements depends mil only 011 N inputs and fertilized area, but also
011 a large number of variables, including organic C availability, oxygen gas partial pressure, soil moisture content,
pi I. temperature, and irrigation/watering practices. The effect of the combined interaction of these variables 011
N:() flux is complex and highly uncertain.
I Jncertainties exist in both the fertilizer N and sewage sludge application rates in addition to the emission
factors. Uncertainty in the amounts of sewage sludge applied to non-agricultural lands and used in surface disposal
was derived from variability in several factors. The uncertainty ranges around 2005 activity data and emission
factor input variables were directly applied to the 2015 emission estimates.
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 rale, 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 CI 11 emissions from MSW and industrial waste
landfills when the first order decay model is applied. In other words, the first order decay methodology as applied in this
Inventory is mil facility-specific modeling and while this approach may over- or under-estimate CI 11 generation at some
landfills if used at the facility-level, the result is expected to balance out because it is being applied nationw ide. There is also
a high degree of uncertainty and variability associated with the first order decay model, particularly when a homogeneous
waste composition and hypothetical decomposition rates are applied to heterogeneous landfills (IPCC 2006). There is less
uncertainty in the (ilKiRP data because this methodology is facility-specific, uses directly measured CI 11 recovery data
(when applicable), and allows for a variety of landfill gas collection efficiencies, destruction efficiencies, and/or oxidation
factors to be used.
Aside from the uncertainly in estimating landfill CI 11 generation, uncertainty also exists in the estimates of the
landfill gas oxidized. Another significant source of uncertainly lies w ilh the estimates of CI 11 recovered by Hating and gas-
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to-energy projects at MSW landfills. Industrial waste landfills are shown with a lower range of uncertainty due to the smaller
number of data sources and associated uncertainty involved.
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 emissions estimates.
Wastewater Treatment
I Jncertainty associated w ith the parameters used to estimate CI 11 emissions from wastewater treatment include that
of numerous input variables used to model emissions from domestic wastewater, and wastewater from pulp and paper
manufacture, meat and poultry processing, fruits and vegetable processing, ethanol production, and petroleum refining.
Uncertainty associated with the parameters used to estimate N;() emissions include that of sewage sludge disposal, total
I J.S. population, average protein consumed per person, fraction of N in protein. 11011-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.
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) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and
K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
IPCC (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.
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In addition, based on the national QA/QC Management Plan for the Inventory, source-specific QA/QC plans have
been developed for a number of sources. These plans follow the procedures outlined in the national QA/QC plan, tailoring
the procedures to the specific text and spreadsheets of the individual sources. For each greenhouse gas emissions source or
sink included in this Inventory, minimum general QA/QC analysis consistent with Vol. 1, Chapter 6 of the 2006 IPCC
Guidelines has been undertaken. Where QA/QC activities for a particular source go beyond the general level, and include
category-specific checks, further explanation is provided within the respective source category text. Similarly, responses or
updates based on comments from the expert, public and the international technical expert reviews (e.g., UNFCCC) are also
addressed within the respective source or sink category text. For transparency, responses to public review comments are
also posted on the EPA website with the final report.
Figure A-19: U.S. QA/QC Plan Summary
Obtain data in electronic
format (if possible)
Review spreadsheet
construction
Avoid hardwiring
• Use data validation
Protect cells
Develop automatic
checkers for:
• Outliers, negative
values, or missing
data
Variable types
match values
Time series
consistency
Maintain tracking tab for
status of gathering
efforts
Check input data for
transcription errors
Inspect automatic
checkers
Identify spreadsheet
modifications that could
provide additional
QA/QC checks
Contact reports for non-
electronic communications
Provide cell references for
primarydata elements
Obtain copies of all data
sources
~stand location of any
working/external
spreadsheets
Document assumptions
Check citations in
spreadsheetandtextfor
accuracy and style
Check reference docket for
new citations
Review documentation for
any data / methodology
changes
Clearly label parameters,
units, and conversion
factors
Review spreadsheet
integrity
¦ Equations
¦ Units
¦ Inputs and outputs
Develop automated
checkers for:
• Input ranges
• Calculations
• Emission aggregation
Reproduce calculations
Review time series
consistency
Review changes in
data/consistency with IPCC
methodology
Common starting
versions for each
inventory year
Utilize unalterable
summarytab foreach
source spreadsheet for
linkingto a master
summary spreadsheet
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
EPA's Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity, of Information
Disseminated by the Environmental Protection Agency107 and A Summary of General Assessment Factors for Evaluating
the Quality of Scientific and Technical Information.10* This includes evaluating the data and models used as inputs into
107 EPA report #260R-02-008, October 2002, Available online at .
108 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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1 the Inventory against the five general assessment factors: soundness, applicability and utility, clarity and completeness,
2 uncertainty and variability, evaluation and review. Table A-286 defines each factor and explains how it was considered
3 during the process of creating the current Inventory.
4 Table A-286: Assessment Factors and Definitions
General Assessment
Factor
Definition
How the Factor was Considered
Soundness (AF1)
The extent to which the scientific
and technical procedures,
measures, methods or models
employed to generate the
information are reasonable for, and
consistent with their intended
application.
The underlying data, methodologies, and models used to generate the
Inventory of U.S. Greenhouse Gas Emissions and Sinks are
reasonable for and consistent with their intended application, to
provide information regarding all sources and sinks of greenhouse
gases in the United States for the Inventory year, as required per
UNFCCC Annex I country reporting requirements.
The U.S. emissions calculations follow the 2006IPCC Guidelines
developed specifically for UNFCCC inventory reporting. They are
based on the best available, peer-reviewed scientific information, and
have been used by the international community for over 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 analyses
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,
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 source 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 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 from Public Review and Expert
Review are posted on the EPA website with the final report in April. Areas of improvement identified through UN review
for improving consistency with the reporting guidelines can be found in annual review reports posted on the UNFCCC
website169. This review is completed on annual basis following submission of the report. Responses to those comments are
finalized and updated during compilation of the next annual report (i.e. to be published in April 2018).
169 Available online at
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