1

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3

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8

9

10

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12

13

14

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16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

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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

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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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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


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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


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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

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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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


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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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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


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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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2

3

4

5

6

7

8

9

10

11

12

13

14

15

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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


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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.

A-70 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

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.

A-71


-------
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).

A-72 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

43

44

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.

A-73


-------
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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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.

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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

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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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29

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.

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Figure A-3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number

100

95

£
"qj

>¦
_Lj

90 -

_g 85

i_

as

o
"E

ai

£>

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.

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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.

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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).

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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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9

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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

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

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18

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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).

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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.

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36

37

38

39

40

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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


-------
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


-------
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.

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1	References

2	AAM (2009) Diesel Survey. Alliance of Automobile Manufacturers, Winter 2008.

3	API (1990 through 2008) Sales of Natural Gas Liquids and Liquefied Refinery Gases, American Petroleum Institute.

4	ASTM (1985) ASTM and Other Specifications for Petroleum Products and Lubricants, American Society for Testing and

5	Materials. Philadelphia, PA.

6	Boldt, K. 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.

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7

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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.

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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

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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).

A-109


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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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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

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

A-111


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22

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24

25

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27

28

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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9

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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.

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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



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2013

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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.

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Annual production data for each glycol compound used as antifreeze and deicers were obtained from the Guide to
the Business of Chemistry (ACC 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



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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 .

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Table fl-82: C Emitted from Utilization of Food Additives [MMT CO; EqJ



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C Emissions

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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

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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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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.

A-130 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016


-------
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

44	online at

45	.

46	U.S. International Trade Commission (1990-2016) "Interactive Tariff and Trade DataWeb: Quick Query." Available

47	online at . Accessed November 2016.

A-131


-------
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.

A-132 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

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.

A-133


-------
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

A-134 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016


-------
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


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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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2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

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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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

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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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

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


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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.

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23

24

25

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29

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36

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.

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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.

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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

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


-------
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.

A-149


-------
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

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


-------
1

2

3

4

5

6

7

8

9

10

11

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


-------
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

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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.

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Definitions of Emission Control Technologies and Standards

The N2O and CH4 emission factors used depend on the emission standards in place and the corresponding level of
control technology for each vehicle type. Table A-105 through Table A-108 show the years in which these technologies or
standards were in place and the penetration level for each vehicle type. These categories are defined below and were
compiled from EPA (1993, 1994a, 1994b, 1998, 1999a) and IPCC/UNEP/OECD/IEA (1997).

Uncontrolled

Vehicles manufactured prior to the implementation of pollution control technologies are designated as
uncontrolled. Gasoline passenger cars and light-duty trucks (pre-1973), gasoline heavy-duty vehicles (pre-1984), diesel
vehicles (pre-1983), and motorcycles (pre-1996) are assumed to have no control technologies in place.

Gasoline Emission Controls

Below are the control technologies and emissions standards applicable to gasoline vehicles.

Non-catalyst

These emission controls were common in gasoline passenger cars and light-duty gasoline trucks during model
years (1973 -1974) but phased out thereafter, in heavy-duty gasoline vehicles beginning in the mid-1980s, and in motorcycles
beginning in 1996. This technology reduces hydrocarbon (HC) and carbon monoxide (CO) emissions through adjustments
to ignition timing and air-fuel ratio, air injection into the exhaust manifold, and exhaust gas recirculation (EGR) valves,
which also helps meet vehicle NOx standards.

Oxidation Catalyst

This control technology designation represents the introduction of the catalytic converter, and was the most
common technology in gasoline passenger cars and light-duty gasoline trucks made from 1975 to 1980 (cars) and 1975 to
1985 (trucks). This technology was also used in some heavy-duty gasoline vehicles between 1982 and 1997. The two-way
catalytic converter oxidizes HC and CO, significantly reducing emissions over 80 percent beyond non-catalyst-system
capacity. One reason unleaded gasoline was introduced in 1975 was due to the fact that oxidation catalysts cannot function
properly with leaded gasoline.

EPA TierO

This emission standard from the Clean Air Act was met through the implementation of early "three-way" catalysts,
therefore this technology was used in gasoline passenger cars and light-duty gasoline trucks sold beginning in the early
1980s, and remained common until 1994. This more sophisticated emission control system improves the efficiency of the
catalyst by converting CO and HC to CO2 and H2O, reducing NOx to nitrogen and oxygen, and using an on-board diagnostic
computer and oxygen sensor. In addition, this type of catalyst includes a fuel metering system (carburetor or fuel injection)
with electronic "trim" (also known as a "closed-loop system"). New cars with three-way catalysts met the Clean Air Act's
amended standards (enacted in 1977) of reducing HC to 0.41 g/mile by 1980, CO to 3.4 g/mile by 1981 and NOx to 1.0
g/mile by 1981.

EPA Tier 1

This emission standard created through the 1990 amendments to the Clean Air Act limited passenger car NOx
emissions to 0.4 g/mi, and HC emissions to 0.25 g/mi. These bounds respectively amounted to a 60 and 40 percent reduction
from the EPA Tier 0 standard set in 1981. For light-duty trucks, this standard set emissions at 0.4 to 1.1 g/mi for NOx, and
0.25 to 0.39 g/mi for HCs, depending on the weight of the truck. Emission reductions were met through the use of more
advanced emission control systems, and applied to light-duty gasoline vehicles beginning in 1994. These advanced emission
control systems included advanced three-way catalysts, electronically controlled fuel injection and ignition timing, EGR,
and air injection.

EPA Tier 2

This emission standard was specified in the 1990 amendments to the Clean Air Act, limiting passenger car NOx
emissions to 0.07 g/mi on average and aligning emissions standards for passenger cars and light-duty trucks. Manufacturers
can meet this average emission level by producing vehicles in 11 emission "Bins," the three highest of which expire in 2006.

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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


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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.

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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.

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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


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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


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1	References

2	AAR (2008 through 2017) Railroad Facts. Policy and Economics Department, Association of American Railroads, Washington,

3	D.C. Obtained from Clyde Crimmel at AAR.

4	ANL (2016) The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model (GREET 2016). Argonne

5	National Laboratory. October 2016. Available at .

6	APTA (2007 through 2016) Public Transportation Fact Book. American Public Transportation Association, Washington, D.C.

7	Available online at: .

8	APTA (2006) Commuter Rail National Totals. American Public Transportation Association, Washington, D.C. Available online

9	at: .

10	Benson, D. (2002 through 2004) Unpublished data. Upper Great Plains Transportation Institute, North Dakota State University

11	and American Short Line & Regional Railroad Association.

12	Browning, L. (2017) "Updated Methodology for Estimating CH4 andN20 Emissions from Highway Vehicle Alternative Fuel

13	Vehicles". Technical Memo, October 2017.

14	Browning, L. (2005) Personal communication with Lou Browning, Emission control technologies for diesel highway vehicles

15	specialist, ICF.

16	DHS (2008) Email Communication. Elissa Kay, Department of Homeland Security and Joe Aamidor, ICF International. January

17	11,2008.

18	DLA Energy (2017) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense Energy

19	Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, D.C.

20	DOE (1993 through 2016) Transportation Energy Data Book Edition 35. Office of Transportation Technologies, Center for

21	Transportation Analysis, Energy Division, Oak Ridge National Laboratory. ORNL-6978.

22	EDTA (2017) Electric Drive Sales Dashboard. Electric Drive Transportation Association, Washington, D.C. Available at:

23	.

24	EEA (2009) EMEP/EAA Air Pollutant Emission Inventory Guidebook. European Environment Agency, Copenhagen, Denmark.

25	Available online at: EI A (2017a) Monthly Energy Review, October 2017, Energy Information Administration, U.S.

27	Department of Energy, Washington, DC. DOE/EIA-0035(2017/10).

28	EIA (2017d) Natural Gas Annual 2016. Energy Information Administration, U.S. Department of Energy. Washington, D.C.

29	DOE/EIA-0131 (06).

30	EIA (2017e) Alternative Fuels Data Tables. Energy Information Administration, U.S. Department of Energy. Washington,

31	D.C. Available online at: .

32	EIA (2017f) Petroleum Supply Annual 2016. Energy Information Administration, U.S. Department ofEnergy. Washington, D.C.

33	DOE/EIA-810.

34	EIA (1991 through 20\6)Fuel Oil and Kerosene Sales. Energy Information Administration, U.S. Department ofEnergy.

35	Washington, D.C. Available online at: .

36	EPA (2017b,). Motor Vehicle Emissions Simulator (MOVES) 2014a. Office of Transportation and Air Quality, U.S.

37	Environmental Protection Agency. Available online at: .

38	EPA (2017c) Annual Certification Test Results Report. Office of Transportation and Air Quality, U.S. Environmental Protection

39	Agency. Available online at: .

41	EPA (2017d) Confidential Engine Family Sales Data Submitted to EPA By Manufacturers. Office of Transportation and Air

42	Quality, U.S. Environmental Protection Agency.

43	EPA (2016g) "1970 - 2016 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory (NEI)

44	Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available online at:

45	.

46	EPA (2000)Mobile6 Vehicle Emission Modeling Software. Office of Mobile Sources, U.S. Environmental Protection Agency,

47	Ann Arbor, Michigan.

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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

EPA (1999b) Regulatory Announcement: EPA's Program for Cleaner Vehicles and Cleaner Gasoline. Office of Mobile Sources.
December 1999. EPA420-F-99-051. Available online at:

.

EPA (1998b) Emissions of Nitrous Oxide from Highway Mobile Sources: Comments on the Draft Inventory of U.S. Greenhouse
Gas Emissions and Sinks, 1990-1996. Office of Mobile Sources, Assessment and Modeling Division, U.S. Environmental
Protection Agency. August 1998. EPA420-R-98-009.

FAA (2017) Personal Communication between FAA and John Steller, Mausami Desai, and Vincent Camobreco for aviation
emissions estimates from the Aviation Environmental Design Tool (AEDT). January 2017.

FFIWA (1996 through 2017) Highway Statistics. Federal Highway Administration, U.S. Department of Transportation,
Washington, D.C. Report FE[WA-PL-96-023-annual. Available online at:
.

FHWA (2015) Off-Highway and Public-Use Gasoline Consumption Estimation Models Used in the Federal Highway
Administration, Publication Number FHWA-PL-17-012. Available online at:



Gaffney, J. (2007) Email Communication. John Gaffney, American Public Transportation Association and Joe Aamidor, ICF
International. December 17, 2007.

ICF (2004) Update of Methane and Nitrous Oxide Emission Factors for On-Highway Vehicles. Final Report to U.S.
Environmental Protection Agency. February 2004.

ICF (2017a) Updated On-highway CH4 and N2O Emission Factors for GHG Inventory. Memorandum from ICF to Sarah Roberts
and Justine Geidosch, Office of Transportation and Air Quality, U.S. Environmental Protection Agency. October 2017.

ICF (2017b) Updated Non-Highway CH4 and N20 Emission Factors for U.S. GHG Inventory. Memorandum from ICF to Sarah
Roberts and Justine Geidosch, Office of Transportation and Air Quality, U.S. Environmental Protection Agency. October
2017.

Santoni, G., B. Lee, E. Wood, S. Herndon, R. Miake-Lye, S Wofsy, J. McManus, D. Nelson, M. Zahniser (2011) Aircraft

emissions of methane and nitrous oxide during the alternative aviation fuel experiment. Environ Sci Technol. 2011 Aug 15;
45(16): 7075-82.

Whorton, D. (2006 through 2014) Personal communication, Class II and III Rail energy consumption, American Short Line and
Regional Railroad Association.

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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

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

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21

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

~i	1	1	1	1	1	1—i—i—r

rMro^tLOVDr^oocno

	1	1	1	1	1

(N CO LD ID

CTicncricricncricncricncriOOOOOOOOOO
cncr>cr>cr>cr>cr>cx>cr>cncr>ooooooooooooooooo

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

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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).

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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.

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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


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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


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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


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Table A-126: Annual Goal Production (Thousand Short Tons)

Basin

1990

2005

2008

2009

2010

2011

2012

2013

2014

2015

2016

Underground























Coal























Production

423,556

368,611

357,074

332,061

337,155

345,607

342,387

341,216

354,705

306,820

251,771

N. Appalachia

103,865

111,151

105,228

99,629

103,109

105,752

103,408

104,198

116,700

103,578

94,679

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.

A-194 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

ACR (2017) Project Database. American Carbon Registry. Available online at .

Creedy, D.P. (1993) Chemosphere. Vol. 26, pp. 419-440.

DMME (2017) DGO Data Information System. Department of Mines, Minerals and Energy of Virginia. Available online
at .

EIA (2017) Annual Coal Report 2015. Table 1. Energy Information Administration, U.S. Department of Energy.

El Paso (2009) Shoal Creek Mine Plan, El Paso Exploration & Production.

EPA (2017) Greenhouse Gas Reporting Program (GHGRP) 2015 Envirofacts. Subpart FF: Underground Coal Mines.
Available online at .

EPA (2005) Surface Mines Emissions Assessment. E)raft. U.S. Environmental Protection Agency.

EPA (1996) Evaluation and Analysis of Gas Content and Coal Properties of Major Coal Bearing Regions of the United
States. U.S. Environmental Protection Agency. EPA/600/R-96-065.

Geological Survey of Alabama State Oil and Gas Board (GSA) (2017) Well Records Database. Available online at
.

IEA (2017) Key World Energy Statistics. Coal Production, International Energy Agency.

IPCC (2011) Use of Models and Facility-Level Data in Greenhouse Gas Inventories. Report of IPCC Expert Meeting on
Use of Models and Measurements in Greenhouse Gas Inventories 9-11 August 2010, Sydney, Australia. Eds:
Eggleston H.S., Srivastava N., Tanabe K., Baasansuren J., Fukuda M. IGES.

JWR (2010) No. 4 & 7 Mines General Area Maps. Walter Energy: Jim Walter Resources.

King, B. (1994) Management of Methane Emissions from Coal Mines: Environmental, Engineering, Economic and
Institutional Implication of Options, Neil and Gunter Ltd., Halifax, March 1994.

MSHA (2017) Data Transparency at MSHA. Mine Safety and Health Administration. Available online at
.

Mutmansky, Jan M., and Yanbei Wang (2000) Analysis of Potential Errors in Determination of Coal Mine Annual
Methane Emissions. Department of Energy and Geo-Environmental Engineering, Pennsylvania State University.
University Park, PA.

Saghafi, Abouna (2013) Estimation of fugitive emissions from open cut coal mining and measurable gas content, 13th
Coal Operators' Conference, University of Wollongong, The Australian Institute of Mining and Metallurgy & Mine
Managers Association of Australia, 2013, 306-313.

USBM (1986) Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins. Circular 9067, U.S.
Bureau of Mines.

West Virginia Geological & Economic Survey (WVGES) (2017) Oil & Gas Production Data. Available online at
.

A-195


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3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

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).

A-196 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016


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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

48

49

50

51

()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

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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.

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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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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

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.

6	B( )ICM (2014) Year 201 1 (iullwide Emission Inventory Study. ()CS Study B( )ICM 2014-666. Available online at <

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

9	lCnlbreement, U.S. Department of Interior.

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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

32	.

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

37	Information Administration, U.S. Department of I Cnergy. Washington. DC. Available online at <

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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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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18

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
Lnvironmental Affairs Department, Report No. DR140, l 'ebruary 1992.

()(i.l (1997-2014) "Worldwide (ias Processing." Oil .

PI 1MSA (2016a) "Annual Report Mileage for Natural Cias Transmission and Gathering Systems." Pipeline and I Iazardous
Materials Safety Administration. I J.S. Department of Transportation. Washington. DC. Available (inline at:
.

PI 1MSA (2016b) "Annual Report Mileage for Natural Cias Distribution Systems." Pipeline and I Iazardous Materials
Safely Administration. U.S. Department of Transportation. Washington, DC. Available online at:
.

Wyoming (2016) Wyoming Oil and Cias Conservation Commission. Available (inline at:

.

/.immerle. el al. (2015) "Methane Emissions from the Natural Cias Transmission and Storage System in the United States."
Environmental Science and Technology. I 'ol. 49 9374-9383.

A-208 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2016


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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

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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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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

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.

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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.

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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.

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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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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


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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

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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


-------
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


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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


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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


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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


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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

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


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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.

A-247


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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

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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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 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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21

22

23

24

25

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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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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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


-------
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


-------
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


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N. Car.

293

7,814

287

1,012

2,822

1,962

34,163

1,011

2,724

999

842

167

54,095

N. Dakota

100

2,106

78

275

4,757

4,707

82,253

2,378

6,419

5,820

5,624

1,835

116,351

Ohio

1,672

35,095

1,625

5,732

2,378

1,485

25,946

704

1,899

5,279

1,520

7,204

90,538

Oklahoma

249

5,602

368

1,299

13,624

10,160

176,931

6,065

16,345

24,151

11,382

11,367

277,542

Oregon

780

17,222

824

2,905

4,155

3,063

52,746

1,713

4,593

4,930

3,527

3,558

100,016

Penn.

3,306

77,283

4,431

15,624

2,440

813

14,156

797

2,147

4,038

1,721

4,036

130,789

R. Island

6

121

7

26

10

8

142

7

20

14

6

2

368

S. Car.

94

2,249

80

281

1,362

919

15,999

433

1,167

250

374

57

23,265

S. Dakota

617

13,582

845

2,980

9,513

8,482

148,221

5,698

15,384

18,408

14,896

16,426

255,053

Tenn.

293

6,850

398

1,405

5,839

4,718

82,160

2,022

5,448

3,331

2,027

410

114,901

Texas

2,932

73,299

3,683

12,987

31,140

22,319

388,683

10,830

29,187

69,955

43,346

107,136

795,495

Utah

599

14,257

659

2,324

2,285

1,890

32,552

1,215

3,257

2,330

2,150

1,047

64,566

Vermont

823

19,144

813

2,869

293

65

1,132

58

156

111

156

24

25,645

Virginia

580

14,817

685

2,416

3,892

3,442

59,949

1,588

4,281

4,303

1,310

858

98,123

Wash.

1,728

41,902

1,867

6,585

1,870

1,155

19,893

841

2,255

5,229

4,535

8,876

96,736

W. Virg.

56

1,136

58

205

1,269

1,003

17,459

478

1,288

1,225

626

180

24,983

Wisconsin

7,953

177,016

9,492

33,473

3,330

1,448

25,301

1,055

2,849

10,151

1,368

11,152

284,588

Wyoming

37

830

65

229

4,155

4,048

69,725

3,006

8,059

4,033

4,770

3,217

102,177

A-267


-------
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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1	References

2	Archibeque, S. (2011) Personal Communication. Shawn Archibeque, Colorado State University, Fort Collins, Colorado

3	and staff at ICF International.

4	Crutzen, P. J., I. Aselmann, and W. Seiler (1986) Methane Production by Domestic Animals, Wild Ruminants, Other

5	Herbivores, Fauna, and Humans. Tellus, 38B:271-284.

6	Donovan, K. (1999) Personal Communication. Kacey Donovan, University of California at Davis and staff at ICF

7	International.

8	Doren, P.E., J. F. Baker, C. R. Long and T. C. Cartwright (1989) Estimating Parameters of Growth Curves of Bulls, J

9	Animal Science 67:1432-1445.

10	Enns, M. (2008) Personal Communication. Dr. Mark Enns, Colorado State University and staff at ICF International.

11	ERG (2016) Development of Methane Conversion Rate Scaling Factor and Diet-Related Inputs to the Cattle Enteric

12	Fermentation Model for Dairy Cows, Dairy Heifers, and Feedlot Animals. ERG, Lexington, MA. December 2016.

13	Galyean and Gleghorn (2001) Summary of the 2000 Texas Tech University Consulting Nutritionist Survey. Texas Tech

14	University. Available online at . Accessed September 2010.

17	ICF (2006) Cattle Enteric Fermentation Model: Model Documentation. Prepared by ICF International for the

18	Environmental Protection Agency. June 2006.

19	ICF (2003) Uncertainty Analysis of2001 Inventory Estimates of Methane Emissions from Livestock Enteric Fermentation

20	in the U.S. Memorandum from ICF International to the Environmental Protection Agency. May 2003.

21	IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth

22	Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen,

23	M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United

24	Kingdom 996 pp.

25	IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories

26	Programme, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and

27	K. Tanabe (eds.). Hayama, Kanagawa, Japan.

28	Johnson, D. (2002) Personal Communication. Don Johnson, Colorado State University, Fort Collins, and ICF

29	International.

30	Johnson, D. (1999) Personal Communication. Don Johnson, Colorado State University, Fort Collins, and David Conneely,

31	ICF International.

32	Johnson, K. (2010) Personal Communication. Kris Johnson, Washington State University, Pullman, and ICF International.

33	Kebreab E., K. A. Johnson, S. L. Archibeque, D. Pape, and T. Wirth (2008) Model for estimating enteric methane

34	emissions from United States dairy and feedlot cattle. J. Anim. Sci. 86: 2738-2748.

35	Lippke, H., T. D. Forbes, and W. C. Ellis. (2000) Effect of supplements on growth and forage intake by stacker steers

36	grazing wheat pasture. J. Anim. Sci. 78:1625-1635.

37	National Bison Association (2011) Handling & Carcass Info (on website). Available online at:

38	. Accessed August 16, 2011.

39	National Bison Association (1999) Total Bison Population—1999. Report provided during personal email communication

40	with Dave Carter, Executive Director, National Bison Association July 19, 2011.

41	NRC (1999) 1996 BeefNRC: Appendix Table 22. National Research Council.

42	NRC (1984) Nutrient requirements for beef cattle (6th Ed.). National Academy Press, Washington, DC.

43	Pinchak, W.E., D. R. Tolleson, M. McCloy, L. J. Hunt, R. J. Gill, R. J. Ansley, and S. J. Bevers (2004) Morbidity effects

44	on productivity and profitability of stacker cattle grazing in the southern plains. J. Anim. Sci. 82:2773-2779.

45	Platter, W. J., J. D. Tatum, K. E. Belk, J. A. Scanga, and G. C. Smith (2003) Effects of repetitive use of hormonal implants

46	on beef carcass quality, tenderness, and consumer ratings of beef palatability. J. Anim. Sci. 81:984-996.

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1	Preston, R.L. (2010) What's The Feed Composition Value of That Cattle Feed? Beef Magazine, March 1, 2010. Available

2	at: .

3	Skogerboe, T. L., L. Thompson, J. M. Cunningham, A. C. Brake, V. K. Karle (2000) The effectiveness of a single dose of

4	doramectin pour-on in the control of gastrointestinal nematodes in yearling stacker cattle. Vet. Parasitology 87:173 -

5	181.

6	Soliva, C.R. (2006) Report to the attention of IPCC about the data set and calculation method used to estimate methane

7	formation from enteric fermentation of agricultural livestock population and manure management in Swiss

8	agriculture. On behalf of the Federal Office for the Environment (FOEN), Berne, Switzerland.

9	USDA (2017) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S. Department of

10	Agriculture. Washington, D.C. Available online at .

11	USDA (2016) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S. Department of

12	Agriculture. Washington, D.C. Available online at . Accessed August 1, 2016.

13	USDA (2007) Census of Agriculture: 2007 Census Report. United States Department of Agriculture. Available online at:

14	.

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.

A-271


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through 2006, 2008 through 2011, and 2013 through 2015 were interpolated and extrapolated based on the 1987, 1992,
1997, 2002, 2007, and 2012 Census data.

American Bison: Annual American bison population data by state were available for 2002, 2007, and 2012 (USDA
2014a). Data for 1990 through 1999 were obtained from the Bison Association (1999). Data for 2000, 2001, 2003 through
2006, 2008 through 2011, and 2013 through 2015 were interpolated and extrapolated based on the Bison Association and
2002, 2007, and 2012 Census data.

Poultry: The USDA provides population data for hens (one year old or older), pullets (hens younger than one year
old), other chickens, and production (slaughter) data for broilers and turkeys (USDA 1995a, 1995b, 1998, 1999, 2004a,
2004b, 2009b, 2009c, 2009d, 2009e, 2010a, 2010b, 2011a, 2011b, 2012a, 2012b, 2013a, 2013b, 2014b, 2014c, 2015a,
2015b, 2016b, and 2016c). All poultry population data were adjusted to account for states that report non-disclosed
populations to USDA NASS. The combined populations of the states reporting non-disclosed populations are reported as
"other" states. State populations for the non-disclosed states were estimated by equally distributing the population attributed
to "other" states to each of the non-disclosed states.

Because only production data are available for boilers and turkeys, population data are calculated by dividing the
number of animals produced by the number of production cycles per year, or the turnover rate. Based on personal
communications with John Lange, an agricultural statistician with USDA NASS, the broiler turnover rate ranges from 3.4
to 5.5 over the course of the inventory (Lange 2000). For turkeys, the turnover rate ranges from 2.4 to 3.0. A summary of
the livestock population characterization data used to calculate CH4 and N2O emissions is presented in Table A-182.

Step 2: Waste Characteristics Data

Methane and N2O emissions calculations are based on the following animal characteristics for each relevant
livestock population:

•	Volatile solids (VS) excretion rate;

•	Maximum methane producing capacity (B0) for U.S. animal waste;

•	Nitrogen excretion rate (Nex); and

•	Typical animal mass (TAM).

Table A-183 presents a summary of the waste characteristics used in the emissions estimates. Published sources
were reviewed for U.S.-specific livestock waste characterization data that would be consistent with the animal population
data discussed in Step 1. The USDA's Agricultural Waste Management Field Handbook (AWMFH; USDA 1996, 2008) is
one of the primary sources of waste characteristics for non-cattle animal groups. Data from the 1996 and 2008 USDA
AWMFH were used to estimate VS and Nex for most non-cattle animal groups across the time series of the Inventory, as
shown in Table A-184 (ERG 2010b and 2010c). The 1996 AWMFH data were based on measured values from U.S. farms;
the 2008 AWMFH data were developed using the calculation method created by the American Society of Agricultural and
Biological Engineers (ASABE), which is based on U.S. animal dietary intake and performance measures. Since the values
from each of the two AWMFHs result from different estimation methods and reflect changes in animal genetics and nutrition
over time, both data sources were used to create a time series across the Inventory as neither value would be appropriate to
use across the entire span of Inventory years. Expert sources agreed interpolating the two data sources across the time series
would be appropriate as each methodology reflect the best available for that time period and the more recent data may not
appropriately reflect the historic time series (ERG 2010b). Although the AWMFH values are lower than the IPCC values,
these values are more appropriate for U.S. systems because they have been calculated using U.S.-specific data. Animal-
specific notes about VS and Nex are presented below:

•	Swine: The VS and Nex data for breeding swine are from a combination of the types of animals that make up
this animal group, namely gestating and farrowing swine and boars. It is assumed that a group of breeding
swine is typically broken out as 80 percent gestating sows, 15 percent farrowing swine, and 5 percent boars
(Safley 2000). Differing trends in VS and Nex values are due to the updated Nex calculation method from
2008 AWMFH. VS calculations did not follow the same procedure and were updated based on a fixed ratio
of VS to total solids and past ASABE standards (ERG 2010b).

•	Poultry: Due to the change in USDA reporting of hens and pullets in 2005, new nitrogen and VS excretion
rates were calculated for the combined population of hens and pullets; a weighted average rate was calculated
based on hen and pullet population data from 1990 to 2004.

•	Goats, Sheep, Horses, Mules and Asses: In cases where data were not available in the USDA documents, data
from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) or the 2006 IPCC
Guidelines were used as a supplement.

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The method for calculating VS excretion and Nex for cattle (including American bison, beef and dairy cows, bulls,
heifers, and steers) is based on the relationship between animal performance characteristics such as diet, lactation, and weight
gain and energy utilization. The method used is outlined by the 2006IPCC Guidelines Tier II methodology, and is modeled
using the CEFM as described in the enteric fermentation portion of the inventory (documented in Moffroid and Pape 2013)
in order to take advantage of the detailed diet and animal performance data assembled as part of the Tier II analysis for
cattle. For American bison, VS and Nex were assumed to be the same as beef NOF bulls.

The VS content of manure is the fraction of the diet consumed by cattle that is not digested and thus excreted as
fecal material; fecal material combined with urinary excretions constitutes manure. The CEFM uses the input of digestible
energy (DE) and the energy requirements of cattle to estimate gross energy (GE) intake and enteric CH4 emissions. GE and
DE are used to calculate the indigestible energy per animal as gross energy minus digestible energy plus the amount of gross
energy for urinary energy excretion per animal (2 or 4 percent). This value is then converted to VS production per animal
using the typical conversion of dietary gross energy to dry organic matter of 18.45 MJ/kg, after subtracting out the ash
content of manure. The current equation recommended by the 2006 IPCC Guidelines is:

VS production (kg) = [(GE - DE) + (UE x GE)] x l~ASH

18.45

where,

GE	= Gross energy intake (MJ)

DE	= Digestible energy (MJ)

(UE x GE)	= Urinary energy expressed as fraction of GE, assumed to be 0.04 except for feedlots

which are reduced 0.02 as a result of the high grain content of their diet.
ASH	= Ash content of manure calculated as a fraction of the dry matter feed intake (assumed

to be 0.08).

18.45	= Conversion factor for dietary GE per kg of dry matter (MJ per kg). This value is

relatively constant across a wide range of forage and grain-based feeds commonly
consumed by livestock.

Total nitrogen ingestion in cattle is determined by dietary protein intake. When feed intake of protein exceeds the
nutrient requirements of the animal, the excess nitrogen is excreted, primarily through the urine. To calculate the nitrogen
excreted by each animal type, the CEFM utilizes the energy balance calculations recommended by the 2006IPCC Guidelines
for gross energy and the energy required for growth along with inputs of weight gain, milk production, and the percent of
crude protein in the diets. The total nitrogen excreted is measured in the CEFM as nitrogen consumed minus nitrogen retained
by the animal for growth and in milk. The basic equation for calculating Nex is shown below, followed by the equations for
each of the constituent parts.

N

excreted

= N

consumed

-(n

growth ^ milk ,

where,

N excreted
N consumed
N growth

N milk

= Daily N excreted per animal, kg per animal per day.

= Daily N intake per animal, kg per animal per day
= Nitrogen retained by the animal for growth, kg per animal per day
= Nitrogen retained in milk, kg per animal per day

The equation for N consumed is based on the 2006IPCC Guidelines, and is estimated as:

( 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

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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.

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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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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

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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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

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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12

13

14

15

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


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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


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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


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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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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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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]

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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

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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).

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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

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Similarly, DayCent model results have been compared to trace gas N2O fluxes for a number of native and managed systems
(Del Grosso et al. 2001, 2005, 2010) (Figure A-13). In general, the model simulates accurate emissions, but some bias and
imprecision does occur in predictions, which is reflected in the uncertainty associated with DayCent model results.
Comparisons with measured data showed that DayCent estimated N2O emissions more accurately and precisely than the
IPCC Tier 1 methodology (IPCC 2006) (See Agricultural Soil Management, QA/QC and Verification Section). The linear
regression of simulated vs. measured emissions for DayCent had higher r2 values and a fitted line closer to a perfect 1:1
relationship between measured and modeled N2O emissions compared to the IPCC Tier 1 approach (Del Grosso et al. 2005,
2008). This is not surprising, since DayCent includes site-specific factors (climate, soil properties, and previous
management) that influence N2O emissions. Furthermore, DayCent also simulated NO3- leaching (root mean square error =
20 percent) more accurately than IPCC Tier 1 methodology (root mean square error = 69 percent) (Del Grosso et al. 2005).
Volatilization of N gases that contribute to indirect soil N2O emissions is the only component that has not been thoroughly
tested, which is due to a lack of measurement data. Overall, the Tier 3 approach has reduced uncertainties in the agricultural
soil C stock changes andN20 emissions compared to using lower Tier methods.

Figure A-13: Comparisons of Results from DayCent Model and Measurements of Soil Nitrous Oxide Emissions

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.

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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


-------
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

A-319


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Undrained	0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Total

0.21

0.21

0.19

0.19

0.19

0.19

0.19

0.19

0.19

Sub-Tropical

Cultivated Cropland



















(high drainage)

0.23

0.22

0.22

0.21

0.21

0.22

0.22

0.18

0.18

Managed Pasture



















(low drainage)

0.10

0.10

0.10

0.10

0.10

0.10

0.10

0.10

0.10

Undrained

0.01

0.01

0.01

0.01

0.02

0.00

0.00

0.03

0.03

Total

0.33

0.33

0.33

0.33

0.32

0.32

0.32

0.32

0.32

Note: In the current Inventory, NRI data only provide land use and management statistics through 2012. Additional data will be incorporated in the future to extend the time
series for the land use and management data.

The harvested area for rice cultivation is estimated from the NRI points based on survey locations classified as flooded rice
(Table A-200). Ratoon crops occur in the Southeast with a second season of rice during the year. Ratoon cropping also
occurs in Louisiana (LSU 2015 for years 2000 through 2013, 2015) and Texas (TAMU 2015 for years 1993 through 2014),
averaging 32 percent and 48 percent of rice acres planted, respectively. Florida also has a large fraction of area with a ratoon
crops (45 percent), but ratoon cropping is uncommon in Arkansas occurring on relatively small fraction of fields estimated
at about 1 percent. No data are available on ratoon crops in Missouri or Mississippi, and so the amount of ratooning is
assumed similar to Arkansas. Ratoon rice crops are not grown in California.

Table fl-2

DO: Total Rice Harvested Area 1



Land Areas (Million Hectares)

Year

Tier 1

Tier 3

Total

1990

0.16

1.54

1.70

1991

0.16

1.60

1.76

1992

0.17

1.67

1.84

1993

0.17

1.63

1.80

1994

0.17

1.53

1.70

1995

0.15

1.56

1.71

1996

0.15

1.56

1.72

1997

0.15

1.52

1.67

1998

0.17

1.43

1.60

1999

0.31

1.49

1.80

2000

0.33

1.51

1.84

2001

0.18

1.44

1.62

2002

0.18

1.60

1.79

2003

0.15

1.47

1.62

2004

0.17

1.53

1.69

2005

0.18

1.65

1.83

2006

0.14

1.33

1.48

2007

0.12

1.45

1.57

2008

0.14

1.27

1.41

2009

0.14

1.57

1.71

2010

0.15

1.61

1.76

2011

0.13

1.32

1.45

2012

0.11

1.18

1.29

Note: In the current Inventory, NRI data only provide land use and management statistics through 2012.
Additional data will be incorporated in the future to extend the time series of the land use and management data.

Step 1b: Obtain Management Activity Data for the Tier 3 Method to estimate Soil C Stock Changes, CH4 and 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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46

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48

49

50

51

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.

A-321


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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.

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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.

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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


-------
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

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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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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

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)

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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

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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.

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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


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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


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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


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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)

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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


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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


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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


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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.

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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

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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

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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.

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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

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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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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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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


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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).

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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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1

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.

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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.

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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).

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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.

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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 .

A-409


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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.

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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

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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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Barlaz, M.A. (1998) "Carbon Storage During Biodegradation of Municipal Solid Waste Components in Laboratory-scale
Landfills." Global Biogeochemical Cycles, 12(2): 373-380, June 1998.

BioCycle (2010) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and N. Themelis.
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Chartwell (2004) Municipal Solid Waste Directory. The Envirobiz Group.

Czepiel, P., B. Mosher, P. Crill, and R. Harriss (1996) "Quantifying the Effect of Oxidation on Landfill Methane Emissions."
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EIA (2007) Voluntary Greenhouse Gas Reports for EIA Form 1605B (Reporting Year 2006). Available online at
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EPA (2017a) Landfill Methane Outreach Program (LMOP). 2017 Landfill and Project Level Data. June 2017. Available online
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EPA (2017b) Greenhouse Gas Reporting Program (GHGRP). 2017 Envirofacts. Subpart HH: Municipal Solid Waste Landfills.
Available online at: .

EPA (2016) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. August 2015.

EPA (2015) Greenhouse Gas Reporting Program (GHGRP). 2015 Envirofacts. Subpart HH: Municipal Solid Waste Landfills.
Available online at: .

EPA (2008) Compilation of Air Pollution Emission Factors, Publication AP-42, Draft Section 2.4 Municipal Solid Waste
Landfills. October 2008.

EPA (1998) Compilation of Air Pollution Emission Factors, Publication AP-42, Section 2.4 Municipal Solid Waste Landfills.
November 1998.

EPA (1993) Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress, U.S.

Environmental Protection Agency, Office of Air and Radiation. Washington, D.C. EPA/430-R-93-003. April 1993.

EPA (1988) National Survey of Solid Waste (Municipal) Landfill Facilities, U.S. Environmental Protection Agency. Washington,
D.C. EPA/530-SW-88-011. September 1988.

EREF (The Environmental Research & Education Foundation) (2016). Municipal Solid Waste Management in the United States:
2010 & 2013.

ERG (2017) Draft Production Data Supplied by ERG for 1990-2016 for Pulp and Paper, Fruits and Vegetables, and Meat.
August.

ERG (2014) Draft Production Data Supplied by ERG for 1990-2013 for Pulp and Paper, Fruits and Vegetables, and Meat.
August.

FAO (2016). FAOStat database 2016. Available at http://faostat3.fao.Org/home/index.html#DOWNLOAD, Accessed on July 18,
2016.

Flores, R.A., C.W. Shanklin, M. Loza-Garay, S.H. Wie (1999) "Quantification and Characterization of Food Processing
Wastes/Residues." Compost Science & Utilization, 7(1): 63-71.

Heath, L.S. et al. 2010. Greenhouse Gas and Carbon Profile of the U.S. Forest Products Industry Value Chain. Environmental
Science and Technology 44(2010) 3999-4005.

IPCC (2006) 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.

Jensen, J.E.F., and R. Pipatti (2002) "CH4 Emissions from Solid Waste Disposal." Background paper for the Good Practice
Guidance and Uncertainty Management in National Greenhouse Gas Inventories.

Kraft, D.L. andH.C. Orender (1993) "Considerations for Using Sludge as a Fuel." Tappi Journal, 76(3): 175-183.

Lockwood-Post Directory of Pulp and Paper Mills (2002). Available for purchase at
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Mancinelli, R. and C. McKay (1985) "Methane-Oxidizing Bacteria in Sanitary Landfills." Proc. First Symposium on

Biotechnical Advances in Processing Municipal Wastes for Fuels and Chemicals, Minneapolis, MN, 437-450. August.

Miner, R. (2008). "Calculations documenting the greenhouse gas emissions from the pulp and paper industry." Memorandum
from Reid Minor, National Council for Air and Stream Improvement, Inc. (NCASI) to Becky Nicholson, RTI International,
May 21, 2008.

Mintz C., R. Freed, and M. Walsh (2003) "Timeline of Anaerobic Land Disposal of Solid Waste." Memorandum to T. Wirth
(EPA) and K. Skog (USDA), December 31, 2003.

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Emissions from the Pulp and Paper Industry." Memorandum to R. Nicholson (RTI).

National Council for Air and Stream Improvement, Inc. (NCASI) (2005) "Calculation Tools for Estimating Greenhouse Gas
Emissions from Pulp and Paper Mills, Version 1.1." July 8,2005.

Peer, R., S. Thorneloe, and D. Epperson (1993) "A Comparison of Methods for Estimating Global Methane Emissions from
Landfills." Chemosphere, 26(l-4):387-400.

RTI (2018) Methodological changes to the scale-up factor used to estimate emissions from municipal solid waste landfills in the
Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA). In progress.

RTI (2017) Methodological changes to the methane emissions from municipal solid waste landfills as reflected in the public
review draft of the 1990-2015 Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA).
March 31, 2017.

RTI (2015) Investigate the potential to update DOC and k values for the Pulp and Paper industry in the US Solid Waste
Inventory. Memorandum prepared by K. Bronstein and M. McGrath for R. Schmeltz (EPA), December 4,2015.

RTI (2014) Analysis of DOC Values for Industrial Solid Waste for the Pulp and Paper Industry and the Food Industry.
Memorandum prepared by J. Coburn for R. Schmeltz (EPA), October 28,2014.

RTI (2011) Updated Research on Methane Oxidation in Landfills. Memorandum prepared by K. Weitz (RTI) for R. Schmeltz
(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.

Solid Waste Association of North America (SWANA) (1998) Comparison of Models for Predicting Landfill Methane Recovery.
Publication No. GR-LG 0075. March 1998.

Sonne, E. (2006) "Greenhouse Gas Emissions from Forestry Operations: A Life Cycle Assessment." J. Environ. Qual. 35:1439-
1450.

Upton, B., R. Miner, M. Spinney, L.S. Heath (2008) "The Greenhouse Gas and Energy Impacts of Using Wood Instead of
Alternatives in Residential Construction in the United States." Biomass and Bioenergy, 32: 1 -10.

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Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2016. Available online at:
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Waste Business Journal (WBJ) (2016) Directory of Waste Processing & Disposal Sites 2016.

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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.

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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


-------
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


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4

5

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10

11

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13

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18

19

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23

24

25

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32

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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 .

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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 .

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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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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 .

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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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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)

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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.

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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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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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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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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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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


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1

6.5. Constants, Units, and Conversions

2	Metric Prefixes

3	Although most activity data for the United States is gathered in customary U.S. units, these units are converted

4	into metric units per international reporting guidelines. Table A-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


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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


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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


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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


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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


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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


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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


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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

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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

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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.

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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%

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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.

A-467


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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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29

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.

A-469


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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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