EPA430-D-II-003
DRAFT: Global Anthropogenic Non-CO2
Greenhouse Gas Emissions: 1990 - 2030
August 201 I
Office of Atmospheric Programs
Climate Change Division
U.S. Environmental Protection Agency
1200 Pennsylvania Avenue, NW
Washington, DC 20460
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How to Obtain Copies
You may electronically download this document from the U.S. EPA's webpage at
http://www.epa.gov/nonco2/econ-inv/international.html.
How to Obtain the Data
You may electronically download the data compiled for this report in .xls format from the U.S.
EPA's webpage at:
http://www.epa.gov/nonco2/econ-inv/international.html.
For Further Information:
If you have questions or would like to provide comments on this draft report, contact Jameel
Alsalam (alsalam.jameel(g),epamail.epa.gov) or Shaun Ragnauth (ragnauth.shaun(g),epa.gov). Climate
Change Division, Office of Atmospheric Programs, U.S. Environmental Protection Agency.
Expert Reviewed Document
This report has been reviewed by external experts from the private sector, academia, non-
governmental organizations, and other government agencies. The report is being published as a draft
to allow further review and development before being finalized. Readers are encouraged to provide
comments on the report to EPA.
August 201 I Front Matter Page ii
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Acknowledgements
This report was prepared under a contract between the U.S. Environmental Protection Agency
(USEPA) and IGF International (IGF).
We thank the following external reviewers: Shawn Archibeque (Colorado State University), Chris
Bayliss (International Aluminum Institute), Jean Bogner (Landfill, Inc), E. Lee Bray (USGS), Jim
Crawford (Trane Company), Stuart Day (CSIRO), Dr. John Freney (CSIRO), Maureen Hardwick
(International Pharmaceutical Aerosol Consortium), Mike Jeffs (ISOPA), Kris Johnson (Washington
State University), Deborah Kramer (USGS), Lambert Kuijpers, Jan Lewandrowski (USDA),Dr.
Changsheng Li (University of New Hampshire), Kennth J. Martchek (Alcoa, Inc.), May Massoud
(American University of Beirut), Mack McFarland (DuPont), Cythia Murphy (University of Texas),
Bob Ridgeway (Air Products), Silvio Stangherlm (CIGRE),Takeshi Yokota (Toshiba, CIGRE).
Although these individuals participated in the review of this analysis, their efforts do not constitute
an endorsement of the report's results or of any USEPA policies and programs.
August 201 I Front Matter Page iii
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August 201 I Front Matter Page iv
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Acronyms
AI Annex I
AE anode effects
AR4 Fourth Assessment Report
BAU business as usual
BOD biological oxygen demand
C AG R compound annual growth rate
CDM Clean Development Mechanism
CEH Chemical and Economics Handbook
CEIT countries with economies in transition
C FC chlorofluorocarbon
C p4 perfluoromethane
C2p6 hexafluoroethane
CsFa perfluoropropane
c-C4Pa perfluorocyclobutane
CH4 methane
CIA Central Intelligence Agency
CO2 carbon dioxide
C RF Common Reporting Format
CVD chemical vapor deposition
CWPB Center-Worked Prebake
DOC degradable organic carbon
EDGAR Emission Database for Global Atmospheric Research
EF emission factor
EIA Energy Information Administration
EMF-22 Energy Modeling Forum 22
EPA U.S. Environmental Protection Agency
ESI A European Semiconductor Industry Association
EU European Union
F-GHG fluorinated greenhouse gas
FAO Food and Agriculture Organization of the United Nations
FOD first order decay
FPD flat panel display
FSU Former Soviet Union
GDP gross domestic product
CS gigagram
GHG greenhouse gas
G WP global warming potential
HCFC hydrochlorofluorocarbon
HCFC-22 chlorodifluoromethane
HFC-23 trifluoromethane
H FC hydrofluorocarbon
HFE hydrofluoroethers
August 2011
Front Matter
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HSS Horizontal Stud Soderberg
HTOC Halon Technical Options Committee
I Al International Aluminium Institute
IEA International Energy Agency
I FA International Fertilizer Industry Association
IFPRI International Food Policy Research Institute
IMA International Magnesium Association
IMPACT International Model for Policy Analysis of Agricultural Commodities and Trade
IPCC Intergovernmental Panel on Climate Change
IRRI International Rice Research Institute
JEITA Japan Electronic and Information Technology Industries Association
Jl Joint Implementation
Kg kilogram
KSIA Korean Semiconductor Industry Association
MDI metered dose inhalers
MtCC^e million metric tons of carbon dioxide equivalent
MSW municipal solid waste
N Nitrogen
N2O nitrous oxide
NAI non-Annex I
NC National Communication
N Fs nitrogen trifluoride
NIK not-in-kind
NIR National Inventory Report
NOX Nitrogen oxides
ODP ozone-depleting potential
ODS ozone-depleting substance
OECD The Organization for Economic Cooperation and Development
OEM Original Equipment Manufacturers
OX Oxidation
PFC perfluorocarbons
PFPB Point Feed Prebake
PRP pasture, range, and paddock
PV photovoltaic
SAR Second Assessment Report
SF6 sulfur hexafluoride
Si Silicon
SIA U.S Semiconductor Industry Association
SO2 sulfur dioxide
SRES Special Report on Emissions Scenarios
SWPB Side-Worked Prebake
SWDS solid waste disposal site
TAR Third Assessment Report
August 2011
Front Matter
Page vi
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TEAR Technology and Economic Assessment Panel
TJ terajoule
TMLA total manufacture layer area
TSIA Taiwan Semiconductor Industry Association
UNEP United National Environmental Programme
UNFCCC United Nations Framework Convention on Climate Change
USDA U.S. Department of Agriculture
USGS U.S. Geological Survey
VSS Vertical Stud Soderberg
WEO World Energy Outlook
WFW World Fab Watch
WLICC World LCD Industry Cooperation Committee
WSC World Semiconductor Council
WWT wastewater treatment
VAIP Voluntary Aluminum Industrial Partnership
August 2011
Front Matter
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Table of Contents
1 Introduction and Overview 1-1
1.1 Introduction 1-1
1.2 Overview of Non-CO2 Greenhouse Gas Emissions 1-1
1.3 Emission Sources 1-3
1.4 Region Groupings 1-4
1.5 Approach 1-7
1.6 Limitations 1-8
1.7 Organization of this Report 1-10
2 Summary Results 2-1
2.1 Summary Estimates 2-1
2.2 Trends by Region 2-2
2.3 Trends by Gas, Sector, and Source Category 2-5
2.4 Other Global Datasets 2-7
3 Energy 3-1
3.1 Natural Gas and Oil Systems (CH4) 3-3
3.2 Coal Mining Activities (CH4) 3-5
3.3 Stationary and Mobile Combustion (CH4, N2O) 3-8
3.4 Biomass Combustion (CH4, N2O) 3-10
3.5 Other Energy Sources (CH4, N2O) 3-13
4 Industrial Processes 4-1
4.1 Adipic Acid and Nitric Acid Production (N2O) 4-5
4.2 Use of Substitutes for Ozone Depleting Substances (HFCs) 4-6
4.3 HCFC-22 Production (HFCs) 4-9
4.4 Electric Power Systems (SF6) 4-12
4.5 Primary Aluminum Production (PFCs) 4-13
4.6 Magnesium Manufacturing (SF6) 4-16
4.7 Semiconductor Manufacturing (HFCs, PFCs, SF6, NF3) 4-17
4.8 Flat Panel Display Manufacturing (PFCs, SF6, NF3) 4-20
4.9 Photovoltaic Manufacturing (PFCs, NF3) 4-22
4.10 Other Industrial Processes Sources (CH4, N2O) 4-24
5 Agriculture 5-1
5.1 Agricultural Soils (N2O) 5-3
5.2 Enteric Fermentation (CH4) 5-6
5.3 Rice Cultivation (CH4) 5-8
5.4 Manure Management (CH4, N2O) 5-9
5.5 Other Agriculture Sources (CH4, N2O) 5-13
6 Waste 6-1
6.1 Landfillmg of Solid Waste (CH4) 6-2
6.2 Wastewater (CH4) 6-4
6.3 Human Sewage Domestic Wastewater (N2O) 6-7
6.4 Other Waste Sources (CH4, N2O) 6-8
7 Methodology 7-1
7.1 Energy 7-3
7.2 Industrial Processes 7-11
7.3 Agriculture 7-47
August 201 I Front Matter Page i
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8 R«
8.1
8.2
8.3
8.4
8.5
8.6
8.7
Aooenc
aferences
Introduction and Overview
Summary Results
Enerpv
^ &7
Industry
Agriculture
Waste
Methodology
dices
8-1
8-1
8-1
8-2
8-3
8-4
8-5
8-5
1
August 201 I Front Matter Page ii
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Appendices
Appendix A: Total Emissions by Country
Appendix B: Energy Sector Emissions
Appendix C: Industrial Processes Sector Emissions
Appendix D: Agriculture Sector Emissions
Appendix E: Waste Sector Emissions
Appendix F: Methodology Applied to Develop Source Emissions
Appendix G: Data Sources Used to Develop Non-Country-Reported Emissions
Estimates
Appendix H: Future Mitigation Measures Included in Developing Non-Country-
Reported Estimates
Appendix I: Regional Definitions
Appendix J: U.S. EPA Vintaging Model Framework
August 201 I Front Matter Page iii
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I Introduction and Overview
I.I Introduction
This report provides historical and projected estimates of emissions of non-carbon dioxide (non-
CO2) greenhouse gases (GHGs) from anthropogenic sources. The report provides a consistent and
comprehensive estimate of non-CO2 greenhouse gases for 92 individual countries and eight regions.
The analysis provides information that can be used to understand national contributions of GHG
emissions, historical progress on reductions, and mitigation opportunities. Although this document
is being published by the EPA, the U.S. projections are generated using the same methodologies
used for all countries, and is based on IPCC Tier 1 calculations supplemented with country reported
inventory data where available. The dataset compiled for this report is available in spreadsheet (.xls)
format on the U.S. EPA's webpage at: http://www.epa.gov/nonco2/econ-inv/international.html.
The gases included in this report are the direct non-CO2 GHGs covered by the United Nations
Framework Convention on Climate Change (UNFCCC): methane (CH4), nitrous oxide (N2O), and
the high global warming potential (high-GWP) gases. The high-GWP gases include
hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6). In addition,
nitrogen fluoride (NF3) is considered. Compounds covered by the Montreal Protocol are not
included in this report, although many of them are also high-GWP gases. Historical estimates are
reported for 1990, 1995, 2000, and 2005 and projections of emissions are provided for 2010, 2015,
2020, 2025, and 2030. Projections reflect the currently achieved impact of sector-specific climate
policy programs, agreements, and measures that are already in place, but exclude GHG reductions
due to additional planned activities and economy-wide programs whose impacts on individual
sectors are less certain.
To develop estimates included in this report, the U.S. Environmental Protection Agency (EPA)
collected emission estimates from publicly available nationally-prepared GHG reports consistent
with the Revised 1996 Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas
Inventories (IPCC Guidelines) (IPCC, 1997), the IPCC Good Practice Guidance and Uncertainty Management
in National Greenhouse Gas Inventories (IPCC Good Practice Guidance) (IPCC, 2000), and the Revised
2006 Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories (IPCC
Guidelines) (IPCC, 2006). If national estimates were unavailable from nationally-prepared GHG
reports, EPA estimated non-CO2 GHG emissions in order to produce a complete global inventory.
EPA's calculated emission estimates are prepared in a consistent manner across all countries using
IPCC default methodologies, international statistics for activity data, and the IPCC Tier 1 default
emission factors.
1.2 Overview of Non-CO2 Greenhouse Gas Emissions
As shown in Exhibit 1-1, global emissions of CH4, N2O, and high-GWP gases account for
approximately 28 percent of global radiative forcing since the pre-industrial era of GHGs covered by
the UNFCCC (IPCC, 2007). Emissions of non-CO2 GHGs contribute significantly to radiative
forcing1 since they are more effective at trapping heat than CO2. The IPCC uses the concept of the
1 Radiative forcing is the change in the balance between radiation coming into and going out of the atmosphere. Positive
radiative forcing tends on average to warm the surface of the Earth, and negative forcing tends on average to cool the
surface (IPCC, 2007).
August 201 I I. Introduction and Overview Page I-1
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global warming potential (GWP) to compare the ability of different gases to trap heat in the
atmosphere relative to CO2. Emissions of non-CO2 gases are converted to a CO2-equivalent basis
using the 100-year GWPs published in the IPCC's Second Assessment Report (SAR) (IPCC, 1996).
Table 1-1 shows GWPs of select gases from IPCC's Second Assessment Report.2 These GWPs, as
well as GWPs for additional gases (see Table 4-1) were used in this report.
Exhibit I-I: Contribution of Anthropogenic Greenhouse Gas Emissions to Global Radiative Forcing
(W/m2)
Source: IPCC, 2007: Table 2.1
Table I-I: Global Warming Potentials
Gas GWP"
Carbon dioxide (CO2)
Methane (CH4)
Nitrous Oxide (N2O)
HFC-23
HFC-32
HFC-125
HFC-l34a
HFC-l43a
HFC-l52a
HFC-227ea
HFC-236fa
I
21
310
I 1,700
650
2,800
1,300
3,800
140
2,900
6,300
High GWPs
0.7%
2 Although the GWPs have been updated by the IPCC in the Third Assessment Report (TAR) (IPCC, 2001) and again in
the Fourth Assessment Report (AR4) (IPCC, 2007), estimates of emissions in this report continue to use the GWPs
from the Second Assessment Report (SAR) (IPCC, 1996), in order to be consistent with international reporting
standards under the UNFCCC. However, some of the high-GWP gases estimated in this report do not have GWPs in
the SAR. In these cases, this report uses the TAR GWPs or other published data(see Table 4-1 for additional gases).
August 2011
I. Introduction and Overview
Page 1-2
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Gas
HFC-43IOmee
CF4
C2F6
C4F|0
C6FI4
SF6
GWP"
1,300
6,500
9,200
7,000
7,400
23,900
Source: IPCC, 1996
1 100 year time horizon.
EPA estimates that global non-CO2 GHG emissions in 2005 were 10,883 million metric tons of
carbon dioxide equivalents (MtCO2e3). When compared to a global CO2 estimate for 2005 of
approximately 31,613 MtCO2e (WRI, 2010), anthropogenic non-CO2 emissions sources represent 25
percent of the global GHG emissions emitted annually on a CO2 equivalent basis.
1.3 Emission Sources
This report focuses exclusively on anthropogenic sources of non-CO2GHGs. Table 1-2 lists the
source categories discussed in this report. All anthropogenic sources of CH4 and N2O are included
(with a few exceptions noted in Section 1.6). The major sources are considered individually and
emissions from minor sources are combined under "Other" categories, listed in Table 1-2. The high-
GWP sources include substitutes for ozone-depleting substances (ODS) and industrial sources of
HFCs, PFCs, and SF6.
Table 1-2: Sources Included in this Report
Sector/Source
Energy
Natural Gas and Oil Systems
Coal Mining Activities
Stationary and Mobile Combustion
Biomass Combustion
Other Energy Sources
Waste Combustion
Fugitives from Solid Fuels
Fugitives from Natural Gas and Oil Systems
Industrial Processes
Adipic Acid and Nitric Acid Production
Use of Substitutes for Ozone Depleting Substances
HCFC-22 Production
Electric Power Systems
Primary Aluminum Production
Magnesium Manufacturing
Semiconductor Manufacturing
Flat Panel Display Manufacturing
Photovoltaic Manufacturing
CH4
CH4
CH4, N2O
CH4, N2O
CH4, N2O
N2O
N2O
A
N2O
HFCs
HFCs
SF6
PFCs
SF6
HFCs, PFCs, SF6, NF3
PFCs, SF6, NF3
PFCs, NF3
3 One MtCC>2 is equivalent to one megatonne or teragram of CC>2.
August 2011
I. Introduction and Overview
Page 1-3
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Sector/Source
Other Industrial Processes Sources
Chemical Production
Iron and Steel Production
Metal Production
Mineral Products
Petrochemical Production
Silicon Carbide Production
Solvent and Other Product Use
Agriculture
Agricultural Soils
Enteric Fermentation
Rice Cultivation
Manure Management
Other Agriculture Sources
Agricultural Soils
Field Burning of Agricultural Residues
Prescribed Burning of Savannas
Open Burning from Forest Clearing
Waste
Landfilling of Solid Waste
Wastewater
Human Sewage - Domestic Wastewater
Other Waste Sources
CH4
CH4
CH4, N2O
CH4
CH4
CH4
N2O
N2O
CH4
CH4
CH4, N2O
CH4
CH4, N2O
CH4, N2O
CH4
CH4
CH4
N,O
Miscellaneous Waste Handling Processes
CH4, N2O
Sources of Non-CO2 Greenhouse Gas Emissions Not Included in This Estimate
Due to methodological limitations, a few anthropogenic sources have not been fully included in this
analysis. These include CH4 from hydroelectric reservoirs and abandoned coal mines, N2O from
industrial wastewater, and high-GWP emissions from the manufacture of electrical equipment.
Information on these sources is partially included because historical and projection data taken from
country-reported inventories and national communication may include emissions data from one or
more of these sources. EPA did not calculate tier 1 estimates for these sources where it was missing,
nor subtract out values from country reports where it was included. For this reason, the sources
covered by the wastewater, electric power systems, and coal mine estimates may be slightly different
between countries with country-reported emissions versus tier 1 estimates. In addition, natural
sources of non-CO2 emissions are not included in this report because policies focus on
anthropogenic emissions sources as opposed to natural sources which include long-term
background levels of GHG emissions.4
1.4 Region Groupings
Countries in this report have been grouped for the purpose of charts and analysis. These regions are
defined based on a combination of geographic regions and OECD membership status:
4 For more information see EPA Report 430-R-10-001 "Methane and Nitrous Oxide Emissions from Natural Sources."
August 2011
I. Introduction and Overview
Page 1-4
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OECD
non-OECD Asia,
non-OECD Europe and Eurasia,
Africa,
Central and South America, and
the Middle East.
OECD membership status is used as of November, 2010. At that time, Chile, Israel, and Slovenia
had recently joined the OECD. Chile and Israel are included in the OECD as opposed to Central
and South America and Middle East regions. Likewise, Slovenia is included in the OECD as
opposed to the non-OECD Europe and Eurasia region.
August 201 I I. Introduction and Overview Page 1-5
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Exhibit 1-2: Regional Groupings
OECD
Australia A Germany AE Luxembourg AE Slovenia AE
Austria A E Greece A E Mexico South Korea
Belgium AE Hungary AE' Netherlands A E Spain A E
Canada A Iceland A New Zealand A Sweden A E
Chile Ireland AE Norway A Switzerland A
Czech Republic A E Israel Poland AE Turkey A
Denmark A E Italy A E Portugal A E United Kingdom (UK) A E
Finland AE Japan A SlovakiaA E United States (U.S.) A
France A E
V
f~ "*X
Non-OECD Europe &
Eurasia
Albania
Armenia
Azerbaijan
Belarus A
Bulgaria A E
Croatia A
Estonia AE
Georgia
Kazakhstan
Kyrgyzstan
Latvia A E
Lithuania AE
Macedonia
Moldova
K A A
Monaco
_ A F
Romania '
Russia A
Tajikistan
Turkmenistan
Ukraine A
Uzbekistan
"Rest of Non-OECD
Europe & Eurasia" ''2
Africa
Algeria °
Congo (Kinshasa)
Egypt
Ethiopia
Nigeria °
Senegal
South Africa
Uganda
"Rest of Africa" ' 2
Central and South
America
Argentina
Non-OECD Asia
Bangladesh
Burma
Cambodia
China
India
Indonesia
Laos
Mongolia
Nepal
North Korea
Pakistan
Philippines
Singapore
ai an
Victrism
"Rest of Non-OECD Asia"
Bolivia
Brazil
Colombia Middle East
Ecuador ° ! o
Iran
Peru imn o
v,i u Iraq
Uruguay Jordan
Venezuela ° Kuwait °
"Rest of Central and South Saudi Arabia °
America"1' 2 United Arab Emirates °
"Rest of Middle East" ' 2
V J V s
Codes:
A - Annex 1 Countries E - European Union Countries O - OPEC Countries
Notes:
1 . The complete list of countries included in the "Rest of groupings can be found in Appendix 1.
\
/
X
1,2
^
2. In this report, when emissions totals are presented for a region, the regional sum includes the estimates for all of the individually
reported countries and the aggregated value for the "Rest of countries. For example, the emissions total for the "Middle
East"
found in the graphs and Appendices A through D, includes the sum of Iran, Iraq, Israel, Jordan, Kuwait, Saudi Arabia, the United
Arab Emirates and the smaller emitters already aggregated under "Rest of Middle East".
These regional country groupings are further defined in Exhibit 1-2 and Appendix I.
Page 1-6
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1.5 Approach
In this report, EPA presents historical emission estimates for individual countries for 1990, 1995,
2000, and 2005. Projected emissions, assuming no additional reduction measures, were estimated
from 2010 to 2030, also at five-year intervals. In addition to the individual country data, EPA
presents overall trends by region, gas, and source category and explanations for why these trends are
projected.
The general approach for developing the estimates used a combination of country-prepared,
publicly-available reports of emissions and calculations based on activity data and default emission
factors. The base year for projections was 2005. Estimates from 1990 to 2005 are the historical
period and estimates of actual emissions. Estimates from 2010 to 2030 are projections. Emissions
projections required a range of assumptions about economic activity, technology development, and
emissions reductions, and other factors.
The projections represent a business as usual (BAU) scenario where currently achieved reductions
are incorporated and future mitigation actions are included only if either a well-established program
or an international sector agreement is in place. Estimates in this report are presented at the source
category level; therefore, only policies and programs that affect source level emissions directly were
reflected in the BAU projections. For example, the reductions attributable to the EU landfill
directive regulations, U.S. sector level voluntary programs, and international sector agreements such
as the World Semiconductor Council agreement were reflected in BAU projections presented here.
The reductions associated with Kyoto commitments and Copenhagen reduction pledges were not
reflected in projections by GHG or source category because these are country level goals that are
difficult to disaggregate to the required degree.
Data Sources
The three primary types of data used in this report are country-prepared emissions reports, activity
data, and default emission factors. Country-reported data include Annex I inventory submissions to
the UNFCCC Secretariat which consist of a National Inventory Report (NIR) and Common
Reporting Format (CRF), National Communications to the UNFCCC, and/or other country
prepared reports. The preferred source for historical data was the UNFCCC flexible query system
(UNFCCC, 2009) since this database provides updated GHG emission estimates for most Annex I
Parties and to a lesser extent the latest GHG emission estimates for non-Annex I Parties.5 National
Communications were the preferred source for projections and non-Annex I historical emission
estimates. The Fifth National Communications were available for most Annex I Parties. For non-
Annex I countries, a majority have submitted their First National Communications, 29 had Second
National Communications, and one country had both a Third and Fourth National
Communications. The estimates in the UNFCCC inventory submissions and National
Communications for each reporting Party are comparable because they rely on the IPCC
methodologies and are reported for IPCC-designated source categories which generally follow the
categories shown in Table 1-2.
For most Annex I Parties, a full historical time series of emissions inventories was available from
national inventory reports. In some cases, this report also used emissions projections provided by
5 Annex I Parties include the industrialized countries that were members of the OECD in 1992, plus countries with
economies in transition (the CEIT Parties), including the Russian Federation, the Baltic States, and several Central and
Eastern European States. Annex I countries are noted in Exhibit 1-2.
August 201 I I. Introduction and Overview Page 1-7
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Annex I Parties in their National Communications. However, in many cases emissions projections
from National Communications use aggregated or differing categories which make them difficult to
use for disaggregated source-specific projections. Non-Annex I Parties do not file yearly national
inventory reports, but they do produce National Communications. Those National Communications
include historical inventories and projections in some cases. However, most non-Annex I countries
provided their most recent National Communication prior to 2005, meaning some historical period
emissions data use projections and calculations.
In addition to country-reported data, this report utilized international activity data sources and
default emission factors. For example, activity data sources included coal and oil production
compiled by the International Energy Agency, primary aluminum production compiled by the U.S.
Geological Survey, fertilizer usage and crop production compiled by the Food and Agriculture
Organization, and population and GDP data and projections. Information on data sources used for
each emissions source can be found in Section 7. Activity data were used with default emission
factors provided in IPCC emissions calculation guidelines to estimate emissions. In some cases,
projections of activity data were available. In other cases, growth rates were extrapolated from
historical data.
Emissions Calculations
If nationally developed emission estimates were unavailable or if the data were insufficient, EPA
estimated historical emissions and projections using the default methodologies presented in the
IPCC Guidelines (available at: http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html) and the
IPCC Good Practice Guidance (available at: http://www.ipcc-nggip.iges.or.jp/public/gp/english/).
EPA used IPCC Tier 1 methodologies and available country or region-specific activity data to
estimate emissions. Some of these calculations relied on population estimates provided by the U.S.
Census International Database and GDP estimates from the U.S. Department of Agriculture.
IPCC guidelines provide three tiers of calculation methods which provide different levels of
accuracy based on available data. Tier 1 methodologies are the simplest methods, requiring the least
data but have the greatest uncertainty. Tier 1 estimates usually involve activity data statistics
multiplied by a default emission factor.
Many sources and countries had some years for which country-reported data is available, and others
for which calculations were necessary. In most of these cases, growth rates were calculated using
Tier 1 methodology (historical or projected activity data and Tier 1 emission factors); these rates
were then applied to country reported estimates to project emissions. One advantage of this
approach is that it avoids reporting discontinuities or changes in emissions because of changes in
methodology. It also implicitly uses emission factor information from country-reported emissions
data, which may use more accurate methodologies than the Tier 1 calculations. The disadvantage,
however, is that some emission estimates are a hybrid of country-reported and calculated emissions.
A detailed description of the methodology used for each country and source category can be found
in Section 7 and Appendix F.
1.6 Limitations
Although careful and consistent methods have been used to produce the emissions estimates in this
report, they have limitations. First, some data were not incorporated into the estimates due to
methodological and time limitations. In addition, the methods entail significant uncertainty. Third,
August 201 I I. Introduction and Overview Page 1-8
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policies and economic development are likely to diverge from the business-as-usual assumptions that
were used to construct the projections.
This report primarily uses recent information available as of April 2010. More recent estimates of
emissions and activity data are available for some countries and sectors, but were not incorporated
due to time limitations. These more recent information include GHG emission estimates from
Annex I national inventory report submissions for 2010, several non-Annex I National
Communications, emission estimates from biomass burning from EDGAR (the Emission Database
for Global Atmospheric Research), energy and fuel use data from IEA (Energy Balances of OECD
and Non-OECD Countries), projections of energy and fuel use from lEA's World Energy Outlook
and EIA's International Energy Outlook, population estimates from the U.S. Census, and GDP
estimates from USDA. In addition, some data sources were not used because of methodological
limitations or because time was not available to develop calculations to utilize those sources. For
example, National Communications often present aggregated emissions projections, which are
difficult to use to project emissions by source.
The projections are sensitive to changes in key assumptions regarding technological changes and
production/consumption patterns. For example, the emission rates of new equipment using ODS
substitutes are likely to be much lower than the emission rates of older equipment. This newer
equipment is only now being phased in, and the long-term emission characteristics are not yet well
known. In the agriculture sector, the effect of changing consumer preferences on product demand,
such as increased beef consumption, is difficult to predict and creates large uncertainties in the
projected emissions from many of the agricultural sources. In general, Tier 1 calculations include
significant uncertainty because they do not utilize detailed information but instead use average
emission rates for a category.
While efforts have been made to provide projected emissions on a consistent basis, the distinction
between currently achieved GHG reductions from climate mitigation measures in place and those
from additionally planned activities is not always clearly defined in the reported data. The inclusion
of incidental GHG reductions in projected emissions as a result of climate related actions or
government polices still in development is a possibility in some isolated cases. However, due to the
consistent approaches established for reporting projected data and policies and measures in the
National Communications, the information developed from these sources is generally considered
comparable.
The projections in this report used BAU assumptions. However, many countries have already
committed to actions to reduce their emissions below the BAU level. The extent to which actions
will affect CO2 and non-CO2 emissions is uncertain. In addition, the projections used constant
emission factors, which do not account for future changes in emission rates due to technological
development (such as low-emissions technologies).
For all these reasons, uncertainty in the emissions projections is significant. Care should be used in
examining emissions projections for a single country or source, especially in examining small
changes for which uncertainty can alter conclusions. Nonetheless, EPA believes that these estimates
and projections represent a reasonable and detailed approximation with the data and resources
available.
August 201 I I. Introduction and Overview Page 1-9
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1.7 Organization of this Report
The remainder of this report expands upon the results of this analysis in six main sections. Section 2
presents a summary of global emissions and briefly discusses global trends. Sections 3 through 6
present source descriptions and emission estimates for CH4, N2O, and high-GWP emissions for
each of the following sectors: energy, industrial processes, agriculture, and waste. Within each of
these chapters, the discussion is divided into key sources that contribute to non-CO2 GHG
emissions. These source category discussions present an overview of global emissions for that
category and regional trends for 1990 to 2030. Section 7 presents the methodology used to collect
the most recent emissions inventory and projection data, and the data sources and methods used to
adjust the available data for each country. The appendices include detailed emission estimates by
country, sector and source; a description of methodologies applied for each country and source; data
sources used; future mitigation measures included for some sources; regional definitions; and a
description of EPA's Vintaging Model Framework used to estimate emissions of ODS substitutes in
the U.S.
August 201 I I. Introduction and Overview Page I -10
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2 Summary Results
2.1 Summary Estimates
Between 1990 and 2005, global non-CO2 emissions grew by 10 percent from 9,909 to 10,928
MtCO2e and are expected to grow approximately 45 percent from 2005 to 2030. This projection
represents a BAU scenario in which currently achieved reductions are incorporated but future
mitigation actions are included only if either a regulation, well-established program, or an
international sector agreement is in place.1 Historical emissions of CH4 have increased 8 percent
(from 6,304 to 6,837 MtCO2e), N2O emissions increased 4 percent (from 3,355 to 3,477 MtCO2e),
and high-GWP emissions increased 146 percent (from 249 to 613 MtCO2e) from 1990 to 2005.
Emissions of high-GWP gases are projected to increase 377 percent from 2005 to 2030, much faster
than CH4 (25 percent) andN2O (25 percent).
Historical emission trends for CH4 and N2O are the cumulative effect of several drivers. Although
basic activities (waste generation and landfilling, energy production and consumption, etc.) have
predominantly increased, several factors have mitigated emission growth. First, recovery and use of
CH4 has reduced these emissions in many countries. Second, sectoral level restructuring has
decreased emissions. Finally, economic restructuring in several countries, such as Russia and
Germany, caused a decrease in emissions in the 1990s. Since 2000, emissions have increased due to a
number of factors, driven largely by 1) economic and sectoral growth in recently restructured
countries and sectors, and 2) only partial mitigation coverage in the BAU projections (as described
above). High-GWP emissions, although relatively small in 1990, have increased substantially as
HFCs have been deployed as substitutes for the ozone-depleting substances (ODS) that are being
phased out globally under the Montreal Protocol. This historical deployment of HFCs has taken
place primarily in developed countries, where hydrofluorocarbon (HCFC) phaseout regulations have
been promulgated, although emissions are also now present in developing countries where HFCs are
being used as direct replacements for the globally-phased out chlorofluorocarbons (CFCs) in some
technologies (e.g., air conditioning for passenger cars).
Projections of future growth in emissions of non-CO2 gases are driven by several factors. Countries
with fast-growing economies and populations are expected to contribute more to the global CH4
and N2O totals as their economies grow, energy consumption increases, and waste generation rates
increase. Countries with more steady-state economies, and small or even declining population
growth rates, are likely to experience minimal growth in CH4 and N2O emissions. The large increase
in high-GWP emissions stems predominately from the increase in use of HFCs as substitutes for
ozone depleting substances. While this trend has largely been observed only for OECD countries to
2005, throughout the projection period all regions are projected to have increases in HFC emissions,
as more countries transition away from ODSs amidst strong global growth in demand expected for
refrigeration and air conditioning and other technologies that utilize HFCs in lieu of ODSs. While
emissions of HFCs used as substitutes for ODSs are increasing, the ODSs which HFCs replace are
1 Estimates in this report are presented at the source category level, therefore, only policies and programs that affect
source level emissions directly are reflected in the BAU projections. For example, the reductions attributable to the EU
landfill directive regulations, U.S. sector level voluntary programs, and international sector agreements such as the World
Semiconductor Council agreement are reflected in BAU projections presented here. The reductions associated with
Kyoto commitments and Copenhagen targets are not taken into account because these are country level goals that are
difficult to disaggregate to the source category level.
August 201 I 2. Summary Results Page 2-1
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also greenhouse gases, in many cases more potent than the substitutes. Thus, although emissions of
HFCs used as substitutes of ODSs are increasing, the radiative forcing from the CFCs and HCFCs
they replace would have been much higher had the phaseout of ODSs not taken place.2
Exhibit 2-1: Global Non-CO2 Emissions, by Gas (MtCO2e)
18,000
o
U
c
o
HighGWPs
DN2O
CH4
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
2.2 Trends by Region
Exhibit 2-2 shows the regional contribution of emissions from 1990 to 2030. Between 1990 and
2005, emissions grew from Africa, Central and South America, the Middle East, and non-OECD
Asia, while falling from the OECD and non-OECD Europe and Eurasia regions. By 2030, BAU
emissions of non-CO2 GHGs are projected to increase in every region compared to 2005 emissions.
Emissions are projected to grow the fastest in non-OECD Asia, the Middle East, and the OECD.
Table 2-1 displays decadal growth rates by region from 1990 to 2030.
2 For an estimate of the climate benefits of phasing out ODSs, see Velders et al. (2007).
August 2011
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Exhibit 2-2: Total Global Non-CO2 Emissions, by Country Grouping (MtCO2e)
18,000
o
I/I
c
o
D Middle East
DCentral and South America
D Africa
Non-OECD Europe & Eurasia
ONon-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
Table 2-1: Percent Change in Total Global Non-CO2 Emissions, by Decade and Region
Region
OECD
Non-OECD Asia
Non-OECD Europe & Eurasia
Africa
Central and South America
Middle East
Total
1990-2000
1.2%
12.1%
-30.2%
0.6%
10.2%
47.0%
1.5%
2000-2010
2.3%
26.9%
9.8%
20.5%
19.8%
24.9%
16.3%
2010-2020
14.6%
17.2%
11.1%
10.5%
10.1%
15.6%
14.0%
2020-2030
15.8%
29.1%
9.8%
11.5%
9.2%
18.4%
18.5%
1990-2030
37.3%
1 1 5.2%
-6.5%
49.2%
58.8%
151.3%
59.4%
Non-CO2 emissions from the OECD decreased by 2 percent from 1990 to 2005 (2,795 to 2,752
MtCO2e), while GDP grew by 44 percent.3 Several initiatives took place during this period which
had the effect of reducing emissions. Some of the most significant were increasing control of
emissions from nitric acid, adipic acid, and HCFC-22 manufacturing facilities, tailpipe emissions
from vehicles, and capture and combustion of landfill gas. Coal production declined significantly in
the EU, which decreased emissions from coal mining. Emissions from OECD countries are
projected to increase 39 percent (from 2,752 to 3,837 MtCO2e) from 2005 to 2030. This scenario
does not take into account economy-wide programs to control GHG emissions or country
emissions reduction pledges. While some emissions reduction activities that have been successful in
3 EIA, 2009. GDP is expressed in constant 2005 dollars, at market exchange rates. Table A4 from the International
Energy Outlook 2009.
August 2011
2. Summary Results
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the OECD in the past will likely continue to be significant, large additional reductions in those areas
are less likely since many-cost effective options have already been implemented.
The non-OECD Europe and Eurasia region includes many countries from the former Soviet Union
which underwent significant economic changes since 1990. Non-CO2 emissions from this region
dropped 27 percent between 1990 and 1995, and stayed at approximately this level through 2005.
The emissions decline can be attributed to economic contraction, with GDP in 2005 2 percent lower
than 1990, as well as changes in industry structure that accompanied the change to market
economies. From 2005 to 2030, emissions from this region are projected to grow 28 percent, which
would result in emission totals nearly reaching 1990 levels.
Non-OECD Asia has grown quickly from 1990 to 2005, both in terms of economy and emissions.
Over this period, non-CO2 emissions grew 28 percent (2,752 to 3,525 MtCO2e), while GDP grew by
178 percent, nearly tripling the previous level. International offset projects have been concentrated
in this region, and especially in the HCFC-22 manufacturing sector, but emissions in this sector have
continued to increase. Because national inventory reports are not available from the largest emitters
in this region, historical emissions have been estimated using activity data and IPCC default emission
factors. Recent initiatives to close small mines in China may be reducing CH4 emissions from the
coal mining sector. From 2005 to 2030, non-CO2 emissions are projected to grow by 101 percent,
with GDP more than quadrupling (increasing by 327 percent). Two factors are expected to cause
ODS substitute emissions to grow significantly: the phase-out of ODSs and the increasing use of air
conditioning and refrigeration as economies grow. Emissions from many industries are expected to
grow in parallel with economic expansion.
Non-CO2 emissions from Africa grew 11 percent between 1990 and 2005. GDP in Africa grew 57
percent over the same period. The pattern of emissions is quite different in Africa than other
regions. Sources with significant emissions and growth over this period include savanna burning
(included in other agricultural sources), biomass burning, natural gas and oil, stationary and mobile
combustion, landfills and wastewater. Emissions from Africa are projected to increase 34 percent
from 2005 to 2030, while GDP is expected to triple over this time. As African economies develop,
technologies used are likely to change substantially, impacting non-CO2 emission trajectories. Such
changes aren't generally accounted for in the BAU projections.
Between 1990 and 2005, emissions from Central and South America4 grew 33 percent, while GDP
grew by 55 percent. About 83 percent of non-CO2 emissions in Central and South America are
attributed to the Agriculture sector, a much higher proportion than other regions. By 2030,
emissions from the region are projected to increase 20 percent, a slower rate than any other region
except the non-OECD Europe and Eurasia region. GDP is expected to grow 157 percent over the
projection period, slower than any of the other non-OECD regions.
Emissions from the Middle East region grew 68 percent from 1990 to 2005. While, this rate of
growth is the highest of any region, emissions from the Middle East comprise only 4 percent of the
world total in 2005. Over half of non-CO2 emissions from the Middle East (on a CO2 equivalent
basis) are CH4 emissions from the natural gas and oil sector; thus the emissions trend for the region
4 The Central and South America region excludes Chile, which recently joined the OECD and is included in that region.
5 The Middle East region excludes Israel, which recently joined the OECD and is included in that region.
August 201 I 2. Summary Results Page 2-4
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is highly correlated with trends in oil and gas production. From 2005 to 2030, emissions from the
region are projected to grow by 50 percent.
2.3 Trends by Gas, Sector, and Source Category
Emissions sources are grouped into four economic sectors: energy, industrial processes, agriculture
and waste. While CO2 emissions are concentrated in the energy sector, agriculture accounts for the
largest share of non-CO2 emissions (56 percent of emissions in 2005). The energy, waste, and
industrial processes sectors account respectively for 25 percent, 12 percent, and 7 percent of
emissions in 2005. However, emissions from industrial processes are growing at a faster rate than
emissions from the other sectors.
The agricultural sector is the largest source of non-CO2 emissions, as illustrated in Exhibit 2-3.
Emissions from agricultural sources accounted for 60 percent of global non-CO2 emissions in 1990,
and remain the largest contributor of emissions in 2030. However, by 2030 the sector's share is
expected to decrease to 46 percent. Agricultural sector emissions have increased 3 percent between
1990 and 2005 (from 5,920 to 6,112 MtCO2e). Emissions from Africa, the OECD, and non-OECD
Europe and Eurasia have declined, while emissions from Central and South America, the Middle
East, and non-OECD Asia have increased. Emissions from the agricultural sector are projected to
further increase 20 percent by 2030 (to 7,314 MtCO2e). Emissions from all regions are expected to
grow between 2005 and 2030. The largest emissions sources within the agricultural sector are N2O
emissions from agricultural soils and CH4 from enteric fermentation, which combined account for
60 to 68 percent of non-CO2 emissions from agriculture. Exhibit 2-4 shows trends for the largest
sources of non-CO2 emissions.
Exhibit 2-3: Total Global Non-CO2 Emissions, by Sector (MtCO2e)
18,000
I 6,000
o
U
o 8,000 -
*<7i
VI
'I 6,000
UJ
D Waste
Industrial Processes
D Energy
Agriculture
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
August 2011
2. Summary Results
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Energy sector emissions are the second largest source of non-CO2 emissions, accounting for 23 to
25 percent of non-CO2 emissions in the 1990 to 2030 period. Emissions from the energy sector
increased 17 percent between 1990 and 2005 (from 2,316 to 2,699 MtCO2e). Natural gas and oil
systems contributed to over 50 percent of the non-CO2 emissions from the energy sector in 2005.
From 2005 to 2030, energy sector emissions are projected to increase 40 percent (to 3,777 MtCO2e).
The industrial processes sector includes all emissions of high-GWP gases. The sector also includes
N2O emissions from nitric and adipic acid production and other industrial process sources. In 1990,
nitric and adipic acid production accounted for 38 percent of non-CO2 emissions from the sector.
Between 1990 and 2005, emissions from nitric and adipic acid declined significantly due to the
installation of abatement equipment. However, emissions from production of HCFC-22 and ODS
substitutes increased over the same time period. Emissions from the industrial processes sector as a
whole have increased 56 percent between 1990 and 2005 (from 525 to 818 MtCO2e) and are
projected to grow even faster, nearly quadrupling between 2005 and 2030 (from 818 to 3,143
MtCO2e). This sectoral growth is driven by growth in emissions from ODS substitutes over this
period, due to the phase out of ODSs under the Montreal Protocol and strong predicted growth in
traditional ODS applications (e.g., refrigeration and air conditioning). As ODSs are phased out,
other gases, including HFCs and to a limited extent PFCs, are substituted. The rate of growth is
uncertain, however, because the choice of chemicals and potential new technologies or operating
procedures could eliminate or diminish the need for these gases. However, under the BAU scenario
without further controls, it is assumed that most users will switch to HFCs.
In the waste sector, the two largest sources of non-CO2 emissions are landfilling of solid waste and
wastewater, together contributing 92 percent of emissions throughout the 1990 to 2030 period. CH4
from landfills accounts for approximately 60 percent of emissions from waste. Increases in waste
generation and population drive global waste emissions upward but increases in waste-related
regulations and gas recovery and use are expected to temper this increase. Emissions from
wastewater are projected to grow more quickly than those from landfills, and are projected to
account for 34 percent of waste emissions by 2030. Projected wastewater emissions are driven by
population growth and the underlying assumption that growing populations in the developing world
are largely served by latrines and open sewers, rather than advanced wastewater treatment systems.
Exhibit 2-4 displays the breakdown of global non-CO2 emissions by source. Thirteen sources are
expected to contribute almost all (95 percent) of non-CO2 emissions in 2030. Four of these
sourcesagricultural soils, enteric fermentation, ODS substitutes, and natural gas and oil systems
are projected contribute over half (56 percent) of the global total.
August 201 I 2. Summary Results Page 2-6
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Exhibit 2-4: Global Non-CO2 Emissions, by Source (MtCO2e)
18,000
o
y
z
I/I
E
UJ
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
D Remaining I I Sources
D Biomass Combustion
D Manure Management
Wastewater
D Stationary and Mobile Combustion
Rice Cultivation
DCoal Mining Activities
Landfillingof Solid Waste
DHCFC-22 Production
D Other Agricultural Sources
D Natural Gas and Oil Systems
Use of ODS Substitutes
D Enteric Fermentation
Agricultural Soils
2.4 Other Global Datasets
Although non-CO2 global emissions data are not as prevalent as CO2 data, other datasets exist and
EPA has included information on those datasets for comparison. It should be noted that in some
cases, those datasets rely partly on either segments or earlier versions of the dataset presented in this
report. Additionally, the dataset presented in this report includes data on biomass burning taken
from the Emission Database for Global Atmospheric Research (EDGAR).
Table 2-2 and Exhibit 2-5 present global historical and projected emissions of CH4, N2O, and high-
GWP gases for 2000, 2010, 2020, and 2030 from the following sources:
Energy Management Forum 22 (EMF-22) Analysis (EMF-22, 2009).6
CCSP Synthesis and Assessment Product 2.1 - Scenarios of Greenhouse Gas Emissions and
Atmospheric Concentrations (CCSP, 2007).7
Emission Database for Global Atmospheric Research (EDGAR) 4.1 (EC-JRC, 2010).
The data compiled for EMF-22 share many of the data sources and methods EPA employed in this
report for CH4 and N2O. SAP 2.1 presents 15 scenarios that make different assumptions about
(among other things) economic and population growth rates, energy sources, environmental policies,
and future technologies. This report uses the three reference scenarios in the comparison table
6 Used "Reference" scenario for all models, which include ETSAP-TIAM, FUND, GTEM, MERGE Optimistic,
MERGE Pessimistic, MESSAGE, MiniCAM - BASE, MmiCAM - Lo Tech, POLES, SGM, and WITCH.
7 Ranges depicted include estimates for the three reference scenarios, IGSM, MERGE, and MINICAM.
August 2011
2. Summary Results
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below. The EDGAR 4.1 estimates emissions by country and source applying technology-based
emission factors that take into account assumptions for country-specific activity data and abatement
technologies. For EMF-22 and CCSP SAP 2.1, minimum and maximum values of reference
scenarios are compared against, which varies by model. Although there are differences among
individual numbers, the trends and relative magnitudes are similar.
Table 2-2: Comparison of non-CO2 Emission Estimates in this Report (EPA 201 I) to Other Global
Inventories (MTCO2e)
Source
EPA (2011)
EMF-22 (2009)a
CCSP SAP 2.1 (2007)b
EDGAR 4.1 20IOC
2000
10,060
7,164-10,826
9,438-11,327
9,804d
2010
1 1 ,702
8,538-12,857
9,939-12,687
NE
2020
13,337
7,891-14,758
11,348-15,205
NE
2030
1 5,800
8,685-17,188
12,268-17,064
NE
Codes: NE indicates "not estimated."
Notes:
1 Energy Management Forum 22 (EMF-22) Analysis (EMF-22, 2009).
bCCSP Synthesis and Assessment Product 2.1 - Scenarios of Greenhouse Gas Emissions and Atmospheric
Concentrations (CCSP, 2007) - Ranges depicted include estimates for the three reference scenarios (IGSM, MERGE,
and MINICAM)
c Emission Database for Global Atmospheric Research (EDGAR) 4.1 (EC-JRC, 2010).
d 97 metric tons of CyFie not included in total; unknown GWP.
Comparison of non-CO2 Emission Estimates in EPA (201 I) to Other Global Inventories
EPA(2011)
D EMF-22 Analysis (2009) min
D EMF-22 Analysis (2009) max
CCSP SAP 2.1(2007) min
CCSP SAP 2.1(2007) max
EDGAR 4.1(2010)
2000
2010
2020
2030
August 2011
2. Summary Results
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3 Energy
This chapter presents global CH4 and N2O emissions for 1990 to 2030 for the following energy
sector sources:
Natural Gas and Oil Systems (CH4)
Coal Mining Activities (CH4)
Stationary and Mobile Combustion (CH4, N2O)
Biomass Combustion (CH4, N2O)
Other Energy Sources (CH4, N2O), including:
Waste Combustion (CH4, N2O)
Fugitives from Solid Fuels (N2O)
Fugitives from Natural Gas and Oil Systems (N2O)
The energy sector is the second largest contributor to global emissions of non-CO2 greenhouse
gases, accounting for 25 percent of emissions in 2005. In 1990, the energy sector accounted for
2,316 MtCO2e of non-CO2 GHG emissions. Between 1990 and 2005, non-CO2 emissions from the
energy sector have grown 17 percent, to 2,699 MtCO2e. Emissions from this sector are projected to
further increase 40 percent by 2030 to 3, 777 MtCO2e. Exhibit 3-1 shows energy sector emissions by
source. Fugitive emissions from natural gas and oil systems are the largest source of non-CO2 GHG
emissions from the energy sector, accounting for 54 percent of energy-related emissions in 2005.
The next largest source in this sector is emissions from coal mining activities, accounting for 19
percent of energy related emissions in that year.
August 201 I 3. Energy Page 3-1
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Exhibit 3-1: Total Non-CO2 Emissions from the Energy Sector, by Source (MtCO2e)
4,000
DOther Energy Sources
D Biomass Combustion
Stationaryand Mobile Combustion
DCoal Mining Activities
Natural Gas and Oil Systems
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
Several key factors play a role in emission trends from the energy sector as a whole: economic
restructuring in Eastern Europe and the Former Soviet Union (FSU), and several key coal mining
countries; a shift from coal to natural gas as an energy source in several regions; and expansive
growth in energy consumption in less developed regions. These effects are further discussed within
each source discussion.
Exhibit 3-2 displays energy sector emissions by region. In 1990, the regions with the most emissions
were non-OECD Europe and Eurasia and the OECD, accounting for 29 percent and 28 percent
respectively of global emissions. Between 1990 and 2005, this pattern shifted, however, as emissions
declined in these two regions while increasing in other regions. In 2005, non-OECD Asia accounted
for 26 percent of global emissions. Emissions in all regions are expected to increase over the
projection period of 2005 to 2030, but emissions from non-OECD Asia, Africa, and Central and
South America will grow more quickly than non-OECD Europe and Eurasia or OECD regions.
August 2011
3. Energy
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Exhibit 3-2: Total Non-CO2 Emissions from the Energy Sector, by Region (MtCO2e)
4,000
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
3.1 Natural Gas and Oil Systems (CH4)
3.1.1 Source Description
CH4 is the principal component of natural gas (95 percent of pipeline quality natural gas) and is
emitted from natural gas production, processing, transmission and distribution. Oil production and
processing upstream of oil refineries can also emit CH4 in significant quantities since natural gas is
often found in conjunction with petroleum deposits. In both oil and natural gas systems, CH4 is a
fugitive emission from leaking equipment, system upsets, and deliberate flaring and venting at
production fields, processing facilities, natural gas transmission lines and compressor stations,
natural gas storage facilities, and natural gas distribution lines.
Emissions calculations for this source utilize international statistics on production and consumption
of natural gas and oil. Default emission factors relate emissions to energy product flows through
different industry segments. Default emission factors differ between developed and developing
countries.
The emissions projections presented in this report rely on IPCC Tier 1 calculations and country
reported inventory data. Specifically, the U.S. projections are based on the updated emissions data
published in the 2011 U.S. Greenhouse Gas Inventory. The 2011 U.S. Inventory incorporates
updated industry data and several improvements in emission calculation methodologies for a
number of source categories. With respect to natural gas and oil systems, these enhancements lead
to a significant increase in methane emissions in all years from this source category. It is important
to note that this update was not applied to other countries historical or projected data.
August 2011
3. Energy
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International voluntary programs encourage measures which can reduce CH4 emissions without
reducing energy production, but those mitigation programs are not explicitly included in the
estimates. Mitigation measures include installing equipment designed to minimize CH4 emissions,
retrofitting existing equipment and conducting inspection and maintenance regimes to identify,
quantify and repair leaks.
3.1.2 Source Results
Between 1990 and 2005, global CH4 emissions from natural gas and oil systems are estimated to
have increased by about 26 percent, from 1,165 to 1,463 MtCO2e (see Table 3-1). Underlying this
trend have been increases in natural gas and oil production. Over this time period, emissions have
declined modestly in OECD countries (see Exhibit 3-3). Emissions declined in non-OECD Europe
and Eurasia between 1990 and 1995, but have risen gradually since then. Significant percentage
increases in emissions have occurred in other regions, especially in Africa and the Middle East,
where emissions nearly doubled between 1990 and 2005.
From 2005 to 2030, emissions are projected to increase by about 35 percent, from 1,463 to 1,972
MtCO2e. This projection corresponds to increases in natural gas and oil production from 2005 to
2030. Emissions are expected to increase in all regions. Emissions from non-OECD regions are
expected to grow about twice as fast as those from the OECD over the projection period.
Table 3-1: Total CH4 Emissions from Natural Gas and Oil Systems (MtCO2e)
Gas
1990
1995
2000
2005
2010
2015
2020
2025
2030
Total CH4 |,|65.4 1,219.8 1,325.7 1,462.6 1,595.2 1,699.5 1,788.9 1,887.0 1,971.6
Exhibit 3-3: CH4 Emissions from Natural Gas and Oil Systems 1990 - 2030 (MtCO2e)
2,500
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
D Non-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
Emissions in OECD countries are expected to grow more slowly from 2005 to 2030 than emissions
August 2011
3. Energy
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in non-OECD regions. Natural gas production is expected to increase in countries such as the
United States and Australia, whereas production is expected to decline in European OECD
countries. In the United States, advances in production technology have allowed exploitation of vast
shale gas reserves to production. By contrast, in Europe production of tight gas, shale gas, and
coalbed CH4 are not sufficient to offset declining production. Most oil production has already
matured in the OECD. However, it is expected to increase in the U.S. and Canada because of
expanded use of enhanced oil recovery and unconventional production such as from oil sands.
Increasing consumption of natural gas also contributes to future increases in emissions from natural
gas and oil systems in the OECD countries. (EIA, 2009)
Non-OECD Europe and Eurasia emit more from this source than any other region, and are
expected to grow 33 percent from 2005 to 2030. Russia accounts for most natural gas production in
this region, and has larger reserves of natural gas than any other country in the world. Production of
natural gas and oil in Russia are expected to increase, driving the emissions increase 27 percent by
2030.
In the Middle East and Africa, emissions have grown by over 80 percent from 1990 to 2005. Natural
gas consumption has increased substantially in recent years. Consumption is expected to continue to
grow, but not as quickly as in recent years. The Middle East accounts for 40 percent of proved
natural gas reserves, and future production increases are expected in the Middle East and Africa.
(IEO 2010)
The largest natural gas consumption increases are expected in non-OECD Asia, particularly in China
and India. Natural gas consumption is also growing quickly in Central and South America.
(IEO2010) This growth partially accounts for expected emissions growth of 39 percent and 36
percent, respectively in these regions, from 2005 to 2030.
Actual future emissions may differ from these projections for several reasons. Efforts are underway
to modernize gas and oil facilities in Russia and many Eastern European countries, which could help
reduce fugitive emissions. In areas where gas production is projected to increase, emissions will not
necessarily increase at the same rate. As the world becomes more concerned with the emissions of
greenhouse gases, new legislation and voluntary carbon markets are developing to increase energy
production efficiency in the natural gas and oil industry. Projections of oil and natural gas
production and consumption are, by nature, highly uncertain. The uncertain future of gas prices
adds an additional level of uncertainty.
Current emissions calculations are based on quantity of oil and gas production and consumption.
However, leakage and venting do not necessarily increase linearly with throughput, and newer
equipment tends to leak less than older equipment. More accurate estimation methodologies would
make use of counts of equipment and country-specific emission factors, but such information is not
readily available for many countries. Even when more accurate methodologies are used, estimates
for this source have significant uncertainty.
3.2 Coal Mining Activities (CH4)
3.2.1 Source Description
CH4 is stored within the coal seams and the surrounding rock strata and is liberated when the
pressure above or surrounding the coal bed is reduced as a result of natural erosions, faulting, or
mining. CH4 is produced during the process of coalification, where vegetation is converted by
August 201 I 3. Energy Page 3-5
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geological and biological forces into coal. Because CH4 is explosive, it must be removed from
underground mines high in CH4 as a safety precaution.
The quantity of gas emitted from mining operations is a function of two primary factors: coal rank
and coal depth. Coal rank is a measure of the carbon content of the coal, with higher coal ranks
corresponding to higher carbon content and generally higher CH4 content. Coals such as anthracite
and semianthracite have the highest coal ranks, while peat and lignite have the lowest. Pressure
increases with depth and prevents CH4 from migrating to the surface and, as a result, underground
mining operations typically emit more CH4 than surface mining (EPA, 1993). In addition to
emissions from underground and surface mines, post-mining processing of coal and abandoned
mines also release
Emissions calculations for this source use international statistics on production of hard coal and
lignite, which are assumed to correspond to underground and surface mining, respectively. Default
emission factors are used which relate the quantity of coal mined to CH4 emissions. Abandoned
mines are not considered in this analysis due to a lack of data.
Voluntary programs encourage capture and utilization of coalbed CH4. The value of captured
methane is dependent on proximity to an end user or pipeline and the quality of gas extracted. This
analysis accounts for some CH4 recovery and use, although not all coal mine CH4 projects may be
accounted for (please see Section 7.1.2). The projection assumes that mitigation activities will
continue in the countries where coal mine methane projects have been documented.
As shown in Table 3-2, global CH4 emissions from coal mining are estimated to have increased 1
percent, from 512 MtCO2e to 515 MtCO2ebetween 1990 and 2005. Over this time period, total
primary coal production has increased. In 2005, coal mine methane projects in 12 countries
prevented emissions of about 35 MtCO2e, accounting for part of this divergence. The geographic
dispersion of emissions has shifted over the historical period between regions. Coal mine CH4
emissions have declined in the OECD, non-OECD Europe and Eurasia, while they have increased
in non-OECD Asia. Emissions in the Middle East, Africa, and Central and South America are small
compared to the other regions.
From 2005 to 2030, CH4 emissions from coal mines are projected to increase by 53 percent, from
515 MtCO2e to 790 MtCO2e. This projection assumes significant increases in coal production by
2030. While emissions in all regions are expected to increase, the rise in emissions is expected to be
much more significant in some regions than in others. Emissions in non-OECD Asia are expected
to increase relatively more quickly than in OECD and non-OECD Europe and Eurasia over the
projection period.
The non-OECD Asia region's CH4 emissions from coal mining have nearly doubled between 1990
and 2005, and are expected to increase by about 75 percent by 2030. This region includes China,
which has extensive coal resources and coal mining. China is expected to account for a majority of
the increase in world coal production over the projection period. The Chinese economy is growing
1 While emissions from abandoned coal mines were not explicitly estimated in this report, some countries report
emissions from abandoned mines within this source category. In these cases, this source category includes these
emissions.
August 201 I 3. Energy Page 3-6
-------
quickly and much of the increased electric power and industrial demand will be met by coal. The
decrease in coal mining CH4 emissions from 1995 to 2000 is caused primarily by mine closures and a
significant reduction in coal production during this time period.2 Between 1998 and 2002, the
government of China closed tens of thousands of small mines (Andrews-Speed et al, 2005). While
EPA's methodology captures the impact of these closures on overall production, the methodology
does not distinguish between mining at large and small mines. It is unclear how emissions intensity
may differ at various types of mines, and the extent to which production shifted from small to large
mines. Moreover, EPA does not estimate emissions from abandoned mines, so emissions resulting
from these closures are not reflected in the estimates. China and India, among other countries, have
extensive uncontrolled fires in their coal mining regions which may add to fugitive emissions, but are
not included in the estimates (Stracher and Taylor, 2004).
Table 3-2: Total CH4 Emissions from Coal Mining Activities (MtCO2e)
Gas
1990
1995
2000
2005
2010
2015
2020
2025
2030
Total CH4
511.5
444.5
392.9
515.3
583.8
628.6
673.6
730.9
790.2
Exhibit 3-4: CH4 Emissions from Coal Mining Activities 1990 - 2030 (MtCO2e)
900
800
-------
the U.S. decreased between 1900 and 2005, they are projected to follow an increasing trend after
2005.
The non-OECD Europe and Eurasia region also experienced a significant decrease in emissions
between 1990 and 2005, although emissions began rising again after 2000. In Russia and in Eastern
European coal producing countries, restructuring of the energy industries caused many of the
gassiest underground mines to close during the 1990s resulting in the decrease in emissions.
Emissions in this region are expected to increase through 2015, at which point they expected to
begin to level off.
Reductions due to CH4 recovery and use of coal mine CH4 will likely impact future emission
estimates. Reductions from coal mine CH4 projects could help slow or even decrease, emissions for
some countries even when coal production increases. Projecting the abatement due to future coal
mine CH4 projects is challenging (please see Section 7.1.2 for a discussion of how EPA has
accounted for some coal mine CH4 projects, and areas of uncertainty from such projects).
Emissions calculations in this section are based on coal production statistics, divided into hard coal
and lignite production. However, CH4 emissions are not necessarily directly related to production.
CH4 emissions occur not just during mining, but also during the pre-mining stage and after mining is
completed. In addition, the actual gas levels of a mine can vary significantly based on geologic
factors. More accurate estimation would include information on the gas levels of mines in particular
regions and mine operations in the pre-mining and post-mining stages.
3.3 Stationary and Mobile Combustion (CH4, N2O)
3.3.1 Source Description
N2O is a product of the reaction between nitrogen and oxygen during combustion of fossil fuels.
Both mobile and stationary sources emit N2O, and the volume emitted varies according to the type
of fuel, combustion technology, size and vintage (model year for mobile combustion), pollution
control equipment used, and maintenance and operating practices. Stationary and mobile
combustion also result in CH4 emissions and are primarily a function of the CH4 content of the fuel
and the combustion efficiency. However, combustion is a relatively minor contributor to overall
CH4and N2O emissions, representing just over 3 percent and 7 percent of global CH4 emissions in
2005, respectively.
Mobile combustion sources such as automobiles and airplanes emit N2O as an exhaust emission
from a variety of engine and fuel configurations. As with stationary sources, N2O emissions are
closely related to air-fuel mixtures and combustion temperature, as well as pollution control
equipment on transportation vehicles. Key factors affecting fuel consumption and, ultimately
emissions, for mobile sources include the distance traveled for vehicles, hours of operation for off-
road equipment, age of vehicles, and mode of operation. Road transport accounts for the majority
of mobile source fuel consumption, and as a result, the majority of mobile N2O emissions.
3.3.2 Source Results
Between 1990 and 2005, CH4 and N2O emissions from stationary and mobile have increased 13
percent, from 419 MtCO2e to 472 MtCO2e (Table 3-3). Total fossil fuel consumption has increased
over this time period. Emissions have decreased about 5 percent in OECD countries and by 10
percent among EU countries, while they have increased in other regions.
August 201 I 3. Energy Page 3-8
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From 2005 to 2030, CH4 and N2O emissions from stationary and mobile combustion are projected
to increase 53 percent, from 472 MtCO2e to 724 MtCO2e. This projection assumes steady increase
in fossil fuel consumption over the projection period. Emissions are expected to increase in all
regions except the OECD. CH4 and N2O emissions from combustion are expected to double in
non-OECD Asia during the projection period. The results for stationary and mobile combustion are
shown in Table 3-3, Exhibit 3-5, and Exhibit 3-6.
The increasing emissions in non-OECD Asia are driven by higher demand for and production of
energy and the increased use of automobiles. China and India are the main drivers of growth in this
region, and their emissions are expected to grow by 89 percent and 116 percent respectively in the
projection period.
In OECD countries, CH4 and N2O emissions from stationary and mobile combustion have
historically declined despite increasing energy use. This has been achieved through improvements in
combustion technologies and pollution controls. Unlike CO2 emissions, CH4 and N2O emissions
from combustion are highly dependent upon combustion conditions and not directly proportional
to fuel quantities combusted. Emissions in the OECD are expected to continue to decline despite
increasing energy use.
Table 3-3: CH4 and N2O Emissions from Stationary and Mobile Combustion (MtCO2e)
Gas
1990
1995
2000
2005
2010
2015
2020
2025
2030
CH4
N2O
Total
220.4
198.5
418.9
214.5
221.4
435.9
209.3
235.3
444.6
225.6
246.0
471.7
244.7
263.1
507.7
265.2
282.1
547.3
289.9
305.2
595.1
319.5
333.7
653.2
355.3
368.2
723.5
Exhibit 3-5: CH4 Emissions from Stationary and Mobile Combustion 1990-2030 (MtCO2e)
400
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
D Non-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
August 2011
3. Energy
Page 3-9
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Exhibit 3-6: N2O Emissions from Stationary and Mobile Combustion 1990-2030 (MtCO2e)
400
350
0
(N
0
C
UJ
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECD Asia
OECD
50
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
3.4 Biomass Combustion (CH4, N2O)
3.4.1 Source Description
CH4 and N2O are produced as a result of incomplete biomass combustion. Fuel wood, charcoal,
agricultural residues, agricultural waste, and municipal waste combustion are the major contributors
to CH4 and N2O emissions within this category. Biomass combustion in developing countries often
refers to the combustion of biofuels in small-scale combustion devices for heating, cooking, and
lighting purposes. In general, for developing countries the combustion of biomass in the residential
sector is the leading contributor of emissions for this source. In developed countries, biomass
combustion primarily refers to the combustion of biofuels in large-scale industrial processes (e.g.,
wood and wood products, pulp and paper), and to a lesser extent, in residential applications. Because
of the wide variety in the types and conditions under which these fuels are burned, estimates for this
category are highly uncertain and difficult to predict.
3.4.2 Source Results
Between 1990 and 2005, CH4 and N2O emissions from biomass combustion are estimated to have
increased by 13 percent, from 217 to 245 MtCO2e (Table 3-4). Over this time period, underlying
biomass combustion grew on an energy content basis. Liquid biofuel use has grown quickly, but
remains smaller than solid biomass or charcoal usage, which grew more slowly. Greenhouse gas
emissions from biomass combustion have grown significantly in Africa, while they have grown more
slowly in Central and South American and non-OECD Asia and declined in the OECD and non-
OECD Europe and Eurasia.
August 2011
3. Energy
Page 3-10
-------
As shown in Exhibit 3-7 and Exhibit 3-8, CH4 and N2O emissions from biomass combustion are
projected to increase by 17 percent from 2005 to 2030, from 245 to 288 MtCO2e. Underlying
biomass usage is assumed to increase over the same time period. Biomass combustion emissions are
expected to increase most quickly in OECD countries, while biomass emissions in other regions
grow more slowly. In OECD countries, projected emissions increase as a result of a projected
threefold increase in biomass use for combined heat and power production and in electricity-only
power plants (IEA, 2009). Despite being one of the largest contributors, total biomass emissions in
the non-OECD Asia region are projected to remain essentially flat between 2005 and 2030 due to a
decrease in biomass consumption in the residential sector. This decline is a result of the increased
industrialization in the region, and fuel switching from biomass to fossil fuels. The non-OECD Asia
region is set to play an increasingly important role in global energy markets as energy consumption
on a whole is projected to grow rapidly due to rapid economic and population growth, and
continuing urbanization and industrialization (IEA, 2009).
Table 3-4: Total CH4 and N2O Emissions from Biomass Combustion (MtCO2e)
[990F9952000200520102015202020252030
176.5 184.9 189.4 197.9 203.7 209.1 215.1 221.8 229.2
N2O 40.7 42.9 44.8 47.5 49.8 51.8 53.9 56.3 58.9
Total 217.2 227.8 234.2 245.4 253.5 260.9 269.0 278.1 288.1
August 201 I 3. Energy Page 3-1 I
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Exhibit 3-7: CH4 Emissions from Biomass Combustion 1990 - 2030 (MtCO2e)
250
200
l/l
C
O
UJ
ISO
100
50 -
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
Exhibit 3-8: N2O Emissions from Biomass Combustion 1990 - 2030 (MtCO2e)
70
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
D Non-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
August 2011
3. Energy
Page 3-12
-------
3.5 Other Energy Sources (CH4, N2O)
3.5.1 Source Description
This category includes emissions from the energy sector that contribute only a small fraction of total
overall emissions, but are reported by specific countries to the UNFCCC and are thus grouped
together in this report. The data presented here include the following three sources of CH4 and
N2O:
Waste Combustion (CH4,N2O)
Fugitives from Solid Fuels (N2O)
Fugitives from Natural Gas and Oil Systems (N2O)
3.5.2 Source Results
The results for this source are presented in Table 3-5. The OECD is by far the largest contributor to
this category, accounting for an average of 87 percent of emissions from 1990 through 2030. The
data presented in Table 3-5, are not fully comparable to data in the remainder of this report.
Emissions are included only for those countries which reported emissions, as opposed to other
sources which use a combination of calculated and country-reported data. Please see the
methodology section for further discussion of this source category.
Table 3-5: Total CH4 and N2O Emissions from Other Energy Sources (MtCO2e)
Gas
CH4
N2O
Total
1990
0.5
2.6
3.1
1995
0.6
3.1
3.7
2000
0.5
3.4
3.9
2005
0.5
3.5
4.1
2010
0.5
3.4
3.9
2015
0.5
3.4
3.9
2020
0.5
3.4
3.9
2025
0.5
3.4
3.9
2030
0.5
3.4
3.9
Exhibit 3-9 and Exhibit 3-10 illustrate trends in CH4 and N2O emissions for this source category.
August 201 I 3. Energy Page 3-13
-------
Exhibit 3-9: CH4 Emissions from Other Energy Sources 1990 - 2030 (MtCO2e)
0.7
0.6
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
D Middle East
DCentraland South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECDAsia
OECD
Exhibit 3-10: N2O Emissions from Other Energy Sources 1990 - 2030 (MtCO2e)
4.0
-------
4 Industrial Processes
This section presents non-CO2 emissions from the industrial processes sector for 1990 to 2030. The
industrial processes sector includes industrial sources of N2O and CH4, along with several sources of
high-GWP gases. High-GWP emissions covered in this section include HFCs used as substitutes for
ozone-depleting substances (ODSs) and industrial sources of HFCs, PFCs, and SF6. Initial estimates
of NF3 emissions from electronics manufacturing processes are also included as new sources in this
update. The categories and their GHG emissions presented in this section are as follows:
Adipic Acid and Nitric Acid Production (N2O)
Use of Substitutes for Ozone-Depleting Substances (HFCs)
HCFC-22 Production (HFCs)
Electric Power Systems (SF6)
Primary Aluminum Production (PFCs)
Magnesium Manufacturing (SF6)
Semiconductor Manufacturing (HFCs, PFCs, SF6, NF3)
Flat Panel Display Manufacturing (PFCs, SF6, NF3)
Photovoltaic Manufacturing (PFCs, NF3)
Other Industrial Processes Sources (CH4, N2O), including:
Chemical Production (CH4)
Iron and Steel Production (CH4)
Metal Production (CH4, N2O)
Mineral Products (CH4)
Petrochemical Production (CH4)
Silicon Carbide Production (CH4)
Solvent and Other Product Use (N2O)
The industrial processes sector was the smallest contributor to global emissions of non-CO2
greenhouse gases in 1990, accounting for only 5 percent of total emissions, but it has also grown the
fastest of all sectors. Between 1990 and 2005, non-CO2 GHG emissions from industrial processes
grew by 56 percent, and now account for 7 percent of global emissions. Emissions are projected to
grow even more quickly, nearly quadrupling between 2005 and 2030 to 3,143 MtCO2e (20 percent of
the global total). Exhibit 4-1 shows the industrial processes sector emissions by source. In 1990 and
1995, the largest source of non-CO2 emissions from this sector was adipic acid and nitric acid
production, which accounted for 38 percent of emissions in 1990. Between 1990 and 2005, HFC
emissions of substitutes for ODSs and HFC-23 emissions from HCFC-22 production have become
the most important sources within the sector. The increase in emissions of HFCs used as ODS
substitutes corresponds to decreasing use of CFCs and HCFCs, which they replace. CFCs and
August 201 I 4. Industrial Processes Page 4-1
-------
HCFCs are potent GHGs but, following international convention, their emissions are not included
here.
By 2030, emissions from adipic and nitric acid production are projected to account for only 5
percent of the sector's emissions, due to the mitigation efforts begun in the 1990s as well as large
increases in emissions from other sources.
Exhibit 4-1: Total Non-CO2 Emissions from the Industrial Processes Sector, by Source (MtCO2e)
D Other Industrial Processes Sources
Photovoltaic Manufacturing
D Flat Panel Display Manufacturing
Magnesium Manufacturing
D Semiconductors Manufacturing
D Primary Aluminum Production
D Operation of Electric Power Systems
HCFC-22 Production
D Use of Substitutes for Ozone
Depleting Substances
Adipic Acid and Nitric Acid
Production
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
During the 40-year period from 1990 to 2030, the replacement of ODSs with HFCs (and other
substitutes) will lead to decreases in emissions of CFCs and HCFCs and increases in emissions of
HFCs used as substitutes for ODSs. HFCs have a wide variety of applications, including use as
refrigerants, aerosol propellants, solvents, foam blowing agents, medical sterilization carrier gases,
and fire extinguishing agents. It should be noted that the ODSs themselves are greenhouse gases;
however, following international conventions, the emissions of these substances are not included in
the baseline emissions presented in this report. Only emissions of non-ozone-depleting fluorinated
gases used as substitutes for ODSs are included in the baseline emissions. Had the phaseout of
ODSs not occurred, more warming would have occurred because many ODSs are more potent
GHGs than the HFCs and other substitutes now being used or introduced.
Emissions of HFCs used as substitutes for ODSs have grown dramatically between 1990 and 2005,
from zero1 to 308 MtCO2e (38 percent of sector total). HFC emissions from ODS substitutes are
expected to increase by a factor of six between 2005 and 2030, driven by strong demand for
refrigeration and air conditioning equipment in developing countries. Emissions from HCFC-22
production are projected to more than triple during the same time period. Emissions from
magnesium manufacturing are projected to decrease 46 percent over this period.
1 In 1990, emissions for this category were negligible, with U.S. emissions accounting for less than 0.5 MtCC>2e.
August 2011
4. Industrial Processes
Page 4-2
-------
Exhibit 4-2 displays industrial processes sector non-CO2 emissions by region. In 1990, 70 percent of
sector emissions were from the OECD region. However, emissions in the OECD have grown
relatively slowly between 1990 and 2005, and now constitute 53 percent of the sector total while
emissions from non-OECD Asia now account for 25 percent of the global total. By 2030, the
relative share of emissions from these two regions is expected to roughly switch. Non-OECD Asia
will account for 49 percent of emissions while the OECD will account for 35 percent. This trend is
largely due to projected increases in emissions from ODS substitutes and HCFC-22 production in
China.
Exhibit 4-2: Total Non-CO2 Emissions from the Industrial Processes Sector, by Region (MtCO2e)
3,500
3,000
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
D Non-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
Table 4-1 lists the high-GWP gases included in this analysis of the industrial sector with their
atmospheric lifetime, global warming potentials (GWP), and associated uses or emission sources.
Although the GWPs have been updated by the IPCC in the Third Assessment Report (TAR) and
again in the Fourth Assessment Report (AR4), estimates of emissions in this report continue to use
the GWPs from the Second Assessment Report (SAR) in order to be consistent with international
reporting standards under the United Nations Framework Convention on Climate Change
(UNFCCC). However, some of the high-GWP gases estimated in this report did not have GWPs
listed in the SAR. In these cases, this report uses the TAR GWPs.
Table 4-1: High-GWP Chemicals - Partial List
Chemical
Life-
time
(yrs)
GWP
(100-
y)
Use
Hydrofluorocarbons (MFCs)
Byproduct of HCFC-22 production, used in very low temperature
HFC-23 264 11,700 refrigeration, blend component in fire suppression, and plasma etching
and cleaning in semiconductor production.
August 2011
4. Industrial Processes
Page 4-3
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Life- GWP
Chemical time (100-
(ys) yr)
HFC-32 5.6 650
HFC-41 3.7 ISO
HFC- 125 32.6 2,800
HFC- 134 10.6 1,000
HFC-l34a 14.6 1,300
HFC-l52a 1.5 140
HFC- 143 3.8 300
HFC-l43a 48.3 3,800
HFC-227ea 36.5 2,900
HFC-236ea I0.0a I200a
HFC-236fa 209 6,300
HFC-245ca 6.6 560
HFC-245fa 7.2a 950a
^,FrC" 9.9a 890a
365 mfc
V«FC~43~ 17.1 1,300
lOmee
Use
Blend component of numerous refrigerants.
Not in commercial use today.
Blend component of numerous refrigerants and a fire suppressant.
Not in commercial use today.
Most widely used HFC refrigerant, blend component of other
refrigerants, propellant in metered-dose inhalers and aerosols, and foam
blowing agent.
Blend component of refrigerant blends, propellant in aerosols, foam
blowing agent.
Not in commercial use today.
Refrigerant blend component.
Fire suppressant, foam blowing agent, and propellant for metered-dose
inhalers.
Not in commercial use today.
Refrigerant and fire suppressant.
Not in commercial use today.
Foam blowing agent and under consideration as a refrigerant.
Foam blowing agent.
Cleaning solvent.
Perfluorocarbons (PFCs)
CF4 50,000 6,500
C2F6 10,000 9,200
C3F8 2,600 7,000
C4FIO 2,600 7,000
c-C4F8 3,200 8,700
C5FI2 4,100 7,500
C6FI4 3,200 7,400
Byproduct of aluminum production. Plasma etching and cleaning in
semiconductor production and component of low temperature
refrigerant blends.
Byproduct of aluminum production. Plasma etching and cleaning in
semiconductor production.
Component of low-temperature refrigerant blends and fire suppressant.
Used in plasma cleaning in semiconductor production.
Fire suppressant.
Not in much use, if at all, today. Emerging for plasma etching in
semiconductor production.
Not in much use, if at all, today.
Precision cleaning solvent.
Nitrogen Trifluoride (NF3)
NF3 740b 8,000b
Plasma cleaning in semiconductor production.
Sulfur Hexafluoride (SF6)
SF,
3,200 23,900
Cover gas in magnesium production and casting, dielectric gas and
insulator in electric power equipment, used to test fire suppression
discharge in military systems and civilian aircraft, atmospheric and
subterranean tracer gas, sound insulation, process flow-rate
measurement, medical applications, and formerly an aerosol propellant.
Used for plasma etching in semiconductor production.
Table excludes ozone-depleting substances controlled by the Montreal Protocol.
GWPs and atmospheric lives are reprinted from the Intergovernmental Panel on Climate Change, Second Assessment Report
(IPCC, 1996), except as noted below.
a IPCC, 2001. Third Assessment Report.
August 2011
4. Industrial Processes
Page 4-4
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b Molina, L.T., P.J. Woodbridge, and M. Molina, 1995.
4.1 Adipic Acid and Nitric Acid Production (N2O)
4.1.1 Source Description
N2O is emitted during the production of adipic and nitric acids, both of which are feedstocks or
components to the manufacture of a variety of commercial products.
Adipic acid (hexane-1, 6-dioxic acid) is a white crystalline solid used as a feedstock in the
manufacture of synthetic fibers, coatings, plastics, urethane foams, elastomers, and synthetic
lubricants. Commercially, it is the most important of the aliphatic dicarboxylic acids, which are used
to manufacture polyesters. Worldwide, the largest single use of adipic acid is carpet manufacturing,
accounting for 30 percent of the market (Chemical Week, 2007). By treating nitrogen oxides (NOx)
and other regulated pollutants in the waste gas stream, N2O emissions can be reduced. Studies
confirm that these abatement technologies can reduce N2O emissions by more than 95 percent,
depending on plant specifications (Riemer et al., 1999). Emissions calculations for this source use
adipic acid production plant capacity and default emission factors to estimate growth.
Nitric acid (HNO3) is an inorganic compound used primarily to make synthetic commercial
fertilizer. It is also a major component in the production of adipic acid and explosives. During the
catalytic oxidation of ammonia, N2O is formed as a byproduct and released from reactor vents into
the atmosphere. Calculations for this source use projected fertilizer use to estimate growth in nitric
acid production. N2O emissions estimates for adipic and nitric acid are combined in this chapter
because country-reported data often combines these sources.
4.1.2 Source Results
Between 1990 and 2005, N2O emissions from production of nitric and adipic acid has decreased 34
percent, from 199 MtCO2e to 131 MtCO2e (see Table 4-2). Over this time period, production of
nitric and adipic acid has increased. The decline in historical emissions is mostly due to widespread
installation of abatement technologies in the adipic acid industry (Reimer et al, 1999). Most
production capacity in these industries has been located in the OECD, but the proportion of
emissions in the OECD has declined. In 1990, the OECD accounted for 87 percent of global N2O
emissions from this source, whereas the OECD is estimated to account for 75 percent of global
emissions in 2005.
From 2005 to 2030, N2O emissions from nitric and adipic acid production are projected to increase
8 percent. This projection assumes continued increase in production, but does not assume further
mitigation. The regional shift of emissions away from the OECD is expected to continue. The
OECD is projected to account for 67 percent of N2O emissions from this source in 2030, down
from 75 percent in 2005.
Table 4-2: Total N2O Emissions from Adipic Acid and Nitric Acid Production (MtCO2e)
GasT990F9952000200520102015202020252030
Total N2O 199.4 198.3 135.3 131.1 117.5 116.7 124.9 134.3 141.7
August 201 I 4. Industrial Processes Page 4-5
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Exhibit 4-3: N2O Emissions from Adipic Acid and Nitric Acid Production 1990 - 2030 (MtCO2e)
250
200
VI
C
O
UJ
ISO
100
50
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECDAsia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
The U.S., EU, and Canada began ramping up efforts to reduce N2O emissions from adipic acid
production in the late 1990s. Their effects can be seen in Exhibit 4-3 in the substantial reduction in
emissions from 1995 to 2000. These control technologies can significantly reduce emissions, and
their long-term effects may be even greater than illustrated in Exhibit 4-3 for countries with high
technology penetration rates. Capacity expansions to meet increased global demand for adipic acid
are expected in Asia, while market restructuring is expected to continue in Western Europe and
North America (SRI, 2009; Chemical Week, 2007).
Fertilizer demand, and thus nitric acid use, is expected to continue to decline in Western Europe and
increase elsewhere. The decline in several regions including Western Europe is due in part to
concerns about nitrates in the water supply.
4.2 Use of Substitutes for Ozone Depleting Substances (MFCs)
4.2.1 Source Description
HFCs are used as alternatives to several classes of ozone-depleting substances (ODSs) that are being
phased out under the terms of the Montreal Protocol. PFCs and hydrofluoroethers (HFEs) are also
used as alternatives, but to a substantially lesser extent than HFCs. Emissions from these gases are
thus not estimated in this report. ODSs, which include chlorofluorocarbons (CFCs), halons, carbon
tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs), have been used in a
variety of industrial applications including refrigeration and air conditioning equipment, aerosols,
solvent cleaning, fire extinguishing, foam production, and sterilization. Although the HFCs that
would replace the ODSs are not harmful to the stratospheric ozone layer, they are powerful
greenhouse gases.
August 2011
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Calculations of HFC emissions from the use of substitutes for ODSs are modeled by end use and
country. End uses are expected to transition from ODSs to HFCs (and other substitutes) in
response to the ODS phaseout required under the Montreal Protocol. For more information on the
modeling approach, see section 7.2.2.
This section reports increases in emissions of HFCs used as substitutes for ODSs. However, the
ODSs which HFCs are replacing are also greenhouse gases, in many cases more potent than the
substitutes now being used. Thus, although emissions of HFCs used as substitutes of ODSs are
increasing, the radiative forcing from the CFCs and HCFCs they replace would have been much
higher had the phaseout of ODSs not taken place.2
4.2.2 Source Results
Table 4-3, Exhibit 4-4, and Exhibit 4-5 illustrate the rapid growth expected in the emissions for this
source. In 1995, HFC emissions from ODS substitutes were only 63 MtCO2e, but by 2005, global
emissions are estimated to have grown to 308 MtCO2e. The growth in emissions up to 2005 is
primarily driven by the transition to HFCs under the Montreal Protocol in OECD nations, which
account for three quarters of 2005 emissions.
Table 4-3: Total HFC Emissions from Substitutes for Use of Ozone-Depleting Substances (MtCO2e)
Gas T990 T995 2000 2005 2010 2oTs 2020 2025 2030
Total HFCs - 615 \8\A307.7 442.8 660.2 935.6 1,451.0 1,902.7
2 For an estimate of the climate benefits of phasing out ODSs, see Velders et al. (2007).
3 1990 emissions for ODS substitutes were not estimated for all countries and are not presented here. In 1990, emissions
for this category were negligible, with U.S. emissions accounting for less than 0.5 MtCO2e.
August 201 I 4. Industrial Processes Page 4-7
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Exhibit 4-4: HFC Emissions from Use of Substitutes for Ozone-Depleting Substances 1990 - 2030 by
Region (MtCO2e)
2,000
-------
emitted at service and disposal events. Enhanced recovery and reuse, transitions to more efficient
equipment, and the use of low- or no-GWP alternatives could avert these projected emissions
increases.
Exhibit 4-5: HFC Emissions from Use of Substitutes for Ozone-Depleting Substances 1990 - 2030 by
Sector (MtCO2e)
C
O
UJ
DFire Extinguishing
DMDI Aerosols
Solvents
DFoam Blowing
Refrigeration/ Air Conditioning
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
4.3 HCFC-22 Production (MFCs)
4.3.1 Source Description
Trifiuoromethane (HFC-23) is generated and emitted as a byproduct during the production of
chlorodifluoromethane (HCFC-22). HCFC-22 is used primarily as a feedstock for production of
synthetic polymers and, secondarily, in emissive applications (primarily air conditioning and
refrigeration). Because HCFC-22 depletes stratospheric ozone, its production for non-feedstock
uses is scheduled to be phased out under the Montreal Protocol. However, feedstock production is
permitted to continue indefinitely. Estimates in this section are associated with both types of HCFC-
22 production.
HFC-23 emissions from HCFC-22 production can be avoided through thermal destruction and
reduced through process optimization. Destruction of HFC-23 from this source in non-Annex I
countries is a major source of credits in the CDM program. All producers in Annex I countries have
implemented process optimization and/or thermal destruction to reduce HFC-23 emissions. In a
few cases, HFC-23 is collected and used as a substitute for ozone-depleting substances, mainly in
very-low temperature refrigeration and air conditioning systems. Emissions from this use are
quantified under air conditioning and refrigeration and are therefore not included here. HFC-23
exhibits the highest global warming potential of the HFCs, 11,700 under a 100-year time horizon,
with an atmospheric lifetime of 264 years.
August 2011
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4.3.2 Source Results
As shown in Table 4-4, global HFC-23 emissions from HCFC-22 production grew by 95 percent
between 1990 and 2005, driven by 98 percent growth in global HCFC-22 production during that
period. Emissions grew at a slower rate than production due to the implementation of thermal
destruction and process optimization in Europe and the United States. Recent research such as
Miller et al. (2010) uses atmospheric measurements to estimate total emissions of HFC-23, allowing
comparison between top-down measurements and the bottom-up analysis presented in this report.
While bottom-up emission estimates prior to 2006 fall within the uncertainty range of global
estimates as published by Miller et al., atmospheric measurements for 2009 indicate that the
projection methodology used in this report may not fully account for recent mitigation efforts.
Table 4-4: Total HFC-23 Emissions from HCFC-22 Production (MtCO2e)
Gas 1990 1995 2000 2005 2010 2015 2020 2025 2030
Total HFC-23 90.6 96.8 123.8 177.0 308.9 326.3 370.5 448.1 568.9
Between 2005 and 2030, world HFC-23 emissions from HCFC-22 production are expected to more
than double. This projection includes a phaseout of non-feedstock HCFC-22 production in
developed countries between 2015 and 2020, which results in a temporary reduction in HFC-23
emissions over that period. HCFC-22 production is expected to increase through 2030 because of
feedstock uses.
Exhibit 4-6 reveals a striking shift of the majority of emissions from OECD countries to non-
OECD Asia between 1990 and 2005. This is due to (1) a combination of increased use of emission
controls and the phaseout of HCFC-22 under the Montreal Protocol in OECD countries and (2)
increased HCFC-22 production in China and India. Thus, while HFC-23 emissions from OECD
countries have declined by half, emissions from non-OECD Asia increased from a negligible level in
1990 to 74 percent of world emissions in 2005. Over the projection period, emissions are expected
to grow in both regions, but will grow much more quickly in non-OECD Asia than in the OECD.
Emissions from other regions are minor compared to these two regions. In 1990, the three largest
emitters for this source were the U.S., Russia, and Japan, which together accounted for over 70
percent of all emissions. In 2030, the three largest emitters are projected to be China, India, and
Mexico. It is anticipated that these nations will account for 94 percent of all HFC-23 emissions,
while China alone is expected to be the world's major HFC-23 emitter, accounting for over 50
percent of total emissions.
August 201 I 4. Industrial Processes Page 4-10
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Exhibit 4-6: HFC-23 Emissions from HCFC-22 Production 1990 - 2030 (MtCO2e)
600
o
UJ
500
400
300
200
100 -
D Middle East
DCentral and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
In the OECD, HFC-23 emissions decreased between 1990 and 2005 due to process optimization
and thermal destruction. The U.S. and the European Union (EU) drove these trends. Although
emissions increased in the EU between 1990 and 1995 due to increased production of HCFC-22, a
combination of process optimization and thermal oxidation led to a sharp decline in EU emissions
after 1995, resulting in a net decrease in emissions of 74 percent for this region between 1990 and
2005. U.S. emissions declined by 57 percent during the same period, despite a 12 percent increase in
HCFC-22 production.
As illustrated in Exhibit 4-6, HFC-23 emissions in developed countries are predicted to increase
between 2005 and 2030 due to increasing production and use of HCFC-22 for feedstock purposes.
Several factors mitigate the emissions increase: (1) Japan's implementation of either thermal
abatement or HFC-23 capture (for use) for 100 percent of its production beginning in 2005 (JICOP,
2006); (2) 100 percent implementation of thermal abatement in all EU countries; (3) closure of the
HCFC-22 production plants in Greece, France, Italy, and the U.K. between 2006 and 2008; and (4)
the HCFC-22 production phaseout scheduled under the Montreal Protocol, which is occurring
gradually between 2000, 2015, and 2020.
In non-OECD Asia, particularly in China, emissions have increased quickly due to a rapid increase
in the production of HCFC-22 over the historical period. This production is meeting growing
demand for unitary air conditioning, for commercial refrigeration, and for substitutes to
chlorofluorocarbons currently being phased out in developing countries under the Montreal
Protocol, as well as demand for HCFC-22 as a feedstock in the manufacture of
polytetrafluoroethylene (PTFE) also known by its brand name Teflon (UNEP, 2003 and 2007).
Emissions of HFC-23 from HCFC-22 production are expected to continue to increase in non-
OECD Asia through 2030. Emissions increase through 2015, as HCFC-22 non-feedstock
August 2011
4. Industrial Processes
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production is essentially unrestricted. After 2015, the emission growth slows as HCFC-22 non-
feedstock production is restricted by the Montreal Protocol. Emissions begin growing at a faster rate
around 2025 as HCFC-22 feedstock production outgrows non-feedstock production.
4.4 Electric Power Systems (SF6)
4.4.1 Source Description
SF6 is used as both an arc quenching and insulating medium in electrical transmission and
distribution equipment. SF6 emissions from electrical equipment used in transmission and
distribution systems occur through leakage and handling losses. Leakage losses can occur at gasket
seals, flanges, and threaded fittings, and are generally larger in older equipment. Handling emissions
occur when equipment is opened for servicing, SF6 gas analysis, or disposal. The manufacture of
equipment for electrical transmission and distribution can also result in SF6 emissions, but this
source is not included in this report.4
Several factors affect SF6 emissions from electrical equipment, including the type and age of SF6-
containing equipment, and the handling and maintenance protocols used by electric utilities.
Historically, approximately 20 percent of total global SF6 sales have been attributed to electric power
systems, where the SF6 is believed to have been used primarily to replace emitted SF6.
Approximately 60 percent of global sales have gone to manufacturers of electrical equipment, where
the SF6 is believed to have been mostly banked in new equipment (Smythe, 2004).
Calculations of SF6 emissions from this source use electricity usage projections as a proxy for the
amount of electrical transmission and distribution equipment being used and the estimated
emissions from that equipment. Voluntary programs encourage practices to reduce emissions of SF6
from electrical equipment, but enhanced future mitigation from these programs are not explicitly
included in the estimates.
4.4.2 Source Results
Global emissions from electric power systems are believed to have decreased 15 percent between
1990 and 2005, from 50 to 43 MtCO2e (see Table 4-5 and Exhibit 4-7). This emissions decline is
based on declining SF6 sales to utilities and estimated equipment retirements. The cost of SF6 gas
increased significantly in the mid-1990s, which motivated electric utilities to implement improved
management practices to reduce their use of SF6. However, sales of SF6 increased by over 37 percent
between 2000 and 2003, reversing the trend observed in the previous decade (Smythe, 2004). In
addition, equipment retirements (based on a 40-year equipment lifetime) are estimated to have more
than doubled between 2000 and 2003. Together, these two trends result in an increase in global
emissions beginning in 2003. The global increase in SF6 emissions is reflected in the trends of the
individual regions except for the U.S., the EU, and Japan. Country-reported data for these three
regions shows that SF6 emissions from electric power systems declined from 1990 through 2003.
Table 4-5: Total SF6 Emissions from Operation of Electric Power Systems (MtCO2e)
GasT990F9952000200520102015202020252030
Total SF6 5O3 4T5307 4Z8472 5ZO 56^6 62J 67.7
4 While these emissions were not explicitly estimated in this report, some countries report emissions from the
manufacture of equipment for electrical transmission and distribution equipment manufacture within this source
category. In these cases, this source category includes these emissions.
August 201 I 4. Industrial Processes Page 4-12
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Exhibit 4-7: SF6 Emissions from Electric Power Systems 1990 - 2030 (MtCO2e)
80
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
From 2005 to 2030, SF6 emissions from electric power systems are projected to increase 58 percent,
from 43 to 68 MtCO2e. This increase is driven by rapid projected electricity usage increases in non-
OECD regions. In the U.S. and the EU, emissions are expected to continue to decline as utilities,
through government-sponsored voluntary and mandatory programs, implement reduction measures
such as leak detection and repair and gas recycling practices.
In contrast, emissions from non-OECD Asia, Africa, Central and South America, and the Middle
East are expected to continue to increase over the projection period. In these countries, it is
assumed that SF6-containing equipment has been installed relatively recently, and that all equipment
is new. Consequently, as infrastructure expands to meet the demands of growing populations and
economies, emissions are estimated to grow at a rate proportional to country- or region-specific net
electricity consumption (EIA, 2009). By 2030, non-OECD regions are expected to account for 72
percent of total emissions, up from 46 percent in 2005 and 12 percent in 1990.
4.5 Primary Aluminum Production (PFCs)
4.5.1 Source Description
Emissions of the perfluorocarbons CF4 and C2F6 are generated during brief process upset conditions
in the aluminum smelting process. During the aluminum smelting process, when the alumina (A12O3)
in the electrolytic bath falls below critical levels required for electrolysis, rapid voltage increases
occur. These voltage excursions are termed "anode effects" (AEs). Anode effects produce CF4 and
5 Electricity consumption growth rates are assumed to equal the growth rates in world total net electricity generation
from central producers, as provided by EIA, 2009.
August 2011
4. Industrial Processes
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C2F6 emissions when carbon from the anode, instead of reacting with alumina, as it does during
normal operating conditions, combines with fluorine from the dissociated molten cryolite bath
combine. In general, the magnitude of emissions for a given level of production depends on the
frequency and duration of these anode effects; the more frequent and long-lasting the anode effects,
the greater the emissions.
Calculations of PFC emissions from this source are based on historical and expected levels of
aluminum production and anode effect rates from historical experience. Emission factors vary by
aluminum production technology. Voluntary programs encourage practices to reduce the frequency
and duration of anode effects and PFC emissions, but enhanced future mitigation from these
programs is not included here. Anode effect minute data for PFC emissions calculations in this
section were taken from International Aluminum Institute (IAI) survey results (IAI, 2010). The IAI
estimate of global emissions used plant-by-plant data not incorporated in this report.
Five different electrolytic cell types are used to produce aluminum: Vertical Stud Soderberg (VSS),
Horizontal Stud Soderberg (HSS), Side-Worked Prebake (SWPB), Center-Worked Prebake (CWPB),
and Point Feed Prebake (PFPB), which is considered the most technologically-advanced process to
produce aluminum. PFPB systems can be further improved through the implementation of
management and work practices, as well as improved control software. Facilities using VSS, HSS,
SWPB, and CWPB cells can reduce emissions by retrofitting smelters with emission-reducing
technologies such as computer control systems and point feeding systems, by shifting production to
Point-Feed Prebake (PFPB) technology, and by adopting management and work practices aimed at
reducing PFC emissions. This analysis accounts for the historical reduction in the frequency and
duration of anode effects realized by facilities but does not assume that aluminum producers have
conducted retrofits or will continue to introduce technologies and practices aimed at reducing PFC
emissions.
4.5.2 Source Results
Table 4-6 and Exhibit 4-8 present total PFC emissions from aluminum production under the
analysis from 1990 to 2030. Between 1990 and 2005, global emissions declined from 84 to 51
MtCO2e. This significant decline was the result of voluntary measures undertaken by global smelters
to reduce their AE minutes per cell day. These measures included incremental improvements in
smelter technologies and practices, and a shift in the share of SWPB-related production to more
state-of-the-art PFPB facilities. Emission reductions were offset by a 62 percent increase in global
aluminum production between 1995 and 2005. The IAI estimates of PFC emissions from aluminum
manufacture are significantly lower than the estimates presented here, and may reflect more updated
information on the mix of production technologies currently in use.
Table 4-6: Total PFC Emissions from Primary Aluminum Production (MtCO2e)
Gas T990 [9952000 2005 2010 2015 2020 2025 2030
Total PFCs 83.8 68.8 62.0 51.4 47.3 53.1 59.3 66.1 73.9
August 201 I 4. Industrial Processes Page 4-14
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Exhibit 4-8: PFC Emissions from Primary Aluminum Production 1990-2030 (MtCO2e)
90
80
-------
intensity (i.e., anode effect minutes per cell day) will remain constant at 2010 values; consequently,
emissions will be driven by increasing aluminum production.
4.6 Magnesium Manufacturing (SF6)
4.6.1 Source Description
The magnesium metal production and casting industry uses SF6 as a cover gas to prevent the
spontaneous combustion of molten magnesium in the presence of air. Fugitive SF6 emissions occur
primarily during three magnesium manufacturing processes: primary production, die-casting, and
recycling-based production. Additional processes that may use SF6 include sand and gravity casting;
however, these are believed to be minor sources and are not included in this analysis.
Emissions calculations in this section use magnesium production statistics and default emission
factors. Although recent studies indicate some destruction of SF6 in its use as a cover gas (Bartos et
al., 2003), this analysis follows current IPCC guidelines (IPCC, 2006), which assumes that all SF6
used is emitted to the atmosphere.
4.6.2 Source Results
Between 1990 and 2005, SF6 emissions from magnesium manufacturing have decreased 20 percent,
from 12 to 10 MtCO2e. Over this time period, magnesium production has increased, but this growth
has been offset by major initiatives to phase-out the use of SF6 in magnesium production in
numerous countries. Total SF6 emissions from magnesium manufacturing are displayed in Table 4-7
and Exhibit 4-9.
From 2005 to 2030, emissions from this source are projected to decrease further from 10 to 5
MtCO2e, a decrease of about 50 percent. Emissions from OECD countries decrease significantly in
the short term because of facility closures in North America and SF6 phase-out efforts (USGS,
2010). As a result, the OECD share of global SF6 emissions from magnesium manufacturing is
projected to decrease from 68 percent in 2005 to 12 percent in 2030. Major SF6 phase-out efforts are
driven by the EPA's voluntary partnership in the United States and regulatory directives in Japan
and Europe.
Table 4-7: Total SF6 Emissions from Magnesium Manufacturing (MtCO2e)
GasT990[9952000200520102015202020252030
Total SF* IZ2 IO3 93935J 465J 4351~
August 201 I 4. Industrial Processes Page 4-16
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Exhibit 4-9: SF6 Emission from Magnesium Manufacturing 1990 - 2030 (MtCO2e)
-------
emissions of CF4 also result when a fraction of the heavier consumed gases is converted during the
manufacturing process. Fluorinated greenhouse gases (F-GHGs) and N2O are also used as heat
transfer fluids. Total PFC, HFC, and SF6 emissions from this source vary by process and device
type.7
Emission calculations for this source were developed using semiconductor production capacity
statistics, capacity utilization assumptions, and default emission factors. PFC, HFC, and SF6
emissions from this source can be reduced using chemical substitution, process optimization, and
equipment to destroy these compounds in waste gas streams. Voluntary programs encourage
adoption of these mitigation technologies. These projections assume reductions that have resulted or
are anticipated to result from international voluntary climate commitments.
4.7.2 Source Results
Table 4-8 and Exhibit 4-10 show the emission estimates for the semiconductor manufacturing
industry.
Between 1990 and 2005, PFC emissions from the semiconductor manufacturing industry have
increased 54 percent, from 13 to 20 MtCO2e. This increase in emissions reflects underlying growth
in semiconductor production partially offset by mitigation efforts.
In April 1999, the semiconductor manufacturing industry set an aggressive target to reduce PFC
emissions. The World Semiconductor Council (WSC) then agreed to reduce PFC emissions to 10
percent below 1995 levels by the year 2010. WSC members include the industry organizations for
the European countries, China8, Japan, Korea, and the U.S. Since WSC members account for
production of over 90 percent of the world's semiconductors9, the goal is expected to have dramatic
effects in decreasing emissions from semiconductor manufacturing over time. The WSC is currently
considering post-2010 targets but these are not publicly available.
Table 4-8: Total High-GWP Emissions from Semiconductor Manufacturing (MtCO2e)
Gas T990 [9952000 2005 2010 2015 2020 2025 2030
Total High-GWPs IZ6 Tl6 247 2O2 \63 176 \8A I9X) 19.6
7 Note that while the term PFC (strictly referring to only perfluorocarbon compounds) does not include all of the
fluorinated compounds emitted from this source, the semiconductor industry commonly refers to the mix of fluorinated
compounds as PFCs; this report adopts the same convention.
8 Although China joined the WSC in 2006, it has not yet committed to a reduction goal.
9 According to the EPA's website on PFC Reduction/Climate Partnership for the Semiconductor Industry:
http://www.epa.gov/semiconductor-pfc/international.html.
August 201 I 4. Industrial Processes Page 4-18
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Exhibit 4-10: High-GWP Emissions from Semiconductors Manufacturing 1990 - 2030 (MtCO2e)
30
25
-------
4.8 Flat Panel Display Manufacturing (PFCs, SF6, NF3)
4.8.1 Source Description
Flat panel display (FPD) manufacturing uses SF6, PFCs including CF4, and NF3, in the etching and
chamber cleaning processes.11 These high-GWP greenhouse gases are used for chemical vapor
deposition (CVD) cleaning processes and plasma dry etching during manufacture of arrays of thin-
film transistors on glass substrates, which switch pixels of liquid crystal displays and organic light
emitting diode displays.
In order to reduce emissions, this sector may employ abatement technologies, including fueled
combustion, plasma and catalytic technologies explicitly intended for F-GHG abatement. FPD
manufacturing is a new source category in this report. Emissions calculations for this source use data
on flat panel manufacturing capacity and industry growth trends. The projections for this sector
assume continued rapid growth in a currently fast-growing industry, due to continued demand for
and evolving generations of electronics products (e.g., televisions and computer monitors).
Additionally the growth is predicated on the fact there will be increased demand for these newer
technologies, particularly in developing nations such as China.
4.8.2 Source Results
Flat panel display manufacturing is a relatively new industry sector. By 2005, industry emissions have
grown to about 3.9 MtCO2e. Underlying this growth, flat panel displays have grown to over half of
the electronic display market. In 2005, the OECD and non-OECD Asia regions accounted for 54
percent and 46 percent of high-GWP emissions from flat panel display manufacturing, respectively.
The total emissions from the manufacture of FPDs are displayed in Table 4-9 below. China12, Japan,
Singapore, and South Korea contributed significantly to FPD manufacturing emissions.
From 2005 to 2030, emissions from this source are expected to grow by a factor of forty, to 162
MtCO2e in 2030. Between 2005 and 2010, FPD manufacturing capacity grew by a factor of 9, or an
annual growth rate of more than 50 percent. This projection assumes large growth in the FPD
industry, tapering from an assumed annual growth rate about 30 percent in 2010 to about 15 percent
in 2030.13 The OECD and non-OECD Asia are expected to remain dominant in the industry, while
Africa, Central and South America, and non-OECD Europe and Eurasia do not contribute
significantly to emissions from FPD manufacturing.
The OECD's emissions have continued to grow in absolute terms; however global totals have
increased at a faster rate, resulting in the OECD emitting only 1 percent of the global FPD
emissions in 2030. This is in part due to the assumed use of abatement technologies in some OECD
11 Note that while the term PFC (strictly referring to only perfluorocarbon compounds) does not include all of the
fluorinated compounds emitted from this source, specifically NFj, the electronics manufacturing industry commonly
refers to the mix of fluorinated compounds as PFCs. This report follows this convention. The GWP used for NFj was
from IPCC AR4.
12 For purposes of this report, emissions presented for China include emissions from manufacture in China and Taiwan,
however emissions for these countries were estimated separately because Taiwan is a member of the WLICC.
13 The annual growth rate of 15-30% assumed for the flat panel display industry is lower than the recent growth rate for
the industry, but much higher than overall economic growth. For this reason, the emissions estimates for this industry
can be thought of an upper bound for emissions from a fast-growing industry. If the industry grows much slower than it
has in the past, then emissions would be lower.
August 201 I 4. Industrial Processes Page 4-20
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countries, and because of a large increase in FPD manufacturing in China by 2030. The contribution
of emissions by China, as a percent of world emissions from FPD manufacturing, increased from 18
percent in 2000 to 45 percent in 2005, and by 2010 China's emissions were 1.8 MtCO2e, accounting
for 53 percent of global emissions from FPD manufacturing.
Table 4-9: Total SF6, PFC, and NF3 Emissions from Flat Panel Display Manufacturing (MtCO2e)
Gas T990 [9952000 2005 2010 2015 2020 2025 2030
Total SF6, PFC,
and NF,
O.I
0.2
0.5
3.9
3.0
7.4
34.8
82.2 162.3
Exhibit 4-11: SF6, PFC, and NF3 Emissions from Flat Panel Display Manufacturing 1990 - 2030
(MtCO2e)
180
160
140
^ 120
0
y
o
UJ
100
80
60
40
20
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
The share of global emissions from China is projected to drastically increase to 98 percent by 2030,
increasing to 158.5 MtCO2e. This increase is a result of two key drivers. First, there is an expected
increase in China's domestic demand for FPDs, and much of this demand will be met through
domestic production (DisplaySearch, 2010). Second, in the later years of this analysis, China's share
of world emissions is projected to steeply increase partly because other countries with large FPD
manufacturing capacities are expected to meet and maintain a voluntary emissions reductions goal
set by the World LCD Industry Cooperation Committee (WLICC). The WLICC is comprised of
three member associations representing Taiwan,14 Japan, and South Korea. The WLICC goal, which
was agreed to by all three member associations, is to meet and maintain an aggregate 2010 F-GHG
emission target of 10 percent of the projected business-as-usual 2010 emissions, or 0.82 MMTCE
(3.01 MtCO2e). The WLICC member associations are estimated to have 96 percent of the world's
FPD manufacturing capacity in 2010. By 2030 WLICC countries are still expected to maintain 82
14 See footnote 12.
August 2011
4. Industrial Processes
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percent of world FPD manufacturing capacity. In contrast in 2010, the WLICC countries are
expected to emit only 79 percent of world high-GWP emissions from FPD manufacturing, and 2
percent in 2030. This low share of emissions versus capacity for the WLICC in 2030 is a direct result
of the voluntary WLICC emission reduction goal and increasing FPD manufacturing capacity in
China to meet domestic and global demand. In addition, in part because of the WLICC goal, the
OECD and SE Asian countries' emissions are projected to remain steady and slightly increase,
respectively, from 2015 to 2030.
4.9 Photovoltaic Manufacturing (PFCs, NF3)
4.9.1 Source Description
Photovoltaic (PV) manufacturing causes emissions of PFCs, including CF4 and C2F6, as well as NF3,
from etching and chamber cleaning processes used during the manufacture of PV cells.15
Photovoltaic (PV) manufacturing is a new source category in this report.
Emissions depend on the particular substrate and process used in the production of PV cells.
Substrates used in the industry include crystalline silicon, amorphous silicon, and other thin-films.
CF4 and C2F6 are used during manufacture of crystalline silicon (c-Si) PV cells; NF3 is used during
manufacture of amorphous silicon (a-Si) and tandem a-Si/nanocrystaline (nc) silicon PV cells.
Etching and cleaning processes for PV cells manufactured on other thin films do not utilize GHGs.
Calculations in this section utilize statistics on PV production capacity which take into account
projected increases in renewable energy use.
4.9.2 Source Results
Historically, PV manufacturing has not resulted in significant GHG emissions. For 1990 and 1995,
PV manufacturing, and as a result emissions, were assumed to be negligible. In the base year 2005,
PFC emissions are estimated to have been about 0.5 MtCO2e, based on PV production capacity of
about 2,200 MW or 13.8 million meters squared of substrate.
The trends for the PV manufacturing industry used for this report were based on the assumption
that demand for, and therefore production of PV cells rapidly increases through 2030. This
projection assumes rates of growth in this sector will remain high due to the increasing demand for
electric power, efforts to reduce dependence on fossil fuels, and a growing understanding of the
environmental effects of traditional sources of energy. The estimates developed for this report do
not explicitly take into account any current or future policies (renewable energy standards), as it is
uncertain at this point how to quantify the effect on demand for PV cells.
PFC emissions from PV manufacturing are estimated to grow quickly between 2005 and 2030, from
0.5 to 127 MtCO2e. This projection assumes very large growth in solar energy usage to about 200
GW installed PV capacity in 2030, from 13 GW global installed PV capacity in 2008. Although this
assumption is very large, the PV industry is growing quickly and one purpose of this projection is to
understand the possible emissions that would result should that growth occur.16 Total PFC
15 Note that while the term PFC (strictly referring to only perfluorocarbon compounds) does not include all of the
fluorinated compounds emitted from this source, specifically NFj, the electronics manufacturing industry commonly
refers to the mix of fluorinated compounds as PFCs. Therefore NFj emissions are included in this analysis.
16 The projection assumes an annual industry growth rate approximately 19%. This growth rate is lower than the PV
industry has achieved in some recent years, but much higher than total economic growth. For this reason, the emissions
August 201 I 4. Industrial Processes Page 4-22
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emissions for the world from PV manufacturing are projected to grow by over 130 percent for each
of the 5 year periods starting in 2000 through 2030. The emissions from the PV manufacturing
industry for in 5-year increments from 1990 through 2030 are shown below in Table 4-10.
The OECD and non-OECD Asia country groups are projected to account for nearly all emissions
from this source from 2005 through 2030 (see Exhibit 4-12). In 2005, the OECD countries
contributed 76 percent of total PFC emissions from PV manufacturing. In 2010, China is expected
to become the largest contributor of PV manufacturing emissions, accounting for 47 percent of
world PFC emissions, while the OECD's share of PFC emissions from PV manufacturing decreases
to 40 percent of global emissions. Overall, the non-OECD Asia region contributes 57 percent of
PFC emissions in 2010 from the manufacture of PVs. Other than the OECD and non-OCED Asia
regions, the only other regions that manufacture PVs are the Middle East and non-OECD Europe
and Eurasia, which combined contribute less than 1 percent of global PFC emissions from PV
manufacturing through 2030. By the year 2030, EPA projected that China will be the highest
contributor of PFC emissions from PV manufacturing, emitting an estimated 55.6 MtCO2e, with
Japan, Germany, and Malaysia emitting 15.0, 12.8, and 10.2 MtCO2e, respectively.
Table 4-10: Total PFC and NF3 Emissions from Photovoltaic Manufacturing (MtCO2e)
Gas T990 [995 2000 2005 2010 2015 2020 2025 2030
Total PFCs and
_ _ QQ Q5 ^ 9Q ^27 ^ |26J.
NF3
estimates in this section can be thought of as an upper bound for possible future emissions from a currently fast-
growing industry. If the PV industry grows much more slowly than it has in the past, then emissions, then emissions
would be lower.
August 201 I 4. Industrial Processes Page 4-23
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Exhibit 4-12: PFC and NF3 Emissions from Photovoltaic Manufacturing 1990-2030 (MtCO2e)
140
120
0
z
C
O
UJ
100
80
60
40
20
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECDAsia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
The significant global increase in PFC emissions from PV manufacturing is due to an expected
increase in demand for clean, renewable energy, which equates to a large growth in PV
manufacturing capacity. This demand is a result of future national GHG reduction regulations,
increasing costs and risks of securing traditional energy supplies, the increasing need for energy in
industrialized nations with growing populations, and a growing understanding of the environmental
effects of traditional sources of energy. While PFC abatement was not explicitly considered in this
analysis due to limited information, it may provide a potential option to reduce the estimated large
future increases in PFC emissions from PV manufacturing.
4.10Other Industrial Processes Sources (CH4, N2O)
4.10.1 Source Description
This source category includes emissions from the industrial processes sector that are relatively small
and are thus grouped together. The data presented here include the following sources of CH4 and
N2O:
Chemical Production (CH4)
Iron and Steel Production (CH4)
Metal Production (CH4, N2O)
Mineral Products (CH4)
Petrochemical Production (CH4)
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4. Industrial Processes
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Silicon Carbide Production (CH4)
Solvent and Other Product Use (N2O '
4.10.2 Source Results
The results for this source are presented in Table 4-11. Africa is the main contributor to emissions
from this category, accounting for an average of 78 percent of emissions from 1990 to 2030. The
OECD is the other major contributor for other industrial sources, accounting for an average of 17
percent of emissions from 1990 to 2030. The data in Table 4-11, below, are not fully comparable to
data in the remainder of this report since emissions are not calculated for all countries.
Table 4-11: Total CH4 and N2O Emissions from Other Industrial Processes Sources (MtCO2e)
Gas
1990
1995
2000
2005
2010
2015
2020
2025
2030
CH4
N2O
Total
7.3
69.2
76.4
6.5
69.0
75.5
6.7
69.0
75.7
6.5
67.7
74.3
6.7
68.0
74.7
6.7
68.0
74.7
6.7
68.0
74.7
6.7
68.0
74.7
6.7
68.0
74.7
Exhibit 4-13 and Exhibit 4-14 illustrate trends in CH4 and N2O emissions for this source category.
Exhibit 4-13: CH4 Emissions from Other Industrial Processes Sources 1990 -2030 (MtCO2e)
8
C
O
.2 3
UJ
2
I
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECDAsia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
August 2011
4. Industrial Processes
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Exhibit 4-14: N2O Emissions from Other Industrial Processes Sources 1990 - 2030 (MtCO2e)
80
70
60 -
-------
5 Agriculture
This section presents global CH4 and N2O emissions for 1990 to 2030 for the following agricultural
sources:
» Agricultural Soils (N2O)
» Enteric Fermentation (CH4)
» Rice Cultivation (CH4)
» Manure Management (CH4, N2O)
» Other agricultural sources, including:
Agricultural Soils (CH4)
Field Burning of Agricultural Residues (CH4, N2O)
Prescribed Burning of Savannas (CH4, N2O)
Open Burning from Forest Clearing (CH4)
The agricultural sector is the largest contributor to global emissions of non-CO2 greenhouse gases,
accounting for 56 percent of emissions in 2005 (6,211 MtCO2e). Exhibit 5-1 shows agricultural
sector emissions by source. The sector is dominated by N2O emissions from agricultural soils and
CH4 emissions from enteric fermentation, which accounted for 32 percent and 30 percent
respectively of agricultural emissions in 2005. Emissions from agricultural soils are projected to
increase by 34 percent by 2030, with its share of the sector's total emissions growing to 36 percent.
Enteric fermentation emissions are expected to grow by 23 percent from 2005 to 2030, and its
relative share of agricultural emissions will increase to 31 percent.
CH4 emissions from rice cultivation, CH4 and N2O emissions from manure management, and other
smaller agricultural sources constitute the remaining non-CO2 emissions from this sector. Emissions
from rice cultivation and manure management are projected to grow by 4 percent and 17 percent,
respectively, from 2005 to 2030. This growth is moderate compared to the larger sources. The
emissions from these and all other agricultural sources combined represent 32 percent of total
agricultural emissions in 2030, while agricultural soils and enteric fermentation are expected to
contribute the majority (68 percent).
August 201 I 5. Agriculture Page 5-1
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Exhibit 5-1: Total Non-CO2 Emissions from the Agricultural Sector, by Source (MtCO2e)
8,000
DOtherAgricultural Sources
D Manure Management
Rice Cultivation
D Enteric Fermentation
Agricultural Soils
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
Exhibit 5-2 displays agricultural sector emissions by region. As shown in this exhibit, emissions are
split fairly evenly among regions. In 2005, emissions from non-OECD Asia were larger than from
other regions, at 36 percent of the agriculture total. Emissions from the OECD, Central and South
America, and Africa each contributed about 20 percent.
August 2011
5. Agriculture
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Exhibit 5-2: Total Non-CO2 Emissions from the Agricultural Sector, by Region (MtCO2e)
8,000
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
The key driver for this sector is agricultural production, which is expected to increase to meet the
demand of fast-growing population centers in non-OECD Asia, Central and South America, and
Africa. Increases in both population and income in many areas of these regions will cause
consumption of agricultural products to rise quickly. Also, changes in diet preferences, such as an
increase in per-capita meat consumption, are expected to increase consumer demand for a variety of
agricultural products. Increases in consumption will be met by domestic production gains from
increased yields, livestock herds, and agricultural acreage, as well as imports from traditionally high-
producing countries. Increased commercialization of production in less developed regions is also
expected to increase fertilizer usage and livestock production capacity.
5.1 Agricultural Soils (N2O)
5.1.1 Source Description
N2O is produced naturally in soils through the microbial process of denitrification and nitrification.
A number of anthropogenic activities add nitrogen to the soils, thereby increasing the amount of
nitrogen available for nitrification and denitrification, and ultimately the amount of N2O emitted.
Anthropogenic activities may add nitrogen to the soils either directly or indirectly.
Direct additions of nitrogen occur from the following activities:
Various cropping practices, including: (1) application of fertilizers; (2) incorporation of
crop residues into the soil, including those from nitrogen-fixing crops (e.g., beans,
pulses, and alfalfa); and (3) cultivation of high organic content soils (histosols); and
Livestock waste management, including: (1) spreading of livestock wastes on cropland
and pasture, and (2) direct deposition of wastes by grazing livestock.
August 2011
5. Agriculture
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Indirect additions occur through volatilization and subsequent atmospheric deposition of ammonia
and oxides of nitrogen that originate from (a) the application of fertilizers and livestock wastes onto
cropland and pastureland, and (b) subsequent surface runoff and leaching of nitrogen from these
same sources.
Calculations in this section utilize international statistics and projections of crop production,
synthetic fertilizer use, and livestock production. Synthetic fertilizer, crop residues and manure are
sources of applied nitrogen which cause N2O emissions. Emissions from this source can be
mitigated by reducing or increasing the efficiency of fertilizer use, but mitigation is not assumed for
this source. IPCC default factors relate
5.1.2 Source Results
Between 1990 and 2005, N2O emissions from agricultural soil management have increased 10
percent, from 1,804 to 1,984 MtCO2e. Underlying this trend are increasing crop production and
increasing use of fertilizer and other nitrogen sources such as crop residues. Emissions from this
source have grown in Central and South America, non-OECD Asia, Africa and the Middle East.
Emissions in the OECD have remained flat, while emissions in non-OECD Europe and Eurasia
have dropped by half between 1990 and 2005. Total N2O emissions from agricultural soils are
presented in Table 5-1.
From 2005 to 2030, N2O emissions from agricultural soils are projected to increase by 34 percent,
from 1,984 to 2,666 MtCO2e. This projection assumes continued increases in fertilizer usage. Over
the projection period emissions are expected to increase in all regions. These regional increases are
driven largely by projected emission increases in China, the United States, India, Brazil, Argentina,
and Pakistan. Among OECD countries, growth will be driven by the U.S., Canada, Turkey, New
Zealand, and Australia.
Table 5-1: Total N2O Emissions from Agricultural Soils (MtCO2e)
Gas T990 F995 2000 2005 2010 2oTs 2020 2025 2030
Total N2O T80^2 T789J T85I6 I^SlS 2,124.0 2,287.4 2,408.3 2,533.8 2,665.9
August 201 I 5. Agriculture Page 5-4
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Exhibit 5-3: N2O Emissions from Agricultural Soils 1990 - 2030 (MtCO2e)
3,000
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECDAsia
OECD
500
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
The primary factor for the increase in emissions illustrated in Exhibit 5-3 is the expected increase in
crop and livestock production, with expanded use of synthetic fertilizers, to meet the growing
fertilizer consumption requirements of non-OECD Asia, Central and South America, and Africa.
Emission increases in these areas are somewhat offset by declining or slower growth in OECD
countries (such as the EU and U.S.) due to constant agricultural acreage, economic and
environmental agricultural policies, and the changing world market for goods. Due to the
complexities of agricultural product markets and the influences of disruptions in the industry (such
as food safety issues), many of these factors are hard to predict. The following paragraphs explain
some of the relevant developments that influence emissions from agricultural soils.
Overall, expected modest increases in emissions from much of the EU and more robust but slowing
growth in the United States, Canada, Australia, New Zealand, and Turkey, result in a projected 36
percent rate of growth over the study period for the OECD. Many OECD countries (especially in
the EU) have little opportunity for expanding crop acreage for key crops (e.g., wheat, corn) and
therefore most growth in production is in the form of yield growth, which tends to have less of an
impact on emissions growth than acreage increases. The market restructuring during the early 1990s
in Eastern Europe, as well as in the non-EU FSU countries, resulted in an economic downturn in
those countries. Because of lower farm income due to economic restructuring, farmers purchased
and used less fertilizer, a main driver for emissions from this category, as well as keeping fewer
livestock, leading to lower manure emissions. In the U.S., the 1990s were characterized by increases
in synthetic fertilizer usage, crop and forage production, and manure production. During the
projected 2010 to 2030 period, fertilizer use is expected to increase in most parts of the OECD and
FSU (except Russia), leading to increases in emissions, while manure production is expected to
decrease in Eastern Europe, slightly offsetting this growth.
August 2011
5. Agriculture
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In non-OECD Asia, Africa, Central and South America, the anticipated growth from 2010 to 2030
in agricultural soils emissions has several causes. Increases in population as well as per-capita
income, particularly in China, India, and parts of Central and South America, will increase the
demand for agricultural products such as cereal grains, milk, oilseed products, and meat. In addition,
livestock operations are expected to become more advanced in these areas, thereby increasing
demand for high-quality feed crops (e.g., corn-based). While some of this demand will be addressed
in the short term through increases in imports, long term expansion of domestic production
capabilities is expected. The increased commercialization of the livestock industries in these growing
countries is also expected to increase livestock productive capacity and the production of livestock
manure, an important component of N2O emissions for this source category.
5.2 Enteric Fermentation (CH4)
5.2.1 Source Description
Normal digestive processes in animals result in CH4 emissions. Enteric fermentation refers to a
fermentation process whereby microbes in an animal's digestive system ferment food. CH4is
produced as a byproduct and can be exhaled by the animal.
Domesticated ruminants such as cattle, buffalo, sheep, goats, and camels account for the majority of
CH4 emissions in this sector. Other domesticated non-ruminants such as swine and horses also
produce CH4as a byproduct of enteric fermentation, but emissions per animal species vary
significantly. Total emissions are driven by the size of livestock populations and the management
practices in use, particularly the feed regime used. The quantity, quality, and type of feed are
significant determinants of CH4 emissions. Feed intake varies by animal type, as well as by weight,
age, and growth patterns for individual animals.
Calculations in this section are based on population estimates and growth projections for livestock
divided among various species. Emission factors for each species are used from the 2006 IPCC
guidelines. Emission factors varied between developed and developing country, and in some cases
by region. No mitigation is assumed. Emission factors are held constant through the projection
period despite the likelihood that changes in management practices will change average emission
factors, due to the difficulty in anticipating how management practices will change over time. CH4
emission factors from this source tend to be higher from more industrialized regions due to higher
productivity per animal.
5.2.2 Source Results
Global CH4 emissions from enteric fermentation increased by 6 percent between 1990 and 2005,
from 1,755 11,864 MtCO2e. Over this time period, global livestock populations have increased. CH4
emissions from this source have increased most quickly in Africa and Central and South America.
Emissions in non-OECD Europe and Eurasia have decreased by 48 percent between 1990 and
2005.
From 2005 to 2030, CH4 emissions from enteric fermentation are projected to increase 23 percent,
from 1,864 to 2,289 MtCO2e. This projection assumes further increases in livestock production. It
does not account for possible changes in emissions per head of livestock due to changes in
management practices such as a move towards more concentrated feeding operations. The largest
increases in emissions are expected in Africa and non-OECD Asia.
August 201 I 5. Agriculture Page 5-6
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Between 1990 and 2005, emissions from enteric fermentation decreased in the OECD and non-
OECD Europe and Eurasia, while they increased in the other regions. Emissions in all regions are
expected to grow over the 2030 projection period, but will grow most quickly in Africa (49 percent),
non-OECD Asia (35 percent) and the Middle East (34 percent), continuing the trend of a larger
portion of world emissions shifting away from OECD countries towards non-OECD countries. In
2005, the largest five emitting countries of CH4 from enteric fermentation were Brazil, China, India,
the U.S. and Argentina.
Table 5-2: Total CH4 Emissions from Enteric Fermentation (MtCO2e)
Gas
1990
1995
2000
2005
2010
2015
2020
2025
2030
Total CH4 1,754.5 1,789.0 1,784.3 1,864.2 1,905.6 2,015.7 2,103.2 2,195.3 2,288.6
Exhibit 5-4: CH4 Emissions from Enteric Fermentation 1990 - 2030
(MtCO2e
2,500
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
D Non-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
Since beef, dairy, and buffalo are responsible for the majority of the world enteric fermentation
emissions, historical trends in enteric fermentation CH4 emissions follow the production cycles of
these animal types. Despite the recent setbacks in the dairy and beef industries due to the global
economic slowdown, the markets have started to recover, and world projections for the period 2009
through 2019 show increases in both meat and dairy product consumption, production, and trade
(FAPRI, 2010). Advancing domestic beef and dairy production capabilities in some key developing
countries, in combination with the maintenance of relatively high levels of production (but not
necessarily high productivity growth) for large exporting countries, are expected to shape the
emissions projections for this source.
Increases in per capita income are expected to drive the increase in livestock product demand,
particularly in developing countries, which in turn drives domestic livestock populations and thus
enteric fermentation emissions. Also, the anticipated transformation of management systems from
August 2011
5. Agriculture
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dispersed, pasture operations to larger-sized, commercialized production is expected to increase
breeding herd productivity, animal size, and overall meat production. Such transformations are
occurring now throughout the developing world and will likely increase emissions, particularly in
Africa and Central and South America.
In many developed countries, CH4 emissions from enteric fermentation are expected to decline
through 2030. In the EU, cattle inventories are projected to decrease, mainly in the dairy industry, as
yields increase and as consumption decreases (FAPRI, 2010). During the 1990s, the farm industries
in many non-OECD Europe and Eurasian countries reduced their livestock production significantly
as part of their transition to market economies; however this trend slowed in 2000, and production
is expected to gradually increase through 2030. A decrease in emissions for the U.S. occurred
between 1990 and 1995, resulting from increased production efficiencies, such as those occurring in
the dairy industry. Recovery has been slow due to the dampening effect on export production
between 2003 and 2005 due to bovine spongiform encephalopathy (BSE) cases in the industry and
the current economic downturn. In China, demand and production of both meat and milk have
been growing rapidly, and despite decreased milk exports following the milk scandal in 2008,
emissions are projected to decline only slightly between 2005 and 2010, and then increase through
2030.
5.3 Rice Cultivation (CH4)
5.3.1 Source Description
The anaerobic decomposition of organic matter in flooded rice fields produces CH4. When fields are
flooded, aerobic decomposition of organic material gradually depletes the oxygen present in the soil
and flood water, causing anaerobic conditions in the soil to develop. Once the environment
becomes anaerobic, CH4 is produced through anaerobic decomposition of soil organic matter by
methanogenic bacteria. Several factors influence the amount of CH4 produced, including water
management practices and the quantity of organic material available to decompose.
Calculations in this section utilize statistics on land area under rice cultivation and rice season length
and management practices. No mitigation is assumed for this source.
5.3.2 Source Results
CH4 emissions from rice production have increased 6 percent between 1990 and 2005, from 670 to
710 MtCO2e (see Table 5-3). Underlying this trend has been a similar increase in land area of
harvested rice. In 2005, 90 percent of CH4 emissions from this sector were from non-OECD Asia.
From 2005 to 2030, CH4 emissions from this source are projected to increase 4 percent from 710 to
739 MtCO2e. This projection assumes a further increase in rice area harvested over the projection
period. The increase is primarily attributed to increased demand for rice due to expected population
growth in rice consuming countries. Total global rice consumption is expected to rise in the
projection years; however, this increase is slower than population growth because per-capita
consumption decreases over the next 10 years (FAPRI, 2010). Emissions growth has also been
tempered by innovations that increased rice production without increasing rice acreagethe most
important determinant of rice CH4 emissions. It is anticipated that yield growth, as opposed to
acreage growth, will continue to be the main source of the production growth, with the continued
development and adoption of higher-yielding rice varieties in many producing countries (FAPRI,
2010).
August 201 I 5. Agriculture Page 5-8
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Table 5-3: Total CH4 Emissions from Rice Cultivation (MtCO2e)
Gas
1990
1995
2000
2005
2010
2015
2020
2025
2030
Total CH4
670.4
683.2
708.3
710.4
732.3
729.9
732.4
735.5
739.1
Exhibit 5-5: CH4 Emission from Rice Cultivation 1990 - 2030 (MtCO2e)
800
0
y
700
600
500
400
C
_O
.2 300
UJ
200
100
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECDAsia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
The non-OECD Asia region produces the vast majority of CH4 emissions from rice cultivation,
accounting for more than 90 percent of the emissions for this source in 2005, as illustrated in
Exhibit 5-5. The single largest contributors in this region are India, China, Indonesia, Thailand,
Vietnam, and Burma. Emissions from non-OECD Asia are projected to increase 6 percent between
2005 and 2030. Emissions from China are expected to decrease over the projection period, while
they increase from other major emitting counties in non-OECD Asia.
Thailand, Viet Nam, India, and Pakistan are projected to dominate global rice exports through the
2005 to 2030 projection period, with an estimated 75 percent or greater share of the global export
market. Continued yield growth in Viet Nam and Pakistan and both yield and area growth in
Thailand, Myanmar, and India is expected to increase production in those key rice-producing
countries. China is expected to continue to be a significant contributor, but at a lower rate of growth
due to decreases in production area (FAPRI, 2010).
5.4 Manure Management (CH4, N2O)
5.4.1 Source Description
Manure management produces CH4 and N2O. Methane is produced during the anaerobic
decomposition of manure, while N2O is produced by the nitrification and denitrification of the
organic nitrogen content in livestock manure and urine. Emissions from only the managed
August 2011
5. Agriculture
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collection, handling, storage, and treatment of manure are included here; emissions from the
distribution of manure on pastures, ranges, and paddocks are included with agricultural soils
emissions and are discussed in Section 5.2.
The quantity of CH4 emitted from manure management operations is a function of three primary
factors: the type of treatment or storage facility, the ambient climate, and the composition of the
manure. When manure is stored or treated in liquid systems such as lagoons, ponds or pits,
anaerobic conditions can often develop and the decomposition process results in CH4 emissions.
Ambient temperature and moisture content also affect CH4 formation, with higher ambient
temperature and moisture conditions favoring CH4 production. The composition of manure is
directly related to animal types and diets. For example, milk production in dairy cattle is associated
with higher feed intake, and therefore higher manure excretion rates than non-dairy cattle. Also,
supplemental feeds with higher energy content generally result in a higher potential for CH4
generation per unit of waste excreted than lower quality pasture diets. However, some higher energy
feeds are more digestible than lower quality forages, which can result in less overall waste excreted.
Ultimately, a combination of all these factors affects the actual emissions from manure management
systems.
Nitrous oxide generation is a function of the composition of the manure, the type of bacteria
involved in the decomposition process, and the oxygen and liquid content of manure. Nitrous oxide
emissions occur through the processes of nitrification and denitrification, where the manure is first
treated aerobically (nitrification) and then handled anaerobically (denitrification). Nitrous oxide
generation is most likely to occur in dry manure handling systems that can also create pockets of
anaerobic conditions.
Calculations in this section are based on population estimates and growth projections for livestock
divided among various species. Nitrogen excretion and emission factors for each species are used
from the 2006 IPCC guidelines and emission factors varied between regions. No mitigation is
assumed. Emission factors are held constant through the projection period despite the likelihood
that adoption of mitigation and changes in management practices will change average emission
factors, due to the difficulty in anticipating how those changes will occur. CH4 emission factors from
this source tend to be higher from more industrialized regions due to higher productivity per animal,
while nitrogen excretion per 1,000 pounds of animal are lower due to more efficient nutrient
conversion.
Between 1990 and 2005, CH4 and N2O emissions from manure management decreased by 5 percent,
from 408 to 389 MtCO2e. This decline was driven by the non-OECD Europe and Eurasia region,
where emissions from this source decreased by 61 percent between 1990 and 2005 due to a general
decline in livestock production as a result of market restructuring. Emissions increased in other
country groupings.
Global CH4 and N2O emissions from manure management are projected to increase by 17 percent
from 2005 and 2030 (see Table 5-4. Emissions are projected to increase significantly in Africa,
Central and South America and the Middle East. Historically, the largest portion of GHG emissions
from manure management is from the OECD, which accounted for 43 percent of all emissions in
August 201 I 5. Agriculture Page 5-10
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2005. Emissions from the OECD are projected to increase by just 1 percent between 2005 and
2030. In contrast, the expected growth rates are significantly higher in other regions: Africa (43
percent), non-OECD Asia (39 percent), Middle East (10 percent), and Central and South America
(30 percent). Although these regions have significantly higher growth rates, the OECD remains the
top emitting region through 2030.
Table 5-4: Total CH4 and N2O Emissions from Manure Management (MtCO2e)
Gas
CH4
N2O
1990
219.2
188.7
1995
215.9
176.4
2000
214.2
158.8
2005
226.2
162.8
2010
236.8
167.7
2015
243.0
173.6
2020
249.2
179.3
2025
256.0
185.5
2030
263.6
191.8
Total
407.9
392.3
373.0
389.0
404.5
416.6
428.5
441.5
455.4
Exhibit 5-6: CH4 Emissions from Manure Management 1990 - 2030 (MtCO2e)
300
0
(N
0
VI
O
UJ
250
200
ISO
100
50
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
D Non-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
August 2011
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Exhibit 5-7: N2O Emissions from Manure Management 1990 - 2030 (MtCO2e)
250
200
-------
to increase both production and livestock population. In particular, this transformation is expected
to take place in countries such as China and Brazil, which are both expected to have high growth
rates over the next decade (FAPRI 2010). In addition, larger commercialized operations tend to
utilize more liquid-based manure management systems, which generate more CH4 emissions than
smaller, individual feedlot operations. In the U.S., one of the largest and most commercialized pork
producing countries in the world, swine are responsible for almost half of the CH4 emissions from
manure management primarily because a large portion of the manure is handled with liquid-based
systems. As other key pork producing countries transform to larger management systems, the trend
will likely be toward increasing CH4 emissions.
5.5 Other Agriculture Sources (CH4, N2O)
5.5.1 Source Description
This category includes emission sources from the agricultural sector that are relatively small
compared to the sector overall. The data presented in this chapter include the following sources of
CH4 and N2O:
Agricultural Soils (CH4)
Field Burning of Agricultural Residues (CH4, N2O)
Prescribed Burning of Savannas (CH4, N2O)
Open Burning from Forest Clearing (CH4)
Field burning, prescribed burning, and open burning constitute the majority of emissions for this
source category, whereas agricultural soils contribute a small fraction of emissions.
5.5.2 Source Results
Total emissions from other agricultural sources are shown in Table 5-5. Africa is the largest
contributor of emissions for this source category, accounting for about 46 percent of emissions in
2005. Central and South America and non-OECD Asia are the second and third largest contributors
for this source category, contributing an average of 26 percent and 22 percent in 2005, respectively.
Data for other agricultural sources are based only on country reports, and so are not fully
comparable between countries or to data in the remainder of this report since emissions are not
calculated for countries not reporting emissions data.
Table 5-5: Total CH4 and N2O Emissions from Other Agricultural Sources (MtCO2e)
Gas
CH4
N2O
Total
1990
505.9
776.7
1,282.6
1995
419.3
743.0
1,162.3
2000
343.4
699.3
1,042.6
2005
420.4
744.1
1,164.4
2010
420.4
744.1
1,164.4
2015
420.4
744.1
1,164.4
2020
420.4
744.1
1,164.4
2025
420.4
744.1
1,164.4
2030
420.4
744.1
1,164.4
Exhibit 5-8 and Exhibit 5-9 illustrate trends in CH4 and N2O emissions for this category.
August 201 I 5. Agriculture Page 5-13
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Exhibit 5-8: CH4 Emissions from Other Agricultural Sources 1990-2030 (MtCO2e)
600
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
Exhibit 5-9: N2O Emissions from Other Agricultural Sources 1990-2030 (MtCO2e)
900
800
100
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
D Non-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
August 2011
5. Agriculture
Page 5-14
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6 Waste
This section presents global CH4 and N2O emissions for 1990 to 2030 for the following waste sector
sources:
Landfillmg of Solid Waste (CH4)
Wastewater (CH4)
Human Sewage Domestic Wastewater (N2O)
Other Waste Sources (CH4, N2O), including:
Miscellaneous Waste Handling Processes (CH4 N2O).
The waste sector accounted for 12 percent of total non-CO2 emissions in 2005, and is anticipated to
drop to 10 percent of emissions by 2030. Exhibit 6-1 shows the waste sector emissions by source.
As shown in Exhibit 6-1, the two largest sources of non-CO2 GHG emissions within the waste
sector are landfilling of solid waste and wastewater, together contributing 92 percent of emissions
throughout the 1990 to 2030 period. Landfilling of solid waste contributed 59 percent of total waste
sector emissions in 2005, while wastewater contributed 33 percent of emissions. Out of all sources,
landfilling was the fourth largest individual source of non-CO2 GHG emissions in 2005, at 767
MtCO2e.
Exhibit 6-1 : Total Non-CO2 Emissions from the Waste Sector, by Source (MtCO2e)
1,800
D Other Waste Sources
Human Sewage - Domestic
Wastewater
D Wastewater
Landfill ing of Solid Waste
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
Exhibit 6-2 shows waste sector emissions by region. The OECD and non-OECD Asia were the
largest contributors to waste sector non-CO2 emissions, accounting respectively for 35 percent and
30 percent of emissions in 2005. Non-CO2 emissions from the OECD are expected to decrease to
31 percent of the total in 2030, still the largest-emitting region.
August 2011
6. Waste
Page 6-1
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Exhibit 6-2: Total Non-CO2 Emissions from the Waste Sector, by Region (MtCO2e)
D Middle East
HI Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECD Asia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
6.1 Landfilling of Solid Waste (CH4)
6.1.1 Source Description
CH4 is produced and emitted from the anaerobic decomposition of organic material in landfills. The
major drivers of emissions are the amount of organic material deposited in landfills, the extent of
anaerobic decomposition, the thickness as well as the physical and chemical properties of the landfill
cover materials, the seasonal variation in methane oxidation rates1, and the level of landfill CH4
collection and combustion (e.g., energy use or flaring)2. The amount of waste deposited in landfills
can be affected by waste-reduction and recycling efforts. Because organic material deep within
landfills takes many years to completely decompose, past landfill disposal practices greatly influence
present day emissions. Developed countries are experiencing a stabilization or decline in landfill
wastes due to regulations that encourage such practices. Developing countries, on the other hand,
are expected to face increasing rates of landfill methane due to increased urbanization and a parallel
increase in controlled landfilling (IPCC, 2007). However, public scrutiny of GHGs from landfilling
(and other waste management activities) is increasing in both developed and developing countries
(Bogner and Spokas, 2010).
Emissions projections for this source utilize National Communications projections where available
and a combination of activity data and emission factors to project emission estimates where country
1 Landfill methane oxidation reflects the amount of methane that is oxidized or converted to CC>2 in the soil or other
materials that cover the landfilled waste.
2 For additional information on landfill methane emissions refer to IPCC, 2007; Bogner and Spokas, 2010; and Scheutz
et al, 2009.
August 2011
6. Waste
Page 6-2
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reported projected data was not available. Emission factors were generated using the IPCC 2006
Waste Model.
International voluntary programs encourage measures which can reduce CH4 emissions through the
capture and beneficial reuse of landfill CH4 gas, but those programs are not explicitly included in
these estimates. Waste reduction programs, as well as CH4 recovery and use impact the amount of
CH4 that is actually released to the atmosphere. Mitigation measures include installing landfill gas
collection systems. The collected landfill CH4 gas can then be flared, used to generate heat and/or
electricity, or sold for pipeline injection. Over the last couple of decades, although landfill methane
emissions have continued to increase, growth in these emissions has declined due to decreasing
landfilling rates, particularly in Europe, and increasing landfill gas recovery rates in many countries
(IPCC 2007). Additional mitigation measures contributing to landfill gas recovery include increased
use of biocovers and geomembrane composite covers to enhance CH4 oxidation.
The IPCC 2006 Guidelines recommend using a first-order decay (FOD) method for the simplest tier
1 estimates, replacing the previous mass balance method recommended by IPCC 1996 Guidelines
and used for the GER 2006 report. Emissions calculations for non-reporting countries use the Tier
1 FOD method; however, it is possible that not all country-reported data has used this relatively
recent change in the methodological guidance, thus limiting comparability across country estimates.
6.1.2 Source Results
Between 1990 and 2005, global CH4 emissions from landfilling of solid waste are estimated to have
increased by about 9 percent, from 705 to 767 Mt (see Table 6-1). Driving factors for landfill
emission trends are growing populations, increases in personal incomes, and expanding
industrialization, all of which can lead to increases in the amount of solid waste generated for a
country. Over this time period emissions have decreased in OECD countries. Emissions in all other
regions have increased (see Exhibit 6-3).
From 2005 to 2030 emissions are projected to increase by about 19 percent from 767 to 910
MtCO2e (see Table 6-1). The projected increase in emissions shows significant shifts in
contributions to landfill emissions. Emissions from the OECD are projected to increase by just 3
percent between 2005 and 2030, decreasing from 45 percent to 39 percent of the global emissions
for this source. By 2030, the following two regions are projected to contribute more than a 10
percent share of global emissions: Africa (13 percent) and non-OECD Asia (22 percent). Countries
with fast-growing economies and populations are expected to contribute more to the global CH4
total from landfills as their economies grow and waste generation rates increase. Countries with
more steady-state economic growth, and small or even declining population growth rates, are likely
to experience minimal growth in landfill emissions. The OECD countries emitted about 45 percent
of the global CH4 produced from the landfilling of solid wastes in 2005, as shown in Exhibit 6-3. In
that same year, the remaining regions each contributed less than 20 percent of the CH4 emissions for
this source category. Within the OECD, the U.S. is the largest source of emissions from the
landfilling of solid waste. In 2005, the U.S. emitted 128 MtCO2e. of CH4, which is about 17 percent
of the global total.
Table 6-1: Total CH4 Emissions from Landfilling of Solid Waste (MtCO2e)
GasT990T9952000200520102oTs202020252030
Total CH4 705.0 757.7 752.2 767.0 799.0 826.3 855.2 883.5 910.1
August 201 I 6. Waste Page 6-3
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Exhibit 6-3: CH4 Emissions from Landfilling of Solid Waste 1990 - 2030 (MtCO2e)
-------
wastewater treatment to handle their domestic wastewater, so that CH4 emissions are small and
incidental. However, in developing countries with little or no collection and treatment of
wastewater, anaerobic systems or disposal environments such as latrines, open sewers, or lagoons
are more prevalent. Industrial wastewater can also be treated anaerobically, with significant CH4
being emitted from those industries with high organic loadings in their wastewater stream, such as
food processing and pulp and paper facilities. While country-reported estimates include both
domestic and industrial wastewater, the emissions estimates calculated using Tier 1 methodology
only include domestic wastewater.3
Emissions projections for this source utilize National Communications projections were available.
Where NC data was not available, emissions were projected from UNFCCC historical data using
population growth rates.
CH4 emissions from wastewater can be reduced through improved wastewater treatment practices
include reducing the amount of organic waste anaerobically digested and by flaring or using CH4
from anaerobic digesters for cogeneration or other beneficial reuse. Such emission reduction
activities are not widespread are not explicitly included in these estimates. The estimates do not
account for possible future modernization of domestic wastewater handling that may see a shift to
aerobic treatments and the implementation of CH4 capture from anaerobic digesters that would
result in a reduction of emissions.
6.2.2 Source Results
Between 1990 and 2005, global CH4 emissions from wastewater are estimated to have increased by
about 20 percent, from 354 to 425 MtCO2e (see Table 6-2). The main driver for increasing domestic
wastewater emissions is population growth, particularly growth associated with countries that rely on
anaerobic treatment and collection systems such as latrines, septic tanks, open sewers, and lagoons.
Most developed countries have an extensive infrastructure to collect and treat urban wastewater, in
which the majority of systems rely on aerobic treatment with minimal CH4 production and thus less
effect on the emissions trend. In contrast, there is widespread use of less advanced, anaerobic
systems in some of the fastest growing parts of the world. Consequently, the largest growth in
emissions has been in Africa, the Middle East, and Central and South America (see Exhibit 6-3).
From 2005 to 2030 emissions are projected to increase by about 25 percent from 425 to 531
MtCO2e (see Table 6-2). The projected rate of increase is expected to be highest in the same regions
where emissions grew most quickly over the historical period: Africa, the Middle East, and Central
and South America. Emissions from Africa are projected to increase by about 58 percent between
2005 and 2030, 50 percent for the Middle East, and 35 percent for Central and South America.
Table 6-2: Total CH4 Emissions from Wastewater (MtCO2e)
GasT990[9952000200520102015202020252030
Total CH4 354.2 372.9 400.9 425.3 449.8 472.7 494.4 514.0 531.4
3 While industrial wastewater emissions were not explicitly estimated in this report, some countries report industrial
wastewater emissions within this source category. In these cases, this source category includes these emissions.
August 201 I 6. Waste Page 6-5
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Exhibit 6-3: CH4 Emissions from Wastewater 1990 - 2030 (MtCO2e)
600
500
% 400
C
O
UJ
300
200
100
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECDAsia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
A majority of domestic wastewater goes uncollected and untreated in large portions of the non-
OECD Asia and Africa regions, with an even larger share in rural areas. Much of this untreated
wastewater is found in open sewers, pits, latrines, or lagoons where there is greater potential for CH4
production. For example, nearly 74 percent of China's domestic wastewater emissions are estimated
to come from latrines, with the majority of wastewater generated in rural China being untreated. The
largest share of India's estimated emissions also comes from latrines (62 percent), but open sewers
contribute a sizable amount as well (34 percent). Like India, most of Indonesia's emissions come
from latrines and open sewers. As long as populations grow significantly without large scale
advances in wastewater treatment, these areas will continue to have a major influence on the upward
trend in wastewater CH4 emissions. The impact of urban center growth in these regions, however,
may offset this trend if migrating rural populations are served by more advanced urban treatment
systems.
Less advanced treatment systems are still widely used in some developed countries. In the U.S., for
example, septic tanks are estimated to be responsible for 65 percent of the domestic wastewater
emissions, though only 25 percent of treatment. Septic tanks are utilized in many parts of the
developed world where centralized sewer infrastructure is not available; however, their usage is not
expected to increase significantly in the future since there are economic and site considerations that
limit their widespread applicability.
August 2011
6. Waste
Page 6-6
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6.3 Human Sewage - Domestic Wastewater (N2O)
6.3.1 Source Description
Domestic wastewater is also a source of N2O emissions. Domestic wastewater includes human waste
as well as flows from shower drains, sink drains, washing machines and other domestic effluent. The
wastewater is transported by a collection system to an on-site, decentralized wastewater treatment
(WWT) system, or a centralized WWT system. Decentralized WWT systems are septic systems and
package plants. Centralized WWT systems may include a variety of processes, ranging from
treatment in a lagoon to advanced tertiary treatment technology for removing nutrients. After
processing, treated effluent may be discharged to a receiving water environment (e.g., river, lake,
estuary) applied to soils, or disposed of below the surface.
N2O may be generated during both nitrification and denitrification of the nitrogen present in the
wastewater effluent, usually in the form of urea, ammonia, and proteins. These are converted to
nitrate via nitrification, an aerobic process converting ammonia-nitrogen into nitrate (NO3-).
Denitrification occurs under anoxic conditions (without free oxygen), and involves the biological
conversion of nitrate into dinitrogen (N2). N2O can be an intermediate product of both processes,
but is more often associated with denitrification.
Emissions projections for this source utilize National Communications projections were available.
Where NC data was not available, emissions were projected from UNFCCC historical data using
population growth rates. Emissions may be linked to treatment type (lagoons versus advanced
treatment such as nitrification/denitrification plant), however not enough information is available to
account for advanced treatment methods. The IPCC default methodology uses the same emission
factor for all wastewater generated. Therefore, the total quantity of wastewater generated, regardless
of treatment type, is the principle factor.
Some industries produce wastewater with significant nitrogen loadings that is discharged to the city
sewer, where it mixes with domestic, commercial, and institutional wastewater. However, emissions
from these sources have not been estimated, unless countries have reported these emissions within
either the human sewage or wastewater source categories. This methodology does not take into
account changes to dietary standards over time in developing countries, which could lead to
emissions increases.
6.3.2 Source Results
Between 1990 and 2005, global N2O emissions from human sewage are estimated to have increased
by about 20 percent, from 67 to 80 MtCO2e (see Table 6-4). The main driver for human sewage
emissions is population increase.
From 2005 to 2030 emissions are projected to increase by about 22 percent from 80 to 97 MtCO2e
(see Table 6-4). Emissions from this source are projected to rise most quickly in Africa, the Middle
East, and Central and South America. Emissions from Africa are projected to increase by about 59
percent between 2005 and 2030, 42 percent for the Middle East, and 29 percent for Central and
South America. In 2030, non-OECD Asia and the OECD continue to be the largest contributing
regions to N2O emissions from human sewage, while declining slightly in their overall share of world
emissions to 35 percent and 25 percent respectively. In 2030, Africa is projected to contribute 16
percent of emissions, and Central and South America is projected to contribute 11 percent.
August 201 I 6. Waste Page 6-7
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Table 6-4: Total N2O Emissions from Human Sewage - Domestic Wastewater (MtCO2e)
Gas
1990
1995
2000
2005
2010
2015
2020
2025
2030
Total N2O
66.7
69.1
74.7
79.9
83.9
87.7
91.3
94.5
97.4
Exhibit 6-4: N2O from Human Sewage - Domestic Wastewater 1990 - 2030 (MtCO2e)
120
100
-------
Table 6-6: Total CH4 and N2Ofrom Other Waste Sources (MtCO2e)
Gas
CH4
N2O
Total
1990
13.4
8.8
22.2
1995
13.6
9.5
23.1
2000
14.6
10.4
24.9
2005
15.1
11.0
26.1
2010
15.3
II. 1
26.4
2015
15.3
II. 1
26.4
2020
15.3
II. 1
26.4
2025
15.3
II. 1
26.4
2030
15.3
II. 1
26.4
Exhibit 6-5: CH4 Emissions from Other Waste Sources 1990 - 2030 (MtCO2e)
18
16
14 -
% 12
0
y
10
C
O
UJ
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECDAsia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
August 2011
6. Waste
Page 6-9
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Exhibit 6-6: N2O Emissions from Other Waste Sources 1990 - 2030 (MtCO2e)
12
10
0
(N
0
C
O
UJ
D Middle East
D Central and South America
D Africa
Non-OECD Europe & Eurasia
DNon-OECDAsia
OECD
1990 1995 2000 2005 2010 2015 2020 2025 2030
Year
August 2011
6. Waste
Page 6-10
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7 Methodology
This chapter outlines the methodologies used to compile and estimate category and country-specific
historical and projected emissions of CH4, N2O, and high-GWP gases. The preferred approach for
estimating historical and projected emissions is to use a hierarchy of country-prepared, publicly-
available reports. If country-supplied data are not available, EPA estimates emissions consistent with
the Revised 1996IPCC Guidelines for National Greenhouse Gas Inventories (IPCC Guidelines) (IPCC,
1997), the IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories
(IPCC Good Practice Guidance) (IPCC, 2000), and the 2006 IPCC Guidelines for National Greenhouse
Gas Inventories (IPCC Guidelines) (IPCC, 2006).
A primary source of data for historical emission estimates was the UNFCCC flexible query system
data (UNFCCC, 2009). The UNFCCC flexible query system contains historical CH4and N2O
emission estimates for Annex I (Al) and non-Al countries, reported to the UNFCCC (from Al
National Inventories Common Reporting Format files and non-Al National Communication
reports). The CRF data obtained through the UNFCCC flexible query system (UNFCCC, 2009)
contain reported national inventory data from 1990 through 2007. Data for non-Al countries
obtained through the UNFCCC flexible query system contained data reported through country
National Communication reports. As identified by the UNFCCC, Annex I countries include all
OECD countries in 1992, plus countries with economies in transition and most of Central and
Eastern Europe. Annex I countries are noted in Exhibit 1-2 and Appendix I. The hierarchy of data
sources and an overview of the methods used to augment missing historical and projected estimates
are discussed below followed by a detailed discussion of the methodology associated with each
source category and gas.
This report does not describe in detail the methodology used to generate the publicly-available data.
However, the CRF inventory data obtained through the UNFCCC flexible query system are
generally comparable across countries because they are based on IPCC methodologies and are
reported for a standard list of IPCC source categories. Although the CRFs provide the latest
historical GHG emissions data for Annex I countries, they do not contain projected emissions. A
preferred source for projected emissions is the National Communications. The National
Communications are documents that were submitted by each Party to the UNFCCC Secretariat to
report on steps taken to implement the Convention; they contain emissions and projections to
various years, up to 2035. EPA used the Fifth National Communications for Annex I countries and
the First, Second, and/or Third National Communications for non-Annex I countries for this
analysis. The Fifth National Communications for Annex I countries were submitted primarily in
2009 and 2010. The non-Annex I countries have a more flexible schedule, with submissions of First,
Second, and/or Third National Communications from 1997 to 2009. The projected information
from the National Communication is adjusted to be compatible with the most recent inventory data,
if necessary.
Data Sources for Historical and Projected Emissions
CH4 and N^O General Methodology
The preferred approach for estimating historical and projected emissions is to use country-prepared,
publicly-available reports. EPA applied an overarching methodology to estimate emissions across all
sectors, and deviations to this methodology are discussed in each of the source-specific
methodology sections. EPA applied the following general methodology to estimate global non-CO2
emissions.
August 201 I 7. Methodology Page 7-1
-------
Historical Emissions (1990, 1995, 2000, 2005)
Annex I Countries
The UNFCCC flexible query system (UNFCCC, 2009) provides emission estimates for Al countries
from Common Reporting Format (CRF) files, submitted with annual national inventories. The full
or partial time series of source disaggregated data is available for Al countries from 1990 through
2007. The time series is complete for the majority of sources; however there are gaps in the time
series for some countries and categories and data for missing years were supplemented. The
methodology used by each source to interpolate, backcast, or forecast depends on the availability of
CRF data and the distribution of that data over time. In general, the following methodology was
applied to interpolate, backcast, or forecast data:
When two years are reported such that a year requiring an estimate (e.g., 1995) occurred
between the reported years (e.g., 1993 and 1997), EPA interpolates the missing estimate
(1995) using reported estimates.
EPA backcasted or forecasted emission estimates to complete the historical series for
1990, 1995, 2000, and 2005 on a source by source basis. For each source, EPA used
growth rates for available activity data believed to best correlate with emissions (e.g.,
production, consumption). If either 1) more than one type of activity data should be
used, 2) the emission factor will vary over time, or 3) the relationship between the
activity data and emissions is not linear (i.e., exponential), then EPA used Tier 1 growth
rates. This involves estimating emissions for 1990, 1995, 2000, and 2005 using a Tier 1
approach, then using the rate of growth of this emission estimate to backcast and
forecast the country-reported emissions.
If a country reported an estimate for an individual source for one year, but reported
aggregate estimates for other years, EPA disaggregated the estimates using the percent
contribution of the individual source in the latest reported year.
Non-Al Countries
Historical emissions data from non-Al countries were available in the UNFCCC flexible query
system as well, but generally these reported data do not constitute a full time series. The
methodology for interpolating or backcasting missing historical data used by each source will follow
the same general guidelines outlined in the Al section above. Because the data for non-Al countries
from the UNFCCC flexible query system do not generally have a complete time series, it is likely
that non-Al sources will rely more heavily on Tier 1 calculated growth rates or activity data growth
rates for backcasting and forecasting emissions between 1990 and 2005.
Projected Emissions (2010, 2015, 2020, 2025, and 2030)
Emission projections by source and country were obtained from National Communications (NCs)
reports. For Al countries, this refers to the Fifth NCs currently being released. For non-Al
countries, EPA reviewed the most recent NCs submitted to the UNFCCC.
If an NC had projections for a sector but not a source, EPA used the relative proportion of
emissions for the latest year of historical emissions to disaggregate projected emissions for a source.
For example, if France projected CH4 emissions from agriculture to 2030 but does specify what
portion is from manure management, EPA took the proportion of emissions that manure
August 201 I 7. Methodology Page 7-2
-------
contributes to agriculture CH4 emissions in France's 2007 GHG Inventory, assume this proportion
remains constant for 2030, and apply this to the 2030 agriculture estimate.
If projections for a sector are not available from a NC, EPA used activity data drivers or Tier 1
growth rates, specific to each source. The specific methodology followed by each source category is
outlined in each sector's methodology description.
High Global Warming Potential Gas Emissions
For most countries, emissions and projections are not available for the sources of high-GWP gases.
Therefore, EPA estimates high-GWP emissions and projections using detailed source
methodologies described later in this chapter.
7.1 Energy
7.1.1 Natural Gas and Oil Systems (CH4)
If country reported emission estimates were not available or the data are insufficient, EPA used the
2006 IPCC Tier 1 methodology (IPCC, 2006) to estimate emissions. The Tier 1 basic equation to
estimate fugitive CH4 emissions from oil and natural gas production, transmission, and distribution
systems is as follows:
Fugitive CH4 Emissions = (Annual Oil Production x Emission Factors + Annual Crude Oil defined x Emission
Factor) + (-Annual Natural Gas Production x Emission Factors + Annual Natural
Gas Consumption x Emission Factors)
Assuming that the emission factors do not change, the driver for determining fugitive CH4
emissions from oil and natural gas is the respective production and consumption of these fuels.
Historical Emissions
Activity Data
EPA obtained historical natural gas and oil production and consumption data, and
refinery capacity data from U.S. Energy Information Agency (EIA) for 1990 through
2005 (EIA, 2009). EPA assumed that refinery utilization is equal to the ratio of oil
production to refinery capacity.
Emission Factors and Emissions
EPA used 2006 IPCC Guidelines default factors for natural gas production (IPCC,
2006), natural gas consumption, oil production, oil refining, and venting and flaring for
1990, 1995, 2000, and 2005 emissions.
EPA multiplied appropriate oil and natural gas production and consumptions and
refining statistics for 1990, 1995, 2000, and 2005 by IPCC (IPCC, 2006) default factors.
If country-provided historical data combined oil and natural gas emissions into one
estimate, EPA determined the percentage of emissions generated from each industry
sector using the IPCC Tier 1 methodology. EPA applied this percentage to country-
provided historical data to determine the approximate emissions associated with each
industry.
For missing historical years, EPA extrapolated emissions based on changes in oil and
natural gas production and consumption from EIA (EIA, 2009).
August 201 I 7. Methodology Page 7-3
-------
If emissions are not reported and EIA production data are not available, EPA assumed zero
emissions for this source.
Projected Emissions
Activity Data
Projections of natural gas and oil production and consumption were available from the EIA (EIA,
2009). EPA used growth rates as provided by EIA "reference case" projections for 2005-2010,
2010-2015, 2015-2020, 2020-2025, and 2025-2030. These growth rates were available by country or
region.
Emissions
EPA applied EIA consumption projected growth rates to activity factors closely related to
consumption (such as transportation of fuels), and applied EIA production projected growth rates
to activity factors (such as production and processing of oil and gas) which are closely related to
production for each time period and region. Where emissions were estimated using IPCC Tier 1
emission factors, the emission factors were applied directly to the projected activity data. For
countries that submitted National Communication data, production growth rates in barrels of oil
equivalents were used to project their historical emission data. Specifically for the U.S., projected
emissions utilize the updated 2011 U.S. Greenhouse Gas Inventory data as a basis for 2010
projections and Tier 1 emission factor projections from 2015 to 2030.
Uncertainties
The greatest uncertainties are due to the use of default emission factors, and difficulties in projecting
oil and natural gas consumption and production through 2030 for rapidly changing global
economies such as those in the FSU and developing Asia. In addition, CH4 emissions from oil and
natural gas systems are not linearly related to throughput, so the IPCC Tier 1 methodology and
emission factors can lead to overestimates.
Table B-2 presents historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.1.2 Coal Mining Activities (CH4)
The basic equation to estimate fugitive CH4 emissions from underground, surface, and post-mining
operations is as follows:
Fugitive CH4 Emissions = (Annual Hard Coal Production xEFHAPDCOAiJ + (Annual Soft Coal Production x
^-T SOFT COALJ
Unless otherwise noted, EPA assumed that hard coal is produced in underground coal mines and
soft coal is produced in surface mines. Because a default methodology for fugitive emissions from
abandoned mines is not currently available, this source is not considered in this report, unless it is
included in country-reported emissions.
Historical Emissions
Historical emission estimates were based on available country-reported data obtained from the
UNFCCC flexible query system from 1990 through 2007 (UNFCCC, 2009). The full time series was
August 201 I 7. Methodology Page 7-4
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available for most Al countries; however, gaps existed in the time series for many of the NA1
countries. The time series was completed by applying growth rates as follows:
EPA forecasted and backcasted reported estimates using production growth rates
calculated from EIA's International Energy Statistics Portal (EIA, 2010). This method
was used when two years were reported such that a year requiring an estimate (e.g., 1995)
occurred between the reported years (e.g., 1993 and 1997), as well as when a year
requiring an estimate (e.g., 1990) occurred outside the reported years (e.g. 1993-1997).
If EIA data were not available to calculate growth rates for countries with some
UNFCCC reported data, EPA calculated estimates in non-reported years using linear
interpolation/extrapolation.
When UNFCCC flexible query data were not available for any years, EPA calculated historical
emissions using the Tier 1 equation above, and activity data and emission factors as outlined below.
Activity Data
EPA used coal production estimates for total primary production, hard coal production,
and lignite production for 1990-2008 from EIA's International Energy Statistics Portal
(EIA, 2010).
EPA disaggregated production into above-ground mines and underground mines,
assuming that hard coal is produced in underground mines, and lignite, or soft coal, is
produced in aboveground mines.
Where 2005 estimates1 were calculated by EPA, EPA accounted for coal mine CH4
recovery projects by adjusting the estimates to account for CH4 abatement at projects
reported in the EPA International Coal Mine Methane Projects Database (U.S. EPA
2010). Note that some country-reported estimates may already account for recovery
projects. However, EPA does not know which country-reported estimates account for
CH4 recovery, and did not adjust 2005 country-reported estimates to account for CH4
recovery.
If historical data were unavailable for a particular country through the UNFCCC flexible
query system or EIA's International Energy Statistics Portal, EPA assumed that coal
mining emissions were zero.
Emission Factors and Emissions
Where IPCC Tier 1 methodology was used, EPA determined CH4 emissions from coal
mining activities by multiplying activity data (i.e., soft and hard coal production) by
default Tier 1 IPCC emission factors from the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC, 2006). The IPCC guidelines provide low, average,
and high tier 1 emission factors. The average emission factors were used for
underground and surf.
Projected Emissions
EPA estimated future emissions by adjusting historical emissions based on the projected changes in
coal production in each country's region.
1 2005 is the first year for which EPA has estimates on abatement from coal mine QHU projects.
August 201 I 7. Methodology Page 7-5
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EPA estimated CH4 abated by coal mine CH4 projects starting in 2005. Since historical estimates
were used to develop future estimates, EPA did not adjust emission estimates for any country that
self-reported estimates in 2005. Rather, it is assumed that countries that self-reported estimates had
the opportunity to account for coal mine CH4 projects in their own estimates, and that any country-
made adjustment for coal mine CH4 projects is captured when projecting emissions forward. For
countries that did not self-report estimates in 2005, EPA adjusted estimates to account for CH4
abatement due to coal mine CH4 projects. Based on these criteria, EPA adjusted estimates for three
countries: China, Mexico, and South Africa.
» EPA projected emissions by adjusting historical estimates based on projected changes in
country coal production from EIA's International Energy Outlook (EIA, 2009). If EIA
did not report country-specific coal production forecasts, EPA used EIA's estimates for
the country's region. In some cases, EIA provided estimates for a few countries within a
region, and then an estimate for the "rest of the region. Where appropriate, EPA used
these "rest of estimates of forecasted coal production.
» Estimates for abated CH4 for 2010 and 2015 were developed using information from
EPA's Coal Mine Methane Projects database (U.S. EPA, 2010). EPA then estimated
post-2015 abatement by assuming that the percentage of a country's coal mine CH4
emissions that is abated remains constant starting in 2015.
Uncertainties
EPA used several methodologies to calculate historical emissions, depending on data availability for
a given country. While this approach allowed EPA to develop more detailed estimates than under a
general, one-size-fits all approach, it introduces some uncertainty to the estimates.
Emissions were projected using regional coal mining growth rates, and for the most part were not
customized to individual countries. While this approach allows regional trends to be consistent with
trends projected by EIA, it introduces uncertainty into emissions for individual countries.
Furthermore, emission estimates were calculated by projecting emissions rather than calculating
emissions based on production using the Tier 1 equation. This approach introduces uncertainty as it
would not capture any shift in surface to underground mining (or vice versa), which are associated
with different emission factors.
Finally, EPA did not adjust estimates for countries who self-reported estimates in 2005, the first year
for which EPA has information on coal mine CH4 projects. It is assumed that countries had the
opportunity to incorporate abatement from CH4 projects into their self-reported estimates; however,
whether countries actually accounted for coal mine CH4 projects in their estimates is unknown. In
addition, for countries whose estimates EPA &/adjust for coal mine CH4 projects, EPA assumed
that the percentage of a country's emissions abated by these projects remained constant starting in
2015; the extent that this assumption will hold true in the future is an additional source of
uncertainty in emissions.
Table B-3 presents historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
August 201 I 7. Methodology Page 7-6
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7.1.3 Stationary and Mobile Combustion (CH4, N2O)
If historical N2O and CH4 emissions dataware not available or the data were insufficient, EPA
developed emissions using fuel consumption data from the International Energy Agency's (IEA)
Energy Balances (IEA, 2009a; IEA, 2009b) and the IPCC Tier 1 methodology. If projections were
not available, EPA developed projections by applying projected growth rates of energy consumption
from lEA's World Energy Outlook (WEO) (IEA, 2009c) to historical emission estimates.
The basic equations to estimate emissions from mobile and stationary sources are as follows:
CH4 Emissions = Annual Fuel Consumption (by sector and fuel type)
x Emission Factor (by sector and fuel type)
N2O Emissions = Annual Fuel Consumption (by sector and fuel type)
x Emission Factor (by sector and fuel type)
For mobile sources, emission factors varied by the different transportation modes such as aviation,
road, railway, and navigation. The main driver for determining N2O and CH4 emissions from
stationary and mobile sources is fuel consumption, assuming that the emission factors do not change
over time.
Table 7-1 presents the IEA- and IPCC-defmed sectors and modes that constitute stationary and
mobile combustion. Table 7-1 shows how the IEA categories fit into the IPCC-defined sectors.
Table 7-1: IEA- and IPCC-Defined Sectors and Modes for Stationary and Mobile Combustion
lEA-Defined Sectors IPCC-Defined Sectors
I. Energy Industries'1
2. Total Industry Sector
3. Total Transport Sector
- International Civil Aviation
- Domestic Air Transport
- Road
-Rail
- Pipeline Transport
- Internal Navigation
- Non-specified Transport
4. Total Other Sectors
- Agriculture
- Commercial and Public Services
- Residential
- Non-specified Other
I. Energy Industries
2. Manufacturing Industries and Construction
3. Transport
(not used, bunker fuels)
- Aviation
- Road
- Railways
(used EF for Manufacturing Industries and Construction)
- Navigation
(assumptions depends on fuel type)
4. Total Other Sectors
- Agriculture/Forestry/Fishing
- Commercial/Institutional
- Residential
(used EF for residential or agriculture)
August 2011
7. Methodology
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a This sector comprises an aggregate of categories assumed to consume fuel primarily for the generation of heat
and power. This determination was made after consultation with both IEA and ICF energy experts. The following
categories are included: electricity plants, combined heat and power (CHP) plants, and heat plants, and own use.
Plants primarily selling to the public and primarily operating for on-site use (autoproducers) of each of these types
are included.
Historical Emissions
Historical estimates were based on emissions data obtained from the UNFCCC flexible query
system where data are available from 1990 through 2007 (UNFCCC, 2009). The time series was
available for most Al countries, however there are gaps in the time series for the majority of the
NA1 countries. The remainder of the historical time series is based on applying growth rates to this
base year estimate as follows:
When two years are reported such that a year requiring an estimate (e.g., 1995) occurred
between the reported years (e.g., 1993 and 1997), EPA interpolated the missing estimate
(1995) using reported estimates.
EPA backcast or forecast emission estimates to complete the historical series for 1990,
1995 and 2000, and 2005 based on Tier 1 growth rates. This involves estimating
emissions for 1990, 1995, 2000, and 2005 using a Tier 1 approach, then using the rate of
growth of this emission estimate to backcast and forecast.
If the historical time series of emissions was incomplete, EPA used calculated Tier 1 annual growth
rates for energy consumption from lEA's Energy Balances (IEA, 2009a; IEA, 2009b) to backcast
and forecast emissions to the missing years.
If historical emission estimates were not available, EPA estimated emissions for a country and/or
region using the IPCC Tier 1 methodology. This methodology allows for an estimate of emissions
by sector and primary fuel type. The inputs used to estimate emissions are discussed in the following
sections.
Activity Data
Fossil fuel consumption data by country, fuel product, and sector were collected from
ISA's Energy Balances for all major fuel types (IEA, 2009a; IEA, 2009b). The sectors
included in the analysis are listed in Table 7-1. The main fuel categories include coal, oil,
and natural gas (see Table 7-2 for a listing of product categories). Biomass combustion
emissions were not included in these calculations as they are included in the Biomass
Combustion chapter, and discussed in methodology Section 7.1.4.
Table 7-2: Fuel Types Included Under Main Fossil Fuel Categories
Coal
Natural Gas
Oil
Hard Coal
Brown Coal
Coke Oven Coke
Gas Coke
Peat
Brown Coal/Peat Briquettes (BKB)
Natural Gas
Refinery Gas (in metric tons)
Ethane
Liquefied Petroleum Gases
Gas Works Gas
Coke Oven Gas
Blast Furnace Gas
Crude
Motor Gasoline
Aviation Gasoline
Gasoline - Type Jet Fuel
Kerosene - Type Jet Fuel
Kerosene
Gas/Diesel Oil
August 2011
7. Methodology
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Oxygen Steel Furnace Gas
Residual Fuel Oil
Petroleum Coke
Non-specified Petroleum Products
Naphtha
Patent Fuel
Source: IEA, 2009a; IEA, 2009b
To calculate emissions, EPA multiplied the IEA fuel consumption data by the IPCC Tier
1 N2O and CH4 uncontrolled emission factors for each fuel type and sector from IPCC,
2006.
Projected Emissions
EPA projected emissions based on forecasts of coal, oil, and natural gas consumption
for each region/country, by sector, provided by IEA WEO (IEA, 2009c).2 Use of IEA
WEO data assumes that countries within the same region have the same growth rate.
EPA applied the forecasted annual growth rate of fuel consumption to emissions, based
on the following scenarios:
For 2010, 2015, 2020, 2025, and 2030: EPA forecasted emissions using the 2007-2030
annual growth rate for energy consumption for the appropriate region, by sector and
fuel type.
Table B-4 and Table B-5 present historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
Uncertainties
A high degree of uncertainty is associated with the IPCC Tier 1 default emission factors used to
calculate emissions from both stationary and mobile combustion. For stationary combustion
sources, this high degree of uncertainty is a result of lack of relevant measurements, uncertainties in
measurements, or an insufficient understanding of the emission generating process (IPCC, 2006).
The 2006 IPCC Good Practice Guidance estimates uncertainty for the stationary CH4 combustion
emission factors at +50 to 150 percent. Uncertainty for stationary combustion N2O combustion
emission factors are highly uncertain due to limited testing data on which the factors are based. In
addition, the use of uncontrolled stationary IPCC default emission factors may overestimate
emissions in those developing countries that have adopted some level of emission control strategies
for combustion sources.
Uncertainty in N2O and CH4 emission factors for mobile combustion are relatively high and depend
on a number of factors including uncertainties in fuel composition, fleet age distribution and other
vehicle characteristics, and maintenance patterns of the vehicle stock, to name a few (IPCC, 2006).
Higher certainty is associated with the aggregate fuel consumption data on which estimates are
based, due to established statistical approaches and surveys used to collect IEA data. Estimates of
2 The regions and countries are: Transition Countries, Russia, China, South Asia, India, East Asia, Latin America, Brazil,
Africa, and the Middle East.
August 2011
7. Methodology
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uncertainty for fossil fuel consumption data can range from ±1 to 10 percent depending on the
collection method used to acquire activity data (IPCC, 2006).
Table B-4 and Table B-5 present historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.1.4 Biomass Combustion (CH4, N2O)
The basic equation to estimate emissions from biomass combustion is as follows:
Emissions = Emission Factor * Activity
Where:
The emission factor is specific to each fuel type (solid biomass, charcoal, liquid biomass,
other) and sector (such as energy industries and manufacturing).
The activity is the energy input in terajoules (TJ) or metric tons of fuel.
Historical Emissions
Historical estimates were based on emissions data obtained from the UNFCCC flexible query
system where data were available from 1990 through 2007 (UNFCCC, 2009). The time series was
available for most Al countries, however there are gaps in the time series for the majority of the
NA1 countries. The remainder of the historical time series is based on applying growth rates to this
base year estimate as follows:
When two years were reported such that a year requiring an estimate (e.g., 1995)
occurred between the reported years (e.g., 1993 and 1997), EPA interpolated the missing
estimate (1995) using reported estimates.
EPA applied regional (or country-specific when available) annual growth rates to the
emission estimates to complete the historical time series of emissions. Compound
growth rates are directly from Annex A of the International Energy Agency's (IEA)
World Energy Outlook, Biomass and Waste category (IEA, 2009c), for 2007 through
2030.
Activity Data
EPA established historical energy demand for each country, using 1990, 1995, 2000,
2005, and 2007 consumption data from the IEA Energy Statistics for OECD and non-
OECD countries (IEA 2009a, IEA 2009b). Consumption data are presented for the
following sectors and subsectors: total solid biomass composed of industry (energy and
manufacturing), and transportation; other (which is composed of residential, commercial,
agricultural, and unspecified other); liquid biomass; charcoal; and industrial waste.
EPA forecasts 2007 emissions by applying annual growth rates from Annex A of lEA's
World Energy Outlook (IEA, 2009c) Biomass and Waste category through 2030. EPA
applied country-specific growth rates when they were available through the World
Energy Outlook (WEO); otherwise the regional growth rates were applied to the 2007
estimate. In projecting consumption, the distribution of energy supplied by biomass into
the relevant subsector is assumed to remain constant.
August 201 I 7. Methodology Page 7-10
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Emission Factors and Emissions
EPA determined CH4 and N2O emissions from biomass combustion by multiplying
activity data (i.e., biomass fuel consumption by sector for each country) by uncontrolled,
default Tier 1 IPCC emission factors from IPCC, 2006.
Projected Emissions
Activity Data
EPA used 2007 as base year to project biomass fuel consumption in 2010, 2015, 2020,
2025, and 2030. Annual growth rates are directly from Annex A of IEA, 2009c, Biomass
and Waste category, through 2030.
Emission Factors and Emissions
The emission factors used to calculate projected emissions are the same IPCC default
factors used in the historical time series calculations.
Uncertainties
Emission factors for biomass fuel are not as well developed as those for fossil fuels due to limited
test data for the variety of types and conditions under which these fuels are burned. Uncertainties
are at least as great as those for fossil fuel CH4 and N2O factors (+ 50 to 150 percent).
Activity data for biomass fuel combustion also tends to be much more uncertain than fossil fuels
due to the smaller, dispersed and localized collection and use of these fuels, which makes tracking
consumption more difficult. Estimates in IPCC Good Practice Guidance suggest uncertainties in the
range of +10 to 100 percent.
Table B-6 and Table B-7 present historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.1.5 Other Energy Sources (CH4, N2O)
Emission estimates for the "Other Energy Sources" emissions category are based on UNFCCC-
reported data. Projected emissions from this source are assumed to remain constant at the value for
the last reported year. Similarly, values before the first reported year are assumed to equal that year's
value and values between two reported values are calculated using a linear interpolation. Emissions
were not estimated for countries that did not report emissions in any year. As a result, estimates are
mostly available only for Annex I countries.
Table B-8 and Table B-9 present historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.2 Industrial Processes
7.2.1 Adipic Acid and Nitric Acid Production (N2O)
Estimates for N2O emissions from adipic and nitric acid production rely first on country-reported
emissions data. Where gaps exist in country-reported historical estimates and/or projections, EPA
used the IPCC Tier 1 methodology to estimate emissions in order to develop annual growth rates,
which are then applied to reported data in order to complete the historical and projected time series
(IPCC, 2006).
August 201 I 7. Methodology Page 7-1 I
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The basic Tier 1 equation used to estimate emissions from adipic acid production is as follows:
N2O emissions Adipic Acid Production * Unabated Emission Factor
The basic Tier 1 equation used to estimate emissions from nitric acid production is as follows:
N2O emissions Nitric Acid Production * Unabated Emission Factor
Historical Emissions -Adipic Acid Production
Activity Data
Where country reported emissions data were unavailable, production data were estimated
based on adipic acid plant capacity figures and estimated capacity utilization. Capacity
utilization was assumed to be 75 percent in 1990, 80 percent in 1995, 90 percent in 2000
and 2005, and 82 percent in 2010 through 2030 (SRI, 2010; Chemical Week, 2007, 1999).
Emission Factors and Emissions
The IPCC uncontrolled default emission factor for N2O generation is 300 kilograms
N2O per metric ton adipic acid (IPCC, 2006). This factor is applied to all countries where
Tier 1 calculations are used.
Projected Emissions - Adipic Acid Production
Activity Data
Global adipic acid consumption was forecasted to increase by 3.5 percent annually for
the period 2008 through 2013 (SRI, 2010). In this analysis, projections of global adipic
acid consumption are used as a surrogate for production projections, and the 3.5 percent
growth rate is applied through 2030.
Emission Factors
Emission factors used for projections are the same as those used in historical time series
calculations.
Historical Emissions - Nitric Acid Production
Activity Data
Production data are estimated by apportioning global nitric acid production to the
country level using country-specific fertilizer consumption data (FAO, 2010; SRI, 2007,
1999).
Emission Factors and Emissions
The unabated emission factor used for Tier 1 calculations is 9 kilograms N2O per metric
ton nitric acid (IPCC, 2006).
Projected Emissions - Nitric Acid Production
Activity Data
Emissions from nitric acid production are projected based on changes in estimated long-
term fertilizer consumption (Tenkorang & Lowenberg-DeBoer, 2008) as discussed in the
agricultural soils section (see Section 7.2.6).
Emission Factors
Emission factors used for projections are the same as those used in the historical time
series calculations.
August 201 I 7. Methodology Page 7-12
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Uncertainties
In general, IPCC default adipic acid emission factors are more certain than nitric acid emission
factors because they are derived from stoichiometry of the process chemical reaction. The 2006
IPCC Guidelines (IPCC, 2006) estimate an uncertainty range for the unabated adipic acid emission
factor of +10 percent. The uncertainty range given for the unabated nitric acid emission factor is
+40 percent. A more thorough understanding of country-specific production processes and control
technologies would reduce uncertainty in these estimates by allowing the use of more specific
emission factors. Regarding activity data, estimates of nitric acid production derived in part from
national fertilizer consumption are much more uncertain than reported estimates. While estimates of
nitric acid production described above are used to inform the trend in actual nitric acid production,
they may not reflect true annual production.
Table C-2 presents historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.2.2 Use of Substitutes for Ozone Depleting Substances (MFCs)
EPA used a modeling approach to determine emissions from the various ODS substitute end-use
sectors (refrigeration/air-conditioning, foams, aerosols, fire extinguishing, and solvents). Although
some nations have made significant efforts to track and project use and emissions of HFCs from
ODS substitutes, the methodologies used, scope covered, and the level of aggregation presented
have varied and so are not used in this report. To estimate emissions, EPA modeled HFC emissions
based upon reported ODS consumption data. Nations that have ratified the Montreal Protocol are
required to report ODS consumption by chemical "group" (e.g., CFCs) to the United Nations
Environmental Programme (UNEP) Ozone Secretariat; and as of this report, 196 nations had
ratified the Montreal Protocol.
ODSs and their substitutes are first consumed during manufacture (e.g., to charge a refrigerator).
These gases are then mostly emitted to the atmosphere over time from equipment leaks, services,
and disposals. Some consumption may be recovered or recycled, depending upon the end use and
country. The relationship between initial consumption and eventual emission is complex and
uncertain. Comparing modeled emission estimates to atmospheric measurements is beyond the
scope of this report.
First, EPA used a bottom-up "Vmtaging Model" (EPA, 2010) of ODS- and ODS-substitute-
containing equipment and products to estimate the use and subsequent emissions of ODS
substitutes in the U.S. Emissions from non-U.S. countries were then estimated for each ODS-
consuming sector. In developing these estimates, EPA initially assumed that the transition from
ODSs to HFCs follows the same substitution patterns as the U.S. The U.S.-based substitution
scenarios were then customized to each region or country using adjustment factors that take into
consideration differences in historical and projected economic growth, the timing of the ODS
phase-out, the type of alternatives employed, and the distribution of ODSs across end-uses in each
region or country. This methodology is described in more detail in the following sections.
Estimating ODS Substitute Emissions in the U.S.
EPA used the Vintaging Model of ODS- and ODS substitute-containing equipment and products to
estimate the use and emissions of ODS substitutes in the U.S. The model tracks the use and
emissions of each of the substances separately for each of the ages or "vintages" of equipment. The
August 201 I 7. Methodology Page 7-13
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Vintaging Model is used to produce the ODS Substitute emission estimates in the official U.S. GHG
Inventory, and is updated and enhanced annually. For this analysis, the Vintaging Model was
adapted slightly to include data sources common to each source category (e.g., GDP). The model
and the equations used to estimate emissions are discussed in more detail in Appendix}.3
The consumption of ODS and ODS substitutes was modeled by estimating 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 over time. The model estimates emissions
by applying an emissions profile (e.g., annual leak rates, service emission rates, and disposal emission
rates for air conditioning and refrigeration end-uses) to each population of equipment. The model
estimates and projects annual use and emissions of each compound over time by aggregating the
consumption and emission output from approximately 60 different end uses.
For this analysis, the model calculated a "business as usual" (BAU) case that does not incorporate
measures to reduce or eliminate the future emissions of these gases, other than those regulated by
U.S. law or otherwise largely practiced in the current market. Furthermore, the model does not
project future market transitions, including those anticipated by industry. There is significant
uncertainty as to what compounds will replace HFCs in ODS substitutes applications, particularly in
developing countries.
The major end-use sectors defined in the Vintaging Model for characterizing ODS use in the U.S.
are refrigeration and air-conditioning, aerosols (including metered-dose inhalers (MDI)), solvent
cleaning, fire extinguishing equipment, foam production, and sterilization. The Vintaging Model
estimates the use and emissions of ODS substitutes by taking the following steps:
/. Gather historical emissions data. The Vintaging Model is populated with information on each
end-use, taken from published and confidential sources and industry experts.
2. Simulate the implementation of new, non-ODS technologies. The Vintaging Model uses detailed
characterizations of the historical and current uses of the ODSs, as well as data on how the
substitutes are replacing the ODSs, to simulate the implementation of new technologies that
ensure compliance with ODS phase-out policies. As part of this simulation, the ODS
substitutes are introduced in each of the end uses over time as seen historically and as
projected for the future considering the need to comply with the ODS phase-out.
3. Estimate emissions of the ODS substitutes. The chemical use is estimated from the amount of
substitutes that are required each year for the manufacture, installation, use or servicing of
products. The emissions are estimated from the emission profile for each vintage of
equipment or product in each end-use. By aggregating the emissions from each vintage, a
time profile of emissions from each end-use is developed.
Estimating ODS Substitute Emissions in Other Countries
After U.S. emissions are calculated using the Vintaging Model, EPA developed emission estimates
for non-U.S. countries by building on the detailed U.S. assessment. The general methodology and
assumptions used by EPA are discussed below, although the methodology was modified for several
3 A discussion of the Vintaging Model can also be found in the U.S. Inventory of Greenhouse Gas Emissions and Sinks
(EPA, 2010).
August 201 I 7. Methodology Page 7-14
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sectors where necessary. Specific deviations from this basic methodology are discussed following the
general methodology description.
The following general steps are applied to estimate country-specific emissions. Steps 1 through 7
results in preliminary emission estimates calculated by Equation 1, below. The preliminary estimates
were adjusted based on a series of factors discussed in Steps 8 through 11.
/. Gather base ODS consumption data for each country. UNEP (UNEP, 2010) provided reported
ODS consumption in terms of ozone depletion potential (ODP)-weighted totals for the
major types of ODSs: CFCs, HCFCs, halons, carbon tetrachloride, and methyl chloroform.
The base year for estimates was 1989; when data for 1989 was unavailable, the earliest
available data was used as a proxy because, in general, ODS substitution had not yet taken
place. Since data was only available in ODP-weighted totals by ODS "group", groups were
divided into component chemicals (e.g., CFC-11, CFC-12, etc) according to 1990 U.S.
percentages as modeled in the Vintaging Model. After disaggregating the ODP-weighted
consumption by chemical, ODPs were used to determine the total consumption in metric
tons.
2. Calculate the percent of base ODS consumption of each chemical group used in each end-use sector. The
amount of ODS use in various industrial sectors differs by country. Data on the end-use
distributions of ODS in 1990 were available for the following countries:
» U.S. from the Vintaging Model,
» United Kingdom (U.K.) from U.K. Use and Emissions of Selected Halocarbons, prepared
for the Department of the Environment (March, 1996), and
» Russia from Phaseout ofO^one Depleting Substances in Russia, prepared for the Ministry for
Protection of the Environment and Natural Resources of The Russian Federation and
the Danish Environmental Protection Agency (Russian Federation, 1994).
The 1990 end-use sector distribution for the U.S. was applied to Canada and Japan.
The U.K.'s distribution was applied to the EU-154, non-EU Western Europe5,
Australia, and New Zealand. Russia's distribution was applied to the Former Soviet
Union and Eastern European countries. For developing countries, data on the 1990
consumption of ODS were available for many nations6 by sector and substance from
4 The EU-15 is defined as these European Union (EU) members: Austria, Belgium, Denmark, Finland, France,
Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and the United Kingdom.
5 Iceland, Liechtenstein, Monaco, Montenegro, Norway, and Switzerland.
6 Algeria, Antigua and Barbuda, Argentina, Bahrain, Bangladesh, Barbados, Belize, Benin, Bolivia, Brazil, Burkina Faso,
Burma, Cameroon, Chile, China, Columbia, Costa Rica, Croatia, Cuba, Dominica, Dominican Republic, Ecuador, Egypt,
El Salvador, Ethiopia, Georgia, Ghana, Grenada, Guatemala, Guyana, Honduras, India, Indonesia, Iran, Jamaica, Jordan,
Kenya, Lebanon, Lesotho, Macedonia, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritius, Mexico,
Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Panama, Paraguay,
Peru, Philippines, Saint Lucia, South Korea, Sri Lanka, Sudan, Swaziland, Syria, Thailand, Togo, Trinidad and Tobago,
Tunisia, Turkey, Uganda, Uruguay, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe.
August 201 I 7. Methodology Page 7-15
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the Multilateral Secretariat. For developing countries that did not have data available,
EPA used a representative average.
3. Calculate the base consumption of ODS for each end-use sector. This step involves multiplying the
amount of consumption of each chemical group from Step 1 by the end-use sector
distribution percentages from Step 2.
4. Obtain conversion ratios. Ratios of HFC consumption to base ODS consumption, and HFC
emissions to base HFC consumption, were obtained from the Vintaging Model for each
given year, chemical, and end-use. These ratios are used to convert ODS consumption to
HFC emissions.
5. Estimate HFC consumption in metric tons. This step involves multiplying the country-specific
base level consumption of ODS (Step 3) by the ratio of HFC consumption to base level
ODS consumption (Step 4).
6. Estimate HFC emissions in metric tons. This step requires multiplying the HFC consumption
(Step 5) by the ratio of HFC emissions to HFC consumption (Step 4).
7.
Estimate GWP-weighted ODS substitute emissions in metric tons ofCO2 equivalent. This step involves
multiplying HFC emissions (Step 6) by an average GWP to derive GWP-weighted HFC
emissions. The average GWP, which varies by sector, is determined by examining the
estimated ODS substitute emissions in 2012 in the U.S., as obtained from the Vintaging
Model. The year 2012 is used as a representative average; the U.S. HFC market is assumed to
be mature by this date and, under a business-as-usual scenario, the mix of HFCs and other
ODS substitutes (and hence the average GWP) is not expected to change significantly
thereafter. For instance, this year is beyond the recent (January 1, 2010) U.S. and Montreal
Protocol HCFC phaseout step.
I:
HFC Emissions
(MtCO2e)
[country, year]
ODS
Consumption
(MT)
[country, 1989 or
as available]
Step 3
X
HFC
Consumption
(MT)
[U.S., year]
ODS
Consumption
(MT)
[U.S., 1989]
Step 5
HFC
Emissions
(MT)
[U.S.,^
X
HFC
X
on
(MT)
[U.S.,^
Step 6
GWP of
HFC Emissions
(MtCO2e/MT)
[U.S., 2012]
Step 7
This methodology is followed for each country, given year, and end-use category (e.g.,
refrigeration). This equation thus produces preliminary estimates based on the general
assumption that all countries will transition away from ODS in a similar manner as the U.S.
(For example, CFC-12 mobile air conditioners transitioned to HFC-134a beginning in 1994
in the U.S. Thus, as a first estimation, it is assumed that CFC-12 mobile air conditioners
transition to HFC-134a in other countries). In many cases, options for ODS substitutes in
August 2011
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each end-use are technically limited to the same set of alternatives, regardless of geographic
region. Furthermore, alternative technologies used in the U.S. are available and in many cases
are used worldwide. These assumptions may be adjusted in later steps to account for
differences between the U.S. and other countries, as explained below.
8. Develop and apply adjustment factors. In this analysis EPA applied adjustment factors to modify
the emission estimates for countries based on what is known qualitatively about how their
transition to alternatives and technology preferences will likely differ from that of the U.S.
For example, EPA multiplied the estimates produced in step 7 by adjustment factors of less
than one to refrigeration and air-conditioning end-uses, because some nations have been
more likely to use hydrocarbon refrigerants than HFCs and/or because some nations may
choose less emissive designs or practices. Table 7-3 shows the adjustment factors used for
each sector and country grouping.
Table 7-3: Adjustment Factors Applied in Each Sector/Country
Australia/New Zealand
China/Economies in Transition
European Union
Non-EU Europe
Japan
Rest of World
Ref/AC
0.90
0.80
0.70
0.80
0.70
0.80
Aerosols
1.00
1.00
1.00
1.00
1.00
1.00
Foams
1.00
1.00
1.00
0.00
1.00
0.00
Solvents
1.00
1.00
1.00
1.00
1.00
1.00
Fire-Ext.
1.00
1.00
1.00
1.00
1.00
1.00
9. Develop timing factors. Since most developing countries will transition to substitutes more
slowly, EPA reduced the adjusted emission estimates by multiplying the results in each year
by a timing factor to reflect the assumed delay in their transition. In the Montreal Protocol,
developing countries are listed under Article 57. Timing factors for CFCs start at 25 percent
in 1995 and increase by 25 percent at each 5-year interval, until they reach 100 percent in
2010, when they are assumed to have caught up to the developed countries. Article 5
countries also have a delayed phase-out of HCFCs, to account for the fact that these
countries can continue consuming new HCFCs through 2040 with specific step-downs
based on the 2007 Adjustment to the Montreal Protocol. These factors are outlined in Table
7-4.
Table 7-4: Timing Factors Used For Developing (Article 5) Countries
CFCs
HCFCs
1995
0.25
0.00
2000
0.50
0.00
2005
0.75
0.00
2010
1.00
0.00
2015
1.00
0.11
2020
1.00
0.35
2025
1.00
0.68
2030
1.00
0.98
10. Develop economic growth factors. Since other countries' economies are growing at different rates
than the U.S., EPA altered emissions based on comparisons between U.S. and regional
historical and projected GDP. These GDP growth factors are shown in Table 7-5 (USDA,
2009).
7 A complete list of Article 5 countries is available at
http://ozone.unep. org/Ratification_status/list_of_article_5_parties.shtm.
August 201 I 7. Methodology Page 7-17
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Table 7-5: GDP Growth Factors (Relative to U.S.)
Region
Africa
Asia
Australia/New Zealand
Brazil
Canada
Central/South America
China
Eastern Europe
Economies in Transition
EU
Europe (non-EU)
India
Japan
Mexico
Middle East
South Korea
1995
0.91
1.24
1.04
1.03
0.96
1.09
1.58
0.87
0.55
0.95
0.95
1.14
0.95
0.95
1.04
1.29
2000
0.87
1.17
1.02
0.93
0.97
1.00
1.95
0.83
0.49
0.90
0.90
1.24
0.82
1.02
1.02
1.31
2005
0.96
1.31
1.07
0.95
0.98
1.04
2.75
0.90
0.59
0.87
0.87
1.52
0.78
0.99
1.13
1.46
2010
1.11
1.49
1.11
1.07
0.97
1.21
4.11
0.98
0.64
0.84
0.84
2.06
0.72
0.99
1.25
1.54
2015
1.23
1.66
1.14
1.15
1.02
1.28
5.32
1.04
0.71
0.80
0.80
2.61
0.69
1.03
1.37
1.66
2020
1.36
1.85
1.16
1.22
1.02
1.37
6.68
1.10
0.80
0.76
0.76
3.24
0.66
1.08
1.49
1.79
2025
1.49
2.03
1.18
1.30
1.02
1.46
8.40
1.15
0.89
0.72
0.72
3.94
0.64
1.14
1.63
1.92
2030
1.61
2.22
1.19
1.38
1.02
1.55
10.51
1.19
0.99
0.68
0.68
4.77
0.62
1.19
1.76
2.06
/ /. Estimate adjusted HFC emissions in metric tons ofCO2 equivalent in a given year by country. EPA
estimated emissions and projections for each year by multiplying the estimates in Step 7 by
the adjustment factors (Step 8), the timing factors (Step 9), and the growth factor (Step 10).
Sector-Specific Adjustments to General Methodology for ODS Substitutes
In addition to the adjustments discussed above, EPA adjusted the methodology for some sectors to
account for information that was available on a country or regional scale. These adjustments are
discussed by sector in more detail below.
Fire-Extinguish ing
EPA adjusted global emissions in the fire extinguishing sector by region by developing Vintaging
Model scenarios that were representative of country- and region-specific substitution data. In
addition, EPA adjusted emissions in the EU to account for the rapid halon phase-out due to
regulation. Details of these adjustments include the following:
/. To estimate baseline emissions, information collected on current and projected market
characterizations of international total flooding sectors was used to create country-specific
versions of the Vintaging Model (i.e., country-specific ODS substitution patterns). For this
report, current and projected market information was obtained on new total flooding
systems in which halons have been previously used. Information for Australia, Brazil, China,
India, Japan, Russia, and the U.K. was obtained from Halon Technical Options Committee
(HTOC) members from those countries.8 Information for the U.S. was taken from the
Vintaging Model. General information was also collected on Northern, Southern, and
8 Fire protection experts in these countries provided confidential information on the status of national halon transition
markets and average costs to install the substitute extinguishing systems in use (on a per volume of protected space
basis) for 2001 through 2020.
August 201 I 7. Methodology Page 7-18
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Eastern Europe. Baseline emission information from some of these countries was used to
adjust the substitution patterns for all other countries not listed above, as described below:
Australia: proxy for New Zealand.
Brazil: proxy for countries in Latin America and the Caribbean.
India: proxy for all other developing countries.
Eastern, Northern, and Southern Europe: proxies for European countries (based on
geography).
Russia: proxy for economies in transition.
An adjustment factor was applied to EU countries to account for European Regulation
2037/2000 on Substances that Deplete the Ozone Layer, which mandates the
decommissioning of all halon systems and extinguishers in the EU-15 by the end of 2003
(with the exception of those applications that are defined as critical uses). To reflect this, the
methodology assumes that all halon systems in the EU-15 will be decommissioned by 2004.
No adjustments were made to the 10 countries that joined the EU in May 2004, because the
regulation makes exceptions for these countries.
Refrigeration and Air-Conditioning
EPA adjusted estimates for the refrigeration and air-conditioning sector to account for less
refrigerant recovery (i.e., more venting) in developing countries. These estimates assume that
recovery does not occur in these countries in any small refrigeration and air-conditioning units, but
does occur in larger units, such as chillers. The resulting adjustment factors are shown in Table 7-6.
Table 7-6: Recycling Adjustment Factors Applied to Refrigeration Emission Estimates
T995 2000 2005 2010 2oTs 2020 2025 2030
LOO L02 L06 L09 L09 L09 L22 L26
Aerosols
Since the ban on CFC use in MDI aerosols caused the U.S. to transition out of CFCs earlier than
other countries, the U.S. consumption of ODS in 1990 for non-MDI aerosols is assumed to be
equal to zero. In order to determine a non-zero denominator for the ratio calculated in step 4, it was
assumed that 15 percent of the non-MDI aerosols ODS consumption transitioned to HFCs, while
the remainder was assumed to transition to not-in-kind (NIK) or hydrocarbon alternatives.
Foams
Most global emissions were estimated in the foam-blowing sector by developing Vintaging Model
scenarios that were representative of country- or region-specific substitution and consumption
patterns. To estimate baseline emissions, current and projected characterizations of international
total foams markets were used to create country or region-specific versions of the Vintaging Model.
The market information was obtained from Ashford (2004), based on research conducted on global
foam markets. Scenarios were developed for Japan, Europe (both EU and non-EU countries
combined), other developed countries (excluding Canada), countries with economies in transition
(CEITs), and China. It was assumed that other non-Annex I countries would not transition to HFCs
during the scope of this analysis, as reflected by the foams adjustment factor (step 8 above). Once
the Vintaging Model scenarios had been run, the emissions were disaggregated to a country specific
level based on estimated 1989 CFC consumption for foams developed for this analysis. Emission
August 20 1 I 7. Methodology Page 7- 1 9
-------
estimates were adjusted slightly to account for relative differences in countries' economic growth as
compared to the U.S. (step 9 above).
Table C-3 presents historical and projected emissions for all countries for ODS substitutes in each
sector: aerosols (MDI), aerosols (non-MDI), fire-extinguishing, foams, refrigeration and air
conditioning, and solvents.
7.2.3 HCFC-22 Production (MFCs)
Trifiuoromethane (HFC-23) is generated and emitted as a byproduct during the production of
chlorodifluoromethane (HCFC-22). HCFC-22 is used, primarily, as a feedstock for production of
synthetic polymers and, secondarily, in emissive applications (primarily air conditioning and
refrigeration). Because HCFC-22 depletes stratospheric ozone, its production for non-feedstock
uses is scheduled to be phased out under the Montreal Protocol. However, feedstock production is
permitted to continue indefinitely.
All producers in developed countries have implemented process optimization and/or thermal
destruction to reduce HFC-23 emissions. In a few cases, HFC-23 is collected and used as a
substitute for ozone-depleting substances, mainly in very-low temperature refrigeration and air
conditioning systems. Emissions from this use are quantified under air conditioning and
refrigeration and are therefore not included here. HFC-23 exhibits the highest global warming
potential of the HFCs, 11,700 under a 100-year time horizon, with an atmospheric lifetime of
264 years.
Estimating Historical HFC-23 Emissions
EPA estimated historical HCFC-22 production and used an emission rate to estimate the HFC-23
emissions, subtracting any emissions that were abated through technology. Country-specific HCFC
production data as reported to the United Nations Environmental Program (UNEP) Ozone
Secretariat (UNEP 2010); 2001, 2004, and 2007 country-specific production capacity information
from the Chemical and Economics Handbook (CEH) (CEH 2001; Will et al., 2004; Will et al.,
2008); and field data on HFC-23 emissions from HCFC-22 production (Montzka et al., 2010) were
used to estimate historical HFC-23 emissions from HCFC-22 production. HFC-23 emissions were
estimated for the following countries:
Argentina
Australia (historical only)
Brazil (historical only)
Canada (historical only)
China
France (historical only)
Germany
Greece (historical only)
India
Italy (historical only)
Japan
Mexico
Netherlands
Russian Federation
South Africa (historical only)
South Korea
Spain
United Kingdom (historical only)
United States
Venezuela
Activity Data
Estimating Production in Europe
Information on historical HCFC-22 production was used to estimate HFC-23 emissions. According
to Will et al. (2004), Greece's, the Netherlands', and Spain's HCFC production is only HCFC-22
(based on plant capacities). UNEP (2010) reports total non-feedstock HCFC production by country
in ODP-weighted tons. As a result, non-feedstock HCFC-22 production for these countries is
assumed to be the total reported for each country in UNEP (2010) after "un-weighting" the
August 2011
7. Methodology
Page 7-20
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production estimates by HCFC-22's ODP (0.055). The ratio of non-feedstock production to
feedstock production is then used to grow non-feedstock HCFC-22 production to total HCFC-22
production, without exceeding the CEH (2001) and Will et al. (2004) reported production capacities.
The ratio of non-feedstock production to feedstock production as shown in Table 7-7 was estimated
over the time series based on data for 1990 from EPA (2006), data for 1996 and 2007 from Montzka
et al. (2010), and by linearly interpolating the intervening years.
This total is subtracted off Will et al. (2004) reported Western Europe production across the time
series and the remaining HCFC-22 production for Western Europe is allocated to France, Germany,
Italy, and the United Kingdom based on total HCFC-22 production capacity for each country as
reported in CEH (2001, 2008) and Will et al. (2004). EPA assumed that for all European countries,
production from 1990 through 2003 could not exceed 2001 reported capacity, that production in
2004 through 2006 could not exceed 2004 reported capacity, and that production in 2007 could not
exceed 2007 reported capacity.
Table 7-7: Portion of Total HCFC-22 Production that is Feedstock HCFC-22 Production for Annex I
(AI) countries
1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
AI 20% 26% 28% 31% 33% 36% 39% 41% 44% 47% 50% 52% 55% 58%
Estimating Production in the Rest of the World
According to Will et al. (2004) Mexico's, Argentina's, Venezuela's and India's HCFC production is
also only HCFC-22 (based on plant capacities). Again, UNEP (2010) reported HCFC production is
assumed to be the total non-feedstock HCFC-22 production reported for each country by "un-
weighting" the production estimates by dividing the total production by HCFC-22's ODP of 0.055.
For South Korea, 33 percent of total HCFC production capacity is HCFC-22 (Will et al. 2004,
2008). This percent is applied across the UNEP-reported non-feedstock HCFC production time
series to estimate non-feedstock HCFC-22 production totals. The ratio of non-feedstock production
to feedstock production is then used to grow non-feedstock HCFC-22 production to total HCFC-22
production.
Will et al. (2008) reports China's apparent production for 2000 through 2007. EPA used these
estimates and back casted HCFC-22 production using the ratio of total HCFC-22 production
reported in Will et al. (2008) to UNEP-reported non-feedstock HCFC production for 2000. This
ratio was applied across the UNEP-reported time series for 1990 to 1999 to estimate China's
HCFC-22 production for those years. The ratio of non-feedstock production to feedstock
production across the time series for China and other non-Annex I countries and Russia is shown in
Table 7-8 below.
Table 7-8: Portion of Total HCFC-22 Production that is Feedstock HCFC-22 Production for Non-
Annex I (NAI) Countries
1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
NAI 20% | 31% 33% 32% 31% 30% 29% 28% 27% 26% 26% 25% 24% 23%
Historical Emissions Calculation
To estimate emissions of HFC-23, the HCFC-22 production levels estimated above were multiplied
by emission rates (i.e., tons of HFC-23 emitted per ton of HCFC-22 produced). In some cases the
August 201 I 7. Methodology Page 7-21
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emission estimate was reduced due to assumed market penetrations of thermal abatement
technologies. The emission rate for Annex I countries was assumed to be 2 percent across the entire
time series (Montzka et al., 2010). The emission rate for non-Annex I countries and Russia was
assumed to be 3 percent from 1990 through 2005 (EPA, 2006) and 2.4 percent from 2006 through
2007 (Montzka et al., 2010). The decreased emission rate takes into account any HFC-23 emission
offsets from Clean Development Mechanism (CDM) projects in these countries and the Joint
Implementation (JI) project at Russia's HCFC-22 plant in Perm.
To reflect the adoption of thermal oxidation technology between 1995 and the present, EPA
assumed that current emission rates had been reduced relative to historical emission rates in some
regions. The following market penetrations were incorporated into the analysis:
In 2000, the baseline market penetration of thermal oxidation was estimated to be
100 percent in Germany and Italy, and 75 percent in the U.K (Harnisch and Hendriks,
2000). Except for the U.K., these levels were assumed to be maintained through 2030.
In 2005, the baseline market penetration of thermal oxidation in the U.K. was estimated
to be 87.5 percent. This was intended to reflect the 2005 commissioning of a thermal
oxidizer at the one U.K. plant that had not had one previously (Campbell, 2006). For
2006 through 2008, the level of baseline market penetration in the U.K. was estimated to
be 100 percent. No emissions were estimated for the U.K. after 2008 as a result of their
two HCFC-22 plants closing during the course of 2008 (MacCarthy et al., 2010)
Where UNFCCC-reported HFC-23 emission estimates were available, these estimates were used in
place of estimates calculated using production data (UNFCCC, 2009). Countries for which
UNFCCC historical emission estimates (1990 through 2007) were used are: France, Greece, the
Netherlands, Russia, Spain, and the United States; partial time series emission estimates from the
UNFCCC were available for Australia (1990), Canada (1990 and 1995), Italy (1990 and 1995), Japan
(1995 through 2007), and Brazil (1990).
Estimating Projected HFC-23 Emissions
Activity
For all countries except the U.K., France, Italy and the United States, HFC-23 emissions from 2007
were used as a baseline to project future emissions. Non-feedstock and feedstock related emissions
were projected separately.
The method for projecting HFC-23 emissions was as follows:
Project Non-Feedstock Production Portion of Emissions: To project the non-feedstock portion of HFC-23
emissions, EPA applied the following assumptions:
For developed countries other than Australia, Canada, the U.K., France, Italy and the
United States; emissions from non-feedstock production were assumed to decrease
linearly from 2007 so that no emissions resulted from HCFC-22 non-feedstock
production by the 2020 phaseout date under the Montreal Protocol.
For Australia and Canada, UNFCCC reported emissions of HFC-23 were zero beginning
in 2000 and 1995, respectively. No further data was available on Australia, so EPA
assumed Australia will not produce HCFC-22 in the future. Will et al. (2004) reports that
August 201 I 7. Methodology Page 7-22
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Canada only produces one HCFC, HCFC-123, so EPA assumed that Canada will not
produce HCFC-22 in the future.
For the U.K., France, and Italy; HCFC-22 production was assumed to end and therefore
emissions were set equal to zero.
For the United States, National Communications projections of emissions were then
used for 2010-2020 (UNFCCC, 2009). Emissions trends were used to project HFC-23
emissions from 2025 through 2030.
For developing countries, non-feedstock production was assumed to increase linearly at
25 percent per year until 2013, the date when developing countries must begin phasing
out HCFCs (Montzka et al., 2010). After 2013, this production was assumed to decrease
linearly so that complete phaseout occurred by 2030.
Project Feedstock Production Portion of Emissions: To project the feedstock production portion of HFC-23
emissions, EPA applied the 5 percent global growth rate of feedstock HCFC-22 production as
reported in Montzka et al. (2010) for all countries.
Uncertainties and Sensitivities
In developing these emission estimates, EPA made use of, multiple international data sets, country-
specific information on abatement levels (where available), and the IPCC guidance on estimating
emissions from this source. Nevertheless, uncertainties exist in both the activity data and the
emission rates used to generate these emission estimates. Although EPA used four separate sources
to estimate country-by-country production of HCFC-22 (UNEP-reported, country-specific HCFC
production; country-by-country production capacities from the Chemical and Economics
Handbook; field data on HFC-23 emissions from HCFC-22 production; and the IPCC/TEAP
Special Report on Safeguarding the Ozone Layer and the Global Climate System), none of these
sources is comprehensive. Specifically, none provide country-by-country production of HCFC-22
for all countries. As a result, EPA used different ratios to estimate total HCFC-22 production over
time for several countries (e.g., percent of total HCFC production capacity that is HCFC-22 for
South Korea). These ratios may add uncertainty to the extent that the ratios fluctuate over time.
Future emission and abatement levels are particularly uncertain. Future policies (e.g., under the
Montreal Protocol) could affect total production of HCFC-22 and therefore emissions of HFC-23.
Changing emission rates may also have a significant impact on emissions. There is a significant
probability that many of these emissions will be averted, either through CDM or other mechanisms.
In this case, HFC-23 emissions will be lower than projected in this analysis.
Table C-4 presents historical and projected emissions for all countries for this source.
7.2.4 Electric Power Systems (SF6)
Historical Emissions
Country reported emission estimates available from the UNFCCC flexible query system (UNFCCC,
2009) were used for historical estimates. Where UNFCCC reported data were not available, EPA
estimated historical global emissions using the 2004 RAND survey (Smythe, 2004) of global SF6
sales to electric utilities and equipment manufacturers, estimates of net electricity consumption, and
August 201 I 7. Methodology Page 7-23
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the following equation, which is derived from the equation for emissions in the IPCC Good Practice
Guidance (IPCC, 2000):9'10
Emissions = SF6 purchased to refill existing equipment + nameplate capacity of retiring equipmentu
Note that the above equation holds true whether the gas from retiring equipment is released or
recovered. Recovered gas is used to refill existing equipment, lowering the amount of SF6 purchased
by utilities for this purpose.
Gas purchases by utilities and equipment manufacturers from 1961 to 2003 were available from the
2004 RAND survey (Smythe, 2004). For the SF6 markets represented in the RAND survey (believed
to include all SF6-consuming countries except Russia and China), SF6 purchased to refill existing
equipment in a given year was assumed to be approximately equal to the SF6 purchased by utilities in
that year.12'13 To estimate the quantity of SF6 released or recovered from retiring equipment, the
nameplate capacity of retiring equipment in a given year was assumed to equal 77.5 percent of the
amount of gas purchased by electrical equipment manufacturers 40 years previous (e.g., in 2000, the
nameplate capacity of retiring equipment was assumed to equal 77.5 percent of the gas purchased by
original equipment manufacturers (OEMs) in I960).14 The remaining 22.5 percent was assumed to
have been emitted at the time of manufacture. The 22.5 percent emission rate is an average of IPCC
9 Emission estimates based on RAND sales data do not include SFe emissions from electrical equipment manufacturing.
However, some of the UNFCCC reported data that was used does include emissions from the manufacture of electrical
equipment.
10 Guidance from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2006) was not used because the
methods contained in the 2006 Guidelines are not well suited to estimate global SFe emissions from electric power
systems given the type of data available on global SFe use.
11 According to the 2000 IPCC Good Practice Guidance, emissions from electrical equipment can be summarized by the
following equation:
Emissions = Annual Sales ofSF6 Net Increase in nameplate (SF6) capacity of equipment SF6 stockpiled or destroyed
Where:
Annual Sales = SFe purchased to fill new equipment + SFe purchased to refill existing equipment;
Net Increase in nameplate capacity - nameplate capacity of new equipment-nameplate capacity of retiring
equipment; and
SF6 stockpiled or destroyed SFe stockpiled or recovered from electrical equipment and destroyed
In general, the quantity of SFe destroyed is believed to be small compared to the other quantities in the equation. In
addition, if no gas from retiring equipment is used to fill new equipment, then the quantity of new SFe used to fill new
equipment is equal to the nameplate capacity of the new equipment. In this case, the IPCC equation simplifies the
expression above.
12 Communications with electrical equipment manufacturers indicated that beginning in the late 1990s, a small but
increasing fraction of new equipment was being filled with gas purchased by utilities rather than by equipment
manufacturers. In this analysis, EPA assumed that in 1999, one percent of new equipment was filled using gas purchased
by utilities and that by 2003; this fraction had grown to five percent. This assumption has the effect of decreasing
estimated global refills and emissions by 11 percent in 2003.
13 See the country-by-country emissions section for information on how emissions were estimated for Russia and China.
14 The volume of SFe sold for use in new equipment before 1961 was assumed to have increased linearly from 0 tons in
1950 to 91 tons in 1961, the first year for which the RAND survey has data.
August 201 I 7. Methodology Page 7-24
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SF6 emission rates for Europe and Japan before 1996 (IPCC, 2000). The 40-year lifetime for
electrical equipment is from Reductions ofSF6 Emissions from High and Medium Voltage Electrical
Equipment in Europe (Ecofys, 2005). To reduce the potential impact of inventory fluctuations on the
estimates, EPA applied three-year smoothing to both the utility and the OEM sales figures. The
results of the two components of the above equation were then summed to yield estimates of total
SF6 emissions for all of the countries represented in the RAND survey from 1990 to 2003.
For 2005 historical emissions, EPA extrapolated the 2003 emission estimates based on the change in
world net electricity consumption from 2003 to 2005, as provided by EIA (EIA, 2008). It was
necessary to use extrapolation for 2005 emissions because RAND ceased publication of their survey
in 2004, so 2003 was the last year for which RAND survey data were available.
Country-Specific Historical Emissions Methodology
United States
Historical emissions data for the United States used in this analysis were available through the
UNFCCC flexible query system (UNFCCC, 2009).
EU
Emissions for the EU were based on UNFCCC reported data, where available (UNFCCC, 2009).
When data were not available, emissions were based on those provided for equipment use and
decommissioning in Reductions ofSF6 Emissions from High and Medium Voltage Electrical Equipment: Final
Report to CAPIEL (Ecofys, 2005). The Ecofys study relied on bottom-up estimates of emission rates
and of the SF6 bank in equipment, both of which varied by region and over time. The study
supplemented published information and national reporting with surveys of electrical equipment
manufacturers and users.
The Ecofys report provided estimates on a regional level for 1995, 2003, 2010, and 2020. For this
analysis, estimates were extrapolated or interpolated to obtain values for 1990, 2000, 2005, 2015,
2020, 2025, and 2030, and regional totals were disaggregated to the country level using either
country-specific data (for Germany) or GDP (for all other countries).15 To estimate 1990 emissions,
trends for Germany between 1990 and 1995 were applied to EU-1516 and Norway, Switzerland, and
Iceland. Emissions in 1990 from the EU-1017 were assumed to be equal to the 1995 estimates.
Japan
Historical emissions data for the Japan used in this analysis were available through the UNFCCC
flexible query system (UNFCCC, 2009).
15 Ecofys indicated that within the three European regions, GDP was a slightly better predictor of emissions than net
electricity consumption.
16 The EU-15 includes these European Union (EU) members: Austria, Belgium, Denmark, Finland, France, Germany,
Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and the United Kingdom.
17 The EU-10 includes these EU members: Poland, Hungary, Czech Republic, Slovak Republic, Lithuania, Latria,
Slovenia, Estonia, Cyprus, and Malta.
August 201 I 7. Methodology Page 7-25
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All Other Countries
For all countries except the U.S., Japan, the EU, and nine other miscellaneous countries that had
historical data reported through the UNFCCC flexible query system (UNFCCC, 2009),18 historical
emissions (1990 through 2003) from electrical equipment were estimated using world sales of SF6 to
electrical utilities and country-level net electricity consumption data (Smythe, 2004; EIA, 2008).
To estimate world sales of SF6 that should be allocated to the "all other countries" category,
emissions for the U.S., the EU, and Japan were deducted from the global SF6 sales to electric utilities
value from the RAND survey. This global SF6 sales value first had to be adjusted to include sales for
China and Russia, which were not included in the RAND survey. To make this adjustment, EPA
assumed Russian and Chinese SF6 sales were proportional to the net electricity consumption of these
countries. Estimates of net electricity consumption were available from the Energy Information
Administration (EIA, 2008). To obtain a global sales value that included China and Russia, the total
sales for the countries represented in the RAND survey were multiplied by the ratio of total global
net electricity consumption (including Russia and China) to global net electricity consumption
excluding Russia and China.
The emissions for the EU and Japan that were subtracted from the RAND global sales were not the
same UNFCCC reported values that are presented as estimated emissions for the EU and Japan in
this report. Instead, Ecofys data was used for all the EU countries even for countries that had
UNFCCC reported data and emissions for Japan were estimated from the paper Recent Practice for
1 Reduction ofSF6 Gas Emission from GIS e> GCB in Japan (Yokota et al., 2005).
These alternative emission estimates were necessary because much of the UNFCCC reported data
includes emissions from electrical equipment manufacturing, but the method for estimating
emissions from RAND sales data only applies to electric utilities. To be consistent, the values
deducted for EU-23+3 and Japan from the RAND global sales needed to only include emissions
from electric utilities and not emissions from manufacturing. The Ecofys data as well as the Japan
estimates from Yokota et al. were for electrical utilities only.
The amount of RAND sales remaining after the deduction for the U.S., EU-25+3, and Japan were
assumed to equal the total emissions from all other countries.19 This amount was allocated to the
remaining countries according to each country's share of world net electricity consumption.
Country-specific electricity consumption data for the period 1990 to 2003 was obtained from the
International Energy Annual 2006 (EIA, 2008).
Country-Specific Projected Emissions Methodology
Since the mid-to-late 1990s various developed countries have implemented voluntary (and in some
cases, mandatory) programs aimed at reducing SF6 emissions from electric power systems. These
countries include the U.S., Japan, and the EU. The successful attainment of developed country SF6
reduction goals are accounted for in the emission projections based on the following methodology.
18 Other countries with UNFCCC reported historical data were Australia, Belarus, Bulgaria, Canada, Croatia, New
Zealand, Romania, Russia, and Turkey.
19 In countries outside of the U.S., EU, and Japan, it is uncommon for electric utilities to purchase SFe for filling new
equipment (this SFe is usually supplied by the equipment manufacturer). Therefore, most SFe purchased is used to fill
equipment that is leaking and will therefore be a reasonable indicator of SFe emissions from electric utilities.
August 201 I 7. Methodology Page 7-26
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United States
For the U.S., EPA assumed that emissions would decline over time as new, small, leak-tight
equipment gradually replaced old, large, leaky equipment, and as many utilities implemented
reduction measures under EPA's SF6 Emissions Reduction Partnership for Electric Power
Systems.20 These assumptions are built into the U.S. emission projections provided in the U.S. Fifth
National Communication and used in this analysis for the years 2010 through 2020 (U.S. State
Department, 2010). Because the Fifth U.S. National Communication does not provide projections
past 2020, linear regression was used to extrapolate emissions to 2030 (based on 2010 through 2020
emissions).
EU
For the EU emissions projection rates for 2010 to 2020 are based on those presented for equipment
use and decommissioning in the "Additional Voluntary Action" scenario of the Ecofys study
(Ecofys, 2005). These projection rates reflect the increasing implementation of reduction measures
both historically (starting in 1995) and in the future. Implementation is assumed to be complete by
2010. The measures include operator training, equipment repair and replacement, improved gas
recycling techniques (deep recovery), and a decommissioning infrastructure. As in the U.S., the
projections also reflect the increasing leak-tightness of new equipment. Since the Ecofys study did
not provide scenarios for beyond 2020, linear regression was used to extrapolate emissions to 2030
(based on 2010 through 2020 emissions).
In July of 2006, the European Parliament and Council enacted a regulation on fluorinated
greenhouse gases that required both operator training and "proper" recovery of SF6 during
equipment servicing and decommissioning. It is assumed that these training and recovery measures
are reflected in the "Additional Voluntary Action" scenario used from the Ecofys study.
Japan
For Japan, projection rates were obtained from T. Yokota (2006) and reflect the increasing
implementation of reduction measures both historically (starting in 1995) and in the future.
Emissions were assumed to remain constant at their 2005 level through 2030, based on T. Yokota's
projections through 2020 (Yokota et al., 2005). Because the SF6 bank in Japan is expected to grow
substantially in the future, EPA assumed that implementation of reduction measures would increase
in order to maintain the 2005 emission level through 2030.
Other Developed Countries
For all developed countries except the U.S., Japan, and the EU, EPA assumed that emissions would
remain constant from 2010 levels through 2030. That is, any system growth was expected to be
offset by decreases in the equipment's average SF6 capacity and emission rate as new, small, leak-
tight equipment gradually replaced old, large, leaky equipment.
Developing Countries
For developing countries, which began to install SF6 equipment relatively recently, all current
equipment was assumed to be new. Consequently, as infrastructure expanded, emissions from
developing countries were estimated to grow at the same rate as country- or region-specific net
electricity consumption projections (EIA, 2009).
20 More information on EPA's SFe Emission Reduction Partnership for Electric Power Systems is available at:
http://www.epa.gpv/electricpower-sf6/index.html
August 201 I 7. Methodology Page 7-27
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Uncertainties
In developing emission estimates for this source, EPA used multiple international data sets and
IPCC guidance. The robustness of the bottom-up estimates used for the U.S., Japan, and the EU are
believed to have improved from EPA (2006) due to the use of UNFCCC reported data in this
updated version of the report (UNFCCC, 2009). Nevertheless, this analysis is subject to a number of
uncertainties that affect both global and country-specific emission estimates, particularly estimates
for countries other than the U.S., Japan, and the EU.
First, the SF6 producers represented in the RAND survey do not represent 100 percent of global SF6
production and consumption. EPA accounted for unreported Chinese and Russian SF6 production,
consumption, and emissions by assuming a relationship between net electricity consumption and SF6
emissions (i.e., SF6 consumption/net electricity consumption). However, this assumption is subject
to uncertainty. One source of this uncertainty is the fact that net exports from or imports into
Russia and China affect the relationship between SF6 consumption and net electricity consumption
in the rest of the world. Net exports from Russia and China would make the "consumption factor"
(SF6 consumption/net electricity consumption) in the rest of the world appear to be smaller than it
actually is, while net imports would have the opposite impact. Information from manufacturers of
electrical equipment indicates that exports from Russia and China have fluctuated over time, peaking
around 2000 and declining more recently. Thus, the apparent dip in global emissions between 1995
and 2000, and the subsequent rise between 2000 and 2005, may be partly an artifact of these export
trends rather than purely a result of changes in emissions from electric power systems.21 Another
source of uncertainty is that the relationship between SF6 emissions and net electricity consumption
varies from country to country, even when imports and exports are properly accounted for.22
Second, the RAND survey's attribution of SF6 sales to particular end uses is also uncertain, since SF6
producers frequently sell to distributors rather than directly to end-users. Although producers would
be expected to have a reasonably good understanding of their markets, this understanding is not
always accurate. Thus, some of the SF6 sales that the survey attributes to utilities could have actually
have been to other uses, or vice versa.
Third, the typical lifetime of electrical equipment, and therefore the amount of equipment that is
now being retired, is uncertain. This analysis uses a lifetime of 40 years (Ecofys, 2005); however,
other publications have estimated the lifetime at 30 years (IPCC, 2000). The difference is important
because the amount of equipment manufactured 40 years ago is considerably smaller than
equipment manufactured 30 years ago. If the average lifetime of equipment were assumed to be less
than 40 years, then the estimate of 2003 global emissions would increase.
Fourth, for countries other than the U.S., Japan, EU-25+3, and countries that have reported to the
UNFCCC, EPA assumes that each country's share of past and current global emissions is directly
proportional to that country's share of past and current global net electricity consumption. In fact, as
21 The bottom-up studies cited above indicate that emissions from this sector declined between 1995 and 2000, and
atmospheric studies confirm that emissions declined globally (Maiss and Brenninkmeijer, 2000). Other atmospheric
studies indicate that emissions increased after 2000 (Peters et. al, 2005). However, the post-2000 increase may be from
other sectors, e.g., magnesium or electronics.
22 S. Reiman and M. Vollmer of EMPA have performed a preliminary analysis of this relationship, comparing the SFe
emission reported through national inventories to the net electricity consumption reported by EIA. They find that the
ratios between these two values vary by more than a factor of ten.
August 201 I 7. Methodology Page 7-28
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noted above, the relationship between emissions and electricity consumption varies between regions
and over time, particularly as regions make efforts to reduce their emission rates. Thus, there is an
associated uncertainty in the allocation of global emissions to individual regions within this analysis.
Finally, emission projections are based on the assumptions that emissions in developing countries
will increase with increasing net electricity consumption. However, the application, design, and
maintenance of equipment all affect equipment banks and emission rates. These factors may change
over time, which may alter the trends observed to date. For example, switchgear dimensions have
changed since the 1970's resulting in a reduction in the amount of SF6 required in switchgear
(Ecofys 2010).
Table C-5 presents historical and projected emissions for all countries.
7.2.5 Primary Aluminum Production (PFCs)
EPA used reported emissions from the UNFCCC flexible query system (UNFCCC, 2009) and
National Communications reports for all countries where data were available.
For countries for which there was no reported UNFCCC emissions data, EPA calculated country-
specific emission estimates from primary aluminum production using historical and forecasted
country-specific production data and cell type-specific emission factors. This section first discusses
the historical and projected activity data utilized and then discusses the methodology used to
develop PFC emission factors for historical and projected emissions.
Historical Activity Data
EPA estimated primary aluminum production for all aluminum-producing countries based on data
from USGS Mineral Yearbooks for Aluminum (USGS, 1995 through 2009). Country-specific
aluminum production was disaggregated to cell type using information derived from EPA's 2006
Global Emissions Report (U.S. EPA, 2006).
Projected Activity Data
For 2010, country-specific production estimates were based on a combination of proxy data for
2009 from the USGS 2009 Mineral Yearbook: Aluminum (USGS, 2011), estimates for certain countries
available for 2010 from the USGS 2011 Mineral Commodity Summary: Aluminum (USGS, 20 lib), and
for China from the IAI statistics for China's Primary Aluminum Production (IAI, 2011).
Country-specific projections from 2015 to 2030 were estimated based on a combination of either
applying the global aluminum production compounded annual growth rate of 2.5 percent per year as
reported by the IPCC (Martchek, 2006) to the 2010 country-specific production estimate, or for
certain countries, specific production projections provided in comments from USGS (USGS,
201 Ic). For countries with newly developed primary aluminum production (e.g. Qatar and Saudi
Arabia) or newly re-commissioned primary production (e.g. Nigeria), the production projections
were based on expected production capacity in future years.
Emission Factors and Related Assumptions
EPA estimated PFC emission factors using the Intergovernmental Panel for Climate Change (IPCC)
Tier 2 methodology for calculating PFC emissions from primary aluminum production (IPCC,
2006). These emission factors were derived from smelter operating parameters that describe anode
effect (AE) duration and frequency and a slope-coefficient, which relates the parameters to actual
August 201 I 7. Methodology Page 7-29
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cell type-specific PFC emissions. AE duration and frequency were combined into an overall AE
minutes-per-cell-day value. The slope coefficient is the parameter that, when multiplied by the AE
minutes per cell day, provides the specific emission estimates in kg CF4 or kg C2F6 per metric ton of
aluminum.
Historical Emission Factors and Related Assumptions
Cell type-specific default values for AE minutes-per-cell-day and slope coefficients were used.
Average global cell type-specific AE minutes-per-cell-day data for 1990 through 2010 were provided
by the International Aluminum Institute (IAI, 2011) and are based on IAI surveys. Table 7-9
illustrates these production-weighted AE minutes per cell-day by cell type used for 1990 through
2010 emission estimates. The reduction in AE minutes between 1990 and 2010 was the result of
several factors, including incremental improvements in smelter technologies and practices, and the
construction of state-of-the-art facilities.
Table 7-9: Cell Type Specific Production Weighted AE Minutes per Cell Day
Cell Type
vss
HSS
SWPB
CWPB
PBPF
1990
6.8
3.0
5.5
2.8
2.6
1995
6.4
2.9
5.6
1.8
I.I
2000
4.4
2.6
4.3
1.2
0.8
2005
3.2
2.2
3.0
0.4
0.7
2010
1.7
1.3
1.9
0.3
0.3
Source: U.S. EPA, 2006
Table 7-10 illustrates slope coefficient information for each cell type that was obtained from IPCC
(2006).
Table 7-10: Slope Coefficients by Cell Type (kg PFC/metric ton AI/AE minutes/cell day)
Cell Type VSS HSS SWPB CWPB
CF4
C2F6
0.092
0.005
0.099
0.008
0.272
0.069
0.143
0.017
0.143
0.017
Source: IPCC, 2006
Projected Emission Factors and Related Assumptions
In the analysis, the emission factors for each cell technology were assumed to remain constant from
2010 through 2030. The analysis is intended to model the hypothetical scenario in which no further
action is taken by the aluminum industry to reduce their emission rates below the 2010 levels.
Although this scenario represents a break from the historical trend, future action by the aluminum
sector is not guaranteed and the rate of decline in emission intensities (metric ton CO2e/metric ton
Al) has decreased in recent years (i.e., since 2005). However, IAI member surveys note the
significant reductions in AE duration and frequency for all cell-types compared to 1990 through
2009there has been an 88 percent improvement in anode effect PFC emissions per metric ton
since 1990. The IAI had previously established a voluntary goal of reducing global PFC emission
intensity by 80 percent by 2010, compared to 1990 levels. Following the achievement of its previous
target in 2006, the IAI endorsed a new voluntary target in 2008 of further reducing PFC emissions
intensity by at least 50 percent by 2020 as compared to 2006 (equivalent to a reduction of 93 percent
compared to 1990). Thus, it is unlikely that actual emissions will be as high as those presented in the
analysis. Nevertheless, the analysis does provide an upper-bound estimate of future global emissions.
August 2011
7. Methodology
Page 7-30
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Uncertainties and Sensitivities
In developing these emission estimates, EPA used multiple international data sets and the most
recent IPCC guidance on estimating emissions from this source. Nevertheless, uncertainties exist in
both the activity data and the emission rates used to generate these emission estimates.
First, while this study incorporated recent data on total aluminum production by country from
USGS Mineral Yearbooks, in order to disaggregate aluminum production by cell type EPA used
information developed for EPA's 2006 Global Emissions Report (U.S. EPA, 2006). This
information was gathered several years ago and relied primarily on the 2000 TEA report, Greenhouse
Gas Emissions from the Aluminum Industry (IEA, 2000). In its 2000 report, TEA accounted for expected
plant openings and closings, but these may not be occurring as expected, particularly given the large
increase in Chinese aluminum production since 2000. When EPA previously compared the IEA data
on regional production by cell type with more recent industry data on global production by cell type
(IAI, 2005; Marks, 2006), it found that the IEA projections had not fully captured either (1) a sharp
decline in production from VSS, HSS, and SWPB smelters (the most emissive type), or (2) a sharp
increase in production by PFPB plants (the least emissive type). Cell type is important because
emissions per ton of aluminum can vary by a factor of five or more across different cell types
(IPCC, 2000). For EPA's 2006 Global Emissions Report, EPA compensated for this by modeling a
shift from SWPB to PFPB between 1990 and 2005. In addition, EPA modeled increasing levels of
adoption of complete retrofits, which essentially convert VSS, HSS, CWPB, and SWPB cells to
PFPB cells. However, even with the adjustment, EPA appeared to be underestimating global
production by PFPB. This may have a significant impact on emission estimates because PFPB is the
least emissive cell type.
Second, EPA used a single aluminum production compounded annual growth rate to project
country-specific production through 2030 for the majority of countries for which individual
projections are not estimated. Future production in individual countries is likely to follow actual
trends not reflected by an annual growth rate and the value of an individual country's annual growth
rate might be significantly different from that of the global rate. This may have a significant impact
on emission estimates because production growth may actually be significantly higher than the
global rate in countries with major production (e.g., China) or significantly lower in countries
traditionally using more emissive cell-technology types (e.g., Russia).
Third, the analysis does not assume that the new IAI goal (i.e., a 93 percent reduction in PFC
emission intensity by 2020 from 1990 levels) will be attained (there are no further improvement in
PFC intensity levels assumed after 2010). However, it is possible that additional improvement will
occur due to changes in the technology mix and continued operational improvements. If this is the
case, the analysis may overestimate emissions.
Fourth, EPA used information from IAI (2011) for anode effect minutes (AE minutes). The IAI
surveys while representative do not cover the entire global primary aluminum production sector.
Therefore, these data may under- or overestimate the true global AE minutes (and hence the
emissions) for the analysis.
Fifth, to estimate emissions, EPA used slope coefficients from the 2006 IPCC Guidelines for
National Greenhouse Gas Inventories (IPCC, 2006). The CF4 and C2F6 slope coefficients
recommended in the draft 2006 IPCC Guidelines are technology specific factors from measurement
data available as of March 2005. Therefore, while the slope coefficients from the 2006 Guidelines are
August 201 I 7. Methodology Page 7-3 I
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representative of the technology in place through 2005, these slope coefficients may not truly
represent values that would be obtained from measurement date for the technology in place through
2030.
Table C-6 presents historical and projected emissions for all countries for this source for the
analysis.
7.2.6 Magnesium Manufacturing (SF6)
EPA developed SF6 baseline emissions for three magnesium metal processes: primary production,
die-casting, and recycling-based or secondary production. Country-specific emission estimates are
expressed as projections based on reported UNFCCC data or as the product of process-specific
emission factors and historical and forecasted production. This section first discusses the historical
and projected activity data utilized, specifically country-specific production and anticipated market
trends (projections), such as future plans to expand, shift, or curtail production. Next, it discusses
the process-specific emission factors used to estimate historical and projected emissions.
In the absence of emission control measures, the rapid growth of the magnesium manufacturing
industry would be expected to result in significantly increased future SF6 emissions from magnesium
production and processing. However, efforts in recent years to eliminate the use of SF6 in this
application around the world have reduced this potential growth in emissions. In 2003, the U.S.
partnership catalyzed a global industry commitment through the International Magnesium
Association (IMA), which represents approximately 80 percent of magnesium production and
processing outside of China, to eliminate SF6 emissions from magnesium operations by the end of
2010 (EPA, 2010). Regulatory efforts in Europe and Japan, and clean development mechanism
(CDM) projects in Brazil and Israel have resulted in significantly reduced emissions.
Historical Emissions
Activity Data
Historical estimates were based on emissions data obtained from the UNFCCC flexible query
system where data were available from 1990 through 2007 (UNFCCC, 2009). The time series was
available for most Al countries, however gaps existed in the time series for the majority of the NA1
countries. For the remainder of the historical time series, EPA utilized the follows projection
methodology:
When data for an incremental reporting year was not available, the next adjacent reporting
year value was utilized as a proxy (e.g., data reported for 1996 was utilized for 1995).
When data for an incremental reporting year was not available, the value from the next
available reporting year was utilized (e.g., data reported for 1995 was held constant for the
1990 value).
This section summarizes process-specific production data used to estimate historical emissions.
Primary Production
Countries for which EPA estimated emissions from primary magnesium production include: Brazil,
China, the Czech Republic, Israel, Kazakhstan, Portugal, the Russian Federation, Spain, Ukraine,
and the United Kingdom. Data for primary magnesium production for all countries for 1990 to
2008 were obtained from the U.S. Geological Survey (USGS, 2007 and 2009).
August 201 I 7. Methodology Page 7-32
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Die-Casting Production
» European Union (E U). For Portugal, Spain, and the United Kingdom, EPA estimated
historical SF6 emissions using information derived from Harnisch and Schwarz (2003). 2001
emissions were estimated as the product of a region-specific emission factor and country-
specific data on SF6-based magnesium casting from Harnisch and Schwarz (2003). For 1990,
emissions were estimated using the 1995 estimates and two trends between 1990 and 1995:
(1) EU auto production and (2) the quantity of magnesium used per car in the U.S. Between
1990 and 1995, the quantity of magnesium used per car in the EU were estimated to have
increased by 30 percent. Thus, casting SF6 emissions in the EU based on car production
were assumed to have increased by 30 percent between 1990 and 1995, since emission
factors were believed to have remained constant over the same period. 1995 emission
estimates were derived from the 1995 emissions presented by Harnisch and Schwarz for the
EU as a whole; country-specific emissions were calculated by multiplying the aggregate EU
emission estimate (20 metric tons SF6) by each country's share of total SF6-based EU die-
casting production in 2001. 2000 emissions were estimated by linearly interpolating between
the 1995 and 2001 data. For years 2000 through 2035, emission estimates were simply
calculated as a product of emission factor and die-casting. For 1996 to 2000, estimates were
based on linear interpolation.
» China (2000, 2005 and 2010 only). EPA utilized Chinese casting volume for the years 2000,
2005 and 2010 from Edgar (2004).
« Other Countries. Casting estimates for other countries and other historical years were not
readily available. Consequently, die-casting for the years 1990 to 2008 was estimated as a
function of automobile production. For example, for Brazil, China (except 2000, 2005 and
2010), Russia, and Ukraine, casting was estimated using the ratio of country-specific
automobile production to U.S. automobile production. This ratio was multiplied by U.S. die-
casting production to obtain an estimate of die-casting production in each country.
Automobile production for 1990 to 2000 was obtained from Ward's Motor Vehicle Data
(Ward's, 2001) and 2001 to 2008 production data was obtained from (OICA, 2010). For
countries that do not produce automobiles but have growing casting industries such as
Kazakhstan and Israel (IMA, 2002), production was estimated from the ratio of primary
production to casting production for a similar country. Russia was used as a proxy for
estimating production in Kazakhstan, while the U.S. was used as a proxy for Israel. Taiwan is
estimated to acquire 50 percent of Japan's die casting activity starting in 2005.
Recycling-based Production
Recycling-based production, or secondary production, for Brazil, China, Russia, and the UK was
estimated using die casting activity and a "remelt factor" of 30 percent. The secondary production to
die casting ratio can range from 30 to 55 percent across countries that actively recycle scrap
magnesium (Edgar, 2006) and 30 percent was chosen as a conservative default for those countries
where emissions are calculated for this source. The Czech Republic was reported to have a new
recycling plant come online in 2002 and is expected to have an annual growth rate of 3.4 percent
through 2010 and then 1.7 percent from 2011 to 2035 (Webb, 2005). Table 7-11 presents the growth
rates used in this analysis.
August 201 I 7. Methodology Page 7-33
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Table 7-11: Annual Growth Rates for Primary, Casting and Recycling Production (Annual Percent
Increase)
Year
2000-2005
2005-2010
Casting Annual Growth Rates
(percent)
Asia Europe"
9.6 3.4
9.6 3.4
Recycling Annual Growth Rates
(percent)
World
Same as Casting
Same as Casting
1 Limited projection efforts conducted to fill historical projection gaps in the automobile production benchmarking
approach described above.
Historical Emission Factors and Related Assumptions
In this analysis, SF6 emissions are conservatively assumed to be equivalent to SF6 consumption (i.e.,
it is assumed that no SF6 is destroyed during the metal processes). This may overstate emissions, as
recent EPA studies have shown that 5 to 20 percent of the SF6is degraded during its use as a cover
gas during at least one type of casting process (Bartos et al., 2003). For all countries that EPA
estimated emissions for, Table 7-12 and Table 7-13 summarize the emission factors utilized to
estimate historical emissions for each of the production processes. The emission factor for primary
production was based on measurements made in 1994 and 1995 by U.S. producers. Due to the
similarity between the primary and recycling production processes, the emission factor for recycling
production was assumed to be the same as that for primary production. The emission factor for die-
casting was drawn from a 1996 international survey of die-casters performed by Gjestland and
Magers (1996).
Table 7-12: Emission Factors for Primary Casting and Recycling Production (1990 - 1995)
I Emission Factor
Process
(kg SF6/metric ton Mg produced)"
Source
~
Primary Production
Casting
Recycling
1.10
4.10
1.10
EPA, 2010
Gjestland and Magers, 1996
EPA, 2010
"Emission factors utilized to estimate emissions from Brazil, China, the Czech Republic, Israel, Kazakhstan, Portugal,
Russia, Spain, Ukraine, and United Kingdom as appropriate.
Table 7-13: Emission Factors for Primary Casting and Recycling Production (2000 - 2005)
I Emission Factor
Process Source
(kg SF6/metric ton Mg produced)"
Primary Production
Casting
Recycling
0.75
1.00
0.75
EPA, 2010
EPA, 2010
EPA, 2010
"Emission factors utilized to estimate emissions from Brazil, China, the Czech Republic, Israel, Kazakhstan, Portugal,
Russia, Spain, Ukraine, and United Kingdom as appropriate.
In China, in 1990 and 1995 the main cover gas mechanism for primary production was sulfur
dioxide (SO^ generated from the application of solid sulfur powder. Therefore, China's SF6
emissions from magnesium primary production in 1990 and 1995 are assumed to be zero. In 2000,
SF6 usage is estimated to account for 10 percent of primary production and the remaining was SO2.
For 1990 to 2000 SF6 is estimated to account for 50 percent of recycling production in China; the
share of SF6 for recycling drops to 10 percent in 2005 and zero in 2010. For 2000 and 2005 SF6 is
estimated to account for 50 percent of die casting production, dropping to 10 percent in 2010.
August 2011
7. Methodology
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Die casting activity using SF6 in Portugal and Spain is estimated to account for 60 percent and 10
percent of die casting production in 2005 and 2010, respectively, under the EU phase-out. Similarly,
magnesium recyclers in the U.K. have switched to SO2 since 2000, and U.K.'s SF6 emissions from
magnesium recycling from 2000 to 2035 are therefore assumed to be zero. Kazakhstan, Portugal,
Spain, and Ukraine do not recycle magnesium in significant quantities.
Projected Emissions
Projected emission estimates were based on emissions data obtained from National
Communications (NC), where available. Estimates for some years were available for four countries
(Argentina, Australia, Macedonia, and New Zealand). Voluntary SF6 cover gas use phase-out is
assumed by 2010 for Austria, Denmark, France, Germany, Italy, Norway, Poland, Sweden, and
Switzerland in compliance with the EU phase-out schedule. U.S. phase-out is assumed to be
implemented by a majority of companies in 2010 under the U.S. Magnesium Industry Partnership
goal (EPA, 2010). Canada and Japan are assumed to phase-out SF6 usage from 2010 through 2020.
These estimates were incorporated into the time-series as follows:
When data for projected years was not available for countries with small emissions,
emissions were held constant from the most recent year reported (e.g., 2005);
European Union countries were projected to have emissions in 2010 that were 10 percent of
estimated emissions in 2005; emissions from 2015 to 2030 were assumed to be zero;
Canada was projected to have emissions in 2010 through 2020 that were 50 percent of
estimated emissions in 2005; emissions from 2025 to 2030 were assumed to be zero;
Japan was projected to have emissions in 2010 through 2020 that were 50 percent of
estimated emissions in 2005; emissions from 2025 to 2030 were assumed to be zero; and
« The United States was projected to have emissions reductions in 2010 by 40 percent relative
to the reported 2005 emissions; emissions were projected to be reduced by 25 percent in
2015 and 2020, then hold constant at the 2020 level to 2030.
This section summarizes the process-specific activity data and emission factors used to estimate
projected emissions in the absence of NC data. Projected emissions were calculated by EPA for
Brazil, China, the Czech Republic, Israel, Kazakhstan, Portugal, Russia, Spain, Ukraine, and the
United Kingdom.
This section discusses the regional growth rates and country-specific assumptions used to forecast
magnesium primary production, casting, and recycling-based production from 2010 through 2035.
Growth rates are summarized in Table 7-14. In general, annual growth rates used in this analysis
were assumed to account for new facility construction as well as facility capacity expansion driven by
growing global demand for magnesium in applications such as automotive lightweighting to improve
fuel economy. Primary production and die-casting growth rates were based on information supplied
by Webb (2005) for the rest of the countries' estimates. Recycling is linked to die casting and the
associated growth rates for that production process.
Primary Production
« Growth Rates. In all countries where EPA projected emissions (i.e., Brazil, the Czech
Republic, Israel, Kazakhstan, Portugal, Russia, Spain, Ukraine and the U.K.) except China,
August 201 I 7. Methodology Page 7-35
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EPA assumed primary production will grow 3.4 percent per year between 2001 and 2010.
Between 2011 and 2020, growth was assumed to decrease to an annual rate of 1.7 percent.
From 2000 to 2005, Chinese primary production more than doubled, however, based on
data reported in USGS (2009) production contracted due to the global economic downturn.
Primary production in China was projected to grow at 5 percent from 2010 through 2020
and then hold steady at 2020 levels to 2030.
Die-Casting
Growth Rates. In Asia (except China) and Russia, die casting is expected to grow at 9.6
percent from 2006 to 2010, and 4.8 percent from 2011 to 2035 (Webb, 2005). For Europe
and other countries such as Brazil, Israel, Kazakhstan and Ukraine, die casting is estimated
to grow at 3.4 percent from 2006 to 2010, and 1.7 percent from 2011 to 2035. The decrease
after 2010 reflects the likelihood that the recent period of growth will not continue
indefinitely. For China, casting is assumed to grow annually at approximately 10 percent
from 2005 to 2010 (Edgar, 2004). From 2010 to 2035, casting in China is estimated to grow
at 5 percent, or half of the 2005 to 2010 rate. This growth is spurred by increasing
investments by western, Japanese and Taiwanese companies in China to meet domestic
demand for camera, computers, and automobile parts.
Recycling-based Production
Growth Rates. For all countries where EPA estimated emission projections, recycling growth
rates were set equal to casting growth rates.
Global Activity Growth Rates
Table 7-14 presents the growth rates used in this analysis.
Table 7-14: Annual Growth Rates for Primary Casting and Recycling Production (Annual Percent
Increase)"
Year
2006-2010
2011-2035
Primary
Production Annual
Growth Rate"
(percent)
China ROW
3.5 3.4
5.5C 1.7
Casting Annual Growth Rate
(percent)
ROW Asia China Europe Russia
3.4 9.6 10.0 3.4 9.6
1.7 4.8 5.0 1.7 4.8
Recycling Annual
Growth Rate"
(percent)
World
Same as Casting
Same as Casting
1 See text above.
b Source: Primary and casting growth rates are based on Webb (2005). For recycling, it is assumed that growth rates will
be driven by increased use in automotive applications; consequently, growth rates will be the same as casting estimates.
c Annual growth for China estimated to be 5.5 percent through 2020 and then held at zero for 2020 through 2035.
Projected Emission Factors
EPA assumed the projected emission factors remain constant from 2010 to 2035. EPA's emission
projections are intended to model the hypothetical scenario in which no additional action is taken by
magnesium producers or processors to reduce their SF6 emission rates below the levels observed
during the late 1990s. In fact, many producers and processors have already taken significant steps to
reduce their emission rates and to achieve the IMA goal of eliminating SF6 emissions from
magnesium operations by the end of 2010. These include research programs in several countries
and, in some cases, the adoption of alternative cover gases such as HFC-134a and SO2.
Table 7-15 summarizes the emission factors EPA used to estimate emissions for this scenario from
2010 to 2035 where data was obtained from the EPA's SF6 Emission Reduction Partnership for the
August 2011
7. Methodology
Page 7-36
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Magnesium Industry. The 2000 emission factor for primary production, which is held constant from
2010 to 2035, was based on measurements made by four producers (i.e., producers with domestic
U.S. and international operations) (EPA, 2010).
In China, it is assumed that some Chinese magnesium producers have begun to utilize SF6 in an
effort to produce better quality magnesium for the world market. Between 2000 and 2005, the
fraction of Chinese magnesium producers using SF6 is assumed to have grown from zero to
10 percent. From 2005 through 2035, SF6 cover use is assumed to remain at 10 percent of total
market cover gas usage, with the remaining Chinese primary producers still using SO2 (Edgar, 2006).
Those Chinese producers using SF6 are assumed to emit at the rate shown in Table 7-15.
For all countries except the U.K., the emission factor for recycling was conservatively assumed to be
the same as primary production. For the U.K., SO2 will continue to be the primary cover gas system,
so emissions from these sources will be zero. For all countries including China, the emission factors
for die-casting were estimated based on reports from U.S. die-casters, and a report on emissions
from European die-casters (Harnisch and Schwarz, 2003).
In Brazil and Israel, CDM projects are projected to significantly reduce emissions starting in 2010.
RIMA, a large scale magnesium production and processing facility in Brazil implemented a full
conversion so SO2 for its primary, die casting, and recycling activities (UNFCCC, 201 Oa). Dead Sea
Magnesium, in Israel, implemented a conversion of its primary production to HFC-134a (UNFCCC,
2010b); because HFC-134a has a GWP of 1,300, these emissions were included with an estimated
mass usage ratio of 50 percent that of SF6.
Table 7-15: Emission Factors for Primary Casting and Recycling Production (2010 - 2035)
Emission Factor
(kg SF6/metric ton Mg produced)"
Primary Production
Casting
Recycling
0.75
1.00
0.75
EPA 20 10
EPA, 2010
EPA, 20 10
1 Emission factors utilized to estimate emissions from Brazil, China, the Czech Republic, Israel, Kazakhstan, Portugal,
Russia, Spain, Ukraine, and United Kingdom as appropriate.
Uncertainties and Sensitivities
In developing these emission estimates, EPA used multiple international data sets and the most
recent IPCC guidance on estimating emissions from this source (IPCC, 2006). Nevertheless, the
resulting emission estimates are subject to considerable uncertainty.
Historical and current emissions from this source are affected by both activity levels and emission
rates. Although country-specific activity levels are fairly well known for primary production, they are
less well known for recycling-based production (particularly the share consisting of magnesium-base
alloys) and for casting. In addition, emission rates vary widely across different processes and over
time. EPA accounted for these variations (e.g., the decline in emission rates that occurred between
1995 and 2000), but some regional and process-based variability may exist).
Projected emissions from magnesium production and processing are sensitive to (1) estimated
activity growth rates, and (2) assumptions regarding the adoption and/or retention of alternate melt
protection technologies. EPA has used relatively high activity growth rates to project emissions;
therefore, slight changes in these rates could lead to large changes in projected emissions. Second,
August 201 I 7. Methodology Page 7-37
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this analysis assumes that some but not all Chinese magnesium producers have adopted SF6 in place
of solid sulfur as they seek to increase the quality of their metal. Because China is currently the
world's largest producer of magnesium, greater penetration of the Chinese market by SF6 could
significantly increase both Chinese and global emissions. On the other hand, penetration of the
Chinese casting market by alternate cover gases would lower Chinese emissions below those
projected in this analysis.
Finally, this analysis does not account for the potentially significant impact of unannounced
mitigation projects funded by developed countries under the Clean Development Mechanism
(CDM) of the Kyoto Protocol. While projects in Brazil and Israel have been accounted for,
additional CDM projects could decrease SF6 emissions from magnesium production and processing
in China and other developing countries.
Table C-8 presents historical and projected emissions for all countries for this source for the
analysis.
7.2.7 Semiconductor Manufacturing (MFCs, PFCs, SF6, NF3)
PFC, HFC, and SF6 emissions are from two repeated activities in semiconductor manufacturing: (1)
cleaning of chambers used to deposit thin layers of insulating materials, a process referred to as
chemical vapor deposition (CVD) chamber cleaning, and (2) etching intricate patterns into
successive layers of insulating films and metals, a process referred to as plasma etching. Film
deposition and etching processes begin with the semi-conductive crystalline silicon (Si) wafer and
continues as successive films (layers) are deposited and etched to form and complete a device (i.e.,
the connection of all the elements of the device). Industry reports indicate that approximately 70 to
80 percent of emissions result from chamber cleaning processes and 20 to 30 percent from etching
processes (IPCC, 2002; Beu and Brown, 1998).
The absence of emission control measures, the rapid growth of the semiconductor industry (11 to
12 percent per year through the late 1990s) and the increasing complexity of microchips could
potentially result in significantly increased projected emissions from semiconductor manufacturing.
Due to this possibility, the U.S. EPA and the U.S. semiconductor industry launched a voluntary
partnership to reduce PFC emissions in 1996. In 1999, the U.S. partnership catalyzed a global
industry commitment through the World Semiconductor Council (WSC). Most WSC member
countries - the U.S., EU, Japan, South Korea, and Taiwan23 - have voluntarily committed to reduce
PFC emissions to 90 percent of 1995 levels by 2010.24 For this analysis it was assumed that all of
these WSC countries met and maintained the WSC goal25 (ITRS, 2009 and WSC, 2010). While China
joined the WSC in June 2006, it has not yet committed to a reduction goal. EPA assumed though in
23 For purposes of this report, emissions presented for China include emissions from manufacture in China and Taiwan,
however emissions for these countries were estimated separately as they are treated separately under the WSC and have
different industry associations.
24 For the U.S. Semiconductor Industry Association (SIA), Japan Electronic and Information Technology Industries
Association (JEITA) and European Semiconductor Industry Association (ESIA), the baseline year is 1995; for the
Korean Semiconductor Industry Association (KSIA), the baseline year is 1997; and for the Taiwan Semiconductor
Industry Association (TSIA), the baseline is the average of the emission values in 1997 and 1999.
25 These assumptions are based on the WSC Joint Statement (May 2010) which indicated that the WSC is on track to
meet their reduction goals, and information from the ITRS 2009 (Table ESH3a or b) which indicates that the WSC goal
will be maintained through 2024.
August 201 I 7. Methodology Page 7-38
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this analysis that China will set and achieve a reduction target. EPA based this assumption by
analyzing multiple alternative emissions reduction scenarios/growth scenarios of total manufacture
layer area (TMLA) for semiconductor devices for China presented in the article Modeling China's
semiconductor industry fluorinated compound emissions and drafting a roadmapfor dim ate protection (Bartos et al,
2008). Based on EPA's analysis of how the various scenarios align with China's historical emissions
and other world historical emissions and projections, 2012 was selected as the reduction baseline
year for China with a 10 percent reduction goal by 2010.26
Historical Emissions (1990 through 2005)
Historical country-reported emissions (1990 through 2005) from the manufacture of
semiconductors were available through the UNFCCC for most Annex I countries. EPA, where
possible, elected to use the Annex I reported emissions data for this analysis. However, a large share
of world semiconductor manufacturing capacity is represented in many non-Annex I countries, such
as China, Taiwan, and Singapore. To achieve as much consistency as possible while using the
UNFCCC emissions data, EPA summed the total amount of reported emissions from Annex I
countries for PFCs, HFC, and SF6 separately. These three totals, one for PFC, one for HFC, and
one for SF6, were then each scaled up using country-specific capacity shares to determine total
emissions for the world. This method is demonstrated in the following equation:
Total World Emissions^ = Total Reported Annex I Emissions'v /
Total Capacity Share Reporting Annex I Countriesv
Where:
Total World Emissions^ = estimated total world emissions of gas type i in year ji
Total Reported Annex I = total reported emissions for Annex I countries of gas
Emissions^ type i in year j
Total Capacity Share Reporting = total capacity share of the world for reporting Annex
Annex I countries^ I countries for gas type i in yearj
i = gas type (PFC, HFC, or SF6)
j = year
Total world emissions for 1990, 1995, 2000, and 2005, along with estimated country-specific
capacity shares were used to determine country-specific historical emissions for all non-Annex I
countries and Annex I countries without reported historical emissions using the following equation:
Country-Specific Historical Emissionsv = Total World Emissionsv * Country-Specific Capacity Sharev
Historical Country-Capacity Shares
Global activity data comprise historical and projected global Si consumption by linewidth and device
type (i.e., memory vs. logic) provided by VLSI Research, Inc. (VLSI, 2003). For 1990 through 2005,
this activity was apportioned to individual countries and regions using information from the World
Fab Watch (WFW) databases on manufacturing capacity by linewidth and country (WFW, July 1996,
2001, 2002 and April 2003 Editions) to determine country-specific capacity shares for 1995, 2000,
26 This assumes that China's TMLA grows at an intermediate rate (13.5 percent per year) in future years.
August 201 I 7. Methodology Page 7-39
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and 2005.2V In using capacity shares to apportion emissions EPA made the assumption that TMLA
is the basic unit of activity and that the distribution of F-GHG reduction technologies during this
period does not vary appreciably across countries.
Projected Emissions (2010 through 2030)
For countries that are not members of the WSC, emissions from 2010 to 2030 were estimated by
growing each PFC, HFC, and SF6 emissions at a rate equivalent to the 5 year compound annual
growth rate of each country's gross domestic product (GDP). GDP growth rates were determined
using raw GDP data from the US Department of Agriculture (USDA, 2009).
For all WSC member countries, as discussed above, EPA assumed that they each individually
achieved the voluntary emissions reduction goal of 10 percent below the voluntarily agreed-upon
baseline year by the year 2010, for each PFC, HFC, and SF6 emissions (WSC, 2010). EPA assumed
that the WSC countries consistently met this goal in all subsequent years (2015-2030)28 (ITRS, 2009).
For years prior to 2020, China's PFC, HFC, and SF6 emissions were estimated to grow at a rate
equivalent to the 5 year compound annual growth rate of their GDP. As discussed above, EPA
assumed that China will commit to a 2012 reduction baseline year and achieve a 10 percent
reduction goal by 2020. EPA assumed this under the condition that China's future TMLA will
growth at a rate of 13.5 percent annually (Bartos et al, 2008). Due to limited information, it was
assumed that in 2025 and 2030 China will maintain emissions at their assumed reduction goal level.
Uncertainties and Sensitivities
EPA based projected sector emission growth rates on a one-to-one scale with county GDP growth
rates. However, it may be appropriate to scale the country GDP growth rates by some factor before
applying them to determine future emissions for the semiconductor manufacturing sector. EPA may
consider these potential scaling factors in future analyses.
This analysis also projects emissions assuming that the current semiconductor manufacturing
process continues and that currently available abatement technologies are used to reduce the
resulting fluorinated greenhouse gas emissions. It does not model a possible future in which
fluorinated greenhouse gases are no longer used in semiconductor manufacturing at all. Thus, this
analysis may overestimate emissions. Alternatively, there is a possibility that the analysis
underestimates emissions by assuming that China sets and achieves a voluntary reduction goal with
the WSC. If this does not materialize, China's emissions, and hence total world emissions, may be
substantially higher than projections calculated in this analysis.
Table C-7 presents historical and projected emissions for all countries for this source for the
analysis.
7.2.8 Flat Panel Display Manufacturing (SF6, PFCs, NF3)
The flat panel display (FPD) sector is a new source category in this report. Country reported
emission estimates were not available for this sector and, as a result, EPA used the IPCC Tier 1
27 Country-specific capacity shares in 1990 were assumed to be equivalent to those in 1995.
28 The ITRS 2009 indicates that the 10 percent absolute reduction from a baseline year will be maintained through 2024.
EPA assumes this goal is also met in 2030.
August 201 I 7. Methodology Page 7-40
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methodology for estimating emissions from the manufacture of FPDs (IPCC, 2006). The basic Tier
1 equation for estimating emissions is as follows:
FCt = EFt *CU*CD
Where:
FCt = Emissions of gas i (mass)
EFt = Emission factor for gas i (mass/m2)
CU = Fraction of annual plant production capacity utilization (°/o)29
CD = Annual maximum design capacity of substrate processed (m2)
The main source of data for this source category is the Display/Search Q4- '09 Quarterly FPD Capacity
Database & Trends Report ("DisplaySearch database") (DisplaySearch, 2009). This database supplies
historical and projected annual data through 2012 about all FPD facilities in the world, including
location (country), maximum design capacity for substrate processing of a facility (in 1,000 m2), and
in some cases the utilized capacity of a facility (percent).
As discussed in Section 4.8 of this report, SF6 and PFCs, including CF4 and NF3, are used for
chemical vapor deposition cleaning process during the manufacture of FPDs. Additionally the gases
are used in plasma dry etching during manufacture of arrays of thin-film transistors on glass
substrates, which switch pixels of liquid crystal displays and organic light emitting diode displays.
Historical and Projected Activity Data
The activity data for emission estimates from FPD manufacturing is utilized capacity (m2) of FPD
area produced. This is derived from maximum design capacity expressed in area (1,000 m2) for each
country and the world. This maximum design capacity is converted to utilized capacity (m2) by
applying a utilized capacity factor (%). For simplicity, a single, global average utilized capacity factor
of 88 percent was applied to all countries and to the world for all years. This factor was derived by
taking a simple average of the world utilized capacity factors (%) for all years provided in the
DisplaySearch FPD database (DisplaySearch, 2009).30
Total maximum design capacities are determined by the following various methods:
» 2000, 2005, and 2010: EPA extracted total maximum design capacities by country and for
the world in 2000, 2005, and 2010 directly from the DisplaySearch Q4 2009 PV database
(DisplaySearch, 2009).
» 1990, 1995, 2015, 2020, 2025, and 2030: EPA determined total world maximum design
capacities in each of these years by applying 5 year, global compound annual growth rates
29 CU is assumed to be equivalent to 88 percent. See footnote 30.
30 In the DisplaySearch FPD database capacity utilizations (%) were only available for the years 2005-2010. The capacity
utilization provided for the world in each of these years was simply averaged together to get the capacity utilization
factor used in this analysis (88 percent). While the DisplaySearch databases provided some country specific capacity
utilizations for specific fabs in a country, there were many gaps in this data. Therefore using the database may have lead
to an underestimation of actual emissions.
August 201 I 7. Methodology Page 7-41
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(CAGRs) for each period. These 5-year world CAGRs were assumed based on expert
judgment about past demand in the FPD market.
Using the world maximum design capacity estimate for each year as well as country-specific
shares of world capacity (or "capacity shares"), country-specific CAGRs for each five year
interval are determined using the following equation:
-1
1/5
rACn _ WorldManufacturedCapacity(Yf)*CountrySharej(Yf)
WorldManufacturedCapacity(Yo) * CountrySharei (Yd)
Where:
Yf = future year
Yo = initial year
i - country index
Country-specific capacity shares for 1990 and 1995 were assumed to be equivalent to the
2000 country-specific capacity shares, which were determined using country and world
capacity data extracted from the DisplaySearch Q4 2009 FPD database (DisplaySearch,
2009). Country-specific capacity shares for 2015 through 2030 were assumed based on
expert judgment of how the market may look though 2030.31
Maximum design capacity for each country was then forecasted or backcasted by applying a
country-specific 5 year CAGR to maximum design capacity in the appropriate adjacent time
period.
As noted above, once total maximum design capacities were determined for each country and the
world for the 1990-2030 time series, these values are converted to utilized capacity (m2) using a
world average utilized capacity factor.
Emission Factors and Related Assumptions
To determine emissions for each country, the total utilized capacity (m2) is converted to PFC and
SF6 emissions (MtCO2e) using IPCC Tier 1 emission factors for PFCs and SF6 (MTCOjC/m2)
(IPCC, 2006).
Use of Abatement Strategies
Without incentives and or emissions targets, it is assumed that the FPD sector does not employ
abatement technologies. The World LCD Industry Cooperation Committee (WLICC) goal, which is
voluntarily established, creates reason for Japan, South Korea, and Taiwan to employ abatement
technologies at facilities in their countries in 2010 and beyond. The WLICC goal, formed in 2003,
established a fluorinated- GHG (F-GHG emission) target of 0.82 MtCO2e, equivalent to 10 percent
of the projected business-as-usual 2010 emissions (Bartos, 2010).32
31 It was assumed that the competition between Taiwan and South Korea leads to the equal country shares in 2030.
China's share is expected to increase to meet rising domestic (internal) demand for FPDs.
32 The WLICCC is a group of the three participating countries' LCD trade organizations whose main purpose is to
ensure the future of the LCD industry through collaboration on environmental issues such as emissions and waste. This
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Therefore, as part of the emissions projections in this report, it was assumed that abatement
strategies were used to achieve the WLICC goal in Japan, South Korea and Taiwan. The goal of was
assumed to be split equally between the three countries involved, meaning each country is projected
to emit less than 0.273 MtCO2e over a given year. To determine emissions with the use of abatement
to meet the WLICC goal in any given year, EPA used the following equation:
TotalEmissionsg i (Abatement) = TotalEmissionsg i (NoAbatemenf) * (1 - ag i * dg i)
Where:
tf; = fraction of gas g emissions abated in country i
d i - abatement efficiency for gas g in country i
g - gas index (PFC or SF6)
i - country index
Due to limited availability about abatement practices in WLICC countries, as a starting point, EPA
assumed 90 percent abatement efficiency for PFCs for each country, for each year. This abatement
efficiency is the default abatement efficiency value published in the 2006 IPCC Guidelines (IPCC,
2006). The abatement efficiency used as starting point for SF6 for each country for each year is
assumed to be an achievable 100 percent because SF6 is straightforward, that is, SF6 has the highest
GWP and it is equally cost effective to abate compared to PFCs.
Next, EPA determined the fraction of emissions abated and the abatement efficiency that WLICC
countries must achieve to meet the goal in 2010-2030. EPA used the following algorithm:
1. An emissions goal was set at for each country for 2010-2030 at one third of the total WLICC
goal; this equated to approximately 1.00 MtCO2e per member-country.
2. The fraction of SF6 emission abated was assumed to be 100 percent at an abatement
efficiency of 100 percent, equating to zero SF6 emissions.33 This results in all of the WLICC
countries' goal emissions to be allocated to PFC emissions.
3. While holding the PFC abatement efficiency constant at 90 percent for each country for
each year, the fraction of emissions abated was varied until a value was reached that resulted
in the WLICC goal being met (i.e., until emission of PFCs were equivalent to 1.00 MtCO2e)
If a WLICC country's emissions were already below the WLICC goal, it was assumed that
the fraction of emissions abated was zero.
4. In the instance that setting a fraction of emissions abated equivalent to 100 percent (with an
abatement efficiency of 90 percent) does not result in the WLICC goal being met in a
country, the abatement efficiency is then varied until the goal is met.
goal was set in response to the increasing growth in the F-GHG emissions due to the 96 percent share of the global
FPD manufacturing market that these three countries hold.
33 This assumption was made, again, because SFe is because of its higher GWP and as cost effective to abate as PFCs.
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Through using this method EPA ensured that the WLICC goal could be realistically met based on
the estimated emissions without the use of abatement.
Uncertainties and Sensitivities
These global emissions projections are highly sensitive to the assumption that China's domestic
demand for FPDs will substantially increase in the future (DisplaySearch, 2010); thereby increasing
Chinese domestic capacity and production of FPDs, and hence increasing emissions. If actual
domestic demand in China varies in the future, China's large contribution to global emissions may
change.
Table C-9 presents historical and projected emissions for all countries for this source for the
analysis.
7.2.9 Photovoltaic Manufacturing (PFCs, NF3)
The photovoltaic manufacturing (PV) sector is a new source category in this report, and country-
reported emission estimates are not available for this sector. Due to the lack of country-reported
data, EPA used the IPCC Tier 1 methodology for estimating emissions from etching and cleaning
processes used at PV manufacturing facilities (IPCC, 2006). The basic Tier 1 equation for estimating
emissions is as follows:
FQ = EF: *Cu*Cd
Where:
FCt = Emissions of gas i (mass)
EFt = Emission factor for gas i (mass/m2)
CU = Fraction of annual plant production capacity utilization (%)34
CD = Annual maximum design capacity of substrate processed (m2)
The main source of data for this source category is the DisplaySearch Q1'09 Quarterly PV Cell Capacity
Database & Trends Report ("DisplaySearch database") (DisplaySearch, 2009). This database supplies
historical and projected annual data through 2013 about all PV facilities in the world, including
location (country), type of technology manufactured at a facility (crystalline silicon, amorphous
silicon, or other thin film), maximum design capacity (megawatts) of a facility, and in some cases
conversion efficiency of the PV technology manufactured at a facility.
As discussed in section 4.10, and shown in the DisplaySearch database, there are a variety of
substrates used in the production of PV cells, including crystalline silicon, amorphous silicon, and
other thin-films. Manufacturing processes of PV cells with other thin film technologies do not
utilize F-GHGs, where as manufacturing processes of PV cells with crystalline silicon (c-Si) PV cells
and amorphous silicon (a-Si) and tandem a-Si/nanocrystaline (nc) silicon PV cells do use F-GHGs.
Therefore for this analysis the PV market considered was limited to c-Si and a-Si PV cells.
34 Cu is assumed to be equivalent to 100 percent; that is the maximum design capacity is assumed to be utilized.
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Historical Activity Data
The activity data for emission estimates from PV manufacturing is area (m2) of PV panels produced,
which is derived from maximum design capacities expressed in total peak power production (MW)
for each country and the world.
Historical maximum design capacities35, in units of MW, are determined by the following various
methods:
1990 and 1995: Maximum design capacities in these years are assumed to be 0 MW
because the sector was so small in this time period that any associated manufacturing
emissions would be negligible.
2000, 2005, and 2010: Maximum design capacities by country and for the world in 2000,
2005, and 2010 are extracted directly from the DisplaySearch database (DisplaySearch,
2009).
Maximum design capacity is converted to area of produced PV panels (m2), the activity data, using
technology-specific and time-varying market shares and average electrical conversions efficiencies
for c-Si and a-Si, and the expected power produced per unit of solar power absorbed at the Earth's
equator at noon (0.001 W/m2). The equation used for this conversion is as follows:
Area of PV Panel Produced (m2) = Maximum Design Capacity (MW) / ^(Market Share of Technology t (%) *
Average Electrical Conversion Efficiency of Technology t (%)) * Expected Power Produced (.001 MW/ m2)]
Technology market shares36 and average conversion efficiencies37 are determined using data from
the DisplaySearch database (DisplaySearch, 2009). In instances where data was not available to
calculate these values (i.e. DisplaySearch information was incomplete or for future years) technology
conversion efficiencies and market shares are assumed based on historical data and expert judgment.
Projected Activity Data
Projected maximum design capacities38, in units of MW, are determined by the following various
methods:
2015: World maximum design capacity in 2015 is extrapolated using the average annual
absolute growth in capacity for the world from 2010 through 2013. World maximum
design capacities for 2010 and 2013 are extracted directly from the DisplaySearch
database (DisplaySearch, 2009).
35 Includes maximum design capacity for crystalline and amorphous silicon, the two technologies that use PFCs in their
manufacturing processes.
36 For this report technology market shares are calculated based on a PV market that is assumed to only include c-Si and
a-Si technologies.
37 Technology conversion efficiencies are supplied for some years for both c-Si and a-Si technologies in the
DisplaySearch database. For each year this information is supplied a simple average of the available conversion
efficiencies is taken for each technology.
38 Includes capacity for crystalline and amorphous silicon, the two technologies that use PFCs in their manufacturing
processes.
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Using the world maximum design capacity estimate for 2015 as well as country-specific
shares of world maximum design capacity (or "capacity shares"), country-specific compound
annual growth rates (CAGRs) for 2010 through 2015 are estimated using the following
equation:
rACn _ WorldMaxDesignCapacity(2Q\5}*CountrySharei(20l5)
WorldMaxDesignCapacity(2Q\G) * CountrySharei (2010
-1
Country-specific capacity shares for 2010 are determined using the 2010 country and world
maximum design capacity data extracted from the DisplaySearch database (DisplaySearch,
2009). Country-specific capacity shares for 2015 were assumed to be equivalent to the shares
for 2013, which are also determined using data extracted from the DisplaySearch database
(DisplaySearch, 2009).
Maximum design capacity for each country in 2015 is calculated by applying country-specific
5 year compound annual growth rates (CAGRs) for 2010-2015 to maximum design capacity
for each country in 2010.
2020, 2025, and 2030: World maximum design capacities in 2020, 2025, and 2030 are
determined by applying 5 year CAGRs for each period. These 5 year CAGRs were
assumed to be equivalent to the 2010 through 2015 CAGR.
The methodology that is used to estimate maximum design capacity for each country in 2015
is also used to estimate maximum design capacity for each country in 2020 through 2030.
Country-specific capacity shares are held constant through 2030 at 2015 (2013) levels.
Maximum design capacity is converted to area of produced PV panels (m2), using the conversion
equation as described in the previous section.
Emission Factors and Related Assumptions
Area of PV panels (m2) for each country and the world are converted to emissions (MtCO2e) using
the emission factors (MtCO2e/m2) for c-Si and a-Si, and the respective market shares of each
technology in a given year. CF4 and C2F6 are used during manufacture of c-Si PV cells. Tier 1
emission factors both of these PFCs for PV manufacturing are published in the 2006 IPCC
Guidelines (IPCC, 2006).
NF3 is also used during manufacture of a-Si PV cells; however there is no published emission factor
for NF3 used during PV manufacturing. However NF3 is used routinely for cleaning during the
manufacture of a-Si PV cells, and the emissions are not negligible, depending on emissions
abatement practices. Therefore EPA developed an emission factor for NF3 using recently measured,
unpublished NF3-usage and NF3-emissions data for currently operating a-Si PV manufacturing
facilities.
Uncertainties and Sensitivities
Projections
In developing global projections of PFC emissions from the PV sector, a broad perspective was
adapted to determine future capacity for manufacturing PV cells. This forecast was framed by the
fast-growing renewable energy sector, which, in turn is embedded in the relatively slow-growing
energy sector. An effort was made to take into account, the use of alternative renewable energy
technologieswind, hydro, geothermal and solar thermal technologiesthat serve as alternatives to
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both conventional fossil fuels and PV solar. Pressure to develop sources of clean, renewable energy
is growing because of the increasing costs and risks of securing traditional energy supplies, the
increasing need for more energy as countries like China and India industrialize, and a growing
understanding of the environmental effects of traditional sources of energy.
While this perspective was useful in framing these projections, there are many uncertainties that
surround it. First and foremost are uncertainties in future GHG policy, which is one of the main
drivers in the use of renewable energy. Demand for renewable energy is highly dependent upon the
design of such policies, and what these policies will look like is some developed nations as well as
developing nations is still unknown.
Another uncertainty is a longer-term shift away from centralized sources of electricity generation to
more distributed sources of electricity. It is this distributive benefit that gives solar, over the long
term, an edge relative to other renewable sources of energy. This edge, however, might not become
evident in trends until 2030 or sometime thereafter.
Use of Abatement Systems
Emissions estimated in these projections do not explicitly consider PFC abatement. Abatement may
occur when point of use (POU) abatement systems are used at a manufacturing facility for PFCs.
Additionally all NF3 used during chamber cleaning passes through required silane abatement systems
for safety purposes, which are capable without modification of abating NF3 and more capable with
some modification. Emission estimates will be sensitive to the use of abatement. This sensitivity may
be considered in future versions of this report, when more information about this newly emerging
sector is available.
Table C-10 presents historical and projected emissions for all countries for this source for the
analysis.
7.2.10 Other Industrial Processes Sources (CH4, N2O)
Historical emission estimates for the "Other Industry Sources" emissions category are based on
UNFCCC-reported data. Projected emissions are assumed to remain constant at the value for the
last reported year. Similarly, values before the first reported year are assumed to equal that year's
value and values between two reported values are calculated using a linear interpolation. Emissions
were not estimated for countries that did not report emissions in any year.
Table C-ll and Table C-12 present historical emission estimates and projections for all countries.
7.3 Agriculture
7.3.1 Agricultural Soils (N2O)
If country-reported estimates were not available, EPA used the IPCC Tier 1 methodology to
estimate emissions. EPA estimated the following six components of N2O emissions from
agricultural soils:
Direct emissions from commercial synthetic fertilizer application
Indirect emissions from commercial synthetic fertilizer application
Direct emissions from the incorporation of crop residues
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Indirect emissions from the incorporation of crop residues
Direct emissions from manure (pasture, range and paddock and all applied manure)
Indirect emissions from manure
This section describes the methodology used to estimate N2O emissions from agricultural soils, and
is arranged by commercial fertilizer application, crop residues, and manure (including pasture, range
and paddock and all applied manure).
Direct and Indirect Emissions from Commercial Synthetic Fertilizer Application
Historical Activity Data
EPA obtained commercial synthetic fertilizer consumption data from the International Fertilizer
Industry Association (IFA) database of fertilizer statistics, known as IFADATA (IFA, 2010), and
from the Food and Agriculture Organization of the United Nations (FAO) database of agricultural
statistics, known as FAOSTAT (FAO, 2010). IFA data was the preferred source of activity data, and
where IFA data were unavailable, FAO data were used. One of these activity data sources was
available for most countries from 1990 through 2005. Specifically, EPA used the consumption of
nitrogenous fertilizers data, reported in metric tons of N39 (FAO) or thousand metric tons of N
(IFA). EPA used the following assumptions for countries with incomplete data:
Eritrea before 1993. In 1993, the former People's Democratic Republic of Ethiopia (Ethiopia PDR)
divided into Ethiopia and Eritrea. Data for Ethiopia for 1990 through 2005 were available from
IFA, but data for Eritrea were not. To estimate the fertilizer consumption of Eritrea in 1990, EPA
determined the relative ratio of the fertilizer consumption of the current Eritrea and Ethiopia in
1993. This ratio (two percent for fertilizer consumption) was then applied to the fertilizer
consumption of Ethiopia PDR to estimate the fertilizer consumption of Eritrea for 1990. This
method assumes that the IFA data for Ethiopia in 1990 included only the portion of Ethiopia PDR
that would become Ethiopia, and not the portion that would become Eritrea.
^-Luxembourg before 2000. In 2000, Belgium and Luxembourg began reporting separately to
FAO, rather than together, as had previously been the case. The distribution of fertilizer
consumption between these two countries in 2000 was assumed to be the same for 1990 and 1995.
Consequently, Belgium-Luxembourg consumption data in 1990 and 1995 was allocated between
Belgium and Luxembourg by their relative percentages in 2000.
The former Yugoslavia before 1995. In 1995, Yugoslavia divided into separate countries. The distribution
of fertilizer consumption among the former Yugoslav countries in 1995 was assumed to be the same
for 1990. Consequently, Yugoslavia consumption data in 1990 was allocated among the former
Yugoslav countries according to their relative percentages in 1995. Montenegro was not reported
separately from Serbia at any point, and it was assumed that this country had zero synthetic fertilizer
consumption (i.e., all consumption was allocated to Serbia).
The former Chechoslovakia before 1993. In 1993, Czechoslovakia divided into the Czech and Slovak
Republics. The distribution of fertilizer consumption between these two countries in 1993 was
39 In the FAO online database, fertilizer data appear to be reported in metric tons, but data are actually reported in
metric tons of N. This was corroborated by paper copies of the FAO statistics.
August 201 I 7. Methodology Page 7-48
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assumed to be the same for 1990. Consequently, Czechoslovakia consumption data in 1990 was
allocated between the Czech and Slovak Republics by their relative percentages in 1993.
IFA reported data for former Soviet Union (FSU) states dating back to 1990 (before the break-up of
the Soviet Union), so there was no need to separate out Soviet Union data for 1990, as would have
to be done with FAO data, which are not reported separately in 1990.
Portions of the FAO time series for particular countries were determined to be outliers because they
differed significantly from other parts of the time series and did not line up with trends in other
parts of the time series. In such cases, the rest of the time series was extrapolated to replace the
outlier data point. This was the case for Benin, Oman, and United Arab Emirates for 2005. In
addition, the entire FAO time series for Bahrain and Samoa were not used because of significant and
extreme variations in reported fertilizer use. In these two cases no other data were available and
fertilizer use was assumed to be zero.
Projected Activity Data
EPA estimated the growth rate of fertilizer consumption from 2010 to 2030 by using the regional N
fertilizer consumption projections available from Tenkorang & Lowenberg-DeBoer (2008). This
publication provided regional fertilizer use for 2005, 2015, and 2030, and EPA interpolated fertilizer
use for 2010, 2020, and 2025. The consumption projections were then used to calculate average
annual growth rates for the five-year increments between 2005 and 2030, which in turn were used to
project fertilizer use by country. Countries were assigned to regions based on Annex I of Tenkorang
& Lowenberg-DeBoer (2008).
Historical and Projected Emissions
As recommended in the 2006IPCC Guidelines (IPCC, 2006) EPA assumed that one percent of all
nitrogen from fertilizer consumption is directly emitted as N2O. Therefore, direct emissions were
calculated as follows:
Direct emissions from synthetic fertiliser (Gg N2O) = FSN x EF1 x 44/28
Where:
FSN = the annual amount of synthetic fertilizer N applied to soils (Gg N)
EF1 = emission factor (equal to 0.01 Gg N2O-N/Gg N input)
44/28 = conversion of N2O-N to N2O
EPA also followed the IPCC (2006) Tier 1 methodology for calculating indirect emissions from
synthetic fertilizer consumption, using the following equation:
Indirect emissions from synthetic fertiliser (Gg N2O) = [(FSN x FmcGASP x EF4) + (FSN x Frackach x EF5)] x
44/28
Where:
FSN = annual amount of synthetic fertilizer N applied to soils (Gg N)
FracGASP = fraction of synthetic fertilizer N that volatilizes as NH3 and NOX (equal to
0.10 Gg N volatilized/Gg N applied)
August 201 I 7. Methodology Page 7-49
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EF4 = emission factor for N2O emissions from N volatilization (equal to 0.01 Gg
N2O-N/(Gg NH3-N + NOx-N volatilized))
Fmckacb = N lost from leaching and runoff (equal to 0.30 Gg N/Gg N applied)
EFS = emission factor for N2O emissions from N leaching and runoff (equal to
0.0075 Gg N2O-N/Gg N leached or runoff)
44/28 = conversion of N2O-N to N2O
Direct and Indirect Emissions from the Incorporation of Crop Residues
Residues from crops are typically incorporated into soils. Incorporation of crop residues directly
adds nitrogen to the soil, resulting in an increase in N2O emissions.
Historical Activity Data
FAO provided historical production and acreage statistics for the following major crops (residues of
which are typically incorporated into soils): barley, maize, pulses,40 rice, sorghum, soybeans, and
wheat. Historical production and area data for these crops were available for most countries for
1990 through 2005 (FAO, 2010). For countries where data were not available, EPA assumed zero
production. For countries without complete data, EPA used the following assumptions:
The former Soviet Union (FSU) before 1993. In 1993, the Soviet Union divided into separate countries (in
the context of FAO reportingthe political dissolution occurred in 1991). The distribution of
fertilizer consumption among the FSU countries in 1993 was assumed to be the same for 1990.
Consequently, Soviet consumption data in 1990 was allocated among the FSU countries by their
relative percentages in 1993.
The former Yugoslavia before 1995. In 1995, Yugoslavia divided into separate countries. The distribution
of fertilizer consumption among the former Yugoslav countries in 1995 was assumed to be the same
for 1990. Consequently, Yugoslavia consumption data in 1990 was allocated among the former
Yugoslav countries according to their relative percentages in 1995. Montenegro was not reported
separately from Serbia at any point, and it was assumed that this country had zero synthetic fertilizer
consumption (i.e., all consumption was allocated to Serbia).
The former Chechoslovakia before 1993. In 1993, Czechoslovakia divided into the Czech and Slovak
Republics. The distribution of fertilizer consumption between these two countries in 1993 was
assumed to be the same for 1990. Consequently, Czechoslovakia consumption data in 1990 was
allocated between the Czech and Slovak Republics by their relative percentages in 1993.
Ethiopia andEritrea before 1993. In 1993, the People's Democratic Republic of Ethiopia (Ethiopia
PDR) divided into Ethiopia and Eritrea. The distribution of fertilizer consumption between these
two countries in 1993 was assumed to be the same for 1990. Consequently, Ethiopia PDR
consumption data in 1990 was allocated between Ethiopia and Eritrea by their relative percentages
in 1993.
'Belgium-Luxembourg before 2000. In 2000, Belgium and Luxembourg began reporting separately to
FAO, rather than together, as had previously been the case. The distribution of fertilizer
40 Pulses include lentils, dry beans, dry broad beans, dry horse beans, chickpeas, and pulses not elsewhere specified.
August 201 I 7. Methodology Page 7-50
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consumption between these two countries in 2000 was assumed to be the same for 1990 and 1995.
Consequently, Belgium-Luxembourg consumption data in 1990 and 1995 was allocated between
Belgium and Luxembourg by their relative percentages in 2000.
Projected Activity Data
EPA estimated the growth rate of crop area and production for 2010 to 2030 by using the country
and regional crop area and production projections available from FAPRI (2010). Projected crop
production and area data through the 2019/2020 agricultural year were available from FAPRI for all
crops except pulses (projections for rice were available through 2018/2019). For pulses, EPA
calculated and applied an average crop growth rate for all other crops. Projected data were available
for world regions for all key countries by crop, and for "Rest of World." For example, country-
specific crop data were available for Viet Nam for rice, since it is a major rice producing country, but
Viet Nam country-specific data were not available for soybeans, since it is not a major soybean
producer. For soybeans, Viet Nam was grouped with "Rest of World." For barley, maize, and wheat,
"rest of [region]" data were available for countries not specified.
These area and production projections were used to calculate average annual growth rates for the
five-year increments between 2005 and 2030. EPA used the 2015 to 2020 growth rate for the 2020
to 2025 and 2025 to 2030 periods. For rice, the 2015 to 2020 growth rate was based on data from
2015 through 2019. For countries for which specific data were unavailable, EPA used the five-year
growth rates for the relevant region or "Rest of World." EPA then used the growth rates to project
crop area and production by country.
Historical and Projected Emissions
EPA used IPCC (2006) Tier 1 methodology to estimate emissions from crop residues. The direct
emissions calculation used the following equation:
Direct emissions from crop residues (GgN2O) = FCR x EF, x 44/28 x 1(f
Where:
FCK = the annual amount of N in crop residues and forage/pasture renewal (kg
N)
EF1 = emission factor,(equal to 0.01 kg N2O-N/kg N input)
44/28 = conversion of N2O -N to N2O
/ (f - conversion from kg to Gg
Indirect N2O emissions from crop residues used the following calculation:
Indirect emissions from crop residues (Gg N2O) = FCR x Fracleacb x EF3 x 44/28 x 1(f
Where:
FCR = the annual amount of N in crop residues and forage/pasture renewal (kg
N)
Fmckacb = N lost from leaching and runoff (equal to 0.30 kg N/kg N applied)
August 201 I 7. Methodology Page 7-5 I
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EF3 = emission factor for N2O emissions from N leaching and runoff (equal to
0.0075 kg N2O-N/kg N leached or runoff)
44/28 = conversion of N2O -N to N2O
/ (f - conversion from kg to Gg
N additions to soils from crop residues depend on the crop type and yield, since different crop types
have different N contents and different amounts of residue typically left in the soil. The equation for
FCRis:
FCR (Gg N20) = X (Yield Fnsbr x DRYT xST + IT) x AreaT x (Na£(r) + Rb£_BIO m x Nb£(T))
Where:
T = crop or forage type
Yield Fresh = fresh weight yield of crop (kg fresh weight/ha)
DRY = dry matter fraction of harvested crop (kg dry matter/kg fresh weight)
S = Slope for above-ground residue dry matter
I = Intercept for above-ground residue dry matter
Area = total annual area harvested (ha)
N^ = N content of above-ground residues (kg N/kg dry matter)
R^BIO = ratio of belowground residues to above ground biomass
Ntg = N content of below-ground residues (kg N/kg dry matter)
EPA used the crop residue factors by crop type shown in Table 11.2 in the 2006IPCC Guidelines
(IPCC, 2006). If a default factor was not available for a particular crop, EPA used a proxy. Nbg for
rice and Rbg_Bio f°r sorghum were based on the general "grains" category in the 2006 IPCC Guidelines
(IPCC, 2006).
Direct and Indirect Emissions from Manure (Pasture, Range, and Paddock, and All
Applied Manure)
Direct N2O emissions result from livestock manure that is applied to soils through daily spread
operations, through application to soils of the residues of already-managed manure, or through
direct deposition on pasture, range, and paddock (PUP) by grazing livestock.
Historical Activity Data
EPA obtained animal population data for 1990, 1995, 2000, and 2005 through 2008 from FAO
(2010). Populations of non-dairy cattle are obtained by subtracting FAO dairy cattle populations
from FAO total cattle populations. In 1990, animal population data were not available for certain
countries that were formed after the breakup of the Former Soviet Union (FSU) (Armenia,
Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Russian
Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan), Yugoslavia (Bosnia, Croatia,
Macedonia, Slovenia, and Serbia and Montenegro), Czechoslovakia (Czech Republic and Slovakia),
August 201 I 7. Methodology Page 7-52
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and Ethiopia (Ethiopia and Eritrea). In addition, Belgium and Luxembourg were reported jointly
until 2000. Therefore, for each region, EPA determined the percent contribution of each country to
its regional total using 1995 (1993 for Czechoslovakia) or 2000 animal population data. EPA then
applied these percentages to estimate 1990 and/or 1995 animal population for these countries. The
animal types included were dairy cows, other cattle, buffalo, sheep, goats, pigs, chickens, turkeys,
ducks, geese, horses, mules, asses, camels, and other camelids (assumed to be llamas and alpacas).
Projected Activity Data
EPA projected emissions from 2010-2030 based on livestock product growth rates developed by the
International Food Policy Research Institute's (IFPRI) International Model for Policy Analysis of
Agricultural Commodities and Trade (IMPACT) model (IFPRI, 2009).41 The IMPACT model
projects growth rates by country for the demand of beef, pork, lamb, and milk for the years 2005
through 2030, in five year increments. These estimates are used to proxy average annual growth
rates for the livestock species, non-dairy cattle, swine, sheep, and dairy cattle, respectively. For the
remaining livestock types, the average population growth rate from 2005 through 2008 in the FAO
data was used to project population growth through 2030.42
Starting with the historical year 2005 FAO animal population statistics, growth rates were applied to
calculate projected populations for 2010, 2015, 2020, 2025, 2030, and 2035 for each livestock
species.
Historical and Projected Emissions
EPA assigned countries to regions (Africa, Asia, Eastern Europe, Indian Subcontinent, Latin
America, Middle East, North America, Oceania, and Western Europe) and development categories
(developed, developing). EPA then used IPCC default nitrogen excretion rates by region and
development category to estimate N excretion per head by country for each animal type, based on
the country's region and development category (IPCC, 2006).
EPA then used the IPCC guidance methodology on "Coordination with reporting for N2O
emissions from managed soils," found in Section 10.5.4 of the 2006 IPCC Guidelines, to determine
the amount of N that remains in manure following management in manure management systems.
The amount of N remaining corresponds to the amount available for application to agricultural soils.
Using IPCC Equation 10.34, EPA estimated managed manure N available for application to
managed soils as follows:
41 The IFPRI IMPACT model incorporates supply and demand parameters to determine the estimated growth rates.
These parameters include the feed mix applied according to relative price movements, international trade, national
income, population, and urban growth rates as well as anticipated changes in these rates over time.
42 Basing livestock population growth on the 2005 2008 historical trends led to unrealistically high growth rates in
some countries that have experienced large livestock increases in recent years. In countries where the growth between
2008 and 2035 was greater than 200 percent, the trend was adjusted to draw on a longer historical period. Where
possible, the period used was 1990 - 2008; however, in some cases, a shorter period was necessary in order to keep
growth as close as possible to the range considered reasonable (i.e., 200 percent or less).
August 201 I 7. Methodology Page 7-53
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Where:
= amount of managed manure nitrogen available for application to managed
soils or for feed, fuel, or construction purposes (kg N yr4)
= number of head of livestock species/category T in the country
= annual average N excretion per animal of species/category T in the
country (kg N animal4 yr"1)
= fraction of total annual nitrogen excretion for each livestock
species/category T that is managed in manure management system S in the
country (dimensionless)
FracLassMS = amount of managed manure nitrogen for livestock category T that is lost
in the manure management system S (%)
NteMngMS = amount of nitrogen from bedding (to be applied for solid storage and deep
bedding MMS if known organic bedding usage) (kg N animal4 yr4).
S = manure management system
T = species/category of livestock
Uncertainties
The greatest uncertainties are associated with the completeness of the activity data used to derive the
emission estimates. Emissions from fertilizers are estimated from only synthetic fertilizer use. In
reality, organic fertilizers (other than the estimated manure and crop residues) also contribute to
N2O emissions from soils, but this activity is not captured in these estimates. Crop residues from
crops other than those covered (including from nitrogen-fixing crops other than soybeans and
pulses) may be left on the field, thus resulting in N2O emissions. The identity and quantity of these
crops vary among the different countries.
The livestock nitrogen excretion values, while based on detailed population statistics, and using
regional nitrogen excretion factors, do not accurately reflect country-to-country variations in animal
weight or feeding regimes. Any contribution of animal bedding materials to manure N was not
considered. The "other" category for manure management is a large unknownEPA assumed no
emissions from this category, except for from poultry, where the "other" category was assumed to
represent an average of the "poultry with litter" and "poultry without litter" management systems.
Finally, emissions from histosols, sewage sludge, asymbiotic fixation of soil nitrogen, and
mineralization of soil organic matter are not calculated or included in these estimates. The last two
sources, in particular, can be a significant component of agricultural soil emissions.
Uncertainty also exists in the projected emissions. For some subcategories, projections are not
available to 2030, and so projections from earlier periods are used. Additionally, in some cases
projections are on a regional level, not a country-specific level and using regional projections
increases uncertainty.
August 201 I 7. Methodology Page 7-54
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Table D-2 presents historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.3.2 Enteric Fermentation (CH4)
The basic equation to estimate emissions from enteric fermentation is as follows:
Emission Factor (kg/ head/jr) x .Animal Population (head) / (1(f kg/Gg) = Emissions (Gg/jr)
The default emission factors are taken from the IPCC Guidelines (IPCC, 2006) and the animal
population data were obtained from the Food and Agriculture Organization (FAO, 2010). The
primary driver for determining CH4 emissions from enteric fermentation was animal population. It
was assumed that the animal characteristics upon which the default emission factors are based do
not change significantly over time.
Historical Emissions
If reported estimates were not available, EPA used the IPCC Tier 1 methodology for each country
for which FAO animal population data were available. If reported emissions were available only for
a portion of the timeframe, emissions were interpolated using the available data in conjunction with
the growth rate associated with the estimated Tier 1 emissions calculated for the country.
Activity Data
EPA obtained 1990, 1995, 2000, and 2005 through 2008 animal population data from
FAO (2010). Populations of non-dairy cattle were calculated by subtracting FAO dairy
cattle populations from FAO total cattle populations. The FAO population data is
further modified in instances where country data was aggregated for part of the time
series. For example, in 1990, animal population data were not available for certain
countries that were formed after the breakup of the Former Soviet Union (FSU)
(Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia,
Lithuania, Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, and
Uzbekistan), Yugoslavia (Bosnia, Croatia, Macedonia, Slovenia, and Serbia and
Montenegro), Czechoslovakia (Czech Republic and Slovakia), and Ethiopia (Ethiopia
and Eritrea). In addition, Belgium and Luxembourg were reported jointly until 2000.
Therefore, for each region, EPA determined the percent contribution of each country to
its regional total using 1995 (1993 for Czechoslovakia) or 2000 animal population data.
EPA then applied these percentages to estimate 1990 and/or 1995 animal population for
these countries.
Emission Factors
Tier 1 default emission factors from the 2006 IPCC Guidelines were used in the
calculated emissions (IPCC, 2006). For buffalo, sheep, goats, camels, horses, mules and
asses, deer, alpacas, and swine, the appropriate enteric fermentation emission factors for
either "developed" or "developing" countries were used. For dairy and non-dairy cattle,
enteric fermentation emission factors for world regions were used, with factors assigned
to countries based on the region in which they are located.
August 201 I 7. Methodology Page 7-55
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Projected Emissions
Activity Data
EPA used reported estimates for 2010, 2015, 2020, 2025, 2030, and 2035 if available
through the UNFCCC flexible query system (UNFCCC, 2009). If projections were not
available, EPA projected emissions from 2005-2035 based on livestock product growth
rates developed by the International Food Policy Research Institute's International
Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) model
(IFPRI, 2009).43 The IMPACT model projects growth rates by country for the demand
of beef, pork, lamb, and milk for the years 2005 through 2035, in five year increments.
These estimates were used to proxy average annual growth rates for the livestock species,
non-dairy cattle, swine, sheep, and dairy cattle, respectively. For the remaining livestock
types, the average population growth rate from 2005-2008 in the FAO data were
applied.44
The growth rates described above were applied to the 2005 FAO animal populations to
calculate projected populations for 2010, 2015, 2020, 2025, 2030, and 2035 for each
livestock species.
Emission Factors
Emission factors used for calculating projections were the same as those described above
for the historical time series calculations.
Uncertainties
The greatest uncertainties are associated with the use of default emission factors due to the lack of
information on country-specific animal diets. Emission estimates for countries with a variety of
animal diets could be inaccurate, particularly when projecting emissions since there is a lack of
information on potential changes in the quality, quantity, and type of feed that could affect
emissions in projected years. Also, the impacts of world markets and consumption patterns on
national livestock production patterns are often difficult to predict, further increasing the uncertainty
of projected emissions from this source.
Table D-3 presents historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.3.3 Rice Cultivation (CH4)
The 2006 IPCC Guidelines (IPCC, 2006) provides the following overall equation for the calculation
of CH4 emissions from rice production:
43 The IFPRI IMPACT model incorporates supply and demand parameters to determine the estimated growth rates.
These parameters include the feed mix applied according to relative price movements, international trade, national
income, population, and urban growth rates as well as anticipated changes in these rates over time.
44 Basing livestock population growth on the 2005 2008 historical trend led to unrealistically high growth rates in some
countries that have experienced large livestock increases in recent years. In countries where the growth between 2008
and 2035 was greater than 200 percent, the trend was adjusted to draw on a longer historical period. Where possible, the
period used was 1990 - 2008; however, in some cases, a shorter period was necessary in order to keep growth as close as
possible to the range considered reasonable (i.e., 200 percent or less).
August 201 I 7. Methodology Page 7-56
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CH4emissions from rice cultivation (Gg C//4) = y (£"Fjj fc x tjj fc x Aijik x 10 6)
Where:
EF^k = a daily emission factor for i,j, and k conditions (kg CH4 ha4 day"1)
/w£ = cultivation period of rice for i,j, and k conditions (days)
A^i/k = annual harvested area of rice for i,j, and k conditions (ha yr"1)
i,j, and k - represent different ecosystems, water regimes, type and amount of organic
amendments, and other conditions under which CH4 emissions from rice
may vary
Rice emissions vary according to the conditions under which rice is grown. Using the approach
outlined above, the harvested area can be subdivided by different growing conditions (e.g., water
management regime) and multiplied by an emission factor appropriate to the conditions. The sum of
these individual products represents the total national estimate.
In practice, it is difficult to obtain specific emission factors for each commonly occurring set of rice
production conditions in a country, so the IPCC Guidelines instruct countries to first obtain a
baseline emission factor (EFC) for continuously flooded fields without organic amendments.
Different scaling factors are then applied to this seasonally integrated emission factor to obtain an
adjusted seasonally integrated emission factor for the harvested area as follows:
= EFe * SFa *SFo * SFs
Where:
EFt = Adjusted seasonally integrated emission factor for a particular harvested area
EFC = Seasonally integrated emission factor for continuously flooded fields without
organic amendments
SFa = Scaling factor to account for the differences in ecosystem and water
management regime
SF0 = Scaling factors for organic amendments (should vary for both type and
amount of amendment applied)
SFS = Scaling factor for soil type, if available.
Historical Emissions
If no estimates were available, EPA used the IPCC Tier 1 methodology for each country/region, as
detailed below:
August 201 I 7. Methodology Page 7-57
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Activity
EPA obtained data on area harvested for rice cultivation from 1990 through 2005 (FAO,
2010). If the harvested area was not available through FAO statistics, EPA assumed that
the country does not grow rice.
« EPA obtained information on type of water management regime (irrigated, rainfed
lowland, upland, or deepwater) from the International Rice Research Institute (IRRI,
2009).
EPA obtained information on the length of the rice-growing season in each country
(IRRI, 2009).
Country-applicable daily emission factors were developed for each of the five main water
management types: irrigated, rainfed lowland, upland, or deepwater. The starting point (baseline)
emission factor (1.3 kg CH4/ha-day) obtained from IPCC Guidelines (IPCC, 2006) assumes fields
with no flooding for less than 180 days prior to rice cultivation, and continuously flooded during
rice cultivation without organic amendments. Scaling factors from IPCC Guidelines (IPCC, 2006)
are then applied to adjust the starting point emission factor for each of the other water regimes. The
scaling factors 0.78, 0.28, 0.31, and 0, are used for irrigated, regular rainfed (lowland), deepwater, and
upland, respectively. A scaling factor of 1.22 was used for all water regimes except upland
cultivation.
The combination of all the above adjustment factors provided the adjusted country-
specific emission factors used in the emission equation above.
A weighted average of the water-regime-based emission factors for each country was
calculated based on the percentage of each regime in that country. This weighting gives
the combined final daily emission factor for each country.
If a country-specific emission factor was not available and a country was used as a proxy
for season length, the same country proxy was used. Otherwise the baseline emission
factor (1.3) was used. The following country proxies were applied:
Madagascar's emission factor was applied to Comoros.
Malaysia's emission factor was applied to Brunei Darussalam.
Nepal's emission factor was applied to Bhutan.
Pakistan's emission factor was applied Afghanistan.
Irrigated Land: Due to limited information, EPA assumed that all irrigated land is
continuously flooded with no aeration. This assumption is conservative and could lead
to overestimates in emissions.
Country-applicable season lengths were based on IRRI data (IRRI, 2009, Appendix Table 4). Season
lengths were given as month ranges for planting and harvest (e.g., Planting: February through
March, Harvest: Mid-June through Mid-July). To estimate the number of days corresponding to the
given range, the following assumptions were made:
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EPA assumed that a single month given (e.g., March, rather than a range, March-April)
refers to the 15th of that month; "Mid" refers to the 15th of the month; "Early" refers
to the 1st of the month; and "Late" refers to the last day of the month.
EPA assumes that a range of months refers to the 1st or 15th, day of the month, falling
in the approximate middle of the range, as applicable. For example, April May would
return May 1st; April June would return May 15th; Late November January would
return Jan 1st.
For countries with more than one season per year (i.e. "main", "second"), EPA added
the season lengths. For countries with early and late seasons, EPA used the longer of the
two seasons. For countries where IRRI identifies different rice-growing regions, EPA
averaged the regions.
For some countries where FAO indicated that rice is grown, no season length data were
available, and for some countries the available data was problematic (e.g. planting dates
overlapped with harvest dates). In both these cases, countries in the same region deemed
to have similar climates or rice-growing schemes were used as proxies. Table 7-16
displays the country season lengths that were used as proxies.
Table 7-16: Growing Season Length Proxies
Proxy Country (Season Length)
Proxy Country Applied To:
Bulgaria
Democratic Republic of Congo
Dominican Republic
Guinea
Indonesia
Madagascar
Malaysia
Mozambique
Nepal
Nicaragua
Pakistan
Solomon Islands
Uganda
Macedonia
Angola
Jamaica, Saint Vincent and the Grenadines.
Guinea-Bissau
Timor-Leste
Comoros
Brunei Darussalam
South Africa, Swaziland, Zambia, Zimbabwe
Bhutan
Costa Rica
Afghanistan
Fiji, Micronesia, Papua New Guinea
Kenya
Emissions
EPA multiplied area harvested for 1990, 1995, 2000, and 2005 by the combined final
daily emission factor and by the season length.
If reported emissions or FAO production data were not available, EPA assumed zero emissions
from this source.
Projected Emissions
If projections were not available, EPA used the following methodology to project emission
estimates:
August 2011
7. Methodology
Page 7-59
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Activity Data
Projected rice area harvested data for selected countries through 2018/2019 were
available from the Food and Agriculture Policy Research Institute (FAPRI, 2010). EPA
calculated growth rates for the periods 2005 through 2010, 2010 through 2015, and 2015
through 2020 (using the 2018/2019 data as a proxy for 2020 data). EPA assumed that
the growth rate from 2015 through 2020 applied through 2030.
For countries where projected area data were not available, EPA used the "Rest-of-
World" area growth rates from the same FAPRI report (FAPRI, 2010).
Emissions
EPA applied the five-year area growth rates to the historical emissions attributed to rice
cultivation to develop projections at five-year intervals.
Uncertainties
Significant uncertainties exist in the CH4 emission estimates from rice cultivation. The greatest
uncertainties are associated with the use of default emission factors. The IPCC emission factors are
not country-specific and are adjusted for some parameters (e.g., water management), but not
adjusted for other parameters (e.g., rationing). There were many countries where water regime
information was not available, and using the default emission factor for these countries may lead to
an overestimate of emissions. In addition, country-specific information is not readily available on the
amount flooding and aeration in irrigated areas, so EPA had to develop assumptions based on
known country conditions.
Also, no scaling adjustment was made to account for organic amendments, due to a lack of data on
the use of such amendments. This may result in an underestimate of emissions.
The rice season length is also an area of uncertainty, as many assumptions were made (detailed
above) to turn a rough estimate of month ranges into a specific number of days. In addition, a
number of countries were proxied due to lack of data, and these proxies for season length might not
be accurate. Lastly, since projections beyond 2020 were based on growth rates from 2015 through
2020, increased uncertainty is introduced through these assumptions.
Table D-4 presents historical emissions and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.3.4 Manure Management (CH4, N2O)
Many developing countries report estimates of CH4 emissions and some countries also report N2O
emissions for manure management; however, there is generally less coverage of N2O emissions in
the published inventory data.
The basic equation to estimate emissions from manure management is as follows:
Emission Factor (kg/ head/yr) x Animal Population (head)/ (10s kg/Gg) = Emissions (Gg/yr)
The default manure management emission factors are either taken directly or derived from the data
provided in the 2006 IPCC Guidelines (IPCC 2006) and livestock population data are obtained from
the Food and Agriculture Organization (FAO, 2010). The primary driver for determining CH4
August 201 I 7. Methodology Page 7-60
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emissions from enteric fermentation is animal population, assuming that waste management and
animal characteristics do not change significantly over time.
Historical Emissions
If country reported estimates were not available, EPA used the IPCC Tier 1 methodology for each
country where FAO animal population data were available (IPCC, 2006). If reported emissions were
available only for a portion of the time series, emissions were interpolated using the available data in
conjunction with the growth rate associated with the estimated Tier 1 emissions calculated for the
country.
Activity
EPA obtained 1990, 1995, 2000, and 2005 through 2008 animal population data from
FAO (2010). Populations of non-dairy cattle were estimated by subtracting FAO dairy
cattle estimates from FAO total cattle estimates. The FAO population data is further
modified in instances where country data was aggregated for part of the time series. For
example, in 1990, animal population data are not available for certain countries that have
since been established after the breakup of the Former Soviet Union (FSU) (Armenia,
Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania,
Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan),
Yugoslavia (Bosnia, Croatia, Macedonia, Slovenia, and Serbia and Montenegro),
Czechoslovakia (Czech Republic and Slovakia), and Ethiopia (Ethiopia and Eritrea). In
addition, Belgium and Luxembourg were reported jointly until 2000. Therefore, for each
region, EPA determined the percent contribution of each country to their regional total
using 1995 (1993 for Czechoslovakia) or 2000 animal population data. EPA then applied
these percentages to estimate 1990 and/or 1995 animal populations for these countries.
Emission Foctors
For sheep, goats, camels and other camelids, horses, mules and asses, and poultry, CH4
emission factors for both "developed" and "developing" countries were obtained from
the 2006 IPCC Guidelines (IPCC, 2006) by climate type (i.e., cool, temperate, or warm).
For cattle, swine and buffalo, CH4 emission factors from the 2006 IPCC Guidelines were
used, and were selected based on region and average annual temperature (provided in
increments of one degree Celsius) for the country.
According to IPCC (2006) Tier 1 default assumptions, N2O manure emission factors for
animal categories other than cattle, buffalo, swine and poultry is assumed to be managed
in pasture and grazing operations and is therefore not included in the manure
management estimates. Therefore manure management emissions from these animal
types were assumed to be zero and are estimated under N2O from agriculturally managed
soils.
For cattle, buffalo, swine, and poultry all default data was obtained from the 2006 IPCC
Guidelines (IPCC, 2006). Nitrogen (N) excretion rates (kg N per 1,000 kg animal mass)
were obtained by animal type and region, and were used in conjunction with typical
animal mass estimates (in kg, available by animal type and region for cattle, swine, and
buffalo and by developed or developing country designation for poultry) to calculate an
N excretion rate per head per year for each animal type and region, and also by
developed or developing country designation for poultry. The N excretion rate was used
August 201 I 7. Methodology Page 7-61
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with default manure management system usage estimates and the associated emission
factors for each management system to calculate default emission factors per head per
year by animal type and region for cattle, buffalo, and swine, and by region and
developed or developing country designation for poultry.
EPA estimated climate type for most countries using data from the Global Historical
Climatology Network, which is published by the National Climatic Data Center and
contains annual average temperatures for most country's capital/major cities. These
annual averages are for a range of years, which vary by country. Given the lack of animal
population data by areas within a country, EPA assumes that 100 percent of the animal
populations are located in a climate defined by the average temperature of the country
capital.
Projected Emissions
Activity Data
EPA used reported estimates for 2010, 2015, 2020, 2025, and 2030 if available through
National Communications (UNFCCC, 2009). If projections were not available, EPA
projected emission estimates from 2005 to 2030 based on livestock product growth rates
developed by the International Food Policy Research Institute's International Model for
Policy Analysis of Agricultural Commodities and Trade (IMPACT) model (IFPRI,
2009).45 The IMPACT model projects growth rates by country for the demand of beef,
pork, lamb, and milk for the years 2005 through 2035, in five year increments. These
estimates are used to proxy average annual growth rates for the livestock species, non-
dairy cattle, swine, sheep, and dairy cattle, respectively. For the remaining livestock types,
the average population growth rate from 2005-2008 in the FAO data were applied.46
The growth rates described above were applied to the 2005 FAO animal populations to
calculate projected populations for 2010, 2015, 2020, 2025, and 2030 for each livestock
species.
Emission Factors
Projected emission factors were the same as those described above for the historical time
series calculations.
Uncertainties
The default emission factors represent the greatest source of uncertainty due to the lack of
information on country-specific manure management systems and the geographic concentration of
animal populations, which affects the climate zone assignment. Considerable uncertainty in
projected emissions is due to the lack of information on potential changes to management system
types and animal feeding characteristics that could affect emissions in the projected years.
45 The IFPRI IMPACT model incorporates supply and demand parameters to determine the estimated growth rates.
These parameters include the feed mix applied according to relative price movements, international trade, national
income, population, and urban growth rates as well as anticipated changes in these rates over time.
46 Basing livestock population growth on the 2005 2008 historical trend led to unrealistically high growth rates in some
countries that have experienced large livestock increases in recent years. In countries where the growth between 2008
and 2035 was greater than 200 percent, the trend was adjusted to draw on a longer historical period. Where possible, the
period used was 1990 - 2008; however, in some cases, a shorter period was necessary in order to keep growth as close as
possible to the range considered reasonable (i.e., 200 percent or less).
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Additionally, the impacts of world markets and livestock product consumption patterns on national
livestock production patterns are often difficult to predict, further increasing the uncertainty of
projected emissions from this source.
Table D-5 and Table D-6 present historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.3.5 Other Agriculture Sources (CH4, N2O)
The sources included in this category are prescribed burning of savannas, field burning of
agricultural residues, and open burning from forest clearing. This category also includes small
amounts of country-reported emissions data on CH4 from agricultural soils. However, biomass
burning constitutes the majority of emissions for this source.
Emissions from biomass burning were obtained from the Emission Database for Global
Atmospheric Research (EDGAR), Version 4.0 (EC-JRC, 2009). EDGAR contains historical
emissions data for 1990 to 2005. Similar to the remaining "Other" sources, 2010 through 2035
emission estimates are set equal to the 2005 estimates. EDGAR contains historical data for the
following biomass burning sources:
Savanna Burning (IPCC Category 4E)
Agricultural Waste Burning (IPCC Category 4F)
Forest Fires (IPCC Category 5A)
Grassland Fires (IPCC Category 5C)
Forest Fires - Post Burn Decay (IPCC Category 5F2)
Table D-7 and Table D-8 present historical emission estimates and projections for all countries.
7.4 Waste
7.4.1 Landfilling of Solid Waste (CH4)
If country reported estimates were not available or country reported activity data were insufficient,
EPA used the 2006 IPCC Guidelines for National GHG Inventories Tier 1 methodology and the
associated simple spreadsheet model (IPCC Waste Model) to estimate emissions (IPCC, 2006).4V The
emission estimates for this source, calculated in the IPCC Waste Model, are based on the IPCC First
Order Decay (FOD) method primarily using default activity data and default parameters. As per the
2006 IPCC Guidelines for National GHG Inventories, this method assumes that the degradable organic
carbon (DOC) in waste decays slowly throughout a few decades releasing CH4 over time. This
updated IPCC 2006 Tier 1 methodology, used in cases where country-reported data was missing, is
different from the IPCC 1996 Tier 1 methodology used in the 2006 GER report because it includes
the temporal dimension for CH4 emissions associated with the slow decay of organic matter over
time.
47 Due to modeling limitations in the IPCC Waste Model and data availability issues, EPA was unable to model certain
drivers such as cover material characteristics, seasonal fluctuation in CH4 oxidation rates, and landfill gas recovery.
August 201 I 7. Methodology Page 7-63
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CH4 emissions from Solid Waste Disposal Sites (SWDS) for a single year can be estimated using the
Tier 1 equation below from the 2006 IPCC Guidelines. CH4 is generated due to degradation of
organic material under anaerobic conditions. Part of the CH4 generated is oxidized or can be
recovered for energy or flaring and as a result, the CH4 actually emitted will be less than the amount
generated.
CH ^Emissions t =
generatedxT -R
*(l-OXT)
Where:
CH4
-------
GDP data in Real 2005 Dollars obtained from U.S. Department of Agriculture (USDA,
2009) were used to estimate and project industrial waste generation.50
IPCC Waste model defaults were used in most cases, such as "waste per capita" and the
composition percentages of household waste going to SWDSs.
Climate zones were selected for the "CH4 generation rate" input in the IPCC waste
Model using IPCC 2003 Good Practice Guidance for LULUCF (Section 3.1) (IPCC, 2003)
and relevant default values were used for the selected region.
An industrial waste generation proxy country was selected to assume a "waste generation
rate" if default values were not available. This selection was performed according to the
guidance in Section 2.2.3 of 2006 IPCC Guidelines for National GHG Inventories (IPCC,
2006). A proxy country was selected, based on similar circumstances, from a list of
countries provided in Table 2.2 (Industrial Waste generation for Selected Countries) of
section 2.2.3.
Based on 2006 IPCC Guidelines for National GHG Inventories (Section 2.2.3), the percent of
industrial waste generated and sent to landfills (% to SWDS) was assumed to be the
same as the IPCC regional default for percent of MSW sent to landfills.
Emission Factors
The IPCC 2006 Waste Model was used to calculate emissions for countries that did not report
historical emission estimates (mainly non-Annex I countries). The following assumptions were made
with respect to emission factors:
DOC (mass of degradable organic carbon), DOCf (fraction of DOC dissimilated), k
(CH4 generation rate), were based on IPCC default values (IPCC, 2006). The values are
primarily based on the selected climate zone and geographic region.
Oxidation (OX) and recovery (R) were assumed to equal zero.51 However, Annex-I
countries that report emissions may be assuming non-zero numbers for these rates.
IPCC default values were used for estimated distribution of site types (managed or
unmanaged, deep or shallow, and uncategorized) and distribution of waste by site type.
Projected Emissions
If projections from National Communications were not available, EPA used the following
methodology to project emission estimates:
Activity Data
If a portion of the projected time series was reported, EPA used the following to project emission
estimates from 2007 to 2030:
50 Proxy GDP was assumed based on similar size or geographical regions for countries that did not provide GDP data in
the USDA data.
51 EPA recognizes that programs such as the Landfill Methane Outreach Program (LMOP) are encouraging landfill gas
recovery and use for energy in Non-Annex 1 countries leading to significant emission reductions. EPA will consider
incorporating this data for future revisions.
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EPA interpolated between projected emissions values and extrapolated out to 2030
based on the last 5-year interval projections as indicated through National
Communications.
If country reported projected data were not available and a historical emission estimate was
reported, EPA used the following activity data to project emission estimates:
Population data obtained from the U.S. Census International Database (Census, 2009) were
used to estimate and project landfill CH4 emissions by applying population growth rates
to the reported emission estimates.52
If country reported data were not available for the entire historical and projected time
series, the IPCC 2006 Waste Model (IPCC, 2006) was run for these countries to calculate
both historical and projected emissions from 1950 to 2030. The assumptions regarding
inputs into the model are outlined in the historical emissions section above.
Emission Factors
The IPCC 2006 Waste Model (IPCC, 2006) was used to calculate emissions for countries
that did not report historical emission estimates. The assumptions regarding emission
factor inputs into the model are outlined in the historical emissions section above.
Uncertainties
Uncertainties in the estimation of CH4 emissions from landfills are due in large part to the lack of
one or more country-specific values for the following parameters: MSW generation per capita,
percent to MSW, percent to managed landfills, DOC fractions, oxidation factors, and recovery.
Also, while the drivers for projections were selected to capture future trends in the movement of
waste to MSW landfills, there is considerable uncertainty, particularly in the developing regions of
the world, in predicting landfill utilization. Finally, although the methodology for projecting
landfilling CH4 emissions from waste disposal using population growth is acceptable as per the
IPCC 2006 Guidelines for a Tier 1 approach, waste disposal is likely influenced by multiple drivers
including economic and population growth.
Table E-2 presents historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.4.2 Wastewater (CH4)
The basic equation to estimate emissions from wastewater is as follows:
CH^Emissions =
TOW
Where:
CH4 Emissions = CH4 emissions per year, kg CH4/yr
52 Proxy populations were assumed based on similar size or geographical regions for countries that did not provide
population data in the US Census database.
August 201 I 7. Methodology Page 7-66
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TOW = total organics in wastewater per year, kg BOD/yr
Ut = fraction of population in income group i
TV = degree of utilization of treatment/discharge pathway or system,^
for each income group fraction, i
i income group: rural, urban high income, urban low income
j each treatment, discharge pathway or system
EFj = emission factor for treatment/discharge pathway or system,^ kg
CH4/kg BOD
The emission factors are a product of maximum CH4 producing capacity (kg CH4/kg biochemical
oxygen demand (BOD)) and a CH4 correction factor specific to each treatment or discharge
pathway or system. The maximum CH4 producing capacity used in this analysis is 0.6 kg CH4/kg
BOD, which is the default value in the 2006 IPCC guidelines. The above equation differs from the
2006 IPCC Guidelines in that estimates for organics removed as sludge and CH4 recovery were not
feasible to estimate by country on a global scale.
Total organics in wastewater is calculated by multiplying population by biochemical oxygen demand
(BOD) per person.
Historical Emissions
Historical estimates were based on emissions data obtained from the UNFCCC flexible query
system where data were available from 1990 through 2007 (UNFCCC, 2009). The time series was
available for most Al countries, however gaps existed in the time series for the majority of the NA1
countries. For the remainder of the historical time series, EPA applied growth rates to the 2007 base
year estimate as follows:
* When two years were reported such that a year requiring an estimate (e.g., 1995)
occurred between the reported years (e.g., 1993 and 1997), EPA interpolated the missing
estimate (1995) using linear interpolation of the reported estimates.
» EPA applied population growth rates calculated from the U.S. Census International Data
Base (Census, 2009) to the reported emission estimates to complete the historical time
series of emissions.
ilo
Population data were from the U.S. Census International Data Base (Census, 2009). The
U.S. Census International Data Base does not provide population data for Holy See or
Niue. For these countries, EPA used population estimates from the CIA World
Factbook (CIA, 2010) and assumed a constant population from 1990 to 2035.
BOD data by region/country, CH4 generation capacity, wastewater treatment pathways
by region/country, and urbanization scenarios were based on IPCC 2006 Guidelines
default factors for domestic wastewater (IPCC, 2006). The Holy See is assumed to have
100 percent of its population in Urban High conditions.
August 201 I 7. Methodology Page 7-67
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Emission Factors and Emissions
EPA calculated CH4 emissions from wastewater by multiplying activity data (i.e., BOD
data, wastewater treatment pathways) by default Tier 1 IPCC emission factors from
IPCC, 2006.
The UNFCCC-reported emissions for South Korea decreased by 99 percent from 1990
to 2000. To address this anomaly, the emissions for South Korea are projected from the
reported 1990 value using population data. The reported value for 2000 was not used.
Projected Emissions
Projected emission estimates were based on emissions data obtained from National
Communications (NC), where available. Estimates for some years were available for six countries
(Germany, Greece, Italy, Poland, Slovakia, and the United Kingdom). These estimates were
incorporated into the time-series as follows:
EPA projected emission estimates using NC data similar to the methodology followed to
estimate historical estimates using UNFCCC data. When two years were reported such
that a year requiring an estimate (e.g., 2010) occurred between the NC reported year
(e.g., 2015) and the UNFCCC reported year (e.g. 2000), EPA interpolated the missing
estimate (2010) using linear interpolation of the reported estimates.
EPA applied population growth rates calculated from the U.S. Census International Data
Base (Census, 2009) to the NC-reported emission estimates to complete the projected
time series of emissions.
Where NC data were not available for countries with UNFCCC reported historical
emissions, historical emissions were projected using population growth rates calculated
from the U.S. Census International Data Base (Census, 2009).
Activity Data
Population data were from the U.S. Census International Data Base (Census, 2009),
which provides annual population estimates through 2050. The U.S. Census
International Data Base does not provide population data for Holy See or Niue. For
these countries, EPA used population estimates from the CIA World Factbook (CIA,
2010) and assumed population remains constant across the time period.
BOD data by region/country, CH4 generation capacity, wastewater treatment pathways
by region/country, and urbanization scenarios were based on 2006 IPCC Guideline
default factors (IPCC, 2006). The Holy See was assumed to have 100 percent of its
population in Urban High conditions.
Emission Factors and Emissions
The emission factors used to calculate projected emissions are the same IPCC default
factors used in the historical time series calculations (IPCC, 2006).
Uncertainties
Significant uncertainty exists in this methodology in that as developing countries modernize or
change their domestic wastewater handling in the future, the shift to aerobic treatment will reduce
emissions. Other uncertainties exist with respect to population projections and linear interpolation
projections of UNFCCC reported data for individual countries.
August 201 I 7. Methodology Page 7-68
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Table E-3 presents historical emission estimates and projections for all countries.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.4.3 Human Sewage - Domestic Wastewater (N2O)
The basic equation to estimate N2O emissions from human sewage is as follows:
N2O Emissions = NSEVAGE xEFSEVAGEx44/ 28
Where:
N2Ofs) = N2O emissions from human sewage (kg N2O/yr)
~ Nitrogen in human sewage (kg N/yr)
EFSEVAGE = Emission factor for N2O emissions from human sewage (default =
0.005 kg N2O-N/kg N)
The factor 44/28 is the conversion of kg N2O-N into kg N2O.
The nitrogen content of human sewage is calculated according to the equation below:
NSEUTAGE = Px Protein x FNPR
Where:
NSEWAGE = total annual amount of nitrogen in human sewage, kg N/yr
P = country population
Protein = annual per capita protein consumption, kg/person/yr
Fj^-^ = fraction of nitrogen in protein (default = 0.16 kg N/kg protein)
Historical Emissions
Historical estimates were based on emissions data obtained from the UNFCCC flexible query
system where data were available from 1990 through 2007 (UNFCCC, 2009). The time series was
available for most Al countries, however gaps existed in the time series for the majority of the NA1
countries. For the remainder of the historical time series EPA applied growth rates to the 2007 year
estimate as follows:
When two years were reported such that a year requiring an estimate (e.g., 1995) occurred
between the reported years (e.g., 1993 and 1997), EPA interpolated the missing estimate
(1995) using linear interpolation of the reported estimates.
EPA applied population growth rates calculated from the U.S. Census International Data
Base (Census, 2009) to the reported emission estimates to complete the historical time series
of emissions.
Activity Data
Population data were from the U.S. Census International Data Base (Census, 2009), which
provides annual population from 1950 through 2035. The U.S. Census International Data
August 20 1 I 7. Methodology Page 7-69
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Base does not provide population data for Holy See or Niue. For these countries, EPA used
population estimations from the CIA World Factbook (CIA, 2010) and assumed population
remains constant across the time period.
Protein consumption data by country were taken from the Food and Agriculture
Organization (FAO) of the 2009 United Nations Statistical Yearbook (FAO, 2009). FAO
provides protein consumption values for three periods: 1994-1996, 1999-2001, and 2003-
2005. These values were used for the 1995, 2000, and 2005 estimates, respectively. Protein
consumption values for 1990 were assumed equal to the values for 1995.
The 2009 FAO Statistical Yearbook did not provide protein consumption data for a number
of countries. For these countries, EPA used geographically adjacent countries as a proxy for
protein consumption, as indicated in Table 7-17 below.
Table 7-17: Countries Used to Estimate Protein Consumption in Countries Missing Data
Country Missing Protein Consumption Data: Protein Consumption Assumed Equal to:
Afghanistan
Andorra
Bahrain
Bhutan
Cook Islands
Djibouti
Equatorial Guinea
Grenada
Holy See
Iraq
Kiribati
Liechtenstein
Maldives
Marshall Islands
Micronesia (Federated States of)
Monaco
Montenegro
Nauru
Niue
Oman
Palau
Papua New Guinea
Qatar
San Marino
Serbia
Singapore
Tonga
Tuvalu
Iran
average of France and Spain
Saudi Arabia
Nepal
Solomon Islands
Ethiopia
Gabon
Trinidad and Tobago
Italy
Iran
Solomon Islands
average of Austria and Switzerland
Sri Lanka
Solomon Islands
Solomon Islands
France
Bosnia and Herzegovina
Solomon Islands
Solomon Islands
Saudi Arabia
Solomon Islands
Indonesia
Saudi Arabia
Italy
Bosnia and Herzegovina
Malaysia
Solomon Islands
Solomon Islands
August 2011
7. Methodology
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Emission Factors and Emissions
EPA calculated N2O emissions from human sewage by multiplying activity data (i.e., protein
consumption, population) by default Tier 1 IPCC factors from IPCC, 2006. These default
factors include FNPR, the fraction of nitrogen in protein; 44/28, the conversion of kg N2O-N
into kg N2O; and the emission factor for N2O emissions from human sewage.
The 1990 UNFCCC-reported estimate for Paraguay was two orders of magnitude higher
compared to other estimates by Paraguay, as well as similar countries; therefore 1990
emissions were calculated by backcasting the 1994 country-reported estimate.
Projected Emissions
Projected emission estimates were based on emissions data obtained from National
Communications (NC), where available. Projections for some years were available for six countries
(Germany, Greece, Ireland, Italy, Poland, and Slovakia). These estimates were incorporated into the
time-series as follows:
EPA projected emission estimates using NC data similar to the methodology followed to
estimate historical estimates using UNFCCC data. When two years were reported such that a
year requiring an estimate (e.g., 2010) occurred between the NC reported year (e.g., 2015)
and the UNFCC reported year (e.g. 2000), EPA interpolated the missing estimate (2010)
using linear interpolation of the reported estimates.
EPA applied population growth rates calculated from the U.S. Census International Data
Base (Census, 2009) to the NC-reported emission estimates to complete the projected time
series of emissions.
Where NC data were not available for countries with UNFCCC reported historical
emissions, historical emissions were projected using population growth rates calculated from
the U.S. Census International Data Base.
Activity Data
Population data were from the U.S. Census International Data Base (Census, 2009), which
provides annual population estimates, by country through 2050. The U.S. Census
International Data Base does not provide population data for Holy See or Niue. For these
countries, EPA used population estimates from the CIA World Factbook (CIA, 2010) and
assumed population remains constant across the time period.
Protein consumption data by country is taken from the Food and Agriculture Organization
(FAO) of the 2009 United Nations Statistical Yearbook (FAO, 2009). Protein consumption
values for 2010-2030 are assumed equal to the FAO reported values for 2003-2005.
The 2009 FAO Statistical Yearbook did not provide protein consumption data for a number
of countries. For these countries, EPA used geographically adjacent countries as a proxy for
protein consumption, as indicated in Table 7-17 above.
Emission Factors and Estimates
The emission factors used to calculate projected emissions are the same IPCC default factors
used in the historical time series calculations (IPCC, 2006).
August 201 I 7. Methodology Page 7-71
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Greece's NC-reported projections were two orders of magnitude smaller than the historical
UNFCCC data indicated. Therefore, projections for Greece are calculated by forecasting
UNFCCC-reported data using population growth, rather than using NC-reported data.
Uncertainties
Significant uncertainty exists in this methodology in that as developing countries modernize and
change their dietary standards, an increase in protein consumption will increase emissions; this
uncertainty is particularly applicable to China and India with very large populations and economic
growth potential. Other uncertainties exist with respect to population projections and linear
interpolation projections of UNFCCC reported data for individual countries.
Table E-4 presents historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
7.4.4 Other Waste Sources (CH4, N2O)
Emission estimates for the "Other Waste Sources" emissions category are based on UNFCCC-
reported data. Future emissions are assumed to remain constant at the value for the last reported
year. Similarly, values before the first reported year are assumed to equal that year's value and values
between two reported values are calculated using a linear interpolation. No emissions are estimated
for countries that did not report emissions in any year.
Table E-5 and Table E-6 present historical and projected emissions for all countries for this source.
Appendix F and Appendix G describe the methodologies and data sources used for each country.
August 201 I 7. Methodology Page 7-72
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8 References
8.1 Introduction and Overview
Census. 2009. U.S. Census International Data Base. Online Database Accessed: October 2009.
Available online at: http://www.census.gov/ipc/www/idb/.
IPCC. 1996. Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate
Change. Edited by J.T. Houghton, L.G. Meira Filho, B.A. Callender, N. Harris, A.
Kattenberg, and K. Maskell. Cambridge, UK: Cambridge University Press.
IPCC. 1997. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. Paris:
Intergovernmental Panel on Climate Change, United Nations Environment Programme,
Organization for Economic Co-Operation and Development, International Energy Agency.
IPCC. 2000. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories
Programme, Montreal, IPCC-XVI/Doc.10 (1.IV.2000). May 2000.
IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National
Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change,
H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.). Hayama, Kanagawa,
Japan.
IPCC. 2007. Climate Change 2007: Working Group I: The Physical Science Basis. Intergovernmental Panel
on Climate Change. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt,
M. Tignor and H.L. Miller (eds.) Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA.
UNFCCC. 2009. United Nations Framework Convention on Climate Change Flexible GHG Data
Queries. Online Database Accessed: Fall 2009. Available online at:
.
USDA. 2009. Real GDP (2005 dollars) Historical International MacroeconomicData Set. United States
Department of Agriculture Economic Research Service. Available online at
.
WRI. 2010. Climate Analysis Indicators Tool (GAIT) Version 7.0. World Resources Institute.
Washington, DC.
8.2 Summary Results
CCSP. 2007. Synthesis and Assessment Product 2.1: Scenarios of Greenhouse Gas Emissions and
Atmospheric Concentrations (Part A) and Review of Integrated Scenario Development and
Application (Part B). A Report by the U.S. Climate Change Science Program and the
Subcommittee on Global Change Research [Clarke, L., J. Edmonds, J. Jacoby, H. Pitcher, J.
Reilly, R. Richels, E. Parson, V. Burkett, K. Fisher-Vanden, D. Keith, L. Mearns, C.
Rosenzweig, M. Webster (Authors)]. Department of Energy, Office of Biological &
Environmental Research, Washington, DC., USA.
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EMF-22. 2009. EMF 22: Climate Change Control Scenarios. Energy Modeling Forum. Stanford
University, Stanford, California, USA. .
EC-JRC. 2010. European Commission, Joint Research Centre QRC)/Netherlands Environmental
Assessment Agency (PEL). Emission Database for Global Atmospheric Research
(EDGAR), release version 4.1. Available online at
IPCC. 2001. Climate Change 2001: The Scientific Basis, Intergovernmental Panel on Climate
Change. Edited by J.T. Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X.
Dai, C.A.Johnson, and K. Maskell. Cambridge, UK: Cambridge University Press. Available
online at .
Velders et al. 2007. The importance of the Montreal Protocol in protecting climate. Proceedings of
the National Academy of Sciences (PNAS), 104(12), 4814-4819.
8.3 Energy
8.3.1 Natural Gas and Oil Systems
None.
8.3.2 Coal Mining Activities
EPA. 1993. Anthropogenic Methane Emissions in the United States: Estimates for 1990, Report to
Congress. Atmospheric Pollution Prevention Division, Office of Air and Radiation, US
Environmental Protection Agency. EPA/430/R/93/012. Washington, DC.
EPA. 1999. US Methane Emissions 1990-2002: Inventories, Projections, and Opportunities for
Reductions. Climate Protection Division, Office of Air and Radiation, US Environmental
Protection Agency. EPA/430/R/99/013. Washington, DC.
Stracher, G.B., Taylor, T.P., 2004. Coal fires burning out of control around the world:
thermodynamic recipe for environmental catastrophe. International Journal of Coal Geology
59, 7-17.
8.3.3 Stationary and Mobile Combustion
None.
8.3.4 Biomass Combustion
IEA. 2009. World Energy Outlook 2009. International Energy Agency. 2009 ed. November 2009.
8.3.5 Other Energy Sources
None.
August 201 I 8. References Page 8-2
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8.4 Industry
IPCC. 1996. Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate
Change. Edited by J.T. Houghton, L.G. Meira Filho, B.A. Callender, N. Harris, A.
Kattenberg, and K. Maskell. Cambridge, UK: Cambridge University Press.
IPCC. 2001. Climate Change 2001: The Scientific Basis, Intergovernmental Panel on Climate Change; Edited by
J.T. Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, C.A. Johnson,
and K. Maskell. Cambridge, UK: Cambridge University Press.
Molina, L.T., P.J. Woodbridge, and M. J. Molina. 1995. Atmospheric Reactions and Ultraviolet and
Infrared Absorptivities of Nitrogen Trifluoride. Geophysical Research Eetters. 22, no. 14, 1873-
76.
8.4.1 Adipic Acid and Nitric Acid Production
Chemical Week. 2007. Product Focus: Adipic Acid. Chemical Week. August 1-8, 2007.
Reimer, R.A., Slaten, C.S., Seapan, M., Koch, T.A. and Triner, V.G. 1999. Implementation of Technologies
for Abatement of N2Q Emissions Associated with Adipic Acid Manufacture. Proceedings of the 2nd
Symposium on Non-CO2 Greenhouse Gases (NCGG-2), Noordwijkerhout, The
Netherlands, 8-10 Sept. 1999, Ed. J. van Ham eta!., Kluwer Academic Publishers,
Dordrecht, pp. 347-358.
SRI. 2009. World Petrochemical Report: Adipic Acid. SRI Consulting. Access Intelligence LLC Inc.
January, 2010. Abstract available online at
.
8.4.2 Use of Substitutes for Ozone Depleting Substances
Velders et al. 2007. The importance of the Montreal Protocol in protecting climate. Proceedings of the
National Academy of Sciences (PNAS), 104(12), 4814-4819.
8.4.3 HCFC-22 Production
JICOP. 2006. Mr. Shigehiro Uemura of Japan Industrial Conference for Ozone Layer Protection
(JICOP), emails to Deborah Ottmger Schaefer of U.S. EPA, May 9, 2006.
UNEP. 2003. Report of the Technology and Economic Assessment Panel. United Nations Environment
Programme (UNEP) HCFC Task Force Report. May 2003.
UNEP. 2007. Response to Decision XVIII/12: Report of the Task Force on HCFC Issues (With Particular
Focus on the Impact of the Clean Development Mechanism) and Emissions Reduction benefits Arisingfrom
Earlier HCFC Phase-Out and Other Practical Measures. United Nationals Environment
Programme (UNEP) Technology and Economic Assessment Panel. August 2007.
8.4.4 Operation of Electrical Power Systems
EIA. 2009. International Energy Outlook 2009. Energy Information Administration, U.S.
Department of Energy, Washington, DC. Report# DOE/EIA-0484(2009). Available online
at .
August 201 I 8. References Page 8-3
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Smythe. K. 2004. Trends in SF6 Sales and End-Use Applications: 1961-2003. International
Conference on SF6 and the Environment: Emission Reduction Technologies, December 1-3,
2004, in Scottsdale, Arizona.
8.4.5 Primary Aluminum Production
None.
8.4.6 Semiconductor Manufacturing
Bartos, S.C., et al. 2008. Modeling China's semiconductor industry fiuorinated compound emissions
and drafting a roadmap for climate protection. International Journal of Greenhouse Gas
Control: April 2008.
ITRS. 2009. International Technology Roadmap for Semiconductors: 2009 Edition. Available online
at .
WSC. 2010. Joint Statement of the 14th Meeting of the World Semiconductor Council (WSC), May
2010. Available online at < http://www.sia-
online.org/gallenes/Publications/WSC%202010%20Final%20Jomt%20Statement.pdf>
8.4.7 Magnesium Manufacturing
Bartos S., J. Marks, R. Kantamaneni, C. Laush. 2003. Measured SF6 Emissions from Magnesium Die
Casting Operations. Magnesium Technology 2003, Proceedings of the Minerals, Metals &
Materials Society (TMS) Conference, March 2003.
IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National
Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change,
H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.). Hayama, Kanagawa,
Japan.
8.4.8 Flat Panel Display Manufacturing
DisplaySearch. 2010. DisplaySearch Q4- '09 Quarterly FPD Supply I Demand and Capital Spending Report.
DisplaySearch, LLC.
8.4.9 Photovoltaic Manufacturing
None.
8.4.10 Other Industrial Processes Sources (CH4, N2O)
None.
8.5 Agriculture
8.5.1 Agricultural Soils
None.
August 201 I 8. References Page 8-4
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8.5.2 Enteric Fermentation
FAPRI. 2010. U.S. and World Agricultural Outlook. Food and Agricultural Policy Research Institute,
Iowa State University, and University of Missouri-Columbia. Ames, Iowa. January 2010.
8.5.3 Rice Cultivation
FAPRI. 2010. U.S. and World Agricultural Outlook. Food and Agricultural Policy Research Institute,
Iowa State University, and University of Missouri-Columbia. Ames, Iowa. January 2010.
8.5.4 Manure Management
FAPRI. 2010. U.S. and World Agricultural Outlook. Food and Agricultural Policy Research Institute,
Iowa State University, and University of Missouri-Columbia. Ames, Iowa. January 2010.
8.5.5 Other Agricultural Sources
None.
8.6 Waste
Bogner, J., and K. Spokas. 2010. Landfills. Methane and Climate Change. Reay, D., Smith, P., and Van
Amstel, A., eds. Earthscan Publishers. London & Washington, DC.
IPCC. 2007. Climate Change 2007: Working Group III: Mitigation of Climate Change. 4th Assessment
Report. Intergovernmental Panel on Climate Change. Bogner, J., M. Abdelrafie Ahmed, C.
Diaz, A. Faaij, Q. Gao, S. Hashimoto, K. Mareckova, R. Pipatti, T. Zhang. Waste
Management. In Climate Change 2007: Mitigation. Contribution of Working Group III to
the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz,
O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)]. Cambridge University Press.
Cambridge, United Kingdom and New York, NY, USA.
Scheutz C., P. KjeldsenJ.E. Bogner, A. De Visscher, J. Gebert, H.A. Hilger, M. Huber-Humer, and
K. Spokas. 2009. Microbial methane oxidation processes and technologies for mitigation of
landfill gas emissions. Waste Management and Research. 27: 409-455. Available online at:
8.7 Methodology
IPCC. 1997. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. Paris:
Intergovernmental Panel on Climate Change, United Nations Environment Programme,
Organization for Economic Co-Operation and Development, International Energy Agency.
IPCC. 2000. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories
Programme, Montreal, IPCC-XVI/Doc.10 (1.IV.2000), May 2000.
IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National
Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change,
H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.). Hayama, Kanagawa,
Japan.
August 201 I 8. References Page 8-5
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UNFCCC. 2009. United Nations Framework Convention on Climate Change Flexible GHG Data
Queries. Online Database Accessed: Fall 2009. Available online at:
.
8.7.1 Energy
Natural Gas and Oil Systems
EIA. 2009. Natural Gas Annual Data. U.S. Energy Information Agency (EIA), August 2010.
IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National
Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change,
H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.). Hayama, Kanagawa,
Japan.
Coal Mining Activities
Andrews-Speed et al. 2005. Economic responses to the closure of small-scale coal mines in
Chongqing, China. Resources Policy: 30, 39-54. Available online at
.
EIA. 2009. International Energy Outlook 2009. Energy Information Administration, U.S. Department
of Energy, Washington, DC. Report# DOE/EIA-0484(2009). Available online at
.
EIA. 2010. Energy Information Administration International Energy Statistics Data Portal. Online
Database Accessed April 12, 2010. Available online at:
.
EPA. 2010. Methane to Markets International Coal Mine Methane (CMM) Projects database. Online
database Accessed: Summer 2010. http://www2.ergweb.com/cmm/index.aspx
IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National
Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change,
H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.). Hayama, Kanagawa,
Japan.
Stracher, G.B., Taylor, T.P., 2004. Coal fires burning out of control around the world:
thermodynamic recipe for environmental catastrophe. International Journal of Coal Geology
59, 7-17.
UNFCCC. 2009. United Nations Framework Convention on Climate Change Flexible GHG Data
Queries. Online Database Accessed: Fall 2009. Available online at:
.
Stationary and Mobile Combustion
IEA. 2009a. Energy Valances ofNon-OECD Countries 1971-2007. International Energy Agency. 2009
ed. CD-ROM. Pans, France.
IEA. 2009b. Energy Valances ofOECD Countries 1960-2007. International Energy Agency. 2009 ed.
Paris, France.
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IEA. 2009c. World Energy Outlook 2009. International Energy Agency. 2009 ed. November 2009.
IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National
Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change,
H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.). Hayama, Kanagawa,
Japan.
UNFCCC. 2009. United Nations Framework Convention on Climate Change Flexible GHG Data
Queries. Online Database Accessed: Fall 2009. Available online at:
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Biomass Combustion
IEA. 2009a. Energy Statistics ofNon-OECD Countries 1971-2007. International Energy Agency. 2009
ed. CD-ROM, Accessed March 23, 2010. Pans, France.
IEA. 2009b. Energy Statistics ofOECD Countries 1960-2007. International Energy Agency. 2009 ed.
Pans, France. 2009 ed. CD-ROM, Accessed March 23, 2010. Pans, France.
IEA. 2009c. World Energy Outlook 2009. International Energy Agency. 2009 ed. November 2009.
IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National
Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change,
H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.). Hayama, Kanagawa,
Japan.
UNFCCC. 2009. United Nations Framework Convention on Climate Change Flexible GHG Data
Queries. Online Database Accessed: Fall 2009. Available online at:
.
8.7.2 Industrial Processes
Adipic Acid and Nitric Acid Production
Chemical Week. 2007. Product Focus: Adipic Acid. Chemical Week. August 1-8, 2007.
Chemical Week. 1999. Product Focus: Adipic Acid/Adiponitrile. Chemical Week, p. 31. March 10,
1999.
FAO. 2010. FAOSTAT Statistical Database. Food and Agriculture Organization of the United
Nations. Available online at .
IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National
Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change,
H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.). Hayama, Kanagawa,
Japan.
SRI 2010. World Petrochemical Report: Adipic Acid. SRI Consulting. Access Intelligence LLC Inc.
January, 2010. Abstract available online at
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SRI. 2007. CEH Report: Nitric Acid. SRI Consulting. Access Intelligence LLC Inc. January, 2010.
September, 2007. Abstract available online at
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SRI. 1999. Quoted in Product focus: Adipic Acid/Adipomtrile. ChemicalWeek. March 10, 31. SRI
Consulting. Menlo Park, CA. Available online at
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18, 1999.
Tenkorang, F. and J. Lowenberg-DeBoer (2008) Forecasting Long-term Global Fertiliser Dem and. Food
and Agriculture Organization of the United Nations. Rome, 2008.
Use of Substitutes for Ozone Depleting Substances
Ashford, P. 2004. Peer review comments on U.S. EPA Draft Report, Draft Analysis of International
Costs of Abating HFC Emissions from Foams. Caleb Management Services Ltd. March 3, 2004.
EPA. 2010. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2008. United States
Environmental Protection Agency (EPA), Office of Atmospheric Programs. April 2010.
EPA-43-R-10-006. Washington, DC. Available online at
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March. 1996. UK Use and Emissions of Selected Hydrocarbons. A. Study for the Department of the Environment.
March Consulting Group, HMSO, London, 1996.
Russian Federation. 1994. Phaseout ofO^pne Depleting Substances in Russia. Prepared for the Ministry for
Protection of the Environment and Natural Resources of the Russian Federation and the
Danish Environmental Protection Agency, August 1994, x-xi, 27-28.
UNEP. 2010. Production and Consumption ofO^pne Depleting Substances, 1986-2007. United Nations
Environment Programme (UNEP) Ozone Secretariat. Nairobi, 2010. Available online at <
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USD A. 2009. Real GDP (2005 dollars) Historical InternationalMacroeconomic Data Set. United States
Department of Agriculture Economic Research Service. Available online at
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HCFC-22 Production
Campbell. 2006. Nick Campbell of Arkema, emails to Deborah Ottinger Schaefer of U.S. EPA,
April 24, 2006.
Chemical and Economics Handbook (CEH). 2001. Fluorocarbons CEH Marketing Research
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Harnisch and Hendricks. 2000. Harnish, Jochen and Chris Hendriks. Economic Evaluation of
Emission Reductions of HFCs, PFCs and SF6 in Europe. Contribution to the study Economic
Evaluation of Sectoral Emission Reduction Objectives for Climate Change. Commission of the
European Union, Directorate General Environment. April 2000.
MacCarthy, et al. 2010. J. MacCarthy, J. Thomas, S. Choudrie, N. Passant, G. Thistlethwaite, T.
Murrells, J. Watterson, L. Cardenas, and A. Thomson. UK Greenhouse Gas Inventory, 1990 to
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Miller et al. 2010. B.R. Miller, M. Rigby, L.J.M. Kuijpers, P.B. Krummel, L.P. Steele, M. Leiste, P.J.
Fraser, A. McCulloch, C. Harth, P. Salameh, J. Muhle, R.F. Weiss, R.G. Pnnn, R.H.J. Wang,
S. O'Doherty, B.R. Greally, and P.G. Simmonds. "HFC-23 (CHF3) emission trend response
to HCFC-22 (CHCIF^ production and recent HFC-23 emission abatement measures."
Atmos. Chem. Phys. 10: 7875-7890. 25 August 2010.
Montzka et al. 2010. Stephen A. Montzka, Lambert Kuijpers, Mark O. Battle, Murat Aydin Kristal
Verhulst, Eric S. Saltzman, and David W. Fahey. "Recent increases in global HFC-23
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UNEP. 2010. Data Access Centre. HCFC Production. Available online at:
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Will et al. 2004. R. Will, A. Kishi and S. Schlag. CEH Marketing Research Report: Fluorocarbons.
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Electric Power Systems
Ecofys. 2010. Update on Global SF6 Emissions Trends from ElectricalEquipment Edition 1.1. July 2010.
Ecofys. 2005. Reductions ofSF6 Emissions from High and Medium Voltage Electrical Equipment in Europe,
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EIA. 2009. International Energy Outlook 2009. Energy Information Administration, U.S. Department
of Energy, Washington, DC. Report# DOE/EIA-0484(2009). Available online at
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EIA. 2008. International Energy Annual 2006. Energy Information Administration, U.S. Department of
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EPA. 2006. Global Anthropogenic Non-CO2 Greenhouse Gas Emissions: 1990 - 2020. Office of
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IPCC. 2000. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories
Programme, Montreal, IPCC-XVI/Doc.10 (1.IV.2000), May 2000.
IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National
Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change,
H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.). Hayama, Kanagawa,
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Maiss, M. and C.A.M. Brenninkmeijer. 2000. A. Reversed Trend in Emissions ofSF6 into the Atmosphere.
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Peters, W., E. Dlugokencky, J. Olivier, G. Dutton, and K. Smythe. 2005. Surface measurements
show a 17 percent increase in the release of sulfur-hexafluoride (SF6) to the atmosphere in
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Smythe, K. 2004. Trends in SF6 Sales and End-Use Applications: 1961-2003. International
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U.S. State Department. 2010. U.S. Climate Action Report 2010. Washington: Global Publishing
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Yokota, T., K. Yokotsu, K., Kawakita, H. Yonezawa, T. Sakai, T, Yamagiwa. 2005. Recent Practice for
Huge Reduction ofSF6 Gas Emission from GIS&GCB in Japan. CIGRE SC A3 & B3 Joint
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Yokota, T. 2006. E-mail from Takeshi Yokota, T&D Power Systems, Toshiba Corporation, to
Debbie Ottmger, U.S. EPA, April 9, 2006.
Primary Aluminum Production
EPA. 2006. Global Mitigation ofNon-CO2 Greenhouse Gases. United States Environmental Protection
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IAI. 2011. Personal communication with Chris Bayliss, International Aluminum Institute.
IAI. 2005. The International Aluminum Institute's Report on the Aluminum Industry's Global Perfuorocarbon
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IEA. 2000. Greenhouse Gas Emissions from the Aluminum Industry. International Energy Agency (TEA)
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IPCC. 2000. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
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IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse
Gas Inventories Programme, The Intergovernmental Panel on Climate Change, H.S.
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Martchek, K.J. 2006. Modelling More sustainable Aluminium: Case Study. Int. J. LCA 11(1) 2006:4.
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USGS. 201 Ic. Personal communication with E. Lee Bray, U.S. Geological Survey Mineral
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Semiconductor Manufacturing
Bartos, S.C., et al. 2008. Modeling China's semiconductor industry fluorinated compound emissions
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Magnesium Manufacturing
Bartos S., J. Marks, R. Kantamaneni, C. Laush. 2003. Measured SF6 Emissions from Magnesium Die
Casting Operations. Magnesium Technology 2003, Proceedings of The Minerals, Metals &
Materials Society (TMS) Conference, March 2003.
Edgar, B. 2004. SF6 Usage in the Chinese Magnesium Industry: 2000-2010. Report prepared for U.S.
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Edgar, B. 2006. Personal Communication with Bob Edgar, former executive at Norsk Hydro
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EPA. 2010. Information from U.S. EPA's SF6 Emission Reduction Partnership for the Magnesium
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Harnisch and Schwarz. 2003. Costs of the Impact on Emissions of Potential Regulatory Framework
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Gjestland H., and D. Magers. 1996. Practical Usage of Sulfur Hexafluoride for Melt Protection in
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OICA 2010. 2009 Production Statistics. Organisation Internationale des Constructeurs
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Japan.
Tenkorang, F. and J. Lowenberg-DeBoer (2008) ForecastingLong-term Global Fertiliser Demand. Food
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http://beta.irri.org/solutions/index.php?option=com_content&task=view&id=250>.
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August 201 I 8. References Page 8-16
-------
Appendices
Appendix A: Total Emissions by Country
Table A-1: Total Non-CO2 Emissions by Country (MtCO2e)
Table A-2: Total CH4 Emissions by Country (MtCO2e)
Table A-3: Total N2O Emissions by Country (MtCO2e)
Table A-4: Total High-GWP Emissions by Country (MtCO2e)
Appendix B: Energy Sector Emissions
Table B-l: Total Non-CO2 Emissions from the Energy Sector by Country (MtCO2e)
Table B-2: CH4 Emissions from Natural Gas and Oil Systems by Country (MtCO2e)
Table B-3: CH4 Emissions from Coal Mining Activities by Country (MtCO2e)
Table B-4: CH4 Emissions from Stationary and Mobile Combustion by Country (MtCO2e)
Table B-5: N2O Emissions from Stationary and Mobile Combustion by Country (MtCO2e)
Table B-6: CH4 Emissions from Biomass Combustion by Country (MtCO2e)
Table B-7: N2O Emissions from Biomass Combustion by Country (MtCO2e)
Table B-8: CH4 Emissions from Other Energy Sources by Country (MtCO2e)
Table B-9: N2O Emissions from Other Energy Sources by Country (MtCO2e)
Appendix C: Industrial Processes Sector Emissions
Table C-l: Total Non-CO2 Emissions from the Industrial Processes Sector by Country (MtCO2e)
Table C-2: N2O Emissions from Adipic Acid and Nitric Acid Production by Country (MtCO2e)
Table C-3: HFC and PFC Emissions from Use of Substitutes for Ozone-Depleting Substances by
Country (MtCO2e)
Table C-4: HFC-23 Emissions from HCFC-22 Production by Country (MtCO2e)
Table C-5: SF6 Emissions from Electric Power Systems by Country (MtCO2e)
Table C-6: PFC Emissions from Primary Aluminum Production by Country (MtCO2e)
Table C-7: High-GWP Emissions from Semiconductor Manufacturing by Country (MtCO2e)
Table C-8: SF6 Emissions from Magnesium Manufacturing by Country (MtCO2e)
Table C-9: SF6 and PFC Emissions from Flat Panel Display Manufacturing by Country (MtCO2e)
Table C-IO: PFC Emissions from Photovoltaic Manufacturing by Country (MtCO2e)
Table C-l I: CH4 Emissions from Other Industrial Processes Sources by Country (MtCO2e)
Table C-l2: N2O Emissions from Other Industrial Processes Sources by Country (MtCO2e)
Appendix D: Agriculture Sector Emissions
Table D-l: Total Non-CO2 Emissions from the Agriculture Sector by Country (MtCO2e)
Table D-2: N2O Emissions from Agricultural Soils by Country (MtCO2e)
Table D-3: CH4 Emissions from Enteric Fermentation by Country (MtCO2e)
Table D-4: CH4 Emissions from Rice Cultivation by Country (MtCO2e)
Table D-5: CH4 Emissions from Manure Management by Country (MtCO2e)
Table D-6: N2O Emissions from Manure Management by Country (MtCO2e)
Table D-7: CH4 Emissions from Other Agricultural Sources by Country (MtCO2e)
Table D-8: N2O Emissions from Other Agricultural Sources by Country (MtCO2e)
Appendix E: Waste Sector Emissions
Table E-l: Total Non-CO2 Emissions from the Waste Sector (MtCO2e)
Table E-2: CH4 Emissions from Landfilling of Solid Waste by Country (MtCO2e)
Table E-3: CH4 Emissions from Wastewater by Country (MtCO2e)
Table E-4: N2O Emissions from Human Sewage - Domestic Wastewater by Country (MtCO2e)
Table E-5: CH4 Emissions from Other Waste Sources by Country (MtCO2e)
Table E-6: N2O Emissions from Other Waste Sources by Country (MtCO2e)
Appendix F: Methodology Applied to Develop Source Emissions
Table F-l: Methodology Applied to Develop Energy Sector Source Emissions, by Country
Table F-2: Methodology Applied to Develop Industrial Processes Sector Source Emissions, by Country
August 201 I Appendices Page A-1
-------
Table F-3: Methodology Applied to Develop Other Industrial Processes Sector Source Emissions, by
Country
Table F-4: Methodology Applied to Develop Agriculture Sector Source Emissions, by Country
Table F-5: Methodology Applied to Develop Waste Sector Source Emissions, by Country
Appendix G: Data Sources Used to Develop Non-Country Reported Emissions Estimates
Appendix H: Future Mitigation Measures Included in Developing Non-Country-Reported Estimates
Appendix I: Regional Definitions
Appendix J: U.S. EPA Vintaging Model Framework
August 201 I Appendices Page A-2
-------
EPA430-D-II-003
DRAFT: Appendices to the Report
Global Anthropogenic Non-CO2 Greenhouse Gas Emissions: 1990 - 2030
-------
Table of Contents
Appendix A: Total Emissions by Country
Table A-l: Total Non-CO2 Emissions by Country (MtCO2e) A-l
Table A-2: Total CH4 Emissions by Country (MtCO2e) A-3
Table A-3: Total N2O Emissions by Country (MtCO2e) A-5
Table A-4: Total High GWP Emissions by Country (MtCO2e) A-7
Appendix B: Energy Sector Emissions
Table B-l: Total Non-CO2 Emissions from the Energy Sector by Country (MtCO2e) B-l
Table B-2: CH4 Emissions from Natural Gas and Oil Systems by Country (MtCO2e) B-3
Table B-3: CH4 Emissions from Coal Mining Activities by Country (MtCO2e) B-5
Table B-4: CH4 Emissions from Stationary and Mobile Combustion by Country (MtCO2e) B-7
Table B-5: N2O Emissions from Stationary and Mobile Combustion by Country (MtCO2e) B-9
Table B-6: CH4 Emissions from Biomass Combustion by Country (MtCO2e) B-l 1
Table B-7: N2O Emissions from Biomass Combustion by Country (MtCO2e) B-13
Table B-8: CH4 Emissions from Other Energy Sources by Country (MtCO2e) B-l 5
Table B-9: N2O Emissions from Other Energy Sources by Country (MtCO2e) B-17
Appendix C: Industrial Processes Sector Emissions
Table C-l: Total Non-CO2 Emissions from the Industrial Processes Sector by Country (MtCO2e) 1
Table C-2: N2O Emissions from Adipic Acid and Nitric Acid Production by Country (MtCO2e) 3
Table C-3: HFC and PFC Emissions from Use of Substitutes for Ozone-Depleting Substances by Country
(MtCO2e) 5
Table C-4: HFC-23 Emissions from HCFC-22 Production by Country (MtCO2e) 7
Table C-5: SF6 Emissions from Operation of Electric Power Systems by Country (MtCO2e) 9
Table C-6: PFC Emissions from Primary Aluminum Production by Country (MtCO2e) 11
Table C-7: SF6 Emissions from Magnesium Manufacturing by Country (MtCO2e) 13
Table C-8: High GWP Emissions from Semiconductor Manufacturing by Country (MtCO2e) 15
Table C-9: SF6 and PFC Emissions from Flat Panel Display Manufacturing by Country (MtCO2e) 17
Table C-10: PFC Emissions from Photovoltaic Manufacturing by Country (MtCO2e) 19
Table C-ll: CH4 Emissions from Other Industrial Processes Sources by Country (MtCO2e) 21
August 201 I Appendices Page ii
-------
Table C-12: N2O Emissions from Other Industrial Processes Sources by Country (MtCO2e) 23
Appendix D: Agriculture Sector Emissions
Table D-l: Total Non-CO2 Emissions from the Agriculture Sector by Country (MtCO2e) D-l
Table D-2: N2O Emissions from Agricultural Soils by Country (MtCO2e) D-3
Table D-3: CH4 Emissions from Enteric Fermentation by Country (MtCO2e) D-5
Table D-4: CH4 Emissions from Rice Cultivation by Country (MtCO2e) D-7
Table D-5: CH4 Emissions from Manure Management by Country (MtCO2e) D-9
Table D-6: N2O Emissions from Manure Management by Country (MtCO2e) D-ll
Table D-7: CH4 Emissions from Other Agricultural Sources by Country (MtCO2e) D-13
Table D-8: N2O Emissions from Other Agricultural Sources by Country (MtCO2e) D-l 5
Appendix E: Waste Sector Emissions
Table E-l: Total Non-CO2 Emissions from the Waste Sector (MtCO2e) E-l
Table E-2: CH4 Emissions from Landfilling of Solid Waste by Country (MtCO2e) E-3
Table E-3: CH4 Emissions from Wastewater by Country (MtCO2e) E-5
Table E-4: N2O Emissions from Human Sewage - Domestic Wastewater by Country (MtCO2e) E-7
Table E-5: CH4 Emissions from Other Waste Sources by Country (MtCO2e) E-9
Table E-6: N2O Emissions from Other Waste Sources by Country (MtCO2e) E-l 1
Appendix F: Methodology Applied to Develop Source Emissions
Appendix G: Data Sources Used to Develop Non-Country-Reported Emissions
Estimates
Appendix H: Future Mitigation Measures Included in Developing Non-Country-
Reported Estimates
Appendix I : Regional Definitions
Appendix J: U.S. EPA Vintaging Model Framework
August 201 I Appendices Page iii
-------
Appendix A: Total Emissions by Country
Table A-1: Total Non-CO2 Emissions by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
4.8
23.2
138.4
3.9
160.4
16.9
17.7
49.7
27.8
20.4
42.0
534.6
31.5
162.6
22.7
138.7
15.1
1,006.0
78.0
237.0
8.4
30.6
16.3
12.9
35.4
5.1
73.5
14.3
161.2
13.3
172.4
22.6
26.9
1.2
766.7
273.8
66.9
16.0
23.0
9.1
82.0
94.5
7.3
83.6
48.8
8.4
11.5
7.5
13.1
1.0
4.5
153.5
6.1
trt^i i ^wTTjTU
IT
24.7
147.7
3.4
150.0
16.4
13.2
54.7
18.6
21.3
40.2
558.6
22.3
180.4
25.3
190.6
21.5
1,135.6
85.8
212.3
6.1
21.9
15.7
15.0
41.1
3.0
71.3
13.4
158.4
7.9
152.7
23.7
18.5
0.8
817.6
302.3
76.8
18.8
24.0
11.4
86.3
104.5
9.4
52.9
83.6
4.3
12.4
3.5
6.8
1.0
3.1
168.0
4.1
3.5
29.6
160.0
3.4
172.8
14.9
16.1
58.7
19.9
20.7
39.0
560.2
19.5
139.1
24.0
162.3
28.0
1,143.9
90.4
176.5
6.2
20.1
14.9
14.8
46.5
3.1
78.6
12.6
145.8
8.5
119.8
24.1
19.8
1.0
854.5
311.4
84.2
19.0
24.2
14.4
90.7
103.5
10.3
42.2
86.8
4.1
10.1
3.3
7.3
I.I
3.0
195.1
2.4
MtC02e
2005 2010
2.9
34.7
171.9
3.8
153.1
13.7
20.9
63.7
23.0
17.9
53.0
756.8
17.1
144.1
34.0
171.4
35.8
1,404.7
101.0
167.2
7.0
19.7
13.4
15.6
53.9
3.0
95.4
11.9
132.6
8.8
115.8
20.9
19.6
0.8
898.4
362.7
96.7
19.4
22.5
86.8
94.4
11.7
47.0
106.1
4.0
16.3
3.8
8.6
I.I
3.2
211.6
2.3
2.8
39.1
181.2
4.1
158.2
14.0
34.1
70.8
23.9
16.7
55.0
715.9
16.9
151.6
34.2
180.9
37.8
1,586.5
107.9
169.3
7.3
19.4
10.9
16.1
62.2
3.1
116.2
12.4
135.7
8.2
116.6
17.2
18.3
1.0
1,004.6
383.5
102.7
19.9
22.2
16.4
80.7
93.7
14.7
53.5
109.0
4.4
17.3
4.0
9.4
I.I
3.7
240.4
2.1
KB
2.8
47.0
193.8
4.5
168.6
14.6
41.6
78.0
23.8
17.6
57.0
745.8
16.9
156.8
35.7
199.8
40.2
1,734.8
115.7
171.3
7.6
19.8
11.3
17.2
69.0
3.0
128.8
13.1
144.6
8.4
109.0
16.4
18.9
I.I
1,058.5
401.6
108.1
21.6
22.8
20.5
85.5
106.1
15.6
57.3
lll.l
4.8
18.0
4.3
9.7
1.2
4.3
251.9
2.2
^vTj/TjV ^EZlZ±3
I8~
51.8
203.9
4.8
177.2
15.0
44.5
86.7
23.9
18.4
58.8
761.8
16.9
162.5
36.0
213.5
42.7
1,953.7
122.8
173.5
7.9
20.2
11.4
18.3
76.2
3.1
139.3
13.5
149.0
8.7
112.6
16.0
19.6
1.2
1,124.0
419.0
113.5
23.6
22.9
25.1
89.9
122.4
17.4
59.8
109.7
5.1
18.7
4.6
10.0
1.2
4.8
267.9
2.3
2.9
56.8
215.1
5.2
187.4
15.5
46.5
97.1
24.0
19.5
60.8
779.7
17.2
168.8
36.7
230.6
45.7
2,339.6
129.6
175.9
8.4
21.1
12.0
19.7
84.8
3.1
150.2
13.8
153.9
9.4
119.4
15.7
20.5
1.2
1,211.4
438.8
122.5
25.8
23.2
33.9
95.1
144.8
19.5
62.5
117.7
5.4
19.5
5.2
10.8
1.2
5.8
295.0
2.5
1
16"
61.1
225.8
5.5
197.1
15.8
49.2
109.2
24.0
20.7
62.8
794.5
17.4
175.5
37.6
244.4
48.7
2,818.3
137.7
178.5
8.8
21.6
12.2
20.9
93.1
3.1
161.3
14.1
156.7
9.9
127.8
15.5
21.1
1.3
1,315.2
462.9
132.3
28.1
23.3
39.8
99.3
165.1
21.6
66.0
128.8
5.8
20.4
5.7
11.3
1.3
6.4
325.7
2.7
August 2011
Appendices
Page A-1
-------
Country
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
Non-OECD Asia
1990 1995 2000
0.0
12.1
23.5
51.6
36.4
140.6
34.2
14.8
86.3
24.7
42.8
91.1
15.7
70.9
854.3
23.3
9.0
1.3
11.4
3.8
73.5
43.4
61.0
16.1
8.4
6.8
85.7
62.2
28.1
16.7
206.5
12.7
174.5
1,044.6
24.0
69.8
69.4
57.4
951.1
128.6
81.5
116.2
20.3
1,560.0
1,052.7
256.5
2,795.0
2,752.5
Non-OECD Europe & | 1,492.4
EU I 1,141.7
OPEC 620.4
0.0
12.8
26.2
53.0
37.1
146.4
25.5
11.8
99.0
26.4
47.7
80.5
17.1
51.2
617.1
27.0
9.8
2.1
8.6
3.7
71.4
47.6
65.5
16.1
7.8
8.2
91.5
79.0
20.8
17.3
131.3
15.0
150.4
1,073.8
26.3
85.9
78.0
69.6
941.1
124.5
101. 1
131. 1
19.0
1,535.6
1,102.5
331.7
2,852.1
3,033.8
1,086.3
1,036.0
691.5
0.0
13.0
29.3
43.3
39.6
168.6
23.2
11.7
113.4
30.9
53.1
68.6
18.7
40.7
609.0
31.4
11.4
3.8
8.2
3.8
71.9
62.0
79.2
15.3
7.6
7.2
89.6
86.4
29.5
17.1
98.7
17.6
119.2
1,078.6
23.8
92.7
85.4
82.0
968.7
155.8
127.6
135.1
21.4
1,568.9
1,160.3
377.0
2,827.7
3,084.4
1,041.4
939.8
768.0
2005 2010 2015 2020
0.0
9.7
30.6
36.0
41.4
190.0
25.9
10.8
131.6
31.2
57.4
67.1
20.8
44.0
622.6
37.1
12.5
4.7
8.8
3.9
75.2
73.2
72.4
14.8
7.6
8.7
101.2
83.6
40.5
22.5
95.5
20.9
96.6
1,058.2
26.2
99.7
85.0
92.5
1,086.0
154.6
138.7
147.9
20.8
1,737.3
1,395.2
430.7
2,751.8
3,525.5
1,087.1
874.1
887.7
0.0
13.7
33.2
34.4
41.5
201.3
27.1
9.7
154.5
33.1
62.7
69.3
20.4
43.2
647.9
41.3
13.6
5.6
6.8
4.0
81.6
84.1
73.0
14.0
8.0
9.3
112.5
89.8
43.9
24.3
91.1
24.1
92.5
1,152.0
28.1
110.5
84.8
98.8
1,182.3
168.5
159.0
157.1
20.4
1,890.0
1,390.6
470.8
2,892.8
3,913.8
1,143.9
857.5
985.3
0.0
16.8
36.9
35.8
43.7
222.9
29.2
10.3
169.4
35.5
68.3
71.9
20.9
43.5
668.9
46.4
14.8
7.3
6.8
4.3
88.4
95.8
76.4
14.0
8.4
10.0
118.5
96.0
50.0
25.5
90.9
26.6
96.3
1,251.3
31.1
122.9
90.4
104.8
1,233.2
181.6
191.7
171.6
21.4
2,001.0
1,467.9
521.1
3,094.8
4,206.1
1,198.7
880.3
1,063.2
0.0
20.9
40.8
36.9
45.6
239.9
32.6
10.9
184.3
37.7
75.2
74.6
21.3
43.7
718.6
51.6
16.0
9.8
7.0
4.5
95.6
II 1.6
78.6
14.1
8.7
10.8
125.7
102.2
53.7
26.7
91.5
28.7
99.1
1,360.1
33.6
131.2
100.6
110.4
1,269.7
193.8
200.0
186.7
22.5
2,088.7
1,531.2
544.5
3,314.9
4,586.9
1,271.1
906.1
1,111.0
2025 2030
0.0
26.6
45.4
38.4
47.8
256.4
39.1
11.7
201.5
40.4
85.7
77.2
21.6
44.4
764.2
59.5
17.2
15.0
7.4
4.9
109.4
143.3
81.3
14.5
9.1
11.6
138.0
109.2
55.5
28.0
92.4
31.8
102.2
1,486.6
36.7
135.0
112.7
117.3
1,329.1
209.5
218.9
208.8
24.2
2,207.8
1,604.2
595.7
3,604.7
5,189.2
1,336.4
941.5
1,192.5
O.I
34.6
50.9
40.1
50.0
272.5
47.2
12.8
220.4
43.1
96.3
80.3
21.8
45.1
803.1
66.9
18.5
22.1
7.7
5.2
119.5
170.1
83.8
14.6
9.4
12.6
150.7
115.7
57.7
29.4
93.5
34.9
103.8
1,570.7
39.6
138.9
123.7
125.4
1,394.0
223.9
232.2
235.5
25.8
2,327.9
1,672.0
644.6
3,837.4
5,922.1
1,395.6
972.0
1,277.2
World Totals 9,909.0 9,942.0 10,059.8 10,927.6 11,701.8 12,489.6 13,337.3 14,537.9 15,799.5
August 2011
Appendices
Page A-2
-------
Table A-2: Total CH4 Emissions by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
^^^^|
I6~
18.3
80.4
3.2
122.1
9.2
15.2
37.9
16.2
10.0
19.7
283.3
20.3
86.2
18.6
76.6
12.0
727.1
47.5
98.9
3.5
18.5
5.7
9.3
22.0
3.2
44.3
6.3
65.4
II. 1
96.1
9.1
11.3
0.5
576.2
189.4
46.5
7.9
13.5
7.4
42.0
32.8
6.8
64.8
48.6
6.3
6.5
3.7
5.9
0.5
1.7
110.7
4.1
0.0
^T^B^
k£f^H
Is"
19.5
85.2
2.9
116.1
8.6
11.4
39.4
12.4
9.5
19.2
285.8
16.1
94.3
20.4
107.0
12.8
798.7
51.0
81.9
2.9
13.7
6.0
10.7
25.0
2.0
44.0
6.1
65.7
6.8
77.8
9.1
9.3
0.4
599.3
199.6
53.5
8.1
13.8
8.8
44.3
30.4
8.8
43.1
83.3
3.2
6.9
2.1
3.6
0.5
1.8
128.8
2.8
0.0
MtCO2e
2000 2005 2010
2.4
23.1
87.8
2.9
123.9
7.7
14.1
42.0
12.2
8.5
18.2
280.7
13.7
71.7
19.8
99.6
12.9
767.8
51.0
64.4
2.7
12.1
5.9
10.3
27.7
2.0
48.9
5.4
61.4
6.9
61.2
9.0
9.5
0.5
614.4
199.0
57.6
8.8
13.5
9.3
44.3
26.4
9.6
34.7
86.2
2.9
5.6
1.8
3.1
0.5
1.8
146.6
1.7
0.0
2.1
27.1
92.5
3.0
112.9
7.2
18.4
44.1
14.0
6.9
25.7
413.3
12.0
81.1
26.9
104.5
13.7
879.0
58.2
58.4
3.2
11.6
5.7
II. 1
31.2
2.1
58.7
4.5
55.0
7.0
46.2
8.1
8.9
0.4
626.9
220.0
67.0
9.8
13.3
6.1
39.7
23.4
10.8
38.5
104.8
3.0
8.8
1.9
3.1
0.5
2.0
154.4
1.5
0.0
2.0
29.1
96.8
3.2
116.6
6.9
31.4
47.5
14.8
6.6
27.2
383.8
11.3
87.1
26.6
110.8
14.0
924.5
62.5
60.3
3.0
11.2
5.7
11.3
35.4
2.1
71.5
4.4
54.1
6.3
40.6
7.9
8.2
0.5
660.8
234.6
69.9
10.9
12.8
6.7
37.1
22.4
13.3
43.9
107.0
3.3
9.5
1.9
3.1
0.5
2.0
155.6
1.2
0.0
16"
34.1
102.1
3.5
122.6
7.0
38.7
51.0
14.5
6.6
28.5
398.5
II. 1
90.8
27.6
120.3
14.7
967.2
67.3
62.1
2.9
II. 1
5.7
11.9
38.6
2.0
79.3
4.5
54.0
6.3
37.5
7.5
8.1
0.5
684.5
247.1
71.1
12.1
12.7
7.3
36.7
21.7
13.9
47.1
108.1
3.5
10.0
1.9
3.0
0.5
2.0
156.6
1.2
0.0
1
2.1
36.8
106.6
3.6
127.4
7.0
41.3
55.4
14.3
6.5
29.7
403.8
10.7
95.1
27.4
125.7
15.4
1,016.2
71.9
64.1
2.9
II. 1
5.7
12.5
41.9
2.0
85.9
4.5
53.9
6.3
35.3
7.1
8.0
0.6
712.4
258.8
71.9
13.6
12.6
7.7
36.6
21.2
15.3
48.7
105.1
3.8
10.5
1.9
3.0
0.5
2.1
160.7
1.2
0.0
2.1
38.5
II 1.3
3.7
133.0
7.0
43.0
60.4
14.1
6.5
31.1
406.5
10.4
99.7
27.3
132.5
16.2
1,073.1
75.3
66.3
2.9
II. 1
5.8
13.1
45.4
2.0
92.7
4.5
53.8
6.4
33.7
6.9
8.0
0.6
742.7
271.3
74.8
15.1
12.6
8.2
36.4
20.5
16.7
50.4
109.7
4.0
11.0
1.8
2.9
0.5
2.1
168.3
1.2
0.0
^^VAjjlii^H
2.1
40.0
116.1
3.7
139.2
7.0
45.4
66.0
13.9
6.4
32.5
408.5
10.1
104.7
27.3
138.9
16.9
1,116.1
80.7
68.6
2.9
II. 1
5.8
13.7
49.0
1.9
99.6
4.5
53.5
6.4
32.2
6.7
8.0
0.6
772.3
287.4
78.9
16.8
12.6
8.6
36.3
19.8
18.1
52.8
117.7
4.2
11.6
1.8
2.9
0.5
2.2
177.3
1.2
0.0
August 2011
Appendices
Page A-3
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
Non-OECD Asia
Non-OECD Europe &
EU
OPEC
1990 1995 2000
6.9
19.9
25.4
25.4
116.3
21.7
4.6
60.2
16.0
31.3
53.0
10.2
40.6
582.7
16.5
5.8
0.9
4.8
2.3
46.4
28.9
28.9
7.3
4.4
3.7
66.7
38.1
25.7
8.0
151.7
10.4
104.3
636.8
14.1
56.7
50.3
47.7
479.7
77.2
74.4
94.5
16.0
839.7
597.8
211.2
1,624.1
1,991.6
1,039.8
598.1
464.0
7.3
22.1
24.0
25.7
121.0
17.0
4.8
67.1
17.0
35.1
49.1
11.3
30.7
446.2
20.3
6.4
I.I
4.3
2.2
46.6
28.7
31.9
7.3
4.0
2.7
70.4
51.9
19.1
8.6
96.1
12.3
91.2
635.8
15.6
74.3
60.7
55.9
483.9
80.1
93.4
106.9
15.3
837.0
625.3
279.8
1,640.9
2,141.4
798.0
540.7
532.3
7.5
24.7
19.7
27.0
139.1
15.4
4.8
77.8
19.4
38.1
38.1
11.8
25.2
440.5
22.7
7.4
1.9
4.4
2.2
49.2
31.8
36.7
6.7
3.7
2.4
71.4
59.1
27.6
8.4
77.6
14.4
69.9
614.6
14.7
81.7
67.7
63.8
517.0
94.6
119.0
II 1.3
15.8
885.2
644.4
318.3
1,588.6
2,132.1
773.7
475.2
596.5
2005 2010 2015 2020
5.5
26.0
17.1
27.2
157.1
17.2
4.4
89.0
19.8
40.2
36.2
13.7
26.5
468.2
25.8
8.2
2.3
4.6
2.2
52.2
33.8
38.0
6.3
3.5
4.0
79.0
58.5
37.3
10.6
74.3
16.7
51.2
599.9
15.0
89.4
64.3
72.2
597.5
88.5
127.7
120.8
15.0
1,000.9
788.4
362.6
1,519.8
2,339.0
826.3
423.3
695.8
7.6
27.9
17.0
27.1
167.0
18.2
4.2
106.7
20.9
42.9
34.9
13.7
25.7
483.2
27.7
8.9
2.6
4.5
2.2
53.8
36.1
37.8
5.9
3.6
5.1
86.3
64.0
40.4
11.5
70.3
18.8
45.7
642.3
16.0
99.2
60.4
76.0
672.7
96.0
145.4
127.0
15.0
1,110.2
774.7
393.0
1,561.5
2,485.8
868.4
402.7
778.5
9.1
30.9
16.9
28.0
186.2
18.6
4.2
117.6
22.3
45.4
35.8
13.8
25.2
495.7
30.0
9.8
3.0
4.4
2.2
55.0
36.6
37.9
5.7
3.6
5.4
88.3
67.3
46.2
12.1
69.4
20.1
44.4
656.5
17.5
II 1.0
63.1
79.6
703.0
102.4
175.0
137.0
15.4
1,180.2
813.6
430.3
1,592.9
2,607.6
908.1
397.0
837.4
1 I.I
33.8
16.9
28.9
200.7
19.5
4.3
128.5
23.5
48.3
36.8
13.9
24.8
532.6
31.9
10.7
3.3
4.3
2.2
55.8
36.9
38.0
5.6
3.5
5.7
90.4
70.5
49.6
12.6
69.4
20.9
43.2
681.5
18.8
118.6
69.8
82.9
719.4
108.0
180.2
145.6
16.0
1,227.9
844.6
438.8
1,634.1
2,739.0
960.4
393.0
864.7
2025 2030
13.8
37.2
16.9
29.9
214.3
21.4
4.4
140.7
24.7
51.4
37.2
13.9
24.7
548.3
33.9
11.6
3.7
4.3
2.1
57.2
37.1
38.0
5.7
3.5
6.0
92.3
73.5
51.1
13.2
69.1
21.8
42.4
709.5
20.2
121.6
75.5
87.0
754.1
113.9
194.1
153.8
16.6
1,293.3
871.6
466.1
1,684.4
2,886.8
984.3
390.2
913.7
17.6
41.3
16.8
31.0
227.2
24.7
4.5
154.7
25.9
54.8
38.6
14.0
24.7
563.1
36.3
12.5
4.1
4.3
2.1
58.6
36.9
38.0
5.8
3.5
6.3
94.9
76.3
52.9
13.8
69.2
22.6
41.7
725.0
21.5
124.7
81.0
92.0
792.9
119.6
202.6
164.0
17.4
1,362.2
899.4
492.8
1,724.2
3,033.5
1,009.7
388.3
968.4
World Totals 6,304.2 6,322.4 6,342.4 6,837.0 7,193.7 7,532.7 7,844.7 8,186.4 8,521.9
August 2011
Appendices
Page A-4
-------
Table A-3: Total N2O Emissions by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
pv
1.2
4.9
56.3
0.6
32.7
6.2
2.5
11.8
11.6
10.4
22.3
246.9
11.2
76.4
4.1
52.9
3.1
273.6
30.4
138.1
3.9
12.0
10.5
3.6
12.6
1.9
29.2
7.8
89.2
2.2
69.8
12.3
15.4
0.3
188.6
83.8
20.1
8.1
9.5
1.7
37.7
31.0
0.4
18.6
0.2
2.1
5.0
3.8
7.1
0.5
1.3
37.5
2.0
0.0
0.9
5.1
62.0
0.4
30.2
6.6
1.8
15.3
6.2
11.3
21.0
259.9
6.2
86.1
4.8
74.1
8.7
326.2
34.7
130.4
3.1
8.1
9.4
4.2
15.3
1.0
27.4
7.1
86.0
I.I
66.8
II. 1
8.9
0.3
210.2
102.2
23.0
10.6
9.9
1.9
38.5
31.9
0.6
9.4
0.2
I.I
5.5
1.4
3.2
0.5
1.3
36.2
1.3
0.0
^fT^^H
l.l
6.1
71.1
0.4
43.9
6.2
1.9
16.7
7.7
11.2
20.8
271.1
5.6
67.4
4.2
49.8
14.9
329.8
39.1
112.1
3.4
7.6
8.3
4.4
17.9
1.0
29.7
6.8
74.4
1.5
48.3
10.8
9.7
0.3
221.2
II 1.9
25.9
10.2
10.1
1.8
39.9
28.1
0.6
7.2
0.2
I.I
4.5
1.3
4.1
0.6
1.0
40.5
0.6
0.0
MtCO2e
2005 2010
0.7
6.5
77.3
0.6
32.3
5.3
2.3
19.5
8.9
9.5
27.2
333.0
4.8
63.1
7.1
52.5
21.6
385.1
42.0
108.8
3.6
7.5
6.7
4.3
21.0
0.9
36.7
6.9
66.4
1.6
55.4
9.9
9.6
0.3
232.9
141.7
28.1
9.5
8.7
1.9
37.9
23.7
0.7
7.7
0.3
1.0
7.5
1.5
5.1
0.5
0.7
39.6
0.7
0.0
0.7
6.9
79.8
0.6
31.8
5.5
2.5
22.8
8.9
8.4
27.8
320.8
5.0
64.6
7.5
54.8
22.9
415.9
44.2
109.0
3.7
7.2
4.1
4.4
24.6
1.0
44.7
7.5
69.8
1.5
60.0
8.5
8.6
0.3
246.5
147.3
30.5
9.0
8.8
2.1
33.3
22.9
0.8
8.7
0.3
I.I
7.7
1.6
5.7
0.5
0.7
44.6
0.6
0.0
^^IrAJj^^^B
07"
7.4
85.8
0.6
32.9
5.7
2.5
26.2
9.0
8.7
28.4
332.0
5.1
65.9
7.9
59.7
24.3
447.3
46.7
109.2
3.8
7.3
4.1
4.7
27.4
1.0
49.4
8.0
75.5
1.5
50.8
7.9
8.7
0.4
263.4
151.7
33.7
9.4
9.4
2.3
35.1
22.5
0.9
9.2
0.4
1.2
7.9
1.6
5.8
0.6
0.7
48.2
0.7
0.0
1
6J1 as"
7.9
89.8
0.6
33.6
5.8
2.6
30.2
9.1
8.8
29.0
337.7
5.2
67.4
8.4
63.0
25.5
475.3
48.5
109.3
3.9
7.3
3.9
5.0
30.1
1.0
53.3
8.1
76.0
1.5
50.2
7.5
8.8
0.4
278.5
156.5
37.0
9.9
9.5
2.5
35.5
22.2
1.0
9.8
0.4
1.3
8.2
1.6
5.9
0.6
0.8
50.8
0.7
0.0
8.5
93.2
0.7
34.4
5.9
2.7
35.2
9.1
8.8
29.6
342.4
5.3
69.0
9.0
66.4
26.9
505.6
50.4
109.5
4.0
7.4
3.9
5.4
32.9
1.0
57.3
8.3
76.6
1.6
49.7
7.0
8.9
0.4
294.7
162.1
40.6
10.5
9.5
2.7
36.0
22.0
I.I
10.6
0.4
1.4
8.4
1.6
5.9
0.6
0.8
53.4
0.7
0.0
^^^^|
O8~
9.2
96.5
0.7
35.1
5.9
2.8
41.4
9.2
8.8
30.2
346.9
5.5
70.8
9.7
69.8
28.4
537.7
52.0
109.8
4.1
7.4
3.8
5.8
35.8
I.I
61.5
8.4
77.1
1.6
49.1
6.6
9.0
0.4
311. 1
168.3
44.0
II. 1
9.6
2.8
36.6
21.8
1.2
11.6
0.5
1.5
8.7
1.7
6.0
0.6
0.8
55.9
0.8
0.0
August 2011
Appendices
Page A-5
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
5.2
3.6
19.3
10.4
24.2
12.4
4.7
26.0
8.7
11.4
37.9
5.5
28.2
239.6
6.6
3.1
O.I
6.3
1.3
26.0
13.4
28.7
8.3
3.7
3.0
18.9
23.6
2.4
8.7
53.6
1.7
64.5
317.6
10.0
13.0
14.4
9.7
469.5
51.1
6.4
21.6
4.2
716.4
443.8
43.5
985.3
Non-OECD Asia 752.3
Non-OECD Europe &
EU
OPEC
414.1
505.5
150.6
5.5
4.1
20.5
11.2
25.4
8.5
4.3
31.9
9.4
12.4
30.8
5.7
18.7
144.9
6.4
3.3
0.7
4.2
1.2
22.2
14.6
27.0
8.1
3.5
2.4
20.8
26.6
1.7
8.8
34.4
2.2
53.7
332.5
10.7
11.5
14.1
13.7
455.7
44.0
7.0
24.0
3.4
693.6
460.0
49.9
991.4
871.7
255.2
446.2
154.4
5.5
4.6
18.1
12.0
29.4
7.5
4.5
35.3
11.3
14.1
29.5
6.6
14.9
129.9
7.7
4.0
0.8
3.6
1.3
18.6
16.9
33.1
7.6
3.4
2.0
17.4
26.5
1.9
8.7
20.5
2.5
41.9
329.0
9.0
10.8
15.4
18.1
449.8
60.2
7.7
22.8
4.1
676.3
502.4
54.7
947.2
881.9
221.9
402.7
165.6
2005 2010 2015 2020
4.1
4.7
16.3
13.3
32.8
8.1
4.7
41.9
11.0
15.1
28.7
6.5
16.6
II 1.8
8.8
4.3
0.9
3.9
1.3
16.1
18.1
30.1
7.2
3.2
1.9
20.0
23.5
3.2
11.8
20.0
3.5
36.4
314.9
10.9
10.0
17.5
20.0
485.5
63.7
9.1
24.2
4.0
723.4
586.8
59.9
904.2
995.6
207.5
384.0
180.8
6.1
5.3
14.6
13.2
34.1
7.9
4.3
46.7
11.6
16.4
31.2
6.1
16.6
116.4
9.4
4.6
1.0
1.9
1.3
17.4
21.4
30.0
6.9
3.3
2.5
22.0
23.8
3.5
12.7
19.4
4.2
37.3
345.2
11.7
10.9
18.2
22.1
505.4
68.1
10.5
25.1
4.0
759.4
586.6
64.7
941.7
1,064.8
215.4
381.2
186.8
7.6
6.0
15.1
14.0
36.4
8.8
4.5
50.3
12.3
17.8
31.4
6.3
17.1
116.2
10.2
4.9
I.I
1.9
1.4
18.2
23.9
31.8
6.6
3.5
2.6
23.3
26.1
3.7
13.4
19.7
4.9
39.7
377.8
12.9
11.3
19.4
24.2
523.8
72.7
11.7
26.2
4.1
790.0
614.9
71.1
996.2
1,135.6
218.2
387.1
196.5
9.7
6.9
15.2
14.6
38.5
9.9
4.6
53.7
13.0
19.2
31.6
6.4
17.5
117.3
11.0
5.1
I.I
2.0
1.5
18.8
26.7
32.1
6.4
3.5
2.9
24.5
28.2
4.0
14.0
19.9
5.7
40.1
400.9
13.9
11.7
20.3
26.1
541.2
76.8
13.0
27.4
4.2
818.5
634.0
78.0
1,033.9
1,203.0
222.2
388.7
206.1
2025 2030
12.7
8.0
15.2
15.3
40.8
II. 1
4.7
57.3
13.8
20.8
31.9
6.4
17.9
118.5
11.8
5.4
1.3
2.1
1.6
19.5
30.1
32.3
6.2
3.5
3.1
25.8
30.5
4.3
14.7
20.2
6.6
40.6
424.2
15.0
12.2
21.3
28.2
560.2
81.3
14.5
28.7
4.2
848.9
652.4
85.5
1,073.2
1,278.0
226.7
390.8
216.5
16.9
9.4
15.3
16.1
43.4
12.6
4.8
61.2
14.6
22.6
32.4
6.5
18.3
119.6
12.7
5.7
1.5
2.1
1.7
19.7
33.8
32.6
5.9
3.5
3.4
27.2
32.7
4.7
15.5
20.5
7.6
41.0
447.9
16.1
12.7
22.3
30.6
581.6
86.0
16.1
30.3
4.3
882.1
670.3
93.3
1,113.4
1,360.1
231.5
393.2
227.3
World Totals 3,355.4 3,321.9 3,284.5 3,477.4 3,632.7 3,825.9 3,989.6 4,164.7 4,350.7
August 2011
Appendices
Page A-6
-------
Table A-4: Total High GWP Emissions by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
o6~
0.0
1.7
0.0
5.6
1.4
0.0
0.0
0.0
0.0
0.0
4.4
0.0
0.0
0.0
9.1
0.0
5.3
O.I
0.0
0.9
O.I
0.0
0.0
0.8
0.0
O.I
6.6
0.0
6.5
1.2
0.3
0.4
1.9
0.7
0.3
0.0
0.0
O.I
2.4
30.7
0.0
0.3
0.0
0.0
0.0
0.0
0.0
0.0
1.5
5.4
0.0
-
|Q9
0.0
O.I
0.5
0.0
3.7
1.2
0.0
0.0
0.0
0.4
0.0
12.9
0.0
0.0
0.0
9.5
O.I
10.8
O.I
0.0
0.2
O.I
0.3
0.0
0.8
0.0
0.0
0.2
6.7
0.0
8.0
3.5
0.3
O.I
8.1
0.5
0.4
0.0
0.2
0.7
3.6
42.3
0.0
0.4
O.I
0.0
0.0
O.I
0.0
0.0
0.0
3.0
0.0
0.0
^fT^^H
oo~
0.4
1.1
O.I
5.1
I.I
0.0
O.I
O.I
1.0
0.0
8.4
0.2
0.0
0.0
12.9
0.2
46.3
0.3
0.0
0.2
0.4
0.7
O.I
0.9
0.0
0.0
0.3
9.9
O.I
10.3
4.3
0.7
0.2
18.9
0.5
0.7
0.0
0.6
3.3
6.5
49.0
O.I
0.3
0.4
0.0
0.0
0.2
O.I
O.I
0.2
8.0
O.I
0.0
MtCO2e
2005 2010
0.0
I.I
2.1
0.2
7.9
1.3
0.2
0.2
O.I
1.4
0.0
10.6
0.3
0.0
O.I
14.3
0.6
140.6
0.8
0.0
0.3
0.6
1.0
0.2
1.7
0.0
0.0
0.4
11.2
0.3
14.2
2.8
I.I
O.I
38.6
1.0
1.6
O.I
0.6
5.7
9.3
47.2
0.3
0.7
1.0
0.0
0.0
0.3
0.4
O.I
0.5
17.6
O.I
0.0
0.0
3.1
4.6
0.3
9.9
1.6
0.3
0.5
0.2
1.7
0.0
11.4
0.5
0.0
O.I
15.3
0.9
246.1
1.3
0.0
0.6
1.0
I.I
0.4
2.2
0.0
0.0
0.5
11.9
0.4
16.0
0.8
1.5
O.I
97.4
1.7
2.4
O.I
0.5
7.6
10.4
48.5
0.5
0.8
1.7
0.0
0.0
0.6
0.6
O.I
1.0
40.1
0.2
0.0
illlfl.
0.0
5.5
5.9
0.4
13.2
1.9
0.4
0.9
0.3
2.3
O.I
15.2
0.8
0.0
0.2
19.8
1.3
320.3
1.7
0.0
0.9
1.4
1.4
0.6
3.1
0.0
0.0
0.6
15.1
0.6
20.7
1.0
2.1
0.2
110.7
2.7
3.3
O.I
0.7
11.0
13.7
61.8
0.9
1.0
2.7
O.I
0.0
0.8
0.9
O.I
1.5
47.0
0.3
0.0
1
ooj ^oT
7.1
7.5
0.6
16.2
2.3
0.6
I.I
0.5
3.1
O.I
20.3
1.0
0.0
0.2
24.8
1.8
462.2
2.4
O.I
I.I
1.8
1.8
0.8
4.3
O.I
O.I
0.8
19.2
0.8
27.1
1.4
2.7
0.2
133.0
3.6
4.6
O.I
0.8
14.9
17.8
78.9
1.2
1.2
4.1
O.I
0.0
I.I
1.2
0.2
2.0
56.4
0.4
0.0
9.8
10.6
0.9
20.1
2.7
0.8
1.5
0.7
4.2
O.I
30.8
1.5
O.I
0.4
31.7
2.7
760.9
3.9
O.I
1.6
2.6
2.3
1.2
6.5
O.I
O.I
I.I
23.6
1.4
36.0
1.8
3.5
0.3
174.0
5.5
7.2
0.2
1.0
23.0
22.7
102.3
1.7
1.5
7.5
O.I
0.0
1.8
1.9
0.2
2.9
73.2
0.6
0.0
^^^^|
oT
11.9
13.2
1.2
22.8
2.9
1.0
1.8
0.9
5.5
O.I
39.1
1.8
O.I
0.6
35.6
3.4
1,164.5
5.0
O.I
1.9
3.1
2.5
1.5
8.3
O.I
0.2
1.2
26.1
1.9
46.5
2.2
4.1
0.3
231.8
7.1
9.4
0.2
I.I
28.4
26.5
123.5
2.2
1.7
10.6
O.I
0.0
2.2
2.4
0.2
3.4
92.5
0.8
0.0
August 2011
Appendices
Page A-7
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
0.0
0.0
6.9
0.7
0.0
O.I
5.6
O.I
0.0
O.I
0.2
0.0
2.1
32.0
0.2
-
0.2
0.3
0.3
I.I
1.2
3.4
0.5
0.2
0.0
O.I
0.5
0.0
-
1.2
0.6
5.7
90.2
0.0
O.I
4.7
0.0
1.9
0.2
0.7
O.I
O.I
3.9
II. 1
1.8
185.7
Non-OECD Asia 8.5
Non-OECD Europe & | 38.5
EU 38.1
OPEC | 5.9
0.0
0.0
8.5
0.3
0.0
O.I
2.6
O.I
0.0
0.2
0.5
O.I
1.8
26.0
0.3
0.0
0.3
0.2
0.3
2.6
4.2
6.6
0.7
0.2
3.1
0.3
0.5
0.0
0.0
0.8
0.5
5.5
105.5
0.0
O.I
3.2
0.0
1.5
0.3
0.7
0.2
0.3
5.0
17.2
2.0
219.7
20./
33.1
49.0
4.7
0.0
0.0
5.5
0.5
O.I
0.2
2.5
0.3
0.2
0.8
I.I
0.3
0.6
38.6
1.0
0.0
1.2
O.I
0.2
4.1
13.2
9.3
0.9
0.5
2.8
0.9
0.8
0.0
0.0
0.6
0.8
7.5
134.9
O.I
O.I
2.3
O.I
1.9
1.0
0.9
I.I
1.5
7.4
13.5
3.9
291.9
70.4
45.8
62.0
5.9
2005 2010 2015 2020
0.0
0.0
2.6
0.9
0.2
0.6
1.6
0.7
0.4
2.1
2.1
0.5
0.9
42.6
2.5
0.0
1.5
0.3
0.4
6.9
21.3
4.4
1.3
0.8
2.8
2.3
1.6
0.0
0.0
1.2
0.7
9.1
143.5
0.3
0.3
3.1
0.3
3.1
2.5
1.9
2.9
1.8
13.0
20.0
8.2
327.8
190.9
53.3
66.8
11.0
0.0
0.0
2.9
1.2
0.2
I.I
1.2
I.I
0.6
3.4
3.3
0.6
1.0
48.3
4.1
O.I
2.0
0.4
0.5
10.4
26.6
5.2
1.3
1.0
1.7
4.2
2.0
0.0
0.0
1.3
I.I
9.4
164.6
0.5
0.5
6.2
0.7
4.2
4.4
3.1
5.0
1.4
20.3
29.3
13.1
389.6
363.2
60.0
73.7
20.0
0.0
O.I
3.7
1.7
0.4
1.8
1.5
1.5
0.9
5.1
4.7
0.8
1.2
57.0
6.2
O.I
3.3
0.6
0.7
15.2
35.2
6.6
1.6
1.3
1.9
6.9
2.6
O.I
0.0
1.7
1.7
12.3
217.0
0.7
0.6
7.9
I.I
6.5
6.5
4.9
8.4
1.8
30.8
39.4
19.8
505.6
462.9
72.5
96.2
29.4
0.0
O.I
4.9
2.1
0.7
3.2
2.0
2.1
1.2
7.7
6.2
1.0
1.4
68.8
8.7
0.2
5.4
0.7
0.9
20.9
48.0
8.5
2.1
1.7
2.2
10.8
3.4
O.I
0.0
2.2
2.2
15.7
277.7
1.0
0.8
10.5
1.5
9.1
9.0
6.7
13.8
2.3
42.3
52.7
27.6
646.9
644.9
88.5
124.4
40.2
2025 2030
O.I
0.2
6.4
2.5
1.3
6.5
2.6
3.5
2.0
13.5
8.1
1.3
1.8
97.4
13.8
0.3
10.1
1.0
1.2
32.7
76.1
11.0
2.6
2.1
2.5
19.9
5.3
O.I
O.I
3.1
3.4
19.2
352.9
1.5
1.2
15.8
2.1
14.8
14.3
10.3
26.2
3.3
65.7
80.2
44.1
847.0
1,024.4
125.3
160.5
62.4
O.I
0.2
8.0
2.8
1.9
9.9
3.6
4.6
2.6
18.9
9.4
1.4
2.0
120.5
17.9
0.3
16.5
1.2
1.4
41.3
99.4
13.3
2.9
2.4
2.8
28.6
6.7
O.I
O.I
3.8
4.7
21.1
397.8
2.0
1.5
20.4
2.7
19.5
18.3
13.5
41.1
4.1
83.6
102.2
58.5
999.8
1,528.5
154.4
190.5
81.4
World Totals 249.4 297.7 432.9 613.2 875.5 1,131.0 1,503.0 2,186.8 2,927.0
August 2011
Appendices
Page A-8
-------
Appendix B: Energy Sector Emissions
Table B-l: Total Non-CO2 Emissions from the Energy Sector by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
1.8
10.9
9.7
1.7
26.8
1.4
9.5
2.2
2.0
2.1
1.2
17.7
2.8
3.5
0.7
45.1
4.2
251.3
7.6
6.1
1.6
10.6
0.6
1.2
8.5
1.3
8.3
1.3
15.2
2.7
38.0
2.2
3.7
0.0
94.9
56.8
29.5
0.0
1.2
0.2
13.8
12.4
0.2
39.8
48.0
1.8
0.5
0.7
1.0
O.I
0.3
37.4
1.5
0.3
11.7
12.4
1.7
28.8
1.5
6.7
2.3
1.7
1.9
2.0
16.9
2.6
3.6
1.5
57.1
4.1
290.1
8.6
6.3
1.4
7.9
1.0
1.4
9.3
0.8
9.7
1.5
14.8
0.5
29.1
2.3
3.5
0.0
106.5
65.3
36.6
0.0
1.3
0.4
14.2
13.0
0.2
25.1
82.7
0.6
0.5
0.6
0.5
O.I
0.3
41.6
0.7
^EJTJU ^Emil^l
02
14.5
13.5
1.8
30.2
1.6
8.7
2.4
1.9
2.0
1.0
20.3
2.3
4.4
1.6
64.7
4.0
249.0
9.1
6.7
1.4
7.0
I.I
1.5
10.8
0.9
12.3
1.6
12.2
0.5
25.0
2.7
3.4
O.I
107.0
69.9
41.2
0.0
1.5
0.4
13.9
13.3
0.2
18.3
85.5
0.5
0.6
0.5
0.5
0.2
0.3
46.7
O.I
0.3
18.0
14.4
1.8
30.5
1.8
12.1
2.7
2.1
1.9
2.3
24.2
2.3
7.7
1.8
64.3
4.5
384.8
16.4
7.3
1.7
7.0
I.I
1.7
13.1
1.0
14.3
1.7
8.8
0.5
20.0
2.7
3.3
O.I
124.3
66.1
51.2
0.0
1.5
0.7
12.9
12.0
0.3
17.1
104.0
0.6
0.7
0.6
0.6
0.2
0.3
47.8
0.2
^^^^V^^^H
^KTJTTIV i ^vTjT^V
oT
19.4
15.4
1.9
36.7
1.7
24.6
2.8
2.2
2.1
2.7
27.6
2.6
8.2
2.0
71.5
4.8
435.0
19.1
7.9
1.5
6.7
1.0
1.8
15.1
I.I
16.2
1.8
8.3
0.5
18.9
2.6
2.9
O.I
140.9
72.5
54.8
0.0
1.3
0.9
12.8
II. 1
0.4
20.8
106.0
0.5
0.9
0.6
0.6
0.2
0.3
39.5
0.2
0.3
23.7
15.7
2.2
39.5
1.8
31.8
2.8
2.2
2.2
2.8
30.7
2.7
8.9
2.2
79.7
5.2
474.7
21.4
8.2
1.5
6.5
1.0
1.7
16.5
1.0
17.3
1.9
8.6
0.5
18.3
2.5
2.9
O.I
153.1
79.0
56.6
0.0
1.3
1.0
12.8
10.5
0.5
22.9
106.9
0.6
1.0
0.6
0.6
0.2
0.3
33.6
0.2
|
oT
25.6
17.0
2.3
41.6
1.8
34.3
2.9
2.3
2.2
3.0
34.1
2.8
9.4
2.4
84.0
5.7
522.6
24.3
8.5
1.5
6.5
I.I
1.8
18.1
1.0
18.6
1.9
8.9
0.6
17.7
2.5
2.9
O.I
167.3
85.2
58.0
0.0
1.3
I.I
12.9
10.0
0.6
23.4
103.8
0.7
1.2
0.7
0.6
0.2
0.3
31.6
0.2
0.4
26.4
19.1
2.4
44.6
1.9
36.0
2.9
2.4
2.2
3.4
37.5
2.9
9.9
2.7
89.8
6.2
582.4
26.2
8.9
1.5
6.5
I.I
1.9
19.7
I.I
20.0
2.0
9.3
0.6
17.7
2.5
2.9
O.I
184.3
92.0
61.7
0.0
1.3
1.2
13.2
9.5
0.6
23.9
108.2
0.7
1.4
0.7
0.7
0.2
0.3
33.4
0.3
oT
27.2
21.7
2.5
48.2
2.0
38.4
3.0
2.4
2.3
3.7
41.0
3.1
10.5
3.0
95.5
6.9
634.4
30.4
9.2
1.6
6.5
1.2
2.0
21.4
I.I
21.6
2.0
9.8
0.7
17.9
2.5
3.0
O.I
201.7
102.4
67.0
0.0
1.3
1.3
13.5
9.1
0.7
25.0
115.9
0.8
1.7
0.8
0.8
0.2
0.4
37.2
0.3
August 2011
Appendices
Page B-l
-------
Country
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
1990 1995 2000
0.0
0.5
2.7
2.9
0.9
55.3
19.2
0.9
10.4
2.0
4.2
22.1
I.I
23.9
420.1
2.1
0.3
-
1.8
0.7
10.9
10.2
5.3
1.7
0.9
1.0
4.6
8.6
22.4
2.6
92.3
9.2
37.7
343.0
O.I
46.7
28.5
8.3
172.7
7.0
62.2
23.2
2.3
275.7
74.9
Middle East 151.2
OECD 1 654.2
Non-OECD Asia 483.1
Non-OECD Europe & | 677.0
EU 1 193.3
OPEC 277.6
0.0
0.4
4.0
3.3
0.9
51.7
15.2
1.3
12.3
2.1
5.1
22.5
1.2
18.2
327.2
2.6
0.3
0.0
1.7
0.7
12.4
8.2
5.8
1.9
0.8
0.4
6.4
8.6
16.3
3.4
56.3
10.4
31.1
344.4
0.2
63.3
36.9
11.2
186.3
8.0
80.2
30.4
2.7
291.2
88.5
212.8
656.4
554.8
528.0
170.1
338.0
0.0
0.5
5.7
2.5
I.I
61.7
14.4
1.5
14.4
2.2
5.2
20.0
1.6
13.0
325.7
2.8
0.4
0.6
1.8
0.7
13.1
8.9
6.5
1.7
0.7
0.2
9.9
8.5
24.4
3.4
54.5
11.5
22.4
347.6
0.2
70.5
42.4
13.4
210.9
10.6
103.9
36.0
3.1
333.9
100.7
245.1
657.1
534.9
529.6
146.7
388.7
2005 2010 2015 2020
0.0
0.6
6.0
2.5
I.I
72.7
16.1
1.4
17.5
2.2
5.1
19.1
2.3
12.6
362.3
3.3
0.4
0.7
1.6
0.7
15.0
9.4
7.4
1.8
0.7
0.2
13.5
7.0
32.6
3.7
53.7
13.6
16.5
308.6
0.2
76.7
36.4
19.3
272.6
11.5
IIO.I
37.4
3.2
417.1
109.2
282.6
603.1
704.5
582.4
13 1.9
472.3
0.0
0.7
6.6
2.8
1.3
72.4
16.7
1.4
19.8
2.5
5.3
17.9
2.4
11.9
376.6
3.2
0.4
0.8
1.6
0.7
15.3
11.4
7.3
1.9
0.7
0.3
15.1
7.5
35.6
4.1
52.0
15.3
13.2
334.4
0.2
85.1
30.9
22.1
337.0
12.3
125.5
38.0
2.7
487.9
112.5
305.4
629.3
787.4
621.7
538.9
0.0
0.8
7.2
2.8
1.4
83.2
17.3
1.4
21.6
2.7
6.1
17.9
2.5
11.6
391.0
3.6
0.4
1.0
1.6
0.8
16.1
12.2
7.3
2.0
0.7
0.3
16.5
7.6
41.1
4.4
51.8
16.3
12.5
345.2
0.2
96.2
31.3
23.1
349.3
12.9
153.2
41.8
2.7
519.2
119.4
337.1
645.0
857.1
662.2
1 23.7
581.6
0.0
0.9
8.0
2.8
1.5
89.4
18.7
1.4
23.0
2.9
7.1
18.3
2.5
11.3
430.2
3.8
0.5
I.I
1.6
0.8
16.6
12.5
7.4
2.1
0.7
0.3
17.4
7.7
44.2
4.8
52.5
16.9
12.1
367.2
0.2
103.1
36.2
24.6
351.0
13.9
156.4
44.4
2.7
533.0
133.4
339.4
672.6
936.5
715.6
593.8
2025 2030
0.0
I.I
8.9
2.8
1.5
94.8
21.1
1.4
24.5
3.2
8.4
18.5
2.6
11.4
448.4
4.1
0.5
1.2
1.7
0.9
17.8
12.8
7.5
2.2
0.7
0.4
18.1
7.9
45.3
5.2
53.0
18.0
11.9
391.3
0.2
105.3
40.0
27.0
370.1
15.1
168.1
46.8
2.7
563.3
146.5
360.6
709.5
1,032.6
740.5
125.9
627.9
0.0
1.3
10.0
2.9
1.7
99.5
25.0
1.4
26.4
3.4
10.1
19.5
2.8
11.6
465.9
4.4
0.5
1.3
1.7
1.0
18.9
12.8
7.7
2.4
0.7
0.4
19.4
8.1
46.7
5.6
53.9
19.1
11.9
403.3
0.2
107.8
43.7
30.3
392.5
16.5
174.3
51.1
2.8
596.4
162.6
381.5
738.2
1,131.5
767.2
129.3
668.3
World Totals 2,316.2 2,331.7 2,401.2 2,699.0 2,944.2 3,140.1 3,330.5 3,553.1 3,777.4
August 2011
Appendices
Page B-2
-------
Table B-2: CH4 Emissions from Natural Gas and Oil Systems by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
^^^^|
o6~
10.6
8.3
1.7
7.0
0.4
9.3
0.0
1.2
0.5
0.7
1.0
0.6
0.9
-
30.1
1.9
2.4
2.4
I.I
1.2
0.9
0.0
0.4
3.1
0.8
0.0
2.8
0.2
7.5
O.I
1.5
11.8
36.9
25.7
O.I
0.0
7.3
0.2
O.I
12.6
48.0
I.I
0.3
0.4
0.0
0.0
30.6
I.I
-
ITT^B
^^^>^H
06"
11.4
10.2
1.7
7.0
0.5
6.5
0.0
1.2
0.5
1.6
1.2
0.7
0.9
-
40.7
1.6
2.7
2.8
I.I
I.I
0.7
O.I
0.6
3.4
0.4
O.I
2.2
0.2
7.7
O.I
2.0
14.4
42.5
31.4
O.I
O.I
6.8
0.3
O.I
6.8
82.7
0.4
0.2
0.2
0.0
0.0
34.0
0.5
-
2000 2005 2010
0.0
14.2
10.8
1.7
5.9
0.6
8.5
0.0
1.5
0.4
0.7
2.0
0.6
1.5
-
47.7
1.2
3.1
3.0
1.0
I.I
0.7
O.I
0.6
3.3
0.4
O.I
2.0
0.2
7.5
O.I
2.0
15.5
41.7
35.0
O.I
O.I
6.4
0.3
O.I
5.2
85.5
0.3
0.2
0.2
0.0
0.0
38.3
-
-
0.0
17.5
11.6
1.7
4.9
0.7
11.9
0.0
1.6
0.4
1.9
2.6
0.6
4.4
-
48.5
1.6
3.7
8.4
0.7
1.3
0.7
O.I
0.8
4.2
0.5
O.I
1.9
0.2
7.0
O.I
2.0
18.1
33.8
43.5
O.I
0.4
5.7
0.3
0.2
5.2
104.0
0.4
O.I
0.2
0.0
0.0
39.1
-
-
0.0
18.9
12.2
1.8
5.3
0.6
24.4
0.0
1.7
0.4
2.2
3.4
0.7
4.8
-
55.3
1.5
4.2
8.7
0.9
I.I
0.6
O.I
0.8
4.6
0.6
O.I
1.9
O.I
7.2
0.2
1.7
21.1
33.9
45.8
O.I
0.5
5.4
0.4
0.3
6.2
106.0
0.3
0.2
0.2
O.I
0.0
30.3
-
-
oo~
23.1
12.2
2.1
5.6
0.6
31.6
0.0
1.6
0.5
2.3
4.7
0.7
5.5
-
63.1
1.6
4.1
8.4
0.9
1.0
0.6
O.I
0.6
5.1
0.5
O.I
1.9
0.2
6.9
0.2
1.7
24.4
36.6
45.9
O.I
0.6
5.3
0.4
0.3
8.1
106.9
0.3
0.2
0.2
O.I
0.0
24.9
-
-
1
oo~
25.0
13.1
2.2
6.0
0.6
34.1
0.0
1.7
0.5
2.4
6.1
0.7
5.9
-
67.2
1.8
4.2
8.9
0.9
1.0
0.6
O.I
0.6
5.6
0.5
O.I
1.9
0.2
6.5
0.2
1.7
27.4
38.0
45.4
O.I
0.6
5.3
0.5
0.3
8.7
103.8
0.4
0.2
0.2
O.I
0.0
23.2
-
-
0.0
25.8
14.8
2.3
6.2
0.6
35.7
0.0
1.7
0.5
2.7
6.9
0.7
6.3
-
72.7
2.0
4.4
10.3
1.0
1.0
0.6
O.I
0.6
6.1
0.5
O.I
1.9
0.2
6.6
0.2
1.7
31.3
38.6
47.6
O.I
0.7
5.4
0.5
0.4
9.1
108.2
0.4
0.2
0.2
O.I
0.0
24.7
-
-
^^VAjjlii^H
o6~
26.5
16.9
2.3
6.5
0.6
38.0
0.0
1.8
0.5
3.0
7.7
0.7
6.9
-
78.0
2.1
4.7
12.0
1.0
1.0
0.6
O.I
0.6
6.4
0.5
O.I
2.0
0.2
6.7
0.2
1.7
31.5
40.9
50.6
O.I
0.7
5.5
0.5
0.4
9.7
115.9
0.4
0.2
0.2
O.I
0.0
28.1
-
-
August 2011
Appendices
Page B-3
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
1.6
0.3
17.3
0.0
0.3
3.9
O.I
0.0
3.1
O.I
19.4
335.1
1.6
0.5
O.I
0.0
0.3
0.6
0.0
0.4
0.8
2.4
I.I
22.1
31.3
7.8
10.3
189.8
0.0
45.7
28.0
O.I
83.6
0.2
62.0
19.8
0.9
115.7
41.2
145.1
299.6
Non-OECD Asia 1 78.2
Non-OECD Europe & | 485.6
1.6
0.3
19.2
0.0
0.6
5.0
O.I
0.0
3.2
O.I
12.8
274.6
2.1
0.0
0.0
0.6
O.I
0.2
1.8
0.8
0.0
0.3
0.3
4.1
1.2
16.2
23.6
8.6
9.7
198.4
0.0
62.8
36.3
0.2
94.9
0.7
79.9
26.4
1.6
130.3
53.5
204.9
323.0
96.3
411.8
51.0
OPEC | 231.1 | 295.2
0.8
0.4
21.9
0.0
0.7
6.3
O.I
3.5
0.2
9.2
280.3
2.2
0.0
0.6
0.7
0.0
0.2
3.3
0.8
0.0
0.2
O.I
7.6
1.2
24.2
21.9
9.5
7.9
209.3
0.0
70.0
41.7
0.3
112.2
1.8
103.6
30.6
0.9
152.8
60.6
235.8
342.6
107.2
426.6
44.6
336.9
2005 2010 2015 2020
0.8
0.4
27.7
0.0
0.6
7.9
O.I
O.I
4.3
0.9
8.8
311.5
2.6
0.0
0.7
0.7
0.0
0.2
5.7
0.9
0.0
0.2
O.I
10.8
1.6
32.4
23.9
10.8
5.8
190.4
0.0
76.3
35.7
0.5
162.7
1.9
109.8
31.6
0.8
213.2
63.1
270.8
326.0
II 1.7
477.8
42.5
412.6
0.7
0.6
25.8
0.0
0.5
10.0
0.2
O.I
4.2
1.0
8.0
314.8
2.4
0.0
0.8
0.7
0.0
0.2
7.8
0.9
0.0
0.2
O.I
12.3
1.7
35.4
23.2
11.8
4.1
221.2
0.0
84.7
30.2
0.4
215.3
2.1
125.2
31.6
0.2
265.7
59.7
291.4
355.3
119.3
503.8
39.6
475.1
0.7
0.7
33.2
0.0
0.4
11.6
O.I
O.I
4.4
1.0
7.6
324.2
2.7
0.0
1.0
0.7
0.0
0.3
8.5
1.0
0.0
0.2
O.I
13.5
1.7
40.9
22.8
12.1
3.7
229.8
0.0
95.8
30.5
0.5
218.7
2.0
152.8
34.4
0.3
281.2
60.8
320.7
366.9
131.7
538.1
38.4
511.7
0.7
0.7
35.7
0.0
0.4
12.7
0.2
O.I
4.9
1.0
7.3
363.0
2.7
0.0
I.I
0.7
0.0
0.3
8.9
1.0
0.0
0.2
O.I
14.3
1.7
43.9
23.9
12.0
3.3
250.9
0.0
102.7
35.3
0.5
210.3
2.2
155.9
35.8
0.3
277.9
68.7
320.2
391.2
140.1
590.9
37.9
517.2
2025 2030
0.7
0.8
37.1
0.0
0.4
13.7
0.2
O.I
4.9
1.0
7.3
381.1
2.8
0.0
1.2
0.8
0.0
0.3
9.3
1.0
0.0
0.2
O.I
14.7
1.7
44.9
24.4
12.2
3.2
272.3
0.0
104.9
39.0
0.5
218.1
2.5
167.5
36.5
0.3
288.4
77.1
338.6
420.7
147.3
614.9
38.2
544.4
0.7
0.8
37.5
0.0
0.4
15.0
0.2
O.I
5.7
1.0
7.3
396.3
3.0
0.0
1.3
0.8
0.0
0.3
9.3
1.0
0.0
0.2
0.2
15.7
1.7
46.3
24.7
12.3
3.2
278.5
0.0
107.3
42.6
0.5
227.9
2.9
173.7
38.8
0.3
299.6
86.0
355.9
437.4
155.4
637.3
39.4
576.5
World Totals 1,165.4 1,219.8 1,325.7 1,462.6 1,595.2 1,699.5 1,788.9 1,887.0 1,971.6
August 2011
Appendices
Page B-4
-------
Table B-3: CH4 Emissions from Coal Mining Activities by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
oir
0.0
0.2
15.9
0.0
-
0.3
1.2
1.6
0.0
-
1.9
0.6
126.1
1.9
0.0
0.0
7.6
-
0.3
-
4.3
O.I
18.4
I.I
0.7
11.2
O.I
0.3
0.0
-
O.I
2.8
24.9
-
0.3
0.0
-
-
0.2
1.3
O.I
-
|Q9
O.I
0.0
0.3
17.4
0.0
-
0.0
I.I
1.5
0.0
-
1.7
0.3
162.3
2.3
0.0
0.0
5.8
-
0.2
-
4.4
0.0
12.6
1.2
0.3
14.5
0.5
0.3
0.0
-
0.0
1.3
15.6
-
0.0
0.0
-
-
0.2
1.4
0.0
-
^fT^^H
oo~
0.2
19.6
0.0
-
0.0
1.5
1.2
O.I
-
0.9
O.I
134.7
3.4
0.0
5.0
-
0.0
0.2
-
2.5
0.0
9.7
1.3
0.3
16.8
1.0
0.3
-
-
0.0
0.8
11.0
-
0.0
O.I
-
-
0.2
1.7
-
-
2005 2010
0.0
0.0
20.9
0.0
-
0.0
1.4
I.I
0.3
-
0.7
0.2
257.1
5.2
0.0
4.7
-
0.0
0.3
-
0.0
0.0
5.7
1.5
0.0
21.5
2.3
0.3
-
-
0.0
O.I
11.0
-
0.0
O.I
-
-
O.I
1.4
-
-
0.0
O.I
26.8
-
-
-
1.6
1.3
0.3
-
0.8
O.I
299.5
7.4
0.0
4.3
-
0.0
0.3
-
-
0.0
3.7
1.4
0.0
26.5
3.9
0.3
-
-
0.0
13.5
-
0.0
O.I
-
-
O.I
1.5
-
-
oo~
O.I
29.1
-
-
-
1.4
1.3
0.3
-
0.9
0.2
332.6
9.7
0.0
4.1
-
0.0
0.3
-
-
0.0
3.6
1.3
0.0
27.4
3.9
0.5
-
-
0.0
13.6
-
0.0
O.I
-
-
O.I
0.9
-
-
1
oo~
0.2
30.6
-
-
-
1.4
1.3
0.3
-
0.9
0.2
371.7
11.8
0.0
4.1
-
0.0
0.3
-
-
0.0
3.5
1.3
0.0
28.9
4.1
0.8
-
-
0.0
13.3
-
0.0
O.I
-
-
O.I
0.4
-
-
0.0
0.2
33.1
-
-
-
1.6
1.3
0.4
-
0.9
0.2
420.1
12.0
0.0
4.0
-
0.0
0.3
-
-
0.0
3.4
1.3
0.0
30.1
4.7
0.5
-
-
0.0
13.2
-
0.0
O.I
-
-
O.I
0.4
-
-
^^^^|
o6~
0.2
36.0
-
-
-
1.8
1.3
0.5
-
1.0
0.2
457.6
14.0
0.0
4.0
-
0.0
0.3
-
-
0.0
3.4
1.3
0.0
32.2
5.8
0.5
-
-
0.0
13.3
-
0.0
O.I
-
-
O.I
0.5
-
-
August 2011
Appendices
Page B-5
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
Non-OECD Asia
Non-OECD Europe &
1990 1995 2000
0.2
0.3
10.3
12.7
O.I
0.8
0.0
0.2
14.7
O.I
3.7
67.2
-
0.6
0.3
6.8
4.8
1.8
0.0
-
O.I
0.2
1.4
55.4
-
18.3
84.1
0.0
0.5
0.0
0.7
1.0
-
-
0.0
1.0
18.1
3.4
0.3
181.3
152.4
156.1
73.7
OPEC 1 10.6
O.I
0.0
0.3
2.3
8.6
O.I
0.9
0.0
0.2
13.2
3.9
43.6
-
0.6
0.3
7.0
1.6
1.5
-
0.0
0.4
1.4
30.1
-
12.6
67.1
0.0
0.3
0.0
1.2
I.I
-
-
0.0
0.8
10.4
3.7
0.3
145.1
188.8
96.2
58.1
2.6
O.I
0.0
0.3
0.3
8.2
O.I
1.0
0.0
0.2
11.4
2.7
39.2
-
0.6
0.3
7.7
1.2
1.2
-
0.0
0.3
1.6
31.4
-
7.0
60.5
0.0
0.2
O.I
1.7
0.8
-
-
0.0
1.9
8.9
5.2
°'3
126.3
164.3
88.1
43.5
0.7
2005 2010 2015 2020
O.I
0.0
0.3
0.9
9.5
0.0
1.5
0.0
0.4
9.6
2.5
44.5
-
0.3
0.3
8.3
0.8
0.9
-
0.0
0.4
1.5
28.5
-
4.1
57.1
0.0
O.I
O.I
4.7
0.7
-
-
O.I
2.1
10.0
6.7
°'3
109.9
298.0
90.3
30.9
1.3
0.2
0.0
0.3
1.0
10.7
O.I
I.I
O.I
0.4
8.3
2.5
55.2
-
0.3
0.2
8.2
0.8
0.5
-
0.0
0.4
1.8
27.4
-
2.8
59.0
O.I
O.I
6.0
0.8
-
-
O.I
2.1
10.0
9.3
0.3
112.6
349.0
102.6
25.6
1.3
0.2
0.0
0.3
1.0
10.7
O.I
I.I
O.I
0.4
7.9
2.6
59.8
-
0.3
0.2
8.6
0.8
0.5
-
0.0
0.4
1.7
27.6
-
2.6
60.8
O.I
O.I
6.0
0.9
-
-
O.I
2.1
10.6
11.5
0.5
115.4
383.1
107.5
24.8
1.7
0.2
0.0
0.3
1.0
11.4
O.I
1.2
O.I
0.4
7.8
2.5
59.8
-
0.3
0.2
8.7
0.8
0.5
-
0.0
0.4
1.7
27.0
-
2.6
61.2
O.I
0.2
6.4
0.9
-
-
O.I
2.1
10.7
13.7
0.8
116.6
425.2
106.6
24.5
2.0
2025 2030
0.2
0.0
0.3
I.I
13.0
0.0
1.3
O.I
0.4
7.7
2.5
59.5
-
0.3
0.2
9.5
0.8
0.5
-
0.0
0.4
1.7
26.8
-
2.6
63.2
O.I
0.2
7.4
1.0
-
-
O.I
2.1
11.6
14.0
0.5
120.7
478.3
105.8
24.0
1.8
0.3
0.0
0.4
1.2
15.9
0.0
1.6
O.I
0.5
7.6
2.5
61.2
-
0.3
0.2
10.2
0.8
0.5
-
0.0
0.5
1.7
27.1
-
2.5
68.0
O.I
0.2
9.0
1.0
-
-
O.I
2.1
12.5
16.3
0.5
128.5
524.2
108.2
23.9
1.9
World Totals 511.5 444.5 392.9 515.3 583.8 628.6 673.6 730.9 790.2
August 2011
Appendices
Page B-6
-------
Table B-4: CH4 Emissions from Stationary and Mobile Combustion by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
H2fl
0.9
O.I
0.4
0.0
0.7
O.I
O.I
0.0
0.4
0.4
0.2
7.0
O.I
-
0.6
2.3
0.6
47.6
1.4
1.9
O.I
1.4
O.I
0.4
1.8
O.I
3.5
O.I
0.8
2.2
4.2
O.I
0.3
0.0
32.2
6.8
1.3
-
0.2
O.I
1.3
0.8
0.0
1.5
-
0.3
0.5
O.I
0.2
0.0
O.I
2.5
O.I
0.0
O.I
O.I
0.9
0.0
0.8
O.I
0.2
0.0
0.2
0.3
0.2
6.4
O.I
-
0.5
2.5
0.8
44.5
1.6
1.8
0.0
0.6
0.4
0.4
1.8
0.0
4.1
O.I
0.7
O.I
1.4
0.2
0.2
0.0
35.2
8.0
1.8
-
O.I
O.I
1.4
0.9
0.0
1.9
-
O.I
0.5
0.0
O.I
0.0
O.I
2.6
0.0
0.0
jWjIjB
O.I
O.I
1.2
0.0
0.9
O.I
O.I
0.0
0.2
0.3
O.I
8.0
0.0
-
0.5
3.2
0.9
29.7
1.4
1.8
0.0
0.4
0.4
0.5
2.3
0.0
5.7
O.I
0.6
0.2
0.8
0.2
O.I
0.0
29.8
11.3
2.2
-
O.I
O.I
I.I
0.9
0.0
1.3
-
O.I
0.5
0.0
0.0
0.0
O.I
2.6
0.0
0.0
2005 2010
O.I
O.I
1.6
0.0
0.9
O.I
O.I
0.0
O.I
0.2
0.2
9.3
0.0
-
0.6
3.0
1.0
34.7
1.4
2.0
O.I
0.2
0.4
0.5
2.9
0.0
6.3
O.I
0.5
O.I
0.6
O.I
0.2
0.0
36.7
12.4
2.4
-
O.I
O.I
0.8
0.9
0.0
0.6
-
0.2
0.6
0.0
O.I
0.0
0.0
2.6
0.0
0.0
O.I
O.I
1.6
0.0
0.9
O.I
O.I
0.0
0.2
0.2
0.3
10.5
0.0
-
0.7
3.0
I.I
35.1
1.6
2.1
O.I
0.2
0.3
0.6
3.2
0.0
7.1
O.I
0.5
0.2
0.6
O.I
O.I
0.0
42.3
15.6
3.0
-
O.I
O.I
0.8
0.8
0.0
0.7
-
0.2
0.7
0.0
O.I
0.0
0.0
2.5
0.0
0.0
O.I
0.2
1.8
0.0
0.8
0.0
O.I
0.0
0.2
0.2
0.3
11.4
O.I
-
0.8
2.9
1.2
36.6
1.7
2.1
O.I
0.2
0.3
0.6
3.5
0.0
7.7
O.I
0.5
0.2
0.6
O.I
O.I
0.0
47.7
18.4
3.4
-
O.I
O.I
0.8
0.7
O.I
0.8
-
0.2
0.8
0.0
O.I
0.0
O.I
2.5
0.0
0.0
1
oT
0.2
2.0
0.0
0.8
0.0
0.2
0.0
0.2
0.3
0.3
12.5
O.I
-
1.0
2.9
1.3
38.5
1.9
2.1
O.I
0.2
0.2
0.7
3.8
0.0
8.3
O.I
0.4
0.2
0.5
O.I
O.I
0.0
54.3
21.8
3.9
-
O.I
O.I
0.8
0.7
O.I
0.9
-
0.2
1.0
0.0
O.I
0.0
O.I
2.4
0.0
0.0
O.I
0.2
2.3
0.0
0.8
0.0
0.2
0.0
0.2
0.2
0.3
13.7
O.I
-
I.I
2.8
1.4
40.7
2.1
2.2
O.I
0.2
0.2
0.7
4.1
0.0
9.1
O.I
0.4
0.3
0.5
O.I
O.I
0.0
62.6
26.0
4.5
-
O.I
O.I
0.8
0.6
O.I
1.0
-
0.3
I.I
0.0
O.I
0.0
O.I
2.4
0.0
0.0
O.I
0.2
2.5
0.0
0.8
0.0
0.2
0.0
0.2
0.2
0.4
15.0
O.I
-
1.4
2.8
1.5
43.5
2.3
2.2
O.I
0.2
0.2
0.8
4.5
0.0
9.9
O.I
0.4
0.3
0.5
O.I
O.I
0.0
73.1
31.1
5.2
-
O.I
O.I
0.8
0.6
O.I
1.2
-
0.3
1.4
0.0
O.I
0.0
O.I
2.4
0.0
0.0
August 2011
Appendices
Page B-7
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
Non-OECD Asia
Non-OECD Europe &
1990 1995 2000
0.2
0.8
0.7
O.I
9.4
0.9
O.I
O.I
0.8
1.3
2.2
O.I
0.3
8.7
0.2
O.I
0.4
O.I
0.5
0.4
0.5
O.I
O.I
0.0
0.0
3.0
O.I
1.2
3.8
O.I
2.7
7.3
0.0
0.4
0.2
1.5
39.8
2.2
O.I
1.6
0.0
58.3
12.6
1.7
34.1
94.2
19.5
16.7
OPEC 1 11.8
0.3
1.7
0.8
O.I
9.7
0.9
O.I
O.I
1.0
1.9
3.1
O.I
O.I
4.2
0.2
O.I
0.2
O.I
1.0
0.4
0.5
O.I
O.I
0.0
O.I
2.9
0.0
1.7
1.6
O.I
1.9
6.9
0.0
O.I
0.2
2.9
40.3
2.7
O.I
2.1
0.0
60.6
13.4
2.3
30.6
98.6
9.0
12.8
12.6
0.3
2.9
0.9
O.I
15.3
0.9
O.I
O.I
I.I
2.2
1.7
O.I
O.I
2.1
0.3
0.2
0.2
0.0
0.8
0.5
0.4
O.I
O.I
0.0
O.I
2.6
O.I
1.5
0.6
O.I
1.6
6.0
0.0
O.I
0.2
3.6
44.8
3.6
0.2
3.2
0.0
72.5
16.2
2.8
27.3
85.2
5.3
9.5
18.8
2005 2010 2015 2020
0.4
3.0
0.8
O.I
16.1
0.9
O.I
0.2
1.0
2.1
1.8
O.I
O.I
2.5
0.3
0.2
O.I
0.0
1.5
1.0
0.5
O.I
0.0
0.0
O.I
1.0
O.I
1.4
0.6
O.I
I.I
5.1
0.0
O.I
0.2
5.2
45.2
4.0
0.2
3.4
0.0
75.7
18.3
3.1
23.6
100. 1
4.9
8.1
19.8
0.4
3.3
1.2
O.I
16.7
0.8
0.2
0.3
I.I
2.3
1.8
O.I
O.I
2.5
0.3
0.2
O.I
0.0
1.7
1.0
0.5
O.I
0.0
0.0
O.I
I.I
O.I
1.5
0.6
0.2
1.0
4.7
0.0
0.2
0.3
5.9
48.7
4.1
0.2
3.8
0.0
81.3
20.0
23.1
II 1.4
5.2
8.0
21.2
0.5
3.8
1.2
O.I
18.4
0.8
0.2
0.3
1.2
2.8
1.6
O.I
O.I
2.6
0.4
0.2
O.I
0.0
1.7
0.9
0.5
O.I
0.0
0.0
O.I
1.0
O.I
1.6
0.6
0.2
0.9
4.6
0.0
0.2
0.3
6.4
52.2
4.3
0.2
4.6
0.0
87.6
21.7
22.4
123.6
5.5
7.7
23.5
0.6
4.4
1.2
O.I
20.4
0.8
0.2
0.4
1.3
3.4
1.4
O.I
0.2
2.7
0.4
0.2
O.I
0.0
1.7
0.9
0.5
O.I
0.0
0.0
O.I
0.9
O.I
1.8
0.7
0.2
0.9
4.5
0.0
0.2
0.3
6.8
56.2
4.7
0.2
5.6
0.0
94.6
23.7
22.0
138.7
6.0
7.5
26.1
2025 2030
0.7
5.0
1.2
O.I
22.5
0.9
0.2
0.5
1.5
4.2
1.3
O.I
0.2
2.7
0.5
0.2
O.I
O.I
1.7
0.9
0.5
O.I
0.0
0.0
0.2
0.8
O.I
1.9
0.7
0.3
0.9
4.5
0.0
0.2
0.3
7.4
60.7
5.0
0.3
6.8
0.0
102.6
25.9
21.6
157.4
6.4
7.4
29.1
0.9
5.8
1.2
O.I
24.9
0.9
0.2
0.6
1.6
5.2
1.2
O.I
0.2
2.8
0.6
0.2
O.I
O.I
1.8
0.9
0.5
O.I
0.0
0.0
0.2
0.8
O.I
2.0
0.8
0.3
0.9
4.4
0.0
0.2
0.4
8.1
65.7
5.4
0.3
8.4
0.0
II 1.5
28.4
21.3
180.7
7.0
7.3
32.4
World Totals 220.4 214.5 209.3 225.6 244.7 265.2 289.9 319.5 355.3
August 2011
Appendices
Page B-8
-------
Table B-5: N2O Emissions from Stationary and Mobile Combustion by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
O.I
0.2
0.6
0.0
1.3
0.4
O.I
0.0
0.2
0.8
O.I
2.4
0.4
-
O.I
7.9
0.4
11.8
0.4
0.6
O.I
0.6
0.3
0.2
3.4
O.I
0.6
0.9
2.7
O.I
7.5
0.7
I.I
0.0
2.9
1.6
2.1
-
0.9
O.I
4.5
6.9
O.I
0.8
-
O.I
0.0
O.I
0.3
O.I
0.0
1.2
0.2
0.0
HK*£f^l
OO"
0.2
0.8
0.0
1.6
0.5
O.I
0.0
O.I
0.9
O.I
2.8
0.4
-
O.I
9.5
0.6
16.6
0.5
0.5
O.I
0.7
0.4
0.2
3.9
0.0
0.9
1.0
2.9
0.0
7.0
0.7
0.8
0.0
3.7
1.9
2.9
-
I.I
0.2
5.1
8.4
O.I
0.9
-
0.0
0.0
O.I
O.I
O.I
0.0
1.5
O.I
0.0
^TPPh^
BilH
oTj oTj ""oT
0.2
1.0
O.I
2.2
0.6
O.I
0.0
O.I
I.I
O.I
3.4
0.3
-
O.I
10.1
0.8
18.7
0.5
0.4
O.I
0.8
0.4
0.2
4.9
0.0
1.3
I.I
3.2
0.0
6.5
0.9
0.7
O.I
4.2
2.8
3.6
-
1.3
0.2
5.3
9.0
O.I
0.7
-
0.0
0.0
O.I
O.I
O.I
0.0
2.2
O.I
0.0
0.3
1.0
O.I
2.6
0.6
O.I
0.0
O.I
I.I
O.I
3.7
0.3
-
0.2
9.5
0.8
29.1
0.5
0.5
0.2
I.I
0.4
0.2
5.7
O.I
2.1
1.2
3.3
0.0
6.0
0.9
0.8
O.I
4.9
3.6
4.8
-
1.3
0.2
5.1
8.3
O.I
0.3
-
0.0
O.I
O.I
O.I
O.I
0.0
2.8
O.I
0.0
0.3
1.2
O.I
2.4
0.5
O.I
0.0
O.I
1.3
O.I
4.2
0.4
-
0.2
9.6
1.0
38.2
0.6
0.6
0.2
I.I
0.4
0.3
7.0
O.I
2.7
1.3
3.2
0.0
6.5
0.8
0.7
O.I
6.5
4.5
5.6
-
I.I
0.3
4.9
7.6
O.I
0.4
-
0.0
O.I
O.I
O.I
O.I
0.0
3.0
O.I
0.0
O.I
0.4
1.3
O.I
2.4
0.5
O.I
O.I
O.I
1.3
O.I
4.6
0.5
-
0.3
9.5
I.I
45.1
0.6
0.6
0.3
I.I
0.3
0.3
7.7
O.I
3.0
1.3
3.1
0.0
6.2
0.8
0.7
O.I
8.1
5.3
6.4
-
I.I
0.3
4.8
7.0
0.2
0.4
-
O.I
O.I
O.I
0.2
O.I
O.I
3.0
O.I
0.0
1
oT
0.4
1.4
O.I
2.3
0.5
O.I
O.I
O.I
1.4
0.2
5.0
0.6
-
0.3
9.4
1.3
53.7
0.7
0.7
0.3
1.0
0.3
0.4
8.4
O.I
3.3
1.3
3.1
0.0
5.9
0.8
0.7
O.I
10.3
6.4
7.5
-
I.I
0.4
4.8
6.5
0.2
0.5
-
O.I
O.I
O.I
0.2
O.I
O.I
2.9
0.2
0.0
0.2
0.4
1.6
0.2
2.3
0.5
O.I
O.I
O.I
1.4
0.2
5.6
0.7
-
0.4
9.3
1.4
64.2
0.8
0.7
0.4
1.0
0.3
0.4
9.2
O.I
3.6
1.2
3.1
0.0
5.8
0.7
0.7
O.I
13.0
7.7
8.7
-
I.I
0.4
4.7
6.0
0.2
0.6
-
O.I
O.I
O.I
0.2
O.I
O.I
2.9
0.2
0.0
O2~
0.5
1.8
0.2
2.2
0.5
O.I
O.I
O.I
1.4
0.2
6.2
0.8
-
0.5
9.2
1.6
77.2
0.9
0.7
0.5
1.0
0.3
0.4
10.0
0.2
4.0
1.2
3.0
O.I
5.7
0.7
0.7
O.I
16.6
9.3
10.2
-
I.I
0.5
4.7
5.6
0.2
0.7
-
O.I
0.2
0.2
0.3
O.I
O.I
2.8
0.2
0.0
August 2011
Appendices
Page B-9
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
0.0
0.2
0.5
O.I
2.3
5.4
0.3
0.2
0.2
0.5
1.9
0.4
0.3
5.6
0.3
0.0
0.3
O.I
1.6
4.0
1.5
I.I
0.3
0.0
0.2
1.0
0.2
1.5
1.5
1.4
6.1
54.4
0.0
0.2
0.2
0.4
35.2
1.0
O.I
0.4
0.0
45.4
5.1
3.9
110.2
Non-OECD Asia 1 23.5
Non-OECD Europe & | 10.4
EU 33.5
OPEC | 6.7
World Totals
0.0
0.4
0.7
O.I
2.3
5.4
0.3
0.2
0.3
0.8
2.0
0.6
0.3
3.2
0.3
0.0
0.2
0.2
1.9
3.8
2.3
1.2
0.3
0.0
0.3
I.I
O.I
1.7
0.8
1.7
6.6
64.7
0.0
O.I
0.2
0.6
35.0
1.2
O.I
0.4
0.0
46.3
6.0
5.0
127.0
30.4
6.6
35.6
7.7
0.0
0.6
0.7
0.2
3.7
4.9
0.4
0.2
0.3
1.0
2.4
0.8
0.2
2.7
0.3
0.0
0.2
0.2
2.0
3.4
3.2
1.0
0.4
0.0
0.3
1.3
O.I
1.9
0.4
1.9
5.6
65.1
0.0
O.I
0.2
1.0
36.2
1.5
O.I
0.6
0.0
50.8
7.3
6.0
131.4
34.4
5.5
36.9
10.1
2005 2010 2015 2020
0.0
0.6
0.7
0.2
5.0
5.2
0.4
0.3
0.3
1.0
2.3
0.9
0.2
2.6
0.4
0.0
0.3
0.2
2.3
1.3
4.2
1.0
0.3
O.I
0.4
1.2
0.2
2.3
0.5
2.7
5.3
49.3
0.0
O.I
0.2
1.7
44.7
1.7
O.I
0.8
0.0
62.9
8.0
8.1
113.9
47.9
5.3
37.5
13.6
O.I
0.7
0.6
0.2
4.1
4.7
0.5
0.4
0.4
1.2
2.5
0.8
0.3
2.9
0.5
0.0
0.2
0.3
2.5
1.3
4.4
0.9
0.3
O.I
0.4
1.3
0.2
2.6
0.6
3.4
5.1
42.3
0.0
O.I
0.3
2.2
51.7
2.1
O.I
0.9
0.0
71.6
9.1
9.7
106.6
59.9
6.1
37.8
14.5
O.I
0.8
0.6
0.2
4.6
5.2
0.5
0.4
0.4
1.4
2.7
0.8
0.3
3.1
0.6
0.0
0.2
0.3
2.7
1.3
4.3
0.9
0.3
O.I
0.5
1.3
0.2
2.8
0.7
4.0
5.0
41.7
0.0
O.I
0.3
2.7
56.2
2.2
0.2
I.I
0.0
77.9
9.9
11.3
105.0
71.2
6.8
37.6
16.5
O.I
1.0
0.6
0.2
5.0
5.7
0.5
0.5
0.5
1.8
2.7
0.8
0.4
3.4
0.7
0.0
0.2
0.4
2.9
1.2
4.3
0.9
0.3
O.I
0.6
1.2
0.2
3.0
0.8
4.7
4.9
41.2
0.0
O.I
0.3
3.3
61.2
2.4
0.2
1.4
0.0
85.0
10.9
13.2
103.4
85.1
7.7
37.3
18.9
2025 2030
O.I
1.2
0.6
0.2
5.6
6.4
0.5
0.7
0.5
2.2
2.9
0.8
0.4
3.6
0.8
0.0
0.2
0.5
3.2
1.2
4.2
0.9
0.3
O.I
0.7
1.2
0.3
3.3
1.0
5.5
4.9
40.8
0.0
O.I
0.4
4.0
67.0
2.6
0.2
1.7
0.0
92.9
12.0
15.4
102.3
102.4
8.7
37.3
21.8
O.I
1.4
0.6
0.2
6.2
7.1
0.5
0.8
0.6
2.7
3.1
0.8
0.5
3.9
0.9
0.0
0.2
0.5
3.4
1.2
4.2
0.9
0.3
O.I
0.9
1.2
0.3
3.6
I.I
6.5
4.8
40.3
0.0
0.2
0.4
4.9
73.4
2.8
0.2
2.1
0.0
101.9
13.2
18.0
101.4
124.0
9.8
37.5
25.0
198.5 221.4 235.3 246.0 263.1 282.1 305.2 333.7 368.2
August 2011
Appendices
Page B-10
-------
Table B-6: CH4 Emissions from Biomass Combustion by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
O.I
0.0
O.I
0.0
1.7
0.3
0.0
1.8
O.I
0.0
0.2
4.5
O.I
2.2
-
2.1
0.5
52.9
1.2
2.0
O.I
O.I
O.I
0.2
0.2
0.0
3.5
0.2
3.8
O.I
0.3
O.I
O.I
-
29.9
9.3
O.I
0.0
0.0
0.0
0.2
0.0
0.0
O.I
0.0
0.0
-
0.2
O.I
0.0
-
1.5
0.0
0.0
|Q9
O.I
0.0
O.I
0.0
1.7
0.3
0.0
1.9
O.I
O.I
O.I
3.7
O.I
2.2
0.7
2.0
0.7
53.6
I.I
2.4
O.I
0.0
O.I
O.I
0.2
O.I
3.9
0.2
3.8
0.2
0.3
O.I
O.I
-
31.3
10.0
O.I
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.0
0.0
-
0.2
O.I
0.0
0.0
1.6
0.0
0.0
^fT^^H
O.I
0.0
0.2
0.0
1.4
0.2
0.0
2.0
0.2
O.I
O.I
3.8
O.I
2.3
0.8
2.0
0.8
52.5
0.7
2.8
O.I
O.I
O.I
O.I
0.2
O.I
4.4
0.2
3.0
0.2
0.4
O.I
O.I
-
33.0
10.7
O.I
0.0
0.0
0.0
0.4
0.0
0.0
0.0
-
0.0
-
0.2
0.2
0.0
O.I
1.6
0.0
0.0
2005 2010
O.I
0.0
O.I
0.0
1.0
0.3
0.0
2.2
0.2
O.I
O.I
4.9
O.I
2.5
0.9
2.0
0.8
50.3
0.6
3.2
O.I
0.3
0.2
O.I
0.2
O.I
5.0
0.2
2.1
0.2
0.4
O.I
0.2
-
34.9
11.6
0.2
0.0
0.0
0.0
0.5
0.0
0.0
0.0
-
0.0
-
0.3
0.2
0.0
0.0
1.6
0.0
0.0
O.I
0.0
O.I
-
I.I
0.3
0.0
2.3
0.2
O.I
O.I
5.2
O.I
2.6
0.9
2.1
0.8
48.5
0.7
3.5
O.I
0.3
0.2
O.I
0.2
O.I
5.4
0.2
1.8
O.I
0.5
O.I
O.I
-
36.1
12.0
0.2
0.0
0.0
0.0
0.7
0.0
0.0
0.0
-
0.0
-
0.3
0.2
0.0
0.0
1.7
0.0
0.0
^^IMIIK^^H
O.I
0.0
0.2
-
1.3
0.3
0.0
2.3
0.2
O.I
O.I
5.6
0.2
2.6
0.9
2.4
0.9
47.0
0.7
3.6
O.I
0.4
0.3
O.I
0.2
O.I
5.6
0.2
2.1
O.I
0.6
O.I
0.2
-
36.9
12.1
0.2
0.0
0.0
0.0
0.8
0.0
0.0
0.0
-
0.0
-
0.3
0.2
0.0
0.0
1.9
0.0
0.0
1
oT
0.0
0.2
-
1.5
0.4
0.0
2.3
0.2
O.I
O.I
5.9
0.2
2.6
0.9
2.7
0.9
45.6
0.8
3.8
O.I
0.4
0.3
O.I
0.2
O.I
5.9
0.3
2.4
O.I
0.6
O.I
0.2
-
37.6
12.2
0.3
0.0
0.0
0.0
0.9
0.0
0.0
0.0
-
0.0
-
0.3
0.2
0.0
0.0
2.2
0.0
0.0
O.I
0.0
0.2
-
1.8
0.4
0.0
2.3
0.2
O.I
O.I
6.3
0.2
2.6
0.9
3.0
1.0
44.3
0.8
4.0
O.I
0.5
0.3
O.I
0.2
O.I
6.1
0.3
2.7
O.I
0.7
O.I
0.2
-
38.4
12.4
0.3
0.0
0.0
0.0
1.0
O.I
0.0
0.0
-
0.0
-
0.3
0.2
0.0
0.0
2.4
0.0
0.0
^^VAjjlii^H
O.I
0.0
0.2
-
2.1
0.5
0.0
2.4
0.2
O.I
O.I
6.8
0.2
2.7
1.0
3.4
1.0
43.0
0.9
4.2
O.I
0.6
0.4
O.I
0.3
O.I
6.4
0.4
3.0
O.I
0.8
O.I
0.2
-
39.1
12.5
0.4
0.0
0.0
0.0
1.2
O.I
0.0
0.0
-
0.0
-
0.3
0.2
0.0
0.0
2.7
0.0
0.0
August 2011
Appendices
Page B-1 I
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
0.0
1.4
O.I
0.0
13.2
0.2
O.I
4.4
0.7
1.8
0.2
0.4
0.2
2.5
0.0
O.I
-
0.0
O.I
1.6
0.3
0.7
0.3
O.I
-
1.4
1.8
-
-
0.2
-
O.I
4.8
O.I
0.0
O.I
4.8
10.9
2.7
0.0
1.2
0.4
31.6
9.5
0.2
19.9
Non-OECD Asia 1 1 1.2
Non-OECD Europe & | 4.0
EU 7.5
OPEC | 14.3
World Totals
0.0
1.6
O.I
0.0
15.1
0.2
O.I
5.0
0.6
1.8
0.9
0.3
0.9
1.2
0.0
O.I
-
O.I
O.I
1.8
0.3
0.6
0.3
O.I
-
1.2
1.6
-
-
0.2
0.0
O.I
4.6
O.I
0.0
O.I
5.3
12.5
2.7
0.0
1.2
0.2
36.0
8.7
0.2
20.5
II 6.1
3.5
9.2
16.3
0.0
1.8
O.I
O.I
16.8
0.3
O.I
5.6
0.5
1.5
0.8
0.3
0.7
0.8
0.0
0.2
-
O.I
O.I
2.0
0.3
0.6
0.3
O.I
-
1.2
1.5
-
-
O.I
0.0
O.I
4.0
O.I
0.0
O.I
5.7
14.1
2.8
0.0
1.2
0.3
40.4
8.3
0.2
18.9
118.5
3.0
8.4
18.2
2005 2010 2015 2020
0.0
2.1
O.I
O.I
19.0
0.4
0.2
6.3
0.5
1.2
0.8
0.3
0.8
0.7
0.0
0.2
-
0.2
O.I
2.1
0.3
0.6
0.3
O.I
-
1.4
1.4
-
-
O.I
0.0
O.I
4.1
O.I
0.0
O.I
6.1
16.0
3.1
0.0
1.3
0.3
45.7
9.6
0.2
18.3
121.0
3.1
8.4
20.5
0.0
2.2
O.I
O.I
20.4
0.4
0.2
6.6
0.6
I.I
0.9
0.4
0.8
0.7
0.0
0.2
-
0.2
O.I
2.2
0.3
0.7
0.4
O.I
-
1.5
1.4
-
-
O.I
0.0
O.I
4.5
O.I
0.0
O.I
6.3
17.1
3.3
0.0
1.3
0.3
49.0
10.3
0.2
19.4
121.7
3.0
8.7
22.1
0.0
2.2
O.I
O.I
21.4
0.5
0.2
6.7
0.7
I.I
1.0
0.4
0.8
0.8
0.0
0.2
-
0.2
O.I
2.3
0.3
0.8
0.5
O.I
-
1.5
1.6
-
-
O.I
0.0
0.2
5.1
O.I
0.0
O.I
6.3
17.9
3.5
O.I
1.3
0.3
51.2
11.0
0.3
22.0
121.5
3.2
9.7
23.2
0.0
2.2
O.I
O.I
22.4
0.6
0.2
6.7
0.7
1.2
I.I
0.5
0.8
0.8
0.0
0.2
-
0.2
O.I
2.4
0.3
0.9
0.6
O.I
-
1.5
1.8
-
-
O.I
0.0
0.2
5.8
O.I
0.0
O.I
6.4
18.7
3.7
O.I
1.3
0.3
53.6
11.7
0.3
24.9
121.3
3.3
10.9
24.3
2025 2030
0.0
2.2
0.2
O.I
23.4
0.7
0.2
6.8
0.8
1.2
1.3
0.5
0.9
0.9
0.0
0.2
-
0.3
O.I
2.5
0.3
1.0
0.6
O.I
-
1.5
2.0
-
-
O.I
0.0
0.2
6.5
O.I
0.0
O.I
6.4
19.5
4.0
O.I
1.3
0.3
56.0
12.5
0.4
28.2
121.2
3.5
12.2
25.4
0.0
2.3
0.2
O.I
24.5
0.9
0.2
6.9
0.8
1.2
1.4
0.6
0.9
1.0
0.0
0.2
-
0.3
O.I
2.6
0.3
1.2
0.7
O.I
-
1.5
2.3
-
-
O.I
0.0
0.2
7.4
O.I
0.0
O.I
6.5
20.4
4.3
O.I
1.3
0.3
58.6
13.3
0.5
31.9
121. 1
3.7
13.7
26.7
176.5 184.9 189.4 197.9 203.7 209.1 215.1 221.8 229.2
August 2011
Appendices
Page B-12
-------
Table B-7: N2O Emissions from Biomass Combustion by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
o6~
0.0
O.I
0.0
0.2
O.I
0.0
0.4
0.0
0.0
0.0
1.6
0.0
0.4
-
0.5
O.I
10.4
0.3
0.5
0.0
0.0
O.I
0.0
O.I
0.0
0.7
O.I
0.6
0.0
O.I
0.0
0.0
-
6.9
2.1
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
-
0.0
0.0
0.0
-
0.4
0.0
0.0
|Q9
0.0
0.0
O.I
0.0
0.2
O.I
0.0
0.4
0.0
0.0
0.0
1.6
0.0
0.5
O.I
0.5
0.2
10.5
0.3
0.6
0.0
0.0
O.I
0.0
O.I
0.0
0.8
O.I
0.6
0.0
O.I
0.0
0.0
-
7.3
2.3
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
-
O.I
0.0
0.0
0.0
0.4
0.0
0.0
^fT^^H
oo~
0.0
O.I
0.0
0.2
O.I
0.0
0.4
0.0
0.0
0.0
1.7
0.0
0.5
0.2
0.6
0.2
10.3
0.2
0.7
0.0
0.0
O.I
0.0
O.I
0.0
0.9
0.2
0.6
0.0
0.2
O.I
0.0
-
7.7
2.5
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.0
-
0.0
-
0.0
0.0
0.0
0.0
0.4
0.0
0.0
2005 2010
0.0
0.0
O.I
0.0
0.2
0.2
0.0
0.4
O.I
0.0
0.0
2.3
0.0
0.5
0.2
0.6
0.2
9.9
0.2
0.8
0.0
O.I
O.I
0.0
O.I
0.0
1.0
0.2
0.6
0.0
0.3
0.0
O.I
-
8.1
2.6
0.0
0.0
0.0
0.0
0.4
0.0
0.0
0.0
-
0.0
-
O.I
0.0
0.0
0.0
0.4
0.0
0.0
0.0
0.0
O.I
-
0.2
0.2
0.0
0.4
O.I
0.0
0.0
2.8
0.0
0.5
0.2
0.7
0.2
9.5
0.2
0.8
0.0
O.I
O.I
0.0
O.I
0.0
I.I
0.2
0.6
0.0
0.5
0.0
O.I
-
8.4
2.6
0.0
0.0
0.0
0.0
0.6
0.0
0.0
0.0
-
0.0
-
O.I
0.0
0.0
0.0
0.4
0.0
0.0
0.0
0.0
O.I
-
0.3
0.3
0.0
0.5
O.I
0.0
0.0
2.9
0.0
0.5
0.2
0.7
0.2
9.3
0.2
0.9
0.0
O.I
O.I
0.0
O.I
0.0
I.I
0.2
0.7
0.0
0.5
O.I
O.I
-
8.6
2.7
O.I
0.0
0.0
0.0
0.6
0.0
0.0
0.0
-
0.0
-
O.I
0.0
0.0
0.0
0.4
0.0
0.0
1
oo~
0.0
O.I
-
0.3
0.3
0.0
0.5
O.I
O.I
0.0
3.1
0.0
0.5
0.2
0.8
0.3
9.0
0.2
0.9
0.0
O.I
O.I
0.0
O.I
0.0
I.I
0.2
0.8
0.0
0.6
O.I
O.I
-
8.8
2.7
O.I
0.0
0.0
0.0
0.7
0.0
0.0
0.0
-
0.0
-
O.I
0.0
0.0
0.0
0.5
0.0
0.0
0.0
0.0
O.I
-
0.4
0.3
0.0
0.5
O.I
O.I
0.0
3.3
0.0
0.5
0.2
1.0
0.3
8.7
0.3
1.0
0.0
O.I
0.2
0.0
O.I
0.0
1.2
0.3
0.9
0.0
0.7
O.I
O.I
-
9.0
2.7
O.I
0.0
0.0
0.0
0.8
0.0
0.0
0.0
-
0.0
-
O.I
0.0
0.0
0.0
0.6
0.0
0.0
o6~
0.0
O.I
-
0.4
0.4
0.0
0.5
O.I
O.I
O.I
3.6
O.I
0.5
0.2
I.I
0.3
8.5
0.3
1.0
0.0
0.2
0.2
0.0
O.I
0.0
1.3
0.3
1.0
0.0
0.8
O.I
0.2
-
9.1
2.8
O.I
0.0
0.0
0.0
0.9
0.0
0.0
0.0
-
0.0
-
O.I
0.0
0.0
0.0
0.6
0.0
0.0
August 2011
Appendices
Page B-13
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
0.0
0.3
0.0
0.0
2.8
0.0
0.0
1.0
O.I
0.4
O.I
O.I
0.0
0.9
0.0
0.0
-
0.0
0.0
0.4
0.0
0.2
0.2
0.0
-
0.3
0.3
-
-
0.0
-
0.0
2.6
0.0
0.0
0.0
0.9
2.2
0.9
0.0
0.3
O.I
6.7
3.1
0.0
OECD | 6.2
Non-OECD Asia 23.5
Non-OECD Europe & | 1.2
EU 2.0
OPEC | 3.1
World Totals
0.0
0.3
0.0
0.0
3.2
0.0
O.I
I.I
O.I
0.4
0.2
O.I
O.I
0.5
0.0
0.0
-
0.0
0.0
0.4
O.I
0.2
0.3
0.0
-
0.4
0.3
-
-
0.0
0.0
0.0
2.8
0.0
0.0
0.0
1.0
2.5
0.7
0.0
0.3
0.0
7.6
3.0
0.0
6.8
24.6
0.8
2.4
3.5
0.0
0.4
O.I
0.0
3.6
O.I
0.0
1.2
O.I
0.3
0.2
O.I
O.I
0.4
0.0
0.0
-
0.0
0.0
0.4
O.I
0.2
0.3
0.0
-
0.4
0.3
-
-
0.0
0.0
0.0
2.7
0.0
0.0
0.0
I.I
2.8
0.8
0.0
0.3
O.I
8.5
3.0
0.0
7.0
25.3
0.9
2.7
3.9
2005 2010 2015 2020
0.0
0.4
O.I
O.I
4.1
O.I
0.0
1.4
O.I
0.3
0.2
O.I
0.2
0.4
0.0
0.0
-
0.0
0.0
0.5
O.I
0.2
0.4
0.0
-
0.5
0.3
-
-
0.0
0.0
0.0
2.5
0.0
0.0
0.0
1.2
3.2
0.8
0.0
0.3
O.I
9.7
3.6
0.0
7.4
25.8
1.0
3.4
4.4
0.0
0.4
O.I
O.I
4.4
O.I
O.I
1.4
O.I
0.3
0.3
0.2
0.2
0.3
0.0
0.0
-
0.0
0.0
0.5
O.I
0.2
0.4
0.0
-
0.5
0.3
-
-
0.0
0.0
O.I
2.8
0.0
0.0
0.0
1.2
3.4
0.8
0.0
0.3
O.I
10.3
4.1
O.I
8.5
26.0
0.8
4.1
4.8
0.0
0.4
O.I
O.I
4.6
O.I
O.I
1.5
O.I
0.3
0.3
0.2
0.2
0.3
0.0
0.0
-
0.0
0.0
0.5
O.I
0.2
0.5
0.0
-
0.5
0.3
-
-
0.0
0.0
O.I
3.1
0.0
0.0
0.0
1.2
3.6
0.9
0.0
0.3
O.I
10.8
4.4
O.I
9.6
26.0
0.9
4.6
5.0
0.0
0.4
O.I
O.I
4.8
O.I
O.I
1.5
O.I
0.3
0.3
0.2
0.2
0.4
0.0
0.0
-
0.0
0.0
0.5
O.I
0.3
0.5
0.0
-
0.5
0.4
-
-
0.0
0.0
O.I
3.6
0.0
0.0
0.0
1.2
3.7
0.9
0.0
0.3
O.I
11.3
4.7
O.I
10.9
26.0
0.9
5.2
5.2
2025 2030
0.0
0.4
0.2
O.I
5.0
0.2
O.I
1.5
0.2
0.3
0.4
0.2
0.2
0.4
0.0
0.0
-
O.I
0.0
0.6
O.I
0.3
0.6
0.0
-
0.5
0.4
-
-
0.0
0.0
O.I
4.0
0.0
0.0
0.0
1.3
3.9
1.0
0.0
0.3
O.I
11.8
5.0
O.I
12.3
26.0
1.0
5.8
5.5
0.0
0.4
0.2
O.I
5.3
0.2
O.I
1.5
0.2
0.3
0.4
0.3
0.2
0.4
0.0
0.0
-
O.I
0.0
0.6
O.I
0.4
0.7
0.0
-
0.5
0.5
-
-
0.0
0.0
O.I
4.6
0.0
0.0
0.0
1.3
4.1
1.0
0.0
0.3
O.I
12.4
5.3
O.I
14.0
26.1
I.I
6.6
5.7
40.7 42.9 44.8 47.5 49.8 51.8 53.9 56.3 58.9
August 2011
Appendices
Page B-14
-------
Table B-8: CH4 Emissions from Other Energy Sources by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.2
-
0.0
-
-
-
-
-
0.2
0.0
-
-
-
-
-
-
0.0
0.0
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.2
-
0.0
-
-
-
-
-
0.3
0.0
-
-
-
-
-
-
0.0
0.0
MtCOie
2000 2005 2010
.
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.2
-
0.0
-
-
-
-
-
0.3
0.0
-
-
-
-
-
-
0.0
0.0
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.2
-
0.0
-
-
-
-
-
0.3
0.0
-
-
-
-
-
-
0.0
0.0
. .
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.2
-
0.0
-
-
-
-
-
0.3
0.0
-
-
-
-
-
-
0.0
0.0
^^j££^J
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.2
-
0.0
-
-
-
-
-
0.3
0.0
-
-
-
-
-
-
0.0
0.0
Ki3H EZ!z±9
^^^^^_
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.2
-
0.0
-
-
-
-
-
0.3
0.0
-
-
-
-
-
-
0.0
0.0
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.2
-
0.0
-
-
-
-
-
0.3
0.0
-
-
-
-
-
-
0.0
0.0
-^^B-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.2
-
0.0
-
-
-
-
-
0.3
0.0
-
-
-
-
-
-
0.0
0.0
August 2011
Appendices
Page B-15
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
-
-
-
0.0
-
-
0.0
-
-
-
-
0.0
-
-
-
-
-
-
-
-
-
0.0
-
0.0
-
-
-
-
-
-
-
O.I
-
-
-
-
-
0.0
-
-
-
0.0
0.0
-
-
-
-
0.0
-
-
0.0
-
-
-
-
0.0
-
-
-
-
-
-
-
-
-
0.0
-
0.0
-
-
-
-
-
-
-
O.I
-
-
-
-
-
0.0
-
-
-
0.0
0.0
-
-
-
-
0.0
-
-
0.0
-
-
-
-
0.0
-
-
-
-
-
-
-
-
-
0.0
-
0.0
-
-
-
-
-
-
-
0.0
-
-
-
-
-
0.0
-
-
-
0.0
0.0
-
2005 2010 2015 2020
-
-
-
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
-
-
-
-
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
-
2025 2030
-
-
-
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
-1 -1 -1 -1 -1 -1 -1
OECD | 0.5 | 0.6 1 0.5 | 0.5
Non-OECD Asia
Non-OECD Europe & | 0.0
EU 0.5
OPEC |
World Totals
0.0
0.5
-
0.0
0.5
-
0.0
0.5
-
0.5
-
0.0
0.5
-
0.5
-
0.0
0.5
-
0.5
-
0.0
0.5
-
0.5
-
0.0
0.5
-
0.5
-
0.0
0.5
-
0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5
August 2011
Appendices
Page B-16
-------
Table B-9: N2O Emissions from Other Energy Sources by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
^^^^|
0.0
0.0
0.0
-
0.0
0.0
-
-
-
0.2
-
-
-
-
0.0
0.0
-
0.0
0.0
0.2
0.0
0.0
0.0
-
-
-
-
-
O.I
1.5
-
-
-
0.0
-
0.0
-
0.0
^T^B^
k£J^H
0.0
0.0
0.0
-
0.0
0.0
-
-
-
O.I
-
-
-
-
0.0
0.0
-
O.I
0.0
O.I
0.0
0.0
0.0
-
-
-
-
-
O.I
2.0
-
-
-
0.0
-
0.0
-
0.0
2000 2005 2010
0.0
0.0
0.0
-
0.0
0.0
-
-
-
O.I
-
-
-
-
0.0
0.0
-
O.I
0.0
0.2
0.0
0.0
0.0
-
-
-
-
-
O.I
2.2
-
-
-
0.0
-
0.0
-
0.0
0.0
0.0
0.0
-
0.0
0.0
-
-
-
O.I
-
-
-
-
0.0
0.0
-
0.0
0.0
0.2
0.0
0.0
0.0
-
-
-
-
-
O.I
2.4
-
-
-
0.0
-
0.0
-
0.0
0.0
0.0
0.0
-
0.0
0.0
-
-
-
O.I
-
-
-
-
0.0
0.0
-
0.0
0.0
0.2
0.0
0.0
0.0
-
-
-
-
-
O.I
2.3
-
-
-
0.0
-
0.0
-
0.0
0.0
0.0
0.0
-
0.0
0.0
-
-
-
O.I
-
-
-
-
0.0
0.0
-
0.0
0.0
0.2
0.0
0.0
0.0
-
-
-
-
-
O.I
2.3
-
-
-
0.0
-
0.0
-
0.0
1
0.0
0.0
0.0
-
0.0
0.0
-
-
-
O.I
-
-
-
-
0.0
0.0
-
0.0
0.0
0.2
0.0
0.0
0.0
-
-
-
-
-
O.I
2.3
-
-
-
0.0
-
0.0
-
0.0
0.0
0.0
0.0
-
0.0
0.0
-
-
-
O.I
-
-
-
-
0.0
0.0
-
0.0
0.0
0.2
0.0
0.0
0.0
-
-
-
-
-
O.I
2.3
-
-
-
0.0
-
0.0
-
0.0
0.0
0.0
0.0
-
0.0
0.0
-
-
-
O.I
-
-
-
-
0.0
0.0
-
0.0
0.0
0.2
0.0
0.0
0.0
-
-
-
-
-
O.I
2.3
-
-
-
0.0
-
0.0
-
0.0
August 2011
Appendices
Page B-17
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
-
-
0.0
0.0
-
-
0.0
-
-
-
0.0
0.0
-
O.I
-
-
-
0.0
-
-
0.3
0.0
0.0
0.0
-
-
-
-
-
0.0
-
O.I
-
-
-
0.0
-
0.0
-
-
-
0.0
0.0
0.0
-
-
0.0
0.0
-
-
0.0
-
-
-
0.0
0.0
-
O.I
-
-
-
0.0
-
-
0.3
0.0
0.0
0.0
-
-
-
-
-
0.0
-
O.I
-
-
-
0.0
-
0.0
-
-
-
0.0
0.0
0.0
-
-
0.0
0.0
-
-
0.0
-
-
-
0.0
0.0
-
O.I
-
-
-
0.0
-
-
0.3
0.0
0.0
0.0
-
-
-
-
-
0.0
-
O.I
-
-
-
0.0
-
0.0
-
-
-
0.0
0.0
0.0
2005 2010 2015 2020
-
-
0.0
0.0
-
-
0.0
-
-
-
0.0
0.0
-
O.I
-
-
-
0.0
-
-
0.3
0.0
0.0
0.0
-
-
-
-
-
0.0
-
O.I
-
-
-
0.0
-
0.0
-
-
-
0.0
0.0
0.0
1 1
OECD | 2.4 1 2.9 1 3.2 | 3.4
Non-OECD Asia - - - 1
Non-OECD Europe & | 0. 1
EU 0.4
OPEC | 0.0
World Totals
O.I
0.5
0.0
0.2
0.6
0.0
O.I
0.5
0.0
-
-
0.0
0.0
-
-
0.0
-
-
-
0.0
0.0
-
O.I
-
-
-
0.0
-
-
0.3
0.0
0.0
0.0
-
-
-
-
-
0.0
-
O.I
-
-
-
0.0
-
0.0
-
-
-
0.0
0.0
0.0
-
3.2
-
O.I
0.5
0.0
-
-
0.0
0.0
-
-
0.0
-
-
-
0.0
0.0
-
O.I
-
-
-
0.0
-
-
0.3
0.0
0.0
0.0
-
-
-
-
-
0.0
-
O.I
-
-
-
0.0
-
0.0
-
-
-
0.0
0.0
0.0
-
3.2
-
O.I
0.5
0.0
-
-
0.0
0.0
-
-
0.0
-
-
-
0.0
0.0
-
O.I
-
-
-
0.0
-
-
0.3
0.0
0.0
0.0
-
-
-
-
-
0.0
-
O.I
-
-
-
0.0
-
0.0
-
-
-
0.0
0.0
0.0
-
3.2
-
O.I
0.5
0.0
2025 2030
-
-
0.0
0.0
-
-
0.0
-
-
-
0.0
0.0
-
O.I
-
-
-
0.0
-
-
0.3
0.0
0.0
0.0
-
-
-
-
-
0.0
-
O.I
-
-
-
0.0
-
0.0
-
-
-
0.0
0.0
0.0
-
3.2
-
O.I
0.5
0.0
-
-
0.0
0.0
-
-
0.0
-
-
-
0.0
0.0
-
O.I
-
-
-
0.0
-
-
0.3
0.0
0.0
0.0
-
-
-
-
-
0.0
-
O.I
-
-
-
0.0
-
0.0
-
-
-
0.0
0.0
0.0
-
3.2
.
O.I
0.5
0.0
2.6 3.1 3.4 3.5 3.4 3.4 3.4 3.4 3.4
August 2011
Appendices
Page B-18
-------
Appendix C: Industrial Processes Sector Emissions
Table C-l: Total Non-CO2 Emissions from the Industrial Processes Sector by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
0.0
0.2
1.8
0.0
5.7
2.6
0.0
0.0
0.4
3.8
0.0
7.1
2.4
0.0
0.0
21.0
0.2
9.9
O.I
0.0
1.8
1.6
I.I
0.0
I.I
0.0
-
1.8
28.1
0.7
32.1
2.3
3.7
0.4
4.1
0.7
0.8
0.0
I.I
0.6
10.0
39.6
0.0
0.3
0.0
0.0
0.0
0.0
0.8
0.0
1.5
6.0
O.I
0.0
0.4
0.7
0.0
3.8
2.3
0.0
0.0
0.3
4.9
0.0
17.0
2.1
0.0
0.0
21.4
0.4
16.0
0.2
0.0
0.9
1.5
1.2
0.0
I.I
0.0
0.0
1.7
29.7
0.3
34.7
4.4
1.8
O.I
11.0
0.5
I.I
0.0
I.I
1.2
11.7
51.2
0.0
0.4
O.I
0.0
0.0
O.I
0.6
0.0
O.I
3.6
0.0
^EiiH ^Emil^l
^^^^yj^^^M
0.0 1 0.0 | 0.0
1.2
1.2
O.I
5.2
2.3
0.0
O.I
0.4
5.5
0.0
13.9
1.6
0.0
0.0
15.2
0.5
51.7
0.4
0.0
1.0
1.7
1.7
O.I
1.2
0.0
0.0
1.7
19.3
0.6
17.1
5.1
2.6
0.2
22.0
0.5
1.7
0.0
1.4
3.7
15.5
54.2
O.I
0.3
0.4
0.0
0.0
0.2
1.5
O.I
0.3
8.3
O.I
1.7
2.3
0.2
8.0
1.7
0.2
0.2
0.7
4.8
0.0
16.5
1.4
0.0
O.I
18.4
0.9
147.0
0.9
0.0
1.0
1.9
1.0
0.2
2.0
0.0
0.0
2.1
17.1
0.8
29.7
3.4
3.0
O.I
42.0
1.0
2.6
O.I
0.6
6.1
17.9
48.9
0.3
0.7
1.0
0.0
0.0
0.4
2.4
O.I
0.6
17.7
0.2
3.7
4.9
0.3
10.0
2.1
0.3
0.5
0.8
3.6
0.0
18.4
1.9
0.0
O.I
18.5
1.2
253.4
1.4
0.0
1.4
2.1
I.I
0.4
2.6
0.0
0.0
2.2
16.4
1.0
27.1
1.2
2.5
O.I
101.2
1.7
3.4
O.I
0.5
8.0
12.7
49.8
0.5
0.9
1.7
0.0
0.0
0.6
3.2
O.I
I.I
40.3
0.3
0.0
6.1
6.2
0.4
13.3
2.4
0.4
0.9
0.9
4.2
O.I
23.1
2.2
0.0
0.2
23.6
1.6
328.3
1.9
0.0
1.7
2.5
1.5
0.6
3.4
0.0
0.0
2.5
19.7
1.2
23.5
1.4
3.1
0.2
114.7
2.7
4.5
O.I
0.7
11.4
16.0
63.2
0.9
1.0
2.7
O.I
0.0
0.8
3.6
O.I
1.6
47.2
0.3
^vTjyTjV ^BTiM^B"
ON ^oT
7.8
7.8
0.6
16.3
2.8
0.6
I.I
1.0
5.0
O.I
29.4
2.4
0.0
0.2
29.1
2.1
470.9
2.5
O.I
2.0
2.9
1.9
0.8
4.6
O.I
O.I
2.8
23.8
1.5
30.0
1.7
3.7
0.2
137.3
3.6
5.9
O.I
0.8
15.4
20.1
80.4
1.2
1.2
4.1
O.I
0.0
I.I
3.9
0.2
2.0
56.6
0.4
10.6
10.9
0.9
20.2
3.2
0.8
1.5
1.3
6.2
O.I
41.1
2.9
O.I
0.4
36.7
3.0
770.4
4.0
O.I
2.4
3.7
2.3
1.2
6.9
O.I
O.I
3.1
28.3
2.1
39.0
2.1
4.6
0.3
178.5
5.5
8.7
0.2
1.0
23.5
25.0
103.9
1.7
1.5
7.5
O.I
0.0
1.8
4.7
0.2
3.0
73.4
0.7
O.I
12.8
13.5
1.2
22.9
3.4
1.0
1.8
1.5
7.5
O.I
50.8
3.2
O.I
0.6
41.2
3.8
1,174.4
5.1
O.I
2.7
4.2
2.6
1.5
8.6
O.I
0.2
3.3
30.9
2.6
49.5
2.5
5.2
0.3
235.8
7.2
10.6
0.2
I.I
28.8
28.8
125.2
2.2
1.7
10.6
O.I
0.0
2.2
5.2
0.2
3.5
92.7
0.8
August 2011
Appendices
Page C-l
-------
Country
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
1990 1995 2000
-
0.0
0.0
13.7
0.7
0.0
0.4
7.7
O.I
0.0
O.I
4.3
0.6
7.1
36.9
0.4
-
0.2
1.4
0.3
3.0
7.1
6.7
1.4
0.5
0.0
O.I
0.7
0.0
-
2.9
0.6
30.5
131.7
0.0
1.9
5.2
0.0
60.6
1.4
0.7
O.I
O.I
64.9
15.7
Middle East 2.6
OECD | 369.2
Non-OECD Asia 15.7
0.0
0.0
0.0
15.2
0.4
0.0
0.4
4.3
O.I
0.3
0.2
4.9
0.8
5.4
29.1
0.5
0.0
0.9
1.3
0.3
4.9
9.7
9.5
1.5
0.5
3.1
0.3
5.7
0.0
0.0
1.7
0.6
20.5
151.8
0.0
1.6
3.7
0.0
60.3
0.6
0.7
0.2
0.3
66.7
22.5
3.0
403.3
29.6
Non-OECD Europe & | 5^" 46.4
EU 157.4 157.4
OPEC 1 7.4 6.5
0.0
0.0
0.0
11.8
0.7
O.I
0.6
4.3
0.3
0.4
0.8
5.6
0.8
4.0
42.1
1.2
0.0
1.9
1.2
0.3
6.5
20.3
12.2
1.7
0.7
2.8
0.9
5.1
0.0
0.0
1.7
0.8
13.1
170.1
O.I
1.5
2.7
O.I
60.9
1.2
1.0
I.I
1.5
69.9
20.0
5.1
409.2
80.0
59.7
128.0
8.2
2005 2010 2015 2020
0.0
0.0
0.0
8.6
1.0
0.2
1.2
3.7
0.7
0.6
2.1
7.1
I.I
4.1
47.0
2.7
0.0
2.3
1.7
0.4
8.8
30.9
6.5
1.9
1.0
2.8
2.3
3.3
0.0
0.0
2.4
0.8
12.0
174.2
0.3
1.9
3.6
0.3
61.6
2.7
1.9
2.9
1.8
74.4
27.1
9.4
436.8
202.1
68.7
131.0
13.3
0.0
0.0
0.0
6.3
1.3
0.2
1.8
2.6
I.I
0.8
3.4
8.4
0.7
4.0
53.1
4.3
O.I
2.9
0.5
0.6
12.9
38.8
5.8
1.7
1.2
1.7
4.2
2.0
0.0
0.0
2.6
I.I
12.1
199.4
0.5
2.1
6.6
0.7
62.8
4.6
3.1
5.0
1.4
82.3
37.5
14.4
481.0
375.8
76.7
117.9
22.3
0.0
0.0
O.I
7.1
1.7
0.4
2.7
2.9
1.5
I.I
5.1
9.9
0.9
4.3
61.9
6.4
O.I
4.1
0.6
0.7
17.8
49.5
7.3
2.0
1.5
1.9
6.9
2.6
O.I
0.0
3.0
1.7
14.8
253.6
0.7
2.3
8.3
I.I
65.0
6.7
5.0
8.4
1.8
93.0
48.6
21.2
593.3
476.6
89.6
132.4
31.9
0.0
0.0
O.I
8.2
2.1
0.7
4.2
3.4
2.1
1.4
7.7
11.3
1.2
4.6
73.7
8.9
0.2
6.3
0.8
0.9
23.6
64.8
9.2
2.5
1.8
2.2
10.8
3.5
O.I
0.0
3.4
2.2
18.2
316.3
1.0
2.5
10.9
1.5
67.7
9.2
6.7
13.8
2.4
104.7
63.0
29.2
740.1
659.7
105.9
160.9
42.9
2025 2030
0.0
O.I
0.2
9.7
2.6
1.3
7.7
4.1
3.5
2.2
13.5
13.3
1.4
5.1
102.4
14.0
0.3
11.2
I.I
1.3
35.5
95.8
11.7
3.0
2.2
2.5
19.9
5.3
O.I
O.I
4.4
3.4
21.6
393.5
1.5
2.9
16.3
2.1
73.4
14.5
10.3
26.2
3.3
128.2
92.0
45.8
946.3
1,040.6
0.0
O.I
0.2
11.4
2.9
1.9
11.2
5.0
4.6
2.8
18.9
14.5
1.6
5.4
125.5
18.1
0.3
17.8
1.3
1.5
43.7
122.6
13.9
3.3
2.5
2.8
28.6
6.7
O.I
O.I
5.0
4.8
23.5
440.7
2.0
3.1
20.9
2.7
78.1
18.5
13.6
41.1
4.1
145.7
115.2
60.0
1,105.4
1,545.1
142.9 172.0
197.3 227.6
65.4 84. 1
World Totals 525.2 571.6 644.0 818.5 1,067.7 1,322.4 1,702.6 2,395.8 3,143.4
August 2011
Appendices
Page C-2
-------
Table C-2: N2O Emissions from Adipic Acid and Nitric Acid Production by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
0.0
0.2
O.I
-
-
0.9
-
-
0.3
3.6
-
2.6
2.3
-
-
11.7
O.I
4.6
O.I
-
0.8
I.I
1.0
-
0.3
-
-
1.7
21.4
0.7
23.5
I.I
3.2
-
2.2
0.0
0.5
-
1.0
0.5
6.7
8.3
-
-
-
-
-
-
0.8
-
-
0.5
-
-
0.0
0.3
O.I
-
-
0.9
-
-
0.3
4.3
-
4.1
1.9
-
-
11.7
0.2
5.2
O.I
-
0.7
1.0
0.9
-
0.3
-
-
1.5
22.9
0.3
25.0
0.9
1.3
-
2.8
0.0
0.7
-
0.8
0.5
7.2
8.2
-
-
-
-
-
-
0.5
-
O.I
0.5
-
-
0.0
0.7
O.I
-
-
1.0
-
-
0.3
4.2
-
5.4
1.3
-
-
2.1
0.3
5.3
O.I
-
0.7
1.0
1.0
-
0.3
-
-
1.4
9.3
0.5
5.5
0.8
1.8
-
3.1
0.0
0.9
-
0.8
0.4
7.9
4.7
-
-
-
-
-
-
1.3
-
O.I
0.3
-
-
2005 2010
0.0
0.6
0.2
-
-
0.3
-
-
0.5
3.1
-
5.9
1.0
-
-
3.9
0.3
6.4
O.I
-
0.7
1.0
-
-
0.3
-
-
1.6
5.8
0.6
14.3
0.5
1.7
-
3.3
0.0
0.9
-
-
0.4
7.8
1.3
-
-
-
-
-
-
2.0
-
O.I
O.I
-
-
0.0
0.6
0.3
-
-
0.3
-
-
0.4
1.6
-
6.9
1.3
-
-
3.0
0.3
7.3
O.I
-
0.8
0.8
-
-
0.3
-
-
1.7
4.4
0.7
9.9
0.4
0.9
-
3.8
0.0
1.0
-
-
0.4
1.5
0.9
-
-
-
-
-
-
2.6
-
O.I
O.I
-
-
0.0
0.7
0.3
-
-
0.3
-
-
0.4
1.6
-
7.9
1.3
-
-
3.5
0.3
8.0
O.I
-
0.8
0.8
-
-
0.3
-
-
1.8
4.5
0.7
1.6
0.4
0.9
-
4.0
0.0
1.2
-
-
0.4
1.4
1.0
-
-
-
-
-
-
2.7
-
O.I
O.I
-
-
ooj oo"
0.7
0.3
-
-
0.3
-
-
0.4
1.6
-
9.0
1.3
-
-
4.0
0.3
8.7
O.I
-
0.8
0.8
-
-
0.3
-
-
1.9
4.6
0.7
1.7
0.4
1.0
-
4.2
0.0
1.3
-
-
0.5
1.5
I.I
-
-
-
-
-
-
2.7
-
O.I
O.I
-
-
0.8
0.3
-
-
0.3
-
-
0.4
1.7
-
10.3
1.3
-
-
4.6
0.3
9.5
O.I
-
0.8
0.8
-
-
0.3
-
-
2.0
4.6
0.7
1.8
0.4
1.0
-
4.4
0.0
1.4
-
-
0.5
1.5
1.2
-
-
-
-
-
-
2.8
-
O.I
O.I
-
-
o6~
0.9
0.3
-
-
0.3
-
-
0.4
1.7
-
11.6
1.3
-
-
5.3
0.3
9.9
O.I
-
0.8
0.8
-
-
0.3
-
-
2.1
4.7
0.7
1.8
0.4
1.0
-
3.9
0.0
1.2
-
-
0.4
1.5
1.3
-
-
-
-
-
-
2.8
-
O.I
O.I
-
-
August 2011
Appendices
Page C-3
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
-
-
6.3
-
-
0.4
2.1
-
-
-
3.5
0.6
5.0
3.6
-
-
-
I.I
-
1.8
5.8
2.9
0.8
0.2
-
-
O.I
-
-
-
-
24.6
35.3
-
1.8
0.5
-
0.2
1.0
-
-
-
2.5
4.3
0.5
OECD | 169.8
Non-OECD Asia 7.1
Non-OECD Europe & | 15.2
EU 113.1
OPEC | I.I
World Totals
-
-
6.3
-
-
0.3
1.6
-
0.2
-
3.9
0.6
3.6
2.1
-
-
0.6
I.I
-
2.3
5.2
2.4
0.7
0.2
-
-
5.1
-
-
-
-
14.9
39.6
-
1.6
0.4
-
0.3
O.I
-
-
-
3.2
4.9
0.7
169.5
8.9
II. 1
102.6
1.4
-
-
5.9
-
-
0.4
1.7
-
0.2
-
4.0
0.4
3.4
2.4
-
-
0.7
1.0
0.0
2.4
6.7
2.3
0.6
O.I
-
-
4.3
-
-
-
-
5.5
28.1
-
1.4
0.3
-
0.5
0.0
-
-
-
4.0
6.1
0.9
103.3
9.5
11.4
60.7
1.9
2005 2010 2015 2020
-
-
5.7
-
-
0.5
2.0
-
0.2
-
4.5
0.6
3.2
3.2
-
-
0.8
1.3
0.0
1.9
9.2
1.9
0.4
O.I
-
-
1.8
-
-
-
-
2.8
24.6
-
1.6
0.4
-
O.I
0.0
-
-
-
2.9
6.8
0.9
96.7
II. 1
12.7
59.4
1.9
-
-
3.1
-
-
0.7
1.4
-
0.2
-
4.6
O.I
3.0
3.5
-
-
0.8
0.0
-
2.4
11.8
O.I
0.3
O.I
-
-
-
-
-
-
-
2.6
28.6
-
1.6
0.3
-
O.I
0.0
-
-
-
3.4
7.8
1.0
78.8
12.6
13.9
39.2
2.0
-
-
3.0
-
-
0.8
1.4
-
0.2
-
4.6
O.I
3.2
3.5
-
-
0.9
0.0
-
2.6
13.9
O.I
0.3
O.I
-
-
-
-
-
-
-
2.4
30.5
-
1.7
0.4
-
O.I
0.0
-
-
-
3.6
8.8
1.2
75.1
13.6
14.3
31.1
2.2
-
-
3.0
-
-
1.0
1.4
-
0.2
-
4.6
O.I
3.2
3.6
-
-
0.9
0.0
-
2.7
16.4
O.I
0.3
O.I
-
-
-
-
-
-
-
2.4
32.4
-
1.7
0.4
-
O.I
0.0
-
-
-
3.8
10.0
1.3
80.5
14.8
14.5
31.4
2.4
2025 2030
-
-
3.0
-
-
I.I
1.4
-
0.2
-
4.6
O.I
3.3
3.7
-
-
I.I
0.0
-
2.8
19.4
O.I
0.3
O.I
-
-
-
-
-
-
-
2.3
34.5
-
1.7
0.4
-
O.I
0.0
-
-
-
4.1
11.3
1.4
86.6
16.1
14.7
31.8
2.6
-
-
3.0
-
-
1.3
1.4
-
0.2
-
4.6
O.I
3.3
3.7
-
-
1.3
0.0
-
2.4
22.8
O.I
0.3
O.I
-
-
-
-
-
-
-
2.3
36.7
-
1.6
0.3
-
O.I
0.0
-
-
-
3.7
12.6
1.2
93.1
16.5
14.7
32.1
2.4
199.4 198.3 135.3 131. 1 117.5 116.7 124.9 134.3 141.7
August 2011
Appendices
Page C-4
-------
Table C-3: HFC and PFC Emissions from Use of Substitutes for Ozone-Depleting Substances by Country
(MtCO2e)
Country |99Q |995 200Q 20Q5 20|Q 2Q|5 202Q 2Q25 203Q
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0
O.I
O.I
0.0
I.I
0.3
0.0
0.0
0.0
0.4
0.0
0.3
0.0
0.0
0.0
2.2
0.0
1.0
O.I
0.0
0.0
O.I
0.3
0.0
O.I
0.0
0.0
O.I
2.4
0.0
3.4
0.2
O.I
0.0
O.I
0.0
O.I
-
O.I
0.6
2.3
9.6
0.0
0.0
O.I
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.4
0.5
O.I
3.5
0.6
0.0
0.0
O.I
0.9
0.0
1.7
0.2
0.0
0.0
6.6
0.2
7.6
0.3
0.0
O.I
0.3
0.6
O.I
0.4
0.0
0.0
0.3
5.8
O.I
7.9
0.4
0.3
0.0
0.6
0.2
0.4
-
0.2
2.3
5.5
24.2
O.I
0.0
0.4
0.0
0.0
O.I
O.I
0.0
0.2
1.9
O.I
0.0
1.0
1.2
0.2
5.8
0.9
O.I
O.I
O.I
1.4
0.0
3.8
0.3
0.0
O.I
9.6
0.4
23.5
0.7
0.0
0.3
0.6
0.9
0.2
1.0
0.0
0.0
0.4
8.7
0.2
11.8
0.6
0.8
O.I
1.7
0.5
1.0
-
0.3
4.6
8.3
38.2
0.2
O.I
0.9
0.0
0.0
0.3
0.4
O.I
0.5
4.3
O.I
0.0
3.0
2.3
0.3
7.8
I.I
0.2
0.4
0.2
1.6
0.0
6.5
0.5
0.0
O.I
10.6
0.7
58.2
I.I
0.0
0.6
1.0
I.I
0.3
1.6
0.0
0.0
0.5
10.3
0.4
13.8
0.7
1.4
O.I
5.5
1.2
1.7
-
0.4
6.9
9.8
41.8
0.5
0.2
1.6
0.0
0.0
0.6
0.6
O.I
1.0
8.1
0.2
0.0
5.3
3.5
0.4
10.9
1.4
0.3
0.8
0.3
2.2
0.0
9.7
0.8
0.0
0.2
14.7
I.I
110.0
1.6
0.0
0.9
1.4
1.4
0.5
2.4
0.0
0.0
0.6
13.7
0.6
18.2
1.0
2.0
O.I
11.3
2.0
2.6
-
0.6
10.1
13.0
53.9
0.8
0.3
2.5
0.0
0.0
0.8
0.9
O.I
1.5
12.5
0.3
0.0
6.9
4.7
0.6
13.6
1.8
0.5
1.0
0.5
2.8
0.0
14.1
1.0
0.0
0.2
19.2
1.6
187.8
2.2
0.0
I.I
1.8
1.8
0.7
3.5
O.I
O.I
0.8
17.8
0.8
23.4
1.2
2.6
O.I
18.0
2.9
3.8
-
0.7
14.0
16.8
69.3
I.I
0.5
4.0
0.0
0.0
I.I
1.2
0.2
2.0
16.7
0.4
O.I
9.6
7.2
0.9
17.2
2.2
0.7
1.3
0.7
3.5
O.I
23.7
1.4
0.0
0.4
25.6
2.4
377.6
3.7
0.0
1.6
2.6
2.3
1.2
5.7
O.I
O.I
I.I
22.2
1.4
29.2
1.5
3.5
O.I
32.8
4.5
6.3
-
0.9
21.9
21.0
89.1
1.7
0.7
7.3
0.0
0.0
1.7
1.9
0.2
2.9
24.8
0.6
O.I
11.7
9.1
I.I
19.5
2.4
0.9
1.7
0.9
3.9
O.I
31.1
1.7
0.0
0.6
28.9
3.2
598.9
4.8
O.I
1.8
3.0
2.5
1.5
7.3
O.I
0.2
1.2
24.5
1.9
32.2
1.7
4.0
0.2
50.1
6.0
8.4
-
1.0
27.2
23.2
100.4
2.1
0.9
10.4
O.I
0.0
2.2
2.4
0.2
3.4
30.9
0.8
August 2011
Appendices
Page C-5
-------
Country
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
1990 1995 2000
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0
0.0
0.0
0.6
O.I
0.0
0.0
O.I
0.0
0.0
O.I
0.2
O.I
O.I
2.2
O.I
0.0
0.0
0.0
0.0
0.3
0.8
1.0
0.2
O.I
0.0
O.I
O.I
0.0
0.0
O.I
0.0
2.0
28.5
0.0
0.0
O.I
0.0
O.I
0.2
O.I
0.2
O.I
0.6
0.8
0.0
0.0
0.0
1.4
0.5
0.0
0.2
0.3
0.2
O.I
0.8
0.8
0.3
0.2
6.5
0.8
0.0
0.2
O.I
O.I
1.9
4.7
2.4
0.6
0.3
0.0
0.8
0.3
0.0
0.0
0.2
O.I
4.7
73.3
O.I
O.I
0.8
O.I
0.7
0.9
0.4
1.0
0.2
3.4
2005 2010 2015 2020
0.0
0.0
0.0
2.0
0.8
O.I
0.6
0.5
0.5
0.3
1.9
1.8
0.5
0.3
13.5
2.0
0.0
0.5
0.2
0.2
4.7
11.5
3.6
1.0
0.6
0.0
1.9
0.7
0.0
0.0
0.5
0.2
7.1
103.2
0.2
0.2
1.8
0.2
1.7
2.2
I.I
2.4
0.5
8.6
4.5 1 10.5
Middle East - 1 0.3 | 2.2 | 5.5
OECD |
Non-OECD Asia
Non-OECD Europe & 1
EU
OPEC
World Totals
57.4
1.7
2.7
14.0
0.5
151.4
11.6
8.4
34.1
3.0
231.6
33.8
17.7
52.8
7.6
0.0
0.0
0.0
2.4
I.I
0.2
1.0
0.6
0.8
0.5
3.1
3.1
0.6
0.4
19.7
3.5
O.I
0.8
0.4
0.4
8.7
17.6
4.3
I.I
0.9
0.0
3.7
1.2
0.0
0.0
0.8
0.4
8.6
131. 1
0.4
0.3
3.2
0.5
3.3
4.1
2.4
4.3
0.8
16.9
18.6
10.1
291.0
79.5
26.7
65.0
14.4
0.0
0.0
0.0
3.1
1.6
0.3
1.7
0.8
I.I
0.8
4.7
4.5
0.8
0.6
28.9
5.4
O.I
1.5
0.5
0.6
13.3
25.5
5.7
1.4
1.2
0.0
6.2
1.8
0.0
0.0
I.I
0.7
11.4
185.4
0.7
0.5
4.7
0.9
5.5
6.2
4.0
6.9
I.I
27.0
27.6
16.0
402.9
147.3
39.3
87.0
22.7
0.0
0.0
O.I
4.0
2.0
0.5
3.1
I.I
1.7
I.I
7.0
5.9
1.0
0.8
40.5
7.8
0.2
2.7
0.7
0.8
18.9
36.8
7.4
1.8
1.5
O.I
10.0
2.6
0.0
0.0
1.5
I.I
14.8
246.5
0.9
0.6
6.8
1.2
8.0
8.7
5.6
II. 1
1.5
38.1
39.3
23.4
533.1
246.8
54.9
112.9
32.6
2025 2030
0.0
0.0
O.I
5.0
2.4
I.I
6.4
1.4
3.0
1.9
12.4
7.8
1.3
1.2
64.6
12.7
0.3
5.7
1.0
I.I
30.6
62.4
9.2
2.3
1.8
O.I
18.9
4.4
O.I
O.I
2.4
2.1
18.6
323.3
1.5
1.0
11.4
1.8
13.5
14.0
9.2
20.8
2.3
61.0
64.6
39.4
713.5
485.9
86.6
143.8
53.6
0.0
0.0
0.2
5.6
2.8
1.7
9.7
1.5
4.0
2.5
16.5
9.0
1.4
1.5
82.2
16.7
0.3
8.8
1.2
1.3
39.0
81.3
10.1
2.6
2.0
O.I
27.3
5.8
O.I
O.I
3.1
3.1
20.5
366.9
2.0
1.3
14.9
2.3
18.1
17.9
12.3
29.7
2.8
78.5
83.8
53.1
822.2
755.8
109.5
70.9
63.5 181.4 307.7 442.8 660.2 935.6 1,451.0 1,902.7
August 2011
Appendices
Page C-6
-------
Table C-4: HFC-23 Emissions from HCFC-22 Production by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
^^^^|
-
I.I
-
-
-
1.4
-
-
0.8
-
-
-
-
-
-
-
-
1.6
2.5
0.9
-
-
-
-
-
-
0.4
13.2
-
-
-
-
-
4.4
-
-
ITT^B
^^^>^H
-
0.7
-
-
-
4.3
-
-
-
-
3.2
-
-
-
-
-
-
0.2
1.5
3.3
-
6.4
-
-
-
-
0.4
17.0
-
-
-
-
-
2.4
-
-
2000 2005 2010
O.I
-
-
-
-
-
-
-
-
-
27.7
-
-
-
-
-
-
0.3
O.I
3.7
-
16.8
-
-
-
-
O.I
12.4
-
-
-
-
-
5.1
-
-
0.5
-
-
-
-
-
-
-
-
-
91.3
-
-
-
-
-
-
0.3
0.2
2.2
-
34.2
-
-
-
-
0.2
0.5
-
-
-
-
-
12.6
-
-
1.8
-
-
-
-
-
-
-
-
-
155.9
-
-
-
-
-
-
-
0.3
-
88.7
-
-
-
-
0.2
-
-
-
-
-
31.2
-
-
1.9
-
-
-
-
-
-
-
-
-
167.5
-
-
-
-
-
-
-
0.4
-
95.3
-
-
-
-
0.3
-
-
-
-
-
33.5
-
-
1
2.2
-
-
-
-
-
-
-
-
-
192.9
-
-
-
-
-
-
-
0.4
-
109.7
-
-
-
-
0.4
-
-
-
-
-
38.6
-
-
2.7
-
-
-
-
-
-
-
-
-
235.7
-
-
-
-
-
-
-
0.5
-
134.1
-
-
-
-
0.9
-
-
-
-
-
47.2
-
-
3.5
-
-
-
-
-
-
-
-
-
300.8
-
-
-
-
-
-
-
0.6
-
171.2
-
-
-
-
2.1
-
-
-
-
-
60.2
-
-
August 2011
Appendices
Page C-7
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
Non-OECD Asia
1990 1995 2000
4.4
-
-
-
-
-
-
14.8
-
-
-
-
2.4
-
-
-
-
-
3.4
36.4
-
2.7
-
-
-
-
-
-
-
4.1
71.6
Non-OECD Europe & | 14.8
EU 15.7
5.8
-
-
-
-
-
-
6.9
-
-
-
1.2
2.1
4.6
-
-
-
-
-
2.0
33.0
-
1.8
-
-
-
-
-
-
1.2
6.1
72.9
9.7
17.8
OPEC 1 2.7 ~TJf
World Totals
2.4
-
-
-
-
-
-
12.5
-
-
-
-
5.6
6.3
-
-
-
-
-
I.I
28.6
-
0.7
-
-
-
-
-
-
-
0.8
66.0
44.5
12.5
14.2
0.7
2005 2010 2015 2020
0.2
-
-
-
-
-
-
12.1
-
-
-
-
5.2
0.3
-
-
-
-
-
0.8
15.8
-
0.8
-
-
-
-
-
-
-
1.3
38.2
125.4
12.1
4.1
0.8
0.2
-
-
-
-
-
-
10.8
-
-
-
-
5.0
0.4
-
-
-
-
-
11.8
-
2.6
-
-
-
-
-
-
-
4.4
49.2
244.6
10.8
1.0
2.6
0.2
-
-
-
-
-
-
7.9
-
-
-
-
5.4
0.4
-
-
-
-
-
10.6
-
2.8
-
-
-
-
-
-
-
4.7
50.9
262.8
7.9
1.0
2.8
0.3
-
-
-
-
-
-
5.3
-
-
-
-
6.2
0.5
-
-
-
-
-
10.8
-
3.2
-
-
-
-
-
-
-
5.4
57.;
302.6
5.3
I.I
3.2
2025 2030
0.3
-
-
-
-
-
-
6.8
-
-
-
-
7.5
0.6
-
-
-
-
-
7.9
-
3.9
-
-
-
-
-
-
-
6.6
64.9
369.8
6.8
1.4
3.9
0.4
-
-
-
-
-
-
8.7
-
-
-
-
9.6
0.8
-
-
-
-
-
6.0
-
4.9
-
-
-
-
-
-
-
8.4
79.8
472.0
8.7
1.8
4.9
90.6 96.8 123.8 177.0 308.9 326.3 370.5 448.1 568.9
August 2011
Appendices
Page C-8
-------
Table C-5: SF6 Emissions from Operation of Electric Power Systems by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
TTRnK
^^^^H
ao~
0.0
O.I
0.0
0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.5
0.0
0.0
0.0
1.5
0.0
1.6
O.I
0.0
0.0
O.I
0.0
0.0
O.I
0.0
-
O.I
0.9
0.0
1.0
0.0
0.0
0.0
0.5
O.I
O.I
0.0
0.0
0.0
0.2
11.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
-
E£ifl_
0.0
0.0
O.I
0.0
0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.0
0.0
0.0
1.5
0.0
1.6
O.I
0.0
0.0
O.I
0.0
0.0
O.I
0.0
-
O.I
1.0
0.0
I.I
0.0
0.0
0.0
0.5
O.I
O.I
0.0
0.0
0.0
0.5
11.0
0.0
O.I
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
-
^fT^^H
oo~
0.0
O.I
0.0
0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.5
0.0
0.0
0.0
1.5
O.I
2.1
O.I
0.0
0.0
O.I
0.0
0.0
O.I
0.0
-
0.0
0.8
0.0
1.2
0.0
O.I
0.0
0.6
O.I
O.I
0.0
0.0
O.I
0.3
3.1
0.0
O.I
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.0
-
2005 2010
0.0
O.I
0.3
0.0
0.5
0.0
O.I
O.I
0.0
0.0
0.0
1.3
0.0
0.0
0.0
1.2
0.2
8.8
O.I
0.0
0.0
0.0
0.0
0.0
0.3
0.0
-
0.0
0.8
0.0
0.8
0.0
0.2
0.0
1.7
0.4
0.5
O.I
0.0
0.2
0.3
0.9
0.0
0.2
O.I
0.0
0.0
0.0
0.0
0.0
0.0
0.7
0.0
-
0.0
O.I
0.4
0.0
0.5
0.0
O.I
O.I
0.0
0.0
0.0
1.7
0.0
0.0
0.0
1.2
0.2
13.4
0.2
0.0
0.0
0.0
0.0
0.0
0.4
0.0
-
0.0
0.6
0.0
0.9
0.0
O.I
-
2.3
0.4
0.6
O.I
0.0
0.2
0.3
0.9
0.0
0.2
O.I
0.0
0.0
0.0
0.0
0.0
0.0
0.8
0.0
-
oo~
O.I
0.4
0.0
0.5
0.0
O.I
O.I
0.0
0.0
0.0
2.0
0.0
0.0
0.0
1.2
0.2
16.3
0.2
0.0
0.0
0.0
0.0
0.0
0.4
0.0
-
0.0
0.5
0.0
0.8
0.0
O.I
0.0
2.8
0.5
0.6
O.I
0.0
0.2
0.2
0.9
0.0
0.2
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.9
0.0
-
1
oo~
0.2
0.4
0.0
0.5
0.0
O.I
O.I
0.0
0.0
0.0
2.3
0.0
0.0
0.0
1.2
0.2
19.8
0.2
0.0
0.0
0.0
0.0
O.I
0.5
0.0
-
0.0
0.4
0.0
0.6
0.0
O.I
0.0
3.4
0.7
0.7
O.I
0.0
0.2
0.2
0.9
0.0
0.2
0.2
0.0
0.0
0.0
0.0
0.0
0.0
I.I
0.0
-
0.0
0.2
0.4
0.0
0.5
0.0
O.I
O.I
0.0
0.0
0.0
2.6
0.0
0.0
0.0
1.2
0.2
23.8
0.2
0.0
0.0
0.0
0.0
O.I
0.5
0.0
-
0.0
0.3
0.0
0.5
0.0
O.I
0.0
3.9
0.8
0.8
0.2
0.0
0.2
O.I
0.9
O.I
0.2
0.2
0.0
0.0
0.0
0.0
0.0
0.0
1.2
0.0
-
^^VAjjlii^H
o6~
0.2
0.4
0.0
0.5
0.0
O.I
0.2
0.0
0.0
0.0
2.9
0.0
0.0
0.0
1.2
0.2
27.6
0.2
0.0
0.0
0.0
0.0
O.I
0.6
0.0
-
0.0
0.2
0.0
0.4
0.0
O.I
0.0
4.4
0.9
0.9
0.2
0.0
0.3
O.I
0.9
O.I
0.2
0.2
0.0
0.0
0.0
0.0
0.0
0.0
1.4
0.0
-
August 2011
Appendices
Page C-9
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
0.0
0.0
0.2
0.0
0.0
O.I
O.I
O.I
0.0
O.I
0.0
0.0
0.0
0.0
0.2
-
0.0
0.0
0.0
0.4
0.2
O.I
O.I
O.I
0.0
O.I
0.2
0.0
-
0.6
0.0
0.6
26.9
0.0
O.I
O.I
0.0
O.I
O.I
O.I
O.I
O.I
0.7
1.0
0.5
OECD | 44.2
Non-OECD Asia 2.7
Non-OECD Europe & | 1.3
EU 3.4
OPEC | 0.6
World Totals
0.0
0.0
O.I
0.0
0.0
0.0
O.I
O.I
0.0
0.0
0.0
0.0
0.0
0.0
O.I
-
0.0
0.0
0.0
0.3
0.3
O.I
O.I
O.I
0.0
O.I
0.2
0.0
-
0.3
0.0
0.8
21.6
0.0
O.I
O.I
0.0
O.I
O.I
0.0
O.I
O.I
0.5
0.8
0.4
39.6
2.6
0.6
4.1
0.5
0.0
0.0
O.I
0.0
0.0
0.0
O.I
O.I
0.0
O.I
0.0
0.0
0.0
0.0
0.2
-
0.0
0.0
0.0
0.3
0.4
0.2
0.0
O.I
0.0
O.I
0.3
0.0
-
0.2
O.I
0.6
15.1
0.0
O.I
O.I
0.0
O.I
O.I
O.I
O.I
O.I
0.5
0.9
0.5
25.1
3.2
0.5
3.7
0.6
2005 2010 2015 2020
0.0
0.0
O.I
0.0
O.I
O.I
O.I
0.3
O.I
0.2
0.0
0.0
0.0
O.I
0.5
-
O.I
0.0
0.0
0.8
1.3
0.3
0.0
O.I
O.I
0.4
0.7
0.0
-
0.5
0.2
0.5
14.1
0.0
0.2
0.3
0.2
0.3
0.2
0.2
0.3
O.I
1.6
2.4
1.7
23.1
12.6
1.4
3.2
2.0
0.0
0.0
O.I
0.0
O.I
O.I
0.0
0.3
O.I
0.2
0.0
0.0
0.0
O.I
0.6
-
O.I
0.0
0.0
0.9
1.4
0.2
0.0
O.I
O.I
0.5
0.7
0.0
-
0.5
0.2
0.5
12.1
0.0
0.2
0.3
0.2
0.4
0.3
0.3
0.4
O.I
1.8
2.9
1.9
21.1
18.1
1.4
2.9
2.3
0.0
0.0
O.I
0.0
O.I
O.I
0.0
0.4
O.I
0.2
0.0
0.0
0.0
O.I
0.7
-
O.I
0.0
0.0
1.0
1.7
0.2
0.0
O.I
O.I
0.6
0.7
0.0
-
0.5
0.2
0.5
12.1
0.0
0.2
0.3
0.2
0.4
0.3
0.3
0.5
O.I
2.1
3.3
2.2
21.1
22.0
1.4
2.5
2.5
0.0
0.0
0.0
0.0
O.I
O.I
0.0
0.4
O.I
0.3
0.0
0.0
0.0
O.I
0.7
-
O.I
0.0
0.0
1.2
1.9
O.I
0.0
O.I
O.I
0.7
0.7
0.0
-
0.5
0.3
0.5
II. 1
0.0
0.2
0.3
0.3
0.5
0.3
0.3
0.6
O.I
2.4
3.7
2.4
20.2
26.6
1.4
2.2
2.8
2025 2030
0.0
0.0
0.0
0.0
O.I
O.I
0.0
0.5
O.I
0.3
0.0
0.0
0.0
O.I
0.8
-
O.I
0.0
0.0
1.3
2.1
O.I
0.0
O.I
O.I
0.9
0.7
0.0
-
0.5
0.3
0.2
10.8
0.0
0.2
0.3
0.3
0.5
0.3
0.4
0.7
O.I
2.7
4.1
2.7
19.6
31.7
1.4
1.6
3.1
0.0
0.0
0.0
0.0
O.I
0.2
O.I
0.6
O.I
0.4
0.0
0.0
0.0
O.I
0.9
-
O.I
0.0
0.0
1.4
2.3
O.I
0.0
O.I
O.I
1.0
0.7
0.0
-
0.5
0.3
0.2
10.3
0.0
0.2
0.4
0.4
0.6
0.4
0.4
0.8
O.I
2.9
4.5
3.0
19.1
36.8
1.4
1.2
3.5
50.3 44.5 30.7 42.8 47.2 52.0 56.6 62.1 67.7
August 2011
Appendices
Page C-10
-------
Table C-6: PFC Emissions from Primary Aluminum Production by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
ThTSEifli
.
-
1.6
-
4.0
I.I
-
-
-
-
-
2.2
-
-
-
6.5
-
2.8
-
-
0.9
-
-
-
0.7
-
-
-
3.0
-
2.5
0.3
0.3
0.4
1.3
0.6
0.2
-
-
-
1.7
O.I
-
-
-
-
-
-
-
-
1.5
0.7
-
-
-
0.4
-
1.3
-
0.0
-
-
-
-
7.4
-
-
-
5.5
-
3.8
-
-
O.I
-
-
-
0.6
-
-
-
1.8
-
1.6
O.I
0.2
O.I
1.0
0.4
0.2
-
-
-
0.3
O.I
-
-
-
-
-
-
-
-
-
O.I
-
-
2000 2005 2010
.
-
0.3
-
I.I
-
-
-
-
-
-
6.0
-
-
-
4.3
-
4.5
-
-
0.0
-
-
-
0.4
-
-
-
1.6
-
0.4
O.I
0.2
O.I
0.9
0.2
0.2
-
-
-
0.2
0.0
-
-
-
-
-
-
-
-
-
0.6
-
-
-
O.I
-
1.5
-
O.I
-
-
-
-
5.0
-
-
-
3.3
-
9.4
-
-
-
-
-
-
0.4
-
-
-
0.7
-
0.3
O.I
0.2
0.0
0.8
O.I
O.I
-
-
-
0.2
0.0
-
-
-
-
-
-
-
-
-
-
-
-
-
O.I
-
1.6
-
0.0
-
-
-
-
3.1
-
-
-
3.3
-
10.0
-
-
-
-
-
-
0.2
-
-
-
0.5
-
0.2
0.0
-
O.I
0.7
O.I
O.I
-
-
-
0.2
0.0
-
0.0
-
-
-
-
-
-
-
-
-
-
^^^^^^
-
O.I
-
1.8
-
0.0
-
-
-
-
3.5
-
-
-
3.8
-
11.3
-
-
-
-
-
-
0.3
-
-
-
0.5
-
0.2
0.0
-
O.I
0.8
O.I
O.I
-
-
-
0.2
0.0
-
0.0
-
-
-
-
-
-
-
-
-
-
Ki3H EZ!z±9
^^^^^_
-
0.2
-
2.0
-
0.0
-
-
-
-
4.0
-
-
-
4.3
-
12.8
-
-
-
-
-
-
0.3
-
-
-
0.5
-
0.2
0.0
-
O.I
0.9
O.I
O.I
-
-
-
0.2
0.0
-
O.I
-
-
-
-
-
-
-
-
-
-
-
0.2
-
2.3
-
0.0
-
-
-
-
4.5
-
-
-
4.8
-
14.5
-
-
-
-
-
-
0.4
-
-
-
0.5
-
0.2
0.0
-
O.I
1.0
O.I
O.I
-
-
-
0.2
0.0
-
O.I
-
-
-
-
-
-
-
-
-
-
-^^B-
-
0.2
-
2.6
-
O.I
-
-
-
-
5.1
-
-
-
5.5
-
16.4
-
-
-
-
-
-
0.4
-
-
-
0.5
-
0.2
O.I
-
O.I
I.I
O.I
O.I
-
-
-
0.3
0.0
-
O.I
-
-
-
-
-
-
-
-
-
-
August 2011
Appendices
Page C-1 I
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
-
-
2.2
0.6
-
-
3.4
-
-
-
0.2
-
2.1
15.3
-
-
-
0.3
0.3
0.7
.
0.9
0.4
O.I
-
-
0.3
-
-
-
0.5
1.3
18.5
-
-
1.8
-
1.8
O.I
0.6
-
-
3.2
5.7
1.3
OECD | 49.0
Non-OECD Asia 4.7
Non-OECD Europe & | 19.9
-
-
1.9
O.I
-
-
2.0
-
-
-
0.3
-
1.8
15.8
-
-
-
O.I
0.3
0.9
.
0.8
0.3
0.0
3.1
-
0.3
-
-
0.2
0.5
0.3
11.8
-
-
1.2
-
1.3
O.I
0.5
-
0.2
2.8
9.1
1.3
29.1
21.2
EU 16.5 9.7
OPEC 1 2.5 2.0
World Totals
-
-
1.4
O.I
-
-
1.3
-
-
-
0.2
-
0.4
18.5
-
-
-
0.0
O.I
1.9
.
0.4
0.2
0.0
2.8
-
0.2
-
-
0.2
0.6
0.3
8.6
-
-
0.7
-
I.I
-
0.4
-
1.2
3.4
7.0
1.2
21.4
5.6
23.2
5.6
1.5
2005 2010 2015 2020
-
-
O.I
O.I
-
-
0.8
-
-
-
0.2
-
0.6
15.9
-
-
-
0.0
O.I
1.4
.
O.I
0.3
0.0
2.7
-
O.I
-
-
0.2
0.3
O.I
3.0
-
-
0.3
-
I.I
-
0.6
-
1.2
2.9
5.3
1.0
11.3
10.3
20.6
3.0
0.7
-
-
O.I
0.0
0.0
-
0.5
-
-
-
0.2
-
0.5
16.7
-
-
-
0.0
O.I
0.8
.
O.I
0.2
-
1.6
-
O.I
-
-
0.0
0.5
O.I
3.7
-
-
O.I
-
0.5
-
0.5
-
0.5
1.6
3.4
1.0
II. 1
10.7
19.5
2.3
0.8
-
-
O.I
O.I
0.0
-
0.5
-
-
-
0.2
-
0.5
18.9
O.I
-
-
0.0
O.I
0.8
.
0.2
0.2
-
1.9
-
O.I
-
-
O.I
0.7
O.I
3.7
-
-
0.2
-
0.6
-
0.6
-
0.6
1.7
3.8
1.5
-
-
O.I
O.I
0.0
-
0.6
-
-
-
0.3
-
0.5
21.4
0.2
-
-
0.0
O.I
0.8
.
0.2
0.2
-
2.1
-
O.I
-
-
O.I
0.7
O.I
3.6
-
-
0.2
-
0.7
-
0.7
-
0.6
1.8
4.3
1.7
11.9 12.8
1 2.2
22.0
2.4
1.3
13.8
24.9
2.5
1.4
2025 2030
-
-
O.I
O.I
0.0
-
0.7
-
-
-
0.3
-
0.5
24.2
0.2
-
-
0.0
O.I
0.8
.
0.2
0.3
-
2.4
-
O.I
-
-
O.I
0.7
O.I
3.6
-
-
0.2
-
0.7
-
0.8
-
0.7
1.9
4.9
1.8
13.9
15.6
28.1
2.7
1.5
-
-
O.I
O.I
0.0
-
0.8
-
-
-
0.3
-
0.5
27.4
0.2
-
-
0.0
O.I
0.8
.
0.2
0.3
-
2.7
-
O.I
-
-
O.I
0.7
O.I
3.6
-
-
0.2
-
0.8
-
0.8
-
0.8
2.1
5.5
1.9
15.1
17.6
31.7
2.8
1.5
83.8 68.8 62.0 51.4 47.3 53.1 59.3 66.1 73.9
August 2011
Appendices
Page C-12
-------
Table C-7: SF6 Emissions from Magnesium Manufacturing by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
0.0
0.0
0.2
-
-
0.3
-
-
0.2
-
0.0
-
-
0.0
-
-
-
0.8
0.2
-
-
-
-
-
-
0.0
O.I
0.0
-
-
-
-
0.0
-
-
-
0.0
0.0
0.4
-
-
0.4
-
-
0.2
-
0.2
-
-
0.0
-
-
-
0.9
0.2
-
-
-
-
-
-
0.0
O.I
0.3
-
-
-
-
0.0
-
-
-
2000 2005 2010
.
0.0
0.0
0.0
-
-
0.3
-
-
0.5
-
0.7
-
-
0.0
-
-
-
0.8
0.3
-
-
-
-
-
0.8
0.2
1.0
0.2
-
-
-
-
0.0
-
-
-
0.0
0.0
0.0
-
-
0.4
-
-
0.2
-
1.3
-
-
0.0
-
-
-
0.4
0.7
-
-
-
-
-
0.8
O.I
I.I
0.4
-
-
-
-
0.0
-
-
-
0.0
0.0
0.0
-
-
O.I
-
-
O.I
-
1.2
-
-
0.0
-
-
-
0.0
O.I
-
-
-
-
-
0.4
0.0
0.6
0.4
-
-
-
-
0.0
-
-
-
^^^^^^
0.0
0.0
-
-
-
-
-
-
O.I
-
1.6
-
-
-
-
-
-
-
-
-
-
-
-
-
0.4
0.6
0.5
-
-
-
-
0.0
-
-
-
Ki3H EZ!z±9
^^^^^_
0.0
0.0
-
-
-
-
-
-
O.I
-
2.1
-
-
-
-
-
-
-
-
-
-
-
-
-
0.5
0.6
0.5
-
-
-
-
0.0
-
-
-
0.0
0.0
-
-
-
-
-
-
-
-
2.2
-
-
-
-
-
-
-
-
-
-
-
-
-
0.5
0.5
-
-
-
-
0.0
-
-
-
-^^B-
0.0
0.0
-
-
-
-
-
-
-
-
2.3
-
-
-
-
-
-
-
-
-
-
-
-
-
0.6
0.6
-
-
-
-
0.0
-
-
-
August 2011
Appendices
Page C-13
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
-
-
-
0.0
-
-
2.1
-
-
-
0.0
0.0
-
1.8
-
-
-
-
-
-
.
0.0
0.0
0.0
-
-
-
-
-
0.6
-
0.0
5.4
-
-
-
-
-
-
-
-
-
-
0.3
-
9.3
Non-OECD Asia 0.0
Non-OECD Europe & | 2.5
EU 1.3
OPEC |
-
-
-
0.0
-
-
0.4
-
-
-
0.0
0.0
-
I.I
-
-
-
-
-
-
-
0.0
0.0
0.0
-
-
-
-
-
0.3
-
0.0
5.6
-
-
-
-
-
-
-
-
-
-
0.4
-
8.1
0.2
1.6
1.6
-
-
-
-
0.0
-
-
0.8
-
-
-
0.0
0.0
-
0.9
-
-
-
-
-
-
.
0.0
O.I
O.I
-
-
-
-
-
0.0
-
0.0
3.0
-
-
-
-
-
-
-
-
-
-
0.3
-
7.6
0.7
I.I
1.4
-
2005 2010 2015 2020
-
-
-
0.0
-
-
0.2
-
-
-
0.0
0.0
-
1.0
-
-
-
-
-
-
-
0.0
O.I
O.I
-
-
-
-
-
O.I
-
0.0
2.9
-
-
-
-
-
-
-
-
-
-
0.4
-
6.7
1.4
1.3
-
-
-
-
0.0
-
-
0.0
-
-
-
0.0
0.0
-
0.9
-
-
-
-
-
-
.
0.0
0.0
0.0
-
-
-
-
-
O.I
-
0.0
1.2
-
-
-
-
-
-
-
-
-
-
O.I
-
2.4
1.2
1.4
O.I
-
-
-
-
0.0
-
-
-
-
-
-
-
-
-
I.I
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
O.I
-
-
0.3
-
-
-
-
-
-
-
-
-
-
0.0
-
1.4
1.6
1.6
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
-
1.2
-
-
-
-
-
-
.
-
-
-
-
-
-
-
-
O.I
-
-
O.I
-
-
-
-
-
-
-
-
-
-
0.0
-
1.2
2.1
1.8
2025 2030
-
-
-
0.0
-
-
-
-
-
-
-
-
-
1.4
-
-
-
-
-
-
.
-
-
-
-
-
-
-
-
O.I
-
-
O.I
-
-
-
-
-
-
-
-
-
-
0.0
-
0.6
2.2
2.0
-
-
-
0.0
-
-
-
-
-
-
-
-
-
1.6
-
-
-
-
-
-
.
-
-
-
-
-
-
-
-
O.I
-
-
O.I
-
-
-
-
-
-
-
-
-
-
0.0
-
0.6
2.3
2.3
-
World Totals 12.2 10.3 9.8 9.8 5.1 4.6 5.1 4.8 5.2
August 2011
Appendices
Page C-14
-------
Table C-8: High GWP Emissions from Semiconductor Manufacturing by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
-
-
-
-
0.0
O.I
-
-
-
0.0
-
0.0
-
-
-
0.0
-
0.8
-
-
-
0.0
-
-
-
-
-
0.0
0.2
-
0.3
-
0.0
-
O.I
-
-
-
0.0
0.0
O.I
6.2
-
-
-
-
-
0.0
-
-
-
-
-
-
-
0.0
0.5
-
-
-
0.0
-
0.0
-
-
-
0.0
-
0.9
-
-
-
0.0
-
-
-
-
-
0.0
0.4
-
0.3
-
0.0
-
O.I
-
-
-
O.I
O.I
O.I
4.4
-
-
-
-
-
0.0
-
-
-
-
0.0
0.4
0.0
0.0
0.0
3.6
0.0
0.0
0.5
0.4
0.0
O.I
0.3
O.I
0.2
8.1
0.0
0.0
0.0
0.3
0.0
0.0
0.0
4.5
0.0
0.0
0.3
0.3
0.0
O.I
0.2
O.I
0.2
5.8
0.0
£J[jJ
0.0
0.0
0.4
0.0
-
0.0
3.8
0.0
0.0
0.3
0.2
0.0
O.I
O.I
0.2
O.I
4.0
0.0
0.0
0.0
0.4
0.0
-
0.0
4.1
0.0
0.0
0.3
0.2
0.0
O.I
O.I
0.2
O.I
4.0
0.0
0.0
0.0
0.4
0.0
-
0.0
4.4
0.0
0.0
0.3
0.2
0.0
0.2
O.I
0.2
O.I
4.0
0.0
0.0
0.0
0.4
0.0
-
0.0
4.4
0.0
0.0
0.3
0.2
0.0
0.3
O.I
0.3
O.I
4.0
0.0
0.0
0.0
0.4
0.0
-
0.0
4.4
0.0
0.0
0.3
0.2
0.0
0.4
O.I
0.4
O.I
4.0
0.0
August 2011
Appendices
Page C-15
-------
Country
Mexico
Moldova
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
0.0
-
-
O.I
-
-
-
-
-
-
0.0
-
0.2
-
0.0
0.9
0.0
0.0
0.0
-
-
-
-
0.3
2.9
-
-
-
-
-
-
0.0
-
0.0
0.0
,1.4
Non-OECD Asia I.I
Non-OECD Europe & | 0.0
EU 1.3
OPEC |
World Totals
0.0
-
-
O.I
-
-
0.0
-
-
-
0.0
-
0.2
-
0.0
1.0
0.0
0.0
0.0
-
-
-
-
0.4
4.9
-
-
-
-
-
-
0.0
-
0.0
0.0
12.4
1.2
0.0
1.9
-
0.0
-
-
0.2
-
-
0.0
-
-
-
O.I
-
0.9
0.0
-
0.0
2.4
0.0
0.0
O.I
-
-
-
-
0.7
6.2
-
-
-
-
-
-
0.0
-
0.0
20.0
4.6
O.I
3.0
-
2005 2010 2015 2020
-
-
-
O.I
-
-
0.0
-
-
-
O.I
-
0.9
0.0
-
0.0
1.8
0.0
O.I
-
-
-
-
0.6
4.4
-
-
-
-
-
-
0.2
-
0.0
-
14.4
5.6
O.I
2.2
-
-
-
-
O.I
-
-
0.0
-
-
-
O.I
-
1.0
-
0.0
1.4
0.0
0.0
O.I
-
-
-
-
0.3
4.5
-
-
-
-
-
-
0.2
-
0.0
-
11.7
5.1
O.I
1.6
-
-
-
-
O.I
-
-
0.0
-
-
-
O.I
-
1.3
-
0.0
1.4
0.0
0.0
O.I
-
-
-
-
0.3
4.5
-
-
-
-
-
-
0.2
-
0.0
11.7
-
-
-
O.I
-
-
0.0
-
-
-
0.2
-
1.5
-
0.0
1.4
0.0
0.0
O.I
-
-
-
-
0.3
4.5
-
-
-
-
-
-
0.3
-
0.0
11.8
5.7 6.4
0.2
1.6
-
0.2
1.6
-
2025 2030
-
-
-
O.I
-
-
0.0
-
-
-
0.2
-
1.8
-
0.0
1.4
0.0
0.0
O.I
-
-
-
-
0.3
4.5
-
-
-
-
-
-
0.4
-
0.0
-
11.9
6.8
0.2
1.6
-
-
-
-
O.I
-
-
0.0
-
-
-
0.2
-
2.2
-
0.0
1.4
0.0
0.0
O.I
-
-
-
-
0.3
4.5
-
-
-
-
-
-
0.4
-
0.0
:
11.9
7.3
0.3
1.6
-
12.6 13.6 24.7 20.2 16.9 17.6 18.4 19.0 19.6
August 2011
Appendices
Page C-16
-------
Table C-9: SF6 and PFC Emissions from Flat Panel Display Manufacturing by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
0.0
0.2
O.I
-
-
0.9
-
-
0.3
3.6
-
2.6
2.3
-
-
11.7
O.I
4.6
O.I
-
0.8
I.I
1.0
-
0.3
-
-
1.7
21.4
0.7
23.5
I.I
3.2
-
2.2
0.0
0.5
-
1.0
0.5
6.7
8.3
-
-
-
-
-
-
0.8
-
-
0.5
-
-
0.0
0.3
O.I
-
-
0.9
-
-
0.3
4.3
-
4.1
1.9
-
-
11.7
0.2
5.2
O.I
-
0.7
1.0
0.9
-
0.3
-
-
1.5
22.9
0.3
25.0
0.9
1.3
-
2.8
0.0
0.7
-
0.8
0.5
7.2
8.2
-
-
-
-
-
-
0.5
-
O.I
0.5
-
-
0.0
0.7
O.I
-
-
1.0
-
-
0.3
4.2
-
5.4
1.3
-
-
2.1
0.3
5.3
O.I
-
0.7
1.0
1.0
-
0.3
-
-
1.4
9.3
0.5
5.5
0.8
1.8
-
3.1
0.0
0.9
-
0.8
0.4
7.9
4.7
-
-
-
-
-
-
1.3
-
O.I
0.3
-
-
2005 2010
0.0
0.6
0.2
-
-
0.3
-
-
0.5
3.1
-
5.9
1.0
-
-
3.9
0.3
6.4
O.I
-
0.7
1.0
-
-
0.3
-
-
1.6
5.8
0.6
14.3
0.5
1.7
-
3.3
0.0
0.9
-
-
0.4
7.8
1.3
-
-
-
-
-
-
2.0
-
O.I
O.I
-
-
0.0
0.6
0.3
-
-
0.3
-
-
0.4
1.6
-
6.9
1.3
-
-
3.0
0.3
7.3
O.I
-
0.8
0.8
-
-
0.3
-
-
1.7
4.4
0.7
9.9
0.4
0.9
-
3.8
0.0
1.0
-
-
0.4
1.5
0.9
-
-
-
-
-
-
2.6
-
O.I
O.I
-
-
0.0
0.7
0.3
-
-
0.3
-
-
0.4
1.6
-
7.9
1.3
-
-
3.5
0.3
8.0
O.I
-
0.8
0.8
-
-
0.3
-
-
1.8
4.5
0.7
1.6
0.4
0.9
-
4.0
0.0
1.2
-
-
0.4
1.4
1.0
-
-
-
-
-
-
2.7
-
O.I
O.I
-
-
ooj oo"
0.7
0.3
-
-
0.3
-
-
0.4
1.6
-
9.0
1.3
-
-
4.0
0.3
8.7
O.I
-
0.8
0.8
-
-
0.3
-
-
1.9
4.6
0.7
1.7
0.4
1.0
-
4.2
0.0
1.3
-
-
0.5
1.5
I.I
-
-
-
-
-
-
2.7
-
O.I
O.I
-
-
0.8
0.3
-
-
0.3
-
-
0.4
1.7
-
10.3
1.3
-
-
4.6
0.3
9.5
O.I
-
0.8
0.8
-
-
0.3
-
-
2.0
4.6
0.7
1.8
0.4
1.0
-
4.4
0.0
1.4
-
-
0.5
1.5
1.2
-
-
-
-
-
-
2.8
-
O.I
O.I
-
-
o6~
0.9
0.3
-
-
0.3
-
-
0.4
1.7
-
11.6
1.3
-
-
5.3
0.3
9.9
O.I
-
0.8
0.8
-
-
0.3
-
-
2.1
4.7
0.7
1.8
0.4
1.0
-
3.9
0.0
1.2
-
-
0.4
1.5
1.3
-
-
-
-
-
-
2.8
-
O.I
O.I
-
-
August 2011
Appendices
Page C-17
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
-
-
6.3
-
-
0.4
2.1
-
-
-
3.5
0.6
5.0
3.6
-
-
-
I.I
-
1.8
5.8
2.9
0.8
0.2
-
-
O.I
-
-
-
-
24.6
35.3
-
1.8
0.5
-
0.2
1.0
-
-
-
2.5
4.3
0.5
OECD | 169.8
Non-OECD Asia 7.1
Non-OECD Europe & | 15.2
EU 113.1
OPEC | I.I
World Totals
-
-
6.3
-
-
0.3
1.6
-
0.2
-
3.9
0.6
3.6
2.1
-
-
0.6
I.I
-
2.3
5.2
2.4
0.7
0.2
-
-
5.1
-
-
-
-
14.9
39.6
-
1.6
0.4
-
0.3
O.I
-
-
-
3.2
4.9
0.7
169.5
8.9
II. 1
102.6
1.4
-
-
5.9
-
-
0.4
1.7
-
0.2
-
4.0
0.4
3.4
2.4
-
-
0.7
1.0
0.0
2.4
6.7
2.3
0.6
O.I
-
-
4.3
-
-
-
-
5.5
28.1
-
1.4
0.3
-
0.5
0.0
-
-
-
4.0
6.1
0.9
103.3
9.5
11.4
60.7
1.9
2005 2010 2015 2020
-
-
5.7
-
-
0.5
2.0
-
0.2
-
4.5
0.6
3.2
3.2
-
-
0.8
1.3
0.0
1.9
9.2
1.9
0.4
O.I
-
-
1.8
-
-
-
-
2.8
24.6
-
1.6
0.4
-
O.I
0.0
-
-
-
2.9
6.8
0.9
96.7
II. 1
12.7
59.4
1.9
-
-
3.1
-
-
0.7
1.4
-
0.2
-
4.6
O.I
3.0
3.5
-
-
0.8
0.0
-
2.4
11.8
O.I
0.3
O.I
-
-
-
-
-
-
-
2.6
28.6
-
1.6
0.3
-
O.I
0.0
-
-
-
3.4
7.8
1.0
78.8
12.6
13.9
39.2
2.0
-
-
3.0
-
-
0.8
1.4
-
0.2
-
4.6
O.I
3.2
3.5
-
-
0.9
0.0
-
2.6
13.9
O.I
0.3
O.I
-
-
-
-
-
-
-
2.4
30.5
-
1.7
0.4
-
O.I
0.0
-
-
-
3.6
8.8
1.2
75.1
13.6
14.3
31.1
2.2
-
-
3.0
-
-
1.0
1.4
-
0.2
-
4.6
O.I
3.2
3.6
-
-
0.9
0.0
-
2.7
16.4
O.I
0.3
O.I
-
-
-
-
-
-
-
2.4
32.4
-
1.7
0.4
-
O.I
0.0
-
-
-
3.8
10.0
1.3
80.5
14.8
14.5
31.4
2.4
2025 2030
-
-
3.0
-
-
I.I
1.4
-
0.2
-
4.6
O.I
3.3
3.7
-
-
I.I
0.0
-
2.8
19.4
O.I
0.3
O.I
-
-
-
-
-
-
-
2.3
34.5
-
1.7
0.4
-
O.I
0.0
-
-
-
4.1
11.3
1.4
86.6
16.1
14.7
31.8
2.6
-
-
3.0
-
-
1.3
1.4
-
0.2
-
4.6
O.I
3.3
3.7
-
-
1.3
0.0
-
2.4
22.8
O.I
0.3
O.I
-
-
-
-
-
-
-
2.3
36.7
-
1.6
0.3
-
O.I
0.0
-
-
-
3.7
12.6
1.2
93.1
16.5
14.7
32.1
2.4
199.4 198.3 135.3 131. 1 117.5 116.7 124.9 134.3 141.7
August 2011
Appendices
Page C-18
-------
Table C-l 0: PFC Emissions from Photovoltaic Manufacturing by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
2000 2005 2010
.
-
-
-
-
-
-
-
-
-
0.0
-
0.0
0.0
-
-
-
0.0
0.0
-
0.0
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.0
-
-
0.0
-
-
-
-
-
O.I
-
0.0
0.0
-
-
-
0.0
O.I
-
0.0
-
-
-
-
0.2
-
-
-
-
-
-
-
-
0.0
-
-
0.0
-
0.0
-
-
-
2.0
-
0.0
0.0
-
-
-
0.0
0.5
0.0
-
O.I
0.0
-
-
-
O.I
0.5
-
-
-
-
-
-
-
-
^^^^^^
0.0
-
-
O.I
-
0.0
-
-
-
4.3
-
0.0
0.0
-
-
-
0.0
1.0
0.0
-
0.4
0.0
-
-
-
0.2
1.2
-
-
-
-
-
-
-
-
Ki3H EZ!z±9
^^^^^_
0.0
-
-
0.3
-
0.0
-
-
-
10.0
-
0.0
0.0
-
-
-
O.I
2.3
O.I
-
0.8
0.0
-
-
-
0.5
2.7
-
-
-
-
-
-
-
-
O.I
-
-
0.7
-
0.0
-
-
-
23.5
-
0.0
0.0
-
-
-
0.2
5.4
0.2
-
2.0
0.0
-
-
-
1.2
6.4
-
-
-
-
-
-
-
-
-^^B-
0.2
-
-
1.6
-
O.I
-
-
-
55.6
-
0.0
0.0
-
-
-
0.5
12.8
0.4
-
4.7
O.I
-
-
-
2.8
15.0
-
-
-
-
-
-
-
-
August 2011
Appendices
Page C-l9
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
-
-
0.0
-
0.0
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
-
-
-
2005 2010 2015 2020
-
-
0.0
-
-
-
0.0
-
-
0.0
-
-
-
0.0
-
-
-
-
-
-
0.0
0.0
-
0.0
-
0.0
-
-
-
-
0.0
-
0.0
-
-
-
-
-
-
-
-
-
-
-
- 1 - 1 0.0
OECD | -| -| 0.0 | 0.4
Non-OECD Asia - - 0.0 O.I
Non-OECD Europe & | ^^ ^^ 0.0 0.0
EU - - 0.0 O.I
OPEC | - 1 - 1 - | 0.0
World Totals - - 0.0 0.5
-
-
O.I
-
-
-
0.0
-
-
O.I
-
0.0
-
0.0
-
-
0.0
-
-
-
O.I
O.I
-
0.0
-
0.0
-
-
-
-
0.0
-
0.3
-
-
-
-
-
-
-
O.I
0.0
-
-
0.0
1.8
2.4
0.0
0.8
0.0
-
-
O.I
-
-
-
O.I
-
-
0.2
-
0.0
-
0.0
-
-
0.3
-
-
-
0.3
0.2
-
0.0
-
0.0
-
-
-
-
0.0
-
0.5
-
-
-
-
-
-
-
0.8
0.0
-
-
0.0
3.8
5.9
0.0
1.7
0.0
-
-
0.3
-
-
-
0.2
-
-
0.3
-
0.0
-
0.0
-
-
0.6
-
-
-
0.7
0.4
-
0.0
-
0.0
-
-
-
-
O.I
-
1.2
-
-
-
-
-
-
-
1.8
0.0
-
-
O.I
8.8
13.7
O.I
4.0
O.I
2025 2030
-
-
0.7
-
-
-
0.5
-
-
0.8
-
0.0
-
O.I
-
-
1.5
-
-
-
1.6
0.9
-
O.I
-
O.I
-
-
-
-
0.2
-
2.7
-
-
-
-
-
-
-
4.3
O.I
-
-
0.2
20.7
32.3
0.3
9.5
0.2
-
-
1.8
-
-
-
1.2
-
-
1.9
-
0.0
-
0.3
-
-
3.6
-
-
-
3.8
2.0
-
0.2
-
0.3
-
-
-
-
0.6
-
6.5
-
-
-
-
-
-
-
10.2
0.3
-
-
0.6
49.0
76.4
0.6
22.4
0.6
4.2 9.8 22.7 53.5 126.5
August 2011
Appendices
Page C-20
-------
Table C-l I: CH4 Emissions from Other Industrial Processes Sources by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
0.0
O.I
0.0
0.0
0.0
O.I
O.I
-
-
0.0
-
0.0
0.0
O.I
-
-
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-
0.0
-
-
O.I
0.4
0.0
-
-
0.0
0.0
0.0
O.I
0.0
-
0.0
O.I
0.0
0.0
0.0
O.I
O.I
-
-
0.0
-
0.0
0.0
O.I
-
-
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-
0.0
-
-
O.I
0.3
0.0
-
-
0.0
0.0
0.0
O.I
0.0
-
2000 2005 2010
.
0.0
O.I
0.0
0.0
0.0
O.I
O.I
-
-
0.0
-
0.0
0.0
O.I
-
-
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-
0.0
-
-
O.I
0.2
0.0
-
-
0.0
0.0
0.0
O.I
0.0
-
0.0
O.I
0.0
0.0
0.0
O.I
0.0
-
-
0.0
-
0.0
0.0
O.I
-
-
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-
0.0
-
-
O.I
O.I
0.0
-
-
0.0
0.0
0.0
O.I
0.0
-
0.0
O.I
0.0
0.0
O.I
O.I
0.0
-
-
0.0
-
0.0
0.0
O.I
-
-
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-
0.0
-
-
O.I
O.I
0.0
-
-
0.0
0.0
0.0
O.I
0.0
-
^^^^^^
0.0
O.I
0.0
0.0
O.I
O.I
0.0
-
-
0.0
-
0.0
0.0
O.I
-
-
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-
0.0
-
-
O.I
O.I
0.0
-
-
0.0
0.0
0.0
O.I
0.0
-
^
-^^B-
0.0
O.I
0.0
0.0
O.I
O.I
0.0
-
-
0.0
-
0.0
0.0
O.I
-
-
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-
0.0
-
-
O.I
O.I
0.0
-
-
0.0
0.0
0.0
O.I
0.0
-
0.0
O.I
0.0
0.0
O.I
O.I
0.0
-
-
0.0
-
0.0
0.0
O.I
-
-
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-
0.0
-
-
O.I
O.I
0.0
-
-
0.0
0.0
0.0
O.I
0.0
-
-^^B-
0.0
O.I
0.0
0.0
O.I
O.I
0.0
-
-
0.0
-
0.0
0.0
O.I
-
-
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-
0.0
-
-
O.I
O.I
0.0
-
-
0.0
0.0
0.0
O.I
0.0
-
August 2011
Appendices
Page C-21
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
-
-
0.3
0.0
0.0
0.0
0.0
-
0.0
0.0
0.4
0.0
0.0
0.8
0.2
-
-
0.0
0.0
O.I
O.I
O.I
0.0
0.0
-
0.0
0.0
-
-
1.3
0.0
0.2
1.9
-
0.0
O.I
-
0.5
0.0
0.0
-
-
0.6
0.2
0.3
OECD | 3.8
Non-OECD Asia O.I
Non-OECD Europe & | 2.3
EU 1.3
OPEC | 0.4
World Totals
-
-
0.3
O.I
0.0
0.0
0.0
-
0.0
0.0
0.4
0.0
0.0
0.5
0.2
-
-
0.0
0.0
0.0
0.2
O.I
0.0
0.0
-
0.0
0.0
-
-
0.5
0.0
0.2
2.1
-
0.0
O.I
-
0.5
0.0
0.0
-
-
0.5
0.2
0.3
4.2
O.I
1.2
1.3
0.4
-
-
0.3
O.I
0.0
0.0
0.0
-
0.0
0.0
0.4
0.0
0.0
0.6
0.2
-
-
0.0
0.0
0.0
0.4
O.I
0.0
0.0
-
0.0
0.0
-
-
0.7
0.0
O.I
2.2
-
0.0
O.I
-
0.5
0.0
0.0
-
-
0.5
0.2
0.3
4.2
O.I
1.5
I.I
0.4
2005 2010 2015 2020
-
-
0.3
0.0
0.0
0.0
0.0
-
0.0
0.0
0.4
0.0
0.0
0.7
0.2
-
-
0.0
0.0
0.0
0.4
O.I
0.0
0.0
-
0.0
0.0
-
-
0.8
0.0
O.I
1.8
-
0.0
O.I
-
0.5
0.0
0.0
-
-
0.5
0.2
0.3
3.7
O.I
1.7
1.2
0.4
-
-
0.3
0.0
0.0
0.0
0.0
-
0.0
0.0
0.4
0.0
0.0
0.8
0.2
-
-
0.0
0.0
0.0
0.4
O.I
0.0
0.0
-
0.0
O.I
-
-
0.9
0.0
O.I
1.7
-
0.0
O.I
-
0.5
0.0
0.0
-
-
0.5
0.2
0.3
3.7
O.I
1.9
1.2
0.4
-
-
0.3
0.0
0.0
0.0
0.0
-
0.0
0.0
0.4
0.0
0.0
0.8
0.2
-
-
0.0
0.0
0.0
0.4
O.I
0.0
0.0
-
0.0
O.I
-
-
0.9
0.0
O.I
1.7
-
0.0
O.I
-
0.5
0.0
0.0
-
-
0.5
0.2
0.3
3.7
O.I
1.9
1.2
0.4
-
-
0.3
0.0
0.0
0.0
0.0
-
0.0
0.0
0.4
0.0
0.0
0.8
0.2
-
-
0.0
0.0
0.0
0.4
O.I
0.0
0.0
-
0.0
O.I
-
-
0.9
0.0
O.I
1.7
-
0.0
O.I
-
0.5
0.0
0.0
-
-
0.5
0.2
0.3
3.7
O.I
1.9
1.2
0.4
2025 2030
-
-
0.3
0.0
0.0
0.0
0.0
-
0.0
0.0
0.4
0.0
0.0
0.8
0.2
-
-
0.0
0.0
0.0
0.4
O.I
0.0
0.0
-
0.0
O.I
-
-
0.9
0.0
O.I
1.7
-
0.0
O.I
-
0.5
0.0
0.0
-
-
0.5
0.2
0.3
3.7
O.I
1.9
1.2
0.4
-
-
0.3
0.0
0.0
0.0
0.0
-
0.0
0.0
0.4
0.0
0.0
0.8
0.2
-
-
0.0
0.0
0.0
0.4
O.I
0.0
0.0
-
0.0
O.I
-
-
0.9
0.0
O.I
1.7
-
0.0
O.I
-
0.5
0.0
0.0
-
-
0.5
0.2
0.3
3.7
O.I
1.9
1.2
0.4
7.3 6.5 6.7 6.5 6.7 6.7 6.7 6.7 6.7
August 2011
Appendices
Page C-22
-------
Table C-l 2: N2O Emissions from Other Industrial Processes Sources by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
-
0.0
0.2
O.I
0.2
-
O.I
-
0.2
-
-
-
0.0
0.2
0.0
-
-
O.I
O.I
2.1
0.2
0.0
-
-
-
-
-
0.8
0.3
-
-
0.0
-
0.0
-
-
-
0.0
0.2
O.I
0.2
-
0.0
-
0.2
-
-
-
0.0
0.2
0.0
-
-
O.I
O.I
1.7
0.2
0.0
-
-
-
-
-
0.8
0.4
-
-
0.0
-
0.0
-
-
-
2000 2005 2010
.
0.0
0.2
O.I
0.3
-
0.0
-
0.2
-
-
-
0.0
0.2
0.0
-
-
O.I
O.I
1.3
O.I
0.0
-
-
-
-
-
1.0
0.3
-
-
0.0
-
0.0
-
-
-
0.0
0.2
O.I
0.2
-
0.0
-
0.2
-
-
-
0.0
0.2
0.0
-
-
0.0
O.I
1.2
O.I
0.0
-
-
-
-
-
0.8
0.3
-
-
0.0
-
0.0
-
-
-
0.0
0.2
O.I
0.2
-
0.0
-
0.3
-
-
-
0.0
0.2
0.0
-
-
0.0
O.I
1.2
O.I
0.0
-
-
-
-
-
0.8
0.2
-
-
0.0
-
0.0
-
-
-
^^^^^^
0.0
0.2
O.I
0.2
-
0.0
-
0.3
-
-
-
0.0
0.2
0.0
-
-
0.0
O.I
1.2
O.I
0.0
-
-
-
-
-
0.8
0.2
-
-
0.0
-
0.0
-
-
-
Ki3H EZ!z±9
^^^^^_
0.0
0.2
O.I
0.2
-
0.0
-
0.3
-
-
-
0.0
0.2
0.0
-
-
0.0
O.I
1.2
O.I
0.0
-
-
-
-
-
0.8
0.2
-
-
0.0
-
0.0
-
-
-
0.0
0.2
O.I
0.2
-
0.0
-
0.3
-
-
-
0.0
0.2
0.0
-
-
0.0
O.I
1.2
O.I
0.0
-
-
-
-
-
0.8
0.2
-
-
0.0
-
0.0
-
-
-
-^^B-
0.0
0.2
O.I
0.2
-
0.0
-
0.3
-
-
-
0.0
0.2
0.0
-
-
0.0
O.I
1.2
O.I
0.0
-
-
-
-
-
0.8
0.2
-
-
0.0
-
0.0
-
-
-
August 2011
Appendices
Page C-23
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
-
-
0.2
0.0
-
-
0.0
-
-
-
O.I
-
-
0.6
-
-
-
0.0
0.0
-
-
0.4
O.I
O.I
-
-
-
-
-
0.4
-
0.0
4.4
-
-
-
-
58.0
0.2
-
-
0.0
58.0
0.2
-
-
0.2
0.0
-
-
0.0
-
-
-
O.I
-
-
0.5
-
-
-
0.0
0.0
-
-
0.4
O.I
O.I
-
-
-
-
-
0.4
-
0.0
4.6
-
-
-
-
58.0
0.2
-
-
0.0
58.0
0.2
-
-
O.I
0.0
-
-
0.0
-
-
-
O.I
-
-
0.5
-
-
-
0.0
0.0
-
-
0.4
O.I
O.I
-
-
-
-
-
0.4
-
0.0
4.9
-
-
-
-
58.0
0.2
-
-
0.0
58.0
0.2
2005 2010 2015 2020
-
-
O.I
0.0
-
-
0.0
-
-
-
O.I
-
-
0.5
-
-
-
O.I
0.0
-
-
0.2
O.I
O.I
-
-
-
-
-
0.3
-
0.0
4.4
-
-
-
-
58.0
0.2
-
-
0.0
58.0
0.2
1 ' 1
OECD | 9.9 | 9.9 | 9.8 1 8.6
Non-OECD Asia ^B - -
Non-OECD Europe & | L^ 1.0
EU 4.9 4.5
OPEC | - |
1.0
4.2
-
1.0
3.6
-
-
-
O.I
0.0
-
-
0.0
-
-
-
0.2
-
-
0.5
-
-
-
O.I
0.0
-
-
0.5
O.I
O.I
-
-
-
-
-
0.3
-
0.0
4.4
-
-
-
-
58.0
0.2
-
-
0.0
58.0
0.2
-
8.9
-
1.0
3.8
-
-
-
O.I
0.0
-
-
0.0
-
-
-
0.2
-
-
0.5
-
-
-
O.I
0.0
-
-
0.5
O.I
O.I
-
-
-
-
-
0.3
-
0.0
4.4
-
-
-
-
58.0
0.2
-
-
0.0
58.0
0.2
-
8.9
-
-
O.I
0.0
-
-
0.0
-
-
-
0.2
-
-
0.5
-
-
-
O.I
0.0
-
-
0.5
O.I
O.I
-
-
-
-
-
0.3
-
0.0
4.4
-
-
-
-
58.0
0.2
-
-
0.0
58.0
0.2
-
8.9
1.0
3.8
-
1.0
3.8
-
2025 2030
-
-
O.I
0.0
-
-
0.0
-
-
-
0.2
-
-
0.5
-
-
-
O.I
0.0
-
-
0.5
O.I
O.I
-
-
-
-
-
0.3
-
0.0
4.4
-
-
-
-
58.0
0.2
-
-
0.0
58.0
0.2
-
8.9
-
1.0
3.8
-
-
O.I
0.0
-
-
0.0
-
-
-
0.2
-
-
0.5
-
-
-
O.I
0.0
-
-
0.5
O.I
O.I
-
-
-
-
-
0.3
-
0.0
4.4
-
-
-
-
58.0
0.2
-
-
0.0
58.0
0.2
-
8.9
-
1.0
3.8
World Totals 69.2 69.0 69.0 67.7 68.0 68.0 68.0 68.0 68.0
August 2011
Appendices
Page C-24
-------
Appendix D: Agriculture Sector Emissions
Table D-l: Total Non-CO2 Emissions from the Agriculture Sector by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
2.4
7.8
117.5
1.5
109.1
9.3
6.1
35.1
22.0
11.4
40.7
490.7
13.7
150.9
21.7
54.1
8.9
577.2
62.2
225.6
4.4
15.8
13.0
10.3
19.3
3.0
62.7
7.2
108.5
3.7
61.9
13.7
16.2
0.6
646.1
169.9
28.8
11.2
19.2
1.7
41.2
32.0
0.8
40.3
0.2
4.8
10.8
6.0
9.6
0.8
1.9
76.8
3.8
2.6
7.8
123.9
I.I
99.4
9.3
4.5
38.5
13.7
11.5
37.5
503.6
6.3
167.9
23.5
92.6
15.1
652.0
68.1
199.7
3.1
9.8
11.9
12.0
23.9
1.5
58.8
6.4
102.4
2.6
54.6
12.6
9.7
0.5
676.5
185.5
30.7
13.4
19.9
1.9
40.7
30.7
1.0
23.8
0.2
2.7
11.6
2.1
4.1
0.8
1.9
72.9
2.8
^^^^^^^
27"
8.7
131.2
1.0
121.6
8.5
5.2
40.9
13.9
II. 1
36.7
503.4
6.3
125.0
22.1
62.4
21.4
657.7
71.4
162.7
3.3
8.6
10.6
11.6
26.9
1.3
63.3
6.0
104.2
3.1
55.6
12.4
10.1
0.5
699.6
186.1
32.5
12.8
19.6
1.9
40.3
27.9
0.9
19.3
0.3
2.8
9.1
1.8
3.9
0.8
1.5
76.9
1.6
2.0
9.4
140.4
1.3
100.2
7.9
6.5
44.2
14.9
10.0
49.2
691.8
5.2
125.8
31.8
67.8
28.2
681.9
73.6
151.7
3.6
7.8
10.0
11.8
30.2
1.3
77.7
5.6
97.8
3.5
52.9
11.6
9.5
0.5
704.1
236.8
33.9
12.2
18.7
2.0
37.4
26.8
1.0
21.1
0.4
2.7
15.1
2.0
4.3
0.7
1.4
78.1
1.5
^^^^V^^^H
^KTJTTIV
MF
10.1
145.4
1.4
96.5
8.1
7.1
49.7
14.9
10.0
50.8
644.1
4.9
132.2
31.7
69.2
29.4
702.4
76.7
151.7
3.5
7.5
7.4
11.9
35.1
1.2
96.4
5.9
102.7
2.7
60.3
10.2
9.2
0.5
732.3
246.8
35.2
11.6
18.3
2.2
39.1
27.0
1.2
23.5
0.5
3.1
15.9
2.0
4.2
0.7
1.4
88.7
1.2
1.8
10.9
155.5
1.4
100.0
8.3
7.1
55.3
14.8
10.2
52.5
664.7
4.9
135.9
32.9
74.0
31.0
731.4
81.2
151.8
3.6
7.5
7.4
12.7
38.8
1.2
107.3
6.3
107.8
2.6
58.9
9.6
9.3
0.6
758.5
253.7
37.3
12.2
18.6
2.4
41.0
26.6
1.4
24.9
0.5
3.3
16.4
2.0
4.2
0.8
1.5
95.2
I.I
^vTTTjV ^vTTTI
He"
11.8
162.0
1.4
102.5
8.3
7.2
62.4
14.7
10.1
53.9
669.7
4.9
140.4
32.9
77.0
32.4
756.4
84.1
152.0
3.6
7.5
7.1
13.4
42.3
1.2
II 6.1
6.3
107.7
2.6
57.9
9.1
9.4
0.6
785.2
260.4
39.3
12.9
18.5
2.5
41.5
26.2
1.6
26.6
0.5
3.5
16.9
2.0
4.2
0.8
1.5
100.1
I.I
1.8
12.8
167.3
1.4
105.1
8.3
7.3
71.0
14.6
10.0
55.5
671.3
4.9
145.4
33.1
80.0
33.9
781.5
87.1
152.1
3.6
7.6
7.1
14.1
46.1
1.2
124.9
6.3
107.5
2.6
56.9
8.8
9.4
0.7
812.7
268.0
41.5
13.9
18.5
2.6
42.0
25.9
1.8
28.5
0.6
3.7
17.4
2.0
4.1
0.8
1.5
105.0
I.I
1.7
14.0
172.3
1.4
107.8
8.3
7.4
81.4
14.5
9.9
57.0
671.8
4.9
150.8
33.5
82.9
35.4
804.7
89.6
152.2
3.7
7.6
7.0
14.8
50.2
1.2
133.9
6.4
107.1
2.6
55.8
8.4
9.5
0.7
840.2
276.4
43.8
14.9
18.5
2.7
42.6
25.5
2.0
30.8
0.7
4.0
18.0
2.0
4.1
0.8
1.6
109.5
I.I
August 2011
Appendices
Page D-l
-------
Country
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
1990 1995 2000
-
11.5
20.2
22.5
32.5
44.6
12.9
4.4
71.0
17.3
32.0
50.4
8.2
37.0
342.4
7.5
6.7
0.2
7.1
2.2
44.4
14.9
41.6
9.5
6.1
5.0
79.8
39.9
5.1
13.8
102.9
1.0
54.7
392.8
22.7
17.1
30.3
46.7
676.9
95.3
10.8
51.7
11.7
1,101.9
887.0
Middle East 60.3
OECD 1 1,288.1
Non-OECD Asia | 1,937.8
Non-OECD Europe & j 644.5
EU I 584.3
OPEC 252.6
-
12.2
21.7
23.5
33.7
48.7
8.2
4.5
81.3
18.2
35.1
38.1
8.3
24.0
213.6
7.3
7.1
0.2
4.4
2.1
37.5
15.6
40.5
9.3
5.7
4.1
83.6
37.3
3.8
13.7
64.7
1.5
52.7
402.9
24.8
16.7
31.4
55.7
647.3
87.8
11.3
54.5
9.5
1,044.5
907.1
65.6
1,281.5
2,108.0
409.2
507.4
252.1
-
12.4
23.0
20.4
35.8
55.2
6.4
4.5
93.0
22.0
39.0
34.8
9.8
18.6
188.9
8.2
8.3
O.I
3.5
2.2
34.5
15.9
48.3
8.8
5.4
3.8
77.5
35.9
4.3
13.4
33.8
2.0
50.2
406.2
22.1
16.2
33.8
65.7
644.3
113.0
12.6
47.3
10.0
1,017.1
945.1
69.5
1,282.0
2,105.0
343.2
498.0
264.3
2005 2010 2015 2020
-
9.0
23.9
18.5
37.5
59.4
6.8
4.3
107.2
21.6
41.1
33.1
9.8
20.8
152.8
9.2
8.9
0.2
3.3
2.0
32.8
15.1
45.3
8.6
5.3
5.1
84.1
35.2
7.0
18.4
30.2
2.4
45.8
415.1
24.2
16.4
38.0
69.9
693.1
106.8
15.3
51.9
8.6
1,081.7
1,157.5
74.3
1,252.5
2,233.7
312.1
470.7
282.9
-
12.9
25.8
19.6
37.1
64.9
6.8
4.4
127.2
22.6
44.0
34.9
9.8
21.1
155.3
9.5
9.5
0.2
2.3
2.1
34.1
15.9
45.9
8.1
5.5
6.7
91.8
38.0
7.4
19.7
27.2
2.6
46.2
454.9
25.9
18.4
39.6
72.9
717.5
115.4
17.5
53.4
8.7
1,139.0
1,132.4
78.2
1,317.5
2,345.8
317.8
482.1
292.3
-
15.8
28.8
20.1
38.7
69.3
7.4
4.7
139.5
24.2
46.1
35.4
9.9
21.4
154.6
10.2
10.2
0.2
2.2
2.1
35.5
16.0
47.8
7.6
5.6
7.1
93.7
41.1
7.9
20.5
27.0
2.8
49.0
489.0
28.6
19.3
42.5
77.3
746.9
123.4
19.5
55.5
8.8
1,191.3
1,185.2
83.9
1,384.7
2,448.5
320.5
494.3
305.6
-
19.8
31.8
20.0
40.1
73.6
7.8
4.8
151.9
25.4
48.4
35.8
10.0
21.6
155.0
11.0
10.8
0.2
2.2
2.1
36.2
16.1
48.1
7.1
5.6
7.6
96.0
44.0
8.5
21.3
26.8
3.1
49.7
512.3
30.8
20.2
44.7
80.8
771.9
129.7
21.7
57.4
8.8
1,235.9
1,213.9
90.1
1,423.5
2,548.7
324.7
493.7
318.0
2025 2030
-
25.3
35.4
19.9
41.6
77.8
8.3
4.9
166.0
26.9
50.9
36.2
10.0
21.9
155.5
11.8
11.4
0.2
2.3
2.1
36.9
16.3
48.4
6.8
5.5
8.0
98.6
47.0
9.2
22.0
26.6
3.3
50.4
535.6
33.3
21.2
47.1
84.6
799.2
136.6
24.1
59.6
8.8
1,283.2
1,239.1
97.0
1,463.6
2,658.0
329.6
494.2
331.0
-
33.0
39.6
19.8
43.4
82.5
9.1
5.0
181.6
28.3
53.5
36.7
10.0
22.2
155.7
12.7
12.0
0.2
2.4
2.1
37.6
16.6
48.7
6.5
5.5
8.6
101.2
50.1
9.9
22.8
26.4
3.6
51.1
559.0
35.7
22.2
49.4
88.6
829.4
143.5
26.7
62.1
8.9
1,334.6
1,262.3
104.3
1,503.3
2,774.0
334.9
494.4
344.7
World Totals 5,919.6 5,815.9 5,761.8 6,111.9 6,330.7 6,614.1 6,836.9 7,070.5 7,313.5
August 2011
Appendices
Page D-2
-------
Table D-2: N2O Emissions from Agricultural Soils by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
1.0
3.5
50.2
0.5
13.5
3.3
1.7
9.6
10.7
4.5
3.9
132.0
7.2
5.0
I.I
25.5
0.2
219.0
23.2
1.5
2.5
8.9
8.3
3.0
8.0
1.5
19.2
4.3
56.2
1.2
31.4
9.7
8.3
0.2
168.6
21.0
16.1
3.3
7.0
0.7
19.4
7.9
0.3
8.7
O.I
1.6
0.8
3.0
5.0
0.4
1.0
30.9
1.6
-
0.7
3.3
56.5
0.3
14.0
3.6
I.I
13.0
5.6
4.5
4.4
150.5
3.1
5.8
1.4
27.6
6.1
262.6
27.6
1.3
1.9
5.4
7.3
3.6
10.3
0.7
17.9
3.8
51.5
0.6
28.0
8.7
5.0
0.2
188.2
24.7
17.9
3.2
7.5
0.7
19.3
7.2
0.5
5.6
O.I
0.9
0.9
1.0
2.0
0.4
1.0
28.6
1.0
-
0.6
3.7
65.2
0.3
16.6
3.1
I.I
14.3
6.9
4.3
5.0
169.8
2.9
6.9
1.5
29.7
12.0
263.4
32.1
I.I
2.1
4.8
6.2
3.6
11.6
0.7
19.7
3.5
53.4
0.8
29.7
8.4
5.4
0.2
196.4
24.6
19.8
3.3
7.4
0.7
19.2
6.7
0.4
3.1
0.2
0.9
0.9
0.9
2.2
0.4
0.7
30.5
0.4
-
2005 2010
0.5
4.0
72.1
0.4
16.3
2.9
1.5
16.8
7.9
3.9
5.8
189.2
2.7
7.2
1.6
29.7
18.0
308.6
36.5
I.I
2.3
4.5
5.7
3.6
13.8
0.6
25.6
3.2
49.0
0.8
28.4
7.8
5.5
0.2
207.2
29.3
20.8
2.6
6.8
0.7
18.0
6.4
0.5
4.0
0.2
0.8
1.0
1.2
2.5
0.3
0.4
28.8
0.4
-
0.5
4.4
74.3
0.4
15.8
3.0
1.6
20.0
8.0
4.1
6.3
173.5
2.6
8.3
1.8
32.9
18.8
329.2
38.6
I.I
2.3
4.4
3.0
3.6
16.0
0.6
32.8
3.6
53.9
0.6
36.3
6.5
5.4
0.2
218.3
33.1
22.3
3.0
7.1
0.9
19.5
6.7
0.6
4.9
0.3
0.9
1.2
1.2
2.5
0.4
0.4
33.5
0.4
-
oT
4.7
80.1
0.4
16.8
3.3
1.7
23.3
8.1
4.4
6.9
182.5
2.7
9.3
2.0
37.0
19.9
351.9
41.0
I.I
2.3
4.5
3.1
3.9
18.1
0.6
37.0
4.0
59.5
0.6
35.7
6.0
5.5
0.3
233.0
36.2
24.3
3.3
7.7
1.0
21.3
6.9
0.6
5.3
0.3
1.0
1.3
1.2
2.5
0.4
0.4
36.9
0.4
-
OS] OS"
5.1
83.9
0.4
17.5
3.3
1.7
27.2
8.2
4.4
7.5
185.6
2.7
10.3
2.3
39.7
20.9
369.8
42.7
1.2
2.4
4.6
2.8
4.2
20.0
0.7
40.3
4.0
60.0
0.6
35.3
5.6
5.6
0.3
245.4
39.4
26.4
3.6
7.8
1.0
21.6
7.0
0.7
5.8
0.3
I.I
1.5
1.2
2.5
0.4
0.4
39.4
0.4
-
5.5
87.0
0.4
18.2
3.3
1.8
32.0
8.2
4.4
8.0
187.6
2.7
11.4
2.7
42.4
22.0
388.1
44.5
1.3
2.4
4.6
2.8
4.5
21.9
0.7
43.8
4.1
60.5
0.6
34.8
5.2
5.7
0.3
258.3
43.0
28.5
4.0
7.8
I.I
21.8
7.1
0.8
6.5
0.4
1.2
1.7
1.2
2.5
0.4
0.4
41.9
0.4
-
^^^^J
0.5
6.0
90.2
0.4
18.9
3.4
1.9
38.0
8.2
4.4
8.6
189.1
2.8
12.7
3.1
45.0
23.1
406.4
46.0
1.3
2.4
4.7
2.7
4.8
23.8
0.7
47.3
4.1
60.9
0.7
34.4
4.7
5.8
0.3
271.2
47.1
30.7
4.3
7.9
1.2
22.0
7.3
0.9
7.3
0.4
1.3
1.9
1.2
2.6
0.4
0.4
44.3
0.4
-
August 2011
Appendices
Page D-3
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
Non-OECD Asia
Non-OECD Europe &
EU
OPEC
1990 1995 2000
3.4
2.1
II. 1
10.0
13.0
5.2
2.0
21.5
5.4
5.7
21.9
3.4
19.9
157.2
3.5
2.0
0.0
3.6
0.7
18.5
0.3
19.1
5.2
2.4
2.8
8.7
21.0
2.1
3.2
42.4
0.2
30.4
200.3
9.8
10.2
8.5
5.3
97.4
31.1
5.8
6.6
3.4
166.4
266.9
29.4
572.4
483.7
285.4
294.1
54.0
3.7
2.3
12.2
10.8
14.0
1.2
2.1
26.9
5.9
6.0
15.9
3.4
12.5
97.2
2.9
2.3
0.0
2.0
0.8
14.8
1.7
17.4
5.2
2.2
2.2
10.1
18.9
1.6
3.5
26.0
0.4
29.0
202.3
10.4
9.1
8.6
8.4
106.1
24.1
6.3
7.1
2.8
173.5
291.7
31.3
553.3
562.5
176.8
250.3
56.8
4.6
2.4
10.2
11.6
16.2
1.8
2.0
29.8
7.0
7.0
15.0
3.7
9.2
77.0
3.2
2.6
0.0
1.7
0.8
11.0
3.1
22.4
4.9
2.2
1.9
10.3
19.4
1.7
4.1
14.8
0.5
27.4
204.5
8.7
8.5
9.2
12.5
122.3
31.1
6.9
8.2
2.8
192.4
331.7
34.3
571.2
584.6
139.5
248.0
62.9
2005 2010 2015 2020
3.7
2.3
8.8
12.8
18.5
1.9
2.0
36.1
7.6
7.5
14.5
2.9
11.0
71.8
3.3
2.7
O.I
1.7
0.7
7.3
4.5
18.9
4.8
2.1
1.6
11.5
19.0
3.0
4.8
14.7
0.6
25.2
210.6
10.7
7.4
10.4
12.4
135.7
37.8
8.2
9.4
3.4
213.6
373.5
36.3
564.7
656.8
138.9
231.8
67.4
5.6
2.7
9.6
12.7
20.0
2.1
2.1
40.5
8.0
8.2
16.8
3.0
II. 1
76.4
3.5
3.0
O.I
I.I
0.8
7.7
5.0
20.1
4.6
2.2
2.1
12.7
20.8
3.2
5.3
14.4
0.7
26.6
242.8
11.4
8.1
11.0
13.7
147.1
42.2
9.5
10.1
3.5
237.4
369.1
39.7
624.1
707.8
145.9
248.0
72.2
7.1
3.1
10.2
13.5
21.5
2.4
2.4
43.8
8.6
8.9
16.8
3.2
11.5
76.1
3.9
3.3
O.I
1.2
0.8
8.2
5.2
22.1
4.3
2.4
2.3
13.5
23.0
3.4
5.7
14.7
0.8
29.2
273.0
12.6
8.5
12.1
15.0
159.6
45.9
10.6
10.8
3.5
259.2
393.6
43.9
681.5
761.7
147.7
261.9
78.5
9.1
3.7
10.3
14.1
22.8
2.7
2.4
46.7
9.1
9.5
16.8
3.3
11.8
77.2
4.3
3.6
O.I
1.3
0.8
8.5
5.5
22.4
4.0
2.4
2.5
14.3
25.1
3.7
6.0
14.9
0.8
29.8
293.5
13.6
8.9
12.9
16.1
170.5
49.1
11.9
11.5
3.6
278.0
408.6
48.0
712.9
809.8
151.0
263.0
84.4
2025 2030
12.0
4.4
10.3
14.8
24.1
3.2
2.5
49.9
9.8
10.2
16.8
3.3
12.1
78.4
4.7
3.8
O.I
1.4
0.8
8.7
5.7
22.6
3.7
2.4
2.7
15.2
27.3
4.0
6.3
15.1
0.9
30.3
314.2
14.7
9.3
13.8
17.2
182.4
52.6
13.2
12.4
3.7
297.7
422.5
52.4
744.5
861.9
154.8
264.1
90.5
16.2
5.3
10.4
15.6
25.7
3.7
2.6
53.3
10.5
11.0
16.9
3.3
12.5
79.6
5.2
4.1
O.I
1.5
0.8
9.0
6.0
22.9
3.4
2.4
3.0
16.0
29.5
4.3
6.6
15.3
1.0
30.8
334.8
15.8
9.7
14.6
18.4
196.0
56.2
14.7
13.4
3.7
319.8
435.8
57.1
776.4
917.9
158.9
265.4
97.1
World Totals 1,804.2 1,789.1 1,853.6 1,983.8 2,124.0 2,287.4 2,408.3 2,533.8 2,665.9
August 2011
Appendices
Page D-4
-------
Table D-3: CH4 Emissions from Enteric Fermentation by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
1.2
3.4
57.4
0.9
63.9
3.8
3.4
8.2
10.0
4.1
7.6
184.9
3.8
12.3
2.9
16.9
5.8
185.9
25.9
1.5
1.2
4.9
3.3
5.9
6.8
I.I
28.8
1.9
31.1
2.2
21.8
2.9
3.1
0.3
181.8
18.7
10.4
2.6
9.5
0.6
12.2
7.7
0.4
14.1
O.I
2.4
2.1
2.1
3.1
0.3
0.7
38.8
1.7
-
1.7
3.3
59.0
0.7
59.4
3.6
2.6
8.9
7.2
4.1
8.7
200.4
1.8
13.0
3.6
20.0
6.7
221.4
29.1
1.3
0.8
3.0
3.1
6.7
7.6
0.6
27.9
1.7
30.2
1.7
19.0
2.9
1.9
0.2
190.1
20.2
10.4
2.0
9.6
0.7
12.3
7.6
0.5
12.1
O.I
1.5
2.5
0.8
1.5
0.3
0.7
36.5
1.5
-
2000 2005 2010
.
1.5
3.7
57.5
0.7
60.4
3.4
3.2
9.1
6.1
3.9
9.9
207.8
1.7
14.5
3.7
21.5
7.1
231.1
28.2
I.I
0.7
2.6
2.9
6.3
8.5
0.4
30.5
1.7
29.9
2.0
18.3
3.0
1.8
0.2
191.4
18.6
10.4
2.1
9.5
0.7
12.2
7.4
0.4
9.5
O.I
1.6
2.3
0.6
1.2
0.3
0.6
36.3
0.9
-
1.3
3.9
60.5
0.8
60.3
3.2
3.9
10.0
6.1
3.6
10.1
251.8
1.4
16.1
3.8
23.8
6.9
217.5
29.8
1.0
0.8
2.4
2.7
6.6
10.0
0.4
38.6
1.6
28.4
2.3
17.2
2.9
1.6
0.2
193.0
18.1
10.4
2.2
9.2
0.7
10.8
7.1
0.4
10.3
O.I
1.7
2.5
0.6
1.3
0.2
0.8
38.9
0.8
-
. .
1.2
4.3
62.9
0.9
57.4
3.2
4.3
11.5
6.0
3.5
11.0
219.9
1.2
18.7
4.4
22.4
6.9
212.5
31.1
I.I
0.8
2.3
2.7
6.7
12.2
0.4
49.6
1.5
28.8
1.8
16.8
2.8
1.5
0.2
195.1
21.2
10.4
2.3
8.8
0.7
11.0
7.1
0.6
11.8
O.I
1.9
2.9
0.6
1.2
0.3
0.8
44.7
0.6
-
1.2
4.7
67.0
0.9
59.6
3.2
4.3
13.5
5.9
3.4
12.1
230.6
1.2
21.1
4.8
22.9
7.3
225.3
33.1
I.I
0.8
2.3
2.6
7.2
13.7
0.4
56.1
1.5
28.6
1.8
16.3
2.7
1.5
0.3
202.4
23.8
10.4
2.4
8.5
0.8
11.0
7.1
0.7
12.7
0.2
2.1
3.2
0.6
1.2
0.2
0.8
47.8
0.5
-
KI5H EZ!z±9
.^^HHHH^^I,
1.2
5.2
69.7
0.9
61.4
3.2
4.3
15.8
5.7
3.4
13.0
231.7
1.2
23.6
5.3
23.2
7.6
234.5
34.4
1.2
0.8
2.2
2.5
7.6
15.3
0.4
61.2
1.5
28.3
1.8
15.7
2.6
1.5
0.3
209.0
26.1
10.4
2.6
8.4
0.8
II. 1
7.1
0.8
13.7
0.2
2.2
3.4
0.6
I.I
0.2
0.8
50.2
0.5
-
. .
I.I
5.8
71.6
0.9
63.1
3.2
4.4
18.7
5.6
3.3
13.9
230.5
1.2
26.3
5.7
23.4
7.9
243.4
35.6
1.2
0.7
2.2
2.5
8.1
17.1
0.4
66.5
1.5
28.0
1.7
15.4
2.7
1.5
0.3
215.8
28.7
10.4
2.8
8.4
0.8
11.2
7.0
0.9
14.9
0.2
2.3
3.7
0.6
I.I
0.2
0.9
52.5
0.5
-
,_^^HHHH^^H_
l.l
6.4
73.4
0.9
65.0
3.2
4.4
22.1
5.5
3.3
14.8
228.6
1.2
29.3
6.2
23.6
8.2
250.3
36.6
1.3
0.7
2.2
2.5
8.5
19.2
0.4
71.7
1.5
27.6
1.7
15.1
2.8
1.5
0.3
222.3
31.7
10.4
3.1
8.4
0.9
11.4
7.0
1.0
16.3
0.2
2.4
4.0
0.5
I.I
0.2
0.9
54.6
0.5
-
August 2011
Appendices
Page D-5
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
5.1
10.4
7.5
21.8
20.1
I.I
1.9
40.6
7.2
6.5
15.6
2.6
10.3
95.3
1.6
2.6
0.0
2.0
0.7
19.2
3.0
11.8
3.1
2.5
1.8
13.3
17.0
2.4
3.9
34.5
0.8
18.4
133.2
12.1
5.8
13.1
6.2
115.2
35.6
4.6
9.5
6.0
201.6
349.9
20.5
474.0
Non-OECD Asia I 504.4
Non-OECD Europe & | 204.2
EU 181. 1
OPEC | 61.4
5.6
11.3
7.3
22.2
22.8
0.9
2.0
44.8
7.8
7.1
10.8
2.8
6.9
68.6
1.8
2.9
0.0
1.4
0.7
17.5
2.9
12.0
3.1
2.4
1.5
13.2
16.5
1.8
4.2
24.4
I.I
18.1
143.6
13.3
6.5
14.9
7.3
127.3
35.3
4.5
10.0
4.8
214.9
375.2
20.5
468.7
560.0
149.7
159.7
66.5
6.6
11.9
6.5
23.5
26.4
0.8
2.0
51.6
8.5
8.1
9.7
3.0
5.7
43.9
1.6
3.2
0.0
I.I
0.7
18.5
2.7
13.4
2.9
2.3
1.4
7.7
14.5
2.2
4.8
13.8
1.5
17.4
134.4
12.2
6.6
15.5
8.3
146.1
33.4
5.3
9.9
3.9
242.8
379.4
21.5
456.9
575.6
108.0
153.7
71.6
2005 2010 2015 2020
4.7
12.9
6.3
23.9
29.3
0.9
1.9
58.5
9.1
8.7
8.9
3.0
5.9
39.4
1.6
3.4
0.0
1.0
0.7
18.1
1.9
13.5
2.8
2.3
2.8
9.0
14.0
3.4
5.7
10.8
1.8
15.9
136.0
12.2
7.9
16.9
11.3
162.7
34.5
6.5
10.3
3.5
272.7
431.5
23.0
453.7
577.2
106.1
145.7
76.9
6.6
14.5
6.4
23.6
31.9
1.0
1.8
71.8
9.6
9.1
9.1
2.9
6.0
37.3
1.5
3.7
0.0
0.7
0.7
18.9
2.4
12.5
2.6
2.4
3.7
10.4
14.9
3.5
6.4
8.4
1.9
15.1
140.6
13.1
9.1
18.0
13.4
174.1
39.1
7.5
11.2
3.4
302.1
411.3
24.4
458.3
604.4
105.0
142.8
81.2
8.0
16.8
6.3
24.4
34.7
I.I
1.9
79.8
10.5
9.7
9.3
2.9
6.0
37.1
1.7
4.0
0.0
0.6
0.7
19.7
2.5
12.7
2.4
2.4
3.9
10.9
15.6
3.8
6.8
8.0
2.0
15.3
145.0
14.5
9.6
19.7
14.8
188.7
42.5
8.3
12.2
3.4
329.5
437.3
25.7
469.6
647.5
106.1
141.7
87.5
9.8
19.3
6.2
25.1
37.5
1.3
1.9
88.5
11.2
10.2
9.5
3.0
6.0
36.7
1.8
4.4
0.0
0.6
0.7
20.2
2.7
12.9
2.2
2.3
4.2
11.4
16.4
4.1
7.2
7.7
2.2
15.5
147.7
15.6
10.0
21.1
16.0
201.6
45.2
9.2
13.3
3.4
353.7
449.4
27.2
478.0
687.6
107.3
140.8
93.3
2025 2030
12.3
22.3
6.1
26.0
40.3
1.6
2.0
98.5
12.0
10.9
9.7
3.0
6.0
36.3
2.0
4.7
0.0
0.6
0.7
20.6
2.9
13.1
2.2
2.3
4.4
11.8
17.2
4.4
7.6
7.4
2.3
15.8
150.4
16.8
10.5
22.5
17.4
215.6
48.0
10.2
14.5
3.4
379.4
459.1
28.9
487.3
731.7
108.8
140.9
99.4
15.8
25.6
6.1
26.9
43.3
1.9
2.0
109.7
12.8
11.5
9.9
3.0
6.0
35.8
2.2
5.0
0.0
0.5
0.7
21.0
3.1
13.3
2.2
2.3
4.7
12.3
18.0
4.8
8.0
7.1
2.5
16.0
153.0
18.0
11.0
23.9
18.9
230.8
50.6
11.4
15.9
3.4
406.7
467.2
30.8
495.9
777.6
110.4
140.6
105.6
World Totals 1,754.5 1,789.0 1,784.3 1,864.2 1,905.6 2,015.7 2,103.2 2,195.3 2,288.6
August 2011
Appendices
Page D-6
-------
Table D-4: CH4 Emissions from Rice Cultivation by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
0.0
0.0
0.4
0.5
-
0.0
14.6
0.2
5.0
O.I
22.3
13.0
-
O.I
139.1
3.5
0.2
-
0.7
4.0
-
O.I
O.I
O.I
264.8
44.0
2.2
0.3
-
1.6
7.0
0.5
-
0.0
0.8
-
-
-
0.3
-
-
0.0
0.6
0.6
-
0.0
13.9
0.2
5.9
0.0
28.2
13.5
-
O.I
129.1
3.0
0.2
-
1.0
5.4
0.0
O.I
O.I
0.0
265.5
47.9
2.4
0.7
-
1.7
7.1
0.4
-
0.0
0.7
-
-
0.0
0.2
-
-
0.0
0.6
0.7
-
0.0
15.1
0.3
7.6
0.0
29.5
13.4
-
0.2
125.7
2.3
0.2
-
0.9
6.0
O.I
O.I
O.I
0.0
277.4
49.4
2.3
0.4
-
1.4
6.0
0.4
-
0.0
0.9
-
-
0.0
0.2
-
-
2005 2010
0.0
1.0
0.3
-
0.0
14.7
0.3
8.8
0.0
34.6
16.9
-
0.2
120.8
1.7
0.2
-
1.0
5.6
0.0
O.I
O.I
0.0
270.8
49.6
2.7
0.4
-
1.5
5.8
0.4
-
0.0
1.0
-
-
0.0
O.I
-
-
. .
0.0
1.3
O.I
-
0.0
15.3
0.3
6.7
0.0
36.1
16.0
-
0.2
124.6
1.6
0.2
-
0.9
5.9
0.0
O.I
O.I
0.0
285.1
51.4
2.5
0.3
-
1.4
5.5
0.3
-
0.0
0.9
-
-
0.0
O.I
-
-
^^BHHH^^H
0.0
1.4
0.2
-
0.0
15.4
0.3
6.7
0.0
35.8
16.4
-
0.2
116.8
1.7
0.2
-
0.9
6.0
0.0
O.I
O.I
0.0
288.3
51.8
2.5
0.4
-
1.4
5.0
0.3
-
0.0
0.9
-
-
0.0
O.I
-
-
^^EZS£±^I
0.0
1.5
0.3
-
0.0
15.9
0.3
6.7
0.0
36.2
15.5
-
0.2
113.5
1.6
O.I
-
0.9
5.9
0.0
O.I
O.I
0.0
294.9
52.1
2.5
0.4
-
1.4
4.6
0.3
-
0.0
0.9
-
-
0.0
O.I
-
-
0.0
1.6
0.3
-
0.0
16.5
0.3
6.6
0.0
36.6
14.7
-
0.2
110.3
1.5
O.I
-
0.8
5.8
0.0
O.I
O.I
0.0
301.6
52.4
2.6
0.5
-
1.4
4.2
0.3
-
0.0
0.8
-
-
0.0
O.I
-
-
VTOTV
0.0
1.6
0.4
-
0.0
17.1
0.2
6.6
0.0
37.0
13.9
-
O.I
107.2
1.4
O.I
-
0.8
5.8
0.0
O.I
O.I
0.0
308.6
52.7
2.7
0.6
-
1.4
3.9
0.3
-
0.0
0.8
-
-
0.0
O.I
-
-
August 2011
Appendices
Page D-7
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
-
6.2
-
-
1.9
3.4
-
4.6
I.I
11.8
-
0.2
O.I
1.6
-
0.2
-
-
-
0.0
8.7
0.2
-
-
O.I
42.8
0.2
O.I
O.I
0.2
-
-
7.1
0.5
0.3
0.5
29.3
8.2
3.9
0.0
11.8
-
14.4
15.8
2.5
OECD | 26.2
Non-OECD Asia 608.5
Non-OECD Europe & | 2.9
EU 2.4
OPEC | 5.6
-
6.4
-
-
2.8
3.3
-
4.7
1.2
13.4
-
O.I
0.0
0.9
-
0.2
-
-
-
0.0
7.7
O.I
-
-
O.I
44.3
0.2
O.I
O.I
O.I
-
-
7.6
0.6
0.3
0.8
32.8
10.0
4.3
0.0
12.1
-
18.6
17.8
3.1
25.9
615.9
1.9
2.2
7.7
-
6.7
-
-
3.4
3.1
-
5.1
1.6
14.4
-
0.3
0.0
0.9
-
0.2
-
-
-
0.0
7.3
0.3
-
-
0.2
48.1
0.2
O.I
O.I
O.I
-
-
7.5
0.7
0.2
0.6
37.1
12.6
4.7
-
11.8
-
22.5
19.3
H2.7
2005 2010 2015 2020
-
6.6
-
-
3.8
3.3
-
5.7
2.0
14.5
-
0.3
0.0
0.7
-
0.2
-
-
-
0.0
6.7
0.3
-
-
O.I
49.7
0.4
O.I
0.2
O.I
-
-
6.8
1.0
O.I
1.0
35.5
15.2
4.7
-
12.5
-
25.2
21.5
3.1
24.2 22.7
637.7
2.0
2.2
7.5
636.3
1.6
2.3
8.9
-
6.3
-
-
4.8
3.2
-
6.5
1.9
15.5
-
0.3
0.0
0.7
-
0.2
-
-
-
0.0
6.3
0.3
-
-
O.I
53.8
0.5
O.I
O.I
O.I
-
-
6.5
1.0
O.I
0.9
34.8
14.5
4.4
-
12.4
-
25.8
19.2
2.8
21.2
661.8
1.5
2.2
9.5
-
6.4
-
-
4.8
3.2
-
6.9
2.0
15.8
-
0.3
0.0
0.7
-
0.2
-
-
-
0.0
5.7
0.3
-
-
O.I
53.6
0.5
O.I
O.I
O.I
-
-
5.9
I.I
O.I
0.9
36.0
14.8
4.5
-
12.6
-
26.2
19.6
2.8
19.8
660.0
1.6
2.2
9.5
-
6.1
-
-
4.8
3.1
-
6.9
1.9
16.2
-
0.3
0.0
0.7
-
0.2
-
-
-
0.0
5.4
0.3
-
-
O.I
53.9
0.5
O.I
O.I
O.I
-
-
6.0
1.2
O.I
0.9
36.7
14.0
4.3
-
12.5
-
25.3
19.1
3.0
19.2
664.4
1.5
2.2
9.6
2025 2030
-
5.7
-
-
4.8
2.9
-
6.9
1.8
16.6
-
0.3
0.0
0.6
-
0.2
-
-
-
0.0
5.1
0.3
-
-
O.I
54.3
0.5
O.I
O.I
O.I
-
-
6.2
1.3
O.I
0.8
37.4
13.3
4.1
-
12.4
-
24.4
18.7
3.1
18.7
669.2
1.4
2.2
9.6
-
5.4
-
-
4.7
2.7
-
7.0
1.7
17.0
-
0.3
0.0
0.6
-
0.2
-
-
-
0.0
4.7
0.3
-
-
O.I
54.6
0.4
O.I
O.I
O.I
-
-
6.3
1.4
O.I
0.8
38.1
12.6
3.8
-
12.3
-
23.5
18.3
3.3
18.2
674.4
1.3
2.2
9.6
World Totals 670.4 683.2 708.3 710.4 732.3 729.9 732.4 735.5 739.1
August 2011
Appendices
Page D-8
-------
Table D-5: CH4 Emissions from Manure Management by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
TrzSnK
O.I
0.2
1.3
O.I
1.5
I.I
0.6
0.9
1.2
1.7
0.3
7.1
1.5
I.I
0.4
2.4
0.5
16.0
0.6
O.I
0.2
1.0
0.8
0.2
0.5
O.I
1.0
0.2
13.9
0.2
6.2
0.5
2.3
0.0
18.6
2.9
-
0.4
2.3
0.2
3.5
3.1
0.0
1.9
0.0
0.2
0.3
0.3
0.4
O.I
O.I
1.2
0.3
-
^^£1^1
oT
0.2
1.3
0.0
1.7
1.0
0.5
0.9
0.9
1.8
0.3
7.8
0.7
1.3
0.5
2.7
0.6
18.8
0.7
O.I
0.2
0.7
0.9
0.2
0.6
O.I
1.0
0.2
13.8
0.2
5.5
0.5
1.4
0.0
20.2
3.3
-
0.4
2.4
0.2
3.3
2.9
0.0
1.7
0.0
0.2
0.4
O.I
0.2
O.I
O.I
I.I
0.2
-
oTj oTj oT
0.2
1.2
0.0
2.0
0.9
0.6
1.0
0.8
1.8
0.4
7.2
0.6
1.7
0.5
2.9
0.6
19.5
0.8
O.I
0.2
0.6
1.0
O.I
0.6
O.I
I.I
0.3
14.1
0.2
5.6
0.5
1.4
0.0
20.0
2.4
-
0.4
2.3
0.2
3.3
2.7
0.0
1.4
0.0
0.2
0.3
O.I
0.2
O.I
O.I
I.I
0.2
-
0.2
I.I
0.0
2.0
0.9
0.8
I.I
0.7
1.6
0.4
8.1
0.5
2.3
0.6
3.1
0.9
19.1
0.7
O.I
0.2
0.5
1.0
O.I
0.7
O.I
1.4
0.3
14.0
0.2
5.5
0.5
I.I
0.0
20.5
3.0
-
0.4
2.3
0.3
3.2
2.5
0.0
1.2
0.0
0.2
0.4
O.I
0.2
O.I
0.0
I.I
O.I
-
0.2
1.3
0.0
1.9
0.9
0.8
1.2
0.8
1.6
0.4
8.8
0.5
3.1
0.6
2.9
1.0
20.1
0.6
O.I
0.2
0.3
I.I
O.I
0.9
O.I
1.8
0.3
13.7
O.I
4.8
0.5
1.0
0.0
21.2
3.5
-
0.4
2.0
0.3
3.2
2.5
0.0
1.3
0.0
0.2
0.6
O.I
0.2
O.I
O.I
I.I
O.I
-
|
O.I
0.2
1.3
0.0
1.9
0.9
0.8
1.4
0.8
1.5
0.5
9.2
0.4
3.3
0.6
2.9
1.0
20.4
0.6
O.I
0.2
0.3
1.2
O.I
1.0
O.I
2.0
0.3
13.4
O.I
4.6
0.5
1.0
0.0
22.2
3.9
-
0.4
2.0
0.3
3.2
2.5
0.0
1.4
0.0
0.2
0.6
O.I
0.2
O.I
O.I
I.I
O.I
-
1
oT
0.2
1.4
0.0
2.0
0.9
0.8
1.7
0.7
1.5
0.5
9.6
0.4
3.5
0.7
2.9
I.I
20.7
0.6
O.I
0.2
0.3
1.3
O.I
1.0
O.I
2.2
0.3
13.1
O.I
4.5
0.5
1.0
0.0
23.3
4.4
-
0.5
1.9
0.3
3.1
2.4
0.0
1.5
0.0
0.2
0.7
O.I
0.2
O.I
O.I
1.2
O.I
-
O.I
0.3
1.5
0.0
2.1
0.9
0.8
1.9
0.7
1.5
0.5
9.9
0.4
3.8
0.7
2.9
I.I
21.1
0.6
O.I
0.2
0.3
1.3
O.I
I.I
O.I
2.4
0.3
12.8
O.I
4.4
0.5
1.0
0.0
24.4
4.9
-
0.5
1.9
0.3
3.1
2.4
0.0
1.5
0.0
0.2
0.7
O.I
0.2
O.I
O.I
1.2
O.I
-
O.I
0.3
1.5
0.0
2.1
0.9
0.8
2.3
0.7
1.4
0.5
10.3
0.4
4.1
0.8
2.9
I.I
21.4
0.6
O.I
0.2
0.3
1.3
O.I
1.3
O.I
2.6
0.3
12.5
O.I
4.2
0.5
1.0
0.0
25.5
5.5
-
0.5
1.9
0.3
3.0
2.3
0.0
1.6
0.0
0.2
0.8
O.I
0.2
O.I
O.I
1.2
O.I
-
August 2011
Appendices
Page D-9
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
0.2
0.7
3.0
0.6
1.0
0.8
0.3
3.7
0.2
2.6
3.4
1.2
3.8
7.4
0.2
0.2
O.I
0.4
0.5
1.8
0.9
6.2
0.3
0.6
0.2
2.7
0.6
0.5
O.I
17.7
0.0
3.6
30.4
0.3
0.4
0.7
2.2
11.4
2.4
0.2
2.6
1.7
16.3
13.1
0.9
OECD | 94.4
Non-OECD Asia 55.7
0.2
0.7
3.1
0.6
1.2
0.4
0.3
4.1
0.2
2.9
3.6
1.2
2.4
5.3
0.2
0.2
O.I
0.3
0.5
1.7
0.6
7.1
0.4
0.5
0.2
3.0
0.7
0.3
0.2
7.9
0.0
3.5
34.5
0.3
0.5
0.7
2.9
12.4
2.3
0.2
3.0
1.5
17.4
14.0
0.9
97.8
62.6
Non-OECD Europe & | 38.9 23.2
EU 58.5 54.9
OPEC | 3.0 | 3.3
0.2
0.9
2.8
0.7
1.4
0.4
0.3
5.2
0.3
3.4
3.3
1.2
1.8
3.6
0.2
0.2
0.0
0.2
0.5
1.7
0.4
8.4
0.4
0.5
0.2
3.4
0.7
0.3
0.2
1.0
0.0
3.3
37.9
0.3
0.5
0.7
3.5
14.5
2.2
0.3
2.3
1.5
20.1
13.1
1.0
101.9
64.7
13.5
54.7
3.5
2005 2010 2015 2020
0.2
0.9
2.6
0.7
1.7
0.5
0.3
5.9
0.3
3.9
3.6
1.2
2.0
3.1
0.2
0.2
O.I
0.2
0.4
1.7
0.2
8.8
0.5
0.5
0.4
3.8
0.8
0.5
0.2
0.9
0.0
2.9
41.8
0.3
0.6
0.7
4.8
16.6
2.4
0.3
2.6
1.3
22.9
14.2
I.I
105.3
69.6
13.2
54.1
3.9
0.2
1.0
2.7
0.7
1.9
0.3
0.3
7.1
0.3
4.3
3.1
1.2
2.1
3.6
0.2
0.3
O.I
0.2
0.4
1.8
0.2
9.2
0.4
0.5
0.5
4.1
0.8
0.5
0.3
0.9
0.0
2.8
44.6
0.3
0.6
0.7
4.8
18.1
2.6
0.4
2.6
1.4
25.3
15.1
I.I
106.5
74.9
14.0
52.5
4.1
0.3
I.I
2.7
0.7
2.0
0.4
0.3
7.7
0.3
4.6
3.4
I.I
2.0
3.5
0.2
0.3
O.I
0.2
0.4
1.8
0.2
9.0
0.4
0.5
0.5
4.6
0.9
0.5
0.3
0.9
0.0
2.8
44.2
0.3
0.7
0.8
5.1
19.6
2.7
0.4
2.8
1.4
27.3
15.9
1.2
105.7
79.0
14.0
52.0
4.3
0.3
1.2
2.6
0.7
2.1
0.4
0.3
8.2
0.4
4.9
3.4
I.I
2.0
3.5
0.3
0.3
O.I
0.2
0.4
1.8
0.2
8.9
0.4
0.5
0.5
5.0
0.9
0.6
0.3
0.8
0.0
2.7
43.7
0.3
0.7
0.8
5.5
21.0
2.9
0.4
2.9
1.4
29.1
16.6
1.3
104.6
83.6
14.0
51.3
4.6
2025 2030
0.4
1.3
2.6
0.8
2.2
0.4
0.3
8.9
0.4
5.3
3.5
I.I
2.0
3.4
0.3
0.3
O.I
0.2
0.4
1.8
0.2
8.7
0.4
0.5
0.6
5.5
1.0
0.6
0.3
0.8
0.0
2.7
43.3
0.3
0.7
0.9
5.9
22.5
3.0
0.5
3.1
1.4
31.1
17.3
1.4
103.5
88.5
14.1
50.5
4.9
0.5
1.5
2.5
0.8
2.3
0.5
0.3
9.7
0.4
5.7
3.6
I.I
2.0
3.4
0.3
0.4
O.I
0.2
0.4
1.9
0.2
8.5
0.4
0.5
0.6
6.1
I.I
0.6
0.4
0.8
O.I
2.7
42.8
0.4
0.8
0.9
6.2
24.1
3.2
0.5
3.3
1.4
33.3
18.1
1.5
102.5
94.0
14.2
49.8
5.2
World Totals 219.2 215.9 214.2 226.2 236.8 243.0 249.2 256.0 263.6
August 2011
Appendices
Page D-10
-------
Table D-6: N2O Emissions from Manure Management by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
o6~
0.8
O.I
O.I
0.5
1.0
0.2
0.2
0.0
1.0
0.0
5.8
1.0
1.9
1.0
3.5
1.8
11.9
0.0
0.0
0.4
0.7
0.7
0.2
0.0
0.3
O.I
0.7
6.9
O.I
2.9
0.3
2.2
0.0
0.3
3.0
0.0
4.6
0.4
0.2
3.9
5.7
0.0
0.4
0.0
0.0
0.4
0.6
0.9
0.0
O.I
0.0
0.2
-
^^£1^1
oo~
1.0
O.I
O.I
0.9
1.0
0.2
0.2
0.0
1.0
0.0
6.4
0.5
2.1
1.3
4.2
1.2
14.5
0.0
0.0
0.2
0.5
0.6
0.3
0.0
0.2
O.I
0.6
6.6
O.I
2.6
0.3
1.3
0.0
0.3
3.5
0.0
7.2
0.4
0.2
3.8
5.2
0.0
0.3
0.0
0.0
0.5
0.2
0.4
0.0
O.I
0.0
O.I
-
^fT^^H
oo~
1.1
0.2
O.I
1.3
0.9
0.2
0.2
0.0
1.0
0.0
5.9
0.4
2.3
1.3
4.6
1.3
15.1
0.0
0.0
0.2
0.4
0.6
0.2
0.0
O.I
0.2
0.6
6.5
O.I
2.5
0.3
1.3
0.0
0.3
3.1
0.0
6.6
0.4
0.3
3.9
5.0
0.0
0.3
0.0
0.0
0.4
0.2
0.3
0.0
O.I
0.0
O.I
-
2005 2010
0.0
1.2
O.I
O.I
1.5
0.9
0.2
0.3
0.0
0.9
0.0
6.5
0.4
2.6
1.4
5.0
2.0
13.7
0.0
0.0
0.2
0.4
0.6
0.2
O.I
O.I
0.2
0.5
6.1
0.2
2.4
0.3
1.2
0.0
0.3
3.2
0.0
6.6
0.4
0.3
3.7
4.8
0.0
0.3
0.0
0.0
0.5
0.2
0.3
0.0
0.2
0.0
O.I
-
0.0
1.2
0.2
O.I
1.6
0.9
0.3
0.3
0.0
0.8
0.0
7.8
0.3
2.9
1.6
4.8
2.3
13.8
0.0
0.0
0.2
0.3
0.6
0.2
O.I
O.I
0.2
0.5
6.1
O.I
2.9
0.3
I.I
0.0
0.3
3.9
0.0
5.7
0.4
0.3
3.9
5.0
0.0
0.3
0.0
0.0
0.6
0.2
0.3
0.0
0.2
0.0
0.0
-
1 1
0.0
1.2
0.2
O.I
1.7
0.9
0.3
0.4
0.0
0.8
0.0
8.2
0.3
3.3
1.7
4.9
2.4
14.9
0.0
0.0
0.2
0.3
0.5
0.2
O.I
O.I
0.3
0.5
6.0
O.I
2.9
0.3
I.I
0.0
0.3
4.3
0.0
5.7
0.4
0.3
4.0
5.0
0.0
0.4
0.0
0.0
0.6
0.2
0.3
0.0
0.2
0.0
0.0
-
1
ooj 06"
1.3
0.2
O.I
1.7
0.9
0.3
0.4
0.0
0.8
0.0
8.7
0.3
3.7
1.9
5.0
2.5
15.7
0.0
0.0
0.2
0.3
0.5
0.2
O.I
O.I
0.3
0.5
5.9
O.I
2.8
0.3
I.I
0.0
0.3
4.7
0.0
5.8
0.4
0.3
4.1
5.0
0.0
0.4
0.0
0.0
0.7
0.2
0.3
0.0
0.2
0.0
0.0
-
1.3
0.2
O.I
1.7
0.9
0.3
0.5
0.0
0.8
0.0
9.3
0.3
4.2
2.0
5.1
2.6
16.5
0.0
0.0
0.2
0.3
0.5
0.2
O.I
O.I
0.3
0.5
5.9
O.I
2.8
0.3
1.0
0.0
0.3
5.1
0.0
6.0
0.4
0.4
4.3
4.9
0.0
0.4
0.0
0.0
0.7
0.2
0.3
0.0
0.2
0.0
0.0
-
^^^^|
o6~
1.3
0.2
O.I
1.7
0.9
0.3
0.6
0.0
0.8
0.0
9.8
0.3
4.6
2.2
5.2
2.7
17.2
0.0
0.0
0.2
0.3
0.5
0.2
O.I
O.I
0.3
0.5
5.8
O.I
2.7
0.3
1.0
0.0
0.4
5.6
0.0
6.3
0.4
0.4
4.6
4.9
0.0
0.4
0.0
0.0
0.8
0.2
0.3
0.0
0.2
0.0
0.0
-
August 2011
Appendices
Page D-1 I
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
0.4
0.6
0.8
0.0
0.5
0.0
O.I
0.4
0.6
3.3
9.2
0.6
2.8
48.4
2.2
0.0
0.0
I.I
0.3
0.4
1.9
2.5
0.7
0.4
0.2
5.8
-
0.0
0.2
7.6
0.0
2.2
12.1
0.0
0.3
0.4
0.9
6.8
6.6
O.I
1.0
0.5
8.9
13.8
6.8
64.3
Non-OECD Asia 30.9
Non-OECD Europe & | 63.9
EU 43.4
OPEC | 8.8
World Totals
0.4
0.6
0.9
0.0
0.6
0.0
O.I
0.5
0.6
3.6
7.5
0.6
2.0
33.0
2.4
0.0
0.0
0.7
0.2
0.0
2.6
2.7
0.6
0.4
O.I
6.0
-
0.0
0.2
5.9
0.0
2.0
12.9
0.0
0.2
0.5
I.I
7.2
8.1
O.I
1.0
0.4
9.3
16.0
9.7
61.9
35.4
44.0
37.3
12.1
0.5
0.7
0.9
O.I
0.8
0.0
O.I
0.5
0.7
4.2
6.5
0.6
1.6
20.7
3.2
O.I
0.0
0.5
0.2
0.0
2.4
2.8
0.6
0.4
O.I
3.9
-
0.0
0.2
3.6
0.0
2.0
14.0
0.0
0.2
0.4
1.3
6.0
7.6
O.I
0.8
0.3
8.4
15.1
9.9
62.0
28.7
35.1
12.4
2005 2010 2015 2020
0.2
0.8
0.9
O.I
0.9
0.0
O.I
0.6
0.8
4.5
5.9
0.6
1.7
19.3
4.0
O.I
0.0
0.4
0.2
0.0
1.7
3.0
0.5
0.4
O.I
4.4
-
0.0
0.3
3.1
0.0
1.7
14.2
0.0
0.3
0.5
1.8
7.8
10.6
O.I
0.9
0.3
10.6
18.8
10.6
60.6
35.2
27.0
33.1
13.5
0.4
0.9
0.9
O.I
1.0
0.0
O.I
0.8
0.9
4.8
5.7
0.6
1.7
18.8
4.2
O.I
0.0
0.3
0.2
0.0
2.1
2.9
0.5
0.4
0.2
5.1
-
0.0
0.3
2.7
0.0
1.6
14.8
0.0
0.3
0.5
2.0
8.7
10.2
O.I
1.0
0.3
11.7
19.7
9.9
61.9
38.3
26.1
33.1
12.9
0.4
1.0
0.9
O.I
I.I
0.0
O.I
0.9
0.9
5.1
5.7
0.6
1.7
18.6
4.4
O.I
0.0
0.2
0.2
0.0
2.2
2.9
0.5
0.4
0.2
5.4
-
0.0
0.4
2.6
0.0
1.6
15.3
0.0
0.3
0.5
2.2
9.2
10.8
O.I
I.I
0.3
12.4
20.9
10.1
62.7
41.6
25.9
32.9
13.2
0.4
I.I
0.9
O.I
I.I
0.0
O.I
1.0
1.0
5.4
5.8
0.6
1.6
18.4
4.5
O.I
0.0
0.2
0.2
0.0
2.3
2.9
0.5
0.4
0.2
5.7
-
0.0
0.4
2.5
0.0
1.6
15.7
O.I
0.3
0.6
2.4
9.8
11.4
O.I
1.2
0.3
13.0
22.2
10.5
63.3
44.7
25.6
32.8
13.7
2025 2030
0.5
1.3
0.8
O.I
1.2
0.0
O.I
1.2
I.I
5.8
5.9
0.6
1.6
18.2
4.8
O.I
0.0
0.2
0.2
0.0
2.4
2.9
0.5
0.4
0.2
6.1
-
0.0
0.5
2.5
0.0
1.6
16.0
O.I
0.4
0.6
2.6
10.3
12.0
O.I
1.3
0.3
13.8
23.5
10.9
64.1
48.0
25.3
32.8
14.3
0.5
1.4
0.8
O.I
1.2
0.0
O.I
1.4
1.2
6.1
6.1
0.6
1.5
17.9
5.0
O.I
0.0
0.2
0.2
0.0
2.5
2.8
0.5
0.4
0.2
6.4
-
0.0
0.5
2.4
0.0
1.6
16.4
O.I
0.4
0.6
2.8
10.9
12.7
O.I
1.4
0.3
14.5
24.9
11.4
64.8
51.4
24.8
32.9
14.9
188.7 176.4 158.8 162.8 167.7 173.6 179.3 185.5 191.8
August 2011
Appendices
Page D-12
-------
Table D-7: CH4 Emissions from Other Agricultural Sources by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
o6~
0.0
3.7
0.0
12.6
O.I
0.0
0.6
0.0
0.0
10.4
57.0
O.I
39.6
1.5
2.8
0.2
2.5
3.3
86.9
0.0
0.2
0.0
O.I
0.0
0.0
5.6
0.0
0.2
0.0
(0.5)
O.I
O.I
0.0
6.4
25.7
0.0
0.0
0.0
0.3
0.3
0.0
6.5
0.0
0.2
2.6
0.0
0.0
0.0
0.0
2.6
0.0
-
^^£1^1
oo~
0.0
2.9
0.0
9.9
0.0
0.0
0.6
0.0
0.0
7.5
41.9
O.I
40.0
1.5
18.5
0.2
2.5
2.4
68.9
0.0
O.I
0.0
O.I
0.0
0.0
5.0
0.0
O.I
0.0
(0.5)
O.I
O.I
0.0
6.5
17.7
0.0
0.0
0.0
0.2
0.3
0.0
1.7
0.0
O.I
2.6
0.0
0.0
0.0
0.0
3.1
0.0
-
^fT^^H
02
0.0
3.0
0.0
17.6
0.0
0.0
0.4
0.0
0.0
5.7
24.4
0.3
12.7
0.9
2.1
O.I
1.3
2.8
50.6
O.I
O.I
0.0
0.2
0.0
0.0
4.9
0.0
O.I
0.0
(0.5)
O.I
O.I
0.0
7.0
10.6
0.0
0.0
0.0
0.2
O.I
0.0
2.0
0.0
O.I
1.2
0.0
0.0
0.0
0.0
3.4
0.0
-
MtCO2e
2005 2010
0.0
0.0
2.7
0.0
8.7
0.0
0.0
0.5
0.0
0.0
11.6
106.6
O.I
10.8
3.8
3.3
0.2
1.0
1.2
43.1
0.0
0.0
0.0
0.2
0.0
0.0
5.1
0.0
O.I
0.0
(0.5)
0.0
O.I
0.0
6.0
32.3
0.0
0.0
0.0
O.I
O.I
0.0
2.1
0.0
0.0
3.9
0.0
0.0
0.0
0.0
3.8
0.0
-
0.0
0.0
2.7
0.0
8.7
0.0
0.0
0.5
0.0
0.0
11.6
106.6
O.I
10.8
3.8
3.3
0.2
1.0
1.2
43.1
0.0
0.0
0.0
0.2
0.0
0.0
5.1
0.0
O.I
0.0
(0.5)
0.0
O.I
0.0
6.0
32.3
0.0
0.0
0.0
O.I
O.I
0.0
2.1
0.0
0.0
3.9
0.0
0.0
0.0
0.0
3.8
0.0
-
0.0
0.0
2.7
0.0
8.7
0.0
0.0
0.5
0.0
0.0
11.6
106.6
O.I
10.8
3.8
3.3
0.2
1.0
1.2
43.1
0.0
0.0
0.0
0.2
0.0
0.0
5.1
0.0
O.I
0.0
(0.5)
0.0
O.I
0.0
6.0
32.3
0.0
0.0
0.0
O.I
O.I
0.0
2.1
0.0
0.0
3.9
0.0
0.0
0.0
0.0
3.8
0.0
-
1
oo~
0.0
2.7
0.0
8.7
0.0
0.0
0.5
0.0
0.0
11.6
106.6
O.I
10.8
3.8
3.3
0.2
1.0
1.2
43.1
0.0
0.0
0.0
0.2
0.0
0.0
5.1
0.0
O.I
0.0
(0.5)
0.0
O.I
0.0
6.0
32.3
0.0
0.0
0.0
O.I
O.I
0.0
2.1
0.0
0.0
3.9
0.0
0.0
0.0
0.0
3.8
0.0
-
0.0
0.0
2.7
0.0
8.7
0.0
0.0
0.5
0.0
0.0
11.6
106.6
O.I
10.8
3.8
3.3
0.2
1.0
1.2
43.1
0.0
0.0
0.0
0.2
0.0
0.0
5.1
0.0
O.I
0.0
(0.5)
0.0
O.I
0.0
6.0
32.3
0.0
0.0
0.0
O.I
O.I
0.0
2.1
0.0
0.0
3.9
0.0
0.0
0.0
0.0
3.8
0.0
-
o6~
0.0
2.7
0.0
8.7
0.0
0.0
0.5
0.0
0.0
11.6
106.6
O.I
10.8
3.8
3.3
0.2
1.0
1.2
43.1
0.0
0.0
0.0
0.2
0.0
0.0
5.1
0.0
O.I
0.0
(0.5)
0.0
O.I
0.0
6.0
32.3
0.0
0.0
0.0
O.I
O.I
0.0
2.1
0.0
0.0
3.9
0.0
0.0
0.0
0.0
3.8
0.0
-
August 2011
Appendices
Page D-13
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
1.0
0.2
0.0
0.0
3.4
1.0
0.0
0.2
1.0
1.5
0.2
O.I
O.I
14.6
O.I
0.7
0.0
O.I
0.0
1.9
0.0
0.8
0.0
O.I
0.0
3.2
0.7
0.0
2.6
0.3
0.0
O.I
5.2
0.0
0.0
2.6
1.7
176.3
6.5
O.I
7.4
O.I
277.5
84.7
O.I
OECD | 26.4
Non-OECD Asia 95.0
Non-OECD Europe & | 22. 1
EU 2.0
OPEC | 46.9
World Totals
1.0
0.2
0.0
0.0
3.0
1.0
0.0
0.2
0.8
1.5
0.2
O.I
O.I
4.0
0.0
0.6
0.0
0.0
0.0
1.5
0.0
0.5
0.0
O.I
0.0
3.4
0.7
0.0
2.3
0.2
0.0
O.I
1.2
0.0
0.0
1.9
1.9
146.4
5.2
O.I
7.0
O.I
227.7
62.6
O.I
34.9
87.6
6.4
1.2
37.3
0.2
0.3
0.0
0.0
2.9
0.2
0.0
0.4
1.5
1.3
0.2
0.4
0.2
20.3
0.0
0.8
0.0
0.0
0.0
1.4
0.0
0.5
0.0
0.0
0.0
2.2
0.7
0.0
1.7
0.2
0.0
O.I
4.1
0.0
O.I
2.5
1.9
128.1
16.4
O.I
2.4
0.6
190.4
56.5
O.I
29.4
42.8
24.2
1.8
37.0
2005 2010 2015 2020
0.0
0.3
0.0
0.0
2.1
O.I
0.0
0.3
0.5
1.5
O.I
0.7
O.I
8.6
0.0
0.9
0.0
0.0
0.0
2.4
0.0
0.3
0.0
0.0
0.0
2.9
0.8
0.0
3.0
0.4
0.0
0.0
2.9
0.0
O.I
2.9
2.5
129.0
5.8
O.I
4.4
O.I
185.8
131.5
O.I
20.9
70.3
11.7
1.5
37.8
0.0
0.3
0.0
0.0
2.1
O.I
0.0
0.3
0.5
1.5
O.I
0.7
O.I
8.6
0.0
0.9
0.0
0.0
0.0
2.4
0.0
0.3
0.0
0.0
0.0
2.9
0.8
0.0
3.0
0.4
0.0
0.0
2.9
0.0
O.I
2.9
2.5
129.0
5.8
O.I
4.4
O.I
185.8
131.5
O.I
20.9
70.3
11.7
1.5
37.8
0.0
0.3
0.0
0.0
2.1
O.I
0.0
0.3
0.5
1.5
O.I
0.7
O.I
8.6
0.0
0.9
0.0
0.0
0.0
2.4
0.0
0.3
0.0
0.0
0.0
2.9
0.8
0.0
3.0
0.4
0.0
0.0
2.9
0.0
O.I
2.9
2.5
129.0
5.8
O.I
4.4
O.I
185.8
131.5
O.I
20.9
70.3
11.7
1.5
37.8
0.0
0.3
0.0
0.0
2.1
O.I
0.0
0.3
0.5
1.5
O.I
0.7
O.I
8.6
0.0
0.9
0.0
0.0
0.0
2.4
0.0
0.3
0.0
0.0
0.0
2.9
0.8
0.0
3.0
0.4
0.0
0.0
2.9
0.0
O.I
2.9
2.5
129.0
5.8
O.I
4.4
O.I
185.8
131.5
O.I
20.9
70.3
11.7
1.5
37.8
2025 2030
0.0
0.3
0.0
0.0
2.1
O.I
0.0
0.3
0.5
1.5
O.I
0.7
O.I
8.6
0.0
0.9
0.0
0.0
0.0
2.4
0.0
0.3
0.0
0.0
0.0
2.9
0.8
0.0
3.0
0.4
0.0
0.0
2.9
0.0
O.I
2.9
2.5
129.0
5.8
O.I
4.4
O.I
185.8
131.5
O.I
20.9
70.3
11.7
1.5
37.8
0.0
0.3
0.0
0.0
2.1
O.I
0.0
0.3
0.5
1.5
O.I
0.7
O.I
8.6
0.0
0.9
0.0
0.0
0.0
2.4
0.0
0.3
0.0
0.0
0.0
2.9
0.8
0.0
3.0
0.4
0.0
0.0
2.9
0.0
O.I
2.9
2.5
129.0
5.8
O.I
4.4
O.I
185.8
131.5
O.I
20.9
70.3
11.7
1.5
37.8
505.9 419.3 343.4 420.4 420.4 420.4 420.4 420.4 420.4
August 2011
Appendices
Page D-14
-------
Table D-8: N2O Emissions from Other Agricultural Sources by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
oo~
0.0
4.5
0.0
16.6
O.I
0.0
1.0
0.0
0.0
18.2
98.8
O.I
68.7
1.8
2.8
0.2
3.0
5.6
135.3
0.0
0.2
0.0
O.I
0.0
0.0
7.8
0.0
O.I
0.0
O.I
O.I
O.I
0.0
5.6
54.6
0.0
-
0.0
0.0
0.3
0.3
0.0
8.3
0.0
0.3
3.8
0.0
O.I
0.0
0.0
2.9
0.0
-
^^£1^1
oo~
0.0
3.4
0.0
12.9
0.0
0.0
1.0
0.0
0.0
16.3
90.6
O.I
77.4
1.8
19.6
O.I
3.0
5.2
127.9
0.0
O.I
0.0
O.I
0.0
0.0
6.8
0.0
O.I
0.0
O.I
O.I
O.I
0.0
5.7
68.2
0.0
-
0.0
0.0
0.2
0.3
0.0
2.1
0.0
O.I
4.0
0.0
0.0
0.0
0.0
3.3
0.0
-
ttjMij^
(D~
0.0
3.5
0.0
22.9
0.0
0.0
0.9
0.0
0.0
15.5
80.7
0.4
57.3
1.0
1.8
O.I
1.7
5.3
109.7
O.I
O.I
0.0
0.2
0.0
0.0
6.8
0.0
O.I
0.0
O.I
O.I
O.I
0.0
7.2
77.4
0.0
-
0.0
0.0
0.2
O.I
0.0
2.7
0.0
O.I
3.0
0.0
0.0
0.0
O.I
5.3
0.0
-
2005 2010
0.0
0.0
2.7
0.0
11.0
0.0
0.0
0.9
0.0
0.0
21.1
120.9
O.I
52.3
3.5
2.9
O.I
1.2
3.7
106.2
0.0
0.0
0.0
O.I
0.0
0.0
6.9
0.0
O.I
0.0
0.0
0.0
O.I
0.0
6.3
101.3
0.0
-
0.0
0.0
O.I
O.I
0.0
2.8
0.0
0.0
5.8
0.0
0.0
0.0
0.0
5.5
0.0
-
0.0
0.0
2.7
0.0
11.0
0.0
0.0
0.9
0.0
0.0
21.1
120.9
O.I
52.3
3.5
2.9
O.I
1.2
3.7
106.2
0.0
0.0
0.0
O.I
0.0
0.0
6.9
0.0
O.I
0.0
0.0
0.0
O.I
0.0
6.3
101.3
0.0
-
0.0
0.0
O.I
O.I
0.0
2.8
0.0
0.0
5.8
0.0
0.0
0.0
0.0
5.5
0.0
-
0.0
0.0
2.7
0.0
11.0
0.0
0.0
0.9
0.0
0.0
21.1
120.9
O.I
52.3
3.5
2.9
O.I
1.2
3.7
106.2
0.0
0.0
0.0
O.I
0.0
0.0
6.9
0.0
O.I
0.0
0.0
0.0
O.I
0.0
6.3
101.3
0.0
-
0.0
0.0
O.I
O.I
0.0
2.8
0.0
0.0
5.8
0.0
0.0
0.0
0.0
5.5
0.0
-
1
oo~
0.0
2.7
0.0
11.0
0.0
0.0
0.9
0.0
0.0
21.1
120.9
O.I
52.3
3.5
2.9
O.I
1.2
3.7
106.2
0.0
0.0
0.0
O.I
0.0
0.0
6.9
0.0
O.I
0.0
0.0
0.0
O.I
0.0
6.3
101.3
0.0
-
0.0
0.0
O.I
O.I
0.0
2.8
0.0
0.0
5.8
0.0
0.0
0.0
0.0
5.5
0.0
-
0.0
0.0
2.7
0.0
11.0
0.0
0.0
0.9
0.0
0.0
21.1
120.9
O.I
52.3
3.5
2.9
O.I
1.2
3.7
106.2
0.0
0.0
0.0
O.I
0.0
0.0
6.9
0.0
O.I
0.0
0.0
0.0
O.I
0.0
6.3
101.3
0.0
-
0.0
0.0
O.I
O.I
0.0
2.8
0.0
0.0
5.8
0.0
0.0
0.0
0.0
5.5
0.0
-
o6~
0.0
2.7
0.0
11.0
0.0
0.0
0.9
0.0
0.0
21.1
120.9
O.I
52.3
3.5
2.9
O.I
1.2
3.7
106.2
0.0
0.0
0.0
O.I
0.0
0.0
6.9
0.0
O.I
0.0
0.0
0.0
O.I
0.0
6.3
101.3
0.0
-
0.0
0.0
O.I
O.I
0.0
2.8
0.0
0.0
5.8
0.0
0.0
0.0
0.0
5.5
0.0
-
August 2011
Appendices
Page D-15
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
1.4
O.I
0.0
0.0
4.7
1.3
0.0
O.I
1.8
0.7
0.2
O.I
0.0
17.9
0.0
1.0
0.0
0.0
0.0
2.6
0.0
1.0
0.0
O.I
0.0
3.4
0.3
0.0
3.7
0.2
0.0
O.I
4.5
0.0
O.I
4.5
1.2
261.6
9.2
0.0
12.8
0.0
416.8
142.9
O.I
OECD | 30.4
Non-OECD Asia 159.6
Non-OECD Europe & | 27.0
EU 2.8
OPEC | 72.8
World Totals
1.4
O.I
0.0
0.0
4.2
1.3
0.0
O.I
1.7
0.7
O.I
O.I
0.0
4.6
0.0
0.9
0.0
0.0
0.0
2.0
0.0
0.6
0.0
O.I
0.0
3.6
0.3
0.0
3.2
O.I
0.0
O.I
0.8
0.0
0.0
4.0
1.3
237.9
8.4
0.0
14.3
0.0
383.0
129.8
O.I
39.0
7.1
1.8
68.4
0.2
0.2
0.0
0.0
4.1
0.2
0.0
0.3
2.3
0.5
O.I
0.6
0.2
22.4
0.0
1.2
0.0
0.0
0.0
1.8
0.0
0.6
0.0
0.0
0.0
2.0
0.3
0.0
2.3
O.I
0.0
0.0
4.0
0.0
O.I
4.9
1.0
214.7
17.7
0.0
11.8
0.8
340.6
130.0
0.0
36.5
164.8
27.3
2.6
69.3
2005 2010 2015 2020
0.0
0.2
0.0
0.0
3.1
O.I
0.0
0.2
1.4
0.7
O.I
1.0
0.0
9.9
0.0
1.3
0.0
0.0
0.0
3.2
0.0
0.4
0.0
0.0
0.0
2.7
0.3
0.0
4.2
0.3
0.0
0.0
2.7
0.0
O.I
5.5
1.6
226.0
II. 1
0.0
11.6
0.0
351.0
166.5
O.I
24.6
13.5
2.1
74.6
0.0
0.2
0.0
0.0
3.1
O.I
0.0
0.2
1.4
0.7
O.I
1.0
0.0
9.9
0.0
1.3
0.0
0.0
0.0
3.2
0.0
0.4
0.0
0.0
0.0
2.7
0.3
0.0
4.2
0.3
0.0
0.0
2.7
0.0
O.I
5.5
1.6
226.0
II. 1
0.0
11.6
0.0
351.0
166.5
0.0
0.2
0.0
0.0
3.1
O.I
0.0
0.2
1.4
0.7
O.I
1.0
0.0
9.9
0.0
1.3
0.0
0.0
0.0
3.2
0.0
0.4
0.0
0.0
0.0
2.7
0.3
0.0
4.2
0.3
0.0
0.0
2.7
0.0
O.I
5.5
1.6
226.0
II. 1
0.0
11.6
0.0
351.0
166.5
0.0
0.2
0.0
0.0
3.1
O.I
0.0
0.2
1.4
0.7
O.I
1.0
0.0
9.9
0.0
1.3
0.0
0.0
0.0
3.2
0.0
0.4
0.0
0.0
0.0
2.7
0.3
0.0
4.2
0.3
0.0
0.0
2.7
0.0
O.I
5.5
1.6
226.0
II. 1
0.0
11.6
0.0
351.0
166.5
O.I O.I | O.I
24.6
188.3
13.5
2.1
74.6
24.6
188.3
13.5
2.1
74.6
24.6
188.3
13.5
2.1
74.6
2025 2030
0.0
0.2
0.0
0.0
3.1
O.I
0.0
0.2
1.4
0.7
O.I
1.0
0.0
9.9
0.0
1.3
0.0
0.0
0.0
3.2
0.0
0.4
0.0
0.0
0.0
2.7
0.3
0.0
4.2
0.3
0.0
0.0
2.7
0.0
O.I
5.5
1.6
226.0
II. 1
0.0
11.6
0.0
351.0
166.5
O.I
24.6
188.3
13.5
2.1
74.6
0.0
0.2
0.0
0.0
3.1
O.I
0.0
0.2
1.4
0.7
O.I
1.0
0.0
9.9
0.0
1.3
0.0
0.0
0.0
3.2
0.0
0.4
0.0
0.0
0.0
2.7
0.3
0.0
4.2
0.3
0.0
0.0
2.7
0.0
O.I
5.5
1.6
226.0
II. 1
0.0
11.6
0.0
351.0
166.5
O.I
24.6
188.3
13.5
2.1
74.6
776.7 743.0 699.3 744.1 744.1 744.1 744.1 744.1 744.1
August 2011
Appendices
Page D-16
-------
Appendix E: Waste Sector Emissions
Table E-l: Total Non-CO2 Emissions from the Waste Sector (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
0.6
4.3
9.4
0.6
18.7
3.6
2.1
12.4
3.3
3.1
0.2
19.1
12.6
8.1
0.2
18.5
1.9
167.6
8.1
5.3
0.6
2.6
1.5
1.4
6.4
0.8
2.5
3.9
9.4
6.2
40.4
4.5
3.3
0.2
21.6
46.4
7.8
4.7
1.5
6.6
17.1
10.5
6.3
3.2
0.6
1.9
0.3
0.8
1.8
O.I
0.8
33.4
0.8
0.6
4.8
10.7
0.5
17.9
3.2
2.0
13.9
2.8
2.9
0.7
21.0
11.3
8.9
0.3
19.5
2.0
177.5
8.9
6.3
0.7
2.7
1.6
1.6
6.9
0.7
2.8
3.9
11.4
4.5
34.3
4.4
3.5
0.2
23.7
51.0
8.4
5.3
1.7
7.9
19.7
9.7
8.1
3.5
0.5
1.0
0.3
0.7
1.5
O.I
0.8
49.9
0.5
2000
0.6
5.2
14.0
O.b
15.8
2.6
2.1
15.4
3.7
2.2
1.2
22.6
9.2
9.7
0.3
19.9
2.1
185.5
9.5
7.1
0.6
2.8
1.5
1.7
7.6
0.8
3.0
3.3
10.0
4.2
22.1
4.0
3.7
0.2
25.9
54.9
8.7
6.2
1.6
8.4
21.0
8.1
9.1
4.3
0.6
0.7
0.4
0.8
1.4
O.I
0.8
63.1
0.5
MtC02e
2005 2010
0.6
5.6
14.7
0.5
14.4
2.3
2.1
16.7
5.3
1.2
1.4
24.3
8.1
10.6
0.3
20.9
2.3
191.0
10.1
8.2
0.8
3.0
1.4
1.9
8.5
0.8
3.3
2.4
8.8
4.1
13.1
3.2
3.8
0.2
28.1
58.8
8.9
7.1
1.8
4.9
18.7
6.5
10.2
8.1
0.7
0.7
0.4
0.8
1.3
O.I
0.9
67.9
0.5
0.6
5.9
15.5
0.5
15.1
2.1
2.2
17.8
6.1
1.0
1.5
25.8
7.5
11.3
0.4
21.7
2.4
195.8
10.7
9.7
0.9
3.1
1.4
2.0
9.4
0.8
3.7
2.4
8.3
4.0
10.3
3.2
3.7
0.2
30.2
62.6
9.2
8.2
2.0
5.4
16.1
5.9
12.4
8.3
0.9
0.7
0.5
0.8
1.3
O.I
0.9
71.9
0.5
^^^|
O6~
6.3
16.3
0.5
15.9
2.1
2.3
19.0
6.0
1.0
1.6
27.3
7.2
12.0
0.4
22.5
2.5
200.4
11.3
11.3
0.9
3.2
1.4
2.2
10.3
0.8
4.1
2.4
8.5
4.0
8.3
2.9
3.6
0.2
32.2
66.2
9.7
9.3
2.2
5.8
15.7
5.8
12.9
8.4
1.0
0.8
0.5
0.8
1.3
O.I
0.9
75.8
0.5
1 1
07"
6.6
17.0
0.5
16.8
2.1
2.4
20.3
5.8
1.0
1.8
28.6
6.9
12.7
0.4
23.3
2.5
203.8
11.8
13.0
0.9
3.3
1.4
2.3
11.3
0.7
4.5
2.4
8.7
4.0
7.1
2.6
3.6
0.2
34.1
69.8
10.2
10.5
2.3
6.2
15.5
5.7
14.1
8.5
1.2
0.8
0.6
0.8
1.3
O.I
0.9
79.6
0.5
0.7
6.9
17.7
0.5
17.6
2.1
2.4
21.7
5.7
1.0
1.9
29.8
6.5
13.4
0.5
24.1
2.6
205.2
12.3
14.9
0.8
3.3
1.4
2.5
12.1
0.7
5.0
2.4
8.8
4.0
5.8
2.3
3.5
0.3
35.9
73.3
10.6
11.8
2.4
6.6
15.0
5.5
15.4
8.6
1.3
0.9
0.6
0.7
1.3
O.I
0.9
83.2
0.5
1
07"
7.1
18.3
0.5
18.3
2.1
2.5
23.0
5.6
1.0
2.0
30.8
6.2
14.1
0.5
24.7
2.7
204.8
12.7
17.0
0.8
3.3
1.4
2.6
13.0
0.7
5.6
2.4
8.9
4.1
4.6
2.0
3.5
0.3
37.6
77.0
10.8
13.0
2.5
6.9
14.4
5.3
16.7
8.5
1.5
0.9
0.7
0.7
1.2
O.I
0.9
86.4
0.5
August 2011
Appendices
Page E-l
-------
Country
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
1990 1995 2000
0.0
O.I
0.5
12.5
2.4
40.6
1.7
1.8
4.9
5.4
6.5
14.3
5.8
2.9
54.9
13.3
2.0
0.9
1.0
0.6
15.2
11.2
7.3
3.5
0.9
0.7
1.2
13.0
0.6
0.2
8.4
1.8
51.5
177.1
1.2
4.1
5.4
2.4
40.9
24.9
7.7
41.2
6.2
117.5
75.0
Middle East 42.3
OECD | 483.5
Non-OECD Asia 315.9
Non-OECD Europe & | 113.7
OPEC 82.9
0.0
O.I
0.6
11.0
2.2
46.0
1.8
1.7
5.3
5.9
7.3
15.0
6.9
3.6
47.2
16.6
2.4
I.I
1.2
0.6
16.7
14.1
9.7
3.3
0.8
0.6
1.2
27.5
0.7
0.2
8.5
2.5
46.0
174.7
1.4
4.3
6.0
2.6
47.2
28.1
8.9
46.0
6.4
133.2
84.3
50.3
510.9
341.4
102.7
201.1
0.0
O.I
0.7
8.6
2.0
51.7
1.8
1.5
5.7
6.4
8.1
8.1
6.6
5.1
52.3
19.2
2.7
1.3
1.7
0.7
17.8
16.9
12.2
3.1
0.7
0.5
1.3
36.8
0.7
0.3
8.7
3.2
33.5
154.6
1.5
4.5
6.6
2.8
52.6
31.0
10.1
50.7
6.8
148.0
94.5
57.2
479.4
364.6
109.0
167.1
2005 2010 2015 2020
0.0
O.I
0.7
6.5
1.8
57.7
1.8
1.3
6.1
6.8
9.0
7.7
7.5
6.5
60.4
21.9
3.1
1.5
2.3
0.7
18.7
17.7
13.1
2.6
0.6
0.5
1.3
38.2
0.8
0.4
9.3
4.1
22.3
160.2
1.5
4.7
7.1
3.0
58.6
33.6
11.3
55.7
7.2
164.1
101.3
64.4
459.4
385.2
123.9
94.9 106.9 119.3
0.0
O.I
0.8
5.7
1.8
63.8
1.9
1.3
6.5
7.2
10.0
8.1
7.5
6.3
62.9
24.2
3.6
1.8
2.4
0.7
19.4
18.0
14.0
2.3
0.6
0.6
1.4
42.1
0.8
0.4
9.3
5.0
21.0
163.3
1.6
4.9
7.6
3.2
65.0
36.2
12.9
60.8
7.6
180.8
108.1
72.9
465.0
404.7
127.6
132.8
131.7
0.0
O.I
0.8
5.8
1.9
70.1
1.9
1.3
6.8
7.6
11.0
8.7
7.6
6.2
61.4
26.2
4.1
2.0
2.4
0.7
19.0
18.1
14.0
2.4
0.6
0.6
1.4
44.7
0.9
0.5
9.0
5.8
20.0
163.5
1.6
5.2
8.2
3.3
72.0
38.6
13.9
65.9
8.1
197.6
114.7
78.9
471.7
423.9
126.3
129.9
144.2
0.0
0.2
0.9
5.9
1.9
76.3
1.9
1.3
7.1
7.9
11.9
9.1
7.6
6.1
59.7
27.9
4.6
2.2
2.3
0.7
19.1
18.2
13.8
2.4
0.6
0.7
1.4
47.0
0.9
0.6
8.7
6.5
19.1
164.3
1.6
5.4
8.7
3.5
79.1
41.0
15.2
71.1
8.6
215.2
120.9
85.6
478.8
442.0
124.9
127.3
156.4
2025 2030
0.0
0.2
1.0
6.0
2.0
82.5
1.9
1.3
7.5
8.2
12.9
9.3
7.6
6.0
57.9
29.6
5.1
2.5
2.3
0.6
19.2
18.2
13.7
2.4
0.6
0.7
1.5
49.0
1.0
0.7
8.4
7.1
18.2
166.2
1.7
5.6
9.3
3.6
86.5
43.3
16.4
76.2
9.3
233.1
126.6
92.2
485.3
457.9
123.3
124.1
168.2
0.0
0.2
1.0
6.0
2.1
88.6
1.9
1.4
7.8
8.5
13.8
9.6
7.5
5.9
56.0
31.7
5.7
2.7
2.2
0.6
19.3
18.1
13.5
2.4
0.6
0.8
1.5
50.8
1.0
0.9
8.1
7.5
17.3
167.7
1.7
5.8
9.8
3.7
94.1
45.4
17.5
81.2
10.1
251.2
131.8
98.8
490.4
471.5
121.5
120.7
180.0
World Totals 1,148.0 1,222.8 1,252.7 1,298.3 1,359.2 1,413.1 1,467.3 1,518.5 1,565.2
August 2011
Appendices
Page E-2
-------
Table E-2: CH4 Emissions from Landfilling of Solid Waste by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
^^^^|
(D~
3.8
4.4
0.5
14.9
3.4
1.3
1.2
2.3
2.6
O.I
13.0
10.7
3.7
O.I
17.7
1.5
40.7
6.7
5.2
0.2
1.7
1.3
0.8
5.5
0.6
0.5
3.6
7.4
2.6
35.9
1.8
2.3
O.I
11.3
19.2
5.9
2.7
1.3
6.3
13.3
8.3
6.2
2.0
0.3
1.6
0.2
0.3
I.I
0.0
0.7
16.3
0.7
-
^T^B
^^^>^H
(D~
4.2
5.2
0.5
14.3
2.9
1.4
1.3
1.9
2.4
0.4
14.4
9.9
4.1
O.I
18.7
1.6
43.1
7.3
6.1
0.3
2.0
1.3
0.9
6.0
0.5
0.6
3.6
9.1
2.4
30.7
2.0
2.5
0.2
12.5
21.5
6.3
3.1
1.6
7.5
15.8
7.7
8.0
2.4
0.4
0.8
0.3
0.4
1.2
0.0
0.7
25.8
0.5
-
MtCO2e
2000 2005 2010
0.3
4.6
7.5
0.4
12.5
2.3
1.5
1.5
2.7
1.7
0.7
15.6
8.2
4.5
O.I
19.1
1.7
44.7
7.8
6.9
0.4
2.2
1.2
1.0
6.6
0.7
0.7
3.0
7.6
2.2
18.7
2.2
2.8
0.2
13.6
23.8
6.6
3.6
1.5
8.0
16.8
6.4
8.9
3.2
0.4
0.6
0.3
0.5
I.I
0.0
0.7
32.5
0.5
-
0.3
4.9
7.9
0.4
II. 1
2.0
1.5
1.6
4.4
0.8
0.8
16.7
7.1
4.9
0.2
20.0
1.8
46.0
8.3
8.0
0.6
2.3
I.I
I.I
7.3
0.6
0.8
2.1
6.3
2.0
9.7
2.4
3.0
0.2
14.8
26.0
6.7
4.2
1.6
4.1
14.4
5.1
10.0
6.8
0.5
0.5
0.3
0.5
0.9
0.0
0.8
36.2
0.5
-
0.3
5.2
8.3
0.4
11.5
1.7
1.6
1.7
5.2
0.6
0.9
17.8
6.5
5.3
0.2
20.7
1.9
47.1
8.9
9.4
0.6
2.5
I.I
1.2
8.1
0.5
1.0
2.1
5.7
2.0
7.0
2.1
2.9
0.2
15.9
28.3
6.9
4.8
1.9
4.5
11.6
4.5
12.2
6.9
0.5
0.6
0.4
0.5
0.9
0.0
0.8
38.4
0.5
-
0.3
5.5
8.7
0.4
12.2
1.7
1.6
1.8
5.1
0.6
1.0
18.7
6.3
5.7
0.2
21.5
1.9
48.2
9.4
10.9
0.6
2.6
I.I
1.3
8.9
0.5
I.I
2.1
5.8
2.0
5.0
1.8
2.9
0.2
16.9
30.5
7.3
5.5
2.0
4.9
11.0
4.4
12.6
7.0
0.6
0.6
0.4
0.5
0.9
0.0
0.8
40.5
0.5
-
1
0.3
5.8
9.1
0.4
12.8
1.7
1.7
2.0
5.0
0.6
I.I
19.7
6.0
6.1
0.2
22.3
2.0
49.0
9.9
12.7
0.6
2.6
I.I
1.4
9.7
0.5
1.3
2.1
5.9
2.0
3.7
1.5
2.9
0.2
17.9
32.9
7.7
6.3
2.1
5.2
10.6
4.3
13.9
7.1
0.7
0.6
0.5
0.5
0.9
0.0
0.8
42.5
0.5
-
0.3
6.0
9.5
0.4
13.4
1.7
1.8
2.1
4.9
0.6
I.I
20.5
5.7
6.5
0.2
23.0
2.1
49.4
10.3
14.5
0.6
2.6
I.I
1.5
10.5
0.5
1.5
2.1
6.0
2.0
2.5
1.2
2.8
0.2
18.9
35.3
8.0
7.1
2.2
5.5
10.2
4.2
15.2
7.2
0.9
0.7
0.5
0.5
0.9
0.0
0.8
44.4
0.4
-
oT
6.2
9.8
0.4
13.9
1.7
1.8
2.2
4.7
0.6
1.2
21.2
5.4
6.9
0.2
23.7
2.1
49.3
10.6
16.5
0.6
2.7
I.I
1.5
11.2
0.4
1.8
2.0
6.1
2.1
1.3
0.9
2.7
0.2
19.8
37.8
8.1
8.0
2.2
5.8
9.7
4.0
16.4
7.1
1.0
0.7
0.6
0.4
0.8
0.0
0.8
46.1
0.4
-
August 2011
Appendices
Page E-3
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
O.I
0.2
12.0
2.1
3.7
1.4
1.7
1.8
1.9
3.9
11.5
3.0
2.4
24.4
12.5
1.6
0.5
0.5
0.3
14.1
4.2
4.7
2.9
0.7
0.7
0.4
6.4
0.2
O.I
5.3
1.6
49.8
149.2
1.0
3.3
4.9
1.3
21.7
8.6
3.6
33.7
5.0
56.1
41.3
32.9
388.9
Non-OECD Asia 119.7
Non-OECD Europe & | 66.2
EU 174.9
OPEC | 37.9
O.I
0.2
10.5
1.9
4.2
1.5
1.6
2.0
2.1
4.4
12.1
3.8
3.2
28.2
15.6
1.9
0.6
0.6
0.4
15.4
6.7
6.8
2.7
0.5
0.6
0.4
20.3
0.2
O.I
5.8
2.1
44.2
144.3
1.0
3.5
5.5
1.4
25.9
10.3
4.2
37.3
5.3
64.3
47.1
39.7
405.9
130.7
70.1
170.3
44.2
O.I
0.2
8.1
1.7
4.7
1.5
1.4
2.2
2.2
4.9
6.1
4.0
4.5
31.4
18.1
2.2
0.8
1.2
0.4
16.4
9.2
8.8
2.4
0.4
0.4
0.4
29.0
0.2
O.I
6.1
2.8
31.6
122.3
I.I
3.7
5.9
1.5
28.8
II. 1
4.9
41.0
5.7
70.8
53.0
45.2
366.9
141.3
75.0
137.9
49.9
2005 2010 2015 2020
O.I
0.2
6.1
1.5
5.3
1.5
1.2
2.5
2.4
5.4
5.6
4.9
5.5
35.4
20.6
2.5
1.0
1.8
0.5
17.3
9.9
9.2
1.9
0.3
0.5
0.5
29.8
0.3
O.I
6.7
3.5
20.3
127.8
I.I
3.8
6.4
1.6
32.2
11.8
5.5
44.8
6.0
78.3
56.5
51.0
344.9
151.4
84.8
110.9
55.7
O.I
0.3
5.3
1.5
5.8
1.6
1.2
2.7
2.6
6.0
5.9
5.0
5.3
37.1
22.8
2.8
1.2
1.9
0.5
17.9
10.0
9.8
1.7
0.3
0.5
0.5
33.1
0.3
O.I
6.8
4.3
18.9
129.7
I.I
4.0
6.9
1.7
35.8
12.5
6.3
48.8
6.5
86.1
60.1
57.9
345.5
161.7
87.6
102.2
61.4
O.I
0.3
5.4
1.5
6.4
1.6
1.2
2.9
2.7
6.6
6.5
5.0
5.3
36.2
24.7
3.2
1.4
1.8
0.4
17.6
10.1
9.8
1.7
0.3
0.5
0.5
35.2
0.3
O.I
6.6
5.0
17.8
128.4
I.I
4.2
7.4
1.8
39.9
13.2
7.1
52.8
6.9
93.7
63.6
62.8
347.4
171.8
87.0
98.9
67.0
O.I
0.3
5.5
1.6
7.0
1.6
1.2
3.1
2.9
7.2
6.8
5.0
5.2
35.2
26.2
3.7
1.6
1.8
0.4
17.7
10.1
9.7
1.7
0.3
0.6
0.5
37.0
0.3
O.I
6.4
5.6
16.9
127.7
1.2
4.4
7.9
1.9
44.0
13.8
7.8
56.9
7.5
102.0
66.8
68.2
350.0
181.8
86.4
96.2
72.4
2025 2030
O.I
0.3
5.6
1.7
7.6
1.6
1.3
3.3
3.0
7.7
7.0
5.0
5.1
34.1
27.9
4.1
1.8
1.7
0.4
17.7
10.1
9.5
1.7
0.3
0.6
0.5
38.6
0.3
0.2
6.1
6.1
16.0
128.0
1.2
4.6
8.4
2.0
48.3
14.4
8.5
60.9
8.2
110.4
69.8
73.6
352.8
191.3
85.6
93.1
77.6
O.I
0.4
5.6
1.7
8.1
1.6
1.3
3.5
3.1
8.3
7.2
5.0
5.0
33.0
29.8
4.5
2.1
1.7
0.4
17.8
10.1
9.4
1.7
0.3
0.7
0.5
39.9
0.3
0.2
5.9
6.4
15.1
128.0
1.2
4.7
8.8
2.0
52.8
15.0
9.3
64.8
8.9
119.1
72.5
79.1
354.4
200.2
84.8
89.9
82.9
World Totals 705.0 757.7 752.2 767.0 799.0 826.3 855.2 883.5 910.1
August 2011
Appendices
Page E-4
-------
Table E-3: CH4 Emissions from Wastewater by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
^^^^|
02"
0.3
4.3
0.0
3.3
O.I
0.4
10.6
0.7
0.2
0.0
2.5
1.7
4.1
0.0
0.2
0.2
114.0
0.6
0.0
0.3
0.7
O.I
0.5
O.I
O.I
0.3
0.2
0.8
0.6
2.2
2.3
0.8
0.0
8.2
17.9
0.6
1.9
0.0
0.2
2.0
2.1
O.I
0.8
0.2
0.2
0.0
0.5
0.6
0.0
O.I
15.5
0.0
0.0
^T^B^
b£f^H
02"
0.3
4.7
0.0
3.2
O.I
O.I
11.8
0.7
0.2
O.I
2.7
1.3
4.5
0.0
0.2
0.2
120.7
0.7
0.0
0.4
0.6
0.2
0.6
O.I
O.I
0.4
O.I
1.0
0.5
0.9
2.1
0.8
0.0
9.0
19.5
0.6
2.0
0.0
0.2
2.2
1.9
O.I
0.7
0.2
O.I
0.0
0.3
0.3
0.0
O.I
22.3
0.0
0.0
2000 2005 2010
0.2
0.3
5.5
0.0
2.8
O.I
O.I
13.0
0.7
0.2
0.4
2.9
0.9
4.8
0.0
0.2
0.2
125.4
0.7
0.0
0.2
0.5
0.2
0.6
0.2
O.I
0.4
O.I
1.2
0.5
0.2
1.4
0.7
0.0
9.8
21.1
0.7
2.3
0.0
0.2
2.3
1.6
O.I
0.7
0.2
O.I
0.0
0.3
0.3
0.0
0.0
28.7
0.0
0.0
0.2
0.3
5.8
0.0
2.7
0.0
O.I
14.0
0.7
O.I
0.4
3.1
0.9
5.2
0.0
0.3
0.2
128.8
0.8
0.0
O.I
0.4
0.3
0.7
0.2
O.I
0.5
O.I
1.2
0.5
O.I
0.4
0.6
0.0
10.7
22.6
0.7
2.7
0.0
0.6
2.3
1.4
O.I
1.0
0.2
O.I
0.0
0.3
0.3
0.0
0.0
29.6
0.0
0.0
0.3
0.4
6.1
0.0
2.9
0.0
O.I
15.0
0.7
O.I
0.4
3.3
0.8
5.5
0.0
0.3
0.3
132.0
0.8
0.0
0.2
0.4
0.3
0.7
0.2
O.I
0.6
O.I
1.3
0.5
O.I
0.7
0.6
0.0
11.5
24.0
0.7
3.0
0.0
0.6
2.5
1.4
0.2
1.0
0.3
O.I
0.0
0.2
0.4
0.0
0.0
31.4
0.0
0.0
^KM«E^H
0.3
0.4
6.5
0.0
3.1
0.0
O.I
16.0
0.7
O.I
0.5
3.5
0.8
5.7
0.0
0.3
0.3
135.1
0.8
0.0
0.2
0.4
0.3
0.8
0.2
O.I
0.7
O.I
1.3
0.5
O.I
0.7
0.5
0.0
12.2
25.2
0.7
3.4
0.0
0.7
2.8
1.3
0.2
I.I
0.3
O.I
0.0
0.2
0.4
0.0
0.0
33.1
0.0
0.0
1
0.3
0.4
6.7
0.0
3.3
0.0
O.I
17.2
0.6
O.I
0.5
3.7
0.8
6.0
0.0
0.3
0.3
137.4
0.9
0.0
0.2
0.4
0.3
0.8
0.2
O.I
0.8
O.I
1.3
0.5
O.I
0.7
0.5
0.0
13.0
26.4
0.8
3.8
0.0
0.7
3.0
1.3
0.2
I.I
0.4
O.I
0.0
0.2
0.4
0.0
0.0
34.7
0.0
0.0
0.3
0.4
7.0
0.0
3.4
0.0
O.I
18.3
0.6
O.I
0.6
3.8
0.7
6.3
0.0
0.3
0.3
138.4
0.9
0.0
0.2
0.4
0.3
0.9
0.3
O.I
1.0
O.I
1.3
0.4
O.I
0.7
0.5
0.0
13.7
27.5
0.8
4.1
0.0
0.8
2.9
1.3
0.2
I.I
0.4
O.I
0.0
0.2
0.4
0.0
0.0
36.3
0.0
0.0
O3~
0.4
7.3
0.0
3.6
0.0
0.2
19.4
0.6
O.I
0.6
4.0
0.7
6.5
0.0
0.3
0.3
138.1
1.0
0.0
0.2
0.4
0.3
0.9
0.3
O.I
I.I
O.I
1.3
0.4
O.I
0.7
0.5
0.0
14.3
28.5
0.8
4.5
0.0
0.8
2.9
1.2
0.2
I.I
0.5
O.I
0.0
0.2
0.3
0.0
0.0
37.7
0.0
0.0
August 2011
Appendices
Page E-5
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
0.0
0.0
0.2
0.2
35.7
O.I
0.0
0.2
2.6
1.7
1.7
2.4
0.4
25.0
O.I
0.4
0.3
0.4
0.2
0.4
6.2
1.2
0.6
0.0
0.0
0.3
5.8
0.4
0.0
1.6
0.2
0.7
23.5
O.I
0.3
0.2
0.0
II. 1
15.0
3.7
6.9
1.0
48.3
25.7
6.7
OECD | 74.2
Non-OECD Asia 164.4
Non-OECD Europe & | 34.8
EU 20.3
OPEC 1 40.6
0.0
0.0
0.2
0.2
40.4
O.I
0.0
0.2
2.9
1.9
1.8
2.8
0.2
15.3
O.I
0.4
0.4
0.4
O.I
0.5
6.5
1.5
0.6
0.0
0.0
0.3
6.3
0.4
0.0
1.7
0.3
0.7
24.8
0.2
0.3
0.2
0.0
12.6
16.5
4.2
7.8
0.9
54.8
28.5
7.5
82.2
176.4
23.5
18.5
45.9
0.0
0.0
0.2
0.2
45.5
O.I
0.0
0.2
3.2
2.2
1.0
2.2
0.4
17.3
O.I
0.5
0.4
0.4
0.2
0.5
6.8
1.8
0.6
0.0
0.0
0.3
6.9
0.4
0.0
1.6
0.4
0.8
25.2
0.3
0.3
0.2
0.0
14.5
18.4
4.7
8.8
1.0
61.9
32.2
8.5
86.9
186.3
25.0
16.0
51.6
2005 2010 2015 2020
0.0
0.0
0.2
0.2
50.8
O.I
0.0
0.3
3.4
2.4
1.0
2.3
0.7
21.1
O.I
0.5
0.5
0.4
0.2
0.5
6.9
2.1
0.6
0.0
0.0
0.4
7.4
0.5
0.0
1.5
0.5
0.8
24.3
0.3
0.3
0.2
0.0
16.5
20.3
5.2
9.8
1.0
69.5
34.9
9.5
87.1
194.8
29.6
15.7
57.7
0.0
0.0
0.2
0.2
56.2
O.I
0.0
0.3
3.6
2.7
I.I
2.2
0.7
22.0
0.2
0.6
0.5
0.4
0.2
0.5
7.0
2.3
0.6
0.0
0.0
0.4
7.9
0.5
O.I
1.5
0.6
0.8
25.1
0.3
0.3
0.2
0.0
18.6
22.0
5.8
10.8
1.0
77.2
37.6
10.7
91.1
202.8
30.4
16.3
64.0
0.0
0.0
0.2
0.2
61.8
O.I
0.0
0.3
3.8
2.9
1.2
2.2
0.7
21.4
0.2
0.7
0.5
0.4
0.2
0.5
7.1
2.3
0.6
0.0
0.0
0.4
8.4
0.5
O.I
1.4
0.7
0.9
26.3
0.3
0.3
0.2
0.0
20.8
23.7
6.1
11.8
1.0
85.2
40.2
11.6
95.3
210.6
29.8
16.7
70.4
0.0
0.0
0.2
0.2
67.4
O.I
0.0
0.3
4.0
3.2
1.2
2.2
0.7
20.8
0.2
0.8
0.5
0.4
0.2
0.5
7.1
2.3
0.6
0.0
0.0
0.4
8.9
0.6
O.I
1.4
0.8
0.9
27.6
0.3
0.4
0.3
O.I
23.1
25.4
6.5
12.8
1.0
93.4
42.6
12.5
99.2
217.5
29.2
16.9
76.8
2025 2030
0.0
0.0
0.2
0.2
72.9
O.I
0.0
0.4
4.2
3.4
1.3
2.2
0.7
20.2
0.2
0.9
0.5
0.4
0.2
0.5
7.1
2.3
0.6
0.0
0.0
0.4
9.2
0.6
O.I
1.3
0.8
0.9
28.9
0.3
0.4
0.3
O.I
25.6
27.0
6.9
13.9
1.0
101.6
44.9
13.5
102.6
223.0
28.4
16.9
83.0
0.0
0.0
0.2
0.2
78.2
O.I
0.0
0.4
4.4
3.7
1.4
2.2
0.7
19.6
0.2
1.0
0.5
0.4
0.2
0.5
7.1
2.2
0.7
0.0
0.0
0.4
9.6
0.6
O.I
1.3
0.9
0.9
30.2
0.3
0.4
0.3
O.I
28.0
28.4
7.2
14.8
0.9
109.7
47.1
14.3
105.6
226.9
27.7
16.7
89.1
World Totals 354.2 372.9 400.9 425.3 449.8 472.7 494.4 514.0 531.4
August 2011
Appendices
Page E-6
-------
Table E-4: N2O Emissions from Human Sewage - Domestic Wastewater by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
0.0
0.3
0.8
0.0
0.5
O.I
0.2
0.7
0.2
0.3
O.I
3.6
0.2
0.3
O.I
0.5
0.2
12.9
0.3
O.I
O.I
0.2
O.I
O.I
0.7
0.0
0.4
O.I
I.I
O.I
2.2
0.3
0.2
0.0
2.0
1.4
1.4
0.2
O.I
O.I
1.8
O.I
0.0
0.5
0.0
O.I
0.0
O.I
O.I
0.0
O.I
1.6
0.0
0.0
0.0
0.3
0.9
0.0
0.5
O.I
0.2
0.7
0.2
0.3
O.I
3.9
0.2
0.3
O.I
0.6
0.2
13.7
0.3
0.2
O.I
0.2
O.I
O.I
0.8
0.0
0.5
O.I
I.I
0.0
2.3
0.4
0.2
0.0
2.2
1.5
1.5
0.2
O.I
0.2
1.7
O.I
0.0
0.4
0.0
O.I
0.0
O.I
O.I
0.0
O.I
1.8
0.0
0.0
0.0
0.3
1.0
0.0
0.5
0.2
0.2
0.9
0.2
0.3
O.I
4.2
0.2
0.4
O.I
0.6
0.2
15.3
0.3
0.2
O.I
0.2
0.0
O.I
0.9
0.0
0.6
O.I
1.0
0.0
2.3
0.4
0.2
0.0
2.4
1.6
1.5
0.3
O.I
0.2
1.9
0.0
0.0
0.4
0.0
O.I
0.0
O.I
O.I
0.0
O.I
1.9
0.0
0.0
2005 2010
0.0
0.4
1.0
0.0
0.6
0.2
0.2
1.0
0.2
0.3
O.I
4.5
O.I
0.5
0.2
0.7
0.2
16.2
0.4
0.2
O.I
0.2
0.0
O.I
1.0
0.0
0.6
O.I
0.9
O.I
2.3
0.4
0.2
0.0
2.6
1.7
1.5
0.3
O.I
0.2
1.9
0.0
O.I
0.3
0.0
O.I
O.I
0.0
O.I
0.0
O.I
2.0
0.0
0.0
O.I
0.4
I.I
0.0
0.6
0.2
0.3
I.I
0.2
0.3
0.2
4.8
O.I
0.5
0.2
0.7
0.2
16.6
0.4
0.3
O.I
0.2
0.0
O.I
I.I
0.0
0.8
O.I
0.9
O.I
2.4
0.4
0.2
0.0
2.8
1.8
1.6
0.4
0.2
0.2
1.9
0.0
O.I
0.3
0.0
O.I
O.I
0.0
O.I
0.0
O.I
2.1
0.0
0.0
O.I
0.4
I.I
0.0
0.6
0.2
0.3
1.2
0.2
0.3
0.2
5.0
O.I
0.5
0.2
0.7
0.3
17.0
0.4
0.3
O.I
0.2
0.0
O.I
1.2
0.0
0.9
O.I
1.0
0.0
2.4
0.4
0.2
0.0
3.0
1.9
1.7
0.4
0.2
0.3
1.9
0.0
O.I
0.3
0.0
O.I
O.I
0.0
O.I
0.0
O.I
2.3
0.0
0.0
^^EZS£±^I
oT
0.4
1.2
0.0
0.7
0.2
0.3
1.2
0.2
0.3
0.2
5.3
O.I
0.6
0.2
0.7
0.3
17.3
0.4
0.4
O.I
0.2
0.0
O.I
1.3
0.0
1.0
O.I
1.0
0.0
2.4
0.4
0.2
0.0
3.2
2.0
1.8
0.5
0.2
0.3
1.9
0.0
O.I
0.3
0.0
O.I
O.I
0.0
O.I
0.0
O.I
2.4
0.0
0.0
O.I
0.5
1.2
0.0
0.7
0.2
0.3
1.3
0.2
0.3
0.2
5.5
O.I
0.6
0.2
0.8
0.3
17.4
0.4
0.4
O.I
0.2
0.0
O.I
1.4
0.0
1.2
O.I
1.0
0.0
2.3
0.4
0.2
0.0
3.4
2.1
1.8
0.5
0.2
0.3
1.9
0.0
O.I
0.3
O.I
O.I
O.I
0.0
O.I
0.0
O.I
2.5
0.0
0.0
O.I
0.5
1.3
0.0
0.7
0.2
0.3
1.4
0.2
0.3
0.2
5.7
O.I
0.6
0.2
0.8
0.3
17.4
0.5
0.5
O.I
0.2
0.0
O.I
1.5
0.0
1.4
O.I
1.0
0.0
2.3
0.4
0.2
0.0
3.5
2.2
1.9
0.5
0.2
0.3
1.9
0.0
O.I
0.3
O.I
O.I
O.I
0.0
O.I
0.0
O.I
2.6
0.0
0.0
August 2011
Appendices
Page E-7
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
0.0
0.3
0.3
O.I
0.8
0.2
O.I
1.0
0.2
0.9
I.I
0.3
0.2
5.4
0.7
O.I
O.I
O.I
O.I
0.7
0.8
I.I
0.0
0.2
0.0
0.5
0.8
0.0
O.I
1.6
0.0
1.0
3.7
O.I
0.4
0.4
I.I
3.7
0.9
0.4
0.6
0.2
6.9
6.4
2.8
19.2
0.0
0.4
0.2
O.I
0.9
0.2
O.I
I.I
0.2
1.0
I.I
0.3
0.2
3.8
0.9
O.I
O.I
O.I
O.I
0.8
0.9
1.0
0.0
0.2
0.0
0.5
0.9
0.0
O.I
I.I
O.I
1.0
4.0
O.I
0.5
0.4
1.2
4.2
1.0
0.5
0.9
0.2
7.9
7.0
3.1
19.9
Non-OECD Asia 22.0 1 23.9
Non-OECD Europe & | 9.5 7.4
EU 1 I.I 10.9
OPEC | 3.9 | 4.4
World Totals 66.7 69. 1
0.0
0.4
0.2
O.I
1.0
0.2
O.I
1.3
0.3
I.I
I.I
0.3
0.2
3.6
1.0
O.I
O.I
O.I
O.I
0.9
0.9
I.I
0.0
0.2
0.0
0.5
0.9
0.0
0.2
1.0
O.I
1.2
4.5
O.I
0.5
0.4
1.2
4.8
I.I
0.5
1.0
0.2
9.0
7.6
3.5
20.9
26.6
7.2
11.2
4.9
2005 2010 2015 2020
0.0
0.4
O.I
O.I
I.I
0.2
O.I
1.4
0.3
1.2
I.I
0.4
0.3
3.9
I.I
O.I
O.I
O.I
O.I
0.9
0.9
1.2
0.0
0.2
O.I
0.5
1.0
O.I
0.2
I.I
O.I
1.2
4.8
O.I
0.6
0.5
1.3
5.5
1.2
0.6
I.I
0.2
10.1
8.2
3.8
21.7
28.5
7.6
11.5
5.4
0.0
0.5
O.I
O.I
1.3
0.2
O.I
1.5
0.3
1.3
I.I
0.4
0.3
3.9
1.3
O.I
O.I
O.I
O.I
1.0
1.0
1.2
0.0
0.2
O.I
0.5
I.I
O.I
0.3
1.0
O.I
1.3
5.0
O.I
0.6
0.5
1.4
6.1
1.3
0.7
1.2
0.2
11.2
8.7
4.2
22.4
29.8
7.6
11.7
5.8
0.0
0.5
O.I
O.I
1.4
0.2
O.I
1.6
0.3
1.5
I.I
0.4
0.3
3.8
1.4
O.I
O.I
O.I
O.I
1.0
1.0
1.2
0.0
0.2
O.I
0.6
I.I
O.I
0.3
1.0
0.2
1.3
5.3
O.I
0.6
0.5
1.5
6.8
1.4
0.8
1.3
0.2
12.4
9.2
4.5
23.0
31.1
7.5
11.7
6.3
0.0
0.5
O.I
O.I
1.5
0.2
O.I
1.7
0.3
1.6
I.I
0.4
0.3
3.7
1.5
O.I
O.I
O.I
O.I
1.0
1.0
1.2
0.0
0.2
O.I
0.6
1.2
O.I
0.4
1.0
0.2
1.3
5.5
O.I
0.6
0.6
1.5
7.4
1.5
0.9
1.3
0.2
13.6
9.7
4.9
23.5
32.2
7.4
11.7
6.8
2025 2030
0.0
0.6
O.I
O.I
1.6
0.2
O.I
1.8
0.3
1.7
I.I
0.4
0.3
3.6
1.5
0.2
O.I
O.I
O.I
1.0
1.0
1.2
0.0
0.2
O.I
0.6
1.2
O.I
0.5
0.9
0.2
1.3
5.8
O.I
0.7
0.6
1.6
8.1
1.6
1.0
1.4
0.2
14.8
10.1
5.2
24.0
7.3
11.6
7.2
0.0
0.6
O.I
O.I
1.8
0.2
O.I
2.0
0.4
1.9
1.0
0.4
0.3
3.5
1.7
0.2
O.I
O.I
O.I
1.0
1.0
1.2
0.0
0.2
O.I
0.6
1.3
O.I
0.5
0.9
0.2
1.3
6.0
O.I
0.7
0.6
1.6
8.8
1.6
1.0
1.5
0.2
16.0
10.5
5.4
24.3
33.9
7.2
11.5
7.6
83.9 87.7 91.3 94.5 97.4
August 2011
Appendices
Page E-8
-------
Table E-5: CH4 Emissions from Other Waste Sources by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
^^^^|
-
-
0.0
-
0.0
-
-
-
-
-
-
-
-
0.0
-
0.0
0.9
0.0
0.0
3.0
0.0
0.0
-
7.8
-
-
-
0.0
0.0
-
-
0.0
-
0.0
-
-
-
^T^B
^^^>^H
-
-
0.0
-
0.0
-
-
-
-
-
-
-
-
0.0
-
0.0
1.0
0.0
0.0
1.5
0.3
0.0
-
8.4
-
-
-
0.0
0.0
-
-
0.0
-
0.0
-
-
-
2000 2005 2010
-
-
0.0
-
0.0
-
-
-
-
-
-
-
-
0.0
-
0.0
1.0
0.0
O.I
1.5
0.6
0.0
-
8.4
-
-
-
0.0
0.0
-
-
0.0
-
0.0
-
-
-
-
-
0.0
-
0.0
-
-
-
-
-
-
-
-
0.0
-
0.0
1.0
O.I
O.I
1.5
0.6
0.0
-
8.4
-
-
-
0.0
0.0
-
-
0.0
-
0.0
-
-
-
-
-
0.0
-
0.0
-
-
-
-
-
-
-
-
0.0
-
O.I
1.0
O.I
O.I
1.5
0.5
0.0
-
8.4
-
-
-
0.0
0.0
-
-
0.0
-
0.0
-
-
-
-
0.0
-
0.0
-
-
-
-
-
-
-
-
0.0
-
O.I
1.0
O.I
O.I
1.5
0.5
0.0
-
8.4
-
-
-
0.0
0.0
-
-
0.0
-
0.0
-
-
-
1
-
0.0
-
0.0
-
-
-
-
-
-
-
-
0.0
-
O.I
1.0
O.I
O.I
1.5
0.5
0.0
-
8.4
-
-
-
0.0
0.0
-
-
0.0
-
0.0
-
-
-
-
0.0
-
0.0
-
-
-
-
-
-
-
-
0.0
-
O.I
1.0
O.I
O.I
1.5
0.5
0.0
-
8.4
-
-
-
0.0
0.0
-
-
0.0
-
0.0
-
-
-
-
0.0
-
0.0
-
-
-
-
-
-
-
-
0.0
-
O.I
1.0
O.I
O.I
1.5
0.5
0.0
-
8.4
-
-
-
0.0
0.0
-
-
0.0
-
0.0
-
-
-
August 2011
Appendices
Page E-9
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
1990 1995 2000
-
-
0.0
-
0.4
-
-
-
0.3
-
-
-
-
-
-
-
-
0.0
-
-
-
0.3
-
0.0
-
-
-
-
-
-
-
-
0.3
-
-
-
-
0.0
O.I
-
0.0
0.0
1.3
0.4
-
-
O.I
-
0.4
-
-
-
0.3
-
-
-
-
-
-
-
-
0.0
-
-
-
0.4
-
0.0
-
-
-
-
-
-
-
-
0.7
-
-
-
-
0.0
O.I
-
0.0
0.0
1.4
0.4
-
-
O.I
-
0.4
-
-
-
0.3
-
-
-
-
-
-
-
-
0.0
-
-
-
0.5
-
O.I
-
-
-
-
-
-
-
-
1.3
-
-
-
-
O.I
O.I
-
0.0
0.0
1.5
0.4
2005 2010 2015 2020
-
-
O.I
-
0.4
-
-
-
0.3
-
-
-
-
-
-
-
-
0.0
-
-
-
0.6
-
O.I
-
-
-
-
-
-
-
-
1.6
-
-
-
-
O.I
O.I
-
0.0
0.0
1.5
0.4
1 ' 1
OECD | 0.7 | 1.7
Non-OECD Asia 7.8
Non-OECD Europe & | 3.0
EU 0.4
OPEC | 0.4
World Totals
8.5
1.5
0.9
0.4
2.7
8.5
1.5
1.4
0.4
3.2
8.5
1.6
1.5
0.4
-
-
O.I
-
0.4
-
-
-
0.3
-
-
-
-
-
-
-
-
0.0
-
-
-
0.7
-
O.I
-
-
-
-
-
-
-
-
1.7
-
-
-
-
O.I
O.I
-
0.0
0.0
1.5
0.4
-
3.3
8.5
1.6
1.6
0.4
-
-
O.I
-
0.4
-
-
-
0.3
-
-
-
-
-
-
-
-
0.0
-
-
-
0.7
-
O.I
-
-
-
-
-
-
-
-
1.7
-
-
-
-
O.I
O.I
-
0.0
0.0
1.5
0.4
-
3.3
8.5
1.6
1.6
0.4
13.4 13.6 14.6 15.1 15.3 15.3
-
-
O.I
-
0.4
-
-
-
0.3
-
-
-
-
-
-
-
-
0.0
-
-
-
0.7
-
O.I
-
-
-
-
-
-
-
-
1.7
-
-
-
-
O.I
O.I
-
0.0
0.0
1.5
0.4
-
3.3
8.5
1.6
1.6
0.4
2025 2030
-
-
O.I
-
0.4
-
-
-
0.3
-
-
-
-
-
-
-
-
0.0
-
-
-
0.7
-
O.I
-
-
-
-
-
-
-
-
1.7
-
-
-
-
O.I
O.I
-
0.0
0.0
1.5
0.4
-
3.3
8.5
1.6
1.6
0.4
-
-
O.I
-
0.4
-
-
-
0.3
-
-
-
-
-
-
-
-
0.0
-
-
-
0.7
-
O.I
-
-
-
-
-
-
-
-
1.7
-
-
-
-
O.I
O.I
-
0.0
0.0
1.5
0.4
-
3.3
8.5
1.6
1.6
0.4
August 2011
Appendices
Page E-10
-------
Table E-6: N2O Emissions from Other Waste Sources by Country (MtCO2e)
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
-
-
0.0
0.2
-
-
-
-
-
-
-
0.6
-
0.0
-
0.0
0.3
0.0
O.I
0.0
0.0
-
-
-
-
-
0.0
-
-
0.0
-
0.0
-
-
-
-
0.0
0.2
-
-
-
-
-
-
-
0.6
-
0.0
-
0.0
0.3
0.0
O.I
O.I
0.0
-
-
-
-
-
0.0
-
-
0.0
-
0.0
-
-
-
2000 2005 2010
.
-
O.I
0.2
-
-
-
-
-
-
-
0.6
-
0.0
-
0.0
0.3
0.0
0.2
0.3
0.0
-
-
-
-
-
0.0
-
-
0.0
-
0.0
-
-
-
-
O.I
0.2
-
-
-
-
-
-
-
0.6
-
0.0
-
0.0
0.3
O.I
0.3
0.3
0.0
-
-
-
-
-
0.0
-
-
0.0
-
0.0
-
-
-
-
O.I
0.2
-
-
-
-
-
-
-
0.6
-
0.0
-
O.I
0.3
O.I
0.3
0.3
0.0
-
-
-
-
-
0.0
-
-
0.0
-
0.0
-
-
-
^^^^^^
-
O.I
0.2
-
-
-
-
-
-
-
0.6
-
0.0
-
O.I
0.3
O.I
0.3
0.3
0.0
-
-
-
-
-
0.0
-
-
0.0
-
0.0
-
-
-
Ki3H EZ!z±9
^^^^^_
-
O.I
0.2
-
-
-
-
-
-
-
0.6
-
0.0
-
O.I
0.3
O.I
0.3
0.3
0.0
-
-
-
-
-
0.0
-
-
0.0
-
0.0
-
-
-
-
O.I
0.2
-
-
-
-
-
-
-
0.6
-
0.0
-
O.I
0.3
O.I
0.3
0.3
0.0
-
-
-
-
-
0.0
-
-
0.0
-
0.0
-
-
-
-^^B-
-
O.I
0.2
-
-
-
-
-
-
-
0.6
-
0.0
-
O.I
0.3
O.I
0.3
0.3
0.0
-
-
-
-
-
0.0
-
-
0.0
-
0.0
-
-
-
August 2011
Appendices
Page E-1 I
-------
Country
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Rest of Africa
Rest of Central and South
Rest of Middle East
Rest of Non-OECD Asia
Rest of Non-OECD Europe and
Africa
Central and South America
Middle East
OECD
1990 1995 2000
-
-
0.0
-
O.I
-
-
2.0
0.4
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.4
O.I
-
-
-
4.4
0.3
-
0.0
0.0
4.8
1.3
0.5
Non-OECD Asia 2.0
Non-OECD Europe & | 0.2
EU O.I
OPEC | O.I
World Totals
-
-
0.0
-
O.I
-
-
2.0
0.4
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
0.8
O.I
-
-
-
4.4
0.3
-
0.0
0.0
4.8
1.3
1.2
2.0
0.2
0.3
O.I
-
-
0.0
-
O.I
-
-
2.0
0.4
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
1.4
O.I
-
-
-
4.4
0.3
-
0.0
0.0
4.8
1.3
2.0
2.0
0.2
0.6
O.I
2005 2010 2015 2020
-
-
0.0
-
O.I
-
-
2.0
0.4
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
1.7
O.I
-
-
-
4.4
0.3
-
0.0
0.0
4.8
1.3
-
2.6
2.0
0.3
0.9
O.I
-
-
0.0
-
O.I
-
-
2.0
0.4
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
1.8
O.I
-
-
-
4.4
0.3
-
0.0
0.0
4.8
1.3
2.7
2.0
0.3
0.9
O.I
-
-
0.0
-
O.I
-
-
2.0
0.4
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
1.8
O.I
-
-
-
4.4
0.3
-
0.0
0.0
4.8
1.3
2.7
0.3
0.9
O.I
-
-
0.0
-
O.I
-
-
2.0
0.4
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
1.8
O.I
-
-
-
4.4
0.3
-
0.0
0.0
4.8
1.3
2.7
2.0
0.3
0.9
O.I
2025 2030
-
-
0.0
-
O.I
-
-
2.0
0.4
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
1.8
O.I
-
-
-
4.4
0.3
-
0.0
0.0
4.8
1.3
-
2.7
2.0
0.3
0.9
O.I
-
-
0.0
-
O.I
-
-
2.0
0.4
-
-
-
-
-
-
-
-
0.0
-
-
-
-
-
0.0
-
-
-
-
-
-
-
-
1.8
O.I
-
-
-
4.4
0.3
-
0.0
0.0
4.8
1.3
-
2.7
2.0
0.3
0.9
O.I
8.8 9.5 10.4 11.0 II. 1 II. 1 II. 1 II. 1 II. 1
August 2011
Appendices
Page E-12
-------
Appendix F: Methodology Applied to Develop Source Emissions
Appendix F provides a brief overview of the methodologies used to estimate historical and projected emissions
of methane (CH4), nitrous oxide (N2O), and high global warming potential (high GWP) gases by country and
source. The tables in this appendix correspond to the four sectors studied in this report, with the Industrial
Processes sector split into two tables for primary and "other" sources. The contents of this appendix are as
follows:
Table F-l: Methodology Applied to Develop Energy Sector Source Emissions, by Country
Table F-2: Methodology Applied to Develop Industrial Processes Sector Source Emissions, by
Country
Table F-3: Methodology Applied to Develop Other Industrial Processes Sector Source Emissions, by
Country
Table F-4: Methodology Applied to Develop Agriculture Sector Source Emissions, by Country
Table F-5: Methodology Applied to Develop Waste Sector Source Emissions, by Country
For each source and country within each of the sectors, Appendix F indicates whether the estimates are based
on country-reported estimates (CR* or CR), IPCC Tier 1 estimates (Tl), or other methodologies (O). If
emissions for a given country and source are not estimated or are equal to zero, the corresponding cell is left
blank.
CR* Designation. A "CR*" designation indicates that a country reported emissions from the given source for
one or more years, including data for 2005 or later. Historical and projected time-series for these countries are a
combination of country reported estimates, where available, interpolated values based on country-reported
estimates, and extrapolated values based on country reported estimates and derived growth rates or other
drivers, as indicated in the Methodology Chapter. A majority of the reported data was derived from the
UNFCCC flexible query system; however some sources relied on other country specific reports for emissions
data. The specific data collected for each source are outlined in the Methodology Chapter.
CR Designation. A "CR" designation indicates that a country reported emissions from the given source for
one or more years, but did not report data for 2005 or later. Historical and projected time-series for these
countries are a combination of country reported estimates, where available, interpolated values based on
country-reported estimates, and extrapolated values based on country reported estimates and derived growth
rates or other drivers, as indicated in the Methodology Chapter. A majority of the reported data was derived
from the UNFCCC flexible query system; however some sources relied on other country specific reports for
emissions data. The specific data collected for each source are outlined in the Methodology Chapter.
Tl Designation. A "Tl" designation indicates that EPA developed Tier 1 emissions estimates consistent with
methodologies outlined in the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC
Guidelines) (IPCC, 1997), the IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas
Inventories (IPCC Good Practice Guidance) (IPCC, 2000), and the 2006 IPCC Guidelines for National Greenhouse
Gas Inventories (IPCC Guidelines) (IPCC, 2006).
O Designation. An "O" designation indicates that emissions estimates are not based on country-reported data
and that EPA developed emissions estimates using a methodology other than Tier 1 (e.g., Tier 2, Vintaging
Model). The specific methodology used for each source is outlined in the Methodology Chapter.
Blank Cells. A cell is left blank if emissions for the given source and country were not estimated or are equal to
zero across the entire time-series.
August 201 I Appendices Page F-1
-------
Table F-1: Methodology Applied to Develop Energy Sector Source Emissions, by Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
CR
CR
CR
CR
CR*
CR*
CR
CR
CR*
CR*
CR
CR
CR*
Tl
CR*
Tl
CR
CR
Tl
CR*
CR*
CR*
CR
CR
CR*
CR*
CR*
Tl
CR*
CR*
CR*
CR
CR
CR
CR*
Tl
CR*
CR*
Tl
CR*
Tl
CR*
CR
Tl
CR
CR*
CR*
CR
CR
CR*
Tl
CR*
Tl
CR
CR
Tl
CR
CR*
Tl
Tl
CR*
Tl
CR*
CR*
CR*
CR
CR
CR
Tl
CR*
CR*
CR*
CR*
H
(/> U CD
Other Energy Sources
Efl H^E!
I/) Z LL
CH4 N2O CH4 N2O CH4 N2O N2O N2O
CR
CR
CR
CR
CR*
CR*
CR
CR
CR*
CR*
CR
CR
CR*
CR
CR*
CR
CR
CR
CR
CR*
CR*
CR*
CR
CR
CR*
CR
CR*
CR*
CR
CR*
CR*
CR*
CR*
CR
CR
CR
CR*
CR*
CR*
CR*
CR
CR*
CR*
CR
Tl
CR
CR
CR*
CR*
CR
CR
CR*
CR*
CR
CR
CR*
CR
CR*
CR
CR
CR
CR
CR*
CR*
CR*
CR
CR
CR*
CR
CR*
CR*
CR
CR*
CR*
CR*
CR*
CR
CR
CR
CR*
CR*
CR*
CR*
CR
CR*
CR*
Tl
Tl
Tl
Tl
CR*
CR*
Tl
Tl
CR*
CR*
Tl
Tl
CR*
Tl
Tl
CR*
Tl
Tl
Tl
Tl
CR*
CR*
CR*
Tl
Tl
CR*
Tl
CR*
CR*
Tl
CR*
CR*
CR*
Tl
Tl
Tl
Tl
CR*
Tl
CR*
CR*
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
CR*
CR*
Tl
Tl
CR*
CR*
Tl
Tl
CR*
Tl
Tl
CR*
Tl
Tl
Tl
Tl
CR*
CR*
CR*
Tl
Tl
CR*
Tl
CR*
CR*
Tl
CR*
CR*
CR*
Tl
Tl
Tl
Tl
CR*
Tl
CR*
CR*
Tl
Tl
Tl
Tl
CR*
CR*
CR*
CR*
CR*
CR*
CR
CR*
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR
CR*
CR*
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
August 2011
Appendices
Page F-2
-------
C-,
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
2221
CR*
CR*
CR*
CR
CR
CR
CR*
CR*
CR
Tl
CR*
CR
CR
CR
CR*
CR*
CR*
CR*
CR
CR
Tl
CR*
CR*
CR
CR
CR*
CR*
CR*
CR
CR
Tl
CR
CR*
CR
CR*
CR*
CR
CR*
CR
CR
!!!
Tl
CR
CR
Tl
CR
Tl
CR*
CR
CR
CR*
CR
CR
CR
CR*
CR
CR*
CR*
CR*
CR*
CR
CR
CR*
Tl
CR
CR
CR*
CR*
CR*
CR*
CR
CR*
CR
CR
KB
&
HI
Hb^C^^l
^
~~ CR
CR*
CR*
CR*
CR
CR
CR
CR*
CR
CR
CR*
CR*
CR
CR
CR*
CR
CR
CR
CR*
CR*
CR*
CR*
CR
CR
CR*
CR*
CR
CR
CR*
CR*
CR*
CR
CR
CR*
CR
CR
CR*
CR
CR*
CR*
CR
CR*
CR
CR
CR
CR*
CR*
CR*
CR
CR
CR
CR*
CR
CR
CR*
CR*
CR
CR
CR*
CR
CR
CR
CR*
CR*
CR*
CR*
CR
CR
CR*
CR*
CR
CR
CR*
CR*
CR*
Tl
CR
CR*
CR
CR
CR*
CR
CR*
CR*
CR
CR*
CR
CR
Other Energy Sources
^2^1
Efl B%i8
CH4 N20 CH4 N20 N20
CR*
CR*
CR*
Tl
Tl
Tl
CR*
Tl
Tl
CR*
CR*
Tl
Tl
CR*
Tl
Tl
Tl
CR*
CR*
CR*
CR*
Tl
Tl
CR*
CR*
Tl
Tl
CR*
CR*
CR*
Tl
CR*
CR*
Tl
CR*
CR*
Tl
Tl
Tl
Tl
CR*
CR*
CR*
Tl
Tl
Tl
CR*
Tl
Tl
CR*
CR*
Tl
Tl
CR*
Tl
Tl
Tl
CR*
CR*
CR*
CR*
Tl
Tl
CR*
CR*
Tl
Tl
CR*
CR*
CR*
Tl
CR*
CR*
Tl
CR*
CR*
Tl
Tl
Tl
Tl
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR
CR*
CR*
CR*
CR*
CR*
Z li
N20
CR*
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR
August 2011
Appendices
Page F-3
-------
Table F-2: Methodology Applied to Develop Industrial Processes Sector Source Emissions, by Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Tl
Tl
Tl
Tl
Tl
CR*
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
CR*
Tl
CR*
Tl
Tl
Tl
Tl
Tl
Tl
Tl
CR*
CR*
CR*
CR*
CR*
Tl
CR*
CR*
Tl
Tl
Tl
CR*
CR*
CR
Tl
CR*
CR*
Tl
CR*
CR*
CR*
Tl
Tl
Tl
CR
Tl
CR*
CR*
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
Tl
CR
Tl
CR*
Tl
CR*
Tl
CR
CR*
Tl
Tl
Tl
Tl
CR*
CR*
Tl
Tl
CR*
CR*
Tl
Tl
CR*
Tl
Tl
CR*
Tl
Tl
Tl
Tl
CR*
CR*
CR*
Tl
Tl
CR*
CR*
CR*
Tl
CR*
CR*
CR*
CR*
Tl
Tl
Tl
Tl
CR*
Tl
CR*
CR*
Tl
Tl
Tl
CR
CR*
O
CR
O
CR*
CR*
CR*
CR*
CR*
O
O
O
CR*
CR*
O
O
O
O
CR
O
CR*
CR*
O
O
CR*
O
CR*
CR*
O
O
CR*
O
CR*
O
CR*
CR*
O
O
CR*
O
CR*
CR*
O
O
CR*
O
CR*
O
CR*
CR*
O
O
CR*
O
CR*
CR*
Tl
CR*
Tl
CR
CR*
CR*
Tl
CR*
CR*
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
August 2011
Appendices
Page F-4
-------
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Tl
Tl
Tl
Tl
Tl
Tl
Tl
CR
Tl
CR
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
CR*
CR*
Tl
Tl
CR*
Tl
Tl
CR*
Tl
CR*
Tl
CR*
CR*
CR*
CR*
Tl
CR*
CR*
Tl
Tl
CR*
CR*
CR*
CR*
CR*
CR*
Tl
Tl
O
0
0
O
O
O
O
O
O
O
O
O
O
O
O
0
0
0
0
0
0
0
0
0
0
0
0
O
O
O
O
O
O
O
O
O
O
O
O
0
0
0
0
0
0
0
Tl
CR*
CR*
Tl
Tl
CR*
Tl
CR*
Tl
Tl
Tl
CR*
CR*
CR*
Tl
Tl
Tl
Tl
Tl
Tl
CR*
Tl
Tl
CR*
Tl
Tl
Tl
CR*
CR*
CR*
CR*
Tl
Tl
CR*
CR*
Tl
Tl
CR*
CR*
CR*
Tl
Tl
CR*
Tl
Tl
Tl
CR*
CR*
Tl
Tl
Tl
Tl
0
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
0
CR*
CR*
CR*
O
CR*
O
O
CR*
CR*
0
0
0
O
O
0
0
0
0
0
0
CR
O
O
CR*
0
0
O
O
CR*
0
0
0
0
0
CR
CR
O
CR*
0
0
O
CR*
0
0
0
0
0
0
CR
CR
O
CR*
CR
CR
CR*
CR*
Tl
Tl
Tl
CR*
CR*
Tl
Tl
CR*
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
Tl
August 2011
Appendices
Page F-5
-------
Table F-3: Methodology Applied to Develop Other Industrial Processes Sector Source Emissions, by
Country
1
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Other Industrial Processes Sources
1
9 EH
Mr
CR
CR*
CR*
CR*
CR*
CR
CR*
CR
CR
CR*
CR*
CR*
CR*
CR
CR*
CR
CR
CR*
CR*
CR*
E£fl
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
£
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
H
UUUil
Z Q.Q. 55 a. in a. ^^^H
CH4 CH4 CH4 N2O ^^M
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
August 2011
Appendices
Page F-6
-------
Kyrgyzstan
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Other Industrial Processes Sources
1
{iflU
u .±0. i. i. a. a. vi a. vi a.
CH4 CH4 CH4 N2O CH4 CH4 CH4 N2O
CR*
CR
CR
CR*
CR*
CR
CR*
CR
CR
CR*
CR*
CR*
CR*
CR
CR*
CR*
CR
CR
CR*
CR*
CR*
CR
CR*
CR*
CR
CR*
CR*
CR*
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR*
August 2011
Appendices
Page F-7
-------
Table F-4: Methodology Applied to Develop Agriculture Sector Source Emissions, by Country
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
Laos
Tl
Tl
CR
Tl
CR*
CR*
Tl
Tl
CR*
CR*
Tl
CR
CR*
Tl
Tl
CR*
CR
Tl
CR
Tl
CR*
CR*
CR*
Tl
Tl
CR*
Tl
CR*
CR*
Tl
CR*
CR*
CR*
CR*
Tl
Tl
Tl
Tl
CR*
Tl
CR*
CR*
Tl
Tl
Tl
Tl
Tl
CR
CR
CR
CR
CR*
CR*
CR
CR
CR*
CR*
CR
CR
CR*
Tl
CR
CR*
CR
CR
CR
CR
CR*
CR*
CR*
CR
CR
CR*
CR
CR*
CR*
Tl
CR*
CR*
CR*
CR*
CR
CR
CR
Tl
CR*
CR
CR*
CR*
CR
CR*
Tl
CR*
CR
Tl
Tl
CR
CR*
CR
CR
CR
CR
CR*
Tl
Tl
CR
CR
CR
Tl
Tl
CR
Tl
CR*
CR*
CR*
Tl
CR
CR
Tl
CR*
CR*
CR*
CR*
Tl
CR
CR
CR
CR
CR*
CR*
CR
CR
CR*
CR*
CR
CR
CR*
Tl
CR
CR*
Tl
CR
CR
CR
CR*
CR*
CR*
CR
CR
CR*
CR
CR*
CR*
Tl
CR*
CR*
CR*
CR*
CR
Tl
Tl
CR*
CR
CR*
CR*
CR
CR*
Tl
CR*
CR
CR
CR
CR
Tl
CR*
CR*
Tl
Tl
CR*
CR*
CR
CR
CR*
Tl
CR
CR*
CR
CR
CR
CR
CR*
CR*
CR*
Tl
Tl
CR*
Tl
CR*
CR*
Tl
CR*
CR*
CR*
CR*
CR
Tl
CR
Tl
CR*
CR
CR*
CR*
Tl
CR*
Tl
CR*
Tl
Other Agncultur
Sources
CR*
CR
CR*
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
August 2011
Appendices
Page F-8
-------
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
CR*
CR*
CR*
CR
Tl
Tl
Tl
Tl
CR*
CR*
Tl
Tl
CR*
Tl
Tl
Tl
CR*
CR*
CR*
CR*
Tl
Tl
Tl
CR*
CR*
CR
CR
CR*
CR*
CR*
CR
Tl
Tl
Tl
Tl
CR*
Tl
CR*
CR*
CR
CR*
Tl
Tl
CR*
CR*
CR*
CR
CR
CR
CR
CR
CR*
CR*
CR
CR
CR*
CR
CR
CR
CR*
CR*
CR*
CR*
CR
CR
Tl
CR*
CR*
CR
CR
CR*
CR*
CR*
CR
CR
CR*
CR
CR
CR*
CR
CR*
CR*
CR
CR*
CR
CR
CR
CR
CR
Tl
CR
CR
CR
CR
CR*
CR*
CR*
Tl
Tl
CR
CR*
CR
CR
CR*
CR
Tl
CR*
CR*
CR
CR*
CR
CR
CR*
CR*
CR*
CR
CR
CR
Tl
CR
CR*
CR*
CR
CR
CR*
CR
CR
Tl
CR*
CR*
CR*
CR*
CR
Tl
Tl
CR*
CR*
CR
CR
CR*
CR*
CR*
CR
CR
CR*
CR
CR
CR*
Tl
CR*
CR*
CR
CR*
CR
CR
CR*
CR*
CR*
CR
CR
Tl
Tl
CR
CR*
CR*
Tl
CR
CR*
Tl
CR
CR
CR*
CR*
CR*
CR*
CR
Tl
Tl
CR*
CR*
CR
CR
CR*
CR*
CR*
Tl
CR
CR
Tl
CR*
CR
CR*
CR*
Tl
CR*
CR
Tl
O
o
O
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
August 2011
Appendices
Page F-9
-------
Table F-5: Methodology Applied to Develop Waste Sector Source Emissions, by Country
Country
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bolivia
Brazil
Bulgaria
Burma
Cambodia
Canada
Chile
China
Colombia
Congo (Kinshasa)
Croatia
Czech Republic
Denmark
Ecuador
Egypt
Estonia
Ethiopia
Finland
France
Georgia
Germany
Greece
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Japan
Jordan
Kazakhstan
Kuwait
Kyrgyzstan
CR
CR
CR
CR
CR*
CR*
CR
CR
CR*
CR*
CR
CR
CR*
Tl
CR
CR*
CR
CR
CR
CR
CR*
CR*
CR*
CR
CR
CR*
CR
CR*
CR*
Tl
CR*
CR*
CR*
CR*
CR
Tl
CR
Tl
CR*
CR*
CR*
CR*
CR
CR*
Tl
CR*
1
B9
H
Other Waste Sources
0
I
I
^^1
CH4 N2O CH4 N2O
Tl
CR
CR
CR
CR*
CR*
CR
Tl
Tl
CR*
CR
CR
CR*
Tl
CR
CR*
CR
CR
CR
CR
CR*
CR*
CR*
CR
CR
Tl
CR
CR*
CR*
Tl
CR*
CR*
CR*
CR*
CR
Tl
CR
Tl
CR*
CR*
CR*
CR*
CR
CR*
Tl
CR*
CR
CR
CR
Tl
CR*
CR*
CR
Tl
CR*
CR*
CR
CR
CR*
Tl
CR
CR*
CR
Tl
Tl
CR
CR*
CR*
CR*
Tl
Tl
CR*
CR
CR*
CR*
Tl
CR*
CR*
CR*
CR*
CR
Tl
CR
Tl
CR*
CR*
CR*
CR*
Tl
CR*
Tl
CR*
CR*
CR*
CR*
CR*
CR
CR*
CR*
CR
CR*
CR*
CR
CR*
CR*
CR*
CR
CR
CR*
CR*
CR
CR*
CR*
CR*
CR*
CR*
August 2011
Appendices
Page F-10
-------
Laos
Latvia
Lithuania
Luxembourg
Macedonia
Mexico
Moldova
Monaco
Mongolia
Nepal
Netherlands
New Zealand
Nigeria
North Korea
Norway
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
Senegal
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Tajikistan
Thailand
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela
Vietnam
Other Waste Sources
BrK
KKfl
> I Q XI
CH4 CH4 N20 CH4 N20
CR
CR*
CR*
CR*
CR
CR
CR
CR
CR
CR*
CR*
CR
CR
CR*
CR
CR
CR
CR*
CR*
CR*
CR*
CR
CR
Tl
CR*
CR*
CR
CR
CR*
CR*
CR*
CR
CR
CR*
CR
CR
CR*
CR
CR*
CR*
CR
CR*
CR
CR
CR
CR*
CR*
CR*
CR
CR
CR
Tl
CR
CR
CR*
CR*
CR
CR
CR*
CR
Tl
CR
CR*
CR*
CR*
CR*
CR
CR
Tl
CR*
CR*
CR
CR
CR*
Tl
CR*
CR
CR
Tl
Tl
CR
CR*
CR
CR*
CR*
CR
CR*
CR
CR
1 1
CR*
CR*
Tl
CR
CR
Tl
CR*
Tl
CR
CR*
CR*
Tl
Tl
Tl
Tl
Tl
CR
CR*
CR*
CR*
CR*
CR
Tl
CR
Tl
CR*
CR
CR
CR*
CR*
CR*
Tl
Tl
Tl
Tl
Tl
CR*
CR
CR*
CR*
CR
CR*
CR
CR
CR*
CR*
CR*
CR
CR
CR*
CR*
CR*
CR*
CR*
CR*
CR*
CR
CR
CR
CR*
CR*
CR*
CR
August 2011
Appendices
Page F-1 I
-------
Appendix G: Data Sources Used to Develop Non-Country-Reported Emissions Estimates
Emissions Source
Energy
Natural Gas and Oil Systems
Coal Mining Activities
Stationary and Mobile
Combustion
Biomass Combustion
Other Energy Sources
Data Type
Data Source
Production, Consumption,
Refinery Capacity
Historical Coal Production
CH4 Abatement
Projected Coal Production
Fuel Consumption
Projected Energy
Consumption
Biomass Fuel Consumption
Growth Rates
None
EIA 2010. Energy Information Administration International Energy Statistics Data Portal.
EIA 2009. Natural Gas Annual Data. U.S. Energy Information Agency.
EIA 2010. Energy Information Administration International Energy Statistics Data Portal.
EPA. 2010. Methane to Markets International Coal Mine Methane (CMM) Projects database.
EIA 2009. International Energy Outlook 2009. Energy Information Administration.
IEA 2009a. Energy Balances of Non-OECD Countries 1971-2007. International Energy Agency.
IEA 2009b. Energy Balances of OECD Countries 1960-2007. International Energy Agency.
IEA 2009c. World Energy Outlook 2009. International Energy Agency.
IEA 2009a. Energy Statistics of Non-OECD Countries 1971-2007. International Energy Agency.
IEA 2009b. Energy Statistics of OECD Countries 1960-2007. International Energy Agency.
IEA 2009c. World Energy Outlook 2009. International Energy Agency.
Country-reported data only.
Industrial Processes
Adipic Acid and Nitric Acid
Production
Adipic Capacity Utilization SRI. 2010. World Petrochemical Report: Adipic Acid. SRI Consulting.
Fertilizer Consumption
Long-term Fertilizer
Consumption
Chemical Week. 2007. Product Focus: Adipic Acid.
Chemical Week. 1999. Product Focus: Adipic Acid/Adiponitrile.
FAO. 2010. FAOSTAT Statistical Database. Food and Agriculture Organization of the United Nations.
SRI. 2007. CEH Report: Nitric Acid. SRI Consulting.
SRI. 1999. Quoted in Product focus: Adipic Acid/Adiponitrile. Chemical Week. SRI Consulting.
Tenkorang and Lowenberg-DeBoer. 2008. Forecasting Long-term Global Fertilizer Demand. Food and
Agriculture Organization of the United Nations.
August 2011
Appendices
PageG-1
-------
Emissions Source
Data Type
Use of Substitutes for Ozone Vintaging Model
Depleting Substances
ODS Consumption
End-Use Distributions of
ODS
GDP Growth Factors
Foam Market Information
Data Source
EPA. 2010. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2008.
UNEP. 2010. Production and Consumption of Ozone Depleting Substances, 1986-2007. United
Nations Environment Programme (UNEP) Ozone Secretariat.
March. 1996. UK Use and Emissions of Selected Hydrocarbons. March Consulting Group, HMSO,
Russian Federation. 1994. Phaseout of Ozone Depleting Substances in Russia.
USDA. 2009. Real GDP (2005 dollars) Historical International Macroeconomic Data Set. United States
Department of Agriculture Economic Research Service.
Ashford. 2004. Peer review comments on U.S. EPA Draft Report, Draft Analysis of International Costs
of Abating HFC Emissions from Foams.
HCFC-22 Production
HCFC-22 Production
Thermal Oxidation Market
Penetration
UNEP. 2010. Data Access Centre. HCFC Production.
CEH. 2001. Fluorocarbons CEH Marketing Research Report. Chemical and Economics Handbook.
Will et al. 2004. CEH Marketing Research Report: Fluorocarbons. Chemical Economics HandbookSRI
Consulting.
Montzka, et al. (2010). "Recent increases in global HFC-23 emissions."
EPA. 2006. Global Anthropogenic Non-CO2 Greenhouse Gas Emissions: 1990 - 2020.
IPCC/TEAP. 2005. Special Report: Safeguarding the Ozone Layer and the Global Climate System:
Issues Related to Hydrofluorocarbons and Perfluorocarbons.
Harnisch, J. and C. Hendricks. 2000. Economic Evaluation of Emission Reductions of HFCs, PFCs and
SF6 in Europe.
EPA. 2006 Global Anthropogenic Non-CO2 Greenhouse Gas Emissions: 1990 - 2020. Office of
Atmospheric Programs: Climate Change Division.
August 2011
Appendices
Page G-2
-------
Emissions Source
Electric Power Systems
Data Type
Data Source
SF6 Sales to Utilities and
Manufacturers
Emission Rate
Electrical Equipment
Lifetime
Electricity Consumption
Japanese Emissions
U.S. Projections
Electricity Consumption
Projections
Japanese Projections
Smythe. 2004. Trends in SF6 Sales and End-Use Applications: 1961-2003.
IPCC. 2000. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas
Inventories. Intergovernmental Panel on Climate Change.
Ecofys. 2005. Reductions of SF6 Emissions from High and Medium Voltage Electrical Equipment in
Europe.
EIA 2008. International Energy Annual 2006. Energy Information Administration.
Yokota et al. 2005. Recent Practice for Huge Reduction of SF6 Gas Emission from GIS&GCB in Japan.
U.S. State Department. 2010. U.S. Climate Action Report 2010.
EIA 2009. International Energy Outlook 2009. Energy Information Administration.
Yokota. 2006. E-mail from Takeshi Yokota.
Primary Aluminum
Production
Primary Aluminum
Production
Global Aluminum
Production Growth Rate
USGS. 1995 through 2009. Mineral Yearbook: Aluminum. U.S. Geological Survey.
Martchek. 2006. Modelling More Sustainable Aluminium: Case Study.
Magnesium Manufacturing
Primary Magnesium
Production
EU Historical Emissions
Chinese Casting Volume
Automobile Production
Recycling-based
Production
US 2010 Phased-out Goal
CDM Projects
USGS. 2007. Minerals Yearbook 2007: Magnesium. United States Geological Survey (USGS).
USGS. 2009. Minerals Yearbook 2009: Magnesium. United States Geological Survey (USGS).
Harnisch and Schwarz. 2003. Costs of the Impact on Emissions of Potential Regulatory Framework for
Reducing Emissions of Hydrofluorocarbons, Perfuorocarbons, and Sulphur Hexafluoride.
Edgar. 2004. SF6 Usage in the Chinese Magnesium Industry: 2000-2010.
Ward's. 2001. Ward's World Motor Vehicle Data.
OICA20IO. 2009 Production Statistics. Organisation Internationale des Constructeurs d'Automobiles.
Webb. 2005. Magnesium Supply and Demand 2004.
USEPA 2010. SF6 Emission Reduction Partnership for the Magnesium Industry.
UNFCCC. 2010. Clean Development Mechanism (CDM) Project Information
August 2011
Appendices
Page G-3
-------
Emissions Source
Semiconductor
Manufacturing
Data Type
Data Source
GDPWSC Reduction
GoalsModeling China's
semiconductor industry
fluorinated compound
emissions and drafting a
roadmap for climate
protection ("The China
Paper")
USDA. 2009. Real GDP (2005 dollars) Historical International Macroeconomic Data Set. United States
Department of Agriculture Economic Research Service.WSC. 2010. Joint Statement of the 14th Meeting
of the World Semiconductor Council (WSC).ITRS. 2009. International Technology Roadmap for
Semiconductors: 2009 Edition. Bartos, S.C., et al. 2008. Modeling China's semiconductor industry
fluorinated compound emissions and drafting a roadmap for climate protection.
Flat Panel Display
Manufacturing
Photovoltaic Manufacturing
Other Industrial Processes
Sources
Maximum design capacities DisplaySearch. 2009. DisplaySearch Q4'09 Quarterly FPD Supply/Demand and Capital Spending Report
Country capacity shares Database.
WLICC Goal
Maximum design capacities DisplaySearch. 2009. DisplaySearch Q4'09 Quarterly PV Cell Capacity Database & Trends Report.
Maximum design capacities
Country capacity shares
PV technology shares
None
Country-reported data only.
Agriculture
August 2011
Appendices
Page G-4
-------
Emissions Source
Agricultural Soils
Data Type
Data Source
Commercial Synthetic
Fertilizer Consumption
Regional N Fertilizer
Consumption Projections
Major Crop Production
and Acreage
Projected Crop
Production and Acreage
Animal Population
Livestock Product Growth
Rates
IFA. 2010. IFADATA Statistical Database. International Fertilizer Industry Association.
FAO. 2010. FAOSTAT Statistical Database. Food and Agriculture Organization of the United Nations.
Tenkorang and Lowenberg-DeBoer. 2008. Forecasting Long-term Global Fertilizer Demand. Food and
Agriculture Organization of the United Nations.
FAO. 2010. FAOSTAT Statistical Database. Food and Agriculture Organization of the United Nations.
FAPRI. 2010. U.S. and World Agricultural Outlook. Food and Agricultural Policy Research Institute.
FAO. 2010. FAOSTAT Statistical Database. Food and Agriculture Organization of the United Nations.
IFPRI. 2009. International Food Policy Research Institute, Impact Model Growth Rate Spreadsheet.
Enteric Fermentation
Rice Cultivation
Animal Population Data
Livestock Product Growth
Rates
Area Harvested for Rice
Cultivation
Water Management
Regime Type
Length of Rice-growing
Season
Projected Rice Harvesting
FAO. 2010. FAOSTAT Statistical Database. Food and Agriculture Organization of the United Nations.
IFPRI. 2009. International Food Policy Research Institute, Impact Model Growth Rate Spreadsheet.
FAO. 2010. FAOSTAT Statistical Database. Food and Agriculture Organization of the United Nations.
IRRI. 2009. World Rice Statistics. International Rice Research Institute.
IRRI. 2009. World Rice Statistics. International Rice Research Institute.
FAPRI. 2010. U.S. and World Agricultural Outlook. Food and Agricultural Policy Research Institute.
Manure Management
Livestock Population Data FAO. 2010. FAOSTAT Statistical Database. Food and Agriculture Organization of the United Nations.
Livestock Product Growth IFPRI. 2009. International Food Policy Research Institute, Impact Model Growth Rate Spreadsheet.
Rates
Other Agriculture Sources Biomass Burning Emissions EC-JRC. 2009. Emission Database for Global Atmospheric Research (EDGAR), release version 4.0.
Landfilling of Solid Waste
Population
GDP
Climate Zones
Census, 2009. U.S. Census International Data Base.
USDA. 2009. Real GDP (2005 dollars) Historical International Macroeconomic Data Set. United States
Department of Agriculture Economic Research Service.
IPCC. 2003. Good Practice Guidance for Land Use, Land-Use Change and Forestry. The
Intergovernmental Panel on Climate Change.
August 2011
Appendices
Page G-5
-------
Emissions Source
Data Type
Data Source
Wastewater
Human Sewage - Domestic
Wastewater
Other Waste Sources
Population
Population
Protein Consumption
None
Census. 2009. U.S. Census International Data Base.
CIA. 2010. The World Factbook. Central Intelligence Agency.
Census. 2009. U.S. Census International Data Base.
CIA. 2010. The World Factbook. Central Intelligence Agency.
FAO. 2009. FAO Statistical Yearbook 2009. Food and Agriculture Organization of the United Nations.
Country-reported data only.
August 2011
Appendices
Page G-6
-------
Appendix H: Future Mitigation Measures Included in Developing Non-Country-
Reported Estimates
Emissions Source
Energy
Natural Gas and Oil Systems
Coal Mining Activities
Stationary and Mobile Combustion
Biomass Combustion
Other Energy Sources
Industrial Processes
Adipic Acid and Nitric Acid Production
Use of Substitutes for Ozone Depleting
Substances
HCFC-22 Production
Operation of Electric Power Systems
Primary Aluminum Production
Magnesium Manufacturing
Semiconductors Manufacturing
Future Mitigation Measures Included
None.
Methane Abatement Projects.
None.
None.
None.
M
None.
None.
Thermal Oxidation Technology Abatement.
Successful Attainment of Developed Country SF6 Reduction Goals; replacement of
existing SF6-insulated equipment with newer equipment that holds less SF6 and is more
leak-tight (in developed countries only).
None.
Voluntary SF6 cover gas use phase-out is assumed by 2010 for Austria, Denmark,
France, Germany, Italy, Norway, Poland, Sweden, and Switzerland in compliance with
the EU phase-out schedule.
U.S. phase-out is assumed to be implemented by a majority of companies in 2010
under the U.S. Magnesium Industry Partnership goal.
Canada and Japan are assumed to phase-out SF6 usage from 2010 through 2020.
WSC Goal assumed to be attained individually by all WSC countries (except China) in
2010, and maintained through 2030. WSC member China assumed to set and achieve
a WSC goal of a 10% reduction from a baseline year of 2012 for 2020 through 2030.
Flat Panel Display Manufacturing
Photovoltaic Manufacturing
Other Industrial Processes Sources
Assumed abatement strategies are used to achieve the WLICC goal in Japan, South
Korea and Taiwan in 2010 through 2030.
None.
None.
Agriculture
Agricultural Soils
Enteric Fermentation
Rice Cultivation
Manure Management
Other Agriculture Sources
None.
None.
None.
None.
None.
Waste
Landfilling of Solid Waste None.
Wastewater None.
Human Sewage - Domestic Wastewater None.
Other Waste Sources None.
August 2011
Appendices
Page H-1
-------
Appendix I : Regional Definitions
Algeria °
Congo (Kinshasa)
Angola °
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African
Republic
Chad
Comoros
Congo (Brazzaville)
^^
Argentina
Bolivia
Africa
Egypt Nigeria °
Ethiopia Senegal
Rest of Africa
Cote d'lvoire Libya0
Djibouti Madagascar
Equatorial Guinea Malawi
Eritrea Mali
Gabon Mauritania
Gambia Mauritius
Ghana Morocco
Guinea Mozambique
Guinea-Bissau Namibia
Kenya Niger
Lesotho Rwanda
Liberia
Central and South America
Brazil Ecuador °
Colombia Peru
South Africa
Uganda
Sao Tome and Principe
Seychelles
Sierra Leone
Somalia
Sudan
Swaziland
Tanzania
Togo
Tunisia
Zambia
Zimbabwe
J
Uruguay
Venezuela °
Rest of Central and South America
Antigua and Barbuda
Bahamas
Barbados
Belize
Costa Rica
Cuba
^^
Iran0
Iraq0
Bahrain
Lebanon
xles:
| A - Annex 1 Countries
Dominica Haiti
Dominican Republic Honduras
El Salvador Jamaica
Grenada Nicaragua
Guatemala Panama
Guyana Paraguay
Middle East
Jordan
Kuwait °
Rest of Middle East
Oman
Qatar0
| E - European Union Countries
Saint Kitts and Nevis
Saint Lucia
Saint Vincent and the
Grenadines
Suriname
Trinidad and Tobago
Saudi Arabia °
United Arab Emirates °
Syria
Yemen
| O - OPEC Countries |
Note: In this report, when emissions totals are presented for a region, the regional sum includes the estimates for all of the individually
reported countries and the aggregated value for the "Rest of' countries. For example, the emissions total for the "Middle East" found in
the graphs and Appendices A through D, includes the sum of Iran, Iraq, Israel, Jordan, Kuwait, Saudi Arabia, the United Arab Emirates and
the smaller emitters already aggregated under "Rest of Middle East".
August 2011
Appendices
Page I-1
-------
r
Australia A
Austria A E
Belgium A E
Canada A
Chile
Czech Republic AE
Denmark AE
Finland AE
France AE
V
OECD
Germany A E Mexico
Greece AE Netherlands AE
Hungary AE New Zealand A
Iceland A Norway A
Ireland AE Poland A E
Israel Portugal A E
Italy A E SlovakiaA E
Japan A Slovenia A E
Luxembourg AE
South Korea
Spain A E
Sweden A E
Switzerland A
Turkey A
United Kingdom (UK) A E
United States (U.S.) A
"Rest of OECD" ' 2
^t
Bangladesh
Burma
Cambodia
China
India
Afghanistan
Bhutan
Brunei
Cook Islands
Fiji
Kiribati
Malaysia
Non-OECD Asia
Indonesia
Laos
Mongolia
Nepal
North Korea
Rest of Non-OECD Asia
Maldives
Marshall Islands
Micronesia (Federated States of)
Nauru
Niue
Palau
Papua New Guinea
%
Pakistan
Philippines
Singapore
Thailand
Vietnam
Samoa
Solomon Islands
Sri Lanka
Timor-Leste
Tonga
Tuvalu
Vanuatu
f
Albania
Armenia
Azerbaijan
Belarus A
Bulgaria A E
Croatia A
Estonia AE
Non-OECD Europe & Eurasia
Georgia
Kazakhstan
Kyrgyzstan
Latvia A E
Lithuania AE
Macedonia
Moldova
^
Monaco A
Romania AE
Russia A
Tajikistan
Turkmenistan
Ukraine A
Uzbekistan
Rest of Non-OECD Europe & Eurasia
Andorra
Bosnia and Herzegovina
Cyprus E
V
Holy See
Liechtenstein A
Malta A E
Montenegro
San Marino
Serbia
_>
August 2011
Appendices
Page 1-2
-------
Appendix J: U.S. EPA Vintaging Model Framework
Vintaging Model Overview
The Vintaging Model estimates emissions from six industrial sectors: refrigeration and air-
conditioning, foams, aerosols, solvents, fire extinguishing, and sterilization. Within these sectors,
over 60 independently modeled end-uses exist. The model requires information on the market
growth for each of the end-uses, as well as a history of the market transition from ozone-depleting
substances (ODS) to alternatives. 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 in each of the end-uses. Emissions are estimated by applying annual leak rates, service
emission rates, and disposal emission rates to each population of equipment. By aggregating the
emission and consumption output from the different end-uses, the model produces estimates of
total annual use and emissions of each chemical. For the purpose of projecting the use and
emissions of chemicals into the future, the available information about probable evolutions of the
end-use market is incorporated into the model.
The following sections discuss the forms of the estimation equations used in the Vintaging Model
for each broad end-use category. These equations are applied separately for each chemical used
within each of over 60 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 extrapolating
forward in time from the amount of regulated ODS used in the early 1990s, adjusted for factors that
might affect ODS substitute consumption, such as different charge sizes and lower emission rates.
Using data gleaned from a variety of sources, assessments are made regarding which alternatives will
likely be used, and what fraction of the ODS market in each end-use will be captured by that
alternative. By combining this information with estimates of the total end-use market growth, a
consumption value is estimated for each chemical used within each end-use.
Emissions Equations
Refrigeration and Air-Conditioning
For refrigeration and air conditioning products, emission calculations are split into two categories:
emissions during equipment lifetime, which arise from annual leakage and service losses, and
disposal emissions, which occur at the time of discard. Equation 1 calculates the lifetime emissions
from leakage and service, and Equation 2 calculates the emissions resulting from disposal of the
equipment. These lifetime emissions and disposal emissions are added to calculate the total
emissions from refrigeration and air-conditioning (Equation 3). As new technologies replace older
ones, it is generally assumed that there are improvements in their leak, service, and disposal emission
rates. In addition, the charge size assumed for equipment using an ODS substitute may be different
than that for equipment using the ODS.
August 201 I Appendices Page J-1
-------
Lifetime emissions from any piece of equipment include both the amount of chemical leaked during
equipment operation and during service, including recharges. Emissions from leakage and servicing
can be expressed as follows:
ESJ = (la + IJ x EQcrM fori=1 ->k Eq. 1
Where:
ESJ -Emissions from equipment serviced. Emissions in year/' from normal leakage and
servicing (recharging) of equipment.
4 = Annual leak rate. Average annual leak rate during normal equipment operation
(expressed as a percentage of total chemical charge).
ls - Service leak rate. Average leakage during equipment servicing (expressed as a
percentage of total chemical charge).
Qc -Quantity of chemical in new equipment. Total amount of a specific chemical used to
charge new equipment in a given year, by weight.
k - Eifetime. The average lifetime of the equipment.
j - Year of emission.
i - Counter. Runs from 1 to lifetime (k).
The disposal emission equations assume that a certain percentage of the chemical charge will be
emitted to the atmosphere when that vintage is discarded. Disposal emissions are thus a function of
the quantity of chemical contained in the retiring equipment fleet and the proportion of chemical
released at disposal:
Ed} = QCJ-M X [1 -(rmx re)] Eq. 2
Where:
Ed: = Emissions from equipment disposed. Emissions in year/' from the disposal of
equipment.
Qc -Quantity of chemical in new equipment. Total amount of a specific chemical used to
charge new equipment one lifetime (k) ago (e.g.,/- k+\\ by weight.
rm - Chemical remaining. Amount of chemical remaining in equipment at the time of
disposal (expressed as a percentage of total chemical charge)
re - Chemical recovery rate. Amount of chemical that is recovered just prior to
disposal (expressed as a percentage of chemical remaining at disposal (rm))
k - Eifetime. The average lifetime of the equipment.
j - Year of emission.
Ej = ESj + Edj Eq. 3
August 20 1 I Appendices Page J-2
-------
Where:
Ej = Total emissions. Emissions from refrigeration and air conditioning equipment in
year/'.
Es = Emissions from equipment serviced. Emissions in a given year from normal leakage
and servicing (recharging) of equipment.
Ed = Emissions from equipment disposed. Emissions in a given year from the disposal of
equipment.
j - Year of emission.
All HFCs used in aerosols are assumed to be emitted in the year of manufacture. Since there is
currently no aerosol recycling, it is assumed that all of the annual production of aerosol propellants
is released to the atmosphere. Equation 4 describes the emissions from the aerosols sector.
Ej=Qfj Eq.4
Where:
Ej ^Emissions. Total emissions of a specific chemical in year/' from use in aerosol
products, by weight.
Qc -Quantity of chemical. Total quantity of a specific chemical contained in aerosol
products sold in a given year, by weight.
j - Year of Emission.
Generally during the solvent cleaning process, a portion of used solvent is assumed to remain in the
liquid phase and is not emitted as gas. Thus, emissions are considered "incomplete," and are set as a
percentage of the amount of solvent consumed in a year. The remainder of the consumed solvent is
assumed to be reused or disposed without being released to the atmosphere. Equation 5 calculates
emissions from solvent applications.
E} = I X QCj Eq. 5
Where:
Ej = Emissions. Total emissions of a specific chemical in year/' from use in solvent
applications, by weight.
/ -Percent leakage. The percentage of the total chemical that is lost to the
atmosphere, assumed to be 90%.
Qc -Quantity of chemical. Total quantity of a specific chemical sold for use in solvent
applications in a given year, by weight.
j - Year of emission.
August 201 I Appendices Page J-3
-------
Fire
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 (Equation 6). For modeling purposes, it is assumed
that fire extinguishing equipment leaks at a constant rate for an average equipment lifetime.
E} = r x ZQCj_l+1 fori=1^>k Eq. 6
Where:
E = Emissions. Total emissions of a specific chemical in year/' for fire extinguishing
equipment, by weight.
r = Percent Released. The percentage of the total chemical in operation that is
released to the atmosphere.
Qc -Quantity of chemical. Total amount of a specific chemical used in new fire
extinguishing equipment one lifetime (/£) ago (e.g.,/- k+\\ by weight.
i = Counter. Runs from 1 to lifetime (/£).
j - Year of emission.
k - Ufetime. The average lifetime of the equipment.
Foams are given emission profiles depending on the foam type (open cell or closed cell). Open cell
foams are assumed to be 100% emissive in the year of manufacture, as described in Equation 7
below. 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 post-disposal, as described in Equations 8 through 12, below.1
Open-Cell Foam
Ej=8fj Eq-?
Where:
E. = Emissions. Total emissions of a specific chemical in year/' used for open-cell
foam blowing, by weight.
Qc = Quantity of chemical. Total amount of a specific chemical used for open-cell
foam blowing in a given year, by weight.
j - Year of emission.
Closed-Cell Foam
Emissions from closed-cell foams occur at many different stages, including manufacturing, lifetime,
disposal and post-disposal.
1 Emissions from foams may vary because of handling and disposal of the foam; shredding of foams may increase
emissions, while landfilling of foams may abate some emissions (Scheutz and Kjeldsen, 2002; Scheutz and Kjeldsen,
2003). Average annual emissions are assumed in the model, which may not fully account for the range of foam handling
and disposal practices.
August 201 I Appendices Page J-4
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Manufacturing emissions occur in the year of foam manufacture, and are calculated as presented in
Equation 8.
fax Qcj Eq. 8
Where:
ErMj = Emissions from manufacturing. Total emissions of a specific chemical in year/' due
to manufacturing losses, by weight.
Im -Eoss Rate. Percent of original blowing agent emitted during foam manufacture.
Qc - Quantity of chemical. Total amount of a specific chemical used to manufacture
closed-cell foams in a given year.
j - Year of emission.
Lifetime emissions occur annually from closed cell foams throughout the lifetime of the foam, as
calculated using Equation 9.
EUJ = lu x ZQCj_l+1fori = 1 -> k Eq. 9
Where:
EUJ = Emissions 'from lifetime losses. Total emissions of a specific chemical in year/' due
to lifetime losses during use, by weight.
lu -Eeak Rate. Percent of original blowing agent emitted during lifetime use.
Qc - Quantity of chemical. Total amount of a specific chemical used to manufacture
closed-cell foams in a given year.
k - Eifetime. Average lifetime of foam product.
i Counter. Runs from 1 to lifetime (M).
j - Year of Emission.
Disposal emissions occur in the year the foam is disposed, and are calculated as presented in
Equation 10.
Edj = ldxQcj± Eq.10
Where:
Ed- -Emissions from disposal. Total emissions of a specific chemical in year/' at
disposal, by weight.
Id -Eoss Rate. Percent of original blowing agent emitted at disposal.
Qc = Quantity of chemical. Total amount of a specific chemical used to manufacture
closed-cell foams in a given year.
August 201 I Appendices Page J-5
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k = Lifetime. Average lifetime of foam product.
j - Year of emission.
Post-disposal emissions occur in the years after the foam is disposed, and are assumed to occur
while the disposed foam is in a landfill. Currently, the only foam type assumed to have post-disposal
emissions is polyurethane appliance foam, which is expected to continue to emit for 32 years post-
disposal, and are calculated as presented in Equation 11.
Epj = lpx ZQc^for m = k -> k + 32 Eq.11
Where:
Epj = Emissions post disposal. Total post-disposal emissions of a specific chemical in
year j, by weight.
Ip -Leak rate. Percent of original blowing agent emitted post disposal.
Qc - Quantity of chemical. Total amount of a specific chemical used in closed-cell
foams in a given year.
k - Lifetime. Average lifetime of foam product.
m - Counter. Runs from lifetime (M) to (k + 32).
j - Year of emission.
To calculate total emissions from foams in any given year, emissions from all foam stages must be
summed, as presented in Equation 12.
+ EUj + Ed} + Epj Eq. 12
Where:
Ej = Total emissions. Total emissions of a specific chemical in year/, by weight.
= Emissions from manufacturing losses. Total emissions of a specific chemical in year/'
due to manufacturing losses, by weight.
= Emissions from lifetime losses. Total emissions of a specific chemical in year/' due
to lifetime losses during use, by weight.
= Emissions at disposal. Total emissions of a specific chemical in year/' due to
disposal, by weight.
Epj = Emissions post disposal. Total post-disposal emissions of a specific chemical in
year/', by weight.
Sterilization
For sterilization applications, all chemicals that are used in the equipment in any given year are
assumed to be emitted in that year, as shown in Equation 13.
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Where:
Ej = Emissions. Total emissions of a specific chemical in year/' from use in
sterilization equipment, by weight.
Qc -Quantity of chemical. Total quantity of a specific chemical used in sterilization
equipment in a given year, by weight.
j - Year of emission.
Model Output
By repeating these calculations for each year from 1985-2050, the Vintaging Model creates annual
profiles of use and emissions for ODS and ODS substitutes. The results can be shown for each
year in two ways: 1) on a chemical-by-chemical basis, summed across the end-uses, or 2) on an end-
use basis. Values for use and emissions are calculated in metric tons, ozone depleting tons, and in
million metric tons of carbon dioxide equivalents (MtCO2eq). The conversion of metric tons of
chemical to MtCO2eq is accomplished through a linear scaling of tonnage by the global warming
potential (GWP) of each chemical. The model can produce the values for use and emissions in
MtCO2Eq. using chemical GWPs from IPCC's Second, Third, or Fourth Assessment Reports
(IPCC, 1996; IPCC, 2001; IPCC, 2007).
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