&EPA
United States
Environmental Protection
Agency
Potic
And Evaluation*
(PM-221)
21P-2003.3
December 1990
Policy Options For
Stabilizing Global Climate
Report To Congress
Technical Appendices
Printed on Recycled Paper
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POLICY OPTIONS FOR STABILIZING GLOBAL CLIMATE
REPORT TO CONGRESS
Appendices
Editors: Daniel A. Lashof and Dennis A. Tirpak
United States Environmental Protection Agency
Office of Policy, Planning and Evaluation
December 1990
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This document has been reviewed in accordance with the U.S.
Environmental Protection Agency's and the Office of Management and
Budget's peer and administrative review policies and approved for
publication. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
Publisher's Note:
Policy Options for Stabilizing Qlobal Climaie, Report to Congress has been
published in three parts:
21P-2003.1 MAIN REPORT (includes Executive Summary)
21P-2003.2 EXECUTIVE SUMMARY
21 P-2003.3 TECHNICAL APPENDICES
Those who wish to order the Main Report or Technical Appendices should
inquire at the address below:
Publications Requests
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Office of Policy, Planning and Evaluation
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
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TABLE OF CONTENTS
Page
APPENDIX A
MODEL DESCRIPTIONS
PREFACE A-l
INTRODUCTION A-2
Purpose and Structure of the Atmospheric Stabilization Framework A-2
Emissions Modules A-4
Atmospheric Composition Module , A-6
Ocean Circulation and Uptake Module ^ A-7
Algorithms Used to Estimate Increases in Radiative Forcing A-7
Unknown Sink A-7
ENERGY EMISSIONS MODULE A-7
Introduction A-7
Regional Supply and Demand Models A-8
Energy Flow: Primary Production to End Use A-9
Basis for Determining Energy Prices A-ll
Estimating Primary Energy Supply A-12
Fossil Fuels A-12
Hydropower A-13
Nuclear Energy A-13
Solar Energy A-15
Commercial Biomass A-15
Calculating Prices for Primary and Secondary Energy A-15
Estimating Synfuel Conversion A-16
Modeling Electricity Generation A-16
Estimating Energy Demand A-19
Bottom-Up Approach: Energy Demand Through 2025 A-20
Top-down Approach: Energy Demand Beyond 2025 A-26
Interface to the End-Use Models A-27
Implementation of Capital Stock A-28
Estimating Greenhouse And Related Emissions A-29
INDUSTRIAL EMISSIONS MODULE '. A-31
Estimating Emissions of CFCs and Halons A-31
Estimating Emissions of CH4 from Landfills A-32
Estimating CO2 from the Production of Cement A-32
AGRICULTURAL EMISSIONS MODULE A-35
Introduction A-35
Estimating Agricultural Activities through 2050: The Basic Linked System A-35
National Models A-38
Regional Models A-42
Treatment of Agricultural Variables A-42
Completing And Expanding The Estimates Through 2100 A-44
Estimating Emissions of Trace Gases A-46
Methane from Rice A-46
Methane Emissions from Enteric Fermentation in Domestic Animals A-47
N2O Emissions from Fertilizer Use and Legumes A-49
Emissions from the Burning of Agricultural Wastes A-49
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LAND-USE CHANGES AND NATURAL EMISSIONS MODULE A-49
Estimating Natural Emissions of Trace Gases A-50
Estimating Emissions from Changing Land Use A-50
Flux of CO^ Between the Atmosphere and Land Resulting from
Deforestation and Reforestation A-51
Emissions of N2O, CH^ NO^ and CO A-54
ATMOSPHERIC COMPOSITION MODULE A-57
Estimating the Atmospheric Content of Long-Lived Trace Gases A-57
Measuring Changes in Climate A-61
The Stratosphere . A-63
Tropospheric Chemistry A-65
Feedbacks A-65
CO2 Uptake by the Oceans A-66
Methane Emissions > A-66
CO2 Emissions t A-66
Vegetation Albedo .J . A-66
OCEAN CIRCULATION AND UPTAKE MODULE A-66
Integrated Box-Diffusion Model A-67
Alternative CO2/Ocean Uptake Models A-68
REFERENCES A-69
APPENDIX B
IMPLEMENTATION OF THE SCENARIOS
DESCRIPTIVE OVERVIEW OF THE SCENARIOS B-l
Scenarios with Unimpeded Emissions Growth B-3
Scenarios with Stabilizing Policies and Accelerated Emissions B-4
MACROECONOMIC ASSUMPTIONS FOR THE ATMOSPHERIC
STABILIZATION FRAMEWORK B-5
Population Growth Rates B-5
Economic Growth Rates B-6
Oil Prices : . B-9
ENERGY ....;.. B-9
Energy Demand . ........ B-9
Transportation B-9
Residential and Commercial Sectors B-ll
Industrial and Agricultural Sectors B-ll
Energy Supply B-19
Production Costs for Fossil Fuels B-19
Gas Flaring Rates B-23
Refinery Efficiencies and Costs B-23
Hydroelectric Resources B-26
Solar Energy Costs B-26
Nuclear Power Costs B-27
Biomass Energy Costs and Availability B-27
Synthetic Fuel Costs ..',.• ... B-29
Transportation Costs in the Atmospheric Stabilization Framework B-29
Distribution Cost Assumptions For The Atmospheric Stabilization Framework . . . B-31
Generation Efficiency B-31
Emission Control Assumptions . B-32
Carbon Fees B-36
vi
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Results of the Energy Scenarios B-36
Energy Prices , B-36
Energy Use and Emissions B-36
CHLOROFLUOROCARBON AND HALON EMISSIONS B-38
DEFORESTATION B-38
AGRICULTURE B-41
GREENHOUSE GAS EMISSIONS B-41
REALIZED AND EQUILIBRIUM WARMING B-43
NOTES B-46
REFERENCES : B-46
c.
APPENDIX C
SENSITIVITY ANALYSES
FINDINGS C-l
INTRODUCTION . , C-6
ASSUMPTIONS ABOUT THE MAGNITUDE AND TIMING OF
GLOBAL CLIMATE STABILIZATION STRATEGIES C-6
No Participation by the Developing Countries C-6
Delay in Adoption of Policies C-9
ASSUMPTIONS AFFECTING RATES OF TECHNOLOGICAL CHANGE C-9
Availability of Non-Fossil Technologies C-9
Cost and Availability of Fossil Fuels C-ll
High Coal Prices C-ll
Alternative Oil and Natural Gas Supply Assumptions C-ll
Availability of Methanol-Fueled Vehicles C-17
ATMOSPHERIC COMPOSITION: COMPARISON OF MODEL RESULTS
TO ESTIMATES OF HISTORICAL CONCENTRATIONS C-17
ASSUMPTIONS ABOUT TRACE-GAS SOURCES AND STRENGTHS C-18,
Methane Sources C-18
Nitrous Oxide Emissions From Fertilizer C-21
Anhydrous Ammonia C-21
N2O Leaching From Fertilizer C-21
N2O Emissions From Combustion C-24
UNCERTAINTIES IN THE GLOBAL CARBON CYCLE C-24
Unknown Sink In Carbon Cycle C-24
Amount of CO2 From Deforestation C-27
Alternative CO2 Models of Ocean Chemistry and Circulation C-31
ASSUMPTIONS ABOUT CLIMATE SENSITIVITY AND TIMING C-31
Sensitivity of the Climate System C-31
Rate of Heat Diffusion C-33
vii
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ASSUMPTIONS ABOUT ATMOSPHERIC CHEMISTRY:
A COMPARISON OF MODELS C-33
Model Descriptions , C-36
Assessment Model for Atmospheric Composition • C-36
Isaksen Model G-36
Thompson et al. (1989) Model , , C-37
Results from the Common Scenarios C-37
EVALUATION OF UNCERTAINTIES USING AMAC '. C-40
Atmospheric Lifetime of CFC-11 C-40
Interaction of Chlorine with Column Ozone C-44
Sensitivity of Tropospherie Ozone to CH4 Abundance , C-44
Sensitivity of OH to NOX C-44
BIOGEOCHEMICAL FEEDBACKS C-46
Ocean Circulation C-46
Methane Feedbacks : C-46
Combined Feedbacks C-46
NOTES C-51
REFERENCES C-51
via
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LIST OF FIGURES
APPENDIX A
A-l Structure of the Atmospheric Stabilization Framework . A-3
A-2 Geopolitical Regions of Climate Analyses A-5
A-3 Energy Flows A-10
A-4 Supply Model A-14
A-5 Capital Stock Approach A-18
A-6 Typical Outline of a National Model A-40
A-7 Tropical Forest Response Curves A-53
APPENDIX B
B-l
CO2 Emissions from Tropical Deforestation B-42
APPENDIX C
C-l Increase in Realized Warming When Developing Countries
Do Not Participate C-8
C-2 Increase in Realized Warming Due to Global Delay in Policy Options C-10
C-3 Availability of Non-Fossil Energy Options C-12
C-4 Impact of 1% per Year Real Escalation in Coal Prices C-13
C-5 Impact of Higher Oil Resources on Total Primary Energy Supply C-15
C-6 Impact of Higher Natural Gas Resources on Total Primary Energy Supply C-16
C-7 Realized Warming Through 1985 C-19
C-8 Increase in Realized Warming Due to Changes in the Methane Budget C-23
C-9 Change in Atmospheric Concentration of N2O Due to Leaching C-25
C-10 Change in Atmospheric Concentration of N2O Due to Combustion C-26
C-ll Impact on Realized Warming Due to Size of Unknown Sink C-28
C-12 CO2 From Deforestation Assuming High Biomass C-29
C-13 Impact of High Biomass Assumptions on Atmospheric Concentrations of CO2 . . C-30
C-14 Comparison of Different Ocean Models C-32
C-15 Impact of Climate Sensitivity on Realized Warming C-34
C-16 Increase in Realized Warming Due to Rate of Ocean Heat Uptake C-35
C-17 Regional Differences for Urban Areas with Different Emissions
of CO and NO C-41
C-18 OH and Ozone Perturbations in the Isaksen and Hov Model C-42
C-19 Sensitivity of Atmospheric Concentration of CFC-11 to Its Lifetime C-43
C-20 Increase in Realized Warming Due to Rate of Interaction of Clx
With Ozone C-45
C-21 Increase in Realized Warming Due to Change in Ocean Circulation C-47
C-24 Increase in Realized Warming Due to Methane Feedbacks C-48
C-25 Increase in Realized Warming Due to Change in Combined Feedbacks C-50
IX
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LIST OF TABLES
APPENDIX A
A-l Sector/Subsector Disaggregation in Industrialized Countries A-21
A-2 Sector/Subsector Disaggregation in Developing Countries A-25
A-3 Differences in Emission Rate by Sector A-30
A-4 Release Profiles for CFC-11 A-33
A-5 Assumptions Concerning Methane Emissions from Landfills A-34
A-6 Regional Disaggregation of BLS A-36
A-7 Agricultural and Non-Agricultural Commodity Classes A-39
A-8 Structure and Approach Used to Estimate Fertilizer Use A-43
A-9 Structure and Approach Used to Estimate Rice Acreage A-44
A-10 Structure and Approach Used to Estima.te Ruminants A-45
A-ll 1984 Animal Populations and Emission Estimates A-48
A-12 Estimates of Current Emissions from Burning of Agricultural Wastes A-50
A-13 Estimates of Current Emissions from Natural Sources A-51
A-14 Fate of Carbon in Undisturbed Ecosystems After Land is Cleared
for Agriculture '.....' A-52
A-15 Carbon in Vegetation and Soils of Different Land-Use Categories in
the World's Major Tropical Regions A-54
A-16 Annual Rates of Deforestation (1975-80) A-55
A-17 Estimates of Current Emissions from Land-Use Change A-56
A-18 Participants, Contributors, and Reviewers Workshop: A Model for
Atmospheric Composition A-58
A-19 Long-Lived Gases A-59
A-20 Short-Lived and Implicitly Solved Species A-60
A-21 Global Lifetime Assumptions for Long-Lived Gases A-62
A-22 Models of Changes in Forcing A-64
APPENDIX B
B-l Overview of Major Scenario Assumptions B-2
B-2 Global Population Estimates: 1985-2100 B-7
B-3 World Bank GDP Growth Assumptions: 1986-1995 B-8
B-4 GDP Growth Assumptions B-10
B-5 Assumptions on Vehicle Ownership and Amount of Travel B-12
B-6 Average Fuel Efficiency of Cars and Light Trucks B-13
B-7 Demographic Changes in the Residential Sector for Industrialized Countries .... B-14
B-8 Average Improvements in Energy Intensity in the Residential/Commercial
Sector in Industrialized Countries . . .s B-15
B-9 Key Assumptions in the Residential Sector of the Developing Countries
Through 2025 B-16
B-10 Key Assumptions in the Commercial Sector of the Developing Countries
Through 2025 B-17
B-ll Per Capita Production of Basic Materials in Industrialized Countries B-18
B-12 Energy Efficiency Improvement in the Industrial Sector B-20
B-13 Key Assumptions in the Industrial Sector of the Developing Countries
Through 2025 B-22
B-15 Minimum Extraction Cost Curves for Natural Gas B-24
B-16 Minimum Extraction Cost Curves for Oil . B-25
B-17 Minimum Extraction Cost Curves for Coal B-25
B-18 Hydroelectric Resources B-26
B-19 Future World Wide Biomass Energy Potential B-28
B-20 Cost of Synthetic Fuel Technologies B-30
B-21 Emission Rate Differences by Sector B-33
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B-22 Emission Control Performance B-34
B-23 Energy Prices B-37
B-24 Primary Energy Supply in the SCW B-49
B-25-31 Primary Energy Supply by Oil, Gas, Coal, Biomass, Hydroelectric, Nuclear,
and Solar, Respectively, in the SCW B-49
B-32 Primary Energy Consumption in the SCW B-51
B-33 Secondary Energy Consumption: Fuel Versus Electricity in the SCW B-51
B-34-36 Secondary Fuel Consumption by Oil, Gas, and Solids, Respectively, in
the SCW , B-52
B-37 Residential/Commercial Energy Consumption: Fuel Versus Electricity
in the SCW ' B-53
B-38 Industrial Energy Consumption: Fuel Versus Electricity in the SCW B-54
B-39 Transportation Energy Consumption: Fuel Versus Electricity in the SCW B-55
B-40 Electric Utility Energy Consumption B-56
B-41 Energy Conversion Efficiency at Electric Utility Power Plants in the SCW B-56
B-42 Synthetic Production of Oil and Gas in the SCW B-56
B-43 Energy Used for Synthetic Fuel Production by Type in the SCW B-57
B-44 CO2 Emissions from Fossil Fuel in the SCW B-58
B-45 CO Emissions from Fossil Fuel in the SCW B-58
B-46 NOX Emissions from Fossil Fuel in the SCW B-58
B-47-69 Same tables as B-24-46 in the RCW Case B-59
B-70-92 Same tables as B-24-46 in the RCWA Case B-69
B-93-115 Same Tables as B-24-46 in the SCWP Case B-79
B-116-138 Same Tables as B-24-46 in the RCWP Case B-89
B-139-161 Same Tables as B-24-46 in the RCWR Case B-99
B-162 Chlorofluorocarbon Emissions by Scenario B-39
B-163 Production of Wheat in the SCW and SCWP B-109
B-164 Production of Rice in the SCW and SCWP B-109
B-165 Production of Coarse Grains in the SCW and SCWP B-109
B-166 Production of Meats in the SCW and SCWP B-109
B-167 Production of Dairy Products in the SCW and SCWP B-110
B-168 Production of Other Animals and in the SCW and SCWP B-110
B-169 Nitrogenous Fertilizer Use in the SCW and SCWP B-110
B-170 Land Under Rice Cultivation in the SCW and SCWP B-110
B-171-178 Same Tables as B-163-170 in the RCW, RCWA, RCWP, and RCWR Cases B-lll
B-179 CO2 Emissions by Type in the SCW B-113
B-180 N2O Emissions by Type in the SCW B-113
B-181 CH4 Emissions by Type in the SCW B-113
B-182 NOX Emissions by Type in the SCW B-113
B-183 CO Emissions by Type in the SCW B-114
B-184-188 Same Tables as B-179-183 in the RCW Case B-115
B-189-193 Same Tables as B-179-183 in .the RCWA Case B-117
B-194-198 Same Tables as B-179-183 in the SCWP Case B-119
B-199-203 Same Tables as B-179-183 in the RCWP Case B-121
B-204-208 Same Tables as B-179-183 in the RCWR Case B-123
B-209 Realized Warming for 1.5-5.5°C Sensitivities B-44
B-210 Equilibrium Warming for 1.5-5.5°C Sensitivities B-45
APPENDIX C
C-l Impact of Sensitivity Analyses on Realized Warming and Equilibrium Warming . . . C-3
C-2 Comparison of Model Results to Concentrations in 1986 C-20
C-3 Low and High Anthropogenic Impact Budgets for Methane C-22
C-4 Comparison of Emission Estimates for EPA #1-8, RCW and SCW Cases C-38
C-5 Comparison of Results from Atmospheric Chemistry Models for the Year
2050 Compared to 1985 C-39
XI
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APPENDIX A
MODEL DESCRIPTIONS
PREFACE
This appendix describes the approaches
and modeling techniques used to estimate
future global warming. The appendix is
intended to serve as a summary; detailed
descriptions of the different models can be
obtained from the papers and reports cited
here. Data sources and scenario specifications
are presented in Appendix B.
This appendix draws heavily from
separate papers and reports prepared for U.S.
EPA and includes some edited sections of
these reports. We would like to cite the
following works that faU within this category:
• Frohberg, Klaus K., Phil R. Van de
Kamp, 1988, Results of Eight
Agricultural Policy Scenarios for
Reducing Agricultural Sources of Trace
Gas Emissions. Center for
Agricultural and Rural Development,
Iowa State University, Ames, Iowa;
• Houghton, R.A. 1988. The Flux of
CO2 between Atmosphere and Land as
a Result of Deforestation and
Reforestation from 1850 to 2100. The
Woods Hole Research Center;
ICF Incorporated, 1988. Global
Macro-Energy Model Summary Paper.
ICF Incorporated;
Mintzer, I.M. 1988. Projecting Future
Energy Demand in Industrialized
Countries: An End-Use Oriented
Approach. World Resources Institute,
Washington, D.C.;
Prather, M. 1989. An Assessment
Model for Atmospheric Composition.
Proceedings of a workshop held at
NASA Goddard Institute for Space
Studies, January 10-13, 1988, NASA
Conference Publication 3023, New
York, 64 pp.; and
Sathaye, J.A., A.N. Ketoff, LJ.
Schipper, and S.M. Lele. 1988. An
End-Use Approach to Development of
Long-Term Energy Demand Scenarios
for Developing Countries. International
Energy Studies Group, Energy Analysis
Program, Lawrence Berkeley
Laboratory, Berkeley, CA.
A-l
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Policy Options for Stabilizing Global Climate
INTRODUCTION
Purpose and Structure of the Atmospheric
Stabilization Framework
This appendix describes how the
Atmospheric Stabilization Framework (ASF)
estimates the magnitude of possible future
greenhouse warming and the impact of
different strategies to stabilize climate -
identifying the different natural, physical, and
economic processes that affect future
warming, and then .describing how those
processes are modeled.
The primary purpose of the ASF is to
provide a tool that can estimate the
magnitude of future greenhouse warming
under a wide variety of assumptions about
variables that effect trace gas emissions,
atmospheric chemistry, and temperature rise.
The ASF allows the user to measure the
relative impacts of different climate
stabilization policies, as well as the
importance of uncertainties that are inherent
in the data and parameter assumptions, within
a forfhat that is internally consistent.
The different assumptions required for
the,ASF that can effect estimates of future
warming fall into three categories: data;
parameters, algorithms, and models; and
stabilization strategies. The data assumptions
range from estimates of energy resources and
production costs to assumptions about future
growth in population, growth in income, and
current emissions of trace gases from the
different natural and anthropogenic processes.
The assumptions about parameters,
algorithms, and models include variations in
how energy supply and demand can be
modeled, the elasticity of energy supply and
demand to income and prices, the impact of
changes in emissions of the different trace
gases on concentrations of short-lived gases
such as tropospheric ozone, and the rate of
change of CO2 and heat uptake by the oceans
over time as CO2 concentrations and radiative
forcing change. Stabilization strategies
include the use of control technologies to
reduce emissions from energy combustion and
strategies to shift between energy sources,
reduce energy consumption, and reduce
emissions from livestock and rice production.
The ASF explicitly deals with the gases
identified in the literature as the most
important contributors, either directly or
indirectly, to future greenhouse warming.
With the exception of water vapor arid clouds,
the ASF estimates atmospheric concentrations
of the gases. The impact of water vapor and
clouds on climate warming is captured in the
parameter that defines climate sensitivity.
Trace gases explicitly represented in the ASF
that directly affect greenhouse warming include
CO2, CH4, N2O, CFC-ll, CFC-12, HCFC-22,
CFC-113, CC14, CH3CC13, halon 1301, and
tropospheric ozone; those that haye an
indirect impact on warming include NOX and
CO. Volatile Organic Compounds (VOCs)
are included in the atmospheric composition
module, but changes in emissions over time
are not included at this time. For simplicity,
all of the gases will be referred to in this
appendix as greenhouse gases, although
strictly speaking, NOX and CO are not
greenhouse gases.
The ASF combines input data, user
scenario specifications, and different models
to estimate trace gas emissions, changes in the
atmospheric concentrations of the trace gases,
ocean uptake of heat and CO2, and
temperature rise. The ASF provides a
structure so that emissions of the different
trace gases from the different sources are
consistent with input assumptions such as
assumptions of future population, income,
efficiency, etc. The ASF is designed to run
on a personal computer where all but a few
of the components are completely specified
and run during a session. Several of the
components are run on separate computers
due to program size and complexity, and
output from these runs is combined through
data files (e.g., agricultural activities are
estimated by an integrated set of 34 models
on a minicomputer and the results of these
models are transferred to the ASF through
data files).
Figure A-l illustrates the overall
structure of the ASF. It consists of four
emissions modules ~ energy, industrial,
agricultural, and land-use change and natural
emissions ~ which estimate emissions of the
different trace gases based on input data
assumptions on population, income, energy
A-2
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Appendix A: Model Descriptions
FIGURE A-l
STRUCTURE OF THE ATMOSPHERIC STABILIZATION FRAMEWORK
Inputs
Base case
Assumptions
Resources
Population
Growth
Productivity
Technology
Alternative
Strategies
Emissions
Forecasting
Modules
Concentration
•• *
Determination
Modules
Atmospheric
Composition
i
1
r
Ocean
Outputs
Atmospheric
Concentrations
and
Temperature
Change
Feedbacks
A-3
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Policy Options for Stabilizing Global Climate
resources and costs, control technologies,
control strategies, and'current emissions. The
atmospheric composition and ocean CO2 and
heat uptake modules estimate the changes in
the atmospheric concentrations of greenhouse
gases, radiative forcing, and global
temperature. A feedback parameterization
estimates changes in emissions rates due to
changes in global temperatures.
Figure A-2 illustrates the regional
structure of the ASF. It disaggregates
emissions into four regions consisting of
developed nations and five regions consisting
of developing nations:
Developed Nations
• U.S.
• Western OECD Canada and
Western Europe
Eastern OECD
• Centrally-
Planned Europe
Developing Nations
• Centrally-
Planned Asia
• Middle East
• Africa
• Latin America
• South and East
Asia
Japan, Australia,
New Zealand, and
other eastern
OECD nations
USSR and Eastern
Europe
China, North
Korea, Vietnam,
and other
Centrally-Planned
Asian Economies
Indian Pakistan,
Bangladesh,
Thailand,
Indonesia, and the
remainder of Asia
not included in the
other regions
All of the models within the energy module
provide energy supply, demand, and/or
emission estimates for each of these regions
separately. The agricultural module provides
detailed estimates of agricultural activities for
the 34 regions that reflect the regional
structure of the agricultural activities model
(Basic Linked System), but provides emissions
estimates for the nine regions listed above.
The other emissions modules provide
emission estimates for these nine regions, for
the northern\southern hemisphere, or for the
world as a whole, depending on the module,
the trace gas, and the emission source.
The primary method of implementing
the interface between the different modules is
the use of scenario description files and the
integrating model interface files and routines.
For each scenario, the scenario description
file contains an identifier for the scenario, a
brief description of the scenario, and a list of
all of the data files used to implement the
scenario. The integrating model interface
files contain detailed output from the
different modules, including trace gas
emissions, atmospheric concentrations, and
data on such activities as energy production
and consumption, fertilizer use, and
agricultural production. The integrating
model interface routines provide the interface
between the models and the interface file.
Each of the individual pieces that make
up the ASF is described below. The
remainder of the appendix describes each of
the components in some detail; these detailed
descriptions are qualitative and refer the
reader to Appendix B for more detail on the
data assumptions and sources used to
implement the different scenarios and
sensitivities.
Emissions Modules ^
Energy Module. The energy module
estimates the emissions of trace gases
resulting from the production, transportation,
distribution, and consumption of fossil fuels.
It is comprised of a set of detailed global
energy end-use models (DEMAND) and the
Global Energy Supply Model (SUPPLY),
which estimates energy supply and locates the
energy supply/demand balance.
The end-use models provide a detailed
picture of energy consumption in the nine
regions based on future population and
income. In developing countries, the end-use
A-4
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Appendix A: Model Descriptions
FIGURE A-2
GEOPOLITICAL REGIONS OF CLIMATE ANALYSES
KEY:
1. United States
2. OECD Europe/Canada
3, OECD Pacific
4. USSR/Centrally Planned Europe
5. Centrally Planned Asia
• 6. Middle East ^
7. Africa
8, Latin America
9. South and East Asia
Source: Adapted from Edmonds & Reiily, 1,983a, in Mintter, 1988.
A-5
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Policy Options for Stabilizing Global Climate
models use current energy-consumption
patterns of different countries to represent
the level of energy use at different levels of
income and economic growth. As the
income and population increase over time, the
energy patterns in the five developing regions
change to reflect this growth and any changes
projected due to increases in energy prices
and/or improvements in energy efficiency.
The Global Energy Supply Model
combines the end-use models with a supply
model within an equilibrium framework that
adjusts energy prices to balance energy supply
and demand. The supply model estimates
future energy supply of fossil, biomass,
nuclear, solar, and hydro energy sources. The
model accounts for such factors as resource
depletion, technology improvements, energy
prices, constraints to proliferation, and
production constraints.
The Global Energy Supply Model
estimates emissions of trace gases using a
capital stock approach that accounts for the
inherent inertia resulting from the investment
in energy supply systems and the
infrastructure to use that energy. The
approach keeps track of different vintages of
technologies that consume energy and allows
for the future use of more energy-efficient
stock, technologies that produce fewer
emissions, and the implementation of
emission controls.
Industrial Emissions Module. The
industrial non-energy-related emissions
include three categories of emissions: CFCs
and halons, CH4 from landfills, and CO2 from
cement production. The U.S. EPA Integrated
Assessment Model is used to estimate future
emissions of CFCs and halons. This model
accounts for emissions related to the chemical
production and use of these substances and
for emissions that result from product
decomposition. The model allows for the
implementation of various control strategies
and accounts for alternative scenarios of
compliance with those strategies. Estimates
of CH4 from landfills and CO2 from cement
production reflect assumptions concerning
current emissions and the relationships among
population, income, and growth in the
emissions.
Agricultural Emissions Module.
Agricultural emissions include CH4 from rice
cultivation, CH4 from enteric fermentation in
domestic animals, N2O emissions resulting
from the use of nitrogenous fertilizer, and
CH4, N2O, NOX, and CO emissions resulting
from the burning of agricultural wastes. The
agricultural module combines a detailed
regional agricultural model with an emissions
model that applies emission coefficients to the
detailed rice paddy area, meat and dairy
production, and fertilizer use from the
agricultural model. The emissions model ties
emissions resulting from the burning of
agricultural wastes to land use from the
agricultural model.
Land-Use Changes and Natural
Emissions Module. The land-use changes and
natural emissions module captures those
emission sources not represented within the
other modules. These emissions sources
range from CH4 from termites and small
herbivores and naturally occurring N2O
emissions from land and ocean sources to
emissions of gases from deforestation and
non-agricultural biomass burning. The
approach used to estimate future emissions
varies considerably by emissions source; the
Marine Biological Laboratory/ Terrestrial
Carbon Model (MBL/TCM) is used to
estimate emissions of CO2 from deforestation
and reforestation (Houghton, 1988).
Atmospheric Composition Module
The atmospheric composition module
uses the emissions estimates produced by the
emissions modules and estimates changes in
the atmospheric concentrations of the
greenhouse gases (Prather, 1989). The model
is highly parameterized and considers
feedbacks of increased emissions, including
changes to stratospheric ozone, increased
penetration of solar UV radiation, and
subsequent increased destruction of some of
the long-lived gases. Other feedbacks include
the impact of emissions on the levels of
tropospheric ozone and OH and the
subsequent impact on the oxidization of gases
such as CH4.
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Appendix A: Model Descriptions
Ocean Circulation and Uptake Module
The ASF accounts for the net flux of
CO2 and heat between the atmosphere and
the oceans with a box-diffusion formulation
introduced by Oeschger et al. (1975) and
utilized by Hansen et al. (1984, 1988). Four
alternative models of CO2 uptake by the
ocean are included in this module to account
for some of the uncertainty in modeling this
process.
The box-diffusion model represents
through a diffusion equation the turnover of
carbon and the flux of heat below an ocean
depth of 110 meters. The model includes a
mixed layer and a thermocline but no deep
ocean. The coupling between climate change
and CO2 uptake is captured through
equations for CO2 solubility and carbonate
chemistry.
The four alternative models provide the
capability to test the sensitivity of the results
to different approaches, to modeling the
uptake of CO2 by the ocean. Unlike the box-
diffusion model which is directly coupled to
the atmospheric composition model (see
above), the alternative models do not capture
the coupling between ocean uptake of CO2
and climate change. These models include an
alternate box-diffusion formulation, which
includes a deep ocean, an advective-diffusive
model based on work by Bjorkstrom (1979),
a 12-compartment regional model based on
work by Bolin et al. (1983), and an outcrop-
diffusion model based on work by
Siegenthaler (1983).
Algorithms Used to Estimate Increases in
Radiative Forcing
The formulation used to estimate
radiative forcing due to increases in
atmospheric concentrations of trace gases is
based on calculations from a one-dimensional
radiative convectfve model (Hansen et al.,
1981; Hansen et al., 1988; Ramanathan et al.,
1985). Radiative forcing is translated to
temperature change using the zero-
dimensional formulation described by
Dickinson (1986):
Q - A AT = F
where Q is radiative forcing, A is the climate
feedback parameter, AT is the realized
warming, and F is the flux of heat into the
ocean. The model allows testing of different
levels of climate feedback by adjusting the
parameter A.
Unknown Sink
The model is calibrated using estimates
from Rotty and Masters (1985) of historical
CO2 emissions from fossil-fuel combustion
and estimates from Houghton (1988) of
historical CO2 emissions from deforestation.
Differences between CO2 concentrations
estimated by the models and from historical
measurements are resolved through the
unknown sink. Behavior of the unknown sink
is allowed to vary in the future.
ENERGY EMISSIONS MODULE
Introduction
The Global Macro-Energy Model
selected for use in the ASF consists of a
modified version of the IEA/ORAU Long-
Term Global Energy-CO2 Model (Edmonds
and Reilly, 1986) in combination with detailed
end-use models. The IEA/ORAU Long-Term
Global Energy-CO2 Model was developed by
Jae Edmonds and John Reilly to run on a
personal computer and was released in 1985.
U.S. EPA modified the model to change the
time steps, improve the energy supply
component, add emissions of four trace gases
(CO, CH4, N2O, and NOX), interface with
detailed end-use models, and interface with
the ASF. The detailed end-use models were s
developed at the World Resources Institute
(Mintzer, 1988) and Lawrence Berkeley
Laboratory (Sathaye et al., 1988).
The time period of interest, 1985 to
2100, imposed certain restrictions on the
design of the energy model that would not
necessarily be imposed on energy models with
shorter time horizons. First, conventional
recoverable resources of oil and gas will likely
be exhausted by 2100 assuming that current
trends in consumption continue. It was
necessary, therefore, that the model keep
track of resources and resource depletion and
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Policy Options for Stabilizing Global Climate
simulate shifts away from the use of
conventional oil and gas to other primary
energy sources, such as unconventional oil
and gas, coal, synthetic fuels, and non-fossil
forms of electricity. The energy model had to
simulate how emissions of trace gases would
change as a result of the shift in energy
supply. This change in emissions could come
about as a result of a reliance on fuels with a
higher or lower carbon content per unit of
energy or on those that require a substantial
amount of energy to convert them to a useful
form, such as synthetic fuels. In addition, the
model had to allow for the future use of non-
fossil technologies that may not currently
exist, such as fusion or large-scale biomass
projects:
The extent of energy consumption over
the long term will be driven by a number of
factors, including growth in income,
development of industries, development of
electric power capacity, and investment in
energy-consuming projects such as road
building and the development of mass transit
systems. In addition, future energy
consumption will depend on the development
and/or improvement of end-use technologies,
such as steel-manufacturing processes, and the
energy-consumption efficiencies of vehicles,
home appliances, and newly designed
buildings. Finally, the model simulations had
to capture the potential differences in energy
consumption patterns in different parts of the
world as incomes grow and end-use
technologies improve.
Regional Supply and Demand Models
In the time frame of interest, 1985 to
2100, energy markets can be expected to
undergo major fundamental changes. These
changes include what energy sources are
exploited, where the energy sources are
produced, how they are converted to useful
energy for end^use, how the energy is used,
and where the energy is used. The model
captures these changes with regional supply
and demand models within a framework that
addresses both the regional flow of energy
and the conversion of energy from primary
energy sources to end use.
For each model run, an internally
consistent estimate of energy supply and
demand is generated. Energy prices provide
the key variables used by the model to create
this consistent picture. Through a series of
calculations that account for transportation
costs, refining costs, distribution costs, synfuel
conversion costs, and electricity generation
costs, the model captures the relationship
between prices for energy at the point of
production (e.g., wellhead prices for oil and
gas) and prices for the energy seen by end
users. Energy supply reflects supply prices
(prices at the point of production) and the
energy demand reflects secondary energy
prices (prices seen by end users).
As illustrated in Figure A-2, the model
breaks the energy market down into regions,
thus allowing consideration of regional
differences, which are expected to influence
future energy supply and demand. As with all
models, the regional disaggregation represents
a balance between the significance of the
effect that regional factors have on the
results, uncertainties inherent in the model,
and the difficulty of designing and using a
model that incorporates greater or less detail.
Regional factors that affect energy
supply include regional differences in energy
prices, resource endowments, extraction or
production costs, and transportation
constraints. The most important factors
determining regional energy prices are the
existence of a global energy market and the
costs of transporting energy from the
producing areas to the consuming areas. The
global market for crude oil provides a good
example of regional energy price variations.
For example, for crude oil of similar quality,
prices will generally run higher in the U.S.
than in the Middle East. This pattern reflects
the role of the Middle East in setting oil
prices and the cost of transporting the oil
from the Middle East to the U.S. Regional
prices also vary within the natural gas market
primarily because of the high costs of
transporting natural gas either overseas or
over long distances by land. Large identified
reserves of natural gas remain unexploited
while gas reserves that are more expensive to
develop and produce, but are closer to the
demand markets, continue to be developed.
The regional endowments of energy
resources as well as the costs of producing
those resources can vary considerably. For
example, the Middle East contains large
A-8
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Appendix A: Model Descriptions
resources of conventional crude oil that can
be produced for well under $20 per barrel.
The U.S. and Latin America together contain
nearly as much unconventional oil (e.g., oil
shale or bituminous tar sands); however, those
resources, for the most part, cost well over
$20 per barrel to produce. The cost of
producing hydroelectric power also varies by
region. Both India and Brazil contain large
untapped hydro resources, but the extent to
which each country utilizes its hydro resources
will depend on the costs of transporting the
electricity long distances, environmental
constraints, capital constraints, and the
availability of markets for the electricity.
Energy Flow: Primary Production to End Use
The model's simulation of the flow of
energy from primary energy production to end
use can account for future shifts in the types
of energy dominating the energy markets as
well as the introduction of new technologies.
As shown in Figure A-3, the flow of energy
starts with the production of primary energy
of the following eight types:
• conventional oil;
• unconventional oil (includes enhanced
oil recovery, tar sands, and shale oil);
• natural gas (conventional and
unconventional);
• coal;
• hydropower;
• nuclear energy (fission and fusion);
• solar energy; and
• commercial biomass energy.
?
The model keeps track of a number of
steps involving transportation, refining, and
distribution of energy, along with the
conversion of primary energy to secondary
energy suitable for end use. Each of these
steps imposes costs on the final product and
may result in losses of energy. As an
example, costs of transporting crude oil or
natural gas from the wellhead to refineries or
end-use markets will reflect the proximity of
the supply source to the destination and
whether processing, such as conversion to
liquified natural gas (LNG), is required.
Energy use for transportation usually
represents a small fraction of the energy
transported (e.g., average 3% for natural gas
in the U.S.). Refining of crude oil to
petroleum products such as gasoline, distillate,
kerosene, and residual fuel oil incurs both
fuel and non-fuel costs and involves the
consumption of energy (approximately 9% of
the energy refined). Distribution costs equal
the costs of delivering the energy to
individual consumers and can represent a
large share of the total costs of energy costs
to the consumer (e.g., distribution costs
represented 20% of the average delivered
price of gas in the U.S. in 1985).
Conversion of primary energy to
secondary energy includes also the conversion
of coal or biomass fuels to liquid or gaseous
fuels and the conversion of primary energy to
electricity. These conversion activities can
lead to energy losses of over 70% as well as
substantial non-fuel costs. Conversion
activities captured in the model include the
following:
• conversion of coal and/or biomass to
liquid or gaseous fuels;
• conversion of coal, liquids, and gases to
electricity; and
• conversion of hydro, nuclear, and solar
energy to electricity.
The cost and efficiency of these conversions
have changed substantially during the past
several years and can be expected to change,
in the fu'ture as well! For example, current
technologies for converting gas to electricity
can be as much as 25% more efficient than
technologies that existed in 1978. Similarly,
breakthroughs in fuel-cell technology could
lead to greater gains in conversion efficiency
in the future.
The model completes the flow of
energy by converting four types of secondary
energy - liquids, gases, solids, and electricity
~ to end-use energy. The term end-use
energy can refer to transportation, heat used
for cooking, cooling produced by refrigeration,
feedstock uses of energy, and to many other
applications. The conversion of secondary
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Policy Options for Stabilizing Global Climate
FIGURE A-3
ENERGY FLOWS
Coal
Conversion
'Losses
Electricity
Generation
-End-Use
Natural Gas
;
Synfuel Conversion
C
\.
-t^- Losses
/
V
Gases
-End-Use
Solids
-End-Use
^-End-Use
Losses
A-10
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Appendix A: Model Descriptions
energy to end-use energy can incur widely
varying losses of energy as well as non-fuel
costs associated with the conversion.
Basis for Determining Energy Prices
Although a wide variety of factors
(including population, income, and regional
GNP) are assumed to influence future supply
and demand, the model assumes that
balancing the supply and demand for energy
ultimately will be achieved by adjusting
energy prices to reduce or increase supply or
demand. Energy prices differ by region to
reflect the regional market conditions, and by
type of energy to reflect supply constraints,
conversion costs, and the value of the energy
to end users.
The model estimates this supply and
demand balance with an iterative search
technique to determine supply prices. Using
specified initial supply prices for oil, natural
gas, and coal, the model locates prices that
result in energy supply and demand where the
supply of each primary energy type equals the
demand for the energy.
All energy prices estimated by the
model are based on the supply prices for the
fossil fuels, that is, the price that energy
producers can expect to receive for the fuels
at the wellhead or at the mine. The prices
reflect a marginal price about which all but
the marginal producers can adjust their prices
over their production costs. The price
structure does not, however, account for
differences in quality of the product (e.g., ash
content, sulfur content, specific gravity).
Prices of similar types of crude can vary
by region by as much as $3.80 per barrel
(1988 U.S. dollars), and the price of natural
gas can vary by as much as $2.65 per
gigajoule (1988 U.S. dollars) (ICF, 1988). In
actual energy markets, such differences in
supply prices reflect whether the regions are
importers or exporters and a region's
relationship to the other import and export
regions. These relationships can change as
supply and demand and the roles of regions
as importers or exporters change over time.
The model simplifies this complex
relationship for each type of energy by
selecting a region that will act as a major
exporter of the energy form and a region that
will act as a major importer of the energy, by
estimating the costs of transporting the fuel
from the selected exporting region to the
selected importing region, and by applying a
set of rules concerning the relationship of the
supply prices in other regions based on the
prices in these two regions. Using crude oil
as an example, the marginal supply and
demand regions selected were the Middle East
and the U.S., respectively. Our estimate of
the cost of transporting the crude from the
Middle East to the U.S. equalled $3.80 per
barrel (1988 U.S. dollars). The rules for
assigning regional supply prices then followed:
• Crude oil supply prices in all oil-
exporting countries equaled the price of
crude oil in the Middle East; and
• Crude oil supply prices in all non-
exporting regions were at least the
price of crude oil in the Middle East
and at most that price plus the
marginal transportation cost of $3.80
per barrel.
For natural gas, the marginal transportation
costs equaled $2.65 per gigajoule (1988 U.S.
dollars), which reflected the costs of
liquefaction, transporting the LNG from the
marginal exporter (Middle East) to the
marginal importer (U.S.), and regasification.
The average transportation costs for coal were
assumed to be $0.66 per gigajoule.
This simplification represents a trade-
off between the structure originally
incorporated in the IEA/ORAU model, which
allowed no regional differences in supply
prices due to transportation costs, and a more
complex representation of the factors that
determine these differences. The inability of
the original model to account for regional
differences due to transportation cost resulted
in understating the price that producers could
receive from markets within that region. The
approach we selected to determine regional
supply prices allowed us to reduce the error
in the regional price differentials without
completely restructuring the model.
Secondary energy prices in each region
are based on the supply price for the marginal
export region, the interregional transportation
cost, refining and distribution costs, and
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Policy Options for Stabilizing Global Climate
regional tax policies. For electricity, the
secondary prices will reflect the relative
proportions of each fuel used to produce the
electricity, the secondary prices of those fuels,
the non-fuel costs of converting the fuels to
electricity, and the efficiency of the
conversions.
Estimating Primary Energy Supply
The approaches used to estimate future
energy supply vary according to the type of
energy because of fundamental differences in
how the energy source may be exploited over
time and how the energy is used. The way in
which fossil fuels are treated within the model
reflects the limitations in the resource base
and the behavior of available supply and
extraction costs as the resource is exploited.
The model's treatment of hydro, nuclear, and
solar sources reflect constraints on the annual
supply of each energy type, estimates of how
the costs of producing the energy will change
over time, and the impact of relative energy
costs on the share of electricity produced
from each source. Its treatment of biomass
sources reflects the potential availability of
this energy source and competition of biomass
energy with other commercial energy sources.
Fossil Fuels
For crude oil, natural gas, and natural
gas liquids, the model provides a
simplification of the physical and economic
factors that influence the production of these
resources over time. The factors captured in
the model include the relationship between
prices and extraction costs, the impact of
exploiting finite resources, technological
improvements, and constraints on the rate of
production.
Resources are defined within the model
according to the concept of technically
recoverable' resources (which should not be
confused with the concept of oil-in-place or
gas-in-place) and represent the total estimated
production from the resource base that is
economic at the prices estimated by the
model. Recoverable resources are categorized
according to estimates of the cost of locating,
developing, and producing them. Estimates of
oil, gas, and natural gas liquid resources and
of the costs of developing and producing
these resources are taken from the literature
(e.g., World Energy Conference, 1980; ICF,
1982; U.S. DOE, 1988). Details of these
resource cost curves are reproduced in
Appendix B.
The model simulates the exploration,
development, and production activities
through a number of steps, which start with
dividing the resource base into four
categories: cumulative production, reserves,
economic undeveloped resources, and
uneconomic undeveloped resources. For a
selected year in the time horizon, cumulative
production equals all energy produced from
the resource prior to that year, and reserves
at the beginning of the year equal reserves at
beginning of the previous year plus additions
to the reserves minus production. Given a
cost at the wellhead that producers are willing
to incur, the undeveloped resources can be
divided into economic and uneconomic
resources. The fraction of resources that are
economic will change over time as the price
changes and as technological improvements
are realized in locating, developing, and
producing the resources.
Resource production involves a series
of steps, including locating, developing, and
producing reserves, as well as ongoing
maintenance and development. The rate at
which these activities will take place is
influenced by a numbei of factors, including
the ability to locate all economic resources,
the lead time necessary before construction
can begin, the availability of capital, and the
rate at which the resources are produced from
wells. The model simplifies these factors by
estimating the fate at which economic
undeveloped resources are proved and
converted to reserves, and by using a
production-to-reserves ratio to estimate the
rate at which reserves are produced.
The process for estimating coal
production is very similar, but the factors that
influence production and production costs are
very different. Unlike oil and gas, coal
resources are often close to the surface and,
in many places, the locations are known with
more certainty. Also, unlike many oil and gas
fields, which are developed to exploit the
resources within 8 to 20 years (thereby
maximizing revenues and not necessarily
minimizing costs), a coal field is developed to
minimize costs or to supply energy to a large
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Appendix A: Model Descriptions
electricity-generating or industrial project
usually resulting in a longer useful life.
Figure A-4 illustrates the approach
used to estimate future fossil-fuel supplies.
The model starts with an estimate of
recoverable resources and a marginal cost
curve. The recoverable resources include only
those resources not produced by 1985, and
the marginal cost curve represents how much
of the resource can be economically recovered
at different supply prices. The marginal cost
curve can change over time as technological
improvements reduce the costs of locating,
developing, and producing the resource.
For each year within the analysis, the
procedure simulates resource production
through a series of calculations that identify
the following:
• Economic Resources -- from the
marginal cost curve using the supply
.-,-. price;
• Cumulative Production -- through the
last period and remaining reserves in
the last period;
• Reserve additions; and
• Production.
Reserve additions represent a specified
fraction of the economic resources minus
cumulative production and remaining reserves.
The rate of production equals reserves times
the production-to-reserves ratio.
The estimates of recoverable resources,
existing reserves, and marginal eost curves
were developed from a number of different
sources, including ICF (1982), EIA (1986),
and World Energy Conference (1980).
Hydropower
Hydropower provides a very attractive,
cost-competitive source of electricity. In
many developed countries, hydropower has
been extensively developed, and few
opportunities for further development remain.
Conversely, growth in hydropower provides a
major future source of energy in many
developing countries. The energy model
treats hydropower differently from all other
sources of energy, taking these factors into
account by using an exogenously specified
path for the rate of increase in hydropower
for each region.
Two sources comprise the basis on
which the rate of increase is derived:
historical hydro production from the EIA
(1986) and technically feasible resources from
Goldemberg et al. (1987). (Estimates of
hydro resources for each region are outlined
in Appendix B.) For each region, regressions
were used to fit a logistic equation relating
historical hydro production and technically
feasible resources:
pt = r * s * [e(c+v'0 /
In the equation, c and v represent input
variables, which are specified for each region
and estimated using historical hydroelectric
production and technically feasible hydro
resources. These variables describe how much
of the hydro resources have been developed
and how fast they will continue to be
developed. The variable t represents the
number of years from 1985. The variable r
represents the technically feasible hydro
resources, and the variable s represents the
fraction of the technically feasible resources
that will ultimately be developed. The
variable pi represents the hydro production
for year t.
Nuclear Energy
The future of nuclear energy is very
uncertain due to a number of political,
technical, and economic factors. To
determine the extent to which nuclear energy
will be used in the future, the model accounts N
for two of these factors: the cost
competitiveness of nuclear energy and
political constraints on the proliferation of
nuclear energy. Political constraints include
restrictions on the export of nuclear
technology to developing countries,
moratoriums on the construction of new
plants, and restrictions on the development of
breeder reactors. Other constraints include
private investors' aversion to investing in
high-risk, high -cost nuclear projects.
Estimated future costs of nuclear energy
reflect expectations about technological
improvements (e.g., lower construction costs,
the use of commercial breeder reactors, and
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Policy Options for Stabilizing Global Climate
FIGURE A-4
SUPPLY MODEL
PRICE
PRODUCED
RESERVES
ECONOMIC
UNDERDEVELOPED
UNECONOMIC
COST CURVE
ECONOMIC RESOURCES
A-14
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Appendix A: Model Descriptions
breakthroughs in fusion technology), costs
associated with compliance with stricter
environmental regulations, and anticipated
increases in the cost of fuels. Further details
of the nuclear cost assumptions are outlined
in Appendix B.
The cost factor is captured through
exogenous input of nuclear electricity costs, a
set of factors that allows the costs to decline
over time, and an environmental cost factor
that can be used to slowly increase or
decrease nuclear generation costs. The
political and other factors can be captured in
the fuel share weights used to determine the
mix of energy types that generate electricity
(see Modeling Electricity Generation below).
Solar Energy
Solar energy is potentially a major
source of energy in the future. The major
uncertainties affecting future growth are the
cost of harnessing solar energy and concerns
over electric system load management and
reliability. Solar power is used for two types
of energy: heating (e.g., home heating and
water heating) and electricity generation. The
model treats these two sources separately.
Home and water heating are dealt with in the
demand component, while solar electricity is
handled in the supply component.
Currently, the cost of cultivating solar
energy is much greater than for other
commercial electricity sources. Furthermore,
solar power is available only for portions of
the day, and the source can be unreliable due
to climatic factors. As a result, dependence
on solar energy requires either substantial
electricity storage facilities, such as batteries
or pumped storage, or substantial
conventional backup systems. The energy
model captures these factors by requiring that
electricity generation by solar power compete
with fossil and nuclear electricity generation
on a cost-competitive basis. Solar electricity
costs are allowed to decline over time to
capture technological improvements. Details
of the costs assumed for solar energy are
given in Appendix B.
Commercial Biomass
Commercial biomass energy supplies
may also play a major role in the future.
Biomass energy technologies include
exploitation of fuelwood, forest residues,
agricultural residues, municipal solid wastes,
animal wastes, and energy plantations. Solid
biomass can be converted to either gaseous or
liquid fuels, but generally with a loss of
energy and substantial fixed costs. Existing
conversion projects include methanol
production from sugar cane (Brazil) and
methane recovery from waste (China).
Current biomass energy use is not entirely
sustainable and is one factor contributing to
net deforestation, particularly in Africa.
To represent biomass energy, the energy
model uses a supply curve of annual biomass
energy that would be available at different
prices. Biomass is treated as a solid fuel that,
like coal, may be converted to liquid or
gaseous fuels, assuming a fixed cost and net
loss in energy. Biomass competes with liquid
petroleum products or natural gas based on
the supply prices of these fuels and the cost
of producing and converting the biomass.
Biomass competes with solid fuels based on
production costs and on relative efficiencies
and costs of end use. Appendix B provides
further detail on the numerical cost
assumptions for biomass.
Calculating Prices for Primary and Secondary
Energy
As described earlier, the model keeps
track of three different levels of prices:
supply prices, primary energy prices, and
secondary energy prices. For fossil-fuel
energy, the model estimates supply prices, or
the value that producers can expect to receive
for the fuel at the wellhead or the mine. The
model assigns primary energy prices to the
fossil fuels and to nuclear, solar, and hydro
energy sources. Primary energy prices for the
fossil fuels represent the value of the fuel in
the demand market and contain the
adjustment for inter-regional transportation
(e.g., price of crude oil landed at the U.S.
Gulf Coast). Primary energy prices for
nuclear, solar, and hydro energy represent the
marginal costs of producing electricity.
The model estimates secondary energy
prices for four fuels: liquids, gases, solids,
and electricity. Secondary energy prices for
the first three fuels are based on the primary
energy prices for fossil fuels plus refining and
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Policy Options for Stabilizing Global Climate
distribution costs and, for electricity, are set
to the overall average costs of generating
electricity (including transmission and
distribution costs). The refining and
distribution costs for fossil fuels reflect
estimates of the average costs of processing
and distributing the secondary energy to end
users. These costs can include fixed non-fuel
costs and fuel costs (e.g., gas used for
compression). The approach used in the
model involves estimating the non-fuel costs
on a dollar basis (to be included in the
secondary price) and accounting for the fuel
use in the energy demand routines. Further
details can be found in Appendix B.
The model sets the secondary price of
electricity independently for each region to a
weighted average cost of generating the
electricity. This average captures the input
energy costs, conversion efficiencies, capital
costs, and shares allocated to each fuel type.
Estimating Synfuel Conversion
The conversion of coal to synthetic oil
or synthetic gas involves both substantial fuel
and non-fuel costs, and the amount of
synthetic fuel production will depend heavily
on these costs, as well as on the cost of
producing the coal and the value of the
synthetic fuels. For example, current
estimates of the non-fuel costs of producing
synthetic fuels average $7/gigajoule of
produced gas (1988 U.S. dollars) and
$8/gigajoule of produced oil (1988 U.S.
dollars). Estimates of the energy efficiency of
the conversion process average around 67%.
Additional details of the costs of the
conversion process are outlined in Appendix
B.
To estimate synfuel production, coal or
biomass production is allocated to three
different uses based on the relative value of
coal for each use: (1) direct secondary energy
consumption (including consumption for the
generation of electricity), (2) conversion to a
liquid fuel, and (3) conversion to a gaseous
fuel. The value of coal for each use (V;,
where the index i =c, o, or g) is based on the
supply prices of the three fuels adjusted to
reflect differences in end-use taxes (for all
equations, the subscripts c, o, and g refer to
coal, oil, and gas, respectively).
The cost to convert the coal to
synthetic fuel, adjusted for taxes, (Vc;, where
the index /= o or g) accounts for fuel costs,
taxes, conversion efficiency, and capital costs.
The weighting factor for allocating coal
production to the three uses is specified by:
Cj / V;)a
(i = o or g),
where a is an elasticity control parameter
(which is negative) and S-{ is the fuel share
weight. Finally, the allocation of coal
production to each use is determined by:
q =
j / (sc + s0
+ s) * c
where C is the total coal produced, and C; is
the coal allocated to either coal consumption
for end-use and electricity generation,
synthetic oil production, or synthetic gas
production.
As seen from the above equation, as
the value of the oil or gas increases, the share
of coal used to produce synfuels increases.
For example, if the cost of producing
synthetic oil and gas equaled the value of the
oil and gas respectively, then equal shares of
coal production would be assigned to the
three uses. The rate at which the fuel shares
change depends on the elasticity control
parameter where an elasticity with larger
absolute values yields faster rates of change.
Modeling Electricity Generation
The approach used to model electricity
generation captures the relative cost of using
different fuels, the impact of the mix on
electricity demand, and the impact of
changing generation technology on the
relative cost. The approach can be separated
into two activities: selecting fuels and
estimating generation costs, efficiencies, and
emissions.
Fuel Shares. The fuel mix selection
involves a three-step process that reflects the
basic assumption that fuel selection for
electricity production will be based on relative
cost and exogenous factors, such as the desire
to maintain a diverse mix of technologies, and
that hydroelectric production will be specified
exogenously (underlying this is the assumption
that hydropower may be cheaper than the
A-16
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Appendix A: Model Descriptions
other sources but is constrained by resource,
political, and environmental factors). The
first step uses an estimate of the share of
electricity production that will come from
hydro sources. The model allocates the
remaining electricity production to the other
fuels based on costs of producing electricity
from the different sources and exogenously
specified fuel-share weights. The model
calculates the costs as follows:
p. *
ri
p. _ p.
S ri
(for i = o, g, and c) and
C- = V.
*-i vi
(for i = n and s).
In these equations, the index, i, represents the
fuel type (o = oil, g = gas, c = coal, n =
nuclear, and s = solar). The variable P{ is
the secondary price of the fossil fuels, and the
variable F-t represents a price discount that
large electric generation users receive for fuels
over the average price to all users. £j is the
efficiency factor, which represents the
multiplicative inverse of the marginal
efficiency of converting the fossil fuel to
electricity. H^ represents the marginal non-
fuel costs of producing the electricity. The
variable Vi represents the costs of producing
electricity from nuclear and solar sources.
The variable C; represents the marginal unit
cost of producing electricity from the
appropriate fuel type.
After estimating the marginal costs of
producing the electricity, the share of
electricity produced from each source is
estimated as follows:
Si =
W—
~
(W0 + Wg + Wc + Wn + Ws)
In these equations, the variable Jfj is the
exogenously specified fuel-share weight. The
variable a is the logit substitution parameter.
W{ is the calculated fuel-share weight
reflecting relative cost differentials, and the
variable S; is the share of electricity
production, excluding hydroelectricity, that is
produced from energy type i.
As can be seen from these equations,
three factors influence the estimates of the
fuel shares: the costs of electricity
production, the exogenous fuel-share weights,
and the logit substitution parameter. For
fossil fuels, the costs of electricity production
will change over time as the prices of the
fuels change and as technological
improvements or emission controls change the
marginal efficiencies and non-fuel costs. For
the other fuels, solar and nuclear, the costs of
producing electricity over time will be
specified exogenously. The exogenous fuel-
share weights provide a way of reproducing
the 1985 fuel selection and a way of
restricting the use of certain energy sources
(e.g., nuclear energy in less-developed
countries). In general, the exogenous fuel-
share weights are initialized to represent the
current fuel selection but are modeled so that
fuel selection is solely based on economics
after 40 years. The substitution elasticity
captures the variations that will occur in fuel
selection due to a number of factors,
including system reliability and location-
specific environmental regulations or
economic factors.
Fuel Use and Emissions. In the second
step of the generation/fuel selection process,
the model estimates the price of electricity
using the fuel shares, fuel costs, and non-fuel
costs as described above and accesses the
demand routines to estimate the demand for
electricity. The third step involves readjusting
the fuel shares to reflect the fixed amount of
hydroelectricity production and the estimated
demand. In the third step, the model
estimates the average efficiency and non-fuel
costs for producing the electricity for each
fuel.
The model uses a capital stock
approach to estimating trace gas emissions
along with marginal and average efficiencies
and non-fuel costs of electricity production.
Figure A-5 illustrates how the capital stock
approach works over time. For each fuel
type, the model starts out with an existing mix
of technologies that are used to convert the
fuel to electricity. Over time the initial mix
of technologies is retired and new
technologies are added to replace the retired
stock and to satisfy increases in demand. For
the initial stock, the model retires the stock
in equal shares over its useful life. All capital
stock added after the first period is retired
immediately after its useful life has been
exceeded.
A-17
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Policy Options for Stabilizing Global Climate
FIGURE A-5
Capital Stock Approach
e
IB
3
"O
tu
0>
o
111
1985 1990 1995 2000 2005 2010 2015 2020 2025 2030
Existing New 1985-1990 New 1990-1995 N»w 1995-2000
A-18
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Appendix A: Model Descriptions
For example, in 1985 gas-fired
generating technologies consisted of gas
turbines, gas boilers, and gas combined-cycle
units. Each unit is described according to its
useful life, non-fuel costs, generation
efficiency, and trace gas emissions per
gigajoule of energy consumed by the unit. If
the useful life of these units is assumed to be
40 years, then 1/40 of the initial stock is
retired in each year through 2025. If we
assume that one exajoule of electricity is
produced from gas units in 1985 and that in
1990 this increases to 1.05, then the existing
stock would be able to produce 0.875
exajoules of electricity and the remaining
0.175 would be produced with new stock.
The model would allow the 0.175 exajoules to
be produced through a combination of
technologies that might be comprised of
simple gas turbines used for peaking and gas
combined cycles. In addition, in 1990 the
model could allow the user to estimate the
effect of applying control technologies to a
specified percentage of the existing units as
well as to all new units. The control
technologies will affect the costs and
efficiencies, as well as the emissions that
result from the combustion.
In the above example, the marginal
costs and efficiencies used to determine the
share of electricity produced by the different
fuels would be based solely on the
combination of technologies and controls on
the new units and not the existing stock.
Both the new and the existing technologies,
however, would be considered for calculating
total fuel use and emissions of trace gases.
The exogenous fuel-share weights and
the existing combination of technologies are
based on data from a number of different
sources. The report on combustion emissions
prepared by Radian Corporation (Radian,
1990) provided the basis for the different
technologies available, the efficiencies, the
non-fuel costs, and the trace gas emissions.
The combination of technologies currently
used in the U.S. is based on data from the
U.S. Department of Energy (EIA, 1983; EEA,
1983). For the rest of the world, we used the
same mix of technologies for each fuel type
but adjusted the efficiencies based on
efficiency estimates from various sources. The
fuel-share weights are based on energy
consumption data from the United Nations
(1987), EIA (1986), and the detailed end-use
analyses for developing countries provided by
the Lawrence Berkeley Laboratory (Sathaye et
al., 1988). The combination of technologies
that will be used in the future is based on
current trends in the U.S. and on scenario
assumptions.
Estimating Energy Demand
One of the key parameters affecting the
level of future emissions is the demand for
energy and how that demand will change over
time. Strictly speaking, consumers do not
demand energy per se, but the type of services
that energy can provide. Throughout the
world, energy is used for many different
purposes, such as heating, cooling, cooking,
transportation, and lighting. Energy is also a
key component in many industrial processes;
for example, energy is used for producing
steam, providing heat, and as a catalyst for
chemical reactions such as those occurring
with the use of coke in steel production. The
pattern of future energy use that results from
consumer demands for these services will
depend on many factors, including the rate of
growth in population, changes in income as
determined by the rate of economic growth,
technological changes in end-use equipment,
the introduction of technologies that alter the
amount and type of energy demanded, and the
regional pattern of demand for products that
require energy for their production.
The energy model uses a combination
of two different approaches to estimate the
future demand for energy: a top-down
approach and a bottom-up approach. The
bottom-up approach (the end-use models)
looks in detail at how the income will be
distributed, how these distributions will affect
consumption of products and use of
transportation, and how industry will change
to meet the change in demand. The bottom-
up approach is used to estimate energy
demand through 2025. The top-down
approach relates the growth in demand for
energy services to growth in population,
income, and energy prices through income
and price elasticities and is used to extend the
estimates of energy demand from 2025
through the end of the model's time horizon.
A-19
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Policy Options for Stabilizing Global Climate
Bottom-Up Approach:
Through 2025
Energy Demand
End-Use Model: Industrialized Countries.
The industrialized countries end-use model
estimates the demand for end-use energy in
the industrialized countries through 2025; it
was developed by the World Resources
Institute (Mintzer, 1988) and modified by
Lawrence Berkeley Laboratory/ICF specifically
for use in the ASF. The model utilizes an
end use-oriented, bottom-up approach to
estimating energy demand in the industrialized
countries for each of two energy forms,
electricity and fuels. Fuels represent all
commercial energy sources, excluding
electricity, and include all petroleum products,
natural gases, natural gas liquids, and coal.
Non-commercial biomass fuels are not
considered. Population changes, increases in
GNP, and saturation effects drive changes in
major energy-using activities. Parameters that
represent energy intensity per unit o;f output,
or service delivered to the activity level, are
applied as the basis for deriving estimates of
energy use.
The model disaggregates energy end use
into three sectors:
• Residential/Commercial;
• Industrial/Agricultural; and
• Transportation.
The model further disaggregates the industrial
and transportation sectors into subsectors
representing different types of industrial
output and modes of transportation. Table
A-l summarizes this disaggregation.
Data on historical energy use and.
activity patterns are used to estimate future
activity levels, energy intensity, and energy use
by country. Regional estimates of energy use
by sector, which equal the sum of country-
specific estimates, are calculated for every
five-year period from 1985 to 2025. Estimates
of future activity and intensity levels are
functions of the assumed rate of growth in
population, real GNP growth, rates of changes
in energy prices, rates of improvement in
engineering efficiency of energy use, and
estimates of the elasticity of energy demand
to changes in income and price.
Estimates of future energy use in the
combined residential and commercial sector are
tied to changes in the total space
requirements for domestic households and the
efficiency of energy use relative to domestic
energy use in 1985. Future residential floor
area reflects the estimated population per
household, area required per capita, and total
population. Demographic trends for each
country provide the basis for estimating
population per household and floor area per
capita in 2025. Users specify the level of
improvements in energy efficiency per unit of
floor area, which reflect scenario and policy
assumptions. Values for floor space and
efficiency improvements in the years between
1985 and 2025 are interpolated using logistic
growth curves.
The model uses the following equations
to estimate energy use in the combined
residential/commercial sector separately for
fuels and electricity. First, total residential
floor area, Fat, is estimated based on
population, persons per household, and
average floor area per household as follows:
Fat = (Fht/Hot)*Popt.
In this equation, the variable Fht represents
the average floor area per household. The
variable Hol represents the average number of
persons per household, and the variable Popt
represents the population in the region. All
three variables (Fhv Hov and Popt) are
exogenously specified.
Total energy use, £t, in the residential
and commercial sectors is then calculated as
follows:
Et = [Em1985*(l-efft)]*Fat
The index t represents the energy form (fuel
or electricity). The variable Em1985
represents the energy use in the combined
sector in 1985 per square meter of residential
floor space. The variable efft represents the
cumulative gains in average efficiency from
1980 to year t. Efficiency improvement, effv
in the combined sector is exogenously
specified and reflects a number of factors,
including the vintage of the building stock,
assumptions on older units that are
A-20
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Appendix A: Model Descriptions
TABLE A-l
Sector and Subsector Disaggregation for Industrialized Countries
Sector
Subsector Breakout
Residential/Commercial
Industrial
Transportation
None
Basic Materials
Iron and Steel
Non-ferrous metals
Chemicals and Feedstocks
Paper and Pulp
Stone, clay, and glass
Other
Automobiles and light trucks
Trucks and buses
Air travel
Rail travel
A-21
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Policy Options for Stabilizing Global Climate
remodeled, and the energy intensity of new
and replacement stock.
Energy demand in the industrial sector
is modeled as the sum of energy used in the
production of basic materials and energy used
in fabrication, finishing, agriculture, and other
non-manufacturing activities. The model
estimates the energy used during the
production of five separate categories of basic
materials: iron and steel; non-ferrous metals;
chemicals and feedstocks; paper and pulp; and
stone, clay, and glass. Production of these
commodities represents 70% of industrial
energy use in the U.S. in 1980 and the
majority of industrial energy use for most of
the industrialized countries included in the
demand model.
For each basic material, the demand for
future energy use is estimated as a function of
the per capita production, the fuel and
electricity intensity per ton of manufactured
product, population, and price elasticities of
demand. Historical production data provide
the starting point for the analysis and future
output is indexed to the level of production
in 1985. For countries for which historical
data is unavailable, data applying to countries
that are similar in terms of level of economic
development, population, etc., are used as a
basis for deriving the production estimates.
Future energy intensities and per capita
production are specified for 2025.
Interpolation using a logistic curve provides
values for the intervening years between 1985
and 2025. Assumed decreases in energy
intensity reflect scenario assumptions and are
based on analyses of currently available
technologies. Assumptions of per capita
production in 2025 reflect historical trends
and saturation effects.
Estimates of the energy use in
industrial activities other than production of
basic materials are set as a fraction of the
estimated energy used in the production of
basic materials. This fraction is assumed to
increase over time reflecting the shift from
commodity manufacturing to services and
other industrial activities. The fraction for
1985 for each country is based on historical
data, if such data are available, or on data
from countries with similar economies, if
historical data for that country are not
available.
As shown in Table A-l, the model
disaggregates energy demand in the
transportation sector into four categories:
automobiles and light trucks, trucks and
buses, air travel, and rail travel. Vehicles
falling under the first three categories use
liquid fuels exclusively, while rail vehicles are
modeled as using both fuel and electricity.
The procedure for estimating energy
use for automobiles and light trucks factors in
variables such as energy use per mile traveled,
average number of miles traveled per vehicle,
and number of vehicles per capita. The
model estimates energy use for each country
separately. First, the model estimates average
vehicle miles traveled per vehicle, Vmv based
on miles traveled in 1980, and then adjusts
this figure to reflect the impact of prices and
income as follows:
Vmt = Vm1980 * (l+Pgt*Pe)(<-198°)
In this equation, the variables are defined as
follows:
Vmt -- average number of miles
traveled per vehicle;
Pgt - growth in fuel prices;
Pe - price elasticity of demand
for transportation services;
Igt - growth in income; and
le -- income elasticity of
demand.
. \
Given the vehicle miles traveled, the following
equation is used to estimate the
transportation demand for energy:
Ft = Eit * Vmt * Vct * Popt
The variables in this equation are defined as
follows:
Ft -- fuel used in the
automobile and light truck
category;
Ei,
energy use per vehicle mile;
A-22
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Appendix A: Model Descriptions
Vc,
number of vehicles per
capita; and
Pop, -- population in year I for
the country.
Energy intensity for 1985 is derived
from historical data collected by the Motor
Vehicle Manufacturers Association and the
United Nations. The user specifies the energy
intensity in 2025, consistent with the intent of
the scenario, and values in the intermediate
years are interpolated using a logistics curve.
Users also specify changes in the number of
vehicles per capita.
The procedure used in the model to
estimate energy demand for trucks and buses
is similar to the approach taken to derive this
estimate for automobiles and light trucks.
The only difference is that the price elasticity
of demand is assumed to be zero.
The approaches to estimating energy
use by the rail and air transport sectors are
similar and reflect estimates of future demand
for travel and transport and the energy
intensity of providing the transportation
service. For passenger travel, the model
estimates the total distance of travel required,
converts the distance to ton-km, and uses an
energy intensity (energy per ton-km) to obtain
energy use. For freight, the model estimates
future activity (ton-km) directly. The model
translates passenger kilometers to ton-km
using conversion factors (of 0.11 and 0.08 ton-
km per passenger-kin for rail and air
transport, respectively) which account for the
estimated weight of the vehicles.
Future demand for passenger travel,
kilometers, is a function of demand in 1985,
growth in real income, and the income
elasticity of demand. Freight traffic is a
function of freight traffic in 1985, growth in
real income, the income elasticity of demand,
growth in real prices, and the price elasticity
of demand. Users specify the energy intensity
of the transportation service exogenously to
be consistent with the intent of the scenario.
Electricity use and fuel use for rail transport
are calculated separately.
End-Use Model: Developing Countries,
The model of energy demand in developing
countries through 2025 was developed by the
International Studies Group, Energy Analysis
Program of the Lawrence Berkeley Laboratory
(Sathaye et al., 1988). This model uses an
end use, bottom-up approach to estimate
demand that emphasizes the fuel uses and
circumstances facing the developing countries.
The developing countries represent a
diverse group of countries that has
experienced steady and rapid growth (4.7%
annually) in the use of modern energy (coal,
oil, natural gas, nuclear, hydro, and
geothermal sources) since 1973. The
countries included in this category range from
some of the poorest countries in the world
such as Bangladesh and Ethiopia to some of
the richest countries such as Saudi Arabia and
Kuwait. They include exporters and importers
of energy.
Within each country, urbanization and
industrialization has characterized modern
economic development and has led to this
growth in energy demand. Factors that have
influenced urbanization include the wealth of
the country, whether it imports or exports
energy, and whether the country is centrally-
planned or market-oriented. For example, the
share of the population in Saudi Arabia living
in urban centers increased from 39% in 1965
to 72% in 1985. In the Ivory Coast,
population in urban centers grew almost twice
as fast as the overall growth rate.
Urbanization facilitates the adoption of
modern lifestyles. Adoption of modern
lifestyles facilitates increased ownership of
appliances and vehicles which, in turn, results
in increases in the consumption of electricity
and other modern fuels. This shift to modern
fuels is accompanied by a shift from
traditional biomass fuels,.which is sustained
even during times of economic adversity.
Biomass and other renewables represent
a large share of energy use in many
developioe countries (41% in India and 68%
in Bangladesh). Fuelwood is a major form of
biomass energy, and current rates of fuelwood
use exceed the annual biomass increment
leading to deforestation and positive net
fluxes of CO2 to the atmosphere. Energy use
derived from biomass is, in addition,
extremely inefficient compared with that
achieved through the use of modern fuels.
A-23
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Policy Options for Stabilizing Global Climate
The modeling approach used for the
developing countries disaggregates energy
demand into five regions, three fuel types, and
six sectors. The five regions are consistent
with the regional disaggregation used in the
ASF and consist of Latin America, Africa,
Middle East, Centrally-Planned Asia, and
South and East Asia. The three energy forms
are electricity, biomass and renewables, and
modern fuels. The six sectors are further
broken down into subsectors that represent
different types of activities that occur at
different stages of economic development.
Table A-2 illustrates the sector/subsector
disaggregation along with examples of the
types of activities included in the sector and
the forms of energy used.
The model estimates the shifts between
the different subsectors as modern economic
development is pursued. For example, in the
agricultural sector, economic development
results in the shift from traditional agriculture
methods of production, which use human and
animal motive power, to modern techniques,
which involves mechanized power and makes
use of fast-growing varieties of crops that
require regular and large amounts of fertilizer
and water.
The transition between the subsectors
from traditional activities to more modern
ones can be very rapid as can the increase in
the use of modern energy (e.g., in South
Korea the transition spanned a period of
three decades). The pattern of growth
reflects a wide range of factors. As an
example, the emergence and rapid growth of
modern transportation characterizes the early
stages of economic growth. Use of
transportation services by the lower and
middle income brackets depends heavily on
decisions concerning road and rail
infrastructure, settlement patterns, and
location of industry. Also, energy intensity,
which increases during the initial stages of
economic development, may later decline as
more energy-efficient processes and machinery
replace less efficient processes and outdated
energy-intensive machinery.
The models vary among regions but
contain a number of common elements.
GDP, in real terms, acts as an indicator of
economic activity. The composition of GDP
changes as manufacturing and service
industries assume a greater role, thus reducing
the role of agriculture. While mining and
energy processing currently account for a
large share of energy use in industry,
manufacturing will assume a larger share in
the future. Demand in 1985, by region and
sector, is estimated either by extrapolating
data from groups of countries that represent
a large share of the region or by allocating
energy supply data obtained from the United
Nations and similar sources according to
energy use patterns of a few countries in the
region (Africa, Middle East).
The models estimate future energy use
by estimating ftiture activity levels such as
vehicle use and output of raw materials and
by applying factors representing the energy
intensity of these activities. Activity levels
reflect population growth, income, and the
distribution of income to the population. The
future energy intensity of these activities and
changes in energy intensity reflect historical
data as well as exogenously specified
assumptions.
Within each region, population is
separated into quintiles based on historical
income per capita. The distribution of
population in the quintiles is kept constant
over time, and the average income per capita
for each quintile is shown to grow consistent
with assumptions on real GDP growth.
The models estimate activity levels in
2025 by mapping the income per capita in
each of the quintiles to activity levels per
capita and by multiplying those results by the
estimated population in each of the quintiles.
The mapping from income to activities is
based on current patterns of energy use in
different countries at different levels of
income. This mapping is adjusted for 2025 to
reflect the impact of the costs of energy-
intensive goods (automobiles, appliances, etc.)
and the decline of these costs in real terms.
The composition of energy end use is
consistent between regions. Residential
energy use is divided into various end uses,
including cooking, water heating, space
heating, lighting, and appliance use.
Transportation is divided into two modes,
land and air, and land transport is further
divided by vehicle type. The model estimates
the level of activity and/or ownership and
A-24
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Appendix A: Model Descriptions
TABLE A-2
Sector/SubSector Disaggregation in Developing Countries
Seetor/Subsector
Energy-Using Activities
Main Form of Energy
Agriculture
Traditional
Mechanized
Fisheries
Non-motorized
Motorized
Industry
Handicraft
Light
Heavy
Energy Intensive
Feedstocks
Transportation
Personal
Informal Public
Formal Public
Light Truck
Heavy Truck
' Rail
Air
Residential
Rural
Urban
Commercial
Buildings
Ploughing and irrigation
Powering of pumps and tractors
Powering of:
Nets, canoes
Fleets
Production of:
Weaving Baskets
Shoes, textiles
Metal processing
.Cement, Aluminum
Fertilizers, chemicals
Powering of:
Cars, motorcycles
Jitneys
Buses, rail, transit
Cooking, lighting
Cooking, lighting
Appliances Operation
Lighting/space heating and
cooling, etc., in offices,
hotels, restaurants
Animals
Electricity, Diesel
Diesel
Electricity, fuel oil,
natural gas, coal
Gasoline
Diesel (mostly)
Diesel (mostly)
Gasoline, diesel
Diesel (mostly)
Coal, diesel, electric
Jet fuel (mostly)
Biomass, kerosene,
electricity
LPG, kerosene,
electricity, biomass
Electricity, natural gas
Electricity (mostly)
Source: Sathaye et al, 1988.
A-25
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Policy Options for Stabilizing Global Climate
multiplies by the energy intensity of the
activities to obtain energy end use.
Industrial energy use is divided into
electricity and non-electricity components.
The current ratio of each component is
calculated for 1985 and estimated for 2025.
Energy use is estimated by applying these
ratios to the estimated level of industrial
value-added. For appropriate regions,
consumption of fossil fuels in the refining,
chemical feedstocks, and fertilizer industries,
as well as in other energy-processing
industries is accounted for separately.
Current fuel and electricity intensities
in the commercial sector are based on data
from those countries where the data are
reported separately from the residential
energy use. Estimates of future energy use in
these sectors are based on similar ratios and
estimates of future value added in these
sectors. The approach taken to estimate
energy end use for the agricultural sector is
similar to the approach for the commercial
and services sector but is based on rough
estimates of the fuel and electricity use in
those sectors.
The model relies on a number of
assumptions concerning the impact of energy
prices, the development of the energy market,
and the rate and role of technological
innovation. Although resource constraints
may drive prices up, the model assumes that
these constraints will not be a bottleneck to
economic growth. Energy markets will
develop in an orderly manner. Higher
economic growth combined with higher energy
prices will result in technological innovation
and improvements in the efficiency of energy
use, which will offset a large part of the costs
of the more expensive energy. Unproven
technologies are not included in the view of
the world in 2025.
Top-down Approach: Energy Demand Beyond
2025
The top-down approach to estimating
future energy demand, for both industrialized
and developing countries, ties future demand
for energy to changes in population, income,
and energy prices through a set of simple
relationships that utilize income and price
elasticities. The model selects from different
fuels to satisfy demand based on the relative
costs of meeting the demand and keeps track
of the stock of technologies used to satisfy
demand in order to change efficiencies, costs,
and emissions.
The basis for the modeling of energy
demand is the demand for end-use energy.
End-use energy may be defined in many
different ways for different applications of
energy and even for the same application.
For example, the end-use service provided by
a conventional fireplace, a furnace, and a heat
pump is temperature control in the house,
but the fuels used, the cost, and the energy
efficiencies can ' vary considerably. In a
conventional fireplace, most of the energy
from the combustion of wood escapes through
the chimney. With conventional oil and gas
furnaces, over 30% of the energy is lost
through the flue. With a pulse gas furnace,
however, the amount of energy lost is less
than 5%. A heat pump can, on average,
provide the same type of end-use energy as a
pulse gas furnace but with up to 75% less
secondary energy (coefficient of performance,
COP, of 1.7, although more primary energy is
required to produce the electricity).
The model disaggregates the f energy
demand for each region into three end-use
sectors: residential/commercial,
transportation, and industrial, where the
relationship between population, income, and
prices vary between sectors. The first step is
to estimate the demand for end-use energy for
each region and each sector as follows:
Residential/Commercial and Transportation
\
EEt = EEl * Cgta * Igtb * Pgt;
Industrial
EEt = EEl * Cgta * Ggtb.
The variables in the above equations
represent the following:
EE
demand for end-use energy
in year t;
demand for end-use energy
in the starting period;
A-26
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Appendix A: Model Descriptions
Cgt -- growth in the cost of
providing the end-use
energy;
a -- price elasticity;
Igt -- growth in per capita
income;
b -- income elasticity;
Pgt -- growth in population; and
Ggt — growth in regional GNP.
The growth in the cost of providing the end-
use energy will reflect the secondary prices of
energy, the non-fuel costs of providing the
secondary energy, the efficiency of providing
the secondary energy, and the fuel shares.
The approach used to calculate fuel
shares and the efficiency with which the end-
use energy is provided is the same as that
used for calculating fuel shares and efficiency
for the generation sector. Using the capital
stock approach, the model determines the „
marginal efficiency and non-fuel costs of
converting each type of secondary energy to
end-use energy. Note that the marginal
efficiency refers to the average efficiency of
the combination of technologies used to
satisfy new demand or replace retired stock.
Combining the secondary prices of energy
with the marginal efficiencies and non-fuel
costs, the model estimates the cost of
providing the end-use energy with each type
of fuel. Using these costs, end-use fuel-share
weights, and elasticity control parameters, the
model can estimate the share of end-use
energy satisfied by each fuel (see Electricity
Generation above).
Given these shares and the demand for
end-use energy, the model then uses a capital
stock approach to estimate the average
efficiency of providing the end-use energy, the
consumption of secondary fuels, and emissions
resulting from the consumption. As with
electricity generation, the user can introduce
new technologies and efficiency improvements
over time as well as apply emission controls.
Interface to the End-Use Models
The end-use models are run separately
and require input of a wide range of
assumptions concerning the stock of energy-
using equipment (e.g., vehicles, building,
appliances, electric utility powerplants), as
well as population, income, energy prices, and
efficiency. The models produce a set of
outputs that include the demand for
secondary energy and income and price
elasticities. The demand for secondary energy
is broken down into three categories: fuels,
electricity, and biomass; and the demand for
fuels is not further divided by fuel type (e.g.,
demand for fuels represents the total demand
for oil, natural gas, and coal). The income
and price elasticities allow the global model
to deviate from the input price assumptions
for the end-use models in order to balance
supply and demand.
In order to interface with the end-use
models, the energy demand estimates,
elasticities, efficiency assumptions, price
assumptions, and income assumptions made in
the end-use models must be put into a format
suitable for use in the global model. The
efficiency assumptions must be replicated
within the framework of the global model,
and conversion factors must be specified that
allow the demand estimates to be converted
into estimates of end-use energy for each of
the three sectors. For each period through
2025, the global model uses the estimate of
energy demand from the end-use model and
converts the demand to end-use energy using
the efficiency assumptions and the specified
conversion factor. It then adjusts the end-use
to reflect changes in income and energy
prices. The model allocates the end-uses
energy to secondary fuels and converts the
end-use energy back to energy demand -using
the same procedures as in the top-down
approach, with the exception that the fuel
share for electricity is specified by the end-use
model. If the efficiency assumptions, the
income assumptions, and the energy price
solutions replicate the input assumption of
the end-use model, then the global model will
reproduce the results from the end-use
models.
A-27
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Policy Options for Stabilizing Global Climate
After 2025 the model estimates energy
demand using the top-down approach, basing
all estimates on the end-use energy estimate
in 2025 and changes in prices, population, and
income from 2025.
Implementation of Capital Stock
The model calculates changes in end-
use fuel and non-fuel costs and emissions
resulting from end-use energy using the same
capital stock approach as is used for
estimating costs for electricity generation. As
with the generation side, the model keeps
track of different vintages of capital,
application of emission controls, and
efficiency improvements. The model
maintains different sets of capital stock for
each of the end-use sectors and allows
different assumptions concerning emission
controls and efficiency improvements for each
sector and for different technologies within
each sector.
As with the generation sector, the
assumptions about the existing mix of
technologies are based primarily on
information on energy consumption in the
U.S. and in other OECD countries. The
approach differs depending on the end-use
sector and fuel type. The sectors are
residential, commercial, industrial, and
transportation. The fuel types are liquids,
gases, solids, and electricity. The basic
approach for developing the mix of
technologies and fuel types for the year 1985
is discussed below.
Primary energy consumption estimates
were taken from United Nations (1987), and
secondary energy consumption estimates were
developed from OECD (1988) for the OECD
countries and from Sathaye et al. (1988) for
the developing countries. For the
USSR/Eastern Europe, information from the
OECD countries was used to apportion
secondary demand, since detailed information
on the centrally-planned European countries
was not available. This step categorized each
region's energy consumption by fuel type and
end-use sector. Within each sector the
following steps were taken to categorize
demand in greater detail.
Residential energy use was classified by
type of application and fuel source using data
from El A (1988). This source contained
information for the U.S. for the year 1984
(1985 data was not yet available). The
categories by type of application were space
heating, air conditioning, water heating, and
appliances. The fuel sources were natural gas,
distillate fuel oil and kerosene, liquified
petroleum gas (LPG), and electricity. This
breakdown was also applied to other
industrialized countries (on a percentage
basis) since comparably detailed information
was not available for these regions. The
breakdown for the developing countries was
developed from information provided by
Sathaye et al. (1988).
Commercial energy demand by fuel type
was based on OECD (1988) and Sathaye et
al. (1988). Type of application was
determined from EIA (1978), which
categorized energy consumption in the
commercial sector by type of fuel and end-use
application for the United States. Major end-
use applications included space conditioning,
water heating, cooking, lighting, and
refrigeration. This information was also
applied to other regions to classify their
commercial energy consumption by end-use
category.
Transportation energy demand was
categorized using OECD (1988). For the
OECD countries this source provided
information on energy use by mode of
transportation (e.g., rail, road, air, etc.) and by
type of energy (i.e, coal, gas, oil, etc.).
Sathaye et al. (1988) was used for categorizing
demand in the developing countries.
End-use classification in the industrial
sector was based on information for the U.S.s
in OECD (1988), which categorized coal
demand by industry. The following
sources/assumptions were used to apportion
this demand to end-use categories: (1)
metallurgical coal demand for use in coke
ovens for steel-making, from EIA (1988); (2)
coal demand for cement kilns and other
minor applications, also from EIA (1988); and
(3) the remaining demand was assumed to be
consumed in boilers.
U.S. industrial consumption of oil and
natural gas was not classified by industry in
the OECD (1988) data. For these fuels
aggregate demand was disaggregated by end
A-28
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Appendix A: Model Descriptions
use using information in EIA (1978), which
indicated the pattern of fuel consumption
within the U.S. industrial secior. For each
fuel type, percentage esiimates were
determined for (1) the amouni of fuel used as
a raw material, i.e., oil used in asphalt
production and natural gas used in fertilizer
production; (2) the amount of fuel used in
transportation, e.g., diesel or gas used to fuel
vehicles in construction or mining; (3) the
amount of fuel consumed in boilers used to
produce steam; and (4) the amount of fuel
used in other applications, e.g., dryers, ovens,
etc. The quantity of oil or gas consumed in
boilers and in other applications, such as
dryers, was allocated to industry based on
information in EIA (1983), which indicated
the proportion of fuel use consumed by each
industry. This detailed classification for the
U.S. was also applied to the aggregate
demand estimates for the other regions since
this information was unavailable.
Estimating Greenhouse And Related
Emissions
The production and consumption of
energy discussed previously generates a variety
of emissions that affect global climate. In the
model these emission estimates are generated
once energy use is determined from the
equilibration of supply and demand. The
greenhouse gases for which estimates are
provided in the energy model include carbon
dioxide (CO2), nitrous oxide (N2O), and
methane (CH4); the model also estimates
emissions for carbon monoxide (CO) and
nitrogen oxides (NOX), which, although not
greenhouse gases, indirectly affect global
climate as a result of their interactions with
other gases in the atmosphere. The emission
estimation procedure is discussed below in
greater detail.
The type and quantity of emissions will
depend not only on the amount and type of
energy consumed, but also on the manner in
which the energy is consumed. In the model,
consumer demand for end-use energy
produces emissions from several types of
energy, including fossil fuels such as oil,
natural gas, and coal. These emissions are
estimated on a sectoral basis by primary
energy application. That is, as discussed
earlier in the energy demand section, energy
use is first categorized by type of energy
within each of the major energy-consuming
sectors that are included in the model -
industrial/agricultural, residential/commercial,
transportation, and the utility sector for
electricity generation. Within each of these
sectors, energy is consumed to provide some
type of service. For example, electric utility
powerplants consume fossil fuels to provide
electricity (which is then consumed by the
other sectors); automobiles, trucks, and buses
consume gasoline or diesel fuel; industrial
boilers consume residual oil or natural gas to
produce steam; furnaces consume natural gas
or oil to provide heating in homes and
buildings; etc. In the energy model this level
of detail reflects the different methods by
which energy may be consumed and hence,
the different methods by which emissions can
be generated.
This level of disaggregation is necessary
to capture the various inefficiencies that occur
in the production of end-use energy for
different applications and how these
efficiencies may change over time. In the
model these parameters are specified by
representative cost, efficiency, and emissions
characteristics. Table A-3 summarizes these
key characteristics for some of the major
fossil-fuel-burning technologies in each sector.
In the energy model several types of
non-fossil energy can also be chosen to supply
end-use energy demands. The quantities of
emissions from these other energy sources
depend on the specific energy type. For
example, nuclear energy is assumed to have
zero emissions for the five trace gases
indicated in Table A-3 (CO2, CO, CH^, N2O,
and NOX). Special consideration is given to
biomass energy as a renewable resource.
Consumed biomass does emit various
quantities of CO2; however, if consumed on a
sustainable basis, the carbon from which this
gas is formed has been^stored in the biomass
material during the plant's growth. To
capture the net recycling of carbon, CO2
emissions from biomass are assumed to be
zero.
For emissions of other gases from the
combustion of fuelwood and other biomass
energy sources, the model uses emission
estimates for 1985 and ties changes in the
quantities of these emissions to changes in
the use of biomass in the developing
A-29
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Policy Options for Stabilizing Global Climate
TABLE A-3
Differences in Emission Rate By Sector
(grams per gigajoule)
Efficiei
Source (%)
Electric Utility (g/GJ delivered electricity)
Gas Turbine Comb. Cycle
Residual Oil Boilers
Coal - PC Wall Fired
42.0
32.4
31.3
Industrial (g/GJ delivered steam for boilers; energy
Coal-Fired Boilers
Gas-Fired Boilers
Kilns - Coal
Residential/Commercial (g/GJ energy output)
Distillate Oil Furnaces
Gas Heaters
Transportation (g/GJ energy input)
Automobiles
Trucks
80
85
ncy
C02
120,300
230,000
330,000
output for kilns)
130,000
57,000
65-75 300,000-
75
70
350,000
111,000
101,000
Trace Gas
CO
70
43
42
110
18
75
17
13
54,900 10,400
73,300
600
CH4
13
2.2
2
2.9
1.5
1
7
1
36
8
N20
20
44
45
18
3.5
2
NA
NA
0.5
NA
NOX
400
590
1400
390
71
500
65
61
400
1,200
Source: Radian, 1990.
NA = Not Available.
A-30
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Appendix A: Model Descriptions
countries, which have been derived from the
energy model.
INDUSTRIAL EMISSIONS MODULE
The Industrial Emissions Module
provides estimates of trace gas emissions from
three different activities: use of CFCs and
haltins, decay of organic matter in landfills
(CH4), and cement production (CO2). The
factors that affect future emissions vary
considerably as do the mechanisms to reduce
future emissions.
Estimating Emissions of CFCs and Halons
The CFC emissions component of the
U.S. EPA Integrated Assessment Model (U.S.
EPA, 1988) provides estimates of emissions of
CFCs and halons under different economic
scenarios, policy objectives, and compliance
scenarios through the year 2100. Future
production of CFCs reflect assumptions about
future demographic and economic trends,
regional and global control strategies to
reduce the production and use of these
chemicals, and compliance with regulatory
controls. Estimates of emissions resulting
from the production and use of CFCs account
for the different uses and rates of release
associated with those uses.
The CFC emissions component
disaggregates production and emissions of
CFCs into ten compounds, eight applications,
or end uses, of CFCs, and ten global regions.
The regional disaggregation conforms closely
to that used throughout the ASF (Canada and
Western Europe are further disaggregated in
the CFC model) where production scenarios,
control strategies, and compliance with
controls can be specified separately for each
region. The ten compounds are as follows:
CFC-11;
CFC-12;
HCFC-22;
CFC-113;
CFC-114;
CFC-115;
Carbon Tetrachloride (CC14);
Methyl chloroform (CH3CC13);
Halon 1211; and
Halon 1301.
Each of these compounds is associated with
as many as six different end uses, where these
end uses and possible substitutions influence
the production of CFCs and the release rates.
There are a total of eight end-use categories
included in the model:
aerosol propellants;
flexible foam;
rigid polyurethane foam;
rigid nonurethane foam;
refrigeration;
solvent;
fire extinguishants; and
miscellaneous.
The model estimates production and
emissions starting from 1931; the time frame
can be extended to 2165, although the ASF
utilizes emissions only through 2100.
The overall procedure for estimating
emissions involves, first, estimating global
production of the different compounds, which
reflects demographic and economic
assumptions of the scenario. Global
production is allocated to regions and
applications using sharing rules. Regional
shares vary both historically and in the
projections, reflecting shifts in global
production due to differing rates of economic
development and other factors.
Policy alternatives are then applied to
the production scenarios to reduce or shift
the relative uses of the different compounds.
These policy alternatives can reflect a number
of issues, including constraints on the
production of different compounds,
compliance with these constraints, shifts
between compounds as a result of the
constraints, and the impact of technological
development, which may encourage the use of
alternative compounds in regional and global
production.
Releases of the different compounds to
the atmosphere are then estimated based on
the projections of production by type of
compound and type of application. For some
uses of CFCs, such as for aerosol propellants,
the compounds are released into the
atmosphere soon after the production of the
compound. In other applications the
A-31
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Policy Options for Stabilizing Global Climate
compound is released slowly. Releases from
refrigeration depend on the integrity and
useful life of the refrigeration system, and
releases from rigid foams depend on the rate
at which the rigid foams decay. As an
example, production of CFC-11 for use in
rigid polyurethane foam can contribute to
releases of CFC-11 20 years later.
The model simulates these delayed
releases, or banking, of compounds by
applying release profiles to the production of
each compound by end use. These release
profiles match the production of the chemical
in a given year to emissions in future years.
The release profiles are based on Quinn
(1986).
Table A-4 illustrates the release profiles
for CFC-11. The release profiles that allocate
the CFCs range from almost immediate
release, in the case of aerosol propellants, to
release profiles that span 20 years. In all
cases, 100% of the compound produced is
eventually released to the atmosphere. A
limitation of the model is that it does not
allow revision of the release profiles over
time to reflect technological development that
results in reduced emissions.
Estimating Emissions of CH4 from Landfills
Approximately 80% of municipal solid
wastes collected in urban areas around the
world is deposited in landfills or open dumps
(Bingemer and Crutzen, 1987). Sanitary
landfilling (compaction of wastes, followed by
daily capping with a layer of clean earth) is
used primarily in urban centers in
industrialized countries. A large portion of
these waste disposal sites develop anaerobic
conditions resulting in the decay of organic
matter to CH4.
Future disposal of solid wastes will be
driven by a number of factors, including the
amount of available land suitable for sanitary
landfilling, the switch to incineration as a
means of disposing of wastes, increased
urbanization and waste generation in
developing countries, and policies such as
waste minimization and CH4 recovery to
reduce wastes or emissions. Waste dumping
rates in the industrial world are now
beginning to level off. However, because of
strong population growth and increasing
urbanization, CH4 production from waste
dumps in the developing world can be
expected to grow in the future (Bingemer and
Crutzen, 1987).
The approach used to estimate future
emissions of CH4 from landfills is based on
estimates of the current level of emissions as
well as on future population and GNP
growth. Our approach is threefold. First, the
literature provided estimates of current global
emissions of CH4 from landfills, which can
range from 30 to 70 Tg CH4 per year
(Bingemer and Crutzen, 1987). The global
emissions estimate is then subdivided by
region based on the amount of carbon
disposed of in each region (Bingemer and
Crutzen, 1987). Estimates of future regional
emissions were developed for each of the
nine integration model regions separately,
assuming a close relationship between
emissions measured on a per capita basis and
average GNP/capita (see Table A-5).
For the U.S., emissions from landfills
are assumed to remain flat. For the rest of
OECD, emissions are expected to increase
slightly as a result of increased population
and increased GNP/capita. For the
developing countries, emissions rise rapidly
because of rapid increases in population,
GNP, and urbanization.
Estimating CO2 from the Production of
Cement
The CO2 emissions resulting from
cement manufacture occur during the
production of clinker. A mixture of cement
rock, limestone, clay, and shale are crushed
and blended to a mixture that is
approximately 80% limestone by weight. This
mixture is fed into a kiln where it is exposed
to progressively higher temperatures. The
emissions of CO2 occur during the calcination
process when the limestone (CaCO3) is
converted to lime (CaO) and CO2.
Approximately 0.14 ton of carbon is emitted
per ton of cement produced (Rotty, 1987).
For the developed regions (U.S.,
OECD-West, OECD-East, and the USSR and
Centrally-Planned Europe), future emissions
of CO2 from cement production through 2025
are tied to assumptions made in the end-use
models on growth in economic output from
A-32
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Appendix A: Model Descriptions
TABLE A-4
Release Profiles for CFC-11
(percent)
End Use
Years after Initial Use
2 345
10 15 20
Aerosol Propellent
annual release
cumulative released
Flexible Foam
annual release
cumulative released
Rigid Polyurethane Foam
annual release
cumulative released
Rigid Nonurethane Foam
annual release
cumulative released
Refrigeration
annual release
cumulative released
Miscellaneous
annual release
cumulative released
100,0
100,0
100.0
100.0
14.5
14.5
4.5
19.0
4.5
23.5
4.5
28.0
4.5
32.5
4.5
55.0
4.5
77.5
4.5
100.0
60,0
60.0
19,0
19.0
100
100
40.0
100.0
8.1
27.1
7.3
34.4
65.6
100.0
Source: Quinn, 1986.
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Policy Options for Stabilizing Global Climate
TABLE A-5
Assumptions Concerning Methane Emissions from Landfills
Region
U.S., Canada, Australia
Other OECD
USSR & E. Europe
Developing Countries
TOTAL
Waste C
Dumped
(106 t C/yr)a
37
19
13
16
85
Regional Emissions
Total
(Tg/yr)
13.0
6.7
4.6
5.7
30.0a
Per Capita
(K)6g/yr)
46.4
12.6
11.0
1.6
Average 1985 ,
GNP/Capitab
(103, S1988)
18
10
5
0.7
a Bingemer and Crutzen, 1987.
b World Bank, 1987.
A-34
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Appendix A: Model Descriptions
the stone, clay, and glass sectors. After 2025,
the rate. of growth is reduced to reflect
reductions in the rate of economic growth in
those regions. For the developing countries,
cement production is allowed to grow
consistently with growth in GNP until it
reaches levels of production consistent with
production in the developed countries.
AGRICULTURAL EMISSIONS MODULE
Introduction
The Agricultural Module of the ASF
estimates emissions of trace gases resulting
directly from the production of agricultural
products. This module provides a
comprehensive look at changes in land area
under cultivation, regional production of
different crops, meat and dairy production,
and fertilizer use, and then ties these activities
to emission coefficients to produce estimates
of annual emissions.
The agricultural module is used to
estimate emissions of four trace gases that
result from four agricultural activities: rice
cultivation (CH4), nitrogenous fertilizer use
(N2O), animal husbandry (CH4), and burning
of agricultural wastes (N2O, CH4, CO, and
NOX). The module ties CH4 emissions
resulting from anaerobic decomposition in
flooded rice fields to the estimated land under
paddy rice cultivation. Methane released
through enteric fermentation in domestic
animals follows the production estimates of
meat and dairy products and population
estimates of domestic animals used for labor.
Nitrous oxide that evolves from the
application of fertilizer follows projections of
nitrogenous fertilizer use. Emissions of all
four of the gases resulting from the
combustion of agricultural wastes follow
estimates of the use of cultivated land.
Carbon dioxide emissions resulting from the
combustion of agricultural wastes are assumed
to net to zero within each year due to
recycling during plant growth.
The agricultural module consists of two
components: an agricultural activities model
and an emissions model. The agricultural
activities model is a detailed regional model
of agricultural production, land use, fertilizer
use, and product consumption through 2050
(Frohberg et al., 1988; Fischer et al., 1988)
with a simplified approach to extending the
projections through 2100. The emissions
model applies emission coefficients to the
results from the activities model, allowing for
changes in these coefficients to represent the
impact of policies to reduce emissions.
Estimating Agricultural Activities through
2050: The Basic Linked System
Agricultural activities through 2050
were estimated by the Center for Agricultural
and Rural Development (CARD) at Iowa
State University through use of the Basic
Linked System (BLS) (Frohberg et al., 1988).
The BLS is a tool developed and used
primarily for analyzing policies to improve the
agricultural production and distribution
system over a medium-term horizon (for the
1980s and 1990s). The origins of the BLS
lie with the Food and Agriculture Program
at the International Institute for Applied
Systems Analysis, Laxenburg, Austria, in
cooperation with the Center for World Food
Studies, Amsterdam. These two
organizations, with the participation of
researchers from around the world, took the
lead in conceptualizing and constructing the
BLS, which was then transferred to several
research institutions including CARD.
CARD has subsequently extended the time
horizon to 2050 for U.S. EPA's use in the
ASF.
The BLS combines 34 national and
regional models (see Table A-6) within an
integrating framework that uses prices and
flows of capital to balance global supply,
demand, and trade. Twenty national models,
which represent 80% of the world's
population and production, are used xto
estimate agricultural activities for specific
countries (or in two cases, groups of
countries). The rest of the world is
represented in 14 simplified regional models.
Each regional model represents a set of
countries with similar income levels and
import and export characteristics with respect
to agricultural products and crude oil.
The integration of the 34 national and
regional models includes an equilibrating
mechanism that addresses the major factors
affecting the global market including prices
for the different agricultural products,
imports, exports, and level of stocks. At the
A-35
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Policy Options for Stabilizing Global Climate
TABLE A-6
Regional Disaggregation of BLS
Japan
Mexico
Nigeria
Pakistan
Turkey
European Community
Group 2 Group 3
Kenya CMEA (USSR & Centrally Planned
Europe)
New Zealand China
Thailand India
United States
National Models
Group 1
Argentina
Australia
Austria
Brazil
Canada
Egypt
Indonesia
Regional Models
Africa
1 oil exporters (Algeria, Angola, Congo, Gabon)
2 medium-income calorie exporters (Ghana, Ivory Coast, Senegal, Cameroon, Mauritius,
Zimbabwe)
3 medium-income calorie importers (Morocco, Tunisia, Liberia, Mauritania, Zambia)
4 low-income calorie exporters (Benin, Gambia, Togo, Ethiopia, Malawi, Mozambique, Uganda,
Sudan)
5 low-income calorie importers (Guinea, Mali, Niger, Sierra Leone, Burkina Faso, Central
African Republic, Chad, Zaire, Burundi, Madagascar, Rwanda, Somalia, Tanzania)
Latin America
1 high-income calorie exporters (Costa Rica, Panama, Cuba, Dominican Republic, Ecuador,
Surinam, Uruguay)
2 high-income calorie importers (Jamaica, Trinidad, Tobago, Chile, Peru, Venezuela)
3 medium-/low-income (El Salvador, Guatemala, Honduras, Nicaragua, Colombia, Guyana,
Paraguay, Haiti, Bolivia)
Asia
1
2
SE Asia high-/medium-income calorie exporters (Malaysia, Philippines)
SE Asia high-/medium-income calorie importers (Republic of Korea, Laos, Vietnam, Korea,
DPR, Kampuchea)
3 Asia low-income calorie importers (Nepal, Burma, Sri Lanka, Bangladesh)
4 SW Asia high-income oil exporters (Libya, Iran, Iraq, Saudi Arabia, Cyprus, Lebanon, Syria)
5 SW Asia medium-/low-income calorie importers (Jordan, Yemen Arab, Yemen Democratic,
Afghanistan)
Rest of the World -- Developed Countries
Albania, Andorra, Faeroe Islands, Finland, Gibraltar, Greece, Greenland, Hong Kong, Iceland,
Israel, Liechtenstein, Malta, Monaco, Norway, Portugal, San Marino, Singapore, South Africa,
Spain, Sweden, Switzerland, Vatican City, Yugoslavia
A-36
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Appendix A: Model Descriptions
TABLE A-6 (Continued)
Regional Disaggregation of BLS
Rest of the World - Developing Countries
1 Africa: Botswana, British Indian Territory, Cape Verde, Comoros, Equatorial Guinea,
Djibouti, Guinea-Bissau, Lesotho, Namibia, Reunion, St. Helena, Sao Tome, Seychelles,
Spanish North Africa, Swaziland, Western Sahara
1 Central America: Antigua, Bahamas, Barbados, Belize, Bermuda, Cayman Islands, Dominica,
Grenada, Guadeloupe, Martinique, Montserrat, Netherland Antilles, Panama Canal Zone,
Puerto Rico, St. Kitts-Nevis, St. Lucia, St. Pierre and Miquelon, St. Vincent, Turks and
Caicos, Virgin Islands (UK), Virgin Islands (USA)
3 South America: Falkland Islands, French Guinea
4 Asia: Bahrain, Bhutan, Brunei, East Timor, Gaza Strip, Kuwait, Macau, Maldives, Mongolia,
Oman, Qatar, Sikkim, United Arab Emirates
5 Oceania: American Samoa, Canton and Enderbury Islands, Christmas Island, Cocos Islands,
Cook Island, Fiji, French Polynesia, Gilbert Islands, Guam, Johnston Island, Midway Islands,
Nauru, New Caledonia, New Hebrides, Niue Islands, Norfolk Islands, Pacific Islands, Papua
New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, Wake Island, Wallis
and Futuna Islands
A-37
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Policy Options for Stabilizing Global Climate
beginning of each year, the model estimates
regional prices for the different agricultural
products. The national and regional models
then base their decisions to allocate capital
and labor based on these prices. At the end
of the year, the model determines the
wholesale and retail prices, determines
consumption, and estimates the level of trade.
Export restrictions and tariffs can affect
regional prices as well as the flow of goods.
Prices for agricultural products and the level
of trade form a major component in the flow
of capital between regions.
National Models
The national models are aggregated
into three groups based on the structural
formulation of each model. The structure
of the models within Group 1 are similar;
those within Group 2 are similar (and closely
related to those in Group 1); the structures of
the models within Group 3 are different for
each nation (USSR and Centrally-Planned
Europe are treated as a single nation).
The national and regional models
address the specific factors that have an
impact on agricultural production and
consumption in each of the countries/regions.
FOr production, these factors include decisions
by domestic producers, allocation of primary
inputs (land, labor, and capital) and allocation
of interine~diate inputs (feed and fertilizer).
For consumption and trade, these factors
include stock holding, sectorial gross GDP
and investment, and allocation of family
income. These factors also reflect region-
specific policies and the relative impact of
these policies. The approach varies between
groups of models, which reflects the specific
characteristics of the region.
The availability and allocation of capital
provide a major component of the structure
of the domestic models. Consumers can
spend no more than their after-tax income
plus government transfers. Government
revenues come from taxes or from the
ownership of production. Agricultural
production depends on allocation and
investment of capital.
Group 1 and Group 2 Models. The
models for the Group 1 and Group 2
countries estimate the behavior of producers,
consumers, and the government and how this
behavior will change over time due to
changing economic conditions and policies.
These models assume that agricultural
production and the consumption of
agricultural goods are determined in large
part by efforts of producers to maximize
profit and of consumers to maximize utility,
as well as by policies set by the government.
The parameters of these models are estimated
using data from the time period 1961 to 1976.
The models contain an agricultural
sector and a non-agricultural sector.
Production from the agricultural sector is
aggregated into nine commodity classes as
shown in Table A-7. The non-agricultural
sector represents the rest of the economy and
is used as both a sink and source of capital
and is also used to process and distribute
agricultural products. Figure A-6 illustrates
the typical structure within the national
models.
For each region, demand for
agricultural products consists of human
consumption, feed, stocks, and for industrial
use, seed and waste. The estimates of human
consumption are based on past consumption
patterns and reflect income, tastes, and habits.
Changes in income and prices allow
consumption patterns to change.
Producer decision and agricultural
production depend both on prices and the
availability of the primary inputs: land, labor,
and capital. While land is used in only the
agricultural sector, both the agricultural and
non-agricultural sectors compete for labor and
capital. For each year and for each crop, the
models estimate yields, optimal fertilizer
application rates, and least-cost feed
application rates and allocates the primary
and intermediate inputs in order to maximize
net income (revenue minus costs). For crops,
yield is a function of fertilizer application.
For livestock, yield is a function of feeding
intensity.
Although labor and capital are treated
differently in the models, they are assumed to
be homogenous inputs to the production
model and mobile between the two sectors.
Labor is not disaggregated based on skill
levels or whether it is family or hired. Within
the agricultural sector, labor is allowed to
A-38
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Appendix A: Model Descriptions
TABLE A-7
Agricultural and Non-Agricultural Commodity Classes
Commodity'Class Main Components Type of Measurement
Agricultural
Wheat Total weight
Rice, milled Total weight
Coarse grains Total weight
Bovine and ovine meat Carcass weight
Dairy products Milk equivalent
Other animal products Pork, poultry, eggs, fish Protein equivalent
Protein feed Oilcakes, fish/meat meal Protein equivalent
Other food Oils, fats, sugar, vegetables, . Unit value of exports
fruits, coffee, cocoa, tea (expressed in US S)
Nora-food agriculture Clothing, fiber, industrial Unit value of exports
crops (expressed in US S)
Non-Agricultural All non-agriculture outputs Domestic prices
(expressed in US S)
A-39
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Policy Options for Stabilizing Global Climate
FIGURK A-6
TYPICAL OUTLINE OF A NATIONAL MODEL
r Stocki 4- pollci««
Excess demand
trad* deficit
Source: Parikhctal., 1988.
A-40
-------
Appendix A: Model Descriptions
move freely among the various enterprises
(associated with the different commodities).
Capital is accumulated through
investment and depreciation. Once
investment in each sector is determined, the
capital within the sector is fixed. Over time,
the capital is freed for reinvestment through
depreciation. Capital is mobile between the
different enterprises in the agricultural sector,
although a distinction is made between crop
enterprises and livestock enterprises. Capital
is more mobile between the crop enterprises
than among the livestock subsectors although
a gradual shift between crop enterprises and
livestock enterprises is possible.
The model allows the amount of
available land for cropping and pasture to
change, but it is very inelastic to changing
economic conditions. Technical progress is
captured in the yield functions.
Group 3 Models. Four models (U.S..
China, CMEA, and India) make up the
Group 3 category. The structure of each
model is different.
The agricultural component of the U.S.
model is Michigan State University's
intermediate model of U.S. agriculture, which
is an econometrically based supply model.
The model has some similarities to the Group
1 and Group 2 models but incorporates
specific U.S. policies such as domestic price
policies, trade quotas, land-set-aside policies,
and wheat and coarse-grain stock policies.
The China model differs considerably
from the other regional models primarily
because of the lack of data available from
China. As a result, the main purpose of the
China model is to check for consistency in the
agricultural sector for the scenarios generated.
The China model differs from the
models in Group 1 and Group 2 in terms of
how yield is treated, how available land is
calculated and allocated to the different
sectors, and how data on animal production
and human consumption is derived. Yield is
specified as both a function of fertilizer use
and irrigation practices. Cultivated acreage is
a function of arable land, irrigated land, and
horse power available. Animal production is
based on trends and the availability of feed,
and human consumption is based on
government-set target levels of consumption
that are adjusted based on trade and deficit
rcali/aiions.
The USSR and Centrally-Planned Europe
(CMEA) model is similar to the other
country/regional models but contains funda-
mental differences that reflect the specific
features of the centrally-planned economies.
Like the Group 1 and Group 2 models, the
production model uses the same methodology
and is based on the same data (FAO). The
differences lie in that agricultural policy and
policy goals are determined by a centrally
planned economy and are an integral part of
the central plan for the whole economy. The
internal market is separated from the global
market and producer prices reflect production
expenses as opposed to market value.
Consumer prices reflect wage and income
targets and are not set to balance supply and
demand.
The differences between the CMEA
model and the Group 1 and Group 2 models
include treatment of growth in the economy,
production bounds, consumption trends,
relationship to the global market, and
treatment of land. Users specify the desired
growth in the economy and allocation of
investment. Lower and upper bounds on
production assure levels of supply and limit
growth Consumption estimates are based on
data from FAO (1981) and published data on
targets for private consumption. The
interface with the global market is performed
by first adjusting stocks, modifying
investments in the rest of the economy,
modifying investments in the agricultural
sector, adjusting private consumption of non-
agricultural products, and, finally, modifying
food consumption. Land use is not
considered in the model because of the lack
of data in this area.
The differences between the India
model and the Group 1 and Group 2 models
are related to the disaggregation of
production into groups, the methods for
estimating future production, and the
determination of demand. Like the other
models, the India model includes an
agricultural sector and non-agricultural sector,
but the non-agricultural sector is divided into
two components: rural and urban. Income is
A-41
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Policy Options Cor Stabilizing Global Climate
generated endogenously, and its allocation is
based on the distribution of assets such as
land, livestock, and implements, as well as on
tenancy structure.
Production is aggregated into 16 major
crops, nine minor crops, and several animal
products and is estimated using an
econometric approach that bases land
allocation on relative differences in expected
revenues. Yields are a function of irrigation,
fertilizer, rainfall, time, and prescribed rates
of adoption of high-yielding varieties of crops.
Consumer demand is aggregated into
the nine commodities used with the other
national models, and the more detailed supply
results are combined for consumption
purposes. Demand is estimated separately for
each of ten income classes and reflects an
optimal allocation of income.
Policy instruments available to the
government include tariffs, as in the other
countries/regions, but include some specific
regional capabilities. These include subsidies
on trade, buffered stock releases of
agricultural products, support prices,
procurement levels, and procurement prices.
The model also operates a food-rationing
system for the urban population.
Regional Models
Each of the 14 regional models
represents a group of countries at comparable
levels of income with similar relationships to
the world market. The basis for the supply
and demand projections for these models are
the results from the moderate economic
growth scenario of FAO's study Agriculture:
Toward 2000 (FAO, 1981). But since the
FAO projections are based on constant prices,
the models for the low- and medium-income
regions allow adjustments to the forecast
using price elasticities based on the behavior
of the national models in the BLS for the
developing countries. The models ensure
consistent physical and financial balances with
an exchange component that allocates income
for consumption purposes. For the regional
models in the remaining high-income
countries, the approach is the same, but the
supply and demand target levels follow the
trend of 1961-1980 historical data.
Treatment of Agricultural Variables
Nitrogenous Fertilizer. Inorganic
nitrogenous fertilizer is an explicit decision
variable in all models except the regional
models. The regional models determine
nitrogenous fertilizer use from output of
agricultural products using a statistical
relationship based on data from the early
1980s. All models consider only inorganic
nitrogen. Table A-8 summarizes the approach
used within the different types of models.
Organic nitrogen is not explicitly considered
in the models and is not part of the
production or yield functions.
For the national models, nitrogen use
is determined on a per hectare basis, although
the approach varies by model. The Group 1
models, along with the China, India, and U.S.
models use nitrogen response (yield)
functions. The Group 2 models and those of
CMEA use production functions and
determine nitrogen use by crop. Total
nitrogen use for Group 2 and CMEA models
equals acreage times per-hectare use, summed
over all crops.
Acreage Used in Rice Production.
Group 1 models and models of the U.S.,
China, and India, determine acreage used in
the production of rice. In the Group 1
models, rice acreage is a function of inputs,
including labor and machinery, while in the
U.S., China, and India models, rice acreage
directly follows changes in relative prices.
The Group 2 models, regional models, and
the CMEA model simulate rice production
directly as a function of the relative changes
in prices.
Rice acreage includes both dryland rice
and paddy rice. Only paddy rice contributes
to methane production. Table A-9
summarizes the approach within the different
types of models.
Ruminants. Ruminant populations are
estimated in the models using the same
procedures used to calculate rice production.
As a result, the numbers of ruminants are
directly calculated within Group 1 models and
the U.S., China, and India models. Two types
of ruminants are calculated in these models:
dairy cows and all other bovine and ovine
A-42
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Appendix A: Model Descriptions
TABLE A-8
Structure and Approach Used to Estimate Fertilizer Use
Model
Approach
Group 1 Models
Group 2 Models
Group 3 Models
China
India
U.S. '
CMEA
Regional Models
Individual crops -- except fruits and roughage, which have a nitrogen
balance built in -- use yield response functions to nitrogen application
Response to nitrogen use is modelled on the basis of total production
rather than yield per hectare
Individual crops use yield response functions to nitrogen fertilizer,
including manure application, but only one average per hectare application
rate (for all crops)
Most of the important crops use yield response functions to nitrogen
application
Wheat, corn, soybeans, and cotton have yield response functions to
nitrogen application, while the nitrogen balance of the remaining crops are
calculated
Response to nitrogen use is modeled on the basis of total production
Balance of nitrogen calculated after the level of crop production is
determined
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Policy Options for Stabilizing Global Climate
TABLE A-9
Structure and Approach Used to Estimate Rice Acreage
Model
Approach
Group 1 Models
Group 2 Models
Group 3 Models
China
India
U.S.
CMEA
Regional Models
Acreage allocated to rice according to relative profitability
Production determined directly and rice acreage not estimated
Acreage allocated to rice determined based on past trends
Acreage allocated to rice, of which there are three different types of crops,
based on relative profitability
Acreage allocated to rice based on relative profitability
Production determined directly and rice acreage not estimated
Production determined directly and rice acreage not estimated
animals used for meat production. Table A-
10 summarizes the approach within the
different types of models.
Completing And Expanding The Estimates
Through 2100
For the purposes of the emissions
model, the estimates of rice acreage, ruminant
populations, and nitrogenous fertilizer use
must be completed for all of the regions and
then extended through 2100. As explained in
the previous section, the acreage used to
produce rice is not estimated for all of the
regions. Fertilizer use is reported in several
different units and must be converted to
grams of nitrogen.
The first step performed by the model
is to estimate the rice acreage for those
countries/regions where the acreage is not
solved for directly. For each time period
between 1985 and 2050, the model calculates
the average yield per acre for the countries
where both rice acreage and production are
specified. The model then applies this global
average yield estimate to production of rice
for those countries/regions where the acreage
has not been directly calculated.
Next the model estimates country/
regional production of the nine agricultural
product types through 2100 by first estimating
regional demand through 2100 and then esti-
mating regional production to satisfy that
demand. Using the detailed demand
estimates through 2050 for each of the nine
agricultural product types, the model
calculates total demand in the year 2050 for
each country/ region where the total demand
includes human consumption, feed, seed,
industrial use, and waste. Using the
projections of regional population through
2100, the model estimates the rate of
population growth and applies these rates of
growth to total demand in 2050, which results
in estimates of demand per country/ region
for each time period from 2050 to 2100.
Summation of the country and regional
results provides global estimates of demand.
Country and regional production for the
period 2050 to 2100 are estimated by first
growing production consistent with growth in
population and then normalizing the
A-44
-------
Appendix A: Model Descriptions
TABLE A-10
Structure and Approach Used to Estimate Ruminants
Model
Approach
Group 1 Models
Group 2 Models
Group 3 Models
China
India
U.S.
CMEA
Regional Models
Bovine and ovine animals and dairy cows identified endogenously along
with slaughter weight and milk yield.
Production determined directly and ruminant animals not estimated
Ruminant animals determined based on past trends
Production determined directly and ruminant animals not estimated
Production determined directly and ruminant animals not estimated
Production determined directly and ruminant animals not estimated
Production determined directly and ruminant animals not estimated
production estimates so that global
production equals global demand.
Rice yields for each country and region
are held constant after 2050. These yields are
applied to estimated production, resulting in
estimated rice acreage for each country/region
through 2100.
Estimation of fertilizer use after 2050
involves several steps. First, detailed model
results for the period 1985 to 2050 were used
to estimate the relationship between growth
in production of rice, wheat, and coarse grains
and growth in fertilizer use. The model then
applies this relationship to estimated
production from 2050 to 2100 resulting in
estimates of fertilizer use by region.
Fertilizer use is tied only to increases in
production of three crops for several reasons.
First, the countries/regions where fertilizer use
are reported by crop, wheat, rice, and coarse
grains account for around 70% of fertilizer
use. Second, the components of the other
commodity classes include a wide variety of
products where the fertilizer use per unit of
product might not be homogeneous between
regions (e.g., clothing, fiber, and industrial
crops measured in U.S. dollars).
The functional form of the equation
used to estimate regional nitrogenous
fertilizer use, Fft, follows:
Fr,t = Zr,2050 + a(Wr,t ' Wr,205o)
+ b(Rr t - RF)205o) + C(Cr,f ' Cr,205o)'
where
r = index for the country/region,
t = index for the year (e.g.,
2075),
Z t = fertilizer use estimated by
the BLS (1985 through
2050) for each region and
period,
r,t
= estimated fertilizer use after
2050,
Wrt = production of wheat,
A-45
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Policy Options for Stabilizing Global Climate
R
r.t
•'r.t
production of rice, and
production of coarse
grains.
The parameters a, b, and c represent fertilizer
use per unit of production. For the regions
Turkey and CMEA, this functional form was
applied starting in 2000 due to the high
increases in fertilizer use in the original BLS
results, which were inconsistent with
production levels.
The parameters for fertilizer use per
unit of production were estimated using the
results of the BLS through 2050. The model
selects parameters a, b, and c in order to
minimize the sum of the squares of the error
term Erp where the error term is defined as
follows:'
Er,t = Zr,1985 + a(Wr,t " Wr,198s)
+ b(Rr,t - Rr,1985)
+ c(Cfit - Cr)1985) - Zr.t
The summation of the squares of the error
term is over all regions (except Turkey and
the USSR, which display growth rates well
above the rates for all other regions) and over
the time periods 1985 to 2025, in five-year
increments, and 2050.
Total land use is extrapolated to 2100
only for those regions where total land use is
estimated by the BLS country/regional models.
The model extrapolates the trend in land use
per capita from 2025-2050 to the period 2050-
2100.
Estimating Emissions of Trace Gases
The emissions of trace gases estimated
by the agricultural component include CH4
emissions associated with rice production,
N2O from nitrogenous fertilizer use, CH4
from enteric fermentation in domestic
animals, and CH4, N2O, NOX, and CO from
burning of agricultural wastes. Emissions of
CO2 from the burning of agricultural wastes
are assumed to net to zero each year, since
the carbon released to the atmosphere is
recycled during plant growth.
Methane from Rice
Methane emissions associated with rice
production occur as a result of anaerobic
decomposition in flooded rice fields, which
allows CH4 to escape to the atmosphere
through ebullition (bubbling) through the
water column, diffusion across the water/air
interface, and transport through the rice
plants. Research suggests that the amount of
CH4 released to the atmosphere is a function
of rice species, number and duration of
harvests, temperature, irrigation practices, and
fertilizer use, although quantification of these
factors is poor since few measurements have
been taken.
Our approach to estimating CH4 from
rice production applies an emission coefficient
to land area under rice cultivation.
Measurements of rice paddy CH4 flux have
yielded average emission coefficients of 25 g
CH^n^/harvest for a study in California
(Cicerone et al., 1983) to 54 g CH4/m2/
harvest for a study in Italy (Holzapfel-Pschorn
and Seller, 1986). Using the flux-temperature
relationship derived from the Italian data,
Holzapfel-Pschorn and Seiler (1986) derived
emission coefficients from approximately 45 to
120 g CH4/m2/harvest for the tropical and
sub-tropical regions, where over 95% of the
global rice acreage is located. We adopted a
mid-range emission coefficient of 75
g/m2/harvest, which when multiplied by the
harvested rice paddy acreage in 1985 of 144.7
million hectares (FAO, 1986), yields an
annual emission of about 110 Tg, the same
number as that given by Cicerone and
Oremland (1988) for this source. The
emission coefficient is applied to estimates of
future land area under cultivation (double-^
cropped areas are counted twice), which
accounts for land each time it is harvested.
However, the land area estimates from BLS
include estimates of land area for dry rice as
well as land area for paddy rice. Dry (or
upland) rice fields are not flooded and do not
emit CH4. Also, land used for upland rice
represents only a small fraction of the land
area under rice cultivation (less than 9% in
Asia, where over 90% of the world's rice is
grown). The model adjusts for this by
reducing the land acreage under rice
cultivation.
A-46
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Appendix A: Model Descriptions
Methane Emissions from Enteric Fermentation
in Domestic Animals
Methane is a by-product of enteric
fermentation in herbivores, a digestive process
by which carbohydrates are broken down by
microorganisms into simple molecules for
adsorption into the bloodstream. Although
some non-ruminant animals produce CH4, the
highest losses come from ruminants. The
quantities of CH4 emissions depend on the
type, age, weight, and energy expenditure of
the animal, as well as the quality and quantity
of feed.
The approach to estimating future
emissions involves several steps: estimating
animal populations, selecting regional
emission coefficients and applying the
emission coefficients to the animal
populations, and allowing these emission
estimates to increase over time using
estimates of future agricultural activities.
FAO data (FAO, 1986) provided animal
populations by type (cattle, dairy cows, sheep,
buffalo, goats, pigs, horses, camels, mules, and
asses) and by country. For each animal type,
emission coefficients were obtained from the
literature (Crutzen et al., 1986, and Lerner et
al., 1988). Estimates of the appropriate
coefficients to apply to the animal
populations for each country were based on
factors such as the uses to which the animals
were put and the feeding practices. Applying
the emission coefficients to the animal
populations resulted in estimates of emissions
by animal type and by country. Table A-ll
summarizes these emission estimates by
region and animal type. The model then
aggregated these emissions into four
categories for each of the 34 countries/regions
represented by the individual models within
BLS:
• emissions related to bovine and ovine
meat production, which equaled the
emissions from cattle and sheep;
• emissions related to dairy production,
which equaled the emissions from dairy
cows;
• emissions related to other meat
production, which equaled the
emissions from pigs; and
• all other emissions from domestic
animals.
The method for estimating emissions
over time reflected the data available in all of
the individual models within the BLS. Some
of the country models included information
on animal stocks and feed usage, but since
bovine and ovine animals were combined and
the animal stock was available for only a
portion of the countries/regions, the following
approach was used for each country/region:
• growth in emissions from bovine and
ovine meat production are consistent
with the growth in meat production
from the BLS;
• growth in emissions from dairy cows
are consistent with the growth in dairy
production from the BLS;
• growth in emissions related to pigs are
consistent with growth in other meat
production from the BLS; and
• emissions from all other sources are
kept flat.
Several major issues concerning the emissions
forecasts should be addressed. First, impacts
on emissions due to changes in feeding
practices over time will not be captured
within this approach because information on
the feed use for the animals and animal
stocks is limited. Second, the combination of
cattle and sheep into one category assumes
that the ratio of these two animal populations
remains constant within each region. Annual
emission coefficients range from 35 to 55
kg/animal for cattle to 5 to 8 kg/animal for
sheep (Crutzen et al., 1986), and meat yield
varies from 100 to 300 kg/animal for cattle to
5 to 30 kg/animal for sheep (FAO, 1986). In
Brazil and the U.S., for example, this
assumption can lead to an error of up to 15%
and 20%, respectively, in the emissions per
A-47
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Policy Options for Stabilizing Global Climate
TABLE A-U
1984 Animal Populations and Emission Estimates
Cattle
Region*
ARGENTINA
AUSTRALIA
AUSTRIA
BRAZIL
CANADA
EGYPT
INDONESIA
JAPAN
MEXICO
NIGERIA
PAKISTAN
TURKEY
EEC
KENYA
NEW ZEALAND
THAILAND
CMEA
CHINA
INDIA
U.S.
AFRICA-1
AFRICA-2
AFRICA-3
AFRICA-4
AFRICA-5
LATIN AMER-1
LATIN AMER-2
LATIN AMER-3
ASIA-1
ASIA-2
ASIA-3
ASIA-4
ASIA-5
REST OF
WORLD
TOTAL
Pop.
50530
20426
1652
118101
10556
870
6615
3209
28600
10620
13672
11000
51705
9200
5791
4609
94519
57660
155160
102840
4016
12390
5658
47913
44654
22707
16622
38551
2437
6891
47904
7901
3306
32286
1050571
Emis.
(10* g)
2729
1103
58
6377
581
30
232
113
1001
372
479
385
1825
322
204
161
3337
2018
5431
5656
141
434
198
1677
1563
795
582
1349
85
241
1677
277
116
' 1361
42908
Sheep
Pop.
(10J H)
30000
139242
214
17500
791
1450
4790
22
6400
12000
24272
48707
62184
6700
70344
22
185605
98916
40890
11411
15085
8134
22507
47018
30745
26108
21197
13186
99
365
4949
65330
23823
100180
1140186
Emis.
(10* g)
150
696
2
88
6
7
24
0
32
60
121
244
497
34
563
0
1485
495
204
91
75
41
113
235
154
131
106
66
0
2
25
327
119
725
6917
Dairy Cows
Pop.
(10fH)
2970
1735
981
14700
1728
955
185
1473
8900
1180
2680
6200
28073
2800
2119
11
57551
857
27000
11200
859
669
1984
6522
5735
2551
2750
4347
58
325
6684
3357
1559
10660 ,
221358
Emis.
(10* g)
160
94
88
794
145
33
6
133
312
41
94
217
2527
98
191
0
5180
30
945
941
30
23
69
228
201
89
96
152
2
11
234
117
55
843
14180
Pigs
Pop.
(10J H)
3800
2527
3881
33000
10760
15
3620
10423
18370
1300
0
11
78703
100
420
4150
143467
298693
8650
55819
657
2179
401
1590
2996
8301
5846
8207
9829
19909
3230
289
0
45297
786440
All Other
Emis.
(10* g)
4
3
6
33
16
0
4
16
18
1
0
0
118
0
1
4
215
299
9
84
1
2
0
2
3
8
6
8
10
20
3
0
0
67
959
Pop.
(103 H)
6403
719
74
17520
409
5847
10828
81
21962
26968
45586
19678
3997
8932
208
6198
17933
12840
147880
11755
5192
8205
12955
47034
54829
3554
7227
11504
5645
5323
25555
25793
9991
32618
721243
Emis.
(10* g)
70
9
1
172
7
133
169
1
152
136
842
135
40
78
2
308
231
1528
3681
193
34
43
121
412
641
42
51
93
174
217
544
169
69
254
10751
Total
Pop.
(103 H)
93703
164649
6802
200821
24244
9137
26038
15208
84232
52068
86210
85596
224662
27732
78882
14990
499075
568966
379580
193025
25809
31577
43505
150077
138959
63221
53642
75795
18068
32813
88322
102670
38679
221041
3919798
Emis.
(1° g)
3113
1905
155
7464
755
204
434
262
1515
610
1536
980
5008
532
960
474
10447
4370
10270
6965
280
543
501
2554
2561
1065
841
1669
272
492
2482
890
358
3249
75715
* For an explanation of the regions, see Table A-6.
Sources: FAO, 1985; Crutzen et al., 1986; Lerner et al., 1988; Fung, pers. communication.
-------
Appendix A: Model Descriptions
ton meat production from the two animals.
Also, the ratio of meat production to animal
stocks is assumed to remain constant within
each region.
Nf> Emissions from Fertilizer Use and
Legumes
Application of nitrogenous fertilizer
enhances the rate of flux of N2O released
through microbial processes in soils both
through nitrification and denitrification. The
enhanced emissions vary considerably due to
type of fertilizer used, application practices,
soil conditions, rainfall, and other factors.
Research has also shown that use of legumes
to fix nitrogen will also enhance the flux of
N2O to the atmosphere. It has been
estimated that enhanced levels of N2O
emissions result from the leaching of
nitrogenous fertilizers into surface water and
ground waters.
The approach to estimating the
fertilizer-induced emissions of N2O involves
disaggregating nitrogenous fertilizer use into
five categories, applying emission coefficients
(fraction of N applied that evolves as N2O) to
each of these categories to estimate the direct
emissions from the soils, and applying a
separate emission coefficient to approximate
the emissions resulting from leaching. The
five categories of nitrogenous fertilizer, which
were selected based on similarities in emission
rates, are as follows:
• Ammonium Nitrate and Ammonium
Salts;
• Nitrate;
• Urea;
• Other Nitrogenous and Other Complex;
and
• Anhydrous Ammonia.
The allocation of the fertilizers to these
categories were based on statistics from FAO
(FAO, 1987), and the relative shares were
kept constant over time unless policy
objectives included the conversion to other
fertilizer types.
The emission coefficients were obtained
from the literature (Galbally, 1985; Fung,
pers. communication) and are reproduced in
Appendix B. Since estimates from different
sources (Eichner, 1988; Breitenbeck, pers.
communication) vary considerably, the model
allowed sensitivity analysis using different
coefficients. The literature addressing
enhanced fluxes from leaching of fertilizer and
from human and animal wastes provided a
wide range of emissions, from approximately
0.5% to over 3% of nitrogen evolved as N2O
(Conrad et al., 1983; Kaplan et al., 1978;
Ronen et al., 1988). Due to the large
uncertainties and the wide range of factors
involved, one coefficient (2% of nitrogen
evolved as N2O) was applied to all fertilizer
types equally and allowed to vary between
runs to test the sensitivity of the results to
the different assumptions.
Enhanced N2O emissions from legumes
are as highly uncertain, ranging from 0.02 to
0.3 Tg N per year (Eichner, 1988). For the
purpose of this analysis, these emissions were
assumed to lie towards the lower range, i.e.,
0.02 Tg N.
Emissions from the Burning of Agricultural
Wastes
Emissions of N2O, CH4, NOX, and CO
result from the burning of agricultural wastes
such as rice straw (emissions of CO2 are
recycled in the plant growth). The model
estimates emissions using estimates of current
global emissions of the different gases from
the literature (Logan et al., 1981; Crutzen,
1983; Logan, 1983; Bolle et al., 1986; Crutzen
et al., 1979; Seiler and Crutzen, 1980; Seiler
and Conrad, 1987; Cicerone and Oremland,
1988) and allows these emissions to grow over
time consistent with growth in total land use
for agriculture as predicted by the BLS.
Since land use is not provided for all regions
within the BLS, the total land use for those *
regions where it is specified is used as a
proxy. Table A-12 illustrates the range of
emission estimates from these sources.
LAND-USE CHANGES AND NATURAL
EMISSIONS MODULE
Natural sources of emissions of
greenhouse gases include anaerobic
decomposition in wetlands and enteric
fermentation in wild ruminants (CH4),
nitrification and denitrification in soils (N2O),
natural processes in oceans and fresh waters
(N2O), and lightning (NOX). Emissions from
changes in land use include CO2, N2O, CH4,
A-49
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Policy Options for Stabilizing Global Climate
TABLE A-12
Estimates of Current Emissions from Burning of Agricultural Wastes
Lower Range
Model
Upper Range
N2O
(Tg N)
0.3
0.4
0.6
CH4
(Tg CH4)
14
15
28
NOX
(TgN)
1
4
7
CO
(TgC)
30
67
110
Sources:
N2O = Crutzen, 1983; Seller and Crutzen, 1980.
CH4 = Cicerone and Oremland, 1988; Seiler and Crutzen, 1980.
NOX = Logan, 1983; Seiler and Crutzen, 1980.
CO = Logan et al, 1981; WMO 1985; Seiler and Crutzen, 1980.
NOX, and CO from biomass burning during
deforestation, and enhanced N2O emissions
from disturbance of soils.
Estimating Natural Emissions of Trace Gases
The procedures for estimating emissions
of trace gases from natural sources involved
the selection of estimates of current emissions
from the literature and making sure that these
estimates were consistent with other
parameters and emission estimates within the
model. These estimates were held constant
over time except where modified by feedbacks
from changes in CO2 concentrations or from
changes in realized warming (see discussion in
Feedbacks below).
Table A-13 summarizes the different
natural sources of trace gases included in the
analysis. The table provides references for
the different estimates and also provides
ranges on the emissions estimates from the
literature. The emission estimates under the
heading labeled Model refer to the estimates
used in the six scenarios described in
Appendix B and in the rest of the report.
Estimating Emissions from Changing Land
Use
Emissions of trace gases from changing
land use include releases of CO2 from
burning and/or decomposition of organic
matter during land clearing and deforestation;
releases of CO and
N2O
due to soil
disturbance after land clearing; and releases of
N2O, CH4, NOX, and CO from the
combustion of organic matter (biomass
burning) due to prescribed forest
burning of savanna' '" ~aSS~~HvsSS
idefbrestatjSn; ^ 1ba^d»~~xle*afiSg and
deforestation are driven by a number x of
factors; the importance of each varies by
region and by country. Inj^opicajLA-frica and
South and Southeast Asia, jra]*M jpopulation
growtfiT™ajppears ^o^^^^ "siitical- factor
affecfinj^deK^tafibn. The niajority of the
population engages in agricultural practices,
arid most of the increases in agricultural
production have come from increases in the
area under cultivation through deforestation.
Seventy percent of Africa's deforestation
stems from swidden agriculture.
A-50
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Appendix A: Model Descriptions
TABLE A-13
Estimates of Current Emissions from Natural Sources
Trace
Gas
Source
Emission Estimates
Model Low High
Reference
CR,
Wetlands
(Tg CH4) Wild Ruminants &
Small Herbivores
Termites
Oceans
Freshwater
Wildfires
O Natural Lands
g N) Oceans/Freshwater
Wildfires
115 100 200 Cicerone and Oremland, 1988
4 26 Crutzen et al., 1986
40 10 100 Cicerone and Oremland, 1988
10 5 20 Cicerone and Oremland, 1988
5 1 25 Cicerone and Oremland, 1988
2 24 Cicerone and Oremland, 1988;
Seiler and Crutzen, 1980
6 Bolle et al., 1986
2 Bolle et al., 1986
0.05 0.04 0.07Crutzen, 1983;
Seiler and Crutzen, 1980
NOX
(TgN)
CO
(TgQ
Soils
Lightning
Wildfires
Oceans
Wildfires
12.5
3.5
0.5
20
10
4
2
0.1
10
5
16
20
0.9
35
20
Logan, 1983; WMO, 1985
Logan, 1983; WMO, 1985
Seiler and Crutzen, 1980
Logan et al., 1981; WMO, 1985
Logan et al., 1981; WMO, 1985
The procedures for estimating current
and future emissions of trace gases from these
sources involves implementation of a
terrestrial carbon model and parameterization
of emissions of N2O, CH4, NOX, and CO
based on the literature. Several scenarios of
tropical deforestation and plantation
establishment were developed (see
APPENDIX B and Houghton, 1988), and the
Marine Biological Laboratory/ Terrestrial
Carbon Model (MBL/TCM) was used to
forecast CO2 emissions from
deforestation/reforestation and associated land
disturbance. Current emissions of the other
four gases resulting from soil disturbance and
biomass burning were taken from the
literature, and then either kept flat over time
or tied to future emissions of CO2 from land
clearing.
Flux of CO2 Between the Atmosphere and
Land Resulting from Deforestation and
Reforestation
The model of the net flux of CO2
between the atmosphere and terrestrial
biosphere (MBL/TCM) acts as a bookkeeping
device that uses scenarios of changes in the
use of land as a key input to changes in the
balance of carbon in the soils and vegetation
(Houghton et al., 1983). The model requires
estimates of the rate of land clearing, the fate
of the cleared land, and the amount of
biomass stored both in the vegetation and in
the soils.
When forested land is cleared, the
carbon stored within the vegetation is
oxidized quickly through burning either to
A-51
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Policy Options for Stabilizing Global Climate
dispose of the biomass or use as a fuel, or the
vegetation is oxidized slowly through decay
and decomposition. The model captures
these different rates of oxidization by
allocating the cleared land to three major
categories:
• cleared for fuelwood;
• cleared for use as crops or pasture; or
• cleared for harvest and industrial use.
A response curve for each type of ecosystem
and each category of clearing activity maps
emissions of carbon over time, including after
the land is abandoned and the vegetation is
allowed to regrow. Furthermore, the land
cleared for harvest and industrial use is
further categorized according to whether the
products are used for paper and paper
products (40% of the category) or for lumber
or industrial wood. The rate of oxidation for
vegetation cleared for fuelwood is assumed to
be within 1 year of clearing; paper and paper
products decay within 10 years, and lumber
and industrial wood can take up to 100 years
to decay. Cleared vegetation from different
land types decays at various rates (see Table
A-14). Figure A-7 illustrates for tropical
forests the response curve for changes in the
carbon content in vegetation following
clearing for agriculture, as well as what
happens if the land is abandoned.
Response curves also show the changes
in the content of carbon in the soils after
changes in land use (see Figure A-7). Unlike
the carbon content in vegetation, the carbon
content in soils may increase shortly after the
land is cleared but then declines gradually as
the biomass decays.
The MBL/TCM considered only fluxes
from tropical regions (tropical Africa,
America, and Asia) and two types of forests
(open and closed). Emissions and
accumulation of carbon were projected from
1985 through 2100. Land-use changes
included deforestation to create permanent
croplands and afforestation/ reforestation,
which consisted of the formation of
plantations. The maximum error introduced
by not considering abandonment of cropland,
shifting agriculture, and no shifting to
pastures is less than 10% (Houghton,
unpublis-hed data). Clearing from temporal
and boreal regions were not included because
recent estimates suggest that the net flux in
these regions is currently low.
The MBL/TCM used a low and a high
estimate of the amount of carbon stored in
the vegetation and soils of different land-use
types. These estimates are based on the data
shown in Table A-15. In cases where two
estimates are shown in the table (i.e., carbon
TABLE A-14
Fate of Carbon in Undisturbed Ecosystems
After Land is Cleared for Agriculture
Years
Required for Soil
Fraction Left
Fraction Oxidized
Ecosystem Dead in Soils By 1st Year
Tropical Moist Forest
Tropical Seasonal Forest
Tropical Woodland/Scrubland
Tropical Grassland
.33
.33
.50
.50
.40
.40
.40
.50
By 10th Year
.67
.67
.50
.50
to Reach Minimum
Carbon Content
15
15
15
15
Source: Houghton et al., 1983.
A-52
-------
Appendix A: Model Descriptions
FIGURE A-7
TROPICAL FOREST RESPONSE CURVES
a
£
c **
o e
JQ o
« **
,*5 n
Time
Clear
Abandon
Time
Clear
Abandon
A-53
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Policy Options for Stabilizing Global Climate
TABLE A-15
Carbon in Vegetation and Soils of Different Land-Use
Categories in the World's Major Tropical Regions
Carbon in Vegetation*
(IP3 kg
Carbon in Soils
(IP3 kg hectare'1*)
Tropical
Region
America
Africa .
Asia
Type of
Forest
Moist
Seasonal
Dry
Moist
Seasonal
Dry
Moist
Seasonal
Dry
Undisturbed
Forest
82 (176)
85 (158)
27 (27)
124 (210)
62 (160)
15 (90)
135 (250)
90 (150)
40 (60)
Mature
Fallow
33 (70)
34 (63)
11 (11)
50 (84)
'25 (64)
6 (36)
90 (90)
50 (50)
35 (35)
Agriculture
5
5
5
5
5
5
5
5
5
Undisturbed
Forest
100
100
69
100
100
69
120
80
50
Mature
Fallow
90
90
62
90
90
62
108
72
45
Agriculture
70
70
48
70
70
48
84
56
35
* Values outside parentheses are derived from volumes of growing stock. Values inside
parentheses are based on direct measurements of carbon stocks.
Source: Houghton et al., 1985.
in vegetation of undisturbed forest and
mature fallow), the values outside parentheses
were used for the low biomass estimates, and
the values inside parentheses were used for
the high biomass estimates.
Table A-16 summarizes the assumptions
used for the rate of deforestation from 1975
through 1980, which were used in the base
year (1985) estimates of CO2 emissions. In
scenarios associated with high biomass, areas
of fallow forests were converted to
permanently cleared land at rates around 60%
higher than the rates used with the low
biomass assumptions. Also, when the high
biomass assumptions are used, higher rates of
clearing of tropical forests are used; these
higher rates include estimates of tropical
forests cleared by the landless, which may not
be included in FAO statistics (Houghton,
1988; Houghton et al., 1983).
Emissions of N2O, CH^ NOX and CO
Current estimates of emissions of N2O
resulting from the gain of cultivated land are
based on estimates in the literature (Bolle et
al., 1986; see Table A-17) and are grown
according to patterns of the net flux of CO2
from tropical deforestation as projected by the
MBL/TCM. This ties the emissions of N2O
closely to the model estimates of the amounts
of tropical land cleared in each year.
Estimates of emissions of N2O, CH4,
NOX, and CO resulting from biomass burning
were disaggregated into several different
categories (Seiler and Crutzen, 1980; see
Table A-17):
• Industrial combustion of biomass and
fuelwood;
A-54
-------
Appendix A: Model Descriptions
Table A-16
Annual Rates of Deforestation (1975-80)
(106 hectare/year)
Region
Tropical America
Tropical Africa
Tropical Asia
Total
Closed Forest
4.4
1.3
1.8
7.5
Open Forests
1.3
2.3
0.2
3.8
Fallow Forests
2.8
1.5
5.7
10.0
The low biomass assumptions were based on rates of deforestation in the closed and open forests;
the high biomass assumptions were based on rates of deforestation in closed, open, and fallow
forests.
Sources: Houghton et al., 1985; Houghton et al., 1987.
A-55
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Policy Options for Stabilizing Global Climate
TABLE A-17
Estimates of Current Emissions from Land-Use Change
Emission Estimates
Trace Gas
CH4
(Tg CH4)
N2O
(TgN)
NOX
(TgN)
CO
(TgC)
Source
Shifting agriculture, population
increase and colonization
Prescribed fires, savanna and
bush burning
Gain in cultivated land
Shifting agriculture, population
increase and colonization
Prescribed fires, savanna and
bush burning
Shifting agriculture, population
increase and colonization
Prescribed fires, savanna and
bush burning
Shifting agriculture, population
increase and colonization
Prescribed fires, savanna and
bush burning
Model
19.8
9.5
0.4
0.5
0.3
5
2
160
50
Low
18
9
0.2
0.4
0.2
1
1
85
25
High
35
18
0.6
0.7
0.4
9
4
300
90
References
Cicerone and Oremland,
1988; Seiler and Crutzen, 1980
Cicerone and Oremland,
1988; Seiler and Crutzen 1980
Bolle et al., 1986
Crutzen, 1983;
Seiler and Crutzen, 1980
Crutzen, 1983;
Seiler and Crutzen, 1980
Logan, 1983;
Seiler and Crutzen, 1980
Logan, 1983;
Seiler and Crutzen, 1980
Logan, 1981; WMO, 1985;
Seiler and Crutzen, 1980
Logan, 1981; WMO, 1985;
Seiler and Crutzen, 1980
• Burning of agricultural wastes;
• Wildfires;
• Shifting agriculture, population, and
colonization; and
• Prescribed forest fires, and
burning of savanna and bushland
Procedures for estimating future emissions
due to industrial biomass and fuelwood use,
burning of agricultural wastes, and wildfires
were described in the sections of this
appendix that discuss emissions from energy-
related sources, and natural and agricultural
sources. Table A-17 summarizes the
estimates along with references for these
emissions. x
Estimates of future emissions of these
gases from shifting agriculture, population
pressures, and colonization are tied to
patterns of net fluxes of CO2 resulting from
tropical deforestation which, for each
deforestation scenario, closely reflect the rate
of land clearing in the tropics and also reflect
differences in biomass stored in the
vegetation. Emissions from prescribed forest
fires, and burning of savanna and bushland
are kept flat over time.
A-56
-------
Appendix A: Model Descriptions
ATMOSPHERIC COMPOSITION MODULE
The At mospheric Composition Module
estimates changes in atmospheric concentra-
tions of trace gases using a highly para-
meterized model developed for this study.
The Assessment Model for Atmospheric
Composition (AMAC) was developed by
Michael Prather of NASA/GISS, in coopera-
tion with members of the atmospheric
sciences community (see Table A-18). This
model was then closely integrated with an
ocean uptake model to determine CO2 and
heat uptake by the ocean, and with the entire
ASF to achieve complete specification of
emissions of the different trace gases.
The atmospheric composition model is
a highly parameterized model for estimating
atmospheric composition. The model uses
first- and second-order relationships between
parameters to estimate the effects of
emissions on the global atmosphere and on
global warming. Annual and globally
integrated quantities are used to define first-
order effects on climate, stratospheric ozone,
and tropospheric oxidants in so far as they
control atmospheric composition (Prather,
1989). The primary advantages of the model
are that it represents the interactions between
chemistry, composition, and climate and that
it can be run for a large number of scenarios.
Its primary limitations pertain to the high
parameterization and simplification of the
physical processes occurring in the
atmosphere.
To estimate atmospheric concentrations
or perturbations to concentrations of different
long- and short-lived species the model
combines estimates of emissions of chemically
and radiatively active trace gases with an
ocean CO2 and heat uptake model, a model
of increased forcing due to increases in
atmospheric concentrations of radiatively
active gases, and a model of emission
feedbacks. The model uses global emissions
estimates of 14 long-lived gases and estimates
of emissions of CO, NOX, and non-methane
hydrocarbons (NMHCs) by hemisphere. The
ocean CO2 and heat uptake model is a box
diffusion formulation introduced by Oeschger
et al. (1975) and utilized by Hansen et al.
(1988). The model of increased forcing is
based on calculations from a one-dimensional
radiative convective model (Hansen et al.,
1981; Hansen et al., 1988; Ramanathan et al.,
1985). The model of feedbacks is based on
work by Lashof (1989) and captures increased
fluxes of CH4 from rice paddies, bogs, and
from CH4 hydrates due to increases in
temperature; uptake of CO2 due to increased
fertilization from increased CO2
concentrations; and increases in CO2
emissions resulting from increased respiration
under higher temperatures.
Table A-19 lists the long-lived gases
included in the model formulation. The long-
lived gases include major radiatively active
gases, such as N2O, CH4, CO2, CFC-11, CFC-
12, HCFC-22, carbon tetrachloride (Ccl4),
methyl chloroform (CH3CC13), and halon
1301 (CF3Br), as well as gases that affect the
composition of the stratosphere, such as
N2O, CFC-11, CFC-12, HCFC-22, CFC-113,
Ccl4, CH3CC13, methyl chloride (CH3C1),
halon 1301 (CF3Br), halon 1211 (CF2ClBr),
and methyl bromide (CH3Br),
Table A-20 lists the short-lived and
implicitly solved species included within the
model. They include water vapor, NOX, CO,
OH, tropospheric O3, column O3,
stratospheric O3, and variables for total
inorganic chlorine, inorganic bromine, and
odd nitrogen. NOX and NMHCs are treated
differently in the model than the other
implicitly solved species in that all estimates
of the impact of changes related to
perturbation of these gases in the atmosphere
are based directly on changes in emissions.
Uncertainty surrounding the estimates
of future atmospheric composition and
changes in climate are captured by varying the
key parameters in the model. These
parameters define the climate feedback, the
coupling between chemically active gases and
the short-lived and implicitly solved species,
and the lifetimes of long-lived gases. While
the variations used do not represent a
detailed and complete uncertainty analysis,
they demonstrate the range of uncertainty
surrounding the results and the relative
importance of the different assumptions.
Estimating the Atmospheric Content of Long-
Lived Trace Gases
The model determines the global
atmospheric content of the long-lived gases,
A-57
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Policy Options for Stabilizing Global Climate
TABLE A-18
Participants, Contributors, and Reviewers Workshop:
A Model for Atmospheric Composition
Name
Affiliation
-Dan Albritton
Robert Dickinson
Inez Fung
Richard Gammon
James Holton
Ivar Isaksen
Malcolm Ko
Andrew Lacis
Dan Lashof
Shaw Liu
Jennifer Logan
Jerry Mahlman
Pauline Midgley
.Michael Prather
Ron Prinn
Nien Dal Sze
Anne Thompson
Dennis Tirpak
Don Wuebbles
NOAA, Colorado
NCAR, Colorado
NASA, New York
NOAA, Washington
U. Wash,, Washington
U. of Oslo, Norway
AER, Massachusetts
NASA, New York
U.S. EPA, Washington, D.C.
NOAA, Colorado
Harvard, Massachusetts
NOAA, New Jersey
ICI, Delaware
NASA, New York
MIT, Massachusetts
AER, Massachusetts
NASA, Maryland
U.S. EPA, Washington, D.C.
LLNL, California
Source: Prather, 1989.
A-58
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Appendix A: Model Descriptions
TABLE A-19
Long-Lived Gases
Gas
Nitrous Oxide
Methane
Carbon Dioxide
CFC-11
HCFC-12
CFC-22
.CFC-113
Carbon Tetrachloride
Methyl Chloroform
Methyl Chloride
Halon 1301
Halon 1211
Methyl Bromide
Carbon Tetrafluoride
Chemical
Symbol
N2O
CH4
CO2
CFC13
CF2C12
CHF2C1
qF3ci3
Ccl4
CH3CC13
CH3C1
CF3Br
CF2ClBr
CH3Br
CF4
Reference
Concentration
300 ppb
1600 ppb
345 ppm
220 ppt
375 ppt
80 ppt
30 ppt
100 ppt
110 ppt
600 ppt
2 ppt
2 ppt
10 ppt
60 ppt
Source: Prather, 1989.
A-59
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Policy Options for Stabilizing Global Climate
TABLE A-20
Short-Lived and Implicitly Solved Species
Reference
Species State Description
trop-OH (%)1 mean perturbation to global OH levels
NH-OH (%Y mean perturbation to OH levels in the northern hemisphere
SH-OH (%y mean perturbation to OH levels in the southern hemisphere
NH-O3 (%) mean perturbation to tropospheric ozone levels in the northern
hemisphere
SH-O3 (%)1 mean perturbation to tropospheric ozone levels in the southern
hemisphere
NH-CO (100 ppb) mean concentrations of CO in the northern hemisphere
SH-CO (60 ppb) mean concentrations of CO in the southern hemisphere
trop-H2O (%)! perturbation to mean tropospheric water vapor
col-O3 (%)1 perturbation to total ozone column
upp-O3 (%Y perturbation to ozone column above 30 km
str-NO (18 ppb) (HNO3+NO+NO2+NO3+2xN2O5+HNO4+ClNO3 at ~ 35 km)
str-CL. (2.78 ppb) (HCl+Cl+ClO+2xCl2+HOCl+ClNO3 at ~ 40 km)
str-Brx (12.9 ppt) (BrO+Br+HBr+HOBr+BrNO3 at ~ 25 km)
str-H2O (3 ppm) mean concentrations at tropopause
1 The reference states of these species are modeled as % changes from their values in the reference
year (1985).
Source: Prather, 1989.
A-60
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Appendix A: Model Descriptions
excluding CO2, on an annual basis by adding
annual sources and removing (subtracting)
annual losses. Annual sources are defined by
the four emissions models and are checked
for consistency with assumptions on global
lifetimes and observed increases in
atmospheric concentrations. Annual losses
reflect atmospheric destruction and are based
on global lifetimes of the gases. The
atmospheric content of CO2 is treated
separately with an ocean uptake model, which
models the dominant sink of carbon.
If global lifetimes and observed
increases in atmospheric concentrations for a
long-lived trace gas are specified for the
reference year, 1985, then the annual source
in that year equals the annual loss (e.g.,
atmospheric burden divided by global lifetime)
plus the annual increment. If the emission
estimates from the emissions modules do not
equal this value then the difference is noted
and the emission estimates are scaled to the
estimated annual source described above.
The atmospheric composition model
obtains the annual sources of the long-lived
gases from the four emissions modules. For
all gases, excluding CO2, the emission
estimates include all anthropogenic and
natural sources of IneZgases. For CO2, the
estimate' includes only the anthropogenic
sources of CO2. The model interpolates the
-values provided by the emissions modules,
which are provided for 12 time periods (in 5-
year increments from 1985 to 2025, and in 25-
year increments thereafter), to obtain annual
values needed for the annual integration
performed in the atmospheric composition
model.
The global lifetime, defined as the
global content of the species in the
atmosphere divided by the global losses,
represents an approximation of a more
complex destruction process that depends on
variables that differ considerably over the
globe. The model addresses this uncertainty
in the estimates of the global lifetimes by
allowing specification of ranges for the
lifetimes. Global lifetimes for the
perhalogenated hydrocarbons (CFC13, CF2C12,
C^gClg, Ccl4, CF3Br, and CF2ClBr) assume
stratospheric loss only. Global lifetimes for
CH4, CH3CC13, CHFC12, CH3C1, and CH3Br
assume that dominant loss is in the
troposphere by reaction with OH, and the
lifetimes have been set to be consistent with
models for global OH. Stratospheric losses
are also considered for CHF2C1 and for CH4.
Table A-21 summarizes the assumptions used
for the different long-lived gases.
The annual integration for global
content of the long-lived gases involves adding
the annual source and removing the annual
losses associated with both stratospheric and
tropospheric destruction. Losses from
stratospheric destruction account for the lag
in transport as follows:
Xt = Xt4 + S, - X/TL,
' Xt-lag/SLt>
where Xt is the global concentration in year t,
St is the annual source, TLt is the lifetime
associated with tropospheric sinks, SLt is the
lifetime associated with stratospheric sinks,
and t-lag reflects the amount of time needed
for the increased concentrations to transport
to the stratosphere and contribute to the
stratospheric destruction. The model updates
the global lifetimes annually to reflect
perturbations in column ozone, stratospheric
transport, realized temperatures, and OH
concentrations.
Measuring Changes in Climate
Changes in climate are measured
through estimates of changes in radiative
forcing at the top of the troposphere due to
changes in greenhouse gases, modeling of heat
uptake of the oceans, and estimates of
changes in tropospheric temperature required
to restore radiative equilibrium at the topxof
the troposphere. In the model we have
assumed a climate sensitivity of 2.0-4.0°C
although we have tested a range of sensitivity
from 1.5 to 5.5°C.
The change in radiative forcing at the
top of the troposphere is instantaneous and is
estimated from changes in atmospheric
concentrations of greenhouse gases from pre-
industrial levels. The change in radiative
forcing is based on calculations from a one-
dimensional radiative convective model,
assuming that no climate feedbacks occur
(Hansen et al., 1981; Ramanathan et al., 1985;
Hansen et al. 1988). These yield an increase
A-61
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Policy Options for Stabilizing Global Climate
TABLE A-21
Global Lifetime Assumptions for Long-Lived Gases
Gas
Global
Lifetime
Assumptions
N2O
160 yr
CK,
9.6 yr
Perhalogenated Hydrocarbons
CFC13
CF2C12
CC14
CF2ClBr
CF3Br
CF4
Hydrohalocarbons
CH3CC13
CHF2C1
CH3C1
CH3Br
65 yr
140 yr
90 yr
50 yr
15 yr
110 yr
large
6.3 yr
15.5 yr
1.5 yr
1.6 yr
destroyed predominately in the stratosphere.
Reductions in upp-O3 lead to increased penetration of
solar UV and to shorter lifetimes, and vice versa.
Increases in stratospheric mixing rates lead to higher
concentrations in the photodissociation region and
shorter lifetimes
approximately 95% destroyed in troposphere with
reaction with OH. The lifetime responds immediately
to changes in OH and to changes in realized warming
like N2O
like N2O
like N2O
like N2O
like'N2O
like N2O
loss over the next century is insignificant
loss dominated by reaction with tropospheric OH and lifetime
responds to changes in OH
like CH3CC13, lifetime for reference atmosphere based on
scaling of lifetime for CH3CC13
same as above
same as above
A-62
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Appendix A: Model Descriptions
of 4.3 waus/meter2 at the top of the
troposphere for a doubling of CO2 or 1.26°C
in global surface air temperature. Table A-22
summarizes the equations used to estimate
the changes in forcing for the different gases.
Equilibrium surface temperature (for a
specified year) represents the amount of
warming expected to occur if the atmospheric
concentrations were to stabilize (at the levels
in that year) and the global climate was
allowed to reach equilibrium. For a doubling
of CO2, which would provide an increase of
1.26°C in the global surface air temperature
with no feedbacks, the increase with
feedbacks is expected to range from 1.5-5.5°C.
For each scenario, the atmospheric
composition model is solved for two different
estimates of the feedbacks, one at 2.0°C and
one at 4.0°C, by using different values of the
feedback parameter, A. The relationship
between radiative forcing and warming is:
F = Q - AAT
where F is the flux of heat into the ocean, Q
is the radiative forcing, A is the feedback
parameter, and AT is the global mean surface
air temperature which is assumed to be the
temperature of the mixed layer of the ocean.
Changes to the mean tropospheric
temperature represent the mean surface air
temperature and are used in the model as a
surrogate for climate change. This variable
depends on the equilibrium temperature and
heat uptake by the ocean and is calculated by
the box-diffusion model. The variable directly
affects tropospheric chemistry through the
temperature dependence of kinetic rates and
abundance of water vapor.
Tropospheric water vapor abundance is
assumed to respond instantaneously to
changes in tropospheric temperatures and to
maintain a constant distribution of relative
humidity. Perturbations to water vapor are
calculated relative to the reference state
(1985) and are set at a 6.2% increase per
degree centigrade near 25°C. Tropospheric
water vapor affects tropospheric chemistry
directly in the model. The feedback of
changes in tropospheric water vapor on
equilibrium temperatures is captured in the
feedback coefficient, A.
The Stratosphere
The model represents stratospheric
ozone with two variables: (1) col-O3 -- the
total stratospheric plus tropospheric column,
and (2) upp-O^ - ozone in the upper
atmosphere, the column above 30 km.
Different processes control the state of each
of these variables and they also have different
impacts on the lifetimes of the long-lived
gases. Ozone is the primary source of
chemically reactive species in the atmosphere.
It competes for solar ultraviolet radiation that
destroys many long-lived gases and has a
direct radiative effect on stratospheric
temperatures. The approach in the model is
aimed solely at determining changes in the
stratospheric loss rates of long-lived gases and
in the tropospheric chemistry that is
controlled by the stratospheric ozone column
(i.e., OH).
The model uses two variables, str-C^
and str-Brx, to represent total inorganic
chlorine and bromine mixing ratios. They
equal the sum of all chlorine and bromine
atoms, respectively, contained in the source
gases list in Table A-19. For chlorine, the
mixing ratio, measured in ppb, represents the
average mixing ratios at 40 km over the
different latitudes and seasons. The mixing
ratio for bromine is measured in ppt and
represents the average mixing ratios at 25 km
over the different latitudes and seasons.
The model uses the variable str-NO to
represent the levels of odd-nitrogen in the
stratosphere. It is initialized to the maximum
average mixing ratio of about 18 ppb
presently occurring in the tropics between 30
and 40 km. The model estimates changes in
odd nitrogen based on changes in the
abundance of N2O, applying a time lag of 2.5
years to the mean tropospheric concentration
of N2O. Perturbations due to other sources,
such as tropospheric lightning, thermospheric
and mesospheric NO, and ionization by
cosmic rays, are not included.
The model currently does not estimate
changes in the abundance of stratospheric
water or of the rate of circulation in the
stratosphere. The model does allow
exogenous specification of changes in the
mixing ratio of stratospheric water (str-H2O)
A-63
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Policy Options for Stabilizing Global Climate
TABLE A-22
Models of Changes in Forcing
Gas
Reference
Units
Cone.
Equation for Net Radiative Forcing (w/m )
CO.
CR,
N20
CH4/N20
ppm
ppm
ppm
285
1.02
.2853
CFC13
CF2C12
CHF2C1
£2^3^13
CC14
CH3CC13
CH3C1
CF3Br
CF2ClBr
CH3Br
CF4
trop-O3
ppb
ppb
ppb
ppb
ppb
ppb
ppb
ppb
ppb
ppb
ppb
% change
0
0
0
0
0
0
0
•0
0
0
0
Fc-F2g5 where c is the concentration of CO2 &
Fc =' ln[l + .942*c/(l+.00062*c) + .0088*c2+
3.26*10-6c3 + .156*c13*e-c/76°] * (4.3/1.26)
see below
see below
(g(x,y)-g(x0,y0)) * (4.3/1.26) where x and y are the
concentrations of CH4 and N2O and x0 and y0 are
the reference concentrations of these gases and
g(x,y) = [0.394x0-66+0.16xe-1-6x]/[l+0.169x°'62]
+ 1.5561n[l +y°-77(109.8+3.5y)/(100+0.14y2)] '
\1.52n
-0.0141n[l+0.636(xy)u-/:>+0.007x(xy)
0.23 *c (where c is concentration of gas)
0.29*c
0.10*c
0.39*c
0.17*c
0.03*c
0.34*c
.01 (% change in tropospheric O3)
A-64
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Appendix A: Model Descriptions
and changes in the rate of circulation in the
stratosphere (circ).
Tropospheric Chemistry
The primary focus of the tropospheric
chemistry component of the model is to
estimate changes in the global mean levels of
OH and tropospheric O3. These changes
then determine the oxidizing capacity of the
troposphere and the global lifetimes of CH4,
CO, CH3CC13, and HCFC-22. Prediction of
trends in global tropospheric models is, at
present, a difficult research problem
complicated by a lack of knowledge about the
global distribution of NOX; the estimates of
changes in O3, therefore, must be assigned
large uncertainties.
The model simulates the two
hemispheres separately due to significant
asymmetries observed in many of the shorter-
lived gases such as CO, NOX, and NMHCs,
which play a major role in the budgets of OH
and O3. Averaging even over a hemisphere
may not adequately represent the interactions
of highly variable species such as OH with the
other trace gases.
The model represents the perturbation,
from the annual mean value for tropospheric
concentrations of OH, from the reference
state (e.g., 1985) with three variables: SH-OH,
NH-OH, and trop-OH. The variables SH-OH
and NH-OH represent the perturbations to
the concentrations in the southern and
northern hemispheres, respectively, and trop-
OH represents (an equally weighted)
combination of these two values:
trop-OH = 0.5*NH-OH + 0.5*SH-OH
Perturbations to tropospheric ozone
will directly affect radiative forcing and
indirectly affect the long-lived source gases
controlled by OH. The model assumes that
the sources of tropospheric ozone
(tropospheric chemical reactions and
stratospheric ozone) respond to atmospheric
.composition and that the loss frequencies
(photochemical and surface reactions) remain
constant. Tropospheric ozone is
disaggregated into two hemispheres due to its
short life (a few months).
The model uses variables that represent
annual mean concentrations of CO in each
hemisphere (NH-CO and SH-CO).
Interhemispheric transport from the northern
to the southern hemisphere is modeled using
a single coefficient and an exchange residence
time of one year. Sources of CO include the
oxidization of CH4, which is proportional to
OH levels, oxidization of NMHC, which is
proportional to the annual flux of NMHC,
and direct emissions of CO. Loss of CO is
proportional to OH levels in each
hemisphere.
Due to the extremely short lifetime of
NOX, from hours to weeks, and to the wide
variation of concentrations of NOX, over three
orders of magnitude, the model uses only the
emissions estimates of NOX and does not
estimate concentrations. The model estimates
the impact of perturbations in NOX on the
formation of O3 directly from changes in the
emissions of NOX. The sources of NOX are
disaggregated by hemisphere and the effective
flux is represented in the model as NH-NOX
and SH-NOX.
The model estimates the impact of
NMHCs on tropospheric chemistry in a
manner similar to that used with NOX. All
impacts are based on changes in the annual
•flux of NMHCs (NH-NMHC and SH-
NMHC). The difficulty of estimating the
impacts of NMHCs is due to a number of
factors, including the fact that the category
NMHCs includes a number of gases (e.g.,
Cy-^, Cy-Ig, isoprene, etc.) that have different
concentrations and different reactivities with
OH.
Feedbacks
The ASF allows the incorporation of a
number of possible feedbacks from climate
warming. These can include increased
stability of the thermocline and therefore
reduced uptake of CO2 from the ocean (see
OCEAN CIRCULATION AND UPTAKE
MODULE below) and major changes in ocean
circulation resulting in massive changes in
CO2 uptake and possible net fluxes from the
ocean. The model also allows changes in
emissions of greenhouse gases from natural
sources as realized temperatures increase.
A-65
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Policy Options for Stabilizing Global Climate
C02 Uptake by the Oceans
Changes in ocean chemistry, mixing,
biology, and general circulation have the
potential to change the amount of
anthropogenic emissions taken up by the
oceans. The ASF captures these. feedbacks
through a number of automatic and optional
•features that are closely tied into the
atmospheric composition model.
The model addresses feedbacks on
ocean chemistry by including in the ocean
model equations for CO2 solubility and
carbonate chemistry (Lashof, 1989). The
model adjusts the partial pressure of CO2
between the sea surface and the atmosphere
based on the temperature of the mixed layer.
The model optionally allows the
modeling of reduced CO2 uptake due to
increased stability of the thermocline. When
selected, the eddy diffusion coefficients are
assumed to be inversely proportional to the
square root of the temperature gradient at the
top of the thermocline. (See OCEAN
CIRCULATION AND UPTAKE MODULE
discussion of the thermocline and the eddy
diffusion coefficient.)
The model optionally allows the
investigation of the results of major changes
in ocean biology and circulation. This
feedback is one of the reasons suggested for
the rapid changes in CO2 concentrations
during the glacial-intergjacial transitions. The
feedback is modeled by setting the eddy
diffusion coefficient to zero (or near zero)
when the realized temperature reaches a
certain level.
Methane Emissions
The literature suggests there are a
number of feedbacks from increased tempera-
tures that affect emissions of CH4 from
different sources by increasing rates of
microbial activity and emissions of CH4
hydrates from continental slope sediments.
These feedbacks are modeled by increasing
emissions as a linear function of increases.in
realized temperature, allowing for time lags in
the response. The increased emissions are
included within the atmospheric composition
model and affect future atmospheric
chemistry, composition, and forcing. The
following categories of emissions include
feedback formulations:
• methane emissions from rice paddies;
• methane emissions from wetlands; and
• releases of methane hydrates due to
warming of the oceans.
CO2 Emissions
Changes in the emissions of CO2 have
been proposed due to increased fertilization
resulting from higher atmospheric CO2
concentrations and from the disruption of
existing ecosystems, resulting in reductions in
biomass and soil carbon. The procedure for
modeling increased CO2 fertilization is to tie
the increase in carbon stored in the terrestrial
biosphere linearly to increases in atmospheric
concentrations of CO2 (Lashof, 1989).
Annual net fluxes equal the change in carbon
stored. The procedure for modeling increased
CO2 fluxes due to disruption of existing
ecosystems is similar to the approach used for
CH4 emission feedbacks. Increased emissions
of CO2 are specified as a linear function of
realized temperatures where a time lag is
allowed.
Vegetation Albedo
The approach to modeling the feedback
of climate warming to changes in vegetation
albedo is to implement the gain through the
climate feedback factor (A, units
watts/meter2/degrees Kelvin), -see OCEAN
CIRCULATION AND UPTAKE MODULE
below). In order to implement a 1% increase
in the planetary albedo with a climate
sensitivity of 3°C for a doubling of CO2, the
factor A would change from 1.43 (4.3
watts/meter2/3°C) to 1.20.
OCEAN CIRCULATION AND UPTAKE
MODULE
The net flux of CO2 between the ocean
and the atmosphere and the role of the
oceans in slowing the rate of warming are
handled within a separate component of the
ASF that is closely integrated with the
Atmospheric Composition Model. The
approach utilizes a box-diffusion model that
was developed at GISS (Hansen et al., 1984)
A-66
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Appendix A: Model Descriptions
and modified for use in the ASF. Alternate
models of CO2 uptake by the oceans were
implemented for use in the ASF, including an
alternative box-diffusion formulation, an
advective diffusive model, a 12-compartment
regional model, and an outcrop-diffusion
model (Moore and Ringo, 1988).
Integrated Box-Diffusion Model
The integrated model of ocean heat and
CO2 uptake utilizes a box-diffusion approach
introduced by Oeschger et al. (1975). In the
model the ocean is divided into a mixed layer
and a thermocline, which is further divided
into nine compartments (the deep ocean is
not included in the formulation). The
approach to modeling the diffusion of heat
and CO2 is similar, and the model addresses
the coupling of climate and CO2 uptake.
For heat, diffusion into the mixed layer
is a function of the net forcing to the sea
surface (F, units w/m2), the heat capacity of
the mixed layer (He, units joules/[m3*K]),
amount of time the forcing is applied (t, units
seconds), and the depth of the mixed layer (4
units meters) as follows:
heat flux (K) = (t/Hc)*F/d
The net forcing (F) is a function of the
increase in forcing over pre-industrial levels
(Q, units w/m2), the increase in temperature
of the mixed layer from pre-industrial levels
(T, units K), and the feedback factor (A, units
w/[m2*K]), which converts forcing to long-run
temperature equilibrium, as follows:
F = Q - A*T.
The diffusion of heat to the
thermocline for the specified time period is
accomplished by dividing the mixed layer and
thermocline into ten vertical zones (the mixed
layer is the top zone) and estimating the
diffusion of heat between adjacent zones.
The rate of change in temperature (dT/ds) for
one of the zones (/) is a function of the
diffusion from the zone above and diffusion
to the zone below as follows:
dT/ds = ed/L, * [Ofa-
(vrv,+1)/DJ,
where ed is the eddy diffusion coefficient
(m2/s), v, is the temperature of the zone, L, is
the vertical incremental depth of the zone,
and £>[ is the difference between the middle of
zone / and the next deeper zone. The model
approximates the diffusion over time by
dividing the time steps into discrete intervals
and solving a set of simultaneous linear
equations that approximate the integration of
the change in temperature over time. The
value for the eddy diffusion coefficient for the
reference cases was 0.55* 10"4 m2/s and values
ranging from 2*10'5 to 2*10'4 m2/s were
examined.
The approaches used to model the
diffusion of CO2 to the mixed layer and the
thermocline are similar to the approaches
used to model heat. For diffusion from the
atmosphere to the mixed layer, the flux is
dependent on the difference in the partial
pressure of CO2. The units for CO2 content
in the ocean mixed layer are moles/m3. The
flux of CO2 between the ocean and the.
atmosphere (Fc, units moles/m3) is a function
of the atmospheric concentrations (ate, units
ppm), the solubility of CO2 in sea water (a,
units moles/[m3*atm]), the current
concentration of CO2 in the mixed layer (cml,
units moles/m3), the piston velocity (pv, units
m/s), the elapsed time (t, units seconds), and
the depth of the mixed layer (d, unit meters):
Fc = pv * t * (a*atc*l(T6 - cml) / d.
In the formulation, the solubility of CO2 in
the mixed layer (a) is calculated as a function
of temperature using the carbonate chemistry
equations of Takahashi et al. (1980). The
only differences in the approach (from the
one used for heat) for modeling diffusion to
the thermocline are that the operative
variable, vt, represents the CO2 concentration
in moles/m3 and the eddy diffusion
coefficients are different (ed = l.'7*W4 m2/s).
The start of the time horizon for the
model is 1830, which allows 155 years for the
model to initialize the starting atmospheric
concentration and heat and carbon content of
the mixed layers and thermocline. Estimates
of historic fossil-fuel emissions are based on
data from Rotty and Masters (1985) and
estimates of historic emissions from land-use
changes are based on results from the
A-67
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Policy Options for Stabilizing Global Climate
MBL/TCM (see LAND USE CHANGES AND
NATURAL EMISSIONS MODULE) where
different historic estimates are used depending
on assumptions on the amount of carbon
stored in the terrestrial biosphere. The model
is calibrated to historic CO2 concentrations
through implementation of an "unknown
sink". For each year, prior to 1985, the
estimated atmospheric CO2 concentration is
compared to historical measurements. The
unknown, sink is set to the difference,
estimated atmospheric concentration minus
observed concentration, and the atmospheric
concentration is reduced by that amount. The
unknown sink is generally assumed to remain
constant, at 1985 levels, in the future.
Alternatively, it can be specified to increase in
proportion to atmospheric CO2
concentrations or decline over time.
The model also allows modeling of a
feedback on the eddy diffusion coefficient due
to increased stability of the thermocline as the
temperature increases. When specified, the
model makes the eddy diffusion coefficient
inversely proportional to the square root of
the temperature gradient at the top of the
thermocline.
Alternative CO2/Ocean Uptake Models
Four alternative models of ocean
uptake of CO2 were implemented in order to
test the sensitivity of the CO2 concentrations
to different approaches and formulations. As
with the implemented version of the box-
diffusion model, an unknown sink provides
the mechanism for calibrating the results of
the model to historic CO2 concentrations.
These models are not closely integrated with
the atmospheric concentration model and do
not provide feedbacks on realized
temperatures and atmospheric chemistry.
Impacts of the alternative results are
measured only against warming commitment
and are based only on changes in atmospheric
concentrations of CO2.
The first of the models is an alternative
box-diffusion model. The differences between
this model and the one described above is
that the mixed layer is only 75 meters (instead
of 110) and the model includes a deep ocean
component. The model captures all of the
ocean physics with an eddy diffusion
coefficient and does not explicitly capture
deep water formation. In this version, the
eddy diffusion coefficient can vary with depth
based on calibration with steady-state carbon-
14 data.
The advective-diffusive model is
structured to be more realistic than the
simple diffusive assumptions in the box-
diffusion models. The surface ocean is
divided into cold and warm components, and
water downwells directly from the cold surface
compartment into intermediate and deep
layers. The ocean physics are captured
primarily by an advective approach.
The 12-compartment regional model
divides both the Atlantic and Pacific-India
oceans into surface, intermediate, deep, and
bottom water compartments. The Arctic and
Antarctic are divided into surface and deep
water compartments. The model is calibrated
against multiple tracer distributions. The
model has both advective and eddy
diffusivities and a number of differences from
the other models as a result of the different
geometrical configuration.
The outcrop-diffusion model allows
direct ventilation of the intermediate and
deep oceans at high latitudes by incorporatings
outcrops for the sublayers into the box-
diffusion formulation. This model is the most
efficient in taking up CO2.
A-68
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Appendix A: Model Descriptions
REFERENCES
Bingemer, H.G., and P.J. Crutzen. 1987. The
production of methane from solid wastes.
Journal of Geophysical Research 92:2182-2187.
Bjorkstorm, A. 1979. A model of CO2
interaction between atmosphere, oceans, and
land biota. In Bolin, B., E. Degens, S.
Kempe, and P. Ketner, eds. The Global
Carbon Cycle. Scope 13. John Wiley and
Sons, New York.
Bolin, B., A. Bjorkstrom, K. Holemen, and B.
Moore. 1983. The simultaneous use of
tracers for ocean circulation studies. Tellus
358:206-236.
Bolle, H.J., W. Seiler, and B. Bolin. 1986.
Other greenhouse gases and aerosols. In
Bolin, B., B.R. Doos, J. Jager, and R.A.
Warrick, eds. The Greenhouse Effect, Climate
Change, and Ecosystems. Scope 29. John
Wiley and Sons, Chichester. 157-203.
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APPENDIX B
IMPLEMENTATION OF THE SCENARIOS
This appendix describes in greater detail
the scenarios presented in Chapter VI. As
seen in Chapter VI, we have constructed six
scenarios of future patterns of economic and
technological development starting with
alternative assumptions about the rate of
economic growth and the adoption of policies
that influence climatic change (see Table B-
1). These six scenarios cannot capture all the
possibilities, of course. Rather, they allow us
to explore likely climatic outcomes and the
impact of strategies for stabilizing the
atmosphere. The sensitivity of the results to a
wide range of specific assumptions has been
tested and is discussed in Appendix C. In this
appendix a brief narrative description of each
scenario is provided first. These descriptions
are followed by a detailed discussion of the key
assumptions underlying the scenario results.
DESCRIPTIVE OVERVIEW OF THE
SCENARIOS
Two scenarios explore alternative
pictures of how the world may evolve in the
future assuming that policy choices allow
unimpeded growth in emissions of greenhouse
gases (these are referred to as the "No
Response" scenarios). One of these scenarios,
called the Rapidly Changing World (RCW),
assumes rapid economic growth and technical
change; the other, called the Slowly Changing
World (SCW), assumes more gradual change.
In other words we have invented two futures:
one with relatively high and robust economic
growth and the other with a more pessimistic
view of the evolution of the world's
economies. The first world would likely
illustrate the upper half of the potential range
of future greenhouse gas emissions because, in
general, higher economic activity means higher
total energy use and emissions. Conversely,
the second world could serve as a useful guide
to the lower half of the range. In either case,
our scenarios are first constructed as if there
were no interventions motivated by global
climate problems.
In constructing these two worlds/
scenarios, we have borne two important ideas
in mind. First, there is evidence that with
more rapid economic growth, energy efficiency
improves more rapidly than with slower
growth (Schurr, 1982). This occurs because
innovation proceeds more rapidly and because
older, less efficient systems are more rapidly
replaced with new technology. History shows,
for example, that for almost every country,
energy efficiency in industry increases with
increasing incomes, as sophistication and scale
win over brute force. At the same time,
higher incomes allow people to spend more
money on two key energy-intensive uses, space
conditioning (heating and air conditioning)
and automobiles. Thus, not all of the
technological benefits of rapid economic
growth put the brakes on overall energy use.
But more rapid economic growth allows
society to put resources aside to improve the
efficiency of both space comfort and personal
transportation. Similar patterns can . be
expected in other economic sectors.
Conversely, slower economic growth
retards innovation, in part because both
consumers and producers do not see bright
economic times that make innovation and
expansion into new technologies useful.
Comfort and mobility still manage to increase
as important drivers of personal energy
demand, but at a slower rate. When these two
paths are compared, the effect of more rapid
efficiency increases in the higher growth world
is to narrow the difference in greenhouse gas
emissions; that is, the likely difference between
emissions in the Rapidly and Slowly Changing
Worlds is less than the differences in Grosss
National Product. This result makes our
scenarios somewhat more robust than one
might otherwise think.
The second idea concerns energy prices.
In a world of high and robust economic
growth, which we have assumed in the Rapidly
Changing scenario, energy demand will likely
increase, and in the medium term, so will
energy prices. Yet if energy efficiency
increases, then energy costs can increase more
rapidly than the rate of economic growth and
still not consume an increasing share of
national wealth and income. In other words,
energy prices can rise without putting the
B-l
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Policy Options for Stabilizing Global Climate
TABLE B-l
Overview of Major Scenario Assumptions
Slowly Changing World
Slow GNP Growth
Continued Rapid Population Growth
Minimal Energy Price Increases
Slow Technological Change
Carbon-Intensive Fuel Mix
Increasing Deforestation
Montreal Protocol/Low Participation
Rapidly Changing World
Rapid GNP Growth
Moderated Population Growth
Modest Energy Price Increases
Rapid Technological Improvements
Very Carbon-Intensive Fuel Mix
Moderate Deforestation
Montreal Protocol/High Participation
Slowly Changing World
with Stabilizing Policies
Slow GNP Growth
Continued Rapid Population Growth
Minimal Energy Price Increases/Taxes
Rapid Efficiency Improvements
Moderate Solar/Biomass Penetration
Rapid Reforestation
CFC Phaseout
Rapidly Changing World
with Stabilizing Policies
Rapid GNP Growth
Moderated Population Growth
Modest Energy Price Increases/Taxes
Very Rapid Efficiency Improvements
Rapid Solar/Biomass Penetration
Rapid Reforestation
CFC Phaseout
Rapidly Changing World
with Accelerated Emissions
High CFC Emissions
Cheap Coal
Cheap Synfuels
High Oil and Gas Prices
Slow Efficiency Improvements
High Deforestation
High-Cost Solar
High-Cost Nuclear
Rapidly Changing World
with Rapid Emissions Reductions
Carbon Fee
High MPG Cars
High Efficiency Buildings
High Efficiency Powerplants
High Biomass Penetration
Rapid Reforestation
B-2
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Appendix B: Implementation of the Scenarios
brakes on economic growth, as long as the
price increases are gradual (CONAES, 1980).
But in a world of sluggish economic growth,
energy demand rises more slowly, so that
energy prices would rise very little. This
relationship is an additional reason why we
believe that energy efficiency increases more
rapidly in the high-growth scenarios (RCW)
than in the low-growth scenario (SCW).
With these ideas in mind, we can build
scenarios of world energy demand by end use
and region as well as levels of other activities
that emit greenhouse gases. The scenarios are
not exact predictions, but serve as guides to
the level of emissions associated with each
important purpose or end use in the worlds we
constructed.
This approach allows us to compare the
utilization efficiencies that we assume for the
No Response scenarios with those we believe
achievable if more than just market forces
were acting. Two additional scenarios
(referred to as the "Stabilizing Policy"
scenarios) start with the same economic and
demographic assumptions, but examine the
effect that policies could have on global
warming. These scenarios are called the
Slowly Changing World with Stabilizing
Policies (SCWP), and the Rapidly Changing
World with Stabilizing Policies (RCWP). In
addition, we add a variant of the RCWP case
called the Rapidly Changing World with Rapid
Emissions Reductions (RCWR); for this
scenario, we assume more aggressive policies
to contend with global climate change than are
adopted in the RCWP scenario. A fourth
additional scenario considers the effect of
policy choices that directly conflict with
concerns about global warming and that
therefore, would accelerate emissions; this
scenario is called the Rapidly Changing World
with Accelerated Emissions (RCWA). This
scenario is more pessimistic than the RCW
scenario since policy choices increase the rate
of greenhouse gas buildup.
Using our best information about
technologies that could become available, or
technologies that are already available but not
taken up by the market because of market
failures or other reasons, we can reconstruct
activity patterns that are still consistent with
our overriding economic assumptions, but
produce much lower (or higher) levels of
greenhouse gas emissions. Key changes are
assumed in energy efficiency, the energy supply
mix, land-clearing rates, and other factors that
might be changed by government policies or
other means.
In other words, we keep the basic
scenarios but, for example, manipulate
important energy use patterns within these
scenarios. These manipulations can only be
carried out if greenhouse gas emissions in each
scenario are constructed from the bottom up,
i.e., by specifying the level of each major-
emitting activity, as well as the emissions per
unit of activity (e.g., total harvested rice paddy
area and methane emissions per square meter
of paddy).
Thus, the scenarios we constructed are
a necessary step towards illustrating both
ranges of greenhouse gas emissions under two
quite different assumptions about economic
growth, and where there is scope for reducing
emissions through a variety of strategies. In
the final analysis, our work can be turned
around: we can consider the levels of
emissions that under the best and worst
assumptions about how emissions are coupled
to climatic change leave the world's climate
tolerable.
Scenarios with Unimpeded Emissions Growth
In the SCW scenario, we consider the
possibility that the recent experience of
modest economic growth will continue
indefinitely, with no concerted policy response
to the risk of climatic change. In this scenario^
we assume that the aggregate level of
economic activity (as measured by GNP)
increases relatively slowly on a global basis.
Per capita income is stagnant for some time in
Africa and the Middle East as rapid
population growth continues. Modest
increases in per capita income occur
elsewhere, and per capita growth rates increase
slightly over time in all developing countries as
population growth rates slowly decline. The
share of global income going to the developing
world increases with time, but not
dramatically. The population engaged in
traditional agriculture and shifting cultivation
continues to increase, as do demand for
fuelwood and speculative land clearing. These
B-3
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Policy Options for Stabilizing Global Climate
factors lead to accelerated deforestation until
tropical forests are virtually eliminated toward
the middle of the next century.
In industrialized countries economic
growth is sluggish, although per capita income
reaches about $40,000 by 2100 in the OECD.
Because of slack demand, real energy prices
increase slowly. Correspondingly, existing
capital stocks turn over slowly and production
efficiency in agriculture and industry improve
at only a moderate rate. The energy efficiency
of buildings, vehicles, and consumer products
also improves at a slow rate.
In the RCW scenario, we assume that
rapid economic growth and structural change
occur and that little attention is given to the
global environment. Per capita income rises
rapidly in most regions and consumer demand
for energy increases, putting upward pressure
on energy prices. On the other hand, there is
a high rate of innovation in industry, and
capital stocks turn over rapidly, which leads to
an accelerated reduction in energy required
per unit of industrial output. An increasing
share of energy is consumed in the form of
electricity, produced mostly from coal. The
fraction of global economic output produced
in the developing world increases dramatically
as post-industrial structural change continues
in the industrialized world. As educational
and income levels rise, population growth
declines more rapidly than in the SCW
scenario. Deforestation continues at about
current rates, spurred by land speculation and
commercial logging, despite reduced rates of
population growth. Energy efficiency is not
much of a factor in consumer decisions, as
incomes increase faster than real energy prices.
Private vehicle ownership increases rapidly in
developing countries while air travel increases
rapidly in wealthier ones. Nonetheless,
significant reductions in energy intensity occur
with technological innovation and structural
change.
Scenarios with Stabilizing Policies and
Accelerated Emissions
Three variants of the above scenarios
explore the impact of policy choices aimed at
reducing the risk of global warming. These
scenarios, labelled Slowly Changing World
with Stabilizing Policies (SCWP), Rapidly
Changing World with Stabilizing Policies
(RCWP), and Rapidly Changing World with
Rapid Emissions Reductions (RCWR), start
with the same economic and demographic
assumptions used in the SCW and RCW
scenarios, respectively, but assume that
government leadership is provided to ensure
that limiting greenhouse gas emissions
becomes a consideration in investment
decisions beginning in the 1990s. We assume
that policies to promote energy efficiency in
all sectors succeed in substantially reducing
energy demand relative to the No Response
scenarios, and the efforts to expand the use of
natural gas increase its share of primary energy
supply relative to other fossil fuels in the near
term. Research and development into non-
fossil energy supply options such, as
photovoltaics (solar cells) and biomass-derived
fuels (fuels made from plant material) assure
that these options are available and begin to
become competitive after 2000. In addition,
the RCWR case considers the imposition of
even more aggressive policies (compared to
the RCWP case) such as a substantial carbon
fee and rapid reforestation. In all three
scenarios, non-fossil energy, sources meet a
substantial fraction of total demand in later
periods. The Montreal Protocol to reduce
CFC emissions is assumed to be strengthened,
leading to a phaseout of fully halogenated
compounds and a freeze on methyl
chloroform. A global effort to reverse
deforestation transforms the biosphere from a
source to a sink for carbon, and technological
innovation and controls reduce agricultural,
industrial, and transportation emissions of
greenhouse gases.
x
While the general policy assumptions
apply to the SCWP, RCWP, and RCWR cases,
the degree and speed of improvement are
higher in the Rapidly Changing variants
because technological innovation and capital
stock replacement are greater in these cases.
In the long time frame of our analysis,
lifestyles will certainly change, although the
policies we consider do not restrict basic living
patterns. For example, energy use in buildings
is greatly reduced in the Stabilizing Policy
scenarios relative to the No Response
scenarios, but the floor space available per
person and the amenity levels provided are
assumed to be the same. The technological
strategies and policy options available to
B-4
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Appendix B: Implementation of the Scenarios
achieve the Stabilizing Policy scenarios are
discussed in detail in Chapters V, VII, and
VIII.
The fourth policy case considers a
Rapidly Changing World with Accelerated
Emissions (RCWA). In this scenario,
concerns over climate change are not only
ignored, but also other policies adopted
actually exacerbate the buildup of greenhouse
gas emissions. For example, current U.S.
energy policy seeks to increase coal production
and use to reduce dependence on imported oil
and boost employment; the U.S. Department
of Energy has made numerous suggestions
concerning various policies to increase the role
of coal in relative and absolute terms (U.S.
DOE, 1987; National Coal Council, 1987).
Furthermore, recent initiatives in utility
regulation and alternative fuels may also
increase greenhouse gas emissions (see
CHAPTER VII).
Improving the efficiency of coal combus-
tion in so-called "clean coal" technologies may
reduce greenhouse gas emissions relative to
the current generation of coal-burning plants.
Over the long run, however, more efficient
coal-burning technologies may increase
greenhouse gas emissions by making coal
economically attractive relative to other fuels.
(This proposition is tested in the modeling
analysis presented below.) Numerous policy
proposals have also been made to increase
U.S. coal exports in order to improve the
balance of trade. A recent proposal by U.S.
DOE coal advisory committee would link
exports of clean-coal technology to an
agreement to purchase U.S. coal, a policy that
might slow the adoption of more efficient
technology for burning less expensive domestic
coal in some developing countries like China
(National Coal Council, 1987).
The need to consider more carefully the
potential impact of government decisions on
greenhouse warming is evident from analyses
of two recent policies with ambiguous impact
on greenhouse warming. The Alternative
Motor Fuels Act of 1988 (Public Law 100-494)
creates incentives for auto manufacturers to
produce vehicles powered by methanol,
ethanol, and substitutes for gasoline. This
program was adopted to lessen dependence on
imported oil and to improve urban air quality.
However, during Congressional debates
concern was expressed that if methanol were
produced in large quantities from coal, the
result would be a significant increase in
greenhouse gas emissions. Congress therefore
included provision for study of this
relationship. (The potential effect of
accelerated synfuels development is discussed
in APPENDIX C.)
Another example of a policy with
ambiguous, but potentially significant, effects
on greenhouse gas emissions is rule changes
proposed by the Federal Energy Regulatory
Commission (FERC) to facilitate non-utility
power production. The draft environmental
impact statement (DEIS) on these rules
concluded that coal-fired technologies have, so
far, played a limited role in the development
of independent power projects relative to
resource recovery, hydroelectric power, and
natural gas. As a result of the FERC
proposals, coal could assume a much larger
role in the future because of proposed
elimination of requirements for cogeneration
incompatible with the most economic coal
technologies and because larger firms with the
resources necessary to undertake large-scale
projects that increase the attractiveness of coal
technologies may find the power market more
attractive. Alternative assumptions imply
natural gas will grow much more than coal,
however (FERC, 1988).
MACROECONOMIC ASSUMPTIONS FOR
THE ATMOSPHERIC STABILIZATION
FRAMEWORK
Population Growth Rates
\
This section presents the population
assumptions used in the Atmospheric
Stabilization Framework. The population
estimates for the RCW scenario were
developed from Zachariah and Vu (1988) of
the World Bank; for the SCW scenario,
estimates were based on U.S. Bureau of the
Census (1987). These two sources agree quite
closely on the size of the world's population
through 2000, then diverge thereafter due to
different assumptions on the rate at which the
global population will stabilize. Discussions
with representatives of the U.S. Bureau of the
Census and the World Bank indicated that
there is a very high degree of uncertainty
B-5
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Policy Options for Stabilizing Global Climate
concerning population trends in the next
century due to differing expectations over the
rate at which current population growth rates
will decline.1 Zachariah and Vu (1988)
assume that population growth rates in
developing countries will begin to decline
markedly after 2000, achieving a net
reproduction rate of unity in every country by
2040.2 The U.S. Bureau of the Census (1987)
assumes that global population stability will
occur at a later date, with developing countries
experiencing rapid population growth rates
until the middle of the next century. The
lower population estimates from Zachariah
and Vu (1988) were used for the RCW to
represent a more rapid rate of change
consistent with this basic scenario, while the
higher population estimates from the U.S.
Bureau of the Census (1987) were used for the
SCW to represent future rates of growth that
are more consistent in the longer term with
recent trends. These two population estimates
are summarized in Table B-2 for each of the
nine regions in the Atmospheric Stabilization
Framework.
Economic Growth Rates
The primary source for the economic
growth rate estimates was the World Bank
(1987). In this report, Gross Domestic
Product (GDP) forecasts were provided for the
1986-1995 period for several different types of
country groups. Most countries could be
classified into one of three general categories:
low income, middle income, or industrialized.
In addition, the World Bank defined several
other more select groups for which separate
growth rates were estimated, including oil
exporters, exporters of manufactures, highly
indebted countries, and sub-Saharan Africa:
• Oil Exporters, which included Algeria,
Arab Republic of Egypt, Cameroon, Ecuador,
Gabon, Indonesia, Iraq, Islamic Republic of
Iran, Mexico, Nigeria, Oman, People's
Republic of the Congo, Syrian Arab Republic,
Trinidad and Tobago, and Venezuela.
• Exporters of Manufactures, which
included Brazil, China, Hong Kong, Hungary,
India, Israel, Poland, Portugal, Republic of
Korea, Romania, Singapore, and Yugoslavia.
• Highly Indebted Countries, which
included Argentina, Bolivia, Brazil, Chile,
Colombia, Costa Rica, Ivory Coast, Ecuador,
Jamaica, Mexico, Morocco, Nigeria, Peru,
Philippines, Uruguay, Venezuela, and
Yugoslavia.
• Sub-Saharan Africa, which included all
countries south of the Sahara excluding South
Africa.
For each of these groups, the World
Bank provided a range of GDP growth
estimates from 1986-1995. The low estimates
were used for this analysis because these
estimates were more in line with recent
historical trends and with other forecasts (e.g.,
projected growth from 1986-95 was 3.9% for
developing countries and 2.5% for industrial
countries). The GDP assumptions used by the
World Bank for each of these groups is
indicated in Table B-3.
Since these country groupings did not
match the regional definitions used in the
Atmospheric Stabilization Framework, some
method was required to transform the World
Bank's estimates to be consistent with the
regions used in the Atmospheric Stabilization
Framework. To do this, each country was
assigned a GDP growth rate based on the
average growth rates provided in Table B-3.
Generally, if a country fell into one of the four
special categories discussed above (i.e., the last
four groups in Table B-3), the growth rate for
that group was used for that country. If a
country was not part of one of these
groupings, the growth rate for that country's
general category (i.e., low income, middle
income, or industrialized) was assumed.
In cases when a country fell into two or
more categories, e.g., oil exporter and exporter
of manufactures, an average of the two growth
rates was assumed. The only exception to this
rule occurred when such averaging would
increase/decrease a country's GDP growth rate
in a direction that would seem
counterintuitive. For example, if a country
were both sub-Saharan and highly indebted, a
simple average would have assigned a growth
rate of 3.35%, which would have increased its
rate of growth above the rate of growth for
B-6
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Appendix B: Implementation of the Scenarios
TABLE B-2
Global Population Estimates: 1985-2100
(millions of people)
United States
OECD Western Europe
OECD Pacific
USSR/Eastern Europe
China/CP Asia
Middle East
Africa
Latin America
South/Southeast Asia
TOTAL
1985
239
430
144
416
1140
111
570
402
1417
4869
Slowly Changing World
2000 2025 2050 2075
268
462
158
457
1351
181
886
577
1925
6265
297
482
164
514
1638
359
1679
787
2731
8651
299
466
158
533
1762
602
2658
967
3359
10804
296
461
159
545
1918
738
3600
1129
3958
12804
2100
296
461
160
557
1942
781
3963
1169
4166
13495
United States
OECD Western Europe
OECD Pacific
USSR/Eastern Europe
China/CP Asia
Middle East
Africa
Latin America
South/Southeast Asia
TOTAL
1985
239
430
144
416
1140
111
570
402
1417
4869
Rapidly Changing World
2000 2025 2050 2075
262
458
158
454
1408
172
871
533
1859
6175
285
483
166
500
1727
277
1498
720
2534
8190
280
478
165
521
1865
359
2026
834
2999
9527
278
476
164
536
1918
399
2336
874
3195
10176
2100
278
479
165
545
1932
411
2436
893
3281
10420
Sources: U.S. Bureau of the Census (1987) for the Slowly Changing World; Zachariah and
Vu (1988), for the Rapidly Changing World.
B-7
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Policy Options for Stabilizing Global Climate
Table B-3
WORLD BANK GDP GROWTH ASSUMPTIONS: 1986-1995
(real average annual percent change)
Country Group GDP Growth Rate
Low income countries 4.6%
Middle income countries 3.6%
Industrial countries 2.5%
Oil exporters 3.6%
Exporters of manufactures 4.3%
Highly indebted countries 3.5%
Sub-Saharan Africa 3.2%
Source: World Bank, 1987.
B-8
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Appendix B: Implementation of the Scenarios
other sub-Saharan countries because it is
highly indebted. Since this assumption seems
implausible, the lower of the two rates was
assumed.
Once a GDP growth rate was assigned
to each country, this growth rate was applied
to the country's 1985 GDP level to determine
the resulting size of its economy in 1995. The
resulting GDP estimates were then aggregated
according to the nine regions in the
Atmospheric Stabilization Framework to
determine the magnitude of each region's
GDP in 1995. This value was compared to the
1985 GDP estimate for that region to
determine the average annual real rate of
growth in GDP over the 1985-1995 period.
The only region for which the World Bank did
not have any estimates was Eastern Europe/
USSR. For this region, the World Bank's
assumption for middle income economies
(3.6% per year) was assumed.
For the RCW (SCW) scenario these
initial values were generally increased
(decreased) by one percentage point for
developing countries, Eastern Europe, and the
USSR and one-half percentage point for
OECD countries to reflect the greater
uncertainty regarding future growth in
developing and centrally-planned economies.
The growth rates were applied for the period
1985-2000, and were generally reduced by one-
half percentage point each 25-year period,
beginning in 2000, to reflect structural change
and the decline in population growth rates
over time. Nonetheless, GDP per capita
continues to increase throughout the
projection period, although the rate of growth
is substantially lower in the SCW scenario.
The economic growth assumptions are
summarized in Table B-4.
Oil Prices
The oil prices used in this analysis were
taken from U.S. DOE (1988b), which supplied
a range of oil price forecasts. The Middle
Price forecast from U.S. DOE was used for the
RCW scenario (by 2000 the world oil price is
about $32/barrel in 1988 dollars), while the
Low Price forecast was used for the SCW
scenario (oil prices by 2000 were about
$26/barrel in 1988 dollars). Since U.S. DOE
price forecasts did not extend beyond 2000, oil
prices were derived from the SUPPLY model
of the Atmospheric Stabilization Framework
(see APPENDIX A); in each scenario prices
escalated about 0.8% annually from 2000-2100.
ENERGY
Energy Demand
The energy demand estimates were
developed using an end-use approach for each
of the nine regions in the Atmospheric
Stabilization Framework. This section
presents the major assumptions used to
develop these estimates. Two reports provide
the basis for most of these assumptions:
• Mintzer, I. 1988. Projecting Future
Energy Demand in Industrialized Countries: An
End-Use Oriented Approach, draft, World
Resources Institute.
• Sathaye, Jayant A, Andrea N. Ketoff,
Leon J. Schipper, and Sharad M. Lele, 1988.
An End-Use Approach to Development ofLong-
Term Energy Demand Scenarios for Developing
Countries, draft, International Energy Studies
Group, Energy Analysis Program, Lawrence
Berkeley Laboratory.
Mintzer (1988) was used for the
industrialized countries, i.e., the U.S.; Canada
and Western Europe; Japan, Australia, New
Zealand, and other Pacific Rim countries; and
the USSR and Eastern Europe. Sathaye et al.
(1988) was used for the developing countries,
i.e., China and other centrally-planned Asian
economies, the Middle East, Africa, Latin
America, and South and Southeast Asia. Key
assumptions for determining energy demancL
within each region for each of the major
energy-consuming sectors - transportation,
residential, commercial, industrial, and
agriculture - are provided below.
Transportation
Transportation energy use is expected to
increase over time as population increases and
incomes rise, affording people a greater
opportunity to purchase their own vehicles and
to use their leisure time to travel. The types
of vehicles that people use for transportation,
the number of vehicles owned per capita, and
the distance travelled will vary from one
B-9
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Policy Options for Stabilizing Global Climate
TABLE B-4
GDP Growth Assumptions
(real average annual percent change)
Slowly Changing World
1985-2000 2000-2025 2025-2050 2050-2075 2075-2100
U.S.
OECD WESTERN EUROPE
OECD PACIFIC
USSR
CHINA
MIDDLE EAST
AFRICA
LATIN AMERICA
ASIA
2.0
2.0
2.5
2.6
3.5
3.3
3.0
2.7
3.3
1.5
1.5
1.5
2.1
3.0
2.8
2.6-
2.2
2.8
1.0
1.0
1.0
1.6
2.5
2.1
2.1
1.7
2.3
1.0
1.0
•1:0
1.6
2.5
2.1
2.1
1.7
2.3
1.0
1.0
1.0
1.6
2.5
2.1 '
2.1
1.7
2.3
Rapidly Changing World
U.S: '
OECD WESTERN EUROPE
OECD PACIFIC
USSR
CHINA
MIDDLE EAST
AFRICA
LATIN AMERICA
ASIA
3.0
3.0
3.5
4.6
5.5
4.1
4.5
4.7
'5.3
2.5
2.5
2.5
4.1
5.0
4.6
4.0
4.2
4.8
2.0
2.0
2.0
3.1
4.5
3.6
3.5
3.7
4.3
1.5
1.5
1.5
2.6
4.0
3.1
3.0
- 3.2
3.8
1.0
1.0
1.0
2.1
3.5
2.6
2.5
2.7
3.3
B-10
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Appendix B: Implementation of the Scenarios
region to the next. These behavioral factors
were captured explicitly in each region; key
assumptions on vehicle ownership and amount
of travel are defined in Table B-5. The rate at
which vehicles consume energy is also assumed
to change over time; these assumptions on
vehicle energy efficiency for cars and light
trucks are summarized in Table B-6.
Residential and Commercial Sectors
Energy use in the residential and
commercial sectors varies significantly from
one region to another due to differences in
income levels, climate, extent of infrastructure
development, government policies, available
energy resources, among other factors. Due to
the vast differences in energy usage patterns,
there are substantial differences in the
approaches used to determine energy use in
the industrialized countries compared to
developing countries. Key assumptions for
each are summarized below.
For industrialized countries,
energy use in the commercial and residential
sectors was modelled as one category.
Changes in the amount of energy used in these
sectors depend on several factors, including
the rate of new construction, the rate of
retrofitting in existing buildings, number of
people per household, amount of floor space
per capita, and changes in energy efficiency
over time. Key demographic parameters are
summarized in Table B-7 for the period from
1985 to 2025; Table B-8 summarizes key
assumptions on energy intensity improvements
in the residential/commercial sectors from
1985 to 2025. In industrialized countries after
2025, the annual rate of improvement in
energy efficiency (energy/$ GNP) was assumed
to be 0.7-1.9% in the SCW, 0.9-1.9% in the
RCW, and 0.9-1.5% in the RCWA; in the
Stabilizing Policies scenarios, the annual rate
of improvement in energy efficiency (energy/$
GNP) was assumed to be 0.9-1.9% in the
SCWP, 1.3-2.2% in the RCWP and 1.7-2.7%
in the RCWR.
Patterns of energy use in
developing countries are quite different from
those in industrialized countries due to such
factors as current reliance on traditional fuels
(e.g., biomass), different , construction
techniques, and the early stage of development
for these sectors in many developing countries.
Due to reliance on traditional fuels in the
residential sector in many developing
countries, there are also significant differences
between the two sectors. Consequently, the
residential and commercial sectors are treated
separately. Key assumptions for the
residential sector through 2025 are
summarized in Table B-9; electricity intensity
assumptions for the commercial sector through
2025 are summarized in Table B-10. For the
residential/commercial sectors from 2025 to
2100, annual rates of efficiency improvement
(energy/$ GNP) in the SCW were assumed to
be 0.3-1.1% and 0.6-1.4% in the RCW; total
additional overall improvements of 35-55% in
the SCWP and 35-45% in the RCWP were
assumed over the period from 2025 to 2100.
For the RCWR case, the annual rate of energy
efficiency gains was increased by 0.6% relative
to the RCWP case over the period 2025 to
2100. The rates of efficiency improvement for
the RCWA case were assumed to occur only
half as rapidly as in the RCW case; the rates
were therefore decreased to 0.2-0.8% per
annum for industrialized countries and 0.3-
0.7% per annum for developing countries.
The lower rates of improvement are similar to
assumptions in recent projections for U.S.
DOE's National Energy Policy Plan (U.S.
DOE 1988b).
Industrial and Agricultural Sectors
The amount and type of energy
use devoted to the industrial and agricultural
sectors vary depending on the stage of
industrial development for each region, the
age of the capital stock, the types of industrial
activities, types of commodities produced, etc.
For example, countries that are just beginning
to industrialize often develop basic, energy-
intensive industries such as petrochemicals or
steel, while post-industrialized countries are in
the process of reducing their dependence on
these types of industries as information
services and other higher value-added activities
become increasingly important.
For the industrialized countries,
changes in per capita consumption of basic
materials are summarized in Table B-ll (all
values are relative to U.S. consumption in
1985, which is set to an indexed value of 1.00);
the overall improvements in energy efficiency
B-ll
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Policy Options for Stabilizing Global Climate
TABLE B-5
Assumptions on Vehicle Ownership and Amount of Travel
Region
Cars and
USA
W. Europe and Canada
OECD Pacific
USSR and E. Europe
Asia
China
Africa
Latin America
Middle East
2025
1985 RCW RCWP SCW
Light Trucks (No. owned/1000 people)
550 645 570 600
360 445 380 420
250 340 280 315
60 270 210 215
5.7 19.8 14 9.5
NA NA NA NA
12 40 38 20
56 214 171 132
43 137 110 75
SCWP
600
420
315
215
8.1
NA
18
120
70
Cars and Light Trucks (km driven/yr/vehicle)
USA
W. Europe and Canada
OECD Pacific
USSR and E. Europe
Asia
China
Africa
Latin America
Middle East
16,000 14,020 13,420 14,040
11,980 11,190 10,440 11,400
10,950 10,900 9,730 10,960
10,080 12,460 13,390 10,640
12,000 8,000 9,000 10,000
NA NA NA NA
18,000 13,714 11,885 15,542
15,000 12,000 12,600 13,200
18,000. 13,000 13,500 15,500
14,040
11,400
10,960
10,640
10,000
NA
14,628
13,200
14,000
Commercial Trucks and Buses (No, owned/1000 people)
USA
W. Europe and Canada
OECD Pacific
USSR and E. Europe
Commercial
USA
W. Europe and Canada
OECD Pacific
USSR and E. Europe
170 190 160 180
43 70 50 65
150 190 160 170
35 80 65 70
Trucks and Buses (km driven/yr/vehicle)
18,770 25,500 25,500 22,640
31,820 37,100 37,100 33,870
13,100 15,380 15,380 13,940
26,170 37,270 37,270 31,800
180
65
170
70
22,640
33,870
13,940
31,800
B-12
-------
Appendix B: Implementation of the Scenarios
TABLE B-6
Average Fuel Efficiency of Cars and Light Trucks
(kilometers/liter/vehicle)
2025
Region 1985 RCW RCWP SCW SCWP RCWA RCWR
USA 7 12 18 11 15 10 20
W. Europe and Canada 8 11 18 11 15 10 20
OECD Pacific 8 11 19 11 15 10 21
USSR and E. Europe 6 10 16 9 13 9 17
Asia 10 16 21 13 14 14 22
China NA NA NA NA NA NA NA
Africa 8 11 17 10 12 9 18
Latin America 7 13 19 11 13 11 20
Middle East 7 13 17 11 13 11 18
B-13
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Policy Options for Stabilizing Global Climate
TABLE B-7
Demographic Changes in the Residential Sector for Industrialized Countries1
MODE
Slowfy Changing World Scenarios2
People per Household - 1985
People per Household - 2025
Square Meter per Capita - 1985
Square Meter per Capita - 2025
Rapidly Changing World Scenarios2
People per Household - 1985
People per Household - 2025
Square Meter per Capita - 1985
Square Meter per Capita - 2025
United
States
2.7
2.6
73
78
2.6
2.4
73
109
OECD
Europe/
Canada
2.7
2.7
29
34
2.7
2.4
29
46
OECD
Pacific
3.6
3.5
25
32
3.6
3.0
25
38
Centrally-
Planned
Europe
3.9
3.8
15
24
3.9
3.5
14
28
1 Figures shown are population-weighted averages for the countries in each region.
2 Household size and household area are assumed to remain constant in the two Rapidly
Changing World cases and in the Slowly Changing World cases, respectively.
Sources: Schipper et al., 1985; United Nations, 1986; Sagers and Tretyakova, 1987a.
B-14
-------
Appendix B: Implementation of the Scenarios
TABLE B-8
Average Improvements in Energy Intensity in the
Residential/Commercial Sector in Industrialized Countries
(1985 to 2025)
Average Improvement Average Improvement in
Scenario in Fuel Intensity (%) Electricity Intensity (%)
RCW
United States 39 -4
OECD-Europe/Canada 27 5
OECD-Pacific 27 5
Centrally-Planned Europe 39 -90
RCWP
United States 64 36
OECD-Europe/Canada 62 40
OECD-Pacific 62 40
Centrally-Planned Europe 56 59
SCW
United States 22 -9
OECD-Europe/Canada 24 5
OECD-Pacific 24 0
Centrally-Planned Europe 15 -73
SCWP
United States 35 4
OECD-Europe/Canada 33 25
OECD-Pacific 38 20
Centrally-Planned Europe 44 44
Sources: IEA, 1987; Sagers and Tretyakova, 1987a; United Nations, 1987;
Schipper and Ketoff, 1987.
B-15
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Policy Options for Stabilizing Global Climate
TABLE B-9
Key Assumptions in the Residential Sector of
the Developing Countries Through 2025
Parameter
Household Size
(Persons/Household)
Electrification
(Percent of Households
with Electricity)
Biomass Energy Use
(GJ/Capita That Use Biomass
for Cooking and Water
Heating)
Efficiency of Biomass Use
(Percent)
Residential Electricity Use
(kwh/Electricity Capita)
Residential Fuel Use
(GJ/Capita That Use Fuel for
Cooking and Water Heating)
Space Heating
(GJ/Heated Capita)
Year/
Scenario
1985
2025
sew
RCW
SCWP
RCWP
1985
2025
sew
RCW
SCWP
RCWP
1985
2025
sew
RCW
SCWP
RCWP
1985
2025
sew
RCW
SCWP
RCWP
1985
2025
sew
RCW
. SCWP
RCWP
1985
2025
sew
RCW
SCWP
RCWP
1985
2025
sew
RCW
SCWP
RCWP
Centrally-
Planned
Asia
NA
NA
NA
NA
NA
35%
70%
82%
70%
82%
9
6
7
5
6
9%
15%
15%
17%
17%
69
190
227
166
170
3.0
3.0
2.5
3.0
2.5
7.8
6.3
5.1
5.6
3.9
Middle
East
6.0
5.0
4.5
5.0
4.5
65%
85%
95%
85%
95%
8
6
6
4
4
7%
10%
10%
14%
15%
297
412
632
365
474
3.1 .
3.0
5.6
3.0
4.2
NA
NA
NA
NA
NA
Africa
6.0
5.0
4.3
5.0
4.3
25%
40%
55%
40%
55%
10
6
7
5
6
6%
10%
12%
15%
16%
298
396
481
286
298
3.8
4.4
4.3
3.6
3.3
NA
NA
NA
NA
NA
Latin
America
4.5
3.8
3.4
3.8
3.4
78%
92%
98%
92%
98%
13
8
6
. 7
6
6%
10%
12%
12%
14%
308
413
684
359
469
4.4
3.9
5.0
3.9
4.4
NA
NA
NA
NA
NA
South
& East
Asia
5.9
5.4
4.9
5.4
4.9
35%
70%
82%
70%
82%
8
7
8
6
6
8%
12%
12%
16%
16%
136
153
245
134
184
3.0
3.0
2.5
3.0
2.5
12.8
12.2
9.5
11.2
8.0
Sources: Lang, 1988; Leach, 1987; Bangladesh Statistical Yearbook, 1985; Mu, 1988;
Trocki et al., 1985; Joshi, 1985.
B-16
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Appendix B: Implementation of the Scenarios
TABLE B-10
Key Assumptions in the Commercial Sector of
the Developing Countries Through 2025
2025
Region
Centrally-Planned Asia
Middle East
Africa
Latin America
South and East Asia
1985
NA
110
224
126
205
sew
NA
165
118
114
191
RCW
NA
187
135
101
226
SCWP
NA
143
108
95
178
RCWP
NA
131
80
76
181
Source: Sathaye et al., 1988.
B-17
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Policy Options for Stabilizing Global Climate
TABLE B-ll
Per Capita Production of Basic Materials in Industrialized Countries
USA
OECD
Europe/
Canada
OECD
Pacific
Centrally-
Planned
Europe
Iron and Steel
1985
SCW-2025
RCW-2025
SCWP-2025
RCWP-2025
Non-Ferrous Metal
1985
SCW-2025
RCW-2025
SCWP-2025
RCWP-2025
Chemicals
1985
SCW-2025
RCW-2025
SCWP-2025
RCWP-2025
Pulp and Paper
1985
SCW-2025
RCW-2025
SCWP-2025
RCWP-2025
Stone. Cement, and Glass
1985
SCW-2025
RCW-2025
SCWP-2025
RCWP-2025
1.00
1.05
0.76
1.00
0.71
1.00
0.95,
1.19
1.05
1.29
1.00
0.81
1.05
0.81
0.91
1.00
1.29
1.43
1.14
1.29
1.00
0.90
1.09
0.86
1.05
1.31
1.25
1.06
1.18
1.00
1.05
1.00
0.80
1.15
1.40
0.64
0.58
0.68
0.55
0.64
2.01
2.11
2.40
2.01
2.21
1.41
1.49
1.56
1.42
1.49
2.49
2.25
2.01
2.13
1.90
0.93
0.89
1.11
1.02
1.24
0.60
0.54
0.63
0.52
0.60
1.05
1.10
.1.25
1.05
1.15
1.77
1.85
1.94
1.77
1.85
1.62
1.47
1.31
1.31
1.16
0,65
0.75
0.83
0.77
0.90
0.19
0.22
0.24
0.21
0.21
1.11
1.91
2.17
1.64
1.91
1.44
1.30
1.35
1.23
1.30
Per capita production of each commodity is indexed to United States per capita production of
that commodity in 1985.
Source: Mintzer, 1988.
B-18
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Appendix B: Implementation of the Scenarios
by 2025 are summarized for the major
energy-consuming industries in Table B-12.
After 2025 the annual rate of improvement in
energy efficiency (energy/S GNP) was assumed
to range from 0.3-1.1% in the SCW and 0.6-
1.4% in the RCW. In the Stabilizing Policies
scenarios, total additional improvements of 35-
55% in the SCWP and 35-45% in the RCWP
were assumed over the period from 2025 to
2100.
For the developing countries, the
industrial and agricultural sectors were treated
separately through 2025. The key energy
parameters for the industrial sector are
summarized in Table B-13 for each region and
each scenario; the key energy intensity
parameters for the agricultural sector through
2025 are summarized in Table B-14. From
2025 to 2100, annual rates of improvement in
energy intensity were assumed to range from
0.4-1.4% in the SCW and 0.9-2.3% in the
RCW in these sectors. In the Stabilizing
Policies scenarios, total additional
improvements of 35-45% were assumed to
occur in the SCWP and RCWP scenarios over
the period from 2025 to 2100. In the RCWR
scenario, annual rates of efficiency gains for
this sector were assumed to increase by 0.6%
relative to the RCWP case, for both
industrialized and developing countries. The
efficiency gains for the RCWA case were
decreased to half the values of those for the
RCW scenario and, therefore, ranged from 0.3-
0.7% annually for industrialized countries and
0.4-1.1% annually for developing countries.
Energy Supply
This section documents the amount of
energy resources available in each of the nine
regions through 2100, the cost of producing
these resources, and the costs associated with
transporting fossil fuel supplies. The
combined impact of these assumptions
establishes the cost framework that determines
the delivered cost of energy to the end-user
and, therefore, the mix of fuels used.
Production Costs for Fossil Fuels
This section documents the fossil fuel
resource estimates used in the Atmospheric
Stabilization Framework.
The initial source for the oil and natural
gas resource estimates was ICF (1982). In this
report oil and natural gas resource estimates
were developed from several sources, and the
extraction costs for these resources were
estimated in order to develop extraction cost
curves for these two fossil fuels. Since this
was the only readily available public source
that not only identified the amount of each
resource available, but also the cost at which
the resource would be supplied, the
information from this report was used in the
Atmospheric Stabilization Framework to
represent oil and natural gas availability
worldwide. The production costs for these
resources were reduced by 0.5% per annum in
order to incorporate assumed technical
advances.
The estimates of natural gas resources
in the USSR and Eastern Europe were
augmented with additional information
contained in EIA (1986a). The adjustments
were made because the gas resource estimates
for these countries in ICF (1982) did not
reflect more recent information on the size of
the resource base in these countries,
particularly the USSR.
The resource data presented in ICF
(1982) were not disaggregated by the nine
regions utilized in the Atmospheric
Stabilization Framework. For example, the
gas resource information was provided for the
U.S., Canada, Latin America (Mexico and
Venezuela), Africa, Asia, Middle East,
Centrally-Planned Economies (CPE), and the
Rest of World (non-CPE). For regions such
as the U.S., Latin America, Africa, and the
Middle East, the resource estimates were used
as indicated in ICF (1982). For other regions
various methods were employed to reallocate
the data according to the Atmospheric
Stabilization Framework regions. For
example, the CPE data was proportioned
between two regions - E.Europe/USSR and
China/CPE — using each region's percentage
of each resource according to the country-by-
country resource estimates provided in WEC
(1980). The data for the Rest of the World
were proportioned among the remaining
regions - W.Europe/ Canada, OECD Pacific,
and S. & S.E. Asia — using a similar
approach. The natural gas resource estimates
B-19
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Policy Options for Stabilizing Global Climate
TABLE B-12
Energy Efficiency Improvement in the Industrial Sector
(% improvement by 2025 over 1985 intensities)
United
States
OECD
Europe/
Canada
OECD
Pacific
Centrally-
Planned
Europe
Iron and Steel
Non-Ferrous
Chemicals
sew
RCW
SCWP
RCWP
Metal
sew
RCW
SCWP
RCWP
sew
RCW
SCWP
RCWP
23
23
36
36
18
23
26
30
9
14
22
27
22
22
27
27
18
23
23
27
14
18
18
23
14
18
18
23
9
14
18
25
9
14
18
18
27
36
32
41
23
27
27
32
14
23
18
27
Pulp and Paper
Stone, Clay,
sew
RCW
SCWP
RCWP
and Glass
sew
RCW
SCWP
RCWP
18
18
32
37
18
31
36
54
6
15
14
23
18
28
32
36
14
23
18
27
9
26
18
31
31
39
35
46
18
27
23
41
Projected improvements in efficiency of industrial production assume that only technologies
available commercially today or now in prototype testing will be used between 1985 and 2025.
Estimates of future efficiency improvement vary among scenarios as a function of the assumed
rates of stock turnover and penetration of these new technologies. Policies are assumed to
accelerate turnover rate of capital stock and thus to improve average efficiency.
Sources: Goldemberg et al., 1987; Leach et al., 1986; Kahane, 1985; IEA, 1987; Sagers and
Tretyakova, 1987a, 1987b.
B-20
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Appendix B: Implementation of the Scenarios
TABLE B-13
Key Assumptions in the Industrial Sector of
the Developing Countries Through 2025
Parameter
Industrial Value Added/GDP
(Percent Share)
Fuel Intensity
(TJ/Million Dollars)
Electricity Intensity
(MWh/Million Dollars)
Year/
Scenario
1985
2025
sew
RCW
SCWP
RCWP
1985
2025
sew
RCW
SCWP
RCWP
1985
2025
sew
RCW
SCWP
RCWP
Centrally-
Planned
Asia
42%
40
40
40
40
59
48
34
43
28
2160
2160
2242
1944
1906
Middle
East
49%
45
50
45
50
13
11
9
10
8
582
611
640
611
611
Africa
38%
33
39
33
39
11
10
11
9
8
790
883
916
818
798
Latin
America
29%
32
38
32
38
26
23
16
19
14
1163
1163
1163
1047
930
South
&East
Asia
32%
35
40
35
40
25
20
18
14
12
955
955
991
860
843
B-21
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Policy Options for Stabilizing Global Climate
TABLE B-14
Key Assumptions in the Agricultural Sector of
the Developing Countries Through 2025
Parameter
Year/
Scenario
Centrally-
Planned
Asia
Middle
East Africa
Latin
America
South
& East
Asia
Electric Intensity
(kWh/$1000 Value Added)
Fuel Intensity
(TJ/Million Dollars)
1985
2025
sew
RCW
SCWP
RCWP
1985
2025
sew
RCW
SCWP
RCWP
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
58
116
157
113
110
90
135
180
126
126
23
57
85
52
68
18
35
45
30
35
170
204
238
187
204
80
64
56
56
48
202
234
271
222
244
50
59
68
56
61
B-22
-------
Appendix B: Implementation of the Scenarios
resulting from this approach are provided in
Table B-15 according to the minimum
extraction cost at which these resources could
be economically produced.3
A similar approach was employed for
the oil resource curves. In ICF (1982)
resource estimates were provided for the U.S.,
Canada, Latin America (Mexico and
Venezuela), Middle East, and the rest of the
world (non-centrally planned economies only).
The U.S., Latin America, and the Middle East
resource estimates were used as indicated in
ICF (1982). The resource estimates for the
rest of the world were proportioned among the
remaining non-CPE regions -- W.Europe/
Canada, OECD Pacific, S. & S.E. Asia, and
Africa using the same procedure outlined
above for the gas resource curves. Data for
centrally-planned economies were not available
from ICF (1982), and the Africa estimates
were incomplete. For these regions the
resource estimates provided in WEC (1980)
were used. The oil extraction cost curves
resulting from this approach are provided in
Table B-16 by minimum extraction cost.
The coal resource estimates were taken
from WEC (1980). This report contained
country-by-country estimates of indigenous
coal resources, which were developed at the
World Energy Conference from information
supplied by experts familiar with each
country's resources. The coal resource
estimates resulting from this approach are
provided in Table B-17 by minimum extraction
cost. The extraction costs were based on the
costs originally documented for The
IEA/ORAU Long-Term Global Energy-CO2
Model (Edmonds and Reilly, 1986).
Gas Flaring Rates
During the production of oil and gas
resources some portion of natural gas is either
vented or flared rather than produced for
commercial use. The amount of gas that is
not recovered was determined from EIA
(1986a). This source provided country-by-
cduntry estimates of the amount of natural gas
that was vented or flared in 1984. The total
amount of natural gas vented or flared in each
of the nine regions was determined, and this
value was converted to a percentage of total
natural gas production in that region. This
percentage was assumed to be the amount of
natural gas that was initially flared or vented.
Over time the value of natural gas will
increase and the market infrastructure,
including the distribution systems, will become
more highly developed in many regions. As a
result, we assumed that the amount of venting
or flaring would decrease over time to a level
equal to the amount of flaring in the U.S.
currently, i.e., 0.5% of gross natural gas
production.4 The rate of decrease was varied
depending on the amount of venting and/or
flaring occurring currently, with regions
currently venting and/or flaring a larger
amount of natural gas assumed to take a
longer time to achieve the 0.5% level. For
example, in the Middle East and Africa where
total venting and flaring is over 15%, it was
assumed to take 50 years to reach 0.5% of
total production. For most other regions this
value was assumed to be 10-20 years.
Refinery Efficiencies and Costs
The cost of refining is partially tied to
the price of oil since about one-third of
refining costs are fuel-related. This cost per
barrel, based on ICF (1984), can be expressed
as:
$3.63 + Fuel Cost (all costs are in 1988
dollars)
where Fuel Cost is defined as 0.083 barrels of
residual fuel oil per barrel of Saudi light
crude, or about 521,000 Btu of fuel consumed
for each barrel refined (this fuel cost varies
based on the acquisition cost of crude and
resulting residual oil costs).
From U.S. DOE sources cited above,
residual oil prices are about 0.95 the price of
crude oil. Assuming a crude oil price of $20
per barrel ($0.48 per gallon) in 1988 dollars,
the price for residual oil would be $19 per
barrel ($0.45 per gallon). The refining cost
equation above becomes:
$3.63 + [0.083 barrels * 0.95 * $ per
barrel of crude] = $5.21 per barrel
Each barrel of crude is assumed to contain 5.8
million Btu, or 6.119 gigajoules. At a cost of
B-23
-------
Policy Options for Stabilizing Global Climate
TABLE B-15
Minimum Extraction Cost Curves For Natural Gas
(1988 $/gigajoule)
Region
1.02
TOTAL
2112
3.38
5.58
7.78
15.64
United States
Western OECD
Eastern OECD
USSR & E. Europe
C-Planned Asia
Middle East
Africa
Latin America
S and E Asia
340
200
14
1000
10
388
40
60
60
594
433
66
2286
276
1401
347
560
154
910
746
111
2583
334
1936
531
673
261
1071
944
129
2711
359
2171
657
718
304
1307
1109
147
2807
377
2315
761
779
348
6117
8085
9064
9950
Sources: ICF, 1982; EIA, 1986a; WEC, 1980.
B-24
-------
Appendix B: Implementation of the Scenarios
TABLE B-16
Minimum Extraction Cost Curves for Oil
(1988 $/gigajoule)
Region
2.80
3.00
3.46
4.60
5.76
8.06
10.70
United States
Western OECD
Eastern OECD
USSR & E. Europe
C-Planned Asia
Middle East
Africa
Latin America
S and E Asia
173
201
10
384
112
2928
346
514
105
443
351
12
1225
458
4413
967
539
189
532
461
16
1336
499
4571
.1257
608
246
621
610
19
1440
538
4666
1499
712
293
710
779
23
1710
639
4855
1837
1074
359
1291
881
31
1851
669
5406
2137
3858
439
13276
892
53
2161
915
5760
2546
3863
639
TOTAL
4773 8597 9526 10398 11986 16563 30105
Sources: ICF, 1982; WEC, 1980.
TABLE B-17
Minimum Extraction Cost Curves For Coal
(1988 $/gigajoule)
Region
0.70
0.80
1.60
3.20
TOTAL
5.40
United States
Western OECD
Eastern OECD
USSR & E. Europe
C-Planned Asia
Middle East
Africa
Latin America
S and E Asia
600
300
120
750
600
1
120
18
180
7964
2459
1891
13536
4054
2
607
121
365
17727
5474
4209
30128
9024
4
1352
269
812
31601
9758
7503
53706
16086
7
2410
480
1447
50613
15629
12018
86017
25764
11
3860
769
2318
2689 30999 68999 122998 196999
Sources: WEC, 1980; Edmonds and Reilly, 1986.
B-25
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Policy Options for Stabilizing Global Climate
$5.21 per barrel, the cost of refining crude oil
is $0.85 per gigajoule (1988 dollars).
Hydroelectric Resources
The amount of hydroelectric power
available for development was based on
.estimates of the technical potential for
hydropower as presented in WRI (1987). In
this report the total amount of hydroelectric
power available in the world was estimated by
country. These estimates were used to
establish the total amount of hydroelectric
power technically available in each region.
Within the Atmospheric Stabilization
Framework the total amount of this technical
potential that could be utilized was arbitrarily
limited to 75% of each country's technical
potential. The reason for limiting
hydroelectric development is based on the
argument that various environmental,
economic,, social, and political factors will
preclude the development of all hydroelectric
potential. The realistic level of development
in each country cannot be estimated. The
75% restriction is arbitrary, but seems like a
.reasonable upper bound estimate when
compared to the U. S. situation. For example,
based on information obtained from the U. S.
Department of the Interior, the United States
has currently developed about 50% of its
hydroelectric potential. Some additional
development in the future is likely, although
certain hydroelectric sites will undoubtedly
never be developed.
The rate of hydroelectric development
allowed in the Atmospheric Stabilization
Framework was limited in order to avoid the
addition of an unreasonable amount of
hydroelectric power within a very short time
frame. The allowed rate of development was
limited to the historical rate of development of
these resources within each country, as
determined from WRI (1987) and EIA
(1986a). Table B-18 presents the 1985
hydroelectric production levels and the
technically feasible resource amounts by
region. (See APPENDIX A for further
discussion.)
Solar Energy Costs
. The cost of renewable resources depends
on many factors, including the current costs of
Table B-18
Hydroelectric Resources
(exajoules delivered electricity)
Region
1985 Production
TOTAL
7.03
Technical Potential
75% • 100%
United States
Western OECD
Eastern OECD
USSR & E. Europe
C-Planned Asia
Middle East
Africa
Latin America
S. & E. Asia
1.11
2.75
0.40
0.80
0.32
0.03
0.07
1.09
0.46
1.6
' 3,7
0.6
1.2
3.9
0.2 :
2.4
6.9
4.2
2.1
4.9
0.8
1.6*
5.2
0.3
3.2.
9.2
5.6
24.7
32.9
15-26
-------
Appendix B: Implementation of the Scenarios
production, the level to which these
production costs will fall as the technologies
mature, and the rate at which commercial
penetration occurs. These factors can be
varied within the Atmospheric Stabilization
Framework. To determine the basic cost
inputs for solar energy resources, information
was obtained from the Solar Energy Research
Institute (SERI) based on industry data and
U.S. DOE technology development goals.
SERI indicated that the current cost of
electricity from renewables is about $0.10-
0.11/kwh (in 1988 dollars, based primarily on
the cost of wind generating systems and recent
solar thermal demonstration projects).
Without a significant emphasis on further
research and development, however, this cost
is estimated to decline only to about $0.08/kwh
in the long run in the No Response scenarios
as solar technologies mature. In the
Stabilizing Policies scenarios, the costs decline
to about $0.06/kwh by the year 2030, which is
the level consistent with the cost objectives
U.S. DOE has set for its solar research
program. Solar power was assumed to play a
small role in the RCWA case. The costs,
therefore, reflect those of the No Response
scenarios. The solar energy costs for the
RCWR case were assumed to be similar to the
Stabilizing Policies scenarios, declining to
about S0.06/kwh by 2030.
Nuclear Power Costs
Nuclear fission is a technology which is
currently widely used and growing in its
contribution to global energy supply due to
completion of powerplants ordered during the
1970s. However, high capital costs and
concerns about safety, nuclear weapons
proliferation and radioactive waste disposal
have brought new orders to a halt in most
countries. It is technically feasible to expand
the contribution of this energy source beyond
current projections if these problems are
resolved, and steps have been taken to deal
with some of the constraints on the nuclear
power industry (for example, U.S. DOE
programs aimed at developing improved
reactor designs).
The costs associated with nuclear energy
in the No Response and Policy scenarios
reflect these characteristics of the nuclear
industry. Costs start at 6.1 cents per kwh in
1985 and rise to 7.6 cents per kwh by 2050 for
the RCW, SCW and RCWA scenarios,
indicating the constraints faced by the industry.
Costs for the RCWP, SCWP, and RCWR
scenarios start at 6.1 cents per kwh in 1985
and decline to 5.5 cents per kwh under the
assumption that the policy programs targeting
the industry's problems are successful.
Biomass Energy Costs and Availability
The availability and cost of biomass for
commercial energy applications were based on
U.S. DOE (1988a). There is a substantial
amount of land worldwide that could be
dedicated to biomass development projects.
For purposes of this analysis, the U.S. DOE
assumed that 10% of total forest and
woodland area plus 10% of total cropland area
would technically be available for biomass
energy development. Based on different
assumptions on the rate of improvement in
productivity, energy yields per hectare were
assumed to increase up to about three times
current levels. Table B-19 summarizes the
amount of energy potentially available from
biomass under different productivity
assumptions, with scenario A assuming a 65%
improvement in current energy plantation
productivity estimates, scenario B assuming a
150% improvement, and scenario C assuming
an improvement of over 500% (a level
potentially achievable by 2050). Although
these estimates indicate that about 675 EJ
annually could potentially be developed from
biomass with sufficient research and
development and commitment of land area, in
all except one of our scenarios the total
amount of additional energy from biomass was
limited to about 275 EJ (less than the amount
available under scenario A after subtracting
current levels of biomass consumption). It was
assumed for the RCWR scenario that twice
this amount, or 540 EJ, was available (an
estimate consistent with the potential under
scenario B).
Based on cost and performance data
provided in U.S. DOE (1988a), the energy
conversion efficiency for solid biomass to
gaseous or liquid fuels was assumed to be 75%
after 2010. With sufficient research and
development, the average cost of gaseous fuels
from biomass after 2010 was assumed to be
about S4.35/GJ in 1988 dollars on a well-head
B-27
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Policy Options for Stabilizing Global Climate
TABLE B-19
Future World Wide Biomass Energy Potential
Region
World
Africa
U.S./Canada
C. America
S. America
Asia
Europe
USSR
Oceania
Total Land
Area
(10*113)
13081
2966
1839
300
1753
2679
473 '
2227
843
Cropland
(10*ha)
1472
183
236
38
139.
455
140
232
48
Forest &
Woodlands
(lO^ha)
4091
703
591
69
927
• 558
155
928
159
Energy
Plantations1
(106ha)
556
88
83
11
107
101
29
116
21
%Land
Area (Energy
Plantations1
Total Land
Area
4.3%
3.0
4.5
3.7
6.1
3.8
6.1
5.2
2.5
Energy Production
From Biomass (E3)
Potential
1988
50
12.6
3.4
1.2
6.6
21.5
1.7
2.5
0.2
A
338
69
33
8
83
79
12
45
8
B
506
103
49
13
126
118
17
69
13
Cl
675
137
65
17
167
158
23
91
17
1 Assumed area of biomass energy plantations = 10% of total forest & woodland area plus 10% of cropland area.
2 1988 data is estimated actual use; scenarios A, B, and C use the following biomass productivity values (assuming all of the potential energy plantation
hectares shown above are used at these intensive cultivation productivity levels); current energy plantation production/year for the U.S./Canada, Europe,
USSR, & Oceania = 14.8 dry tons/heetare/year _(dt/ha/yr) (7.4 dt/ha/yr in conventional forests); with R&D, assumed energy plantation productivity for
these temperature climate countries are; scenario A = 24.7 dt/ha/yr, scenario B = 37.1 dt/ha/yr, scenario C = 49.4 dt/ha/yr, for Africa, C. & S. America,
and Asia current productivity = approx. 29.7 dt/ha/yr, scenario A assumes 49.4 dt/ha/yr, scenario B = 74.1 dt/ha/^r, and scenario C = 98.8 dt/ha/yr for the
tropical regions.
f
Source : U.S. DOE, 1988a.
B-28
-------
Appendix B: Implementation of the Scenarios
equivalent basis; the cost of liquid fuel (i.e.,
gasoline) after 2010 from biomass was assumed
to be about S6.00/GJ on a refinery gate basis.5
These values were used in the SCWP and
RCWP scenarios. As a result, biomass
contributes about 250 EJ by 2100 in the
SCWP (about 48% of total primary energy)
and 275 EJ in the RCWP by 2100 (about 32%
of total primary energy). It was assumed for
the RCWR scenario that the fixed cost of
converting biomass to fuel was reduced by a
third of the above values; this resulted in a
biomass contribution of about 470 EJ by 2100.
In the SCW, RCW, and RCWA scenarios, it
was assumed that a lack of research and
development into biomass energy potential
and an unwillingness/inability to commit land
for biomass development prevented biomass
from competing with more traditional fuels to
a greater extent. Nevertheless, biomass still
makes some contribution (about 50 EJ in the
SCW and 70 EJ in the RCW and RCWA by
2100, which is 7%, 5%, and 3%, respectively of
total primary energy in the SCW, RCW, and
RCWA scenarios).
Synthetic Fuel Costs
There are several synthetic fuel
technologies that were included in the
Atmospheric Stabilization Framework,
including coal gasification and liquefaction, oil
shale development, and tar sands development.
To determine the costs at which energy could
be supplied from these technologies, two
sources were used:
• Technical Assessment Guide: Volume I
- Electricity Supply, Electric Power Research
Institute, 1986.
• Synthetic Fuels Report, Pace Engineers,
December 1987.
The key synthetic fuel technologies and
their associated costs are summarized in Table
B-20. The conversion efficiencies of synthetic
fuel development technologies were based on
Radian (1990), and averaged about 65%.
These values were used for all the scenarios
with one exception; in order to represent the
accelerated development of synthetic fuels in
the RCWA case, the fixed costs of conversion
were decreased by 50% of their original values.
Transportation Costs in the Atmospheric
Stabilization Framework
In addition to the costs of producing
energy, the Atmospheric Stabilization
Framework contains interregional costs for
transporting fossil fuels. This section
documents the transportation cost assumptions
made for each fossil fuel in the Atmospheric
Stabilization Framework.
Oil. The oil transportation costs were
developed from ICF (1979), which estimated
long-term, full cost pricing for oil transporta-
tion. Actual estimates of transportation rates
were not used because the world shipping
markets have been quite depressed due to
excess shipping capacity. This situation has
caused shipping rates in recent years to decline
toward short-term variable costs. Since the
Atmospheric Stabilization Framework should
reflect long-term, full cost pricing by shippers,
current rate estimates would be inappropriate.
ICF (1979) was designed to reflect transporta-
tion rates in an equilibrium market, i.e., long-
term pricing, including an adequate return on
capital. Based on this information, oil
transportation costs were derived from the
following formula:
Transportation = 0.022 * Price of Crude
Cost + [0.28 per 1000
nautical miles]
This equation is in 1988 dollars, and assumes
a 120,000 DWT tanker (a size typically used
for many international shipments, although
not supertanker size). The mileage estimate is
based on the distance one-way fully loaded; the
transportation cost function, however, includes
the cost of the return trip.
The Atmospheric Stabilization
Framework currently operates with only one
transportation rate assumption for oil. To
estimate a single rate using the above formula,
a crude price of $20 and a "typical" shipment
from the Middle East to the United States (a
distance of about 12,200 miles) was assumed.
Therefore, the oil transportation costs between
the Middle East and the U.S. would be
S3.86/barrel in 1988 dollars, or $0.63 per
gigajoule (assuming an energy content of
about 6.1 gigajoules per barrel).
B-29
-------
Policy Options for Stabilizing Global Climate
TABLE B-20
Cost of Synthetic Fuel Technologies
Technology
Tar Sands3
Oil Shale3
Coal Gasification
Commercially Demonstrated
High Btu Gas «<;
Medium Btu Gas
Facility
Size
50,000b
50,000b
250C
250C
Capital Cost
(1988 S/kw-yr)
2,350
2,800
2,730
2,330
O & M
(1988$/kw-yr)
14.2
14.2
10.6
9.3
Advanced Gasifier
Medium Btu Gas
Coal Liquefaction
Direct
250C
50,000b
1,700
2,810
8.0
11.7
3 Within the Atmospheric Stabilization Framework, oil production from tar sands and oil
shale is treated as unconventional oil resources rather than synthetic fuel production.
b Barrels per day.
c Billions of Btus per day. '*
Source: Pace Engineers, 1987.
B-30
-------
Appendix B; Implementation of the Scenarios
Coal. Coal transportation costs were
developed from several presented in
Coal Transportation: 1984, proceedings from
the Third International Coal Trade and
Transportation and Handling Conference, held
in London on October 1-3, 1984. In
particular, two sources were used: Penfold
(1984) and Portheine (1984),
Based on these sources, the average coal
transportation cost is assumed to be Sl7/ton in
1988 dollars. At 24 million Btu per ton, or
25.32 per ton, the cost is S0.67 per
in dollars.
Natural Gas. The transportation costs
for natural gas in the Atmospheric
Stabilization Framework are based on the cost
of transporting liquefied natural gas (LNG),
and therefore, include not only the cost of
transporting the LNG but also the cost of
liquefying and regasifying the natural gas.
costs were developed ICF (1982).
la this report the of liquefying,
transporting, and regasifying natural gas
identified as:
LNG cost = $0,7S/mcf for processing
H- 15% loss
Transport = $0.44/mcf for each 1,000
cost miles round trip
+ loss
These are in 198S dollars. An
average wellhead gas price of $0.55 per mcf
(1988 dollars) and an average distance of 4700
mite (based on distance from U. S. to
Europe) was assumed, leading to a transporta-
tion cost for natural gas of $2.90 per mcf in
1988 dollars, or $2.70 per gigajoule.
Distribution Cost Assumptions For The
Atmospheric Stabilization Framework
In addition to the interregional of
energy transportation, the Atmospheric
Stabilization Framework also includes costs for
toaregional transportation costs (referred to
s$ distribution costs). These costs are basically
the costs to transport fossil fuels from the
mine, wellhead, or port facility to the end user.
Since the current version of the Atmospheric
Stabilization Framework can accept only one
value for each fuel, several simplifying
ass ympt ions made,
Oil. Two primary sources were used for
estimating the distribution costs for oil: E1A
(1987a), which provided data on crude oil
prices and various prices for products refined
by the oil industry, and EIA (1986b), which
provided data on the quantity of products
produced by refineries. Using 1985 data, an
average price paid for all oil products by end
users was estimated. In 1988 dollars, this
price was about $0.91 per gallon. This
petroleum product was then
compared to the refiner's average cost of
production, which was determined by adding
the refiner's acquisition cost for crude oil
(S30.08 per barrel in 1988 dollars) to the cost
for refining the crude oil into the various
petroleum products (estimated at S6.44 per
barrel in 1988 dollars). The refiner's average
cost would then be $36,52 per barrel in 1988
dollars, or $0.87 per gallon. The difference
between these two values ($0,04 per gallon, or
$1.68 per barrel) assumed to be the cost
of distributing oil products to the end user.
This distribution cost would be S0.28 per
gigajoule.
Natural Gas. The source used for gas
distribution costs was U.S. DOE (1987). In
this report the average retail price of gas was
reported to be $5,15/mcf in 1988 dollars. The
average wellhead price was reported to be
S2.74/mcf. The difference between these two
is $2.41/ntef (or $2.24 per gigajoule),
is the average cost of distribution for
natural gas.
Coal, The source used for coal
distribution costs was EIA (1987b). In this
report the average F.O.B. mine price during
1985 was estimated to be about $27 per ton
(1988 dollars). The average price paid by all
consumers was about $38 per ton, indicating a
transportation cost of about $11 per ton.
Assuming a heat content of 22 million Btu per
ton, or 23.21 gigajoales per ton, the
distribution cost for coal would be $0.47 per
gigajoule.
Generation Efficiency
In the Atmospheric Stabilization
Framework, a number of input parameters
B-31
-------
Policy Options for Stabilizing Global Climate
define the rate of change in energy
conservation and efficiency. This section
presents the major assumptions that have been
made in this area for the electric utility sector.
In many instances, these input parameters
have been developed from U.S. sources due to
the lack of readily available data on energy
conservation and efficiency trends in other
parts of the world.
The technologies that are used to
generate electricity differ in their ability to
convert the fuel supply into useful electrical
energy. The efficiencies of electrical
generation technologies were determined from
Radian (1990). In this report conversion
efficiencies were provided for most existing
and emerging fossil fuel combustion
technologies.
Radian (1990) also provided information
on the current conversion efficiencies of
electrical generation for different regions of
the world. These efficiency values differ by
region due to several factors, including
differences in fuel quality and differences in
technological design (such as operating
pressure or the inclusion of more energy-
efficient reheat technologies). Over time the
efficiency of electricity generation is expected
to improve as new technologies are developed.
For example, beginning in 1985 we assumed
that electricity generation in the developing
countries was 15% less efficient than
generation in the developed countries. This
differential was assumed to decline through
2025, at which point generation efficiencies of
new units in the developing countries would
equal generation efficiencies in the developed
countries. Additionally, generation efficiencies
for new powerplants were assumed to increase
gradually over time. In the No Response
scenarios, oil-fired units were assumed to
improve their efficiency to 40% after 2000 and
45% after 2025 (compared with initial
assumptions for the efficiency ratings of new
units of 35% in 1986). Most new gas-fired
units (combined-cycle) were assumed to be
45% efficient in 1986, with no efficiency
changes thereafter. Goal-fired units were 38%
efficient after 2000 (based on the efficiency of
fluidized-bed combined-cycle units), improving
to an efficiency rating of 44% after 2025. In
the Stabilizing Policies scenarios, the rates of
efficiency improvement were assumed to
increase relative to the No Response cases,
thus, oil-fired powerplants were 43% efficient
after 2000, and 48% efficient after 2025. Gas-
fired units were assumed to demonstrate a
similar improvement with a 50% efficiency
rating after 2025. The efficiency of coal-fired
units in these scenarios changed from 38%
after 2000 to 47% after 2025.
Emission Control Assumptions
The consumption of energy often
generates a variety of greenhouse gas
emissions, including CO2, CO, NOX, CH4, and
N2O. The type and amount of greenhouse gas
emissions will depend on the combustion
technology and the extent, if any, of emission
controls. Emission rates for different
combustion technologies were determined
from Radian (1990) and Marland and Rotty
(1984). In addition, emission factors for N2O
were decreased to remain consistent with more
recent information on N2O formation. Table
B-21 summarizes the uncontrolled emission
rates for the major combustion technologies
(uncontrolled means that no emission controls
are assumed). To reduce the amount of emis-
sions from different combustion technologies,
however, there are different types of emission
controls that can be applied. Table B-22
summarizes the size of emission reductions
that could occur if various emission controls
were applied to the technologies in Table
B-21, as determined from Radian (1990).
In constructing the scenarios, assump-
tions were made about the level of emission
controls that would be adopted. In the No
Response scenarios, the major assumptions
were:
• New utility and industrial coal-fired
boilers in the industrialized countries would
use low NOX burners starting in 1985 (to be
consistent with the New Source Performance
Standard for nitrogen oxides from boilers);
developing countries would not use any
controls.
• New light-duty gasoline vehicles in the
U.S. use 3-way catalysts by 1985; the rest of
the OECD uses oxidation catalysts on light-
duty gasoline vehicles; developing countries
have no emission control devices installed.
B-32
-------
Appendix B: Implementation of the Scenarios
TABLE B-21
Emission Rate Differences by Sector
(grams per gigajoule)*
Source
Efficiency
C02
CO
CH
N2O
NO
• Electric Utility (g/GJ delivered electricity)
Gas Turbine Comb. Cycle
Gas Turbine Simp. Cycle
Residual Oil Boilers
Coal - F. Bed Comb. Cycle
Coal - PC Wall Fired
Coal - PC Cyclone
Coal - Integrated Gas
42.0
26.4
32.4
35.0
31.3
31.3
27.3
Industrial (g/GJ delivered steam for boilers; energy output
Boilers
Coal-Fired
Residual Oil-Fired
Natural Gas-Fired
Kilns - Coal
Dryer - Natural Gas
Dryer - Oil
Dryer - Coal
. Residential/Commercial (g/GJ energy
Wood Stoves
Coal Stoves
-Distillate Oil Furnaces
Gas Heaters
Wood Boilers
Gas Boilers
Residual Oil Boilers
Coal Boilers
Transportation (g/GJ energy input)
Rail
Jet Aircraft
Ships
Light Duty Gasoline Vehicle
Light Duty Diesel Vehicle
Light Duty Compressed
N. Gas Vehicle
80
85
85
65-75
30-65
30-65
30-65
output)
50
50
75
70
67.5
80.9
84.9
75.9
NA
NA
NA
NA
NA
NA
120,300
191,400
230,000
290,000
330,000
330,000
253,600
for others)
130,000
88,000
57,000
300,000-350,000
75,000-170,000
100,000-240,000
155,000-340,000
[150,000]
198,000
111,000
101,000
[138,000]
61,800
86,000
135,000
69,900
72,800
70,000
54,900
73,750
50,200
70
110
43
NA
42
42
222
110
17
18
75
10
15
170
17,600
3,400
17
13
280
10.6
19
244
570
120
320
10,400
340
4
13
20
2.2
1.8
2.0
2.0
NA
2.9
3.3
1.5
1
1
1
1
70
NA
7
1
21
1.4
1.8
13
13
2
20
36
2
120
20
30
44
40
45
45
51
18
16
3.5
2
NA
NA
NA
NA
NA
NA
NA
6
2.7
14
16
NA
NA
NA
0.5
20
7
400
640
590
690
1,400
2,600
760
390
180
71
500
' ' 52
160
215
190
170
65
61
47
53
183
295
xl,640
290
830
: 400
300
140
* All emission rates are based on total molecular weight.
NA = Not Available
[ ] =" No Net CO2 if based on sustainable yield
Source: Radian Corporation, 1990; except N2O data, which is based on unpublished EPA data. N2O emission
coefficients are highly uncertain and currently undergoing further testing and review.
B-33
-------
Policy Options for Stabilizing Global Climate
TABLE B-22
Emission Control Performance
Efficiency3
Loss
Technology
CO2
Reduction
CO
Reduction
CH4
Reduction
NOX
Reduction
Date
Available
Utility
Low NOX Burner
Coal
Coal Tangentially Fired
Oil
Gas
Selective Catalytic Reduction
Coal.
Oil, AFBC
Gas
CO2 Scrubbing
Coal
Oil
Gas
Industrial Boiler
Low NOjj Burner
Coal
Oil
Gas
Selective Catalytic Reduction
Coal
Oil
Gas
Kilns. Ovens and Dryers
Low NOX Burner
Kilns, Dryers
0.25
0.25
0.25
0.25
22.5
16.0
11.3
0.25
0.25
0.25
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
90
90
90
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
8
8
8
NA
NA
NA
Negligible
Negligible
Negligible
Negligible Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
NA
NA
NA
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
Negligible
35
35
35
50
80
80
80
NA
NA
NA
35
35
50
80
80
80
35
1980
1980
1980
1980
1985
1985
1985
2000
2000
2000
1980
1980
1980
1985
1985
1985
1985
B-34
-------
Appendix B: Implementation of the Scenarios
TABLE B-22 (cent.)
Emission Control Performance
Mobile Source
Selective Catalytic Reduction
Coke Oven
Light Dutv Gasoline Vehicle
Engine Control
Oxidation Catalyst
3-Way Catalyst
Heavy Duty Gasoline Vehicle
Engine Control
Oxidation Catalyst
3-Way Catalyst
Light Duty Diesel Vehicle
Low NOX Control
Heavy Duty Diesel Vehicle
Low NO, Control
Efficiency3
Loss
(%)
1
NA
NA
NA
NA
NA
NA
NA
NA .
CO2
Reduction
(%)
Negligible
-11
-17
-23
-25
-66
-71
Negligible
Negligible
CO
Reduction
(%)
8
36
57
78
35
90
97
11
8
CH4
Reduction
(%)
Negligible
Negligible
33
44
52
70
69
-16
Negligible
NOS
Reduction
' (%)
80
8
23
44
31
33
41
24
41
Date
Available
1979
1968
1975
1980
1978
1985
1998
1985
1987
3 Efficiency loss as a percent of end-user energy conversion efficiency.
Source: Radian, 1990.
8-35
-------
Policy Options for Stabilizing Global Climate
• Heavy-duty gasoline vehicles in the U.S.
use basic engine controls.
Emission controls in the No Response
scenarios were limited to controls already
approved or highly likely to be approved under
current laws or regulations. In response to
concerns over global warming, additional
emission controls were assumed in the
Stabilizing Policies scenarios:
• New utility boilers in the OECD use
Selective Catalytic Reduction (SCR) beginning
in 2000, while all existing units install low NOX
burners. In the rest of the world all new units
use low NOX burners beginning in 2000 and
SCR in 2025, with 50% of all existing units
using low NOX burners beginning in 2000.
• Industrial boilers in the OECD install
SCR beginning in 2000; the rest of the world
installs low NOX burners beginning in 2000
and SCR beginning in 2025.
• Kilns and dryers in the industrial sector
in the OECD employ low excess air (LEA) by
2000, with all new facilities using low NOX
burners beginning in 2000; the rest of the
world employs LEA beginning in 2000 and low
NOX burners beginning in 2025.
• Coke ovens use SCR beginning in 2000
in the OECD; the rest of the world adopts
SCR beginning in 2025.
• Beginning in 1985 the non-OECD
countries install engine controls on all light-
duty gasoline vehicles, which is intended to
capture some countries that adopt catalyst
technology, while others continue not to use
any controls. Beginning in 2000 all of the
OECD uses 3-way catalysts on light-duty
gasoline vehicles, oxidation catalysts on all
heavy-duty gasoline vehicles, and low NOX
controls on heavy-duty diesel vehicles. The
rest of the world uses oxidation catalysts on
light-duty gasoline vehicles and engine controls
on heavy-duty gasoline vehicles. Beginning in
2025,3-way catalysts are installed on all heavy-
duty gasoline vehicles in the OECD; the rest
of the world installs 3-way catalysts on light-
duty and heavy-duty gasoline vehicles and low
NOX controls on heavy-duty diesel vehicles.
Carbon Fees
Carbon fees were imposed on the
production of fossil fuels in proportion to
their CO2 emissions potential in order to
reflect a more aggressive response strategy to
reduce the rate of emissions growth. The
RCWP and SCWP scenarios assumed fees of
$25/ton of coal, S4.80/barrel of oil (S0.80/GJ),
and S0.54/GJ for natural gas. The fees for the
RCWR scenario were increased to SlOO/ton of
coal, $19.20/barrel of oil and S2.16/GJ for
natural gas.
Results of the Energy Scenarios
This section presents the detailed results
for the No Response and Stabilizing Policies
scenarios,
Energy Prices
Given the level of energy demand
resulting from the assumptions discussed
above and the types of energy supplies
assumed to meet this demand, the
Atmospheric Stabilization Framework
estimates energy prices. These prices are
summarized in Table B-23 for oil, natural gas,
and coal for all six scenarios. These prices
should be viewed as the energy cost to the
marginal consumer to purchase the energy at
its point of production, i.e., at the wellhead or
mine-mouth from the marginal producer (e.g.,
oil prices are typically the acquisition cost of
crude from the Middle East),
Energy Use and Emissions
The following tables present the results
for the SCW scenario for each of the nine
regions (these tables can be found at the end
of Appendix B following REFERENCES):
• Table B-24: Primary Energy Supply;
• Tables B-25 to B-31: Primary Energy
Supply by Resource, i.e., oil, gas, coal,
biomass, hydroelectric, nuclear, and
solar, respectively;
Table B-32:
Consumption;
Primary Energy
B-36
-------
Appendix B: Implementation of the Scenarios
TABLE B-23
Energy Prices
(1988$/Gigajoule)
YEAR
RCWP
RCW
SCWP
sew
RCWR
RCWA
1985
2000
2025
2050
2075
2100
3,04
3.40
4.42
4.90
5.62
6.44
3.04
4,18
7.06
7.88
8.74
9.50
Crude Oil Prices
3.04
3.44
5.28
4.68
5.04
5.24
3.04
3.82
5.88
6.74
7.86
8.56
3.04
3.68
3.50
3.88
4.02
4.06
3.04
5.40
5.42
5.88
6.54
8.90
1985
2000
2025
2050
2075
2100
Natural Gas Prices
(wellhead)
1.20
2.10
1.04
1.42
2.04
2.76
1.20
2.40
3.82
4.94
6.46
7.34
1.20
2.06
2.04
1.12
1.32
1.44
1.20
2.12
2.96
3.40
4.34
5.28
1.20
2.34
0.46
0.78
0.90
0.94
1.20
3.12
4.28
4.92
5.66
7.92
Coal Prices
(mine-mouth)
1985
2000
2025
2050
2075
2100
0.70
0.82
1.40
1.24
1.10
0.98
0.70
0.66
0.62
0.56
0.80
1.08
0.70
0.82
1.34
1.24
1.10
0.96
0.70
0.66
0,60
0.52
0.54
0.60
0.70
1.36
2.06
2.58
2.58
2.42
0.70
0.74
1.08
1.28
1,68
3.20
1-37
-------
Policy Options for Stabilizing Global Climate
• Table B-33: Secondary Energy
Consumption, broken down into fuel
versus electricity;
• Tables B-34 to B-36: Secondary Fuel
Consumption by Type (i.e., oil, gas,
solids);
• Table B-37: Residential/Commercial
Energy Consumption: Fuel versus
Electricity;
• Table B-38: Industrial Energy
Consumption: Fuel versus Electricity;
• Table B-39: Transportation Energy
Consumption: Fuel versus Electricity;
• Table B-40: Electric Utility Energy
Consumption;
• Table B-41: Energy Conversion
Efficiency at Electric Utility
Powerplants;
• Table B-42: Synthetic Production of
Oil and Gas;
• Table B-43: Energy Used for Synthetic
Fuel Production by Type;
• Table B-44: CO2 Emissions from
Energy Consumption (in petagrams of
carbon);
• Table B-45: CO Emissions from Energy
Consumption (in teragrams of carbon);
and
• Table B-46: NOX Emissions from
Energy Consumption (in teragrams of
nitrogen).
This same information is provided for
each of the scenarios. Tables B-47 to B-69
summarize the RCW case. Tables B-70 to
B-92 summarize the RCWA case. Tables
B-93 to B-115 summarize the SCWP case.
Tables B-116 to B-138 summarize the RCWP
case. Tables B-139 to B-161 summarize the
RCWR case.
CHLOROFLUOROCARBON AND HALON
EMISSIONS
The CFC and halon emission estimates
were based on Regulatory Impact Analysis:
Protection of Stratospheric Ozone, U.S. EPA
(1988), which was developed by U.S. EPA to
support U.S. participation in the Montreal
Protocol. Emission estimates from U.S. EPA
(1988) consistent with the Montreal Protocol
were used in the SCW case since the economic
growth rates in the SCW case were similar to
U.S. EPA (1988) assumptions. In the SCW it
was assumed that the U.S. would comply with
the Montreal Protocol 100%, other developed
countries would average 94% participation,
and developing countries would average 65%
participation. For the RCW case, the rate of
growth was increased 75% to reflect the higher
economic growth rates. Also, the rate of
participation was increased to reflect a higher
rate of technological improvement that would
make it easier to comply with the terms of the
Montreal Protocol, i.e., 100% of the developed
countries and 75% of the developing countries
were assumed to participate. For the SCWP,
RCWP, and RCWR cases, the Montreal
Protocol is strengthened to produce a
complete phaseout of CFCs in participating
countries by 2003. Participation rates were the
same for all Stabilizing Policies scenarios, with
100% of the developed countries and 85% of
the developing countries participating. The
RCWA case assumes a low level of
participation in and compliance with the
Montreal Protocol; the assumptions used in
this case are similar to the "Low Case" analysis
described in U.S. EPA (1988), i.e., 75% of the
developed countries and 40% of the
developing countries were assumed to
participate. The CFC and halon emission
estimates from each of the six scenarios are
summarized in Table B-162.
DEFORESTATION
Net carbon flux projections due to
deforestation and afforestation were based on
a model described by Moore et al. (1981) and
Houghton et al. (1983). For our analyses
(which were based on Houghton (1988)) only
B-38
-------
Appendix B: Implementation of the Scenarios
TABLE B-162
Chlorofluorocarbon Emissions By Scenario
(Gigagrams)
SCENARIO
1985 2000
2025
2050
2075
2100
CFC-11
sew
SCWP
RCW
RCWP
RCWA
RCWR
CFC-12
sew
SCWP
RCW
RCWP
RCWA
RCWR
HCFC-22
sew
SCWP
RCW
RCWP
RCWA
RCWR
CFC-113
sew
SCWP
RCW
RCWP
RCWA
RCWR
363.8
363.8
363,8
363.8
363.8
363.8
73.8
73.8
73.8
73.8
73.8
73.8
150.5
150.5
150.5
150.5
150.5
150.5
419.5
402.9
500.2
491.4
614.6
491.4
206.1
206.1
263,0
263.0
263.0
263.0
121.3
112.7
178.4
174.7
248.6
174.7
393.5
50.6
450.3
83.5
857.2
83.5
407.0
407.0
,830.7
830.7
830.7
830.7
124.9
8.8
170.5
19.9
371.7
19.9
420.0
64.5
508.7
87.9
1400.0
87.9
754.5
754.5
2425.8
2425.8
2425.8
2425.8
142.2
14.0
195.3
26.1
618.7
26.1
297.4
53.5-
327.1
57.9
1056.4
57.9
426.5
68.5
519.1
91.2 -
1483.1
91.2
879.1
879.1
3124.5
3124.5-
3124.5
3124.5
142.2
14.0
195.3
26.1
618.7
26.1
297.4
53.5
327.1
57.9
1056.4
. 57.9
426.5
68.5
519.1
91.2
1483.1
91.2
879.1
879.1
3124.5
3124.5
3124.5
3124.5
142.2
14.0
195.3
26.1
618.7
26.1
B-39
-------
Policy Options for Stabilizing Global Climate
the tropical regions were considered since the
net flux of carbon from the temperate regions
was close to zero for 1980. Three tropical
regions of the world were evaluated (tropical
America, Asia, and Africa), two forests types
(closed and open forests), and two types of
land-use changes (deforestation to permanent
croplands, and afforestation or the formation
of plantations). A low estimate for the
amount of carbon stored in terrestrial
ecosystems (forests and soils) was used, such
that the net flux of carbon to the atmosphere
for the base year (1980) is about 0.4 Pg (1 Pg
= 1 petagram = 1015 grams = 109 metric tons
= 1 gigaton = 1 Gt).
Based on Hough ton (1988), three
scenarios for projecting the net flux of carbon
from terrestrial ecosystems from 1980 to 2100
were developed:
• Scenario 1: Deforestation as a function of
population size. In this projection
unsustainable agricultural practice and rapid
population growth lead to continuously
increasing pressure on tropical forests. The
rate of deforestation 'is assumed to increase
exponentially, based on population growth in
each of the three tropical regions, from 11
million ha/yr (1 ha/yr = 1 hectare per year =
2.461 acres per year) in 1980 to 34 million
ha/yr in 2047, when the available area of
forests in Asia is exhausted. The rate of
establishment of tree plantations in the tropics
is assumed to be zero. These assumptions
result in a rapid increase in net carbon
emissions from 0.7 Pg C/yr to more than 2 Pg
C/yr in 2047 before the Asian forests are
exhausted. Latin American and African
forests are exhausted by 2075, reducing
emissions drastically. The total net release of
carbon between 1980 and 2100 is 138 Pg C.
• Scenario 2: Exponential increase in
tropical deforestation. In this case,.clearing of
forested lands for agriculture, pasture, logging,
and speculation continues, although at a
somewhat slower rate than in Scenario 1
because of improved agricultural practices and
the substitution of modern fuels for traditional
uses of wood. As a result, tropical
deforestation increases gradually, reaching 15
million ha/yr in 2097. The rate of
establishment of tree plantations in the tropics
again is assumed to be zero. These
assumptions result in emissions that total
almost the same amount as in the previous
case, although they are spread out over a
longer period. Annual emissions are close to
1 Pg C/yr from 2000 to 2100. The total flux of
carbon between 1980 and 2100 is 118 Pg C.
• Scenario 3: High reforestation. In the
third case, it is assumed that a combination of
policies succeed in stopping deforestation by
2025, while more than 1000 million ha are
reforested by 2100. Only land that once
supported forests and is not intensively
cultivated is assumed to be available for
reforestation. These lands include 85% of the
area currently involved in shifting cultivation
(370 million ha), under the assumption that
this practice is replaced by sustainable low
input agriculture (Sanchez and Benites, 1987).
In addition, fallow agricultural land in the
temperate zone (250 million ha), planted
pasture in Latin America (100 million ha), and
degraded land in Africa and Asia (400 million
ha) is assumed to be reforested. Of the
reforested land, about 380 million ha are
assumed to be in plantations; the rest absorbs
carbon at a much lower rate but reaches a
higher level of average biomass. In this case,
the^biosphere becomes a sink for carbon by
2000 and reaches its peak absorption of 0.7 Pg
C/yr before 2025. The size of the sink
gradually declines after 2025 as forests reach
their maximum size and extent. This case
results in a total release of carbon due to
deforestation of 12 Pg, and a total
accumulation of carbon due to the three
reforestation activities of 38 Pg. Therefore the
net accumulation of carbon on land for this
case is 26 Pg.
\
Scenario 1 was used in the SCW case
and the RCWA case to represent worlds where
population growth remains quite high and the
rate of technological diffusion low such that
heavy reliance on biomass for cooking and
heating continues. Scenario 2 was used in the
RCW case to represent a world that continues
to utilize its biomass resources at an
unsustainable rate for many years, although
the depletion rate is lower than in Scenario 1
since population growth is lower and the
transformation to more modern fuels occurs
more quickly. Scenario 3 was used in both the
RCWP and SCWP cases to represent a world
committed to halting net deforestation,.
B-40
-------
Appendix B: Implementation of the Scenarios
including the adoption of agricultural practices
that do not require significant amounts of
slash and burn agriculture or land-clearing and
the development of alternative energy sources
for current biomass consumers. Figure B-l
summarizes the CO2 emissions from
deforestation (in terms of petagrams of
carbon) for these three scenarios. An
additional scenario, Scenario 4 was created for
the RCWR case. It was similar to Scenario 3
in representing a world committed to halting
net deforestation, but also assumed an
increased uptake of CO2 equivalent to a
maximum of 1 gigaton of carbon per year.
AGRICULTURE
As discussed in Appendix A, global
estimates of agricultural production and
fertilizer consumption were derived from the
Basic Linked System (BLS) at the Center for
Agricultural and Rural Development at Iowa
State University. Data were available from the
BLS only through 2050; therefore, to
determine values for the 2050-2100 period, the
trends indicated from the pre-2050 results
were extrapolated to 2100 and adjusted based
on changes in population (see APPENDIX A
for further discussion).
Two scenarios were devised for the
SCW and RCW cases. Key results for the
SCW and SCWP cases for each of the nine
regions are summarized as follows (tables are
at the end of this document):
• Table B-163: Production of Wheat;
• Table B-164: Production of Rice;
• Table B-165: Production of Coarse
Grains;
• Table B-166: Production of Meats;
• Table B-167: Production of Dairy
Products;
• Table B-168: Production of Other
Animals (e.g., pork, poultry, eggs, fish);
• Table B-169: Amount of Fertilizer Use;
and
• Table B-170: Amount of Land Under
Rice Cultivation.
• The corresponding information for the
RCW, RCWA, RCWP, and RCWR cases is
provided in Tables B-171 to B-178.
The assumptions on grain production,
animal production, and fertilizer use were not
varied from the No Response scenarios to the
Stabilizing Policies scenarios (e.g., from the
SCW to the SCWP). Since the population
estimates were the same in both cases, we
assumed that basic consumption habits would
not change, i.e., people would consume the
same types of foods regardless of policies to
stabilize the atmosphere. However, we did
assume that policies would be implemented to
reduce the quantity of greenhouse gas
emissions from agricultural activities. Key
assumptions included the following:
• Changes in types of fertilizers and
method of application were assumed to reduce
the quantity of N2O evolved from nitrogenous
fertilizers. (It is possible that policies could
also encourage the development of fertilizers
that would alter the total quantity of fertilizer
required; however, this possibility was not
modelled in this analysis.)
• Changes in rice cultivation practices and
types of rice cultivars were assumed to reduce
the amount of CH4 from rice production.
• Changes in meat and dairy production
techniques, such as the use of additives like
methane-inhibiting ionophores, diet changes,
or alterations in animal waste management
methods, were assumed to reduce the amount
of CH4 from meat and dairy production.
For all three types of activities, emission
rates were assumed to decline 0^5% per year in
the Stabilizing Policies scenarios and remain
constant in the No Response scenarios.
GREENHOUSE GAS EMISSIONS
The human activities portrayed in the
previous tables, including energy production
and consumption, agricultural activities, CFC
consumption, and other industrial activities,
B-41
-------
Policy Options for Stabilizing Global Climate
FIGURE B>1
CO2 EMISSIONS FROM TROPICAL DEFORESTATION
Global Total
2.5
1.5 -
n
X
o
.a
n
o
«
£
n
0>
n
1 •-
•5 0.5
Q.
o
-0.5
i
-1
1950
Stabilizing Policy Scenarios
1980 2010 2040 2070 2100
Year
B-42
-------
Appendix B: Implementation of the Scenarios
cause a number of greenhouse gas emissions.
Additionally, various greenhouse gases are also
produced from natural processes, such as CH4
production from wetlands. These emission
forecasts are summarized below by scenario for
each major emission category. For example,
forecasts for the SCW scenario are (all tables
can be found at the end of this document):
• Table B-179: CO2 Emissions by Type
of Activity (in petagrams of carbon);
• Table B-180: N2O Emissions by Type
of Activity (in teragrams of nitrogen);
Table B-181: CH
of Activity (in teragrams of CH4);
4 Emissions by Type
• Table B-182: NOX Emissions by Type
of Activity (in teragrams of nitrogen);
and
• Table B-183: CO Emissions by Type of
Activity (in teragrams of carbon).
This information is provided for each of
the scenarios. Tables B-184 to B-188
summarize emissions from the RCW case,
Tables B-189 to B-193 summarize the RCWA
case, Tables B-194 to B-198 summarize the
SCWP case, and Tables B-199 to B-203
summarize the RCWP case. Tables B-204 to
B-208 summarize the RCWR case.
REALIZED AND EQUILIBRIUM WARMING
The emissions estimates for each
scenario were used to determine the amount
of realized and equilibrium warming. The
extent of warming will depend not only on the
amount of greenhouse gas emissions, but also
on the sensitivity of the climate system to
increases in greenhouse gas concentrations.
As discussed in Chapter III, the sensitivity of
the climate system is often expressed in terms
of the amount of warming that would result
from an equivalent doubling of CO2
concentrations in the atmosphere (typically
expressed as 2XCO2). There is some debate
over the amount of warming that would result;
in our analyses we have considered a range of
1.5-5.5°C (see CHAPTER HI for further
discussion of this range).
Based on the amount of greenhouse gas
emissions and resulting atmospheric concentra-
tions, the extent of realized and equilibrium
warming is presented for each scenario for
climate sensitivities of 1.5°C, 2.0°C, 3.0°C,
4.0°C, and 5.5°C. Table B-209 presents
realized warming and Table B-210 presents
equilibrium warming for the six scenarios.
B-43
-------
Policy Options Tor Stabilizing Global Climate
TABLE B-209
Realized Warming for l.5°-5.5*C Sensitivities
(Degrees Celsius)
Sensitivity
sew
1.5
2.0
3.0
4.0
5.5
RCW
1.5
2.0
3.0
4.0
5.5
SCWP
1.5
2.0
3.0
4.0
5.5
RCWP
1.5
2.0
3.0
4.0
5.5
RCWA
1.5
2.0
3.0
4.0
5.5
RCWR
1.5
2.0
3.0
4.0
5.5
1985
0.4
0.5
0.6
0.7
0.8
0.4
0.5
0.6
0.7
0.8
0.4
0.5
0.6
0.7
0.8
0.4
0.5
0.6
0.7
0.8
0.4
0.5
0.6
0.7
0.8
0.4
0.5
0.6
0.7
0.8
2000
0.6
0.7
0.9
1.0
1.2
0.6
0.7
0.9
1.0
1.2
0.6
0.7
0.9
1.0
1.2
0.6
0.7
0.9
1.0
1.2
0.6
0.7
0.9
1.1
1.2
0.6
0.7
0.9
1.0
1.2
2025
1.0
1.2
1.5
1.8
2.0
1.0
1.3
1.6
1.9
2.1
0.8
0.9
1.2
1.4
1.7
0.8
1.0
1.3
1.5
1.7
1.2
1.5
1.9
2.1
2.4
0.7
0.9
1.2
1.4
1.6
2050
1.3
1.7
2.2
2.6
3.1
1.6
2.0
2.6
3.0
3.5
0.9
1.1
1.5
1.7
2.1
0.9
1.2
1.6
1.9
2.2
2.3
2.8
3.6
4.2
4.8
0,7
0.9
1.3
1.5
1.8
2075
1.7
2.2
2.9
3.4
4.0
2.3
2.9
3.8
4.4
5.2
0.9
1.2
1.6
1.9
2.3
1.1
1.4
1.9
2.2
2.7
3.7
4.5
5.8
>6.0*
>6.0*
0.7
0.9
1.2
1.5
1.8
2100
2.1
2.6
3.5
4.2
5.0
3.1
3.8
5.0
6.0
>6.0*
1.0
1.2
1.7
2.1
2.5
1.2
1.5
2.1
2.5
3.0
5.0
>6.0*
>6.0*
>6.0*
>6.0*
0.6
0.8
1.1
1.4
1.8
* Estimates of equilibrium warming commitments greater than 6°C represent extrapolations beyond the range tested
in most climate models, and this warming may not be fully realized because the strength of some positive feedback
mechanisms may decline as the Earth warms. These estimates are represented by >6°C.
B-44
-------
Appendix B: Implementation of the Scenarios
TABLE B-210
Equilibrium Warming for L5°-5.5°C Sensitivities
(Degrees Celsius)
Sensitivity
sew
1.5
2.0
3.0
4.0
5.5
RCW
1.5
2.0
3.0
4.0
5.5
SCWP
1.5
2.0
3.0
4.0
5.5
RCWP
1.5
2.0
3.0
4.0
5.5
RCWA
1.5
2.0
3.0
4.0
5.5
RCWR
1.5
2.0
3.0
4.0
5.5
1985
0.6
0.7
1.1
1.5
2.1
0.6
0.7
1.1
1.5
2.1
0.6
0.7
1.1
1.5
2.1
0.6
0.7
1.1
1.5
2.1
0.6
0.7
1.1
1.5
2.1
0.6
0.7
1.1
1.5
2.1
2000
0.8
1.1
1.6
2.2
3.0
0.8
1.1
1.7
2.2
3.0
0.7
1.0
1.5
2.0
2.7
0.8
1.0
1.5
2.0
2.8
0.9
1.1
1.7
2.3
3.1
0.7
1.0
1.5
2.0
2.7
2025
1.3
1.7
2.6
3.5
4.8
1.4
1.9
2.8
3.8
5.2
0.9
1.2
1.9
2.5
3.4
1.0
1.3
2.0
2.6
3.6
1.8
2.4
3.5
4.7
>6.0*
0.9
1.2
1.7
2.3
3.2
2050
1.8
2.3
3.5
4.7
>6.0*
2.2
2.9
4.3
5.8
>6.0'
1.0
1.4
2.0
2.7
3.7
1.2
1.5
2.3
3.1
4.2
3.3
'4.4
>6.0*
>6.0*
>6.0*
0.8
1.1
1.6
2.1
2.9
2075
2.1
2.8
4.2
5.7
>6.0*
3.0
4.0
6.0
>6.0*
>6.0*
1.1
1.4
2.1
2.8
3.9
1.3
1.7
2.6
3.4
4.7
4.8
>6.0*
>6.0*
>6.0*
>6.0*
0.7
1.0
1.4
1.9
2.6
2100
2.5
3.3
4.9
>6.0*
>6.0*
3.8
5.1
>6.0*
>6.0*
>6.0*
1.1
1.4
2.1
2.8
3.8
1.4
1.8
2.8
3.7
5.0
>6.0*
>6.0*
>6.0*
>6.0»
>6.0*
0.6
0.8
1.3
1.6
2.2
' Estimates of equilibrium wanning commitments greater than 6°C represent extrapolations beyond the range tested
in most climate models, and this warming may not be fully realized because the strength of some positive feedback
mechanisms may decline as the Earth warms. These estimates are represented by >6°C.
B-45
-------
Policy Options for Stabilizing Global Climate
NOTES
1. The World Bank estimates are very similar
to estimates that could be obtained from the
United Nations due to reliance on the same
data sources. The World Bank estimates
(Zachariah and Vu, 1988) were chosen since
they were the most recently published
estimates available at the time.
2. A net reproduction rate of unity indicates
that people of child-bearing age have children
at a replacement rate; it eventually leads to a
stable population level.
3. Each cost level indicates the cost at which
resources could first be economically extracted.
Not all of the resources available at a specific
extraction cost would be made available
immediately since not all potentially economic
resources are discovered immediately or
produced instantaneously once discovered.
See Appendix A for further details.
4. Flaring was not assumed to decline any
further in the U.S. since flaring occurs
primarily during well testing and maintenance
operations. Some additional reductions may
be possible, but these potential improvements
in the U.S. were not assumed to occur.
Additionally, the flaring value for the OECD
Pacific countries was reported to be 0.1%; this
value was not used as a lower bound, however,
because very little natural gas production
occurs in these countries compared to the size
of the U.S. market.
5. When implementing these assumptions, a
small amount of biomass was assumed to be
available at a lower cost in order, to ensure
that the amount of biomass supplied globally
in the Atmospheric Stabilization Framework
increased smoothly rather than becoming
available immediately once these cost levels
were reached. In this sense, these cost
assumptions are average costs for biomass at a
given point in time, although some biomass
supplies may be available at costs lower than
the average.
6. All participation rates are applied to total
estimated production for the region; e.g., if
65% of the developing countries participate in
the Montreal Protocol, then 65% of the
production was subject to the terms of the
agreement.
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Schipper, L. and A Ketoff. 1987. Residential
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Energy 1986. Annual Review Press, Palo Alto.
Schurr, S. 1982. Energy efficiency and
productive efficiency: Some thoughts based on
the American experience. Energy Journal
1985. Central
Trocki, Booth, and Umana.
America Energy Study;
United Nations. 1987. Statistical Yearbook:
1985. United Nations, New York.
U.S. Bureau of the Census. 1987. World
Population Profile: 1987. U.S. Department of
Commerce. U.S. Bureau of the Census,
Washington, D.C.
U.S. DOE (U.S. Department of Energy).
1987. Natural Gas Monthly. U.S. DOE,
Washington, D.C.
U.S. DOE (U.S. Department of Energy).
1988a. Letter from Donald R. Walter of U.S.
DOE to Daniel A Lashof, U.S. EPA May 26.
U.S. DOE (U.S. Department of Energy).
1988b. Long Range Energy Projections to 2010.
Office of Policy, Planning and Analysis.
Washington, D.C.
U.S. EPA (U.S. Environmental Protection
Agency). 1988. Regulatory Impact Analysis:
Protection of Stratospheric Ozone. Office of Air
and Radiation, U.S. EPA. Washington, D.C.
WEC (World Energy Conference). 1980.
Survey of Energy Resources 1980. Prepared for
the llth World Energy Conference, Munich,
8-12 September 1980, World Energy
Conference, London. 352+ pp.
World Bank. 1987. World Development Report
1987. Oxford University Press, New York. 285
pp.
WRI (World Resources Institute). 1987.
World Resources: 1987. International Basic
Books, New York.
Zachariah, K.C., and M.T. Vu. 1988. World
Population Projections, 1987-1988 Edition.
World Bank, Johns Hopkins University Press,
Baltimore. 440 pp.
United Nations. 1986. Statistical Yearbook:
1983-84. United Nations, New York.
B-48
-------
Appendix B: Implementation of the Scenarios
sew
REGION
TABLE B-24
PRIMARY ENERGY SUPPLY
(Exajoules/Yr)
1985
2000
2025
2050
2075
TOTAL
301.6
364.6
457.9
506.0
574.3
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
63,
47.
8,
81.
25.
23.
16.
20.
14.
,7
.6
,9
.2
.6
.7
.3
.5
.1
58.
51.
11
91
38
49
24
22
16
,7
.8
.8
.3
.2
.0
.1
.9
.8
57.
52.
12.
103.
64.
63.
37.
39.
26.
5
9
8
6
0
4
7
1
9
61.
51.
13.
108.
73.
60.
45,
61.
29.
7
6
9
.4
.4
2
.9
.4
.5
71.
54.
17.
122.
88.
62.
53.
73.
31.
7
3
3
,2
,3
.1
.4
,7
.3
93.6
63.4
23.1
165.9
102.6
50.3
48.4
67.5
36.0
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
117.9
TABLE B-25
PRIMARY OIL SUPPLY
(Exajoules/Yr}
2000
129.3
2025
2050
136.4
125.8
2075
103.7
2100
20.8
11.9
1.1
26.0
5.2
22.4
10.8
14.1
5.6
12.0
9.5
.6
21.8
6.8
46.2
16.9
11.0
4.5
7.3
8.2
.2
17.7
6.8
55.6
20.6
16.2
3.8
7.8
6.1
.0
12.4
5.1
44.2
17.5
29.7
3.0
8.1
4.2
.0
8.0
3.3
30.0
12.6
35.2
2.3
12.0
2.6
.1
5.6
2.6
19.1
8.8
26.3
2.0
79.1
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 58.6
TABLE B-26
PRIMARY GAS SUPPLY
(Exajoules/Yr)
2000
71.8
2025
2050
79.9
76 ,0
2075
65.5
2100
16.3
9.7
.7
24.0
.5
1.2
1.3
2.5
2.4
15.5
11.9
1.6
29.2
1.4
2.6
2.5
4.8
2.3
11.6
10.6
1.6
29.4
3.9
6.4
5.1
8.0
3.3
8.1
8.2
1.1
26.4
4.0
12.2
5.8
6.8
3.4
5.3
5.0
.6
13.4
2.7
26.0
5.8
4.7
2.0
4.3
3.5
.4
6.4
1.5
22.6
4.4
2.8
1.2
47.1
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
87.3
TABLE B-27
PRIMARY COAL SUPPLY
(Exajoules/Yr}
2000
114.6
2025
2050
158.8
180.9
2075
246.6
2100
19,
9,
3,
26,
18,
4,
4,
=s
.4
.4
,9
.7
.9
.0
.0
.6
.4
SSSZSIX
22,
12,
5,
33,
27
4
7
.8
.7
.6
.9
,5
.0
.3
.8
.0
26.
16.
7.
44.
43.
8.
1,
11.
8
.1
,1
4
,4
.0
,2
4
,4
31.
18.
8.
52.
48.
12.
2.
7.
1
1
3
6
7
0
3
1
7
— — — -
40.
23.
10.
80.
62,
19,
3,
5,
szsss
,9
,4
,9
,4
.9
.0
.3
.3
.5
===_
56.
32.
15,
130,
76,
14,
3
5
,8
,5
.4
.4
.1
.0
.2
.4
.4
334.2
B-49
-------
Policy Options for Stabilizing Global Climate
-SCW
REGION
1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL
TABLE B-28
PRIMARY BIOMASS SUPPLY
(Exajoules/Yr)
2000
2025
2050
7.4
25.7
2075
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
1.
1.
2.
1.
6
4
2
1
5
0
4
2
0
2,
1.
3.
1.
5.
7
3,
.1
.5
.7
.8
,8
.0
.0
.5
.3
2
2
1
5
2
7
10
4
.9
.1
.1
.5
.6
.0
.2
.7
.7
3.
2,
1.
7,
3,
9.
13
6
.8
.7
.4
,1
.3
.1
.3
.9
.1
47.7
REGION
United States
OECD Europe/Canada
OECD Pacific /
Centrally Planned Europe
Centrally /Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-29
PRIMARY HYDROELECTRIC SUPPLY
{,Exajoules/Yr)
1985
21.2
20,00
2025
2050
29.6
45.1
59.2
2075
69.4
2100
3,
8.
1,
2.
1.
3,
1,
,3
.2
.2
,5
.0
.1
.2
.3
.4
3
9
1
3
2
6
2
.8
.6
.3
.1
.3
.2
.4
.3
.6
4,
10,
1,
3,
6.
1.
11,
5,
.3
.6
.3
.6
.4
.4
.3
.3
.9
4.
11.
1.
3.
10.
3.
15.
9.
,6
,0
,4
,7
,0
,6
,2
,3
,4
4.
11.
1.
3.
11.
5.
19.
. 11.
7
,1
4
.7
3
7
3
8
4
4.
11.
1.
3.
11.
6.
20.
12,
,8
,1
,4
,7
.6
,7
.6
.6
,1
72.6
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-30
PRIMARY NUCLEAR SUPPLY
(Exajoules/Yr)
1985
16.5
2000
2025
2050
17.9
23.8
26.2
2075
33.6
2100
3
8,
2,
2
.8
,4
.0
.0
.0 .
.0
.0
.0
.3
4,
7,
2,
2.
.2
.8
.6
.8
.1
.0
.0
.0
.4
==£3
5,
6,
2,
5,
2
1
.3
.2
.0
.5
.2
.7
.8
.0
.1
=sssst
5.
5.
1.
6.
2.
2.
1.
1,
,2
,3
.7
,2
.5
.1
,4
,0
.8
sxx:
6.
6.
2.
6.
3.
3.
2.
3.
0
,5
2
9
.4
3
0
,0
,3
=SiS=
6.
8.
2.
7.
4.
4.
2.
5,
8
0
8
3
3
,5
9
,3
,3:
42.2
REGION 1985
United States .1
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East ! .0
Africa .0
Latin America .0
South and^East Asia ..0
TOTAL . 1
TABLE B-31
PRIMARY SOLAR SUPPLY
(Exajoules/Yr)
2000
1.4
2025
6.5
2050
2075
12.2
18.7
2100
.4
.3
.1
.5
/I
.0
.0
.0
.0
1.6
.8
.4
1.9
.8
.3
.3
.0
.4
2
1
3
1
1
.8
.4
.7
.3
.3
.1
.7
.0
.9
3.
2.
1.
4.
2.
2.
1.
2.
8
0
1
3
1
1
2
0
1
5
3
1
5,
3,
3.
2.
3.
.1
.0
.6
,4
,2
3
2
2
9
27.9
B-50
-------
Appendix B: Implementation of the Scenarios
sew
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-32
PRIMARY ENERGY CONSUMPTION
(Exajoules/Yr)
1985
300.2
2000
2025
2050
2075
365.1
459.1
505.5
574.4
2100
74
67.
19
71.
23
5
7
15
15
.9
.0
.3
.1
.8
.8
.6
.6
.1
82.
72.
21.
88.
35.
9,
11.
21.
22.
.1
7
,9
,7
.4
,0
,1
.9
.3
86.
72.
21.
105.
60.
17.
20.
36.
38.
7
4
.5
0
.4
.4
.9
.8
,0
84.
71.
22.
103.
70.
25.
30.
45.
52.
,2
.5
,1
.5
.6
.1
.7
,0
.8
85
74
25
106
82
32
44
52
71
.9
.4
.1
.2
.2
.0
.5
.3
.8
92.
81.
30.
114.
92.
39.
54.
54.
91.
.2
.9
.1
.3
.0
.5
.7
.8
.4
650.9
TABLE B-33
SECONDARY ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
194.4
FUEL CONSUMPTION
2000
2025
2050
232.9
286.5
306.9
2075
323.8
2100
48.
42.
11.
44.
17.
3.
4.
11,
10.
.2
.4
.9
,6
.0
.9
.7
,3
,4
. 51.
45,
13.
54,
24,
6,
7,
15,
15,
.5
.5
.2
.7
.4
.0
.0
.6
.0
52,
44.
12.
65.
38.
11.
12,
24,
23,
.8
.6
.7
.0
.7
.2
.7
.9
.9
49.
42.
12.
63.
44.
15.
17.
27.
32.
.8
,2
.6
.9
5
.8
,9
.7
5
47,
41,
13,
.60,
47.
18.
23,
29,
42.
.3
.6
.5
.0
.6
.9
.5
.2
.2
46
42,
14
55
48
22
28,
28
51
.1
.7
.9
.9
.1
.7
.4
.2
.2
338.2
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
ELECTRICITY CONSUMPTION
1985
32.9
2000
42.3
2025
2050
57.1
65.0
2075
78.1
2100
8,
8,
2,
8,
1,
1,
1
.4
.0
.4
.4
.8
.5
.8
.3
.3
10.
8.
2.
11.
3.
1.
2.
2,
,0
,9
,8
,0
,2
,9
,2
.1
.2
11.
9.
3.
13.
6.
2.
2.
' 3,
4,
.5
3
.0
,5
.9
,1
,5
.8
.5
11.
9.
3.
13.
8.
3.
3.
5,
6.
,7
7
.3
,3
.3
,3
,7
.1 •
.6
12.
10.
3,
14.
10,
4,
5,
6,
9,
.7
.8
.8
.0
.1
.8
,4
.6
.9
13.
12.
4.
14.
11.
6,
7.
7,
13,
,8
,1
,6
.1
.5
.2
.4
,4
.5
90.6
TOTAL ENERGY CONSUMPTION
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 227.3
2000
275.2
2025
2050
2075
'343.6
371.9
401.9
2100
56.
50,
14,
53.
18,
4,
5,
12,
11,
.6
.4
.3
.0
.8
.4
.5
.6
.7
61
54
16
65
27
6
8
17
17
.5
.4
.0
.7
.6
.9
.2
.7
.2
64.
53,
15,
78,
45,
13,
15,
28,
28
.3
.9
.7
.5
.6
.3
.2
.7
.4
61,
51,
15,
77,
52,
19
21,
32
39
.5
.9
.9
.2
.8
.1
.6
.8
.1
60,
52.
17,
74.
57,
23.
28,
35,
52.
.0
,4
.3
.0
7
.7
.9
.8
.1
59,
54
19,
70,
59,
28,
35,
35
64
.9
.8
.5
.0
.6
.9
.8
.6
.7
428.8
B-51
-------
1'olicy Options for Stabilizing Global Climate
.sew
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-34
SECONDARY OIL CONSUMPTION
(Exajoules/Yr)
1985
100.6
2000
2025
2050
116.2
128.1
131.9
2075
147.6
2100
28.
25.
8.
14.
2.
3.
3.
8.
6.
.8
8
0
6
2
4
1
6
1
s
29.
27.
8.
19.
3,
5.
4
11.
7.
.9
.0
.4
.1
,0
.3
.2
.4
,9
28
24
7
21
4
9
6
15
9
.4
.9
.5
.7
.5
.0
.9
.6
.6
25.
22.
7
21.
6.
12
8
16
12,
.0
.2
.2
.1
. 4
.4
.8
.3
.5
24.
22.
7,
22,
9,
15,
11,
17
17,
.4
.3
,8
,2
.4
.0
.8
.5
.2
24
23,
9,
23.
14.
19.
15,
ie.
24.
.9
.8
.2
.9
.2
.0
,1
.1
.1
172.3
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South arid East Asia
TOTAL
TABLE B-35
SECONDARY GAS CONSUMPTION
(Exajoules/Yr)
1985
48.8
2000
53.0
2025
65.9
2050
2075
68.7
62.7
2100
15.
10.
1.
17.
2.
1.
.6
,3
.3
.1
.3
.5
,4
1
,2
16,
10.
1
18.
2
1
.2
.3
.4
.8
.3
.7
.7
.9
.7
17,
10,
1,
23,
2,
1
6,
2,
.6
.4
.4
.8
.5
.2
.1
.0
.9
17.
10.
1.
23.
3.
1.
7.
3.
5
2
4
1
6
4
5
1
9
15,
9,
1,
19,
3,
1,
6,
4,
.3
.0
.4
.6
.5
.9
.6
.9
.5
13,
8,
1
16,
3
1
5
4,
.5
.3
.3
.2
.5
.7
.8
.8
.8
55.9
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-36
SECONDARY SOLIDS CONSUMPTION
(Exajoules/Yr)
1985
2000
2025
2050
2075
2100
3,
6,
2,
12,
14,
1,
3,
45,
.8
.3
.6
.9
.5
.0
.2
.6
.1
.0
5.
8.
3,
16.
21.
2.
1.
5.
63.
,4
2
.4
,8
1
,0
.1
,3
,4
,7
6.
9,
3,
19,
33,
4
3,
11
92
,8
.3
.8
.5
.7
.0
.7
.3
.4
.5
7
9,
4,
19
37
7
4
16
106
.3
.8
.0
.7
.5
.0
.6
.3
.1
.3
7
10
4
18
37
10
4
20
113
.6
.3
.3
.2
.7
.0
.1
.8
.5
.5
7
10,
4,
15
33,
11,
4,
22,
110,
.7
.6
.4
.8
.4
.0
.5
.3
.3
.0
B-52
-------
Appendix B: Implementation of the Scenarios
sew
TABLE B-37
RESIDENTIAL/COMMERCIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
47.9
FUEL CONSUMPTION
2000
54.6
2025
2050
2075
69.6
75.8
77.0
2100
11.
12
1.
13,
it.
1.
2
.5
.9
.6
.2
.3
,2
.it
.6
.2
11.
13,
2.
15.
5,
2,
3,
,5
6
,0
,6
.it
.it
.9 .
1
,1
12,
13,
2.
23,
6
1,
2,
3,
5,
,3
.3
,0
,9
.8
.0
.2
.1
.0
12,
12
2.
23,
9.
1
3,
it.
7
.1
.6
,0
.1
.5
.5
,6
,0
.it
10,
11
1,
20,
11,
2
it.
it
9
.6
.it
.9
.3
.5
.0
.9
.5
.9
9
10
2
17
12
2
5
it
11
.6
.8
.0
.3
.it
.5
.9
.7
.9
77.1
ELECTRICITY CONSUMPTION
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 13.7
2000
17.6
2025
26.5
2050
30.3
2075
36.9
2100
5
it.
1
1
.2
.5
.1
.3
.2
.1
.3
.5
.5
6,
5.
1,
2,
,2
,0
,it
.2
.5
.2
.5
.8
.8
7.
5.
1.
it.
1.
1.
1.
1.
8
7
,7
7
.it
.7
.2
,5
,8
7.
5.
1.
it.
2.
1.
1,
2.
2.
,9
,9
,9
.6
,0
,1
,9
,1
9
8.
6.
2.
it.
2.
1.
2,
3.
it.
.it
.it
.2
,9
,8
,8
.9
,0
.5
9.
7,
2,
it.
3,
2.
it.
3.
6,
.0
,0
.7
.9
.7
.8
.0
.7
.5
44.3
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TOTAL ENERGY CONSUMPTION
1985 2000 2025
61.6
72.2
2050
2075
96.1
106.1
113.9
2100
16,
17,
2,
lit.
it.
2.
2,
:==
.7
.it
.7
.5
.5
.3
.7
.1
.7
=SSSi
17.
18.
3.
17.
5.
1.
2,
3.
===:===
,7
6
.it
,8
,9
,6
.it
9
,9
20.
19.
3,
28,
8,
1.
3,
>t.
6,
,1
.0
,7
.6
,2
,7
.it
.6
.8
20,
18.
3.
27.
11.
2,
5
6,
10.
,0
,5
.9
,7
.5
.6
,5
.1
.3
19,
17.
it.
25,
U.
3,
7,
7,
14,
.0
,8
.1
.2
.3
.8
.8
.5
.it
==SS=
18,
17.
4,
22,
16,
5,
9,
8,
18,
.6
.8
,7
.2
.1
.3
.9
.4
.4
S=s:
121.4
B-53
-------
Policy Options for Stabilizing Global Climate
sew
TABLE B-38
INDUSTRIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
86.1
FUEL CONSUMPTION
2000
107.3
2025
2050
2075
129.5
U0.it
137.5
2100
15
13
5
23
11
3
2
5
5.
.6
.9
.4
.7
.5
.0
.1
.5
.4
17,
15,
6,
27,
17,
it.
3,
8,
8,
,9
.2
,0
.3
.1
.5
.0
.0
.3
17,
13,
5,
25,
28,
8,
It.
13,
13,
.7
.1
.2
.it
.2
.1
.7
.4
,7
17
13
5
24
29
11
6
1*
17
.5
.0
.2
.7
.5
.5
.8
.6
.6
16,
12,
5,
21.
27,
12,
a.
13,
20
.2
.7
.3
.5
.it
.0
.2
.8
.it
15.
12.
'5.
18.
22.
11.
9,
11.
20,
. 1.
,6
,3
,1
,2
,1
.1
,3
.9
125.7
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
ELECTRICITY CONSUMPTION
1985
19.2
2000
2025
2050
24.7
30.6
34.5
2075
40.7
2100
3.
3.
1.
7.
1,
,2
.5
.3
.1
.6
.4
.5
.8
.8
3
3,
1
8
2
1
1
.8
.9
.4
.8
.7
.7
.7
.3
.4
3.
3.
1.
8.
5.
1.
1.
2.
2,
.7
,6
.3
.8
,5
.4
.3
.3
.7
3
3,
1,
8
6,
2
1
3
3
.8
.8
.4
.7
.1
.2
.8
.0
.7
4,
4.
1.
9,
7,
3,
2,
3
5
.3
.4
.6
.1
.0
.0
.5
.6
.2
4.
5.
1.
9.
7.
3,
3.
3,
6.
,8
.1
.9
.2
.3
.4
.4
.7
.7
45.5
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TOTAL ENERGY CONSUMPTION
1985
105.3
2000
132.0
2025
2050
2075
160.1
174.9
178.2
2100
18.
17.
6.
30.
13.
3.
2.
6.
6.
3SKS=
,8
,4
,7
,8
.1
,4
,6
.3
,2
21.
19,
7,
36.
19.
5,
3,
9.
9,
.7
,1
.4
.1
.8
.2
.7
.3
.7
21
16
6
34
33
9
6
15
16
.4
.7
.5
.2
.7
.5
.0
.7
.4
SS35S5S
21.
16.
6.
33.
35.
13.
8.
17.
21.
,3
.8
,6
,4
,6
.7
.6
.6
.3
20.
17,
6,
30,
34,
15,
10,
17
25
.5
.1
.9
.6
.4
.0
.7
.4
.6
19,
17,
7,
27,
29,
14,
12,
15,
27
,9
.7
.2
.3
.5
.5
.5
.0
.6
171.2
B-54
-------
Appendix B: Implementation of the Scenarios
SCH
TABLE B-39
TRANSPORTATION ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
1985
FUEL CONSUMPTION
2000 2025
2050
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
ELECTRICITY CONSUMPTION
1985
======
.0
.0
.0
.0
.0
.0
.0
.0
.0
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0
2025
.0
.0
.0
.0
.0
.0
.0
.0
.0
2050
.0
.0
.0
.0
.2
.0
.0
.0
.0
2075
2075
.0
.0
.0
.0
.3
.0
.0
.0
.2
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
21.
15
4.
7
1
2
A
2
60
.1
.6
.9
.7
.2
.7
.2
.2
.8
.4
22.
16.
5,
11,
1,
1
3,
5.
3.
71.
.1
.7
,2
,8
.9
.1
,1
,5
.6
.0
22,
18.
5,
15,
3,
2,
5,
8,
5
87,
,8
.2
.5
,7
.7
.1
.8
.4
.2
.A
20
16
5
16
5
2
7
9
7
90
.2
.6
.4
.1
.5
.8
.5
.1
.5
.7
20.
17.
6.
18.
8.
4.
10.
10,
11.
109.
5
.5
.3
2
,7
.9
.4
.9
.9
.3
21.4
19.3
7.6
20.5
13.5
9.1
13. A
12.2
18.4
135.4
2100
.0
.0
.0
.0
.5
.0
.0
.0
.3
TOTAL
REGION
United States
OECD Europe/Canada
OECD Pacific
-Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TOTAL ENERGY CONSUMPTION
1985 2000 2025
60.A
71.0
2050
2075
87.4
90.9
109.8
2100
21.
15.
A.
7,
1.
2.
4,
2,
SSSS
.1
.6
.9
7
.2
7
.2
,2
.8 •
22
16.
5.
11.
1,
1,
3
5
3
.1
.7
.2
.8
.9
.1
.1
.5
.6
22,
18,
5,
15
3,
2,
5
8
5
===
.8
.2
.5
.7
.7
.1
.8
.4
.2
20,
16,
5,
16,
5.
2.
7.
9
7.
,2
,6
,4
.1
.7
,8
,5
.1
.5
20
17
6
18
9
4
10
10
12
.5
.5
.3
.2
.0
.9
.A
.9
.1
21
19
7
20
14
9
13
12
18
.4
.3
.6
.5
.0
.1
.4
.2
.7
136.2
B-55
-------
Policy Options for Stabilizing Global Climate
sew
TABLE B-40
ELECTRIC UTILITY ENERGY CONSUMPTION
(Exajoules/Yr)
REGION
1985
2000
2025
2050
2075
TOTAL
105.3
131.9
168.5
185.2
215.2
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
26
24
7
26
6
1
2
4
4
.6
.6
.4
.4
.8
.9
.7
.3
.6
30
27
8
34
11
2
4
6
7
.5
.1
.7
.0
.0
.9
.1
.3
.3
33.
27.
8.
39.
21.
6.
7.
11,
13.
3
4
6
.2
,2
,0
.8
.3
.7
32.
27,
9,
37,
23
9
10
15
19
,7
,8
,1
.2
.9
.3
.8
.3
.1
34.
30.
10.
37.
28,
13,
14.
19
27
1
1
4
3
.0
.1
.9
.8
.5
36.8
33.9
12.5
37.8
31.8
16.7
20.3
22.0
36.9
248.7
TABLE B-41
ENERGY CONVERSION EFFICIENCY AT ELECTRIC UTILITY FOWERPLANTS*
(percent)
REGION
1985
2000
United 'States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
* Includes transmission and distribution losses
2025
2050
2075
2100
31.2
32:.5
32.4
31.4
26.5
26.3
22.2
30,. 2
26.1
32.5
32.5
32.2
' 32.4
29.1
27.6
29.3
33.3
30.1
34.2
33.6
33.7
34.2
32.5
31.7
32.1
33.6
32.8
35.5
35.3
36.3
36.0
33.9
35.5
35.2
33.3
34.6
37.2
35.9
37.5
37.3
36.1
36.6
36.2
33.3
35.6
37.2
36.0
36.0
37.3
36.5
37.1
36.5
33.2
36.3
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TABLE B-42
SYNTHETIC PRODUCTION OF OIL AND GAS
(Exajoules/Yr)
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
OIL FROM SYNFUELS
2000 2025
2050
2075
2100
.4
.2
.1
.7
.7
.0
.1
.0
.2
2.
A.
3.
1.
1.
1.
3
8
.6
1
.2 .
,0
,5
,9
1
8.
4.
2.
16.
12.
3.
3,
2.
.3
,9
.3
,3
,0
.0
.3
.7
.2
16
9
4
38
21
4
4
2
.9
.8
.7
.4
.8
.0
.9
.2
.3
TOTAL
2.4
15.5
53.0
103.0
REGION 1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL . 0
GAS FROM SYNFUELS
2000
.0
2025
5.5
2050
12.9
2075
16.2
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
.4
.3
.2
.8
.4
.0
1.1
• 1.6
.7
1.
1.
2.
3.
1.
0
7
4
9
9
0
5
8
7
1.
2.
1.
3.
4.
2.
.3
9
5
5
.3
0
.1
,6
,0
2.
1.
4.
2.
4.
6.
• 3.
,4
6
.8
.8
,4
.0
.6
,7
,0
26.3
B-56
-------
Appendix B: Implementation of the Scenarios
sew
TABLE B-43
ENERGY USED FOR SYNTHETIC FUEL PRODUCTION BY TYPE
(Exajoules/Yr)
REGION 1985
United States .0
OECD Europe/Canada ,0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL .0
COAL
2000
.0
2025
2050
4.0
16.1
2075
67.7
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
.7
.4
.2
1.1
1.1
.0
.2
.0
.3
2.
1,
4.
4
1
.8
.6
.7
.7
.3
.0
.1
.2
.7
11.
6.
3.
22.
17.
5.
1,
.2
.4
.0
.1
.3
.0
.3
9
.5
24.
14.
6.
56.
33.
6,
1,
2.
.7
,1
,7
,7
,1
,0
,2
.5
.4
145.4
REGION 1985
TOTAL
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
BIOMASS
2000
2025
2050
7.4
25.7
2075
36.8
2100
0
0
0
0
0
0
0
0
0
1.
1
2
1
.6
.4
.2
.1
.5
.0
.4
.2
.0
2.
1.
3.
1.
5.
7,
3,
.1
,5
7
.8
.8
,0
,0
.5
.3
2
2
1
5
2
7
10
4
.9
.1
.1
.5
.6
.0
.2
.7
.7
3.
2.
1.
7.
3,
9.
13,
6,
.8
.7
,4
,1
.3
,1
,3
.9
.1
47.7
REGION 1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL
TOTAL
2000
2025
2050
2075
41.8
104.5
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
1.
2.
1.
1.
2.
1.
3
8
4
2
6
0
.6
.2
.3
4
3
1
8
6
6
7
4
.9
.1
.4
.5
.1
.0
.1
.7
.0
14.
8.
4.
27.
19.
12.
11.
6,
.1
,5
.1
.6
.9
,0
.5
.6
.2
28.
16,
8.
63.
36.
15,
15,
8,
,5
,8
,1
.8
,4
,1
.5
.4
.5
=ss:
193.1
B-57
-------
Policy Options for. Stabilizing (Ilobal Climate
sew
TABLE B-44
C02 EMISSIONS FROM FOSSIL FUEL
(Petagrams C/Yr)
REGION
1985
2000
2025
2050
2075
TOTAL
5.1
6.2
7.6
7.9
9.0
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned
Centrally Planned
Middle East
Africa
Latin America
Europe
Asia
South and East Asia
1.3
.9
.3
1.3
.6
.1
.1
.2
.3
1.4
1.0"
.It
1.6
.8
.1
.2
.3
.4
1
1
1
1
.5
.0
.it
.8
.2
.3
.4
.It
.6
1.
1.
1.
1.
4
.0
.4
7
.3
.4
.It
.5
,8
1.
1.
1.
1,
1.
A
.1
. 4
.8
.6
.5
.6
,5
,1
1
1
2
' 1
1
.6
.2
.5
.1
.7
.6
.7
.5
.3
10.
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-45
CO EMISSIONS FROM FOSSIL FUEL
(Teragrams C/Yr)
1985
185.8
2000
2025
2050
188.3
249.5
273.6
2075
342.7
2100
51
it>t
14
31
6
2
8
16
11
.0
.7
.1
.1
.0
.9
.5
.3
.2
29
38
11
47
9
4
12
21
14
.2
.2
.9
.1
.1
.3
.3
.6
.4
30
41
12
62
17
8
22
32
21
.3
.5
.6
.8
.1
.3
.9
.9
.2
26.
38.
12.
64.
24.
11.
29.
35.
30.
.9
.1
,4
.3
.6
,1
.8
,7
7
27.
40.
14.
72.
37.
19.
41.
42.
48.
,3
,2
.4
,3
.3
,3
,3
7
.0
28.
44.
17.
81.
55.
35.
53.
48.
73.
=_:
.5
,1
.2
,2
.9
.6
. 1
.0
.6
437.2
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-46
NOx EMISSIONS FROM FOSSIL FUEL
(Teragrams N/Yr)
1985
24.2
2000
2025
2050
2075
27.9
32.9
34.1
37.9
2100
6.
4,
1,
5.
2.
1.
1
.1
.6
.7
.8
.6
.4
.8
.0
.3
5.
4,
1.
7,
3.
1,
1,
'1,
,8
.7
,7
.0
.8
.5
.2
,3
.8
5.
4.
1,
7,
5.
1,
2,
2,
2.
4
.7
.7
.4
.7
.0
,0
2
.8
4.
4.
1.
7.
6.
1.
2.
2.
3.
8
.4
7
0
1
3
6
5
7
4.
4
1
7.
6.
1
3,
• 2,
5,
.8
.6
.9
.0
.6
.7
.4
,8
.1
5.
5
2.,
7
7.
2
4,
' 3
6
.0
.0
.1
.1
.0
.5
.2
.1
.8
B-58
-------
Appendix B: Implementation of the Scenarios
ROW
TABLE B-47
PRIMARY ENERGY SUPPLY
(Exajoules/Yr)
REGION
198;
2000
20Z5
2050
2075
TOTAL
301.6
403.6
646.2
926.6
1209.6
210D
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
63.
47.
8.
81.
25.
23.
16.
20.
14.
7
6
9
2
6
7
3
5
1
63
55
13
99
46
52
26
25
20
.2
.6
, it
.9
.0
.8
.7
.6
.4
80
66
17
148
114
75
49
62
32
.0
.5
.0
.3
.3
.0
.9
.3
.9
110
74
24
243
181
89
64
93
45
.7
.0
.6
.5
.7
.0
.2
.2
.7
196
91
44
395
183
66
66
94
71
.4
.7
.1
.9
.2
,4
.7
.1
.1
305,
110,
70.
472,
209.
51,
71.
91,
102,
ss=:
7
3
,D
.0
.7
,3
.6
,1
,3
1484.0
REGION
1985
TABLE B-48
PRIMARY OIL SUPPLY
(Exajoules/Yr)
2000
2025
2050
TOTAL
117.9
135.1
156.5
143.9
2075
112.4
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
g~.Ma
20,
11,
1,
26.
5.
22.
10.
14,
5.
soffl
.8
9
1
. 0
2
,4
,8
,1
.6
12
9
22
6
49
17
11
4
.2
.8
.6
.3
.9
.3
.4
.9
.7
9
8
18
7
58
22
27
4
.7
.7
.2
,4
.2
.3
.3
.5
.2
10,
6.
12
5
45
18
41
3
.4
.4
.0
.8
.2
.5
.4
.9
.3
18,
3,
8.
4.
29,
13.
31,
2,
EZD
,4
9
,2
.8
,0
,6
.2
,5
.8
iLjiuim.g3.-n
26.2
2.3
.2
6.4
3.4
18.5
9.5
19.2
2.5
88,2
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 58.6
TABLE B-49
PRIMARY GAS SUPPLY
(Exajoules/Yr)
2000
78.7
2025
2050
97.5
100.1
2075
60.2
2100
16,
9,
24,
1,
1,
2,
2,
.3
.7
.7
.0
.5
.2
.3
,5
.4
16,
12,
1,
31.
2,
3.
3.
5,
3,
,0
.4
.8
.2
,1
.3
.2
,7
.0
12,
11,
1
31,
4
14
6
10,
4
,6
.8
.7
,7
.6
.1
.3
.5
.2
8.
8,
1
24,
4,
34,
7
7
2
,8
,4
.1
.6
.2
.8
.8
.5
.9
6
5
12
2
21
5
4
1
.4
.5
.5
.6
.0
.6
.7
.1
,8
Z
3
5
1
10
3
2
.2
.1
.3
.0
.0
.a
.4
.0
.9
28.7
REGION
1985
TABLE B-50
PRIMARY COAL SUPPLY
(Exajoules/Yr)
2000
2025
2050
TOTAL
87.3
136.6
275.3
467.2
2075
713.7
2100
United States
OECD Europe /Can ad a
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
19.
9.
3.
26.
18.
4.
4,
.4
,4
,9
7
.9
.0
o
.6
s.
25,
14
6
39,
34
5,
1
9
.9
.4
.5
.4
.3
.0
.6
.1
.4
42
23
10
83
86
14
3
11
.4
.9
.3
.2
.6
.0
.8
.1
.0
70.
33,
16.
177.
139,
19.
3.
7.
.0
,5
,8
.4
1
,0
.6
.8
.0
144,
51.
34,
331,
123.
17,
3,
8,
-laa—gai
,1
.1
,2
.6
.2
,0
,8
,5
,2
244.5
68.8
58.0
404.8
130.8
.0
19.3
3.8
10.4
wi-TfnfMiiHpaBi
940.4
B-59
-------
Policy Options for Stabilizing Global Climate
RCW
TABLE B-51
PRIMARY BIOMASS SUPPLY
(Exajoules/Yr)
REGION
1985
2000
2025
2050
TOTAL
13.A
A3. 8
2075
61.7
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
1.
2.
2.
3.
1.
1
8
A
0
9
0
6
9
7
3
2
1
6
3
8
' 12
5
.5
.5
.3
.5
.1
.0
.5
.8
.6
A.
3.
1.
9
A,
12.
18.
7.
.9
,5
.8
.2
.3
.1
.0
.0
.9
5.
3.
2.
10.
A.
13,
19.
8.
A
9
.0
1
.8
.2
.3
.9
.7
68.3
REGION
1985
TABLE B-52
PRIMARY HYDROELECTRIC SUPPLY
(Exajoules/Yr)
2000
2025
2050
TOTAL
21.2
30.2
A8.7
63.1
2075
70.0
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
3
8
1
2
1
3
1
.3
.2
.2
.5
.0
.1
.2
.3
.A
3
9
1
3
2
6
2
.8
.6
.3
.1
.3
.2
.A
.9
.6
A,
10.
1.
3.
6,
1.
1A.
5.
.3
.6
,3
.6
,A
A
.3
.9
.9
A
11
1
3
10
3
19
9
.6
.0
.A
.7
.0
.6
.2
.2
.A
A
11
' 1
3
11
5
20
11
.7
.1
.A
.7
.3
.7
.3
.A
.A
A.
11.
1.
3.
11.
6.
20.
12.
8
1
A
7
6
7
6
6
1
72.6
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
1985
3.
8.
2.
2,
.8
,A
.0
,0
,0
.0
,0
,0
.3
2000
A. 8
9.1
3.0
3. A
.2
.0
.1
.0
.6
TOTAL
16.5
TABLE B-53
PRIMARY NUCLEAR SUPPLY
(Exajoules/Yr)
2025 2050
7.6
9. A
2.6
7.0
6. A
1.5
1.8
1.7
A. 3
A2.3 72.6
2075
2100
21.2
120.3
167.9
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin. America
South and East Asia
TOTAL
1985
TABLE B-5A
PRIMARY SOLAR SUPPLY
(Exajoules/Yr)
2000
2025
1.8
12.5
2050
2075
35.9
71.3
2100
.5
.3
.2
.5
.2
.0
.0
.0
.1
2.
1.
2.
2.
1,
3
.3
5
,A
.2
,7
.8
,7
,6
A,
2.
1,
6.
7
2
2
2
6
.7
.6
.2
.A
.0
.8
.3
.8
.1
6.
A.
2.
11,
1A,
5,
A,
6,
15
.9
.0
,0
.6
.8
.6
.9
.A
.1
9.
5,
3.
17,
2A,
9.
8,
10,
28,
,6
,7
.0
,9
,7
.0
,3
,9
.8
117.9
B-60
-------
Appendix B: Implementation of the Scenarios
RCW
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-55
PRIMARY ENERGY CONSUMPTION
(Exajoules/Yr)
1985
300.2
2000
404.2
2025
2050
647.7
926.1
2075
1209.9
2100
7A.
67.
19,
71.
23.
5,
7,
15,
15,
9
,0
,3
. 1
.8
,8
. 6
.6
,1
85.
76,
2A,
96,
A3,
9,
1A
27.
28
.8
.6
.0
.0
.A
.A
.0
.0
.0
102
88
25
115
110
27
37
68
72
.7
.3
.5
.7
.3
.7
.5
.0
.0
112,
96,
31
162
176,
A5
63
105
132
.7
.0
.6
.2
.1
.A
.6
.8
.7
128.
103.
38.
221.
230.
61.
85.
133.
207.
7
1
6
6
5
A
1
9
0
157.
113.
48.
272.
274.
75.
IDA.
155.
283.
I—;
A
9
1
1
0
0
0
5
9
1483.9
TABLE B-56
SECONDARY ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
FUEL CONSUMPTION
REGION
1985
2000
2025
2050
TOTAL
194.4
251.0
369.9
480.5
2075
540.9
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
48
42
11
44
17
3
4
11
10
.2
.4
.9
.6
.0
.9
.7
.3
.A
52
47
14
57
28
6
8
18
17
.7
.2
.2
.5
.6
.1
.5
.5
.7
57.7
52.6
14.9
66.0
60.9
16.5
21.3
42.0
38.0
58.4
52.9
16.9
82.6
88.0
25.6
32.7
57.7
65.7
55,
50
17,
90,
99
32
39
65
90,
.5
.7
.5
.4
.9
.1
.A
.1
.3
56
51
18
98
104
36
43
70
112
.2
.7
.7
.5
.6
.9
.2
.7
.7
593.2
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 32.9
ELECTRICITY CONSUMPTION
2000
49.0
2025
2050
3.5
137.9
2075
201.0
2100
8.
8.
2.
8.
1.
1.
1.
,4
,0
.A
.A
,8
,5
.8
,3
,3
10
9
3
12
A
1
1
2
3
.9
.6
.1
.6
.2
.0
.7
.8
.1
1A,
11,
3,
15,
15,
3,
A,
a:
10
.7
.7 .
.5
.7
.1
.8
.8
.3
.9
16,
13,
A,
22,
26,
7,
8,
15,
23,
.9
,6
.6
.A
.2
.0
,7
.A
.1
s=s=
19.
15.
5.
30.
Al.
10.
1A.
22.
Al.
0
8
,8
,3
,0
,6
,0
,8
,7
==±=
20.
17.
6.
36.
52.
13.
18.
28.
61.
7
5
8
5
8
8
7
A
1
256.3
REGION
1985
TOTAL ENERGY CONSUMPTION
2000 2025
2050
2075
TOTAL
227.3
300.0
AS'S.A
618.A
7A1.9
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
56,
50,
1A,
53
18
A
5
12,
11
.6
.A
.3
.0
.8
.A
.5
.6
.7
63.
56.
17.
70.
32.
7.
10.
21.
20.
,6
,8
,3
,1
,8
,1
,2
,3
,8
72.
6A.
18.
81.
76.
20.
26.
50.
AS,
A
,3
,A
,7
,0
,3
,1
.3
,9
75.
66.
21.
105.
11A.
32.
Al.
73.
88.
3
,5
5
,0
,2
,6
,A
,1
,8
7A.
66.
23.
120.
1AO.
A2.
53.
87.
132.
5
5
3
7
9
7
A
9
0
76.9
69.2
25.5
135.0
157. A
50.7
61.9
99.1
173.8
8A9.5
B-61
-------
Policy Options for Stabilizing Global Climate
RCW
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
1985
TOTAL
100.6
TABLE B-57
SECONDARY OIL CONSUMPTION
(Exajoules/Yr)
2000
120.4
2025
2050
161.4
209.7
2075
259.3
2100
28,
25.
8.
14.
2.
3.
3.
8.
6.
:=s:=
=s==
g
.8
.0
.6
.2
.4
.1
.6
.1
=====
=====
29.
26,
8,
20,
3.
5,
4,
13
8
S====
.1
.9
.8
,0
.4
.3
.9
.1
.9
sssssss
29,
29,
9,
25,
7,
12.
11,
24,
13,
S===
.3
,1
.2
,3
.2
.7
,2
.0
.4
29,
28,
10,
32,
13.
19.
16,
34,
25.
,4
,7
,1
,3
.6
.9
.4
.1
.2
29.
28,
10,
38,
23.
25.
20.
41.
41.
,3
.1
.7
.5
.6
.9
.4
.4
.4
=====
30.
30,
11.
46.
37,
30,
24,
47,
62,
,9
.2
,9
,7
,7
,5
.2
.9
.0
322.0
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
48.8
TABLE B-58
SECONDARY GAS CONSUMPTION
(Exajoules/Yr)
2000
2025
2050
57.7
79.4
93.4
2075
94.0
2100
fSK=S=
15.
10.
1.
17.
2,
1,
sssss
.6
.3
,3
,1
,3
.5
,4
.1
.2
======
17.
11.
1.
19.
3.
2.
.4
.2
.6
.6
.4
.8
.8
.8
.1
=======
19.
12.
1,
22,
3,
1,
12,
5,
.7
,4
.5
,3
,8
.8
.8
.'0
.1
19,
11.
1,
27,
1.
5,
2.
15.
8.
.3
.8
.8
.1
.2
.7
.6
.5
.4
SS===S=
16
10.
1
28.
1
6
3
15
11
=====
.8
.2
.6
.1
.4
.1
.0
.8
.0
16.
9.
1.
30.
1.
6.
3.
16.
13.
4
8
6
0
6
3
5
3
8
99.3
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 45.0
TABLE B-59
SECONDARY SOLIDS CONSUMPTION
(Exajoules/Yr)
2000
72.9
2025
2050
2075
129.1
177.4
187.6
2100
3
6
2
12
14
1
3
.8
.3
.6
.9
.5
.0
.2
.6
.1
6.
9,
3.
17.
24.
2,
1.
6,
.2
,1
,8
.9
.8
,0
.8
.6
.7
8,
11.
4,
18,
52,
8,
6.
19.
.7
,1
.2
.4
.9
.0
.3
.0
.5
9.
12,
5,
23.
73,
13,
8,
32,
.7
.4
.0
.2
.2
.0
.7
.1
.1
9
12.
5.
23
74.
16
7.
37
.4
.4
.2
,8
.9
.1
.0
.9
.9
8
11
5
21
65
15
6
36
.9
.7
.2
.8
.3
.1
.5
.5
.9
171.9
B-62
-------
Appendix B: Implementation of the Scenarios
RCW
TABLE B-60
RESIDENTIAL/COMMERCIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
FUEL CONSUMPTION
REGION
1985
2000
2025
2050
TOTAL
47.9
56.4
76.0
99.9
2075
113.2
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
11.
12.
1.
13.
4.
1.
2.
5
.9
6
2
3
2
4
6
2
11.
14.
2
15,
6.
1
2
3.
.6
.6
.2
.0
,1
,4
.0
.3
.2
13.
17.
2.
20.
9.
1.
2.
4.
5.
1
5
3
4
4
5
3
1
4
12.
16.
2.
25.
17.
2.
4.
7.
10.
9
5
7
,6
7
7
2
0
6
11.
14.
2.
27,
23,
3.
5.
9.
15.
.4
5
.7
,3
.7
,8
.5
.0
.3
10.9
13.5
2.8
28.4
25.8
4.5
6.1
10.4
18.2
120.6
REGION
1985
ELECTRICITY CONSUMPTION
2000
2025
2050
2075
TOTAL
13.7
19.7
37.3
56.4
81.8
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
5.2
4.5
1.1
1.3
.2
.1
.3
.5
.5
6.
5.
1.
2.
1.
1.
,6
4
.5
7
.5
2
7
.0
.1
10.
7.
2.
6.
2.
1.
2.
2.
3.
1
8
0
1
.0
1
0
4
.8
11
8
2
8
4
2
3
5
8
.4
.6
.7
.8
.5
.3
.9
.3
.9
12.
9.
3,
11.
8,
4.
6.
9,
17.
.4
.4
,3
.7
,5
,0
:s
.0
.0
13.
10.
3.
13.
12.
5.
8,
12.
26.
.2
.0
.8
,9
,9
.9
.9
.5
.1
107.2
REGION
1985
TOTAL ENERGY CONSUMPTION
2000
2025
2050
TOTAL
61.6
76.1
113.3
156.3
2075
195.0
2100
United States
OECD Europe/Canada
,OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
16
17
2
14
4
2
2
.7
.4
.7
.5
.5
.3
.7
.1
.7
18,
20,
3.
17.
6,
1.
3.
4,
=====
.2
,0
.7
.7
.6
.6
.7
.3
.3
23
25
4
26
11
2
4
6
9
===
.2
.3
.3
.5
.4
.6
.3
.5
.2
24
25
5
34
22
5
8
12
19
.3
.1
.4
.4
.2
.0
.1
.3
.5
23,
23,
6.
39,
32,
7,
12,
18,
32,
;=:=
.8
,9
.0
.0
,2
.8
.0
.0
.3
24.1
23.5
6.6
42.3
38.7
10.4
15.0
22.9
44.3
227.8
B-63
-------
Policy Options for Stabilizing Global Climate
RCW
TABLE B-61
INDUSTRIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
1985
TOTAL
86.1
FUEL CONSUMPTION
2000
122.0
2025
2050
2075
182.6
233.3
240.3
2100
15.
13.
5.
23.
11.
3,
2,
5.
5,
6
9
A
7
5
,0
,1
. 5
,A
20,
16,
6,
29,
20
A
3
10
10
.0
.1
.6
.5
.A
.7
.9
.2
.6
20.
13.
5.
25.
A5.
12,
9.
25.
25,
9
5
5
0
7
A
,2
,3
.1
21,
14
6
30
58
18
1A
31
39
.A
.5
.2
.1
.2
.0
.2
.A
.3
19
14
6
30
5A
20
16
31
' 46
.9
.5
.3
.6
.6
.2
.2
.2
.8
19,
14,
6,
29,
43,
19,
15
28
48
.A
,2
,3
,5
.3
.6
.9
.A
.0
22A . 6
REGION 1985
United States
OECD Europe/Canada
OECD Pacific ;
Centrally Planned Europe
Centrally Planned Asia
Middle 'East
Africa
Latin America
South and East Asia
TOTAL 19.2
ELECTRICITY CONSUMPTION
2000 2025
2050
29.3
51.0
80.9
2075
117.8
2100
3,
3,
1
7
1
.2
.5
.3
.1
.6
.A
.5
.8
.8
4,
A,
1,
9,
3,
1
1,
2
.3
,2
.6
.9
.7
.8
.0
.8
.0
A.
3.
1.
9.
12.
2,
2,
5,
7,
6
9
,5
,6
,9
,7
,8
,9
,1
5,
5
1
13
21
A
A
10
1A
,5
.0
.9
.6
.3
.7
.8
.1
.0
6
6
2
18
31
6
7
13
24
=====±!
.6
.A
.5
.6
.6
.6
.5
.8
.2
=====
7.
7.
3.
22,
38.
7.
9.
15.
3A.
:s=ss=:ss
,5
,5
,0
.6
,3
,9
,8
,8
2
ss:;
1A6.6
REGION
1985
TOTAL ENERGY CONSUMPTION
2000
2025
2050
TOTAL
105.3
151.3
233.6
31A.2
2075
358.1
2100
^United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
18.
17.
6.
30.
13.
3.
2.
6,
6.
==S=
8
A
7
.8
.1
4
,6
,3
.2
2A.
20,
8,
39,
2A
5,
A,
12,
12
.3
,3
.2
.A
.1
.5
.9
.0
.6
=S=KS
25.
17.
7.
3A.
58.
15.
12.
31.
32,
5
A
0
,6
6
1
0
,2
,2
:s==;
26,
19,
8.
A3.
79,
22,
19,
Al,
53
,9
,5
,1
.7
,5
.7
.0
.5
.3
s==±
26
20
8
A9
86
26
23
A5
71
.5
.9
.8
.2
.2
.8
.7
.0
.0
26.9
21.7
9.3:
52.1
81.6
27.5
25.7
AA.2
82.2
371.2
B-64
-------
Appendix B: Implementation of the Scenarios
_RCW
TABLE B-62
TRANSPORTATION ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
FUEL CONSUMPTION
REGION
1985
2000
2025
2050
2075
TOTAL
60.4
72.6
111.3
147.3
187.4
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
21.
15.
4.
7.
1.
2,
4.
2.
1
6
,9
,7
2
.7
.2
.2
.8
21
16,
5
13
2
1
3
6
3
.1
.5
.4
.0
.1
.0
.6
.0
.9
23.
21.
7.
20.
5.
2.
9.
12.
7.
7
6
1
6
8
6
8
6
5
24.
21
8
26
12
4.
14
19
15
.1
,9
.0
.9
.1
.9
.3
.3
.8
24
21
8
32
21
8
17
24
28
.2
.7
.5
.5
.6
.1
.7
.9
.2
25.9
24.0
9.6
40.6
35.5
12.8
21.2
31.9
46.5
248.0
ELECTRICITY CONSUMPTION
REGION 1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL .0
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0
2025
.0
.0
.0
.0
.2
.0
.0
.0
.0
2050
.0
.0
.0
.0
.4
.0
.0
.0
.2
2075
.0
.0
.0
.0
.9
.0
.0
.0
.5
1.4
2100
.0
.0
.0
.0
1.6
.0
.0
.1
.8
2.5
REGION
1985
TOTAL ENERGY CONSUMPTION
2000 2025 2050
2075
TOTAL
60.4
72.6.
111.5
147.9
188.8
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
21.
15,
4 .
7.
1.
2,
4
2,
;=s=
.1
,6
,9
,7
.2
.7
.2
.2
.8
-— —
~= —
21
16
5
13
2
1
3
6
3
.1
.5
.4
.0
.1
.0
.6
.0
.9
23.
21.
7.
20.
6.
2.
9,
12.
7.
7
6
.1
6
.0
.6
,8
,6
.5
s==
24
21
8
26
12
4
14
19
16
.1
.9
.0
.9
.5
.9
.3
.3
.0
24.
21,
8,
32,
22.
8,
17,
24,
28,
.2
,7
.5
.5
.5
.1
.7
,9
.7
25.9
24.0
9.6
40.6
37.1
12.8
21.2
32.0
47.3
250.5
B-65
-------
Policy Options for Stabilizing Global Climate
RCW
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-63
ELECTRIC UTILITY ENERGY CONSUMPTION
(Exajoules/Yr)
1985
105.3
2000
152.9
2025
2050
263.5
387.4
2075
545.2
2100
26
24
7
26
6,
1
2
4
4
.6
.6
.4
.4
.8
.9
.7
.3
.6
==:=
33.
29.
9,
38,
14.
3.
5,
8.
10,
2
,4
,7
,5
,8
,2
,4
,4
,3
43
34
10
45
"45
11
14
24
33
.1
.6
.2
.9
.8
.1
.7
.9
.2
47.
38.
12
61,
74,
19,
24
44
64.
,0
.8
.8
.6
.1
.8
.7
.0
.6
51
44
15
81
110
29
38
63
112
.1
.0
.7
.0
.6
.2
.0
.4
.2
56.
49,
18,
98
142
38
50
78
165
0
,1
.4
.6
.9
.0
.7
.4
.2
697.3
TABLE B-64
ENERGY CONVERSION EFFICIENCY AT ELECTRIC UTILITY POHERPLANTS*
(percent)
REGION
1985
2000
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
* Includes transmission and distribution losses
2025
2050
2075
2100
31.
32.
32.
31.
26.
26.
22.
30.
26.
,2
,5
,4
,4
,5
,3
,2
,2
.1
33.
32,
30.
32,
28,
28,
29,
32,
30,
.1
.7
.9
.7
.4
.1
,6
.1
.1
34.
33.
34.
33.
32,
33,
32.
33.
32,
,1
,5
,3
,8
.8
.3
,7
,3
,8
36.
35,
36,
36,
35,
35,
35,
35,
35,
.0
.3
.7
.4
.5
.4
.6
.0
.6
37
35
36
37
37
36
37
36
37
.2
.7
.9
.5
.2
.3
.1
.0
.1
37,
35,
37.
37.
36.
36.
36.
36.
36.
.0
,8
,0
,1
,9
.3
9
2
9
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-65
SYNTHETIC PRODUCTION OF OIL AND GAS
(Exajoules/Yr)
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
:===
.0
OIL FROM SYNFUELS
2000 2025
2050
2075
2100
3.
1,
6.
6',
.1
.8
,8
,1
.2
.0
.8
.2
.8
13.
5.
3.
32.
24.
2.
2.
1.
1
,7
2
,7
.9
,0
,7
.5
,9
35
12
8
81
29
3
2
1
.6
.8
.5
.8
.3
.0
.3
.2
.5
70.
20.
16.
116.
35.
3.
2.
2.
0
0
7
1
8
0
6
5
0
19.8
86.7
175.0
266.7
REGION
1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL
GAS FROM SYNFUELS
2000
2025
10.1
2050
28.5
2075
72.4
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
1.
1.
•2.
1.
8
6
3
5
8
.0
,9
,9
,3
2.
1,
5,
2.
5,
7,
3
.5
.7 „-
.8
,1
.8
.0
.0
.3
.3
S=S5
9.
4.
2.
21.
8.
8,
11,
5,
7
,6
,7
,0
,4
,0
,6
.9
.5
===
21.
7,
5,
36,
12.
10,
13
6
,8
7
,6
.8
9
.1
,0
.1
.•4
±=;
114.4
B-66
-------
Appendix B: Implementation of the Scenarios
RCH
TABLE B-66
ENERGY USED FOR SYNTHETIC FUEL PRODUCTION BY TYPE
(Exajoules/Yr)
COAL
REGION
1985
2000
2025
2050
2075
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
REGION
United -States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
BIOMASS
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0
L, .
2,
1.
9.
9.
1.
1.
30,
202
1.
2,
2,
3.
1,
.7
.6
. 1
.2
.6
.0
.6
.3
,2
,3
>5
.1
,8
.A
.0
.9
.0
.6
.9
7
129.9
2050
309.6
2075
13.A
2100
131.6
37.0
31.3
217.9
70. A
.0
10.A
2.1
5.6
506.3
2100
61.7
68.3
REGION
1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL
TOTAL
2000
2025
A3.7
2050
173.7
2075
371.3
2100
0
0
0
0
0
0
0
0
0
5.
3.
1.
11.
10,
A,
A.
2,
,8
.A
.5
.2
.5
.0
.2
.2
.9
22,
11
6
55,
Al,
1A,
13,
7
.9
.8
.0
.8
.8
.0
.0
.9
.5
67
25
16
153
57
19
19
11
.A
.7
.6
.1
.8
.1
.7
.5
.A
137
AO
33
228
75
23,
22,
1A.
.0
.9
.3
.0
.2
.2
.7
.0
.3
57A.6
B-67
-------
Policy Options for Stabilizing Global Climate
RCW
TABLE B-67
C02 EMISSIONS FROM FOSSIL FUEL
(Petagrams C/Yr)
REGION
1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
5.1
2000
2025
2050
2075
7.0
11.2
15.6
20.5
2100
1
1
.3
.9
.3
.3
.6
.1
.1
.2
.3
1
1
1
-• 1
.5
.1
.4
.8
.0
.2
.3
.3
.5
1
1
2
2
1
.8
.3
.4
.0
.3
.5
.7
.9
.3
2.
1.
2.
3.
1.
2.
0
4
5
9
6
7
9
4
1
2
1
4
4
1
1
3
.5
.6
.7
.5
.4
.9
.2
.7
.1
3,
1.
1
5.
4.
1.
1
1
4
5
.8
.0
, 7,
.9
.0
.4
.8
.1
25.0
TABLE B-68
CO EMISSIONS FROM FOSSIL FUEL
(Teragrams C/Yr)
REGION 1985 2000 2025 2050 2075 2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
,,T0TAL 185.8 197.6 334.9 477.5 636.0 862.7
51
44,
14,
31,
6,
2
8
16
11,
.0
.7
,1
.1
.0
.9
.5
.3
.2
28,
37.
12.
51,
10.
4.
14,
23.
15,
.0
.7
.3
,9
.2
.0
.4
.6
.6
31.
49.
16.
81.
26.
10.
38.
49,
30.
5
3
2
5
.9
,5
8
7
7
32.
50.
18,
106,
53,
19,
56,
76.
64,
.2
.1
.1
.8
.5
.7
.6
.0
,5
32
49,
19,
129,
91,
32
70
97,
113,
.5
.8
.5
.0
.5
.0
.3
.9
.5
35.
54,
22.
160,
145,
50,
84,
125.
184.
,1
,9
.1
,8
,4
,2
.1
,.3
,9
REGION
TABLE B-69
NOx EMISSIONS FROM FOSSIL FUEL
(Teragrams N/Yr)
1985
2000
2025
2050
TOTAL
24.2
31.0
47.0
62.7
2075
77.5
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
6.
4,
1.
5.
2.
1,
1.
,1
,6
.7
.8
,6
,4
,8
.0
.3
=±s —
6.
4.
1.
7.
4.
1.
1.
2.
0
8
8
7
7
6
.5
.6
,3
6.
5,
2.
8.
10.
1,
3,
4.
5,
.3
,6
.0
.5
.2
,5
.6
.0
,4
6,
5,
2,
10
14,
2,
5,
6
9
.2
.9
,3
.6
.3
.3
.2
.3
.5
- — ss
6
6
2
12,
17,
3
6
8
14,
.5
.0
.6
.9
.5
.1
.6
.1
.2
==:=
7.2
6.5
3.0
15.2
19.4
4.0
7.5
9.6
19.1
91.5
B-68
-------
RCWA
Appendix B: Implementation of the Scenarios
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
301.6
TABLE B-70
PRIMARY ENERGY SUPPLY
(Exajoules/Yr)
2000
2025
2050
2075
432.9
972.2
1607.6
2261.6
2100
63
47
8
81
25
23.
16
20
14
.7
.6
.9
.2
.6
.7
.3
.5
.1
69,
56.
13.
113.
56,
48.
26.
28,
20.
,3
.5
.3
.5
,1
.4
.7
.6
.5
130.
86.
27.
305.
219.
57.
56.
50.
36.
,4
.9
.5
.8
.7
.9
.4
.8
.8
218
124
46
669
301
53
78
67
47
.4
.6
.9
.0
.7
.6
.7
.6
.1
391.
203
84
982.
333
47
80
75
61
.1
.5
.9
.6
.4
.8
.6
.8
.9
744.
205.
184.
930,
222,
61,
66.
99.
79.
.3
.4
.9
.8
.5
,2
.1
.3
.7
2594.2
REGION
United .States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia ..
TOTAL
1985
117.9
TABLE B-71
PRIMARY OIL SUPPLY-
(Exajoules/Yr)
2000
126.7
2025
2050
110.9
97.9
2075
80.4
2100
20.
11.
1.
26,
5.
22.
10.
14.
5,
,8
,9
,1
,0
,2
,4
.8
,1
,6
12.
9,
22
6.
44,
13
12,
4
.5
.5
.6
.1
.9
.0
.8
.7
.6
7.
7.
15.
6.
41.
14.
14.
3.
,0
,3
.2
,5
.1
,9
,7
,7
,5
5,
5,
11,
4.
32,
18,
17,
2,
.2
.7
.0
.5
.8
.8
.2
.1
.6
4
3
7
3
25
13
19
1
.9
.8
.0
.9
.3
.7
.2
.7
.9
9,
3,
7,
3.
29,
11.
32,
2,
.3
.0
.0
.0
.2
.3
.9
.5
.5
98.7
REGION
1985
TABLE B-72
PRIMARY GAS SUPPLY
(Exajoules/Yr)
2000
2025
2050
TOTAL
58.6
84.8
94.1
67.2
2075
44.0
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
16.
9.
24.
1.
1.
2.
2.
.3
.7
7
.0
,5
,2
.3
,5
,4
17.
13.
1.
30.
2.
4.
4.
7.
2.
.1
.6
,9
,2
.8
.2
.3
.8
.9
12.
12.
1,
27.
4,
14,
7,
9,
4
,9
,1
.7
.5
,8
.8
.0
.0
.3
8
7
12
3
17
6
7
2
.1
.6
.7
.9
.5
.5
.9
.1
.9
ssssst •
2.
4,
8.
1,
15,
4,
3,
1,
.6
,5
.5
.4
.9
.9
.8
.8
.6
4.0
3.1
.5
13.1
1.1
19.5
3.6
2.0
1.0
47.9
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL ' 87.3
TABLE B-73
PRIMARY COAL SUPPLY
(Exajoules/Yr)
2000
171.9
2025
2050
2075
666.0
1284.9
1914.7
2100
19,
9,
3,
26,
18,
4
4,
.4
.4
.9
.7
.9
.0
.0
.6
.4
31.
15.
6.
54.
44.
8.
1.
9.
8
2
8
8
0
0
2
2
9
98.
46.
21.
249.
196.
28.
5.
18.
8
9
6
5
7
0
9
6
0
191
90
41
623
270
39
8
20
.2
.1
.5
.7
.3
.0
.9
.2
.0
366.
169.
78.
936,
288.
43,
8,
23.
.4
,6
,4
,0
,7
.1
.0
.7
.8
706.
163.
175.
858.
139.
20.
4.
11.
3
9
2
3
9
0
8
2
6
2080.2
B-69
-------
Policy Options for Stabilizing (Global Climate
RCWA
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
TABLE B-74
PRIMARY BIOMASS.SUPPLY
(Exajoules/Yr)
2000
2025
2050
18.5
43.6
2075
52.1
2100
0
0
0
0
0
0
0
0
0
I.
1.
2.
1.
3.
5,
2.
5
1
5
7
3
0
.6
,4
.4
3.
2.
1.
6.
3.
8.
12.
5.
5
5
.2
5
1
,0
.5
.7
.6
4.
3.
1.
7.
3.
10.
15.
6.
1
0
5
7
7
1
1
2
7
5.
3.
2.
10,
4
13
19
8
A
,9
,0
,1
.8
.2
.3
.9
.7
68.3
REGION
United States
OECD Europe/Canada
OECD Pacific
Ce'ntcally Planned Europe
'Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-75
PRIMARY HYDROELECTRIC SUPPLY
(Exajoules/Yr)
1985
21.2
2000
30.2
2025
2050
48.7
63.1
2075
70.0
2100
3,
8,
1,
2.
1,
3.
1
.3
,2
.2
.5
.0
.1
.2
.3
.4
3.
9,
1,
3,
2,
6.
2,
.8
.6
,3
,1
,3
,2
.4
.9
.6
4.
10.
1.
3.
6.
1,
14
5.
.3
.6
.3
.6
.4
.4
.3
.9
.9
4,
11,
1,
3,
10
3,
19.
9.
.6
.0
.4
.7
.0
.6
.2
.2
.4
4,
11.
1,
3.
11.
5.
20,
11,
.7
.1
.4
.7
.3
.7
.3
.4
.4
4.
11.
1.
3.
11.
6.
20.
12.
.8
1
4
7
.6
7
6
6
.1
72.6
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
16.5
TABLE B-76
PRIMARY NUCLEAR SUPPLY
(Exajoules/Yr)
2000
18.8
2025
2050
2075
31.4
43.8
83.9
2100
3.
8.
2.
2.
.8
.4
.0
.0
.0
.0
.0
.0
.3
===
3
8
2
3
.9
.5
.7
.1
.1
.0
.0
.0
.5
5.
8.
2.
6,
4,
1,
2,
.4
.5
.1
.3
.0
.8
.8
.1
.4
4
7
1
9
8
2
1
2
5
.9
.1
.8
.1
.5
.3
.7
.8
.6
6,
10,
2,
15
20
4
3
6
13
9
.4
.7
.6
.2
.4
.5
.6
.6
==;=
11.
18.
4.
30.
49,
9,
7
16,
35,
.6
,1
,8
,9
.6
,2
,9
.1
.1
s=:
183.3
REGION 1985
United States. .1
OECD Europe/Canada .0
OECD Pacific .0
.Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0.
TOTAL
TABLE B-77
PRIMARY SOLAR SUPPLY
(Exajoules/Yr)
2000
.2
.1
.0
.2
.0
.0
.0
.0
.0
2025
2050
2075
2100
.5
.4
.1
.7
.4
.0
.1
.1
.3
1.
1.
1,
,9
6
3
.6
.5
,4
,3
.5
.0
1.
1.
3.
4.
1.
2.
5
.1-
4
3
3
9
7
,4
.9
2.
2.
1,
. 7,
12.
2.
2,
4.
8.
9
,3
,0
,7
.3
,3
.0
.0
.7
2.6
7.1
16.5
43.2
B-70
-------
Appendix B: Implementation of the Scenarios
RCWA
TABLE B-78
PRIMARY ENERGY CONSUMPTION
(Exajoules/Yr)
REGION
1985
2000
2025
2050
2075
TOTAL
300.2
432.7
970.5
1607.3
2261.8
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
74
67
19
71
23
5
7
15
15
.9
.0
.3
.-1
.8
.8
.6
.6
.1
91.
81.
24.
113,
45.
9.
13.
26.
28
.2
,2
,9
.0
,9
.1
,1
.3
.0
154.
128.
40.
233.
• 170.
32.
44.
79.
87.
2
9
0
1
0
,5
3
9
6
198
163.
55.
406
319
59
77
144
182
.9
.6
.3
.8
.9
.0
.2
.5
.1
248.
194.
69.
561.
480,
87.
107,
205.
307.
,2
.5
.7
,4
.7
.2
.4
.4
.3
317.9
193.0
91.3
587.1
539.5
104.4
116.4
238.0
406.1
2593.7
TABLE B-79
SECONDARY ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION ' 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 194.4
FUEL CONSUMPTION
2000
2025
2050
2075
264.0
493.8
752.8
950.8
2100
48,
42.
11.
44,
17,
3.
4,
11,
10,
,2
.4
.9
.6
.0
.9
.7
.3
.4
54.
49.
14.
65.
30,
5.
8,
18,
17,
.9
,3
,5
.8
,2
.9
,0
,0
,4
77.
71.
20.
108.
79.
19.
23.
48.
44.
2
3
8
6
.1
8
6
6
.8
89.
83.
26.
159.
142,
36.
40,
82,
92,
.8
.5
.4
.1
.9
.2
.2
.2
.5
93.
86,
28,
195,
191,
52.
51,
108,
143,
.3
.7
.4
.6
,9
,1
,5
,0
.3
87.
79.
26.
201.
180.
58.
51.
109.
165.
7
3
9
2
9
,8
9
2
.9
961.8
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 32.9
ELECTRICITY CONSUMPTION
2000
2025
2050
53.9
124.7
220.6
2075
351.6
2100
8.
8.
2.
8,
1.
1,
1,
.4
.0
,4
.4
.8
.5
.8
,3
.3
11
10
3
15
4
1
1
2
3
.8
.5
.4
.3
.5
.0
.5
.7
.2
21.
17.
5.
29,
19.
4,
4,
•9.
13.
,2
,2 .
.4
.2
,6
.4
,8
.8
.1
26.
22.
7.
47.
46.
8.
9.
20.
31,
3
8
.7
,2
,1
,6
,7
.8
.4
30,
27,
9,
67,
89
13.
16
34
61
,7
.6
.7
.5
.6
.7
.7
.6
.5
30
29
10
78
127
17
21
46
91
.5
.3
.3
.4
.8
.7
.9
.9
.•6
454.4
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
227.3
TOTAL ENERGY CONSUMPTION
2000 2025
2050
2075
317.9
618.5
973.4
1302.4
2100
56.
50.
14.
53.
18.
4.
5.
12.
11.
6
4
,3
0
.8
.4
,5
.6
.7
66.
59.
17,
81,
34,
6.
9,
20
20,
.7
.8
.9
.1
.7
.9
.5
.7
.6
98
88
26
137
98
24
28
58
57
.4
.5
.2
.8
.7
.2
.4
.4
.9
116
106
34
206
189
44
49
103
123
.1
.3
.1
.3
.0
.8
.9
.0
.9
124.
114.
38.
263.
281.
65.
68,
142,
204,
,0
,3
,1
,1
,5
.8
,2
.6
.8
118,
108.
37.
279.
308.
76
73
156
257
.2
.6
.2
.6
.7
.5
.8
.1
.5
1416.2
B-71
-------
Policy Options for Stabilizing Global Climate
RCWA
TABLE B-80
SECONDARY OIL CONSUMPTION
CExajoules/Yr)
REGION 1985
United States
OECD Europe/C,anada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 100.6
2000
2025
2050
2075
112.A
229.0
352.5
AA3.7
2100
28.
25.
8.
1A.
2
3
3
8
6
.8
,8
.0
.6
.2
.A
.1
.6
.1
27
25.
8.
19
2
5
A
11
7
,9
.6
.0
.3
.8
.0
.A
.8
.6
AO.
AO.
12.
38.
10.
17,
1A.
33.
21,
5
.9
.7
,0
.0
.1
,A
.8
.6
A6.
A7,
16.
55,
23,
31,
2A,
57,
A8,
,5
,7
.7
,9
,3
,2
,A
.9
.9
A5.
A7,
17,
65,
38.
AA,
30.
7A.
80.
.6
.A
.6
,7
.5
.A
.1
.2
.2
A2.
AA.
17,
69,
50,
A9
31
7A
101
.A
,3
.1
.A
.1
.1
.6
.5
.3
A79.8
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East \
Africa
Latin America
South and East 'Asia
sg—=:—;jig=;=s=is===:====—=:a:===
TOTAL
1985
TABLE B-81
SECONDARY GAS CONSUMPTION
(Exajoules/Yr)
A8.8
2000
63.2
2025
2050
2075
10A.8
157.3
21A.9
2100
15.6
10.3
1.3
17.1
.3
.5
.A
2.1
1.2
19.1
12.1
1.6
22. A
.3
.9
.7
A.I
2.'0
26.3
16. A
2.2
38.7
1.1
2.7
1.8
10.3
5.3
31.9
20.0
2.8
61. A
2.7
5.0
3. A
18.0
12.1
36. A
23.1
3.5
8A.5
A. 9
7.7
5.5
27.0
22.3
36.1
22.2
3.5
9A.7
6.1
9.7
6.7
29.8
28.7
237.5
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-82
SECONDARY SOLIDS CONSUMPTION
(Exajoules/Yr)
1985
A5.0
2000
88. A
2025
2050
2075
160.0
2A3.0
292.2
2.100.
3.
6.
2.
12,
H,
1
3
.8
.3
.6
.9
.5
.0
.2
.6
.1
7,
11.
A,
2A,
27,
2,
2,
7,
,9
.6
.9
.1
,1
.0
.9
.1
.8
10
1A.
5.
31,
68,
7
A
17
.A
.0
.9
.9
.0
.0
.A
.5
.9
11.
15.
6.
/Al.
116,
12.
6.
31,
A
,8
9
.8
,9
,0
,A
.3
.5
11.
16.
7,
A5,
1A8
15
6
AO
.3
.2
.3
.A
.5
.0
.9
.8
.8
9.
12.
6.
37.
124.
13.
A.
35.
.2
.8
.3
,1
7
.0
,6
9
9
2AA.5
B-72
-------
Appendix B: Implementation of the Scenarios
RCHA
TABLE B-83
RESIDENTIAL/COMMERCIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 47.9
FUEL CONSUMPTION
2000
2025
2050
2075
62.2
111.A
172.A
233.1
2100
11.
12.
1.
13.
4.
1.
2.
5
9
6
2
3
2
4
6
2
12
15
2
17
7
2
3
.6
.6
.3
.3
.1
.A
.9
.5
.5
18.
2A,
3.
33
15
1,
2
5
7
.6
,0
.A
.A
.2
,5
.3
.6
.A
22.
27.
A.
51.
32.
3,
A.
10,
16,
,1
, A
.A
.7
.2
,1
.A
.9
.2
2A.
29.
5.
68.
50.
5.
6.
16.
26.
3
5
1
3
A
1
7
9
8
22
26
A
71
A8
6
6
18
31
.7
.3
.7
.7
.6
.1
.8
.2
.1
236.2
REGION
1985
ELECTRICITY CONSUMPTION
2000
2025
2050
2075
TOTAL
13.7
21.3
53.3
88.6
136.5
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
5
A
1
1.
.2
.5
.1
.3
.2
.1
.3
.5
.5
7
5
1
3
1
1
.2
.8
.6
.1
.6
.2
.6
.0
.2
1A,
10.
2,
10.
3.
1,
2.
3
5,
.5
.9
.9
.1
,2
.1
.0
.3
.3
17.
13.
A.
16.
8.
2.
A.
7,
13.
,8
,8
,1
,2
.7
,5
.2
.9
,4
— _ — ;
20
16
5
22
19
A
7
14
27
.A
.0
.1
.6
.2
.A
.A
.3
.1
19.5
16.1
5.1
2A.8
29.1
5.8
9. A
19.5
39.9
169.2
REGION
United States
OECD Europe/Canada
OECD Pacific
-Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
61.6
TOTAL ENERGY CONSUMPTION
2000
2025
2050
2075
83.5
16A.7
261.0
369.6
2100
16,
17,
2.
1A.
A,
2
2
.7
.A
.7
.5
.5
.3
.7
.1
.7
19.
21.
3.
20,
7,
1,
3,
A,
,8
.A
.9
,A
,7
.6
.5
.5
.7
33
34
6
A3
18
2
A
8
12
.1
.9
.3
.5
.A
.6
.3
.9
.7
39,
Al,
8.
67.
AO.
5,
8,
18,
29,
.9
.2
.5
.9
.9
.6
.6
.8
.6
44.
A5,
10,
90,
69.
9.
1A.
31,
53,
.7
.5
.2
.9
.6
,'5
.1
.2
.9
42.
42,
9,
.96,
77,
11,
16,
37,
71,
,2
.4
.8
.5
.7
.9
.2
.7
.0
405.4
B-73
-------
Policy Options for Stabilizing Global Climate
RCWA
TABLE B-8A
INDUSTRIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 86 .1
FUEL CONSUMPTION
2000
2025
2050
130.6
250.9
388.A
2075
490.8
2100
15.
13.
5,
23.
11.
3,
2
5
5
.6
.9
, A
.7
.5
.0
.1
.5
.A
21.
17,
6.
35.
21,
A,
3,
9.
10.
,6
,5
.9
.8
.1
,5
,5
.6
.1
30...
21.
9.
50.
57.
15.
9.
28.
28.'
6
8
0
9
1
2
7
0
6
36.
27,
11,
72,
95.
26.
17.
A6.
55,
,3
,6
.6
.A
.0
.7
,1
.0
,7
39,
30,
13,
88
115.
37
23.
60.
82.
.7
,9
.0
.0
.3
.3
.3
.8
.5
37.
27.
11 '.
85,
9A,
38,
22,
56,
85 ,
.1
.2
,8
.9
.9
.9
.2
.5
,5
460.0
ELECTRICITY CONSUMPTION
REGION
United States
OECD Europe/Canada ^
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle-East
Africa
Latin America
South and East Asia
1985
2000
2025
2050
2075
2100
3.
3.
1.
7.
1.
2
5
3 „
1
.6
4
,5
,8
.8
4.
4,
1.
12,
3,
1,
2,
.6
.7
.8
.2
.9
.8
.9
.7
.0
6.
6.
2.
19.
16.
3,
2.
6,
7.
,7
,3
5
,1
,2
,3
,8
,5
,7 -.
8,
9
3,
31
36.
6,
5.
12.
17.
.5
,0
.6
.0
.9
.1
.5
.9
.7 ,
10
11
A
4A
69
9
9
20
33
.3
.6
.6
.9
.5
.3
.3
.3
.9
11.
13.
5.
53,
96,
11,
. .12.
27,
50.,
.0
.2
.2
.6
.9
.9
.5
.3..
.8
TOTAL
19.2
32.6
71.1
131.2
213.7
282.4
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin .America
South and East Asia
TOTAL 105.3
TOTAL ENERGY CONSUMPTION
2000
163.2
2025
2050
322.0
519.6
2075
704.5
2100
18,
17.
6.
30.
13.
3.
2.
6.
6,
,8
,4
,7
,8
,1
,4
,6
,3
,2
26,
22,
8
48,
25
5,
4,
11,
12,
.2
.2
.7
.0
.0
.3
.A
.3
.1
37.
28.
11.
70.
73.
18.
12.
34.
36.
3
,1
,5
,0
,3
,5
,5
.5
,3
44,
36,
15.
103.
131.
32.
22.
58.
73,
.8
.6
.2
.A
,9
.8
.6
.9
,4
50,
42,
17.
132.
184.
46.
32.
81.
116.
.0
.5
,6
.9
.8
.6
.6
.1
.4
48,
40,
. 17,
, - 139,
191,
50,
. 34,
83,
136',
..1
.i4
.0
,5.
.'8
.8
.7
.8
.3
742.4
B-74
-------
Appendix B: Implementation of the Scenarios
RCWA
TABLE B-85
TRANSEORTATION ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
FUEL CONSUMPTION
REGION
1985
2000
2025
2050
TOTAL
60.4
71.2
131.5
192.0
2075
226.9
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
21.
15.
4.
7,
1.
2
it.
2.
.1
,6
.9
.7
.2
.7
,2
.2
.8
20
16
5
12
2
1
3
5
3
.7
.2
.3
.7
.0
.0
.6
.9
.8
28
25
8
24
6
3
11
15
a
.0
.5
.4
.3
.8
.1
.6
.0
.8
31.
28
10
35
15
6
18
25
20
.4
.5
.4
.0
.7
.4
.7
.3
.6
29
26
10
39
26
9
21
30
34
.3
.3
.3
.3
.2
.7
.5
.3
.0
27.9
25.8
10.4
43.6
37.4
13.8
22.9
34.5
49.3
265.6
ELECTRICITY CONSUMPTION
REGION 1985
United States .0
OECD Europe'/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0
2025
.0
.0
.0
.0
.2
.0
.0
.0
.1
2050
=IS::SS535=
.0
.0
.0
.0
.5
.0
.0
.0
.3
2075
.0
.0
.0
.0
.9
.0
.0
.0
.5
==;=5=
1.4
2100
.0
.0
.0
.0
1.8
.0
.0
.1
.9
2.8
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
-Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 60.4
TOTAL ENERGY CONSUMPTION
2000 2025
2050
2075
71.2
131.8
192.8
228.3
2100
21.
15.
4.
7.
1
2.
4.
2.
.1
.6
.9
,7
,2
.7
.2
.2
.8 •
20.
16.
5.
12.
2.
1.
3,
5.
3.
.7
.2
.3
,7
.0
.0
,6
.9
.8
28.
25.
8.
24.
7.
3.
11.
15.
8.
,0
.5
.4
.3
,0
.1
.6
.0
.9
31.
28.
10.
35.
16,
6.
18
25.
20,
.4
.5
.4
.0
.2
.4
.7
.3
.9
29
26,
10
39
27
9
21
30
34
.3
,3
.3
.3
.1
.7
.5
.3
.5
27
25
10
43
39
13
22
34
50
.9
.8
.4
.6
.2
.8
.9
.6
.2
268.4
B-75
-------
Policy Options for Stabilizing Global Climate
RCWA
REGION
TABLE B-86
ELECTRIC UTILITY ENERGY CONSUMPTION
(Exajoules/Yr)
1985
2000
2025
2050
TOTAL
105.3
168.6
591.
2075
900.1
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
26.
24.
7,
26,
6.
1.
2.
4.
4,
.6
.6
,4
,4
,8
.9
,7
,3
,6
36
31
10
47
15
3
5
8
10
.3
.8
.4
.1
.8
.2
.1
.3
.6
60
49
15
84
59
12
14
28
39
.8
.9
.6
.1
.1
.7
.6
.9
.1
70,
62.
20,
124,
123.
22.
26
56,
84
,9
.0
,6
,6
.5
,8
.4
.6
.0
78.
72.
24.
170.
228,
35
43
91
157
.4
.2
.7
,7
.1
.0
.0
.0
.0
78.6
77.9
26.8
200.7
327.6
45.6
56.9
123.0
235.5
1172.6
TABLE B-87
ENERGY CONVERSION EFFICIENCY AT ELECTRIC UTILITY POWERPLANTS*
(percent)
REGION
1985
2000
United States .
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
Includes transmission and distribution losses
2025
2050
2075
2100
31
32
32
31
26
26
22
30
26
.2
.5
.4
.4
.5
.3
.2
.2
.1
32.
32.
32.
32,
29.
31
29
32
30
.5
.7
.7
.3
.1
.2
.4
.5
.2
34.
34.
34
34
33
34.
32
34
33
.9 .
.5
.6
.7
.0
.6
.9
.3.
.2
37
36
37
37
37
37
37
36
37
.0
.8
.4
.8
.3
.7
.5
.7
.3
39.
38
39.
39,
39,
39,
38,
38,
39
.2
.4
.3
.4
.2
.1
.8
.1
.2
38,
37.
38,
39,
39.
38,
38.
38.
38.
,9
.7
.8
.1
.0
.8
,7
.2
,9
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TABLE B-88
SYNTHETIC PRODUCTION OF OIL AND GAS
(Exajoules/Yr)
TOTAL
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
OIL FROM SYNFUELS
2000
2025
2050
2075
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
24,
11.
5.
62,
49,
6,
1
4
.8
,8
,4
,7
.4
.0
.7
.2
.4
51.
24,
11,
168,
73,
9
1
5
.7
.4
.2
.6
.0
.0
.9
.3
.2
97.
45.
20.
248.
76.
9,
1,
5.
3
.1
.8
,7
.6
.0
,9
,5
.9
190.
44,
47,
231
37
6
1
3
.2
.2
.2
.3
.8
.•0
.0
.7
.4
.0
166.4
345.3
505.8
561.8
REGION 1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL .0
GAS FROM SYNFUELS
2000
2025
59.1
2050
185.3
2075
319.8
2100
,0
.0
.0
.0
.0
.0
.0
.0
.0
7.
4.
1,
19,
14,
4
• 4,
3
.8
.0
.9
.0
.3
.0
.7
.4
.0
25.
12.
5.
78.
34.
11.
10.
6,
3
6
9
9
,4
,0
,1
,5
,6
56.
27.
12.
143.
45.
13,
12,
8.
9
1
6
,1
,1
,0
,9
.6
.5
114.
28.
28.
141.
25.
12.
15.
&,
6
5
9
8
A
,1
,8
,0
1
375.2
B-76
-------
Appendix 1$: Implementation of the Scenarios
RCWA
TABLE B-89
ENERGY USED FOR SYNTHETIC FUEL PRODUCTION BY TYPE
(ExajouLes/Yr)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Pla
Centrally Pla
Middle East
Africa
Latin America
TOTAL
in ad a
med Europe
med Asia
- Asia
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
COAL
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0
2025
47
22
10
119
94
13
2
8
.3
.4
.4
.4
. 1
.0
.9
.7
.6
2050
111
52
24
364
157
23
4
11
.6
.6
.2
.0
.8
.0
.3
.8
.7
2075
226
104
48
579
178
26
5
14
.6
.9
.5
.0
.6
.0
.6
.4
.7
2100
451
104
111.
548.
89.
13.
2.
7
.1
.7
.9
.2
.3
.0
.3
.7
.4
.0
.0
750.0
1184.3
1328.6
REGION 1985
United States .0
OECD Europe/Canada .0
.OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East . .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL . 0
BIOMASS
2000 2025
18.5
2050
43.6
2075
52.1
2100
0
0
0
0
0
0
0
0
0
1,
1.
2.
1.
3,
5.
2.
.5
.1
.5
,7
.3
,0
,6
,4
,4
3.
2.
1.
6.
3.
8.
12.
5.
.5
.5
.2
.5
,1
.0
.5
,7
.6
4
3
1
7
3
10
15
6
.1
.0
.5
.7
.7
.1
.1
.2
.7
5.
3.
1
10
4
13
19
8
.4
.9
.9
.1
.8
.1
.2
.9
.7
68.0
TOTAL
REGION - 1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0
2025
2050
2075
2100
48,
23.
10.
122,
95,
17,
8,
11,
.8
.5
,9
.1
,4
,0
,5
.1
.0
115.
55.
25.
370.
160.
31.
17.
17.
.1
.1
.4
,5
.9
,0
.8
.5
,3
230.
107,
50.
586.
182.
36.
20.
21.
.7
,9
.0
.7
.3
,1
.7
.6
.4
456
108
113.
558,
94.
26
22
16
.5
.6
.8
.3
. 1
.1
.5
.6
.1
337.3
793.6
1236.4
1396.6
B-77
-------
Policy Options for Stabilizing Global Climate
.RCHA
TABLE B-90
C02 EMISSIONS FROM FOSSIL FUEL
(Petagrams C/Yr)
REGION
1985
2000
2025
2050
2075
TOTAL
5.1
7,8
19.8
34.6
49.6
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned
Centrally Planned
Middle Ea'st
Africa
Latin America
Europe
Asia
South and East Asia
1.3
.9
.3
1.3
.6
.1
.1
.2
.3
1.6
1.2
.4
2.2
1.1
.2
.3
.3
.5
3.
2,
5,
4.
1
1.
.1
.3
.8
.2
.2
.6
.9
.1
.7
4
3,
1.
10
7.
1
1
2
3
.4
.2
.2
.1
.8
.0
.4
.1
.4
6
it
1
14
11
1
2
3
5
.1
.1
.6
,5
.1
.5
.0
.1
.7
8
3
2
14.
10
1.
1.
3
7,
.9
.8
.4
.2
.8
.7
.9
.5
.0
54.4
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-91
CO EMISSIONS FROM FOSSIL FUEL
(Teragrams C/Yr)
1985
185.8
2000'
195.4
2025
2050
399.7
631.6
2075
789.8
2100
51.0
44.7
14.1
31.1
6.0
2.9
8.5
16.3
11.2
27.6
37.3
12.1
51.5
10.2
4.0
14.1
23.1
15.4
37.7
58.6
19.4
97.8
32.9
12.3
45.9
58.9
36.1
42.8
65.8
24.0
142.7
73.4
25.6
73.8
99.8
83.7
41.2
61.4
24.2
161.9
119.1
38.8
85.4
119.9
137.9
40.9
60.1
24.7
178.2
160.1
54.6
90.6
136.2
197.6
942.9
REGION
TABLE B-92
NOx EMISSIONS FROM FOSSIL FUEL
(Teragrams N/Yr)
1985
2000
2025
2050
2075
TOTAL
24.2
33.7
66.7
107.3
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
6.
4.
1.
5.
2.
1.
1.
,1
,6
.7
.8
.6
,4
.8
.0
,3
6,
5,
2,
9,
5.
1.
1.
2
.4
.2
.0
.1
.0
.5
.4
.6
.4
9
8
3
15
14
1
4
4
6
,1
.2
.0
.0
.0
.9
.1
.9
.6
10.
10.
4.
23.
25.
3.
6.
9.
13.
9
2
0
5
9
4
7
2
4
12.
11.
4.
30.
38.
5.
8.
12.
21.
5
1
6
8
3
0
.7
.7
.9
14.4
10.6
5.2
31.9
39.8
6.0
9.1
14.5
29.3
160.8
B-78
-------
Policy Options for Stabilizing Global Climate
SCWP
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-97
PRIMARY BIOMASS SUPPLY
(Exajoules/Yr)
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
2000
2025
2050
.0
34.5
155.7
2075
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
2.
2.
1.
5.
2.
6.
10.
4.
8
0
0
1
4
0
7
,1
A
12,
8,
4,
23.
10
30,
45
19,
,4
.9
.5
.1
.9
.3
.3
.4
.9
16
11
5
30
14
39
59
26
.3
.7
.9
.5
.4
.5
.9
.8
.3
19.
14.
7.
36.
17.
48.
72.
31.
7
1
1
8
4
5
2
.3
7
205.3
247.8
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America '
South and East Asia
TABLE B-98
PRIMARY HYDROELECTRIC SUPPLY
(Exajoules/Yr)
1985
TOTAL
21.2
2000
29.3
2025
2050
43.7
55.7
2075
62.9
2100
3.
8,
1,
2.
1,
3,
1.
,3
.2
,2
,5
,0
.1
.2
.3
.4
3.
9.
1.
3.
2.
6.
2.
8
6
.3
,1
,3
.2
4
,0
,6
4.
10.
1.
3.
6.
1.
9.
5.
3
6
3
6
,4
,4
,3
9
,9
4,
11,
1,
3,
10,
3
11,
9,
,6
.0
.4
.7
.0
.6
.2
.8
.4
4,
11,
1,
3.
11,
5
13,
11
,7
.1
.4
.7
.3
.7
.3
.3
.4
4.
11.
1.
3.
11.
6.
12.
12.
8
1
4
7
6
7
6
6
1
64.6
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-99
PRIMARY NUCLEAR SUPPLY
(Exajoules/Yr)
1985
16.5
2000
18.1
2025
2050
25.5
32.8
2075
38.0
2100
3.
8,
2.
2,
,8
.4
,0
.0
.0
.0
.0
.0
.3
4.
7.
2.
3.
7
3
5
1
1
0
0
0
4
6.
5.
2.
6.
2.
1
0
6
1
,2
,6
,8
9
.0
,3
6
4
2
8
4
2
2
2
.5
.8
.2
.2
.0
.6
.0
.0
.5
6.
5,
2,
8,
4,
3,
2,
3
,8
.2
.5
.6
.8
.5
.7
.0
.9
7.
5.
2.
8,
5,
3,
2.
5
,1
,5
,9
.6
.2
.9
.9
.0
.0
41.1
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-100
PRIMARY SOLAR SUPPLY
(Exajoules/Yr)
1985
.1
.0
.0
.0
.0
.0
.0
.0
.0
.1
2000
2025
2050
2075
2100
6
3
2
6
2
0
0
0
0
3.
1.
3,
1,
,2
,3
8
.7
,6
.6
.7
.0
.9
6.
2.
1.
8.
4.
2.
2.
2.
4
4
7
1
0
.6
,0
,0
,5
6.
2.
1.
7.
4.
3.
2.
3,
0
3
,8
,5
,2
,1
.4
.0
,4 '
5
2
1
6
4
3
2
3
.6
.2
.8
.7
.1
.1
.3
.0
.9
1.9
12.8
29.7
30.7
29.7
B-80
-------
Appendix B: Implementation of the Scenarios
SCWP
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-101
PRIMARY ENERGY CONSUMPTION
(Exajoules/Yr)
1985
300.2
2000
2025
2050
2075
335.9
394.7
479.7
501.9
2100
74
67
19
71
23
5
7
15
15
.9
.0
.3
.1
.8
.8
.6
.6
.1
lit
66
19
80
33
8
10
20
21
.3
.8
.9
.6
.9
.8
.1
.4
.1
71
62
18
83
54
15
19
33
36
.3
.3
.5
.6
.2
.3
.2
.4
.9
73.
62.
20.
91.
65.
25.
34.
49.
56.
7
8
5
2
1
9
9
4
2
68
60
21
85
67
29
45
54
69
.6
.9
.3
.0
.7
.2
.1
.9
.2
68
61
22
81
67
30
49
54
77
.3
.5
.9
.7
.0
.0
.9
.7
.2
513.2
TABLE B-102
SECONDARY ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
FUEL CONSUMPTION
1985 2000
194.4
212.2
2025
2050
2075
242.0
276.0
277.8
210.0
48.
42.
11.
44.
17.
3,
4
11
10
.2
.4
.9
.6
.0
.9
.7
.3
.4
45
42.
11
49
23
5
6
14
14
.1
.0
.9
.1
.4
.7
.3
.4
.3
42.
38.
11.
50.
34.
9.
11.
21.
23.
1
.8
.0
.1
,7
.7
,2
.0
.4
42.
38.
11.
52.
37.
16.
17.
26.
32.
.9
.7
.5
.4
.8
.4
.2 -
.3 .
.8
39.
36,
11.
47,
38,
17,
21.
26,
39,
.0
.2
.8
.2
.0
.9
.6
.8
.3
38
36,
12
44
36
18
23
24
43
.3
.2
.7
.4
.1
.5
.1
.2
.0
276.5
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
ELECTRICITY CONSUMPTION
1985 2000 2025
8.4
8.0
2.4
8.4
1.8
1.3
1.3
2050
2075
2100
9.
8.
2.
10.
3.
1.
2.
2.
7
3
6
4
1
,9
,2
,0
,1
9.
.7,
2,
11,
6,
2.
2
3
4
.9
.9
.5
.3
.2
.0
.1
.3
.1
9.
7.
2.
11.
8.
3.
3.
3,
6.
9
6
8
8
4
4
6
9
3
9,
7
2.
11
9
4
4
4
8
.4
.6
.9
.2
.3
.1
.9
,4
.2
9.
7.
3,
10,
9,
4,
5,
4,
9
,2
.5
,1
.3
.4
.2
.3
.2
.3
32.9
40.3
49.3
57.7
62.0
62.5
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TOTAL ENERGY CONSUMPTION
1985
227.3
2000
252.5
2025
2050
291.3
333.7
2075
339.8
2100
:==:
56
50
14
53
18
4
5
12
11
.6
.4
.3
.0
.8
.4
.5
.6
.7
54
50
14
59
26
6
7
16
16
.8
.3
.5
.5
.5
.6
.5
.4
.4
52.
46,
13,
61.
40.
11.
13
24
27
.0
.7
.5
.4
.9
.7
.3
.3
.5
S=S=:BS
52,
46.
14.
64.
46,
19,
20,
30,
39,
.8
.3
,3
.2
.2
.8
.8
.2
.1
48
43
14
58
47
22
26
31
47
.4
.8
.7
.4
.3
.0
.5
.2
.5
ssss
47.
43.
15.
54.
45.
22.
28.
28.
52.
,5
,7
,8
,7
,5
,7
,4
,4
,3
S3S3S
339.0
B-81
-------
Policy Options for Stabilizing Global Climate
SCHP
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-103
SECONDARY OIL CONSUMPTION
(Exajoules/Yr)
1985
100.6
2000
2025
2050
107.3
113.3
130.2
2075
134.8
2100
28
25
8
14
2
3
3
8
6
.8
.8
.0
.6
.2
.4
.1
.6
.1
26.
25
7
17
2
5
3
10
7
.4
.1
.8
.6
.9
.1
.8
.7
.9
23.
22
6
18
4
7
6
13.
11
.0
.5
.7
.4
.5
.6
.2
.4
.0
21.
21
7
19.
6
12.
9.
16.
16
.7
.3
.3
.1
.8
.4
.3
.1
.2
19,
19,
7,
18,
8,
13.
. 11.
16,
19,
.7
.8
,5
,2
.5
..6
,5
.2
.8
20
. 20
.. • 8
•19
11
.14
13
15
,23
.3
...5,
...4--
:. 4r
.4;
,6
.2
.2
.5
146.5
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-104
SECONDARY GAS CONSUMPTION
(Exajoules/Yr)
1985
48.8
2000
47.6
2025
2050
2075
62.6
81.4
78.2
2100
15,
10.
1,
17,
2.
1.
,6
,3
,3
,1
.3
.5
,4
,1
,2
14.
9.
1.
16.
2.
1.
,2
,7
,2
,8
.3
.6
,6
,6
.6
15.
10.
1.
20.
2.
1.
5.
4.
,7
,6
.7
,3
,8
,1
,5
,9
.0
18.
12.
2.
24
1
4
2
8
6
.5
.9
.0
.4
.3
.0
.8
.6
.9
16.
11.
2.
21.
1.
4.
3,
8.
8.
.8
.9
.0
.3
.3
.3
.6 .
.8
.2
•15.
11
2.
18
1.
. 3,
, ,, 3.
7.
8,
.6
.3
.0
.6
.3
.9
.6
.6
.7
72.6
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-105
SECONDARY SOLIDS CONSUMPTION
(Exajoules/Yr)
1985
45.0
2000
57.3
2025
2050
66.1
64.4
2075
64.8
2100
3,
6,
2,
12,
14,
1,
3,
.8
.3
.6 .
.9
.5
.0
.2
,6
.1
4.
7,
2.
14.
20.
1,
1,
4,
.5
.2
,9
.7
.2
.0
.9
.1
.8
3.
5.
2.
11.
29.
3.
1.
8.
,4
.7
.6
,4
,4
.0
,5
.7
.4
2
4
2
8.
29.
5.
1.
9,
.7
.5
.2
.9
.7
.0
.1
.6
.7
2,
4.
2.
7.
28.
6.
1.
11.
,5
.5
.3
7
2
0
5
.8
.3
. 2.
.4.
2,
6,
.. . 23,,
6,
1,
10
.4
.4
.3
,4
,4
.0
.3
.4
.8
57.4 .
B-82
-------
Appendix B: Implementation of the Scenarios
SCWP
TABLE B-106
RESIDENTIAL/COMMERCIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
FUEL CONSUMPTION
1985 2000
A7.9
2025
56.2
2050
6A.7
2075
2100
11.
12.
1.
13.
A.
1.
2.
.5
9
.6
2
3
.2
.A
,6
,2
10
13
1
12
5
2
3
.1
.3
.7
.9
.3
.A
.9
.2
.1
10,
11.
1.
15.
6.
1.
2.
3
A
.3
.7
.5
.A
.1
.0
.1
.2
.9
10.
11.
1.
16.
7 c
1.
3.
A.
7.
.7
.5
.6
.A
.7
.8
.A
,5
,1
9
10
1
1A
8
2
A
A
8
.2
.0
.6
.1
.7
.2
.5
.9
.7
3.
' ' 9.
1
12
8
2.
A
A
9
.2
.0
.5
.1
.9
.3
.A
.6
.1
60.1
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
ELECTRICITY CONSUMPTION
1985
13.7
2000
2025
2050
2075
16.A
21:9
25.2
27.1
2100
5
A.
1.
1.
.2
.5
.1
.3
.2
.1
.3
.5
.5
6
A
1
2
.0
.A
.2
.0
.5
.2
.5
.8
.8
6,
A.
1,
3,
1.
1.
1.
,7
.6
.3
.8
.2
.6
.9
.2
.6
6.
A.
1.
3.
1.
1.
1.
1.
2.
.7
.3
.5
.9
.8
,1
.6
,7
.6
6.
A.
1.
3.
2.
1,
2.
2,
3.
.2
.1
,5
.7
.3
,5
.3
.0
.5
5,
3
1
3
2
1
2
2
A
.9
.9
.6
.3
.7
.8
.5
.1
.1
27.9
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TOTAL ENERGY CONSUMPTION
1985
61.6
2000
66.3
2025
2050
78.1
2075
91.0
2100
16.
17.
2.
1A.
A.
2.
2.
7
A
7
5
.5
3
7
1
7
16
17
2
1A
5
1
3
3
.1
.7
.9
.9
.8
.6
.A
.0
.9
17.
16.
2,
19
7.
1.
3
A
6
.0
.3
.8
.2
.3
.6
.0
.A
.5
17
15
3
20
9
2
5
6
9
.A
.8
.1
.3
.5
.9
.0
.2
.7
15.
1A.
3.
17.
11.
3.
6.
6.
12.
A
1
.1
,8
.0
.7
.8
,9
,2
1A.
12.
3.
15.
11.
A.
6.
6.
13.
.1
.9
.1
.A
.6
.1
,9
.7
,2
88.0
B-83
-------
Policy Options for Stabilizing Global Climate
SCWP
REGION
TABLE B-107
INDUSTRIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
2050
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
1985
15
13
5
23
11
3
2
5
5
.6
.9
.4
.7
.5
.0
.1
.5
.4
FUEL CONSUMPTION
2000
16.6
14.5
5.8
26.2
16.6
4.4
2.8
7.7
8.1
2025
13.7
11.6
4.7
21.3
25.5
7.1
4.4
11.1
13.4
TOTAL
86.1
102.7
112.8
18.1
130.4
2075
2100
125.8
19.2
110.3
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
ELECTRICITY CONSUMPTION
1985 2000 c 2025
19.2
2050
23.9
27.4
32.4
2075
34.7
2100
3.
3.
1
7
1.
.2
.5
.3
.1
.6
.4
.5
.8
.8
3.
3.
1,
8.
2.
1.
1.
7
.9
4
.4
,6
.7
.7
.2
.3
3,
3,
1,
7,
5,
1.
1,
2,
2,
.2
.3
.2
.5
.0
.4
.2
,1
.5
3.
3.
1.
7,
6.
2.
2,
2,
3,
.2
,3
,3
,9
.5
,3
,0
.2
,7
3.
3.
1.
7.
6.
2.
2.
2.
4.
2
5
,4
.5
8
.6
.6
4
7
3,
3,
1,
7,
6.
2.
2.
2.
5.
.3
.6
.5
.0
,4
.4
.8
,1
.1
34.2
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TOTAL ENERGY CONSUMPTION
1985
105.3
2000
126.6
2025
2050
140.2
162.8
2075
160.5
2100
18.
17.
6.
30.
13.
3.
2.
6.
6.
8
4
7
8
1
4
6
3
2
20.
18.
7,
34.
19.
5.
3,
8
9
.3
,4
.2
.6
.2
.1
.5
.9
.4
16.
14.
5.
28.
30.
8.
5.
13.
15.
9
.9
9
8
.5
5
.6
.2
.9
18,
15.
6,
29,
31,
14.
9,
16.
21.
,2
.6
,2
,5
.8
,5
.0
,2
,8
17.
15.
6.
26.
29.
14.
11.
15.
24.
0
3
3
1
4
6
1
9
8
16,
15,
6.
22.
23.
12.
11.
12.
24.
,3
,0
,3
,9
7
2
0
,8
,3
144.5
B-84
-------
Appendix B: Implementation of the Scenarios
SCWP
TABLE B-108
TRANSPORTATION ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
FUEL CONSUMPTION
1985
21
15
4
7
1
2
4
2
.1
.6
.9
.7
.2
.7
.2
.2
.8
2000
18
14
4
10
1
2
4
3
.4
.2
.4
.0
.5
.9
.6
.5
.1
2025
18
15
4
13
3
1
4
6
5
.1
.5
.8
.4
.1
.6
.7
.7
.1
2050
17.
14.
5.
14.
4.
2,
6.
7
7
.2
.9
.0
.4
.8
.4
.8
.8
.6
2075
16,
14
5,
14
6
3
8
8
10
.0
.4
.3
.5
.7
.7
.6
.4
.5
2100
17
15
6
16
9
6
10
8
14
.1
.8
.4
.4
.9
.4
.5
.9
.7
60.4
59.6
73.0
80.9
88.1
106.1
ELECTRICITY CONSUMPTION
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0
TOTAL
2025
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
2050
.0
.0
.0
.0
.1
.0
.0
.0
.0
.1
2075
.0
.0
.0
.0
.2
.0
.0
.0
.0
.2
2100
.0
.0
.0
.0
.3
.0
.0
.0
. .1
.It
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TOTAL ENERGY CONSUMPTION
1985
60.4
2000
59.6
2025
2050
.2075
73.0
81.0
88.3
2100
21.
15.
4.
7.
1.
2.
it.
2:
,1
6
.9
,7
.2
,7
.2
.2
,8
18.
14.
it
10.
1,
2.
4.
3.
,4
.2
.4
.0
,5
,9
.6
.5
.1
18.
15.
4.
13.
3.
1.
4.
6.
5.
1
5
8
4
1
6
7
7
1
17
14
5
14
4
2
6
7
7
.2
.9
.0
.4
.9
.4
.8
.8
.6
16.
14.
5,
14,
6.
3,
8.
8.
10,
.0
.4
.3
.5
.9
.7
.6
.4
.5
17
15,
6.
16,
10
6,
10,
8,
14,
.1
.8
.4
.4
.2
.4
.5
.9
.8
106.5
B-85
-------
Policy Options for Stabilizing Global Climate
SCHP
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-109
ELECTRIC UTILITY ENERGY CONSUMPTION
CExajoules/Yr)
1985
105.3
2000
2025
2050
123.6
144.0
165.2
2075
173.8
2100
26,
24.
7.
26.
6.
1.
2.
4,
4,
,6
,6
4
,4
8
,9
.7
.3
,6
29
24
8.
31
10
3
3
6
6
.3
.8
.0.
.4
.5
.0
.7
.0
.9
28,
22,
7,
32.
18.
5,
6,
9,
12,
,5
.9
,3
,2
,7
,6
.4
,9
,5
27.
22,
7,
33.
24.
9,
10,
11.
18,
,7
,0
,9
.0
.5
.4
.4
,8
,5
25.
21.
8.
30.
26.
11,
13.
13,
23.
8
8
1
3
,2
,1
,7
.3
.5
25,
21,
8
28,
26
11
14
12
26
.2
,9
.6
.4
.6
.4
.9
.6
.4
176.0
TABLE B-110
ENERGY CONVERSION EFFICIENCY AT ELECTRIC UTILITY POWERPLANTS*
(percent)
REGION
1985
2000
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
Includes transmission and distribution losses
2025
2050
2075
2100
31.
32.
32.
31.
26.
26.
22.
30.
26.
,2
,5
,4
,4
,5
,3
,2
.2
,1
33.
33.
32.
32.
29.
26,
29.
33.
31.
,4
,5
,5
,8
,5
,7
,7
,3
,9
34
34,
35,
34,
32,
35.
32,
33,
33,
.7
.1
.6
.8
.6
.7
.8
.3
.6
35
35
35
35
33
36
36
33
34,
.4
.0
.4
.5
.9
.2
.5
.1
.1
37.
34.
37,
37.
35.
36.
36,
33,
35.
,2
,9
,0
,0
,9
,0
.5
,1
.3
36.
34.
37.
36.
35.
36.
36.
33.
35.
5
7
2
6
,7
0
,2
.3
2
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-lll
SYNTHETIC PRODUCTION OF OIL AND GAS
(Exajoules/Yr)
OIL FROM SYNFUELS
1985 2000 2025 2050
.0 .0
.0 .0
.0 .0
.0 .0
.0 .0
.0 .0
.0 .0
.0 .0
.0 .0
.0 .0 .0 44.5
2075
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
3.6
2.5
1.3
6.6
3.. 1
.0
8.7
13.0
5.7
6.7
4.8
2.4
12.5
5.9
.2
16.3
24.5
10.8
9.4
6.7
3.4
17.5
8.3
.3
22.8
34.2
15.0
84.1
117.6
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
GAS FROM SYNFUELS
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
2000
2025
26.0
2050
72.6
2075
70.4
2100
0
0
0
0
0
0
0
0
0
2
1
3
1
5
7
3
.1
.5
.7
.9
.8
.0
.1
.6
,3
5,
4,
2,
10,
5,
14,
21,
9,
.8
.1
.1
.8
.1
,2
,1
.1
,3
5.
4,
2,
10,
4,
13,
20,
9
.6
.0
.0
.5
.9
.2
.7
.5
.0
5
3
2
10
4
13
20
8
.5
.9
.0
.3
.8
.2
.4
.2
.9
69.2
B-86
-------
Appendix B: Implementation of the Scenarios
SCWP
TABLE B-112
ENERGY USED FOR SYNTHETIC FUEL PRODUCTION BY TYPE
(Exajoules/Yr)
REGION
United States
OECD- Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
• Africa
Latin America
South and East Asia
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
COAL
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0
2025
.0
.0
.0
.0
.1
.0
.0
.0
.0
2050
.0
.0
.0
.0
.0
.0
.0
.0
.0
2075
.0
.0
.0
.0
.0
.0
.0
.0
.0
2100
.0
.0
.0
.0
.1
.0
.0
.0
.0
TOTAL
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
BIOMASS
2000
2025
2050
2075
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
2,
2.
1,
5.
2,
6.
10,
4.
,8
.0
.0
.1
.4
.0
.7
.1
.4
12
8.
4
23
10
30
45
19
.4
.8
.5
.1
.9
.3
.3
.4
.9
16.
11
5
30
14
39
59
26
.3
.7
.9
.5
.4
.5
.9
.8
.3
19.
14.
7.
36.
17
48.
72
31.
.7
.1
.1
.8
.4
.5
.2
.3
.7
TOTAL
34.5
155.6
205.3
REGION
United States
OECD Europe/Canada
OECD Pacific
-Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
TOTAL
2000
2025
2050
2Q75
2100
0
0
0
.0
o •
0
0
0
0
2
2
1
5
2
6
10
4
.8
.0
.0
.1
.5
.0
.7
.1
. /t
12,
8,
4,
23.
10,
30,
45,
19,
,4
.8
.5
.1
.9
,3
.3
.4
.9
16,
11.
5
30
14.
39.
59.
26
.3
.7
.9
.5
.4
.5
.9
.8
.3
19.
14,
7,
36,
17,
48,
72,
31,
,7
,1
.1
.8
.5
.5
.2
,3
.7
34.6
205.3
247.9
B-87
-------
Policy Options for Stabilizing Global Climate
-SCWP
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-113
C02 EMISSIONS FROM FOSSIL FUEL
(Petagrams C/Yr)
1985
5.1
2000
2025
2050
5.6
5.5
4.2
2075
3.3
2100
1
1
.3
.9
.3
.3
.6
.1
.1
.2
.3
1.2
.9
.3
1.4
.8
.1
.2
.2
.4
1
1
1
.0
.8
.3
.2
.0
.2
.2
.3
.5
.8
.7
.2
.9
.9
.3
.0
-.1
.5
.6
.6
.2
.6
.8
.4
-.0
-.3
.5
.5
.5
.2
•• .'5
.7
.4
-.1
~* . 6
.5
2.6
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-114
CO EMISSIONS FROM FOSSIL FUEL
(Teragrams C/Yr)
1985
185.8
2000
135.5
2025
2050
102.1
64.6
2075
69.7
2100
51.
44,
14.
31.
6.
2.
8.
16.
11.
.0
,7
.1
.1
.0
.9
.5
.3
.2
24.
32.
10.
29,
6,
2,
7
12,
9.
.4
.4
,1
,9
,2
,6
.5
.9
.5
14
12
3
26
8
3
9
13
10
.1
.4
.8
.7
.4
.3
.5
.2
.7
12.
10.
3.
11.
6.
2.
5.
6.
6.
3
9
.7
.3
,0
.0
.6
,3
.6
11.
10.
3.
11.
7.
2.
7.
6.
8.
5
5
.9
,2
.3
.9
.0
,7
.8
12,
11,
4
12,
9.
4,
8
7
11.
.2
.4
.7
.4
.3
.7
.3
.0
.7-
81.6
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
.TABLE B-115
NOx EMISSIONS FROM FOSSIL FUEL
(Teragrams N/Yr)
1985
24.2
2000
2025
2050
24.6
22.0
18.3
2075
18.2
2100
6,
4.
1.
5.
2,
1,
1,
,1
,6
.7
,8
.6
.4
.8
.0
.3
5
4
1
6
3
1
1
1
.1
.0
.5
.2
.5
.5
.0
.1
.6
3
2
4
4
1
1
2
.2
.6
.9
.7
.4
.7
.5
.7
.3
2
2
3
2
1
2
2
.7
.4
.9
.1
.4
.7
.8
.1
.1
2.
2.
2.
2.
1.
2.
2.
4
3
9
9
4
8
9
1
4
2
2
1
3
2
1
2
2
2
.5
.4
.0
.0
.6
.0
.1
.1
.9
19.
B-88
-------
Appendix B: Implementation of the Scenarios
RCWP
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
301.6
TABLE B-116
PRIMARY ENERGY SUPPLY
(Exajoules/Yr)
2000
353.0
2025
2050
526.3
680.4
2075
790.2
2100
63
47
8
81
25
23
16
20
14
.7
.6
.9
.2
.6
.7
.3
.5
.1
52
47
11
86
41
48
22
23
19
.1
.7
.1
.0
.7
.2
.8
.7
.7
48
45
12
93
87
63
55
72
47
.7
.9
.6
.4
.0
.5
.6
.1
.5
59.
50,
16.
118.
122.
56.
80.
102.
7/t
z^Zi
.8
,4
.1
.8
.1
.5
.1
.3
.3
71
55
18
133
142
51
94
123
99
.0
.9
.8
.3
.8
.1
.5
.3
.5
77.
57.
20.
139,
152.
52.
98,
134,
119,
;=:
.9
.5
,8
,6
.3
.4
,0
.1
.4
852.0
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
117.9
TABLE B-117
PRIMARY OIL SUPPLY
(Exajoules/Yr)
2000
2025
2050
123.3
117.9
91.8
2075
57.4
2100
20.8
11.9
1.1
26.0
5.2
22.4
10.8
14.1
5.6
11.6
8.8
.6
20.9
6.5
45.2
15.1
10.3
4.3
5.9
7.2
.2
16.2
6.5
52.4
16.5
9.6
3.4
3.5
5.8
.0
12.0
5.1
37.4
16.6
8.8
2.6
2.0
3.6
.0
7.2
3.1
23.4
10.8
5.7
1.6
2.3
2.2
.0
4.4
1.8
16.3
6.9
8.8
1.1
43.8
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 58.6
TABLE B-118
PRIMARY GAS SUPPLY
(Exajoules/Yr)
2000
2025
2050
71.9
61.6
48.4
2075
45.9
2100
16.
9.
24.
1.
1.
2.
2.
3
7
7
,0
.5
.2
.3
.5
,4
15.
11.
1.
28.
1,
2.
2.
4,
3,
,3
.6
.6
.4
9
,8
.6
.6
,1
7.
6.
1.
25.
4,
8.
2.
3,
2,
.8
.5
.1
,6
.4
.2
.0
,3
.7
4.
4.
24.
3.
8.
1.
4
1
5
0
6
9
4
7
8
5.
5.
15.
2.
12.
1.
2.
1,
1
.5
.1
.0
.3
,8
,2
.1
.8
4.
5.
8.
• 1.
17,
3.
5,
1.
,8
,1
.0
,1
.3
,5
,0
.5
,4
46.7
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 87.3
TABLE B-119
PRIMARY COAL SUPPLY
(Exajoules/Yr)
2000
2025
2050
2075
105.2
107.3
122.0
140.2'
2100
19.
9,
3
26
18
4
4
.4
.4
.9
.7
.9
.0
.0
.6
.4
16.
10.
4.
29.
30.
4.
1.
8.
.0
.4
.9
.4
.2
.0
,5
.1
.7
11
6
3
17
49
6
2
10
.4
.3
.4
.2
.7
.0
.5
.1
.7
14
5
3
20
59
8
3
6
.4
.9
.4
.3
.1
.0
.5
.6
.8
19
6
3
30
63
10
2
4
.1
.2
.7
.8
.2
.0
.4
.6
.2
•SSTTTT
23.
6.
4.
38.
62.
11.
1.
3.
0
9
7
8
8
0
5
6
2
152.5
B-89
-------
Policy Options For Stabilizing Global Climate
-RCWP
REGION 1985
United States .0
OECD Europe/Canada .0
OECD Pacific ' • .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL ' .0
TABLE B-120
PRIMARY BIOMASS SUPPLY
(Exajoules/Yr)
2000
2025
135.8
2050
2075
215.
272.6
2100
0
0
0
0
0
0
0
0
0
10.
7.
3.
20,
9,
26.
39,
17.
.8
.7
.9
,2
.5
.3
.4
.6
.4
17
12.
6
32,
15
41
62
27
.1
.2
.2
.0
.1
.5
.9
.8
.6
21
15,
7
'••'• 40
19,
53
79
34
.7
.5
.8
.5
.1
.6
.0
.5
.9
21,
15.
7.
•'• 40,
" 19:
53,
79
34,
.7
:5
.8
.5
.1;
.6
.0
.5
.9
272.6
REGION
TABLE B-121
PRIMARY HYDROELECTRIC SUPPLY
(Exajoules/Yr)
1985
2000
2025
2050
TOTAL
21.2
31.9
53.4
66;9
2075
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
3,
8.
1.
2.
1,
3,
1.
,3
.2
.2
.5
.0
.1
.2
.3
.4
3.
9.
1.
3.
2.
7.
2.
9
7
3
2
6
2
5
7
8
SS=
4.
10.
1.
3.
7.
1.
16.
6.
4 '
.8
:3
.7
,6
,5
7
,5
.9
===
4.
11.
1,
3,
10,
4,
20.
10.
.7
,0
.4
,7
.8
.6
.2
.0
.5
4.
11.
1,
3.
11
6
20
11
.7
.1 '
.4
.7
.5
.7
.2
.6
.9
=S5SS
4.8
11.1
1.5
3.7
11.6
.7
7.0
20.7
12.4
73.5
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
16.5
TABLE B-122
PRIMARY NUCLEAR SUPPLY
(Exajoules/Yr)
2000
18.4
2025
2050
2075
31.2
70.6
110.2
2100
3.
8.
2.
2.
,8
4
0
0
.0 •
,0
.0
,0
.3
4.
6,
2.
3.
,7
9
,5
.4
.2
,0
.1
,0
.6
5.
5.
1.
6,
5.
1.
1,
3,
,2
.8
,9
,2
.5
.1
.3
.5
.7
7.
7.
2.
13.
14.
4.
4.
3.
12.
9
,6
.6
.5
,3
.6
.3
.2
,6
9;
9,
3.
19,
23,
7,
6,
6.
24,
.8 '
,7
,4
,2
.2
.2
.9
.8
.0
11.
12.
4.
24.
31.
9.
9.
10,
37,
,9
.0
;2
.7
.2
.7
.3
,1
.2
150.3
REGION 1985
United States .1
OECD Europe/Canada .0
OECD Pacific ' .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL . .1
TABLE B-123
PRIMARY SOLAR SUPPLY
(Exajoules/Yr)
2000
.6
.3
.2
.7
.3
.0
.0
.0
.2
2.3
2025
2050
2075
2100
3.
1.
4.
3.
i;
1.
2.
2
6
8
3
8
0
2
5
7
7,
3
2,
13,
14,
4.
4,
3,
12,
.8
.8
.0
.3
.1
.5
.2
.2
.4
8
4
2
16
20
6
6
6
21
.6
.3-
.4
.9
.4
.4
.0
.0
.1
=:=3
9,
4,
2,
19
24
7,
7
7
29
.4
,7
.6
.4
.5
.6
.3
.9
.2
19.1
65.3
92.1
112.6
B-90
-------
Appendix B: Implementation of the Scenarios
RCWP
REGION
TABLE B-124
PRIMARY ENERGY CONSUMPTION
CExajoules/Yr)
1985
2000
2025
2050
2075
TOTAL
300.2
353.2
526.7
680.0
789.3
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
74.
67.
19.
71.
23.
5.
7.
15.
15.
9
0
3
1
8
8
6
6
1
71.
64
20,
84
40
9
12
24
26
.8
.1
.1
.8
.4
.3
.2
.5
.0
71
62
19.
92.
90
24
34
64
67
.2
.6
,7
.4
.7
.2
.0
.7
.2
69
60.
20
111
128
35
51
89
114
.2
.9
.5
.2
.0
.2
.0
.9
.1
67.
60.
21.
125,
152,
40,
62
103
155,
,6
,9
.3
,0
,2
.7
.7
.6
.3
68.9
62.2
22.3
131.6
164.6
43.0
67.9
107.1
185.9
853.5
TABLE B-125
SECONDARY ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
1985
TOTAL
194.4
FUEL CONSUMPTION
2000
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
48
42.
11.
44
17
3
4
11
10
.2
.4
.9
.6 .
.0
.9
.7
.3
.4
42.3
40.1
12.2
50.3
26.7
5.?
7.4
16.8
16.4
218.1
2025
286.6
2050
329.7
2075
341.8
2100
39.
35.
11.
49.
50.
14,
16,
35,
34,
.1
.2
.1
,4
.9
,5
.5
.1
.8
33
30.
10
53
64
19
21
43
53.
.4
.1
.2
.9
.3
.4
.6
.3
.5
29,
27.
9.
55.
68.
19,
23,
43,
65.
.2
,0
.7
.4
,3
.9
.6
.7
.0
27
25
9.
53
67
18
23
40
68
.6
.4
.4
.5
.0
.3
.4
.6
.4
333.6
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
ELECTRICITY CONSUMPTION
2000 2025
2050
2075
2100
8
8
2
8
1
1
1
32
.4
.0
.4
.4
.8
.5
.8
.3
.3
.9
9.
8.
2.
11.
4.
1.
1.
2.
2.
43.
8
0
6
4
.0
.0
4
.6
.9
.7
10.
8,
2.
13,
12,
3,
3,
6,
9,
71.
.4
,8
.7
.4
.8
.4
,8
.7
.7
.7
11.
9.
3.
17.
21.
5,
6.
10,
19,
104,
.3
.6
.1
.9
.2
.5
.6
.6
.0
.8
12
10
3
21
28
7
9
14
29
137
.0
.5
.5
.8
.7
.5
.3
.0
.7
.0
12.
11.
3.
24.
33.
8.
11.
16.
39.-
161.
8
4
9
5
3
8
1
4
3
5
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
227.3
TOTAL ENERGY CONSUMPTION
2000
261.8
2025
2050
2075
358.3
434.5
478.8
2100
56.
50.
14.
53.
18.
4.
5.
12,
11,
.6
.4
.3
,0
,8
.4
,5
,6
,7
52.
48,
14,
61,
30.
6.
8,
19,
19,
,1
,1
.8
.7
,7
,9
.8
.4
.3
49.
44.
13.
62.
63.
17.
20.
41.
44,
.5
.0
.8
,8
.7
,9
.3
,8
,5
44,
39,
13,
71,
85,
24,
28,
53,
72,
.7
.7
.3
.8
.5
.9
.2
.9
.5
41.
37.
13.
77.
97.
27.
32.
57.
94,
.2
.5
.2
.2
.0
.4
.9
J
.7
40
36
13
78,
100,
27,
34,
,57.
107
.4
.8
.3
.0
.3
.1
.5
.0
.7
495.1
B-91
-------
Policy Options for Stabilizing Global Climate
-RCWP
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
TABLE B-126
SECONDARY OIL CONSUMPTION
(Exajoules/Yr)
100.6
2000
108.5
2025
28.
25.
8.
14.
2.
3.
3.
8,
6.
8
8
0
6
2
4
. 1
6
,1
24.
23.
7.
17,
3.
5.
i, c
12,
9,
.6
.7
,9
,7
.3
.3
.4
.5
,1
19
19
6
16
7
10
9
20
16
.2
.2
.9
.7
.4
.8
.4
.9
.5
127.0
2050
132.0
2075
132.6
2100
14.
14.
5.
16,
9.
14.
10,
23,
24,
,0
,3
,7
,1
,8
,0
. 4
,7
,0
11.
12.
5,
16,
11.
14,
10.
22,
28,
.6
,0
,1
,2
.9
,1
,4
,8
,5
11.
11.
5,
18,
15.
13,
10,
21,
32
,5
5
.0
,2
,1
,2
.6
.8
.2
139.1
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 48.8
TABLE B-127
SECONDARY GAS CONSUMPTION
(Exajoules/Yr)
2000
2025
2050
2075
48.9
86.4
107.5
112.6
2100
15.6
10.3
1.3
17.1
.3
.5
.4
2.1
1.2
13.3
9.5
1.3
17.9
.4
.6
.8
332
1.9 .
17.7
12.0
2.0
24.9
1.9
3.7
2.9
12.5
8.8
17.2
11.8
2.2
30.1
2.9
5.4
4.8
17.7
15.4
15.3
10.8
2.1
30.9
3.3
5.8
5.7
18.9
19.8
13.6
9.5
1.8
26.3
3.3
5.1
5.3
16.9
18.7
100.5
TABLE B-128
SECONDARY SOLIDS CONSUMPTION
(Exajoules/Yr)
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 45 .0
2000
2025
2050
60.7
73.2
90.2
2075
96.6
2100
3,
6,
2,
12.
14,
1,
3
.8
.3
.6
.9
.5
.0
.2
.6
.1
4,
6.
3.
14.
23.
2,
1,
5,
.4
.9
,0
.7
.0
,0
.2
.1
.4
2.
4,
2,
7
41,
4,
1,
9
.2
,0
,2
,8
.6
,0
.2
.7
.5
2,
4.
2.
7.
51.
6,
1.
14,
.2
.0
,3
.7
.6
.0
.4
.9
.1
2
4
2
8
53
7
2
16
.3
.2
.5
.3
.1
.0
.5
.0
.7
2,
4,
2.
9.
48.
7.
1.
17.
.5
,4
,6
,0.
,6
0
5
9
5
94.0
B-92
-------
Appendix B: Implementation of the Scenarios
RCWP
TABLE B-129
RESIDENTIAL/COMMERCIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
FUEL CONSUMPTION
REGION
1985
2000
2025
2050
TOTAL
47.9
47.5
54.5
2075
78.4
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
11.
12.
1.
13.
4.
1.
2,
. 5
.9
.6
.2
.3
.2
.4
.6
.2
7
12
1
13
5
2
3
.9
.4
.7
.0
.7
.4
.9
.3
.2
8
9
1
15
7
1
2
3
5
.1
.4
.2
.5
.8
.2
.1
.9
.3
7.
8,
1.
18.
13.
2.
3.
6
9
.8
.5
.3
.0
.1
.0
.5
.2
.4
6
7
1
18
17
2
4
7
12
.9
.6
.2
.3
.5
.4
.4
.6
.5
6.
6.
1.
16.
19.
2.
4.
7.
13.
0
6
0
8
8
5
5
6
7
78.5
REGION
1985
ELECTRICITY CONSUMPTION
2000
2025
2050
TOTAL
13.7
16.4
26.7
2075
50.2
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
5
4
1
1
.2
.5
.1
.3
.2
.1
.3
.5
.5
5.
4.
1,
2.
1
.9
.0
.1
.3
.5
.2
.5
.9
•°»
6
5
1
5
1
1
1
3
.5
.2
.3
.3
.4
.8
.3
.8
.1
7.
5,
1.
7.
3.
1.
2.
3,
6.
.0
.4
.5
.0
.0
,5
.4
.4
.6
7
5
1
8
5
2
3
5
11
.3
.7
.7
.4
.0
.4
.6
.1
.0
7.6
6.1
1.9
9.3
7.0
3.1
4.5
6.5
15.2
61.2
REGION
1985
TOTAL ENERGY CONSUMPTION
2000
2025
2050
TOTAL
61.6
63.9
81.2
107.6
2075
128.6
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
16
17
2
14
4
2
2
.7
.4
.7
.5
.5
.3
.7
.1
.7
13
16
2
15
6
1
3
4
.8
.4
.8
.3
.2
.6
.4
.2
.2
14
14
2
20
9
2
3
5
8
.6
.6
.5
.8
.2
.0
.4
.7
.4
14.
13.
2,
25,
16.
3,
5.
9.
16.
.8
.9
.8
.0
.1
.5
.9
.6
.0
14
13
2.
26.
22
4
8
12
23
— sssssi — =
.2
.3
.9
.7
.5
.8
.0
.7
.5
13.
12.
2.
26.
26.
5.
9.
14.
28.
.6
,7
.9
.1
.8
,6
.0
.1
,9
==:
139.7
B-93
-------
Policy Options for Stabilizing Global Climate
RCWP
TABLE B-130
INDUSTRIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 86.1
FUEL CONSUMPTION
2000
2025
2050
112.9
160.8
194.7
2075
195.7
2100
15
13
5
23
11
3
2
5
5
.6
.9
.4
.7
.5
.0
.1
.5
.4
17,
14.
6,
27,
19,
4
3,
9
10
,3
.8
.2
,6
.3
.6
,6
.5
.0
16.
13,
5.
22,
38.
11,
7,
21,
24.
.4
,0
.5
,2
.9
,2
,7
.8
.1
15,
12
5,
24,
45,
14
11,
27,
36
.4
.7
.5
.8
.5
.9
.5
.6
.8
13.
11.
5.
25.
43.
14,
12.
26,
42,
8
,9
4
.5
,0
.4
.7
, 5
,5
12,
10,
5,
22,
35,
11,
11,
21,
39
.7
.9
.0
.2
.5
.4
.6
.8
.7
170.8
REGION
1985
ELECTRICITY CONSUMPTION
2000
2025
2050
2075
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
3
3
1
7
1
19
.2
.5
.3
.1
.6
.4
.5
.8
.8
.2
3.
4.
1.
9.
3.
1.
:t
27,
9
0
.5
1
,5
.8
,9
,7
,9
,3
3.
3.
1.
8.
11.
2.
2.
4.
6.
44.
9
6
4
1
2
,6
.5
.9
,5
,7
4.
4.
1.
10.
18.
4,
4,
7,
12,
66,
,3
,2
,6
,9
,0
,0
,2
,2
,2
.6
4,
4.
1,
13,
23,
5
5
8
18,
86
.7
.8
.8
.4
.3
.1
.7
.9
.4
.1
5.2
5.3
2.0
15.2
25.7
5.7
6.6
9.9
23.6
99.2
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 105.3
TOTAL ENERGY CONSUMPTION
2000
140.2
2025
2050
205.5
261.3
2075
281.8
2100
18
17
6
30
13
3
2
6
6
.8
.4
.7
.8
.1
.4
.6
.3
.2
21,
18,
7.
36,
22.
5,
it
11,
11.
.2
,8
.7
,7
.8
.4
,5
.2
,9
20.
16.
6.
30.
50.
13.
10.
26.
30.
3
6
9
3
1
8
2
7
6
19,
16.
7.
35.
63.
18.
15.
34.
49,
,7
.9
,1
.7
.5
.9
,7
.8
.0
18,
16,
7,
38,
66,
19,
18.
35
60,
.5
.7
.2
.9
.3
.5
.4
.4
.9
17
16
7
37
61
17
18
31
63
.9
.2
.0
.4
.2
.1
.2
.7
.3
270.0
B-94
-------
Appendix B: Implementation of the Scenarios
RCWP
TABLE B-131
TRANSPORTATION ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD Europe/Canada .
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
60.4
FUEL CONSUMPTION
2000
57.7
2025
2050
71.3
65.2
2075
67.7
2100
21.
15.,
4.
7.
1,
2.
4.
2,
.1
.6
.9
.7
,2
.7
.2
,2
.8
17
12
4.
9.
1.
2
5
3.
.1
.9
.3
.7
.7
.9
.9
.0
.2
14
12
4
11
4
2
6
9
5
.6
.8
.4
.7
.2
.1
.7
.4
.4
10.
8.
3,
11.
5.
2.
6
9
7
.2
.9
.4
.1
.7
.5
.6
.5
.3
8.
7.
3.
11,
7,
3,
6.
9.
10
.5
.5
, 1.
.6
,8
.1
.5
.6
.0
8.
7,
3.
14,
11,
4,
7,
11
15
.9
.9
,4
.5
.7
,4
.3
.2
.0
84.3
ELECTRICITY CONSUMPTION
REGION
1985
2000
2025
2050
2075
2100
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South.and East Asia .0
TOTAL
.0
.0
.0
.0
.2
.0
.0
.0
.2
.4
.0
.0
.0
.0
.6
.0
.0
.0
.5
1.1
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL : 60.4
TOTAL ENERGY CONSUMPTION
2000
57.7
2025
2050
71.6
65.6
2075
68.4
2100
21
15
4
7
1
2
4
2
.1
.6
.9
.7
.2
.7
.2
.2
.8 •
17.
12.
4.
9.
1.
2.
5.
3.
,1
,9
,3
,7
,7
,9
.9
,0
.2
14.
12,
4,
11,
4,
2.
6.
9,
5,
.6
,8
.4
.7
.4
.1
.7
.4
.5
10.
8.
3.
11,
5.
2.
6.
9,
7.
,2
.9
.4
,1
,9
,5
.6
,5
,5
8.
7.
3,
11.
8.
3,
6.
9,
10.
.5
,5
.1
.6
.2
,1
,5
.6
,3
8,
7,
3,
14,
12.
4.
7.
11,
15,
,9,
.9
,4
.5'
.3
.4
,3
.2
.5
85.4
B-95
-------
Policy Options for Stabilizing Global Climate
RCWP
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-132
ELECTRIC UTILITY ENERGY CONSUMPTION
(Exajoules/Yr)
1985
105.3
2000
134.9
2025
2050
206.4
297 .4
2075
380.1
2100
26
24
7
26
6
1
2
4
4
.6
.6
.4
.4
.8
.9
.7
.3
.6
==-—
29
24.
7
34
13
3
4
7
9
.6
.0
.9
.5
.7
.2
.8
.7
.5
29.
25.
7.
38.
37.
9.
11.
19.
28.
5
4
6
0
4
6
0
9
0
:as:s
31.
27.
8.
49.
60.
15.
19.
31.
53.
6
9
9
3
1
7
0
2
7
33.
30.
9.
59.
78.
20.
26.
40.
81.
.1
1
9
.4 . '
.8
,7
.1
.4
6
35,
32.
11.
67.
92,
24,
31.
46,
108,
.8
.7
.0
.8
.4
.5
.0
.8
,8
450.8
TABLE B-133
ENERGY CONVERSION EFFICIENCY AT ELECTRIC UTILITY POWERPLANTS*
(percent)
REGION
1985
2000
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
* Includes transmission and distribution losses
2025
2050
2075
2100
31.2
32.5
32.4
31.4
26.5
26.3
22.2
30.2
26.1
33.4
33.3
32.9
33.0
29.2
25.0
29.2
3-3.8
29.5
35.6
34.3
35.5
35.3
34.0
35.4
34.5
34.2
34.3
35.8
34.8
37.1
36.3
35.4
35.7
34.7
34.6
35.4
36.6
34.9
37.4
36.5
36.2
36.2
36.0
35.1
36.4
36.0
34.6
36.4
36.3
36.3
35.9
35.8
35.0
36.0
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-134
SYNTHETIC PRODUCTION OF OIL AND GAS
(Exajoules/Yr)
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
==:=
.0
OIL FROM SYNFUELS
2000 2025.
2050
2075
2100
0
0
0
0
0
0
0
0
0
1
1
3
'I
4
6
2
.8
.3
.6
.3
.6
.0
.3
,4
.8
4.
3.
1,
- .7.
3.
10,
15";
6,
,2
.0
,5
,8
.8 .
.1
.2
.3
.7
7.
5,
2,
13,
'6,
17.
25.
11
,1
.0
.'5
.2
.5 '
.2
.1
.6
.2
8,
6.
3.
16.
8,
20
• - 30
13
.9
.1
.1
.5
.7
.2
.6
.8
.6
.0
22,1
52.6
88.4
108.5
REGION
1985
GAS FROM SYNFUELS
2000
2025
2050
TOTAL
.0
79.6
109.3
2075
117.3
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
6.3
4.5
2.3
11.8
5.6
.2
15.5
• 23.2
10.2
8.7
6.2
3.1
16.2
7.7
.2
21.3
31.9
14.0
9.3
6.7
3.4
17.4
8.2
.3
22.8
34.2
15.0
7.9
5.6
2.8
14.8
7.0
.2
19.3
29.0
12.7
99.3
B-96
-------
Appendix B: Implementation of the Scenarios
-RCWP
TABLE B-135
ENERGY USED FOR SYNTHETIC FUEL PRODUCTION BY TYPE
(Exajoules/Yr)
REGION
1985
COAL
2000
2025
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
REGION
United .States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
BIOMASS
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0
20
10
7
3
20
9
26
39
17
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
25
.8
.7
.9
.1
.5
.3
.3
.4
.3
2050
.0
.0
.0
.0
.1
.0
.0
.0
.0
.1
2050
2075
.1
.0
.0
.2
.5
.0
.0
.0
.0
2075
2100
.7
,2
.1
1.2
2.0
.0
.4
.0
.0
4. 6
2100
TOTAL
.0
135.3
215.4
272.6
272.6
TOTAL
REGION 1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL .0
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0
2025
2050
2075
2100
10.
7
3.
20.
9
26.
39.
17.
.8
.7
.9
.1
.5
.3
,3
.4
.3
17.
12.
6,
32.
15.
41,
62,
27.
.1
.2
.2
,0
,2
,5
,9
,8
.6
21.
15,
7.
40.
19,
53,
79.
34.
,8
,5
.8
.7
.6
,6
,0
,5
.9
22.
15,
7.
41.
21.
53.
79,
34.
.4
,7
,9
.7
,1
.6
,4
,5
9
135.3
215.5
273.4
277.2
B-97
-------
1'olicy Options for Stabilizing Global Climate
RCWP
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TABLE B-136
C02 EMISSIONS FROM FOSSIL FUEL
(Petagrams C/Yr)
1985
TOTAL
5.1
2000
5.9
2025
2050
2075
5.7
5.3
5.0
2100
1.3
.9
.3
1.3
.6
.1
.1
.2
.3
1.2
.9
.3
1.5
.9
.2
.2
.3
.5
.8
.7
.2
1.1
1.5
.3
.1
.2
.7
.6
.5
.2
.9
1.8
.4
.0
.0
1.0
.4
,4
.1
.8
1.8
.it
-.0
-.2
1.2
.4
.4
.1
.8-
1.9
.4
-.0
-.2
1.4
5.2
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-137
CO EMISSIONS FROM FOSSIL FUEL
(Teragrams C/Yr)
1985
185.8
2000
133.A
2025
2050
109.6
58.A
2075
2100
51,
44
14
31,
6
2
8
16
11
.0
.7
.1
.1
.0
.9
.5
.3
.2
22,
29,
9.
29,
6,
2,
8.,
14'
9,
,7
,6
.8
,1
.9
.6
.6
;'5
.7 ;
11,
10,
3.
23,
11,
4,
13
19,
11,
.6
.3
.6
.6
.7
.3
.7
.1
.8
7.
6.
2.
9.
8.
2.
5.
8.
7.
.6
,8
,6
,2
,8
.1
,8
,1
,3
6.
5,
2,
9,
10,
: 2,
6
8,
9
.4
.8
.4
.7
.9
.6
.0
.3
.7
6.
6,
2,
11
13
3.
6
9
13
.6
.1
.6
.6
.5
.4
.4
.2
.2
72.5
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-138
NOx EMISSIONS FROM FOSSIL FUEL
(Teragrams N/Yr)
1985
24.2
2000
25.8
2025
2050
2075
30.6
22.7
22.7
2100
:==
6,
4,
1,
5,
2,
1,
1
,1
.6
.7
.8
.6
.4
.8
.0
.3
4.
3.
1.
6.
4.
1.
1.
2.
9
8
5
4
2
5
2
3
0
3
2
6
7
1
2
3
3
.1
.4
.9
.3
.2
.1
.6
.2
.8
2.
1.
3,
4.
1.
2.
3.
3.
0
8
7
4
5
0
3
3
5
1
1
3
4
2
3
3
.8
.7
.7
.8
.4
.9
.3
.3
.9
==s;s=
1.
1.
4,
4.
2,
3.
5,
.7
.6
,7
,6
.7
,9
.2
,2
,1
24.7
B-98
-------
Appendix B: Implementation of the Scenarios
.RCHR
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-139
PRIMARY ENERGY SUPPLY
(Exajoules/Yr)
1985
301.6
2000
334.3
2025
2050
2075
519.6
744.2
2100
63.
47,
8,
81.
25,
23,
16.
20,
14.
.7
.6
.9
,2
,6
,7
.3
,5
,1
49.
44.
10,
80,
38.
49.
21,
22.
18,
.1
.8
.4
,8
.1
.2
,0
,7
,2
48
44
14,
91
60,
33,
68,
102,
55,
.4
.2
.3
.5
,2
.9
.6
.9
.6
52.
45,
17,
105.
78,
24,
92.
142,
85,
,8
.3
.6
.8
.0
.5
.2
.5
.7
54.
47.
19.
118,
108.
23.
107.
159.
105.
5
8
2
,1
5
,4
,4
,5
.8
55,
50
20
120,
121,
23,
117,
168.
121
,8
.0
.5
.6
.6
.2
.5
.0
.7
798.9
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
117.9
TABLE B-140
PRIMARY OIL SUPPLY
(Exajoules/Yr)
2000
2025
2050
2075
121.4
62.7
18.0
18.9
2100
20
11
1
26
5
22
10
14
5
.8
.9
.1
.0
.2
.4
.8
.1
.6
11.
8.
20.
6.
45.
14.
10 .
4.
.5
,6
,6
.4
.2
,8
,0
Pl
.2 ;
4,
4.
10,
4.
23,
9.
5.
2,
.2
.0
.2
.1
.4
.4
.2
.2
.0
1,
1,
2
3
6
2
1,
.1
.0
.0
.6
.0
.1
.4
.3
.5
1,
1,
4,
2.
1,
4,
1,
1.
.2
.6
.0
.6
.6
.6
.9
.3
.1
1
2
5
2
8
2,
1,
.1
.0
.0
.3
.5
.4
.4
.3
.6
23.6
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 58.6
TABLE B-141
PRIMARY GAS SUPPLY
(Exajoules/Yr)
2000
2025
2050
2075
70.6
44.0
28.1
28.6
2100
16,
9.
24.
1.
1,
2.
2.
.3
.7
,7
.0
.5
.2
.3
.5
.4
15.
11.
1.
27.
1.
3.
2.
4.
3.
,1
.1
.6
,7
,9
,2
.5
.4
,1
5.
4.
17.
2.
7.
1,
2.
1,
,9
.5
.8
.7
,1
.7
.3
.2
.8
1,
13,
1,
9,
.2
.9
.2
.7
.7
.2
.3
.5
.4
15
2
10
.2
.2
.0
.2
.6
.1
.0
.0
.3
9,
1.
10.
,3
.3
,0
,9
,8
,2
,0
,0
6
23.1
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-142
PRIMARY COAL SUPPLY
(Exajoules/Yr)
1985
19,
9
3
26,
18,
4,
4
.4
.4
.9
.7
.9
.0
.0
.6
.4
2000
12.
8,
4.
24.
26.
3,
7
.9
,4
.1
,9
.9
,0
,8
.9
.3
2025
5
3
2
11
19
2
5
.9
.9
.1
.7
.6
.0
.6
.6
.4
2050
2
1
5
8
1
2
.6
.7
.9
.2
.7
.0
.2
.3
.4
2075
1,
2,
23.
2,
.2
.8
.7
,3
.3
.0
.5
.1
.5
S
2100
1.
3.
29.
5.
6
0
9
6
5
0
6
0
6
87.3
89.2
51.8
23.0
31.'4
41.8
B-99
-------
Policy Options for Stabilizing Global Climate
RCWR
TABLE B-143
PRIMARY BIOMASS SUPPLY
(Exajoules/Yr)
REGION 1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
TOTAL .0
2000
.0
2025
2050
2075
265.8
396.1
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
21
15
7
39
18
51
77
34
.2
.1
.6
.5
.6
.6
.7
.5
.0
31.
22.
11.
58.
27.
77.
115.
50.
.5
.5
.3
.9
.8
.9
,0
.5
.7
35
25
12
66
31
1
86
129
56
.4
.3
.7
.0
.1
.0
.4
.6
.9
37
26
13
69
32
1
90
136
59
.2
.6
.4
.4
.7
.0
.8
.2
.8
467.1
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TABLE B-144
PRIMARY HYDROELECTRIC SUPPLY
(Exajoules/Yr)
1985
TOTAL
21.2
2000
2025
2050
2075
31.5
53.4
66.9
71.8
2100
3
8
1
2,
1
3
1
.3
.2
.2
.5
.0
.1
.2
.3
.4
3.
9.
1.
3.
2.
7'.
2.
9
7
3
2
6
2
5
3
8
4
10
1
3
7
1
16
6
.4
.8
.3
.7
.6
.5
7
.5
.9
4
11.
1.
3.
10.
4
20
10
.7
.0
.4
.7
.8
.6
,2
.0
.5
4.
11.
1,
3,
11,
6.
20.
11.
.7
.1
.4
.7
.5
.7
7,
.6
.9
4.
11.
1.
3.
11.
7.
20.
12.
.8
.1
.5
.7
,6
.7
n
.7
.4
73.5
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 16.5
TABLE B-145
PRIMARY NUCLEAR SUPPLY
(Exajoules/Yr)
2000
2025
2050
2075
19.0
25.8
58.2
81.0
2100
3,
8,
2.
2,
.8
,4
.0
.0
.0 •
.0
.0
.0
.3
5,
6.
2.
3.
.0
.7
.6
.8
.2
,0
,1
,0
.6
4
4
1
5
4
1
3
.2
.6
.6
.2
.6
.9
.1
.5
.1
5.
5.
2.
10.
13.
3.
3.
2.
10.
9
.5
1
9
1
.9
.6
.5
,7
6
6
2
14
19
5
5
4
17
.3
.1
.6
.0
.9
.3
.0
.2
.6
6,
6.
2.
16,
24.
6,
6.
4,
23,
,6
5
,9
.1
.4
.1
.0
.9
4
96.9
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
.1
.0
.0
.0
.0
.0
.0
.0
.0
.1
TABLE B-146
PRIMARY SOLAR SUPPLY
(Exajoules/Yr)
2000
2.6
2025
2050
2075
16.1
54.1
68.1
2100
.7
.3
.2
.8
.3
.0
.1
.0
.2
2,
1.
3,
3.
1,
2,
.6
.3
.7
,6
.3
.8
.0
.4
.4
. 5.
2.
1.
10.
12.
3.
3,
2.
10,
,8
,7
.7
.8
,9
.8
.5
.4
.5
5.
2.
1.
12.
17.
4,
4.
3,
15,
.5
.7
',&
,3
.5
.7
.4
.7
.5
5
2
1
12
19
4
4
3
18
.2
.5
.8
.6
.1
.8
.7
.9
.3
72.9
B-100
-------
Appendix B: Implementation of the Scenarios
RCHR
REGION
TABLE B-147
PRIMARY ENERGY CONSUMPTION
(Exajoules/Yr)
1985
2000
2025
2050
TOTAL
300..2
334.3
520.1
643.3
2075
742.6
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
74.
67.
19
71
23.
5.
7.
15.
15.
.9
.0
.3
.1
.8
.8
.6
.6
.1
68
60
19
80
37
9
11
23
24
.2
.8
.4
.0
.1
.0
.7
.7
.4
66
58
19
88
85
24
36
72
68
.6
.4
.3
.6
.7
.4
.9
.1
.1
62
55
19
101
112.
34
52
93.
110
.8
.4
.8
.5
.9
.5
.8
.4
.2
61.
55.
21.
113.
132.
40,
64.
109.
144.
.7
.5
.0
.7
.6
.1
.3
.2
.5
62.5
56.5
22.4
121.4
141.3
41.7
70.2
114.7
168.7
799.4
TABLE B-148
SECONDARY ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
194.4
FUEL CONSUMPTION
2000
2025
2050
207.9
267.2
292.5
2075
326.6
2100
48,
42.
11
44
17
3,
4
11
10
.2
.4
.9
.6
.0
.9
.7
,3
.4
40.
38.
11.
47.
24.
5.
7.
16.
15.
.9
.2
.7
,6
.6
.8-
.1 .
,4
.6
35.
31.
10,
44,
46,
14,
15,
34,
33,
.4
.8
.3
.4
.8
.8
.1
.8
.8
30
26
9
47
49
20
19
39
50
.0
.8
.4
.0
.8
.2
.2
.3
.8
29
26
9
51
52
23
23
45
65
.4
.1
.9
.7
.3
.1
.0
.9
.2
30,
26
10,
.55,
52.
23.
24.
47.
75.
.0
.7
.6
,5
.3
.7
.5
.5
.3
346.1
REGION
ELECTRICITY CONSUMPTION
1985
2000
2025
2050
2075
TOTAL
32.9
41.1
68.2
94.2
114.9
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
8
8
2
8
1
1
1
.4
.0
.4
.4
.8
.5
.8
.3
.3
9
7
2
10
3
1
1
2
2
.1
.5
.5
.8
.7
.0
.4
.4
.7
9.
8.
2
12,
12,
3,
3
' 6
9
.6
.1.
.5
.8
.1
.5
.5
.8
.3
9.
8.
2.
15.
20.
5.
5.
9,
17.
.2
.2
,8
.3
.5
.1
.9
.8 .
.4
8
8
3
17
27
6
7
11
24
.9
.2
.0
.5
.0
.2
.8
.7
.6
8.7
8.2
3.1
18.4
30.0
6.6
8.7
12.2
29.1
125. 0
REGION
1985
TOTAL ENERGY CONSUMPTION
2000
2025
2050
2075
TOTAL
227.3
249.0
•335.4
386.7
441.5
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
56
50
14
53
18
4
5
12
11
.6
.4
.3
.0
.8
.4
.5
.6
.7
50
45
14
58
28
6
8
18
18
.0
.7
.2
.4
.3
.8
.5
.8
.3
45.
39.
12.
57,
58,
18.
18,
41,
43,
,0
.9
.8
.2
.9
.3
.6
.6
.1
39
35
12
62
70
25
25
49
68
.2
.0
.2
.3
.3
.3
.1
.1
.2
38.
34.
12,
69,
79,
29,
30,
57,
89,
.3
.3
.9
.2
.3
.3
.8
.6
.8
38.7
34.9
13.7
73.9
82.3
30.3
33.2
59.7
104.4
471.1
B-101
-------
Policy Options for Stabilizing Global'Climate
RCWR
TABLE B-149
SECONDARY OIL CONSUMPTION
(Exajoules/Yr)
REGION
1985
2000
2025
2050
TOTAL
100.6
106.9
122.8
134.1
158,2
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South- and East Asia
28
25
8
14
2
3
3
8
6
.8
.8
.0
.6
,2
.4
.1
.6
.1
24
23
7
17
3
5
4
12
9
.2
.3
.7
.4
.4
.2
.4
.3
.0
16
17
6
14
a
11
8
21
18
.8
.1
.4
.8
.3
.4
.5
.4
.1
12
13.
5.
14
11.
14
10
23
27
.7
.5
.8
.7
.5
,9
.6
.2
.2
12.
13
6.
17.
15
17.
12
26
36
.8
.4
.2
.8
.6
.0
.9
.4
.1
13.
14.
6.
21.
20,
17.
14.
27.
43.
7
2
8
7
6
5
2
8
7
180.2
TABLE B-150
SECONDARY GAS CONSUMPTION
(Exajoules/Yr)
REGION 1985 2000 2025 2050 2075 2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Afric-a
Latin America
South and East Asia
TOTAL 48.8 47.8 87.7 103.8 117.3 121.5
15.
10.
1.
17.
2,
1.
,6
,3
,3
,1
.3
.5
.4
.1
2
13,
9.
1,
17,
s'
1.
.0
.1
.3
.5
.4
.6
,8
.''2
.9'
17.
12.
2.
24.
2,
3.
3,
12,
9.
,1
,1
.1
.6
,7
.4
,8
.4
.5
16,
11,
2,
28,
4
5,
5,
15,
16.
,2
.2
,2
.2
.0
.3
.2
.1
.4
15.
10.
2,
30,
4,
6,
6,
18,
21,
.7
.9
,4
.2
.9
.1
.6
.6
.9
15.
10.
2.
30.
5.
6.
7,
19,
25,
.4
.7
5
.3
.3
.2
.1
,0
.0
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-151
SECONDARY SOLIDS CONSUMPTION
(Exajoules/Yr)
1985
45.0
2000
2025
2050
2075
53.2
56.7
54.6
51.1
2100
3.
6,
2,
12,
14,
1.
3,
,8
.3
.6
.9
.5
.0
.2
.6
.1
3,
5.
2.
12.
20.
1,
4,
,7
.8
.7
.7
.8
,0
.9
.9
.7
1.
2,
1.
5.
35.
2,
1.
6,
,5
.6
,8
,0
.8
.0
,8
.0
,2
1.
2,
1,
4,
34
3.
1
7,
.1
.1
.4
.1
.3
.0
,4
.0
,2
1.
1.
' 3,
31,
3,
7,
.9
.8
.3
.7
.8
.0
.5
.9
.2
1,
1,
3,
26,
3.
6.
.9
.8
.3
.5
.4
.0
,2
.7
,6
44.4
B-102
-------
Appendix B: Implementation of the Scenarios
RCWR
TABLE B-152
RESIDENTIAL/COMMERCIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD 'Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
47.9
FUEL CONSUMPTION
2000
42.1
2025
2050
2075
48.0
2100
11
12
1
13
4
1
2
.5
.9
.6
.2
.3
.2
.4
.6
.2
7.
11.
1.
11.
4.
2.
2.
0
1
.6
.5
.8
.4
.8
.0
,9
6
7
1
12
5
1
1
3
4
.7
.8
.1
.8
.9
.0
.7
.3
.4
5.
6.
12
7
1
2
4
6
.6
.1
.9
.3
.1
.5
.3
.5
.5
4.
5.
11.
8.
1.
2.
5.
7,
8
.1
8
.8
,0
.8
.7
.1
.9
4.
4.
10.
8,
1,
2
5
8
.1
.5
.8
.6
.1
,8
.6
.2
.3
46.0
REGION
1985
ELECTRICITY CONSUMPTION
2000 2025
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
5
4
1
1
.2
.5
.1
.3
.2
.1
.3
.5
.5
5
3
1
2
.3
.6
.0
.0
.4
.2
.5
.8
.>9
5.
4,
1.
4.
1,
1.
1.
2.
.4
.3
.1
.4
.1
.7
.0
.5
.5
TOTAL
13.7
14.7
22.0
2050
4.6
25.6
2075
6.6
29.4
2100
4.8
3.8
1.1
4.5
1.9
1.0
1.6
2.3
4.4
3.4
1.1
4.6
2.8
1.4
2.1
3.0
3.9
3.1
1.0
4.3
3.4
1.6
2.3
3.3
7.7
30.6
REGION
1985
TOTAL ENERGY CONSUMPTION
2000 2025
2050
2075
TOTAL
61.6
56.8
66.7
72.4
77.4
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
16.
17.
2.
14.
4.
2.
2.
.7
.4
.7
.5
,5
,3
.7
.1
.7
12.
14.
2.
13.
5,
1.
2,
3,
,3
.7
,6
.5
.2
.6
.3
.8
.8
==5S
12.
12.
2.
17.
7,
1.
2,
4,
6.
.1
.1
.2
.2
,0
,7
,7
.8
.9
zxsss
10.
9.
2.
16.
9.
2.
3,
6.
11.
.4
.9
0
8
.0
5
.9
.8
,1
9.
8.
1.
16.
10,
3.
4,
8.
14.
.2
.5
.9
.4
.8
.2
.8
.1
.5
8.0
7.6
1.8
14.9
11.5
3.4
4.9
8.5
16.0
76.6
B-103
-------
Policy Options for Stabilizing Global Climate
RCWR
TABLE B-153
INDUSTRIAL ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
FUEL CONSUMPTION
REGION
1985
2000
2025
2050
2075
TOTAL
86.1
103.5
165.2
190.2
205.6
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
15.
13.
5.
23.
11.
3.
2.
5.
5.
6
9
4
7
5
0
1
5
4
16
14
5
26
13
4
3
9
9
.9
.3
.9
.4
.1
.5
.4
.4
.6
16
13
5
22
37
12
3
23
25
.9
.7
.7
.2
.6
.1
.0
.9
.1
15
13
5
25
37
16
11
26
38
.7
.1
.6
.3
.9
.6
.3
.7
.0
15
13
5
27
35
17
13
30
46
.5
.0
.3
.4
.9
.9
.2
.4
.5
15.7
13.2
5.9
23.2
30.6
16.9
13.5
29.4
49.6
203.0
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
ELECTRICITY CONSUMPTION
1935 2000 2025
19.2
26.4
2050
45.9
63.2
2075
84.9
2100
3
3
1
7
1
.2
.5
.3
.1
.6
.4
.5
.3
.8
3.
3.
1.
8.
3.
1.
i:
8
9
5
8
,3
8
,9
.6
"8
4.
3.
1,
8,
10,
2,
2
5
6
.2
.3
.4
.4
.3
.8
.5
.3
.7
4
4
1
10
18
4
4
7
12
.4
.4
.7
.8
.4
.1
.3
.5
.6
4.
4.
1.
12.
23.
4,
5.
3,
17,
5
,8
,9
,9
,9
,8
,7
,7
.7
4.
5.
2.
14,
26,
5
6,
8.
21,
,8
.1
.1
.1
.0
.0
.4
,9
.0
93.4
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TOTAL ENERGY CONSUMPTION
1935 2000 2025
105.3
134.9
2050
2075
211.1
253.4
290.5
2100
18
17
6
30
13
3
2
6
6
.8
.4
.7
.8
.1
.4
.6
.3
.2
20.
13.
7.
35
21
5
4
11
11
.7
.2
.4
.2
.4
.3
.3
.0
.4
21.
17.
7,
30.
48.
14,
10,
29,
31,
.1
,5
.1
.6
.4
.9
.5
.2
.3
20.
17.
7.
36.
56.
20.
15.
34.
50.
1
5
3
1
3
7
6
2
6
20
17
7
40
59
22
18
39
64
.0
.8
.7
.3
.8
.7
.9
.1
.2
20,
13,
8,
42,
56
21
19
38
70
.5
.3
.0
.3
.6
.9
.9
.3
.6
'296.4
B-104
-------
Appendix B: Implementation of the Scenarios
RCWR
TABLE B-154
TRANSPORTATION ENERGY CONSUMPTION: FUEL VERSUS ELECTRICITY
(Exajoules/Yr)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
60.4
FUEL CONSUMPTION
2000
2025
2050
2075
57.3
57.3
55.5
73.0
2100
21
15.
4.
7
1
2
4.
2
.1
.6
.9
.7
.2
.7
.2
.2
.8
17
12.
4,
9
1
2
5
3
.0
.8
.2
.7
.7
.9
.9
.0
.1
11
10
3
9
3
1
5
7
4
.8
.3
.5
.4
.3
.7
.4
.6
.3
8
7
2
9
4
2
5
8
6
.7
.6
.9
.4
.8
.1
.6
.1
.3
9.
8.
3.
12.
8,
3.
7
10
10
.1
.0
.3
.5
.4
.4
.1
.4
.8
10.
9.
3.
16.
13,
5.
8.
12
17,
2
0
9
,7
,6
,0
.4
.9
.4
97.1
ELECTRICITY CONSUMPTION
REGION ' 1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe .0
Centrally Planned Asia .0
Middle East .0
Africa .0
Latin America .0
South and East Asia .0
2000
.0
.0
.0
.0
.0
.0-,
.0
.0
.0
2025
.0
.0
.0
.0
.2
.0
.0
.0
.1
2050
.0
.0
.0
.0
.2
.0
.0
.0
.2
TOTAL
2075
.0
.0
.0
.0
.3
.0
.0
.0
.3
.6
2100
.0
.0
.0
.0
.6
.0
.0
.0
.4
1.0
REGION
.United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TOTAL ENERGY CONSUMPTION
1985 2000 2025
60.4
2050
2075
5 7.. 3
57.6
55.9
73.6
2100
21.
15.
4,
7,
1.
2,
4,
2,
,1
,6
,9
.7
,2
.7
.2
.2
.8 •
— — .:
17
12
4
9
1
2
'5
3
.0
.8
.2
.7
.7
.9
.9
.0
.1
11,
10,
3,
9,
3.
1,
5,
7
4.
.8
.3
.5
.4
.5
.7
.4
.6
.4
8
7
2
9
5
2
5
8
6
.7
.6
.9
.4
.0
.1
.6
.1
.5
9,
8
3,
12,
8,
3,
7
10,
11,
.1
.0
.3
.5
.7
.4
.1
.4
.1
10
9
3.
16,
14.
5.
8,
12.
17.
.2
.0
,9
.7
.2
,0
.4
.9
.8
98.1
B-105
-------
Policy Options for Stabilizing Global Climate
RCHR
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-155
ELECTRIC UTILITY ENERGY CONSUMPTION
(Exajoules/Yr)
1985
105.3
2000
2025
2050
2075
126.0
193.0
264.6
314.9
2100
26.
24.
7.
26.
6.
1.
2.
4.
4.
6
6
.4
.4
.8
9
7
,3
,6
27
22
7
32
12
3
4
7
8
.3
.5
.6
.6
.5
.0
.5
.3
.7
26
23
7
35
• 34
9
10
19
26
.4
.2
.1
.3
.6
.6
.3
.9
.6
25.
23.
8.
41.
57.
14.
16.
29.
48.
8
7
0
6
2
1
9
0
3
24
23
8
46
73
16
21
33
66
.2
.7
.3
.9
.1
.8
.8
.7
.4
23.
23.
8.
49.
81.
17.
24,
34,
79,
,6
,6
,7
,5
,3
.7
. 1'
.8
.2.
342.5
TABLE B-156
ELECTRICITY CONVERSION EFFICIENCY AT ELECTRIC UTILITY POWERPLANTS*
(percent)
REGION
1985
2000
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
* Includes transmission and distribution losses
2025
2050
2075
2100
31,
32,
32,
31,
26,
26,
22,
30
26,
.2
.5
.4
.4
.5 ,
.3
.2
.2
.1
33
32
31
33
29
26
28
32
29
.3
.9
.6
.7
.6
.7
.9
.9
;9
36,
34.
33
36
34
37
35
33
35
.4
.9
.8
.0
.7
.5
.0
.7
.0
35,
34,
36
36,
36,
36.
36,
34,
36,
.7
.6
.3
.8
.0
.2
.1
.5
.0
36,
35,
37,
37,
36,
37,
37,
35,
37,
.8
.0
.3
.5
.8
.5
.2
.0
.0
36.
35,
36,
37.
37.
36,
36.
34.
36.
.9
.2
.8
.4
.0
,7
,5
,8
.7
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-157
SYNTHETIC PRODUCTION OF OIL AND GAS
(Exajoules/Yr)
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
===
.0
OIL FROM SYNFUELS
2000 2025.
2050
2075
:2100
0
0
.0
.0
,0
,0
,0
,0
,0
5.9
4.2
2.1
11.1
' 5.2
.2
14.5
21.7
9.5
10.3
7.4
3.7
19.2
9.1 .
.3
25.2
37.8
16.6
12.3
8.8
4.4
23.0 -
10.9
.3
30.1
45.2
19.9
13.7
•9.8
4.9
25v5
12.1
-.4
33.4
50.2
22.0
.0
74.4
129.6
154.9
172.0
REGION 1985
United States .0
OECD Europe/Canada .0
OECD Pacific .0
Centrally Planned Europe . 0
Centrally Planned Asia .0
Middle East .0
Africa . 0
Latin America .0
South and East Asia .0
TOTAL
GAS FROM SYNFUELS
2000
.0
2025
105.5
2050
133.7
2075
152.9
2100
.0
.0
.0
.0
.0
.0
.0
.0
.0
8.
6.
3.
15.
7.
20.
• 30.
13.
4
0
0
7
4
2
5
8
5
10
7
3
19
9
26
39
17
.6
.6
.8
.9
.4
.3
.0
.0
.1
12
8
4
22
10
29
44
19
sstss
.2
.7
.4
.7
.7
.3
.7
.6
.6
=:===i
13.
9.
4.
24.
11.
32.
48.
21.
1
4
7
5
5
4
0
1
1
164.8
B-L06
-------
Appendix B: Implementation of the Scenarios
RCWR
TABLE B-158
ENERGY USED FOR SYNTHETIC FUEL PRODUCTION BY TYPE
(Exajoules/Xr)
COAL
REGION 1985 2000 2025 2050 2075 2100
United States .0 .0 .0 .0 .0 .0
OECD Europe/Canada .0 .0 .0 .0 .0 .0
OECD Pacific .0 .0 .0 .0 .0 .0
Centrally Planned Europe .0 .0 .0 .0 .0 .0
Centrally Planned Asia .0 .0 .0 .0 .0 .0
Middle East .0 .0 .0 .0 .0 .0
.Africa .0 .0 .0 .0 .0 .0
Latin America .0 .0 .0 .0 .0 .0
South and East Asia .0 .0 .0 .0 .0 .0
TOTAL
.0
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
1985
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
BIOMASS
2000
.0
.0
.0
.0
.0
.0
.0
.0
.0 »•
.0
TOTAL
2000
,0
.0
.0
.0
.0
.0
.0
.0
.0
2025
19.1
13.6
6.9
35.6
16.8
.5
46.6
69.8
30.7
239.6
2025
19.1
13.6
6.9
35.6
16.8
.5
46.6
69.8
30.7
2050
2075
350.1
2050
27.9
19.9
10.0
52.0
24.5
68,
102.
.0
239.6
44.8
350.1
409.6
2075
32.6
23.3
11.7
60.9
28.7
.9
79.6
119.5
52.4
409.6
2100
19
13
6
35
16
46
69
30
.1
.6
.9
.6
.8
.5
.6
.8
.7
27.
19.
10.
52.
24.
68.
102.
44.
,9
.9
.0
.0
,5
.8
.1
,1
.8
32.
23.
11.
60.
28.
79.
119.
52.
,6
.3
.7
.9
.7
,9
,6
,5
.4
35
25
12
66
31
1
87
130
57
.7
.5
.8
.5
.4
.0
.1
.6
.3
447.9
2100
447.9
B-107
-------
Policy Options for Stabilizing Global Climate
RCWR
TABLE B-159
C02 EMISSIONS FROM FOSSIL FUEL
(Petagrams C/Yr)
REGION
1985
2000
2025
2050
TOTAL
5.1
5.5
2.9
1.0
2075
1.2
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
1.3
.9
.3
1.3
.6
.1
.1
.2
.3
1.1
.8
.3
1.4
.8
.1
.2
.3
.A
.6
.5
.2
.6
1.2
.3
-.3
-.5
.A
.2
.2
.0
.2
1.1
.A
-.6
-.9
.A
.1
.2
.0
.2
1.2
.A
-.7
-1.0
.7
.1
.2
.0
.3
1.1
.5
-. 7
-1.0
.9
1.5
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE E-160
CO EMISSIONS FROM FOSSIL FUEL
(Teragrams C/Yr)
1985
185.8
2000
2025
2050
132.1
88.0
A5.9
2075
61.6
2100
51.
AA.
1A.
31.
6.
2.
8.
16.
11.
0
7
1
1
0
,9
.5
.3
.2
22,
29,
9,
28,
6,
2,
8,
1A,
9,
.5
.A
,7
.8
.6
.6
.5
.A
.5
8.
7,
2,
19.
9.
3,
11,
15,
9,
.8
,7
.7
.0
.6
,5
.2
.8
.6
6.
5.
2.
7.
6.
1.
A.
6.
5.
1 '
,3
.1
,2
,5
,8
,7
,7
7
6,
6
2
9
9
2,
6
8,
9
.8
.0
.5
.9
.0
.8
.1
.9
.A
7.
6.
2.
12,
12.
3,
7,
10,
1A,
6
.7
.9
,8
,A
,9
,1
,8
,0
78.3
TABLE B-161
NOx EMISSIONS FROM FOSSIL FUEL
(Teragrams N/Yr)
REGION 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 24.2
2000
24.2
2025
2050
27.8
20.A
2075
21.3
2100
6
A
1,
5,
2,
1
1,
.1
.6
.7
.8
.6
.A
.8
.0
.3
A.
3.
1,
6.
3.
1.
lc
1,
,6
,6
A
,0
.8
, 5
.1
,3
.8
2.
2.
A.
6.
1.
2.
3.
3.
.7
.1
.9
.1
,7
,1
8
.9
,5
1.
1.
2.
3.
1,
2.
3.
3.
,9
,6
,7
.7
.7
.0
.3
.3
.1
1.
1.
3.
3.
1,
2,
3,
3,
.9
.6
,7
,1
,5
.0
.A
.7
,A .
2.
1,
3
3,
1
2
A
A
.0
.7
.8
.6
.8
.1
.7
.1
.3
2A.O .
B-108
-------
Appendix B: Implementation of the Scenarios
SLOW
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-163
PRODUCTION OF WHEAT
(Million Metric Tons)
1985
481.1
2000
2025
2050
2075
627 .it
855.3
1060.2
1064.0
2100
72.
108.
16.
142.
40.
12.
11.
17.
59.
6
1
3
6
7
3
0
7
9
97
131
24
164
46
14
15
23
109
.5
.9
.1
.6
.8
.6
.0
.9
.0
128.
187.
37
190
54.
18.
22.
37.
178.
.5
.3
.8
.6
.4
.0
.1
.9
.5
146
219
49
212
60
21
30
85
233
.8
.1
.8
.2
.6
.5
.6
.8
.9
134.
196.
43.
200.
59.
30.
42.
95.
261.
0
6
5
7
2
9
3
6
2
127.
190,
41.
197.
61.
36.
53.
106.
294.
.0
.1
.9
.5
,7
.1
,8
.9
.7
1109.5
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
277.9
TABLE B-164
PRODUCTION OF RICE
(Million Metric Tons)
2000
375.7
2025
2050
538.5
671.8
2075
786.2
2100
3.
2,
10.
4.
100.
1.
7
10.
137.
.9
.0
.1
.0
.8
.4
,3
,7
.7
3
2
10
5
115
2
13
13
208
.8
.7
.5
.7
.5
.0
.3
.9
-:.3
6
3
9
6
123
2
24
19
341
.2
.8
.5
.4
.8
.8
.9
.5
.7
8,
4.
7,
6.
123.
2,
35,
20.
462,
.8
.1
.5
,8
.9
.8
,4
.5
.0
8.
4.
7
6.
129
4
53
24
547
.5
.3
.0
.9
.6
.4
.9
.3
.2
7.
4.
6.
6.
126.
4.
65.
25.
576,
.5
.2
3
.4
.9
.9
.2
.4
.0
822.8
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-165
PRODUCTION OF COARSE GRAINS
(Million Metric Tons)
1985
809.9
2000
2025
2050
1073.1
1453.3
1750.2
2075
1928.1
2100
233.
139.
10.
162.
82.
6.
56,
69,
50,
.3
,5
,3
,3
.3
.6
.0
.0
.5
277 .
205.
25.
203.
100.
9.
85,
102.
62,
.9
.7
.9
.6
,6
.7
.6
.1
.1
304,
286,
55.
267,
112,
14,
131.
203,
77
,6
.0
.1
.8
.0
.7
.9
.3
.8
319.
233,
81,
311.
119.
17.
161
409
96
.8
.5
.4
.4
.6
.9
.0
.2
.4
310.
234.
75.
315.
124.
26.
240,
485,
114,
,9
,1
,7
.7
,9
,9
.0
.5
.5
286,
229.
71.
304,
127.
30.
299,
528.
125,
.9
.6
.0
.6
.1
.6
.6
.4
.8
2003.6
REGION
TABLE B-166
PRODUCTION OF MEATS
(Million Metric Tons of Carcass Weight)
1985
TOTAL
68.0
2000
87.1
2025
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
11.
14.
4,
12.
4.
5.
11,
3,
.0
,6
,8
.2
.4
,9
.1
.6
.4
13
16
5
14
5
1
8
16
5
.8
.5
.7
.0
.4
.6
.5
.4
.2
13.
19,
7.
15.
6.
2,
15,
24,
9
.9
.2
.5
.3
.6
.8
.6
.5
.1
114.5
2050
130.3
2075
151.-1
2100
7.0
22.0
9.0
14.7
7.4
4.0
23.4
29.9
12.9
6.8
22.0
8.3
15.0
7.7
6.1
34.8
35.1
15.2
6.0
20.9
7.5
14.2
7.6
6.8
42.0
36.8
16.1
157.9
B-109
-------
Policy Options for Stabilizing Global Climate
SLOW
TABLE B-167
PRODUCTION OF DAIRY PRODUCTS
(Million Metric Tons of Milk Equivalent)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
508.6
2000
2025
2050
2075
634.1
851.1
1067.5
1200.0
2100
61.
140,
23,
154
8,
6,
20
38
54
.5
.3
.0
.7
,9
,7
.0
,8
.7
71.
151.
28.
178.
14.
10.
32.
61.
85.
6
,5
2
.9
0
8
.0
8
.1
104,
165.
33.
188
19,
17,
54
101.
166
,5
.9
.1
.2
.8
.3
.5
.6
.1
127
167
31
177
24
22
75
133
307
.5
.0
.7
.9
.0
.3
.7
.9
.6
124.
168.
29.
181.
25.
34,
111.
159.
366,
2
3
5
,5
,1
,8
,4
,2
,2
113.
163.
27.
173.
25.
39,
136.
171.
397,
4
7
4
,8
,3
.2
,5
,3
.9
1248.5
TABLE B-168
PRODUCTION OF OTHER ANIMALS
(Million Metric Tons of Protein Equivalent)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
19.5
2000
2025
2050
2075
27.3
42.8
64.3
72.1
2100
2
3
2
3
4
1,
1
.2
.8
.0
.0
.4
.2
.9
.3
.8
2.
4.
2.
3,
6.
1,
2,
3
.3
.6
.8
.5
.9
.4
.6
.3
.0
2.
5.
4.
3.
13.
2,
4,
5,
,4
,2
,8
,8
,5
,7
,6
,6
,2
2.
4.
9.
3.
26.
3.
7.
7.
1
5
2
4
1
8
2
5
6
2,
4,
8,
3,
28.
1.
5,
9
9,
.1
.7
.8
.6
.1
.3
.0
.2
.2
2
4
8
3
28
1
6
10
10
.0
.6
.3
.5
.'6
.5
.2
.0
.1
74.9
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-1&9
NITROGENOUS FERTILIZER USE
(Million Metric Tons N)
1985
63.7
2000
100.4
2025
2050
141.1
166.3
2075
180.5
2100
10,
11,
1,
13.
12,
1,
2,
2,
8,
,4
.9
,0
.5
.1 .
.0
.1
.9
.7
11.
18.
1.
22,
17.
1.
4.
4,
18,
9
,6
,1
4
.0
,5
,4
.7
,8
12,
24.
1,
25,
20,
2,
9,
8
37.
.8
.2
.8
.4
.1
.4
.1
.0
.2
10,
22
2,
27
21,
3
14.
10
53
.6
.6
.2
.6
.2
.1
.7
.5
.8
9.
21.
1.
27.
21.
3.
18.
13.
62.
,8
,8
7
,3
,7
.9
9
,4
,1
8,
21.
1.
26.
21.
4,
22.
15,
65,
.8
,4
,5
,8
,7
,2
,0
.1
,9
187.3
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-170
LAND UNDER RICE CULTIVATION
(Million Hectares)
1985
146.7
2000
167.9
2025
2050
2075
200.2
221.2
235.9
2100
1.1
.9
2.4
2.1
43.1
.7
3.5
8.0
84 ..9
1.0
.9
2.3
2.5
43.8
.9
5.1
8.6
102.7
1.4
1.0
1.8
2.4
43.8
1.0
7.5
9.6
131.7
1.8
.9
1.3
2.2
42.7
.9
8.5
8.5
154.4
1.6
.9
1.0
2.3
44.5
1.5
11.5
9.4
163.2
1.3
.9
.8
2.1
43.3
, 1.6
12'. 7
9.2
154.2
226.1
B-110
-------
Appendix B: Implementation of the Scenarios
RAPID
REGION
TABLE B-171
PRODUCTION OF WHEAT
(Million Metric Tons)
1985
2000
2025
2050
2075
TOTAL
481.1
628.2
843.1
966.3
975.1
2100
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
72.
108.
16.
142.
40,
12.
11.
17.
59,
.6
.1
.3
,6
,7
,3
,0
.7
;9
97.
132,
24,
164,
47.
14,
15
23
109
.6
.0
.3
.6
.0
.6
.1
.9
.1
127.
180.
34.
190.
54.
17.
22.
36.
180.
4
5
0
6
6
3
0
1
8
141
166
34
211
60
19
26
60
246
.7
.1
.3
.9
.6
.0
.4
.0
.2
129
155
31
206
61
22
32
64
271
.7
.6
.8
.2
.0
.5
.2
.7
.4
126
154
' 31
209
61
24
36
66
284
.8
.0
.1
.3
.8
.5
.2
.8
.9
995.3
REGION '" 1985
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL 277.9
TABLE B-172
PRODUCTION OF RICE
(Million Metric Tons)
2000
376.5
2025
2050
2075
536.9
655.7
708.9
2100
3.
2,
10,
4,
100
1
7
10
137
.9
.0
.1
.0
.8
.4
.3
.7
.7
3.
2.
. 10.
5,
115.
2.
13,
14.
208-
.8
.7
.6
,7
,5
.0
.3
.0
.9
6.
3,
9,
6,
123,
2,
24,
19.
341,
.2
.7
.3
.4
.8
.8
.4
.2
.0
8.
4,
9.
6.
125,
3.
35,
22.
439,
.9
.7
.5
.9
.7
.3
.6
.4
.0
8.
4.
8.
6,
126.
3.
44.
24.
481,
,1
,6
.7
.7
.7
,9
.5
,0
.7
7
4
8
6
125
4
49,
24,
494
.7
.5
.4
.7
.9
,1
.4
.3
.3
725.3
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-173
PRODUCTION OF COARSE GRAINS
(Million Metric Tons)
1985
809.9
2000
2025
2050
1074.6
1433.3
1728.4
2075
1851.6
2100
233
139
10
162
82
6
56
69
50
.3
.5
.3
.3
.3
.6
.0
.0
.5
278
206
26
203
99
9
85
102
62
.2
.6
.2
.6
.9
.7
.8
.3
.4
308
279
50
267
111
14
127
194
79
.2.
.7
.7
.7
.1
.5
.8
.6
.0
314.
292.
86.
311.
119.
18.
155.
352.
78.
2
6
1
6
3
,2
.0 .
,7
,8
304,
294.
84.
321,
127,
22,
202,
402,
91,
.0
.9
,3
.9
.3
.6
.7
.4
.5
295
293
82
325
128
24
227
413-
95,
.8
.1
.1
.6
.5
.4
.4
.3
.6
1885.7
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle' East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-174
PRODUCTION OF MEATS
(Million Metric Tons of Carcass Weight)
1985
68.0
2000
87.2
2025
2050
2075
113.4
134.9
143.0
2100
11.
14,
4.
12,
4,
5,
11,
3,
.0
,6
,8
.2
,4
.9
.1
,6
.4
13
16
5
14
5
1
8
16
5
.8
.5
.7
.0
.4
.6
.5
.5
.2
ISiSSK
13
18
7
15
6
2
• 15
24
9
K=SS
.5
.9
.3
.3
.6
.8
.2
.5
.2
8,
22,
9,
14.
7,
4,
22,
31.
13,
.8
.6
.5
,8
.5
.1
.8
.7
.2
8,
21,
8,
14,
7,
4
28
34
14
.0
.8
.8
.6
.6
.9
.4
.3
.5
7c
21.
8.
14.
7.
5,
31,
34,
' 14,
,7
.5
,5
,6
.5
.3
,4
.7
.9
146.2
B-lll
-------
Policy Options for Stabilizing Global Climate
RAPID
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-175
PRODUCTION OF DAIRY PRODUCTS
(Million Metric Tons MiLk Equivalent)
1985
508.6
2000
634.5
2025
2050
2075
856.2
1110.6
1161.9
2100
61.
140.
23.
ISA.
8.
6.
20.
38,
54.
5
3
0
,7
,9
.7
0
,8
,7
71.
151.
28.
178.
14.
10,
32,
62.
85,
,6
.7
,3
.9
.0
,8
,0
.0
,1
10A.
168.
32.
188,
19,
17,
5A,
IDA,
167
.6
,0
,8
.3
.8
,7
.1
.1
.1
139,
177,
33,
178,
24
23
76,
143
314
,6
,8
.2
.0
.0
.3
,2
.7
.8
129
172
31
176
24
28
94
156
350
.0
.4
.0
.0
.5
.1
.2
.5
.2
124,
171,
30,
177,
24,
30
104
159
363
,8
,1
.1
.2
.6
.3
.7
.8
.7
1186.1
TABLE B-176
PRODUCTION OF OTHER ANIMALS
(Million Metric Tons of Protein Equivalent)
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
1985
19.5
2000
2025
2050
27.4
43.2
65.7
2075
68.0
2100
:=:
2
3
2
3
4
1
1
.2
.8
.0
.0
.4
.2
.9
.3
.8
2,
4
2
3
6
1
2
3
==SS
.3
.6
.8
.5
.9
.4
.6
.4
>0
2.
5,
4.
3.
13,
2,
4,
5,
,4
,4
.9
.8
,5
.7
.6
,6
.2
2,
5,
9,
3.
26,
3,
7,
7
,2
.2
.4
,4
.1
.9
.4
.2
.9
2,
5
8,
3
26,
1
4
7
8
.1
.1
.8
.4
.7
.1
.2
.9
.7
2
5
8
3
27
1
4
8
9
.0
.1
.6
.5
.0
.2
.7
.1
.1
69.3
REGION
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-177
NITROGENOUS FERTILIZER USE
(Million Metric Tons N)
1985
63.7
2000
100.5
2025
2050
2075
139.8
147.7
155.8
2100
10.4
11.9
1.0
13.5
12.1
1.0
2.1
2.9
8.7
:=KSRS=
11.9
18.7
1.1
22.4
17.0
1.5
4.4
4.7
18.8
12.7
23.3
1.6
25.4
20.1
2.4
8.1
8.3
37.9
10.9
21.8
2.0
27.6
21.2
3.1
10.8
11.0
39.3
10.0
21.5
1.8
27.7
21.5
3.4
13.1
12.8
44.0
9.7
21.4
1.7
27.9
21.5
3.6
14.4
13.2
45.6
158.9
REGION
========
United States
OECD Europe/Canada
OECD Pacific
Centrally Planned Europe
Centrally Planned Asia
Middle East
Africa
Latin America
South and East Asia
TOTAL
TABLE B-178
LAND UNDER RICE CULTIVATION
(Million Hectares)
1985
2000
2025
2050
2075
2100
1.
2,
2.
43,
3
8,
84.
.46
,1
,9
,4
.1
.1
.7
.5
.0
.9
.7
1.
2,
2,
43.
5,
8
103,
168,
,0
.9
.3
.5
.9
.9
.2
.7
.1
.4
1.
1,
1,
2,
43,
1,
7
9
131
199
,4
,0
.7
.4
.8
.0
.3
.4
.2
.2
1.
1.
1.
2.
43.
1.
8.
9.
155.
225.
8
0
6
4
6
1
8
1
7
1
1.
1.
1.
2.
43,
- 1,
10,
9
156
227
5
0
,3
.3
.8
.3
.1
.1
.8
.1
1.
1.
2.
43.
1.
10.
8.
148,
217,
3
9
1
,3
2
.4
4
,7
.1
.5
B-112
-------
Appendix B: Implementation of the Scenarios
sew
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
TROPICAL DEFORESTATION
CEMENT PRODUCTION
TABLE B-179
C02 EMISSIONS BY TYPE
(Petagrams C/Yr)
TOTAL
1985
5.1
.7
.1
6.0
2000
6.2
1.2
.2
7.6
2025
7.6
1.8
.2
9.6
2050
7.9
1.7
.2
9.9
2075
9.0
.4
.3
9.6
2100
10.7
ACTIVITY
TABLE B-180
N20 EMISSIONS BY TYPE
(Teragrams N/Yr)
1985
2000
2025
2050
TOTAL
12.5
14.3
16.5
17.0
2075
15.9
2100
COMMERCIAL ENERGY COMBUSTION
FERTILIZER USE
GAIN OF CULTIVATED LAND
BIOMASS BURNING
NATURAL LAND EMISSIONS
OCEANS/FRESHWATER
1.0
1.6
.4
1.4
6.0
2.0
1.4
2.5
.6
1.8
• 6.0
2.0
1.6
3.5
1.0
2.3
6.0
2.0
1.6
4.1
.9
2.4
6.0
2.0
1.8
4.5
.2
1.4
6.0
2.0
1.9
4.6
.0
1.1
6.0
2.0
15.6
TABLE B-181
CH4 EMISSIONS BY TYPE
(Teragrams CH4/YD
ACTIVITY 1985
COMMERCIAL ENERGY COMBUSTION
FUEL PRODUCTION & TR,
ENTERIC FERMENTATION
RICE PRODUCTION
BIOMASS BURNING
LANDFILLS
WETLANDS
OCEANS /FRESHWATER
WILD RUMINANTS AND TERMITES
TOTAL 510.7
2000
2025
2050
581.0
687.9
748.4
2075
783.9
2100
BUSTION 2.
NSMISSION 60.
75.
109.
55.
30.
115.
15,
RMITES 44.
BILIZATION 5,
.0
.0
.2
,4
,1
,0
.0
.0
.0
.0
2.
76.
94,
125,
68.
35,
115,
15,
44.
5,
.2
.7
.4 '
.3
.1
.5
.0
.0
.0
.0
2
97
125,
149
87,
46
115,
15
44
5
.8
.9
.0
.4
.4
.4
.0
.0
.0
.0
3
105
151
165
86,
58,
115.
15
44
5
.0
.0
.4
.1
.9
.1
.0
.0
.0
.0
3
127
171
176
50
76
115
15
44
5
.6
.0
.7
.0
.5
.2
.0
.0
.0
.0
4
154
178
168
40,
104,
115,
15
44
5,
.4
.2
.7
.7
.2
.5
.0
.0
.0
.0
829.7
TABLE B-182
NOx EMISSIONS BY TYPE
(Teragrams N/Yr)
ACTIVITY 1985
COMMERCIAL ENERGY COMBUSTION
BIOMASS BURNING
NATURAL LAND EMISSIONS
LIGHTNING
TOTAL 54.2
2000
61.2
2025
32.9
22.1
12.5
3.5
71.1
2050
34.1
22.0
12.5
3.5
2075
2100
42.8
10.2
12.
3,
72.2
66.7
69.0
B-113
-------
Policy Options for Stabilizing Global Climate
sew
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
TROPICAL DEFORESTATION
AGRICULTURAL BURNING
HOOD USE
WILDFIRES
OCEANS
TOTAL
1985
160.0
110.0
20.0
10.0
20.0
TABLE B-183
cc EMISSIONS BY'TYPE
(Teragrams C/Yr)
2000
188,
254.
117.8
20.5
10.0
20.0
2025
249.5
394.5
130.0
21. tf
10.0
20.0
82S.4
2050
273.6
379.2
137.1
22.3
10.0
20.0
842.2
2075
614.1
2100
437.2
S-.6-
126.9'
24.3
10.0
20.0
623.0
B-114
-------
RCW
Appendix B: Implementation of the Scenarios
ACTIVITY
COMMERICIAL ENERGY COMBUSTION
TROPICAL DEFORESTATION
CEMENT PRODUCTION
TOTAL
TABLE B-18A
C02 EMISSIONS BY TYPE
(Petagrams C/Yr)
.985
5.1
.7
.1
2000
7.0
.9
.2
2025
11.2
1.0
.3
2050
15.6
1.0
.3
2075
20.5
1.1
.A
2100
25.0
.8
.A
6.0
8.1
12. A
16.9
22.0
26.1
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
FERTILIZER USE
GAIN OF CULTIVATED LAND
BIOMASS BURNING
NATURAL LAND EMISSIONS
OCEANS/FRESHWATER
TOTAL
TABLE B-185
N20 EMISSIONS BY TYPE
(Teragrams N/Yr)
1985
12.5
2000
1A.2
2025
2050
16.1
17.2
2075
18.1
2100
1.0
1.6
.A
l.A
6.0
2.0
1.5
2.5
.5
1.7
6.0
2.0
2. A
3.5
.5
1.7
6.0
2.0
3.2
3.7
.6
1.8
6.0
2.0
3.8
3.9
.6
1.8
6.0
2.0
A. 2
3.9
.A
1.6
6.0
2.0
18.1
ACTIVITY
TABLE B-186
CHA EMISSIONS BY TYPE
(Teragrams CHA/Yr)
1985
2000
2025
2050
2075
TOTAL
510.7
590.1
731.9
901.1
10AA.5
2100
COMMERICAL ENERGY COMBUSTION
FUEL PRODUCTION & TRANSMISSION
ENTERIC FERMENTATION
RICE PRODUCTION
BIOMASS BURNING
LANDFILLS
WETLANDS
OCEANS/FRESHWATER
WILD RUMINANTS AND TERMITES
METHANE HYDRATE DESTABILIZATION
2
60,
75.
109
55,
30,
115,
15,
AA
5,
.0
.0
.2
.A
.1
.0
.0
.0
.0
.0
2.
88.
9A.
125.
60.
39.
115.
15.
AA.
5.
3
5
5 •
7
A
6
0
0
0
0
3,
152.
12A,
1A8.
63,
60.
115,
15.
AA.
5,
.7
.1
.6
.7
.6
.3
,0
.0
.0
.0
5.
230,
156,
167.
65.
97.
115,
15,
AA,
5,
. 1
.2
.0
.9
.7
.2
.0
.0
.0
.0
6,
311.
163.
169.
66.
1A7.
115,
15,
AA,
5,
.A
.9
.6
.5
.A
.6
.0
.0
.0
.0
8. A
389. A
166.8
162.3
55.3
16A.9
115.0
15.0
AA.O
5.0
1126.1
TABLE B-187
NOx EMISSIONS BY TYPE
(Teragrams N/Yr)
ACTIVITY 1985
COMMERICIAL ENERGY COMBUSTION
BIOMASS BURNING
NATURAL LAND EMISSIONS
LIGHTNING
TOTAL 5A.2
2000
62.A
2025
2050
79.2
95.A
2075
110.A
2100
2A.2
13.9
12.5
3.5
31.0
15.3
12.5
3.5
A7.0
16.2
12.5
3.5
62.7
16.7
12.5
3.5
77.5
16.9
12.5
3.5
91.5
1A.O
12.5
3.5
121.6
B-115
-------
Policy Options for Stabilizing Global Climate
RCW
TABLE B-188
CO EMISSIONS BY TYPE
(Teragrams C/Yr)
ACTIVITY . 1985 2000 2025 2050 2075 2100
COmERCIAL ENERGY COMBUSTION 185.8 197.6 334.9 477.5 636.0 862.7
TROPICAL DEFORESTATION 160.0 195.1 210.4 225.8 241.1 164.4
AGRICULTURAL BURNING 110.0 119.5 130.6 134.0 128.0 118.6
WOOD USE 20.0 19.5 18.7 18.0 17.2 16.5
OCEANS 20.0 20.0 • 20.0 20.0 20.0 20.0
WILDFIRES 10.0 10.0 10.0 10.0 10.0 10.0
TOTAL 505.8 561.7 724.6 885.3 1052.3 1192.2
B-116
-------
RCHA
Appendix B: Implementation of the Scenarios
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
TROPICAL DEFORESTATION
CEMENT PRODUCTION
TABLE B-189
C02 EMISSIONS BY TYPE
(Petagrams C/Yr)
1985
5.1
.7
.1
TOTAL
6.0
2000
7.8
1.2
.2
9.1
2025
19.8
1.8
.3
21.9
2050
36.6
2075
49.6
.4
.4
50.3
2100
5it.it
.0
.4
54.8
TABLE B-190
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
FERTILIZER USE
GAIN OF CULTIVATED LAND
BIOMASS BURNING
NATURAL. LAND EMISSIONS
OCEANS/FRESHWATER
N20 EMISSIONS BY TYPE
(Teragrams N/Yr)
1985
TOTAL
12.5
2000
1.7
2.5
.6
1.9
6.0
2.0
14.7
2025
18.5
2050
5.7
3.7
.9
2.4
6.0
2.0
;==:=::==:—
20.7
2075
7.9
3.9
.2
1.4
6.0
2.0
21.3
2100
9.0
3.9
.0
1.1
6.0
2.0
22.0
TABLE B-191
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
FUEL PRODUCTION & TRANSMISSION
ENTERIC FERMENTATION
RICE PRODUCTION
BIOMASS BURNING
LANDFILLS
WETLANDS
OCEANS/FRESHWATER
WILD RUMINANTS AND TERMITES
METHANE HYDRATE DESTABILIZATION
TOTAL
CH4 EMISSIONS BY TYPE
(Teragrams CH4/Yr)
1985
2
60
75.2
109.4
55.1
30.0
115.0
15.0
44.0
5.0
510.7
2000
2025
614.4
5.0
911.9
2050
7,
544,
156,
167.9
86.5
97.2
115.
15,
44,
5.0
1237.6
2075
8.9
786.5
163.6.
169.5
48.9
147.6
115.0
15.0
44.0
5.0
1504.0
2100
10.4
854.6
166.8
162.3
39.0
164.9
115,
15.
44.
5.0
1576.9
TABLE B-192
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
BIOMASS BURNING
NATURAL LAND EMISSIONS
LIGHTNING
TOTAL
NOx EMISSIONS BY TYPE
(Teragrams N/Yr)
1985
54.2
2000
67.1
2025
104.9
2050
145.3
2075
174.2
2100
186.8
B-117
-------
Policy Options for Stabilizing Global Climate
RCWA
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
TROPICAL DEFORESTATION
AGRICULTURAL BURNING
HOOD USE
WILDFIRES
OCEANS
TOTAL
TABLE B-193
CO EMISSIONS BY TYPE
(Teragrams C/Yr)
1985 2000 2025
185.8 195.4
160,
110,
20.
10,
20.0
2050
2075
399.7
394.5
130.6
21.4
10.0
20.0
631.6
379.2
134.0
22.3
10.0
20.0
789.8
78.9
128.0
23.3
10.0
20.0
2100
942.9
619.6
976.2
1197.1
1050.1
1122.3
B-118
-------
Appendix B: Implementation of the Scenarios
SCWP
ACTIVITY
TABLE B-194
C02 EMISSIONS BY TYPE
(Petagrams C/Yr)
1985
COMMERCIAL ENERGY COMBUSTION 5.1
TROPICAL DEFORESTATION .7
CEMENT PRODUCTION .1
TOTAL 6.0
2000
5.6
-.2
.1
5.6
2025
5.5
-.5
.2
5.2
2050
4.2
-.3
.2
4.0
2075
3.3
-.2
.1
3.3
2100
2.6
-.2
.1
2.6
ACTIVITY
COMMERICAL ENERGY COMBUSTION
FERTILIZER USE
GAIN OF CULTIVATED LAND
BIOMASS BURNING
NATURAL LAND EMISSIONS
OCEANS/FRESHWATER
TOTAL
TABLE B-195
N20 EMISSIONS BY TYPE
(Teragrams N/Yr)
1985
12.5
2000
12.9
2025
2050
13.1
13.1
2075
13.0
2100
1.0
1.6
.4
1.4
6.0
2.0
1.2
2.3
.2
1.1
6.0
2.0
1.2
2.9
.0
1.0
6.0
2.0
1.1
3.0
.0
1.0
6.0
2.0
1.1
2.8
.0
1.0
6.0
2.0
1.2
2.6
.0
1.0
6.0
2.0
12.8
ACTIVITY
TABLE B-196
CH4 EMISSIONS BY TYPE
(Teragrams CH4/Yr)
1985
2000
2025
2050
TOTAL
510.7
528.1
544.8
527.9
2075
518.0
2100
COMMERCIAL ENERGY COMBUSTION
FUEL PRODUCTION & TRANSMISSION
ENTERIC FERMENTATION
RICE PRODUCTION
BIOMASS BURNING
LANDFILLS
WETLANDS
OCEANS/FRESHWATER
WILD RUMINANTS AND TERMITES
METHANE HYDRATE DESTABILIZATION
2.
60.
75.
109.
55.
30.
115.
15.
44.
5.
=K=±SSS
0
0
2
4
1
0
0
0
0
0
1
68
88
116
44
30
115
15
44
5
.9
.6
.2
.2
.0
.2
.0
.0
.0
.0
ZSSSSSS
1.
64.
104.
122.
38.
34.
115.
15.
44.
5.
=55=
.8
.6
0
,3
.5
.6
0
.0
,0
.0
1.
47.
112.
119.
38.
30.
115.
15.
44.
5.
=====
,8
.0
,0
2
.4
.6
.0
.0
.0
.0
5SS3S
1.
45.
112.
112'.
38.
28.
115.
15.
44.
5.
=5=
9
4
9
1
3
4
0
0
0
0
2.2
39.2
104.7
94.8
36.0
27.3
115.0
15.0
44.0
5.0
483.0
TABLE B-197
NOx EMISSIONS BY TYPE
(Teragrams N/Yr)
ACTIVITY 1985
COMMERCIAL ENERGY COMBUSTION
BIOMASS BURNING
NATURAL LAND EMISSIONS
LIGHTNING
TOTAL 54.2
2000
51.8
2025
2050
47.8
44.0
2075
43.9
2100
24.2
13.9
12.5
3.5
24.6
11. 1
12.5
3.5
22.0
9.7
12.5
3.5
18.3
9.6
12.5
3.5
18.2
9.7
12.5
3.5
19.6
9.1
12.5
3.5
44.7
B-119
-------
Policy Options for Stabilizing Global Climate
SCOT
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
TROPICAL DEFORESTATION
AGRICULTURAL BURNING
WOOD USE
WILDFIRES
OCEANS
TOTAL
1985
185.8
160.0
110.D
20.0
10.0
20.0
5Q5.8
TABLE B-198
CO EMISSIONS BY TYPE
(Teragrams C/Yr)
2000
135,5
63,6
117.8
19.4
10.0
20.0
366.3
2025
102.1
8.8
130.0
18.5
10.0
20.0
289.4
2050
64.6
2.2
137.1
17.6
10.0
20.0
251.5
2075
69.7
2.2
139.2
16,8
10.0
20.0
257.9
2100
'81.6
.0
126.9
15.0
10.0
20.0
254.5
B-120
-------
Appendix 1$: Implementation of the Scenarios
RCWP
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
TROPICAL DEFORESTATION
CEMENT PRODUCTION
TABLE B-199
C02 EMISSIONS BY TYPE
(Petagrams C/Yr)
1985
5.1
.7
.1
TOTAL
6.0
2000
5.9
.2
5.9
2025
5.7
-.5
.2
5.4
2050
5.3
-.3
.3
5.3
2075
5.0
-.2
.3
5.2
2100
5.2
.3
5.3
ACTIVITY
COMMERICIAL ENERGY COMBUSTION
FERTILIZER USE
GAIN OF CULTIVATED LAND
BIOMASS BURNING
NATURAL LAND EMISSIONS
OCEANS/FRESHWATER
TOTAL
TABLE B-200
N20 EMISSIONS BY TYPE
(Teragrams N/Yr)
1985
12.5
2000
12.9
2025
2050
13.3
12.9
2075
12.7
2100
1.0
1.6
.it
1.4
6.0
2.0
1.3
2.3
.2
1.2
6.0
2.0
1.4
2.8
.0
1.0
6.0
2.0
1.3
2.6
.0
.9
6.0
2.0
1.3
2.4
.0
.9
6.0
2.0
1.5
2.2
.0
.9
6.0
2.0
12.6
ACTIVITY
TABLE B-201
CH4 EMISSIONS BY TYPE
(Teragrams CH4/Yr)
1985
2000
2025
2050
TOTAL
510.7
536.7
561.2
567.3
2075
548.9
2100
COMMERCIAL ENERGY COMBUSTION
FUEL PRODUCTION & TRANSMISSION
ENTERIC FERMENTATION
RICE PRODUCTION
BIOMASS BURNING
LANDFILLS
WETLANDS
OCEANS/FRESHWATER
WILD RUMINANTS AND TERMITES
METHANE HYDRATE DESTABILIZATION
2,
60,
75,
109,
55,
30,
115,
15
44
5
.0
,0
.2
.4
.1
.0
.0
.0
.0
.0
1.
73.
88,
116,
43.
34,
115,
15,
44.
5,
,9
,0
,3
.6
,7
,3
.0
.0
.0
.0
2
69
103
121
37
48
115
15
44
5
.0
.2
.7
.6
.3
.4
.0
.0
.0
.0
1.
69.
115,
121,
36.
44.
115.
15.
44,
5,
.8
,6
,3
,2
.0
,5
.0
.0
,0
,0
1
75,
107
'107
34
42
115
15
44
5
.9
.9
.7
.9
.3
.3
.0
.0
.0
.0
2.1
81.2
98.0
91.2
32.0
41.2
115.0
15.0
44.0
5.0
524.6
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
BIOMASS BURNING
NATURAL LAND EMISSIONS
LIGHTNING
TOTAL
TABLE B-202
NOx EMISSIONS BY TYPE
(Teragrams N/Yr)
1985
54.2
2000
52.9
2025
2050
56.1
47.8
2075
47.4
210*
24.2
13.9
12.5
3.5
25.8
11.1
12.5
3.5
30.6
9.5
12.5
3.5
=====3S
22.7
9.1
12.5
3.5
22.7
8.6
12.5
3.5
24.7
8.1
12.5
3.5
48.8
B-121
-------
Policy Options for Stabilizing Global Climate
HCWP
TABLE B-203
CO EMISSIONS BY TYPE
CTeragraras C/Yr)
ACTIVITY 1985 2000 2025 2050 2073 2100
COMMERCIAL ENERGY COMBUSTION 185.8 133.4 109.6 58.4 61.8, 72,5'
TROPICAL DEFORESTATION 160.0 63.6 8.8 2.2 2.2 . ,.0
AGRICULTURAL BURNING 110.0 119,5 130.6 134,0 128.0 118.6
WOOD USE 20,0 18.1 15.3 12.9 10.9 9.2
WILDFIRES 10.0 10.0 10.0 10.0 10.0 10.0
OCEANS 20.0 20.0 20.0 20.0 20.0 20.0
TOTAL 505.8 364.5 294.2 237.5 232.9 230.3
B-122
-------
Appendix B: Implementation of the Scenarios
RCWR
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
TROPICAL DEFORESTATION
CEMENT PRODUCTION
TOTAL
TABLE B-204
C02 EMISSIONS BY TYPE
(Petagrams C/Yr)
1985
2000
6.0
5.2
2025
2.1
2050
2075
1.2
-1.0
,3
.5
2100
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
FERTILIZER USE
GAIN OF CULTIVATED LAND
BICMASS BURNING
NATURAL LAND EMISSIONS
OCEANS/FRESHWATER
TOTAL
TABLE B-205
N20 EMISSIONS BY TYPE
(Teragrams N/Yr)
1985
12.5
2000
12.8
2025
2050
13.1
12.7
2075
12.6
2100
1.0
1.6
.4
1.4
6.0
2.0
1.2
2.3
.2
1.2
' 6.0
2.0
1.2
2.8
.0
1.0
6.0
2.0
1.1
2.6
.0
.9
6.0
2.0
1.2
2.4
.0
.9
6.0
2.0
1.4
2.2
.0
.9
6.0
2.0
12.5
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
FUEL PRODUCTION & TRj
ENTERIC FERMENTATION
RICE PRODUCTION
BIOMASS BURNING
LANDFILLS
WETLANDS
OCEANS/FRESHWATER
WILD RUMINANTS AND TERMITES
METHANE HYDRATE DESTABILIZATION
TOTAL
TABLE B-206
CH4 EMISSIONS BY TYPE
(Teragrams CH4/Yr)
1985
510.7
2000
2025
2050
529.6
520.8
500.2
2075
484.1
2100
ON 2
SION 60
75,
109.
55.
30.
115.
15.
S 44
AT ION 5
.0
.0
,2
4
1
0
0
0
.0
.0
1.
66.
88.
116.
43.
34.
115.
15.
44.
5.
,8
.0
3
6
7
3
0
0
0
.0
1
29
103
121,
37.
48,
115.
15,
44
5
.7
.1
.7
.6
.3
.4
0
.0
.0
.0
1
2
115
121
36
44.
115,
15,
44
5
.6
.7
.3
.2
.0
.5
.0
.0
.0
.0
2.
10,
107.
' 107,
34,
42.
115.
15.
44,
5.
.0
.9
,7
9
,3
,3
0
,0
,0
,0
2.
19
98
91.
32
41,
115.
15.
44.
5.
.4
.0
.0
.2
.0
.2
.0
.0
.0
,0
462.7
ACTIVITY
COMMERCIAL ENERGY COMBUSTION
BIOMASS BURNING
NATURAL LAND EMISSIONS
LIGHTNING
TOTAL
TABLE B-207
NOx EMISSIONS BY TYPE
(Teragrams N/Yr)
1985
24.2
13.9
12.5
3.5
54.2
2000
2025
51.3
53.2
2050
45.5
2075
46.0
2100
48.1
B-123
-------
Policy Options for Stabilizing Global Climate
RCWR
TABLE B-206
CO EMISSIONS BY TYPE
CTeragrams C/Yr)
ACTIVITY 1985 2000 2025 2050 2075 2100
COMMERCIAL ENERGY COMBUSTION 185.8 132.1 88.0 45,9 61.6 78.3
TROPICAL DEFORESTATION 160.0 63.6 8.8 2.2 2.2 .0
AGRICULTURAL BURNING 110.0 119.5 130.6 134.0 128.0 118.6
WOOD USE - . 20.0 18.1 15.3 12.9 10.9 9.2
WILDFIRES 10.0 10.0 10.0 10.0 10.0 10.0
OCEANS 20.0 20.0 20.0 20.0 20.0 20.0
TOTAL 505.8 363,2 272.6 225.0 232.7 236.1
B-124
-------
APPENDIX C
SENSITIVITY ANALYSES
FINDINGS
* The degree of participation by
developing countries in policies to limit
warming is one of the most important factors
affecting equilibrium temperatures in the year
2100. If only industrialized countries adopt
policy measures, equilibrium temperatures
could increase by 40% or more relative to
scenarios with global cooperation. This
suggests that, despite uncertainties about
future economic growth rates, developing
countries will be a significant determinant in
the ultimate level of global warming.
• Delaying any response to global
warming by OECD, U.S.S.R., and Eastern
Europe until the year 2010 and by developing
countries until 2025 might increase the
equilibrium warming commitment in 2050 by
40-50%.
• The sensitivity of the climate system to
a given increase in greenhouse gases is one of
the most important causes for uncertainty
about the ultimate magnitude of global
warming. For most of the analysis in this
report, we have assumed that the climate
sensitivity to doubling CO2 is 2.0 to 4.0°C;
broadening the range of climate sensitivity to
between 1.5 and 5.5°C for a CO2 doubling
causes the estimated range for equilibrium
warming in 2050 to become 2.2-7.9°C in the
Rapidly Changing World (RCW) scenario.
The impact on realized warming is less: the
estimated range for 2050 increases from 2.0-
3.0°C to 1.6-3.5°C. This uncertainty has
important implications for the timing and
stringency of policy responses. Even the lower
values, when considered with information on
the impacts of global warming, suggest a need
for caution about future emissions.
• Uncertainties in biogeochemical
feedbacks appear to be potentially the most
important reason to suspect that global
warming may ultimately be greater than
predicted by current general circulation
models. Changes in the ocean circulation,
methane releases from hydrates, bogs, and rice
cultivation and other positive feedbacks could
amplify realized warming in 2100 by 20-40%
for a climate sensitivity of 2.0-4.0°C. These
estimates are speculative, they are based on
the fragmentary evidence currently available,
and these positive feedbacks may not occur or
may be delayed until the latter part of the next
century, but the potentially large impact on
the magnitude of warming suggests that even
more drastic policy measures than those
considered in the Rapidly Changing World
with Stabilizing Policies (RCWP) scenario
might be needed.
• Sensitivity analyses with four ocean
models for CO2 uptake suggest that the path
of atmospheric concentrations could follow
somewhat different trajectories, but very little
difference is observed in equilibrium warming
for the year 2100. These equilibrium
temperatures differ by at most 13% depending
on the type of ocean model. More complex
ocean circulation models currently in the
research stage could broaden or decrease this
range in the future.
• Assumptions about the total supply of
oil and gas are among the least significant
factors affecting global warming in the year
2100. While gas may be desirable as a
transition fuel, sensitivity tests that assume
very optimistic estimates of oil and gas
availability at each price level suggest only
small changes in global warming. A larger
impact could occur if policy measures were
adopted to take advantage of the assumed
increases in gas resources.
• The sources of methane are subject to
considerable uncertainty. Estimates of some
individual emission sources vary by a factor of
two to three. Sensitivity tests that consider
extreme assumptions about anthropogenic
methane emission sources suggest that
uncertainties in this budget could cause
equilibrium warming commitments in 2100 to
vary by about 5%. These results should not be
interpreted to mean that methane is not an
important greenhouse gas, but simply that
uncertainties in the current budget do not
greatly affect the ultimate temperatures
derived in this report.
C-l
-------
Policy Options for Stabilizing Global Climate
• A . comparison between current
atmospheric concentrations and growth rates
for the greenhouse gases and those calculated
with the atmospheric composition model,
based on estimates of pre-industrial
concentrations and past emissions, indicates
agreement. The largest discrepancies are for
relatively short-lived gases where emissions
have been increasing rapidly in recent years,
such as HCFC-22 and carbon tetrachloride.
• Non-greenhouse gases such as NOX, CO,
and non-methane hydrocarbons (NMHC)
affect the lifetimes and concentrations of
tropospheric ozone and methane. A
comparison of different chemistry models
suggests that increases in methane
concentrations may vary by approximately a
factor of three to four for similar assumptions
about NOX/CO/NMHC. This range may be
attributed to differences in initial budgets and
modeling approaches and may ultimately
increase or decrease as other models become
available.
• The most important determinant of
future atmospheric concentrations of methane
appears to be the growth rate of methane
sources. While NOX and CO affect the
lifetime of methane, model studies suggest that
assumptions about the emissions of these gases
are less important than assumptions about the
direct emissions of methane. However,
considerable research is needed to further our
understanding of the chemistry of the
atmosphere.
• There is considerable uncertainty about
future concentrations of tropospheric ozone
and about changes in composition at different
altitudes. While model comparisons all
suggest that increases in ozone are likely, the
effect of these changes in global temperatures
is difficult to predict.
• For the major sensitivity analyses
presented in this appendix, Table C-l
summarizes the impact on realized warming
and equilibrium warming by 2050 and 2100
(assuming a 3.0°C climate sensitivity).
Throughout this appendix, results are
discussed for 2.0-4.0°C climate sensitivities for
the Rapidly Changing World Scenario, with
any figures using the midpoint of this range,
i.e., a 3.0°C climate sensitivity, unless stated
otherwise.
C-2
-------
\p[K'iidi\ {': Sen.sitivitv Analyses
TABLE €-1
Impact of Sensitivity Analyses on Ri-aliztd Warming
and Equilibrium Warming
(degrees Celsius — 3,0°C climate sensitivity)
2050
Rapidly Changing World - No Response (RCW)
Rapidly Changing World - Stabilizing Policies (RCWP)
Sensitivity Case Assumptions
No Participation by Developing Countries3
Global Delay in Adopting Policies*
Non-Fossil Technolo^1
Fossil Resources
High Coal Prices'1
High Oil Supply*
High Gas Supp!yf
Methane Budget8
N2O From Fertilizer
Anhydrous Ammonia15
NjO Leaching1
NjO From Combustion*
CO2 From Biomass*
CO2 Models1
Oeschger et al.°
Bolin et ai,n
Bjorkstrom0
Siegenthalerp
Unknown Sink**
1.5-5.5"C Sensitivityr
Heat Diffusion4
Prather Model
CFC-11 Lifetime1
Chlorine/Col O,u
Trop Oj/CH/ "
OH/NOX*
Feedbacks
Ocean Circulation*
Methane*
COj/CHyUptake1
Realised
2.6a
1.6
2.1
2.2
2.4-2.5
2.4
2.6
2.6
2.5-2.7
2.6
2.6
2.6
2.5
-
-
2.4-2.6
1,6-3.5
2.0-2.9
2.6
2.5
2,6
2.5-2.6
3.5
2.8
3.0
Equilibrium
4.3°
23
3.2-3.4
3.4
3.9-4.1
3.8
4.3
4.4
4.2-4.5
4.3
4.3
4.3
4.3
4.2
4,3
4.3
3.9
4.0-4.5
2.2-7,9
4.3
4.3
4.1
4.4
4.3-4.4
4.5
4.7
5.0
2100
Realized
5.0°
2.1
3.3-3.7
2.9
4.2-4.5
4.1
4.8
5.0
4.9-5.3
5.0
5.0
5.0
5.0
-
-
4.4-5.2
3.1-7.0
4.1-5.7
5.0
4.6
5.1
4.9-5.1
7.4
5.6
6.4
Equilibrium
7.6°*
2.8
4.7-5.4
3.8
6.3-6.7
6.0
7.3*
7.6*
7.3-8.0*
7,6*
7.6*
7,6*
7.5*
7.3
7.5
7.5
6.6
6.5-7.8*
3,8-13.9*
7.6*
7.6*
7.0
7.7*
7.5-7.7*
8.1*
8.6*
9.1*
* Estimates of equilibrium warming commitments greater than 6° C represent extrapolations beyond the range
tested in most climate models, and this warming may not be fully realized because the strength of some positive
feedback mechanisms may decline as the Earth warms.
C-3
-------
Policy Options for Stabilizing Global Climate
TABLE C-l -- NOTES
Developing countries were assumed to not participate in climate stabilization policies. The range represents
uncertainty in the rate of technological diffusion; that is, even if developing countries do not participate, they
will indirectly benefit from technological improvements as a result of stabilization policies among the developed
countries.
Impact if developed countries do not respond to global warming until 2010; developing countries delay to 2025.
These ranges represent modest to optimistic assumptions about future commercial availability of non-fossil
technologies, e.g., solar photovoltaics, advanced nuclear power designs, and synthetic fuel production from
biomass. Solar photovoltaic costs decline to 6 cents/kwh (1988$) by 2030 in the optimistic scenario and by 2050
in the modest assumptions. Nuclear costs decline 0.5% annually with the optimistic assumptions and remain
relatively flat in the modest assumptions. The cost of producing and converting biomass to modern fuels
reaches $4.35/gigajoule for gas and $6.00 (gigajoule) for liquids by 2030 in the optimistic assumptions and by
2050 in the modest assumptions. The total amount of fuel available from biomass is 205 EJ.
The impact of an escalation in coal prices above the RCW case by about 3% annually from 1985 to 2025 and
about 1% annually from 2025 to 2100.
The impact of an increase in global oil resources to 25,000 EJ, more than double the estimate in the RCW
case, assuming proportionate increases in resource availability at each cost level.
The impact of an increase in global natural gas resources to 27,000 EJ, more than 2.5 times the estimate in
the RCW case, assuming proportionate increases in resource availability at each cost level.
These ranges represent assumptions about the'felative sizes of anthropogenic versus non-anthropogenic sources
of methane emissions, thereby affecting growth in emissions over time, i.e., high emission levels (373 Tg CH4)
from anthropogenic activities such as fuel production and landfilling with low emission levels (137 Tg CH4)
from natural processes such as oceans and wetlands, versus low anthropogenic emissions (245 Tg CH4) with
high natural emissions (265 Tg CH4).
The impact of elevating the emission coefficient for the anhydrous ammonia fertilizer type (the percent of N
evolved as N2O) from 2.5% to 4.0%.
The impact of assuming reduced N2O emissions from fertilizer leaching into surface water and ground water,
modeled by decreasing all the fertilizer emission coefficients by 2 percentage points.
The impact of higher emission coefficients for N2O from combustion; assumes that N2O emissions are about
20-25% of NOj emissions and the N2O emissions from combustion sources in 1985 equaled 2.2 Tg N, over
2 times the level assumed in the RCW case.
The impact of assuming a higher estimate for the amount of carbon initially contained in forest vegetation and
soils (roughly a 50-100% increase) and a more rapid rate of change in land use, resulting in emissions of carbon
of 281 Pg from 1980 and 2100 compared to 118 Pg C in the RCW scenario.
\
Realized warming was not calculated in these tests.
This box-diffusion model represents carbon turnover below 75 meters as a purely diffusive process.
A 12-compartment regional model that divides the Atlantic and Pacific-Indian Oceans into surface-,
intermediate-, deep-, and bottom-water compartments and divides the Arctic and Antarctic Oceans into
surface- and deep-water compartments.
An advective-diffusive model that divides the ocean into cold and warm compartments; water downwells directly
from the cold surface compartment into intermediate and deep layers.
An outcrop-diffusion model that allows direct ventilation of the intermediate and deep oceans in high latitudes
by incorporating an outcrop connecting all sublayers to the atmosphere.
These ranges represent the impact of alternative assumptions about the "unknown carbon sink" that absorbs
the unaccounted-for carbon in the carbon cycle. Two sensitivities were analyzed: 1) a high case, where the
size of the unknown sink increases at the same rate as atmospheric CO2 levels compared with pre-industrial
levels; and 2) a low case, where the size decreases to zero exponentially at 2% per year.
Atmospheric response to a doubling QfCO2 was varied from 1.5 to 5.5° C.
C-4
-------
Appendix C: Sensitivity Analyses
TABLE C-l -- NOTES (continued)
Heal diffusion in the oceans is modeled as a purely diffusive process. To capture some of the uncertainty
regarding actual heat uptake, ihe base case eddy-diffusion coefficient of 0.55x 10 m2/sec was increased to 2xlO'4
and decreased to 2x10° nr/sec.
The atmospheric lifetime of CFG-11, 65 years in the RCW case, was varied from 55 to 75 years. Increases
or decreases in the atmospheric concentration of CFC-11, however, tend to be offset by corresponding
decreases or increases in atmospheric concentrations of other trace gases, such as other CFCs and CH4.
The amount of stratospheric ozone depletion due to chlorine contained in CFCs was increased from a 0.03%
to 0.20% decline in total column ozone/(ppb)2 of stratospheric chlorine.
The rate at which tropospneric ozone forms as a result of CH4 abundance was increased. In the RCW case,
this variable for the Northern Hemisphere is a 0.2% change in tropospheric ozone for each percentage change
in CH4 concentration; it was changed to 0.4% in the sensitivity analysis.
Tropospheric OH formation is affected by the level of NO, emissions. A 0.1% OH change for every 1%
change in NO, emissions for the Northern Hemisphere was assumed in the RCW case; in the sensitivity
analysis, a range of 0.05% to 0.2% was evaluated.
For this analysis we assumed that a 2°C increase in realized warming would alter ocean circulation patterns
sufficiently to shut off net uptake of CO2 and heat by the oceans.
We assumed that with each 1°C increase in temperature, an additional 110 Tg CH4 from methane hydrates,
12 Tg CH4 from bogs, and 7 Tg CH4 from rice cultivation would be released.
This case illustrates the combined impact of several types of biogeochemical feedbacks: 1) methane emissions
from hydrates, bogs, and rice cultivation (see footnote above); 2) increased stability of the thermocline, thereby
slowing the rate of heat and CO2 uptake of the deep ocean by 30% due to less mixing; 3) vegetation albedo,
which is a decrease in global albedo as a result of changes in the distribution of terrestrial ecosystems by 0.06%
per 1°C warming; 4) disruption of existing ecosystems, resulting in transient reductions in biomass and soil
carbon at the rate of 0.5 Pg C per year per 1°C warming; and 5) CO2 fertilization, which is an increase in the
amount of carbon stored in the biosphere in response to higher CO2 concentrations by 0.3 Pg C per ppm.
C-5
-------
Policy Options for Stabilizing Global Climate
INTRODUCTION
The Rapidly Changing World (RCW)
and Slowly Changing World (SCW) scenarios
presented in Chapter VI describe two signifi-
cantly different futures for the global
community. Although these two potential
paths capture a wide range of uncertainty, they
do not represent all possible outcomes. Alter-
native assumptions are clearly possible for
many of the parameters specified in these
scenarios; these alternative specifications could
alter the timing and magnitude of global
climate change described in the RCW and
SCW scenarios. To understand the
importance of these alternative assumptions,
this appendix examines how changes in key
parameters affect our portrayal of the rate and
magnitude of global climate change. These
sensitivity analyses include alternative
assumptions about the magnitude and timing
of global policies to combat climate change,
rates of technological change, trace-gas source
strengths and emission coefficients, the carbon
cycle, sensitivity of the climate system,
atmospheric chemistry, and feedbacks.
The sensitivity analyses discussed in this
appendix are generally run relative to the
Rapidly Changing World scenario (specifically
the RCW and RCWP cases), unless specified
otherwise.
ASSUMPTIONS ABOUT THE
MAGNITUDE AND TIMING OF GLOBAL
CLIMATE STABILIZATION STRATEGIES
The analyses of the Stabilizing Policy
scenarios presented in this Report are based
on the assumption that the global community
takes immediate, concerted action to contend
with the consequences of climate change.
Potential actions, which are discussed in
Chapters V, VII, arid VIII, include reducing
the amount of energy required to meet the
world's increasing needs, developing
alternative technologies that do not require
the consumption of fossil fuels, halting
deforestation, and making changes in
agricultural practices, among others. For
many reasons, however, the world may not
respond to the threat of climate change in a
timely fashion. This section explores the
consequences of other possibilities, particularly
the unwillingness or inability of some
countries to participate in climate stabilization
programs and the implications of delaying
global action until a later date.
No Participation by the Developing Countries
Most of the greenhouse gas emissions
currently committing the world to climate
change can be traced to activities by the
industrialized countries. Although the
quantity of emissions generated by developing
countries has been increasing, the argument is
sometimes made that since the greenhouse
problem has been largely caused by the
industrialized countries, these countries should
be responsible for solving the problem. Also,
despite the potential environmental
consequences of global climate change, other
problems facing the developing countries, such
as poverty, inadequate health care, and other
apparently more pressing environmental
problems may make it difficult for developing
countries to commit any resources to climate
stabilization policies.
Regardless of the merits of these
arguments, for this sensitivity analysis we have
assumed that developing countries do not
participate in any climate stabilization
activities; that is, only developed countries
adopt policies to limit global climate change.
For this analysis the developing countries
include China and centrally-planned Asian
economies, the Middle East, Africa, Latin
America, and South/Southeast Asia. We have
assumed that industrialized countries (i.e., the
U.S., the rest of the OECD countries, and the
USSR and Eastern Europe) follow the path
assumed in the Rapidly Changing World with
Stabilizing Policies (RCWP) scenario, while
developing countries follow the path assumed
in the Rapidly Changing World No Response
(RCW) case, in which the entire global
community does not respond to climate
change.
Even if developing countries do not
participate in global stabilization policies,
however, policies adopted by the industrialized
countries are likely to lead to technological
advancements, altered market conditions, etc.,
that indirectly reduce emissions in the
developing countries as well. For example,
advancements by the developed countries in
automobile fuel efficiency or fuel supply
C-6
-------
Appendix C: Sensitivity Analyses
technologies may be partly adopted by the
developing countries, tangentially allowing for
some climate stabilization benefits. If the
developing countries do not participate,
however, they may tend to adopt technological
advances more slowly and at a higher cost than
if they had participated from the start. This
slower rate of technological diffusion could
occur for many reasons -- for example, if the
industrialized countries take actions that
prevent easy access to improved technologies
or they are unwilling or unable to make the
necessary capital available for investment, or if
developing countries decide to invest their
limited resources in other areas.
Since we cannot be certain of the
direction that non-participation by the
developing countries might take, we analyzed
two cases to capture the potential range of
likely possibilities. In the first case, little
technological diffusion was assumed, resulting
in a future path of energy consumption and
investment trends for developing countries
similar to those assumed in the RCW scenario.
In the second case, developing countries were
assumed to have greater access to the
efficiency improvements and technological
advances assumed for the RCWP case as a
result of policies by the industrialized
countries to make these improvements
available and extend the credit necessary for
investment by the developing countries in
these improvements.
In this analysis key assumptions for the
developing countries included the following:
(1) rates of energy efficiency improvements for
all sectors are the same as in the RCW case or
midway between the RCW and RCWP case;
for example, automobile efficiency levels,
which by 2050 in developing countries were 6.7
liters/100 km (35 mpg) in the RCW case and
3.1 liters/100 km (75 mpg) in the RCWP case,
were varied from 5.9-4.1 liters/100 km (40-58
mpg); (2) CFCs are not phased out (although
compliance with the Montreal Protocol would
still occur); (3) agricultural practices that
cause methane emissions from rice and enteric
fermentation and nitrous oxides from
fertilizers do not change or would show
modest improvements; (4) deforestation
continues as in the RCW case with an
exponential decline in forest area; (5) non-
fossil energy supply technologies developed by
the industrialized countries arc available to
developing countries at a later date and a
higher cost than assumed in the RCWP case;
for example, technological di(fusion of biomass
gasification technology would occur 10 years
later than it would in the RCWP case, but
feedstock costs would remain high due to a
lack of investment by the developing countries
in highly productive energy plantations; and
(6) no additional incentives are provided for
increased use of natural gas.
Without the participation of the
developing countries to stabilize the
atmosphere, the amount of greenhouse gas
emissions will increase substantially: In the
analysis considered here, CO2 emissions are
3.9-5.3 Pg C higher than in the" RCWP case by
2050 and 4.6-8.5 Pg C higher by 2100
(emissions by 2100 are 12.3 to 16.2 Pg C lower
than in the RCW case since industrialized
countries adopt climate stabilization policies);1
other greenhouse gas emissions are also
higher. These emission increases are sufficient
to increase realized warming by 0.4-0.6°C in
2050 compared with the RCWP case and 1.2-
1.6°C by 2100 (see Figure C-l), with
equilibrium warming by 2100 up to 1.9-2.6°C
higher. Figure C-l also shows the results for
the SCW scenario. In this scenario, emission
increases are sufficient to increase realized
warming by 0.4-0.5°C in 2050 compared with
the SCWP case and 0.8-1.0°C by 2100, with
equilibrium warming by 2100 up to 1.2-1.6°C
higher.
The implications of these results are
clear: even if the industrialized countries
adopt very stringent policies to counteract the
effects of climate change, the atmosphere
continues to warm at a rapid rate. As a result,
unilateral action by the industrialized countries
can significantly slow the rate and magnitude
of climate change, but because of the
growing impact that developing countries have
on the global climate, without the
participation of the developing countries,
substantial global warming is unavoidable.
Because most of the world's population resides
in these countries, their role in climate
stabilization becomes increasingly important as
the demand for resources to feed and clothe
their growing population and improve their
standard of living expands.
C-7
-------
Policy Options for Stabilizing Global Climate
FIGURE C-l
INCREASE IN REALIZED WARMING
WHEN DEVELOPING COUNTRIES DO NOT PARTICIPATE
(Based on 3.0 Degree Sensitivity)
Slowly Changing World
4 -
I 3
m
SCW
SCWP with
No Participation
by Developing
Countries
SCWP
1986 2000 2026 2060 2076 2100
Year
Rapidly Changing World
5 -
4 -
3 -
RCWP with
No Participation
by Developing
Countries
1986 2000 2025 2050 2076
2100
C-8
-------
Appendix C: Sensitivity Analyses
Delay in Adoption of Policies
The Stabilizing Policy cases (including
the RCWR case) presented in Chapter VI
assume that the global community takes
immediate action to respond to the dangers
posed by climate change. For this sensitivity
analysis we have assumed that the global
community delays any response to the threat
of climate change, with developed countries
(i.e, the United States, the rest of the OECD
countries, the USSR and Eastern European
economies) delaying action until 2010, and the
developing countries delaying action until
2025. Additionally, once regions do initiate
action to combat global warming, they do so at
a slower rate than assumed in the RCWP case.
This slower approach assumes a minimum 25-
year delay in attaining the policy goals of the
RCWP case; that is, levels of technological
improvement, availability of alternative energy
supply technologies, etc., will be achieved at
least 25 years later. For example, in the
RCWP case, automobile efficiency reaches 3,1
liters/100 km (75 mpg) by 2050; in the Delay
case industrialized countries reach 3.9
liters/100 km (60 mpg) by 2050, while
developing countries reach 4.7 liters/100 km
(50 mpg); the rate of energy efficiency
improvement for the residential, commercial,
and industrial sectors is unchanged from the
rates assumed in the RCW case, through 2010
for industrialized countries and through 2025
for developing countries. After these years,
energy efficiency improvements occur at the
same rate assumed in the RCWP case. In
addition, the implementation of production
and consumption taxes on fossil fuels from the
RCWP case is delayed until 2010 for
developed countries and until 2025 for
developing countries.
Delaying the adoption of policies to
stabilize the atmosphere significantly increases
the Earth's commitment to global warming.
With delay by the industrialized countries until
2010 and by the developing countries until
2025, the increase in realized warming
compared to that assumed in the RCWP case
is 0.5-0.7°C by 2050 and 0.6-0.9°C by 2100;
equilibrium warming is 0.7-1.4'C higher by
2050 and 0.7-1.4°C higher by 2100 (based on
climate sensitivities of 2.0-4.0°C; see Figure C-
2), Figure C-2 also shows the results for the
Slowly Changing World scenarios. If global
delays do occur, the increase in realized
warming compared to that assumed in the
SCWP case is 0.4-0.6°C by 2050 and 0.4-0.6°C
by 2100; equilibrium warming is 0.5-1.1°C
higher by 2050 and 0.4-0.8°C higher by 2100
(based on climate sensitivities of 2.0-4.0°C).
ASSUMPTIONS AFFECTING RATES OF
TECHNOLOGICAL CHANGE
The extent of global warming will
depend on the availability of energy supplies
and technologies that minimize dependence on
carbon-based fuels, nitrogen-based fertilizers,
and other sources of greenhouse gas emissions.
The availability of non-fossil fuel technologies
and the development of new production
methods that significantly increase the supply
of natural gas could have an impact on the
rate of change in greenhouse gas emissions.
Alternative assumptions regarding these
factors are presented below.
Availability of Non-Fossil Technologies
Most technologies in use currently rely
on fossil fuels to supply their energy needs. In
the RCW, fossil-fuel-based technologies
continue to dominate throughout the next
century: by 2100 fossil fuels still supply over
70% of primary energy needs. However, if
non-fossil technologies can be commercialized
earlier, the magnitude of global climate change
can be reduced because these technologies do
not emit the greenhouse gases that cause
global warming. To evaluate the implications
of the availability of non-fossil technologies,
two different scenarios were analyzed: (1) an
Early Non-Fossil case, in which non-fossil
technologies, specifically solar photovoltaics,
advanced nuclear power designs, and
production of synthetic fuels from btomass, are
commercially available by 2000 at a rate faster
than that assumed in the RCWP case; and (2)
an Intermediate Non-Fossil case, in which
non-fossil technologies are widely available by
the middle of the next century (i.e., greater use
of non-fossil technologies than in the RCW
case, but less than in the RCWP case). The
intent of these two cases is to capture a range
of possible roles for non-fossil technologies,
with the first case reflecting very optimistic
assumptions on non-fossil availability and the
second case reflecting more modest
assumptions.
C-9
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Policy Options for Stabilizing Global Climate
FIGURE C-2
INCREASE IN REALIZED WARMING
DUE TO GLOBAL DELAY IN POLICY OPTIONS
(Based on 3.0 Degree Sensitivity)
Slowly Changing World
6 -
1985 2000 20ZS 2050 2075 2100
Rapidly Changing World
1986 2000 2025 2060 207S 2100
C40
-------
Appendix C: Sensitivity Analyses
In the Early Non-Fossil case, non-fossil
energy sources increase their share of total
primary energy supply from 12% in 1985 to
about 40% by 2025 and 55% by 2100, while in
the Intermediate Non-Fossil case the share for
non-fossil technologies increases to about 20%
by 2025 and about 50% by 2100 (see Figure
C-3a). As shown in Figure C-3a, the non-
fossil share of total energy is lower in the long
run compared with the share in the RCWP
case; this is because other policies that were
included in the RCWP case to discourage the
use of fossil fuels were not included in this
case. In both cases, however, an increased role
for non-fossil technologies can affect the
amount of global warming. As shown in
Figure C-3b, for the two cases presented here
the amount of realized warming compared
with the RCW case could be reduced about
0.1-0.2°C by 2050 and 0.4-0.9°C by 2100;
equilibrium warming could be reduced about
0.2-0.6°C by 2050 and 0.6-1.7°C by 2100 (based
on 2.0-4.0°C climate sensitivities).
Cost and Availability of Fossil Fuels
As discussed in Chapters IV and V,
there is significant uncertainty over the
amount of fossil-fuel resources available
globally and the cost at which these resources
could be produced. The development of the
fossil energy resource estimates and the
associated extraction costs used in this analysis
are documented in ICF (1988). Given the
uncertainties about the cost and availability of
fossil energy supplies, several sensitivity cases
were analyzed. These are discussed below.
High Coal Prices
In the RCW case from 1985 to 2050
there was no real escalation in coal prices.
Given the vast quantity of coal resources
available worldwide, and the rate of
productivity improvements in coal extraction
that have helped to contain cost increases, coal
prices may not escalate in real terms (e.g.,
from 1949 to 1987, U.S. coal prices declined
an average of 0.2% annually [EIA, 1988]).
Since the longer-term price path for coal is
highly uncertain, however, we analyzed the
impacts of a high price coal case where coal
prices escalated about 1% annually from 1985
to 2100.
As illustrated in Figure C-4a, increasing
coal prices have a significant impact on the
amount of primary energy consumed; for
example, by 2100 total primary energy demand
is more than 15% lower compared with this
demand in the RCW case. Most of this
reduction in energy demand is due to the
decline in coal use as consumers respond to
the escalating prices. Because coal is a major
energy resource for electricity production and
synthetic fuel production, the impact on the
level of greenhouse gas emissions is fairly
substantial. For example, CO2 emissions are
reduced nearly 40% by 2100. The reductions
in greenhouse gas emissions have a significant
impact on global warming, as shown in Figure
C-4b, which indicates a decline in realized
warming from the RCW case of 0.2-0.3°C by
2050 and 0.7-1.0°C by 2100 (assuming 2.0-
4.0°C climate sensitivities). The corresponding
decrease in equilibrium warming by 2100 is
Alternative Oil and Natural Gas Supply
Assumptions
There are many uncertainties concerning
the amount of oil and natural gas supplies
available worldwide. As discussed in Chapter
V, for example, the viability of increased use
of natural gas as a near-term option for
reducing greenhouse gas emissions critically
depends on the amount of natural gas
available, its price, the length of time over
which adequate supplies can be secured, etc.
To explore how sensitive the level of
greenhouse gas emissions may be to the
amount of oil and natural gas supplies, two
sensitivity cases assuming higher global
supplies have been analyzed: (1) a high oil
resource case and (2) a high natural gas
resource case. The higher oil resource
estimates were derived from Grossling and
Nielsen (1985), who indicated that resources
may be more than double the estimates used
in the base case analyses (which were about
12,000 EJ of conventional oil resources).2 For
this analysis we assumed conventional oil
resources of about 25,000 EJ. Natural gas
estimates were derived from Hay et al. (1988),
which assumed in-place resources of about
150,000 EJ. For purposes of this sensitivity
case, we assumed that technological
improvements in gas extraction would permit
C-ll
-------
Policy Options for Stabilizing Global Climate
FIGURE C-3
AVAILABILITY OF NON-FOSSIL ENERGY OPTIONS
(a) Non-Fossil Share Of Total Primary Energy Supply
100
u
o
0.
80
60
40
20
RCWP
Non-Fossil
Energy Options
RCW
(b)
w
1 4
4)
o
W o
4)
Q
Increase In Realized Warming
(Based on 3.0 Degree Sensitivity)
1985 2000
2025
2050
Year
2075
RCW
Non-Fossil
Energy Options
RCWP
2100
C-12
-------
Appendix C: Sensitivity Analyses
FIGURE C-4
IMPACT OF 1% PER YEAR REAL ESCALATION IN COAL PRICES
(a)
Total Primary Energy Demand
1500
1250
» 1000
o
s 75°
UJ
500
250
0
RCW
High Coal Prices
RCWP
(b)
Increase In Realized Warming
(Based on 3.0 Degree Sensitivity)
1985 2000
2025 2050
Year
2075
RCW
High Coal Prices
^ RCWP
2100
C-13
-------
Policy Options for Stabilizing Global Climate
an additional 10% of in-place resources to be
economically recovered. This amount was
added to the baseline estimates of pioved
reserves and economically recoverable
resources, for a total resource base of about
27,000 EJ. We must emphasize that these
sensitivity cases do not examine policy options
that encourage greater use of oil and natural
gas; rather, they only attempt to examine how
current uncertainties concerning the size of the
resource base for these energy supplies can
directly affect the rate and magnitude of global
climate change. Policy options encouraging
greater use of these fuels in conjunction with
higher resource estimates would have a
substantially different impact.
' High Oil Resources. An increase in
global oil resources to 25,000 EJ is more than
double the resource estimates assumed in the
RCW case. These additional resources were
assumed to be available at the same economic
costs, such that the amount of oil available at
any given price was twice the amount assumed
in the RCW case. This increase in oil
resources had two major impacts: (1) the
amount of synthetic production of liquid fuels
from coal declined substantially since
conventional oil supplies were available at a
competitive price to meet this demand; and (2)
total demand for energy, mainly oil, increased
as. consumers responded to the increased
availability of oil supplies at the same price
(since twice the amount of oil was available at
a price equal to that in the RCW case). The
net effect of these impacts is a small increase
in total primary energy demand in the first
half of the next century (a 2% increase by
2050) followed by a small decrease, a major
shift from coal (primarily for synthetic fuel
production) to oil, and a decrease in the
portion of total primary energy supplied by
non-fossil resources since oil is more plentiful
and competitive; for example, non-fossil fuels
supply about 22% of all energy by 2050
compared with 23% in the RCW case (see
Figure C-5). The net effect of these factors is
a decrease in CO2 emissions of 0.2 Pg C by
2050 and 1.2 Pg C by 2100. The decline in
coal production also lowered methane (CH4)
emissions since the amount of CH4 emitted
during coal mining decreased substantially
(e.g., by 2100 CH4 emissions from fuel
production declined from about 390 Tg in the
RCW case to 260 Tg), resulting in a modest
decline of about 0.2°C in realized warming by
2100 compared with the RCW case warming
(assuming 2.0-4.0°C climate sensitivities).3
High Natural Gas Resources. For the
high natural gas resource case, natural gas
resources were increased from about 10,000 EJ
to 27,000 EJ. As in the high oil resource case,
higher natural gas resource estimates result in
two major impacts: (1) an increase in demand
for energy, particularly for gas, since natural
gas is more plentiful compared with the
amount available in the RCW case; and (2) a
decline in the conversion of coal to synthetic
gas, since natural gas supplies are available to
meet the demand.
Overall, by the end of the 21st century
the amount of primary energy consumed
changes very little from the RCW case. In the
near term, energy demand increases slightly
compared with the RCW case since natural gas
is more plentiful (e.g., by 2025 energy demand
is about 3% higher compared with the RCW
case; see Figure C-6). However, the total
amount of energy required in the long run is
less because a greater portion of end-use
energy demand is met with natural gas
rather than with synthetic gas from coal. This
increase in conventional natural gas
consumption reduces the total primary energy
required to satisfy demand because the decline
in synthetic fuel demand from the RCW case
reduces the amount of energy required for
synthetic fuel conversion, although this impact
is small: by 2100 primary energy demand is
lower by about 1%.
The amount of natural gas consumed
does increase significantly; for example, in
2050 natural gas consumption increases, to 210
EJ compared with 100 EJ in the RCW case.
However, the increased availability of natural
gas also reduces the portion of energy supplied
by non-fossil fuels; for example, by 2050 non-
fossil energy sources supply about 20% of total
demand compared with 23% in the RCW case.
The net impact on CO2 emissions due to these
factors is quite small: a decline of 0.2 Pg by
2050 and 1.1 Pg by 2100. The impact on
realized and equilibrium wanning is negligible
(less than 0.1°C).
C-14
-------
Appendix C: Sensitivity Analyses
FIGURE C-5
IMPACT OF HIGHER OIL RESOURCES ON
TOTAL PRIMARY ENERGY SUPPLY
Rapidly Changing World
1500
1250
Biomass
Solar
Nuclear
Hydro
Gas
Oil
Coal
High Oil Resources
Biomass
Solar
Nuclear
Hydro
Gas
Coal
1985 2000
2025 2050
Year
2075
2100
C-15
-------
Policy Options for Stabilizing Global Climate
1500
FIGURE C-6
IMPACT OF HIGHER NATURAL GAS RESOURCES ON
TOTAL PRIMARY ENERGY SUPPLY
Rapidly Changing World
1500 <—
1250
0 1000
>
\
M
•2 750
uj 500
250
High Natural Gas Resources
Biomass
Solar
1985 2000
2025 2050
Year
2075
Coal
Biomass
Solar
2100
C-16
-------
Appendix C: Sensitivity Analyses
Availability of Methanol-Fueled Vehicles
The transportation sector throughout
the world is heavily dependent on petroleum-
based fuels. This dependence, particularly on
gasoline and diesel fuel, produces substantial
quantities of greenhouse gases (see CHAPTER
IV). A variety of non-petroleum-based
alternatives are under development, including
the use of methanol. There are many
potential advantages to using methanol as a
transportation fuel rather than gasoline;
according to recent research, advanced
methanol-fueled vehicles could be 20-40%
more energy efficient, emit much lower levels
of CO, and reduce non-methane hydrocarbon
(NMHC) reactivity up to 95% (Gray, 1987).
Methanol's potential to reduce NMHC
reactivity could reduce levels of urban ozone,
which would improve ambient air quality in
urban areas. These reductions could be on the
order of about 5-20% of peak ozone levels
(DeLuchi et al., 1988). However, it is not
clear how reductions in urban ozone levels
may translate to reductions in average
tropospheric ozone and, therefore, changes in
radiative forcing. Current understanding of
these atmospheric processes attributes urban
ozone changes primarily to NMHC and NOX
flux, while tropospheric ozone changes depend
primarily on (in descending order of
importance) CH4, CO, NOX flux, and NMHC
flux (Prather, 1989). Interactions between
urban air quality and the rest of the
troposphere cannot be evaluated with the
aggregate model used here.
Since the ability of methanol to affect
tropospheric ozone levels cannot be reliably
estimated, we cannot reflect all of the
potential advantages of using methanol as a
transportation fuel. It is useful to note,
however, that in addition to reducing
emissions of CO and other gases, methanol
can be produced from different types of
feedstocks, such as natural gas, coal, or
biomass. When biomass is the feedstock, the
carbon emitted during the combustion process
is recycled from the environment as the
biomass is grown. As a result, the net CO2
emissions are zero when biomass is used.
Greenhouse gas emissions from methanol,
however, can be greater than those from
gasoline if coal is used as the feedstock
because additional emissions will be generated
during the methanol production process.
According to one analysis, methanol
production from coal would generate about
twice the amount of CO2-equivalent emissions
(based on their radiative effect) compared to
gasoline from crude oil, while methanol from
natural gas would only be slightly better
(about 3%) than petroleum-based fuels
(DeLuchi et al., 1988). From a global
warming perspective, DeLuchi et al. (1988)
concluded that only biomass-derived methanol
would substantially reduce the amount of
radiative forcing from transportation fuels,
although as mentioned above, this argument
does not incorporate any potential benefits
from reductions in urban ozone levels.
ATMOSPHERIC COMPOSITION:
COMPARISON OF MODEL RESULTS TO
ESTIMATES OF HISTORICAL
CONCENTRATIONS
The atmospheric composition model was
applied to estimates Of historical emissions of
trace gases and the results compared to
historical data on atmospheric composition.
This exercise provides insight on how the
model performed under conditions much
different from the reference year, 1985, and
provided one mechanism to validate the
model. The exercise included the development
of a single scenario of historical emissions of
trace gases and application of the model using
different assumptions on climate sensitivity
and chemistry parameters in the model.
The scenario of historical emissions of
trace gases is based on estimates of natural
sources from the Atmospheric Stabilization
Framework described in Chapter VI, estimates
from a study by Darmstadter et al. (1987) on
historical emissions from various
anthropogenic sources, and estimates of
historical CO2 emissions from Rotty (1987)
and Houghton (1988). For natural emission
sources, historical emissions were assumed to
be constant from 1870 to 1985 at the levels
assumed in the scenarios described in Chapter
VI. The exception is emissions of CH4 from
wetlands, which were assumed to be larger in
1870 by 50% and to decline to current levels
due to destruction of wetlands. The estimates
of historical emissions of CFCs and halons
were taken from U.S. EPA's Regulatory
C-17
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Policy Options for Stabilizing Global Climate
Impact Analysis on Stratospheric Ozone
Protection (U.S. EPA, 1988).
The alternative scenarios of historical
atmospheric composition and global warming
reflect a range of assumptions concerning the
climate sensitivity and the first- and second-
order relationships assumed in the model.
Figure C-7 illustrates the increase in realized
warming projected from 1840 to 1985, which
ranges from 0.4°C to 0.8°C based on a range of
climate sensitivities (from 1.5 to 5.5°C for
doubled CO2). These results compare well
with results from Wigley et al. (1986), who
estimated a global temperature increase of 0.3-
0.7°C in the last century, and Hansen et al.
(1988), who estimated a global temperature
increase of 0.4-0.8°C during the same period.
The model produced estimates of atmospheric
concentrations of CO2, CH4, N2O, CO, and
CFC-12 within 1.5% and estimates of
concentrations of CFC-11 within 3.5% of
observed values in 1985. In addition, the
pattern of estimated atmospheric
concentrations over time conformed well with
historical measurements for CO2, N2O, and
CH4. Estimates of concentrations of some
. gases such as HCFC-22 varied from the
historical measurements to a greater extent,
which reflects their more recent introduction
and rapid growth in atmospheric
concentrations. Table C-2 summarizes the
results for the long-lived gases.
For CO2, the atmospheric concentration
over time matched the Mauna Loa and Ice
Core measurements by design through the use
of the unknown sink in the model (see
Unknown Sink in Carbon Cycle). The
unknown sink is zero through 1940 and then
slowly rises to 1.9 Pg C per year by 1985,
which represents about one-third of the
estimated anthropogenic emissions.
The estimates of CH4 concentrations
match atmospheric and ice core measurements
well, especially given the uncertainties in the
emissions estimates and the historical
measurements. The model shows somewhat
higher than expected growth in the late 19th
century, which may reflect the uncertainties
surrounding the scenario of historical
emissions. Using the reference assumptions,
the model achieves an atmospheric
concentration of 1671 ppb in 1985 compared
to the observed value of 1675 ppb. The CH4
concentrations vary considerably in the
sensitivity analyses and range from 1650 ppb
to 1750 ppb for alternative chemistry
parameters.
Of the three dominant greenhouse
gases, the estimates of N2O concentrations
vary the most from historical measurements.
The model predicts concentrations of 314 ppb
in 1985 compared to 308-310 ppb cited in the
literature. From 1979 to 1986, the model
estimates growth in N2O concentrations of
310-314 ppb compared to measurement data
that suggests growth of 303 to 310 ppb. One
of the possible explanations of these results is
that the relative share of emissions of N2O
from anthropogenic sources is larger than
estimated in the model. A larger
anthropogenic source combined with lower
natural emissions or a shorter atmospheric life
would be needed to reduce the overall
concentrations and obtain the growth in.
concentrations seen from 1979 to 1986. These
results suggest that the model' may
underestimate future atmospheric
concentrations of N2O.
The model "predicts" very little deviation
from current levels for the short-lived gases,
including OH, O3, and CO. The results for
levels in 1870 include higher levels of OH by
14-26%, lower levels of tropospheric O3 by 19-
29%, lower concentrations of CO by
approximately 50%, and increased levels of
upper stratospheric ozone by 4.5%.
ASSUMPTIONS ABOUT TRACE-GAS
SOURCES AND STRENGTHS
Among the various greenhouse gases
there is some uncertainty over the quantity of
emissions that can be attributed to specific
sources and the ability of these gases to modify
the atmosphere. The most critical of these
uncertainties are examined below.
Methane Sources
The available evidence on CH4 indicates
that annual production ranges from 400-640
Tg of methane (based on known sources and
sinks, its atmospheric lifetime, and current
atmospheric concentrations). Within this
C-18
-------
Appendix C: Sensitivity Analyses
0.9 h
0.8 -
0.7 -
0.6 h
e
o
u>
o
0.4
0.3 -
0.2
0.1
FIGURE C-7
REALIZED WARMING THROUGH 1985
(Based on 1.5-5.5 Degree Sensitivity)
1840 1865
1890 1915 1940 1965 1985
Year
e-19
-------
Policy Options for Stabilizing Global Climate
TABLE C-2
Comparison of Model Results to Concentrations in 1986
Trace Gas (units)
CO2 (ppm)
N20(ppb)
CH4 (ppb)a
CFC-11 (ppt)
CFC-12 (ppt)
HCFC-22 (ppt)
CC14 (ppt)b
CH3CC13 (ppt)
Halon 1211 (ppt)
a 1987 value.
b 1982 value.
Model
Model Results Growth Rates
346
314
1650-1750
212-222
391
37
70
186
0.4
0.4%
0.27%
1%
4%
4%
14%
0.6%
12%
100%
Atmospheric Observed
Measurements Growth Rates
346
310
1675
226
392
100
121
125
2
0.4%
0.2-0.3%
1%
4%
4%
7%
1.3%
6%
>10%
C-20
-------
Appendix C: Sensitivity Analyses
budget, however, there is much dispute over
the size of individual sources. For example,
research indicates that current CH4 emissions
from rice paddies could be 60-170 Tg;
similarly, estimated emissions from biomass
burning range from 50-100 Tg (Cicerone and
Oremland, 1988).
To account for these uncertainties, the
initial CH4 budget was varied to construct two
cases: (1) a high anthropogenic impact case,
where the starting methane budget was biased
toward anthropogenic sources by assuming
that anthropogenic activities such as fuel
production and landfilling caused higher
emission levels than assumed in the RCW
case, while lower emission estimates were
assumed from natural processes such as
oceans, wetlands, wildfires, and wild ruminants;
and (2) a low anthropogenic impact case, by
assuming lower emissions from anthropogenic
activities such as fuel production, enteric
fermentation, and rice cultivation, with
corresponding emission increases from natural
processes such as oceans and wetlands. The
specific emission assumptions for the starting
budget are summarized in Table C-3.
The alternative starting budgets in Table
C-3 result in different growth paths for CH4,
since emissions from anthropogenic sources
increase by different amounts over time.
These differences alter the atmospheric
concentration of CH4: by 2100 the
atmospheric concentration is about 3600-3800
ppb in the Low Impact case and 5450-5700
ppb in the High Impact case (compared with
4300-4500 ppb in the RCW case and assuming
2°-4°C climate sensitivities). The increase
(decrease) in CH4 also increases (decreases)
the amount of tropospheric ozone. The
impact on realized warming is summarized in
Figure C-8, which indicates a decline of 0.1-
0.2°C by 2100 in the Low Impact case
compared with the RCW case and an increase
of 0.2-0.3°C by 2100 in the High Impact case.
The corresponding effects on equilibrium
warming by 2100 are a decline of 0.2-0.4°C in
the Low Impact case and an increase of 0.3-
0.6°C in the High Impact case (based on 2°-
4°C climate sensitivities).
Nitrous Oxide Emissions From Fertilizer
N2O is naturally produced in soils by
microbial processes during denitrification and
nitrification. When nitrogen-based fertilizers
are applied, N2O emissions from the soil can
increase as a result of the additional nitrogen
source. The amount of fertilizer nitrogen
evolved as N2O is highly variable and
uncertain. We have used the emission
estimates developed by Galbally (1985) in our
base cases: 0.5% for anhydrous ammonia,
0.1% for ammonium nitrate, 0.1% for
ammonium salts, 0.5% for urea, and 0.05% for
nitrates. An N2O emission pathway not
included in Galbally's estimates is leaching
from the fields into the ground water or
surface water because of fertilizer application.
Conrad et al. (1983) and Kaplan et al. (1978)
have suggested that the amount of N2O
evolved due to leaching may be as large as
N2O from the denitrification/nitrification
processes in the soil (i.e., 0.5-2.0%). Ronen et
al. (1988) have calculated that the N2O flux
from these sources is 10-20% of the global
production of N2O annually, which is an
estimate greater than 3% of nitrogen evolved
as N2O. Alternative assumptions on N2O
emissions are explored below.
Anhydrous Ammonia
One of the key uncertainties concerns
the emission coefficient for anhydrous
ammonia. A review of the scientific literature
on measurements of N2O emissions by
fertilizer type indicates that the percentage of
anhydrous ammonia evolved as N ranges from
0.05-6.84%, with most measurements ranging
from 0.5-2.0% (Eichner, 1988). The impact of
this uncertainty was evaluated by changing the
anhydrous ammonia coefficient by 1.5%. This
change increased the amount of N2O from
fertilizer applications by about 0.06-0.07 Tg of
N annually, which was too small to affect the
amount of global warming.
N2O Leaching From Fertilizer
As discussed above, in the RCW case
N2O emissions from fertilizer were based on
C-21
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Policy Options for Stabilizing Global Climate
TABLE C-3
Low and High Anthropogenic Impact Budgets For Methane
(teragrams/year as of 1985)
Source of Methane
Fuel Production
Enteric Fermentation
Rice Cultivation
Landfills
Oceans
Wetlands
Biomass Burning - Anthropogenic
Biomass Burning — Natural
Wild Ruminants
Other Sources
TOTAL
Low Impact
50
70
60
30
45
' 150
53
2
44
7
511
RCW
60
75
109
30
15
115
53
2
44
7
511
High Impact
95
75
109
58
6
100
48
2
10
7
511
C-22
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Appendix C: Sensitivity Analyses
FIGURE C-8
INCREASE IN REALIZED WARMING
DUE TO CHANGES IN THE METHANE BUDGET
(Degrees Celsius; Based on 3,0 Degree Sensitivity)
w
9
O
10
CD
4)
Q
2 f
1985 2000
High Methane
Low Methane
2025 2050
Year
2075
2100
C-23
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Policy Options for Stabilizing Global Climate
estimates by Galbally (1985) with a 2%
increase in these estimates to allow for
leaching. The rate of emissions related to
leaching is, however, highly uncertain;
therefore, a no leaching case was analyzed by
decreasing the emission coefficients by two
percentage points (to remove the impact of
the leaching assumed in the base cases). In
order to account for this change and maintain
the N2O budget at its original level, N2O
emissions from wetlands were increased
correspondingly. The consequent lower rate of
N2O from fertilizer resulted in a decrease in
emissions of about 1.0-2.0 Tg N annually.
Atmospheric N2O concentrations
decrease about 25 ppb by 2100 compared with
the RCW case (from 430 to 404 ppb; see
Figure C-9). While N2O concentrations
decrease when leaching is eliminated, the
impact on global wanning is not as certain. In
this case, global warming was slightly increased
(less than 0.001°C). Specifically, lower N2O
levels in the stratosphere increase the amount
of stratospheric ozone, which in turn allows
less ultraviolet (UV) radiation to penetrate to
lower elevations. The reduced UV radiation
decreases the amount of CFC destruction,
which increases the contribution of CFCs to
global warming. None of these reactions are
very strong, since the change in N2O emissions
due to leaching does not have a major effect
on atmospheric concentrations, but they are
sufficient to counteract the effect of lower
N2O concentrations alone.
N2O Emissions From Combustion
During the combustion process,
chemical interactions downstream from the
combustion chamber can lead to N2O
formation from nitrogen oxides. The rate of
this formation is highly uncertain, although
recent evidence indicates that it is likely to be
fairly small. In the RCW case these low
emission coefficients were assumed (see
CHAPTER II). To ascertain the impact of
higher emission coefficients, N2O coefficients
from combustion were increased such that
emissions from energy in 1985 were 2.2 Tg N
rather than 1.0 Tg N as obtained in the RCW
case. The higher N2O emission levels
increased atmospheric concentrations about 30
ppb by 2100 (as shown in Figure C-10); the
resulting impact on global warming was
negligible (less than 0.1°C) for the same
reasons discussed above under leaching from
fertilizer.
UNCERTAINTIES IN THE GLOBAL
CARBON CYCLE
The global carbon cycle, which regulates
the flow of carbon through the environment,
including the atmosphere, biosphere, and
hydrosphere, was discussed in Chapters II and
III. Uncertainties in the size of the various
sources and sinks for carbon and the
interactions that govern the flow of carbon
increase the difficulty of estimating the impact
of anthropogenic activities on global climate.
In this section the major uncertainties in the
global carbon cycle are evaluated. The first
part focuses on the impact of deforestation on
CO2 emissions. The second part discusses the
ability of the oceans to absorb CO2 and heat.
Currently, the oceans are the dominant sink
for anthropogenic CO2 emissions, with the
mixed layer alone containing about as much
carbon as the atmosphere. The oceans' ability
to operate as a net sink for carbon and heat is
an important component of the global climate
system; any changes in this absorption ability
could have profound effects on global climate
(see CHAPTER III).
Unknown Sink In Carbon Cycle
Atmospheric CO2 concentrations have
changed historically because of an imbalance
between the sources and sinks for carbon. If
the production of carbon exceeds the ability of
the various carbon sinks to absorb it, then the
atmospheric CO2 concentrations will increase
(and vice versa). When analyzing the amount
of carbon produced from various sources in
the past, atmospheric scientists have been
unable to balance the carbon cycle. That is,
given current estimates of carbon sources, it
would appear that atmospheric CO2
concentrations would have to be higher than
currently measured, since all known sinks do
not appear to be able to absorb all of the
carbon produced. To account for this
imbalance, we have assumed the existence of
an "unknown sink" that absorbs the
unaccounted-for carbon. The size of this
unknown sink depends on the assumed
magnitude of known sources and sinks - by
definition, the unknown sink is simply:
C-24
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Appendix C: Sensitivity Analyses
FIGURE C-9
CHANGE IN ATMOSPHERIC CONCENTRATION OF N2O
DUE TO LEACHING
(Based on 3.0 Degree Celsius Sensitivity)
450
400 -
c 350
_o
EE
ffl
i_
o
Q.
CO
a
0.
300
250 -
200
RCW
Leaching
1985 2000
2025
2050
Year
2075
2100
C-25
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Policy Options for Stabilizing Global Climate
FIGURE €-10
CHANGE IN ATMOSPHERIC CONCENTRATION OF N20
DUE TO COMBUSTION
(Based on 3,0 Degree Celsius Sensitivity)
500
450
400
jo
ffl
a
Q.
350
300
250
200
Combustion
RCW
1985 2000
2025 2050
Year
2075 2100
C-26
-------
Appendix C: Sensitivity Analyses
sources minus sinks minus atmospheric
accumulation.
For our base cases, the size of the
unknown sink was kept constant at 1.6 Pg
annually, based on as calculated value (from
the model) for 1975-1985. However,
alternative assumptions are plausible. To
capture these uncertainties, two sensitivities
were analyzed: (1) a high case, where the size
of the unknown sink increases at the same rate
as atmospheric CO2 levels compared with pre-
industrial levels (this increase might occur,
e.g., because the size of the unknown sink is
related to the fertilization of terrestrial
ecosystems by increasing CO2); and (2) a low
case, where the size decreases to zero
exponentially at 2% per year (e.g., because the
process responsible for the unknown sink has
a limited capacity).
When the unknown sink is assumed to
increase in proportion to CO., concentrations
in the RCW case, the amount of carbon
absorbed by the unknown sink increases to
11.9 Pg annually by 2100. This rate of carbon
absorption results in a decline in CO2
concentrations relative to the RCW scenario,
which reduces realized warming by 0.1-0.2°C in
2050 and 0.5-0.7°C in 2100;" equilibrium
warming is reduced in 2050 by 0.2-0.5 C and in
2100 by 0.7-1.5CC (based on 2.0-4.0°C climate
sensitivities).
In the low case, that is, when the
unknown sink decreases to zero, the estimated
impact on warming is significantly lower, since
the unknown sink was only 1.6 Pg annually to
start. As a result, CO2 concentrations do
increase, but the increase in realized warming
is less than 0.1°C in 2050 and 0.1-0.2°C in 2100
(based on 2.0-4.0°C climate sensitivities; see
Figure C-ll).
Amount of CO2 From Deforestation
Estimates of the amount of CO2 emitted
from deforestation activities vary because of
different assumptions on the rate of
deforestation, the fate of the deforested lands,
and the amount of carbon contained in the
forest vegetation and soils. In the base cases
we used the lower carbon estimates (i.e., lower
biomass estimates) given by Houghton (1988);
for 1980 the resulting net flux of carbon to the
atmosphere was about 0.4 Pg of carbon.
Higher estimates of initial biomass have also
been analyzed by Houghton (1988); with these
estimates the nei flux of carbon to the
atmosphere in 1980 would have been about 2.2
Pg. These higher biomass estimates are
evaluated here for the three deforestation
scenarios discussed in Chapter VI. The net
flux of carbon for each of these scenarios is
presented in Figure C~12.
In the RCW case the rate of CO2
emissions from deforestation was based on an
exponential decline in forest area using the
lower biomass assumptions. If the higher
biomass estimates are used, the total carbon
flux from deforestation from 1980 to 2100 is
281 Pg compared with 118 Pg using the low
estimates of carbon stocks (Houghton, 1988).
Similarly, in the population-based
deforestation scenario the total carbon flux to
the atmosphere from 1980 to 2100 is about
138 Pg using the lower biomass estimates and
324 Pg using the higher biomass estimates. In
the reforestation scenario, the total
accumulation of carbon from the atmosphere
was 38 Pg using the lower biomass estimates
and 59 Pg using the higher biomass estimates.
Despite the substantial increase in the
amount of carbon from deforestation when the
higher biomass estimates are used (e.g., by
2050 CO2 emissions from deforestation are 2.3
Pg compared with 1.0 Pg in the RCW with the
lower estimates), the resulting atmospheric
concentration of CO2 is slightly lower (see
Figure C-13 for the differences in the RCW
case, i.e., forest area declines exponentially).
This result is due to the larger size of the
"unknown carbon sink" in our model when
higher deforestation emissions are assumed
(see Unknown Sink In Carbon Cycle above).
In our analysis the increase in the size of the
unknown sink was sufficient to absorb some of
the additional carbon when the higher biomass
estimates are used, assuming that the size of
the unknown sink remains constant at its
average 1975-1985 value (i.e., 2.6 Pg C with
high biomass vs. 1.6 Pg C with low biomass).
The decrease in CO2 concentrations decreased
realized wanning and equilibrium warming less
than 0.10C by 2100 compared with the RCW
case warming (assuming 2.0-4.0°C climate
sensitivities).
C-27
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Policy Options for Stabilizing Global Climate
FIGURE C-ll
IMPACT ON REALIZED WARMING DUE TO
SIZE OF UNKNOWN SINK
(Based on 3.0 Degree Sensitivity)
.2
co
"3
o
(0
a>
a>
D)
O
a
2% Decline
RCW
Proportional
Increase
1985 2000
2025
2050
Year
2075
2100
C-28
-------
Appendix C: Sensitivity Analyses
FIGURE C-12
CO 2 FROM DEFORESTATION ASSUMING HIGH BIOMASS
(Petagrams of Carbon/Year)
4 -
/ i
sew/
/
/
/
/
RCW
Stabilizing
Policy Scenarios
1950 1980 2010 2040
Year
2070
2100
C-29
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Policy Options for Stabilizing Global Climate
FIGURE C-13
IMPACT OF HIGH BIOMASS ASSUMPTIONS ON
ATMOSPHERIC CONCENTRATIONS OF C02
(Based on 3.0 Degree Celsius Sensitivity)
1000
900 -
800 -
c
o
= 700
GO
-------
Appendix C: Sensitivity Analyses
Alternative CO2 Models of Ocean Chemistry
and Circulation
In the RCW case ocean chemistry was
represented using a diffusion model of the
ocean (the Modified GISS model) based on
the model described by Hansen et al. (1988).
Several other approaches have also been
developed and adopted for the U.S. EPA
framework by W. Emmanuel and B. Moore.
These include:
• Box-Diffusion Model introduced by
Oeschger et al. (1975), which represents the
turnover of carbon below 75 meters as a
purely diffusive process.
• 12-Compartment Regional Model by
Bolin et al. (1983), which divides the Atlantic
and Pacific-Indian Oceans into surface-,
intermediate-, deep-, and bottom-water
compartments and divides the Arctic and
Antarctic Oceans into surface- and deep-water
compartments.
• Advective-Diffusive Model by
Bjorkstrom (1979), which divides the surface
ocean into cold and warm compartments;
water downwells directly from the cold surface
compartment into intermediate and deep
layers.
• Outcrop-Diffusion Model by
Siegenthaler (1983), which allows direct
ventilation of the intermediate and deep
oceans at high latitudes by incorporating
outcrops connecting all sublayers to the
atmosphere.
Because each of these models uses a
different approach to evaluate ocean
chemistry, the resulting impact on atmospheric
CO2 concentrations could vary from one
approach to the next. To determine how
comparable these models were, the RCW case
was evaluated using each model in turn.
The estimates of future CO2
concentrations from each model are
summarized in Figure C-14a. These results
indicate that the Modified GISS model tends
to project higher atmospheric CO2
concentrations than the other models; for
example, by 2100. CO2 concentrations are
about 3% higher than concentrations
estimated by Bolin et al. or Bjorkstrom, about
5% higher that those estimated by Oeschger et
al., and about 23% higher than those
estimated by Siegenthaler. There are two
basic reasons for these differences: (1) The
Modified GISS model, unlike the other
models, incorporates temperature feedback
that alters ocean carbonate chemistry; that is,
as the mixed layer of the oceans warms due to
atmospheric warming, the amount of carbon
that can be absorbed by the oceans decreases;
and (2) The Modified GISS model does not
incorporate any heat or CO2 transfer between
the thermocline and the deep ocean (below
1,000 meters); to the extent heat or CO2 is
transported to the ocean depths in the long
run, the Modified GISS model understates the
oceans' absorption capacity.
Siegenthaler's Outcrop-Diffusion Model
estimates lower CO2 concentrations than any
of the other models. This result is anticipated
because the Outcrop-Diffusion Model allows
CO2 to be absorbed from the atmosphere to
the deep layers rather than diffuse through the
intervening layers, so that, in this model,
carbon is absorbed more quickly in the oceans
than in the other models. By 2100 equilibrium
warming using Siegenthaler's model is about
1°C lower than the RCW case (see Figure C-
14b for warming estimates from all five
models).
ASSUMPTIONS ABOUT CLIMATE
SENSITIVITY AND TIMING
Sensitivity of the Climate System
A general benchmark for comparing
atmospheric models is their response to a
doubling of CO2 concentrations (2xCO2; see
CHAPTER HI). Put simply, this benchmark
describes how much warming would be
expected once the atmosphere stabilizes
following a twofold increase in CO2
concentrations. In our analyses we have used
the range from 2.0-4.0°C. As discussed in
Chapter III, there is a great deal of uncertainty
about the strength of internal climate
feedbacks, and, in some cases, whether a
feedback will be positive or negative. If cloud
and surface albedo changes produce large
positive feedbacks, as suggested by some
analyses, the climate sensitivity could be 5.5°C
or greater. On the other hand, these feed-
C-31
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Policy Options for Stabilizing Global Climate
FIGURE C-14
COMPARISON OF DIFFERENT OCEAN MODELS
1000
o
900 -
800 -
700
o
• 600
a
o.
500
400
300
"5
"5
o
M
o>
»
Q
Concentrations
Bjorkstrom
Bolln
Slegenthaler
ROW
Oeschger
Impact on Equilibrium Warming
RCW
Oeschger
1985 2000
2025 2050
Year
2075
2100
C-32
-------
Appendix C: Sensitivity Analyses
backs could be weak and cloud feedbacks
could be negative, resulting in a climate
sensitivity as low as 1.5°C. For the sensitivity
analysis, therefore, we have evaluated the
extent of global warming using 1.5 and 5.5°C
as lower and upper bounds, respectively. The
impact of these assumptions on realized
warming is summarized in Figure C-15 for the
RCW and SCW cases. In the RCW case, the
range of realized warming for a 1.5-5.5°C
climate sensitivity would be 1.6-3.5°C by 2050
and 3.1-7.0°C by 2100, compared with a range
of 2.0-3.0°C by 2050 and 3.8-6.0°C by 2100
when the climate sensitivity is bounded by 2.0-
4.0°C. The corresponding values for
equilibrium warming for a 1.5-5.5°C climate
sensitivity are 2.2-7.9°C by 2050 and 3.8-13.9°C
by 2100, compared with 2.9-5.8°C by 2050 and
5.1-10.10C by 2100 for a 2.0-4.0°C climate
sensitivity. In the SCW case, the range of
realized warming for a 1.5-5.5°C climate
sensitivity would be 1.4-3.1°C by 2050 and 2.1-
5.0°C by 2100, compared with a range of 1.7-
2.6°C by 2050 and 2.6-4.2°C by 2100 when the
range of climate sensitivity is 2.0-4.0°C. The
corresponding values for equilibrium warming
for a 1.5-5.5°C climate sensitivity are 1.8-6.5°C
by 2050 and 2.5-9.0°C by 2100, compared with
2.3-4.7°C by 2050 and 3.3-6.6°C by 2100 for a
2.0-4.0°C climate sensitivity.
Rate of Heat Diffusion
CO2 and heat are currently transferred
from the atmosphere to the oceans and within
the ocean itself as a result of many complex
chemical and physical interactions. One of
these interactions is the transfer of heat from
the mixed layer to the thermocline, thereby
delaying global warming. Additionally,
changes in ocean mixing and circulation
patterns as a result of climate change could
alter the capacity of the oceans to absorb heat
(see BIOGEOCHEMICAL FEEDBACKS below
for further discussion). The rate at which heat
is absorbed only affects the rate of realized
warming, not the rate of equilibrium warming,
because the oceans cannot absorb heat
indefinitely.
In our model the rate at which mixing
occurs between the mixed layer and the
thermocline is parameterized with an eddy-
diffusion coefficient (see CHAPTER HI). The
value of the eddy-diffusion coefficient in the
base cases was assumed to be 0.55 x 10"4
m2/sec. For purposes of this sensitivity
analysis alternative values of 2 x 10"5 and 2 x
10"4 have been evaluated.
As shown in Figure C-16 the rate at
which the oceans absorb heat can noticeably
affect the amount of realized warming by 2100.
If the rate of heat absorption is greater than
that assumed in the base cases (i.e., if the
eddy-diffusion coefficient is 2 x 10"4 m2/sec),
realized warming by 2100 would be 0.5-1.2°C
less than in the RCW case (assuming 2.0-4.0°C
climate sensitivities). For the smaller eddy-
diffusion coefficient of 2 x 10"5 m2/sec, realized
warming by 2100 would be 0.3-0.9°C higher.
ASSUMPTIONS ABOUT ATMOSPHERIC
CHEMISTRY: A COMPARISON OF
MODELS
As discussed in Chapters II and III, the
chemistry of the future troposphere is one of
the uncertainties in the prediction of
atmospheric composition. The principal
factors contributing to this uncertainty are:
(1) the complexity and tremendous natural
variability of chemistry in the troposphere,
especially regarding oxidant formation; (2) the
range of interactions between tropospheric
chemistry and radiation perturbed by climate
change and changes in stratospheric
composition; and (3) the range of uncertainties
in future emissions of CH4, CO, NOX, and
non-methane hydrocarbons (NMHC). This
section focuses on the first two aspects of
uncertainty in atmospheric composition.
Recognizing the uncertainty in
tropospheric chemistry, U.S. EPA sponsored a
workshop on atmospheric composition to
discuss recent modelling efforts among
members of the atmospheric sciences
community and to construct a parameterized
atmospheric chemistry model that would
incorporate the latest scientific findings. The
end result was the Assessment Model for
Atmospheric Composition (AMAC), the
model used to obtain the findings discussed in
this report. AMAC was developed by Prather
of NASA/GISS as a result of the workshop,
which was held in January 1988 (see Prather,
1989). To obtain insight into the uncertainties
made by the AMAC and to ensure results that
are comparable to current, more detailed
C-33
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Policy Options for Stabilizing Global Climate
FIGURE C-15
7 -
IMPACT OF CLIMATE SENSITIVITY ON
REALIZED WARMING
(Based on 1.5 - 5.5 Degree Sensitivity)
Slowly Changing World
1985 2000 2025 2050 2075 2100
Rapidly Changing World
1985 2000 2025 2050 2075 2100
5.5'
4.0'
c
•
ta
2.0'
1.5*
C-34
-------
Appendix C: Sensitivity Analyses
FIGURE C-16
INCREASE IN REALIZED WARMING
DUE TO RATE OF OCEAN HEAT UPTAKE
(Based on 3.0 Degree Sensitivity)
6
w
"35
"5
O
« o
O 3
O
O)
O
o
2x10
-, RCW
2x10
-4
1985 2000
2025 2050
Year
2075
2100
C-35
-------
Policy Options for Stabilizing Global Climate
modeling efforts, a set of common scenarios of
CH4> CO, and NOX emissions were analyzed in
the AMAC, as well as in two current research
models: a 2-D tropospheric chemistry model
developed by Isaksen (Isaksen and Hov, 1987);
and a multi-box photochemical model of the
global troposphere developed by Thompson
and co-workers at NASA/Goddard (Thompson
et al., 1989).
Model Descriptions
Assessment Model for Atmospheric Composition
The focus of interest in tropospheric
composition is on O3, CH4, and OH, because
the two former gases are key greenhouse
absorbers and OH (together with ozone)
determines the oxidizing capacity of the
atmosphere and the abundance of many gases
such as methane, carbon monoxide, methyl
chloroform, and HCFC-22 (CHF2C1).
For the simulation of the troposphere in
this model, the Northern and Southern
Hemispheres (NH & SH) are treated
separately because significant asymmetries are
observed in many of the important shorter-
lived gases, such as CO and NOX. These
species play a major role in the budgets for
- CH4, O3, and OH in each hemisphere.
In the AMAC, tropospheric OH can be
treated as a steady-state variable as it responds
immediately to the annual average values of
the trace gases. To derive perturbations to
OH, a non-linear system is solved equating a
"production" term to a "loss" term. OH losses
are partitioned among the predicted gases
(CH4, CO), the specified fluxes (NMHC), and
self-reactions (OH). The production side of
the equation includes a positive response to
increases in UV radiation (i.e., loss in column
ozone) and in tropospheric H2O, O3 and NOX
fluxes. Coefficients for variations in either the
production or loss terms with respect to
column O3, tropospheric water vapor, trop-O3,
CO, CH4, and fluxes of both NMHCs and NOX
are based on results from 1-D and 2-D models
(Liu et al., 1987; Thompson and Cicerone,
1986; Isaksen and Hov, 1987; Isaksen et al.,
1988). Major sources of uncertainty in
calculating OH are the spatial averaging for
this highly variable constituent and the
nonlinearity in perturbation coefficients,
especially with respect to NOX distribution.
Perturbations to tropospheric ozone
affect both tropospheric temperatures and the
long-lived source gases controlled by OH. A
significant fraction of tropospheric ozone
originates in the stratosphere and is destroyed
by surface deposition; it is sufficiently short-
lived (a few months) that the AMAC
calculates ozone perturbations separately for
each hemisphere. Changes in tropospheric
ozone are associated with perturbations to the
total ozone column, and to tropospheric
chemical reactions, which are evaluated with
sensitivity coefficients, dln(O3)/dln(X),
ascribed to the precursor gases (Column -O3,
0.8; CH4, 0.2; CO, O.I; NOX flux, O.I; NMHC
flux, O.I). The coefficients are based on
detailed photochemical models for typical
tropospheric air parcels (Liu et al., 1987;
Thompson et al., 1989), but their uncertainties
are large, by approximately a factor of 2. Also,.
the efficiency of O3 production varies widely
with the NOX levels (Liu et al., 1987), which in
turn cannot be adequately characterized
throughout the entire troposphere due to their
large dynamic range. A similar concern
applies to the simplified treatment of non-
methane hydrocarbons.
Isaksen Model
The Isaksen model is a 2-D transport
model that calculates absolute concentrations
for O3 and OH (and several dozen other trace
chemical constituents in the troposphere) as
functions of altitude and latitude, as emissions
are varied over time (Isaksen and Hov, 1987;
Isaksen et al., 1988). Unlike the AMAC, thisx
model resolves latitudinal and altitude
distributions, and emission changes are
introduced with latitudinal discrimination.
The transport of longer-lived constituents can
locate key areas of tropospheric ozone and
OH change that the AMAC will miss; because
a 2-D model resolves altitude, the effects of
high-altitude aircraft emissions on NOX and
ozone or cloud perturbations to radiation
fields, for example, can be explored. The
Isaksen model differs from the AMAC in that
the troposphere is not coupled to the
stratosphere, so that the impact of changing
climate or perturbations on stratospheric
C-3<6
-------
Appendix C: Sensitivity Analyses
ozone are.not included. Methane flux changes
are included in annual updates of the model.
Thompson et al. (1989) Model
The Thompson model couples the result
of 1-D model calculations of the time history
for eight chemically coherent global regions,
which are then averaged to estimate net global
changes. A steady-state method is used:
emissions are specified in simulations to
represent conditions at 5-year intervals. This
is somewhat inadequate for lifetime changes,
although test runs show that this introduces
discrepancies in calculated mixing ratios of at
most 15%.
The description of chemically coherent
regions offers insight into regional variability,
a feature lacking in the version of the Isaksen
model used here, which does not have the
longitudinal variation needed to treat
emissions that are restricted to the source area
but can have extensive effects on ozone and
OH. Like the Isaksen model, the Thompson
model includes a more complete set of
chemical constituents than does the AMAC
and can identify other effects and interactions
of climate perturbation. For example, the
oxidants that contribute to sulfuric acid
formation in clouds and rain (HO2 and H2O2)
are very sensitive to changes in stratospheric
ozone and tropospheric water vapor.
Results from the Common Scenarios
It is not easy to compare the models
because the structure, input, and derived
quantities from the three models are not
treated comparably. Nevertheless, insights
into uncertainties can be obtained by
comparing selected results from each model.
U.S. EPA supplied eight scenarios of
alternative estimates of CH4, CO, and NOX for
evaluation in each model. In this section one
of these scenarios is discussed (U.S. EPA
Scenario #2), which assumes low CH4, low
CO, and high NOX growth in sources, a rapid
growth scenario for CO2 and N2O from
combustion, and a CFC and halon scenario
consistent with the Montreal Protocol. Table
C-4 summarizes the emission estimates for the
U.S. EPA scenarios and compares them to
estimates from the RCW and SCW scenarios.
The RCW and SCW cases could not be
explicitly included for this model comparison
because the development of these cases
occurred simultaneously with the model
comparison. Table C-5 summarizes the results
for all eight scenarios.
The U.S. EPA #2 emission estimates
are in the same range as those of the other
two cases except for CO emissions. These
estimates are much lower than both the RCW
and SCW cases and are similar to the
Stabilizing Policy cases due to stringent
control assumptions on transport vehicle
emissions. For the other emissions, the CO2
emission estimates in U.S. EPA #2 fall
between the RCW and SCW cases for most of
the time periods, approaching the RCW
estimates by 2100. The CH4 and NOX
estimates are similar to those for the SCW
case, except that NOX estimates after 2050 fall
between the RCW and SCW estimates.
The AMAC's troposphere is basically a
parameterized 2-box model: it reports mean
tropospheric values (ppb) for CH4, and
separate perturbations (% change) to OH and
O3 in each hemisphere. For the global
average perturbation to OH and O3, Northern
and Southern Hemisphere results are averaged
with equal weight. In addition to the
perturbed species discussed here with the
tropospheric chemical models, the AMAC
calculated other significant perturbations, such
as a 12% decrease in column ozone, a 2K rise
in mean tropospheric temperature, along with
a 10% increase in tropospheric water vapor.
These perturbations have an impact on
tropospheric OH, O3, CO, and CH4.
Additionally, unlike the other two models,
which provide point estimates, the AMAC
produces a range of trace-gas scenarios in
response to specified uncertainties in the
model coefficients.
The Thompson model averages over
eight "chemically coherent regions." This
approach is probably adequate for short-lived
species such as OH, and possibly for
tropospheric O3. However, it makes it
difficult to interpret CH4 calculations, which
predict different CH4 concentrations among
the boxes, when in fact the long lifetime of
CH4 ensures that it is well mixed throughout
the troposphere. The methane results in
Table C-5 have been averaged over the eight
C-37
-------
Policy Options lor Stabilizing (.'lobsil Climate
TABLE C-4
Comparison of Emission Estimates For U.S. EPA,
RCW, and SCW cases
(in teragrams, unless indicated otherwise)
Trace Gas
1985
Emissions Estimates by Year
2000
2025
2050
2075
2100
C02 (Pg C)
U.S. EPA #1
U.S. EPA #2
U.S. EPA #3
U.S. EPA #4
U.S. EPA #5
U.S. EPA #6
U.S. EPA #7
U.S. EPA #8
RCW
SCW
6.3
6.3
7.9
7.9
6.3
6.3
7.9
7.9
6.0
6.0
7.1
73
8.7
9.0
7.1
7.3
8.7
9.0
8,1
7.6
7.4
10.4
9.3
113
7.4
10.4
9.3
123
12.4
9.6
6.9
13.7
9.0
15.9
6.9
13.7
9.0
15.9
16.9
9.9
6.3
17.9
8.8
20.9
6.3
17.9
8.8
20.9
22.0
9.6
6.3
25.2
8.9
28.5
6.3
25.2
8.9
28.5
26.1
10.7
CO (as C)
U.S. EPA #1
U.S. EPA #2
U.S. EPA #3
U.S. EPA #4
U.S. EPA #5
U.S. EPA #6
U.S. EPA #1
U.S. EPA #8
RCW
SCW
315.5
315.5
750.5
750.5
315.5
315.5
750.5
750.5
505.8
505.8
225.3 :
225.6
696.3
696.5
225.3
225.6
696.3
696.5
561.7
610.8
183.6
194.5
701.4
734.9
183.6
194.5
701.4
734.9
724.6
825.4
168.6
191.8
699.3
778,0
168.6
191.8
699.3
778.0
885.3
842.2
148.1
177.5
684.4
807.0
148.1
177.5
684.4
807.0
1052.3
614.1
143.5
192.1
691.6
892.7
143.5
192.1
691.6
892.7
1192.2
625.0
CH,
U.S. EPA #1
U.S, EPA #2
U.S. EPA #3
U.S. EPA #4
U.S. EPA #5
U.S. EPA #6
U.S.JEPA#7
U.S. EPA #8
RCW
SCW
389.2
389.2
389.2
389.2
419.0
419.0
419.0
419.0
510.7
510.7
434.5
437.5
443.8
446.8
517.1
52Z5
522.2
527.6
590.1
581.0
508.4
530.2
531.6
553.6
674.5
726.7
6875
740.4
731.9
687.9
566.5
610.9
601.6
647.0
795.2
9185
815.6
941.1
901.1
748.4
618.7
669.0
664.1
717.6
903.6
1096.0
930.1
1131.7
. 1044.5
783.9
628.6
698.8
683.4
757.4
945.1
1252.9
978.7
1126.1
1126.1
829.7
NOX (as N)
U.S. EPA #1
U.S. EPA #2
U.S. EPA #3
U.S. EPA #4
U.S. EPA #5
U.S. EPA #6
U.S. EPA #7
U.S. EPA #8
RCW .
SCW
40.4
59.4
40.4
59.4
40.4
59.4
40.4
59.4
54.2
54.2
345
58.6
35.5
60.1
345
58.6
355
60.1
62.4
61.2
28.0 -
64.1
31.2
68,9
28.0
64.1
31.2
68.9
79.2
71.1
26.2
72.1
31.0
80.2
26.2
72.1
31,0
80.2
95.4
72.2
23.3
824
29.9
95.2
23.3
82.4
29.9
95.2
110.4
66.7
23.2
103.6
30.7
120.1
23.2
103.6
30.7
120.1
121.6
69.0
C-38
-------
Appendix C: Sensitivity Analyses
TABLE C-5
Comparison of Results From Atmospheric Chemistry Models for the Year 2050
Compared to 1985
MODEL TEST CASE RESULTS
LOW CH.
HIGH CH4
Increases in Methane (ppb)
Low NOX
Prather - average
(minimum/maximum)
Isaksen
Thompson et al.
High NOX
Prather - average
' (minimum/maximum)
Isaksen
Thompson et al.
Percent Change in CO
Low NOX
Prather
Isaksen
Thompson et al.
High NOX
Prather
Isaksen
Thompson et al.
Percent Change in OH
Low NOX
Prather -- average
(minimum/maximum)
Isaksen
Thompson et al.
High NOX
Prather - average
(minimum/maximum)
Isaksen
Thompson et al.
Percent Change in O3
Low NOX
Prather -- average
(minimum/maximum)
Isaksen
Thompson et al.
High NOX
Prather - average
(minimum/maximum)
Isaksen
Thompson et al.
Low CO
U.S. EPA#1
806
(669/943)
400
1112
U.S. EPA#2
801
(658/944)
350
1331
U.S. EPA#1
16
-13
-12
U.S. EPA#2
17
-9
-1.5
U.S. EPA#1
-9
(-15/-2)
5
-9.6
U.S. EPA#2
-2
(-8/4)
8
-8.9
U.S. EPA#1
1
(-7/8)
-1
4.3
U.S. EPA#2
5
(-2/13)
5
12
High CO
U.S. EPA#3
1031
(901/1160)
720
1498
U.S. EPA#4
1048
(898/1197)
550
1740
U.S. EPA#3
43
8
25
U.S. EPA#4
44
8
36
U.S. EPA#3
-14
(-20/-9)
- 1
-16
U.S. EPA#4
-9
(-15/-2)
4
-15
U.S. EPA#3
8
(-1/17)
2
11
U.S. EPA#4
13
(5/22)
8
19
Low CO
U.S. EPA#5
1750
(1586/1914)
950
2555
U.S. EPA#6
2082
(1901/2264)
1010
3305
U.S. EPA#5
55
0
14
U.S. EPA#6
70
8
32
U.S. EPA#5
-23
(-30/-17)
1
-21
U.S. EPA#6
-22
(-29/-16)
5
-22
U.S. EPA#5
21
(8/34)
3
15
U.S. EPA#6
33
(18/47)
10
27
High CO
U.S. EPA#7
2022
(1734/2052)
1220
4306
U.S. EPA#8
2242
(2056/2427)
1200
3989
U.S.EPA#7
82
17
77
U.S. EPA#8
99
19.
78
U.S. EPA#7
-26
(-32/-20)
-2
-31
U.S. EPA#8
-25
(-32/-19)
3
-27
U.S. EPA#7
27
(13/41)
5
28
U.S. EPA#8
39
(24/55)
13
34
C-39
-------
Policy Options for Stabilizing Global Climate
regions and scaled to account for the CH4
lifetime changes occurring in the perturbed
atmosphere. Also summarized are percent
changes in CO (surface mixing ratios) and OH
and O3 (column-integrated from 0-15 km).
The CH4 changes obtained by the Thompson
• model are much higher than those obtained
with the AMAC, while the CO changes are
somewhat less than the AMAC. Although not
shown in the global averages in Table C-5, the
most useful results of the regional calculations
are localized estimates of OH and O3 changes
in each chemically defined region where CO
and NOX growth rates may differ considerably.
The differences between areas with controlled
emissions (Urban 1) and without controlled
emissions (Urban 2) are very striking (see
Figure C-17).
The Isaksen model calculates
perturbations as a function of latitude, altitude,
(0-16 km), and time of year. The increase in
CH4 is distributed uniformly throughout the
troposphere as expected. There is a problem
with the implementation of the U.S. EPA #2
scenario in that the CH4 concentrations
decline at the beginning of the model
integration. This may be due to the low
estimate of global CO flux. Initial fluxes were
- scaled in the AMAC and Thompson models to
obtain a steady-state of current concentrations.
CH4 does not recover to its initial
concentration for at least 20 years into the
scenario, and this is probably the reason for
the Isaksen model predicting such a small
increase in CH4. The patterns for OH and O3
perturbations are distinct (see Figure C-18).
The greatest changes in O3 are below 2 km
altitude: there is a large increase between 0°
and 35°N and a small decrease centered at
50°N. The spatial pattern of changes in OH
are interesting: in the upper troposphere
between 12 and 16 km the OH increases by
10-30% in the Northern Hemisphere, whereas
throughout most of the Southern Hemisphere
OH decreases. Both of these changes may be
driven by increases in CH4. In the dry upper
troposphere in the presence of NOX, CH4
increases the OH concentration during its
atmospheric oxidation, but in the lower
troposphere the CH4 provides merely a sink
for OH.
Overall, all three models predict similar
increases in tropospheric O3. The Thompson
et al. and AMAC models predict decreases in
tropospheric OH, while the Isaksen model
reports a globally averaged increase. This
discrepancy may be explained by the large
increases in OH above 12 km as noted above,
something that is also calculated by the
Thompson model. However, most of the
difference in OH levels seems attributable to
the lower CO and CH4 concentrations
calculated by Isaksen compared with the other
two models. As shown in Table C-5 for all
eight scenarios, none of the increases in CO by
2050 are more than 15-20% in Isaksen,
whereas Thompson et al. and AMAC show
CO increases of 80-100% (see scenarios #7
and #8). Some of the CO and OH differences
between Isaksen et al. and the other two
models are due to the difference in
initialization described above, but most of the
OH difference may be due to how CO behaves
in each model. This may be one of the more
prominent uncertainties in predicting future.
tropospheric composition. CO has a moderate
lifetime (typically about a few months) with
considerable spatial variability that is not well
resolved in any of the models. Perhaps the
Isaksen model gives a lower limit to CO and
OH changes, and the other two models
estimate the largest expected changes.
EVALUATION OF UNCERTAINTIES
USING AMAC
Comparing the results of AMAC to
other models given identical scenarios provides
one approach to evaluating uncertainties
related to atmospheric chemistry. Valuable
information can also be obtained by testing the
robustness of the AMAC results to changes inN
critical model parameters. This section
examines these impacts by varying key
parameters within AMAC and then comparing
the results to the RCW scenario.
Atmospheric Lifetime of CFC-11
The assumed atmospheric lifetime for
CFC-11 in the AMAC for the RCW case was
65 years. Its atmospheric lifetime, however,
may range from 55 to 75 years (Prather, 1989);
these estimates were evaluated to determine
the impact on atmospheric chemistry. The
changes in atmospheric concentration for
CFC-11 are summarized in Figure C-19, which
indicates that concentration levels may vary
C-40
-------
Appendix C: Sensitivity Analyses
FIGURE C-17
REGIONAL DIFFERENCES FOR URBAN AREAS
WITH DIFFERENT EMISSIONS OF CO AND NO
Fractional Change: 1985-2060
urDan 1
Fractional Change: 1985-2050
Source: Thompson et al, 1989.
C-41
-------
Policy Options for Stabilizing Global Climate
FIGURE C-18
2.00-j
OH AND OZONE PERTURBATIONS
IN THE ISAKSEN AND HOV MODEL
(Percent Change)
Ozone
33 80 10 SO SO 40 SO 20 <3 0 -to -20 -SO -40 -SO -SO -10 -80 -90
LATITUCt'
OH
2.00
SO 80 10 SO SO 40 SO 20 to 3
-
-------
Appendix C: Sensitivity Analyses
FIGURE C-19
SENSITIVITY OF ATMOSPHERIC CONCENTRATION
OF CFC-11 TO ITS LIFETIME
(Based on 3.0 Degree Celsius Sensitivity)
900
800
700
600
c
o
I- 500
9
a.
ta
400
300
200
100
1985 2000
2025 2050
Year
...4 75 Years
RCW (65 Years)
55 Years
2075 2100
C-43
-------
Policy Options for Stabilizing Global Climate
from about 690 to 860 ppt by 2100. Increases
(decreases) in the atmospheric concentration
of CFC-11, however, tend to be offset by
corresponding decreases (increases) in
atmospheric concentrations of .other trace
gases, such as other CFCs and CH4. That is,
the increase (decrease) in the lifetime of CFC-
11 increases (decreases) the amount of
stratospheric ozone depletion, which increases
(decreases) the amount of UV radiation; these
higher (lower) UV levels increase (decrease)
the rate of destruction of these other gases.
As a result, the impacts on global warming are
negligible (less than 0.1°C).
Interaction of Chlorine with Column Ozone
Chlorine in the stratosphere has a
negative, non-linear impact on total column
ozone. This chemical interaction is one of the
primary causes of stratospheric ozone
depletion due to the chlorine contained in
CFCs; this interaction has been included in the
AMAC, however, primarily for its ability to
affect the rate of tropospheric ozone
formation. In the RCW case this relationship
was defined as a 0.03% decline in total column
ozone/(ppb)2 of stratospheric chlorine. A
higher value, 0.20%, was evaluated, which
would increase the rate of column ozone
destruction.
With the 0.20% assumption, total
column ozone depletion was 47-48% by 2050
(assuming 2.0-4.0°C climate sensitivities)
compared with a total column ozone depletion
of 17.5% with the lower value (i.e., the -0.03%
value used in the RCW case). The increase in
total column ozone depletion has a positive
feedback on the tropospheric OH levels due to
the increase in UV radiation. The resulting
OH interactions with other trace gases
substantially reduces the atmospheric
concentration of CH4, HCFC-22, methyl
chloroform, and methyl chloride, and reduces
the rate of tropospheric ozone formation.
(The role of O3 is problematic, Oj at 10-12
km probably would increase. At this altitude,
O3 probably has the largest greenhouse effect;
see CHAPTER II.) These impacts reduce the
amount of global warming; as shown in Figure
C-20, the decline in realized wanning is 0.1°C
by 2050 compared with the RCW case, and
0.3-0.5°C by 2100; the decline in equilibrium
warming by 2100 is 0.4-0.8°C (assuming 2.0-
4.0°C climate sensitivities).
Sensitivity of Tropospheric Ozone to CH4
Abundance
Tropospheric ozone formation is
affected by the amount of CH4 present,
although the rate at which tropospheric ozone
forms as a result of CH4 abundance is subject
to some uncertainty. In the RCW case, this
variable for the Northern Hemisphere was
assumed to be a 0.2% change in tropospheric
ozone for each percentage change in CH4
concentration; other evidence suggests that a
higher value, 0.4%, is possible (Prather, 1989).
Using this higher value increases the
change in tropospheric ozone in 2100 by about
50% over the RCW case (tropospheric ozone
increases by about 69% compared with about.
46% when a value of 0.2% is assumed). The
increase in tropospheric ozone indirectly
results in a decrease in CH4 concentrations
since the tropospheric ozone increase also
increases OH formation, which destroys CH4.
Due to this partially offsetting effect, the
increase in global warming is less than 0.1°C.
Sensitivity of OH to NOX
Tropospheric OH formation is affected
by the level of NOX emissions, although the
rate of OH formation is uncertain. In the
RCW case, we assumed a 0.1% OH change for
every 1.0% change in NOX emissions for the
Northern Hemisphere. We evaluated a range
of uncertainty from 0.05% to 0.2%.
An increase (decrease) in the amount of
OH due to a higher (lower) sensitivity to NOX
emissions results in less (more) tropospheric
ozone formation as well as lower (higher)
levels of CO and CH4. The higher sensitivity
value of 0.2% reduces realized warming about
0.1°C by 2100 compared with the RCW case
(assuming 2.0-4.0°C climate sensitivities;
equilibrium warming is about 0.1-0.2°C lower
by 2100), while the lower sensitivity value of
0.05% increases realized warming less than
0.1°C by 2100 (the equilibrium warming
increase is also less than 0.1°C by 2100).
C-44
-------
Appendix C: Sensitivity Analyses
FIGURE C-20
INCREASE IN REALIZED WARMING
DUE TO RATE OF INTERACTION OF Clx WITH OZONE
(Based on 3.0 Degree Sensitivity)
w
la
"i
u
(A
e
o
D)
0)
Q
RCW
Clx/Ozone
Interaction
1985 2000
2025
2050
Year
2075
2100
C-45
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Policy Options for Stabilizing Global Climate
BIOGEOCHEMICAL FEEDBACKS
The sensitivity of the climate system to
anthropogenic perturbations is determined by
a combination of feedbacks that amplify or
dampen the direct radiative effects of
.increasing concentrations of greenhouse gases.
Several important internal climate feedbacks,
such as those resulting from changes in water
vapor, clouds, and sea ice albedo, are included
in the estimates of climate sensitivity discussed
throughout this report. There are a number of
feedbacks of a biogeochemical origin, however,
that may also play an important role in
climatic change that were not included in the
analyses on which this range is based.
Biogeochemical sources of feedback include
releases of methane hydrates; changes in ocean
chemistry, biology, and circulation; and
changes in the albedo of the global vegetation.
Any attempt to quantify the impact of
biogeochemical feedbacks is necessarily quite
speculative at this time; however, it does
appear that they could have an important
impact on global climate. For example,
Lashof (1989) has estimated that the gain from
biogeochemical feedbacks ranges from 0.05-
0.29 compared with a 0.17-0.77 gain from
internal climate feedbacks. (The gain is
defined as the portion of global equilibrium
temperature change attributable to the
feedback divided by the total global
equilibrium temperature when the feedback is
included). Some of these key feedbacks were
incorporated into the AMAC for these
sensitivity cases to determine the magnitude of
their impact on global warming.
Ocean Circulation
As mentioned above, the oceans are
currently a major sink 'for heat and CO2.
Concerns have been raised, however, that the
basic circulation patterns that allow these
processes to continue could be significantly
altered as the global climate changes. This
possibility is suggested by the rapid rate of
atmospheric CO2 change during past periods
of climate change (e.g., see CHAPTER III). If
circulation patterns did change, it is plausible
that the oceans would no longer be a net sink
for heat and CO2.
It is not known at what point ocean
circulation would be altered. For this analysis
we assumed that a 2°C increase in realized
warming would alter ocean circulation patterns
sufficiently to shut off net uptake of CO2 and
heat by the oceans. This would increase
atmospheric CO2concentrations from 10-13%
by 2100, and would reduce the difference
between realized and equilibrium warming as
the atmosphere warmed more quickly due to
the oceans' inability to continue to act as a
heat sink. As shown in Figure C-21, this
feedback is sufficient to increase realized
warming up to 1.6°C by 2050 and 1.3-3.6°C by
2100 compared with the warming estimated for
the RCW case.
Methane Feedbacks
Increases in global temperature could
increase the amount of CH4 emissions due to
several feedback processes: (1) release of
methane from hydrates, which are methane
compounds contained in continental slope
sediments, as increasing temperatures
destabilize the formations; (2) additional
methane from high-latitude bogs due to longer
growing seasons and higher temperatures; and
(3) increased rate of methanogenesis from rice
cultivation. The amount of CH4 that could be
released from each of these feedback
processes, and the rate at which any releases
might occur, are highly speculative. For each
process we have assumed that the rate of CH4
release is linearly related to the increase in
temperature, with each 1°C increase leading to
an additional 110 Tg from methane hydrates,
12 Tg from bogs, and 7 Tg from rice
cultivation (Lashof, 1989). These methane,
feedbacks could have a major impact on
atmospheric CH4 concentrations: by 2100
concentrations would increase to about 6900-
8050 ppb, compared with 4300-4550 ppb in the
RCW case. As shown in Figure C-22, this
increase in CH4 would be sufficient to increase
realized wanning relative to the RCW case
about 0.1-0.3°C by 2050 and 0.4-0.8°C by 2100
(assuming 2.0-4.0°C climate sensitivities).
Combined Feedbacks
In addition to the two separate
feedbacks discussed above, we analyzed the
C-46
-------
Appendix C: Sensitivity Analyses
FIGURE C-21
INCREASE IN REALIZED WARMING
DUE TO CHANGE IN OCEAN CIRCULATION
(Based on 3.0 Degree Sensitivity)
1985 2000
2025 2050
Year
2075
1 Ocean
/ Circulation
RCW
2100
C-47
-------
Policy Options for Stabilizing Global Climate
FIGURE C-22
5 l
10
3
o
I 3
«
O>
O
INCREASE IN REALIZED WARMING
DUE TO METHANE FEEDBACKS
(Based on 3.0 Degree Sensitivity)
Methane
•'•' Feedbacks
RCW
1985 2000
2025 2050
Year
2075
2100
C-48
-------
Appendix C: Sensitivity Analyses
combined impact of several types of
biogeochemical feedbacks. The following
specific feedbacks were included: (1) methane
from hydrates, bogs, and rice cultivation, as
previously discussed; (2) increased stability of
the thermocline, thereby slowing the rate of
heat and CO2 uptake by the deep ocean by
30% due to less mixing; (3) vegetation albedo,
which is a decrease in global albedo as a result
of changes in the distribution of terrestrial
ecosystems by 0.06% per 1°C warming; (4)
disruption of existing ecosystems, resulting in
transient reductions in biomass and soil carbon
at the rate of 0.5 Pg C per year per 1°C
warming; and (5) CO2 fertilization, which is an
increase in the amount of carbon stored in the
biosphere in response to higher CO2 concen-
trations at the rate of 0.3 Pg C per ppm. See
Lashof (1989) for further discussion.
The combined impact of these feedbacks
on realized warming is an increase of 0.3-0.7°C
by 2050 and 0.7-2.2°C by 2100 relative to the
RCW case (assuming 2.0-4.0°C climate
sensitivities; see Figure C-23); the increase in
equilibrium warming is 0.2-1.3°C by 2050 and
0.6-2.8°C by 2100. These preliminary analyses
strongly suggest that biogeochemical feedbacks
could have a major impact on the rate of
climatic change during the next century.
C-49
-------
Policy Options for Stabilizing Global Climate
FIGURE C-23
INCREASE IN REALIZED WARMING
DUE TO CHANGE IN COMBINED FEEDBACKS
(Based on 3,0 Degree Sensitivity)
6 —
to
| 4-
O
«
*
1)
O)
2 h
1985 2000
Combined
Feedbacks
RCW
2025 2050
Year
2075 2100
C-50
-------
Appendix C: Sensitivity Analyses
NOTES
1. Pg = petagram; 1 petagram = 1015 grams.
2. EJ = exajoule; 1 exajoule = 1018 joules.
3. Tg = teragram; 1 teragram = 1012 grams.
REFERENCES
Bjorkstrom, A. 1979. A model of CO2
interaction between atmosphere, oceans, and
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