investigating greenhouse gas abat ement pathways in … · 2017. 9. 25. · investigating...
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INVESTIGATING GREENHOUSE GAS ABATEMENT PATHWAYS IN SELECTED OECD COUNTRIES USING A
HYBRID ENERGY-ECONOMY APPROACH by
Suzanne Goldberg
BCom (Hon), McMaster University, 2005
RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF RESOURCE MANAGEMENT
In the School of Resource Environmental Management
Project No.464
© Suzanne Goldberg 2009
SIMON FRASER UNIVERSITY
Spring 2009
All rights reserved. This work may not be reproduced in whole or in part, by photocopy
or other means, without permission of the author.
ii
APPROVAL
Name: Suzanne Goldberg
Degree: Master of Resource Management
Title of Thesis: Investigating Greenhouse Gas Emission Pathways in Selected OECD Countries Using a Hybrid Energy-Economy Approach
Project No.: 464
Examining Committee:
Chair: Steven Groves Master of Resource Management Candidate
______________________________________
Mark Jaccard Senior Supervisor Professor, School of Resource and Environmental Management Simon Fraser University
______________________________________
John Nyboer Supervisor Adjunct Professor, School of Resource and Environmental Management Simon Fraser University
Date Defended/Approved: March 3, 2009
Last revision: Spring 09
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iii
ABSTRACT
This report outlines the development and analysis of CIMS OECD-EPM. CIMS
OECD-EPM is a hybrid energy-economy model that forecasts energy
consumption and GHG emissions in 28 OECD countries from 2005 to 2050. In
the absence of climate change mitigation policy, growth forecasts for energy
consumption and GHG emissions are moderate, far below that projected for non-
OECD regions. With its unique modelling structure, which incorporates
technological detail, macroeconomic feedbacks and behavioural realism, CIMS
OECD-EPM is used to simulate the impact of abatement policies on the region.
Initial results suggest that significant emission reductions can be achieved.
Development of carbon capture and storage, nuclear and energy-efficient
technologies in the electricity and industrial sectors are the primary drivers of
abatement in the region. Overall, abatement activity in OECD-EPM is likely to be
more costly than in other world regions; high marginal abatement costs and high
levels of technological development limit incremental mitigation activity.
Keywords: Hybrid energy-economy models, Climate change policy, OECD, Marginal abatement costs, Greenhouse gas abatement Subject Terms: Climatic changes – Mathematical models; Energy policy – Mathematical models; Climatic changes – Government policy; Climatic changes– Economic aspects; Environmental policy – Economic aspects
iv
ACKNOWLEDGEMENTS
I would like to thank my family and friends for supporting me throughout
the entire project process. Your encouragement and guidance were instrumental
to my success. I would especially like to thank my parents, Steve Groves and the
CIMS Global team -- Noel Melton, Michael Wolinetz, and Nygil Goggins. I would
also like to acknowledge Mark Jaccard, John Nyboer, Jotham Peters and Chris
Bataille for guiding me through the academic process and motivating me along
the way. Additionally, I would like to thank EMRG, SFU and SSHRC for providing
the funding for this research effort.
v
TABLE OF CONTENTS
Approval .............................................................................................................. ii
Abstract .............................................................................................................. iii
Acknowledgements ........................................................................................... iv
Table of Contents ............................................................................................... v
List of Figures .................................................................................................. viii
List of Tables ...................................................................................................... x
Glossary ............................................................................................................ xii
Chapter 1 Introduction and background ...................................................... 1
1.1 The Global Climate Change Debate ................................................... 1
1.1.1 The Science .................................................................................... 1 1.1.2 The Policy ....................................................................................... 2
1.2 Energy-Economy Models ................................................................... 3 1.2.1 Hybrid Models ................................................................................. 5 1.2.2 CIMS: An Integrated Framework..................................................... 6
1.3 CIMS-Global ....................................................................................... 6 1.3.1 Research Objectives and Questions ............................................... 7
1.4 Structure of Report ............................................................................. 8
Chapter 2 Methodology.................................................................................. 9
2.1 Introduction to the CIMS Framework .................................................. 9
2.1.1 Model Structure............................................................................... 9 2.1.2 Model Sequencing ........................................................................ 11 2.1.3 Market Share Algorithm ................................................................ 12
2.1.4 Behavioural Parameters ............................................................... 14 2.1.5 Endogenous Technological Change ............................................. 16
2.2 Supporting Data ............................................................................... 17 2.3 Empirical Basis for Parameter Values .............................................. 17
2.3.1 Technology Parameters ................................................................ 17
2.3.2 Behavioural Parameters ............................................................... 18 2.3.3 Macroeconomic Feedback Parameters ........................................ 18
2.4 Critical Assumptions ......................................................................... 18 2.4.1 Population and GDP Forecasts .................................................... 18 2.4.2 Demand Forecasts ........................................................................ 19
2.4.3 Climate Policy ............................................................................... 19 2.4.4 Trade ............................................................................................ 20
2.5 Analysis ............................................................................................ 20
vi
2.5.1 BAU Forecast................................................................................ 20 2.5.2 GHG Abatement Pathways ........................................................... 20 2.5.3 Marginal Abatement Costs ............................................................ 21 2.5.4 Uncertainty .................................................................................... 22
Chapter 3 Overview of the Energy Sector .................................................. 23
3.1 Energy Trends .................................................................................. 23 3.2 Trade ................................................................................................ 25 3.3 Greenhouse Gases .......................................................................... 25 3.4 Sources of Primary Energy .............................................................. 27
3.4.1 Oil ................................................................................................. 27 3.4.2 Natural Gas ................................................................................... 29 3.4.3 Coal .............................................................................................. 29 3.4.4 Electricity ...................................................................................... 30 3.4.5 Nuclear ......................................................................................... 31 3.4.6 Renewables .................................................................................. 32
3.5 Total Final Consumption .................................................................. 35 3.5.1 Industrial Sector ............................................................................ 35 3.5.2 Transportation Sector ................................................................... 37 3.5.3 Residential Sector ......................................................................... 38 3.5.4 Commercial Sector ....................................................................... 40
3.6 Carbon Capture and Storage ........................................................... 41
Chapter 4 Simulation Results ...................................................................... 43
4.1 Calibration of BAU Run .................................................................... 43 4.2 Details of BAU .................................................................................. 44
4.2.1 Total Energy Consumption ........................................................... 44 4.2.2 Total Final Energy Consumption ................................................... 46 4.2.3 GHG Emissions ............................................................................ 49 4.2.4 Electricity Generation .................................................................... 50 4.2.5 Intensity Trends ............................................................................ 51
4.3 Policy Runs ...................................................................................... 52 4.3.1 Marginal Abatement Cost Curves ................................................. 52 4.3.2 Target Abatement Policy Run ....................................................... 57
4.4 Sensitivity Analysis ........................................................................... 67 4.4.1 Demand Sector Growth ................................................................ 68
4.4.2 Nuclear Power Generation ............................................................ 69
Chapter 5 Discussion ................................................................................... 72
5.1 Regional Marginal Abatement Cost Curve Comparison ................... 72
5.2 Implications of Regional Marginal Abatement Cost Variation ........... 75 5.3 Key Modelling Challenges ................................................................ 78
Chapter 6 Conclusion .................................................................................. 81
6.1 Summary of Key Findings ................................................................ 81 6.2 Limitations ........................................................................................ 85 6.3 Recommendations for Further Research ......................................... 86
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Appendix 1: Geographic Coverage ................................................................. 90
Appendix 2: Industrial Sector Data Sources .................................................. 91
Appendix 3: Drivers of Energy Demand ......................................................... 93
Appendix 4: Comparison of MACCs-All CIMS Regions (2050) ..................... 94
Appendix 5: MACCs For All CIMS Sectors in 2050 ........................................ 95
Appendix 6: BAU Energy and GHG Forecasts ............................................... 96
Appendix 7: Policy Energy and GHG Forecasts ............................................ 98
References ...................................................................................................... 100
viii
LIST OF FIGURES
Figure 1: CIMS model structure ................................................................... 10
Figure 2: Population and growth assumptions for 2010-2015 ...................... 19
Figure 3: Policy runs: Emission price pathways ........................................... 21
Figure 4: 2005 Total final consumption and total primary energy supply ............................................................................................ 24
Figure 5: Historical CO2 emissions in OECD-EPM, 1971-2005 .................... 26
Figure 6: Estimate of remaining oil resources under three distinct resource-use efficiency scenarios ................................................. 28
Figure 7: Comparison of BAU nuclear energy production forecasts ............. 32
Figure 8 Composition of renewable energy supply in 2005, by region ............................................................................................ 33
Figure 9: Total final consumption+ by fuel and sector ................................... 35
Figure 10: Industrial sub-sector output growth forecast ................................. 37
Figure 11: Transportation demand forecast, by mode .................................... 38
Figure 12: Historical and forecasted housing stock ........................................ 39
Figure 13: Forecast of commercial floor space .............................................. 41
Figure 14: Total primary and secondary energy consumption, by fuel ................................................................................................ 45
Figure 15: Total final consumption in BAU, by sector and by fuel .................. 47
Figure 16: Composition of GHG emission projections, by sector ................... 50
Figure 17: Primary energy consumption in the electricity sector, 2005-2050 ..................................................................................... 51
Figure 18: Marginal abatement cost curves for CIMS OECD-EPM in selected years ............................................................................... 53
Figure 19: Marginal abatement cost curves for energy demand sectors in 2050 .............................................................................. 55
Figure 20: Marginal abatement cost curves of energy demand sectors in 2050 (% below BAU)..................................................... 56
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Figure 21: Composition of fuel consumed and total generation in the electricity sector in 2050, by emission charge ......................... 57
Figure 22: Comparison of total energy consumption in BAU and policy, by fuel ................................................................................ 59
Figure 23: Wedge diagram, abatement by activity ......................................... 62
Figure 24: Captured GHGs using carbon capture and storage (Mt CO2e), by sector ..................................................................... 64
Figure 25: Electricity sector MACCs with varying nuclear development constraints ............................................................... 70
Figure 26: Regional MACC for selected CIMS-Global regions in 2050 .............................................................................................. 73
Figure 27: Comparison of absolute MACC for selected CIMS regions in 2050.............................................................................. 74
Figure 28: Regional and market MACCs in 2050 ........................................... 77
x
LIST OF TABLES
Table 1: Major emission abatement initiatives of OECD countries ................ 3
Table 2: Regional aggregation of CIMS-Global ............................................. 7
Table 3: Final and intermediate goods and services produced by the sector models .......................................................................... 11
Table 4: Default discount rate in CIMS ....................................................... 15
Table 5: Summary of sensitivity analysis .................................................... 22
Table 6: Comparison of regional energy consumption indicators (2005) ............................................................................................ 23
Table 7: GHG emissions by sector in 2005 ................................................. 27
Table 8: Total primary energy supply, by region (2005) .............................. 27
Table 9: Electricity production by fuel (2005) .............................................. 31
Table 10: Annual growth in transportation demand ....................................... 38
Table 11: Current, planned and potential CCS development, 2005-2030 ..................................................................................... 42
Table 12: Comparison of energy consumption and GHGs in 2005 and 2030, by sector ....................................................................... 44
Table 13: Total primary energy supply in 2005, 2030 and 2050, by fuel ................................................................................................ 46
Table 14: Shares of total final consumption, by fuel and by sector ............... 48
Table 15: Energy efficiency and GHG intensity in the electricity sector ............................................................................................ 50
Table 16: Economy-wide GHG intensity ....................................................... 52
Table 17: Target abatement policy run emission charge schedule ............... 59
Table 18: Comparison of GHG intensity in the BAU and Policy forecasts ....................................................................................... 60
Table 19: Cumulative emission reductions (2005-2050), by sector ............... 61
Table 20: Composition of energy consumption in the electricity sector, by fuel, in the policy run ..................................................... 63
Table 21: Output changes in the policy run ................................................... 65
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Table 22: Estimated effect of the policy run on GDP for sectors covered by CIMS in 2025 and 2050 .............................................. 67
Table 23: Results of demand sector growth sensitivity analysis, presented as percentage change from the reference run in 2050 BAU and Emission Charge* ............................................. 69
Table 24: Fuel mix in the electricity sector for varying nuclear development constraints in 2050, by emission charge .................. 71
Table 25: Regional marginal abatement costs and reductions associated with a target of 30% below BAU by 2050- assuming no trading ...................................................................... 77
xii
GLOSSARY
AMELA BAU DA CCS CGE CO2e EC EIA EJ EPM GDP GHGs Gt IEA IEO LCC MACC Mt OECD
Africa, Middle East and Latin America Business as Usual Developing Asia -- Asia excluding China Carbon Capture and Storage Computable General Equilibrium Model Carbon Dioxide Equivalent European Commission Energy Information Administration Exajoule Europe, Pacific and Mexico Gross Domestic Product Greenhouse Gases Gigatonne (109 tonnes) International Energy Agency International Energy Outlook 2007 Life Cycle Cost Marginal Abatement Cost Curve Megatonne (106 tonnes) Organization for Economic Co-operation and Development
xiii
PPM PPP TE TEC TFC TPES RPP WEO WETO
Parts Per Million Purchasing Power Parity Transitioning Economies -- Former Soviet Union and non-OECD Europe Total Primary and Secondary Energy Consumption Total Final Energy Consumption Total Primary Energy Supply Refined Petroleum Products World Energy Outlook 2006 World Energy Technology Outlook- 2050
1
CHAPTER 1 INTRODUCTION AND BACKGROUND
1.1 The Global Climate Change Debate
It is now clear that climate change is “unequivocal” and that anthropogenic
activity is the main driver of this change (IPCC, 2007). Moreover, there is a
growing consensus across scientific, economic, and political communities that
climate change is an urgent global threat (Aldy et al., 2003). Consequently, world
leaders have acknowledged that immediate and corrective global action is
imperative.
Such corrective action requires countries to take accountability for past
and present emission output and to take concerted action to reduce emissions.
Despite acknowledgement of the necessity of action, many nations are not
aggressively pursuing abatement strategies. Some countries fear that abatement
efforts will negatively affect their economic health. Other countries, with lower
economic status, believe that their per capita emissions output is too low to
warrant aggressive abatement efforts. Debates over costs, equity and
accountability have stalled progress in global climate change efforts.
Unfortunately, as many scientists suggest, this issue is time sensitive and cannot
afford further delay. Nations must work together to reduce emissions because
the impacts of climate change have global consequences.
1.1.1 The Science
Global greenhouse gas (GHG) emissions have risen more than 70% since
the 1970’s (IPCC, 2007). 1 As a result, the atmospheric concentration of carbon
dioxide has increased from its pre-industrial level of 280ppm to 380ppm in 2005
1 GHGs are gases that contribute to the warming if the planet. The six major GHGs regulated
under the 1997 Kyoto Protocol are carbon dioxide, methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons and sulphur hexafluoride. It is common practice to measure and group these gases in terms of their equivalency to the warming potential of carbon dioxide (CO2e).
2
(IPPC, 2007). During the same period, global average surface temperature has
warmed 0.13oC per decade. The Intergovernmental Panel on Climate Change
(2007) states with very high confidence that there is a link between
anthropogenic activity and climate change, as well as a link between increased
GHG atmospheric concentrations and increased global average surface
temperature. 2 To avoid further warming, GHG concentrations in the atmosphere
need to stabilize. Although there is no agreed upon GHG limit that countries
should work toward achieving, the United Nations Framework Convention on
Climate Change, Organization of Economic Co-operation and Development and
the European Commission advocate stabilization of atmospheric GHG
concentrations at 550ppm of carbon dioxide equivalent (CO2e). Abatement
activity over the next two decades will play a defining role in the challenges our
society will face in the future.
1.1.2 The Policy
The Kyoto Protocol (1997) is an agreement under the United Nations
Framework Convention on Climate Change that aims to combat climate change
by stetting targets to reduce greenhouse gas emissions. In February of 2005, the
protocol came into force, requiring all developed nations that have ratified it and
accepted abatement obligations to reduce GHG emissions an average 5% below
1990 levels in the first commitment period: 2008-2012. The protocol has helped
to stimulate a number of domestic climate change policies in industrialized
countries. Table 1 identifies a selection of emission abatement initiatives in
OECD countries.
2 The IPCC defines high confidence as a 90% chance of being correct (IPCC, 2007).
3
Table 1: Major emission abatement initiatives of OECD countries
Frameworks Scope Status Targets3 Binding
European Union Emission Trading Scheme (EU ETS) (January 2005)
Multi-country (EU) and multi-sector
In force
6.5% below 2005 by 2012
Yes
Asia-Pacific Partnership on Clean Development and Climate (APP) (July 2005)
Multi-country (Australia, India, Japan, China, South Korea, and Canada)
Negotiations None No
Japanese Voluntary Emission Trading Scheme (May 2005)
National, select industrial and power generation facilities
In force Pledged voluntary commitments
No
Swiss Emissions Trading Scheme and Tax (January 2008)
National, multi-sector In force 10 % below 1990 by 2012
Yes
Australia Emission Reduction Trading Scheme (November 2007)
National, multi-sector Design and development
60% of 2000 by 2050
Yes
New Zealand Trading Scheme (September 2007)
National, multi-sector Design and development
Unclear Yes
New South Wales Abatement Scheme (January 2003)
Provincial, electricity sector
In force 5% below 1990 by 2012
Yes
1.2 Energy-Economy Models
Energy-economy models are used to investigate the relationship between
an energy system and its economy. The majority of models used for climate
change policy analysis can be categorized into two conceptual frameworks: top-
down and bottom-up.
Top-down models simulate the impacts of policies on the aggregate
indicators of the economy, such as GDP, prices, investment, employment and
trade. One type of top-down model used for climate policy analysis is the
computable general equilibrium model (Hourcade et al., 2006). In these models,
flows of production (inputs and outputs) are tracked between households and
firms. These flows are described by utility and production functions, which define
the consumption and production behaviour of the economy. In each simulation
3 Unless specified, all targets are to be met by the end of the first Kyoto commitment period,
2012.
4
period, the economy is assumed to reach an equilibrium state where supply
equals demand and utility and production functions are maximized.
Top-down models are most appropriate for analyzing the economic
impacts of policies. The strength of the top-down model is its representation of
microeconomic and macroeconomic feedbacks, and its representation of a
diverse group of decision makers. However, top-down models lack the
technological detail necessary to reflect the full suite of technological options
available to decision makers. In top-down models, technologies are represented
implicitly with elasticities of substitution and autonomous energy efficiency
improvement variables derived from historical data. Thus, top-down models are
not responsive to future technological innovation that might alter these
relationships. Additionally, top-down models assume efficient resource allocation
at market equilibrium, leaving little room for cost effective abatement activity.
Given these two limitations, top-down models often overestimate the costs
associated with enforcing climate change policies.
The second major approach is the bottom-up model. Bottom-up models
are characterized by technology detail. These models describe the economy in
terms of its demand for energy services. In the model, suites of technologies,
capable of providing these energy services, are from selected to fulfil demand.
Technology selection is driven by the apparent financial costs of individual
technologies. In a conventional bottom-up method, the model may focus on
technologies that minimize the financial cost of providing energy services to the
economy.
Bottom-up models describe the technological changes in an economy that
could result from policy implementation. Bottom-up models are rich in
technological detail and are able to provide estimates of technology diffusion
within an economy. Given this capability, bottom-up models can depict changes
in fuel choice, efficiency and emission intensity generated by policies. A major
limitation of the bottom-up model is its reliance on financial costs, such as
equipment and maintenance costs. Consequently, the model ignores important
non-financial costs that represent real-world differences in consumer behaviour,
5
such as brand loyalty or lack of product information. Additionally, bottom-up
models do not account for the macroeconomic changes that accompany policy
implementation. Therefore, bottom-up models often produce low cost estimates
for emission abatement activity.
Model Comparison
Both frameworks, although effective in their specific domains, possess
limitations that prevent them from being comprehensive tools capable of
examining a wide spectrum of policy options. A more comprehensive approach is
likely to incorporate three important modelling characteristics: technological
explicitness, behaviour realism, and equilibrium feedbacks:
1. Technological explicitness– the level of detail used to describe individual
technologies (i.e., energy intensity factors, emissions produced or financial
costs).
2. Behavioural realism– the diverse nature of consumer and firm preferences
(i.e., the risks associated with purchasing new technologies).
3. Equilibrium Feedbacks– the equilibrium effects of a policy (i.e., a carbon
tax that affects the price of goods, market demand and capital investment
flows).
Top-down models lack technological explicitness and, to some extent,
behavioural realism. Conversely, bottom-up models lack both behavioural
realism and equilibrium feedbacks.
1.2.1 Hybrid Models
Hybrid models address the limitations of both the top-down and bottom-up
frameworks by seeking to maximize all three characteristics of effective
modelling. To date, most hybrid modelling efforts represent varying levels of
integration between top-down and bottom-up models.
Fig 3
6
1.2.2 CIMS: An Integrated Framework
CIMS is an integrated energy-economy model that simulates the
interaction of energy supply and demand, as well as the macroeconomic
performance of key sectors in the economy (Rivers & Jaccard, 2005). The
model’s framework is biased toward the major energy supply and demand
sectors of an economy. CIMS differs from most attempts at hybrid modelling
because it explicitly incorporates empirically estimated behavioural parameters
into its modelling framework. A fundamental component of CIMS is its ability to
endogenously model the evolution of technological change over time. This
function allows CIMS to simulate fuel and technology choices that accompany
capital stock turnover.
1.3 CIMS-Global
Until recently, the CIMS framework housed three distinct national models
for Canada, the US, and China. Through the collaboration of four researchers in
the Energy and Materials Research Group at Simon Fraser University, the scope
of the CIMS framework has been expanded to cover the entire globe. The
aggregation of CIMS-Global is primarily based on the regional structure of the
Global Multi-regional MARKAL model developed by the Paul Scherrer Institute
(Rafaj et al., 2006). CIMS-Global differs from the Global MARKAL model’s five-
region aggregation in that CIMS-Global has country specific models for Canada,
the US, and China. CIMS-Global is divided into seven distinct regional models
(see Table 2). At present, the models are not linked with one another. It is
expected that the linkage of these models will follow the completion of all seven
regional models. With its comprehensive framework, the CIMS model is well
positioned to inform international policies and negotiations.
7
Table 2: Regional aggregation of CIMS-Global
Economic Regions CIMS-Global Regions
OECD (2000) Canada US OECD Europe, Pacific4 and Mexico (OECD-EPM)
Transitioning Economies Non-OECD Europe and the Former Soviet Union (TE)
Developing Economies China Developing Asia (DA) Africa, the Middle East and Latin America (AMELA)
1.3.1 Research Objectives and Questions
The aim of this study is to develop a hybrid energy-economy model for
OECD Europe, Pacific and Mexico using the CIMS framework. The research
objectives and questions guiding this study include:
Research objectives:
1. To accurately represent the future energy and GHG emission flows of
OECD Europe, Pacific and Mexico (OECD-EPM).
2. To create a single-region hybrid energy-economy simulation model that
includes technological explicitness, behavioural realism and equilibrium
feedbacks.
3. To create a regional model to be used in conjunction with other CIMS
models to complete a global CIMS model.
4. To compare and contrast the regional impacts of GHG abatement on
OECD-EPM with other regions.
Research questions:
1. What are the impacts of GHG abatement on the economy and energy
system of OECD-EPM?
4 OECD Pacific includes Australia, Japan, New Zealand and South Korea
8
2. What mix of technologies and fuels will be required to achieve this
abatement?
3. What price signal(s) will stimulate substantial GHG abatement in OECD-
EPM?
4. How do the marginal abatement costs of other regions differ from the
marginal abatement costs of OECD-EPM?
1.4 Structure of Report
This report focuses on one region of CIMS-Global: OECD-EPM. Chapter 2
examines the methodology of CIMS OECD-EPM, outlining details of model
structure, supporting data and model interpretation. Chapter 3 describes the
characteristics of OECD-EPM’s energy system, and highlights basic modelling
assumptions. Chapter 4 summarizes simulation results from the business-as-
usual and policy runs; this section also includes a brief discussion on model
sensitivity. Chapter 5 explores how the results from Chapter 4 compare to model
results from other CIMS regions; the chapter also includes a brief discussion on
modelling challenges. The paper concludes with a summary of the report,
followed by recommendations for further research (Chapter 6).
9
CHAPTER 2 METHODOLOGY
2.1 Introduction to the CIMS Framework
CIMS was developed by the Energy and Materials Research Group in the
School of Resource and Environmental Management at Simon Fraser University.
CIMS enables decision makers to explore how policies might change producers’
incentives and influence consumers’ preferences, with respect to technological
decision-making. The following section includes a brief overview of the structure
and function of CIMS.
2.1.1 Model Structure
CIMS is a technology-rich simulation model that focuses on the energy
service requirements of major energy supply and end-use demand sectors in the
economy. As illustrated in Figure 1, the three main components of the model are
the macroeconomic, the energy supply and the energy demand modules. The
modules are linked together in an integrated framework. Each module is
characterised by a distinct set of algorithms and functions that define how the
three modules interact. CIMS houses over 2800 technologies that compete to
provide energy services to all model sectors and sub-sectors. The organization of
this competition is determined by the service requirements of each sector. Table
3 displays a selection of the major service requirements in CIMS.
As illustrated in Figure 1, CIMS represents five energy supply sectors,
natural gas extraction, coal mining, oil extraction, oil refining and electricity
production (including renewable energy); and four end-use demand sectors,
residential, commercial, transportation and industry. The industrial sector is
comprised of seven sub-sectors: chemical products, industrial minerals, iron and
steel, metal smelting, mining, other manufacturing, and pulp and paper.The
energy service requirements of each sector drive total energy demand. Within
10
each sector, specific energy services are required to carry out the primary
functions of that sector. For example, the commercial sector requires lighting,
heating and air conditioning to function as effective retail and service outlets.
CIMS defines service nodes according to the energy demands they satisfy, such
as heated commercial floor space.
Figure 1: CIMS model structure
Energy Supply & Conversion
Model
NG, Oil & Coal Markets
Renewables Elec. Generation
Oil Refining NG Processing
Energy Demand Model
Residential Commercial
Industry Transportation
Macro-Economic Model
Demand Elasticities Employment Consumption Investment
Trade
Global Data
Structure
11
Table 3: Final and intermediate goods and services produced by the sector models
Sector Models Final and intermediate goods and services produced*
Commercial Refrigeration, cooking, hot water, plug load Transportation Marine, road, rail, single- and high-occupancy vehicles, public transit Residential Refrigeration, dishwashers, freezers, ranges, clothes washers/dryers Iron and Steel Slabs, blooms, billets Pulp and Paper Newsprint, linerboard, uncoated and coated paper, tissue , market pulp Metal Smelting Lead, copper, nickel, titanium, magnesium, zinc, aluminum Chemical Production
Chlor-alkali, sodium chlorate, hydrogen peroxide, ammonia, methanol, polymers
Mining Open-pit, underground, potash Industrial Minerals Cement, lime, glass, bricks
Other Manufacturing
Food, tobacco, beverages, rubber, plastics, leather, textiles, clothing, wood products, furniture, printing, machinery, transportation equipment, electrical, electronic equipment
Petroleum Refining Gasoline, diesel, kerosene, naptha, aviation fuel, petroleum coke Electricity Prod. electricity Natural Gas Production Natural gas, natural gas liquids Coal Mining Lignite, sub-bituminous, bituminous, anthracite coal Crude Extraction Oil Production
Source: Bataille et al. (2006) * Includes space heating and cooling, pumping, compression, conveyance, hot water, steam, air displacement and motor drive services, as applicable.
2.1.2 Model Sequencing
CIMS simulations occur in cycles of 5-year periods. Each simulation run
follows the following five steps:
1. Assessment of Demand: Exogenous forecasts of service demands
from both the energy demand and energy supply modules are
calculated.
2. Retirement: A portion of the technology stock from the previous
simulation run is retired according to an age-dependent function.
Residual capacity is compared with forecasted demand to
determine investments in new technology stock.
3. Competition for new demand: Perspective technologies compete to
gain market share in the acquisition of new stock. Technologies are
12
defined according to a variety of attributes, which include financial
and energy costs, as well as monetized behavioural costs and
benefits. The distribution of market share achieved by each
technology is a function of the market share algorithm as described
in section 2.1.3.
4. Equilibrium of supply and demand: Once forecasted demand has
been satisfied, the model iterates between the energy supply and
demand models until an equilibrium price is achieved.
5. Output: The model generates values for total energy consumption,
emission output, as well as policy and energy costs for each
simulation run. The scale of this output ranges from economy and
sector-wide to technology and production-specific. Differences
between businesses-as usual output and policy output reflect the
impacts of policy on the economy.
2.1.3 Market Share Algorithm
CIMS is a technology vintage model. CIMS tracks the evolution of
technological stock over time through retirements, retrofits and new purchases,
with consumers and producers making sequential decisions with limited foresight
about the future (Rivers & Jaccard, 2005). In each simulation period, CIMS
determines the amount of new stock required to meet energy demand in the
following simulation period. New stock requirements are equal to the energy
service demand forecasts of each sector plus retirement of old technologies,
minus existing stock. At each service node technologies compete for market
share. The allocation of market share for individual technologies is determined by
the slope of a logistical function, which compares the relative life-cycle costs
(LCC) of competing technologies. The formula used in CIMS to simulate this
competition is the market share algorithm:
13
Equation 1
K
k
v
k
v
j
j
LCC
LCCMS
1
where: MSj is the market share of technology j for new equipment stock, LCCj is the annualized life-cycle cost of technology j, v is the variance parameter, and k is the total number of technologies competing to meet service
demands. Life-cycle costs (Equation 2) are defined as annualized capital costs
(which include all financial and up-front intangible costs) divided by annual output,
plus energy and operating costs. The distribution of capital cost over the life of a
technology is determined by the discount rate and lifespan of the technology.
Equation 2
jj
j
njj
j ECOMSO
r
riCC
LCC11
)(
where: CCj is the capital costs of technology j, i is the intangible cost of technology j, SOj is the annual service output of technology j, OMj is the operating and maintenance costs of technology j , ECj is the energy costs of technology j, r is the discount rate, and n is the lifespan of the technology.
14
2.1.4 Behavioural Parameters
CIMS uses three distinct parameters to simulate the behaviour of
consumers and firms: intangible cost (i), private discount rate (r) and market
heterogeneity (v). The i parameter captures the non-cost attributes of
technologies. This parameter represents perceived costs and benefits,
highlighting differences in technologies that provide the same service. For
example, compact fluorescent light bulbs carry a positive intangible cost because
some people may find that the light they produce is unattractive (Rivers et al.,
2003). Even though the compact fluorescents provide lighting services at lower
energy costs, conventional light bulbs are often favoured because the issues
described above present a competitive disadvantage.
The r parameter, or the discount rate, represents the time preference of
decision makers when purchasing technologies. In other words, the discount rate
represents a decision marker’s preference for consumption in one period relative
to later periods. Consumers and firms have very distinct discount rates and thus
it is important to represent these differences in the modelling structure. In CIMS, r
values for consumers range from 30% to 65%, and from 20% to 50% for firms.
All technologies that provide the same service have equivalent r values.
However, r values differ among service categories (service nodes) to
characterize who is making the purchase decision and the type of service being
demanded. Values for both i and r are derived from primary and secondary
research (discrete choice models and consumer behaviour reports). Table 4
shows the complete range of discount rates used in the model.
15
Table 4: Default discount rate in CIMS
Sector Technology Discount rate %
Commercial Building HVACs 20 Cogeneration
Other 25 30
Residential Space heat/shell 35 Other appliances 35 Refrigeration 65 Industrial Process 35 Auxiliary 50 Electricity Generation 20 Transportation Private vehicle 30 Buses outside urban areas 12.5 Urban public transit 8 Source: Batille, 2005
As mentioned above, the penetration of one technology (j) relative to all
other technologies (k) is dependent on the value of the v parameter, the market
heterogeneity parameter. When v has a larger value, for example 100, the
technology with the lowest life-cycle cost will capture almost the entire market
share, reflecting a homogenous market. This functional relationship is most
similar to that of a traditional linear programming optimization model where
cheaper technologies capture 100% of the market share. Conversely, in a very
heterogeneous market (v=1), the distribution of market share is almost
insensitive to differences in life-cycle costs. As a result, market share is evenly
distributed among competing technologies. Therefore, the distribution of market
share becomes less sensitive to relative life-cycle costs as v values decrease.
Empirical analysis reveals that consumers are fairly heterogeneous. To reflect
this heterogeneity, CIMS uses a v equal to 10 as its default value. With a v equal
to 10, a technology that has a competitive advantage of 15% -- in terms of a
lower life-cycle cost relative to the life-cycle costs of all other competing
technologies -- will capture about 85% of market share. CIMS OECD-EPM uses
the CIMS default value.
16
2.1.5 Endogenous Technological Change
Technological change, defined as the ratio of inputs to outputs of
technologies, has the potential to lower the cost of GHG reductions through
innovations and improvements in efficiency (Löschel, 2002). Until recently, most
models treated technological change as an exogenous factor, assuming a
prescribed rate of efficiency/innovation over time. However, empirical evidence
has revealed that technological change is intrinsically linked to market factors,
such as production, diffusion, research and development (Löschel, 2002).
Policies that motivate changes in these market factors may increase the impact
of climate change policy.
CIMS utilizes two functions to simulate endogenous technological change:
declining capital cost and declining intangible costs. The declining capital cost
function, also known as learning by doing, describes the relationship between
financial costs in future simulation periods and cumulative production (Jaccard,
2005). In CIMS, the declining capital cost function is described by the progress
ratio, which is a prescribed decrease in capital costs associated with a doubling
of production. For example, a progress ratio of 0.8 means that the cost of new
capital stock will decrease by 20% following a doubling of cumulative production.
The relationship between long-term costs and cumulative production has been
well documented in modelling literature (Jaccard, 2005; Kouvaritakis et al., 2000;
McDonald & Schrattenholzer, 2001). Typical progress ratio values range from
0.75 to 0.95 (Jaccard, 2005). The declining intangible cost function represents
the neighbour effect. It describes the relationship between a technology’s market
share in a previous period and its intangible cost in a given period. Essentially,
intangible costs decline as a technology gains market share because it is
assumed that enhanced availability of information and decreased perception of
risk accompany technological diffusion (Jaccard, 2005). CIMS defines the
neighbour effect as the percentage reduction in intangible costs associated with
a percentage change in market share. Unlike the progress ratio, the neighbour
parameter has received less empirical investigation. The values used in CIMS
17
are based on a composite of discrete choice model studies, which quantify the
trade-offs consumers make when purchasing technologies (Axsen, 2006).
2.2 Supporting Data
In the model, OECD Europe, Pacific and Mexico are treated as a single
region, forming OECD-EPM. The aggregation of these regions is based on
membership in the Organization of Economic Co-operation and Development
(OECD).5 Data gathered for this model have been aggregated to fit this one-
region structure. The model is calibrated to 2005 IEA energy consumption data
and runs out to 2050 (IEA 2007d, IEA 2008a).6
The model runs in an integrated fashion to balance energy supply and
demand. Many exogenous parameters, which represent key modelling
assumptions, shape the expression of endogenous model functions and
simulation results. The major assumptions embedded in CIMS OECD-EPM
include consumer behaviour, discount rates, technology characteristics,
demographics, demand forecasts and energy prices. The following section
addresses the primary assumptions of this model.
2.3 Empirical Basis for Parameter Values
2.3.1 Technology Parameters
Technical and market literature provide the basis for the technological
parameter values used in the model. Basic technology-specific data, such as
efficiencies, financial costs, unit sizes and technology lifespans, are based on
Canadian data. Since the technologies used in energy intensive industries are
quite standardized across the globe, Canadian data are used as a proxy for
OECD-EPM data. Where significant differences existed, parameter values were
adjusted to reflect regional specifications.
5 The OECD is an organization that unites countries committed to a market-based economy and
democracy, to facilitate the exchange of information and policy expertise. 6 The model simulates from 2000-2050, running in 5-year increments.
18
2.3.2 Behavioural Parameters
Behaviour parameters i, r and v are estimated from a comprehensive
review of literature, meta-analysis, discrete-choice surveys and expert opinion
(Axsen, 2006; Rivers & Jaccard 2005). CIMS OCED-EPM uses the default
values of CIMS. Adjustments were made to the i parameter where appropriate.
2.3.3 Macroeconomic Feedback Parameters
CIMS uses own price elasticities and cross-price elasticities to describe
how changes in the cost of producing goods and service affect demand and the
balance of traded goods, respectively. The cross price elasticity for traded goods
uses an Armington specification to describe how the volume of traded goods
changes in response to the relative price differences between domestic and
international goods. The Armington specification assumes that a minimum
portion of domestic goods are inelastic to price changes. Price elasticities in the
model are set to CIMS default values. These values are empirically supported by
Bataille (2005).
2.4 Critical Assumptions
2.4.1 Population and GDP Forecasts
According to the International Energy Agency, OCED- EPM had a
population of 843 million and a GDP of $18,794 billion (2005 USD) in 2005 (IEA,
2008a). Figure 2 displays the population and GDP growth forecasts used in
CIMS OECD-EPM. The region’s population is expected to experience slow
growth until 2030. For the remainder of the simulation period (2030-2050)
population growth is expected to decrease (EC, 2006). The rate of economic
growth is expected to maintain relatively steady growth out to 2050, with an
average increase of 2% per year (EIA, 2007).
19
Figure 2: Population and growth assumptions for 2010-2015
Source: Projected population growth based on growth rate assumptions from the World Energy Technology Outlook (EC, 2006). Projected GDP growth based on assumptions from the 2007 International Energy Outlook (2007b).
2.4.2 Demand Forecasts
Demand projections originate from a varied mix of sources, most of which
provide forecasts out to 2030. Where 2050 forecasts are not available, growth
estimates for 2030-2050 are extrapolated. The primary sources of forecast data
include the International Energy Agency, European Commissions, Energy
Information Association, US Geological Survey and Euromonitor (see Appendix
2).
2.4.3 Climate Policy
In the business-as-usual run, no existing climate policies are applied to the
model. The effects of current national policies are implicitly included to the extent
that they have been considered in exogenous forecast data. In this report, policy
runs are restricted to emission charges.
0
50
100
150
200
250
300
350
400
2010 2015 2020 2025 2030 2035 2040 2045 2050
Ind
ex (
2005=
100%
)
Population GDP
20
2.4.4 Trade
Intraregional trade is included in the model insofar as it is reflected in input
data. Interregional trade is represented by CIMS’s default trade elasticities,
whereby OECD-EPM is assumed to be trading with an aggregated rest of the
word economy. For computational reasons, this function is turned off in most
policy runs.
2.5 Analysis
2.5.1 BAU Forecast
Establishing a business as usual forecast (BAU) is an essential element in
any form of policy analysis. The BAU forecasts describe the trajectory a system
may pursue in the absence of policy. With respect to energy-economy modelling,
the BAU describes the economic and environmental state of a system, given
assumptions about economic growth, population, energy prices and
technological development, in the absence of government intervention that alters
underlying energy trends (OECD, 2007). Common measures of BAU forecasts
include energy demand, GDP, GHG emission output and fuel consumption.
The BAU forecast is also an indicator of policy effectiveness, as it is the
scenario that all policies are evaluated against. A comparison between model
runs (BAU and policy simulations) indicates the potential responses that may be
expected in the real world from the policies examined (OECD, 2007).
2.5.2 GHG Abatement Pathways
In CIMS, the cost of a unit of emission reduction is simulated using GHG
emission charges. An emission change is the price associated with emitting one
unit of a pollutant -- in this case GHGs. This report explores the reduction
potential of OECD-EPM using various levels of emission charges. Figure 3
summarizes the emission pathways explored in this report. All emission
pathways begin in 2011 and extend out to 2050 (Chapters 4, and 5 discuss these
simulation results in further detail). Each pathway describes a linear progression
21
to a targeted emission charge maximum. By increasing at a rate consistent with
natural capital stock turnover, emission charges implemented in this fashion
circumvent large economic losses associated with the premature retirement of
equipment (Jaccard, 2007).
Figure 3: Policy runs: Emission price pathways
2.5.3 Marginal Abatement Costs
As mentioned in Chapter 1, many climate change policies have been
proposed by OECD countries, each with a unique policy framework. Regardless
of approach, all policies can be viewed through the lens of marginal abatement
costs. A marginal abatement cost is the cost incurred to abate the last unit of
emissions. In other words, it is the added cost of reducing emissions by one unit
(Field & Olewiler, 2005). In most energy-economy models, marginal abatement
costs are developed from emission charges associated with an emission
constraint (Klepper & Peterson, 2006). A summation of emission charges
associated with different levels of abatement, at one point in time, form the
marginal abatement cost curve. These curves can be used to compare one
region with another.
050
100150200250300350400450
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
$/t
CO
2e (
2005 U
SD
)
Simulation Year
22
2.5.4 Uncertainty
Models simulate current trends and anticipated developments based on a
limited understanding of how economic and social factors drive change (OECD,
2007). They are sensitive to uncertainty, as any unpredicted future changes can
significantly alter the validity of modelling output. Section 4.4 explores model
uncertainty in further detail. Table 5 lists the assumptions explored in chapter 4.
Table 5: Summary of sensitivity analysis in Section 4.4
Parameter Assumptions Tested
Demand sector growth Growth- high and low
Nuclear Nuclear program development- heavy restriction, no restriction
23
CHAPTER 3 OVERVIEW OF THE ENERGY SECTOR
As stated previously in Section 2.2, CIMS OECD-EPM treats OECD
Europe, Pacific and Mexico as a single region (see Appendix 1 for geographic
coverage). The base year energy data presented in the following sections
adheres to this aggregate structure. Table 6 shows the energy consumption
behaviour of both OECD Europe and OECD Pacific is quite similar in terms of
energy consumption per capita (TPES/Cap), and energy efficiency (TPES/GDP).
The energy consumption behaviour of Mexico, on the other hand, is similar to
OECD Europe and Pacific in terms of energy efficiency, but significantly different
in terms of energy consumption per capita. However, the relative contribution of
Mexico to total energy consumption in OECD-EPM is quite small -- approximately
6%.
Table 6: Comparison of regional energy consumption indicators (2005)
Region TPES/Cap (toe/population)
TPES/GDP PPP (toe/thousand 2000 USD)
OECD
Mexico 1.68 0.18
Pacific 4.4 0.17
Europe 3.5 0.15
World 1.78 0.21
Non-OECD 1.09 0.24
Source: IEA (2008a)
3.1 Energy Trends
OECD countries are the world’s largest consumers of energy, consuming
approximately 2.3 times more energy per capita than the global average (IEA,
2008). With only 13% of the world’s population, OECD-EPM produces a
24
disproportionate amount of energy and wealth, 26% and 46% respectively.7 The
region’s high level of energy consumption is linked to the characteristics of its
economic development: large industry and service sectors, high rates of vehicle
ownership, and almost 100% electrification. OECD-EPM is one of the most
energy-efficient regions in the word in terms of energy consumption per GDP
(IEA, 2007d). Because of advanced technological development in the industrial,
energy supply and end-use appliance sectors, the region consumed roughly 0.16
toe/GDP PPP8 (thousand 2000 USD) in 2005, significantly less than the world
average of 0.21, and even smaller than the Non-OECD average of 0.24.
In 2005, total primary energy supply and total final consumption were 122
EJ and 86 EJ, respectively (Figure 4). Despite the impacts of two major oil
shocks in the 1970’s and 1980’s, oil continues to provide the largest share of
energy supply to the region at 41%. The remaining conventional fuels -- natural
gas, coal and uranium (nuclear) -- account for an additional 52%, leaving
renewables with only 7%.
Figure 4: 2005 Total final consumption and total primary energy supply
Source: IEA (2007d)
7 Energy is measured in exajoules and wealth is measured in GDP PPP. 8 PPP is the acronym for Purchasing Power Parity, which is a currency conversion method that
equalizes the purchasing power of two or more currencies. PPP measures the purchasing power of per capita income in different countries.
Coal5%
RPP52%
Natural Gas19%
Combustible Renewables
4% Electricity20%
Coal18%
Oil 41%
Natural Gas21%
Nuclear13%
Hydro2%
Other Renewables
1%
Combustible Renewables
4%
25
3.2 Trade
OECD-EPM is a net importer of energy. Oil, natural gas, and coal
represent over 75% of total energy imports. Oil is the largest energy product
imported into the region. Although the OECD-EPM has major oil production
facilities in Norway, the UK and Mexico, consumption overwhelms domestic
supply, requiring the region to depend heavily on imports. France, Germany and
the Netherlands are the largest oil importers in the region (with supply originating
from the Middle East, North Africa and Russia). The second largest energy
import is natural gas (with supply originating from Algeria, the Middle East and
Russia). The International Energy Outlook (2007) projects an increase in the
import of fossil fuels over the next two decades. Given an expected decline in
domestic oil and natural gas production, the proportion of imports to domestic
production is projected to increase significantly between 2010 and 2030 (EIA,
2007).
3.3 Greenhouse Gases
Since 1990, global greenhouse gas (GHG) emissions have grown 29%,
largely driven by growth in non-OECD countries. Between 1990 and 2005, OECD
and Non-OECD regions experienced an increase in GHG emissions of 16% and
43% respectively (IEA, 2007a). However, cumulative CO2 emissions (1850-2002)
from the developed world (primarily members of the OECD) remain over 3 times
greater than that from the developing world (Baumert et al., 2005). Over the next
two decades the balance of GHG emissions output is likely to transfer from the
developed to the developing world.
In 2005, regional GHG emissions were 6,645 Mt -- primarily from the
combustion of fossil fuels (IEA, 2007a). The GHG contribution of each OECD-
EPM region is quite proportional to each region’s share of total energy
consumption. Since the mid 1990’s, GHG growth in all three regions has
stabilized at very low levels (Figure 5). In 2005, the largest producer of GHGs
was the electricity sector, accounting for 35% of total emissions in the region
(Table 7). Although emissions increased rapidly in OECD-EPM following the
26
industrial revolution, emissions growth has slowed considerably over the last
decade. Factors affecting this trend include technological development,
environmental regulations, improved agricultural practices and a shift to a service
based economy.
Business-as-usual (BAU) projections for GHG emissions over the
simulation period are quite diverse, ranging from an average growth of -1% to 1%
a year (EC, 2006; IEA 2004; IIASA, 2007). Since these projections rely heavily
on assumptions about drivers of GHG output like technological development and
deployment, GDP growth and environmental regulation, a diverse range of
projections are produced. For example, the World Energy Technology Outlook
(2006) assumes a moderate carbon tax in BAU, projecting a decline in emissions
over time. The World Energy Outlook (2006) assumes no carbon tax in BAU and,
as a result, projects an increase in emissions over time. A variety of assumptions
shapes the emission projections of CIMS OECD-EPM. Sections 2.4 and 3.3
outline some of the critical modelling assumption.
Figure 5: Historical CO2 emissions in OECD-EPM, 1971-2005
Source: IEA (2007a)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
19
71
19
75
19
80
19
85
19
90
19
95
20
00
20
02
20
3
20
04
20
05
Mt
C0
2
Mexico
OECD Pacific
OECD Europe
27
Table 7: GHG emissions by sector in 2005
Electricity Transport Industry & Commercial Residential
35% 24% 31% 10% Source: IEA (2007a)
3.4 Sources of Primary Energy
The composition of a region’s primary energy supply is a key driver in its
GHG emissions output. In the absence of abatement technologies, energy
consumption is positively correlated with GHG emissions. Policies aimed at
emission reduction can alter this relationship by stimulating energy efficiency,
fuel substitution and emission capture and storage.
As Table 8 shows, energy shares in OECD-EPM are quite similar to
shares in OECD Europe, Pacific and Mexico. Across all regions, fossil fuels
provide over 90% of total supply. Oil dominates each region with shares of 39%,
43% and 56% for OECD Europe, Pacific and Mexico, respectfully. This implies
that GHG reduction policies are likely to have similar effects on all regions.9 The
following section will provide further detail on the composition of energy supply in
the OECD-EPM region.
Table 8: Total primary energy supply, by region (2005)
OECD Region
Coal Oil Natural Gas
Nuclear Hydro Other Renewables
Combustible Renewables
Europe 17% 39% 24% 13% 2% 1% 4% Pacific 25% 43% 14% 14% 1% 1% 2% Mexico 5% 56% 27% 2% 1% 4% 5%
Source: IEA (2007d)
3.4.1 Oil
In 2005 regional energy supply was dominated by oil, accounting for 41%.
Domestic production (21 EJ) contributed approximately 40% of total regional
supply. Currently, crude oil production in OECD-EPM is approximately 8.6 million 9 Once again, Mexico differs slightly in terms of overall fuel composition in contrast to the other
two regions. However, these differences have a minimal impact on aggregate model results.
28
barrels per day -- about 15% of global production (IEA, 2006d). The majority of
this production is offshore extraction concentrated in the North Sea (Norway and
the UK) and the Gulf of Mexico. Combined, both extraction sites generate about
90% of total domestic production (EIA, 2007). Reserves in OECD-EPM are
estimated to be approximately 29 billion barrels, representing only 2.5% of global
oil reserves (PennWell Corporation, 2007). As production in the North Sea is
reported to have peaked in 2000, the bulk of future domestic production will have
to be supplied by other oil producers in the region (BP, 2008; EIA, 2007b).
Domestic production is projected to decrease approximately 2.5% per year
between 2005 and 2030 as regional reserves dwindle (IEA, 2006d). By 2050, the
majority of proven reserves are projected to be near depletion. Simultaneously,
demand for oil is projected to grow over the simulation period, putting even
greater emphasis on oil imports. Figure 6 illustrates IIASA’s projections for
remaining resources under three different resource-use efficiency scenarios
(IIASA, 2007). The figure shows that all three scenarios project resources
exhaustion or near exhaustion by the end of the century.
Figure 6: Estimate of remaining oil resources under three distinct resource-use efficiency scenarios
Source: IIASA (2007)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
EJ (
00
0)
Senario 1 (low)
Scenario 2 (meduim)
Senario 3 (high)
29
3.4.2 Natural Gas
In 2005, natural gas accounted for 21% of primary energy. In the same
year, the region produced 14 EJ of natural gas, representing only 15% of global
production (IEA, 2007d). Norway, UK, Netherlands, Mexico and Australia are the
top producers, generating 85% of total natural gas supply in OECD-EPM (IEA,
2007c). Despite domestic production, high demand for natural gas in the region
facilitated the import of 12 EJ of natural gas in 2005. Regional reserves are
estimated to be between 6,300 and 9,000 billion cubic meters; the largest
uncertainty associated with reserves in OECD Pacific (EIA, 2007). At current
production levels, these reserves are projected to last for approximately 50 years
(BP, 2008).
The World Energy Outlook forecasts that demand for natural gas in
OECD-EPM will increase steadily from 2005 to 2030 (IEA, 2006c). In response to
this demand, production in Australia and New Zealand is projected to increase
4.3% a year from 2004 to 2030 (EIA, 2007). However, large production declines
in Europe are projected to overwhelm anticipated growth, deepening the region’s
reliance on natural gas imports (IEA, 2006b).
3.4.3 Coal
OECD-EPM represents some of the largest producers and consumers of
coal in the OECD. In 2004, Australia was the world’s leading coal exporter, while
Japan and South Korea were the world’s leading importers (EIA, 2007). The top
coal producers in the region are Australia, Germany, Spain, the UK and Poland.
In 2004, OECD-EPM produced 1003 million tonnes of coal, approximately 18%
of the world’s total coal supply (WEO, 2006). In 2005, coal accounted for 18% of
total primary energy supply. In that year, demand for coal was slightly greater
than domestic supply, requiring the import of 14 EJ coal (IEA, 2007b).10
Reserves in the region are abundant, estimated at 909 billion tonnes
(approximately 13% of global reserves). Consequently, the reserve-to-production
10 Import values include intraregional trade within OECD Europe and Pacific, but may exclude
trade between these regions.
30
ratio is high, estimated at 164 years (BP, 2008).11Demand for coal is projected to
grow over the simulation period for the following reasons: anticipated increases
in oil and natural gas prices, concerns over energy security, and the development
of clean coal technology. The IEA projects that future demand will stimulate
growth in domestic production, reducing the region’s reliance on imports (IEA,
2006c).
3.4.4 Electricity
Electricity consumption accounted for 20% of total final energy
consumption in 2005 (IEA, 2008a). OECD-EPM’s electricity sector is
characterized by high efficiency, and low transmission losses, which have been
estimated at 6.9% -- significantly lower than losses reported in non-OECD
regions (IEA, 2007d). In fact, due to large investments in technological
development, conversion efficiencies in OECD-EPM are some of the highest in
the world (IEA, 2007). Additionally, the portion of primary energy supply used for
electricity production, relative to other energy uses, has increased rapidly over
the last two decades, as the region’s economy has transitioned from an industrial
to an electricity-intensive, service based economy (IEA, 2007d). Table 9 shows
that the majority of electricity generation is produced from coal, nuclear and
natural gas.
Over the last three decades, the fuel composition of electricity production
has changed markedly: a rapid decline in oil and an equally rapid rise in nuclear
and natural gas (EIA, 2007). In 2005, oil’s share of electricity production was only
7%. The remaining conventional fuels (coal, natural gas and nuclear), accounted
for over 75% of electricity generation in 2005: 30%, 21%, and 26%, respectively,
for coal, natural gas and nuclear. Electricity generation from renewable energy
was 16% in 2005. The largest renewable energy source was hydro, at 12% of
total production. Other renewable energy sources, which include combustible
11 The reserve-to-production ratio measures the number of years that reserves will last if current
rates of production are maintained (WRI, 2008).
31
renewables, solar, wind, geothermal and tidal, generated only 4% of total
production.
Table 9: Electricity production by fuel (2005)
Conventional Renewables
Coal Oil Natural Gas
Nuclear Hydro Combustible Renewables
Other Renewables
30% 7% 21% 26% 12% 2% 2%
Source: IEA (2007c)
3.4.5 Nuclear
After the initial oil shock in the 1970’s, OECD countries turned to nuclear
power as a solution to dependence on oil imports. Between 1971and 1990, the
production of nuclear energy increased an average of 16% per year; however,
since the 1990’s, growth has slowed to approximately 1.8% a year (IEA, 2007).
In 2005, nuclear energy represented 13% of primary energy supply in the region.
The region’s production of nuclear energy is concentrated in four countries:
France, Germany, Japan and South Korea. Projections of future nuclear capacity
are modest amid fears of nuclear weapon proliferation and environmentally
driven moratoriums.12 Both the World Energy Outlook (2006) and the
International Energy Outlook (2007) project less than 20% growth in additional
nuclear capacity from 2005 to 2030, while less conservative studies, such as the
World Energy Technology Outlook (2006) project growth slightly above 20% over
the same period. The assumptions of the aforementioned studies have been
considered in CIMS BAU forecast. As illustrated in Figure 7, the model is
constrained so that the production of nuclear energy does not exceed World
Energy Outlook and International Energy Outlook projections by more than 6% in
2030.
12 Several countries in OECD Europe have proposed policies to phase out nuclear over the next
two decades (EIA, 2007).
32
Figure 7: Comparison of BAU nuclear energy production forecasts
Sources: EIA (2007) & IEA (2006c)
3.4.6 Renewables
In 2005, total primary energy supply of renewable energy was 5.3 EJ.
Figure 8 describes the composition of total renewable energy supply in all three
regions of OECD-EPM. In 2005, Iceland, Norway and New Zealand had the
highest shares of renewables in their total energy supply, at 73%, 40%, and 29%,
respectively (EIA, 2007). Over the last decade there has been significant growth
in renewable energy in OECD countries; the most notable growth being solar and
wind energy in Europe (IEA, 2007e).
0
2
4
6
8
10
12
14
16
18
20
2000 2005 2010 2015 2020 2025 2030 2035
EJ
WEO
EIA
CIMS
33
Figure 8 Composition of renewable energy supply in 2005, by region
Source: IEA (2007e)
3.4.6.1 Hydro
In 2005, approximately 2.3 EJ of hydropower was produced in OECD-
EPM. The region’s largest hydro producer is Norway, which is also the third
largest producer of hydropower in the world. As the majority of feasible sites in
the region have been exploited, growth potential is forecasted to be minimal.
Future growth is projected to be driven by small-scale and mini hydro projects
(IEA, 2006c; Lauzon et al., 2007).
3.4.6.2 Combustible Renewables
Combustible renewables dominate renewable energy supply in the region,
contributing approximately 60% of total renewable energy supply in 2005.
Combustible renewable energy includes wood, wood waste and other forms of
waste products (gas, liquids, and solids); municipal waste, along with liquid and
gas biomass, are the most common sources in the region (IEA, 2007e).
Consumption of combustible renewables is projected to decrease over the study
period as other renewables gain market share (EIA, 2007).
0
1
2
3
4
5
6
Mexico OECD Europe OECD Pacific
EJ
Combustibles
Geothermal
Solar/Tidal
Wind
Hydro
34
3.4.6.3 Other Renewables
Other renewables include solar, wind and geothermal energy. In 2005,
primary energy for other renewables was 1.2 EJ. Although other renewables only
accounted for 1% of total supply in 2005, shares of other renewables are
projected to increase significantly over the study period due to market stimulation
policies in the EU, Australia, New Zealand and Japan (EIA, 2007).
Wind
Seven of the ten largest markets for wind-powered electricity generation
are OECD-EPM countries, which produced 65% of global installed capacity in
2006 (EIA, 2007). Because of government policies, wind energy is the fastest-
growing renewable energy source in the region (GWEC, 2006). In 2005, installed
capacity in OECD-EPM was 41,872 MW (BP, 2008). The region has some of the
best wind resources in the world, particularly in Australia and Mexico where total
potential is estimated to be over 3,000 MW and 141 MW respectively (GWEC,
2006). The Global Wind Energy Association projects a marked increase in the
generation of wind energy in the region over the study period.
Solar
Despite experiencing significant growth from 2000 to 2005, solar energy
contributed the least to renewable energy supply in 2005 (Figure 8). In that year,
Japan and Germany were the world’s leaders in solar energy, in terms of growth
and capacity (BP, 2008). Solar energy is projected to experience significant
growth over the next three decades as technological costs decline and
governments implement market stimulus policies.
Geothermal
Geothermal energy is the second largest renewable energy source in the
region. In 2005, 0.82 EJ of geothermal energy was produced. In the same period,
installed capacity was 2,982 MW (BP, 2008). Ninety percent of the region’s
geothermal energy is produced in Mexico, Italy, Japan, Iceland and New
35
Zealand. Growth of large-scale geothermal energy is expected to be low over the
study period due to financial, technological and geological limitations (IEA,
2008c; Lauzon et al., 2007). Geothermal activity has been constrained in the
model to reflect these limitations.
3.5 Total Final Consumption
Total final consumption in 2005 was 86 EJ. Figure 9 illustrates the
distribution of total final consumption across all energy demand sectors in the
region.
Figure 9: Total final consumption+ by fuel and sector
Source: IEA (2008a) *Other sectors include agriculture, fishing and other non-specified commercial activities. +This calculation does not include heat, non-energy fuel consumption and energy used in the transformation sector.
3.5.1 Industrial Sector
In 2005, industry’s share of consumption was 28%, making it the largest
energy-consuming sector in the region. In that year, electricity and natural gas
provided over half of the energy consumed in the sector. Aside from the general
manufacturing sub-sector, the iron and steel sub-sector was the largest industrial
consumer of energy in the region.
0
5
10
15
20
25
30
Industry Transport Other* Residential Commercial and Public Services
EJ
Renewables
Electricty
Gas
RPP
Coal
36
In CIMS, total industrial sector energy demand represents the energy
service requirements of seven sub-sectors: chemical products, industrial
minerals, iron and steel, metal smelting, mining, other manufacturing and pulp
and paper. In addition to these sub-sectors, the energy demands of the energy
supply sectors are also included in the final calculation of total sector
consumption.13 In each sub-model, energy demand is driven by either physical or
monetary output; for example, energy demand in the iron and steel sub-sector is
driven by million tonnes of steel produced.
Industrial output is projected to grow at an average rate of 1.63% per year
over the study period, slightly lower than projected increases in GDP (EC, 2006;
EIA, 2007; IIASA, 2007). This trend is characterized by declining growth in the
industrial minerals and crude extraction sub-sectors, and high growth in the
manufacturing, pulp and paper, and metal smelting sub-sectors. The US
Geological Survey (2005), UNIDO International Yearbook of Industrial Statistics
(2002), and the Global Market Information Database (2008) were the primary
sources used to inform the production forecasts for all sub-sectors. Figure 10
depicts the output forecasts for each sub-sector over the simulation period.
Despite producing diverse products, a set of common services define the
energy needs of all sub-sectors: compression, conveyance, machine and direct
drive, lighting, space conditioning and industry specific equipment services. A
variety of additional sources were used to detail the specific energy consumption
characteristics of each sector. For a complete list of these sources, see
Appendix 2.
13 The energy supply sectors included in this calculation are coal mining, crude extraction, crude
refining and natural gas extraction. Electricity is not included in this calculation, as its fuel consumption is reported separately.
37
Figure 10: Industrial sub-sector output growth forecast
3.5.2 Transportation Sector
The transportation sector was the second largest energy demand sector in
2005, consuming approximately 25 EJ, primarily refined petroleum products.
CIMS OECD-EPM’s transportation model is based on the IEA/SMP
transportation model (IEA/WBCSD, 2004).14Data from the IEA/SMP provides the
basis for all forecasts and assumptions.
The CIMS OECD-EPM transportation model is defined by passenger
demand for personal vehicle, rail, bus and air services, as well as freight demand
for rail, truck and marine services. Within each service node both traditional and
low-emission technologies, such as hydrogen-powered buses and plug-in electric
cars, compete to fulfil service demand.
Transportation demand, in terms of personal kilometres travelled and
tonnes kilometres travelled, is projected to increase minimally over the study
period: on average 0.8% a year (Table 10). Figure 11 illustrates the
transportation demand forecast by mode from 2000 to 2050. Due to vehicle
14
The IEA/SMP model was developed by the IEA in conjunction with the World Business Council for Sustainable Development in 2004. A detailed explanation of this model is available on-line at: http://www.wbcsd.org.
80
100
120
140
2000 2010 2020 2030 2040 2050 2060
Gro
wth
Ind
ex
(20
05
=10
0)
Chemical Products
Industrial Minerals
Iron and Steel
Metal Smelting
Mineral Mining
Paper Manufacturing
Other Manufacturing
Petroleum Refining
Petroleum Crude Extraction
Natural Gas Extraction
Coal Mining
38
ownership saturation and declining population growth, growth in personal
transportation demand is expected to peak in 2010; after 2010, growth is
expected to slow, eventually stabilizing in 2035. Demand for air and rail travel is
the primary driver of growth in personal transportation over the study period, as
demand for personal vehicles is expected to stabilize by 2020. Growth in freight
transportation is driven by increasing demand for marine travel, while rail and
truck demand are expected to remain fairly stable throughout the study period.
Table 10: Annual growth in transportation demand
2005 2010 2015 2020 2025 2030 2035 2040 2045
Annual Growth
0.92% 1.13% 0.99% 0.87% 0.77% 0.72% 0.56% 0.56% 0.56%
Source: IEA/WBCSD (2004)
Figure 11: Transportation demand forecast, by mode
Source: IEA/WBCSD (2004)
3.5.3 Residential Sector
Total final energy consumption in the residential sector was approximately
18 EJ in 2005. Gas, followed by electricity and oil supplied over 80% of the
0
1
2
3
4
5
6
7
8
9
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Trill
ion
Pas
sen
ger
and
To
nn
e K
ilom
eter
Tra
velle
d
Personal Vehicles Bus Rail Pers Air Trucks Marine Rail Frt
39
energy consumed in the sector. Housing stock, defined as number of
dwellings/households, drives demand in the residential sector. In CIMS, housing
stock is categorized into shells, which are characterized by occupancy and floor
space. Each shell has specific service requirements that include space
conditioning, lighting, water heating and powering appliances. Housing stock and
appliance usage data were obtained from the Global Market Information
Database (2008) and United Nations Bulletin of Housing Statistics (2002). Like
CIMS-Canada, there is no distinction between rural and urban households
because it is assumed that most rural dwellings in the region have similar energy
service requirements as urban households.
Due to declining growth in population forecasted over the study period,
housing stock is projected to experience nominal growth. As illustrated in Figure
12, from 2005 to 2020 housing stock increases on average 1% a year; after
2020, housing stock experiences minimal growth and stabilises at 0.8% a year.
Adoption rates of major appliances experience a similar stabilization in 2020, as
most households are assumed to have reached 100% saturation.
Figure 12: Historical and forecasted housing stock
Source: Actual housing stock based on EI (2008). Forecasts based ENRA (2006) and EC (2006)
0
50000
100000
150000
200000
250000
300000
350000
400000
2000 2010 2020 2030 2040 2050
Ho
use
ho
lds
(00
0)
Actual Forecast
40
3.5.4 Commercial Sector
The commercial sector accounted for 12% of total final consumption in
2005. Electricity, followed by natural gas and oil supplied the majority of energy
demanded. Service requirements in the commercial sector are heating, cooling,
lighting, cooking and refrigeration. Demand for energy in the commercial sector is
driven by cubic meters of commercial floor space. Floor space estimates were
calculated using commercial sector data from CIMS US adjusted with a scalar.15
In CIMS, total floor space is distributed among several building types
representative of the economy’s service industry: retail, educational, hospitality
and healthcare facilities. Data informing this distribution were derived from
Australian and European building studies (McLennan, 2000; Pink, 2008; UNEP,
2007).
Over the simulation period, commercial floor space is projected to
increase at an average rate of 2% per year (Figure 13). Growth projections are
based on a GDP growth factor derived from GDP growth projections of the World
Energy Technology Outlook and energy growth assumptions of the International
Energy Agency (EC, 2006; EIA, 2007).
15 The relative difference between average floor space of residential dwellings in OECD-EPM and
the US, as defined by the UNECE (2002).
41
Figure 13: Forecast of commercial floor space
Source: Floor space estimates based on UNECE (2002). Growth projects based on EIA (2007) and EC (2006).
3.6 Carbon Capture and Storage
As of 2005 only two major carbon capture and storage (CCS) facilities
were operating in the region, one in Norway and one in the Netherlands. The
Sleipner demonstration project in Norway was established in 1996 and stores
1Mt CO2 a year in deep saline aquifers (IEA, 2008c). The K12b project in the
Netherlands was developed in 1994 and stores 1.2 Mt CO2 a year.
According to the International Energy Agency, in absence of policy, CCS
potential in the OECD-EPM region is small, projected at maximum of 7.5 Mt CO2
a year. The greatest potential for CCS development is in OECD Europe. In
addition to its current projects, the region has plans to add an additional 2.5 Mt
CO2 of CCS capacity by the end of the decade. Potential for CCS development in
OECD Pacific is minimal: at most, 2 Mt CO2 a year (IEA, 2004). Currently there
is no CCS development in the region; however, two projects in Japan and
Australia are in the early stages of development. Table 11 outlines the maximum
regional potential for CCS projected for 2030 by the OECD/IEA (2004).
Technically, the region has enormous potential for enhanced coal bed methane
0
2000
4000
6000
8000
10000
12000
14000
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
mc
(mill
ion
s)
42
projects (ECBM) in Japan and Australia, and enhanced oil recovery (EOR) in the
North Sea (IEA, 2004). However, in the absence of policy, high development
costs and liability issues prevent the full technical potential of CCS from being
realized. In the BAU forecast, CCS has been constrained to reflect the current
and future potential of CCS within the region, as noted in the CO2 capture and
storage section of the Energy Technology Perspectives Report produced by the
International Energy Agency (2006a).
Table 11: Current, planned and potential CCS development, 2005-2030
Region Time Amount (Mt CO2/year) Type
Europe Current Planned Possible
2.2 2.5 2.2-5
Aquifers EOR Elec and EOR
Pacific Current Planned Possible
None ~1 1+
ECBM ECBM/Elec
Mexico Possible Unknown EOR
Source: IEA (2004) & IEA (2006a)
43
CHAPTER 4 SIMULATION RESULTS
The composition of a region’s energy supply influences the design and
effectiveness of climate change policies. OECD-EPM’s use of fossil fuels is the
key driver of its GHG emissions output. In the absence of abatement activities,
energy consumption in OECD-EPM will continue to be tied to increases in GHG
emissions. However, emission reduction policies can alter this relationship by
stimulating mitigation activities such as fuel switching, energy efficiency and
emission capture and storage.
This chapter presents the results from the BAU and policy runs. The
chapter begins with a discussion of model calibration in Section 4.1, followed by
a detailed analysis of the BAU forecast in Section 4.2. Section 4.3 examines the
impacts of carbon constraining policies on the region’s energy system. The
chapter concludes with a brief sensitivity analysis, examining the effects of
growth forecasts and nuclear constraints on modelling results.
4.1 Calibration of BAU Run
Modellers use calibration to test the validity of their models. Calibration
involves comparing model results to a set of acceptable criteria, which are
representative of the system being modelled. If model output is consistent with
the “acceptable criteria”, it is assumed that the model accurately represents the
characteristics of that system (Michigan Government Department of
Environmental Quality, 2008). CIMS OECD-EPM is calibrated to energy data
from the International Energy Agency (IEA). IEA Energy Balances 2004/2005
(2007d) serve as the “acceptable criteria” for the base year, 2005. IEA Energy
Balances data represents actual energy demand and supply activity in the
OCED-EPM region for that year. For subsequent simulation periods, data from
the World Energy Outlook (WEO) and the International Energy Outlook (IEO)
44
provide a benchmark for BAU simulations out to 2030. Table 12 compares CIMS
OECD-EPM BAU energy consumption to that of the previously mentioned
calibration and benchmark sources. In both 2005 and 2030, differences in total
energy consumption are approximately 2%. This small difference indicates model
consistency and validates CIMS OECD-EPM BAU simulation results. In 2030,
World Energy Outlook projections for total energy consumption in the residential
and commercial sectors differ significantly from projections for CIMS OECD-
EPM. The primary cause of this contrast is diverse assumptions about the future
service demands of these sectors. Given the assumptions used in CIMS OECD-
EPM, energy consumption projections appear to be more consistent with
projections from the International Energy Outlook. Thus, these data provide the
benchmark for forecasts in the residential and commercial sectors, instead of
World Energy Outlook data.
Table 12: Comparison of energy consumption and GHGs in 2005 and 2030, by sector
EJ
2005 IEA/SMP
CIMS
Difference
2030 WEO/IEO
CIMS
Difference
Residential and Services+ 26 26 0% 29+ 29 0% Industry 27 27 2% 35 36 3% Transport* 26* 28 4% 30 31 3% Total GHGs
80 6544
81 6548
2%
0%
95 7717
97 7445
2%
-4%
Sources: EIA (2007), IEA (2006c), IEA (2007d) & IEA/WBCSD (2004)
*Data from the Sustain Mobility Project are used to calibrate the transportation sector in base year.
+IEO 2007 is used as a benchmark for residential and commercial energy consumption forecasts.
4.2 Details of BAU
4.2.1 Total Energy Consumption
Consumption of primary and secondary energy in the BAU forecast
increases 39% over the simulation period, from 126 EJ in 2005 to 176 EJ in 2050
(Figure 14). Average annual growth from 2005 to 2030 is approximately 0.7%,
slightly less than the World Energy Outlook projections (0.8%). Renewables
experience the largest growth among all fuel types. Consumption of renewables
45
increases 134% over the simulation period, primarily from demand in the
electricity and industrial sectors. Despite this growth, fossil fuels continue to
dominate total energy consumption within the region. Electricity consumption
increases 56% over the simulation period, driven by demand growth in the
commercial and industrial sectors. In 2050, electricity accounts for 16% (28 EJ)
of total energy consumption.
Figure 14: Total primary and secondary energy consumption, by fuel
Table 13 shows the evolution of total primary energy consumption over
the simulation period. In 2050, BAU oil consumption is 55 EJ, 37% of total
primary energy supply. With increasing demand and decreasing domestic
production, oil imports in BAU are projected to increase at a rate of 13% per year
from 2005 to 2050. Coal consumption grows at an average rate of 0.82% a year
over the simulation period. In 2050, coal consumption is projected to account for
17% of total supply (25 EJ). Consumption of natural gas remains fairly constant
over the simulation period, growing only 19% from 2005 to 2050. In 2050, total
demand for natural gas is 29 EJ.
0
20
40
60
80
100
120
140
160
180
200
2005 2015 2025 2035 2045
EJ
Electricity
Other
Renewables
Nuclear
Coal
Natural Gas
Oil
46
Table 13: Total primary energy supply in 2005, 2030 and 2050, by fuel
2005
(EJ)
Share
(%)
2030
(EJ)
Share
(%)
2050
(EJ)
Share
(%)
2005-2030
Growth
2030-2050
Growth
Natural Gas 24 22 27 21 29 19 13% 5%
Coal 17 16 21 17 25 17 20% 19%
Oil 43 40 47 37 55 37 10% 16%
Nuclear 15 14 18 14 20 13 21% 8%
Renewables 9 8 14 11 20 13 62% 45%
Total 108 1 127 1 149 1
18% 17%
4.2.2 Total Final Energy Consumption
Figure 15 disaggregates total final energy consumption by sector and fuel
for 2005 and 2050. The industrial sector experiences the largest growth among
all sectors, with energy consumption rising from 27 EJ in 2005 to 46 EJ in 2050.
Demand in the chemical, pulp and paper, and metal smelting sub-sectors are the
primary drivers of this growth. The commercial sector experiences little
fluctuation in its share of energy consumption over the simulation period,
increasing from 10 EJ in 2005 to only 13 EJ in 2050 -- an annual increase of less
than 1%. Energy consumption in the residential and transportation sectors
experience moderate increases in consumption, rising from 17 EJ and 28 EJ in
2005 to 21 EJ and 34 EJ in 2050, respectively. Despite this growth, both sectors’
share of total final consumption drops from 13% and 22% in 2005 to 12% and
19% in 2050, for the residential and transportation sectors respectively.
47
Figure 15: Total final consumption in BAU, by sector and by fuel
Table 14 presents the evolution of fuel composition in each sector over the
simulation period. The fuel mix in the residential and commercial sectors
experiences dramatic changes over the simulation period. The most significant of
these changes is a reduction in natural gas consumption resulting from price
increases projected over the forecast period. Between 2005 and 2050, shares of
natural gas decrease from 35% to 32%, and 21% to 15% of total consumption, in
the residential and commercial sectors respectively. In these sectors, decreased
natural gas consumption is offset by an increase in electricity and refined
petroleum products (RPP) consumption. The drivers of these changes are the
technologies providing services to the residential and commercial sectors. In the
residential sector, increasing adoption of appliances contributes to a rise in
energy consumption; by 2050, most appliances have reached 100% saturation in
all households. As energy consumption increases, low- and standard-efficiency
technologies begin to lose market share to higher-efficiency technologies. For
example, low- and standard-efficiency dishwashers captured 77% of market
share in 2005, but only 27% of market share in 2050. The commercial sector, on
the other hand, does not experience significant adoption of higher-efficiency
0
5
10
15
20
25
30
35
40
45
50
Res Comm Ind Trans Res Comm Ind Trans
2005 2050
EJOther
Other Renewable
Ethanol
Biodiesel
Hydrogen
Nuclear
Elec
RPP
Coal
NG
48
technologies. The majority of services in the commercial sector are projected to
be provided by standard-efficiency technologies.
Table 14: Shares of total final consumption, by fuel and by sector
Fuel shifts in the industrial sector over the simulation period are small. The
most notable changes in this sector are a reduction in coal and an increase in
renewable energy. Shares of coal in the industrial sector decrease from 14% in
2005 to 9% in 2050 because of stagnant growth in the iron and steel sub-sector
and declining production in the industrial minerals sub-sector. Shares of
renewable energy double from 2005 to 2050, due to hog fuel and wood
consumption in the pulp and paper and other manufacturing sub-sectors. In
contrast to other sectors, the transportation sector experiences the least amount
of compositional change. Despite significant growth in electricity, biodiesel and
Year Share (% of total) 2005 Residential Commercial Industry Transportation Natural Gas 35 32 26 0 Coal 0 0 14 0 RPP 21 32 24 99 Electricity 38 36 29 0 Nuclear 0 0 0 0 Renewables 6 0 6 0 2050 Residential Commercial Industry Transportation Natural Gas 21 15 26 0 Coal 0 00 9 0 RPP 26 37 25 95 Electricity 42 48 26 2 Nuclear 0 0 0 0 Renewables 11 0 12 3 Change
Residential Commercial Industry Transportation
Natural Gas -14 -17 0 0
Coal 0 0 -5 0
RPP 5 5 1 -5
Electricity 4 12 -3 2
Nuclear 0 0 0 0
Renewables 5 0 6 3
49
ethanol, lower emission fuels represent only 5% of total consumption in the
transportation sector in 2050. Consequently, refined petroleum products continue
to dominate fuel consumption in the sector with shares of 99% and 95% in 2005
and 2050 respectively. In 2050, around 90% of personal vehicles are powered by
fossil fuels. Despite the reliance on fossil fuels, energy efficiency in the sector
increases significantly over the simulation period as hybrid and plug-in hybrid
vehicles gain market share over standard gasoline and diesel vehicles. In 2050,
shares of hybrid and plug-in hybrids are 14% and 13% of total personal vehicle
stock, respectively. Similar efficiency gains are experienced for all modes of
freight transportation. For example, high-efficiency trucks represent almost 50%
of total market share in 2050.
4.2.3 GHG Emissions
Over the simulation period, GHG emissions grow an average of 0.6% a
year, increasing from 6,548 Mt CO2e in 2005 to 8,570 Mt CO2e in 2050. This
growth is in sharp contrast to the annual growth rate projected for non-OECD
economies over the same period: between 2 and 4% (IIASA, 2007). These
economies are expected to experience higher growth in most energy-intensive
sectors than in the OECD-EPM region. As a result, the distribution of GHGs
across sectors in non-OECD economies is likely to change dramatically over the
21st century. However, a similar shift is unlikely to occur in OECD-EPM as the
region represents some fairly stable and mature economies. Figure 16 shows
that the distribution of GHGs remains reasonably constant over the simulation
period. The electricity, transportation and industrial sectors contribute relatively
equally to total GHG emissions, while the residential and commercial sectors
capture only a 15% share. The residential and commercial sectors experience
the lowest growth (in terms of Mt CO2e) over the simulation period, as gains in
energy efficiency reduce emissions associated with fuel consumption.
Consequently, its share of total GHG emissions falls to 13% in 2050.
50
Figure 16: Composition of GHG emission projections, by sector
4.2.4 Electricity Generation
Both the generation of electricity and the consumption of primary energy in
the electricity sector increase modestly from 2005 to 2030, at an average rate of
1.22% and 0.67% per year respectively. After 2030, electricity generation gains
momentum, increasing at an average rate of 1.34% a year out to 2050. During
the same period, growth in primary energy consumption is minimal relative to that
of generation. As a result, the amount of energy consumed per unit of electricity
generated decreases over time. As Table 15 shows, both the energy efficiency
and GHG intensity of the sector improve over the simulation period. The increase
in energy efficiency can be attributed to the natural evolution of capital stock
turnover, whereby old retiring conversion technologies are replaced by newer
and more efficient technologies. This efficiency combined with growth in nuclear
and renewable, produce a decrease in the carbon intensity of the sector over the
simulation period.
Table 15: Energy efficiency and GHG intensity in the electricity sector
2005 2020 2030 2040 2050
Efficiency (input-EJ/output)
2.60
2.42
2.32
2.27
2.17
GHG Intensity (Mt CO2e/EJ) 0.10 0.09 0.09 0.09 0.08
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2005 2030 2050
Electricity
Transportation
Industry
Residential and commercial
51
Figure 17 displays the composition of primary energy consumption in the
electricity sector from 2005 to 2050. Throughout this period, shares of coal,
natural gas and nuclear remain relatively stable. The most notable change in fuel
composition over the simulation period is an increase in renewable energy.
Renewable energy experiences the largest growth rate among all primarily fuels
consumed in the sector, increasing from 5.8 EJ in 2005 to 11.1 EJ in 2050. The
greatest contributor to this growth is wind energy, which, along with solar power
receives subsidies in many of the OECD-EPM countries. However, with higher
capital and operating costs, and smaller initial market shares, solar technology
experiences significantly less growth over the same period. As mentioned in
section 3.4.5, nuclear power is constrained to reflect anticipated phase-out
policies in certain OECD countries. Despite this constraint, nuclear energy
continues to be a significant source of electricity throughout the simulation
period.
Figure 17: Primary energy consumption in the electricity sector, 2005-2050
4.2.5 Intensity Trends
Table 16 show how the GHG intensity of OECD-EPM evolves from 2005
to 2050. GHG intensity, in terms of Mt CO2e per EJ of total primary and
0
10
20
30
40
50
60
70
2005 2015 2025 2035 2045
EJ
Renewables
Nuclear
Elec
Oil
Coal
Natural Gas
52
secondary energy consumption, decreases over the simulation period. In
response to the adoption of emission capture and storage, energy-efficient
technologies and fuel switching, the overall intensity of GHG declines over time.
Most sectors in the region experience constant or declining intensity values from
2005 to 2050. However, the industrial minerals and petroleum refining sectors
experience slight increases in intensity due to rising consumption of petroleum-
derived fuels and low capital stock turnover.
Table 16: Economy-wide GHG intensity
Intensity 2,005 2,020 2,030 2,040 2,050
Mt CO2e/TEC (EJ)
0.052
0.050
0.049
0.049
0.049
4.3 Policy Runs
While several emission abatement pathways were tested with this model
over the course of its development, only a fraction of simulation results will be
presented in the remainder of this report. The following section will provide a
detailed analysis of the policy simulations performed on CIMS OECD-EPM. .
4.3.1 Marginal Abatement Cost Curves
Economy-wide Marginal Abatement Cost Curves
As stated in section 2.5.3, marginal abatement costs represent the cost
associated with reducing the last unit of GHG emissions. The marginal
abatement cost is often referred to as the abatement cost, emission price or
emission charge; these terms will be used interchangeably. The marginal
abatement cost curves (MACCs) displayed below were developed from the
aggregation of simulation results from emission charges that increase linearly
over time.
Figure 18 presents three marginal abatement cost curves for CIMS
OECD-EPM in 2020, 2030 and 2050, with abatement costs (2005 USD/t CO2e)
on the y-axis and GHG reduction (Gt CO2e) on the x-axis. Since these curves are
static, meaning that they are a snapshot of emission reductions at various points
53
in time, emissions reductions are presented as the difference between BAU and
policy in that year for given emission charges.
Figure 18: Marginal abatement cost curves for CIMS OECD-EPM in selected years
As illustrated in Figure 18, emission reductions increase over time. For
example, at an emission price of $100 a tonne of CO2e, annual reductions are 2
Gt in 2020, 2.5 Gt in 2040 and 3 Gt in 2050. Essentially, the charge required to
reach the same level of abatement decreases with time. For example, to achieve
a reduction of 3 Gt CO2e, demands a charge of $250 in 2020, $150 in 2030, and
$100 in 2050. This temporal disparity is attributed to the length of time the
economy has to adapt to the charge and the abatement options available within
that time span. With short policy adoption periods (1-15 years), emission
abatement options are limited to new investments, replacing retiring technologies
with short lifespans (1-10years) and reductions in output. However, the bulk of an
economy’s emissions originate from capital-intensive sources that have long
lifespans (30-50 years). Replacing these technologies with new, more efficient
technologies before their natural retirement would be extremely costly. Thus, it is
not feasible to deploy most of these abatement options in the short-term, even
0
50
100
150
200
250
300
350
0 1 2 3 4 5 6
20
05
USD
/t C
O2e
GHG Reduction (Gt CO2e)
2020
2030
2050
54
when confronted with substantial emission charges; hence, the steep slope of the
curve. However, over longer periods the economy has sufficient time to adjust to
a policy. Consumers and firms are better able to make substantial investments
and have access to a wider array of abatement options, resulting in more
significant emission reductions. For this reason, the 2050 MACC is flatter than
the 2020 MACC. To illustrate this point consider the production activity of the
chemical sector. The majority of emissions in this sector are associated with
steam production and petrochemicals processing services. The equipment used
to provide these services has an average lifespan of 30 years. In the short-term,
emission reductions are minimal (less than 1% of BAU in 2020) -- limited by the
replacement of retiring equipment. However, with 45 years of adjustment,
emissions are reduced by 21%,16 primarily through investment in cogeneration,
improved efficiency and emission capture technologies.
Sectoral Marginal Abatement Cost Curves
An economy’s ability to abate emissions is defined by a wide array of
factors that both enable and constrain emission reductions. In the previous
section, time is a constraining factor in emissions reductions; the longer an
economy has to adjust to a policy, the more emission reductions it is able to
achieve. This section reveals what factors constrain emission reductions in the
energy demand sectors.
Economy-wide emission reductions are limited by the emission reduction
potential of each sector within the economy. Figure 19 presents the sectoral
MACCs in terms of absolute reductions. The emission reduction potential of the
residential and commercial sectors is small. The constraining factor for these
sectors is the relative size of their emission output (13% of BAU GHG emissions
in 2050). Even with aggressive abatement activity, emission reductions in this
sector will be small relative to other sectors, hence the steep appearance of their
MACCs. The transportation sector, on the other hand, is constrained by emission
charges below $150 a tonne of CO2e. For example, at $100 a tonne of CO2e,
16 Percentage reduction from BAU in 2050 when a charge of $200 a tonne of CO2e is applied.
55
emission reductions are 0.3 Gt; however, at $250 a tonne of CO2e, reductions
are 0.8 Gt -- almost three times greater. Since emission reduction options in the
transportation sector are quite expensive, high emission charges are required to
catalyse substantial change. Conversely, the industrial sector experiences the
least amount of constraint, delivering the greatest amount of emission reductions
at all price levels. For example, with a charge of $100 a tonne of CO2e, the
industrial sector delivers 0.7 Gt of reductions in 2050, approximately 50% of the
total abatement in that period.17
Figure 19: Marginal abatement cost curves for energy demand sectors in 2050
The residential and commercial sectors are constrained by absolute
emissions in BAU; however, viewing the same data through the lens of the
relative performance of individual sectors (as a percentage reduction from BAU),
reveals that both sectors actually experience a greater percentage of emission
reduction than the other sectors. In Figure 20, the MACCs of the transportation
and industrial sector are steeper than the residential and commercial sectors; a
17 Total abatement refers to the sum of emission reductions in the residential, commercial,
industrial, and transportation sectors.
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contrary scenario to that presented in Figure 19. For example at an emission
charge of $100 a tonne of CO2e, emission reductions are 11%, 24%, 40%, and
47% for the transportation, industrial, commercial and residential sectors,
respectively. The primary causes of this difference are the availability and cost of
emission reduction technologies in the residential and commercial sectors: a
wider variety of cheaper emission reduction options is available. As a result,
these sectors are able to achieve a greater percentage of abatement with
equivalent emission charges.
Figure 20: Marginal abatement cost curves of energy demand sectors in 2050 (% below BAU)
In the previous MACCs, the electricity sector is included insofar as its use
contributes to emission reductions within each sector. Figure 21 presents
electricity consumption increasing with rising emission prices on the right vertical
axis (z-axis). Because emissions related to electricity generation are not directly
associated with end-use, it is common for energy demand sectors to increase
electricity consumption as a means of lowering their emissions output.
Consequently, electricity generation increases; often well above BAU projections,
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increasing the energy consumption requirements of the sector. Figure 21 also
illustrates how various levels of carbon prices affect the composition of fuels
used to produce energy in 2050. At emission prices above $150 a tonne of CO2e,
consumption of coal increases as carbon capture and storage technologies gain
market share, offsetting growth in nuclear and renewables. However, at emission
prices below $150 a tonne of CO2e, the situation is reversed; growth in nuclear
and renewables offsets reductions in coal consumption. For example, at an
emission price of $100 a tonne of CO2e, shares of nuclear and renewable are
45% and 24%of total consumption, respectively, compared to 41% and 22% at a
price of $350 a tonne of CO2e. Natural gas and oil are generally unaffected by
increasing emission prices, experiencing only small fluctuations in market share.
Figure 21: Composition of fuel consumed and total generation in the electricity sector in 2050, by emission charge
4.3.2 Target Abatement Policy Run
The previous section provides an overview of the abatement potential in
OECD-EPM by examining marginal abatement cost curves. Section 4.3.2 will
provide an in-depth analysis of this abatement potential by focusing on a specific
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58
emission reduction target. The following paragraphs will discuss the abatement
activity employed to achieve the target.
The abatement target is a 60% reduction below BAU emissions by 2050. I
will refer to this target as the target abatement policy run, or the policy. This
target was chosen because it is closely aligned with the regional targets
prescribed by the “sovereign approach” to global GHG stabilization (Böhringer &
Weslch, 2006).
The “sovereign approach” is an emission entitlement rule for allocating
global emission reductions among world regions. To achieve the climate
stabilization target mentioned in Chapter 1 (550ppm C02e), global average per
capita emission should be 0.48 tonnes of carbon by 2050. To achieve this target,
a reduction of 62% from BAU in 2050 is required from the OECD-EPM region
(Böhringer & Weslch, 2006. p.988). The “sovereign approach” allocates emission
reductions (percentage reduction below business-as-usual) in a relatively uniform
manner across all regions (i.e., OECD Pacific is allocated a 63% reduction, while
China is allocated a 64% reduction). While the fairness of this approach may be
disputed, the sovereign approach supports a target that is more cost-effective for
OECD-EPM when compared to targets from other entitlement regimes. To
achieve stabilization with a more egalitarian entitlement distribution, emission
reductions would need to be around 90% below BAU by 2050, requiring emission
charges well above $400 a tonne of CO2e in a no-trade scenario. Emission
prices of this magnitude are likely to result in significant losses in GDP and
threaten the economic health of the region. For these reasons, the policy run
target in this study is set to 60% below BAU by 2050.18
To achieve the target, various emission charges were simulated until the
target was achieved. Table 17 illustrates the emission price pathway that
achieves a reduction of 60% below BAU in 2050. The following two sections will
explore the policy run in more detail.
18 The 62% target, as prescribed by the sovereign entitlement regime, has been rounded down to
60% for the purpose of simplicity.
59
Table 17: Target abatement policy run emission charge schedule
2005 2011 2020 2025 2030 2035 2040 2045 2050
2005 USD/t CO2e 0 35 80 120 160 190 215 240 280
4.3.2.1 Energy Consumption
Figure 22 compares total primary and secondary energy consumption in
the BAU and policy forecasts. As illustrated below, energy consumption varies
considerably from BAU when the policy is applied. Over the simulation period,
total energy consumption in the policy forecast is an average of 9% lower than
the BAU forecast. In 2050, total energy consumption is 166 EJ in the policy
forecast, compared to 176 EJ in the BAU forecast. However, it is the composition
of energy consumption that is more affected by the policy. The most dramatic
impact is the shift from carbon-intensive to low-carbon fuels.
Figure 22: Comparison of total energy consumption in BAU and policy, by fuel
In 2050, shares of renewables and nuclear are 19% and 18% of total
consumption, respectively, compared to 11% and 11% in BAU. In the same
period, shares of coal and oil decrease from 14% and 31%, respectively, in BAU,
to 8% and 16% in the policy run, a combined decline of 50%. While oil follows a
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relatively linear decline over the simulation period, coal does not, and fluctuates
dramatically. Between 2005 and 2030, coal consumption experiences a rapid
decline (11%-5%); however, from 2030 to 2050 shares begin to increase (5%-
8%) as carbon capture and storage technologies gain market share. Due to
these changes in fuel composition, GHG intensity drops dramatically. As
illustrated in Table 18, GHG intensity falls more than 50% from the BAU forecast
in 2040.
Table 18: Comparison of GHG intensity in the BAU and Policy forecasts
Mt CO2e/TEC (EJ) 2005 2020 2030 2040 2050
BAU
0.052
0.050
0.049
0.049
0.049
Policy 0.050 0.039 0.030 0.024 0.021
4.3.2.2 GHG Abatement Portfolio
Over the simulation period, the policy produces 133 Gt CO2e of cumulative
emission reductions. Table 19 shows how each sector contributes to total
cumulative reductions. The electricity sector captures the largest share of
cumulative abatement (43%), reaching 56 Gt CO2e in 2050. The industrial sector
generates 26% of total abatement, primarily from reductions in the chemical, iron
and steel, and other manufacturing sub-sectors. Emission capture and storage,
and energy-efficient technologies drive abatement in these sub-sectors.
Reductions in the energy supply sector are minimal due to the low production
capacity in the region. The transportation sector produces 20 Gt CO2e of
reductions. Investments in high-efficiency, biodiesel and ethanol vehicles are the
fundamental drivers of this abatement. At the same time, demand for high-
occupancy vehicles and public transportation rises, decreasing demand for
single-occupancy vehicles. Demand for high-occupancy vehicles and public
transportation increase 12% and 24%, respectively, over the simulation period.
61
Table 19: Cumulative emission reductions (2005-2050), by sector
Reductions (2005-2050) Gt CO2e Share (%)
Energy Demand Sectors
Residential 12 9 Commercial 9 6 Transportation 20 15 Industry 28 21
Energy Supply Sectors
Other Energy Supply + 7 5
Electricity 56 43
Total 133 100
+Includes: Coal mining, natural gas extraction, petroleum crude, and petroleum refining
Individually, the commercial and residential sectors deliver the lowest
portion of emission reduction when compared to the electricity, industrial and
transportation sectors. However, both sectors combined generate 15% of total
cumulative reductions. Fuel switching from fossil fuels to electricity is the
principle reduction activity in these sectors. The adoption of higher-efficiency
technologies also plays a key role in reducing the emission output of these
sectors. High-efficiency appliances and alternative fuel furnaces, such as wood
furnaces and ground-source heat pumps, are the key abatement technologies in
both sectors.
In 2050, GHG emissions are 3.5 Gt CO2e, meeting the abatement target
of 60% below BAU (a reduction of 8.6 Gt CO2e). Figure 23 presents a wedge
diagram showing the various actions employed to reach this target. Carbon
capture and storage, and fuel switching capture over 50% of total abatement in
2050 -- 36% and 21% respectively. As you can see from the diagram, fuel
switching dominates abatement in the initial periods of the policy run; however,
after 2020, carbon capture and storage gains market share, replacing fuel
switching as the dominant abatement activity. The remaining 43% of reductions
62
(2.3 Gt CO2e) are achieved through energy-efficiency improvements, output
reductions, and other GHG control technology.19
Figure 23: Wedge diagram, abatement by activity
Note: CCS Energy Efficiency Penalty is the additional energy required to capture and store carbon, which
effectively decreases energy efficiency.
Electricity Sector
Table 20 shows how the composition of energy consumption within the
electricity sector changes over time in response to the policy run. Renewables
experience the most significant growth over the simulation period, increasing 160%
from 2005 to 2050. Wind energy, followed by waste fuels, are responsible for the
majority of this growth. Despite rapid development of renewables, the bulk of
emission reductions come from coal and natural gas powered conversion
technology with carbon capture and storage (CCS). In 2050, over 50% of total
abatement in the sector is generated by CCS technologies. The adoption of CCS
in the electricity sector precipitates an increase in coal consumption in 2025,
effectively reversing declines in consumption experienced in the previous
simulation periods. Shares of coal increase from 0.8 in 2020 to 0.16 of total 19 Other GHG controls include activities such as removing PFC’s from aluminium production and
avoided methane flaring from oil and natural gas production.
63
consumption in 2050. Consequently, CCS development slows growth in nuclear
generation, reducing its share from 0.46 in 2020 to 0.41 in 2050.
Table 20: Composition of energy consumption in the electricity sector, by fuel, in the policy run
Share of total 2010 2020 2030 2040 2050
Natural Gas 0.21 0.21 0.20 0.19 0.19 Coal 0.17 0.08 0.09 0.13 0.16 RPP 0.05 0.03 0.01 0.01 0.01 Nuclear 0.39 0.46 0.47 0.45 0.41 Renewables 0.18 0.22 0.23 0.22 0.23
Carbon Capture and Storage
As mentioned above, CCS plays a dominant role in emission reduction for
the majority of the simulation periods. Figure 24 presents total captured GHG
emissions in the region over the simulation period. The electricity sector captures
the largest market share, generating almost 70% of GHGs captured in 2050.
CCS from coal combustion using single-cycle and integrated gasification
combined-cycle conversion technologies, are the main technologies deployed in
the sector. Captured emissions in the industrial sector come from the chemical
and industrial minerals sub-sectors. Steam production technologies fueled by
natural gas and coal are responsible for over 90% of the emissions captured in
each sub-sector. CCS deployment in the energy supply sector is minimal, due to
low production output and minimal investment in new capital stock.
64
Figure 24: Captured GHGs using carbon capture and storage (Mt CO2e), by sector
Macroeconomic Impact
Two key indicators of the macroeconomic impacts of the policy run are
output changes and GDP. Due to the partial equilibrium structure of CIMS,
results presented in this section only apply to the sectors covered in CIMS. For
this report, output changes are measured as a percentage reduction from the
BAU forecast. Table 21 illustrates how output in each sector responds to the
policy run. In terms of the energy demand sectors, the greatest output losses
come from the industrial minerals, pulp and paper, and mineral mining sub-
sectors, producing a combined loss of 22% from BAU in 2050. The transportation
sector experiences negligible losses over the simulation period because energy
prices experience only small increases from BAU when the policy is applied -- on
average less than 10%. The energy supply sector is the only sector to
experience gains in output. Growth in electricity demand drives an increase in
generations of 22% from BAU in 2050. In the other energy supply sectors, output
drops to 8% below BAU in 2030, propelled by massive output losses in the coal
mining sector at 62% below BAU -- a reduction of 649 million tonnes. As evident
from the wedge diagram above, total output losses have a minimal impact on
economy-wide abatement.
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Table 21: Output changes in the policy run
% Change from BAU 2020 2030 2040 2050
Residential 9% 9% 7% 5% Commercial 3% 3% 3% 4% Transportation 0% 0% 0% 0% Industry 9% 10% 11% 12% Chemical Products 7% 9% 9% 8%
Industrial Minerals 47% 48% 48% 48%
Iron and Steel 4% 6% 7% 9%
Metal Smelting 7% 9% 9% 11%
Mineral Mining 6% 11% 13% 15%
Paper Manufacturing 25% 18% 13% 14%
Other Manufacturing 5% 7% 8% 8%
Electricity -1% -9% -17% -22% Other Energy Supply*..... 6% 8% 8% 8%
*Other energy supply includes coal mining, crude extraction, natural gas extraction, and petroleum refining.
Gross domestic product (GDP) is a widely accepted measure of economic
health and is often used as an indicator of the economic impact of policies. GDP
is the sum of expenditures on capital, labour and natural resources, required to
produce all goods and services in an economy, in a given year. CIMS is a partial
equilibrium model representing only the key energy consuming sectors in the
economy. In CIMS, calculations of GDP hold activities in all other sectors of the
economy constant, assuming they are unaffected by activities in CIMS sectors. In
reality, this assumption does not hold as sectors not covered by CIMS, such as
the employment and the capital market sector, may be affected by changes in
CIMS’s sectors. Thus, values produced by CIMS should not be interpreted as
GDP impacts on the entire economy, but rather as GDP impacts on sectors of
the economy represented in CIMS.
Table 22 presents the estimated GPD effect of the policy run on sectors
covered by CIMS in 2025 and 2050. In 2025, total GDP losses are estimated at
$89.7 billion (2005 USD), which is approximately 0.3% of the GDP growth
projected from 2020 to 2025 (EIA, 2007). In 2050, GDP impacts are projected to
be positive, with an increase in GDP of 0.2% from the BAU forecast -- $75.4
billion (2005 USD). In both 2025 and 2050, the energy demand sectors
experience losses in GDP. However, with the exception of the industrial sector,
66
GDP losses decrease over time as the emission charge rises. The primary cause
of this decline is investment in energy efficiency measures that reduce energy
costs, thus increasing GDP. GDP impacts also appear to decrease over time in
the other energy supply sector. The primary cause of this decline is investment in
carbon capture and storage technology in the petroleum crude sub-sector. The
electricity sector, on the other hand, experiences significant gains in GDP in both
simulation periods. The reason for this gain is twofold. First, income in the
electricity sector increases because of growth in demand. As mentioned in
Section 4.3.1, demand for electricity generation increases in response to carbon
constraints as demand sectors switch from technologies powered by fossil fuels
to technologies powered by electricity. Second, the policy creates market
momentum for abatement conversion technologies like renewable and carbon
capture and storage. Investments in abatement technologies increase capital
expenditures, and thus increase GDP.20 Overall, it appears that the policy run
has a positive impact on the GDP of sectors represented in CIMS OECD-EPM.
However, this result should be viewed with caution in light of the caveats listed
above.
20 In each sector, GDP can be calculated from total income in a sector. In the electricity sector,
income equals the price of electricity multiplied by the sales of electricity, minus the expenditures on intermediate inputs. When the policy is applied, both the price of electricity increases, due to increased expenditures on abatement, and the sales of electricity increase, due to fuel switching in the energy demand sectors. As a result, the GDP in the electricity sector increases (C.Bataille, personal communication, October 21, 2008).
67
Table 22: Estimated effect of the policy run on GDP for sectors covered by CIMS in 2025 and 2050
Millions (2005USD) 2025 2050
Total -89,684 75,374 % Change from BAU -0.3% 0.2% Demand Sectors
Residential -23,577 -17,122 Commercial -17,084 -14,689 Industry -35,086 -54,922 Transportation………….. -
126,597 -
108,633 Supply Sectors
Other Energy Supply* -11,510 -7,351 Electricity 124,171 278,089
*Includes: Coal extraction, crude extraction, natural gas extraction, and petroleum refining.
4.4 Sensitivity Analysis
Models are built around assumptions about how systems function.
Because models are incapable of representing these systems in their entirety,
model output is always subject to uncertainty (Oreskes, 2003). Uncertainty can
be addressed by identifying and testing key areas of uncertainty, thereby making
model limitations more transparent. A common tool for testing model uncertainty
is sensitivity analysis. A sensitivity analysis involves evaluating the impact that
varying input parameters have on model output.
As mentioned in Chapter 2, CIMS relies on a variety of assumptions in its
depiction of OECD-EPM’s energy system and economy. Major modelling
assumptions include energy price forecasts, macroeconomic growth, behavioural
parameters and abatement technologies. The sensitivity of the model to energy
prices and behavioural parameters has been explored by Nyboer (1997) and Tu
(2004). Their findings suggest that model results may be fairly insensitive to
variation in these parameters. In light of previous analysis, the following sections
will focus on assumptions not addressed in past research: demand forecasts and
nuclear power generation.
68
4.4.1 Demand Sector Growth
Energy consumption in each energy demand sector -- residential,
commercial, industrial and transportation - - is driven by inputs that define the
sector’s main activity. For example, tonnes of non-ferrous metals produced drive
energy consumption in the metal smelting sector. Projections of growth in energy
demand sectors have a significant impact on energy and GHG emission
forecasts. Because these values are exogenous to the model, care was taken to
insure that reliable forecast data were used. However, as mentioned in Section
2.4.2, data were often limited to 2030 and subsequent growth was projected
through extrapolation. Thus, these projections are subject to uncertainty.
To test this uncertainty, growth estimates in the energy demand sectors
are both increased and decreased. Over the simulation period, exogenous
forecasts for each energy demand sector receive an annual increase or decrease
of 5% from its BAU values. Table 23 illustrates the impacts of these changes on
energy consumption and GHG emissions: a 5% variation in demand forecasts
produces a difference of less than 5% in energy consumption and GHGs
production when compared to the BAU reference run. Moreover, both sensitivity
analyses result in equivalent magnitudes of change when compared to BAU.
To explore the impact of these changes on policy effectiveness, an
emission charge is applied. The outcome of this analysis is presented in the third
and forth column of the table below. When output is reduced 5%, emission
reductions (percentage from BAU) are equal to that achieved in the reference
run: 34%. However, increasing output 5% causes a slight decrease in policy
effectiveness. In 2050, emission reductions are 33%, 1% less than the emission
reductions achieved in the reference run. Overall, within the range of variation
considered, it appears that policy effectiveness is fairly insensitive to changes in
demand projections.
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Table 23: Results of demand sector growth sensitivity analysis, presented as percentage change from the reference run in 2050 BAU and Emission Charge*
% Change from reference run
BAUoooooo0oo -5%
+5%
Emission Charge* -5%
+5%
Energy Consumption Industry
-3%
3%
-3%
3%
Residential -1% 1% -1% 1% Commercial -2% 2% -2% 2% Transportation -2% 2% -2% 2% GHGs Total
-2%
2%
-2%
2%
Reduction from BAU 0% 1%
Emission Charge*- A linearly increasing carbon tax starting at $13 a tonne of CO2e in 2011 and increasing to $100 a tonne of CO2e in 2050.
4.4.2 Nuclear Power Generation
OECD-EPM is the largest producer of nuclear power out of all CIMS-
Global regions (IEA, 2008a). Nuclear power plays a major role in the region’s
electricity production, accounting for over 30% of total energy consumption in the
sector in 2005.
Nuclear power generates electricity with virtually no emissions. Given its
low cost per Gwh and reliability, it is currently the most extensively adopted low-
emission electricity production technology in the world. However, fears about
health and safety are currently hindering further development in the region (EIA,
2007). In fact, several countries in OECD Europe have proposed policies to
phase out nuclear power (EIA, 2007). As mentioned in Section 3.4.5, nuclear
power has been constrained to reflect these conditions. Observations from the
marginal abatement cost curve runs indicate that increases in nuclear energy
accompany increases in emission charges. For example, at a charge of $100 a
tonne of CO2e, shares of nuclear power are 46% of total electricity generation in
2040, compared to only 36% in BAU. In light of the uncertainty surrounding the
proposed nuclear power phase-out policies in OECD Europe, and the future
development of nuclear power in the region, both tighter and more relaxed
constraints are explored in the sensitivity analysis. To model this analysis,
nuclear market share constraints were both increased and decreased by 50%.
70
Figure 25 illustrates that relaxing nuclear constraints increases the
region’s ability to reduce GHGs in 2050. In 2050, nuclear’s share of total energy
consumption in the electricity sector is 0.54. While this scenario may not be
realistic, given public concerns, it provides an indication of nuclear power’s
technical potential as an abatement option. When nuclear constraints are
tightened, reflecting nuclear phase-out, abatement in the electricity sector
decreases. At a price of $100 a tonne of CO2e, GHG reductions in the electricity
sector are only 58%, compared to 67% and 72% from BAU in the reference and
relaxed run, respectively. However, as the emission price increases, this gap
decreases because of carbon capture and storage technology development.
Figure 25: Electricity sector MACCs with varying nuclear development constraints
Table 24 shows that in the tight scenario, at an emission price of $200 a
tonne of CO2e, the development of carbon capture and storage increases
dramatically to compensate for the nuclear constraints. In this scenario, shares of
nuclear decrease 24% from the reference run. Consequently, shares of
renewables and coal (to be used with carbon capture and storage) increase from
0.21 and 0.17 of total consumption in the reference run, to 0.23 and 0.23 in the
tight scenario, respectively. The sensitivity analysis reveals that emission
reductions in OECD-EPM are fairly sensitive to nuclear constraints when carbon
charges are below $150 a tonne CO2e. Moreover, the composition of energy
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consumption in the sector is quite sensitive to nuclear constraints at all price
levels. Therefore, the model should be continually updated to reflect the current
nuclear policies of the OECD-EPM region.
Table 24: Fuel mix in the electricity sector for varying nuclear development constraints in 2050, by emission charge
Relaxed
Ref Tight
Share of total ($/CO2e) 50 100 200 50 100 200 50 100 200
Natural Gas 0.18 0.17 0.16 0.20 0.21 0.23 0.26 0.28 0.24 Coal 0.09 0.06 0.06 0.24 0.23 0.17 0.16 0.15 0.23 RPP 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 Nuclear 0.49 0.54 0.55 0.35 0.35 0.38 0.31 0.31 0.29 Renewables 0.23 0.23 0.22 0.20 0.20 0.21 0.25 0.25 0.23
CCS (Mt) 52 230 457 73 348 853 105 250 1210
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CHAPTER 5 DISCUSSION
5.1 Regional Marginal Abatement Cost Curve Comparison
As stated in Section 1.3, the goal of this research project is to create a
regional CIMS model for the OECD-EPM region in an effort to develop a CIMS-
Global model. A global CIMS model will facilitate international energy and GHG
emission forecasts, as well as interregional policy analysis. Although CIMS-
Global is currently represented by individual regional models, a preliminary
interregional policy analysis can be conducted by comparing the marginal
abatement cost curves (MACCs) of each region. The shape of a MACC reveals
an economy’s ability to reduce emissions at various price levels. Variations
between regions result from differences in energy prices, technological capacity,
path dependence, energy infrastructure, macroeconomic growth and economic
status.
Figure 26 compares the marginal abatement cost curves of the four
aggregate CIMS regional models. On the x-axis, GHG reductions are defined as
percentage reduction relative to the BAU forecast. OECD-EPM and the
Transitioning Economies (TE) represent the regions with the highest and lowest
emission reduction potential. OECD-EPM has the steepest MACC, suggesting
that less GHG reductions are achieved for a given emission price than when
compared with other regions. For example, at a charge of $125 a tonne of CO2e,
GHG reductions in OECD-EPM are 38% of BAU, compared to 43%, 46% and
50% in Africa, Middle East and Latin America (AMELA), Developing Asia (DA),
and TE respectively. Looking at the situation from another perspective, higher
emission charges are required to achieve equivalent percentages of emission
reductions. For example, OECD-EPM requires an emission price of $150 a tonne
of CO2e to reach a target of 40% below BAU in 2050. However, in DA the same
target is achieved with a much lower emission price: $100 a tonne of CO2e.
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Figure 26: Regional MACC for selected CIMS-Global regions in 2050
Sources: CIMS AMELA (Melton, 2008), CIMD DA (Goggins, 2008), CIMS TE (Wolinetz, 2008)
Growth projections, both economic and demographic, along with structural
differences in each economy are the primary drivers of MACC variation in Figure
26. Growth in the industrializing and developing economies of TE, DA and
AMELA is projected to increase at a rate far greater than OECD-EPM. Slow GDP
growth is projected in OECD-EPM due to declining population growth and
declining production in certain industrial sub-sectors. The majority of emission
reductions in TE, DA and AMELA come from investments in new capital stock;
however, given slow growth in OECD-EPM’s economy, emission reductions from
new investments are restricted to a minimal amount of new capital stock. In
terms of economic structure, the OECD-EPM is one of the most energy efficient
economies in the world (IEA, 2008a; IEA, 2008c). Thus, there is less opportunity
for significant efficiency improvements, as many of the affordable options have
already been exploited.
Figure 27 presents the MACCs of each region in terms of absolute
abatement potential, where GHG reductions are defined as Gt of CO2e. As
shown in the figure, the shape and order of the regional MACCs change
0
20
40
60
80
100
120
140
160
0 10 20 30 40 50 60
20
05
USD
/t C
O2e
GHG Reduction (% Below BAU )
AMELA
DA
TE
OECD-EPM
74
dramatically from Figure 26 to Figure 27. For example, the TE region, which
displays the greatest emission reduction potential in Figure 26, achieves the least
amount of reductions in Figure 27. OECD-EPM shifts from the steepest curve in
Figure 26 to the second steepest curve in Figure 27. Its new position indicates
that OECD-EPM is able to abate more emissions than the TE for equivalent
emission charges, but less than the other two regions. In contrast to developing
regions, where GHG emissions are projected to increase -- driven by growth in
industrial output and population, GHG emissions in OECD-EPM are projected to
experience slow growth -- driven reductions in certain industrial sub-sectors and
declining population growth. Because the forecasted BAU emission growth in
OECD-EPM is lower than in DA and AMELA, absolute reductions are smaller at
most price levels. For example, at an emission price of $100 a tonne of CO2e,
OECD-EPM reduces emissions by approximately 3Gt, while AMELA reduces
emissions by approximately 4Gt.
Figure 27: Comparison of absolute MACC for selected CIMS regions in 2050
Sources: CIMS AMELA (Melton, 2008), CIMD DA (Goggins, 2008), CIMS TE (Wolinetz, 2008)
0
20
40
60
80
100
120
140
160
0 1 2 3 4 5 6
20
05
USD
/CO
2e
GHG Reduction- Gt C02e
AMELA
DA
TE
OECD-EPM
75
The preceding analysis provides insight into the price signals required to
achieve various policy targets by generating marginal abatement costs. However,
these costs do not represent total policy costs. Total policy costs are a function of
absolute emission reductions and the emission charge. The area under a MACC
between 0 and the quantity of emissions reduced (Gt Co2e) provides a crude
estimate of total policy costs. By comparing the policy costs of different regions,
important opportunities for emission transfers can be identified.21 While
emissions permit trading is not a focus of this paper, the next section will provide
a brief examination of trading opportunities between CIMS-Global regions.
5.2 Implications of Regional Marginal Abatement Cost Variation
Until now, I have assumed that each region is acting alone in their
abatement effort: domestic reduction with no interregional emissions permit
trading. However, this scenario is unrealistic. Any international or sustained
regional effort is likely to involve some form of emissions permit trading.
Emissions permit trading is beneficial to international cooperation in global
emission reductions because it “allows a group of sources to reach a specific
emission target at the lowest cost” (UNEP/UNCCEE/UNCTAD, 2002, p.5). The
OECD-EPM, with both large historical emissions and high marginal abatement
costs, could benefit greatly from the purchase of emission permits. Deriving
policy costs using the method described in Section 5.1 may overestimate true
costs, as it does not account for the economic benefits associated with emissions
permit trading.
The current version of CIMS OECD-EPM does not endogenously simulate
emissions permit trading. The following paragraphs will identify potential
opportunities for emissions permit trading between CIMS OECD-EPM and other
CIMS-Global regions. The analysis uses the methodology applied by Criqui et al.
21 The term, emission transfers, commonly refers to the transfer of emission permits. However it
can also refer to the transfer of abatement technologies and knowledge. For the purposes of this report, emission transfers will refer exclusively to emission permit transfers.
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(1999), whereby cost efficiency drives emission transfers. Assuming perfect
competition, no barriers to entry, no transaction costs and no permit ceilings, the
international permit price (market marginal abatement cost) is the equilibrium of
market supply and demand of aggregate emission reductions22 (Criqui et al,
1999). According to the methodology, regional economies will be driven to abate
emissions up to the point where their regional MACCs intersect the aggregate
market marginal abatement cost; this point may be above or below a region’s
individual abatement target. If an economy’s abatement cost is below the market
abatement cost, it will sell emission permits. Conversely, if an economy’s
abatement cost is above the market abatement cost it will purchase emission
permits. Given this exchange, participating economies collectively achieve
aggregate emission abatement targets in an economically efficient manner.
To illustrate this theory, consider a scenario where four CIMS-Global
regions have been given an emission target of 30% below BAU by 2050. Due to
differences in abatement abilities and emission profiles, each region’s absolute
reduction requirements will be different. Table 25 presents the emission price
and reduction required if each region is to achieve its target solely through
domestic action. If trading is present, the international permit price represents the
emission price necessary for all economies to achieve the aggregated emission
reduction target. The international permit price (P*) is reached when the
aggregate abatement target (QT) intersects with the market MACC. Figure 28
shows that P* is $60 a tonne of CO2e. As you can see from the graph, P* is
greater than the individual abatement costs of DA and TE, but less than that of
OECD-EPM (given a 30% reduction target). According to the theory, both TE and
DA will abate in access of their individual targets, while OECD-EPM will abate
less. Thus, there will be a transfer of emission credits from DA and TE to OECD-
EPM.
22 “Aggregate emission reductions” refers to the summation of individual abatement targets for
regions participating in a prescribed policy. For example, policy 123 imposes targets of 2 Gt and 3 Gt of CO2e on countries X and Y, respectively. Aggregate emission reductions for 123 are, therefore, 5 Gt of CO2e.
77
Table 25: Regional marginal abatement costs and reductions associated with a target of 30% below BAU by 2050- assuming no trading
30% Below BAU
Price required ($/t CO2e)
Reduction required (Gt CO2e)
Graph Label
OECD-EPM 75 3 QO AMELA 75 3 QA DA 50 2 QD TE 50 1 QTE
Total (Market)
9 QT
Figure 28: Regional and market MACCs in 2050
Sources: CIMS AMELA (Melton, 2008), CIMD DA (Goggins, 2008) & CIMS TE (Wolinetz, 2008)
All parties gain from this exchange. The permit sellers, TE and DA receive
revenue from the sale of permits, while the permit buyer, OECD-EPM gains from
reduced abatement costs. According to this analysis, if OECD-EPM participates
in emissions permit trading, total abatement costs will be lower than if OECD-
EPM acts alone.
The purpose of this exercise is to explore how CIMS-Global can be used
to identify potential opportunities for emission transfers. Analysis results may not
represent realistic permit flows and therefore the following caveats apply:
0
20
40
60
80
100
120
140
160
0 2 4 6 8 10 12 14 16
2005
USD
/t C
O2
e
GHG Reductions (Gt C02e)
AMELA
DA
TE
OECD-EPM
Market
QO
QD
QT
P*-$60
QT
QA
20
05
USD
/t C
O2e
20
05
USD
/t C
O2e
C
O2e
GHG Reduction (Gt CO2e)
TE
78
The analysis ignores important macroeconomic feedbacks
associated with trading.
The analysis does not address the equity or fairness of the
prescribed permit allocations and emission targets.
The analysis assumes no restrictions on the purchase of emission
permits.
5.3 Key Modelling Challenges
CIMS was initially developed for policy simulation in Canada and its
current framework is biased towards that economy. Despite similarities between
Canada and OECD-EPM, several challenges are present when applying CIMS to
the OECD-EPM region.
Partial equilibrium framework: Böhringer and Weslch (2006) identify the
drawbacks of partial equilibrium frameworks as “the neglect of economy-
wide market interaction and income effects” (p.982). One key interaction
identified by the authors is the impacts of carbon constraints on fossil fuel
prices. CIMS is able to capture this effect in two ways: directly through the
endogenous pricing of electricity and refined petroleum products, and
indirectly through increased production costs -- when production
processes involve the consumption of fossil fuels, carbon charges can
increase energy costs and thus production costs. However, CIMS is
unable to capture price changes in fossil fuels caused by shifts in global
demand. Given the size of the OECD-EPM region, it is likely that demand
shifts in the region will have global impacts. In addition to changes in fuel
prices, CIMS is not capable of modelling the income and market affects of
carbon constraints.
Regional aggregation: CIMS OECD-EPM represents 28 countries.
Although all countries are members of the OECD, their economies are
quite diverse, ranging from large, stable economies like the UK and
79
Japan, to smaller, less stable economies like Mexico and Turkey. Two
problems arise from this aggregation: data representation and policy
implementation. Of all 28 countries, technology-specific data were the
most readily available for the EU. As such, CIMS OECD-EPM is biased
towards Western European OECD countries. Secondly, the model
assumes that climate policies apply equally to all countries in the region.
However, not all countries are equally able to respond to climate change
policies. In fact, some OECD-EPM countries have been identified by the
United Nations Framework Convention on Climate Change, as having a
constrained ability to respond to climate change measures. Aggregating
these countries may distort the financial impacts of policies in these
regions, as some OECD-EPM are not as capable of responding to carbon
constraints as others. Moreover, these countries span over a large
geographic area with many different climate zones. For example, average
winter temperatures range from 20° to 24°C in Mexico, to 4° to 7°C in
Ireland (Microsoft Encarta, 2008). This type of diversity presents a
challenge for modelling heating, ventilation and cooling services. The
greatest obstacle is in the residential sector where these services
represent a significant portion of total energy consumption in the sector.
Carbon Leakage: Carbon leakage refers to a situation where domestic
carbon policy causes an increase in emissions in countries outside that
region (Reinaud, 2008). CIMS OECD-EPM uses Armington elasticities to
measure changes in output resulting from policy implementation. The
model assumes that a certain portion of reduction in domestic production
is replaced by imports, serving as an indicator of carbon leakage.
However, this approach presents problems. First, the Armington elasticity
may not fully represent the dynamics of carbon leakage. The Armington
elasticity captures the degree of substitution between domestic and
imported goods. This substitution is determined by the relative price of
each good, whereby the demand for imports rise as its price falls relative
80
to the price of a domestically produced substitute (Bataille, 2007).
However, factors, aside from direct price changes that result from carbon
charges, may influence demand. Examples of such factors include foreign
investment risks, relocation costs, union contracts and domestic trade
policy. Consequently, the substitution of goods may be less sensitive to
policy than implied by the Armington specification. Secondly, the
Armington elasticity is estimated from historical data. Therefore, any future
developments that deviate from historical trends will not be captured.
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CHAPTER 6 CONCLUSION
6.1 Summary of Key Findings
CIMS OECD-EPM is a technologically explicit and behaviourally realistic
energy-economy model that forecasts energy consumption and GHG emissions
from 2005 to 2050. The development of CIMS OECD-EPM is one component of
a research initiative that aims to develop a global CIMS model. The objectives of
this research effort are twofold: to explore the regional impacts of abatement
policies in OECD-EPM, and to investigate how other regions respond to
equivalent abatement policies.
To guide these objectives, several research questions were posed in
Chapter 2. In the following section, I return to each.
Research questions
1. What are the impacts of GHG abatement on the economy and energy system of OECD-EPM? What mix of technologies and fuels is required to achieve this abatement?
Results from the target abatement policy run indicate that GHG emissions
in the region are reduced by 60% below BAU in 2050. To achieve this reduction,
a linearly increasing carbon tax is applied to the economy, beginning at $35 a
tonne of CO2e in 2011 and rising to $280 a tonne of CO2e in 2050. The price
pathway for the policy run is aggressive, producing 133 Gt CO2e of cumulative
reductions over a 45-year period. Carbon capture and storage, and fuel switching
cause over 50% of total abatement in 2050. The remaining reductions, 43%,
come from energy efficiency, output reductions and other GHG control
technologies, such as reduced methane flaring from oil and natural gas
production. Overall GHG intensity (Mt CO2e/TEC) falls more than 50% from the
BAU forecast by 2050.
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The electricity sector generates the largest portion of cumulative
reduction, abating 56 Gt CO2e over the simulation period. Accelerated
developments of carbon capture and storage, nuclear and renewables are the
primary drivers of emission reductions in the sector. In the residential,
commercial and industrial sectors, fuel switching followed by energy efficiency
produce the majority of reductions. Significant declines in oil and coal
consumption in these sectors are offset by electricity and renewable
consumption. In the transportation sector, the use of low-emission fuels grows
rapidly; however, refined petroleum products continue to dominate total
consumption in 2050.
Total energy consumption decreases approximately 2% a year over the
simulation period because of output reductions and energy efficiency. The
composition of energy consumption varies considerably over the simulation
period. The most dramatic changes include a rapid increase in the consumption
of renewable energy, over 200% from 2005 to 2050; a rapid decline in oil
consumption, 40% from 2005 to 2050; and fluctuating coal consumption, a
decline of 51% from 2005 to 2025 and an increase of 105% from 2025 to 2050.
Consumption of coal resurges in 2025 as coal-fuelled CCS technology quickly
gains market share.
Results show that, out of all the abatement technologies in the region,
carbon capture and storage technologies deliver the largest GHG reductions.
The electricity sectors, followed by the industrial sector, produce the majority of
captured GHGs in the economy, 66% and 25% respectively. Coal (single-cycle
and integrated gasification combined-cycle) and natural gas (combined-cycle)
fired conversion technologies in the electricity sector, along with coal fired boilers
in the chemical and industrial minerals sub-sectors, are the primary technologies
used in conjunction with carbon capture and storage. In addition to capturing
emissions, the industrial sector invests heavily in high-efficiency technologies
fuelled by natural gas, renewables and electricity. For example, in the pulp and
paper sub-sector there is significant adoption of steam technologies powered by
hog fuel. With the exception of the electricity sector, little investment occurs in
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high-efficiency and emission capture and storage technologies in the energy
supply sectors because of declining production. In the transportation sector, the
replacement of low-efficiency gasoline vehicles with higher-efficiency and zero-
emission vehicles generate the bulk of emission reductions. Additionally, there is
a significant switch from single-occupancy vehicles to high-occupancy vehicles
and public transportation. From 2005 to 2050, total personal kilometres travelled
in high- occupancy vehicles and public transportation increase 12% and 24%,
respectively. In the residential sector, the bulk of emission reductions come from
the adoption of high-efficiency appliances and alternative fuel furnaces such as
wood-fuelled furnaces and ground-source heat pumps.
The GDP impacts of the policy run are varied. In the first half of the
simulation period, GDP is reduced 0.3% from the BAU forecast; however, in later
simulation periods GDP rebounds, increasing 0.2% from the BAU forecast in
2050 due to GDP growth in the electricity sector. As mentioned in Section 4.3,
these results should be viewed with caution, as the CIMS framework does not
support full equilibrium analysis. Overall, the policy produces substantial
emission reduction in 2050 without significantly damaging the long-term
economic growth of the sectors covered in CIMS.
2. What price signal will stimulate substantial GHG abatement in OECD- EPM? I define substantial reduction as a reduction of over 30% from BAU in
2050. According to the analysis, any price pathway resulting in an emission price
of over $75 a tonne of CO2e in 2050 will cross this threshold. The marginal
abatement cost curves explored in Section 4.2 reveal that above $250 a tonne of
CO2e the reduction potential of the region experiences diminishing returns to
scale, meaning that incremental abatement declines as charges are increased.
Above $150 a tonne of CO2e, carbon capture and storage technologies achieve
widespread adoption throughout the economy, which further increases the
abatement potential of the region.
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Time is a major enabler of abatement potential in OECD-EPM.
Substantial abatement targets suggest the need for emissions charges that
gradually rise over time. The longer an economy has time to adjust to a market-
based policy, the greater its abatement because the policy is in line with the
natural rate of capital stock turnover. Ideally, an emission charge will start at a
moderate level and gradually rise to a more aggressive level.
3. How do the marginal abatement costs of other regions differ from the
marginal abatement costs of OECD-EPM?
In terms of relative abatement (percentage below BAU), marginal
abatement costs appear to be highest in OECD-EPM. At every price level, all
other CIMS regions achieve greater percentages of reduction. The slope of
OECD-EPM’s marginal abatement cost curves in both Figure 26 and Figure 27
steepens at charges above $100 a tonne of CO2e. This suggests that lower-cost
abatement opportunities are limited. In terms of absolute reductions, developing
nations --DA, AMELA, and China -- exhibit a greater capacity to reduce
emissions than OECD-EPM (see Appendix 4). One explanation for this contrast
is that forecasted BAU GHG emissions are projected to be higher in these
regions than in OECD-EPM. With higher GHG emission forecasts, these regions
have more opportunities to engage in abatement activities.
Whether these developing regions have the financial capacity or
obligation to pursue such activities is subject to much uncertainty. What is certain
is that opportunities exist for emission permit transfers between developing
countries and OCED-EPM. As mentioned in Section 5.2, this report identifies
these opportunities solely on an economic basis and does not comment on the
structure of this transfer. The development and design of such a system
addresses issues such as equity, fairness and political acceptance, which are all
beyond the scope of this paper. With further research and the development of
CIMS-Global, questions regarding international abatement policy and emission
transfers could be more appropriately addressed.
85
Policy Discussion
According to IPCC, global emissions should be reduced by approximately
60% by 2050. Under an entitlement rule where all countries assume fairly equal
abatement targets, OECD-EPM is responsible for a 62% reduction by 2050. The
target abatement policy run attempts to simulate this scenario, implementing a
reduction target of 60% below BAU by 2050.23 Results show that OECD-EPM is
able to achieve this target with substantial price signals. Preliminary analysis
indicates that economic health is unlikely to be significantly threatened.
Furthermore, the report suggests that OECD-EPM could benefit from emission
permit trading. It is recommended that the OECD-EPM engage in moderate
carbon constraining policies in the near-term, gradually ramping up to more
aggressive carbon constraints in the future. It is also recommended that some
level of permit trading and technology transfer be part of such a policy. Moreover,
the results of the policy run suggest that it may be possible for the region to
pursue more aggressive carbon constraints, along the lines of those proposed by
other emission entitlement regimes (Berk, 2001; Böhringer & Weslch, 2006; den
Elzen et al., 2005).
6.2 Limitations
Over the past decade, researchers using CIMS have worked closely with
industry and other experts to improve the CIMS modelling framework. While this
effort has enhanced the model, it has done so in a Canadian context. Although
OECD-EPM is similar to Canada in terms of economic status and political
system, significant differences exists in their energy systems and consumption
behaviors. Region-specific data that characterize OECD-EPM’s economy is
necessary to accurately represent these differences. Inputting quality data at the
level of detail required in CIMS is essential to building an OECD-EPM model with
equal integrity to its Canadian counterpart.
23 The 62% target, as prescribed by the sovereign entitlement regime, has been rounded down to
60% for the purpose of simplicity.
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As mentioned in Chapter 2, acquiring high level technological data for the
entire region was challenging. Despite best efforts to acquire the most accurate
data, time and representation limited a more comprehensive data search. In
contrast to other CIMS-Global regions, data for the OECD-EPM region was
accessible, but only for a selection of OECD-EPM countries. As a result, many
sectors are biased towards those countries. For example, technological input
data in the commercial sector comes from Japanese and Australian sources.
Information overload was also a problem. Because of the data requirements of
CIMS, a large portion of time was spent locating data. Sometimes searches
proved to be unsuccessful or the validity of the source questionable. Given that
there are several well-established models for many of the OCED-EPM countries,
partnerships should be established to capitalize on the expertise of these
research groups.
Large amounts of uncertainty are embedded in CIMS OECD-EPM.
Simulation models like CIMS provide insight into future trends and are useful
tools for climate and energy policy analysis. While no simulation model will ever
be 100% accurate, its utility should not be dismissed. I conclude this report with a
brief section of recommendations for future research, notably improvements to
the CIMS model.
6.3 Recommendations for Further Research
This research effort was the first attempt to create a CIMS model for the
OECD-EPM region. The model will require ongoing attention to elevate it to the
integrity level of CIMS Canada. The following are key issues requiring the
attention of future research:
Establish strategic partnerships: As mentioned above, the data
requirements of CIMS are numerous. Locating and verifying all the
technology specific data required by CIMS demands large amounts
of resources and time. To acquire data in an efficient manner,
linkages should be established with institutions already in
87
possession of such information, such as universities and statistical
organizations located in OECD-EPM countries. Additionally,
strategic alliances should be formed with developers of other
models that represent OECD-EPM countries. Some entities that
would be compatible for partnerships include EuroStat, the
European Commission, the Australian Bureau of Statistics, and the
statistics branch of the Japanese government.
Verify behavioural parameters: Using the default CIMS behavioural
parameters in this research may not be a valid assumption. Due to
differences in technological development, energy supply and
energy costs between Canada and OECD-EPM, the parameters
(intangible costs and discount rates) may not be equivalent. A
comprehensive literature review should be performed to verify
these assumptions. If this effort reveals assumptions to be invalid,
then a study similar to that of John Axsen (2006), which estimated
behavioural parameters using discrete choice modelling, is
recommended.
Develop a full equilibrium model: Several sections of this report
mention the macroeconomic limitations of CIMS’s partial
equilibrium structure. Connecting CIMS to a computable general
equilibrium model is an effective way to incorporate full equilibrium
analysis. Deriving elasticities of substitution from CIMS output
would be one option for soft linking CIMS to a computable general
equilibrium model. Adding full equilibrium analysis to CIMS will
produce a more realistic picture of policy impacts and costs. Ideally,
the model would also serve as a link between all CIMS regional
models. Additionally, adding a full equilibrium analysis component
to CIMS will facilitate the simulation of transfers, such as energy
88
trade, emissions permit trading and carbon leakage, between
CIMS-Global regions.
Create several climate zone nodes in the residential sector: As
mentioned in Section 5.3, there is a wide range of climatic variation
between countries in the OECD-EPM. Presently, CIMS OECD-EPM
aggregates all countries in the residential sector, assuming an
aggregate demand level for heating and cooling services. In reality,
each country has different heating and cooling demands. Given the
importance of these services to total energy consumption in the
sector, ignoring these differences may produce erroneous
forecasts. To rectify this problem, I suggest creating different
residential nodes to capture the unique service requirement of the
major climate zones in the region.
Enhance the consistency of CIMS models: As noted in Melton
(2008), modelling consistency is instrumental to any meaningful
interregional policy analysis. Current regional CIMS models are
quite diverse from one another in terms of major modelling
assumption, sectors represented, and the presence and availability
of abatement options. For example, the Canadian CIMS model
includes an agriculture and ethanol sector. However, these sectors
are not represented in other regions. Comparing marginal
abatement cost curves between Canada and other CIMS regions
would produce unbalanced results; abatement options in Canada
would be greater than the other regions. Future interregional policy
analysis should ensure that there is consistency in the areas
mentioned above. Using regional models with equivalent numbers
of energy supply and demand sectors or with consistent
assumptions about world energy price trends are two examples of
such improvements.
89
Incorporate an emissions permit trading sector: CIMS OECD-EPM
projects high marginal abatement costs. Concerns over severe
economic losses have generated support for emissions permit
trading, as trading may reduce the overall cost of compliance.
CIMS OECD-EPM assumes no emissions permit trading, and thus
may overestimate marginal abatement costs. As mentioned in
Section 5.2, it is unlikely that any sustained global or regional
abatement effort will omit emissions permit trading. Thus, it is
important that we include this very realistic abatement option into
our modelling efforts. One suggestion is to include an emission
permit module in the proposed computable general equilibrium
model. Adding this module to a CIMS model with full equilibrium
analytical capabilities would require minimal effort, but provide
maximum benefit to the model’s utility.
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APPENDIX 1: GEOGRAPHIC COVERAGE
CIMS OECD-EPM represents selected countries that are members of the Organization for Economic Cooperation and Development (OECD). The United States and Canada, members of OECD North America, are excluded as individual CIMS models exist for both regions. The regional boundaries of this analysis are defined according to the IEA Energy Balances 2000/2001 (2003): OECD Europe Austria, Belgium, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Poland, Portugal, the Slovak Republic, Spain, Sweden, Switzerland, Turkey and the United Kingdom. OECD Pacific Australia, Japan, Korea and New Zealand OECD North America Mexico
91
APPENDIX 2: INDUSTRIAL SECTOR DATA SOURCES
Sector Source
Iron and Steel C02 in the Iron and Steel Industry (Gielen & Moriguchi, 2002) Direct Reduce Iron and Iron (Chemlink Pty Ltd, 2001) Energy Intensity in the Iron and Steel Industry (Worrell, et al., 1997) Eurofer Forecasts Modest (Metal Center News, 2006) European Steel Technology Platform Report (EUROFER, 2006)
Japan Iron and Steel Federation: Statistics and Analysis (JISF, 2008) Steel In Figures (WSO, 2008) Tracking Industrial Energy Efficiency and CO2 Emissions (IEA, 2007f)
Industrial Minerals
European Lime Association (EULA, 2007) Tracking Industrial Energy Efficiency and CO2 Emissions (IEA, 2007f)
UNIDO Industry Statistics Yearbook (UNIDO, 2002) US Geological Survey (USGS, 2007) Chemicals Manufacturing Industry 1995-2003 (Steinbach et al., 2006) Rosy outlook for chemicals output despite ongoing high energy costs
(Adams, 2006) Tracking Industrial Energy Efficiency and CO2 Emissions (IEA, 2007f) UNIDO Industry Statistics Yearbook (UNIDO, 2002) Metals Energy and reduction in energy use (AFFG, 2005) International Aluminum Institute (IAI, 2008) International Copper Fact Book (ICSG, 2008) International Zinc Association (IZA, 2008) International Zinc and Lead Study Group (IZLSG, 2008) Tracking Industrial Energy Efficiency and CO2 Emissions (IEA, 2007f) UK Minerals Survey (Heatherington et al., 2008) UNIDO Industry Statistics Yearbook (UNIDO, 2002) USGS Mineral Yearbook (USGS, 2005) & (USGS, 2007) Mining International Aluminum Institute (IAI, 2008) Tracking Industrial Energy Efficiency and CO2 Emissions (IEA, 2007f) UK Minerals Survey (Heatherington et al., 2008) UNIDO Industry Statistics Yearbook (UNIDO, 2002) USGS Mineral Yearbook (USGS, 2005) & (USGS, 2007) Continued…
92
Other Manufacturing
Global Market Information Database (EI, 2008) European Competitiveness Report 2002 (EC, 2002) IEA Energy Balances 2000/2001 (IEA, 2003)
Structural statistics for industry and services (OECD/IEA, 2000) Technological Change and the Environment (Jaffe et al. 2001) Tracking Industrial Energy Efficiency and CO2 Emissions (IEA, 200f) UNIDO Industry Statistics Yearbook (UNIDO, 2002) Pulp and Paper
Confederation of European Paper Industry (CEPI, 2008) ForeStats (FAO, 2008) Global Market Information Database (EI, 2008) IEA Energy Balances 2000/2001 (IEA, 2003)
Integrated Pollution Prevention and Control (EC, 2001) Tracking Industrial Energy Efficiency and CO2 Emissions (IEA, 2007f) UNIDO Industry Statistics Yearbook (UNIDO, 2002) Petroleum Refining Global Market Information DatabFase (EI, 2008) Oil Information 2002 (IEA, 2002) Tracking Industrial Energy Efficiency and CO2 Emissions (IEA, 2007f) World Energy Outlook (IEA, 2006) Worldwide refining survey (Stell, 2002)
93
APPENDIX 3: DRIVERS OF ENERGY DEMAND
Physical and Monetary Output Summary by Sector
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Residential ( '000 thousand households) 305,20 320,77 337,13 350,83 365,09 376,12 381,77 387,50 393,32 399,22 Commercial (million m2 floorspace) 7,633 8,095 8,631 9,269 9,786 10,333 10,801 11,291 11,803 12,338 Transportation Personal (billion pkt) 9,143 9,875 10,665 11,518 12,439 13,435 14,106 14,671 15,257 16,478 Transportation Freight (billion tkt) 9,230 9,753 10,235 10,679 11,092 11,491 11,814 12,147 12,488 12,838 Chemical Products (million tonnes) 329 361 396 434 475 521 571 626 687 753 Industrial Minerals (million tonnes) 500 497 494 493 492 491 490 489 488 487 Iron and Steel (million tonnes) 393 413 434 456 480 504 522 540 559 579 Metal Smelting (million tonnes) 22 24 28 32 37 42 50 57 67 78 Mineral Mining (million tonnes) 657 708 752 824 905 995 1,096 1,208 1,334 1,474 Paper Manufacturing (million tonnes) 98 112 124 137 151 167 184 204 225 248 Other Manufacturing (10 billion $2005 GDP) 2,731 3,011 3,319 3,659 4,034 4,316 4,618 4,941 5,287 5,657 Electricity (TWh) 5,496 5,725 5,896 6,101 6,426 6,710 7,000 7,288 7,613 8,007 Petroleum Refining (10 million m3) 1,261 1,279 1,332 1,357 1,337 1,354 1,402 1,479 1,580 1,723 Petroleum Crude Extraction (thousand barrels per day) 8,561 7,566 6,471 5,940 5,409 4,878 4,474 4,085 3,730 3,407 Natural Gas Extraction (billion m3) 411 420 429 441 458 481 511 551 602 667 Coal Mining (million tonnes) 1,003 1,054 1,086 1,126 1,220 1,301 1,374 1,457 1,570 1,772
94
APPENDIX 4: COMPARISON OF MACCs- .......................ALL CIMS REGIONS (2050)
0
20
40
60
80
100
120
140
160
0 20 40 60 80
20
05
USD
/t C
O2e
GHG Reduction (% Below BAU )
AMELA
DA
TE
OECD-EPM
China
Canada
0
20
40
60
80
100
120
140
160
0 2 4 6 8
20
05
USD
/CO
2e
GHG Reduction (Gt C02e)
AMELA
DA
TE
OECD-EPM
China
Canada
95
APPENDIX 5: MACCs FOR ALL CIMS SECTORS IN 2050
0
50
100
150
200
250
300
350
400
0 20 40 60 80 100
20
05
USD
/t C
O2
e
GHG Reduction (% Below BAU)
Residential
Commercial
Transpiration
Industry
Electricity
0
50
100
150
200
250
300
350
400
0.0 0.5 1.0 1.5 2.0
20
05
USD
/t C
O2
e
GHG Reduction (Gt CO2e)
Residential
Commercial
Transpiration
Industry
Electricity
96
APPENDIX 6: BAU ENERGY AND GHG FORECASTS
Energy Consumption (EJ)
2005 2010 2020 2030 2040 2050
Total Primary Energy Consumption
Natural Gas 24.2 23.7 25.0 27.5 27.6 28.8 Coal 17.4 17.8 18.2 20.9 22.6 24.9 Oil 43.3 44.2 46.7 47.4 50.9 55.0 Nuclear 15.1 15.8 17.1 18.3 19.2 19.7 Renewables 6.6 7.8 9.5 11.1 13.3 16.3 Other 2.0 2.1 2.5 2.8 3.3 3.8 Total 108.6 111.5 119.0 128.0 136.9 148.5
Electricity
Natural Gas 8.1 8.2 8.6 9.2 9.9 10.2 Coal 13.5 13.9 14.3 16.9 18.5 20.6 RPP 2.7 2.3 1.4 0.6 0.7 0.9 Nuclear 15.1 15.8 17.1 18.3 19.2 19.7 Renewables 5.8 6.4 7.0 7.7 9.1 11.1 Total 45.1 46.6 48.4 52.7 57.5 62.5
Total Final Energy Consumption
Natural Gas 16.2 15.5 16.4 18.2 17.7 18.6 Coal 3.9 3.9 3.9 4.0 4.1 4.3 Oil 40.6 41.9 45.3 46.9 50.2 54.1 Electricity 17.8 18.9 20.3 22.7 25.3 27.8 Renewables 0.8 1.4 2.5 3.4 4.2 5.2 Other 2.0 2.1 2.5 2.8 3.3 3.8 Total 81.3 83.7 90.9 98.0 104.7 113.8
Residential
Natural Gas 5.8 4.8 5.0 6.0 4.9 4.4 Oil 3.5 4.0 3.3 3.2 4.4 5.3 Electricity 6.3 7.0 7.7 8.1 8.4 8.6 Renewables 1.1 1.3 1.7 1.8 2.0 2.3 Total 16.7 17.1 17.7 19.0 19.7 20.7
Continued…
97
Energy Consumption (EJ)
2005 2010 2020 2030 2040 2050
Commercial
Natural Gas 3.0 3.1 2.9 2.5 2.0 1.9 Oil 3.1 3.3 3.8 4.1 4.4 4.7 Electricity 3.5 3.5 3.9 4.6 5.4 6.1 Renewables 0.0 0.0 0.0 0.0 0.0 0.0 Total 9.6 9.9 10.6 11.3 11.8 12.6
Industry*
Natural Gas 7.2 7.6 8.5 9.7 10.8 12.3 Coal 3.9 3.9 3.9 4.0 4.1 4.3 Oil 6.6 6.7 8.2 9.1 10.4 11.8 Electricity 8.0 8.3 8.6 9.5 10.7 12.3 Renewables 0.6 1.1 1.8 2.5 2.9 3.5 Other 1.1 1.2 1.4 1.6 1.9 2.2 Total 27.4 28.8 32.4 36.3 40.7 46.4
Transportation
Natural Gas 0.1 0.1 0.0 0.0 0.0 0.0 RPP 27.4 27.8 30.1 30.6 31.1 32.4 Electricity 0.0 0.0 0.1 0.5 0.7 0.8 Ethanol 0.0 0.0 0.0 0.3 0.5 0.7 Biodiesel 0.0 0.0 0.0 0.1 0.1 0.1 Hydrogen 0.0 0.0 0.0 0.0 0.0 0.1 Total 27.5 27.9 30.2 31.4 32.4 34.1
GHGs (Mt)
Residential 570 563 527 570 610 661 Commercial 382 397 421 429 422 439 Transportation 1,996 2,051 2,208 2,211 2,244 2,329 Industrial* 1,832 1,881 2,081 2,266 2,499 2,790 Electricity 1,769 1,787 1,772 1,969 2,154 2,352 Total 6,548 6,677 7,008 7,445 7,928 8,570
*Includes both energy supply and demand sectors
98
APPENDIX 7: POLICY ENERGY AND GHG FORECASTS
Energy Consumption (EJ)
2005 2010 2020 2030 2040 2050
Total Primary Energy Consumption
Natural Gas 25.0 25.0 26.0 28.0 29.0 31.0 Coal 13.0 11.0 6.0 7.0 10.0 13.0 Oil 42.0 41.0 38.0 30.0 27.0 26.0 Nuclear 16.0 17.0 20.0 24.0 28.0 30.0 Renewables 7.0 9.0 12.0 17.0 22.0 28.0 Other 2.0 2.0 3.0 3.0 4.0 5.0 Total 105.0 104.0 105.0 110.0 120.0 132.0
Electricity
Natural Gas 9.0 9.0 9.0 10.0 12.0 13.0 Coal 10.0 7.0 4.0 5.0 8.0 12.0 RPP 3.0 2.0 1.0 1.0 1.0 1.0 Nuclear 16.0 17.0 20.0 24.0 28.0 30.0 Renewables 6.0 8.0 10.0 12.0 14.0 16.0 Total 43.0 43.0 44.0 52.0 62.0 72.0
Total Final Energy Consumption
Natural Gas 17.0 16.0 17.0 18.0 17.0 18.0 Coal 4.0 3.0 3.0 2.0 2.0 2.0 Oil 39.0 39.0 37.0 29.0 26.0 25.0
Electricity 18.0 19.0 20.0 25.0 30.0 34.0 Renewables 1.0 1.0 2.0 5.0 8.0 11.0 Other 2.0 2.0 3.0 3.0 4.0 5.0 Total 80.0 80.0 82.0 83.0 87.0 94.0
Residential
Natural Gas 6.0 5.0 4.0 4.0 2.0 1.0 Oil 3.0 4.0 2.0 0.0 0.0 0.0 Electricity 6.0 7.0 8.0 9.0 10.0 10.0 Renewables 1.0 1.0 2.0 3.0 3.0 3.0 Total 17.0 17.0 15.0 15.0 15.0 15.0
Continued…
99
Energy Consumption (EJ)
2005 2010 2020 2030 2040 2050
Commercial
Natural Gas 3.0 3.0 3.0 3.0 2.0 1.0 Oil 3.0 3.0 2.0 1.0 0.0 0.0 Electricity 3.0 4.0 5.0 6.0 8.0 9.0 Renewables 0.0 0.0 0.0 0.0 0.0 0.0 Total 10.0 10.0 10.0 10.0 10.0 10.0
Industry*
Natural Gas 8.0 8.0 10.0 12.0 13.0 15.0 Coal 4.0 3.0 3.0 2.0 2.0 2.0 Oil 6.0 6.0 6.0 5.0 5.0 5.0 Electricity 8.0 8.0 8.0 9.0 11.0 13.0 Renewables 0.0 1.0 1.0 2.0 3.0 3.0 Other 1.0 1.0 1.0 1.0 2.0 2.0 Total 26.0 27.0 28.0 32.0 36.0 41.0
Transportation
Natural Gas 0.0 0.0 0.0 0.0 0.0 0.0 RPP 27.0 27.0 28.0 23.0 20.0 20.0 Electricity 0.0 0.0 0.0 1.0 1.0 2.0 Ethanol 0.0 0.0 0.0 1.0 2.0 2.0 Biodiesel 0.0 0.0 0.0 1.0 3.0 5.0 Hydrogen 0.0 0.0 0.0 0.0 0.0 0.0 Total 27.0 27.0 28.0 26.0 26.0 28.0
GHGs (Mt)
Residential 565.0 529.0 351.0 262.0 187.0 157.0 Commercial 374.0 366.0 293.0 206.0 119.0 86.0 Transportation 1960.0 1972.0 2036.0 1643.0 1467.0 1416.0 Industrial* 1741.0 1700.0 1576.0 1406.0 1276.0 1259.0 Electricity 1477.0 1241.0 670.0 503.0 497.0 534.0 Total 6116.0 5808.0 4925.0 4020.0 3546.0 3453.0
*Includes both energy supply and demand sectors
100
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