2. Long-term Scenarios based on AIM Model
Tsuneyuki Morita1, Kejun Jiang2, Toshihiko Masui1, Yuzuru Matsuoka3, andAshish Rana1
Summary. In order to respond to climate change, it is essential to describe
possible future trajectories of greenhouse gas (GHG) emissions in terms of bothnonintervention and intervention strategies. This chapter analyzes long-term GHG
emissions scenarios according to alternative development paths for the world andmajor regions, based on the nonintervention emissions scenarios quantified by the
Asian-Pacific Integrated Model (AIM). AIM has been revised and applied to thequantification of story lines for scenarios of socioeconomic development, and
GHG emissions from energy use, land use changes, and industrial production
processes are simulated. A wide range of mitigation policies have been adopted as
responses to climate change. The results show that to achieve stabilization at adifferent GHG concentration level, it is essential to have a policy package to reachthe target concentration level, rather than a single policy. Energy efficiency
improvements and renewable energy introduction make a key contribution to the
reduction of GHG emissions as a result of such a policy package. The mitigation
costs could be small without a significant reduction in economic growth. The
developing world could substantially reduce GHG emissions compared withnonintervention scenarios with sufficient knowledge transfer from the developed
countries.

2.1 Introduction
The Asia-Pacific region, which covers Asia plus the Oceania region, excluding the
Middle East, has half the world’s population and is experiencing high economic
growth, making it a major growth center in the global economy. Many countries in
the region share problems that arise from rapid industrialization, population
growth, and the increasing concentration of people in cities. With energy
consumption increasing rapidly, the region is a major and growing driver ofclimate change. On the other hand, the region will suffer significant damage fromclimate change in terms of its impact on water resources, agriculture, ecosystems,
and natural disasters. The Asia-Pacific region has been emerging as a dominant
force for human responses to climate change.
To enable the Asia-Pacific region to respond to the climate change issue, thefirst step is to forecast the regional scenarios for greenhouse gas emissions in
1

National Institute for Environmental Studies, Tsukuba 305-8506, Japan
Energy Research Institute, State Development Planning Commission, Guohong Building,
A-11 Muxidi Beili, Xicheng District, Beijing 100038, China
3
Kyoto University, Kyoto 606-8501, Japan2

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Morita, T. et al.

relation to world emissions. Future emissions scenarios are mostly dependent on
regional development patterns, and this region has a wide range of options for its
development path. This means that future GHG emissions could be diverse,
depending on the future development path. The recognition of such diverse nonintervention scenarios is very important in assessing the policy options forresponding to climate change, since any reduction in the level of GHG emissions
depends not only on the target climatic stabilization level, but also on the rate ofincrease in GHG emissions in a baseline non-intervention scenario.
It is known that a lot of emissions scenarios have already been quantified or
published, including ones for the Asia-Pacific region. The most popular scenarios
are the IS92 scenarios published by the IPCC in 1992 (IPCC 1992), and thenumber of other quantified scenarios comes to more than 150 for China, 30 for
India, and 20 for South Asia (Morita et al. 1994; Morita and Lee 1998). However,none of these scenarios was explicitly analyzed from the viewpoint of future
alternative paths for development in the Asia-Pacific region. Only some scenarios
clarified the relationship between development patterns and emissions at the
global level (Lashof and Tirpak 1990; WEC 1993). The IPCC activities for theSpecial Report on Emissions Scenarios (SRES) (Nakicenovic et al. 2000) gave us
an opportunity to analyze the Asia-Pacific emissions scenarios with explicit
consideration of the regional paths for future development.
This chapter presents some results from the AIM model simulations foremissions scenarios for the Developing Asia-Pacific countries and the world withand without climate change policies. In the next section, SRES scenarios arebriefly introduced. This is followed by a description of the model. Following this,the results, findings and conclusions are given.

2.2 SRES Scenarios
Each scenario links one of four “story lines” with one particular quantitativemodel interpretation. All the scenarios based on a specific story line constitute a
scenario “family.” The following paragraph describes four story lines, driving
forces of the SRES scenarios and their relationships. Each story line represents theplaying out of different social, economic, technological and environmental
developments (or paradigms), which may be viewed positively by some people
and negatively by others. Possible “surprise” and “disaster” scenarios were
excluded.
The main characteristics of the four SRES story lines and scenario families are:
• The A1 story line and scenario family describes a future world of very rapid
economic growth, low population growth and the rapid introduction of new andmore efficient technologies. Major underlying themes are convergence among
the regions, capacity building and increased cultural and social interaction, witha substantial reduction in regional disparities in per capita income.
Scenarios in the A1 family were categorized into four groups according to theirtechnological emphasis—on coal (A1C), oil and gas (A1G), non-fossil fuel2. Long-term Scenarios based on AIM Model

19

energy sources (A1T) or a balanced mix of all three (A1B). The last group, abalanced mix, is simply noted in this chapter as “A1”.
• The A2 story line and scenario family describes a very heterogeneous world.
The underlying theme is self-reliance and preservation of local identities.
Fertility patterns across regions converge very slowly, resulting in highpopulation growth. Economic development is primarily regionally oriented, andper capita economic growth and technological change are more fragmented andslow compared to other story lines.
• The B1 story line and scenario family describes a convergent world with rapidchange in economic structures toward a service and information economy,
reduction in materials intensity and the introduction of clean and resourceefficient technologies. The emphasis is on global solutions for economic, socialand environmental sustainability, including improved equity, but without
additional climate initiatives.
• The B2 story line and scenario family describes a world in which the emphasis
is on local solutions to economic, social, and environmental sustainability. It isa world with less rapid, and more diverse technological change, but with astrong emphasis on community initiatives and social innovation to find local
and regional solutions. While policies are also oriented towards environmental
protection and social equity, they are focused on the local and regional levels.

2.3 Model Description
In order to quantify GHG emissions from various sources, a new linkage moduleof the integrated assessment model was developed and comprehensive storylinesof development were established. Future projections were made using the
integrated assessment model for energy use, energy production, industrial processes, land-use changes, agricultural production, livestock, etc. from 1990 to
2100 according to the storylines. These projections were finally converted to the
GHG emissions scenarios.
A model framework called the AIM/Emission-Linkage model was developed
for this emissions scenario study. It links several models to calibrate the data andperform scenario quantification. An important point to note is that thedevelopment pattern of the developing Asia-Pacific region should be analyzed inrelation to the global regime since international issues will strongly influence theregion’s future environment, economy, and energy activities. Scenarios for thedeveloping Asia-Pacific region should also be closely related to scenarios forother regions. Hence, the model framework adopted was a global model divided
into key regions.
Major emission sources including energy activities, industries, land use,
agriculture, and forests can be simulated in the model framework. The structure of
the AIM/Emission-Linkage model comprises two kinds of top-down models –energy and land-use – and a set of bottom-up models as shown in Fig. 1. The
GHG emissions from energy consumption and energy production are simulated by20

Morita, T. et al.

Socioeconomic Scenari os
GDP

Population

Resource Base

Lifestyle

AIM/Emission-Linkage
Bottom-up Model
End-UseEnergyEfficiency

SocialEnergyEfficiency

GDP

Resource
Base

Feedback

Population

EnergyServiceDemandSocialEnergyEfficiency
ChangeTechnology
ChangeFood
Consumption
Pattern

Exploitation
Technology

Industrial
Process
ChangeIndustrial
Production

GDP

Other
InputsPopulation

EnergyResource
Goods &
ServiceDemandEnergy Price

Final Energy
DemandFinal Energy
SupplyPrimary
EnergySupplyEnergyConversion
Technology
Efficiency

End-UseTechnology
Efficiency

Biomass
EnergyDemandGoods &
Service
SupplyGoods andService Price
Land Input
CroplandEnergyConversion
Technology

End-UseTechnology

Energy-Economic Model

PastureForestBiomass Farms
Other Land

Land Equilibrium Model
GHG Emissions

AIM/ Climate Model

AIM/ Impact Model

Fig. 1. Outline of the AIM/Emission-Linkage model

the energy model. GHG emissions from land use are derived from the land use
model, while GHGs from other emission sources are calculated by simplified
industry process models that describe the relationship between GDP per capita andindustrial product outputs.
The energy sector top-down module was developed based on the revised
Edmonds-Reilly-Barns (ERB) model (Edmonds et al. 1983; Edmonds et al. 1995),
which is widely used for the analysis of emissions. The top-down model for theenergy sector provides a consistent, conditional representation of economic,

2. Long-term Scenarios based on AIM Model

21

demographic, technical, and policy factors as they affect energy use andproduction. It is a macroeconomic partial-equilibrium model that deals withenergy activities and forecasts energy demand over the long term. It uses the grossdomestic product (GDP) and population as future development drivers, combinedwith other energy-related parameters to forecast energy demand based on the
supply and demand balance. Three end use sectors—industrial, residential andtransportation—and one energy conversion sector— the power generation
sector—are specified in the model. Energy efficiency is described by bothtechnology efficiency and social efficiency improvements. A number oftechnologies in these four sectors are listed in the model to present different
possibilities of technological progress. A link between the bottom-up energy
model and the top-down energy model has been developed. A detailed energy use
analysis for the developing Asia-Pacific region from the bottom-up model drives
the energy use pathway before 2030, while a simplified linkage is presented forother regions in the model. The linked AIM/Enduse model and the energy topdown model comprise the energy model in the model framework.
The top-down land use model is based on the Global Trade Analysis Project(GTAP), which was established in 1992 (Hertel 1997). This model is an applied
general-equilibrium model that divides the world into multiple regions. For thesake of this analysis, the land uses for agriculture, livestock, and forests are
considered, and the biomass energy demand is considered exogenously. It is
designed to explicitly model agriculture and land use, endogenously determine
emissions resulting from land use changes, and explore the use of biomass as anelement of a strategy for anthropogenic carbon emissions.
The bottom-up models were prepared using the original AIM bottom-up
components, which can reproduce detailed processes of energy consumption,industrial production, land use changes, and waste management as well as
technology development and social demand changes. The AIM/Enduse model ispart of the Asian-Pacific Integrated Model (AIM), which was developed by the
National Institute for Environmental Studies (NIES) and Kyoto University (AIM
Project Team 1996; Hibino et al. 1996). It is a bottom-up, energy technology
model. Based on detailed descriptions of energy services and technologies, itcalculates the total energy consumption and production in a bottom-up manner.
This model has been used to analyze several key countries in the Asian region,
including China, India, Indonesia, and Japan. AIM/Enduse models for key Asiandeveloping countries have been constructed, and the results of analyses using thismodel have been reported (Jiang et al. 1998; Hu et al. 1996). Among theadvantages of bottom-up models, the most important is that their results can beinterpreted clearly because they are based on detailed descriptions of changes inhuman activities and technologies.
The AIM/Emission-Linkage model combines these various components tocalculate future GHG emissions in a relatively wide ranging analysis. For the
purpose of the model, the world is divided into nine regions: USA, Western
Europe, OECD countries and Canada, Pacific OECD, Eastern Europe and theformer Soviet Union, Centrally Planned Asia and China, South and East Asia, theMiddle East, Africa, and Central and South America. The model has a time

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Morita, T. et al.

horizon extending from 1990 to 2100. The time steps are in units of 5 years up to2030, followed by time steps at 2050, 2075, and 2100. The GHGs covered in thenonintervention emissions scenarios are CO2, N2O, CO, NOx, and CH4. Since SO2
has a strong influence on climate change and is an important pollutant in local
areas (Gan 1998; Qi et al. 1995), it is also included. CO2 emissions are analyzed in
the intervention scenarios.

2.4 Emissions Scenarios without Climate Change Policies
2.4.1 Assumptions
The data has been compiled from several sources. Based on the descriptions ofdevelopment patterns (story lines), the quantified key scenario drivers for eachscenario used in our model for the Developing Asia-Pacific and the world arelisted in Table 1.
The population data resources include estimations by the United Nations and
IIASA. There are three patterns for population growth: low (for scenarios A1 andB1), medium (for scenario B2), and high (for scenario A2). The high and low
Table 1. Key scenario drivers assumed for the Developing Asia-Pacific countries and theWorld
Developing AsiaPacific population
Developing AsiaPacific GDP
growth rate
World population

World GDP

GDP/capita trends

A1
4.2 billion in
2050; 2.9 billion
in 2100
6.4% from 1990 to
2050, 4.6% from1990 to 2100
9 billion in 2050;
7 billion in 2100

$550 trillion in
2100, High growthin non-OECD
countries
OECD: more than$100,000 by 2100;
Non-Annex I:>$70,000 by 2100,$14,000 by 2040

AEEI
International trade

1.2%-1.6%
High trade
Low trade cost

Urbanization

Rapid increase

A2
5.8 billion in
2050; 7.3 billion
in 2100
3.9% from 1990 to
2050, 3.4% from1990 to 2100
15 billion in 2100,
Higher growth in
non-OECD
countries$250 trillion in
2100

B1
4.2 billion in
2050; 2.9 billion
in 2100
5.6% from 1990 to
2050, 4.0% from1990 to 2100
9 billion in 2050;
7 billion in 2100

B2
4.7 billion in
2050; 5.0 billion
in 2100
5.7% from 1990 to
2050, 3.8% from1990 to 2100
11.7 billion in
2100$350 trillion in
2100$250 trillion in
2100

Lower growth in
non-OECD
countries
Disparity rises.

Annex-I: morethan $90,000 by
2100; Non-AnnexI: >$30,000 by
2100; Global$40,000
1.6%-1.8%
High trade
Low trade cost

Disparity remains;
GDP/capita ofOECD is 7 times
that of non-OECD(now 13 times).

0.8%-1.0%
Low trade acrossregions, high trade
within regions;High trade cost
Increase in nonOECD
Decrease inOECD

Increase

1.0%-1.2%
Low trade acrossregions
High trade cost
Decrease2. Long-term Scenarios based on AIM Model

23

population assumptions were adopted from the research output of IIASA, while
the medium population assumptions were taken from the medium case of theWorld Bank population forecast.
Energy resources in the model include not only conventional energy such ascoal, crude oil and natural gas, but also unconventional sources of oil and gas. Theenergy resources in the simulation are ultimately the recoverable reserves. Theenergy reserves available for exploitation are determined by progress in energy
production technologies, which are described in the story lines. For example, in
the A1 scenario, available energy reserves are taken to be plentiful by assuming
high levels of improvements in the efficiency of energy exploitation technologies
that will make unconventional forms of energy available. Due to the large volume
of energy supply, several sub-scenarios were defined according to this possible
pathway of energy supply. The A1C scenario describes large amounts of available
coal reserves. Oil and gas supply is high in the A1G scenario, while the A1T
scenario assumes progress in high technology for renewable energy. A1B is abalanced scenario taking elements from these A1 scenarios. Energy resource
availability in Scenario A2 mainly relies on energy resources distribution among
the regions. The low technological progress assumption for energy production in
scenario A2 means that energy exploitation mainly relies on conventional energy.
Due to concern for environment, energy resource pressure is not so great for theB1 and B2 scenarios.
The grades of energy resources used in the model differ on the basis of
exploitation costs. When combined with the level of improvements in exploitation
technology efficiency (expressed as the rate of improvement in the marginal costof producing energy), the graded energy resource exploitation cost determines theprimary energy production cost (price). Table 2 shows the total for the energy
resources assumed in the model.
Improvements in energy efficiency mainly rely on the parameters of social
efficiency improvements and technological efficiency improvements based on theenergy market. Social efficiency improvements were determined according tofactors such as changes in the economic structure, the trend towarddematerialization, patterns in people's lifestyles, transportation, etc. Technologicalefficiency improvements were set up according to the introduction of different
technologies. For example, on the demand side, efficiency was assumed to be
Table 2. Assumed total energy resources
Conventional Conventional
oil
gas
OECD
1,271
2,186
CIS & EE
1606
2,679
Developing
912
657
Asia-Pacific
ROW
7,325
3,449
World
11,114
8,971
Unit: Exa (1018) joule.

Coal
56,80862,43920,385
3,368
143,000

UnconventionaUnconventiona
l oil
l gas
12,70973,062451
36,628
556
9,379
4,403
18,11945,026
164,095

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Morita, T. et al.

relatively low in the A1 scenarios since low energy prices provide very little
incentive to improve end use energy efficiencies and high income levels willencourage people to pursue comfortable and convenient lifestyles (especially inthe household, services, and transportation sectors). This will result in the
consumption of much more energy. Efficient technologies are not fully introduced
into the end use side, dematerialization processes in the industrial sector are notwell promoted, lifestyles become energy intensive, and there is greater use ofprivate motor vehicles in developing countries as per capita GDP increases. Thus,
the final energy use in scenario A1 is much higher than in the other scenarios (A2,
B1, B2), while the difference in per capita final energy use between Annex 1
countries and non-Annex 1 countries in 2100 is small. In determining energy
efficiency in the Developing Asia-Pacific, attention was paid to the processes ofsocial and economic restructuring that have been underway since the 1980s. Thisis a key issue for short- and medium-term analyses of the Developing Asia-Pacificcountries.
2.4.2 Quantified scenarios
From the parameters used for the inputs, outputs were obtained from the model onenergy use and GHG emissions.
Global primary energy use will keep increasing up to 2100, except in scenario
B1, in which primary energy starts to decrease after 2075. The range for primaryenergy use by the end of the next century is quite large and is 6.7 times that in1990 for A1C, while it is only 2.3 times for B1. The primary energy intensity has
a similar range, which is from 2.41 GJ/MUS$ for B1 to 7.7 GJ/MUS$ for A2,
while it was 16.5 GJ/MUS$ in 1990.
With regard to primary energy demand in the Developing Asia-Pacific, all of
the scenarios show increases to support the demand for economic development atleast up to 2075, and then a decrease is found for scenario B1. The highest
primary energy demand in 2100 (scenario A1C) is 10.5 times that in 1990, while
the lowest (scenario B1) is 3.7 times. The growth rate for primary energy use in
the Developing Asia-Pacific is higher than the global average. Per capita primaryenergy use ranges from 75 to 274 GJ, compared with 181 to 403 GJ for OECD
countries. A significant catch-up effect is seen in per capita primary energy use
although it is still lower than that of the OECD countries. The primary energy
mixes also show highly significant changes during the next century. However, thechanges are quite different for each scenario, in accordance with the conditions
described in the story lines. Figure 2 shows the primary energy mix for scenario
A1B, in which renewable energy becomes dominant.
GHG emissions follow the pattern of energy use in the case of CO2 and SO2
emissions. Due to the introduction of renewable energy, CO2 emissions reach apeak between 2030 and 2050 (Fig. 3), except for scenario A2 for the world and A2
for the Developing Asia-Pacific in which coal use will continue and renewableenergy use will be limited. Per capita CO2 emissions in the Developing AsiaPacific rise from 0.56 tons of carbon (t-C) in 1990 to range from 0.97 to 1.9 t-C in2. Long-term Scenarios based on AIM Model

25

2030, and 0.84 to 4.8 t-C in 2100, while the corresponding data for the OECD
countries are 3.34 t-C in 1990, 2.55 to 4.71 t-C in 2030, and 1.13 to 5.73 t-C in
2100. All the scenarios show that per capita CO2 emissions do not reach the levelof the OECD countries in 2100.
By the year 2100, the range of most GHG emissions scenarios expands
significantly for both the world and the Developing Asia-Pacific. For example,
CO2 emissions in these scenarios for the world in 2100 range from 6.0 Giga tonsof carbon (Gt-C) per year to 36.8 Gt-C per year, a factor of more than 6 (Fig. 4).
They are 2.4 Gt-C, 13.9 Gt-C and 5 Gt-C for the respective scenarios for theDeveloping Asia-Pacific. Consequently, these results highlight how future
emissions estimates can vary according to different development pathways.
Global SO2 emissions reach a peak between 2020 and 2050, and then start todecrease (Fig. 5). The emissions fall below the 1990 level by 2050 except for
scenario A1C. The global SO2 emissions trajectory follows that of the developing
countries. The Kuznets curve was introduced to calculate SO2 and NOx emissions.
Based on the historical data, per capita GDP is a key factor in controlling the levelof SO2 emissions. Since the per capita GDP in the Developing Asia-Pacific is
rapidly increasing, all SO2 emissions decrease after 2030 (Fig. 6). Emissions are
2500
Renewables

Exa joule

2000

Nuclear

1500

Gas

1000

Oil

500

Coal

0
1990 2000

2050
Year

2100

Gt-C

Fig. 2. Global primary energy in A1B scenario
40
35
30
25
20
15
10
5
0
1980

A1B
A1C
A1G
A1T
A2
B1
B2
2000

2020

Fig. 3. Global CO2 emissions

2040 2060
Year

2080

2100

2120

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Morita, T. et al.

higher in scenario A1C due to the large amount of coal used. The range of SO2
emissions in 2100 is relatively smaller than that of other gases. This is the result ofdomestic environmental policies to control pollutant emissions rather than climatechange policies.

Gt-C

16
14

A1B

12
10

A1C

8
6
4
2
0
1980

A1T

A1G

A2
B1
B2
2000

2020

2040 2060
Year

2080

2100

2120

Fig. 4. CO2 emissions in Developing Asia-Pacific
200
A1B
A1C

Mt-S

150

A1G
100

A1T
A2

50
0
1980

B1
B2
2000

2020

2040

2060

2080

2100

2120

Year

Fig. 5. Global SO2 emissions

100

A1B

Mt-S

80

A1C

60

A1G

40

A1T

20

A2

0
1980

B1
2000

2020

2040 2060
Year

Fig. 6. SO2 emissions in Developing Asia-Pacific2080 2100

2120

B2

2. Long-term Scenarios based on AIM Model27

It is interesting to look at the A1 scenario emissions family. This scenariofamily has a quite wide diversity. Scenarios A1B and A1T have low emissions,indicating that it is possible for the Developing Asia-Pacific to maintain a higheconomic growth rate with low emissions. Energy end use technology
improvements and large-scale renewable energy recovery will play key roles inthe trajectory. Scenarios A1C and A1G have high emissions due to the huge fossil
fuel consumption. Their emissions are among the highest of all the groups. If theworld follows either of these paths, new technologies for emissions control andnew policies will have to be introduced. The same applies to NOx emissions.

2.5 Emissions Scenarios with Climate Policies
Scenarios in the previous section (SRES scenarios) do not include any explicit
mitigation or stabilization policies or measures. As such, they include scenarios
ranging from rapidly increasing to decreasing emissions over the next one hundredyears. New scenarios based on the wide range of SRES scenarios were quantified
as a set of mitigation (policy intervention) scenarios for stabilizing atmospheric
GHG concentrations. Therefore, the policy/technology measures assumed in thesescenarios are strongly affected by baseline emission trajectories of SRES scenarios
as well as by their socio-economic assumptions. They describe mitigation
measures and policies (the additional climate initiatives) that would have to beundertaken, in each SRES scenario “world,” to achieve stabilization at different
levels (450, 550, 650, and 750 ppmv). As a result, the analysis and comparison of
scenarios with climate policies (AIM stabilization scenarios) can supply very
systematic data to clarify the relationship between the relative contribution of thedevelopment path and climatic policy/technology measures. Knowledge of these
relationships can in turn enable us to assess robust policy/technology options fordifferent future development paths.
2.5.1 Policy package design for stabilizing global climate
The AIM stabilization scenarios were simulated to quantify the various pathways
to reach the desired target for global GHG concentrations by the end of the 21st
century. A policy package was designed for this quantification based on thediverging baseline scenarios.
The policy package used in the AIM stabilization scenarios is as follows:• Improved transportation efficiency. Higher transportation technology efficiency
and the introduction of advanced transport technologies, such as electricvehicles and fuel cell vehicles, are included.
• Social efficiency gains. Efficiency improvements from industrial structurechanges and lifestyle changes are considered.
• Improved power generation efficiency. More advanced power generation
technologies are introduced.

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Morita, T. et al.

• Improved end use efficiency. Higher end use technology efficiency
improvements are adopted.
• Nuclear power progress. Advanced nuclear power generation technologies suchas the fast breeder reactor (FBR) are emphasized.
• Incentives for natural gas use.
• Carbon tax. A carbon tax is levied on the basis of carbon emissions.
• Renewable energy incentives. Solar, wind, geothermal, and ocean energy will
be well developed.
• Synthesized fuel production.
• Commercial biomass: early introduction, larger share. Commercial biomass
will involve low cost technology to bring it to the market.
• Preference for forests.
Population and GDP growth are not designed to be reduced for mitigation,although there will be some reduction in the GDP due to the introduction of theabove policies.
All these policies are incorporated in the AIM stabilization scenario analysis
based on the merits of each baseline scenario. In the A1B baseline scenario,
successful economic development, social prosperity, human equity, etc., are thekey factors. Consequently end use technology efficiency improvements and social
efficiency improvements are emphasized in the A1B stabilization scenario
analysis. Intergenerational equity is considered in the A1 mitigation scenarios to
avoid major pressure on CO2 emissions reductions after 2050. In the A2 scenario
failed economic development results in inequity and little improvement intechnological efficiency. Hence, technological efficiency improvements,
commercial renewable energy utilization, and nuclear technology incentives are
adopted in the A2 stabilization scenario simulation. A neutral policy level wasmaintained for the stabilization scenario analysis of the B2 world, since the B2baseline scenario already includes an understanding of the importance of humanwelfare and inequity, as well as environmental solutions. There is no majorpressure for policies in the AIM B1 stabilization scenario for a 550 ppmv
stabilization level analysis.
In the A1B stabilization scenario family, much stricter policies are required forthe 450 ppmv stabilization analysis. A wider range of policies has to be
introduced, and strong policies have to be considered in order to attain the large
reduction in CO2 emissions. Early reduction is essential to avoid substantial
pressure on social development and technological progress in the latter part of the
21st century. Investment in technology R&D will contribute to CO2 emissions
reductions over the next several decades. High carbon tax rates must also be
adopted at an early stage even in the developing countries.
By examining all the policies adopted in the AIM stabilization scenario
analyses, some policies such as carbon taxes, end use efficiency improvements,
and renewable incentives are seen in all the stabilization scenario analyses. All
these policies could be regarded as robust policies.
The quantified policies in this study are shown in Table 3 based on the model
parameters.

2. Long-term Scenarios based on AIM Model

29

Table 3. Policy option package for stabilization at 550 ppmv
Policy options
Transport
efficiency
improvements

A1B
Vehicle fuel use
efficiency
improvement
rate will be
0.14% higher
than the BaU
case for all
regions, startingfrom 2000

A2
Vehicle fuel use
efficiency
improvement
rate will be
0.14% higher
than the BaU
case for all
regions, startingfrom 2000

B1
Vehicle fuel use
efficiency
improvement
rate will be 0.1%
higher than thebaseline case forall regions,starting from
2000

Other end use
technology
efficiency
improvementsPowergeneration
efficiency
Socialefficiency
improvement--

0.1% higher
efficiency
improvements

0.13% higher
efficiency
improvement
0.3% higher
energyefficiency
improvement,
additional 0.2%
higher energy
efficiency
improvement indeveloping
countries from2030 to 2050

0.15% higher
efficiency
improvement
0.3% higher
energyefficiency
improvement,
additional 0.2%
higher energy
efficiency
improvement indeveloping
countries from2030 to 2050

0.1% higher
efficiency
improvement
0.1% higher
energyefficiency
improvement,
starting from
2000; additional
0.1% higher
energyefficiency
improvement indeveloping
countries from2030 to 2075
(efficiency
improvement
rate will be
0.1%, 0.2%,
0.1% higher in
2000, 2030 to2075 2100

B2
Vehicle fuel use
efficiency
improvement
rate will be
0.1% higher
than thebaseline casefor all regions,starting from2000
0.15% higher
efficiency
improvements
0.1% higher
efficiency
improvement
0.2% higher
energyefficiency
improvement,
starting from
2000; additional
0.2% higher
energyefficiency
improvement indeveloping
countries from2030 to 2070

2.5.2 Quantified stabilization scenarios
This section presents the quantified results from AIM/Emission-Linkage for thestabilization scenarios.
Among the same target concentration level stabilization scenarios—for
example, the 550 ppmv stabilization group—there is no significant difference inCO2 emissions trajectories (Fig. 7). Rather, the CO2 emissions reductions differ
due to the different baseline emissions trajectories. They show that CO2 emissions
will increase first then start to decrease in the second half of the 21st century.

30

Morita, T. et al.

Table 3. Policy option package for stabilization at 550 ppmv (continued)
Policy options
Carbon tax

A1B
US$50/t-C
Annex 1countries startfrom 2000, nonAnnex 1
countries startfrom 2030

Nuclear power
incentives-

Natural gas
incentives-

Syn-oil

0.1% higher
marginal
production cost
improvementrate
0.1% higher
marginal
production cost
improvementrate
0.1% higher
marginal
production cost
improvementrate-

Syn-gasBiomass
incentives

Solar energy

A2
B1
US$80/t-C
US$15/t-C
Carbon tax starts Annex 1
from 2000.
countries startfrom 2000, nonAnnex 1
countries startfrom 2030
0.5% higher
marginal
production cost
improvementrate
0.4% higher
0.1% higher
marginal
marginal
production cost production cost
improvement
improvement
rate
rate
0.15% higher
0.1% higher
marginal
marginal
production cost production cost
improvement
improvement
rate
rate
0.16% higher
0.1% higher
marginal
marginal
production cost production cost
improvement
improvement
rate
rate
0.2% higher
0.1% higher
marginal
marginal
production cost production cost
improvement
improvement
rate
rate
0.4% higher
marginal
production cost
improvement
rate (3.5cents/kWh)

B2
US$60/t-C
Annex 1countries startfrom 2000, nonAnnex 1
countries startfrom 2030
0.2% higher
marginal
production cost
improvementrate
0.2% higher
marginal
production cost
improvementrate
0.15% higher
marginal
production cost
improvementrate
0.16% higher
marginal
production cost
improvementrate
0.2% higher
marginal
production cost
improvementrate
0.1% higher
marginal
production cost
improvementrate

To achieve CO2 stabilization at a given level, CO2 abatement is mainly
achieved through a mix of technological progress in the energy end use sector andsupply sector, structural changes in the economy with a trend towarddematerialization and lifestyle changes. End use technology efficiency
improvements and lifestyle changes are favored mitigation measures in the A1B
baseline scenarios. In order to avoid possible damage from climate change toprevent a greater welfare loss, people may invest more in end use technology
R&D to attain higher efficiency improvements, and give up their energy-intensive
consumption patterns. Advanced energy end use technology could be introduced

Gt-C

2. Long-term Scenarios based on AIM Model

40
35
30
25
20
15
10
5
0
1980 2000 2020 2040 2060 2080 2100 2120
Year

31

A1B 550
A2 550B1 550B2 550A1B Base line
A2 Base line
B1 Base line
B2 Base line

Fig. 7. Global CO2 emissions in stabilization scenarios

to save energy, especially fossil fuels. In the A1B world, in order to reach the 450
ppmv stabilization level, early action to reduce GHG emissions becomes essential
due to the large reduction needed. If the reduction of GHG emissions is delayed,
there will be critical pressure for reductions in the latter half of the 21st century,
which may cause social and economic losses. In the A2 baseline scenario, due tothe energy resource limitations in the baseline scenarios, CO2 abatement is mainly
through progress towards zero carbon technologies such as renewable energy
utilization technologies, nuclear power generation technology, etc. Fossil fuel use
could be reduced due to the increase in renewable energy and nuclear energy
production, when the cost of such technologies decreases as a result of the large
demand for them. End use technology efficiency improvements are also a key
countermeasure for CO2 abatement. The results show that in the A2 world, early
GHG emissions reduction is also essential. In the B1 baseline scenario, there isrelatively little pressure for the CO2 emissions reduction to reach the 550 ppmv
stabilization level, so the target could be reached by price incentive policies, suchas a carbon tax. In the B2 baseline scenario, progress in both energy end use
technology and energy supply technology is emphasized.
Technological progress is thus a key issue for CO2 emissions abatement in the
AIM mitigation emissions scenarios. This is because these scenarios embrace theperspective of induced technical change; i.e., an additional environmental
constraint accelerates the rates of technological change already implicit in the
scenario baseline.
Examining the policies used for emissions reductions in this study, it is seenthat some of them are not necessarily adopted in response to climate change,
especially in developing countries. For example, technological efficiency
improvements in both energy production and energy end use, social efficiencychanges, and low carbon technology incentives (nuclear and renewable energy,etc.) have been widely adopted in pursuit of sustainable development, as has been
the case in China.
As a result, primary energy will decrease with energy efficiency improvements
and the introduction of energy price incentive policies, and the primary energy32

Morita, T. et al.

mix will tend to shift to low carbon energy sources such as natural gas, renewableenergy, nuclear energy, etc. (Fig. 8).
Cost analyses were simulated by the AIM/Emission-Linkage model. Table 4shows the GDP loss for each mitigation scenario and different target level in 2050
and 2100. The results reveal that the GDP loss ranges from 0.1% to 5.9% acrossthe scenarios. Obviously the costs rely on the target level and baseline emissions
trajectory. The largest loss occurs in the A1B-450 scenario, at 5.9%.
Applying the designed robust policies to different scenarios results in different
CO2 emissions levels (Fig. 9). Some commonly used policies in the AIM
mitigation scenarios could be recommended as essential countermeasures in
response to climate change, while they also have benefits unrelated to the climatechange concept. Policies such as technological progress in end use and energy
supply, social efficiency improvements, renewable energy incentives and carbon
taxes can be regarded as robust policies.

2100

1200
1000
800
600
400
200
0
1990 2000

2050
Year

2100

1200
1000
800
600
400
200
0
1990 2000

Renewables

Nuclear

Gas

2050
Year
Scenario B1 550

1000
800
600

2050
Year
Scenario B2 550

2100

2050
Year

2100

Exa joule

Exa joule

Scenario A2 550

Exa joule

Exa joule

Scenario A1B 550
1600
1400
1200
1000
800
600
400
200
0
1990 2000

400
200
0
1990 2000

Oil

Coal

Fig. 8. Global primary energy in AIM stabilization scenarios

Table 4. GDP loss for each scenario at different target levels
A1B-550
A1B-650
A1B-450
A2-550B1-550B2-550

2050
1.0%
0.6%
3.2%
1.3%
0.3%
0.9%

2100
2.0%
1.0%
5.9%
3.2%
0.1%
1.2%

2. Long-term Scenarios based on AIM Model

33

25

Gt-C

20

A1B 550

15

A2

10

B1

5

B2

0
1980 2000

2050
Year

2100

Fig. 9. CO2 emission reductions with robust policies

2.6 Findings
In this chapter, a model framework to analyze long-term emissions scenarios forthe global and the Developing Asia-Pacific was described. Four development
patterns were simulated to generate scenarios. Several key findings have beenobtained from the results, as follows:
1. The large range of CO2 emissions in 2100 was simulated for the global and theDeveloping Asia-Pacific countries.
2. The trend in the Developing Asia-Pacific emissions is similar to that for theworld, and the Asia-Pacific future would affect the global future significantly.
3. All the scenarios show that CO2 emissions in 2100 will be above the level in1990 for the Developing Asia-Pacific, while some scenarios present thepossibility that they will be below 1990 CO2 emissions at the global level.
4. The growth rate of GHG emissions is higher in the Developing Asia-Pacificcountries than the global rate.
5. Half of the scenarios present a decrease in CO2 emissions after 2050.
6. Technological progress will contribute substantially to low CO2 emissions.
7. Per capita CO2 emissions of the Developing Asia-Pacific countries will be
below the level of the OECD countries over the next 100 years.
8. SO2 and NOx emissions of the Developing Asia-Pacific countries will decrease
rapidly after 2020.
9. The high economic growth scenarios (scenarios A1) give a wider range of CO2
emissions trajectories than for the low economic growth scenarios.
10.The global market and global governance are especially important key factors
for CO2 emissions scenarios.
Furthermore, a set of mitigation scenarios was simulated by the AIM/EmissionLinkage model based on the nonintervention emissions scenarios. Key findings
from the results of this modeling are as follows:34

Morita, T. et al.

1. The targeted stabilization levels could be reached through the adoption ofvarious policies. All the mitigation scenarios from AIM show a trend towardvarious stabilization levels.
2. Wide-ranging policy packages are needed, rather than a single policy, in orderto mitigate the socioeconomic effects of responses to climate change.
3. In the A1 and A2 world views as well as for 450 ppmv stabilization, an early
reduction in GHG levels is essential to avoid serious pressure on social
development and technological progress in the second half of the 21st century.
4. Integration between global climate policies and domestic environmental
policies could effectively reduce GHG levels in the developing regions for the
next two or three decades.
5. Technological progress and lower energy consumption play a very importantrole in stabilization.
6. Knowledge transfer to developing countries is a key issue that should beemphasized to motivate developing countries to participate in early CO2emissions reductions.
7. Technological efficiency improvements for both energy use technology andenergy supply technology, social efficiency improvements, renewable energy
incentives and the introduction of energy price incentives, such as a carbon tax,
can be regarded as robust policies.
8. Robust technology/policy measures include efficiency improvements in end use
technologies and social systems, as well as the introduction of renewableenergy.

2.7 Conclusions
Based on the above findings, the following points form our conclusions from the
Asian viewpoint:
1. The future development of the Asia-Pacific region has a significant influenceon global emissions scenarios. The Developing Asia-Pacific countries willachieve a dominant position in climate change issues.
2. It is possible for the Developing Asia-Pacific to continue high economic growth
while maintaining GHG emissions at a low level.
3. Technological progress and technology transfer should be emphasized tomaintain low GHG emissions in the economic development of the Developing
Asia-Pacific.
4. It is important for the Developing Asia-Pacific to introduce sophisticatedmeasures to control GHG emissions before 2030.
5. Robust policy options should be designed to respond to a very wide range ofalternative development paths.
From the global common viewpoint, the major conclusions can be summarized
as follows:

2. Long-term Scenarios based on AIM Model

35

1. Different development paths require different technology/policy measures andinvolve different costs of mitigation to stabilize atmospheric CO2
concentrations at the same level.
2. Secondly, no single type of measure will be sufficient for the timely
development, adoption and diffusion of mitigation options for CO2
stabilization. Policy integration across an array of technologies, sectors andregions is the key to the successful promotion of climate change policies.
3. The level of technology/policy measures in the beginning of the 21st century
will be significantly affected by the choice of the development path over the
next one hundred years.
4. Several robust policy options across the different worlds are identified forstabilization. Technological efficiency improvements for both energy use
technologies and energy supply technologies, social efficiency improvements,
renewable energy incentives and the introduction of energy price incentives,
such as a carbon tax, can be regarded as robust policies.
5. Large and continuous energy efficiency improvements and afforestation arecommon features of mitigation scenarios in all the different SRES worlds. The
introduction of low-carbon energy is also a common feature of all scenarios,
especially the introduction of biomass energy over the next one hundred years,
as well as the introduction of natural gas in the first half of the 21st century.

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