79

Global Low-Carbon Society Scenario Analysis based on
Two Representative Socioeconomic Scenarios
Shinichiro FUJIMORI1*, Toshihiko MASUI1 and Yuzuru MATSUOKA2
1

Center for Social and Environmental Systems Research, National Institute for Environmental Studies,
16-2 Onogawa, Tsukuba, Ibaraki 305-8506 Japan2
Department of Urban and Environmental Engineering, Kyoto University
Kyoto daigaku-Katsura, Nishikyo-ku, Kyoto 615-8540, Japan*e-mail: Fujimori.shinichiro@nies.go.jp

Abstract
This paper analyzes an ambitious climate mitigation target, namely to halve global GHG (greenhousegas) emissions by 2050 relative to those of 1990. Two representative socioeconomic scenarios are considered.
A CGE (Computable General Equilibrium) model is used as the analytical tool. Although there is great
uncertainty about the future, we could ascertain several things. 1) The emission reduction target could cause a
GDP loss of more than 1.2% globally, with large variation among countries and regions. 2) Renewable energy
and CCS (Carbon Capture and Storage) are fundamental technologies for achieving a great GHG emission
reduction. 3) Asian regions cannot avoid putting as great an effort into reducing GHG emissions as othercountries. These findings imply that such a large reduction is feasible within our scenario assumptions;however, technological progress and diffusion should be supported in order to make those needed
fundamental technologies available. The regions which would suffer a large GDP (Gross Domestic Product)
loss should be aided and an international policy framework needs to be developed with such losses
considered.
Key words: CGE model, climate mitigation, low-carbon society, renewable energy, scenario analysis

1. Introduction
The 4th IPCC (Intergovernmental Panel in Climate
Change) Assessment Report concluded, with very highconfidence, that the global average net effect of humanactivities since 1750 has been one of warming. In
addition, it assessed the future impacts of climate changeon the natural ecosystem and human society, within alarge uncertainty range, to be highly significant. Reacting
to such scientific information, some countries have been
motivated to take mitigating measures and pledge theirown greenhouse gas reduction targets. This movement ismainly being led by the developed countries, but many
developing countries are also joining it. One of theframeworks for reducing GHG emissions has been the
Kyoto Protocol and another was produced at the Group of
Eight (G8) Summit held in Heiligendamm, Germany. At
that summit, the G8 countries agreed to consider certain
proposals, which included reducing global emissions byat least half by 2050. In 2009, COP 15 (Conference of theParties to the United Nations Convention on Climate
Change) was held in Copenhagen, at which the participants re-confirmed the necessity of long-term cooperaGlobal Environmental Research
17/2013: 79-87
printed in Japantion to limit the global temperature increase to two degrees. What, then, does this imply for the energy systemor macro economy?
Although there is a range of emission pathways forachieving the two-degree target given the uncertainty ofclimate model parameters and feasible emissions reduction velocity, the 2050s emissions range is narrow and it
can be interpreted as halving global GHG emissions by
2050 relative to 1990 and being approximately consistent
with a two-degree target and stabilization of CO2equivalent concentration at 450 ppm (Rogelj et al., 2011;
Meinshausen et al., 2009). In the last few years IAMs
(Integrated Assessment Models) have been used to assess
strong climate mitigation policy scenarios such as stabilizing atmospheric CO2 concentration at a 450 ppm
equivalent. Clarke et al. (2009) compiled around ten research results and showed how the difficulties of climate
mitigation differed depending on the CO2 concentration
stabilization level (such as 450 or 550 ppm) and thetime-path of international participation (such as whether
developing countries’ participation could be delayed ornot). Clarke et al. (2009) summarized the results of the
22nd Energy Modeling Forum, EMF 22. Most of the©2013 AIRIES80

S. FUJIMORI et al.

models provided solutions for a 550 ppm CO2-equivalentconcentration target but not for a 450 ppm target. Six ofthe ten models provided feasible solutions for 450 ppm,
but their carbon prices varied greatly. Currently, manymodel comparison projects are ongoing or finished suchas EMF27, AMPERE (Assessment of Climate Change
Mitigation Pathways and Evaluation of the Robustness of
Mitigation Cost Estimates), and AME (Asian Modeling
Exercise). These mostly argue about climate mitigationand its cost. The carbon price in 2050 is expected to beapproximately 170$/tCO2eq. The Energy Technology
Perspective (IEA, 2010) also assessed a similar scenario
called BLUE map, which took into account only energyrelated emissions. The emissions in 2050 would be
14GtCO2/year, slightly higher than half of 1990’s emissions. The carbon price in 2050, however, would be
175$/tCO2. Rogelj et al., (2011) collected past IAM scenarios and discussed the two-degree target. Although
they did not indicate a specific carbon price corresponding to the two-degree target, they concluded that theemissions peak should be around 2020 and follow apathway to half the current emissions by 2050.
Asia is one of the most important regions foreseen in
the next couple of decades to have great growth in economic magnitude and amount of GHG emissions. We
have not yet been able clearly to see what would happen
to Asia in incorporating the two-degree target, although
global studies have been conducted as we discussed
above. Thus, this paper aims to provide insight into thefollowing questions: 1) how difficult it would be toachieve that reduction target, and 2) what the Asian role
would be in that reduction target. Obviously, the answers
strongly depend on the underlying scenario assumptions
and a huge number of future scenarios could be used to
explore the world of 2050. To provide straightforward
messages related to climate policy, however, we need tolimit the number of scenarios while paying attention to
the fundamental social dimensions in the scenarios wechoose. This paper, therefore, focuses on two representative socioeconomic assumptions, characterized by
the words “advanced” and “conventional” (Kawase &Matsuoka, 2013). The advanced world tries to solve
social, economic and environmental issues aggressively
and the “conventional” world does the opposite. The
scenarios are explained in later sections in detail.
Section 2 describes the methodology adopted in thispaper, the scenarios and assumptions. Section 3 shows
the results of this analysis, and finally the conclusions are
given in Section 4.

2. MethodFirstly, the scenarios are prepared, then the quantitative information according to the narrative scenarios is
inputted into a CGE model and finally the simulation
results are analyzed. The scenarios are represented by thefollowing matrix (Table 1). Two socioeconomic scenarios and two climate policy assumptions have been
adopted. The former are defined in Kawase & Matsuoka

Table 1 Scenario matrix.
ADV (Advanced)
Baseline (BAU;
business as usual) ADV_BAU
Mitigation (MIT;ADV_MITmitigation)

CNV (Conventional)
CNV_BAU
CNV_MIT(2013) and the latter are defined as a non-emission
constrained (what we call the “baseline scenario”) and anemission-constrained scenario (what we call the “climate
mitigation scenario”).
The CGE model incorporated in this paper is based on
Fujimori et al. (2012). This CGE model is a recursivetype dynamic general equilibrium model, which covers
all parts of the world, classified into 35 regions. In orderto analyze climate mitigation policy and the energy
demand and supply, the energy sectors, especially power
sectors, are disaggregated in detail. Generally, CGE
models have the advantage of being able to describe
economic activity consistently and completely. This advantage makes it possible to calculate GHG emission
prices and GDP losses, and is the reason we chose theCGE model for this analysis. Projections are from 2005
to 2050 annually on the global scale.
2.1 CGE modelThis study uses a global CGE model. This CGE
model is a multi-region, multi-sector-type model. The
CGE model represents the circular flow of goods andservices in the economy associated with monetary flows.
Production sectors input intermediate goods, including
energy commodities, and labor, capital and land as factors. Household, government and capital formulation are
the institutional sectors which purchase goods and services as final demand. Their incomes are circulated fromproduction factors such as labor wages, capital return andtax revenues. Trade flows are also described in this modeland formulated as both exports and imports. To assessclimate mitigation policy, energy commodities aretreated as physical volume while other goods are accounted for as monetary value. GHG emissions areassociated with energy commodity consumption. All
production, final demand, trade and income flows areformulated as mathematical equations. The model issolved recursively in one-year time steps.
The world is classified into 35 geographical regions,
within which Asian countries are classified into ten single countries and five aggregated regions. Industry isclassified into 32 sectors. The industrial classification ischaracterized by the energy sectors, such as power sectors, being disaggregated in detail into fifteen sectors,
since the main topic of this study is climate policy andenergy analysis. This CGE model was developed basedon the IFPRI’s CGE model named the “Standard CGE
model” (Lofgren et al., 2002). The details of the model
structure and mathematical formulas are described in
Fujimori et al. (2012).
Several points have been updated from Fujimori et al.
(2012) as explained below.
1) The capital market is assumed to be the same as inGlobal Low-Carbon Society Scenario Analysis based on Two Representative Socioeconomic Scenarios

ENVISAGE (The ENVironmental Impact andSustainability Applied General Equilibrium (van der
Mensbrugghe, 2008) which has only one rate of
return on capital for each region. Only if no newcapital is allocated to a sector (called a declining
sector in van der Mensbrugghe (2008)), is the rate of
return on capital lower than in the other sectors and, in
this case, the operation ratio decreases as well. The
details are described in van der Mensbrugghe (2008).
2) Generally many CGE models describe fuel substitution by using the CES function (Babiker, 2001);
ENVISAGE (van der Mensbrugghe, 2008)). However,
the CES function does not necessarily capture theenergy balance, though it is easy to interpret theoretically. This paper puts great weight on the energy balance of supply and demand. Therefore, we adopted
the logit function (Clarke et al., 1993).
3) Household consumption is formulated by the
AIDADS (An Implicit Direct Additive Demand
System; Yu et al., 2003) function, except for energy
goods. Energy consumption in households is determined by income. Fuel selection is determined by thelogit function.
4) Transport is split into freight and passenger transport
and includes all modes, such as cars, railways, ships
and so on.
2.2 Scenario framework
Two different representative socioeconomic scenarios
are assumed as mentioned above. The “ADV” is described as a positive social system in which institutions
tend to be designed for positively tackling various social
issues and assisting the creation of innovative technologies. As a result, technological progress is relatively high,so economic growth and energy technology are also
relatively advanced. In contrast, the “CNV” is characterized as slow-to-change social systems, institutions andtechnologies, hesitating to risk incurring transition costs
to change what they are accustomed to. Governance andeconomic levels stay relatively low. Each country
pursues short-term profits and, as a result, technicalimprovements and the economic growth rate also stayrelatively low. The narrative scenario assumptions arequantified for utilization as the CGE model parameters,
as shown in Table 2.
The medium variant of the latest UN population prospectus (UN, 2011) is provided for both scenarios as the
population assumption. GDP growth is assumed to berelatively high in ADV and low in CNV. In ADV, theTable 2 Scenario characteristics.

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energy service intensity (energy service per output) isrelatively low in terms of service quantity per GDP. Thisimplies that the ADV world would supposedly be using
relatively less heavy materials such as iron and cement or
be a less transport-oriented society while providing
similar service levels. People in it would tend to prefer a
low-energy lifestyle while enjoying high income.
Energy-efficient technological development would be
relatively more optimistic in ADV than CNV. As a result,the ADV scenario would have relatively low energy
intensity. In addition, in ADV, the energy technology
would be relatively more sensitive to energy pricechanges than in CNV. If the prices of the energy sourceschange (e.g., coal becomes quite expensive when carbon
pricing is introduced), ADV would respond to that pricechange more flexibly. CCS technology would be unavailable in CNV since the CNV people may be interpreted as tending not to accept CCS technology, while
those in ADV would be able to install it once the carbon
price reached the technological cost.
2.3 Quantification of narrative scenario information
Based on the previous section, the narrative scenarios
are quantified as input information for CGE model
application. As mentioned above, the medium case of the
UN’s population prospects is used for both scenarios.
Labor endowment is changed in proportion to population
changes. The GDP assumption is based on Kawase andMatsuoka (2013). In ADV the worldwide average growthrate is 3.27% and in CNV, 2.24%. The parameter representing total factor productivity is updated recursively
based on accumulated capital and labor endowment,
according to GDP assumptions. This parameter treatment
increases capital and labor productivity for each production sector. The mechanism of how the energy service
demand generated is differentiated among industry,transport and household sectors is as follows. Passenger
transport service demand and household energy service
demand are determined using income and its elasticity.
The basic parameters of income elasticity are chosen
based on the IEA’s perspective (IEA, 2010). The freighttransport value is generated by multiplying each sector’s
output (e.g., steel production) and each unit’s coefficient(e.g., ton-km freight demand per output). ADV assumes
that the logistics system improves so this coefficientshows a 2% annual improvement while CNV maintains
this coefficient as constant. The industrial sectors’ energy
demand is based on two factors. One is output itself (e.g.,steel output) and the other is energy efficiency. In addi-

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tion, energy efficiency is improved by two factors. One isenergy-price- driven energy technological improvement
and the other is AEEI (Autonomous Energy Efficiency
Improvement)1). The former, price-oriented technological improvement, is associated with energy price elasticity, while the latter is represented by multiplication with acoefficient. Numbers from Babiker et al. (2001) are used
for ADV, and values 0.5% lower for CNV. The energy
source share is determined by the logit function as explained. The parameter which represents the base year’s
share is recursively updated in a similar way to van der
Mensbrugghe’s (2008) method. This implicitly assumes
that a price change triggers the relatively cheap installation of energy technology. This mechanism is applied
only to ADV.
2.4 Emission constraint
The global GHG emissions in 2050 are assumed to behalf the level of emissions in 1990. To ascertain theemission pathway to 2050, each region’s emission pathway is predetermined for each year. While many global
studies have only global total emission constraints, thisstudy specifies each single country’s emission pathway
since some countries’ emission targets have been alreadyagreed on in the Kyoto protocol and pledged according to
the Copenhagen Accord and they are incorporated in thisanalysis. The way to determine emission constraints is
described below.
Firstly, the countries participating in the Kyoto
Protocol, which prescribed allowable emission amounts
from 2008 to 2012, set their own targets. For the postKyoto Protocol period, many countries have pledged anemissions target for 2020. The pledges contain not only
one specific target (like a 20% reduction) but also otheroptions according to how other countries participate intheir own reduction efforts. This study assumes that all of
the conditions are satisfied and that the strongest emis-

sions constraints are assumed for all regions. For instance,
Japan is supposed to reduce its emissions by 25% relative
to 1990 and China, to decrease its emission intensity by
45% relative to 2005.
After 2020, all regions are supposed to follow allowable emission targets. The allowable emission amount in
2050 equalizes per capita GHG emissions over the wholeworld, halving global emissions relative to the 1990 level.
The emissions of 2020 and target emissions of 2050 areconnected linearly and converted to an allowable emissions level for each region. This post-2020 regime isbased on the Contraction & Convergence (C&C) approach (Meyer, 2000). This regime has advantages in that
it is simple, transparent, easy to understand and highly
cost-effective, though it also has the disadvantages ofmonitoring problems in low-development countries, arisk of surplus emissions for Annex I countries, politicalresistance to the resource-sharing concept and so on (denElzen et al., 2006).
Emissions trading is fully allowed for all regions,
therefore, the emission price is globally equalized throughemission rights trading.

3. Results
3.1 Global BAU overview
Global population, GDP, total primary energy supply(TPES) and energy-related CO2 emissions in the BAU
cases are shown first to clarify the outline of the scenarios
in Fig. 1. AR4 (Fourth Assessment Report of IPCC)’s(IPCC, 2007) nonintervention scenarios are put together
in this figure. The four indicators show that our two
scenarios are in the range of the AR4, ADV is locatedrelatively high in the range and CNV is low. CO2
emissions seem relatively high in the range and this ismainly driven by a relatively high GDP. In 2050, the GDP,
TPES and CO2 of ADV are 1.60, 1.31 and 1.22 times as

Fig. 1 Global BAU population (a), GDP (b), primary energy supply (c) and energy-related CO2 emissions (d).
(blue line: ADV, red line: CNV, grey lines: AR4 nonintervention scenarios)Global Low-Carbon Society Scenario Analysis based on Two Representative Socioeconomic Scenarios

83much as those of CNV. The GDP shows the largest difference whereas the other indicators are not as large as theGDP. This result is the consequence of the scenario assumption in which the energy service demand intensity(per GDP) of ADV is relatively high. This causes highenergy- intensity improvement and offsets the GDP increase of ADV to some degree.

scenarios relative to the BAU scenarios are 73% and 74%,
respectively. They do not differ much from the global
reduction rate. The absolute amount of emissions in each
mitigation scenario differs between the ADV and CNV
scenarios since their emissions trading mechanisms work
differently. Asia becomes a net importer of emission
rights from other regions such as Africa after 2040.

3.2 GHG emissions
Total global and Asian GHG emissions are shown in
Fig. 2. In the non-intervention cases these gradually increase for the whole simulation period. In 2050, global
emissions are 99.0 (ADV) and 80.5 (CNV) GtCO2/year,
which are 2.2 and 1.8 times the 2005 level, respectively.
Looking at mitigation scenarios, the emission levels in
2050 are exactly the same since the emissions target iscommon to both of these scenarios. The reduction rates
from the BAU scenarios are 81% (ADV) and 76% (CNV).
Their emission pathways, however, are slightly different.
This is due to different assumptions until 2020. Somecountries have declared an emission intensity target inaccordance with the Copenhagen Pledge, and that produces different emissions in 2020, since different
GDPs and energy efficiency have been assumed for the
scenarios. In addition, the non-pledged countries have noemission constraints until 2020, so their emission levels
then could also be different.
The rate of increase of Asia’s GHG emissions ishigher than that of the world total. The emissions in 2050
are 2.6 (ADV) and 2.1 (CNV) times the 2005 level. Thisis mainly driven by the relatively high GDP growth of
Asia. The emission reduction rates of both mitigation

3.3 Total primary energy supply and power supply
This section discusses the breakdown of the total primary energy supply and power supply to understandchanges in energy structure. Primary energy is anindicator that captures the total energy system, as it issubsequently consumed directly by the final demandsource or transformed to secondary energy such as
electricity. The source of the electricity is one of the fundamental factors determining CO2 emissions. Figure 3
shows the global and Asian total primary energy supply
for all scenarios in 2050. The world total primary energy
in 2050 BAU maintains almost the same energy mix as in2005 in both ADV and CNV scenarios. The same can beseen in the Asian figure.
On the other hand, the mitigation scenarios show
absolutely different energy mixes in 2050. The ADV
scenario expands the biomass share, whereas the fossil
fuel share, particularly coal, decreases. The total energy
volumes of the mitigation scenarios are slightly lower
than those of BAU, whereas the energy supply of the
CNV mitigation scenario is apparently lower than that of
BAU. In the ADV scenario CCS use is allowed and it
provides an option for using fired power plants, including
fossil fuel and biomass. Therefore, the decrease in energy

Fig. 2 Total GHG emissions (left: global, right: Asia).

Fig. 3 Global and Asian total primary energy supply (left: global, right: Asian).

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supply is small. This tendency is clearly seen in the
power supply structure in Fig. 4. Fired power with CCS
appears in the ADV mitigation scenario and fossil-fuelfired powers without CCS have mostly disappeared.
Biomass CCS is also introduced at the top of the bar. The
global and Asian shares are 17% and 15%, respectively.
Focusing on the CNV scenario, a decrease in thevolume of the energy supply is required, and in addition,
the share of nuclear increases. These tendencies happen
not only in the global figures but also in the Asian.
Almost the same can be seen in the right-hand figure.
From this analysis, we find that Asia is not special in thecontext of energy supply structure. Fossil-fuel-fired
power plants should be drastically decreased andnon-fossil fuels should take their place as major powersources. If CCS technology installation is allowed, fired
power plants can be kept to some degree with CCS.
3.4 GHG emission price
The GHG emission price is shown in Fig. 5. Bothscenarios’ emission prices take off from 2020, the year
when strong emission constraints are introduced. The
movements after 2030 differ sharply between the two
scenarios. The ADV scenario carbon price is higher inthe 2030s; however, it falls after 2040 while in CNV it
keeps increasing. This is mainly due to availability of
CCS technology. Once CCS technology is introduced
and diffused in ADV, it will be able to reduce the emis-

sion price. Biomass with CCS, in particular, creates negative emissions and dramatically affects the emission price.
On the other hand, the CNV scenario is restricted in its
use of CCS and this scenario involves only one choice for
reducing emissions, namely setting a high emission price.
3.5 Macroeconomic responses to mitigation
Mitigation scenarios involve changes in GDP from
BAU. Figure 6 shows world and Asian GDP losses in the
mitigation scenarios relative to the BAU scenarios. The
GDP loss would increase in both ADV and CNV scenarios. The global GDP loss is projected to be 1.2% and1.9%, respectively. The GDP loss starts to increase
particularly from 2020 in both scenarios. Comparing the
ADV and CNV scenarios, the GDP loss in ADV seems toreach saturation around 2040 while in CNV it keeps rising. An almost similar tendency to that of the GHG
emission price can be seen (Fig. 5) and the GDP response
seems to follow the GHG emission price. Looking at
Asia, the tendency is similar to the global one. Asia’s
ADV and CNV GDP losses in 2050 are 1.4% and 2.7%,
respectively. The latter period of the Asian CNV scenario
differs from the global one due to trends in China, andaround two thirds of the loss comes from China in CNV.
The trajectory also heavily depends on China’s GDP loss.
When we look at individual regions’ GDP losses in
Asia, they show large variation. Table 3 shows the GDP
losses of Asian regions in 2050. For instance, in China’s

Fig 4 Global and Asian power supply (left: global, right: Asian).

Fig. 5 GHG emission price.

Fig. 6 World and Asia GDP losses.

Global Low-Carbon Society Scenario Analysis based on Two Representative Socioeconomic Scenarios

ADV and CNV scenarios they are 0.2% and 0.3%,
respectively, with loss percentages small and almost
unchanged. On the other hand, Thailand and Malaysia
show more than 4% in all scenarios. Not only the degreeof difference between the two scenarios but also the basic
magnitude of the GDP loss itself is quite different. The
GDP loss is generated by a complicated mechanism andthe GHG emission price, economic structure, income
level, emission allowance and mitigation capacity are allimportant factors. Although it is hard to identify thecontributions of these factors, there are several tendencies. One is that the developed countries, such as Japanand Korea, tend to have low economic losses. For thesecountries energy expenditures are relatively lower thanother expenditures, and the effect of pricing carbon is low.
The least developed regions like Other Oceania, Other
South Asia, India and the Philippines also show low ornegative GDP losses. Their emission targets are relatively easier since their current emission levels are relatively low. As a result, they become emission rights
exporters. However, there are exceptions, for instance
Other Southeast Asia. This region’s emissions are dominated by land-use-change emissions and the economic
losses are strongly dependent on them. In this paper, wecannot go in detail, though. Further analysis andidentification of the factors generating GDP losses areneeded.

3.6 Decomposition of GHG emission reduction
The decomposition of GHG emissions reduction isshown in Fig. 7 for the ADV and CNV scenarios2). Firstof all, reduction through use of renewable energy makes
a substantial contribution in both scenarios. As alreadyseen in the primary energy figures, biomass and solar
energy play important roles. Non-energy-related GHG
emission reduction is also a key reduction measure. Suchemissions themselves comprised about 25% of global
emissions in 2005 so their contribution to emission
reduction would also be great. In the ADV scenario(Fig. 7, left), the CCS contribution appears from around2030, becoming a major portion in 2050. This is also
discussed in the power supply section, and most fired
power plants are expected to install CCS in 2050. On theother hand the CNV scenario is constrained in utilizationof CCS, therefore other measures such as nuclear andpower efficiency are substituted for that contribution.
Focusing on the energy-end-use side, energy efficiency
steadily contributes throughout the period after 2020.
The contribution of energy fuel switches (coal to oil andgas) becomes larger in the latter period while that ofelectricity demand is larger in the former period. Fossilfuel-fired power plants account for a large share in theformer period but less in the latter. Thus, although one
emission reduction measure for the former period iselectricity demand decrease, it turns out no longer to be a
reduction measure in the latter period since the power

Table 3 GDP losses of Asian regions in 2050.
(positive values indicate losses and negative values, gains)
China
Indonesia
India
Japan
Korea
Malaysia
Philippines
Singapore

ADV
2.3%
1.4%
0.5%
0.7%
0.6%
4.9%
-0.7%
6.0%

CNV
4.5%
-0.6%
-0.4%
1.1%
3.0%
9.2%
-7.1%
22.7%

85

ThailandTaiwanVietnam
Other East Asia
Other Oceania
Other South Asia
Other South-east Asia
Asia
World

ADV
5.0%
2.0%
1.8%
6.0%
-2.7%
-5.9%
3.8%
1.4%
1.2%

CNV
5.2%
4.4%
-5.5%
2.7%
-10.0%
-6.7%
4.6%
2.7%
1.9%

Negative values indicate that the region benefits from climate mitigation. This scenario analysis
allows trading of emission allowances. Thus some countries could gain revenue by selling emission
allowances.

Fig. 7 Decomposition of Asian GHG reduction (left: ADV, right: CNV).

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supply will already be decarbonized.
Figure 7 illustrates Asia’s emission reduction decomposition. In general, the figures differ little from theglobal ones. Renewable energy, non-energy-related GHG
reduction and CCS play important roles, as do global
reduction measures. An obvious difference between the
global and Asian figures can be seen in emission rights
importation (at the bottom of the measures). In 2050,
importation in both scenarios accounts for more than
30% of emissions.

4. Discussion and Conclusion
The uncertainties and limitations of this study are
discussed in this section. Problematic points are 1)
uncertainty in the socioeconomic scenarios, 2) controversy over allocation regimes of emission allowances, 3)
uncertain availability of CCS, including biomass CCS
and 4) the models’ structural limitations. Firstly, thesocioeconomic scenario includes uncertainties. The
scenarios incorporated in this paper are simply representative of a wide range of possible futures. There is no
guarantee ADV will be realized, since this scenario assumes a good educational system, high technology andlow energy service demand, and these might not be
achieved without political or institutional efforts to promote them. In addition, we arbitrarily assume CCS isavailable in ADV but not in CNV. It is possible to consider scenarios in which CCS is available for both.
Sensitivity analysis is required for the socioeconomic
assumptions, including technological availability. The
emission allowance allocation regimes are also controversial. den Elzen et al. (2006) argued extensively about
related concerns. Just one simple scheme called C&C istentatively used in this paper. However, much deeper
analysis would be required in order to put forth more
useful policies and describe their relevant implications.
As is discussed in the results in Section 3, CCS technology is one of the most important factors, as it is expected
to reduce CO2 emissions greatly. On the other hand, its
technological feasibility and social acceptability are
uncertain. Biomass CCS is also controversial since itwould compete with food production. It is noted that thispaper uses a top-down CGE approach. Although power
supply technology is disaggregated, other end-use technologies are not. Thus, energy technology might not be
fully covered and this treatment could overestimate theGDP loss. Thus further research is required coupled withbottom-up information.
In conclusion, while recognizing these uncertainties,
we would like to make several politically important
points.
1) Halving GHG emissions globally by 2050 relative to1990 is quite an ambitious target and would cause
substantial changes in energy systems. The GHG
emission price would not be low and GDP losses
could exceed 1.2 % globally.
2) The reduction target is feasible in both scenarios using a CGE model. However, a technologically richanalysis incorporating a bottom-up-type model would
be required in order to assess energy technologicalfeasibility more concretely.
3) Renewable energy plays an important role in mitigation scenarios. CCS is also a fundamental reduction
contributor in the ADV scenario. The emission target
is also achieved in the CNV scenario even though
CCS is assumed to be unavailable. Renewable energy
such as wind and solar are substituted for the role of
CCS in the CNV scenario but the carbon price would
reach about 450$/tCO2. Technological progress isneeded, particularly for those renewable energy
source technologies, and policy should support themsince the future cost of renewable energy sources inthis model is assumed to decrease, rapidly based on
IEA (2010). Otherwise, the carbon price could bemuch higher than in this analysis, even though it isquite uncertain whether this price would be acceptable in terms of real political decisions or not.
4) Asia is expected to be an important region in theworld in the next a few decades. Strong emission
reduction will force Asia’s energy systems to change
drastically along with global ones. This implies that
Asia cannot avoid a great effort to reduce GHG
emissions if the global target is to be kept. The reduction measures are similar to the global ones.
Emissions trading would be a net beneficial reduction
measure for Asia.
5) Although the average Asian GDP loss is similar tothat of the global results, GDP losses vary greatly
among regions. An international policy frameworkneeds to be determined considering these losses. For
instance, in this study a C&C scheme is applied, butother schemes considering cost aspects should betaken into account.

Acknowledgments
This study was supported by “Global Environmental
Research Fund” S-6 of the Ministry of the Environmentof Japan. The authors would like to acknowledge thegenerosity of this fund.
Notes
1)

AEEI is a heuristic representation of non-price-driven improvements in technology that create a progressively energy-saving
bias in technical change (Babiker et al., 2001)
2)
The technical decomposition methodology is described in
Fujimori et al.(2012).

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87

Shinichiro FUJIMORI
Shinichiro FUJIMORI is a researcher at the National
Institute for Environmental Study in Japan. Dr.
Fujimori got his Bachelor’s, Master’s and PhD
degrees from the Department of Environmental
Engineering, Kyoto University. Now he is amember of the AIM (Asia-Pacific integrated
Assessment Model) team, where he is working on
development and application of a CGE model. In the early period of his
career, he tackled development of a method to create a consistent social
accounting matrix and energy balance table. He later applied that method
to the world, classifying it into more than 100 regions spanning the last
three decades, and using more than 30 international data sets, e.g.: national
accounts, trade statistics, energy statistics, etc. The reconciled data are usedin the CGE model or a bottom-up-type energy model. During the last
couple of years, he has been working on development and application of a
CGE model. The model will find wide use, for instance, in global climate
mitigation assessment, development of new IPCC socioeconomic
scenarios, and national level climate mitigation policy.

Toshihiko MASUI
Toshihiko MASUI is Chief of the Integrated
Assessment Modeling Section at the Center forSocial and Environmental Systems Research,
National Institute for Environmental Studies(NIES). He received his Ph.D. from Osaka University in 1997, and he has worked at NIES since1998. He has also been a Coordinate Associate
Professor at the Tokyo Institute of Technology from 2000. He is in chargeof development of a computable general equilibrium model as part of the
Asia-Pacific Integrated Model team in NIES. He is a lead author of theAR5 report of IPCC Working Group II.

Yuzuru MATSUOKA
Yuzuru MATSUOKA is a Professor at the Graduate
School of Engineering, Kyoto University. He
specializes in environmental engineering. He
started researching global environmental issues in
the late 1980s. He has developed various kinds ofintegrated models, including bottom-up and topdown models. Recently he has been leading
projects for development of Asian low-carbon societies.

(Received 22 June 2012, Accepted 29 November 2012)