109

Feasibility of Low-Carbon Development in China
Michiko NAMAZU*1, Shinichiro FUJIMORI2, Kejun JIANG3 and Yuzuru MATSUOKA1
1

Department of Environmental Engineering, Graduate School of Engineering, Kyoto University
Kyoto daigaku-Katsura, Nishikyo-ku, Kyoto 606-8540, Japan
2
Center for Social and Environmental Systems Research, National Institute for Environmental Studies
16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
3
Energy Research Institute, National Development and Reform Commission, China*e-mail: namazu.michiko.22m@st.kyoto-u.ac.jp

Abstract
This study analyses the energy system and macro-economic responses to emissions reduction in China
from 2005 to 2050 using a dynamic recursive Computable General Equilibrium (CGE) model. To explore
plausible climate mitigation measures, two socioeconomic scenarios are assumed: Advanced (ADV; active
acceptance of innovations and changes) and Conventional (CNV; conservative and passive response toinnovations and changes). The assumed emissions reduction target is a 68% reduction from the 2005 level by
2050 derived from a target to halve global emissions. In total, four cases are considered, including two
reference cases without emissions reduction and two mitigation cases with emissions reduction. The results,
especially in the mitigation cases, are as follows:
In the ADV scenario, the GDP in 2050 is thirteen times larger than that in 2005 even with emissions constraint. The maximum emissions price is $375/tCO2eq in 2036, and the maximum GDP loss is 4.6% in 2035.
Electrification is accelerated, and electricity is supplied by various power sources including renewableenergies. In the CNV scenario, the GDP in 2050 is 7.5 times larger than that in 2005 even under emissions
constraint. The emissions price increases significantly and becomes $1,932/tCO2eq, with a GDP loss of 10%
in 2050. Unavailability of CCS technology forces to reduce fossil fuel consumption while electrification isaccelerated. Electricity is mainly supplied by hydroelectric, biomass and nuclear power.
The results show that there are feasible low-carbon development pathways for China, although great
uncertainty exists in China’s future development. To achieve these pathways, efficient and appropriate
governance is necessary, and the introduction of new technologies can contribute to minimizing thesocioeconomic impacts of emissions reduction.
Key words: CGE model, China, economic effects, greenhouse gas (GHG), mitigation

1. Introduction
In recent years, global warming has been one of themost critical problems in the world. The Intergovernmental Panel on Climate Change (IPCC) says that thesigns of warming of the climate system are unequivocal,
and most of the observed increase in global averagetemperatures since the mid-20th century are very likely
due to the observed increase in anthropogenic greenhouse gas (GHG) concentrations (IPCC, 2007). Emissions reduction, especially in the developing world with
rapid economic growth, is crucial for preventing drasticchanges in the global climate system. The GHG emissions from China were 3,770 Mt CO2 eq in 1990, rising to7,707 Mt CO2eq in 2005, the largest in the world,
overtaking the US. (European Commission, 2011). In
order to overcome climate change problems and takesteps to acheive a “low-carbon society,” one of the mostGlobal Environmental Research
17/2013: 109-118
printed in Japanintense future concerns is to see how China achieves asignificant emissions reduction. Several studies focusing
on the achievement of a low-carbon society in China
have been conducted. Liu et al. (2009) clarified the maintechnologies to achieve such a society in China using abottom-up type model. On the other hand, Ke et al.
(2009) developed a top-down type model dealing withthe relationship between investment and technology
improvement, and simulated the effects of reduction
measures. Although both studies managed to evaluate
China’s future quantitatively, neither of them consideredboth the global emissions halving target and theuncertainties of China’s future development. Bothinternational and domestic institutions, which are listed
in Appendices 1 and 2, have published various scenarios
of China’s future development; however, often huge
differences are seen among the scenarios, and it clearlyshows that there are great uncertainties in terms of©2013 AIRIESM. NAMAZU et al.

3.1 Two socioeconomic scenarios
This study assumes two different socioeconomic
scenarios, advanced (ADV) and conventional (CNV).
The concept of “ADV” is a society that is proactive andwilling to take risks to innovate social systems, institutions and technologies for realizing next-generationsocieties. In contrast, “CNV” is a society that is cautious
about changing social systems, institutions and technologies, putting more focus on transition costs forrealizing next-generation societies.
3.1.1 Population
Figure 1 shows population estimates in China. The assumptions range between 1.1 billion (United Nations,2010, low scenario) and 2.0 billion (Netherlands Environmental Assessment Agency, 2006, A2 scenario) in
2050. In our study, we have used the medium scenario
from United Nations Population Prospects 2010 (2010).
In this scenario, the population growth shows a downward trend after 2027, with the total population in 2050
finally becoming almost same as that in 2005. Although
the scenario is slightly lower than the average of all thecollected scenarios, the latter references assume lower
scenarios, so this study reflects the latest trend in
scenarios.
3.1.2 GDP
Assumed GDP scenarios range between 9.2 trillion(Japan Center for Economic Research, 2007) and44.6 trillion US dollars (China Energy Research Institute,2009a, reference scenario in Chapter 2). This study refers
to Kawase and Matsuoka (under review), using an
econometric model associated with a governance index(Kaufman et al., 2010), average education period, andlabor participants for target GDPs. The study assumes
2,500
2,000
1,500
1,000

2050

2045

2040

year

2035

2030

2025

0

2020

United Nations Population
Prospects 2010 Medium

500

2015

The model used in this study is a dynamic recursive
CGE model, in which equilibriums are found every year.
A detailed description of the model is available in
Fujimori et al. (2011), Chapter 7 in this journal (Fujimori
et al., 2013) and the AIM/CGE [basic] manual (Fujimori
et al., 2012). The model encompasses 21 commodities,
30 industrial sectors, government, households and enterprises. A commodity is able to be produced by several
different sectors, for example, electricity is generated by
sectors including wind and solar photovoltaic power
sectors. The total capital investment in a year is determined based on the target GDP growth. The investment
in one year becomes the new capital in the next year andis allocated to the production sectors. The amount of newcapital for each sector depends on the capital rate of return while old capital is fixed to its original sectors, andeach sector has a capital stock with 4% annual depletion.
Trade is formulated as per the Armington assumption
with constant elasticity of substitution (CES) (Arrowet al., 1961) and constant elasticity of transformation(CET) (Powel & Gruen, 1968) functions for imports and3. Scenarios

2010

2. Model Description

exports, respectively. The international goods price is an
exogenous parameter for this model and global CGE
model results (Fujimori et al., 2013) are used.
The base year’s social accounting matrix (SAM) andthe energy balance table were compiled from several
sources, for example, national accounts, industry statistics and energy statistics, with adjustments of inconsistencies among these data (Fujimori et al. 2011).

2005

socioeconomic assumptions and mitigation measures.
This study takes into account the global emissions
halving target and the uncertainties of China’s future
development. Two different socioeconomic scenarios for
China, named advanced (ADV) and conventional (CNV),
are assumed. While ADV describes a society that is open
to innovation and changes, CNV describes a society thatis conservative and passive about innovation and changes.
The GHG emissions constraints used for this study arederived from the proposals for a global target of halving
emissions and for construction and convergence ofemissions, i.e., halving global GHG emissions by 2050
from the 1990 level with equal emissions per capita
allowed globally in 2050. Based on these ideas, theemissions reduction target for China in 2050 would
become a 68% reduction from the 2005 level. In thisstudy, China tries to achieve this target by utilizing
mitigation measures. Combining the two socioeconomic
scenarios and the reduction target results in four cases:
two pairs of cases with and without achieving the emissions reduction target. The emissions reduction measures
are set for the two mitigation cases based on the concepts
and characteristics of the scenarios and the country,
China. The macroeconomic impacts, especially in thetwo mitigation cases, caused by emissions reductions
from 2005 to 2050 are quantitatively evaluated using
a computable general equilibrium (CGE) model. The
model is a recursive dynamic CGE model dealing with
CO2, CH4 and N2O emissions as GHG emissions in both
energy-related and non-energy-related sectors.
An outline of the model is presented in Section 2.
Section 3 introduces the scenarios, including detailed
settings and emissions reduction targets with mitigation
measures. Section 4 shows results from the model andfinally Section 5 presents the conclusions.

population (mil. people)

110

Fig. 1 Population prospects (references listed in Appendix 1).

Feasibility of Low-Carbon Development in Chinathat in ADV, the governance index would improve from
–2.5 in 2005 to –1.4 in 2050 and the average education
period would improve from 7.1 years in 2005 to 12.0
years in 2050. On the other hand, in CNV, the governanceindex would improve to –1.7 and the average education
period would improve to 9.7 years in 2050. The averageannual GDP growth rates in the period between 2005 and2050 would be 6.1% and 4.9% in ADV and CNV,
respectively, and with these growth rates, the total GDP
in 2050 can be assumed to be around 14 and 8 times
greater than the 2005 level in ADV and CNV, respectively when emissions constraints are not considered. The
two scenarios almost trisect the other scenarios (Fig. 2).
3.1.3 AEEIThis study assumes autonomous energy efficiency
improvement (AEEI), which is energy efficiency improvement that occurs in the absence of any energy pricechange or emissions constraints. According to various
references, in 2050, China’s total primary energy supply
and total final energy consumption are assumed to be
2,400-5,400 and 1,700-3,600 Mtoe per year, respectively.
This study sets the AEEI parameter values at 0% to 4%
per year depending on the energy sources (Table 1). The
current energy efficiency in China is relatively low: in
2007, China’s energy intensity was 50% below the world
average (IEA, 2010). This implies the possibility thatenergy efficiency can be improved significantly in China;
therefore, this study assumes a relatively high AEEI
value, especially for ADV. With the high AEEI value andwithout considering emissions reduction, in both ADV
and CNV total primary energy supplies would be 3,800
and 3,400 Mtoe per year, respectively, and total final
energy consumption would be 2,400 and 2,000 Mtoe per
year, respectively, in 2050.
3.1.4 Intermediate input efficiency improvement
Although material demand used for social capital likesteel and cement grows with economic development, thisdemand gradually decreases following the maturation ofa society. This study reflects this through constant reduction of the input coefficients for iron and steel, mineral products including cement, and non-ferrous products.
The reduction rates are determined based on the directoryof iron and steel statistics (The Japan Iron and SteelFederation, 2010).

Fig. 2 GDP scenarios (references listed in Appendix 2).

111

3.1.5 Power generation (fossil fuel)According to an IEA report (2010), the investment
costs for coal and natural gas fuel-burning plants are
expected to decrease by 0.5% - 0.6% per year during 2010
to 2050. This study assumes the cost reduction to be 0.6%
per year.
3.1.6 Power generation (non-fossil fuel)Although the costs for introducing solar photovoltaic
and wind power are relatively expensive currently, they
are assumed to decrease drastically in the future. According to IEA (2010), during the period between 2010
and 2050 the cost of investing in solar photovoltaic andwind power is expected to decrease from US$3,5005,600 to 1,000-1,600/kW and from US$1,450-2,200
to 1,200-1,600/kW respectively. Based on this assumption, this study assumes the cost reduction rates forsolar photovoltaic and wind power generation shown in
Table 1.
Hydroelectric and nuclear power are often managed
by governments. Therefore, this study has establishedfixed amounts of introduction of the two exogenously.
According to China Energy Research Institute (2009a,
2009b) and Chinese Academy of Engineering (2011), in
2050, the amounts of hydroelectric and nuclear power
introduced will range from 106 to 378 and 40 to 452
Mtoe per year, respectively. This study assumes that 206
and 210 Mtoe per year of hydroelectric power and 156
and 378 Mtoe per year of nuclear power are introduced in
ADV and CNV, respectively, in 2050. Note that theassumed rates of introducing hydroelectric and nuclear
power are strongly intense. The domestic scenarios
referred to assume strong extension of hydroelectric andnuclear power utilization, at least twice and 30 times
larger, respectively, by 2050 than the current level.
3.1.7 Household energy consumption
This study assumes that households increase theirenergy consumption as income increases and energy
prices decrease. Households decide which energy types
to consume based on the price of each energy type whilesatisfying their total energy needs using the logit function(Clark & Edmonds, 1993).
3.1.8 Industrial sector energy consumption
Except in the case of energy transformation sectors
such as power generation, industrial sectors have production functions in which value-added and energy
composite are aggregated by the CES function and theenergy composite is determined using the logit function.
Energy commodities consumed by industrial sectors are
selected depending on their prices, just as with households.
3.1.9 Transport
According to China Energy Research Institute (2009a,
2009b) and Chinese Academy of Engineering (2011), in
2050 passenger transport volumes by road, rail andaviation are expected to become 3.1-4.4, 3.1-5.0 and112

M. NAMAZU et al.

9.8-23.8 times those in 2010, respectively, and freighttransport volumes by road, rail and navigation transport
will become 2.1-6.3, 3.4 -4.0, 3.1-5.0 times those in 2010,
respectively. We considered not only the above transportvolume assumptions but also GDP and population assumptions presented in the reports, and set the scenarios
for transport in ADV and CNV as below.
Household passenger transport by private car increases as household income rises, and its sensitivity to
income increase would be lower in ADV than in CNV.
China Energy Research Institute (2009a) assumes thatthe economic efficiency of transport energy consumption
improves by 30%-60% from 2010 to 2050. This study
assumes that the improvement in energy efficiency forhousehold passenger car use will be 2% per year in ADV
and 1.3% per year in CNV. The energy types consumedby each form of transport are determined based on energy
prices, using the logit function.
Other passenger transport also increases with anincrease in GDP. In ADV, bus and rail passenger transport would exhibit higher sensitivity to increasing GDP
while car transport would have lower sensitivity to suchincreases, compared with in CNV. The freight transportvolume was calculated based on transport service
demand from institutions.
3.2 Emissions reduction targets
Although currently China has a reduction target only
for 2020, this study considers both that target and theglobal halving target by 2050 to obtain the emissions
constraints used here. The emissions constraint for 2020
is determined on the basis of China’s national reduction
target (Department of Climate Change, China, 2010), a
40%-45% reduction of CO2 intensity in 2020 comparedwith the 2005 level. The emissions constraint in 2050 isassumed based on the global halving target, with the ideaof construction and convergence, i.e., halving global
GHG emissions by 2050 from the 1990 level, allotting
each country’s emissions allowance based on equal
emissions per capita all over the world in 2050. Based on
the two targets, this study assumes China’s emissions
reduction target in 2020 to be a 43% reduction of GHG
intensity and the target in 2050 to be a 68% reduction of
GHG emissions compared with the 2005 level. China isTable 1 Scenario catalog.
Concept
GHG emissions
reduction targets
Population
Potential GDP growthrate(% per year)
AEEI (% per year)
Intermediate input
efficiency
improvement(% per year)

Advanced (ADV)
Conventional (CNV)
A society that is proactive and willing to take risks to innovate A society that is cautious of changing social systems, institutionssocial systems, institutions and technologies for realizing and technologies, putting more focus on transition costs fornext-generation societies
realizing next-generation societies
In 2020: 43% reduction of GHG emissions per GDP from the 2005 level
In 2050: 68% reduction of GHG emissions from the 2005 level
Peak in 2027 with 1.4 billion people and gradually reducing after 2027
2006-2015: 10.0, 2016-2030: 5.7, 2031-2050: 4.2
2006-2015: 10.0
2016-2030: 4.0
2031-2050: 2.9
Coal: 4.0, Oil: 3.0, Gas: 2.0, Electricity: 1.0
Coal: 3.0, Oil: 2.0, Gas: 1.0
Electricity: 0.0
Construction
Construction
Iron and steel: 4.0
Iron and steel: 2.1
Mineral products (cement): 4.0
Mineral products (cement): 2.0
Other manufacturing
Other manufacturing
Iron and steel: 5.9
Iron and steel: 4.0
Non-ferrous metals: 2.0
Non-ferrous metal: 2.0
Cost reduction rate: 0.6% per year

Power generation(fossil fuel)GHG emissions mitigation measures
Household energy
Demand decreases as energy prices rise and increases as household income grows. The energy commodities consumed are
consumption
determined using the logit function.
Industrial energy con- Energy demand is determined by the CES function and the energy commodity share is determined by the logit function.
sumption (non-energy
transformation sector)
Transport
Household passenger transport volume by private car rises as household income increases and the sensitivity to increases in ADV
is smaller than that in CNV. The energy efficiency change in ADV is 2% per year while that in CNV is 1.3% per year.
Other passenger transport volume rises as GDP increases, and the sensitivity to this for bus and train transport is higher in ADV
than in CNV, while the sensitivity for car transport is lower in ADV than in CNV.
The freight transport volume is calculated based on transport service demand from industrial sectors and institutions. The energy
efficiency improves by 0.8% per year for cars, buses and trucks, and 0.2% per year for the other transport types. Depending on theenergy price, additional energy efficiency improvement may occur.
Energy commodity allocation is determined by the logit function.
Power generation
The rate of cost reduction for solar photovoltaic energy is 3.7% per year in ADV and 2.0% per year in CNV, while the rate of cost
reduction for wind power is 2.0 per year in ADV and 0.8% per year in CNV.
The amount of nuclear power introduced in 2050 is 210 Mtoe per year in ADV and 206 Mtoe per year in CNV while the amountof hydro power introduced in 2050 is 156 Mtoe per year in ADV and 378 Mtoe per year in CNV.
CCS technology is available only in ADV from 2020 with a 3% per year maximum introduction rate. The assumed additionalcosts for introducing CCS technology are shown below.
CCS technology introduction costs
Fuel-burning power plants
Coal
Oil
Gas
Biomass$/tCO250
70
70
70
Industry
Mineral products
Paper and pulp
Coal Refineries$/tCO2200
150
100
Non-energy-related
The reduction of non-energy-related GHG is determined depending on the GHG emissions price or scenarios.
GHG

Feasibility of Low-Carbon Development in China

assumed to start reducing its emissions from 2013, andthe study assumes a constant rate of reduction of GHG
intensity during the periods from 2013 to 2019 and from2021 to 2049.
By combining the two socioeconomic scenarios andemissions reduction targets, four cases were obtained:
two reference cases without the emissions reduction targets and two mitigation cases with the emissions reduction targets. This study considers the four scenarios,
focusing mainly on the two mitigation cases.
3.3 Mitigation measures
Several mitigation measures were considered in thisstudy, and the detailed settings differed between the two
mitigation cases in accordance with their respective
socioeconomic factors.
3.3.1 GHG emissions taxation
A GHG emissions tax is that which is imposed perunit of GHG emissions on the end-use emitter, andhereinafter, this tax price, calculated per ton of GHG
emissions in CO2eq, is called the emissions price. Thistax inhibits activities involving GHG emissions, resulting
in acceleration of a switch to higher energy efficiency
equipment and introduction of lower-carbon energy. In
this study, the revenue from the GHG emissions tax isassumed to be allocated to households.
3.3.2 CCS technology
Carbon capture and storage (CCS) technology istechniques of capturing CO2 from large emissions
sources and injecting it into deep geological formationsin this study. According to China Energy Research Institute (2009a), CCS is expected to be introduced
experimentally starting around 2020, with practicalapplication starting around 2025 in China. This study
assumes that such technology will be available only in
ADV after 2020. CCS technology can be introduced toconventional fuel-burning power plants, coal refineries,
and the mineral products and paper-pulp industries with amaximum introduction speed of 3% per year. The
additional cost for the introduction of CCS technology is
set based on IEA (2004) assumptions.
3.3.3 Non-energy-related GHG reduction
This study assumes that if emissions are constrained,
the GHG emissions from non-energy sources except land
use and land use change will decrease depending on theemissions price, and the coefficient is set based on
Hasegawa and Matsuoka (2010). Without GHG constraints, GHG emissions from land use and land usechange are assumed to follow Riahi et al. (2007), and with
GHG constraints, the emissions become zero in 2050.
3.4 Overview of the scenarios
The scenarios mentioned above are summarized inTable 1. One of the common characteristics between thetwo socioeconomic scenarios is a dramatic change in
potential GDP growth rate: both scenarios show a rapid

113

slowdown of GDP growth rate, especially between the
periods of 2006-2015 and 2016-2030. This means that
China is currently facing a dramatic shift in its economic
system. Another turning point is the year of 2027, when
the population is expected to peak. After 2027, the population will start to decrease. Because the emissions
reduction target in 2050 is calculated based on population,a smaller population results in a stricter emissions reduction target as a whole. High energy efficiency improvement, seen especially in the ADV scenario, is also
one of the characteristics. Additionally, active introduction of new energy is also an important characteristic in
ADV. The power generated from hydroelectric and nuclear sources in 2050 exceeds 6 times and 35 times, respectively, than that of 2005 in ADV. Based on the concepts of the scenarios, the ADV society intends to introduce various renewable energies while the CNV society
mainly focuses on utilizing conventional renewables.

4. Results
In this section, cases without GHG emissions reduction are shown under names which include the letters
REF (reference), while cases with GHG emissions
reduction are shown under names which include MIT(mitigation). All prices below are to be taken at the 2005
value of the US dollar, unless stated otherwise.
4.1 GHG emissions
Without emissions constraints, GHG emissions will
become 15 to 16 Gt of CO2eq in 2050 (Fig. 3, left). Even
though ADV does involve more rapid economic development than CNV, the emissions in the REF scenarios arealmost same. This is because rapid energy efficiency
improvement is assumed for ADV and as a result, theenergy consumption in the two cases becomes close:
primary energy consumption in ADV_REF is only
1.1 times that in CNV_REF. Another factor is that theemissions in the two REF cases become almost constantbecause of the slowdown in economic growth and energy
efficiency improvement.
In the mitigation cases, the emissions are constrained
as mentioned in the previous section, to a 43% reduction
in GHG intensity by 2020 and 68% reduction in GHG
emissions by 2050 from the 2005 levels. Because thisstudy does not consider emissions trading, the amounts ofemissions in the two cases in 2050 are exactly the same,
2.4 Gt of CO2eq.
4.2 The GHG emissions price
The maximum emissions price in ADV_MIT is$375/tCO2eq in 2036 and the price finally decreases to$223/tCO2eq in 2050 (Fig. 3, right). The price in
CNV_MIT is lower than that in ADV_MIT until 2036,
because the GHG emissions of CNV_REF are almost
constant while those of ADV_REF are continuously
increasing. After 2036, the emissions price in CNV_MIT
continuously increases while that in ADV_MIT decreases gradually. In the year 2050, the emissions price in114

M. NAMAZU et al.

CNV_MIT grows to $1,932/tCO2eq. The emissions priceof ADV_MIT decreases because new technologies will
have matured enough and their introduction will be
accelerated. On the other hand, in CNV_MIT without
CCS technology, consumption of fossil fuel, especiallycoal, is strictly constrained. To consume less fossil fuel,electrification is accelerated; however, thermal power
plants, except those burning biomass, are again strictly
constrained. In addition, the introduction of hydroelectricand nuclear power is exogenously managed; therefore, in
CNV_MIT, relatively expensive renewable energies
would need to be utilized while reducing energy demandand increasing the price of electricity. That would resultin a significant increase in the emissions price.
4.3 GDP and GDP loss
The total GDP achieved in ADV_MIT in 2050 is almost twice as large as that in CNV_MIT (Fig. 4, left).
Figure 4 (right) shows that the GDP loss in CNV_MIT isgreater than that in ADV_MIT throughout the period.
Ultimately, the GDP loss in CNV_MIT reaches 10% in
2050. The maximum GDP loss in ADV_MIT is 4.6% in
2035, declining to 4.3% in 2050.
These trends in GDP loss are similar to those in the
GHG emissions price (Fig. 3, right). In CNV_MIT, strict
emissions reduction without efficient mitigation measures like CCS technology results in high emissions andelectricity prices. These increases would cause a decreasein production in some industries. The overall effects ofstrict emissions reduction eventually result in a significant GDP loss. On the other hand, ADV_MIT reaches aturning point in 2035: the GDP loss starts to decrease
after that point almost the same as the GHG emissions
price.

4.5 Total Final Energy Consumption
The share of final energy consumption in the two
scenarios is similar throughout the period (Fig. 6). After
2012, GHG emissions constraints are applied to bothscenarios and final energy consumption starts to decrease.
Although consumption in ADV_MIT increases after
2035, the consumption in CNV_MIT continuously decreases through to 2050. The reason why final energy
consumption increases in ADV_MIT after 2035 is because several mitigation technologies like CCS become
available and practical.
4.6 Household energy consumption
Household energy consumption decreases drasticallyin both scenarios, especially in 2035, after which it increases in ADV_MIT while it continuously decreases in
CNV_MIT (Fig. 7, left). This follows the same trend as
the other energy parameters, as CNV_MIT is required toachieve its emissions reduction target by decreasingenergy demand and fossil fuel dependence. In bothscenarios, electricity becomes the main energy source forhouseholds in 2050, while biomass, often called traditional biomass, decreases significantly. The decrease in
traditional biomass is a common trend among developing
countries.
4.7 Industrial energy consumption
Similar to household energy consumption, electricity
penetration in the industrial sectors is accelerated, especially in the CNV_MIT scenario after 2035 (Fig. 7, right).
In 2050, electricity would account for more than 80% of
total industrial energy consumption in CNV_MIT.

ADV_MIT

2050

2045

2040

2035

2030

2025

0

2020

500

2050

2045

2040

2035

2030

2025

2020

2015

CNV_MIT

CNV_MIT

1,000

2015

ADV_MIT

1,500

2010

CNV_REF

2,000

2005

ADV_REF

Emission price(2005 US$/t CO2 eq)

2,500

2010

18
16
14
12
10
8
6
4
2
0

2005

GHG emissions(GtCO2 eq/ year)

4.4 Total Primary Energy Supply
The primary energy supply in ADV_MIT is greaterthan that in CNV_MIT during the entire period (Fig. 5).
This is mainly because the GDP in ADV_MIT is muchhigher than that in CNV_MIT. In both scenarios, thebiomass and nuclear energy supply increase while thefossil fuel energy supply decreases. Because CCS technology is not available in CNV_MIT, coal consumptionin CNV_MIT decreases to near zero in 2050 while 13%of primary energy supply in ADV_MIT would be coal.

12

30
25
ADV_REF
ADV_MIT
CNV_REF
CNV_MIT

20
15
10
5

GDP loss (%/year)

35

10
8
6

ADV_MIT

4

CNV_MIT

2

Fig. 4 GDP (left: GDP, right: GDP loss).

2050

2045

2040

2035

2030

2025

2020

2015

2010

2050

2045

2040

2035

2030

2025

2020

2015

2010

2005

0

0

2005

GDP (tril. 2005US$/year)

Fig. 3 GHG emissions (left: GHG emissions, right: GHG emissions price).

Feasibility of Low-Carbon Development in China

4.8 Power generation
Total power generation follows almost identicaltrends in the two scenarios (Fig. 8). In ADV_MIT,
however, electricity is generated by a variety of powersources, for example, not only conventional energies but
also wind power and biomass fire power with CCS
technology. On the other hand, in CNV_MIT, hydroelectric, biomass fire and nuclear power generate more

4.9 GHG emissions reduction measures
Figure 9 shows shares of GHG emissions reduction
measures in ADV_MIT and CNV_MIT. In ADV_MIT, in
addition to renewable power and non-energy-related
GHG reduction, CCS technology accounts for a large
share. CCS technology would become one of the main
reduction measures from around 2035, as mentioned inprevious sections. In CNV_MIT, the introduction ofrenewable power is the largest contributor, and nonenergy-related GHG reduction is the second.

20202020
2020 2035
2050
20352035 20502050
CNV ADV CNV ADV CNV ADV_MIT _MIT _MIT _MIT _MIT _MIT
Coal
Oil
Gas
Bio
Ely

Industtry Energy Consumption(Mtoe/year)

Household Energy Consumption(Mtoe/year)

Fig. 6 Total final energy consumption.

2005

than 80% of total power in 2050. Although both cases areunder emissions reduction targets, ADV_MIT would still
be able to utilize fossil fuel energy because of CCS
technology, so the share of renewable power in
ADV_MIT is lower than that in CNV_MIT.

4.10 CCS
It seems that CCS technology would have a significant economic effect in China; therefore, this study has
also assumed several additional CCS settings to comparewith the original scenario results.
Although originally CCS would not be available in
CNV_MIT, if it were possible to introduce it after 2020 at
maximum speed of 3% per year as in the case of
ADV_MIT, the GHG emissions price would decreasesignificantly. The maximum emissions price would be$270/tCO2eq in 2038, and in 2050, it would become$170/tCO2eq. The GDP loss would decrease to 5.0%
from the original 10% in the CNV_MIT without CCS.
On the other hand, if the maximum speed of CCS
introduction were 1% per year instead of the 3% per year
in ADV_MIT, the GHG emissions price increase and the
maximum price would become $625 in 2041, up significantly from $375 in 2036.

Fig. 5 Total primary energy supply.

450
400
350
300
250
200
150
100
50
0

115

900
800
700
600
500
400
300
200
100
0

2005

202020202020 2035
2050
20352035 20502050
CNV ADV CNV ADV CNV ADV_MIT _MIT _MIT _MIT _MIT _MIT
Coal

Oil

Gas

Ely

Fig. 7 Energy consumption (left: household, right: industry).

Fig. 8 Power generation.
(from left: ADV_MIT 2005-2050, ADV_REF 2050, CNV_MIT 2005-2050, CNV_REF 2050).

116

M. NAMAZU et al.
18

Enduse_efficiency
12000

Enduse_fuel_switch
10000
Electricity_demand
8000
Power_efficiency
6000
4000
Renewable
2000
Nuclear

2050

2045

2040

CCS

2035

2050

2045

2040

2035

0

2030

0
2025

2
2020

2
2015

4

4

2010

Mt CO2-eq/year

6

2030

6

8

2025

8

10

2020

10

14000

12

2015

12

16000
Non-energy
GHG

14

2010

14

18000
Other

16

2005

GHG emissions reuction(Gt CO2 -eq/year)

16

2005

GHG emissions reuction(Gt CO2 -eq/year)

18

0

EmissionFig. 9 GHG emissions reduction measures (left: CNV_MIT, right: ADV_MIT).

5. Discussion and Final Remarks
This study analysed the economic effects of GHG
emissions reduction in China during the period from2005 to 2050 using a CGE model. Two socioeconomic
scenarios were assumed for China: ADV, which describes a society open to innovation and changes, and
CNV, which describes a society conservative and passive
about innovation and changes. The emissions reduction
target in 2050 calls for a 68% reduction from the 2005
level and is based on the idea that the global halving
target and equal emission amounts per capita all over theworld. GHG mitigation measures were set in accordancewith the two socioeconomic scenario concepts. A
dynamic recursive CGE model was applied and theeconomic impacts of GHG emissions reductions wereanalyzed quantitatively.
Based the results, the two societies are described as
below:<ADV scenario>– Rapid economic growth is achieved and the GDP in
2050 is $28 trillion, 12.6 times greater than that in2005.
– GHG emissions reduction results in a maximum
emissions price of $375/tCO2eq in 2036, and the GDP
loss becomes 4.6% in 2035.
– CCS introduction is accelerated and renewable energy
technologies become economically practical, especially after 2035, so energy consumption starts to increase again and the economic loss gradually decreases.
– The electricity supply is supported by various powersources including renewable energies, and in 2050,
67% of total electricity is supplied by renewable energies.
<CNV scenario>– In 2050, China achieves a GDP of $17 trillion, 7.5
times as large as that in 2005.
– Emissions reductions have a significant economic
impact, and the GDP loss is 10% in 2050 with an emissions price of $1,932/t CO2eq.
– Because CCS technology is unavailable, fossil fuelconsumption decreases and electrification is accelerated rapidly.
– Electricity is mainly supplied by hydroelectric, biomass fire and nuclear power, which supply 84% of thetotal in 2050.
– In the case that CCS technology is available, there is apossibility of reducing these economic effects significantly.
The descriptions of the two scenarios bring up threepoints for discussion: energy system shift, energy efficiency improvement and economic impacts.
One of the characteristic of China’s energy system is
its dependence on fossil fuels, especially coal. Fossil
fuels account for more than 80% of its total primaryenergy supply, and 75% of those fossil fuels was provided by coal in 2005. In order to achieve a low-carbon
society, China needs to realize a significant energy system change, from a system relying on fossil fuel to one
relying on renewable and nuclear power. CCS technology would be able to ease the change because with it,
fossil fuel could be utilized even under strict emissions
constraints. The results from the model clearly show theimportance of CCS technology in China.
This study assumes a relatively high degree of energy
efficiency improvement, especially for the ADV_MITcase. This assumption is based on reports from Chinese
domestic institutes including one from the China Energy
Research Institute (2009b). In order to achieve this assumed energy efficiency improvement, appropriate government plans and strategies will be necessary.
The final point for discussion is the economic impactsof emissions reduction. The results show that the GDP
loss ranges between 4% and 10%. Taking into accountthe total GDP growth, there may be compensation for thisloss; however, there is still room to consider strategies tominimize the negative impacts. This study does not consider some of the emissions reduction measures thatmight be available, for example, international emissions
trading, which would allow China to buy and sell emission credits internationally. Although they might beessential countermeasures for China, appropriate efforts
should be made and support should be provided by thegovernment to realize other countermeasures such asconcrete international carbon market systems.
In conclusion, this study has analyzed the feasibilityof low-carbon development in China. Although great
uncertainty exists, especially in China’s future development, there are feasible low-carbon development pathways. In order to achieve these pathways, efficient andappropriate governance is necessary, and the introductionof some new technologies can contribute to minimizing
the impacts of emissions reduction in China.

Feasibility of Low-Carbon Development in China

Acknowledgments
This research was supported by the “Environment
Research and Technology Development Fund of the
Ministry of the Environment Japan” S-6-1.
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117

Michiko NAMAZU
Michiko NAMAZU is a master’s degree student at
the Department of Urban and Environmental
Engineering, Kyoto University. Ms. Namazu got
her Bachelor’s of Engineering from the Department of Global Engineering, Kyoto University.
She has been conducting research for the Asianpacific Integrated Modeling (AIM) team since her
senior year. Her field of study is climate change modeling, especiallycomputable general equilibrium (CGE) modeling. Currently she is working
on quantitative evaluation of the impact of greenhouse gas emission
reduction in Asian regions, using the AIM/CGE [basic] model. She hasapplied the CGE model to Japan, India and China so far, and will apply it toother Asian regions as well. Through application of the model, she isdeveloping scenarios for Asian regions toward a reduced GHG emissions
society, i.e., the so-called low-carbon society.

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.

Kejun JIANG
Kejun JIANG began researching climate change
relative to energy policy analysis, with a focus onenergy technology policy assessment, energy
supply policy assessment, renewable energy development and energy conservation. Since 1994,
he has worked on integrated assessment model
development for energy and GHG emission
policy scenarios, focusing on analysis of China and the world. At present
he is mainly working on energy and environment policy assessment,
serving as leader of the Integrated Policy Assessment Model for China(IPAC) team. His major focus includes energy and emissions scenarios,
energy policy energy systems, energy market analysis, and climate change.
Started from 1997, he has worked with the IPCC on the Special Report on
Emission Scenario and Working Group III Third Assessment Report,
serving as lead author for IPCC WGIII AR4 Chapter 3, and lead author for
GEO-4 Chapter 2. Now he is the Coordinating Lead Author for IPCC AR5.
His recent research projects include energy and emissions scenarios for2030, low-carbon emissions scenarios up to 2050, assessment of energy
taxes and fuel taxes, the potential of energy targets in China, developmentof an integrated policy assessment model, and other projects. He got his
Ph.D. from the Social Engineering Department of the Tokyo Institute ofTechnology.

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 12 July 2012, Accepted 13 November 2012)

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M. NAMAZU et al.

Appendices
Appendix 1 Reference list for Fig. 1.
Title
Year
Author2050 China Energy and CO2 Emission Report (CEACER)
2009a China Energy Research Institute, National Development and Reform Commission
2009b China Energy Research Institute, National Development and Reform Commission
China’s Low-Carbon Development Pathways by 2050:
Scenario Analysis of Energy Demand and Carbon Emissions(CEACES)
Transportation Energy Intensity and Consumption Forecasts.
2012 China Energy Research Institute, National Development and Reform Commission
Energy Science & Technology in China: A Roadmap to 2050
2010 Chinese Academy of Sciences
Energy Revolution Sustainable Energy Global Energy Outlook 2010 Greenpeace
2nd Edition
The GGI Scenario Database (version 2.0)
2006 International Institute for Applied Systems Analysis
2006 Netherlands Environmental Assessment Agency
Downscaling Drivers of Global Environmental Change –Enabling Use of Global SRES Scenarios at the National andGrid Levels
Development of Long-term Socioeconomic Scenarios
2011 Research Institute of Innovative Technology for the Earth–Population, GDP –China’s Low-Carbon Development Scenario in 2050, 2050
2009 Study Team of 2050 China Energy and CO2 Emissions Report
China Energy and CO2 Emissions Report
World Population to 2300
2004 United Nations Population Division
World Population Prospects 2008
2008 United Nations Population Division
World Population Prospects 20102011 United Nations Population Division
US Census International Database2010 United States Census Bureau

Appendix 2 Reference list for Fig. 2.
Title
Year
AuthorEnergy Outlook for Asia and the Pacific 20092009 Asia-Pacific Economic Cooperation, Asian Development BankCountry-level GDP and Downscaled Projections
2002 Center for International Earth Science Information Network
CEPII BASELINE V1.0 GDP
2009 Centre D’etudes Prospectives et D’infomarions Internationales (CEP)The Long Term Growth Prospects of the World
2006 Center D’etudes Prospectives et D’infomarions Internationales (CEP)
Economy-Horizon 2050
2050 China Energy and CO2 Emission Report (CEACER)
2009a China Energy Research Institute, National Development and Reform Commission
China’s Low-Carbon Development Pathways by 2050 Scenario 2009b China Energy Research Institute, National Development and Reform Commission
Analysis of Energy Demand and Carbon Emissions (CEACES)
China’s Mid- and Long-term (2030, 2050) Energy
2011 Chinese Academy of Engineering
Development Strategy
Energy Science & Technology in China: A Roadmap to 2050
2010 Chinese Academy of Sciences
The Prospects of China’s Economic Development in2010 Development Research Center of State Council, P. R. China
2030-Based on the DRC-CGE Model
International Energy Outlook 2010.
2010 Energy Information Administration
Potential Secure, Low-Carbon Growth Pathways for the2011 Energy Research Institute, National Development and Reform Commission
Chinese Economy
The GGI Scenario Database (version 2.0)
2006 International Institute for Applied Systems Analysis
Energy Technology Perspectives.
2010 International Energy Agency
Long-term Forecast of Global Economy and Population2007 Japan Center for Economic Research
2006-2050 Demographic Change and the Asian Economy
2006 Netherlands Environmental Assessment Agency
Downscaling Drivers of Global Environmental
Change-Enabling use of Global SRES Scenarios at the National
and Grid Levels
Development of Long-term Socioeconomic Scenarios
2011 Research Institute of Innovative Technology for the Earth–Population, GDP–
China’s Low-Carbon Development Scenario in 2050, 2050
2009 Study Team of 2050 China Energy and CO2 Emissions Report
China Energy and CO2 Emissions Report
Asia/World Energy Outlook 2009 –The role of Technology
2009 The Institute of Energy Economics, Japan
towards the Resolution of Energy & Environmental Issues in
Asia–
Asia/World Energy Outlook 2010 –The Role of Technology
2010 The Institute of Energy Economics, Japan
Towards the Resolution of Energy & Environmental Issues in
Asia and the World–
Asia/World Energy Outlook 2011 –Growing Uncertainty over2011 The Institute of Energy Economics, Japan
International Energy Trends and the Future of Asia–