53

Identifying Factors for Promoting Renewable Energy Projects
through the Clean Development Mechanism inChina, India and ASEAN Countries
Akihisa KURIYAMA1 and Minoru MORITA2
1

Climate and Energy Area, Institute for Global Environmental Strategies(IGES),
2108-11, Kamiyamaguchi, Hayama Kanagawa, 240-0115, Japan
2
Faculty of Political Science and Economics, Waseda University,
1-6-1, Nishiwaseda, Shinjyuku-ku, Tokyo, 169-8050, Japan*email: kuriyama@iges.or.jp

Abstract
This study analysed the greenhouse gas (GHG) abatement cost of renewable energy projects developed
through the Clean Development Mechanism (CDM) in China, India and ASEAN countries. The results fromthis study show that the average abatement cost for all renewable energy technology including biomass,hydro, wind and solar renewable energy in ASEAN countries was the highest among these three countries
due to the absence of a “scale of economy,” lower penetration rate of new technology such as PV and wind
power, and lower magnitude of grid emission factors. To encourage the implementation of renewable energy
in ASEAN countries towards a decarbonised economy, the following research topics could be examined in
the future. First, it will be necessary to analyse the “learning curve” for new types of technology, i.e., PV andwind power technology. Second, effective mitigation mechanisms and incentives need to be examinedbecause as the results of this study imply, the magnitude of grid emission factors could affect the abatement
cost and business conditions for investments in renewable energies through mitigation mechanisms. Lastly, it
is important to discuss the overall policy arrangements since ASEAN countries implement not only feed-intariffs for renewable energy, but also subsidies for fossil fuels, which provide some advantages to fossil-fuelpower plants and affect estimations of CO2 abatement costs. Apparently, climate policies interact so closelywith energy policies that it is worth proposing comprehensive climate and energy policies with analyticalinsights into energy-related technologies and policies in ASEAN countries.
Key words: abatement cost, ASEAN, China, India, renewable energy, scale of economy

1. Introduction
In December 2015, the Conference of Parties (COP)
adopted the Paris Agreement to strengthen the global
response to the threat of climate change, aiming to keep
the global temperature rise this century to well within
two degrees Celsius over pre-industrial levels. The Paris
Agreement requires all countries including developing
countries to make their best efforts to implement
mitigation actions through nationally determined
contributions (NDCs).
Among the developing countries, China and India
emitted 9.1 billion tonnes and 2.0 billion tonnes of CO2,
respectively, from fossil fuel combustion in 2014,
accounting for 34% of the world’s total CO2 emissions
from fossil fuel combustion in 2015. Also, among the
Association of Southeast Asian Nations (ASEAN)
countries, Indonesia, Thailand, and Malaysia had GHG
Global Environmental Research
21/2017: 053–059
printed in Japanemissions positioned within world’s top 30 (World Bank,2015). Furthermore, the final energy consumption of thewhole ASEAN region is expected to increase by 2.2%
per annum, which indicates an increase from 549 Mtoe
in 2011 to 1,004 Mtoe in 2035. The Asian Development
Bank (ADB) (2013) has forecasted the amount ofelectricity generation to increase to 1,879 TWh in 2035
from 696 TWh in 2011, which indicates a 4.2% annual
increase. In particular, it is expected that the use of coal
will increase in Viet Nam, the Philippines and Lao PDR
which have not used fossil fuels as electricity sources in
the past (ADB, 2013). Therefore, it is important to takethe best actions for mitigation in the electricity sector,
especially in ASEAN countries, in order to achieve theultimate goal of the Paris Agreement.
Until the end of the first commitment period of the
Kyoto Protocol in 2012, the Clean Development
Mechanism (CDM), which is a baseline and credit©2017 AIRIES54

A. KURIYAMA and M. MORITA

Number of registered projects

1,200
1,000

Other
Industrial gas

800

Biomass
600

MethaneEnergy Efficiency

400

Hydro power
2000PV, etc
Wind power2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Fig. 1 Annual estimated emission reductions through CDM project activities, categorised by project type and project starting date.
Source: author, based on IGES (2014).

mechanism in developing countries authorised under the
Kyoto Protocol, provided significant incentives topromote mitigation measures in developing countries.
By the end of May 2014, the CDM had registered 7,490
projects and issued 1.5 billion tons of certified emission
reductions (CERs) (IGES, 2016).
Figure 1 illustrates trends of annually estimatedemission reductions through CDM projects. As shown in
Fig. 1, project developers promoted energy efficiency
CDM projects mainly for waste heat recovery,hydropower generation, methane recovery andavoidance, and fossil fuel substitution by using biomass
and industrial gases such as HFC and N2O from 2004 to
2007. From 2007 onwards, registration of those projecttypes has become less active, while renewable energy
project types such as wind power and photovoltaic
power have increased. As a result, renewable energy
projects accounted for 64% of the total registeredprojects that had been developed by 2014. In particular,
China and India hosted 60% and 27% of those renewableenergy projects, respectively. This indicates that China
and India hosted 38% and 17%, respectively, of all CDM
projects. This result can be explained by looking at thebusiness incentives for CDM project developers. In
other words, most project developers were interested ina project that would bring a higher profit, or in a country
where the business market could be expected to expand.
Regarding the attractiveness of the investment for hostcountries, Jung (2006) evaluated the potential of CDM
project formulations from three aspects: reduction
potential in each country, institutional framework in hostcountries, and investment in climate change. Among
Asian countries, Jung (2006) concluded the assessment
for China, India, Thailand, Indonesia, Mongolia andMalaysia was “very attractive” or “attractive.” Oleschak& Springer (2011) also evaluated the attractiveness ofproject investments in the host countries from three
aspects: the institutional framework for the Kyoto

100%
80%
60%

12%
4%
11%

29%
6%
13%

40%
20%
0%

China

73%

53%
Share of CO2 emissions
from power sector
India

ASEAN

Share of CER issuancefrom renewableenergy projects
Other non-Annex B countries

Fig. 2 CO2 emissions in the electricity sector and CER issuancestatus of renewable energy projects.
Source: author, based on IEA (2014a), IGES(2016).

Mechanism, legal regulations in host countries, andeconomic activity. As a result, India was evaluated as
the most attractive country for investment by project
developers, with China in second place. Winkelman and
Moore (2011) also state that total GHG emissions, theemission intensity of the economy, domestic humancapital and increased electricity demand are importantfactors in hosting a CDM project.
Indeed, ASEAN countries including Indonesia,
Thailand and Viet Nam have a high potential to host a
considerable number of CDM projects because thosecountries have sufficient institutional capacity and a huge
potential for GHG emissions reduction owing to therapid increase in economic activity. However, as Fig. 2
shows, CERs in the electricity sector have not been
issued there in contrast to India and China when it
comes to considering the ASEAN countries’ share of
CO2 emissions in the power sector. A possilble factor
hindering the development of CDM projects in thosecountries could be high GHG abatement and transaction
costs in ASEAN countries since, generally speaking, theIdentifying Factors for Promoting Renewable Energy Projects through the CDM in ASEAN Countries

cost issue is one of the major concerns for CDM project
developers. This study, however, focuses solely on
analysing GHG abatement costs because the transaction
costs for renewable energy projects account for only
14.4% of the total (Michaelowa & Jotzo, 2003).
As shown in Equation (1), the CO2 abatement cost
per ton can be calculated by dividing the abatement costby the amount of emission reductions (Rahman et al.,2009). The abatement cost is defined as the difference
between the baseline cost and the project cost, andemission reduction is identified by the difference
between baseline emissions and project emissions.

55

literature, Rahman et al. (2012) define the abatement cost
as the difference between the project cost and baseline
cost, charged for supplying the same amount ofelectricity in the absence of the renewable energy
technology proposed under the CDM. In particular,
Castro and Michaelowa (2010) and Wetzelaer and Linden(2007) define the baseline cost as the net present value ofelectricity sales, and the project cost as the net present
value consisting of investment costs and operation costs.
When project developers calculate the net present value,
they can usually use the discount rate as a value for theirbusiness. Therefore, this study follows the same approach
identifying theabatement cost of CDM projects using
Projecttocost-Baseline
cost(1)
Average GHG emission abatement costthebenchmarkrate
Baseline emissoin-Project emissionas shown in Equation (2). GHG
emission
reductions
arereferred
to by the numbers
cost-Baseline
Project Projectcost-Baseline
cost cost(1)
n abatement
cost
described
in
each
PDD.
Using
the
project
period outlined
(1)
ment
cost
emissoin-Projectemission
BaselineBaseline
emissoin-Projectemissionin the PDDs, emission reductions during the assessment
The cost of a CDM project consists of investment
period are calculated using Equation (3). The duration
costs and operating costs for the project. The baseline
used in evaluating the net present value is same as thecost is defined as the cost of generating electricity using
project period for each project.
conventional power plants that would be operated in theabsence of the CDM. For a renewable energy project,
the baseline cost is defined as the selling price ofelectricity (Rahman et al., 2012). The effect of(3)
technological innovation can also be considered, in which
where “t” denotes the year of the project activity
the cost of GHG reduction falls in proportion to thestarting date, “n” represents the project period, “i” denotes
decrease in the cost of the project. (Lantz et al., 2012;ith project serial number, and “r” indicates the discount
IEA, 2014b).
rate defined by the value of the benchmark described in
A review of past studies reveals that there are few
the PDDs. “ACi” represents the net present value of GHG
studies analysing the CO2 abatement costs using data
abatement costs without social costs for project i.
from actual projects in China, India and ASEAN“OMCi,t” denotes the operation cost of project i at time t,
countries. Rahman et al. (2012) calculated the GHG“FCi,t” represents the fuel costs of project i at time t,
abatement cost for all CDM projects that had been
“OTCi,t” represents other costs of project i at time t, suchregistered up to 2011 in Annex B countries but which
as water usage rights and insurance. “IEi,t” represents
did not have any focus on ASEAN countries. Simon etincome from electricity sales of project i at time t.
al. (2016) investigated the abatement cost structure of
For projects which do not provide a benchmark value,
CDM projects in India. Therefore, this study analyses
this study uses the lending interest rate given by the World
the reasons ASEAN countries failed to harness a
Bank (2015). To collect cost data, this study examines
considerable number of CDM projects compared to theireach PDD that is made available on the UNFCCC
mitigation potentials, from the two perspectives of “scale
website. Since the PDDs provide cost data using severalof economy,” which causes the average cost of
currencies, this study unified all the cost data into USD
producing something to fall as the volume of its output
for the year 2013 using the exchange rate and consumer
increases, and renewable energy technology type. The
price index provided by the World Bank (2015). Table 1
discussion section analyses possible impact by level ofsummarises the basic statistics for all the data.
grid emission factors.
“Abatement Cost” indicates the GHG abatement
Section 2 introduces data preparation for projectcost. “Abatement” denotes the amount of emission
costs and abatement costs. Section 3 discusses the
reduction during the project period. “Biomass,” “pv_analytical approach, Section 4 describes the analyticalsolar” and “wind” represent the dummy variables ofresults, Section 5 draws conclusions and discusses them,biomass power, PV or solar power and wind powerand Section 6 remarks on prospects for further studies.
projects, respectively. The numbers for 2006 to 2012
denote the dummy variables for each year. “Project
2. Data
Duration” indicates project operation period. “India” andThis study developed data for analysing CO2“ASEAN” denote the country dummy variables.
abatement costs for renewable energy projects under the
CDM, based on the information used in the investment
analysis to demonstrate additionality. This information isprovided in the project design documents (PDDs) for
each renewable energy project. In a review of existing

56

A. KURIYAMA and M. MORITA

Table 1 Summary statistics.
Variable

Obs

Mean

Std. Dev.

Min

Max

Abatement
Cost

3381

15.4

1.32

2.6

20.1

Abatement
(Log)

3842

14.1

1.11

10.0

18.3

biomass

3895

0.1

0.23

0.0

1.0

pv_solar

3895

0.0

0.19

0.0

1.0

wind

3895

0.5

0.50

0.0

1.0

2006

3895

0.1

0.29

0.0

1.0

2007

3895

0.1

0.33

0.0

1.0

2008

3895

0.1

0.33

0.0

1.0

2009

3895

0.1

0.33

0.0

1.0

2010

3895

0.2

0.36

0.0

1.0

2011

3895

0.2

0.39

0.0

1.0

2012

3895

0.1

0.31

0.0

1.0

Project
Duration
(Log)

3895

3.1

0.19

1.9

3.9

India

3895

0.2

0.40

0.0

1.0

ASEAN

3895

0.1

0.27

0.0

1.0

3. Methodology
The cost function for emissions abatement throughCDM refers to existing studies on pollution abatement
costs. Hartman et al. (1997), Goldar, et al. (2001), andRahman et al. (2012) note that the abatement costs ofprojects under pollution control can be explained by the
first and second powers of emission reduction ofpollutants as shown in Equations (4) and (5). If thecoefficient of the second power of emission reductions
is negative, the result can imply that the abatement costhas a scale of economy.
(4)(5)
where “ACi” denotes the abatment cost (USD)
derived by Equation (2), “Ai” represents the amount ofemission reduction during the project period (tCO2),“Xi” denotes explanatory variables and “εi” denotes theerror term.

4. Estimation Results for Abatement Costs in
China, India and ASEAN Countries
Table 2 presents the estimation results of this study.
Model 1 shows the results for all of the countries
including China, India and ASEAN countries. The
coefficient of abatement is positive at a significancelevel of 1% while the coefficient of squared abatement
is negative at a significance level of 5% . This result
implies that abatement costs in all of the countries havea scale of economy. All the models include the dummy

variables of project types, which enable us to discuss theimpact of cost increases compared to the base project.
Hydropower is selected as the base project because it is
the only the type of renewable energy technology that is
widely implemented across the regions in this study.
Regarding abatement cost differences by project type,abatement costs for biomass power, PV and solar
thermal power (hereinafter, solar) and wind power CDM
projects are around 30%, 16% and 64% higher than thecost of hydropower projects, respectively. Considering
the effect of the project starting year, the abatement costhas increased over the years. Looking at the country’s
effect on CO2 abatement costs, the abatement cost of a
CDM project in India is 12－16% higher than in China.
On the other hand, the abatement cost in ASEAN is
estimated to be 47－48% lower than in China. This result
is consistent with the consequences of investment in
CDM projects, whereby China and India host a large
number of CDM projects and ASEAN countries have
had less investment through the CDM despite their
mitigation potential as discussed in Section 1.
Models 2, 3 and 4 show the country-specific results.
For China, the coefficient of abatement in Model 2.1 isestimated to be 0.745 at a 1% significance level. In
Model 2.2, however, the coefficient of abatement andsquared abatement are not significant. This result
implies that renewable energy projects in the CDM
did not have a scale of economy. The cost of biomass,solar and wind power projects in China are 103－106%,
181－183% and 71－73% higher than the cost of hydropower
projects, respectively. The abatement costs of renewableenergy projects have been continuously increasing over
the years.
For India, the coefficient of abatement in Model 3.1
is estimated to be 0.985 at a 1% significance level. Also,in Model 3.2 the coefficient of abatement is 1.941 at 1%
significance levels while the coefficient of squared
abatement is －0.036 at 5% significance levels. This result
implies that renewable energy projects in India have ascale of economy effect. The cost of biomass power is52－55% lower than the cost of hydropower plants, while
the cost of solar and wind power plants are 97－100 and
42% higher than the cost of hydropower plants,
respectively. Regarding the dummy variable of a
project’s starting year, variables for 2007, 2008 and 2011are positively significant. The weak trend of increasing
CO2 abatement costs as years pass is consistent with paststudies conducted by Simon et al. (2016)
Regarding ASEAN countries, the results do notindicate a scale of economy effect. This can be seen inin Model 4.2, where neither the coefficients of abatement
nor squared abatement are significant. Compared to theresults in China and India, the abatement costs of solar
and wind power plants in ASEAN countries are morethan 200% higher compared to the cost of hydropower
plants. This result implies that solar and wind power in
ASEAN countries are still much more costly thanconventional renewable technology such as hydropowerand biomass power. Unlike the results for China and57

Identifying Factors for Promoting Renewable Energy Projects through the CDM in ASEAN Countries
Table 2 Estimation results for all countries and by country.
Abatement Cost
(Log)
Abatement (Log)
Squared Abatement (Log)
Renewable Energy Dummies:
biomass
pv & solar
wind
Project’s Start Year:
2006
2007
2008
2009
2010
2011
2012 and After
Project Duration (Log)
India
ASEAN
_cons
Number of obs.
Adjusted R2

ALL (Model 1)
Model 1.1
Model 1.2
Coef.
Coef.
0.868 ***
1.446 ***
(0.02)(0.23)－-0.021 **(0.01)CHINA (Model 2)
Model 2.1
Model 2.2
Coef.
Coef.
0.745 ***
0.081
(0.03)(0.48)
0.023(0.02)INDIA (Model 3)
Model 3.1
Model 3.2
Coef.
Coef.
0.985 ***
1.941 ***
(0.03)(0.39)－-0.036 **(0.02)

ASEAN (Model 4)Model 4.1
Model 4.2
Coef.
Coef.
0.933 ***
1.006
(0.05)(0.81)－-0.003(0.03)

0.312 ***
(0.10)
1.630 ***
(0.17)
0.642 ***
(0.04)

0.294 ***
(0.10)
1.609 ***
(0.17)
0.642 ***
(0.04)

1.026 ***
(0.08)
1.834 ***
(0.27)
0.706 ***
(0.05)

1.055 ***
(0.08)
1.811 ***
(0.28)
0.733 ***
(0.05)－-0.518 ***(0.14)
0.995 ***
(0.28)
0.417 ***
(0.13)－-0.545 ***(0.14)
0.974 ***
(0.27)
0.415 ***
(0.13)－-0.040(0.46)
2.183 ***
(0.26)
2.069 ***
(0.26)－-0.043(0.47)
2.182 ***
(0.26)
2.064 ***
(0.27)

0.112(0.09)
0.183 **
(0.08)
0.228 ***
(0.08)
0.337 ***
(0.08)
0.410 ***
(0.08)
0.442 ***
(0.08)
0.348 ***
(0.09)－-0.390 ***(0.14)－-0.161 ***(0.05)
0.478 ***
(0.07)
3.699 ***
(0.39)

0.108(0.09)
0.182 **
(0.08)
0.232 ***
(0.08)
0.346 ***
(0.08)
0.410 ***
(0.08)
0.436 ***
(0.08)
0.341 ***
(0.09)－-0.336 **(0.15)－-0.118 **(0.06)
0.472 ***
(0.07)－-0.524(1.75)
3,360
0.61

0.077
(0.10)
0.141(0.10)
0.202 **
(0.09)
0.497 ***
(0.09)
0.516 ***
(0.09)
0.485 ***
(0.08)
0.430 ***
(0.09)
0.214(0.18)-

0.080(0.10)
0.141(0.10)
0.197 **
(0.09)
0.495 ***
(0.09)
0.515 ***
(0.09)
0.487 ***
(0.08)
0.429 ***
(0.09)
0.217(0.18)-

0.291(0.21)
0.380 **
(0.19)
0.444 **
(0.18)
0.259(0.18)
0.327 *
(0.19)
0.557 ***
(0.19)
0.186(0.21)－-1.340 ***(0.31)-

0.293(0.21)
0.401 **
(0.19)
0.477 **
(0.19)
0.283(0.18)
0.339 *
(0.19)
0.574 ***
(0.19)
0.208(0.21)－-1.277 ***(0.32)-－-0.170(0.44)－-0.193(0.43)－-0.055(0.51)－-0.135(0.46)－-0.015(0.41)－-0.221(0.44)－-0.141(0.47)－-0.302(0.38)-－-0.169(0.44)－-0.192(0.43)－-0.055(0.51)－-0.135(0.46)－-0.014(0.41)－-0.221(0.44)－-0.142(0.48)－-0.306(0.40)
-

-

-

-

-

-

-

0.61

3.510 ***
(0.45)
0.52

8.366 **
(3.55)
2,403
0.52

5.130 ***
(1.06)
0.61－-1.287(3.01)
697
0.61

3.337 **
(1.31)
0.62

260

2.828
(5.35)
0.61

Note: Each model shows the results both with and without the variable of Squared Abatement (Log). For all models, the base technology is hydropower.
For Models 1.1 and 1.2, the base country is China.

India, none of the year variables of ASEAN countries
were significant, meaning that the abatement costs in
ASEAN countries have not increased over the years.

5. Discussion and Limitations of This Study
As discussed above, mitigation by renewable energy
projects under the CDM was higher than the costs in
China and India. Three reasons can be considered forthis result. First, scale of economy could have resulted
in the lower CO2 abatement costs of renewable energy
projects in India. Indeed, India has the lowest average
abatement cost for the whole country among the threegroups. On the other hand, only the coefficients ofabatement are significant for China and ASEAN
counties. Furthermore, while China’s coefficient is
0.745, the coefficient of ASEAN is 0.955 which implies
that abatement costs increase in proportion to increased
abatement. This result could indicate that CDM project
developers in ASEAN countries have been unable tobenefit from scale of economy.
Second, the incremental cost of ASEAN’s wind andPV technology compared to hydropower plants is larger
than that of the incremental costs in China and India.
The results from our empirical model show that the costof wind power technology in India is only 42% higher
than the cost of hydropower plants. On the contrary, thecost of wind technology in ASEAN countries is 207%
greater than the cost of hydropower plants. Similarly, thecost of solar technology in ASEAN countries is higher
than the cost in China and India. This result could be
explained by the fact that CDM project developers have
implemented a large number of solar and wind power
projects in these two countries. In fact, China had hosted
1,427 wind power projects and 118 solar projects by
2014. India had hosted 570 wind power projects and 20
solar projects by 2014. Therefore, China and India could
benefit from the “learning curve” from suchtechnological innovation, which has contributed to a
reduction in the initial cost of renewable energy
technologies.
Third, the grid emission factor of ASEAN countries
is lower than those in India and China. In fact, thebaseline emissions of renewable energy projects which

58

A. KURIYAMA and M. MORITA

displace electricity from electricity grids are obtained bymultiplying the amount of electricity generation (MWh)by the grid emission factor (tCO2/MWh). For example,
the average grid emission factors in China and India are
0.90 tCO2/MWh and 0.88 tCO2/MWh, respectively, which
are higher than those of other CDM project hostcountries according to data from IGES (2015). On theother hand, the grid emission factors of Indonesia, Viet
Nam and Thailand are 0.76 tCO2/MWh, 0.56 tCO2/MWh
and 0.55 tCO2/ MWh, respectively, which are mid orlower level among the host countries. The differences ingrid emission factors among China, India and ASEAN
countries mean that CDM project developers in China
and India gain 1.6 times the CERs compared to ASEAN
countries even though they generate the same amount ofelectricity by renewable energy. Thus, even if the power
generation costs per kilowatt hour are at the same level
among those countries, CO2 abatement costs fromrenewable energy are not uniform owing to differencesin GHG emission reductions. While the magnitude ofgrid emission factors could have a significant impact on
the abatement costs of renewable energy, there is no
sophisticated method for identifying grid emission
factors, and in fact there is room for improvement. In
fact, Hawkes (2014) pointed out that quantification of
CO2 emissions from electricity use is usuallyaccomplished using short-run emission factors, but theseshort-run factors do not take account of structuralchanges in power systems. He also suggested that
long-run marginal emission factors need to be analysed
to quantify CO2 emissions. In particular, although the“tool to calculate the emission factor for an electricity
system” (UNFCCC, 2013) takes into account structuralchanges in the electricity system, it does not consider
how the newly-built or avoided power stations would
have operated in the absence of the CDM. Harmsen andGraus (2013) pointed out that an average CO2 intensity
approach cannot be used to estimate future electricity
savings in situations where the change in both electricity
generation and the emission intensity occur
simultaneously. In this case, they recommend applying amarginal CO2 intensity approach with a scenario-based
analysis.
Indeed, the result of our model also supports the factthat ASEAN countries have enormous potential to
implement renewable energy because the abatement
costs using renewable energy technology in China andIndia have increased over the years while the cost in
ASEAN countries has not increased. Also, as discussed
above, if the project costs of renewable energy decrease
further owing to the scale of economy, or methods for
identifying CO2 emission reduction from electricity use
are improved to take into account possible increases offossil-fuel power plants, then the abatement costs ofrenewable energy in ASEAN countries would decrease.

6. Prospects for Climate and Renewable
Energy Policies in ASEAN Countries
In this concluding section, this study identifies
possible research topics to be examined in the future.
First, it would be interesting to examine the learning
effect or learning curve of a new type of technology, e.g.,
PV and wind power technology. As predicted by various
research (Taylor, et al., 2015), the levelised cost ofelectricity (LCOE) for that technology would be lower
than for conventional power plants. However, there arestill few studies on how technology has been developed
using actual data.
Second, an effective mitigation mechanism thatprovides appropriate incentives in line with long-termemission reduction goals needs to be examined. The
results of this study imply that the degree of the gridemission factor could affect the abatement costadvantage. The limitation of current grid emission
factors, however, is that they can only reflect historical
CO2 emissions or short-term trends in electricity sectors
as discussed in Section 5. The number of fossil fuelfired power plants, including coal-fired power
technology, however, is expected to increase in ASEAN
countries. In this case, the mitigation impact by
renewable energy could be higher if it takes into accountlong-term energy forecasts. Therefore, an appropriate
method that promotes renewable energy technology at
an early stage as possible needs to be considered tofulfill the two-degree target, which is the ultimate
objective of the Paris Agreement.
Third, it is necessary to discuss a comprehensive
policy framework. In order to calculate CO2 abatement
costs in this study, the abatement cost was defined as thedifference between project and baseline costs. The
baseline cost was determined by the electricity sales,consisting of electricity generation and tariffs. In fact,
those elements could be affected by certain energy
policy interventions. For example, if a government
implemented feed-in tariffs, the baseline cost would
increase. As a consequence, the abatement cost would
decrease. On the other hand, some ASEAN countries
have introduced fossil fuel subsidies for energy,
especially for oil prices. In this case, the fuel cost forelectricity generation is lower than that of business-asusual. As a consequence, the electricity price is lower
than the normal level, and tariffs for electricity also
decrease. In this case, though, abatement costs increase.
Above all, the mitigation costs of renewable energy
depend on the level of technology development, thedesign of mitigation mechanisms and incentives, andpolicy arrangements. Renewable energy for ASEAN
countries, however, is critical for achieving adecarbonising society, so the development of thesetechnologies should be constantly promoted with stablepolicy support. For that reason, a policy proposal basedon analytical policy research and fundamental policy
discussion would have a significant impact on promotionof steady mitigation actions in ASEAN countries.

Identifying Factors for Promoting Renewable Energy Projects through the CDM in ASEAN Countries

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Akihisa KURIYAMA
Akihisa Kuriyama obtained his master’s degree inJapan from the Graduate School of Science andEngineering(International
Development
Engineering), Tokyo Institute of Technology on
environmental economics and econometrics.
After that, he worked for IGES to implement
capacity building programs for mitigation
activities in Asian countries inlcuding Cambodia, Lao PDR, the Philippines
and Indonesia. He also conducts quantitative research on the Kyoto
mechanisms and CO2 emissions from the electricity sector.

Minoru MORITA
Minoru Morita obtained his Ph.D. in Economics
from Sophia University in Japan. Currently, he is anassistant professor in the Faculty of Political
Science and Economics at Waseda University. His
research interests include climate change policies,
energy policies and indoor air pollution indeveloping countries.

(Received 2 June 2017, Accepted 22 August 2017)