2015-25
Determinants of the efficiency of CDM and JCM projects:
Viewing from financial and environmental outcomes
Mai Huong Hoang Mohammad Tazul Islam
Michiyuki Yagi Katsuhiko Kokubu

Determinants of the efficiency of CDM and JCM projects:
Viewing from financial and environmental outcomes

Mai Huong Hoang*, Mohammad Tazul Islam *, Michiyuki Yagi*, Katsuhiko Kokubu*
ホァン マイ フォン・タズル イスラム・八木迪幸・國部克彦

Abstract
With the purpose of promoting clean development in developing countries, as well as increasing
mitigation toward global warming issue, efforts have been made between government andcompanies through implementing Clean Development Mechanism and Joint Credit Mechanism
projects. This study targets at identifying the determinants of the financial and environmental
outcomes of CDM and JCM pilot projects, focusing on host party, project type, and project status.
The CDM project data is collected for 11 years (2004 – 2014) from the Institute of Global
Environmental Strategies whereas the JCM pilot project data covers 2 years (2013 – 2014) from
Global Environment Centre Foundation. Result of this study shows that regarding JCM pilot projects,
project type is a crucial determinant for environmental outcome. Meanwhile, regarding CDM
projects, statistically significant determinants of environmental outcome are host party, project type,
and project status. In terms of CDM financial outcome, only project type and project status show
significant effects. It implies that there is no need for considering which country to implement CDM
projects if the target is financial outcome. Instead of that, if aiming at projects’ environmental
outcome, for either CDM or JCM, it is necessary for companies to take project type/ sector into
consideration.
Key words: Clean development mechanism; Joint crediting mechanism; Internal Rateof Return; greenhouse gas emissions; country and industry effects
JEL classification: F21, O13, Q54*

Graduate School of Business Administration, Kobe University
Address: 2-1, Rokkodai-cho, Nada-ku, Kobe-city, 657-8501, Japan
Email: tazul_ctgu08@yahoo.com

11. INTRODUCTION
Global warming has been viewed as one of the greatest threats to the survival of not only
human beings but also other species in the world over the last few decades. Efforts have been madeto reduce the emission of greenhouse gases (GHGs) by different means. Many developed countries
have engaged in endeavors to reduce the amount of GHGs through modern technology. In terms oftechnology transfer related to the climate change issue, the Clean Development Mechanism (CDM)
under the Kyoto Protocol has been widely implemented in developing countries since 2001. CDM
allows emission-reduction projects in developing countries to earn certified emission reduction(CER) credits. As of June 2015, 1.6 billion CERs have been issued in the United Nations Framework
Convention on Climate Change (UNFCCC) (2015).
However, along with the advantages of such actions, many drawbacks have been reported.
For instance, large transaction costs make it difficult for projects to be accepted by host parties(Burniaux et al., 2009). In addition, the types of projects have also been a controversial topic ofdiscussion. Recent studies on CDM also point out the role of host parties, which count a lot for the
success of a CDM project (Winkelman and Moore, 2010). As there has arisen great criticism toward
the real outcome of CDM, in recent years, a new mechanism regarding emission reduction has beendeveloped. Originally developed by the Japanese Government, the Joint Credit Mechanism (JCM) isa means to facilitate the diffusion of leading low-carbon technologies, products, systems, services,
and infrastructure as well as to implement mitigation actions regarding global environmental issues
in developing countries. 1 As they were launched just in 2011 and carried on in 2014, more time isneeded to see the real effectiveness of JCM projects.
The purpose of this research is to investigate the determinants of the success of bothtypes of projects. Based on the literature review, several potential factors that affect the outcome ofthose projects are taken into consideration. So far, there have been a limited number of empirical1

See explanation by United Nations Environment Programme<http://www.cdmpipeline.org/overview.htm> [accessed in 19th July 2015].

2

studies taking financial or environmental outcomes of such projects into account; therefore, we study
this aspect. Due to the lack of data, analysis of this study is divided into two elements. One is to
examine the determinants of the annual reduction of GHG emissions in both CDM and JCM projects,
carefully comparing CDM with JCM in terms of determinants such as the effects of country, industry,and project types. Another is to examine the determinants of the investment effectiveness of CDM.
Specifically, this study focuses on the internal rate of return (IRR) of CDM projects. We consider thefindings to be beneficial in forming the expectations of investment returns in JCM projects.
This paper is structured as follows. Section 2 explains the CDM and JCM and reviews
the literature examining the successful determinants of CDM projects. Section 3 explains themethodology of this study. Section 4 shows empirical results and Section 5 concludes.

2. BACKGROUNDS
2.1

Clean Development Mechanism
In an endeavor to encourage developing countries to take part in the global mission of

reducing GHG emissions, some environmental mechanisms have been developed by the UNFCCC.
Along with the Joint Implementation and Emissions Trading, for a long time CDM has been
considered an effective flexibility mechanism, which allows emission reduction projects indeveloping countries to earn CER credits, each equivalent to one tonne of CO2 (IPCC, 2007). The
CER credits then can be traded on the global market by industrialized countries to help them meettheir goal of emissions reduction (UNFCCC, 1998).
The purpose of CDM is twofold. First, it supports parties which are not included in Annex
I (developed countries) to achieve sustainable development and prevent dangerous climate change,
which is the main objective of UNFCCC. Second, CDM assists Annex I countries to comply with
the quantified emission limitation commitment (UNFCCC, 1998). By allowing the Annex I countries
to buy CERs from emission reduction projects in developing countries, CDM enables the countries
to reach part of their emission reduction commitment. Under CDM, industrialized countries could3choose and buy the cheapest CERs (Grubb, 2003).
In accordanace with the Kyoto Protocol, CDM is designed based on the idea of emissions
reduction production (Toth et al., 2001). The baseline, which defines the emission amount when no
CDM project is proceeded, is set up by the CDM executive board. Upon subtracting the actual
emission by the baseline, the CERs are calculated. As of July 2015, there have been more than 7647
registered 2. Projects that attract the most investment are those that focus on energy and waste sectors.
India and China are the biggest host parties, with the total share of more than 50% of the projects.
By 2015, CDM projects have issued nearly 1.6 billion CERs.

2.2

Joint Credit Mechanism
The JCM was developed by the Japanese Government in an attempt to facilitate the diffusionof leading low carbon technologies, products, systems, services, and infrastructure as well as theimplimentation of mitigation actions and contribution to sustainable development of developing
countries. GHG emission reduction is calculated by applying measurement, reporting, andverification (MRV) methodologies, leading to contributions of Japan (Government of Japan, 2014).
Unlike the CDM, the JCM requires representatives from both governments. In the case of theJCM, rules and guidelines for the implementation of JCM projects are developed by the Joint
Committee (JC). The JC determines if the project methodologies are approved or rejected. It thendesignates the third party entities to verify the projects. Based on the third party’s validation, JC
decides whether to register the project or not. Taking the insurance of credits by JC, governments
issue the notified amount of credits to the registry. In addition, in contrast to CDM projects in which
credits are tradable, JCM is a non-tradable credit type mechanism at the beginning. Bothgovernments continue to consult until the mechanism reaches the transition to a tradable credit typemechanism.
Japan already signed a bilateral agreement of the JCM with several countries, including2

See the information platform managing by the program of Ministry of the Environment, Japan<http://www.mmechanisms.org/e/index.html> [accessed in 19th July 2015].

4

Vietnam, Mongolia, Palau, Bangladesh, Ethiopia, Kenya, Maldives, Laos, Costa Rica, andCambodia. The first project was registered on October 31, 2014 in Indonesia. So far, 4 projects,
mainly focused on energy saving, have been registered (Table 1).

2.3

Literature review
This sub-section reviews previous studies about CDM projects in terms of empirical studies.

As JCM is a new mechanism and has just come into practice lately, there is no empirical literature
about JCM published yet. Past research on the determinants of CDM success covers a wide range ofissues. The following is dedicated to give brief descriptions of each of the studies.
Although there are several types of projects listed for CDM, one common characteristic is thatthe way of registering projects was imbalanced in terms of project types. Thomas et al. (2009) try tofind the constraints to the development of each CDM project, especially focusing on the neglect
toward afforestation and reforestation (AR). The results from the study reveal that the main reasonsfor the scarce number of projects on AR are limited financial support, poor administration, andgovernment concern. By studying each of the 4 registered AR projects, it was implicated thatsuccessful CDM AR applications have the following two aspects. One is that large organizations
with strong technical expertise guide initial funding support, design, and implementation of projects.
Another is that revenue from CERs must be directed back to local communities. As a result, it isclaimed that CDM should be reformed in order to support more development for AR projects.
Another issue recently addressed by researchers is that CDM projects are also unequallydistributed among host countries. For example, most CDM projects have gone to emerging markets
such as China, India, and Brazil, while very few host parties are the least developed countries.
Winkelman and Moore (2010) investigate why CDM activities are differently distributed acrosscountries in order to identify the country-level determinants for CDM projects to be chosen. Taking
the sample of 58 host countries, the authors point out that human capital and GHG emission levels
are the criteria that determine if a country is able to host a CDM project or not. By setting the5number of projects and projects’ CERs as a dependent variable, the authors find that countries thatoffer a growing market for CDM co-products, such as electricity, are more likely to become host
CDM projects while those with higher carbon intensity levels have greater CER production.
Jung (2006) and Oleschak and Springer (2007) mark efforts of those researchers in ranking thehost country attractiveness for CDM investment. Jung uses cluster analysis to rank 114 countries’
attractiveness according to four levels: very attractive, attractive, attractive to a limited extent, andvery unattractive. The three dimensions Jung provided are “the mitigation potential,” “the CDM
institutional capacity,” and “the general investment climate.” On the other hand, the study by
Oleschak and Springer only takes investment risk factor into account. The risk factors of theircategory is summarized as follows: 1) CDM institutions including Kyoto Protocol ratification anddesignated national authority establishment, 2) national communications submitted to UNFCCC, 3)
the number of capacity-building programs, understanding with other countries, 4) the presence of
CDM in national communications, 5) CDM experience, and 6) the regulatory environment in the
country.
In addition, according to Michaelowa and Jotzo (2005), CDM project size is affected by thehuge transaction cost that mostly resulted from the difficulty in proving the additionality of projects.
Evidence from emerging CDM projects shows that transaction costs can account for a significant
share of the total cost of CDM projects. Furthermore, smaller projects will be at a disadvantage as
fixed cost becomes a major factor. Additionally, as transaction costs are assumed to rise with thepermit price in the market, some developing countries may decide not to enter CDM market if
permit prices are low.
As discussed above, the success of CDM projects is largely affected by country and industry
conditions. The difference in these conditions is shown as the fact that CDM projects are unequallydistributed. Therefore, this study examines what factors, such as country, industry, and project type,affect financial (investment return) and environmental (i.e., expected CER size) outcomes. In
examining these determinants of CDM and JCM, this study helps to form expectations regarding
what types of policies or supports are needed in JCM in the near future. The following section

6

explains our methodology to assess the determinants.
3. METHODOLOGY
3.1 Data
The study uses the CDM project data from the Institute of Global Environmental Strategies(IGES), 3 and the JCM pilot project data from the Global Environment Centre Foundation (GEC)4.
While the CDM consists of 51 countries with 4051 observations that play the role of host party
countries, the JCM contains 15 countries with 68 observations. The study analyzes 11 years(2004-2014) of data for the CDM and 2 years (2013-2014) of data for the JCM.
Table 2 shows descriptive statistics, and Figures 1, 2, and 3 show the numbers of cases byregistration year (Fig.1), registration by types (Fig.2), and CER insurances by type (Fig. 3). Thesefigures support the literature on the imbalance of project distribution by type. While there is a hugeamount of projects on power and energy recovery, the number of afforestation, leak reduction, PFC
reduction, and SF6 replacement projects are still limited.

3. 2

Regression methodology
This study aims to study the environmental and financial outcomes of the CDM and JCM

projects. As proxy for the environmental and financial outcomes, this study adopts annual emission
reduction (AER) and projects’ IRR, respectively. For JCM, as projects have not been tackled yet,there is no IRR data for it; however, there is AER data (i.e., expected GHG reduction), calculated
and provided by GEC.
To evaluate the CDM project, this study uses the following equations:lnAER =

3
4(1)

http://www.iges.or.jp/en/
http://gec.jp/

7

(2)

where D denotes dummy variables.

denotes a dummy of country i.

denotes adummy of project type j, which consists of following 14 types (baseline is energy efficiency): AR,
bio-fuels, biogas, biomass, cement, energy efficiency, fuel switch, hydro power, methane avoidance,methane recovery and utilization, other renewable energies, PFC reduction and substitution, SF6
replacement, waste gas/heat utilization, and wind power.
takes 1 if the project is large scale.

stands for a dummy variable that

denotes a dummy variable of project status (baseline isregistered): rejected (l = 2) or withdrawn (l = 3). e denotes an error term.
This study next examines the determinants of the environmental outcome of the JCM pilot
projects. Specifically, this study uses the expected GHG emissions reduction (i.e., AER) in the log
form (lnGHG), which is reported in each project, as a dependent variable in the following regression
model:(3)

wheredenotes the effects of year t. Note that, while the study used the projects’ country (hostparty), type, status, and scale as independent variables for the CDM, country, sector, and year havebeen considered as independent variables for the JCM.

denotes a dummy variable of projecttype j, which is reducing emission from deforestation and forest degradation (REDD+), renewableenergy, transport, and waste management/biomass utilization (baseline is energy efficiency
improvement).

4. Result
4.1 CDM projects’ environmental efficiency (AER)

8

Table 3 shows the relationships between determinants of host party, project type, projectscale, and project status with CDM projects’ environmental outcome (lnAER). Firstly, the regression
result demonstrates that determinants of the host party play an important role in determining theCDM project outcome. Thus, setting China as the baseline, Egypt, Iran, Nigeria, Moldva, Qatar, andSouth Korea show statistically significantly positive coefficients with AERs earned. In contrast,
Bhutan-India, Brazil, Costa Rica, Cuba, El Salvador, Honduras, India, Kenya, Malaysia, Mexico,
Syria, Thailand, the former Yugoslav Republic of Macedonia, United Arab Emirates, and Vietnam
perform less productively in reaching a high CDM project environmental outcome.
In addition, the result also points out that the project type should definitely be appointed
as one determinant of CDM project environmental outcome. As reported by Table 3, compared toenergy efficiency chosen as a baseline, the AR project shows less AERs earning, whereas otherprojects (biofuels, biogas, biomass, fuel switch, methane recovery and utilization, other renewableenergies, PFC reduction and substitution, SF6 replacement, waste gas/heat utilization, and wind
power) bring more environmental outcome. Among those, biofuel and methane recovery andutilization raise the highest outcomes (i.e., largest coefficients) comparing to those others that are
higher than China’s.
Regarding project scale, large projects are found to produce more AERs than small ones.
Likewise, in terms of project status, there is no difference in the environmental outcome among theregistered, rejected, or withdrawn statuses.

4.2 CDM financial outcome (IRR)
Table 4 shows the regression result of using IRR. In terms of the coefficients of the hostparty dummies, the table indicates that there is little diversity among host parties. Among the 49 hostparties surveyed (due to missing values), only the coefficient of Honduras is statisticallysignificantly negative, indicating that Honduras is less efficient in the financial outcome compared tothe baseline (i.e., China). On the other hand, the coefficients of the other host parties are not9statistically significant, indicating that IRR is not different from that of China.
Regarding project type, setting energy efficiency as a baseline, biogas, biomass, andwind power are shown significantly negative coefficients, indicating that these 3 types are lesseffective in terms of investment. In addition, the scale dummy is not statistically significant,indicating that size does not relate to the financial outcome of CDM. Meanwhile, projects withrejected status are, like the environmental outcome analysis above, proven to be statisticallysignificantly positive, indicating that the rejected projects may be overstated on purpose to pass thecertification.

4.3 JCM projects’ environmental outcome
Table 5 shows the relationship between determinants of the host party, the sectors, and theireffects on GHG reduction. From the table, it can be speculated that the determinant of the host party
has no effect on the environmental outcome of JCM projects. Similarly, the determinant of the year
(2014) has no significant effect on the amount of AERs earned from this project type. Meanwhile,regarding the project sector, compared to the baseline chosen as energy efficiency, REDD +(afforestation and forest degradation) is expected to see a 557 percent greater increase in AER thanthe baseline. This indicates that sector is a crucial determinant to predict JCM environmental
outcome.

5. CONCLUSION AND DISCUSSION
This study aims at identifying the determinants for efficient CDM and JCM projects from theviewpoint of project environment (AER) and investment (IRR) outcomes. This study finds that thedeterminants of AER in CDM and JCM differ. Regarding CDM environmental outcome (AER), theregression result shows that the coefficients of country dummies (baseline: China) are statisticallysignificantly positive in 6 countries, significantly negative in 16 countries, and insignificant in 28

10

countries. In addition, the coefficients of CDM project type are also statistically significantly
positive in 10 types, significantly negative in 1 type, and insignificant in 3 types. It indicates that theenvironmental outcome of CDM highly varies among countries and project type. On the other hand,in terms of the environmental outcome of JCM projects, the coefficients of host party dummies are
not statistically significant. In addition, only the project sector REDD+ has a statistically significant
positive coefficient with AER earned (baseline: Energy efficiency). This finding indicates that, atleast in the current situation, the environmental outcome of JCM project is not considerably affectedby country and type effects.
In terms of CDM investment outcome (IRR), country dummies (baseline: China) are
statistically significantly positive in none of the countries, significantly negative in 1 countries(Honduras), and insignificant in the other countries. The coefficients of CDM project type are also
statistically significantly negative in 3 types and insignificant in 11 types. This indicates that the IRR
is not so different among the projects. In addition, the coefficient of the rejected status is statisticallysignificantly positive, indicating that it may be overstated on purpose to pass the certification.
From the regression results, the following can be concluded. Firstly, compared to the CDM’s
AR, the JCM’s REDD+ projects are expected to bring more environmental outcomes for companies.
Because of this potential, there is greater opportunity for companies to invest in JCM instead of
CDM in forestry projects. Also, if aiming at projects’ environmental outcome, for either CDM or
JCM, it is necessary for companies to take project type/sector into consideration. Secondly, thedifference of host parties has no effect on CDM projects’ IRR. Hence, there is no need to consider
which country would implement CDM projects if the target is a financial outcome. Instead,companies should pay attention to project types, because certain project types, which are biogas,
biomass, and wind power in this study, may not be financially efficient.
We also identify some limitations of this study. Firstly, there are some deficiencies in thedataset due to a lack of information. Data for JCM is estimated based on expectation, as JCM
projects have not been implemented yet. It is still not precise and therefore lowers the credibility ofcomparison between the two mechanisms’ projects. Hence, future research should be supported by

11

actual data collected after the JCM projects are officially finished.

ACKNOWLEDGEMENT
This study was carried out as part of the SESAMI project in Kobe University and was supported
by research grant from the Research Institute of Advanced Management. We would like to thank
Masashi Yasuda, Ryuji Shimono, and all concerned members of Panasonic Corp. for arranging andcoordinating our research survey.

12References
Bolscher H., J. van der Laan, S. Slingerland, J. Sijm, S. Bakker, T. Mikunda, Y. Wehnert, W. Sterk,
J. Hoogzaad, M. Wemaere and D. Conway (2012) “Design Options for Sectoral Carbon Market
Mechanism,”, CLIMA.B.3/SER/2011/0029 Final Report to European Commision-DG Climate
Action, 1-117
Boyd, E., Hultman, N., Timmons Roberts, J., Corbera, E., Cole, J., Bozmoski, A., Ebeling, J.,
Tippman, R., Mann, P., Brown, K., Liverman, D.M. (2009) “Reforming the CDM forsustainable development: lessons learned and policy futures” Environmental Science and Policy
12, 820-831
Buen, J., (Fni), F.N.I. (2013) CDM Criticisms: Don’t Throw the Baby out with the Bathwater. FNIClimatePolicyPerspectives
8<
http://www.fni.no/climatepolicyperspectives/FNICPP-08.html>.
Burniaux J. P., Chateau J., Dellink R., Duval R. and Jamet S., (2009) “The Economics of Climate
Change Mitigation: How to build necessary global action in a cost-effective manner “ Paris:
OECD
Government of Japan, 2014, Recent Development of The Joint Crediting Mechanism (JCM)<http://www.meti.go.jp/policy/energy_environment/global_warming/pdf/201409_JCM_ENG.p
df> [accecced in 19th July 2015].
Grubb M. (2003) “The economics of the Kyoto Protocol” World Economics Vol4 No3
July-September 2003, 143-189
IPCC (2007) Metz B., Davidson O. R., Bosch P. R., Dave R., Meyer L. A. (eds) “Contribution ofWorking Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change” Cambridge University Press, Cambridge, UK and New York, NY, USA
Jung, M., 2006 Host country attractiveness for CDM non-sink projects. Energy Policy 24,
2173-2184
Lloyd B. & Subbarao (2009) “Development challenges under the Clean Development Mechanism(CDM) – Can renewable energy initiatives be put in place before peak oil?” Energy policy(2009), 237-245
Michaelowa A. and Jotzo F., 2005 Transaction costs, institutional rigidities and the size of the clean
development mechanism Energy Policy 33 (2205) 511-523
Oleschak, R., Springer, U., 2007 Measuring host countriy risk in CDM and JI projects: a composite
indicator. Climate Policy 7, 470-487
Schroeder M. (2009) “ Utilizing the clean development mechanism for the deployment of renewableenergies in China” Applied energy 86 (2009) 237-242
Sterk, W., Bolscher, H., van der Laan, J., Hoogzaad, J., Sijm, J., 2014. Developing a sectoral newmarket mechanism: insights from theoretical analysis and country showcases. Climate Policy
14, 417-437
Thomas S., Dargusch P., Harrison S. and Herbohn J. Why are there so few
afforestation andreforestation Clean Development Mechanism projects? Land use policy 27 (2010), 880-887
UNFCCC (1998) ”Kyoto Protocol to the United Nations Framework Convention on Climate
Change”
UNFCCC (2015) “CERs issued by host party” Data as of 30 June 201513

Figure 1 : Time series, number of cases (Registration) (Sources: IGES)

14

Figure 2 : Project type – Number of cases (Registration)

15(Source : IGES)

Figure 3 : Project type – number of cases (CER issuance)

16(Sources: IGES)

Table 1 : Registered JCM projects

Country

SectorDate

Project Title

Emission reduction(average ton/year)

Indonesia

Energy31

EnergySavingfor 114demandOctober,

Air-Conditioning and Process

2014

CoolingbyIntroducing

High-efficiency

Centrifugal

ChillerIndonesia

Energy29

Project of Introducing High 120

demandMarch,

Efficiency Refrigerator to a

2015

Food Industry Cold Storage
in Indonesia

Indonesia

Energy29

Project of Introducing High 21

demandMarch,

Efficiency 227Refrigerator to2015

a Frozen Food Processing
Plant

Palau

Energy21 April, Small

industry

2015

scale

plants

for

solar

power

commercial

facilities in island states

Source: Progress of Financing Programme for JCM Model Projects and Feasibility Studies for JCM
Projects by MOEJ in 2015 [accecced 19th July 2015].
http://gec.jp/jcm/jp/images/publications/JCM_booklet_2015June.pdf

17

Table 2. Desciptve statistics
Variable

Obs.

(CDM) IRR without CER

4439(CDM) IRR with CER

Std.Dev.

Min

Max

0.703

0.188

−0.513

9.460

4082

0.116

0.058

−0.321

0.878(CDM) IRR Benchmark rate

4488

0.099

0.028

0

0.3

(CDM) Annual ERs (ton CO2/year)

4478

131649.2

2233282181

3016714(JCM) GHG reduction (ton CO2/year)

66153799.7

5257361.73050000

18

Mean

Table 3

CDM projects (lnAER)Country dummies (baseline: China)
Albania
Argentina
Armenia
Bangladesh
Bhutan-India
Bolivia
BrazilChile
Colombia
Costa Rica
Cuba
CyprusDominican Republic
EcuadorEgypt
El Salvador
GuatemalaHonduras
India
Indonesia
Iran
IsraelJamaica
JordanKenya
Lao PDR
Madagascar
Malaysia
MexicoMoldova
Mongolia
MoroccoNicaragua
Nigeria
Pakistan
PanamaPeru
Qatar
Republic of Korea
Singapore
South Africa
South Korea
Sri Lanka

19

Coef.

S.E.

1.003
0.141
0.849
−0.600−2.831***−0.414−0.362***−0.285−0.403−1.643***−1.456*−0.287−0.317−0.6480.732*−1.023**−0.226−1.332***−0.545***−0.0041.569**−0.377−0.740−0.246−1.577**−0.0230.267−0.350***−0.399**1.431*−0.1390.474
0.079
3.304***
0.409−0.5590.232
3.254***−0.285−0.5320.033
1.657**−0.921(0.746)(0.431)(0.748)(0.530)(0.746)(0.528)(0.116)(0.334)(0.249)(0.528)(0.747)(0.431)(0.745)(0.746)(0.431)(0.432)(0.336)(0.287)(0.049)(0.084)(0.745)(0.377)(0.527)(0.754)(0.746)(0.282)(0.746)(0.096)(0.191)(0.835)(0.528)(0.431)(0.431)(0.745)(0.431)(0.527)(0.239)(0.745)(0.216)(0.530)(0.336)(0.750)(0.746)

SyriaThailandThe Dominican Republic
The former Yugoslav Republic of Macedonia
The Philippines
United Arab Emirates
Viet Nam
Project type dummies (baseline: energy efficiency)
Afforestation & reforestation
Biofuels
BiogasBiomass
CementFuel switch
Hydro Power
Methane avoidance
Methane recovery & utilization
Other renewable energies
PFC reduction and substitution
SF6 replacement
Waste gas/heat utilization
Wind power
Other dummies
Large scale
Rejected Status
Withdrawn Status
Constant
obs
year
Adj R-squared−1.140**−0.346***−0.703−1.956***−0.193−1.144**−0.505***(0.530)(0.088)(0.747)(0.754)(0.176)(0.528)(0.054)−0.812**
1.539**
0.383***
0.420***
0.012
0.998***
0.771
0.863
1.387***
0.253***
0.271***
0.576*
0.148*
0.787***(0.378)(0.746)(0.071)(0.058)(0.561)(0.148)(0.746)(0.746)(0.116)(0.039)(0.031)(0.343)(0.089)(0.059)1.301***
0.049
0.166
10.176***4478
2004-2014
0.4293(0.030)(0.082)(0.174)(0.034)

Note: ***, **, and * stand for statistically significant levels at 1%, 5%, and 10%,respectively.

20

Table 4 : CDM project (IRR)
Base IRRCountry dummies (baseline: China)
Albania
Argentina
Armenia
Bangladesh
Bhutan-India
Bolivia
BrazilChile
Colombia
Costa Rica
Cuba
CyprusDominican Republic
EcuadorEgypt
El Salvador
GuatemalaHonduras
India
Indonesia
Iran
IsraelJamaica
JordanKenya
Lao PDR
Madagascar
Malaysia
MexicoMoldova
Mongolia
MoroccoNicaragua
Nigeria
Pakistan
PanamaPeru
Qatar
Republic of Korea
Singapore
South Africa
South Korea

21

Coef.
0.861***

S.E.
(0.155)

0.017
0.004

(0.186)(0.107)−0.0580.026
0.021−0.026−0.012−0.004−0.058−0.005−0.022−0.0190.011−0.0640.001−0.097−0.126*−0.012−0.029−0.163−0.062−0.0090.049
0.018−0.0030.017−0.041−0.034(0.187)(0.186)(0.132)(0.032)(0.083)(0.070)(0.131)(0.186)(0.107)(0.185)(0.186)(0.107)(0.108)(0.094)(0.075)(0.014)(0.023)(0.186)(0.108)(0.131)(0.188)(0.186)(0.076)(0.186)(0.026)(0.048)−0.0230.016−0.006−0.1120.010−0.028
0.008
0.017
0.008
0.027
0.040
−0.227(0.131)(0.107)(0.107)(0.186)(0.108)(0.131)(0.060)(0.185)(0.062)(0.132)(0.132)(0.186)

Sri Lanka
Syria
ThailandThe Dominican Republic
The former Yugoslav Republic of Macedonia
The Philippines
United Arab Emirates
Viet Nam
Project type dummies (baseline: energy efficiency)
Afforestation & reforestation
Biofuels
BiogasBiomass
CementFuel switch
Hydro Power
Methane avoidance
Methane recovery & utilization
Other renewable energies
PFC reduction and substitution
SF6 replacement
Waste gas/heat utilization
Wind power
Other dummies
Large scale
Rejected Status
Withdrawn Status
Constant
obs
year
Adj R-squared

0.025−0.046−0.0070.027
0.017−0.0260.005
0.002(0.186)(0.133)(0.023)(0.187)(0.188)(0.047)(0.185)(0.015)−0.010−0.097−0.045**−0.032**−0.038−0.036−0.032−0.041−0.025−0.013−0.0050.000−0.028−0.063***(0.094)(0.186)(0.018)(0.015)(0.139)(0.038)(0.186)(0.186)(0.029)(0.010)(0.008)(0.132)(0.023)(0.015)0.003
0.213***
0.018−0.0094432
2004-20140.0297(0.008)(0.021)(0.043)(0.016)

Note: ***, **, and * stand for statistically significant levels at 1%, 5%, and 10%,respectively.

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Table 5. JCM projects (lnAER)Country dummies (baseline: Indonesia)
Bangladesh
Cambodia
Costa Rica
EthiopiaIndonesia & Myanmar
Kenya
Lao PDR
Maldives
Mongolia
MyanmarPalau
Sri Lanka
Thailand
Vietnam
Project type dummies (baseline: energy efficiency)
REDD+
Renewable Energy
Transport
Waste Management /Biomass Utilisation
Year dummy of 2014
Constant

obs
year
Adj R-squared

Coef.

S.E.

−1.489
0.458
2.511
3.347
0.826
1.551
0.346
−2.2891.142
0.263−2.742
2.081
2.407
1.557

(1.440)(2.024)(3.208)(2.792)(2.774)(1.943)(1.826)(2.118)(1.120)(1.758)(2.075)(2.023)(1.961)(1.016)5.570***
0.926−2.6481.452
0.276
6.964***
66
2013-2014
0.238(1.366)(0.978)(1.795)(1.110)(0.717)(0.730)Note: *** stands for statistically significant levels at 1%.
[2015.11.5 1210]

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