Download 1 Impact of the Clean Development Mechanism on Host

Impact of the Clean Development Mechanism on Host Countries: The Relevance of
Absorption Capacities
Iva Hristova1
This version: January 2014.
Preliminary and Incomplete: Please do not distribute.
Abstract
The present paper aims to apply the FDI literature theoretical framework to the CDM case when
dealing with host countries absorption capacities. These could be defined such as the conditions
easing the investments’ integration and, thus, providing greater benefits for host countries.
Therefore, they can be considered as the determinants that would optimize the positive spill-over
effects. Three models try do define the links that might occur between host country absorption
capacities and the issuance of CERs. The first one provides details on the determinants that
promote greater positive spill-over effects. The second and the third one study those that would
induce greater increases of these effects. For the estimation, four datasets for the sample period
2004-2010 are applied: one encompassing all the concerned countries, one including only the
major CERs emitter countries (with and without China) and one focusing only on small CERs
emitter countries.
The results for Model1 tend to demonstrate the relevance of renewable energies, skilled
workforce and initial income level. Model2 confirms the importance of the first two mentioned
variables, while host country’s GDP levels are negatively correlated with CERs evolution. Model 3
highlights the significance of skilled and highly skilled labour force.
Highlights
•
•
•
•
We define the conditions involving greater benefits for host countries under CDM.
We test three models defining the impact of these conditions on the issuance of CERs.
Renewable energies, skilled workforce, GDP allow for greater CERs issuances in levels.
CERs growth is negatively correlated to GDP level.
1
Iva Hristova, PhD, CGEMP (Centre de Géopolitique de l`Energie et des Matières Premières), Université ParisDauphine, Place du Maréchal de Lattre de Tassigny, 75016, Paris, FRANCE.
[Tel : 00 33 1 44 05 43 53 ; Fax : 00 33 1 44 05 44 84 ; E-mail: [email protected]].
1
Impact of the Clean Development Mechanism on Host Countries: The Relevance of
Absorption Capacities
Iva Hristova
1. Introduction
Several studies have focused recently on the analysis of Clean Development Mechanisms (CDM) and their
economic impacts on host countries, through the occurring technology transfers (Dechezlepretre et al.
(2008), Schneider et al. (2008), Dechezlepretre et al. (2009), Seres et al. (2009), Doranova et al. (2009),
Schmid (2011), Chatterjee (2011), Das (2011), Lema and Lema (2013), Glachant and Ménière (2010), Der
Gaast et al. (2009)) and the generated effects in terms of sustainable development (Olsen (2007), Sutter
and Parreño (2007), Nussbaumer (2009), Watson and Fankhauser (2009), Boyd et. alii (2009),Alexeew et al.
(2010), Disch (2010), Lee and Lazarus (2011), Subbarao and Lloyd (2011), Huang et al. (2012a)). Another
part of the relevant economic literature has focused on CDM determinants or on the economic conditions
that would involve a greater probability for a project implementation (Jung (2005); Dolsak et al. (2007);
Dinar et al. (2008); Wang and Firestone (2009); Ketterer (2009); Flues (2010)).
The purpose of the present is to propose a complementary study (with respect to the above mentioned
literature) by focusing on the host countries conditions (absorption capacities) impacting Certified Emission
Reductions (CERs) issuances and also the added value created by the implemented CDM projects. We
choose this approach in order to avoid the underestimation of the on-going effects for those countries
having hosted a small number of important size projects. Thus, we aim to obtain some clear insights on the
mechanisms that would involve greater economic impacts for host countries.
The absorption capacities notion has been developed within the FDI literature. According to Kalotay (2000),
the “absorptive capacity” of a country defines the maximum amount of FDI that can be hosted and
assimilated or integrated by the economy. In that sense, FDI is not only a flow of capital, but also a flow of
technology and knowhow (De Mello (1997)). The difference between FDI determinants and the FDI
absorption capacities lies within the fact that the first group focuses on the conditions that should be
respected in order to attract more FDI, while the second one concerns the conditions to be fulfilled in order
to exploit the benefits from FDI. Thus, the absorption capacities are defined as those determinants that
would foster greater spill-over effects for the recipient country.
The theory on absorption capacities has been developed through different works focusing on: the
importance of qualified workforce (Cohen & Levinthal (1990); Borensztein et al. (1998)), the significance of
a minimum level of economic development (Nunnemkamp (2004); Blomstrom and et al. (1994)) or the
availability of a developed domestic financial system easing the diffusion of technology (Hermes & Lensink
(2003)).
This notion captures the idea of the “social capacity” (K. Ohzawa and H. Rosovsky (1973)) of a country to
integrate FDI. Criscuolo and Narula (2002) define it as the performance of the economical, financial,
political and educational system. Nurbel and Ahamada (2008) consider the absorption capacities through a
2
bi-dimensional analysis, concerning human capital on the one hand, and the organisational aspects on the
other one (within the firm itself and between the firm and its environment).
Thus, several factors can be identified as absorption capacities and most of them have been summarised by
H. Nguyen et al. (2011) through their photosynthesis model. They point out six major factors:
•
the development of the absorption capacities at a micro-level (i.e. firms);
•
the availability of skilled workforce (human capital and education) needed to absorb and adapt
foreign technology;
•
the research and development capacity representing the firm’s ability to adapt and develop the
external knowledge;
•
the presence of a developed financial system needed in the process of the technology spread;
•
the availability of an institutional framework (through investment-friendly policies and an
administrative framework (Kalotay (2000)), but also through property rights and corruption
regulation (Durham (2004) or a good governance level (Kaufmann, Kraay, Zoido-Lobaton, (1999 and
2002));
•
and the possibility to rely on a developed infrastructure system.
The availability of all these factors should lead to greater spill-over effects through the channels of skill
acquisition, technology transfer, management improvements and competitiveness (Kokko (1992); Damijan,
Kell, Majcen and Rojec (2003); Görg and Greenaway (2004)).
The factors presented above seem quite similar to the typical ones characterising the literature on FDI
determinants. Nevertheless, the dependent variable differs. Indeed, in the latter case the studies are not
focusing on the occurrence of FDI or on the received FDI financial flows, but on the positive spill-over
effects for the host country (for example, through an eventual productivity growth; (Narula and Marin
(2003)). This theory applied to the case of CDM, would allow us to focus on the created Certified Emission
Reductions (CERs) resulting from the investments made within the CDM framework instead of the
probability that a country would host a project or not.
We can therefore deepen the analysis of CDM, and its impacts on developing countries, by focusing on the
added value created by the implemented CDM projects. This approach has also an additional benefit, as it
allows to consider from an alternative perspective the availability of infrastructure and the country’s
institutions, for which the CDM determinants literature has found contradictory results.
The present paper is organised as follows. Section 2 proposes an adaptation of the existing theories to the
present study case, i.e. to the CDM projects framework and the main assumptions to be tested. It also
describes the model, the used estimation methodology and data sets. Section 3 presents the estimations
results and Section 4 focuses on their analysis, while Section 5 concludes.
2. Absorption capacities and Emission reductions analysis
2.1 Absorption capacities
Following the classification established by H. Nguyen et al. (2011) and taking into account the relevance of
a minimum level of economic development, the six major absorption capacities could be expressed in the
present case, as follows:
•
The level of economic development would be represented by the GDP level (denoted
);
3
•
•
•
•
•
the availability of skilled workforce would be summarised by the availability of qualified human
capital ( , standing for Human Capital);
the research and development capacity of a country is expressed by the R&D expenditure or by the
availability of very high qualified skilled labour force. In our case, given data availability constraints,
the second solution is chosen (labelled
);
the presence of a developed financial system: this factor is usually proxied by the Stock market
capitalisation or through the availability of credit to private non- financial entities. In the present
case, the second possibility is chosen (identified
) as our study focuses only on developing
and emerging economies where the stock market capitalisation concerns a restrained number of
countries;
the availability of an institutional framework, represented by a global index describing the
economic freedom (
);
the possibility to rely on a developed infrastructure system, expressed through the commonly used
Gross Fixed capital Formation (
).
Two other CDM specific absorption capacities are added to the six above mentioned ones: host countries’
reduction potential and renewable energies availability (denoted
and
). Thus, the set of
selected variables can be divided into three groups: the first one concerns the mitigation characteristics of
the country; the second one focuses on the economic potential and institutional environment of the
recipient/host country and the last one summarises the technology availability and innovative capacity. The
relevance of these three sets of absorption capacities would be tested further within the framework of
three different models that would provide informations, first, on the determinants that promote greater
positive spill-over effects, and second, a greater increase of these effects.
2.1.1
Host country’s mitigation characteristics
The first set of absorption capacities refers directly to the CDM-specific determinants and it aims at
describing the reduction potential of a country per se and also the availability of renewable energies.
The reduction potential of a country is defined as the Greenhouse Gases (GHG) intensities of its GDP. All
GHG emissions are taken into account and not only CO2 emissions, given that a large part of CDM projects
concern also methane (CH4), hydrofluorocarbons (HFC) and nitrous oxide (N2O) reductions. The more a
country is emitting, per created GDP unit, the more important emission cuts can be provided through the
implementation of a cleaner technology within a CDM project.
We create this variable in such a way to represent the reduction potential of a given country with respect
to the reduction potential of the entire sample of countries. This procedure thus allows us to consider not
only the reduction potential of a country per se, but also to make it in comparison to the entire group of
studied countries.
The “Renewables” variable focuses on the already existing availability of renewable energies. In the present
case the Renewable and Waste Energy Supply (expressed in Ktoe) provided by the International Energy
Agency is used. A positive dependence between this variable and CER flows would be expected.
Thus, we have the following:
Assumption 1 to be tested: The mitigation capacities of a host country would induce greater positive spillover effects from the implementation of a CDM project
4
2.1.2
Host country’s economic potential and institutional environment
The second set of variables includes all the variables that could allow defining the general capacity of a
country to host and to allow for the development of a project.
The initial income and GDP growth reflects the economical potential of a given economy. While GDP
reflects an economical threshold level, its growth defines the economic capacities of a country. We would
expect a positive relationship between these variables and CERs issuance, if we assume that the projects’
registration procedure and the final issuance of CERs might be quite demanding and involving several costs.
The availability of credit to private non financial institutions is often used as a proxy for financial
development in developing countries. For example, King and Levine (1993) examine the statistical
significance of variables such as bank credit or credits of the central bank when they try to assess the
impact of financial systems on economic growth within developing countries.
Gross Fixed Capital Formation (GFCF) is used as a proxy for the available infrastructure. In the CDM
determinants literature, Flues (2010) finds a negative link between GFCF and the implementation of a CDM
project. However, one might expect that the presence of a developed infrastructure within a country
should represent a positive determinant for attracting more CDM and it should also reinforce the country’s
capacity to obtain a greater share of CERs.
The INSTITUTIONS variable is represented by the Index of Economic Freedom, provided by Heritage
Foundation. It considers every country, following 10 categories of economic freedom: Business Freedom,
Trade Freedom, Fiscal Freedom, Government Spending, Monetary Freedom, Investment Freedom, Financial
Freedom, Property Rights, Freedom from Corruption, Labor Freedom. The scale runs from 0 to 100, and the
country with the highest value is considered as the one the most involved in promoting economic freedom
policies and disposing of the freest economic environment.
Assumption 2 to be tested: The economic potential, a developed infrastructure framework and a good
institutional environment of a host country induce a greater positive spill-over effect from the
implementation of a CDM project.
2.1.3
Host country’s technology potential
The implementation and the development of a project would require a certain threshold of available skilled
workforce and a certain technology capacity. In order to capture this technology potential we have focused
on the availability of human capital (HK) and its qualification. Thus, in order to control for the technology
and skill endowment of a country, two variables are used: HK and TECHNOPOT.
Assumption 3 to be tested: Developed technologic capacities and high skilled endowment induce greater
CERs issuance.
2.2 Emission reductions analysis
2.2.1. General model framework
5
Given the characteristics of CERs data and the procedure of CERs issuing, we have adopted a lagged period
estimation. More precisely, the issuing of CERs in 2004 (for example) is the result of the validation of the
project at least 3 years before (i.e. in 2001) and the validation results from investment decisions applied
(on average) 2 years before (thus, in 1999 in our example). These decisions are based on the economic
and mitigation characteristics of the considered country over at least 5 years before (since end 1994). Thus,
the quantity of CERs that would be issued in 2004 depends on countries characteristics from 1994 to 2001
(once that the project is validated).
We specify a regression equation of the form:
Or, more precisely:
= +
(i= 1, 2,…, 7 or 8 or 40 or 48; t = 2004, …., 2010)
= +
(1)
where
is the dependent variable, representing the total emission reductions induced by CDM for
country i at date t.
is the date t-9 vector of the explanatory variables, is the vector of the
explanatory variables’ parameters and
is the vector of Gaussian i.i.d. noise terms with ( ) = 0 and
( )=
.
If all the explanatory variables presented above are considered, the regression equation is given by:
&
=
!
+ "
+ '
+ + (
+
#
+
+
$
+
%
+
(2)
This model will be estimated for three datasets: one including all the CERs emitting countries for which
there was available data, one focusing only on the major eight CERs emitters (China, India, Brazil, Mexico,
South Korea, Honduras, Chile and Argentina) and one containing all the small emitter countries. The idea is
to test the relevance of the absorption capacities given the high level of asymmetry within the emitter
countries. Indeed, more than 80% of the emitted CERs are provided by the second group of countries. The
first dataset would allow for the determination of a general (average) type of absorption capacities. The
second one would focus on the more relevant of them and the third one should ease the understanding of
the potential differences or similarities between the two first datasets.
Within the second dataset, given the predominant importance of China, a control set of estimations is run
without this country. Thus, four datasets will be considered with, respectively 7, 8, 40 and 48 countries (see
section 2.2.4 for further details).
2.2.2. Analysed Models
For all of the datasets a first estimation is run with a cross-country regression framework. More precisely,
the explained variable and the explanatory variables represent the log of the average levels over the
studied period (i.e. 2004 - 2010). Thus, the model described above can be re-written in the following
manner (this model will be named Model 1 for ease of presentation):
6
)*+,
$ )*+,
( )*+,
, !!$
,"
%
!"!
=
+
" )*+,
)*+,
%
," & !!' +
!
!"! + ,"
,"
%
&
+ )*+,
+
& )*+,
!"!
!!'
,"
,"
&
&
+ # )*+,
!!' + ' )*+,
!!'
,"
&
,"
!!',
&
+
!!'
+
(3)
where lnav stands for the log value of the average levels of a given variable (and country) over the period
specified in the subscripts. The retained period for both of the HK and TECHNOPOT variables is slightly
different from the one applied to the rest of the variables, but this is due to the particular structure of the
relevant database (the observations are reported over five-year periods).
An estimation of a system of 48, 40, 8 and 7 equations (for the four datasets described above) is
undergone, where every equation concerns a given country. The system of equations is estimated using the
Feasible Generalised Least Squares (FGLS) method described in the following sub-section.
Additionally, a second set of estimations is run for the restricted dataset encompassing only the major host
countries (with and without China). This estimation aims to go beyond the level variables effect and the
factors that would induce the emission of more CERs, the present setting would provide a complementary
understanding of the mechanisms leading to more important CERs issuance growth. In other words, the
estimations would try to define the absorption capacities that would induce a more important growth of
the CERs emissions.
In this case, the explained variable would be CERs growth rate over the studied period and the explanatory
variables would remain the same. Therefore the model would take the following form and it will be named
Model 2:
ln 0
$ )*+,
( )*+,
, !!$
,"
%
=
!"!
!
!"! + + " )*+,
% )*+,
," & !!' +
,"
,"
%
&
+ )*+,
+
& )*+,
!"!
!!'
,"
,"
&
&
+ # )*+,
!!' + ' )*+,
!!'
,"
&
,"
!!',
&
+
!!'
+
(4)
where lng CERs represents the log value of the growth rate of this variable.
At last, a final set of estimations is run in order to account for those absorption capacities which evolution
has induced a greater CERs emission. In result, we will obtain a clear vision not only on those capacities that
would ease CERs emissions and on those that could explain that some countries have had a greater
evolution of their CERs emitting pattern, but also on the evolution of those that have mostly contributed to
this. Hence, the model to be tested would be such as:
ln 0
$ )*0
( )*0
, !!$
,"
%
!"!
= ! + " )*0
+ % )*0
!"!
,"
&
!!' +
,"
,"
%
&
+ )*0
+ & )*0
!!'
!"!
,"
,"
&
&
!!'
!!'
+
+
# )*0
' )*0
,"
&
,"
!!',
&
+
!!'
+
(5)
The last set of estimations concerns only the countries representing the bulk of the emitted CERs.
Hereafter, this model will be named Model 3.
Given the structure of the CERs emitting patterns of the 48 concerned countries, it is impossible to apply
the last two methodologies to the entire dataset. Indeed, CERs issuance is not continuously observed over
time at a given frequency (yearly, say). Once that a project is implemented, there is certain time lag before
the final issuance of emission reductions, which does not mean that investments are not made on a regular
basis during the entire period. Thus, for most of the countries, that are not in the group of the major CDM
7
attracting countries (like China, India, Brazil, South Korea, Chile, Honduras, Mexico), we observe a spotted
character of the issued CERs.
As for the first setting of estimations the retained econometric methodology is the SUR approach,
described below.
2.2.3. Estimation technique
The model is estimated by Feasible Generalised Least Squares (FGLS). In the first stage, an ordinary least
squares regression is run for the model 1:
1
)*+,
)*+,
)*+,
⋮
6"
0 0 0
"
0
0
6
5 = 1 0 ⋮ ⋮
⋮ 51 ⋮ 5 + 1 ⋮ 5 =
⋮
64
0 0 0
24
4
4
2"
2
"
+6
(6)
where, n ∈ 87, 8, 40, 48< respectively, for the four datasets described earlier, )*+,
2 is a scalar,
is a
1×9 row vector of observed explanatory variables, and is a 9×1 vector of parameters,6 is a i.i.d Gaussian
distributed white noise.
For model 2 and 3, the system would take the form:
1
)*0
)*0
)*0
⋮
6"
0 0 0
"
0
0
6
5 = 1 0 ⋮ ⋮
⋮ 51 ⋮ 5 + 1 ⋮ 5 =
⋮
64
0 0 0
24
4
4
2"
2
"
+6
(7)
where, n ∈ 87, 8< respectively, for the two datasets concentrating the major CERs emitters.
2.2.4. Data sets
For most of the adopted variables (Reduction potential, GDP, Gross Fixed Capital Formation, Credit to
private non-financial institutions) the used data within this study is issued from the World Bank Indicators
(2010, 2011). Observations concerning the renewable energies profile of each country (renewable energy
supply in Ktoe) are extracted from the IEA Dataset: World - Renewable and Waste Energy Supply (Ktoe)
(2012).
The human capital (HK) and TECHNOPOTENTIAL variables are derived from the Barro and Lee database
“Educational Attainment for Total Population, 1950-2010” (2011). The used proxys for these two variables
are, respectively, the average years of secondary schooling and the average year of tertiary schooling as a
more highly educated labour force should ease technological progress.
The UNFCCC 2010 database and the IGES CDM Project Database were used for the information concerning
the calculation of the explained variable (i.e. CDM flows) in terms of physical units. Regarding the
valorisation of the latter, yearly mean prices for CERs were applied (data on that was provided by the World
Bank team working on the “State and Trends of Carbone Market”). The Heritage Foundation database has
8
been used for its Economic Freedom Index. This index is split into ten composite sub-indexes concerning
the freedom for investing and developing a business in a given country.
The analysis concerns the following 50 countries: Albania, Armenia, Argentina, Bangladesh, Bolivia, Brazil,
Cambodia, Cameroon, China, Chile, Colombia, Costa Rica, Cote d'Ivoire, Cyprus, Ecuador, Egypt, El Salvador,
Guatemala, Honduras, India, Indonesia, Iran, Jordan, Kenya, Malaysia, Mali , Senegal , Mauritania, Moldova,
Mongolia, Morocco, Mexico, Nepal, Nicaragua, Pakistan, Panama, Paraguay, Peru, Singapore, South Korea,
Sri Lanka, Syria, Tanzania, Thailand, The Dominican Republic, The Philippines, Tunisia, Uruguay, Vietnam,
Zambia.
Mali, Senegal and Mauritania are considered as a group of countries, given that CDM projects data is
available for the whole group and not country by country. Therefore, all the necessary transformations
have been made within all the explanatory variables in order to take into account this feature.
The first dataset includes all of the countries, the second one only the eight major CERs emitters (China,
India, Brazil, Mexico, South Korea, Honduras, Chile and Argentina). The third dataset excludes China from
the group of the major emitters, in order to avoid the risk of a possible pollution of the results given its
considerable predominance. The last dataset regroups all the small emitters (40 countries).
Because of reasons of poor data availability, 16 CDM emitter countries have not been integrated to this
study: Bhutan, Ethiopia, Fiji, Georgia, Guyana, Israel, Macedonia, Nigeria, Uzbekistan, Jamaica, Lao PDR,
Papua New Guinea, Rwanda, South Africa, Uganda, and United Arab Emirates.
3. Empirical Results
As announced in the previous section, we proceed to the estimations using the four types of datasets: one
encompassing all the countries in the sample; one focusing on a restricted number of countries that have
hosted CDM projects and have a continuous issuance of ERs; one similar to the latter but excluding China;
and one regrouping all the small emitter countries. The idea is to check the relevance of the obtained
results and to avoid an eventual misinterpretation either due to the predominant importance of China or to
the highly concentrated character of CERs emissions (more than 80% issued only by 8 countries, against 48
countries in the entire sample).
These four types of datasets are used to estimate three models: the first one where all the variables are
represented by the average levels over the studied period; the second one where only the explained
variable is a log variation value and the third one where all the variables are in log variation. As it was
explained before, these three models would provide details, first, on the determinants that promote
greater positive spill-over effects, and then on those that have involved a more important increase of these
effects. Thus, the aim is not only to define the level effects but also the progression effects.
As far as the first model is concerned, all datasets are adopted. Concerning the second and the third
model, only the eight countries’ dataset is used given the issues related to the data structure explained
above. The second model is also applied to the sample without China. The latter sample of countries is not
applied to the third model, as the reduced size of the sample (in terms of countries and observations)
makes the estimation procedure unfeasible. In result, seven sets of estimations are obtained. The first four
concern model 1.
9
3.1 Results from model 1
In table 1 (see the Appendix) are presented the estimated parameters of the first model using the dataset
containing all countries. Columns 1 to 5 correspond to the different regressions that have been run for
different sets of explanatory variables that have been selected in order to test the robustness of the
obtained results.
First of all, one can notice the predominant significant positive effect of the renewable energies variable,
while, most of the other variables seem to have insignificant effect, which might be due to the presence of
a too important number of explanatory variables for such a temporally restrained dataset. For that reason,
a more restricted (in the number of explanatory variables) version of the model is presented in column 3.
Also, in that case, the GDP variable confirms its positive and significant effect on the dependant variable.
The obtained results are consistent with the assumptions described in Section 2.
However, the large number of insignificant variables is an issue to be considered carefully. This situation
can result either from the inclusion of too many explanatory variables or from the limited number of the
CERs emission data observations. For that reason further tests are proposed through the estimations using
the restricted datasets: those including only small emitters (Table.2 in Appendix) and those encompassing
only the major CERs emitter countries (Tables 3 and 4 also in Appendix).
The results in Table 2 can be considered as a confirmation of those appearing in Table 1. The presence of
renewable energies is supposed to ease the positive spill-over effects of the implementation of CDM
projects and these estimations coherently find and confirm that they have a positive and significant impact
on CERs emissions. Since the number of explanatory variables is reduced (column 3), the detected positive
impact of the income level becomes more reliable.
In column 6 we test another reduced set of explanatory variables for the above mentioned model,
excluding this time not the CREDIT and GFCF variables, but the TECHNOPOT and CREDIT variables. The first
variable is positively correlated with HK (the magnitude of correlation is 0.80) and thus, given also the
restricted size of the studied period, it might lead to a misleading interpretation of the significance of both
variables. The CREDIT variable is also omitted as it might be correlated positively with GDP and following
the same reasoning as above, its presence might lead to an incorrect interpretation of the estimated
regression coefficients.
In result, the variable HK demonstrates a positive and significant effect, which is consistent with the idea
that if the recipient country concentrates a more important quantity of skilled workforce, it would more
easily benefit from greater spill-over effects. Concerning the relevance of the GDP variable, it is not
confirmed in this regression, as it is positively correlated with the variable GFCF (correlation of 0.98). The
results in column (7) confirm this suggestion and therefore for this dataset (containing only small emitter
countries) it seems that the model should be restricted to the first four explanatory variables, all of them
having a positive and significant impact on CERs emissions.
In Table 3 are presented the results for the dataset concentrating all the major CERs emitters. Here, the
renewables and the initial level of income, as well as the presence of qualified labour force, confirm their
positive and significant role on CERs emissions. Only the INSTITUTIONS and GFCF variables are statistically
insignificant.
10
We also observe that, the variable concerning the potential to reduce GHG emissions as well as CREDIT to
private non-financial institutions provide a positive impact to the explained variable. The large correlation
between HK and TECHNOPOT is confirmed (magnitude level of 0.88). Nevertheless, it seems that model 1 is
quite relevant for the major CERs emitters with the exception of the INSTITUTIONS and the GFCF variables.
In order to be sure that these results are not “polluted” by the predominance of China within the sample,
another set of regressions is proposed below in Table 4. Variables such as REN, GDP and HK confirm their
positive relevance in the CERs emission process, while the CREDIT variable seems to lose its importance.
The institutions promoting the ease of doing business are still of no considerable importance. However, one
should bear in mind the relatively important reduction of the studied sample in the present case and
therefore the difficulties in terms of statistical relevance that might arise.
A more complicated situation is observed for REDUCPOT and TECHNOPOT as they present a negative
impact on the issuance of CERs once that China is excluded from the sample (a more detailed discussion on
this feature would be developed in Section 4.1). Before continuing further, it is important to precise that
this sample is the most restricted one (containing only 7 countries and thus the system to be estimated
contains 7 equations) and for reasons concerning the degrees of freedom it is impossible to run the model
with all the explanatory variables at the same time.
A deeper analysis of the CDM absorption capacities characteristics is proposed through the results from
model 2 and 3, presented below.
3.2 Results from Model 2
In this model the explained variable is the variation of the CERs emissions, as it has been presented in
Section 2. It seems that for this framework, the presence of a certain level of human capital and renewable
energies would ease a greater progression in terms of CERs issuance (Table 5).
However the predominance of China might influence the final results given that it is the only country
presenting a positive evolution of its CERs emissions during the studied period. This induces the necessity
to run the same model but excluding China from the sample (the results are presented further in Table 6).
Even tough, up to now, most of the assumptions presented in Section 2 seem to be fulfilled.
Similarly to model 1, the results tend to demonstrate that the countries that have undergone a more
important rise of their CERs emissions are not those having the freest economic system and this is quite in
accordance with the data (indeed, China and India are ranked at the two lowest levels in the sample).
The negative impact of the initial income levels (through the variable GDP) highlights the main issue about
model 2 and, namely, the negative CERs growth profile for all of the countries but one. Even though China
has seen its CERs emissions growing and even if it has the largest GDP level, the predominance of countries
with important incomes also but undergoing CERs decreases seems to be more important.
All these conclusions have lead to the application of Model 2 to a sample excluding China, in order to
“clean” the eventual suspected “pollution effects” (Table 6).
Since China is excluded from the sample, no significant change is observed concerning the relevance of HK,
GDP and INSTITUTIONS . All of the three variables maintain their effects on CERs progression.
11
Variables such as TECHNOPOT and CREDIT also confirm their non-significant impact presented in the
previous case (Table.5). However, the presence of significant infrastructures involves a positive effect on
CERs progression. At last, the variable REDUCPOT also presents a negative impact on the explained variable
and this tends to involve that the countries with the lowest CERs decreases do not present the most
efficient capacities for GHG mitigation.
These findings might be considered as “surprising” given the theoretic cases presented in Section 2.
However, sometimes, theory should be considered carefully on the light of the real situation. Hence, the
progression of CERs depends not only from the absorption capacities available in the country, but also from
the evolution of the registration of projects. Indeed, the predominance of China is clearly influencing the
final results. Indeed, it is the only country that has concentrated a growing majority of the projects (during
the studied period) and therefore has experienced the more substantial CERs evolution (to the detriment of
the other countries), even though its absorption capacities are not the most considerable ones.
3.3 Results from Model 3
Model 3 aims at specifying the relation between the evolution (namely, growth rates) of the absorption
capacities and the evolution of the CERs emission patterns. Thus, it aims to study the progression of those
absorption capacities that would lead to a stronger growth of the positive spill-over effects through the
issuance of CERs. The estimations are presented in Table 7. For this model we will apply only the dataset
including China due to restricted freedom degrees reasons.
For this framework, the most important CERs emitters have undergone within the studied period a
deterioration of their levels of economic freedom and therefore the impact of the variable INSTITUTIONS is
significantly negative.
During the same period, all of the countries have seen their HK capacities rising, which have involved an
improvement of the conditions easing the implementation of CDM projects and also allowing for more
important positive effects. The same evolution is observed within the data concerning TECHNOPOT which
sustains the idea that a more highly-qualified workforce might have a positive impact on the
implementation of a project and on the optimisation of the potential spill-over effects, or at least it can
reduce the negative trend.
Concerning the relevance of GDP, from an economic point of view, we would have expected that a more
active economy would ease the spread of more spill-over effects and thus involve greater CERs issuance.
However, the results seem to demonstrate the opposite, namely a significant negative link between these
two variables.
Concerning the previous models, the variable REN has always played a positive role on CERs emissions.
However, since the growth rate effect is considered (and no longer the level effect), the variable presents a
significant negative impact. The same pattern is observed for the reduction potential variable.
A more deep analysis of the results presented above is provided in the next section.
4. Discussion
4.1 Results from model 1
12
In summary, the results for model 1 can vary slightly according to the studied datasets. For the general case
(the entire sample of CERs emitters’ countries), the capacities that might play a crucial role are those
concerning the existence of renewable energies use and a certain development threshold level. These
findings are relevant also for the sample including only small CERs emitters’ countries. For these countries,
it seems that the availability of skilled workforce has also contributed positively. For the sample regrouping
the major CERs emitters, credit facilities and large mitigation capacities can be added also to the previous
list.
Concerning the negative impact of the REDUCPOT variable, in the restricted sample of countries containing
only the major CDM emitters, since China is excluded, there are two possible explanations of that
relationship: one purely statistical and one economical. First, the countries that have issued the largest
shares of CERs are India, Brazil, South Korea and Mexico and the last two are also those that demonstrate
one of the lowest levels of REDUCPOT. Besides, countries with a good potential for GHG mitigation, like
Argentina or Honduras, concentrate very poor CERs emissions.
Second, from an economic point of view, the countries presenting one of the highest possibilities for GHG
reductions are not those that have benefitted from the highest levels of CERs issuances. Indeed, for the
cases of Argentina and Honduras, they have hosted also fewer projects than the other countries in the
sample. This finding suggests that a certain level of CDMs’ inefficiency might be detected from this point of
view. It does also highlight the country specific features and thus the necessity to consider the situation on
a country by country basis. Unfortunately, the restricted data availability, over the time series dimension,
does not allow such an analysis at the moment, but it has to be considered for further developments.
The TECHNOPOT variable, within the same restricted sample, presents another interesting point. If the
entire sample is considered, the countries with the highest level of intensively qualified workforce present
the lowest CERs emissions profiles (Mexico and South Korea) , while Brazil and India (main CERs emitters, if
China is excluded) can be situated more in the middle of the sample distribution. Thus, the observed
negative dependence between this variable and the explained one seems to be quite evident even though
a positive link was expected.
The impact of infrastructures and the available credit facilities are also uncertain as they present
correlations with some of the other explanatory variables. A possible solution might represent a study
including country specific effects or a larger time horizon. Unfortunately, the restricted size of the sample in
terms of observations and in terms of concerned countries does not allow for the application of such a
methodology.
4.2 Results from Model 2
As far as the results obtained from Model 2 are concerned, the countries that have undergone a more
considerable growth of their CERs emissions have been helped by their capacities in terms of renewable
energies, available skilled workforce and infrastructure. On the contrary, a more important GDP level, more
developed institutions and credit facilities, as well as the availability of highly qualified labour force did not
promote more important CERs growth rates. On the contrary, they had a negative impact or a non
significant impact for the last mentioned absorption capacity.
These results are quite logic since the major emitter countries are also those presenting significant levels of
renewable energies use. The situation is a little bit more mitigated about HK, given the low levels presented
by India and Brazil.
13
However, the presence of significant infrastructures involves a positive effect on CERs progression as well.
This is in line with the expectation that the countries with the lowest CERs decrease present significant
levels of this absorption capacity.
From these findings two interpretations can be drawn. First, on a general basis, the countries with better
conditions in terms of business facilitating initiatives, and income levels, have not been able to inverse the
decreasing slope of their CERs emissions. Second, on a country-specific basis, given the very restricted size
of the sample, in some cases the predominance of a country might influence the final result.
For example, South Korea is among the less concerned countries by CERs decrease and it is also the one
with the strongest institutions level but, at the same time, a country like India with a better situation in
terms of CERs2 presents the lowest level (for the entire sample) in terms of institutions.
Concerning the TECHNOPOT variable, the previously presented situation about INSTITUTIONS is reproduced
(India denotes one of the lowest levels of highly-skilled work force). Therefore, the non-significant results
can lead to the conclusion that a higher degree of very qualified population would not lead to a more
important progression in terms of CERs emissions.
From an economic point of view, this interpretation could be linked to the fact that the investments that
are done within the CDM framework might not be extremely technology intensive. The study presented by
Das (2011) points out that the most important technology transfers have concerned mainly the abatement
of industrial gases (N2O and HFC, both leading to the most substantial CERs issuances) or agriculture related
emissions. According to the author, from the 1000 studied projects, a little bit more than 20% has led to a
technology transfer and among these transfers, more than 90% could be qualified as being of type III.
Namely, this implies that: “technological learning and capability building are restricted only to the level of
operation and maintenance of an imported technology”. Thus, the main technology transfers that have
operated within the CDM framework did not require the availability of highly-skilled workforce.
Concerning GDP and CREDIT, the situation is quite similar. Indeed, Brazil (one of the most affected
countries by CERs decrease) presents one of the most important levels of income and credit facilities. From
that point of view, it seems evident that country specific effects should be taken into account.
Unfortunately, given the limited number of observations over the time series dimension, such a study
would be quite difficult to be provided for the moment.
Lastly, the variable REDUCPOT also presents a negative impact on the explained variable regardless the
presence of China in the sample. Within the theoretical section, we have assumed that the more a country
has an important reduction potential (high GHG intensity), more projects could be implemented, more
CERs would consequently be issued, thus suggesting a positive link between these two variables. However,
this assumption was considered in terms of levels of the concerned variables and not in terms of variation.
This result tends to involve that the countries with the lowest CERs reductions do not present the most
efficient capacities for GHG mitigation. Indeed, these countries do not dispose of the largest GHG/GDP
ratios due to their important GDP levels (compared to the other countries in the sample). Thus, a certain
2
If one considers the evolution of the explained variable once that China is not included, the countries can be ranked
according to their CERs (from the less concerned to the most concerned) in the following manner: Mexico, India,
South Korea, Honduras, Argentina, Brazil and Chile.
14
contradiction concerning the CDM investment flows’ implementation might be detected (even when China
is excluded from the sample) since one might expect that greater reduction capacities would allow for more
abatement and thus for a greater progression in terms of CERs issuances.
Besides the purely statistical explanation, another technical explanation exists: the mitigation of every GHG
leads to the issuance of different proportions of CERs, according to the polluting capacity of the avoided gas
emissions. More precisely, some gases that are present in less important proportions than CO2 lead to the
obtainment of more CERs. Thus, countries like South Korea that have benefitted from these types of
projects present a rather good situation in terms of CERs issuance progression even if they do not present
the more important GHG/GDP ratio.
At last, similarly to the results obtained with model 1, it seems that the absorption capacity involving the
reduction potential of the host country (once that China is not considered) did neither involve more
important CERs issuances levels nor has lead to more consequent CERs growth. Thus, from this point, a
certain level of CDMs’ inefficiency might be suspected.
4.3 Results from Model 3
Regarding Model 3, it seems that the evolution of skilled and highly-skilled workforce availability has lead
to more important CERs issuances. However, the improvements in terms of renewable energies use, or in
terms of institutional framework or of economic activity have not contributed to greater positive effects. At
last, the results obtained about the reduction potential impact tend to demonstrate that the CERs growing
trends have been concentrated in countries (for ex: China) with more important GHG emissions per se and
not in terms of their weight within the economy. This last feature highlights the need for further
developments excluding the predominant importance of this country.
However, the obtained significant negative effect of REDUCPOT can be motivated, first, through the
significant GHG intensities decrease observed for all of the countries in the sample (during the studied time
period) and second, through the commonly observed pattern attributing the most considerable slowdowns
to the least concerned countries in terms of CERs reductions. Thus, the economical interpretation linking
greater GHG abatement to more consequent Emission Reductions issuances seems to be predominant at
that stage.
The situation about GDP can be considered as quite puzzling per se. However, since most of the countries
within the sample have undergone a decrease of their CERs emissions during the studied period, the
explanation that might be drawn is that greater economic growth levels have not been able to inverse the
decreasing CERs patterns.
Nevertheless, given the reduced size of the sample and, therefore, the impossibility to include all of the
explanatory variables at the same time, a certain instability of the estimated parameters can results.
Therefore, a reassessment of the analysis should be undergone with a larger dataset.
For the previous models, the variable REN has always played a positive role on CERs emissions. However,
when the growth rate effect is considered (and no longer the level effect), the variable presents a
significant negative impact (in terms of variation). The explanation that could be found is that the country
that has undergone the more considerable progression in terms of Renewable energies use (Brazil) has also
undergone the more substantial deterioration in terms of CERs emissions growth. From an economic point
of view, a positive dependence should be expected, as more renewable energies facilities should promote
15
greater GHG reductions and thus lead to a greater progression of CERs. However, this finding has to be
considered in the light of the CERs issuance features, mentioned above and in terms of the observed
different GHG polluting capacities. Thus, the obtained result might be interpreted such as the
implementation of more renewable energies projects does not impact, per se, the abatement of highly
polluting industrial gases leading to more consequent CERs emissions growth.
Besides, in the present case, the country specific effect might influence the results and a more appropriate
methodology should be considered for further developments, once that a larger data availability would be
present.
In summary, it seems that, within model 3, the REDUCPOT variable influences negatively CERs issuances,
but the reasoning behind this finding differs from the one presented previously in model 1 and 2. Indeed, in
model 1 and within the restricted sample excluding China, the REDUCPOT variable has also presented a
negative sign. However, while in the present case (model 3) the explanatory variable describes the inverse
correlation between diminishing GHG intensities and increasing CERs issuances, in the case of model 1, the
negative relationship suggests that higher GHG abatement potentials do not involve, per se, the
implementation of more projects and the issuance of more CERs. While the result for model 3 is quite
natural (reduced abatement capacities indicate reduced GHG intensities and involve greater GHG
abatement that would lead to more important CERs progression), the result for model 1 is counterintuitive
and it might reveal a certain level of CDM’s inefficiency.
5. Conclusions and Policy Implications
The aim of this paper is to draw a description of the main host country characteristics that have eased the
issuance of CERs within the period 2004-2010. The analysis is based on the estimation of three models. The
first one tries to define the absorption capacities that have lead to greater CERs emissions levels (in result
of the implementation of CDM projects). The second one studies the conditions that have eased their more
rapid growth. The last one tries to link the evolution of these capacities to a stronger growth of the positive
spill-over effects (through the issuance of CERs).
For the estimation of these models, four datasets are applied, since several considerable discrepancies exist
within the entire sample of concerned countries. Thus, the first one is split into two parts: one including
only the major CERs emitter countries and one focusing only on the small CERs emitter countries (datasets
2 and 3). The second sample encompasses eight countries including China. As the predominance of this
country might influence the final results an additional dataset excluding China is considered.
From the results obtained for Model 1, the absorption capacities that have played a major positive role are:
the use of renewable energies, the availability of a certain level of economic activity, the presence of a
certain level of human capital, as well as developed credit facilities. However, for model 1, in the case
where all the countries are included or when only the small emitter countries are concerned, it seems that
only the renewables have a significant impact on CERs issuances.
In Model 2, the relevance of renewables and human capital is maintained. However, some changes are
observed for GDP and credit facilities. Both are due to the fact that even though most of the major emitter
countries have undergone an important progression in terms of economic growth and credit availability,
they have seen their CERs emissions decreasing during the studied period. Within this framework, the
institutional level attains negatively the evolution of CERs issuance.
16
The main conclusions that can be drawn from the results obtained within the framework of Model 3 are
that countries with a more significant progression of their qualified populations have been able to extract
greater spill-over effects.
Given the strong country particularities within the restricted sample containing the major CERs emitters, a
methodology taking into account the country specific effects might provide very interesting insights.
Unfortunately, the restricted size of the sample in terms of observations and in terms of concerned
countries does not allow for the application of such a methodology for the moment. Thus, regressions with
larger databases over the times series dimension should be considered in the future.
Within the presented paper, a strong assumption has been made concerning the impact of the investments
undergone in the framework of the Clean Development project mechanism: we have supposed that these
investments would only lead (in terms of spill-over effects) to the issuance of a certain amount of emission
reductions (CERs). This assumption was motivated by the fact that CERs are easily quantifiable and less
uncertain than the other spill-over effects that might occur, and therefore they are more suitable for our
models.
However, precisely the other types of spill-over effects (like the technology and various know-how
transfers) are supposed to ease the implementation of an endogenous growth process, where the limits of
the decreasing returns to scale should be transcended by the improvement of productivity. Indeed, these
transfers leading to a greater convergence in terms of growth would also involve an evolution of the used
technologies and therefore might allow for more sustainable growth paths.
Thus, further developments should concern the technology and skill transfer issues, in order to complete
the findings presented above.
References:
Alexeew, J., Bergset, L., Meyer, K., Petersen, J., Schneider, L., Unger, C., 2010. An analysis of the relationship
between the additionality of CDM projects and their contribution to sustainable development,
International Environmental Agreements. 103): pp.233–248.
Blomstrom, M., & al., 1994. What Explains Developing Country Growth?, National Bureau of Economic
Research Working Paper 4132.
17
Boyd, E., Hultman, N., Roberts, J., Corbera, E., Cole, J., Bozmoski, A., Ebeling, J., Tippman, R., Mann, P.,
Brown, K. and Liverman, D., 2009. Reforming the CDM for sustainable development: lessons learned and
policy future, Environmental Science & Policy, 12 7. pp. 820-831.
Borensztein, E., De Gregorio, J., & Lee, J. W. 1998). How does foreign direct investment affect economic
growth? Journal of International Economics, 451. 115-135.
Cohen, W. M., & Levinthal, D. A. 1990. Absorptive capacity: A new perspective on learning and innovation,
Administrative Science Quarterly, 351. 128-152.
Chatterjee, B., 2011. Briefing paper Technology Transfer through the CDM, AEA, ED56638 , Issue Number 1
.
Criscuolo, P. and R., Narula, 2002. A novel approach to national technological accumulation and absorptive
capacity : aggregating Cohen and Levinthal, MERIT, 2002-016, 25 p.
Damijan, P. J., Knell, M., Majcen, B., & Rojec, M., 2003. Technology transfer through FDI in Top-10 transition
countries: How important are direct effects, horizontal and vertical spillovers?, The William Davidson
institute, The university of Michigan business school.
Das, K., 2011. Technology Transfer Under the Clean Development Mechanism: An Empirical Study of 1000
CDM Projects, The Governance of Clean Development Working Paper Series, ESRC Climate Change
Leadership Fellowship scheme at School of International Development DEV) at the University of East Anglia
UEA).
Dechezlepretre, A., Glachant, M., Meniere, Y., 2008. The clean development mechanism and the
international diffusion of technologies: an empirical study, Energy Policy 36 4. 1273–1283.
Dechezlepretre, A., Glachant, M., Meniere, Y., 2009. Technology transfer by CDM projects: a comparison of
Brazil, China, India and Mexico, Energy Policy 37 2. 703–711.
Der Gaast, W., et alii 2009. Promoting sustainable energy technology transfers to developing countries
through the CDM, Applied Energy, 2009, vol. 86, issue 2, pages 230-236.
Dinar et al. 2008. Factors Affecting Levels of International Cooperation in Carbon Abatement Projects, The
World Bank, Development Research Group, Sustainable Rural and Urban Development Team, WP.
Disch, D., 2010. A comparative analysis of the development dividend of Clean Development Mechanism
projects in six host countries, Climate and Development, 21. pp. 50–64.
Doranova, A., Costa, I., Duysters, G., 2010. Knowledge base determinants of technology sourcing in clean
development mechanism projects, Energy Policy 38 10. 5550–5559.
Durham, J. B. J. B. 2004. Absorptive capacity and the effects of foreign direct investment and equity foreign
portfolio investment on economic growth, European Economic Review, 482. 285-306.
Flues, F., 2010. Who hosts the Clean Development Mechanism? Determinants of CDM project distribution ,
University of Zurich, Center for Comparative and International Studies CIS) and ETH WP
18
Glachant, M. and Y., Ménière, 2010. Project Mechanisms and Technology Diffusion in Climate Policy,
Environmental and Resource Economics, Volume 49, Issue 3, pp 405-423.
Görg, H., & Greenaway, D. 2004. Much ado about nothing? Do domestic firms really benefit from foreign
direct investment?, The World Bank Research Observer, vol. 19, no. 2,
Haites E., Duan M.S., Seres S., 2006. Technology transfer by CDM projects, Climate Policy 6, 327-344.
Hermes, N., and R., Lensink, 2003. Foreign Direct Investment, Financial Development and Economic
Growth, Journal of Development Studies, 401. 142-163.
Huang, Y., et alii, 2012. Is the Clean Development Mechanism Promoting Sustainable Development?,
Working Paper No. 2012/72, United Nations University, World Institute for Development Economics
Research UNU-WIDER. Helsinki, Finland.
Jung, M., 2006. Host country attractiveness for CDM non-sink projects. Energy Policy, 34 2006): 2173-2184.
Kalotay, K., 2000. Is the sky the limit? The absorptive capacity of Central Europe for FDI. Transnacional
Corporations, 93).
Kaufmann D., Kraay A., Zoido-Lobaton P., 1999. Governance matters, World Bank Policy Research Working
Paper, n°2196, 64 p.
Kaufmann D., Kraay A., Zoido-Lobaton P.,2002. Governance matters II. Updated indicators for 2000/01,
World Bank Policy Research Working Paper, n°2272, 47 p.
Kokko, A., 1992. Foreign direct investment, host country characteristics, and spillovers, Stockholm:
Stockholm School of Economics, Economic Research Institute.
Ketterer, J., 2009.
Workshop Venice.
Determinants of Clean Development Mechanism Projects, International Energy
Lee, C., and M., Lazarus, 2011. Bioenergy Projects and Sustainable Development: Which Project Types Offer
the Greatest Benefits?, Stockholm Environment Institute Reports, Seattle.
Lema, A., and R., Lema, 2013. Technology Transfer in the Clean Development Mechanism: Insights from
Wind Power, Global Environmental Change.
Narula,R., and A., Marin, 2003. FDI spillovers, absorptive capacities and human capital development:
evidence from Argentina, Research Memoranda 018, Maastricht : MERIT, Maastricht Economic Research
Institute on Innovation and Technology
Nguyen, H., Duysters, G., Patterson J., and H., Sander, 2011. Attracting And Benefiting from Foreign Direct
Investment Under Absorptive Capacity Constraints, PhD Thesis, Maastricht School of Management.
Nunnenkamp, P. and J., Spatz, 2003. Foreign Direct Investment and Economic Growth in Developing
Countries: How Relevant Are Host-country and Industry Characteristics?, Kiel Working Paper, 1176.
Nunnenkamp, P. 2004. To what extent can foreign direct investment help achieve international
development goals?, Blackwell Publishers, Oxford, United Kingdom.
19
Nurbel A. and I., Ahamada, 2008. Investissements directs étrangers entrants et développement : l'enjeu de
la capacité d'absorption, Mondes en Développement, vol.36, 2008/3, n°143.
Nussbaumer, P., 2009. On the contribution of labelled Certified Emission Reductions to sustainable
development: A multi-criteria evaluation of CDM projects, Energy Policy, 2009, vol. 37, issue 1, pages 91101.
Ohzawa K. and H., Rosovsky, 1973. Japanese Economic Growth, Stanford University Press.
Olsen , K. H., 2007. The Clean Development Mechanism’s contribution to sustainable development: A
review of the literature, Climatic Change, 841. pp. 59-73.
Seres S., Haites. E., 2008. Analysis of technology transfer in CDM Projects, Prepared for UNFCCC
Registration & Issuance Unit CDM/SDM.
Seres, S., Haites, E., Murphy, K., 2010. The Contribution of the Clean Development Mechanism under the
Kyoto Protocol to Technology Transfer, United Nations Framework Convention on Climate Change, Bonn,
Germany.
Schneider M., Holzer A., Hoffmann V., 2008. Understanding the CDM’s contribution to technology transfer,
Energy Policy 368. 3862-3871.
Schmid, G., 2011. Technology transfer in the Clean Development Mechanism: the role of host country
characteristics, Climate Policy, Vol. 12, Issue 6, pp. 722-740.
Subbarao, S. and B., Lloyd, 2011. Can the Clean Development Mechanism CDM) deliver?, Energy Policy, 39,
pp. 1600–1611.
Sutter, C. and J.C., Parreño , 2007. Does the current clean development mechanism CDM) deliver its
sustainable development claim? An analysis of officially registered CDM projects, Climatic Change, 84, pp.
75–90.
Wang, H. and J., Firestone, 2009. The analysis of country-to-country CDM permit trading using the gravity
model in international trade, Working Paper .
Watson , C. and S., Fankhauser, 2009. The Clean Development Mechanism: too flexible to produce
sustainable development benefits?, Working Paper No. 3, Centre for Climate Change Economics and Policy,
London School of Economics and Political Science, London.
20
Appendix
Table 1: Results from model 1 applied to the all countries dataset
(1)
(2)
(3)
(4)
(5)
3.354341 1.291309 -2.428581 0.421250 1.935259
0.430060 0.323464 -0.342686 0.110081 0.521216
(0.6695)
(0.7480)
(0.7336)
(0.9129)
(0.6050)
REDUCPOT
-0.087663 -0.091836 -0.014930 -0.088257 -0.076742
-0.619063 -0.650877 -0.111123 -0.622525 -0.560148
(0.5395)
(0.5188)
(0.9121)
(0.5370)
(0.5784)
REN
0.309500* 0.309806* 0.242452* 0.303504* 0.297759*
2.602976 2.603072 2.100801 2.543213 2.567885
(0.0130)
(0.0129)
(0.0419)
(0.0149)
(0.0140)
GDP
-0.531325 -0.557052 0.608207* -0.461184 -0.628364
-0.662699 -0.697907 3.903293 -0.582795 -0.802935
(0.5114)
(0.4893)
(0.0003)
(0.5632)
(0.4266)
HK
0.210060 0.218017 0.373353 0.484841
0.417716 0.433685 0.734410 1.414116
(0.6784)
(0.6668)
(0.4669)
(0.1649)
TECHNOPOT
0.254493 0.230249 0.263947
0.331814
0.775850 0.722387 0.787906
1.531676
(0.4425)
(0.4743)
(0.4353)
(0.1333)
GFCF
0.632501 0.706599
0.574026 0.782044
0.802409 0.940468
0.783633 1.067868
(0.4272)
(0.3526)
(0.4378)
(0.2918)
CREDIT
0.359135 0.322129
0.371490 0.320698
1.317389 1.315322
1.571082 1.307041
(0.1954)
(0.1959)
(0.1238)
(0.1985)
INSTITUTIONS
-0.429682
0.084312
-0.307779
0.065836
(0.7599)
(0.9478)
R-square
0.747275 0.746776 0.728023 0.744023 0.745784
Durbin-Watson stat
2.296966 2.272746 2.265754 2.370919 2.265939
In italic appear the t-statistics
In brackets are notified the P-value
The variables having a significant impact are notified with: *denotes significance at the 5% level
and ** denotes significance at the10% level
const
21
Table 2: Results from model 1 applied to the small emitters countries dataset
(1)
(2)
const
4.940622
0.537222
(0.5949)
5.035955
1.011706
(0.3193)
-0.420751 3.458970
-0.054557 0.713873
(0.9568)
(0.4803)
5.440704
1.161853
(0.2536)
0.417765
0.091487
(0.9276)
(7)
-0.103665
-0.023712
(0.9812)
REDUCPOT
0.139723
0.506445
(0.6161)
0.340061*
2.509229
(0.0175)
-0.138036
-0.128058
(0.8989)
0.129148
0.238739
(0.8129)
0.138965
0.516691
(0.6089)
0.340048*
2.509206
(0.0174)
-0.139838
-0.130938
(0.8966)
0.128633
0.238498
(0.8130)
0.252443
1.103996
(0.2776)
0.310339*
2.349401
(0.0249)
0.559775*
3.146960
(0.0035)
0.174793
0.324331
(0.7477)
0.148152
0.556195
(0.5818)
0.332783*
2.518292
(0.0168)
-0.179858
-0.170408
(0.8657)
0.080842
0.295634
(0.7693)
0.291000*
2.092830
(0.0439)
0.167409
0.154461
(0.8782)
0.721900**
1.863447
(0.0711)
0.141539
0.630304
(0.5326)
0.277547*
2.057520
(0.0471)
0.581558*
3.234828
(0.0027)
0.765157*
2.058755
(0.0470)
0.403037
1.104072
(0.2781)
0.246992
0.260535
(0.7962)
0.403574
1.113503
(0.2738)
0.245878
0.260546
(0.7961)
0.483945
1.340437
(0.1893)
REN
GDP
HK
TECHNOPOT
GFCF
(3)
(4)
0.069448
0.261447
(0.7954)
0.329596*
2.401006
(0.0221)
-0.222329
-0.205520
(0.8384)
0.547436
1.394695
(0.1724)
0.223046
0.232826
(0.8173)
(5)
0.463853
1.784362
(0.0836)
0.288733
0.311437
(0.7574)
(6)
0.380140
0.387465
(0.7008)
0.317431 0.319442
0.421161 0.317254
0.975158 1.134075
1.556684 1.126105
(0.3370)
(0.2652)
(0.1291)
(0.2682)
INSTITUTIONS
0.020038
0.797821
0.012328
0.559776
(0.9902)
(0.5794)
R-square
0.496396 0.496394 0.482402 0.480783 0.495677 0.449328
0.447261
Durbin-Watson stat 2.105958 2.107065 2.062355 2.271865 2.102035 2.196426
2.243931
In italic appear the t-statistics
In brackets are notified the P-value
The variables having a significant impact are notified with: *denotes significance at the 5% level
and ** denotes significance at the10% level
CREDIT
22
Table 3: Results from model 1 applied to the big emitters countries dataset
const
REDUCPOT
REN
GDP
HK
(1)
25.24866
1.520557
(0.3703)
2.156994
3.074848
(0.2002)
0.487279
3.621260
(0.1715)
0.376276
1.859816
(0.3141)
1.609449
2.672910
(0.2279)
1.071589
1.272658
(0.4240)
(2)
5.844089
0.810452
(0.5028)
1.400933*
3.424658
(0.0757)
0.371590*
3.415562
(0.0761)
0.590835*
4.817306
(0.0405)
1.852601*
2.958673
(0.0978)
(3)
0.755601
0.455287
(0.7280)
0.965266
4.657826
(0.1346)
0.235641
4.398166
(0.1423)
0.371402
1.596893
(0.3562)
0.821907
3.611622
(0.1720)
(4)
10.33337
47.36233
(0.0134)
1.735048*
82.16050
(0.0077)
0.288863*
72.78893
(0.0087)
0.063771
3.729164
(0.1668)
GFCF
-0.109519
-0.404012
(0.7556)
0.932184*
64.84346
(0.0098)
0.081084
4.146955
(0.1506)
CREDIT
0.484541
4.739178
(0.1324)
0.496279*
75.54311
(0.0084)
TECHNOPOT
INSTITUTIONS
-2.364185
-0.984163
(0.5051)
0.988893
2.513157
-0.270241
-0.140800
(0.9009)
0.986644
2.545180
R-square
0.997372
0.999987
Durbin-Watson stat
3.160120
2.721600
In italic appear the t-statistics
In brackets are notified the P-value
The variables having a significant impact are notified with: *denotes significance at the 5% level
and ** denotes significance at the10% level
23
Table 4: Results from model 1 applied to the big emitters countries dataset without China
const
REDUCPOT
REN
GDP
HK
TECHNOPOT
(1)
-123.5058
-11.42759
(0.0556)
-5.045924*
-8.744150
(0.0725)
1.383711*
16.92197
(0.0376)
1.340847*
19.34742
(0.0329)
29.91898*
13.17393
(0.0482)
-19.84331*
-12.22029
(0.0520)
(2)
20.96183
1.810924
(0.3212)
1.884173
3.905261
(0.1596)
0.577880
3.532837
(0.1756)
0.379418
2.179890
(0.2738)
3.397528
3.011799
(0.2041)
-0.196889
-0.416230
(0.7489)
0.713284
4.733069
(0.1326)
GFCF
CREDIT
INSTITUTIONS
(3)
-2.192316
-0.833663
(0.5576)
0.681073
1.906087
(0.3076)
0.097711
1.486118
(0.3771)
0.375474
0.892593
(0.5361)
(4)
14.46387
2.610969
(0.2329)
1.851027
3.270989
(0.1889)
0.418644
4.558236
(0.1375)
-0.428383
-1.009055
(0.4971)
1.422212
4.327241
(0.1446)
0.916613
2.248668
(0.2664)
-2.822940
-1.185725
(0.4460)
0.983168
2.483022
R-square
0.999095
0.988142
0.986447
Durbin-Watson stat
0.523552
3.464168
2.307648
In italic appear the t-statistics
In brackets are notified the P-value
The variables having a significant impact are notified with: *denotes significance at the 5% level
and ** denotes significance at the10% level
24
Table 5: Results from model 2 applied to the big emitters countries dataset
Const
REDUCPOT
REN
GDP
HK
TECHNOPOT
(1)
89.44941
2.965119
(0.2071)
-1.995213
-1.565540
(0.3619)
0.840449
3.437903
(0.1802)
-0.715606
-1.946873
(0.3021)
13.57090*
12.40554
(0.0512)
-3.237885
-2.116632
(0.2810)
(2)
148.0818
9.923902
(0.0100)
0.289284
0.341739
(0.7651)
1.190012*
5.285905
(0.0340)
-1.363912*
-5.373965
(0.0329)
12.83619*
9.906539
(0.0100)
8.937146
3.538898
(0.1753)
-1.821100
-1.911916
(0.3068)
GFCF
CREDIT
INSTITUTIONS
(3)
5.914993
0.382569
(0.7674)
0.737087
0.381784
(0.7678)
1.037206
2.078012
(0.2855)
-7.514022
-3.467899
(0.1787)
3.644581
1.719055
(0.3354)
-27.91930*
-6.397208
(0.0987)
0.979126
2.513157
(4)
-20.52395
-0.617941
(0.6476)
-1.630200
-0.507093
(0.7012)
0.220410
0.364837
(0.7773)
-7.191426
-2.762489
(0.2211)
-1.010693
-0.461827
(0.7246)
8.267129
2.777448
(0.2200)
-0.554615
-0.554570
(0.6777)
-34.24631*
-8.622520
(0.0132)
0.967437
2.538771
R-square
0.870112
0.826752
Durbin-Watson stat
3.160120
2.721600
In italic appear the t-statistics
In brackets are notified the P-value
The variables having a significant impact are notified with: *denotes significance at the 5% level
and ** denotes significance at the10% level
25
Table 6: Results from model 2 applied to the big emitters countries dataset without China
const
REDUCPOT
REN
GDP
HK
TECHNOPOT
(1)
82.88532
0.388054
(0.7643)
4.128949
0.362046
(0.7789)
-1.490859
-0.922549
(0.5256)
0.215061
0.157019
(0.9008)
-24.14672
-0.537990
(0.6858)
15.18224
0.473098
(0.7187)
(2)
106.2128
5.247588
(0.1199)
-1.049064
-1.243493
(0.4312)
0.618688
2.163066
(0.2757)
-0.778388
-2.557551
(0.2373)
8.557475
4.338310
(0.1442)
7.376017*
16.94168
(0.0375)
-1.861604*
-13.42116
(0.0473)
GFCF
CREDIT
INSTITUTIONS
(3)
-20.49928
-8.469310
(0.0748)
-2.749800*
-8.361270
(0.0758)
0.295668
4.885830
(0.1285)
-5.408759*
-13.96988
(0.0455)
(4)
-46.19297
-2.463715
(0.2455)
-4.146041
-2.164698
(0.2755)
-0.321141
-1.033107
(0.4896)
-3.422841
-2.382136
(0.2530)
-2.514059
-2.260057
(0.2652)
4.273813
3.097791
(0.1988)
-27.17653*
-6.528123
(0.0968)
0.901599
2.483022
R-square
0.324137
0.980793
0.703147
Durbin-Watson stat
0.523552
3.464168
2.307648
In italic appear the t-statistics
In brackets are notified the P-value
The variables having a significant impact are notified with: *denotes significance at the 5% level
and ** denotes significance at the10% level
26
Table 7: Results from model 3 applied to the big emitters countries dataset
Const
REDUCPOT
REN
GDP
HK
TECHNOPOT
(1)
-5.919139
-35.14882
(0.0181)
-15.95370*
-22.44516
(0.0283)
-1.416999*
-7.690005
(0.0823)
-5.913027*
-10.01372
(0.0634)
8.273935*
23.15465
(0.0275)
5.361870*
29.84598
(0.0213)
(2)
-7.055114
-4.057511
(0.0557)
-6.082111
-0.912059
(0.4580)
0.406048
0.220358
(0.8460)
4.989849
1.014728
(0.4170)
5.180205
1.429151
(0.2892)
GFCF
CREDIT
INSTITUTIONS
-10.20268*
-29.16018
(0.0218)
0.998627
1.070191
(3)
-6.557224
-3.949171
(0.1579)
-1.322766
-0.193223
(0.8785)
0.960283
0.368237
(0.7754)
10.99858
1.943972
(0.3025)
2.160517
0.736674
(0.5958)
(4)
-6.557224
-3.949171
(0.2152)
-1.322766
-0.193223
(0.4936)
0.960283
0.368237
(0.7320)
10.99858
1.943972
(0.9242)
2.160517
0.736674
(0.8904)
-0.281745
-0.131859
(0.9165)
-1.810874
-1.488285
(0.3766)
-0.281745
-0.131859
(0.3447)
-1.810874
-1.488285
(0.6834)
-4.383889
-1.423582
(0.2906)
0.845780
2.213509
R-square
0.850311
0.857957
Durbin-Watson stat
1.896855
2.233333
In italic appear the t-statistics
In brackets are notified the P-value
The variables having a significant impact are notified with: *denotes significance at the 5% level
and ** denotes significance at the10% level
27