Technology transfer in clean development mechanism (CDM) projects: lessons from China.
Xie, Linna ; Zeng, Saixing ; Zou, Hailiang 等
JEL Classification: O33, P48, Q55, Q56.
Introduction
With the rapid economic growth, anthropogenic greenhouse gas (GHG)
emissions from developing countries have become one of the main concerns
around the world (La Rovere et al. 2011; Streimikiene, Esekina 2008).
Technology transfer (TT) is expected to play an important role in
mitigating GHG emissions for developing countries (Halsnass, Garg 2011;
Kim et al. 2011; Marconi, Sanna-Randaccio 2011; Schneider et al. 2008).
Clean Development Mechanism (CDM) is one of the international
instruments facilitating such transfers (Gangale, Mengolini 2011).
The CDM fosters sustainable developments by channelling new
financial resources to promote the use of technologies currently not
available in the host countries (Reynolds 2012; Schneider et al. 2008).
As one of the largest C[O.sub.2] emission countries and one of the
fastest developing countries, China and its government face the dilemma
between economic growth and environmental conservation similar as most
of the developing countries (Ward, Shively 2011). China has made use of
CDM, not only to receive financial assistance, but also to obtain
advanced technologies from developed countries (Wang 2010). Currently,
China has become the largest CDM host country in the world
(Richerzhagen, Scholz 2008). The number of registered and registering
projects hosted in China is about 48% of all 7,520 registered and
registering projects around the world (UNFCCC 2013). The expected
average annual certified emission reductions (CERs) of Chinese
registered projects are about 64% of the total 905,682 ktC[O.sub.2]e per
year (UNFCCC 2013).
The TT claims for CDM projects have been extensively studied
(Dechezlepretre et al. 2008; Seres et al. 2009; Wang 2010). However,
this research is different from the previous studies in several
important aspects. First, it attempts to differentiate the effects of
CDM on TT, including: (1) form of ownership of Chinese participants; (2)
foreign participants more than credit buyers; and (3) regional
disparities of China. Second, it investigates the trends beyond
individual projects and individual aspects. Third, it identifies several
aspects that can be improved in the Chinese GHG mitigation activities.
The aims are to gain an insight into the mitigation of TT and further
understanding in the CDM at its current or modified form in the new
negotiation stage.
1. Literature review
The Intergovernmental Panel on Climate Change (IPCC) defined TT as
"a broad set of processes covering flows of know-how, experience
and equipment for mitigating and adapting climate change amongst
different stakeholders such as governments, private sector entities,
financial institutions, non-governmental organizations (NGOs) and
research/ education institutions'' (IPCC 2000). Technologies
consist of not only the "hardware" such as machineries and
equipment, but also the "software" including knowledge,
skills, know-how, management arrangements and goods or services (Tebar
Less, McMillan 2005). A project can involve both hardware and software
(Dechezlepretre et al. 2008). In CDM, "technology transfer"
reported in the project design documents (PDDs) are not based on a
specific or identical definition, but based on the interpretation of TT
by project participants. According to former research, in general, it
can be assumed that TT means "the use of equipment and/or knowledge
not previously available in the host country" (Haites et al. 2006;
Seres, Haites 2008; Seres et al. 2009).
An extensive body of literature has reported economic, political,
methodological and sustainable development aspects of the CDM project
performance (Olsen 2007; Schneider et al. 2008). Currently, there are
two main streams of literature studying TT in CDM projects. The first
stream reports empirical analyses which study TT in CDM projects using
the data from the UNEP Risoe Center CDM Pipeline and the project design
documents (PDDs), which explore TT on parameters such as project sizes,
project types, host countries, technology suppliers, local technology
capabilities and partnerships. The second stream collects information
from interviews, case studies, policy documents, government information
and other sources, using qualitative approaches rather than
quantitative, giving us a wide open mind and a big map of TT in CDM
projects.
In the studies of the first stream, some significant results are
found. The data show that TT takes place in less than half of the CDM
projects (Dechezlepretre et al. 2008; Seres, Haites 2008; Seres et al.
2009, 2010). Large projects are more likely to involve TT than
unilateral or small-scale projects (Dechezlepretre et al. 2008; Haites
et al. 2006; Seres et al. 2010). Slightly varying with the samples, TT
possibility is high in agriculture, energy efficiency (EE) own
generation, landfill gas, N2O, HFCs and wind projects, and low in
biomass energy, cement, fugitive, hydro, and transportation projects
(Das 2011; Dechezlepretre et al. 2008; Seres, Haites 2008; Seres et al.
2009). This reflects the variation of average sizes under different
project types and technology characteristics. As more projects of a
given type are located in the host country, subsequent projects rely
more on local knowledge and equipment than imported technologies
(Dechezlepretre et al. 2008; Seres et al. 2009, 2010). Meanwhile,
countries with more experience in the development and applications of
mitigation technologies tend to rely on domestic technologies or
developing technologies accompanied by foreign partners (Doranova et al.
2010). These indicate that the well-developed local technology
facilitates the use of foreign technologies and implies availability of
technology locally (Dechezlepretre et al. 2008, 2009). A host country
can influence TT in CDM through their criteria for approving CDM
projects and other factors such as tariff, and protection of
intellectual property rights (Seres, Haites 2008; Seres et al. 2009,
2010). Projects hosted by the subsidiary of a foreign company or with
foreign consultants are more inclined to involve TT (Das 2011; Dechezle
pretre et al. 2008, 2009; Doranova et al. 2010).
In the second stream, insights about TT in CDM projects are
displayed. Four TT barriers: (1) lack of commercial viability; (2) lack
of access to capital; (3) lack of information; and (4) lack of
institutional framework are identified and the CDM does contribute to TT
by lowering these barriers except the last one (Schneider et al. 2008).
The four barriers are used by other researchers to explain project
characteristics' effects on TT in CDM projects (Dechezlepretre et
al. 2009; Seres et al. 2009). Besides the four barriers, local
technology capability is identified by Schneider et al. (2008) to
explain the TT distribution across geographies and project types. But
they could not identify low local technology capability as the fifth
barrier of TT in CDM projects. Local technology capability already can
be measured by standard index, such as the ArCo technology index
(Dechezlepretre et al. 2008, 2009), or a set of indicators specially
made for research purposes (Doranova et al. 2010).
Although the studies focusing on CDM projects hosted in China are
of paucity, some features of TT in CDM projects in China still could be
found. When the certified emission reduction (CER) income is low and
most of the technologies are locally available, time effect, technology
diffusion, governmental involvement, and investors' and
brokers' participation play an important role in deciding TT (Wang
2010). The domestic regulations and policies have important influences
on the CDM projects, e.g. CDM projects are heavily concentrated in
government priority areas such as renewable energy and energy efficiency
in industrial applications (Marconi, Sanna-Randaccio 2011). It also
points out that China's technology localization strategies will
eventually reduce TT in CDM projects and advance the level of
technologies adopted (Wang 2010). Overall, this subject is not
sufficiently discussed by previous researchers. This research follows
the empirical analyses from the first stream and also inspired by the
findings of the second stream researches.
2. Hypothesis development
2.1. Project sizes
According to previous empirical research, project sizes are one of
the most important factors that influence the possibility of TT in CDM
projects. It is suggested that the larger the project sizes, the more
possible the project involving TT (Dechezlepretre et al. 2008, 2009;
Doranova et al. 2010; Haites et al. 2006; Seres et al. 2010). Therefore:
Hypothesis 1: The project size is positively associated with TT in
CDM.
2.2. Project types
The TT claim varies widely across project types (Das 2011). On
average, there are 40% projects claiming TT, but the share of TT ranges
from 13% to 100% across different project types (Seres et al. 2010).
First, industrial regulations and policies directly affect TT in
specific project types. For example, the Chinese government implements
the large-scale wind farm plan, requiring about 70% local contents for
the eligibility of concession bidding, which significantly spurs the
localization of wind turbine manufacture (Wang 2010). Second, as the
number of CDM projects in a given type grows in a host country, TT
probability will gradually be reduced in later projects (Dechezlepretre
et al. 2008; Seres et al. 2009, 2010). It can be suggested that TT is
more likely to happen in some project types than others. Hence, we
propose that:
Hypothesis 2: The attribute of project types is positively
associated with TT in CDM.
2.3. Form of ownership of Chinese participants
It is restricted by National Development and Reform Commission
(NDRC) and three other ministries of Chinese government that only
wholly-owned Chinese companies or Chinese holding companies are eligible
for CDM projects in China (NDRC et al. 2005, 2011). There must be at
least 51% of the company owned by Chinese entities (Wang 2010). In this
condition, the CDM owners in China can be classified into three types:
state-owned enterprises (SOEs), collective-owned enterprises (COEs) and
private-owned enterprises (POEs) (Nee 1992).
In China, although the SOEs undertake more complex socio-economic
mission (Nolan 2001) and more commonly-faced multiple tasks than
non-SOEs (Bai et al. 2000, 2006), they have the facilities in accessing
capital resources (Brandt, Li 2003) and gaining political support from
the state and local governments (Li, Zhou 2005). The COEs have fewer
advantages than SOEs, but are still ranked higher in terms of accessing
political and financial support than POEs (Poncet et al. 2010). They
have structural advantages over both SOEs and POEs (Xia et al. 2009;
Xin, Pearce 1996), because they are affiliated with and are able to gain
protection from the local government (Kung, Lin 2007; Nee 1992; Peng et
al. 2004) and meanwhile they sell products in competitive markets that
encourage efficiency (Kornai 1986; Kung, Lin 2007). COEs did benefit
from their structural advantages in the early years of reformation (Xia
et al. 2009). After undergoing a decline in the mid-1990s as the SOEs
(Jefferson, Su 2006), COEs have experienced transformations under the
central government policy since 1995, including both privatization and
corporatization (Lin, Zhu 2001). In the transformation times, local
governments may still play an important role in dealing with the agency
problems as the controllers of COEs, and the close relationship with the
government becomes an obstacle rather than advantages in improving firm
performance (Xia et al. 2009; Zeng et al. 2012). The POEs gain less
local policy support from the government and having weak influential
power (Xin, Pearce 1996). They cannot enter into certain industries, and
are commonly with less tax relief (ADB 2002; Ralston et al. 2006). They
are harder to obtain loans from state-owned banks (Poncet et al. 2010),
have less access to market information and have more problems in getting
land which is owned by the state and other resources from the government
(ADB 2002; Gregory et al. 2000; Ralston et al. 2006). They can also be
large and sophisticated, with much more discretion on hiring, firing
people, and exercising market responses than SOEs (Pyke et al. 2000).
The alternative resources, such as reputation and relationships, are
used by POEs as alternative financing channels and governance mechanisms
to overcome the imbalance among the three sectors (Allen et al. 2005).
It is also suggested that the situation is changing over time, the
institutional and market infrastructure inspiring both SOEs and non-SOEs
are starting to be established in China (Carney et al. 2009). In
general, the more support they could gain from the government and the
stronger their financial power, the easier they can lower the four
barriers of TT.
It can be supposed that TT is likely to happen in SOEs, less likely
in COEs, and least likely in POEs. It is hypothesized that:
Hypothesis 3: The form of ownership of Chinese participants is
associated with TT in CDM.
2.4. Foreign participants
Cooperating with foreign companies can increase the possibility of
TT in CDM projects. Beside technology suppliers, there are at least four
types of foreign participants, such as project developers, financers,
consultants and credit buyers. One foreign entity can play one or more
roles at one time in a CDM project. The project developer clearly
favours TT, if it is the subsidiary of a company from an Annex I country
(1) (Das 2011; Dechezlepretre et al. 2008, 2009; Doranova et al. 2010).
The subsidiaries have stronger effects on TT than credit buyers
(Dechezlepretre et al. 2008). And, the involvement of foreign
consultants can also increase the possibility of TT in CDM projects (Das
2011). Thus:
Hypothesis 4: The Involvement of foreign companies is positively
associated with TT in CDM.
2.5. Disparity of host regions
In this paper, China is the host country of the investigated
projects. In China, the degree of reform and openness, geographical
locations and infrastructure investments significantly affect economic
growth performance across provinces (Demurger 2001). The heavy industry
development strategies in China formed a rural-urban gap in the
pre-reform period, while openness and decentralization induce and
exacerbate the inland-coastal disparity in the reform period (Kanbur,
Zhang 2005).
The regional disparity of China may affect TT in CDM projects by
lowering barriers on commercial viability, accessing to capital,
information and institutional framework. First, the CDM projects located
in the economically developed regions can take advantage of active
business activities and facility of access to capital. Second, the
comparatively developed regions in China have better local technology
capability (Qi et al. 2012), as they have better educated engineers and
better trained skilled workers, and more financial support on research
and development. High technology capabilities are necessary to adopt new
technologies, but it also implies that the technologies needed may
already be available in the local market (Dechezlepretre et al. 2008,
2009). It is hypothesized that:
Hypothesis 5: The regional disparity is positively associated with
TT in CDM.
3. Methodology 3.1. Data collection
In this paper, 500 recently-registered projects were chosen from
the CDM pipeline by the end of 2010. There are 27 projects without
credit buyers (unilateral project), or located in more than one province
of China which were excluded and replaced by another 27
recently-registered projects. The information about host regions,
project types, credit buyers, consultants, methods and planned annual
reductions were listed in the CDM pipeline (2). TT claims made by CDM
projects participants can be seen in "Section A.4.3. Technology to
be employed by the project activity" of their PDDs and other
possible parts of PDDs are also covered in "Section A.2.",
"Section A.4.2.", "Section B.5.", "Section
B.7.", "Section E.", "Annex", et al. (3)
Although a recent survey suggests that the actual rate of TT may be
higher than it is reported in PDDs (Kirkman et al. 2013), this research
follows the former empirical analyses using the TT information from
PDDs, and only the projects with information confirming TT are coded as
involving TT. The form of ownership of Chinese participants and foreign
project participants are verified by searching the Internet with the
information of project owners and foreign participants in "Section
A.3. Project participants" of PDDs.
3.2. Measurements
3.2.1. Technology transfer
The projects in number and estimated annual emission reductions can
be used as crude proxies to measure TT (Seres, Haites 2008). This paper
analyses the TT claims made by CDM projects participants in their PDDs.
In the following regression analysis, if any foreign equipment and/or
knowledge are employed in the project activities, a value of
'1' will be assigned, '0' otherwise.
Overall, there are 195 projects out of the 500 CDM projects (about
39%) claimed TT. These 39% projects account for almost 61% of the
estimated annual emission reductions which are 49,346 ktC[O.sub.2]e per
year out of all 80,948 ktCO2e per year of the sampled 500 projects. The
average size of projects involving TT and not involving TT are about
253.06 and 103.61 ktCO2e per year, significantly different under
independent-samples T test, p < 0.01.
3.2.2. Project sizes
In this paper, the project size is measured by the estimated annual
emission reductions in terms of ktC[O.sub.2]e per year. The projects are
classified into small-scale and large-scale based on the methodologies
used in calculating emission reductions. There are two projects used
both small- and large-scale methodologies, which are classified as
large-scale projects. Both the natural log of the estimated annual
emission reductions and the classification of small-scale and
large-scale projects based on the methodology of calculating emission
reductions are used as the proxies of project size in the following
regression analysis. The results of project size analysis are shown in
Table 1.
Table 1 indicates that over 74% of projects are large-scale,
accounting for more than 94% of the annual emission reductions. More
than 88% TT happen in large-scale projects. About 46% large-scale
projects and only 17% small-scale projects involve TT, which means
large-scale projects are indeed more likely to involve TT than
small-scale projects. In large-scale projects, about 46% projects
involve TT accounting for about 63% annual emission reductions of all
large-scale projects. In small-scale projects, about 17% projects
involve TT which account for about 20%. The average sizes of small-scale
and large-scale projects are 34.09 and 205.41 ktC[O.sub.2]e respectively
per year, significantly different under independent-samples T test, p
< 0.001.
3.2.3. Project types
According to UNFCCC, the CDM projects can be classified into 26
different project types, of which 15 are involved in this study. Limited
by this sample, they are classified into four categories: 252 hydro
projects, 156 wind projects, 27 EE projects and 65 other projects. (4)
Three dummy variables are used to measure Hydro, EE and the Others with
Wind as the base category. If the project is from one of the three
categories except Wind, a value '1' is assigned, '0'
otherwise. The variable Similar which is natural log of the projects in
number using the same technology of the 15 project types in China, is
used as a proxy of local mitigation technology availability of China in
the regression analysis. Project type analysis is reported in Table 2.
From Table 2, Hydro and Wind are the two main CDM project types in
China, account for more than 81% projects. Hydro projects have the
lowest rate of projects involving TT, only 25%. Though the TT rate of
Wind projects drops in the recent years (Wang 2010), it still apportions
about 46%. The EE and the Other projects have the highest TT rate, more
than 63% and 66% of the projects involving TT and respectively account
for about 84% and 91% of annual emission reductions. Except the high
rate of TT in EE projects, the average size of EE projects is not much
different with Hydro and Wind projects. The average size of the Other
projects is significantly larger than Hydro, Wind and EE projects,
tested by one-way ANOVA, p < 0.05. The Other projects category
contains some very large projects such as HFCs and fossil fuel switch
which all involve TT.
In the end of 2012, 3,479 CDM projects in China are registered.
There are 1,445 Wind and 1,244 Hydro projects, which are 42% and 36% of
the 3,479 projects. The other projects are the 191 EE own generation
projects, 132 solar projects and 121 biomass energy projects. The rest
projects are less than 100 in each project type. The Wind projects are
significantly growing in China. In the end of 2010, there were only 338
Wind projects registered. In 2011 and 2012, 477 and 473 Wind projects
started their first CDM comment, and 274 and 833 Wind projects were
registered. (5) The dynamic changes of Wind project located in China
from 2005 to2012 are shown in Figure 1.
3.2.4. Form of ownership of Chinese participants
The CDM project involves the effect of the ownership of the project
owners. According to the entities from the Chinese side, they are
classified into three basic types of ownerships including SOEs, COEs and
POEs. Two dummy variables are used to measure different ownerships in
the following regression analysis with COEs as the base category. If the
project is from one of the two categories except COEs, a value of
'1' is assigned, '0' otherwise. The results of
ownership analysis are reported in Table 3.
From Table 3, over 73% CDM project hosts are SOEs, accounting for
the same amount of annual emission reductions. Over 80% TT projects are
hosted by SOEs. But the projects hosted by POEs have the highest portion
of projects involving TT, more than 44% and accounting for 83% annual
emission reductions. The average size of the projects hosted by POEs is
larger than projects hosted by SOEs, and significantly larger than
projects hosted by COEs, tested by one-way ANOVA, p < 0.1.
3.2.5. Foreign participants
In this research, all the 500 samples are the projects with at
least one credit buyer. There are 184 projects which involve foreign
consultants in this sample. In 105 projects, the foreign consultants act
as both the consultants and the credit buyers. There are 17 projects in
which the foreign participants act as project developers or financers.
(6) Thirteen out of the 17 projects hire foreign consultants, the rest 4
projects hire local consultants, and in two projects the foreign
consultants also act as credit buyers. All the 17 projects are
classified into the category of projects with foreign project
developers. All samples are classified into the four categories. They
are consultants (including 68 projects), consultants and buyers
(including 103 projects), project developers (including 17 projects) and
the Other projects (including 312 projects). Three dummy variables are
used to measure different foreign participants in the following
regression analysis with the Other 312 projects as the base category. If
the project is from one of the three categories except the Other
projects, a value of '1' is assigned, '0' otherwise.
The results of foreign participant analysis are reported in Table 4.
Except the technology suppliers and credit buyers, there are about
38% of the projects involve other types of foreign participants
(consultants, consultants and buyers, and project developers). These
projects have higher TT shares than the Other projects, and the TT
projects in these three categories account for larger portions of
emission reductions than the Other projects. Especially the projects in
consultants and project developers categories, the TT projects account
for 81% and 93% of the annual emissions reductions in each category. The
average sizes of the project with foreign participants are much larger
than the Other projects.
3.2.6. Disparity of host regions
In the host country, the regions or provinces have different
economic development level and technology capabilities, which can
influence TT in the projects. The GDP per capita (in 10 thousand of RMB)
and the GDP growth are used as the proxies of local economic
development, which are the average data of each province from 2006 to
2010. (7) The research and development (R&D) investment (the average
percentage of R&D per GDP from 2006 to 20107) of each province and
the ArCo technology indexes of each province (8) are used as the proxies
of local technology capabilities. The variable Similar in province which
is the natural log of the projects in number adopting the same type of
technology in each province is used as the proxy of local mitigation
technology availability in provinces in the following regression
analysis.
In this paper, the CDM projects involve 30 regions/provinces in
China. As National Bureau of Statistics of China stated, all the
regions/provinces can be classified into four economic regions (NBSC
2011) according to their geographical locations, economic development
and institutional environment. (9) The East is a region with a
comparatively high level of economic and institutional development. The
West is a region with comparatively low level of economic and
institutional development. The development level of Midland is between
the East and West. And the Northeast is a region of traditional heavy
industry and large-scale agricultural production. There are 56% of the
projects that are located in the West. The West region can be further
classified into two sub-regions, which are the Mid-west (including Inner
Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan and Shaanxi) and
the Wild-west (including Gansu, Xinjiang, Ningxia, Qinghai and Xizang).
The results of host region analysis are reported in Table 5 and Table 6.
From Table 5, more than 42% of the CDM projects are located in the
Mid-west and account for about 36% of the annual emission reductions.
Only 20% of the projects are located in the East but account for 31% of
the annual emission reductions. More than one third of the TT projects
are located in the Mid-west, but the highest TT rates are in the East
region. Over 54% of the projects in the East involve TT which account
for 87% of the annual emission reductions in this region. The average
size of projects located in the East is much larger than other regions.
Table 6 shows the regional disparity of China in economic
development, local technology capabilities and local mitigation
technology availability described by five variables. The GDP per capital
of the East is 3.45 which is significantly higher than all other four
regions. The GDP growth of the Wild-west is 11.39 which is significantly
smaller than all other four regions. The R&D investment of the
Mid-west and the Wild-west are both 0.82 which is significantly lower
than other three regions. And the ArCo index of the East is 0.40 which
is significantly higher than all other four regions. In the opposite,
the values of the Similar in the provinces of the Mid-west and Wild-west
are significantly larger than other three regions. All are tested by
one-way ANOVA, p < 0.001.
The dynamic changes of the distribution of Wind projects and the
distribution of all CDM projects in China are shown in Figure 1. Most of
the 1,445 registered Wind projects are located in Inner Mongolia, Hebei,
Shandong, Liaoning, and Ningxia. The number of Wind projects was growing
very fast in these regions during 2011 and 2012. The Wind projects
located in other provinces are fewer than 100 in each province. Most of
the registered CDM projects are located in the Mid-west, the Wild-west,
the Bohai Rim (Shandong, Hebei and Liaoning) and Hunan province.
4. Results and analysis
Table 7 shows correlations among variables. All the bivariate
correlations are lower than the recommended 0.7 threshold. The variance
inflation factors (VIFs) are below the recommended ceiling of 10 (Cohen
et al. 2003). The individual variables can be used in the regression
analysis.
The dependent variable has values of either '0' or
'1'. The logistic regression analysis is used for the
following regression analysis. The results are reported in Table 8.
[FIGURE 1 OMITTED]
From Table 8, the results of Model 1 indicate that TT commonly
increases with project sizes. EE projects are significantly more likely
to involve TT than Wind projects. TT is significantly more likely to
happen in the project hosted by SOEs and POEs than COEs. The projects
with foreign participants which act as consultants, both the consultants
and credit buyers, and the project developers are significantly more
likely to involve TT. The projects hosted in the regions with high GDP
per capita are significantly more likely to involve TT, such as projects
in Chinese Eastern provinces. But the TT possibility is significantly
negatively related to the host province's GDP growth rate. Of the
five hypotheses, four are well supported by the results of Model 1
except Hypothesis 5.
In Model 2, the variable Similar is added. The results of Model 1
are stable, except the significance of project types which becomes weak.
Similar is a variable which has stronger influence on TT in CDM projects
than project type. TT is significantly negatively related to the number
of projects using the same type of technology.
In Model 3, the R&D and Local technology capability are added.
The results of Model 1 and Model 2 are stable. The TT possibility is
significantly positively related to the R&D investment of the host
province, but significantly negatively related to Local technology
capability.
In Model 4, the variable Similar is substituted by Similar in
province. The result of Model 1 and Model 2 are stable. The TT
possibility is significantly negative related to Similar in province. In
Model 5, both Similar and Similar in province are added. The
significance of Similar in province becomes weak. Other results are
stable.
In Table 9, the regression models only include 156 Wind projects.
The relation between TT and Similar in province becomes not significant,
but the Similar is still significantly related to TT.
Robust tests are conducted. When the GDP per capita is substituted
by log of GDP, log of FDI, or log of export and import, the results of
the regression models are stable. And it is shown in Model 1 that Hydro
projects are significantly less inclined to involve TT than Wind
projects. When the Local technology capability is substituted by the
percentage of tertiary educated local population, the results of the
regression models are stable. Other influence of regional disparity have
also been considered, such as the percentage of fossil fuel in local
energy consumption, the local resource consumption (the log of energy
consumption and the log of water consumption), the local pollution
control investment, and the local emission compliance (the pollution
control percentage of industrial wastewater discharge, industrial and
domestic emissions, industrial solid waste disposal, and domestic
rubbish disposal). (10) The variables are added in the regression model,
the results are still stable. TT is significantly positively related to
fossil fuel percentage and energy consumption, but negatively related to
pollution control investment, the pollution control percentage of
industrial and domestic emissions, and the pollution control percentage
of industrial solid waste disposal. Four dummy variables East,
Northeast, Mid-west and Wild-west are used as the proxies of regional
disparity, with Midland as the base category. When other regional
variables are substituted by these four dummy variables, the regression
results are still stable. It is shown that Hydro projects are
significantly less inclined and projects located in the East region are
more inclined to involve TT.
5. Discussion
Project sizes are one of the main factors affecting TT in CDM
projects. Large-scale projects can obtain more financial support and
have more opportunities in gaining investment to use advanced mitigation
technologies because large-scale projects can supply CERs more steadily
than small-scale projects. Globally, about 40% of CDM projects are
small-scale projects, about 25% of small-scale projects involve TT and
overall 40% of projects involve TT (Seres et al. 2010; UNFCCC 2013). In
China, only 25% are small-scale projects, 17% of small-scale projects
involve TT, and the overall percentage of TT is about 39% which is close
to the global level. The percentages of small-scale projects and its TT
in China are comparatively low. This means that the large entities in
China are more likely to implement CDM projects and involve TT than
small entities. This also means that the mitigation potential of
small-scale projects is not fully explored in China. In small-scale CDM
projects, the transaction costs of CDM and TT has a higher impact on its
commercial viability than in large-scale projects at current CER price
(Das 2011; Dechezlepretre et al. 2008, 2009; Schneider et al. 2008). And
CDM is criticized for its project-by-project crediting process which is
inefficient to avoid dangerous climate change (Lewis 2010). To reduce
the transaction costs of mitigation activities and in response to these
criticisms, bundling and programming are allowed in CDM (Lewis 2010).
But these forms of activities are not widely used in CDM. Till the end
of June 2013, there were only 220 programme activities registered and
only 33 of them were located in China (UNFCCC 2013). The increasing
implementation of these reformed activities will help to explore the
mitigation potential of China. But it still needs flexible and
diversiform emission reductions. To maintain the integrity of the
emission reduction credits in the more flexible and diversiform
crediting forms, the future mechanism should devote to the reduction of
the asymmetric information between the participants and regulator
(MacKenzie, Ohndorf 2012).
TT is different among CDM project types in China. EE projects are
more likely to involve TT than wind projects. This category includes the
project types (EE own generation and EE households projects) which are
new in China, for more than 81% CDM projects in China are Hydro and Wind
projects. The technologies of these new types of projects are not
available in the local market or inefficient as foreign technologies.
China is the main hydro technology supplier for CDM projects in the
world (Seres et al. 2010). Most of the hydro technologies are locally
available. The Hydro projects hosted in China do not incline to adopt
foreign technologies than other project types. There are still about 25%
of Hydro projects involving TT. This is the result of the efficiency,
quality or other virtues of foreign technologies. Wind projects are more
likely to involve TT than Hydro projects, but still less likely involve
TT than EE and Other projects. The large-scale wind farm plans and the
local content requirements of the Chinese government significantly spur
the localization of wind turbine manufacture, but the inferiority of
components' quality still troubles the local turbine providers in
China (Wang 2010). Hence, there are still about 46% of the Wind projects
involving TT. The Other projects category includes some very large
projects, such as HFC and [N.sub.2]O projects. But it doesn't show
that the project types have any significant influence on TT in CDM
projects. There are about 66% of projects in this category involving TT
for their large project sizes, not for their project types. The
influence of project types becomes weak when the variable Similar is
added into the regression model. Similar is a proxy of local mitigation
technologies availability of the 15 project types in our sample. It
shows that TT is significantly negatively related with the number of
projects in the same type. The more projects are implemented, the later
projects in this type are more inclined to use local technology,
especially for wind projects which grow very fast in China. The GHG
mitigation potential of EE and Other project types have not been fully
explored in China. Giving policy support of TT in these project types
will help explore the mitigation potential. When the number of projects
of certain types is small, the policies, such as tax relief for these
projects or lowering tariff on foreign equipment, will help spur their
development and increase the possibility of involving TT. When the
number of projects in this type is growing, policies, such as the
requirements of local content levels or the use of local equipment, will
help spur the localization of foreign advanced technologies and promote
local technology diffusion.
The form of ownership of Chinese participants is a significant
factor which influences TT in CDM projects in China. Projects hosted by
SOEs and POEs are more likely to involve TT than COEs. Most SOEs are
very large and in a monopoly position nationally or locally. They have
advantages in financing mitigation activities (Xu et al. 2012; Zeng et
al. 2012), gaining support and resources from the government, and
obtaining investment and long term loans, which make them more capable
to involve TT than POEs. POEs can gain the least support from the
government and banks, but they are more likely to involve TT than COEs.
There are only 9% of the 500 CDM projects hosted by POEs and only 20 of
them involve TT. It is hard to exclude that these projects may actually
be very special and they may be hosted by POEs which have some similar
attributes as SOE, e.g. some EE and cement projects are hosted by
large-scale POEs. The advantages of SOEs cannot be learned entirely by
other enterprises. POEs and COEs should implement mitigation projects
according to their actual needs and capabilities. If the government
supports the mitigation activities according to the importance and
actual need of projects other than enterprise's ownership, the
results will be better for both mitigation activities and public
resources efficiency. Nearly three quarters of the CDM projects in China
are hosted by SOEs. The participations of non-SOEs in mitigation
activities are insufficient. The institutional and market infrastructure
which inspire both SOEs and non-SOEs are very important to promote
economic developments in China, and are also important to fully explore
the mitigation potential of China.
In addition to the credit buyers, some types of foreign
participants are important to increase TT possibility in bilateral CDM
projects, which are foreign consultants, both consultants and buyers,
and project developers. The entities from the Annex I countries which
participate in CDM projects more than being credit buyers in these ways,
cannot only promote the transfer of advanced mitigation technologies to
developing countries, but also reduce the asymmetric information between
CER buyers and suppliers. It is good for enhancing the environmental
integrity of the CER suppliers, but may increase the risk of losing
control of the integrity of the CER buyers and widening the information
gaps between the participants and regulators. Today, only Chinese or
Chinese holding companies can be the host of CDM projects in China (NDRC
et al. 2005, 2011). The Chinese government should encourage additional
foreign participants to engage in mitigation activities in China, but
should also enhance their abilities to supervise these activities.
The projects located in the developed regions, such as Chinese
Eastern provinces, can easily involve TT. The general explanations are
that the developed regions have better financial ability to support the
utilization of foreign technologies, have better ability to access the
information of these foreign technologies, are much easier to access
capital, gain more support from the government for their environmental
conservation activities, and even have better local technology
capabilities which facilitates the use of foreign technologies than
other regions (Zeng et al. 2010a, b). However, additional findings have
been obtained in this research. The high level of economic development
indeed can increase the possibility of TT in CDM projects. But high
economic growth may reduce this possibility. This might be for the
reason that the high economic growth is earned by increasingly investing
resources in economic growth and reducing the investment of resources in
environmental conservation. The local technology capability in China is
in a relatively high level which can reduce the possibility of TT,
because the technologies are already available locally. But the R&D
investment is still one of the positive factors that influence TT in CDM
projects. The influence of Similar in province on TT is much weaker than
Similar. This implies that the mitigation technologies are diffused at
the country level rather than the province level, though it might be
diffused at the province level first, such as the number of Wind
projects first increases in Inner Mongolia then in the whole country as
shown in Figure 1. In general, the advantages of the developed regions
cannot be copied by all other local governments and enterprises of the
undeveloped regions, which are limited by their geographical locations,
technology capability, as well as economic and institutional development
(Xu et al. 2012). Cooperating with companies or institutions from
developed regions is a good way of CDM project hosts from other regions
to take advantage of economic, technological and institutional
development of the developed regions. The cooperation of different
regions can help explore the mitigation potential of undeveloped regions
and promote the mitigation technology diffusion in China.
Conclusion
This article focused on GHG mitigation technologies transferred by
the CDM in China. China as one of the biggest developing countries with
fast economic growth plays a significant role in GHG mitigation.
Technology is an important and irreplaceable factor in striking a
balance between economic development and environmental conservation. The
Chinese government has tried its best to promote the mitigation of TT to
China.
CDM project characteristics (such as project sizes, project types,
the form of ownership of Chinese participants, and the participation of
foreign companies) and the characteristics of host regions (such as
economic development levels, local technology capabilities, and local
mitigation technologies availability) can affect the possibility of TT
in CDM projects. The projects in large sizes, of comparatively new
project types (like EE own generation and EE households projects),
hosted by SOEs, or with foreign participants (such as foreign consultant
and project developer), are more likely to involve TT. It was clear that
the projects located in the comparatively-developed regions such as
Eastern China are more likely to involve TT than in other regions,
because of their advantages in economic development and R&D
investment.
In addition to the above results, three lessons were identified.
First, the non-SOEs rarely participate in mitigation activities in
China. More than 73% of CDM projects were hosted by SOEs. The non-SOEs
are significantly growing in China, which nearly 72% of the industrial
outputs were produced by non-SOEs in 2008 (NBSC 2009). If mitigation
activities of non-SOEs are supported by the government policies and
banks equally as SOEs, these activities and technology transfer of
non-SOEs will immensely be spurred. Second, the mitigation potential of
CDM projects in EE and Other projects categories was not fully explored
in China. More than 81% of CDM projects in China were Hydro and Wind
projects with only 18% of CDM projects being EE and Other projects. The
mitigation potential of these CDM projects can extensively be explored
by the transfer, utilization and development of new mitigation
technologies. Third, some projects are growing very fast and the
technologies are widely diffused and localized in China, such as Wind
projects. It is important to develop mitigation technologies locally and
promote the mitigation activities, but the Wind projects in recent years
are growing too fast in China (Li 2012). The rate of abandoned wind
energy is very high, which is about 11.12% on average and about 22.99%
in Inner Mongolia at the end of 2011 in official documents (Li 2012),
and is unofficially estimated as high as 40% to 50% in the middle of
2012 (Tong 2012). The current crisis of the photovoltaic industry (Liu
2012) and the recent deficit of wind turbine manufacture industry
combining with the high rate of abandoned wind energy in China
demonstrate that with the absence of general plans and industrial
regulations on the mitigation activities, the fast growth rate can end
in tragedy. When the government signals the policy preference on
mitigation technologies and green energy, the capital will flow into
these industries and trigger rapid growth in these industries, which may
result in overcapacity and industry crises. If the government has not
prepared for the fast growth rate and no general plan to regulate the
growth, the faster the growth, the more serious the crises it will
trigger.
Other developing countries can learn from China. First, the
mitigation activities should cooperate with the country's resource
endowment and industrial development. Before the implementation of CDM
projects, the Chinese government has invested a lot of money in
investigating and exploiting hydro resource. The CDM helps further
explore the mitigation potential of hydro power in China. If the country
has no such resource endowment or has no adequate local technology
capability to use the mitigation technology, the CDM cannot help much to
explore the mitigation potential of the country just using CER income or
TT. Second, the implementation of mitigation activities should
accommodate the country's development. The expansion of large wind
farms in China is much beyond the absorption capability of the Chinese
market. The CDM does support the development of local mitigation
activities, but it cannot support the development of the whole industry
of a country, such as wind energy in China.
CDM was criticized for its problems in excluding new projects, its
inefficiency in GHG emission reductions, its insufficiency in supporting
sustainable development, and its negative effects on the development of
domestic low carbon policies in developing countries and the low-carbon
transformation in developed countries (Lewis 2010; Vasa, Neuhoff 2011).
Some new mechanisms need to be effective, encourage mitigation actions,
eliminate bottlenecks and bureaucracy, and maintain the integrity of
emission reduction credits (Lewis 2010). When the new mechanisms are not
established and matured, CDM will still play a significant role in GHG
emission reduction in its present or reformed structures (Bakker et al.
2011).
Further research is required in several areas. Some factors which
may have important influence on TT are not sufficiently discussed in our
study, such as the regulation of local government on environmental
conservation activities, the regional disparity of institutional
development, the technology capability of project owners with different
ownerships. Local mitigation technology capability is not directly
measured only using the number of similar projects as a crude proxy, and
it has not been sufficiently discussed in this study. Besides these
issues, improving the efficiency of TT in/by CDM and the reforming of
CDM are interesting issues which need to be explored in further studies.
Caption: Fig. 1. The distribution of wind projects in cumulated
numbers from 2005 to 2012 and the all registered CDM projects in China
Note: The numbers in the maps are the cumulated numbers of registered
projects located in each province; these numbers are divided equally
into eight ranks coloured by blue; the data is collected from the CDM
pipeline.
doi: 10.3846/20294913.2013.879751
Acknowledgements
We acknowledge the helpful comments and suggestions given by two
anonymous reviewers which substantially improved this article. This
research is supported by the National Natural Science Foundation of
China (Grant No. 71025006, 71373161, 71390525), and the Ministry of
Education of China (Grant No. 20120073110030).
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Linna XIE. A PhD student in Antai School of Management at Shanghai
Jiaotong University, China. She has received her Master's degree in
2010 and passed the PMP certification in 2008. She has published four
chapters in An Experimental Course in Project Management by China Renmin
University Press in 2010. Research interests include technology
management, project management.
Saixing ZENG. Doctor, Head and Professor in Antai School of
Management at Shanghai Jiaotong University, China. As a researcher in
Technology Management and related fields, he has been in charge of a
large number of research projects, and has published more than 100
journal and conference papers, books, and reports on technology
management and project management. Research interests include technology
management, project management.
Hailiang ZOU. A PhD student in Antai School of Management at
Shanghai Jiaotong University, China. He has received his Master's
degree in 2009. He has been involved in two research projects supported
by the National Natural Science Foundation of China. Research interests
include technology management, environmental management.
Vivian W. Y. TAM. Doctor, A Senior Lecturer in School of Computing,
Engineering and Mathematics at University of Western Sydney, Australia.
She has received her PhD in City University of Hong Kong. She is a
Principal Supervisor for doctoral projects, and has published more than
160 international refereed journal papers. Research interests include
environmental management, sustainable construction.
Zhenhua WU. A graduated student in Antai School of Management at
Shanghai Jiaotong University, China. He has received his Master's
degree in 2011. He is currently working in the China Securities
Depository and Clearing Corporation Limited. He is responsible for the
technical development and maintenance of the security depository and
clearing system. Research interests include project management.
Linna XIE (a), Saixing ZENG (a), Hailiang ZOU (a), Vivian W. Y. TAM
(b), Zhenhua Wu (a)
(a) Antai School of Management, Shanghai Jiaotong University,
200052 Shanghai, China
(b) School of Computing, Engineering and Mathematics, University of
Western Sydney, Locked Bag 1797, NSW2751 Penrith, Australia
Received 11 November 2012; accepted 02 November 2013
Corresponding author Saixing Zeng
E-mail: zengsaixing@sjtu.edu.cn
(1) According to Seres (2010) and a list of Annex I counties
available on UNFCCC website (http://unfccc.int/
parties_and_observers/parties/annex_i/items/2774.php) which include the
United States and Canada. This paper will include the United States and
Canada for the analysis of the Annex I counties.
(2) CDM pipeline can be downloaded from the website of UNEP RIS0
CENTRE (http://cdmpipeline.org/).
(3) See in the PDDs download from UNFCCC website using the titles
of sampled projects. If there is no statement which confirms TT in
"Section A.4.3.", the keywords "technology",
"technologies", "transfer", "equipment",
"service", "train", "serve"
"supplier", "import", "manufacturer" are
used to make this process more efficient. If no information is found,
the whole PDDs are covered and been read accordingly. If there is any
equipment and/ or knowledge from another country involved, it is
recorded as involving TT.
(4) The EE projects include 25 EE own generation and 2 EE
households projects. The rest 65 projects include 13 landfill gas, 12
biomass energy, 11 coal bed/mine methane, 8 fossil fuel switch, 7 N2O, 5
methane avoidance, 3 solar, 2 cement, 2 reforestation, 1 fugitive and 1
HFCs project. The four categories are classified with the limit of the
sample.
(5) Data are collected from CDM pipeline. URL:
http://cdmpipeline.org/
(6) In four of these 17 projects, the projects' foreign
developers are also the financers.
(7) Data is collected from China Statistical Yearbook published on
the website of National Bureau of Statistics of China
(http://www.stats.gov.cn/tjsj/ndsj/).
(8) The ArCo technology index includes eight sub-indexes (a1
patens, a2 scientific articles, b1 internet penetration, b2 telephone
penetration, b3 electricity consumption, c1 tertiary science and
engineering enrolment, c2 mean years of schooling, c3 literacy rate)
(Archibugi, Coco 2004). We can only acquire data of sub-indexes from
2009 and 2010. From the limited available data, we use the average of
two years standardized indicators to compute the local technology
capability.
(9) NBSC (2011) stated that the East includes Beijing, Tianjin,
Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and
Hainan; the Midland includes Shanxi, Anhui, Jiangxi, Henan, Hubei and
Hunan; and the West includes Inner Mongolia, Guangxi, Chongqing,
Sichuan, Guizhou, Yunnan, Xizang, Shaanxi, Gansu, Qinghai, Ningxia and
Xinjiang; the Northeast includes Liaoning, Jilin and Heilongjiang.
(10) The average of five years' data is used, which from 2006
to 2010. Most of the data is from China Statistical Yearbook published
on the website of National Bureau of Statistics of China. URL:
http://www.stats.gov.cn/tjsj/ ndsj/. The fossil fuel consumption data is
from China Energy Yearbook 2011.
Table 1. Project size analysis
Size Number of Annual Number
projects % emission of TT
reductions % projects %
Small-scale 25.40 5.35 11.28
Large-scale 74.60 94.65 88.72
Total 100.00 100.00 100.00
TT projects as
percentage of
Size Number of Annual Average size
projects% emission (ktC[O.sub.2]e
reductions % per year)
Small-scale 17.32 19.89 34.093
Large-scale 46.38 63.28 205.412
Total 39.00 60.96 161.897
Table 2. Project type analysis
Type Number of Annual Number
projects % emission of TT
reductions % projects %
Hydro 50.40 36.28 32.31
Wind 31.20 25.65 36.92
EE 5.40 4.52 8.72
Others 13.00 33.55 22.05
Total 100.00 100.00 100.00
TT projects as
percentage of
Type Number of Annual Average size
projects % emission (ktC[O.sub.2]e
reductions % per year)
Hydro 25.00 40.93 116.547
Wind 46.15 46.06 133.118
EE 62.96 83.69 135.383
Others 66.15 90.95 417.796
Total 39.00 60.96 161.897
Table 3. Ownership analysis
Ownership Number of Annual Number
projects % emission of TT
reductions % projects %
SOEs 73.40 73.15 80.51
COEs 17.60 11.73 9.23
POEs 9.00 15.12 10.26
Total 100.00 100.00 100.00
TT projects as
percentage of
Ownership Number of Annual Average size
projects % emission (ktC[O.sub.2]e
reductions % per year)
SOEs 42.78 61.52 161.343
COEs 20.45 29.48 107.867
POEs 44.44 82.71 272.069
Total 39.00 60.96 161.897
Table 4. Foreign participant analysis
Foreign participant Number of Annual Number
projects % emission of TT
reductions % projects %
Consultant 13.60 17.70 16.41
Consultant and buyer 20.60 23.46 27.69
Project developer 3.40 6.61 6.67
Other projects 62.40 52.23 49.23
Total 100.00 100.00 100.00
TT projects
as percentage of
Foreign participant Number of Annual Average size
projects % emission (ktC[O.sub.2]e
reductions % per year)
Consultant 47.06 81.46 210.709
Consultant and buyer 52.43 70.46 184.370
Project developer 76.47 93.42 314.936
Other projects 30.77 45.64 135.500
Total 39.00 60.96 161.897
Table 5. Host region analysis
Number of Annual Number
projects % emission of TT
reductions % projects %
Region
Wild-west 12.80 10.46 11.79
Mid-west 42.80 36.02 35.90
East 20.40 30.64 28.72
Midland 16.40 15.13 14.36
Northeast 7.60 7.74 9.23
Total 100.00 100.00 100.00
TT projects
as percentage of
Number Annual Average size
of projects % emission (ktC[O.sub.2]e
reductions % per year)
Region
Wild-west 35.94 54.40 132.334
Mid-west 32.71 42.00 136.262
East 54.90 87.23 243.136
Midland 34.15 60.32 149.398
Northeast 47.37 55.37 164.958
Total 39.00 60.96 161.897
Table 6. Regional disparity of China
China Min. Max. East
GDP per capita 2.23 0.97 6.45 3.45
GDP growth 13.43 10.58 17.60 12.77
R&D 1.00 0.26 5.35 1.39
ArCo index 0.24 0.12 0.84 0.40
Similar in province 2.78 0.00 5.02 1.85
Midland Northeast Midwest Wild-west
GDP per capita 1.84 2.63 1.95 1.48
GDP growth 13.23 14.23 14.29 11.39
R&D 1.04 1.17 0.82 0.82
ArCo index 0.20 0.27 0.19 0.19
Similar in province 2.00 2.06 3.69 2.62
Table 7. Means, standard deviations, VIFs, and correlations (a)
Mean Std. 1 2 3
Deviation
1 TT 1.000
2 Project sizes 11.41 0.95 0.337 1.000
3 Small-scale projects 0.25 0.44 -0.259 -0.663 1.000
4 Hydro 0.5 0.5 -0.289 -0.271 0.358
5 EE 0.05 0.23 0.117 -0.043 -0.017
6 Others 0.13 0.34 0.215 0.233 -0.062
7 SOEs 0.73 0.44 0.126 0.137 -0.107
8 POEs 0.09 0.29 0.043 -0.076 0.087
9 Consultants 0.14 0.34 0.066 -0.045 0.077
10 Consultants and 0.21 0.4 0.140 0.094 -0.025
credit buyers
11 Project developers 0.03 0.18 0.144 0.103 -0.084
12 GDP per capita 2.23 1 0.243 0.155 -0.185
13 GDP growth 13.43 1.9 0.006 0.069 -0.124
14 Similar 5.15 1.27 -0.301 -0.230 0.133
15 R&D 1 0.5 0.145 0.010 0.091
16 Local technology 0.24 0.12 0.228 0.113 -0.095
capability
17 Similar in province 2.78 1.42 -0.227 -0.025 0.033
Variance Inflation 2.072 2.096
Factors VIFs (b)
4 5 6 7 8
1 TT
2 Project sizes
3 Small-scale projects
4 Hydro 1.000
5 EE -0.241 1.000
6 Others -0.390 -0.092 1.000
7 SOEs 0.043 -0.048 -0.024 1.000
8 POEs -0.088 0.179 0.102 -0.546 1.000
9 Consultants 0.020 0.112 0.090 -0.051 0.132
10 Consultants and -0.108 0.010 -0.006 0.009 0.037
credit buyers
11 Project developers -0.167 -0.045 0.321 -0.006 0.008
12 GDP per capita -0.557 0.105 0.104 0.024 0.067
13 GDP growth -0.275 -0.061 -0.114 0.071 -0.122
14 Similar 0.536 -0.234 -0.676 0.037 -0.184
15 R&D -0.037 0.113 0.137 -0.005 0.076
16 Local technology -0.420 0.138 0.174 -0.019 0.140
capability
17 Similar in province 0.400 -0.246 -0.558 0.015 -0.150
Variance Inflation 2.818 1.477 3.550 1.481 1.584
Factors VIFs (b)
9 10 11 12 13
1 TT
2 Project sizes
3 Small-scale projects
4 Hydro
5 EE
6 Others
7 SOEs
8 POEs
9 Consultants 1.000
10 Consultants and -0.202 1.000
credit buyers
11 Project developers -0.074 -0.096 1.000
12 GDP per capita -0.028 0.110 -0.026 1.00
13 GDP growth -0.125 0.168 -0.046 0.407 1.000
14 Similar -0.128 0.020 -0.263 0.226 0.037
15 R&D 0.089 -0.019 0.028 0.383 -0.147
16 Local technology 0.034 0.065 -0.018 0.606 0.100
capability
17 Similar in province -0.095 0.064 -0.215 0.229 0.235
Variance Inflation 1.120 1.120 1.175 6.797 2.453
Factors VIFs (b)
14 15 16 17
1 TT
2 Project sizes
3 Small-scale projects
4 Hydro
5 EE
6 Others
7 SOEs
8 POEs
9 Consultants
10 Consultants and
credit buyers
11 Project developers
12 GDP per capita
13 GDP growth
14 Similar 1.000
15 R&D -0.144 1.000
16 Local technology -0.268 0.579 1.000
capability
17 Similar in province 0.695 -0.305 -0.390 1.000
Variance Inflation 4.551 1.886 5.157 2.811
Factors VIFs (b)
Notes: (a) N = 500; (b) None of these correlations exceed 0.70 and
all of the VIFs are much less than the recommended maximum threshold
of 10.
Table 8. Results of logistic regression analyses
Variables Model 1 Model 2
Constants -8.329 *** (2.15) -6.061 *** (2.34)
Project sizes 0.685 *** (0.17) 0.695 *** (0.17)
Small-scale projects -0.382 (0.38) -0.385 (0.39)
Project types: Hydro -0.323 (0.30) -0.132 (0.32)
EE 0.891 * (0.49) 0.343 (0.54)
Others 0.463 (0.39) -0.608 (0.58)
Ownerships: SOEs 0.921 *** (0.32) 0.866 *** (0.32)
POEs 0.829 * (0.45) 0.653 (0.46)
Foreign participants: 0.689 ** (0.31) 0.661 ** (0.31)
Consultants
Consultants and credit 0.849 *** (0.27) 0.913 *** (0.27)
buyers
Project developers 1.522 ** (0.66) 1.487 ** (0.66)
GDP per capita 0.469 *** (0.14) 0.472 *** (0.15)
GDP growth -0.151 ** (0.06) -0.159 ** (0.06)
Similar -0.419 ** (0.17)
R&D
Local technology
capability
Similar in province
Pseudo [R.sub.2] 0.3479 0.3577
Correctly classified 81.00% 82.40%
Variables Model 3 Model 4
Constants -5.039 ** (2.46) -7.975 *** (2.27)
Project sizes 0.666 *** (0.17) 0.738 *** (0.18)
Small-scale projects -0.432 (0.39) -0.369 (0.39)
Project types: Hydro -0.046 (0.35) -0.072 (0.35)
EE 0.298 (0.54) 0.648 (0.51)
Others -0.617 (0.59) 0.110 (0.45)
Ownerships: SOEs 0.848 *** (0.32) 0.858 *** (0.32)
POEs 0.698 (0.46) 0.832 * (0.46)
Foreign participants: 0.667 ** (0.31) 0.725 ** (0.32)
Consultants
Consultants and credit 0.954 *** (0.28) 0.935 *** (0.27)
buyers
Project developers 1.445 ** (0.67) 1.384 ** (0.67)
GDP per capita 1.143 ** (0.49) 1.269 ** (0.49)
GDP growth -0.232 ** (0.09) -0.197 ** (0.09)
Similar -0.454 *** (0.17)
R&D 0.547 * (0.31) 0.447 (0.30)
Local technology -6.621 * (3.92) -7.952 ** (4.03)
capability
Similar in province -0.248 ** (0.12)
Pseudo [R.sub.2] 0.3642 0.3597
Correctly classified 82.80% 81.60%
Variables Model 5
Constants -5.689 ** (2.55)
Project sizes 0.706 *** (0.18)
Small-scale projects -0.396 (0.39)
Project types: Hydro 0.025 (0.35)
EE 0.280 (0.54)
Others -0.629 (0.59)
Ownerships: SOEs 0.832 *** (0.32)
POEs 0.704 (0.46)
Foreign participants: 0.694 ** (0.32)
Consultants
Consultants and credit 0.974 *** (0.28)
buyers
Project developers 1.402 ** (0.67)
GDP per capita 1.262 ** (0.50)
GDP growth -0.222 ** (0.09)
Similar -0.369 ** (0.19)
R&D 0.513 * (0.31)
Local technology -7.785 * (4.09)
capability
Similar in province -0.136 (0.13)
Pseudo [R.sub.2] 0.3658
Correctly classified 82.80%
Notes: Standard errors are in parentheses; N = 500; * p < 0 .10; **
p < 0.05; *** p < 0 .01.
Table 9. Results of logistic regression analyses, with Wind projects
only
Variables Model 6
Constant 4.372 (5.70)
Project sizes 0.262 (0.40)
Small-scale projects -0.076 (1.23)
Ownerships: SOEs 0.565 (0.46)
POEs -0.780 (1.02)
Foreign participants: Consultants -0.210 (0.72)
Consultants and credit buyers 1.175 *** (0.44)
Project developers 0.406 (1.09)
GDP per capita 1.328 * (0.73)
GDP growth -0.290 * (0.15)
Similar -1.238 ** (0.55)
R&D -0.036 (0.56)
Local technology capability -4.537 (6.09)
Similar in province
Pseudo [R.sup.2] 0.2711
Correctly classified 77.95%
Variables Model 7
Constant -2.777 (4.82)
Project sizes 0.311 (0.40)
Small-scale projects -0.266 (1.24)
Ownerships: SOEs 0.681 (0.45)
POEs -0.799 (1.02)
Foreign participants: Consultants -0.248 (0.71)
Consultants and credit buyers 0.921 ** (0.42)
Project developers 0.375 (1.11)
GDP per capita 1.668 ** (0.82)
GDP growth -0.248 (0.15)
Similar
R&D -0.069 (0.54)
Local technology capability -6.871 (6.45)
Similar in province -0.308 (0.28)
Pseudo [R.sup.2] 0.2519
Correctly classified 74.10%
Variables Model 8
Constant 5.159 (6.27)
Project sizes 0.240 (0.41)
Small-scale projects -0.056 (1.22)
Ownerships: SOEs 0.562 (0.46)
POEs -0.768 (1.01)
Foreign participants: Consultants -0.219 (0.72)
Consultants and credit buyers 1.168 *** (0.44)
Project developers 0.419 (1.09)
GDP per capita 1.198 (0.85)
GDP growth -0.299 * (0.16)
Similar -1.358 ** (0.68)
R&D 0.010 (0.59)
Local technology capability -3.692 (6.70)
Similar in province 0.109 (0.36)
Pseudo [R.sup.2] 0.2715
Correctly classified 78.59%
Notes: Standard errors are in parentheses; N = 500; * p < 0 .10; **
p < 0.05; *** p < 0 .01.
