metadata: authors: 0: author: Dinar, Ariel
1: author: Mahfuzur, Shaikh
2: author: Larson, Donald
3: author: Ambrosi, Philippe
count: World
docna: 0: docna: Factors affecting levels of international
            cooperation in carbon abatement projects
docty: Policy Research Working Paper
subtopic: Science of Climate Change,Climate Change and Health,Climate Change and Environment,Rural and Renewable Energy,International Trade and Trade Rules,Energy Demand,Energy and Environment,Energy and Mining,Renewable Energy,Global Environment
teratopic: Energy,Macroeconomics and Economic Growth,Environment
abstracts: cdata!: The Clean Development Mechanism, a
            provision of The Kyoto Protocol, allows countries that have
            pledged to reduce their greenhouse gas emissions to gain
            credit toward their treaty obligations by investing in
            projects located in developing (host) countries. Such
            projects are expected to benefit both parties by providing
            low-cost abatement opportunities for the investor-country,
            while facilitating capital and technology flows to the host
            country. This paper analyzes the Clean Development Mechanism
            market, emphasizing the cooperation aspects between host and
            investor countries.  The analysis uses a dichotomous
            (yes/no) variable and three continuous variants to measure
            the level of cooperation, namely the number of joint
            projects, the volume of carbon dioxide abatement, and the
            volume of investment in the projects.  The results suggest
            that economic development, institutional development, the
            energy structure of the economies, the level of country
            vulnerability to various climate change effects, and the
            state of international relations between the host and
            investor countries are good predictors of the level of
            cooperation in Clean Development Mechanism projects. The
            main policy conclusions include the importance of
            simplifying the project regulation/clearance cycle;
            improving the governance structure host and investor
            countries; and strengthening trade or other long-term
            economic activities that engage the countries.
repnme: repnme: Factors affecting levels of international
            cooperation in carbon abatement projects
display_title: Factors affecting levels of
            international cooperation in carbon abatement projects
listing_relative_url: /research/2008/11/10049439/factors-affecting-levels-international-cooperation-carbon-abatement-projects
topicv3: Renewable Energy m1326170 525,Energy and Extractives m1326595 507,Climate Change m1326829 567,Trade m1326924 698,Environment, Natural Resources and Blue
            Economy m1327391 566
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Energy,Macroeconomics and Economic Growth,Environment
no_of_pages: 40
model_extractions: WPS4786 Policy ReseaRch WoRking PaPeR 4786 Factors Affecting Levels of International Cooperation in Carbon Abatement Projects _Ariel Dinar Shaikh Mahfuzur Rahman Donald Larson Philippe Ambrosi_ The World Bank Development Research Group Sustainable Rural and Urban Development Team November 2008
Policy ReseaRch WoRking PaPeR 4786

# **Abstract**

The Clean Development Mechanism, a provision of The Kyoto Protocol, allows countries that have pledged to reduce their greenhouse gas emissions to gain credit toward their treaty obligations by investing in projects located in developing (host) countries. Such projects are expected to benefit both parties by providing low-cost abatement opportunities for the investor-country, while facilitating capital and technology flows to the host country. This paper analyzes the Clean Development Mechanism market, emphasizing the cooperation aspects between host and investor countries. The analysis uses a dichotomous (yes/no) variable and three continuous variants to measure the level of cooperation, namely the number of joint projects, the volume of carbon dioxide abatement, and the volume of investment in the projects. The results suggest that economic development, institutional development, the energy structure of the economies, the level of country vulnerability to various climate change effects, and the state of international relations between the host and investor countries are good predictors of the level of cooperation in Clean Development Mechanism projects. The main policy conclusions include the importance of simplifying the project regulation/clearance cycle; improving the governance structure host and investor countries; and strengthening trade or other long-term economic activities that engage the countries.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to mainstream research on climate change. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at adinar@ucr.edu, shaikh.m.rahmnan@ ttu.edu, dlarson@worldbank.org, pambrosi@WorldBank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **FACTORS AFFECTING LEVELS OF INTERNATIONAL COOPERATION IN CARBON ABATEMENT PROJECTS**

Ariel Dinar<sup>1</sup> , Shaikh Mahfuzur Rahman<sup>2</sup> , Donald Larson<sup>3</sup> , and Philippe Ambrosi<sup>4</sup>

> 1 University of California, Riverside; at the time of this research World Bank, Development Economics Research Group.<sup>2</sup> Texas Tech University, Lubbock, and consultant to the World Bank.<sup>3</sup> World Bank, Development Economics Research Group.<sup>4</sup> World Bank, Carbon Finance Unit. This paper is one of the products of the study “Local Actions for Global Impact Inference of the Role of the Flexible Mechanisms in Reducing GHGs” conducted in the World Bank DECRG-RU that was funded by the Knowledge for Change Trust Fund. We thank Rama Chandra Reddy for useful comments.
# **1. INTRODUCTION**

The Clean Development Mechanism (CDM)<sup>2</sup> has come a long way since its launch in Marrakech in 2001. As of December 31, 2007, 2,966 CDM projects have been submitted to the CDM Board for validation (UNFCCC, 2008). It is expected that by 2012, the transition year when the Kyoto Protocol terminates and a new protocol is reckoned to be in place, the CDM Board will have issued Certified Emission Reductions (CERs) equivalent to 800-1,150 million tons of CO2 (UNDP, 2006:11). Of these, 42 percent represent partnerships between developed and developing countries.<sup>3</sup> This would be an impressive achievement, and it is believed and expected that the interest in CDM projects by governments and private sector entrepreneurs will extend beyond 2012. While the future of CDM depends on political decisions, it is also affected by the performance of that market, and by the fulfillment of the basic CDM objectives, namely greenhouse gas emission reductions that can be credited to the investor country and at the same time support sustainable development in the host country, as is discussed below. Thus, CDM projects provide a-priori incentives for cooperation between host and investor countries.

The potential of CDM has been studied by different disciplines, addressing different aspects such as efficiency, sustainability, institutions, and development. The views regarding the contribution of CDM towards the development objective are mixed. In a review of the literature on the (potential) impact of the CDM to sustainable development Olsen (2007:84) suggests that “...left to the market forces, the CDM does not significantly contribute to sustainable development.” Sirohi (2007) arrives at a similar conclusion for the case of CDM project portfolio in India, but sees the prospect of CDM in developing the energy sector. Sirohi states that (p. 105) “The renewable energy projects may not be able to make any observable decline in the incidence of poverty ... but they have immense importance in fostering development of energy resources in India” (that have indirect contribution to poverty reduction and growth). A similar conclusion is reached by Da Cunha et al. (2007) who attempt to assess the potential contribution of the CDM towards the achievement of MDGs in Brazil’s remote regions. Their conclusion is that under certain conditions, such as improved market accessibility and synergy

> 2 According to Article 12 of the Kyoto Protocol, CDM is designed as a regulated market with the premise of cooperation among the parties to the convention and the resulting anticipated payoffs to mitigation efforts involving Annex I and Non-Annex I countries.

> 3 The remaining projects are presented to the Board “unilaterally” by the host country and do not necessarily involve foreign investors.

2
impact of other rural development policies, CDM projects could help minimize distortions in the access to clean energy resources in remote rural areas. However, Sutter and Parreno (2007:75) conclude that “... there are currently no UNFCCC registered CDM projects that are likely to fulfill the ... twofold objectives ... of emission reduction and contributing to sustainable development.” Despite the above skepticism, the number of CDM projects submitted to the Board has grown at an increased rate during the 5 years since this program started (Figure 1). Are there additional attributes that make the CDM attractive to both host and investor countries? From the perspective of the host countries (Annex I), investment in CDM projects can be seen as a means of development assistance, with all derived benefits to them. Thus, certain development attributes may play important roles in explaining levels of investment in CDM projects. Moreover, with the expectation that the demand for CERs would further increase beyond 2012, it would make sense to examine the factors that determine the location and extent of investment in CDM projects by investor countries. However, the CDM may face a loss of interest from carbon investors given the lead time required in developing and implementing a project. Without a clear signal from policy-makers on post-2012 prospects, the CDM may experience a significant slow down of activity in the near future (Cosbey et al., 2005).

With a development objective, CDM trends can be explained, using several supply-side variables such as export promotion, political hegemony, donor budget allocations, and donor internal politics, which are mainly political economy variables (Michaelowa and Michaelowa, 2007). But, CDM trends can also be explained using demand side considerations, where the developing countries formulate policies to Annex B countries’ investment in CDM projects (Brechet and Lussis, 2006).

The literature summarizing the various carbon investment mechanisms and the development of Activities Implemented Jointly (AIJ), Joint Implementation (JI), and CDM projects over time is quite large. Larson and Breustedt (forthcoming) provide a detailed history and background work related to the pilot program of (AIJ) between 1992 and 2001. The history, status, and prospects of the CDM are articulated in detail in Lecocq and Ambrosi (2007) and in the references they list. And Larson et al. (1998) review carbon market policies and their recent development. These sources review the extensive literature on the nature and functioning of 3
CDM and the role regulation plays in its evolution. The literature in these sources allow comprehension of the relationship between global environmental agreement on climate and its actual operation in the real world and its relationship to the rest of the economy in terms of promoting low carbon growth.

In this paper we focus on identifying factors that affect levels of cooperation in carbon abatement projects between governments of Annex B (hereafter investor) countries and developing (hereafter host) countries. We focus on examining bilateral and multilateral CDM projects that were submitted to UNFCCC between 2003-2007 in order to provide useful assessment of policy interventions for possible enhancement and extension of the CDM mechanism beyond 2012. We are interested in understanding the grouping of countries in the CDM market, or in other words, what explains cooperation and cooperation level in CDM between certain dyads of countries. We model level of cooperation in CDM to explain incidence in projects between hosts and investor countries, and to understand why certain countries are heavily involved while others are not. We use also different measures of cooperation levels, including the number of joint CDM projects, the amount of CO2 abatement in the CDM projects<sup>4</sup> and the level of investment in CDM projects.

The remainder of the paper is structured as follows. The next section provides the conceptual framework used for determining the cooperation level in joint CDM investment projects among host and investor countries. Section 3 presents the data sources, the assumptions and procedures used in variable construction, and the empirical specifications of the estimated relationships. The hypotheses regarding the relationship between the various variables in their different estimation contexts are spelled in Section 4. Estimation procedures and empirical results are presented in Section 5. Finally, Section 6 summarizes, highlights caveats, concludes, and addresses environmental and development policy implications.

# **2. CONCEPTUAL FRAMEWORK**

We look at CDM joint investment projects between two countries as a cooperative investment decision that is affected by both domestic factors and by economic and political interactions between these countries. We borrow from the literature on foreign direct investment (FDI), as

> 4 Actually, investor country’s ultimate interest is in project CER credits, while host country interest is in the investment in the project in their countries and the likely multiplier effect.  Both interest lead to the CDM cooperation that we observe.

4
CDM is a subset of FDI. FDI stock or flow represent the level of cooperation among dyad states. We also rely on theories in the literature on international cooperation developed in the international relations and international economics literature, using variables that explain the level of other interactions between dyads of countries, and applied to the CDM market. In the remainder of the section we relate variables within these categories.

In general, the determinants of cooperation relate to the bilateral characteristics of the pairing – for example trade, or unilateral characteristics of the investor and the host – for example characteristics of their respective energy sectors. With this is mind, cooperation between a given country dyad _i_ and _h_ can be written as:

[1] where C is level of cooperation, D represents the bilateral characteristics of a given host-investor pair and _<u>I</u>_ and _<u>H</u>_ are vectors of variables strictly attributable to investor and host country _i_ and _h,_ respectively. We expect some interaction effects between _i_ and _h_ because certain variables in host countries are linked to considerations by investor countries.

The CDM raised expectations as it seeks to bridge technology gap between developing and developed countries. The Kyoto mechanisms are expected to serve as feedback loop for reducing economic and financial burden of host countries, which is expected to strongly reflect in the fast growing non-OECD countries. Furthermore, CDM is expected to evolve into a form of FDI with opportunities for collaboration for project developers in investor countries to interact with host country investors (UNEP/GRID-Arendal, 2000).

In order to analyze the level of engagement in CDM projects by investor and host countries, we apply the theory used for explaining FDI flows and stocks. Dunning (2002), Nonnemberg (2004), Nunnenkamp (2002), and Siegel et al. (2008) provide a wide review of theories used to support the empirical literature, linking variables in host countries and considerations by investor countries. While we include in this section a wide list of variables based on previous relevant work, we will restrict the set of variables we use in the empirical estimation of the level of cooperation to only variables that apply to both host and investor countries.

5
Several factors enhance investor and host country interest in CDM projects. According to Dunning (1996) the desire of firms in investor countries to seek foreign investments is affected by tax policy in the investor country; transaction cost in investor country and in the host country; and the size and status of the market for the particular product/technology transferred (for discussion on additional variables see Michaelowa and Michaelowa, 2007). In the case of the CDM market, one should also add the opportunity cost of meeting the Kyoto CO2 reduction quota in the investor country. This depends on the country economy structure and growth trajectory, the energy dependency of the economy, the level of CO2 emission of the economy, and the clean energy resources it possesses (Velasco, 2007; UNEP/GRID-Arendal, 2000).

Vulnerability to climate change and natural disasters an incentive for needed related actions has been well documented in the literature (e.g., Tsur and Zemel, 2008). Agents that that face harsher environmental conditions would be early adopters of technologies, implement policies, and look for partners to sign treaties to ease their situation, in the case of states sharing a water body (Dinar 2009).

But cooperation in the CDM market is not only dependent on country-specific variables. Joint investment in CDM projects, is a venture between international partners. Higher levels of joint investment indicate a higher degree of cooperation and no investment suggests no cooperation. Therefore, it is conceivable that international relations theories are likely to play some role alongside the profit maximization motives of the parties as in any international cooperation such as trade and FDI. We follow the literature on trade and FDI in the international conflict and cooperation literature (e.g., Polacheck et al., 2007; Dunning 2002). Below we provide the rationale behind each of the sets of the additional variables we include in the cooperation equation (e.g., trade and governance level).

# _International Trade_

Several empirical studies argue that the extent of trade between countries provides an appropriate measure of their overall relations. Trade is a measure for both openness of a country to the global economy and the interaction between countries. The international relations literature’s assessment of the link between trade, conflict, and cooperation has been quite mixed. On the one hand, there has been the general claim that increased trade between countries reduces incidents of militarized conflict between them and promotes peace (Russett and Oneal, 2001). The fear of 6
losing the gains from trade deters conflict. Along the same lines, it has been argued that nations with cooperative political relations will engage in more trade, while conflictive nations are expected to trade less (Pollins, 1989). On the other hand, there has been the conjecture that high international trade, interdependence, and conflict are positively related (Waltz, 1979). Higher interdependence increases frictions among the countries, and therefore may lead to conflict. Barbieri (2002:121), for example, finds that the higher the interdependence and trade between countries the higher the likelihood of militarized conflict.

International trade also acts as a contract enforcing mechanism. Stein (2003), who argues that trade increases the likelihood of disputes between countries, also claims that it provides countries with an opportunity to resolve them at a lower level of international conflict. In essence, the coercive potential of trade reduces conflict, the occurrence of political crisis, and the need for militarized actions.

The above examination of the literature leads us to suppose that the level of relations among countries, measured by the extent of trade among them, is an appropriate measure for assessing the likelihood of cooperation negotiations (Neumayer, 2002). In particular, these studies suggest that the likelihood of a CDM will be relatively higher in the case of better or stronger relations among countries, and will be relatively lower in the case of poorer or weaker relations among countries (Sigman, 2004).

# _Enabling Environment: Governance, Regulations, Business Climate_

When considering international cooperation, in general, and international investment in specific projects, in particular, domestic institutions may play a major role in either facilitating or inhibiting success of the cooperation-project in question. Dinar et al. (2007) suggest that political, legal, and economic institutions—enabling environment—often sustain the functioning of the state both domestically and internationally. It reflects not only the state’s interest in the project but also its ability to enter into, and honor, an investment agreement, which may require financial investments and costs (Congleton 1992: 412-413). The political stability and enabling institutions of a given state are, therefore, a principal mode to judge the viability of its domestic institutions, its general inclination to negotiate a project agreement and its capacity to support the project.

7
Politically unstable countries have less institutional capacity to carry a project, and more politically stable countries may in turn have little interest in cooperative ventures with those. Similarly, investments are not secure and property rights poorly defined in unstable countries characterized by political turmoil (Deacon 1994). Participating in an agreement requires both competence (also in terms of appropriate investment climate and supporting regulation) and stability inherent in a particular polity, which will in turn be able to honor the signed project agreement (Young 1989:365; Young 1982:287).

# _Categories of Variables to Be Used in the Analysis_

Based on the discussion in the literature, for the right hand side of the estimated relationship we will construct variables that represent variants of the level of economic activity; variables that represent the energy dependency; variables that measure the level of renewable energy endowments; variables that measure the cost of transactions; variables that measure vulnerability to climate change; variables that measure governance level; and variables that measure international trade among the host and investor countries. In addition, for level of cooperation we will calculate measures CDM incident and variables related to the CDM activities.

# **3. DATA DESCRIPTION, VARIABLE CONSTRUCTION, AND EMPIRICAL SPECIFICATION**

Data used in this study are derived from several different sources. Collected data are then combined and transformed for the purpose of empirical analysis. In particular, individual CDM project-level data are combined with corresponding host and investor country-specific macroeconomic and environmental variables, and alternative empirical models are specified.

# **_Description of Data and Variable Construction for the Empirical Analysis_**

A dataset consisting of all CDM projects that have been sent to UNFCCC for validation up until December 31, 2007 is obtained from the CDM/JI Pipeline Analysis and Database of the United Nations Environment Programme (UNEP) Risoe Center (2007). From December 2003 to December 2007, 2,966 CDM projects were sent to UNFCCC for validation. The dataset provides detailed information about each individual CDM project. That information includes project name, type, and current status, host country, expected emission reduction (ktCO2 per year and total CO2 reduction up until 2012 and 2030), credit buyers, potential energy outputs, etc. The projects in the CDM pipeline are at various stages in the projects cycle. Of the nearly 3,000 8
projects, 1,966 are at validation, 806 are already registered, 42 were rejected (36) or withdrawn (6) and the rest are in various stages of evaluation. The dataset includes 1,246 (42%) unilateral projects, mainly in India, China, and Brazil, and 1,592 (54%) bilateral projects. The rest (128) are multilateral projects with 3-9 investor countries. Because our focus is on cooperation, unilateral projects are excluded from the analysis. We also drop projects that were withdrawn and four projects that include 5 or more country partners. For the remaining multilateral projects, project activities are equally divided and attributed to all plausible dyads. For example, for a CDM project with _n_ investor countries, _n_ separate dyads are formed with the same host. Amount of carbon abatement and capital costs are then equally divided and attributed to _n_ investor countries in the dyads. However, one project is accounted for each of the _n_ dyads because the single project is indivisible. We keep the projects that were subsequently rejected by the CDM board because they indicate propensity to cooperate, which is the subject of this paper. For a subset of projects (1199), we also have information about capital costs (Seres 2007).<sup>5</sup> For the empirical model, we construct four measures of cooperation from this basic data for pairs of countries i and h. We first use a dichotomous variable to distinguish between pairs that do have joint CDM projects and pairs that do not have any project at all. The variable CDM Incidence ( _CDMI_ ) will get a value of 0 if there is not CDM activity among the countries and a value of 1 if the number of CDM projects is greater than 0. Then we measure the cooperation level, using three variables, namely, Number of Projects ( _NPRJ_ ), Total CO2 Abatement ( _Total CO2 Abatement_ ) in million tones of Certified Emissions Reduction (CER) of CO2 equivalent and Volume of Investment ( _VINV_ ) in million constant US dollars. When direct information on project costs is missing (we have only 1199 projects with direct investment cost data), we categorized projects by type (9 types) and size (2 sizes: small and big) and calculated for each and average investment cost value. We then used average investment cost to extrapolate to projects for which we did not have investment cost data.

As discussed in the context of equation 1, we use both pair-wise and country characteristics as determinants in our statistical models. For the bilateral measure, we use the level of bilateral trade among the countries ( _TRD_ ). We also include five additional country characteristics for both host and investor: i) the economic development of the countries

> 5 See Appendix 1 for details.

9
(measured in either _GDP,_ or _ENR_ ), ii) the energy sources status ( _REN_ ), iii) climate vulnerabilities of the countries ( _IVUL_ ), iv) governance level of the countries ( _GOVR_ ), and v) Ease of Doing Business in the countries ( _EDB_ ).

While we use only six groups of right hand side variables, we still need to calculate some of them, and the dependent variable, using additional variables. Therefore, we report here all data we have used and which variables have used which variables in their construction. Annual GDP (both in current and 2000 constant US$ absolute and per capita terms), energy and electricity production, import, and consumption (both in absolute and per capita terms), and the volume of CO2 emissions (in terms of total kiloton of oil equivalent, per capita CO2, and CO2 per dollar of 2000 PPP GDP) data for all countries of the world during 1960-2003 are obtained from the World Development Indicators (WDI, World Bank, 2007).<sup>6</sup> In addition, country level estimates of total energy available from nonrenewable sources (e.g., coal, oil, gas, oil shale, and bitumen) and annual energy available from renewable (e.g., solar, onshore and offshore wind, hydro, geothermal, and biofuels) sources are obtained from Buys et al (2007).

These data are used to construct variables reflecting economic development, available energy resources, and emission intensity of the host and investor countries. Economic development variables include _Total GDP_ ( _GDP_TOT_ ), _Average GDP_ ( _GDP_AVG_ ), and _GDP per Capita_ ( _GDP_PC_ ). Energy resources of each country are constructed by adding up potential non-renewable and renewable energy resources and are represented by two variables: _Nonrenewable Energy_ ( _NREN_ ) and _Renewable Energy_ ( _REN_ ). In addition, two separate variables are constructed to reflect intensity of energy use in each country: _Avr Annual Energy Use_ ( _ENR_AVG_ ) and _Annual per Capita Energy Use_ ( _ENR_PC_ ). See Appendix 3 for further detailed description of the variables considered for this study and their specification.

Buys et al (2007) provide measures for countries’ vulnerability to climate change and emissions reduction mandates. They first construct individual indices for alternative sources of vulnerability and then calculate composite index values by categorizing the sources of vulnerability into two major groups: impact and source vulnerabilities. We use the first one in our empirical analysis. Impact vulnerability refers to the country’s ability to sustain climate change impacts such as weather damage and sea level rise. The quantitative score (scale of 1 to

> 6 Data on CO2 per dollar of 2000 PPP GDP were not available prior to 1975.

10
100) of the _Impact Vulnerability_ ( _IVUL_ ) for the host and investor countries of the CDM projects are obtained from Buys et al (2007) and used in our empirical analysis.

Estimates of six dimensions of governance of the host and investor countries are obtained from Kaufmann et al (2007). The six dimensions of governance are: Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. Kaufmann et al estimate these dimensions of governance of 212 countries during the period of 1996-2006 based on several hundred individual variables measuring the perceptions of governance reflecting the views of public sector, private sector and NGO experts, as well as thousands of citizen and firm survey respondents worldwide. Kaufmann et al (2007) aggregate these individual measures into the above six categories, using a statistical methodology known as unobserved components model. In particular, they first rescale the individual indicators from each underlying source in order to make them comparable across data sources. Then they construct a weighted average of each of these rescaled data sources to construct the aggregate indicators of governance. The weights they assign to each data sources are based on the estimates of the precision of each source that are produced by the unobserved components model. Combining the six governance measures of Kaufmann et al. (2007), a _Governance_ variable ( _GOVR_ ) is constructed, using a Principal Component Analysis (PCA) that reflects overall governance level of the host and investor countries of each of the CDM projects.

We used the ease of doing business (EDB) indicators as a proxy for the transaction costs associated with the CDM projects implementation. EDB indicators, comparing business regulations and protection of property rights across 178 countries and over time are obtained from World Bank (2008). The EDB index ranks economies from 1 to 178 by measuring regulations affecting 10 stages of a business’s life: starting a business, dealing with licenses, employing workers, registering property, getting credit, protecting investors, paying taxes, trading across borders, enforcing contracts, and closing a business. The index reflects the ranking of simple average of country percentile rankings on each of the 10 topics, with a higher rank indicating less favorable business atmosphere. The ranking on each of the 10 topics is the simple average of the percentile ranking on its component indicators.<sup>7</sup> The rankings remain almost the

> 7 The choice of aggregation method has little influence on the ranking. More complex aggregation methods such as principal components and unobserved components yield nearly identical rankings (Djankov and others, 2005).

11
same over the years, from 2003 to 2007. However, a simple average of the rankings over the years is used to calculate the overall _Ease of Doing Business_ ( _EDB_ ) in each country.

Data on annual bilateral trade between all countries in the world are obtained from International Monetary Fund (IMF, 2007), for the period of 1960-2003. Using the sum of the volumes of bilateral trade and _GDP_ (in current US$) of the host and investor countries, two alternative trade variables, _Trade_ 1 and _Trade_ 2 ( _TRD_ 1 and _TRD_ 2) are constructed following Dinar et al (2007).<sup>8</sup> The first trade variable ( _TRD_ 1) expresses total trade (the sum of the volume of bilateral imports and exports) between the host and investor countries as a fraction of the sum of the countries’ GDPs. The second trade variable ( _TRD_ 2) measures the total trade (the sum of the volume of bilateral imports and exports) between the host and investor countries as a fraction of their trade with the rest of the world.<sup>9</sup> _TRD_ 1 is referred to as trade importance and _TRD_ 2 is referred to as trade dependency. For a group of _N_ ≥2 countries the _TRD_ 1 and _TRD_ 2 ( _Trade_ 1 and _Trade_ 2) are: Where _IMP_ and _EXP_ are import and export, respectively, _t_ is year, _w_ is the rest of the world, not including any country _j_ and _k_ in _N_ .

> 8 The definitions of these variables are available in Dinar et al (2007). Since the IMF trade data are in current $US; country-level GDP in current $US (collected from the WDI of the World Bank) are used to construct the trade variables.

> 9 For multiple investor countries, _TRD_ 1 expresses trade as the sum of total volume of exports and imports between the host country and each investor country as a fraction of the sum of GDP of each of the country involved in the CDM project. On the other hand, _TRD_ 2 measures the total trade (the sum of the volume of bilateral imports and exports) between the host and each investor country as a fraction of their trade with the rest of the world.

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# **4. HYPOTHESES**

We selected a particular set of variables for inclusion in the regression analysis. These variables were selected because, at least theoretically, each can be attributed to explaining both investor and host countries’ desire to cooperate. From the above discussion and based on previous work on FDI and AIJ determinants that resemble behavioral patterns similar to those of CDM (e.g., Agarwal, 1980; Dunning 2002; Nonnemberg et al., 2005; Nonnenkamp 2002; and Larson and Breustedi, 2007), it is reasonable to hypothesize that, while all other variables are held constant, CDM incident and the level of cooperation between host and investor countries will increase (decrease) with higher (lower) level of economic development; will increase (decrease) with higher (lower) levels of energy use, and thus emission intensities, of its economy. We hypothesize that for an investor country the level of cooperation will decrease (increase) and for host country it will increase (decrease) with more renewable energy resources available in that country; And the more (less) vulnerable to climate change the economies are, the higher (lower) will the cooperation be.

Additional variables included in the empirical models are trade, business environment, and governance. Based on our conceptual framework we expect that CDM incidence and the level of cooperation between host and investor countries will increase (decrease) as trade widens (shrinks); will decrease (increase) as transaction cost of doing business are higher (lower); will increase (decrease) as governance level in the country increases (decreases); will increase (decrease) as vulnerability level in the country increases (decreases); will increase (decrease) as development level in the country increases (decreases); and will decrease (increase) as renewable energy resource levels in the country are higher (lower).

To be specific, we expect ∂ _Ci_ ∂ _TRD_ > 0 , ∂ _Ci_ ∂ _GOVR_ > 0 , ∂ _Ci_ ∂ _IVUL_ > 0 , ∂ _Ci_ ∂ _GDP_ > 0 , ∂ _Ci_ ∂ _EDB_ < 0 , ∂ _Ci_ ∂ _REN_ < 0 , where _Ci_ is the cooperation variant ( _i_ = _CDMI_ , _NPRJ_ , _VINV_ , _TCO2_ ).

This paper focuses on a relatively narrow set of hypotheses, mainly related to the cooperation aspect of CDM. Additional hypotheses could be about the growth pattern of CDM and whether or not it is affected by the regulation of the carbon product. For example, does growth of CDM follow the same trend as the international trade/FDI between countries or does it follow a curtailed growth path considering the strong role of regulation in approving the carbon 13
assets for trade in CDM. This and similar hypotheses are very relevant and will be addressed in a different paper.

# **5. ESTIMATION PROCEDURES AND RESULTS**<sup>**10**</sup>

Depending on the characteristics of the dependent variables, appropriate econometric procedures are applied to estimate the incidence and level of CDM cooperation according to the models specified above. The incidence of host and investor countries in CDM activity is represented by a dichotomous variable indicating whether a particular dyad has any CDM activity (the dependent variable is set equal to 1 for those dyads having CDM activity and equal to 0 for those without any CDM activity). The probability that host and investor countries will jointly engage in CDM activity is estimated by regressing the characteristics of the host and investor countries and dyads on the dichotomous variable. Both logit and probit models are employed to estimate the probability.

As described earlier, the level of CDM cooperation between host and investor countries is represented by three alternative measures: the number of CDM projects, amount of CO2 abatement, and volume of capital investments. When the level of cooperation (the dependent variable) is measured by the number of CDM projects (i.e., _NPRJ_ ), Poisson regression models are estimated. In this case, the _i_ th observation on _NPRJ_ is assumed to be a conditional draw from a Poisson distribution, the natural logarithm of the mean of which is a linear function of explanatory variables. Maximum likelihood estimates are calculated using the density function of the Poisson distribution with equal mean and variance.

Dependent variables representing total carbon abatement ( _TCO2_ ) and volume of investment ( _VINV_ ) have observations at 0, corresponding to dyads that do not cooperate in CDM activity. The OLS estimates based on such a censored sample are likely to be biased. Since values of explanatory variables are also available for the countries not participating in the CDM activity, a tobit model is appropriate that determine both the probability of participating in the CDM activity and the levels of _TCO2_ and _VINV_ when these are positive. (See Appendix 2 for definitions of variables used in the regression analyses.) Several specifications of the Poisson and tobit models are examined.

> 10 See Appendix 2 for variable definition.

14
# **_Descriptive Statistics Results_**

We start by providing a descriptive statistics of project-level variables. Note that we created more variables than we use in the regression analysis because of the interest these variables create. Those variables are not given acronyms. We then move to describe country-level and project-related variables, both for host and investor countries. Finally we provide descriptive statistics of main variables for the dyad-level variables.<sup>11</sup>

# _Project Level_

Table 2 includes the descriptive statistics for variables in the project-level dataset. In this table we include all reported projects, including unilateral ones. Some additional descriptive results may be of interest. There are 2966 projects that have been submitted to the UNFCCC. Of these, 1966 are at validation, 806 are already registered, 42 were rejected (36) or withdrawn (6) and the rest are in various stages of evaluation. The dataset includes 1246 (42%) unilateral projects, mainly in India, China, and Brazil, and 1592 (54%) bilateral projects. The rest (128) are multilateral projects with 3-9 participating countries. We dropped the projects that were withdrawn and the projects that include 5 or more country partners (4). We kept the projects that were rejected because they indicate propensity to cooperate, which is the subject of this paper. Therefore our dataset includes 2956 project observations. Eighty two percent of the projects are associated with power generation; 37 percent create hydro, solar and wind energy; and 29 percent create energy from agricultural biomass. Project life ranges between 7 and 30 years. Mean investment per 1,000 tons of CO2 abatement is $38,004. Mean electricity generation capacity per project is 31.2 Mwh. And mean per annum abatement of CO2 is 140.6 kiloton of oil equivalent.

# _Country Level_

In the dataset there are 170 host countries (including island countries) and 36 investor countries, many of which are involved in CDM investment. We present the data separately for the host and for the investor countries, in Tables 3 and 4, respectively.

> 11 It is important to note that project-level and country-level descriptive statistics results are reported for all available observation in each category while dyad-level descriptive statistics are reported only for the observations included in the regression analysis.

15
The mean number of projects per host and investor country is 17 and 54, respectively, but it varies between 0 and 960 for host countries and 0 and 720 for investor countries, (Note that India and China host many projects that are funded by local investors.) Mean values of governance in host countries are quite low with a mean of -2.49. Source vulnerability for host and investor countries are quite similar (56.25 and 55.93 respectively), but host countries are much more vulnerable than investor countries 49.95 and 27.50, respectively). Some of the variables deserve a mention. First, mean sample values suggest that CO2 per capita abatement levels of host countries are about 50 percent of investor country levels. A similar value was found for GDP per capita and energy per capita. Finally, mean per capita values for nonrenewable energy resources are about 400 times higher for host countries, while they are only 14 times richer in renewable energy resources per capita than investor countries.

# _Dyad Level_

With 34 investor countries (excluding Australia and the USA) and 175 host countries, there are 5,950 plausible host-investor pairs.<sup>12</sup> Only 195 of the host-investor pairs have CDM project activity, while 46 host countries have unilateral projects. Unilateral CDM projects were removed from the dyad-level analysis. Dyads without any CDM projects can be regarded as the non-cooperation dyads. Because we use 3 different left-hand side variables the missing values problem results in different sample size for each dependent variable and making it harder to compare the results. Therefore, we use only 2,771 dyad-level observations that allow comparisons among the various dependent variables.

The descriptive statistics of dyad-based variables used in our analysis are presented in Table 5. _Annual CO2_ abatement level values are nearly 113 kilotons, ranging between 0 and nearly 89,793 kilotons. _Annual Volume of Investment_ cost ( _VINV_YR_ ) values are $19.6 million, and mean trade ratio values are 0.00037 and 0.00090 for _TRD1_ and _TRD2_ respectively. The independent variables are presented for host and investor country, separately. The interaction variables (host × investor) are not presented. Several results are worth mentioning. First, we note main differences in the various variables between host and investor countries in the dyads.

> 12 Australia and the USA are excluded from the dataset because Australia did not ratify Kyoto until 2005 and the USA is yet to ratify Kyoto. Few host countries are also omitted from the dataset because they are very small and country-level data are not available for those.  (Our descriptive statistics in Table 3 includes Australia which brings the number of Dyads to 6125.)

16
Investor countries have higher _Average GDP_ , higher _Average Annual Energy Use_ , better ranking of _Ease of Doing Business_ , better _Governance_ , and lower _Impact Vulnerability._ However, host countries have significantly much higher endowments of _Renewable Energy_ than do investor countries. As was hypothesized, earlier, we expect that this difference is attractive to investor countries.

# **_Results for the Cooperation Estimates_**

To reduce complications associated with having the dyad as the observation unit, our variables capture the bilateral nature of the dyad. All variables incorporate values for the two countries in the dyad that are engaged in the CDM activity. In the case of all other variables except Trade, we use values for host, for investor, and the interaction between investor and the host country. In the case of Trade, we have two variants and both incorporate the values of the host and investor countries into the variable.

We present results for a set of 2,771 country dyads that have all the needed data in all the regression specifications. We also report, in Appendix 3, the results of regressions that have a larger number of observations due to availability of data. All our equation estimates (Tables 6-9) have significant fit with Likelihood Ratio values that are significant at the 1 percent level in all estimates.

Results are presented in Table 6 for the dichotomous _CDM Incidence_ ( _CDMI_ ). For both the logit (column 1) and probit (column 2) estimation procedures results are as expected. Higher economic development levels measured as _Average annual energy use_ ( _ENR_AVG_ ) of both host and investor countries are significant and positive; _Governance_ level ( _GOVR_ ) and impact vulnerability ( _IVUL_ ) are both significant and positive. The trade variable ( _TRD_ 1) is positive and significant. The _Ease of doing business_ ( _EDB_ ) behaves also as expected with a negative and significant sign assigned to the host country and an insignificant coefficient assigned to the investor country. The _Renewable Energy_ ( _REN_ ) endowment of the host country has a positive and significant coefficient and that of the investor country has a negative and significant one. In column 3 of Table 6 we present also the marginal effect calculated at the sample mean as the change in the probability for an infinitesimal change in each independent. The marginal effect of bilateral trade has by far the highest impact, where a 1 percent change in dyad trade increases the 17
likelihood of CDM incidence by close to 5%. All other independent variables are much less effective but among them governance has the highest effect.

Results are presented in tables 7, 8, and 9 for the 3 variants of level of cooperation, namely number of projects ( _NPRJ_ ), CO2 abatement ( _TCO2_ ) and volume of investment ( _VINV_ ), respectively. Most interaction terms are statistically insignificant. In all functional forms, all country economic development variables ( _GDP_AVG_ ; _ENER_AVG_ ) are positive and have a significant sign, meaning that more cooperation is expected as both the investor and the host countries are more developed. Renewable energy profile of the _RENRG_ variable is significant in all regressions. It is positive in the case of a host country ( _hst_RENERG_ ), while it is negative in the case of the investor country ( _inv_RENRG_ ). This suggests that renewable energy resources operate in opposite directions in host and investor countries, which is consistent with the relevant hypothesis. For a host country, abundant renewable resources imply more CDM project to offer. For an investor country, abundant renewable resources imply opportunities to invest in carbon abatement projects at home and less incentive for investments abroad.

_Ease of Doing Business_ ( _EDB_ ) has the expected negative and significant coefficients in estimates for both host and investor countries in Table 6 where the dependent variable is the number of projects ( _NPRJ_ ). It has expected negative and significant coefficients for the host country ( _hst_EDB_ ) in the cases of the other two dependent variables, _TCO2_ and _VINV_ (Table 7 and 8). However, it has a non-significant coefficient for the investor country in these two tables. Our interpretation is that EDB is a very important consideration in host countries for CDM investment. It is less important in the case of investor countries. It is important to indicate that higher levels of that variable mean more difficulty of doing business in that country.

_Governance_ ( _GOVR_ ) is also an important variable in our analysis. It has positive and significant coefficients for _inv_Governance_ variable in all equations, and positive, but not significant coefficients for the _hst_Governance_ variable in several equations. Better governance leads to higher cooperation. The coefficient of the impact vulnerability variable ( _IVUL_ ) was positive and significant, as expected, in all estimated regressions for both host and investor countries. This means that the higher the vulnerability level of the country, either a host or an investor, the more it will be willing to cooperate in CDM investment for different reasons. For a host country such cooperation means improvement of its development opportunity, and for an 18
investor country this means a lower cost of meeting its CO2 reduction obligations, and a longterm contribution towards reduction of GHGs.

# **6. SUMMARY, CONCLUSIONS AND POLICY IMPLICATIONS**

In this paper we analyzed the CDM market from the point of view of cooperation between developing (host) and developed (investor) countries. We used a dichotomous variable to measure incidence of country involvement in CDM projects and three variants to measure cooperation level, namely number of joint CDM projects, volume of CO2 abatement that can be realized from the CDM projects, and volume of investment in the CDM projects. All cooperation equation estimates provided robust and consistent results, for all four cooperation variants. We used linear functional forms in the independent variables. We used linear and log transformation for the investment variable. Our results suggest that the variables we used – namely economic development, institutional development, energy structure of the economies, level of country vulnerability to various climate change effects, and international relationship between the host and investor countries – are good predictors of the level of cooperation in CDM projects we may expect. Coefficients of the interaction terms of these variables between the host and investor countries, in most cases, have not been significant.

In deriving conclusions from the analysis, we will start by comparing our results to those of other studies that attempt to explain the level of cooperation between countries. We used trade to explain the level of cooperation in CDM projects. The two trade variables we constructed suggest that countries with strong trade relations, that imply also other types of international relations ties, will be more likely to cooperate over CDM projects. Trade was found in many other studies to be a key in explaining cooperation in water treaties and in FDI. Trade is only an example of economic ties among the dyad countries. Therefore, the overall conclusion is that any type of active relationships among the dyad countries would lead to higher likelihood of cooperation over CDM.

Another variable that has similar impact in other studies of international cooperation is the level of governance in the country. It is frequently suggested that governance matters and that institutional strength is a prerequisite for better performance, both domestic and international. Other studies that were reviewed earlier suggest as well that higher level of governance makes countries better parties for cooperation over many issues. A similar 19
interpretation can be attributed to the variable measuring ease of doing business. Our results suggest, as it is expected, that the situation in the investor country does not matter. What matters is the level of ease of doing business in the host country. The more difficult it is to do business in a host country, the less it will attract domestic and international investors. It is quite straightforward, but needed empirical proof. Studies on FDI demand and supply identified ease of doing business in the investor (source) country to be negatively correlated with FDI supply. This was explained by having local entrepreneurs in developing countries look for other markets because they can minimize their transaction cost of doing business at home. Our results suggest the same impact only in the case of using the number of projects as an indicator for the level of cooperation. In other cases this variable’s coefficient was not significant.

All other variables measure the natural endowment of a country and thus may be less affected by policy interventions. Our policy discussion would therefore be focused only on the suggestion that international development institutions focus mainly on the strengthening of multilateral interactions between countries and on domestic structural changes and reforms to economies, so that they are better prepared not only to adapt to climate change but also to be able to better cooperate in the CDM market and take advantage of the CDM dividend— development—that results from CDM joint investment.

There are several variables in the cooperation equation specification that are state variables in the sense their level cannot be controlled, at least directly, by policy makers. This leaves the range of policy interventions narrower. Still, _Governance_ and _EDB_ leave sufficient degrees of freedom to policy makers. One issue that has been raised in Carbon Finance discussions is the complications of the CDM project clearance process, which becomes a barrier to CDM project development. Simplifying the CDM project clearance cycle is an important policy option. _EDB_ goes hand in hand with _Governance_ although they are not necessarily correlated. Improved governance in the host and in the investor countries means higher political stability and trust between the countries for business. Finally, trade or other long-term economic activities that connect the countries is an important inhibitor for CDM cooperation. Having a regional or international trade agreement or the like will increase the likelihood of CDM incidence and higher level of cooperation on CDM projects.

20
Thus, all three variables, _EDB_ , _Trade_ , and _Governance_ have a significant impact, increasing future viability of the CDM market. These variables represent both state-level and international-level policy interventions, which governments and international development institutions have already identified as important directions for their future commitment. The analysis in this paper further highlights and quantifies these declared commitments.

21
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Table 1: Correlation Matrix for Dependent Variables Used in the Cooperation Equations

||_NPRJ_|_TCO2_|_VINV_|_TCO2YR_|
|---|---|---|---|---|
|Numberof Projects|1||||
|TotalCO2 Abatement|0.812|1|||
|Volume of Investment|0.913|0.946|1||
|TotalCO2 Abatement peryear|0.757|0.995|0.919|1|
|Annual Investment cost|0.870|0.961|0.993|0.945|

Table 2: Project-level descriptive statistics

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|AnnualCO2abatement (ktCO2e)|2956|140.64|487.32|4|10437|
|Total CO2 abatement during<br>projectlife (ktCO2e)|2956|1120.42|3583.17|3.5|73059|
|CDM-based electricity generation<br>capacity (MW)|2474|31.20|100.09|0|1560|
|Investment cost<sup>a</sup> (US$)|1199|3.31E07|6.22E07|7222|7.87E08|

Notes:<sup>a</sup> All monetary values in this paper are 2000 constant US$.

26
Table 3: Country-level descriptive statistics (Host countries)

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|Number ofprojects|175|16.89|99.53|0|960|
|Annual CO2 abatement(ktCO2e)<sup>a</sup>|175|2375.75|18177.86|0|233211|
|TotalCO2abatement during projectlife|175|18925.58|137625.67|0|1735068|
|Annual investment cost(Mill.US$)|175|20.6|133|0|1570|
|Total investment cost (Mill. US$)|175|182|1230|0|14900|
|AnnualGDP(Mill. US$ Const.2000)|153|42100|122000|4390|17900000|
|GDPper capita(Constant 2000 US$)|152|3383|5139|101|23903|
|Climate changeimpact vulnerability|170|49.95|21.45|1|91.28|
|Governance|116|-2.40|4.96|-14.12|11.34|
|Ease of DoingBusiness|142|103.15|45.98|1|178|
|Renewable energy per capita (ton oil<br>eqv.)|168|15.29|116.89|0|1506.21|

Note:<sup>a</sup> ktCO2e = Kiloton of oil equivalent.

27
Table 4: Country-level data descriptive statistics (Investor countries)

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|Number ofprojects|34|54.11|132.52|0|720|
|Annual CO2 Abatement(ktCO2e)|34|9299.81|23109.15|0|124150.1|
|Annual investment cost(MillUS$)|34|212|508|0|2670|
|Renewable energy percapita (tonoileqv.)|34|6.64|18.57|0.22|106.03|
|Climate changeimpact vulnerability|34|27.50|12.72|1|50.38|
|Ease of DoingBusiness|34|37.39|33.70|2|139|

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Table 5: Descriptive statistics results for dyad-level variables in the analysis

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|_CDMI_|2771|0.065|0.247|0|1|
|_Number of Projects_|2771|0.657|8.04|0|321|
|_Total CO2 Abatement_|2771|866.50|14820.18|0|672990.5|
|_AnnualCO2 Abatement_|2771|112.80|1977.78|0|89793.5|
|_Total Volume of Investment_|2771|1.96E+07|3.35E+08|0|1.53E+10|
|_Annual Volume of Investment_|2771|2574199|4.45E+07|0|2.03E+09|
|_Trade_1|2771|0.00037|0.00101|0|0.02397|
|_Trade_2|2771|0.00090|0.00294|0|0.05724|
|_hst_Averag GDP_|2771|42233.17|79493.96|864.5967|407822.8|
|_inv_Averag GDP_|2771|289208.43|559055|5154.85|2914536|
|_hst_Average Annual Energy Use_|2771|33.03|95.20|0.885|790.7972|
|_inv_Average Annual Energy Use_|2771|82.97|129.83|1.69|640.80|
|_hst_Ease of Doing Business_|2771|98.90|42.00|1|178|
|_inv_Ease of Doing Business_|2771|37.46|33.26|2|139|
|_hst_Governance_|2771|-2.39|4.45|-12.45|11.34|
|_inv_Governance_|2771|7.91|4.74|-4.11|13.48|
|_hst_Renewable Energy_|2771|5516.84|12786.63|0|62220|
|_inv_Renewable Energy_|2771|113.96|353.60|1|1937|
|_hst_Impact Vulnerability_|2771|46.44|16.88|1|80.72|
|_inv_Impact Vulnerability_|2771|27.51|12.53|1|50.38|

Note: See Appendix 2 for units of measurement. Interaction terms are not shown.

29
Table 6: Regression results for CDM Engagement (No interaction trms)

|Equation|1|2|3|
|---|---|---|---|
|Estimationprocedure|Logit|Pr|obit|
|Dependet variable|_CDM_<br>_Engagement_|_CDM E_|_ngagement_<br>|
||||_Marginal Effect_<sup>_a_</sup>|
|_hst_Average Annual Energy Use_|2.04E-03***<br>(2.56)|1.24E-03***<br>(2.79)|0.0000495|
|_inv_Average Annual Energy Use_|8.79e-03***<br>(9.63)|4.62E-03***<br>(9.43)|0.0001845|
|_hst_Ease of Doing Business_|-9.47E-03***<br>(-3.33)|-4.59E-03***<br>(-3.06)|-0.0001833|
|_inv_Ease of Doing Business_|0.046<br>(0.59)|3.11E-02<br>(0.77)|0.000124|
|_hst_Governance_|0.077***<br>(2.77)|0.037***<br>(2.65)|0.0015142|
|_inv_Governance_|0.368***<br>(6.04)|0.185***<br>(5.93)|0.0073955|
|_hst_Renewable Energy_|1.26E-03***<br>(4.17)|6.02E-04***<br>(3.58)|0.000024|
|_inv_Renewable Energy_|-1.02E-03***<br>(-2.73)|-5.88E-04***<br>(-3.07)|-0.0000235|
|_hst_Impact Vulnerability_|4.06E-02***<br>(6.01)|1.90E-02***<br>(5.13)|0.0007592|
|_inv_Impact Vulnerability_|4.34E-02***<br>(5.18)|2.05E-02***<br>(5.13)|0.0008188|
|_Trade1_|209.61***<br>(2.70)|118.60***<br>(3.03)|4.726333|
|_Constant_|-10.05***|-5.12***||
||(-9.88)|(-9.96)||
|Observations|2771|2771||
|LogLikelihood|-461.01|-461.86||
|LR|420.91***|419.21***||
|PseudoR<sup>2</sup>|0.313|0.312||
|Overallobserved probability|||0.065|
|Predicted Probability|||0.016|

In parentheses are t-values; *** (=0.01); ** (=0.05); * (=0.10). aMarginal effect: the change in the probability for an infinitesimal change in each independent.

30
Table 7: Regression results for CDM projects cooperation level (Dependent variable is number of projects)

|Equation|1<sup>a</sup>|2|3<sup>b</sup>|4|
|---|---|---|---|---|
|Estimationprocedure|Poisson|Poisson|Poisson|Poisson|
|Dependet variable|_Number of_<br>_Projects_|_Number of_<br>_Projects_|_Number of_<br>_Projects_|_Number of_<br>_Projects_|
|_hst_Averag GDP_|7.47E-06***<br>(24.79)||8.35E-06***<br>(24.56)||
|_inv_Averag GDP_|8.46E-07***<br>(20.03)||1.06E-06***<br>(20.24)||
|_hst_inv_Averag GDP_|||-1.07E-12***<br>(-6.16)||
|_hst_Average Annual Energy Use_||2.98E-03***<br>(17.72)||3.18E-03***<br>(17.39)|
|_inv_Average Annual Energy Use_||7.95E-03***<br>(31.52)||8.60E-03***<br>(26.67)|
|_host_inv_Average Annual Energy Use_||||-1.70E-06***<br>(-2.58)|
|_hst_Ease of Doing Business_|-9.92E-03***<br>(-10.71)|-7.81E-03***<br>(-7.62)|-8.40E-03***<br>(-5.99)|-7.47E-03<br>(-4.79)|
|_inv_Ease of Doing Business_|-0.016***<br>(-5.63)|-2.13E-02***<br>(-7.64)|-1.33E-02**<br>(-1.96)|-1.92E-02***<br>(-2.96)|
|_host_inv_Ease of Doing Business_|||-5.32E-05|-2.55E-05|
||||(-0.76)|(-0.32)|
|_hst_Governance_|0.082***|0.160***|0.050|0.088*|
||(6.74)|(14.08)|(0.99)|(1.78)|
|_inv_Governance_|0.301***|0.290***|0.292***|0.303***|
||(15.42)|(16.74)|(14.34)|(16.36)|
|_host_inv_Governance_|||2.99E-03|6.47E-03|
||||(0.66)|(1.45)|
|_hst_Renewable Energy_|1.09E-03***|1.56E-03***|1.12E-03***|1.66E-03***|
||(12.84)|(17.55)|(12.76)|(18.38)|
|_inv_Renewable Energy_|-8.31E-04***|-1.89E-03***|-3.65E-05|-0.01***|
||(-5.25)|(-10.94)|(0.17)|(-4.74)|
|_host_inv_Renewable Energy_|||-1.28E-06***<br>(-4.72)|-1.35E-06***<br>(-4.50)|
|_hst_Impact Vulnerability_|0.074***<br>(25.03)|0.063***<br>(21.41)|0.114***<br>(14.11)|0.110***<br>(12.74)|
|_inv_Impact Vulnerability_|0.018***<br>(8.18)|0.023***<br>(9.20)|0.087***<br>(6.42)|0.104***<br>(7.18)|
|_host_inv_Impact Vulnerability_|||-1.22E-03***|-1.40E-03***|
||||(-5.32)|(-5.80)|
|_Trade1_|82.15***|35.95***|132.63***|54.82***|
||(6.94)|(2.96)|(8.46)|(3.37)|
|_Constant_|-8.72***|-8.01***|-11.26***|-11.08***|
||(-25.49)|(-25.38)|(-19.46)|(-18.52)|
|Observations|2771|2771|2771|2771|
|LogLikelihood|-2760.34|-2630.94|-2715.74|-2596.95|
|LR<br>|10076.69***|10335.49***|10165***|10403.48***|
|Pseudo R<sup>2</sup>|0.646|0.662|0.651|0.667|

In parentheses are t-values; *** (=0.01); ** (=0.05); * (=0.10).

Note:<sup>a</sup> See Appendix 3, equation 1 for regression results of 3411 observations;<sup>b</sup> See Appendix 3, equation 2 for regression results of 3411 observations.

31
Table 8: Regression results for CDM projects cooperation level (Dependent variable is project CO2 abatement level)

|Equation|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Estimationprocedure|Tobit|Tobit|Tobit|Tobit|Tobit|Tobit|
|Dependet variable|_Total CO2_<br>_Abatement_|_Total CO2_<br>_Abatement_|Ln(_Total_<br>_CO2_<br>_Abatement)_|Ln(_Annual_<br>_CO2_<br>_Abatement)_|_Total CO2_<br>_Abatement_|Ln(_Total_<br>_CO2_<br>_Abatement)_|
|_hst_Average Annual_|114.15***|114.90***|0.021***|0.018***|64.93***|0.022**|
|_Energy Use_|(5.48)|(5.53)|(2.67)|(2.64)|(2.92)|(2.38)|
|_inv_Average Annual_|218.51***|215.16***|0.090***|0.077***|175.52***|0.091***|
|_Energy Use_|(7.95)|(7.66)|(8.33)|(8.46)|(6.48)|(8.26)|
|_host_inv_Average_|||||0.554***|-1.03E-06|
|_Annual Energy Use_|||||(4.49)|(-0.21)|
|_hst_Ease of Doing_|-195.70***|-206.87***|-0.095***|-0.077***|-228.42**|-0.113***|
|_Business_|(-2.45)|(-2.60)|(-3.23)|(-3.09)|(-2.21)|(-2.84)|
|_inv_Ease of Doing_|59.17|148.23|0.085|0.055|-151.26|-0.029|
|_Business_|(0.27)|(0.68)|(1.10)|(0.84)|(-0.39)|(-0.21)|
|_host_inv_Ease of_|||||1.77|0.001|
|_Doing Business_|||||(0.49)|(0.84)|
|_hst_Governance_|1435.95**|1488.29**|0.799***|0.657***|1946.27|0.996|
||(1.86)|(1.93)|(2.82)|(2.72)|(0.71)|(1.00)|
|_inv_Governance_|8680.39***|9433.50***|3.91***|3.17***|8067.71***|3.69***|
||(5.13)|(5.51)|(6.07)|(5.83)|(5.00)|(5.73)|
|_host_inv_Governance_|||||-50.55|-0.020|
||||||(-0.20)|(-0.23)|
|_hst_Renewable_|24.98***|24.67***|0.011***|0.010***|29.69***|0.011***|
|_Energy_|(2.91)|(2.88)|(3.65)|(3.67)|(3.63)|(3.43)|
|_inv_Renewable_|-27.68***|-26.01***|-0.010***|-9.601E-3***|-18.30*|-0.011***|
|_Energy_|(-2.69)|(-2.53)|(-2.89)|(-3.00)|(-1.62)|(-2.76)|
|_host_inv_Renewable_|||||-0.063***|2.44E-6|
|_Energy_|||||(-2.89)|(0.32)|
|_hst_Impact_|784.79***|751.08***|0.361***|0.314***|874.22*|0.470***|
|_Vulnerability_|(4.44)|(4.28)|(5.46)|(5.55)|(1.91)|(2.59)|
|_inv_Impact_|975.92***|1016.79***|0.430***|0.355***|1210.89*|0.547**|
|_Vulnerability_|(4.52)|(4.74)|(5.36)|(5.16)|(1.74)|(2.08)|
|_host_inv_Impact_|||||-5.47|-3.10E-03|
|_Vulnerability_|||||(-0.42)|(-0.61)|
|_Trade1_|6.91E06***|||1651.83***|4.12E06***|1879.84**|
||(3.82)|||(2.50)|(1.96)|(2.29)|
|_Trade2_||1.85E6***<br>(3.46)|441.40**<br>(2.04)||||
|_Constant_|-2.53E5***|-2.60E5***|-110.46***|-93.66***|-2.35E5***|-111.55***|
||(-8.58)|(-8.69)|(-9.37)|(-9.43)|(-6.42)|(-7.41)|
|Observations|2771|2771|2771|2771|2771|2771|
|Uncensored obs.|182|182|182|182|182|182|
|LogLikelihood|-2477.73|-2477.86|-1113.53|-1083.83|-2467.98|-1112.32|
|LR|431.28***|431.04***|422.78***|426.03***|450.79***|425.21***|
|Pseudo R<sup>2</sup>|0.080|0.080|0.159|0.164|0.083|0.160|

In parentheses are t-values; *** (=0.01); ** (=0.05); * (=0.10).

32
Table 9: Regression results for CDM projects cooperation level (Dependent variable is Volume of investment)

|Equation|1<sup>a</sup>|2<sup>b</sup>|3|4|5|
|---|---|---|---|---|---|
|Estimationprocedure|Tobit|Tobit|Tobit|Tobit|Tobit|
|Dependet variable|_Total Volume_<br>_of Investment_|_Annual Volume_<br>_of Investment_|_Ln(Total_<br>_Volume of_<br>_Investment)_|_Ln(Annual_<br>_Volume of_<br>_Investment)_|_Ln(Total_<br>_Volume of_<br>_Investment)_|
|_hst_Averag GDP_|||||6.30E-05**<br>(2.56)|
|_inv_Averag GDP_|||||2.09E-05***<br>(8.25)|
|_hst_inv_Averag GDP_|||||1.99E-11<br>(1.07)|
|_hst_Average Annual Energy Use_|2.92E06***|3.90E05***|0.034***|0.033***||
||(6.22)|(6.25)|(2.52)|(2.63)||
|_inv_Average Annual Energy Use_|5.43E06***|7.22E05***|0.152***|0.139***||
||(8.69)|(8.68)|(8.41)|(8.32)||
|_hst_Ease of Doing Business_|-5.26E06***|-7.05E05***|-0.152***|-0.145***|-0.179***|
||(-2.90)|(-2.93)|(-3.06)|(-3.20)|(-2.70)|
|_inv_Ease of Doing Business_|3.32E06|4.46E05|0.116|0.134|0.108|
||(0.67)|(0.68)|(0.89)|(1.13)|(0.46)|
|_hst_inv_Ease of Doing Business_|||||1.81E-03<br>(0.87)|
|_hst_Governance_|3.91E07**|5.10E06**|1.30***|1.22***|1.43|
||(2.22)|(2.18)|(2.73)|(2.82)|(0.79)|
|_inv_Governance_|2.19E08***|2.90E07***|6.29***|6.04***|6.97***|
||(5.62)|(5.62)|(5.86)|(6.09)|(5.93)|
|_host_inv_Governance_|||||-0.034<br>|
||||||(-0.21)|
|_hst_Renewable Energy_|5.63E05***|7.52E04***|0.019***|0.017***|0.012*|
||(2.87)|(2.88)|(3.55)|(3.55)|(1.83))|
|_inv_Renewable Energy_|-7.55E05***|-1.00E05***|-0.019***|-0.017***|-0.004|
||(-3.12)|(-3.11)|(-3.01)|(-2.92)|(0.54)|
|_host_inv_Renewable Energy_|||||1.05E-05<br>(0.76)|
|_hst_Impact Vulnerability_|1.76E07***|2.3026***|0.626***|0.561***|0.763***|
||(4.37)|(4.33)|(5.59)|(5.51)|(2.59)|
|_inv_Impact Vulnerability_|2.51E07***|3.33E07***|0.700***|0.663***|0.907**|
||(5.09)|(5.09)|(5.17)|(5.37)|(2.02)|
|_host_inv_Impact Vulnerability_|||||-4.77E-03<br>(-0.58)|
|_Trade1_|||3105.00**<br>(2.35)||2106.44<br>(1.40)|
|_Trade2_||||657.55**<br>(1.98)||
|_Constant_|-5.99E9***|-7.95E7***|-178.73***|-166.58***|-191.54|
||(-8.79)|(-8.78)|(-9.11)|(-9.19)|(-7.48)|
|Observations|2771|2771|2771|2771|2771|
|Uncensored obs.|182|182|182|182|182|
|LogLikelihood|-4306.05|-3938.14|-1207.96|-1191.85|-1207.47|
|LR|423.80|424.20|420.42***|420.13***|421.38***|
|Pseudo R<sup>2</sup>|0.047|0.051|0.148|0.149|0.148|

In parentheses are t-values; *** (=0.01); ** (=0.05); * (=0.10).

Note: See Appendix 3, equation 3 for regression results of 2856 observations; See Appendix 3, equation 4 for regression results of 2856 observations.

33
<!-- Start of picture text --> 3500<br>2960<br>3000<br>2500<br>2000<br>1359<br>1500<br>1000 509<br>500 5 59<br>0<br>2002 2003 2004 2005 2006 2007 2008<br>Year<br>(accumulated)<br>Number of Projects<br><!-- End of picture text --> Figure 1: Accumulative number of CDM submitted projects 2003-2007.

34
# **APPENDIX 1: METHODOLOGY FOR THE DATA COLLECTION ON THE CAPITAL COST DATA FROM CLEAN DEVELOPMENT MECHANISM PROJECT ACTIVITY**<sup>13</sup>

Reporting project capital cost data is not required in for CDM registration, but the information is sometimes used to demonstrate additionality. One consequence is that the way the information is presented in official CDM documents is not standardized and some interpretation is required.

Of those PDDs that contain capital cost information, it is often reported as “Capital costs” or “fixed costs” for the project, and as a single number either in host country currency units or USD. From what is reported, capital cost in the CDM PDDs generally includes: (1) procurement of any plant and/or machinery dedicated to the realization of the CDM project, (2) construction and civil works, and (3) engineering consultation (non-ongoing). In some cases, the following were included in the capital costs: (1) Costs incurred for the validation, registration, and verification of the project as a CDM project, (2) Contingency and margin money for working capital, (3) interest during construction, and (4) licenses.

Unfortunately, it was not possible to disaggregate the costs as only one (all inclusive) number was reported. In some cases, project participants reported all costs, including variable costs in a table. For these cases, capital cost had to be reconstituted into a single number in order to record it into the dataset.<sup>14</sup>

# **_Methods in obtaining capital cost data_**

As stated above, information on project costs is sometimes used in the demonstration of “additionality” of the CDM project. The section of the PDD where participants prove the additionality of the project is section B.5.

In each of the PDDs, this section was thoroughly reviewed to determine if capital cost data was included. In addition, the entire PDD was searched using key words such as USD, $, investment, cost, capital, and currency acronym for the host country (ex: for projects in china, key words included, CNY, RMB, yuan).

> 13 We thank Stephen Seres for helping with this Annex.

> 14 Most PDDs provided one single capital cost value which generally included construction, equipment, and engineering costs.  Where detailed tables were included, only the values for construction, equipment, and engineering costs were summed to produce a single value for the dataset with hopes at maintaining comparability.

35
All cost data was recorded in the spreadsheet in the currency units used in the PDD. All cost data were converted into USD using the spot exchange rate on the 20<sup>th</sup> of November 2007. The exchange rates used were included in the dataset.

# **_Perspective in capital cost data_**

It may be important to note two facts with regard to the capital cost data from CDM project activity.

First, it should not be assumed that the CDM projects have been implemented yet and so capital cost outlays may not have occurred. The CDM project data represents all projects that have been put forth for validation and registration. This may, and often does, occur prior to commitments on capital purchases have been made. However, it is largely expected that these projects will be implemented.

Second, it should not be assumed that the reported capital expenditures on CDM projects are solely attributable to the CDM. In many cases, capital expenditures would have taken place in its absence. For instance, wind farm and hydro projects are implemented to increase the host country’s power generation capacity. In the absence of the CDM, it is likely that capital expenditures would have taken place regardless, in order to increase the host country’s power generation capacity, albeit with a different technology and less of a capital outlay. However, for certain project types, where there is no revenue stream other than CDM credits, i.e. landfill gas and animal waste flaring projects, it would be fair to assume that the capital cost expenditures are solely attributable to the CDM.

36
# **APPENDIX 2: DEFINITION OF VARIABLES USED IN THE REGRESSION ANALYSES**

|**Variabl**|**e**|**Description**|**Unit of Measurement**|
|---|---|---|---|
|_Ease of Doing_|_EDB_|Ease of doing business, a relative ranking|Smaller value indicates less|
|<br>_Business_||of the countries reflecting the state of<br>business regulation|favorable business environment|
|_CDM Incidence_|_CDMI_|A dichotomous variable with 0 if there<br>are no, and 1 if there are any number of<br>CDM dyad projects.|0 or 1|
|_Avr Annual Energy_<br>_Use_|_ENR_AVG_|<br>Average annual energy use over t he<br>period 1960-2003|Million tons of oil equivalent|
|_Annual Per Capita_<br>_Energy Use_|_ENR_PC_|Annual per capita energy consumption|Tons of oil equivalent|
|<br>_GDP_|_GDP_TOT_|Total gross domestic product for the<br>period 1960-2003.|Million US $ (constant 2000)|
|_GDP per Capita_|_GDP_PC_|<br>Gross domestic product per capita for the<br>period 1960-2003.|Thousand US $ (constant 2000)|
|_Average GDP_|_GDP_AVG_|<br>Average annual gross domestic product<br>for the period 1960-2003|Million US $ (constant 2000)|
|_Governance_|_GOVR_|<br>Indicator reflecting the overall<br>governance level, a principal component<br>product of 6 governance indicators|Smaller value indicates poorer<br>level of governance|
|_Impact_|_IVUL_|Impact vulnerability index (reflecting|Scale of 1 to 100 (1= lowest,|
|_Vulnerability_||country vulnerability in terms of various<br>impacts of climate change)|100=highest)|
|_Number of Project_|_NPRJ_|<br>Number of CDM projects||
|_Nonrenewable_<br>_Energy_|_NREN_|Non-renewable energy resources (total)<br>available per capita|Million tons of oil equivalent|
|_Renewable Energy_|_REN_|Renewable energy resources (annual)<br>available per capita.|Million tons of oil equivalent|
|_Total CO2_<br>_Abatement_|_TCO2_TOT_|<br>Total amount of CO2 abatement (CERs)<br>until 2012.|Kiloton of oil equivalent|
|_Annual CO2_|_TCO2_YR_|Annual amount of CO2 abatement|Kiloton of oil equivalent|
|_Abatement_||(CERs) until 2012.||
|_Trade1_|_TRD_1|<br>Total trade (the sum of the volume of<br>bilateral imports and exports) between<br>the host and investor countries as a<br>fraction of the sum of the countries’<br>GDPs|Share|
|_Trade2_|_TRD2 _|Total trade (the sum of the volume of<br>bilateral imports and exports) between<br>the host and investor countries as a<br>fraction of their trade with the rest of the<br>world|Share|
|_Volume of_<br>_Investment_|_VINV_|.<br>Volume of investment in CDM projects:<br>TOT_reflecting total investment and<br>_YR_reflecting annual investment|US $ (constant 2000)|
|_hst_*_|_hst_*_|_ <br>Variable related to the host country|* represents the variables<br>described above|
|_inv_*_|_inv_*_|Variable related to the investor country|* represents the variables<br>described above|
|_hst_inv_*_|_hst_inv_*_|An interaction term|* represents the variables<br>described above|

37
# **APPENDIX 3: REGRESSION RESULTS WITH AN EXTENDED NUMBER OF OBSERVATIONS.**

|Equation|1|2|3|4|
|---|---|---|---|---|
|Estimationprocedure|Poisson|Poisson|Tobit|Tobit|
|Dependet variable|Number of<br>Projects|Number of<br>Projects|Total Volume<br>of Investment|Annual<br>Volume of<br>Investment|
|_hst_Averag GDP_|8.68E-06***<br>(29.53)|9.63E-06***<br>(29.04)|||
|_inv_Averag GDP_|8.43E-07***<br>(20.07)|1.06E-06***<br>(20.96)|||
|_hst_inv_Averag GDP_||-1.18E-12***<br>(-6.93)|||
|_hst_Average Annual Energy Use_|||3076.01***<br>(6.54)|409.74***<br>(6.57)|
|_inv_Average Annual Energy Use_|||5389.33***<br>(8.70)|715.44***<br>(8.70)|
|_hst_Ease of Doing Business_|-8.90E-03***<br>(-9.64)|-7.41E-03***<br>(-5.35)|-4.70E06***<br>(-2.60)|-6.32E05***<br>(-2.63)|
|_inv_Ease of Doing Business_|-0.016***|-1.4E-02***|3.37E06|4.52E05|
||(-5.59)|(-2.16)|(0.69)|(0.69)|
|_hst_inv_Ease of Doing Business_||-4.19E-5<br>(-0.61)|||
|_hst_Governance_|0.042***<br>(3.73)|0.042<br>(0.89)|3.15E07*<br>(1.81)|4.09E06*<br>(1.77)|
|_inv_Governance_|0.300***<br>(15.46)|0.286***<br>(14.23)|2.18E08***<br>(4.65)|2.89E07***<br>(2.72)|
|_host_inv_Governance_||1.64E-04<br>(0.04)|||
|_hst_Renewable Energy_|8.63E-04***<br>(10.63)|8.81E-04***<br>(10.47)|5.32E05***<br>(2.70)|7.11E04***<br>(2.72)|
|_inv_Renewable Energy_|-8.38E-04***<br>(-5.30)|-9.82E-06<br>(-0.05)|-7.44E05***<br>(-3.10)|-9.88E04***<br>(-3.10)|
|_host_inv_Renewable Energy_||-1.23E-06***<br>(-4.63)|||
|_hst_Impact Vulnerability_|0.061***<br>(23.68)|0.099***<br>(12.99)|1.50E07***<br>(3.87)|1.97E06***<br>(3.83)|
|_inv_Impact Vulnerability_|0.018***<br>(8.29)|0.083***<br>(6.38)|2.49E07***<br>(5.09)|3.30E06***<br>(5.20)|
|_host_inv_Impact Vulnerability_||-1.15E-03***<br>(-5.24)|||
|_Trade1_|85.71***<br>(7.32)|146.45***<br>(9.28)|||
|_Constant_|-8.29***|-10.68***|-5.94E09***|-7.87E08***|
||(-24.56)|(-19.28)|(-8.79)|(-8.79)|
|Observations|3411|3411|2856|2856|
|Uncensored obs.|||182|182|
|LogLikelihood|-2898.2|-2849.64|-4313.30|-3945.42|
|LR|10573.85***|10670.97***|419.20***|419.69***|
|Pseudo R<sup>2</sup>|0.646|0.651|0.046|0.050|

In parentheses are t-values; *** (=0.01); ** (=0.05); * (=0.10).

38