Climatic Change

, Volume 118, Issue 3, pp 521–536

Do we know each other? Bilateral ties and the location of clean development mechanism projects

Authors

    • Interdisciplinary Arts and Sciences; and School of Marine and Environmental AffairsUniversity of Washington
  • Emily Bowerman Crandall
    • Interdisciplinary Arts and SciencesUniversity of Washington Bothell
Article

DOI: 10.1007/s10584-013-0694-7

Cite this article as:
Dolšak, N. & Crandall, E.B. Climatic Change (2013) 118: 521. doi:10.1007/s10584-013-0694-7

Abstract

This paper examines how bilateral ties between developed (home) countries and developing (host) countries influence the location of Clean Development Mechanism projects (CDMs). With the home-host country pair as the unit of analysis (2,058 country-pairs), we employ a logistic regression model to analyze decisions of home countries in selecting the location for their CDMs. We are most interested in examining how home countries’ familiarity with the host country influences CDM location decisions. The familiarity factors are: (1) colonial history; (2) bilateral trade; and (3) bilateral aid. Using a binary logistical model, we find that that bilateral familiarity factors strongly influence CDM location decisions. Further, with respect to host country characteristics, we find that total carbon dioxide emissions and UNFCCC specific domestic institutions influence CDM location decisions, but not general investment institutions or high carbon intensity of host country economies.

1 Introduction

International environmental cooperation literature increasingly examines effectiveness of international environmental regimes (Young 1999; Miles et al. 2002; Breitmeier et al. 2006). Implementation of the global climate change regime is found to be challenging due to high economic and political cost of action, important equity concerns, and uncertainty (Dolšak et al. 2003; Victor 2001; Luterbacher and Sprinz 2001). This paper contributes to the literature on climate change regime effectiveness. Specifically, we examine a concrete policy measure, Clean Development Mechanism projects (hereafter CDMs), that evolved from the United Nations Framework Convention on Climate Change (hereafter UNFCCC) regime. In particular, we explore how the CDM instrument has been used in practice. Home countries investing in CDMs can earn emission reduction credits by virtue of their investment abroad. The puzzle is: how do developed countries decide where to locate their CDMs?

This location decision is important as it influences the extent to which the objectives of the CDM as a policy instrument are being met. CDMs have multiple objectives such as stimulating sustainable development in developing countries, fostering technology transfer from developed to developing countries, providing cost-efficient ways for developed countries to meet their emission reduction obligations, and creating mechanisms that enable developing countries to reduce their greenhouse gas emissions without additional financial burden (IPCC 2001).1

How might CDMs meet the above objectives? Developing country firms tend to employ technologies which are highly polluting. This can be attributed to the lack of stringent domestic laws (or their poor enforcement) and to the lack of capital required for less polluting and/or energy-efficient technologies. In contrast, developed country firms tend to be quite familiar with such technologies. Thus, if developed country firms can be encouraged to transfer such technologies to developing countries, and, at the same time, take credit for consequent emission reductions, this might create a win-win situation for both sets of actors. CDMs create new energy sources, and increase the efficiency of existing ones. As a result, host countries are able to increase economic activity at lower incremental pollution. CDMs create other benefits associated with foreign direct investment, such as increased domestic capital formation, reduced balance of payment deficits, and the production of positive technological and political spillovers for the host economy.2 In reality, however, whether or not a CDM will result in technology transfer depends on the scale of the project (Dechezlepretre et al. 2008) and technological capacities of host countries (Seres et al. 2009). Similarly, developmental, environmental, and community benefits to host countries are provided at varying levels. While some find that CDMs reduce host country’s long-term carbon dioxide emissions (Huang and Barker 2012) and have positive impacts on sustainable development in some industries (McNish et al. 2009), others find that sustainable development benefits are lacking (Drupp 2011). The bottom line is that notwithstanding their shortcomings, CDMs offer a route for technology transfer to at least a subset of developing countries in ways which serve the economic and environmental objectives of both developed and developing countries.

As per UNFCCC, almost 5,000 projects have been registered since 2004 when the modalities of CDMs were established in Marrakech Accords to the Kyoto Protocol. Location of CDMs is, however, uneven. Among almost hundred developing countries parties to the UNFCCC, four countries have been able to attract CDMs from more than half of the investor countries while 15 countries have been able to attract investments from only 1 developed country and 38 have not been able to attract any. The question then is what explains how developed countries decide where to locate their CDMs?

To model CDM location decisions, we propose viewing CDMs as a specific type of foreign direct investment (hereafter FDI). The Organization for Economic Cooperation and Development (OECD) defines foreign direct investors as actors seeking to establish a long-term relationship (in the form of a subsidiary, associate or branch office) in a foreign country along with exerting a significant influence on the management of this new enterprise.3 Unlike portfolio investors, who can move their funds in and out of the country quickly at low costs to markets that provide them with higher returns, FDI is less mobile because it has higher exit costs. As a consequence, foreign director investors tend to pay close attention to the economic, political, and social environment of their host country when deciding where to invest (Dunning 1981). Further, investors are able to more correctly assess investment risks when they are familiar with or have prior experience in dealing with a potential host country. This “familiarity”, or prior experience can be attributed to colonial ties, bilateral trade, and aid relationships.

Drawing on the literature on transaction costs and foreign direct investment, we focus on how “familiarity” between home and host country influences location decisions.

At the same time, we control for another category of factors, “attractiveness” factors. These focus on host country characteristics. Empirically, using a logistic regression model, we investigate bilateral CDM location decisions across 2,058 home-host country pairs for the period 2004–2006. We find support for the argument that developed countries locate CDMs in developing countries they are most familiar with due to their colonial history, trade, or aid disbursements. Importantly, we do not find support for the argument that CDMs are viewed as environmental aid or that they gravitate towards countries which offer the lowest marginal costs of pollution reduction (greenhouse gas intensive economies).

The paper proceeds as follows. The next section identifies theoretical arguments that bear upon the location puzzle. The third section describes the data and provides an empirical test of the analytical model. The concluding section discusses implications of our results for a broader study of international environmental cooperation.

1.1 Theoretical perspectives on CDM location decisions

CDMs are a mechanism for cost-effective emission reductions in ways that both developing and developed countries have incentives to participate in the exchange. The question we pose is: why are some host countries more likely selected to host CDMs than others? Analytically, we focus on dyadic or home-host pair characteristics (familiarity). At the same time, our model examines host country characteristics (attractiveness factors) which arguably can also influence CDM location. We begin our theoretical discussion by first presenting “attractiveness” factors, and then “familiarity” variables.

1.1.1 Host country attractiveness

The host country “attractiveness” factors have economic and political dimensions. If CDM location decisions are influenced by the desire to (eventually) produce a global public good (Ostrom et al. 2002) (climate change mitigation), then CDMs would be located in host countries that significantly contribute to global emissions. Similarly, home country investors may view CDMs as mechanisms to access new markets for emission reduction technologies. CMDs can lock host countries into specific technologies, providing future home country investors with a captive market for successive generations of their pollution reduction technologies. The higher the levels of greenhouse gas emissions in a host country, the larger is the size of the potential market which the home country investors might tap in the future. Therefore, we hypothesize that:

H1:

Developing countries with higher levels of greenhouse gas emissions will be more likely to host CDMs.

Along with absolute emission levels, another factor influencing the attractiveness of a host country is the potential to reduce pollution at low marginal costs. Emission reduction costs are among the key factors impacting climate change mitigation (Dolšak 2009). Mitigation costs can be assessed by examining the emission intensity of economic activities. It is likely that host countries with higher greenhouse gas emissions per dollar of their gross domestic product would have more of the “low-hanging fruit” projects available, and therefore become more attractive locations for CDMs. Therefore, we hypothesize that:

H2:

The higher the host country’s greenhouse gas emissions per unit of their gross domestic product, the higher the likelihood of CDMs being located in this host country.

CDMs have to be designed following guidelines provided in various UNFCCC subsidiary bodies. These procedures require expertise and detailed information on sources of greenhouse gases in each country, their future emission scenarios, climate change impacts and other related environmental impacts in the country. Collection of this information is greatly aided if the developing country has submitted a “national communication” to the UNFCCC Secretariat (Jung 2006). Therefore, we hypothesize that:

H3:

Developing countries that submitted National Communications to the UNFCCC secretariat are more likely to host a CDM project than those that have not yet submitted this document.

Because CDMs are commercial contracts, the investing actors would seek to design, monitor and enforce them at low costs (North 1990). Projects under the Activities Implemented Jointly (AIJ) mechanisms were the precursors to CDMs (Michaelowa 2002). If a developing country has hosted AIJ projects, then is it likely that its domestic institutions are sufficiently developed to support formal contractual arrangements required by the UNFCCC. Prior engagement of a developing country with AIJs is therefore a credible signal to potential investors about the above institutions. Therefore, we hypothesize that:

H4:

Host countries that had been engaged in AIJs are more likely to host a CDM project.

CDMs reflect varying levels of investment risks depending on contract enforcement environment of the host economy. When the enforcement of commercial contracts is costly, CDM investors are likely to find these locations less attractive (World Bank 2002; Dunning 1981). Arguably, informal institutions which foster trust and communication might serve as low cost vehicles to enforce contract (Axelrod 1984; McMillan and Woodruff 2000). Because foreign investors are less likely to be able to draw on these informal institutions, they will be dependent on availability of state institutions for protection of their investments (Greif et al. 1994) and for making business transactions easier (Georgiou et al. 2008). Following this logic, we hypothesize that:

H5:

Developing countries providing a low cost domestic investment climate for foreign investors are more likely to host CDMs.

The factors discussed above focus on the characteristics of potential host countries which influence their attractiveness to any investor seeking to locate its 4 CDMs. What the existing literature examining CDMs has not yet addressed—to the best of our knowledge—is how the characteristics specific to each home-host country pair might drive the location decision. These dyadic characteristics, familiarity factors, as we term them, result from a home country’s interactions with a potential host country.

1.1.2 Familiarity and CDM location

We draw on the literature on foreign direct investment to model firms’ decisions regarding the location of their CDMs. Foreign investors are likely to favor locations which offer them considerable returns at low risk (Dunning 1981; Caves 1996). Dunning’s (1981) ownership, location, and internalization (OLI) framework focuses on firm’s benefits of investing in a host country as well as risks associated with realizing these benefits. While the first two elements focus on the benefits of the CDM and are related to host country’s characteristics, the third one reflects the risks associated with locating a CDM in a given host country. Risks associated with investing in a particular country can be minimized by knowing domestic institutions of the host countries. Internalization, the third element of the OLI framework, is most relevant for our argument here and is, therefore, discussed in detail below.

Risks of realizing benefits of a CDM located in a particular host country could be political (insufficient protection of property rights), cultural (absence of a common language), or technical. As the costs of exiting a host country once the factory has been established are high, foreign investors tend to value familiarity with a host country as it increases their ability to identify and respond to political, economic, and social risks. Arguably, prior knowledge of or experience in the host country would substantially lower the transaction costs of operating CDMs, that tend to be substantial (Powell et al. 1997; Springer 2003; Michaelowa 2002; Michaelowa and Jotzo 2005; Dolšak and Dunn 2006).

To account broadly for familiarity, our model includes trade flows between a host and a home country, aid provided by the home country to the host country, and historic colonial relationships. As recent research shows, higher extent of familiarity with the frameworks of rules and norms in a country increases bilateral trade with this country (Tadesse and White 2009; De Groot et al. 2004) and trade further reduces information asymmetries between potential investor and host country (Bengal 2008). Therefore, we hypothesize:

H6:

The higher the past bilateral trade between a home and a potential host country, the more likely this pair of countries will engage in CDMs.

Foreign investors benefit from information about political and economic institutions in the host country. They access this information through a variety of mechanisms, including prior aid relationships between the host and the home country (Selaya and Sunesen 2012; Mody et al. 2003; Kimura and Todo 2010). Therefore, we hypothesize that

H7:

The higher the past development aid given by a home country to a potential host country, the more likely this pair of countries will engage in CDMs.

FDI location decision is also influenced by similarity between home and host countries’ cultures. When home and host country share a language and culture, costs of information dissemination (Davidson 1980), negotiations (Campbell et al. 1988) and marketing decisions (Tse et al. 1988) are lower. Therefore, we hypothesize that

H8:

If the host country was a colony of the home country, then it is likely to serve as a location of home country’s CDMs.

2 Empirical model

Given that our dependent variable (presence or absence of a CDM in a given dyad) is dichotomous, we employ a logistic regression model to analyze factors influencing a home country’s CDM location decisions. The unit of analysis is a home-host pair. The country-pairs that had at least one CDMs are coded as 1, those with no project as 0.

We collected information on CDMs from the UNFCCC website that contains a database of all CDMs. Most information about the individual projects was taken from the Project Design Document (PDD), an official UNFCCC document template that serves as the major proposal document for each project. Each CDM project is identified using its unique title and reference number, assigned by the CDM administrator upon electronic submission in accordance with Procedures for the Registration of a Proposed CDM Project Activity. We examined CDMs submitted during years 2004–2006. In 2007, the UNFCCC changed the institutional context that may bear upon transactions costs of identifying a partner for CDM. Namely, the UNFCCC developed a centralized clearing house for information on potential sellers and buyers of emission reduction credits emanating from CDMs. Therefore, we restrict our analysis to the preceding years.

Given the focus of this analysis, we included bilateral projects only, not multilateral or unilateral ones. A project is considered bilateral when the financing party is located in one or two developed countries (home country) and the party implementing the CDM technology is located in a single host country. These represented about 80 % of the total CDMs. We did not examine multilateral projects in which the investor is a multilateral organization, such as the World Bank, or those in which investing parties are located in several home countries. These projects represented about 8 % of all projects. Similarly, we did not examine unilateral projects in which an entity in a developing country is fully financing the project with the purpose of acquiring certified emission reduction credits to be offered in the carbon market (representing about 12 % of projects).

2.1 CDM locations

Table 1 summarizes geographic location of CDMs. As already noted in the literature (Dechezlepretre et al. 2008), small number of host countries are able to attract CDMs from a large number of home countries. These include China, India, Brazil, and Thailand. On the other hand, there are about 15 host countries that host CDMs from only one home country and 38 countries that do not attract a single CDM. 5
Table 1

Host countries’ involvement in bilateral clean development mechanisms

Host Country

Number of Home Country Partners

Host Country

Number of Home Country Partners

Argentina

8

Madagascar

2

Armenia

3

Malaysia

8

Azerbaijan

1

Mali

1

Bolivia

5

Mauritania

1

Brazil

10

Mauritius

1

Chile

1

Mexico

9

Cambodia

3

Mongolia

1

Cameroon

1

Morocco

3

Chile

9

Nepal

2

China

15

Nicaragua

3

Columbia

8

Nigeria

2

Costa Rica

4

Pakistan

6

Cote de Ivory

3

Panama

3

Dominican Republic

3

Papua New Guinea

1

Ecuador

6

Paraguay

2

Egypt

7

Peru

7

El Salvador

3

Philippines

7

Eq. Guinea

1

Senegal

2

Fiji

2

Singapore

1

Georgia

2

South Korea

6

Guatemala

4

South Africa

6

Honduras

6

Sri Lanka

3

India

13

Syria

1

Indonesia

5

Tanzania

1

Israel

3

Thailand

12

Jamaica

1

Vietnam

1

Jordan

2

  

Kenya

2

  

Laos

2

  

Dyads with at least one CDM project with a home country are listed here

UNFCCC CDM database: http://cdm.unfccc.int/Registry/index.html

Can familiarity explain this uneven distribution of CDMs? Though there are several elements of familiarity included in our model, we focus only the impact of colonial history in this discussion. To illustrate the impact of familiarity, we use Spain’s CDMs. We have chosen Spain because one third of CDM hosts are Spain’s former colonies.

Colonial past plays an important role in CDM location decisions. Among 98 developing countries included in our analysis, 20 are former Spain’s colonies and 78 are not. Spain located its CDMs in 21 countries. It located its CDMs in 14 of the 20 former colonies and in 7 countries among 78 that are not its former colonies. It appears that Spain’s former colonies are more likely to be chosen to host a Spain’s CDM. This holds even when we include host countries’ attractiveness. Developing countries can be grouped into clusters from very attractive hosts, moderately attractive, somewhat attractive, and not attractive (Jung 2006). If we juxtapose this variable with the Spanish colonial history, we get a 4 × 2 matrix presented in Table 2. This allows us to examine differences in the number of dyads with- (top of each cell) and without- a CDM (bottom of each cell) with respect to these two characteristics.
Table 2

Number of observed dyads between Spain and developing countries with and without at least one Spain’s CDM

 

Very attractive host countries

Moderately attractive host countries

Somewhat attractive host countries

Not attractive host countries

Spanish colonies (n-20)

yes:3

yes:4

yes:6

yes:1

 

no:1

no:4

no:1

Not Spanish colonies (n = 78)

yes:4

 

yes:2

yes:1

no:1

no:6

no:13

no:51

Among 8 very attractive host countries, 3 are Spain’s former colonies and 5 are not. Spain located CDMs in all former colonies and in 4 out of 5 non-colonies. It appears, then, that Spain opts for a CDM project in a very attractive country no matter what its familiarity with this country is. However, at decreasing levels of attractiveness, familiarity becomes more important. Among 11 moderately attractive countries, 4 former Spain’s colonies are hosting at least one Spain’s CDM, but 6 non-colonies are not. Among 25 somewhat attractive countries, 6 former Spain’s colonies are hosting at least one CDM, while 13 non-colonies are not hosting any Spain’s CDMs.

2.2 Models

The data on independent variables are either from the mid 1990s (bilateral development aid and bilateral trade) or from year 2000 (emissions, domestic institutions, gross domestic product). This ensures that our independent variables are temporally prior to our dependent variable. For a detailed description of independent variables, see Appendix. Descriptive statistics of the independent variables are presented in Table 3.
Table 3

Descriptive statistics

Variable

Mean

St. Dev.

Min

Max

Attractiveness

HostAIJ

0.287

0.452

0

1

HostCO2Emissions

88119.160

474963.4

41.000

4215022

HostCO2GDP

5.01e-7

5.40e-7

1.81e-8

3.16e-6

HostInvestment

51.709

17.607

10

90

HostNationalCommunication

0.725

0.447

0

1

Very attractive hosts

0.070

0.255

0

1

Moderately attractive hosts

0.095

0.293

0

1

Somewhat attractive hosts

0.209

0.407

0

1

Familiarity

TradeLn

15.777

4.110

0

25.134

Aid

17.941

137.326

0

5037.36

Hostcolony

0.037

0.188

0

1

We begin with a base model (Model 1 in Table 4) which examines influence of attractiveness characteristics, on CDM location decisions. If CDMs are instruments to create global public goods, then investors may tend to locate them in host countries that potentially have high impact on global climate change. We measure this impact via host country’s annual greenhouse gas emissions. Host countries may also be perceived as more attractive if they have not yet invested in the low-cost emission reduction technologies. The extent of the availability of “low-hanging fruit” opportunities for emission reductions is proxied by total greenhouse gas emissions per GDP dollar. Following Jung (2006), we operationalize the ability of domestic institutions to protect foreign investment by employing Heritage Foundation data on the openness of foreign capital flows. We also account for presence of domestic institutions specific to the UNFCCC, required to prepare national communications or host AIJ projects.
Table 4

Unstandardized coefficients for logistic regression of CDM projects

 

Model 1

Model 2

Model 3

Model 4

Attractiveness

HostCO2emissions

1.18e-06*** (1.95e-07)

8.08e-07*** (2.22e-07)

  

HostCO2GDP

−486114.1 (441301.7)

−589022.5 (551204.8)

  

HostNat.Communication

1.592*** (0.614)

1.840*** (0.677)

  

HostAIJ

0.641* (0.351)

0.616* (0.347)

  

HostInvestment

−0.002 (0.010)

−0.010 (0.010)

  

Very attractive host

  

3.694*** (0.498)

3.428*** (0.538)

Moderately attractive host

  

1.972*** (0.590)

2.091*** (0.610)

Somewhat attractive host

  

1.807*** (0.529)

1.941*** (0.545)

Familiarity

Aid

 

0.001* (0.000)

 

0.001** (0.000)

Ln Trade

 

0.250*** (0.065)

 

0.179*** (0.058)

Hostcolony

 

1.180** (0.463)

 

1.530*** (0.477)

Model

LR Chi-2 [df]

67.260[5]

103.20 [8]

81.288[3]

115.69[6]

Adjusted McFadden R2

0.117

0.181

0.156

0.216

BIC

−15252.451

−15265.506

−15281.738

−15293.251

N = 2,058; Standard errors for regression coefficients are reported in parentheses

* p ≤ 0.10, **p ≤ 0.05, ***p ≤ 0.01 (two-tail)

As a specification check (Model 3 in Table 4), we also combined all aspects of attractiveness into three dummy variables to measure four levels of attractiveness from very attractive, to moderately attractive, somewhat attractive, and not attractive to host non-sink CDMs (Jung 2006). Non-attractive and non-ranked countries are represented with values 0 on all three above dummy variables.

In Models 2 and 4, we present our main model which includes the base model and the group of familiarity variables. These variables are country-pair relationships that potentially influence the perceptions of risks and costs of doing business in host countries. As a proxy for transaction costs, we measure aid and trade flows between a home and a host country in each country-pair in years prior to the commencement of the CDMs. We use bilateral trade data (log transformed) published by the OECD for the mid 1990s. Further, we also employ a dichotomous variable indicating whether the host country was the home country’s colony.

In process of providing foreign aid, developed countries often seek to assess the institutional realities in developing countries. Often elaborate mechanisms are established to ensure that aid is used wisely. As a result, developed countries come to acquire considerable knowledge about local institutions and politics of the host, aid receiving country. This knowledge is likely to flow through network of elites. Often, the foreign embassies serve as meeting grounds for various home country actors working for private, governmental, or nonprofit sectors. MNCs (the entities that undertake foreign direct investment) often employ former government officials to take advantage of their expertise about the politics and institutions of specific foreign countries. Thus, transaction costs involved in establishing and managing CDM projects are likely to be lower if home countries have provided aid to specific host countries. To measure bilateral aid flows, we use the OECD dataset for the mid 1990s.

3 Results

Table 4 presents the results of binary logistic regression analysis. While Models 1 and 3 include only the characteristics of host countries, Models 2 and 4 also include familiarity variables.

3.1 Attractiveness of host countries

Our analysis supports findings of other scholars indicating the importance of host country attractiveness factors. As reported in Model 1, host countries with higher greenhouse gas emissions are more likely to host a CDM (H1), holding other variables constant. Specifically, an increase in host country’s annual carbon dioxide emissions by about 475 million tons of carbon dioxide (approximately, the annual carbon dioxide emissions of South Korea) increases the odds of a developing country hosting a CDM by 49 %. Importantly, our hypothesis about the impact of carbon intensity of host countries’ economies (H2) is not supported by our analysis.

We also examined the impact of three categories of host countries’ domestic institutions on the likelihood of hosting a CDM. While the results support our hypotheses about the impact of domestic institutions specific to the global climate change regime (H3 and H4), the hypothesis about the second types of institutions, the ones which protect foreign investments (H5), is not supported. A host country that had submitted a National Communication to the secretariat of the UNFCCC has odds of hosting a CDM that are almost 400 % higher than those of a country that had not invested resources in building these institutions (H3). Similarly, a developing country that had hosted an Activities Implemented Jointly project (a precursor to CDMs) has the odds of hosting a CDM that are 90 % higher than the odds of a developing country without this experience (H4). Clearly then, domestic institutions specialized to global climate change policies, the required monitoring and reporting, importantly increase the likelihood that a host country will attract a CDM.

Our hypotheses about the impact of host country’s attractiveness as measured by Jung (2006) are also supported by the analysis (Model 3). The odds of a very attractive host country (Jung cluster 1) hosting a CDM are 40 times higher than the odds of any other country.

3.2 Familiarity variables

As hypothesized (H6-8), familiarity, operationalized as bilateral trade, bilateral aid, and colonial relationship, is an important driver of CDM location decisions (see Models 2 and 4). By reducing transaction costs, previous interactions, relationships, and exchanges enable home country investors to map out the terrain in the potential host countries and provide confidence that their CDMs will not be hampered by local institutional or political idiosyncrasies. Thus, what matters for foreign investor is not the overall quality of domestic institutions (which are not a significant predictor of CDM location decision), but the prior interactions with the host country that allows it to understand the political, economic, and social terrain in the host country. Irrespective of the operationalization of the attractiveness variables, familiarity variables have statistically significant and strong impact on the location of a CDM. As reported in Model 2, one standard deviation increase in bilateral aid (approximately equal to an annual bilateral aid provided by Italy to Egypt; 137 million US$) from the home to the host country increases the odds of a CDM in this dyad by 9 %. As the log of trade increases by a standard deviation (about a sixth of the value for U.S.A.—China trade), the odds of a CDM occurring in this dyad increase by 170 %. The odds of a former colony to host a CDM of its former colonizer are 230 % higher than the odds of developing county hosting a CDM from a developed country without common colonial history. Results from Model 4 similarly suggest that, holding variables measuring attractiveness at their means, a standard deviation increase in bilateral aid increases the odds of this dyad hosting a CDM project by 13 %. A standard deviation increase in trade between a home and a host country increases the odds of this home country locating its CDMs in this host country by 107 %. Lastly, dyads of a colonial power and its former colony have odds of hosting CDM project that are 362 % higher than the odds of a CDM in a pair without colonial history.

We can further illustrate the impact of familiarity using predicted probabilities. Predicted probabilities of a host country hosting at least one CDM project of a home country are presented in Table 5. The logistic model predicts that a very attractive host country that was not a colony of the home country has a 15 % probability of hosting this home country’s CDM, holding other variables at their means. In contrast, a very attractive host country that was the home country’s colony is predicted to host its CDMs with three times higher probability, that is 44 %. Similarly, the probability that a moderately attractive former colony will host a CDM project from its former colonial ruler is about 4 times higher than that of non-colony (18 % vs. 4.5 %).
Table 5

Predicted probabilities for at least one CDM project occurring in a dyad

 

Very attractive host

Moderately attractive host

Somewhat attractive host

Host country was a colony of the home country

0.4443

0.1801

0.1294

Host country was NOT a colony of the home country

0.1475

0.0454

0.0312

Values of all other independent variables are held at their means

The impact of familiarity variables on the CDM project location can also be measured in terms of their impact on the total “goodness of fit” of the model. There are several measures of “goodness of fit” available for binary logistic regression. Following Long (1997) and Raftery (1996), we compute two measures: McFadden’s pseudo R2 (adjusted to account for number of variables) 6 and Bayesian Information Criterion (BIC). We report these measures in Table 4. Both measures suggest that the models including familiarity and attractiveness variables (Model 2 and Model 4) are a better fit than the models with only attractiveness variables (Model 1 and Model 3). McFadden’s pseudo R2 are is 0.181 (Model 2) and 0.117 (Model 1). Similarly, McFadden’s pseudo R2 for Model 4 is 0.216 whereas it is only 0.156 for Model 3. Measure BIC indicates the same. The difference (13.054) in BIS values between Model 2 and Model 1 suggests that including familiarity variables results in a “very strong” improvement over the base model with only attractiveness variables (Raftery 1996). The same holds for Model 4 vs. Model 3 (difference in BIC of 11.512).

Given that CDM mechanisms are not only targeting carbon dioxide emissions, but also methane and nitrous oxide emissions, we estimated the above models for methane, nitrous oxides, and all greenhouse gases. Our results hold across these specifications. These results can be obtained from the authors upon request.

4 Conclusions

Global warming is among the most serious global environmental problems. Given the reliance on fossil fuels to power industrial activity, reductions in greenhouse gas emissions will require significant restructuring of industrial processes. The effect of this restructuring will vary across industry sectors. CDMs are a mechanism to reduce costs and therefore political opposition to climate change mitigation. Further, CDMs also serve as a mechanism to transfer new pollution reduction technologies to developing countries in ways which are win-win for both developing and developed countries.

However, as with most policy instruments distributional challenges are inevitable. Most of the CDMs have gravitated towards a few advanced developing countries. This paper has sought to examine CDM location decisions. We find that prior trading, aid, and colonial relationships play an important role in shaping the location decisions. From a global equity perspective this can be potentially problematic because the poorest and most disadvantaged countries may not attract investors from a substantial number of developed countries. Furthermore, the attractiveness variables tend to be beyond the control of policy makers, at least in the short run. Hence, our paper calls for policy steps to correct the potential inequities that CDM projects may create, inequities that tend to mirror structural disadvantages that poor countries face in the contemporary global economy.

This paper raises issues for policy design. Given that developed countries’ climate change investments are influenced not only by cost-savings in emission reductions, but also by transaction costs of undertaking and sustaining foreign investment abroad, when devising mechanisms to implement any global environmental regime, policy makers should pay special attention to transaction costs facing relevant actors. The bottom line is that developed countries may not invest in CDM projects where the marginal emission reduction costs are the lowest, thereby not realizing all the cost-saving potentials. While the Marrakech Accords seek to reduce transactions costs related to the CDM implementation guidelines, the regime may need to find ways of helping reduce transactions costs related to unfamiliarity of home countries with domestic institutions in host countries. Those seeking to enhance cost-effectiveness of multilateral cooperation via international regimes should therefore pay close heed to both the costs of emission reduction technologies and to transaction costs related to devising, monitoring, and enforcing contracts in host countries.

Footnotes
1

In addition to the objectives stated in the regime, CDMs also may foster interests of the home country’s industries as these projects enable contractors from the home country to do work in the host country.

 
2

For empirical studies on positive spillovers see Ahlquist and Prakash (2008).

 
5

Similar concentration is found in sectors, such as sugar industry (McNish et al. 2009) and wind energy (Georgiou et al. 2008).

 
6

While there is no clear interpretation of values other than 0 and 1 and no clear standard by which to judge it, we can say that models with a higher R2McF are preferred (Long 1997).

 

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© Springer Science+Business Media Dordrecht 2013