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External sources of clean technology: Evidence from the Clean Development Mechanism


New technology is fundamental to sustainable development. However, inventors from industrialized countries often refuse technology transfer because they worry about reverse-engineering. When can clean technology transfer succeed? We develop a formal model of the political economy of North–South technology transfer. According to the model, technology transfer is possible if (1) the technology in focus has limited global commercial potential or (2) the host developing country does not have the capacity to absorb new technologies for commercial use. If both conditions fail, inventors from industrialized countries worry about the adverse competitiveness effects of reverse-engineering, so technology transfer fails. Data analysis of technology transfer in 4,894 projects implemented under the Kyoto Protocol’s Clean Development Mechanism during the 2004–2010 period provides evidence in support of the model.

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  1. Formally, this can be seen by simply relabeling the “project developer” as a “third party” and reiterating the equilibrium analysis. Equilibrium behavior remains unchanged.

  2. Empirically, absorptive capacity could also reduce technology transfer simply because there is less need for advanced technology. The empirical analysis accounts for this.

  3. It is important not to overestimate the importance of this concern. All members of the World Trade Organization have committed to implementing the Agreement on Trade Related Intellectual Property Rights, which increases the homogeneity of intellectual property rights across member states, regardless of their absorptive capacity.

  4. See Accessed on March 2, 2012.

  5. See the Online Appendix at this journal’s webpage for a more detailed description of the CDM.

  6. See for the project design document. Accessed on March 12, 2012.

  7. See for the project design document. Accessed on March 12, 2012.

  8. Unfortunately, we do not have the 2000 and 2010 numbers for all major sectors of renewable electricity generation. Concentrating solar thermal power increased from 355 to 1,095 megawatts between 2007 and 2010. Growth was slower for geothermal power and hydropower. Since hydropower is a major CDM sector, we also estimated our model without hydropower projects.

  9. While hydroelectricity (1,362 projects) and wind projects (921 projects) account for more than 75 % of all renewable projects, biomass projects (636 projects) also make up a considerable part of total renewable CDM projects.

  10. Indeed, separating between the effects of GDP per capita and population, the composite terms of GDP, does not seem warranted here. GDP per capita should only have a positive effect if population is large, and vice versa.

  11. See “List of Top Wind Power/Turbine Companies/ Stocks—Chinese Rising” at Accessed on March 12, 2012.

  12. When both GDP and patents are included, the effects of GDP are generally stronger than the effects of patents. Qualitatively, however, both variables have the expected effects.

  13. Since time fixed effects induce bias in limited dependent variable models, as Beck et al. (1998) and Chamberlain (1980) show, we opt for a specification with a cubic time polynomial (Carter and Signorino 2010). We are thankful to one of the reviewers for pointing this out.

  14. The variable is coded by the CDM/JI Pipeline Database. The measure does not distinguish between projects that had, and had not, a foreign partner at the very beginning. We also cannot follow the formation of partnerships at later stages since our data for technology transfer are from the time of registration. For other definitions of unilateral projects, see Lütken and Michaelowa (2008).

  15. The data for carbon dioxide emissions are from the World Bank’s World Development Indicators.

  16. See Accessed on March 2, 2012.

  17. The data come from the 1976 Patent Cooperation Treaty, available from the OECD website at Accessed on April 18, 2012.

  18. See previous footnote for data source.

  19. Summary statistics and correlation matrices are all based on our fixed effects models.

  20. Popp (2011, 147) notes that, apart from China, South Korea implemented policies to promote technology transfer. One of our robustness checks in the Online Appendix, therefore, excludes South Korea as a host country. Our findings are robust to the exclusion of 72 Korean CDM projects.

  21. We acknowledge that testing interactive hypotheses in nonlinear models not necessarily requires interactive product terms in the statistical model specification. Following the recommendation in Berry et al. (2010), we run likelihood ratio tests for all our main models with and without the interaction term. For all our models, we can reject the null hypothesis that the models with and without interaction term are identical with p < 0.001.

  22. Formally, the probability ratio is given as \(\dfrac{\text{Pr(TT=1|renewables=1,\textbf{M})}}{\text{Pr(TT=1|renewables=0,\textbf{M})}}\), where M is the matrix of all covariates except for the renewable project dummy.

  23. This number refers to our complete sample. For our models, the loss of observations ranges from about 16 % in model (2) to about 33 % in model (6).

  24. For our purposes we aggregate demand and supply side projects on energy efficiency, which are separately coded in the CDM/JI Pipeline Database.

  25. See the Online Appendix for a discussion of how these covariates were chosen for matching.

  26. These factors may differentially condition technology transfer for renewable and non-renewable projects, but since we do not expect the effect of these factors to depend on GDP, we did not include complex triple interactions.

  27. See Accessed on April 18, 2012.

  28. The data are from the United States Energy Information Administration. See and select “Total Non-Hydroelectric Renewables.” Accessed on April 18, 2012. To avoid losing data, the value of this variable is lagged by two years.

  29. See Accessed on April 18, 2012.


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This paper was written during a research stay funded by an ERP fellowship of the Studienstiftung des deutschen Volkes. Patrick Bayer gratefully acknowledges this generous funding and is thankful for the hospitality of Columbia University. We thank Christopher Marcoux for help with data collection and management. We also thank Thomas Hale, Richard Perkins, Eri Saikawa, and two anonymous reviewers for helpful comments on a previous draft.

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Correspondence to Patrick Bayer.

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Bayer, P., Urpelainen, J. External sources of clean technology: Evidence from the Clean Development Mechanism. Rev Int Organ 8, 81–109 (2013).

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  • Technology transfer
  • Political economy
  • Clean Development Mechanism

JEL Classification

  • D02
  • F55
  • O33
  • Q54
  • Q56