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Explaining differences in sub-national patterns of clean technology transfer to China and India

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Abstract

The Kyoto Protocol’s Clean Development Mechanism (CDM) has the capacity to incentivize the international transfer of environmentally sound technologies. Given that both countries are expected to have similar incentives when managing the distribution of technology transfer within the country, why do sub-national patterns in the allocation of projects with technology transfer differ? Using comparable political–economic data compiled for China and India, we offer an explanation for these differences. In China, where the government regards the CDM as a tool for achieving sustainable development, technology transfer is concentrated in provinces that need it the most and that are most conducive to receiving transfers (i.e., economically less developed, yet heavily industrialized provinces). In India, where the government takes on a “laissez-faire” approach to the CDM, neither level of economic development nor that of industrialization affects clean technology transfer. In this regard, although the incentives are similar, the capacity to pursue them is not comparable. We test these hypotheses using data on CDM technology transfer across Chinese provinces and Indian states during the 6-year period from 2004 to 2010.

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Notes

  1. See the research design section for data sources.

  2. Article 10(c) of the Kyoto Protocol does specify the need for all Parties involved to cooperate in the development, application, diffusion, and transfer of environmentally sound technologies in the public domain.

  3. At the firm level, technology transfer often results from cooperation between multinational companies, such as joint ventures (Heller and Shukla 2003; Ivarsson and Alvstam 2005). In the absence of a strong national policy, technology transfer is expected to be higher for most developed Indian states because they already host branches of internationally operating technology leaders.

  4. Officially, China administers 33 sub-national divisions, but CDM project data are not available for Tibet and the two special administrative regions, Hong Kong and Macau. This leaves us with 30 “provinces.”

  5. In India, there are 35 sub-national administrative units: 28 states and 7 Union Territories. However, with the exception of Delhi, data for the Union Territories (i.e., Andaman and Nicobar Islands, Chandigarh, Dadra and Nagar Haveli, Daman and Diu, Lakshadweep, and Pondicherry) and for the states of Mizoram and Nagaland is not available. Accessed June 16, 2012.

  6. CDM data are available from http://cdmpipeline.org. Accessed July 11, 2012. Of all the 6,977 CDM projects available in the CDM/JI Pipeline database for years 2004–2010, the projects implemented in China and India jointly account for about 65 % of the data.

  7. The data for CDM projects with technology transfer are available from UNFCCC (2010) upon request.

  8. As shown in the supplementary appendix, correlation coefficients vary from \(r=+0.497\) for total Chinese CDM projects and 2012 CERs to \(r=+0.839\) for total Indian CDM projects and 2012 CERs. All correlation coefficients are strongly positive and highly statistically significant.

  9. In fact, the UNFCCC (2010) report also distinguishes between the presence of equipment transfers, knowledge transfer, and both. To avoid unstable estimation, we do not use this information to construct separate dependent variables.

  10. See http://chinadataonline.org. Accessed March 20, 2012.

  11. See http://planningcommission.nic.in/data/datatable/. Accessed April 25, 2012.

  12. For the data on Chinese provinces, we use real GDP growth data provided by China Data Online to account for inflation. For the data on Indian states, we use price data taken from the Directorate of Economics and Statistics. These sources account for price increases in provinces and states, respectively. Finally, we used exchange rate data from the Reserve Bank of India (http://www.rbi.org.in/scripts/PublicationsView.aspx?id=13734) and the China–US foreign exchange rate provided by the Board of Governors of the Federal Reserve System (http://research.stlouisfed.org/fred2/series/AEXCHUS?cid=32219), to convert these values into 2005 US$. Accessed July 7, 2012.

  13. The variables used are “Total Output” (India) and “Gross Industrial Output” (China). For India, see http://mospi.nic.in/mospi_new/upload/asi/ASI_main.htm?status=1&menu_id=88. Accessed July 7, 2012.

  14. The deflators were provided by the IMF. See http://elibrary-data.imf.org/public/FrameReport.aspx?v=3&c=20840382. Accessed July 7, 2012.

  15. For China, the data are from China Data Online. For India, the data are from the Ministry of Power. See http://pib.nic.in/newsite/erelease.aspx?relid=84206 and http://pib.nic.in/newsite/erelease.aspx?relid=30158. Accessed on July 7, 2012.

  16. Election data come from the Election Commission of India and are available from http://eci.nic.in/eci_main1/ElectionStatistics.aspx. Accessed on April 13, 2014.

  17. In our dataset, 70 out of 298 province-years have large-scale projects. This accounts for about one-quarter of the dataset.

  18. Overall, only 38 out of 298 province-years (13 %) do not have a single renewable energy project.

  19. See the supplementary appendix for a histogram.

  20. See http://planningcommission.nic.in/data/datatable/index.php?data=datatab. Accessed May 20, 2012.

  21. For summary statistics and a correlation matrix for the entire sample, see the supplementary appendix.

  22. For models (1) to (3) in Table 3, the normally distributed test statistics are \(z=2.24\), \(z=2.20\), and \(z=1.44\), respectively. This yields corresponding \(p\) values of \(p<0.012\), \(p<0.013\), and \(p<0.077\), supporting the choice of zero-inflated models over non-inflated count models.

  23. While dropping the first year reduces the number of CDM projects by only eleven, seven of which feature technology transfer, excluding projects from 2010, reduces the sample size by about 11 %. Out of 4,460 CDM projects, 939 are implemented in 2010 with 56 or about 6 % carrying technology transfers.

  24. See the supplementary appendix for data sources and the exact construction of these renewable potential controls.

  25. A full list of Chinese provinces with CDM service centers and Indian states with CDM cells as well as the regression results can again be found in the appendix.

  26. The full results tables for these additional robustness checks can be found in the supplementary appendix.

  27. We exclude these project types mainly because they are the largest categories in our dataset. Renewable projects account for 67 % of the data (3,005 projects), supply side energy efficiency projects for 14 % of the data (634 projects), and projects concentrating on methane, coal, and cement emissions for 9 % of the data (397 projects).

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Acknowledgments

This article 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. He is also thankful for a postdoctoral fellowship at Washington University in St. Louis from the German Academic Exchange Service (DAAD). We thank Valerie Pinkerton and Sung Eun Kim for comments on a previous draft.

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Authors and Affiliations

  1. Department of Political Science, Program in International and Area Studies, Washington University in St. Louis, One Brookings Drive, St. Louis, MO, 63130, USA

    Patrick Bayer

  2. Department of Political Science, Columbia University, 420 W 118th Street, 712 IAB, New York City, NY, 10027, USA

    Johannes Urpelainen

  3. Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA, 02138, USA

    Alice Xu

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  1. Patrick Bayer
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Correspondence to Patrick Bayer.

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Bayer, P., Urpelainen, J. & Xu, A. Explaining differences in sub-national patterns of clean technology transfer to China and India. Int Environ Agreements 16, 261–283 (2016). https://doi.org/10.1007/s10784-014-9257-2

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