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Laissez faire and the Clean Development Mechanism: determinants of project implementation in Indian states, 2003–2011

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Abstract

India is the world’s second-largest host of projects implemented under the Kyoto Protocol’s Clean Development Mechanism (CDM). There is, however, considerable variation in the distribution of CDM projects implemented across different Indian states. While a large body of the literature examines cross-national variation in the implementation of CDM projects, few studies have analyzed the determinants of sub-national variation in different national contexts. We theorize that given India’s laissez-faire approach to CDM project implementation the availability of profitable climate mitigation opportunities and the political stability are two factors that promote CDM project implementation. Using sub-national data collected from a variety of sources, we conduct systematic analysis that provides empirical support for a set of hypotheses regarding the effects of these variables on project implementation. First, we find that states with a lot of public electricity-generating capacity and industrial capital implement more CDM projects than other states. Additionally, project developers rarely propose CDM projects during election years as a result of high levels of political uncertainty associated with those years. Our findings show that India’s liberal approach prevents the central government from using the CDM to promote sustainable development in less developed states. In India and other host countries where coordinated national policies to maximize their gains from CDM projects is absent, there is a paucity of project implementation in states that need it the most.

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Notes

  1. Data from CDM/ JI Pipeline Database. Available at http://www.cdmpipeline.org. Accessed May 27, 2012. For our analysis, we consider CDM projects that are registered, waiting for registration, or at the validation stage of the CDM project cycle.

  2. Erlewein and Nüsser (2011) conduct a descriptive analysis of the effectiveness of large hydropower CDM projects in the Indian state of Himachal Pradesh by examining planning documents and carrying out expert interviews. Michaelowa and Purohit (2007) study a sample of 52 CDM project design documents to determine if these documents address additionality.

  3. Other jurisdictions of India did not implement any CDM projects during the study period.

  4. One CER is equivalent to the abatement of one metric ton of carbon dioxide emissions. These credits can be traded and sold to industrialized countries with reduction commitments under the Kyoto Protocol.

  5. For details, see http://cdm.unfccc.int/Projects/diagram.html. Accessed June 11, 2012.

  6. Non-Annex I countries are mostly developing countries not listed in Annex I of the United Nations Framework Convention on Climate Change. For a complete list of Annex I countries, see http://unfccc.int/parties_and_observers/parties/annex_i/items/2774.php. Accessed June 11, 2012.

  7. See http://www.cdmindia.gov.in/constitution.php. Accessed June 6, 2012.

  8. An assessment of various hydroelectric CDM project development documents proposed for the state of Himachal Pradesh shows that the Ministry’s role is limited to the protection of forest land.

  9. Approximately one third of the CDM projects in our sample are classified as large-scale projects, while the rest is considered small-scale. In accordance with the official CDM guidance document, we classify projects as small based on the used methodology. Specifically, we use methodologies with the prefix, “AMS,” to categorize projects as small. See http://www.cdmpipeline.org/publications/GuidanceCDMpipeline.pdf. Accessed June 11, 2012.

  10. For further details, see the project design document at http://cdm.unfccc.int/Projects/Validation/DB/UD85KL6SWWV357TS8A262T76Z49P1E/view.html. Accessed May 31, 2012.

  11. The total amount of CO2 emission reductions of this single project over its entire ten year lifespan matches almost CO2 emissions of the Philippines in a single year like 2008. See the World Development Indicators for emissions data at http://data.worldbank.org/indicator/EN.ATM.CO2E.KT. Accessed May 31, 2012.

  12. Overall, in India, there are 35 sub-national units, 28 states, and 7 Union Territories. Since we lack non-zero CDM project data for all Union Territories except Delhi (Andaman and Nicobar Islands, Chandigarh, Dadra and Nagar Haveli, Daman and Diu, Lakshadweep, and Pondicherry) and for two states (Mizoram and Nagaland), our sample consists of only 27 sub-national units. See http://india.gov.in/knowindia/state_uts.php. Accessed May 31, 2012.

  13. See http://www.cdmpipeline.org. Accessed December 31, 2011.

  14. Again, it bears mentioning that our dataset includes all CDM projects that are either registered, waiting for registration, or at the validation stage.

  15. See http://www.powermin.nic.in/reports/annual_report.htm. Accessed April 24, 2012.

  16. Distributions of privately and publicly owned electricity-generating capacities can be found in the supplementary appendix.

  17. See http://mospi.nic.in/mospi_new/upload/asi/ASI_main.htm?status=1&menu_id=88. Accessed May 13, 2012.

  18. The supplementary appendix provides a histogram showing the distribution of the industrial capital base variable. To account for the fact that the capital base needs to be renewed over time, we also estimate regression models with both the level and the annual net change in the capital base included, without any consequences for our main results.

  19. See http://eci.nic.in/eci_main1/ElectionStatistics.aspx. Accessed June 6, 2012.

  20. To avoid bias from potential anticipation and backlog effects from elections called at the beginning or the end of a year, we also estimate models with pre- and post-election year dummies, without any changes to our results.

  21. Population data come from the census of the Office of the Registrar General of India. See http://censusindia.gov.in/. Accessed May 20, 2012.

  22. Data are from the Directorate of Economics and Statistics of the respective state governments. See http://planningcommission.nic.in/data/datatable/. Accessed April 25, 2012.

  23. Data are from the Databook for DCH. See http://planningcommission.nic.in/data/datatable/. Accessed May 20, 2012.

  24. In a robustness check, we also estimate our main models with state fixed effects instead of regional ones, without any changes to our findings.

  25. Specifically, we have the Northern Zonal Council (Chandigarh, Delhi, Haryana, Himachal Pradesh, Jammu and Kashmir, Punjab, Rajasthan), the Central Zonal Council (Chhattisgarh, Madhya Pradesh, Uttarakhand, Uttar Pradesh), the Eastern Zonal Council (Bihar, Jharkhand, Orissa, and West Bengal), the Western Zonal Council (Dadra and Nagar Haveli, Daman and Diu, Goa, Gujarat, Maharashtra), the Southern Zonal Council (Andhra Pradesh, Karnataka, Kerala, Pondicherry, and Tamil Nadu), and the North Eastern Council (Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura). The Indian Union Territories Andaman and Nicobar Islands and Lakshadweep are not part of any of India’s six official Zonal Councils, so they are not listed in Table 1. This classification comes from the Ministry of Home Affairs of the Indian Government. See http://www.mha.nic.in/zonal_council for additional information. Accessed June 5, 2012.

  26. All estimation results can be found in the supplementary appendix.

  27. Data on state-level transmission and distribution losses (%) are made available for the years 2002–2008 by the Central Electricity Authority’s General Review 2007, 2008. We use the logarithm of this variable, and extrapolate the data beyond 2008 to allow analysis for CDM projects implemented during later years. Data on state-level corruption cases, or total cases brought in for investigation, are available from 2000–2009 and are taken from the Crime in India annual reports collated by the PRS Legislative Research. See http://ncrb.nic.in/ciiprevious/main.htm. Accessed May 20, 2012. The logarithm of total corruption cases is used, and the data are also extrapolated.

  28. See the supplementary appendix for the substantive effects plots.

  29. See the supplementary appendix for the substantive effects plots.

  30. The difference between the correlation coefficients is highly statistically significant with \(p<0.000\).

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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 West 118th Street, 712 International Affairs Building, New York, NY, 10027, USA

    Johannes Urpelainen

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

    Alice Xu

Authors
  1. Patrick Bayer
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  2. Johannes Urpelainen
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  3. Alice Xu
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Corresponding author

Correspondence to Johannes Urpelainen.

Additional information

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 Michaël Aklin, S.P. Harish, and the anonymous reviewers for helpful comments on a previous draft.

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Bayer, P., Urpelainen, J. & Xu, A. Laissez faire and the Clean Development Mechanism: determinants of project implementation in Indian states, 2003–2011. Clean Techn Environ Policy 16, 1687–1701 (2014). https://doi.org/10.1007/s10098-014-0746-3

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