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Renewable electricity generation in India—A learning rate analysis

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  • Partridge, Ian
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    Abstract

    The cost of electricity generation using renewable technologies is widely assumed to be higher than the cost for conventional generation technologies, but likely to fall with growing experience of the technologies concerned. This paper tests the second part of that statement using learning rate analysis, based on large samples of wind and small hydro projects in India, and projects likely changes in these costs through 2020. It is the first study of learning rates for renewable generation technologies in India, and only the second in any developing country—it provides valuable input to the development of Indian energy policy and will be relevant to policy makers in other developing countries.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0301421513003704
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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Policy.

    Volume (Year): 60 (2013)
    Issue (Month): C ()
    Pages: 906-915

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    Handle: RePEc:eee:enepol:v:60:y:2013:i:c:p:906-915

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    Web page: http://www.elsevier.com/locate/enpol

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    Keywords: Renewable energy; Learning rate analysis; India;

    References

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    1. Severin Borenstein, 2011. "The Private and Public Economics of Renewable Electricity Generation," NBER Working Papers 17695, National Bureau of Economic Research, Inc.
    2. Qiu, Yueming & Anadon, Laura D., 2012. "The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization," Energy Economics, Elsevier, vol. 34(3), pages 772-785.
    3. Cropper, Maureen & Gamkhar, Shama & Malik, Kabir & Limonov, Alex & Partridge, Ian, 2013. "The Health Effects of Coal Electricity Generation in India," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150290, Agricultural and Applied Economics Association.
    4. Alberth, Stephan & Hope, Chris, 2007. "Climate modelling with endogenous technical change: Stochastic learning and optimal greenhouse gas abatement in the PAGE2002 model," Energy Policy, Elsevier, vol. 35(3), pages 1795-1807, March.
    5. William D. Nordhaus, 2009. "The Perils of the Learning Model For Modeling Endogenous Technological Change," Cowles Foundation Discussion Papers 1685, Cowles Foundation for Research in Economics, Yale University.
    6. Paul L. Joskow, 2011. "Comparing the Costs of Intermittent and Dispatchable Electricity Generating Technologies," EUI-RSCAS Working Papers 45, European University Institute (EUI), Robert Schuman Centre of Advanced Studies (RSCAS).
    7. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.
    8. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    9. Donovan, Charles & Nuñez, Laura, 2012. "Figuring what’s fair: The cost of equity capital for renewable energy in emerging markets," Energy Policy, Elsevier, vol. 40(C), pages 49-58.
    10. Ibenholt, Karin, 2002. "Explaining learning curves for wind power," Energy Policy, Elsevier, vol. 30(13), pages 1181-1189, October.
    11. Nemet, Gregory F., 2009. "Interim monitoring of cost dynamics for publicly supported energy technologies," Energy Policy, Elsevier, vol. 37(3), pages 825-835, March.
    12. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
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    Cited by:
    1. Tian Tang & David Popp, 2014. "The Learning Process and Technological Change in Wind Power: Evidence from China's CDM Wind Projects," CESifo Working Paper Series 4705, CESifo Group Munich.

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