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Renewable electricity generation in India—A learning rate analysis

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  • Partridge, Ian

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.

Suggested Citation

  • Partridge, Ian, 2013. "Renewable electricity generation in India—A learning rate analysis," Energy Policy, Elsevier, vol. 60(C), pages 906-915.
    • Handle: RePEc:eee:enepol:v:60:y:2013:i:c:p:906-915
    DOI: 10.1016/j.enpol.2013.05.035
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    References listed on IDEAS

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    1. Ibenholt, Karin, 2002. "Explaining learning curves for wind power," Energy Policy, Elsevier, vol. 30(13), pages 1181-1189, October.
    2. Cropper, Maureen & Gamkhar, Shama & Malik, Kabir & Limonov, Alex & Partridge, Ian, 2012. "The Health Effects of Coal Electricity Generation in India," RFF Working Paper Series dp-12-25, Resources for the Future.
    3. Coulomb, L. & Neuhoff, K., 2006. "Learning curves and changing product attributes: the case of wind turbines," Cambridge Working Papers in Economics 0618, Faculty of Economics, University of Cambridge.
    4. 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.
    5. Severin Borenstein, 2012. "The Private and Public Economics of Renewable Electricity Generation," Journal of Economic Perspectives, American Economic Association, vol. 26(1), pages 67-92, Winter.
    6. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    7. Nemet, Gregory F., 2009. "Interim monitoring of cost dynamics for publicly supported energy technologies," Energy Policy, Elsevier, vol. 37(3), pages 825-835, March.
    8. 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.
    9. Paul L. Joskow, 2011. "Comparing the Costs of Intermittent and Dispatchable Electricity Generating Technologies," American Economic Review, American Economic Association, vol. 101(3), pages 238-241, May.
    10. 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.
    11. 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.
    12. William D. Nordhaus, 2014. "The Perils of the Learning Model for Modeling Endogenous Technological Change," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    13. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
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    Cited by:

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    3. 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.
    4. Lin, Boqiang & He, Jiaxin, 2016. "Learning curves for harnessing biomass power: What could explain the reduction of its cost during the expansion of China?," Renewable Energy, Elsevier, vol. 99(C), pages 280-288.
    5. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    6. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    7. Elia, A. & Taylor, M. & Ó Gallachóir, B. & Rogan, F., 2020. "Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers," Energy Policy, Elsevier, vol. 147(C).
    8. Elia, A. & Kamidelivand, M. & Rogan, F. & Ó Gallachóir, B., 2021. "Impacts of innovation on renewable energy technology cost reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    9. Singh, Kewal & Singh, Anoop & Prakash, Puneet, 2022. "Estimating the cost of equity for the regulated energy and infrastructure sectors in India," Utilities Policy, Elsevier, vol. 74(C).

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