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Interim monitoring of cost dynamics for publicly supported energy technologies

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  • Nemet, Gregory F.

Abstract

The combination of substantial public funding of nascent energy technologies and recent increases in the costs of those that have been most heavily supported has raised questions about whether policy makers should sustain, alter, enhance, or terminate such programs. This paper uses experience curves for photovoltaics (PV) and wind to (1) estimate ranges of costs for these public programs and (2) introduce new ways of evaluating recent cost dynamics. For both technology cases, the estimated costs of the subsidies required to reach targets are sensitive to the choice of time period on which cost projections are based. The variation in the discounted social cost of subsidies exceeds an order of magnitude. Vigilance is required to avoid the very expensive outcomes contained within these distributions of social costs. Two measures of the significance of recent deviations are introduced. Both indicate that wind costs are within the expected range of prior forecasts but that PV costs are not. The magnitude of the public funds involved in these programs heightens the need for better analytical tools with which to monitor and evaluate cost dynamics.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Policy.

Volume (Year): 37 (2009)
Issue (Month): 3 (March)
Pages: 825-835

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Handle: RePEc:eee:enepol:v:37:y:2009:i:3:p:825-835

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

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Keywords: Technology policy Experience curves Learning curves;

References

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Citations

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Cited by:
  1. Nemet, Gregory F., 2010. "Robust incentives and the design of a climate change governance regime," Energy Policy, Elsevier, vol. 38(11), pages 7216-7225, November.
  2. Rüther, Ricardo & Zilles, Roberto, 2011. "Making the case for grid-connected photovoltaics in Brazil," Energy Policy, Elsevier, vol. 39(3), pages 1027-1030, March.
  3. Schmid, Eva & Pahle, Michael & Knopf, Brigitte, 2013. "Renewable electricity generation in Germany: A meta-analysis of mitigation scenarios," Energy Policy, Elsevier, vol. 61(C), pages 1151-1163.
  4. Dinica, Valentina, 2011. "Renewable electricity production costs--A framework to assist policy-makers' decisions on price support," Energy Policy, Elsevier, vol. 39(7), pages 4153-4167, July.
  5. 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.
  6. Rigter, Jasper & Vidican, Georgeta, 2010. "Cost and optimal feed-in tariff for small scale photovoltaic systems in China," Energy Policy, Elsevier, vol. 38(11), pages 6989-7000, November.
  7. Edenhofer, Ottmar & Hirth, Lion & Knopf, Brigitte & Pahle, Michael & Schlömer, Steffen & Schmid, Eva & Ueckerdt, Falko, 2013. "On the economics of renewable energy sources," Energy Economics, Elsevier, vol. 40(S1), pages S12-S23.
  8. Arnaud De La Tour & Matthieu Glachant, 2013. "How do solar photovoltaic feed-in tariffs interact with solar panel and silicon prices? An empirical study," Working Papers hal-00809449, HAL.
  9. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
  10. Partridge, Ian, 2013. "Renewable electricity generation in India—A learning rate analysis," Energy Policy, Elsevier, vol. 60(C), pages 906-915.

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