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Learning-by-Doing and the Optimal Solar Policy in California

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Listed:
  • Arthur van Benthem
  • Kenneth Gillingham
  • James Sweeney
Abstract
Much policy attention has been given to promote fledgling energy technologies that promise to reduce our reliance on fossil fuels. These policies often aim to correct market failures, such as environmental externalities and learning¥by-doing (LBD). We examine the implications of the assumption that LBD exists, quantifying the market failure due to LBD. We develop a model of technological advancement based on LBD and environmental market failures to examine the economically efficient level of subsidies in CaliforniaÕs solar photovoltaic market. Under central-case parameter estimates, including nonappropriable LBD, we find that maximizing net social benefits implies a solar subsidy schedule similar in magnitude to the recently implemented California Solar Initiative. This result holds for a wide range of LBD parameters. However, with no LBD, the subsidies cannot be justified by the environmental externality alone.

Suggested Citation

  • Arthur van Benthem & Kenneth Gillingham & James Sweeney, 2008. "Learning-by-Doing and the Optimal Solar Policy in California," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 131-152.
  • Handle: RePEc:aen:journl:2008v29-03-a07
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    References listed on IDEAS

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    1. Paul Joskow & Nancy L. Rose, 1985. "The Effects of Technological Change, Experience, and Environmental Regulation on the Construction Cost of Coal-Burning Generating Units," RAND Journal of Economics, The RAND Corporation, vol. 16(1), pages 1-17, Spring.
    2. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 51-72.
    3. van der Zwaan, Bob & Rabl, Ari, 2004. "The learning potential of photovoltaics: implications for energy policy," Energy Policy, Elsevier, vol. 32(13), pages 1545-1554, September.
    4. Stephen H. Schneider & Lawrence H. Goulder, 1997. "Achieving low-cost emissions targets," Nature, Nature, vol. 389(6646), pages 13-14, September.
    5. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    6. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    7. Duke, Richard & Williams, Robert & Payne, Adam, 2005. "Accelerating residential PV expansion: demand analysis for competitive electricity markets," Energy Policy, Elsevier, vol. 33(15), pages 1912-1929, October.
    8. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    9. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    10. Loschel, Andreas, 2002. "Technological change in economic models of environmental policy: a survey," Ecological Economics, Elsevier, vol. 43(2-3), pages 105-126, December.
    11. Goulder Lawrence H., 1995. "Effects of Carbon Taxes in an Economy with Prior Tax Distortions: An Intertemporal General Equilibrium Analysis," Journal of Environmental Economics and Management, Elsevier, vol. 29(3), pages 271-297, November.
    12. Papineau, Maya, 2006. "An economic perspective on experience curves and dynamic economies in renewable energy technologies," Energy Policy, Elsevier, vol. 34(4), pages 422-432, March.
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