IDEAS home Printed from https://ideas.repec.org/a/sae/enejou/v29y2008i3p131-152.html
   My bibliography  Save this article

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

Author

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 onfossilfuels. 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 California’s 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, , vol. 29(3), pages 131-152, July.
    • Handle: RePEc:sae:enejou:v:29:y:2008:i:3:p:131-152
    DOI: 10.5547/ISSN0195-6574-EJ-Vol29-No3-7
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5547/ISSN0195-6574-EJ-Vol29-No3-7
    Download Restriction: no
    File URL: https://libkey.io/10.5547/ISSN0195-6574-EJ-Vol29-No3-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, , vol. 28(3), pages 51-72, July.
    4. 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.
    5. 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.
    6. Stephen H. Schneider & Lawrence H. Goulder, 1997. "Achieving low-cost emissions targets," Nature, Nature, vol. 389(6646), pages 13-14, September.
    7. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    8. Loschel, Andreas, 2002. "Technological change in economic models of environmental policy: a survey," Ecological Economics, Elsevier, vol. 43(2-3), pages 105-126, December.
    9. 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.
    10. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    11. 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.
    12. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    2. Hong, Sungjun & Chung, Yanghon & Woo, Chungwon, 2015. "Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea," Energy, Elsevier, vol. 79(C), pages 80-89.
    3. 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.
    4. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    5. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    6. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    7. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    8. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    9. Elofsson, Katarina, 2014. "International knowledge diffusion and its impact on the cost-effective clean-up of the Baltic Sea," Working Paper Series 2014:06, Swedish University of Agricultural Sciences, Department Economics.
    10. Witajewski-Baltvilks, Jan & Verdolini, Elena & Tavoni, Massimo, 2015. "Bending the learning curve," Energy Economics, Elsevier, vol. 52(S1), pages 86-99.
    11. Li, Sheng & Zhang, Xiaosong & Gao, Lin & Jin, Hongguang, 2012. "Learning rates and future cost curves for fossil fuel energy systems with CO2 capture: Methodology and case studies," Applied Energy, Elsevier, vol. 93(C), pages 348-356.
    12. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    13. Heuberger, Clara F. & Rubin, Edward S. & Staffell, Iain & Shah, Nilay & Mac Dowell, Niall, 2017. "Power capacity expansion planning considering endogenous technology cost learning," Applied Energy, Elsevier, vol. 204(C), pages 831-845.
    14. Nemet, Gregory F., 2009. "Interim monitoring of cost dynamics for publicly supported energy technologies," Energy Policy, Elsevier, vol. 37(3), pages 825-835, March.
    15. Harashima, Taiji, 2012. "A Theory of Intelligence and Total Factor Productivity: Value Added Reflects the Fruits of Fluid Intelligence," MPRA Paper 43151, University Library of Munich, Germany.
    16. Rout, Ullash K. & Blesl, Markus & Fahl, Ulrich & Remme, Uwe & Voß, Alfred, 2009. "Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model," Energy Policy, Elsevier, vol. 37(11), pages 4927-4942, November.
    17. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    18. van Blommestein, Kevin & Daim, Tugrul U. & Cho, Yonghee & Sklar, Paul, 2018. "Structuring financial incentives for residential solar electric systems," Renewable Energy, Elsevier, vol. 115(C), pages 28-40.
    19. Elofsson, Katarina & Gren, Ing-Marie, 2014. "Cost-efficient climate policies for interdependent and uncertain carbon pools," Working Paper Series 2014:7, Swedish University of Agricultural Sciences, Department Economics.
    20. Candelise, Chiara & Winskel, Mark & Gross, Robert J.K., 2013. "The dynamics of solar PV costs and prices as a challenge for technology forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 96-107.

    More about this item

    Keywords

    Market failure; Solar; learning-by-doing; diffusion; induced technological change; optimal policy; California;
    All these keywords.

    JEL classification:

    • F0 - International Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:enejou:v:29:y:2008:i:3:p:131-152. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.