IDEAS home Printed from https://ideas.repec.org/p/enp/wpaper/eprg0601.html
   My bibliography  Save this paper

Learning Curves and Changing Product Attributes: the Case of Wind Turbines

Author

Listed:
  • Louis Coulomb

    (Faculty of Economics, University of Cambridge)

  • Karsten Neuhoff

    (Faculty of Economics, University of Cambridge)

Abstract

The heuristic concept of learning curves describes cost reductions as a function of cumulative production. A study of the Liberty shipbuilders suggested that product quality and production scale are other relevant factors that affect costs. Significant changes of attributes of a technology must be corrected when assessing the impact of learning-by-doing. We use an engineering-based model to capture the cost changes of wind turbines that can be attributed to changes in turbine size. We estimate the learning curve and turbine size parameters using more than 1500 price points from 1991 to 2003. The fit between model and empirical data confirms the concept.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Louis Coulomb & Karsten Neuhoff, 2006. "Learning Curves and Changing Product Attributes: the Case of Wind Turbines," Working Papers EPRG 0601, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg0601
    as

    Download full text from publisher

    File URL: https://www.jbs.cam.ac.uk/wp-content/uploads/2023/12/eprg-wp0601.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Goldemberg, Jose, 1996. "The evolution of ethanol costs in Brazil," Energy Policy, Elsevier, vol. 24(12), pages 1127-1128, December.
    2. Peter Thompson, 2001. "How Much Did the Liberty Shipbuilders Learn? New Evidence for an Old Case Study," Journal of Political Economy, University of Chicago Press, vol. 109(1), pages 103-137, February.
    3. 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.
    4. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    5. Isoard, Stephane & Soria, Antonio, 2001. "Technical change dynamics: evidence from the emerging renewable energy technologies," Energy Economics, Elsevier, vol. 23(6), pages 619-636, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peter Hartley, Kenneth B. Medlock III, Ted Temzelides, Xinya Zhang, 2016. "Energy Sector Innovation and Growth: An Optimal Energy Crisis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    2. Wilson, Charlie, 2012. "Up-scaling, formative phases, and learning in the historical diffusion of energy technologies," Energy Policy, Elsevier, vol. 50(C), pages 81-94.
    3. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    4. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    5. Marc Baudry & Clément Bonnet, 2016. "Demand pull isntruments and the development of wind power in Europe: A counter-factual analysis," Working Papers 1607, Chaire Economie du climat.
    6. Yu, Yang & Li, Hong & Che, Yuyuan & Zheng, Qiongjie, 2017. "The price evolution of wind turbines in China: A study based on the modified multi-factor learning curve," Renewable Energy, Elsevier, vol. 103(C), pages 522-536.
    7. Peter R. Hartley & Kenneth B. Medlock III, 2017. "The Valley of Death for New Energy Technologies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    8. Neuhoff, Karsten & Ehrenmann, Andreas & Butler, Lucy & Cust, Jim & Hoexter, Harriet & Keats, Kim & Kreczko, Adam & Sinden, Graham, 2008. "Space and time: Wind in an investment planning model," Energy Economics, Elsevier, vol. 30(4), pages 1990-2008, July.
    9. Partridge, Ian, 2013. "Renewable electricity generation in India—A learning rate analysis," Energy Policy, Elsevier, vol. 60(C), pages 906-915.
    10. Dosi, Giovanni & Grazzi, Marco & Mathew, Nanditha, 2017. "The cost-quantity relations and the diverse patterns of “learning by doing”: Evidence from India," Research Policy, Elsevier, vol. 46(10), pages 1873-1886.
    11. Ambec, Stefan & Crampes, Claude, 2012. "Electricity provision with intermittent sources of energy," Resource and Energy Economics, Elsevier, vol. 34(3), pages 319-336.
    12. Marc Baudry & Clément Bonnet, 2019. "Demand-Pull Instruments and the Development of Wind Power in Europe: A Counterfactual Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(2), pages 385-429, June.
    13. Oswald, James I. & Oswald, Andrew J. & Ashraf-Ball, Hezlin, 2009. "Hydrogen Transport and the Spatial Requirements of Renewable Energy," Economic Research Papers 271297, University of Warwick - Department of Economics.
    14. Stefan Ambec & Claude Crampes, 2010. "Electricity Production with Intermittent Sources of Energy," LERNA Working Papers 10.07.313, LERNA, University of Toulouse.
    15. 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.
    16. Hong, Soonpa & Yang, Taeyong & Chang, Hyun Joon & Hong, Sungjun, 2020. "The effect of switching renewable energy support systems on grid parity for photovoltaics: Analysis using a learning curve model," Energy Policy, Elsevier, vol. 138(C).
    17. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    18. 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.
    19. 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).
    20. Berry, David, 2009. "Innovation and the price of wind energy in the US," Energy Policy, Elsevier, vol. 37(11), pages 4493-4499, November.
    21. Grafström, Jonas & Poudineh, Rahmat, 2021. "A review of problems associated with learning curves for solar and wind power technologies," Ratio Working Papers 347, The Ratio Institute.
    22. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    23. 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.

    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. Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
    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. Kahouli, Sondès, 2011. "Effects of technological learning and uranium price on nuclear cost: Preliminary insights from a multiple factors learning curve and uranium market modeling," Energy Economics, Elsevier, vol. 33(5), pages 840-852, September.
    4. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    5. 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.
    6. Anelí Bongers, 2017. "Learning and forgetting in the jet fighter aircraft industry," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    7. 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.
    8. Greaker, Mads & Lund Sagen, Eirik, 2008. "Explaining experience curves for new energy technologies: A case study of liquefied natural gas," Energy Economics, Elsevier, vol. 30(6), pages 2899-2911, November.
    9. Gregory F. Nemet, 2006. "How well does Learning-by-doing Explain Cost Reductions in a Carbon-free Energy Technology?," Working Papers 2006.143, Fondazione Eni Enrico Mattei.
    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. John R. Boyce & Diane P. Bischak, 2010. "Learning by Doing, Knowledge Spillovers, and Technological and Organizational Change in High-Altitude Mountaineering," Journal of Sports Economics, , vol. 11(5), pages 496-532, October.
    12. Lehmann, Paul, 2009. "Climate policies with pollution externalities and learning spillovers," UFZ Discussion Papers 10/2009, Helmholtz Centre for Environmental Research (UFZ), Division of Social Sciences (ÖKUS).
    13. Rout, Ullash K. & Fahl, Ulrich & Remme, Uwe & Blesl, Markus & Voß, Alfred, 2009. "Endogenous implementation of technology gap in energy optimization models--a systematic analysis within TIMES G5 model," Energy Policy, Elsevier, vol. 37(7), pages 2814-2830, July.
    14. Dosi, Giovanni & Grazzi, Marco & Mathew, Nanditha, 2017. "The cost-quantity relations and the diverse patterns of “learning by doing”: Evidence from India," Research Policy, Elsevier, vol. 46(10), pages 1873-1886.
    15. Schauf, Magnus & Schwenen, Sebastian, 2021. "Mills of progress grind slowly? Estimating learning rates for onshore wind energy," Energy Economics, Elsevier, vol. 104(C).
    16. 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.
    17. Lehmann, Paul, 2013. "Supplementing an emissions tax by a feed-in tariff for renewable electricity to address learning spillovers," Energy Policy, Elsevier, vol. 61(C), pages 635-641.
    18. Wüstemeyer, Christoph & Madlener, Reinhard & Bunn, Derek W., 2015. "A stakeholder analysis of divergent supply-chain trends for the European onshore and offshore wind installations," Energy Policy, Elsevier, vol. 80(C), pages 36-44.
    19. 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.
    20. 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.

    More about this item

    Keywords

    Learning curve; Turbine scale; Wind turbines;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • N70 - Economic History - - Economic History: Transport, International and Domestic Trade, Energy, and Other Services - - - General, International, or Comparative
    • L64 - Industrial Organization - - Industry Studies: Manufacturing - - - Other Machinery; Business Equipment; Armaments
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

    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:enp:wpaper:eprg0601. 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: Ruth Newman (email available below). General contact details of provider: https://edirc.repec.org/data/jicamuk.html .

    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.