IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v184y2023ics1364032123003490.html
   My bibliography  Save this article

A hypothesis for experience curves of related technologies with an application to wind energy

Author

Listed:
  • Hernandez-Negron, Christian G.
  • Baker, Erin
  • Goldstein, Anna P.

Abstract

We develop a novel hypothesis around the impact of a related technology on the development of an experience curve. We explore the implications of this hypothesis in the case of wind energy, which has been historically developed onshore and is currently experiencing rapid growth in deployment offshore. We look at the impact of modelling offshore wind energy as (1) a fully new technology, (2) a direct offshoot of onshore wind, and (3) a hybrid. Focusing on the levelized cost of electricity of offshore wind, we find that assumptions about its relatedness to onshore wind are equally important as assumptions about future growth scenarios. This research highlights a previously neglected factor in experience curve analysis, which may be especially important for technologies, such as offshore wind energy, that are expected to contribute significantly to climate change mitigation.

Suggested Citation

  • Hernandez-Negron, Christian G. & Baker, Erin & Goldstein, Anna P., 2023. "A hypothesis for experience curves of related technologies with an application to wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:rensus:v:184:y:2023:i:c:s1364032123003490
    DOI: 10.1016/j.rser.2023.113492
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032123003490
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2023.113492?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    2. Toke, David, 2011. "The UK offshore wind power programme: A sea-change in UK energy policy?," Energy Policy, Elsevier, vol. 39(2), pages 526-534, February.
    3. Rubin, Edward S. & Yeh, Sonia & Antes, Matt & Berkenpas, Michael & Davison, John, 2007. "Use of experience curves to estimate the future cost of power plants with CO2 capture," Institute of Transportation Studies, Working Paper Series qt46x6h0n0, Institute of Transportation Studies, UC Davis.
    4. Laura Díaz Anadón & Erin Baker & Valentina Bosetti, 2017. "Publisher Correction: Integrating uncertainty into public energy research and development decisions," Nature Energy, Nature, vol. 2(12), pages 980-980, December.
    5. Fernando F. Suárez & James M. Utterback, 1995. "Dominant designs and the survival of firms," Strategic Management Journal, Wiley Blackwell, vol. 16(6), pages 415-430.
    6. Green, Richard & Vasilakos, Nicholas, 2011. "The economics of offshore wind," Energy Policy, Elsevier, vol. 39(2), pages 496-502, February.
    7. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    8. Franklyn Kanyako & Erin Baker, 2021. "Uncertainty analysis of the future cost of wind energy on climate change mitigation," Climatic Change, Springer, vol. 166(1), pages 1-17, May.
    9. Utterback, James M & Abernathy, William J, 1975. "A dynamic model of process and product innovation," Omega, Elsevier, vol. 3(6), pages 639-656, December.
    10. Miketa, Asami & Schrattenholzer, Leo, 2004. "Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes; first results," Energy Policy, Elsevier, vol. 32(15), pages 1679-1692, October.
    11. Rubin, Edward S & Taylor, Margaret R & Yeh, Sonia & Hounshell, David A, 2004. "Learning curves for environmental technology and their importance for climate policy analysis," Energy, Elsevier, vol. 29(9), pages 1551-1559.
    12. Malte Jansen & Iain Staffell & Lena Kitzing & Sylvain Quoilin & Edwin Wiggelinkhuizen & Bernard Bulder & Iegor Riepin & Felix Müsgens, 2020. "Offshore wind competitiveness in mature markets without subsidy," Nature Energy, Nature, vol. 5(8), pages 614-622, August.
    13. Nemet, Gregory F., 2012. "Inter-technology knowledge spillovers for energy technologies," Energy Economics, Elsevier, vol. 34(5), pages 1259-1270.
    14. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    15. 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.
    16. Nemet, Gregory F., 2009. "Interim monitoring of cost dynamics for publicly supported energy technologies," Energy Policy, Elsevier, vol. 37(3), pages 825-835, March.
    17. Grubler, Arnulf, 2010. "The costs of the French nuclear scale-up: A case of negative learning by doing," Energy Policy, Elsevier, vol. 38(9), pages 5174-5188, September.
    18. Sagar, Ambuj D. & van der Zwaan, Bob, 2006. "Technological innovation in the energy sector: R&D, deployment, and learning-by-doing," Energy Policy, Elsevier, vol. 34(17), pages 2601-2608, November.
    19. 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.
    20. Ryan Wiser & Karen Jenni & Joachim Seel & Erin Baker & Maureen Hand & Eric Lantz & Aaron Smith, 2016. "Expert elicitation survey on future wind energy costs," Nature Energy, Nature, vol. 1(10), pages 1-8, October.
    21. Raymond Vernon, 1966. "International Investment and International Trade in the Product Cycle," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 80(2), pages 190-207.
    22. Kobos, Peter H. & Erickson, Jon D. & Drennen, Thomas E., 2006. "Technological learning and renewable energy costs: implications for US renewable energy policy," Energy Policy, Elsevier, vol. 34(13), pages 1645-1658, September.
    23. Ferioli, F. & Schoots, K. & van der Zwaan, B.C.C., 2009. "Use and limitations of learning curves for energy technology policy: A component-learning hypothesis," Energy Policy, Elsevier, vol. 37(7), pages 2525-2535, July.
    24. 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.
    25. Laura Díaz Anadón & Erin Baker & Valentina Bosetti, 2017. "Integrating uncertainty into public energy research and development decisions," Nature Energy, Nature, vol. 2(5), pages 1-14, May.
    26. Kavita Surana & Claudia Doblinger & Laura Diaz Anadon & Nathan Hultman, 2020. "Effects of technology complexity on the emergence and evolution of wind industry manufacturing locations along global value chains," Nature Energy, Nature, vol. 5(10), pages 811-821, October.
    27. Dubaric, Ervin & Giannoccaro, Dimitris & Bengtsson, Rune & Ackermann, Thomas, 2011. "Patent data as indicators of wind power technology development," World Patent Information, Elsevier, vol. 33(2), pages 144-149, June.
    28. 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.
    29. Jing Meng & Rupert Way & Elena Verdolini & Laura Diaz Anadon, 2021. "Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(27), pages 1917165118-, July.
    30. Clarke, Leon & Weyant, John & Birky, Alicia, 2006. "On the sources of technological change: Assessing the evidence," Energy Economics, Elsevier, vol. 28(5-6), pages 579-595, November.
    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. 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).
    2. 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.
    3. 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).
    4. 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).
    5. Wiser, Ryan & Millstein, Dev, 2020. "Evaluating the economic return to public wind energy research and development in the United States," Applied Energy, Elsevier, vol. 261(C).
    6. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    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. Schauf, Magnus & Schwenen, Sebastian, 2021. "Mills of progress grind slowly? Estimating learning rates for onshore wind energy," Energy Economics, Elsevier, vol. 104(C).
    9. Choi, Donghyun & Kim, Yeong Jae, 2023. "Local and global experience curves for lumpy and granular energy technologies," Energy Policy, Elsevier, vol. 174(C).
    10. Criqui, P. & Mima, S. & Menanteau, P. & Kitous, A., 2015. "Mitigation strategies and energy technology learning: An assessment with the POLES model," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 119-136.
    11. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    12. Gunther Glenk & Rebecca Meier & Stefan Reichelstein, 2021. "Cost Dynamics of Clean Energy Technologies," Schmalenbach Journal of Business Research, Springer, vol. 73(2), pages 179-206, June.
    13. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng, 2022. "Effects of learning curve models on onshore wind and solar PV cost developments in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    14. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng & Vaz-Serra, Paulo, 2022. "Economic and environmental impacts of public investment in clean energy RD&D," Energy Policy, Elsevier, vol. 168(C).
    15. 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).
    16. Duffy, Aidan & Hand, Maureen & Wiser, Ryan & Lantz, Eric & Dalla Riva, Alberto & Berkhout, Volker & Stenkvist, Maria & Weir, David & Lacal-Arántegui, Roberto, 2020. "Land-based wind energy cost trends in Germany, Denmark, Ireland, Norway, Sweden and the United States," Applied Energy, Elsevier, vol. 277(C).
    17. Doblinger, Claudia & Surana, Kavita & Li, Deyu & Hultman, Nathan & Anadón, Laura Díaz, 2022. "How do global manufacturing shifts affect long-term clean energy innovation? A study of wind energy suppliers," Research Policy, Elsevier, vol. 51(7).
    18. Shayegh, Soheil & Sanchez, Daniel L. & Caldeira, Ken, 2017. "Evaluating relative benefits of different types of R&D for clean energy technologies," Energy Policy, Elsevier, vol. 107(C), pages 532-538.
    19. Odam, Neil & de Vries, Frans P., 2020. "Innovation modelling and multi-factor learning in wind energy technology," Energy Economics, Elsevier, vol. 85(C).
    20. 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.

    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:eee:rensus:v:184:y:2023:i:c:s1364032123003490. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.