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Innovation modelling and multi-factor learning in wind energy technology

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  • Odam, Neil
  • de Vries, Frans P.

Abstract

Learning curves are frequently cited to justify the subsidization of new technologies to facilitate market competitiveness. The main literature has focused on improving the specification of the basic learning curve model by augmenting it to control for technological development measured by public R&D expenditures. In addition to employing R&D expenditures, the purpose of this paper is to assess the robustness of an augmented multi-factor learning curve model by estimating learning rates in a panel framework utilising patent data on relevant wind power technologies in Germany, Denmark, Spain and the UK. Results indicate that both innovation proxies are qualitatively identical and generate consistent learning estimates. The paper also aims at exploring the presence of unit roots in learning curves and alerts to the possibility of spurious estimations. Renewable energy policy guided by learning curve estimates should therefore be implemented with caution.

Suggested Citation

  • Odam, Neil & de Vries, Frans P., 2020. "Innovation modelling and multi-factor learning in wind energy technology," Energy Economics, Elsevier, vol. 85(C).
    • Handle: RePEc:eee:eneeco:v:85:y:2020:i:c:s0140988319303895
    DOI: 10.1016/j.eneco.2019.104594
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    6. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying endogenous learning models in energy system optimization," Papers 2106.06373, arXiv.org.
    7. Xin-gang, Zhao & Wei, Wang & Jieying, Wang, 2022. "The policy effects of demand-pull and technology-push on the diffusion of wind power: A scenario analysis based on system dynamics approach," Energy, Elsevier, vol. 261(PA).
    8. Che, Xiao-Jing & Zhou, P. & Wang, M., 2022. "The policy effect on photovoltaic technology innovation with regional heterogeneity in China," Energy Economics, Elsevier, vol. 115(C).
    9. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying Endogenous Learning Models in Energy System Optimization," Energies, MDPI, vol. 14(16), pages 1-21, August.
    10. 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.

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    More about this item

    Keywords

    Technical change; R&D; Learning curves; Renewables; Patents; Knowledge stock; Unit roots;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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