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Learning-by-doing, Learning Spillovers and the Diffusion of Fuel Cell Vehicles


  • Malte Schwoon

    () (Statkraft, Duesseldorf)


Fuel cell vehicles (FCVs) running on hydrogen do not cause local air pollution. Depending on the energy sources used to produce the hydrogen they may also reduce greenhouse gases in the long-term. Besides problems related to the necessary investments into hydrogen infrastructure, there is a general notion that current fuel cells costs are too high to be competitive with conventional engines, creating an insurmountable barrier to introduction. But given historical evidence from many other technologies it is highly likely that learning by doing (LBD) would lead to substantial cost reductions. In this study we implement potential cost reductions from LBD into an existing agent based model that captures the main dynamics of the introduction of the new technology together with hydrogen infrastructure build up. Assumptions about the learning rate turn out to have a critical impact on the projected diffusion of the FCVs. Moreover, LBD could imply a substantial first mover advantage. We also address the impact of learning spillovers between producers and find that a government might face a policy trade off between fostering diffusion by facilitating learning spillovers and protecting the relative advantage of a national technological leader.

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  • Malte Schwoon, 2006. "Learning-by-doing, Learning Spillovers and the Diffusion of Fuel Cell Vehicles," Working Papers FNU-112, Research unit Sustainability and Global Change, Hamburg University, revised Jun 2006.
  • Handle: RePEc:sgc:wpaper:112

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    References listed on IDEAS

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    Cited by:

    1. Gary W. Yohe & Richard S.J. Tol, 2007. "Precaution And A Dismal Theorem: Implications For Climate Policy And Climate Research," Working Papers FNU-145, Research unit Sustainability and Global Change, Hamburg University, revised Aug 2007.
    2. Bosetti, Valentina & Longden, Thomas, 2013. "Light duty vehicle transportation and global climate policy: The importance of electric drive vehicles," Energy Policy, Elsevier, vol. 58(C), pages 209-219.
    3. Ma, Tieju & Chen, Huayi, 2015. "Adoption of an emerging infrastructure with uncertain technological learning and spatial reconfiguration," European Journal of Operational Research, Elsevier, vol. 243(3), pages 995-1003.
    4. Martin Zsifkovits & Markus Günther, 2015. "Simulating resistances in innovation diffusion over multiple generations: an agent-based approach for fuel-cell vehicles," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(2), pages 501-522, June.
    5. Zhao, Wei, 2013. "Estimating Dynamic Merger Effciencies with an Application to the 1997 Boeing-McDonnell Douglas Merger," MPRA Paper 63184, University Library of Munich, Germany, revised 11 Sep 2014.

    More about this item


    Fuel cell vehicles; Hydrogen; Learning by doing; Agent based modeling;

    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
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory

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