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Development of a market penetration forecasting model for Hydrogen Fuel Cell Vehicles considering infrastructure and cost reduction effects

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  • Park, Sang Yong
  • Kim, Jong Wook
  • Lee, Duk Hee

Abstract

In order to cope with climate change, the development and deployment of Hydrogen Fuel Cell Vehicles (HFCVs) is becoming more important. In this study, we developed a forecasting model for HFCVs based on the generalized Bass diffusion model and a simulation model using system dynamics. Through the developed model, we could forecast that the saturation of HFCVs in Korea can be moved up 12 years compared with the US. A sensitivity analysis on external variables such as price reduction rates of HFCVs and number of hydrogen refueling stations is also conducted. The results of this study can give insights on the effects of external variables on the market penetration of HFCVs, and the developed model can also be applied to other studies in analyzing the diffusion effects of HFCVs.

Suggested Citation

  • Park, Sang Yong & Kim, Jong Wook & Lee, Duk Hee, 2011. "Development of a market penetration forecasting model for Hydrogen Fuel Cell Vehicles considering infrastructure and cost reduction effects," Energy Policy, Elsevier, vol. 39(6), pages 3307-3315, June.
    • Handle: RePEc:eee:enepol:v:39:y:2011:i:6:p:3307-3315
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    References listed on IDEAS

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    1. Oecd, 2001. "The Development of Broadband Access in the OECD Countries," OECD Digital Economy Papers 56, OECD Publishing.
    2. Clark II, Woodrow W. & Rifkin, Jeremy, 2006. "A green hydrogen economy," Energy Policy, Elsevier, vol. 34(17), pages 2630-2639, November.
    3. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    4. McDowall, William & Eames, Malcolm, 2006. "Forecasts, scenarios, visions, backcasts and roadmaps to the hydrogen economy: A review of the hydrogen futures literature," Energy Policy, Elsevier, vol. 34(11), pages 1236-1250, July.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Collantes, Gustavo O, 2007. "Incorporating stakeholders' perspectives into models of new technology diffusion: The case of fuel-cell vehicles," Institute of Transportation Studies, Working Paper Series qt9bm1w968, Institute of Transportation Studies, UC Davis.
    7. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    8. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
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