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Hydrogen-Fuel Infrastructure Investment with Endogenous Demand: A Real Options Approach

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  • Y. Li
  • C.J.M. Kool
  • P.J. Engelen

Abstract

In this paper, we explicitly incorporate the impact that realized investment in new infrastructure has on adoption speed in a real options framework for investment decisions and analyze the consequences of this dependence for optimal investment. For the adoption diffusion process, we use a modified Generalized Bass Model. As an illustration, we apply the combined model to the case of the introduction of hydrogen- cars in the Netherlands. We perform a scenario analysis for six different infrastructure investment strategies combined with four different parameterizations. The results show that ignoring the potential interaction between the speed with which the required infrastructure will become available and the adoption process may lead to sub-optimal decisions with respect to the optimal timing of investment spending as well as with respect to the assessment of the feasibility of the project in general.

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  • Y. Li & C.J.M. Kool & P.J. Engelen, 2016. "Hydrogen-Fuel Infrastructure Investment with Endogenous Demand: A Real Options Approach," Working Papers 16-12, Utrecht School of Economics.
    • Handle: RePEc:use:tkiwps:1612
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    References listed on IDEAS

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    1. Zhao, Jimin & Melaina, Marc W., 2006. "Transition to hydrogen-based transportation in China: Lessons learned from alternative fuel vehicle programs in the United States and China," Energy Policy, Elsevier, vol. 34(11), pages 1299-1309, July.
    2. 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.
    3. Eckhause, Jeremy & Herold, Johannes, 2014. "Using real options to determine optimal funding strategies for CO2 capture, transport and storage projects in the European Union," Energy Policy, Elsevier, vol. 66(C), pages 115-134.
    4. Köhler, Jonathan & Whitmarsh, Lorraine & Nykvist, Björn & Schilperoord, Michel & Bergman, Noam & Haxeltine, Alex, 2009. "A transitions model for sustainable mobility," Ecological Economics, Elsevier, vol. 68(12), pages 2985-2995, October.
    5. Zhang, M.M. & Zhou, D.Q. & Zhou, P. & Liu, G.Q., 2016. "Optimal feed-in tariff for solar photovoltaic power generation in China: A real options analysis," Energy Policy, Elsevier, vol. 97(C), pages 181-192.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Reid, Sergey & Spence, David B., 2016. "Methodology for evaluating existing infrastructure and facilitating the diffusion of PEVs," Energy Policy, Elsevier, vol. 89(C), pages 1-10.
    8. Peter N. Golder & Gerard J. Tellis, 1997. "Will It Every Fly? Modeling the Takeoff of Really New Consumer Durables," Marketing Science, INFORMS, vol. 16(3), pages 256-270.
    9. Hans T.J. Smit, 2003. "Infrastructure Investment as a Real Options Game: The Case of European Airport Expansion," Financial Management, Financial Management Association, vol. 32(4), Winter.
    10. Pless, Jacquelyn & Arent, Douglas J. & Logan, Jeffrey & Cochran, Jaquelin & Zinaman, Owen, 2016. "Quantifying the value of investing in distributed natural gas and renewable electricity systems as complements: Applications of discounted cash flow and real options analysis with stochastic inputs," Energy Policy, Elsevier, vol. 97(C), pages 378-390.
    11. Malte Schwoon, 2006. "Simulating the adoption of fuel cell vehicles," Journal of Evolutionary Economics, Springer, vol. 16(4), pages 435-472, October.
    12. 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.
    13. van Benthem, A.A. & Kramer, G.J. & Ramer, R., 2006. "An options approach to investment in a hydrogen infrastructure," Energy Policy, Elsevier, vol. 34(17), pages 2949-2963, November.
    14. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    15. Malte Schwoon, 2005. "Simulating the Adoption of Fuel Cell Vehicles," Working Papers FNU-59, Research unit Sustainability and Global Change, Hamburg University, revised Feb 2006.
    16. Dan Horsky & Leonard S. Simon, 1983. "Advertising and the Diffusion of New Products," Marketing Science, INFORMS, vol. 2(1), pages 1-17.
    17. Christopher J. Easingwood & Vijay Mahajan & Eitan Muller, 1983. "A Nonuniform Influence Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 2(3), pages 273-295.
    18. Noppers, Ernst H. & Keizer, Kees & Milovanovic, Marko & Steg, Linda, 2016. "The importance of instrumental, symbolic, and environmental attributes for the adoption of smart energy systems," Energy Policy, Elsevier, vol. 98(C), pages 12-18.
    19. Engelen, Peter-Jan & Kool, Clemens & Li, Ye, 2016. "A barrier options approach to modeling project failure: The case of hydrogen fuel infrastructure," Resource and Energy Economics, Elsevier, vol. 43(C), pages 33-56.
    20. Shin, Jungwoo & Lee, Chul-Yong & Kim, Hongbum, 2016. "Technology and demand forecasting for carbon capture and storage technology in South Korea," Energy Policy, Elsevier, vol. 98(C), pages 1-11.
    21. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    22. Sarkar, Sudipto, 2009. "Optimal fishery harvesting rules under uncertainty," Resource and Energy Economics, Elsevier, vol. 31(4), pages 272-286, November.
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