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Strategic niche management of alternative fuel vehicles: A system dynamics model of the policy effect

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  • Kwon, Tae-hyeong

Abstract

Using a system dynamics model, this study investigates the market barriers to increasing the market share of alternative fuel vehicles (AFVs) and possible policy options to overcome them, in particular strategic niche management (SNM). The model assumes that the operating costs of AFVs, including fuel supply and vehicle maintenance costs, have a positive feedback relationship with the scale of overall car stocks sold. System dynamics modelling is a useful approach to model a feedback effect like this situation. According to the simulation results, if there is a strong network effect on vehicle operating costs, it is difficult to achieve the shift to alternative fuel vehicles, even in the long term, without policy intervention. Although SNM alone may not be enough to sustain the rise of the market share of AFVs with a strong network effect, SNM seems to be very effective in strengthening the policy effect of financial incentives. A relatively small budget devoted to SNM can result in a substantial difference in AFV market share. However, the effectiveness of SNM also depends on the magnitude of the network effect. SNM is more effective when applied to AFVs with a strong network effect.

Suggested Citation

  • Kwon, Tae-hyeong, 2012. "Strategic niche management of alternative fuel vehicles: A system dynamics model of the policy effect," Technological Forecasting and Social Change, Elsevier, vol. 79(9), pages 1672-1680.
    • Handle: RePEc:eee:tefoso:v:79:y:2012:i:9:p:1672-1680
    DOI: 10.1016/j.techfore.2012.05.015
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    Cited by:

    1. Grazia Cecere & Nicoletta Corrocher & Cédric Gossart & Muge Ozman, 2014. "Lock-in and path dependence: an evolutionary approach to eco-innovations," Journal of Evolutionary Economics, Springer, vol. 24(5), pages 1037-1065, November.
    2. Sheng, Mingyue & Sreenivasan, Ajith Viswanath & Sharp, Basil & Wilson, Douglas & Ranjitkar, Prakash, 2020. "Economic analysis of dynamic inductive power transfer roadway charging system under public-private partnership–Evidence from New Zealand," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    3. Antti Lajunen & Panu Sainio & Lasse Laurila & Jenni Pippuri-Mäkeläinen & Kari Tammi, 2018. "Overview of Powertrain Electrification and Future Scenarios for Non-Road Mobile Machinery," Energies, MDPI, vol. 11(5), pages 1-22, May.
    4. Auke Hoekstra & Maarten Steinbuch & Geert Verbong, 2017. "Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation," Complexity, Hindawi, vol. 2017, pages 1-23, December.
    5. Gao, Jiayang & Zhang, Tao, 2022. "Effects of public funding on the commercial diffusion of on-site hydrogen production technology: A system dynamics perspective," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Huth, Christian & Kieckhäfer, Karsten & Spengler, Thomas Stefan, 2015. "Make-or-buy strategies for electric vehicle batteries—a simulation-based analysis," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 22-34.
    7. Oliveira, Gabriela D. & Roth, Richard & Dias, Luis C., 2019. "Diffusion of alternative fuel vehicles considering dynamic preferences," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 83-99.
    8. Demeulenaere, Xavier, 2019. "The use of automotive fleets to support the diffusion of Alternative Fuel Vehicles: A Rapid Evidence Assessment of barriers and decision mechanisms," Research in Transportation Economics, Elsevier, vol. 76(C).
    9. Harrison, Gillian & Thiel, Christian, 2017. "An exploratory policy analysis of electric vehicle sales competition and sensitivity to infrastructure in Europe," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 165-178.
    10. Shen, Yung-Shuen & Huang, Guan-Ting & Chang-Chien, Chien-Li & Huang, Lance Hongwei & Kuo, Chien-Hung & Hu, Allen H., 2023. "The impact of passenger electric vehicles on carbon reduction and environmental impact under the 2050 net zero policy in Taiwan," Energy Policy, Elsevier, vol. 183(C).
    11. Ricciardi, Francesca & De Bernardi, Paola & Cantino, Valter, 2020. "System dynamics modeling as a circular process: The smart commons approach to impact management," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    12. Federico Cosenz & Guido Noto, 2016. "Applying System Dynamics Modelling to Strategic Management: A Literature Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 33(6), pages 703-741, November.
    13. Mohammadreza Zolfagharian & Bob Walrave & A. Georges L. Romme & Rob Raven, 2020. "Toward the Dynamic Modeling of Transition Problems: The Case of Electric Mobility," Sustainability, MDPI, vol. 13(1), pages 1-23, December.
    14. Chang, Ruidong & Cao, Yuan & Lu, Yujie & Shabunko, Veronika, 2019. "Should BIPV technologies be empowered by innovation policy mix to facilitate energy transitions? - Revealing stakeholders' different perspectives using Q methodology," Energy Policy, Elsevier, vol. 129(C), pages 307-318.

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