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How to Improve the Market Penetration of New Energy Vehicles in China: An Agent-Based Model with a Three-Level Variables Structure

Author

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  • Mo Chen

    (School of Management, Tianjin University of Commerce, Tianjin 300134, China)

  • Rudy X. J. Liu

    (School of Management, Tianjin University of Commerce, Tianjin 300134, China
    Research Center for Management Innovation and Evaluation, Tianjin University of Commerce, Tianjin 300134, China)

  • Chaochao Liu

    (School of Management, Tianjin University of Commerce, Tianjin 300134, China
    Research Center for Management Innovation and Evaluation, Tianjin University of Commerce, Tianjin 300134, China)

Abstract

This paper develops an agent-based model with linking variables (ABML) to investigate the influencing factors for the new energy vehicles (NEVs) market in China. The ABML is a framework with three-level variables including micro, linking, and macro variables, which can reduce the complexity of the simulation. The emergence from bottom to top occurs between linking and macro variables, while the best–worst scaling describes the mapping between micro and linking variables. In the case study, Rookie, Veteran, and New Generation consumers are assumed as the three types of consumers in China’s market. A specification of the three types of variables is presented, where the value of linking variables obeys uniform distribution. By introducing the population density and the interaction frequency, the number of agents is determined with an experiment. All parameters in the model are estimated by calibrating the realistic vehicle sales. We compare different scenarios and obtain some management insights for improving the market penetration of NEVs in China.

Suggested Citation

  • Mo Chen & Rudy X. J. Liu & Chaochao Liu, 2021. "How to Improve the Market Penetration of New Energy Vehicles in China: An Agent-Based Model with a Three-Level Variables Structure," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12307-:d:674268
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    1. Giacomarra, Marcella & Tulone, Antonio & Crescimanno, Maria & Galati, Antonino, 2019. "Electric mobility in the Sicilian short food supply chain," Studies in Agricultural Economics, Research Institute for Agricultural Economics, vol. 121(2), August.
    2. Liu, Dunnan & Xiao, Bowen, 2018. "Exploring the development of electric vehicles under policy incentives: A scenario-based system dynamics model," Energy Policy, Elsevier, vol. 120(C), pages 8-23.
    3. Melton, Noel & Axsen, Jonn & Goldberg, Suzanne, 2017. "Evaluating plug-in electric vehicle policies in the context of long-term greenhouse gas reduction goals: Comparing 10 Canadian provinces using the “PEV policy report card”," Energy Policy, Elsevier, vol. 107(C), pages 381-393.
    4. Langbroek, Joram H.M. & Franklin, Joel P. & Susilo, Yusak O., 2016. "The effect of policy incentives on electric vehicle adoption," Energy Policy, Elsevier, vol. 94(C), pages 94-103.
    5. Zhang, Jing & Reed Johnson, F. & Mohamed, Ateesha F. & Hauber, A. Brett, 2015. "Too many attributes: A test of the validity of combining discrete-choice and best–worst scaling data," Journal of choice modelling, Elsevier, vol. 15(C), pages 1-13.
    6. Noori, Mehdi & Tatari, Omer, 2016. "Development of an agent-based model for regional market penetration projections of electric vehicles in the United States," Energy, Elsevier, vol. 96(C), pages 215-230.
    7. Eppstein, Margaret J. & Grover, David K. & Marshall, Jeffrey S. & Rizzo, Donna M., 2011. "An agent-based model to study market penetration of plug-in hybrid electric vehicles," Energy Policy, Elsevier, vol. 39(6), pages 3789-3802, June.
    8. Silvia, Chris & Krause, Rachel M., 2016. "Assessing the impact of policy interventions on the adoption of plug-in electric vehicles: An agent-based model," Energy Policy, Elsevier, vol. 96(C), pages 105-118.
    9. Antonini, Gianluca & Bierlaire, Michel & Weber, Mats, 2006. "Discrete choice models of pedestrian walking behavior," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 667-687, September.
    10. Shafiei, Ehsan & Thorkelsson, Hedinn & Ásgeirsson, Eyjólfur Ingi & Davidsdottir, Brynhildur & Raberto, Marco & Stefansson, Hlynur, 2012. "An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland," Technological Forecasting and Social Change, Elsevier, vol. 79(9), pages 1638-1653.
    11. Wolinetz, Michael & Axsen, Jonn, 2017. "How policy can build the plug-in electric vehicle market: Insights from the REspondent-based Preference And Constraints (REPAC) model," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 238-250.
    12. Ma, Shao-Chao & Fan, Ying & Feng, Lianyong, 2017. "An evaluation of government incentives for new energy vehicles in China focusing on vehicle purchasing restrictions," Energy Policy, Elsevier, vol. 110(C), pages 609-618.
    13. Ajanovic, Amela & Haas, Reinhard, 2016. "Dissemination of electric vehicles in urban areas: Major factors for success," Energy, Elsevier, vol. 115(P2), pages 1451-1458.
    14. Wang, Lei & Fu, Zhong-Lin & Guo, Wei & Liang, Ruo-Yu & Shao, Hong-Yu, 2020. "What influences sales market of new energy vehicles in China? Empirical study based on survey of consumers’ purchase reasons," Energy Policy, Elsevier, vol. 142(C).
    15. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
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