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The effect of government policies and consumer green preferences on the R&D diffusion of new energy vehicles: A perspective of complex network games

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  • Fan, Ruguo
  • Bao, Xuguang
  • Du, Kang
  • Wang, Yuanyuan
  • Wang, Yitong

Abstract

Consumer preferences and government policies are important factors that affect the diffusion of new energy vehicles (NEVs). Based on the complex network evolutionary game theory, this paper constructs a R&D diffusion model of NEVs considering the emission trading scheme (ETS), and studies the effect of consumer green preferences and related government policies on the R&D diffusion of NEVs. The simulation analysis shows that: (1) consumer green preferences and the quota system have duality to the R&D diffusion of NEVs, which means that while increasing the proportion of NEV enterprises, they inhibit the R&D diffusion among NEV enterprises. (2) NEV enterprises are more inclined to invest in R&D projects with a low success probability, rather than those with a high success probability. (3) When the carbon price reaches a certain threshold, the ETS will facilitate the R&D diffusion of NEVs. However, with the further increase of the carbon price, the promotional effect will weaken. (4) When the R&D tax incentives reach a certain threshold, the increase in R&D tax incentives will greatly promote the R&D diffusion of NEVs. However, the promotional effect has an upper limit.

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  • Fan, Ruguo & Bao, Xuguang & Du, Kang & Wang, Yuanyuan & Wang, Yitong, 2022. "The effect of government policies and consumer green preferences on the R&D diffusion of new energy vehicles: A perspective of complex network games," Energy, Elsevier, vol. 254(PA).
  • Handle: RePEc:eee:energy:v:254:y:2022:i:pa:s0360544222012191
    DOI: 10.1016/j.energy.2022.124316
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    1. Fan, Ruguo & Dong, Lili, 2018. "The dynamic analysis and simulation of government subsidy strategies in low-carbon diffusion considering the behavior of heterogeneous agents," Energy Policy, Elsevier, vol. 117(C), pages 252-262.
    2. M. E. J. Newman & D. J. Watts, 1999. "Renormalization Group Analysis of the Small-World Network Model," Working Papers 99-04-029, Santa Fe Institute.
    3. Wenqiang Xiao & Yi Xu, 2012. "The Impact of Royalty Contract Revision in a Multistage Strategic R&D Alliance," Management Science, INFORMS, vol. 58(12), pages 2251-2271, December.
    4. Li, Yaoming & Zhang, Qi & Liu, Boyu & McLellan, Benjamin & Gao, Yuan & Tang, Yanyan, 2018. "Substitution effect of New-Energy Vehicle Credit Program and Corporate Average Fuel Consumption Regulation for Green-car Subsidy," Energy, Elsevier, vol. 152(C), pages 223-236.
    5. Stéphan Marette, 2007. "Minimum safety standard, consumers’ information and competition," Journal of Regulatory Economics, Springer, vol. 32(3), pages 259-285, December.
    6. Li, Jingjing & Jiao, Jianling & Tang, Yunshu, 2019. "An evolutionary analysis on the effect of government policies on electric vehicle diffusion in complex network," Energy Policy, Elsevier, vol. 129(C), pages 1-12.
    7. Kong, Deyang & Xia, Quhong & Xue, Yixi & Zhao, Xin, 2020. "Effects of multi policies on electric vehicle diffusion under subsidy policy abolishment in China: A multi-actor perspective," Applied Energy, Elsevier, vol. 266(C).
    8. Ingrid Moons & Patrick De Pelsmacker, 2015. "An Extended Decomposed Theory of Planned Behaviour to Predict the Usage Intention of the Electric Car: A Multi-Group Comparison," Sustainability, MDPI, vol. 7(5), pages 1-34, May.
    9. Shi, Yingying & Wei, Zixiang & Shahbaz, Muhammad & Zeng, Yongchao, 2021. "Exploring the dynamics of low-carbon technology diffusion among enterprises: An evolutionary game model on a two-level heterogeneous social network," Energy Economics, Elsevier, vol. 101(C).
    10. Chen, Feng & Wu, Bin & Lou, Wenqian, 2021. "An evolutionary analysis on the effect of government policies on green R & D of photovoltaic industry diffusion in complex network," Energy Policy, Elsevier, vol. 152(C).
    11. Axsen, Jonn & TyreeHageman, Jennifer & Lentz, Andy, 2012. "Lifestyle practices and pro-environmental technology," Ecological Economics, Elsevier, vol. 82(C), pages 64-74.
    12. Li, Jizi & Ku, Yaoyao & Yu, Yue & Liu, Chunling & Zhou, Yuping, 2020. "Optimizing production of new energy vehicles with across-chain cooperation under China’s dual credit policy," Energy, Elsevier, vol. 194(C).
    13. Encarnação, Sara & Santos, Fernando P. & Santos, Francisco C. & Blass, Vered & Pacheco, Jorge M. & Portugali, Juval, 2018. "Paths to the adoption of electric vehicles: An evolutionary game theoretical approach," Transportation Research Part B: Methodological, Elsevier, vol. 113(C), pages 24-33.
    14. Makena Coffman & Paul Bernstein & Sherilyn Wee, 2017. "Electric vehicles revisited: a review of factors that affect adoption," Transport Reviews, Taylor & Francis Journals, vol. 37(1), pages 79-93, January.
    15. Yu, Yugang & Han, Xiaoya & Hu, Guiping, 2016. "Optimal production for manufacturers considering consumer environmental awareness and green subsidies," International Journal of Production Economics, Elsevier, vol. 182(C), pages 397-408.
    16. Poullikkas, Andreas, 2015. "Sustainable options for electric vehicle technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1277-1287.
    17. Ganesh Iyer & David A. Soberman, 2016. "Social Responsibility and Product Innovation," Marketing Science, INFORMS, vol. 35(5), pages 727-742, September.
    18. Ji, Shou-feng & Zhao, Dan & Luo, Rong-juan, 2019. "Evolutionary game analysis on local governments and manufacturers' behavioral strategies: Impact of phasing out subsidies for new energy vehicles," Energy, Elsevier, vol. 189(C).
    19. Herman C. Quirmbach, 1993. "R&D: Competition, Risk, and Performance," RAND Journal of Economics, The RAND Corporation, vol. 24(2), pages 157-197, Summer.
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