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Optimizing Green Strategy for Retired Electric Vehicle Battery Recycling: An Evolutionary Game Theory Approach

Author

Listed:
  • Yi Zheng

    (School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China)

  • Yaoqun Xu

    (Institute of Systems Engineering, Harbin University of Commerce, Harbin 150028, China)

Abstract

As the global new energy vehicle (NEV) industry rapidly expands, the disposal and recycling of end-of-life (EOL) power batteries have become imperative. Efficient closed-loop supply chain (CLSC) management, supported by well-designed regulations and strategic investments, plays a crucial role in sustainable waste power battery recycling. In this study, an evolutionary game theory (EGT) methodology is used to construct a tripartite game model to investigate the interactions among manufacturers, recyclers, and the government to study the decision-making dynamics of green investments. In addition, numerical simulations are performed to evaluate the sensitivity of the relevant parameters on the stability of the evolution of the system. The results reveal that government green subsidies can stimulate early period investments in advanced recycling technologies. However, as the battery recycling industry matures, a ‘free-rider’ behavior emerges among enterprises, which can be mitigated through the imposition of a carbon tax. Eventually, as the industry reaches maturity, manufacturers and recyclers autonomously invest for enhanced profitability. This research provides valuable insights for government policy formulation, facilitating the formal recycling of retired batteries and fostering sustainability in the NEV sector.

Suggested Citation

  • Yi Zheng & Yaoqun Xu, 2023. "Optimizing Green Strategy for Retired Electric Vehicle Battery Recycling: An Evolutionary Game Theory Approach," Sustainability, MDPI, vol. 15(21), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15464-:d:1271228
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    References listed on IDEAS

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    Cited by:

    1. Zhu, Lu & He, Juan, 2026. "Effects of multiple incentive policies on carbon information disclosure strategies of power battery: A complex network evolutionary game analysis," International Journal of Production Economics, Elsevier, vol. 292(C).
    2. Liu, Songsong & Li, Tingting & Chen, Yurui & Lyu, Chang & Zhai, Fengyong, 2025. "Government subsidy strategies for new energy vehicle charging infrastructure considering consumers’ low-carbon preference: A tripartite evolutionary game model," Energy Policy, Elsevier, vol. 205(C).
    3. Cheng, Lefeng & Yu, Feng & Huang, Pengrong & Liu, Guiyun & Zhang, Mengya & Sun, Runbao, 2025. "Game-theoretic evolution in renewable energy systems: Advancing sustainable energy management and decision optimization in decentralized power markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).

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