In the context of tightening energy constraints and increasing resource security risks, inadequate collaborative governance in reverse logistics networks has become a critical bottleneck constraining the efficiency of urban mining. However, the literature on reverse logistics governance remains largely confined to traditional supply-chain cooperation paradigms, offering limited insight into the synergistic governance mechanisms among the four parties under green innovation cooperation. This study constructs a four-party evolutionary game model involving the government, recycling enterprises, remanufacturing enterprises, and consumers from the perspective of green innovation cooperation. Green innovation spillover effects, subsidy intensity, and product pricing are integrated into a unified evolutionary framework, where system dynamics are examined through replicator dynamics, Jacobian stability analysis, and numerical simulations. The results reveal four distinct stable equilibrium states within the system. Notably, green innovation spillovers exhibit a significant threshold effect, whereby excessive spillovers undermine incentive compatibility constraints and induce opportunistic behavior. Furthermore, government subsidies exhibit stage-dependent incentive effects, with system stability requiring both internal transfer prices and terminal market prices within effective boundaries. The system converges toward an optimal collaborative equilibrium only when government incentives, enterprise cooperation, and consumer support are synergistically enhanced. These findings provide theoretical insights and practical implications for designing optimal reverse logistics policy frameworks and constructing sustainable urban mining development models.
Citation: Shuming Liu, Xiang Liu, Xianghua Liu, Ziquan Li, Jiahui Huang, Qiuzhi He. Exploring the e-waste reverse logistics governance dynamics: A four-party evolutionary game model for green innovation cooperation[J]. Journal of Industrial and Management Optimization, 2026, 22(8): 3864-3896. doi: 10.3934/jimo.2026138
In the context of tightening energy constraints and increasing resource security risks, inadequate collaborative governance in reverse logistics networks has become a critical bottleneck constraining the efficiency of urban mining. However, the literature on reverse logistics governance remains largely confined to traditional supply-chain cooperation paradigms, offering limited insight into the synergistic governance mechanisms among the four parties under green innovation cooperation. This study constructs a four-party evolutionary game model involving the government, recycling enterprises, remanufacturing enterprises, and consumers from the perspective of green innovation cooperation. Green innovation spillover effects, subsidy intensity, and product pricing are integrated into a unified evolutionary framework, where system dynamics are examined through replicator dynamics, Jacobian stability analysis, and numerical simulations. The results reveal four distinct stable equilibrium states within the system. Notably, green innovation spillovers exhibit a significant threshold effect, whereby excessive spillovers undermine incentive compatibility constraints and induce opportunistic behavior. Furthermore, government subsidies exhibit stage-dependent incentive effects, with system stability requiring both internal transfer prices and terminal market prices within effective boundaries. The system converges toward an optimal collaborative equilibrium only when government incentives, enterprise cooperation, and consumer support are synergistically enhanced. These findings provide theoretical insights and practical implications for designing optimal reverse logistics policy frameworks and constructing sustainable urban mining development models.
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Figures(8) / Tables(4)
Shuming Liu, Xiang Liu, Xianghua Liu, Ziquan Li, Jiahui Huang, Qiuzhi He. Exploring the e-waste reverse logistics governance dynamics: A four-party evolutionary game model for green innovation cooperation[J]. Journal of Industrial and Management Optimization, 2026, 22(8): 3864-3896. doi: 10.3934/jimo.2026138
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