Tripartite evolutionary game model for recycling strategies of decommissioned wind and solar energy equipment

The global volume of decommissioned components from wind turbines and solar photovoltaic (PV) equipment is projected to exceed 30 million tons by 2030, posing a significant challenge for their disposal and recycling. The issue of managing these components remains largely unresolved. While existing research primarily focuses on technical aspects, few studies address policies and recycling mechanisms. This research introduces an evolutionary game model to study the recycling of retired wind and PV equipment, exploring strategic interactions and policy effects over time by constructing a tripartite model involving the government, manufacturers, and dismantlers. Through numerical simulations, the impact of initial willingness and government subsidies is analyzed. The findings indicate that (1) government involvement is pivotal, as its initial commitment significantly enhances collaboration between recyclers and dismantlers, accelerating the recycling process; (2) when manufacturers' initial willingness to recycle reaches 0.9, collaboration with dismantlers improves markedly, requiring minimal government subsidies to maximize overall benefits; (3) subsidies should be dynamically adjusted, with a 1:6 ratio between manufacturers and recyclers providing optimal economic efficiency. This study quantifies various recycling scenarios and provides policy recommendations based on an evolutionary game model, offering insights into optimizing the management of wind and PV equipment recycling.

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Publication Details

Journal
Waste Management
Published
2025-08-20
DOI
https://doi.org/10.1016/j.wasman.2025.115077
Citations
3
Primary Topic
Recycling and Waste Management Techniques
Type
article
Field-Weighted Citation Impact
2.99

Funders

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article

Tripartite evolutionary game model for recycling strategies of decommissioned wind and solar energy equipment

Bei Sun, Minxi Wang, Yifei Zhao, Xin Li et al.
3 citations
Waste Management
Recycling and Waste Management Techniques
2.99
article

Tripartite evolutionary game model for recycling strategies of decommissioned wind and solar energy equipment

Bei Sun, Minxi Wang, Yifei Zhao, Xin Li, Jing Lin
article en
3 citations

Abstract

The global volume of decommissioned components from wind turbines and solar photovoltaic (PV) equipment is projected to exceed 30 million tons by 2030, posing a significant challenge for their disposal and recycling. The issue of managing these components remains largely unresolved. While existing research primarily focuses on technical aspects, few studies address policies and recycling mechanisms. This research introduces an evolutionary game model to study the recycling of retired wind and PV equipment, exploring strategic interactions and policy effects over time by constructing a tripartite model involving the government, manufacturers, and dismantlers. Through numerical simulations, the impact of initial willingness and government subsidies is analyzed. The findings indicate that (1) government involvement is pivotal, as its initial commitment significantly enhances collaboration between recyclers and dismantlers, accelerating the recycling process; (2) when manufacturers' initial willingness to recycle reaches 0.9, collaboration with dismantlers improves markedly, requiring minimal government subsidies to maximize overall benefits; (3) subsidies should be dynamically adjusted, with a 1:6 ratio between manufacturers and recyclers providing optimal economic efficiency. This study quantifies various recycling scenarios and provides policy recommendations based on an evolutionary game model, offering insights into optimizing the management of wind and PV equipment recycling.

Waste ManagementVol. 206
University of Electronic Science and Technology of China (CN), Chengdu University of Information Technology (CN), Chengdu University of Technology (CN), Chengdu University (CN)
Chengdu University of Technology
Affordable and clean energy
Openalex Percentile: Top 9%
Recycling and Waste Management Techniques
2.99
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