Game Analysis of the Recycling Efficiency of WEEE Based on Edge Computing and AI: Impact of Government and Enterprise Interaction on Recycling Efficiency

The recycling of waste electrical and electronic equipment (WEEE) poses significant challenges that necessitate effective management strategies to optimize resource recovery. Based on evolutionary game theory, this study analyzes the nonlinear interaction between the government, manufacturers, and recyclers, especially the application of AI and edge computing. By combining edge computing and AI, a dynamic game model is constructed to analyze the evolution of the behavioral decisions of all parties and their impact on the recycling process. An evolutionary game model is developed to assess the impacts of government regulatory measures, the willingness of enterprises to engage, and the prevailing market conditions. The study found that the integration of AI and edge computing significantly optimized information flow and decision-making efficiency. With the support of government policies, punitive measures are more effective in promoting the use of formal recycling channels, while subsidy measures have a more limited effect. At the same time, manufacturers’ recycling decisions are mainly driven by the market and have a limited response to government intervention. This study reveals how to promote more efficient recycling and resource reuse through the combination of technological innovation and policy design.