Revolutionizing industrial park waste classification with artificial intelligence: A behavioral economics and evolutionary game theory perspective

Abstract

Industrial waste classification is vital for sustainable manufacturing; however, traditional methods face challenges such as high costs, behavioral inertia, and fragmented strategies that overlook dynamic interdepartmental collaboration. Artificial intelligence (AI) presents transformative potential to overcome these barriers, but existing studies fail to integrate behavioral economics and evolutionary dynamics, limiting their ability to stabilize compliance in diverse industrial parks. This study proposes a novel framework combining behavioral economics with evolutionary game theory (EGT) to explore AI-enhanced waste management dynamics across various industrial park types. Behavioral insights examine how cognitive biases, such as loss aversion and social proof, influence employee compliance with waste classification policies in AI-driven environments. Concurrently, EGT simulations model interdepartmental cooperation and strategy evolution in waste management. Results indicate that AI-based behavioral interventions, including real-time feedback and incentive mechanisms, increase compliance rates by 22 % and foster interdepartmental collaboration to maximize eco-efficiency. In gated industrial parks, strict regulation combined with AI-driven monitoring accelerates the stabilization of compliant behaviors, achieving equilibrium 40 % faster than traditional models. In open parks, social and moral incentives are essential, requiring a moral constraint threshold (θ) of 0.8 for stable compliance. A case study at Suzhou Industrial Park demonstrates a 52 % reduction in mixed waste disposal, 90 % accuracy in AI-driven classification, and a 30 % reduction in policy costs, underscoring the framework’s scalability. This study provides actionable insights for policymakers and industry leaders seeking to optimize waste management in diverse regulatory environments and highlights AI’s transformative role in promoting sustainable manufacturing.