Collaborative governance for urban waste management: A case study using evolutionary game theory

Abstract

The rapid escalation in municipal solid waste generation presents a critical challenge, compounded by delays in implementing source-separated household waste management systems. This study addresses the lack of comprehensive stakeholder-oriented planning in government-led waste classification efforts—characterized by overreliance on administrative enforcement, imbalanced resource allocation, and inadequate coordination mechanisms—which has led to rising administrative costs and suboptimal outcomes. By employing an evolutionary game model, this study explores the dynamic interactions between key stakeholders—governments, property service enterprises, and residents—while incorporating subsidy and transfer payment mechanisms to establish a collaborative governance framework. The empirical analysis, grounded in data from Shanghai, China, reveals that a multi-agent collaborative model improves cost efficiency by 25 % compared to a government-only approach. The research findings indicate that when government subsidies exceed an 80 % distribution ratio to real estate service enterprises, a cooperative strategy is achieved, although it may lead to a non-cooperative state among residents. Reducing the cooperation costs of property service enterprises enhances their willingness to cooperate but has limited influence on residents. In contrast, residents' strategies are more sensitive to changes in government subsidies, and increasing subsidies led to evolutionary stability points characterized by dual and tripartite cooperation modes. These findings demonstrate how properly designed subsidy structures can address macro-level planning deficiencies by creating incentive alignment among stakeholders. The study also finds that reducing urban household garbage sorting costs by 30 % contributes to optimal tripartite cooperation. Although the study focuses on Shanghai, its findings apply to other cities with similar waste management policies, offering universal insights for improving urban waste classification systems. The study advocates for robust regulations and comprehensive monitoring systems to sustain long-term collaborative waste management practices.