Addressing the Collective Action Dilemma in Resident-Led Urban Regeneration: Designing and Verifying a Multi-Dimensional Policy Lever System Through Evolutionary Game Theory

Against the backdrop of urban stock development worldwide, resident-led urban regeneration and in-situ demolition-and-reconstruction models are crucial for advancing sustainable urban regeneration. However, these initiatives often stall due to collective action dilemmas arising from complex interactions among governments, residents, and contractors. To address this, we develop a tripartite evolutionary game model that incorporates a novel multi-dimensional policy lever system. This system integrates the following: (1) resource-allocation levers (area-expansion coefficient, w; expansion benefit-sharing coefficient, v), (2) cost-sharing levers (expansion-purchase coefficient, p; original-area reconstruction payment coefficient, q), and (3) behavioral-intervention levers (cost-burden perception coefficient, e; accident-risk perception coefficient, d), the latter quantifying behavioral economics principles like loss aversion and probability weighting. Through numerical simulations, we identify the nonlinear effects, critical thresholds, and interaction mechanisms of these levers. The results demonstrate that resource-allocation and cost-sharing levers exhibit critical ranges, whereas behavioral-intervention levers are characterized by perception thresholds and saturation effects. Crucially, coordinated optimization of all parameters—rather than one-sided incentives—is essential to steer the system towards the ideal cooperative equilibrium (government guidance, contractor participation, and resident engagement). This study provides a systematic theoretical framework and practical pathway for crafting targeted urban regeneration policies, emphasizing that aligning economic incentives with behavioral interventions can simultaneously enhance compactness, feasibility, and equity, thereby contributing to the achievement of Sustainable Development Goal 11.