Bridging Economic Efficiency and Perceived Fairness: A New Framework for Collaborative Disaster Material Reserves.

Urban agglomerations increasingly confront disaster risks that overwhelm individual cities' reserve capacities. Collaborative emergency reserves offer a potential remedy but also introduce coordination burdens and distributional concerns. To address this, this study formulates a multi-objective optimization problem that integrates economic utility with perceived fairness. The model explicitly accounts for residents' shortage and delay experiences alongside managers' perceptions of coordination costs, reserve burdens, and cross-city disparities. It is solved using an enhanced multi-objective particle swarm optimization algorithm coupled with the ε-constraint method and driven by Monte Carlo disaster scenario generation. A numerical illustration based on 21 mainland prefecture-level cities in Guangdong Province evaluates the framework under heterogeneous risk, budget, and coordination conditions. Sobol's global sensitivity analysis identifies regional budget, inter-city collaboration experience, disaster intensity, and nonlinear perceived-loss parameters as dominant drivers of collaborative advantage, whereas transportation costs and standalone city resilience exert weaker effects. The results demonstrate that collaboration generally outperforms independent reserves, especially under severe disasters and fiscal constraints. This advantage diminishes when disasters are widespread or when transaction and coordination costs are prohibitive. Distributional stress tests further reveal that resource-abundant hub cities may perceive lower fairness when acting primarily as providers yet stand to gain substantially when severe shocks occur locally. This research provides a decision-making framework for jointly evaluating efficiency and perceived fairness. It highlights mutual-aid agreements, fiscal compensation, contribution-credit mechanisms, and joint exercises as institutional complements to optimized reserve strategies.