Policy modeling for urban energy resilience to extreme heat: A multi-agent simulation framework in Chongqing, China

Climate change is increasing extreme heat events, intensifying stress on urban energy systems. Chongqing, a megacity in China, has seen a notable rise in urban heat island intensity (∼0.5 °C per decade), driving peak cooling loads up by 15 %. While previous research emphasized energy efficiency, resilience under compounded extreme heat remains underexplored. To fill this gap, we develop an agent-based model with four agent types exhibiting adaptive thermal behavior, simulating building energy demand under four high-temperature scenarios (36∼42 °C) with non-linear responses. A multi-indicator framework is employed to evaluate three strategies: adjusting temperature thresholds, establishing public cooling centers (PCCs), and electricity rationing. Results indicate: 1) Proportional electricity rationing is more cost-effective and better mitigates oversupply risks than uniform restrictions. 2) Increasing temperature thresholds from 26 °C to 28 °C improves resilience (relative closeness: 56 %) with only marginal cost increases, although economic burdens remain notable. Expanding PCCs provides negligible resilience benefits (<0.1 % improvement). 3) A TOPSIS-based cost-resilience trade-off analysis further confirms that temperature threshold adjustment consistently offers superior performance in balancing resilience outcomes and economic feasibility. Our findings highlight the importance of incorporating economic and practical feasibility into urban energy resilience strategies.