Enhancing Flood Resilience by Retrofitting Old Communities for Sustainable Urban Renewal: A Robust Causal Diagnostic Framework

As China’s Sponge City Program (SCP) shifts towards retrofitting old communities, enhancing flood resilience is critical for sustainable urban renewal. However, engineering practice often encounters a performance inversion—characterized by high design evaluation scores but low operational efficiency. This issue largely stems from relying on the static importance of indicators while neglecting dynamic driving forces within the socio-technical system. To address this, this study aims to construct a Robust Causal Diagnostic Framework integrating Improved AHP, DEMATEL, and K-means clustering. Through a quadrant positioning and cluster locking mechanism, it identifies Hidden Leverage Factors (HLFs)—critical indicators typically assigned low weights but exerting strong driving forces. To demonstrate the practical application of this framework, an empirical analysis of H City’s DG Community was conducted, identifying residents’ willingness and design pertinence as the project’s HLFs. Optimization strategies based on this diagnosis were simulated using SWMM. Results show that the Annual Runoff Volume Capture Ratio increased by 44.45%, with significant improvements in peak flow reduction and water purification. This study facilitates a shift from empirical evaluation to precision diagnosis, offering a quantitative reference for enhancing urban flood resilience under complex social constraints.