Evaluating Waterlogging Risk Inequality in a Megacity

Urban waterlogging poses increasing threats to megacities; however, the spatial distribution of waterlogging risk inequality remains poorly understood. In this work, a comprehensive framework to evaluate urban waterlogging risk and its spatial inequality in Beijing is developed. The framework integrates machine learning-based waterlogging susceptibility, urban waterlogging resilience quantified using the entropy weight method, and population exposure data. The Gini index and Lorenz curves are employed to quantify risk inequality at both district and township scales. The central urban area is characterized by high- and very-high-susceptibility levels, while low-susceptibility areas are mainly concentrated in the outer suburbs. A distinct “core-periphery” pattern characterizes urban resilience: high resilience concentrates in the central urban area, while resilience drops significantly in the outer suburbs. The integration of susceptibility, resilience, and exposure reveals a profound spatial waterlogging risk disparity. The urban core has the highest waterlogging risk, but the burden is evenly spread across its dense population (Gini < 0.4). In contrast, outer suburbs and mountainous areas show severe inequality (Gini > 0.6), where a tiny fraction of the population bears most of the risk. This framework supports region-specific strategies to improve control efficiency and achieve environmental distributive justice.