脆弱性(计算)
操作化
风险评估
脆弱性评估
环境资源管理
危害
弹性(材料科学)
环境规划
减少灾害风险
社区复原力
心理弹性
气候变化
地理
城市复原力
不透水面
风险分析(工程)
准备
环境科学
基线(sea)
相互依存
危害分析
压力源
适应能力
风险管理
影响评估
城市化
毒物控制
作者
Wonmin Sohn,Zhicheng Xu,Guanqi Lu,Jun-Hyun Kim
标识
DOI:10.1016/j.landurbplan.2026.105620
摘要
• Coupled vulnerability and resilience shaped distinct neighborhood risk profiles. • Exposure is the primary driver of vulnerability in high-risk census tracts. • Strengthening disaster response capacity is most effective for high-risk areas. • A sensitivity analysis identifies actionable risk factors for targeted adaptation. • Impervious surface reduction is a key intervention for mitigating hazard exposure. Climate-related hazards are intensifying, exposing persistent limitations in prevailing risk assessment approaches that rely on static or exposure-centered formulations. The inadequate treatment of interactions between vulnerability and time-structured resilience capacities obscures policy-actionable mechanisms, limiting interpretability for local planning. These limitations are particularly pronounced in depopulating Midwestern Rust Belt cities, where long-term socioeconomic decline and infrastructure deterioration intersect with climate stressors in spatially heterogeneous patterns not resolved by static or aggregated risk measures. The goal of this study was to develop and apply an integrated climate risk assessment framework that distinguishes vulnerability and resilience as analytically distinct yet interdependent dimensions and examine their coupled dynamics in shaping neighborhood-scale risk profiles. Vulnerability and resilience were operationalized using spatially explicit indicators spanning five capital assets–social, economic, environmental, infrastructure, and health–to identify distinct vulnerability–resilience configurations. The framework was applied to census-tract-level data in Detroit and Cincinnati. The results reveal heterogeneous pathways of climate risk across urban contexts, demonstrating that neighborhoods with comparable exposure and sensitivity experienced different risk outcomes, depending on their phase-specific resilience capacities. Machine-learning analyses identified dominant policy-relevant drivers associated with higher-risk configurations, highlighting the influence of exposure conditions alongside the importance of disaster response and recovery processes in mitigating risk. Priority interventions include impervious surface reduction, wetland expansion, emergency services employment, and educational access. By modeling the coupled dynamics of vulnerability and resilience, this study advances a more interpretable and planning-oriented approach to climate risk assessment, supporting spatially targeted adaptation strategies in structurally disadvantaged urban environments.
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