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From data to actionable risk information: A community-based comprehensive disaster risk assessment framework
Journal article   Open access   Peer reviewed

From data to actionable risk information: A community-based comprehensive disaster risk assessment framework

Li Li, Zhongchao Shi, Yuxi Chen, Luna Zhao, Banggu Liao, Jianping Yan, Qiqi Li, Jianli Liu, Tongfei Tian and Jiahong Wen
Climate Services, Vol.44, pp.1-13
2026
DOI: https://doi.org/10.1016/j.cliser.2026.100698
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From data to actionable risk information: A community-based comprehensive disaster risk assessment framework6.82 MBDownloadView
Published Version Open Access CC BY-NC-ND V4.0

Abstract

Actionable risk information Community-based disaster risk assessment Multi-scale risk aggregation Risk components Shanghai Urban risk governance
Climate change and rapid urbanization are intensifying natural disaster risks in cities, creating an urgent need for fine-scale assessments that can directly inform resilience planning. Yet existing approaches often fall short in terms of assessment scale, the completeness of risk-element coverage, and their ability to translate rapidly expanding datasets into actionable risk information for governance. To address this gap, we develop a community-based assessment framework that organizes hazard, exposure, vulnerability and coping capacity into a structured modeling process and aggregates the resulting indicators across administrative levels to generate actionable risk information for disaster risk governance. Using 6286 communities in Shanghai as a case study, we show that the framework effectively captures spatial variations in disaster risk and their underlying drivers. Only 8.68% of communities fall into the high overall risk category, primarily located in suburban areas. In terms of risk formation mechanisms, community vulnerability is mainly associated with infrastructure deficiencies and aging population, while insufficient coping capacity stems from low levels of public participation and grassroots mobilization. Classification of dominant risk mechanisms further identifies coping capacity deficit type communities as the most common high-risk type (70.51%). Multi-level aggregation shows that community-level information can be systematically translated into subdistrict and district level profiles, supporting differentiated interventions and targeted resource allocation. Overall, the framework provides a practical pathway for transforming large, heterogeneous datasets into actionable risk information and offers a robust methodological foundation for strengthening disaster risk assessment and urban risk governance.

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