A framework of evidence-based multi-hazard risk screening for disaster risk reduction and resilience planning

Multi-hazard risk assessment is essential for informed decision-making in disaster risk reduction (DRR). However, conducting fine-scale disaster risk assessments for a large number of elements at risk remains challenging. This study proposes a bottom-up, evidence-based framework for multi-hazard risk screening. It develops a risk index system for individual elements at risk based on the UNDRR’s disaster risk definition and statistically aggregates these indices to generate risk profiles at various administrative levels. The framework enables rapid identification of exposure, vulnerability, coping capacity, and risk hotspots, ranging from individual elements to entire administrative regions. By applying this approach, over 4,100 schools in Shanghai were assessed with data from the First National Comprehensive Natural Disaster Risk Census. Results indicate that 46.31% and 5.43% of schools exhibit medium-high to high exposure and vulnerability, respectively. Additionally, 13.12% and 11.43% face significant asset and overall risk, respectively. Moreover, 9.63% of schools have low to medium-low coping capacity. Hotspots were identified at the township and district levels, with the top 100 highest-risk schools, 10 most vulnerable townships, and 5 highest-risk districts highlighted. These findings provide a crucial foundation for risk-informed DRR and resilience planning, while also validating the proposed assessment framework.

Authors

Institutions

Publication Details

Journal
Geomatics Natural Hazards and Risk
Published
2025-12-21
DOI
https://doi.org/10.1080/19475705.2025.2605119
Primary Topic
Disaster Management and Resilience
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A framework of evidence-based multi-hazard risk screening for disaster risk reduction and resilience planning

Jiahong WEN, Y. T. Chen, Jianping Yan, Tongfei Tian et al.
Geomatics Natural Hazards and Risk
Disaster Management and Resilience
article

A framework of evidence-based multi-hazard risk screening for disaster risk reduction and resilience planning

Jiahong WEN, Y. T. Chen, Jianping Yan, Tongfei Tian, Banggu Liao, Li Li, Zhongchao Shi
article en

Abstract

Multi-hazard risk assessment is essential for informed decision-making in disaster risk reduction (DRR). However, conducting fine-scale disaster risk assessments for a large number of elements at risk remains challenging. This study proposes a bottom-up, evidence-based framework for multi-hazard risk screening. It develops a risk index system for individual elements at risk based on the UNDRR’s disaster risk definition and statistically aggregates these indices to generate risk profiles at various administrative levels. The framework enables rapid identification of exposure, vulnerability, coping capacity, and risk hotspots, ranging from individual elements to entire administrative regions. By applying this approach, over 4,100 schools in Shanghai were assessed with data from the First National Comprehensive Natural Disaster Risk Census. Results indicate that 46.31% and 5.43% of schools exhibit medium-high to high exposure and vulnerability, respectively. Additionally, 13.12% and 11.43% face significant asset and overall risk, respectively. Moreover, 9.63% of schools have low to medium-low coping capacity. Hotspots were identified at the township and district levels, with the top 100 highest-risk schools, 10 most vulnerable townships, and 5 highest-risk districts highlighted. These findings provide a crucial foundation for risk-informed DRR and resilience planning, while also validating the proposed assessment framework.

Geomatics Natural Hazards and RiskVol. 17(1)
National Science and Technology Center for Disaster Reduction (TW), University of the Sunshine Coast (AU), Shanghai Normal University (CN), Risk Management Solutions (United Kingdom) (GB)
Climate action
Openalex Percentile: Top 24%
Disaster Management and Resilience
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.