Urban Flood Resilience in a Megacity Context: Multidimensional Assessment and Spatial Differentiation in Shenzhen

This study assesses urban flood resilience at the subdistrict scale in Shenzhen, China, addressing the lack of fine-grained spatial analysis in existing city-level models. A multidimensional framework integrating natural geography, infrastructure, socioeconomics, emergency management, and risk exposure was constructed, with indicator weights derived from a hybrid Analytic Hierarchy Process–Entropy Weight Method. Spatial autocorrelation analysis (Moran’s I = 0.475, p < 0.001) revealed distinct “resilience fault lines,” with high-resilience clusters in central districts and low-resilience clusters in peripheral industrial belts. Geodetector identified economic intensity (q = 0.46), elevation (q = 0.39), and emergency shelter density (q = 0.37) as dominant drivers, with strong interaction effects. These findings highlight significant resilience inequality, emphasizing the need for targeted, multidimensional interventions to enhance adaptive capacity and inform climate adaptation strategies in rapidly urbanizing coastal megacities.

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Publication Details

Journal
Sustainability
Published
2025-08-31
DOI
https://doi.org/10.3390/su17177852
Citations
1
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
1.11
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article

Urban Flood Resilience in a Megacity Context: Multidimensional Assessment and Spatial Differentiation in Shenzhen

Xinyan Huang, Dawei Wang
1 citations
Sustainability
Flood Risk Assessment and Management
1.11
article

Urban Flood Resilience in a Megacity Context: Multidimensional Assessment and Spatial Differentiation in Shenzhen

Xinyan Huang, Dawei Wang
article en
1 citations

Abstract

This study assesses urban flood resilience at the subdistrict scale in Shenzhen, China, addressing the lack of fine-grained spatial analysis in existing city-level models. A multidimensional framework integrating natural geography, infrastructure, socioeconomics, emergency management, and risk exposure was constructed, with indicator weights derived from a hybrid Analytic Hierarchy Process–Entropy Weight Method. Spatial autocorrelation analysis (Moran’s I = 0.475, p < 0.001) revealed distinct “resilience fault lines,” with high-resilience clusters in central districts and low-resilience clusters in peripheral industrial belts. Geodetector identified economic intensity (q = 0.46), elevation (q = 0.39), and emergency shelter density (q = 0.37) as dominant drivers, with strong interaction effects. These findings highlight significant resilience inequality, emphasizing the need for targeted, multidimensional interventions to enhance adaptive capacity and inform climate adaptation strategies in rapidly urbanizing coastal megacities.

SustainabilityVol. 17(17)
University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 20%
Flood Risk Assessment and Management
1.11
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Urban Flood Resilience in a Megacity Context: Multidimensional Assessment and Spatial Differentiation in Shenzhen — Xinyan Huang, Dawei Wang · Sustainability (2025) | TGRS Research Map | TGRS