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A Coupled Coordination and Network-Based Framework for Optimizing Green Stormwater Infrastructure Deployment: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area
A Coupled Coordination and Network-Based Framework for Optimizing Green Stormwater Infrastructure Deployment: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area
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A Coupled Coordination and Network-Based Framework for Optimizing Green Stormwater Infrastructure Deployment: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area
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A Coupled Coordination and Network-Based Framework for Optimizing Green Stormwater Infrastructure Deployment: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area
A Coupled Coordination and Network-Based Framework for Optimizing Green Stormwater Infrastructure Deployment: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area

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A Coupled Coordination and Network-Based Framework for Optimizing Green Stormwater Infrastructure Deployment: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area
A Coupled Coordination and Network-Based Framework for Optimizing Green Stormwater Infrastructure Deployment: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area
Journal Article

A Coupled Coordination and Network-Based Framework for Optimizing Green Stormwater Infrastructure Deployment: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area

2025
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Overview
Green Stormwater Infrastructure (GSI), as a nature-based solution, has gained widespread recognition for its role in mitigating urban flood risks and enhancing resilience. Equitable spatial distribution of GSI remains a pressing challenge, critical to harmonizing urban hydrological systems and maintaining ecological balance. However, the complexity of matching GSI supply with urban demand has limited comprehensive spatial assessments. This study introduces a quantitative framework to identify priority zones for GSI deployment and to evaluate supply–demand dynamics in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) using a coupled coordination simulation model. Clustering and proximity matrix analysis were applied to map spatial relationships across districts and to reveal underlying mismatches. Findings demonstrate significant spatial heterogeneity: over 90% of districts show imbalanced supply–demand coupling. Four spatial clusters were identified based on levels of GSI disparity. Economically advanced urban areas such as Guangzhou and Shenzhen showed high demand, while peripheral regions like Zhaoqing and Huizhou were characterized by oversupply and misaligned allocation. These results provide a systematic understanding of GSI distribution patterns, highlight priority intervention areas, and offer practical guidance for large-scale, equitable GSI planning.