On the Spatial Supply-Demand Balance of Green Stormwater Infrastructure (Gsi) and Mitigation of Flood Vulnerability: A Case Study in Guangdong, Hong Kong and Macao Greater Bay Area (Gba)

Abstract

Green Stormwater Infrastructure (GSI), a nature-based approach, has gained prominent recognition for its vital role in mitigating flooding vulnerability and urban resilience. Ensuring the fair distribution of GSI throughout urban areas is a critical concern, which is a vital step for harmonizing urban water resources and achieving ecosystem balance. However, up to now, few studies have been conducted to investigate GSI spatial allocation because of the intricate GSI supply-demand relationship. This study proposed a quantitative framework to identify priority areas of GSI and examine the complex matching of supply-demand In the Greater Bay Area (GBA), through a coupled coordination simulation model. The assessment clustering graphing of supply and demand relationships was interpreted through clustering numerous districts, using proximity matrix analysis mapped by network reference diagrams. The results indicated a spatial heterogeneity in supply and demand of GSI in GBA, with 90% of the districts in a condition of imbalance in coupling coordination. The districts were classified into four spatial clusters based on their similar levels of GSI supply-demand imbalances. Most of the economically developed urban areas of Guangzhou and Shenzhen exhibited a high GSI demand level, while Zhaoqing and Huizhou presented a state of GSI oversupply, with spatial allocation mismatches and resource mismatches. The findings of this study offer a comprehensive understanding of the unique distribution of GSI, the disparities between supply and demand, and the key areas for prioritizing GSI construction. This insight serves as a theoretical foundation and practical guidance for planning and implementing GSI initiatives on a macro-scale.

Keywords: Green Stormwater Infrastructure, Supply-Demand, Coupled Coordination Model, Proximity Matrix Analysis, Towns