Supply-Demand Evaluation of Green Stormwater Infrastructure (GSI) Based on the Model of Coupling Coordination

Int J Environ Res Public Health. 2022 Nov 9;19(22):14742. doi: 10.3390/ijerph192214742.

Abstract

The rational spatial allocation of Green Stormwater Infrastructure (GSI), which is an alternative land development approach for managing stormwater close to the source, exerts a crucial effect on coordinating urban development and hydrological sustainability. The balance between the supply and demand of urban facilities has been an influential standard for determining the rationality of this allocation. However, at this stage, research on evaluating planning from the perspective of supply-demand in GSI is still limited. This study proposed an evaluation method for assessing supply-demand levels in GSIs in Guangzhou, China, using the coupling coordination model consisting of Coupling Degree (CD) and Coupling Coordination Degree (CCD). Furthermore, the spatial distributions of supply-demand balance and resource mismatch were identified. The results indicated that the supply and demand levels of GSI exhibited significant spatial differences in distribution, with most streets being in short supply. The GSI exhibited a high CD value of 0.575 and a poor CCD value of 0.328, implying a significant imbalance in facility allocation. A lot of newly planned facilities failed to effectively cover the streets in need of improvement, so it became essential to adjust the planning scheme. The findings of this study can facilitate the decision-makers in assessing the supply-demand levels in GSI and provide a reference of facility allocation for the sustainable construction of Sponge City.

Keywords: coupling coordination model; green stormwater infrastructure; sponge city; supply–demand.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China
  • Cities
  • Urban Renewal*

Grants and funding

This research was funded by the Natural Science Foundation of Guangdong Province, China [grant number 2019A1515010873], and the Science and Technology Program of Guangzhou, China [grant number 202201010431], and Guangzhou University [grant number PT252022023].