Fine identification of the supply–demand mismatches and matches of urban green space ecosystem services with a spatial filtering tool

Abstract

Metropolitan environments have been confronted with green space configuration issues, which is reflected not only in quantitative deficits of resources, but also in spatial mismatches between ecosystem service (ES) supply and demand. Investigating on whether ES supply meets demand provides support for urban green space (UGS) planning and management. However, scientific spatial quantification methods for UGS-ES supply–demand matching status at fine scale remain insufficient, especially from a lack of practice on addressing extraterritorial effects in ES flows. Therefore, this research proposed a systematic framework for the fine-scale identification of mismatches and matches between UGS-ES supply and demand. Five UGS-ESs, i.e., cooling, air purification, noise reduction, landscape aesthetics, and outdoor recreation, were selected for a case study in the downtown area of Hangzhou, China. The framework comprised three key steps: (1) mapping UGS-ES supply and demand, (2) applying a demand-oriented geographically weighted regression (GWR) model to modify the spatial patterns of supply–demand matching degrees, and (3) identifying supply–demand mismatches and matches. The results showed that cooling and landscape aesthetics had the largest mismatched areas overall. Upon zoning by UGS social functions, commercial and public service lands had the largest the mismatches of cooling and outdoor recreation; residential areas had the largest mismatches of cooling and landscape aesthetics; yet few mismatches were found in public green spaces. UGSs in matched areas had more reasonable proportion of vegetation types and spatial configurations compared to the average. The comprehensive multiservice analysis of mismatches and matches helped identify priority areas and generate optimization suggestions based on trade-offs among ESs, vegetation types, and urban functions. This study demonstrates how fine-scale ES supply–demand matching analysis can support decision-making, thus promoting the integration of ES knowledge to achieve optimal UGS configurations in the future.