Interaction between ecosystem service supply and urbanization in northern China

Abstract

Rapid urbanization has profoundly changed ability of ecosystem to supply welfare and resulted a series of environmental issues, in turn, such results not only affected the economic growth, but also the quality of sustainable development. Therefore, deeply understanding the interaction between ecosystem service (ES) supply and urbanization is of great significance. As a case study of northern China, we attempt to build an effective framework to evaluate past and future states of the interaction between ES supply and urbanization from quantification spatiotemporal pattern of coupling coordination degree (CD), decoupling path exploration, driving factor analysis to grey prediction model construction. It tries to better improve the interaction between ES supply and urbanization, as well as provide some effective policies for local condition. Results showed that the synthesis coefficient of urbanization (URSC) and CD has been significantly improved from 1995 to 2020 at province scale, while the synthesis coefficient of ES supply (ESSC) and decoupling index score are fluctuating. All of indicators revealing differences between the west and east regions. The east region has the highest levels of ESSC, URSC, CD and decoupling index score during the last twenty-five years. The driving factors including annual precipitation (Precipitation), normalized difference vegetation index (NDVI), cropland area (Cropland), grassland area (Grassland), unused land area (Unused land) all have a spatiotemporal impact on the CD with divergent significances, among which the effect of NDVI is the most significant. Finally, the grey prediction model was applied to predict the ESSC, URSC, CD and decoupling index in the future. The findings reveal a growing state for ESSC and CD in province scale, while a decline state of URSC. Based on the aforementioned results, some scientific policies were proposed for different regions to improve sustainable development.