The spatio-temporal dynamic evolution and variability pattern of urban green resilience in China based on multi-criteria decision-making

Abstract

Environmental challenges significantly impact urban areas, making cities vulnerable to extreme climatic events. Hence, this study introduces an urban green resilience index system and employs a novel multi-criteria decision-making method to measure green resilience across Chinese cities. Utilizing exploratory spatio-temporal data analysis, Dagum Spatial Gini coefficient, and geographical detector methods, we examine spatio-temporal dynamic evolution and variability pattern. Key findings are as follows: (1) The overall level of green resilience in Chinese cities has significantly increased over the past decade. (2) Urban green resilience exhibits significant spatial clustering and dependence, with high-high mode in central and eastern China, and low-low mode in the southwest and northwest. (3) Urban green resilience aligns along a northeast-southwest axis, with its center of gravity corresponding to major population and economic centers. (4) There is a spatial convergence in urban green resilience across eastern, central, and western China, with declining Dagum Spatial Gini coefficients suggesting reduced spatial inequality. (5) Exploratory spatio-temporal data analysis indicates stable local spatial structures, but inter-city collaboration remains insufficient for fostering a cooperative developmental pattern for urban green resilience. This study examines the capacity and potential of cities to adapt their development strategies and achieve sustainable growth amidst environmental challenges and uncertainties, including extreme weather events.