Spatial and Temporal Evolution and Driving Factors of Urban Ecological Well-Being Performance in China

Extensive development leads to the decline of ecological well-being, and it is necessary to improve the urban ecological well-being performance (EWP). This paper adopted the Super-slack-based measure (Super-SBM) model to evaluate the EWP of 285 Chinese prefecture level cities from 2011 to 2017. The exploratory spatial data analysis method (ESDA) was used to explore the spatial and temporal evolution characteristics of the EWP, and then the spatial Durbin model (SDM) was adopted to analyze the driving factors of the EWP. The results show that the trend of the overall average EWP has experienced a stage evolution process of upward -> downward -> upward. The urban EWPs have significant spatial agglomeration and path dependence. The economic development level and technological progress had the positive impacts on the EWP, and the urbanization level, economic extroversion and industrial structure had the negative impacts on the EWP. The result reveals that there was a U-shaped relationship existing between urbanization level and the EWP. The negative spatial spillover effect of urbanization level on the EWP was significant. The corresponding policy implications were put forward. This study will provide strategic guidance for policy makers to optimize and enhance the urban EWP.

Automated summary

Extensive development has led to the decline of ecological well-being, and it is necessary to improve the urban ecological well-being performance (EWP). This paper evaluates the EWP of 285 Chinese prefecture level cities from 2011 to 2017 using the Super-slack-based measure (Super-SBM) model. The findings suggest that economic development level and technological progress have positive impacts on the EWP, while urbanization level, economic extroversion, and industrial structure have negative impacts on the EWP.