Spatio-temporal evolution characteristics and influencing factors of environmental welfare performance in Chinese...

In the process of China's urbanization, issues such as air pollution, water pollution, soil pollution, and noise pollution have become increasingly prominent, severely constraining the sustainable development of cities. The resultant decline in environmental welfare performance (EWP) not only affects residents' quality of life but may also lead to public health issues, increasing healthcare costs, and subsequently impacting social stability and economic development.

This paper incorporates factors closely related to environmental pollution, such as residents' health and social welfare, into the analytical framework of environmental welfare performance. Using the Hybrid-Network-DEA model, we measure the EWP of 240 cities in China, and then investigate the spatial distribution characteristics and spatio-temporal evolution patterns of EWP. Finally, empirical testing of the factors influencing EWP is conducted using spatial econometric methods.

The overall level of EWP in 240 Chinese cities from 2004 to 2019 is relatively low, but it generally shows a wavy upward trend. Meanwhile, notable regional disparities exist in EWP, with the highest average performance in the east, followed by the west, and the lowest in the central. The main source of regional differences in EWP lies in inter-regional disparities. The greatest internal disparities are found in the east, while the largest inter-regional disparities are between the east and the west. A pronounced positive spatial autocorrelation is observed in the EWP among Chinese cities. Economic development, opening-up, financial development, digital infrastructure, and population density significantly promote the local EWP, whereas the industrial structure and transportation structure have exerted opposite effects. Additionally, the enhancement of EWP in neighboring regions is also notably facilitated by economic development, opening-up, financial development, and digital infrastructure. Within the three major regions, the direct and indirect effects of various influencing factors exhibit significant differences.

Based on these insights, we suggest comprehensively improving environmental welfare efficiency, narrowing regional disparities, strengthening spatial agglomeration effects, optimizing industrial structure, and strengthening financial support and digital infrastructure construction.