长三角区域工业污染治理效率的时空演化及其空间关联网络

Under the background of deepening the integration of the Yangtze River Delta, regional industrial pollution control is increasingly moving towards networking. The overall improvement of regional industrial pollution control efficiency is influenced by its spatial network structure. Based on panel data of 41 cities in the Yangtze River Delta (YRD) region from 2011 to 2022, this paper uses the super SBM-DEA model to measure the industrial pollution control efficiency and visualize its spatio-temporal evolutionary characteristics. Furthermore, the imput-output-efficiency classification is used to analyze the differences in industrial pollution control among cities. On this basis, through the social network analysis, the structural characteristics of the spatial correlation network for industrial pollution control in the YRD are recognized, while the driving factors of the spatial correlation network are explored. The results show that: (1) From 2011 to 2022, the temporal change of the industrial pollution control level in the study area presents a fluctuating upward trend, with its spatial distribution showing a pattern of high efficiency cities as the mainstay and low efficiency cities distributed in contiguous areas. The spatial agglomeration degree and spatial disparity of industrial pollution control efficiency is gradually weakening, and the spatial dependence is not stable. (2) In the spatial correlation network of industrial pollution control efficiency in the YRD, the “human”shaped area formed by the Taizhou-Tongling line and the Taizhou-Shaoxing line is the backbone of the network, and the connections within provinces are the branches. The network structure characteristics tend to be stable and the hierarchical structure is not strict. (3) The Yangtze River Delta can be divided into four major sectors based on the network position. The isolation between each sector is strong, and the internal connections within the sectors dominate the correlation relationships. (4) The formation of the spatial correlation network is greatly influenced by geographical location and economic development level, and is also significantly affected by differences in industrial structure, foreign direct investment scale and command-and-control environmental regulation intensity during the development process.