Effects of industrial agglomeration and environmental regulation on urban ecological efficiency: evidence from 269 cities in China

Abstract

The ecological environment and economic development are double-edged swords. Nevertheless, we can still achieve green and coordinated development through environmental regulations and industrial agglomeration. Based on the panel data from 269 cities in China from 2008 to 2017, using the SBM-DEA model, the Malmquist-Luenberger (ML) index, and the spatial Durbin model (SDM) under different weight matrices, this paper explored the spatial pattern of ecological efficiency, the internal evolution mechanism, and the spillover effects of industrial agglomeration and environmental regulation on ecological efficiency. The results demonstrated that China’s urban ecological efficiency had an obvious spatial pattern of “high in the east and low in the west.” Due to the different life cycles of cities, the internal evolution mechanism of urban ecological efficiency had significant differences. Pure technological efficiency (PEFFCH), technological progress (TECH), and scale efficiency (SECH) have contributed the most to the ecological efficiency of the eastern, central, and western regions, respectively. Furthermore, a significant U-shaped relationship existed between industrial agglomeration and ecological efficiency. In particular, urban ecological efficiency will be improved when the industrial agglomeration level exceeds a certain scale. However, the spillover effects of industrial agglomeration were more sensitive to distance factors, leading to failure of the significance test under the economic distance and asymmetric economic distance matrix. The “innovation compensation effect” of environmental regulation was greater than the “compliance cost,” which verified the applicability of the “Porter Hypothesis” in urban ecological efficiency to a certain extent. Finally, the geographical detector showed that each variable had a certain impact on the urban ecological efficiency, and the impact of the interaction term was greater than that of a single variable.