The spatiotemporal evolution of the effect of industrial agglomeration on industrial green economic efficiency: empirical evidence from pollution-intensive industries in China

Using basic data from China’s 30 provincial regions from 2005 to 2015, the slacks-based measure of super-efficiency in data envelopment analysis (super-efficiency SBM-DEA) is used to measure industrial green economic efficiency (IGEE). Moreover, the temporal change and spatial equilibrium of IGEE are analyzed, and the panel quantile regression model (PQRM) is used to empirically analyze the impacts of the degree of agglomeration of pollution-intensive industries (PIIs) and other influencing factors on IGEE. The results reveal the following. (1) The kernel density distribution curve of China’s IGEE exhibits a double-peaked distribution, and the overall peak value of the curve changes relatively greatly, exhibiting obvious spatial inequality. (2) Based on their trajectories, the centers of gravity of IGEE are all distributed in the central region. The moving speed of the centers of gravity exhibits a trend of “accelerating–decreasing–quickly accelerating.” Judging from the changes of the standard deviation ellipse (SDE), the spatial distribution pattern of China’s IGEE generally presents a northeast–southwest pattern, and it has exhibited the trend of shifting to a north–south pattern in recent years. (3) The agglomeration level of PIIs has a significant positive effect on the development of China’s IGEE at each quantile level, and the most significant impact on China’s IGEE is at the 75th quantile level. This paper empirically analyzes the impact of the agglomeration level of PIIs on IGEE to aid in the greening of industrial economic efficiency and the sustainable development of the ecological environment in China and other developing countries.