Industrial wastewater is one of the three industrial wastes. If substandard industrial wastewater is discharged into the environment, there will be a serious impact on the environmental quality. Excessive emissions also indicate that water resources utilization is unreasonable. Therefore, it is of great significance to study the changing trends and influencing factors of industrial wastewater discharge in order to effectively conserve resources and improve the environmental quality. In this study, the spatial autocorrelation and the environmental Kuznets curve were used to study the spatial-temporal changes and characteristics of industrial wastewater discharge at the provincial scale and prefectural scale in China in 2004-2015. Then, the Logarithmic Mean Divisia Index was used to analyze the influencing factors of wastewater discharge in this period. China's total industrial wastewater discharge showed a trend of increasing at the beginning and then decreasing, and more than half provinces or cities show this trend of decoupling from economic development. Moreover, wastewater discharge was higher in the east region and lower in the west region at both the provincial scale and prefectural scale, but the aggregation degree on the prefectural scale is more obvious than that on the provincial scale. The technical effect has a general inhibitory effect on industrial wastewater discharge, but it also promoted the discharge in a few cities; the structure effect on industrial wastewater discharge has generally changed from promotion to inhibition during the study period; and economic effect and population effect were mainly to promote industrial wastewater discharge. Therefore, a few cities should accelerate technology upgrading and industrial restructuring in recent years in order to change the promoting effect, and most cities need to strengthen the implementation of economic measures and improve the residents' environmental awareness.
Keywords: Environmental Kuznets curve; Industrial wastewater; Logarithmic Mean Divisia Index; Prefectural scale; Spatial autocorrelation.