Evaluating spatial characteristics and influential factors of industrial wastewater discharge in China: A spatial econometric approach. (February 2021)

• Record Type: Journal Article
• Authors: Bu, Yan; Wang, Erda; Jiang, Ziyu
• Is Part Of: Ecological indicators. Volume 121(2021)
• Publication Date: 2021-02
• Subjects: Industrial wastewater discharge -- Spatial autocorrelation test -- Spatial econometric approach -- China; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2020.107219
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: The spatial Durbin model is employed to investigate factors of China's IWD. There is evident spatial autocorrelation of province-level IWD. The EKC relationship between IWD and GDP is verified. The spatial spillover effects of socioeconomic factors of IWD are investigated. Abstract: Although China's total industrial wastewater discharge (IWD) has shown a downward trend in recent years, it is still at a relatively high level. Based on the panel data gathered from 30 Chinese provinces from 2000 to 2017, this study attempts to investigate the spatial characteristics and influential factors which could potentially pose primary contribution to the IWD. Specifically, the spatial autocorrelation test is used to depict the spatial characteristics of IWD. All statistical test results seem to support that using the spatial Durbin model (SDM) with spatial and time period fixed effects could be properly utilized in assessing over the factors of being considered of affecting the IWD. Those assumed factors involve with economic development, population growth, industrial structure, intensity of environmental governance and the level of technological innovation. The results show that the provincial industrial wastewater effluents exhibit a significant spatial autocorrelation as well as agglomeration characteristics. The relationship between IWD and GDP shows up an inverted U-shape, suggesting a typical dynamic of the Environmental Kuznets Curve (EKC). Perhaps, due to a lagging effect, the government's investments in IWD governance at the current stage is somehow positively correlated with the volume of IWD while technological innovation and industrial structure are negatively correlated with IWD, and yet it appears no connection between population growth and IWD. Furthermore, the estimated direct and indirect effects indicate that economic development, technological innovation, and environmental governance intensity all impose negative spatial spillover effects on IWD. However, it seems to present a significant positive spatial spillover effect being exerted by the industrial structure. Accordingly, we put forward the general measures and specific measures to address China's IWD problems.