Decomposition Analysis of Factors Affecting Changes in Industrial Wastewater Emission Intensity in China: Based on a SSBM-GMI Approach

Abstract

This paper investigated the factors driving the changes in industrial wastewater emission intensity (IWEI) across provinces in China. To do this, we proposed a Super-efficiency Slacks-based Measure-Global Malmquist Index (SSBM-GMI) to decompose the change in IWEI into the effects from efficiency change (ECE), technological change (TCE), capital–wastewater substitution (KWE) and labor–wastewater substitution (LWE). The method was applied to conduct an empirical study using Chinese provincial data from 2003–2015. The main findings include the following: firstly, TCE was the dominant driving force behind the reduction in IWEI with an average annual contribution of −6.4% at the national level, followed by KWE (−5.3%), LWE (−1.8%) and ECE (1.2%). Secondly, significant differences exist in the driving factors behind the reduction in IWEI across regions. The reduction in IWEIs in the Northeast area and the Great Northwest area was mainly driven by productivity growth, while the reduction in IWEIs in the other areas was mainly driven by factor substitution. Thirdly, the shortage of KWE and LWE has impeded IWEI reduction in the Great Northwest area, the Middle Reaches of the Yellow River, the Northeast area and the North area. Finally, some particular policy implications were also recommended for reducing industrial wastewater emission in China.

Total-Factor Eco-Efficiency and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration, China

“…The coefficients for FDI are negative but not significant in the YRDUA, suggesting that FDI has not promoted the growth in eco-efficiency in this region. This result is similar to Wen [29], who suggested that the impacts of FDI on total productivity differed by region in China. The reason may be that foreign investment mainly focused on manufacturing, consequently bringing about tremendous pressure on the resources and environment in the YRDUA.…”

“…Thus, the PPS of the global frontier is given by:PPSGlb = {PPS1∪ PPS2∪ …∪ PPST} where PPSGlb denotes the specific technologies of the global frontier (i.e., best practice frontier) [29]. The production technology is assumed to follow all the standard axioms of production theory, including the assumptions of bounded set, bounded convexity, etc.…”

Decomposition and attribution analysis for assessing the progress in decoupling industrial development from wastewater discharge in China

“…Only Beijing, Shanghai and Gansu showed strong decoupling in this time period. The finding that Beijing and Shanghai experienced strong decoupling in the 10th FYP is not surprising, as they were the very few regions in China with advanced technology and equipment (Chen et al, 2016;Wu et al, 2018a), optimized industrial structure (Geng et al, 2014) and preferable environmental policies (Cheng et al, 2018;Chen et al, 2019). However, for the less developed inland province, Gansu, it may be due to that its Zhangye City was selected as the first pilot city for building Water-saving Society in 2002, which helps improve water use efficiency and reduce wastewater discharges of Gansu.…”

Machine Learning-Based Predominant Driving Factors Impacting Urban Industrial Wastewater Discharge in the Yellow River Basin

“…8 It is necessary to promote the implementation of water-saving policies and coordinate the relationship between the subsystems. 9 Shortages of capital and labor reduced the IWD intensity of cities in the middle of the Yellow River, 10 and urban wastewater posed an ecological burden to the Bohai Sea. 11 Economic factors are the main factors affecting IWD in all provinces, and the logarithmic mean divisia index method is normally used to evaluate the spatiotemporal characteristics and driving forces of IWD in China.…”