Dynamic Environmental Efficiency Assessment of Industrial Water Pollution

Abstract

In the face of severe water pollution, all provinces and cities in China have actively invested in water environment management funds driven by the goals of national energy conservation and emissions reduction. However, due to differences in natural environment, economic and technological levels, industrial structure, and other aspects in provinces and cities, their water environment management effects are also different across time and space. Under economic development and environmental regulation policies, it can be seen that the change in industrial GDP is not completely consistent with that of industrial wastewater discharge. How to improve desirable outputs and reduce undesirable outputs under the limited investment in water pollution control are key issues when investigating the efficiency of industrial water pollution control. This study uses the Dynamic SBM (Slacks-Based Measure) model to assess wastewater resources for research samples covering the 30 regions of China. There are two output variables, two input variables, and one carry-over variable. The output variables are industrial wastewater treatment and industrial output, the two input variables are industrial water consumption and facility operation cost, and the carry-over variable is industrial waste. This study concludes with implications for theory research, as these variables may lead to a better understanding and merging with the input variables, output variables, and carry-over variable of recent studies. The empirical results show that from the efficiency rank changes of the 30 regions for 2011–2015, regions with higher industrial output do not appear to have improved versus other regions, such as for Shandong, Guangdong, Jiangsu, Qinghai, and Zhejiang. The 30 regions’ efficiency scores show some volatility, with 13 regions’ efficiency score volatility clustering close to 0, like Beijing, Chongqing, Shandong, Guangdong, and Sichuan. In contrast, for Anhui, Inner Mongolia, Zhejiang, and Xinjiang, their efficiency scores fell more than other regions in this period and thus should adjust their input/output variables to increase their efficiency scores. This study further presents that many lower-/middle-/high-industrial output regions do not achieve a balance between industrial output and industrial wastewater treatment. How to find a balance between these two factors for any region is a vitally important issue for industrial wastewater treatment policy makers. Under such a circumstance, an industrial output region may not actually be highly efficient at doing this.

Green Mining Efficiency and Improvement Countermeasures for China's Coal Mining Industry

“…An empirical analysis was organized to examine the primary factors that cause performance loss in energy-saving and emissions reduction (Wang et al, 2015). Zhang et al used dynamic SBM models to evaluate the overall efficiency of decision-making units (DMUs) for the whole term period, as well as the term period efficiencies in industrial water pollution (Zhang et al, 2019). This proposed dynamic DEA model incorporates carryover activities and helps measure a period's specific efficiency based on long-term optimization during the whole period.…”

“…About a decade ago, Franks, Brereton, and Moran and Mamurekli evaluated the environmental cumulative effects of coal resource development and utilization and suggested that the authorities involved should play significant roles in improving impact assessment and institutional formulation (Franks et al, 2010;Mamurekli, 2010). Using evidence from China's coal sector, a recent study by Zhang et al addressed the relationship of energy-price regulations and price fluctuations by building simultaneous equations for coal price and coal supply and constructing a forward-looking coefficient to evaluate different coal pricing policies from 2008 to 2016 (Zhang et al, 2019). Sueyoshi and Yuan focused on the unintended consequences of China's coal capacity cut policy and revealed that the capacity cut policy should be differentiated across regions due to the fragmentation of the coal markets, unbalanced distribution of resources, and a mismatch between production and demand centers (Sueyoshi and Yuan, 2018).…”

Efficiency Evaluation of Atmospheric Pollutants Emission in Zhejiang Province China: A DEA-Malmquist Based Approach

“…Therefore, many scholars often use atmospheric pollutant emissions as input indicators in empirical research to conduct empirical measures of atmospheric environmental performance [21,22], atmospheric environmental efficiency [11,19,23,24] and measure and analysis of atmospheric pollution emission efficiency [25][26][27]. From the evaluation method, the environmental efficiency measure based on the data envelopment analysis (DEA) method and its improved model is widely used [28][29][30]. After Charnes et al proposed the DEA method in 1978 [31], from the traditional radial framework to the non-radial framework considering slack variables, different scholars have proposed improved models such as the SBM (Slacks-Based Measure)-DEA model [32,33], Super-SBM model [10,34,35], and non-radial directional distance function (NRDDF) DEA model [11,25].…”

Industrial Efficiency Evaluation in China: A Nonparametric Production-Frontier Approach

“…Zhang et al [27] investigated the dynamic carbon emissions performance of China's industrial sectors using the Malmquist-type index. Zhang et al [28] used the dynamic slacks-based measure (SBM) model to assess the environmental efficiency of industrial water pollution. More details of dynamic efficiency evaluations can be found in Chen and Golley [29] and Yao et al [30].…”