 Dynamic Environmental Efficiency Assessment of Industrial Water PollutionRonggang Zhang,
Ching-Cheng Lu,
Jen-Hui Lee,
Ying Feng and
Yung-Ho Chiu
Sustainability, 2019, vol. 11, issue 11, 1-12 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. Keywords: dynamic DEA (Data Envelopment Analysis); SBM; water pollution; environment; efficiency (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:11:p:3053-:d:235589 Access Statistics for this article Sustainability is currently edited by Ms. Alexandra Wu More articles in Sustainability from MDPI |
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