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Dynamic Environmental Efficiency Assessment of Industrial Water Pollution

Author

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  • Ronggang Zhang

    (Business College, Northwest University of Political Science and Law, No. 558 West Chang An Road, Chang An District, Xi’an 710122, China)

  • Ching-Cheng Lu

    (Department of Economics, Soochow University, 56, Kueiyang St., Sec. 1, Taipei 100, Taiwan)

  • Jen-Hui Lee

    (Department of Economics, Soochow University, 56, Kueiyang St., Sec. 1, Taipei 100, Taiwan)

  • Ying Feng

    (Business College, Institute of Resource Conflict and Utilization Northwest University of Political Science and Law, No. 558 West Chang An Road, Chang An District, Xi’an 710122, China)

  • Yung-Ho Chiu

    (Department of Economics, Soochow University, 56, Kueiyang St., Sec. 1, Taipei 100, Taiwan)

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.

Suggested Citation

  • Ronggang Zhang & Ching-Cheng Lu & Jen-Hui Lee & Ying Feng & Yung-Ho Chiu, 2019. "Dynamic Environmental Efficiency Assessment of Industrial Water Pollution," Sustainability, MDPI, vol. 11(11), pages 1-12, May.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3053-:d:235589
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    References listed on IDEAS

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    Cited by:

    1. Linlin Zhao & Lin Zhang & Yong Zha, 2019. "Industrial Efficiency Evaluation in China: A Nonparametric Production-Frontier Approach," Sustainability, MDPI, vol. 11(18), pages 1-23, September.
    2. Ying-yu Lu & Yue He & Bo Wang & Shuang-shuang Ye & Yidi Hua & Lei Ding, 2019. "Efficiency Evaluation of Atmospheric Pollutants Emission in Zhejiang Province China: A DEA-Malmquist Based Approach," Sustainability, MDPI, vol. 11(17), pages 1-19, August.
    3. Zhirong Li & Kaiheng Zheng & Qikang Zhong, 2022. "Comprehensive Evaluation and Spatial-Temporal Pattern of Green Development in Hunan Province, China," Sustainability, MDPI, vol. 14(11), pages 1-21, June.
    4. Xiao-Ning Li & Ying Feng & Pei-Ying Wu & Yung-Ho Chiu, 2021. "An Analysis of Environmental Efficiency and Environmental Pollution Treatment Efficiency in China’s Industrial Sector," Sustainability, MDPI, vol. 13(5), pages 1-25, February.
    5. Manli Cheng & Zhen Shao & Changhui Yang & Xiaoan Tang, 2019. "Analysis of Coordinated Development of Energy and Environment in China’s Manufacturing Industry under Environmental Regulation: A Comparative Study of Sub-Industries," Sustainability, MDPI, vol. 11(22), pages 1-20, November.
    6. Ethel Ansaah Addae & Dongying Sun & Olivier Joseph Abban, 2023. "Evaluating the effect of urbanization and foreign direct investment on water use efficiency in West Africa: application of the dynamic slacks-based model and the common correlated effects mean group e," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 5867-5897, July.

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