Dynamic Environmental Efficiency Assessment of Industrial Water Pollution

被引:31
|
作者
Zhang, Ronggang [1 ]
Lu, Ching-Cheng [2 ]
Lee, Jen-Hui [2 ]
Feng, Ying [3 ]
Chiu, Yung-Ho [2 ]
机构
[1] Northwest Univ Polit Sci & Law, Business Coll, 558 West Chang An Rd, Xian 710122, Shaanxi, Peoples R China
[2] Soochow Univ, Dept Econ, 56 Kueiyang St,Sec 1, Taipei 100, Taiwan
[3] Northwest Univ Polit Sci & Law, Inst Resource Conflict & Utilizat, Business Coll, 558 West Chang An Rd, Xian 710122, Shaanxi, Peoples R China
关键词
dynamic DEA (Data Envelopment Analysis); SBM; water pollution; environment; efficiency; DATA ENVELOPMENT ANALYSIS; TREATMENT PLANTS; WASTE; PRODUCTIVITY; MANAGEMENT; COMPANY;
D O I
10.3390/su11113053
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] Relations between upgrading of industrial structure, innovation of green technology and water environmental pollution: estimation based on dynamic simultaneous equation
    Wang, Xin-yu
    Zhao, Dong-sheng
    Zhang, Li-li
    Hu, Han-qing
    Ma, Ya-dong
    Ma, Jiao-yue
    DESALINATION AND WATER TREATMENT, 2021, 218 : 80 - 86
  • [22] ENVIRONMENTAL PROBLEMS OF INDUSTRIAL POLLUTION OF ENVIRONMENT
    Pavlova, Svetlana
    11TH INTERNATIONAL SCIENTIFIC CONFERENCE ON ENGINEERING FOR RURAL DEVELOPMENT, VOL 11, 2012, : 184 - 188
  • [23] INDUSTRIAL POLLUTION - AIR AND WATER
    VAILLANT, G
    CHIMIE AND INDUSTRIE GENIE CHIMIQUE, 1972, 105 (15): : C52 - &
  • [24] Water environmental safety assessment and dynamic evolution in China
    Wang, Xin Yu
    Fu, Zhi Yu
    Wu, Si Jian
    DESALINATION AND WATER TREATMENT, 2020, 188 : 309 - 315
  • [25] Assessment on Efficiency of Environmental Pollution Control in Hebei Province Based on DEA Model
    Ji, Weizhuo
    Liu, Yinghui
    Li, Dandan
    RENEWABLE AND SUSTAINABLE ENERGY, PTS 1-7, 2012, 347-353 : 444 - 447
  • [26] Consequences of Decentralization: Environmental Impact Assessment and Water Pollution Control in Indonesia
    Bedner, Adriaan
    LAW & POLICY, 2010, 32 (01) : 38 - 60
  • [27] POLLUTION STATUS AND HEALTH IMPACT ASSESSMENT OF ANTIBIOTICS IN ENVIRONMENTAL - AND DRINKING WATER
    Zheng, Guihua
    Yang, Yiyi
    Deng, Linghui
    Zhang, Ting
    Zhu, Feiyun
    Sun, Jing
    Li, Shuangjiang
    Feng, Xiaonan
    FRESENIUS ENVIRONMENTAL BULLETIN, 2022, 31 (02): : 1779 - 1787
  • [28] Urban and industrial environmental pollution control in China: An analysis of capital input, efficiency and influencing factors
    Guo, Ke
    Cao, Yuequn
    Wang, Zongfang
    Li, Zhengyang
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2022, 316
  • [29] Environmental proteomics in pollution assessment
    Cristobal, S.
    Amelina, H.
    Apraiz, I
    Bayat, N.
    Danielsson, G.
    INTEGRATIVE AND COMPARATIVE BIOLOGY, 2012, 52 : E38 - E38
  • [30] Dynamic Assessment of Comprehensive Water Quality Considering the Release of Sediment Pollution
    Wang, Tianxiang
    Xu, Shiguo
    Liu, Jianwei
    WATER, 2017, 9 (04)