Efficiency Evaluation of Water Consumption in a Chinese Province-Level Region Based on Data Envelopment Analysis

被引:26
作者
Hu, Ping [1 ,2 ]
Chen, Na [1 ]
Li, Yongjun [3 ]
Xie, Qiwei [2 ]
机构
[1] Hubei Univ, Fac Math & Stat, Wuhan 430062, Hubei, Peoples R China
[2] Beijing Univ Technol, Sch Econ & Management, Beijing 100000, Peoples R China
[3] Univ Sci & Technol China, Sch Management, Hefei 230000, Anhui, Peoples R China
基金
中国国家自然科学基金;
关键词
data envelopment analysis; water efficiency; China; output improvement; water consumption; DEA WINDOW ANALYSIS; ECO-EFFICIENCY; ENVIRONMENTAL EFFICIENCY; EU COUNTRIES; PERFORMANCE; INDUSTRY; SYSTEM; PRODUCTIVITY; INEFFICIENCY; IMPROVEMENT;
D O I
10.3390/w10060793
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Due to the large volume of sewage in China, the efficiency of water consumption evaluated by the traditional model may be inaccurate. This paper evaluates the water consumption efficiency more scientifically. First, this paper uses the CCR model to evaluate the resource efficiency and environmental efficiency separately. The latter is generally lower than the former, which means the issue of water pollution is more serious than the problem of water resource consumption. Then, the water consumption efficiency is integrally evaluated by an eco-inefficiency model which focuses on undesirable outputs. The results are in good agreement with the results of the CCR model: (1) Only Beijing, Tianjin, and Shanghai are eco-efficient in terms of water consumption, water consumption efficiency in the southeastern coastal areas is higher than in the Midwest, and the overall water environment is bad; (2) China needs to focus on reducing industrial wastewater; (3) the output of water consumption has a lot of room for improvement; and (4) the output improvement schemes of all provinces have some similarities and are related to many features. So, this paper has made a clustering analysis of the improvement schemes and given detailed suggestions for improving the eco-efficiency of water consumption in China according to the clustering result.
引用
收藏
页数:21
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