4.7 Article

Evaluating the construction efficiencies of urban wastewater transportation and treatment capacity: Evidence from 70 megacities in China

期刊

RESOURCES CONSERVATION AND RECYCLING
卷 128, 期 -, 页码 373-381

出版社

ELSEVIER
DOI: 10.1016/j.resconrec.2016.08.020

关键词

Data envelopment analysis; Construction efficiency; Sewers; Wastewater treatment capacity; China

资金

  1. National Natural Science Foundation of China [71325006, 71373153, 71403120, 71461137008]
  2. Program for New Century Excellent Talents in University [NCET-13-0890]
  3. Jiangsu Natural Science Foundation [BK20151351]
  4. Key Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province [2015ZDIXM039]
  5. Shanghai Philosophy and Social Science Fund Project [2014BJB001]
  6. Shanghai Education Development Foundation
  7. Shanghai Municipal Education Commission [145G32]
  8. Taizhou University Research Project [TZXY2014ZDKT003, TZXY2014YBKT008]

向作者/读者索取更多资源

Although China's urbanization process requires corresponding infrastructure development, it has reached a consensus that wastewater transportation and wastewater treatment capacity are not satisfactory in China. We use an input-oriented non-radical Data Envelopment Analysis (DEA) model to investigate the construction efficiencies of urban sewers and wastewater treatment capacity in China's 70 megacities during 2006-2012. The results show that in 2012, the average construction efficiency of urban sewers is 0.76, more than the average construction efficiency of wastewater treatment capacity that is 0.68. During the sample period, Hefei, Liuzhou, Shenzhen, and Wuxi present better construction efficiencies than other cities, while Jilin, Wenzhou, and Xianyang have relatively lower construction efficiencies. 26 cities should develop infrastructure for both urban sewers and wastewater treatment capacity. The growth rates of both urban sewers and wastewater treatment capacity are lower than those of urban population in Xiamen. More attentions should be paid by relevant government agencies. (C) 2016 Elsevier B.V. All rights reserved.

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