4.7 Article

Efficiency evaluation of regional energy saving and emission reduction in China: A modified slacks-based measure approach

期刊

JOURNAL OF CLEANER PRODUCTION
卷 140, 期 -, 页码 1313-1321

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2016.10.021

关键词

Energy saving and emission reduction; Efficiency evaluation; M-SBM model; Data envelopment analysis

资金

  1. National Natural Science Funds of China [71222106, 71571173]
  2. Research Fund for the Doctoral Program of Higher Education of China [20133402110028]
  3. Foundation for the Author of National Excellent Doctoral Dissertation of P. R. China [201279]
  4. Top-Notch Young Talents Program of China
  5. State Scholarship Fund by the Office of China Scholarship Council [201606340054]

向作者/读者索取更多资源

China's economic reform and opening policy has resulted in China's rapid growth, especially in terms of economy. Unfortunately, the growth has brought about huge consumption of energy and increasing emission of environmental pollution. Little research has been done which pays close attention to the efficiency evaluation of regional energy saving and emission reduction even though such evaluation is considered a crucial method in the fight to save resources and protect the environment. Aiming at evaluating the efficiency of regional energy saving and emission reduction of China, we firstly construct an evaluation index system for input and output selection. Then a modified slacks-based measure model is applied to measure the performance of energy saving and emission reduction. An empirical study is done on 30 of mainland China's provincial-level regions. The results show that the energy saving and emission reduction efficiencies of the regions in China are generally at a low level. More precisely, China's regional regions performed really badly in energy saving although their emissions results are passable, barely. To resolve this problem, several policy recommendations are proposed to improve the efficiency of regional energy saving and emission reduction of China. (C) 2016 Elsevier Ltd. All rights reserved.

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