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Title: Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model

Abstract

As an energy-intensive industry, the industrial sector consumes 70% of energy consumption and causes serious environmental pollution in China. Also, the government emphasized the promotion of R&D investment in the industrial sector in China's National Plan on Climate Change (2014–2020). It is meaningful and contributes to assessing energy and environmental performance, as well as R&D and industrial pollution control (IPC) investment strategies of China's industrial sector. A non-radial DEA model, as with natural and managerial disposability, was adopted to evaluate this from provincial and regional perspectives during the 2008–2012 period. Energy and environmental performance was evaluated by unified efficiency under natural disposability (UEN), unified efficiency under managerial disposability (UEM), and unified efficiency under natural and managerial disposability (UENM). The empirical results indicated that Shandong and Hainan were efficient under natural and managerial disposability, while other provinces had the potential to improve their energy and environmental performance. The number of provinces that was fit for investments of R&D and IPC increased from 2008 to 2010, then decreased in 2011 and 2012. In spite of this, many provincial industrial sectors should make efforts to reduce pollution by investment on technology. Tianjin, Heilongjiang, Jiangxi and Henan were especially the best investment objects becausemore » investments of R&D and IPC turned to be effective for them during the whole study period. Moreover, western China had the highest average UENM, followed by eastern China and central China. Eastern China and central China were rewarding to expand investments. Coal consumption was the main factor to negatively affect unified efficiency whereas the increase in economic development level was primarily responsible for the improvement of unified efficiency. According to the results, differentiated suggestions to further improve energy and environmental performance were proposed.« less

Authors:
 [1];  [1];  [2]
  1. College of Management and Economics, Tianjin University, Tianjin 300072 (China)
  2. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 210016 (China)
Publication Date:
OSTI Identifier:
22589265
Resource Type:
Journal Article
Resource Relation:
Journal Name: Environmental Impact Assessment Review; Journal Volume: 60; Other Information: Copyright (c) 2016 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; CHINA; CLIMATIC CHANGE; ECONOMIC DEVELOPMENT; ENERGY CONSUMPTION; INVESTMENT; PERFORMANCE; POLLUTION CONTROL

Citation Formats

Wang, Juan, E-mail: wangjuan_tju@163.com, Zhao, Tao, and Zhang, Xiaohu. Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model. United States: N. p., 2016. Web. doi:10.1016/J.EIAR.2016.05.002.
Wang, Juan, E-mail: wangjuan_tju@163.com, Zhao, Tao, & Zhang, Xiaohu. Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model. United States. doi:10.1016/J.EIAR.2016.05.002.
Wang, Juan, E-mail: wangjuan_tju@163.com, Zhao, Tao, and Zhang, Xiaohu. Thu . "Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model". United States. doi:10.1016/J.EIAR.2016.05.002.
@article{osti_22589265,
title = {Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model},
author = {Wang, Juan, E-mail: wangjuan_tju@163.com and Zhao, Tao and Zhang, Xiaohu},
abstractNote = {As an energy-intensive industry, the industrial sector consumes 70% of energy consumption and causes serious environmental pollution in China. Also, the government emphasized the promotion of R&D investment in the industrial sector in China's National Plan on Climate Change (2014–2020). It is meaningful and contributes to assessing energy and environmental performance, as well as R&D and industrial pollution control (IPC) investment strategies of China's industrial sector. A non-radial DEA model, as with natural and managerial disposability, was adopted to evaluate this from provincial and regional perspectives during the 2008–2012 period. Energy and environmental performance was evaluated by unified efficiency under natural disposability (UEN), unified efficiency under managerial disposability (UEM), and unified efficiency under natural and managerial disposability (UENM). The empirical results indicated that Shandong and Hainan were efficient under natural and managerial disposability, while other provinces had the potential to improve their energy and environmental performance. The number of provinces that was fit for investments of R&D and IPC increased from 2008 to 2010, then decreased in 2011 and 2012. In spite of this, many provincial industrial sectors should make efforts to reduce pollution by investment on technology. Tianjin, Heilongjiang, Jiangxi and Henan were especially the best investment objects because investments of R&D and IPC turned to be effective for them during the whole study period. Moreover, western China had the highest average UENM, followed by eastern China and central China. Eastern China and central China were rewarding to expand investments. Coal consumption was the main factor to negatively affect unified efficiency whereas the increase in economic development level was primarily responsible for the improvement of unified efficiency. According to the results, differentiated suggestions to further improve energy and environmental performance were proposed.},
doi = {10.1016/J.EIAR.2016.05.002},
journal = {Environmental Impact Assessment Review},
number = ,
volume = 60,
place = {United States},
year = {Thu Sep 15 00:00:00 EDT 2016},
month = {Thu Sep 15 00:00:00 EDT 2016}
}