Research on the dynamic evolution and influence factors of industrial energy efficiency in China Yangtze River Economic Belt

被引:1
作者
You, Jiansheng [1 ]
Zhao, Rui [2 ,3 ]
机构
[1] Shandong Univ Technol, Sch Management, Zibo, Peoples R China
[2] Univ Int Business & Econ, Sch Govt, Beijing, Peoples R China
[3] Univ Int Business & Econ, Sch Govt, Beijing 100029, Peoples R China
关键词
YREB; industrial energy efficiency; super-EBM; spatial Dubin model; influence factors; ENVIRONMENTAL-REGULATION; BIOENERGY INDUSTRY; CONSUMPTION; PRODUCTIVITY; URBANIZATION; DETERMINANTS; INTENSITY; PROMOTE;
D O I
10.1177/0958305X231177750
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Improving the Yangtze River Economic Belt's industrial energy efficiency is not only an important measure to alleviate China's energy shortages but also a drive to promote green economic development. The Super-EBM model, Malmquist productivity index, exploratory spatial data analysis, and Spatial Dubin model are used in this article to investigate the spatial-temporal dynamic development characteristics and influencing factors of industrial energy efficiency in 108 cities of the Yangtze River Economic Belt from 2011 to 2020. The findings demonstrate that the industrial energy efficiency of the Yangtze River Economic Belt and its three urban agglomerations went through three stages, including the "oscillation period," "stability period," and "enhancement period," and decreases from east to west in the spatial dimension. The Yangtze River Delta urban agglomeration has the highest industrial energy efficiency, followed by the Middle-reach Yangtze River urban agglomeration, and the Chengdu-Chongqing urban agglomeration is the lowest. Further, this article identifies seven influencing factors including government intervention, industrial structure, degree of openness, R&D investment, urbanization, economic development, and environmental regulation. This article provides suggestions for industrial energy efficiency improvement.
引用
收藏
页码:4133 / 4155
页数:23
相关论文
共 48 条
[1]   Bioenergy efficiency change and its determinants in EU-28 region: Evidence using Least Square Dummy Variable corrected estimation [J].
Abdulwakil, Muhammad Mansur ;
Abdul-Rahim, Abdul Samad ;
Alsaleh, Mohd .
BIOMASS & BIOENERGY, 2020, 137
[2]  
Alsaleh M, 2016, Int J Energy Econ Pol, V6, P290
[3]  
Alsaleh M., 2016, Soc. Sci., V11, P5318, DOI [10.3923/sscience.2016.5318.5332, DOI 10.3923/SSCIENCE.2016.5318.5332, DOI 10.36478/SSCIENCE.2016.5318.5332]
[4]   Productivity growth and its determinants of the bioenergy industry in the EU28 region: Empirical evidence using Malmquist productivity index [J].
Alsaleh, Mohd ;
Zubair, Azeem Oluwaseyi ;
Abdul-Rahim, Abdul-Samad .
BUSINESS STRATEGY AND DEVELOPMENT, 2020, 3 (04) :531-542
[5]   Determinants of cost efficiency of bioenergy industry: Evidence from EU28 countries [J].
Alsaleh, Mohd ;
Abdul-Rahim, A. S. .
RENEWABLE ENERGY, 2018, 127 :746-762
[6]   Determinants of technical efficiency in the bioenergy industry in the EU28 region [J].
Alsaleh, Mohd ;
Abdul-Rahim, A. S. ;
Mohd-Shahwahid, H. O. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 78 :1331-1349
[7]   A PROCEDURE FOR RANKING EFFICIENT UNITS IN DATA ENVELOPMENT ANALYSIS [J].
ANDERSEN, P ;
PETERSEN, NC .
MANAGEMENT SCIENCE, 1993, 39 (10) :1261-1265
[8]   Energy efficiency, sustainability and economic growth [J].
Ayres, Robert U. ;
Turton, Hal ;
Casten, Tom .
ENERGY, 2007, 32 (05) :634-648
[9]   Are too many natural resources to blame for the shape of the Environmental Kuznets Curve in resource-based economies? [J].
Badeeb, Ramez Abubakr ;
Lean, Hooi Hooi ;
Shahbaz, Muhammad .
RESOURCES POLICY, 2020, 68
[10]   Research on the efficiency of the mining industry in China from the perspective of time and space [J].
Chen, Jiabin ;
Wen, Shaobo ;
Liu, Yuchen .
RESOURCES POLICY, 2022, 75