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Spatial-temporal pattern evolution and driving factors of China's energy efficiency under low-carbon economy

文献类型：期刊论文

作者Zhang, Yan2; Wang, Wei3; Liang, Longwu4,5; Wang, Daoping6; Cui, Xianghe1; Wei, Wendong7
刊名SCIENCE OF THE TOTAL ENVIRONMENT
出版日期2020-10-15
卷号739页码:12
关键词Low-carbon economy Energy efficiency Super-efficiency SBM model EOF GTWR Spatial-temporal heterogeneity
ISSN号0048-9697
DOI10.1016/j.scitotenv.2020.140197
通讯作者Liang, Longwu(lianglw.17s@igsnrr.ac.cn) ; Wei, Wendong(wendongwei@sjtu.edu.cn)
英文摘要Improving energy efficiency and building a low-carbon economy are the important ways to resolve the current contradiction between economic growth and the environment in China. In this paper, we use the super efficiency Slack-Based Measure model (super-efficiency SBM model) to measure the energy efficiency of 30 provinces in China, and then conduct Empirical Orthogonal Function (EOF) to analyze its spatial-temporal evolution. Moreover, we use the Geographically and Temporally Weighted Regression (GTWR) to analyze the spatial-temporal heterogeneity of its driving factors. The results showthat: (i) during the sample period, China's energy efficiency shows a rapidly upward trend, accompanied by the gradually strengthening spatial pattern of the "eastern>central>western"; (ii) the spatial pattern of the "southern>northern" exhibited by the annual growth rate of energy efficiency experienced a process of weakening first and then gradually strengthening; (iii) the influencing effects of market openness, relative energy price and industry structure on energy efficiency have no significant heterogeneity as a whole; (iv) the effects of environmental regulation intensity, the marketization level, the technical level, energy consumption structure and economic development level have significant spatial heterogeneity, and the effects of energy conservation and emission reduction policies has significant temporal heterogeneity. (C) 2020 Published by Elsevier B.V.
WOS关键词GEOGRAPHICALLY WEIGHTED REGRESSION ; LEVEL PM2.5 CONCENTRATIONS ; SLACKS-BASED MEASURE ; REGIONAL ENERGY ; ENVIRONMENTAL EFFICIENCY ; EMISSION PERFORMANCE ; TECHNICAL PROGRESS ; EMPIRICAL-ANALYSIS ; DIOXIDE EMISSION ; SUPER-EFFICIENCY
资助项目National Natural Science Foundation of China[71904125] ; Shanghai Sailing Program[18YF1417500] ; MOE (Ministry of Education of China) Special Funds for National and Regional Studies[19GBQY055] ; Philosophy and Social Science Project of Shanghai[2018EGL003]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000562956600003
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Shanghai Sailing Program ; MOE (Ministry of Education of China) Special Funds for National and Regional Studies ; Philosophy and Social Science Project of Shanghai
源URL[http://ir.igsnrr.ac.cn/handle/311030/158030]  
专题中国科学院地理科学与资源研究所
通讯作者Liang, Longwu; Wei, Wendong
作者单位1.Nankai Univ, Sch Econ, Tianjin 300071, Peoples R China
2.Northwest Univ, Sch Econ & Management, Xian 710127, Shaanxi, Peoples R China
3.Shandong Univ, Ctr Econ Res, Jinan 250100, Shandong, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
5.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
6.Shanghai Univ Finance & Econ, Sch Urban & Reg Sci, Shanghai 200433, Peoples R China
7.Shanghai Jiao Tong Univ, Sch Int & Publ Affairs, Shanghai 200030, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yan,Wang, Wei,Liang, Longwu,et al. Spatial-temporal pattern evolution and driving factors of China's energy efficiency under low-carbon economy[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2020,739:12.
APA Zhang, Yan,Wang, Wei,Liang, Longwu,Wang, Daoping,Cui, Xianghe,&Wei, Wendong.(2020).Spatial-temporal pattern evolution and driving factors of China's energy efficiency under low-carbon economy.SCIENCE OF THE TOTAL ENVIRONMENT,739,12.
MLA Zhang, Yan,et al."Spatial-temporal pattern evolution and driving factors of China's energy efficiency under low-carbon economy".SCIENCE OF THE TOTAL ENVIRONMENT 739(2020):12.

入库方式： OAI收割

来源：地理科学与资源研究所

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