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中国省际能源尾效:测度、时空格局及影响因素
引用本文:谢品杰,穆卓文,王绵斌.中国省际能源尾效:测度、时空格局及影响因素[J].北京理工大学学报(社会科学版),2019,21(6):51-62.
作者姓名:谢品杰  穆卓文  王绵斌
作者单位:上海电力学院 经济与管理学院, 上海 200090,上海电力学院 经济与管理学院, 上海 200090,国网冀北电力有限公司 经济技术研究院, 北京 100053
基金项目:国家自然科学基金青年项目“外商直接投资对我国二氧化碳排放绩效的影响效应研究及政策选择”(71103120);上海市社科规划一般项目“新时代我国新型城镇化与碳排放绩效的交互机制及治理对策研究”(2018BGL019);国家自然科学基金青年项目“家庭智能用电任务调度优化及其对电网负荷影响分析模型”(51507099)
摘    要:探究能源尾效对经济发展的约束与其空间效应,识别能源尾效的关键影响因素,可为中国经济可持续发展提供新思路。测算中国省际1997—2016年各期能源尾效,分析其空间格局和集群特征,利用空间计量模型对全国及七大区域的尾效影响因素展开实证分析。研究表明:(1)各省份普遍存在着能源尾效对经济发展的约束,且具有显著的空间自相关性,高(低)尾效与高(低)尾效省份之间呈现出相互聚集的特征;(2)全国实证结果表明,经济发展水平、能源价格和开放水平对尾效增长具有正向贡献,而产业结构、科技投入水平、城市化水平和能源结构具有负向贡献;(3)区域实证结果表明,能源价格对尾效的推动作用更为显著,其余变量则在不同空间区域显示出不同的作用效果。

关 键 词:能源尾效  经济增长约束  偏最小二乘法  空间自回归模型
收稿时间:2018/11/21 0:00:00

China's Growth Drag of Energy: Measurement, Spatial Pattern and Influencing Factors
XIE Pinjie,MU Zhuowen and WANG Mianbin.China's Growth Drag of Energy: Measurement, Spatial Pattern and Influencing Factors[J].Journal of Beijing Institute of Technology(Social Sciences Edition),2019,21(6):51-62.
Authors:XIE Pinjie  MU Zhuowen and WANG Mianbin
Institution:College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China,College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China and Economics and Technology Research Institute, State Grid Jibei Electric Power Co. Ltd, Beijing 100053, China
Abstract:Exploring the spatial pattern of growth drag of energy and identifying the key influencing factors of drag can provide new ideas for the sustainable development of China''s economy in the new era. This paper measured the growth drag of each province in China from 1997 to 2016 using provincial panel data and PLS method. And the spatial pattern and the agglomeration effect of growth drag were analyzed. Based on this, the SAR model was used to conduct an empirical analysis of the factors affecting the growth drag of the whole nation and seven regions. The research shows that:(1)Growth drag of energy is common in all provinces, and there is significant spatial correlation. Provinces of high(low)growth drag tend to cluster.(2) The national results show that the level of economic development, energy price and openness have a positive contribution to growth drag, while industrial structure, level of scientific and technological input and level of urbanization have negative contributions.(3) The regional results show that energy price has a more significant effect on growth drag. The other variables show different effects in different regions.
Keywords:growth drag of energy  influencing factors of economic growth  partial least squares method  spatial auto-regression model
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