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中国省域碳排放的空间特征及影响因素
引用本文:赵巧芝,闫庆友,赵海蕊. 中国省域碳排放的空间特征及影响因素[J]. 北京理工大学学报(社会科学版), 2018, 20(1): 9-16. DOI: 10.15918/j.jbitss1009-3370.2018.1281
作者姓名:赵巧芝  闫庆友  赵海蕊
作者单位:华北电力大学经济管理系,河北保定,071003;华北电力大学经济与管理学院,北京,102202
基金项目:国家社科基金资助项目“多情景模拟下统一碳交易对我国出口竞争力的传导效应与政策研究”(17BGL252)
摘    要:通过核密度分布和莫兰指数对中国2000—2015年30省份碳排放强度的动态趋势及集聚特征进行测度,并利用空间杜宾模型对其主要影响因素进行分析。结果显示:(1)中国30省份碳排放强度呈下降趋势,新常态以来低碳步伐加快;(2)碳排放强度的空间集聚性具有高水平集中、低水平集聚特征,空间溢出效应不断增强;(3)本省经济规模、产业结构对本省碳排放强度具有显著的正向影响,专利产出具有显著的负向影响;相邻省份的外商投资规模及能源消费结构变化对本省碳排放具有显著的空间溢出作用。因此,未来中国加快产业结构调整幅度、优化相邻省份间的产业空间布局以及大力发展绿色技术进步是中国促进区域低碳转型的主要方向,同时生态城镇化以及继续改善外商直接投资质量也是减排潜力因子,省域间的减排空间溢出效果不容忽视。

关 键 词:碳排放  核密度分布  空间自相关系数  空间面板模型
收稿时间:2017-05-29

Research on Spatial Characteristics and Influencing Factors of Provincial Carbon Emissions in China
ZHAO Qiaozhi,YAN Qingyou and ZHAO Hairui. Research on Spatial Characteristics and Influencing Factors of Provincial Carbon Emissions in China[J]. Journal of Beijing Institute of Technology(Social Sciences Edition), 2018, 20(1): 9-16. DOI: 10.15918/j.jbitss1009-3370.2018.1281
Authors:ZHAO Qiaozhi  YAN Qingyou  ZHAO Hairui
Affiliation:1.Department of Economics and Management, North China Electric Power University, Baoding Hebei 071003, China2.School of Economics and Management, North China Electric Power University, Beijing 102202, China
Abstract:Kernel density distribution and Moran''s Index methods were utilized to indicate the dynamic evolution trend and spatial cluster characteristics of carbon emissions among 30 provinces in China during 2000-2015. Spatial Durbin Model was constructed to explore the key influencing factors. The results are as follows:(1)Carbon emission density keeps a decreasing trend in this period and the low transformation trend has been accelerated since the New Normal Stage;(2)Spatial cluster characteristics of carbon emissions density in 30 provinces are mainly divided into "High-High" and "Low-Low" types. Moreover, this spatial spillover effects show a growing trend;(3)The economic scale and industrial structure of a province have a significant positive effect upon its carbon emission density while patent output scale has a significant negative effect. FDI scale and energy consumption structure of its neighborhood exert spatial spillover effects on its emission density significantly. On the one hand, to accelerate the pace of industrial structure adjustment, to optimize industrial spatial layouts and to develop green technology are the main ways in the future to stimulate regional low carbon transformation in China. Meanwhile, ecological town construction and continuously improving FDI quality are the potential factors to drive carbon emissions down. Spatial spillover effects among provinces in carbon emission reductions shouldn''t be neglected.
Keywords:carbon emissions  kernel density distribution  spatial autocorrelation index  spatial panel model
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