中国能源消费碳排放的空间计量分析(英文)

编号 zgly0001585802

文献类型 期刊论文

文献题名 中国能源消费碳排放的空间计量分析(英文)

作者 揣小伟  黄贤金  王婉晶  文继群  陈强  彭佳文 

作者单位 SchoolofGeographicandOceaographicSciences  NanjingUniversity  LandDevelopmentandConsolidationTechnologyEngineeringCenterofJiangsuProvince  HousingandUrbanConstructionBureau  Gaochun 

母体文献 Journal of Geographical Sciences 

年卷期 2012年04期

年份 2012 

分类号 X502 

关键词 carbonemissions  temporospatialchange  spatialautocorrelation  spatialregression  China 

文摘内容 Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support,this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy con-sumption,spatial autocorrelation analysis of carbon emissions,spatial regression analysis between carbon emissions and their influencing factors.The analyzed results are shown as follows.(1) Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly.(2) The global spatial autocorrelation of carbon emissions from energy consumption in-creased from 1997 to 2009,the spatial autocorrelation analysis showed that there exists a polarization phenomenon,the centre of High-High agglomeration did not change greatly but expanded currently,the centre of Low-Low agglomeration also did not change greatly but narrowed currently.(3) The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population,R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population.The contribution of population to carbon emissions in-creased but the contribution of GDP decreased from 1997 to 2009.The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population,so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.