Journal of Geographical Sciences ›› 2012, Vol. 22 ›› Issue (4): 630-642.doi: 10.1007/s11442-012-0952-z

• Ecology and Environment • Previous Articles     Next Articles

Spatial econometric analysis of carbon emissions from energy consumption in China

CHUAI Xiaowei1, HUANG Xianjin1,2, WANG Wanjing1, WEN Jiqun1, CHEN Qiang3, PENG Jiawen1   

  1. 1. School of Geographic and Oceaographic Sciences, Nanjing University, Nanjing 210093, China;
    2. Land Development and Consolidation Technology Engineering Center of Jiangsu Province, Nanjing 210093, China;
    3. Housing and Urban Construction Bureau, Gaochun, Nanjing 211300, China
  • Received:2011-07-21 Revised:2011-12-28 Online:2012-08-15 Published:2012-07-10
  • Supported by:

    National Social Science Foundation of China, No.10ZD&M030; Non-profit Industry Financial Program of Ministry of Land and Resources of China, No.200811033; A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China, No.40801063; No.40971104

Abstract:

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 consumption, 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 increased 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 increased 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.

Key words: carbon emissions, temporospatial change, spatial autocorrelation, spatial regression, China