Resources Science ›› 2018, Vol. 40 ›› Issue (6): 1297-1306.doi: 10.18402/resci.2018.06.19

• Orginal Article • Previous Articles     Next Articles

Temporal-spatial patterns and factors affecting indirect carbon emissions from urban consumption in the Central Plains Economic Region

Qinqin SHI1(), Fengxian LU2, Hai CHEN1(), Lijun ZHANG2, Rongwei WU3, Xiaoying LIANG1   

  1. 1. College of Urban and Environmental Science, Northwest University, Xi’an 710127, China
    2. Key Laboratory of Geospatial Technology for the Middle & Lower Yellow River Regions, Ministry of Education, College of Environment and Planning, Henan University, Kaifeng 475004, China
    3. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
  • Received:2017-12-03 Revised:2018-02-28 Online:2018-06-25 Published:2018-06-22


The Central Plains Economic Region is undergoing rapid urbanization and industrialization. Living standards and the consumption demands of residents will need to be improved for some time, but indirect carbon emissions from consumption have rigid requirements. From the perspective of consumption, little research has explored historical growth, spatial heterogeneity and factors affecting indirect carbon emissions from urban residential consumption. Understanding these patterns is significant to the development of low carbon cities and formation of reasonable consumption patterns. Here, we calculated the indirect carbon emissions of urban residential consumption in the Central Plains Economic Region using an input-output model, spatial self-correlation and spatial panel modeling. We found that indirect carbon emissions increased from 2002 to 2014. From the consumption structure, the carbon emissions from survival consumption (clothing, food and living) grew faster; development consumption (traffic, education, entertainment and healthcare) grew slower. From spatial association patterns, the spatial correlation type of per capita carbon emissions from urban residential consumption is mainly composed of HH and LL from 2002 to 2014. Spatial Durbin model estimation results show that income level, consumption structure, industrial structure and consumption consciousness are the main influencers of indirect residential carbon emissions. Factors such as consumption consciousness and consumption structure have spatial spillover effects.

Key words: indirect carbon emissions, temporal-spatial patterns, influence factors, STIRPAT Model, Spatial Panel Model, Central Plains Economic Region