Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (3): 327-349.doi: 10.1007/s11442-021-1839-7

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Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

ZHANG Yongnian1(), PAN Jinghu2,*(), ZHANG Yongjiao1, XU Jing3   

  1. 1. School of Economics, Lanzhou University, Lanzhou 730000, China
    2. College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
    3. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  • Received:2019-12-20 Accepted:2020-02-17 Online:2021-03-25 Published:2021-05-25
  • Contact: PAN Jinghu;
  • About author:Zhang Yongnian (1991-), specialized in spatial economic analysis and industrial development strategy. E-mail:
  • Supported by:
    National Natural Science Foundation of China Youth Science Foundation Project(41701170);National Natural Science Foundation of China(41661025);National Natural Science Foundation of China(42071216);Fundamental Research Funds for the Central Universities(18LZUJBWZY068)


In 2007, China surpassed the USA to become the largest carbon emitter in the world. China has promised a 60%-65% reduction in carbon emissions per unit GDP by 2030, compared to the baseline of 2005. Therefore, it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies. This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000-2013 using nighttime lighting data and carbon emission statistics data. By applying the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework, this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001-2013. The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units. The results show that, firstly, high accuracy was achieved by the model in simulating carbon emissions. Secondly, the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82% and 5.72%, respectively. The overall carbon footprints and carbon deficits were larger in the North than that in the South. There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units. Thirdly, the relative lengths of the Local Indicators of Spatial Association (LISA) time paths were longer in the North than that in the South, and they increased from the coastal to the central and western regions. Lastly, the overall decoupling index was mainly a weak decoupling type, but the number of cities with this weak decoupling continued to decrease. The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.

Key words: nighttime lighting data, carbon footprint, carbon deficit, exploratory spatial-temporal data analysis, spatial-temporal interaction characteristics, decoupling effect