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

doi:10.1007/s11442-021-1839-7

Abstract

Abstract:

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

ZHANG Yongnian, PAN Jinghu, ZHANG Yongjiao, XU Jing. Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data[J].Journal of Geographical Sciences, 2021, 31(3): 327-349.

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t/t+1	HH (Deep pressure type)	LH (Sunken type)	LL (Weak pressure type)	HL (Polarization type)
HH (Deep pressure type)	Type 0 (0.251)	Type I (0.007)	Type III (0.002)	Type II (0.002)
LH (Sunken type)	Type I (0.009)	Type 0 (0.121)	Type II (0.013)	Type III (0.000)
LL (Weak pressure type)	Type III (0.003)	Type II (0.019)	Type 0 (0.514)	Type I (0.005)
HL (Polarization type)	Type II (0.004)	Type III (0.000)	Type I (0.003)	Type 0 (0.047)