A comprehensive evaluation of the spatiotemporal variation of CO2 and its driving forces over China

Abstract

With the improved accuracy and high spatiotemporal resolution, satellite remote sensing has provided an alternative way for monitoring the variations of CO2 in remote areas where field observations are inadequately sampled but the emissions of CO2 are increasing rapidly. Based on CO2 estimates from satellite remote sensing and the atmospheric tracer transport model, this study assessed the spatiotemporal patterns of atmospheric CO2 and its driving forces across China. Results show a consistent increase in CO2 at all levels of the troposphere, with the growth rate exceeding 2.1 ppm/year. Among them, the near surface witnessed obvious spatial heterogeneity with the highest concentrations of CO2 occurring in East China and the lowest in Northwest China. This strong spatial differentiation disappeared with increase in altitude and is replaced by a distinct south–north gradient difference at the upper troposphere. With regard to vertical variations, the concentration and growth rates of CO2 at the lower troposphere are generally higher than those at the upper troposphere. The driving mechanism analysis indicates that the variation of CO2 at the near surface is primarily caused by anthropogenic and biogenic activities, while air motion dominates the distribution of CO2 at the upper troposphere. The findings of the present study could provide a valuable reference for understanding regional carbon cycles and formulating carbon emission reduction strategies on a national scale.

Spatiotemporal Pattern and Driving Factors of Carbon Emissions in Guangxi Based on Geographic Detectors

“…Recently, studies on carbon emissions carried out by scholars from home and abroad have mainly focused on the analysis of spatiotemporal differences in carbon emissions and the influencing factors of carbon emissions. From the perspective of research scale, this study mainly concerns the spatiotemporal patterns of carbon emissions at the national, provincial, and municipal levels [5][6][7][8][9][10], with less research on the spatiotemporal patterns of carbon emissions at the county level [11]. In terms of studying the spatiotemporal patterns of carbon emissions, most scholars at home and abroad use the methods of Moran index, Gini coefficient, and Thiel coefficient to analyze the regional differences and agglomeration effects of carbon emissions, as well as the evolution of spatial patterns [12][13][14][15][16].…”