[Spatiotemporal Evolution and Influencing Factors of Land Use Carbon Emissions at County Level in China Based on...

Accurately estimating the carbon emissions from land use at the county scale is an important prerequisite for rationally formulating carbon reduction measures. Limited by the acquisition of energy statistics， previous studies on land use carbon emissions at the county scale mainly estimated energy consumption carbon emissions using night light data. The integration of multi-source remote sensing data to estimate land use carbon emissions remains to be further explored. Therefore， based on multi-source remote sensing data such as energy statistics， nighttime light data， and XCO₂ data， this study adopted methods such as the energy consumption carbon emission model， standard deviation ellipse analysis， and exploratory spatiotemporal data analysis to depict the spatiotemporal characteristics of carbon emissions from county land use in China from 2010 to 2020. Furthermore， the geographic detector model was adopted to explore the influencing factors of carbon emissions from land use in counties of China. The results show that： ① From 2010 to 2020， the carbon emissions from land use in China's counties decreased， with significant regional differences. The carbon emissions in the eastern region were the highest， followed by the central and northeastern regions， and the carbon emissions in the western region were relatively low. ② The carbon emissions from land use in China's counties showed a trend of dispersion along the "northeast-southwest" direction and aggregation along the "northwest-southeast" direction， with obvious directionality and the center of gravity shifting towards the northeast. Its spatial distribution was significantly positively correlated， with obvious spatial heterogeneity and agglomeration， and the spatial correlation was relatively stable. ③ The carbon emissions from land use in counties of China were mainly affected by urbanization and economic development， and the intensity of the interaction between the two was higher than that of a single influencing factor. The results of this study can provide a reference for the formulation of carbon emission reduction targets in China.