The spatiotemporal evolution and impact mechanism of energy consumption carbon emissions in China from 2010 to 2020...

The spatiotemporal evolution patterns of carbon emissions and their influence mechanisms are important topics for regional climate change monitoring and research on sustainable development goals. At present, due to the limitation of statistical data collection scale, it is difficult to analyze the spatiotemporal variation of carbon emission and its influence mechanism at a finer scale in China. With the development of new remote sensing platforms and technologies, multisource remote sensing data such as nighttime light remote sensing data and XCO₂ concentration data have become important information resources for carbon emission monitoring. Therefore, this study monitors the spatiotemporal evolution of carbon emissions in China based on multisource remote sensing data and conducts impact mechanism research. The main conclusions of this study include: (1) The partial least squares carbon emission estimation model and the downscaled inversion model estimate carbon emissions with high accuracy. The estimated carbon emissions of both have high correlation with statistical carbon emissions, with R² of 0.86 and 0.87, respectively, and no significant overestimation or underestimation. (2) The overall spatial pattern of energy consumption carbon emissions in China from 2010 to 2018 is high in the east and low in the west and high in the north and low in the south, but this spatial distribution pattern is gradually weakening. China's energy consumption carbon emissions varied considerably from 2010 to 2018, with an overall slow positive growth trend. (3) The mechanisms of population growth, economic development, urbanization and industrialization on carbon emissions are more complex, and most of their influencing factors promote carbon emission generation, while carbon emission impacts have spatial spillover. This study designs and studies a regional energy consumption carbon emission estimation model in China based on multisource remote sensing data, and explores the characteristics of regional multiscale carbon emission spatiotemporal variation and its influence mechanism, so as to provide scientific references for China's carbon emission reduction targets.