Spatially explicit carbon emissions by remote sensing and social sensing.

Scientific simulation of carbon emissions is an important prerequisite for achieving low-carbon green development and carbon peak and carbon neutralization. This study proposed a carbon emissions spatialization method based on nighttime light (NTL) remote sensing and municipal electricity social sensing. First, the economics-energy comprehensive index (EECI) was proposed by integrating the NTL and municipal electricity consumption (EC) data. Second, the carbon emissions were spatialized at a fine scale based on NTL, EC, and EECI, respectively. Finally, the geographical detector model was applied to quantify the influencing factors on carbon emissions from the perspectives of individuals and interactions. Results show that combining remote sensing and social sensing data helps depict carbon emissions accurately. The factor analysis found that GDP and population were the basis of carbon emissions, while the secondary industry and urbanization rate were the direct factors. This study is expected to provide constructive suggestions and methods for emission reduction, carbon peak, and carbon neutrality in high-density cities in China.