High resolution CO2 emissions inventory and investigation of driving factors for China using an advanced dynamic estimation model

Abstract

Developing a high-resolution CO₂ emissions inventory for China is challenging because of limited detailed parameter information in bottom-up approaches. This study integrated socioeconomic attributes, point emission data, industrial heat sources, and improved night-time light data to develop an advanced top-down dynamic CO₂ emissions estimation model. Using this model, a 0.01° resolution CO₂ emissions inventory for China from 2012 to 2022 was created. The results demonstrated that the model enhances spatial precision, distribution accuracy, and timeliness. Spatiotemporal dynamics help identify high emission periods and regions, and reflect the impact of geographical and social activities. The driver factor analysis indicated that GDP per capita, energy intensity, and carbon emissions intensity were the main drivers of changes in emissions. Each region should develop emission-reduction strategies based on the dynamic variations of these drivers. This study offers a reliable tool for carbon emissions inventory research, supporting accurate carbon emissions estimation and policy formulation.