Examining spatiotemporal dynamics of CO₂ emission at multiscale based on nighttime light data

Carbon emissions have increasingly been the focus of all governments as countries throughout the world choose carbon neutrality as a national development strategy. The analysis of the spatiotemporal dynamics of CO₂ emission has emerged as a significant research topic considering the dual-carbon goal. In this research, we explore the spatiotemporal changes of CO₂ emission at different scales based on nighttime light data. The Chinese Academy of Science's Earth Luminous Dataset, CO₂ emission data from Carbon Emission Accounts and Datasets, and basic national geographical data are used for analysis. A linear regression model between nighttime light data and CO₂ emission is constructed. Thereafter, the global Moran's I index of exploratory spatial data analysis is used to verify the spatial parameters of all provinces. The trend value method is utilized to analyze the changing trend of CO₂ emission at multiscale levels, covering the Chinese mainland, three major economic regions, and six largest agglomerations from 2012 to 2019. Experimental results show a significant positive correlation between the CO₂ emission and nighttime light data from 2012 to 2019. The nighttime light data could be used to effectively estimate the total CO₂ emission at the provincial and municipal levels in China. The growth rate of CO₂ emissions in China is stable and has decreased in 2015. Furthermore, the spatiotemporal dynamics of CO₂ emission in different agglomerations vary. Our work provides a scientific basis for the different provinces and cities to develop feasible emission reduction policies.