Dynamic spatiotemporal evolution and spatial effect of carbon emissions in urban agglomerations based on nighttime light data

Exploring the issues related to carbon emissions (CEs) in urban agglomerations (UAs) is an effective way to address global warming and achieve sustainable development. We propose a method to measure CEs using nighttime light data to characterize grid-scale CEs with high accuracy. Using seven UAs in the Yellow River Basin of China as examples, the dynamic evolution trend and dynamic spillover effect are discussed. The results show that there are significant correlations between CEs and nighttime light data, the R2 values of the fitting function are all greater than 0.9, and the maximum error is 0.1942%, indicating that the fitting results are good. The CEs of UAs exhibit significant club convergence and siphon effects, revealing certain spatial agglomeration characteristics during the study period. In addition, CEs exhibit a significant spatial spillover effect, their long-term impact is greater than their short-term impact, and they display a significant snowball effect. This study further expands the application of the spatial scale of CE measurement, and the research results provide new ideas for developing countries to formulate adaptive CE reduction measures.