Analysis of Spatiotemporal Changes in Energy Consumption Carbon Emissions at District and County Levels Based on Nighttime Light Data—A Case Study of Jiangsu Province in China

Approximately 86% of the total carbon emissions are generated by energy consumption, and the study of the variation of energy consumption carbon emissions (ECCE) is of vital significance to regional sustainable development and energy conservation. Currently, carbon emissions accounting mainly focuses on large and medium-scale statistics, but at smaller scales (district and county level), it still remains unclear. Due to the high correlation between nighttime light (NTL) data and ECCE, this study combines “energy inventory statistics” with NTL data to estimate ECCE at smaller scales. First, we obtained city-level statistics on ECCE and corrected the NTL data by applying the VANUI index to the original NTL data from NPP-VIIRS. Second, an analysis was conducted on the correlation between the two variables, and a model was created to fit the relationship between them. Under the assumption that ECCE will be consistent within a given region, we utilized the model to estimate ECCE in districts and counties, eventually obtaining correct results at the county-level. We estimated the ECCE in each district and county of Jiangsu Province from 2013 to 2022 using the above-proposed approach, and we examined the variations in these emissions both spatially and temporally across the districts and counties. The results revealed a significant degree of correlation between the two variables, with the R2 of the fitting models exceeding 0.8. Furthermore, ECCE in Jiangsu Province fluctuated upward during this period, with clear regional clustering characteristics. The study’s conclusions provide information about how carbon emissions from small-scale energy use are estimated. They also serve as a foundation for the creation of regional energy conservation and emission reduction policies, as well as a small-scale assessment of the present state.