Against the background of global warming, a scientific assessment of the spatiotemporal patterns of regional carbon emissions is crucial for achieving the "Dual Carbon" goals (carbon peak and carbon neutrality). Using counties in Jiangsu Province as a typical case study, a county-level carbon emission estimation model was constructed based on DMSP-OLS and NPP-VIIRS nighttime light data and the IPCC carbon emission coefficient method. The model was validated using energy consumption statistics from the China Carbon Accounting Database, showing a high goodness of fit (R² = 0.966) with statistical data and a mean relative error of 4.48%, indicating high reliability. On this basis, methods including spatial autocorrelation analysis and carbon emission economic contribution coefficient were employed to systematically reveal the spatiotemporal evolution characteristics of carbon emissions. Six indicators—Regional GDP, Industrial Structure, Total Population, Urbanization Rate, Energy Structure, and Energy Intensity—were selected to explore the driving factors of carbon emission expansion using the geographical detector technique, identifying both single-factor and interactive effects. The results demonstrate that between 2000 and 2019, carbon emissions in Jiangsu Province generally exhibited a spatial distribution pattern of "high in the south and low in the north," with high-value areas concentrated in the economic core zone of Southern Jiangsu, while the global spatial agglomeration effect showed a weakening trend. Analysis reveals that the level of economic development and industrial structure are the dominant factors driving the spatial heterogeneity of carbon emissions, with significant interactive enhancement effects observed between factors. Finally, considering the combined effects of market regulation and environmental policy, differentiated emission reduction strategies were proposed to provide a scientific basis for regional low-carbon transition and sustainable development.