[Spatiotemporal Patterns of County-level Carbon Emissions in Jiangsu Province Based on Nighttime Light Imagery]

Against the backdrop of global climate warming， scientifically assessing the spatiotemporal patterns of regional carbon emissions is of significant importance for achieving the "dual carbon" goals （carbon peaking and carbon neutrality）. As a highly developed economic region in China， the study of carbon emissions in Jiangsu Province not only provides critical guidance for regional low-carbon transformation but also serves as a reference for formulating carbon reduction policies in other similar regions. Taking the county-level units of Jiangsu Province as a case study， this research analyzes the spatiotemporal evolution patterns and driving mechanisms of carbon emissions； explores the synergistic relationship between carbon emissions， economic development， and ecological protection； and provides a scientific basis for regional low-carbon transformation and sustainable development. Based on NPP-VIIRS nighttime light data and land use data from 2003 to 2023， this study constructs a county-level carbon emission estimation model using the IPCC carbon emission coefficient method. Spatial autocorrelation analysis （Moran's I index）， the economic contribution coefficient of carbon emissions （ECC）， and the ecological carrying coefficient of carbon emissions （ESC） are employed to systematically reveal the spatiotemporal characteristics of carbon emissions. The results indicated that： ① Significant spatial heterogeneity of carbon emissions： Jiangsu Province exhibited a "high in the south， low in the north" spatial pattern of carbon emissions. Southern regions such as Nanjing and Suzhou， characterized by dense economic activities and industrial concentration， formed the core high-emission zones， accounting for over 30% of the province's net carbon emissions in 2023. In contrast， northern regions like Xuzhou experienced accelerated industrialization， leading to a significant increase in carbon emissions， with some counties approaching the emission levels of southern areas. ② Dynamic evolution of spatial agglomeration effects： The global Moran's I index decreased from 0.34 to 0.220， indicating a weakening of the global agglomeration effect of carbon emissions. The proportion of local high-high agglomeration areas decreased to 2.3%， while low-low agglomeration areas increased to 8.2%， reflecting the regulatory impact of regional emission reduction policies on spatial patterns. ③ Regional development contradictions revealed by economic and ecological indicators： Southern Jiangsu showed a significant economic contribution from carbon emissions （ECC > 1） but a weak ecological carrying capacity （ESC < 0.5）. Central Jiangsu， including regions like Yancheng and Yangzhou， benefitted from abundant ecological resources， resulting in stronger ecological carrying capacity （ESC > 1）. Based on these findings， this study proposes measures such as differentiated policy regulation， regional collaborative emission reduction， and ecological restoration to promote low-carbon transformation. The results provide methodological references for fine-grained carbon emission research at the county level and offer a scientific basis for carbon reduction policy formulation in Jiangsu Province and other similar regions.