基于夜间灯光数据的苏南小城镇碳排放时空演变及低碳优化策略研究OACHSSCD

With the increasingly serious problem of global climate change,reducing carbon emissions has become a top priority.Small towns are also the major carriers of carbon emissions as the spatial carriers of human life,work,and rest,but research on refined townships or even raster scales has not yet been thoroughly carried out.The study takes 151 small towns in Southern Jiangsu as the research object,integrates the CEADs database and VIIRS nighttime light data to construct a carbon emission raster dataset from 2012 to 2021,and applies spatial autocorrelation analysis methods to systematically reveal the spatiotemporal evolution characteristics of carbon emissions,and proposes classification optimization strategies.The results show that:(1)the total amount of carbon emissions shows phased growth,and the spatial distribution shows a pattern of"high in the south and low in the north,high in the east and low in the west,"with the high-value area expanding to the economically active zone;(2)the global Moran's I index indicates that there is significant spatial agglomeration of carbon emissions,with H-H hotspots concentrated along the Taihu Lake Economic Zone and L-L coldspots contracting to the west;(3)differentiated optimization paths are proposed for the four types of small towns(coordinated/green/focused/innovation zones).This study provides methodological innovations for managing carbon emissions on a refined scale,which is of practical guidance for realizing the"dual-carbon"goal in the urban agglomeration of the Yangtze River Delta.