This study delves into the intricacies of the county-level carbon emission spatial correlation network within the BTH (BTH) region, employing Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP) to reveal key structural traits and influential factors. Our findings can be summarized as follows: The spatial correlation network of carbon emissions in the BTH region displays a multifaceted, multi-threaded structure. Notably, it exhibits limited overall correlations, tending towards loose connectivity – a state characterized by “moderate central density with western sparseness.” Furthermore, the carbon emissions’ spatial correlation network assumes a distinctive “segmented” configuration, featuring well-defined boundaries and a proclivity for “each region to operate autonomously with localized centers.” This network adheres to a “core-periphery” distribution model, with pivotal regions such as the Beijing Ring, Tianjin Ring, Shijiazhuang city center, Beijing-Tianjin axis, and Beijing-Guangzhou axis occupying central roles. These areas wield substantial influence over collaborative carbon reduction efforts in urban clusters. In contrast, regions at the periphery of the BTH, such as Chengde, Zhangjiakou, Qinhuangdao, Handan, and Cangzhou, exert limited impact within the spatial correlation network of carbon emissions. Lastly, geographical distance and population size differences positively correlate with the spatial correlation network of carbon emissions in the BTH region. Conversely, disparities in the development levels of secondary and tertiary industries, along with variations in technological levels, manifest negative correlations within this network. Our study employs SNA and QAP to unravel these complexities, offering insights vital for coordinated carbon reduction efforts in this crucial region.