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The critical transmission hubs in global supply chain networks have great potential to reduce carbon emissions, yet this has not received much attention from existing studies. This research combines the structural path betweenness, an extended structural path analysis, and a multi-level modularity optimization algorithm to identify key transmission sectors, paths, and carbon communities in a global supply chain network using the environmentally extended multi-regional input-output analysis. The results show that (1) China's metal industry, power industry, and non-metallic industry are the most crucial transmission centers, and some sectors, such as the paper industry and textile industry in China, are also key transmission hubs, yet they are ignored by traditional accounting methods. From a national viewpoint, China is the most key transmission hub, and its high betweenness is mainly caused by domestic demand. Meanwhile, the CO 2 emissions transferred by China to satisfy foreign demand are mainly related to the US, ROW (especially the Asia-Pacific region), and Japan. (2) The transmitted carbon emission is mainly concentrated in the first four production layers, accounting for 69.46 % of the total. (3) The global carbon emission network can be divided into 20 carbon communities. And the largest communities are mainly led by mainland China. Most of the communities contain various national sectors, which offer evidence for international cooperation strategies. The results offer policymakers a new perspective on mitigating environmental stress. [Display omitted] • Critical sectors having higher betweenness are identified from a global level. • Key supply chain paths causing the high betweenness are identified. • Some critical carbon communities in the global carbon emission network are detected. • An extended structural path betweenness is proposed to find key supply chain path. [ABSTRACT FROM AUTHOR]