In the context of global carbon neutrality targets, the biopharmaceutical industry—characterized by complex processes, extensive outsourcing, and high resource intensity—has emerged as a critical sector for supply chain carbon footprint management. While life cycle assessment (LCA) is widely used for product-level carbon quantification, existing studies often overlook the structural distribution and attribution of carbon emissions across supply chain networks. This study develops an integrated framework that combines LCA with social network analysis (SNA) to systematically attribute carbon emissions within biopharmaceutical supply chains. By constructing life cycle-based supply chain network models for representative pharmaceutical products, we identify carbon emission hotspots, key responsibility nodes, and the impact of network structure (e.g., centrality, structural holes, coupling degree) on carbon distribution. We propose a “structure–attribution–governance” analytical framework, introducing the responsibility contribution index (RCI) to rank nodes based on both emission intensity and structural influence. The framework is empirically validated using three representative products—acarbose, bailing capsule, and polymyxin B—demonstrating pronounced carbon concentration at structurally pivotal nodes and revealing the effectiveness of network-driven governance strategies. The results reveal significant carbon concentrations at specific network nodes, where structural position amplifies their impact on overall supply chain carbon performance. The study provides differentiated decarbonization pathways and governance strategies tailored to network characteristics. These findings enrich the theoretical dimensions of carbon footprint modeling in cleaner supply chains and offer actionable insights for biopharmaceutical companies seeking to optimize low-carbon governance and improve carbon performance in complex supply networks.
Chen et al. (Sun,) studied this question.