The Interplay of Carbon Policy and Social Dynamics in Supply Chain Decarbonization: A Network Systems Approach

Abstract

Decarbonizing supply chains is a complex systemic challenge where institutional constraints such as carbon policies intersect with behavioral responses including social dynamics within intricate network structures. Existing studies often examine these factors in isolation, overlooking their coupled systemic interactions. This paper adopts a network systems approach to investigate how carbon quota constraints and conformity behavior jointly shape the evolution of low-carbon transitions within supply chain industrial clusters. Using a Watts–Strogatz small-world network as the structural carrier, we develop an evolutionary system model that integrates individual economic rationality with social learning dynamics. By combining analytical derivations with Monte Carlo simulations, we reveal the critical systemic thresholds arising from this interplay. The results indicate that while carbon policy sets the economic baseline, its effectiveness is profoundly modulated by social dynamics. Specifically, conformity exhibits a critical threshold effect: moderate conformity slows diffusion but ultimately allows economic incentives to prevail; however, extreme conformity overwhelms economic signals, leading to a systemic high-carbon lock-in. Furthermore, network topology acts as a crucial regulator: small-world structures with moderate rewiring optimally balance local coordination and global spread, effectively buffering the negative impacts of conformity. Carbon pricing demonstrates a strict economic threshold, above which the system undergoes a phase transition towards full decarbonization, though with diminishing marginal returns. Finally, core nodes can act as breakthrough agents under moderate conformity but lose their advantage under extreme social pressure. These findings provide a systems-level understanding of supply chain decarbonization, offering actionable insights for designing carbon policies that account for behavioral dynamics and optimizing industrial network structures to foster sustainable transitions.