Under the background of the coordinated advancement of the strategy for building a strong transportation nation and the “dual carbon” goals, this paper focuses on the impact of subway network development on regional carbon emission reduction. By integrating data on subway line planning and the opening sequence of stations, the topological structure of urban subway networks is constructed on an annual basis. The complex network analysis method is employed to calculate the station centrality index, and a quantitative system for subway network accessibility is established. Further matching with county-level panel data from 2011 to 2023, this paper empirically investigates the impact of subway network development on per capita carbon emissions. The empirical results show that at the district and county level, each one-unit increase in subway network accessibility is associated with an average reduction of 0.908 tons in local per capita carbon emissions. Mechanism testing reveals a dual-driven pathway: On the consumption side, subway networks guide residents towards low-carbon travel modes, reduce household transportation expenditures, and optimize consumption structures, thereby contributing to household carbon reduction; on the production side, subway networks promote industrial spatial restructuring, forming an optimized layout characterized by service industry agglomeration and industrial decentralization. Furthermore, this emission reduction effect is synergistically amplified by local low-carbon regulations, land price differentials, industrial agglomeration, green technology innovation, and energy efficiency. Further analysis indicates that intercity subway construction, through its functional decentralization and network expansion effects, can amplify the low-carbon efficacy of central cities.

The marginal contributions of this paper are threefold: First, using a sample of cities with operational subway systems across China, it covers different city sizes, development stages, and network characteristics, exploring the intrinsic logic between subway network development and carbon emission reduction, thereby enhancing the generalizability and external validity of the findings. Second, in terms of indicator measurement, it employs social network analysis to construct a subway network accessibility index, capturing network layout, transfer convenience, and operational efficiency, thus addressing the shortcomings of traditional indicators. Third, by building a multi-level analytical framework that integrates consumption-based, production-based, and transportation carbon emissions, it reveals the unique emission reduction pathways of subway networks.