Decarbonizing the transportation sector is a critical global imperative for achieving carbon neutrality, and optimizing freight infrastructure has emerged as a key pathway to mitigate carbon emissions. However, existing literature on the relationship between rail freight networks and regional carbon emissions remains insufficient—most studies overlook the network characteristics of rail freight and fail to systematically explore the nonlinear effects and spatial spillover mechanisms. Clarifying the inherent correlation between rail freight network and carbon emissions is therefore a prerequisite for exploring low-carbon transition of the transportation sector, which is of far-reaching significance for accelerating the construction of a modern logistics system, advancing the achievement of carbon peaking and carbon neutrality goals, and propelling the development of Chinese modernization through the building of a country with strong transportation network. Drawing on interprovincial origin-destination (OD) freight flow data in China from 2015 to 2023, this paper constructs a national rail freight network, and employs the system generalized method of moments (System GMM) model to empirically examine the impact of rail freight network on local carbon emissions. The results show that: (1) carbon emissions exhibit a distinct spatial distribution pattern, with higher emission levels concentrated in the eastern and central regions and lower levels in the western region; (2) rail freight network exhibits an inverted U-shaped effect on emissions; (3) the effect operates through two channels: transportation structure upgrading and regional industrial agglomeration; (4) further analysis using spatial econometric models confirms that rail freight network generate significant spatial spillover effects on carbon emissions.
Liu et al. (Sat,) studied this question.