Toward low-carbon multimodal freight networks: Insights from uncertain carbon prices and transfer times

Multimodal transport offers a key strategy for freight decarbonization. To address the dual uncertainties of carbon prices and transfer times in multimodal freight networks, this study constructs a multi-objective fuzzy stochastic programming model. It incorporates conditional value-at-risk to quantify carbon-price risk under triangular fuzzy distributions and employs chance-constrained programming to address uncertain transfer times. An NSGA-III combined with the TOPSIS is developed to solve the model. A case study of the Yangtze River Economic Belt in China validates the methodology's applicability. The results indicate that, under a low-carbon orientation, transport enterprises will prioritize rail-water multimodal transport and rail-only transport schemes to improve the economic-environmental performance. Sensitivity analysis reveals a critical carbon price threshold that triggers a modal shift from multimodal to rail-only transport, reducing both carbon emissions and transportation time. Furthermore, deteriorating transfer conditions diminish carbon pricing effectiveness, highlighting the priority of infrastructure upgrades. Policy and managerial insights emphasize three priorities: (i) constructing a dynamic risk-based decision-making framework for carbon prices, (ii) improving transfer efficiency through coordinated infrastructure development, and (iii) leveraging technological innovations to build an integrated optimization strategy framework. This study offers robust decision-support insights for the design of adaptive and low-carbon freight networks under conditions of evolving market dynamics and operational uncertainties.