Optimization and Implementation Strategy for Low-Carbon Multimodal Transport Routes: A Collaborative Approach between Government and Transport Enterprises

Abstract

Existing studies on intermodal transportation path optimization have largely overlooked the coordination between government and enterprise objectives. To address this gap, we propose a multi-objective path optimization model that balances the government's carbon reduction goals with enterprises' cost-control requirements. The model aims to minimize both carbon emissions and operational costs while evaluating the impacts of subsidy policies, carbon tax mechanisms, and energy structure on these objectives. To account for demand fluctuations, we incorporate fuzzy demand, which effectively captures uncertainties in transportation needs. The optimization problem is reformulated into a single-objective framework through a carbon tax and is solved using an Improved Sparrow Search Algorithm (ISSA)—an enhanced metaheuristic technique designed for improved efficiency and accuracy. A case study validates both the proposed model and algorithm, demonstrating that significant reductions in CO₂ emissions can be achieved with a moderate increase in cost, thereby balancing the interests of governments and enterprises. Sensitivity analysis reveals a complementary relationship between government subsidies and carbon tax rates. In the early stages of policy implementation, high subsidies combined with low tax rates effectively curb carbon emissions. However, as subsidies decrease, a gradual increase in carbon tax rates becomes necessary. Additionally, prioritizing an increase in the proportion of electric trucks and electric locomotives is proving more cost-effective than increasing green penetration. This study provides a scientific basis for policymakers and transportation enterprises to collaboratively design and adjust low-carbon policies.