Low-carbon route optimization model for multimodal freight transport considering value and time attributes

As the international community increasingly focuses on climate change, optimizing low-carbon transportation routes in the multimodal freight transport system has become a pressing issue. However, due to the variability in cargo properties and the influence of various factors on transportation route decisions, formulating a low-carbon and economical multimodal freight transport plan remains a significant challenge. To address the issue, this study considered cargoes with different attributes in terms of both value and time attributes. Triangular fuzzy numbers were employed to represent the uncertain demand for cargo, with confidence levels introduced for clarification. A low-carbon route decision optimization model for multimodal freight transport was established to minimize the combined transportation carbon emission and time costs. The catastrophe adaptive genetic algorithm, based on Monte Carlo sampling, was employed to solve the model using arithmetic examples. Finally, parameter sensitivity analysis revealed that adjustments to carbon tax values and changes in the proportion of electric trucks and green electricity supply had the most significant impact on the low-carbon route decision-making plan for multimodal freight transport. For low value-added and timeliness-strong cargo, a 60 % increase in carbon tax value shifted the mode of transportation from road to railway. When the carbon tax increased by more than 140 %, the transportation mode shifted from railway to waterway. Additionally, when the proportion of electric trucks and green electricity supply both exceeded 80 %, the transportation mode between some city nodes shifted from railway to road. When these proportions increased beyond 90 %, road transportation became the predominant mode.