Carbon reduction and peaking pathway evaluation of container intermodal network: From the perspective of policy sentiment

Carbon peaking and carbon neutrality represent the ambitious commitments of humans to harmonise society and nature. Achieving carbon peaking in intermodal network requires attention to the coordinated development of economic benefits and carbon reduction as well as the collaboration of network elements in dynamic environments based on current policies. Thus, this study proposes an intermodal network carbon-reduction improvement methodology based on policy sentiment analysis, incorporating a machine learning-based sentiment analysis of carbon-reduction policies into the multi-objective optimisation framework. Scenario parameters are integrated into model constraints to generate network transport mode choices and path allocation schemes for various scenarios. In this way, this study evaluates the impacts of policy sentiment, network connectivity, and energy structure on network carbon-reduction improvement and carbon peaking performance. Through the case study of the Yangtze River Delta’s intermodal network, the results reveal that shifting transport modes based on carbon peak targets involves more than the low-emission transport modes replacing high-emission ones. Various transport modes are constantly optimised and adjusted to achieve the optimal compatibility ratio with the changing network structure and advancements in energy technology. From the government’s carbon-reduction sentiment perspective, appropriately tightening carbon policies can improve the quality of carbon peak, achieving it sooner and at a lower level. However, overly strict policies may not offer substantial benefits in terms of effectiveness. The findings provide a theoretical framework to guide government decision-making in reducing transportation carbon emissions.