Research on energy transport optimization under low carbon policy background

Abstract Driven by the “dual carbon” goal, the low-carbon transformation of energy transportation systems has become a key link in achieving carbon neutrality. However, traditional energy transportation optimization models often focus on cost minimization, lack systematic embedding of carbon emission constraints, and are difficult to balance the game relationships among multiple stakeholders. Therefore, this study proposes a low-carbon energy transportation decision-making framework (LCETO) based on multi-objective optimization, which integrates carbon quota constraints, path selection elasticity, and multimodal transport coordination mechanisms. It aims to construct a transportation optimization model that balances economic and environmental benefits. The model uses an improved NSGA-II algorithm to solve the Pareto front and combines fuzzy decision-making to determine the optimal solution. Taking the coal transportation network in the Beijing Tianjin Hebei region as an example for verification, the results show that compared to traditional models, LCETO reduces carbon emission intensity by 18.7% while transportation costs only increase by 4.2%; The proportion of multimodal transportation has increased to 36.5%, and the congestion rate at key nodes has decreased by 22.3%. This study provides policymakers with scientific decision-making tools to assist in the construction of a low-carbon transportation system.