Driven by the global carbon neutrality target and China’s dual-carbon strategic deployment, the transportation industry has become a key field for energy conservation and emission reduction. As a low-carbon, high-capacity, and stable transportation mode, railway undertakes more than 70% of domestic bulk cargo transportation including coal, iron ore and grain. However, traditional railway bulk cargo transportation organization still suffers from unreasonable train marshalling, low wagon loading rate, high empty running ratio and unscientific operation scheduling, resulting in redundant energy consumption and excessive carbon emissions, which restricts the green upgrading of railway freight systems. This paper takes the actual operation scenarios of domestic railway bulk cargo transportation as the research object, constructs a complete railway freight carbon emission accounting model based on IPCC national greenhouse gas inventory guidelines and railway industry actual operation parameters, and establishes a multi-objective transportation organization optimization model aiming at minimum carbon emission, minimum transportation cost and maximum operation efficiency. A hybrid genetic algorithm integrating particle swarm optimization is adopted to solve the nonlinear constrained optimization problem. All statistical data spanning 2020-2025 are sourced from China State Railway Group freight big data platform covering five core bulk trunk lines; raw data adopts daily statistical granularity with total valid sample volume of 192,600 groups, abnormal data from temporary line overhaul and emergency suspension is eliminated via 3σ outlier screening during preprocessing. Based on the authentic statistical operation data of China State Railway Group from 2020 to 2025, empirical verification and scheme comparison are carried out. The research results show that the optimized organization scheme significantly improves the overall operation level of bulk railway freight. After optimization, the total carbon emission of bulk cargo transportation is reduced by 12.5%, the comprehensive transportation cost is decreased by 9.5%, the average wagon loading rate is increased by 5.1 percentage points, and the empty driving rate is reduced to 9.2%. The proposed optimization strategy can effectively solve the low-carbon operation bottleneck of traditional railway bulk freight, provide practical technical support for railway green transportation organization scheduling, and offer a reference for low-carbon transformation of comprehensive freight transportation systems.
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