Amid escalating global concerns over climate change and sustainable development, carbon emissions have emerged as a critical issue for the international community. The control of carbon dioxide (CO₂) emissions is particularly crucial for meeting the objectives of the Paris Agreement. This study applied the LMDI decomposition method and a BP neural network model to thoroughly analyse the factors influencing carbon emissions in Henan Province’s transportation sector and forecast future trends. Our core contribution is the development of an integrated model that quantifies the impact of key factors on carbon emissions and offers policy recommendations. This study concludes that by optimizing the energy structure and enhancing energy efficiency, China can meet its carbon peak and neutrality targets, thereby providing scientific guidance for sustainable regional development.

In order to improve the operation efficiency of technical station, it is necessary to study the identification method and resolution strategy of the operation capacity bottleneck of technical station. Based on relevant research, this paper analyzed the utilization of the operation capacity of the technical station, considered the service capacity and level of the equipment inside the station, established the capacity bottleneck identification model, and put forward the methods and steps of capacity identification. Besides, aiming at the bottleneck of technical station's ability to handle the transfer and reclassifying operation, raised the corresponding solving strategy. With a lower investment, through the use of optimized transportation organization technology and the efficient use of technical equipment, to improve the capacity of bottleneck and compress the detention time of railcars in the technical station. Therefore, accelerated the turnover of locomotive and rolling stock, and improved the reliability and service quality of the whole road network.

Transportation is a major contributor to carbon emissions, and achieving carbon peaking in this sector is essential for China’s Dual Carbon goals. This study investigates transportation carbon emission dynamics and future pathways in Sichuan Province, a representative western inland region with complex geographical and transport characteristics. A top-down carbon accounting approach was applied to estimate emissions during 2005–2020, while LMDI decomposition and an extended STIRPAT-based scenario framework were employed to analyze historical emission contributions and future trajectories under four scenarios. The results show that transportation carbon emissions increased from 12.47 million tons in 2005 to 24.02 million tons in 2020, with emission growth mainly associated with vehicle expansion and energy consumption, while improvements in energy structure and transport efficiency partially offset this trend. Scenario analysis shows that emissions peak around 2035 under both the Low-Carbon Development and Enhanced Control scenario, with the Low-Carbon Development scenario achieving a substantially lower peak level and stronger mitigation effect compared with other scenarios. The findings highlight the importance of region-specific strategies, including transport electrification, green urbanization, cleaner energy substitution, and low-carbon logistics development, for supporting transportation decarbonization in Sichuan and similar western inland regions.