There is a plethora of methods in the global literature that can be used to measure CO₂ emissions from electrified transport. But are these methods reliable, and do they offer us a true view of how much exactly of this greenhouse gas is being produced by electric rail transport? We answer this question by proposing an improved CO₂ emission estimation model based on cargo transport. Unlike other works, our studies include four crucial steps: (1) estimation of energy consumption in electrified rail cargo transport; (2) estimation of energy losses in the railway traction system and high voltage transmission lines; (3) CO₂ emission estimation in traditional powerhouses; and (4) determination of the intensity of the CO₂ emissions from electrified rail cargo transport. Based on our method, we concluded that the intensity of CO₂ depends not only on the type of fossil fuel used for energy production but also on the parameters of the cargo train, such as its length and weight or the total number of wagon axles (which depend on wagon type). The achieved intensity of CO₂ emissions in electrified rail cargo transport slightly varies from those reported in the global literature. Among the most important reasons responsible for this are the conditions under which these tests were conducted. Nevertheless, our results shed new light on how CO₂ should be measured. We proved that the decarbonization of electrified rail cargo transport will never be possible without infrastructure modernization. In addition, based on a case study, we also delivered knowledge on how to reduce the environmental impact of electrified rail cargo transport.

Numerous studies address the estimation of energy consumption at intermodal terminals, with a primary focus on existing facilities. However, a significant research gap lies in the lack of reliable methods and tools for the ex ante estimation of energy consumption in transshipment systems. Such tools are essential for assessing the energy demand and intensity of intermodal terminals during the design phase. This gap presents a challenge for intermodal terminal designers, power grid operators, and other stakeholders, particularly in an era of growing energy needs. The authors of this paper identified this issue in the context of a real business case while planning potential intermodal terminal locations along new railway lines. The need became apparent when power grid designers requested energy consumption forecasts for the proposed terminals, highlighting the necessity to formulate and mathematically solve this problem. To address this challenge, a three-stage model was developed based on a pre-designed intermodal terminal. Stage I focused on establishing the fundamental assumptions for intermodal terminal operations. Key parameters influencing energy intensity were identified, such as the size of the transshipment yard, the types of loading operations, the number of containers handled, and the selection of handling equipment. These parameters formed the foundation for further analysis and modeling. Stage II focused on determining the optimal number of machines required to handle a given throughput. This included determining the specific parameters of the equipment, such as speed, span, and efficiency coefficients, as well as ensuring compliance with installation constraints dictated by the terminal layout. Stage III focused on estimating the energy consumption of both individual handling cycles and the total consumption of all handling equipment installed at the terminal. This required obtaining detailed information about the operational parameters of the handling equipment, which directly influence energy consumption. Using these parameters and the equations outlined in Stage III, the energy consumption for a single loading cycle was calculated for each type of handling equipment. Based on the total number of loading operations and model constraints, the total energy consumption of the terminal was estimated for various workload scenarios. In this phase of the study, numerous test calculations were performed. The analysis of testing parameters and the specified terminal layout revealed that energy consumption per cycle varies by equipment type: rail-mounted gantry cranes consume between 5.23 and 8.62 kWh, rubber-tired gantry cranes consume between 3.86 and 7.5 kWh, and automated guided vehicles consume approximately 0.8 kWh per cycle. All handling equipment, based on the adopted assumptions, will consume between 2200 and 13,470 kWh per day. Based on the testing results, a methodology was developed to aid intermodal terminal designers in estimating energy consumption based on variations in input parameters. The results closely align with those reported in the global literature, demonstrating that the methodology proposed in this article provides an accurate approach for estimating energy consumption at intermodal terminals. This method is also suited for use in ex ante cost–benefit analysis. A sensitivity analysis revealed the key variables and parameters that have the greatest impact on unit energy consumption per handling cycle. These included the transshipment yard’s dimensions, the mass of the equipment and cargo, and the nominal specifications of machinery engines. This research is significant for present-day economies heavily reliant on electricity, particularly during the energy transition phase, where efficient management of energy resources and infrastructure is essential. In the case of Poland, where this analysis was conducted, the energy transition involves not only switching handling equipment from combustion to electric power but, more importantly, decarbonizing the energy system. This study is the first to provide a methodology fully based on the design parameters of a planned intermodal terminal, validated with empirical data, enabling the calculation of future energy consumption directly from terminal technical designs. It also fills a critical research gap by enabling ex ante comparisons of energy intensity across transport chains, an area previously constrained by the lack of reliable tools for estimating energy consumption within transshipment terminals.

Currently, many logistics operators operate in both domestic and foreign markets using various forms of transport organization. Choosing a corresponding technology and appropriate form of transport has an influence not only delivery time and costs, but also has an impact on the environment as a whole. There is a plethora of public research available in global literature discussing various ways of exploiting transport. On the other hand, there is a lack of complex studies detailing carbon emissions coming from transport activity. Specifically, where a theory of organic fuel combustion in the form of a chemical reaction with oxygen is considered. To fill this gap, we offer an innovative Emission Model of Industrial Sources (EMIS) method. This method makes it possible to determine the amount of CO2 emitted into the atmosphere during various transport methods. It also enables us to estimate, in terms of CO2 output, a threshold where transport of containers via combined mode becomes more favourable for the environment, than road transport. We ran a simulation of our algorithm to create boundary conditions. This let us prepare a regression function of CO2 emission, for intermodal and road transport as a function of various transport distances. The simulation results suggest that our approach may be used by supervisory institutions, which are responsible further developing and utilizing combined transport.

This article examines the impact of intermodal wagon technical specifica-tions and railway infrastructure parameters on electricity consumption in rail freight transport. To conduct this investigation, a three-stage analytical model was developed. The first stage establishes core assumptions, encompassing train lengths, rolling stock types, container configurations, infrastructure constraints, and the characteristics of the energy-consumption model. The second stage identifies technical constraints of specific wagons, determines representative train compositions, and executes loading simulations. The third stage focuses on evaluating energy efficiency across diverse loading scenarios. The case study demonstrates that specific energy consumption varies significantly with wagon type, train mass, and route characteristics, challenging the use of static energy-consumption values prevalent in current literature. Results indicate that 40-foot wagons incur high energy penalties due to their tare weight and axle count, despite maximizing loading capacity. While 60-foot wagons consume less energy, they result in a high frequency of empty slots under a 20 t/axle limit. Conversely, 80-foot wagons emerge as the most energy-efficient, particularly at a 22.5 t/axle limit. Mixed consists offer a balance of operational flexibility and competitive performance. Inter-estingly, extending train length from 600 m to 730 m increases volume but does not inherently reduce unit energy consumption. These findings underscore the necessity of aligning wagon fleet selection with infrastructure capabilities and cargo characteris-tics. Ultimately, this study provides actionable recommendations for planning ener-gy-efficient intermodal operations.

This article examines the impact of intermodal wagon technical specifications and railway infrastructure parameters on electricity consumption in rail freight transport. For this purpose, a three-stage analytical model was developed. The first stage defines the core assumptions, including train length, rolling stock types, container configurations, infrastructure constraints, and the characteristics of the energy consumption model. The second stage identifies the technical constraints of specific wagons, determines representative train compositions, and performs loading simulations. The third stage evaluates energy efficiency across different loading scenarios. The case study shows that specific energy consumption varies significantly with wagon type, train mass, and route characteristics. This findings challenge the use of static energy consumption values commonly applied in the literature. The results indicate that 40-foot wagons incur high energy penalties due to their tare weight and axle count, despite offering high loading capacity. While 60-foot wagons consume less energy, they lead to a high share of empty slots under a 20 t/axle limit. In contrast, 80-foot wagons are the most energy-efficient, particularly at a 22.5 t/axle limit. Mixed consists provide a balance between operational flexibility and competitive performance. Extending train length from 600 m to 730 m increases volume but does not automatically reduce unit energy consumption. These findings highlight the need to align wagon fleet selection with infrastructure capabilities and cargo characteristics. This study therefore provides practical recommendations for planning energy-efficient intermodal operations.