AI can balance cost and emissions in global transport systems

A new study published in the journal Energies suggests that AI-driven decision frameworks could offer a critical pathway to balance economic efficiency with environmental sustainability in multimodal transport systems.

Researchers Tomasz Neumann and Paweł Wierzbicki of Gdynia Maritime University examine this transition in their latest work, "Artificial Intelligence for Energy and Cost Resilience in Sustainable Supply Chains: A Dynamic LCA/TCO Approach to Multimodal Transport." The study presents a data-driven framework that integrates artificial intelligence into life cycle assessment and total cost of ownership models to evaluate transport strategies under varying economic and environmental conditions.

AI-driven lifecycle modeling reshapes transport decision-making

The study highlights a structural problem facing global logistics systems. Traditional models used to assess transport performance rely on static assumptions, such as fixed emission factors and average cost estimates. These models often fail to capture real-world variability, including fluctuating fuel prices, evolving carbon policies, and changing energy mixes.

To address this limitation, the researchers introduce a dynamic framework that uses artificial intelligence to generate scenario-specific inputs in real time. Instead of relying on generalized assumptions, the model adjusts calculations based on changing conditions, offering a more accurate representation of both environmental impact and economic performance.

This approach is particularly relevant for multimodal transport systems, where goods move through a combination of road, rail, and terminal operations. Each segment has distinct cost structures and emission profiles, making it difficult to optimize the system as a whole using traditional methods. The AI-enhanced framework enables decision-makers to evaluate how different combinations of transport modes perform under specific conditions.

A key feature of the model is its integration of life cycle assessment with total cost of ownership. Life cycle assessment captures the full environmental impact of transport activities, including emissions generated during operations and infrastructure use. Total cost of ownership, on the other hand, accounts for both capital expenditures and operational costs. By combining these perspectives, the framework provides a comprehensive view of trade-offs between cost efficiency and environmental performance.

The study demonstrates that AI can act as a bridge between these two dimensions, enabling more informed decision-making. Rather than treating sustainability and profitability as competing goals, the framework identifies conditions under which both can be achieved simultaneously.

Partial electrification emerges as a practical transition strategy

The study asserts that full decarbonization, while environmentally optimal, is not always economically viable under current conditions. Fully electrified transport systems deliver the lowest emissions but often require high upfront investment and may face operational constraints depending on energy infrastructure.

On the other hand, the research identifies partial electrification strategies as a more balanced and immediately feasible solution. These configurations typically involve electrifying specific segments of the transport chain, such as road pre-haulage, while combining them with low-carbon rail transport and partially electrified terminal operations.

The results show that such intermediate configurations can achieve strong environmental performance while maintaining cost competitiveness. This makes them particularly attractive in the short to medium term, where businesses must navigate financial constraints alongside sustainability targets.

The study also highlights the value of policy and market conditions in determining the viability of different strategies. Factors such as electricity prices, carbon pricing, and grid carbon intensity play a decisive role in shaping outcomes. For example, lower electricity costs and higher carbon prices tend to favor deeper electrification, while higher energy costs can limit its economic appeal.

Another critical insight relates to the role of inland transport segments, especially road pre-haulage. Despite representing a smaller portion of total transport distance, these segments contribute disproportionately to both emissions and costs. This makes them a priority target for decarbonization efforts.

By focusing on these high-impact segments, companies can achieve significant reductions in emissions without overhauling entire transport systems. The study suggests that targeted interventions, supported by AI-driven analysis, can deliver meaningful progress toward sustainability goals while preserving operational efficiency.

Energy systems and policy dynamics shape decarbonization outcomes

The effectiveness of electrification strategies is closely tied to the broader energy system, particularly the carbon intensity of electricity supply. The study finds that electrification delivers its full environmental benefits only when supported by low-carbon energy sources. In regions where electricity is still generated from fossil fuels, the environmental advantage of electrified transport is reduced.

This interdependence highlights the need for coordinated policy approaches that align transport decarbonization with energy system transitions. Without such alignment, efforts to reduce emissions in logistics may yield limited results.

The research also underscores the role of carbon pricing in driving change. Higher carbon costs make emission-intensive transport options less competitive, encouraging investment in cleaner technologies. At the same time, the study notes that achieving cost parity for advanced electrification strategies often requires supportive policy frameworks, including subsidies or incentives.

AI plays a critical role in navigating these complexities. By modeling different scenarios and incorporating real-time data, the framework allows decision-makers to anticipate how changes in policy and market conditions will affect transport performance. This capability is particularly valuable in an environment characterized by uncertainty and rapid change.

The study's robustness analysis further reinforces the reliability of its findings. By testing multiple combinations of key variables, the researchers demonstrate that certain strategies, particularly those involving partial electrification, remain effective across a wide range of conditions. This suggests that the framework can provide stable guidance even when underlying assumptions vary.

Toward resilient and sustainable supply chains

The study points to a broader transformation in how supply chains are managed. As environmental and economic pressures intensify, companies are increasingly required to balance competing objectives, including cost control, emissions reduction, and operational resilience.

The integration of artificial intelligence into lifecycle-based assessments represents a significant step toward achieving this balance. By enabling dynamic, data-driven decision-making, AI can help organizations adapt to changing conditions and optimize performance across multiple dimensions.

The study also highlights the importance of data quality and availability. The effectiveness of AI-driven models depends on access to reliable and representative datasets. Expanding data collection and integrating real-time information will be essential for improving model accuracy and decision relevance in future applications.

The framework could be adapted to cover full Door-to-Door transport chains, as well as additional environmental impact categories such as air pollutants, noise, and resource use.

The findings also suggest that AI-driven approaches could play a key role in evaluating emerging technologies, where empirical data is limited. By generating scenario-specific inputs, the framework provides a way to assess the potential impact of new solutions before they are widely deployed.