Scope of this paper This white paper investigates how AI and other advanced analytics tools can enhance operational efficiencies, capacity utilization and modal shifts to decarbonize transportation, with a particular focus on freight logistics. Artificial intelligence (AI) and machine learning (ML) are subsets of advanced analytics, referring to the use of sophisticated techniques and tools to analyse data and extract actionable insights, enabling improved decision-making and operational efficiency. These technologies can enhance predictive capabilities, optimize operations and support strategic priorities across various industries. This paper looks beyond the operational efficiency gains that can be made through digitalization alone, for example transitioning from manual administrative processes to computer-based programmes. Throughout the report, AI is used to refer to all computational applications including advanced analytics. The analysis is focused primarily on freight logistics – in other words, the global transportation of goods or cargo by road, sea, air and rail – as this sector has significant decarbonization potential, addressable operational scope and growing investor and regulatory pressure to decarbonize. The paper includes examples from the passenger rail and commercial aviation sectors, due to their significance in contributing to global carbon emissions and the potential role that digital technologies can play in driving meaningful decarbonization in these sectors in the short term. However, passenger travel (e.g. passenger cars, motorbikes, passenger boats) has been excluded from the analysis as decarbonization efforts in this area largely depend on behaviour change, which is tied to entrenched consumer preferences. While AI could play a role in supporting more capex- heavy transformations, the focus in this report is on non-capex-intensive use cases that leaders can implement in day-to-day operations. In the medium to long term, AI has the potential to fundamentally transform the freight logistics industry in ways that have not even been anticipated. This report focuses on the short-term, low-capex operational gains that companies can make as a no-regret move. Similarly, AI-powered solutions are only one set of levers that companies can explore in their decarbonization journey. Capital-intensive technological shifts will likely have to be made in the long term to successfully decarbonize transportation, for example through fleet electrification and advanced fuels. Many industry leaders in freight logistics are already using AI to enhance efficiency, automate decision- making and deliver cost savings, making its adoption a win-win strategic move. This report aims to demonstrate how recent developments in AI and ML can offer cost-effective measures to drive lower- carbon practices in freight logistics, contributing to the broader goal of steering the sector towards a 1.5ºC pathway through technological and operational improvements. While it is also important to consider the just transition in relation to such technological advancements, this subject lies outside the scope of the current paper. Intelligent Transport, Greener Future: AI as a Catalyst to Decarbonize Global Logistics 6

Intelligent Transport Greener Future 2025