AI can help address empty capacity and reduce emissions2.1 The financial and climate impacts of empty capacity are significant. Analysis conducted for this report indicates that in the US, the trucking industry loses over $150 billion annually due to empty capacity.21 Much of this empty capacity problem is around the following structural issues: –Trade imbalances, where reciprocal demand is lacking, means that vehicles return empty after delivering full loads. –Specialized cargo requirements can further exacerbate this issue, as certain freight types may have limited return demand. –Strict delivery restrictions can compel freight operators to depart with less-than-full loads to meet tight timelines, which can reduce overall efficiency. –Market fragmentation is another challenge; smaller freight logistics companies often struggle with limited visibility of regional demand and lack collaboration opportunities to consolidate loads. –Volume and load constraints can prevent effective capacity utilization, as vehicles transporting lightweight goods may operate at full volumetric capacity while underutilizing their weight capacity. Freight logistics are complicated, involving fluctuating demand, last-minute bookings and the need to efficiently allocate space across various transportation modes. Addressing these issues requires a complex optimization equation. AI- powered solutions can help to predict demand, optimize loading practices, generate capacity demand scenarios and suggest the best routes to minimize empty space. By dynamically consolidating freight loads, these systems may help reduce emissions and improve overall efficiency. The case study of a European freight forwarder highlighted in Box 3 demonstrates how some of the issues causing empty freight capacity can be addressed. Analysis suggests that if such approaches were adopted across the US trucking industry, empty capacity could be cut by as much as 50%. This could prevent the emission of 43 billion kg of CO2 annually, equivalent to avoiding the combustion of 16 billion litres of diesel fuel.22 We truly see already today in the market that sustainable fuels and electric vehicles are the most scalable and competitive decarbonization levers – reducing GHG emissions by up to 90% per load. At the same time, advanced analytics and AI/ML are generating business opportunities for us, which then drive improvements across the transportation network we operate – including load bundling, load recommendation and predictive load matching. These move the carbon needle at the edge, but primarily improve the business case for our trucking partners, who then generate further opportunities for decarbonization as green business becomes recognized as good business. Graham Major-Ex, Senior Director of Green Business & eMobility, sennderImproving capacity utilization2 Reducing inefficient use of space in freight can lead to a 2-4% reduction in global freight logistics emissions. Intelligent Transport, Greener Future: AI as a Catalyst to Decarbonize Global Logistics 14

Intelligent Transport Greener Future 2025