Key drivers of empty capacity for a freight forwarder in Europe BOX 3 FIGURE 3In 2020, around one-fifth of total road freight kilometres in the European Union was carried out by empty vehicles.23 A European freight forwarder interviewed for this report faced an average of 45% empty capacity in its trucking operations (see Figure 3). While about one-third of this was due to structural challenges including trade imbalances, the remainder was caused by factors such as the difficulty of finding shippers to fill available volume, urgent delivery deadlines and inefficient loading. By applying AI solutions for dynamic freight consolidation, intelligent routing and capacity planning, this company was able to significantly reduce empty capacity. Capacity utilization – AI can help address empty capacity Sources: Eurostat, McKinsey expert interviews.Key drivers of empty capacity for a freight forwarder in Europe % of total payload volume (illustrative only) 100% Addressable empty capacity (31%)Not finding shipper to fill volume or payload Volumetric "maxing out" at low payload Urgent delivery or cut-off times Unsophisticated loading and steering Structural issues (e.g. trade imbalances, specialized cargo requirements)Loads cannot be stacked Vehicle compatibility requirements (e.g. refrigeration) Non-addressable empty capacity (14%) Current truck utilization (55%)6% 13% 6% 6% 11% 55%1%2% 2-4% Potential reduction in global freight emissions through using AI to improve capacity utilization The potential for AI in optimizing capacity utilization extends to other transport modes. Air freight experiences particularly high rates of empty capacity – often up to 40-50%, according to experts interviewed for this white paper.24 A cargo division of a large commercial airline addressed this by implementing AI-based demand and capacity management. Using a “show rate estimation” model, the company accurately predicted booked cargo capacity over time, considering fluctuations such as weight changes, cancellations and new bookings. This approach led to an 8% increase in load factor during a 12-week pilot programme. If scaled-up, such an intervention could reduce CO2 emissions by 80,000 to 85,000 tonnes across all relevant cargo routes.These examples highlight how AI tools are already helping companies reduce empty capacity and unlock meaningful reductions in emissions, while also lowering costs. On a larger scale, AI may help the freight logistics industry to tackle structural inefficiencies such as consolidating freight loads in trucking or predicting demand in air freight. By leveraging real-time data and predictive analytics, businesses can be equipped to make smarter decisions – with better outcomes for decarbonization and the bottom line. As these technologies continue to evolve, their potential to transform the freight logistics sector and contribute to global climate goals will only grow. Intelligent Transport, Greener Future: AI as a Catalyst to Decarbonize Global Logistics 15

Intelligent Transport Greener Future 2025