Intelligent Transport Greener Future 2025

Three key levers to reduce emissions across the global freight sector FIGURE 7 ~4-7% Estimated potential reduction in emissions through AI, % of global freight emissions2 ~2-4% ~3-4% <0% 0-1% 1%-3% 3%-5%Enhance operational efficiency Improve capacity utilization Optimize modal shifts1 Level of relative emissions reduction impact (% of global freight emissions) ~10-15%Road freight has the highest impact potential, responsible for ~70% of freight transport emissions 4-5% 2-3% 4-5%0-1% 0-1% 0% to -1%0-1% 0-1% 0-1%0-1% 0-1% 0% to -1% 1. Assumes a modal volume shift from road and air to rail and maritime for calculations, resulting in a reduction of emissions in high-intensity modes (air, road), but an increase of emissions in low-intensity modes (maritime, rail – hence negative values shown in white squares). 2. Totals might not equate exactly to values in heatmap graphic due to rounding to one decimal place. Note: Discount factor assumptions were applied to the decarbonization impact potential of each initiative to account for possible double counting across levers. For example, a reduction in dwell times would allow for better capacity utilization for perishable goods, while routing optimization would aid in enabling modal shifts. Source: McKinsey expert interviews informed modelling. Intelligent Transport, Greener Future: AI as a Catalyst to Decarbonize Global Logistics 27