Intelligent Transport Greener Future 2025

Four ways AI can reduce emissions through identifying operational efficiencies Recent developments in AI, including large language models (LLMs), have accelerated computational capacities to process large amounts of data to obtain actionable insights faster. As a result, AI could help companies improve operational efficiency through applications such as real-time data analytics, predictive maintenance and dynamic routing. These technologies could enable more efficient resource allocation, reduce fuel consumption and minimize dwell time. Small, incremental improvements in high-emission areas may have a substantial impact on overall emissions and costs. Four key areas for potential improvement include dwell time optimization, route optimization, driver behaviour change and vehicle maintenance (see Figure 2).8 Operational efficiencies – four ways AI can help reduce emissions FIGURE 2: ~1.5-2.0% Potential reduction in emissions through AI, % of global freight emissionsIncremental, cross-cutting efficiency gains~1.5-2.0% ~0.5-1.5% ~0.5-1.5% <0.1 0.1 0.2 1.0 1.5 >2.0Extended dwell times Route optimization Driver behaviour Vehicle maintenance Level of relative emission reduction impact (% of global freight emissions) ~4-7%1 1. Range calculated considering rounding errors Source: McKinsey expert interviews.Enhancing operational efficiencies 1 Enhancing day-to-day operations across all transportation modes could reduce emissions from the global freight logistics sector by 4-7% relative to the current baseline. Intelligent Transport, Greener Future: AI as a Catalyst to Decarbonize Global Logistics 10