This is how the transport industry can harness AI for decarbonization

The global transport sector stands at a pivotal crossroads. Responsible for 16-25% of global greenhouse gas (GHG) emissions, it plays a critical role in both economic growth and climate impact. Freight logistics alone – moving goods by road, sea, air and rail – accounts for nearly half of the sector’s emissions, contributing 7-8% globally.

Artificial intelligence (AI) has emerged as a powerful catalyst for decarbonization, capable of improving operational efficiency and reducing emissions today, not years from now. However, without robust, equitable governance, its potential risks being diluted by fragmentation, inefficiency and inequity.

Innovation thrives within clear rules. Just as rules transform a simple ball into an engaging sport, thoughtful governance converts AI from a promising technology into a powerful tool that benefits all. Aligning AI-driven business models in the real economy with supportive, climate-conscious regulation is essential for sustainable, inclusive growth.

The white paper on transport and AI – Intelligent Transport, Greener Future: AI as a Catalyst to Decarbonize Global Logistics – outlines how the technology can reduce transport emissions by up to 15% through enhanced efficiency.

Three key levers stand out:

AI algorithms can analyze real-time data – including traffic patterns, weather conditions and delivery schedules – to identify the most fuel-efficient routes for road, air and maritime freight.

This reduces unnecessary mileage and idle times, translating directly into lower emissions. For example, UPS’s ORION system saves 10 million gallons of fuel annually by optimizing delivery routes with AI.

AI enables smart load planning for ships, trains and trucks, ensuring vehicles travel closer to full capacity and minimizing empty trips. A major airline’s cargo division, for instance, increased load capacity by 8% through AI-based demand and capacity management during a 12-week pilot programme.

AI-driven logistics planning can shift freight from higher-emission modes (such as trucking) to lower-emission options (such as rail or sea). Predictive analytics also helps forecast demand fluctuations and disruptions, enabling proactive adjustments to smooth operations, thereby saving money and minimizing wasted resources.

For example, DHL’s multimodal solution optimizing trucking operations between Spain and Germany with existing train routes reduced costs by 13% and emissions by 58% per tonne-kilometre per trip.

While the logistics industry increasingly recognizes the potential of AI to create value and support climate objectives, its use remains largely underleveraged. According to Eurostat, only 3.72% of EU enterprises used AI technologies for logistics in 2024.

This growing momentum is not limited to the private sector. Governments are also acknowledging the benefits of AI for the transport sector.

However, increased uptake must go hand in hand with robust governance to enable scalable and responsible deployment. In their recent recommendations, the 69-member governments of the International Transport Forum (ITF) emphasized the importance of adopting a governance framework that ensures AI data security, accuracy and privacy.

Applying these principles to the operational insights from the white paper is essential. Without clear data-sharing agreements and ethical guidelines, trust and collaboration may falter, hindering the scale-up of AI-powered solutions.

Achieving net-zero emissions in transport requires an integrated approach. Combining cleaner fuels, electrification and new vehicle technologies with AI-driven efficiencies underpinned by sound governance offers the best chance to meet ambitious climate-aligned growth targets.

Practical next steps include:

By weaving together technology, policy and real economy business models, we can build smarter transport systems that are also responsible, inclusive, earn the public’s trust and serve the planet’s needs.