In the age of rapid urbanization and booming e-commerce, last-mile delivery has become a defining element of the logistics sector. It is also one of its most carbon-intensive components, contributing as much as 41% of total supply chain emissions in urban freight transport. In dense cities, the last mile can result in more than 4x higher emissions per package compared to upstream transportation due to short, frequent, and fragmented trips. As cities grapple with congestion, pollution, and climate targets, the decarbonization of last-mile logistics has emerged as a critical imperative, and artificial intelligence (AI) is playing a transformational role in making it happen.
The Carbon Burden of the Last Mile
The last mile is inherently inefficient from a carbon perspective. Delivery vans often traverse fragmented routes, idling in traffic or circling for parking. According to a University of Washington Urban Freight Lab study, delivery drivers in dense urban areas can spend up to 28% of their time searching for parking, contributing to increased fuel consumption and congestion. As same-day and next-day delivery become the norm, vehicles make more trips with fewer packages, increasing emissions per parcel. The World Economic Forum estimates that urban last-mile delivery emissions could rise by over 30% in the top 100 cities by 2030 without intervention.
The key drivers of carbon emissions in the last mile include route inefficiencies, underutilized vehicle capacities, idling and traffic congestion, non-electrified delivery fleets and reverse logistics. Tackling these challenges requires a blend of operational changes, policy shifts, and technological innovation, with AI serving as a core enabler.
AI’s Decarbonization Toolbox for the Last Mile
AI offers logistics providers and retailers unprecedented capabilities to reduce emissions while improving efficiency and customer service. Below are the primary levers through which AI supports decarbonization:
- Dynamic Route Optimization: Traditional static route planning tools cannot adapt to real-time variables like traffic congestion, weather, or delivery constraints. AI-powered dynamic routing engines overcome these issues by recalculating the most efficient path for each vehicle using real-time and historical data.
- UPS’s ORION system, launched in 2013, optimizes delivery routes and saves an estimated 10 million gallons of fuel annually, avoiding around 100,000 metric tons of CO₂ emissions. Since launch, ORION has saved approximately 100 million miles and 10 million gallons of fuel each year.
- Walmart’s AI route optimization eliminated 30 million unnecessary miles and bypassed 110,000 inefficient paths, helping avoid 94 million pounds of CO₂ emissions.
- Delivery Slot Optimization and Demand Forecasting: By analyzing customer behavior, geographic factors, and historical demand patterns, AI can offer optimal delivery windows that encourage customers to select time slots clustered geographically or aligned with existing delivery routes.
- Ocado, a UK-based online grocer, uses machine learning to recommend delivery slots that align with local deliveries.
- This strategy improved vehicle utilization and reduced carbon intensity per 100,000 orders from 489 to 458 metric tons of CO₂ equivalent, a 6 percent reduction.
- Micro-Fulfillment and Network Optimization: AI helps determine the ideal placement of fulfillment centers to shorten the distance between inventory and the end consumer. By analyzing customer density, order frequency, and transportation patterns, AI guides decisions on where to deploy micro-fulfillment centers (MFCs) or dark stores.
- Walmart uses AI simulations within its store network to strategically place micro-fulfillment centers, reducing last-mile distances and increasing same-day delivery coverage.
- Electric and Autonomous Fleet Management: While electrification addresses the hardware side of decarbonization, AI manages electric fleet operations by optimizing route planning to account for battery range and scheduling intelligent charging. AI also powers autonomous delivery bots and drones, which provide low or zero-emission alternatives for short-distance deliveries.
- Amazon’s Scout delivery robot is an example of a battery-powered autonomous delivery bot being piloted for short-distance deliveries, offering a cleaner alternative to vans.
- Smart Returns Management: AI reduces return volumes through better product recommendations, improved sizing tools, and fraud detection mechanisms. Additionally, it optimizes reverse logistics by aggregating returns or redirecting them to secondary markets closer to customers.
- Returns contribute significantly to carbon emissions, with round-trip shipments emitting between 5 to 2.5 kilograms of CO₂ per package. eCommerce return rates range from 20–30%, making this an important area for AI intervention.
- Zalando uses AI to recommend better-fitting products, which has led to reduced return rates and fewer emissions from reverse logistics.
The Road Ahead: Building a Carbon-Conscious AI Ecosystem
The future of last-mile logistics lies at the intersection of sustainability, technology, and customer experience. To fully harness AI’s potential for decarbonization, industry leaders must adopt sustainability KPIs alongside cost and speed in AI optimization models. Collaboration across ecosystems, including retailers, logistics providers, and cities, is essential to share data and infrastructure effectively. Additionally, investing in workforce upskilling is critical to ensure that humans-in-the-loop can interpret and act on AI insights, maximizing the benefits of these advanced technologies.
AI, when used ethically and intentionally, can deliver a future where rapid delivery does not come at the cost of planetary health. Companies that embed decarbonization goals into their AI-driven last-mile strategies will not only meet evolving regulatory and customer demands, but also lead the charge toward a net-zero logistics ecosystem.
Conclusion
Decarbonizing the last mile is one of the toughest challenges in the global supply chain. Yet, it also represents a profound opportunity. AI is more than just a technological trend, it is an essential enabler in building efficient, resilient, and low-emission delivery networks. Whether through smarter routing, predictive demand, or autonomous electric vehicles, AI can help reimagine the last mile not as a bottleneck of emissions, but as a benchmark of sustainable innovation.
About the author:
Nitin Natesh Kumar is an experienced executive specializing in logistics, transportation, fulfillment network design, network optimization, and operational strategy. He currently serves as Director of Planning & Analytics at Fanatics and has held key leadership roles at OnTrac, Shopify, Walmart, Wayfair, and John Deere. Throughout his career, Nitin has successfully led transformational initiatives focused on optimizing delivery networks, improving operational efficiency, enhancing service performance, and enabling scalable growth. His expertise includes last-mile delivery, demand forecasting, transportation planning, network design, and network optimization within complex, high-volume operations. He is also skilled at helping small and medium businesses scale up by aligning operational capabilities with business goals, while maintaining a strong focus on customer experience and financial discipline. Nitin holds an MBA from the University of Maryland, College Park, and a Bachelor’s degree in Mechanical Engineering from Visvesvaraya Technological University. He also advises startups and emerging companies on logistics strategy, growth acceleration, and operational excellence.
