AI-Powered Fuel-Efficient Routing for Sustainable Transportation
United States
Environment
Implementing Organisation
United States, California, Mountain View
Implementing Point of Contact
Jonathan Turnbull
Environment x AI Sustainability Program Manager
Contributor of the Impact Story
International Energy Agency
Year of implementation
2021
Problem statement
For years, navigation technology has focused on time efficiency, often not accounting for the energy cost of stop-and-go traffic, steep inclines, and variable engine performance. Sector-wide inefficiencies arise from an "information gap" regarding how different vehicles consume energy under varying conditions. Traditional routing lacks the ability to account for complex variables such as road grade or engine-specific efficiency. This lack of transparency means drivers can unknowingly choose routes that maximize fuel consumption. The sector faces the systemic hurdle of "accidental" emissions - extra carbon released simply because individuals lack personalized data to make more sustainable travel choices. This creates a powerful need for AI solutions that can transform vast, disparate data points into actionable insights for the average driver.
Submission Overview
Google is a global technology leader known for its search engine, digital mapping services, cloud computing, and artificial intelligence innovations. Google Maps is one of the world's most widely used navigation platforms, serving billions of users globally with real-time traffic information, route optimization, and location-based services. Google has pioneered the integration of AI and machine learning across its product suite to improve user experience, efficiency, and sustainability outcomes.
AI Technology Used
Key Outcomes
Resource Efficiency
Resilience & Risk Reduction
Google's AI-powered fuel-efficient routing leverages machine learning at planetary scale to address the "information gap" that leads to accidental emissions from suboptimal route choices. Developed in partnership with the U.S. Department of Energy's National Renewable Energy Laboratory, the solution analyzes road inclines, traffic patterns, and vehicle-specific powertrain characteristics to recommend routes balancing fuel efficiency with convenience. Deployed across 40+ countries and serving billions of users—from car owners to motorcycle riders in Southeast Asia—it enabled the reduction of over 2.7 million metric tonnes of greenhouse gas emissions in 2024. By embedding sustainability into everyday navigation without requiring users to sacrifice time or purchase new technology, Google Maps demonstrates how AI can democratize climate action at scale, turning individual routing decisions into collective decarbonization while helping drivers save money on fuel.
Impact Metrics
Total greenhouse gas emissions avoided through fuel-efficient routing recommendations
Baseline Value
Estimated fuel consumption and emissions on fastest routes without fuel-efficient routing Metric tonnes CO₂ equivalent
Post-Implementation
Over 2.7 million metric tonnes of GHG emissions reduced in 2024 Metric tonnes CO₂ equivalent
Users actively selecting fuel-efficient routes when offered as an alternative to the fastest route
Baseline Value
NA users
Post-Implementation
Billions of route requests processed globally with fuel-efficient alternatives offered when applicable Route requests/selections
Expansion from initial deployment to multiple vehicle types and geographies
Baseline Value
Limited deployment in select countries for cars only Countries, vehicle types
Post-Implementation
40 + countries, expanded to include motorcycles and scooters in Southeast Asia
Implementation Context
Global deployment across North America, Europe, Asia, Southeast Asia, and other regions
Google Maps users globally, including private car owners, motorcycle and scooter riders, particularly in Southeast Asia, commercial vehicle operators, and urban and rural commuters across diverse socioeconomic backgrounds
Key Partnerships
United States' Department of Energy's National Renewable Energy Laboratory (NREL)
Replicability & Adaptation
1. Localize road network data and traffic patterns to regional conditions 2. Adapt vehicle fleet composition models to reflect local automotive markets (e.g., higher motorcycle/scooter usage in Southeast Asia vs. North America) 3. Integrate with local fuel pricing and energy cost structures 4. Calibrate fuel consumption models for regional driving behaviors and conditions 5. Partner with local transportation and environmental agencies for validation 6. Adapt user interface for local languages and cultural preferences 7. Adjust carbon accounting methodologies to align with regional emissions standards 8. Integrate with local electric vehicle charging infrastructure data where applicable 9. Customize for left-hand vs. right-hand drive markets 10. Account for regional road quality and infrastructure variations 11. Validate AI model performance against local vehicle efficiency standards 12. Ensure data privacy compliance with regional regulations 13. Consider local climate and weather pattern impacts on fuel efficiency 14. Integrate with regional public transportation data for multimodal routing
The technology is already deployed globally across diverse geographies, road networks, vehicle types, and user demographics. The AI models are designed to be scalable and adaptable to different regional conditions.
Supporting Materials
* The data presented is self-reported by the respective organisations. Readers should consult the original sources for further details.