Leveraging machine learning (ML) and artificial intelligence (AI) to address climate change and help build a sustainable future for all.
About the team
Climate change is an urgent threat to humanity with far-reaching societal and economic consequences. Our climate and sustainability teams harness the power of AI to address this challenge, as part of Google's long-standing commitment to climate action.
AI has expanded the type of information we can apply to deep computing power and unlocked an increasing number of methods for interpreting information and creating groundbreaking innovations. It enables us to identify previously impossible solutions to climate challenges — from reducing greenhouse gas emissions in cities to building models that improve our ability to predict and respond to climate-driven natural disasters.
Google Research is leading multiple climate and sustainability efforts in various stages of development, and we continue to explore innovations to accelerate progress in this space. As part of our mission to build a safer, more sustainable future for all, we collaborate closely with cities, governments, startups and aid organizations.
Our Climate and Sustainability team is global, with researchers, partners and projects spanning the US, Europe, Africa, Latin America and Asia. Learn more below about how we are using AI to improve the lives of billions of people worldwide.
Team focus summaries
Highlighted projects
Optimizing traffic lights to reduce vehicle emissions in cities, helping to mitigate climate change and improve urban mobility.
Leveraging computer vision to mitigate aviation’s climate impact.
Using AI models to predict when floods will occur and help make critical flood forecasting information accessible to all.
Mentoring and technological support for impact startups working towards the Sustainable Development Goals.
Wildfire modeling and the detection of wildfire boundaries in partnership with fire authorities.
Providing users with the most fuel-efficient route to their destination based on factors such as engine type and traffic conditions.
Featured publications
ICML workshop on Climate Change 2021 (2021)
IEEE Spectrum Magazine, vol. August 2021 (2021)
Energy and Environmental Science (EES) (2022)
Remote Sensing of Environment, vol. 285 (2023)
Tackling Climate Change with Machine Learning, NeurIPS 2022 Workshop
Self-Supervised Learning - Theory and Practice, NeurIPS 2022 Workshop
IEEE Transactions on Geoscience and Remote Sensing, vol. 60 (2022), pp. 1-13