Climate and sustainability

Leveraging machine learning (ML) and artificial intelligence (AI) to address climate change and help build a sustainable future for all.

Landscape with clouds in the background

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

Featured publications

A Neural Encoder for Earthquake Rate Forecasting
Oleg Zlydenko
Brendan Meade
Alexandra Sharon Molchanov
Sella Nevo
Yohai bar Sinai
Scientific Reports (2023)
Flood forecasting with machine learning models in an operational framework
Asher Metzger
Chen Barshai
Dana Weitzner
Frederik Kratzert
Gregory Begelman
Guy Shalev
Hila Noga
Moriah Royz
Niv Giladi
Ronnie Maor
Sella Nevo
Yotam Gigi
Zvika Ben-Haim
HESS (2022)
AI Increases Global Access to Reliable Flood Forecasts
Asher Metzger
Dana Weitzner
Frederik Kratzert
Guy Shalev
Martin Gauch
Sella Nevo
Shlomo Shenzis
Tadele Yednkachw Tekalign
Vusumuzi Dube
arXiv (2023)
Estimates of broadband upwelling irradiance from GOES-16 ABI
Sixing Chen
Vincent Rudolf Meijer
Joe Ng
Geoff Davis
Carl Elkin
Remote Sensing of Environment, 285 (2023)
Multimodal contrastive learning for remote sensing tasks
Umangi Jain
Alex Wilson
Self-Supervised Learning - Theory and Practice, NeurIPS 2022 Workshop
Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data
Fantine Huot
Lily Hu
Matthias Ihme
Yi-fan Chen
IEEE Transactions on Geoscience and Remote Sensing, 60 (2022), pp. 1-13