Sustainability and AI
AI for Sustainability explores how AI can advance sustainable development
Programme Summary
AI for Sustainability is a pioneering research programme, dedicated to leveraging AI to tackle urgent societal, environmental, and economic challenges. This interdisciplinary programme explores how AI can drive sustainable outcomes—ranging from sustainable production and consumption to climate adaptation, biodiversity conservation, and the design of energy-efficient AI systems. We investigate how AI can support sustainability goals such as environmental risk prediction, the discovery and modelling of sustainability pathways, systemic policy strategies, and global efforts in conservation and biodiversity. At the same time, we examine how AI itself can evolve sustainably, through the development of low-energy techniques and equitable infrastructures. Our work champions responsible, transparent, and cross-disciplinary approaches that maximise co-benefits across domains—ensuring AI serves both people and the planet.
Featured
Sustainable Innovation Lab (SIL)
The SIL commits to finding real-world pathways to sustainability.
Our interdisciplinary research includes:
- Modelling behaviours to identify actionable and evidence-based sustainability pathways
- Innovating technologies that enable scalable and practical solutions
- Strategising systemic approaches for implementing real-world changes
Lab Lead: Lili Jia
Collective Intelligence & Design Group
How does individual behaviour scale into collective decisions for environmental sustainability and climate action?
Based at the University of Cambridge, the Collective Intelligence & Design group combine systems thinking, data science, AI and sociotechnical design to decode this process. Using multi-domain data and human-centered AI, they build decision architectures for climate action and democratic futures.
Group Lead: Ramit Debnath
Aardvark Weather
Aardvark reimagines current weather prediction methods offering the potential to make weather forecasts faster, cheaper, more flexible and more accurate than ever before, helping to transform weather prediction in both developed and developing countries using artificial intelligence. This fully AI driven approach means predictions that were once produced using many models – each requiring a supercomputer and a large support team to run – can now be produced in minutes on a desktop computer.
Project Lead: Richard Turner
Cambridge Centre for Carbon Credits
The Cambridge Centre for Carbon Credits (4C) is creating digital tools and a trusted, decentralized platform to help purchasers of carbon credits to confidently and directly fund trusted deforestation avoidance projects, bringing together corporate funders and conservationists via automated and transparent smart contracting tools.
Principal Investigator: Anil Madhavapeddy
