The aim of this wiki is to help foster impactful research to tackle the climate crisis, by identifying problems where ML can be useful. The scope of solutions to address the climate crisis goes far beyond the intersection we address here; the problems of climate change require cooperation between diverse stakeholders, and action in many forms. But whether you are machine learning researcher looking to apply your skills to combat climate change, a young researcher aiming to have impact in your career, a practitioner in one of these areas looking to apply ML to your problem, or for any other reason are interested in the intersection of climate change and ML, we hope these pages can help inform and facilitate your research!
We welcome your contributions and feedback! See editing guidelines here.
This wiki is maintained and moderated by members of CCAI.
Please see the pages below for an overview of topics at the intersection of climate change and machine learning, accompanied by relevant readings, datasets, conferences, and organizations.
- Electricity systems
- Buildings and cities
- Forestry and other land use
- CO2 removal and negative emissions technologies
- Climate science
- Climate change adaptation
- Biodiversity, ecosystems, and conservation
- Solar geoengineering