Welcome to the Climate Change AI Wiki
The aim of this wiki is to help foster impactful research to tackle climate change, by identifying areas for a useful implementation of machine learning (ML).
The scope of machine learning solutions to address climate change goes far beyond the intersection we address here. Tackling climate change requires cooperation between diverse stakeholders, domain scientists, and action in many forms. Whether you are a machine learning researcher looking to apply your skills to combat climate change, or an early career researcher aiming to have a meaningful impact in your career, a practitioner in one of the domain science areas looking to apply ML to your problem, or for any other reason you 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! This wiki is maintained and moderated by members of CCAI.
See guide on contributing to the CCAI Wiki. Feel free to start suggesting changes to any of the following pages!
If you would like to discuss your ideas for additional pages or gain moderator privileges, feel free to reach out to CCAI (firstname.lastname@example.org with WIKI in the subject line).
Topics by Application Area
The pages below provide overviews and resources on topics at the intersection of climate change and machine learning. Mitigation refers to reducing emissions in order to lessen the extent of climate change, while adaptation refers to preparing for the effects of climate change. We also provide overviews of various tools for action -- such as policy, economics, education, and finance -- that can help enable mitigation and adaptation strategies.
- Electricity systems
- Buildings and cities
- Forestry and other land use
- CO2 removal and negative emissions technologies
Tools for Action
- Public policy and decision science
- Climate and environmental economics
- Climate finance
- Tools for individuals
Topics by Cross-cutting Theme
There are several cross-cutting themes and research problems that recur across the topic areas above.