Revision as of 20:35, 17 December 2020 by EbudeY (talk | contribs) (Reverted edits by EbudeY (talk) to last revision by Drolnick)

The aim of this wiki is to help foster impactful research to tackle the climate crisis, by identifying problems where ML can be useful. This wiki is maintained and moderated by members of CCAI.

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.

General Resources

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.

Mitigation

Adaptation

Tools for Action

Topics by Cross-cutting Theme

There are several cross-cutting themes and research problems that recur across the topic areas above.