Welcome to the Climate Change AI Wiki

From Climate Change AI Wiki
Revision as of 09:44, 3 December 2020 by NikolaMD (talk | contribs) (added cross-cutting themes)

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.

General resources

Topics organized by Application Area

Here pages are organized by the field or area which studies an area to which ML can be applied. Mitigation is preventing or reducing the effects of climate change, while Adaptation is changing (technology, society, or other systems) to deal with the effects of climate change. Social Impacts & Tools for Action refer to fields which study the problems of climate change at a meta-level, describing and quantifying effects and suggesting concrete ways to make change.



Social Impacts & Tools for Action

Topics organized by Cross-cutting Theme

Here pages are organized by some cross-cutting themes or research problems we have identified as having potential impacts for many of the application areas identified above.

Topics organized by Machine Learning Area

Here pages are organized by the area or type of machine learning research. If you are an ML researcher in one of these areas, this provides a quick way to see which problems of climate change potentially best align with your expertise.