Welcome to the Climate Change AI Wiki: Difference between revisions

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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 [https://www.climatechange.ai/about CCAI].
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 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. Whether you are a machine learning researcher looking to apply your skills to combat climate change, or a young researcher aiming to have impact in your career, or a practitioner in one of these 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!
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! See editing guidelines [[Guidelines|here]].
We welcome your contributions and feedback! This wiki is maintained and moderated by members of [https://www.climatechange.ai/about CCAI].

See '''[[Guidelines|editing guidelines]]'''. 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 ([[Mailto:info@climatechange.ai|info@climatechange.ai]]).


=== Quick start ===
=== Quick start ===

Revision as of 13:36, 12 June 2021

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 editing guidelines. 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 ([[1]]).

Quick start

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

Climate science

Tools for Action

Topics by Cross-cutting Theme

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