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/ CCAI].
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!▼
 
▲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 [[Guidelines|here]].
 
=== General resourcesResources ===▼
This wiki is maintained and moderated by members of [https://www.climatechange.ai/ 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 ===
 
*[[General Resources]] page
*[https://arxiv.org/abs/1906.05433 Tackling Climate Change with Machine Learning] review paper
 
=== Topics organized by Application Area ===
HereThe pages arebelow organizedprovide byoverviews theand fieldresources oron areatopics whichat studiesthe anintersection areaof toclimate whichchange MLand canmachine be appliedlearning. ''Mitigation'' isrefers preventing orto reducing emissions in order to lessen the effectsextent of climate change, while ''Adaptationadaptation'' is changing (technology, society, or other systems)refers to dealpreparing withfor the effects of climate change. ''SocialWe Impactsalso &provide Toolsoverviews forof various Action''tools referfor toaction'' fields-- whichsuch studyas thepolicy, problemseconomics, ofeducation, climateand changefinance at-- athat meta-level,can describinghelp andenable quantifying effectsmitigation and suggestingadaptation concrete ways to make changestrategies.
 
==== Mitigation ====
 
*[[Electricity Systems|Electricity systems]]
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*[[Agriculture]]
*[[Forestry and Other Land Use|Forestry and other land use]]
*[[Negative Emissions Technologies|CO2CO<sub>2</sub> removal and negative emissions technologies]]
 
==== Adaptation ====
 
*[[Climate Science|Climate science]]
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*[[Solar Geoengineering|Solar geoengineering]]
 
==== Social Impacts & Tools for Action ====
 
*[[Public Policy and Decision Science|Public policy and decision science]]
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*[[Tools for Individuals|Tools for individuals]]
 
=== Topics organized by Cross-cutting Theme ===▼
<br />
There are several cross-cutting themes and research problems that recur across the topic areas above.
 
▲=== 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.
 
* [[Accelerated Science|Accelerated science]]
* [[Remote Sensing|Remote sensing]]
* [[Predictive Maintenance|Predictive maintenance]]
*[[Efficient sensing]]
*[[Surrogate modeling]]
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<br />
 
<!-- === 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.
 
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* [[Uncertainty quantification and Bayesian methods]]
* [[Unsupervised and Self-supervised learning]]
-->
 
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Revision as of 21:58, 6 December 2020

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.



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