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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).
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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. 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!


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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]].


This wiki is maintained and moderated by members of [https://www.climatechange.ai/ CCAI].
We welcome your contributions and feedback! This wiki is maintained and moderated by members of [https://www.climatechange.ai/about CCAI].


See '''[[Contributing_to_the_CCAI_Wiki|guide on contributing to the CCAI Wiki]]'''. Feel free to start suggesting changes to any of the following pages!
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.


If you would like to discuss your ideas for additional pages or gain moderator privileges, feel free to reach out to CCAI at [mailto:wiki@climatechange.ai wiki@climatechange.ai].
=== General resources ===


=== Quick start ===
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*[[General Resources]] page
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*[https://arxiv.org/abs/1906.05433 Tackling Climate Change with Machine Learning] review paper


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* [[General Resources]] page
=== Topics organized by Application Area ===
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* [https://arxiv.org/abs/1906.05433 Tackling Climate Change with Machine Learning] review paper or explore its [https://www.climatechange.ai/summaries interactive summary]!
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.
* Explore the [https://www.climatechange.ai/papers Climate Change AI Workshop papers]


==== Mitigation ====
=== 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====


*[[Electricity Systems|Electricity systems]]
*[[Electricity Systems|Electricity systems]]
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*[[Agriculture]]
*[[Agriculture]]
*[[Forestry and Other Land Use|Forestry and other land use]]
*[[Forestry and Other Land Use|Forestry and other land use]]
*[[Negative Emissions Technologies|CO2 removal and negative emissions technologies]]
*[[Negative Emissions Technologies|CO<sub>2</sub> removal and negative emissions technologies]]


==== Adaptation ====
====Adaptation====


*[[Climate Science|Climate science]]
*[[Climate Change Adaptation|Climate change adaptation]]
*[[Climate Change Adaptation|Climate change adaptation]]
*[[Biodiversity]]
*[[Biodiversity, Ecosystems, and Conservation|Biodiversity, ecosystems, and conservation]]
*[[Solar Geoengineering|Solar geoengineering]]
*[[Solar Geoengineering|Solar geoengineering]]


====Climate science====
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==== Social Impacts & Tools for Action ====
*[[Climate_Modeling_and_Analysis|Climate modeling and analysis]]
*[[Weather prediction|Weather forecasting]]

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====Tools for Action====


*[[Public Policy and Decision Science|Public policy and decision science]]
*[[Public Policy and Decision Science|Public policy and decision science]]
*[[Climate and Environmental Economics|Climate and environmental economics]]
*[[Economics]]
*[[Education]]
*[[Education]]
*[[Climate Finance|Climate finance]]
*[[Climate Finance|Climate finance]]
*[[Tools for Individuals|Tools for individuals]]
*[[Tools for Individuals|Tools for individuals]]


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===Topics by Cross-cutting Theme ===
<br />
There are several cross-cutting themes and research problems that recur across the topic areas above.

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=== 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|Accelerated science]]
* [[Remote Sensing|Remote sensing]]
*[[Remote Sensing|Remote sensing]]
* [[Predictive Maintenance|Predictive maintenance]]
*[[Predictive Maintenance|Predictive maintenance]]
*[[Efficient sensing]]
*[[Efficient sensing]]
*[[Surrogate modeling]]
*[[Surrogate modeling]]
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<br />
<br />


=== Topics organized by Machine Learning Area ===
<!-- === 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.
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]]
* [[Uncertainty quantification and Bayesian methods]]
* [[Unsupervised and Self-supervised learning]]
* [[Unsupervised and Self-supervised learning]]
-->

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<br />

Latest revision as of 17:27, 26 August 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 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 at wiki@climatechange.ai.

Quick start[edit | edit source]

Topics by Application Area[edit | edit source]

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[edit | edit source]

Adaptation[edit | edit source]

Climate science[edit | edit source]

Tools for Action[edit | edit source]

Topics by Cross-cutting Theme[edit | edit source]

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



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