Editing Welcome to the Climate Change AI Wiki

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 [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! 

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].

=== Quick start ===

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

=== 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]]
*[[Transportation]]
*[[Buildings and Cities|Buildings and cities]]
*[[Industry]]
*[[Agriculture]]
*[[Forestry and Other Land Use|Forestry and other land use]]
*[[Negative Emissions Technologies|CO<sub>2</sub> removal and negative emissions technologies]]

====Adaptation====

*[[Climate Change Adaptation|Climate change adaptation]]
*[[Biodiversity]]
*[[Solar Geoengineering|Solar geoengineering]]

====Climate science====
*[[Climate_Modeling_and_Analysis|Climate modeling and analysis]]
*[[Weather prediction|Weather forecasting]]
*[[Remote sensing]] 

====Tools for Action====

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

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

*[[Accelerated Science|Accelerated science]]
*[[Remote Sensing|Remote sensing]]
*[[Predictive Maintenance|Predictive maintenance]]
*[[Efficient sensing]]
*[[Surrogate modeling]]

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<!-- === 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.

* [[Causal inference]]
* [[Computer vision]]
* [[Interpretable models]]
* [[Natural language processing (NLP)]]
* [[Reinforcement learning (RL), Bandits, and Control]]
* [[Time-series analysis]]
* [[Transfer learning and Generalization]]
* [[Uncertainty quantification and Bayesian methods]]
* [[Unsupervised and Self-supervised learning]]
-->
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