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 climate change, by identifying areas for a useful implementation of machine learning (ML). |
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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! |
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We welcome your contributions and feedback! See editing guidelines [[Guidelines|here]]. |
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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]. |
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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! |
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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. |
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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]. |
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=== General resources === |
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* Explore the [https://www.climatechange.ai/papers Climate Change AI Workshop papers] |
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=== Topics by Application Area=== |
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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. |
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====Mitigation==== |
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*[[Electricity Systems|Electricity systems]] |
*[[Electricity Systems|Electricity systems]] |
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*[[Agriculture]] |
*[[Agriculture]] |
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*[[Forestry and Other Land Use|Forestry and other land use]] |
*[[Forestry and Other Land Use|Forestry and other land use]] |
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*[[Negative Emissions Technologies| |
*[[Negative Emissions Technologies|CO<sub>2</sub> removal and negative emissions technologies]] |
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====Adaptation==== |
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*[[Climate Change Adaptation|Climate change adaptation]] |
*[[Climate Change Adaptation|Climate change adaptation]] |
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*[[Biodiversity]] |
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*[[Biodiversity, Ecosystems, and Conservation|Biodiversity, ecosystems, and conservation]] |
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*[[Solar Geoengineering|Solar geoengineering]] |
*[[Solar Geoengineering|Solar geoengineering]] |
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====Climate science==== |
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*[[Climate_Modeling_and_Analysis|Climate modeling and analysis]] |
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*[[Weather prediction|Weather forecasting]] |
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====Tools for Action==== |
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*[[Public Policy and Decision Science|Public policy and decision science]] |
*[[Public Policy and Decision Science|Public policy and decision science]] |
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*[[Climate and Environmental Economics|Climate and environmental economics]] |
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*[[Economics]] |
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*[[Education]] |
*[[Education]] |
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*[[Climate Finance|Climate finance]] |
*[[Climate Finance|Climate finance]] |
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*[[Tools for Individuals|Tools for individuals]] |
*[[Tools for Individuals|Tools for individuals]] |
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===Topics by Cross-cutting Theme === |
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There are several cross-cutting themes and research problems that recur across the topic areas above. |
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*[[Remote Sensing|Remote sensing]] |
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*[[Predictive Maintenance|Predictive maintenance]] |
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*[[Efficient sensing]] |
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*[[Surrogate modeling]] |
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<!-- === Topics organized by Machine Learning Area === |
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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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* [[Causal inference]] |
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* [[Computer vision]] |
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* [[Interpretable models]] |
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* [[Natural language processing (NLP)]] |
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* [[Reinforcement learning (RL), Bandits, and Control]] |
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* [[Time-series analysis]] |
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* [[Transfer learning and Generalization]] |
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* [[Uncertainty quantification and Bayesian methods]] |
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* [[Unsupervised and Self-supervised learning]] |
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--> |
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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
- General Resources page
- Tackling Climate Change with Machine Learning review paper or explore its interactive summary!
- Explore the 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
- Transportation
- Buildings and cities
- Industry
- Agriculture
- Forestry and other land use
- CO2 removal and negative emissions technologies
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.