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Tackling Climate Change with Machine Learning
descriptionPublicationkeyboard_double_arrow_right Article , Preprint 2022Embargo end date: 01 Jan 2023 Germany, United States, Switzerland Publisher:Association for Computing Machinery (ACM)Funded by:NSERC, NSF | PostDoctoral Research Fel...
NSERC ,
NSF| PostDoctoral Research Fellowship
Authors: David Rolnick; Priya L. Donti; Lynn H. Kaack; Kelly Kochanski; +18 Authors
David Rolnick; Priya L. Donti; Lynn H. Kaack; Kelly Kochanski; Alexandre Lacoste; Kris Sankaran; Andrew Slavin Ross; Nikola Milojevic-Dupont; Natasha Jaques; Anna Waldman-Brown; Alexandra Sasha Luccioni; Tegan Maharaj; Evan D. Sherwin; S. Karthik Mukkavilli; Konrad P. Kording; Carla P. Gomes; Andrew Y. Ng; Demis Hassabis; John C. Platt; Felix Creutzig; Jennifer Chayes; Yoshua Bengio;
doi: 10.1145/3485128 , 10.3929/ethz-b-000573494 , 10.48550/arxiv.1906.05433 , 10.14279/depositonce-15739
arXiv: 1906.05433
handle: 1721.1/146383
doi: 10.1145/3485128 , 10.3929/ethz-b-000573494 , 10.48550/arxiv.1906.05433 , 10.14279/depositonce-15739
arXiv: 1906.05433
handle: 1721.1/146383
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
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The following results are related to Energy Research. Are you interested to view more results? Visit OpenAIRE - Explore.

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Tackling Climate Change with Machine Learning
descriptionPublicationkeyboard_double_arrow_right Article , Preprint 2022Embargo end date: 01 Jan 2023 Germany, United States, Switzerland Publisher:Association for Computing Machinery (ACM)Funded by:NSERC, NSF | PostDoctoral Research Fel...
NSERC ,
NSF| PostDoctoral Research Fellowship
Authors: David Rolnick; Priya L. Donti; Lynn H. Kaack; Kelly Kochanski; +18 Authors
David Rolnick; Priya L. Donti; Lynn H. Kaack; Kelly Kochanski; Alexandre Lacoste; Kris Sankaran; Andrew Slavin Ross; Nikola Milojevic-Dupont; Natasha Jaques; Anna Waldman-Brown; Alexandra Sasha Luccioni; Tegan Maharaj; Evan D. Sherwin; S. Karthik Mukkavilli; Konrad P. Kording; Carla P. Gomes; Andrew Y. Ng; Demis Hassabis; John C. Platt; Felix Creutzig; Jennifer Chayes; Yoshua Bengio;
doi: 10.1145/3485128 , 10.3929/ethz-b-000573494 , 10.48550/arxiv.1906.05433 , 10.14279/depositonce-15739
arXiv: 1906.05433
handle: 1721.1/146383
doi: 10.1145/3485128 , 10.3929/ethz-b-000573494 , 10.48550/arxiv.1906.05433 , 10.14279/depositonce-15739
arXiv: 1906.05433
handle: 1721.1/146383
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
downloaddonwloadFull-Text
DSpace@MIT (Massachu...arrow_drop_down
DSpace@MIT (Massachusetts Institute of Technology)
Article . 2022
License: CC BY
Full-Text: https://doi.org/10.1145/3485128
Data sources: Bielefeld Academic Search Engine (BASE)
ACM Computing Surveys
Article
Data sources: UnpayWall
https://dx.doi.org/10.14279/de...
Article . 2022
License: CC BY
Data sources: Datacite
arXiv.org e-Print Archive
Preprint . 2019
Data sources: arXiv.org e-Print Archive
Research Collection
Article . 2023
License: CC BY
Data sources: Research Collection
DepositOnce
Article . 2022
License: CC BY
Data sources: DepositOnce
ACM Computing Surveys
Article . 2022 . Peer-reviewed
License: ACM Copyright Policies
Data sources: Crossref
Research Collection
Article . 2023
Data sources: Datacite
https://dx.doi.org/10.48550/ar...
Article . 2019
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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shareshareShare
citeCite
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For further information contact us at helpdesk@openaire.eu
Access Routes
Green
bronze
316
citations 316
popularity Top 0.1%
influence Top 1%
impulse Top 0.1%
Powered by BIP!
more_vert
downloaddonwloadFull-Text
DSpace@MIT (Massachu...arrow_drop_down
DSpace@MIT (Massachusetts Institute of Technology)
Article . 2022
License: CC BY
Full-Text: https://doi.org/10.1145/3485128
Data sources: Bielefeld Academic Search Engine (BASE)
ACM Computing Surveys
Article
Data sources: UnpayWall
https://dx.doi.org/10.14279/de...
Article . 2022
License: CC BY
Data sources: Datacite
arXiv.org e-Print Archive
Preprint . 2019
Data sources: arXiv.org e-Print Archive
Research Collection
Article . 2023
License: CC BY
Data sources: Research Collection
DepositOnce
Article . 2022
License: CC BY
Data sources: DepositOnce
ACM Computing Surveys
Article . 2022 . Peer-reviewed
License: ACM Copyright Policies
Data sources: Crossref
Research Collection
Article . 2023
Data sources: Datacite
https://dx.doi.org/10.48550/ar...
Article . 2019
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
linkLink to
shareshareShare
citeCite
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Please grant OpenAIRE to access and update your ORCID works.
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