Policy Document · 2026-05-01
24/7 Carbon-Free Energy for All: Towards a Resilient Energy System
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Scientific Paper · source-verified
Tackling Climate Change with Machine Learning
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 · 2022-02-07
Machine learning can help combat climate change by reducing emissions and aiding adaptation through applications like smart grids and disaster management.
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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. 2022-02-07. Tackling Climate Change with Machine Learning. https://dl.acm.org/doi/10.1145/3485128 (AI & Environment Resource Hub; record paper-013; collection snapshot 2026-09-15).
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- 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
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- AI for Climate Mitigation Strategies
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- ACM Journals
Provenance and review
Source sheet: Scientific Paper · Row 17 · Original ID: paper-013.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Related through shared environmental topics
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Shared topics: Energy and electricity; Climate and greenhouse gases; Weather, hazards, and adaptation
Shared topics: Energy and electricity; Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-05-21
A Foundation Model for the Earth System
Aurora is a large-scale AI foundation model that significantly outperforms traditional forecasting systems across multiple Earth system domains, like air quality and cyclone tracking, while using far less computational power, marking a major advancement in accessible, efficient environmental prediction.
Shared topics: Energy and electricity; Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-07-17
Accelerating Climate Change Mitigation and Conserving Biodiversity – The Role of Advanced Digitalization for a Fast and Environmentally Sound Energy Transition Linking SEA and EIA
This article examines how advanced digitalization and AI can enhance spatial energy planning and environmental assessments in Austria, offering solutions to data deficiencies that currently hinder biodiversity-friendly renewable energy transitions.
Shared topics: Energy and electricity; Climate and greenhouse gases; Weather, hazards, and adaptation
Policy Document · 2025-09-18
AI and Climate Change: The Global South Facing the New Geopolitics of Innovation
We are happy to share this strategic report–AI and Climate Change: The Global South Facing the New Geopolitics of Innovation–written by Lori Regattieri and jointly launched by CIPÓ and the Green Screen Coalition. The report articulates key connections between energy infrastructures, climate justice and purported AI solutions for the “energy transition”. Their work argues for multilateral, pluriversal, and multisectoral approaches to digital infrastructure and industrial policy. The report names the violence of the “logic of expansion” and the fallacies of transition, which “simultaneously drives energy regimes and artificial intelligence systems”. Lori’s critique on the entanglement of many different social and political issues in Brazil is critical in a time where the technology industry lays the groundwork to be the saviors of COP30 in Belem, Brazil. With COP less than two months away, the report provides a powerful framing for those curious about the industrial dynamics in the region, and the ontological logic underpinning them.