Policy Document · 2024-12-01
2024 Climate Risk Landscape Report
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Multimedia
Stanford Energy Seminar: AI & Climate Change Mitigation
Stanford ENERGY · 2025-01-01
What it covers
The second edition of the Artificial Intelligence for Climate Change Mitigation Roadmap (November 2024) examines AI's role in addressing the climate crisis, identifying key sectors where AI can have the greatest impact, and weighing the potential emissions reductions against the increased power demand from AI systems, with a discussion led by the report's authors.
Who it helps
Intended audience has not been documented.
Limitations
Source-specific limitations have not been reviewed. Inclusion and topic labels do not establish effectiveness or reliability.
Show citation
Stanford ENERGY. 2025-01-01. Stanford Energy Seminar: AI & Climate Change Mitigation. https://www.youtube.com/watch?v=su3R8lo9a7M (AI & Environment Resource Hub; record video-005; collection snapshot 2026-10-04).
Classification and context
Included in the original Multimedia collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
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Available source metadata
- Author or creator
- Stanford ENERGY
- Publisher
- youtube.com
- Publication date
- 2025-01-01
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source access
- Excluded from automated checks
- Metadata confidence
- Required metadata corroborated
- Last verified
- 2026-09-15T16:13:40.977Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Climate & Weather
- Legacy subdomain
- Unknown or not supplied
Provenance and review
- Source access
- Excluded from automated checks
- Metadata confidence
- Required metadata corroborated
- Editorial relevance
- Relevance has not been separately reviewed.
- Evidence quality
- Evidence quality has not been separately assessed.
Source sheet: Multimedia · Row 9 · Original ID: video-005.
Formats and evidentiary standards vary; evaluate each original source. Automated checks excluded by website or access policy. Existing metadata evidence is retained; exclusion is not a quality assessment. The import date is not the original date added.
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Related through shared environmental topics
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Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-01-19
3D Cloud Reconstruction Through Geospatially-Aware Masked Autoencoders
Self-supervised learning enhances 3D cloud reconstruction, improving climate predictions by reducing uncertainties in climate models.
Shared topics: 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: Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-04-23
A Hackathon for Flood Map Prediction From Geospatial Data With Parsimonious Machine Learning Models
This study showcases a machine learning hackathon in France where a CNN-based model accurately predicted flood risk evolution using geospatial and climate data, even without streamflow input, emphasizing trustworthy AI in data-scarce regions.