AI & Environment Resource HubLearning, workforce development, and professional skills supporting work across this field.
Browse all 57 resources →Counts reflect provisional metadata classification, not measured environmental impact. A record may belong to multiple topics.
Share of the 57 records in this view. Pathways can overlap; bars do not add to 100%.
Research papers and scholarly literature on impacts, applications, and methods.
The Artificial Intelligence Forecasting System (AIFS), developed by ECMWF, leverages graph neural networks and transformers to produce highly skilled medium-range weather forecasts, running alongside traditional numerical models and providing open-access predictions.
Engaging students in future school scenarios fosters critical thinking on AI’s environmental impact, addressing gaps in policy and industry narratives.
Employers in AI roles are shifting toward skill-based hiring in the green field, reducing emphasis on formal degrees while valuing AI skills with a 23% wage premium.
A machine learning weather model, ACE2, trained on reanalysis data, demonstrates skillful global seasonal forecasting, comparable to leading physics-based models—especially in predicting the North Atlantic Oscillation, suggesting ML's potential in near-term climate prediction.
Missing metadata is a curation task, not evidence of absent research or activity. The Resource Hub does not currently contain a validated incident dataset, expert survey, or measurements of net environmental benefit.