AI & Environment Resource HubAuthor or publisher unknown · 2025-03-21
This research leverages AI, big data, and mechanistic modeling to uncover how microbial activity and environmental factors shape soil carbon storage, aiming to develop innovative soil-based climate mitigation strategies.
Author unknown. 2025-03-21. AI-Assisted Discoveries in Soil Carbon Cycle: Mechanisms and Predictions Toward Global Sustainability. https://www.youtube.com/watch?v=TZEuOSaP9uQ&t=9s (AI & Environment Resource Hub; record video-078; collection snapshot 2026-09-15).
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Source sheet: Multimedia · Row 82 · Original ID: video-078.
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Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Agriculture and food; Weather, hazards, and adaptation
Himanshu Gupta and Eleonore Fournier-Tombs discuss AI’s role in climate adaptation, from predicting floods to growing resilient crops, while addressing the ethical dilemma of its high carbon footprint and environmental impact.
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Agriculture and food; Weather, hazards, and adaptation
The full film celebrating the recent Bright Tide Sustain.AI accelerator programme in collaboration with Hogan Lovells (HL BaSE), featuring our Sustain.AI Ambassador, Lord Ranger, alongside several of the innovative ventures (Gamaya, Synature and Ecodetect) from the cohort. Sustain.AI is designed to empower the next generation of entrepreneurs harnessing artificial intelligence for positive environmental impact—from biodiversity monitoring and regenerative agriculture to climate resilience and nature restoration.
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Weather, hazards, and adaptation
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Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Weather, hazards, and adaptation
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.