AI & Environment Resource HubAuthor or publisher unknown · Publication date unknown
We use AI and geoanalytics to turn raw satellite data into actionable insights on energy, supply chains, physical risks, nature and the environment, working with businesses and governments to make energy systems more efficient, harden supply security, boost climate resilience, and manage environmental impacts while enabling investors, insurers, traders and other stakeholders to make faster, smarter decisions.
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Author unknown. Date unknown. Kayrros. https://www.linkedin.com/company/kayrros/ (AI & Environment Resource Hub; record org-650; collection snapshot 2026-10-04).
Included in the original Organization collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Source sheet: Organization · Row 653 · Original ID: org-650.
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Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Research tools and geospatial methods; Energy and electricity; Climate and greenhouse gases; Weather, hazards, and adaptation
This preprint examines computing demands and model skill in Earth system science and proposes joint reporting of predictive performance and energy use, including for machine learning weather models.
Shared topics: Research tools and geospatial methods; Energy and electricity; Climate and greenhouse gases
Sustainable AI Group's public dashboard compares modeled AI inference energy and CO₂-eq by model, provider and session, including data center overhead assumptions. Water estimates and API are marked coming soon.
Shared topics: Research tools and geospatial methods; Energy and electricity; Climate and greenhouse gases
The study compares performance, energy use, processing time, and carbon emissions across dictionaries, trained classifiers, and self-hosted open LLMs for three text-analysis tasks.
Shared topics: Research tools and geospatial methods; Energy and electricity; Climate and greenhouse gases
Proposes per-token energy, water and CO₂-eq estimates using open-model GPU measurements and modeled closed-model assumptions, regression and Monte Carlo uncertainty. Submitted v1 Sep 27; v2 Sep 29, 2026.