AI & Environment Resource HubFabio Ciulla; Andre Santos; Preston Jordan; Timothy Kneafsey; Sebastien C. Biraud; Charuleka Varadharajan · 2024-12-04
A neural network model identifies undocumented orphaned wells, helping locate methane-leaking sites and aiding environmental monitoring efforts.
Fabio Ciulla; Andre Santos; Preston Jordan; Timothy Kneafsey; Sebastien C. Biraud; Charuleka Varadharajan. 2024-12-04. A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: A Case Study for California and Oklahoma. https://pubs.acs.org/doi/10.1021/acs.est.4c04413 (AI & Environment Resource Hub; record paper-012; collection snapshot 2026-09-15).
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Shared topics: Cross-cutting sustainability; Energy and electricity
Get a snapshot of the shifting landscape of data center energy storage, with a focus on cost, safety, and sustainability. The 2024 report explores industry perceptions, technology adoption, and key priorities—laying the groundwork for how operators began adapting to growing demands and emerging trends.
Shared topics: Cross-cutting sustainability; Energy and electricity
Explore how AI, sustainability, and rising power demands are reshaping the future of data center energy storage. The 2025 report highlights key trends, evolving strategies, and emerging technologies—offering a forward-looking view into how data centers are rethinking power, backup, and energy infrastructure.
Shared topics: Cross-cutting sustainability; Energy and electricity
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Shared topics: Cross-cutting sustainability; Energy and electricity
Accurate energy measurement is essential for reducing ML’s carbon footprint, requiring standardized tools, best practices, and improved adoption among researchers.