The Data Heat Island Effect: Quantifying the Impact of AI Data Centers in a Warming World
Andrea Marinoni; Erik Cambria; Weisi Lin; Mauro Dalla Mura; Jocelyn Chanussot; Edoardo Ragusa; Chi Yan Tso; Yihao Zhu; Benjamin Horton · 2026-03-21
Uses remote sensing data to quantify that AI data centers increase surrounding land surface temperature by 2 degrees C on average after operations begin, potentially affecting over 340 million people globally.
Source identity and required public metadata were editorially reviewed on 2026-09-15.
Andrea Marinoni; Erik Cambria; Weisi Lin; Mauro Dalla Mura; Jocelyn Chanussot; Edoardo Ragusa; Chi Yan Tso; Yihao Zhu; Benjamin Horton. 2026-03-21. The Data Heat Island Effect: Quantifying the Impact of AI Data Centers in a Warming World. https://arxiv.org/pdf/2603.20897 (AI & Environment Resource Hub; record paper-442; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Andrea Marinoni; Erik Cambria; Weisi Lin; Mauro Dalla Mura; Jocelyn Chanussot; Edoardo Ragusa; Chi Yan Tso; Yihao Zhu; Benjamin Horton
Publisher
arXiv
Publication date
2026-03-21
Date precision
day
Geographic scope
Unknown
Language
unknown
Peer review
unknown
Source status
verified
Last verified
2026-09-15T17:00:21.094Z
Snapshot import
2026-09-06
Legacy domain
Cross-Cutting Sustainability
Legacy subdomain
Data Center Heat Impact & Urban Climate
journal
arXiv
Provenance and review
Selected fields checked: authors · 2026-09-06. The arXiv paper supplies the full author list and corrects the misspelled lead surname. Verification source ↗
Source sheet: Scientific Paper · Row 446 · Original ID: paper-442.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
In the Environmental Impacts of Data Centers 101 course, you will learn to analyze the environmental impacts of data centers using a life cycle assessment (LCA) perspective that goes beyond what you see in the news. This will include energy, water, land use, carbon emissions, global supply chains, e-waste concerns, ecological impacts, and environmental justice case studies. Whether you work in tech, sustainability, policy, or are just curious about how AI systems operate behind the scenes, this course gives you the clarity and frameworks to understand these impacts from end-to-end.
Nathaniel Burola
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Land use, heat, and noise; Built environment and infrastructure
AI data centers are becoming grid-scale flexible loads whose siting and operation affect electricity demand, carbon emissions, cooling energy, water use, and regional infrastructure adequacy. This paper presents GAIA-DC, a feasibility-first framework for sustainable AI data centers that couples facility energy performance, grid flexibility, regional environmental carrying capacity, and workload placement.
SSRN
Shared topics: Cross-cutting sustainability; Research tools and geospatial methods; Climate and greenhouse gases; Built environment and infrastructure
The National Academies hosted a two-day workshop bringing together experts from industry, utilities, and government to examine AI data centers' electricity usage, exploring its energy demands, mitigation strategies, regional siting considerations, and renewable resource availability.
