The paper reviews 194 papers on using artificial intelligence to optimize data center energy use, coding 63 of them. It finds that most control studies validate only in simulation, none consider water withdrawal or embodied carbon, and savings estimates overlap across methods, preventing ranking. The authors propose CLEAR‑DC, a framework that links control and workload demand through elasticity, reports net benefits, and records energy, carbon, water, embodied share, and validation venue.
By Mohammed Basharath Ullah, Summaiya Unnisa Begum, Mohammed Nadeem Ullah
arXiv:2606. 05420v1 Announce Type: new Abstract: The rapid proliferation of hyperscale data centers (HDCs) in the US, mainly driven by the adoption of artificial intelligence, has raised concerns about this industry's environmental footprint.
By Gianluca Guidi, Francesca Dominici, Tiziano Squartini, Callaway Sprinkle, Jonathan Gilmour, Kevin Butler, Eric Bell, Scott Delaney, Falco J. Bargagli-Stoffi
SustainAI is a water‑aware, closed‑loop framework that embeds environmental accountability into AI deployment. It combines real‑time water metering, a hallucination‑aware penalty model, and a water‑aware routing algorithm that considers regional water stress. In tests with small language models, water footprints varied 11‑fold across data centers, and 1,335 inference runs consumed about 399 mL of water but yielded only 240 correct outputs, highlighting the resource cost of inaccurate responses.
By Farnaz Farid, Tashfia Towkee, Sania Nasreen, Sami bin Azad
arXiv:2606. 00811v1 Announce Type: cross Abstract: Data centers now account for 4.
By Dana Golden, Aruna Balasubramanian, Niranjan Balasubramanian
arXiv:2608. 12915v1 Announce Type: cross Abstract: The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality.
By Nicoletta Tsiopani, Moysis Symeonides, George Pallis, Marios D. Dikaiakos
arXiv:2609.23421v1 Announce Type: cross
Abstract: Standard data center sustainability metrics, including Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), and Carbon Usage Effectivene...
By Sharifa Sultana, Syed Ishtiaque Ahmed
arXiv:2607. 17391v1 Announce Type: cross Abstract: As hyperscale and colocation AI data centers continue to expand, the electric grid is increasingly required to support large, concentrated loads, with individual facilities ranging from 500 MW to 2 GW.
By Soham Ghosh, Nabil Mohammed, Mohammad Ashraf Hossain Sadi
arXiv:2412. 03716v3 Announce Type: replace Abstract: Artificial intelligence (AI) computing and data centers consume large amounts of freshwater, both directly for cooling and indirectly for electricity generation.
By Noah Shumba, Opelo Tshekiso, Pengfei Li, Giulia Fanti, Shaolei Ren
arXiv:2606. 10660v1 Announce Type: cross Abstract: AI inference services -- API subscriptions, enterprise chat tools, and SaaS products with embedded AI features -- fall unambiguously within Scope 3 Category 1 under the Corporate Sustainability Reporting Directive (CSRD), which requires disclosure for fiscal years starting January 2024.
By Guillermo Llopis (SOMA AI, Barcelona)
arXiv:2607. 05408v1 Announce Type: cross Abstract: The environmental impact of training large language models (LLMs) is increasingly scrutinised, yet most published estimates focus on operational energy and disclose little about manufacturing (embodied) emissions, water consumption, or the underlying highperformance computing (HPC) infrastructure.
By Marc L\'eobet, Pierre-Fran\c{c}ois Lavall\'ee, Jean-Pierre Lorr\'e
arXiv:2606. 15709v1 Announce Type: new Abstract: Jordan faces severe water scarcity with 50\% of water produced is lost to leakage, theft and metering issues also known as non-revenue water (NRW).
By Mohammed Fasha, Nahel Al-Maayta, Bilal Sowan, Mohammad Athamneh, Husam Barham
arXiv:2606. 14707v1 Announce Type: cross Abstract: AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions.
By Yuxin Chen (University of Helsinki, Finland), Hao Gao (Independent Researcher), Chujie Zou (University of Helsinki, Finland)