The DOI matches its Crossref record; 5 of the 6 references it cites were found in a registry or an index.
The growing energy footprint of artificial intelligence
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In the citing paper
“a recent study suggests that the global AI could consume 85–134TWh of electricity in 2027,6”
From Making AI Less 'Thirsty' (2025) · cites this paper as [6]
In the citing paper
“contributes to AI's carbon footprint (Section 3.2.4;[42])”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [42]
In the citing paper
“The significant energy footprint of training and running AI models presents a 'Sustainability Paradox' [18,42,54,55].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [42]
In the citing paper
“Environmental Sustainability confronts the paradox of AI's own significant energy consumption and carbon footprint [5,42].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [42]
In the citing paper
“addressing calls for greater accountability for AI's environmental impact [5,42]”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [42]
In the citing paper
“Individual AI training clusters already draw hundreds of megawatts, with the largest announced sites approaching a gigawatt 3”
From Grid topology and the spatial cost of AI data-centre siting (2026) · cites this paper as [3]
Citations
Cites
Why Are There Still So Many Jobs? The History and Future of Workplace Automation
Autor, D. H. (2015) · Journal of Economic Perspectives · doi:10.1257/jep.29.3.3 · Cited by 3,917
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
Patterson, D., Gonzalez, J., Holzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D. R., Texier, M., & Dean, J. (2022) · Computer · doi:10.1109/mc.2022.3148714 · Cited by 400
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
Luccioni, A. S., Viguier, S., & Ligozat, A.-L. (2022) · arXiv · doi:10.48550/arxiv.2211.02001 · Cited by 378
A systematic review of Green AI
Verdecchia, R., Sallou, J., & Cruz, L. (2023) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1507 · Cited by 375
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Research works printed but not linked: 8
- Heaven, 2023, Google just launched Bard, its answer to ChatGPT—and it wants you to make it better, MIT Technology Review
- Dastin, 2023, Focus: For tech giants, AI like Bing and Bard poses billion-dollar search problem, Reuters
- Mehta, 2023, Nvidia adds jet fuel to AI optimism with record results, $25 billion buyback, Reuters
- Leswing, 2023, Meet the $10,000 Nvidia chip powering the race for A.I., CNBC
- Bary, 2023, Nvidia is ‘dominating’ and could unlock $300 billion in AI revenue by 2027, analyst says, MarketWatch
- Chen, 2023, TSMC new CoWoS packaging plant to start volume production in mid-2027, DigiTimes
- Syed, 2023, The Demand Elasticity Paradox: More than Meets the AI, Medium
- Moses, 2023, Mining 2.0 Trends as Defunct Crypto Mining Rigs Tap into the AI Boom, Cryptopolitan
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Metadata
- Authors
- de Vries, A.
- Venue
- Joule
- Publisher
- Elsevier BV
- Published
- 2023-10
- Volume
- 7
- Issue
- 10
- Pages
- 2191-2194
- ISSN
- 2542-4351 (ISSN-L)
- OpenAlex
- W4387494819
- Cited by
- 597
- References
- 6
- Citations by year
- 2026: 275 · 2025: 199 · 2024: 117 · 2023: 6
- FWCI
- 44.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 383 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Joule (publishedVersion)
- VU Research Portal (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- artificial intelligence, energy consumption, environmental impact, carbon footprint, sustainability, digital technologies
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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