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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Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Cryptocurrencies on the road to sustainability: Ethereum paving the way for Bitcoin
de Vries, A. (2022) · Patterns · doi:10.1016/j.patter.2022.100633 · Cited by 110
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
Cited by
Sorted by citations
Integrating industry 4.0 for enhanced sustainability: Pathways and prospects
Khan, M. I., Yasmeen, T., Khan, M., Hadi, N. U., Asif, M., Farooq, M., & Al‐Ghamdi, S. G. (2024) · Sustainable Production and Consumption · doi:10.1016/j.spc.2024.12.012 · Cited by 255
ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review
Khowaja, S. A., Khuwaja, P., Dev, K., Wang, W., Nkenyereye, L., & Fortino, G. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10285-1 · Cited by 235
Li, P., Yang, J., Islam, M. A., & Ren, S. (2025) · Communications of the ACM · doi:10.1145/3724499 · Cited by 176
Large language models for reticular chemistry
Zheng, Z., Rampal, N., Inizan, T. J., Borgs, C., Chayes, J. T., & Yaghi, O. M. (2025) · Nature Reviews Materials · doi:10.1038/s41578-025-00772-8 · Cited by 134
EU AI sovereignty: for whom, to what end, and to whose benefit?
Mügge, D. (2024) · Journal of European Public Policy · doi:10.1080/13501763.2024.2318475 · Cited by 160
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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