Does not compute: Avoiding pitfalls assessing the Internet's energy and carbon impacts
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Past projections of rising energy use due to technology in the dot-com boom of the early 2000s failed to materialize.
“as Koomey and Masanet [65] cogently argue, this is not the first time the alarm has been raised about rising energy use due to technology, and that similar projections made in the dot-com boom of the early 2000s failed to materialize.”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [65]
The Introduction (page unavailable) says extrapolations of 1990s Internet-traffic growth led to excess network capacity around 2000, with significant energy implications. It also describes later projections of data-center energy use that overestimated growth. It does not say that projections of rising energy use made during the dot-com boom failed to materialize. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPoor data availability, flawed methodology, and inaccurate reporting caused inaccurate projections in data center energy use that did not take into account significant improvements in data center efficiency.
“They cite poor data availability, flawed methodology, and inaccurate reporting as causes for inaccurate projections in data center energy use that ultimately did not take into account significant improvements in data center efficiency.”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [65]
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Recalibrating global data center energy-use estimates
Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020) · Science · doi:10.1126/science.aba3758 · Cited by 1,545 · see citing works
Self-citationOn Global Electricity Usage of Communication Technology: Trends to 2030
Andrae, A., & Edler, T. (2015) · Challenges · doi:10.3390/challe6010117 · Cited by 1,334 · see citing works
Flagged first, then by citations
Research works printed but not linked: 10
- [1]1. Koomey, J. Separating Fact from Fiction: A Challenge For The Media IEEE Consumer Electronics Magazine. 2014; 3:9-11 Crossref Scopus (2) Google Scholar
- [3]3. Dreazen, Y.J. (2002) Behind the Fiber Glut. The Wall Street Journal. September 26, 2002. B1. Google Scholar
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- [9]9. Aslan, J. ∙ Mayers, K. ∙ Koomey, J.G. ... Electricity Intensity of Internet Data Transmission: Untangling the Estimates J. Ind. Ecol. 2018; 22:785-798 Crossref Scopus (105) Google Scholar
- [10]10. Koomey, J. Turning Numbers into Knowledge: Mastering the Art of Problem Solving Analytics Press, 2017 Google Scholar
- [11]11. Mora, C. ∙ Rollins, R.L. ∙ Taladay, K. ... Bitcoin emissions alone could push global warming above 2°C Nat. Clim. Chang. 2018; 8:931-933 Crossref Scopus (243) Google Scholar
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Cited by
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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 · see citing works
The Cloud Is Material: On the Environmental Impacts of Computation and Data Storage
Monserrate, S. G. (2022) · MIT Case Studies in Social and Ethical Responsibilities of Computing · doi:10.21428/2c646de5.031d4553 · Cited by 145 · see citing works
Luccioni, A. S., Strubell, E., & Crawford, K. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732007 · Cited by 71 · see citing works
“Similar uncertainty is clouding the prediction of AI’s energy use, and we are well aligned with Koomeyand Masanet [65], Masanet et al. [74] and others in calling for more granular transparent data from technology firms and service providers, and more rigorous analysis of available data.”
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
Patterson, D., Gonzalez, J., Hölzle, U., Le, Q. H., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., & Dean, J. (2022) · doi:10.36227/techrxiv.19139645 · Cited by 3 · see citing works
- Authors
- Koomey, J., & Masanet, E.
- Affiliations
- University of California, Santa Barbara (US)
- Corresponding author
- Koomey, Jonathan
- Venue
- Joule
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 5
- Issue
- 7
- Pages
- 1625-1628
- ISSN
- 2542-4351 (ISSN-L)
- OpenAlex
- W3173702736
- Cited by
- 53
- References
- 2
- Citations by year
- 2026: 11 · 2025: 10 · 2024: 10 · 2023: 4 · 2022: 15 · 2021: 3
- FWCI
- 2.35
- 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, 385 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)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- McCormick School of Engineering, Northwestern University
- Lawrence Berkeley National Laboratory
- Northwestern University
- 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 › Cloud Computing and Resource Management
- Keywords
- data centers, CO2 emissions, sustainability indicators
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