The DOI matches its Crossref record; 3 of the 4 references it cites were found in a registry or an index, and 1 are not checked yet.
Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins
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“While one largescale generative AI model would not cause much environmental damage, if numerous companies developed models with slight di ff erences for various purposes -such as detecting extortion, electoral disinformation, and AI sexual abuse content -each used by millions of users, the high demand for energy could undeniably become unsustainable (Saenko 2023).”
From Generative AI and deepfakes: a human rights approach to tackling harmful content (2024) · cites this paper as Saenko 2023
Citations
Cites
Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., & McCallum, A. (2019) · arXiv · doi:10.48550/arxiv.1906.02243 · Cited by 3,735
Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1355 · Cited by 3,684
Carbon Emissions and Large Neural Network Training
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021) · arXiv · doi:10.48550/arxiv.2104.10350 · Cited by 486
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
Research works printed but not linked: 1
- 10.48550/arxiv.2104.10350 · DOI · Paper page
Cited by
Sorted by citations
Generative AI and deepfakes: a human rights approach to tackling harmful content
Romero Moreno, F. (2024) · International Review of Law, Computers & Technology · doi:10.1080/13600869.2024.2324540 · Cited by 117
Huang, J., Bibri, S. E., & Keel, P. E. (2025) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2025.100526 · Cited by 108
The Future of Research in an Artificial Intelligence-Driven World
Kulkarni, M., Mantere, S., Vaara, E., van den Broek, E., Pachidi, S., Glaser, V. L., Gehman, J., Petriglieri, G., Lindebaum, D., Cameron, L. D., et al. (2024) · Journal of Management Inquiry · doi:10.1177/10564926231219622 · Cited by 80
Bibri, S. E., & Huang, J. (2025) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2025.100591 · Cited by 75
From E-Waste to Eco-Wonder: Resurrecting Computers for a Sustainable Future
Reyna, J., Hanham, J., & Orlando, J. (2024) · Sustainability · doi:10.3390/su16083363 · Cited by 5
Making Bricks Without Clay: Generative AI and Reading
Gavin, D. J. (2025) · Springer eBooks · doi:10.1007/978-3-032-06534-6_3
Metadata
- Authors
- Saenko, K.
- Affiliations
- Boston University (US)
- Corresponding author
- Saenko, Kate
- ORCID
- Saenko, Kate
- Publisher
- The Conversation Media Group Ltd
- Published
- 2023-05-23
- OpenAlex
- W4413603376
- Cited by
- 6
- References
- 3
- Citations by year
- 2025: 3 · 2024: 3
- Type
- other
- Language
- en
- Open access
- closed
- 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 › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- generative AI, carbon footprint, ChatGPT, environmental impact of AI, large language models, energy consumption
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