Rebound Effects in Methods of Artificial Intelligence
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Direct economic rebound effects are the most common type of indirect effects discussed in conjunction with many technological innovations.
“They are also the most common type of indirect effects that are discussed in conjunction with many technological innovations (see [63, 135]).”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [135]
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
The Circular Economy–A new sustainability paradigm?
Geissdoerfer, M., Savaget, P., Bocken, N. M. P., & Hultink, E. J. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.12.048 · Cited by 9,360 · see citing works
Cradle to cradle: remaking the way we make things
Braungart, M., & McDonough, W. F. (2002) · Choice Reviews Online · doi:10.5860/choice.40-0914 · Cited by 3,850 · see citing works
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 · see citing works
1.1 Computing's energy problem (and what we can do about it)
Horowitz, M. (2014) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2014.6757323 · Cited by 2,217 · see citing works
Quantifying the Carbon Emissions of Machine Learning
Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019) · arXiv · doi:10.48550/arxiv.1910.09700 · Cited by 408 · see citing works
Benchmarking TPU, GPU, and CPU Platforms for Deep LearningPreprint
Wang, Y. E., Wei, G.-Y., & Brooks, D. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.10701 · Cited by 236 · see citing works
NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural NetworksPreprint
Cai, E., Juan, D.-C., Stamoulis, D. S., & Marculescu, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.05420 · Cited by 110 · see citing works
Google's Training Chips Revealed: TPUv2 and TPUv3
Norrie, T., Patil, N. P., Yoon, D. H., Kurian, G. T., Li, S., Laudon, J. P., Young, C., Jouppi, N. P., & Patterson, D. A. (2020) · IEEE Hot Chips Symposium · doi:10.1109/hcs49909.2020.9220735 · Cited by 34 · see citing works
LCI Modeling Challenges and Solutions for a Complex Product System: A Mid-Sized Automobile
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International Consortium of Investigative Journalists (ICIJ)
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Flagged first, then by citations
2 of the 16 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
AI beyond efficiency, navigating the rebound effect in AI-driven sustainable development
Mhlanga, D. (2025) · Frontiers in Energy Research · doi:10.3389/fenrg.2025.1460586 · Cited by 26 · see citing works
More sustainable artificial intelligence systems through stakeholder involvement?
Kunkel, S., Schmelzle, F., Niehoff, S., & Beier, G. (2023) · GAIA - Ecological Perspectives for Science and Society · doi:10.14512/gaia.32.s1.10 · Cited by 20 · see citing works
Vergallo, R., & Mainetti, L. (2024) · Future Internet · doi:10.3390/fi16090334 · Cited by 17 · see citing works
Wu, C.-T., Li, H.-R., & Yuan, B.-B. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126607 · Cited by 5 · see citing works
Vergallo, R., Casciaro, S., & Mainetti, L. (2025) · Advances in finance, accounting, and economics book series · doi:10.4018/979-8-3693-8186-1.ch002 · Cited by 4 · see citing works
Rebound Effects in the Use of Rare Earth Metals in ICT
Willenbacher, M. (2022) · International Journal of Environmental Sciences & Natural Resources · doi:10.19080/ijesnr.2022.30.556277 · Cited by 4 · see citing works
Self-citationBalancing AI’s energy consumption with commitments to clean energy (SDG 7): Is there a trade-off?
Glavas, D. (2025) · International Journal of Green Energy · doi:10.1080/15435075.2025.2546047
- Authors
- Willenbacher, M., Hornauer, T., & Wohlgemuth, V.
- Affiliations
- Leuphana University of Lüneburg (DE)
- HTW Berlin - University of Applied Sciences (DE)
- Corresponding author
- Willenbacher, Martina
- Venue
- Progress in IS
- Publisher
- Springer International Publishing
- Published
- 2021-12-11
- Pages
- 73-85
- ISSN
- 2196-8705 (ISSN-L)
- 2196-8713
- OpenAlex
- W4200395034
- Cited by
- 7
- References
- 16
- Citations by year
- 2025: 4 · 2024: 1 · 2023: 1 · 2022: 1
- FWCI
- 1.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Progress in IS (publishedVersion)
- Multilingual Matters (Channel View Publications) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, and Transportation Policies
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- rebound effect, artificial intelligence, energy consumption, CO2 emissions, energy efficiency, sustainability, digitalization, energy conservation
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