The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
E-waste Challenges of Generative Artificial Intelligence
Open accessPreprint — published as 10.1038/s43588-024-00712-6
Abstract
Generative artificial intelligence (GAI), a subset of artificial intelligence, requires substantial computational hardware resources for data processing and model training. However, the electronic-waste (E-waste) toll of GAI remains underexplored and overlooked. Here, we propose a Computational Power-driven Material Flow Analysis (CP-MFA) model to measure GAI-related E-waste generation, with a specific focus on large language models. By quantifying server requirements and E-waste generation of GAI under different scenarios, we find that this emerging waste stream will grow at a rapid pace (16 million tons cumulative waste by 2030) with deleterious environmental impacts. Accordingly, we call for an early adoption of circular economy measures among server manufacturers and data center operators. This study reveals significant hardware-linked environmental implications in the context of GAI boom.
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“one recent study estimates that AI will generate an additional 1.2-5 million metric tons of e-waste by 2030 [134].”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [134]
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Cites
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Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083
Journal of International Business Studies
(1975) · Maritime Studies and Management · doi:10.1080/03088837500000054 · Cited by 2,456
Learning Structured Sparsity in Deep Neural NetworksPreprint
Wen, W. X., Wu, C., Wang, Y., Chen, Y., & Li, H. H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.03665 · Cited by 1,924
Life Cycle Assessment of Metals: A Scientific Synthesis
Nuss, P., & Eckelman, M. J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101298 · Cited by 777
Aligning artificial intelligence with climate change mitigation
Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01377-7 · Cited by 570
The rise of techno-geopolitical uncertainty: Implications of the United States CHIPS and Science Act
Luo, Y., & Assche, A. V. (2023) · Journal of International Business Studies · doi:10.1057/s41267-023-00620-3 · Cited by 402
AI and the transformation of social science research
Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P. E., & Cunningham, W. A. (2023) · Science · doi:10.1126/science.adi1778 · Cited by 377
An analog-AI chip for energy-efficient speech recognition and transcription
Ambrogio, S., Narayanan, P., Okazaki, A., Fasoli, A., Mackin, C., Hosokawa, K., Nomura, A., Yasuda, T., Chen, A., Friz, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06337-5 · Cited by 269
Digital product passports as enabler of the circular economy
Walden, J., Steinbrecher, A., & Marinkovic, M. (2021) · Chemie Ingenieur Technik · doi:10.1002/cite.202100121 · Cited by 252
Health consequences of exposure to e-waste: an updated systematic review
Parvez, S. M., Jahan, F., Bruné, M., Gorman, J., Rahman, M. J., Carpenter, D. O., Islam, M. Z., Rahman, M. M., Aich, N., Knibbs, L. D., et al. (2021) · The Lancet Planetary Health · doi:10.1016/s2542-5196(21)00263-1 · Cited by 227
Cited by
Sorted by citations
We Reject the Use of Generative Artificial Intelligence for Reflexive Qualitative Research
Jowsey, T., Braun, V., Clarke, V., Lupton, D. A., & Fine, M. (2025) · Qualitative Inquiry · doi:10.1177/10778004251401851 · Cited by 127
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
Radiology AI and sustainability paradox: environmental, economic, and social dimensions
Koçak, B., Ponsiglione, A., Romeo, V., Ugga, L., Huisman, M., & Cuocolo, R. (2025) · Insights into Imaging · doi:10.1186/s13244-025-01962-2 · Cited by 61
Efficiency Is Not Enough: A Critical Perspective on Environmentally Sustainable AI
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Enhancing Sustainable Energy Through Cutting-Edge Waste Biorefinery Technologies
Lee, Y., Masimukku, S., Li, I.-C., Keharika, K., Pothu, R., Boddula, R., Al-Qahtani, N. H. S., Huang, B.-W., & Chang‐Chien, G. (2024) · Reactions · doi:10.3390/reactions5040059 · Cited by 41
Metadata
- Authors
- Wang, P., Zhang, L.-Y., Tzachor, A., Masanet, E., & Chen, W.-Q.
- Affiliations
- Chinese Academy of Sciences (CN)
- Institute of Urban Environment, Chinese Academy of Sciences
- Reichman University (IL)
- University of California, Santa Barbara (US)
- Corresponding author
- Wang, Peng
- Tzachor, Asaf
- Venue
- Nature Computational Science
- Published
- 2024-03-13
- Volume
- 4
- Issue
- 11
- Pages
- 818-823
- ISSN
- 2662-8457 (ISSN-L)
- OpenAlex
- W4403828159
- Cited by
- 122
- References
- 17
- Citations by year
- 2026: 67 · 2025: 48 · 2024: 7
- FWCI
- 33.04
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Computational Science (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (72274187, 71961147003)
- Topics
- Physical Sciences › Environmental Science › Waste Management and Disposal › Recycling and Waste Management Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Environmental Science › Waste Management and Disposal › Municipal Solid Waste Management
- Keywords
- electronic waste, generative AI, large language models, material flow analysis, computational power, circular economy, e-waste management, semiconductor supply chain, AI value chain
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