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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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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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