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E-waste challenges of generative artificial intelligence

Nature Computational Science volume 4, pages 818–823 (2024)Cite this article

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Abstract

Generative artificial intelligence (GAI) requires substantial computational resources for model training and inference, but the electronic-waste (e-waste) implications of GAI and its management strategies remain underexplored. Here we introduce a computational power-driven material flow analysis framework to quantify and explore ways of managing the e-waste generated by GAI, with a particular focus on large language models. Our findings indicate that this e-waste stream could increase, potentially reaching a total accumulation of 1.2–5.0 million tons during 2020–2030, under different future GAI development settings. This may be intensified in the context of geopolitical restrictions on semiconductor imports and the rapid server turnover for operational cost savings. Meanwhile, we show that the implementation of circular economy strategies along the GAI value chain could reduce e-waste generation by 16–86%. This underscores the importance of proactive e-waste management in the face of advancing GAI technologies.

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Fig. 1: Hierarchical framework of our computational power-driven material flow analysis model and the corresponding scenario results regarding LLM-related waste generation without interventions.
Fig. 2: Circular economy strategies and their potential impacts on GAI-related e-waste generation.

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Data availability

This paper analyzes existing and publicly available data. All data sources used in this research are referenced in the main text or in Supplementary Information17. Source data for Figs. 1b,c and 2b,c are available with this paper.

Code availability

The main code of our approach (as well as datasets to run the code) is available17.

References

  1. Crawford, K. Generative AI’s environmental costs are soaring—and mostly secret. Nature 626, 693 (2024).

    Article  Google Scholar 

  2. Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023).

    Article  Google Scholar 

  3. Grossmann, I. et al. AI and the transformation of social science research. Science 380, 1108–1109 (2023).

    Article  Google Scholar 

  4. Jia, Z. et al. The importance of resource awareness in artificial intelligence for healthcare. Nat. Mach. Intell. 5, 687–698 (2023).

    Article  Google Scholar 

  5. Lannelongue, L. et al. GREENER principles for environmentally sustainable computational science. Nat. Comput. Sci. 3, 514–521 (2023).

    Article  Google Scholar 

  6. Mytton, D. & Ashtine, M. Sources of data center energy estimates: a comprehensive review. Joule 6, 2032–2056 (2022).

    Article  Google Scholar 

  7. Masanet, E., Shehabi, A. & Koomey, J. Characteristics of low-carbon data centres. Nat. Clim. Change 3, 627–630 (2013).

    Article  Google Scholar 

  8. Computing 2030: Building a Fully Connected, Intelligent World (Huawei, 2021).

  9. Baldé, C. P., et al. Global E-waste Monitor 2024 (ITU/UNITAR, 2024); https://ewastemonitor.info/the-global-e-waste-monitor-2024/

  10. Parvez, S. M. et al. Health consequences of exposure to e-waste: an updated systematic review. Lancet Planet. Health 5, e905–e920 (2021).

    Article  Google Scholar 

  11. Data Centres and Data Transmission Networks https://www.iea.org/energy-system/buildings/data-centres-and-data-transmission-networks (IEA, 2023).

  12. Ambrogio, S. et al. An analog-AI chip for energy-efficient speech recognition and transcription. Nature 620, 768–775 (2023).

    Article  Google Scholar 

  13. Wen, W. et al. Learning structured sparsity in deep neural networks. In Proc. 30th International Conference on Neural Information Processing Systems 2082–2090 (Curran, 2016).

  14. Jonathan, O. Who’s making chips for AI? Chinese manufacturers lag behind US tech giants. Nature https://doi.org/10.1038/d41586-024-01292-1 (2024).

  15. Nuss, P. & Eckelman, M. J. Life cycle assessment of metals: a scientific synthesis. PLoS ONE 9, e101298 (2014).

    Article  Google Scholar 

  16. Walden, J., Angelika, S. & Maroye, M. Digital product passports as enabler of the circular economy. Chem. Ing. Tech. 93, 1717–1727 (2021).

    Article  Google Scholar 

  17. Johnly233. E-waste-Challenges-of-Generative-Artificial-Intelligence: revised version (V1.1). Zenodo https://doi.org/10.5281/zenodo.13790035 (2024).

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Acknowledgements

This research was financially supported by the National Natural Science Foundation of China (72274187 to P.W., 71961147003 to W.-Q.C.) and CAS IUE Research Program (IUE-JBGS-202202 to P.W.). We thank E. Masanet and our other colleagues for their contributions, which have improved this study.

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Author notes

  1. These authors contributed equally: Peng Wang, Ling-Yu Zhang.

Authors and Affiliations

  1. Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China

    Peng Wang, Ling-Yu Zhang & Wei-Qiang Chen

  2. University of Chinese Academy of Sciences, Beijing, China

    Peng Wang & Wei-Qiang Chen

  3. School of Sustainability, Reichman University, Herzliya, Israel

    Asaf Tzachor

  4. Centre for the Study of Existential Risk, University of Cambridge, Cambridge, UK

    Asaf Tzachor

Authors
  1. Peng Wang
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  2. Ling-Yu Zhang
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  3. Asaf Tzachor
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  4. Wei-Qiang Chen
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Contributions

P.W. and L.-Y.Z. designed the research; L.-Y.Z., P.W. and A.T. led the drafting of the manuscript. P.W., L.-Y.Z. and W.-Q.C. contributed to the methodology; L.-Y.Z., P.W. and A.T. interpreted the results. All authors contributed to the final writing of the article.

Corresponding authors

Correspondence to Peng Wang, Asaf Tzachor or Wei-Qiang Chen.

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The authors declare no competing interests.

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Peer review information

Nature Computational Science thanks Loïc Lannelongue, Mengmeng Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Discussion, Figs. 1–8, Tables 1–4 and equations 1–7.

Supplementary Data 1

Table of studied scenarios, giving a brief description and key parameter configurations for each scenario studied in the main text.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

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Wang, P., Zhang, LY., Tzachor, A. et al. E-waste challenges of generative artificial intelligence. Nat Comput Sci 4, 818–823 (2024). https://doi.org/10.1038/s43588-024-00712-6

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