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.
“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]
The paper estimates substantial LLM-related server e-waste through 2030, so it supports the general point. But the results section gives cumulative estimates of 8 million tons in its most conservative scenario and about 16 million tons in its optimistic scenario, not the cited 1.2–5 million-ton range. The cited sentence does not specify a different measure, such as waste generated in 2030 alone. (LLM, conf=0.84)
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Cites
Large language models encode clinical knowledge
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 · see citing works
Journal of International Business Studies
(1975) · Maritime Studies and Management · doi:10.1080/03088837500000054 · Cited by 2,456 · see citing works
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 1,861 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 355 · see citing works
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 · see citing works
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 242 · see citing works
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 · see citing works
Sources of data center energy estimates: A comprehensive review
Mytton, D., & Ashtine, M. (2022) · Joule · doi:10.1016/j.joule.2022.07.011 · Cited by 183 · see citing works
Generative AI’s environmental costs are soaring — and mostly secret
Crawford, K. (2024) · Nature · doi:10.1038/d41586-024-00478-x · Cited by 175 · see citing works
Digitalization and the Anthropocene
Creutzig, F., Acemoglu, D., Bai, X., Edwards, P. N., Hintz, M. J., Kaack, L. H., Kilkis, S., Kunkel, S., Luers, A., Milojevic-Dupont, N., et al. (2022) · Annual Review of Environment and Resources · doi:10.1146/annurev-environ-120920-100056 · Cited by 112 · see citing works
GREENER principles for environmentally sustainable computational science
Lannelongue, L., Aronson, H. G., Bateman, A. G., Birney, E., Caplan, T., Juckes, M. N., McEntyre, J., Morris, A. D., Reilly, G., & Inouye, M. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00461-y · Cited by 90 · see citing works
The importance of resource awareness in artificial intelligence for healthcare
Jia, Z., Chen, J., Xu, X., Kheir, J. N., Hu, J., Xiao, H., Peng, S., Hu, X. S., Chen, D., & Shi, Y. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00670-0 · Cited by 85 · see citing works
Assessment of toxic metals in waste personal computers
Kolias, K., Hahladakis, J. Ν., & Gidarakos, E. (2014) · Waste Management · doi:10.1016/j.wasman.2014.04.020 · Cited by 69 · see citing works
Characteristics of low-carbon data centres
Masanet, E. R., Shehabi, A., & Koomey, J. (2013) · Nature Climate Change · doi:10.1038/nclimate1786 · Cited by 65 · see citing works
Self-citationReporting electricity consumption is essential for sustainable AI
Debus, C., Piraud, M., Streit, A., Theis, F. J., & Götz, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00750-1 · Cited by 52 · see citing works
Evaluation of pre-training large language models on leadership-class supercomputers
Yin, J., Dash, S., Gounley, J. P., Wang, F., & Tourassi, G. D. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05479-7 · Cited by 19 · see citing works
An Eco-Friendly Approach for Reducing Carbon Emissions in Cloud Data Centers
Aldossary, M., & Alharbi, H. A. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.026041 · Cited by 11 · see citing works
Who’s making chips for AI? Chinese manufacturers lag behind US tech giants
O'Callaghan, J. (2024) · Nature · doi:10.1038/d41586-024-01292-1 · Cited by 1 · see citing works
Johnly233/E-waste-Challenges-of-Generative-Artificial-Intelligence: Revised version
Johnly233 (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.13755986 · Cited by 1 · see citing works
Building resilience in high-tech supply chains
Aguilar-Hernandez, G. A., Singhvi, A., Böcher, C., & Zhong, X. (2023) · Nature Electronics · doi:10.1038/s41928-023-01015-w
The toxicological implications of e-waste
Lein, P., & Bruun, D. A. (2023) · Open Access Government · doi:10.56367/oag-039-9559
Flagged first, then by citations
Cited by
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Jowsey, T., Braun, V., Clarke, V., Lupton, D. A., & Fine, M. (2025) · Qualitative Inquiry · doi:10.1177/10778004251401851 · Cited by 127 · see citing works
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 · see citing works
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 58 · see citing works
Efficiency Is Not Enough: A Critical Perspective on Environmentally Sustainable AI
Wright, D., Igel, C., Samuel, G. N., & Selvan, R. (2025) · Communications of the ACM · doi:10.1145/3724500 · Cited by 45 · see citing works
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 40 · see citing works
Xue, T., Xu, G., Liu, C., Li, R., Wu, Y., Guo, Y., Gong, S., Shao, Z., Cai, X., Zou, H., et al. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202500092 · Cited by 38 · see citing works
Yang, Q., Han, J., Li, S., Chen, M., Wang, S., & Zhang, T. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202516067 · Cited by 34 · see citing works
Gao, Z., Zhuang, M., & Geng, Y. (2026) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2026.108430 · Cited by 27 · see citing works
Data Centers, Critical Minerals, Energy, and Geopolitics: The Foundations of Artificial Intelligence
Stacciarini, J. H. S., & Gonçalves, R. J. D. A. F. (2025) · Sociedade & natureza · doi:10.14393/sn-v37-2025-77215 · Cited by 26 · see citing works
Darzi, M. A. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.123999 · Cited by 24 · see citing works
Kim, J., Kim, J., Sovacool, B. K., Griffiths, S., Bazilian, M., Creutzig, F., Rio, D. D. F. D., Agarwala, M., Choi, M., & Debnath, R. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.116861 · Cited by 24 · see citing works
Bibri, S. E., Huang, J., Omar, O., Krogstie, J., & Yiğitcanlar, T. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107408 · Cited by 20 · see citing works
Environmental footprint of GenAI – Changing technological future or planet climate?
Moravec, V., Gavurová, B., & Kováč, V. (2025) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2025.100691 · Cited by 19 · see citing works
Sustainable and selective recovery of precious metals from e-waste: Catalyzing a circular economy
Jin, L., Shen, B., Du, H., Hao, D., Wang, M., Zhang, S., & Wang, Q. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.173153 · Cited by 18 · see citing works
AI-driven sustainability assessment for greener lithium-ion batteries
Chen, Q., Lai, X., Zhang, Y., Chen, J., Zheng, Y., Song, X., Han, X., & Ouyang, M. (2025) · Carbon Footprints · doi:10.20517/cf.2025.14 · Cited by 16 · see citing works
To be Artificial Intelligence for sustainability or not to be sustainable Artificial Intelligence
Rizzo, S. A. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116063 · Cited by 15 · see citing works
Xiao, J., Niu, B., Li, P., Chen, W., & Xu, Z. (2025) · Green Chemistry · doi:10.1039/d5gc02804c · Cited by 15 · see citing works
Self-citationYang, Q., Li, S., Han, J., Chen, M., Zhao, W., Wang, S., Rodriguez, R. D. D., & Zhang, T. (2026) · Advanced Materials · doi:10.1002/adma.72446 · Cited by 15 · see citing works
Ethical perspectives on AI Agents and Agentic AI
Hahn, M., Tretter, M., & Dabrock, P. (2026) · AI and Ethics · doi:10.1007/s43681-026-01027-0 · Cited by 14 · see citing works
Wirtu, Y. D., Godana, U. A., & Tucho, G. T. (2025) · Environmental Reviews · doi:10.1139/er-2024-0133 · Cited by 14 · see citing works
Smart sensors and AI: Sustainable solutions for environmental pollution challenges
Mutlag, F., Elaibi, H. K., Mahious, R., Halvaci, E., & Şen, F. (2025) · Next research. · doi:10.1016/j.nexres.2025.101195 · Cited by 13 · see citing works
Wu, Y., Guo, Y., Xue, T., Shao, Z., Xu, L., Kuang, J., Li, R., Xu, G., Chen, P., Hao, W., et al. (2026) · Angewandte Chemie International Edition · doi:10.1002/anie.202526042 · Cited by 12 · see citing works
Hernández-Ramírez, R., & Ferreira, J. B. (2024) · She ji · doi:10.1016/j.sheji.2024.11.002 · Cited by 11 · see citing works
Ashraf, W. M., Panda, R., Jadhao, P. R., Pant, K. K., & Dua, V. (2025) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2025.109237 · Cited by 11 · see citing works
Copara, M., Pilamunga, A., Ibarra-Torres, F., Mora, S. M. O., Morales-Urrutia, D., & Córdova, P. (2025) · Sustainability · doi:10.3390/su17146402 · Cited by 11 · see citing works
AIdeology : Unpacking the Ideology of Artificial Intelligence and Its Spaces
Cugurullo, F. (2025) · Antipode · doi:10.1111/anti.70065 · Cited by 11 · see citing works
Xiao, T., & You, F. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c09990 · Cited by 11 · see citing works
Fuinhas, J. A., Khan, Z., Koengkan, M., Irshad, H. M., & Domingos, H. A. (2026) · Energy Policy · doi:10.1016/j.enpol.2026.115136 · Cited by 11 · see citing works
The politics of artificial intelligence supply chains
Muldoon, J., Valdivia, A., & Badger, A. (2025) · AI & Society · doi:10.1007/s00146-025-02625-y · Cited by 10 · see citing works
Sustainable AI: Emerging Trends, Impacts, and Future Challenges
Philipo, A. G., Ning, H., Sarwatt, D. S., Mohamed, J. A., Yusufu, A. S., Shi, F., Urenje, S., & Ding, J. (2025) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2025.3611272 · Cited by 9 · see citing works
Christensen, E. L. (2025) · Public Relations Inquiry · doi:10.1177/2046147x251377853 · Cited by 9 · see citing works
Yang, Q., Han, J., Li, S., Chen, M., Wang, S., & Zhang, T. (2025) · Angewandte Chemie · doi:10.1002/ange.202516067 · Cited by 9 · see citing works
Rohde, F., Nasruddin, Z., Kotova, E., & Ammon, S. (2026) · AI and Ethics · doi:10.1007/s43681-026-01008-3 · Cited by 9 · see citing works
Lal, A., & You, F. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.4c11477 · Cited by 8 · see citing works
Valdivia, A. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732027 · Cited by 8 · see citing works
Zhao, X., Chow, C. L., & Lau, D. (2025) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2025.144034 · Cited by 8 · see citing works
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Worrell, T., & Carlson, B. L. (2025) · Somatechnics · doi:10.3366/soma.2025.0468 · Cited by 8 · see citing works
AI for translation and interpreting: A roadmap for users and policymakers
Peeters, K., Daems, J., Plieseis, C., Ginel, M. I. R., & Şahin, M. (2026) · Interpreting and Society · doi:10.1177/27523810251409377 · Cited by 8 · see citing works
Peykani, P., Emrouznejad, A., Ghanidel, S., Javadi-Sisi, I., & Mirjalili, S. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10546-2 · Cited by 8 · see citing works
BALAKRISHNAN, S., CHITTINENI, J., MAHESWARI, P., & SAHILA, C. (2025) · Journal of Research Innovation and Technologies · doi:10.57017/jorit.v4.2(8).03 · Cited by 6 · see citing works
Esho, E. O., Akinyelu, A. A., & Dinis, M. A. P. (2026) · Frontiers in Sustainability · doi:10.3389/frsus.2026.1726832 · Cited by 6 · see citing works
Modeling the increase of electronic waste due to generative AI
Lannelongue, L. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00726-0 · Cited by 6 · see citing works
Heiland, H., Schütt, M., Sieron, S., & Syrovatka, F. (2024) · PROKLA Zeitschrift für kritische Sozialwissenschaft · doi:10.32387/prokla.v54i217.2167 · Cited by 5 · see citing works
Jithish, J., Mahalingam, N., Wang, B., & Yeo, K. S. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101730 · Cited by 5 · see citing works
Ding, M., Ji, D., & Hu, W. (2025) · FlexMat. · doi:10.1002/flm2.70021 · Cited by 4 · see citing works
Green AI: Navigating sustainability and environmental impact from an Indian perspective
Martin, N., & Sharma, V. L. (2025) · E3S Web of Conferences · doi:10.1051/e3sconf/202563202010 · Cited by 4 · see citing works
E-Waste Wars: The Catalyst Awakens
Paone, E., & Mauriello, F. (2024) · Sustainable Chemistry · doi:10.3390/suschem5040022 · Cited by 4 · see citing works
A Deployment-Aware Framework for Carbon- and Water- Efficient LLM Serving
Hoxha, J., Thanasi-Boçe, M., & Khalifa, T. (2025) · Sustainability · doi:10.3390/su172310473 · Cited by 3 · see citing works
Navigating AI–nature frictions: Autonomous vehicle testing and nature-based constraints
Das, P., Woods, O., & Kong, L. (2025) · Big Data & Society · doi:10.1177/20539517251406123 · Cited by 3 · see citing works
For a Political Ecology of Communication: AI and Ecological Imperialism
Downey, J., & Wang, W. (2025) · Global Media and China · doi:10.1177/20594364251400876 · Cited by 3 · see citing works
Xue, T., Xu, G., Liu, C., Li, R., Wu, Y., Guo, Y., Gong, S., Shao, Z., Cai, X., Zou, H., et al. (2025) · Angewandte Chemie · doi:10.1002/ange.202500092 · Cited by 2 · see citing works
GenAI’s silent cost: the looming environmental crisis
Nie, D., Li, X., & Su, X. (2026) · Integrated Environmental Assessment and Management · doi:10.1093/inteam/vjaf114 · Cited by 2 · see citing works
Recycling power semiconductor devices
Sun, P., Zeng, Z., Pan, X., Ran, L., Hu, Y., Huang, S., Wang, Y., & Li, J. (2026) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-025-00242-x · Cited by 2 · see citing works
Chen, Z., & Song, Y. C. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103233 · Cited by 2 · see citing works
Recalibrating global artificial intelligence e-waste estimates
de Vries, A. (2026) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2026.108872 · Cited by 2 · see citing works
Bringing Uncle Chatty Gee (ChatGPT) into the classroom: a lens from Critical Indigenous Studies
Worrell, T., & Roberts, Z. (2026) · Curriculum Perspectives · doi:10.1007/s41297-026-00321-5 · Cited by 2 · see citing works
Lakatos, E. S., Ferenci, S., & Szabó, L. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117152 · Cited by 2 · see citing works
Steering open-source AI to accelerate the sustainable development goals
Chen, M., Wu, K., Pradhan, P., Allen, C., Nativi, S., Hubacek, K., Voinov, A., Creutzig, F., Filatova, T., Boers, N., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73866-8 · Cited by 2 · see citing works
A comprehensive review on green AI methods, strategies & measurement frameworks
Bayoumi, R., Nazmy, T., Salem, A. M., & Hindy, H. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00603-y · Cited by 2 · see citing works
Frimpong, V. (2025) · Qeios · doi:10.2139/ssrn.6991298 · Cited by 2 · see citing works
Friese, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5690262 · Cited by 2 · see citing works
Towards a sustainable use of GPUs in Graphics Research
Yu, E., Michel, E., Crespel, O., Paris, A., & Hähnlein, F. (2025) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3721239.3734084 · Cited by 1 · see citing works
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Quttainah, M. A., Singh, P., & Rana, S. (2025) · المجلة العربية للعلوم الإدارية. · doi:10.34120/ajas.2025.1387 · Cited by 1 · see citing works
Artificial Intelligence and Its Environmental Sustainability: Challenges and Opportunities
HOHENDORFF, R. V., & Engelmann, W. (2025) · Sustainable development · doi:10.5772/intechopen.1012513 · Cited by 1 · see citing works
Wu, K., Han, M., Huang, C., Qiu, H., Sethanan, K., & Tseng, M. (2025) · Journal of Business Logistics · doi:10.1111/jbl.70046 · Cited by 1 · see citing works
Martínez, J. S., Gallego, D., & Silva, J. H. O. (2025) · RSC Advances · doi:10.1039/d5ra06041a · Cited by 1 · see citing works
Chakraborty, A., Mandal, P., & Kumar, R. R. (2025) · Journal of the Operational Research Society · doi:10.1080/01605682.2025.2579853 · Cited by 1 · see citing works
LEGO-like three-dimensional integrated stretchable electronics
Wang, Q., Yang, X., Hu, Z., Li, Y., Zhang, X., Liu, B., Yang, P., Huang, L., & Wu, Y. (2025) · Science China Materials · doi:10.1007/s40843-025-3615-1 · Cited by 1 · see citing works
Celik, S. E., Chen, Z., Yldzbasi, A., & Sarkis, J. (2025) · International Journal of Generative Artificial Intelligence in Business · doi:10.1504/ijgaib.2025.10074843 · Cited by 1 · see citing works
AI and Machine Learning Driven Circular Design for Smart Manufacturing and Zero Waste Production
Kapoor, S. K., Sankhla, K., Khullar, S., Mamodiya, U., & Khattri, V. (2026) · Information systems engineering and management · doi:10.1007/978-3-032-10122-8_1 · Cited by 1 · see citing works
MXene/Polymer Composite Fibers in Flexible Electronics
Liu, L., Zhang, Q., Ming, J., Huang, X., & Bai, S. (2026) · Fibers and Polymers · doi:10.1007/s12221-025-01305-4 · Cited by 1 · see citing works
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Wang, B., & You, F. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.5c15514 · Cited by 1 · see citing works
Valdivia, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-09748-4_2 · Cited by 1 · see citing works
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Collinson, I. (2026) · Future of business and finance · doi:10.1007/978-3-032-07605-2_19 · Cited by 1 · see citing works
Domingo, J. L., & Shomar, B. (2026) · Human and Ecological Risk Assessment An International Journal · doi:10.1080/10807039.2026.2670401 · Cited by 1 · see citing works
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Hariyani, D., Hariyani, P., Choudhary, S., & Mishra, S. (2026) · Environmental Management Engineering and Technology · doi:10.1016/j.jemet.2026.100007 · Cited by 1 · see citing works
Lupton, D. A. (2026) · Health An Interdisciplinary Journal for the Social Study of Health Illness and Medicine · doi:10.1177/13634593261451272 · Cited by 1 · see citing works
Artificial Intelligence and Circular Economy: An Exploration of the Ecological Infosphere
Plociennik, C., Reich, R. H., Berthelot, A., Ruskowski, M., Acker, K. V., & Schien, D. (2026) · Procedia CIRP · doi:10.1016/j.procir.2026.05.012 · Cited by 1 · see citing works
Heidari, A., & Passerone, R. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10648-x · Cited by 1 · see citing works
Erol, İ., Pakdel, J., & Öztel, A. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124801 · Cited by 1 · see citing works
Realigning AI technology towards the Sustainable Development Goals
Feng, R., Li, Z., Fu, B., Guo, H., Liu, J., & Bengio, Y. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01271-3 · Cited by 1 · see citing works
Perturbed Perceptron's Input to Derive Schrödinger Equation in Artificial Neural Networks
Nieto–Chaupis, H. (2024) · International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) · doi:10.1109/contesa64738.2024.10891273 · Cited by 1 · see citing works
Green AI and AI Act: pioneering legislation or merely an environmental statement?
Silva, S. R., Chinarro, D., & Prieto‐Andrés, A. (2025) · Revista Catalana de Dret Ambiental · doi:10.17345/rcda4122 · Cited by 1 · see citing works
Adverse Environmental and Public Health Effects of Artificial Intelligence: A Narrative ReviewPreprint
Domingo, J. L. (2026) · Qeios · doi:10.32388/x3o2n6.3 · Cited by 1 · see citing works
Networks associative with circular economy research as a model of sustainability
Castro, Y. G., Durán, O. M., & Silva-Aragon, J. A. (2025) · Journal of Infrastructure Policy and Development · doi:10.24294/jipd11509
Gonçalves, Y. L., da Silva, M. L. C. D. P. M., Silva, M. E. B., Mossato, F. D. S., de Oliveira, L. R., & Berardi, R. C. G. (2025) · Anais do Workshop sobre Educação em Computação (WEI) · doi:10.5753/wei.2025.8171
Geopolítica, minerais críticos e energia: a infraestrutura invisível que alimenta a IA
Stacciarini, J. H. S., & Gonçalves, R. J. D. A. F. (2025) · doi:10.64628/ade.naaj7yevf
Raza‐Ullah, T., Trinchini, L., Yousaf, A., & Amin, I. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80262-963-720251007
Oversight of AI’S Dual Impact on Sustainability
Torre, F., Engstam, L. H., & Teigland, R. (2025) · Business guides on the go · doi:10.1007/978-3-032-04087-9_5
Correlating Artificial Intelligence and Sustainable Development in Africa
Ouboumlik, A., Khafif, H., & Touhami, N. O. (2025) · International Conference on Optimization and Applications (ICOA) · doi:10.1109/icoa66896.2025.11236818
Rabiei, M., Jafari, A., & Jahromi, H. B. (2025) · Assessment and Practice in Educational Sciences · doi:10.61838/japes.3.1.10
Wang, Y., Qin, M., Miao, Q., & Wang, X. (2025) · Carbon Management · doi:10.1080/17583004.2025.2595769
Adverse Environmental and Public Health Effects of Artificial Intelligence: A Comprehensive ReviewPreprint
Domingo, J. L. (2026) · Qeios · doi:10.32388/x3o2n6
Adverse Environmental and Public Health Effects of Artificial Intelligence: A Narrative ReviewPreprint
Domingo, J. L. (2026) · Qeios · doi:10.32388/x3o2n6.2
Tahir, F., & Al‐Ghamdi, S. G. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-32880-0.00015-2
The Climate Impact Paradox of Artificial Intelligence: Literature Review and Research Prospects
Chen, Z., Xiong, Q., Hu, Z., Liu, S., & Ma, J. (2026) · Journal of Energy and Climate Change · doi:10.3724/j.issn.2097-4981.jecc-2025-0385
Marwala, T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36322-1.00028-6
Wu, J., Xiao, Y., Bi, H., & Fang, H. (2026) · Green Technologies and Sustainability · doi:10.1016/j.grets.2026.100362
Şentürk, Ö. (2025) · Opportunities and Challenges in Sustainability · doi:10.56578/ocs040405
The Dark Side of Generative AI: A Cross‐Domain Systematic Investigation
Al-Hinai, Y. S., Tarhini, A., Aman, W., & Al-Sharafi, M. (2026) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/2320511
- 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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