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The carbon impact of artificial intelligence
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“Similarly, in a world prone to climate crisis, the environmental impact of AI (Corbett 2018;Dhar 2020;Lucivero 2020;Strubell et al. 2020; van Wynsberghe 2021) might well constrain its deployment, at least if sustainability becomes a serious parameter in economic activity (Nishant et al. 2020 for a contrary optimistic view).”
From Artificial intelligence and work: a critical review of recent research from the social sciences (2024) · cites this paper as Dhar 2020
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Items 501–510 of 510
Ahmed, S., Ahmad, A., Ahmad, F., & Shakir, H. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-8490-0.ch008
Sustainable Agentic Intelligence: A Taxonomy, Evidence-Based Analysis, and Research AgendaPreprint
Poongodi, K., Fameen, R., & Meenakshi, K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7358627
AI for Mitigating Climate Change: An Account of Uses, Difficulties, Morality, and Prospects
Malik, H. R., Basit, A., Nawaz, N. A. H., & Ishaq, K. (2026) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1929
Kaleidoscopic contradictions: Ecological paradoxes in AI-powered interactive digital narrative
TOKDİL, E. (2026) · Convergence The International Journal of Research into New Media Technologies · doi:10.1177/13548565261492524
CRISP-ML(QS): Embedding Sustainability in AI-ML Development Lifecycle
Garg, S., & Mittal, G. (2026) · International Journal of Mathematical Engineering and Management Sciences · doi:10.33889/ijmems.2026.11.5.083
Artificial Intelligence in Carbon Capture and Storage Technological Advances and Future Challenges
Wang, J. (2026) · Transactions on Environment Energy and Earth Sciences · doi:10.62051/r8kwn586
AI advancements can advance environmental inequities and health harms
Nishigaki, M., & Jochim, A. (2026) · Journal of the National Medical Association · doi:10.1016/j.jnma.2026.09.008
AI Policy, Innovation, and Green Productivity: Quasi-Experimental Evidence From China
Li, W., & Peng, X. (2026) · SAGE Open · doi:10.1177/21582440261497046
Dale, G., & Drummond, M. (2026) · World sustainability series · doi:10.1007/978-3-032-34574-5_22
Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models
Sel, B., Al-Tawaha, A., Khattar, V., Jia, R., & Jin, M. (2023) · arXiv · doi:10.48550/arxiv.2308.10379 · Cited by 15
Metadata
- Authors
- Dhar, P.
- Corresponding author
- Dhar, Payal
- ORCID
- Dhar, Payal
- Venue
- Nature Machine Intelligence
- Published
- 2020-08-12
- Volume
- 2
- Issue
- 8
- Pages
- 423-425
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3048650942
- Cited by
- 509
- References
- 0
- Citations by year
- 2026: 67 · 2025: 114 · 2024: 125 · 2023: 130 · 2022: 45 · 2021: 28
- Type
- other
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence, carbon footprint, climate change, technology professionals
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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