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the claim

Artificial intelligence computing harms the environment

the verdict
SUPPORTED
the evidence backs this
Recorded sources
5 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that artificial intelligence computing harms the environment?

Yes. Multiple studies demonstrate that artificial intelligence computing, spanning model training, inference, and hardware deployment, generates substantial carbon footprints, high energy consumption, and electronic waste that harm environmental sustainability.

Verdict: SUPPORTED · 5 sources for, 0 against · Last checked

The analysis

The retrieved literature consistently discusses the environmental burdens of AI computing, focusing on greenhouse gas emissions, high energy consumption, carbon footprints, and sustainability challenges during model training and inference. Papers 1, 2, 4, 8, and 11 explicitly document these environmental harms, while the remaining papers either touch on unrelated topics or explore AI used to optimize other sectors. Consequently, the claim is supported by the evidence.

Evidence for · 5
The carbon footprint of artificial intelligence in materials science
2023
The study evaluates greenhouse gas emissions from deep learning in materials science, demonstrating a massive increase in carbon footprint over time.
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Korolev, V. & Mitrofanov, A. (2023). The carbon footprint of artificial intelligence in materials science. doi.org. https://doi.org/10.26434/chemrxiv-2023-zctn1-v3

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More for · 4
Carbon Footprint of Artificial Intelligence in Materials Science: Should We Be Concerned?
2023
This parallel study similarly highlights the environmental impacts and growing carbon footprint of deep learning training and inference in materials science.
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Korolev, V. & Mitrofanov, A. (2023). Carbon Footprint of Artificial Intelligence in Materials Science: Should We Be Concerned?. doi.org. https://doi.org/10.26434/chemrxiv-2023-zctn1

Green artificial intelligence in health applications.
2026
This review highlights high energy consumption, electronic waste, and carbon emissions associated with deploying and training healthcare AI models.
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YI, A. & A, M. (2026). Green artificial intelligence in health applications.. doi.org. https://doi.org/10.1016/j.artmed.2026.103442

From precision to planetary health: the carbon cost of AI in multimodality cardiovascular imaging.
2026
The article outlines the environmental costs and disproportionate computational demand of intensive AI usage in cardiovascular imaging.
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T, P., A, U., J, H., S, S., A, K., C, G., T, G., & S, T. (2026). From precision to planetary health: the carbon cost of AI in multimodality cardiovascular imaging.. doi.org. https://doi.org/10.1093/ehjimp/qyag078

Comparing energy consumption and accuracy in text classification inference.
2026
The study evaluates energy consumption during model inference, noting that large language models consume significant energy and highlighting sustainability concerns in natural language processing.
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J, Z. & T, H. (2026). Comparing energy consumption and accuracy in text classification inference.. doi.org. https://doi.org/10.1038/s41598-026-45023-0

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Receipt History
first checked06 Aug 2026
judged → SUPPORTED · 7706 Aug 2026
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