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Research Guides

Green Research Computing

Tips, tools, and techniques for evaluating and minimizing the environmental impacts of computational research.

What is Green Research Computing?

"Green computing" refers to practices aimed at reducing the environmental impacts of information technology. Those impacts include carbon emissions due to the consumption of electricity, the consumption of freshwater for data center cooling, the embodied energy and material resources needed to manufacture computing equipment, and the generation of electronic waste. The goal of green computing is to make information technology more sustainable.

This guide is intended to help U-M researchers who seek to improve the sustainability of their computational research, so it is focused on these concepts as they apply to research computing and adjacent topics. For more information about green computing and IT sustainability writ large, please see the "Further Reading" boxes on most pages of this guide.

Read This First

Lannelongue, Loïc, et al. “Ten Simple Rules to Make Your Computing More Environmentally Sustainable.” PLOS Computational Biology, vol. 17, no. 9, Sept. 2021, p. e1009324. PLoS Journals, https://doi.org/10.1371/journal.pcbi.1009324.

Further Reading

Goyal, Kamal. “AI Computing Emits CO₂. We Started Measuring How Much.” GAMMA — Part of BCG X, 21 Mar. 2021, https://medium.com/bcggamma/ai-computing-emits-co%E2%82%82-we-started-measuring-how-much-807dec8c35e3.
Lacoste, Alexandre, et al. Quantifying the Carbon Emissions of Machine Learning. arXiv:1910.09700, arXiv, 4 Nov. 2019. arXiv.org, https://doi.org/10.48550/arXiv.1910.09700.
Lannelongue, Loïc, Jason Grealey, and Michael Inouye. “Green Algorithms: Quantifying the Carbon Footprint of Computation.” Advanced Science, vol. 8, no. 12, 2021, p. 2100707. Wiley Online Library, https://doi.org/10.1002/advs.202100707.
Lannelongue, Loïc, Hans-Erik G. Aronson, et al. “GREENER Principles for Environmentally Sustainable Computational Science.” Nature Computational Science, vol. 3, no. 6, June 2023, pp. 514–21. www.nature.com, https://doi.org/10.1038/s43588-023-00461-y.
Lannelongue, Loïc, and Michael Inouye. “Carbon Footprint Estimation for Computational Research.” Nature Reviews Methods Primers, vol. 3, no. 1, Feb. 2023, pp. 1–2. www.nature.com, https://doi.org/10.1038/s43586-023-00202-5.
Lottick, Kadan, et al. Energy Usage Reports: Environmental Awareness as Part of Algorithmic Accountability. arXiv:1911.08354, arXiv, 16 Dec. 2019. arXiv.org, https://doi.org/10.48550/arXiv.1911.08354.
Strubell, Emma, et al. Energy and Policy Considerations for Deep Learning in NLP. arXiv:1906.02243, arXiv, 5 June 2019. arXiv.org, https://doi.org/10.48550/arXiv.1906.02243.
Last Updated: Sep 4, 2025 3:39 PM
Subjects: Engineering, Science