Green Research Computing

“Chip Production’s Ecological Footprint: Mapping Climate and Environmental Impact.” Accessed June 19, 2025. https://www.interface-eu.org/publications/chip-productions-ecological-footprint.

Han, Yuelin, Zhifeng Wu, Pengfei Li, Adam Wierman, and Shaolei Ren. “The Unpaid Toll: Quantifying the Public Health Impact of AI.” arXiv:2412.06288. Preprint, arXiv, December 9, 2024. https://doi.org/10.48550/arXiv.2412.06288.

Hess, Julia Christina, and Anna Semenova. Semiconductor Emission Explorer: Tracking Greenhouse Gas Emissions from Chip Production (2015-2023). Interface. 2025. https://www.interface-eu.org/publications/semiconductor-emission-explorer.

Masanet, Eric, Nuoa Lei, and Jonathan Koomey. “To Better Understand AI’s Growing Energy Use, Analysts Need a Data Revolution.” Joule 8, no. 9 (2024): 2427–36. https://doi.org/10.1016/j.joule.2024.07.018.

O’Donnell, James, and Casey Crownhart. “We Did the Math on AI’s Energy Footprint. Here’s the Story You Haven’t Heard.” MIT Technology Review, May 20, 2025. https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/.

Partida, José Pablo Ortiz. “What Are the Environmental Impacts of Artificial Intelligence?” Energy. The Equation, June 25, 2025. https://blog.ucs.org/pablo-ortiz/what-are-the-environmental-impacts-of-artificial-intelligence/.

Vries-Gao, Alex de. “Artificial Intelligence: Supply Chain Constraints and Energy Implications.” Joule, May 22, 2025, 101961. https://doi.org/10.1016/j.joule.2025.101961.

Electric Power Research Institute (EPRI), Inc. Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption. No. 3002028905. EPRI, 2024. https://www.epri.com/research/products/3002028905.

Hitchcock, Ian, and Merritt Cahoon. Hyperscaler Data Center Buildout: A Sustainability  Bane, Boon, or Both? NI R 25-04. Nicholas Institute for Energy, Environment & Sustainability, Duke University, 2025. https://nicholasinstitute.duke.edu/sites/default/files/publications/hyperscaler-data-center-buildout-a-sustainability-bane-boon-or-both.pdf.

Ross, Martin, Jackson Ewing, Brian Murray, Tim Profeta, Robert Stout, and Michael Yoo. Planning for Growing Electricity Demand  During an Era of Uncertain  Renewables and Climate Policy. NI R 24-06. Nicholas Institute for Energy, Environment & Sustainability, Duke University, 2024. https://nicholasinstitute.duke.edu/sites/default/files/publications/planning-growing-electricity-demand-during-era-uncertain-renewables-and-climate-policy.pdf.

Shaver, Lee. “Powering the Future: Why Michigan’s Data Center Debate Is Critical for Clean Energy and Your Wallet.” Energy. The Equation, July 7, 2025. https://blog.ucs.org/lee-shaver/powering-the-future-why-michigans-data-center-debate-is-critical-for-clean-energy-and-your-wallet/.

Shehabi, Arman, Alex Newkirk, Sarah J. Smith, et al. 2024 United States Data Center Energy Usage Report. LBNL-2001637. Lawrence Berkeley National Laboratory, 2024. https://escholarship.org/uc/item/32d6m0d1.

Siddik, Md Abu Bakar, Arman Shehabi, and Landon Marston. “The Environmental Footprint of Data Centers in the United States.” Environmental Research Letters 16, no. 6 (2021): 064017. https://doi.org/10.1088/1748-9326/abfba1.

Volzer, Helena. A Finite Resource: Managing the Growing Water Needs of Data Centers, Critical Minerals Mining,  and Agriculture in the Great Lakes Region. Alliance for the Great Lakes, 2025. https://greatlakes.org/wp-content/uploads/2025/08/AGL_WaterUse_Report_Aug2025_Final.pdf.

Anderson, Thomas, Adam Belay, Mosharaf Chowdhury, Asaf Cidon, and Irene Zhang. “Treehouse: A Case For Carbon-Aware Datacenter Software.” SIGENERGY Energy Inform. Rev. 3, no. 3 (2023): 64–70. https://doi.org/10.1145/3630614.3630626.

Chung, Jae-Won, Yile Gu, Insu Jang, Luoxi Meng, Nikhil Bansal, and Mosharaf Chowdhury. “Reducing Energy Bloat in Large Model Training.” Proceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles, November 4, 2024, 144–59. https://doi.org/10.1145/3694715.3695970.

Chung, Jae-Won, Yile Gu, Insu Jang, Luoxi Meng, Nikhil Bansal, and Mosharaf Chowdhury. “Reducing Energy Bloat in Large Model Training.” Proceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles (New York, NY, USA), SOSP ’24, Association for Computing Machinery, November 15, 2024, 144–59. https://doi.org/10.1145/3694715.3695970.

Chung, Jae-Won, Jiachen Liu, Jeff J. Ma, et al. “The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization.” arXiv:2505.06371. Preprint, arXiv, May 9, 2025. https://doi.org/10.48550/arXiv.2505.06371.

Strubell, Emma, Ananya Ganesh, and Andrew McCallum. “Energy and Policy Considerations for Deep Learning in NLP.” arXiv:1906.02243. Preprint, arXiv, June 5, 2019. https://doi.org/10.48550/arXiv.1906.02243.

U-M Information and Technology Services. “AI and Sustainability.” April 29, 2025. https://its.umich.edu/computing/ai/ai-and-sustainability.

Wan, Zhongwei, Xin Wang, Che Liu, et al. “Efficient Large Language Models: A Survey.” Transactions on Machine Learning Research, January 15, 2024. https://openreview.net/forum?id=bsCCJHbO8A.

You, Jie, Jae-Won Chung, and Mosharaf Chowdhury. “Zeus: Understanding and Optimizing {GPU} Energy Consumption of {DNN} Training.” 2023, 119–39. https://www.usenix.org/conference/nsdi23/presentation/you.