Aussie AI

Green AI Research

Last Updated 27 August, 2025

by David Spuler, Ph.D.

The widespread use of AI makes it a significant contributor to energy consumption. Hence, AI improvements will reduce its carbon footprint and environmental impacts. All of those code optimizations are also making AI greener.

Survey Papers on Green AI

Survey papers include:

Jingjing Xu, Wangchunshu Zhou, Zhiyi Fu, Hao Zhou, Lei Li, A Survey on Green Deep Learning, Nov 2021, https://arxiv.org/abs/2111.05193 (Extensive survey paper.)
Roberto Verdecchia, June Sallou, Luís Cruz, May 2023, A Systematic Review of Green AI, https://arxiv.org/abs/2301.11047 (Useful and broad literature review with systematic examination.)
T Tornede, A Tornede, J Hanselle, F Mohr, 2023, Towards green automated machine learning: Status quo and future directions, Journal of Artificial Intelligence Research (JAIR), Vol. 77 (2023), https://www.jair.org/index.php/jair/article/view/14340, PDF: https://www.jair.org/index.php/jair/article/download/14340/26937
Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini, 26 Jun 2024 (v2), The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources, https://arxiv.org/abs/2406.16746
Aditi Singh, Nirmal Prakashbhai Patel, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei, 6 Dec 2024, A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges, https://arxiv.org/abs/2412.04782

Research Papers on Green AI

Environmental issues for AI are an active area of research:

Tim Yarally, Luís Cruz, Daniel Feitosa, June Sallou, Arie van Deursen, March 2023, Uncovering energy-efficient practices in deep learning training: Preliminary steps towards green AI. In: International Conference on AI Engineering, Software Engineering for AI, https://arxiv.org/abs/2303.13972
Friederike Rohde, Josephin Wagner, Andreas Meyer, Philipp Reinhard, Marcus Voss, Ulrich Petschow, June 2023, Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems, https://arxiv.org/abs/2306.13686
BCG, Dec 2020, Top AI Experts Create CodeCarbon, a Tool to Track and Reduce Computing's CO2 Emissions, https://www.bcg.com/press/1december2020-top-ai-experts-create-codecarbon
Harrisen Scells, Shengyao Zhuang, and Guido Zuccon. 2022. Reduce, Reuse, Recycle: Green Information Retrieval Research. In SIGIR ’22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 2825–2837. https://doi.org/10.1145/3477495.3531766, PDF: https://ielab.io/publications/pdfs/scells2022greenir.pdf
MZ Naser, 2023, Do We Need Exotic Models? Engineering Metrics to Enable Green Machine Learning from Tackling Accuracy-Energy Trade-offs, Journal of Cleaner Production, https://www.sciencedirect.com/science/article/abs/pii/S0959652622049083
SL Jurj, F Opritoiu, M Vladutiu, 2020, Environmentally-friendly metrics for evaluating the performance of deep learning models and systems, ICONIP 2020: Neural Information Processing, November 23–27, 2020, pp 232–244, https://link.springer.com/chapter/10.1007/978-3-030-63836-8_20
CCJ Kuo, AM Madni, 2022, Green learning: Introduction, examples and outlook, Journal of Visual Communication and Image Representation Volume 90, February 2023, 103685, https://www.sciencedirect.com/science/article/pii/S104732032200205X
Arun Chandrasekaran, May 2024, 3 Bold and Actionable Predictions for the Future of GenAI, Gartner, https://www.gartner.com/en/articles/3-bold-and-actionable-predictions-for-the-future-of-genai
Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Pablo Villalobos, 9 Mar 2022 (v2), Compute Trends Across Three Eras of Machine Learning, https://arxiv.org/abs/2202.05924
M.Tech. Suhas Shivapakash, 2024, Energy Efficient Hardware Architectures for Memory Prohibitive Deep Neural Networks, Ph.D. Thesis, der Technischen Universität Berlin, ORCID: 0000-0002-9173-213X, PDF: https://api-depositonce.tu-berlin.de/server/api/core/bitstreams/865e682e-3d62-4d71-ae48-c79be9793962/content
Jovan Stojkovic, Esha Choukse, Chaojie Zhang, Inigo Goiri, Josep Torrellas, 29 Mar 2024, Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference, https://arxiv.org/abs/2403.20306
You Zhou, Xiujing Lin, Xiang Zhang, Maolin Wang, Gangwei Jiang, Huakang Lu, Yupeng Wu, Kai Zhang, Zhe Yang, Kehang Wang, Yongduo Sui, Fengwei Jia, Zuoli Tang, Yao Zhao, Hongxuan Zhang, Tiannuo Yang, Weibo Chen, Yunong Mao, Yi Li, De Bao, Yu Li, Hongrui Liao, Ting Liu, Jingwen Liu, Jinchi Guo, Xiangyu Zhao, Ying WEI, Hong Qian, Qi Liu, Xiang Wang, Wai Kin (Victor)Chan, Chenliang Li, Yusen Li, Shiyu Yang, Jining Yan, Chao Mou, Shuai Han, Wuxia Jin, Guannan Zhang, Xiaodong Zeng, Nov 2023, On the Opportunities of Green Computing: A Survey, https://arxiv.org/abs/2311.00447 (Extensive survey of environmental and green AI issues, along with a survey of various optimization methods to reduce AI resource requirements in training and inference.)
Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, Carl Yang, Yue Cheng, Liang Zhao, 4 Jan 2024, Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models https://arxiv.org/abs/2401.00625 (A general survey paper with coverage of many techniques including this one.)
MAURICIO FADEL ARGERICH, MARTA PATIÑO-MARTÍNEZ, 2024, Measuring and Improving the Energy Efficiency of Large Language Models Inference, IEEE Access, https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10549890
Cell, 2023, The growing energy footprint of artificial intelligence, Cell, Volume 7, Issue 10, Pages 2191-2194, October 18, 2023, https://www.cell.com/joule/abstract/S2542-4351(23)00365-3
James Vincent, Feb 17, 2024, How much electricity does AI consume? https://www.theverge.com/24066646/ai-electricity-energy-watts-generative-consumption
Alex de Vries, Oct 13, 2023 , The AI Boom Could Use a Shocking Amount of Electricity, Scientific American, https://www.scientificamerican.com/article/the-ai-boom-could-use-a-shocking-amount-of-electricity/
Enrico Barbierato, Alice Gatti, 20 February 2024, Toward Green AI: A Methodological Survey of the Scientific Literature, PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10418137
Minghao Yan, Hongyi Wang, Shivaram Venkataraman, 9 Jan 2024 (v2), PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices, https://arxiv.org/abs/2310.19991 (Faster inference with a focus on pipelining and scheduling of hardware acceleration.)
Yuyi Mao, Xianghao Yu, Kaibin Huang, Ying-Jun Angela Zhang, Jun Zhang, Dec 2023, Green Edge AI: A Contemporary Survey, https://arxiv.org/abs/2312.00333
Q Liang, WA Hanafy, N Bashir, D Irwin, P Shenoy, 2023, Energy Time Fairness: Balancing Fair Allocation of Energy and Time for GPU Workloads, https://lass.cs.umass.edu/papers/pdf/sec2023-energytimefairness.pdf
Grant Wilkins, 3 June 202, Online Workload Allocation and Energy Optimization in Large Language Model Inference Systems, Master of Philosophy in Advanced Computer Science, Churchill College, University of Cambridge, https://grantwilkins.github.io/gfw27_project.pdf
D Wright, C Igel, G Samuel, R Selvan, 2023 Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI, https://arxiv.org/pdf/2309.02065.pdf
Lukas Arno Jakob Cavigelli, Qiuting Huang, et al., Jul 26, 2019, Towards Energy-Efficient Convolutional Neural Network Inference, https://www.amazon.com/Towards-Energy-Efficient-Convolutional-Network-Inference/dp/3866286511/
Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, Xuanzhe Liu, 16 Jan 2024, A Survey of Resource-efficient LLM and Multimodal Foundation Models, https://arxiv.org/abs/2401.08092 Project: https://github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey
Emily Cerf, June 20, 2024, Researchers run high-performing large language model on the energy needed to power a lightbulb, https://news.ucsc.edu/2024/06/matmul-free-llm.html
Benj Edwards, 26 June, 2024, Researchers upend AI status quo by eliminating matrix multiplication in LLMs, https://arstechnica.com/information-technology/2024/06/researchers-upend-ai-status-quo-by-eliminating-matrix-multiplication-in-llms/
Ignacio de Gregorio Noblejas, June 22, 2024, AI's Elephant in the Room: Energy Constraints, https://thetechoasis.beehiiv.com/p/ais-elephant-room-energy-constraints
Yehia Ibrahim Alzoubi, Alok Mishra, 25 August 2024, Green artificial intelligence initiatives: Potentials and challenges, Journal of Cleaner Production Volume 468, 143090, https://www.sciencedirect.com/science/article/pii/S0959652624025393
Grant Wilkins, Srinivasan Keshav, Richard Mortier, 4 Jul 2024, Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems, https://arxiv.org/abs/2407.04014
18 Apr 2024 (v2), The Efficiency Spectrum of Large Language Models: An Algorithmic Survey, Tianyu Ding, Tianyi Chen, Haidong Zhu, Jiachen Jiang, Yiqi Zhong, Jinxin Zhou, Guangzhi Wang, Zhihui Zhu, Ilya Zharkov, Luming Liang, https://arxiv.org/abs/2312.00678
Guanqiao Qu, Qiyuan Chen, Wei Wei, Zheng Lin, Xianhao Chen, Kaibin Huang, July 2024, Mobile Edge Intelligence for Large Language Models: A Contemporary Survey, https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.172115025.57884352
Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri, Josep Torrellas, Esha Choukse, 1 Aug 2024, DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency, https://arxiv.org/abs/2408.00741
Paula Rooney, 19 Aug 2024, AI to go nuclear? Data center deals say it’s inevitable, CIO, https://www.cio.com/article/3487339/ai-to-go-nuclear-data-center-deals-say-its-inevitable.html
Melody Petersen, Aug. 26, 2024, How much more water and power does AI computing demand? Tech firms don’t want you to know, https://www.latimes.com/environment/story/2024-08-26/tech-firms-conceal-water-and-power-demands-of-ai-computing
Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren, 29 Oct 2023 (v3), Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models, https://arxiv.org/abs/2304.03271 https://github.com/Ren-Research/Making-AI-Less-Thirsty
Alex de Vries, 2023, The growing energy footprint of artificial intelligence, Joule, Volume 7, Issue 10, Pages 2191-2194, ISSN 2542-4351, https://doi.org/10.1016/j.joule.2023.09.004 https://www.sciencedirect.com/science/article/abs/pii/S2542435123003653
Lora Kolodny, Aug 28 2024 Elon Musk’s xAI accused of worsening Memphis smog with unauthorized gas turbines at data center, https://www.cnbc.com/2024/08/28/musk-xai-accused-of-worsening-memphis-smog-with-unauthorized-turbines.html
Dustin Wright, Christian Igel, Gabrielle Samuel, Raghavendra Selvan, 5 Sep 2023, Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI, https://arxiv.org/abs/2309.02065
Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini, 26 Jun 2024 (v2), The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources, https://arxiv.org/abs/2406.16746
Matt Egan, September 12, 2024, Mr ChatGPT and other AI power players are going to the White House to discuss AI’s massive thirst for energy, https://edition.cnn.com/2024/09/12/tech/ai-chatgpt-white-house-power-energy/index.html
Yuzhuo Li, Mariam Mughees, Yize Chen, Yunwei Ryan Li, 9 Sep 2024, The Unseen AI Disruptions for Power Grids: LLM-Induced Transients, https://arxiv.org/abs/2409.11416
Pranshu Verma and Shelly Tan, September 18, 2024. A bottle of water per email: the hidden environmental costs of using AI chatbots. AI bots generate a lot of heat, and keeping their computer servers running exacts a toll, https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-data-centers/
Douglas C. Youvan, September 27, 2024, Building and Running Large-Scale Language Models: The Infrastructure and Techniques Behind GPT-4 , https://www.researchgate.net/profile/Douglas-Youvan/publication/384398902_Building_and_Running_Large-Scale_Language_Models_The_Infrastructure_and_Techniques_Behind_GPT-4/links/66f6f4d3906bca2ac3d20e68/Building-and-Running-Large-Scale-Language-Models-The-Infrastructure-and-Techniques-Behind-GPT-4.pdf
Kai Ebert, Nicolas Alder, Ralf Herbrich, Philipp Hacker, 9 Oct 2024, AI, Climate, and Regulation: From Data Centers to the AI Act, https://arxiv.org/abs/2410.06681
Yue Zheng, Yuhao Chen, Bin Qian, Xiufang Shi, Yuanchao Shu, Jiming Chen, 29 Sep 2024, A Review on Edge Large Language Models: Design, Execution, and Applications, https://arxiv.org/abs/2410.11845
Mikael Wiberg, Robin Teigland, Oct 2024, Computing for the 22nd Century, More-than-human to see the environmental footprints of profound technologies, HTTF ’24, October 21–23, 2024, Santa Cruz, CA, USA, https://dl.acm.org/doi/pdf/10.1145/3686169.3686177
D.Breen, Oct 2024, Towards Sustainable CNNs: Tensor Decompositions for Green AI Solutions: Exploring Energy Consumption of Large CNNs, Master's Thesis, Systems and Control & Robotics, Delft University of Technology, https://repository.tudelft.nl/file/File_8208301f-51ef-4edf-bd12-d6ec3d5a8711
Baolin Li, April 2024, Making Machine Learning on HPC Systems Cost-Effective and Carbon-Friendly, Ph.D. Thesis, The Department of Electrical and Computer Engineering, Computer Engineering, Northeastern University, Boston, Massachusetts, https://repository.library.northeastern.edu/files/neu:4f248m902/fulltext.pdf
Ren, S., Tomlinson, B., Black, R.W. et al. Reconciling the contrasting narratives on the environmental impact of large language models. Sci Rep 14, 26310 (2024). https://doi.org/10.1038/s41598-024-76682-6 https://www.nature.com/articles/s41598-024-76682-6 https://www.nature.com/articles/s41598-024-76682-6.pdf
I Haider, TT Bhatti, 2024. The Environmental Cost of Artificial Intelligence, Focus, https://irs.org.pk/Focus/FOct24.pdf
Lynn Greiner, Dec 02, 2024, Data centers go nuclear for power-hungry AI workloads, https://www.networkworld.com/article/3613868/data-centers-go-nuclear-for-power-hungry-ai-workloads.html
Meta, December 3, 2024, Accelerating the Next Wave of Nuclear to Power AI Innovation, https://sustainability.atmeta.com/blog/2024/12/03/accelerating-the-next-wave-of-nuclear-to-power-ai-innovation/
Aditi Singh, Nirmal Prakashbhai Patel, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei, 6 Dec 2024, A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges, https://arxiv.org/abs/2412.04782
Yuelin Han, Zhifeng Wu, Pengfei Li, Adam Wierman, Shaolei Ren, 9 Dec 2024, The Unpaid Toll: Quantifying the Public Health Impact of AI, https://arxiv.org/abs/2412.06288
Jiacheng Liu, Peng Tang, Wenfeng Wang, Yuhang Ren, Xiaofeng Hou, Pheng-Ann Heng, Minyi Guo, Chao Li, 18 Dec 2024, A Survey on Inference Optimization Techniques for Mixture of Experts Models, https://arxiv.org/abs/2412.14219 (Broad survey of MoE inference optimization from hardware to model compression to expert parallelism.)
Anthony Ha, December 29, 2024, AI data centers could be ‘distorting’ the US power grid, https://techcrunch.com/2024/12/29/ai-data-centers-could-be-distorting-the-us-power-grid/
DOE, December 20, 2024, DOE Releases New Report Evaluating Increase in Electricity Demand from Data Centers: Domestic Energy Usage from Data Centers Expected to Double or Triple by 2028, DOE Continues to Accelerate Development and Deployment of Solutions to Meet Growing Demand, https://www.energy.gov/articles/doe-releases-new-report-evaluating-increase-electricity-demand-data-centers (Data centers used 4.4% of US electricity in 2023, with projected doubling or tripling to 6.7-12% by 2028.)
Tianyao Shi, Yanran Wu, Sihang Liu, Yi Ding, 29 Dec 2024, GreenLLM: Disaggregating Large Language Model Serving on Heterogeneous GPUs for Lower Carbon Emissions, https://arxiv.org/abs/2412.20322
Alexandra Sasha Luccioni, Emma Strubell, Kate Crawford, 27 Jan 2025, From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate, https://arxiv.org/abs/2501.16548 (Efficient AI should be greener AI initially, but then can lead to increased usage.)
Tella Rajashekhar Reddy, Palak, Rohan Gandhi, Anjaly Parayil, Chaojie Zhang, Mike Shepperd, Liangcheng Yu, Jayashree Mohan, Srinivasan Iyengar, Shivkumar Kalyanaraman, Debopam Bhattacherjee, 15 May 2025, AI Greenferencing: Routing AI Inferencing to Green Modular Data Centers with Heron, https://arxiv.org/abs/2505.09989
Alokesh Manna and Sujit K. Ghosh, 12 Aug 2025, Bayesian Models for Joint Selection of Features and Auto-Regressive Lags: Theory and Applications in Environmental and Financial Forecasting, https://arxiv.org/abs/2508.10055
Mattia Sabella and Monica Vitali, 23 Jul 2025, Eco-Friendly AI: Unleashing Data Power for Green Federated Learning, https://arxiv.org/abs/2507.17241
Obumneme Zimuzor Nwafor and Mohammed Abdul Majeed Al Hooti, 23 Jul 2025, Artificial Intelligence for Green Hydrogen Yield Prediction and Site Suitability using SHAP-Based Composite Index: Focus on Oman, https://arxiv.org/abs/2507.14219
Giordano d'Aloisio, Tosin Fadahunsi, Jay Choy, Rebecca Moussa, Federica Sarro, 21 Jul 2025, SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models, https://arxiv.org/abs/2507.15663
Jianghang Gu, Ling Wen, Yuntian Chen, Shiyi Chen, 20 Jul 2025, An explainable operator approximation framework under the guideline of Green's function, https://arxiv.org/abs/2412.16644
Bernie Boscoe, Shawn Johnson, Andrea Osbon, Chandler Campbell, Karen Mager, 18 Jul 2025, GreenCrossingAI: A Camera Trap/Computer Vision Pipeline for Environmental Science Research Groups, https://arxiv.org/abs/2507.09410
Harshwardhan Praveen, Jacob Brown, Christopher Earls, 10 Aug 2025, chebgreen: Learning and Interpolating Continuous Empirical Green's Functions from Data, https://arxiv.org/abs/2501.18715
Obumneme Nwafor and Mohammed Abdul Majeed Al Hooti, 21 Jul 2025, Machine Learning Risk Intelligence for Green Hydrogen Investment: Insights for Duqm R3 Auction, https://arxiv.org/abs/2507.19529
Abderaouf Bahi and Amel Ourici, 24 Jul 2025, Deep Reinforcement Learning for Real-Time Green Energy Integration in Data Centers, https://arxiv.org/abs/2507.21153
Bin Deng, Jiatong Bai, Feilong Zhao, Zuming Xie, Maolin Li, Yan Wang, Feng Shu, 15 Jul 2025, DNN-based Methods of Jointly Sensing Number and Directions of Targets via a Green Massive H2AD MIMO Receiver, https://arxiv.org/abs/2507.22906
Enrique Valero-Leal, Pedro Larra\~naga and Concha Bielza, 4 Aug 2025, Actionable Counterfactual Explanations Using Bayesian Networks and Path Planning with Applications to Environmental Quality Improvement, https://arxiv.org/abs/2508.02634
Linda Smail (College of Interdisciplinary Studies, Zayed University, UAE), David Santandreu Calonge (Department of Academic Development, Mohamed bin Zayed University of Artificial Intelligence, UAE), Firuz Kamalov (School of Engineering, Applied Science and Technology, Canadian University Dubai, UAE), Nur H. Orak (Department of Environmental Engineering, Marmara University, T\"urkiye), 5 Aug 2025, Can Large Language Models Bridge the Gap in Environmental Knowledge?, https://arxiv.org/abs/2508.03149
Gabriel Bibbo, Arshdeep Singh, Mark D. Plumbley, 5 Aug 2025, Environmental Sound Classification on An Embedded Hardware Platform, https://arxiv.org/abs/2306.09106
Raphael C. Kim, Falco J. Bargagli-Stoffi, Kevin L. Chen, Rachel C. Nethery, 17 Aug 2025, Towards Optimal Environmental Policies: Policy Learning under Arbitrary Bipartite Network Interference, https://arxiv.org/abs/2410.08362
Jonathan Ethier, Mathieu Chateauvert, Ryan G. Dempsey, and Alexis Bose, 19 Aug 2025, Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction, https://arxiv.org/abs/2501.08306
Yuzhuo Li, Di Zhao, Tingrui Qiao, Yihao Wu, Bo Pang, Yun Sing Koh, 20 Aug 2025, MetaWild: A Multimodal Dataset for Animal Re-Identification with Environmental Metadata, https://arxiv.org/abs/2501.13368
Cooper Elsworth, Keguo Huang, David Patterson, Ian Schneider, Robert Sedivy, Savannah Goodman, Ben Townsend, Parthasarathy Ranganathan, Jeff Dean, Amin Vahdat, Ben Gomes, James Manyika, 21 Aug 2025, Measuring the environmental impact of delivering AI at Google Scale, https://arxiv.org/abs/2508.15734
Ousmane Youme, Jean Marie Demb\'el\'e, Eugene C. Ezin, Christophe Cambier, 21 Aug 2025, Panoptic Segmentation of Environmental UAV Images : Litter Beach, https://arxiv.org/abs/2508.15985

Energy Efficient Research

Energy efficiency is an important part of green AI, perhaps the most important (there's also water). A lot of AI optimization research is also relevant to reducing energy usage. Here are some paper specifically on energy:

Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri, Josep Torrellas, Esha Choukse, 1 Aug 2024, DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency, https://arxiv.org/abs/2408.00741
Andreas Kosmas Kakolyris, Dimosthenis Masouros, Petros Vavaroutsos, Sotirios Xydis, Dimitrios Soudris, 5 Aug 2024, SLO-aware GPU Frequency Scaling for Energy Efficient LLM Inference Serving, https://arxiv.org/abs/2408.05235
Lukas Arno Jakob Cavigelli, Qiuting Huang, et al., Jul 26, 2019, Towards Energy-Efficient Convolutional Neural Network Inference, https://www.amazon.com/Towards-Energy-Efficient-Convolutional-Network-Inference/dp/3866286511/
Tim Yarally, Luís Cruz, Daniel Feitosa, June Sallou, Arie van Deursen, March 2023, Uncovering energy-efficient practices in deep learning training: Preliminary steps towards green AI. In: International Conference on AI Engineering, Software Engineering for AI, https://arxiv.org/abs/2303.13972
Sheila Chiang, Aug 26 2024, This Nvidia partner says it can cut data center energy use by 50% as AI boom strains power grid, https://www.cnbc.com/2024/08/27/nvidia-partner-sustainable-metal-cloud-ai-data-center-energy-consumption.html
CNBC, Jun 12 2024, Data center liquid cooling is accelerating and it’s accelerating now, says Vertiv CEO, https://www.cnbc.com/video/2024/06/11/data-center-liquid-cooling-is-accelerating-and-its-accelerating-now-says-vertiv-ceo.html
Dustin Wright, Christian Igel, Gabrielle Samuel, Raghavendra Selvan, 5 Sep 2023, Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI, https://arxiv.org/abs/2309.02065
Yuzhuo Li, Mariam Mughees, Yize Chen, Yunwei Ryan Li, 9 Sep 2024, The Unseen AI Disruptions for Power Grids: LLM-Induced Transients, https://arxiv.org/abs/2409.11416
Theo Gregersen, Pratyush Patel, Esha Choukse, 26 Sep 2024, Input-Dependent Power Usage in GPUs, https://arxiv.org/abs/2409.18324
Michelle Horton, Oct 16, 2024, Maximizing Energy and Power Efficiency in Applications with NVIDIA GPUs, https://developer.nvidia.com/blog/maximizing-energy-and-power-efficiency-in-applications-with-nvidia-gpus/
Minghao Yan, Hongyi Wang, Shivaram Venkataraman, 9 Jan 2024 (v2), PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices, https://arxiv.org/abs/2310.19991
D.Breen, Oct 2024, Towards Sustainable CNNs: Tensor Decompositions for Green AI Solutions: Exploring Energy Consumption of Large CNNs, Master's Thesis, Systems and Control & Robotics, Delft University of Technology, https://repository.tudelft.nl/file/File_8208301f-51ef-4edf-bd12-d6ec3d5a8711
Baolin Li, April 2024, Making Machine Learning on HPC Systems Cost-Effective and Carbon-Friendly, Ph.D. Thesis, The Department of Electrical and Computer Engineering, Computer Engineering, Northeastern University, Boston, Massachusetts, https://repository.library.northeastern.edu/files/neu:4f248m902/fulltext.pdf
Grant Wilkins, Srinivasan Keshav, Richard Mortier, 4 Jul 2024, Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems, https://arxiv.org/abs/2407.04014
Robert Corwin Nov 2024, Running Large Language Models Privately: A comparison of frameworks, models, and costs, https://towardsdatascience.com/running-large-language-models-privately-a-comparison-of-frameworks-models-and-costs-ac33cfe3a462
Rei Barjami, Antonio Miele, and Luca Mottola. 2024. Intermittent Inference: Trading a 1% Accuracy Loss for a 1.9x Throughput Speedup. In Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems (SenSys '24). Association for Computing Machinery, New York, NY, USA, 647–660. https://doi.org/10.1145/3666025.3699364 https://dl.acm.org/doi/abs/10.1145/3666025.3699364 https://dl.acm.org/doi/pdf/10.1145/3666025.3699364
R. Geens, M. Shi, A. Symons, C. Fang and M. Verhelst, "Energy Cost Modelling for Optimizing Large Language Model Inference on Hardware Accelerators," 2024 IEEE 37th International System-on-Chip Conference (SOCC), Dresden, Germany, 2024, pp. 1-6, doi: 10.1109/SOCC62300.2024.10737844. https://ieeexplore.ieee.org/abstract/document/10737844/
Jiacheng Liu, Peng Tang, Wenfeng Wang, Yuhang Ren, Xiaofeng Hou, Pheng-Ann Heng, Minyi Guo, Chao Li, 18 Dec 2024, A Survey on Inference Optimization Techniques for Mixture of Experts Models, https://arxiv.org/abs/2412.14219 (Broad survey of MoE inference optimization from hardware to model compression to expert parallelism.)
Francisco Durán, Matias Martinez, Patricia Lago, Silverio Martínez-Fernández, 19 Dec 2024, Energy consumption of code small language models serving with runtime engines and execution providers, https://arxiv.org/abs/2412.15441
Anthony Ha, December 29, 2024, AI data centers could be ‘distorting’ the US power grid, https://techcrunch.com/2024/12/29/ai-data-centers-could-be-distorting-the-us-power-grid/
DOE, December 20, 2024, DOE Releases New Report Evaluating Increase in Electricity Demand from Data Centers: Domestic Energy Usage from Data Centers Expected to Double or Triple by 2028, DOE Continues to Accelerate Development and Deployment of Solutions to Meet Growing Demand, https://www.energy.gov/articles/doe-releases-new-report-evaluating-increase-electricity-demand-data-centers (Data centers used 4.4% of US electricity in 2023, with projected doubling or tripling to 6.7-12% by 2028.)
Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri, Esha Choukse, Haoran Qiu, Rodrigo Fonseca, Josep Torrellas, Ricardo Bianchini, 5 Jan 2025, TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms, https://arxiv.org/abs/2501.02600
Z. Chen et al., "An Empirical Study on the Power Consumption of LLMs with Different GPU Platforms," 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 8640-8642, doi: 10.1109/BigData62323.2024.10825662. https://ieeexplore.ieee.org/abstract/document/10825662
Shashikant Ilager, Lukas Florian Briem, Ivona Brandic, 19 Jan 2025, GREEN-CODE: Optimizing Energy Efficiency in Large Language Models for Code Generation, https://arxiv.org/abs/2501.11006 (Energy efficiency via early exit.)
Tom Wallace, Naser Ezzati-Jivan, Beatrice Ombuki-Berman, 16 Jan 2025, Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models, https://arxiv.org/abs/2502.00046
Yueying (Lisa) Li, Zhanqiu Hu, Esha Choukse, Rodrigo Fonseca, G. Edward Suh, Udit Gupta, 7 Feb 2025, EcoServe: Designing Carbon-Aware AI Inference Systems, https://arxiv.org/abs/2502.05043
Shaibal Saha, Lanyu Xu, 26 Feb 2025, Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies, https://arxiv.org/abs/2503.02891
24 Apr 2025 (v2), TeLLMe: An Energy-Efficient Ternary LLM Accelerator for Prefilling and Decoding on Edge FPGAs, https://arxiv.org/abs/2504.16266
Yunho Jin, Gu-Yeon Wei, David Brooks, 20 May 2025, The Energy Cost of Reasoning: Analyzing Energy Usage in LLMs with Test-time Compute, https://arxiv.org/abs/2505.14733

Water Usage of AI

Research papers on water consumption of AI include:

Melody Petersen, Aug. 26, 2024, How much more water and power does AI computing demand? Tech firms don’t want you to know, https://www.latimes.com/environment/story/2024-08-26/tech-firms-conceal-water-and-power-demands-of-ai-computing
Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren, 29 Oct 2023 (v3), Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models, https://arxiv.org/abs/2304.03271 https://github.com/Ren-Research/Making-AI-Less-Thirsty
A. Shaji George, A. S. Hovan George, A. S. Gabrio Martin, 2023, The Environmental Impact of AI: A Case Study of Water Consumption by Chat GPT, https://doi.org/10.5281/zenodo.7855594 https://puiij.com/index.php/research/article/view/39 https://puiij.com/index.php/research/article/download/39/23
Dustin Wright, Christian Igel, Gabrielle Samuel, Raghavendra Selvan, 5 Sep 2023, Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI, https://arxiv.org/abs/2309.02065
Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini, 26 Jun 2024 (v2), The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources, https://arxiv.org/abs/2406.16746
Pranshu Verma and Shelly Tan, September 18, 2024. A bottle of water per email: the hidden environmental costs of using AI chatbots. AI bots generate a lot of heat, and keeping their computer servers running exacts a toll, https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-data-centers/
I Haider, TT Bhatti, 2024. The Environmental Cost of Artificial Intelligence, Focus, https://irs.org.pk/Focus/FOct24.pdf
Noah Shumba, Opelo Tshekiso, Pengfei Li, Giulia Fanti, Shaolei Ren, 4 Dec 2024, A Water Efficiency Dataset for African Data Centers, https://arxiv.org/abs/2412.03716 https://huggingface.co/datasets/masterlion/WaterEfficientDatasetForAfricanCountries/tree/main
Andy Patrizio, Jan 02, 2025, Microsoft raises bar with water-saving data center design, https://www.networkworld.com/article/3631084/microsoft-raises-bar-with-water-saving-data-center-design.html