{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T19:48:27Z","timestamp":1782762507640,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":77,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"China Scholarship Council","award":["202508060181"],"award-info":[{"award-number":["202508060181"]}]},{"name":"Brazilian National Council for Scientific and Technological Development CNPq","award":["311425\/2023-2"],"award-info":[{"award-number":["311425\/2023-2"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,25]]},"DOI":"10.1145\/3805689.3812368","type":"proceedings-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:20:39Z","timestamp":1782231639000},"page":"5194-5218","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["EcoChat Probe: How Energy Feedback Shapes End Users' Interpretations of Everyday Generative AI Use"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7228-3996","authenticated-orcid":false,"given":"Yuheng","family":"Ren","sequence":"first","affiliation":[{"name":"King's College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5506-9473","authenticated-orcid":false,"given":"Luiz Augusto","family":"Morais","sequence":"additional","affiliation":[{"name":"Universidade Federal de Pernambuco, Recife, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8364-0887","authenticated-orcid":false,"given":"Filipe","family":"Calegario","sequence":"additional","affiliation":[{"name":"Universidade Federal de Pernambuco, Recife, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3065-2987","authenticated-orcid":false,"given":"Fr\u00e9d\u00e9rique","family":"Krupa","sequence":"additional","affiliation":[{"name":"L'Ecole de Design Nantes Atlantique, Nantes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9368-2298","authenticated-orcid":false,"given":"Francisco Paulo Magalh\u00e3es","family":"Sim\u00f5es","sequence":"additional","affiliation":[{"name":"Universidade Federal de Pernambuco, Recife, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6631-4609","authenticated-orcid":false,"given":"Nivan","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Universidade Federal de Pernambuco, Recife, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4408-6371","authenticated-orcid":false,"given":"Georgia","family":"Panagiotidou","sequence":"additional","affiliation":[{"name":"King's College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mistral AI. 2025. Our contribution to a global environmental standard for AI. https:\/\/mistral.ai\/news\/our-contribution-to-a-global-environmental-standard-for-ai"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/1461444816676645"},{"key":"e_1_3_2_1_3_1","unstructured":"Anthesis. 2023. What Is 1 Ton Of Carbon Emissions Equivalent To? https:\/\/www.anthesisgroup.com\/insights\/what-exactly-is-1-tonne-of-co2\/"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2007.03051"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3748239.3748247"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2508.08672"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713240"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445922"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1177\/1035719X211058251"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2207676.2208539"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","unstructured":"S. A. Budennyy V. D. Lazarev N. N. Zakharenko A. N. Korovin O. A. Plosskaya D. V. Dimitrov V. S. Akhripkin I. V. Pavlov I. V. Oseledets I. S. Barsola I. V. Egorov A. A. Kosterina and L. E. Zhukov. 2023. Eco2AI: Carbon Emissions Tracking of Machine Learning Models as the First Step Towards Sustainable AI. Doklady Mathematics (Jan. 2023). https:\/\/doi.org\/10.1134\/S1064562422060230","DOI":"10.1134\/S1064562422060230"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1177\/10778004221097055"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.erss.2020.101535"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-025-00616-z"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2013.210"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","unstructured":"Benoit Courty Victor Schmidt Sasha Luccioni Goyal-Kamal MarionCoutarel Boris Feld J\u00e9r\u00e9my Lecourt LiamConnell Amine Saboni Inimaz supatomic Mathilde L\u00e9val Luis Blanche Alexis Cruveiller ouminasara Franklin Zhao Aditya Joshi Alexis Bogroff Hugues de Lavoreille Niko Laskaris Edoardo Abati Douglas Blank Ziyao Wang Armin Catovic Marc Alencon Micha\u0142 St\u0119chty Christian Bauer Lucas Ot\u00e1vio N. de Ara\u00fajo JPW and MinervaBooks. 2024. mlco2\/codecarbon: v2.4.1. https:\/\/doi.org\/10.5281\/zenodo.11171501","DOI":"10.5281\/zenodo.11171501"},{"key":"e_1_3_2_1_17_1","volume-title":"The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence","author":"Crawford Kate","unstructured":"Kate Crawford. 2021. The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. http:\/\/www.jstor.org\/stable\/10.2307\/j.ctv1ghv45t"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-024-00478-x"},{"key":"e_1_3_2_1_19_1","volume-title":"Open Science, and Beyond","author":"Cumming Geoff","unstructured":"Geoff Cumming and Robert Calin-Jageman. 2016. Introduction to the New statistics: Estimation, Open Science, and Beyond. Routledge, USA."},{"key":"e_1_3_2_1_20_1","volume-title":"Inference by eye: confidence intervals and how to read pictures of data. American psychologist 60, 2","author":"Cumming Geoff","year":"2005","unstructured":"Geoff Cumming and Sue Finch. 2005. Inference by eye: confidence intervals and how to read pictures of data. American psychologist 60, 2 (2005), 170."},{"key":"e_1_3_2_1_21_1","unstructured":"Julien Delavande. 2024. Chat UI Energy Score. https:\/\/huggingface.co\/spaces\/jdelavande\/chat-ui-energy"},{"key":"e_1_3_2_1_22_1","volume-title":"Bootstrap confidence intervals. Statistical science 11, 3","author":"DiCiccio Thomas J","year":"1996","unstructured":"Thomas J DiCiccio and Bradley Efron. 1996. Bootstrap confidence intervals. Statistical science 11, 3 (1996), 189\u2013228."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-26633-6_13"},{"key":"e_1_3_2_1_24_1","first-page":"62","article-title":"How Bad Are Bananas? The Carbon Footprint of Everything","volume":"27","author":"Dunec JoAnne L","year":"2012","unstructured":"JoAnne L Dunec. 2012. How Bad Are Bananas? The Carbon Footprint of Everything. Natural Resources & Environment 27, 1 (2012), 62\u201363.","journal-title":"Natural Resources & Environment"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.4337\/9781800887305.00010"},{"key":"e_1_3_2_1_26_1","unstructured":"Cooper Elsworth Keguo Huang David Patterson Ian Schneider Robert Sedivy Savannah Goodman Ben Townsend Parthasarathy Ranganathan Jeff Dean Amin Vahdat Ben Gomes and James Manyika. 2025. Measuring the environmental impact of delivering AI at Google Scale. https:\/\/arxiv.org\/abs\/2508.15734"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702556"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","unstructured":"Ahmad Faiz Sotaro Kaneda Ruhan Wang Rita Osi Prateek Sharma Fan Chen and Lei Jiang. 2024. LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models. https:\/\/doi.org\/10.48550\/arXiv.2309.14393","DOI":"10.48550\/arXiv.2309.14393"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","unstructured":"Sophia Falk David Ekchajzer Thibault Pirson Etienne Lees-Perasso Augustin Wattiez Lisa Biber-Freudenberger Sasha Luccioni and Aimee van Wynsberghe. 2025. More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU. https:\/\/doi.org\/10.48550\/arXiv.2509.00093","DOI":"10.48550\/arXiv.2509.00093"},{"key":"e_1_3_2_1_30_1","unstructured":"Boris Gamazaychikov and Sasha Luccioni. 2025. Power Heat and Intelligence - AI Data Centers Explained. In Hugging Face Blog. https:\/\/huggingface.co\/blog\/sasha\/ai-data-centers-explained"},{"key":"e_1_3_2_1_31_1","unstructured":"Google. 2023. The next generation of AI for developers and Google Workspace. https:\/\/blog.google\/technology\/ai\/ai-developers-google-cloud-workspace\/"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3706599.3719708"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713198"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3715275.3732088"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","unstructured":"Yuelin Han Zhifeng Wu Pengfei Li Adam Wierman and Shaolei Ren. 2025. The Unpaid Toll: Quantifying and Addressing the Public Health Impact of Data Centers. https:\/\/doi.org\/10.48550\/arXiv.2412.06288","DOI":"10.48550\/arXiv.2412.06288"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/642611.642616"},{"key":"e_1_3_2_1_37_1","unstructured":"IEA. 2024. Electricity 2024 - Analysis. https:\/\/www.iea.org\/reports\/electricity-2024"},{"key":"e_1_3_2_1_38_1","volume-title":"Global Energy Review","author":"IEA.","year":"2025","unstructured":"IEA. 2025. Global Energy Review 2025. https:\/\/www.iea.org\/reports\/global-energy-review-2025"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","unstructured":"Akshaya Jagannadharao Nicole Beckage Sovan Biswas Hilary Egan Jamil Gafur Thijs Metsch Dawn Nafus Giuseppe Raffa and Charles Tripp. 2024. A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning. https:\/\/doi.org\/10.48550\/arXiv.2412.17830","DOI":"10.48550\/arXiv.2412.17830"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","unstructured":"Nidhal Jegham Marwen Abdelatti Lassad Elmoubarki and Abdeltawab Hendawi. 2025. How Hungry is AI? Benchmarking Energy Water and Carbon Footprint of LLM Inference. https:\/\/doi.org\/10.48550\/arXiv.2505.09598","DOI":"10.48550\/arXiv.2505.09598"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3196709.3196779"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461564.3461581"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1080\/1369118X.2016.1153126"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.230604"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202100707"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/s44206-025-00196-5"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3724499"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3630106.3658542"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","unstructured":"Alexandra Sasha Luccioni Emma Strubell and Kate Crawford. 2025. From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate. 13 pages. https:\/\/doi.org\/10.1145\/3715275.3732007","DOI":"10.1145\/3715275.3732007"},{"key":"e_1_3_2_1_50_1","first-page":"1","article-title":"Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model","volume":"24","author":"Luccioni Alexandra Sasha","year":"2023","unstructured":"Alexandra Sasha Luccioni, Sylvain Viguier, and Anne-Laure Ligozat. 2023. Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. Journal of Machine Learning Research 24, 253 (2023), 1\u201315. http:\/\/jmlr.org\/papers\/v24\/23-0069.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2506.15572"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3580675"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","unstructured":"Jacob Morrison Clara Na Jared Fernandez Tim Dettmers Emma Strubell and Jesse Dodge. 2025. Holistically Evaluating the Environmental Impact of Creating Language Models. https:\/\/doi.org\/10.48550\/arXiv.2503.05804","DOI":"10.48550\/arXiv.2503.05804"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-017-7376-8_13"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1080\/13504622.2012.690855"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533133"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","unstructured":"Chester Palen-Michel Ruixiang Wang Yipeng Zhang David Yu Canran Xu and Zhe Wu. 2024. Investigating LLM Applications in E-Commerce. https:\/\/doi.org\/10.48550\/arXiv.2408.12779","DOI":"10.48550\/arXiv.2408.12779"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3596243"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642449"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581641.3584033"},{"key":"e_1_3_2_1_61_1","unstructured":"Hannah Ritchie. 2025. What's the carbon footprint of using ChatGPT? https:\/\/www.sustainabilitybynumbers.com\/p\/carbon-footprint-chatgpt"},{"key":"e_1_3_2_1_62_1","unstructured":"Hannah Ritchie. 2023. Travel and Climate Change. https:\/\/web.archive.org\/web\/20250722040814\/https:\/\/ourworldindata.org\/travel-carbon-footprint"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/1978822.1978834"},{"key":"e_1_3_2_1_64_1","unstructured":"Adrien Banse Samuel Rinc\u00e9 and Valentin Defour. 2025. EcoLogits Calculator. https:\/\/huggingface.co\/spaces\/genai-impact\/ecologits-calculator."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cliser.2025.100631"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9280.2007.01917.x"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2024.3352282"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411763.3451780"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.71468\/P1WC7Q"},{"key":"e_1_3_2_1_70_1","unstructured":"Shubham Singh. 2025. ChatGPT Statistics (2025) - Daily & Monthly Active Users. https:\/\/www.demandsage.com\/chatgpt-statistics\/"},{"key":"e_1_3_2_1_71_1","unstructured":"SUX. 2025. SUX - The Sustainable UX Network. https:\/\/sustainableuxnetwork.com\/"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3714120"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3623509.3633355"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2022.102853"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1609\/aies.v8i3.36753"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","unstructured":"Carole-Jean Wu Ramya Raghavendra Udit Gupta Bilge Acun Newsha Ardalani Kiwan Maeng Gloria Chang Fiona Aga Behram James Huang Charles Bai Michael Gschwind Anurag Gupta Myle Ott Anastasia Melnikov Salvatore Candido David Brooks Geeta Chauhan Benjamin Lee Hsien-Hsin S. Lee Bugra Akyildiz Maximilian Balandat Joe Spisak Ravi Jain Mike Rabbat and Kim Hazelwood. 2022. Sustainable AI: Environmental Implications Challenges and Opportunities. https:\/\/doi.org\/10.48550\/arXiv.2111.00364","DOI":"10.48550\/arXiv.2111.00364"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2024.113893"}],"event":{"name":"FAccT '26: The 2026 ACM Conference on Fairness, Accountability, and Transparency","location":"Montreal QC Canada","acronym":"FAccT '26","sponsor":["ACM\/SIG"]},"container-title":["Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3805689.3812368","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T19:10:48Z","timestamp":1782760248000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805689.3812368"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,25]]},"references-count":77,"alternative-id":["10.1145\/3805689.3812368","10.1145\/3805689"],"URL":"https:\/\/doi.org\/10.1145\/3805689.3812368","relation":{},"subject":[],"published":{"date-parts":[[2026,6,25]]},"assertion":[{"value":"2026-06-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}