Environmental impact of artificial intelligence: a pending debate

Authors

DOI:

https://doi.org/10.3145/infonomy.26.001

Keywords:

Generative artificial intelligence, Environmental impact, Green AI, Sustainability, Energy consumption, Water consumption, CO2e emissions, Communication sciences, Educommunication, Eco-social transition, Global policies

Abstract

The arrival of generative artificial intelligence has sparked important debates within all social spheres, including communication and media education. This research seeks to delve into one of its lesser-known effects: its eco-social impact on the environment. To this end, a documentary review of research seeking to understand this reality has been carried out, analysing more than 40 publications on the subject. The studies focus especially on its energy consumption, with its resulting greenhouse gas emissions, as well as on water consumption. However, other phases of its life cycle, such as the extraction of raw materials or the manufacturing of the equipment that supports artificial intelligence, will not be addressed. We found a lack of studies that take a comprehensive view of the impact of the entire life cycle of artificial intelligence. The results would show a high impact of generative artificial intelligence on the environment, with worse repercussions in the countries of the Global South despite not being its main beneficiaries. Researchers agree on the need to commit to "Green AI" as opposed to the current majority trend of "Red AI", which prioritises performance without taking sustainability into account. This reality should be taken into account when updating media education proposals that seek a more critical and sustainable use of digital tools, as well as when formulating global policy proposals that minimise such impacts.

Author Biographies

Fernando Tucho, Universidad Rey Juan Carlos

José-María García-de-Madariaga, Universidad Rey Juan Carlos

References

Akyürek, Sarah Yasemin; Kieslich, Kimon; Došenović, Pero; Marcinkowski, Frank; Laukötter, Esther (2022). Environmental sustainability of artificial intelligence. How does the public perceive the environmental footprint of artificial intelligence? Factsheet No. 6 of the Artificial Intelligence Opinion Monitor. https://www.cais-research.de/wp-content/uploads/Factsheet-6-KI-Sustainability.pdf

Alnafrah, Ibrahim (2025). The two tales of AI: A global assessment of the environmental impacts of artificial intelligence from a multidimensional policy perspective. Journal of Environmental Management, vol. 392, 126813. https://doi.org/10.1016/j.jenvman.2025.126813

Alonso, Amparo (2024). Inteligencia artificial verde y responsable. Un nuevo paradigma para un futuro más sostenible. Celebrada el 6 de marzo en la Real Academia de Ciencias Exactas, Físicas y Naturales. https://www.youtube.com/watch?v=q6NuWbyY2RE

Altieri, Miguel A.; Nicholls, Clara Inés (2020). Agroecology and the emergence of a post COVID-19 agriculture. Agriculture and Human Values, vol. 37, pp. 525–526. https://doi.org/10.1007/s10460-020-10043-7

Alzoubi, Yehia Ibrahim; Mishra, Alok (2024). Green artificial intelligence initiatives: Potentials and challenges. Journal of Cleaner Production, vol. 468. https://doi.org/10.1016/j.jclepro.2024.143090

Brevini, Benedetta (2020). Black boxes, not green: Mythologizing artificial intelligence and omitting the environment. Big Data & Society, 1–5. https://doi.org/10.1177/2053951720935141

Castro, Daniel (2024). Rethinking concerns about AI’s energy use. Center for Data Innovation. https://www2.datainnovation.org/2024-ai-energy-use.pdf

Crawford, Kate (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.

Dauvergne, Peter (2020). AI in the wild: Sustainability in the age of artificial intelligence. Cambridge, Massachusetts: The MIT Press.

Dauvergne, Peter (2022). Is artificial intelligence greening global supply chains? Exposing the political economy of environmental costs. Review of International Political Economy, 29:3, pp. 696-71. https://doi.org/10.1080/09692290.2020.1814381

De Vries, Alex (2023). The growing energy footprint of artificial intelligence. Joule, 7 (10), pp. 2191-2194. https://doi.org/10.1016/j.joule.2023.09.004

Duarte, Fabio (2025). Number of ChatGPT users (November 2025). https://explodingtopics.com/blog/chatgpt-users

Europa Press (2023). Una consulta en ChatGPT consume tres veces más energía que en el buscador de Google. 28/07/2023.

https://www.europapress.es/portaltic/sector/noticia-consulta-chatgpt-consume-tres-veces-mas-energia-buscador-google-20230728164651.html

European Parliament (2023). Amendments adopted by the European Parliament on 14 June 2023 on the proposal for a regulation of the European Parliament and of the Council on laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. https://www.europarl.europa.eu/doceo/document/TA-9-2023-0236_EN.html

European Union (2021). Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial intelligence act) and amending certain Union legislative acts. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52021PC0206

Fernández-Durán, Ramón; González-Reyes, Luis (2018). En la espiral de la energía. Vol. II, Colapso del capitalismo global y civilizatorio (2ª ed). Libros en Acción; Baladre.

Flores-Vivar, Jesús; García-Peñalvo, Francisco-José (2023). Reflexiones sobre la ética, potencialidades y retos de la inteligencia artificial en el marco de la educación de calidad (ODS4). Comunicar, 74, pp. 37-47. https://doi.org/10.3916/C74-2023-03

González-de-Eusebio, Javier; Carbonell, Alejandro; Gertrudix, Manuel; Tucho, Fernando (2021). La competencia digital docente como clave para un consumo sostenible de las TIC en el contexto de la crisis climática global. Revista de educación ambiental y sostenibilidad, 3(2), 2602. https://doi.org/10.25267/Rev_educ_ambient_sostenibilidad.2021.v3.i2.2602

Guldner, Achim; Murach, Julien (2023). Measuring and assessing the resource and energy efficiency of artificial intelligence of things devices and algorithms. In: Wohlgemuth, V., Naumann, S., Behrens, G., Arndt, HK., Höb, M. (eds). Advances and New Trends in Environmental Informatics. Enviroinfo 2022. Progress in IS. Cham: Springer. https://doi.org/10.1007/978-3-031-18311-9_11

Guo, Qingbin; Peng, Yanqing; Luo, Kang (2025). The impact of artificial intelligence on energy environmental performance: Empirical evidence from cities in China. Energy Economics, vol. 141, 108136. https://doi.org/10.1016/j.eneco.2024.108136

Henderson, Peter; Hu, Jieru; Romoff, Joshua; Brunskill, Emma; Jurafsky, Dan; Pineau, Joelle (2020). Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research 21, pp. 1-43. https://arxiv.org/abs/2002.05651

International Energy Agency (2017). Digitalisation & Energy. https://www.iea.org/reports/digitalisation-and-energy

Jackson, Tim (2016). Prosperity without growth. 2nd edition. London: Routledge. https://doi.org/10.4324/9781315677453

Kshetri, Nir (2024). The environmental impact of artificial intelligence. IT Professional, vol. 26, n. 3. https://doi.org/10.1109/MITP.2024.3399471

Kwame Nti, Emmanuel; Cobbina, Samuel Jerry; Attafuah, Eunice Efua; Opoku, Evelyn; Gyan, Michael Amoah (2022). Environmental sustainability technologies in biodiversity, energy, transportation and water management using artificial intelligence: A systematic review. Sustainable Futures, vol. 4, 100068. https://doi.org/10.1016/j.sftr.2022.100068

Li, Pengfei; Yang, Jianyi; Islam, Mohammad A.; Ren, Shaolei (2023). Making AI less “thirsty”: Uncovering and addressing the secret water footprint of AI models. arXiv. https://arxiv.org/abs/2304.03271v3

Ligozat, Anne-Laure; Lefevre, Julien; Bugeau, Aurélie; Combaz, Jacques (2022). Unraveling the hidden environmental impacts of AI solutions for environment. Life cycle assessment of AI solutions. Sustainability, 14, 5172. https://doi.org/10.3390/su14095172

Ligozat, Anne-Laure; Luccioni, Sasha (2021). A practical guide to quantifying carbon emissions for machine learning researchers and practitioners. [Research report] MILA; LISN. 2021. hal-03376391. https://hal.science/hal-03376391/document

López, Antonio (2014). Greening media education. New York: Peter Lang.

López, Antonio (2021). Ecomedia literacy: Integrating ecology into media Education. New York: Routledge.

Luccioni, Alexandra Sasha; Hernández-García, Alex (2023): Counting carbon: A survey of factors influencing the emissions of machine learning. arXiv. https://arxiv.org/abs/2302.08476v1

Luccioni, Alexandra Sasha; Viguier, Sylvain; Ligozat, Anne-Laure (2022). Estimating the carbon footprint of BLOOM, a 176b parameter language model. arXiv. https://arxiv.org/abs/2211.02001v1

Luers, Amy; Koomey, Jonathan; Masanet, Eric; Gaffney, Owen; Creutzig, Felix; Lavista Ferres, Juan; Horvitz, Eric (2024). Will AI accelerate or delay the race to net-zero emissions? Nature, 628 (8009), pp. 718–720. https://www.nature.com/articles/d41586-024-01137-x

Maxwell, Robert; Miller, Toby (2012). Greening the media. Oxford: Oxford University Press.

Moore, Jason W. (2020). El capitalismo en la trama de la vida: Ecología y acumulación de capital. Traficantes de Sueños.

Nishant, Rohit; Kennedy, Mike; Corbett, Jacqueline (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, vol. 53, 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104

Noticias Financieras (2024). The environmental impact of artificial intelligence: to write 100 words it consumes the energy of 14 LED lights on for one hour. En: CE Noticias Financieras, English ed.; Miami. 3 oct 2024.

O’Brien, Matt; Fingerhut, Hanna (2023). Artificial intelligence technology behind ChatGPT was built in Iowa — with a lot of water. The Associated Press, September 9, 2023. https://apnews.com/article/chatgpt-gpt4-iowa-ai-water-consumption-microsoft-f551fde98083d17a7e8d904f8be822c4

OECD (2022). Measuring the environmental impacts of AI compute and applications: the AI footprint. https://www.oecd.org/en/publications/measuring-the-environmental-impacts-of-artificial-intelligence-compute-and-applications_7babf571-en.html

Official Journal of the European Union (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending regulations (Ec) No 300/2008, (Eu) No 167/2013, (Eu) No 168/2013, (Eu) 2018/858, (Eu) 2018/1139 And (Eu) 2019/2144 And Directives 2014/90/Eu, (Eu) 2016/797 And (Eu) 2020/1828 (Artificial Intelligence Act). https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:L_202401689

Pascual, Manuel (2024). Los centros de datos quieren tener sus propios reactores nucleares. El País, 30-04-2024. https://elpais.com/tecnologia/2024-04-30/los-centros-de-datos-quieren-tener-sus-propios-reactores-nucleares.html

Pascual, Manuel (2025a). La IA consumirá en EE UU tanta energía como toda España”. El País, 23-01-2025. https://elpais.com/tecnologia/2025-01-23/la-ia-consumira-en-ee-uu-tanta-energia-como-toda-espana.html

Pascual, Manuel (2025b). Amazon pide un 48% más de agua para sus centros de datos de Aragón. El País, 22-03-2025. https://elpais.com/tecnologia/2025-03-22/amazon-pide-un-48-mas-de-agua-para-sus-centros-de-datos-de-aragon.html

Patterson, David; Gonzalez, Joseph; Le, Quoc; Liang, Chen; Munguia, Lluis-Miquel; Rothchild, Daniel; So, David; Texier, Maud; Dean, Jeff (2021). Carbon emissions and large neural network training. arXiv. https://arxiv.org/abs/2104.10350v3

Pigem, Jordi (2021). Pandemia y posverdad: La vida, la conciencia y la cuarta revolución industrial (1ª ed). Barcelona: Fragmenta Editorial.

Rolnick, David; Donti, Priya L.; Kaack, Lynn H.; Kochanski, Kelly; Lacoste, Alexandre; Sankaran, Kris; Ross, Andrew Slavin; Milojevic-Dupont, Nikola; Jaques, Natasha; Waldman-Brown, Anna; Luccioni, Alexandra; Maharaj, Tegan; Sherwin, Evan D.; Mukkavilli, S. Karthik; Kording, Konrad P.; Gomes, Carla; Ng, Andrew Y.; Hassabis, Demis; Platt, John C.; Creutzig, Felix; Chayes, Jennifer; Bengio, Yoshua (2019). Tackling climate change with machine learning. arXiv. https://doi.org/10.48550/arXiv.1906.05433

Sanuy, Ada (2023). Calculan que la industria de la IA consumirá tanta electricidad como Países Bajos en 2027. La Vanguardia, 14/10/2023. https://www.lavanguardia.com/tecnologia/20231014/9297305/calculan-que-la-industria-de-la-ia-consumira-tanta-electricidad-como-paises-bajos-en-2027-pmv.html

Schoormann, Thorsten; Strobel, Gero; Möller, Frederik; Petrik, Dimitri; Zschech, Patrick (2023). Artificial intelligence for sustainability—A systematic review of information systems literature. Communications of the Association for Information Systems, 52. https://doi.org/10.17705/1CAIS.05209

Schwartz, Roy; Dodge, Jesse; Smith, Noah; Etzioni, Oren (2020). Green AI. Communications of the ACM, 63-12, pp. 54–63. https://doi.org/10.1145/3381831

Slimani, Sana; Omri, Anis; Ben Jabeur, Sami (2025). When and how does artificial intelligence impact environmental performance? Energy Economics, vol. 148, 108643. https://doi.org/10.1016/j.eneco.2025.108643

Stokel-Walker, Chris (2023). The Generative AI race has a dirty secret. Wired, 10 Febr. 2023. https://www.wired.com/story/the-generative-ai-search-race-has-a-dirty-secret

Strubell, Emma; Ganesh, Ananya; McCallum, Andrew (2019). Energy and policy considerations for deep learning in NLP. 57th Annual Meeting of the Association for Computational Linguistics (ACL). Florence, Italy, July 2019. https://doi.org/10.48550/arXiv.1906.02243

Taibo, Carlos (2020). Colapso: Capitalismo terminal, transición ecosocial, ecofascismo (4ª ed.: enero). Catarata.

Tucho, Fernando; García-de-Madariaga, José-María; Vicente, Miguel (2024). El reto de la sostenibilidad medioambiental de los medios en la era de las tecnologías digitales. En: Carrasco-Campos, Angel y Candón Mena, José (eds). Sostenibilidad de los medios en la era digital. Economía política de los medios públicos, privados y comunitarios. Comunicación Social. https://doi.org/10.52495/c5.emcs.30.tam5

Tucho, Fernando; Masanet, María-José; Blanco, Saúl (2014). La cuestión medioambiental en la educación mediática: un reto pendiente. ZER: Revista de Estudios de Comunicación, 19(36), pp. 205-219. https://ojs.ehu.eus/index.php/Zer/article/view/13502

Tucho, Fernando; Vicente, Miguel; García-de-Madariaga, José-María (2017). La cara oculta de la sociedad de la información: el impacto medioambiental de la producción, el consumo y los residuos tecnológicos. Chasqui, (136), pp. 45-61. https://revistachasqui.org/index.php/chasqui/article/view/3321

UNCTAD (2024). Informe sobre la economía digital. Naciones Unidas. https://unctad.org/publication/digital-economy-report-2024

Van Rijmenam, Mark (2023). Building a greener future: The importance of sustainable AI. The Digital Speake. https://www.thedigitalspeaker.com/greener-future-importance-sustainable-ai

Van Wynsberghe, Aimee (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI Ethics, 1, pp. 213–218. https://doi.org/10.1007/s43681-021-00043-6

Vinuesa, Ricardo; Azizpour, Hossein; Leite, Iolanda; Balaam, Madeline; Dignum, Virginia; Domisch, Sami; Felländer, Anna; Langhans, Simone Daniela; Tegmark, Max; Nerini, Francesco Fuso (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11:233. https://doi.org/10.1038/s41467-019-14108-y

Wang, Quian; Li, Yuanfan; Li, Rongrong (2024). Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence. Humanities and social sciences communications, 11:1043. https://doi.org/10.1057/s41599-024-03520-5

Winner, Langdon (2020). The whale and the reactor: A search for limits in an age of high technology (2nd edition). Chicago: University of Chicago Press.

Wolff, Lasse F.; Kanding, Benjamin; Selvan, Raghavendra (2020). Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. arXiv. https://arxiv.org/abs/2007.03051

Wu, Carole-Jean; Raghavendra, Ramya; Gupta, Udit; Acun, Bilge; Ardalani, Newsha; Maeng, Kiwan; Chang, Gloria; Behram, Fiona Aga; Huang, James; Bai, Charles; Gschwind, Michael; Gupta, Anurag; Ott, Myle; Melnikov, Anastasia; Candido, Salvatore; Brooks, David; Chauhan, Geeta; Lee, Benjamin; Lee, Hsien-Hsin S.; Akyildiz, Bugra; Balandat, Maximilian; Spisak, Joe; Jain, Ravi; Rabbat, Mike; Hazelwood, Kim (2022): Sustainable AI: Environmental implications, challenges and opportunities. arXiv. https://arxiv.org/pdf/2111.00364v2

Wu, Jie; Liu, Tao; Sun, Jiasen (2025). Impact of artificial intelligence on carbon emission efficiency: evidence from China. Environmental Science and Pollution Research, 32, pp. 19450–19461. https://doi.org/10.1007/s11356-023-31139-7

Yadav, Manish; Singh, Gurjeet (2023). Environmental sustainability with artificial intelligence. EPRA International Journal of Multidisciplinary Research (IJMR), 9(5). https://doi.org/10.36713/epra13325

Yu, Yang; Wang, Jiahui; Liu, Yu; Yu, Pingfeng; Wang, Dongsheng; Zheng, Ping; Zhang, Meng (2024). Revisit the environmental impact of artificial intelligence: the overlooked carbon emission source? Frontiers of Environmental Science & Engineering, 18(12): 158. https://doi.org/10.1007/s11783-024-1918-y

Published

2026-01-20

How to Cite

Tucho, F., & García-de-Madariaga, J.-M. (2026). Environmental impact of artificial intelligence: a pending debate. Infonomy, 4(1). https://doi.org/10.3145/infonomy.26.001

Downloads

Download data is not yet available.

Dimensions