Huella Energética y Ambiental de la Inteligencia Artificial: Revisión Sistemática y Análisis Bibliométrico del Consumo Eléctrico, las Emisiones de Carbono y el Uso de Agua en Sistemas de IA y Centros de Datos, 2015–2025

dc.contributor.advisorForero Becerra, Cristian Camilo
dc.contributor.authorCaro Alvarado, Oscar Julián
dc.contributor.corporatenameUniversidad Santo Tomás
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001756834
dc.contributor.googlescholarhttps://scholar.google.com/citations?hl=es&authuser=2&user=s_zyuVsAAAAJ
dc.date.accessioned2026-09-30T14:15:22Z
dc.date.available2026-09-30T14:15:22Z
dc.date.issued2026-09-29
dc.descriptionEste artículo analiza la literatura científica publicada entre 2015 y 2025 sobre la huella energética y ambiental de los sistemas de inteligencia artificial y los centros de datos. Se propone una revisión sistemática y un análisis bibliométrico para caracterizar la evolución del campo, su estructura conceptual y social, y la evidencia disponible sobre consumo eléctrico, emisiones de carbono y uso de agua.
dc.description.abstractThis article analyzes the scientific literature published between 2015 and 2025 on the energy and environmental footprint of artificial intelligence systems and data centers. It proposes a systematic review and a bibliometric analysis to characterize the evolution of the field, its conceptual and social structure, and the available evidence on electricity consumption, carbon emissions, and water use.
dc.description.degreelevelPregradospa
dc.description.degreenameIngeniero Ambientalspa
dc.description.domainhttp://www.ustatunja.edu.co/investigacion
dc.format.mimetypeapplication/pdf
dc.identifier.citationCaro Alvarado, O. J. (2026). Huella eEnergética y Ambiental de la Inteligencia Artificial: Revisión Sistemática y Análisis Bibliométrico del Consumo Eléctrico, las Emisiones de Carbono y el Uso de Agua en Sistemas de IA y Centros de Datos, 2015–2025 [Trabajo de Grado, Universidad Santo Tomás].Repositorio Institucional
dc.identifier.instnameinstname:Universidad Santo Tomásspa
dc.identifier.reponamereponame:Repositorio Institucional Universidad Santo Tomásspa
dc.identifier.repourlrepourl:https://repository.usta.edu.cospa
dc.identifier.urihttp://hdl.handle.net/11634/74409
dc.language.isospa
dc.publisherUniversidad Santo Tomásspa
dc.publisher.branchCRAI-USTA Tunja
dc.publisher.facultyFacultad de Ingeniería Ambientalspa
dc.publisher.programPregrado de Ingeniería Ambientalspa
dc.relation.referencesT. Xiao, F. F. Nerini, H. D. Matthews, M. Tavoni, and F. You, “Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA,” Nature Sustainability, 2025. [Online]. Available: https://www.scopus.com/pages/publications/105021406559?ori gin=resultslist
dc.relation.referencesJ. Hoxha, M. Thanasi-Boçe, and T. Khalifa, “A deployment-aware framework for carbon- and water-efficient LLM serving,” Sustainability (Switzerland), 2025. [Online]. Available: https://www.scopus.com/pages /publications/105024607381?origin=resultslist
dc.relation.referencesP. Li, J. Yang, M. A. Islam, and S. Ren, “Making AI less ’thirsty’: Uncovering and addressing the secret water footprint of AI models,” arXiv preprint arXiv:2304.03271, 2025. [Online]. Available: https://arxiv.org/abs/2304.03271
dc.relation.referencesN. Jegham, M. Abdelatti, C. Y. Koh, L. Elmoubarki, and A. Hendawi, “How hungry is AI? benchmarking energy, water, and carbon footprint of LLM inference,” arXiv preprint arXiv:2505.09598, 2025. [Online]. Available: https://arxiv.org/abs/2505.09598
dc.relation.referencesA. G. Philipo, H. Ning, D. S. Sarwatt, J. A. Mohamed, A. S. Yusufu, F. Shi, S. Urenje, and J. Ding, “Sustainable AI: Emerging trends, impacts, and future challenges,” IEEE Transactions on Sustainable Computing, 2025. [Online]. Available: https://www.scopus.com/pages/p ublications/105016710503?origin=resultslist
dc.relation.referencesN. Huang, X. Li, Q. Xu, R. Chen, H. Chen, and A. Chen, “Artificial intelligence-based temperature twinning and pre-control for data center airflow organization,” Energies, 2023. [Online]. Available: https: //www.scopus.com/pages/publications/85168783070?origin=resultslist
dc.relation.referencesJ. Winsta, “The hidden costs of AI: A review of energy, e-waste, and inequality in model development,” arXiv preprint arXiv:2507.09611, 2025. [Online]. Available: https://arxiv.org/abs/2507.09611
dc.relation.referencesA. Safari, H. Sorouri, A. Rahimi, and A. Oshnoei, “A systematic review of energy efficiency metrics for optimizing cloud data center operations and management,” Electronics (Switzerland), 2025. [Online]. Available: https: //www.scopus.com/pages/publications/105007730854?origin=resultslist
dc.relation.referencesH. Kahil, S. Sharma, P. Välisuo, and M. Elmusrati, “Reinforcement learning for data center energy efficiency optimization: A systematic literature review and research roadmap,” Applied Energy, 2025. [Online]. Available: https://www.scopus.com/pages/publications/105000763032?ori gin=resultslist
dc.relation.referencesL. Zhang, Z. Zhao, B. Chen, M. Zhao, and Y. Chen, “Zero-carbon development in data centers using waste heat recovery technology: A systematic review,” Sustainability (Switzerland), 2025. [Online]. Available: https://www.scopus.com/pages/publications/105023206801?ori gin=resultslist
dc.relation.referencesN. Asadov, V. C. Coroama, M. Franzil, S. Galantino, and M. Finkbeiner, “Carbon-aware spatio-temporal workload shifting in edge–cloud environments: A review and novel algorithm,” Sustainability (Switzerland), 2025. [Online]. Available: https://www.scopus.com/pages /publications/105011672331?origin=resultslist
dc.relation.referencesJ. Cowls, A. Tsamados, M. Taddeo, and L. Floridi, “The AI gambit: Leveraging artificial intelligence to combat climate change— opportunities, challenges, and recommendations,” AI and Society, 2023. [Online]. Available: https://www.scopus.com/pages/publications/851074 44145?origin=resultslist
dc.relation.referencesOpenAI, “Presentamos ChatGPT,” [Online]. Available: https://openai.c om/es-ES/index/chatgpt/, Nov. 2022, nov. 30, 2022. Accessed: Aug. 27, 2026.
dc.relation.referencesArtificial Analysis, “AI Model & API Providers Analysis,” [Online]. Available: https://artificialanalysis.ai/, 2026, accessed: Aug. 27, 2026.
dc.relation.referencesS. Rivero Silva, D. Chinarro Vadillo, and A. Prieto Andrés, “GREEN AI AND AI ACT: PIONEERING LEGISLATION OR MERELY AN ENVIRONMENTAL STATEMENT?” Revista Catalana de Dret Ambiental, 2025. [Online]. Available: https://www.scopus.com/pages/pub lications/105011298416?origin=resultslist
dc.relation.referencesS. Falk, A. van Wynsberghe, and L. Biber-Freudenberger, “The attribution problem of a seemingly intangible industry,” Environmental Challenges, 2024. [Online]. Available: https://www.scopus.com/pages/p ublications/85203005984?origin=resultslist
dc.relation.referencesA. de Vries-Gao, “The carbon and water footprints of data centers and what this could mean for artificial intelligence,” Patterns, 2025. [Online]. Available: https://doi.org/10.1016/j.patter.2025.101430
dc.relation.referencesM. Herrera, X. Xie, A. Menapace, A. Zanfei, and B. M. Brentan, “Sustainable AI infrastructure: A scenario-based forecast of water footprint under uncertainty,” Journal of Cleaner Production, 2025. [Online]. Available: https://www.scopus.com/pages/publications/105016 086198?origin=resultslist
dc.relation.referencesO. Öztürk, R. Kocaman, and D. K. Kanbach, “How to design bibliometric research: An overview and a framework proposal,” Review of Managerial Science, vol. 18, pp. 3333–3361, 2024.
dc.relation.referencesDepartamento Nacional de Planeación, “CONPES 4144: La hoja de ruta de Colombia en Inteligencia Artificial para los retos actuales y la transformación futura,” [Online]. Available: https://dnp.gov.co/publicaci ones/Planeacion/Paginas/conpes-4144-hoja-de-ruta-colombia-inteligen cia-artificial-retos-actuales-transformacion-futura.aspx, May 2025, may 16, 2025. Accessed: Aug. 31, 2026.
dc.relation.referencesCorpoboyacá, “Actualización Plan de Ordenamiento del Recurso Hídrico PORH 2026–2036 cuenca alta y media del río Chicamocha,” [Online]. Available: https://www.corpoboyaca.gov.co/actualizacion-plan-de-ord enamiento-del-recurso-hidrico-porh-2026-2036-cuenca-alta-y-media-d el-rio-chicamocha/, Feb. 2026, updated: Feb. 11, 2026. Accessed: Aug. 31, 2026.
dc.relation.referencesP. Mongeon and A. Paul-Hus, “The journal coverage of web of science and scopus: A comparative analysis,” Scientometrics, vol. 106, pp. 213– 228, 2016.
dc.relation.referencesM. Visser, N. J. van Eck, and L. Waltman, “Large-scale comparison of bibliographic data sources: Scopus, web of science, dimensions, crossref, and microsoft academic,” Quantitative Science Studies, vol. 2, no. 1, pp. 20–41, 2021.
dc.relation.referencesM. L. Rethlefsen, S. Kirtley, S. Waffenschmidt, A. P. Ayala, D. Moher, M. J. Page, J. B. Koffel et al., “PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews,” Systematic Reviews, vol. 10, no. 1, p. 39, 2021.
dc.relation.referencesPRISMA Statement, “PRISMA 2020 Statement,” [Online]. Available: https://www.prisma-statement.org/prisma-2020, 2020, accessed: Aug. 27, 2026.
dc.relation.referencesY. Wang, S. Liu, S. Liu, and L. Liu, “Electricity-computility integration of data centers and pumped storage driven by cold energy storage in china,” Energy Reports, 2025. [Online]. Available: https: //www.scopus.com/pages/publications/105021934191?origin=resultslist
dc.relation.referencesS. A. Rizzo, “To be artificial intelligence for sustainability or not to be sustainable artificial intelligence,” Renewable and Sustainable Energy Reviews, 2025. [Online]. Available: https: //www.scopus.com/pages/publications/105010454002?origin=resultslist
dc.relation.referencesP. Fraga-Lamas, S. I. Lopes, and T. M. Fernández-Caramés, “Green IoT and edge AI as key technological enablers for a sustainable digital transition towards a smart circular economy: An industry 5.0 use case,” Sensors, 2021. [Online]. Available: https: //www.scopus.com/pages/publications/85113574069?origin=resultslist
dc.relation.referencesT. Li, J. Luo, K. Liang, C. Yi, and L. Ma, “Synergy of patent and open-source-driven sustainable climate governance under green AI: A case study of TinyML,” Sustainability (Switzerland), 2023. [Online]. Available: https://www.scopus.com/pages/publications/85172860572?orig in=resultslist
dc.relation.referencesP. Gao and M. Adnan, “Overview of emerging electronics technologies for artificial intelligence: A review,” Materials Today Electronics, 2025. [Online]. Available: https://www.scopus.com/pages/publications/852163 60018?origin=resultslist
dc.relation.referencesN. Tirel, P. Roose, S. Ilarri, A. Noureddine, and O. Le Goaër, “Workload shifting techniques: From digital inebriation to sobriety,” ACM Computing Surveys, 2025. [Online]. Available: https://www.scopus .com/pages/publications/105026661167?origin=resultslist
dc.relation.referencesK. L. Veigas, A. Chinnici, D. De Chiara, and M. Chinnici, “Towards energy efficiency of HPC data centers: A data-driven analytical visualization dashboard prototype approach,” Electronics (Switzerland), 2025. [Online]. Available: https://www.scopus.com/pages/publications/10 5014418372?origin=resultslist
dc.relation.referencesŁ. Łach and D. Svyetlichnyy, “Advances in numerical modeling for heat transfer and thermal management: A review of computational approaches and environmental impacts,” Energies, 2025. [Online]. Available: https: //www.scopus.com/pages/publications/86000506663?origin=resultslist
dc.relation.referencesR. Koteczki, D. Csikor, and B. E. Balassa, “The role of generative AI in improving the sustainability and efficiency of HR recruitment process,” Discover Sustainability, 2025. [Online]. Available: https: //www.scopus.com/pages/publications/105010069483?origin=resultslist
dc.relation.referencesZ. Cao, X. Zhou, H. Hu, Z. Wang, and Y. Wen, “Toward a systematic survey for carbon neutral data centers,” IEEE Communications Surveys and Tutorials, 2022. [Online]. Available: https: //www.scopus.com/pages/publications/85127077398?origin=resultslist
dc.relation.referencesM. Shi, K. Janowicz, J. Verstegen, K. Currier, N. Wiedemann, G. Mai, I. Majic, Z. Liu, and R. Zhu, “Geography for AI sustainability and sustainability for GeoAI,” Cartography and Geographic Information Science, 2025. [Online]. Available: https: //www.scopus.com/pages/publications/105002157883?origin=resultslist
dc.relation.referencesA. Aili, J. Choi, Y. S. Ong, and Y. Wen, “The development of carbon-neutral data centres in space,” Nature Electronics, 2025. [Online]. Available: https://www.scopus.com/pages/publications/105020469852?ori gin=resultslist
dc.relation.referencesM. Toderas, “Artificial intelligence for sustainability: A systematic review and critical analysis of AI applications, challenges, and future directions,” Sustainability, 2025. [Online]. Available: https: //doi.org/10.3390/su17178049
dc.relation.referencesL. Moreau, C. D. Bernard, P. L. Martin, and A. E. Diallo, “Invisible pollutants revisited: A review of emerging environmental risks from digitalization and data infrastructure,” World Journal of Environmental Biosciences, 2025. [Online]. Available: https: //www.scopus.com/pages/publications/105029168880?origin=resultslist
dc.relation.referencesM. Prazian, “An interdisciplinary approach to powering artificial intelligence (AI),” Nuclear and Radiation Safety, 2025. [Online]. Available: https://www.scopus.com/pages/publications/105003193664?ori gin=resultslist
dc.relation.referencesA. Al-Sowaidi and R. J. Isaifan, “Click, store, emit: The environmental cost of digital infrastructure,” Digital Technologies Research and Applications, 2025. [Online]. Available: https://www.scopus.com/pages/p ublications/105019104057?origin=resultslist
dc.relation.referencesS. M. Nleya and M. Velempini, “Industrial metaverse: A comprehensive review, environmental impact, and challenges,” Applied Sciences (Switzerland), 2024. [Online]. Available: https://www.scopus.com/pages /publications/85198488606?origin=resultslist
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Colombiaen
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_14cb
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.subject.keywordArtificial intelligence
dc.subject.keywordData centers
dc.subject.keywordElectricity consumption
dc.subject.keywordCarbon emissions
dc.subject.keywordWater footprint
dc.subject.keywordGreen AI
dc.subject.keywordBibliometric analysis
dc.subject.proposalInteligencia artificial
dc.subject.proposalCentros de datos
dc.subject.proposalConsumo eléctrico
dc.subject.proposalEmisiones de carbono
dc.subject.proposalHuella hídrica
dc.subject.proposalGreen AI
dc.subject.proposalAnálisis bibliométrico
dc.titleHuella Energética y Ambiental de la Inteligencia Artificial: Revisión Sistemática y Análisis Bibliométrico del Consumo Eléctrico, las Emisiones de Carbono y el Uso de Agua en Sistemas de IA y Centros de Datos, 2015–2025
dc.typebachelor thesis
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.driveinfo:eu-repo/semantics/bachelorThesis
dc.type.localTrabajo de Gradospa
dc.type.versioninfo:eu-repo/semantics/acceptedVersion

Archivos

Bloque original

Mostrando 1 - 3 de 3
Cargando...
Miniatura
Nombre:
2026OscarCaro
Tamaño:
1.81 MB
Formato:
Adobe Portable Document Format
Cargando...
Miniatura
Nombre:
Autorización estudiante
Tamaño:
91.35 KB
Formato:
Adobe Portable Document Format
Cargando...
Miniatura
Nombre:
Autorización facultad
Tamaño:
354.56 KB
Formato:
Adobe Portable Document Format

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
807 B
Formato:
Item-specific license agreed upon to submission
Descripción: