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Yapay Zekânın Çevresel Geleceği: Fırsatlar ve Zorluklar

Yıl 2025, Cilt: 34 Sayı: 4, 294 - 304, 12.09.2025

Öz

Yapay zekâ, insan zekâsını taklit ederek öğrenme, problem çözme ve akıl yürütme gibi süreçleri yerine getiren teknolojilerden oluşur. Geniş bir yelpazede uygulama alanı bulan yapay zekâ, çevresel sürdürülebilirlik için önemli fırsatlar sunar. İklim değişikliği analizleri, atık yönetimi ve enerji tasarrufu gibi konularda kullanımı, çevresel sorunların çözümüne katkı sağlar. Ancak yapay zekâ sistemlerinin büyük veri merkezleri ve donanım üretimi süreçlerinde yoğun enerji kullanımı, sera gazı emisyonlarını artırır. Ayrıca, yüksek su tüketimi, yoğun kaynak kullanımı ve elektronik atık oluşumu gibi faktörler de yapay zekânın çevresel etkileri arasında yer almaktadır. Bu durum, yapay zekânın çevre ile uyumlu ve sürdürülebilir modellerinin geliştirilmesine yönelik çalışmalara yol açmıştır. Bu makalede, yapay zekânın çevresel faydaları ve olumsuz etkileri detaylı olarak ele alınmıştır.

Kaynakça

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The Environmental Future of Artificial Intelligence: Opportunities and Challenges

Yıl 2025, Cilt: 34 Sayı: 4, 294 - 304, 12.09.2025

Öz

Artificial intelligence (AI) encompasses technologies that mimic human intelligence to perform processes such as learning, problem-solving, and reasoning. AI, with its wide range of applications, presents significant opportunities for environmental sustainability. Its use in areas such as climate change analysis, waste management, and energy conservation contributes to addressing environmental challenges. However, the intensive energy consumption of AI systems during the operation of large data centers and hardware production processes increases greenhouse gas emissions. Additionally, factors such as high water consumption, extensive resource usage, and the generation of electronic waste are among the environmental impacts of AI. These challenges have led to efforts to develop environmentally friendly and sustainable AI models. This article provides a detailed examination of the environmental benefits and adverse effects of artificial intelligence.

Kaynakça

  • Russell SJ, Norvig P. Artificial intelligence: A modern approach. 4th ed. Hoboken: Pearson Publishers; 2021.
  • Fan Z, Yan Z, Wen S. Deep learning and artificial intelligence in sustainability: A review of SDGs, renewable energy, and environmental health. Sustainability. 2023;15(18):13493. https://doi.org/10.3390/su151813493
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  • Strubell E, Ganesh A, McCallum A. Energy and policy considerations for modern deep learning research. Proceedings of the AAAI Conference on Artificial Intelligence. 2020;34(09):13693-6. https://doi.org/10.1609/aaai.v34i09.7123
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  • United Nations Environment Programme. UNEP: Artificial intelligence (AI) end-to-end: The environmental impact of the full AI life cycle needs to be comprehensively assessed. Nairobi: UNEP; 2024. Available at: https://www.unep.org/resources/report/artificial-intelligence-ai-end-end-environmental-impact-full-ai-lifecycle-needs-be Accessed November 15,2024
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  • Jogin M, Mohana, Madhulika MS, Divya GD, Meghana RK, Apoorva S. Feature extraction using convolution neural networks (CNN) and deep learning. In: 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Ccommunication Technology (RTEICT). 2018, Bangalore, India, 2319-23
  • Chollet F. Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). 2017;1251-58
  • Dhar P. The carbon impact of artificial intelligence. Nat Mach Intell. 2020;2(8):423-5. https://doi.org/10.1038/s42256-020-0219-9
  • O’neil C. Weapons of math destruction: How big data increases inequality and threatens democracy. Scientific American. 2016:315(2);74
  • Susskind R. The future of the professions: How technology will transform the work of human experts. USA: Oxford University Press; 2015.
  • Federico CA, Trotsyuk AA. Biomedical data science, artificial intelligence, and ethics: Navigating challenges in the face of explosive growth. Ann Rev Biomed Data Sci. 2024;7(1):1-14. https://doi.org/10.1146/annurev-biodatasci-102623-104553
  • Aniko K, Peyman N. Recent applications of AI to environmental disciplines: A review. Sci. Total Environ. 2024;906:167705. https://doi.org/10.1016/j.scitotenv.2023.167705
  • World Health Organization.WHO:Climate change, 2023 Available at: https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health. Accessed: September 18, 2024
  • Coulson A. The environmental impact of AI in the lab: a double-edged sword? BioTechniques. 2024: 76(8);353-56 https://doi.org/10.1080/07366205.2024.2376459
  • Huntingford C, Jeffers ES, Bonsall MB, Christensen HM, Lees T, Yang H. Machine learning and artificial intelligence to aid climate change research and preparedness. Environ. Res. Lett. 2019;14(12):124007. https://doi.org/ 10.1088/1748-9326/ab4e55
  • Cowls J, Tsamados A, Taddeo M, Floridi L. The AI gambit: Leveraging artificial intelligence to combat climate change-opportunities, challenges, and recommendations. AI & Soc. 2023;38(1):283-307. https://doi.org/10.1007/s00146-021-01294-x
  • Larraondo PR, Renzullo LJ, Van Dijk AI, Inza I, Lozano JA. Optimization of deep learning precipitation models using categorical binary metrics. J Adv Model Earth Syst. 2020;12(5):e2019MS001909. https://doi.org/10.1029/2019MS001909
  • Zheng G, Li X, Zhang RH, Lıu B. Purely satellite data–driven deep learning forecast of complicated tropical instability waves. Sci. Adv. 2020;6(29):eaba1482. https://doi.org/ 10.1126/sciadv.aba1482
  • Migallón V, Navarro-González FJ, Penadés H, Penades J, Villacampa Y. A parallel methodology using radial basis functions versus machine learning approaches applied to environmental modelling. J. Comput. Sci. 2022;63:101817. https://doi.org/10.1016/j.jocs.2022.101817
  • Ise T, Oba Y. Forecasting climatic trends using neural networks: An experimental study using global historical data. Front. Robot. AI. 2019;6:446979. https://doi.org/10.3389/frobt.2019.00032
  • Ridwan WM, Sapitang M, Aziz A, Kushiar KF, Ahmed AN, El-Shafie A. Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia. Ain Shams Eng. J. 2021;12(2):1651-63. https://doi.org/10.1016/j.asej.2020.09.011
  • Jaafari A, Zenner EK, Panahi M, Shahabi H. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agric. and For. Meteorol. 2019;266:198-207. https://doi.org/10.1016/j.agrformet.2018.12.015
  • Shrestha M, Manandhar S, Shrestha S. Forecasting water demand under climate change using artificial neural network: a case study of Kathmandu Valley, Nepal. Water Supply. 2020;20(5):1823-33. https://doi.org/10.2166/ws.2020.090
  • Intergovernmental Panel on Climate Change (IPCC). Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change Geneva: IPCC; 2022 Available at: https://www.ipcc.ch/report/ar6/wg3/ Accessed: November 6, 2024
  • Berrigan EM, Wang L, Carrillo H, Echegoyen K, Kappes M, Torres J, et al. Fast and efficient root phenotyping via pose estimation. Plant Phenomics. 2024;6:0175. https://doi.org/10.34133/plantphenomics.0175
  • Ren F, Ding X, Zheng M, Korzinkin M, Cai X, Zhu W, et al. AlphaFold accelerates artificial intelligence powered drug discovery: Efficient discovery of a novel CDK20 small molecule inhibitor. Chem. Sci. 2023;14(6):1443-52. https://doi.org/ 10.1039/D2SC05709C 
  • Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature. 2023;616:673-85. https://doi.org/10.1038/s41586-023-05905-z
  • Jayatunga MK, Xie W, Ruder L, Schulze U, Meier C. AI in small-molecule drug discovery: A coming wave. Nat Rev Drug Discov. 2022;21(3):175-6. https://doi.org/10.1038/d41573-022-00025-1
  • Pandey M, Fernandez M, Gentile F, Isayev O, Tropsha A, Stern AC, et al. The transformational role of GPU computing and deep learning in drug discovery. Nat Mach Intell. 2022;4(3):211-21. https://doi.org/10.1038/s42256-022-00463-x
  • Iyamu H, Anda M, Ho G. A review of municipal solid waste management in the BRIC and high-income countries: A thematic framework for low-income countries. Habitat Int. 2020;95:102097. https://doi.org/10.1016/j.habitatint.2019.102097
  • Mukherjee AG, Wanjari UR, Chakraborty R, Renu K, Vellingiri B, George A, et al. A review on modern and smart technologies for efficient waste disposal and management. J Environ Manage. 2021;297:113347. https://doi.org/10.1016/j.jenvman.2021.113347
  • Andeobu L, Wibowo S, Grandhi S. Artificial intelligence applications for sustainable solid waste management practices in Australia: A systematic review. Sci Total Environ. 2022;834:155389. https://doi.org/10.1016/j.scitotenv.2022.155389
  • Coskuner G, Jassim MS, Zontul M, Karateke S. Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes. Waste Manag. Res. 2021;39(3):499-507. https://doi.org/10.1177/0734242X20935181
  • Ramson SJ, Moni DJ, Vishnu S, Anagnostopoulos T, Kirubaraj AA, Fan X. An IoT-based bin level monitoring system for solid waste management. J. Mater. Cycles Waste Manag. 2021;23:516-25. https://doi.org/10.1007/s10163-020-01137-9
  • Crawford K. Generative AI’s environmental costs are soaring - and mostly secret. Nature. 2024;626:693. https://doi.org/10.1038/d41586-024-00478-x
  • Clutton-Brock P, Rolnick D, Donti PL, Kaack L. Climate change and AI: recommendations for government action. GPAI, Climate Change AI, Centre for AI & Climate; 2021. Available at: https://gpai.ai/projects/responsible-ai/environment/climate-change-and-ai.pdf. Accessed: 20.09.2024
  • Lannelongue L, Grealey J, Inouye M. Green algorithms: Quantifying the carbon footprint of computation. Adv Sci. 2021;8(12):2100707. https://doi.org/10.1002/advs.202100707
  • Lottick K, Susai S, Friedler SA, Wilson JP. Energy usage reports: Environmental awareness as part of algorithmic accountability. 2019. https://doi.org/10.48550/arXiv.1911.08354
  • Budennyy SA, Lazarev VD, Zakharenko NN, Korovin AN, Plosskaya OA, Dimitrov DV, et al. eco2AI: Carbon emissions tracking of machine learning models as the first step towards sustainable AI. Dokl. Math. 2022;106(1):S118-S28. https://doi.org/10.1134/S1064562422060230
  • Anthony LFW, Kanding B, Selvan R. Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. 2020; https://doi.org/10.48550/arXiv.2007.03051
  • Lannelongue L, Inouye M. Environmental impacts of machine learning applications in protein science. Cold Spring Harb Perspect Biol. 2023;15(12): a041473. https://doi.org/10.1101/cshperspect.a041473
  • Crawford K, Joler V. Anatomy of an AI System: The Amazon Echo as an anatomical map of human labor, data and planetary resources. AI Now Institute and Share Lab, 2018 Available at: https://anatomyof.ai. Accessed: 07.10.2024
  • United Nations. The UN Conference on Trade and Development [UNCTAD]: Digital economy report 2024. Available at: https://unctad.org/publication/digital-economy-report-2024 Accessed: 07.10.2024
  • United Nations. The UN Secretary-General’s Panel on Critical Energy Transition Mineral 2024. Available at: https://www.un.org/en/climatechange/critical-minerals Accessed: 07.10.2024
  • Statista. Electronic waste generated worldwide from 2010 to 2022 Available at: https://www.statista.com/statistics/499891/projection-ewaste-generation-worldwide/#statisticContainer. Accessed: 07.10.2024
  • Statista. Management of electronic waste worldwide in 2022, by method. Available at: https://www.statista.com/statistics/1066948/share-of-electronic-waste-disposed-globally/. Accessed: 07.10.2024]
  • World Health Organization (WHO). Electronic waste (e-waste), 2024 Available at: https://www.who.int/news-room/fact-sheets/detail/electronic-waste-(e-waste). Accessed: 07.11.2024
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  • International Energy Agency. Artificial intelligence (AI) for decarbonisation innovation programme 2024 Available at: https://www.iea.org/policies/18347-artificial-intelligence-ai-for-decarbonisation-innovation-programme?s=1. Accessed: 8.11.2024
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  • International Atomic Energy Agency. Artificial Intelligence for accelerating nuclear applications, science and technology. Non-serial Publications. IAEA, Vienna.2022. Available at: https://www.iaea.org/publications/15198/artificial-intelligence-for-accelerating-nuclear-applications-science-and-technology Accessed: 25.09.2024
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Toplam 74 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Çevre Sağlığı
Bölüm Derleme
Yazarlar

Sümeyye Nur Budak 0000-0001-8005-2553

Cavit Işık Yavuz 0000-0001-9279-1740

Yayımlanma Tarihi 12 Eylül 2025
Gönderilme Tarihi 8 Ocak 2025
Kabul Tarihi 11 Eylül 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 34 Sayı: 4

Kaynak Göster

Vancouver Budak SN, Yavuz CI. Yapay Zekânın Çevresel Geleceği: Fırsatlar ve Zorluklar. STED. 2025;34(4):294-30.