Yapay zekanın çevresel ayak izi: Enerji, su ve elektronik atık üzerine çok yönlü bir analiz

Öz

Yapay zeka (Artificial Intelligence - AI), modern teknolojinin en yenilikçi alanlarından biri olarak, endüstriyel süreçlerin optimizasyonundan çevresel sorunların çözümüne kadar geniş bir uygulama potansiyeline sahiptir. Bununla birlikte, hızla artan kullanımı, önemli çevresel etkilerin ortaya çıkmasına yol açmaktadır. Bu çalışma, yapay zekanın çevresel etkilerinden enerji tüketimi, karbon emisyonları, su kullanımı ve elektronik atık konuları çerçevesinde mevcut literatürü derleyerek kapsamlı bir şekilde analiz etmeyi ve bu etkilerin azaltılmasına yönelik sürdürülebilir stratejiler geliştirmeyi amaçlamaktadır.Çalışmada ayrıca, enerji verimliliğine dayalı algoritmaların geliştirilmesi, yenilenebilir enerji kaynaklarının entegrasyonu, şeffaf karbon raporlaması ve sürdürülebilir tasarım yaklaşımları gibi stratejik müdahaleler değerlendirilmiştir. Bu analizler, yapay zekanın çevresel maliyetlerinin azaltılmasının yalnızca teknolojik yeniliklerle değil, aynı zamanda politika geliştirme, düzenlemeler ve paydaşlar arası iş birliği ile mümkün olduğunu ortaya koymaktadır. Bulgular, yapay zekanın çevresel etkilerinin etkin bir şekilde yönetilmesi durumunda, teknolojik ilerlemenin çevresel sürdürülebilirlikle uyumlu bir şekilde sağlanabileceğini göstermektedir. Çalışma, gelecekteki araştırmalar ve politika geliştirme süreçleri için temel bir çerçeve sunmakta ve yapay zekanın sürdürülebilirlik odaklı bir yaklaşımla geliştirilmesi gerektiğini vurgulamaktadır.

Anahtar Kelimeler

• Yapay zeka
• çevresel sürdürülebilirlik
• karbon ayak izi
• enerji tüketimi
• su kullanımı
• elektronik atık

The environmental footprint of artificial intelligence: A multi-aspect analysis of energy, water and electronic waste

Abstract

Artificial intelligence (AI), one of the most innovative areas of modern technology, has a wide range of application potential, from optimizing industrial processes to solving environmental problems. However, its rapidly increasing use leads to significant environmental impacts. This study aims to comprehensively analyze the existing literature on the environmental impacts of artificial intelligence within the framework of energy consumption, carbon emissions, water use, and electronic waste and to develop sustainable strategies to reduce these impacts. The study also evaluated strategic interventions such as developing algorithms based on energy efficiency, integrating renewable energy sources, transparent carbon reporting, and sustainable design approaches. These analyses reveal that reducing the environmental costs of artificial intelligence is possible not only with technological innovations but also with policy development, regulations, and stakeholder cooperation. The findings show that if the environmental impacts of artificial intelligence are effectively managed, technological progress can be achieved in a way that is compatible with environmental sustainability. The study provides a basic framework for future research and policy development processes and emphasizes that artificial intelligence should be developed with a sustainability-oriented approach.

Keywords

• Artificial intelligence
• environmental sustainability
• carbon footprint
• energy consumption
• water usage
• electronic waste

Kaynakça

1. Aderibigbe, A. O., Ani, E. C., Ohenhen, P. E., Ohalete, N. C., Daraojimba, D. O. (2023). Enhancing energy efficiency with AI: A review of machine learning models in electricity demand forecasting. Engineering Science Technology Journal, 4(6), 636–645. https://doi.org/10.51594/estj.v4i6.636
2. Adewumi, A., Okoli, C. E., Usman, F. O., Olu-Lawal, K. A., Soyombo, O. T. (2024). Reviewing the impact of AI on renewable energy efficiency and management. International Journal of Science and Research Archive, 11(1), 1518–1527. https://doi.org/10.30574/ijsra.2024.11.1.0245
3. Ahmad Ibrahim, S. R., Yahaya, J., Sallehudin, H. . (2022). Green software process factors: A qualitative study. Sustainability, 14(18), 11180. https://doi.org/10.3390/su141811180
4. Albers, S. (2019). Sustainable data centers: Balancing energy and water usage. IEEE Transactions on Sustainable Computing, 4(2), 145–157. https://doi.org/10.1109/TSUSC.2019.2897700
5. Ali, S., Choi, B. (2020). State-of-the-art artificial intelligence techniques for distributed smart grids: a review. Electronics, 9(6), 1030. https://doi.org/10.3390/electronics9061030
6. Alkrush, A. A., Salem, M. S., Abdelrehim, O., Hegazi, A. A. (2024). Data Centers Cooling: A Critical Review of Techniques, Challenges, and Energy Saving Solutions. International Journal of Refrigeration..
7. Ardito, L., Procaccianti, G., Torchiano, M., Vetro, A. Understanding green software development: A conceptual framework. IT Professional, 17(4), 16–23. https://doi.org/10.1109/mitp.2015.16
8. Austen, K. (2023). The thirsty future of artificial intelligence. Nature, 603(7901), 366–369. https://doi.org/10.1038/d41586-023-00540-1

9. Austen, M. F., Subroto, A. (2023). Enabling practical decision making for sustainable green data center planning. Jurnal Ekonomi, 28(2), 1–15. https://doi.org/10.24912/je.v28i2.1540
10. Azmi, F., Saleh, A. (2024). Utilizing machine learning techniques for power estimation in residential electricity consumption. International Journal of Research and Review, 11(6), 326–333. https://doi.org/10.52403/ijrr.20240637
11. Bahrami, M., Khashroum, Z. (2023). Review of machine learning techniques for power electronics control and optimization. CRPASE, 9(3), 1–8. https://doi.org/10.61186/crpase.9.3.2860
12. Basmadjian, R., De Meer, H., Lent, R., Giuliani, G. (2015). Cloud computing and its interest in saving energy: The use case of a private cloud. Journal of Cloud Computing, 4(1), 1–25. https://doi.org/10.1186/s13677-015-0032-4
13. Bayer, F. M., Pruckner, M. (2023). Multi-agent reinforcement learning for sustainable building climate control. arXiv preprint arXiv:2309.06940. https://arxiv.org/abs/2309.06940
14. BBC News. (2024). The environmental challenges of AI. BBC News Science Technology. Erişim tarihi: 20 Aralık 2024, https://www.bbc.com
15. Biglari, A., Tang, W. (2023). A review of embedded machine learning based on hardware, application, and sensing scheme. Sensors, 23(4), 2131. https://doi.org/10.3390/s23042131
16. Bolte, L., Vandemeulebroucke, T., Wynsberghe, A. (2022). From an ethics of carefulness to an ethics of desirability: Going beyond current ethics approaches to sustainable AI. Sustainability, 14(8), 4472. https://doi.org/10.3390/su14084472
17. Bouaouda, A., Afdel, K., Abounacer, R. (2024). Unveiling genetic reinforcement learning (GRLA) and hybrid attention-enhanced gated recurrent unit with random forest (HAGRU-RF) for energy-efficient containerized data centers empowered by solar energy and AI. Sustainability, 16(11), 4438. https://doi.org/10.3390/su16114438
18. Celestine, A. D. N., Sulic, M., Wieliczko, M., Stetson, N. T. (2021). Hydrogen-based energy storage systems for large-scale data center applications. Sustainability, 13(22), 12654. https://doi.org/10.3390/su132212654
19. Chavali, D., Baburajan, B., Gurusamy, A., Dhiman, V. K., Katari, S. C. (2024). Regulating artificial intelligence: Developments and challenges. International Journal of Pharmaceutical Science, 2(3), 1250–1261.
20. Chen, T., Wang, X., Giannakis, G. B. (2015). Cooling-aware energy and workload management in data centers via stochastic optimization. IEEE Journal of Selected Topics in Signal Processing, 10(2), 402-415.
21. Cowls, J., Tsamados, A., Taddeo, M., Floridi, L. (2021). The AI gambit: Leveraging artificial intelligence to combat climate change—Opportunities, challenges, and recommendations. AI Society, 38(1), 283–307. https://doi.org/10.1007/s00146-021-01294-x
22. Dayarathna, M., Wen, Y., Fan, R. (2016). Data center energy consumption modeling: A survey. IEEE Communications Surveys Tutorials, 18(1), 732–794. https://doi.org/10.1109/COMST.2015.2481183
23. Desislavov, R. (2021). Compute and energy consumption trends in deep learning inference. arXiv preprint. https://doi.org/10.48550/arxiv.2109.05472
24. Ferreira, L. L., Dupont, C., Stolf, P., Da Costa, G., Pierson, J. M. (2019). Towards energy-efficient, thermal-aware scheduling in data centers. Sustainable Computing: Informatics and Systems, 23, 240–251. https://doi.org/10.1016/j.suscom.2019.08.003
25. George, A. S., George, A. H., Martin, A. G. (2023). The environmental impact of ai: A case study of water consumption by chat gpt. Partners Universal International Innovation Journal, 1(2), 97-104.
26. Ghamkhari, M., Mohsenian-Rad, H., Mohsenian-Rad, A. (2017). Optimal integration of renewable energy resources in data centers with behind-the-meter renewable generator. IEEE Transactions on Sustainable Energy, 8(2), 625–636. https://doi.org/10.1109/TSTE.2016.2623678
27. Goudarzi, S., Anisi, M., Kama, N., Doctor, F., Soleymani, S., Sangaiah, A. (2019). Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm. Energy and Buildings, 196, 83–93. https://doi.org/10.1016/j.enbuild.2019.05.031
28. Gupta, U., Kim, Y. G., Lee, S., Tse, J., Lee, H. H. S., Wei, G. Y., Wu, C. J.  (2021). Chasing carbon: The elusive environmental footprint of computing. 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). https://doi.org/10.1109/hpca51647.2021.00076
29. Gürsakal, N., Çelik, S., Batmaz, B. (2022). Problems and opportunities of artificial intelligence. Akademik Yaklaşımlar Dergisi, 13(1), 203–225. https://doi.org/10.54688/ayd.1104830
30. Hamid, M., Ganne, A. (2023). Artificial intelligence in energy markets and power systems. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets35943
31. Hassan, Q., Sameen, A., Salman, H., Al-Jiboory, A., Jaszczur, M. (2023). The role of renewable energy and artificial intelligence towards environmental sustainability and net zero. https://doi.org/10.21203/rs.3.rs-2970234/v1
32. He, W., Xu, Q., Zhao, S., Liu, S., Li, H. (2023). Performance analysis of data centers applying hybrid renewable energy power systems. Energy Proceedings, 1(1), 1–10. https://doi.org/10.46855/energy-proceedings-10347
33. He, W., Xu, Q., Guo, R., Liu, S., Wang, Y., Guo, H., Jin, S. (2024). Comparative performance analysis of two hybrid energy systems for data centers with different replenishment energy sources. Energy Proceedings, 1(1), 1–10. https://doi.org/10.46855/energy-proceedings-11223
34. Hurson, A., Sarvestani, S., Wisely, M. (2016). Energy-aware virtual machine placement in cloud data centers. Sustainable Computing: Informatics and Systems, 11, 1–13. https://doi.org/10.1016/j.suscom.2016.03.001
35. International Energy Agency. (2024). Electricity 2024: Executive summary. IEA. Retrieved from https://www.iea.org/reports/electricity-2024/executive-summary.
36. International Telecommunication Union (ITU) United Nations Institute for Training and Research (UNITAR). (2024). Global E-Waste Monitor 2024: Electronic waste rising five times faster than documented e-waste recycling. Retrieved from https://unitar.org/about/news-stories/press/global-e-waste-monitor-2024-electronic-waste-rising-five-times-faster-documented-e-waste-recycling.
37. Kang, D. K., Yang, E. J., Youn, C. H. (2018). Deep learning-based sustainable data center energy cost minimization with temporal MACRO/MICRO scale management. IEEE Access, 7, 28888–28899. https://doi.org/10.1109/access.2018.2888839
38. Krumay, B., Brandtweiner, R. (2016). Measuring the environmental impact of ICT hardware. International Journal of Sustainable Development and Planning, 11(6), 1064–1076. https://doi.org/10.2495/sdp-v11-n6-1064-1076
39. Kumar, R., Dahyalal ,S., (2014). Review: Current status of recycling of waste printed circuit boards in India. Journal of Environmental Protection, 5(1), 1–10. https://doi.org/10.4236/jep.2014.51002
40. Kumar, R., Khatri, S., Diván, M. (2022). Optimization of power consumption in data centers using machine learning based approaches: a review. International Journal of Electrical and Computer Engineering (Ijece), 12(3), 3192. https://doi.org/10.11591/ijece.v12i3.pp3192-3203
41. Kummu, M., Guillaume, J. H., de Moel, H., Eisner, S., Flörke, M., Porkka, M., Ward, P. J. (2016). The world’s road to water scarcity: Shortage and stress in the 20th century and pathways towards sustainability. Scientific Reports, 6(1), 1–16. https://doi.org/10.1038/srep38495
42. Lacoste, A., Luccioni, A., Schmidt, V., Dandres, T. (2019). Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700. https://doi.org/10.48550/arxiv.1910.09700
43. Li, D. (2016). Review of recycling and processing of waste electronic equipment. Proceedings of the 2016 International Conference on Electronics, Mechanical Engineering and Computer Science. https://doi.org/10.2991/icemct-16.2016.219
44. Li, G., Luo, T., Liu, R., Song, C., Zhao, C., Wu, S., Liu, Z. (2024). Integration of carbon dioxide removal (CDR) technology and artificial intelligence (AI) in energy system optimization. Processes, 12(2), 402. https://doi.org/10.3390/pr12020402
45. Li, P., Yang, J., Islam, M. A., Ren, S. (2023). Making AI less “thirsty”: Uncovering and addressing the secret water footprint of AI models. arXiv preprint arXiv:2304.03271.
46. Li, X., Zhuang, Y., Yang, S. X. (2017). Cloud computing for big data processing. Intelligent Automation & Soft Computing, 23(4), 545-546.
47. Liu, J., Yu, Q., Y, Y., Yang, Z. (2022). Can artificial intelligence improve the energy efficiency of manufacturing companies? Evidence from China. International Journal of Environmental Research and Public Health, 19(4), 2091. https://doi.org/10.3390/ijerph19042091
48. M, S., Alam, M. M., Hossain, M. S., Andersson, K. (2022). Sustainable data centers: A systematic review of energy and water efficiency techniques. Sustainable Computing: Informatics and Systems, 33, 100623. https://doi.org/10.1016/j.suscom.2021.100623
49. Mallipeddi, R. (2022). Harnessing AI and IoT technologies for sustainable business operations in the energy sector. Asia Pacific Journal of Energy and Environment, 9(1), 735–749. https://doi.org/10.18034/apjee.v9i1.735
50. Mekonnen, M. M., Hoekstra, A. Y. (2016). Four billion people facing severe water scarcity. Science Advances, 2(2), e1500323. https://doi.org/10.1126/sciadv.1500323
51. Naeeni, S.K., Nouhi, N. (2023). The environmental impacts of AI and digital technologies. Aitechbesosci, 1(4), 3–15. https://doi.org/10.61838/kman.aitech.1.4.3
52. Nishant, R., Kennedy, M., Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2020.102104
53. Oladoyinbo, T. O., Olabanji, S. O., Olaniyi, O. O., Adebiyi, O. O., Okunleye, O. J., Ismaila Alao, A. (2024). (2024). Exploring the challenges of artificial intelligence in data integrity and its influence on social dynamics. Asian Journal of Advanced Research and Reports, 18(2), 1–10. https://doi.org/10.9734/ajarr/2024/v18i2601
54. Olatunde, T. M., Okwandu, A. C., Akande, D. O., Sikhakhane, Z. Q. (2024). Reviewing the role of artificial intelligence in energy efficiency optimization. Engineering Science Technology Journal, 5(4), 1243–1256. https://doi.org/10.51594/estj.v5i4.1015
55. OpenAI. (2024). Training large language models and their environmental costs. Scientific American. Retrieved from https://www.scientificamerican.com
56. Oró, E., Depoorter, V., Garcia, A., Salom, J. (2015). Energy efficiency and renewable energy integration in data centres. Strategies and modelling review. Renewable and Sustainable Energy Reviews, 50, 1035–1045. https://doi.org/10.1016/j.rser.2014.10.035
57. Osibo, B., Adamo, S. (2023). Data centers and green energy: Paving the way for a sustainable digital future. International Journal of Latest Technology in Engineering Management Applied Science, 12(1), 1–10. https://doi.org/10.51583/ijltemas.2023.121103
58. Othman, N., Mohammad, R., Kamaruddin, S. A. (2015). Prediction of electronic waste disposals from residential areas in Malaysia. Jurnal Teknologi, 74(1), 1–10. https://doi.org/10.11113/jt.v74.4826
59. Peng, X., Bhattacharya, T., Cao, T., Mao, J., Tekreeti, T., Qin, X.  (2022). Exploiting renewable energy and UPS systems to reduce power consumption in data centers. Big Data Research, 28, 100306. https://doi.org/10.1016/j.bdr.2021.100306
60. Perucica, N., Andjelković, K. (2022). Is the future of AI sustainable? A case study of the European Union. Transforming Government: People, Process and Policy, 16(3), 1–20. https://doi.org/10.1108/tg-06-2021-0106
61. Poff, N. L., Richter, B. D., Arthington, A. H., Bunn, S. E., Naiman, R. J., Kendy, E., Warner, A. (2015). The ecological limits of hydrologic alteration (ELOHA): A new framework for developing regional environmental flow standards. Freshwater Biology, 55(1), 147–170. https://doi.org/10.1111/j.1365-2427.2009.02204.x
62. Qian, K., Mao, L., Liang, X., Ding, Y., Gao, J., Wei, X., Li, J. (2023). Towards Sustainable Urban Planning via Multi-Agent Reinforcement Learning. arXiv preprint arXiv:2310.16772. https://arxiv.org/abs/2310.16772
63. Ramli, M. A., Jambari, D. I. (2018). Cooling system optimization in data centers: A review. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1–4), 1–6.
64. Rojek, I., Mroziński, A., Kotlarz, P., Macko, M., Mikołajewski, D. (2023). AI-based computational model in sustainable transformation of energy markets. Energies, 16(24), 8059. https://doi.org/10.3390/en16248059
65. Rolnick, D., Donti, P., Kaack, L., Kochanski, K., Lacoste, A., Sankaran, K., Bengio, Y. (2022). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), 1–96. https://doi.org/10.1145/3485128
66. Schwartz, R., Dodge, J., Smith, N., Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. https://doi.org/10.1145/3381831
67. Șerban, A., Lytras, M. (2020). Artificial intelligence for smart renewable energy sector in Europe—smart energy infrastructures for next generation smart cities. IEEE Access, 8, 77364–77377. https://doi.org/10.1109/access.2020.2990123
68. Shin, D. (2019). Toward fair, accountable, and transparent algorithms: Case studies on algorithm initiatives in Korea and China. Javnost-The Public, 26(3), 274–290.
69. Sîrbu, A., Babaoğlu, O. (2015). Towards sustainable cloud computing: A survey on achieving energy efficiency in software architectures. Sustainable Computing: Informatics and Systems, 6, 65–85. https://doi.org/10.1016/j.suscom.2015.01.001
70. Stanford University. (2024). Artificial Intelligence Index Report 2024. Stanford Institute for Human-Centered AI. Retrieved from https://aiindex.stanford.edu/wp-content/uploads/2024/04/HAI_2024_AI-Index-Report.pdf.
71. Sun, X., Ansari, N., Wang, R. (2016). Optimizing resource utilization of a data center. IEEE Communications Surveys & Tutorials, 18(4), 2822-2846.
72. Sun, Z., Cao, H., Stojmenovic, M., Stojmenovic, I. (2016). Reducing the energy cost in cloud computing by using energy aware algorithms. IEEE Communications Letters, 20(8), 1524–1527. https://doi.org/10.1109/LCOMM.2016.2574999
73. Tamburrini, G. (2022). The AI carbon footprint and responsibilities of AI scientists. Philosophies, 7(1), 4. https://doi.org/10.3390/philosophies7010004
74. Uzaman, F. U., Alam, M. M., Hossain, M. S., Andersson, K. (2019). Towards sustainable data centers: A review and synthesis. Sustainable Computing: Informatics and Systems, 21, 168–186. https://doi.org/10.1016/j.suscom.2018.12.002
75. Villiers, C., Dimes, R., Molinari, M., (2023). How will AI text generation and processing impact sustainability reporting? Critical analysis, a conceptual framework and avenues for future research. Sustainability Accounting Management and Policy Journal. https://doi.org/10.1108/sampj-02-2023-0097
76. Wang, H., Tang, D. (2022). Challenges and opportunities for the energy management of sustainable data centers in smart grids. IOP Conference Series: Earth and Environmental Science, 984(1), 012005. https://doi.org/10.1088/1755-1315/984/1/012005
77. Wang, X., Ji, K., Xie, T. (2023). AI carbon footprint management with multi-agent participation: A tripartite evolutionary game analysis based on a case in China. Sustainability, 15(11), 9013. https://doi.org/10.3390/su15119013
78. Xiao, D. (2023). Neuroscience-inspired continuous learning: A sustainable approach to AI energy challenge. https://doi.org/10.31219/osf.io/twn9q
79. Xu, H., Wei, Q., Liu, Y. (2019). The exploring of electronic waste recycling in Chongqing. Proceedings of the 2019 International Conference on Humanities and Social Science Research. https://doi.org/10.2991/ichssr-19.2019.146
80. Yang, C., Huang, Q., Li, Z., Liu, K., Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13-53.
81. Yang, J., Xiao, W., Jiang, C., Hossain, M. S., Muhammad, G., Amin, S. U.  (2019). AI-powered green cloud and data center. IEEE Access, 7, 28888–28899. https://doi.org/ 10.1109/ACCESS.2018.2888976
82. Zhang, S., Ding, Y., Liu, B., Pan, D. A., Chang, C. C.,Volinsky, A. A. (2015). Challenges in legislation, recycling system and technical system of waste electrical and electronic equipment in China. Waste Management, 45, 1–12. https://doi.org/10.1016/j.wasman.2015.05.015
83. Zhang, J., Sang, L., Xu, Y., Sun, H. (2023). A Multi-agent Safe Reinforcement Learning Framework for Carbon-Constrained Demand Response. arXiv preprint arXiv:2311.15594. https://arxiv.org/abs/2311.15594
84. Zhao, Y., Fariñas, J. (2022). Artificial intelligence and sustainable decisions. European Business Organization Law Review, 23(2), 215–234. https://doi.org/10.1007/s40804-022-00262-2
85. Zhou, G. L., Zhu, Y. R., Mao, L. N. (2014). Design of the electronic waste recycle network system based on GIS. Applied Mechanics and Materials, 518, 381–385. https://doi.org/10.4028/www.scientific.net/amm.518.381
86. Zu, Y. X., Jia, S. S., Li, Z., Jia, Y.  (2012). Study on the layout and structure of the waste electronic products recycling network. Advanced Materials Research, 518–523, 3613–3617. https://doi.org/10.4028/www.scientific.net/amr.518-523.3613