Ekolojik Krizlerin Çözümünde Yapay Zekânın Rolü: Eleştirel Bir İnceleme

Yıl 2025, Cilt: 3 Sayı: 2, 135 - 156, 30.10.2025

Bedri Sina Güneş

Öz

Bu çalışma, özellikle yapay zekâ (YZ) bağlamında 21. yüzyılda yaşanan teknolojik dönüşümün ekolojik krizlerin çözümündeki potansiyelini eleştirel bir bakış açısıyla incelemektedir. Avcı-toplayıcı toplumlardan feodal ve kapitalist sistemlere kadar uzanan tarihsel ve toplumsal dönüşümler üzerinden, insan-doğa ilişkilerinin nasıl yeniden şekillendiğini ve ekolojik krizlerin üretim ilişkileriyle ne kadar sıkı bir bağ içinde olduğunu ortaya koymaktadır. Makale, su kalitesi, hava kalitesi, biyolojik çeşitlilik ve habitat koruma ile karbon yakalama ve depolama gibi temel ekolojik alanlarda YZ uygulamalarını ele alarak, bu teknolojilerin çevresel sorunları izleme ve hafifletmede etkili bir rol oynayabileceğini göstermektedir. Ancak vaka çalışmaları, YZ’nin potansiyel faydalarının veri altyapısı eksiklikleri, yüksek maliyetler, algoritmik belirsizlikler, toplumsal eşitsizlikler ve yetersiz yönetişim mekanizmaları nedeniyle sınırlı kaldığını ortaya koymaktadır. Çalışma, YZ’nin ekolojik kriz yönetiminde etkinliğinin yalnızca teknik gelişmelere değil, aynı zamanda toplumsal adaptasyona, kapsayıcı yönetişime ve kaynaklara adil erişime bağlı olduğunu vurgulamaktadır. YZ teknolojileri, toplumsal gözetimle ve ortak iyiye odaklanarak kullanıldığında sürdürülebilir ekolojik yönetim için güçlü araçlar haline gelebilir; aksi takdirde düzensiz veya adaletsiz kullanım mevcut krizleri derinleştirme riski taşımaktadır. Bulgular, küresel çevresel sorunlarla başa çıkmada YZ’nin potansiyelini tam olarak değerlendirebilmek için teknik, toplumsal ve ekolojik boyutların entegre edilmesinin önemini vurgulamaktadır.

Anahtar Kelimeler

yapay zeka , ekolojik kriz , çevre yönetimi , biyolojik çeşitliliğin korunması , karbon yakalama ve depolama , veri altyapısı

Etik Beyan

Bu çalışma, tüm etik kurallara uyulmuş olup, çıkar çatışması bulunmamaktadır.

Artificial Intelligence Applications in Addressing the Ecological Crisis: A Critical Review

Öz

This study critically examines the potential of 21st-century technological transformation, particularly artificial intelligence (AI), in addressing ecological crises. Drawing on historical and societal transformations—from hunter-gatherer societies to feudal and capitalist systems—it highlights how human-nature relationships have been reshaped and how ecological crises are closely intertwined with production relations. The paper explores AI applications in key ecological domains, including water quality, air quality, biodiversity and habitat conservation, and carbon capture and storage, demonstrating that these technologies can play an effective role in monitoring and mitigating environmental problems. However, case studies indicate that the potential benefits of AI are constrained by data infrastructure gaps, high costs, algorithmic uncertainties, social inequalities, and insufficient governance mechanisms. The study argues that the effectiveness of AI in ecological crisis management depends not only on technical advancements but also on social adaptation, inclusive governance, and equitable access to resources. When deployed with societal oversight and aligned with collective well-being, AI technologies can serve as powerful tools for sustainable ecological management, whereas unregulated or inequitable use risks deepening existing crises. The findings underscore the importance of integrating technical, social, and ecological considerations to fully leverage AI’s potential in addressing global environmental challenges.

Anahtar Kelimeler

Artificial Intelligence , Ecological Crisis , Environmental Management , Biodiversity Conservation , Carbon Capture , Data Infrastructure

Etik Beyan

This study complies with all ethical standards, and no conflict of interest is declared.

Kaynakça

• Altvater, E., & Mahnkopf, B. (1997). The world market unbound. Review of International Political Economy, 4(3), 448-471. https://soz.ovgu.de/isoz_media/downloads/schrader/globalisierung/AltvaterTheMarketUnbound1997.pdf (14.09.2025).
• Berrisford, L. J., Ribeiro, E., & Menezes, R. (2022). Estimating ambient air pollution using structural properties of road networks. arXiv. https://arxiv.org/abs/2207.14335 (04.09.2025).
• Beevers, S. D., Kitwiroon, N., Williams, M. L., Kelly, F. J., Anderson, H. R., & Carslaw, D. C. (2013). Air pollution dispersion models for human exposure predictions in London. Journal of Exposure Science & Environmental Epidemiology, 23(6), 647-653. https://doi.org/10.1038/jes.2013.6 https://www.nature.com/articles/jes20136 (04.09.2025).
• BEIS (Department for Business, Energy & Industrial Strategy). (2021). White Rose Carbon Capture and Storage Project. Retrieved from Beevers, S. D., Kitwiroon, N., Williams, M. L., Kelly, F. J., Anderson, H. R., & Carslaw, D. C. (2013). Air pollution dispersion models for human exposure predictions in London. Journal of Exposure Science & Environmental Epidemiology, 23(6), 647-653. https://doi.org/10.1038/jes.2013.6 https://www.nature.com/articles/jes20136 (04.09.2025).
• BEIS (Department for Business, Energy & Industrial Strategy). (2021). White Rose Carbon Capture and Storage Project. Retrieved from https://www.gov.uk/government/publications/white-rose-carbon-capture-and-storage-project (05.09.2025).
• Bell, D. (1999). The coming of post-industrial society: A venture in social forecasting. Basic Books.
• Bell, M. L., & Davis, D. L. (2001). Reassessment of the lethal London fog of 1952: Novel indicators of acute and chronic consequences of acute exposure to air pollution. Environmental Health Perspectives, 109(Suppl 3), 389–394. https://doi.org/10.1289/ehp.01109s3389 https://ehp.niehs.nih.gov/doi/10.1289/ehp.01109s3389 (05.09.2025).
• Branco, V. V., Correia, L., & Cardoso, P. (2023). The use of machine learning in species threats and conservation analysis. Biological Conservation, 283, Article 110091. https://doi.org/10.1016/j.biocon.2023.110091 https://www.sciencedirect.com/journal/biological-conservation/vol/283/suppl/C (02.09.2025).
• Burawoy, M. (2005). For public sociology. American Sociological Review, 70(1), 4–28.
• Burkett, P. (2014). Marx and nature: A red and green perspective. Haymarket Books.
• Carson, R. (1962). Silent Spring. Houghton Mifflin. https://library.uniteddiversity.coop/More_Books_and_Reports/Silent_Spring-Rachel_Carson-1962.pdf (14.09.2025).
• Castells, M. (2010). The rise of the network society (2nd ed.). Wiley-Blackwell.
• CEH (Centre for Ecology & Hydrology). (2021). Sustainable water management in rural and urban areas: River water quality monitoring. Retrieved from https://www.ceh.ac.uk/sites/default/files/2021-11/sustainable-water-guide-chap5-river-water-quality-v2.pdf (02.09.2025).
• Defra (Department for Environment, Food & Rural Affairs). (2024). World-first project launched to track hedgehogs and reverse decline. Retrieved from https://defraenvironment.blog.gov.uk/2024/08/22/world-first-project-launched-to-track-hedgehogs-and-reverse-decline/ (02.09.2025).
• Durham University. (2024). National Hedgehog Monitoring Programme. Retrieved from https://www.durham.ac.uk/news-events/latest-news/2024/03/national-hedgehog-monitoring-programme/ (02.09.2025).
• Environment Agency. (2023). Thames River Water Quality Monitoring with AI. Retrieved from https://www.gov.uk/government/organisations/environment-agency (02.09.2025).
• EPA. (2023). Artificial Intelligence Applications for Wildfire Smoke Monitoring. United States Environmental Protection Agency. Retrieved from https://www.epa.gov/air-research (04.09.2025).
• Figueroa, J. D., Fout, T., Plasynski, S., McIlvried, H., & Srivastava, R. D. (2020). Advances in CO₂ capture technology—The U.S. Department of Energy’s Carbon Sequestration Program. International Journal of Greenhouse Gas Control, 4(2), 145–165. https://www.researchgate.net/publication/222431915_Advances_in_CO2_Capture_Technology-The_US_Department_of_Energy's_Carbon_Sequestration_Program (05.09.2025).
• Flórez, M., Becerra, O., Carrillo, E., Villa, M., Álvarez, Y., Suárez, J., & Mendes, F. (2024). Deep learning application for biodiversity conservation and educational tourism in natural reserves. ISPRS International Journal of Geo-Information, 13(10), Article 358. https://doi.org/10.3390/ijgi13100358 https://www.mdpi.com/2992530 (04.09.2025).
• Ford, H. V., Jones, N. H., Davies, A. J., Godley, B. J., Jambeck, J. R., Napper, I. E., Suckling, C. C., Williams, G. J., Woodall, L. C., & Koldewey, H. J. (2022). The fundamental links between climate change and marine plastic pollution. Science of The Total Environment, 806, Article 150392. https://doi.org/10.1016/j.scitotenv.2021.150392 https://www.sciencedirect.com/science/article/pii/S0048969721054693?via%3Dihub (05.09.2025).
• Foster, J. B., Clark, B., & York, R. (2010). The ecological rift: Capitalism’s war on the earth. Monthly Review Press.
• Foster, J. B. (2020). The return of nature: Socialism and ecology. Monthly Review Press.
• German Environment Agency (UBA). (2022). AI-supported Water Monitoring in the Rhine River. Retrieved from https://www.umweltbundesamt.de (02.09.2025).
• German Environment Agency (UBA). (2022). AI-supported Biodiversity Monitoring in the Rhine River Habitats. Retrieved from https://www.umweltbundesamt.de (02.09.2025).
• Geyer, R., Jambeck, J. R., & Law, K. L. (2017). Production, use, and fate of all plastics ever made. Science Advances, 3(7), e1700782. https://www.science.org/doi/10.1126/sciadv.1700782 (02.09.2025).
• Grange, S. K., & Carslaw, D. C. (2019). Using meteorological normalisation to detect interventions in air quality time series. Science of The Total Environment, 653, 578–588. https://doi.org/10.1016/j.scitotenv.2018.10.380 https://www.sciencedirect.com/science/article/pii/S004896971834244X (05.09.2025). Greater London Authority. (2021). London Air Quality Network (LAQN) Annual Report 2021. Retrieved from https://www.londonair.org.uk/ (04.09.2025).
• Gudynas, E. (2020). Extractivisms: Politics, Economy and Ecology. Fernwood Publishing.
• Harvey, D. (2017). Marx, capital and the madness of economic reason. Profile Books.
• Hehmeyer, A. (2024, May 14). How artificial intelligence buys valuable time to protect wildlife. World Wildlife Fund. https://www.worldwildlife.org/stories/how-artificial-intelligence-buys-valuable-time-to-protect-wildlife (02.09.2025).
• IEA (International Energy Agency). (2022). Carbon Capture, Utilisation and Storage: Tracking Report 2022. https://www.iea.org/energy-system/carbon-capture-utilisation-and-storage (05.09.2025).
• IEA (International Energy Agency). (2023). AI and energy efficiency: Emerging opportunities. Paris: IEA. https://www.iea.org/reports/energy-and-ai (27.08.2025).
• IPCC. (2022). Climate Change 2022: Impacts, adaptation and vulnerability. Cambridge University Press.
• Jasanoff, S. (2016). The ethics of invention: Technology and the human future. W. W. Norton & Company.
• Klein, N. (2014). This changes everything: Capitalism vs. the climate. Simon & Schuster.
• Latour, B. (2018). Down to Earth: Politics in the new climatic regime. Polity.
• Lhoumeau, S., Pinelo, J., & Borges, P. A. V. (2025). Artificial intelligence for biodiversity: Exploring the potential of recurrent neural networks in conservation scenarios. Ecological Indicators, 171, Article 113119. https://doi.org/10.1016/j.ecolind.2025.113119 https://www.sciencedirect.com/science/article/pii/S1470160X25000482?via%3Dihub (02.09.2025).
• Li, P., Yang, J., Wierman, A., & Ren, S. (2024). Towards environmentally equitable AI via geographical load balancing. Proceedings of the 15th ACM International Conference on Future Energy Systems (e-Energy ’24), 1–16. https://doi.org/10.1145/3632775.3661938 https://dl.acm.org/doi/10.1145/3632775.3661938 (05.09.2025).
• Marx, K. (2011). Capital: Volume 1. Penguin Classics.
• Marx, K. (2013). Economic and Philosophic Manuscripts of 1844 (Trans. M. Belge). Istanbul: Birikim Publishing.
• Moore, J. W. (2015). Capitalism in the web of life: Ecology and the accumulation of capital. Verso. http://pinguet.free.fr/moore2015.pdf (02.09.2025).
• Morgan, L. H. (1994). Ancient Society (2nd ed., Trans. Ü. Oskay). Istanbul: Payel Publishing.
• Natural Resources Canada. (2021). Machine Learning Applications for Water Quality Monitoring in Ontario. Retrieved from https://www.nrcan.gc.ca (02.09.2025).
• Natural Resources Canada. (2021). Carbon Capture and Storage in Alberta: AI Applications. Retrieved from https://www.nrcan.gc.ca (05.09.2025).
• NETL (National Energy Technology Laboratory). (2021). Petra Nova Carbon Capture Project. https://netl.doe.gov/sites/default/files/publication/NETL-January-2021-Carbon-Capture-Newsletter.pdf (05.09.2025).
• Patel, R., & Moore, J. W. (2017). A history of the world in seven cheap things: A guide to capitalism, nature, and the future of the planet. Verso.
• Polanyi, K. (2024). The great transformation: The political and economic origins of our time. Beacon Press.
• Ponting, C. (2000). A Green History of the World: The Environment and the Collapse of Great Civilizations (Trans. A. Başçı-Sander). Istanbul: Sabancı University Press.
• Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1 https://pure.mpg.de/pubman/item/item_3029184_9/component/file_3282959/BGC3001P.pdf (02.09.2025).
• Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Slavin Ross, A., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., S. Karthik Mukkavilli, K. P. Kording, C. P. Gomes, A. Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, & Yoshua Bengio. (2022). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), Article 42. https://doi.org/10.1145/3485128 https://asross.github.io/publications/RolnickEtAl2022.pdf (02.09.2025).
• Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson. Sætra, H. S. (2021). A shallow defence of a technocracy of artificial intelligence: Examining the political harms of algorithmic governance in the domain of climate change. Technology in Society, 67, 101725. https://ideas.repec.org/a/eee/teinso/v62y2020ics0160791x19305925.html (02.09.2025).
• Saito, K. (2022). Marx in the Anthropocene: Towards the idea of degrowth communism. Cambridge University Press.
• Spectroscopy Online. (2023). Artificial Intelligence and Machine Learning in Assessing Water Quality. Retrieved from https://www.spectroscopyonline.com/view/artificial-intelligence-and-machine-learning-assessing-water-quality (02.09.2025).
• Steffen, W., Richardson, K., Rockström, J., Cornell, S. E., Fetzer, I., Bennett, E. M., Biggs, R., Carpenter, S. R., de Vries, W., de Wit, C. A., Folke, C., Gerten, D., Heinke, J., Mace, G. M., Persson, L. M., Ramanathan, V., Reyers, B., & Sörlin, S. (2015). Planetary boundaries: Guiding human development on a changing planet. Science, 347(6223), 1259855. https://doi.org/10.1126/science.1259855 https://www.science.org/doi/10.1126/science.1259855 (02.09.2025).
• Svampa, M. (2019). Neo-extractivism in Latin America: Socio-environmental conflicts, the territorial turn, and new political narratives. Cambridge University Press.
• Turing Institute. (2025). AI and Autonomous Systems for Assessing Biodiversity and Ecosystem Health. Retrieved from https://www.turing.ac.uk/research/research-projects/ai-and-autonomous-systems-assessing-biodiversity-and-ecosystem-health (02.09.2025).
• US Environmental Protection Agency (EPA). (2022). Artificial Intelligence in Water Quality Management. Retrieved from https://www.epa.gov (02.09.2025).
• Walther, C. (2025). How AI Exclusion Impacts Humankind. Knowledge at Wharton. https://knowledge.wharton.upenn.edu/article/how-ai-exclusion-impacts-humankind/ (05.09.2025). WHO. (2023). Air pollution. World Health Organization. Retrieved from https://www.who.int/health-topics/air-pollution (02.09.2025).
• Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
• Zhu, K., Chen, L., Li, L., Wang, Y., Yan, X., Chen, J., Feng, C., & Shen, Z. (2023). New distributed model for predicting erosion-type pollution by integrating sediment connectivity and watershed model. Environmental Modelling & Software, 157, Article 105662. https://doi.org/10.1016/j.envsoft.2023.105662 https://www.sciencedirect.com/science/article/abs/pii/S1364815223000488?via%3Dihub (04.09.2025).