İklim Değişikliği Araştırmalarında Yapay Zekâ ve Makine Öğrenmesi Uygulamaları: Bibliyometrik Bir Analiz

Abstract

İklim değişikliği günümüzün en acil sorunlarından biri olarak kabul edilmektedir. Artan riskler ve olumsuz etkiler, derin sosyal, ekonomik ve kalkınmasal sonuçlar doğurmaktadır. Bu etkilerin ele alınması ve azaltılması, teknolojik yeniliği temel bir gereklilik haline getirmiştir. Gelişen yaklaşımlar arasında, çevrimiçi verileri kullanarak zamanında ve bağlama özgü bilgiler sağlayan yapay zekâ (YZ) ve makine öğrenimi (MÖ), potansiyel çözümler üretme kapasiteleriyle öne çıkmaktadır. Çevresel etkileri değerlendirmek ve tahminlerde bulunmak için temel araçlar haline gelen YZ ve MÖ odaklı modeller, iklim adaptasyon kapasitesini artırmaktadır. İklim değişkenlerindeki karmaşık, ayrılması zor dinamiklerin analizini kolaylaştırır ve sağlam etki değerlendirmelerini destekler. Veri kümelerini entegre ederek, bu teknolojiler uzun vadeli iklim senaryolarının daha hassas tahminlerini mümkün kılar. Bu çalışmanın temel amacı, YZ ve MÖ tekniklerinin özelliklerini ve doğal sistemlerde önemli kayıplara neden olan bir tehdit olan iklim değişikliğinin ele alınmasındaki potansiyel rollerini incelemektir. Bu çalışma, yapay zekâ ve makine öğrenmesinin iklim değişikliğiyle mücadeledeki rolünü bibliyometrik analiz yoluyla incelemekte ve bu alandaki akademik yayınların zaman içindeki gelişimine odaklanmaktadır. Araştırma sonuçları, yapay zekâ ve makine öğreniminin iklim değişikliğiyle mücadelede hem avantajlar hem de dezavantajlar sunabileceğini ortaya koymaktadır. Faydaları çok daha önemli olsa da fayda ve riskler arasındaki dengeyi kurmak çok önemlidir.

Keywords

• Küresel ısınma literatürü
• çevresel izleme
• sera gazı emisyonları
• öngörücü modelleme
• yayın eğilimleri

Artificial Intelligence and Machine Learning Applications in Climate Change Research: A Bibliometric Analysis

Abstract

Climate change is considered one of today's most pressing problems. Increasing risks and negative impacts have profound social, economic, and developmental implications. Addressing and mitigating these impacts has made technological innovation a fundamental necessity. Among emerging approaches, artificial intelligence (AI) and machine learning (ML), which provide timely and context-specific insights using online data, stand out for their capacity to generate potential solutions. AI- and ML-based models, which have become fundamental tools for assessing environmental impacts and making predictive forecasts, are enhancing climate adaptation capacity. They facilitate the analysis of complex, hard-to-separate dynamics in climate variables and support robust impact assessments. By integrating datasets, these technologies enable more precise predictions of extended climate scenarios. The primary objective of this study is to investigate the characteristics of AI and ML techniques and their potential role in addressing climate change, a threat that causes significant losses in natural systems. The study examines the role of AI and ML in combating climate change through bibliometric analysis, focusing on the evolution of academic publications in the field over time. The findings reveal that AI and ML can offer both advantages and disadvantages in combating climate change. While their benefits are far more significant, weighing the balance between benefits and risks is crucial.

Keywords

• Global warming literature
• environmental monitoring
• greenhouse gas emissions
• predictive modelling
• publication trends

References

1. Şenyapar, H. N. D., Ünal, K., & Kardiyen, F. (2024). Enhancing ecological footprint awareness among academic staff at Gazi University: A sustainability communication approach. Politeknik Dergisi, 27(2), 789–807.
2. United Nations Framework Convention on Climate Change (UNFCCC). (1992). United Nations framework convention on climate change.
3. Demirci, M. (2024). İklim değişikliği ve yapay zekâ teknolojisinde yaşanan gelişmeler ışığında ilerleme düşüncesinin evrimi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 80, 249–269.
4. IPCC. (2023). Synthesis report of the IPCC sixth assessment report (AR6) summary for policy makers.
5. Tuğaç, Ç. (2023). İklim değişikliği ve yapay zekâ: fırsatlar ve sorunlar. Hitit Sosyal Bilimler Dergisi, 16(1), 74–94.
6. Shivanna, K. R. (2022). Climate change and its impact on biodiversity and human welfare. Proceedings of the Indian National Science Academy, 88, 160–171.
7. Yue, X. L., & Gao, Q. X. (2018). Contributions of natural systems and human activity to greenhouse gas emissions. Advances in Climate Change Research, 9, 243–252.
8. Chen, L., Chen, Z., Zhang, Y., Liu, Y., Osman, A. I., Farghali, M., Hua, J., Al-Fatesh, A., Ihara, I., Rooney, D. W., & Yap, P.-S. (2023). Artificial intelligence-based solutions for climate change: A review. Environmental Chemistry Letters, 21(5), 2525–2557.

9. Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., & Yang, H. (2019). Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters, 14(12), 124007.
10. Ejiyi, C. J., Cai, D., Thomas, D., Obiora, S., Osei-Mensah, E., & Acen, C. (2025). Comprehensive review of artificial intelligence applications in renewable energy systems: Current implementations and emerging trends. Journal of Big Data, 12(1), 169.
11. Leal Filho, W., Wall, T., Mucova, S. A. R., Nagy, G. J., Balogun, A. L., Luetz, J. M., & Ng, A. (2022). Deploying artificial intelligence for climate change adaptation. Technological Forecasting and Social Change, 180, 121662.
12. Ise, T., & Oba, Y. (2019). Forecasting climatic trends using neural networks: An experimental study using global historical data. Frontiers in Robotics and AI, 6, 446979.
13. Zheng, G., Li, X., Zhang, R. H., & Liu, L. (2020). Purely satellite data-driven deep learning forecast of complicated tropical instability waves. Science Advances, 6(29), eaba1482.
14. Rasp, S., Pritchard, M. S., & Gentine, P. (2018). Deep learning to represent subgrid processes in climate models. Proceedings of the National Academy of Sciences, 115(39), 9684–9689.
15. Choi, C., Kim, J., Kim, J., Kim, D., Bae, Y., & Kim, H. S. (2018). Development of heavy rain damage prediction model using machine learning based on big data. Advances in Meteorology, 2018(1), 5024930.
16. Jaafari, A., Zenner, E. K., Panahi, M., & Shahabi, H. (2019). Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and Forest Meteorology, 266–267, 198–207.
17. Gohr, C., Rodríguez, G., Belomestnykh, S., Berg-Moelleken, D., Chauhan, N., Engler, J. O., & Fischer, J. (2025). Artificial intelligence in sustainable development research. Nature Sustainability, 8(8), 970-978.
18. Hultin, L., & Mähring, M. (2025). Reimagining AI for sustainability: Cultivating imagination, hope, and response-ability. Information and Organization, 35(3), 100586.
19. Baji, T. (2018). Evolution of the GPU device widely used in AI and massive parallel processing. In Proceedings of the 2018 IEEE 2nd Electron Devices Technology and Manufacturing Conference (EDTM) (pp. 7–9). IEEE.
20. Biesbroek, R., Badloe, S., & Athanasiadis, I. N. (2020). Machine learning for research on climate change adaptation policy integration: An exploratory UK case study. Regional Environmental Change, 20(3), 85.
21. Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.
22. Min, Y., Mukkavilli, S. K., & Bengio, Y. (2019). Predicting ice flow using machine learning. arXiv preprint, arXiv:1910.08922.
23. Öztürk, B., & Bilgiç, G. (2022). Yenilenebilir enerji tahmini için derin öğrenme yaklaşımları. In TEKNOBİLİM-2022: Enerji Krizi ve Yenilenebilir Enerji (p. 111).
24. Chen, Z., Zhu, Z., Jiang, H., & Sun, S. (2020). Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. Journal of Hydrology, 591, 125286.
25. Farghali, M., Osman, A. I., Mohamed, I. M. A., Chen, Z., Chen, L., Ihara, I., & Zhang, L. (2023). Strategies to save energy in the context of the energy crisis: A review. Environmental Chemistry Letters, 21(4), 2003-2039.
26. Aydın, S. G., & Aydoğdu, G. (2022). Makine öğrenmesi algoritmaları kullanılarak Türkiye ve AB ülkelerinin CO₂ emisyonlarının tahmini. Avrupa Bilim ve Teknoloji Dergisi, 37, 42–46.
27. Wang, X., Wang, Y., & Wei, C. (2023). The impact of natural resource abundance on green economic growth in the belt and road countries: The role of institutional quality. Environmental Impact Assessment Review, 98, 106977.
28. Delanoë, P., Tchuente, D., & Colin, G. (2023). Method and evaluations of the effective gain of artificial intelligence models for reducing CO₂ emissions. Journal of Environmental Management, 331, 117261.
29. Moraliyage, H., Dahanayake, S., De Silva, D., Mills, N., Rathnayaka, P., Nguyen, S., Alahakoon, D., & Jennings, A. (2022). A robust artificial intelligence approach with explainability for measurement and verification of energy efficient infrastructure for net zero carbon emissions. Sensors, 22(23), 9503.
30. Al-Othman, A., Tawalbeh, M., Martis, R., Dhou, S., Orhan, M., Qasim, M., & Olabi, A. G. (2022). Artificial intelligence and numerical models in hybrid renewable energy systems with fuel cells: Advances and prospects. Energy Conversion and Management, 253, 115154.
31. Demirelli, E., Solak, H. İ., & Tiryakioğlu, İ. (2023). Makine öğrenmesi algoritmaları ile deprem katalogları kullanılarak deprem tahmini. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 13(4), 979–989.
32. Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C. P., Ng, A. Y., Hassabis, D., Platt, J. C., Creutzig, F., Chayes, J., & Bengio, Y. (2019). Tackling climate change with machine learning. arXiv, arXiv:1906.05433.
33. Bonan, G. B., & Doney, S. C. (2018). Climate, ecosystems, and planetary futures: The challenge to predict life in earth system models. Science, 359(6375), eaam8328.
34. Kadow, C., Hall, D. M., & Ulbrich, U. (2020). Artificial intelligence reconstructs missing climate information. Nature Geoscience, 13, 408–413.
35. Dominguez, D., del Villar, L. D., Pantoja, O., & González Rodríguez, M. (2022). Forecasting Amazon rainforest deforestation using a hybrid machine learning model. Sustainability, 14, 691.
36. Elbeltagi, A., Kushwaha, N. L., Srivastava, A., & Zoof, A. T. (2022). Artificial intelligent-based water and soil management. In Deep learning for sustainable agriculture (pp. 129–142). Academic Press.
37. Swaminathan, B., Palani, S., Vairavasundaram, S., Kotecha, K., & Kumar, V. (2023). IoT-driven artificial intelligence technique for fertilizer recommendation model. IEEE Consumer Electronics Magazine, 12(1), 109–117.
38. Xie, R., Jan, N. M., Hao, K., Chen, L., & Huang, B. (2020). Supervised variational autoencoders for soft sensor modeling with missing data. IEEE Transactions on Industrial Informatics, 16(5), 2820–2828.
39. Perera, Y. S., Ratnaweera, D. A. A. C., Dasanayaka, C. H., & Abeykoon, C. (2023). The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: A critical review. Engineering Applications of Artificial Intelligence, 121, 105988.
40. Sun, K., Wu, X., Xue, J., & Ma, F. (2019). Development of a new multilayer perceptron-based soft sensor for SO₂ emissions in power plants. Journal of Process Control, 84, 182–191.
41. Fernandez de Canete, J., del Saz Orozco, P., Gómez de Gabriel, J., Baratti, A., Ruano, A., & Rivas Blanco, I. (2021). Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach. Computers & Chemical Engineering, 144, 107146.
42. Cohen, Y., Faccio, M., Pilati, F., & Yao, X. (2019). Design and management of digital manufacturing and assembly systems in the industry 4.0 era. International Journal of Advanced Manufacturing Technology, 105(9), 3565–3577.
43. Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), 492.
44. Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F., & Lin, Z. (2021). When machine learning meets privacy: A survey and outlook. ACM Computing Surveys, 54(2), 1–36.
45. Xu, R., Baracaldo, N., & Joshi, J. (2021). Privacy-preserving machine learning: Methods, challenges and directions. arXiv preprint, arXiv:2108.04417.
46. Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2023). The AI gambit: Leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI & Society, 1–25.
47. Ahmed, Q. W., Garg, S., Rai, A., Ramachandran, M., Jhanjhi, N. Z., Masud, M., & Baz, M. (2022). AI-based resource allocation techniques in wireless sensor internet of things networks in energy efficiency with data optimization. Electronics, 11, 2071.
48. Arumugam, K., Swathi, Y., Sanchez, D. T., Mustafa, M., Phoemchalard, C., Phasinam, K., & Okoronkwo, E. (2022). Towards applicability of machine learning techniques in agriculture and energy sector. Materials Today: Proceedings, 51, 2260–2263.
49. Clarivate Analytics. Web of Science Core Collection, https://www.webofscience.com, (Accessed March 1, 2025).
50. Elsevier. Scopus database, https://www.scopus.com, (Accessed March 1, 2025).
51. Google LLC. Google Scholar, https://scholar.google.com, (Accessed March 1, 2025).
52. Karcıoğlu, R., & Öztürk, S. (2023). İklim değişikliği muhasebesi araştırmalarının bibliyometrik analizi. Muhasebe ve Denetime Bakış, 23(70), 23–44.