Araştırma Makalesi

A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends

Cilt: 16 Sayı: 4 30 Kasım 2025
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A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends

Öz

Artificial intelligence (AI) has become one of the most transformative technologies of recent years. By leveraging AI, businesses can enhance their environmental interaction, perform advanced analytics, and make sustainable and equitable decisions. At this point, AI is also recognized as a key driver in the advancement of green information technologies (Green IT). Green IT focuses on enabling organizations to increase productivity and efficiency while minimizing environmental impact. This study aims to identify the key research trends at the intersection of AI and Green IT and to conduct a systematic bibliometric analysis of the existing literature. Based on 246 articles retrieved from the Web of Science database (2010–2025), the study examines the most productive countries, influential journals, and thematic clusters to provide a strategic overview for future research. It was observed that AI significantly contributes to strategies such as energy efficiency, smart grid development, and climate crisis mitigation. Notably, this paper also highlights how the synergy between AI and Green IT can lay the foundation for energy-efficient and sustainable metaverse infrastructures, where immersive technologies and intelligent systems demand green and scalable computing solutions. As one of the few bibliometric studies on this emerging convergence, the paper offers strategic insights for both academia and industry to promote environmentally responsible AI-driven digital ecosystems.

Anahtar Kelimeler

Destekleyen Kurum

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Etik Beyan

In this article, the principles of scientific research and publication ethics were followed. This study did not involve human or animal subjects and did not require additional ethics committee approval.

Teşekkür

We thank the editor, Academic Journal of Information Technology Journal of Editorial Office for their insightful and constructive reviews.

Kaynakça

  1. Akbarzadeh, O., Hamzehei, S., Attar, H., Amer, A., Fasihihour, N., Khosravi, M. R., & Solyman, A. A. (2024). Heating-cooling monitoring and power consumption forecasting using LSTM for energy-efficient smart management of buildings: A computational intelligence solution for smart homes. Tsinghua Science and Technology, 29(1), 143–157. https://doi.org/10.26599/TST.2023.901000
  2. Akter, S., Wamba, S. F., Mariani, M., & Hani, U. (2021). How to build an AI climate-driven service analytics capability for innovation and performance in industrial markets? Industrial Marketing Management, 9, 258–273. https://doi.org/10.1016/j.indmarman.2021.07.014
  3. Al Sallami, N. M., Al Daoud, A., & Al Alousi, S. A. (2013). Load balancing with neural network. International Journal of Advanced Computer Science and Applications, 4(10), 138–145. http://dx.doi.org/10.14569/IJACSA.2013.041021
  4. Alzu’bi, S., Kanan, T., Elbes, M., Kanaan, G., & Trrad, I. (2025). Energy-efficient edge deployment of generative AI models using federated learning. Cluster Computing, 28, 315. https://doi.org/10.1007/s10586-025-05263-7
  5. Aquino-Brítez, S., García-Sánchez, P., Ortiz, A., & Aquino-Brítez, D. (2025). Towards an energy consumption index for deep learning models: A comparative analysis of architectures, GPUs, and measurement tools. Sensors, 25(3), 846. https://doi.org/10.3390/s25030846
  6. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  7. Beghoura, M. A., Boubetra, A., & Boukerram, A. (2017). Green software requirements and measurement: Random decision forests-based software energy consumption profiling. Requirements Engineering, 22, 27–40. https://doi.org/10.1007/s00766-015-0234-2
  8. Bracarense, N., Bawack, R. E., Fosso Wamba, S., & Carillo, K. D. A. (2022). Artificial intelligence and sustainability: A bibliometric analysis and future research directions. Pacific Asia Journal of the Association for Information Systems, 14(2), Article 9. https://doi.org/10.17705/1pais.14209

Ayrıntılar

Birincil Dil

İngilizce

Konular

Yapay Zeka (Diğer)

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

30 Kasım 2025

Gönderilme Tarihi

24 Temmuz 2025

Kabul Tarihi

23 Eylül 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 16 Sayı: 4

Kaynak Göster

APA
Pınar, S., Kurt, K., & Türkeli, S. (2025). A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends. AJIT-e: Academic Journal of Information Technology, 16(4), 323-356. https://doi.org/10.5824/ajite.2025.04.003.x
AMA
1.Pınar S, Kurt K, Türkeli S. A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends. AJIT-e. 2025;16(4):323-356. doi:10.5824/ajite.2025.04.003.x
Chicago
Pınar, Sevcan, Kenan Kurt, ve Serkan Türkeli. 2025. “A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends”. AJIT-e: Academic Journal of Information Technology 16 (4): 323-56. https://doi.org/10.5824/ajite.2025.04.003.x.
EndNote
Pınar S, Kurt K, Türkeli S (01 Kasım 2025) A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends. AJIT-e: Academic Journal of Information Technology 16 4 323–356.
IEEE
[1]S. Pınar, K. Kurt, ve S. Türkeli, “A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends”, AJIT-e, c. 16, sy 4, ss. 323–356, Kas. 2025, doi: 10.5824/ajite.2025.04.003.x.
ISNAD
Pınar, Sevcan - Kurt, Kenan - Türkeli, Serkan. “A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends”. AJIT-e: Academic Journal of Information Technology 16/4 (01 Kasım 2025): 323-356. https://doi.org/10.5824/ajite.2025.04.003.x.
JAMA
1.Pınar S, Kurt K, Türkeli S. A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends. AJIT-e. 2025;16:323–356.
MLA
Pınar, Sevcan, vd. “A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends”. AJIT-e: Academic Journal of Information Technology, c. 16, sy 4, Kasım 2025, ss. 323-56, doi:10.5824/ajite.2025.04.003.x.
Vancouver
1.Sevcan Pınar, Kenan Kurt, Serkan Türkeli. A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends. AJIT-e. 01 Kasım 2025;16(4):323-56. doi:10.5824/ajite.2025.04.003.x