TY  - JOUR
T1  - A Bibliometric Analysis of Artificial Intelligence and Green Information Technologies: Evaluating Future Research Trends
TT  - Yapay Zekâ ve Yeşil Bilgi Teknolojilerinin Bibliyometrik Analizi: Gelecekteki Araştırma Trendlerinin Değerlendirilmesi
AU  - Pınar, Sevcan
AU  - Kurt, Kenan
AU  - Türkeli, Serkan
PY  - 2025
DA  - November
Y2  - 2025
DO  - 10.5824/ajite.2025.04.003.x
JF  - AJIT-e: Academic Journal of Information Technology
JO  - AJIT-e
PB  - Akademik Bilişim Araştırmaları Derneği
WT  - DergiPark
SN  - 1309-1581
SP  - 323
EP  - 356
VL  - 16
IS  - 4
LA  - en
AB  - 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.
KW  - Artificial Intelligence
KW  - Green Information Technologies
KW  - Green Computing
KW  - Digital Transformation
N2  - Yapay zekâ (YZ), son yılların en dönüştürücü teknolojilerinden biri haline gelmiştir. İşletmeler YZ'den yararlanarak çevresel etkileşimlerini artırabilir, gelişmiş analizler gerçekleştirebilir ve sürdürülebilir ve adil kararlar alabilirler. Bu noktada, YZ aynı zamanda yeşil bilgi teknolojilerinin (Yeşil BT) ilerlemesinde önemli bir itici güç olarak kabul edilmektedir. Yeşil BT, kuruluşların çevresel etkiyi en aza indirirken üretkenliği ve verimliliği artırmalarını sağlamaya odaklanır. Bu çalışma, YZ ve Yeşil BT'nin kesişimindeki temel araştırma eğilimlerini belirlemeyi ve mevcut literatürün sistematik bir bibliyometrik analizini yürütmeyi amaçlamaktadır. Web of Science veri tabanından (2010-2025) alınan 246 makaleye dayanan çalışma, gelecekteki araştırmalar için stratejik bir genel bakış sağlamak amacıyla en üretken ülkeleri, etkili dergileri ve tematik kümeleri incelemektedir. YZ'nin enerji verimliliği, akıllı şebeke geliştirme ve iklim krizi hafifletme gibi stratejilere önemli ölçüde katkıda bulunduğu gözlemlenmiştir. Özellikle bu makale, yapay zeka ve yeşil bilişim teknolojileri arasındaki sinerjinin, sürükleyici teknolojilerin ve akıllı sistemlerin yeşil ve ölçeklenebilir bilişim çözümleri gerektirdiği, enerji açısından verimli ve sürdürülebilir meta veri tabanı altyapılarının temelini nasıl oluşturabileceğini de vurgulamaktadır. Bu yeni ortaya çıkan yakınsama üzerine yapılmış birkaç bibliyometrik çalışmadan biri olan makale, hem akademiye hem de endüstriye, çevre dostu yapay zeka destekli dijital ekosistemleri teşvik etmek için stratejik içgörüler sunmaktadır.
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - Chappin, E. J., & Ligtvoet, A. (2014). Transition and transformation: A bibliometric analysis of two scientific networks researching socio-technical change. Renewable and Sustainable Energy Reviews, 30, 715–723. https://doi.org/10.1016/j.rser.2013.11.013
CR  - Choi, W., Duraisamy, K., Kim, R. G., Doppa, J. R., Pande, P. P., Marculescu, D., & Marculescu, R. (2018). On-chip communication network for efficient training of deep convolutional networks on heterogeneous manycore systems. IEEE Transactions on Computers, 67(5), 672–686. https://doi.org/10.1109/TC.2017.2777863
CR  - Debrah, C., Chan, A. P. C., & Darko, A. (2022). Artificial intelligence in green building. Automation in Construction, 137, 104192. https://doi.org/10.1016/j.autcon.2022.104192
CR  - Desheng, L., Jiakui, C., & Ning, Z. (2021). Political connections and green technology innovations under an environmental regulation. Journal of Cleaner Production, 298, 126–178. https://doi.org/10.1016/j.jclepro.2021.126778
CR  - Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
CR  - El Yaacoub, K., Stenhammar, O., Ickin, S., & Vandikas, K. (2024). Continual learning with Siamese neural networks for sustainable network management. IEEE Transactions on Network and Service Management, 21(3), 2664–2674. https://doi.org/10.1109/TNSM.2024.3368928
CR  - Ghayvat, H., Awais, M., Geddam, R., Zuhair, M., Khan, M. A., Milard, M., Nkenyereye, L., & Dev, K. (2024). Digitally enhanced home to the village: AIoMT-enabled multisource data fusion and power-efficient sustainable computing. IEEE Internet of Things Journal, 11(24), 39030–39040. https://doi.org/10.1109/JIOT.2024.3411798
CR  - Grossi, A., Vianello, E., Sabry, M. M., Wootters, M. K., Barlas, M., Grenouillet, L., Coignus, J., Nowak, E., & Mitra, S. (2019). Resistive RAM endurance: Array-level characterization and correction techniques targeting deep learning applications. IEEE Transactions on Electron Devices, 66(3), 1281–1288. https://doi.org/10.1109/TED.2019.2894387
CR  - Guo, C., Zhou, F., Feng, L., & Li, W. (2024). Hierarchical multiple split federated learning for low-carbon resource-constrained user equipment. IEEE Internet of Things Journal, 11(24), 39127–39141. https://doi.org/10.1109/JIOT.2024.3475637
CR  - Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020). Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research, 21, 1–43. https://dl.acm.org/doi/abs/10.5555/3455716.34559
CR  - Huang, Y., Ravichandran, V., Zhao, W., & Xia, Q. (2023). Towards energy-efficient computing hardware based on memristive nanodevices. IEEE Nanotechnology Magazine, 17(5), 30–38. https://doi.org/10.1109/MNANO.2023.3297106
CR  - Jiang, F., Fu, Y., Gupta, B. B., Liang, Y., Rho, S., Lou, F., Meng, F., & Tian, Z. (2020). Deep learning based multi-channel intelligent attack detection for data security. IEEE Transactions on Sustainable Computing, 5(2), 204–212. https://doi.org/10.1109/TSUSC.2018.2793284
CR  - Kumar, A., Das, D., Lin, D. J. X., Huang, L., Yap, S. L. K., Tan, H. K., Lim, R. J. J., Tan, H. R., Toh, Y. T., Lim, S. T., Fong, X., & Ho, P. (2024). Bimodal alteration of cognitive accuracy for spintronic artificial neural networks. Nanoscale Horizons, 9(9), 1522–1531. https://doi.org/10.1039/D4NH00097H
CR  - Laleni, N., Müller, F., Cuñarro, G., Kämpfe, T., & Jang, T. (2024). A high-efficiency charge-domain compute-in-memory 1F1C macro using 2-bit FeFET cells for DNN processing. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 10, 153–160. https://doi.org/10.1109/JXCDC.2024.3495612
CR  - Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging technology and business model innovation: The case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44. https://doi.org/10.3390/joitmc5030044
CR  - Liu, N., Hu, T., Li, L., Hao, B., Tao, X., Yang, L., & Wang, S. (2019). Modeling and simulation of robot inverse dynamics using LSTM-based deep learning algorithm for smart cities and factories. IEEE Access, 7, 173989–173998. https://doi.org/10.1109/ACCESS.2019.2957019
CR  - Machado, E. D., Vicario, J. L., Miranda, E., & Morell, A. (2024). Memristor crossbar array simulation for deep learning applications. IEEE Transactions on Nanotechnology, 23, 512–515. https://doi.org/10.1109/TNANO.2024.3415382
CR  - McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12. https://doi.org/10.1609/aimag.v27i4.1904
CR  - Mesa Fernández, J. M., González Moreno, J. J., Vergara-González, E. P., & Alonso Iglesias, G. (2022). Bibliometric analysis of the application of artificial intelligence techniques to the management of innovation projects. Applied Sciences, 12(22), 11743. https://doi.org/10.3390/app122211743
CR  - Mucha, W. (2024). Real-time operational load monitoring of a composite aerostructure using FPGA-based computing system. Bulletin of the Polish Academy of Sciences: Technical Sciences, 72(1).
CR  - Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, 389–404. https://doi.org/10.1016/j.jbusres.2020.10.044
CR  - Nambi, S., Ullah, S., Sahoo, S. S., Lohana, A., Merchant, F., & Kumar, A. (2021). ExPAN(N)D: Exploring posits for efficient artificial neural network design in FPGA-based systems. IEEE Access, 9, 103691–103708. https://doi.org/10.1109/ACCESS.2021.30987
CR  - Naumann, S., Dick, M., Kern, E., & Johann, T. (2011). The GREENSOFT model: A reference model for green and sustainable software and its engineering. Sustainable Computing: Informatics and Systems, 1(4), 294–304. https://doi.org/10.1016/j.suscom.2011.06.004
CR  - Ooi, K.-B., Tan, G. W.-H., Al-Emran, M., Al Sharafi, M. A., Capatina, A., Chakraborty, A., Dwivedi, Y. K., Huang, T.-L., Kar, A. K., Lee, V.-H., Loh, X.-M., Micu, A., Mikalef, P., Mogaji, E., Pandey, N., Raman, R., Rana, N. P., Sarker, P., Sharma, A., Teng, C.-I., Wamba, S. F., & Wong, L.-W. (2025). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 65(1), 76–107. https://doi.org/10.1080/08874417.2023.2261010
CR  - Paesano, A. (2021). Artificial intelligence and creative activities inside organizational behavior. International Journal of Organizational Analysis. Advance online publication. https://doi.org/10.1108/IJOA-09-2020-2421
CR  - Palos-Sánchez, P. R., Baena-Luna, P., Badicu, A., & Infante-Moro, J. C. (2022). Artificial intelligence and human resources management: A bibliometric analysis. Applied Artificial Intelligence, 36(1), 1–22. https://doi.org/10.1080/08839514.2022.2145631
CR  - Penev, K., Gegov, A., Isiaq, O., & Jafari, R. (2024). Energy efficiency evaluation of artificial intelligence algorithms. Electronics, 13, 3836. https://doi.org/10.3390/electronics13193836
CR  - Redwine, S. T., Jr., & Riddle, W. E. (1985). Software technology maturation. In Proceedings of the 8th International Conference on Software Engineering (pp. 189–200). IEEE Computer Society Press.
CR  - Spanjol, J., Xiao, Y., & Welzenbach, L. (2018). Successive innovation in digital and physical products: Synthesis, conceptual framework, and research directions. Review of Marketing Research, 15, 31–62. https://doi.org/10.1108/S1548-643520180000015004
CR  - Song, A., Schölkopf, B., Kottapalli, S. N. M., & Fischer, P. (2024a). Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light. Nature Communications, 15, 10692. https://doi.org/10.1038/s41467-024-55139-4
CR  - Song, Z., Xie, M., Luo, J., Gong, T., & Chen, W. (2024b). A carbon-aware framework for energy-efficient data acquisition and task offloading in sustainable AIoT ecosystems. IEEE Internet of Things Journal, 11(24), 39103–39113. https://doi.org/10.1109/JIOT.2024.3472669
CR  - Tabbakh, A., Al Amin, L., Islam, M., Mahmud, G. M. I., Chowdhury, I. K., & Mukta, M. S. H. (2024). Towards sustainable AI: A comprehensive framework for green AI. Discover Sustainability, 5, 408. https://doi.org/10.1007/s43621-024-00641-4
CR  - Tuncsiper, Z., Sanlisoy, S., & Aydin, U. (2025). Economic transformation from physical to digital: A bibliometric analysis of the metaverse economy. Journal of Metaverse, 5(1), 25–37. https://doi.org/10.57019/jmv.1608255
CR  - Turing, A. M. (1937). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, s2-42(1), 230–265. https://doi.org/10.1112/plms/s2-42.1.230
CR  - Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433
CR  - Vergallo, R., & Mainetti, L. (2024). Measuring the effectiveness of carbon-aware AI training strategies in cloud instances: A confirmation study. Future Internet, 16(9), 334. https://doi.org/10.3390/fi16090334
CR  - Villar-Rodriguez, E., Arostegi Pérez, M., Torre-Bastida, A. I., Regueiro Senderos, C., & López-de-Armentia, J. (2023). Edge intelligence secure frameworks: Current state and future challenges. Computers & Security, 130, 103278. https://doi.org/10.1016/j.cose.2023.103278
CR  - Welsh, R. (2019). Defining artificial intelligence. SMPTE Motion Imaging Journal, 128(1), 26–32. https://doi.org/10.5594/JMI.2018.2880366
CR  - Xue, F., Hwang, W., Zhang, F., Tsai, W., Fan, D., & Wang, S. X. (2025). High-density STT-assisted SOT-MRAM (SAS-MRAM) for energy-efficient AI applications. IEEE Transactions on Magnetics, 61(4), Article 3400508, 1–8. https://doi.org/10.1109/TMAG.2024.3486616
CR  - Yang, H., Hu, H, Lam, K. Y., Niyato, D., Xiao, L., Xiong, Z., & Poor, H. V. (2022). Lead federated neuromorphic learning for wireless edge artificial intelligence. Nature Communications, 13, 4269. https://doi.org/10.1038/s41467-022-32020-w
CR  - Yaseen, S. G., Alsmadi, A. A., & Eletter, S. F. (2025). A bibliometric analysis of metaverse: Mapping, visualizing and future research trends. Journal of Metaverse, 5(1), 38–50. https://doi.org/10.57019/jmv.1582149
CR  - Yigitcanlar, T., Mehmood, R., & Corchado, J. M. (2021). Green artificial intelligence: Towards an efficient, sustainable and equitable technology for smart cities and futures. Sustainability, 13(16), 8952. https://doi.org/10.3390/su13168952
CR  - Yu, K., Chakraborty, C., Xu, D., Zhang, T., Zhu, H., & Alfarraj, O. (2024). Hybrid quantum classical optimization for low-carbon sustainable edge architecture in RIS-assisted AIoT healthcare systems. IEEE Internet of Things Journal, 11(24), 38987–38998. https://doi.org/10.1109/JIOT.2024.3399234
CR  - Zavieh, H., Javadpour, A., & Sangaiah, A. K. (2024). Efficient task scheduling in cloud networks using ANN for green computing. International Journal of Communication Systems, 37(5), e5689. https://doi.org/10.1002/dac.5689
CR  - Zhou, K., Zhao, C., Fang, J., Jiang, J., Chen, D., Huang, Y., & Zeng, X. (2021). An energy-efficient computing-in-memory accelerator with 1T2R cell and fully analog processing for edge AI applications. IEEE Transactions on Circuits and Systems II: Express Briefs, 68(8), 2932–2936. https://doi.org/10.1109/TCSII.2021.3065697
CR  - Zhu, S., Ota, K., & Dong, M. (2022). Green AI for IIoT: Energy efficient intelligent edge computing for industrial Internet of Things. IEEE Transactions on Green Communications and Networking, 6(1), 79–88. https://doi.org/10.1109/TGCN.2021.3100622
CR  - Zupic, I., & Čater, T. (2014). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
UR  - https://doi.org/10.5824/ajite.2025.04.003.x
L1  - https://dergipark.org.tr/tr/download/article-file/5086791
ER  -