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The Influence of employing artificial intelligence in reducing digital Waste
Corresponding Author(s) : Shireen Ismail Khalil AL Hadiddy
American Journal of Economics and Business Management,
Vol. 8 No. 3 (2025): March
Abstract
The aim of this research is to analyze the role of artificial intelligence in reducing digital waste, applying smart automation techniques, machine learning, and big data analytics for data management where data storage and energy consumption will be reduced. An analytical approach was adopted using a questionnaire directed to a sample of academics and employees considered in Tikrit University; data were analyzed using SPSS 25. The findings collected indicated that artificial intelligence improves the efficiency of data storage and enhances the mechanisms to automatically delete unnecessary files, therefore reducing carbon emissions. Furthermore, it was found that demographic factors such as level of education and digital awareness tend to influence the level of acceptance toward artificial intelligence solutions for data management. The study concluded that adopting artificial intelligence in academic institutions contributes toward digital sustainability, and it recommends establishing supporting policies whereby AI can be used for data management to further enhance operational efficiency.
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- R. Alsabt, W. Alkhaldi, Y. A. Adenle, and H. M. Alshuwaikhat, “Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study,” Cleaner Waste Systems, vol. 8, p. 100158, Jun. 2024. [Online]. Available: https://doi.org/10.1016/j.clwas.2024.100158
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References
R. Alsabt, W. Alkhaldi, Y. A. Adenle, and H. M. Alshuwaikhat, “Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study,” Cleaner Waste Systems, vol. 8, p. 100158, Jun. 2024. [Online]. Available: https://doi.org/10.1016/j.clwas.2024.100158
S. Bai, S. Shi, C. Han, M. Yang, B. B. Gupta, and V. Arya, “Prioritizing user requirements for digital products using explainable artificial intelligence: A data-driven analysis on video conferencing apps,” Future Generation Computer Systems, vol. 158, pp. 167–182, Dec. 2023. [Online]. Available: https://doi.org/10.1016/j.future.2024.04.037
S. E. Bibri, J. Huang, S. K. Jagatheesaperumal, and J. Krogstie, “The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review,” Environmental Science and Ecotechnology, vol. 20, p. 100433, 2024. [Online]. Available: https://doi.org/10.1016/j.ese.2024.100433
P. de Wilde, “Building performance simulation in the brave new world of artificial intelligence and digital twins: A systematic review,” Energy and Buildings, vol. 292, p. 113171, Mar. 2023. [Online]. Available: https://doi.org/10.1016/j.enbuild.2023.113171
A. Di Vaio, R. Palladino, R. Hassan, and O. Escobar, “Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review,” Journal of Business Research, vol. 121, pp. 283–314, Aug. 2020. [Online]. Available: https://doi.org/10.1016/j.jbusres.2020.08.019
S. Du and C. Xie, “Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities,” Journal of Business Research, vol. 129, pp. 961–974, Aug. 2021. [Online]. Available: https://doi.org/10.1016/j.jbusres.2020.08.024
Y. K. Dwivedi et al., “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” International Journal of Information Management, vol. 57, p. 102025, Jul. 2021. [Online]. Available: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
G. Echefu, R. Shah, Z. Sanchez, J. Rickards, and S. A. Brown, “Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities,” American Heart Journal Plus: Cardiology Research and Practice, vol. 48, p. 100479, Oct. 2024. [Online]. Available: https://doi.org/10.1016/j.ahjo.2024.100479
Y. Gao, S. Liu, and L. Yang, “Artificial intelligence and innovation capability: A dynamic capabilities perspective,” International Review of Economics and Finance, vol. 98, p. 103923, Jan. 2025. [Online]. Available: https://doi.org/10.1016/j.iref.2025.103923
A. Gedikli, G. D. Sharma, S. Erdoğan, and S. Hammoudeh, “Artificial intelligence, disruption of financial markets and natural resources economy in the digital era,” Resources Policy, vol. 92, p. 104953, Apr. 2024. [Online]. Available: https://doi.org/10.1016/j.resourpol.2024.104953
R. Hasan and R. Burns, “The life and death of unwanted bits: Towards proactive waste data management in digital ecosystems,” in 2013 3rd Int. Conf. Innovative Computing Technology (INTECH 2013), Nov. 2013, pp. 144–148. [Online]. Available: https://doi.org/10.1109/INTECH.2013.6653665
M. Leyer and S. Schneider, “Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers?” Business Horizons, vol. 64, no. 5, pp. 711–724, 2021. [Online]. Available: https://doi.org/10.1016/j.bushor.2021.02.026
D. R. Manchikanti, “Digital Waste Mitigation in AI and Cloud Computing: A Comprehensive Framework for Environmental Sustainability,” Procedia Computer Science, pp. 1357–1366, 2025.
L. Qiao, X. Zhang, and S. He, “Visual Defect Detection and Analysis of Digital Robot Based on Virtual Artificial Intelligence Algorithm,” Procedia Computer Science, vol. 243, pp. 601–609, 2024. [Online]. Available: https://doi.org/10.1016/j.procs.2024.09.073
D. Sjödin, V. Parida, and M. Kohtamäki, “Artificial intelligence enabling circular business model innovation in digital servitization: Conceptualizing dynamic capabilities, AI capacities, business models and effects,” Technological Forecasting and Social Change, vol. 197, p. 122903, Oct. 2023. [Online]. Available: https://doi.org/10.1016/j.techfore.2023.122903
N. A. Ubina et al., “Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT),” Smart Agricultural Technology, vol. 5, p. 100285, Jul. 2023. [Online]. Available: https://doi.org/10.1016/j.atech.2023.100285
C. Wound, C. Wound, and B. Reversal, “Content index,” Brazilian Journal of Anesthesiology, vol. 61, no. 6, pp. 844–866, 2011. [Online]. Available: https://doi.org/10.1016/s0034-7094(11)70095-2