Awareness of the Impact of IT/AI on Energy Consumption in Enterprises: A Machine Learning-Based Modelling Towards a Sustainable Digital Transformation
Open accessAbstract
The integration of artificial intelligence (AI) and information technology (IT) is transforming business operations while increasing energy demand. A scalable and nonintrusive method for assessing the adoption of energy-conscious IT governance without direct measurements of energy use is lacking. To address this gap, a machine learning framework is developed and validated that infers the presence of energy-conscious IT governance from five indicators of digital maturity and AI adoption. Enterprise survey data were used to train five classification algorithms—support vector machine, logistic regression, decision tree, neural network, and k-nearest neighbors—to identify organizations implementing energy-efficient IT/AI management. All models achieved strong predictive performance, with SVM achieving 90% test accuracy and an F1 score of 89.8%. The findings demonstrate that an enterprise’s technological profile can serve as a reliable proxy for assessing sustainable IT/AI practices, enabling rapid assessment, benchmarking, and targeted support for green digital transformation. This approach offers significant implications for policy design, ESG reporting, and managerial decision-making in energy-conscious governance, supporting the alignment of digital innovation with environmental objectives.
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A machine learning-based classification framework can indirectly infer whether a firm applies energy-responsible IT/AI governance based on five indicators of digital maturity and AI adoption.
“Słoniec et al. [33] proposed and tested a machine learning-based classification framework that is able to indirectly infer whether a firm applies energy-responsible IT/AI governance based on five indicators of digital maturity and AI adoption, without directly measuring energy consumption.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [33]
The SVM model achieved 90% accuracy and an F1 score of 89.8% on the test dataset.
“The best-performing model was SVM, achieving 90% accuracy on the test dataset and an F1 score of 89.8%.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [33]
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Cited by
Sorted by citations
Urbanovič, M., & Holubčík, M. (2026) · Systems · doi:10.3390/systems14030245 · Cited by 9 · see citing works
- Authors
- Słoniec, J., Kulisz, M., Małecka-Dobrogowska, M., Konurbayeva, Z., & Sobaszek, Ł.
- Affiliations
- Lublin University of Technology (PL)
- Bialystok University of Technology (PL)
- D. Serikbayev East Kazakhstan State Technical University (KZ)
- Corresponding author
- Kulisz, Monika
- Konurbayeva, Zhadyra
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2025-10-23
- Volume
- 18
- Issue
- 21
- Pages
- 5573
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W4415489932
- Cited by
- 9
- References
- 50
- Citations by year
- 2026: 9
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Polish Ministry of Science and Higher Education (FN-40/24/25/WZ/KOP, WZ/WIZ-INZ/3/2023)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy Efficiency and Management
- Keywords
- energy consumption, artificial intelligence, information technology, digital maturity, artificial intelligence adoption, green digital transformation, sustainable digital transformation, classification models, support vector machine, ESG reporting, energy efficiency
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