The DOI matches its Crossref record; 112 of the 112 references it cites were found in a registry or an index.
Industry 4.0 and opportunities for energy sustainability
Abstract
Abstract Understanding the interactions of Industry 4.0 and sustainability is a cutting-edge research topic. The present study aims to contribute to this research topic by explaining how Industry 4.0 may contribute to energy sustainability. The present study performs a content-centric qualitative review of the extant digitalization literature to identify the primary energy sustainability functions of Industry 4.0. The interpretive structural modeling technique is further used for mapping the interrelationships among various energy sustainability functions identified. The interpretive model developed, and the Matrice d’Impacts Croises Multiplication Appliquee aun Classement analysis offered exciting insights into the Industry 4.0-energy sustainability interactions. Findings show that Industry 4.0 promotes energy sustainability via a very complex mechanism that involves ten interrelated functions. Contrary to the general opinion, production efficiency offered by the digitalization of the manufacturing industry is not the immediate and most essential energy efficiency outcome of the digital industrial transformation. Industry 4.0 primarily contributes to energy sustainability by enabling the energy industry to reshape its operating landscape and enjoy more advanced, intelligent, and complicated energy production and distribution equipment. The digitalization of the energy demand sector, digitalization of the manufacturing industry, and the introduction of smarter and more sustainable products are among the main opportunities of Industry 4.0 for energy sustainability. Overall, the study and the ISM model of energy sustainability developed explains how Industry 4.0 contributes to energy sustainability via different functions and how each function is placed within the structural model based on its driving and dependence powers.
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Cites
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Industry 4.0: A survey on technologies, applications and open research issues
Lu, Y. (2017) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2017.04.005 · Cited by 3,012
Industry 4.0 technologies: Implementation patterns in manufacturing companies
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.01.004 · Cited by 2,958
Literature review of Industry 4.0 and related technologies
Oztemel, E., & Gursev, S. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1433-8 · Cited by 2,143
Industry 4.0, digitization, and opportunities for sustainability
Ghobakhloo, M. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119869 · Cited by 2,115
Self-citationA comparison of random forest variable selection methods for classification prediction modeling
Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.028 · Cited by 1,561
Industry 4.0 technologies assessment: A sustainability perspective
Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107776 · Cited by 1,417
Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges
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Cited by
Ghobakhloo, M., Iranmanesh, M., Grybauskas, A., Vilkas, M., & Petraitė, M. (2021) · Business Strategy and the Environment · doi:10.1002/bse.2867 · Cited by 462
Self-citationAnalysis of Digital Twin Applications in Energy Efficiency: A Systematic Review
Ba, L., Tangour, F., El Abbassi, I., & Absi, R. (2025) · Sustainability · doi:10.3390/su17083560 · Cited by 96
Metadata
- Authors
- Ghobakhloo, M., & Fathi, M.
- Venue
- Journal of Cleaner Production
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 295
- Pages
- 126427
- ISSN
- 0959-6526 (ISSN-L)
- OpenAlex
- W3130233092
- Cited by
- 253
- References
- 112
- FWCI
- 20.14
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.