Industry 4.0 technologies' effects on environmental sustainability - A systematic literature review

Authors

  • Mohamed El Merroun University of Sopron, H-9400 Sopron, Hungary
  • István János Bartók University of Sopron, H-9400 Sopron, Hungary
  • Osama Alkhlaifat University of Sopron, H-9400 Sopron, Hungary

DOI:

https://doi.org/10.37255/jme.v17i4pp132-152

Keywords:

Sustainability, industry 4.0, Digital transformation, IoT, CPS

Abstract

In the existing literature, Industry 4.0 and its potential impact on environmental sustainability have been studied from different perspectives. However, Industry 4.0 is a concept that gathers different technologies that are not necessarily combined. It is clear that the combination of different technologies is the core value of Industry 4.0. However, the examination of each technology separately is crucial for determining the right combination of technologies for each specific case. Therefore, the following research provides a systematic literature review (SLR) of each technology included in Industry 4.0 and its effects on environmental sustainability aspects based on 107 research papers. 417 articles from the SCOPUS database, which contain the word Industry 4.0 in the title, abstract, and/or in the indexed keywords, were scanned by the command-line program Astrogrep to find the most common Industry 4.0 technologies. The results revealed that the Internet of Things (IoT) was mentioned 252 times, Artificial Intelligence/Machine Learning (AI/ML) 81 times, Simulation 38 times, Blockchain 30 times, Augmented Reality (AG) 27 times, and Additive Manufacturing (3D printers) 23 times. First, the study reviews the potential effects of the six technologies on different aspects of environmental sustainability. Later on, the challenges faced by organizations when applying these technologies for environmental purposes were reviewed, and new research scopes and future research directions were highlighted.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Abadías Llamas, A., Valero Delgado, A., Valero Capilla, A., Torres Cuadra, C., Hultgren, M., Peltomäki, M., Roine, A., Stelter, M., & Reuter, M. A. (2019). Simulation-based exergy, thermo-economic and environmental footprint analysis of primary copper production. Minerals Engineering, 131, 51–65. https://doi.org/10.1016/j.mineng.2018.11.007

Abad-Segura, E., González-Zamar, M.-D., Luque-de la Rosa, A. L. la, & Morales Cevallos, M. B. (2020). Sustainability of Educational Technologies: An Approach to Augmented Reality Research. Sustainability, 12(10), 4091. https://doi.org/10.3390/su12104091

Adaloudis, M., & Bonnin Roca, J. (2021). Sustainability tradeoffs in the adoption of 3D Concrete Printing in the construction industry. Journal of Cleaner Production, 307, 127201. https://doi.org/10.1016/j.jclepro.2021.127201

Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834. https://doi.org/10.1016/j.jclepro.2021.125834

Alahmari, M., Issa, T., Issa, T., & Nau, S. Z. (2019). Faculty awareness of the economic and environmental benefits of augmented reality for sustainability in Saudi Arabian universities. Journal of Cleaner Production, 226, 259–269. https://doi.org/10.1016/j.jclepro.2019.04.090

Alcácer, V., & Cruz-Machado, V. (2019). Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. Engineering Science and Technology, an International Journal, 22(3), 899–919. https://doi.org/10.1016/j.jestch.2019.01.006

Alex, W. (2018). Global DataSphere to Hit 175 Zettabytes by 2025, IDC Says. Datanami, 17, 13237–13244.

Almalki, Faris. A., Alsamhi, S. H., Sahal, R., Hassan, J., Hawbani, A., Rajput, N. S., Saif, A., Morgan, J., & Breslin, J. (2021). Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities. Mobile Networks and Applications. https://doi.org/10.1007/s11036-021-01790-w

Alonso-Rosa, M., Gil-de-Castro, A., Moreno-Munoz, A., Garrido-Zafra, J., Gutierrez-Ballesteros, E., & Cañete-Carmona, E. (2020). An IoT Based Mobile Augmented Reality Application for Energy Visualization in Buildings Environments. Applied Sciences, 10(2), 600. https://doi.org/10.3390/app10020600

Amin Amani, M., & Sarkodie, S. (2022). Mitigating spread of contamination in meat supply chain management using deep learning.

Antony, J., Psomas, E., Garza-Reyes, J. A., & Hines, P. (2021). Practical implications and future research agenda of lean manufacturing: A systematic literature review. Production Planning & Control, 32(11), 889–925.

Avgerinou, M., Bertoldi, P., & Castellazzi, L. (2017). Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency. Energies, 10(10), 1470. https://doi.org/10.3390/en10101470

Batista, N. C., Melício, R., & Mendes, V. M. F. (2017). Services enabler architecture for smart grid and smart living services providers under industry 4.0. Energy and Buildings, 141, 16–27. https://doi.org/10.1016/j.enbuild.2017.02.039

Beier, G., Ullrich, A., Niehoff, S., Reißig, M., & Habich, M. (2020). Industry 4.0: How it is defined from a sociotechnical perspective and how much sustainability it includes – A literature review. Journal of Cleaner Production, 259, 120856. https://doi.org/10.1016/j.jclepro.2020.120856

Bekaroo, G., Sungkur, R., Ramsamy, P., Okolo, A., & Moedeen, W. (2018). Enhancing awareness on green consumption of electronic devices: The application of Augmented Reality. Sustainable Energy Technologies and Assessments, 30, 279–291. https://doi.org/10.1016/j.seta.2018.10.016

Birkel, H., & Müller, J. M. (2021). Potentials of industry 4.0 for supply chain management within the triple bottom line of sustainability – A systematic literature review. Journal of Cleaner Production, 289, 125612. https://doi.org/10.1016/j.jclepro.2020.125612

Bose, B. K. (2017). Artificial Intelligence Techniques in Smart Grid and Renewable Energy Systems—Some Example Applications. Proceedings of the IEEE, 105(11), 2262–2273. https://doi.org/10.1109/JPROC.2017.2756596

Bueno, A., Godinho Filho, M., & Frank, A. G. (2020). Smart production planning and control in the Industry 4.0 context: A systematic literature review. Computers & Industrial Engineering, 149, 106774. https://doi.org/10.1016/j.cie.2020.106774

Burinskiene, A., Lorenc, A., & Lerher, T. (2018). A Simulation Study for the Sustainability and Reduction of Waste in Warehouse Logistics. International Journal of Simulation Modelling, 17(3), 485–497. https://doi.org/10.2507/IJSIMM17(3)446

Buterin, V. (2014). A next-generation smart contract and decentralized application platform. White Paper, 3(37), 2–1.

Çakıroğlu, Ü., Atabaş, S., Aydın, M., & Özyılmaz, I. (2022). Creating concept maps with augmented reality: A case of eclipse of the lunar and solar topic. Research and Practice in Technology Enhanced Learning, 17(1), 16. https://doi.org/10.1186/s41039-022-00191-1

Capellán-Pérez, I., Álvarez-Antelo, D., & Miguel, L. J. (2019). Global Sustainability Crossroads: A Participatory Simulation Game to Educate in the Energy and Sustainability Challenges of the 21st Century. Sustainability, 11(13), 3672. https://doi.org/10.3390/su11133672

Ceschi, A., Sartori, R., Dickert, S., Scalco, A., Tur, E. M., Tommasi, F., & Delfini, K. (2021). Testing a norm-based policy for waste management: An agent-based modeling simulation on nudging recycling behavior. Journal of Environmental Management, 294, 112938. https://doi.org/10.1016/j.jenvman.2021.112938

Chemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A. (2018). State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach. Journal of Power Sources, 400, 242–255. https://doi.org/10.1016/j.jpowsour.2018.06.104

Chen, D., Heyer, S., Ibbotson, S., Salonitis, K., Steingrímsson, J. G., & Thiede, S. (2015). Direct digital manufacturing: Definition, evolution, and sustainability implications. Journal of Cleaner Production, 107, 615–625. https://doi.org/10.1016/j.jclepro.2015.05.009

Chen, X. (2022). Machine learning approach for a circular economy with waste recycling in smart cities. Energy Reports, 8, 3127–3140. https://doi.org/10.1016/j.egyr.2022.01.193

Clarke, J. A., & Hensen, J. L. M. (2015). Integrated building performance simulation: Progress, prospects and requirements. Building and Environment, 91, 294–306. https://doi.org/10.1016/j.buildenv.2015.04.002

Coelho, I. M., Coelho, V. N., Luz, E. J. da S., Ochi, L. S., Guimarães, F. G., & Rios, E. (2017). A GPU deep learning metaheuristic based model for time series forecasting. Applied Energy, 201, 412–418. https://doi.org/10.1016/j.apenergy.2017.01.003

Cohen, Y., Faccio, M., Pilati, F., & Yao, X. (2019). Design and management of digital manufacturing and assembly systems in the Industry 4.0 era. The International Journal of Advanced Manufacturing Technology, 105(9), 3565–3577. https://doi.org/10.1007/s00170-019-04595-0

Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). The AI gambit: Leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI & SOCIETY. https://doi.org/10.1007/s00146-021-01294-x

Dev, N. K., Shankar, R., & Swami, S. (2020). Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system. International Journal of Production Economics, 223, 107519. https://doi.org/10.1016/j.ijpe.2019.107519

Dey, S., Saha, S., Singh, A. K., & McDonald-Maier, K. (2022). SmartNoshWaste: Using Blockchain, Machine Learning, Cloud Computing and QR Code to Reduce Food Waste in Decentralized Web 3.0 Enabled Smart Cities. Smart Cities, 5(1), 162–176. https://doi.org/10.3390/smartcities5010011

Dong, Y., & Hauschild, M. Z. (2017). Indicators for Environmental Sustainability. Procedia CIRP, 61, 697–702. https://doi.org/10.1016/j.procir.2016.11.173

Du, W., Zheng, J., Li, W., Liu, Z., Wang, H., & Han, X. (2022). Efficient Recognition and Automatic Sorting Technology of Waste Textiles Based on Online Near infrared Spectroscopy and Convolutional Neural Network. Resources, Conservation and Recycling, 180, 106157. https://doi.org/10.1016/j.resconrec.2022.106157

Duan, L., & Da Xu, L. (2021). Data Analytics in Industry 4.0: A Survey. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10190-0

Dvorak, F., Micali, M., & Mathieug, M. (2018). Planning and Scheduling in Additive Manufacturing. Inteligencia Artificial, 21(62), 40–52. https://doi.org/10.4114/intartif.vol21iss62pp40-52

Ejsmont, K., Gladysz, B., & Kluczek, A. (2020). Impact of Industry 4.0 on Sustainability—Bibliometric Literature Review. Sustainability, 12(14), 5650. https://doi.org/10.3390/su12145650

El Merroun, M. (2022). Industry 4.0 as an Opportunity to Achieve Environmental Sustainability: The Difference between SMES and Large Companies. International Journal of Information Technology Convergence and Services, 12(01), 1–13. https://doi.org/10.5121/ijitcs.2022.12101

Emmert‐Streib, F., Yli‐Harja, O., & Dehmer, M. (2020). Explainable artificial intelligence and machine learning: A reality rooted perspective. WIREs Data Mining and Knowledge Discovery, 10(6). https://doi.org/10.1002/widm.1368

Erol, I., Murat Ar, I., Peker, I., & Searcy, C. (2022). Alleviating the Impact of the Barriers to Circular Economy Adoption Through Blockchain: An Investigation Using an Integrated MCDM-based QFD With Hesitant Fuzzy Linguistic Term Sets. Computers & Industrial Engineering, 165, 107962. https://doi.org/10.1016/j.cie.2022.107962

Farjam, M., Nikolaychuk, O., & Bravo, G. (2019). Experimental evidence of an environmental attitude-behavior gap in high-cost situations. Ecological Economics, 166, 106434. https://doi.org/10.1016/j.ecolecon.2019.106434

Floridi, L. (2020). The Green and the Blue: A New Political Ontology for a Mature Information Society. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3831094

Ford, S., & Despeisse, M. (2016). Additive manufacturing and sustainability: An exploratory study of the advantages and challenges. Journal of Cleaner Production, 137, 1573–1587. https://doi.org/10.1016/j.jclepro.2016.04.150

Fraga-Lamas, P., Fernández-Caramés, T. M., Blanco-Novoa, O., & Vilar-Montesinos, M. A. (2018). A review on industrial augmented reality systems for the industry 4.0 shipyard. Ieee Access, 6, 13358–13375.

Freitas, D., Almeida, H. A., Bártolo, H., & Bártolo, P. J. (2016). Sustainability in extrusion-based additive manufacturing technologies. Progress in Additive Manufacturing, 1(1–2), 65–78. https://doi.org/10.1007/s40964-016-0007-6

Galbusera, F., Casaroli, G., & Bassani, T. (2019). Artificial intelligence and machine learning in spine research. JOR SPINE, 2(1), e1044. https://doi.org/10.1002/jsp2.1044

Garzon, J., Baldiris, S., Acevedo, J., & Pavon, J. (2020). Augmented Reality-based application to foster sustainable agriculture in the context of aquaponics. 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT), 316–318. https://doi.org/10.1109/ICALT49669.2020.00101

Gbededo, M. A., & Liyanage, K. (2020). Descriptive framework for simulation-aided sustainability decision-making: A Delphi study. Sustainable Production and Consumption, 22, 45–57. https://doi.org/10.1016/j.spc.2020.02.006

Gbededo, M. A., Liyanage, K., & Garza-Reyes, J. A. (2018). Towards a Life Cycle Sustainability Analysis: A systematic review of approaches to sustainable manufacturing. Journal of Cleaner Production, 184, 1002–1015. https://doi.org/10.1016/j.jclepro.2018.02.310

Ghobadian, A., Talavera, I., Bhattacharya, A., Kumar, V., Garza-Reyes, J. A., & O’Regan, N. (2020). Examining legitimatisation of additive manufacturing in the interplay between innovation, lean manufacturing and sustainability. International Journal of Production Economics, 219, 457–468. https://doi.org/10.1016/j.ijpe.2018.06.001

Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869

Ghobakhloo, M., & Fathi, M. (2021). Industry 4.0 and opportunities for energy sustainability. Journal of Cleaner Production, 295, 126427. https://doi.org/10.1016/j.jclepro.2021.126427

Ghosh, P., Westhoff, P., & Debnath, D. (2019). Biofuels, food security, and sustainability. In Biofuels, Bioenergy and Food Security (pp. 211–229). Elsevier. https://doi.org/10.1016/B978-0-12-803954-0.00012-7

Glavič, P., & Lukman, R. (2007). Review of sustainability terms and their definitions. Journal of Cleaner Production, 15(18), 1875–1885. https://doi.org/10.1016/j.jclepro.2006.12.006

Gleim, M. R., Smith, J. S., Andrews, D., & Cronin, J. J. (2013). Against the Green: A Multi-method Examination of the Barriers to Green Consumption. Journal of Retailing, 89(1), 44–61. https://doi.org/10.1016/j.jretai.2012.10.001

Goodland, R. (1995). The concept of environmental sustainability. Annual Review of Ecology and Systematics, 26(1), 1–24.

Gorkhali, A., Li, L., & Shrestha, A. (2020). Blockchain: A literature review. Journal of Management Analytics, 7(3), 321–343. https://doi.org/10.1080/23270012.2020.1801529

Gutowski, T. G., Branham, M. S., Dahmus, J. B., Jones, A. J., Thiriez, A., & Sekulic, D. P. (2009). Thermodynamic Analysis of Resources Used in Manufacturing Processes. Environmental Science & Technology, 43(5), 1584–1590. https://doi.org/10.1021/es8016655

Ham, Y., & Golparvar-Fard, M. (2013). EPAR: Energy Performance Augmented Reality models for identification of building energy performance deviations between actual measurements and simulation results. Energy and Buildings, 63, 15–28. https://doi.org/10.1016/j.enbuild.2013.02.054

Han, Y., He, T., Chang, R., & Xue, R. (2020). Development Trend and Segmentation of the US Green Building Market: Corporate Perspective on Green Contractors and Design Firms. Journal of Construction Engineering and Management, 146(11), 05020014. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001924

Harper, S. (2020). Global Risks and the Resilience of Future Health Care Systems. Journal of Population Ageing, 13(1), 1–3. https://doi.org/10.1007/s12062-020-09262-x

HAYES, A. (2022). 10 Important Cryptocurrencies Other Than Bitcoin. https://www.investopedia.com/tech/most-important-cryptocurrencies-other-than-bitcoin/#:~:text=One%20reason%20for%20this%20is,communities%20of%20backers%20and%20investors.

Hong, T., Langevin, J., & Sun, K. (2018). Building simulation: Ten challenges. Building Simulation, 11(5), 871–898. https://doi.org/10.1007/s12273-018-0444-x

Hu, R., Shahzad, F., Abbas, A., & Liu, X. (2022). Decoupling the influence of eco-sustainability motivations in the adoption of the green industrial IoT and the impact of advanced manufacturing technologies. Journal of Cleaner Production, 339, 130708. https://doi.org/10.1016/j.jclepro.2022.130708

Huber, R., Oberländer, A. M., Faisst, U., & Röglinger, M. (2022). Disentangling Capabilities for Industry 4.0—An Information Systems Capability Perspective. Information Systems Frontiers. https://doi.org/10.1007/s10796-022-10260-x

Hülsen, T., Stegman, S., Batstone, D. J., & Capson-Tojo, G. (2022). Naturally illuminated photobioreactors for resource recovery from piggery and chicken-processing wastewaters utilising purple phototrophic bacteria. Water Research, 214, 118194. https://doi.org/10.1016/j.watres.2022.118194

Ibrahim, A. S., Youssef, K. Y., Eldeeb, A. H., Abouelatta, M., & Kamel, H. (2022). Adaptive aggregation based IoT traffic patterns for optimizing smart city network performance. Alexandria Engineering Journal, 61(12), 9553–9568. https://doi.org/10.1016/j.aej.2022.03.037

Issa, A., Hatiboglu, B., Bildstein, A., & Bauernhansl, T. (2018). Industrie 4.0 roadmap: Framework for digital transformation based on the concepts of capability maturity and alignment. Procedia CIRP, 72, 973–978. https://doi.org/10.1016/j.procir.2018.03.151

Jamwal, A., Agrawal, R., Sharma, M., & Giallanza, A. (2021). Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions. Applied Sciences, 11(12), 5725. https://doi.org/10.3390/app11125725

Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Rab, S. (2021). Role of additive manufacturing applications towards environmental sustainability. Advanced Industrial and Engineering Polymer Research, 4(4), 312–322. https://doi.org/10.1016/j.aiepr.2021.07.005

Javornik, A. (2016). ‘It’s an illusion, but it looks real!’ Consumer affective, cognitive and behavioural responses to augmented reality applications. Journal of Marketing Management, 32(9–10), 987–1011. https://doi.org/10.1080/0267257X.2016.1174726

Jia, S., Yan, G., Shen, A., & Zheng, J. (2017). Dynamic simulation analysis of a construction and demolition waste management model under penalty and subsidy mechanisms. Journal of Cleaner Production, 147, 531–545. https://doi.org/10.1016/j.jclepro.2017.01.143

Jiang, J., & Fu, Y.-F. (2020). A short survey of sustainable material extrusion additive manufacturing. Australian Journal of Mechanical Engineering, 1–10.

Joerß, T., Hoffmann, S., Mai, R., & Akbar, P. (2021). Digitalization as solution to environmental problems? When users rely on augmented reality-recommendation agents. Journal of Business Research, 128, 510–523. https://doi.org/10.1016/j.jbusres.2021.02.019

Kagermann, H. (2015). Change Through Digitization—Value Creation in the Age of Industry 4.0. In H. Albach, H. Meffert, A. Pinkwart, & R. Reichwald (Eds.), Management of Permanent Change (pp. 23–45). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-05014-6_2

Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408–425. https://doi.org/10.1016/j.psep.2018.05.009

Keeble, B. R. (1988). The Brundtland report: ‘Our common future’. Medicine and War, 4(1), 17–25. https://doi.org/10.1080/07488008808408783

Khan, S. A., Koç, M., & Al-Ghamdi, S. G. (2021). Sustainability assessment, potentials and challenges of 3D printed concrete structures: A systematic review for built environmental applications. Journal of Cleaner Production, 303, 127027. https://doi.org/10.1016/j.jclepro.2021.127027

Khatua, P. K., Ramachandaramurthy, V. K., Kasinathan, P., Yong, J. Y., Pasupuleti, J., & Rajagopalan, A. (2020). Application and assessment of internet of things toward the sustainability of energy systems: Challenges and issues. Sustainable Cities and Society, 53, 101957. https://doi.org/10.1016/j.scs.2019.101957

Kouhizadeh, M., Sarkis, J., & Zhu, Q. (2019). At the Nexus of Blockchain Technology, the Circular Economy, and Product Deletion. Applied Sciences, 9(8), 1712. https://doi.org/10.3390/app9081712

Kumari, A., Gupta, R., Tanwar, S., & Kumar, N. (2020). Blockchain and AI amalgamation for energy cloud management: Challenges, solutions, and future directions. Journal of Parallel and Distributed Computing, 143, 148–166. https://doi.org/10.1016/j.jpdc.2020.05.004

LaViola Jr, J. J., Kruijff, E., McMahan, R. P., Bowman, D., & Poupyrev, I. P. (2017). 3D user interfaces: Theory and practice. Addison-Wesley Professional.

Lee, L.-H., Braud, T., Hosio, S., & Hui, P. (2022). Towards Augmented Reality Driven Human-City Interaction: Current Research on Mobile Headsets and Future Challenges. ACM Computing Surveys, 54(8), 1–38. https://doi.org/10.1145/3467963

Leung, C. K.-S. (2019). Big data analysis and mining. In Advanced methodologies and technologies in network architecture, mobile computing, and data analytics (pp. 15–27). IGI Global.

Li, W., Wei, Z., Liu, Z., Du, Y., Zheng, J., Wang, H., & Zhang, S. (2021). Qualitative identification of waste textiles based on near-infrared spectroscopy and the back propagation artificial neural network. Textile Research Journal, 91(21–22), 2459–2467. https://doi.org/10.1177/00405175211007516

Li, Y., Huang, Y., Su, X., Riekki, J., Flores, H., Sun, C., Wei, H., Wang, H., & Han, L. (2017). Gamma-modulated Wavelet model for Internet of Things traffic. 2017 IEEE International Conference on Communications (ICC), 1–6. https://doi.org/10.1109/ICC.2017.7996506

Li, Z., Lin, B., Zheng, S., Liu, Y., Wang, Z., & Dai, J. (2020). A review of operational energy consumption calculation method for urban buildings. Building Simulation, 13(4), 739–751. https://doi.org/10.1007/s12273-020-0619-0

Liu, S., Li, Z., Teng, Y., & Dai, L. (2022). A dynamic simulation study on the sustainability of prefabricated buildings. Sustainable Cities and Society, 77, 103551. https://doi.org/10.1016/j.scs.2021.103551

Liu, S., Lu, B., Li, H., Pan, Z., Jiang, J., & Qian, S. (2022). A comparative study on environmental performance of 3D printing and conventional casting of concrete products with industrial wastes. Chemosphere, 298, 134310. https://doi.org/10.1016/j.chemosphere.2022.134310

Lopes de Sousa Jabbour, A. B., Jabbour, C. J. C., Godinho Filho, M., & Roubaud, D. (2018). Industry 4.0 and the circular economy: A proposed research agenda and original roadmap for sustainable operations. Annals of Operations Research, 270(1), 273–286.

Lu, B., Weng, Y., Li, M., Qian, Y., Leong, K. F., Tan, M. J., & Qian, S. (2019). A systematical review of 3D printable cementitious materials. Construction and Building Materials, 207, 477–490. https://doi.org/10.1016/j.conbuildmat.2019.02.144

Luo, M., Hu, G., Chen, G., Liu, X., Hou, H., & Li, X. (2022). 1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020–2100. Scientific Data, 9(1), 110. https://doi.org/10.1038/s41597-022-01204-w

Luo, T., Xue, X., Wang, Y., Xue, W., & Tan, Y. (2021). A systematic overview of prefabricated construction policies in China. Journal of Cleaner Production, 280, 124371. https://doi.org/10.1016/j.jclepro.2020.124371

Machado, C. G., Despeisse, M., Winroth, M., & da Silva, E. H. D. R. (2019). Additive manufacturing from the sustainability perspective: Proposal for a self-assessment tool. Procedia CIRP, 81, 482–487. https://doi.org/10.1016/j.procir.2019.03.123

Mani, M., Lyons, K. W., & Gupta, S. K. (2014). Sustainability Characterization for Additive Manufacturing. Journal of Research of the National Institute of Standards and Technology, 119, 419. https://doi.org/10.6028/jres.119.016

Mele, M., & Campana, G. (2022). Advancing towards sustainability in liquid crystal display 3D printing via adaptive slicing. Sustainable Production and Consumption, 30, 488–505. https://doi.org/10.1016/j.spc.2021.12.024

Milošević, I., Arsić, S., Glogovac, M., Rakić, A., & Ruso, J. (2022). Industry 4.0: Limitation or benefit for success? Serbian Journal of Management, 17(1), 85–98. https://doi.org/10.5937/sjm17-36413

Minerva, R., Biru, A., & Rotondi, D. (2015). Towards a definition of the Internet of Things (IoT). IEEE Internet Initiative, 1(1), 1–86.

Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91–99. https://doi.org/10.1016/j.segan.2016.02.005

Moffat, A., & Newton, A. (2010). The 21st century corporation: The Ceres roadmap for sustainability. Http://Www. Ceres. Org.

Morelli, J. (2011). Environmental Sustainability: A Definition for Environmental Professionals. Journal of Environmental Sustainability, 1(1), 1–10. https://doi.org/10.14448/jes.01.0002

Morrar, R., & Arman, H. (2017). The Fourth Industrial Revolution (Industry 4.0): A Social Innovation Perspective. Technology Innovation Management Review, 7(11), 12–20. https://doi.org/10.22215/timreview/1117

Müller, J. M., Buliga, O., & Voigt, K.-I. (2018). Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17. https://doi.org/10.1016/j.techfore.2017.12.019

Mylonas, G., Triantafyllis, C., & Amaxilatis, D. (2019). An Augmented Reality Prototype for supporting IoT-based Educational Activities for Energy-efficient School Buildings. Electronic Notes in Theoretical Computer Science, 343, 89–101. https://doi.org/10.1016/j.entcs.2019.04.012

Narciso, D. A. C., & Martins, F. G. (2020). Application of machine learning tools for energy efficiency in industry: A review. Energy Reports, 6, 1181–1199. https://doi.org/10.1016/j.egyr.2020.04.035

Naseri-Rad, M., Berndtsson, R., Aminifar, A., McKnight, U. S., O’Connor, D., & Persson, K. M. (2022). DynSus: Dynamic sustainability assessment in groundwater remediation practice. Science of The Total Environment, 832, 154992. https://doi.org/10.1016/j.scitotenv.2022.154992

Nincarean, D., Alia, M. B., Halim, N. D. A., & Rahman, M. H. A. (2013). Mobile Augmented Reality: The Potential for Education. Procedia - Social and Behavioral Sciences, 103, 657–664. https://doi.org/10.1016/j.sbspro.2013.10.385

Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104

Oberländer, A. M., Röglinger, M., Rosemann, M., & Kees, A. (2018). Conceptualizing business-to-thing interactions – A sociomaterial perspective on the Internet of Things. European Journal of Information Systems, 27(4), 486–502. https://doi.org/10.1080/0960085X.2017.1387714

Oesterreich, T. D., & Teuteberg, F. (2016). Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121–139. https://doi.org/10.1016/j.compind.2016.09.006

Ojstersek, R., Acko, B., & Buchmeister, B. (2020). Simulation Study of a Flexible Manufacturing System Regarding Sustainability. International Journal of Simulation Modelling, 19(1), 65–76. https://doi.org/10.2507/IJSIMM19-1-502

Okoli, C., & Schabram, K. (2010). A guide to conducting a systematic literature review of information systems research.

O’Neill, B. C., & Oppenheimer, M. (2002). Dangerous Climate Impacts and the Kyoto Protocol. Science, 296(5575), 1971–1972. https://doi.org/10.1126/science.1071238

Palomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., Alonso, S., Melero, F. J., Molina, D., Fernández, B., Moral, C., Marchena, R., de Vargas, J. P., & Herrera, F. (2021). A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: Progress and prospects. Applied Intelligence, 51(9), 6497–6527. https://doi.org/10.1007/s10489-021-02264-y

Pandey, A. K., Reji Kumar, R., B, K., Laghari, I. A., Samykano, M., Kothari, R., Abusorrah, A. M., Sharma, K., & Tyagi, V. V. (2021). Utilization of solar energy for wastewater treatment: Challenges and progressive research trends. Journal of Environmental Management, 297, 113300. https://doi.org/10.1016/j.jenvman.2021.113300

Parvathi Sangeetha, B., Kumar, N., Ambalgi, A. P., Abdul Haleem, S. L., Thilagam, K., & Vijayakumar, P. (2022). IOT based smart irrigation management system for environmental sustainability in India. Sustainable Energy Technologies and Assessments, 52, 101973. https://doi.org/10.1016/j.seta.2022.101973

Pasha, M. K., Dai, L., Liu, D., Guo, M., & Du, W. (2021). An overview to process design, simulation and sustainability evaluation of biodiesel production. Biotechnology for Biofuels, 14(1), 129. https://doi.org/10.1186/s13068-021-01977-z

Peukert, B., Benecke, S., Clavell, J., Neugebauer, S., Nissen, N. F., Uhlmann, E., Lang, K.-D., & Finkbeiner, M. (2015). Addressing sustainability and flexibility in manufacturing via smart modular machine tool frames to support sustainable value creation. Procedia CIRP, 29, 514–519.

Pizzi, S., Caputo, A., Venturelli, A., & Caputo, F. (2022). Embedding and managing blockchain in sustainability reporting: A practical framework. Sustainability Accounting, Management and Policy Journal, 13(3), 545–567. https://doi.org/10.1108/SAMPJ-07-2021-0288

Prashar, G., & Vasudev, H. (2021). A comprehensive review on sustainable cold spray additive manufacturing: State of the art, challenges and future challenges. Journal of Cleaner Production, 310, 127606. https://doi.org/10.1016/j.jclepro.2021.127606

Rejeb, A., Rejeb, K., Keogh, J. G., & Zailani, S. (2022). Barriers to Blockchain Adoption in the Circular Economy: A Fuzzy Delphi and Best-Worst Approach. Sustainability, 14(6), 3611. https://doi.org/10.3390/su14063611

Renn, O., Beier, G., & Schweizer, P.-J. (2021). The opportunities and risks of digitalisation for sustainable development: A systemic perspective. GAIA - Ecological Perspectives for Science and Society, 30(1), 23–28. https://doi.org/10.14512/gaia.30.1.6

Rhodes, A. (2020). Digitalisation of Energy. Imperial College London. https://doi.org/10.25561/78885

Rosa, P., Sassanelli, C., Urbinati, A., Chiaroni, D., & Terzi, S. (2020). Assessing relations between Circular Economy and Industry 4.0: A systematic literature review. International Journal of Production Research, 58(6), 1662–1687. https://doi.org/10.1080/00207543.2019.1680896

Ruan, J., Wang, Y., Chan, F. T. S., Hu, X., Zhao, M., Zhu, F., Shi, B., Shi, Y., & Lin, F. (2019). A Life Cycle Framework of Green IoT-Based Agriculture and Its Finance, Operation, and Management Issues. IEEE Communications Magazine, 57(3), 90–96. https://doi.org/10.1109/MCOM.2019.1800332

Runji, J. M., Lee, Y.-J., & Chu, C.-H. (2022). User Requirements Analysis on Augmented Reality-Based Maintenance in Manufacturing. Journal of Computing and Information Science in Engineering, 22(5), 050901. https://doi.org/10.1115/1.4053410

Saade, M. R. M., Yahia, A., & Amor, B. (2020). How has LCA been applied to 3D printing? A systematic literature review and recommendations for future studies. Journal of Cleaner Production, 244, 118803. https://doi.org/10.1016/j.jclepro.2019.118803

Saheb, T., Dehghani, M., & Saheb, T. (2022). Artificial intelligence for sustainable energy: A contextual topic modeling and content analysis. Sustainable Computing: Informatics and Systems, 35, 100699. https://doi.org/10.1016/j.suscom.2022.100699

Salem, H., El-Hasnony, I. M., Kabeel, A. E., El-Said, E. M. S., & Elzeki, O. M. (2022). Deep Learning model and Classification Explainability of Renewable energy-driven Membrane Desalination System using Evaporative Cooler. Alexandria Engineering Journal, 61(12), 10007–10024. https://doi.org/10.1016/j.aej.2022.03.050

Senusi, F., Mahmood, S., & Hasrul Akhmal Ngadiman, N. (2021). Environmental Impact for 3D Bone Tissue Engineering Scaffolds Life Cycle: An Assessment. Biointerface Research in Applied Chemistry, 12(5), 6504–6515. https://doi.org/10.33263/BRIAC125.65046515

Shahbazi, Z., & Byun, Y.-C. (2020). A Procedure for Tracing Supply Chains for Perishable Food Based on Blockchain, Machine Learning and Fuzzy Logic. Electronics, 10(1), 41. https://doi.org/10.3390/electronics10010041

Shaw, R., Howley, E., & Barrett, E. (2022). Applying Reinforcement Learning towards automating energy efficient virtual machine consolidation in cloud data centers. Information Systems, 107, 101722. https://doi.org/10.1016/j.is.2021.101722

Shrouf, F., Ordieres, J., & Miragliotta, G. (2014). Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. 2014 IEEE International Conference on Industrial Engineering and Engineering Management, 697–701. https://doi.org/10.1109/IEEM.2014.7058728

Somayaji, S. R. K., Kaliyaperumal, S., & Velayutham, V. (2020). Managing and Monitoring E-Waste Using Augmented Reality in India. In P. Karrupusamy, J. Chen, & Y. Shi (Eds.), Sustainable Communication Networks and Application (Vol. 39, pp. 32–37). Springer International Publishing. https://doi.org/10.1007/978-3-030-34515-0_4

Strepparava, D., Nespoli, L., Kapassa, E., Touloupou, M., Katelaris, L., & Medici, V. (2022). Deployment and analysis of a blockchain-based local energy market. Energy Reports, 8, 99–113. https://doi.org/10.1016/j.egyr.2021.11.283

Teng, Y., & Pan, W. (2019). Systematic embodied carbon assessment and reduction of prefabricated high-rise public residential buildings in Hong Kong. Journal of Cleaner Production, 238, 117791. https://doi.org/10.1016/j.jclepro.2019.117791

Theodorou, P., Kydonakis, P., Han, G. J., Tsagaki-Rekleitou, E., & Skanavis, C. (n.d.). Waste Management Education tailored to Tourists’ Interests through Augmented Reality.

Tinelli, S., & Juran, I. (2019). Artificial intelligence-based monitoring system of water quality parameters for early detection of non-specific bio-contamination in water distribution systems. Water Supply, 19(6), 1785–1792. https://doi.org/10.2166/ws.2019.057

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.

Trienekens, J. H., Wognum, P. M., Beulens, A. J. M., & van der Vorst, J. G. A. J. (2012). Transparency in complex dynamic food supply chains. Advanced Engineering Informatics, 26(1), 55–65. https://doi.org/10.1016/j.aei.2011.07.007

Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2022). The ethics of algorithms: Key problems and solutions. AI & SOCIETY, 37(1), 215–230. https://doi.org/10.1007/s00146-021-01154-8

Turan, E., Konuşkan, Y., Yıldırım, N., Tunçalp, D., İnan, M., Yasin, O., Turan, B., & Kerimoğlu, V. (2022). Digital twin modelling for optimizing the material consumption: A case study on sustainability improvement of thermoforming process. Sustainable Computing: Informatics and Systems, 35, 100655. https://doi.org/10.1016/j.suscom.2022.100655

Turinsky, P. J., & Kothe, D. B. (2016). Modeling and simulation challenges pursued by the Consortium for Advanced Simulation of Light Water Reactors (CASL). Journal of Computational Physics, 313, 367–376. https://doi.org/10.1016/j.jcp.2016.02.043

Turkyilmaz, A., Dikhanbayeva, D., Suleiman, Z., Shaikholla, S., & Shehab, E. (2021). Industry 4.0: Challenges and opportunities for Kazakhstan SMEs. Procedia CIRP, 96, 213–218.

Turner, C., Okorie, O., Emmanouilidis, C., & Oyekan, J. (2022). Circular production and maintenance of automotive parts: An Internet of Things (IoT) data framework and practice review. Computers in Industry, 136, 103593. https://doi.org/10.1016/j.compind.2021.103593

Tyacke, J., Naqavi, I., Wang, Z.-N., Tucker, P., & Boehning, P. (2017). Predictive Large Eddy Simulation for Jet Aeroacoustics–Current Approach and Industrial Application. Journal of Turbomachinery, 139(8), 081003. https://doi.org/10.1115/1.4035662

Tyacke, J., Vadlamani, N. R., Trojak, W., Watson, R., Ma, Y., & Tucker, P. G. (2019). Turbomachinery simulation challenges and the future. Progress in Aerospace Sciences, 110, 100554. https://doi.org/10.1016/j.paerosci.2019.100554

Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of Artificial Intelligence and Machine learning in smart cities. Computer Communications, 154, 313–323. https://doi.org/10.1016/j.comcom.2020.02.069

Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0 – A Glimpse. Procedia Manufacturing, 20, 233–238. https://doi.org/10.1016/j.promfg.2018.02.034

Varavallo, G., Caragnano, G., Bertone, F., Vernetti-Prot, L., & Terzo, O. (2022). Traceability Platform Based on Green Blockchain: An Application Case Study in Dairy Supply Chain. Sustainability, 14(6), 3321. https://doi.org/10.3390/su14063321

Verma, P., Kumar, V., Daim, T., Sharma, N. K., & Mittal, A. (2022). Identifying and prioritizing impediments of industry 4.0 to sustainable digital manufacturing: A mixed method approach. Journal of Cleaner Production, 356, 131639. https://doi.org/10.1016/j.jclepro.2022.131639

Vikiru, A., Mujera, S., & Kangethe, K. (2019). Waste Management using Augmented Reality. https://doi.org/10.13140/RG.2.2.14780.16009

Wang, J., Yang, W., Du, P., & Niu, T. (2020). Outlier-robust hybrid electricity price forecasting model for electricity market management. Journal of Cleaner Production, 249, 119318. https://doi.org/10.1016/j.jclepro.2019.119318

Wang, K., Tekler, Z. D., Cheah, L., Herremans, D., & Blessing, L. (2021). Evaluating the Effectiveness of an Augmented Reality Game Promoting Environmental Action. Sustainability, 13(24), 13912. https://doi.org/10.3390/su132413912

Weng, Y., Li, M., Ruan, S., Wong, T. N., Tan, M. J., Ow Yeong, K. L., & Qian, S. (2020). Comparative economic, environmental and productivity assessment of a concrete bathroom unit fabricated through 3D printing and a precast approach. Journal of Cleaner Production, 261, 121245. https://doi.org/10.1016/j.jclepro.2020.121245

Wu, H., Mehrabi, H., Karagiannidis, P., & Naveed, N. (2022). Additive manufacturing of recycled plastics: Strategies towards a more sustainable future. Journal of Cleaner Production, 335, 130236. https://doi.org/10.1016/j.jclepro.2021.130236

Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962. https://doi.org/10.1080/00207543.2018.1444806

Xu, X., & Yang, Y. (2022). Municipal hazardous waste management with reverse logistics exploration. Energy Reports, 8, 4649–4660. https://doi.org/10.1016/j.egyr.2022.02.230

Yang, B., Yu, T., Zhang, X., Li, H., Shu, H., Sang, Y., & Jiang, L. (2019). Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition. Energy Conversion and Management, 179, 286–303. https://doi.org/10.1016/j.enconman.2018.10.074

Yeomans, J. S., & Imanirad, R. (2012). Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning. Applied Mathematics, 03(10), 1236–1244. https://doi.org/10.4236/am.2012.330179

Yetis, H., Karakose, M., & Baygin, N. (2022). Blockchain-based mass customization framework using optimized production management for industry 4.0 applications. Engineering Science and Technology, an International Journal, 36, 101151. https://doi.org/10.1016/j.jestch.2022.101151

Yudelson, J. (2010). Greening existing buildings. McGraw-Hill Education.

Zendehboudi, A., Baseer, M. A., & Saidur, R. (2018). Application of support vector machine models for forecasting solar and wind energy resources: A review. Journal of Cleaner Production, 199, 272–285. https://doi.org/10.1016/j.jclepro.2018.07.164

Downloads

Published

2022-12-01

How to Cite

[1]
M. El Merroun, I. J. . Bartók, and O. Alkhlaifat, “Industry 4.0 technologies’ effects on environmental sustainability - A systematic literature review”, JME, vol. 17, no. 4, pp. 132–152, Dec. 2022.