This work is licensed under the Creative Commons Attribution 4.0 International License.
C. Choi, C. Kim, and C. Kim, “Towards Sustainable Environmental Policy and Management in the Fourth Industrial Revolution: Evidence from Big Data Analytics,” The Journal of Asian Finance, Economics and Business, vol. 6, no. 3, pp. 185–192, Aug. 2019, doi: https://doi.org/10.13106/jafeb.2019.vol6.no3.185.Search in Google Scholar
M. Javaid, A. Haleem, R.P. Singh, R. Suman, and E.S. Gonzalez, “Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability,” Sustainable Operations and Computers, vol. 3, no. 1, pp. 203–217, Jan. 2022, doi: https://doi.org/10.1016/j.susoc.2022.01.008.Search in Google Scholar
R.R. Hernandez, M.S. Mayernik, M.L. Murphy-Mariscal, and M.F. Allen, “Advanced Technologies and Data Management Practices in Environmental Science: Lessons from Academia,” BioScience, vol. 62, no. 12, pp. 1067–1076, Dec. 2012, doi: https://doi.org/10.1525/bio.2012.62.12.8.Search in Google Scholar
P.V. Thayyib et al., “State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary,” Sustainability, vol. 15, no. 5, p. 4026, Jan. 2023, doi: https://doi.org/10.3390/su15054026.Search in Google Scholar
H. Berg, “Unlocking the potential of Industry 4.0 to reduce the environmental impact of production,” The Wuppertal Institute for Climate, 2021. Available: https://www.eio-net.europa.eu/etcs/etc-wmge/products/etc-wmge-reports/unlocking-the-potential-of-industry-4-0-to-reduce-the-environmental-impact-of-production/@@down-load/file/Final%20for%20website.pdf.Search in Google Scholar
E. Albelda Pérez, C. Correa Ruiz, and F. Carrasco Fenech, “Environmental management systems as an embedding mechanism: a research note,” Accounting, Auditing & Accountability Journal, vol. 20, no. 3, pp. 403–422, Jun. 2007, doi: https://doi.org/10.1108/09513570710748562.Search in Google Scholar
R. Canestrino, M. Ćwiklicki, P. Kafel, M. Wojnarowska, and P. Magliocca, “The digitalization in EMAS-registered organizations: evidences from Italy and Poland,” The TQM Journal, vol. 32, no. 4, pp. 673–695, May 2020, doi: https://doi.org/10.1108/tqm-12-2019-0301.Search in Google Scholar
R.Z. Szabó, B. Szedmák, A. Tajti, and P. Bera, “Environmental Sustainability, Digitalisation, and the Entrepreneurial Perception of Distances as Drivers of SMEs’ Internationali-sation,” Sustainability, vol. 15, no. 3, p. 2487, Jan. 2023, doi: https://doi.org/10.3390/su15032487.Search in Google Scholar
O. Diófási-Kovács and N. Judit, “Beyond CO2 emissions – The role of digitalization in multi-dimensional environmental performance measurement,” Environmental and Sustainability Indicators, vol. 18, p. 100252, Apr. 2023, doi: https://doi.org/10.1016/j.indic.2023.100252.Search in Google Scholar
European Environment Agency, “environmental management system — European Environment Agency,” www.eea.europa.eu. https://www.eea.eu-ropa.eu/help/glossary/eea-glossary/environmental-management-system (accessed Apr. 01, 2023). Search in Google Scholar
European Commission, “Advanced technologies,” single-market-economy.ec.europa.eu. https://single-market-economy.ec.europa.eu/industry/strategy/advanced-technologies_en.Search in Google Scholar
P. Boucher, “Artificial intelligence: How does it work, why does it matter, and what can we do about it?,” 2020. Available: https://www.europarl.europa.eu/Reg-Data/etudes/STUD/2020/641547/EPRS_STU(2020)641547_EN.pdf.Search in Google Scholar
A. Sestino, M.I. Prete, L. Piper, and G. Guido, “Internet of Things and Big Data as enablers for business digitalization strategies,” Technovation, vol. 98, no. 102173, p. 102173, Dec. 2020, doi: https://doi.org/10.1016/j.technovation.2020.102173.Search in Google Scholar
A. Kuzior and A. Kwilinski, “Cognitive Technologies and Artificial Intelligence in Social Perception,” Management Systems in Production Engineering, vol. 30, no. 2, pp. 109–115, May 2022, doi: https://doi.org/10.2478/mspe-2022-0014.Search in Google Scholar
M. Yazdani, C. Kahraman, P. Zarate, and S.C. Onar, “A fuzzy multi attribute decision framework with integration of QFD and grey relational analysis,” Expert Systems with Applications, vol. 115, pp. 474–485, Jan. 2019, doi: https://doi.org/10.1016/j.eswa.2018.08.017.Search in Google Scholar
S. Kumar, R. Sureka, W.M. Lim, S. Kumar Mangla, and N. Goyal, “What do we know about business strategy and environmental research? Insights from Business Strategy and the Environment,” Business Strategy and the Environment, vol. 30, no. 8, May 2021, doi: https://doi.org/10.1002/bse.2813.Search in Google Scholar
H. Wang, T. Zhang, Y. Quan, and R. Dong, “Research on the framework of the Environmental Internet of Things,” International Journal of Sustainable Development & World Ecology, vol. 20, no. 3, pp. 199–204, Apr. 2013, doi: https://doi.org/10.1080/13504509.2013.783517.Search in Google Scholar
Z. Zhang, H. He, J. Guo, and R. Han, “Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses,” Applied Energy, vol. 280, p. 116001, Dec. 2020, doi: https://doi.org/10.1016/j.apenergy.2020.116001.Search in Google Scholar
S. Drobyazko and T. Hilorme, “Influence of Sustainable Development of Space Activities on Earth Ecology,” ProQuest, vol. 940, Dec. 2021, doi: https://doi.org/10.1088/1755-1315/940/1/012014.Search in Google Scholar
G. Das, S. Li, R.A. Tunio, R.H. Jamali, I. Ullah, and K.W.T. Fernando, “The implementation of green supply chain management (GSCM) and environmental management system (EMS) practices and its impact on market competitiveness during COVID-19,” Environmental Science and Pollution Research, vol. 30, May 2023, doi: https://doi.org/10.1007/s11356-023-27077-z.Search in Google Scholar
K. Fenton, S. Simske, and J. Luu, “Mitigation of Chemical Reporting Liabilities through Systematic Modernization of Chemical Hazard and Safety Data Management Systems,” ACS omega, vol. 8, no. 5, pp. 4928–4936, Jan. 2023, doi: https://doi.org/10.1021/acsomega.2c07244.Search in Google Scholar
R.T. Munodawafa and S.K. Johl, “Design and Development of an Eco-innovation Management Information System to Accelerate Firms’ Digital Transformation Strategy,” IEEE Access, vol. 10, pp. 1–1, 2022, doi: https://doi.org/10.1109/access.2022.3163248.Search in Google Scholar
EUR-Lex, “Document 32013D0131,” eur-lex.europa.eu, 2013. https://eur-lex.eu-ropa.eu/eli/dec/2013/131(1)/oj/engSearch in Google Scholar
M.K. Nasir, R. Md Noor, M.A. Kalam, and B.M. Masum, “Reduction of Fuel Consumption and Exhaust Pollutant Using Intelligent Transport Systems,” The Scientific World Journal, vol. 2014, pp. 1–13, 2014, doi: https://doi.org/10.1155/2014/836375.Search in Google Scholar
M.M. Gandhi, D.S. Solanki, R.S. Daptardar, and N.S. Baloorkar, “Smart Control of Traffic Light Using Artificial Intelligence,” in 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Dec. 2020. doi: https://doi.org/10.1109/icraie51050.2020.9358334.Search in Google Scholar
S. Khan, A. Adnan, and N. Iqbal, “Applications of Artificial Intelligence in Transportation,” IEEE Xplore, Jul. 01, 2022. https://ieeexplore.ieee.org/abstract/document/9872928 (accessed Oct. 05, 2022). Search in Google Scholar
T. C. Truong, “The Impact of Digital Transformation on Environmental Sustainability,” Advances in Multimedia, vol. 2022, pp. 1–12, May 2022, doi: https://doi.org/10.1155/2022/6324325.Search in Google Scholar
E.K. Nti, S.J. Cobbina, E.E. Attafuah, E. Opoku, and M.A. Gyan, “Environmental sustainability technologies in biodiversity, energy, transportation and water management using artificial intelligence: A systematic review,” Sustainable Futures, vol. 4, p. 100068, 2022, doi: https://doi.org/10.1016/j.sftr.2022.100068.Search in Google Scholar
I.P. Chochliouros et al., “V2X Communications for the Support of GLOSA and Intelligent Intersection Applications,” in Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops, 2021, pp. 138–152. doi: https://doi.org/10.1007/978-3-030-79157-5_13.Search in Google Scholar
V. Magaña and M.M. Organero, “Reducing Stress and Fuel Consumption Providing Road Information,” Advances in Intelligent Systems and Computing, vol. 376, pp. 23–31, Jan. 2015, doi: https://doi.org/10.1007/978-3-319-19695-4_3.Search in Google Scholar
G. Guido, D. Rogano, A. Vitale, V. Astarita, and Demetrio Carmine Festa, “Big data for public transportation: A DSS framework,” in 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, Jun. 2017. doi: https://doi.org/10.1109/mtits.2017.8005635.Search in Google Scholar
F. Baumgarte, R. Keller, F. Röhrich, L. Valett, and D. Zinsbacher, “Revealing influences on carsharing users’ trip distance in small urban areas,” Transportation Research Part D: Transport and Environment, vol. 105, p. 103252, Apr. 2022, doi: https://doi.org/10.1016/j.trd.2022.103252.Search in Google Scholar
A. Tsakiri, N. Lampiris, J. Prantalos, P. Mylonas, G. Aifadopoulou, and Michalis Fousteris, “Versatile Car Sharing Modelling for Sustainable Mobility with Embedded Intelligent Modules,” in ACM International Conference Proceeding Series, Sep. 2022. doi: https://doi.org/10.1145/3549737.3549791.Search in Google Scholar
L. Bohm, F. Peters, P. Bossauer, D. Lawo, C. Pakusch, and G. Stevens, “Designing a Mobility Intelligence System for Decision-making with Shared Mobility Data,” in 2022 International Conference on ICT for Sustainability, ICT4S 2022, Jun. 2022. doi: https://doi.org/10.1109/ict4s55073.2022.00017.Search in Google Scholar
H. Gökçe and K.U. Gökçe, “Software Interoperability for Energy Efficient Building Operations Based on the IFC Data Model Standard,” in Progress in Sustainable Energy Technologies Vol II: Creating Sustainable Development, Jan. 2014, pp. 309–318. doi: https://doi.org/10.1007/978-3-319-07977-6_21.Search in Google Scholar
S. Agostinelli, F. Cumo, G. Guidi, and C. Tomazzoli, “Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence,” Energies, vol. 14, no. 8, p. 2338, Apr. 2021, doi: https://doi.org/10.3390/en14082338.Search in Google Scholar
L. Farahzadi and M. Kioumarsi, “Application of machine learning initiatives and intelligent perspectives for CO2 emissions reduction in construction,” Journal of Cleaner Production, p. 135504, Dec. 2022, doi: https://doi.org/10.1016/j.jclepro.2022.135504.Search in Google Scholar
S. Bang and B. Andersen, “Utilising Artificial Intelligence in Construction Site Waste Reduction,” Journal of Engineering, Project, and Production Management, vol. 12, no. 3, Sep. 2022, doi: https://doi.org/10.32738/jeppm-2022-0022.Search in Google Scholar
P. Desai, S. Sandbhor, and A. Kaushik, “AI and BIM-based Construction defects, rework, and waste optimization,” in International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, Mar. 2023, pp. 1–6. doi: https://doi.org/10.1109/esci56872.2023.10099726.Search in Google Scholar
M. I. Shah et al., “Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete,” Sustainability, vol. 13, no. 5, p. 2867, Mar. 2021, doi: https://doi.org/10.3390/su13052867.Search in Google Scholar
L. Farahzadi and M. Kioumarsi, “Intelligent Initiatives to Reduce CO2 Emissions in Construction,” in ECCOMAS: European Community on Computational Methods in Applied Sciences, Scipedia, Jan. 2022. doi: https://doi.org/10.23967/eccomas.2022.150.Search in Google Scholar
C. Gomez-Otero, R. Uría-Martínez, and J. Caffarel, “Cli-mApp: A novel approach of an intelligent HVAC control system,” in Iberian Conference on Information Systems and Technologies, Jun. 2012, pp. 1–6. Search in Google Scholar
Y.-Y. Chen, Y.-H. Lin, C.-C. Kung, M.-H. Chung, and I-Hsuan. Yen, “Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes,” Sensors, vol. 19, no. 9, p. 2047, May 2019, doi: https://doi.org/10.3390/s19092047.Search in Google Scholar
S. Arvidsson, M. Gullstrand, B. Sirmacek, and M. Riveiro, “Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data,” Sensors, vol. 21, no. 4, p. 1036, Feb. 2021, doi: https://doi.org/10.3390/s21041036.Search in Google Scholar
A. Chopde, N. Bharate, S. Bhattar, A. Kunvar, and S. Bhadwal, “Trash Can! An AI system for automatic classification of waste,” IEEE Xplore, May 01, 2022. https://ieeex-plore.ieee.org/abstract/document/9844351?casa_to-ken=-nN6OC4O6roAAAAA:aGlEDxGRh7gUFBL28JWMy-cJuUn4A8p2wMpkDgdCiXURfpbcU4sn3graR-gTiJjmNNS1d9Q56NUQ (accessed May 18, 2023). Search in Google Scholar
S. Sen, Dharmendra Yadeo, P. Kumar, and M. Kumar, “Machine learning and predictive control-based energy management system for smart buildings,” in Artificial Intelligence and Machine Learning in Smart City Planning, Jan. 2023, pp. 199–220. doi: https://doi.org/10.1016/b978-0-323-99503-0.00015-6.Search in Google Scholar
A.M. Suduc and M. Bizoi, “AI shapes the future of web conferencing platforms,” Procedia Computer Science, vol. 214, pp. 288–294, Jan. 2022, doi: https://doi.org/10.1016/j.procs.2022.11.177.Search in Google Scholar
D.E. Blanco, B. Lee, and M.A. Modestino, “Optimizing organic electrosynthesis through controlled voltage dosing and artificial intelligence,” Proceedings of the National Academy of Sciences, vol. 116, no. 36, pp. 17683–17689, Aug. 2019, doi: https://doi.org/10.1073/pnas.1909985116.Search in Google Scholar
Z. Ye, J. Yang, N. Zhong, X. Tu, J. Jia, and J. Wang, “Tackling environmental challenges in pollution controls using artificial intelligence: A review,” Science of The Total Environment, vol. 699, p. 134279, Jan. 2020, doi: https://doi.org/10.1016/j.scitotenv.2019.134279.Search in Google Scholar
W. Qing, “Global Practice of AI and Big Data in Oil and Gas Industry,” in Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies, Jan. 2021, pp. 181–210. doi: https://doi.org/10.1016/b978-0-12-820714-7.00009-1.Search in Google Scholar
K.L. Johnston, M.L. Phillips, N.A. Esmen, and T.A. Hall, “Evaluation of an Artificial Intelligence Program for Estimating Occupational Exposures,” The Annals of Occupational Hygiene, vol. 49, no. 2, Dec. 2004, doi: https://doi.org/10.1093/annhyg/meh072.Search in Google Scholar
M.T. Gaudio, G. Coppola, L. Zangari, S. Curcio, S. Greco, and S. Chakraborty, “Artificial Intelligence-Based Optimization of Industrial Membrane Processes,” Earth systems and environment, vol. 5, no. 2, pp. 385–398, Jun. 2021, doi: https://doi.org/10.1007/s41748-021-00220-x.Search in Google Scholar
J.L. McDonagh, W.C. Swope, R.L. Anderson, M.A. Johnston, and D.J. Bray, “What can digitisation do for formulated product innovation and development?” Polymer International, vol. 70, no. 3, pp. 248–255, Jul. 2020, doi: https://doi.org/10.1002/pi.6056.Search in Google Scholar
R. Arboretti et al., “Machine learning and design of experiments with an application to product innovation in the chemical industry,” Journal of Applied Statistics, vol. 49, no. 10, pp. 2674–2699, Mar. 2021, doi: https://doi.org/10.1080/02664763.2021.1907840.Search in Google Scholar
M. Pournader, Y. Shi, S. Seuring, and S.C.L. Koh, “Block-chain applications in supply chains, transport and logistics: a systematic review of the literature,” International Journal of Production Research, vol. 58, no. 7, pp. 1–19, Aug. 2019, doi: https://doi.org/10.1080/00207543.2019.1650976.Search in Google Scholar
A. Noor, “Adoption of Blockchain Technology Facilitates a Competitive Edge for Logistic Service Providers,” Sustainability, vol. 14, no. 23, pp. 15543–15543, Nov. 2022, doi: https://doi.org/10.3390/su142315543.Search in Google Scholar
W. Li, L. Wang, Y. Li, and B. Liu, “A blockchain-based emissions trading system for the road transport sector: policy design and evaluation,” Climate Policy, vol. 21, no. 3, pp. 337–352, Nov. 2020, doi: https://doi.org/10.1080/14693062.2020.1851641.Search in Google Scholar
Y. Li, M.K. Lim, and C. Wang, “An intelligent model of green urban distribution in the blockchain environment,” Resources, Conservation and Recycling, vol. 176, no. 176, p. 105925, Jan. 2022, doi: https://doi.org/10.1016/j.rescon-rec.2021.105925.Search in Google Scholar
X. Yu and X. Wang, “Research on Carbon-Trading Model of Urban Public Transport Based on Blockchain Technology,” Energies, vol. 16, no. 6, pp. 2606–2606, Mar. 2023, doi: https://doi.org/10.3390/en16062606.Search in Google Scholar
S.A. Renu and B.G. Banik, “Implementation of a Secure Ride-Sharing DApp Using Smart Contracts on Ethereum Blockchain,” International Journal of Safety and Security Engineering, vol. 11, no. 2, pp. 167–173, Apr. 2021, doi: https://doi.org/10.18280/ijsse.110205.Search in Google Scholar
A. Lanko, N. Vatin, and A. Kaklauskas, “Application of RFID combined with blockchain technology in logistics of construction materials,” MATEC Web of Conferences, vol. 170, p. 03032, 2018, doi: https://doi.org/10.1051/matec-conf/201817003032.Search in Google Scholar
L. Pellegrini, S. Campi, M. Locatelli, G. Pattini, G.M. Di Giuda, and L. C. Tagliabue, “Digital Transition and Waste Management in Architecture, Engineering, Construction, and Operations Industry,” Frontiers in Energy Research, vol. 8, Nov. 2020, doi: https://doi.org/10.3389/fenrg.2020.576462.Search in Google Scholar
F. Elghaish, M.R. Hosseini, T. Kocaturk, M. Arashpour, and M. Bararzadeh Ledari, “Digitalised circular construction supply chain: An integrated BIM-Blockchain solution,” Au-tomation in Construction, vol. 148, p. 104746, Apr. 2023, doi: https://doi.org/10.1016/j.autcon.2023.104746.Search in Google Scholar
Y. Matsuda, Y. Yamazaki, H. Oki, Y. Takeda, D. Sagawa, and K. Tanaka, “Demonstration of Blockchain Based Peer to Peer Energy Trading System with Real-Life Used PHEV and HEMS Charge Control,” Energies, vol. 14, no. 22, p. 7484, Nov. 2021, doi: https://doi.org/10.3390/en14227484.Search in Google Scholar
A. Kuzior, M. Sira, and P. Brozek, “Using Blockchain and Artificial Intelligence in Energy Management as a Tool to Achieve Energy Efficiency,” Virtual Economics, vol. 5, no. 3, pp. 69–90, 2022. Search in Google Scholar
W. Du, X. Ma, H. Yuan, and Y. Zhu, “Blockchain technology-based sustainable management research: the status quo and a general framework for future application,” Environmental Science and Pollution Research, vol. 29, no. 39, pp. 58648–58663, Jul. 2022, doi: https://doi.org/10.1007/s11356-022-21761-2.Search in Google Scholar
M. Marzband, E. Yousefnejad, A. Sumper, and J.L. Domínguez-García, “Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization,” International Journal of Electrical Power & Energy Systems, vol. 75, pp. 265–274, Feb. 2016, doi: https://doi.org/10.1016/j.ijepes.2015.09.010.Search in Google Scholar
E. Schiller, E. Esati, S. R. Niya, and B. Stiller, “Blockchain on MSP430 with IEEE 802.15.4,” in Conference on Local Computer Networks, LCN. 45th IEEE Conference on Local Computer Networks, Sydney: LCN, Nov. 2020. doi: https://doi.org/10.1109/lcn48667.2020.9314805.Search in Google Scholar
W. Konhäuser, “Digitalization in Buildings and Smart Cities on the Way to 6G,” Wireless Personal Communications, vol. 121, no. 2, Sep. 2021, doi: https://doi.org/10.1007/s11277-021-09069-9.Search in Google Scholar
G. Ongena, K. Smit, B. Jarno, G. Adams, Y. Roelofs, and P. Ravesteyn, “Blockchain-based Smart Contracts in Waste Management: A Silver Bullet?,” Business Strategy and the Environment, vol. 31, no. 1, Jun. 2018, doi: https://doi.org/10.18690/978-961-286-170-4.23.Search in Google Scholar
S. Shrivastava, A. Tripathi, and R. Yamini, “Blockchain-based Smart Waste Management System,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 11S, pp. 1030–1034, Oct. 2019, doi: https://doi.org/10.35940/ijitee.k1212.09811s19.Search in Google Scholar
L. Alves, E. Ferreira Cruz, S. I. Lopes, P. M. Faria, and A. M. Rosado da Cruz, “Towards circular economy in the textiles and clothing value chain through blockchain technology and IoT: A review,” Waste Management & Research: The Journal for a Sustainable Circular Economy, vol. 40, no. 1, p. 0734242X2110528, Oct. 2021, doi: https://doi.org/10.1177/0734242x211052858.Search in Google Scholar
D. Nallaperuma et al., “Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 12, pp. 4679–4690, Dec. 2019, doi: https://doi.org/10.1109/tits.2019.2924883.Search in Google Scholar
P. Killeen, B. Ding, I. Kiringa, and T. Yeap, “IoT-based predictive maintenance for fleet management,” Procedia Computer Science, vol. 151, pp. 607–613, 2019, doi: https://doi.org/10.1016/j.procs.2019.04.184.Search in Google Scholar
S. Proto et al., “REDTag: A Predictive Maintenance Frame-work for Parcel Delivery Services,” IEEE Access, vol. 8, no. 58, pp. 14953–14964, 2020, doi: https://doi.org/10.1109/access.2020.2966568.Search in Google Scholar
M.Y.L. Chew and K. Yan, “Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management,” Journal of Sensors, vol. 2022, pp. 1–48, Jan. 2022, doi: https://doi.org/10.1155/2022/5975816.Search in Google Scholar
M. Martínez-Rojas, M. J. Gacto, A. Vitiello, G. Acampora, and Jose Manuel Soto-Hidalgo, “An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects,” Sensors, vol. 21, no. 19, pp. 6461–6461, Sep. 2021, doi: https://doi.org/10.3390/s21196461.Search in Google Scholar
J. Kim et al., “Occupant comfort and behavior: High-resolution data from a 6-month field study of personal comfort systems with 37 real office workers,” Building and Environment, vol. 148, pp. 348–360, Jan. 2019, doi: https://doi.org/10.1016/j.buildenv.2018.11.012.Search in Google Scholar
S.A. Hashmi, C.F. Ali, and S. Zafar, “Internet of things and cloud computing-based energy management system for demand side management in smart grid,” International Journal of Energy Research, vol. 45, no. 1, pp. 1007–1022, Oct. 2020, doi: https://doi.org/10.1002/er.6141.Search in Google Scholar
Q. Lele and K. Lihua, “Technical Framework Design of Safety Production Information Management Platform for Chemical Industrial Parks Based on Cloud Computing and the Internet of Things,” International Journal of Grid and Distributed Computing, vol. 9, no. 6, pp. 299–314, Jun. 2016, doi: https://doi.org/10.14257/ijgdc.2016.9.6.28.Search in Google Scholar
European Commission and Executive Agency for Small and Medium-sized Enterprises, Artificial intelligence: critical industrial applications : report on market analysis of prioritised value chains, the most critical AI applications and the conditions for AI rollout. LU: Publications Office of the European Union, 2020. Available: https://op.europa.eu/en/publication-detail/-/publication/09a1b19f-93fa-11ea-aac4-01aa75ed71a1/language-en.Search in Google Scholar
J. Hart, I. Hunt, and V. Shankararaman, “. Environmental management systems-A role for AI?,” in Workshop Binding Environmental Sciences and Artificial Intelligence (BESAI’98), 1998, pp. 1–10. Search in Google Scholar
U. Cortés and M. Sànchez–Marrè, “Binding Environmental Sciences and Artificial Intelligence in Environmental Modelling & Software.” Environmental Modelling and Software, vol. 14, pp. 335–337, Jan. 1999. Search in Google Scholar
U. Cortés, M. Sànchez-Marrè, L. Ceccaroni, I. R-Roda, and M. Poch, “Artificial Intelligence and Environmental Decision Support Systems,” Applied Intelligence, vol. 13, no. 1, pp. 77–91, 2000, doi: https://doi.org/10.1023/a:1008331413864.Search in Google Scholar
Amazon, “Artificial Intelligence Services,” Amazon Web Services, Inc. https://aws.amazon.com/machine-learning/ai-services/?nc1=h_ls (accessed Apr. 06, 2023). Search in Google Scholar
Microsoft, “Azure AI Platform – Artificial Intelligence Service | Microsoft Azure,” azure.microsoft.com. https://az-ure.microsoft.com/en-us/solutions/ai/#overview.Search in Google Scholar
451 Research, “The Carbon Reduction Opportunity of Moving to Amazon Web Services,” 2019. Search in Google Scholar
W. Hoek, “Digitalisation on the cloud must absolutely become a priority.,” www.amcsgroup.com, 2020. https://www.amcsgroup.com/blogs/why-smbs-need-to-move-to-the-cloud-with-amcs-platform/#amcs-platform (accessed Apr. 06, 2023). Search in Google Scholar
European Commission Environment Directorate-General, “What is EMAS?” 2001. Accessed: Jul. 17, 2023. [Online]. Available: https://www.isprambiente.gov.it/content-files/01377800/1377885-fs-iso-en.pdf.Search in Google Scholar
F. Testa, F. Rizzi, T. Daddi, N.M. Gusmerotti, M. Frey, and F. Iraldo, “EMAS and ISO 14001: the differences in effec-tively improving environmental performance,” Journal of Cleaner Production, vol. 68, pp. 165-173, Apr. 2014, doi: https://doi.org/10.1016/j.jclepro.2013.12.061.Search in Google Scholar
A. Erauskin-Tolosa, E. Zubeltzu-Jaka, I. Heras-Saizarbitoria, and O. Boiral, “ISO 14001, EMAS and environmental per-formance: A meta-analysis,” Business Strategy and the En-vironment, vol. 29, no. 3, pp. 1145-1159, Dec. 2019, doi: https://doi.org/10.1002/bse.2422.Search in Google Scholar
F.F. Fagioli, L. Paolotti, and A. Boggia, “Trends in Environ-mental Management Systems Research. A Content Analy-sis,” Environmental and Climate Technologies, vol. 26, no. 1, pp. 46-63, Jan. 2022, doi: https://doi.org/10.2478/rtuect-2022-0005.Search in Google Scholar
F. Gualandri and A. Kuzior, “Home Energy Management Systems Adoption Scenarios: The Case of Italy,” Energies, vol. 16, no. 13, pp. 4946-4946, Jun. 2023, doi: https://doi.org/10.3390/en16134946.Search in Google Scholar
C. Welsch, “As the world goes digital, datacenters that make the cloud work look to renewable energy sources,” Microsoft News Centre Europe, Nov. 23, 2022. https://news.microsoft.com/europe/features/as-the-world-goes-digital-datacenters-that-make-the-cloud-work-look-to-renewable-energy-sources/.Search in Google Scholar
W.-Y. Chang, “A Data Envelopment Analysis on the Performance of Using Artificial Intelligence-Based Environmental Management Systems in the Convention and Exhibition Industry,” Ekoloji, vol. 28, no. 107, pp. 3515–3521, 2019. Search in Google Scholar
J. Lohmer, E. Ribeiro da Silva, and R. Lasch, “Blockchain Technology in Operations & Supply Chain Management: A Content Analysis,” Sustainability, vol. 14, no. 10, p. 6192, May 2022, doi: https://doi.org/10.3390/su14106192.Search in Google Scholar
S. Pu and J. S. L. Lam, “The benefits of blockchain for digital certificates: A multiple case study analysis,” Technology in Society, vol. 72, p. 102176, Feb. 2023, doi: https://doi.org/10.1016/j.techsoc.2022.102176.Search in Google Scholar
World Economic Forum, “How technology is enabling mul-tistakeholder engagement,” World Economic Forum, 2023. https://www.weforum.org/agenda/2023/01/how-technology-is-enabling-multi-stakeholder-engagement-davos2023/.Search in Google Scholar
A. Kuzior, S. Arefiev, and Z. Poberezhna, “Informatization of innovative technologies for ensuring macroeconomic trends in the conditions of a circular economy,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 9, no. 1, pp. 10–20, Mar. 2023, doi: https://doi.org/10.1016/j.joitmc.2023.01.001.Search in Google Scholar
A. Parmentola, A. Petrillo, I. Tutore, and F. De Felice, “Is blockchain able to enhance environmental sustainability? A systematic review and research agenda from the perspective of Sustainable Development Goals (SDGs),” Business Strategy and the Environment, vol. 31, no. 1, Sep. 2021, doi: https://doi.org/10.1002/bse.2882.Search in Google Scholar
T. Kawabata et al., “Blockchain Technology and Environmental Sustainability,” 2020. Available: https://wedocs.unep.org/bitstream/handle/20.500.11822/34226/FB019.pdf?sequence=1&isAl-lowed=y.Search in Google Scholar
Q. Jones, “IoT-Based Environmental Monitoring: Types and Use Cases,” www.digi.com Apr. 15, 2022. https://www.digi.com/blog/post/iot-based-environmental-monitoring.Search in Google Scholar
A. Kawasaki et al., “Data Integration and Analysis System (DIAS) Contributing to Climate Change Analysis and Disaster Risk Reduction,” Data Science Journal, vol. 16, Sep. 2017, doi: https://doi.org/10.5334/dsj-2017-041.Search in Google Scholar
O. Farooq, P. Singh, M. Hedabou, W. Boulila, and B. Benjdira, “Machine Learning Analytic-Based Two-Staged Data Management Framework for Internet of Things,” Sensors, vol. 23, no. 5, p. 2427, Feb. 2023, doi: https://doi.org/10.3390/s23052427.Search in Google Scholar
M. Esposito, L. Palma, A. Belli, L. Sabbatini, and P. Pierleoni, “Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review,” Sensors, vol. 22, no. 6, p. 2124, Mar. 2022, doi: https://doi.org/10.3390/s22062124.Search in Google Scholar
C. Chandrakumar, R. Prasanna, M. Stephens, and M.L. Tan, “Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review,” Frontiers in Sensors, vol. 3, Nov. 2022, doi: https://doi.org/10.3389/fsens.2022.1020202.Search in Google Scholar
OECD, “Enhancing the contribution of digitalisation to the smart cities of the future,” 2019. Search in Google Scholar
G. Grander, L.F. da Silva, and E.D.R. Santibañez Gonzalez, “Big data as a value generator in decision support systems: a literature review,” Revista de Gestão, vol. 28, no. 3, pp. 205–222, Jul. 2021, doi: https://doi.org/10.1108/rege-03-2020-0014.Search in Google Scholar
A. Kuzior and J. Zozuľak, “Adaptation of the Idea of Phronesis in Contemporary Approach to Innovation,” Management Systems in Production Engineering, vol. 27, no. 2, pp. 84–87, Jun. 2019, doi: https://doi.org/10.1515/mspe-2019-0014.Search in Google Scholar
P. Fobel and A. Kuzior, “The future (Industry 4.0) is closer than we think. Will it also be ethical?” in AIP Conference Proceedings, 2019, doi: https://doi.org10.1063/1.5137987.Search in Google Scholar
