Sustainable development of smart cities through municipal waste incinerators: the examples of artificial intelligence in technological entrepreneurship

Czemiel-Grzybowska, Wioletta; Pavlakova-Docekalova, Marie; Ratajczak-Mrozek, Milena; Thompson, Cindy

doi:10.2478/emj-2024-0037

Abstract

The article aims to analyse AI’s use for optimising management processes in urban waste incineration plants, making them consistent with the implementation of the sustainable development goals SDG #11 and SDG #12. The triangulation of research methods was chosen to achieve the most reliable research outcomes. The case study was the selected qualitative method. Among the available techniques, two were selected: Computer-Assisted Web Interviewing (CAWI) and in-depth interviews, both of which were employed to gather the necessary data. Scientific research analysed the AI-based technologies used by individual incinerators. The analysis encompassed trends in waste incineration plants utilising AI for sustainable development in smart cities, particularly for achieving SDG#11 and SDG#12, focusing on leveraging AI to enhance environmental outcomes. The effect of using AI in municipal waste incineration plants for SDG#11 and SDG#12 can be applied to other entities implementing the principles of sustainable development in smart cities. The identified trends underscore the importance of adopting and implementing integrated policies and plans that address inclusion, resource efficiency, climate change mitigation and adaptation.

References

Bibri, S. E. (2021). A novel model for data-driven smart sustainable cities of the future: The institutional transformations required for balancing and advancing the three goals of sustainability. Energy Informatics, 4, 5737. doi: 10.1186/s42162-021-00138-8
Search in Google Scholar Back to article
Campbell, D., & Stanley, J. (2000). Experimental and quasi-experimental designs for research. Rand McNally, Chicago, 196-217. doi: 10.1007/978-1-4615-1401-5_11
Search in Google Scholar Back to article
Chen, C., Hu, Y., Marimuthu, K., & Kumar, P. M. (2021). Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies. Sustainable Energy Technologies and Assessments, 47(3), 101358. doi: 10.1016/j.seta.2021.101358
Search in Google Scholar Back to article
Chui, K. T., Lytras, M. D., & Visvizi, A. (2018). Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies, 11(11), 2869. doi: 10.3390/en11112869
Search in Google Scholar Back to article
Cubric, M. (2020). Drivers, barriers, and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62. doi: 10.1016/j.techsoc.2020.101257
Search in Google Scholar Back to article
Czemiel-Grzybowska, W. (2022). Conceptualization and mapping of predictors of technological entrepreneurship growth in a changing economic environment (COVID-19) from the Polish energy sector. Energies, 15(18), 6543. doi: 10.3390/en15186543
Search in Google Scholar Back to article
Czemiel-Grzybowska, W. (2023). Trendy rozwoju zrównoważonej przedsiębiorczości technologicznej opartej na sztucznej inteligencji. Akademia Zarządzania, 7(4), 126-137. doi: 10.24427/az-2023-0059
Search in Google Scholar Back to article
Ertel, W., & Black, N. T. (2018). Introduction to artificial intelligence. Springer International Publishing, 1-11. doi: 10.1007/978-3-319-58487-4
Search in Google Scholar Back to article
Garbuio, M., & Lin, N. (2019). Artificial intelligence as a growth engine for healthcare startups: Emerging business models. California Business Review, 61(2), 59-83. doi: 10.1177/0008125618811931
Search in Google Scholar Back to article
Golinska-Dawson, P., & Sethanan, K. (2023). Sustainable urban freight for energy-efficient smart cities—Systematic literature review. Energies, 16(6), 2617. doi: 10.3390/en16062617
Search in Google Scholar Back to article
Govindan, K., & Shaw, M. (2021). Social sustainability tensions in multi-tier supply chain: A systematic literature review towards conceptual framework development. Journal of Cleaner Production, 279(1-2), 123075. doi: 10.1016/j.jclepro.2020.123075
Search in Google Scholar Back to article
Gupta, B. B., Gaurav, A., Panigrahi, P. K., & Arya, V. (2023). Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship. Technological Forecasting & Social Change, 186(2), 122152. doi: 10.1016/j.techfore.2022.122152
Search in Google Scholar Back to article
Jha, S. K., Bilalovic, J., Jha, A., Patel, N., & Zhang, H. (2017). Renewable energy: Present research and future scope of artificial intelligence. Renewable and Sustainable Energy Reviews, 77, 297-317. doi: 10.1016/j.rser.2017.04.018
Search in Google Scholar Back to article
Kamel Boulos, M. N., Peng, G., & Vopham, T. (2019). An overview of GeoAI applications in health and health-care. International Journal of Health Geographics, 18(7). doi: 10.1186/s12942-019-0171-2
Search in Google Scholar Back to article
Lazaroiu, G., Androniceanu, A., Grecu, I., Grecu, G., & Neguriță, O. (2022). Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing. Oeconomia Coperni -cana, 13(4), 1047-1080. doi: 10.24136/oc.2022.030
Search in Google Scholar Back to article
López-Blanco, R., Martín, J. H., Alonso, R. S., & Prieto, J. (2023). Time series forecasting for improving quality of life and ecosystem services in smart cities. Lecture Notes in Networks and Systems, 603, 74-85. doi: 10.1007/978-3-031-22356-3_8
Search in Google Scholar Back to article
Ma, Y., Ping, K., Wu, C., Chen, L., Shi, H., & Chong, D. (2020). Artificial intelligence-powered Internet of Things and smart public service. Library Hi Tech, 38(1), 165-179. doi: 10.1108/LHT-12-2017-0274
Search in Google Scholar Back to article
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. AI Magazine, 27(4). doi: 10.1609/aimag.v27i4.1904
Search in Google Scholar Back to article
Mikalef, P., & Pateli, A. (2017). Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLSSEM and fsQCA. Journal of Business Research, 70, 1-16. doi: 10.1016/j.jbusres.2016.09.004
Search in Google Scholar Back to article
Moravec, V., Hynek, N., Gavurova, B., & Kubak, M. (2024). Everyday artificial intelligence unveiled: Societal awareness of technological transformation. Oeconomia Copernicana, 15(2), 367-406. doi: 10.24136/oc.2961
Search in Google Scholar Back to article
Muhammad, K., Lloret, J., & Baik, S. W. (2019). Intelligent and energy-efficient data prioritization in green smart cities: Current challenges and future directions. IEEE Communications Magazine, 57(2), 60-65. doi: 10.1109/MCOM.2018.1800371
Search in Google Scholar Back to article
Navarro-Espinoza, A., López-Bonilla, O. R., García-Guerrero, E. E., Tlelo-Cuautle, E., López-Mancilla, D., Hernández-Mejía, C., & Inzunza-González, E. (2022). Traffic flow prediction for smart traffic lights using machine learning algorithms. Technologies, 10(1). doi: 10.3390/technologies10010005
Search in Google Scholar Back to article
O’Dwyer, E., Pan, I., Acha, S., & Shah, N. (2019). Smart energy systems for sustainable smart cities: Current developments, trends, and future directions. Applied Energy, 237, 581-597. doi: 10.1016/j.apenergy.2019.01.024
Search in Google Scholar Back to article
Ortega-Fernández, A., Martín-Rojas, R., & García-Morales, V. J. (2020). Artificial intelligence in the urban environment: Smart cities as models for developing innovation and sustainability. Sustainability, 12(19), 7860. doi: 10.3390/su12197860
Search in Google Scholar Back to article
Pacelli, V. (2012). Forecasting exchange rates: A comparative analysis. International Journal of Business and Social Science, 3(10), 31-45. doi: 10.12846/j. em.2015.02.06
Search in Google Scholar Back to article
Paiva, S., Ahad, M. A., Tripathi, G., Feroz, N., & Casalino, G. (2021). Enabling technologies for urban smart mobility: Recent trends, opportunities, and challenges. Sensors, 21(6), 1-45. doi: 10.3390/s21062143
Search in Google Scholar Back to article
Park, S., Choi, M. I., Lee, S., Lee, T., Kim, S., Cho, K., & Park, S. (2020). Reinforcement learning-based BEMS architecture for energy usage optimization. Sensors, 20(17), 4918. doi: 10.3390/s20174918
Search in Google Scholar Back to article
Patton, M. (1985). Quality in qualitative research: Methodological principles and recent developments. Journal of the American Educational Research Association, Chicago.
Search in Google Scholar Back to article
Ragab, A., Osama, A., & Ramzy, A. (2023). Simulation of the environmental impact of industries in smart cities. Ain Shams Engineering Journal, 14(6), 102103. doi: 10.1016/j.asej.2022.102103
Search in Google Scholar Back to article
Rani, S., Mishra, R. K., Usman, M., Kataria, A., Kumar, P., Bhambri, P., & Mishra, A. K. (2021). Amalgamation of advanced technologies for sustainable development of smart city environment: A review. IEEE Access, 9, 150060-150087. doi: 10.1109/ACCESS.2021.3125527
Search in Google Scholar Back to article
Serban, A. C., & Lytras, M. D. (2020). Artificial intelligence for smart renewable energy sector in Europe—Smart energy infrastructures for next-generation smart cities. IEEE Access, 8, 77364-77377. doi: 10.1109/ACCESS.2020.2990123
Search in Google Scholar Back to article
Sievers, F., Reil, H., Rimbeck, M., Stumpf-Wollersheim, J., & Leyer, M. (2021). Empowering employees in industrial organizations with IoT in their daily operations. Computers in Industry, 129, 103445. doi: 10.1016/j.compind.2021.103445
Search in Google Scholar Back to article
Singh, S., Sharma, P. K., Yoon, B., Shojafar, M., Cho, G. H., & Ra, I.-H. (2020). Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustainable Cities and Society, 63, 102364. doi: 10.1016/j.scs.2020.102364
Search in Google Scholar Back to article
Skowronek-Mielczarek, A., & Czemiel-Grzybowska, W. (2015). Entrepreneurship research in Poland. Technological and Economic Development of Economy, 23(3), 504-519. doi: 10.3846/20294913.2015.1070770
Search in Google Scholar Back to article
Sousa, M. J., & Rocha, A. (2018). Digital learning: Developing skills for digital transformation of organizations. Future Computer Systems, 91(February), 327-334. doi: 10.1016/j.future.2018.08.048
Search in Google Scholar Back to article
Stawasz, D., & Sikora-Fernandez, D. (2016). Koncepcja smart city na tle procesów i uwarunkowań rozwoju współczesnych miast. Łódź, Poland: Wydawnictwo Uniwersytetu Łódzkiego.
Search in Google Scholar Back to article
Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., & Hager, G. et al. (2016). Artificial intelligence and life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015–2016 Study Panel. Stanford University. Retrieved from http://ai100.stanford.edu/2016-report
Search in Google Scholar Back to article
Subotić, V., Eibl, M., & Hochenauer, C. (2021). Artificial intelligence for time-efficient prediction and optimization of solid oxide fuel cell performances. Energy Conversion and Management, 230, 113764. doi: 10.1016/j.enconman.2020.113764
Search in Google Scholar Back to article
Šulyová, D., & Kubina, M. (2022). Quality of life in the concept of strategic management for Smart Cities. Forum Scientiae Oeconomia, 10(3), 9-24. doi: 10.23762/FSO_VOL10_NO3_1
Search in Google Scholar Back to article
Szpilko, D., Jiménez-Naharro, F., Lăzăroiu, G., Nica, E., & Torre Gallegos, A. d. l. (2023). Artificial intelligence in the smart city — a literature review. Engineering Management in Production and Services, 15(4), 53-75. doi: 10.2478/emj-2023-0028
Search in Google Scholar Back to article
Szpilko, D., Szydło, J., & Winkowska, J. (2020). Social Participation of City Inhabitants Versus Their Future Orientation. Evidence From Poland. WSEAS Transactions on Business and Economics, 17, 692-702. doi: 10.37394/23207.2020.17.67
Search in Google Scholar Back to article
Taddy, M. (2018). The technological elements of artificial intelligence. The Economics of Artificial Intelligence: An Agenda, National Bureau of Economic Research, 61-87. Retrieved from https://ideas.repec.org/h/nbr/nberch/14021.html
Search in Google Scholar Back to article
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. doi: 10.1016/j.comcom.2020.02.069
Search in Google Scholar Back to article
United Nations’ 2030 Agenda. Retrieved from https://sdgs.un.org/2030agenda
Search in Google Scholar Back to article
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Fellader, A., Langhans, S. D., Teg-mark, M., & Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 233. doi: 10.1038/s41467-019-14108-y
Search in Google Scholar Back to article
Walicka, M., & Czemiel-Grzybowska, W. (2015). Technology entrepreneurship – State of the art and future challenges. European Journal of Social Sciences, 3(4), 10-21. doi: 10.15604/ejss.2015.03.04.002
Search in Google Scholar Back to article
Wang, K., Zhao, Y. F., Gangadhari, R. K., & Li, Z. X. (2021). Analyzing the adoption challenges of the Internet of Things (IoT) and artificial intelligence (AI) for smart cities in China. Sustainability, 13(19). doi: 10.3390/su131910983
Search in Google Scholar Back to article
Winkowska, J., Szpilko, D., & Pejić, S. (2019). Smart city concept in the light of the literature review. Engineering Management in Production and Services, 11(2), 70-86. doi: 10.2478/emj-2019-0012
Search in Google Scholar Back to article
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector – Applications and challenges. International Journal of Public Administration, 42(7), 596-615. doi: 10.1080/01900692.2018.1498103
Search in Google Scholar Back to article
Wu, Z., & Chu, W. (2021). Sampling strategy analysis of machine learning models for energy consumption prediction. 2021 9th IEEE International Conference on Smart Energy Grid Engineering (SEGE 2021), 77-81. doi: 10.1109/SEGE52446.2021.9534987
Search in Google Scholar Back to article
Wynsberghe, A. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI and Ethics, 1(4). doi: 10.1007/s43681-021-00043-6
Search in Google Scholar Back to article
Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6). doi: 10.3390/en13061473
Search in Google Scholar Back to article
Yin, R. (2009). Case study research: Design and methods. Thousand Oaks: Sage.
Search in Google Scholar Back to article

Sustainable development of smart cities through municipal waste incinerators: the examples of artificial intelligence in technological entrepreneurship

Abstract

Paradigm

My account