Data-driven approach in sustainable city management

• Autorzy: Radziszewska Aleksandra

Identyfikatory

DOI

10.29119/1641-3466.2024.193.23

• Języki publikacji: EN

Abstrakty

EN

Purpose: This study aims to examine the relationships between the use of data-driven solutions in key areas of sustainable city management (urban planning, mobility and transportation management, and environment protection) and city’s position in the global smart cities ranking (the IESE Cities in Motion Index). Design/methodology/approach: A case study methodology is adopted to examine and compare the possibilities of implementing data-driven approaches in sustainable city management, in order to gain a better understanding of this new urban phenomenon. Data and information about data-driven smart city initiatives have been collected from secondary sources. The presented case studies were explored through desk research using online resources, such as the web pages of smart city initiatives. Smart Cities were selected based on their rankings in the IESE Cities in Motion Index 2022. In addition, multiple regressions were used to identify the relationship between the independent variables (environment protection, mobility and transportation management, urban planning) and dependent variable-value of city’s ranking in the IESE Cities in Motion Index. Findings: The results illustrate that the majority of cities use data-driven solutions in all categories to improve city management, efficiency and achieve sustainability goals. All research hypotheses have been accepted, therefore data-driven solutions implemented in all key areas of sustainable city management (urban planning, mobility and transportation management, and environment protection) positively influence performance of achieving sustainability goals. Research limitations/implications: The selection of a limited number of case studies is a limitation of this research. It is therefore important to explore the potential of data-driven smart city solutions in urban development and city management in more detail by considering more cases. Future research should explore the impacts of other variables related to sustainability, which can determinate performance of sustainable city management. A future study should try to validate the result by using a wider sample. Originality/value: The conducted research combines quantitative and quantitative analysis in order to identify the determinants of effective achievement of sustainable development goals in city management. This study provides a form of grounding for further discussion to debate over big data computing on forms of the operational functioning, planning, design, development, and governance of smart sustainable cities in the future.

Słowa kluczowe

EN

sustainability; data-driven cities; data driven technologies; sustainable city management; smart cities

PL

zrównoważony rozwój; miasta oparte na danych; technologie oparte na danych; zrównoważone zarządzanie miastem; inteligentne miasto

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2024
• Tom: z. 193
• Strony: 405--419
• Opis fizyczny: Bibliogr. 37 poz.

Twórcy

autor

Radziszewska Aleksandra

• Czestochowa University of Technology, Faculty of Management

• https://orcid.org/0000-0002-6390-9487

Bibliografia

• 1. Allam, Z., Dhunny, Z. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80-91.
• 2. Bakıcı, T., Almirall, E., Wareham, J. (2013). A smart city initiative: The case of Barcelona. Journal of the Knowledge Economy, 4(2), 135-148.
• 3. Bibri, S.E., Krogstie, J., Kärrholm, M. (2020). Compact city planning and development: Emerging practices and strategies for achieving the goals of sustainability. Developments in the built environment, 4, 100021.
• 4. Caragliu, A., Del Bo, C., Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology, 18(2), 65-82.
• 5. Cruz, S.S., Paulino, S.R. (2022). Experiences of innovation in public services for sustainable urban mobility. Journal of Urban Management, 11(1), 108-122.
• 6. da Silva, R.R., Santos, G.D., Setti, D. (2022). A multi-criteria approach for urban mobility project selection in medium-sized cities. Sustainable Cities and Society, 86, 104096.
• 7. Dameri, R. (2013). Searching for Smart City Definition: A Comprehensive Proposal. International Journal of Computers & Technology, 11(5), 2544-2551.
• 8. Guo, J., Ma, J., Li, X., Zhang, J., Zhang, T. (2017). An attribute-based trust negotiation protocol for D2D communication in smart city balancing trust and privacy. Journal of Information Science and Engineering, 33(4), 1007-1023.
• 9. Guo, Q., Wang, Y., Dong, X. (2022). Effects of smart city construction on energy saving and CO2 emission reduction: Evidence from China. Applied Energy, 313, 118879.
• 10. Herath, H., Mittal, M. (2022). Adoption of artificial intelligence in smart cities: A comprehensive review. International Journal of Information Management Data Insights, 2(1), 100076.
• 11. Huang, K., Zhang, X., Wang, X. (2017). Block-level message-locked encryption with polynomial commitment for IoT data. Journal of Information Science and Engineering, 33(4), 891-905.
• 12. Khan, S. (2022). Barriers of big data analytics for smart cities development: a context of emerging economies. International Journal of Management Science and Engineering Management, 17(2), 123-131.
• 13. Lai, C., Cole, A. (2023). Measuring progress of smart cities: Indexing the smart city indices. Urban Governance, 3(1), 45-57.
• 14. Lee, J., Hancock, M., Hu, M.-Ch. (2014). Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco. Technological Forecasting & Social Change, 89, 80-99.
• 15. Lee, J., Babcock, J., Pham, T.S., Bui, T.H., Kang, M. (2023). Smart city as a social transition towards inclusive development through technology: a tale of four smart cities. International Journal of Urban Sciences, 27, 75-100.
• 16. Marsal-Llacuna, M., Colomer-Llinàs, J., Meléndez-Frigola, J. (2015). Lessons in urban monitoring taken from sustainable and livable cities to better address the Smart Cities initiative. Technological Forecasting and Social Change, 90, 611-622.
• 17. Nam, T., Pardo, T. (2014). The changing face of a city government: A case study of Philly311. Government Information Quarterly, 31(1), 1-9.
• 18. Olaniyi, O., Okunleye, O.J., Olabanji, S.O. (2023). Advancing data-driven decision-making in smart cities through big data analytics: A comprehensive review of existing literature. Current Journal of Applied Science and Technology, 42(25), 10-18.
• 19. Ortiz-Fournier, L., Márquez, E., Flores, F., Rivera-Vázquez, J., Colon, P. (2010). Integrating educational institutions to produce intellectual capital for sustainability in Caguas, Puerto Rico. Knowledge Management Research and Practice, 8(3), 203-215.
• 20. Pašalić, I.N., Ćukušić, M., Jadrić, M. (2021). Smart city research advances in Southeast Europe. International Journal of Information Management, 58, 102127
• 21. Peng, G., Nunes, M., Zheng, L. (2017). Impacts of low citizen awareness and usage in smart city services: The case of London’s smart parking system. Information Systems and e-Business Management, 15(4), 845-876.
• 22. Razmjoo, A., Gandomi, A.H., Pazhoohesh, M., Mirjalili, S., Rezaei, M. (2022). The key role of clean energy and technology in smart cities development. Energy Strategy Reviews, 44, 100943.
• 23. Sanchez, T.W., Shumway, H., Gordner, T., Lim, T. (2023). The prospects of artificial intelligence in urban planning. International Journal of Urban Sciences, 27(2), 179-194.
• 24. Savastano, M., Suciu, M.C., Gorelova, I., Stativă, G.A. (2023). How smart is mobility in smart cities? An analysis of citizens' value perceptions through ICT applications. Cities, 132, 104071.
• 25. Shahat, E., Hyun, C., Yeom, C. (2021). City Digital Twin Potentials: A Review and Research Agenda. Sustainability, 13, 3386.
• 26. Singh, J., Sajid, M., Gupta, S.K., Haidri, R. . (2022). Artificial Intelligence and Blockchain Technologies for Smart City. Intelligent Green Technologies for Sustainable Smart Cities, 317-330.
• 27. Son, T.H., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J.M., Mehmood, R. (2023). Algorithmic urban planning for smart and sustainable development: Systematic review of the literature. Sustainable Cities and Society, 104562.
• 28. Su, Y., Fan, D. (2023). Smart cities and sustainable development. Regional Studies, 57(4), 722-738.
• 29. Su, Y., Hu, M., Yu, X. (2023). Does the development of smart cities help protect the environment? Journal of Environmental Planning and Management, 66(3), 572-589.
• 30. Tura, N., Ojanen, V. (2022). Sustainability-oriented innovations in smart cities: A systematic review and emerging themes. Cities, 126, 103716.
• 31. United Nations. Habitat III Issue Papers, 21—Smart cities (V2.0) (2015). New York, NY. https://collaboration.worldbank. org/docs/DOC-20778.
• 32. United Nations (2015). Transforming our world: the 2030 agenda for sustainable development. New York, NY. https://sustainabledevelopment.un.org/post2015/ transformingourworld.
• 33. Wang, C., Yin, L. (2023). Defining Urban Big Data in Urban Planning: Literature Review. Journal of Urban Planning and Development, 149(1), 04022044.
• 34. Wang, M., Zhou, T. (2022). Understanding the dynamic relationship between smart city implementation and urban sustainability. Technology in Society, 70, 102018.
• 35. Xia, H., Liu, Z., Efremochkina, M., Liu, X., Lin, C. (2022). Study on city digital twin technologies for sustainable smart city design: A review and bibliometric analysis of geographic information system and building information modeling integration. Sustainable Cities and Society, 84, 104009.
• 36. Zanella, A., Bui, N., Castellani, A., Vangelista, L., Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet of Things Journal, 1(1), 22-32.
• 37. Zhang, D., Pee, L.G., Pan, S.L., Cui, L. (2022). Big data analytics, resource orchestration, and digital sustainability: A case study of smart city development. Government information quarterly, 39(1), 101626.