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Smart city ranking with subjective indicators

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Języki publikacji
EN
Abstrakty
EN
Purpose: The primary purpose of the presented work is to show the impact of the residents’ opinion on the formation of the city's position in the SCR ranking. Another objective was to draw attention to the problem of data shortages in publicly available databases. Design/methodology/approach: The primary database of European countries is the Eurostat database. The research area covered cities with a population of between 200,000 and 800,000, which were not national capitals. Only one city from each country was selected. The proposed SCR covers six areas related to Smart City concept. Two types of meters for each of them are proposed – the first based on objective measures, the second on subjective measures, i.e. the opinions of residents. Each factor was standardised and transformed. The higher the value of the factor, the greater the positive effect on the index. Findings: Cities from the database were identified. General ranking and rankings for both objective and subjective meters were created. The relationship between rankings was investigated, and the impact of subjective variables was shown to be significant. Originality/value: The original method for determining the Smart City index was proposed. It is shown that subjective measures should be included in the rankings. The opinion of the residents should be taken into account when building a ranking regarding Smart City concept.
Rocznik
Tom
Strony
631--641
Opis fizyczny
Bibliogr. 19 poz.
Twórcy
autor
  • Silesian University of Technology, Zabrze
Bibliografia
  • 1. Ahvenniemi, H., Huovila, A., Pinto-Seppä, I., Airaksinen, M. (2017). What are the differences between sustainable and smart cities? Cities, 60, pp. 234-245.
  • 2. Albino, V., Berardi, U., Dangelico, R.M. (2015). Smart cities: Definitions, dimensions, performance, and initiatives. Journal of Urban Technology, 22(1), pp. 3-21.
  • 3. Berrone P., Ricart J.E., Duch A., Carrasco C. (2019). IESE Cities in Motion Index 2019, IESE, ST-509-E, 05/2019 DOI: https://dx.doi.org/10.15581/018.ST-509.
  • 4. Bosch, P., Jongeneel, S., Neumann, H.-M., Branislav, I., Huovila, A., Airaksinen, M., Pinto-Seppä, I. (2017a), Recommendations for a Smart City index. CITYkeys – Smart city performance measurement framework. DOI: 10.13140/RG.2.2.20190.74562, 16.04.2020.
  • 5. Bosch, P., Jongeneel, S., Rovers, V., Neumann, H.-M., Airaksinen, M., Huovila, A. (2017b). CITYkeys indicators for smart city projects and smart cities. CITYkeys – Smart city performance measurement framework. DOI: 10.13140/RG.2.2.17148.23686, 16.04.2020.
  • 6. Giffinger, R., Fertner, C., Kramar, H., Kramar, H., Kalasek, R., Pichler-Milanovic, N., Meijers, E. (2007). Smart Cities. Ranking of European medium-sized cities. Centre for Regional Science, Vienna University of Technology, http://www.smart-cities.eu/download/smart_cities_final_report.pdf, 16.04.2020.
  • 7. Huovila, A., Penttinen, T., Airaksinen, M., Pinto-Seppä, I., Piira, K., Penttinen, T. (2016, September). Smart city performance measurement system. Proceedings of the 41st IAHS World Congress Sustainability Innovation for the Future, Algarve, Portugal, pp. 13-16.
  • 8. Kukuła, K. (1989). Statistical structural analysis and its application in the field of production services for agriculture. Scientific Notebooks AE in Krakow, Special series: Monographs, 89, p. 256.
  • 9. Kukuła, K. (2000) Method of zeroed unitarisation. Warsaw: PWN.
  • 10. Lombardi, P., Giordano, S., Caragliu, A., Del Bo, C., Deakin, M., Nijkamp, P., Kourtit, K. (2011). An advanced triple-helix network model for smart cities performance. Vrije Universiteit Amsterdam, Research Memorandum 2011-45, http://degree.ubvu.vu.nl /repec/vua/wpaper/pdf/20110045.pdf, 15.03.2019.
  • 11. Smart City PROFILES (2013). Ergebnisse. 7.6.2013. http://www.smartcities.at/assets/03-Begleitmassnahmen/SmartCity-PDF-INTRO.pdf, 16.03.2020.
  • 12. Sojda, A., Owczarek, T., Wolny, M. (2018). Smart city in data-oriented terms – Poland in eurostat – database. Zeszyty Naukowe PŚl., Org. Zarz., z. 130, p. 557-566, DOI: 10.29119/1641-3466.2018.130.46.
  • 13. Sojda, A., Wolny, M. (2020). The impact of standardisation method on smart city ranking, Sil. Univ. Technol. Sci. Pap., Organ. Manage., no. 142, pp. 83-94, DOI: 10.29119/1641-3466.2020.142.6.
  • 14. Stankovic, J., Dzunic, M., Džunić, Ž., Marinkovic, S. (2015). A multi-criteria evaluation of the European cities' smart performance: Economic, social and environmental aspects. Zbornik radova Ekonomskog fakulteta u Rijeci, časopis za ekonomsku teoriju i praksu-Proceedings of Rijeka Faculty of Economics. Journal of Economics and Business, 35(2), pp. 519-550.
  • 15. Svítek, M., Skobelev, P. Kozhevnikov (2020). Smart City 5.0 as an Urban Ecosystem of Smart Services. 10.1007/978-3-030-27477-1_33.
  • 16. Szczech-Pietkiewicz, E. (2015). Smart city - sample definition and measurement. Scientific work of the University of Economics in Wroclaw. Local economy in theory and practice, 391.
  • 17. Tahir, Z., Malek, J.A. (2016). Main criteria in the development of smart cities determined using analytical method. Planning Malaysia Journal, 14(5).
  • 18. UCLG (2012). Smart Cities Study: International study on the situation of ICT, innovation and knowledge in cities. Bilbao. http://www.uclg-digitalcities.org/app/uploads/2015/06/en_smartcitiesstudy.pdf, 16.04.2020.
  • 19. United Nations (2014). World urbanization prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352). New York. United States of America, https://doi.org/10.4054/DemRes.2005.12.9.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9b33c5a5-1d8b-4e48-a243-458e7d3e92fb
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