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Ranking of Global Smart Cities Using Dynamic Factor Analysis

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Abstract

In the age of the digital revolution, many cities around the world have made significant investments in planning and implementing smart city initiatives to address the issues of climate change and urbanisation. Concurrently, numerous attempts have been made to evaluate the performance and outcomes of these initiatives. In this study, a static and dynamic ranking of 33 selected cities for the period 2005–2019 is provided using dynamic factor analysis. In this comparative study, cities are evaluated and ranked on various smart drivers including technology, community, infrastructure, community, and governance to identify the best (and worst) smart city and to highlight the changes in smartness over the last 15 years. The results show that urban smartness has changed significantly over the past 15 years, but the smartness of most individual cities in the study has either steadily improved or remained constant. Further, smart practices vary significantly across cities, with Frankfurt, Washington, Munich, Los Angeles, and New York City showing relatively strong urban smartness, compared to the poor performance of Bratislava, Warsaw, and Prague. The findings of this study can serve as an empirical basis for identifying specific strengths and weaknesses of cities and as a knowledge-based decision-making tool for policymakers and metropolitan leaders in selecting policies that can improve the growth and performance of smart cities. The paper recommends several proposals to steer the efforts of policymakers and the private and public sectors towards more effective and efficient strategic planning and implementation of smart city initiatives.

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Notes

  1. The cities according to regions are North American and Oceania: Sydney (Australia), Melbourne (Australia), Brisbane (Australia) Vancouver (Canada), Montreal (Canada), Toronto (Canada), New York City (United States), Washington (United States), Los Angeles (United States). Europe: Vienna (Austria), Brussels (Belgium), Copenhagen (Denmark), Prague (Czech), Tallinn (Estonia), Helsinki (Finland), Paris (France), Munich (Germany), Berlin (Germany), Frankfurt (Germany), Dublin (Ireland), Milan (Italy), Reykjavik (Iceland), Amsterdam (Netherlands), Oslo (Norway), Warsaw (Poland), Madrid (Spain), Barcelona (Spain), Bratislava (Slovakia), Stockholm (Sweden), Zürich (Switzerland), London (United Kingdom), Manchester (United Kingdom). Asia: Tokyo (Japan).

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Acknowledgements

This research is part of a doctorate dissertation at Istanbul University. I would like to thank Begüm Özkaynak from Boğazici University, Taner Akan, and Şeref Bozoklu from Istanbul University for their invaluable insights and contributions to this research.

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  1. Department of Economics, Istanbul University, Istanbul, Turkey

    Tara Vanli

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Appendix A

Appendix A

See Tables 8, 9, and 10.

Table 8 Dynamic scores of urban smartness with respect to the first principal component of DFA on the drivers of smart cities
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Table 9 Dynamic scores of urban smartness with respect to the second principal component of DFA on the drivers of smart cities
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Table 10 Dynamic scores of urban smartness with respect to the third principal component of DFA on the drivers of smart cities
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Vanli, T. Ranking of Global Smart Cities Using Dynamic Factor Analysis. Soc Indic Res 171, 405–437 (2024). https://doi.org/10.1007/s11205-023-03259-7

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