Ranking of Global Smart Cities Using Dynamic Factor Analysis
Social Indicators Research, vol.171, no.2, pp.405-437, 2024 (SSCI)
- Publication Type: Article / Article
- Volume: 171 Issue: 2
- Publication Date: 2024
- Doi Number: 10.1007/s11205-023-03259-7
- Journal Name: Social Indicators Research
- Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, FRANCIS, IBZ Online, International Bibliography of Social Sciences, Periodicals Index Online, ABI/INFORM, Abstracts in Social Gerontology, Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, EBSCO Education Source, EconLit, Geobase, Index Islamicus, Philosopher's Index, Political Science Complete, Psycinfo, Public Administration Abstracts, Social services abstracts, Sociological abstracts, Veterinary Science Database, Worldwide Political Science Abstracts
- Page Numbers: pp.405-437
- Keywords: Dynamic factor analysis, Indicators, Ranking, Smart city, Urban development
- Istanbul University Affiliated: Yes
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.