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• W4282001510 abstract "Although prior studies have proposed some smart city index systems, they primarily focus on generic indicators connected to urban development and fail to reflect the properties of intelligence. To fill this gap, we develop a new index system involving three dimensions of digital infrastructure, smart living, and digital economy. Moreover, compared with studies that use subjective weighting methods to rank the smartness of cities, we combine the Shannon entropy weighting method with three multi-criteria decision-making (MCDM) methods to show the objectiveness of the evaluation process. Through analyzing the quantitative data from nine cities in the Pearl River Delta (PRD) region in China, we find that digital infrastructure is the most important first-level indicator, accounting for 46.92%, followed by the digital economy and smart life accounting for 32.48% and 20.60% respectively. More importantly, when the nine cities in the PRD region are ranked by three MCDM methods, the correlation between the results is over 90%, thus proving robustness. We contribute to the current smart city literature by enriching the components of the smart city index system, as well as evaluation methods. Our findings also guide decision-makers in formulating more targeted smart city construction plans. • Develop a new smart city index system involving three dimensions of digital infrastructure, smart living, and digital economy • Use three multi-criteria decision-making (MCDM) methods to rank the smartness of cities • Data were gathered from online platforms in the Pearl River Delta Region, China. • The correlation between the results from different MCDM methods is >90%. • The practical implications are highlighted." @default.
• W4282001510 created "2022-06-13" @default.
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• W4282001510 date "2022-09-01" @default.
• W4282001510 modified "2023-10-16" @default.
• W4282001510 title "Multi-criteria decision-making models for smart city ranking: Evidence from the Pearl River Delta region, China" @default.
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• W4282001510 doi "https://doi.org/10.1016/j.cities.2022.103793" @default.
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