A strategic sustainability framework for smart city investment prioritization

Abstract

Smart cities focus on sustainable and innovative solutions that integrate technology with urban development, provide quality life to the public, and are environment-focused. Therefore, urban planners and policymakers should focus on investment projects that are more suitable for the smart city concept, considering many factors. However, selecting the most appropriate investment projects remains a complex decision-making problem due to the involvement of diverse and often conflicting sustainability-based criteria. To address this problem, this study aims to address the gap in the literature on smart city investment projects prioritization from a strategic sustainability perspective. A hybrid multi-criteria decision-making (MCDM) methodology that integrates flexible criteria weighting with intuitionistic fuzzy logic to handle uncertainty in expert evaluations is proposed. For this purpose, a comprehensive set of criteria, such as social, technological, economic, environmental, and political (STEEP), for prioritizing different smart city investment projects from a sustainability perspective was determined, and a novel hybrid MCDM methodology combining flexible criteria weighting and intuitionistic fuzzy logic under changing conditions was developed. The integration of the Intuitionistic Fuzzy Step-Wise Weight Assessment Ratio Analysis (IF-SWARA) and Intuitionistic Fuzzy Combinative Distance-based Assessment (IF-CODAS) methods offers an innovative approach to this decision-making process. The IF-SWARA method was used for criteria weighting, and the IF-CODAS method was applied to prioritize alternative investment projects. The results show that Smart Grid Implementation is the most favorable investment, followed by Advanced Waste Management, while Smart Healthcare Infrastructure ranks the lowest. The study offers valuable insights for policymakers and city planners, highlighting the role of prioritizing different smart city investment projects from a sustainability perspective. As the first study to integrate the IF-SWARA and IF-CODAS methods for this decision process, this research contributes to developing sustainable infrastructure and suggests a replicable framework for future research.