## 5 Discussion  
The aim of this study is to identify and analyze the key factors that contribute to smart cities' high performance. Based on a dataset of 45 factors across 140 cities, the random forest (RF) algorithm was used to pinpoint the most influential variables, followed by multiple linear regression analysis to explore their interrelationships. The study's significance lies in its ability to drive strategic decision-making for smart city initiatives so that urban development strategies can be more clearly targeted and effective. This study's findings support a strong correlation of human capital development with a higher-ranked smart city. This highlights the importance of initiatives such as improved healthcare, education, and economic opportunities to promote smart city growth. This finding is consistent with previous studies, which have consistently found that a higher HDI, which measures health, education, and standard of living, is associated with a higher quality of life in urban areas [104; 105; 106]. Thus, policymakers must prioritize strategies that improve the HDI, as they can enhance citizens' overall quality of life and increase a city's global competitiveness. Furthermore, the study's findings revealed that the emergence of platforms for sharing unwanted items is a major factor in smart city success. It highlights the growing significance of digital platforms and collaborative consumption in modern urban environments. These platforms can potentially reduce waste, improve resource efficiency, and help foster a sense of community. They also align with the concept of a circular economy, which aims to minimize waste and maximize resource utilization. By enabling the sharing of goods, these platforms can promote social cohesion and environmental sustainability, both vital components of a smart city. A previous study by [PERSON] [107] has indirectly discussed collaborative lifestyles and sharing platforms, but this study highlights the importance of digital platforms in urban environments and collaborative consumption. Therefore, the HDI and the implementation of shared digital platforms promoting circular economy principles emerge as pivotal factors that distinguish top-ranking smart cities from their counterparts. Further analysis investigated the relationships between these key factors (HDI and platforms for sharing unwanted items) and other city attributes. The first regression model, which had the HDI as the dependent variable, revealed a complex interplay of factors influencing human development. A number of factors contributed to the HDI score, including population size, life expectancy, educational levels, healthcare accessibility, housing affordability, transportation, and various digital services. Some of these factors have already been identified as significant factors by previous studies [108; 109]. These findings emphasize the need for a comprehensive human development approach that includes traditional social welfare programs and innovative digital solutions. The second regression model identified a different set of influential factors related to the availability of platforms for sharing unwanted items as the dependent variable. Life expectancy, education, basic sanitation, healthcare, transportation, green spaces, cultural amenities, job creation, government transparency, citizen participation, and digital services were positively associated with sharing platforms. These results have not been investigated in previous studies. These findings suggest that socio-economic development, environmental quality, and effective governance are essential for fostering a culture of sharing and collaboration. The findings from both regression models provide valuable insights for policymakers seeking to improve their cities' performance. By focusing on the significant factors identified, cities can take targeted actions to improve both the HDI and the adoption of sharing platforms.