Abstract
For the past few years, the concept of urban sustainability and smart city has been viewed as a crucial way to solve the problem regarding urbanization, and the global city thus ranks it as a future development goal. With the implementation of the above strategies, the construction of relevant city assessment tools is essential. However, the assessment framework of urban sustainability focuses on the aspect of environment and society, while smart city puts emphasis on economic and social indicators. Therefore, integrating the two concepts as “Smart Sustainable City” and constructing a related evaluation model would be more comprehensive. Moreover, the development of “Big Data” theory allows city planners to interpret and apply large amounts of data collected from various sources. Under this opportunity, the result of analyzing the actual big data to forecast the variance ratio of each indicator can be used as the objective basis to construct the model, which can increase the accuracy of the city assessment. Based on the big data analysis, this paper will construct an assessment framework of smart sustainable city in line with the future situation, and further conduct the model validation through city evaluation. First, this paper reviews the concepts and assessment framework of sustainable development and smart city in order to sum up the appropriate indicators to construct the model, and applies “Fuzzy Delphi Technique (FDT)” to select the indicators which are considered important regarding the smart sustainable city. In addition, “Data Mining” and “Analytic Network Process (ANP)” are used to predict the future variance ratio of smart sustainable city indicators and to apply the variance ratio regarding the future scenario to determine their weights. Finally, this paper will conduct an empirical analysis by assessing the smart sustainable level of cities, hoping to validate the model and propose related suggestions to promote the idea exchange of urban development.
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