Study on Building a Smart Sustainable City Assessment Framework Using Big Data and Analytic Network Process

摘要

This study identifies indicators of urban sustainability and smart cities and then integrates them into a unified concept. Its aim is to reduce the gap in the literature between sustainable cities and smart cities with respect to urban development guidelines. Moreover, development directions for smart, sustainable, and inclusive urban environmental planning and design strategies are evaluated. To achieve these goals, a static evaluation system was developed and big data technique was used to construct a dynamic model. This model identifies factors that affect sustainable and smart cities and simulates changes in urban built environments under dynamic conditions, such as changes in regional development policies or space structures. Our proposed model is applied to examine the effects of both economic development and environmental issues on urban built environments. Moreover, changes in urban land intensification use mean that this model can be used to identify a set of management strategies that can meet planning targets and yield a sustainable urban built environment. Our proposed model is illustrated through case studies that demonstrate both the state of the art and the state of current practice to planners and decision makers. Our study revealed that the dependency weights of 10 indicators (listed in descending order) were "public transportation availability" (weights being 0.310), "prevalence of technology" (0.175), "greenhouse gas emission" (0.103), "domestic water use" (0.092), "living environment quality" (0.082), "decision making" (0.060), "innovation and internationalization" (0.050), "human resource quality" (0.048), "air pollution and noise" (0.048), and "government transparency" (0.032). Taipei City scored a total of 96 points on its performance as a smart sustainable city, which was weighted as 10.3919 and Singapore scored 105 points, which was weighted as 10.7528 according to the evaluation model, respectively.