This dissertation employs a text-mining approach to systematically examine the smart transformation of cities and communities in the U.S. and how this transformation links to sustainability goals and outcomes. It consists of five journal articles. The first paper overviews existing applications and challenges for advancing urban research by adopting natural language processing. The second paper pilots the method of text-mining municipal websites, showing suggestive evidence that smart cities generally score higher on sustainability outcomes than non-smart cities in 103 U.S. cities. The third paper introduces a complete and manually verified dataset containing information on whether a municipality has an official website and, if so, what its web address is, for all the municipalities in the United States. The fourth paper introduces a Python package that evaluates whether a textual expression aligns with the concept of sustainability as defined by the United Nations Sustainable Development Goals (SDGs). It labels a statement with the 17 SDGs as well as 169 specific targets and categorizes the statement into social, environmental, or economic sustainability. The fifth paper scales up the method tested in the second paper and uses the dataset and the tool mentioned above to study all the municipalities in the U.S. It shows that 397 out of 19,518 municipalities are smart cities, the majority of which are medium- or small-sized cities. In addition, smart cities generally discuss social sustainability goals slightly more than environmental and economic ones on their websites. Furthermore, residents in smart cities are more educated (SDG 4.3), have a higher percentage of households with internet access (SDG 9.c), have a higher percentage of commuters carpooling, using public transport, bicycles, or walking to work (SDG 11.2), and have higher incomes but lower income equality (SDG 10.4). The differences become more pronounced as city population sizes decrease. There are no significant differences in poverty rate (SDG 1.1), access to health insurance (SDG 3.8), and unemployment rate (SDG 8.5). This dissertation contributes to the literature by addressing three major gaps: the lack of understanding of how prevalent smart cities are in local practice, especially for small and mid-sized urban areas; the limited knowledge of what sustainability goals local governments aim to achieve with smart city implementation; and the scarcity of empirical investigation into the relationship between smart cities and sustainability outcomes.
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