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Empirical Evidence of Urban Climate Adaptation Alignment with Sustainable Development: Application of LDA

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dc.contributor.author Jin, Saebom
dc.contributor.author Stokes, Gerald
dc.contributor.author Hamilton, Clovia
dc.date.accessioned 2023-04-12T19:02:52Z
dc.date.available 2023-04-12T19:02:52Z
dc.date.issued 2023 en_US
dc.identifier.uri https://hdl.handle.net/10535/10895
dc.description.abstract "Cities are critical sites for climate action. Population and infrastructure are concentrated in urban areas and their susceptibility to climate change impacts makes them a pivotal place to embark on adaptation plans and strategies. In the Fifth Assessment Report (AR5) the Intergovernmental Panel on Climate Change (IPCC) affirms that urban adaptation allows sustainable development and resilience. However, without evidence, this affirmation fails to acquire credibility and objectivity. As an attempt to provide the evidence for the assertion, this study examines the current actions in urban centers to determine if there is an alignment between adaptation and development. The study employs text mining techniques to analyze 400 urban project descriptions from Cities100 reports (2015–2019) of the C40 network. With Latent Dirichlet Allocation (LDA), a machine learning algorithm for topic model analysis, the study identifies 17 major topics. Using multidimensional scaling and cluster analysis to further characterize the findings, it finds an alignment of adaptation with urban sustainable and resilient development in several major cities. In this way, the paper makes a contribution to a global understanding of urban adaptation as well as demonstrates a way of adopting the grey literature into the urban adaptation studies." en_US
dc.language English en_US
dc.subject urban adaptation en_US
dc.subject urban affairs en_US
dc.subject resilience en_US
dc.subject sustainability en_US
dc.subject climate change en_US
dc.title Empirical Evidence of Urban Climate Adaptation Alignment with Sustainable Development: Application of LDA en_US
dc.type Journal Article en_US
dc.type.published unpublished en_US
dc.type.methodology Modeling en_US
dc.subject.sector General & Multiple Resources en_US
dc.identifier.citationjournal Cities en_US
dc.identifier.citationvolume 136 en_US
dc.identifier.citationnumber 104254 en_US


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