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 stra-tegies. 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 Cit-ies100 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 un-derstanding of urban adaptation as well as demonstrates a way of adopting the grey literature into the urban adaptation studies.
Jin, S; Stokes, G; Hamilton, C
Empirical evidence of urban climate adaptation alignment with sustainable development: Application of LDA
Cities