Abstract
Cities play a leading role in climate change actions and solutions, yet city-level case study sources remain fragmented, biased towards large cities, and inaccessible to local practitioners, notably in the Global South. In response, the Urban Climate Change Research Network (UCCRN) is developing a City Solutions Case Study Atlas (City CSA), a centralized and searchable online platform that integrates diverse case studies focused on climate solutions with an interactive global map containing multiple data layers. The UCCRN City CSA provides an evidence base for academics, urban policymakers, city practitioners, city networks, civil society, and the nancial sector to facilitate equitable knowledge transfer and support the development and implementation of context-specic, science-informed urban climate solutions. This paper presents the framework, metadata, and structure of the UCCRN City CSA and assesses metadata search lters and large language model (LLM)-assisted discourse analysis as complementary tools for three user types.
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