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<p>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 financial sector to facilitate equitable knowledge transfer and support the development and implementation of context-specific, science-informed urban climate solutions. This paper presents the framework, metadata, and structure of the UCCRN City CSA and assesses metadata search filters and large language model (LLM)-assisted discourse analysis as complementary tools for three user types.</p>

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Building and using the evidence base for urban climate action: the UCCRN City Solutions Case Study Atlas

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月4日

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  • Cynthia Rosenzweig,
  • William Solecki,
  • Erin Friedman,
  • Gian Carlo Delgado Ramos,
  • Xiaoshi Xing,
  • Martin Lehmann,
  • Nicola Tollin,
  • Susannah Smetana Kagan,
  • Jaad Benhallam,
  • Maria Dombrov,
  • Daniel Bader,
  • Nick Pelaccio

摘要

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 financial sector to facilitate equitable knowledge transfer and support the development and implementation of context-specific, science-informed urban climate solutions. This paper presents the framework, metadata, and structure of the UCCRN City CSA and assesses metadata search filters and large language model (LLM)-assisted discourse analysis as complementary tools for three user types.