Learning from the Global Adaptation Mapping Initiative for Future Evidence Synthesis

The Global Adaptation Mapping Initiative (GAMI) serves as a pioneering framework for synthesising worldwide climate change adaptation data, originally established to support major international assessments like the IPCC 6th Assessment Report. Through a reflective survey of 59 participating researchers, this source evaluates the project's impact, reliability, and procedural feasibility, noting significant success in community-building and policy influence. The analysis highlights a critical trade-off between broad global coverage and local contextual depth, while also identifying geographic and linguistic biases inherent in relying solely on English-language academic journals. To address these limitations, the authors recommend integrating grey literature and diverse knowledge systems to ensure a more equitable representation of adaptation efforts. Furthermore, the source advocates for the strategic adoption of machine learning and natural language processing to manage the expanding volume of climate research through hybrid human-AI workflows. Ultimately, these findings provide a foundational roadmap for "GAMI 2.0," aiming to create a more inclusive, real-time evidence base for global climate policy.

This podcast draws on the recently article published in the Nature family journal Communications Sustainability, 'Ways forward for global adaptation evidence synthesis building on the global adaptation mapping initiative', https://doi.org/10.1038/s44458-026-00071-5