Ways forward in global adaptation evidence synthesis: Lessons learnt from GAMI and beyond

Evidence of climate change adaptation has grown exponentially in recent years and decades. However, this evidence base is highly heterogeneous in its methods, context-specificity, and relevance for effective risk reduction. The Global Adaptation Mapping Initiative (GAMI) was the first systematic evidence synthesis project that mapped adaptation literature from across the globe and categorised the evidence regarding adaptation types, patterns, and regional and sectoral clusters and gaps. After GAMI has been successfully finished, fed into IPCC AR6 reports, and used for various spin-off scientific papers, it is time to take stock: What did and what did not work well in the GAMI process? By whom, for which purposes and with which methods was the GAMI data used? What are future directions and recommendations for global adaptation evidence synthesis? This paper builds on a survey and group discussion among the GAMI core and advisory teams as well as lead authors of GAMI spin-off papers to collect experiences, amongst others, about the opportunities and limitations of the dataset, extensions and re-analyses of the dataset in the various spin-off projects, and recommendations for more effective adaptation evidence synthesis. The results of this analysis provide the basis for a new initiative for mapping and tracking global adaptation efforts and integration with novel machine learning-assisted methods, which can feed into the Global Stocktake (2029) and 7th IPCC assessment cycle.