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<p>Global evidence of climate change adaptation is increasing faster than assessment capabilities of conventional systematic review approaches. To inform better decisions based on this growing literature, the Global Adaptation Mapping Initiative deployed AI-assisted and crowd-sourced methods for synthesising and tracking adaptation globally, offering important knowledge for the Intergovernmental Panel on Climate Change and the Global Stocktake. Here we present results of a survey with Global Adaptation Mapping Initiative researchers regarding the lessons learned and recommendations for future global evidence syntheses on adaptation: transparently reflect on the compromises regarding breadth vs depth of the synthesis; employ the highest standards of quality assurance for human coding; promote justice and equity within synthesis teams; extend the scope beyond academic literature; and further integrate machine learning methods. Building on these learnings, future work can contribute to a more holistic understanding of adaptation progress and raise the standard of adaptation evidence synthesis.</p>

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Ways forward for global adaptation evidence synthesis building on the Global Adaptation Mapping Initiative

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月14日

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  • Jan Petzold,
  • Matthias Garschagen,
  • Robbert Biesbroek,
  • Elisabeth Gilmore,
  • Elphin Tom Joe,
  • Christine J. Kirchhoff,
  • Alexandra Lesnikowski,
  • AR Siders,
  • Nicholas P. Simpson,
  • Christopher H. Trisos

摘要

Global evidence of climate change adaptation is increasing faster than assessment capabilities of conventional systematic review approaches. To inform better decisions based on this growing literature, the Global Adaptation Mapping Initiative deployed AI-assisted and crowd-sourced methods for synthesising and tracking adaptation globally, offering important knowledge for the Intergovernmental Panel on Climate Change and the Global Stocktake. Here we present results of a survey with Global Adaptation Mapping Initiative researchers regarding the lessons learned and recommendations for future global evidence syntheses on adaptation: transparently reflect on the compromises regarding breadth vs depth of the synthesis; employ the highest standards of quality assurance for human coding; promote justice and equity within synthesis teams; extend the scope beyond academic literature; and further integrate machine learning methods. Building on these learnings, future work can contribute to a more holistic understanding of adaptation progress and raise the standard of adaptation evidence synthesis.