Researchers have developed methods to use large language models and text data to assess the socio-economic impacts of climate hazards like floods and droughts. However, the field currently lacks standardized guidelines for defining impacts, managing data biases, and selecting appropriate analytical models. This paper synthesizes common practices and identifies key challenges, offering recommendations to improve the robustness and comparability of text-derived socio-economic impact datasets for disaster risk management. AI
IMPACT Standardizes methods for using LLMs to analyze climate impact data, improving disaster risk management and attribution studies.
RANK_REASON The cluster contains an academic paper proposing methods and best practices for using NLP and LLMs to analyze socio-economic impacts of climate hazards from text data. [lever_c_demoted from research: ic=1 ai=1.0]
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