Recent advances in Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), enable scalable extraction of spatial information from unstructured text and offer new methodological opportunities for studying climate geography. This study develops a spatial framework to examine how cumulative climate risk relates to multidimensional human flourishing across U.S. counties. High-resolution climate hazard indicators are integrated with a Human Flourishing Geographic Index (HFGI), an index derived from classification of 2.6 billion geotagged tweets using fine-tuned open-source Large Language Models (LLMs). These indicators are aggregated to the US county-level and mapped to a structural equation model to infer overall climate risk and human flourishing dimensions, including expressed well-being, meaning and purpose, social connectedness, psychological distress, physical condition, economic stability, religiosity, character and virtue, and institutional trust. The results reveal spatially heterogeneous associations between greater cumulative climate risk and lower levels of expressed human flourishing, with coherent spatial patterns corresponding to recurrent exposure to heat, flooding, wind, drought, and wildfire hazards. The study demonstrates how Generative AI can be combined with latent construct modeling for geographical analysis and for spatial knowledge extraction.

翻译：生成式人工智能（特别是大型语言模型）的最新进展，使得从非结构化文本中可扩展地提取空间信息成为可能，并为气候地理学研究提供了新的方法论机遇。本研究构建了一个空间分析框架，用以考察美国县级尺度上累积气候风险与多维人类繁荣之间的关联。研究将高分辨率气候灾害指标与人类繁荣地理指数相结合——该指数通过对26亿条地理标记推文使用微调后的开源大型语言模型进行分类而构建。这些指标被聚合至美国县级尺度，并通过结构方程模型映射至整体气候风险及人类繁荣维度，包括：表达性福祉、意义与目标、社会联结、心理困扰、身体状况、经济稳定、宗教性、品格与美德以及制度信任。研究结果揭示了累积气候风险升高与人类繁荣表达水平降低之间存在空间异质性关联，其空间格局与热浪、洪水、风灾、干旱和野火等灾害的反复暴露区域高度吻合。本研究展示了如何将生成式人工智能与潜变量建模相结合，以支持地理分析和空间知识提取。