用生成式AI分析气候风险与人类繁荣的地理差异
Spatial Heterogeneity in Climate Risk and Human Flourishing: An Exploration with Generative AI
- 通过微调开源大模型解析26亿条带位置标签推文，构建人类繁荣地理指数
- 发现气候风险累积与表达幸福感等8个维度繁荣度呈显著空间负相关
- 为气候地理研究提供可扩展的生成式AI方法，适合关注城市规划与气候政策者
生成式人工智能（特别是大语言模型）的发展使从非结构化文本中规模化提取空间信息成为可能，为气候地理学研究提供了新方法。本研究构建了一个空间框架，分析美国各县累积气候风险与多维人类繁荣之间的关系。高分辨率气候灾害指标与人类繁荣地理指数（HFGI）相结合，该指数基于对26亿条地理标记推文的分类，采用微调的开源大语言模型生成。这些指标聚合至美国县一级，并输入结构方程模型，以推断整体气候风险及人类繁荣的多个维度，包括表达幸福感、意义与目标、社会联结、心理困扰、身体健康、经济稳定、宗教性、品格与美德以及制度信任。结果揭示了更高累积气候风险与较低表达人类繁荣水平之间存在空间异质性关联，其空间模式与热浪、洪水、风灾、干旱和野火等反复暴露高度对应。研究展示了生成式AI与潜在构念建模结合在地理分析与空间知识提取中的应用潜力。
原文摘要 · Abstract (English)
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.
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