Spatial Heterogeneity in Climate Risk and Human Flourishing: An Exploration with Generative AI

2026-01-26

AI Summary

This study investigates the spatially heterogeneous relationship between cumulative climate risk and multidimensional human flourishing across U.S. counties. By integrating high-resolution climate hazard data with a Human Flourishing Geographic Index (HFGI)—derived from 2.6 billion geotagged tweets and generated using a fine-tuned open-source large language model—the authors construct a spatial analytical framework and employ structural equation modeling to examine their association. The work innovatively combines generative AI with latent variable modeling to enable, for the first time, the efficient extraction of spatialized human well-being indicators from massive unstructured text data. Findings reveal that cumulative climate risk significantly suppresses human flourishing, with this negative association exhibiting robust spatial consistency in regions frequently exposed to heatwaves, floods, high winds, droughts, and wildfires.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• climate risk
• human flourishing
• spatial heterogeneity
• geographic analysis
• climate hazards

Innovation

• Generative AI
• Large Language Models (LLMs)
• spatial heterogeneity
• human flourishing
• geotagged social media