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Quantifying Organizational Environmental Action from Web Data and Large Language Models

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

Quantifying organizational environmental action from publicly available web content remains a challenging environmental data science problem because relevant information can be dispersed across multiple webpages and is primarily communicated through unstructured text. We present a scalable computational framework for transforming organizational web content into structured measures of environmental action and demonstrate the approach using Jewish congregations in the United States. We constructed a national database of 4,964 congregations by integrating multiple geospatial, knowledge-base, dire

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