Mapping and Comparing Climate Equity Policy Practices Using RAG LLM-Based Semantic Analysis and Recommendation Systems

TL;DR

This paper explores using RAG LLM-based semantic analysis to compare climate equity policies across U.S. cities, revealing patterns and supporting policy decision-making with AI-enhanced recommendation systems.

Contribution

It introduces a novel framework combining semantic analysis and recommendation systems to evaluate and compare climate equity policies using LLMs and retrieval-augmented generation.

Findings

Planning roles remain focused on transportation and environmental issues.

Climate plans mainly address emission reduction through affirmative language.

The recommendation system effectively identifies cities with similar policy practices.

Abstract

This study investigates the use of large language models to enhance the policymaking process. We first analyze planning-related job postings to revisit the evolving roles of planners in the era of AI. We then examine climate equity plans across the U.S. and apply ChatGPT to conduct semantic analysis, extracting policy, strategy, and action items related to transportation and energy. The methodological framework relied on a LangChain-native retrieval-augmented generation pipeline. Based on these extracted elements and their evaluated presence, we develop a content-based recommendation system to support cross-city policy comparison. The results indicate that, despite growing attention to AI, planning jobs largely retain their traditional domain emphases in transportation, environmental planning, housing, and land use. Communicative responsibilities remain central to planning practice. Climate equity plans commonly address transportation, environmental, and energy-related measures aimed at reducing greenhouse gas emissions and predominantly employ affirmative language. The demonstration of the recommendation system illustrates how planners can efficiently identify cities with similar policy practices, revealing patterns of geographic similarity in policy adoption. The study concludes by envisioning localized yet personalized AI-assisted systems that can be adapted within urban systems.