AI-Assisted Identification and Equity Assessment of Vulnerable Population Impacts in U.S. Energy Transition

Abstract

The United States energy transition toward renewable sources presents complex challenges regarding social equity and vulnerable population impacts. This research introduces an innovative AI-assisted framework for identifying and assessing equity implications of energy transformation policies. Our methodology integrates multi-dimensional vulnerability identification algorithms with machine learning-based impact quantification techniques to evaluate disparities across demographic, geographic, and socioeconomic dimensions. The framework employs advanced data analytics to process heterogeneous datasets encompassing employment patterns, health indicators, and environmental justice metrics. Through comprehensive case studies across diverse U.S. regions, our approach demonstrates superior accuracy in vulnerable population classification compared to traditional assessment methods. The results reveal significant regional and demographic disparities in energy transition impacts, with rural communities and minority populations experiencing disproportionate effects. This research contributes to evidence-based policy development by providing quantitative insights into energy justice dynamics and offering actionable recommendations for equitable transition strategies.