Ml4ej: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning

Abstract

Understanding the key factors shaping environmental hazard exposures and their associated environmental injustice issues is vital for formulating equitable policy measures. Traditional perspectives on environmental injustice have primarily focused on the socioeconomic dimensions, often overlooking the influence of heterogeneous urban characteristics. This limited view may obstruct a comprehensive understanding of the complex nature of environmental justice and its relationship with urban design features. To address this gap, this study creates an interpretable machine learning model to examine the effects of various urban features and their non-linear interactions to the exposure disparities of three primary hazards: air pollution, urban heat, and flooding. The analysis trains and tests models with data from six metropolitan counties in United States using Random Forest and XGBoost. The predictability performance of the models is used to measure the extent to which variations of urban features shape disparities in environmental hazard levels. The analysis of feature importance reveals features related to social-demographic characteristics as the most prominent urban features that shape environmental hazard extent. We evaluate the models’ transferability across different regions and hazards. The results highlight limited transferability, underscoring the intricate differences among hazards and regions and way in which urban features shape environmental hazard exposures.

Keywords: environmental injustice, urban inequality, integrated urban design, Machine Learning, equity