Persistent Social Vulnerability in Washington D.C. Communities and Green Infrastructure Clustering

Cities worldwide are presently contending with the intricate task of formulating urban infrastructure that seamlessly blends sustainability and resilience to effectively tackle urgent challenges. An increasingly prominent strategy gaining swift traction is the deployment of green infrastructure (GI), heralding a multitude of advantages for the urban milieu. As a growing body of research highlights the emergence of a new equity issue in our infrastructures from the perspective of environmental justice, it becomes evident that there is a significant gap in comprehensive studies investigating the combined temporal and spatial evolution of green infrastructure (GI) distribution. This research aims to address this gap by adopting a novel approach that explicitly considers the temporal dimension of GI distribution. Unlike previous studies that often rely on cross-sectional snapshots, this study employs a panel data analysis, which allows for a comprehensive examination of how GI distribution evolves over time. The primary research question addressed in this study is whether GI distribution in Washington D.C. exhibits a propensity to concentrate within certain communities. This inquiry delves into the pressing concern of the potential exacerbation of existing disparities through GI implementation. The study's findings may drive evidence-based policy recommendations that foster equitable distribution strategies, guaranteeing that socially vulnerable communities reap the rewards of GI's positive impacts.

Automated summary

Cities worldwide are facing the complex task of formulating urban infrastructure that combines sustainability and resilience. The deployment of green infrastructure (GI) is gaining traction as a strategy, with numerous advantages for urban environments. However, there is a significant gap in comprehensive studies investigating the temporal and spatial evolution of GI distribution. This research aims to address this gap by adopting a novel approach that considers the temporal dimension of GI distribution.