Climate change is expected to alter community dynamics and characteristics in complex ways, with the most severe implications for already vulnerable populations. The theory of climate gentrification provides a lens for investigating the ways in which climate change interacts with community demographics, housing, and socioeconomic characteristics in coastal communities. Here we apply unsupervised machine learning to 51 counties along the United States East Coast to identify four distinct clusters of multifaceted vulnerability related to social, housing, and environmental variables. Two of the four clusters, interpreted as superior investment and disinvestment, may be indicative of different pathways of climate gentrification. We also demonstrate the applicability of our methodology at finer spatial scales using a case study within North Carolina. As coastal climate impacts increase, this work demonstrates the need for adaptation planning that proactively considers compounding vulnerabilities to avoid disproportionate impacts on the most vulnerable.
Typologies of multiple vulnerabilities and climate gentrification across the East Coast of the United States