Cited by 19Environmental Justice and Health DisparitiesClimate Change, Adaptation, MigrationUrban, Neighborhood, and Segregation Studies

Typologies of multiple vulnerabilities and climate gentrification across the East Coast of the United States

Open access
Best, Kelsea B., Jouzi, Zeynab, Islam, Md Sariful, Kirby, Timothy, Nixon, Rebecca, Hossan, Azmal, Nyiawung, Richard A.·2023·Urban Climate

Abstract

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.

Integrity signals

Registry

Crossref · 10.1016/j.uclim.2023.101430

Cites

57 of 57 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A number of researchers have created climate displacement and gentrification indices based on a mix of demographics, physical characteristics, climate risk, and other factors.

    “Moreover, a number of researchers have created climate displacement and gentrification indices based on a mix of demographics, physical characteristics, climate risk, and other factors.”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Best, K. B., Jouzi, Z., Islam, M. S., Kirby, T., Nixon, R., Hossan, A., & Nyiawung, R. A.
Affiliations
  • University of Maryland, College Park (US)
  • Cornell University (US)
  • Virginia Tech (US)
  • Florida International University (US)
  • University of Delaware (US)
  • Colorado State University (US)
  • University of Guelph (CA)
Corresponding author
Best, Kelsea B.
Venue
Urban Climate
Publisher
Elsevier BV
Published
2023-03
Volume
48
Pages
101430
ISSN
2212-0955 (ISSN-L)
OpenAlex
W4318485769
Cited by
19
References
57
Citations by year
2026: 4 · 2025: 8 · 2024: 4 · 2023: 3
FWCI
8.20
Type
journal article
Language
en
Open access
Locations
Urban Climate (publishedVersion, cc-by-nc-nd)
Funders
  • National Socio-Environmental Synthesis Center
  • National Science Foundation (DBI-1639145)
Topics
  • Social Sciences › Sociology and Political Science › Environmental Justice and Health Disparities
  • Social Sciences › Sociology and Political Science › Climate Change, Adaptation, Migration
  • Social Sciences › Sociology and Political Science › Urban, Neighborhood, and Segregation Studies
Keywords
climate gentrification, multiple vulnerabilities, coastal communities, East Coast of the United States, socioeconomic vulnerability, environmental vulnerability, climate adaptation planning, North Carolina

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review