Evidence map›Paper›PMID 38110409›Full record

ArticleNature communications2023

Integrating climate change induced flood risk into future population projections.

Evelyn G Shu, Jeremy R Porter, Mathew E Hauer, Sebastian Sandoval Olascoaga, Jesse Gourevitch, Bradley Wilson, Mariah Pope, David Melecio-Vazquez, Edward Kearns

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Evelyn G ShuFirst Street Foundation, Brooklyn, NY, USA. evelyn@firststreet.org.ORCID http://orcid.org/0009-0005-0647-9889
Jeremy R PorterFirst Street Foundation, Brooklyn, NY, USA.
Mathew E HauerDepartment of Sociology and Center for Demography and Population Health, Florida State University, Tallahassee, FL, USA.ORCID http://orcid.org/0000-0001-9390-5308
Sebastian Sandoval OlascoagaDepartment of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA.
Jesse Gourevitch *Environmental Defense Fund, San Francisco, CA, USA.
Bradley Wilson *First Street Foundation, Brooklyn, NY, USA.
Mariah Pope *First Street Foundation, Brooklyn, NY, USA.ORCID http://orcid.org/0009-0006-0398-5484
David Melecio-Vazquez *First Street Foundation, Brooklyn, NY, USA.ORCID http://orcid.org/0000-0003-1727-6137
Edward Kearns *First Street Foundation, Brooklyn, NY, USA.ORCID http://orcid.org/0000-0002-7286-8736

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Flood exposure has been linked to shifts in population sizes and composition. Traditionally, these changes have been observed at a local level providing insight to local dynamics but not general trends, or at a coarse resolution that does not capture localized shifts. Using historic flood data between 2000-2023 across the Contiguous United States (CONUS), we identify the relationships between flood exposure and population change. We demonstrate that observed declines in population are statistically associated with higher levels of historic flood exposure, which may be subsequently coupled with future population projections. Several locations have already begun to see population responses to observed flood exposure and are forecasted to have decreased future growth rates as a result. Finally, we find that exposure to high frequency flooding (5 and 20-year return periods) results in 2-7% lower growth rates than baseline projections. This is exacerbated in areas with relatively high exposure to frequent flooding where growth is expected to decline over the next 30 years.

Identifiers

PMID38110409
PMCPMC10728110

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.