Evidence map›Paper›PMID 41249800›Full record

ArticleScientific reports2025

Coupling and decoupling of humans and the built environment under disaster disturbances.

Xiyang Wu, Peng Chen

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Xiyang WuCollege of Tourism and Geographical Sciences, Jilin Normal University, Siping, China.
Peng ChenCollege of Tourism and Geographical Sciences, Jilin Normal University, Siping, China. pp11290@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study, based on the context of rainstorm and flood disasters, explored the coupling, coordination, and decoupling between humans and the built environment. Combining the coupling coordination model with the Tapio decoupling model, the coupling situation in 16 prefecture-level cities in Anhui province from 2009 to 2020 was analyzed. A random forest method combined with recursive feature elimination was used to identify key driving factors. Constraint line extraction and elasticity analysis were used to determine the response and threshold of the coupling coordination to these driving factors. The results showed an overall upward trend in the coupling coordination of the 16 prefecture-level cities, and in most years, the decoupling between humans and the built environment exhibited varying degrees of coupling intensity. Using the decoupling index (DI) of 0.8 and 1.2 as classification thresholds, the vast majority of the sample exhibited a developmental decoupling pattern, with only a few cities exhibiting a decline-type decoupling pattern in a few years, which is generally consistent with the changing trend of the coupling coordination. GDP, urbanization rate, number of people with college or higher education per 100,000 people, economic losses from flooding, population affected by floods, length of drainage pipes, registered urban unemployment rate, and number of medical institutions are important drivers of changes in the coupling coordination degree. This study provides important insights into the relationship between people and the built environment in the context of heavy rain and flooding, and offers valuable reference and guidance for research and practice in related fields.

Indexed as

Built EnvironmentDisastersFloodsChinaCitiesHumansModels, TheoreticalUrbanizationDecouplingDriving factor analysisRainstorm flood coupling coordination degreeThreshold analysis

Identifiers

PMID41249800
PMCPMC12623913

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.