Evidence map›Paper›PMID 42012664›Full record

ArticleEnvironmental management2026

The Role of Green Infrastructure in Enhancing Flood Resilience: A Spatial Perspective.

Jinhee Han, Yoomi Kim, Katsuya Tanaka

Abstract read
In one paragraph

Article in Environmental management, 2026. 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

3 authors.

Jinhee HanDepartment of Public Administration, Ewha Womans University, Seoul, Republic of Korea.
Yoomi KimDepartment of Public Administration, Ewha Womans University, Seoul, Republic of Korea. kimym@ewha.ac.kr.
Katsuya TanakaFaculty of Economics, Shiga University, Hikone, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study is to empirically assess the effectiveness of green infrastructure (GI) in South Korea as a way to enhance flood resilience. We applied a spatial analytical approach to examine the impact of GI on flood resilience. Specifically, we tested spatial dependence with Moran's I and local indicators of spatial association, addressing it with spatial lag and spatial error models. We compiled government data on GI and other regional attributes for 226 administrative districts in South Korea. Unlike previous studies that focused only on damage costs, this study incorporated both damage and recovery costs to calculate flood resilience costs. The findings show that flood resilience costs and GI distribution exhibit substantial spatial heterogeneity and significant spatial autocorrelation across local governments. Moreover, GI reduces flood resilience costs, although its effectiveness varies by type. No spatial spillover effect of GI on neighboring areas was identified, indicating that the impact of GI is localized and spatially confined. These results provide empirical evidence that GI plays a crucial role in strengthening local resilience against flood risk. The findings highlight the need to strengthen local resilience through spatially efficient expansion, hydrologically optimized design, and strategic allocation of GI, particularly considering flood vulnerability and interregional GI equity. By quantifying resilience costs and employing spatial analysis, this study advances both the theoretical understanding and the practical application of GI in flood-prone regions.

Indexed as

Conservation of Natural ResourcesFloodsRepublic of KoreaSpatial AnalysisGreen infrastructure, Flood resilience, Flood risk management, Resilience cost, Spatial heterogeneity

Identifiers

PMID42012664
PMCPMC13099833

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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.