Evidence map›Paper›PMID 41018759›Full record

ArticleFrontiers in public health2025

Horizontal ecological compensation and urban resilience: mechanisms of low-carbon transformation.

Zhongyin An, Hongce Xiao, Weiyi Li, Hengli Wang

Abstract read
In one paragraph

Article in Frontiers in public health, 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
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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

4 authors.

Zhongyin AnOffice of Information Technology Development Management, South-Central Minzu University, Wuhan, China.
Hongce XiaoSchool of Mathematics and Statistics, Zhongnan University of Economics and Law, Wuhan, China.
Weiyi LiSchool of Mathematics and Statistics, Zhongnan University of Economics and Law, Wuhan, China.
Hengli WangWuhan University of Cyber Security Preparatory Office, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study investigates the socioeconomic impacts of horizontal ecological compensation (HEC) policies in China, focusing on their role in mitigating environmental degradation and enhancing urban resilience. Methods: We utilize panel data from 180 cities in the Yangtze and Yellow River basins (2007-2022) and construct an Inclusive and Resilient City (IRC) index. Policy effects are evaluated through a multi-period quasi-natural experiment. Results: The results indicate that HEC policies are associated with a 0.3% average increase in the IRC index, primarily driven by improvements in green innovation and industrial upgrading. These mechanisms contribute to reducing pollution-related health risks and enhance urban resilience against environmental stressors. While the average increase appears modest, it represents a meaningful improvement in urban well-being within the constraints of regional development and ecological sustainability. The robustness of these findings is confirmed through multiple tests, including parallel trend analysis and placebo tests. Discussion: This research highlights HEC policies as an innovative policy tool that balances environmental protection with socioeconomic development. To strengthen their impact, policy optimization is recommended, aiming to further alleviate the socioeconomic burdens of environmental degradation and improve public health in urban areas.

Indexed as

Conservation of Natural ResourcesChinaCitiesHumansSocioeconomic Factorsenvironmental degradationgreen innovationhorizontal ecological compensationinclusive and resilient citiesindustrial structure advancementlow-carbon transformation

Identifiers

PMID41018759
PMCPMC12464017

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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.