Evidence map›Paper›PMID 41029535›Full record

ArticleBMC public health2025

Air pollution and fertility potential: evidence from employed women in China.

Qingjun Zhao, Yongmei Zhang

Abstract read
In one paragraph

Article in BMC 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
–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.

Qingjun ZhaoCollege of Economics and Management, Huzhou College, Huzhou, China.
Yongmei ZhangSchool of Economics and Management, Huzhou University, Huzhou, China. 01934@zjhu.edu.cn.

Funding

National Social Science Foundation of China 22BRK036
6 · The paper itself

Abstract

backgroundUnder the dual context of China's universal two-child policy implementation and sustained fertility decline, investigating the underlying causes of fertility intention-behavior discrepancies among employed women has become pivotal for policy optimization. However, environmental determinants, particularly air pollution impacts on fertility potential in developing countries, remain understudied. This study addresses the theoretical and practical implications of air pollution effects on employed women's fertility potential in China.

methodsWe integrated city-level air pollution data with the 2016 China Labor-force Dynamics Survey (CLDS) dataset. Probit regression models coupled with instrumental variable approaches were employed to rigorously assess air pollution impacts on fertility potential. Heterogeneity analyses were conducted across income brackets, childbearing age groups, educational attainment levels, and nation. Mediation analysis further elucidated the mechanistic pathways.

resultsElevated PM

conclusionThe adverse impact of air pollution on the fertility potential of working women in China cannot be ignored. This study not only reveals the profound impact of environmental pollution on population health but also provides new evidence for understanding the environmental drivers of fertility decisions. To mitigate this issue, the government and society should take proactive and effective measures to improve air quality, such as strengthening environmental regulation, promoting clean energy, and raising public awareness of environmental protection.

Indexed as

Air PollutionEnvironmental ExposureFertilityWomen, WorkingAdultChinaFemaleHumansMiddle AgedParticulate MatterSurveys and QuestionnairesYoung AdultParticulate MatterAir pollutionChinaEmployed womenEnvironmental healthFertility potential

Identifiers

PMID41029535
PMCPMC12487350

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.