Evidence map›Paper›PMID 40290504›Full record

ArticleFrontiers in public health2025

The impact of energy poverty on the health capital of middle-aged and older adult residents in rural China.

Cuiting Yu, Tianrun Li, Qin Wan

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. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

3 authors.

Cuiting YuSchool of Economics and Management, Southwest Petroleum University, Chengdu, China.
Tianrun LiSchool of Economics and Management, Southwest Petroleum University, Chengdu, China.
Qin WanSchool of Economics and Management, Southwest Petroleum University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Energy poverty significantly affects the well-being of vulnerable populations, particularly middle-aged and older adult individuals in rural China. This study investigates how energy poverty impacts health capital, using data from the China Health and Retirement Longitudinal Study (CHARLS). Methods: We employed the Multidimensional Energy Poverty Index (MEPI) to measure energy poverty and used an ordered logit model to analyze its effects on self-rated health (SRH) as a proxy for health capital. The study utilized longitudinal data from 2013, 2015, 2018, and 2020, covering 9,464 observations, and included control variables such as age, gender, and chronic disease status. Endogeneity was addressed using instrumental variables and propensity score matching. Results: The findings indicate that energy poverty has a significant negative impact on health capital, with a regression coefficient of -0.221(p<0.01), lowering self-rated health levels. This effect is consistent across physical health, mental health, and daily functioning. Heterogeneity analysis reveals that individuals with lower education levels and those in southern rural areas experience more severe health impacts. Mediation tests confirm that indoor environmental conditions partially mediate this relationship. Discussion: The study underscores the urgent need for targeted interventions to mitigate energy poverty in rural China. Expanding access to clean energy, improving rural infrastructure, and providing financial subsidies are critical. Education and regional policies should also be prioritized to address disparities.

Indexed as

Health StatusPovertyRural PopulationAgedChinaFemaleHumansLongitudinal StudiesMaleMiddle Agedaging populationenergy povertyhealth capitalordered logit modelrural China

Identifiers

PMID40290504
PMCPMC12021915

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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.