Evidence map›Paper›PMID 40046119›Full record

ArticleFrontiers in public health2025

Explore factors influencing residents' green lifestyle: evidence from the Chinese General Social Survey data.

Yifei Li, Jiyue Li, Chuntian Lu

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
–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.

Yifei LiInstitute for Empirical Social Science Research, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Jiyue LiInstitute for Empirical Social Science Research, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Chuntian LuInstitute for Empirical Social Science Research, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This paper explores the multifaceted dynamics and various determinants impacting residents' green lifestyles in China, focusing on drop-off recycling, low-carbon purchasing, and civic advocating. Methods: Using data from the 2021 Chinese General Social Survey and the Chinese Statistical Yearbook, we provide an integrated paradigm to position green lifestyles in a hierarchical framework, explore the individual-level mediating mechanisms nested within macro influences, and elucidate the relative strengths of determinants. Results and discussion: Firstly, the random coefficient regression models show that environmental knowledge and environmental protection intention significantly positively affect green lifestyles. Adopting traditional media could enhance civic advocating, and using new media may bolster low-carbon purchasing tendencies. A higher per capita GDP, increased public expenditure on environmental protection, and a reduced proportion of secondary industry are significant factors that promote drop-off recycling and low-carbon purchasing. Secondly, the multilevel mediational effects identify that people's environmental protection intention is a salient mediating variable within the control of regional macro factors. Additionally, the sheaf coefficient clustering models underscore the prominence of ecological awareness and macro-level factors in shaping drop-off recycling and civic advocating.

Indexed as

Conservation of Natural ResourcesLife StyleRecyclingAdultChinaFemaleHumansMaleMiddle AgedSurveys and Questionnairescivic advocatingdrop-off recyclingenvironmental protection intentiongreen lifestylelow-carbon purchasing

Identifiers

PMID40046119
PMCPMC11880220

What OpenQuestion holds

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LicenceCC BY
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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.