Evidence map›Paper›PMID 39157532›Full record

ArticleFrontiers in public health2024

Green workspace and urban health: exploring the impacts of industrial robotics in pollution emissions and public health.

Shule Yu, Minghan Hu, Jiancheng Li, Xueling Yan

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. 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

4 authors.

Shule YuSchool of Economics, Fudan University, Shanghai, China.
Minghan HuSchool of Economics, Sichuan University, Chengdu, China.
Jiancheng LiSchool of Economics, Sichuan University, Chengdu, China.
Xueling YanSchool of Economics, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study addresses a critical gap in understanding how technological advancements, specifically industrial robots, influence urban pollution emissions and public health. The rapid evolution of technology and changing working conditions significantly affect these areas, yet research has not extensively explored this domain. Methods: Utilizing 2018 China Labor-force Dynamic Survey (CLDS) dataset, this study examines the impact of industrial robots on public health. An analytical framework is employed to assess the correlation between the adoption of eco-friendly industrial robots and improvements in worker health, attributed to the reduction of pollution emissions. Results: The findings reveal that the adoption of industrial robots significantly enhance both public physical and mental health. This study also identifies potential demographic heterogeneity in the effects of industrial robots. The benefits are more pronounced among non-insured manual female workers who are older, have lower education levels, and hold rural hukou. These benefits are closely linked to improvements in the quality of the production environment and reductions in pollution emissions at both macro and micro levels. Discussion: The study underscores the significant potential of industrial robots to positively impact urban health, advocating for strategies that promote the development of safer, greener environments.

Indexed as

IndustryPublic HealthRoboticsAdultAir PollutionChinaEnvironmental PollutionFemaleHumansMaleMiddle AgedSurveys and QuestionnairesUrban HealthWorkplaceenvironmental-friendly productiongreen workplaceindustrial roboticspollution reductionpublic health

Identifiers

PMID39157532
PMCPMC11327072

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.