Evidence map›Paper›PMID 35270732›Full record

ArticleInternational journal of environmental research and public health2022

Air Pollution Control and Public Health Risk Perception: Evidence from the Perspectives of Signal and Implementation Effects.

Yangyang Fan, Liangdong Lu, Jia Xu, Fenge Wang, Fei Wang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Yangyang FanSchool of Management, University of Science and Technology of China, Hefei 230026, China.
Liangdong LuBusiness School, Hohai University, Nanjing 210098, China.
Jia XuBusiness School, Hohai University, Nanjing 210098, China.
Fenge WangWater Resources Management Office, Zhangjiagang Water Conservancy Bureau, Suzhou 215638, China.
Fei WangInternational School of Business & Finance, Sun Yat-sen University, Guangzhou 510275, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The main purposes of government environmental policy include improving the objective natural environment as well as reducing the health risk of the public. A majority of studies have tested the means of achieving the first goal. In this paper, we aimed to gather empirical evidence pertaining to the realization of the second goal by drawing on a quasi-natural experiment that was conducted based on the "Action Plan on Air Pollution Prevention and Control" issued in 2013 (AP2013). The research data came from the tracking data of 17,766 individuals from 112 prefecture-level cities of China in 2012 and 2014. Through ordinal logistic regression and DID analysis, a causal relationship between the AP2013 policy and public health risk perceptions was verified, indicating that this policy can significantly decrease public health risk perceptions. By constructing different subsamples, an inverted U-shaped relationship between the causal effect and the length of the policy implementation window was established, which demonstrated the short-term signal effect and long-term implementation effect of this policy. The conclusions can help with the communication and implementation of a government's policy.

Indexed as

Air PollutantsAir PollutionChinaCitiesHumansParticulate MatterPerceptionPublic HealthAir PollutantsParticulate Matterair pollution controlimplementation effectpublic health risk perceptionsignal effect

Identifiers

PMID35270732
PMCPMC8910315

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.