Evidence map›Paper›PMID 34580622›Full record

ArticleSustainable cities and society2021

Does the joint prevention and control regulation improve the air quality? A quasi-experiment in the Beijing economic belt during the COVID-19 pandemic.

Chenlu Tao, Kent Wheiler, Chang Yu, Baodong Cheng, Gang Diao

Abstract read
In one paragraph

Article in Sustainable cities and society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Chenlu TaoSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.
Kent WheilerSchool of Environment and Forest Science, University of Washington, Seattle, WA 98195, USA.
Chang YuSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.
Baodong ChengSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.
Gang DiaoSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to clarify the correlation between air pollution of cities in Beijing Economic Belt from a time-varying perspective and estimate effects of joint prevention and control regulation of air pollution. The COVID-19 pandemic provides a unique opportunity. Based on daily data of air quality, we used TVP-VAR model and utilize the pandemic as a quasi-experiment to assess the policies. The results show air pollution in surrounding cities does influence Beijing's air quality, but the relationship has been weakening year by year, mainly due to industrial adjustment which have achieved progress on alleviating the path of air pollution. Therefore, it is necessary to implement joint regulation in areas with serious pollution. Specifically, the relationship between the air quality of Beijing and Zhangjiakou, Chengde, Tianjin decreased as the pandemic became worse. In contrast, there was no significant decline in Langfang and Baoding. So unlike Baoding and Langfang, industrial production increased relationships between air quality of Beijing and the other three cities, which highlights the validity of restrictions. However, restrictions implemented on Baoding and Langfang affect economic development but have little effect on Beijing's air governance. Therefore, joint regulation contributes to realizing sustainable cities, but more targeted policies should be formulated.

Indexed as

Beijing economic beltCOVID-19Industrial transferRegional governanceTVP-VAR

Identifiers

PMID34580622
PMCPMC8458618

What OpenQuestion holds

Textmetadata
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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.