Evidence map›Paper›PMID 32605137›Full record

ArticleInternational journal of environmental research and public health2020

Can the New Subway Line Openings Mitigate PM10 Concentration? Evidence from Chinese Cities Based on the PSM-DID Method.

Ying Wang, Jing Tao, Rong Wang, Chuanmin Mi

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

4 authors.

Ying WangCollege of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Jing TaoCollege of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Rong WangSchool of Economics and Management, Nanjing Institute of Technology, Nanjing 211167, China.
Chuanmin MiCollege of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.ORCID 0000-0002-9211-0332

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The large-scale construction of subway systems, which is viewed as one of the potential measures to mitigate traffic congestion and its resulting air pollution and health impact, is taking place in major cities throughout China. However, the literature on the impact of the new subway line openings on particulate matter with a diameter less than 10 µm (PM10) at the city level is scarce. Employing the Propensity Score Matching-Difference-in-differences method, this paper examines the effect of the new subway line openings on air quality in terms of PM10 in China, using the daily PM10 concentration data from January 2014 to Dececember 2017. Our finding shows that the short-term treatment effect on PM10 is more controversial. Furthermore, for different time windows, the result confirms an increase in PM10 pollution during the short term, while the subway line openings improve air quality in the longer term. In addition, we find that the treatment effect results in high PM10 pollution for cities with 1-2 million people, while it improves air quality for cities with over 2 million people. Moreover, for cities with varying levels of GDP, there is evidence of a reduction in PM10 after the subway line openings. Mechanism analysis supports the conclusion that the PM10 reduction originated from substituting the subway for driving.

Indexed as

Air PollutantsAir PollutionRailroadsChinaCitiesHumansParticulate MatterAir PollutantsParticulate MatterPM10 pollutionPSM–DID methodsubwaytraffic congestion

Identifiers

PMID32605137
PMCPMC7369925

What OpenQuestion holds

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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.