Evidence map›Paper›PMID 37053228›Full record

ArticlePloS one2023

Can power market reform reduce air pollution?--Evidence from prefecture-level cities in China.

Xing Li, Zimin Liu, Dan Yang, Yong Wei, Na Gong

Abstract read
In one paragraph

Article in PloS one, 2023. 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

5 authors.

Xing LiCollege of Economics and Management, Southwest University, Beibei District, Chongqing, China.ORCID 0000-0002-3428-983X
Zimin LiuCollege of Economics and Management, Southwest University, Beibei District, Chongqing, China.
Dan YangCollege of Economics and Management, Southwest University, Beibei District, Chongqing, China.
Yong WeiCollege of State Governance, Southwest University, Beibei District, Chongqing, China.
Na GongChongqing Youth Vocational & Technical College, Beibei District, Chongqing, China.ORCID 0000-0002-3389-6283

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The market-oriented reform of China's power market has gradually transformed power prices from government pricing to market regulation, which not only promotes the production efficiency of industrial enterprises, but also inhibits the excessive consumption and waste of power by residential power users. This paper uses the data from 2006-2018 combined with the precious industrial power price data and macroeconomic data of 100 cities in China, takes the marketization reform of the power market in 2015 as a quasi-natural experiment, and uses the difference-in-differences model to empirically study the causal relationship between power market reform and air pollution for the first time. The study found that power market reform can reduce air pollution, and this conclusion is also supported by a number of robustness tests. Mechanism analysis shows that power market reform can reduce air pollution by improving power market efficiency, promoting technological progress, and reducing power consumption. Heterogeneity analysis shows that power market reform can suppress air pollution more significantly in eastern regions, regions with severe air pollution, and regions with larger populations. This paper not only provides new research perspectives and research ideas for air pollution prevention and control, but also provides empirical evidence for the positive externalities of power market reform.

Indexed as

Air PollutantsAir PollutionChinaCitiesEfficiencyEnvironmental PollutionIndustryAir Pollutants

Identifiers

PMID37053228
PMCPMC10101395

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.