Evidence map›Paper›PMID 37362843›Full record

ArticleStochastic environmental research and risk assessment : research journal2023

The impact of COVID-19 on economy, air pollution and income: evidence from China.

Chenggang Li, Ziling Chen, Xiaodong Wang, Yikang Wan, Zhen Zhao

Abstract read
In one paragraph

Article in Stochastic environmental research and risk assessment : research journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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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.

Chenggang LiSchool of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang, 550025 Guizhou China.
Ziling ChenSchool of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang, 550025 Guizhou China.
Xiaodong WangPortsmouth Business School, University of Portsmouth, Portsmouth, PO1 3DE UK.
Yikang WanSchool of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang, 550025 Guizhou China.
Zhen ZhaoSchool of Foreign Languages, Guizhou University of Finance and Economics, Guiyang, 550025 Guizhou China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global pandemic caused by the outbreak of COVID-19 has posed significant risks to our health. Preventive measures such as closed management have greatly affected the economies, environments and societies of various countries. Economy, air pollution and income are three important interconnected aspects of sustainable development. However, current research lacks systematic quantitative analysis of their relationships. To fill the gap, this study adopts monthly data from January 2016 to April 2022 and constructs both a Simultaneous Equation Model (SEM) and a Time Varying Parameter Stochastic Volatility Vector Autoregressive (TVP-SV-VAR) model to empirically analyze the impact of COVID-19 on China's economy, air pollution and income. This study finds that the COVID-19 has a negative impact on China's economy and income, and a positive impact on air pollution, and the impact of the COVID-19 is systematic. In addition, there is an inverted-U shaped relationship between air pollution and economics, and a positive correlation between economic and income. The impact of COVID-19 on the economy, air pollution and income show a process of sharp fluctuations to gradual stabilization that gradually stabilized over time. This process is time-varying in the short-term, medium-term and long-term. The impacts are persistent at three different time points (before, during and after the outbreak of COVID-19), but the negative impact on the economy and income is persistent, while the positive impact on air pollution is limited. This study provides a more systematic and dynamic understanding of the COVID-19 preventive and mitigation measures in China and even the world, which helps to provide insights into the formulation of more comprehensive planning strategies in the future. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-023-02450-z.

Indexed as

Air pollutionCOVID-19EconomyImpactIncomeInverted-U shaped relationship

Identifiers

PMID37362843
PMCPMC10158711

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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.