Evidence map›Paper›PMID 34448216›Full record

ReviewRisk analysis : an official publication of the Society for Risk Analysis2022

Assessing the Impacts of COVID-19 on the Industrial Sectors and Economy of China.

Ling Tan, Xianhua Wu, Ji Guo, Ernesto D R Santibanez-Gonzalez

Open access · bronzeAbstract readReview
In one paragraph

Review in Risk analysis : an official publication of the Society for Risk Analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
5.4field-weighted citation impact, top 3% of its field
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

16 citing papers in PubMed, 75 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Trade-Off between COVID-19 Pandemic Prevention and Control and Economic Stimulus.International journal of environmental research and public health · 2022
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Global systemic risk and resilience for novel coronavirus in postpandemic era.Risk analysis : an official publication of the Society for Risk Analysis · 2022
    Article
  15. When Does Overqualification Affect Bootlegging Positively?Psychology research and behavior management · 2022
    Article
  16. The Impacts of COVID-19 on China's Economy and Energy in the Context of Trade Protectionism.International journal of environmental research and public health · 2021
    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

4 authors at 3 institutions in 2 countries.

Ling TanSchool of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, China.
Xianhua WuSchool of Economics and Management, Shanghai Maritime University, Shanghai, China.
Ji GuoSchool of Economics and Management, Shanghai Maritime University, Shanghai, China.
Ernesto D R Santibanez-GonzalezDepartment of Industrial Engineering, CES4.0, Faculty of Engineering, University of Talca, Curico, Chile.
Shanghai Maritime University · CNNanjing University of Information Science and Technology · CNUniversity of Talca · CL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since December 2019, the COVID-19 epidemic has been spreading continuously in China and many countries in the world, causing widespread concern among the whole society. To cope with the epidemic disaster, most provinces and cities in China have adopted prevention and control measures such as home isolation, blocking transportation, and extending the Spring Festival holiday, which has caused a serious impact on China's output of various sectors, international trade, and labor employment, ultimately generating great losses to the Chinese economic system in 2020. But how big is the loss? How can we assess this for a country? At present, there are few analyses based on quantitative models to answer these important questions. In the following, we describe a quantitative-based approach of assessing the potential impact of the COVID-19 epidemic on the economic system and the sectors taking China as the base case. The proposed approach can provide timely data and quantitative tools to support the complex decision-making process that government agencies (and the private sector) need to manage to respond to this tragic epidemic and maintain stable economic development. Based on the available data, this article proposes a hypothetical scenario and then adopts the Computable General Equilibrium (CGE) model to calculate the comprehensive economic losses of the epidemic from the aspects of the direct shock on the output of seriously affected sectors, international trade, and labor force. The empirical results show that assuming a GDP growth rate of 4-8% in the absence of COVID-19, GDP growth in 2020 would be -8.77 to -12.77% after the COVID-19. Companies and activities associated with transportation and service sectors are among the most impacted, and companies and supply chains related to the manufacturing subsector lead the economic losses. Finally, according to the calculation results, the corresponding countermeasures and suggestions are put forward: disaster recovery for key sectors such as the labor force, transportation sector, and service sectors should be enhanced; disaster emergency rescue work in highly sensitive sectors should be carried out; in the long run, precise measures to strengthen the refined management of disaster risk with big data resources and means should be taken.

Indexed as

IndustryChinaCitiesCOVID-19Economic DevelopmentEpidemicsHumansCOVID-19disasterseconomic lossproduction and supply chainstatic CGE models

Identifiers

PMID34448216
PMCPMC8662127
OpenAlexW3195261074

What OpenQuestion holds

Textmetadata
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.