Evidence map›Paper›PMID 36554178›Full record

ArticleEntropy (Basel, Switzerland)2022

Evaluating the Structural Robustness of Large-Scale Emerging Industry with Blurring Boundaries.

Yang Li, Huajiao Li, Sui Guo, Yanxin Liu

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2022. 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

4 authors.

Yang LiSchool of Economics and Management, China University of Geosciences, Beijing 100083, China.
Huajiao LiSchool of Economics and Management, China University of Geosciences, Beijing 100083, China.ORCID 0000-0002-2071-9149
Sui GuoSchool of Business, Jiangsu Normal University, Xuzhou 221116, China.
Yanxin LiuSchool of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China.

Funding

Fundamental Research Funds for the Central Universities No. 2652019087 and No.2652019241National Natural Science Foundation of China No. 72173119, No.71991481 and No.71991480
6 · The paper itself

Abstract

The present large-scale emerging industry evolves into a form of an open system with blurring boundaries. However, when complex structures with numerous nodes and connections encounter an open system with blurring boundaries, it becomes much more challenging to effectively depict the structure of an emerging industry, which is the precondition for robustness evaluation. Therefore, this study proposes a novel framework based on a data-driven percolation process and complex network theory to depict the network skeleton and thus evaluate the structural robustness of large-scale emerging industries. The empirical data we used are actual firm-level transaction data in the Chinese new energy vehicle industry in 2019, 2020, and 2021. We applied our method to explore the transformation of structural robustness in the Chinese new energy vehicle industry in pre-COVID (2019), under-COVID (2020), and post-COVID (2021) eras. We unveil that the Chinese new energy vehicle industry became more robust against random attacks in the post-COVID era than in pre-COVID.

Indexed as

complex networkemerging industrypercolation processstructural robustness

Identifiers

PMID36554178
PMCPMC9778074

What OpenQuestion holds

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LicenceCC BY
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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.