Evidence map›Paper›PMID 34945881›Full record

ArticleEntropy (Basel, Switzerland)2021

Spatial Correlation Network and Regional Differences for the Development of Digital Economy in China.

Luyang Tang, Bangke Lu, Tianhai Tian

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
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  6. Article
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  8. Article
  9. Has the Digital Economy Reduced Carbon Emissions?: Analysis Based on Panel Data of 278 Cities in China.International journal of environmental research and public health · 2022
    Article
  10. 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

3 authors.

Luyang TangSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China.ORCID 0000-0003-0816-3077
Bangke LuSchool of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China.ORCID 0000-0001-5046-4370
Tianhai TianSchool of Mathematics, Monash University, Melbourne, VIC 3800, Australia.ORCID 0000-0001-6191-0209

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid development of the digital economy is a powerful driving force to promote high-quality economic growth all over the world. Although a number of studies have been conducted to investigate the development of the digital economy in China, these studies pay little attention to the spatial linkages between the 30 provinces in China and the developmental differences between northern and southern China. Using Chinese digital economic data from 2004 to 2019, we propose an index system to measure the developmental levels of the digital economy and obtain the annual developmental levels of these provinces by using the factor analysis method. We analyze the regional differences of developmental levels by using the Theil index and kernel density estimation method. More importantly, the network method is used to analyze the correlations between the developmental levels of the digital economy in all provinces of China. By decomposing regional differences, our study shows that polarized and uncoordinated development is prominent. The development level of the digital economy in the southern region is higher than that in the northern region. In terms of regional correlations, the network study suggests that there are beneficial and spillover effects of the digital economy development between provinces. Based on the analysis results, we propose policies for improving the development of the digital economy in China.

Indexed as

correlation effectdigital economynetwork analysisnorth and southregional differences

Identifiers

PMID34945881
PMCPMC8700219

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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.