Evidence map›Paper›PMID 38431732›Full record

ArticleScientific reports2024

Research on the coupling mechanism and influencing factors of digital economy and green technology innovation in Chinese urban agglomerations.

Xuesi Zhong, Ziyi Duan, Chang Liu, Wei Chen

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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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.

Xuesi ZhongSchool of Economics and Management, Guangxi Normal University, Guilin, 541000, Guangxi, China.
Ziyi DuanSchool of Economics and Management, Guangxi Normal University, Guilin, 541000, Guangxi, China.
Chang LiuSchool of Economics and Management, Guangxi Normal University, Guilin, 541000, Guangxi, China.
Wei ChenBusiness School, Guilin Tourism University, Guilin, 541006, Guangxi, China. 554057442@qq.com.

Funding

Graduate Innovation Project of School of Economics and Management, Guangxi Normal University Grant No.: JG2023010National Social Science Foundation of China Grant No.:20&ZD157The Guangxi Key Laboratory of Landscape Resources Conservation and Sustainable Utilizationin Lijiang River Basin,Guangxi Normal University Grant No.:LRCSU21Z0319
6 · The paper itself

Abstract

This paper examines the coupling coordination degree between digital economy and green technology innovation in 19 urban agglomerations across China from 2011 to 2020. Through the analysis of the coupling coordination degree model, spatial autocorrelation, multi-distance spatial clustering analysis, kernel density analysis and grey correlation model, this study uncovers the mechanism of coupling between digital economy and green technology in Chinese urban agglomerations. Data analysis revealed a significant increase in the coupling coordination between the digital economy and green technology innovation within urban agglomerations. However, there are noticeable spatial imbalances in this trend. Additionally, the multi-distance spatial distance analysis highlights a shift from a random distribution to a clustered distribution of spatial characteristics. The polarization features vary among each urban agglomeration and exhibit a significant positive spatial correlation. Factors such as economic sustainability, creative talent, policy support, digital impetus, and technological support will affect the coupling mechanism of green technology innovation and the digital economy in China's urban agglomerations. Policy recommendations are proposed to foster the development of the digital economy, promote coordinated growth within and beyond urban clusters, and ultimately build a digital ecological civilization that is both green and intelligent.

Indexed as

Coupling coordinationDigital economyGreen technology innovationUrban agglomeration

Identifiers

PMID38431732
PMCPMC10908852

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.