Evidence map›Paper›PMID 37611045›Full record

ArticlePloS one2023

Empirical analysis of the impact of the digital economy on the green transformation of manufacturing: Evidence from China.

Chunjie Jia, Meng Shang, Junwei Cao, Yu Liu

Abstract read
In one paragraph

Article in PloS one, 2023. 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.

Chunjie JiaSchool of Flight, Anyang Institute of Technology, Anyang, China.
Meng ShangSchool of Flight, Anyang Institute of Technology, Anyang, China.
Junwei CaoSchool of Business, Yangzhou University, Yangzhou, China.ORCID 0000-0002-7809-679X
Yu LiuSchool of Flight, Anyang Institute of Technology, Anyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, the entropy method and the Super-SBM model with unexpected output are used at first to calculate the digital economy development index and the level of green transformation in manufacturing. Then, a range of multi-dimensional empirical methods, including fixed effects models, threshold models, and mediation models, are applied to analyze the characteristics shown by the impact of digital economy development on the green transformation of manufacturing. The research results are obtained as follows. Firstly, the digital economy contributes significantly to promoting the green transformation of manufacturing after excluding the macro-system environmental effects, conducting such robustness tests as stepwise regression and introducing instrumental variables. Secondly, there is a nonlinear relationship between the development of the digital economy and the green transformation of manufacturing with an increasing marginal effect. Lastly, it is revealed through mechanism analysis that the digital economy promotes the green transformation of manufacturing by enhancing the capabilities of green technological innovation and rationalizing industrial upgrading, with the partial mediation effects reaching 21.2% and 21.8%, respectively. Despite the contribution of digital economy to the advanced upgrading of industries, there is no mediation effect exhibited. In addition to confirming the path of achieving the green transformation of manufacturing through the digital economy, these results also guide the government on how policies can be formulated and improved to grow the digital economy and promote the green transformation of manufacturing.

Indexed as

CommerceIndustryChinaClimateEconomic Development

Identifiers

PMID37611045
PMCPMC10446184

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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.