Evidence map›Paper›PMID 38890482›Full record

ArticleScientific reports2024

Impact of green innovation on carbon reduction in China.

Haiyan Shan, Shangmiao Shao

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

2 authors.

Haiyan ShanSchool of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China. hyshan@nuist.edu.cn.
Shangmiao ShaoSchool of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China. cwssm0808@outlook.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Green innovation directly encompasses the two major concepts of green and innovation in the new development concepts, which provides a powerful driving force to support Chinese-style modernisation. This paper empirically tests the relationship between green innovation and carbon emission intensity using a double fixed effects model. Based on the panel data of 30 provinces in China, the mediation effect model of "green innovation-big data-carbon emission" is constructed. The result shows that green innovation has a noticeable direct negative effect on urban carbon emission intensity. The conclusions are robust after considering measurement errors and endogenous problems. Furthermore, it is found that big data plays a significant role in strengthening the relationship between green innovation and carbon emission intensity. The findings in this study not only advance the study on green innovation and carbon emissions but also provide a new perspective on the role of big data.

Indexed as

Big dataCarbon emissionGreen innovationMediating effect

Identifiers

PMID38890482
PMCPMC11189581

What OpenQuestion holds

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LicenceCC BY
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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.