Evidence map›Paper›PMID 39455710›Full record

ArticleScientific reports2024

The impact of data factor-driven industry on the green total factor productivity: evidence from the China.

Ping Huang, Xiaohui Chen

Abstract read
PubMed Full text at PMC Publisher
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.

Ping HuangSchool of Economics and Finance, Guizhou University of Commerce, Guiyang, 550000, China.
Xiaohui ChenFaculty of Economics and Business Administration, Yibin University, Yibin, 644000, China. m_cxh@163.com.

Funding

Philosophical Social Planning Project of Guizhou Province, 'Research on the high-quality coupling and coordination mechanism of digital economy and green development in Guizhou' (23GZQN23)
6 · The paper itself

Abstract

Data factors have become an essential factor of production in today's digital era, and provided renewed energy for China's green and high-quality development. This study analyses the impact of data factor-driven industries on green total factor productivity (GTFP) and the underlying mechanisms using panel data of 277 Chinese cities from 2008 to 2020. The results show that: First, the urban data factor-driven industry can enhance GTFP. Second, the data factor-driven industry can increase the input-output ratio and enhance GTFP by promoting AI technological innovation and AI technological entrepreneurship. Third, the heterogeneity analysis show that in regions with high levels of digital technological innovation, the market size of data factor-driven industries is larger and the effect of data factor-driven industries in enhancing GTFP is more significant. And in regions with low levels of savings, the data factor-driven industry has limited capital availability and is more efficient in using capital, which has a stronger effect on GTFP enhancement. This study provides valuable empirical evidence on the relationship between data factor-driven industries and urban GTFP, and important policy implications for fully leveraging the green economic effects of data factors, and promoting green and high-quality urban development.

Indexed as

AI entrepreneurshipAI innovationData factor-driven industryGreen total factor productivity

Identifiers

PMID39455710
PMCPMC11511855

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.