Evidence map›Paper›PMID 40594980›Full record

ArticleScientific reports2025

Impact of AI development on green total factor productivity.

Wenhao Ding, Pan Hu

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

2 authors.

Wenhao DingSchool of Economics and Management, Northeast Agricultural University, Harbin City, Heilongjiang Province, China.
Pan HuSchool of Economics and Management, Northeast Agricultural University, Harbin City, Heilongjiang Province, China. zuopeng202409@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The improvement of GTFP has become an important guarantee to change China's development mode and achieve long-term stable economic growth, while the development of AI is of great significance to the development of GTFP. This paper adopts the SBM-GML method to calculate the GTFP of 30 regions in China from 2011 to 2020, and manually collects AI data to study the impact of AI on GTFP, and systematically analyze the relationship between AI and GTFP using a panel double-fixed model with a faceted threshold model. The experimental results show that: (1) There is a positive and significant effect of AI on GTFP, which increases GTFP productivity by 0.3654% for every 1% increase in AI, which still holds after a robust type test. (2) The development of AI in China's eastern region has a greater promoting effect on GTFP. (3) Further mechanism analysis reveals that RIS and ER are two important channels through which AI influences the improvement of GTFP. (4) Threshold regression shows that AI has a single threshold effect on GTFP based on TI and LNPGDP. The promotion of GTFP by AI is higher when technological innovation is less than the threshold value of 29.95, and the promotion of GTFP by AI is insignificant when the level of economic development is less than the threshold value of 85.45. This paper deepens the knowledge and understanding of the role played in the development of AI at the macro level and provides suggestions for improving GTFP at the provincial level in China.

Indexed as

AIGTFPSystem GMM modelThreshold model

Identifiers

PMID40594980
PMCPMC12217399

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.