Evidence map›Paper›PMID 42555575›Full record

ArticlePloS one2026

Research on the logical mechanism and path of digital new quality productivity enabling common prosperity.

Yujie Cui, Lin Li

Abstract read
In one paragraph

Article in PloS one, 2026. 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.

Yujie CuiCollege of Economic and Management, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang, China.
Lin LiCollege of Economic and Management, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang, China.ORCID https://orcid.org/0000-0002-6292-0473

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precisely measuring the phased achievements of common prosperity and analyzing the spatial distribution characteristics and driving mechanisms of its development level are crucial for promoting high-quality economic development. Based on panel data from 30 provincial regions in China, this study employs the entropy weight TOPSIS method, kernel density estimation, and dynamic qualitative comparative analysis (QCA) to explore the linkage effects and path selection of digital new quality productivity elements on the balanced development of common prosperity. The research findings are as follows: (1) The level of common prosperity in our country has generally exhibited an upward trend, and the spatial distribution pattern characterized by "higher levels in the east and lower levels in the west" is clearly evident. (2) The multi-dimensional elements of digital new quality productivity work in concert to promote the development of common prosperity, forming three configuration pathways: the "digital intelligence foundation - talent traction type," "digital intelligence industry-talent linkage type," and "digital intelligence development - talent collaboration type." (3) Considering the evolving trend of the path toward common prosperity, the process has progressed through three distinct stages: "digital intelligence foundation," "digital intelligence industry," and "digital intelligence development." Spatially, this evolution exhibits both an "East-middle disparity" and an "East-West disparity." This research contributes to a better understanding of the current state of common prosperity, enhances the level of digital new quality productivity, and offers valuable insights for implementing differentiated development strategies to accelerate the reduction of regional economic disparities.

Indexed as

Economic DevelopmentEfficiencyChinaHumans

Identifiers

PMID42555575
PMCPMC13440828

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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.