Evidence map›Paper›PMID 36429659›Full record

ArticleInternational journal of environmental research and public health2022

Analysis on the Spatio-Temporal Evolution Characteristics of the Impact of China's Digitalization Process on Green Total Factor Productivity.

Junwei Zhao, Yuxiang Zhang, Anhang Chen, Huiqin Zhang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Junwei ZhaoCollege of Management Science, Chengdu University of Technology, Chengdu 610059, China.
Yuxiang ZhangCollege of Management Science, Chengdu University of Technology, Chengdu 610059, China.ORCID 0000-0001-9537-0490
Anhang ChenCollege of Management Science, Chengdu University of Technology, Chengdu 610059, China.
Huiqin ZhangCollege of Management Science, Chengdu University of Technology, Chengdu 610059, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Green production is an inevitable choice for China's high-quality economic development. With the rise of the digital technology revolution, China's digital transformation may play an integral and important role in increasing green total factor productivity (GTFP). Based on the panel data of 30 Chinese provinces from 2014-2020, the impact of digitization on GTFP was explored using the model of geographically and temporally weighted regression (GTWR), and the spatial and temporal distribution characteristics and development trends of such effects were further explored. The main findings are as follows: (1) China's digitalization level and GTFP has significant spatial autocorrelation and similar spatial distribution characteristics. (2) Digitalization has a significant positive impact on GTFP, but this impact decreases yearly, and there are noticeable regional differences. Digitalization in the eastern and central regions has a more significant impact on GTFP than in the west. (3) The region where China's digital development has extensively promoted GTFP has shifted from China's southern coastal region to the northwest and northeast regions. (4) The time-series fluctuations of the regression coefficients of the digitization level in each province in China also show agglomeration characteristics. That is, the regression coefficients of neighboring provinces have similar time-series fluctuations.

Indexed as

Economic DevelopmentEfficiencyChinaSpatial AnalysisSpatial Regressiondigitalizationgreen total factor productivityGTWRspatial heterogeneity

Identifiers

PMID36429659
PMCPMC9690314

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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.