Evidence map›Paper›PMID 40813890›Full record

ArticleScientific reports2025

Nonlinear relationship between digital and intelligent transformation and energy conservation and emission reduction in China.

Qiqi Sun, Siyan Liu, Lili Feng, Gang Lu

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

4 authors.

Qiqi SunSchool of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang, Hebei, China.
Siyan LiuSchool of Economics, Beijing Technology and Business University, Beijing, China.
Lili FengSchool of Management, Hebei GEO University, Shijiazhuang, Hebei, China.
Gang LuSchool of Management, Hebei GEO University, Shijiazhuang, Hebei, China. 1031@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The manufacturing industry's continuous digital intelligence transformation has triggered an extensive discussion on the environmental impact. In order to further explore this relationship, the panel data of 30 provinces in China from 2011 to 2021 is used in this analysis, the index system of digital intelligent transformation of manufacturing industry is constructed from two aspects of digitalization and intelligence of manufacturing industry, and the principal component analysis method is used to calculate. The two-way fixed effect model is used to analyze its impact on energy consumption and carbon emissions. The results show that the level of digital intelligence transformation of manufacturing industry in the eastern region is relatively high. In addition, the digital intelligence transformation of the manufacturing industry has a U-shaped impact on the scale and intensity of energy consumption and carbon emissions, and there is a positive linear relationship between the digital intelligence transformation of the manufacturing industry and the energy consumption structure. Further research has found that the impact of different regions and digital transformation indicators on energy consumption and carbon emissions is heterogeneous. This study establishes a foundation for quantitative analysis of the digital intelligence transformation of manufacturing industry, and makes up for the deficiency of existing research that pays more attention to linear relationship. It provides both theoretical basis and policy recommendations for energy conservation and emission reduction in manufacturing.

Indexed as

Carbon emissionsDigital intelligence transformationEnergy consumptionLow-carbon development

Identifiers

PMID40813890
PMCPMC12354722

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.