Evidence map›Paper›PMID 41093948›Full record

ArticleScientific reports2025

Synergistic effects of digital technology and environmental regulation on the green transformation of China's manufacturing industry.

Sujiang Zheng, Yongqing Zhang, Ting Luo, Yinyin Gong

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.

Sujiang ZhengSchool of Management, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Yongqing ZhangSchool of Management, University of Shanghai for Science and Technology, Shanghai, 200093, China. zyq28@usst.edu.cn.
Ting LuoSchool of Management, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Yinyin GongSchool of Management, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study builds a framework for the green transformation of manufacturing from the dual perspectives of "technological empowerment" and "institutional regulation." It integrates digital technology, environmental regulation, and their synergy. Using panel data from 30 Chinese provinces during 2012-2023, we apply a PVAR model to explore the dynamic transmission paths among these factors and the green and low-carbon transformation of manufacturing. The results show: (1) Digital technology has a phased effect on transformation: it inhibits in the short term, improves in the mid-term, and promotes in the long term, reflecting an accumulating response mechanism. (2) Environmental regulation mainly drives transformation efficiency in the short term through incentives, but its effect weakens in the long run. (3) A strong synergy between digital technology and environmental regulation has not yet emerged in the medium and long term, and their joint effects remain limited. (4) The main driving force of green and low-carbon transformation comes from internal development momentum. Compared with environmental regulation, digital technology shows much stronger explanatory power, while the marginal contribution of regulation remains small. This study suggests embedding digital tools into regulatory mechanisms, enhancing the long-term incentives of environmental regulation, and promoting cross-regional data integration to build a dual-driven model for green and low-carbon manufacturing.

Indexed as

Digital technologyDynamic relationshipEnvironmental regulationPVAR modelSynergy mechanism

Identifiers

PMID41093948
PMCPMC12528406

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.