Evidence map›Paper›PMID 35959076›Full record

ArticleFrontiers in psychology2022

Green technology innovation and carbon emissions nexus in China: Does industrial structure upgrading matter?

Pengfei Gao, Yadong Wang, Yi Zou, Xufeng Su, Xinghui Che, Xiaodong Yang

Abstract read
In one paragraph

Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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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

6 authors.

Pengfei GaoSchool of Marxism, Xinjiang University, Ürümqi, China.
Yadong WangSchool of Marxism, Liaoning Petrochemical University, Fushun, China.
Yi ZouSchool of Tourism & Transportation Management, Guang Xi Eco-Engineering Vocational & Technical College, Liuzhou, China.
Xufeng SuSchool of Economics and Management, Xinjiang University, Ürümqi, China.
Xinghui CheSchool of Public Administration, Liaoning University, Shenyang, China.
Xiaodong YangSchool of Economics and Management, Xinjiang University, Ürümqi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compared with traditional technological innovation modes, green technology innovation is more targeted for low carbon development and critical support for countries worldwide to combat climate change. The impact of green technology innovation on carbon emissions is considered in terms of fixed effect and mediating effect models through industrial structure upgrading. For this purpose, the sample dataset of 30 provincial administrative areas in China from 2008 to 2020 is employed. The results demonstrate that green technology innovation exerts significantly inhibitory effects on carbon emissions, whose conclusion still holds after removing municipalities and replacing the dependent variable. Industrial structure upgrading is vital for green technology innovation to diminish carbon emissions. There is significant regional heterogeneity in the effects of green technology innovation on carbon emissions, i.e., the direct and indirect impact of green technology innovation on carbon emission reduction is significant in the eastern-central area, but its effect is insignificant in the western region. Therefore, it is essential to realize carbon emission reduction by further bolstering green technology innovation and accelerating industrial structure upgrading to fulfill the synergy of technology and structure.

Indexed as

carbon emission reductionChinagreen technology innovationindustrial structure upgradingmediating effect

Identifiers

PMID35959076
PMCPMC9362775

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.