Evidence map›Paper›PMID 36674099›Full record

ArticleInternational journal of environmental research and public health2023

Government Intervention, Structural Transformation, and Carbon Emissions: Evidence from China.

Shuhua Zhang, Jian Li, Bao Jiang, Tianmiao Guo

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
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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

4 authors.

Shuhua ZhangSchool of Economics, Ocean University of China, Qingdao 266100, China.
Jian LiSchool of Economics, Ocean University of China, Qingdao 266100, China.ORCID 0000-0002-6256-1183
Bao JiangSchool of Economics, Ocean University of China, Qingdao 266100, China.ORCID 0000-0002-2615-1603
Tianmiao GuoSchool of Economics, Ocean University of China, Qingdao 266100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Government intervention and structural transformation play an important role in both the economy and carbon emissions. Based on provincial panel data from China from 2003 to 2020, this paper employs econometric models to investigate the impact of government intervention and structural transformation on carbon emissions. In particular, structural transformation is divided into two indicators: The rationalization of the industrial structure and the upgrading of the industrial structure. According to the research findings, government intervention has significantly promoted carbon emissions and structural transformation has had dual effects on carbon emissions; meanwhile, the rationalization of the industrial structure has significantly increased carbon emissions, while the upgrading of the industrial structure has slowed down carbon emissions, with these findings passing the corresponding robustness test. The relationship between government intervention, structural transformation, and carbon emissions varies significantly over time and across regions. Further investigations revealed that government intervention and structural transformation have a significant impact on carbon emissions in various panel quantiles. Finally, the paper makes policy recommendations in order to provide empirical support for promoting China's high-quality economic development and achieving the "double carbon" goal.

Indexed as

CarbonGovernmentCarbon DioxideChinaEconomic DevelopmentIndustryCarbonCarbon Dioxidecarbon emissionsgovernment interventionstructural transformation

Identifiers

PMID36674099
PMCPMC9859371

What OpenQuestion holds

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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.