Evidence map›Paper›PMID 38125416›Full record

ArticleHeliyon2023

Have government environmental auditing contributed to the green transformation of Chinese cities?

Wei Wang, Zhidan Wang, Yuanfei Mei

Abstract read
In one paragraph

Article in Heliyon, 2023. 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

3 authors.

Wei WangSchool of Finance, Chongqing Technology and Business University, Chongqing 400067, China.
Zhidan WangSchool of Finance, Chongqing Technology and Business University, Chongqing 400067, China.
Yuanfei MeiSchool of Management Science and Engineering, Chongqing Technology and Business University, Chongqing 400067, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Faced with growing ecological problems, governments around the world are increasingly focusing on improving ecology and the environment. The topic of urban green transformation has attracted a great deal of research. However, not much of it has focused on the effectiveness of government environmental auditing, especially from the perspective of its role in sustainable governance. This article takes 285 cities in China from 2009 to 2020 as the research scale. It innovatively measures government environmental audits with dual indicators and uses System Gaussian Mixture Model (SGMM) to estimate that government environmental audits significantly promote urban green transformation, and the impact of "whether to implement government environmental auditing" is greater than the "intensity of government environmental auditing". The results show that government environmental audit intensity has a stronger impact on urban green transformation in eastern cities. In contrast, environmental audit coverage has a stronger impact in western cities. Moreover, the effect of government environmental auditing on green transformation is more significant in small and medium-sized cities and key environmental protection cities than in large cities and non-key environmental protection cities, respectively. Government environmental auditing could facilitate urban green transformation by restraining local government behavior, forcing green technology innovation, and promoting industrial structural upgrading. In addition, the intensity of government environmental auditing can better act on green transformation through the fore-mentioned mechanisms. It can play a crucial role in green technology innovation.

Indexed as

ChinaDual indicatorGovernment environmental auditingSystem Gaussian Mixture modelUrban green transformation

Identifiers

PMID38125416
PMCPMC10730587

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.