Evidence map›Paper›PMID 38486726›Full record

ArticleHeliyon2024

Environmental regulation, land use efficiency and industrial structure upgrading: Test analysis based on spatial durbin model and threshold effect.

Hu Yu, Chaofan Zheng

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

2 authors.

Hu YuInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Chaofan ZhengInstitute of Digital China, Fuzhou University, Fuzhou, 350100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Environmental regulation promotes industrial structure change and regional economic transformation through land use adjustment, which gets a new way to explore the path of reforming traditional industrialization and urbanization. Based on the panel data of 128 prefecture-level cities in China 's Yangtze River Economic Belt from 2000 to 2020, this paper uses the spatial Dubin model to analyze the impact of environmental regulation and land use efficiency on the upgrading of industrial structure, and sets the panel threshold model to examine the impact of environmental regulation on the upgrading of industrial structure by affecting land use efficiency. The results show that formal environmental regulation has a significant positive spatial effect on the rationalization and upgrading of industrial structure, which are 0.1734 and 0.2854 respectively. Informal environmental regulation has a negative spillover effect on neighboring provinces but not significant. Heterogeneous environmental regulation has obvious "double threshold effect" on industrial upgrading by affecting land use efficiency. When the threshold of environmental regulation intensity is 0.0315-0.0886, environmental regulation still inhibits land use efficiency and industrial structure upgrading. When the threshold value is greater than 0.0886, environmental regulation has a positive impact on land use efficiency but not significant. With the intensity of environmental regulation from weak to strong, it will produce a double threshold effect of "strong inhibition-weak inhibition-interaction promotion" on the upgrading of manufacturing structure through the adjustment of land use efficiency.

Indexed as

China's Yangtze River Economic BeltEnvironmental regulationIndustrial structure upgradingLand use efficiencyThreshold effect

Identifiers

PMID38486726
PMCPMC10938079

What OpenQuestion holds

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LicenceCC BY-NC
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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.