Evidence map›Paper›PMID 35805616›Full record

ArticleInternational journal of environmental research and public health2022

Analysis of the Dynamic Relationship between Green Economy Efficiency and Urban Land Development Intensity in China.

Jiao Hou, Xinhai Lu, Shiman Wu, Shangan Ke, Jia Li

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. 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

5 authors.

Jiao HouCollege of Public Administration, Central China Normal University, Wuhan 430079, China.
Xinhai LuCollege of Public Administration, Central China Normal University, Wuhan 430079, China.ORCID 0000-0002-3368-8636
Shiman WuSchool of Management, Wuhan Institute of Technology, Wuhan 430205, China.
Shangan KeCollege of Public Administration, Central China Normal University, Wuhan 430079, China.
Jia LiCollege of Public Administration, Central China Normal University, Wuhan 430079, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The improvement of green economic efficiency (GEE) should be realized under reasonable urban land development intensity (ULDI). Improving GEE can also help alleviate the negative externalities of excessive or unreasonable ULDI. Clarifying the interactive response mechanism between GEE and ULDI is a key link in regional sustainable development. Therefore, this paper uses the super-efficiency slack-based model (SBM) method, panel entropy method, and panel vector auto regression model to comprehensively analyze the interactive response relationship between GEE and ULDI in 283 prefecture-level cities in China from 2003 to 2019. This paper finds that: (1) during the research period, both the GEE and ULDI showed a relatively obvious upward trend, which is manifested in the fact that ULDI increased year by year while GEE overall increased in volatility. The growth and evolution trend of ULDI and GEE has the characteristics of interaction and coordination; (2) there is a two-way interactive Granger causality between ULDI and GEE, showing a positive interactive response effect; and (3) both ULDI and GEE have positive inertial growth and self-enhancement mechanisms. In the long run, GEE has a greater impact on the change of ULDI.

Indexed as

Conservation of Energy ResourcesEconomic DevelopmentChinaCitiesEfficiencySustainable Developmentgreen economic efficiencyinteractive responseurban land development intensity

Identifiers

PMID35805616
PMCPMC9265710

What OpenQuestion holds

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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.