Evidence map›Paper›PMID 36078467›Full record

ArticleInternational journal of environmental research and public health2022

The Driving Mechanism of Urban Land Green Use Efficiency in China Based on the EBM Model with Undesirable Outputs and the Spatial Dubin Model.

Liangen Zeng

Open access · goldAbstract 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. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
7.9field-weighted citation impact, top 3% of its field
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, 26 citations in OpenAlex.

  1. Article
  2. Article
  3. Research on the Effect of Manufacturing Agglomeration on Green Use Efficiency of Industrial Land.International journal of environmental research and public health · 2023
    Article
  4. 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

1 author at 1 institution in 1 country.

Liangen ZengCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, China.ORCID 0000-0001-8052-0070
Peking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Green development is necessary for building a high-quality modern economic system. The contribution of the article mainly includes the following three parts: First is the study on the urban land green use efficiency (ULGUE) in 30 provinces of China from 2008 to 2018 by adopting the epsilon-based measure (EBM) model with undesirable outputs to yield a more accurate and reasonable assessment result. In addition, the spatial agglomeration characteristics were analysed according to the spatial autocorrelation analysis. Thirdly, the spatial Durbin model was applied to analyse the driving factors of the WRGUE, which considers the spatial effects. The findings are as follows: (1) The regional differences in ULGUE were very significant, with the number decreasing from the coastal region to inland. (2) ULGUE showed a significantly positive spatial autocorrelation, and the spatial homogeneity was more significant than the spatial heterogeneity for ULGUE. (3) Economic development level, technical progress level, and urban population density have a significant impact on ULGUE, while the higher the proportion of the secondary industry in GDP, the lower the level of ULGUE. The research results may be a useful reference point for policymakers.

Indexed as

Economic DevelopmentEfficiencyChinaCitiesIndustryPopulation DensityEBM model with undesirable outputsspatial durbin modelurban land green use efficiency

Identifiers

PMID36078467
PMCPMC9517835
OpenAlexW4293724637

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.