Evidence map›Paper›PMID 34403058›Full record

ArticleEnvironmental science and pollution research international2022

Industrial ecological efficiency of cities in the Yellow River Basin in the background of China's economic transformation: spatial-temporal characteristics and influencing factors.

Chengzhen Song, Guanwen Yin, Zhilin Lu, Yanbin Chen

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. The Impact of Environmental Regulations on Pollution and Carbon Reduction in the Yellow River Basin, China.International journal of environmental research and public health · 2023
    Article
  6. Article
  7. Article
  8. 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.

Chengzhen SongCollege of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
Guanwen YinCollege of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
Zhilin LuCollege of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
Yanbin ChenCollege of Geography and Environment, Shandong Normal University, Jinan, 250358, China. ychen@sdnu.edu.cn.ORCID http://orcid.org/0000-0001-6259-595X

Funding

National Natural Science Foundation of China 41701177
6 · The paper itself

Abstract

At present, China's economic development has entered a "new normal." Exploring industrial ecological efficiency (IEE) in the background of economic transformation is of great significance to promote China's industrial transformation and upgrading and achieving high-quality economic development. Based on the super-efficiency DEA model, this study evaluated the IEE of cities in the Yellow River Basin from 2008 to 2017. Exploratory spatial data analysis methods were used to explore the spatial-temporal evolutionary characteristics, and a panel regression model was established to explore the influencing factors of IEE. The research results showed that the IEE in the Yellow River Basin exhibited an elongated S-shaped evolutionary trend from 2008 to 2017, and the mean IEE of cities presented a trend, whereby Yellow River Basin's regions could be ranked in the following order: lower reaches > middle reaches > upper reaches. There was significant spatial autocorrelation of the IEE in the Yellow River Basin, and the hot and cold spots showed an obvious "spatial clubs" phenomenon. The results of panel regression show that the influence factors of IEE in the Yellow River Basin showed spatial heterogeneity in their effect.

Indexed as

Economic DevelopmentRiversChinaCitiesEfficiencyIndustryEconomic transformationIndustrial ecological efficiencySuper-efficiency DEA modelYellow River Basin

Identifiers

PMID34403058

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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.