Evidence map›Paper›PMID 33076427›Full record

ArticleInternational journal of environmental research and public health2020

Dynamic Changes, Spatiotemporal Differences and Factors Influencing the Urban Eco-Efficiency in the Lower Reaches of the Yellow River.

Yu Zhang, Wenliang Geng, Pengyan Zhang, Erling Li, Tianqi Rong, Ying Liu, Jingwen Shao, Hao Chang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

8 authors.

Yu ZhangResearch Center of Regional Development and Planning, College of Environment and Planning, Institute of Agriculture and Rural Sustainable Development, Henan University, Kaifeng 475004, China.
Wenliang GengResearch Center of Regional Development and Planning, College of Environment and Planning, Institute of Agriculture and Rural Sustainable Development, Henan University, Kaifeng 475004, China.
Pengyan ZhangResearch Center of Regional Development and Planning, College of Environment and Planning, Institute of Agriculture and Rural Sustainable Development, Henan University, Kaifeng 475004, China.
Erling LiResearch Center of Regional Development and Planning, College of Environment and Planning, Institute of Agriculture and Rural Sustainable Development, Henan University, Kaifeng 475004, China.
Tianqi RongResearch Center of Regional Development and Planning, College of Environment and Planning, Institute of Agriculture and Rural Sustainable Development, Henan University, Kaifeng 475004, China.
Ying LiuResearch Center of Regional Development and Planning, College of Environment and Planning, Institute of Agriculture and Rural Sustainable Development, Henan University, Kaifeng 475004, China.
Jingwen ShaoResearch Center of Regional Development and Planning, College of Environment and Planning, Institute of Agriculture and Rural Sustainable Development, Henan University, Kaifeng 475004, China.
Hao ChangResearch Center of Regional Development and Planning, College of Environment and Planning, Institute of Agriculture and Rural Sustainable Development, Henan University, Kaifeng 475004, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The measurement of eco-efficiency is an important tool to evaluate the level of urban sustainable development. Therefore; improving urban eco-efficiency in the lower reaches of the Yellow River ensures the implementation of ecological protection and high-quality development strategies in the Yellow River Basin. In this study; the dynamic changes of urban eco-efficiency and spatiotemporal differences in the lower reaches of the Yellow River were investigated using the Super-SBM (Super-Slack measure model) model with undesirable outputs and standard deviation ellipse. The STIRPAT (Stochastic Impacts by Regression Population; Affluence and Technology) model was introduced to analyze the factors affecting the change in urban eco-efficiency. The results showed that the overall urban eco-efficiency in the lower reaches of the Yellow River has not reached the optimal level. The overall eco-efficiency in the lower reaches of the Yellow River in Shandong Province was higher than that in Henan Province but the gap is narrowing. The spatial differentiation of urban eco-efficiency in the lower reaches of the Yellow River showed the following trends: "blooming in the middle and reverse development at both ends" in the high-value area and gradual decrease in the low-value area. From 2007 to 2018; a direction was notable with respect to the development of urban eco-efficiency in the lower reaches of the Yellow River; with the centripetal force weakening. Although the mean center of urban eco-efficiency located in Shandong Province; it notably shifted to the west during the study period. In terms of driving factors; affluence and technological progress play positive roles in driving eco-efficiency; while investment intensity; industrial structure; and foreign investment intensity hindered the optimization and improvement of urban eco-efficiency in the lower reaches of the Yellow River. The results of this study show that urban eco-efficiency in the lower reaches of the Yellow River is improving; but the regional coordination is poor. The main methods promoting the sustainable development in the study area include changing the mode of extensive investments and the introduction of foreign capital; which improve the energy efficiency and promote faster and better economic development.

Indexed as

Economic DevelopmentRiversChinaEfficiencyIndustrySustainable Developmenteco-efficiencylower reaches of Yellow RiverSTIRPATSuper-SBM

Identifiers

PMID33076427
PMCPMC7602535

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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.