Evidence map›Paper›PMID 40215255›Full record

ArticlePloS one2025

Spatio-temporal dynamics and trend forecasting of urban green high-quality development: An examination of 287 cities in China.

Tonghui Yu, Shanshan Jia, Xufeng Cui

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Tonghui YuSchool of Business, Xinyang Normal University, Xinyang, Henan, China.ORCID 0009-0004-8945-1787
Shanshan JiaSchool of Business, Xinyang Normal University, Xinyang, Henan, China.
Xufeng CuiSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan, Hubei, China.ORCID 0000-0002-8016-5192

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Facing the dual challenges of economic growth and environmental conservation, advancing urban green high-quality development (UGHQD) is crucial for sustainable urban development. Rooted in the concept of UGHQD, this study develops a multidimensional evaluation indicator system encompassing economic development, social progress, and ecological civilization. By applying spatial autocorrelation analysis, cold and hot spots analysis, standard deviation ellipse, and Kernel density estimation, it examines the spatial distribution and dynamic evolution of UGHQD across China and its four major regions (Eastern, Central, Western, and Northeastern) from 2003 to 2020. It also forecasts the trajectory of UGHQD from 2021 to 2025. The research findings indicate: (1) A steady annual increase in the overall level of UGHQD, with a geographic pattern showing high levels in the east, moderate in the center, and low in the west. (2) A spatial agglomeration in UGHQD, predominantly in the eastern region, demonstrating spatio-temporal inertia. (3) Varying degrees of a "right-tail" phenomenon in the UGHQD across China and its four sub-regions, indicating a polarization trend or even a weak multi-polarization trend. (4) A forecast of continuous, steady growth in UGHQD from 2021 to 2025, with the eastern region maintaining its leading position. This study offers insights that enhancing our understanding of the fundamental concepts underlying UGHQD, providing a practical foundation and policy guidance for future collaborative efforts in enhancing urban development quality.

Indexed as

Conservation of Natural ResourcesSustainable DevelopmentUrbanizationChinaCitiesEconomic DevelopmentForecastingHumansSpatio-Temporal Analysis

Identifiers

PMID40215255
PMCPMC11990776

What OpenQuestion holds

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LicenceCC BY
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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.