Evidence map›Paper›PMID 40830628›Full record

ArticleScientific reports2025

Response mechanism of ecological environment quality variation to multifactor coupling at Spatiotemporal scales.

Xi Chen, Yuqing Fang, Yufeng He, Wen Dai, Ling Jiang, Hong Wei, Dong Wang, Chun Wang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

8 authors.

Xi ChenSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, 239000, China.
Yuqing FangSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, 239000, China.
Yufeng HeKey Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, 330022, China.
Wen DaiSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Ling JiangSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, 239000, China. ling.jiang@chzu.edu.cn.
Hong WeiSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, 239000, China.
Dong WangSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, 239000, China.
Chun WangSchool of Geographic Information and Tourism, Chuzhou University, Chuzhou, 239000, China.

Funding

Excellent Scientific Research and Innovation Team of Universities in Anhui Province 2023AH010071Foundation for Top Talents in Disciplines of Anhui Universities gxbjZD2022069Key Projects of Natural Science Research Projects in Colleges and Universities of Anhui Province 2022AH051103; 2023AH040217; 2023AH040219National Natural Science Foundation 42407093; 42401506Open Research Fund of Key Laboratory of Drinking Water Source Protection of the Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences 2024YYSYKFYB06Science Research Projects of Chuzhou University 2022qd004
6 · The paper itself

Abstract

The remote sensing ecological index (RSEI) serves as a pivotal metric for evaluating the regional ecological environment quality (EEQ). Nevertheless, accurately quantifying and identifying its response to multi-factor coupling remain a considerable challenge. Therefore, in this study, an improved Remote Sensing Ecological Index with Local Adaptability (RSEILA) method was employed to analyze the EEQ's spatiotemporal distribution pattern using the Google Earth Engine platform. Then, the Geodetector model was employed to identify the driving mechanisms responsible for EEQ variation under multi-factor coupling. The results show the following: (1) Over the past two decades, the EEQ has consistently achieved moderate to good levels and has exhibited an overall trend of improvement. (2) At the spatial scale, the distribution pattern of the RSEILA in Anhui Province was characterized by high values in the south and low values in the north, which was closely associated with the natural geographic conditions and land use patterns. (3) Multi-factor coupling exerted a significant spatiotemporal scale effect on the drivers of EEQ levels. At the temporal scale, EEQ levels were predominantly influenced by policy measures, while spatially topography and human activities were identified as the primary drivers of the EEQ changes. The findings of this research provide a theoretical foundation for enhancement and administration of the EEQ in Anhui Province and analogous regions.

Indexed as

Driving mechanism analysisEcological environment qualityGeodetector modelRemote sensing ecological indexSpatiotemporal evolution

Identifiers

PMID40830628
PMCPMC12365016

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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.