Evidence map›Paper›PMID 36767204›Full record

ArticleInternational journal of environmental research and public health2023

Exploring Spatio-Temporal Variations of Ecological Risk in the Yellow River Ecological Economic Belt Based on an Improved Landscape Index Method.

Meirui Li, Baolei Zhang, Xiaobo Zhang, Shumin Zhang, Le Yin

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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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3 · Its place in the literature

Who cites it

2 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

5 authors.

Meirui LiCollege of Geography and Environment, Shandong Normal University, Jinan 250014, China.
Baolei ZhangCollege of Geography and Environment, Shandong Normal University, Jinan 250014, China.ORCID 0000-0001-8866-6728
Xiaobo ZhangZaozhuang Municipal Bureau of Natural Resources and Planning, Zaozhuang 277099, China.
Shumin ZhangResearch Institute of Regional Economy, Shandong University of Finance and Economics, Jinan 250014, China.
Le YinCollege of Geography and Environment, Shandong Normal University, Jinan 250014, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intense human activities have led to profound changes in landscape patterns and ecological processes, generating certain ecological risks that seriously threaten human wellbeing. Ecological risk assessment from a landscape perspective has become an important tool for macroecosystem landscape management. This research improves the framework and indices of the ecological risk assessment from a landscape perspective, evaluates the land use pattern and landscape ecological risk dynamics in the Yellow River Ecological Economic Belt (YREEB), analyzes the spatiotemporal variation, and identifies key areas for ecological risk management. The results indicate the following: The main land use types in the region are grassland and cropland, but the area of cropland and grassland decreased during the study period, and with the accelerated urbanization, urban land is the only land use type that continued to increase over the 20-year period. The ecological risk in the YREEB tended to decrease, the area of low ecological risk zones increased, while the area of high ecological risk zones gradually decreased. Most areas are at medium risk level, but the risk in central Qinghai and Gansu is obviously higher, and there is a dispersed distribution of local high- and low-risk zones. A total of 37.7% of the study area is identified as critical area for future risk management, and the potential for increased risk in these areas is high. These results can provide a basis for sustainable development and planning of the landscape and the construction of ecological civilization in ecologically fragile areas.

Indexed as

EcosystemRiversChinaConservation of Natural ResourcesHumansRisk AssessmentSustainable DevelopmentUrbanizationecological risk assessmentlandscape patternspatiotemporal patternYellow River Ecological Economic Belt

Identifiers

PMID36767204
PMCPMC9914419

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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.