Evidence map›Paper›PMID 34770128›Full record

ArticleInternational journal of environmental research and public health2021

Does Coal Mining Have Effects on Land Use Changes in a Coal Resource-Based City? Evidence from Huaibei City on the North China Plain.

Jing Guan, Peng Yu

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. 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
–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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Jing GuanSchool of Geography and Tourism, Anhui Normal University, Wuhu 241002, China.
Peng YuSchool of Tourism and Cuisine, Yangzhou University, Yangzhou 225127, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Continuous coal mining results in dramatic regional land use change, and significantly influences the sustainable development of coal resource-based cities. Present studies pay little attention to the characteristics and regularities of land use change in coal resource-based cities, caused by underground coal mining in high groundwater areas. Based on the Landsat remote sensing images of 1999, 2000, 2010, and 2018 of Huaibei City, a typical coal resource-based city of a high ground water area on the North China Plain, this paper applies the dynamic degree and transition matrix of land use to analyze the land use change characteristics, and identify the regularity between land use type and coal mining production in this coal resource-based city. Results show that the land use change in the research area presents an overall characteristic of a constant increase in water area, urban construction land, and rural settlement land, and a continuous decrease in cultivated land. Cultivated land is converted into a water area, urban construction land, and rural settlement land, and rural settlement land and cultivated land are converted bidirectionally. The land use change in this coal resource-based city demonstrates significant reliance on coal resources, and coal mining is significantly related to the area of cultivated land, water area, and rural settlement land, which demonstrates that continuous large-scale coal mining results in damage to cultivated land, a decrease in rural settlement land, and an increase in water area. The research result contributes to the sustainable land use of coal resource-based cities.

Indexed as

Coal MiningGroundwaterChinaCitiesCoalMiningCoalcoal mininghigh ground water arealand use changeresource-based city

Identifiers

PMID34770128
PMCPMC8583168

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.