Evidence map›Paper›PMID 42173977›Full record

ArticleScientific reports2026

Multi-scenario simulation of spatiotemporal changes of land use and ecosystem services in the plateau mountain city.

Yuanyuan Zhang, Kouqiang Zhang, Shu'na Wei

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

3 authors.

Yuanyuan ZhangSchool of Management, Xi'an Polytechnic University, Xi'an, 710600, China.
Kouqiang ZhangSchool of Logistics and Management Engineering, Yunnan University of Finance and Economics, Kunming, 650221, China. zhangkouqiang@163.com.
Shu'na WeiBusiness School, Yunnan University of Finance and Economics, Kunming, 650221, China.

Funding

the National Natural Science Foundation of China 42062020the Western Program of National Social Science Fund of China 22XJY024
6 · The paper itself

Abstract

The ecosystem of plateau mountain cities is highly vulnerable to damage and possesses limited ecological resilience. Influenced by human activities, they face prominent issues such as ecosystem degradation. Scenario-based analysis of land use and ecosystem services (ESs) is therefore essential for ensuring ecological sustainability in such regions, taking Kunming, a typical plateau mountain city, as the study area. We employed an integrated modeling approach that combined Markov-FLUS and InVEST to examine water yield, soil conservation, carbon storage, and habitat quality under different land use scenarios from 2030 to 2050. Trade-offs/synergies among ESs were examined through Spearman correlation analysis. Results demonstrate that: (1) Under the natural development scenario (NDS), construction land will expand substantially. Under the cropland protection scenario (CPS), construction land will expand slightly, and cropland will increase. Under the ecological protection scenario (EPS), forest land and grassland will increase, while construction land will remain almost unchanged. (2) Under NDS and CPS, water yield will increase, mainly in the metropolitan center of Kunming and its adjacent areas. However, soil conservation, carbon storage, and habitat quality will decrease in these areas. Under EPS, water yield will decrease, while soil conservation, carbon storage, and habitat quality will increase. (3) There will be a trade-off enhancement of ESs in Kunming under NDS, while synergies will be enhanced under CPS and EPS. This study provides a scientific foundation for the adjustment of land use structure, the optimization of ecosystems, and sustainable urban development in cities with similar plateau mountain characteristics.

Indexed as

Ecosystem servicesInVEST modelLand use simulationMarkov-FLUS modelPlateau mountain cityTrade-offs/synergies

Identifiers

PMID42173977
PMCPMC13407852

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LicenceCC BY-NC-ND
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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.