Evidence map›Paper›PMID 41963411›Full record

ArticleScientific reports2026

Ranking land degradation drivers in eastern Inner Mongolia using partial order theory and Hasse diagram analysis.

Kaixin Liu, Batunacun, Yong Mei, Yu Feng, Ruifang Guo, Chang An, Yaxin Wang, Bin Sun

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.

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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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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Kaixin LiuCollege of Geographical Science, Inner Mongolia Normal University, Hohhot, 010022, China.
BatunacunCollege of Geographical Science, Inner Mongolia Normal University, Hohhot, 010022, China. 20210041@imnu.edu.cn.
Yong MeiCollege of Geographical Science, Inner Mongolia Normal University, Hohhot, 010022, China.
Yu FengCollege of Geographical Science, Inner Mongolia Normal University, Hohhot, 010022, China.
Ruifang GuoCollege of Geographical Science, Inner Mongolia Normal University, Hohhot, 010022, China.
Chang AnCollege of Geographical Science, Inner Mongolia Normal University, Hohhot, 010022, China.
Yaxin WangInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China.
Bin SunInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China.

Funding

2023 Ministry of Human Resources and Social Security's Talent Sponsorship Program for Studying Abroad No. Grant No2023 Young Scientific and Technological Talent Development Program (Young Scientific Talent) No. njyt23017Central Guidance Funds for Local science and technology Development projects: Intelligent Ecological Decision-Making: Integrating AI and Partial Order Theory for Land Use Optimization No. 2025ZY0049First-Class Discipline Scientific Research Special Project No. ylxkzx-nsd-028National Natural Science Foundation of China Regional Fund No. 42261048National Natural Science Foundation of China Youth Fund No. 42301362Open Fund Project of State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences No. Grant NoScientific Research Innovation Capability Support Project for Young Faculty No. Grant No
6 · The paper itself

Abstract

Land degradation (LD) poses a major challenge to global sustainable development, with the attainment of land degradation neutrality recognised as a key indicator of Sustainable Development Goal 15.3 (SDG 15.3). This goal focuses on combating desertification, restoring degraded land and soil, and including land affected by desertification, drought, and floods, with the aim of creating a land degradation-neutral world by 2030. Eastern Inner Mongolia (EIM), a typical agro-pastoral transitional zone, has experienced increasingly severe LD in recent decades. Identifying its dominant drivers is essential for improving ecological governance and land use management capacity. This study employed the Global 30 m Land-Cover Dynamics Monitoring Product (GLC_FCS30D) to characterise the spatio-temporal patterns of LD across three periods: 1990-2000, 2000-2010, and 2010-2020. Twelve drivers were identified and categorised into four groups: natural, human activities, economic, and urbanisation. Using Partial Order Theory and the Hasse Diagram Technique, the influence intensity of each driver group was systematically evaluated and ranked for each period. The combination of POT and the HDT enables transparent, interpretable, and reproducible ranking of multiple drivers while accounting for nonlinear, hierarchical, and interdependent relationships, offering advantages over conventional statistical or machine learning methods. The results showed that: (1) from 1990 to 2020, approximately 23% of EIM experienced land cover change, with degradation rates of 0.78%, 0.36%, and 0.41% for the three periods, respectively, and restoration rates of 0.71%, 0.36%, and 0.29%, respectively, the net land degradation and restoration areas were 5.83 × 10

Indexed as

Driving factorsHasse diagramInner MongoliaLand degradationPartial order theory

Identifiers

PMID41963411
PMCPMC13222888

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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.