Optimizing ecological security models in typical arid areas: integrated assessment and sustainable management

• A multidimensional ecological security assessment model is developed. • Hierarchical corridor management improves ecological development efficiency. • An optimized ecological pattern of “three axes, three zones, multiple cores” is proposed. • Provides a scientific basis for sustainable regional ecological development decisions. Amid ongoing climate warming and rapid urbanization, arid and semi-arid ecosystems face heightened threats and degradation. This study highlights the urgent need to construct ecological security pattern (ESP) that preserves critical ecosystem functions and strengthen ecological security in Northwest China. We integrated ecological security assessments—utilizing the Ecological Risk Index (ERI), Ecosystem Health (EH), and Comprehensive Ecosystem Services Index (CESI)—with advanced analytical techniques, including Morphological Spatial Pattern Analysis (MSPA), the entropy weight method, Minimum Cumulative Resistance (MCR), and the Gravity Model (GM). Our analysis spanned the period from 1990 to 2020, enabling the construction and optimization of ESPs for the region. Additionally, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) and Geographically and Temporally Weighted Regression (GTWR) to analyze the driving factors and pathways that influence ecological security. Key findings reveal that: (1) Ecological security zones are predominantly concentrated in the southeastern part of the study area, while ecological mitigation zones are distributed in southern Xinjiang and central Qinghai. These areas, particularly around the Tarim, Turpan–Hami, and Qaidam Basins, represent critical regions for ecological restoration due to their vulnerability to desertification and habitat degradation; (2) Northwest China demonstrates a unique ecological development framework, characterized by “three axes, three regions, and multiple nuclei” which reflects the spatial distribution of ecological resources and development opportunities; (3) Meteorological conditions and human activities are the predominant factors impacting ecological security, with land use intensity identified as the primary driver. The findings may help guide urban planning decisions and regional ecological conservation strategies.

Authors

Institutions

Publication Details

Journal
Ecological Indicators
Published
2025-06-19
DOI
https://doi.org/10.1016/j.ecolind.2025.113768
Citations
9
Primary Topic
Sustainable Agricultural Systems Analysis
Type
article
Field-Weighted Citation Impact
7.74
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing ecological security models in typical arid areas: integrated assessment and sustainable management

Binbin Lu, Chuqiao Han, Linzhi Han, Zhe Wang et al.
9 citations
Ecological Indicators
Sustainable Agricultural Systems Analysis
7.74
article

Optimizing ecological security models in typical arid areas: integrated assessment and sustainable management

Binbin Lu, Chuqiao Han, Linzhi Han, Zhe Wang, Jianghua Zheng, Wenjie Yu, Juan Yang, Jiale Wu
article en
9 citations

Abstract

• A multidimensional ecological security assessment model is developed. • Hierarchical corridor management improves ecological development efficiency. • An optimized ecological pattern of “three axes, three zones, multiple cores” is proposed. • Provides a scientific basis for sustainable regional ecological development decisions. Amid ongoing climate warming and rapid urbanization, arid and semi-arid ecosystems face heightened threats and degradation. This study highlights the urgent need to construct ecological security pattern (ESP) that preserves critical ecosystem functions and strengthen ecological security in Northwest China. We integrated ecological security assessments—utilizing the Ecological Risk Index (ERI), Ecosystem Health (EH), and Comprehensive Ecosystem Services Index (CESI)—with advanced analytical techniques, including Morphological Spatial Pattern Analysis (MSPA), the entropy weight method, Minimum Cumulative Resistance (MCR), and the Gravity Model (GM). Our analysis spanned the period from 1990 to 2020, enabling the construction and optimization of ESPs for the region. Additionally, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) and Geographically and Temporally Weighted Regression (GTWR) to analyze the driving factors and pathways that influence ecological security. Key findings reveal that: (1) Ecological security zones are predominantly concentrated in the southeastern part of the study area, while ecological mitigation zones are distributed in southern Xinjiang and central Qinghai. These areas, particularly around the Tarim, Turpan–Hami, and Qaidam Basins, represent critical regions for ecological restoration due to their vulnerability to desertification and habitat degradation; (2) Northwest China demonstrates a unique ecological development framework, characterized by “three axes, three regions, and multiple nuclei” which reflects the spatial distribution of ecological resources and development opportunities; (3) Meteorological conditions and human activities are the predominant factors impacting ecological security, with land use intensity identified as the primary driver. The findings may help guide urban planning decisions and regional ecological conservation strategies.

Ecological IndicatorsVol. 177
Wuhan University (CN), State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN), Xinjiang University (CN)
Life in Land
Openalex Percentile: Top 2%
Sustainable Agricultural Systems Analysis
7.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.