Spatiotemporal Variations and Driving Factors of Ecological Sensitivity in the West Qinling Mountains, China, Under the Optimal Scale

Qiqi Zhao, Xuelu Liu (), Yingying Wu, Hongyan Liu, Fei Qu, Miaomiao Zhang and Xiaodan Li
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Qiqi Zhao: College of Resources and Environment, Gansu Agricultural University, Lanzhou 730070, China
Xuelu Liu: College of Resources and Environment, Gansu Agricultural University, Lanzhou 730070, China
Yingying Wu: College of Resources and Environment, Gansu Agricultural University, Lanzhou 730070, China
Hongyan Liu: College of Resources and Environment, Gansu Agricultural University, Lanzhou 730070, China
Fei Qu: College of Resources and Environment, Gansu Agricultural University, Lanzhou 730070, China
Miaomiao Zhang: College of Management, Gansu Agricultural University, Lanzhou 730070, China
Xiaodan Li: College of Management, Gansu Agricultural University, Lanzhou 730070, China

Sustainability, 2024, vol. 16, issue 21, 1-23

Abstract: This study selected the five indicators of soil erosion, climate environment, geological hazards, biodiversity, and human disturbances and uses the entropy weight method to calculate the ecological sensitivity of the West Qinling Mountains from 2000 to 2020. The analysis produced a spatiotemporal distribution of ecological sensitivity over the 20-year period. An equal step size of 500 m was used to progressively increase the spatial scale from 500 m to 6 km. The optimal scale for the spatial differentiation of ecological sensitivity in the West Qinling Mountains was determined by analyzing the characteristics of changes at different scales, response mechanisms, and optimal parameters for geographical detector spatial scale identification. Based on this scale, the change in intensity and pattern and the influencing factors of ecological sensitivity were analyzed. The results show the following: (1) The 5.5 km spatial scale balances the requirements of accuracy, spatial heterogeneity, and data adequacy, making it the optimal scale for analyzing the spatiotemporal variation patterns of ecological sensitivity in the West Qinling Mountains. (2) From 2000 to 2020, the mean ecological sensitivity in the West Qinling Mountains exhibited a decreasing trend, indicating an improvement in the ecological environment. Spatially, the ecological sensitivity of the West Qinling Mountains showed a spatial distribution pattern of “low in the west and high in the east, low in the south and high in the north”. During the study period, the ecological sensitivity in the West Qinling region remained generally stable, with no high-frequency changes observed. (3) Population density is the primary driving factor of spatial differentiation of ecological sensitivity in the West Qinling Mountains, while GDP serves as a secondary factor. Overall, socioeconomic factors have the most significant impact on ecological sensitivity. (4) Over 75% of the ecological sensitivity trends exhibit patterns of perennial unchanged and fluctuating unchanged trends, with areas of fluctuating increase smaller than areas of fluctuating decrease. Regions of perennial high sensitivity are primarily concentrated in the northeastern part of the West Qinling Mountains, while areas with increased fluctuation in ecological sensitivity are mainly located in the western and southern parts of the West Qinling Mountains. Future efforts should focus on these regions.

Keywords: driving factor; ecological sensitivity; spatiotemporal variation; spatial scale; West Qinling Mountains; China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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