基于PLS-SEM模型的山西省生态环境质量时空变化及驱动因素

doi:10.13287/j.1001-9332.202603.023

应用生态学报 ›› 2026, Vol. 37 ›› Issue (3): 876-888.doi: 10.13287/j.1001-9332.202603.023

基于PLS-SEM模型的山西省生态环境质量时空变化及驱动因素

毕旭¹, 李建¹, 史凯龙¹, 付勇勇¹, 张宇硕², 史来亮^3,4, 班凤梅^1*

¹山西财经大学资源环境学院, 太原 030006;
²山西财经大学文化旅游与新闻艺术学院(人文教育中心), 太原 030006;
³环境遥感应用山西省重点实验室, 山西运城 044000;
⁴山西省地球物理化学勘查院有限公司, 山西运城 044000

收稿日期:2025-08-26 接受日期:2026-01-10 出版日期:2026-03-18 发布日期:2026-09-18
通讯作者: *E-mail: banfm@163.com
作者简介:毕旭, 女, 1987年生, 副教授, 博士。主要从事土地利用与生态响应研究。E-mail: 20181078@sxufe.edu.cn
基金资助:
山西省中央引导地方科技发展资金项目(YDZJSX2024D066)、国家自然科学基金项目(42107498,42401376)和环境遥感应用山西省重点实验室开放课题(202503)

Spatiotemporal variations and driving factors of ecological environmental quality in Shanxi Province based on the PLS-SEM model

BI Xu¹, LI Jian¹, SHI Kailong¹, FU Yongyong¹, ZHANG Yushuo², SHI Lailiang^3,4, BAN Fengmei^1*

¹College of Resource and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China;
²School of Culture Tourism and Journalism Arts (Humanities Education Center), Shanxi University of Finance and Economics, Taiyuan 030006, China;
³Shanxi Key Laboratory of Environmental Remote Sensing Application, Yuncheng 044000, Shanxi, China;
⁴Shanxi Geophysical and Geochemical Exploration Institute Co., Ltd., Yuncheng 044000, Shanxi, China

Received:2025-08-26 Accepted:2026-01-10 Online:2026-03-18 Published:2026-09-18

摘要/Abstract

摘要： 山西省位于中国黄土高原东部,自然环境空间异质性显著,生态环境脆弱。了解山西省生态环境质量的时空分布格局及其驱动因素,对促进生态保护与经济社会协调发展至关重要。本研究基于Google Earth Engine平台构建遥感生态指数(RSEI),分析了2000—2020年间山西省生态环境质量的时空变化格局;采用Theil-Sen中位数趋势分析、Mann-Kendall检验与Hurst指数等方法评估RSEI的变化趋势和可持续性,并引入地理探测器和偏最小二乘法结构方程模型(PLS-SEM)及中介分析,探讨各因子间的相互作用及其对RSEI的直接和间接效应。结果表明:2000—2020年间,研究区RSEI均值波动在0.44~0.68,整体呈上升趋势,空间分布上表现为山区优于盆地、东南优于西北;研究区生态环境质量呈现显著改善趋势,改善区域占比达81.6%,但Hurst指数显示未来变化趋势存在一定的不确定性和潜在的逆转风险。地理探测器分析表明,海拔、潜在蒸散发和土地利用强度是RSEI空间分异的主要因素,且多因子交互作用的解释力显著强于单因子;PLS-SEM与中介分析表明,潜在蒸散发通过加剧气候胁迫削弱了降水对RSEI的正向作用,夜间灯光指数强化了城镇化和资源开发对RSEI的负向复合作用。本研究深化了生态环境质量演变机制的理解,可为资源型地区的生态治理和可持续转型提供方法支撑和政策借鉴。

关键词: 遥感生态指数, 驱动因素, 地理探测器, PLS-SEM模型

Abstract: Shanxi Province is located in the eastern part of the Loess Plateau in China, characterized by natural geographic spatial heterogeneity and fragile ecological environments. Understanding the spatiotemporal distribution variations of ecological environment quality and their driving factors is crucial for promoting ecological conservation and coordinated socio-economic development in Shanxi. We constructed a remote sensing ecological index (RSEI) on the Google Earth Engine platform and analyzed the spatiotemporal variations in ecological environment quality in Shanxi from 2000 to 2020. Methods such as Theil-Sen median trend analysis, Mann-Kendall test, and Hurst index were employed to assess the trends and sustainability of RSEI changes. Additionally, geographic detectors, partial least squares structural equation modeling (PLS-SEM), and mediation analysis were introduced to explore interactions among factors and their direct and indirect effects on RSEI. The results showed that the mean RSEI fluctuated between 0.44 and 0.68 from 2000 to 2020, showing an overall upward trend with spatial distribution patterns of mountainous areas outperforming basins and southeastern regions outperforming northwestern ones. The ecological environment quality exhibited a significant improvement trend, with 81.6% of the area showing enhancement. The Hurst index suggested uncertainties and potential reversal risks in future trends. Geographic detector analysis revealed that elevation, potential evapotranspiration, and land use intensity were the primary factors driving spatial differentiation in RSEI, with the explanatory power of multi-factor interactions significantly exceeding that of single-factor. PLS-SEM and mediation analysis demonstrated that potential evapotranspiration weakened the positive effect of precipitation on RSEI by intensifying climatic stress, while the nighttime light index amplified the negative combined effects of urbanization and resource exploitation on RSEI. Our results deepened the understanding of ecological environment quality evolution mechanisms and could provide methodological support and policy insights for ecological governance and sustainable transformation in resource-dependent regions.

Key words: remote sensing ecological index, driving factor, Geodetector, PLS-SEM model

毕旭, 李建, 史凯龙, 付勇勇, 张宇硕, 史来亮, 班凤梅. 基于PLS-SEM模型的山西省生态环境质量时空变化及驱动因素[J]. 应用生态学报, 2026, 37(3): 876-888.

BI Xu, LI Jian, SHI Kailong, FU Yongyong, ZHANG Yushuo, SHI Lailiang, BAN Fengmei. Spatiotemporal variations and driving factors of ecological environmental quality in Shanxi Province based on the PLS-SEM model[J]. Chinese Journal of Applied Ecology, 2026, 37(3): 876-888.

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基于PLS-SEM模型的山西省生态环境质量时空变化及驱动因素

Spatiotemporal variations and driving factors of ecological environmental quality in Shanxi Province based on the PLS-SEM model

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