新疆风电场对生态环境的影响特征及其驱动因素分析

doi:10.12118/j.issn.1000-6060.2025.743

Abstract

Abstract:

Against the backdrop of the “dual carbon” strategy, wind power, a leading clean renewable energy, is rapidly expanding into arid regions with abundant resources but fragile ecosystems. Xinjiang, one of China’s most wind-resource-rich regions, urgently requires a quantitative assessment of the potential ecological effects of large-scale wind power development. Taking wind farms in Xinjiang as the study object, this work integrates moderate resolution imaging spectroradiometer data from 2000 to 2023 with meteorological, topographic, and land use data. Using the remote sensing ecological index (RSEI) and the geodetector model, we systematically investigate the spatial distribution of wind farms, temporal trends in ecological environmental changes, and their driving factors. The results indicate that (1) Wind farms in Xinjiang exhibit a clustered spatial distribution, mainly concentrated in low-to-medium altitude regions such as Hami City, Turpan City, and Altay Prefecture. Site selection is primarily constrained by terrain flatness, land use types, and wind energy resources. (2) From 2000 to 2023, approximately 81.8% of wind farm areas show a declining trend in RSEI. This suggests that although overall ecological quality remains relatively stable, localized degradation is evident. (3) Geodetector analysis reveals that mean annual precipitation (q=0.4522) and elevation (q=0.3949) are the dominant factors influencing ecological changes in wind farm areas. Interactions among factors such as mean annual temperature, slope, and wind farm operational duration are significant, with 89.3% of factor combinations exhibiting nonlinear enhancement effects, highlighting the multi-factor coupling characteristics of ecological effects. This study helps fill research gaps regarding the ecological effects of wind farms in Xinjiang. It provides a scientific basis for the coordinated management of wind power development and ecological protection, as well as for ecological assessments of renewable energy in arid regions.

Key words: wind farms, spatiotemporal evolution, remote sensing ecological index (RSEI), geographical detector, Xinjiang

JIAO Shitang, YAN Yang, ZHENG Jianghua, HE Ying, Babierjiang DILIXIATI, WEI Jianxin, LIU Junhao, SHE Yongrui. Impact characteristics and driving factors of wind farms on the ecological environment in Xinjiang[J].Arid Land Geography, 2026, 49(9): 1840-1854.

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数据	名称	年份	时间分辨率	原始空间分辨率	来源
地表温度	地表温度8 d合成产品（MOD11A2）	2000—2025	8 d合成	1 km	谷歌地球引擎（GEE）（https://code.earthengine.google.com/）
植被指数	植被指数16 d合成产品（MOD13A1）	2000—2025	16 d合成	500 m	美国国家航空航天局（NASA）（https://www.earthdata.nasa.gov/）
短波红外波段	地表反射率8 d合成产品（MOD09A1）	2000—2025	8 d合成	500 m	美国国家航空航天局（NASA）（https://www.earthdata.nasa.gov/）
气温	中国1 km分辨率逐月平均气温数据集	1901—2024	逐月	1 km	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）
降水	中国1 km分辨率逐月降水量数据集	1901—2024	逐月	1 km	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）
风速	多源数据融合的中国高分辨多要素气象驱动产品（ChinaMet）	1980—2024	逐月	0.1°	国家冰川冻土沙漠科学数据中心（https://www.ncdc.ac.cn/）
海拔、坡度、地形起伏度	航天飞机雷达地形测绘任务数字高程模型（SRTM DEM）	-	-	30 m	美国国家航空航天局（NASA）（https://www.earthdata.nasa.gov/）
土地利用类型	30 m全球土地覆盖动态数据集（GLC_FCS30D）	-	-	30 m	Zenodo（https://zenodo.org/records）
Sentinel-2	哥白尼Sentinel-2地表反射率产品（COPERNICUS_S2_SR_HARMONIZED）	2023	a	10 m	谷歌地球引擎（GEE）（https://code.earthengine.google.com/）
10 m风速	多年平均风速产品	-	-	10 m	全球风能地图（https://globalwindatlas.info/）

判断依据	交互作用类型
q(x₁∩x₂)<Min[q(x₁),q(x₂)]	非线性减弱
Min[q(x₁),q(x₂)]<q(x₁∩x₂)<Max[q(x₁),q(x₂)]	单因子非线性减弱
q(x₁∩x₂)>Max[q(x₁),q(x₂)]	双因子增强
q(x₁∩x₂)=q(x₁)+q(x₂)	独立
q(x₁∩x₂)>q(x₁)+q(x₂)	非线性增强

类别	ΔRSEI范围	数量占比/%
严重退化	ΔRSEI<-0.05	3.0
轻微退化	-0.05<ΔRSEI<0.00	78.8
基本稳定	ΔRSEI=0	0.0
轻微改善	0.00<ΔRSEI<0.05	18.2
显著改善	ΔRSEI>0.05	0.0

Impact characteristics and driving factors of wind farms on the ecological environment in Xinjiang

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