宁夏区域发展对环境胁迫的时空演变及驱动机制分析

doi:10.12118/j.issn.1000-6060.2023.318

摘要/Abstract

摘要：

区域发展引发的环境胁迫已经成为制约黄河流域高质量发展的重要限制性因素。宁夏回族自治区位于黄河上游地区，生态地位十分重要。以宁夏回族自治区为研究区，构建环境胁迫评价指标体系，运用熵权法对2012—2020年宁夏各县域的环境胁迫指数进行测度，并系统分析环境胁迫指数的空间相关性，在此基础上，借助GWR模型对影响环境胁迫空间分异的因素进行识别。结果表明：（1）虽然宁夏各县域环境胁迫指数的平均值由0.56580降至0.49762，但是环境胁迫指数的最小值由0.056359增至0.091005，最大值也仅由0.948896降至0.911162，区域发展对环境造成的胁迫压力仍然较重，表现为整体环境质量有所改善，但局部环境胁迫压力依然趋紧的特点。（2）环境胁迫指数的空间分布差异明显，较高环境胁迫等级和高环境胁迫等级的县域主要分布在经济发展水平较高、资源相对较丰富的宁夏北部川区，而低环境胁迫等级和较低环境胁迫等级的县域主要分布在社会经济发展相对滞后的宁夏南部山区。Moran’s I指数和Getis-Ord G*指数分析表明，环境胁迫指数的空间聚集性在逐渐增强，且其强度和冷热点区的分布与区域经济社会发展水平存在相关性。（3）人口因素、城镇化因素和能源消耗因素对环境胁迫具有正相关性，产业结构因素和土地利用因素对环境胁迫具有负相关性。为此，宁夏应该有序促进人口在空间合理分布，加快实施新型城镇化战略，对高耗能高污染产业进行改造升级，推进产业结构升级和土地利用结构优化。

关键词: 环境胁迫, 时空分异, 区域发展, 宁夏

Abstract:

Environmental stress caused by regional development has emerged as a critical impediment to the high-quality development of the Yellow River Basin. The Ningxia Hui Autonomous Region, located in the upper reaches of the Yellow River, holds significant ecological status. This study focuses on the Ningxia Hui Autonomous Region, establishing an environmental stress evaluation index system and utilizing the entropy method to systematically calculate the environmental stress index (ESI) across various counties in Ningxia. The results show that during 2012—2020, the minimum ESI value in the county level of Ningxia increased from 0.056359 to 0.091005, the maximum value decreased from 0.948896 to 0.911162, and the average ESI value in the county level of Ningxia decreased from 0.56580 to 0.49762. Overall, Ningxia’s environmental stress level has improved, and the local environmental stress is still tight. Moran’s I index analysis showed a significant spatial correlation between environmental stress during 2012—2020, and the spatial spillover effect of regional development on environmental stress increased, with the spatial aggregation effect becoming more obvious. In addition, the Getis-Ord G* index analysis demonstrated that the distribution of hot and cold spots of environmental stress correlated with economic and social development levels. This paper selected a geographically weighted regression model that could better reflect the change in variable regression coefficient with the trend of geographical location for analysis to identify factors affecting the spatial distribution of environmental stress in Ningxia. The results show that population, urbanization, and energy consumption positively affect environmental stress, while industrial structure and land use have negative effects. Therefore, Ningxia should constantly improve population quality, orderly promote the rational distribution of the population in space, implement a new urbanization strategy, and improve urbanization quality. In addition, Ningxia should make technological improvements to energy-intensive and high-polluting industries to reduce pollutant discharge, accelerate the upgrading of the industrial structure, optimize the land use structure, and continuously raise resource utilization efficiency.

Key words: environmental stress, spatiotemporal differentiation, regional development, Ningxia

马明德, 李俊杰, 薛晨皓. 宁夏区域发展对环境胁迫的时空演变及驱动机制分析[J]. 干旱区地理, 2024, 47(6): 1061-1072.

MA Mingde, LI Junjie, XUE Chenhao. Spatiotemporal evolution and driving mechanism of environmental stress of regional development in Ningxia Hui Autonomous Region[J]. Arid Land Geography, 2024, 47(6): 1061-1072.

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年份	Moran’s I	Z得分	P值
2012	0.267461	2.049678	0.040396
2020	0.304817	2.287351	0.022175

变量	2012年					2020年
变量	系数	标准差	T统计量	P值	VIF	系数	标准差	T统计量	P值	VIF
截距	0.29479	0.14959	1.97072	0.11945	-	0.11934	0.20892	0.57122	0.59654	-
pop	2.11593	2.26502	0.93418	0.25581	2.18531	1.29175	2.16479	0.59671	0.48043	2.37403
ter	-0.10233	0.42182	-0.24260	0.80568	2.96175	-0.10370	0.35312	-0.29367	0.81930	2.91962
urb	0.30963	0.14052	2.20354	0.00183	1.32802	0.42767	0.22280	1.91956	0.02651	1.71465
luc	-0.63028	0.21296	-2.95962	0.00006	1.24904	-0.07109	0.18598	-0.38226	0.48459	1.10865
eng	0.10259	0.02777	3.69467	0.00159	2.22788	0.08271	0.02877	2.87477	0.02260	2.27814

模型参数	2012年			2020年
模型参数	R²	调整R²	AICc	R²	调整R²	AICc
OLS	0.79556	0.73167	-9.86242	0.67916	0.57890	-5.59402
GWR	0.79578	0.73176	-9.85740	0.69508	0.58875	-5.64189