关中平原耕地利用生态效率时空演化及驱动因素分析

doi:10.12118/j.issn.1000-6060.2025.372

Abstract

Abstract:

As a key grain-producing region in western China, the Guanzhong Plain requires a critical balance between preserving the nation’s food security and sustainable land use. This study employed county-level panel data from 2013 to 2022 to evaluate the ecological efficiency of cultivated land use using the Super-SBM model incorporating undesirable outputs. Spatial autocorrelation analysis and the Geodetector method were applied to systematically investigate the spatiotemporal evolution of ecological efficiency and its underlying driving mechanisms. The results reveal a distinct “U-shaped” temporal trajectory in ecological efficiency, accompanied by a spatial gradient pattern characterized by higher efficiency in the central region, followed by the west. Meanwhile, the east saw lower efficiency. High-efficiency clusters were primarily concentrated in Xi’an and Xianyang and exhibited a trend of westward diffusion. Meanwhile, Tongchuan and Weinan remained in persistently low-efficiency states. Significant spatial agglomeration was also observed, with high-efficiency clusters expanding southward from Qian County and low-efficiency clusters consistently located in the eastern plain. This spatial heterogeneity of ecological efficiency has been shaped by a synergistic interplay of natural, socioeconomic, and policy-related factors. The influence of variables like annual precipitation and elevation demonstrated dynamic temporal variability, while the positive effects of urbanization rate and per capita disposable income of rural residents revealed a sustained upward trend. The interactions among these multidimensional factors yielded pronounced reinforcing effects on ecological efficiency.

Key words: Guanzhong Plain, cultivated land use, ecological efficiency, Super-SBM model, geographic detector

JIA Yajuan, GAO Jun, ZHANG Xinqi. Spatiotemporal evolution and driving factors of ecological efficiency in cultivated land use in the Guanzhong Plain[J].Arid Land Geography, 2026, 49(4): 830-840.

Figures/Tables 9

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References 26

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变量类型	指标名称	指标释义
投入	耕地	农作物播种总面积/10³ hm²
	劳动力	耕地生产从业人员/10⁴人
	农机	农业机械总动力/kW
	化肥	农用化肥施用折纯量/t
	农药	农药使用量/t
	柴油	农用柴油使用量/t
	农膜	农用塑料薄膜使用量/t
	水资源	有效灌溉面积/10³ hm²
期望产出	农业产值	农业总产值/10⁴元
期望产出	粮食产量	粮食总产量/t
非期望产出	碳排放	化肥、农药等碳排放量/t
非期望产出	面源污染	氮磷农药等流失量/t

类别	变量名称	变量单位	定义与计算方法
自然	年降水量（X₁）	mm	年平均降水量
	高程（X₂）	m	-
	坡度（X₃）	(°)	-
社会	城镇化率（X₄）	%	城镇人口占总人口比例
社会	人口密度（X₅）	人·km^-2	人口数占区域面积比例
经济	农民人均可支配收入（X₆）	元	农村居民人均可支配收入
经济	第一产业占比（X₇）	%	第一产业占地区生产总值比例
政策	财政支农投入强度（X₈）	%	农林水支出占一般公共预算支出

统计结果	2013年	2014年	2015年	2016年	2017年	2018年	2019年	2020年	2021年	2022年
Moran’s I	0.157	0.423	0.368	0.401	0.348	0.194	0.185	0.294	0.217	0.233
z得分	1.749	4.343	3.796	4.116	3.606	2.104	2.015	3.080	2.333	2.485
P值	0.080	0.000	0.000	0.000	0.000	0.035	0.044	0.002	0.020	0.013

驱动因子	2013年		2022年
驱动因子	q值	P值	q值	P值
年降水量	0.412	0.000	0.267	0.000
高程	0.219	0.004	0.243	0.000
坡度	0.177	0.011	0.190	0.008
城镇化率	0.127	0.130	0.172	0.007
人口密度	0.109	0.219	0.096	0.886
农民人均可支配收入	0.104	0.582	0.169	0.046
第一产业占比	0.303	0.000	0.202	0.005
财政支农投入强度	0.157	0.024	0.143	0.082

Spatiotemporal evolution and driving factors of ecological efficiency in cultivated land use in the Guanzhong Plain

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