中国西北地区农业生态效率时空分异及影响因素研究

doi:10.12118/j.issn.1000-6060.2023.604

摘要/Abstract

摘要：

立足资源禀赋和发展阶段因地制宜提高农业生态效率是农业强国建设的必由之路。在借助非期望非径向SE-SBM模型测度2010—2020年中国西北地区市域尺度农业生态效率的基础上，综合应用差异系数、Dagum基尼系数、全局趋势分析、全局莫兰指数和空间误差模型等指标和方法探讨了中国西北地区农业生态效率时空分异特征及影响因素。结果表明：（1） 2010—2020年中国西北地区农业生态效率的中位数在波动中由0.512上升至1.001，年均增长率约为6.93%，且市域间农业生态效率的差异程度呈现出缩小的态势。（2）中国西北地区农业生态效率呈现出梯次性的圈层空间分布特征，投影趋势线在南北方向呈现出“U型”特征，而东西方向则是在由西向东平滑上升的过程中逐渐显现出“倒U型”特征。（3）劳均耕地面积、农村居民人均可支配收入、城镇化水平对中国西北地区农业生态效率产生正向影响，而农业机械密度与化肥使用强度则对该地区农业生态效率产生负向影响。

关键词: 农业生态效率, SE-SBM模型, 空间误差模型, 中国西北地区

Abstract:

Improving agricultural eco-efficiency based on resource endowment and development stage is the only means to build a strong agricultural country. Based on the measurement of agricultural eco-efficiency at the city scale in northwest China from 2010 to 2020 using the non-expectation nonradial SE-SBM model, the spatial and temporal differentiation characteristics and influencing factors were analyzed using the indices and methods of difference coefficient, Dagum Gini coefficient, global trend analysis, spatial autocorrelation, and spatial error model. The results indicate the following: (1) The median of agricultural eco-efficiency in northwest China increased from 0.512 to 1.001, with an average annual growth rate of approximately 6.93%; however, the difference in agricultural eco-efficiency between cities showed a narrowing trend. (2) The spatial distribution of agricultural eco-efficiency in northwest China shows hierarchical spatial distribution characteristics. The projection trend line shows a “U-shaped” feature in the north-south direction, whereas the east-west direction gradually shows an “inverted U-shaped” feature in the process of smooth rise from west to east. (3) The results of the spatial error model show that the per workforce arable land area, per capita disposable income of rural residents, and urbanization level positively influence the agricultural eco-efficiency in the northwest China, whereas the agricultural machinery density and fertilizer use intensity negatively influence the agricultural eco-efficiency in the region.

Key words: agricultural ecological efficiency, SE-SBM model, spatial error model, northwest China

朱瑷嫒, 殷颂葵, 刘琼慧. 中国西北地区农业生态效率时空分异及影响因素研究[J]. 干旱区地理, 2024, 47(7): 1210-1219.

ZHU Aiai, YIN Songkui, LIU Qionghui. Spatial and temporal differentiation and influencing factors of agricultural ecological efficiency in northwest China[J]. Arid Land Geography, 2024, 47(7): 1210-1219.

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指标类型	指标投入	指标说明
投入	土地投入	农作物播种面积/10³ hm²
	劳动力投入	农林牧渔从业人员/10⁴人
	资本投入	农林牧渔业固定资产投资/10⁴元
	化肥投入	化肥折纯量/10⁴ t
	农业机械投入	农业机械总动力/10⁴ kW
	农业用水量	农业用水总量/10⁸ m³
期望产出	农业总产值	农业生产总值/10⁸元
非期望产出	农业碳排放	农业CO₂排放量/10⁶ t

统计量	2010年	2011年	2012年	2013年	2014年	2015年	2016年	2017年	2018年	2019年	2020年
平均值	1.677	1.732	1.748	1.809	1.863	1.893	1.904	1.714	1.707	1.679	1.870
中位数	1.428	1.483	1.510	1.573	1.643	1.644	1.747	1.458	1.451	1.482	1.628
标准差	1.149	1.182	1.199	1.265	1.231	1.275	1.284	1.169	1.151	1.117	1.268
观测数	51	51	51	51	51	51	51	51	51	51	51

年份	2010年	2011年	2012年	2013年	2014年	2015年	2016年	2017年	2018年	2019年	2020年
中位数	0.512	0.187	0.514	0.608	0.553	0.562	0.585	0.640	1.012	0.851	1.001
差异系数	0.600	1.147	0.596	0.549	0.636	0.558	0.548	0.539	0.447	0.471	0.491
偏度系数	1.097	1.655	0.991	0.977	0.757	1.036	0.950	0.837	0.528	0.501	0.658
峰度系数	0.315	1.594	0.055	0.151	-0.039	0.223	0.180	0.052	-0.056	-0.084	-0.098

年份	基尼系数				贡献率/%
年份	总体	组内	组间	超变密度	组内	组间	超变密度
2010	0.317	0.056	0.141	0.121	17.545	44.382	38.073
2011	0.544	0.104	0.227	0.213	19.161	41.639	39.200
2012	0.318	0.061	0.127	0.130	19.337	39.912	40.751
2013	0.296	0.057	0.112	0.127	19.340	37.779	42.881
2014	0.349	0.060	0.160	0.129	17.305	45.829	36.866
2015	0.297	0.060	0.105	0.133	20.084	35.260	44.657
2016	0.295	0.059	0.095	0.141	19.962	32.280	47.758
2017	0.294	0.062	0.060	0.172	21.041	20.371	58.588
2018	0.247	0.051	0.063	0.134	20.521	25.359	54.120
2019	0.263	0.049	0.080	0.134	18.805	30.394	50.801
2020	0.271	0.057	0.066	0.148	20.908	24.299	54.793

年份	Moran’s I	Z值	P值
2010	0.068	3.555	0.000^***
2011	0.061	3.342	0.000^***
2012	0.053	2.922	0.002^***
2013	0.047	2.690	0.004^***
2014	0.073	3.710	0.000^***
2015	0.041	2.431	0.008^***
2016	0.016	1.473	0.070^*
2017	-0.016	-0.207	0.418
2018	-0.034	-0.523	0.300
2019	0.018	1.546	0.061^*
2020	0.024	1.763	0.039^**
均值	0.032	-	-