新疆塔里木河流域县域农业低碳生产率时空格局及影响效应研究

doi:10.12118/j.issn.1000-6060.2022.358

摘要/Abstract

摘要：

提高农业低碳生产率是保证干旱区生态优先和粮食安全的有效路径。基于非期望产出的超效率考虑松弛变量的测度（Slacks-based measure，SBM）模型测度2000—2020年新疆塔里木河流域42个县（市）的农业低碳生产率，利用趋势面分析及空间自相关刻画县域尺度农业低碳生产率的时空特征，并通过构建空间杜宾模型和地理探测器以揭示影响变量的溢出效应与空间异质性。结果表明：（1）塔里木河流域县域农业低碳生产率呈现“W型”的阶段性特征，县域之间形成“下游-上游-中游”凹形递减的分异格局，且在空间上呈现出集聚性。（2）机械化使用强度和农民收入水平对县域农业低碳生产率具有显著的正向溢出效应;人口城镇化水平对县域农业低碳生产率具有显著的负向溢出效应;工业化水平和财政支农力度对农业低碳生产率具有显著的负向直接效应;经济发展水平和人均生产规模的溢出效应并不显著。（3）县域农业低碳生产率影响变量的交互类型总体表现为增强型，意味着县域农业低碳生产率受多重变量作用的趋势日益显现。因此，探究县域农业低碳生产率的时空格局及影响因素对实现塔里木河流域乃至新疆农业低碳化协调发展具有重要意义。

关键词: 农业低碳生产率, 超效率SBM模型, 地理探测器, 空间溢出, 塔里木河流域

Abstract:

Improving low-carbon productivity in agriculture is an effective path to ensure ecological priority and food security in arid areas. In this study, a super-efficient SBM model based on nonexpected output is used to measure the agricultural low-carbon productivity of 42 counties (cities) in the Tarim River Basin of Xinjiang, China from 2000 to 2020. The model uses trend surface analysis and spatial autocorrelation to portray the spatial and temporal characteristics of agricultural low-carbon productivity at the county scale, and constructs a spatial Durbin model and a geographic detector to reveal the spillover effects of influencing variables and spatial heterogeneity. The results show that: (1) The low-carbon productivity of agriculture in the Tarim River Basin shows a “W-shaped” stage, with a concave decreasing pattern of “downstream-upstream-midstream” between counties, and a spatial clustering. (2) Mechanization intensity and farmers’ income level have significant positive spillover effects on low-carbon productivity in county agriculture; population urbanization has significant negative spillover effects on low-carbon productivity in county agriculture; industrialization level and financial support to agriculture have significant negative direct effects on low-carbon productivity in agriculture; and the spillover effects of economic development level and per capita production scale are not significant. (3) The interaction type of the low-carbon productivity impact variables in county agricultural shows an enhanced type in general, implying the trend that low-carbon productivity in county agriculture is increasingly influenced by multiple variables. Therefore, it is important to explore the spatial and temporal patterns of low-carbon productivity in county agriculture and the influencing factors to achieve a coordinated development of low-carbon agriculture in the Tarim River Basin and even in Xinjiang.

Key words: agricultural low-carbon productivity, super-efficient SBM model, geographic probe, spatial spillover, Tarim River Basin

穆佳薇, 乔保荣, 余国新. 新疆塔里木河流域县域农业低碳生产率时空格局及影响效应研究[J]. 干旱区地理, 2023, 46(6): 968-981.

MU Jiawei, QIAO Baorong, YU Guoxin. Spatial and temporal patterns of agricultural low-carbon productivity and its influence effects in the counties of Tarim River Basin, Xinjiang[J]. Arid Land Geography, 2023, 46(6): 968-981.

图/表 15

表1

表2

表3

图1

图2

表4

2000—2020年塔里木河流域42个县市效率值及分解项"

地区	LCP	PEC	SEC	PTC	STC	地区	LCP	PEC	SEC	PTC	STC
库尔勒市	1.0740	1.0231	1.0622	1.0852	1.1163	乌恰县	1.3924	1.2289	1.0883	0.9745	1.3892
轮台县	1.1680	1.1227	1.0572	1.2722	1.0443	喀什市	1.1952	1.1616	1.0151	1.2058	1.0398
尉犁县	1.1760	1.2154	1.2095	1.1727	1.1206	疏附县	1.0829	1.1083	1.0503	1.2986	0.9593
若羌县	1.0992	1.0062	1.0295	1.0377	1.0047	疏勒县	1.1202	1.1051	1.0241	1.2478	1.0012
且末县	1.1218	0.9921	0.9956	1.2047	1.0059	英吉沙县	1.0559	0.9912	1.0040	1.0421	1.0135
焉耆回族自治县	1.1090	1.0173	1.0264	1.2066	1.0408	泽普县	1.0322	1.1105	1.0848	1.1932	0.9979
和静县	1.1264	1.0807	1.0079	1.2434	0.9659	莎车县	1.3514	1.1377	1.3610	1.2387	0.9543
和硕县	1.0696	1.1488	1.1138	1.2008	1.0470	叶城县	1.2152	1.0957	1.2071	1.2327	0.9868
博湖县	1.1111	1.0465	1.0065	1.1454	1.0097	麦盖提县	1.2668	1.1818	1.0665	1.1993	1.0334
阿克苏市	1.1177	1.1241	1.3160	1.1729	1.1447	岳普湖县	1.0520	1.0366	1.0120	1.1027	1.0067
温宿县	1.1524	1.1635	1.1111	1.4487	0.9071	伽师县	1.3242	0.9983	1.2403	1.1834	1.1818
库车县	1.2105	1.1765	1.0581	1.2387	1.0044	巴楚县	1.1450	1.1316	1.1294	1.3043	1.0310
沙雅县	1.0722	1.1585	1.0143	1.1979	0.9933	塔什库尔干塔吉克自治县	1.7783	1.2006	1.4720	1.0121	1.3735
新和县	1.0546	1.1306	1.0119	1.2201	0.9867	和田市	1.1233	1.0534	1.0473	1.1339	1.0004
拜城县	1.0917	1.0785	0.9925	1.1844	0.9786	和田县	1.1770	1.0902	1.0109	1.2182	0.9923
乌什县	1.1013	1.4090	1.1339	1.2308	1.0097	墨玉县	1.1015	1.0711	1.0612	1.3214	1.0005
阿瓦提县	1.1043	1.1312	1.0070	1.1920	1.0092	皮山县	1.2342	1.0771	1.0381	1.2114	1.0440
柯坪县	1.1762	1.0709	1.1467	1.1610	1.0676	洛浦县	1.0958	1.0983	1.0043	1.1380	0.9982
阿图什市	1.0319	1.0042	0.9994	1.1047	1.0428	策勒县	1.2077	1.1039	1.0240	1.1824	1.0481
阿克陶县	1.0449	0.9831	1.0001	1.0910	1.0273	于田县	1.4609	1.4957	0.9920	1.1708	1.0217
阿合奇县	0.9856	0.9518	0.9895	0.9828	1.1466	民丰县	1.2172	1.0532	1.1023	1.0313	1.2797

表4

图3

图4

表5

表6

表7

表8

表9

表10

表11

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维度	类别	变量说明	单位	平均值	标准差
要素投入	土地投入	农作物播种面积	10³ hm²	48.64	40.47
	劳动投入	农业从业人员	人	43204.09	44175.80
	资本投入	有效灌溉面积	10³ hm²	42.68	180.91
		化肥折纯施用量	t	16705.99	15888.50
		农用机械总动力	kW	172630.30	156521.60
		农药施用量	t	125.44	220.05
		农用塑料薄膜施用量	t	1984.04	2558.92
期望产出	效益产出	以2000年不变价调整的农业总产值	10⁴元	135718.70	139873.00
环境产出	农业碳排放量	农药、化肥、农膜、灌溉和翻耕的碳排放量	t	41943.27	37601.60
	农业面源污染	熵值处理后的化肥氮磷流失量	t	462.09	402.77

变量维度	探测变量	指标	单位	变量分类
农户福利	农民收入水平（X₁）	农民人均纯收入	元	自然断点法
	经济发展水平（X₂）	地区人均GDP	元	自然断点法
	人均生产规模（X₃）	农作物播种面积/乡村从业人员	hm²·人^-1	等间距分类
社会经济	机械化使用强度（X₄）	农业机械总动力/农作物播种面积	kW·(10³hm²)^-1	等间距分类
	工业化水平（X₅）	工业增加值/地区生产总值	%	自然断点法
	人口城镇化水平（X₆）	城镇人口/常住人口	%	<10%;10%~30%;30%~50%;50%~70%;>70%
政府行为	财政支农力度（X₇）	农林水事务支出/地方财政一般预算支出	%	自然断点法

交互作用	交互类型
q(X_a∩X_b)<Min[q(X_a), q(X_b)]	非线性减弱
Min[q(X_a), q(X_b)]<q(X_a∩X_b)<Max[q(X_a), q(X_b)]	单变量非线性减弱
q(X_a∩X_b)>Max[q(X_a), q(X_b)]	双变量增强
q(X_a∩X_b)=q(X_a)+q(X_b)	独立
q(X_a∩X_b)>q(X_a)+q(X_b)	非线性增强

年份	Moran’s I	年份	Moran’s I	年份	Moran’s I
2000	0.349^***	2007	0.331^***	2014	0.301^***
2001	0.364^***	2008	0.323^***	2015	0.278^***
2002	0.367^***	2009	0.316^***	2016	0.310^***
2003	0.365^***	2010	0.364^***	2017	0.324^***
2004	0.361^***	2011	0.370^***	2018	0.300^***
2005	0.354^***	2012	0.379^***	2019	0.299^***
2006	0.339^***	2013	0.368^***	2020	0.285^***

检验方法	统计值	P值
空间滞后模型的拉格朗日乘数检验	105.316	0.000
空间误差模型的拉格朗日乘数检验	111.449	0.000
基于稳健的空间滞后模型拉格朗日乘数检验	21.494	0.000
基于稳健的空间误差模型拉格朗日乘数检验	27.627	0.000
空间滞后模型的沃尔德检验	199.720	0.000
空间滞后模型的似然比检验	181.130	0.000
空间误差模型的沃尔德检验	163.180	0.000
空间误差模型的似然比检验	146.770	0.000
豪斯曼检验	20.230	0.000