中国海外耕地投资规模与地缘经济的耦合协调研究

doi:10.11821/dlxb202309014

地理学报 ›› 2023, Vol. 78 ›› Issue (9): 2338-2357.doi: 10.11821/dlxb202309014

中国海外耕地投资规模与地缘经济的耦合协调研究

潘子纯(), 马林燕, 朱玉春()

西北农林科技大学经济管理学院，杨凌 712100

收稿日期:2022-09-13 修回日期:2023-04-28 出版日期:2023-09-25 发布日期:2023-09-28
通讯作者: 朱玉春(1970-), 女, 北京人, 教授, 博士生导师, 研究方向为农林经济管理。E-mail: zhuyuchun321@126.com
作者简介:潘子纯(1996-), 男, 山西人, 博士生, 研究方向为粮食安全与土地可持续利用。E-mail: panzichun1996@nwafu.edu.cn
基金资助:
中国科协“一带一路”国际科技组织合作平台建设项目(CASTBR201614);科技部“一带一路”创新人才交流外国专家项目(DL2022172001L)

Research on the coupling coordination between the scale of China's overseas farmland investment and geo-economy

PAN Zichun(), MA Linyan, ZHU Yuchun()

College of Economics and Management, Northwest A&F University, Yangling 712100, Shaanxi, China

Received:2022-09-13 Revised:2023-04-28 Published:2023-09-25 Online:2023-09-28
Supported by:
The Belt and Road International Science and Technology Organization Cooperation Platform Construction Foundation for China Association for Science and Technology(CASTBR201614);The Belt and Road Innovative Talent Exchange Foreign Expert Foundation of the Ministry of Science and Technology(DL2022172001L)

摘要/Abstract

摘要：

探究中国海外耕地投资规模与地缘经济耦合协调的空间分异及驱动因素有助于为中国企业科学开展海外耕地投资活动提供参考。本文构建中国海外耕地投资规模及地缘经济的评价体系，借鉴耦合协调模型测算二者耦合协调度，并对其空间分异特征和驱动因素进行分析。结果表明：① 中国海外耕地投资规模及中国与各东道国的地缘经济联系呈高度分散化，投资规模较好、地缘经济较密切的地区位于俄罗斯和东南亚。② 投资规模与地缘经济耦合协调度呈显著正相关，局部地区已形成明显的空间分化，高高集聚区分布于东南亚。③ 耦合协调发展受营商便利指数、国内生产总值、人均淡水资源拥有量、谷物单产、百万人互联网拥有量和法律权利力度指数6个因子的显著影响。④ 以上6个因子对耦合协调发展的影响具有空间差异和增强型交互作用，其中国内生产总值与任意因子作用时产生的增强效应值均较为明显。应从优化海外耕地投资及地缘经济发展的空间格局、探索区域差异化路径、完善规划及关注耦合协调提升的影响因素方面提升二者的耦合协调发展。

关键词: 海外耕地投资, 地缘经济, 空间分异, 耦合协调, 影响因素

Abstract:

Exploring the spatial differentiation and driving factors of the coupling coordination between the scale of China's overseas farmland investment and geo-economy can provide a reference for Chinese enterprises to scientifically implement overseas farmland investment activities. This paper establishes a system to evaluate China's overseas farmland investment scale and geo-economy, measure the degree of coupling coordination between the two using the coupling coordination model, and analyze the spatial differentiation characteristics and driving factors. The results show that: (1) The scale of China's overseas farmland investment and the geo-economic ties between China and each host country are highly decentralized, with the regions having a higher investment scale and closer geo-economy located in Russia and Southeast Asia. (2) The coupling coordination degree of investment scale and geo-economy is significantly and positively correlated, and strong spatial differentiation has been observed in certain localities. The high-high agglomeration is distributed in Southeast Asia. (3) The coupling coordinated development is heavily influenced by six factors, including the ease of doing business index, GDP, per capita freshwater resource ownership, cereal yield, internet ownership of millions of people and legal rights strength index. (4) The effects of six factors, namely the ease of doing business index, GDP, per capita freshwater resource ownership, cereal yield, internet ownership of millions of people and legal rights strength index, has spatial differences and enhanced interaction effects. Among them, the enhancement effect value produced by GDP and any factor is more obvious. The following four aspects should be considered to strengthen the coupling coordination development of overseas farmland investment and geo-economy: optimizing the spatial pattern between the two, exploring regional differentiation paths, improving strategic planning and considering the factors influencing the promotion of the coupling coordination.

Key words: overseas farmland investment, geo-economy, spatial differentiation, coupling coordination, influencing factors

潘子纯, 马林燕, 朱玉春. 中国海外耕地投资规模与地缘经济的耦合协调研究[J]. 地理学报, 2023, 78(9): 2338-2357.

PAN Zichun, MA Linyan, ZHU Yuchun. Research on the coupling coordination between the scale of China's overseas farmland investment and geo-economy[J]. Acta Geographica Sinica, 2023, 78(9): 2338-2357.

图/表 16

图1

表1

表2

研究方法

测度方法	模型	计算公式	模型释义
熵值法	标准化	$正向 : a i j = (x i j - x i j m i n) / (x i j m a x - x i j m i n)$	H_j为第j项指标熵值；n为国家个数；P_ij为第i个国家第j个指标值占比；a_ij为第i个国家第j个指标的标准化值；m为指标个数
	标准化	$负向 : a i j = (x i j m a x - x i j) / (x i j m a x - x i j m i n)$
	熵值	$H j = - 1 / l n n ∑ i = 1 n P i j l n P i j, P i j = a i j / ∑ i = 1 n a i j$
	冗余度	$d j = 1 - H j$
	权重	$w j = d j / ∑ j m d j$
	得分	$U i = ∑ j = 1 m a i j w j$
耦合协调度	耦合度	$C = 2 U 1 U 2 / U 1 + U 2 2$	C为耦合度；U₁、U₂为子系统发展水平评价指数；D为耦合协调度；T为投资规模与地缘经济的综合评价指数；α和β为待定参数，且α+β=1
	发展度	$T = α U 1 + β U 2$
	协调度	$D = C × T$
空间自相关	全局	$M o r a n' s I = ∑ i = 1 n ∑ j = 1 n w i j X i - X - X j - X - S 2 ∑ i = 1 n ∑ j = 1 n w i j$	$x ¯$ 和S²为变量x的均值和标准差；n为样本数；x_i和x_j为空间单元i和j的属性值；W_ij为空间权重矩阵
空间自相关	局部	$I i = x i - x - S 2 ∑ w i j x j - x -$
影响因素分析	空间滞后	$Y = ρ W y + β X + ε$	Y为耦合协调度；X为解释变量矩阵；ρ为空间效应系数；β为模型待估统计量；W为空间权值矩阵；W_y为空间滞后因变量；W_ε为空间误差因变量；ε为随机误差项向量；α为因变量的空间误差系数；μ为正态分布的随机误差向量
	空间误差	$Y = β X + ε, ε = α W ε + μ$
	地理探测	$q = 1 - ∑ h = 1 L N h σ h 2 N σ 2$	q为各显著因子对耦合协调空间分异的解释强度；L为自变量或因变量分层；N、N_h为样本总量和h层样本量；σ²、σ_h²为整体及h层方差

表2

表3

表4

表5

图2

图3

图4

图5

图6

表6

表7

图7

表8

图8

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协调度	协调等级	协调等级亚类
0~0.20	严重失调	U₂-U₁>0.1 U₁-U₂>0.1 \|U₁-U₂\|≤0.1	规模滞后地缘滞后相对协调
0.21~0.40	中度失调
0.41~0.50	基本协调
0.51~0.80	中度协调
0.81~1.00	高度协调

指标名称	含义	单位	最大值	最小值	均值	权重
意向项目数量	达成投资意向的项目	个	64	1	5.63	0.10
意向项目面积	反映意向项目的面积	hm²	5051496	40	208610.66	0.14
合同项目数量	已签约或完成协议的项目	个	64	1	5.49	0.10
合同项目面积	反映合同项目的面积	hm²	5051496	40	208110.55	0.14
生产项目数量	已投入生产的项目	个	19	0	1.90	0.06
生产项目面积	反映生产项目的面积	hm²	274053	0	17470.73	0.10
独立投资面积	反映独立投资的意向项目面积	hm²	627072	0	80292.85	0.09
合作投资面积	反映合作投资的意向项目面积	hm²	4599905	0	128317.81	0.20
投资时长	反映投资东道国的时长	a	21	2	14.24	0.01
合作伙伴来源国数	反映国际合作的多样性情况	个	4	0	0.98	0.06

要素	指标	单位	时段	最大值	最小值	均值	权重
贸易流	出口额	万美元	2013—2020年	7582699.30	20172.40	1038781.30	0.16
贸易流	进口额	万美元	2013—2020年	6222540.00	3428.50	882098.26	0.20
投资流	投资流量	万美元	2013—2020年	171441.40	-1554.10	26954.48	0.11
	投资存量	万美元	2013—2020年	1202875.80	402.40	148547.71	0.08
	对外承包工程营业额	万美元	2013—2020年	571149.90	549.00	217675.43	0.10
文旅流	年均入境游客人数	人	2013—2017年	2842424.60	1054.60	317937.58	0.24
文旅流	年均在华留学生人数	人	2013—2017年	17407.80	8.00	2843.83	0.11

维度	指标	时段	最大值	最小值	均值
市场条件	营商便利指数X₁	2020年	100.00	11.93	42.53
	国内生产总值X₂(亿美元)	2013—2020年	19786.34	42.22	2142.38
	对外直接投资净流入占比X₃(%)	2013—2020年	22.98	0.67	4.12
资源要素	人均耕地面积X₄(hm²)	2018年	0.88	0.03	0.30
	人均淡水资源拥有量X₅(m³)	2017年	310880.29	861.16	18015.84
	谷物单产X₆(%)	2018年	6005.70	579.10	3108.56
成本约束	百万人互联网拥有量X₇(个)	2013—2020年	20565.93	1.34	1379.25
	制度质量X₈	2013—2020年	0.87	-1.58	-0.55
	法律权利力度指数X₉	2014—2019年	10.00	0.00	4.85

Test	MI/DF	Value	P
LM(lag)	1	14.8406	0.0001
R-LM(lag)	1	10.7025	0.0011
LM(error)	1	6.4960	0.0108
R-LM(error)	1	2.3579	0.1247

中国海外耕地投资规模与地缘经济的耦合协调研究

Research on the coupling coordination between the scale of China's overseas farmland investment and geo-economy

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图/表 16

参考文献 47

相关文章 15

Metrics

本文评价

模型	OLS	SLM	SEM
CONSTANT	-3.6508^***	-3.4345^***	-2.9641^***
lnX₁	0.1033	0.0925^*	0.0879^*
lnX₂	0.1270^***	0.1187^***	0.1129^***
X₃	0.0075	0.0047	0.0040
X₄	0.0263	0.0828	0.0821
lnX₅	0.0874^***	0.0965^***	0.0970^***
lnX₆	-0.0356	-0.0707^*	-0.0812^**
lnX₇	-0.0485^**	-0.0453^***	-0.0415^***
X₈	-0.0362	-0.0319	-0.0249
X₉	0.0179^*	0.0176^**	0.0162^**
ρ		0.8468^***
λ			0.8589^***
R²	0.6954	0.7870	0.7709
LogL	28.0670	33.2797	31.8983
AIC	-36.1339	-44.5593	-43.7966
SC	-20.2987	-27.1406	-27.9614

q=A∩B	A+B	比较结果	效应类型	排序
X₁∩X₂=0.775	X₁(0.107)+X₂(0.617)=0.724	q>A+B	非线性增强	4
X₁∩X₅=0.727	X₁(0.107)+X₅(0.180)=0.287	q>A+B	非线性增强	7
X₁∩X₆=0.402	X₁(0.107)+X₆(0.126)=0.233	q>A+B	非线性增强	14
X₁∩X₇=0.389	X₁(0.107)+X₇(0.266)=0.373	q>A+B	非线性增强	15
X₁∩X₉=0.754	X₁(0.107)+X₈(0.120)=0.227	q>A+B	非线性增强	5
X₂∩X₅=0.730	X₂(0.617)+X₅(0.180)=0.797	A+B>q>Max(A, B)	双因子增强	6
X₂∩X₆=0.837	X₂(0.617)+X₆(0.126)=0.743	q>A+B	非线性增强	1
X₂∩X₇=0.786	X₂(0.617)+X₇(0.266)=0.883	A+B>q>Max(A, B)	双因子增强	3
X₂∩X₉=0.836	X₂(0.617)+X₈(0.120)=0.737	q>A+B	非线性增强	2
X₅∩X₆=0.471	X₅(0.180)+X₆(0.126)=0.306	q>A+B	非线性增强	13
X₅∩X₇=0.621	X₅(0.180)+X₇(0.266)=0.446	q>A+B	非线性增强	9
X₅∩X₉=0.545	X₅(0.180)+X₉(0.120)=0.300	q>A+B	非线性增强	11
X₆∩X₇=0.585	X₇(0.126)+X₇(0.266)=0.392	q>A+B	非线性增强	10
X₆∩X₉=0.474	X₇(0.126)+X₉(0.120)=0.246	q>A+B	非线性增强	12
X₇∩X₉=0.649	X₇(0.266)+X₉(0.120)=0.386	q>A+B	非线性增强	8

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