基于网络DEA及SNA模型的中国水资源-能源-粮食纽带系统效率研究

doi:10.11821/dlyj020210814

地理研究 ›› 2022, Vol. 41 ›› Issue (7): 2030-2050.doi: 10.11821/dlyj020210814

基于网络DEA及SNA模型的中国水资源-能源-粮食纽带系统效率研究

郝帅¹(), 孙才志²^,³()

1.辽宁师范大学地理科学学院,大连 116029
2.教育部人文社会科学重点研究基地,辽宁师范大学海洋经济与可持续发展研究中心,大连 116029
3.辽宁省“海洋经济高质量发展”高校协同创新中心,大连 116029

收稿日期:2021-09-13 接受日期:2022-01-24 出版日期:2022-07-10 发布日期:2022-07-07
通讯作者: 孙才志（1970-）,男,山东烟台人,教授,博士生导师,研究方向为水资源经济与海洋经济。E-mail: suncaizhi@lnnu.edu.cn
作者简介:郝帅（1992-）,男,河南安阳人,博士研究生,研究方向为资源环境与区域可持续发展。E-mail: haoshuai0914@126.com
基金资助:
国家社会科学基金重点项目(19AJY010)

Water resources-energy-food nexus system efficiency in China based on network DEA and SNA model

HAO Shuai¹(), SUN Caizhi²^,³()

1. School of Geography, Liaoning Normal University, Dalian 116029, Liaoning, China
2. Key Research Base of Humanities and Social Sciences of the Ministry of Education, Center for Studies of Marine Economy and Sustainable Development, Liaoning Normal University, Dalian 116029, Liaoning, China
3. University Collaborative Innovation Center of Marine Economy High-Quality Development of Liaoning Province, Dalian 116029, Liaoning, China

Received:2021-09-13 Accepted:2022-01-24 Published:2022-07-10 Online:2022-07-07

摘要/Abstract

摘要：

通过对水资源-能源-粮食（WEF）纽带系统效率的研究,对提高区域资源综合利用效率、促进区域资源-经济-社会的可持续发展具有重要意义。本文构建了WEF纽带系统效率评价指标体系,并采用网络DEA模型对1997—2019年中国30个省级行政区的WEF纽带系统效率进行测度,同时利用格兰杰因果检验方法对各省份WEF纽带系统效率间的“关系”进行判定,在此基础上,引入社会网络分析（Social Network Analysis,SNA）方法,对中国WEF纽带系统效率的网络结构特征进行相关分析。结果表明：① 中国省际WEF纽带系统效率整体呈上升趋势,由研究初期的低效率水平提升至研究末期的中等效率水平,空间分布上,中国WEF纽带系统效率呈现自东向西、从沿海向内陆逐渐递减的变化趋势,空间非均衡性特征较为显著。② 中国省际WEF纽带系统效率的网络结构特征具有复杂性的特征,研究对象均被包含于网络结构之中,与此同时,网络整体结构具有较好的稳定性和连通性;此外,网络个体结构特征分析表明,溢出关系较多的地区多集中于中国东部,而且在网络中多位于中心位置,而净受益地区多为中国的中部和西部地区。③ 块模型分析显示,“净溢出”板块由广东、上海等10个省份构成,“双向溢出”板块由江西、河南、河北等6个省份构成,“净受益”板块由广西、甘肃等8个省份构成,“中介人”板块由四川、陕西等6个省份构成,其中“净溢出”板块与“双向溢出”板块在网络结构中均起到“引领”作用,而“净溢出”板块在中国WEF纽带系统效率提升中扮演了“发动机”的角色,多集中于中国东部地区,“净受益”板块多集中于中国中西部地区,在网络中处于边缘位置,“中介人”板块则在网络结构中起到了“中介”和“桥梁”的作用。

关键词: 水资源-能源-粮食纽带系统, 网络DEA, 社会网络分析, 空间网络结构, 中国

Abstract:

The research on the efficiency of water-energy-food (WEF) nexus system is of great significance to improve the efficiency of comprehensive utilization of regional resources. This paper constructs the WEF nexus system efficiency evaluation index system, measures the WEF nexus system efficiency of 30 provincial-level regions (hereafter province) in China from 1997 to 2019 by using the network DEA model. The Granger causality test method based on the VAR structure framework transforms the "attribute data" of WEF nexus system efficiency into "relationship data", and then uses the social network analysis (SNA) method to analyze the spatial correlation network structure characteristics of China's inter-provincial WEF nexus system efficiency. The results show that: (1) the overall efficiency of China's inter-provincial WEF nexus system shows an upward trend. In terms of spatial distribution, the efficiency of China's WEF nexus system shows a gradual decreasing trend from east to west and from coastal to inland areas. The spatial difference is obvious. The overall efficiency is characterized as eastern region > central region > western region, with significant spatial imbalance. (2) The spatial correlation of the efficiency of China's inter-provincial WEF nexus system presents a complex network structure as a whole. All the provinces are in the spatial correlation network, and the network has significant stability and connectivity. The characteristics of individual network structure show that eastern China is an agglomeration area of spillover effect, which plays a key node role in the network structure of WEF nexus system efficiency, while the central and western regions, which are in a marginal position in the network structure, mainly benefit from the effect. (3) Block model analysis showed that the numbers of "net spillover" plate, "two-way spillover" plate, "net benefit" plate and "broker" plate are 10, 6, 8 and 6, respectively. The "net overflow" plate and the "two-way overflow" plate play a leading role in the network structure, and the "net overflow" plate plays an "engine" role in improving the efficiency of China's WEF nexus system, most of them are concentrated in eastern China, and the "net benefit" plate is concentrated in central and western China, which is in a marginal position in the network, while the "broker" plate plays an intermediating and bridging role in the network structure.

Key words: water-energy-food nexus system, network DEA, social network analysis, spatial network structure, China

郝帅, 孙才志. 基于网络DEA及SNA模型的中国水资源-能源-粮食纽带系统效率研究[J]. 地理研究, 2022, 41(7): 2030-2050.

HAO Shuai, SUN Caizhi. Water resources-energy-food nexus system efficiency in China based on network DEA and SNA model[J]. GEOGRAPHICAL RESEARCH, 2022, 41(7): 2030-2050.

图/表 12

图1

表1

中国WEF纽带系统效率空间关联网络结构特征相关计算公式

指标类型		计算公式	公式中变量的含义
整体网络特征	网络密度	$N D = a / b (b - 1) / 2$	a表示实际存在的关系数量;b为研究区个数。
	网络关联度	$N C = 1 - V / b (b - 1) / 2$	V为可达矩阵对角线上“0”的个数。
	网络等级	$N H = 1 - S / m a x (S)$	S表征空间关联网络结构中对称的节点对数。
	网络效率	$N E = 1 - K / m a x (K)$	K是空间关联网络结构中实际多余的关系数量。
个体网络特征	度数中心度	$D C = d / (b - 1)$	d为在空间关联网络中某地区与其他地区之间直接关系的数量。
	接近中心度	$C C = ∑ j = 1 b l i j$	l_ij表示地区i与地区j之间的捷径距离,即在该捷径中包含的关系数量。
	中介中心度	$B C = 2 ∑ p b ∑ q b n p q (i) b 2 - 3 b + 2 n p q (i) = g p q (i) / g p q$	g_pq表示节点p与节点q之间的捷径数量;g_pq(i)为节点p与节点q经过节点i的数量;n_pq(i)则表示节点i处于节点p、q之间捷径数量上的概率,且p≠q≠i,且p<q。

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图7

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	各子系统	指标类型	指标选取（单位）
WEF纽带系统效率评价指标体系	水资源子系统（W）	投入指标	水的生产及水利管理投资（以1997年为基期）（亿元）
			用水总量（生产、生活、生态）（亿m³）
			水的生产及水利管理从业人员（万人）
			水资源生产过程中的能源消耗量（亿t标准煤）
		期望产出指标	国内生产总值（以1997年为基期）（亿元）
		非期望产出指标	废水排放总量（亿m³）
	能源子系统（E）	投入指标	能源工业投资（亿元）
			能源消费总量（亿t标准煤）
			采矿业、电力、热力及燃气生产和供应业人数（万人）
			能源生产过程的水资源消耗量（亿m³）
		期望产出指标	国内生产总值（以1997年为基期）（亿元）
		非期望产出指标	污染指数（无量纲）
	粮食子系统（F）	投入指标	粮食生产投资总额（亿元）
			粮食生产从业人员（万人）
			粮食灌溉用水量（亿m³）
			粮食播种面积（hm²）
			粮食生产过程中的能源消耗（kJ）
			粮食生产过程中地膜使用量（t）
		期望产出指标	粮食生产总值（亿元）
		非期望产出指标	粮食生产过程中的灰水足迹（亿m³）

省份（代码）	度数中心度（DC）				接近中心度（CC）		中介中心度（BC）
省份（代码）	点出度	点入度	中心度	排序	中心度	排序	中心度	排序
北京（BJ）	17	6	75.790	6	70.697	5	12.810	5
天津（TJ）	18	9	79.310	2	71.375	3	12.904	4
河北（HE）	15	10	75.754	7	69.363	9	11.648	8
山西（SX）	9	12	62.069	22	63.759	21	5.150	23
内蒙古（NM）	11	12	68.966	16	67.164	15	9.683	13
辽宁（LN）	13	11	75.682	9	69.569	8	9.920	12
吉林（JL）	8	16	62.009	24	63.215	22	6.138	18
黑龙江（HL）	9	8	65.517	20	65.341	19	5.892	19
上海（SH）	18	8	75.826	5	71.036	4	13.226	3
江苏（JS）	18	10	78.930	3	72.857	2	15.658	1
浙江（ZJ）	22	4	89.655	1	80.625	1	13.906	2
安徽（AH）	13	12	75.610	11	68.378	13	11.601	9
福建（FJ）	12	7	72.414	14	66.716	16	8.173	15
江西（JX）	9	12	65.486	21	62.894	23	5.352	21
山东（SD）	15	7	75.718	8	70.024	7	12.418	6
河南（HA）	13	10	75.646	10	68.378	12	10.445	10
湖北（HB）	11	7	72.379	15	67.829	14	8.298	14
湖南（HN）	9	17	68.867	19	66.368	17	5.659	20
广东（GD）	18	8	75.862	4	70.366	6	12.261	7
广西（GX）	8	23	61.979	25	61.704	25	4.946	24
海南（HI）	10	11	68.933	17	65.934	18	7.271	17
重庆（CQ）	12	12	75.538	13	68.705	11	7.952	16
四川（SC）	12	7	75.574	12	69.033	10	10.317	11
贵州（GZ）	7	17	61.949	26	61.359	26	4.820	26
云南（YN）	5	17	51.699	29	57.442	28	4.810	27
陕西（SN）	10	12	68.900	18	64.359	20	5.164	22
甘肃（GS）	7	16	58.621	27	60.732	27	3.892	28
青海（QH）	6	17	51.724	28	56.476	29	2.439	30
宁夏（NX）	4	16	51.674	30	55.864	30	3.880	29
新疆（XJ）	9	14	62.039	23	62.379	24	4.927	25
平均值	11.6	11.6	69.337	—	66.331	—	8.385	—

	接受关系矩阵（个）				接收关系数（个）		溢出关系数（个）		期望内部关系比例（%）	实际内部关系比例（%）
	板块一	板块二	板块三	板块四	板块内	板块外	板块内	板块外	期望内部关系比例（%）	实际内部关系比例（%）
板块一	42	26	48	43	42	37	42	117	34.48	26.42
板块二	11	8	29	13	8	38	8	53	10.34	13.11
板块三	17	9	28	10	28	118	28	36	27.59	43.75
板块四	9	3	41	11	11	66	11	53	17.24	17.19

	密度矩阵				像矩阵
	板块一	板块二	板块三	板块四	板块一	板块二	板块三	板块四
板块一	0.467	0.520	0.533	0.717	1	1	1	1
板块二	0.220	0.389	0.644	0.433	0	0	1	1
板块三	0.189	0.200	0.389	0.185	0	0	0	0
板块四	0.150	0.100	0.759	0.367	0	0	1	0

基于网络DEA及SNA模型的中国水资源-能源-粮食纽带系统效率研究

Water resources-energy-food nexus system efficiency in China based on network DEA and SNA model

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