中国省际水资源绿色效率空间关联网络研究

doi:10.11821/dlyj020180926

摘要/Abstract

摘要：

本文从群组前沿的角度出发,将中国31个省（市、自治区）分为东中西三大群组,在不同的技术前沿面下利用SBM模型对各省（市、自治区）的水资源绿色效率进行测度,并利用VAR格兰杰因果检验方法将水资源绿色效率“属性数据”转化为“关系数据”,在此基础上利用社会网络分析（SNA）方法对中国水资源绿色效率的空间关联网络特征进行研究。结果表明：① 中国水资源绿色效率区域差异显著,总体表现为中部>东部>西部的特征。② 没有一个地区独立于水资源绿色效率空间关联网络之外,网络整体具有较强的稳定性;个体特征表明,东部地区以溢出效应为主,处于“引领者”地位,而西部地区以接收其他地区的溢出关系为主,在网络结构中处于边缘位置。③ 块模型分析表明,北京、天津等10个地区为“净溢出”板块;青海、新疆等8个地区为“净受益”板块;河北、重庆等6个地区为“双向溢出”板块;河南、陕西等7个地区为“经纪人”板块。研究结果为中国各地区水资源绿色效率的提高及协调发展提供了建议。

关键词: SBM模型, 群组前沿, 社会网络分析, 水资源绿色效率, 空间关联网络

Abstract:

In recent years, China's economic development has maintained rapid growth, and the demand for water resources is also increasing day by day. However, shortage of water resources and inefficient use of water have become an obstacle to the sustainable development of China's social economy. The research on spatial correlation network characteristics of China's water resources green efficiency can lay a foundation for optimizing the overall spatial pattern of China's water resources green efficiency and realizing the cross-regional synergistic promotion of it. Therefore, from the perspective of Group Frontier, this paper divides 31 provincial-level regions in China into three groups: eastern, central and western. It uses SBM model to measure the green efficiency of water resources in different areas, and uses VAR Granger causality test to transform "attribute data" of water resources green efficiency into "relational data". On this basis, the spatial correlation network characteristics of water resources green efficiency in China are studied by means of social network analysis. The results show that the regional differences in China's water resources green efficiency are significant, and the overall performance is characterized by central > eastern > western region. The spatial correlation of the water resources green efficiency of between provinces of China presents a more complex network structure, all regions are in the spatial correlation network of water resources green efficiency, and the network overall has the strong stability. The individual characteristics show that the eastern region is dominated by spillover effect, which plays a role of "engine" in the optimization of national water resources green efficiency, while the western region is dominated by the spillover from other regions, which is in the edge position in the network structure. Block model analysis shows that there are 10 provinces in "net overflow" plate such as Beijing and Tianjin, and 8 in "net benefit" plate such as Qinghai and Xinjiang, and 6 provinces in "two-way overflow" plate such as Hebei and Chongqing, and 7 in "brokers" such as Henan and Shaanxi. The results provide suggestions for the improvement of water resources green efficiency and the coordinated development in different regions of China.

Key words: SBM model, group frontier, social network analysis, green efficiency of water resources, spatial association network

孙才志, 马奇飞. 中国省际水资源绿色效率空间关联网络研究[J]. 地理研究, 2020, 39(1): 53-63.

SUN Caizhi, MA Qifei. Spatial correlation network of water resources green efficiency between provinces of China[J]. GEOGRAPHICAL RESEARCH, 2020, 39(1): 53-63.

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	1997年	2002年	2007年	2012年	2016年		1997年	2002年	2007年	2012年	2016年
北京	0.853	0.824	0.921	0.816	1.000	湖北	0.469	0.545	0.805	1.000	1.000
天津	1.000	1.000	1.000	1.000	1.000	湖南	1.000	0.521	0.553	0.601	0.741
河北	0.259	0.269	0.266	0.243	0.254	内蒙古	1.000	0.628	0.539	0.537	1.000
辽宁	0.940	0.790	0.577	0.429	0.375	广西	0.170	0.224	0.245	0.265	0.277
上海	0.496	0.598	1.000	0.797	1.000	重庆	1.000	0.392	0.545	1.000	1.000
江苏	0.286	0.321	0.354	0.274	0.326	四川	0.119	0.182	1.000	1.000	1.000
浙江	0.405	0.473	0.435	0.367	0.431	贵州	0.186	0.220	0.278	0.356	0.367
福建	0.349	0.480	0.546	0.372	0.400	云南	0.161	0.208	0.259	0.248	0.227
山东	1.000	0.370	0.374	0.239	0.225	西藏	1.000	1.000	0.809	1.000	1.000
广东	0.301	0.372	1.000	0.458	0.360	陕西	0.295	0.328	0.347	0.387	0.425
海南	0.700	1.000	1.000	0.702	1.000	甘肃	0.421	0.393	0.449	0.434	0.442
山西	1.000	1.000	1.000	1.000	1.000	青海	0.617	0.611	0.685	1.000	1.000
吉林	1.000	1.000	1.000	1.000	1.000	宁夏	0.550	0.496	0.572	0.509	0.520
黑龙江	0.536	0.677	1.000	1.000	0.939	新疆	0.393	0.419	0.421	0.415	0.346
安徽	0.448	0.826	1.000	0.839	1.000	东部均值	0.599	0.591	0.679	0.518	0.579
江西	0.526	0.576	0.555	0.637	0.828	中部均值	0.669	0.697	0.803	0.825	0.873
河南	0.370	0.428	0.513	0.520	0.473	西部均值	0.493	0.425	0.512	0.596	0.634

地区	度数中心度				接近中心度		中介中心度
地区	点出度	点入度	中心度	排序	中心度	排序	中心度	排序
北京	12	5	53.333	15	56.928	17	1.881	17
天津	10	6	50.000	18	56.119	21	1.996	16
河北	9	7	46.667	22	55.088	22	1.276	21
山西	4	10	40.000	27	52.059	27	0.324	30
内蒙古	9	14	63.333	6	60.761	8	4.352	6
辽宁	9	14	70.000	4	61.455	5	5.188	5
吉林	7	5	26.667	31	49.300	30	0.647	28
黑龙江	11	12	60.000	11	61.250	7	2.957	11
上海	12	1	43.333	24	48.905	31	0.308	31
江苏	8	3	30.000	30	53.302	25	0.833	25
浙江	13	8	63.333	7	60.217	10	3.429	9
安徽	12	5	50.000	19	56.474	20	1.132	24
福建	9	14	63.333	8	60.761	9	5.586	4
江西	9	11	60.000	12	57.714	15	1.760	19
山东	9	15	63.333	9	61.636	4	3.646	8
河南	11	7	50.000	20	58.914	13	2.466	15
湖北	13	8	56.667	14	59.552	11	2.493	14
湖南	12	18	76.667	2	66.327	2	5.603	3
广东	14	8	53.333	16	59.188	12	2.716	12
广西	9	5	43.333	25	52.112	26	0.804	26
海南	8	6	46.667	23	54.058	24	1.305	20
重庆	18	19	83.333	1	71.469	1	9.636	1
四川	9	7	53.333	17	56.624	18	4.134	7
贵州	8	12	60.000	13	57.885	14	1.244	22
云南	10	10	63.333	10	57.148	16	1.792	18
西藏	4	6	33.333	28	50.126	28	0.424	29
陕西	9	7	43.333	26	56.624	19	2.517	13
甘肃	10	18	66.667	5	64.561	3	6.228	2
青海	10	15	70.000	3	61.455	6	3.408	10
宁夏	6	6	33.333	29	49.617	29	1.169	23
新疆	3	15	50.000	21	54.461	23	0.723	27
均值	9.581	9.581	52.366	—	57.487	—	2.644	—

板块	密度矩阵				像矩阵
板块	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	0.356	0.667	0.100	0.375	1	1	0	1
板块二	0.200	0.167	0.281	0.417	0	0	0	1
板块三	0.329	0.443	0.405	0.300	1	1	1	0
板块四	0.213	0.542	0.471	0.146	0	1	1	0