中国水足迹强度空间关联格局及影响因素分析

doi:10.31497/zrzyxb.20190503

摘要/Abstract

摘要：

水资源短缺已成为制约经济社会发展的重要因素,科学审视水资源利用现状、探索水资源可持续发展的有效动力具有重要的理论和现实意义。基于水足迹视角分别测算2006-2015年中国31个省域的水足迹强度,利用探索性空间数据分析（ESDA）对其时空格局演变特征进行解析,考虑到该方法空间描述的粗略性,通过引入时空跃迁测度法进行细化,并借助空间杜宾模型探讨其影响因素。结果表明：中国水足迹强度空间集聚效应显著且具有跃迁性,但主要以类型Ⅵ为主,其空间结构具有一定的路径依赖特征;人口数量仍然是当前中国水足迹强度的一个主要驱动因子,而城镇化率和对外开放程度则对降低水足迹强度起积极作用;中国水足迹强度存在“倒N型”的Kuznets曲线,且大部分省份水足迹强度处于第一个拐点与第二个拐点之间,北京、天津、上海等区域已越过第二个拐点,处于水足迹强度下降阶段,而部分西部欠发达地区仍未跨越第一个拐点。

关键词: 水足迹强度, 探索性空间数据分析, 时空跃迁测度法, 空间杜宾模型

Abstract:

Shortage of water resources has become an important factor that restricts economic and social development. It is of great theoretical and practical significance to examine the current situation of water resources utilization and explore the effective driving force for sustainable development of water resources. Based on the water footprint perspective, this paper calculates the water footprint intensity of 31 provinces in China from 2006 to 2015 respectively. The spatial data analysis (ESDA) is used to examine the evolution of space-time pattern. Considering the rough nature of the spatial description of the method, the spatial Dubin model is introduced to refine and with the help of the model, we analyse the influencing factors. The result shows that the spatial agglomeration effect of water footprint intensity in China is significant and with transition, however, it is mainly based on type VI, and its spatial structure has a certain path dependence. Currently, population quantity is still a major driving factor of water footprint intensity in China, while the urbanization rate and the degree of opening to the outside world play an active role in reducing the water footprint strength. There is an "inverted N" Kuznets curve in China's water footprint intensity In most provinces, the intensity of water footprint is between the first and second inflection points. Beijing, Tianjin, and Shanghai have crossed the second inflection points, and they are in the stage of the decline of footprint intensity, while some of the underdeveloped areas in the western region have not crossed the first turning point.

Key words: water footprint intensity, exploratory spatial data analysis, space-time transition measure method, spatial Dubin model

张凡凡, 张启楠, 李福夺, 傅汇艺, 杨兴洪. 中国水足迹强度空间关联格局及影响因素分析[J]. 自然资源学报, 2019, 34(5): 934-944.

Fan-fan ZHANG, Qi-nan ZHANG, Fu-duo LI, Hui-yi FU, Xing-hong YANG. The spatial correlation pattern of water footprint intensity and its driving factors in China[J]. JOURNAL OF NATURAL RESOURCES, 2019, 34(5): 934-944.

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变量	变量名称	样本量/个	最大值	最小值	均值	标准差
y	水足迹强度/(m³/万元)	310	10.59	0.67	3.18	1.82
x₁	人均GDP/万元	310	10.63	0.58	3.31	2.05
x₂	人均GDP的平方	310	112.96	0.33	15.17	19.60
x₃	人均GDP的立方	310	1200.55	0.19	88.77	177.43
x₄	城镇化率/%	310	0.90	0.21	0.51	0.15
x₅	人口数量/亿人	310	1.08	0.03	0.43	0.27
x₆	对外开放程度/亿元	310	793.47	0.18	67.44	127.86

省份	农业水足迹	工业水足迹	生活水足迹	生态水足迹	灰水水足迹	总水足迹
安徽	337.59	91.44	32.27	3.02	9.06	473.39
北京	108.16	5.14	16.23	5.22	0.51	135.25
福建	206.45	75.34	28.29	2.11	7.05	319.24
甘肃	142.90	13.66	9.26	2.78	6.23	174.83
广东	573.00	129.83	99.85	6.54	20.07	829.28
广西	263.59	53.16	42.42	4.12	31.23	394.53
贵州	197.10	31.55	15.99	0.59	3.35	248.58
海南	48.67	3.96	6.83	0.15	0.95	60.55
河北	399.36	24.37	23.93	3.46	18.74	469.86
河南	524.19	54.07	34.77	7.36	19.76	640.15
黑龙江	213.00	46.88	17.13	2.46	9.35	288.82
湖北	320.34	101.31	41.83	0.30	12.01	475.79
湖南	364.67	88.00	42.69	3.02	15.91	514.30
吉林	152.64	23.74	13.56	3.90	9.28	203.11
江苏	437.01	212.35	55.89	5.57	19.00	729.81
江西	248.39	57.87	27.80	2.66	9.75	346.49
辽宁	242.29	23.65	24.68	3.86	13.62	308.10
内蒙古	137.50	20.63	12.36	11.97	9.00	191.47
宁夏	35.42	4.15	1.72	1.55	8.30	51.14
青海	31.54	4.23	2.96	0.49	3.34	42.55
山东	533.54	26.49	34.45	5.05	17.68	617.21
山西	196.69	13.73	11.19	2.13	9.58	233.32
陕西	208.24	12.86	15.14	1.51	10.07	247.81
上海	126.29	77.68	24.31	1.06	2.28	231.61
四川	452.10	57.48	44.86	2.92	15.74	573.10
天津	73.64	4.76	5.05	1.28	2.29	87.02
西藏	16.86	1.41	1.79	0.03	0.07	20.16
新疆	122.45	11.04	12.74	10.10	14.49	170.81
云南	256.89	23.73	21.21	2.45	10.76	315.04
浙江	298.69	58.95	44.26	8.50	17.92	428.31
重庆	161.74	41.67	18.77	0.64	6.44	229.25

年份	I	E(I)	z	年份	I	E(I)	z	P-value^*
2006	0.475	-0.033	4.295	2011	0.441	-0.033	3.982	0.000
2007	0.472	-0.033	4.268	2012	0.432	-0.033	3.924	0.000
2008	0.482	-0.033	4.329	2013	0.427	-0.033	3.858	0.000
2009	0.465	-0.033	4.172	2014	0.396	-0.033	3.584	0.000
2010	0.474	-0.033	4.239	2015	0.382	-0.033	3.462	0.001

跃迁类型	时间划分
跃迁类型	2006-2009年	2010-2012年	2013-2015年
类型Ⅰ	LL→HL：黑龙江	LL→HL：河南;HH→LH：重庆	LL→HL：山西
类型Ⅱ	—	—	LL→LH：山东、内蒙古
类型Ⅲ	—	—	—
类型Ⅳ	北京天津河北山西内蒙古辽宁吉林上海江苏浙江安徽福建江西山东河南湖北湖南广东广西海南重庆四川贵州云南西藏陕西甘肃青海宁夏新疆	北京天津河北山西内蒙古黑龙江辽宁吉林上海江苏安徽福建江西山东浙江湖南广东广西海南湖北贵州云南西藏陕西四川青海宁夏新疆甘肃	北京天津河北上海江苏浙江安徽黑龙江辽宁福建江西湖北湖南广东广西山东河南海南重庆四川云南西藏陕西贵州青海宁夏新疆甘肃吉林

变量名称	W^l	W^g	W^e
x₁	-1.636^***	-1.590^***	-1.778^***
x₂	0.354^***	0.336^***	0.367^***
x₃	-0.019^***	-0.018^***	-0.019^***
x₄	-9.676^***	-7.875^***	-5.964^*
x₅	17.588^**	17.991^**	21.671^**
x₆	-0.002^*	-0.001	-0.003^*
W×y	0.345^***	0.668^***	0.165
曲线类型	倒N型	倒N型	倒N型
拐点/(万元/人)	3.0和9.3	3.1和9.2	3.2和9.6
Log-likelihood	-47.881	-11.604	59.678
R²	0.954	0.959	0.953