黄河流域城市化与生态系统服务价值协调性及障碍因素研究

doi:10.12118/j.issn.1000-6060.2021.436

摘要/Abstract

摘要：

关注生态系统服务价值（Ecosystem service value,ESV）与城市化耦合协调互动关系,对黄河流域高质量发展大有裨益。文章运用熵权法、耦合协调模型、空间自相关模型,障碍度模型分析了城市化与ESV的耦合协调关系及主导障碍因素。结果表明:（1） 1995—2018年,黄河流域ESV发生了很大的改善,整体增加了33.05×10⁹元,且以调节服务为主导,草地、林地、耕地服务价值对总ESV贡献率较高。单位面积生态系统服务价值（PE）呈现南高北低、中游高上下游低的空间格局。（2） PE与城市化耦合协调度（Coupling coordination degree,CCD）逐渐改善,轻度耦合协调增加了27.12%,严重失调类型减少了45.46%,耦合亚型从城市化滞后型转变为ESV滞后型;从空间上看,CCD呈现南高北低、中游优于上游和下游的空间格局,协调度具有显著的空间正相关性,存在明显的高-高和低-低集聚特征,高-高集聚区主要分布在ESV高且城市化水平相对较高的中游和下游地区,低-低集聚区主要分布在ESV较低的上游地区。（3） 1995—2018年主导障碍因素未发生明显改变,ESV系统以调节服务为主,而城市化系统以经济子系统和社会子系统为主。基于此,应厘清ESV和城市化耦合协调关系,关注城市化进程对区域生态系统服务能力和生态安全格局的影响,实现流域整体优质协调发展。

关键词: 生态系统服务价值, 城市化, 耦合协调, 障碍度模型, 黄河流域

Abstract:

Investigating the coordinated and interactive relationship between ecosystem service value (ESV) and urbanization is beneficial for the high-quality development of the Yellow River Basin, China. This study uses entropy method, coupling coordination model, spatial autocorrelation model, and obstacle degree model to analyze the coupling coordination relationship between urbanization and ESV and identify dominant obstacle factors. Results show the following: (1) From 1995 to 2018, the ESV of the Yellow River Basin greatly improved, with an overall increase of 33.05×10⁹ yuan RMB, and is dominated by regulating services. The service value of grassland, woodland, and cultivated land has a higher contribution rate to the total ESV. The ESV per unit area (PE) presents a high and low spatial pattern in the south and north, respectively; further, the spatial pattern in the midstream is high and that in the upstream and downstream is low. (2) The coupling coordination degree (CCD) of PE and urbanization gradually improves, but it still exhibits an overall low level of coupling coordination. Light coupling coordination increases by 27.12%, serious disorder significantly decreases by 45.46%, and the coupling subtype develops from urbanization lagging to ESV lagging. Furthermore, the CCD shows a spatial pattern of high in the south and low in the north, with the midstream being superior to the upstream and downstream areas. The coordination degree has a significant positive spatial correlation, and an obvious high-high and low-low agglomeration characteristics are observed. The high-high agglomeration areas are mainly distributed in the middle reaches and downstream areas with high ESV and relatively high urbanization level, and the low-low agglomeration areas are mainly distributed in the upstream areas with low ESV. (3) The dominant barrier factors did not change significantly from 1995 to 2018; the ESV is dominated by regulating services, while the urbanization system is dominated by the economic and social subsystems. On the basis of these findings, the coupled and coordinated relationship between ESV and urbanization should be clarified and the impact of the urbanization process on ecosystem services and ecological security should be focused on for ensuring the overall quality and coordinated development of the Yellow River Basin.

Key words: ecosystem service value, urbanization, coupling and coordination, obstacle degree model, Yellow River Basin

张凯莉,冯荣荣,刘潭,张志成,韩佳宁,刘康. 黄河流域城市化与生态系统服务价值协调性及障碍因素研究[J]. 干旱区地理, 2022, 45(4): 1254-1267.

ZHANG Kaili,FENG Rongrong,LIU Tan,ZHANG Zhicheng,HAN Jianing,LIU Kang. Coordination and obstacle factors of urbanization and ecosystem service value in the Yellow River Basin[J]. Arid Land Geography, 2022, 45(4): 1254-1267.

图/表 16

图1

表1

表2

研究模型及指标"

研究模型	计算公式	模型释义	意义
极值标准化	$X m j = [x m j - m i n (x m j)] / [m a x (x m j) - m i n (x m j)] 正指标 X m j = [m a x (x m j) - x m j] / [m a x (x m j) - m i n (x m j)] 负指标$	X_mj为系统m,指标j归一化后的值;x_mj为各系统的原始取值;max(x_mj)与min(x_mj)分别为各系统j个指标的最大值和最小值	消除数据间屏蔽效应和量纲差异
熵权法	$μ p j = 1 + X p j ∑ i = 1 n (1 + X p j) e j = - 1 l n (n) ∑ i = 1 n μ p j × l n (μ p j) g j = 1 - e j W j = g j ∑ j = 1 m g j$	X_pj为第p个城市j指标的归一化值;W_j为各指标权重; $μ p j$ 为第p个城市j指标的比重;e_j为指标的信息熵;g_j为信息熵冗余度;n为城市样本数量;m为系统指标数量	客观确定指标权重
综合发展指数	$φ = ∑ j = 1 m W j × X p j$	当m取E时, $φ$ 为ESV系统;当m取U时, $φ$ 为城市化系统	获得子系统的综合效益
欧式距离耦合度	$C i t = 1 - ∑ 1 2 w i t - w' i t ∑ 1 2 Q i 2 k$	C_it为两系统耦合度,以w₁_t、 $w' 1 t$ 和w₂_t、 $w' 2 t$ 分别为城市化系统和ESV系统第t年水平的实际值和理想值,取Q₁=Q₂=1为调节系数,一般取k=2^[30-31]	C_it取值在（0, 1）之间,C_it值越大,说明系统实际耦合协调状态与理想状态距离更贴合,耦合度越高
耦合协调度	$C C D i t = C i t × T i t 1 / 2 T i t = α φ E + β φ U 1 / 2$	CCD_it为协调度;T_it为耦合协调发展水平指数,取 $α$ = $β$ =0.5; $φ E$ 为ESV系统; $φ U$ 为城市化系统	具体分级见表3
Moran’I检验	$I = ∑ i = 1 n ∑ j = 1 m w i j x i - x - x j - x - S 2 ∑ i = 1 n ∑ j = 1 m w i j$	I为全局莫兰指数,取值在（-1, 1）之间,大于0代表正相关,即高值与高值临近,小于0代表负相关,即高值与低值临近;w_ij为空间权重矩阵;x_i_、x_j分别为要素i、j的属性值; $x -$ 为要素属性值的平均值;S²为样本方差;m、n分别为相邻指标空间要素数量	空间自相关检验
局部Geary指数	$C = (n - 1) ∑ i = 1 n ∑ j = 1 n w i j (x i - x j) 2 2 (∑ i = 1 n ∑ j = 1 n w i j) ∑ i = 1 n (x i - x -) 2 C * = C - 1 V a r (C)$	C为吉尔里指数（Geary）指数,取值在（0, 2）之间,大于1表示负相关,小于1表示正相关,一般认为其比莫兰指数对局部空间相关更为敏感; $C *$ 为局部Geary指数;Var(C)为Geary指数的方差	局部空间自相关检验,识别空间集聚模式
障碍度	$Q j = I j W j / ∑ j = 1 n I j W j × 100 % I j = 1 - X i j$	Q_j为障碍度;W_j为因子贡献度,一般用指标权重表示;I_j为指标偏离度,指标实际值与最优值之间差距,用1与标准化值X_ij之差表示^[32]	识别影响两系统耦合协调关系的主导障碍因素

表2

表3

图2

表4

图3

图4

表5

图5

图6

表6

图7

表7

表8

表9

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准则层	指标层	单位	权重
人口城市化	人口密度	人∙km^-2	0.041
	城镇人口密度	人∙km^-2	0.054
	非农人口比重	%	0.022
空间城市化	建成区占土地总面积百分比	%	0.063
	每万人的市区面积	km²	0.070
	人均铺装道路面积	m²	0.039
	人均绿地面积	人∙m^-2	0.051
	建成区绿化覆盖率	%	0.013
经济城市化	人均国内生产总值	元	0.063
	二、三产业占GDP比重	%	0.005
	限额以上工业总产值	10⁴元	0.103
	固定资产投资总额	10⁴元	0.088
	人均财政收入	元	0.080
	职工平均工资	元	0.059
社会城市化	人均消费品零售总额	元	0.075
	中小学生在校人数	10⁴人	0.035
	人均公共图书馆藏书	册	0.045
	万人拥有医疗床位	张	0.016
	万人互联网用户	10⁴人	0.076

协调大类	耦合协调度	对比关系	亚类型
严重失调型	0.0≤CCD≤0.2	U-E>0.1	严重失调ESV滞后型
		\|E-U\|≤0.1	严重失调同步型
		E-U>0.1	严重失调城市化滞后型
轻度失调型	0.2<CCD≤0.4	U-E>0.1	轻度失调ESV滞后型
		\|E-U\|≤0.1	轻度失调同步型
		E-U>0.1	轻度失调城市化滞后型
轻度耦合协调	0.4<CCD≤0.6	U-E>0.1	轻度耦合协调ESV滞后型
		\|E-U\|≤0.1	轻度耦合协调同步型
		E-U>0.1	轻度耦合协调城市化滞后型
良好耦合协调	0.6<CCD≤0.8	U-E>0.1	良好耦合协调ESV滞后型
		\|E-U\|≤0.1	良好耦合协调同步型
		E-U>0.1	良好耦合协调城市化滞后型
优质耦合协调	CCD>0.8	U-E>0.1	优质耦合协调ESV滞后型
		\|E-U\|≤0.1	优质耦合协调同步型
		E-U>0.1	优质耦合协调城市化滞后型

土地利用面积变化	年份	耕地	林地	草地	水体	湿地	建筑用地	未利用地
土地利用面积/10⁵hm²	1995	348.65	121.75	369.54	3.75	11.59	36.39	77.50
	2005	348.25	129.00	353.46	4.00	13.00	41.31	80.16
	2015	341.95	129.15	348.40	4.65	12.35	54.34	78.31
	2018	337.29	129.34	348.32	4.99	14.04	58.47	76.62
变化量/10⁵hm²	1995—2005	-0.40	7.25	-16.08	0.25	1.41	4.92	2.66
	2005—2015	-6.30	0.15	-5.06	0.65	-0.65	13.03	-1.85
	2015—2018	-4.66	0.19	-0.08	0.34	1.69	4.13	-1.69
	1995—2018	-11.36	7.59	-21.22	1.24	2.45	22.08	-0.88
变化率/%	1995—2005	-0.11	5.95	-4.35	6.67	12.17	13.52	3.43
	2005—2015	-1.81	0.12	-1.43	16.25	-5.00	31.54	-2.31
	2015—2018	-1.36	0.15	-0.02	7.31	13.68	7.60	-2.16
	1995—2018	-3.26	6.23	-5.74	33.07	21.14	60.68	-1.14

ESV	年份	耕地	林地	草地	水体	湿地	未利用地	汇总
ESV/10⁹元	1995	156.76	183.79	293.68	9.91	82.92	1.09	728.15
	2000	158.98	195.16	283.86	9.56	84.40	1.08	733.04
	2005	157.27	193.59	282.20	10.04	88.34	1.08	732.52
	2010	156.33	201.44	293.60	12.90	78.60	1.04	743.91
	2015	154.76	198.64	288.63	12.67	80.46	0.99	736.15
	2018	152.32	198.99	304.99	13.23	90.58	1.09	761.20
变化量/10⁹元	1995—2000	2.22	11.37	-9.82	-0.35	1.48	-0.01	4.89
	2000—2005	-1.71	-1.57	-1.66	0.48	3.94	0.00	-0.52
	2005—2010	-0.94	7.85	11.40	2.86	-9.74	-0.04	11.39
	2010—2015	-1.57	-2.80	-4.97	-0.23	1.86	-0.05	-7.76
	2015—2018	-2.44	0.35	16.36	0.56	10.12	0.10	25.05
	1995—2018	-4.44	15.20	11.31	3.32	7.66	0.00	33.05
变化率/%	1995—2000	1.42	6.19	-3.34	-3.53	1.78	-0.92	0.67
	2000—2005	-1.08	-0.80	-0.58	5.02	4.67	0.00	-0.07
	2005—2010	-0.60	4.05	4.04	28.49	-11.03	-3.70	1.55
	2010—2015	-1.00	-1.39	-1.69	-1.78	2.37	-4.81	-1.04
	2015—2018	-1.58	0.18	5.67	4.42	12.58	10.10	3.40
	1995—2018	-2.83	8.27	3.85	33.50	9.24	0.00	4.54

全局莫兰检验	1995年	2000年	2005年	2010年	2015年	2018年
Moran’I	0.562	0.570	0.621	0.636	0.651	0.638
Z	5.893	6.086	6.824	7.001	7.267	7.042
P	0.001	0.001	0.001	0.001	0..001	0.001