中国城市群产业协同集聚的时空特征及影响因素

doi:10.3969/j.issn.1004-9479.2023.02.2021680

摘要/Abstract

摘要：

以十四五规划纲要中提及的19个中国城市群共201个城市数据为样本，运用ArcGIS空间分析模型、空间误差模型等方法对城市群2006—2018年产业协同集聚水平的时空特征进行分析，并剖析其影响因素。结果表明：①中国城市群第二、三产业协同集聚水平总体呈现波动下降的态势，其中第二产业区位熵呈先降后升的趋势，第三产业区位熵则持续缓慢下降。②中国城市群第二、三产业协同集聚空间格局保持相对稳定，城市群产业协同集聚水平较高的城市群依旧主要分布于西部地区和沿海地区，而中部地区的产业协同集聚水平相对较为落后。③中国城市群第二、三产业协同集聚水平存在显著的空间正相关性，总体呈现波动下降趋势，集聚态势逐渐减弱、内部差异逐渐增大。④运用空间误差模型进行影响因素分析，发现城镇化、政府调控和外商投资强度对中国城市群第二、三产业协同集聚水平的空间布局影响较为显著，而科技支出强度的影响不显著。⑤产业协同集聚发展需因地制宜，东部地区城市群应发挥好政府的调控作用推动城市群内部的协调分工；中部和东北地区城市群应加快提升第三产业，确保其配套服务业能适应第二产业发展；西部城市群则应加强自身城镇化建设水平，保持产业协同集聚水平稳定上升。

关键词: 产业协同集聚, 时空特征, 空间计量模型, 城市群, 区位熵

Abstract:

Based on the data of 201 cities in 19 Chinese urban agglomerations mentioned in the outline of the 14th Five-Year Plan, this paper analyzed the spatial and temporal characteristics of the level of industrial collaborative agglomeration in urban agglomerations from 2006 to 2018 using ArcGIS spatial analysis model, spatial error model and other methods, and analyzed its influencing factors. The results show that: ①The level of coordinated agglomeration of the secondary and tertiary industries in Chinese urban agglomerations shows a trend of fluctuation and decline, in which the location entropy of the secondary industry decreases first and then increases, while the location entropy of the tertiary industry continues to decline slowly. ②The spatial pattern of collaborative agglomeration of the secondary and tertiary industries in China's urban agglomerations remained relatively stable. The urban agglomerations with a high level of industrial collaborative agglomeration are still mainly distributed in western China and coastal areas, while the level of industrial collaborative agglomeration in central China is relatively backward. ③There is a significant positive spatial correlation between the collaborative agglomeration level of the secondary and tertiary industries in Chinese urban agglomerations, and the overall trend of fluctuation decreases. The trend of industrial agglomeration in urban agglomerations gradually weakens, and the internal differences in urban agglomerations gradually increase. ④Based on the spatial error model, it is found that the urbanization level, government regulation and foreign investment intensity have a significant impact on the spatial distribution of the secondary and tertiary industries in Chinese urban agglomerations, while the intensity of science and technology expenditure has no significant impact. ⑤The development of industrial collaborative agglomeration needs to take measures according to local conditions. The urban agglomeration in eastern China should play a good role of government regulation to promote the coordinated division of labor within the urban agglomeration. The urban agglomeration in central and northeast China should promote the tertiary industry and ensure that the supporting service industry can adapt to the development of the secondary industry. Urban agglomeration in western China should try to raise its urbanization and improve the development of service industry.

Key words: industrial co-agglomeration, spatio-temporal characteristics, Spatial Econometric Model, urban agglomeration, location entropy

王运喆, 张国俊, 周春山. 中国城市群产业协同集聚的时空特征及影响因素[J]. 世界地理研究, 2023, 32(2): 104-114.

Yunzhe WANG, Guojun ZHANG, Chunshan ZHOU. Spatio-temporal characteristics and influencing factors of industrial collaborative agglomeration in urban agglomerations in China[J]. World Regional Studies, 2023, 32(2): 104-114.

图/表 8

表1 城市群所处地区划分

Tab.1 Regional division of urban agglomeration

地区	城市群数量	城市群名称
东部	5	珠三角城市群、长三角城市群、京津冀城市群、山东半岛城市群、海峡西岸城市群
东北	2	辽中南城市群、哈长城市群
中部	3	长江中游城市群、晋中城市群、中原城市群
西部	9	成渝城市群、北部湾城市群、天山北坡城市群、关中平原城市群、宁夏沿黄城市群、滇中城市群、兰西城市群、呼包鄂榆城市群、黔中城市群

图1 2006—2018年中国城市群产业协同集聚水平

Fig.1 Industrial collaborative agglomeration level of urban agglomerations in China from 2006 to 2018

图2 2006年和2018年中国城市群第二三产业协同集聚水平空间分布格局注：所用地图基于国家测绘局地理信息局标准地图服务下载的审图号为GS（2019）1827号的标准地图制作，底图无修改。以下地图同。

Fig.2 Spatial distribution pattern of collaborative agglomeration level of secondary and tertiary industries in China's urban agglomerations in 2006 and 2018

表2 中国城市群第二、三产业协同集聚水平的全局Moran's I 指数

Tab.2 The global Moran's I index of the collaborative agglomeration level of the secondary and tertiary industries in China's urban agglomerations

年份	Moran' I	E(I)	Z值
2006	0.530	-0.005	16.929
2007	0.518	-0.005	16.545
2008	0.471	-0.005	15.062
2009	0.479	-0.005	15.290
2010	0.502	-0.005	16.020
2011	0.456	-0.005	14.692
2012	0.439	-0.005	14.036
2013	0.457	-0.005	14.626
2014	0.455	-0.005	14.544
2015	0.483	-0.005	15.436
2016	0.487	-0.005	15.586
2017	0.456	-0.005	14.584
2018	0.447	-0.005	14.310

图3 2006年和2018年中国城市群第二三产业协同集聚水平冷热点空间分布

Fig.3 Spatial distribution of cold and hot spots in the collaborative agglomeration level of secondary and tertiary industries in China's urban agglomerations in 2006 and 2018

图4 2006年和2018年中国城市群第二三产业协同集聚水平LISA聚类图

Fig.4 LISA cluster chart of collaborative agglomeration levels of secondary and tertiary industries in urban agglomerations of China in 2006 and 2018

表3 影响城市群第二三产业协同集聚水平的变量及其说明

Tab.3 Variables influencing collaborative agglomeration level of secondary and tertiary industries in urban agglomeration and their explanations

变量代码	变量名称	定义
X1	城镇化水平	城镇人口与总人口之比
X2	政府调控	人均公共财政支出
X3	科技支出强度	科技支出占财政支出比重
X4	外商投资强度	人均实际利用外资额

表4 普通最小二乘线性回归模型、空间滞后模型和空间误差模型的估计结果

Tab.4 The estimation results of OLS, SLM and SEM

变量	OLS		SLM		SEM
变量	回归系数	t 统计量	回归系数	z统计量	回归系数	z统计量
常数	1.054 9^***	5.760 6	0.796 6^***	4.384 0	0.892 6^***	5.994 6
X1	0.054 3	1.597 2	0.077 4^**	2.397 6	0.099 9^***	3.579 4
X2	-0.025 8	-0.863 2	-0.026 4	-0.939 2	-0.044 8^*	-1.877 4
X3	0.021 6^*	1.722 0	0.019 3	1.633 3	0.014 8	1.402 2
X4	-0.064 9^***	-3.425 2	-0.063 9^***	-3.579 2	-0.030 7^**	-2.102 8
R²	0.084 0		0.167 8		0.520 1
Breusch-Pagan test	23.174 8^***		19.619 4^***		17.953 6^**
LogL	122.373 0		131.333 0		170.171 3
AIC	-234.747 0		-250.667 0		-330.343 0
SC	-218.230 0		-230.847 0		-313.826 0

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