粤港澳大湾区背景下的珠三角城市群产业—人口—空间交互影响机理

doi:10.18306/dlkxjz.2018.12.007

地理科学进展 ›› 2018, Vol. 37 ›› Issue (12): 1653-1662.doi: 10.18306/dlkxjz.2018.12.007

所属专题：人口与城市研究；粤港澳大湾区

粤港澳大湾区背景下的珠三角城市群产业—人口—空间交互影响机理

刘锦^1,³(), 田银生^1,^2,^*()

1. 华南理工大学建筑学院,广州510640
2. 亚热带建筑科学国家重点实验室,广州510640
3. 广东培正学院管理学院,广州 510830

收稿日期:2017-10-16 修回日期:2018-02-25 出版日期:2018-12-28 发布日期:2018-12-28
通讯作者: 田银生 E-mail:liujin1984yy@126.com;arystian@scut.edu.cn
作者简介:
作者简介：刘锦(1984-),男,湖南岳阳人,博士研究生,讲师,主要研究方向为城乡发展理论,E-mail: liujin1984yy@126.com。
基金资助:
国家自然科学基金项目(51678241);亚热带建筑科学国家重点实验室自主创新基金项目(2017KB08)

Mutual influencing mechanism of industry-population-space in the Pearl River Delta urban agglomeration in the context of the Guangdong-Hong Kong-Macao Greater Bay Area development

Jin LIU^1,³(), Yinsheng TIAN^1,^2,^*()

1. College of Architecture, South China University of Technology, Guangzhou 510640, China
2. State Key Laboratory of Subtropical Building Science, Guangzhou 510640, China
3. Management School of Guangdong Peizheng College, Guangzhou 510830, China

Received:2017-10-16 Revised:2018-02-25 Online:2018-12-28 Published:2018-12-28
Contact: Yinsheng TIAN E-mail:liujin1984yy@126.com;arystian@scut.edu.cn
Supported by:
National Natural Science Foundation of China, No.51678241;Independent Innovation Fund for State Key Laboratory of Subtropical Building Science, No.2017KB08

摘要/Abstract

摘要：

在粤港澳大湾区成为国家战略的背景下,具有经济地理优势的“湾区”地带,因作为推动全球经济发展的重要增长极而举世瞩目。本文以其主要组成部分的“珠三角9市”为研究对象,通过建立向量自回归VAR模型,利用脉冲响应函数和方差分解方法,对该城市群发展进程中的“产业—人口—空间”交互影响机理进行分析。研究结果表明：珠三角城市群的产业发展对人口的吸纳作用在后期近乎消退,人口集聚对产业发展的促进作用在中后期也明显减弱,而空间扩展对人口集聚的影响效果比较有限且后期呈现负影响。基于上述结论可以判断,珠三角城市群产业、人口和空间要素之间具有短期间的互促发展效果,但是尚未形成城市群应具有的长期稳定的互促机制。为推进粤港澳大湾区建设,珠三角城市群应积极推动产业类型不断提升以集聚高素质人口,积极引导空间扩张与人口增长匹配发展,并构筑多层次且协同互补的产业空间,以此深化产业、人口和空间要素的协同发展格局。

关键词: 城市群, 产业—人口—空间;, VAR模型, 交互影响, 珠三角, 粤港澳大湾区

Abstract:

The Guangdong-Hong Kong-Macao Greater Bay Area with economic geographical advantages is deemed to be the growth point that captivated the world for its impetus to the global economy. This article focuses on the nine main cities of the Pearl River Delta, analyzing the mutual influences among industry-population-space during the rapid development process of this urban agglomeration by constructing a vector auto regressive model and impulse response function and variance decomposition analysis. The results show that: The absorption effect for population in the development of industry in the urban agglomeration of the Pearl River Delta faded in the late stage. The acceleration that industry benefitted from population aggregation weakened in the middle and late periods. The spatial expansion effects on population aggregation were limited and have become negative in the late period. It concludes that in general a short-term mutual promoting development mechanism between industry-population-space was formed, but a long-term mechanism is yet to be developed. In order to promote the development of the Greater Bay Area of Guangdong-Hong Kong-Macao, the urban agglomeration of the Pearl River Delta should attract highly-qualified workforce on the basis of industrial upgrading, plan spatial growth according to the population aggregation, and construct a multilevel synergistic industrial space with complementary products and services. With these measures, the overall aims are to strengthen the synergistic development among industry, population, and space and promote the development of the Greater Bay Area of Guangdong-Hong Kong-Macao.

Key words: urban agglomeration, industry-population-space, vector auto regressive (VAR) model, mutual influence, Pearl River Delta, Guangdong-HongKong-Macao Greater Bay Area

刘锦, 田银生. 粤港澳大湾区背景下的珠三角城市群产业—人口—空间交互影响机理[J]. 地理科学进展, 2018, 37(12): 1653-1662.

Jin LIU, Yinsheng TIAN. Mutual influencing mechanism of industry-population-space in the Pearl River Delta urban agglomeration in the context of the Guangdong-Hong Kong-Macao Greater Bay Area development[J]. PROGRESS IN GEOGRAPHY, 2018, 37(12): 1653-1662.

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参考文献 19

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序列	检验形式(C, T, L)	ADF值	10%	5%	1%	P值	检验结果
FNCY	(C, T, 0)	-0.8153	-2.7133	-3.0655	-3.9203	0.7871	非平稳
CZRK	(C, T, 0)	-0.1444	-2.7133	-3.0655	-3.9203	0.9281	非平稳
JCMJ	(C, T, 0)	-1.3951	-2.7133	-3.0655	-3.9203	0.5581	非平稳
lnFNCY	(C, 0, 0)	-3.0448	-2.7316	-3.0810	-3.9591	0.0533	平稳
lnCZRK	(C, 0, 0)	-2.7496	-2.7316	-3.0810	-3.9591	0.0891	平稳
lnJCMJ	(C, 0, 0)	-3.8883	-2.7316	-3.0810	-3.9591	0.0114	平稳

Lag(滞后阶数)	logL	LR	FPE	AIC	SC	HQ
0	68.06314	NA	3.43e-08	-8.675085	-8.533475	-8.676593
1	123.8430	81.81054^*	6.97e-11	-14.91241	-14.34597	-14.91844
2	137.9358	15.03224	4.30e-11^*	-15.59144^*	-14.60017^*	-15.60200^*

原假设	特征根	Trace(特征根迹检验)			Max(最大特征值检验)
原假设	特征根	统计量	5%临界值	P值	统计量	5%临界值	P值
无特征根^*	0.7152	45.9541	29.7970	0.0003	18.8427	21.1316	0.1015
最多1个特征根^*	0.6243	27.1113	15.4947	0.0006	14.6865	14.2646	0.0429
最多2个特征根	0.5632	12.4247	3.8414	0.0004	12.4247	3.8414	0.0004

原假设	F统计量	显著水平
lnFNCY不是lnCZRK的Granger原因	8.5244	0.0069
lnCZRK不是lnFNCY的Granger原因	1.4405	0.2820
lnJCMJ不是lnCZRK的Granger原因	3.6568	0.0443
lnCZRK不是lnJCMJ的Granger原因	1.1543	0.3539
lnJCMJ不是lnFNCY的Granger原因	1.9735	0.1895
lnFNCY不是lnJCMJ的Granger原因	4.1989	0.0474

变量序列	lnCZRK	lnFNCY	lnJCMJ
lnCZRK(-1)	0.952082	0.530069	0.210302
lnCZRK(-1)	(0.23502)	(0.53042)	(0.15275)
lnCZRK(-2)	-0.608563	-1.307026	-0.190160
lnCZRK(-2)	(0.23300)	(0.52587)	(0.15144)
lnFNCY(-1)	-0.127860	0.587268	0.122371
lnFNCY(-1)	(0.11624)	(0.26235)	(0.07555)
lnFNCY(-2)	0.242163	-0.122154	0.044644
lnFNCY(-2)	(0.10134)	(0.22871)	(0.06587)
lnJCMJ(-1)	0.328441	1.150933	0.460311
lnJCMJ(-1)	(0.36196)	(0.81691)	(0.23526)
lnJCMJ(-2)	-0.306956	0.922405	-0.042890
lnJCMJ(-2)	(0.38349)	(0.86551)	(0.24925)
常数项C	4.279901	-4.912637	2.973355
常数项C	(1.74620)	(3.94102)	(1.13495)
R-squared	0.990718	0.998204	0.998442

粤港澳大湾区背景下的珠三角城市群产业—人口—空间交互影响机理

Mutual influencing mechanism of industry-population-space in the Pearl River Delta urban agglomeration in the context of the Guangdong-Hong Kong-Macao Greater Bay Area development

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