城市人口与服务业空间协同的多维机制——以福建省为例

doi:10.13249/j.cnki.sgs.2021.08.006

地理科学 ›› 2021, Vol. 41 ›› Issue (8): 1354-1363.doi: 10.13249/j.cnki.sgs.2021.08.006

城市人口与服务业空间协同的多维机制——以福建省为例

刘钰¹^,²(), 李登辉³, 余卓芮²^,⁴, 张浩⁵, 王振波⁶, 刘岱宁²^,*()

1. 河南大学土木建筑学院，河南开封 475003
2. 河南大学中原发展研究院，河南开封 475003
3. 河南大学黄河文明与可持续发展研究中心，河南开封 475003
4. 澳门城市大学创新设计学院，中国澳门 999078
5. 复旦大学环境科学与工程系，上海 200433
6. 中国科学院地理科学与资源研究所，北京 100101

收稿日期:2020-06-29 出版日期:2021-08-25 发布日期:2021-10-11
通讯作者: 刘岱宁 E-mail:microly@126.com;danis1985@163.com
作者简介:刘钰（1982−），男，河北石家庄人，博士，讲师，主要研究方向为区域空间规划。E-mail: microly@126.com
基金资助:
国家自然科学基金项目（41401183）、河南大学省属高校基本科研业务费专项资金（0000A40454）资助

Multidimensional Mechanism of Spatial Coordination Between Urban Population and Service Industry: A Case of Fujian Province

Liu Yu¹^,²(), Li Denghui³, Yu Zhuorui²^,⁴, Zhang Hao⁵, Wang Zhenbo⁶, Liu Daining²^,*()

1. School of Civil Engineering and Architecture, Henan University, Kaifeng 475003, Henan, China
2. Research Institute for Development in the Central Plains Region, Henan University, Kaifeng 475003, Henan, China
3. Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475003, Henan, China
4. Faculty of Innovation and Design, City University of Macau, Macau 999078, China
5. Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
6. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Received:2020-06-29 Online:2021-08-25 Published:2021-10-11
Contact: Liu Daining E-mail:microly@126.com;danis1985@163.com
Supported by:
National Natural Science Foundation of China (41401183), Fundamental Research Funds for the Provincial Universities of Henan University (0000A40454)

摘要/Abstract

摘要：

采用Hoover系数、空间聚集指数、重心模型、空间耦合指数以及面板数据回归模型，对1995—2015年福建省城市人口与服务业的空间协同机制进行了分析。结果表明：① 城市人口与服务业都有向中心城市集中的特征，服务业的空间集中度高于城市人口。② 城市人口与服务业都有向中心城市周边聚集的趋势，而且服务业的聚集度更强。③ 空间重心轨迹显示两者在东西方向上存在错位，服务业的分布更偏向东部沿海。④ 两者的空间耦合指数呈现持续上升的时序演化特征、由东部沿海向西部内陆逐步升高的空间梯度格局。⑤ 回归模型显示，城镇职工收入、农民收入、建设用地规模和固定资产投资是两者空间协同的重要影响因素。

关键词: 城市人口, 服务业, 空间协同, 多维机制, 福建

Abstract:

The coordination development of urban population and service industry is vitally important for the deepening of urbanization and the improvement of regional competitiveness. However, the mechanism of the spatial coordination between them remains less studied, especially at the regional scale. This study hackled the theories of the spatial coordination between urban population and service in multiple spatial dimensions and conducted an empirical research, taking Fujian Province as the study case. Results showed that: 1) In spatial concentration dimension, Hoover coefficient demonstrated that both of them concentrated in the central city and the service industry concentrated higher. This can be attributable to the advantages of central cities in labor quality and age structure. 2) In spatial agglomeration dimension, spatial agglomeration index showed that both of them gathered around the central cities and the service industry gathered more densely, which reflected the spillover effect of service industry from central cities. 3) In spatial mismatch dimension, the trajectories of the gravity center revealed that, comparing with urban population, service industry is more inclined to the East Coast. This indicated that the eastern coastal area had location advantages in the development of service industry. 4) In spatial coupling dimension, the spatial coupling degree exhibited an upward trend in the process of evolution over time, and a gradual rise from the east coast to the west inland in the spatial pattern. 5) Panel data regression model presented that urban workers’ income, farmers’ income, construction land and fixed asset investment were the driving forces of the spatial coordination between urban population and service industry. At last, this study put forward some policy suggestions for the coordinated development of urban population and service industry in Fujian Province.

Key words: urban population, service industry, spatial coordination, multidimensional mechanism, Fujian Province

中图分类号:

F293.1

刘钰, 李登辉, 余卓芮, 张浩, 王振波, 刘岱宁. 城市人口与服务业空间协同的多维机制——以福建省为例[J]. 地理科学, 2021, 41(8): 1354-1363.

Liu Yu, Li Denghui, Yu Zhuorui, Zhang Hao, Wang Zhenbo, Liu Daining. Multidimensional Mechanism of Spatial Coordination Between Urban Population and Service Industry: A Case of Fujian Province[J]. SCIENTIA GEOGRAPHICA SINICA, 2021, 41(8): 1354-1363.

图/表 4

参考文献 29

[1]	张月友, 董启昌, 方瑾, 等. 人口素质红利时代的中国服务业增长[J]. 经济学家, 2020, (3): 56- 65.
	Zhang Yueyou, Dong Qichang, Fang Jin et al. The impact of population quality on the accelerated growth of service industry[J]. Economist, 2020, (3): 56- 65.
[2]	Yang F F, Yeh A G O, Wang J. Regional effects of producer services on manufacturing productivity in China[J]. Applied Geography, 2018, 97, 263- 274. doi: 10.1016/j.apgeog.2018.04.014
[3]	Elheddad M, Djellouli N, Tiwari A K et al. The relationship between energy consumption and fiscal decentralization and the importance of urbanization: Evidence from Chinese provinces[J]. Journal of Environmental Management, 2020, 264, 110474. doi: 10.1016/j.jenvman.2020.110474
[4]	何永达. 人力资本、知识创新与服务业空间集聚——基于省际面板数据的计量分析[J]. 经济地理, 2015, 35 (9): 120- 125.
	He Yongda. Human capital, knowledge innovation and service industry spatial agglomeration: An panel data analysis based on provincial[J]. Economic Geography, 2015, 35 (9): 120- 125.
[5]	徐海峰, 王晓东. 现代服务业是否有助于推动城镇化?——基于产城融合视角的PVAR模型分析[J]. 中国管理科学, 2020, 28 (4): 195- 206.
	Xu Haifeng, Wang Xiaodong. Can modern service industry promote urbanization? —An PVAR analysis based on the perspective of city-industry integration[J]. Chinese Journal of Management Science, 2020, 28 (4): 195- 206.
[6]	Zhong Y, Wei Y D. Economic transition, urban hierarchy, and service industry growth in China[J]. Tijdschrift Voor Economische En Sociale Geografie, 2018, 109 (2): 189- 209. doi: 10.1111/tesg.12276
[7]	杜宇玮, 刘东皇. 中国城镇化与服务业发展耦合协调度测度[J]. 城市问题, 2015, (12): 52- 61.
	Du Yuwei, Liu Donghuang. Measurement of coupling and coordination degree of urbanization and service industry development in China[J]. Urban Problems, 2015, (12): 52- 61.
[8]	张国俊, 邓鸿鹄. 珠江三角洲地区服务业与城镇化协调关系的时空演变[J]. 地理科学, 2018, 38 (7): 1118- 1128.
	Zhang Guojun, Deng Honghu. Spatial-temporal evolution of the eoordination relationship between service industry and urbanization in the Pearl River Delta Region[J]. Scientia Geographica Sinica, 2018, 38 (7): 1118- 1128.
[9]	王毅, 丁正山, 余茂军, 等. 基于耦合模型的现代服务业与城市化协调关系量化分析——以江苏省常熟市为例[J]. 地理研究, 2015, 34 (1): 97- 108. doi: 10.11821/dlyj201501009
	Wang Yi, Ding Zhengshan, Yu Maojun et al. Quantitative analysis of the coordination relation between modern service industry and urbanization based on coupling model: A case study of Changshu[J]. Geographical Research, 2015, 34 (1): 97- 108. doi: 10.11821/dlyj201501009
[10]	福建省统计局. 福建统计年鉴[M]. 北京: 中国统计出版社, 1991-2016.
	Fujian Statistical Bureau. Fujian statistical yearbook. Beijing: China Statistics Press, 1991-2016.
[11]	国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2019.
	National Bureau of Statistics of China. China statistical yearbook. Beijing: China Statistics Press, 2019.
[12]	任喜萍, 殷仲义. 中国省域人口集聚、公共资源配置与服务业发展时空耦合及驱动因素[J]. 中国人口·资源与环境, 2019, 29 (12): 77- 86.
	Ren Xiping, Yin Zhongyi. Spatial-temporal coupling and driving factors of provincial population agglomeration, public resource allocation and service industry development in China[J]. China Population, Resources and Environment, 2019, 29 (12): 77- 86.
[13]	Rogerson P A, Plane D A. The Hoover Index of population concentration and the demographic components of change: An article in memory of Andy Isserman[J]. International Regional Science Review, 2013, 36 (1): 97- 114. doi: 10.1177/0160017612440811
[14]	Shobugawa Y, Wiafe S A, Saito R et al. Novel measurement of spreading pattern of influenza epidemic by using weighted standard distance method: Retrospective spatial statistical study of influenza, Japan, 1999-2009[J]. International Journal of Health Geographics, 2012, (11): 20. doi: 10.1186/1476-072X-11-20/
[15]	闫东升, 孙伟, 孙晓露. 长江三角洲人口时空格局演变及驱动因素研究[J]. 地理科学, 2020, 40 (8): 1285- 1292.
	Yan Dongsheng, Sun Wei, Sun Xiaolu. Spatial-temporal pattern evolution and driving factors of population in the Yangtze River Delta[J]. Scientia Geographica Sinica, 2020, 40 (8): 1285- 1292.
[16]	王德怀, 李旭东. 贵州乌江流域人口与经济发展协调性研究[J]. 地理科学, 2019, 39 (3): 477- 486.
	Wang Dehuai, Li Xudong. Coordination of population and economic development in the Wujiang River Basin of Guizhou Province[J]. Scientia Geographica Sinica, 2019, 39 (3): 477- 486.
[17]	刘玉, 刘彦随, 郭丽英. 环渤海地区县域人均粮食占有量空间格局演化[J]. 地理科学, 2011, 31 (1): 102- 109.
	Liu Yu, Liu Yansui, Guo Liying. Evolvement of spatial pattern of per capita grain possession at county level in the area along Bohai Rim of China[J]. Scientia Geographica Sinica, 2011, 31 (1): 102- 109.
[18]	陈景华, 王素素. 现代服务业发展的地区差异与影响因素——以山东为例[J]. 山东社会科学, 2018, (8): 153- 158.
	Chen Jinghua, Wang Susu. Regional differences and influencing factors in the development of modern service industry: A case study of Shandong Province[J]. Shandong Social Sciences, 2018, (8): 153- 158.
[19]	申玉铭, 邱灵, 任旺兵, 等. 中国服务业空间差异的影响因素与空间分异特征[J]. 地理研究, 2007, 26 (6): 1255- 1264. doi: 10.3321/j.issn:1000-0585.2007.06.019
	Shen Yuming, Qiu Ling, Ren Wangbing et al. Analysis on influenting factors and characteristics of spatial disparity in Chinese service industry[J]. Geographical Research, 2007, 26 (6): 1255- 1264. doi: 10.3321/j.issn:1000-0585.2007.06.019
[20]	梁文泉, 陆铭. 后工业化时代的城市: 城市规模影响服务业人力资本外部性的微观证据[J]. 经济研究, 2016, 51 (12): 90- 103.
	Liang Wenquan, Lu Ming. Cities in the post-industrial economy: How city size affects human capital externality in service industry?[J]. Economic Research Journal, 2016, 51 (12): 90- 103.
[21]	Foerster-Pastor U S, Golowko N. The need for digital and soft skills in the Romanian business service industry[J]. Management & Marketing Challenges for the Knowledge Society, 2018, 13 (1): 831- 847.
[22]	郭建科, 吴陆陆, 李博, 等. 多功能视角的沿海港口城市体系位序规模结构及耦合类型[J]. 地理科学, 2020, 40 (7): 1050- 1061.
	Guo Jianke, Wu Lulu, Li Bo et al. Rank-size structure and coupling type of coastal port city system based on multifunctional perspective[J]. Scientia Geographica Sinica, 2020, 40 (7): 1050- 1061.
[23]	杜群阳, 俞航东. 2003-2015年中国城市劳动力技能互补、收入水平与人口城镇化[J]. 地理科学, 2019, 39 (4): 525- 532.
	Du Qunyang, Yu Hangdong. Skill complementarity of labor force, income level and urbanization in China’s cities in 2003-2015[J]. Scientia Geographica Sinica, 2019, 39 (4): 525- 532.
[24]	彭博. 人口年龄结构对我国服务业的影响效应及其差异性研究[J]. 商业经济研究, 2020, (1): 178- 181.
	Peng Bo. A study on the effect of population age structure on China’s service industry and its spatial heterogeneity[J]. Journal of Commercial Economics, 2020, (1): 178- 181.
[25]	Hutton T A. Service industries, globalization, and urban restructuring within the Asia-Pacific: New development trajectories and planning responses[J]. Progress in Planning, 2003, 61 (1): 1- 74. doi: 10.1016/S0305-9006(03)00013-8
[26]	黄大为. 广东省服务业空间联系与城镇化空间拟合[J]. 经济地理, 2017, 37 (7): 114- 123.
	Huang Dawei. The space fitting between spatial links of services with spatial distribution of urbanization in Guangdong Province[J]. Economic Geography, 2017, 37 (7): 114- 123.
[27]	孙凯, 董慧, 刘艳春, 等. 中国第三产业发展空间溢出效应的统计检验[J]. 统计与决策, 2019, (10): 116- 120.
	Sun Kai, Dong Hui, Liu Yanchun et al. Statistical test on spatial spillover effect of tertiary industry development in China[J]. Statistics & Decision, 2019, (10): 116- 120.
[28]	Zhang X, Kloosterman R C. Connecting the ‘workshop of the world’: Intra- and extra-service networks of the Pearl River Delta City-Region[J]. Regional Studies, 2016, 50 (6): 1069- 1081. doi: 10.1080/00343404.2014.962492
[29]	Li K, Ma Z, Liu J. A new trend in the space-time distribution of cultivated land occupation for construction in China and the impact of population urbanization[J]. Sustainability, 2019, 11 (18): 5089. doi: 10.3390/su11185089

	OLS模型		面板回归模型
	回归系数	t值	回归系数	t值
常数项	−266.07	−3.82^**	—	—
城市人口	0.95	7.51^**	1.54	9.48^**
城市职工工资	2.23	3.90^**	3.12	5.21^**
农民收入	−1.29	−6.10^**	−1.52	−6.83^**
建设用地	0.67	3.82^**	−0.78	−2.15^*
固定资产	0.64	23.74^**	0.63	24.31^**

城市人口与服务业空间协同的多维机制——以福建省为例

Multidimensional Mechanism of Spatial Coordination Between Urban Population and Service Industry: A Case of Fujian Province

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