知识密集型服务业集聚对城市群旅游创新影响的空间效应

doi:10.11821/dlxb202106014

地理学报 ›› 2021, Vol. 76 ›› Issue (6): 1521-1536.doi: 10.11821/dlxb202106014

知识密集型服务业集聚对城市群旅游创新影响的空间效应

方远平¹^,²(), 毕斗斗³, 陈宏洋⁴, 彭婷²

1. 华南师范大学地理科学学院,广州 510631
2. 华南师范大学旅游管理学院,广州 510006
3. 华南理工大学旅游管理系,广州 510006
4. 中电鸿信信息科技有限公司,南京 210029

收稿日期:2020-03-03 修回日期:2020-12-19 出版日期:2021-06-25 发布日期:2021-08-25
作者简介:方远平(1974-), 男, 湖南桂东人, 博士, 教授, 中国地理学会会员(S110007124M), 研究方向为知识密集型服务业与区域创新、经济地理与城乡规划。E-mail: fyp21cn@163.com
基金资助:
国家自然科学基金项目(41471106);广东省自然科学基金项目(2020A1515010835);广东省自然科学基金项目(2021A1515012248);广东省哲学社会科学“十三五”规划项目(GD19CYJ17)

Spatial effects of knowledge-intensive business services clustering on tourism innovation in urban aggolomerations

FANG Yuanping¹^,²(), BI Doudou³, CHEN Hongyang⁴, PENG Ting²

1. School of Geographical Science, South China Normal University, Guangzhou 510631, China
2. School of Tourism Management, South China Normal University, Guangzhou 510006, China
3. Department of Tourism Management, South China University Technology, Guangzhou 510006, China
4. China Telecom Hongxin Information Technology Co., Ltd, Nanjing 210029, China

Received:2020-03-03 Revised:2020-12-19 Published:2021-06-25 Online:2021-08-25
Supported by:
National Natural Science Foundation of China(41471106);Natural Science Foundation of Guangdong Province(2020A1515010835);Natural Science Foundation of Guangdong Province(2021A1515012248);Project for 13th Five-Year Plan of Philosophy and Social Sciences in Guangdong Province(GD19CYJ17)

摘要/Abstract

摘要：

知识密集型服务业作为国家创新体系的重要组成部分,已经成为促进区域旅游创新发展的关键因素。以中国长三角、珠三角和京津冀城市群为例,运用区位商、数据包络分析法、探索性空间数据分析法和空间计量模型等方法,对知识密集型服务业集聚和旅游创新的时空演变特征与两者之间的空间关联性深入分析。结果表明：① 三大城市群知识密集型服务业集聚特征明显,集聚程度由强到弱依次为珠三角、长三角和京津冀,且城市群内部各城市间的集聚存在不均衡特征。② 三大城市群旅游创新的生产效率变化主要由技术进步推动,旅游创新均表现出一定的空间集聚特征,长三角城市群具有明显空间集聚性,但空间集聚模式存在差异,珠三角和京津冀旅游创新集聚不明显。长三角城市群内部各城市大多属于高高和低低空间集聚模式,珠三角和京津冀城市群内部各城市大多属于高低和低高的空间集聚模式。③ 知识密集型服务业集聚对城市群旅游创新水平的提高具有一定的促进作用但存在地区差异性,三大城市群旅游创新水平呈现空间溢出效应,但其对周边城市的辐射带动作用有待加强。

关键词: 知识密集型服务业, 旅游创新, 城市群, 全要素生产率, 空间面板杜宾模型, 空间溢出效应

Abstract:

Knowledge-intensive business services (KIBS), as a key component of the national innovation system (NIS), has become a crucial driving factor for the regional tourism innovation. Despite ever-increasing overlapping and interaction between tourism and KIBS, there was little literature on the relations of KIBS clustering and regional tourism innovation. In this paper, the authors measured the clustering level of KIBS and total factor productivity (TFP) of regional tourism innovation in China's three mega-city regions (MCR), namely, the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Beijing-Tianjin-Hebei (BTH) urban agglomeration, using the methods of location quotient and data envelopment analysis. Then, the authors examined the spatial distribution and cluster mode of regional tourism innovation TFP in the three MCRs using the spatial data analysis method (ESDA). Finally, the authors evaluated the impact and spatial effect of KIBS clustering, among other factors, on regional tourism innovation on the basis of the panel data (from 2005 to 2015) and Spatial Panel Durbin Model (SPDM). The results show that: (1) KIBS in all the three MCRs show high levels of clustering, though the intensity of clustering exhibits a descending pattern from the PRD, to the YRD, and to the BTH. KIBS clustering mainly takes place in municipalities directly under the central government and first-tier cities to the provincial capitals, with significant regional differences among different cities in these MCRs. (2) The regional tourism innovation TFPs in the three MCRs from high to low are the YRD (1.006), the PRD (0.978), and the BTH (0.960), and the changes in TFP are mainly due to technological advancement. (3) Among the three MCRs, only the YRD shows a significant level of spatial clustering of regional tourism innovation on a global scale, while there have been certain signs of spatial clustering in each of the three MCRs on a local scale. However, different MCRs show different spatial clustering patterns: spatial clustering in most cities in the YRD is in the high-low type, while that in most cities in the PRD and BTH region is in the high-low and low-high types. (4) Despite variations from region to region, KIBS clustering has a positive effect on the level of regional tourism innovation. There have been effects of spatial spillover in all the three MCRs, however, it is necessary to set good examples and create favorable conditions for neighbouring cities. (5) An open policy system and well-paced marketization have a promoting effect on regional tourism innovation TFP. The optimization of industrial structure and improvement in digitalization also plays a positive role in regional tourism innovation, which is the result of multiple innovation factors.

Key words: knowledge-intensive business services (KIBS), tourism innovation, urban agglomeration, total factor productivity, spatial panel Durbin model, spatial spillover effect

方远平, 毕斗斗, 陈宏洋, 彭婷. 知识密集型服务业集聚对城市群旅游创新影响的空间效应[J]. 地理学报, 2021, 76(6): 1521-1536.

FANG Yuanping, BI Doudou, CHEN Hongyang, PENG Ting. Spatial effects of knowledge-intensive business services clustering on tourism innovation in urban aggolomerations[J]. Acta Geographica Sinica, 2021, 76(6): 1521-1536.

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类别	指标名称	具体指标
投入指标	人力资本	第三产业从业人数(万人)
	旅游投资	城市固定资产投资总额(亿元)
	旅游吸引力	评A级景区数量(个)
		星级酒店数量(家)
		旅行社数量(个）
	旅游科研经费	地方科技与教育财政支出乘以旅游总收入与地方生产总值的比重(万元)
产出指标	经济效益	旅游总收入(亿元)
	市场规模	旅游总人数(万人次)
	专利发明	当年旅游专利授权量(个)
	学术论文	当年旅游论文篇数(篇）

变量类型	评价指标	指标描述
被解释变量	旅游创新	旅游全要素生产率
核心解释变量	KIBS集聚	KIBS产业区位商
控制变量	经济发展水平	人均GDP
	产业结构	旅游总收入占GDP比重
	城市化水平	非农业人口占总人口比重
	信息化水平	地区邮电总量占全国邮电总量比重
	交通条件	公路里程+铁路里程占地区面积比重
	市场化程度	入境旅游收入占第三产业增加值比重

区域	Moran's I	P值	Z统计量
长三角城市群	0.1702	0.0080	2.8156
珠三角城市群	-0.1556	0.2320	-0.7118
京津冀城市群	-0.1226	0.4550	-0.1887

区域	第一象限(HH)	第三象限(LL)	第四象限(HL)
长三角城市群	宁波、绍兴、舟山、台州	南通、常州、扬州、镇江、泰州、南京、嘉兴	上海、苏州、无锡、湖州
珠三角城市群	广州	惠州、深圳	珠海、佛山、肇庆、中山
京津冀城市群	承德、张家口		天津、北京

检验方法	长三角城市群	珠三角城市群	京津冀城市群
Hausman test-statistic	53.4666^***	27.8865^***	32.5127^***
Wald-spatial lag	35.4535^***	22.4824^***	28.8459^***
LR-spatial lag	44.7917^***	29.9623^***	31.9171^***
Wald-spatial error	38.0898^***	32.8036^***	33.6019^***
LR-spatial error	47.8319^***	35.2527^***	37.3784^***

知识密集型服务业集聚对城市群旅游创新影响的空间效应

Spatial effects of knowledge-intensive business services clustering on tourism innovation in urban aggolomerations

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变量	长三角城市群			珠三角城市群			京津冀城市群
变量	模型1	模型2	模型3	模型1	模型2	模型3	模型1	模型2	模型3
lnLQ	0.240^***	0.239^***	0.244^***	0.472^***	0.500^***	0.497^***	-0.631	-0.359	-0.582
lnPGDP	-0.174^*	-0.154	-0.159	-0.632	-0.622	-0.641	0.011^**	0.301^**	0.011^***
lnSTRUC	0.382^***	0.379^***	0.380^***	0.390^***	0.435^***	0.431^***	-0.472	-0.295	-0.496
lnURBAN	-0.084	-0.090	-0.072	1.849^***	1.672^***	1.660^***	0.128^**	0.340^**	0.306^***
lnICT	0.166^***	0.169^***	0.160^***	0.034^**	0.102^***	0.083^**	-0.119	-0.095	-0.170
lnTRANS	-0.104	-0.145^*	-0.114	-0.783	-0.919	-0.835	0.064^**	0.191^***	0.093^**
lnMARK	0.037^**	0.049^*	0.041^**	0.146^***	0.160^***	0.167^**	0.091^**	0.023^**	0.077^***
w×lnLQ	0.182^**	0.184	0.257^**	0.585^***	0.384	0.429	0.372	-0.080	0.228^**
w×lnPGDP	0.082	0.203	0.141	0.717^**	0.128	0.173	0.040^*	-0.364	-1.385
w×lnSTRUC	-0.137	-0.326^*	-0.468^**	-0.371^**	0.109	0.070	0.608^***	0.772^**	0.924^**
w×lnURBAN	-0.132	-0.488^*	-0.255	-1.780^***	-2.484^***	-2.436^***	0.379	-0.879	-1.014
w×lnICT	-0.004	-0.062	-0.070	-0.119	0.355	0.268	0.090^**	0.257^*	0.255^**
w×lnTRANS	0.010	0.098	0.114^*	0.961^***	0.750^*	0.883_*	0.107^*	-0.385	-0.238^**
w×lnMARK	-0.071^*	-0.031	-0.027	0.089	0.158	0.118	-0.092	0.175^**	-0.235
w×dep.var	0.568^***	0.475^***	0.538^***	0.603^***	0.252^**	0.388^***	0.623^***	0.231^*	0.432^***
R²	0.7455	0.7357	0.7583	0.7550	0.7698	0.7885	0.7598	0.7503	0.7618
LogL	55.814	57.379	62.388	25.114	34.729	38.174	16.054	20.086	22.048

长三角城市群
变量	直接效应	间接效应	总效应
lnLQ	0.2160^***	-0.2404	-0.0244
lnPGDP	-0.1526	0.1005	-0.0520
lnSTRUC	0.3202^***	-0.4955	-0.1753^**
lnURBAN	-0.1375	-0.5778	-0.7154
lnICT	0.1660^***	0.0345^*	0.2005^**
lnTRANS	-0.0994	0.1061^**	0.0067^*
lnMARK	0.0404^**	-0.0110^***	0.0294^***
珠三角城市群
变量	直接效应	间接效应	总效应
lnLQ	0.4611^***	-0.3648	0.0963^*
lnPGDP	-0.6555	-0.1320	-0.7875
lnSTRUC	0.4710^**	0.3886	0.8596^**
lnURBAN	1.3880^***	-2.6992^*	-1.2812^*
lnICT	0.1280^**	0.4501^**	0.5781^*
lnTRANS	-0.7431	0.8805	0.1373
lnMARK	0.1624^*	-0.0655^**	0.0969^*
京津冀城市群
变量	直接效应	间接效应	总效应
lnLQ	-0.5816	-0.0963	-0.6779
lnPGDP	0.2053^**	-2.2735^**	-2.4789^**
lnSTRUC	-0.3938	1.2097	0.8159
lnURBAN	0.1780^***	-1.5060^*	-1.3279
lnICT	-0.1470	0.3025^**	0.1556^*
lnTRANS	0.0623^**	0.3315^*	0.3938
lnMARK	0.0521^**	-0.3315^***	-0.2795^**

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