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

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。

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

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.