The pattern, evolution, and mechanism of venture capital flows in the Guangdong-Hong Kong-Macao Greater Bay Area, China

Abstract

As an important innovation flow, venture capital has been examined in urban network research. However, the segmentation of capital categories and the cross-scale connection of capital remain scarcely analyzed. This study focuses on the structure and industry differentiation of venture capital flows in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and its cross-scale network characteristics. Based on a venture capital database covering capital amount, investment subject address information, and industry information (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018), this article examines the spatial distribution and network structure of venture capital in the GBA by means of a distance-based test of spatial concentration approach and social network analysis. Key findings show that: (1) Venture capital institutions and startups in the GBA present a high-concentration distribution pattern. In the past 20 years, venture capital activities in the GBA have substantially increased, forming a complex urban network structure with Guangzhou, Shenzhen, and Hong Kong as the core of this network. (2) Different types of venture capital show significantly different urban network structures, with manufacturing, the Internet industry, the financial sector, the cultural media industry, and the medical and health industry as the five industry types with the largest capital flow in the GBA.(3) Cross-scale research on the venture capital network reveals the position of the GBA as a capital hub in China, which forms a dense venture capital connection network with major cities on a national scale. (4) The network structure of venture capital in the GBA is influenced by multi-dimensional proximity, institutional factors, urban economy, and path dependence.Along with these three key mechanisms, the GBA has grown into a national-scale and even global-scale venture capital center.

Mapping the educational Frontier: Unleashing the Potential of artificial intelligence talents through cooperative planning in the Guangdong-Hong Kong-Macao greater bay area

“…The nine cities in Guangdong Province, as important manufacturing centers, may emphasize the application of AI in manufacturing and supply chain management. Hong Kong and Macau have strong financial and service industries and may focus more on applications in finance, banking, and innovative city development [ 25 ]. These differences will affect AI talent's characteristics and skill requirements in each region [ 26 ].…”

“…These have resulted in educational differences among the nine urban agglomerations in Guangdong Province and the two special administrative regions of Hong Kong and Macau in four aspects: education system [ 7 , 8 ], academic cooperation [ 9 , 10 ], industrial integration [ [11] , [12] , [13] , [14] ], and cross-border opportunities [ [15] , [16] , [17] ]. These will profoundly impact the five major factors of educational background [ [18] , [19] , [20] , [21] ], cultural and language [ 22 , 23 ], industry and opportunities [ [24] , [25] , [26] ], collaboration and communication [ 27 , 28 ], policies and support [ [29] , [30] , [31] ] for cultivating AI talents.…”

An Integrated Duranton and Overman Index and Local Duranton and Overman Index Framework for Industrial Spatial Agglomeration Pattern Analysis

“…The central region in Guangdong is the Pearl River Delta, comprising nine cities: Guangzhou, Foshan, Zhaoqing, Shenzhen, Dongguan, Huizhou, Zhuhai, Zhongshan, and Jiangmen. Presently, Guangdong Province grapples with a serious industrial and economic imbalance between its central region and the other regions [61,62]. Preventing the excessive concentration of industries in the central region and fostering a coordinated regional development strategy are pressing concerns [61].…”

“…Presently, Guangdong Province grapples with a serious industrial and economic imbalance between its central region and the other regions [61,62]. Preventing the excessive concentration of industries in the central region and fostering a coordinated regional development strategy are pressing concerns [61]. In addition, in 2022, the value added by the C39 industry in Guangdong Province significantly surpassed that of other industries.…”

Spatial patterns and controlling factors of the evolution process of karst depressions in Guizhou province, China

“…A point density map is particularly useful in analysis of the spatial distribution of karst depressions because it can reflect the spatial density variability of depressions, especially by visualizing the concentration of depressions within a region, with larger KD values indicating a higher depression density within the region. In practice, it is often necessary to set a default search radius, and the algorithm will finally generate a spatial trend surface by searching all depression point data within that radius distance and calculating the density contribution value of each data point based on the kernel density (Wu et al, 2022;Zhao et al, 2022). The kernel density function is expressed as:…”