Spatial linkages and endogenous mechanisms of technology transfer in the Guangdong-Hong Kong-Macao Greater Bay Area

被引：0

作者：

Dai L. ^{[1
]}

Ding Z. ^{[1
]}

Ma H. ^{[2
]}

Cao Z. ^{[3
,4
]}

Wang R. ^{[1
]}

机构：

[1] School of Public Administration, Nanjing University of Finance and Economics, Nanjing

[2] Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing

[3] College of Architecture and Urban Planning, Tongji University, Shanghai

[4] Key Laboratory of Spatial Intelligent Planning Technology, Ministry of Natural Resources of the People's Republic of China, Shanghai

来源：

Dili Xuebao/Acta Geographica Sinica | 2024年 / 79卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Guangdong-Hong Kong-Macao Greater Bay Area; structural dependence; technology transfer; temporal exponential random graph model; urban network;

D O I：

10.11821/dlxb202406009

中图分类号：

学科分类号：

摘要：

Technology transfer is pivotal in narrowing regional disparities, optimizing resource allocation, and fostering collaborative innovation. While existing literature predominantly explores the factors influencing technology transfer based on theories of technology disparity, technology demand, technology absorption, and proximity, this study introduces new insights from the perspective of network symbiotic evolution, with a focus on endogenous structures and micro mechanisms. By analyzing data on invention patent transfers obtained from the China National Intellectual Property Administration, this study constructs spatial linkage networks of technology transfer in the Guangdong-Hong Kong-Macao Greater Bay Area for six time points spanning 2007 to 2018. It then examines the evolutionary characteristics of the spatial patterns and explores the underlying mechanisms through temporal exponential random graph models. The findings reveal the following: (1) The technology transfer network in the Greater Bay Area has evolved from loose and homogeneous linkages to a dual-core pattern and subsequently to a polycentric structure. Shenzhen and Guangzhou are regional technology trade centers, while Dongguan, Foshan, Zhongshan, and Huizhou are second-tier cities. Together, they form a Shenzhen-Dongguan-Huizhou-Guangzhou-Foshan-Zhongshan community characterized by multi-dimensional proximity. Hong Kong and Macao are relatively marginalized cities within the Greater Bay Area urban agglomeration, primarily engaged in one-way technology transfer due to institutional differences and regional division. (2) The scale and structure of the technology transfer network in the Greater Bay Area have significantly improved. The technology transfer path has undergone steady changes and gradual optimization, demonstrating increasing reciprocity. The hierarchical structure of the network tends to converge, exhibiting enhanced connectivity and cohesion as it develops into a balanced, clustering, and polycentric network. (3) Both endogenous and exogenous forces drive the evolution of the technology transfer network in the Greater Bay Area. Endogenous factors can reduce cities' reliance on exogenous factors. The level of economic development, R&D investment, and the ability to transform technological and scientific outputs within a city can promote technology transfer. Moreover, there are sender effects and receiver effects. Institutional proximity facilitates technology transfer, followed by spatial contiguity and technological proximity. Structural dependence and time dependence are crucial endogenous driving forces for the evolution of the technology transfer network in the Greater Bay Area, as evidenced by delayed reciprocity, transfer closure, stability, and innovation. © 2024 Science Press. All rights reserved.

引用

页码：1503 / 1520

页数：17

共 50 条

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