关联度—复杂度框架下外部联系对区域 科学知识多样化的影响 ——基于中国地级市的研究

The development of new scientific knowledge in a region is imperative for the enhancement of regional independent innovation capacity. While extant studies have focused on the endogenous dynamics of regional scientific knowledge diversification, discussions on the role of external linkages are scarce and controversial. In this study, we incorporated external linkages into the relatedness-complexity framework and systematically investigated the impact of external linkages on the diversification of regional scientific knowledge and its heterogeneous characteristics. Based on the data of highly cited papers from CNKI, key research findings are as follows: 1) The scientific knowledge output of Chinese cities is characterized by the spatial feature of "large dispersion and small agglomeration", with municipalities and provincial capitals producing numerous highly cited papers. The entry of new scientific knowledge occurs more often in ordinary prefecture-level cities, and there is a spatial mismatch between the pattern of scientific knowledge introduction and production in cities. 2) The scientific knowledge base of most Chinese cities is characterized by "high-potential, high-quality" and "low-potential, low-quality" types. Cities with high potential for developing new scientific knowledge also possess scientific knowledge of higher complexity. The backbone network of China’s urban scientific knowledge cooperation is characterized by a hierarchical structure. Only cities with high administrative levels, such as municipalities and provincial capitals, have numerous external knowledge linkages, while the external knowledge linkages of ordinary prefecture-level cities are characterized by preferential linkages or geographic proximity. 3) External linkages contribute significantly to regional scientific knowledge diversification, with the share of external linkages contributing to complexity diversification and hindering relatedness diversification, and the geographic diversity of external linkages contributing to relatedness diversification but hindering complexity diversification. 4) The impact of external linkages is characterized by heterogeneity in terms of network location and knowledge base dimensions. The scientific knowledge diversification in the network core region is positively influenced by an increase in geographic diversity of external linkages, while the network peripheral regions are positively influenced by an increase in the share of external linkages. The positive impact of geographic diversity of external linkages is more pronounced than the share of external linkages in the "high-potential, high-quality" regions, while the positive impact of geographic diversity of external linkages is less significant than the share of external linkages in the "low-potential, low-quality" regions. The findings provide policy implications for enhancing scientific knowledge innovation capacity in different regions of China by optimizing external linkages.