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International knowledge flows have become increasingly common and more frequent and are a key driving force promoting the development of global science. However, little attention has been paid to the determinants of international scientific collaboration. Using co‐publication data from the Web of Science database in the period 2000–2014, this paper illustrates spatial patterns of international knowledge flows and estimates the impact of geographical, technological, social, and cultural proximity on the variation of inter‐country collaboration in science. Our findings demonstrate that the coefficients of the four dimensions of proximity are positive and significant in panel estimates and cross‐section estimates—international knowledge flows are facilitated by geographical, technological, social, and cultural proximity. We also find that the effect of geographical and cultural proximity have waned over time, while the impact of social and technological proximity have strengthened.

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There is consensus among scholars that social networks are important mechanisms of knowledge spillovers. By occupying a central and advantageous network position in the collaboration network, it is easy for actors to access intangible external resources. However, empirical studies of the impact of network structure on knowledge production remain scarce. Based on copublication data from the Web of Science database (WoS) from 2000 to 2015, we construct eleven international scientific collaboration networks and empirically estimate the impacts of multiple network properties, comprehensively measured by degree centrality, structure holes, and small‐world quotient, on national knowledge output. Empirical results based on fixed effect negative binomial models suggest positive effects of the three facets of network properties, that is, higher degree centrality, structural holes, and small‐world quotient are beneficial for facilitating and improving national knowledge production, which in turn encourages international academic collaboration.

Technology transfer has become a vital pipeline for acquiring external knowledge. The purpose of this paper is to portray the spatial dynamics of intercity technology transfer networks in China’s three urban agglomerations based on patent right transaction data from 2008 to 2015. The integration of social networks and spatial visualization is used to explore spatial networks and influencing variables of the networks. The results demonstrate that Beijing, Shanghai, and Shenzhen are emerging as hubs in the three urban agglomerations. The spatial distributions of degree and weighted degree are significantly heterogeneous and hierarchical. The larger cities play the role of a knowledge and technology incubator, highly related to their economic scale, research and development (R&D) input, and innovation output. The evolution of intercity technology linkages is driven by the networking mechanisms of preferential attachment, hierarchical and contagious diffusion, path dependence, and path breaking. Moreover, we found that the geographical proximity and technology gaps are determinants of the strength of intercity technology linkages. As a result, it has been discovered that the network in the Beijing–Tianjin–Hebei agglomeration is organized in a tree network, while the Yangtze River Delta features a polycentric network and the Pearl River Delta has multi-star characteristics.

Continuous aggregation of socioeconomic factors is the key issue of sustainable development in urban agglomerations. To date, more attention has been paid to single urban agglomeration than to multiple agglomerations. In this paper, China's 19 urban agglomerations were selected as the case study and their spatial differences in factors aggregating ability were portrayed comparatively. Firstly, the spatial pattern of urban factors aggregating ability is relatively well distributed in all China's cases, most noticeably in the Yangtze River Delta urban agglomeration, closely followed by the Beijing-Tianjin-Hebei and the Pearl River Delta urban agglomerations. However, more significant differences on factors aggregating ability are noticeably seen between cities than among urban agglomerations. Meanwhile, the rank-size structure distribution of factors aggregating ability in China's 19 cases is in line with the Zipf's law of their urban systems, and divided into three types: Optimized, balanced, and discrete. Furthermore, the urban factors aggregation ability in one urban agglomeration is roughly negatively correlated with its primacy ratio of factors aggregating ability distribution. Lastly, urban agglomerations with higher average values of factors aggregating ability are concentrated on the three major urban agglomerations: The Yangtze River Delta, the Beijing-Tianjin-Hebei and the Pearl River Delta. Otherwise, high-high clusters in the three urban agglomerations are distinctly observed as well.Sustainability 2018, 10, 4179 2 of 20 gradually becoming vital growth poles of new-style urbanization development and the main body of China's participation in global competition, which depends on the support of continuous factor flows. Driven by "rent-seeking behavior" [8], variety of factors such as talents, technology, knowledge, information, and capital obey profit-seeking law, which leads to their continuous concentration on advantageous places [9]. As a result, the more competitive urban agglomerations tend to occupy a higher position in national urban systems and thus gain more developing opportunities. Moreover, their competitiveness and potential are accumulatively enlarged under the cyclical cumulative causal effect or Matthew effect. Therefore, the factors aggregating ability has become a key criterion to measure the sustainable competitiveness of urban agglomerations in national and inter-country competition contexts.Although factors aggregating ability is specially measured and documented by few scholars at present, much more attention is paid to theoretically refer to factors and their aggregation, such as factor endowment theory and factor flow theory. Generally, factors were classified to primary production factors (i.e., general natural resources such as land, water, forests, minerals) and created production factors (including knowledge and technology, innovation, capital and facility, etc.) by Porter [10]. Oskenbayev et al. further pointed out that the role of the first factors such as land, oil and coal i...