dc.contributor.author |
Dai, Liang |
dc.contributor.author |
Derudder, Ben |
dc.contributor.author |
Cao, Zhan |
dc.contributor.author |
Ji, Yufan |
dc.date.accessioned |
2024-02-28T19:40:15Z |
dc.date.available |
2024-02-28T19:40:15Z |
dc.date.issued |
2023 |
dc.identifier.citation |
International Journal of Urban Sciences, vol. 27(3), 2023, pp. 371-389. |
dc.identifier.other |
https://doi.org/10.1080/12265934.2022.2042365 |
dc.identifier.uri |
http://repozytorium.umk.pl/handle/item/6994 |
dc.description.abstract |
Drawing on data on scientific co-publications derived from the Web
of Science for the periods 2002–2006 and 2012–2016, we construct
and analyse a key element of China’s intercity knowledge networks
(CIKNs): scientific collaboration networks. Employing networkanalytical
and exponential random graph modelling techniques,
we examine the evolving structures and driving mechanisms
underlying these CIKNs. Our results show that the density of the
CIKNs has significantly increased over time. CIKN flows are dense
in the Southeastern but sparse in the Northwestern part of China,
with the Hu Line acting as a clearly visible border. As the
dominant knowledge centre, Beijing is involved in scientific
collaboration networks throughout the country, with the
diamond-shaped structure anchored by Beijing-Shanghai-
Guangzhou-Chengdu becoming evident. We find that preferential
attachment and transitivity are significant endogenous processes
driving scientific collaboration, while a city’s administrative level
and R&D investment are the strongest exogenous factors. The
impact of GDP and geographical proximity is limited, with
institutional proximity being the most sizable of the well-known
suite of proximity effects. |
dc.description.sponsorship |
The research presented in this paper was financially supported through research project number 2020/38/A/HS4/00312 of the Polish National Science Centre (NCN). |
dc.language.iso |
eng |
dc.rights |
Attribution 4.0 Poland |
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/deed.pl |
dc.subject |
Intercity knowledge network |
dc.subject |
scientific collaboration |
dc.subject |
social network analysis |
dc.subject |
exponential random graph model |
dc.subject |
China |
dc.title |
Examining the evolving structures of intercity knowledge networks: The case of scientific collaboration in China |
dc.type |
info:eu-repo/semantics/article |