Evolution Characteristics of Government-Industry-University-Research Cooperative Innovation Network for China’s Agriculture and Influencing Factors: Illustrated According to Agricultural Patent Case

2018 ◽  
Vol 28 (1) ◽  
pp. 137-152 ◽  
Author(s):  
Erling Li ◽  
Fei Yao ◽  
Jiaxin Xi ◽  
Chunyang Guo
Keyword(s):  
Influencing Factors ◽  
University Research ◽  
Innovation Network ◽  
Evolution Characteristics ◽  
Cooperative Innovation

scholarly journals Analysis on Evolution Characteristics of Industry University Research Innovation Network in Guizhou Province Based on Patent

2021 ◽  
Vol 8 (10) ◽  
pp. 141-144
Author(s):  
Yu Zhao ◽  
◽  
Nana Hu ◽  
Keyword(s):  
Structural Characteristics ◽  
University Research ◽  
Guizhou Province ◽  
Central Position ◽  
Innovation Network ◽  
Cooperative Groups ◽  
Patent Data ◽  
The Core ◽  
Evolution Characteristics ◽  
Evolution Dynamics

Based on the industry-university-research cooperation patent data of Guizhou Province from 1986 to 2020, this paper constructs Guizhou industry-university-research innovation network, and empirically explores the overall structural characteristics of Guizhou industry-university-research innovation network, such as network scale and network density, as well as the time evolution dynamics of nodes and cooperation intensity. It is found that the scale of industry-university-research innovation network in Guizhou Province is gradually expanding, the nodes are gradually increasing, and more cooperative groups have been formed, but the overall network is low density; Guizhou University and other universities and scientific research institutions have always occupied the central position of the network. Although enterprises are not in the core position, the intensity of cooperation with institutions is gradually increasing.

scholarly journals Modeling and Simulation of Knowledge Innovation Diffusion in Weighted Scale-free Industry-University-Research Cooperative Innovation Network

2020 ◽  
Author(s):  
Xia Cao ◽  
Chuanyun Li ◽  
Jinqiu Li ◽  
Yunchang Li
Keyword(s):  
Innovation Diffusion ◽  
Network Structure ◽  
Knowledge Diffusion ◽  
University Research ◽  
Innovation Network ◽  
Knowledge Level ◽  
Growth Trend ◽  
Scale Free ◽  
Knowledge Innovation ◽  
Cooperative Innovation

Abstract With the weighted scale-free cooperative innovation network of industry-university-research established by the weighted evolutionary BBV model as the research object, based on the interaction between knowledge innovation diffusion and network structure, a corresponding model of knowledge innovation diffusion is constructed. By using complex network theory and simulation analysis methods, the evolution law of knowledge innovation diffusion in the industry-university-research cooperative innovation network is analyzed. The results show that the overall knowledge level of the industry-university-research cooperative innovation network shows a rapid growth trend, and the growth rate of knowledge shows a changing trend of slowing first and then increasing abruptly; the greater the degree of the innovator, the higher its knowledge level; the more stable the cooperative relationships between innovator, and the stronger its knowledge diffusion ability; the knowledge diffusion mode and network structure are the reasons for the emergence of sudden changes in the network; the knowledge diffusion constraints and network structure are the keys to knowledge innovation and diffusion; with the passage of time, the knowledge level differentiation among innovators gradually increases, and the role of hub institutions in knowledge innovation diffusion becomes increasingly prominent.

scholarly journals Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

2021 ◽  
Vol 13 (6) ◽  
pp. 1150
Author(s):  
Yang Zhong ◽  
Aiwen Lin ◽  
Chiwei Xiao ◽  
Zhigao Zhou
Keyword(s):  
Influencing Factors ◽  
Yangtze River ◽  
Electrical Power ◽  
Random Forest Algorithm ◽  
Temporal Dynamic ◽  
Urban Agglomerations ◽  
Evolution Characteristics ◽  
Spatio Temporal ◽  
Electrical Power Consumption ◽  
Gwr Model

In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

Sign in / Sign up

Export Citation Format

Share Document