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Industrial Technology Progress

Study on the Mechanism of Establishing a Key Technological In⁃ novation Cooperation Network in the Green Energy Industry 

Kong Lingkai, Hu Qian 

(Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650093, China)

Abstract: The key technological innovations in green energy are essential for achieving "carbon peaking and carbon neutrality goals", promoting rapid development within the green energy  industry, and enhancing the country's new quality productivity. However, due to the complexity  and innovative requirements of key technologies in green energy, it is often difficult for a single  organization to undertake all research and development tasks independently. Therefore, how to  accelerate knowledge and technology exchange through effective organizational cooperation and  promote innovation in key technologies in green energy has become an important issue that  needs to be addressed in this field. This study aims to delve into the mechanism behind the for⁃ mation of a cooperation network for the green energy key technology innovation, uncover the in⁃ teraction patterns among nodes within the network, and offer insights for optimizing resource allo⁃ cation, enhancing cooperation efficiency, and accelerating technological innovation. Ultimately, this will contribute to the sustainable development of the green energy industry. This study used patent data from the green energy industry as its foundation, extracted the  primary and secondary classification numbers of green energy patents, and constructed an asym⁃ metric matrix to form a directed network. Social network analysis methods, along with tools such  as Gephi, were employed to measure degree centrality, betweenness centrality, closeness central⁃ ity, and structural holes for each node. Based on these measurement results, key technologies in  green energy were identified. Following this identification, patents from enterprises that collabo⁃ rated with universities, research institutions, and other entities were selected. By using different  types of patent applicants as nodes, a collaborative network for green energy key technology inno⁃ vation was created. Furthermore, ERGM was used to model and analyze the formation mecha⁃ nism of the cooperation network. This analysis aimed to explore the influence of endogenous  structural variables, such as preferential selection and transitivity, as well as node attributes in⁃ cluding knowledge diversification, the roles of enterprise leaders in industry-universityresearch collaboration, the influence of technology leaders, and the impact of geographical dis⁃ tance on the formation of the cooperation network. The results of the study show that the optimality and transitivity of network structure facili⁃ tate the natural formation of efficient clusters in cooperative networks. High-quality nodes at⁃ tract more cooperation opportunities, and cooperative relationships tend to flow through estab⁃ lished connections, forming a virtuous cycle. Additionally, the diversification of knowledge  among network nodes promotes enterprise cooperation, while industry-university-research col⁃ laboration led by enterprises accelerates the integration of knowledge and practice. Technology  leaders lead the direction of cooperation with their foresight and influence. Furthermore, the re⁃ duction of geographical distance lowers the barriers to cooperation, encourages face-to-face  communication, and provides favorable conditions for the development of cooperation networks. This study uncovers the mechanism behind the formation of a key technological innovation  cooperation network in the green energy industry. Additionally, it offers a scientific foundation  for the government to develop relevant policies, assists enterprises in selecting partners, and  guides scientific research institutions in planning their research directions. 

Key words: green energy industry; key technology identification;innovation cooperation net⁃ work; social network analysis; exponential random graph model