Deconstructing the Formation and Dependency Relationships of Dual “Basic–Applied” Networks in China’s Low-Carbon Technology Innovation

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🔬研究者:Network analysis methodology (ERGMs, MERGMs, NK model) applied to innovation systems; findings on formation factors and dependencies.

🏛政策担当者:Insights on how basic and applied research networks interact, informing differentiated governance of innovation networks and resource allocation.

Low-carbon technology innovation serves as the core driver for multiple countries striving to achieve their dual-carbon goals. Therefore, building efficient low-carbon technology innovation networks and accelerating low-carbon technological innovation have become key focuses of academic research. Leveraging patent and publication data, this study constructs dual networks for low-carbon basic and applied research. It employs Exponential Random Graph Models (ERGMs) and Multilevel Exponential Random Graph Models (MERGMs) to explain the different formation factors and dependency relationships within dual networks. Building on this, this study introduces the NK model to analyze the order of effects of these network formation factors and dependencies. The findings reveal the following: (1) The formation factors of dual low-carbon innovation networks differ significantly. For the basic research network (BRN), the key formation factors—in order of effect—are collaboration stability, transitive closure, partner addition, the Matthew effect, and knowledge siphoning. For the applied research network (ARN), the key formation factors—in order of effect—are historical collaboration, collaboration stability, partner addition, cognitive proximity, and knowledge siphoning. (2) The BRN and ARN exhibit an asymmetric dependency. The dependence of the BRN on the ARN is manifested as structural symbiosis, whereas the ARN, guided by the BRN, demonstrates the transmission of collaborative relationships. This study elucidates the complex formation mechanisms and dependency patterns of low-carbon technology innovation networks, providing a theoretical foundation and decision-making support for the differentiated governance of network structures and the optimized allocation of innovation resources.

• openalex https://doi.org/10.3390/systems14050493first seen 2026-05-17 05:59:00