The synergistic evolution mechanism of green innovation and performance in the power industry under social networks
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    Abstract:

    This study examines how green technology innovation and corporate performance of electric power companies evolve synergistically from the perspective of social networks. In this paper, based on the green patent and basic operation data from power listed companies during 2009~2019, a two-layer network consisting of a decision layer and an influence layer is constructed. Deep learning is applied to fitting the mapping relationship between the two layers to establish a synergistic evolution mechanism model for enterprise green technology innovation and performance. In this model, enterprises act as nodes in each layer of the network, have social network properties, and exchange information in the network. Meanwhile, viewpoint dynamics simulation of inter-firm exchange is utilized to explain the evolutionary process, and the influencing factors and influence mechanisms behind enterprise green science and technology innovation decision are analyzed by comparing different green innovation game relationships among enterprises. It is found that: First, when the marginal utility of green science and technology innovation is high, the influence of inter-firm exchange on enterprise decision making is weak. At this time, enterprises should focus on their own operating conditions and improve their financial performance to provide a solid foundation for green science and technology innovation. Second, when the inter-firm green science and technology innovation relationship resembles a public goods game, the influence of enterprise decision making in the neighborhood is enhanced. The more frequent the inter-firm exchanges, the more active innovation is chosen by enterprises; the more frequent the inter-enterprise communication, the higher the proportion of enterprises choosing positive innovation, and the more significant the influence of enterprise operational ability on positive innovation. This study provides theoretical support for the decision regarding science and technology innovation in electric power enterprises and enriches the theory of green innovation and enterprise co-development.

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  • Online: December 03,2024
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