Improving sustainable performance of China's new energy industry through collaborative innovation network resilience

Qin Liu, Ruming Chen and Qian Yu

Renewable and Sustainable Energy Reviews, 2024, vol. 201, issue C

Abstract: The sustainable development of the new energy industry is crucial and needs support from collaborative innovation networks (CINs). However, CINs may face hindrances or interruptions under multiple risk shocks, impeding their effectiveness in promoting sustainable performance (SP). Enhancing CIN resilience is essential for the normal operation of CINs and requires support from the innovation ecosystem. This study aims to improve CIN resilience by optimizing innovation ecological factors, thereby improving the SP of new energy industry. The study selects China's new energy industry as the empirical object. Firstly, the impacts of CIN resilience on SP are explored through regression analysis. Secondly, the impacts of nine driving factors proposed based on innovation ecosystem on CIN resilience were uncovered by the temporal exponential random graph model. Lastly, network simulation was utilized to study how to optimize CIN resilience through network edge addition strategies and identify key regional cooperations for enhancing CIN resilience more effectively. The results indicate that: (1) CIN resilience can improve SP, with adaptive capacity exerting the most promotional effect. (2) Innovation ecological factors dynamically and heterogeneously drive CIN resilience. Innovative market subjects and technical environments exhibit substantial positive impacts on CIN resilience. (3) Promoting innovation cooperations of intermediary bridge regions can optimize CIN resilience. This study offers practical strategies for Chinese local governments and other countries or regions to formulate effective industrial policies and provides valuable insights for new energy enterprises.

Keywords: New energy industry; Sustainable performance; Innovation network; Network resilience; Innovation ecosystem; Temporal exponential random graph model; Complex network (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2024.114625

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