Exploration and future trends on spatial correlation of green innovation efficiency in strategic emerging industries under the digital economy: A social network analysis.

With digital technological change and the increasing frequency of interregional innovation links, the spatial correlation and diversity of strategic emerging industries' green innovation efficiency (SEI-GIE) need to be explored in depth. This paper innovatively constructs the SEI-GIE input-output index system under digital economy. The proposed grey model FINGBM(1,1) with ω -order accumulation and weighted initial value optimization realizes effective prediction of 7 input-output indicators of 30 provinces in China from 2021 to 2025. Super-SBM-DEA, gravity model, and social network analysis are applied to explore spatial network structure's dynamic process of SEI-GIE from 12th to 14th Five-Year-Plan period (2011–2025). Empirical results show that (1) Under the effect of digital economy, the SEI-GIE in China generally shows a U-shaped fluctuation trend, in which the growth trend in the central region is obvious, and the western region shows significant fluctuations. (2) The spatial correlation network of SEI-GIE presents a complex and stable center-periphery circle. Particularly, the overall increase in network efficiency highlights the strong small-world characteristics. (3) Beijing, Shanghai, Zhejiang and Jiangsu have always been in the leading core position, with strong influence and control; And Tianjin's core position in the network will decline. Additionally, Guangxi and Chongqing have great potential, but Guangdong needs to strengthen its radiation effect. (4) Block model shows that plate-I (Beijing, Tianjin) receive spatial spillovers from others, while plates-III,IV have significant spillover effects. This study provides theoretical reference for policymakers from a network perspective to promote development of China's SEI-GIE. • SEI-GIE input-output index under digital economy is innovatively constructed. • Indicators of China's 30 provinces are effective predicted by novel FINGBM(1,1). • Spatial correlation network dynamic process of SEI-GIE is explored from 2011 to 2025. • Complex center-periphery circles are presented in spatial network of SEI-GIE. • Theoretical reference for policymakers from a network perspective is provided. [ABSTRACT FROM AUTHOR]