Spatial Correlation of Regional Standard Innovation and the Influencing Factors

This study investigates the spatial correlation of regional standard innovation and its influencing factors. It first reviews the theories of standard innovation and cooperation. Using Granger causality tests, it examines interregional influences on standard innovation in China from 2001 to 2023, constructing total, positive, and negative correlation networks. Through network analysis, the study explores structural characteristics and positional blocks. Incorporating variables such as standard innovation cooperation, it employs QAP correlation and regression analysis to identify key influencing factors. The findings indicate that spatial correlation in standard innovation is widespread, with the total correlation network formed by overlapping positive and negative correlations. Centrality and block model analyses reveal regional disparities—eastern provinces show stronger positive correlations but also receive more negative ones, whereas central and western regions follow the opposite trend. Standard cooperation networks play a crucial role, with economically developed regions like Beijing and Guangdong at the core. Additionally, differences in economic development, marketization, and industrial structure significantly impact spatial correlation. Based on these insights, this study recommends optimizing regional standard cooperation networks, reducing disparities in innovation capacity, establishing a collaborative system, and developing a monitoring mechanism for spatial correlations. These measures aim to enhance synergy in regional standard innovation and foster an open, efficient, and cooperative innovation system.