Abstract

Although the social value of science popularization has been increasingly recognized, the specific role of science popularization in regional innovation remains inadequately explored in the existing literature. Most of the related research focuses on traditional factors such as R&D investment. To bridge this gap, this study empirically examines the impact of science popularization personnel on regional innovation by analyzing panel data from 30 provincial-level regions in China from 2013 to 2022. The study employs two-way fixed effects models for baseline analysis, and utilizes threshold regression models, explicitly selecting government funding for science popularization as the threshold variable, to investigate non-linear mechanisms.

The empirical results indicate that human capital investment in science popularization serves as a significant driver of regional innovation capacity, a conclusion that remains robust after addressing endogeneity and conducting rigorous reliability tests. Furthermore, the analysis reveals significant regional heterogeneity in China, with the marginal utility of human capital being most pronounced in the western regions, suggesting a higher potential for catch-up growth. Crucially, the study identifies a structural break: The promotional effect of human capital is contingent upon public funding. A significant threshold effect exists, where the impact of human capital intensifies substantially only after financial investment crosses a specific critical level.

Based on these findings, specific policy recommendations are drawn. Policymakers should prioritize the strategic allocation of science popularization talent towards the western regions in China to leverage their high marginal returns. Moreover, rather than making fragmented, low-level investments, fiscal strategies must ensure that funding for science popularization exceeds critical thresholds. This “critical mass” approach is essential to unlock the full potential of human capital and trigger qualitative leaps in regional innovation capacity.