Can Digital Infrastructure Predict Regional Innovation Capacity? Evidence Based on Machine Learning and the SHAP Explanatory Framework

Shasha Xie, Shusheng Xu, Wei Xu, Guo Yu, Yunli Li, Jianqiu Wu, Minghua Xiao, Xuesong Cheng, Peng Zhang

Digital infrastructure is a critical foundation for promoting regional innovation and achieving sustainable development. However, existing studies have primarily focused on its impact effects, while paying limited attention to whether digital infrastructure can effectively identify future changes in regional innovation levels. Using panel data from 30 Chinese provinces during 2011–2023, this study constructs a comprehensive digital infrastructure index (Digital) and integrates the XGBoost and SHAP methods to evaluate the predictive capability of digital infrastructure for regional innovation. The results indicate that digital infrastructure improves the prediction accuracy of regional innovation and maintains high importance across different models and robustness tests. Further analysis reveals significant nonlinear and heterogeneous characteristics in its predictive contribution, with stronger effects observed in central and western regions, regions with relatively low levels of digital infrastructure development, and regions with higher R&D investment. These findings suggest that strengthening the construction and application of digital infrastructure can enhance regional innovation governance capacity, providing valuable insights for promoting coordinated regional innovation and sustainable development.