Are PPP projects poor fundability in China: Based on ensemble learning

The Public-Private Partnerships (PPP) financability is influenced by both internal and external factors of the project. Given the myriad of factors that influence the participation of social capital in PPP projects, in this study, we examine 14,038 PPP projects from the China Public-Private Partnership Centre (CPPPC) database. We aim to construct an ensemble learning-based prediction model for the financability of PPP project by selecting 61 feature variables across four dimensions, namely, project characteristics, local government, the market environment, and macroeconomics. The experimental results demonstrate that the dragonfly algorithm effectively improves model prediction accuracy through the reduction of feature dimensionality. From a feature combination perspective, the combinations related to the project itself, local government, and macroeconomic factors exhibit superior predictive performance than other combinations. Among the relevant factors, the project's intrinsic characteristics exert the most significant impact on social capital participation in PPPs, followed by local government factors. Notably, the inclusion of market environment variables tends to decrease the level of model accuracy, except when considering the project's intrinsic characteristics in isolation. Thus, a dynamic approach is recommended to capturing instantaneous variables such as the market environment is recommended for enhancing model performance. Additionally, social capital should rely on an assessment of the financial capacity of local governments to ensure effective PPP outcomes.