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This study aims to investigate the determinants of the capital structure of public–private partnership (PPP) projects in China and the nonlinear relationship between them.

First, this study identifies potential factors that can influence the capital structure of PPP projects based on literature and theoretical analysis. Second, this paper collects data from PPP projects in China and empirically investigates them using multiple linear regression and machine learning methods. Finally, for machine learning model results, this paper adopts the Shapley additive explanations to interpret them.

The results show that project size, contract duration, number of sponsors, urbanization level and regional openness are key factors influencing project capital structure, and there is a nonlinear relationship between all these factors and capital structure.

Theoretically, this study complements the influencing factors of PPP project capital structure and reveals their nonlinear relationship. Practically, the findings of this study can help PPP project participants formulate project capital structure more scientifically.

Practically, the findings of this study can help project managers to recognize the important factors affecting the capital structure of PPP projects and formulate capital structure more scientifically. Moreover the results are conducive to policymakers to predict a reasonable capital structure for PPP projects and better control project risks. These research findings can also help creditors make more accurate loan decisions and promote project success to meet the needs of the general public.

Most existing literature has studied the linear relationship between influencing factors and the capital structure of PPP projects. This study uses machine learning models to explore the nonlinear relationship between influencing factors and the capital structure of PPP projects and explains the working principles.