# Research on PPP Enterprise Credit Dynamic Prediction Model
## 1 Introduction  
Local government financing vehicles (LGFVs) are established by the local government and its departments and institutions, etc., through financial grants or injection of assets, such as equity, with investment financing functions and a separate legal entity, to help the local government perform a certain function as the main body of the company. In recent years, the tightening of China's banks' ability to invest in credit to cities has superimposed the high frequency of default events of all local governments. In the debt expansion cycle, the solvency pressure of LGFVs in all local regions has also gradually accumulated. Therefore, how to effectively protect against the hidden debt crisis of LGFVs has become an important problem for the development of high-quality service in public-private partnerships (PPP) [1].  
A good credit relationship is the premise of a smooth pursuit of the PPP model. For a long time, some local governments attached more importance to financing than management, resulting in frequent debt performance disputes among some LGFVs [2]. Moreover, the risk of non-standard default is transmitted along the guarantee chain, resulting in the deterioration of the regional financing environment. China's General Office of the State Council in No. 19, May 2022, _Opinions on Further Revitalizing Stock Assets and Expanding Effective Investment_, pointed out the need to improve the regulatory docking mechanism of social capital investment and financing cooperation and to prevent resolving the riskof potential debt of firms. This indicates that whether social capital parties have sufficient capacity for investment, construction, operation, management, innovation, etc., is a key prerequisite for the success of the PPP project as a whole [3; 4].  
At present, the academic research on the PPP model is mainly focused on the areas of benefit distribution, risk sharing, etc. Credit assessment studies are dominated by methods, including traditional expert empirical methods, discriminant analysis, and machine learning methods, such as the logistic model, SVM model, and BP neural network model, etc.  
[PERSON] [5], [PERSON] [6], and [PERSON] [7] et al. applied machine learning algorithms, such as logistic, IVHFSS-IFAHP, KMV, random forest, SVM, and XGBoost, to identify, evaluate, and predict credit risks. However, models, such as random forests, SVM, and XGBoost are less transparent and lack the interpretability of the models, making plausibility of model results poorly interpreted [8]. Additionally, most scholars only pursue the output results of the model, but do not explain in detail the impact of variables on the model, especially the process of model decision-making.  
Due to the long period of the PPP project, static credit evaluation results are difficult to fit into the authentic credit situation of participating firms [9]. LGFVs also are affected by the regional economy to a greater extent than the local government [10]. Consequently, scientific and rational analysis of risk factors, interpretability of machine learning model issues encourage us to develop further research.  
In this paper, our objectives are as follows: (1) Use the XGBoost-logistic evaluation model to evaluate the credit of listed LGFVs and determine which indicators have the greatest impact on the results. (2) Perform an in-depth analysis of the impact of characteristic variables on the model and the potential risk of default, and explain the model decision-making process. (3) Using K-fold cross-validation, lift, and Gain analysis in-depth and detailed comparison of XGBoost-logistic and logistic evaluation were used to compare XGBoost-logistic and logistic in depth and in detail, and the comprehensive performance of the model was evaluated to determine the feasibility and application value of the model.