Abstract

۱ Introduction

۲ State of the art

۳ Methodology

۴ Result analysis and discussion

۵ Conclusion

Declarations

References

Abstract

     In recent years, public–private partnership (PPP) mode has been developed greatly in China’s infrastructure and public services, which can effectively solve the financial difficulties of local governments. This article selects the residual value risk (RVR) as an important index in the risk control of PPP project and constructs and verifies the pre-optimization method of RVR. A method is presented to control and optimize RVR before the risk occurs. The vulnerability as the breakthrough point of RVR beforehand control through DRCM sensitivity analysis is carried out on the effect of the beforehand control of RVR; then, using the optimal control theory to design RVR beforehand control of optimization process, the correctness and validity of the method are verified by an example.

Introduction

     Public–private partnership (PPP) projects, namely government and social capital cooperation. The Treasury defines it as: ‘‘In the field of infrastructure and public services, government and social capital to establish a cooperative relationship based on contract, aims to use market mechanism to allocate risk, the supply of public products and services to improve the quantity, quality and efficiency’’ (Bodiako et al. 2017; Cui et al. 2018; Tariq and Zhang 2020). Specifically, the PPP model as Build–Operate– Transfer (BOT), Build–Own–Operate (BOO), Build– Lease–Operate–Transfer (BLOT), Design–Build–Operate (DBO), Transfer–Operate–Transfer (TOT) and Private– Finance–Initiative (PFI), and a series of specific financing model of the overall concept. It represents the social capital and the government different degree and different ways of cooperation and focuses on the introduction of social capital investment—construction—operation in infrastructure projects, emphasis on a ‘‘win–win’’ situation of mutual benefit. Its applications range from traditional industries such as roads, bridges, rail transit, gas, water supply, sewage treatment and garbage collection to prisons, schools, hospitals, defense, aerospace and other fields (Xu and Bureau 2016). The earliest application of PPP in China is the BOT project of Shenzhen Shajiao B power plant, and its successful experience has made it a preliminary recognition in China. Subsequently, PPP mode has been tried in various fields, and its specific content is also promoted to TOT, BOO, BOOT and other forms.

Conclusion

     Through the detailed literature, the current research status of PPP mode, PPP project residual value risk (RVR), PPP project risk management, etc., is summarized and reviewed. Then, the research idea of risk control of general engineering project is borrowed, and the risk conduction network of PPP project is constructed and verified. Then, based on the analysis of the risk conduction effect of PPP project, the causal conduction network of the risk conduction network of PPP project is deduced. And by constructing the structural equation model for causal testing, the results show that the model parameter estimation results are ideal, the model and data are fitted with higher fitting degree, and the causal hypothesis is basically correct. Based on the empirical analysis results of RVR simulation in what, risk conduction network simulation of West tunnel project and RVR comparison curve under three risk control methods. The parameter estimation results of SEM model can also provide quantitative explanation for the transmission relationship between risks: The PPP project risk can be constructed using a path coefficient and flow evaluation model (RFEM), and its function is use SEM to remove autocorrelation and purify the advantage of measuring error, easy and quickly to solve internal dynamic complex formed by the risk of cluster risk flow; the influence coefficient Y can be used to solve the transformation degree between the risk flow and provide the quantitative basis for the accurate construction of the correlation model. In the future, we will continuously optimize and improve the current model and compare it with more models.