Apart from the loss of time and money, disputes between public authority and private partner in China’s public-private partnership (PPP) projects are destroying the government’s image of PPP support and the private partner’s investment confidence. This article aims to explore the main causes for PPP disputes, present the results of disputes, and then predict the litigation outcomes. Based on 171 PPP litigation cases from China Judgements Online within 2013–2018, the research identified 17 legal factors and explained how these factors influence the litigation outcomes, which are named as “prediction approach” in this study. Nine machine learning (ML) models were trained and validated using the data from 171 cases. The ensemble model of gradient boosting decision tree (GBDT), k-nearest neighbor (KNN) and multi-layer perceptron neural network (MLP) performed best compared with other nine individual ML models, and obtained a prediction accuracy of 96.42%. This study adds meaningful insights to PPP dispute avoidance, such as high compensation of expected revenues could prevent the government from terminating the contract unilaterally. To some extent, if parties consider the case litigation outcome, they are more likely prefer a rational settlement out of court to avoid further aggravation of the dispute, and would also alleviate the pressure of litigation in China.
Disputes are inevitable in public-private partnership (PPP) projects and generate great losses of time and money in practice. If an in-depth understanding of dispute sources can be obtained beforehand, the process of PPP may become more smooth. This paper aims to identify and assess the causes of PPP disputes between the public and private sectors. First, 15 causes are explored based on the PPP litigation cases from China Judgments Online. Second, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is utilized to provide a holistic understanding of the relative importance and define the cause-effect categories among PPP dispute sources. The results demonstrate that the top three decisive causes of PPP disputes are the repudiation of contracts (result category), lack of expertise and experience (reason category), and unreasonable risk allocation (result category). Further, dispute avoiding strategies are proposed to minimize or completely avoid the occurrence of PPP disputes. The outputs are expected to add meaningful insights to potential sources of dispute and dispute prevention mechanisms in PPPs. To some extent, the investors can develop strategic measures through the findings before entering into PPP markets.
The aim of this paper is to study a classic problem in actuarial mathematics, namely, an optimal reinsurance-investment problem, in the presence of stochastic interest and inflation rates. This is of relevance since insurers make investment and risk management decisions over a relatively long horizon where uncertainty about interest rate and inflation rate may have significant impacts on these decisions. We consider the situation where three investment opportunities, namely, a savings account, a share, and a bond, are available to an insurer in a security market. In the meantime, the insurer transfers part of its insurance risk through acquiring a proportional reinsurance. The investment and reinsurance decisions are made so as to maximize an expected power utility on terminal wealth. An explicit solution to the problem is derived for each of the two well-known stochastic interest rate models, namely, the Ho–Lee model and the Vasicek model, using standard techniques in stochastic optimal control theory. Numerical examples are presented to illustrate the impacts of the two different stochastic interest rate modeling assumptions on optimal decision making of the insurer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.