PPP模式下基础设施项目风险评价研究

发布时间：2018-05-13 13:44

本文选题：PPP + 基础设施　；参考：《中北大学》2017年硕士论文

【摘要】：当前,PPP(Public-Private-Partnerships)模式广泛应用于我国的基础设施领域。但由于其包含了众多的融资模式,且在我国尚处于发展阶段,因此如何对该模式进行风险管理就成为了各参与方在项目管理时的瓶颈问题之一,如果不能在风险因素识别的基础上准确评价各融资模式的项目风险,则情况将变得更加复杂。鉴于此,本文提出基于主成分分析法与BP神经网络相结合的PPP项目风险评价方法。本文从工程项目的视角出发,通过筛选及整理PPP基础设施项目已有文献资料,从而识别出PPP基础设施项目的潜在风险因素,然后设计问卷并展开调研,以获得专家对各风险评价指标发生概率和危害程度的评估数据,并计算得出各指标的影响程度和权重,据此组建了包括4个层级,7个一级指标和36个二级指标的PPP项目风险评价指标体系。然后,选择山西省拟建的13个PPP基础设施项目作为样本,对PCA-BP风险评价方法进行应用研究,并通过MATLAB 2010实现了BP神经网络的运算,结果表明了PCA-BP方法进行风险评价的有效性。为进一步表明PCA-BP神经网络较单一BP神经网络的优越性,本文就同样的样本进行了对比分析,并证明了PCA-BP的高效性。最后,结合风险评价的结果,本文就所选的PPP基础设施项目的融资模式和回报机制所对应的风险大小进行了分析评价,以使项目参与者在评价不同模式下的基础设施项目风险时,有理论依据可循。
[Abstract]:At present, PPPU Public-Private-Partnershipsmodel is widely used in the field of infrastructure in China. However, because it contains many financing models and is still in the developing stage in our country, how to manage the risk of this model has become one of the bottleneck problems in project management. If the project risk of each financing model can not be accurately evaluated on the basis of risk factor identification, the situation will become more complicated. In view of this, this paper proposes a PPP project risk assessment method based on the combination of principal component analysis and BP neural network. From the point of view of engineering projects, this paper identifies the potential risk factors of PPP infrastructure projects by screening and sorting out the literature of PPP infrastructure projects, and then designs a questionnaire and carries out a survey. To obtain expert assessment data on the occurrence probability and hazard degree of each risk evaluation indicator, and to calculate the impact degree and weight of each index, Based on this, a PPP project risk evaluation index system including 4 levels, 7 first-grade indexes and 36 second-level indexes is established. Then, 13 PPP infrastructure projects in Shanxi Province are selected as samples to study the application of PCA-BP risk assessment method, and BP neural network is implemented through MATLAB 2010. The results show that the PCA-BP method is effective in risk assessment. In order to further demonstrate the superiority of PCA-BP neural network compared with single BP neural network, this paper makes a comparative analysis on the same sample and proves the high efficiency of PCA-BP. Finally, based on the results of risk assessment, this paper analyzes and evaluates the risk of the selected PPP infrastructure project financing model and return mechanism. In order to make the project participants evaluate the risk of infrastructure projects under different models, there is a theoretical basis.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F283

【参考文献】