本文選題：PPP + 基礎(chǔ)設(shè)施��； 參考：《中北大學(xué)》2017年碩士論文

【摘要】：當(dāng)前,PPP(Public-Private-Partnerships)模式廣泛應(yīng)用于我國的基礎(chǔ)設(shè)施領(lǐng)域。但由于其包含了眾多的融資模式,且在我國尚處于發(fā)展階段,因此如何對該模式進行風(fēng)險管理就成為了各參與方在項目管理時的瓶頸問題之一,如果不能在風(fēng)險因素識別的基礎(chǔ)上準(zhǔn)確評價各融資模式的項目風(fēng)險,則情況將變得更加復(fù)雜。鑒于此,本文提出基于主成分分析法與BP神經(jīng)網(wǎng)絡(luò)相結(jié)合的PPP項目風(fēng)險評價方法。本文從工程項目的視角出發(fā),通過篩選及整理PPP基礎(chǔ)設(shè)施項目已有文獻資料,從而識別出PPP基礎(chǔ)設(shè)施項目的潛在風(fēng)險因素,然后設(shè)計問卷并展開調(diào)研,以獲得專家對各風(fēng)險評價指標(biāo)發(fā)生概率和危害程度的評估數(shù)據(jù),并計算得出各指標(biāo)的影響程度和權(quán)重,據(jù)此組建了包括4個層級,7個一級指標(biāo)和36個二級指標(biāo)的PPP項目風(fēng)險評價指標(biāo)體系。然后,選擇山西省擬建的13個PPP基礎(chǔ)設(shè)施項目作為樣本,對PCA-BP風(fēng)險評價方法進行應(yīng)用研究,并通過MATLAB 2010實現(xiàn)了BP神經(jīng)網(wǎng)絡(luò)的運算,結(jié)果表明了PCA-BP方法進行風(fēng)險評價的有效性。為進一步表明PCA-BP神經(jīng)網(wǎng)絡(luò)較單一BP神經(jīng)網(wǎng)絡(luò)的優(yōu)越性,本文就同樣的樣本進行了對比分析,并證明了PCA-BP的高效性。最后,結(jié)合風(fēng)險評價的結(jié)果,本文就所選的PPP基礎(chǔ)設(shè)施項目的融資模式和回報機制所對應(yīng)的風(fēng)險大小進行了分析評價,以使項目參與者在評價不同模式下的基礎(chǔ)設(shè)施項目風(fēng)險時,有理論依據(jù)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.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F283

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本文編號：1883383