Bio: Hang Yin is an academic researcher from Southwest Jiaotong University. The author has contributed to research in topics: Risk factor (computing). The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
Topics: Risk factor (computing)
TL;DR: Wang et al. as discussed by the authors built a financing risk index system by adopting the raw data of major risk factors by questionnaire investigation from a key domestic PPP project, and using SPSS and factor analysis method to do an empirical test.
Abstract: As an innovative investment and financing system of urbanization and the key to the local financing platform to resolve the debt risk, PPP projects have great significance to the current Chinese economy. At present, domestic PPP projects lack of benefit sharing standard and the mechanism of risk-sharing and risk factor analysis is the premise of risk-sharing. Adopting the raw data of major risk factors by questionnaire investigation from a key domestic PPP project, and using SPSS and factor analysis method to do an empirical test, a financing risk index system is built. The results of the study also reveal that financing risk factors are the most important ones to a particular PPP project.
TL;DR: In this paper, a study was conducted to identify and determine the risk factors in public-private partnership (PPP) for water supply projects in Iran using failure mode and effect analysis (FMEA).
Abstract: To compensate for the lack of funds for investment in private sector and infrastructure projects, governments may propose public–private partnerships (PPPs) to be able to use share capital and establish the necessary infrastructure of the country. The current study was undertaken to identify and determine the risk factors in PPPs for water supply projects in Iran. After identifying the risk factors using failure mode and effect analysis (FMEA), the risk priority number of each was assessed. This identified the most critical risk factors, which were then categorized into experimental, legal, financial, and technological subcategories. The fuzzy synthetic evaluation (FSE) technique and FMEA method were then blended and the FSE technique was modified for measuring the overall risk level. The computational results show that the levels of risk were ranked as follows (highest to lowest): financial, experimental, technological and legal. The level of risk in the financial subcategory was 6.11, in the experimental was 6.05 and in the technological and legal was 5.94 and 5.83, respectively. The overall risk level in PPPs for Iranian water supply projects considering linguistic variables as the criteria was 5.98, which is high. This level of risk confirms the applicability and suitability of the model.