CBA and probabilistic risk analysis tool for non-revenue generating infrastructure projects. The case of Greece. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Vagdatli, Theodora; Petroutsatou, Kleopatra
• Is Part Of: Case studies on transport policy. Volume 9:Issue 1(2021)
• Page Start: 103
• Page End: 124
• Publication Date: 2021-03
• Subjects: CBA -- Infrastructure projects -- Monte Carlo simulation -- NPV – at risk -- Externalities -- Economic impacts; Transportation and state -- Case studies -- Periodicals; Transportation -- Planning -- Case studies -- Periodicals; Transportation -- Research -- Case studies -- Periodicals; 388.05
• DOI: 10.1016/j.cstp.2020.10.004
• Languages: English
• ISSNs: 2213-624X

Abstract

Abstract: This paper analyzes the evaluation of investment proposals for non-revenue infrastructure projects, so as to narrow the gap in this research field, and demonstrates that projects of this category meet the terms and conditions for joining European co-financing programs. It is being implemented on a real-world infrastructure project 1 + 1 lane highway plus an emergency lane connecting the most important urban center of Northern Greece (Thessaloniki) with its major tourist area (Halkidiki). This application endeavours to quantify all the factors which, according to the European Commission Guide to CBA, should be integrated into the CBA and positively or negatively affect the financial result of the investment, including the environmental impacts that should be a fundamental concern of all involved parties in the light of current sustainable development needs. The use of the CBA method is proposed as the most appropriate tool for this process, which provides an instantaneous perception of the acceptance or rejection of investment plans by both the Public and Private Entities. However, since the transport projects refer to a future long-term period, it is evident that there is a great deal of uncertainty in critical factors such as annual average daily traffic (AADT), economic indicators, general social conditions, etc. It is, therefore, necessary to process the data derived from the CBA with a probabilistic model. In this study, the @Risk software was used to infer the NPV behavior and the most influential parameters affected it through cumulative probability and tornado diagrams respectively. The whole procedure, supported by the quantification of all parameters, allows the selection of the most optimal solution among alternatives and therefore offers enhanced cost efficiency projects with the required value for money to be achieved.