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Purpose – The purpose of this paper is to present a methodology of comparing concession projects developed in different transport sub‐sectors. The methodology is tested in the comparison of three different cases, each of which represent a particular mode of transport: a road development project, a city tramway project and a port lock construction initiative. Design/methodology/approach – A fuzzy logic approach methodology is applied in carrying out the comparison between cases. Granulation is achieved by employing a Contextual (Ws) Risk Analysis Framework, as risks constitute the basis to public private partnership (PPP) structure. Linguistic variables are then used to describe the comparative findings. Findings – The methodology presented allows for the comparison of three cases from different transport sub‐sectors. Identification of similarities provides the potential to transfer experience from one sector to the other. With respect to the three cases studied, it was identified that traffic risk seems to be passed on to the private operators in relation to the level of exclusivity. Finally, PPP projects initiated by central government (as opposed to those initiated by local governments) seem to be more finance‐driven than service‐driven. Research limitations/implications – As the number of cases to be compared increases, quantitative comparative analysis fuzzy set values can be included in order to carry out a full analysis. The present approach should be considered introductory, as fuzzy sets are not generated due to the limited number of surveys (cases) compared (hence the term “pre‐fuzzy”). Practical implications – The methodology presented and the cases tested indicate the possibility for knowledge/experience transfer and the transferability of best practices. Originality/value – Cross‐sub‐sectoral comparisons for transport PPP projects have not been identified in literature.
Built Environment Project and Asset Management – Emerald Publishing
Published: Jan 28, 2014
Keywords: Knowledge transfer; Risk; Concessions; Linguistic fuzzy logic; PPP; Transport sub‐sectors
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