Projects implemented under life cycle contracts have become increasingly common in recent years to ensure the quality of construction and maintenance of energy infrastructure facilities. A key parameter for energy facility construction projects implemented under life cycle contracts is their duration and deadlines. Therefore, the systematic identification, monitoring, and comprehensive assessment of risks affecting the timing of work on the design and construction is an urgent practical task. The purpose of this work is to study the strength of the influence of various risks on the duration of a project implemented on the terms of a life cycle contract. The use of the expert assessment method allows for identifying the most likely risks for the design and construction phases, as well as determining the ranges of deviations from the baseline indicator. Using the obtained expert evaluations, a model reflecting the range and the most probable duration of the design and construction works under the influence of risk events was built by the Monte-Carlo statistical method. The results obtained allow monitoring and promptly detecting deviations in the actual duration of work from the basic deadlines set in the life cycle contract. This will give an opportunity to accurately respond to emerging risks and build a mutually beneficial relationship between the parties to life cycle contracts.

Learning from experience to improve future infrastructure public-private partnerships is a focal issue for policy makers, financiers, implementers, and private sector stakeholders. An extensive body of case studies and “lessons learned” aims to improve the likelihood of success and attempts to avoid future contract failures across sectors and geographies. This paper examines whether countries do, indeed, learn from experience to improve the probability of success of public-private partnerships at the national level. The purview of the paper is not to diagnose learning across all aspects of public-private partnerships globally, but rather to focus on whether experience has an effect on the most extreme cases of public-private partnership contract failure, premature contract cancellation. The analysis utilizes mixed-effects probit regression combined with spline models to test empirically whether general public-private partnership experience has an impact on reducing the chances of contract cancellation for future projects. The results confirm what the market intuitively knows, that is, that public-private partnership experience reduces the likelihood of contract cancellation. But the results also provide a perhaps less intuitive finding: the benefits of learning are typically concentrated in the first few public-private partnership deals. Moreover, the results show that the probability of cancellation varies across sectors and suggests the relative complexity of water public-private partnerships compared with energy and transport projects. An estimated $1.5 billion per year could have been saved with interventions and support to reduce cancellations in less experienced countries (those with fewer than 23 prior public-private partnerships).