Share:


Framework for assessing serviceability and socio-economic risk associated with PPPs projects in Libya

    Mohammed Marzouk Affiliation
    ; Mahmoud El-Hesnawi Affiliation

Abstract

On a global scale, limited financing for the development and operation of infrastructure projects has pushed authorities to encourage private investors to enter public-private partnerships (PPPs). In this respect, procurement of infrastructure projects such as bridges, water plants, airports, and roads has been adopted through PPPs. This has also applied to the oil-rich country of Libya which experienced severe economic and political problems in the past decade. This paper presents a systematic framework for risk assessment and appraisal of PPPs infrastructure projects. This framework is capable of identifying probable adverse effects that represent key influential factors on the private sector in a socio-economic environment and related to key performance indicators (KPIs) in order to assess the operational efficiency in developing and financing infrastructure projects. This framework proposes a new integrated system that comprises of the following: fault tree, artificial neural networks, and analytical network process. The aim of this system is to ensure sustainable availability of finances that are considered essential for the development of PPPs infrastructure projects in Libya. considering different alternative funding models, it suggests a means of auditing PPPs structure to carry out improved performance for PPPs projects in Libya.

Keyword : fault tree, neural networks, analytical network process, risk analysis, public-private partnerships

How to Cite
Marzouk, M., & El-Hesnawi, M. (2018). Framework for assessing serviceability and socio-economic risk associated with PPPs projects in Libya. Journal of Civil Engineering and Management, 24(7), 556-567. https://doi.org/10.3846/jcem.2018.5623
Published in Issue
Nov 13, 2018
Abstract Views
973
PDF Downloads
826
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Aboki, L. 2005. Mabushi BOT market: One bidder’s perspective, in Proceedings of BOT Awareness Seminar, 2005, Abuja Investment and Property Development Company.

Akintoye, A.; Chinyio, E. 2005. Private finance initiative in the healthcare sector: trends and risk assessment, Engineering, Construction and Architectural Management 12(6): 601–616. https://doi.org/10.1108/09699980510634155

Akintoye, A.; Taylor, C.; Fitzgerald, E. 1998. Risk analysis and management of private finance initiative projects, Engineering, Construction and Architectural Management 5(1): 9–21. https://doi.org/10.1108/eb021056

Alasad, R.; Motawa, I. 2015. Dynamic demand risk assessment for toll road projects, Construction Management and Economics 33(10): 799–817.

Attarzadeh, M.; Chua, D. K.; Beer, M.; Abbott, E. L. 2017. Options-based negotiation management of PPP–BOT infrastructure projects, Construction Management and Economics 35(11–12): 676–692. https://doi.org/10.1080/01446193.2017.1325962

Ayeni, K. 2005. Risk identification and management in BOT project implementation, in Proceedings of BOT Awareness Seminar, 2005, Abuja Investment and Property Development Company.

Bedford, T.; Cooke, R. 2001. Probabilistic risk analysis: foundations and methods. Cambridge: Cambridge UP. https://doi.org/10.1017/CBO9780511813597

Dikmen, I.; Birgonul, M. T. 2006. An analytic hierarchy process based model for risk and opportunity assessment of international construction projects, Canadian Journal of Civil Engineering 33(1): 58–68. https://doi.org/10.1139/l05-087

Dixon, T.; Pottinger, G.; Jordan, A. 2005. Lessons from the private finance initiative in the UK: Benefits, problems and critical success factors, Journal of Property Investment & Finance 23(5): 412–423. https://doi.org/10.1108/14635780510616016

Fischer, K.; Leidel, K.; Riemann, A.; Wilhelm Alfen, H. 2010. An integrated risk management system (IRMS) for PPP projects, Journal of Financial Management of Property and Construction 15(3): 260–282. https://doi.org/10.1108/13664381011087515

Fu-zhou, L.; Hong-yuan, G. 2011. The risk assessment model of BT construction engineering project financing, Systems Engineering Procedia 1: 169–173. https://doi.org/10.1016/j.sepro.2011.08.028

Hsueh, S. L.; Perng, Y. H.; Yan, M. R.; Lee, J. R. 2007. On-line multi-criterion risk assessment model for construction joint ventures in China, Automation in Construction 16(5): 607–619. https://doi.org/10.1016/j.autcon.2007.01.001

Ibrahim, A. D.; Price, A. D. F.; Dainty, A. R. J. 2006. The analysis and allocation of risks in public private partnerships in infrastructure projects in Nigeria, Journal of Financial Management of Property and Construction 11(3): 149–164. https://doi.org/10.1108/13664380680001086

Iyer, K. C.; Sagheer, M. 2010. Hierarchical structural of public private partnership (PPP) risks using interpretive structural modelling, Journal of Constructional Engineering and Management 23(3): 195–205. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000127

Kurniawan, F.; Mudjanarko, S. W.; Ogunlana, S. 2015. Best practice for financial models of PPP projects, Procedia Engineering 125: 124–132. https://doi.org/10.1016/j.proeng.2015.11.019

Li, J.; Zou, P. X. 2011. Fuzzy AHP-based risk assessment methodology for PPP projects, Journal of Construction Engineering and Management 137(12): 1205–1209. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000362

Liou, F. M.; Huang, C. P. (2008). Automated approach to negotiations of BOT contracts with the consideration of project risk, Journal of Construction Engineering and Management 134(1): 18–24. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:1(18)

Rodney, W.; Gallimore, P. 2002. Risk assessment in PFI schemes for primary health care, Facilities 20(1/2): 52–60. https://doi.org/10.1108/02632770210414290

Saaty, T. L. 1996. Decision making with dependence and feedback: The analytic network process. Vol. 4922. Pittsburgh: RWS publications.

Sachs, T.; Tiong, R. L. 2009. Quantifying qualitative information on risks: development of the QQIR method, Journal of Construction Engineering and Management 135(1): 56–71. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:1(56)

Shen, J. Y.; Wang, S. Q.; Qiang, M. S. 2005. Political risks and sovereign risks in Chinese BOT/PPP projects: a case study, Chinese Businessman Investment and Finance 1: 50–53.

Sonuga, F.; Aliboh, O.; Oloke, D. 2002. Particular barriers and issues associated with projects in a developing and emerging economy. Case study of some abandoned water and irrigation projects in Nigeria, International Journal of Project Management 20(8): 611–616. https://doi.org/10.1016/S0263-7863(02)00029-7

Thomas, A. V.; Kalidindi, S. N.; Ananthanarayanan, K. A. B. T. 2003. Risk perception analysis of BOT road project participants in India, Construction Management and Economics 21(4): 393–407. https://doi.org/10.1080/0144619032000064127

Wang, J. X.; Roush, M. L. 2000. What every engineer should know about risk engineering and management. CRC Press.

Xenidis, Y.; Angelides, D. 2005. The financial risks in build–operate–transfer projects, Construction Management and Economics 23(4): 431–441. https://doi.org/10.1080/01446190500041552

Xu, Y.; Yeung, J. F.; Chan, A. P.; Chan, D. W.; Wang, S. Q.; Ke, Y. 2010. Developing a risk assessment model for PPP projects in China – A fuzzy synthetic evaluation approach, Automation in Construction 19(7): 929–943. https://doi.org/10.1016/j.autcon.2010.06.006

Yusuf, M. L. 2005. Build Operate Transfer method of projects delivery: The AIPDC experience, in Proceedings of BOT Awareness Seminar, 2005, Abuja Investment and Property Development Company.

Zhou, H. B.; Zhang, H. 2010. Dynamic risk management system for large project construction in China, in GeoFlorida 2010: Advances in Analysis, Modeling & Design, 2010, Orlando, Florida, United States, 1992–2001. https://doi.org/10.1061/41095(365)202

Zichun, Y. 2012. The BP artificial neural network model on expressway construction phase risk, Systems Engineering Procedia 4: 409–415. https://doi.org/10.1016/j.sepro.2012.01.004