Identification and Prioritization of Public-Private Partnership Indicators in Iran’s Water and Wastewater Industry via Data Mining Algorithms

Document Type : Research Paper

Authors

College of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.

Abstract

The restrictions of government resources and the recent alterations in the economy have prompted government agencies to employ the capacities of private sector in all infrastructures. In this regard, a variety of financing methods, including the participatory models, have been applied for many years in the water and wastewater industry of Iran. The aim of this study is to identify and prioritize the Public-Private Partnership (PPP) indicators in the water and wastewater industry of Iran via machine learning techniques. To this end, after collecting, preparing and preprocessing the data, weighted indexing techniques including information gain and Gini index were utilized to prioritize the PPP factors. The results indicated that 93% of the indicators were effective in predicting the success of the projects. To compare the two methods, the precision of Naïve Bayes and Random Forest classifiers were taken into account and the information gain method yielded more reasonable findings with one percent difference. The evaluation of indicators elucidated that "complaints about service quality," "contract type," and "Conventional tariffs" revealed a huge impact on the success of collaborative projects. Among the 15 indicators, eight were directly pertinent to the project financing which is the main concern in this industry.

Keywords

References

  1. Abdul-Aziz, A.R., (2001). Unraveling of BOT scheme: Malaysia's Indah water konsortium. Journal of construction engineering and management, 127(6), 457-460.
  2. Akintoye, A., (2003). Achieving best value in private finance initiative project procurement. Construction Management and Economics, 21(5), p. 461-470.
  3. Böhl, C.G.P. (2007). Development of a knowledge based decision support system for private sector participation in water and sanitation utilities. Oldenbourg Industrieverlag, 189.
  4. Brune, N.G., Garrett, & Kogut, B. (2004). The International Monetary Fund and the global spread of privatization. IMF Staff Papers, 195-219.
  5. Commission, E. (2003). Guidelines for successful public-private partnerships. DG Regional Policy.
  6. Company, i. (2009). National Wastewater Finanving and investment strategy Report.
  7. Delmon, J. (2015). Creating a Framework for Public-Private Partnership Programs.
  8. Dorian, J.P. (2006). Central Asia: A major emerging energy player in the 21st century. Energy Policy, 34(5), 544-555
  9. Golabchi, M., Nourzaee. (2015). Selecting the best PPP method in rail projects by using AHP methods. Journal of  Transportation Engineering, No.6
  10. Hall, D., Lobina. E. (2005). The relative efficiency of public and private sector water. PSIRU Reports.
  11. Han, J., Jian, P., & Kamber, M. (2011). Data mining: concepts and techniques, Elsevier.
  12. Hodge, G.A., Greve, C., & Boardman, A.E. (2010). International Handbook on Public-Private Partnership. Edward Elgar Publishing
  13. Li, B. (2005). Critical success factors for PPP/PFI projects in the UK construction industry. Construction management and economics, 23(5), 459-471.
  14. Liu, Haoyue, MengChu, Zhou, Xiaoyu, Sean Lu. & Cynthia Yao. (2018). Weighted Gini index feature selection method for imbalanced data. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). 1-6. IEEE.
  15. Marzouk, M.  Emad Fayez. (2018). PUBLIC PRIVATE PARTNERSHIP PROJECTS: CONCESSIONAIRE PERFORMANCE MEASUREMENT. Journal of Al Azhar University Engineering Sector
  16. Penelope, B.J., Cowen, B. (1997). The private sector in water and sanitation–how to get started. Washington: Bank, p. 89-92.
  17. Qiao, L. (2001). Framework for critical success factors of BOT projects in China. The Journal of Structured Finance, 7(1). p. 53-61.
  18. Sachs, T., Tiong, R.. & Qing, S. (2007). Analysis of political risks and opportunities in public private partnerships (PPP) in China and selected Asian countries: Survey results. Chinese Management Studies, 1(2). p. 126-148.
  19. Salman, A.F., Skibniewski. M.J., & Basha, I. (2007). BOT viability model for large-scale infrastructure projects. Journal of Construction Engineering and Management, 133(1), 50-63.
  20. Savas, E.S. (2000). Privatization and public-private partnerships. Chatham House New York.
  21. Sun, J., Wanga, R., Wanga, X., & Pinga. J. (2014). Spatial cluster analysis of bursting pipes in water supply networks. Procedia Engineering , 70,1610 – 1618
  22. Tang, L., Shen, Q., & Cheng, E.W. (2010). A review of studies on public–private partnership projects in the construction industry. International Journal of Project Management, 28(7), 683-694.
  23. Tina, R., Patil, S., & Sherekar, S. (2013). Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification
  24. Wei, Y. (2011).  Regulating Municipal Water Supply Concessions. p. 1-0.
  25. Wen,Y., Huang, W., & Wu, J. (2013). Water Consumption analysis System Based On data Mining, Mechanics and Materials, 241-244, 1093-1097
  26. Yong Qiu., Ji.  Li.,  Xia, Huang., & Hanchang, Shi. (2018). A Feasible Data-Driven Mining System to Optimize Wastewater Treatment Process Design and Operation
  27. Yu, I. (2007). Comparable performance measurement system for construction companies. Journal of Management in Engineering, 23(3), 131-139.
  28. Yuan, J. (2009). Performance objectives selection model in public-private partnership projects based on the perspective of stakeholders. Journal of Management in Engineering.
  29. Yuan, J. (2011). Developing key performance indicators for public-private partnership projects: questionnaire survey and analysis. Journal of Management in Engineering, 28(3),. 252-264.
  30. . Zhang, X. (2005). Critical success factors for public–private partnerships in infrastructure development. Journal of construction engineering and managemen.
Iranian Journal of Economic Studies
Volume 8, Issue 2
December 2019
Pages 375-396

Files

History

  • Receive Date: 27 November 2019
  • Revise Date: 08 June 2020
  • Accept Date: 09 June 2020

Share

How to cite

Statistics