Toll road public-private partnerships in Malaysia : using the CLIOS process for policy improvements

Abstract

Malaysia has relied on private sector provision of toll roads for over twenty years using public- private partnerships (PPPs). While the program has been successful in providing close to 1,800 kilometers of highway in that time, it has several shortcomings that prevent it from better meeting societal needs. Lack of transparency, weak environmental protections, exclusion of public involvement, and higher tolls than necessary paid to politically-connected businesses are a few of the criticisms leveled at the Malaysian government's handling of the program. The CLIOS process is proposed as a method to improve the Malaysian toll road program. The CLIOS process as developed at MIT is designed to understand Complex, Large-scale, Integrated, Open, Socio-technical systems and to prepare robust strategies for meeting stakeholder goals for the system. Other researchers have used it for energy distribution, air combat command and control, and regional strategic transportation planning (RSTP). This last application provides a useful base for improving a toll road program but RSTP still requires significant adjustment before it can be applied to toll road PPP's. This thesis explores the application of the CLIOS process to Malaysia's toll road program. The application to a generic nation is explored first so more general results can be used for other nations. A specific program is then developed for Malaysia with the Kuala Lumpur Metropolitan Region as the focus for toll road deployment. The performance of two alternatives to handling the Malaysian toll road program is also explored to provide a well-rounded comparison to the CLIOS process.

(cont.) These alternatives are qualitatively compared using measures for technical, economic, environmental, political, and financial performance. Results show the CLIOS process producing the highest performance but the qualitative nature of the evaluation and limited Malaysian data introduce uncertainty into the results.