The Minimum Revenue Guarantee (MRG) was designed to mitigate the financial risk of private investors that participate in the transportation project as concessionaire under a public-private partnership (PPP) program. The MRG can pose a significant financial burden to governments especially when the contract revenue is set considerably higher than the actual revenue. This may encourage the concessionaire to inflate the traffic forecast to make the project look as if it will be profitable. In order to mitigate this problem, extra conditions for exercising the MRG can be considered. This study examines how these exercise conditions change the economic value of the MRG using the case study based on the urban railway project in the Republic of Korea. By utilizing the real options analysis, the study identified that the exercise conditions have worked to curtail the expected payment from the government, eventually leading to a reduction in the concessionaire’s expectation of revenue. The value of MRG was at a far lower level compared to the concessionaire’s investment because of the low probability of exercising the MRG when the exercise conditions apply. The findings are expected to contribute to the sustainability of the PPP program by recognizing and quantifying liabilities and risks embedded in the concession agreement in advance.
Various risk factors influence the success of public–private partnership (PPP) projects. This study analyzes the risk attributes of PPP projects and develops a regression model based on a 20-year PPP project database to quantitatively analyze the factors affecting the contracted internal rate of return (CIRR) of PPP projects. Although the risk factors of PPP projects have been widely studied, the factors affecting CIRR have not been explored. Information from the intra-info DB system managed by Korea Development Institute was used to calculate the impact of the variables on CIRR. It was observed that the CIRR of Korea’s PPP projects did not reflect the risks associated with the facility types, service area, amount of private investment, and operation period accurately. Financing costs did not demonstrate a statistically significant relationship with the CIRR either. Furthermore, the CIRR of projects with a minimum revenue guarantee option was found to be higher than that of projects without. The CIRR of the current project was found to be closely related to the number of bidding competitors and the CIRR values of previous projects that are similar to the current one. This is attributed to a failure in the bureaucratic negotiation behavior of the parties due to their avoidance of responsibilities.
This research investigated the potential of a distance-based fare structure with a case study of the Utah Transit Authority system in northern Utah. The metrics of evaluation were viewed through demand maximization within a modeling scheme for a distance-based fare structure for all fixed route transit modes. Transit users’ route choices were explicitly modeled in the transit system on the time-expanded network. This modeling scheme was integrated into the lower level of the bi-level programming framework, where the upper level uncovered the optimized fare levels for the distance-based fare structure with a genetic algorithm. Through implementation of the methodology, the distance-based fare levels were evaluated for their effect on increasing transit demand. Using the market segmentation analysis, the study found that a distance-based fare with a no-base fare had the highest potential for increasing the transit demand. A $0.50 base fare was examined and was shown to be feasible in the case that a base fare was not necessary because of agency policy.
Increasing the usage of electric vehicles has been proposed as a policy to decrease aggregate fuel consumption and greenhouse gas (GHG) emissions in an effort to mitigate the causes of climate change. In order to increase the attraction of electric vehicles for consumers, governments have employed a number of incentives. In this study, the relationship between shares of electric vehicle and the presence of government incentives as well as other influential socioeconomic factors were examined. The methodology of this study is based on a crosssectional/time-series (panel) analysis. The developed model is an aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different U.S. states from 2003 to 2011. The model was estimated using different panel data methods and the results were compared. The results demonstrated that electricity prices were negatively associated with EV use while, urban roads and government incentives were positively correlated with states' electric vehicle market share. Sensitivity analysis suggested that of these factors, electricity price affects electric vehicle adoption rate the most. According to the sensitivity analysis of electric vehicle adoption rate, state of Vermont has the most sensitivity with respect to electricity price and New Jersey is the most sensitive state with respect to urban roads and incentives. Moreover, the time trend model analysis found that the electric vehicle adoption has been increasing over time, which is consistent with diffusion of new technology theory.
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