Project risk management using multiple criteria decision-making technique and decision tree analysis: a case study of Indian oil refinery

This study proposes an integrated analytical framework for effective management of project risks using combined multiple criteria decision-making technique and decision tree analysis. First, a conceptual risk management model was developed through thorough literature review. The model was then applied through action research on a petroleum oil refinery construction project in the Central part of India in order to demonstrate its effectiveness. Oil refinery construction projects are risky because of technical complexity, resource unavailability, involvement of many stakeholders and strict environmental requirements. Although project risk management has been researched extensively, practical and easily adoptable framework is missing. In the proposed framework, risks are identified using cause and effect diagram, analysed using the analytic hierarchy process and responses are developed using the risk map. Additionally, decision tree analysis allows modelling various options for risk response development and optimises selection of risk mitigating strategy. The proposed risk management framework could be easily adopted and applied in any project and integrated with other project management knowledge areas.     

Valuing mortality risk reductions from environmental, transport, and health policies: a global meta-analysis of stated preference studies

We conduct, to our knowledge, the first global meta-analysis (MA) of stated preference (SP) surveys of mortality risk valuation. The surveys ask adults their willingness to pay (WTP) for small reductions in mortality risks, deriving estimates of the sample mean value of statistical life (VSL) for environmental, health, and transport policies. We explain the variation in VSL estimates by differences in the characteristics of the SP methodologies applied, the population affected, and the characteristics of the mortality risks valued, including the magnitude of the risk change. The mean (median) VSL in our full data set of VSL sample means was found to be around $7.4 million (2.4 million) (2005 U.S. dollars). The most important variables explaining the variation in VSL are gross domestic product (GDP) per capita and the magnitude of the risk change valued. According to theory, however, VSL should be independent of the risk change. We discuss and test a range of quality screening criteria in order to investigate the effect of limiting the MA to high-quality studies. When limiting the MA to studies that find statistically significant differences in WTP using external or internal scope tests (without requiring strict proportionality), we find that mean VSL from studies that pass both tests tend to be less sensitive to the magnitude of the risk change. Mean VSL also tends to decrease when stricter screening criteria are applied. For many of our screened models, we find a VSL income elasticity of 0.7-0.9, which is reduced to 0.3-0.4 for some subsets of the data that satisfy scope tests or use the same high-quality survey.