Cost benefit analysis model for fire safety and DecaBDE case study

For the past 30 to 40 years, well before the concept of sustainable development became politically correct, regulation of the environment has been covered by Environmental Protection Agencies worldwide. Perhaps one of the most important things that we have learned is that regulations have significant costs, not just benefits, and that analysis of the cost and benefit of proposed rules is an indispensable component of responsible regulation. In the early 1980's this was recognised in the US when Congress required the US Consumer Product Safety Commission to consider costs and benefits in connection with all proposed regulations. Despite the recognition of the importance of cost benefit analysis prior to enacting regulations, this is still a controversial issue, especially in light of moral issues such as establishing the value of a statistical life (VSL) and whether net benefit is always necessary before invoking regulation. Indeed, in 1996 it was estimated that the direct costs of federal environmental, health and safety regulation in the US was US$200 billion annually. In this paper, realand perceived risks associated with exposure to flame retardants and to fires are discussed and a monetary value placed on the costs and benefits associated with these chemicals. The model developed has been called the Fire-CBA model and is applied to a TV containing DecaBDE in the enclosure as a case study. In all, a total of 8 scenarios were tested for the TV set application of the Fire-CBA model developed in this report. In all cases, the benefits of a high level of fire performance in a TV set far outweigh the costs associated with obtaining that high level of fire safety. The net benefit is a function of the choices made in the various scenarios but ranges from $657 to $1,380 million US per year. The various scenarios were chosen to illustrate the significance of the parameters included in the study, as the specific value chosen for each parameter can vary depending on the assumptions made in the model.