Assessment and Management of Environmental Risks Cost - Efficient Methods

This edited volume of articles focuses on the value of risk assessment for policy solutions to environmental problems. It succeeds, through a series of case studies, in illustrating various available methodologies and the implications of the subsequent results. Finally, it serves to update social scientists as to the current state of environmental risk assessment, and thereby provides a useful platform for continued productive work in the field. The collection of articles emphasizes the need for practical application of environmental risk assessment and for further research. Articles consider the application of risk analysis theory to situations constrained by cost, where political situations vary, and where decisions are constrained by other practicalities. The contributors differ, however, in their recognition of the complexities and realistic constraints that otherwise plague the social sciences. Suter, for example, questions the availability of “fullblown risk assessment” for those countries that possess “weak institutions and very basic problems to identify priorities.” Constantinescu et al. in a discussion of nuclear power plant conflicts acknowledge that the problems faced by developing countries are extremely complex. In doing so, Constantinescu et al. offer insight into both the role of cultural evaluation and the effects of policy on individuals, as essential components in risk analysis given practical constraints. The articles differ not only in their conclusions, but in the panoply of risk assessment methodologies that they imply are relevant. Shatkin et al. identify three methodologies that dominate environmental risk assessment: risk management options, risk-related uncertainties, and the value model. Although use of these three methodologies can be found throughout the volume, Salgueiro makes a compelling argument for the third when employing economic valuation techniques for insurance assessment of pollution risks. Quantifying policy alternatives in monetary terms using his simple, yet approachable, methodology should appeal to economists and others who seek practical applications of risk assessment analysis. Some authors seem implicitly to assume that a highly quantitative risk/uncertainty approach is more scientific, and thus more justified. Makarynskyy et al., for example, seem to discount any notion of subjectivity in their analysis. Similarly, many authors approach risk assessment and modeling as if it were orthogonal to the sociopolitical science of applying conclusions to policy decisions. Other contributors, however, such as Valverde, acknowledge that risk analysis, even when highly quantitative with true policy implications, remains a “social” science. Those authors who suggest that risk assessment and the policy decision-making processes build upon one another prove more convincing. Shatkin et al., for example, emphasize this point and suggest that all risk analysis should be policy oriented and done with explicit attention to its ultimate use. Melding the risk and policy components is arguably necessitated by the fact that quantitative modeling relies on subjectivity in the same manner that policy relies on qualitative and quantitative analysis: all quantitative modeling requires assumptions. Additionally, modeling necessarily simplifies reality in order to render it manageable. Even the most sophisticated Monte Carlo simulations and QUAL2E models contain “scores of uncertain parameters” (Goossens et al., p. 411) representing simplifications. Some models presented in this volume oversimplify analysis and, in one example, do not account for the interaction between two pollutants. Much to his credit, MacDonell discusses the importance of including such interactions and the combined risk of components. By understanding the assumptions implicit in the models, one can critically determine how results should inform policy. One clear theme resulting from this volume is that no model, however complex, can provide clairvoyance. Environmental risk analysis, and the resulting conclusions for policy decisions, can only be as good as the data on which the modeling relies. Because not all potential risks have been discovered, and because measurement tools continually improve (e.g., by measuring the presence of two different pollutants in samples of soil, air, or water and their joint risk), the science of risk analysis will always suffer some level of