Suchergebnisse

The concept of "Environmental security" has emerged as one basis for understanding international conflicts. This book is based on the discussions and papers prepared for the NATO Advanced Research Workshop that took place in Eilat, Israel, in April 2004. It applies a risk analytical approach to a variety of environmental problems

During the 21st century, environmental challenges are likely to intensify across the world and possibly lead to violent conflicts. Strategies for conflict avoidance will be incomplete unless they recognize, discuss, and mitigate regional environmental stress factors. Comparative risk assessment (CRA) is one of the most critical tools emerging to influence modern environmental policies and is increasingly used to create a common language to help reconcile competing interests in development and environmental disputes around the world. This article considers the environmental challenges facing the Middle East in light of their "transboundary" nature and proposes CRA as a framework for setting environmental priorities and reducing tensions in the region.

The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often‐overlooked issue of what we call "modeler uncertainty," i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model‐model and model‐data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic‐deliberative process in risk characterization.

Intro -- Climate: Global Change and Local Adaptation -- Preface -- Acknowledgements -- Contents -- Part I: Climate Change Challenges -- Part II: Climate Change Adaptation as a Risk-Based Decision Problem -- Part III: National Security Applications and Needs -- Part IV: Coastal Applications -- Part V: Inland Applications -- Author Index.

Decision making in environmental projects is typically a complex and confusing process characterized by trade-offs between socio-political, environmental, and economic impacts. This title show that the use of comparative risk assessment can provide the scientific basis for environmental sound and cost-efficient policies

AbstractRecent guidelines for risk‐informed decision making (RIDM) provide a gold‐standard for how to incorporate probabilistic risk models in conjunction with other considerations in a decision process. Nevertheless, risk quantification using probabilistic and statistical methods is difficult in situations where threat, vulnerability, and consequences are highly uncertain and risk quantification. In such situations a wider variety of methods could be employed, which we call decision making informed by risk (DMIR) combining risk and decision analytics. Risk informed decision making (RIDM) can be considered as a special case of DMIR. Multi criteria decision analysis (MCDA) often serves as a basis for DMIR in order to flexibly accommodate different levels of analytical detail. DMIR often involves artful use of proxy variables that correlate with, and are more measurable than, underlying factors of interest. This article introduces the notion of DMIR and discusses the use of MCDA in its application in the context of risk‐based problems. MCDA‐based risk analyses identify metrics associated with threats of concern and system vulnerabilities, characterize the way in which alternative actions can affect these threats and vulnerabilities, and ultimately synthesize this information to compare, prioritize, or select alternative mitigation strategies. Simple linear additive MCDA models often integrate these inputs, but the same simplicity can limit such approaches and create pitfalls and more advanced models including multiplicative relationships can be warranted. This essay qualitatively explores the critical practitioner questions of how and when the use of linear multicriteria models creates significant problems, and how to avoid them.

The increasing complexity of societal change in response to the new global economy requires an enhanced capacity for scientific assessment and monitoring, particularly of critical infrastructure such as ports and harbors. Public pressure for decision transparency in government and corporations drives the need for a new framework for thinking about globalization and the prioritization of social and corporate values that reaches beyond the realms of economics, world trade, and corporate management to include environmental protection and social goals. Establishing, maintaining, and enhancing a se