Environmental systems and decision analysis models for aiding environmental policy decisions under deterministic and stochastic settings

This dissertation presents three new environmental system and decision analysis tools, each with an environmental application. Together these studies incorporate techniques from environmental systems and decision analysis to provide decision makers with tools to aid in managing complex, real-world environmental problems.;The first study develops two novel integer programming models for identifying irreplaceable nature reserve sites. Knowing which sites are irreplaceable allows decision makers to target reserves that must be selected in order to achieve a conservation objective. The models efficiently determine irreplaceable sites, but find a general lack of trend between the number of irreplaceable sites and the number of sites available for selection. Moreover, irreplaceability at one resource level may not be a predictor of irreplaceability at a higher or a lower resource level.;The second study develops a model for estimating and correcting attribute-weighting biases that result from the use of value trees to elicit decision makers' preferences. Value trees have been used to aid decision makers selecting among alternative solutions to complex environmental problems. The model is based on the conjecture that attribute weights are influenced by tree structure and a subject's use of the "anchor-and-adjust" heuristic. Weights corresponding to environmental and economic attributes of electric system expansion alternatives are elicited from electric utility employees are used to test the model. The model results support the hypothesis that a bias exists that is consistent with the anchor-and-adjust heuristic and illustrate the value losses caused by using elicited versus model-estimated debiased weight sets.;The third study presents a framework to identify the optimal set of information acquisition and abatement actions to address environmental management when there is uncertainty in the environmental processes, the outcomes of those processes, and the effectiveness of management. The framework combines Bayesian inference with multiobjective programming to select research actions, which improve understanding of the natural system, and management actions, which reduce environmental contamination. The model is applied to the problem of reducing turbidity from nonpoint sediment sources in the Minnesota River basin. The results indicate that the economic value placed on sediment reduction influences the choice of both monitoring and management options.