The Value of Information for Managing Contaminated Sediments

Effective management of contaminated sediments is important for long-term human and environmental health, but site-management decisions are often made under high uncertainty and without the help of structured decision support tools. Potential trade-offs between remedial costs, environmental effects, human health risks, and societal benefits, as well as fundamental differences in stakeholder priorities, complicate decision making. Formal decision-analytic tools such as multicriteria decision analysis (MCDA) move beyond ad hoc decision support to quantitatively and holistically rank management alternatives and add transparency and replicability to the evaluation process. However, even the best decisions made under uncertainty may be found suboptimal in hindsight, once additional scientific, social, economic, or other details become known. Value of information (VoI) analysis extends MCDA by systematically evaluating the impact of uncertainty on a decision. VoI prioritizes future research in terms of expected decision relevance by helping decision makers estimate the likelihood that additional information will improve decision confidence or change their selection of a management plan. In this study, VoI analysis evaluates uncertainty, estimates decision confidence, and prioritizes research to inform selection of a sediment capping strategy for the dibenzo-<i>p</i>-dioxin and -furan contaminated Grenland fjord system in southern Norway. The VoI model extends stochastic MCDA to model decisions with and without simulated new information and compares decision confidence across scenarios with different degrees of remaining uncertainty. Results highlight opportunities for decision makers to benefit from additional information by anticipating the improved decision confidence (or lack thereof) expected from reducing uncertainties for each criterion or combination of criteria. This case study demonstrates the usefulness of VoI analysis for environmental decisions by predicting when decisions can be made confidently, for prioritizing areas of research to pursue to improve decision confidence, and for differentiating between decision-relevant and decision-irrelevant differences in evaluation perspectives, all of which help guide meaningful deliberation toward effective consensus solutions.