Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Information on non-market values has the potential to improve decision making but approaches to measure these values are costly and may be inaccurate. This study develops a Bayesian value of information (VOI) model to evaluate when and if the benefit of conducting a non-market valuation (NMV) study exceeds the cost, and which method of those considered delivers the highest expected net benefit. The approach is illustrated using a water quality improvement decision, with VOI estimated for stated preference, revealed preference and benefit transfer methods, the first two implemented at varying degrees of best practice. Information on the anticipated accuracy of each valuation method is derived via structured expert-elicitation. Results show that the net VOI from NMV studies varies widely and depends on multiple factors, including project scale, the quality of existing knowledge, the accuracy of NMV methods, the type of values measured (e.g., use versus nonuse values) and the costs of applying each method. Findings suggest that familiar narratives regarding the value of NMV estimates may be too simplistic, suggesting that a more nuanced approach to study application is warranted. Although demonstrated for one case study, the approach can be adapted to many decision settings.