Socio-economic value of data-driven eruption forecasts to balance false alarms against catastrophic loss

Abstract The adoption of new forecasting methods for volcanic eruptions typically emphasizes predictive accuracy, often overlooking their potential to reduce overall life and economic losses. We introduce a Potential Economic Value (PEV) framework that evaluates forecast utility by balancing precautionary actions against avoidable catastrophic losses (such as mass casualties). Using machine-learning forecasts from continuous seismic data at five volcanoes, we show that non-forecasted eruptions (missed) have disproportionate consequences, compared to false alarms, which generate recurring and manageable disruption. Retrospective analyses of the 2019 Whakaari (New Zealand) and 2014 Ontake (Japan) eruptions, along with three additional volcanoes, indicate that losses could have been reduced by 30–90%, despite generating numerous false alarms. Across all case studies, effective forecasting prioritizes reducing missed eruptions over maximizing accuracy. This supports the use of more precautionary warning thresholds, provided that the resulting increase in alert frequency is managed in ways that maintain public compliance and trust.