Abstract

In the past twenty years, seafood sustainability certifications have been heralded as a complement to traditional government-based fisheries management. However, certifying organizations such as the Marine Stewardship Council (MSC) have often struggled to reach fisheries in the Global South, which are less likely to already have strong top-down management practices. To further understand how MSC’s recruitment may be inhibited in different regions and nations, we developed a random forest machine-learning model which identified MSC-enrolled and unenrolled fisheries with 79% accuracy based on 36 socioeconomic characteristics. Fisheries from countries with high Human Development Indices (HDI) and those targeting high biomass regions were much more likely to enroll, reflecting a lack of socioeconomic and geographic diversity among program fisheries. Almost a third of all issued MSC certifications have also been voluntarily withdrawn, and using a similar predictive model, we found withdrawals were most likely in fisheries in overharvested regions, regardless of HDI, and those that targeted forage fish species. Results suggest MSC has struggled to recruit fisheries outside of particular regions, such as Alaska and the far North Atlantic, and to retain fisheries in need of harvest controls or other significant changes in management practices. Considering the organization’s Theory of Change and existing case studies, we outline a potential program-wide mismatch between where MSC certifications are most likely to occur, and where they are most likely to successfully generate improvements.

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