Environmental performance of blue foods

The food system is a major driver of environmental change, emittinga quarter of all greenhouse gas (GHG) emissions, occupying half of allice-freeland, and responsible for three quarters of global consumptivewater use and eutrophication3,6. Yet, it still fails to meet global nutrition needs7, with 820 million people lacking sufficient food8and with one inthree people globally overweight or obese9. Asa critical source of nutri-tion8,10generating relatively low average environmental pressures1,2,11,12,
blue foods present an opportunity to improve nutrition with lowerenvironmental burdens, inline with the Sustainable Development Goalsto improve nutrition (Goal 2), ensure sustainable consumption and
production (Goal 12), and sustainably use marine resources (Goal 14).Blue foods, however, are underrepresented in food system environmen-tal assessments13and the stressors considered are limited4such that wehave some understanding of GHG emissions14,15, but less of others such as
land or freshwater use16. Where blue foods are included, they are typicallyrepresented by only one ora few broad categories (see, for example, refs.3,17,18), masking the vast diversity within blue food production. Finally, esti-mates combining results of published lifecycle assessments undertaken for different purposes, and consequently using incompatible methodolo-gies19,20, cannot be compared reliably. It is therefore critical to examine the environmental performance across the diversity of blue foods ina robust,methodologically consistent manner to serve asa benchmark within the rapidly evolving sector as blue food demand increases21, production shifts toward aquaculture and production technologies advance.
Here, we provide standardized estimates of GHG emissions, consump-tive freshwater use (water use), terrestrial land occupation (land use),and nitrogen (N) and phosphorus (P) emissions for blue foods, reportedper tonne of edible weight. We identify a set of key lifecycle inventory data (that is, material and energy input, and farm-level performancedata) from published studies and datasets to which a harmonized meth-odology is applied. We draw on studies that collectively report data from over 1,690 farms and 1,000 unique fishery records around the world.The 23 species groups represented in our results cover over 70% of global blue food production. We then discuss environmental impacts
not covered by the standard stressors, most notably biodiversity loss.
Finally, we leverage our model to identify and quantify improvementopportunities and discuss public and private policy options to realize these improvements. In doing so, these results help to identify current and future opportunities for blue foods within sustainable diets.

Stressor posterior distributions. a, Aquaculture GHG emissions(kgCO2e t−1). b, Aquaculture N(kgNet−1). c, Aquaculture P (kgPe t−1). d, Capture GHG emissions (kgCO2e t−1) e, Aquaculture Water use (m3 t−1). f, Aquaculture land use (m2a t−1). Values represent tonnes of edible weight and use mass
allocation. Dot indicates the median, coloured regions show credible intervals(that is, range of values that have a 95% (light), 80% and 50% (dark) probability of containing the true parameter value). Beige bands represent estimatedchicken minimum to maximum range. See Supplementary Fig 10 for estimates expressed in terms of live weight .