Data Integration for Diet Sustainability Analyses

Diet sustainability analyses are stronger when they incorporate multiple food systems domains, disciplines, scales, and time/space dimensions into a common modeling framework. Few analyses do this well: there are large gaps in food systems data in many regions, accessing private and some public data can be difficult, and there are analytical challenges, such as creating linkages across datasets and using complex analytical methods. This article summarizes key data sources across multiple domains of food system sustainability (nutrition, economic, environment) and describes methods and tools for integrating them into a common analytic framework. Our focus is the United States because of the large number of publicly available and highly disaggregated datasets. Thematically, we focus on linkages that exist between environmental and economic datasets to nutrition, which can be used to estimate the cost and agricultural resource use of food waste, interrelationships between healthy eating and climate impacts, diets optimized for cost, nutrition, and environmental impacts, and others. The limitations of these approaches and data sources are described next. By enhancing data integration across these fields, researchers can be better equipped to promote policy for sustainable diets.

Automated summary

This article emphasizes the importance of integrating multiple domains, disciplines, scales, and time/space dimensions into a common modeling framework for diet sustainability analyses. While focusing on the United States, key data sources and methods for integrating them are summarized.