Advancing Integrated Environmental and Nutritional Life Cycle Assessments for Local Food Systems

Background: Accelerating sustainable food system transitions requires spatially explicit integration of local production conditions and nutritional priorities, yet such assessments remain scarce, particularly for low- and middle-income countries (LMIC). Despite methodological advances, most Life Cycle Assessments (LCA) remain focused on high-income, industrialized production systems, depend on proprietary data and software, and frame impact assessments solely around resource efficiency without reference to safe operating spaces. Methods: We developed a spatially explicit nutritional LCA (nLCA) framework and model: Local Environmental and Nutritional Scoring (LENS). Integrating geospatial environmental data with a comprehensive nutritional score to capture both local agroecological conditions and dietary requirements, LENS normalizes and aggregates environmental impacts against spatially resolved sustainability thresholds. We applied LENS across six environmental impact categories at sub-national scale in Kenya and Rwanda. Findings: Results reveal strong context dependency. Wild-caught seafood and vegetables from low-input systems consistently achieve the highest enviro-nutritional efficiencies across both countries, while starchy staples and poultry tend to rank lowest. In Kenya specifically, many terrestrial animal products score comparably to plant-source foods – a pattern less pronounced in Rwanda. Water use, greenhouse gas emissions, and potential biodiversity loss contribute most to overall scores, with substantial variation within food groups, between co-products, and across geographic landscapes. Interpretation: LENS provides a scalable, open-source template for enviro-nutritional analysis in data-scarce environments. As our case studies show, results are most meaningful at the landscape level rather than as independent benchmarks, making locally grounded assessment essential for informing food system governance and production decisions in LMICs.