LENS (Local Environmental and Nutritional Scoring): A spatially explicit nLCA model

Local Environmental and Nutritional Scoring (LENS) is a fully reproducible, open-source, spatially explicit nutritional Life Cycle Assessment (nLCA) model that evaluates natural resource use and emissions across food supply chains – from production and processing through consumption and waste management – adopting the Nutritional Value Score (NVS) as its functional unit (Beal and Ortenzi 2026).

LENS provides global coverage with default inventory data (base year 2020) and integrates high-resolution (5arcmin) geospatial datasets on climate, soil, water, and land use to capture agroecological heterogeneity at subnational level. It quantifies six impact categories: greenhouse gas (GHG) emissions, water use, water pollution (i.e., marine and freshwater eutrophication potential, ocean acidification potential), biodiversity loss (i.e., potential species loss), and non-GHG air pollution (i.e., particulate matter (PM) emissions). 

The NVS is a compound nutritional score tailored to local dietary patterns and malnutrition profiles, thereby enhancing applicability to smallholder and transitional agricultural systems in low- and middle-income countries. LENS implements a target-based normalization approach that compares foods' enviro-nutritional efficiency against sustainability thresholds using spatially resolved policy goals and safe operating spaces. 

Users can adjust country and regional model parameters, functional unit, allocation, and normalization approach as needed. A full methods description is provided in the accompanying preprint (Damerau et al. 2026).

Version notes (v1.0): includes case studies for Kenya and Rwanda, providing data for 163 domestically produced foods in Kenya and 75 in Rwanda at the national and provincial level, and 18 international food imports. Country folders also include interim files in the Mrgd_output subfolder containing combined raw environmental impact data per hectare, including yield, loss/waste rates, and output ratios, prior to normalization and functional unit adjustments (Food_preparation_combined_(system).csv).