Hourly wages were varied from 0.12 to 42.12 USD•hour -1 representing the lowest hourly wage (Ghana) in the contextual parameter dataset and the highest (Switzerland). Similarly, grid electricity price was varied from 0.01 to 0.378 USD•kWh -1 representing the lowest electricity price (Kyrgyzstan) and the highest (Germany). When varying the two economic parameters, the baseline Biogenic Refinery daily per capita cost ranged from 0.023 to 0.105 USD•capita -1 •day -1 with labor wage being the primary driver of daily per capita cost (Figure ). In terms of affordability, the baseline Biogenic Refinery achieves the economic performance target (0.05 USD•capita -1 •day -1 ) in low-income settings with labor wages less than 12-14 USD•hour -1 , depending on the grid electricity price. The daily per capita costs for the baseline NEWgenerator and Reclaimer were significantly impacted by both the labor wage and grid electricity price variations with the NEWgenerator cost ranging from 0.123 to 0.334 USD•capita -1 •day -1 and the Reclaimer cost ranging from 0.186 to 0.306 USD•capita -1 •day -1 (Figures ). Neither CRT system achieves the economic performance target, underscoring the need for targeted improvements to system design and/or O&M to improve affordability in resource-limited settings. In terms of annual per capita GHG emissions, the carbon intensity of the local grid electricity, caloric intake, and both forms of protein intake (animal and vegetal) all yielded high Spearman's rank order correlation coefficients for at least one NSS system. The effect of caloric intake and animal protein intake variations on annual per capita CI scores was explored using the alternative NSS systems because these systems were more sensitive compared to the baseline systems (Figure ). The alternative Biogenic Refinery was the only NSS system sensitive to protein intake. The median Spearman's rank order correlation coefficients for animal protein and vegetal protein intake were similar (0.56 for animal and 0.58 for vegetal), so animal protein intake was selected for the analysis to understand the impact of meat consumption on decision-making.