Identifying implementation contexts for a specific technology and collecting locality-specific data can be very resource intensive for decision-makers. The results presented in this work can allow decision-makers to make preliminary inferences about the sustainability of a NSS system in a specific locality using the contextual parameter trends and performance typologies. While this study provides insights into the financial viability and environmental implications across varied contexts for NSS implementation, it has several limitations. The contextual parameters selected for the study relied on country-specific averages that do not capture (potentially large) subnational variations, especially variations between urban and rural communities. These variations should be further explored, if relevant, by decision-makers to assess the sustainability of alternatives before deployment. Additionally, the parameters included in the analysis do not capture the full set of conditions that may affect performance. The price level ratio was used to represent the differences in material costs by country. Locally collecting material costs is very resourceintensive, and price level ratio is one way to estimate variability across countries; however, incorporating true locality-specific material prices will lead to better estimates for overall systems costs. In terms of life cycle GHG emissions, variability in country-specific CIs were primarily captured through variations in dietary intake (e.g., direct and chemical impacts) and electricity For example, the potential to process agricultural residues or food waste alongside human excreta could provide additional value streams and improve system economics in certain contexts, further highlighting the importance of local conditions in technology selection and deployment. Ultimately, the intent of the country-specific trends and typologies is not to imply one-size-fits-all solutions for individual countries, but rather to shed light on patterns across countries in terms on economic and environmental performance of NSS systems and to help guide early-stage decision-making related to technology research, development, and deployment.