Principal Component Analysis. Principal component analysis (PCA) was used to help explore broad trends related to the environmental and economic performance of each NSS system across the 77 countries. PCA transforms a dataset of potentially interrelated variables into a set of linearly uncorrelated principal components. The analysis was conducted using 13 contextual parameters related to electricity price and carbon intensity, labor wages (four labor wage types: CRT labor wage, OP operator wage, OP electrician wage, and OP facility manager wage), household size, dietary intake, and food waste. The remaining three wage contextual parameters were excluded from the PCA to reduce redundancy, as their data was shared with one of the four included wage types. To remove differences in scale prior to PCA, each variable was transformed such that its mean equaled zero and standard deviation equaled one. A log transformation was conducted on parameters that do not have a roughly normal distribution. To identify clustering in the PCA, the first two principal components were plotted (representing approximately 70% of the overall variance in the data). Trends related to several country groupings, including Human Development Index (HDI, with HDI 1 being the highest and HDI 4 being the lowest based on UN Development Programme categories), least developed country (LDC) classification , and proportion of the population without at least basic sanitation 8 were investigated. Clusters were most apparent with the HDI country classification grouping, so the relationship between sustainability performance typology and HDI tier was reported in the results. Sustainability performance typologies were characterized to describe how a country performs with respect to other countries for a given NSS system. For economic performance, a country was classified as "high cost" and "low cost" if the cost fell above and below the median daily per capita cost, respectively, for the analyzed NSS system. Likewise, for environmental performance, a country was classified as "high CI" and "low CI", if it fell above and below the median annual per capita CI score, respectively. The PCA results showed distinct clustering by economic performance for all six NSS systems (Figure ) and clustering by environmental performance for the two Biogenic Refinery systems (Figure ). Distinct clusters related to environmental performance were not observed for the NEWgenerator and Reclaimer systems, so environmental typology results are only reported for the Biogenic Refinery.