Defining Economic

and Environmental Typologies across 77 Countries to Prioritize Opportunities for Nonsewered Sanitation

Authors: Hannah A. C. Lohman, Yalin Li, Xinyi Zhang, Victoria L. Morgan, Shion Watabe, Lewis S. Rowles, Roland D. Cusick, Jeremy S. Guest

Abstract

Lack of access to sanitation is a challenge that persists globally, with low sewerage connection rates in many low- and lower-middle-income countries. Engineered nonsewered sanitation (NSS) technologies can meet treatment requirements without sewers, but their relative sustainability varies across potential deployment sites. Here, we characterize the costs and carbon intensity (CI) of three emerging NSS technologies, two community reinvented toilets (CRTs) and one Omni Processor (OP), across 77 countries, identify sustainability performance typologies, and map typology prevalence in countries across the globe. Locality-specific factors such as wages, diet, and material costs drive regional variability in NSS costs by up to 15-fold and CI up to 2-fold within technologies. Across all three NSS technologies and all scenarios evaluated, costs ranged from 0.01 to 0.36 USD·capita^–1^·day^–1^ and CIs ranged from 8 to 269 kg CO2 eq·capita^–1^·year^–1^. Low-cost, low-CI typologies are predominantly in countries with lower human development indices (HDI 2–4), demonstrating alignment between the sanitation need and the NSS opportunity space. Ultimately, the intent of this work is not to imply one-size-fits-all solutions for individual countries; by elucidating key sustainability drivers and defining typologies, this work can support early-stage decision-making for NSS technology research, development, and deployment.

Introduction

Access to sanitation is a universal right; , however, insufficient access continues to persist globally, and we are not on track to meet the 2030 Sustainable Development Goal (SDG) targets to achieve universal sanitation coverage and halve the proportion of untreated wastewater. −

Sanitation services refer to the management of bodily waste (i.e., urine and feces) from initial collection through transport, treatment, and eventual discharge or reuse. Even in regions reporting high sanitation access and treatment, national coverage averages can mask subnational disparities, often with lower access and treatment in marginalized and hard-to-reach communities. Conventional centralized wastewater treatment relying on sewersunderground collection systems that transport used water (including from toilet flushing) to centralized facilitiescan be a mechanism to meet the SDGs; however, many cities often have low sewerage connection rates, with low- and lower-middle-income populations averaging 11.7 and 17.6% sewerage coverage in 2022, respectively. Increasing sewer coverage requires large capital investments and significant maintenanceresources that are often out of reach. −

In the absence of sewer connections, many communities in low- and lower-middle-income countries rely on onsite bodily waste collection and storage via pit latrines or partial treatment via septic tanks, the latter of which serves 7.6% (low income) and 31.8% (lower-middle income) of country populations. Although these conventional onsite alternatives support sanitation access, they often leave bodily waste partially or entirely untreated; −

as of 2022, 75.6 and 52.5% of the low- and lower-middle-income populations remain without access to safely managed sanitation, respectively.

To advance access to safely managed bodily waste, there has been an international effort to establish safety and performance requirements for nonsewered sanitation (NSS) systems that can provide reliable decentralized treatment. , In parallel, the Bill & Melinda Gates Foundation has supported the development and commercialization of NSS technologies through the Reinvent the Toilet Challenge, setting targets for low-cost (targeting 0.05 USD·capita^–1^·day^–1^) and environmentally sustainable systems. Recently developed NSS technologies range in service scale from individual households (household reinvented toilets; HRTs) −

to small communities (community reinvented toilets; CRTs) −

and larger communities of 10,000+ people (Omni Processors; OPs). The existing NSS systems from the Reinvent the Toilet Challenge portfolio incorporate a variety of treatment and recovery techniques, including solid–liquid separation technologies, disinfection, thermal treatment (combustion, pyrolysis, wet oxidation), solids-only treatment, liquids-only treatment, and recovery of water, energy, and nutrients.

Although many of the developed NSS systems can meet International Organization for Standardization (ISO) treatment requirements for NSS systems (ISO 30500) (i.e., log reduction values of human enteric bacterial pathogens, thresholds for effluent chemical oxygen demand, total suspended solids, and pH, as well as nutrient removal requirements for total nitrogen and total phosphorus) and are undergoing bench- and pilot-scale testing, critical questions remain about the financial viability and environmental implications of technology scale-up and deployment across contexts. Existing studies have characterized costs and environmental impacts of individual NSS technologies or compare individual technologies against conventional alternatives, generally without comparing NSS alternatives against each other or across deployment contexts. Anastasopoulou et al. used life cycle assessment (LCA) and quantitative microbial risk assessment to compare the Nano Membrane Toilet (HRT) against a conventional pour flush toilets (with centralized wastewater treatment) and a urine-diverting dry toilet (with composting and urine storage) and found the Nano Membrane Toilet to be the second-best alternative in terms of environmental impact. Trotochaud et al. conducted a techno-economic analysis (TEA) to evaluate the current state of a CRT liquid treatment system and to identify priority areas for improvement. Rowles et al. and Watabe et al. integrated design, simulation, LCA, and TEA to evaluate the Biogenic Refinery (OP) and NEWgenerator (CRT), respectively, and identify key drivers of system sustainability. With a portfolio of NSS alternatives effectively meeting treatment standards and becoming increasingly affordable, it is critical that we understand how the deployment context may influence the relative financial viability and environmental implications of NSS technologies. The impact of context on treatment performance has been and is currently being investigated for these systems through field trials; ,, however, these efforts are not focused on understanding the contextual drivers of costs and emissions that could be used to develop a path for scale-up and implementation. For individual NSS technologies, Rowles et al. and Watabe et al. demonstrated how a subset of locality-specific factors across five countries influence technology-specific costs and greenhouse gas (GHG) emissions. Despite these efforts, to our knowledge, no study has been conducted to elucidate the salient features of deployment contexts that govern NSS economics and life cycle environmental impacts. Identifying and understanding these key contextual drivers is critical to prioritize investment in NSS technologies and support their scale-up and implementation.

The objectives of this work were (1) to assess how implementation context influences the relative economic and environmental sustainability of NSS systems; and (2) to determine sustainability performance typologies, their prevalence in countries across the globe, and their implications for sanitation system decision-making. To this end, the deployment of three NSS technologies, one Omni Processor (Biogenic Refinery) and two community reinvented toilets (NEWgenerator and Reclaimer), was simulated across 77 countries. Using a quantitative sustainable design (QSD) approach, we developed and integrated open-source models for the design, simulation, TEA, and LCA of NSS technologies subject to locality-specific contextual parameters. Contextual parameters included labor wages, energy cost, and carbon intensity, price level ratio, household size, dietary intake parameters, and food waste ratio (Tables S1–S3). Baseline and alternative designs were assessed for each NSS system, including two frontend facilities for the Biogenic Refinery (pit latrine vs urine-diverting dry toilets) and two electricity sources for the community reinvented toilets (grid vs photovoltaic electricity). All designs and scenarios were coded in QSDsan , (an open-source QSD tool) to characterize the daily per capita costs (USD·capita^–1^·day^–1^) and annual per capita GHG emissions (reported as carbon intensity, CI; kg CO2 eq·capita^–1^·year^–1^) under uncertainty. The impact of contextual parameter uncertainties on calculated costs and GHG emissions were evaluated via Spearman’s rank order correlation coefficients. Finally, principal component analysis was used to characterize sustainability performance typologieshigh cost vs low cost, high CI vs low CIand contextual parameter trends among typologies across countries. Overall, the results presented in this study provide insight into the salient features governing location-specific financial viability and CI for the selected NSS systems. Although not comprehensive of all potential technologies and systems that can achieve NSS, the results support the prioritization of research, development, and deployment of NSS technologies in diverse settings across the globe.

Methods

Nonsewered Sanitation (NSS) Systems and Scenarios

Three NSS systems, each of which has been designed and analyzed in prior work, −

were modeled to evaluate the influence of context on economic and environmental performance and identify key performance typologies relevant for sanitation and resource recovery decision-making. The systems were selected because they have been rigorously modeled and analyzed previously and have existing open-source models that include design, simulation, TEA, and LCA. The systems included two household cluster-level CRTs: the NEWgenerator ,, (100 daily users and 25-year lifetime) and the Reclaimer (120 daily users and 20-year lifetime), and one community-scale OP: the Biogenic Refinery (12,000 daily users and 20-year lifetime). The specific components of each system, including user interface and storage, conveyance, solid–liquid separation, liquid treatment, solids treatment, energy sources, and overall process flows, are detailed in the SI (Section S1; Figures S1, S2). The sanitation service chain of each NSS system was modeled to include user interface, onsite storage, conveyance (if offsite treatment), treatment of human excreta, and resource recovery (if applicable). Two performance scenarios were evaluated for each NSS technology, resulting in six total NSS systems in the analysis. The NEWgenerator and Reclaimer were each evaluated under two energy source grid (baseline) and photovoltaic electricity (alternative), and the Biogenic Refinery was evaluated for treatment of mixed excreta from pit latrines (baseline) and source-separated urine and feces from urine-diverting dry toilets (alternative). Design, simulation, economic and environmental impact characterization, and uncertainty and sensitivity analyses were performed in Python (version 3.9) using QSDsan, , an open-source, community-led platform for quantitative sustainable design of sanitation and resource recovery systems. The code for all evaluated NSS systems is publicly available on GitHub. Additional details about each technology, as well as more detailed process flow diagrams showing the components of the complete sanitation service chain included in each NSS system, can be found in the Supporting Information (SI; Section S1). Further details are also available in previously published work that investigated how each system’s design, configuration, and population scenarios (population density, number of users served, etc.) impact overall function and performance (including meeting treatment objectives) as well as the economic and environmental implications of these factors. −

This work does not repeat these efforts but instead builds upon them by leveraging the core design and performance outcomes highlighted in those works to delve deeper into the contextual implications of deployment.

Economic Analysis

Techno-economic analysis , (TEA) incorporating a discounted cash flow was used to calculate the daily per capita cost of each NSS system. Capital, annual operation and maintenance (O&M), consumables, and electricity expenses were included in the system boundary of the analysis. The functional unit for the TEA and the life cycle assessment (discussed below) is the provision of sanitation services for one person meeting the ISO 30500 treatment requirements for NSS systems, where treatment requirements include log reduction values of human enteric bacterial pathogens, thresholds for effluent chemical oxygen demand, total suspended solids, and pH, as well as nutrient removal requirements for total nitrogen and total phosphorus. Initial capital costs were estimated based primarily on documentation provided in prototype bill of materials for each system and distributed over the lifetime of the system, with a 5% discount rate adjusting for the diminishing value of money over time. Because capital costs were estimated based on prototype costs, a learning curve equation , was used to conservatively estimate the capital costs associated with serial production of each system at scale (10,000 units for the Biogenic Refinery and 100,000 units for the NEWgenerator and Reclaimer). O&M labor costs were based on the maintenance activity schedules provided by each design team, and labor wages were assigned based on the skill level required (e.g., construction, electrician). O&M replacement parts were included to account for part lifetimes less than the total system lifetime (e.g., pumps, batteries, solar panels), and consumable costs accounted for any chemicals and materials used within the system (e.g., zeolite, LPG, GAC). Electricity costs were estimated based on the electricity needs reported by each design team and the global average electricity cost per kilowatt-hour. Scenarios relying on grid electricity included the capital expenses necessary to connect to the grid; however, costs associated with the grid system itself were outside of scope of this analysis. All expenses associated with photovoltaic electricity generation were included in the calculated daily per capita costs of each NSS system because it is a modification of the NSS system design itself. Each system expense or revenue was resolved to its equivalent annual worth, and the total cost of each system was normalized to a daily per capita basis (i.e., U.S. dollars·capita^–1^·day^–1^).

Environmental Assessment

Life cycle assessment (LCA) was used to characterize the carbon intensity (i.e., greenhouse gas emissions normalized to kg CO2 equivalents with a 100-year time horizon) from materials (construction and O&M replacement), energy, and direct fugitive emissions from excreta. The transportation of construction materials and consumables and the disposal of the NSS systems at their end of life were considered to be outside the scope of this analysis. Environmental impacts were estimated from each system’s materials and electricity consumption using the Ecoinvent v3.6 database and the U.S. EPA’s Tool for the Reduction and Assessment of Chemicals and Other Environmental Impacts, TRACI 2.1 v1.03. Material requirements were estimated using the prototype bill of materials for each system and vendor websites. Materials and processes for each component were identified, and in many cases, when reported masses could not be found, the mass of a component was estimated using the item’s geometry, density, and characteristics of similar components. Impacts associated with electricity were calculated from each system’s energy requirements and the unit environmental impacts associated with the energy source. In the case of the photovoltaic energy scenarios for the NEWgenerator and Reclaimer, electricity generation impacts during operation were assumed to be 0; , however, impacts associated with solar panels, batteries, etc. are included in the construction impacts. Direct fugitive GHG emissions from excreta included CH4, N2O, and CO2 released from the degradation of bodily waste; these emissions were based on literature estimates of nitrogen and carbon excreted and emissions estimates for each active treatment unit process in the system. , Direct fugitive CO2 emissions were assigned a characterization factor of 0, given that this carbon was biogenic and had recently existed as CO2 before being fixed by plants and entering the food production cycle. Fugitive CH4 and N2O were assigned characterization factors of 28 kg CO2 eq·kg^–1^ and 265 kg CO2 eq·kg^–1^, respectively; although these fugitive emissions also stemmed from biological cycling, these values were nonzero because these molecules exert greater radiative forcing than their recent atmospheric precursors (CO2 and N2, respectively). Overall, total environmental impacts were estimated from each of these three sources (materials, energy, and direct impacts from excreta) over each system’s lifetime. To combine and normalize these impacts, it was assumed that material impacts could be evenly distributed across each year of the lifetime (i.e., total impacts were divided by the lifetime to calculate an impact per year). Total impacts of each system were normalized to a per capita basis over the course of a year (i.e., kg CO2 eq·capita^–1^·year^–1^).

Contextual Parameters

The impact of context and expected deployment location on the economic and environmental performance of NSS systems was evaluated by assigning values representative of conditions in specific countries to TEA and LCA contextual parameters (Tables S1–S3). Energy parameters include the carbon intensity (CI) of a country’s energy source (based on the energy mix of a country; i.e., fraction of energy produced by solar, wind, coal, etc.) and electricity price. Demographic parameters include dietary intake (i.e., calories, vegetal protein, and animal protein), household-level food waste, and household size. Economic parameters include price level ratio, construction and maintenance labor wages, and LPG price. The price level ratio provides a measure of the differences in prices between countries as a ratio of the purchasing power parity conversion factor to the currency exchange rate. Out of the 250 countries/territories assessed, 77 were included in the analysis because country-specific data were available for all contextual parameters. Because data availability limited the evaluation of all 250 countries, the performance of the NSS systems was also evaluated under general case, best-case, and worst-case scenarios to better understand the performance space. The general case relied on global averages for each contextual parameter, while the best-case and worst-case scenarios relied on the minimum and maximum parameter values across all countries. For example, the minimum CI calculation uses a minimum electricity emission factor of 0.012 kg CO2 eq·kWh^–1^ (assuming 100% nuclear-derived electricity, the source with the lowest carbon intensity), and the maximum uses an emission factor of 1.05 kg CO2 eq·kWh^–1^ (assuming 100% fossil fuel-derived electricity, the source with the highest carbon intensity including coal, natural gas, oil, and other gases). ,

Uncertainty and Sensitivity Analyses

A Monte Carlo analysis with Latin Hypercube Sampling was used to account for possible variations in input parameters (including contextual parameters and NSS-specific technological parameters). A total of 1000 simulations were conducted for each system scenario and country combination (i.e., 462 combinations with 6 system scenarios and 77 countries). Input parameters were subjected to 10–50% uncertainty based on the level of confidence in the specific parameter values and to account for variability due to factors such as spatial heterogeneity of material prices and impacts (Tables S1–S3). , The impact of contextual parameter uncertainty on the calculated costs and emissions was evaluated by calculating the Spearman’s rank order correlation coefficients. A total of 77 correlation coefficients were calculated for each contextual parameter within a system scenario. Overall, the correlation coefficients can enable decision-makers to understand how input uncertainty is driving output uncertainty and to make inferences about system performance by using local knowledge regarding contextual parameters. Additional details regarding the uncertainty and sensitivity analyses are provided in Section S1 of the SI.

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 (Figure S3). 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, were investigated. Clusters were most apparent with the HDI country classification grouping, so the relationship between sustainability performance typology and the HDI tier was reported in the results. Sustainability performance typologies were characterized to describe the relative performance of a given NSS system (in terms of costs and emissions) in one country relative to those in other countries. 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 across the 77 countries, 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 across the 77 countries, respectively. The PCA results showed distinct clustering by economic performance for all six NSS systems (Figure S4) and clustering by environmental performance for the two Biogenic Refinery systems (Figure S5). Distinct clusters related to environmental performance were not observed for the NEWgenerator and Reclaimer systems; therefore, environmental typology results are reported only for the Biogenic Refinery. Additional details regarding the PCA methods are provided in Section S1 of the SI.

Statistical Analyses

Statistical analyses were performed to characterize the magnitude of the contextual parameters within each sustainability performance typology (e.g., magnitude of caloric intake in countries with high-cost versus low-cost typologies for the baseline Biogenic Refinery system). The Shapiro-Wilk test for normality was conducted to assess the normality of each data set (Table S4). Because many of the comparison data sets were not normal (p < 0.05), the Mann–Whitney U Test/Wilcoxon Rank Sum Test (nonparametric test to compare outcomes between two independent groups) was selected to determine if two samples are likely to derive from the same population (Table S5). The medians of each sample within a comparison pair with a statistically significant difference (defined as p < 0.05) were calculated to quantify the magnitude of each contextual parameter within a typology (i.e., low, high), while pairs with no statistically significant difference were categorized as “varies”. Results were used to inform the summary table of sustainability performance typologies presented in this work.

Results and Discussion

Performance of NSS Technologies across Contexts

The potential deployment of all three NSS technologies was evaluated under uncertainty with global average data (the general case) and across 77 countries using national-level contextual parameters (Figure ). Additionally, to better capture the full range of potential NSS performance across locations, the best-case and worst-case combinations of contextual parameters were also evaluated by selecting the most favorable and unfavorable combinations of parameters from across the global data set, defining the performance space of each NSS system scenario. To provide additional context for the results of the TEA of the evaluated NSS systems, costs can be compared against the World Bank’s International Benchmarking Network (IBNET) data which reports sanitation tariffs (for centralized wastewater management) across 38 of the 77 countries in this study. Country average tariffs reported in IBNET ranged from 0.01 to 1.86 USD·capita^–1^·day^–1^, with a median value of 0.23 USD·capita^–1^·day^–1^.

Using generalized assumptions for the contextual parameters (i.e., global averages), the Biogenic Refinery treating mixed excreta from pit latrines (baseline scenario) is shown to have a lower cost with higher GHG emissions compared with the treatment of source-separated excreta from UDDTs (alternative scenario). The daily per capita cost of the baseline scenario is estimated to be 0.04 USD·capita^–1^·day^–1^ with a range of 0.03–0.04 USD·capita^–1^·day^–1^ [5th and 95th percentiles will be shown in brackets from this point forward] and 0.08 [0.07–0.10] USD·capita^–1^·day^–1^ for the alternative scenario. The two scenarios exhibit cost-CI trade-offs where the higher cost of the alternative scenario treating source-separated excreta are accompanied by significantly less GHG emissions over the system lifetime; the baseline scenario’s emissions are 38 [20–65] kg CO2 eq·capita^–1^·year^–1^ while the alternative scenario’s emissions are 16 [13–20] kg CO2 eq·capita^–1^·year^–1^. For the CRTs, the scenarios relying on grid electricity result in lower costs but with higher CIs compared to scenarios relying on photovoltaic electricity. The daily per capita costs of the NEWgenerator are 0.14 [0.12–0.18] USD·capita^–1^·day^–1^ for the baseline scenario incorporating grid electricity and 0.14 [0.12–0.19] USD·capita^–1^·day^–1^ for the alternative scenario using photovoltaic electricity. The minor difference in cost is due to the price differences of the photovoltaic electricity unit compared with the grid electricity unit. The NEWgenerator emissions are 78 [63–97] kg CO2 eq·capita^–1^·year^–1^ for the grid scenario and 54 [39–73] kg CO2 eq·capita^–1^·year^–1^ for the photovoltaic scenario. The Reclaimer resulted in higher costs and emissions than the NEWgenerator using general assumptions. The daily per capita costs of the Reclaimer are 0.20 [0.16–0.27] USD·capita^–1^·day^–1^ and 0.22 [0.17–0.28] USD·capita^–1^·day^–1^ for the grid and photovoltaic electricity scenarios, respectively. The annual per capita GHG emissions were estimated to be 182 [134–248] kg CO2 eq·capita^–1^·year^–1^ for the baseline grid electricity scenario and 132 [83–198] kg CO2 eq·capita^–1^·year^–1^ for the alternative photovoltaic electricity scenario. Emissions were higher for the Reclaimer compared to the NEWgenerator due to its higher electricity demands and direct emissions produced in the septic tank.

The general results can be useful for preliminary assessments and decision-making; however, identifying contexts appropriate for the implementation of NSS systems requires a more contextualized evaluation of costs and emissions. When applying country-specific values for contextual parameters, the cost of the Biogenic Refinery ranges from 0.01 to 0.15 USD·capita^–1^·day^–1^ and 0.02 to 0.25 USD·capita^–1^·day^–1^ for the baseline and alternative scenarios, respectively. Key contributors to cost are capital expenses for frontend facilities and operation and maintenance (O&M) labor for excreta collection, conveyance, and processing. While the Biogenic Refinery scenario treating mixed excreta was initially shown to be affordable using general assumptions (i.e., less than the 0.05 USD·capita^–1^·day^–1^ goal set by the Reinvented Toilet Challenge), 21 of the 77 countries evaluated resulted in a daily cost over the affordability target. The country-specific emissions for the Biogenic Refinery scenarios range from 23 to 67 kg CO2 eq·capita^–1^·year^–1^ for the baseline scenario and from 8 to 34 kg CO2 eq·capita^–1^·year^–1^ for the alternative scenario. The differences across the countries can be attributed to differences in local dietary intake by country (i.e., high caloric intake results in higher direct emissions from the pit latrines and higher protein intake results in higher chemical inputs for nutrient recovery from source-separated excreta) and are further explored and quantified in the later sections of this work. The cost of the CRTs exceeded the affordability target for all scenarios using generalized assumptions; however, country-specific values for cost parameters demonstrate the potential for daily per capita cost closer to the 0.05 USD·capita^–1^·day^–1^ goal in some contexts. In the case of the NEWgenerator, cost was largely driven by sludge pasteurization, nutrient capture (via ion exchange), and electrochlorination. Although the NEWgenerator does not achieve the affordability target in any of the 77 countries evaluated, the theoretical cost of the NEWgenerator ranges from 0.03 to 0.36 USD·capita^–1^·day^–1^ and from 0.04 to 0.35 USD·capita^–1^·day^–1^ for the grid electricity and photovoltaic electricity scenarios, respectively. These results could motivate the search for subnational conditions that would enable NEWgenerator deployment at costs below the affordability threshold. The NEWgenerator CI scores range from 46 to 108 kg CO2 eq·capita^–1^·year^–1^ using grid electricity and 46 to 71 kg CO2 eq·capita^–1^·year^–1^ using photovoltaic electricity. Relative to the NEWgenerator (which relies on anaerobic biological degradation), the Reclaimer shifts the burden of solids and organics removal to separations via septic tanks, ultrafiltration, and granular activated carbon. In particular, the ultrafiltration unit is a significant contributor to capital and O&M. The cost of the Reclaimer ranges from 0.14 to 0.30 USD·capita^–1^·day^–1^ for the grid electricity scenario and from 0.15 to 0.29 USD·capita^–1^·day^–1^ for the photovoltaic electricity scenario. In most contexts, the NEWgenerator outperforms the Reclaimer with respect to cost; however, in 11 of the 77 countries, the Reclaimer is the lowest-cost CRT. The Reclaimer GHG emissions range from 109 to 269 kg CO2 eq·capita^–1^·year^–1^ and 108 to 194 kg CO2 eq·capita^–1^·year^–1^ for the grid electricity and photovoltaic electricity scenarios, respectively. For both CRTs, the spread of GHG emissions decreases for photovoltaic electricity scenarios compared to grid electricity scenarios because the only country-specific emissions calculated were direct emissions (varied with dietary intake), whereas the grid electricity scenarios incorporate country-specific electricity carbon intensities. Additionally, the best-case emissions scenario results in almost equal emissions from a system powered by grid electricity (relying entirely on nuclear energy) compared to photovoltaic electricity. These results indicate that context can greatly impact the overall performance of NSS systems, and context-specific cost and CI analyses should incorporate location-specific parameters when available.

Contextual Drivers of Costs and Emissions

Because the performance of the NSS systems has been shown to vary dramatically depending on the implementation context, a sensitivity analysis was conducted to reveal which contextual parameters influenced the outcomes of cost and CI scores across all six systems and 77 countries. The sensitivity analysis was conducted by applying uncertainty to 16 contextual parameters while setting the remaining technological parameters (i.e., parameters that are intrinsic to each NSS system unit operation and component) constant at their central value. This approach allowed for a clear understanding of the impact of contextual parameters without the influence of design/operational decisions and technological parameters (a detailed review of QSD and associated terminology is presented in Li et al.). Of the 16 contextual parameters, liquefied petroleum gas (LPG) price and CRT labor wage were only included in the NEWgenerator and Reclaimer scenarios, and OP wages (six labor wage types) and household size were only included for the Biogenic Refinery scenarios. The seven remaining contextual parameters were included in all six NSS systems. For each NSS scenario and country combination (462 combinations from six systems and 77 countries), 1000 Monte Carlo simulations with Latin Hypercube Sampling were performed to vary the contextual parameters across defined distributions while holding all other model parameters constant at their central value. Spearman’s rank order correlation coefficients were calculated to characterize the sensitivity of per capita daily cost and CI scores to contextual parameter uncertainties. A correlation coefficient was calculated for each country evaluated (i.e., 77 correlation coefficients calculated for a single input and output pair within an NSS system), and correlation coefficients across countries were compiled and plotted to identify generalizable trends (Figure ).

The daily per capita costs of all NSS scenarios were most sensitive to labor wages (OP operator wage for the Biogenic Refinery and CRT labor wage for the NEWgenerator and Reclaimer) and the price level ratio. The Biogenic Refinery relies on a daily operator to manage and oversee the system, while the NEWgenerator and Reclaimer do not require an on-site operator but do require periodic maintenance for treatment operations. The price level ratio was the key contextual parameter used to convert generalized material costs to country-specific costs. The magnitude of the ratio directly alters the expected capital and consumable costs of the system, resulting in a high correlation with the calculated daily per capita cost of each NSS system. Additionally, the daily per capita cost of the baseline Reclaimer was sensitive to the grid electricity price due to its high electricity consumption compared to the other systems (i.e., median electricity demands of 0.408 kWh·capita^–1^·day^–1^ to operate the Reclaimer vs 0.131 kWh·capita^–1^·day^–1^ to operate the NEWgenerator and 0.008 kWh·capita^–1^·day^–1^ to operate the Biogenic Refinery). The annual per capita CI scores were most sensitive to the national diet and grid electricity carbon intensity. Systems with high direct emissions were highly sensitive to caloric intake (all NSS scenarios besides the alternative Biogenic Refinery treating excreta from UDDTs) where higher caloric intake results in a more energy-rich (measured as chemical oxygen demand, COD) excreta stream that can be converted to direct emissions. The Biogenic Refinery scenario treating source-separated excreta from UDDTs has relatively low direct fugitive emissions due to its higher emptying frequency and is more sensitive to vegetal and animal protein intake. Protein intake is directly related to the excreta stream nutrient content, with higher nutrient concentrations requiring greater chemical input requirements to meet treatment standards. Overall, NSS technology developers can better improve sustainability performance estimates by gathering locality-specific estimates for the key contextual parameters driving uncertainty.

Effect of Contextual Drivers on Sustainability Performance

The effect of contextual drivers on daily per capita cost and annual CI scores was explored for the six NSS systems to highlight the impact location-specific variations have on NSS sustainability. The results of the sensitivity analysis were used to select four contextual drivers (two driving the uncertainty in cost and two driving the uncertainty in emissions) to vary for the analysis, while holding all other model parameters constant at their central value. Grid electricity price, labor wage, and price level ratio had high Spearman’s rank correlation coefficients with respect to daily per capita cost for at least one NSS system. Although the daily per capita cost was highly sensitive to the price level ratio for all six systems, it was not analyzed further because it is not a parameter that could be adjusted by sanitation developers due to its reliance on market exchange rates and purchasing power of the implementing country. Although sanitation developers have little control over the electricity price and market labor rates in their implementation context, they are expected to have greater subnational variability and could be influenced by design and operational modifications (e.g., shifting the electricity demand to off-peak hours at a reduced unit charge). Because baseline NSS systems demonstrated greater sensitivity to these parameters (relative to alternative systems; Figure ), the effect of labor wage and grid electricity price variations on daily per capita cost was explored using the baseline NSS systems (Figure A–C). When the two economic parameters were varied (with all other model parameters constant at their central value), 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 A). 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^ (Figure B,C). Neither CRT system achieves the economic performance target, underscoring the need for targeted improvements , to system design and/or O&M as well as consideration of pairing these systems with policy and financial mechanisms such as aid programs, subsidies, and carbon credits 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 with 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. When varying the two dietary intake parameters (with all other model parameters constant at their central value), the alternative Biogenic Refinery annual per capital CIs ranged from 14.1 to 18.5 kg CO2 eq·capita^–1^·year^–1^ with animal protein intake significantly impacting the emissions and caloric intake having less of an impact (Figure D). On the other hand, the CI scores for the alternative NEWgenerator and Reclaimer were more impacted by caloric intake than animal protein intake with the NEWgenerator and Reclaimer emissions ranging from 51.7 to 76.2 and from 137.1 to 214.3 kg CO2 eq·capita^–1^·year^–1^, respectively (Figure E,F). Although the NEWgenerator and Reclaimer CIs results visually appear to have a slight inverse relationship between animal protein intake and emissions, no correlation exists between GHG emissions and animal protein intake for these two systems, and any inverse relationship observed is (in part) due to noise in the uncertainty analysis (the Spearman’s rho for animal protein and CI scores had both positive and negative values across countries, but all were close to zero; Figure ). Although dietary intake is outside of the control of sanitation developers and it is not our intent or expectation for users to alter their diets, these trends can be useful in determining the performance of systems in different subnational settings and highlight the need for location-specific analyses to understand whether the systems, in their current design, have the ability to meet emissions performance goals.

Economic and Environmental Performance Typologies

In addition to understanding the correlation of contextual parameters and expected sustainability performance, economic and environmental performance typologies (Table ) were developed to succinctly summarize contextual parameter characteristics. Typologies focus on performance and trends across countries within a single NSS system and not comparisons across systems. Two cost typologies (high-cost, low-cost) and two emissions typologies (high-CI, low-CI) characterize the range of NSS performance. For each country, the individual cost and emission performance was compared to the median performance across the other countries (i.e., a country with a daily per capita cost lower than the median across the 77 countries for the baseline Biogenic Refinery would be categorized as low-cost). Countries were objectively placed in the high or low grouping and visualized with the contextual parameters using principal component analysis (PCA; Figures S4–S8). Contextual parameter characteristics were classified as “low” and “high” when differences between the median contextual parameter values within each typology were observed with p < 0.05. In the case of parameters that yielded p ≥ 0.05 for some or all NSS systems, contextual parameter characteristics were defined as “varies” because there was no clear trend (low vs high) relevant to all NSS systems (e.g., a significant difference between the two emissions typologies existed for vegetal protein for the alternative Biogenic Refinery, but no significant difference existed for the baseline Biogenic Refinery). The alignment of the economic and environmental performance typologies with Human Development Index (HDI) groupings was also explored to better understand the alignment of low-cost and low-CI typologies with higher HDI deployment contexts.

Contextual parameter magnitudes were characterized for all six NSS systems in relation to the cost performance typologies. High-cost implementations of NSS scenarios were in countries with high grid electricity prices (for systems relying on grid electricity), high price level ratios, high labor wages, high animal protein and caloric intake, high food waste, and low household sizes. Vegetal protein intake was found to not be statistically different (p > 0.05) in each cost typology group. Likewise, high-CI emission implementations of the Biogenic Refinery scenarios were in countries with high animal protein and caloric intake, high food waste, and low household sizes. Electricity carbon intensity and vegetal protein intake were not found to be statistically different (p > 0.05) in each emissions typology group. Specifically, parameters without a clear trend across typology groups should be included in local data collection conducted by decision-makers and technology developers to inform estimates of costs and CI. In the case of country development clustering by typology, countries with very high human development (HDI 1) typically resulted in higher costs and CIs. Higher developed countries typically have higher wages, smaller households, and more calorie- and nutrient-rich diets with higher amounts of food wasted. Although the results were based on a subset of NSS systems and are not a universal representation all possible NSS systems, decision-makers can still use these results to make reasonable preliminary estimates related to the sustainability performance and implementation of NSS systems by comparing their level of community development and key contextual parameter characteristics against global averages. Although the results of this work are uncertain, they can be used by decision-makers to rule out systems that would not be appropriate for implementation without the need to conduct resource-intensive analyses. Additionally, the work can be used to determine opportunities to offset costs and emissions to implement systems in countries with high costs and CIs; strategies could include leveraging financial mechanisms such as aid programs, subsidies, and carbon credits to offset costs and emissions as well as recovering resources such as nutrients, energy, and water.

The CRT Selection Opportunity Space

The previously described results related to sustainability performance, drivers of costs and emissions, and typology trends by context can be used by decision-makers to understand the impact of context on NSS system selection. Although the Biogenic Refinery outcompetes the CRTs in terms of costs and emissions in all countries evaluated, constraints within the implementation context could make a CRT a more feasible option over a large-scale Omni Processor. Because a single Biogenic Refinery serves a population of 10,000+ users, it is best suited for more urban populations with higher population densities. Implementation in smaller communities that do not meet their design capacity would greatly impact daily per capita costs and annual GHG emissions. Additionally, the Biogenic Refinery requires an onsite daily operator as well as transportation of excreta to the centralized treatment location, which could be too labor-intensive for smaller, more dispersed communities. Contextual characteristics of communities or settings appropriate for CRTs dictate the selection between the NEWgenerator and the Reclaimer. The specific combinations of an implementation context’s contextual parameters and constraints that lead to one CRT outperforming competing technologies can be described as the CRT’s opportunity space (Figure ).

If a CRT meets the constraints of the community, labor wages dictate which CRT is the most affordable alternative (Figure S9B). The NEWgenerator requires labor hours higher than those of the Reclaimer and is only more affordable than the Reclaimer when labor wages are less than 23 USD·hour^–1^. Of the 77 evaluated countries, 11 countries have hourly wages above this threshold and are locations where the Reclaimer outperforms the NEWgenerator in terms of daily per capita cost. Within each system, selection of photovoltaic vs grid electricity is determined by the location’s grid electricity price (Figure S9A) and the price level ratio (Figure S9C). Photovoltaic electricity generation becomes the more affordable alternative when electricity prices are high (above 0.16 USD·kWh^–1^ for the NEWgenerator and above 0.20 USD·kWh^–1^ for the Reclaimer) or when the price level ratio (capital expenses) is low (below 0.840 for the NEWgenerator and below 0.611 for the Reclaimer). The NEWgenerator and the Reclaimer can also be the leading CRT in contexts that do not necessarily lead to the lowest cost or emissions. For example, the NEWgenerator has the potential to recover biogas energy to offset LPG requirements for sludge pasteurization and water to be used as flush water. Systems with the capability to recover water and/or energy may be especially beneficial in contexts facing water shortages or high LPG prices, even if the system itself is not as affordable as other alternatives. Although the NEWgenerator outperforms the Reclaimer in many contexts in terms of daily per capita cost and annual CI scores, context-specific analyses show that there are conditions in which the Reclaimer can outperform the NEWgenerator and should be explored before selection of one alternative over another for implementation. Finally, although this study focused on three example NSS systems, many alternative NSS systems exist (e.g., container-based systems) that could be better suited for a particular community and could perform better in specific contexts. The choice of sanitation system for an individual community should be made based on local characteristics including environment (e.g., temperature, rainfall), culture (e.g., washing vs wiping), and resources (e.g., access to an electrical grid), among other considerations. ,

Limitations

While this study provides valuable insights, 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 the performance. The price level ratio was used to represent the differences in material costs by country. Locally collecting material costs is very resource-intensive, and the 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 was primarily captured through variations in dietary intake (e.g., direct and chemical impacts) and electricity use. Although uncertainty applied to material characterization factors should account for variations by location, future work could incorporate country- or region-specific material characterization factors available in Ecoinvent or other life cycle inventory databases. Furthermore, the NSS systems have the potential to provide additional benefits to the communities that were excluded from this study due to data limitations or falling outside the scope of work. Each of the NSS technologies has the potential to recover valuable resources, such as nutrients for fertilizer, energy, and water. Economic and environmental offsets associated with recovered resources has previously been investigated for both the Biogenic Refinery and the NEWgenerator for a subset of countries with available data; however, due to limitations in global country-specific data (e.g., country-specific fertilizer prices), the value of recovered resources, outside of recovered biogas from the NEWgenerator, was excluded from these results. Incorporating recovered resource offsets has the potential to significantly reduce daily per capita costs and CIs as seen in previous work evaluating the Biogenic Refinery and the NEWgenerator. For example, the Biogenic Refinery could achieve 100% cost offsets if carbon credits are valued at 350 USD·Mg^1^ CO2 and 40–55% cost reductions if recovered nutrients are sold at fertilizer market rates. Sanitation decision-makers interested in further reducing costs and emissions should collect local resource values (e.g., fertilizer prices) to effectively incorporate recovered resource offsets. Additionally, future work could explore the capacity of NSS systems to co-treat multiple organic feedstocks. 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 the selection and deployment. Finally, other externalized benefits of NSS systems such as avoided environmental costs and health benefits could be investigated in future work as a justification for higher upfront costs; however, valuation of these benefits has been excluded in this work because they have not been explicitly characterized by the NSS system design teams.

Path Forward

Overall, this study evaluated the economic and environmental performance of baseline and alternative scenarios for three NSS systems, explored key drivers of performance uncertainty, and characterized countries using distinct sustainability performance typologies. The results incorporate robust quantitative sustainable design techniques to evaluate the performance of the technologies. 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 an NSS system in a specific locality using the contextual parameter trends and performance typologies. Technology developers could use the results of this work to target contexts with low-cost and low-CI typologies for the initial deployment of NSS systems. Furthermore, for variable cost and CI contexts, technology developers could collect locality-specific data to reduce the cost and CI uncertainty to better understand performance. Contexts categorized as high-cost or high-CI should focus on cost and emission reducing measures, such as incorporating the value of recovered resources to account for offsets or pairing the implementation of systems with financial mechanisms such as aid programs, subsidies, and carbon credits. 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 of economic and environmental performance of NSS systems and to help guide early-stage decision-making related to technology research, development, and deployment.

Supplementary Material