The Biofactory: Quantifying Life Cycle Sustainability Impacts of the Wastewater Circular Economy in Chile

Abstract

The wastewater circular economy (WW-CE) represents a solution to improving sanitation coverage and management worldwide. However, the transition to circular wastewater treatment plants (WWTPs) requires facilitation to enhance decision-makers’ understanding of the integral sustainability impacts of the WW-CE. This research implemented a Life Cycle Sustainability Assessment (LCSA), combining Life Cycle Assessment, Social Life Cycle Assessment and Life Cycle Costing with a Multi-criteria Decision Making (MCDM) model to quantify the environmental, social, and economic impacts of different WWTPs technologies. Two real WWTPs (Plant A and Plant B) in Chile have embraced alternative WW-CE configurations, adopting the title Biofactories, and are considered as case studies in this investigation. A comparative LCSA considered the service of a 1,000,000-population equivalent, under three scenarios: wastewater discharge without treatment, conventional WWTPs, and biofactory WW-CE configurations. The results demonstrate that the transition to WW-CEs improved integral sustainability, and decreased integrated environmental, social, and economic impacts by 30% in Plant A, demonstrating better performance in terms environmental and social impacts. However, a 58% decrease in integral sustainability impacts for Plant B was achieved via the economic advantage of the thermal hydrolysis pre-treatment of sludge. The urgent need to adopt sustainable decision-making models to improve sanitation coverage and sustainability performance of the sanitation industry across the globe is discussed. The WW-CE in Chile presents an opportunity for this to be achieved.

1. Introduction

Global water and sanitation sectors are under increasing pressure to sustain integrated water management for growing populations, balancing increased domestic and industrial demands with water scarcity and contamination challenges [1]. Global objectives were set to achieve “sustainable development”, and decision-makers in the water sector are faced with the challenge of making “sustainable decisions” [2]. Sustainable development is a complex subject pertaining to multiple definitions, often referring to maximizing economic growth in harmony with ecosystems’ regenerative capacity and societal wellbeing over time [3]. Wastewater treatment plants (WWTPs) are a key infrastructure for achieving integral water management, and for protecting aquatic ecosystems from eutrophication and ecotoxicity [4]. However, chemical, energy, and transport resources are required to achieve minimum compliance with discharge standards, contributing to fossil resource consumption and greenhouse gas emissions (GHG), among other environmental impacts [5]. Sanitation system workers must respond to a wide range of challenges so as to maintain operations and relationships with local authorities, supply chains and local communities [6]. This results in the high investment costs of treatment technology, operation, and maintenance [7]. Therefore, most wastewater discharges around the world are not adequately treated, and more “sustainable” WWTPs are of urgent importance, particularly in developing regions [1]. The Sustainable Sanitation Alliance (SuSanA) declared health, environmental, technology, financial and social objectives for achieving the United Nations’ agenda for sustainable development [8]. Decision-makers, traditionally basing decisions on economic indicators, must now begin to incorporate and comprehend environmental and social aspects as well [9]. The inherently different natures of environmental, economic, and social systems, and the trade-offs that can arise between these, defines sustainable decision-making as a complex, multi-criteria problem [10]. Therefore, the interpretation of the most sustainable choice between alternatives becomes more challenging and time-consuming [11].

The wastewater circular economy (WW-CE) poses a promising solution to achieving sustainability in the water and sanitation sectors through the recovery of treated water, biosolids, nutrients, bioenergy, and biomaterials for use in adjacent economic sectors [12]. Water recovery from treated effluents is being implemented across regions for different applications, such as agriculture, industry, and public services, decreasing freshwater consumption and offering cost-savings to stakeholders [13]. Biosolids products can be recovered through anaerobic digestion and composting for land applications, whereby nutrients and organic matter provide savings related to fertilizer consumption for local farmers, while incineration and pyrolysis processes allow dual energy recovery [14]. Biogas generated through the anaerobic digestion of sludge can be recovered for use as a renewable biofuel, decreasing energy costs and generating revenues [15]. Successful resource recovery from wastewater is highly dependent on economic value, product quality and stakeholder perception, aspects that are geographically unique [16]. These systems can generate environmental, economic, and social benefits; however, no single solution exists for diverse sanitation challenges. Therefore, sustainability must be measured in an integrated way on a case-by-case basis to ensure decision-makers can achieve sustainable integrated water management over time.

To facilitate “sustainable decision making”, a wide variety of decision-making tools based on mathematical and multi-criteria modeling have been developed [9]. Multi-criteria decision making (MCDM) is recommended for addressing the subjective nature of decision-making by attributing importance to influencing decision criterion, and ranking alternatives based on the preferences of the decision-makers [17]. There are around 20 main objective and subjective MCDM processes, with different levels of stakeholder interactions [18]. Rezaei et al. [19] considered economic (Net Present Value), environmental (carbon footprint, Eutrophication Potential), and social (Resource Recovery Value) impacts, assessed by a regret-based decision-making model to assess water reuse applications in Florida. Lohman et al. [20] applied MCDM for technical, resource recovery, environmental, social, and economic criteria, using the Analytical Hierarchy Process (AHP) for establishing criteria weights and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). TOPISIS has been employed widely in this context. Ddiba et al. [21] surveyed computational tools facilitating resource recovery in the sanitation industry; only four tools address MCDM and sustainability based on SuSanA-defined sustainability criteria (SANTIAGO, EVAS, Poseidon and the Sustainable Sanitation Management Tool). Overall, these investigations and computational tools did not consider life cycle aspects, especially social ones, in a robust manner.

Life Cycle Sustainability Assessment (LCSA) is highlighted as the most comprehensive method for quantifying the sustainability performance of systems [10]. LCSA integrates Life Cycle Assessment (LCA), Life Cycle Costing (LCC) and Social Life Cycle Assessment (SLCA) for environmental, economic and social impact quantification [22]. LCA is a standardized methodology establishing the framework for complementary LCC and SLCA methods. This involves setting the goal and scope, establishing system boundaries, life cycle stages (such as cradle, gate, and grave) and scenarios for comparison, compiling life cycle inventory data, and undertaking impact characterization and the interpretation of results [23,24,25]. Few studies have implemented LCSA with MCDM in the sanitation context. Opher [26] considered midpoint environmental impacts (International Reference Life Cycle Data System, 17 criteria) with economic (cash flow) and societal concerns (13 criteria) for water reuse options at various scales of centralization in Israel, implementing the Analytical Hierarchy Process (AHP) and agglomerative hierarchical clustering (like TOPSIS). Safarpour et al. [27] applied AHP to LCSA considering endpoint environmental impacts (three criteria: workers, local community, and consumer issues) as well as economic criteria for assessing water demand management policies in Florida. Liu and Ren [28] used the fuzzy weighted sum MCDM method and game theory to compare theoretical sludge management options in China under environmental (three criteria), cost (cash flow), social (three criteria) and technical aspects (four criteria). Tarpani and Azapagic [29] implemented LCSA and MCDM weighted sums, assuming equal weights for all environmental midpoint (ReCiPe, 18 criteria), economic (cash flow) and social (socio-environmental aspects, 9 criteria) assessments, to advanced water treatment and sludge management scenarios in the United Kingdom. There were no clear methodological trends, and variability arose within the decision criteria selection (LCSA and technical considerations), technology analysis, criteria-weighting methods and MCDM algorithms. This body of work has primarily considered theoretical system configurations. Additionally, no LCSA studies have addressed the impacts of system-wide co-product resource recovery in full-scale WW-CEs compared with conventional WWTPs, or discussed a Latin American context. Further contributions are required to present LSCA decision-making models that consider an appropriate range of impacts applied to integrated resource recovery scenarios in different contexts.

In the Metropolitan Region (MR) of Chile, two WWTPs adopted the concept of the WW-CE, employing different resource recovery configurations. These facilities are known as the Biofactories, responsible for the treatment and recovery of resources from wastewater generated by 7 million people and local industries. The Biofactories were developed in response to a range of environmental and social challenges that required the local water company (LWC) to innovate their systems. Integral sustainability assessments of these plants from a life cycle perspective have not been conducted to determine whether the WW-CE improves environmental, social, and economic impacts. Considering the urgent need for sustainable sanitation, this case study provides an example to evaluate the sustainability performance of different WW-CE configurations implementing co-product recovery. The objective of this study is to implement the LCSA-MCDM assessment of two real WW-CEs located in Chile, so as to assess the integrated environmental, economic and social impacts of the transition from conventional WWTPs. The two WW-CEs were compared to determine the most sustainability performance overall. Recommendations are made regarding strategies for improving the sustainability of the Biofactories. Additionally, the LCSA-MCDM decision-making process was discussed in relation to the real decision-making processes of the participating LWC, presenting industry implications.

2. Methodology

2.1. Life Cycle Sustainability Assessment

2.1.1. Study Sites

The two WW-CEs in Santiago, Chile, referred to as Plant A and Plant B, currently serve total population equivalents (p.e., including domestic and industrial wastewater) of 6,045,292 and 4,188,539, and they have influent flowrates of 8.6 and 7.2 m³/s, respectively. Both plants are the property of the LWC, which manages all potable water and wastewater plants of the MR. Each plant was developed as a response to requirements for improved sanitation coverage and waste management in the MR. They are situated at different locations along the Mapocho River, the main river running through the MR catchment, affecting different local communities. The plants were transitioned from conventional WWTPs to Biofactory WW-CE configurations. Specifically, and in different capacities, treated effluent, biogas, nutrients and biosolids were recovered as products via alternative technological configurations and stakeholder engagements in each plant.

2.1.2. Goal and Scope

The goal of the study was to determine whether integral life cycle sustainability was improved or diminished following the implementation of a WW-CE, combining LCA, LCC and SLCA with MCDM, at the two plants in Chile. Additionally, the plants were compared to determine which Biofactory WW-CE configuration best improved integral sustainability impacts compared to wastewater discharge without treatment and conventional WWTPs. These scenarios represent the transition from no sanitation coverage to the use of a WW-CE to address global sanitation challenges. The integral LSCA methodology was established following the ISO LCA guidelines with complementary SLCA and LCC methodologies [23,25,30]. The life cycle of the plants was estimated at 20–50 years for equipment and over 100 years for civil works, resulting in the low relative contribution of infrastructure and demolitions to environmental and social impacts. Therefore, this study considered the operation stage only for LCA and SLCA [31]. Daily plant impacts were averaged over one year of operation for the treatment of wastewater generated by a 1,000,000 p.e. as the adopted functional unit (FU). This considered 44.5 g Biological Oxygen Demand (BOD₅)/person/day, resulting in a reference flow of 44,500 kg BOD₅/day treated in the wastewater influent. Treated effluent, biogas, biosolids and return flows established corresponding waste and product reference flows for the scenarios established within the system’s boundaries. The allocation of sustainability impacts was modeled by grouping unit processes by product systems, considering either the mass (kg) or energy (kWh) of products. This was facilitated by collaboration with the LWC providing operational data and expert interviews.

2.1.3. System Boundaries

The system boundaries present the inclusion of technologies and stakeholders of alternative systems representing scenarios for comparison across specific life cycle stages. The scenarios analyzed presented gate-to-cradle life cycle transition to gate-to-gate, as implied by the goal and scope of the study. The system boundaries from an integral sustainability perspective included the identification of technologies and stakeholders for each scenario. Figure 1 and Figure 2 show the system boundaries of Plant A and Plant B, respectively, with three scenarios considered for each. The boundary began with the reception of wastewater post-preliminary treatment via the removing of larger contaminants, considered negligible. Plant unit processes were grouped by product systems, as indicated in the figure keys. The SLCA system boundaries determined the inclusion of workers, value chain actors, clients, local communities’ and children’s concerns, wider society and farmers as stakeholders. Farmers were included as a separate stakeholder considering the unique social impacts of the plants on agriculture, given their status as a Biofactory WW-CE. Wider society refers to relationships with regulatory bodies and national policies related to sustainability.

The 0th scenario (S0) involved the direct discharge of wastewater into the environment without treatment, modeled with influent wastewater data for LCA and with values set to 0 for SLCA and LCC. Scenario 1 (S1): Conventional WWTPs established a baseline system of WWTP with no product recovery, delineated by the black line in Figure 1. The technologies considered were primary sedimentation (PS), aerobic reactors (AR), secondary clarification (SC) and disinfection then discharge in both plants. Likewise, sludge treatment involved primary and secondary sludge thickening (PST, SST), anaerobic digestion (AD), and sludge dewatering via centrifuge with 100% of biogas flared and biosolids sent to landfill. The designs of treatment technologies in each plant under this scenario varied and technical specifications are given in Table S1. Stakeholders included on-site workers, value chain actors for chemical and service supplies, clients (the connected populations paying for water services), local communities and children affected by the plants in general, and wider society.

Scenario 2 (S2) represents a Biofactory WW-CE and considers the current configurations of respective plants, expanding the system boundaries (dashed line) to include partial water recovery, biosolid recovery to agriculture, biogas recovery for energy generation and advanced nitrogen removal systems. Plant A provided local farmers with irrigation water for “fertigation” via an effluent raceway, considering avoided water consumption credits (20% of discharged effluent volume). In total, 75% of biosolids produced via conventional AD without pre-treatment were recovered and provided to farmers, via collaboration between workers, value chain actors and the affected rural local communities. The remaining 25% was disposed of to landfill. The quantity recovered demonstrated a variation in biosolids’ quality (class B), assuming that the avoidance of fertilizer consumption by farmers led to environmental credits [32]. The transport of biosolids to landfill and agriculture pastures was considered, excluding the impacts of the heavy vehicles and machinery used for the application of biosolids to agricultural crops. Biogas upgrades for H₂S removal involved chemical absorption scrubbing and biological precipitation in both plants. CO₂ removal via pressurized membrane separation was used to produce a domestic biomethane supply (DBM) in Plant A, avoiding domestic natural gas consumption. These system incorporated additional stakeholders, workers, value chain actors, wider society and clients. Nitrogen removal technologies were implemented in sludge centrifuge return flows, recycling nitrogen to primary treatment. Plant A required <1000 mg/L TS for the “Demon” anammox treatment, coagulation–flocculation for the “Densadeg” technology and a sequencing batch reactor (SBR) for the nitrifier activator. Plant A had higher quantities of N in the influent and effluent, thus requiring higher aeration flowrates and higher Cl₂ dosages in the water line. It also incorporated workers, value chain actors and wider society.

Plant B did not recover treated effluent; however, it did implement additional sludge pre-thickening (P-SST) and thermal hydrolysis pre-treatment (THP) processes, thus improving biogas production and biosolids quality [33]. This allowed for 87% of biosolids to be recovered by farmers, while 13% was disposed of to landfill. In Plant B, the cogeneration of heat and power (CHP) was used to produce electricity, of which 12% was injected to the grid and 88% was used within the plant to ensure self-sufficiency, resulting in an overall value of avoided network energy consumption of 80% across the plant. Twelve percent of the CHP heat was used to provide vapor to the THP process so as to increase the avoidance network energy consumption. Workers, value chain actors and wider society were included as stakeholders. For the nitrogen removal process in Plant B, SBR reactors with high aeration facilitated nitrification and solids removal, followed by the use of the “Demon” anammox systems, and also incorporated workers, value chain actors and wider society.

2.1.4. Integrated Life Cycle Inventories

LCSA inventories consider the environmental inputs and outputs of technologies, as well as the corresponding costs and stakeholder interactions (activity variables). For LCA inventories, material flow analysis (MFA) was conducted on wastewater across all unit processes defined in Figure 1 and Figure 2, thus determining influent, effluent, return and biosolid flows. The MFA substances were TS, volatile solids (VS), total nitrogen (TN) and phosphorous (TP), BOD₅, chemical oxygen demand (COD) and 14 heavy meals (Tables S2 and S3). Chemical consumption, energy consumption, transport processes, atmospheric GHG emissions, products and avoided products were also included in the LCA inventory (Tables S5 and S6). All inventory inputs and outputs were normalized to the FU reference flow. LCC integrated capital investment, maintenance costs, operational costs and income with normalized LCA inventories [34]. The data were compiled from operational data, internal reports, and literature sources (Tables S4 and S5). The social impacts were quantified via the SLCA Type II midpoint characterization of social indicators, measuring relationship between stakeholders with “activity variables” (i.e., training hours) according to recommendations derived from methodological guidelines for stakeholder impact assessments [35]. The quantities of stakeholders involved in each product system were identified by expert workers (internal stakeholders) within the LWC who interacted with external stakeholders (value chain actors, clients, local community and children, wider society, farmers), normalized to the FU (Table S7). The expert workers were interviewed regarding their relationships with external stakeholders, enabling us to quantify respective activity variables. This approach was taken due to the potential sensitivity of the external researchers intervening in the LWCs’ established relationships with stakeholders. SLCA data were verified by field observations, supporting documents and relevant national legislation [36]. Historic documents were used to quantify some indicators related to conventional WWTPs (S1) when the expert workers interviewed were unable to quantify these indicators (Table S8). A generalized structure of the integrated LCSA data inventories is presented in Figure 3.

2.1.5. Impact Characterization

Impact characterization involves the selection of impact categories across environmental, economic and social assessments. For LCA, ecosphere and technosphere flows were modeled using the EcoInvent databases. The impact characterization method was ReCiPe Midpoint (H) (world/2.0), which uses 17 impact indicators, determining the environmental decision criteria. The Type II SLCA methodology included 13 impact indicators quantified as activity variables across affected stakeholders and each product system; they have been summed by scenario. For the Type II methodology, the indicators were assigned monetized units so as to quantify the sub-categories’ impacts (Table S9). For LCC, the Net Present Value (NPV) was calculated based on the cost inventory according to Equation (1) [37]:

$$NPV\left(\$\right)={\sum }_{t=1}^1\frac{I-C_{OpEx}-C_{Main}}{{(1+r)}^t}-C_{CapEx}$$

(1)

where $r$ is the discount rate, set to 8% according to LWC, $t$ is the time period of future cash flow, set to 1 year, $I$ is income, $C_{OpEx}$ is operation costs, and $C_{CapEx}$ and $C_{Main}$ are the initial capital investment and average annual maintenance cost of the respective product systems, normalized by the plants’ life cycles and reference flows. The LCA and LCC cash flows were characterized using SimaPro, whereby Type II SLCA and NPV indicators were calculated externally. Figure 3 presents a summary of the LCSA impact indicators, used as a decision criterion.

2.2. Interpretation with Multi-Criteria Assessment of Sustainability Impacts

2.2.1. Overview of Decision Criteria

MCDM involves establishing alternatives for comparison, as well as performance criteria, criteria weights, and a ranking method [18]. Six alternatives, $i$, were considered in relation to the three scenarios, and compared in both plants. The decision criteria included three levels—indicator criteria ($v_{i,j}$), sub-category criteria ($C_{i,j}$) and sustainability criteria ($S_{i,j}$)—used to determine the overall sustainability score ($T_i)$ according to the decision tree shown in Figure 4. The decision tree was organized so as to facilitate the subjective weighting methodologies and to break down the decision problem into domains that are easier for decision-makers to interpret [38]. The environmental impact results generated in SimaPro have bene considered non-beneficial criteria. Indicators with positive values refer to environmental impacts; therefore, the results of impact categories have been normalized linearly according to [39]:

$$\overline{v_{i,j}}=\frac{v_{i,j}-\min \left(v_{i-n}\right)}{\max \left(v_{i-n}\right)-\min \left(v_{i-n}\right)}$$

(2)

where $i$ is the number of alternatives, $n$. Social and economic indicators have been considered as beneficial criteria, whereby positive values represent social and economic benefits; therefore, the indicator results have been normalized linearly according to:

$$\overline{v_{i,j}}=\frac{v_{i,j}-\max \left(v_{i-n}\right)}{\min \left(v_{i-n}\right)-\max \left(v_{i-n}\right)}$$

(3)

2.2.2. Criteria Weighting

A subjective weighting process was implemented to communicate our results using the Ranked Reciprocal Weighting method (RRW). The use of AHP resulted in an exhaustive list of pairwise comparisons relevant to decision-makers in this context. RRW produces similar weighting factors when compared to AHP [40]. A survey was designed to rank the LCSA indicators, sub-categories, and sustainability criteria in terms of their relative importance (Supplementary Data S1). A panel of experts involved in the decision-making processes at the LWC responded to the survey, and ranked factors according to their understanding of the most important areas to be protected in relation to the WW-CE implemented in the respective Biofactories. The average rankings, $r_j$, and positions of criteria $j$ were calculated, where $m$ is the total number of indicators in each sub-category, the total number of sub-categories per sustainability criteria, and the overall number of sustainability criteria. The following equation was applied to calculate the weighting factors [40]:

$$w_j=\frac{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$r_j$}\right.}{{\sum }_{j=1}^m\left(\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$r_m$}\right.\right)}$$

(4)

2.2.3. Aggregated Sustainability Scores

A wide variety of aggregation measures can be implemented in assessing the performance of alternatives with respect to decision criteria [18]. In this case, multi-attribute value theory (MAVT) was selected due to its common use in wastewater treatment decision problems, with discrete performance criteria, and the uncertainty related to criteria weights is unknown [29,41]. The weighted sum method was applied, following the methodology of [29], to indicator (Equation (5)), sub-category (Equation (6)) and sustainability criteria (Equation (7)):

$$C_{i,j}=\sum _{j=1}^m{w}_{I,j}\overline{v_{i,j}}$$

(5)

$$S_{i,j}=\sum _{j=1}^m{w}_{C,j}{C}_{i,j}$$

(6)

$$T_i=\sum _{j=1}^m{w}_{S,j}{S}_{i,j}$$

(7)

where $w_{I,j}$, $w_{C,j}$ and $w_{S,j}$ are the indicator, sub-category, and sustainability weighting factors, respectively. Weighted sum performance matrices were calculated for the total numbers of criteria in each sub-category, $C_{i,j}$, and sustainability criteria, $S_{i,j}$, to produce a sustainability score, $T_i$, of alternative scenarios, $i$. The lowest score implies improved sustainability according to the normalization equations.

3. Results

3.1. Environmental and Social Impact Indicators

3.1.1. Normalized LCA and SLCA Impact Indicators

Table 1. Normalized environmental and social impact assessment results for Plant A and Plant B across scenarios 0 to 2. The green indicates good (near 0) and red indicates bad (near 1) performance.

			Plant A			Plant B
Criteria Sub-Category		Indicator	S0	S1	S2	S0	S1	S2
Environmental (LCA)	Air	CC	0.00	0.74	0.37	0.00	0.78	1.00
		SOD	0.00	0.97	0.28	0.05	1.00	0.97
		IR	0.48	0.88	0.00	0.48	1.00	0.56
		OF	0.49	0.79	0.53	0.49	1.00	0.00
		PM	0.49	0.81	0.56	0.49	1.00	0.00
	Land	TA	0.42	0.84	0.33	0.42	1.00	0.00
		TET	0.69	0.89	0.23	0.69	1.00	0.00
		LU	0.93	0.97	0.22	0.93	1.00	0.00
		MRS	0.48	1.00	0.00	0.52	0.97	0.48
		FRS	0.51	0.80	0.28	0.51	1.00	0.00
	Water	FE	1.00	0.45	0.00	0.94	0.47	0.51
		ME	0.00	0.02	0.64	0.00	0.03	1.00
		FET	0.74	0.14	0.00	1.00	0.14	0.01
		MET	0.76	0.15	0.00	1.00	0.15	0.00
		WC	0.00	0.25	0.09	0.00	0.71	1.00
	Human Health	HCT	0.49	0.79	0.51	0.49	1.00	0.00
		HNCT	0.50	0.02	0.84	0.53	0.00	1.00
Social (SLCA)	Working Conditions	EO	1.00	0.49	0.29	1.00	0.32	0.00
		IN	1.00	0.70	0.37	1.00	0.00	0.28
		HS	0.00	0.59	0.24	0.00	1.00	0.35
		RI	0.00	0.39	0.42	0.00	0.40	1.00
	Social Responsibility	ANMR	1.00	0.51	0.22	1.00	0.19	0.00
		ID	1.00	1.00	0.29	1.00	1.00	0.00
		ED	1.00	0.74	0.00	1.00	0.91	0.60
	Environmental Responsibility	AMR	1.00	0.99	0.00	1.00	0.99	0.93
		DA	1.00	0.60	0.00	1.00	0.47	0.01
		CO	1.00	0.00	0.00	1.00	0.98	0.98
	Governance	FM	0.00	0.30	1.00	0.00	0.00	0.00
		EN	1.00	0.40	0.00	1.00	1.00	0.99
		CB	1.00	0.56	0.35	1.00	0.25	0.00

shows the normalized environmental and social impact indicator scores according to the LCA and SLCA, respectively. The impacts on the air and land sub-categories increased from S0 to S1 for both plants, while resource consumption, mainly network electricity, increased in conventional WWTPs. However, these factors were improved in S2, where energy and biosolids recovery was enabled via the Biofactory’s WW-CE. Plant B under S2 showed better performance in terms of OF, PM, TA, TET, and LU due to the use of AD-THP and CHP systems, thus avoiding higher rates of network energy and fertilizer consumption. However, CC and SOD were increased in Plant B due to the high emissions of CHP. Plant A showed decreased CC and SOD under S2 as a result of partial water recovery to farmers. Plant A under S2 showed IR and MRS values that decreased to a greater extent than in Plant B. However, Plant B achieved decreased FRS mostly by avoiding network electricity consumption. The water sub-category indicators FE, FET and MET were decreased under S1, wherein conventional WWTPs were used to remove contaminants; the enforcement of S2 led to further reductions in these indicators. FE was improved in Plant A by water recovery, leading to the reduced discharge of TP to the environment. Nitrogen removal systems decreased the TN and TP loads in effluents; however, the increased resource consumption in Plant B increased the impacts of FE under S2. ME was increased due to the reuse of the TN in biosolids for agriculture. WC was reduced in Plant A when water recovery was enforced, and increased in Plant B due to the increased resource consumption and lack of water recovery. HCT was improved to a greater extent in Plant B under S2 due to the avoidance of network energy, and HNCT showed greater impacts in both plants as a result of the increased chemical consumption. The working condition sub-category indicators EO and IN were improved in both plants under both S0 and S2 as a result of the employment of workers, training opportunities, and the employment of women. Plant B had higher employment rate compared to Plant A. HS and RI were more highly impacted when workers were exposed to risks and workplace accidents, and RI increased in line with AD-THP and CHP in Plant B under S2. Rates of accidents were reduced across both plants, and Plant B showed a higher accident frequency. In terms of social responsibility, ANMR, ID and ED were improved under both S1 and S2 in both plants. Plant A is located near urban areas with a greater representation of the local community, and therefore records more instances of community ED, while Plant B generated more jobs and invested more in ID. In terms of environmental responsibility, AMR was improved by investments in infrastructure with community access, as well as quality assurance, crop yield and end of life management. Plant A exhibited greater investments in community infrastructure, and reported better performance in terms of DA and CO compared to Plant B under S2. Plant A contributed to CO as part of its legal obligation to conserve a 14-hectare ecological park, which acts as a habitat for flora and fauna. DA was improved when the coverage of data was extended to product systems, and nitrogen removal systems improved compliance with discharge standards. FM was affected by complaints from local communities due to odours, which increased following the application of biosolids in agricultural and plant operations. EN was higher in Plant A, where more hours of engagement were dedicated to a larger population in the local community, and CB was similar in both plants, but Plant B showed higher rates of unionized workers due to its higher employment rates.

3.1.2. Indicator Criteria Weighted Sum Scores

Figure 5a shows the RRW environmental indicator weighting factors. The preferred air sub-category indicators were CC (0.32) and PM (0.32), and we note the decreased importance of OF, IR and SOD (0.12). As regards land sub-category indicators, LU was the most important, at 0.29, TET was 0.2 and MRS and FRS decreased to 0.16. In the water sub-category, the highest importance was assigned to WC (0.44), while FE had a score of 0.22, and ME (0.13), FET (0.13) and MET (0.09) decreased in importance. For human health, the weights of HCT and HNCT were the same (0.5). The RRW environmental indicator’s weighted sum showed an increase in impact, from S0 to S1, by 23 and 37% for Plant A and Plant B, respectively, as shown in Figure 5b. Plant B showed a 21% higher impact. This contradicts the urgent global interest in sanitation and sustainable development, but also demonstrates the complexities of environmental trade-offs in relation to the increased resource consumption of conventional WWTPs. The LCA performed under S0 could not consider the impacts of biological contaminants on human and ecosystem health; a dearth of data meant that the wider catchment of environmental impacts could not be adequately represented. From S1 to S2, the impacts decreased by 40% in both plants. Between S0 and S2, the use of Biofactory WW-CE decreased the environmental impacts by 22% in Plant A and by only 5% in Plant B. Plant B showed 35% higher environmental impacts compared to Plant A, as a result of the avoided water consumption. Plant B showed a higher normalized avoided electricity and fertilizer credits, but also had higher chemical, energy, and transport consumption rates, as influent BOD₅ loading was smaller here compared to Plant A. The BOD₅/COD ratio was lower in Plant B, and TS loading was higher; therefore, sludge production and resource consumption were greater here than in Plant A (Tables S1 and S2). This aligns with the other results of the LCA, which show that the use of less biodegradable wastewater can impart greater environmental benefits [42].

The social RRW indicator’s weighting factors, shown in Figure 6a, indicate preferences in the working conditions sub-category for RI (0.48), while the score for HS was 0.26. The scores of EO (0.16) and IN (0.12) decreased. In terms of social responsibility, ED (0.26) and ANMR (0.32) were the least important, and ID was prioritized (0.43). The environmental responsibility indicators AMR and CO were reduced to 0.22, and the importance of DA increased (0.56). Finally, the weights of RRW governance indicators decreased for FM (0.27) and CB (0.18), while the greatest importance was allocated to EN (0.55). Social factors were improved across all scenarios considered in both plants. Plant A showed social impacts that were reduced by 26% and 20% between S0 and S1 for Plant A and Plant B, respectively, as shown in Figure 6b. Under S0, all impacts were set to 0, representing an overall lack of stakeholder involvement. Between S1 and S2, Plant A and Plant B showed more benefits, with social impacts decreasing 60% and 30%, respectively, resulting in 70 and 45% lower benefits overall from S0 to S2, respectively. Plant A showed 7 and 45% reductions in impacts compared to Plant B in S1 and S2, respectively. FM showed the greatest contribution to social impacts under S2. Plant B experienced lower improvements in terms of social impacts due to lower instances of CO and higher exposure of workers to RI through AD-THP and CHP systems. However, DA was improved here compared to Plant A, as the influent TN was lower and there were fewer instances of non-compliance. EO was improved when more W was employed, and HS was improved too, as accidents decreased.

3.2. Environmental and Social Sub-Categories

3.2.1. Normalized LCA and SLCA Sub-Categories

The normalized sub-category results refer to the weighted sum scores of the environmental and social indicators grouped in Figure 4 and presented in

Table 2. Normalized environmental and social sub-category assessment results of Plant A and Plant B across scenarios 0 through 2. The green indicates good (near 0) and red indicates bad (near 1) performance.

		Plant A			Plant B
Criteria Sub-Category		S0	S1	S2	S0	S1	S2
Environmental (LCA)	Air	0.27	0.81	0.39	0.28	0.93	0.50
	Land	0.64	0.91	0.22	0.65	1.00	0.08
	Water	0.38	0.24	0.12	0.42	0.45	0.68
	Human Health	0.49	0.41	0.68	0.51	0.50	0.50
Social (SLCA)	Working Conditions	0.50	0.54	0.33	0.50	0.43	0.41
	Social Responsibility	1.00	0.75	0.17	1.00	0.70	0.20
	Environmental Responsibility	1.00	0.53	0.00	1.00	0.82	0.64
	Governance	0.67	0.42	0.45	0.67	0.42	0.33

. Both plants showed improved air and land indicators overall when implementing Biofactory WW-CEs (S2). Plant A showed lower air and water impacts due to the employment of water recovery to local farmers. However, water consumption was increased in Plant B, and lower land impacts were also exhibited due to the benefits of energy recovery in relation to self-sufficiency and the avoidance of network energy consumption. Impacts on human health were increased in Plant A due to increased chemical consumption, whereas the avoidance of energy consumption in Plant B countered impacts, enabling it to maintain the same level across all scenarios. Plant A’s working conditions improved overall, while increased exposure to RI in Plant B affected this category. Social responsibility, environmental responsibility and governance improved across all scenarios in both plants, but Plant A showed better performance than Plant B due to the higher levels of engagement with nearby communities.

3.2.2. Sub-Category Criteria Weighted Sum Scores

Figure 7a shows the weighting factors of the environmental sub-category RRW, wherein decision makers expressed preferences for human health and water sub-categories (0.33), while air and land sub-categories decreased in importance (0.17). The weighted sum scores for environmental sub-categories in Figure 6b show 23 and 37% increases in impacts from S0 to S1 for Plant A and Plant B, respectively. From S1 to S2, Plant A’s employment of Biofactory WW-CE caused reductions in sub-category impacts by 37%, while the same in Plant B reduced the impacts by 31% overall. From S0 to S2, Plant A showed reductions in environmental impacts of 23% overall; conversely, Plant B showed an increase in impacts of 5%. Accordingly, Plant A showed 5, 21 and 25% lower impacts compared to Plant B across S0, S1 and S2, respectively.

Figure 8a shows that the LWC decision-makers considered “working conditions” (0.5) the most important social sub-criteria, while social responsibility, environmental responsibility and governance were assigned the same importance (0.17). Plant A showed reductions in the weighted sum of social sub-categories from S0 to S1 of 10%, and from S1 to S2 of 51%, as shown in Figure 8b. Plant B decreased these impacts by 3 and 15% under S0–S1 and S1–S2, respectively. From S0 to S2, Plant A showed improvements in social impacts of 56%, while Plant B showed an 18% improvement. Plant A showed a better overall social performance compared to Plant B, with improvements of 7 and 45% across S1–2, respectively.

3.3. Overall Sustainability Impact

3.3.1. Normalized LCSA Performance Indicators

Table 3. Normalized sustainability impact assessment results of Plant A and Plant B across scenarios 0 through 2. The green indicates good (near 0) and red indicates bad (near 1) performance.

	Plant A			Plant B
Criteria	S0	S1	S2	S0	S1	S2
Environmental (LCA)	0.44	0.58	0.32	0.48	0.67	0.46
Social (SLCA)	0.79	0.57	0.00	1.00	0.70	0.29
Economic (LCC)	1.00	0.62	0.88	1.00	0.42	0.00

shows the normalized environmental and social weighted sum scores and performances according to economic criteria. Figure 9 shows the normalized LCC and NPV results for both plants across all scenarios. Plant A showed higher chemical consumption costs due to the high price of FeCl, which increased from S1 to S2 due to the incorporation of biogas upgrades and nitrogen removal systems. Energy and transport costs were lower in Plant A compared to Plant B, as were capital investment and maintenance costs. However, the income generated by the treatment of influents and the selling of energy to the grid in Plant B resulted in higher income and a more favorable NPV result. The NPV values for both plants across all scenarios were negative, which indicates non-profitability. LCC was limited to LCA inventories and did not consider the wider financial aspects of the plants’ functioning. Therefore, these results are not indicators of real economic performance. Between S1 and S2, Plant A’s energy, capital and maintenance costs increased as a result of incorporating resource recovery product systems. However, waste transport costs decreased, as the charge for the disposal of biosolids to landfill was minimized through biosolid recovery. Plant A’s income increased as a result of the sale of biomethane to the MR natural gas provider. Plant B’s capital and maintenance costs increased between S1 and S2, while its transport costs decreased, and its avoidance of energy consumption as a result of CHP also decreased energy costs. The income of Plant B increased under S2 due to the higher rate of normalized influent wastewater and higher income compared to Plant A. Therefore, Plant B showed a better overall NPV performance.

3.3.2. Overall Sustainability Impact Weighted Sum Scores

Figure 10a shows that the RRW sustainability weighting factors favored the environmental (0.43), followed by economic (0.32) and social (0.26), criteria. Figure 10b shows the overall sustainability weighted sum score. The contributions of social and economic factors to overall sustainability in S0 were equal for both Plant A and Plant B. The environmental impact contributions were 0.19 and 0.2 for Plant A and Plant B, respectively; therefore, Plant B only showed a 2% higher initial impact under S0. Plant A showed an overall sustainability score that was reduced by 13% between S0 and S1, as environmental impacts increased, and social and economic impacts decreased. From S1 to S2, the sustainability weighted sum was improved by 20%, as environmental and social impacts decreased; however, economic impacts increased. From S0 to S2, the overall improvement was 30% in Plant A. Plant B showed a reduction in its overall sustainability score by 20% from S0 to S1, and environmental impacts contributed the most to its overall sustainability. Social impacts were higher under S1 in Plant B compared to Plant A; however, regarding the economic benefits, Plant B experienced a lower impact (6%). Economic advantages in Plant B under S2 were improved by 48% between S1 and S2, resulting in an overall decrease of 58% from S0 to S1. Plant B showed a 61% lower impact compared to Plant A under S2. Therefore, both plants showed effective reductions in sustainability impacts from S0 to S1 and S2, thus offering positive case studies of WW-CE in Chile. Plant B’s Biofactory WW-CE was more “sustainable”, demonstrating lower impacts than Plant A due to its economic performance.

4. Discussion

4.1. Life Cycle Sustainability Assessment and Improving the Wastewater Circular Economy in Chile

LCSA provides a framework for the effective quantification of a wide range of sustainability impacts, integrated through a simple MCDM model that considers decision-makers’ preferences. However, the LCSA methodology encountered limitations. LCA was unable to capture broader ecological or biological aspects, as reflected in the lower environmental impact scores of S0 compared to S1. The benefits of “fertigation” derived from water recovery and crop production should be accounted for in future studies [43]. Biosolid recovery did not affect crop production or offer benefits to soil quality by the application of organic matter to soil; it could improve environmental performance through nutrient and emission sequestration, as well as the reallocation of environmental burdens to crops [44]. Identifying good or bad performance in the social domain of sustainability is challenging. The data resolution of SLCA components can be improved via the more frequent monitoring of social indicators under the LWC. The use of a real case study and operational data demonstrates that the WW-CE is affected by product quality, geography, and technical capacity, as the 100% recovery of treated effluents, biogases and biosolids was not observed here. Both plants have established systems for improving sustainability impacts via WW-CE.

Net-zero impacts were not achieved, and environmental impact trade-offs should be addressed with further innovations. The operators of Plant B should explore options for water recovery, while Plant A should seek to improve its energy recovery, as only 25% of the biogas was recovered, and it could benefit from AD-THP, as observed in Plant B. Future circular economy configurations should be assessed by LCSA-MCDM. A higher quantity of sludge, and the co-digestion through AD of organic wastes, could be facilitated by THP, thus increasing biogas production and waste management from external sources [45]. The use of nutrient removal systems improved environmental responsibility in both plants due to improvements in compliance with discharge standards. As the nutrient loadings in influent wastewater increase over time, nutrient management should be considered plant-wide, as biosolids, water recovery and nutrient removal technologies are interconnected. Considering the interconnected nature of environmental and social impacts, increasing a plant’s capacity for resource recovery and improving adherence to management systems could achieve further social benefits. Plant A should explore the use of alternative flocculants, as FeCl contributes significantly to chemical costs. Income may increase through improved resource recovery, mainly via biomethane production and biosolid recovery. The wider financial implications of the WW-CE, such as governmental tributary benefits related to good social and environmental performance, have not been captured by the LCC [46], but could be integrated into the NPV [47].

4.2. Sensitivity and Uncertainty Assessment

Due to the novelty of the SLCA methodology, the propagated uncertainty of the sustainability scores could not be determined, which would have enabled us to test the statistical significance of the integrated results. In the LCA investigation, the sensitivity of process parameters, as well as uncertainty using Monte Carlo and a Modified Null Hypothesis Test (MNTH), were assessed [48]. The most sensitive impact categories were HCT and FET, which varied by more than 20% from normal. HCT was primarily influenced by total system energy consumption, rate of water recovery and nitrogen removal in anammox systems, while BOD₅ removal affected Plant B the most in this category. FET showed a high sensitivity to the use of nitrogen removal systems in both plants, as well as to water recovery rates in Plant A and TS concentrations in Plant B. FRS and FE were most highly influenced by the water recovery rate in Plant A, which decreased by 20%, as also occurred for GWP. Contrarily, the GWP increased by 20% when the mass of influent BOD₅ was increased. The MNHT illustrated all the differences in the impacts between scenarios, and comparisons between plants were statistically significant [49]. The application of LCC modeling in SimaPro facilitated the uncertainty analysis of cash flows; however, this could not be extended to NPV characterization calculated externally. The sensitivity analysis of parameters is limited by the fixation of prices, as damage factors in the LCC impact characterization compared to modeling by process parameters. The results of SLCA parameter uncertainty analysis via Monte Carlo and MNHT can be related to the real variations in activity variables and affected stakeholders, i.e., stakeholder participation and engagement hours registrations; however, the time series of these data are not available. The propagation of uncertainty involves improving LCC modeling in SimaPro to incorporate price variations, as well as developing mechanisms for modeling variations in social parameters for SLCA. This must be carried out on the weighted sum results of the sub-categories and sustainability criteria. The ontology of the study may be adjusted to facilitate this process, i.e., removing levels of decision criteria.

LCSA-MCDM assumes linear relationships between components; however, complex sustainability is a dynamic non-linear global system, while other modeling approaches could capture this within an LCSA framework and provide mechanisms for more robust sensitivity analyses [10]. It is important to address uncertainty in relation to the validation of decision-makers’ perceptions of high-risk investments, especially when implementing novel technologies in a WW-CE. However, it is unclear whether uncertainty knowledge supports real-life decision-making processes, and this should be explored further [50]. The overall sustainability scores were directly influenced by the selection of impact categories and corresponding weightings. The comparison of weighting factors across objective and subjective methodologies did not manifest significant differences in the weighted sums of impacts across all decision criteria in this context (Figure S1). Therefore, considering the urgent need for sustainable integral water management, efforts should be focused on LCSA frameworks to inform sanitation design and decision-making, whereby simple decision-making processes such as the weighted sum method can be used to evaluate alternative scenarios. Impact categories, decision criteria, and selection and quantification are not standardized, and so academic communities have been seeking to reach a consensus and thus facilitate the assessment of LCSA methodologies [51]. The lack of consensus regarding the quantification of social impacts worldwide requires significant research and collaboration. The use of SLCA weighted impacts disproves the theory that off-sets between impact categories cannot occur, i.e., good performance in one indicator does not counter bad performance in another [30]. However, this contrasts with the need to facilitate decision-making from a sustainability perspective with single scores. The same notion can be applied to environmental impact categories.

4.3. Global Implications

The decision support models proposed for use in the sanitation sector, and the method of resource recovery in WW-CE, are seldom adopted in industry [21]. Decision-making processes implemented by sanitation sectors around the world depend on local politics [52]. In the context of Chile, the LWC is a private company, where decisions are based on integral asset management risk, NPV, and the return on investment of alternative technologies. Decision-making “criteria” are measured according to the respective ISO management systems used for environmental management, health and safety, energy, quality assurance and integral risk management. ISO management systems are defined by local legislations as measures of compliance. The responsibility for data monitoring is triangulated between authorities, auditors, and workers within the operation of plants. In the public sector, decision-making processes in local governments are generally constrained by investment costs [52]. Therefore, in the future, WW-CE needs to demonstrate treatment configurations that benefit the environment, society, as well as the economy, as exhibited in Plant B. The monetization of environmental and social impacts addresses the primarily economic nature of decision-making [53]. The LCSA methodology must be established at a global level to ensure appropriate sustainability criteria are being measured in a standardized way, relating to national and global goals for sustainable development [54]. This represents a tool that can be used to enhance and synthesize sustainability-reporting schemes, such as ESG and Global Compact, as are implemented by the LWC. LCSA can improve this process by offering methods of database processing and analysis with appropriate automation.

5. Conclusions and Recommendations

The objective of this study was to quantify integral environmental, social and economic impacts using LCSA-MCDM assessments, and to demonstrate how the WW-CE in Chile has improved overall sustainability compared to both discharge without treatment and conventional WWTPs for each respective level of wastewater influent flow. From S0 to S1, the environmental impacts increased by 23 and 37% in Plant A and Plant B, respectively, due to the improvement in air and land impacts. Water and human health impacts were improved in Plant A, resulting in 21% lower environmental impacts than in Plant B. From S1 to S2, the WW-CE led to reductions in water, land and air impacts in Plant A, thus impacting human health, and generating a 37% reduction in environmental impact. Under this scenario, Plant B only showed improved air and land impacts, resulting in a 23% lower environmental impact, and a 27% higher impact compared to Plant A. From S0 to S2, Plant A showed a reduction in environmental impact of 23%, while Plant B showed an increase in impacts of 5%, and showed a 23% greater impact overall. The social impacts in Plant A decreased by 10 and 51% between S0 and S1 and S1 and S2, respectively, as a result of improving social responsibility, environmental responsibility, and governance and working conditions. Plant B showed reductions of 3 and 15% between S0 and S1 and S1 and S2, respectively, whereby social responsibility, environmental responsibility and governance improved, and working conditions were impacted by the greater exposure of workers to risk. Therefore, Plant A showed 7 and 45% lower social impacts compared to Plant B under S1 and S2, respectively. Economic impacts were decreased by 38 and 58% in Plant A and Plant B, respectively, from S0 to S1, whereby Plant A showed greater chemical consumption. From S1 to S2, Plant A’s economic impacts increased by 26% as a result of further increases in chemical consumption and the lower generation of income, as well as operational savings induced by resource recovery, compared to Plant B. Plant B’s economic impact decreased by 42%.

Plant A’s development of a Biofactory WW-CE improved its overall sustainability by 30%, while this value in Plant B was 48%, demonstrating that Plant B achieved a better sustainability performance due to the economic advantages provided by AD-THP and CHP systems. Plant A showed better environmental and social performance compared to Plant B, whereby improved resource recovery aided economic performance. Both plants successfully adopted the WW-CE concept; the LWC’s mission to contribute to sustainability through this medium creates opportunities for both systems to improve upon their current resource recovery configurations, which should be assessed under future scenarios via LCSA. However, LCSA modeling requires improvements in SLCA data resolution, ideally so that it can capture the time series data of stakeholder activity variables, and thus quantify uncertainty and sensitivity. LCC modeling via SimaPro was limited in terms of capturing price variations for sensitivity and uncertainty. Therefore, it is recommended that future investigations focus on improved LCSA modeling so as to facilitate uncertainty propagation and sensitivity analyses of the integrated LCA, LCC and SLCA methodologies. This study has highlighted the critical need for the implementation of decision-making models in real-world contexts, as using LCSA is not currently standard practice, but it remains the most robust tool for conducting sustainability assessments. There are significant opportunities to link LSCA-MCDM models to real-world decision-making contexts, and thus facilitate the development of the WW-CE and achieve global sustainable development in the water and sanitation industries.