A two-stage model of the fuzzy analytic hierarchy process and the fuzzy synthetic evaluation technique to prioritize sustainable sanitation services under uncertainty.

In the context of the Sustainable Development Goals (SDGs), which strive to ensure comprehensive access to fundamental water, sanitation, and hygiene (WASH) services, it is extremely imperative to prioritize communities in need and still disadvantaged. Moreover, tackling the worldwide sanitation crisis entails advancing the development of productive and sustainable sanitation systems and infrastructure. Sanitation planning is a multidimensional exercise encompassing multiple dimensions, stakeholders, and strategies, typically with conflicting objectives. Poor planning, funding obstacles, stakeholder priorities, climatic changes, growing populations, system constraints, and user engagement all complicate the entire process. Intelligent strategic decision-making is crucial, particularly for resource-constrained economies. Multi-criteria decision analysis (MCDA) models offer opportunities to figure out and resolve such conflicts, while also optimizing prioritization and policymaking. These models assist with considering trade-offs, data uncertainty, and arriving at decisions by considering technical, economic, social, and environmental sustainability. In the present work, a two-stage integrated model of the Fuzzy Analytic Hierarchy Process (FAHP) and the Fuzzy Synthetic Evaluation Technique (FSET) was developed, accompanied by a sensitivity analysis, yielding an assessment index designated as the Sanitation Priority Index (SPI). This index is particularly applicable in prioritizing and categorizing communities in need of sanitation infrastructure (e.g., wastewater collection systems and treatment plants), considering competition over scarce resources, reliance on third-party funds, and sustainability factors. The decision-making problem was designed as a hierarchical structure that integrates all problem elements and specifies the key criteria contributing to the SPI. Fuzzy set theory handles data uncertainty, with FAHP evaluating criteria significance in a group decision-making context and FSET designing criteria membership functions. Field data on criteria contributing to the SPI is transformed into fuzzy intervals, synthesized, and defuzzified to derive the SPI at community level. The model's robustness is examined using sensitivity analysis as it is applied to several Palestinian communities lacking sanitation infrastructure. The outcomes show that the demographic criterion has the major impact on the SPI (20.38%), followed by water consumption (16.76%) and wastewater reuse potential (15.40%). Environmental risks account for 12.40%, utilities' competency (11.5%), and industrial wastes risks (8.72%). The socioeconomic context is valued at 5.10%, geographical constraints at 4.51%, and license constraints at 4.8%. Out of the 25 communities investigated, five exhibited SPI values surpassing 60.0%. Eleven communities possessed SPI values between 50.0% and 60.0%, and nine achieved SPI values ranging from 40.0 to 50.0%. The sensitivity analysis application reveals almost complete stability in prioritizing communities. Introducing this model into relevant bodies' sanitation management practices and planning strategies holds the potential to significantly boost sustainable sanitation services as well as the performance of water and wastewater utilities. It further enables the incorporation of additional criteria and the interests of more stakeholders.