Deep treatment is a common approach to enhance pollutant removal for biological wastewater treatment technologies (BWTTs), and life cycle assessment (LCA) holds substantial advantages to support process optimization. However, there lacks of LCA-based benchmarks that cover human-nature nexuses and stakeholder involvement, which limits the guidance and eco-design of BWTTs. This study proposed a decision-support system (DSS) by linking LCA with Water Quality Model and Conjoint Analysis. Three major findings were identified based on a demonstrative case (constructed wetland bioaugmented by dosing different microbial inocula): (1) Increasing bacterial intensities would achieve net environmental improvement, but it might not apply to all cases; (2) Making full use of natural self-purification capacity could partly replace the functions of BWTTs; (3) Stakeholders would concern aquatic environmental improvement when receiving river that had limited environmental capacity. Overall, the DSS provided a data-driven platform for screening options before determinations were made to constrain wastewater treatment sustainability.