A New Social Life Cycle Assessment Framework: An <scp>AI</scp> ‐Driven Optimization Approach and Application

ABSTRACT Social life cycle assessment (SLCA) has increasingly been acknowledged as a tool for assessing the societal impacts of services and products. However, the fragmented nature of current approaches makes it difficult to compare this concept and its practical applications. This article develops a harmonized SLCA approach for the field of waste management by combining five leading SLCA methods (Aparcana–Salhofer [AS], Foolmaun–Ramjeeawon [FR], Ciroth–Franze [CF], the subcategory assessment method [SAM], and Ibáñez–Forés [IF]) to develop a new SLCA framework. The framework was developed using artificial intelligence (AI)‐based semantic rephrasing and clustering methods, and involves systematically aggregating indicators, reducing redundancy, and harmonizing measurement scales. A five‐point Likert scale in the new optimized SLCA tool was used to assess the relevant indicators in order to improve the frameworks' interpretability and ease of operation. The framework was validated through a real case study in Brazil, which focused specifically on waste pickers' organizations in Vitória, Brazil.