Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Sustainable Development

DOI: 10.1002/sd.71517

Authors: Flavio Vassallo Mattos, João Luiz Calmon, Alencar Bravo, Darli Vieira

Institutions: Universidade Federal do Espírito Santo, Université du Québec à Trois-Rivières

Language: English

Topic: Environmental Impact and Sustainability

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Responsible consumption and production

Open Access status: hybrid

Estimated APC paid: $4,250