A dynamic social-life cycle assessment based framework for social risk assessment

The integration of tools for assessing social impacts—such as workers' conditions, access to essential services, and governance sustainability—is essential to building a regenerative and inclusive circular economy. Environmental assessments alone are insufficient; approaches that also consider working conditions, distributive justice, and institutional quality are required to achieve holistic sustainability. In this context, current practices of Social Life Cycle Assessment (S-LCA) reveal three main gaps: (1) the absence of dynamic models capturing how social risks evolve with production scale; (2) limited integration of social indicators with systemic feedback and governance quality; and (3) weak predictive capacity to support decision-making in complex systems. To address these challenges, this research develops a dynamic and quantitative framework based on S-LCA to identify, quantify, and mitigate social risks in Waste Electrical and Electronic Equipment (WEEE) recycling and reuse. The methodological process integrates a systematic literature review following PRISMA guidelines and a Systematic Literature Network Analysis (SLNA) to identify theoretical gaps and conceptual needs. Empirical insights were collected from a real-world case study to ensure operational relevance and applicability. The framework combines Analytic Hierarchy Process (AHP), Bayesian updating, regression analysis, risk matrices, and Causal Loop Diagrams (CLDs) to model social risk dynamics and stakeholder interdependencies. Validated with real data, the model enables social risk mapping and simulation of their evolution under different production scenarios. The results highlight that while increasing production may improve environmental efficiency, it can also amplify social risks, supporting predictive and proactive management aligned with GRI and CSRD standards.