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

Dados Bibliográficos

ID22434026
AutoresMaria Ludovica Acquaviva (University of Naples Federico II), Teresa Murino (0000-0003-2007-5598, University of Naples Federico II), Claudio Sassanelli (0000-0003-3603-9735, Polytechnic University of Bari, autor correspondente)
Ano2026
Volume65
Páginas60-84
Data de publicação2026-06-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoSustainable Production and Consumption (JOURNAL)
Identificadores do periódicoISSN: 2352-5509 • E-ISSN: 2352-5509
EditoraElsevier BV (PUBLISHER)
DOI10.1016/j.spc.2026.03.007
OpenAlexW7140079444
IdiomaEN
Referências citadas33

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.

Dynamic assessment · Impact assessment · Life-cycle assessment · Risk assessment · Social risk · Environmental Impact and Sustainability · Life Cycle Costing Analysis · Sustainable Development and Environmental Policy

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