Purpose
We evaluate methodological approaches of different methods that can offer social assessments of product value chains. By analyzing both product-oriented social life cycle assessment (S-LCA) methods and qualitative, organization-, and project-oriented methods, we provide recommendations towards a clearer, harmonized method to better integrate the social dimension into sustainability assessments of products. This could help make S-LCA more analogous to environmental LCA (E-LCA) and more suitable for implementation in frameworks as life cycle sustainability assessment (LCSA).

Methods
We apply two quantitative S-LCA methods side-by-side with three qualitative social assessment methods on the same case-study of a textile’s value chain. The two quantitative S-LCA methods adopt a quantitative functional unit (FU) approach, use similar data structures and calculation principles as E-LCA and are based on the product social impact life cycle assessment (PSILCA) database. The three qualitative methods applied include two social due diligence approaches — one based on the OECD Due Diligence and UN Guiding Principles for Business and Human Rights and the other on the IFC Performance Standards — and the Subcategory Assessment Method (SAM), a semi-quantitative performance evaluation assessment method based on the UNEP S-LCA Guidelines.

Results
None of the approaches to S-LCA described in the UNEP S-LCA Guidelines can, at present, fully achieve the equivalent goals and scope of E-LCA, specifically in the social domain. Our evaluation of five social assessment methods, including two S-LCA methods, highlights their significant differences in basic structure and logic. Consequently, results differ considerably in nature, depth, and social aspects covered. Current product-oriented S-LCA approaches encounter important limitations as they require quantifiable aspects, whereas many social impacts are often qualitative in nature. Qualitative, organization-focused methods, conversely, make it difficult to link organizational social performance to specific products. Instead, these methods are typically used for social due diligence on suppliers in the company’s supply chain and cover only a small part of the product’s life cycle.

Conclusion
For the purpose of computational integration, LCSA frameworks need an S-LCA method with a quantitative FU approach. However, only some S-LCA approaches are able to comply with this requirement, and these will only be able to cover a limited set of scalable quantitative impact indicators. We conclude by emphasizing the equal importance of product-oriented S-LCA and organization-oriented social assessment methods, while appreciating their fundamentally different goals and scopes.

The energy transition brings about complex environmental and societal changes that require robust science-based policy guidance. Conventional Life Cycle Assessment (LCA) methods to evaluate the implications of these changes often fail to capture the dynamic interactions among energy systems, human behavior, and environmental impacts, leading to potentially misleading or oversimplified conclusions. This study introduces a new framework for integrating societal change into LCA using Agent-Based Modeling (ABM). Our approach leverages LCA metamodels, integrated with the ABM via defined linking parameters, to enable real-time feedback between agent decisions and environmental outcomes, while minimizing computational demands. We illustrate the applications of this framework in a photovoltaic case study, examining how end-of-life choices affect abiotic depletion and climate change. The ABM-LCA integration employs metamodels with high predictive performance, achieving R² values of 0.95 for abiotic depletion and 0.82 for climate change. Linking parameters are based on the number of PV modules entering different end-of-life pathways. Societal change, as modeled in the ABM, is driven by the assumption that increased awareness of environmental impacts promotes circular behaviors. The photovoltaic case study provides an illustrative exploration of how consumer choices may affect environmental impacts, such as abiotic depletion and climate change, and how these impacts, in turn, may shape future consumer behavior. This work highlights the value of using a metamodel-driven, integrated ABM-LCA modeling framework for decision-making in complex systems, uncovering unexpected system behaviors and offering insights into sustainable transitions.