PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 6, 2026Journal of Industrial Ecology0 citationsOpen Access

A critical review of S-LCA methods since the UNEP, 2020 guidelines: lingering challenges in the search for standardization

View Full Paper
MJMegan E. JermakARArjun T. RamshankarSCSarah B. Cribb

Key Points

  • This review evaluates the challenges faced in Social Life Cycle Assessment methods since the 2020 UNEP Guidelines.
  • Conducted a critical review of 30 S-LCA case studies published after the UNEP guidelines.
  • Identified four key methodological gaps hindering standardization.
  • Evaluated the application of qualitative methods in S-LCA.
  • Highlighted lack of transparency and rigor in current methodologies.
  • Found that macro-scale approaches rely on generic data, limiting context-specific insights.
  • Identified the need for improved collaboration and data availability in social assessments.

Abstract

Abstract As the least researched component of holistic Life Cycle Sustainability Assessment (LCSA), Social Life Cycle Assessment (S-LCA) continues to face significant methodological fragmentation—hindering the integration of social dimensions in sustainability research. Despite the methodological consolidation efforts provided by the landmark 2020 United Nations Environment Programme (UNEP) Guidelines, several challenges remain. To lay a foundation for greater coherence in S-LCA, this study conducts a critical review of 30 case studies published since the 2020 UNEP Guidelines to evaluate both emerging practices and enduring limitations. From this, four key methodological gaps are identified: (1) lack of transparency for rigor and replicability, (2) macro-scale approaches with generic data inhibiting contextualized analysis, (3) data availability limitations, and (4) need for more effective application of qualitative methods. To address these challenges, the review offers future directions, including enhanced documentation protocols, adoption of micro-scale boundaries enriched with high-resolution data, and shifting application of databases. Additionally, its discussion highlights the importance of high-fidelity data and collaboration with corporations, alongside qualitative method applications adherent to social science principles. These insights inform a flexible framework that builds from existing standards and guidelines to support rigorous, context-specific S-LCA implementation across diverse sectors and micro-scale applications.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Jermak et al. (2026) studied this question.

synapsesocial.com/papers/69fa983604f884e66b532052https://doi.org/10.1007/s44498-026-00081-5
Ask AI
Helpful
Bookmark
Share
View Full Paper