Integrating the Job Demands–Resources Model into social LCA: the JD-R tool for buffering harmful working conditions

Purpose: This paper introduces the “Job Demands–Resources Tool” as a preliminary and conceptual diagnostic framework to buffer harmful working conditions within the Social Life Cycle Assessment (S-LCA) methodology. The study addresses the prevalent critique regarding the lack of operational cause-effect models (Type II) in S-LCA, proposing a qualitative-to-semi-quantitative screening mechanism for the Social Life Cycle Inventory (S-LCI) phase, rather than a fully operational Type II S-LCIA method. Grounded in the Job Demands–Resources (JD-R) Model, the methodology reinterprets working conditions not as static exposures to harm, but as the dynamic interaction between Job Demands (stressors) and Job Resources (mitigating factors). Method: The Tool was developed through a conceptual synthesis of psychosocial risk theories, applied to a case study within a medium-sized company in the French wine sector. The research employed combined systematic literature review, and 24 semi-structured interviews, to identify and prioritize specific JD-R factors. Results: The findings confirmed that “Job Demands”, perceived as adverse working conditions by workers, such as high workload and physical effort, coexist with organizational “Job Resources”, perceived by workers as a buffer against negative outcomes. Four key Job Resources—autonomy at work, supervisor support, task variety, and a positive social climate—were identified as having a significant buffering potential with respect to Job Demands. Conclusion: The JD-R Tool contributes methodologically to S-LCA by structuring psychosocial risk information in a transparent and reproducible way, supporting screening, organisational learning, and the comparison of alternative organisational scenarios. By explicitly positioning the tool as a conceptual and methodological bridge, this work supports a shift beyond purely descriptive Type I approaches, while laying the groundwork for future developments toward quantitative S-LCIA integration.