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Purpose <p>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).</p> Method <p>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.</p> Results <p>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.&#xa0;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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月20日

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  • Federica Silveri,
  • Luigia Petti

摘要

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.