Rethinking social life cycle assessment for agricultural systems: insights using the pig value chain in the Republic of Ireland as a case study

Abstract

Abstract Purpose Social Life Cycle Assessment (S-LCA) is a tool used to evaluate the social sustainability of products and systems. The performance reference scale is the commonly used S-LCA approach for agricultural systems but has limitations including reliance on social performance assessments, lack of sector-specific value-added activity variables, and dependence on linear reference scoring. These limitations can lead to inaccurate assessments of social issues. We aimed to develop a methodology for the pork value chain and agricultural systems to overcome these shortcomings. Methods Performance reference points were sourced from national and regional pig industry benchmarks, while generic data was used for inventory indicators. Social performance was expressed using ordinal scores which were converted to cardinal scores based on expert judgments. Social performance was converted to social risks using reversed min–max normalization. The “people” activity variable was enhanced by incorporating population and pig densities with pig per capita used to distinguish the local community from society. Social risks were aggregated with social issue weights and activity variables to calculate social risk time, which culminated in the estimation of social hotspot indices. These enhancements were compared using linear and nonlinear scoring methods. Results and discussion Average social risks were highest for pigs (0.57 vs. 0.64) and lowest for society (0.47 vs. 0.44) for linear and nonlinear methods, respectively. The distribution and ranking of social risk time for social issues varied between the scoring methods across all stakeholder groups. Both linear and nonlinear methods identified pig farm as a social risk hotspot (0.57 vs. 0.65) and the consumption value chain stage as a social opportunity hotspot (0.39 vs. 0.31). The linear scoring method showed a lack of granularity and systematic bias in estimating the social risks, while the nonlinear method was more nuanced due to incorporating contextualization into the reference scores. Conclusion The proposed methodology highlighted the importance of including social risks and hotspots, alongside social performance, for an agricultural system S-LCA. It demonstrated the advantages of nonlinear scoring over the linear method, overcoming limitations like lack of granularity and systemic bias in the assessment of social issues. However, a limitation of the nonlinear method lies in potential bias when selecting experts for social issue contextualization. This can be mitigated by carefully selecting stakeholder representatives and conducting a sensitivity analysis.

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