Beyond the fields: Unravelling the social consequences of green pea protein production from a Swedish perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite legume-based proteins being more environmentally sustainable compared to conventional meat proteins, these products need to be backed up by socially sustainable supply chains, as upstream and downstream social impacts may hinder their overall contribution to sustainability. This study shows how a social life-cycle assessment (SLCA) can highlight people-centred issues in an emerging Swedish pea-protein supply chain. Using surveys with farmers and workers in combination with a social risk database, we reveal key social risks and improvement options. A stakeholder survey assessment and cradle-to-factory-gate social life-cycle assessment for farmers, workers, local communities, and society were performed. The Product Social Impact Life Cycle Assessment (PSILCA) 2.0 database was used to perform the assessment within OpenLCA. A comparative scenario analysis was performed with Germany, Canada and China. Methodologically, the study applies a mixed-method approach, combining stakeholder-generated data with social risk modelling, offering a replicable template for future assessments of social sustainability. Results indicate moderate but improvable social performance in Sweden for the stakeholders considered, especially in terms of financial risks, economic support and working hours for farmers. The quantitative assessment reveals upstream impacts in terms of risk of child labour, migration flows, and social security expenditures linked to the non-European origin of fertilizer and chemical pesticides. The study highlights the importance of considering social impacts from agricultural input choices and potential risks when scaling up production. It advances social sustainability assessment by integrating qualitative, real-time stakeholders’ insights with quantitative modelling in emerging supply chains. The findings provide useful guidance for companies and policymakers seeking to develop or scale up socially responsible plant-based supply chains.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it