Beyond the fields: Unravelling the social consequences of green pea protein production from a Swedish perspective
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".