Does Circular Reuse of Chickpea Cooking Water to Produce Vegan Mayonnaise Reduce Environmental Impact Compared with Egg Mayonnaise?
Bibliographic record
Abstract
Consumers are increasingly asking for foods that are healthier, more humane, and environmentally sustainable. Recently, chickpea cooking water—aquafaba—has gained popularity as a potential egg substitute that complies with these criteria. However, research on the environmental impact of this ingredient is lacking. We performed a comparative attributional life cycle assessment (LCA) of mayonnaise made with aquafaba as the emulsifying agent, and traditional mayonnaise made with egg yolk. The vegan mayonnaise was found not to be as environmentally sustainable as the egg-based product. The vegan mayonnaise had a significantly (p < 0.05) lower impact across 4 categories, but a significantly higher impact across 8 categories out of 16, including climate change and resource-use-energy-carriers. The majority of categories under which vegan mayonnaise underperformed were related to the electricity needed for aquafaba processing. These impacts can be mitigated with a “cleaner” electricity grid, or onsite renewable electricity generation. Substituting the Mexican grid, where the aquafaba is currently processed, for the Canadian grid, where the mayonnaise is produced, reduced the carbon footprint of the vegan mayonnaise by 37%, making it similar to the egg-based product. As sunflower oil production was linked to extensive environmental burdens, we performed additional sensitivity analyses around oil processing, sunflower production, and other vegetable oils. Our study shows that substituting egg yolk with aquafaba could cause an increase in the environmental footprint of mayonnaise due to high processing costs, illustrating that vegan options do not always have a smaller environmental footprint, and can represent a trade-off in their comparatively more humane and healthier offer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".