Integrating nutritional and environmental impacts of animal-source foods via nutrition-based life-cycle assessment (nLCA)
Bibliographic record
Abstract
Significant progress has been made in animal production systems to better understand the environmental footprints in animal-source foods by applying life-cycle assessment (LCA). However, prior LCA studies heavily focused on quantifying environmental footprints based on physical units, with less attention on the nutritional value of foods. Given that animal-source foods play a vital role in providing key nutrients, it's critical to integrate both nutrition and environmental impacts to better understand the sustainability of foods. Hence, this study aims to assess the nutritional-based cradle-to-gate environmental impacts of five animal-source foods, including pork sausage, pork ham, pork bacon, beef sausage, and beef steak, via nutrition-based LCA approach. Nutritional-environmental footprint (NEF) was quantified based on three functional units: per serving, per 50 g protein, and per 100 kcal energy. Both ranking and actual value method were applied to assess and compare each food's combined environmental and nutritional footprints. Results show that relative to pork products, beef products generally score higher environmental footprints; however, beef steak tends to rank higher when considering nutrition parameters alone. When nutritional and environmental footprints are integrated into NEF scores, pork bacon tends to receive lower NEF scores than other products under most scenarios. Although the choice of assessment methods and functional units impacts NEF scores and the product ranking, the overall pattern remains consistent. These outcomes provide insights for various stakeholders such as the animal industry to identify sustainability hotspots, policymakers to establish evidence-based product recommendations and certification guidelines, and consumers to make informed decisions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".