Internal causality in agri-food Life Cycle Assessments: Solving allocation problems based on feed energy utilization in egg production
Bibliographic record
Abstract
Physical allocation in Life Cycle Assessment (LCA) should, ideally, be based on underlying causal relationships. While both cause-oriented and effect-oriented causality referred to in LCA literature are forms of external causality, internal causality addresses the actual flow of materials and inputs in a system - in other words, the real behaviour of the system under study. While a number or examples of allocation based on physical causality have been used in poultry LCAs, none of these represent the internal causality (the actual biological processes) in egg production. The current study remedies that gap by proposing such a method. Agri-food LCAs, in particular LCAs of livestock production, were used to identify existing physical allocation approaches consistent with internal causality. The most commonly used approach was found to be based on the allocation of feed energy to support the various physiological functions of the livestock species. A feed energy - Metabolizable Energy (ME) - utilization model for allocation in egg production LCAs is hence similarly proposed. Using the inventory of a previous LCA study of egg production in Canada, allocation ratios for eggs and spent hens were developed. Feed utilization models specific to each unit process were identified. The overall differences between ME utilization (∼95% eggs, 5% spent hens) and gross chemical energy content (92% eggs, 8% spent hens) for allocation were relatively small. Scenario analysis, however, showed that the allocation ratios can be considerably different if the causal relationship is interpreted differently. Differences over ∼20% was seen in a scenario which did not allocate between the co-products of each unit process in the system, but rather to the products at the end of a biological causal chain straddling multiple unit processes. The proposed approach is consistent with the interpretation of LCA as a natural sciences framework, and with the ISO 14044 multi-functionality hierarchy, because it reflects actual biological causality in egg production systems. The study results also underscore that practitioners should not only clearly justify their choice of allocation strategy, but also describe its application in detail, since small differences in methods can result in divergent outcomes.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".