Developing a methodological framework for consequential life cycle assessment with an illustrative application to plant protein extraction
Bibliographic record
Abstract
Consequential Life Cycle Assessment (CLCA) can play an important role in providing insights for decision-makers regarding potential market-mediated resource/environmental consequences stemming from changes in product systems. However, the consistent application of CLCA encounters challenges due to the absence of common guidelines. This systematic review studied different sets of literature to develop a methodological framework for CLCA of plant protein extraction through fractionation, using Canada as an example. Given that economic models are integral components of CLCA, this systematic review offers a comprehensive survey of the economic models employed in 18 CLCA studies, shedding light on their respective strengths and weaknesses. Notably, the study identifies the use of both Computable General Equilibrium and Partial Equilibrium models for enabling the analysis of large-scale and long-term changes. The estimation of land use changes (both direct and indirect) is an integral part and economic models are instrumental in quantifying indirect land use changes. For characterizing the common modelling practices in the agri-food sector, 33 CLCA studies were reviewed to extract information on the decision context, time horizon, identification of marginal/substitutable markets, uncertainty and sensitivity analyses, etc. ‘Increased supply’ and ‘increased demand’ are both found to be important decision contexts. It is a common practice to identify marginal markets based on assumptions, literature, and other sources along with employing economic models for some cases. However, economic models in the agri-food studies were used more often for estimating land use change impacts. To elaborate the case study of plant protein extraction (i.e., pea fractionation), some of the alternative uses of the co-products of pea fractionation processes as a basis for determining probable marginal markets were identified. Building upon these findings, the review culminates in the proposal of a detailed methodological framework for CLCA applied to pea fractionation, incorporating considerations of marginal markets that revolve around utilizing co-products like pea starch and pea fibre. Future research could focus on identifying marginal markets relevant to the Canadian landscape, thereby enhancing the applicability and relevance of CLCA within this region.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".