Analysis of sustainability assessments to understand modeling trends and habits in the Agri-food industry: A Canadian case study
Bibliographic record
Abstract
This paper presents a comprehensive analysis of the agri-food Life Cycle Assessments (LCA) to develop actions geared towards improving the consistency across different regions. A case study was conducted in the Canadian agri-food industry, addressing inconsistencies in adopting LCA tools, methodologies, and impact categories across academic, industrial, and governmental stakeholders. The study identifies trends, gaps, and opportunities in the field, which involves understanding the status and trends of LCA construction and modeling in the industry, identifying the regions and institutions engaged in LCA development, and papering on the tools and methods used by practitioners. Significant findings include regional and institutional deviations in selecting LCA impact methods and categories, limiting the scalability and comparability of results and hindering the integration of LCA outcomes into national and regional sustainability strategies. The deviations in LCA practices across regions, years, and institutions highlight the need for regionalized LCA impact assessment methods. Regional distributions indicate that provinces with high agricultural GDP, such as Ontario, Quebec, and Alberta, are well-represented; however, Saskatchewan has less prominent LCA activity, which is a concerning gap. This study proposes the use of region-specific LCA impact assessment method tailored to diverse agricultural and environmental contexts. Stakeholder-specific strategies to address these gaps, promoting actionable collaboration for more consistent and scalable LCA practices applicable to other sectors, are shared. A Strength, Weakness, Opportunity, and Threats (SWOT) framework is developed to target optimal approaches for stakeholder-specific actions to foster efficient and harmonized LCA collaboration. This paper presents the value of cooperation between all stakeholders to enhance the consistency and applicability of LCA results. To facilitate this, it proposes actionable strategies such as creating centralized knowledge-sharing platforms, inter-institutional partnerships, and standardized data collection and papering protocols.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".