Integrating health, nutrition, and environmental impacts of foods: a life cycle impact assessment and modelling analysis of foods in Canada
Bibliographic record
Abstract
BACKGROUND: Given the urgency of transitioning towards sustainable nutrition, dietary shifts that provide co-benefits to human health and the environment are imperative. There is currently no database of the environmental impacts of foods that reflects Canada's unique geographical and agri-climatic context and regional inputs and emissions. To determine sustainable diets, harmonising nutritional considerations with environmental impacts is also essential for an equitable comparison of foods. We aimed to develop a Canadian Food Life Cycle Inventory database and a multidimensional index to enable a joint assessment of the health and environmental impacts of foods in Canada. METHODS: The Canadian Food Life Cycle Inventory database uses life cycle assessment methodology to evaluate environmental impacts. The datasets mirror Canada's food consumption patterns, averaging the spectrum of agricultural practices weighted by domestic production and import shares. The database is structured according to the nomenclature and categorisation of the Canadian Nutrient File. Environmental sustainability is assessed using a cradle-to-grave approach, including indicators such as greenhouse gas emissions, eutrophication, particulate matter, freshwater usage, land use, non-renewable energy consumption, and food loss and waste. Environmental impacts are quantified through an environmental impact score (EIS) assigned to each impact category for a given food. The EIS-nutrition (EIS-N) integrates the evaluation of nutritional quality with environmental impacts using Nutri-Score, a validated food nutrient-profiling tool. The EIS-N is modelled as a ratio of the EIS to the Nutri-Score values. FINDINGS: Preliminary results show the greatest environmental impacts for animal-based foods, particularly beef, in agreement with current literature. Foods with greater nutritional quality also generally show greater environmental sustainability, with some exceptions for particular impact categories. INTERPRETATION: The database and index have potential to serve as powerful tools to support researchers, policy makers, and consumers, harnessing big data to drive efficient food and climate solutions for systems transformation. FUNDING: Province of Ontario and University of Toronto, CIHR SMART Healthy Cities Training Platform, and University of Toronto's Temerty Faculty of Medicine.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".