Ten-Year Changes in Global Warming Potential of Dietary Patterns Based on Food Consumption in Ontario, Canada
Bibliographic record
Abstract
Many studies have evaluated the life cycle environmental impacts of diets based on a single period, but few studies have considered how environmental impacts of diets change over time, even though dietary patterns (DPs) change due to policy and socio-demographic factors. This study evaluated changes in the global warming potential (GWP) of DPs in the province of Ontario, Canada, using a life cycle assessment. We quantified the farm-to-fork GWP of six DPs (Omnivorous, No Pork, No Beef, No Red Meat, Pescatarian, and Vegetarian), using dietary intake data from a 2014 and 2015 survey. Throughout this period, the biggest decrease in GWP was for DPs containing beef, even though these DPs still have the highest GWP (3203 and 2308 kg CO2e, respectively, based on the annual energy intake of one individual). Across all DPs, plant-based proteins contributed less than 5% to GWP, while meat and fish contributed up to 62% of the total GWP. Ten-year GWP reductions are insufficient to meet climate change and other sustainability goals, and major dietary shifts are needed, particularly substituting animal-based proteins with plant-based proteins. To design effective interventions for shifting towards sustainable diets, research is needed to understand how socio-demographic and regional differences influence individuals’ food choices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".