Life cycle climate impacts of eating patterns of Canadian provinces: Focus on meat and protein intake
Bibliographic record
Abstract
Many studies have assessed the life cycle impacts of national eating patterns, but few have explored the differences in environmental impacts of regional eating patterns within a country, which can differ due to geographical, cultural, socio-economic, and systemic factors such as food policies and food environments. This study characterizes the global warming potential (GWP) of eating patterns across ten provinces in Canada, identifying key foods driving these impacts and evaluating protein intake ratios. Meat consumption varies across regions, with the highest consumed meat being poultry. Across all regions, average intakes of protein-rich plant-based foods (including pulses, nuts, and seeds) are lower than total meat and fish consumption. The GWP of eating patterns ranges from 4.31 to 5.04 kg CO 2 e per 2000 kcal. Animal-based protein foods, particularly beef, contribute to 67 % of the GWP, while protein-rich plant-based foods contribute just 1 %. Average protein intake from animal-based foods is consistently higher than from plant-based foods, with an average ratio of 65:35 across the provinces. A scenario analysis for Ontario, the most populous province in Canada, showed that changing protein intake ratios from 65:35 to 50:50 and 40:60 resulted in GWP reductions of 18 % and 27 %, respectively. Strategies for dietary shifts should focus on increasing plant-based protein consumption while reducing animal-based protein intake to lower the climate change impacts of eating patterns, all while maintaining sufficient protein levels. • Climate impacts and protein sources were evaluated in Canadian region eating patterns. • Types of meat consumption varies considerably across regions. • Intake and global warming contribution of protein-rich plant-based foods is low. • Average ratio of animal- and plant-based food consumption is 65:35. • Shifting the ratio from 65:35 to 50:50 and 40:60 can reduce global warming.
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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 | 0.001 |
| 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 | 0.000 |
| 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".