Carbon Footprint of Canadian Self-Selected Diets: Trade-Offs With Nutrient Intakes and Diet Quality
Bibliographic record
Abstract
Individuals' dietary choices are critical determinants of human and planetary health. Although the environmental impact of animal-based foods typically exceeds that of plants, trade-offs among nutritional outcomes and environmental sustainability in the context of self-selected diets are less understood. The objectives were to estimate the carbon footprint of Canadian self-selected diets and to compare low- and high-GHGE diets in terms of intake of food groups, nutrients, and diet quality. Twenty-four-hour recalls from the 2015 Canadian Community Health Survey (CCHS) – Nutrition were used to determine dietary intake among adults ≥19 y (n = 13,612). Estimates from the database of Food Impacts on the Environment for Linking to Diets were used to link foods and beverages reported in the CCHS to their greenhouse gas emissions (GHGE). Intake of food groups, nutrients, and diet quality based on the Alternative Healthy Eating Index – 2010 were compared between low- and high-GHGE diets (lowest and highest quintiles of dietary GHGE expressed per 1,000 kcal). Dietary GHGE (mean ± SE) was 3.98 ± 0.06 kg CO2-equivalents (eq) per person per d or 2.15 ± 0.03 kg CO2-eq per person per 1,000 kcal. Animal-based foods contributed three-quarters of Canadians' total dietary GHGE, with red and processed meat alone accounting for 47.05 ± 0.82%. High-GHGE diets contained more animal-based foods, but also more vegetables and fruits and miscellaneous foods and beverages; low-GHGE diets contained more cereals, grains, and breads. High-GHGE diet respondents had higher intakes of nutrients of public health concern (iron, potassium, calcium, and vitamin D), but also higher intakes of nutrients to limit (saturated fat and sodium). Moreover, low-GHGE diets had higher diet quality scores compared to high-GHGE diets (55.31 ± 0.49 vs. 47.27 ± 0.46 points; p < 0.0001). Self-selected Canadian diets with the highest GHGE contained more animal-based foods and were characterized by higher intakes of nutrients of concern but a lower overall diet quality. These trade-offs warrant attention in shaping future food policy and dietary guidance in Canada aimed at meeting global targets for climate change. None.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".