Drivers and Barriers to the Implementation of Carbon Footprint Labeling on Foods in Canada
Bibliographic record
Abstract
Voluntary environmental programs, including ecolabels, can invoke consumer sustainable purchasing behavior. Despite their growing presence in international markets, carbon footprint labels are rare in Canadian food products. The present study was planned to explore the drivers and barriers to implementing carbon footprint labels from the perspectives of NGOs working towards advocating for environmental sustainability and climate change in Canada. The study questionnaire was designed based on previous literature on sustainability, ecolabels, and carbon footprint labels. Opinions of thirteen representatives of environmental NGOs were collected through online survey Google Forms and thematically analyzed. Study findings revealed consumer demand; manufacturers' desire to follow the global sustainability trends and promote a green image of manufacturing firms as potential drivers for adopting carbon footprint labeling. The barriers identified by the study are manufacturers' preference for profiting, the excessive cost of investment required for following the labeling scheme, the voluntary status of the carbon footprint labels, and less support from the government sector. The drivers and barriers identified through this study can be helpful for policymakers and manufacturers in formulating strategies for adopting carbon footprint labeling.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".