Implications of carbon pricing on food affordability and agri-food sector in Canada: A scoping review
Bibliographic record
Abstract
• Food affordability is a critical issue in Western countries. • Carbon taxes influence broader economic dynamics. • Limited research in Canada on the effects of carbon taxes on food prices. • As of 2020, over 30 economies worldwide have implemented carbon taxes. • Carbon taxes reduce disposable income and raise food prices. This review delves into the effects of carbon pricing policies on food affordability and the performance of the agri-food sector, with a specific focus on Canada. Against the backdrop of the widespread adoption of carbon pricing as a crucial tool in reducing greenhouse gas (GHG) emissions, the discussion acknowledges potential economic repercussions, particularly for lower-income households. Findings reveal that the implementation of a mandated carbon tax across all provinces in Canada by 2019 led to reduced GHG emissions and an increase in food prices. In addition, this review positions Canada within the global context by examining actions taken by other countries and their impacts. Crucial research gaps are also identified, ultimately serving as a guide for future studies and policy formulation aimed at balancing the necessity of carbon tax implementation with considerations of food affordability.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".