The Political Economy of Carbon Pricing in Canada: Economic Impacts, Governance Challenges, and Policy Evolution
Bibliographic record
Abstract
Canada’s carbon pricing system is one of the most ambitious national efforts to balance environmental sustainability with economic growth and fairness. Established under the Greenhouse Gas Pollution Pricing Act (GGPPA) in 2018, the federal carbon tax sets a standard price for greenhouse gas (GHG) emissions across provinces. It aims to account for environmental costs and encourage clean innovation. However, this policy has developed in a politically and economically challenging environment marked by regional differences, reliance on energy, and debates over the constitution. This paper looks at the political economy of Canada’s carbon tax from two main perspectives: (1) its economic effects on national competitiveness, industry structure, and family welfare; and (2) its political aspects, including federal–provincial relations, public opinion, and policy legitimacy. Using data from Statistics Canada, the International Monetary Fund (IMF), and the Organization for Economic Co-operation and Development (OECD), this study examines how carbon pricing affects GDP growth, inflation, and distribution outcomes from 2018 to 2025. The findings show that Canada’s carbon tax has led to significant emissions reductions and ongoing green investment, with minimal negative effects on GDP. Still, the policy remains politically delicate, reflecting deeper issues in Canada’s federal system and energy landscape. By exploring the connection between economics and politics in carbon pricing, this paper argues that Canada’s experience provides valuable insights for managing fairness, environmental responsibilities, and political stability in climate policy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".