Carbon Taxation: A Tale of Three Countries
Bibliographic record
Abstract
Carbon pricing is considered by most economists as a central dimension to any climate policy. It is assumed to bring simple, transparent, and cost-effective means to change investment and consumption behaviors. The most straightforward method is carbon taxation, but its implementation is more complex. This study provides a comparative analysis of carbon taxation in three countries—Sweden, Canada, and France—aimed at drawing lessons for the future of carbon taxation. Comparing the experience of the three countries reveals that carbon taxes, once in place, do have the intended effect. In this sense, they work well. However, the analysis also reveals very different situations in terms of advances, difficulties, and results, which highlights the need to carefully consider the social and political conditions for the acceptance and effective implementation of such economic instruments. Against this background, the comparative analysis yields four main insights that deserve further research from economics and social scientists: the ability to combine pure economic instruments and other regulation or policies and measures; the management of lobbies and vested interests; the identification of a clear strategy for the recycling of the carbon revenues, whether earmarked or not; and finally, the importance of these three dimensions of carbon taxes in the new settings of zero net emission policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".