Impactful Canadian decarbonization policies in times of an uncertain carbon tax
Bibliographic record
Abstract
The future of the Canadian federal carbon tax is uncertain due to a lack of public and political support, as well as an upcoming federal election. The relationship between the carbon tax and the effectiveness of other decarbonization policies is currently unquantified, whether it be synergistic or antagonistic. Prioritization of the highest impact alternative decarbonizaiton policies could aid in long-term strategy under political uncertainty. We employ the recently released MESSAGE-Canada integrated assessment model to explore decarbonization pathways wiith and without the carbon tax. For both future scenarios, a Morris sensitivity analysis of the 33 currently announced Canadian decarbonization policies will be conducted. Changes in the ranking of impact are assessed for key federal-level system indicators, such as cost and emissions. Further, policy impacts rankings on provincial metrics are also compared by scenario for major energy production and consumption provinces. Lastly, the samples generated are also used to develop a range of feasible pathway projections that better capturing Canada’s decarbonization trajectory under uncertainty. Comparing the ranking of policy impacts indicates the extent to which the carbon tax acts synergistically with other policies to reduce emissions, and thus is the most crucial lever for reaching decarbonization targets. However, this ranking also allows us to prioritize exploration of the next most effective policies in the absence of the tax.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".