The Way Forward for Ontario: Design Principles for Ontario’s New Cap-and-Trade System
Bibliographic record
Abstract
Over the next year, Ontario will design and implement a cap-and-trade system for reducing greenhouse gas emissions.Much public discussion has focused on the effectiveness of cap-and-trade as an overall approach to pricing carbon.While it is possible to debate the inherent advantages and challenges of cap-and-trade compared with other carbon-pricing approaches, the fact is, these differences are small.Effective cap-andtrade systems can, and do, exist.But various problems also exist.In Ontario, as in any other jurisdiction, the success of the cap-and-trade system will hinge on the design details.Drawing on the Ecofiscal Commission's April 2015 report, The Way Forward, this brief outlines four fundamental principles of good cap-and-trade design.It offers a practical roadmap and specific recommendations to Ontario as the province moves toward developing its policy.The same principles could be used as a guide by any province considering the introduction of a cap-and-trade system.A common theme runs through these principles and recommendations: transparency.It is not enough to design a policy that is effective, cost-effective, and fair.It must also be clear, predictable, and immune to political interference.The confidence of Ontarianseveryday consumers and big emitters alike-is critical to the success of the province's new policy.While the principles outlined in this brief do not address every detail of policy the government will need to consider, they offer the basis for a well-designed cap-and-trade system for Ontario.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".