A Methodological Roadmap to Determine the Effectiveness of Carbon Policies
Bibliographic record
Abstract
In the struggle to combat climate change carbon policies have been presented as an efficient and effective method for reducing GHG emissions while minimizing economic impacts. Policy implementation worldwide has grown considerably over the past decade with an ever-growing percentage of global emissions being covered by different forms of carbon policies. There is however a low quantity of evidence-based literature on the effectiveness of the carbon pricing models currently in place. I document a statistical, econometric modeling technique developed and used by the Canadian Energy Research Institute (CERI), to evaluate the Environmental Effectiveness of different Carbon Policies worldwide. A case study using the methodology to evaluate the effectiveness of the British Columbia Carbon tax is presented showing it to be ineffective at increasing emissions effectiveness while having a positive impact on economic growth.
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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 | 0.239 | 0.433 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".