Clearing the air: How carbon pricing helps Canada fight climate change
Bibliographic record
Abstract
We've come a long way in Canada.We have real, working examples of both carbon taxes and cap-andtrade systems that are reducing GHG emissions while maintaining strong economies.Yet the growing consensus around carbon pricing is not yet universal.Some voices have questioned the extent to which carbon pricing will affect GHG emissions.And elections are on the horizon, both nationally and in several provinces, in which carbon pricing could be a source of debate and even a key issue.Such policy debates are healthy and necessary.But debates will support good policy decisions only if they are based on facts and evidence.And there is strong evidence, grounded in solid economics and policy experience, that carbon pricing works.Part of the problem is communication.Governments and policy analysts (including here at the Ecofiscal Commission) haven't always done a good enough job explaining carbon pricing to Canadians.This really matters because carbon pricing affects us all.How we design these policies will influence how we live and how we do business.We all want better understanding.In short, we need a more informed conversation about carbon pricing.So let's have that conversation.Let's clear the air.Done right, carbon pricing changes household and business behaviour, reduces GHG emissions, and provides an incentive for the development and adoption of the technologies that can play a key role in a low-carbon economy.In addition (and this point is also often overlooked), carbon pricing will achieve these outcomes at a lower economic cost than other policies.Together, this means that carbon pricing can support both a clean economy and a prosperous economy.It achieves these goals by changing incentives and unleashing market forces.It lets businesses and individuals identify the best ways to reduce their GHG emissions and at the times and places that are right for them.And it doesn't require governments to identify and enforce specific ways to reduce GHG emissions.This essay unpacks the overall story.What does "working" mean for carbon pricing?Where has carbon pricing worked?Why does carbon pricing work?When does carbon pricing work?Who supports carbon pricing?How do policies put a price on carbon?We provide clear answers to these questions in (mostly) jargon-free language.Just the facts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.005 |
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".