Distributional Effects of Changes in BC's Carbon Tax Revenue-Use
Bibliographic record
Abstract
Carbon taxes can impose a disproportionate burden on low-income households, who often spend a greater share of their income on carbon-intensive goods. Policymakers can improve the fairness of a carbon tax through revenue recycling. British Columbia has deviated from its "textbook" 2008 revenue-neutral carbon tax that returned all revenue to taxpayers through income tax cuts and a means-tested tax credit (the climate action tax credit, or CATC). Using Statistic Canada's Social Policy Simulation Database and Model, I estimate the distributional effects of BC's carbon tax and revenue recycling choices on households. I simulate two revenue-recycling schemes in 2022: (1) replicating BC's 2008 policy, directing 35% of revenue toward each of the CATC and personal income tax cuts; and (2) BC's current policy, with 15.7% and 24.3% of revenue funding the CATC and personal income tax cuts, respectively. BC's current carbon tax is regressive, with and without revenue recycling, where regressivity is measured by the carbon tax paid as a share of household disposable income across deciles. The CATC on its own is progressive, and personal income tax cuts are regressive. By increasing the generosity of the CATC, BC's carbon pricing policy becomes progressive and households in the bottom three income deciles receive net benefits (rebates that exceed carbon taxes). British Columbia can meet the goal of achieving a fair carbon tax regime by amending the way it uses its revenue.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".