The Short-Run Household, Industrial, and Labour Impacts of the Quebec Carbon Market
Bibliographic record
Abstract
Resistance to the implementation of greenhouse gas pricing policies comes in part from fears about the concentrated impacts on certain industries, certain regions, and on less affluent households. These distributional concerns are valid and, to be fair, policy can accommodate some transitional measures to soften the impact of sudden policy changes. However, the carbon pricing policy recently instituted in Quebec, in partnership with California under the Western Climate Initiative, is relatively modest in price targets, gradual in implementation, and has the capacity to spend revenues on transitional and impact-mediating programs for the labour market and households. We analyze the expected short-run impacts of the policy, focusing on equity in two domains: the household income distribution and labour in different industrial sectors. Our analysis focuses on the short-term effects, before capital is significantly reallocated, and before most substitution toward lower-carbon or imported goods has happened. For reasonable prices and pass-through levels, and modelling direct and indirect emissions, we bracket these impacts, finding modest effects in all cases. Generous permit handouts to incumbents are likely to result in some windfall profits. Quebec would benefit from greater transparency in the intended allocation of the Green Fund revenues. Overall, the policy appears tuned to provide a balance of price predictability, steady decarbonisation, and manageable transition costs, but could likely be more aggressive.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".