Bibliographic record
Abstract
The decision in fall 2015 to implement an economy-wide carbon tax is one of the most significant policy shifts ever made by Alberta. The provincial government’s attempt to become a global leader in climate change policy will be a major task made even more difficult as Alberta has both high greenhouse gas emissions and high emissions per capita. One of the biggest challenges facing policymakers will be addressing the impact that a carbon tax has on lowincome households. A vast body of research finds that pricing carbon unfairly targets lowincome households, potentially worsening inequality and forcing those with the lowest ability to pay to bear the tax’s greatest burden. This paper seeks to find the best solution to this problem. The Government of Alberta has chosen to address this issue by introducing a lump-sum transfer to lower-income Albertans alongside the carbon tax. The stated goal of these transfers is to repay the full costs of the carbon tax to the lowest-earning 60 per cent of households. This ‘carbon rebate’ is one approach to providing benefits to lower-income households. Some economists argue that the alternative policy approach of reducing personal income taxes on the lowest earners is superior. The goal of this paper is to evaluate these two policies in order to determine which is best able to achieve Alberta’s stated policy goal. This paper looks first at the potential impact of Alberta’s carbon tax on households. Distributional impacts are central to the analysis and the impacts of carbon pricing are examined by income quintile. An accurate estimate of the costs faced by households across the income distribution is performed using data on each quintile’s expenses on residential fuel, transportation fuel and electricity. The estimate is strengthened by a separation of fixed and variable costs based on electricity and residential fuel billing data. This paper finds that the carbon tax has an uneven impact, hitting the second and third quintiles hardest but leaving the lowest quintile the least impacted of the five income groups. The centerpiece of this paper is the evaluation of two test policies, each designed to represent one approach to providing benefits to lower-income households. The lump-sum transfer is designed to be as close as possible to the government’s carbon rebate. The PIT cuts are introduced using an alternative PIT system that offers tax cuts to the lowest tax bracket. Simulations using Statistics Canada’s SPSD/M program determine the effects of both policies. The evaluation finds that the lump-sum transfer is able to make the lowest and second quintiles better off and the third quintile almost no worse off under a carbon tax. The PIT cuts, on the other hand, fare poorly. PIT cuts provide the greatest benefits to Alberta’s highest-earners, while the lowest-three quintiles receive benefits far below the costs they incur under a carbon tax. This paper concludes that a lump-sum transfer system is the mechanism best suited to meeting the government’s policy goal. However, Alberta’s lump-sum transfer system is likely to fall short of making 60 per cent of Albertans no worse off under a carbon tax.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".