Emissions Cap or Emissions Tax? A Multi-sector Business Cycle Analysis
Bibliographic record
Abstract
In contrast to previous studies, this paper uses a multi-sector setting to assess aggregate and sectoral impacts of reducing carbon dioxide emissions in the presence of stochastic productivity shocks. We develop a multi-sector dynamic stochastic general equilibrium model, calibrated to the U.S. economy, to compare the economic implications of reducing carbon emissions with an emissions cap and with an emission tax. As in previous studies, we find that an emission cap predicts lower volatility of aggregate variables than an emission tax. Still, our results point to the importance of going beyond a single-sector analysis in evaluating the relative merits of the cap and the tax policies. The ranking of the welfare costs under the two regimes depends on the sources of productivity shocks. While there is no difference in the welfare costs of the two regimes for productivity shocks originating from non-energy sectors, we find that an emissions cap policy is more costly than an emission tax policy for shocks that originate from the energy sectors. Moreover, we find that non-energy shocks have distinct sectoral impacts under the two regimes even though there are no significant differences between the two regimes for the aggregate variables.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".