EXPLORING THE IMPACTS OF A NATIONAL U.S. CO<sub>2</sub> TAX AND REVENUE RECYCLING OPTIONS WITH A COUPLED ELECTRICITY-ECONOMY MODEL
Bibliographic record
Abstract
This paper provides a comprehensive exploration of the impacts of economy-wide CO2 taxes in the U.S. simulated using a detailed electric sector model [the National Renewable Energy Laboratory’s Regional Energy Deployment System (ReEDS)] linked with a computable general equilibrium model of the U.S. economy [the Massachusetts Institute of Technology’s U.S. Regional Energy Policy (USREP) model]. We implement various tax trajectories and options for using the revenue collected by the tax and describe their impact on household welfare and its distribution across income levels. Overall, we find that our top-down/bottom-up models affects estimates of the distribution and cost of emission reductions as well as the amount of revenue collected, but that these are mostly insensitive to the way the revenue is recycled. We find that substantial abatement opportunities through fuel switching and renewable penetration in the electricity sector allow the economy to accommodate extensive emissions reductions at relatively low cost. While welfare impacts are largely determined by the choice of revenue recycling scheme, all tax levels and schemes provide net benefits when accounting for the avoided global climate change benefits of emission reductions. Recycling revenue through capital income tax rebates is more efficient than labor income tax rebates or uniform transfers to households. While capital tax rebates substantially reduce the overall costs of emission abatement, they profit high income households the most and are regressive. We more generally identify a clear trade-off between equity and efficiency across the various recycling options. However, we show through a set of hybrid recycling schemes that it is possible to limit inequalities in impacts, particularly those on the lowest income households, at relatively little incremental cost.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".