REVENUE RECYCLING AND COST EFFECTIVE GHG ABATEMENT: AN EXPLORATORY ANALYSIS USING A GLOBAL MULTI-SECTOR MULTI-REGION CGE MODEL
Bibliographic record
Abstract
Carbon pricing generates revenues which can be recycled back into the economy in different ways to help mitigate the economic cost of abatement. These include, lump-sum transfers to households; reducing existing distortionary taxes, such as income taxes on labor and capital; investment in technology funds leading to energy/emissions efficiency improvements; and/or infrastructure developments that help expedite the adoption of low or lower carbon-intensive technologies. In this paper, we undertake illustrative simulations to explore how different revenue recycling options influence the overall economic outcome in terms of broad macroeconomic indicators, such as Gross Domestic Product (GDP) or household welfare. Environment and Climate Change Canada’s (ECCC) multi-sector, multi-region Computable General Equilibrium (CGE) model (EC-MSMR) is used to simulate various revenue recycling options. These simulations are undertaken for the U.S. economy. The main findings of the paper are: (i) using carbon revenue for a general income tax reduction or investment subsidy is more advantageous than a lump-sum transfer to U.S. consumers in terms of welfare or GDP; and (ii) using carbon revenue for a sector-based subsidy such as renewable energy is more disadvantageous than a lump-sum transfer to consumers. In terms of accumulated welfare effects, our results indicate that the best carbon revenue recycling option is the investment subsidy or capital income tax reduction in the longer horizon; labor tax reductions yield the best outcome in the shorter horizons.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".