Alternative Tax Instruments for CO2 Emission Reduction and Effects of Revenue Recycling Schemes
Bibliographic record
Abstract
This study examines the roles of revenue recycling schemes for the selection of alternative tax instruments (i.e., carbon-, sulphur-, energy- and output-tax) to reduce CO, emissions to a specified level in Thailand. A static, single period, multi-sectoral computable general equilibrium (CGE) model of the Thai economy has been developed for this purpose. This study finds that the selection of a tax instrument to reduce CO, emissions would be significantly influenced by the scheme to recycle the tax revenue to the economy. If the tax revenue is recycled to finance cuts in the existing labour or indirect tax rates, carbon tax would be more efficient than the sulphur-, energy- and output-taxes to reduce CO2 emissions. On the other hand, if the tax revenue is recycled to households through a lump-sum transfer, sulphur and carbon taxes would be more efficient than energy and output taxes. The ranking between the sulphur and carbon taxes under the lump sum transfer scheme depends on substitution possibility of fossil fuels. Sulphur tax is found superior over carbon tax at the higher substitution possibility between fossil fuels; the reverse is found true at the lower substitution possibility. In all schemes of revenue recycling considered, the output tax is found to be the most costly (i.e., in welfare terms) despite the fact that it generates two to three times higher revenue than the other tax instruments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 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".