A CGE Analysis of the Economic Impact of Output-Specific Carbon Tax on the Malaysian Economy
Bibliographic record
Abstract
Environmental pollution is an emerging issue in many developing countries and its mitigation is increasingly being integrated into national development policies. One approach to mitigate the problem is by implement pollution control policies in the form of pollution tax or clean technology incentives. Empirical studies for developed countries reveal that imposition of an carbon tax would decrease CO2 emissions significantly and do not dramatically reduce economic growth. However, the same result may not apply for small-open developing countries such as Malaysia. The objective of this study is to quantify the impact of pollution tax on the Malaysian economy under the backdrop of trade liberalization. To examine the economic impact and effectiveness of carbon tax, a single-country, static Computable General Equilibrium model for Malaysia is constructed. The model is extended to incorporate output-specific carbon tax elements. Three simulations were carried out using a Malaysian 2000 Social Accounting Matrix. The first simulation examines the impact of halving the baseline tariff and export duty while the second solely focused on the impact of output-specific carbon tax. The third simulation combines both former scenarios. The model results indicate that the Malaysian economy is not sensitive to further liberalization. The reason could be attributed to the fact that Malaysian export duty is already low. Additionally, simulation results also indicate that while imposition of carbon tax reduces carbon emission, it also results in lower GDP and trade.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".