Optimizing the Implementation of Carbon Tax in Reducing the Impact of Environmental Pollution
Bibliographic record
Abstract
Carbon taxes are one of the climate control tools that help achieve sustainable economic growth. It is a market-based instrument that aims to reduce greenhouse gas emissions by making it more expensive to emit carbon dioxide. The Indonesian government has shown its seriousness in reducing global warming by establishing carbon tax regulations, including a provision in Law No. 7 of 2021 concerning the Harmonization of Tax Regulations. Despite implementing carbon taxes in several countries, their implementation must be reconsidered to ensure the objective is achieved. Therefore, this research aims to optimize the implementation of a carbon tax to reduce the impact of environmental pollution. This paper will investigate whether or not the carbon tax has already reduced emissions or if it is not affected at all. Three factors could cause carbon emissions: Coal, vehicle, and greenhouse gas emissions. Therefore, conducting an expectation study encompassing these policymakers, stakeholders, and researchers can gain insights into the potential outcomes and impacts of optimizing the implementation of a carbon tax in reducing environmental pollution. It can inform decision-making processes and help guide the designing and refining adequate and equitable environmental policies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".