Bibliographic record
Abstract
This article attempts to document the status of environmental fiscal instruments (EFIs) so as to explore relevant international experiences on ecotaxes in the context of India and to examine India’s specificities in these taxes within a wider perspective of other fiscal measures. Environmental levies across 15 countries were reviewed and the countries categorised are into two groups: Annex II and Non-Annex I. The revenues from levies imposed in the countries were also analysed. The most common form of taxes in Annex II countries in the form of energy taxes, followed by transport taxes. For India, energy and transport taxes could prove to be vital types of ecotaxes for addressing issues of climate change. Pollution taxes are difficult to levy for administrative reasons, but resource taxes are imperative because of severe environmental problems associated with mining and related activities. The revenue generated from environmental taxes and charges for all Annex II countries hovered between 2 and 4 per cent of their respective GDPs, except for Canada and the United States of America, whereas for Non-Annex I nations, this ranged only between 0 and 1 per cent. JEL Classification: H23, Q50, Q58
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; both teacher heads agree on what is shown here.
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".