Carbon Taxation in India: A Policy Feasibility Study
Bibliographic record
Abstract
Climate change is a pressing challenge facing 21st-century policymakers, with India facing severe consequences due to its geographical diversity. Rising emissions, coupled with increasing economic inequality, make it crucial to identify policy instruments that can both reduce pollution and support sustainable growth. One such instrument is the carbon tax, which has been implemented in several countries to curb emissions while raising government revenue. The paper examines the case study of Canada and the EU, to analyse the effectiveness of carbon tax. Drawing on these lessons, this study suggests a phased strategy for enacting a carbon tax in India and highlights potential economic barriers like inflation, industry resistance, and enforcement problems. This study suggests an initial tax rate of $10 per tonne of CO2 emissions for India. The study also notes that the rate should be viewed as a baseline for phased implementation, open to future adjustments based on performance. In order to guarantee that the tax is both practical and politically feasible, it highlights the necessity of revenue redistribution, public awareness initiatives, and specific exemptions for industries that are particularly vulnerable. By tailoring global lessons to India’s unique economic and political context, this paper bridges an important gap in the literature. Hence, a well-structured carbon tax could be a major contributor to India’s progress to a net-zero future.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".