Carbon Tax as a Climate Solution: Feasibility and Impacts for India’s Sustainable Future
Bibliographic record
Abstract
Humanity's biggest worries right now are climate change and growing greenhouse gas emissions. The year 2019 was very terrible for Earth. A number of wildfires in the Arctic area, the Australian blaze, and the Amazon rainforest fire were all caused by the extraordinary increase in air temperatures and roaring heat. The world community has decided to act because it recognizes the urgency. Carbon reduction targets have been set by the majority of nations as part of the Paris Climate Agreement and India is not an exception. The Paris Agreement's total mitigation objectives seem insufficient; therefore, nations have begun looking at other ways to reduce carbon emissions. Several economists believe that the most realistic and cost-effective way to slow down climate change and solve the problem of global warming is through carbon taxes. This study intends to provide a solid knowledge of carbon pricing in India using the doctrinal research method, making inferences based on a careful analysis of current legal doctrines, legislative trends, and academic discourse. The feasibility of a carbon tax in India, its compatibility with existing legislative frameworks, and the wider ramifications for mitigating climate change and promoting economic sustainability by reconnoitring on why India has not explicitly implemented a carbon price, what tactics it has impliedly used to reduce carbonization, what potential consequences would arise from introducing carbon tax, and how it might help India reach the objective of net-zero emission by 2070.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".