Compensating for the Fiscal Loss in India’s Energy Transition
Bibliographic record
Abstract
India has committed to ambitious targets, aiming to achieve net-zero emissions by 2070; however, this transition away from fossil fuels presents significant fiscal and institutional challenges, that warrant careful examination. This study primarily explores the dynamics of tax revenues and the fiscal implications of India's transition. As fossil fuel consumption declines over time, government revenues generated from fossil fuels are also expected to decrease relative to GDP. The research delves into the institutional challenges related to enhancing existing tax systems, and considers the viability of implementing a carbon tax as an alternative revenue source to replace fossil fuel taxes. The study assesses various tax revenue options, evaluating their effectiveness in revenue generation, long-term sustainability required institutional changes, and the preservation of state autonomy. Allocating revenue between the union and individual states can be an intricate task. The study highlights the potential of carbon taxes as a valuable medium-term solution to address revenue loss. However, it also underscores the challenges associated with their implementation, including institutional barriers and political-economic complexities, particularly within India's fiscal-federal structure. Active engagement from institutions like the Finance Commission and the GST Council is emphasised, recognising their critical roles in managing this transition and mitigating its impact on state-level fiscal autonomy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".