Bibliographic record
Abstract
This paper examines India’s evolving climate change governance framework, emphasizing the role of its federal structure in shaping national and subnational climate action. Given India’s commitment to ambitious climate goals like achieving net-zero emissions by 2070 and aligning with the Paris Agreement, the study underscores the need for a cohesive, multi-level governance approach to effectively implement these targets. It critically assesses the current institutional landscape, identifying gaps in coordination and sectoral integration of climate action. India’s reliance on sector-specific laws and policies, coupled with the absence of overarching climate legislation, highlights the urgent need for a unified legal framework to mainstream climate considerations into governance. Drawing on international experience, it explores how fiscal federalism principles can strengthen India’s climate governance by empowering state governments and decentralizing climate action, while maintaining national coherence. The paper proposes strategies to optimize institutional support, enhance financial mechanisms, and foster cross-sectoral coordination. It outlines a roadmap for strengthening India's climate governance, focusing on establishing national climate laws, integrating climate change into fiscal and policy planning, and improving coordination between central and state authorities. By offering a pathway for scaling up climate action in India, the paper aims to ensure equity and sustainability in India’s transition to a low-carbon, resilient economy.
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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 | 0.003 | 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 | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".