State-led experimentation or centrally-motivated replication? A study of state action plans on climate change in India
Bibliographic record
Abstract
In 2009, the Government of India asked all Indian states and Union Territories to prepare State Action Plans on Climate Change, making it one of the largest efforts at sub-national climate planning globally. Through an examination of state climate plans in five Indian states, the paper explores the implications of sub-national climate measures by examining two questions: First, how do state action plans on climate change link with India’s national and international climate efforts in the context of multi-level governance of climate change? Second, do these plans serve as laboratories of experimentation in addressing climate change? Through an empirically driven inductive analysis, the paper argues that because state climate plans, at least in the initial stages, followed a centrally driven, and sometimes ambiguous agenda, their scope and room to experiment was circumscribed. While they did initiate a process and a conversation, the scope and impact of the plans was limited because they tended to follow conventional bureaucratic planning processes and were limited by a central mandate. The plan process did create some space for local innovation, particularly by enterprising bureaucrats, but this was limited by both restricted space and time for innovation. As a result, the plans made only initial steps toward bringing climate-resilient sustainability to the forefront of state development planning. There is however scope for improvement as states and stakeholders begin examining the plans with a view to implement recommendations, finance projects and even consider fresh iterations.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".