Small Hydro Development in the Indian Himalaya : Implications for Environmental Assessment Reform
Bibliographic record
Abstract
India is promoting the vast hydropower potential of the Himalayan region, and the northern states of Uttarakhand and Himachal Pradesh are encouraging small, medium and major hydro projects. Our research examined the approval processes for small hydro in these states with a view to making recommendations for policy improvements. We describe local understandings of project impacts, review public participation in project approvals, and discuss extending the national environmental assessment law to small hydro. We used a retrospective case study of three hydro projects, semi-structured qualitative interviews, a review of policy and project-specific case documents, and field observations. We found that residents of affected communities held similar views respecting the positive and negative impacts these projects might have, whether the impacts occurred or not. We canvassed predicted impacts such as job creation, increased access to electricity, improved local infrastructure, loss of cultural assets, and removal of trees. Further, the case study revealed opportunities for earlier, more decentralized, and more active participation in small hydro approval processes. We conclude that the legal exemption for small hydro has left an important gap in India’s environmental assessment regime. Improved project-level assessments, catchment-based cumulative effects assessments, and better local involvement are needed for small hydro development.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".