Risk sharing in hydropower development: case study of the Chukha Hydel Project in Bhutan
Bibliographic record
Abstract
The Himalayan rivers have an enormous hydropower potential that is still not exploited fully for the benefit of the region. Bhutan and Nepal together have an economically feasible potential of 60,000 MW of hydroelectric power generation capacity but are too weak financially to bear the risks associated with the development of their hydro resources alone. India is the only potential market for the electricity supplied from these sources. The power purchase agreement framework for the 336 MW Chukha Hydel Project in Bhutan could serve as a model for the transfer of risks, management of risks and sourcing of finance in exchange for sharing the economic rents associated with such projects. India undertook the costs and risks of constructing the hydroelectric dam and power plant in exchange for a reduced purchase price of electricity from the completed facility. This paper contains a financial and economic assessment of the Chukha Hydel Project. While India is in a position to exercise monopsonic power in this electricity market, this analysis shows that it is possible to have an agreement for sharing the risks and returns between India and the Himalayan countries that is highly beneficial to all the stakeholders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".