An empirical application of probabilistic cost–benefit analysis: three case studies on dams in Malaysia, Nepal and Turkey
Bibliographic record
Abstract
This paper empirically applies cost-benefit analysis (CBA) to much debated hydropower projects in Malaysia, Nepal and Turkey.The study selects an interesting mixture of cases, as the main characteristics of each dam, the geographical locations of each dam, and the development stage of each country differ.The study brings together all the major issues attached to each hydropower project and estimates the quantitative impacts of these controversial dams.The CBA model in this study takes into account the premature decommissioning of dams and the correlation between the parameters of generation capacity, total construction cost, and construction period.The mean cumulative net present value (NPV) at the 100th year of the analysis with the 5% discount rate for the Sharada-Babai Dam in Nepal shows a positive figure, whereas the mean cumulative NPV after 100 years for both the Bakun Dam in Malaysia and the Ilisu Dam in Turkey are negative.The mean cumulative NPV for Sharada-Babai becomes negative when the pure rate of time preference is larger than 6%; for Bakun and Ilisu, it converges to zero as the pure rate of time preference becomes larger.The sensitivity analysis shows the dominant positive impact of the generation capacity parameter on NPV for Bakun, and the parameter expressing initial expected increase in economic output for Sharada-Babai and Ilisu.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".