Economic Analysis of Jatropha Bio-diesel Production in Sri Lanka
Bibliographic record
Abstract
There has been an increasing trend in investments in renewable energy sources in the recent years. This study assesses the economic and financial feasibility of Jatropha production in Sri Lanka under the prevailing policy regime. The nominal protection coefficient and effective protection coefficients were employed to gauge the level of protection for bio-diesel production using Jatropha in Sri Lanka. The cost benefit analysis was performed to assess the feasibility of Jatropha bio-diesel production in Sri Lanka. The conventional measures like NPV, BCR, and IRR were used in financial and economic terms. Nominal Rate of Protection (NPR) was calculated by dividing the local Jatropha bio-diesel price by the border price of biodiesel. The NPR for Bio-diesel implies that nearly 47% of protection at local market level. Effective Protection Rate (EPR) for seed production is 90%, for oil extraction and bio diesel processing it is 128%. Implication of this is that the producers will be protected and they receive returns 47% greater than what they would have received under free market conditions for Jatropha cultivation. Except for the benchmark situation, all other considered scenarios produce a favourable NPV, BCR and IRR for Jatropha bio-diesel production. Economic benefits due to CO2 reduction were also considered in the analysis. KEYWORDS:Cost benefit Analysis, Jatropha Biodiesel, Protection Coefficient
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".