Multi-criteria analysis of renewable energy alternatives in southwest Sumba using TOPSIS method with 5C framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Renewable energy development is important for improving energy security and economic growth in Indonesia. This study identifies the best renewable energy potential in Southwest Sumba, East Nusa Tenggara Province, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on 5C criteria: Consolidated, Controllable, Continue, Clean, and Cheap. The research uses a multi-criteria decision-making approach, using primary data from expert interviews and secondary data from literature reviews. The TOPSIS analysis shows that solar energy has the highest preference value, followed by bioenergy and hydropower. Technical assessments show important implementation requirements for each renewable energy option. The study recommends prioritizing solar energy development, supporting bioenergy projects, improving micro-hydro facilities, and creating clear renewable energy policies. Success depends on cooperation between stakeholders and aligning renewable energy development with regional sustainability and community needs. These efforts can help Southwest Sumba develop its renewable energy sector and contribute to national energy security goals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it