The Implementation of Export-Import (E-I) Subsidies Regulation on Rooftop Photovoltaic Plant System in Indonesia Based on Techno-Economic Point of View: A Study Case in Ogan Komering Ulu Region, South Sumatera, Indonesia
Bibliographic record
Abstract
This study aims to analyze the techno-economic impact of implementing a variety of exportimport subsidies on the rooftop photovoltaic plant based on two Ministerial Energy and Mineral Resource (MEMR) in Indonesia: MEMR 49/2018 and MEMR 26/2021.Five of the economic parameters were used to investigate the effect of two regulations with four E-I scenarios.The result showed that the photovoltaic energy in Indonesia is considered as high potential with an average photovoltaic energy of 4.8 kWh/m 2 .The technical analysis presents that the energy could supply the average society's electricity demand.On the economic analysis, the result showed that all the scenarios based on two MEMR showed a positive net present value (NPV) indicating all the implementations are profitable.However, the scenario based on the new MEMR 26/21 with 100% E-I showed the half time of NPV indicates a less risky project compared to others.On the other hand, the result proved that 100% E-I scenario could be the best scenario to stimulate the RPP development since all the economic parameters showed a massive improvement.The value of Net B/C and ROI with MEMR 26/2021 shifted to approximately 63% and 21.3%, respectively, compared to the scenario on MEMR 49/2018.This result confirmed that the new MEMR 26/2021 could stimulate the growth of rooftop photovoltaic in Indonesia for energy transition to achieve 3.6 Giga-watts Solar Photovoltaic Energy (SPE) Mix by 2025.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".