Unraveling the Sustainability Footprint: A Descriptive Analysis of Co-firing Technologies for Advancing Energy Transition in Indonesia
Bibliographic record
Abstract
Abstract Indonesia has ambitious renewable energy goals of achieving 23% utilization of renewable energy in primary energy mix by 2025 and 31% by 2050. To reduce emissions, the government plans to phase out Coal-Fired Power Plants (PLTU) by 2030 and adopt co-firing technology to decrease coal usage. Co-firing involves burning renewable materials alongside fossil fuels to reduce carbon emissions. However, concerns have arisen regarding this method’s sustainability, considering the environmental impact of various biomass sources. This research utilizes a descriptive analysis method to examine and assess the critical sustainability factors related to co-firing in Indonesia. While biomass can help mitigate GHG emissions, a comprehensive assessment of net emissions from the biomass process is crucial. Careful planning and policies for co-firing implementation are essential to mitigate negative effects and promote a greener future for Indonesia.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".