Production of sorghum pellets for electricity generation in Indonesia: A life cycle assessment
Bibliographic record
Abstract
The current study makes use of life cycle assessment to evaluate the potential greenhouse gas (GHG) savings in coal electricity generation by 5% co-firing with sorghum pellets. The research models the utilization of 100 thousand hectares of under-utilized marginal land in Flores (Indonesia) for biomass sorghum cultivation. Based on equivalent energy content, 1.12 tons of pellets can substitute one ton of coal. The calculated fossil energy ratio of the pellets was 5.8, indicating that the production of pellets for fuel is energetically feasible. Based on a biomass yield of 48 ton/ha·yr, 4.8 million tons of pellets can be produced annually. In comparison with a coal system, the combustion of only pellets to generate 8,300 GWh of electricity can reduce global warming impacts by 7.9 million tons of CO2-eq, which is equivalent to an 85% reduction in GHG emissions. However, these results changed when reduced biomass yield of 24 ton/ha·yr, biomass loss, field emissions, and incomplete combustion were considered in the model. A sensitivity analysis of the above factors showed that the potential GHG savings could decrease from the initially projected 85% to as low as 70%. Overall, the production of sorghum pellets in Flores and their utilization for electricity generation can significantly reduce the reliance on fossil fuels and contribute to climate change mitigation. Some limitations to these conclusions were also discussed herein. The results of this scenario study can assist the Indonesian government in exploring the potential utilization of marginal land for bioenergy development, both in Indonesia and beyond.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".