Evaluation of the life cycle of bioethanol produced from soft carbohydrate-rich and common rice straw in Japan with land-use change
Bibliographic record
Abstract
Abstract This study evaluates the life cycle of ethanol produced from soft carbohydrate (SC)-rich rice straw (cv. Leafstar ) and common rice straw (cv. Koshihikari ) while considering land-use change to estimate CO 2 emissions, energy balance (expressed as Net Energy Ratio, NER), and production costs. Three different pretreatment methods were considered: the DiSC (direct saccharification of culms), RT-CaCCO (room temperature-CaCCO) and CaCCO (calcium capturing by carbonation) processes. Although the reduction in CO 2 emission was found to be 59%, 42% and −3.5% for the DiSC, RT-CaCCO and CaCCO processes, respectively, the CO 2 emission reduction decreased significantly when land-use change was considered. This result clearly shows that the biomass (rice straw) should be obtained from paddy fields without land-use change. The NER values for the bioethanol produced by the DiSC, RT-CaCCO and CaCCO processes were estimated to be 2.7, 2.1 and 1.0, respectively, and the total costs were estimated to be 102, 134 and 151 Yen/L ethanol, respectively (US $1 = 100 Yen). The use of the SC-rich rice straw contributes to a reduction of the environmental load and costs for the pretreatment, enzyme production, and enzymatic hydrolysis processes. Therefore, the use of SC-rich rice straw for bioethanol production reduces the total cost of production, reduces CO 2 emissions, and improves the NER. Our results suggest that the DiSC and RT-CaCCO pretreatment processes are promising pretreatment techniques and that SC-rich rice straw is a promising resource for bioethanol production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".