Greenhouse gas emissions and offset potential from sugarcane straw for bioenergy production in Brazil
Bibliographic record
Abstract
This study aims to assess the additional Greenhouse gas (GHG) emissions affected by straw removal from the soil surface in sugarcane areas, including measurement of short-term soil CO2-C emissions plus emissions associated with the recovery and transport operations of straw bales until to the industry gate (diesel emissions) and estimated soil N2O emission, comparing with leaving all straw on the soil surface. Taking into account the main sources evaluated (soil CO2, diesel and N2O from straw), the total additional GHG emissions from the recovery of 6.9 Mg Dry Matter ha-1 (27%) was estimated at 1423 kg CO2eq ha-1, resulting in a carbon footprint of 206.2 kg CO2eq per megagram (Mg) of straw recovered. Applying the parameters cited in this study for electricity generation (GHG emission and offset potential), our results showed an additional GHG emission of (+) 860 kg CO2eq ha-1. Applying the same parameters for second generation (2G) ethanol production replacing gasoline, an avoided GHG emission of (-) 2316 kg CO2eq ha-1 could be achieved. The route of recovering 27% of sugarcane straw from the soil surface through bale system for bioelectricity production using the technical parameters and industrial efficiency rate of this case study resulted in a C footprint of 347 kg CO2eq MWh-1. Improving the efficiency rate for straw conversion in bioelectricity based on its lower heating value could reduce its C footprint to 62.26 kg CO2eq MWh-1 produced. For sugarcane straw recovery at the first cutting cycle in clay soil, the option of producing ethanol 2G could offset GHG emissions once replacing fossil gasoline, resulting in a C footprint of 0.86 kg CO2eq L-1 of 2G ethanol in the agricultural phase, an option to contribute to better sustainability of sugarcane straw recovery, supporting renewable and sustainable bioenergy systems, and reducing the impacts of Global Climate Change.
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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 | 0.000 |
| 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".