On quantifying sources of uncertainty in the carbon footprint of biofuels: crop/feedstock, LCA modelling approach, land-use change, and GHG metrics
Bibliographic record
Abstract
Biofuel systems may represent a promising strategy to combat climate change by replacing fossil fuels in electricity generation and transportation. First-generation biofuels from sugar and starch crops for ethanol (a gasoline substitute) and from oilseed crops for biodiesel (a petroleum diesel substitute) have come under increasing levels of scrutiny due to the uncertainty associated with the estimation of climate change impacts of biofuels, such as due to indirect effects on land use. This analysis estimates the magnitude of some uncertainty sources: i) crop/feedstock, ii) life cycle assessment (LCA) modelling approach, iii) land-use change (LUC), and iv) greenhouse gas (GHG) metrics. The metrics used for characterising the different GHGs (global warming potential-GWP and global temperature change potential-GTP at different time horizons) appeared not to play a significant role in explaining the variance in the carbon footprint of biofuels, as opposed to the crop/feedstock used, the inclusion/exclusion of LUC considerations, and the LCA modelling approach (p<0.001). The estimated climate footprint of biofuels is dependent on the latter three parameters and, thus, is context-specific. It is recommended that these parameters be dealt with in a manner consistent with the goal and scope of the study. In particular, it is essential to interpret the results of the carbon footprint of biofuel systems in light of the choices made in each of these sources of uncertainty, and sensitivity analysis is recommended to overcome their influence on the result.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".