Additional supporting evidence for significant <scp>iLUC</scp> emissions of oilseed rape biodiesel production in the <scp>EU</scp> based on causal descriptive modeling approach
Bibliographic record
Abstract
Abstract Agro‐economic modeling studies have shown that indirect land‐use change (iLUC) emissions of first‐generation biofuels can be significant, reducing or eliminating the climate change mitigating potential of these fuels. Recognizing this, proposed amendments to the European Union's Renewable Energy Directive (RED) would require reporting iLUC emissions of biofuels. The objective of this paper was to provide additional evidence of the iLUC emissions of oilseed rape (OSR) biodiesel using a noneconomic modeling approach called the causal descriptive (CD) model. The CD model originally developed by E4tech (A Causal Descriptive Approach to Modelling the GHG Emissions Associated with the Indirect Land Use Impacts of Biofuels, 2010, E4tech, London, UK) is one of the first noneconomic modeling approaches used for estimating indirect land‐use change (iLUC). Using the E4tech CD modeling framework, we refine assumptions for key parameters such as yields in marginal land, displacement of OSR oil by palm oil, land availability for OSR expansion in the EU, imports of OSR from Canada and Ukraine, and palm oil expansion on peatland and thereby estimate iLUC GHG emissions for a likely scenario (Central Scenario). We find GHG emissions of OSR biodiesel to be 57 g CO2 eq./MJ for the Central Scenario. To capture the possible range of iLUC GHG emissions, we calculate iLUC GHG emissions by changing assumptions for the Central Scenario and land‐use emission factors. We find that GHG emissions of OSR biodiesel may vary from 18 to 101 CO2 eq./MJ. The results provide additional evidence supporting the previous conclusions derived from agro‐economic modeling studies that iLUC emissions of food‐based biofuels can be expected to be significant compared to potential savings. Hence, to achieve meaningful GHG reductions from biofuel use and avoid policy failure, it is important that the EU should take concrete policy action to target support for biofuels toward those with the lowest expected iLUC emissions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".