Regional variations in life cycle greenhouse gas emissions of canola‐derived jet fuel produced in western Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study investigates the life cycle GHG emissions of jet fuel produced via the hydroprocessed esters and fatty acids (HEFA) pathway from canola grown in western Canada, with a focus on characterizing regional influences on emissions. We examine the effects of geographic variations in soil type, agricultural inputs, farming practices, and direct land use changes on life cycle GHG emissions. We utilize GREET 2016 but replace default feedstock production inputs with geographically representative data for canola production across eight western Canadian regions (representing 99% of Canada's canola production) and replace the default conversion process with data from a novel process model previously developed in ASPEN in our research group wherein oil extraction is integrated with the HEFA‐based fuel production process. Although canola production inputs and yields vary across the regions, resulting life cycle GHG emissions are similar if effects of land use and land management changes (LMC) are not included; 44–48 g CO 2 e/MJ for the eight regions (45%–50% reduction compared to petroleum jet fuel). Results are considerably more variable, 16–58 g CO 2 e/MJ, when including effects of land use and LMC directly related to conversion of lands from other uses to canola production (34%–82% reduction compared to petroleum jet fuel). We establish the main sources of emissions in the life cycle of canola jet fuel (N‐fertilizer and related emissions, fuel production), identify that substantially higher emissions may occur when using feedstock sourced from regions where conversion of forested land to cropland had occurred, and identify benefits of less intense tillage practices and increased use of summerfallow land. The methods and findings are relevant in jurisdictions internationally that are incorporating GHG emissions reductions from aviation fuels in a low carbon fuel market or legislating carbon intensity reduction requirements.
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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 it