Determining the carbon intensity value of renewable diesel in the Canadian market according to the Clean Fuel Regulations
Bibliographic record
Abstract
Canada is committed to achieving net-zero emissions by 2050, which requires a total of 40 % reduction in emissions. The transport sector is responsible for 24 % of Canada’s total greenhouse gas emissions, making it the largest source of emissions in the country. To achieve the emission reductions, Canada published the Clean Fuel Regulations in 2022, with an aim to reduce the carbon intensity of fuels by 15 % by 2030. The theory part of this thesis introduces the Clean Fuel Regulations, and what is required from the perspective of a low carbon intensity fuel producer. The aim of this thesis is to provide a calculation model and determine the carbon intensity of renewable diesel, and this is done in the calculation part of this thesis. The calculations are done according to the Fuel LCA Model Methodology developed by Environment and Climate Change Canada, using the OpenLCA software. \n \nThe carbon intensity calculations are done for an imaginary biofuel plant that uses soybean oil and animal fat as feedstocks to produce renewable diesel, sustainable aviation fuel, renewable naphtha and renewable propane. The carbon intensity of renewable diesel produced from animal fat is 29,3 gCO₂ₑ/MJ and 31,3 gCO₂ₑ/MJ when produced from soybean oil. Compared to other regulations, such as the Low Carbon Fuel Standard of California and the EU’s Renewable Energy Directive, the carbon intensity of renewable diesel produced from soybean oil is significantly lower. Renewable diesel produced from animal fat placed itself between other regulations’ carbon intensities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".