Impact of uncertainty in indirect land-use changes and life-cycle carbon intensity for biofuels under climate legislation: a case study of British Columbia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
British Columbia's (BC) Greenhouse Gas Reduction (GHGR) Targets Act (GHGRTA) contains ambitious goals of reducing province-wide greenhouse gas (GHG) emissions by 80% in 2050 (relative to 2007). With ∼38% of BC's GHG emissions stemming from transportation (in 2012), it is clear that BC's climate goals can only be realized with an effective climate policy for transport fuel activities. Enacted in December 2009, the GHGR Renewable and Low Carbon Fuel Requirement (RLCFR) Act and Regulation have achieved significant life cycle GHG emissions reductions accredited to its enforcement: 904,900 t CO2eq (2012). At face value, this is a great success. However, several accounting issues suggest that these GHG emissions reductions are inaccurate. In this study, the GHG emissions reductions achieved with the RLCFR are first analyzed by fuel source and transport mode. Next, the methodology for determining the life-cycle carbon intensity (CI) factors of each fuel are analyzed, with a particular focus on how indirect land use change (iLUC) is dealt with. The findings of this study suggest that the RLCFR legislation has not been nearly as effective as proclaimed by the BC government. Nevertheless, this transport fuel regulation is essential if BC wants to achieve its future GHG emissions reduction targets. Several recommendations are provided.
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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