Greenhouse Gas Emissions cuts, a set of fuel-based mitigation wedges in Canada
Bibliographic record
Abstract
To combat climate change, a global transition from fossil fuels to renewable energy and low-carbon systems is necessary. The energy sector plays a pivotal role in this transition. This thesis aims to guide the Canadian oil and gas (O&G) industry in developing investment strategies for transitioning to renewable energy sources, supporting the 2030 Emissions Reduction Plan. \n \nMitigation wedges provide an accessible framework for understanding emissions-reducing actions. This thesis derives a set of mitigation wedges for transitioning from fossil fuels to renewable energy, which can be applied in designing O&G companies' strategies towards achieving Canada's GHG emissions reductions. Quantitative methods and statistical analysis are applied to a balanced panel dataset collected from the Canada Energy Regulator (CER) website, including information on provinces, territories, and the entire country from 2005 to 2050, with historical data up to 2020. \n \nThe study demonstrates that end-use energy demand for natural gas in the industrial and transport sectors and refined petroleum products in the industrial and residential sectors positively impacts GHG emissions reduction in the O&G industry. These four mitigation wedges can significantly contribute to the Canadian O&G industry's GHG emissions target of 42% below 2019 levels. This research offers valuable insights for decision-makers at industry and policy levels to support a successful transition to a low-carbon economy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".