Unpacking Canada’s oil and gas dilemma: international leadership challenges on the road to net-zero
Bibliographic record
Abstract
Despite significant policy and regulation efforts by Canada’s federal government since its signature of the Paris Agreement, the specific question of whether Canada can retain its role as an energy production powerhouse while gaining some political capital as an international leader with regard to climate change has continued to plague its GHG reduction ambitions. In this article, I argue that despite an exceptional clean energy resource endowment, options to demonstrate the country’s serious intentions to meet its international climate commitments while keeping a sizeable oil and natural gas production sector come with complex implications well beyond the simplistic economic challenge linked to replacing the sector’s exports and employment levels. I explore three such options to keep oil and gas production high by using techno-economic modeling: compensating with more reductions elsewhere, using CCS in the oil and gas sector to avoid the sector’s emissions, and using negative emission technologies to compensate them. Compared with reducing emissions through large cuts in oil and gas production levels, each of these options comes with both significantly higher costs for society and a much higher risk of not delivering the expected emissions reductions.
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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 | 0.000 | 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 | 0.001 | 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) | 0.002 | 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".