CLE Working Paper No.1/2023--Driving Global Heating to 1.7° and Above: The New Canada Energy Future 2023 Report and Canada's Projected Oil Production to 2050
Bibliographic record
Abstract
The Canada Energy Regulator on June 20, 2023, released its new report Canada’s Energy Future 2023. For the first time the Federal Government’s energy regulator has directly addressed whether the currently projected growth of oil production in Canada to 2040 and 2050 is compatible with keeping increased warming to 1.5°C. The regulator’s analysis is based on three scenarios. Only the CER’s first scenario, the “Global Net-zero Scenario” (stated to be based on the International Energy Agency’s (IEA) “Net-Zero by 2050 Scenario”), is aligned with limiting warming to 1.5°C. That would require a very dramatic reduction in Canada’s existing oil production level which, according to the CER, is currently projected to reach 5.6 million bpd by 2026. Under the Global Net-zero Scenario, starting after 2030 Canada’s oil production must decline sharply to 2.8 million bpd by 2040 and fall to 1.2 million bpd by 2050. The CER’s second scenario, the “Canada Net-zero Scenario”, projects much higher levels of oil and gas production through to 2040 and beyond. The CER acknowledges that its second scenario aligns with warming of 1.7°C. This paper identifies the un-examined assumptions and climate implications that underlie the second scenario, which has been given the comforting and eco-friendly name (“Canada Net-zero”) and is presented by the CER as a plausible and acceptable alternative pathway. I argue that in this case, which involves the most complex expert evidence, policy decisions must be informed by an independent public inquiry process, not by discussions behind the “closed doors” of the CER.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.036 |
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".