Similarities and contrasts: Comparing U.S. and Canadian paths to net-zero
Bibliographic record
Abstract
Canada and the United States (US) have both committed to reaching net zero emissions by 2050 but neither have implemented policy sufficient to reach this target. Knowledge of the technical steps to deep decarbonization is needed alongside an understanding of how each country might be similarly and uniquely impacted by a transition to net zero emissions, contingent on specific technology advancements or policy decisions. We use the computable general equilibrium model, gTech, to simulate sixteen net zero scenarios for Canada and the US varying by technology and policy assumptions as part of the energy modelling forum 37 (EMF37) study. We find that both economies similarly continue to grow in all scenarios out to 2050 with the rate of growth largely determined by assumptions on negative emissions technology. Sectoral impacts differ between countries as a result of current emissions and GDP profiles in combination with assumed net zero scenario policy and technology advancements. In the US, we find that efficient use of electricity is a slightly more important predictor of economic outcomes, while Canada's economy is marginally more responsive to cost and performance improvements in carbon capture technologies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".