Net-zero for Canada: An open-method modeling approach
Bibliographic record
Abstract
Canada is a major oil- and gas-producing country that has committed into law an ambitious goal: net-zero greenhouse gas (GHG) emissions economy-wide by 2050. In this work, transition dynamics for Canada are examined across several net-zero GHG emissions scenarios with detailed policy representation using the open-source Global Change Analysis Model (GCAM). To our knowledge, this study is the first modeling analysis of Canadian net-zero GHG emissions scenarios with extensive policy representation and detailed sensitivity analysis. A major contribution of our open-method modeling approach is making our entire analysis publicly available to facilitate vetting, replicability, precise comparability with other studies, and modification by others to explore additional scenarios. Our results show that net-zero achievement in Canada would demand major technological transformation across all sectors of the economy. Scenarios presented herein highlight considerable gaps between Canada’s current policy actions and its net-zero ambitions. Indeed, the largest gaps between current-policy and net-zero scenarios pertain to rates of end-use electrification, buildout of power sector capacity, deployment of carbon dioxide removal, and accompanying reductions in production and consumption of fossil fuels. The results also highlight the importance of effective policy implementation and the variation in transition dynamics attributable to socioeconomic and technological assumptions, carbon dioxide removal scalability, and non-CO 2 mitigation options.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".