Net-zero for Canada: An open-method modeling approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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 it