Exploring the Potential Trade-Offs of Canada’s Participation in a Global Emissions Trading System Using A Multi-Sector, Multi-Region CGE Model
Bibliographic record
Abstract
Like many nations, Canada faces challenges stemming from climate change. Therefore, it aims to reduce overall emissions, measured in megatonnes of CO2 equivalents (MTCO2eq), by 40-45%, relative to 2005 levels by 2030 and achieve net zero emissions by 2050. This paper introduces Environment and Climate Change Canada’s Multi-Sector, Multi-Region (EC-MSMR) recursive dynamic computable general equilibrium (CGE) model, capable of evaluating multiple pathways to reaching emissions-based targets using market-based policies. CGE models capture direct and indirect effects in response to a policy change, making them excellent tools for evaluating economy-wide environmental policies. The EC-MSMR model delivers granular insights into Canada’s emissions and economic activity on the global stage by incorporating data from seventeen aggregated regions and twenty-three commodity-producing sectors, along with three final demand sectors: Consumption, Investment, and Government Spending. With this model, this paper analyzes Canada’s participation in a global Emissions Trading System (ETS) with perfect commitment versus a domestic carbon pricing schedule that adjusts itself to the shadow price that achieves the 2030 target. Results indicate that participating in a global ETS provides slightly greater economic growth and welfare while reducing reliance on fossil fuels to domestic carbon pricing alone. Although both policy experiments meet Canada’s 2030 target, both scenarios experience lower GDP and welfare outcomes than a baseline consisting solely of existing policies and no additional action.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".