The cost of compliance to the Paris agreement and its distribution: An input-output analysis of Canadaâs commitment
Bibliographic record
Abstract
To fulfill its Paris Agreement commitment, Canada must find a way to substantially reduce its greenhouse gas emissions. Given the Canadian political system, where regulation and taxation powers are divided between different levels of government, a nation-wide action plan against climate change calls for a high level of coordination and agreement between provinces. The goal of this research is to propose a way to limit Canada’s GHG emissions without placing an unacceptable burden on the highly emitting provinces. Using a subnational interregional input-output model with interprovincial trade and GHG emissions, the economic impact of an hypothetical carbon pricing policy is assessed according to three burden allocation scenarios and nine sub-scenarios accounting for technological changes. The first allocation evenly assigns the GHG emission reduction across all industrial sectors. The second and third allocations put the burden of the abatement costs on the largest GHG emitting sectors of the Canadian economy. By simulating these different policy scenarios, this study looks at the trade-offs between their overall economic costs and the geographical distribution of those costs amongst provinces. Results show distributing the GHG emission cut evenly across industrial sectors is the highest cost alternative. On the other hand, targeting only the largest GHG emitting sectors places an excessive burden on the western provinces, preventing the policy to be realistically implemented in Canada. A compromise between these two options is proposed.
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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 | 0.006 |
| 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 | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".