Environmental, economic, and social impact of five COP26 policies: A computable general equilibrium analysis for Canada
Bibliographic record
Abstract
Abstract Along with the main objective of politicians, other economic variables are variously affected by the environmental taxes, policies, and limitations like a price on pollution, clean electricity, the cap on emissions, methane emissions, and nature‐based solutions. The objective of the environmental solutions is to decrease pollutant emissions and energy consumption while reducing labor costs and taxes as the incentives for creating new occupations. An overall equilibrium model was considered in the present study as a nonlinear equations system, which was calibrated for the reference year of 2018 utilizing Canada's economy's data table. The effects of utilizing these environmental policies considered by Canada in COP26 are examined. In all scenarios, minimum, maximum, and optimum values for reducing pollutant emissions are calculated under these policies. According to the simulation results, welfare is reduced by the price of pollution policy. Moreover, the actual consumed budget of the household is reduced by 8%. However, such indices will be incremented by 2% in the nature‐based policy. In all scenarios, the gross domestic product is decreased. However, in the methane emission policy, this reduction is 1.05% in the lowest state. In all scenarios, the consumer price index will be incremented by 3.8%–9.8%. It is concluded that the clean electricity policy is an appropriate policy for reducing greenhouse gas emissions and at the same time adhering to international commitments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".