The Comparative Analysis of Carbon Pricing Policies on Canadian Northwest Territories’ Economy under Different Climate Change Scenarios
Bibliographic record
Abstract
Policymakers in the Northwest Territories have introduced carbon pricing as a strategy to reduce fossil fuel consumption and CO2 emissions across various population segments and industries. This indirect approach, chosen for its acceptability, aims to influence behavior rather than directly limit carbon-intensive products. The main purpose of this study was to evaluate the economic and ecological impacts of this policy and its alignment with intended objectives. Using a CGE macroeconomic model incorporating economic structural and behavioral equations, we assessed the policy’s effects on NWT’s economy in general and on a subset of its key sectors. We also incorporated a few observed and simulated climate data for diverse climate change scenarios. The estimated results revealed that climate variables, especially precipitation, significantly influenced sectors like agriculture, construction, and manufacturing. The standardized precipitation evapotranspiration index (SPEI), which encompasses both temperature and precipitation, notably impacted the agriculture, oil, and gas sectors. However, temperature alone showed limited significance, except in the oil and gas sector. The simulation results indicated that, while carbon pricing reduced economic contributions of fossil fuel sector, household rebates could counteract these effects of the economic growth of NWT. Our findings offer valuable insights for shaping NWT’s environmental policies, aligning them with Canada’s goal of net-zero CO2 emissions by 2050.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".