The Nexus between GHGs Emissions and Clean Growth: Empirical Evidence from Canadian Provinces
Bibliographic record
Abstract
Canada is one of the most emission-intensive economies in the world and the big challenge for Canada and its provinces is in how to mitigate the GHGs while keeping the same pace of economic growth. This paper’s main objective is to examine the relationship between greenhouse gas (GHGs) emissions and clean growth using cross-sectional data for Canadian provinces from 1995 to 2019. Based on the results of the cross-sectional dependence, slope heterogeneity, and Hausman test, the study applied the pooled mean group (PMG) estimators. For the robustness of the results, the study also provided the results of augmented mean group (AMG) estimators. The results of Westerlund’s test show that the variables of the estimated models are cointegrated in the long run except in the case of the carbon intensity (GHGs/Energy) model, while no short-run relationship was observed. The main findings of both estimators show that an inverted U-shaped relationship exists in the case of the carbon intensity model. In contrast, as expected, a U-shaped relationship exists in the case of the energy intensity model. The results also confirmed that Canada reduced its GHGs emissions after 2005 and that GHGs emissions and energy intensity are decreasing over time. At the province level, only Alberta has no long-run relationship as regards carbon intensity and energy intensity, while Nova Scotia and British Colombia have no long-run relationship as regards energy intensity. In terms of tipping points, Canada is in the increasing phase of the inverted U-shaped curve in the case of carbon intensity, while in the decreasing phase of the U-shaped curve in the case of energy intensity. There is a significant decrease in greenhouse gas emissions per capita at the provincial level compared to the 2005 base levels. It is imperative to reduce greenhouse gas emissions per capita in Canada and its provinces over time by gradually rolling out energy-saving incentives rather than by using more efficient energy-saving technology. The government of Canada should shift towards low-carbon energy and renewable sources which emit fewer greenhouse gases per unit of energy produced.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".