Growth-environment nexus in Canada: Revisiting EKC via demand and supply dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Previous research on the growth-environment nexus has predominantly focused on demand-side indicators, disregarding the supply-side dynamic and the environmental Kuznets curve (EKC) hypothesis. This study examines the role of economic growth on environmental quality in Canada, considering various macroeconomic factors such as energy consumption, technology innovation, foreign direct investment, and institutional quality. Using time series data for the period 1990 to 2022, this study employs the dynamic autoregressive distributive lag (DARDL) co-integration model to assess the co-integrating relationship among variables and conduct counterfactual shock analysis. The results demonstrate that economic growth significantly affects demand-side dynamics, leading to increased carbon emissions and ecological footprint, while concurrently reducing the supply-side factor, namely the load capacity factor, in both the short and long run. Notably, these findings include the confirmation of the EKC hypothesis as it relates to environmental safety, measured through energy consumption within the Canadian context. In addition, counterfactual analysis of the DARDL approach examines the effects of (±) 1% and (±) 5% shocks from the independent to dependent variables. For robustness, the kernel regularized least squares machine learning algorithm validates the results obtained from the DARDL estimation technique. The study's findings suggest implementing stringent environmental policies to enhance supply-side environmental parameters while carefully balancing energy consumption to support growth. It is crucial to ensure that economic growth is not achieved at the expense of environmental degradation in Canada.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it