Growth-environment nexus in Canada: Revisiting EKC via demand and supply dynamics
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".