Toward a sustainable future: Determinants of renewable energy utilisation in Canada
Bibliographic record
Abstract
This study investigates the nexus between carbon dioxide (CO₂) emissions, electricity consumption, foreign direct investment (FDI), gross domestic product (GDP), and renewable energy in Canada from 1990 to 2023, using an Autoregressive Distributed Lag (ARDL) approach. The research addresses critical environmental challenges posed by economic growth and energy utilization, primarily understanding how these factors influence CO₂ emissions over time. The results reveal that a 1 % increase in electricity consumption is associated with a 0.02 % rise in CO₂ emissions, underscoring the environmental impact of Canada’s energy-intensive economy. Additionally, FDI shows a negative correlation with emissions, indicating that a 1 % rise in FDI contributes to a 0.05 % decrease in CO₂ emissions. Conversely, renewable energy utilization demonstrates significant potential for emissions reduction; a 1 % increase in renewable energy leads to a 1.15 % decrease in CO₂ emissions, highlighting the efficacy of clean energy in mitigating environmental impacts. The findings reveal that CO₂ emissions significantly decrease renewable energy utilization, electricity consumption, foreign direct investment, and economic growth significantly increase renewable energy utilization. Furthermore, electricity consumption and GDP significantly drive CO₂ emissions, while renewable energy and FDI have mitigating effects. These results imply that while economic growth and energy usage are crucial for Canada's development, transitioning to renewable energy and strategically attracting FDI can reduce carbon emissions. The study offers policy recommendations to support Canada's climate goals by encouraging investments in renewable energy and promoting sustainable economic growth. • This research investigates the determinants of renewable energy in Canada. • The paper applies an ARDL approach. • This study offers new insights specific to Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".