Examining the effects of economic development, trade openness, FDI, and urbanization on energy use in Canada: An ARDL analysis
Bibliographic record
Abstract
This study begins by extending the Marshallian demand framework to examine the long-term and short-term determinants of energy use in Canada. Specifically, it explores the impact of GDP per capita, trade openness, foreign direct investment (FDI), and urbanization on energy use, utilizing annual time-series data from 1990 to 2023. The autoregressive distributed lag (ARDL) bounds-testing approach assesses a cointegrating relationship among the variables. The F-bound cointegration test is applied to verify the long-run association, followed by ARDL model estimation to evaluate both short-run and long-run elasticities. Additionally, a pairwise Granger causality test is conducted to determine the direction of causal interactions between the variables, while various diagnostic tests are performed to validate the model assumptions. In addition to the ARDL long-run findings, three alternative econometric techniques are implemented to ensure their robustness: Dynamic Ordinary Least Squares (DOLS), Canonical Cointegrating Regression (CCR), and Fully Modified Ordinary Least Squares (FMOLS). The results suggest that trade openness and GDP per capita increase energy use, whereas FDI and urbanization decrease it. The ARDL model exhibits significant effects only on GDP and urbanisation, while the FMOLS, DOLS, and CCR models exhibit significant effects on all four variables. The direction of effect stays the same, no matter what method is used, but the coefficients' strengths change. These results highlight the importance of GDP growth and urban expansion in determining Canada's energy needs, as well as the impact of trade openness and FDI. The study employs ARDL, combined with FMOLS, DOLS, and CCR techniques, to provide robust and comparative insights into the impact of economic growth, international trade, foreign investment, and urbanization on long-term energy consumption patterns in Canada.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".