Exploring the Relationship Between GDP, Carbon Dioxide Emissions, Energy Consumption, Population, and Renewable Energy Production Using Canada as a Model Country
Bibliographic record
Abstract
This study explores the complex relationships between population growth, gross domestic productivity (GDP), carbon dioxide (CO2) emissions, primary energy consumption, and renewable energy (RE) production in Canada from 1950 to 2021. Using time-series econometric techniques, including Ordinary Least Squares (OLS), Vector Autoregressive (VAR) models, and cointegration analysis, the research investigates how these variables interact over time and their implications for environmental sustainability and economic development. The results indicate that population and GDP growth significantly increase primary energy consumption and CO2 emissions, emphasizing the need for cleaner energy sources. While the positive correlation between population growth and renewable energy production presents opportunities for reducing carbon footprints and fostering economic resilience, there are also risks of overexploitation of renewable resources if energy demand outpaces sustainable supply. The study highlights the importance of sustainable resource management and policy frameworks to ensure that economic growth does not compromise environmental integrity. These findings provide critical insights for policymakers in balancing economic development with environmental sustainability, advocating for increased investment in renewable energy and implementing energy-efficient practices. Future research should expand this analysis to other countries and explore the differentiated impact of various renewable energy sources on economic and environmental outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".