Interaction Between Decomposed Energy Utilization and Environmental Health in Canada: A Cointegration and Counterfactual Analysis Approach
Bibliographic record
Abstract
Despite the substantial impact of various energy consumption parameters on the generation of nitrous oxide and methane emissions, which contribute to environmental degradation, most prior research has primarily concentrated on carbon emissions and ecological footprint metrics. This study seeks to address this void by examining the intricate link between total energy usage and its disaggregated components, including natural gas, oil, coal, renewable, and nuclear energy sources, and their role in four critical aspects of the environment in Canada. Incorporating time series data spanning from 1990 to 2022, we employ the dynamic autoregressive distributed lag (DARDL) approach, which reveals that total energy consumption, particularly from nonrenewable sources, such as coal, natural gas, and oil, contributes to environmental degradation by accelerating ecological footprint, carbon, nitrous oxide, and methane emissions. Conversely, renewable and nuclear energy sources have the opposite effect, reducing environmental decay. Additionally, counterfactual analysis examines the effects of (±) 1% and (±) 5% shocks from the predictors to predicted variables. Moreover, the study evaluates the robustness of the findings derived from the DARDL estimation technique by employing the Kernel‐based Regularized Least Squares (KRLSs) machine learning algorithm. While the environmental impacts of various energy sources are well‐documented, this study offers a novel contribution by analyzing the differentiated effects of both renewable and nonrenewable energy consumption on CO 2 , CH 4 , and N 2 O emissions in a single framework, with a specific focus on the Canadian context. Unlike previous studies, this research integrates economic policy uncertainty and technological innovation as vibrant variables, revealing their distinct roles in amplifying or mitigating environmental degradation. Finally, important policy implications are discussed.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".