Disentangling the effects of nonrenewable energy consumption on CO <sub>2</sub> emissions in Canada: The moderating role of construction and manufacturing
Why this work is in the frame
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Bibliographic record
Abstract
The role of industrial sectors, including construction (CONS) and manufacturing (MFG), in mitigating carbon dioxide (CO 2 ) emissions is often overlooked. The response of these indicators in environmental sustainability is gaining critical attention among scholars and policymakers. Therefore, this research aims to address this issue by investigating the impact of nonrenewable energy consumption (NREC) under the moderating effects of CONS and MFG on Canada's CO 2 emissions from 1980 to 2021, utilizing both traditional autoregressive distributed lags (ARDLs) and dynamic ARDL simulation methods. The findings reveal that NREC, CONS, and economic growth (GDP) are significant drivers of emissions in both the short and long run. Meanwhile, MFG reduces emissions in the long run with no significant short-run impact. Further analysis using Generalized Kernel-based regularized least squares (gKRLS) and frequency domain causality (FDC) tests confirmed these results. Moreover, examining the moderating role of CONS and MFG exhibits significant long-run positive moderating effects on the NREC-CO 2 relationship, with MFG having a more substantial impact than CONS. However, both sectors show insignificant adverse moderating effects in the short run. Robustness analysis using quantile regression (QREG) and simultaneous quantile regression (SQREG) demonstrates that GDP and MFG consistently mitigate CO 2 emissions across all quantiles, with stronger effects at higher emissions levels. These results underscore the importance of targeted renewable energy policies that balance economic growth with environmental sustainability.
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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 | 0.000 | 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 it