Disentangling the effects of nonrenewable energy consumption on CO <sub>2</sub> emissions in Canada: The moderating role of construction and manufacturing
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".