Pathway to cleaner environment: How effective are renewable electricity and financial development approaches?
Bibliographic record
Abstract
While Sustainable Development Goals (SDGs) 13 and 7 are increasingly being explored in climate change research, financialization remains a fundamental part of the discourse on clean and renewable energy development. This study focuses on a policy reconfiguration that may be necessary to further advance clean environment in Canada. More precisely, the research evaluates the co-movement of carbon dioxide (CO2) emissions, financial development, renewable electricity and economic growth. The data, which encompass the quarterly periods from 1984Q1 to 2021Q4, are analysed via the novel wavelet local multiple correlation method. This method is capable of capturing the effect of two or three independent variables on the dependent variable at different frequencies and periods. In this study, the results show that economic growth intensifies ecological deterioration in all periods even as renewable electricity utilisation and financial development restrict ecological deterioration in the medium and long term. Additionally, financial development and renewable electricity consumption promote economic growth in the short, medium and long term. On the basis of these findings, a policy agenda that builds on the SDGs is proposed. Although this policy framework aims to achieve the objectives of SDG 13 and 7 in Canada, it may be extended to other developed countries.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".