Considering nonlinearity and structural changes in the convergence of clean energy consumption: the case of OECD countries
Bibliographic record
Abstract
Purpose In this study, we aim to test the stochastic convergence of per capita clean energy use in 30 OECD (Organization for Economic Co-operation and Development) countries for the period of 1965–2017. Design/methodology/approach This study employed both linear and nonlinear panel unit root tests, and unlike other studies, this study allowed fractional values in addition to integer values for frequencies in the Fourier functions. Integer values of frequency indicate temporary breaks, while fractional values show permanent breaks. Findings The results of the linear panel unit root test indicate that clean energy use does not converge to group average for almost all OECD countries. However, the results of nonlinear panel unit root tests provide evidence that the stochastic convergence hypothesis of clean energy consumption cannot be rejected for most countries. This study does not find any evidence for stochastic convergence of clean energy use in Australia, Canada, Denmark, Ireland, Norway or Sweden. Therefore, the policies regarding clean energy are mandatory in these countries due to their effectiveness. This study also reveals that there are permanent structural breaks in the convergence process of clean energy consumption in approximately half of OECD countries. Originality/value This study considers temporary and permanent smooth structural shifts in addition to nonlinearity when testing the stationarity of clean energy consumption in a country i relative to the group average. This new method eliminates deficiencies of the previous panel data techniques. Thus, it provides more reliable results compared to existing literature.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".