Convergence in per capita CO<sub>2</sub>emissions: evidence from nonlinear unit root tests in top four oil exporter countries
Bibliographic record
Abstract
Purpose This paper aims to examine the stochastic convergence of the per capita CO2emissions among the top four crude oil exporter countries, namely, Canada, Iraq, Russia and Saudi Arabia, from 1960 to 2017. Assessing the stationarity and unit root properties of the environmental series in these countries is important as their large fossil fuel resources increases the potential for rising CO2emissions compared to other countries. Design/methodology/approach In addition to implementing the conventional unit root tests, the authors also benefit from the application of three nonlinear unit root tests, namely, wavelet unit root test, nonlinear unit root test of Güriş (2019) and the Fourier quantile unit root test. These methods are robust to the presence of possible structural breaks and other forms of nonlinearities, while the wavelet unit root test enables us to examine the stochastic behavior of the variables in both time and frequency domains. Hence, they all provide more reliable inferences on the convergences of the CO2emissions compared to their standard competitors. Findings The standard unit root test results show strong evidence in favor of non-stationarity in all countries. This conclusion supports the results of the other nonlinear unit root tests and the overall findings of the Fourier quantile unit root test. The wavelet unit root test provides a controversial finding. However, due to its limitations, its findings must be interpreted with caution. The details of the Fourier quantile unit root test indicate that per capita CO2emissions follow mean-reverting properties in middle quantile ranges for Canada, Russia and Iraq. This validates the asymmetric behaviors of per capita CO2emissions in these countries. Originality/value The novelty of the work can be stated in two ways. First, among the available studies, this is the first paper to emphasize the importance of examining the convergence of per capita CO2emissions among the top four oil exporters. Second, to the best of the knowledge, no study has yet been undertaken in which all these methods have been simultaneously applied. Sustainable environmental policies depend heavily on the CO2series’ properties. Thus, the findings can provide significant environmental and economic implications for policymakers to construct feasible and optimal policies in climate change mitigation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".