Revisiting heterogeneity in the relationship between <scp>CO<sub>2</sub></scp> per capita emissions and income per capita
Bibliographic record
Abstract
Abstract This study applies various methods to deal with sources of potential misspecification in the drivers of CO 2 per capita emissions within an environmental Kuznets curve (EKC) framework. The proposed methodologies are as follows: (i) Bayesian model averaging analysis as a remedy for omitted variable bias due to model uncertainty, (ii) convergence club analysis to endogenously classify a large size of panel of countries as a remedy for unobserved heterogeneity, (iii) inclusion of lagged regressors as a remedy for simultaneity. The empirical findings show that the EKC is holding for all except for the first group with the highest (or fastest) long‐term income trajectories. Moreover, one observes a clear negative impact of renewable energy consumption, a general positive impact of financial development, and no impact of institutions on emissions. Finally, the findings highlight the importance of constructing homogeneous country clusters.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".