The Environmental Kuznets Curve in a long-term perspective: parametric vs semi-parametric models
Bibliographic record
Abstract
Empirical studies of the EKC hypothesis may be very sensitive to datasets, specifications, and functional forms. The aim of this paper is to investigate the long-run relationship among CO2 emissions, real GDP, and energy consumption using a panel of 9 advanced economies from 1870 to 2008 using both parametric and semi-parametric additive models. While at the panel level the results provide support to the Environmental Kuznets Curve (EKC) only in the post-1950s period, at the individual country level the inverted U-shaped relationship between CO2 and real GDP is validated for a subset of countries only. However, when a semi-parametric regression framework is applied an inverse U-shaped pattern becomes clear for all countries of the sample, except Canada. Empirical findings indicate that relaxing the restrictions associated with parametric regression models may be critical for the question of investigating the existence of the EKC.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".