Bibliographic record
Abstract
This research applies a recently-developed nonlinear panel smooth transition regression (PSTR) model and takes into account the potential endogeneity biases to examine whether Environmental Kuznets Curve (EKC) exists in G7 countries over the period 1991-2008. This research makes three contributions to the CO2 emissions literature. First, we apply the panel smooth transition regression (PSTR) model of González et al. (2005) to investigate the relationship among CO2 emissions per capita, energy use per capita, real gross fixed capital formation, real GDP per capita, and labor participation rate for G7 countries. Second, we complement the existing literature by simultaneously examining the impacts of energy use, real gross fixed capital formation, real GDP, and labor participation rate on CO2 emissions and take into account endogenous determination of real GDP on the PSTR model for CO2 emissions. Third, based on the characteristics of the PSTR model, we can consider the elasticity of CO2 emissions changes with country and time to analyze the elasticity of heterogeneous countries and the potential impacts of structural breaks on the CO2 emissions elasticity in the panel framework. Based on the elasticity of the CO2 emissions with respect to real income per capita, the environmental quality is a necessary good in Japan, the UK, and the USA, but a luxury good in the rest of G7 countries. Thus, there exists an inverted U-shaped relationship between CO2 emissions and real income per capita with the threshold value of US$20,488, which is endogenously determined. This finding supports the existence of EKC in G7 countries. In other words, our results confirm there exists the regime-switching effect of real income on CO2 emissions in G7 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".