The Role of Labor Productivity in Reducing Carbon Emission Utilizing the Method of Moments Quantile Regression: Evidence from Top 40 Emitter Countries
Bibliographic record
Abstract
Global warming has become one of the most serious world-challenging issues nowadays, and much effort is being done to combat its consequences. Therefore, studying the trade-off between carbon emissions and economic activity remains an attractive subject for researchers. In this study, the environmental Kuznets curve (EKC) hypothesis is adopted to verify the trade-off between carbon dioxide emissions per capita and labor productivity in the top 40 emitter countries. Accordingly, a panel data from the top 40 emitter countries is employed from 1992 to 2018, and the novel method of moments quantile regression (MMQREG) is used to analyze the nexus among the variables. In addition, four robustness tests were used to validate the initial results. The findings reveal evidence for the N-shape EKC in the top 40 emitter countries. This indicates that economic growth initially will improve environmental quality up to a certain labor productivity level. However, after reaching a certain turning point, per capita CO2 emission began to fall with rising labor productivity up to the second tipping point, and then, a subsequent phase of deterioration. Heterogeneous characteristics are, however, detected over the N-shape EKC. Like the conclusion reached from the MMQREG, the pooled ordinary least squares (POLS), the fixed-effects (FE), the random-effects (RE), and the fully modified ordinary least squares (FMOLS) all confirmed the existence of the N-shape hypothesis.
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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 | 0.004 | 0.011 |
| 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 | 0.001 | 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".