THE EFFECT OF DEMOGRAPHIC STRUCTURE ON CARBON DIOXIDE (CO2) EMISSIONS: TOP EMITTERS CASE STUDY
Bibliographic record
Abstract
This study aims to analyze how the demographic structure affects carbon dioxide (CO2) emissions in Top Emitters, namely China, the United States, the European Union (EU-28), India, Indonesia, Russia, Brazil, Japan, Canada, and Mexico. This study uses panel data from ten countries stated in Top Emitters for the period 2000-2014 sourced from the World Resource Institute, World Bank and UNESCO Institute for Statistics. This study uses the Panel Data Regression method with the best model chosen is the Random Effect Model (REM) and four demographic structure variables, namely the dependency ratio, sex ratio, higher education ratio, industrial employment ratio. The results of this study indicate that the dependency ratio, sex ratio, higher education ratio, industrial employment ratio have a significant effect on carbon dioxide (CO2) emissions in Top Emitters. The results of this study are expected to provide policies that can be implemented by the government.Keywords: Demographic Structure, Top Emitters, Panel Data Regression MethodJEL : I25, O15, Q5
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".