ENVIRONMENTAL KUZNETS CURVE: THE CASE OF CANADA, SPAIN AND INDONESIAN ON CO2EMISSION
Bibliographic record
Abstract
The Environmental Kuznets Curve is used to investigate the relationship between various indicators of environmental degradation and income per capita. The economic growth measured from the change of income per capita contributes negative externalities to nature, and education contributes to better knowledge for sustainable development. The purpose of this research was to apply the Environmental Kuznets Curve to see the impact of income per capita and education on environmental degradation. The objective of this research was to examine how GDP per capita and education contribute to per capita CO2emission in Canada, Spain, and Indonesia. The research output showed a relationship between per capita GDP with per capita CO2emission in Canada and Spain. Contribution of per capita GDP to per capita CO2emission of Canada higher than Spain. The higher per capita GDP will rise per capita CO2emission. The per capita GDP of Indonesia does did contribute significantly to per capita CO2emission. The was also a significant relationship between education quality and per capita CO2emission in Canada, Spain, and Indonesia. The better education quality in Canada and Spain contribute to lower per capita CO2 emissions. Education quality in Indonesia contributed to the higher per capita CO2emission. Keywords:Environmental Kuznets Curve, Per Capita CO2 Emission, Per Capita GDP, Education
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".