The role of economic freedom in achieving the environmental sustainability for the highest economic freedom countries: testing the environmental Kuznets curve hypothesis
Bibliographic record
Abstract
In the study, the relationship between ecological footprint, economic freedom, renewable energy consumption, and economic growth is analysed under the scope of environmental Kuznets curve (EKC) hypothesis. Here, fifteen countries with the highest economic freedom for the period 1996-2018 are selected to more clearly underline the impact of economic freedom on the environment and examined, i.e., Australia, Canada, Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Singapore, South Korea, Sweden, and Switzerland. The long-term relationship between the variables is examined using the panel cointegration test. According to the test results, it has been observed that the variables in the EKC model act together in the long run. According to the long-term estimation results, it is seen that economic freedom decreases the ecological footprint, namely, environmental degradation, in Canada, while it increases in Estonia. Furthermore, it is concluded that renewable energy reduces the ecological footprint in Australia, Denmark, Luxembourg, Norway, Singapore, and Switzerland. Nevertheless, it has been determined that the EKC hypothesis is valid for Canada, Denmark, and Singapore, but not for other 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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".