GREEN ECONOMY AND GROSS DOMESTIC PRODUCT IN 5 OECD MEMBER COUNTRIES (THE UNITED KINGDOM, UNITED STATES, GERMANY, FRANCE, CANADA)
Bibliographic record
Abstract
The green economy is a global concern in sustainable development as it provides an approach that combines economic growth with environmental conservation. However, not all countries have managed to optimize the contribution of the green sector to economic income. This study aims to analyze the effect of several green economy indicators and other economic factors such as renewable energy, carbon emissions, nuclear energy, population, and foreign direct investment on Gross Domestic Product (GDP) in five OECD member countries in 2008-2023. This study uses quantile panel regression analysis tool. The regression results show that renewable energy variables are only significant in the middle and upper quantiles (0.50 and 0.75), while carbon emissions and population have significant positive effects in all quantiles, namely low, medium and high. Nuclear energy has a significant negative effect, while FDI has no effect and tends to have a negative impact on GDP. The contribution of green economy variables to economic growth varies depending on the income level of the country. Therefore, green economy policies need to be tailored to the economic characteristics of each country to be more effective in promoting sustainable growth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".