Analysis of Fuel Oil Consumption, Green Economic Growth and Environmental Degradation in 6 Asia Pacific Countries
Bibliographic record
Abstract
This study aims to fill the gap of previous research in the form of developing studies between fuel oil consumption, green economic growth and environmental degradation in 6 selected Asia Pacific countries (Australia, China, India, Indonesia, South Korea and Thailand) by considering the determinants during the period 2007-2020 by using a simultaneous panel model approach. The important findings of this study are grouped into 3 analytical models. First, green economic growth, environmental degradation and cleaner energy have a negative effect on fuel oil consumption, while GDP per capita has a positive effect. Second, fuel oil consumption, environmental degradation and militarization have a negative effect on green economic growth, while technological innovation and cleaner energy have a positive effect. Third, green economic growth and cleaner energy have a negative effect on environmental degradation, while fuel oil consumption, health expenditure and poverty have a positive effect. The policy implication that can be applied is to utilize renewable energy such as biofuel oil to implement a clean development mechanism because the increasing demand for fuel oil consumption will result in CO2 emissions which are a factor causing increased environmental degradation and decreased green growth in a country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".