The Nexus of Income Inequality, Growth, and Environmental Degradation in ASEAN Economies
Bibliographic record
Abstract
This research aims to examine income inequality, growth and environmental degradation and their determinants in ASEAN countries by focusing on lower middle income countries during 2010-2022 by applying a simultaneous equation approach.The main finding in this research is that there are endogenous influences on each other, including income inequality and growth, as well as income inequality and environmental degradation.Other findings in this research include, the first includes the analysis that income inequality is negatively and significantly influenced by growth and environmental degradation.Meanwhile, unemployment and human resources have a positive and significant effect.Second, growth analysis is negatively and significantly influenced by income inequality, while capital investment has a positive and significant influence.Third, environmental degradation analysis is positively and significantly influenced by income inequality, growth and number of industries, while renewable energy consumption has a negative and significant influence.The recommendation from this research is to produce policy implications based on variables that have a significant influence on the issues of income inequality, growth and environmental degradation to achieve sustainable development.
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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 | 0.001 | 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".