Effect of Relative Redistribution on Environmental Pollution in Oil-Exporting Countries
Bibliographic record
Abstract
Objective: Recent literature emphasizing the importance of income distribution for pollution. The GINI index is the most common indicator for measuring income inequality in previous studies. The new literature has focused on relative redistribution, which is quantified by the GINI coefficientis defined as the difference between the GINI based on market income and GINI based on disposable income. Thus, this study relies on the redistributive effect of taxes and transfers and its impact on carbon dioxide emissions.Methods: The study is conducted using aggregated data from oil-exporting countries including Canada, the United States, the UK, Mexico, the Netherlands, Russia, and China between 2010 and 2020 by using a simultaneous equations system consisting of two equations so that economic growth and pollution emission are as endogenous variables. Elative redistribution, good governance, oil income, trade openness, and CO2 emission are the exogenous variables. Results: Based on model estimates, income inequality, good governance, and oil income have a positive and significant impact on economic growth over the years studied, while inequalities in human development and population growth rates have a negative impact. Economic growth and trade openness also have a negative and significant impact on the spread of pollutionConclusion: Taxes and transfer payments, as redistribution tools can stimulate economic growth. Relative redistribution as a more equitable way can lead to increased economic growth and economic growth reduces carbon dioxide emissions. Therefore, providing an appropriate standard of income inequality can help to better understand and formulate effective policies for income equality.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".