On income inequality and CO2 emissions in Bangladesh
Bibliographic record
Abstract
• This is the first study that examines the relationship between income inequality and CO 2 emissions in Bangladesh. • In the long run, on average, income inequality tends to increase CO 2 emissions. • We use the political economy approach and the Veblen effect hypothesis to explain our results. • Policymakers could focus on redistributing income to address the widening income gap and emissions in Bangladesh. The United Nations called for a holistic approach to successfully achieve the sustainable development goals. In this study, we examine the association between two of those sustainable development goals, namely income inequality and emissions. More specifically, we analyze if income inequality is dynamically related to per capita CO 2 emissions in Bangladesh. We apply the autoregressive distributed lags technique while accounting for other important factors including national income, price, and urbanization. The dataset used in this study covers the period from 1980 to 2021. The results suggest long run cointegrations, running from income inequality to CO 2 emissions. Importantly, a one percent increase in the income share of the top 1% tends to increase per capita CO 2 emissions by 0.52%. These findings are consistent with the political economy theory and the Veblen effect hyopthesis. We provide policy suggestions which are relevant to Bangladesh and other developing countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".