Asymmetric Effects of Financial Development on CO2 Emissions in Bangladesh
Bibliographic record
Abstract
Depending on how it functions and is organized, the financial system can have a negative, positive, or zero impact on the environment. For Bangladesh, the empirical relationship between financial development and the environment, measured in terms of carbon dioxide (CO2) emissions per capita, is analysed over the period 1980 to 2020. This is the first such analysis for this country. We perform this within a non-linear bound testing framework while controlling for changes in energy consumption, gross domestic product, and trade volume. There are two key findings. One, we find that the relationship between CO2 emissions per capita and financial development is cointegrating, with the direction of cointegration running from financial development to CO2 emissions. Two, we find that positive and negative changes in financial development have asymmetric impacts on CO2 emissions in the long and short run. The implications of these findings are discussed regarding their attendant environmental policy implications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".