Enhancing environmental performance: evidence from SAARC Countries
Bibliographic record
Abstract
This paper examines the influence of population density, economic growth, and regulatory quality on the environmental performance of five SAARC countries from 2000 to 2020. To this end, fixed and random effects models are used. Quantitative data on socio-economic and environmental characteristics reveals notable differences across countries. The results show a significant positive and negative impact of the covariates economic growth and population density, respectively, on environmental performance. While the estimated coefficient of the regulatory quality variable was positive, it was statistically insignificant. The results also suggest a significant inter-country dependency and a significant change in environmental performance across countries over time. Based on the results, several strategic approaches are proposed at both individual and multi-country settings. Furthermore, we emphasize the roles of the government, international and regional agencies, private sector, and civil society organizations. Finally, possible extensions of the present paper are discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.009 |
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; both teacher heads agree on what is shown here.
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".