Environmental Performance and a Nation’s Growth: Does the Economic Status and Style of Governance of a Country Matter?
Bibliographic record
Abstract
The literature abounds with studies on the impact of the growth of nations on the environment. However, studies on the financial materiality of environmental concerns are found less often. This study aims to determine the impact of environmental concerns on a nation’s GDP per capita (GDPC). In addition, the influence of developed nations and democracy is also explored. The data for 106 countries and ten years (2011–2020) are procured from World Bank’s official website. The countries with incomplete data for a balanced panel are not included. Panel data econometrics (quantile regression) is applied to analyze the data. Environmental concerns are measured with the help of environmental efficiency (EE) using data envelopment analysis (DEA). It is found that environmental efficiency (EE) negatively impacts the GDPC for low levels of GDPC. However, no association of EE with GDPC is witnessed in the case of high GDPC levels. In addition, developed nations positively moderate the EE’s impact on the GDPC when the GDPC levels are high. Moreover, democratic nations negatively moderate the EE’s impact on the GDPC when low GDPC levels exist. The main implication of the current study is that developed high GDPC countries could bear a significant chunk of the cost of EE. This way, the adverse impact of an increase in EE on the GDPC (by low GDPC counties) could be dodged, and by the efforts of developed high GDPC countries, EE could be increased significantly without adversely impacting their GDPC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".