Global insights on validating environmental Kuznets curve using economic complexity and environmental efficiency scores
Bibliographic record
Abstract
Abstract Considering changing climatic conditions and victimized economic growth, the present study uses data envelopment analysis to obtain the environmental efficiency scores and regresses them with the economic complexity index, a proxy for economic development, for 4 panels formulated based on geographical proximity. The panels formed are America (USA, Mexico, Canada); Europe (Germany, UK, France, Poland, Italy, Ukraine); Asia (India, China, Japan, Russia, Iran); and Africa (South Africa, Egypt, Algeria, Nigeria). The timeframe has been restricted to 1995–2023 due to the unavailability of data. The study adopts the PRISMA approach to conduct a systematic literature review. Further, random effect regression and the random coefficient for generalized least squares have been employed in the panel data for estimating regression coefficients. The results obtained from examining the impact of economic growth on environmental efficiency are quite startling. America has an inverted-N-shaped EKC, Africa exhibits a downward-sloping EKC, Europe has an N-shaped Environment Kuznets Curve (EKC), and lastly, Asia has an inverted-U-shaped EKC. The different results arise because of differences in the structure and background of each panel. For instance, America is comprised of three developed capital-intensive countries; Europe has a mix of developing and developed nations under the umbrella of the European Union which has the strongest emission trading system coupled with carbon tax; Asia comprises of labor-intensive developing nations with the exception of Japan and lastly, Africa has developing nations with a proposition of ‘grow now , clean later.’ Therefore, every nation must recognize the shared global goal of sustainability, which can be achieved through collective effort rather than by focusing solely on individual countries becoming clean.
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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 | 0.000 | 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 | 0.001 |
| 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".