Unraveling the environmental Kuznets curve: The influence of economic diversity, energy efficiency, and industrial dynamics on carbon emissions in developing economies
Bibliographic record
Abstract
A new path of economic development among emerging and developing nations has a distinct impact on the environment than seen in the past. The current study attempts to examine how these growth patterns in the developing world have impacted the degradation of the environment. This study contends that merely considering GDP per capita and the proportion of manufacturing in GDP fails to encapsulate the complete growth dynamics of developing and emerging countries. Consequently, such an approach does not adequately reflect the impacts of environmental degradation. As a result, the economic complexity index (ECI) is introduced to the model to reflect the full effects of new growth trajectories on CO 2 emissions by using the Panel Fully Modified OLS (PFMOLS) model of 67 emerging and developing countries during 1996–2020. The results indicate that the complexity of developing and emerging economies, on the one hand, raises CO 2 emissions, likely through expanding economic activities (the scale effect). Moreover, ECI reduces CO 2 emissions by moving the economy toward more high-tech and environmentally friendly technologies and industries and favorable changes in the energy mix (the efficiency effect). Overall, the empirical outcomes emphasize that the final impact of ECI on the environment was negative in most samples, indicating an improving impact of economic complexity on environmental degradation, reflecting that the “efficiency effect” outweighed the “scale effect.” The findings imply that technology and knowledge transfer are essential for energy efficiency and sustainability.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".