Global Analysis of the Relationship between Environmental Performance, Economic Development, and the Innovation Index Through the Kuznets Environmental Curve
Bibliographic record
Abstract
This study investigated the interaction between GDP per capita, technological innovation, and environmental performance in 102 countries during the period from 2008 to 2022, considering the hypothesis of the Kuznets Environmental Curve (EKC) and the role of innovation. The aim was to contribute to the understanding of the complex relationships between economic growth, innovation, and environmental sustainability, highlighting the ongoing importance of innovation in achieving balanced development. By applying the traditional Kuznets curve, two significant inflection points were identified in the relationship between GDP per capita and environmental performance, revealing an inverted N-shape. However, upon introducing the innovation variable, we conducted an analysis from the perspective of the modified EKC, incorporating the Claudia Innovation Curve (CIC) and adding a more complex dynamic. According to this innovation-modified curve, two additional inflection points were identified. The first suggests that an increase in innovation, up to a certain point, may lead to a decline in environmental performance, due to the adoption of advanced technologies and increased income. However, the second point indicates a notable improvement in countries with a higher degree of innovation, highlighting the importance of policies that encourage sustainability-focused innovation.
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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 | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".