Harnessing the Power of EKC and RKC: A Sustainable Development Perspective
Bibliographic record
Abstract
Objectives: The primary objective of this study is to examine the validity and applicability of the Environmental Kuznets Curve (EKC) theory within the framework of the Resource Kuznets Curve (RKC). Specifically, the study aims to analyze empirical evidence and underlying factors to understand the relationship between environmental degradation and income levels across six major economies: Germany, France, Japan, Canada, UK, and US, spanning the period of 1961–2018. Methods: To achieve the objectives outlined, this study utilizes empirical analysis techniques. Data from the specified economies are collected and analyzed to discern patterns and relationships between environmental degradation, income levels, and other relevant variables. Statistical methods and econometric modeling are employed to evaluate the shape and dynamics of the relationship, allowing for a comprehensive understanding of the complexities involved. Results: The analysis reveals both an N-shaped and a U-shaped pattern in the relationship between environmental degradation and income levels across the selected economies. These findings suggest that the relationship between environmental degradation and economic development is multifaceted and nonlinear, indicating the presence of critical thresholds and turning points. Furthermore, the study highlights the importance of clean energy consumption and renewable energy adoption in mitigating pollution and fostering sustainable economic growth. Conclusion: The findings of this study contribute to the ongoing debate surrounding the Environmental Kuznets Curve (EKC) theory within the context of the Resource Kuznets Curve (RKC). The identification of an N-shaped and a U-shaped pattern underscores the need for nuanced policy interventions aimed at balancing economic development with environmental sustainability. Policymakers and stakeholders can utilize these insights to formulate effective strategies for promoting clean energy adoption, reducing pollution, and fostering long-term environmental quality and economic growth.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".