Deforestation and the Environmental Kuznets Curve in Developing Countries: A Panel Smooth Transition Regression Approach
Bibliographic record
Abstract
Deforestation is a serious environmental problem in many developing countries. This study re‐examines whether the Environmental Kuznets Curve (EKC) relationship between deforestation and real income exists for 52 developing countries during the 1972–2003 period by applying the recently developed panel smooth transition regression (PSTR) model. This paper also considers the potential endogeneity biases and other explanatory variables as a robustness check of the EKC hypothesis. The empirical results indicate the existence of a strong threshold effect between deforestation and real income, and that evidence of the EKC hypothesis for deforestation is apparent. Along with an increase in real income, deforestation increases initially, and after reaching certain income levels, deforestation drops. The turning points are US$3,021 and US$3,103, which the PSTR model endogenously determines. La déforestation constitue un problème environnemental préoccupant dans de nombreux pays en développement. Dans le présent article, nous avons tenté d’établir s’il existe ou non une relation en U inversé (courbe de Kuznets) entre la déforestation et le revenu réel, en appliquant la nouvelle méthode d’estimation d’effets de seuil avec transition lisse en panel (PSTR) à un échantillon de 52 pays en développement au cours de la période de 1972 à 2003. Nous avons également examiné les biais endogènes possibles et d’autres variables explicatives pour vérifier la robustesse de l’hypothèse de Kuznets. Les résultats empiriques montrent qu’il existe un effet de seuil robuste entre la déforestation et le revenu réel, et que l’évidence de l’hypothèse de Kuznets dans le cas de la déforestation est apparente. Lorsque le revenu réel augmente, la déforestation augmente aussi, mais lorsque le revenu atteint certains niveaux, la déforestation diminue. Les points de retournement sont de 3 021 $US et de 3 103 $US, ce que la méthode PSTR a déterminé de manière endogène.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".