Bibliographic record
Abstract
Brock and Taylor (2010) argue that the Environmental Kuznets Curve (EKC) - a hump shaped relationship between emissions and income per capita - is driven by falling GDP growth rates associated with Solow type convergence. I test the importance of their mechanism as a driver of emissions by performing a "pollution accounting" exercise that decomposes emissions data into pollution intensity and GDP growth e ects. The "Green Solow" framework assumes that emission intensities decline at a constant rate and hence that all changes in emissions growth rates are driven by changes in GDP growth rates. Yet, in the data, emission intensities are hump shaped for a wide range of countries and pollutants, implying declining emission intensity growth rates. Furthermore, this decline in emission intensity growth rates is an order of magnitude larger than changes in GDP growth rates. The Green Solow model - which assigns all the weight to changing GDP growth and ig- nores changes in emission intensity growth in its explanation of emissions - cannot be the right way to think about emissions profiles of countries. Models that aim to explain the EKC, must - first and foremost - explain the hump shape intensity curve and hence falling intensity growth rates. I suggest a simple model of structural transformation as one possible mechanism capable of generating both a hump shaped EKC curve and a hump shaped emission intensity curve.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".