Eco-efficiency and convergence in OECD countries
Bibliographic record
Abstract
This paper assesses the convergence in eco-efficiency of a group of 22 OECD countries over the period 1980-2005. In doing so, three air-pollutants representing the impact on the environment of economic activities are considered, namely, carbon dioxide (CO2), nitrogen oxides (NOX) and sulphur oxides (SOX); furthermore, eco-efficiency scores at both country and air-pollutant-specific levels are computed using Data Envelopment Analysis techniques. Then, convergence is evaluated using the recent approach by Phillips and Sul (2007), which allows testing for the existence of convergence groups. First, we find that, with the exception of NOX emissions, eco-efficiency has improved over the period, the greatest progress corresponding to CO2 emissions. Second, Switzerland is the most eco-efficient country, followed by some Scandinavian economies such as Sweden, Norway, Iceland and Denmark. In contrast, European Mediterranean countries such as Portugal, Spain and Greece, in addition to Hungary, Turkey, Canada or the US, are among the worst performers. Finally, we find that both the most eco-efficient countries and the worst-performing countries also tend to form clubs of convergence among them.
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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 | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".