A Stochastic Frontier Model to Assess Agricultural Eco-efficiency of European Countries in 1990–2019
Bibliographic record
Abstract
This paper aims at assessing agricultural eco-efficiency of 40 European countries, including non-European Union and ex-USSR ones, in the period 1990–2019 (30 years). A stochastic frontier model with a panel translog specification is employed to allow technology to change in time and across countries, and both output elasticities and returns to scale to vary with input levels and time. Our study is original compared to existing ones in the literature because it considers the almost totality of European countries and focuses on a long and recent period. As such, it is able to draw an exhaustive and updatedpictureofagriculturaleco-efficiencyinEuropethatfillsbothtemporalandspatialinformationgapsleftbyexisting studies. In our results, countries with a definitely increasing eco-efficiency in the period 1990–2019 are Albania, Croatia, Iceland, Lithuania, North Macedonia, Portugal and Ukraine, while countries with a definitely decreasing eco-efficiency are Cyprus, Czechia, France, Greece, Hungary, Malta, Romania and Slovakia. All other countries have an approximately constant eco-efficiency in the period 1990–2019, ranging, in average, between 0.93 and 0.95, with the exception of two groups of countries: (i) Denmark, Italy, Serbia-Montenegro, Slovenia and Switzerland, which show a decline of eco-efficiency in recent years; (ii) Ireland and Latvia, which exhibit an upward inversion of the trend in the penultimate decade. These two groups of countries should be monitored in the near future to better establish whether the decline or the increase in eco-efficiency is temporary or permanent. Our study also provides, for the first time, evidence on agricultural eco-efficiency in non-European Union transition economies, specifically it emphasizes the promising performance of Albania, North Macedonia and Ukraine.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".