Environmental Impact, Export Intensity, and Productivity Interactions: An Empirical Index Analysis of the Agri‐Food Industry in Spain
Bibliographic record
Abstract
Recent studies have focused on how exports affect environmental performance or vice versa, while others have concentrated on the productivity of export‐oriented firms. The objective of the present paper is to provide a simultaneous analysis on the aforementioned relationships. The approach followed is a composite equation model in which export performance and productivity components, including an environmental indicator, are endogenously determined, taking as reference exporting firms of the food industry in Southeast Spain, for the period 1994–2006. The results show positive interactions among export intensity, firm efficiency, and environmental productivity, also suggesting that the consideration of endogeneity may enhance the findings of analyses on these issues. Des études récentes se sont concentrées sur les effets des exportations sur la performance environnementale ou vice‐versa, tandis que d’autres se sont concentrées sur la productivité des entreprises à vocation exportatrice. Le présent article vise à fournir une analyse simultanée des liens précités. Nous avons utilisé un modèle àéquations multiples dans lequel les éléments liés à la performance des exportations et à la productivité, y compris un indicateur environnemental, sont déterminés de façon endogène, en prenant comme référence des entreprises agroalimentaires à vocation exportatrice situées dans le sud‐est de l’Espagne, durant la période de 1994 à 2006. Les résultats montrent des interactions positives entre l’intensité des exportations, l’efficacité des entreprises et la productivité environnementale, ce qui autorise à penser que l’endogénéité pourrait améliorer les résultats des analyses sur ces points.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".