Eco-efficiency Assessment in Agriculture: A Literature Review Focused on Methods and Indicators
Bibliographic record
Abstract
Combining economic performance with environmental and social concern has been a developing topic in recent decades. Eco-efficiency analysis is a widely applied tool to assess the efficiency of agricultural systems, while increasingly considering their environmental and social impact. The main objective of this article is to accomplish a literature review on the application of eco-efficiency analysis in agricultural systems, focusing on methods and indicators that are most regarded for the quantitative assessment of agricultural eco-efficiency. The literature review found two main methods most widely applied to assess eco-efficiency: Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA), which are often combined. LCA is generally focused on the assessment of the environmental impacts of products and practices. DEA is mostly used to measure the eco-efficiency of decision-making units, such as farms, regions, or countries, and has no subjective focus on neither technical nor environmental performance. Both methods share a wide range of economic and environmental indicators but fail to incorporate the social dimension of sustainability into the eco-efficiency analysis. A simple framework, based on Data Envelopment Analysis, is offered to assess the eco-efficiency of the Brazilian agriculture, aiming at identifying the benefits and limitations of the analysis.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".