Impact of the Implementation of Environmental Management Systems in Agribusiness Worldwide. A Systematic Review
Bibliographic record
Abstract
The main objective of this research was to determine the most relevant impact on the implementation of Environmental Management Systems (EMS) in agribusiness worldwide according to the most recent research work published. For this, the methodology of systematic review of the literature was used, considering IOP SCIENCE, PROQUEST and SCOPUS DATABASES, and information search strategies under inclusion and exclusion criteria.The results obtained were 38 articles, in which the impacts derived from the application of environmental management systems in agroindustrial activity were evidenced, identifying economic, environmental, and social impacts and research published by country, among which Indonesia, Brazil, Colombia and Greece stand out.It is concluded that the most relevant impact in the environmental field was the reduction and control of emissions, effluents, waste, as well as the reduction of the use of agrochemicals including pesticides and fertilizers.In the economic field, the increase in revenues was reported due to the opening of global markets and increase in product sales prices, reducing energy consumption and reducing losses due to waste, improving productivity, as well as reducing expenses for the application of sanctions due to environmental accidents and waste treatment.In the social sphere, it is reported that recycling practices are adopted, awareness is raised and the knowledge and capacities of stakeholders for environmental care and protection are improved.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".