Analysis of eco-efficiency progress in industrial production
Bibliographic record
Abstract
An eco-efficient business manages to produce high-quality, profitable goods and services, with an environmental impact compatible with the Earth's carrying capacity. Eco-efficiency is currently in a phase of expansion; numerous environmental management systems and tools, support initiatives, and application experiences now exist. To understand how this strategy is being developed and which driving forces enhance the implementation of eco-efficiency, a comparative analysis of case studies both directly and indirectly related to eco-efficiency is presented in this work. These surveys were undertaken in Canada, United Kingdom, Holland, Finland, Spain and Latin America. In addition, an assessment of eco-efficiency performance in small and medium sized business in Venezuela has been conducted. The first findings of this assessment in Venezuela and their comparison to the other case studies are also discussed in this paper. The comparison enables us to draw conclusions regarding the driving forces of ecoefficiency which are: training and motivation of human resources, the market (costumers, suppliers, and competitors), environmental legislation, production technologies and infrastructures, among others. The conclusions of this research highlight the differences and similarities between regions, establishing cause-and-effect relationships between the driving forces and the evidence of eco-efficiency found, allowing for a better understanding of how to promote eco-efficiency in businesses.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".