Eco-Efficiency and Smes in Nova Scotia, Canada
Bibliographic record
Abstract
The report in 1987 by the World Commission onEnvironment and Development (WCED) sparked discussion on sustainabledevelopment and the manner in which it can be achieved.The eco-efficiencyof 25 small- and medium-sized enterprises (SMEs) in Nova Scotia, Canada isexplored in addition to considering the challenges to SMEs in attempting toimplement environmental management tools successfully. The firms considered in this analysis were identified through environmentalreviews conducted by the Eco-Efficiency Centre (EEC), a non-profit educationaland environmental management support center for SMEs in Nova Scotia.Avariety of industrial sectors are represented, including food services, metalsmanufacturing, and entertainment. Thirty-five different actions were identified by EEC as applicable toachieving three environmental objectives: (1) reduce the consumption ofresources, (2) reduce the impact on nature, and (3) implement a systematicapproach.The results show that, on average, the companies were involvedin 9 of the 35 actions. Of the actions undertaken, more than 50 percent fell within the objectivesof reducing the consumption of resources and implementing a systematicapproach.This result implies that cost is a stronger influence thanregulations for these SMEs.As a result of the low levels ofeco-efficiency demonstrated by these SMEs, there is much room for improvementin this area. (SRD)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".