Promoting Pollution Prevention in Small Businesses: Costs and Benefits of the “Enviroclub” Initiative
Bibliographic record
Abstract
The Enviroclub initiative was developed by three federal government agencies—Canada Economic Development for Quebec Regions, Environment Canada, and the National Research Council Canada—and launched in 2001 to assist small and medium-sized enterprises (SMEs) in improving their profitability and competitiveness through enhanced environmental performance. An Enviroclub consists of a group of 10 to 15 SMEs involved in training sessions on environmental management and carrying out at least one profitable in-plant pollution prevention project. The objective of this article is to provide a cost-benefit analysis (CBA) of this original initiative in order to inform policy-makers as to the social desirability of such programs. One of the main social benefits of this initiative is to reduce emissions of various pollutants, so that one of our largest challenges is to place a value on these environmental improvements. To do so, we use the “environmental value transfer” method to obtain values from previous relevant studies. We conduct our CBA at three different levels: we consider the costs and benefits first for the whole of society, then from the participating firms' point of view and, finally, from governments' perspective. We conclude that, whichever perspective we choose, the Enviroclub initiative has been highly profitable.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".