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Record W2092169836 · doi:10.1353/cpp.2012.0019

Promoting Pollution Prevention in Small Businesses: Costs and Benefits of the “Enviroclub” Initiative

2012· article· en· W2092169836 on OpenAlexaffvenueabout
Paul Lanoie, Alexandra Rochon-Fabien

Bibliographic record

VenueCanadian Public Policy · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsProfitability indexBusinessGovernment (linguistics)Value (mathematics)Order (exchange)Political scienceEnvironmental economicsEnvironmental planningEnvironmental resource managementGeographyEconomicsFinance

Abstract

fetched live from OpenAlex

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 10-15 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 the governments' perspective.We conclude that, whichever perspective we choose, the Enviroclub initiative has been highly profitable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.241
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2012
Admission routes3
Has abstractyes

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