Manufacturing Firms’ Demand for Water Recirculation
Bibliographic record
Abstract
Relatively little is known about the factors that influence firms’ water recirculation decisions. This paper estimates an econometric model that jointly considers two facets of firms’ recirculation behavior: first, the discrete decision of whether to recirculate and, second, the decision of how much to recirculate. The model is estimated by applying the Heckman two‐stage estimation procedure to cross‐sectional data from Environment Canada's 1996 Industrial Water Use Survey. In the first stage, long‐run factors, such as relative water scarcity and production technologies, are found to influence the decision whether to recirculate water. In the second stage, the imputed prices of intake water and water recirculation as well as the scale of operations are found to influence the choice of the optimal quantity of water to recirculate. Les facteurs qui influencent les décisions des entreprises concernant le recyclage de l’eau sont assez peu connus. Dans le présent article, nous avons estimé un modèle économétrique qui tient compte de deux aspects du comportement des entreprises envers le recyclage de l’eau : le choix de recycler l’eau ou non et le choix de la quantité d’eau à recycler. Nous avons estimé le modèle à l’aide de la procédure en deux étapes de Heckman que nous avons appliquée à des données transversales tirées de l’Enquête sur l’utilisation industrielle de l’eau, réalisée par Environnement Canada en 1996. À la première étape, des facteurs à long terme tels que la rareté relative de l’eau et les technologies de production influent sur la décision de recycler l’eau ou non. À la deuxième étape, les prix imputés du captage et du recyclage de l’eau ainsi que l’envergure des opérations influent sur le choix de la quantité optimale d’eau à recycler.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".