A panel study of water recirculation in manufacturing plants
Bibliographic record
Abstract
Manufacturing plants routinely recirculate water to meet their process and cooling needs. This ability and willingness to recirculate water distinguishes manufacturing plants from most households and agricultural producers. The motivation for this research is to investigate the factors influencing manufacturing plants’ water recirculation decisions. The paper analyses a unique, balanced panel dataset of 2725 manufacturing plants that responded to the 1986, 1991 and 1996 Industrial Water Use Surveys. Investigation of the raw data shows that manufacturing plants routinely start and stop recirculation activities. Building on previous analysis based on only the 1996 dataset, a statistical model is developed to explain observed variations in the volume of water recirculated and water recirculation intensity (recirculation relative to intake) across the three time periods. Specifically, this paper applies a Heckman two-stage estimation procedure that jointly considers two facets of firms’ recirculation behaviour: first, the discrete decision of whether to recirculate and, second, the decision of how much to recirculate. Potential endogeneity of internal input costs is addressed through instrumental variables. Explanatory variables include the scale of operations, water-use costs and dummy variables that account for plants’ location and technology. Results indicate that water use costs, the scale of operations and the need to treat water prior to its use are important determinants of water recirculation decisions. This paper concludes by considering the policy implications of the empirical findings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".