The Judgment of Garbage: End-of-Pipe Treatment and Waste Reduction
Bibliographic record
Abstract
Many scholars have argued that systems for treating waste impede organizations from preventing waste in the first place. They theorize that “end-of-pipe” (EOP) treatment diminishes the incentive to avoid creating waste in the production process, and obscures the information necessary to devise prevention techniques. This prediction has been accepted widely, influencing both policy and practice, despite both a lack of supporting empirical evidence, and the existence of a counter-prediction. In this paper, we use data describing U.S. manufacturing establishments from 1991 to 2005 to test the link between EOP treatment and waste reduction. Our findings show that EOP treatment is associated with an initial jump in reported waste, followed by ongoing reduction. We analyze these results by exploring mechanisms that may drive this relationship. For practitioners, our paper provides critical guidance about strategies for reducing waste. For scholars of environmental management, our paper provides new insight on when facilities accomplish “source reduction” of process waste. For broader management theories of operations and organizational design, our analysis provides new insight on boundary conditions for extrapolation from existing theories. Finally, our paper provides new guidance for the formulation of effective regulatory policy.
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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 | 0.030 |
| 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 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".