Bad aim: Separating the intentions and effects of American newsprint recycling legislation on landfill space, forest conservation and Canadian newsprint producers
Bibliographic record
Abstract
This paper argues that the American newsprint recycling laws passed during the late 1980s and early 1990s not only failed to achieve their stated environmental objectives, but failed so spectacularly that they actually contributed to further environmental harm. These laws, which imposed a recycled content standard on new newsprint production, had three intentions: to decrease landfill space requirements, to preserve forests, and to encourage the recycling of used newsprint. Insofar as the first two intentions are concerned, this paper finds that the American newsprint recycling laws had a negligible effect on both landfill space and forest conservation. But by succeeding in elevating the amount of newsprint recycling far beyond what it would otherwise be, industry compliance with these laws may have actually increased environmental harm. From a Canadian perspective, these laws essentially encouraged Canadian newsprint producers to import American newsprint waste. In addition, by imposing compliance costs on American and Canadian newsprint producers in the range of several billions of dollars, these laws prevented capital from being productively spent on other environmental initiatives. Surveying alternatives to government mandated recycling legislation, this paper ends by concluding that if governments wish to improve environmental outcomes through newsprint recycling, their best option may be to extend the functioning of markets.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".