Circuits of scrap: closed loop industrial ecosystems and the geography of US international recyclable material flows 1995–2005
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The recycling of scrap material has been identified as an important strategy in the larger theory of industrial ecology. Industrial ecology argues that the traditional model of industrial activity needs to be transformed into a ‘closed loop’ industrial ecosystem where used materials (scrap) and by‐products would substitute for virgin materials during production processes. The recycling of scrap material forms part of this larger effort to reduce the overall environmental impact of production and consumption. A key, but as yet, unresolved question in this process is the geographic scale (local, regional, national, global) at which loop closing should take place. This preliminary empirical research examines the export and import geography of the seven largest (by weight) US scrap commodities (iron and steel, paper, plastics, aluminium, copper, nickel and zinc) between 1995 and 2005 to ascertain the extent to which US scrap flows overseas and how that might affect our understanding of how material loops can close. Other than an integrated export and import relationship with Canada, the results suggest that there are two distinct circuits of scrap flows in the USA. The USA exports a substantial portion of the recyclable scrap generated each year to rapidly developing countries, while importing smaller quantities of scrap from the EU. With the major exception of exporting higher value iron and steel scrap to China, the US tends to export lower value scrap and import higher value scrap. In part this reflects imbalances in the supply and demand for scrap between the USA and the developing world, the lack of potentially available scrap and the absence of a robust recycling infrastructure in the developing world. Although such scrap circuits are probably not ideal, the use of US scrap in the developing world is both a realistic and preferable alternative in the short to medium term than virgin production.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it