International environmental performance measurement in the electronics industry
Bibliographic record
Abstract
The electronics industry is under increasing pressure to reduce its environmental impact. Assemblers demand that component manufacturers document their environmental performance. Performance measures are needed to sort out good from bad environmental performance and practices. We present an approach to characterize the environmental burdens associated with materials, products and processes based on material use and environmental discharge data from manufacturing facilities. Each chemical is weighted by its toxicity, a first order approximation to the health burden on the population. We illustrate this method with a time trend analysis for the electronics industry, using data from the US Toxics Release Inventory (TRI) and the Census of Manufacturers. We find that the sector's environmental emissions are showing a downward trend. Comparable data availability in other countries (e.g., Canada and the Netherlands) are discussed. We examine the possibility of international environmental performance measurement and benchmarking using discharge data from different countries. The limitations of the approach are mentioned. Our indices provide information to electronics firms in making decisions about environmental control investments and plant performance. They facilitate internal and external benchmarking and allow for better environmental information about the industry.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".