Occupational Health Impacts Due to Exposure to Organic Chemicals over an Entire Product Life Cycle
Bibliographic record
Abstract
This article presents an innovative approach to include occupational exposures to organic chemicals in life cycle impact assessment (LCIA) by building on the characterization factors set out in Kijko et al. (2015) to calculate the potential impact of occupational exposure over the entire supply chain of product or service. Based on an economic input-output model and labor and economic data, the total impacts per dollar of production are provided for 430 commodity categories and range from 0.025 to 6.6 disability-adjusted life years (DALY) per million dollar of final economic demand. The approach is applied on a case study assessing human health impacts over the life cycle of a piece of office furniture. It illustrates how to combine monitoring data collected at the manufacturing facility and averaged sector specific data to model the entire supply chain. This paper makes the inclusion of occupational exposure to chemicals fully compatible with the LCA framework by including the supply chain of a given production process and will help industries focus on the leading causes of human health impacts and prevent impact shifting.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; both teacher heads agree on what is shown here.
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".