The use of efficiency metrics for cross-jurisdictional assessment of household hazardous waste collection and recycling
Bibliographic record
Abstract
Household hazardous waste (HHW) has proliferated with the growing consumption of household products, highlighting the importance of an effective management program. Although industrialized nations have employed collection programs for HHW, their efficiencies are not appropriately assessed. North America currently lacks comprehensive studies on the efficiency of HHW programs. This study introduces two metrics: Collection Ratio (CollectRat) and Recycling Ratio (RecylRat), to analyze the efficacy of HHW collection and recycling. The study develops predictive models for these metrics to identify key household characteristics influencing HHW management practices. Management practices are shifting towards recycling, although reuse remains low, peaking at 20.9 % in California and 10.8 % in Texas. By examining the metrics using American and Canadian datasets, results show that collection rates are higher in highly populated regions, unlike recycling rates. Most Canadian HHW programs have adopted the Extended Producer Responsibility (EPR) framework, while California has recently introduced EPR for certain household products, leading to increased public awareness and improved waste management practices. Findings suggest HHW collection ratio alone does not represent waste recycling well. The rate of collection and recycling depends on household characteristics such as family size, educational attainment, and other factors. The use of efficiency metrics in forecasting models helps to understand trends in HHW management in North America and can be applied to other jurisdictions. • Two efficiency metrics are proposed for cross-jurisdictional HHW program assessment. • California has 3.76 times higher HHW collection rates compared to Texas. • We found that HHW collection and recycling rates depend on household characteristics. • HHW collection rates are higher in populated regions, but recycling rates are not. • Canadian HHW programs have adopted extended producer responsibility framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".