System analysis perspectives : lead-acid battery recycling in British Columbia, Canada
Bibliographic record
Abstract
This dissertation aims to use a system thinking approach to describe and evaluate the Lead-Acid Battery Recycling Program in British Columbia, compare it with other provincial regulated recycling programs and identify strategies on how it can be improved. The research is presented in the manuscript based format, comprised of four interrelated chapters. Following the introduction, chapter 2 describes a multiple regression analysis to assess how various factors identified by informed stakeholders have contributed to recycling rate in 14 transportation zones from 1995 to 2005. This study demonstrates that the existing recycling scheme ineffectively promotes recycling as it has achieved an average of 75% over the past 13 years with large fluctuations among transportation zones. The regression also shows that recycling rate of transportation zones are not highly influenced by LME lead prices and Transportation Incentive (which can be explained by the strong market power of the recycling plants responsible for setting up the price of scrap lead to which the collectors respond). Chapter 3 identifies key components that influence the performance of varied recycling systems based on a comparative analysis of provincial recycling systems informed by expert interviews. In chapter 4, comprehensive evaluation criteria for the lead-acid battery recycling program is developed based on objectives and performance measures elicited through an extensive stakeholder consultation process with various individuals and organizations. Fundamental objectives identified by stakeholders include: reduce environmental impacts, reduce occupational health impacts, reduce net costs, increase equity in resource consumption patterns and increase systematic learning. In chapter 5, we use multiple criteria decision analysis (MCDA) to design and assess effective recycling strategies to meet societal objectives previously identified in the chapter 4. Recycling strategies were compiled using the results of chapter 3. The results reveals that the optimal policy for the lead-acid battery recycling system combines a return to retailer program financed through an advanced disposal fee included in the battery price in combination with increased plant or recycling capacity domestically. This research also provides relevant contributions to the refining and application of value-focused thinking and decision analysis methodologies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".