Bibliographic record
Abstract
Bans, taxes, and hybrid ban-tax policies can each be effective at reducing plastic bag consumption, but bans and ban-tax policies tend to attract more opposition from stakeholders and have been argued to do less to change patterns of consumer behaviour regarding bags than tax-based policies. For these reasons my research concludes that while bans and ban-tax policies can be effective, a tax-based plastic bag management policy is the best practice for reducing plastic bag consumption in an urban space. Managing the consumption of single-use plastic bags through restrictive policies is a way for governments to reduce litter and divert garbage from their waste stream. Reducing the consumption of plastic bags will also lower the rates of their production, drawing less on a non-renewable resource already in high demand. This report aims to analyze plastic bag management efforts in urban spaces to identify what can be considered the best management practices for reducing their consumption. An effort is made to connect the best plastic bag management practices with the context of Metro Vancouver, a region that is trying to reduce its waste but currently has no policies regarding plastic bags. The analysis of plastic bag policies in this report is informed primarily by reviewing research on successful and failed plastic bag management policies around the world. Research into the areas of consumer behaviour (specifically modifying consumer behaviour) and the power of government communication as a tool to influence citizens towards following policies have also been useful to flush out some of the workings of legislated restrictive policies. In addition to peer reviewed material, a substantial amount of grey literature (including city council minutes, municipal and federal government reports, environmental impact reports, news publications, and trade journals) was reviewed to investigate the details of plastic bag management practices that have not been so widely studied. This grey literature also helped to identify the status and barriers of plastic bag management practices in Canada, which so far have not been researched thoroughly. My research has found that the best practices for reducing plastic bag consumption are government imposed policies that can change consumer behaviour towards an anti-plastic bag mentality, are supported (or at least are not opposed to) by industry and public stakeholders, and are actually effective at reducing plastic bag consumption.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".