Is Plastic Bag Ordinance Effective? Evidence from Carbon Emissions in China
Bibliographic record
Abstract
China has enacted the ordinance of plastic shopping bags since June 1 of 2008, which stipulates the bag thickness should be greater than or equal to 25 micrometers and consumers should pay for each bag they consume. However, the overall effect of the ordinance is lower-than-expected. This paper qualifies the carbon emissions associated with plastic shopping bag consumption in a fifteen year time series. The results indicate that, except for 2008, carbon emissions are on a continuous increase during this period. With respect to carbon emissions in 2008, there does appear a local minimum. Specifically, the carbon emissions relating to plastic bags in 2008 dropped 0.87% when compared with the emissions of 2007. This is not to suggest that the ordinance is effective in the long term, just that there is a slight carbon emission reduction in 2008. The transitory reduction of carbon emissions should be more attributed to the impact of Beijing Olympic Games than the effectiveness of the ordinance in hindsight. From 2009 on, the amount of carbon dioxide emissions generated by plastic shopping bags grows by 0.7-0.8% annually. Also, a two-child policy has taken into effect since 2011 which results in the net increase rate of entire population by 0.05% annually. When taken Beijing 2008 Olympic Games as well as the two-child population policy effect into consideration, it thus implies that the plastic bag ordinance may not be much effective to curb the bag usage in the long run. The theoretical and practical implications of the findings are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".