Amazon's Plastic Problem Revealed
Bibliographic record
Abstract
Oceana analyzed e-commerce packaging data and found that Amazon generated 465 million pounds of plastic packaging waste in 2019. This includes air pillows, bubble wrap, and other plastic packaging items added to the approximately 7 billion Amazon packages delivered in 2019. The report also found that Amazon’s estimated plastic packaging waste, in the form of air pillows alone, would circle the Earth more than 500 times. By combining the e-commerce packaging data with findings from a recent study published in Science, Oceana estimates that up to 22.44 million pounds of Amazon’s plastic packaging waste entered and polluted the world’s freshwater and marine ecosystems in 2019, the equivalent of dumping a delivery van payload of plastic into the oceans every 70 minutes. Plastic is a major source of pollution and is devastating the world’s oceans. Sea turtles and other ocean animals mistake the kind of plastic used by Amazon as food, which can ultimately prove fatal. Oceana surveyed more than 5,000 Amazon customers in the U.S., Canada, and the UK in 2020 and found that a vast majority were concerned about plastic pollution and its impact on the ocean. Customers want Amazon and other major online retailers to offer plastic-free packing choices at checkout. More than 660,000 customers and others have signed a petition calling on the company to offer plastic-free choices at Change.org/Plastic Free Choice. The report discloses that the type of plastic often used in packaging by Amazon, referred to as plastic film, is effectively not recycled, despite the company’s claims of recyclability. And, unlike other companies, Amazon appears to be prioritizing the increased use of “flexible packaging” made of plastic. The company has stated it uses flexible packaging to help protect the climate and environment but has not publicly disclosed the data underlying this claim. Amazon’s plastic waste and pollution footprint is expected to drastically increase, given analysts’ recent estimates that Amazon’s sales will increase by more than a third in 2020. The rapidly growing plastic pollution crisis needs to be solved by major plastic polluters like Amazon taking steps to reduce plastics. The report calls on Amazon to reduce its plastic footprint by offering plastic-free packaging as an option at checkout, consistently reporting on its plastic footprint, and eliminating plastic packaging.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.009 |
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".