Environmental Responsibility: Impact of Waste-Sorting Regulation on Secondary Market
Bibliographic record
Abstract
Proper waste disposal in an environmentally friendly manner is crucial for protecting both ecosystems and public health. Among various policy tools, waste-sorting regulations and the growth of secondary markets—where consumers resell used goods—offer promising solutions for more sustainable waste management. However, how such regulations affect secondary markets remains unclear, as user motivations and convenience differ from those in the primary market. In this paper, we address this question through a natural experiment: the 2019 implementation of mandatory waste-sorting regulations in Shanghai. Using data on over 362 million resale listings from a leading online platform, we examine the policy’s impact on both resale listings and purchase volume. We employ the synthetic control method to construct a comparable control group and use difference-in-differences to estimate the policy’s impact. We find no significant change in overall resale listings. However, among environmentally responsible younger users, resale listings decrease by 8.43% and purchase volume declines by 1.95%. The effect is particularly pronounced for easily discarded goods and inactive users. Our findings reveal a trade-off: although regulations encourage responsible disposal, they may also unintentionally discourage reuse.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".