Environmental requirements for imported products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines trade‐related environmental measures adopted by World Trade Organization members and assesses their outcomes using Organization for Economic Co‐operation and Development (OECD) indicators. It draws on notifications from the WTO Environmental Database for 2009–2021 and on OECD‐Stat metrics for 2012–2019. Each notification is classified by measure type, sector and environmental objective, tracing trends among major issuers such as the United States, the European Union, Australia, China and Canada. Results show that the agricultural and manufacturing sectors account for the largest share of measures, while technical regulations and subsidies are the most prevalent instruments. Evaluations of PM₂.₅ exposure, greenhouse gas emissions, renewable energy share and environmental policy stringency reveal that countries issuing numerous environmental notifications generally achieve better environmental performance, although variations arise according to development status and policy design. Nations with stricter requirements have demonstrated improvements in their indicators, implying that import regulations form part of a comprehensive green policy rather than solely protectionist intent. China is notable for advancing its indicators despite persistent pollution and emission challenges.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it