Trade Regulations for Climate Action? New Insights from the Global Non-tariff Measures Database
Bibliographic record
Abstract
Over 190 United Nations Member States pledged to reduce greenhouse gas (emissions to limit the increase in global temperatures “well below” 2°C under the Paris Agreement reached in 2015. A quarter of global CO2 emissions are linked to the production and distribution of traded goods and services. Trade regulations can therefore play a critical role in supporting the transition towards a low carbon economy. Over the last decade, countries have been increasingly using non-tariff measures to pursue environmental and climate-change mitigation objectives. Climate change-related regulations are commonly imposed on goods such as fuel, motor vehicles, appliances, renewable energy generation equipment and plastic products. This report presents insights from the global non-tariff measures database collected by UNCTAD and many partners. The database covers over 100 countries and 90 per cent of world trade. Using measure coding, product coding and keyword matching, UNCTAD and ESCAP identified non-tariff measures that are related to climate change mitigation. The results of this matching are presented in the report and show that climate change-related non-tariff measures affect a quarter of global trade.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".