International trade sanctions imposed due to the Russia-Ukraine war may cause unequal distribution of environmental and health impacts
Bibliographic record
Abstract
The geopolitical conflict between Russia and Ukraine has escalated into international trade sanctions targeting Russia’s energy-related products. Here we combine a global general equilibrium model and an atmospheric source-receptor model to explore the environmental, economic, and public-health effects of varying intensities of trade sanction measures. Our results indicate that the trade-offs between economic development and environmental pressure might not be directly proportional, where one-fifth of global regions (e.g., Russia, China, and Central America) would experience a co-harm situation (e.g., decreased GDP and increased emissions and health risks). Intensifying trade sanction measures would reallocate the burdens of energy production and export to less-developed regions (e.g., Europe and Central Asia), thereby exacerbating the uneven distribution of environmental and health outcomes among regions. The global repercussions of the trade sanctions highlight the necessity of considering the consequences on regions beyond the conflict to formulate desired global initiatives for impact mitigation/adaptation and post-conflict restoration. Energy production and export could be relocated to less-developed regions due to international trade sanctions on the Russia-Ukraine war, exacerbating the uneven distribution of carbon dioxide emissions and air pollutants according to an analysis combining economic and atmospheric source-receptor models.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".