TRUMP’S TRADE WAR: EU EXPORTS AT RISK AND ALTERNATIVE MARKETS
Bibliographic record
Abstract
This paper examines the European Union (EU)’s response to President Trump’s 2025 imposition of tariffs on imports of aluminum and steel from the EU. The EU’s response includes bargaining, politically targeted tariffs and internal substitution measures. The EU is not considering external substitution measures, such as alternative export markets for aluminum and steel products threatened. In this paper, we argue that this is an omission. The EU’s response should be twofold: one, at the EU level, to apply retaliatory tariffs and negotiations, and two, to support country-level efforts to minimize the impact of tariffs, including external substitution. We use the case of the Netherlands to illustrate the usefulness of our recommended approach. Using the CEPII BACI reconciled UN COMTRADE data we calculate time-weighted Revealed Comparative Advantage (RCA tw ) and Revealed Trade Advantage (RTA tw ) measures to assess the risk to the Netherlands’ exports to the USA. For high-risk products, we then use a data filtering process to identify alternative export markets. Our findings indicate that while most of the Netherlands’ exports to the USA are at low-to-medium risk, a smaller portion is at high risk. For aluminum and steel products, the high-risk products face exports-at-risk of US$ 245 million, much lower than some current estimates. For these, we identify alternative export opportunities outside the USA and EU. The best opportunities, valued at US$ 12 billion, are in China, Mexico, Canada, Malaysia and India. An implication is that the USA’s trade policies could push the Netherlands and the wider EU toward closer economic ties with other global players, potentially weakening the USA’s geopolitical standing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".