Impact of Environmental Regulations on Trade in the Main EU Countries: Conflict or Synergy?
Bibliographic record
Abstract
Abstract In an increasingly integrated world with declining trade barriers, environmental regulations can have a decisive role in shaping countries’ comparative advantages. The conventional wisdom about environmental protection is that it comes at an additional cost on firms imposed by the government, which may erode their global competitiveness. However, this paradigm has been challenged by some analysts. In particular, Porter and van der Linde argue that pollution is often associated with a waste of resources and that more stringent environmental policies can stimulate innovations that may overcompensate for the costs of complying with these policies. This is known as the Porter hypothesis. While there is a broad empirical literature on the impact of trade on environment, the empirical literature on the impact of environmental regulations on trade flows is relatively scarce, very heterogeneous and presents mixed results. The innovative feature of this paper is its attempts to estimate, in a gravity setting, augmented with a proxi of environmental stringency, the impact of three major multilateral environmental agreements (MEAs) on 15 EU countries’ bilateral exports. According to our estimates, in the period 1988–2008, to be member of MEAs had a positive average impact on EU‐15 bilateral exports. This evidence can be partly explained by a possible trade diversion effect with respect to countries that did not sign MEAs and a corresponding trade creation effect among members of the environmental agreements. Furthermore, evidence coming from interaction effects estimates seems to show that for exporting countries, having signed the United Nations Framework Convention on Climate Change and the Montreal agreements partly mitigates (by the amount of the estimated coefficient) the negative impact of having a relatively more stringent environmental regulation on bilateral trade. This result could have important policy implications for the future international trade–environmental negotiations.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".