Europeanisation and the Uneven Convergence of Environmental Policy: Explaining the Geography of EMAS
Bibliographic record
Abstract
In this paper we seek to advance current understanding of uneven convergence in the context of EU environmental policy, and specifically, the Eco-Management and Audit Scheme (EMAS). Using a large-sample, quantitative methodology, we examine three broad sets of determinants hypothesised to influence geographic patterns of policy convergence: (1) cross-national market integration; (2) compatibility between the domestic regulatory context and European policy requirements; and (3) bottom-up pressure from market and societal actors. Our analysis provides empirical support for all three hypothesised determinants. Measures of import–export ties, regulatory burden, past policy adoptions, environmental demand from civil society, and levels of economic productivity are all found to be statistically significant predictors of national EMAS counts. Against a backdrop of geographically diverse regulatory institutions, societal conditions, and trading relationships, we conclude that unevenness is an inevitable feature of Europeanisation.
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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 | 0.000 |
| 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) | 0.000 | 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".