Non-regression clauses: sufficient to maintain the UK-EU future relationship on environmental standards and regulation?
Bibliographic record
Abstract
Both the UK and the EU have called for non-regression of environmental standards and regulation in their future relationship. As environmental regulation imposes costs, there is an incentive for governments to give their industries a competitive advantage through deregulation. The EU has tried to prevent this problem in existing trade agreements by including a requirement for non-regression of environmental standards. The draft Withdrawal Agreement of November 2018 also includes requirements for non-regression of environmental standards that would apply, as part of the so-called backstop, if a future relationship agreement were not concluded by the end of the transition period. Even if (and when) the backstop is superseded by the future relationship, the UK and the EU have indicated that this relationship will build on these commitments. In this note I first describe why this ‘environmental backstop’ is an innovative hybrid between the full alignment with environmental legislation required in EU Association Agreements and the EEA Agreement, and the arm’s length non-regression requirements that the EU has negotiated in its trade agreements with countries such as Canada and Korea. It also has some unique features. Notably, successful implementation would require substantial reform in UK environmental monitoring and enforcement. I thus examine how it might function in practice, focusing in particular on challenges with enforcement. Finally, I analyse its applicability to different models for the future relationship. The Withdrawal Agreement links environmental non-regression to a specific UK-EU customs union. However, if the UK and EU go beyond this, pursuing deep regulatory alignment, it will also prove a source of fundamental disagreement. The UK’s current position is to push for non-regression to stand in for regulatory alignment, whilst the EU will likely reject such an approach.
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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 | 0.001 | 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) | 0.008 | 0.001 |
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; both teacher heads agree on what is shown here.
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".