Fifty shades of binding: Appraising the enforcement toolkit for the EU’s 2030 renewable energy targets
Bibliographic record
Abstract
In December 2018, the European Union (EU) adopted a recast of the Renewable Energy Directive (RED II), which introduces a new target of 32 percent renewable energy to be reached at the EU level by 2030. This target represents a discontinuity with the one enshrined in the previous Directive (RED I), as it is binding only for the EU as a whole but not for individual Member States. Such a policy shift paves the way to new legal challenges for the deployment of renewable energy. Yet, the contextual approval of the Regulation on the Governance of the Energy Union also provides the European Commission with an enforcement toolkit to respond to Member States’ ambition and delivery gaps in their National Energy and Climate Plans. Providing an appraisal of the RED II and the Governance Regulation, this article argues that, despite the lack of binding renewable energy targets at Member State level, the Commission is equipped with the necessary instruments to ensure the enforcement of the collective 2030 renewable energy target.
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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 | 0.053 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".