Exploring legal frameworks for climate action: a network analysis of Green Deal, EU Directives, and environmental assessment indicators
Bibliographic record
Abstract
Abstract The analysis focuses on investigating the links established between climate and environmental policies within the key policy instruments of the European Union (EU). The specific research objective was to gain a better understanding of the structural relationships between the EU’s climate policy and environmental policy agenda. Using a network perspective to visualize how climate and environmental policies intersect, we identified some of the key climate governance policy instruments and explored the areas of connections and disconnections between them. A dataset of 30 key climate and environmental policy instruments was collected, and they were classified into three main policy instrument categories: climate policies, circular economy policies, and pollution control and biodiversity protection policies. The network analysis also recorded centrality measures to provide an insight into how the policy instrument was interconnected to the policy network and what type of relationships existed between them. Additionally, K-core clustering provided a measure for identifying whether the policy instrument served as an indicator of the roles of key actors in climate-related policies. Our findings highlight that key policies in the network, such as the European Green Deal and EU-ETS, serve as central points of coordination for a variety of sustainability policies. We found that there may be potential complementary governance reflected in the significant overlap around waste and pollution control, yet further study is required to assess the implications of functional redundancy. This research provides valuable information about the relationships within the network of climate and environmental policies and the synergies and gaps within the EU’s sustainability agenda overall.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".