The effects of institutional layering on electricity sector reform: Lessons from Norway's electricity sector
Bibliographic record
Abstract
Institutional layering is a dominant mode of change in shaping energy transitions, whereby new institutional rules or practices are added on top of or alongside existing ones. Characterized by the introduction of new actors, policies, and expanding energy arenas, energy transition processes can become progressively layered, resulting in institutional complexity and potentially undermining energy transition goals. This paper explores the impact of layering on actors and on the outcomes of energy transitions. A conceptual framework that integrates institutional change with a layering typology is developed and applied to Norway's electricity sector reform as a case study. Results show that Norway's energy landscape has become more diversified, leading to complex institutional arrangements, differential growth in the energy sector, and gaps between reform intentions versus outcomes. Whether layering produces the intended energy transition outcomes depends on the complexity of layering, the interaction, coordination, and alignment of the layered elements, and the vested interest of stakeholders. Insights from Norway's experience can guide institutional design to support the rapid expansion of renewable energy investments, or the reform or restructurings of existing energy institutions. • Norway's energy sector has diversified, but with complex institutional arrangements. • Institutional layering can be unsupportive to energy transition. • Layering can lead to unintended, differential growth in the energy sector. • Research is needed on optimal integrated strategies to produce transition outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".