Decentralised energy, decentralised accountability? Lessons on how to govern decentralised electricity transitions from multi-level natural resource governance
Bibliographic record
Abstract
Emerging decentralised electricity systems require new approaches to energy governance. As energy sources shift and technology evolves, electricity governance is shifting from largely centralized models to include multiple decentralised and multi-level sites not bounded in their operations by established democratic processes. New forms of accountability are required to ensure that multi-level electricity systems meet societal needs and expectations. While multi-level governance dynamics are new for many electricity systems, they are common across other resources (e.g. water). This article uses an OECD framework that synthesizes decades of research on multi-level natural resource governance to describe 12 principles for “good” resource governance. These principles are developed and applied to decentralising electricity governance contexts in order to develop mechanisms, and identify potential governance gaps, that are relevant for ensuring accountability in decentralised electricity governance systems. The nature of decentralised electricity systems particularly highlights the need to rescale many governance functions, while paying attention to issues of inclusion, capacity building, coherence, adaptiveness, and transparency.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".