Governance and decentralized energy transitions: a comparative case study of three medium-sized cities in Sweden, Canada, and the United States.
Bibliographic record
Abstract
Aim: This study examines the impact of governance on decentralized energy transitions. Knowledge of how particular jurisdictions and their governance arrangements influence these transitions can help strengthen and contextualize divergent trajectories of decentralized energy transitions and—most importantly—reveal the role of geographical context in policy change. Design: This research gap is addressed in this paper by comparing the uptake of decentralized energy transitions in three cities in three different countries—Luleå (Sweden), Saskatoon (Canada), and Anchorage (United States). The jurisdictions in each city has unique governance contexts pertaining to electric utilities, regulations, public policy, and public acceptance. By comparing these transitions, this study highlights the governance considerations for decentralized energy transitions and asks how does governance impact decentralized energy transitions in cities? To answer this question, actors within various public, private, and sectoral capacities were interviewed to provide their insights on decentralized energy transitions in each jurisdiction. Conclusion: I present five governance dimensions that impact decentralized energy transitions and explain how these factors can be included to provide a more contextual understanding of patterns of decentralized energy transitions in cities. Originality: Much of the literature on decentralized energy and cities has focused on project and sectoral level analysis and hasn’t considered the holistic nature of the energy system transition. A particular gap that would help inform a broader understanding is the jurisdictional governance impacts of decentralization energy transitions. Implications of the Research: In practical terms, the results could be used to inform inter-jurisdictional comparisons of decentralization energy projects. Limitations of the Research: Given that there were three case studies, it is not possible to make generalizable claims from the results.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".