Collaborative governance in “community energy planning”: insights from an intersectoral governance network in Durham Region, Canada
Bibliographic record
Abstract
Collaborative governance (CG) has arisen as a useful template for navigating decentralised energy systems at the local level by involving formalised procedures for governance across a network of organisations. By incorporating CG into community energy planning processes, local governments have a framework for involving diverse actors. CG processes, however, also exist within the context of broader systemic constraints and inequalities that impact how organisations collaborate. Given that CG may be seen as a path toward more just and democratic energy governance, it is important for researchers and practitioners to understand both its opportunities and limitations in-practice.Our case study investigates a network of local organisations in Durham Region, Canada, where CG is being used for implementation of the region’s community energy plan. We use a mixed methods approach, incorporating a quantitative social network analysis and qualitative thematic analysis, to examine how and why organisations are collaborating within the local network. Our study illustrates the complexity of these arrangements. While local governments facilitating CG initiatives are well-positioned to mobilise local actors and build connectivity in their community, they are also limited in addressing broader systemic challenges, including asymmetric power dynamics that impact outcomes and erode social trust; resource gaps that exacerbate challenges and lead to competition between organisations; and energy literacy gaps that impede those lacking expertise. Thus, while CG represents an important framework for local energy governance, its potential is constrained by deep-rooted structural limitations that may require comprehensive solutions beyond the capacity of local actors alone.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.034 | 0.014 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".