Renewable Energy Co-operatives and the Struggle for “Critical” Energy Democracy: The Case of Ontario
Bibliographic record
Abstract
The introduction of the Feed-in tariff (FIT) program in Ontario in 2010, with specific considerations for community-owned renewable energy (CE) initiatives, was a turning point for renewable energy co-operatives (RE co-ops) in the province as their numbers jumped from only two to 111 by 2016. RE co-ops, along with other CE initiatives, are considered to be important actors in the democratization of electricity systems by a growing body of activist and, recently, a field of scholarship called “energy democracy”. In the light of this recent surge of RE co-op activity in Ontario, I ask: In what ways did RE co-ops democratize Ontario’s electricity system and what are their limitations and future potential in doing so? In order to answer this question, I develop a theoretical framework called “critical energy democracy”, rooted in critical theories of capitalist political economy, technology, and democracy. Through this lens, I assess RE co-op activity in Ontario by deploying a novel combination of three research methods: (1) A political economy analysis of the history of electricity governance and infrastructure in Ontario, based on a combination of literature review and documentary research. (2) Semi-structured interviews with what I call “leading RE co-op members”, or those closely involved in the governance of their co-op, to reveal the experiences of RE co-ops in Ontario. And (3) semi-structured interviews with what I call “investor-members”, based on a social learning theory and method, to reveal instances of nonformal, informal, and tacit member learning. Overall, this study reveals that market-based policies under capitalist social relations severely constrain the application of community energy models to affluent communities. Given their inaccessibility to marginalized community members and lack of an explicit anti-capitalist social justice agenda, RE co-ops’ impact and potential in advancing a critical form of energy democracy in Ontario has been limited at best. Further, Ontario’s experiment serves as a lesson to policy-makers, academics, and social movements alike that public policies and programs should by design prioritize the immediate energy-related needs of marginalized communities.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.038 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".