Exploring Motivations for Participation in Different Modes of Urban Community Energy Development in Ontario, Canada
Bibliographic record
Abstract
Urban energy systems are facing disruption at the same time as cities are increasingly focusing on sustainability. Community energy projects are increasingly gaining attention as systems that can deliver on the promise of sustainable growth and may even serve as a model for the future of energy planning, especially in an urban context. With the decreasing cost of modular generation technologies, it is increasingly feasible to generate local energy in urban areas. Not only is there potential for an economic benefit, but people are also empowered from being end of the line consumers to ‘prosumers’. This paper explores the landscape for urban community energy projects with community involvement in ownership and management. Different models of ownership and management are examined and a spectrum of citizen participation in community energy is described. Various motivations for participation in community energy projects are identified. Interviews with representatives of key stakeholder groups were conducted to assess the theoretical foundation of the research and to refine a survey given to 270 residents of households located in the City of Toronto. The results were used to determine consumer/prosumer choices towards participation in local community generation and utilisation of renewable energy. This study shows that there is heterogeneity in the ways citizens can participate in community energy in an urban context. Relying on principal component factor analysis to identify the inferential variables associated with four motivating factors, namely Financial, Social Norms, Environmental and Community Concerns, and Trust in Technology, correlations with certain descriptive variables were examined. Stepwise multiple regression was used to identify respective models of causality between these motivating factors and three common models of community energy participation. The analysis shows that most residents in a Canadian urban centre prefer a more passive participatory role and that the financial factor remains the principle motivator. The results have implications for urban energy planning including the need for more utility and industry collaboration with urban community members.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".