Business Model Evaluation of Renewable Energy Community in Multi Unit Residential Building in Canada net metering, considering the regulatory framework, ownership and organizational structure
Bibliographic record
Abstract
Renewable Energy Community (REC) is an emerging concept that empowers local customers to generate decentralized renewable energy.Integrating RECs into the building industry presents a promising opportunity to support the transition toward net-zero cities.However, the viability of RECs depends on regional regulatory frameworks, permitted governance structures, and very importantly, business models.Compared to pioneering European countries, Canada lacks the regulatory support needed to fully enable REC.This paper proposes and evaluates various business models for potential REC implementations under Canada's existing regulatory constraints.The analyzed models focus on "behindthe-meter" electricity distribution, a multi-stakeholder type of REC, where generation occurs independently of the Distribution System Operator.The study also explores different ownership structures, including resident-led (community-based) ownership and non-occupant (investor-led) ownership, as well as financing strategies.Additionally, it examines various legal structures and income distribution models, which collectively shape business models.A case study was conducted based on a multi-unit residential building in Montreal.The results indicate that Canada's current regulatory framework, particularly Quebec, does not support diverse scales and models of REC.Regulatory changes, including rebates for generator purchase, low-interest loans, and tax incentives, are necessary to facilitate local renewable energy developments.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".