Power to the People: Assessing Renewable Energy Cooperatives in Ontario
Bibliographic record
Abstract
There is need for increased production of electricity from renewable energy technologies. The transition to a low-carbon economy, whilst achieving energy security and meeting the Sustainable Development Goal 7 (SDG7) is an ongoing challenge for many countries. Local communities instituting an energy co-operative model may be instrumental to reducing greenhouse gas emissions to attain the 2030 goal. Renewable energy co-operatives (RECs) are one approach that can contribute to environmentally and socially equitable energy transitions in order to meet the SDG7. This thesis examines the factors that affect the success of RECs within Ontario to better discern how RECs are set up and how government policy affects their development. The main research question of this study is “How can Ontario’s renewable energy co-ops grow, experience long-term viability, be updated or expanded?” This thesis argues that the quest towards energy transition, a low-carbon economy and to achieve both the federal and provincial targets by 2030 should take on a multi-stakeholders approach. In theory, this should reflect community desires, goals and energy equity since a community should have its own supported role in energy generation towards the whole of Ontario. With the absence of provincial support from the removal of the Feed-In Tariff (FIT) program in 2017, it is now imperative that municipal governments become involved in REC developments within their community. The methodological approach of this thesis uses a combination of the Strengths, Weaknesses, Opportunities and Threats (SWOT) and sustainability analyses in order to interpret the data collected from semi-structured interviews with co-ops and policymakers as well as their websites and reports. This study examines the support structures and barriers for the growth of RECs in Ontario and how their growth can contribute to the SDG7. Through document review and interviews with representatives from the co-ops, I discovered that barriers include unstable government policy, inadequate funding, and a lack of support from financial institutions due to the smaller size of the projects developed by power co-ops. Comparisons with REC policy and progress in European countries show Ontario can do more to support RECs. This thesis concludes that one of the many available options for Ontario to contribute substantially to the transition to a low-carbon-economy is through applying the Pan-Canadian Framework on Clean Growth and Climate Change and the SDG7 by supporting citizen-led initiatives like RECs and to encourage large financial institutions to invest in their communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".