Learning by Doing: Key Steps for Improving Ontario's Renewable Energy Programs
Bibliographic record
Abstract
The use of renewable energies (RE) for electricity production can potentially deliver a range of social, environmental and economic benefits. With the passage of the Green Energy and Green Economy Act (GEGEA) of 2009, the Ontario government has introduced a comprehensive set of policy measures to foster the development of renewables and achieve other policy goals. The RE growth in the province has been accompanied by a number of challenges, preventing Ontario from realizing the full potential of its RE resources and capturing the associated benefits. As Ontario expands its RE generation, it is vital that it puts in place a robust support framework that can ensure successful RE implementation. \nThis study examines Ontario's current legal and policy framework for renewables in the power sector and offers policy recommendations for improving this framework A qualitative comparative analysis of RE policies and programs in three jurisdictions - Germany, Denmark and Ontario is used to identify policy parameters and conditions that have proved to be significant for successful RE deployment in Germany and Denmark, and to evaluate Ontario's RE policies against these "success factors". Qualitative expert interviews are employed to elicit experts' perspectives about the performance of Ontario's current RE policy framework, the barriers to RE implementation in the province and potential policy solutions to address these barriers. \n \nThe findings of this study suggest that Ontario has made important progress in establishing favourable conditions for RE development with the introduction of the GEGEA legislation and the FIT program. These policy initiatives kick-started the RE development in the province and have been key to a number of other positive developments, such as clean-tech innovation, emergence of a local RE industry and community power development. However, there have also been adverse consequences and implications, such as local opposition to RE and the perception that FIT costs are excessive. \n \nThe study offers a number of policy recommendations for improving RE policy design and implementation and overcoming other barriers to RES in Ontario. The government should give higher priority to renewables in the energy planning. The specific design elements of FIT policy should be better tailored to RE policy goals. Following best international practices in RE policy design and implementation can help achieve this goal. The public engagement opportunities in RE development should be improved. A concentrated effort is needed to create an organizational culture supportive of RETs in the electricity sector. A longer-term perspective and a more integrated approach to energy policy-making is needed in Ontario. The province should initiative a discussion about the costs and benefits of nuclear refurbishment and the implications of continuing with the current nuclear path. Measures to improve integration of RES into the grid should be strengthened. Support for public outreach, education and provision of evidence-based transparent information can help improve the reputation of renewables and create stronger public support for this energy option.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".