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Record W2595910335

Learning by Doing: Key Steps for Improving Ontario's Renewable Energy Programs

2015· article· en· W2595910335 on OpenAlexaboutno aff
Mariana Eret

Bibliographic record

VenueYork University Digital Library (York University) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsKey (lock)Renewable energyBusinessComputer scienceEnvironmental economicsEngineeringEconomicsComputer securityElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

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.
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\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.
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\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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.464
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.006
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.184
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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