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Record W3158131625 · doi:10.24908/iqurcp.9027

Community Owned Renewable Energy: a Case for Economic Localization in Ontario

2016· article· en· W3158131625 on OpenAlexvenueaboutno aff
Philip Ballyk

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energySoftware deploymentIncentiveSustainabilityBusinessEnvironmental economicsSustainable developmentNatural resource economicsEnvironmental resource managementEconomicsEngineeringMarket economyPolitical science

Abstract

fetched live from OpenAlex

Ontario is making some strides to promote renewable energy generation, but policies and incentives are ignoring the merits of scale of two renewable energy deployment options: community owned renewable energy and large-scale absentee owned renewables. This literature review discusses the sustainability of each option in terms of the three pillars of sustainable development: economic, environmental and social. Community owned renewable energy (CORE) projects are found to be more sustainable due to their participative process and distributive outcome. Studies from Europe and the UK show that CORE decreases opposition to renewable energy integration, has more economic impact than the centralized approach, and is a socially cohesive and empowering undertaking. The policy environment in Ontario, however, does not promote the growth of this deployment method. Changes must be made to transmission incentive structure for utilities to consider distributed generation, and laws concerning the limits of co-operatives as financial institutions, among many other changes, to provide a suitable avenue for sustainable deployment of a new energy system based on renewables

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.224
GPT teacher head0.348
Teacher spread0.124 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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