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Record W1990135710 · doi:10.5539/jsd.v3n3p3

Leveraging Solar Photovoltaic Technology for Sustainable Development in Ontario's Aboriginal Communities

2010· article· en· W1990135710 on OpenAlexafffundvenueabout
Dirk V. P. McLaughlin, Nicole C. McDonald, Ha T Nguyan, Joshua M Pearce

Bibliographic record

VenueJournal of Sustainable Development · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotovoltaic systemRenewable energyTariffFeed-in tariffRevenueSolar energyBusinessElectricityEnvironmental economicsSustainable developmentSoftware deploymentEnvironmental resource managementNatural resource economicsEconomic growthEconomicsPolitical scienceEnergy policyEngineeringFinanceInternational trade

Abstract

fetched live from OpenAlex

The Ontario feed-in tariff (FIT) for solar photovoltaic (PV) technology has provided Ontario's Aboriginal communities with an opportunity to i) weaken the cycle of poverty; ii) directly counteract climate change by producing renewable energy; and iii) become more self-sufficient. This paper critically analyzes the technical, cultural, and economic viability of leveraging the FIT for PV to provide green electricity and revenue to assist First Nations communities in sustainable development. A generalized free GIS-energy-based protocol was developed to determine the PV potential for Aboriginal communities. This model was applied to a case study of the Constance Lake First Nations community and an economic analysis showed financially-viable rates of return over 20 years. By generalizing these findings to Ontario, the potential PV deployment on First Nation rooftops alone is over 200MW, which clearly provides an opportunity for developing pride associated with owning a community-led, environmentally beneficial, local energy project.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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 designObservational
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

Citations6
Published2010
Admission routes4
Has abstractyes

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