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Record W2060531961 · doi:10.3390/su2010030

The Role of Policies in Supporting the Diffusion of Solar Photovoltaic Systems: Experiences with Ontario, Canada’s Renewable Energy Standard Offer Program

2009· article· en· W2060531961 on OpenAlexafffundabout
Chris Adachi, Ian Rowlands

Bibliographic record

VenueSustainability · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsPhotovoltaic systemRenewable energyIncentiveEnvironmental economicsBusinessElectricityPayback periodSolar energyGridNet meteringCapital costEconomicsDistributed generationElectrical engineeringEngineeringMicroeconomicsProduction (economics)

Abstract

fetched live from OpenAlex

Traditionally, high initial capital costs and lengthy payback periods have been identified as the most significant barriers that limit the diffusion of solar photovoltaic (PV) systems. In November, 2006, the Ontario Power Authority (OPA) introduced the Renewable Energy Standard Offer Program (RESOP), offering owners of solar PV systems with a generation capacity under 10 MW a 20 year contract to sell electricity back to the grid at a guaranteed rate of CAD $0.42/kWh. While it is the intent of incentive programs such as the RESOP to begin to lower financial barriers in order to increase the uptake of solar PV systems, there is no guarantee that the level of participation will in fact rise. The "on-the-ground" manner in which consumers interact with such an incentive program ultimately determines its effectiveness. This paper analyzes the relationship between the RESOP and solar PV system consumers. Experiences of current RESOP participants are presented, wherein the factors that are either hindering or promoting utilization of the RESOP and the adoption of solar PV systems are identified.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.003
GPT teacher head0.200
Teacher spread0.198 · 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 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

Citations17
Published2009
Admission routes3
Has abstractyes

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