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Record W2508090158 · doi:10.1145/2934328.2934333

Understanding solar PV and battery adoption in Ontario

2016· article· en· W2508090158 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotovoltaic systemBattery (electricity)Carbon footprintElectricityEnvironmental economicsGridDependency (UML)Computer scienceBusinessAutomotive engineeringGreenhouse gasEngineeringPower (physics)Electrical engineeringEconomics

Abstract

fetched live from OpenAlex

The adoption of solar photovoltaic panels and batteries greatly reduces a grid customer's carbon footprint, while simultaneously reducing their dependency on conventional electricity supply. Given the significance of both outcomes, it is important to understand the potential effect of energy policies on the adoption of these 'PV-battery systems' before they are actually implemented. We therefore design and implement an Agent-Based Model (ABM) that captures the purchase and usage of PV-battery systems. Focusing on Ontario, we use a survey to elicit the responsiveness of residents to potential energy policies. We parameterize the ABM based on survey results to forecast the relative performance of different energy policies. We find that PV-battery system adoption in Ontario is likely to be incremental rather than exponential. Moreover, we find that, of all the policies we evaluated, the most effective way to improve PV-battery system adoption is to significantly reduce its price.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.997

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)0.0040.000

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 head0.401
GPT teacher head0.346
Teacher spread0.055 · 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

Quick stats

Citations18
Published2016
Admission routes3
Has abstractyes

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