Stated innovation diffusion model from stated preference data: The Case of Photo-Voltaic (PV) Solar Cells for Household Electricity Generation
Bibliographic record
Abstract
We focus on predicting the adoption time probabilities of photo-voltaic solar panels by households using discrete choice experiments and an innovation diffusion model. This is a potentially very significant energy source as individual households account for one third of all energy consumption in USA and Canada. In addition, growing energy demand, finite fossil fuel supplies, worries about energy security and environmental concerns are all factors encouraging the increasing use of renewable resources for electricity generation. The primary objective of this research is cohesively mapping the theory of disruptive innovation into diffusion of innovations to aid policy makers by linking two critical uncertainties of new technology: (1) whether households prefer the new attributes of the new technology and how these preferences vary by market segments? and (2) when are they going to adopt (if at all)? Our study uses recent developments in measuring household level preferences for innovations via discrete choice experiments and establishes a causal link between the attributes of the technology, attitudinal constructs and socio-demographics, and adoption time probabilities using the Bass diffusion model. The data was collected from Ontario, a province of Canada. The estimation of the innovation diffusion model showed a number of
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".