HOME ENERGY PREFERENCES & POLICY: APPLYING STATED CHOICE MODELING TO A HYBRID ENERGY ECONOMY MODEL
Bibliographic record
Abstract
In this study I design and administer two discrete choice experiments to 950 homeowners across Canada to better understand consumer preferences for home renovations and heating systems.Using stated preference data from over 600 completed surveys, I estimate discrete choice models that provide market shares, time preferences and intangible costs or benefits for heating system and renovation choices in the residential sector.Overall, respondents prefer energy efficient renovations to renovations without energy retrofits, indicated by a market penetration rate of 59% for the energy efficient renovation.Respondents use an average discount rate of 20.79% when trading off the capital cost of renovations with annual heating cost savings.Assuming consumers perceive the energy efficient renovation to have higher air quality than renovations without energy retrofits, energy efficient renovations have an annual intangible benefit of $1278.Market shares by heating system technology are a s follows: 17% for standard efficiency gas furnaces, 42% for high efficiency gas furnaces, 6% for electric baseboards, 28% for heat pumps and 10% for mid efficiency oil furnaces.For heating system choices, respondents use a discount rate of 9%.I assume that lower efficiency heating systems are less responsive compared to high efficiency heating systems, thus standard efficiency gas and oil furnaces have a $46 annual intangible cost. DEDICATIONTo my father, whose strength in the face of challenge and adversity constantly inspires me to bring those things initially out of reach, well within my @-asp.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".