HOME ENERGY PREFERENCES & POLICY: APPLYING STATED CHOICE MODELING TO A HYBRID ENERGY ECONOMY MODEL
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.
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.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.000 | 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 it