Exploring the factors affecting home energy retrofit adoption - a case study of the EcoENERGY retrofit program
Bibliographic record
Abstract
In the wake of the global financial crisis, the Canadian government created the EcoENERGY Retrofit for Homes program with the stated goal of “Encouraging homes to become more energy-efficient, reduce emissions produced through energy use, and contribute to clean air, water, energy, and a healthy environment for Canadians." However, results varied considerably nationwide. An early review of this data suggests that retrofits were not adopted with \nspatial or temporal uniformity. \n \nPopulation data on were obtained from the 2006 and 2011 censes and the National Household Survey; these were then matched with household pre- and post-retrofit data from the EcoENERGY Retrofit program. Multiple linear regression analysis of the retrofit adoption rate was conducted at the finest spatial resolution common to these datasets. \n \nThis preliminary analysis suggests that income, non-condominium properties, and high shelter costs (greater than 30% of household income) had a significant positive correlation with adoption of retrofit measures at a 99.9% confidence level. Meanwhile, renter-occupied units and participation in the workforce were negatively correlated. Seasonal variation was also observed, with the majority of retrofits occurring in winter months. Further, spatial \nvariation at both the city and neighbourhood level suggests a greater degree of program customisation is required to ensure uniform building stock improvement. \n \nThe findings fit with an emerging pattern that grant programs can be effective at delivering high volumes of savings but have a limited market impact in the post-funding period; ~25% of energy advisors were laid off after the conclusion of the initial program end date of March 2011, tied to a sharp decline in the number of energy audits. This study reinforces the importance of the upfront cost barrier and consistent federal-level support. However, retrofit program design may need to provide different grants in different municipalities to address specific community needs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".