Transparent communication for residential heating decarbonization: A content analysis of heat pump marketing in Canada
Bibliographic record
Abstract
Information transparency helps in building trust between intermediary stakeholders and adopters. Potential adopters seek information as they are learning about a new technology to make an informed decision. To this end, this article analyzes how intermediary installers communicate and market heat pumps, an important technology in residential decarbonization. Marketing communication by heat pump installers has been analyzed using an inductively and deductively developed coding system. Results show that 89 % of the 90 installers did not provide an estimate of the total cost of the equipment that homeowners can expect if they decide to adopt. This highlights areas of information that is missing, known as information gaps, that can be improved to increase heat pump adoption. The results reveal a tendency to over emphasize the energy efficiency improvements and expected utility cost savings from heat pump installation which were discussed by 84 %, and 67 % of the installers respectively. Building on the theories of diffusion of innovation, energy transitions, and corporate social responsibility, these findings reveal critical gaps in information transparency that undermines federal rebate program efforts to increase adoption. • Analyzed 90 Ontario heat pump installer websites using 6 coding themes, with 22 subcodes from prior research and site content. • Installers often highlight energy efficiency (84 %) and cost savings (67 %), but rarely mention contract length (1 %) or total cost (11 %). • Findings show a need for support tools and clear info to help adopters understand heat pump ownership and operational costs.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".