Household Decision-Making Dynamics Associated with the Adoption of High-Involvement Renewable Energy Technologies: A Case Study of Consumer Experiences in the Adoption of Residential Ground Source Heat Pump Systems in Rural Southwestern Ontario (Canada)
Bibliographic record
Abstract
In the province of Ontario, 88 percent of residential energy demand is for space heating, cooling and water heating, accounting for 99 percent of the residential sector's greenhouse gas (GHG) emissions (data are for 2011 and are taken from Natural Resources Canada, 2014a). Amongst the current and viable residential renewable energy technologies (RETs), ground source heat pump (GSHP) systems offer the potential to meet a substantial share of these energy requirements, but current levels of adoption are minimal. Ontario's existing and planned rural housing stock that does not have access to natural gas and relies on oil, propane or electric-based systems for heating and cooling demands, offers an immediate opportunity to achieve large scale reductions in GHG emissions and reduce pressure on the existing electricity infrastructure, while simultaneously reducing home heating and cooling costs. \n \n\tThe purpose of this thesis is to describe and explain the factors that influence the residential adoption of GSHP systems in rural southwestern Ontario. To act on this purpose, 17 face-to-face interviews were conducted with householders that have retrofitted their house with a GSHP system in rural southwestern Ontario areas that do not have access to natural gas. This thesis utilizes Rogers' (2003) Innovation-Decision Process model as a theoretical framework to guide the collection of empirical data on household adoption experiences to achieve this research purpose. \n \n\tThe findings from this study indicate that the residential adoption of GSHP systems in rural southwestern is influenced by five determinants of adoption: (1) prior conditions; (2) characteristics of the decision-making unit; (3) perceived characteristics of the innovation; (4) change agent programs; and (5) communication channels; however, the influence of each determinant of adoption varied across households. Cost savings and the government grant(s) were the most significant drivers for adoption followed by factors related to sustainability and the GSHP system's ability to provide space cooling. The high initial capital cost of the system was identified as the most significant barrier to adoption. By developing a comprehensive understanding of the factors that influence the residential adoption of GSHP systems in rural southwestern Ontario, findings may contribute to a better design of policy instruments and marketing strategies aimed at stimulating the adoption of these systems in rural areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".