Understanding consumer knowledge, perceptions, and preferences regarding pro-environmental technology: The cases of plug-in electric vehicles and utility controlled charging
Bibliographic record
Abstract
Consumer demand is an important aspect of a successful transition to low-carbon technology. In this study I explore consumer knowledge, perceptions, and preference formation for two such technologies: purchasing a plug-in electric vehicle (PEV) and enrolling in a green electricity program (to power the PEV). I explore this through in-depth interviews with 22 households in Metro Vancouver, British Columbia. Results provide several key insights into how consumers perceive and may come to value such technologies. First, I find that participant awareness is very low for both technologies; the majority of participants were confused about plug-in hybrid technology and did not understand the sources of electricity they consume. Secondly, once the technologies were explained to participants, most perceived both technologies according to a wide range of attributes, including functional (e.g., cost and performance), symbolic (e.g., “strangeness” and loss of control), and societal (e.g., pollution reduction). Third, I find that most participants do not have pre-existing preferences for these technologies. Instead, they construct preferences as they learn about them and reflect on their various attributes. Interestingly, the initial lack of awareness is not necessarily a barrier to participants developing positive preferences for the case technologies once explained. These findings suggest that research and policy ought to carefully consider the roles of knowledge and different types of perceptions in consumer preferences for low-carbon technologies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".