Influence of Trust Concerns and Benefits of Visibility on Participation in Green Electricity Programs: a Case-Study of Residential Solar-PV Systems in Ontario
Bibliographic record
Abstract
This study examines two of the reasons that prevent people from taking part in green electricity programs: trust concerns that these programs may raise, and lack of benefits that come with visibility of participants’ involvement. While the current literature takes notice of their influence, in this study it was decided to investigate both factors in more detail. In particular, with the help of a survey, the study focused on the reactions of electricity consumers to the proposition of participation in green electricity programs in a controlled setting, in which levels of trust concerns raised and benefits of visibility provided by the programs could be varied. The study was conducted in Oakville, an affluent southern Ontario (Canada) suburb. The results are based on 160 received responses to 500 questionnaires that were sent out by mail. \nWhile the results of this study point towards the conclusion that both factors have an influence on participation in green electricity programs, their relative strength cannot be estimated by these results. One can, however, claim that the combined influence of trust concerns and benefits of visibility is quite strong. This research shows that at a 95% confidence level, willingness to participate in a program that proposes paying premium for electricity from solar panels installed on a participant’s roof (low trust concerns, high benefits of visibility) is 30% + 19.3% higher than willingness to participate in a program that proposes paying premium for electricity from undisclosed solar farms (high trust concerns, low benefits of visibility). \n\tAdditional data about trust concerns, appreciation of benefits of visibility, and concerns about installation of solar panels on one’s own roof, provided by the survey, are also presented in the text. \nIn conclusion, it is recommended that future research should more clearly separate the strength of influence of trust concerns from the influence of benefits of visibility on green electricity program participation. It is also important to study which features of these programs make them more trustworthy and visible. An important implication of this study for policy makers and green electricity proponents is to concentrate on allaying trust concerns, and enhancing benefits of visibility when designing policies or drafting plans for green electricity programs. The creation of an independent green electricity program certification system and a greater accent on the local presence of such programs is suggested.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".