Consumers and green electricity: profiling potential purchasers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Globally, consumers are beginning to be able to choose their electricity supplier. Increasing concerns about the environment are prompting some of them to consider ‘green’ electricity—that is, electricity that has been generated by more environmentally sustainable means (for example, solar power or wind power). This article profiles the potential purchaser of green electricity. Drawing upon the literature on green product purchasers more generally, three sets of hypotheses are presented—more specifically, it is proposed that those who would pay increasingly higher premiums for green electricity are more likely to possess particular demographic characteristics, attitudinal characteristics and socialization characteristics. Responses from a survey distributed in a major Canadian metropolitan area are then examined. Attitudinal characteristics—specifically ecological concern, liberalism and altruism—best identify the potential purchasers of green electricity. Suggestions for managers and marketers are made following these findings. Directions for future research are also presented. Copyright © 2003 John Wiley & Sons, Ltd. and ERP Environment.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it