Consumers and green electricity: profiling potential purchasers
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.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".