Consumers’ Willingness to Pay for Electricity from Renewable Energy Sources, Queensland, Australia
Bibliographic record
Abstract
The results of a survey of Queensland households regarding their willingness to pay (WTP) for renewable energy and support the Mandatory Renewable Energy Target (MRET) policy are presented. Results suggest that respondents were willing to pay more for the electricity generated from the renewable energy for several reasons including the emissions reduction and the benefits of future generations. On average the respondents were willing to pay about $22/quarter to support the increase in electricity generation from renewable energy sources in support of the MRET. The mean for voluntary contributions to increase the generation of electricity from renewable energy sources was $28/quarter. Furthermore, the respondents who hold the perception that a particular electricity generation technology does negatively affect the environment had a significantly different WTP compared to respondents holding the perceptions that the technology does not negatively affect the environment. Finally, respondents were willing to pay significantly more if voluntary payments were made possible compared to the MRET imposts. Therefore, the consumer driven purchases can, in part, support the future of renewable generation capacity in Queensland. However, the reliance upon other policy alternatives may be needed. The results indicate that a more effective renewable energy policy reflecting these preferences can be put in place.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".