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Record W1811784865

Consumers’ Willingness to Pay for Electricity from Renewable Energy Sources, Queensland, Australia

2012· article· en· W1811784865 on OpenAlexaboutno aff

Bibliographic record

VenueDergiPark (Istanbul University) · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyWillingness to payQuarter (Canadian coin)ElectricityEnvironmental economicsPaymentBusinessAffect (linguistics)Feed-in tariffElectricity generationMains electricityNatural resource economicsEnergy policyPublic economicsEconomicsAgricultural economicsEngineeringPower (physics)FinanceGeographyMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.075
GPT teacher head0.200
Teacher spread0.126 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2012
Admission routes1
Has abstractyes

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