Measuring willingness to pay for electricity: The case of New Brunswick in Atlantic Canada
Bibliographic record
Abstract
Recently, electricity markets around the world have been going through transformation process that eventually leads to a higher competition among electricity providers. In this regard, the role of consumer preferences increases, especially if new electricity products are offered. Traditionally, consumer preferences have been expressed in terms of customers’ willingness to pay. Therefore, the goal of this study is to provide a practical framework for estimation of customers’ willingness to pay for electricity. Specifically, dynamics of regional residential willingness to pay for electricity in the province of New Brunswick in Atlantic Canada is analyzed. First, theoretical framework to evaluate consumer preferences is developed followed by empirical approach to define willingness to pay over period of 1991–2013 on the basis of revealed preferences method. Finally, dynamics of the residential willingness to pay for electricity is analyzed with the help of advanced time series analysis. Our study shows that residential willingness to pay for electricity in the province of New Brunswick had been increasing over study period. Moreover, it has accelerated significantly since 2005. The designed methodology and empirical work will help electricity providers identify new electricity products with the highest willingness to pay by consumers. Overall, implementation of the results of this study can improve economic efficiency of provincial electricity market.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".