Improving the Effectiveness of Time-of-Use Pricing to Make Household Electricity Consumption More Sustainable
Bibliographic record
Abstract
To increase efficiencies and reduce greenhouse gas emissions, policymakers and electric utility providers are increasingly adopting time-of-use (TOU) pricing policies, which charge the most for electricity consumption during on-peak hours, the times when the demand for electricity is greatest. TOU policies aim to disincentivize on-peak electricity use in favor of use during usually low-demand, off-peak periods to reduce the suppliers’ need to augment electricity generated by low- or nonemitting sources (such as hydro-electric and nuclear power) with electricity generated by high-emitting sources (such as coal- or gas-fired power plants). Researchers and policymakers are attempting to apply behavioral science tactics to enhance the effectiveness of TOU pricing by making behavioral science-based changes to electricity bills or delivering personalized information about electricity use and pricing, or doing both. In this article, we describe several studies we conducted in Ontario, Canada, in which we examined customer responses to various bill designs and communications. Simplifying bills and emphasizing the high cost of on-peak use (that is, making on-peak pricing more salient) were effective at shifting behavior, as was the delivery of nudge reports, which compared a household's electricity use with its past consumption, offered conservation tips, and asked customers to make a pledge to reduce consumption. These studies demonstrate that incorporating behavioral tactics into existing consumer-facing communications can be an effective, low-cost, and scalable way to induce customers to increase off-peak electricity use and thus limit greenhouse gas emissions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".