Supporting Domestic Energy Conservation in Ontario through Direct Feedback
Bibliographic record
Abstract
Outside of financial incentives, there is a lack of tools for Ontario households to effectively conserve energy. The traditional and most commonly used policy tools, money and information, are not enough on their own to truly develop a culture of conservation, as set out by Ontario’s Long Term Energy Plan. This paper argues that there are gaps in energy conservation policies and programs in Ontario that can be addressed through insights from the social sciences in order to enhance residential energy conservation programs and policies. A review of behaviour literature and decision models from various disciplines of academia will be explained to describe the ‘behavioural blind spot’ in current policies and programs, thus providing an explanation as to why Ontario is falling short of it’s long-term energy conservation targets. A behavioural intervention called direct feedback will be of particular focus in this paper, as studies have demonstrated that providing feedback can, on average, result in up to 15% in household energy savings. Direct feedback has the ability to change household energy behaviours, as it increases energy literacy, instantly reinforces positive behaviours (energy savings), makes energy use ‘visible’ and is presented in a cognitively stimulating and tailored format. Ontario is well positioned to support a direct feedback program through the recent introduction of the Green Button program and its transition to the smart grid. Behaviourbased energy programs are making traction internationally through the formation of behavioural working groups such as the United State’s Customer Information and Behavior Working Group that focuses on the research and development of behaviour-based energy efficiency programs and the United Kingdom’s Department of Climate Change (DECC) that has worked with researchers to contribute to the knowledge of behaviour change programs and energy conservation. It is recommended that Ontario take a similar approach and create a behavioural working group to contribute to behaviour-based energy research and programs. Supporting direct feedback in the residential sector and forming an energy behaviour working group would significantly assist the Province and Local Distribution Companies to meeting long-term conservation targets.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".