Choosing to Pay More for Electricity: an experiment on the level of residential consumer cooperation
Bibliographic record
Abstract
Reducing energy consumption and carbon emissions are two cornerstones of the fight against climate change. Signaling negative externalities of individual consumption on the environment is at the heart of public policies, and usually materializes through an increase in the price of polluting good and services. However, social resistance typically arises when such policies are implemented. In this experiment, we are interested in testing the context in which individuals would be willing to pay more for electricity. We use the situation of Québec (Canada), where low-cost hydropower sold below market value, akin to a consumption subsidy, leads to high residential consumption. Increasing regulated prices closer to their market value would result in a direct welfare gain and free some green energy, reducing greenhouse gases (GHG) in other sectors. The choice to pay more is a prisoner’s dilemma, and we find in this framework that giving clear and transparent information on the consequences of the price increase induces a majority of people to choose to pay more. In addition to the economic benefit of the public good, the presence of the environmental benefit increases contributions. Participants with a more severe budget constraint tend to contribute less. These results are encouraging for the development of efficient energy policies reducing GHG 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".