The impact of adopting an energy information system on household energy consumption: a dynamic difference-in-differences approach
Why this work is in the frame
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Bibliographic record
Abstract
This study analyzes effects of adopting an energy information system on energy use based on the advanced metering infrastructure (AMI) in South Korea. Adopting the AMI was randomly assigned by the Korean Government. However, the adoption timing varies across household, making it difficult to apply an ordinary difference-in-difference (DID) approach to measure the impact, given the variation in adoption timing across households. We use a dynamic DID that is suitable for estimating a causal effect of a policy whose treatment timing differs depending upon the individual. Results indicate that AMI adoption led to a reduction in household electricity consumption, peaking within 18 months after adoption with a slight rebound effect. Our findings suggest that providing information on energy consumption and cost in the household sector can significantly reduce electricity use by as much as one quarter of average monthly consumption. Reducing electricity use in households can be achieved in several ways, including monetary incentives as well as personal and social feedback provided by adopting the AMI, implying the importance of a dissemination policy.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it