Vintage Capital, Technology Adoption and Electricity Demand-Side Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Demand-side Management (DSM) programs by electricity utilities report substantial energy savings that often receive little support from empirical studies. We argue that this discrepancy results from an inherently static view of technology adoption by utilities when estimating future energy savings. We illustrate this through a simple model of technology adoption, in which households operate different vintages of appliances and have heterogenous forecasts about the rate of future technological progress. An “energy efficiency gap” arises when households under-estimate the true rate of technological progress. We parameterize the model using data on refrigerators and show that a DSM program that subsidizes adoption of energy-efficient refrigerators yields small energy saving that, in most cases, do not justify the cost of the subsidy.
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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 | 0.001 | 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 | 0.001 | 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