Evaluating the Energy-Saving Effects of a Utility Demand-Side Management Program: A Difference-in-Difference Coarsened Exact Matching Approach
Bibliographic record
Abstract
This paper seeks to estimate the energy-saving effect of a Demand-Side Management program, specifically Gainesville Regional Utility’s (GRU) high-efficiency central Air Conditioner (AC) rebate program in which GRU offers incentives to its customers to replace their old, low-efficiency AC unit with a high-efficiency model. We use a difference-in-difference coarsened exact matching approach to reduce the imbalance of pre-treatment characteristics between treated and control households. We find substantial annual energy savings of the high-efficiency AC program. We disaggregate the energy-saving effects into summer peak effects, winter peak effects, and non-peak effects. The results indicate that the summer peak effects are substantial and statistically significant while there are little or no statistically significant effects of the program on winter peak demand. Also, by following program participants over a three-year period, we find that there is no statistically significant rebound effect of the high-efficiency AC rebate program.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".