Sure It Works, But How Long Does It Last? Persistence of Savings After Short-term Participation In Behavioral Programs
Bibliographic record
Abstract
Evaluators have documented the success of behavioral programs administered through regular delivery of paper energy reports in inducing energy savings. However, one remaining question about the programs involves the persistence of savings after program intervention is discontinued. This paper examines whether short term exposure to program intervention also results in continued energy savings after report cessation. In 2011, Connecticut Light & Power (CL&P) piloted a behavioral program that varied intervention duration among the participant group: One sub-group received reports for eight consecutive months (the discontinued sample), while other participant groups received reports for a full calendar year. The research compares the energy reduction rates of the discontinued sample to those receiving reports for a full year, examining energy savings for both the first eight months of the program when all groups received intervention and the remainder of the year after the discontinued sample stopped receiving reports. The analysis relies on monthly billing data for both program participants’ and program control group households. The evaluators examined electricity consumption for the baseline year of 2010 and the treatment period of 2011 and the first quarter of 2012. Regression analysis was used to isolate program savings and determine whether short term program exposure leads to long term energy savings. The results show that the discontinued group achieved statistically significant energy savings while receiving the reports and these savings persisted—albeit at a lower level—for four months after the cessation of treatment. However, by the fifth month post-treatment all significant energy savings were gone.
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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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".