Energy Efficiency and Conservation Block Grant (EECBG): Better Buildings Neighborhood Program Final Report
Bibliographic record
Abstract
The Neighbor to Neighbor Energy Challenge (N2N) brought together a consortium of 14 leading clean energy rural, suburban, and low income communities throughout Connecticut. N2N was awarded $4.2 million from the U.S. Department of Energy (DOE) competitive BetterBuildings Neighborhood Program on August 10, 2010 to run a two-year pilot program (plus one year of transition and evaluation) (Award No. EMCBC- 00969-10). N2N tested innovative program models and hypotheses for improving Connecticut’s existing residential energy efficiency programs that are overseen by the ratepayer fund board and administered by CT utilities. N2N’s original goal was to engage 10 percent of households in participating communities to reduce their energy usage by 20 percent through energy upgrades and clean energy measures. N2N planned for customers to complete more comprehensive whole-home energy efficiency and clean energy measures and to achieve broader penetration than existing utility-administered regulated programs. Since this was an ARRA award, we report the following figures on job creation in Table 1. Since N2N is not continuing in its current form, we do not provide figures on job retention. Table 1 N2N Job Creation by Quarter Jobs Created 2010 Q4 6.65 2011 Q1 7.13 2011 Q2 4.98 2011 Q3 9.66 2011 Q4 5.43 2012 Q1 11.11 2012 Q2 6.85 2012 Q3 6.29 2012 Q4 6.77 2013 Q1 5.57 2013 Q2 8.35 2013 Q3 6.52 Total 85.31 The N2N team encountered several gaps in the existing efficiency program performance that hindered meeting N2N’s and DOE’s short-term program goals, as well as the State of Connecticut’s long-term energy, efficiency, and carbon reduction goals. However, despite the slow program start, N2N found evidence of increasing upgrade uptake rates over time, due to delayed customer action of one to two years from N2N introduction to completion of deeper household upgrades. Two main social/behavioral principles have contributed to driving deeper upgrades in CT: 1. Word of mouth, where people share their experience with others, which leads to others to take action; and 2. Self-herding, where people follow past behavior, which leads to deeper and deeper actions within individual households.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".