Public Rooftop Revolution Third in a series of reports from ILSR on the solar rooftop revolution changing America
Bibliographic record
Abstract
In 2012, ILSR published a pair of reports that projected, by 2021,10 % of electricity in the U.S. could come from solar and at a lower price—without subsidies—than utility-provided electricity. In 2014 and 2015, Environment America’s Shining Cities reports examined how cities were catalysts for solar development. However, there has been a missing piece in the examination of how cities can support solar energy: what city leaders have done and can do to use solar on their own buildings. ILSR estimates that over 5,000 megawatts (MW) of solar could be inexpensively installed almost immediately on municipal property—more than a quarter of the nationwide total solar capacity through September 2014. This includes just the municipal buildings of the approximately 200 cities with 100,000 or greater population and it could save millions in energy costs. But it requires city officials to overcome a few, surmountable barriers. The Public Rooftop Solar Opportunity The opportunity of municipal solar spans financial savings, pollution reductions, and job creation: • Energy Savings: New Bedford, MA, is saving $6 to $7 million per year on electricity through its 16 MW of solar installations on municipal properties, which is 2.5 % of the entire city budget. • Greenhouse Gas Emissions Reductions: Maximizing New York City's solar potential with 410 MW of solar would reduce emissions by 1.78 million metric tons, 3.7 % of the city’s total emissions. • Significant Economic Impact: Maximizing Kansas City’s municipal solar potential of 70 MW could create 1400 jobs and add $175 million to the local economy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".