Green Roof Policy Optimization Algorithms and Microsimulations Benefits and Downsides of Green Roof Incentives and Mandates in San Francisco
Bibliographic record
Abstract
As the 21st Century progresses, developers are becoming more aware of their environmental footprint. As the Green Economy slowly gains its footing, developers will be expected to change current building practices to reflect the increasing demand to adapt to sustainability challenges. One such methodology used by LEED to evaluate the sustainability of a building is the implementation of a green roof, the installment of vegetation on the top of a building. There are many socioecological benefits that justify the implementation of a green roof, which explain why in recent years municipalities have enacted new policies to mandate or incentivize their implementation. Four optimization algorithms were developed to determine the size of a green roof delivering maximum Net Present Value (NPV) based off raw economics, San Francisco policy, Chicago policy, and Toronto policy. The models were then tested on a sample of 2001 random buildings in San Francisco and the results analyzed. The major findings are that incentives as described by the Chicago policy do not increase the amount of green roofing installed, and mandates as enacted by Toronto and San Francisco do not apply to enough buildings to be meaningful.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".