Energy and Carbon-Emission Analysis of Integrated Green-Roof Photovoltaic Systems: Probabilistic Approach
Bibliographic record
Abstract
Roofs are important components of buildings and can be designed and/or retrofitted with photovoltaic (PV) and green-roof (GR) systems to produce energy and to improve stormwater management. Traditionally, GRs and PVs have been viewed as direct competitors vying for the same roof space. However, with correct design, synergy effects arise when combining both technologies (GR-PV). In this paper, a probabilistic analysis is performed to examine the potential energy and carbon emissions of GR, PV, and GR-PV systems. The analysis demonstrates that a GR-PV system is a low-risk investment generating lower energy and carbon-emission payback time in comparison with separate GR and PV systems. Furthermore, the average of net energy of this technology is 7.3 and 1.3 times higher than separate GR and PV systems, respectively. In addition, the installation of GR-PV systems throughout the City of Toronto could supply 16% of the electricity by PVs and reduce 12% of energy demand (i.e., heating and cooling) by GRs. However, extensive modification of the electrical grid would be needed for efficient collection and transmission of PV-generated power.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".