Sustainable Parking Infrastructure: Comparing Building Envelopes Across Renewable Energy-Integrated Prototypes
Bibliographic record
Abstract
This study evaluates the life cycle environmental and energy performance of two parking structure prototypes in London, Ontario, Canada: (1) an automated steel parking tower with photovoltaic (PV) panels on the roof and south-facing façade, and (2) a ground-level concrete parking structure with rooftop PV.Using Life Cycle Assessment (LCA), the study assesses environmental impacts from material production through the operational phase, considering material inputs, energy consumption, and renewable energy generation.Results indicate that Prototype 2 exhibits a lower overall environmental burden.However, differences between the prototypes remain relatively minor across specific impact categories.The study also finds that Prototype 1 performs better in the production phase, while Prototype 2 performs better in the use phase.Furthermore, when optimized with strategic PV positioning, Prototype 1 shows a 10% improvement in ecosystem quality, outperforming Prototype 2. This study underscores the role of parking structures as active contributors to net-zero developments rather than passive urban elements.Future research should explore the economic feasibility of coupling parking structures with buildings, assess a broader range of prototypes, investigate advancements in PV technology, examine the potential for waste heat recovery, and evaluate various end-of-life scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".