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Record W7128091254 · doi:10.22260/crc-csce-2025/0064

Sustainable Parking Infrastructure: Comparing Building Envelopes Across Renewable Energy-Integrated Prototypes

2025· article· W7128091254 on OpenAlexaboutno aff
Zahra Al-Shatnawi, Caroline Hachem-Vermette

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicSolar Energy Systems and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energySustainabilityMatching (statistics)Key (lock)Sustainable development

Abstract

fetched live from OpenAlex

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 faade, 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.234
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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