THE GREENING OF THE PARKING INDUSTRY –BEST PRACTICES, AND A NEW CERTIFICATION PROGRAM FOR PARKING STRUCTURES
Bibliographic record
Abstract
INTRODUCTION Sustainable building design is now mainstream. The United States Green Building Council (USGBC) and the LEED Ratings Systems have moved the market, proving that “a rising tide lifts all boats.” Consumers now factor environmental effect into their buying decisions, a trend that looks to continue and intensify with the millennial generation. Renters and homeowners are willing to pay more for high-performing and better quality housing based on environmental standards. Companies recognize that indoor air quality and daylighting improve employee performance and boost retention rates. And even bottom-line businesses are investing in benchmarking and efficiency projects that have the right return on investment rates. The Urban Land Institute's Greenprint Center for Building Performance serves as a prime example of the ways developers implement benchmarking, energy efficiency, and high-performance standards in the major real estate sectors: office, retail, industrial, multifamily, and hotel. Energy efficiency gains and higher-quality buildings in each of these sectors will be essential to reducing emissions and energy use. While the transportation sector implements its own standards and benchmarking tools to achieve greater levels of sustainability across all modes of transportation, one industry that stands at the nexus of every building type and the transportation modes used to access them is parking. There is a key consideration that links land use and transportation: Parking is often the connection between where we are and where we want to go. Parking may not have been at the forefront of the sustainability discussion or the media surrounding it in previous years, but the professionals who work in the world of parking know exactly how important the role of their industry is to the success of our cities, towns, institutions, and businesses. Parking is big business; it is conservatively estimated to be a $30 billion industry. It directly contributes to the economic vitality and accessibility of where we live, work, and play.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".