A CRITICAL REVIEW OF THE EXISTING METHODOLOGIES FOR THE ESTIMATION OF THE VALUE OF UNDERGROUND SPACE
Bibliographic record
Abstract
Ever since 2020, more than 50% of the world’s population has been concentrated in large urban centers. Coupled with the rapidly growing global population, this shift has exacerbated major issues and challenges within modern cities. These problems span environmental, social, transportation, and energy-related concerns that must be addressed to ensure the sustainable development of emerging „mega-cities.“ One sustainable solution that has gained prominence over the past 30 years is the utilization of Urban Underground Space (UUS). Countries like Singapore and Japan, and cities such as Montreal and Paris, have consistently developed their underground spaces to create metro lines, underground parking lots, and roads to cope with these modern challenges. However, in many other parts of the world, underground projects remain unrealized, often stalled at the decision-making stage. The primary reason for this stagnation is the unfavorable comparison between underground structures and their above-ground counterparts, primarily due to the significantly higher capital costs associated with underground development. However, it is misleading to compare an underground project with an above-ground facility of the same utility and base the decision solely on construction cost estimates. UUS possesses a „hidden“ value, often referred to as the indirect value, which represents the monetary value of the indirect benefits that arise from UUS development. This indirect value is frequently overlooked during the decision-making process because it is challenging to quantify in monetary terms. However, if systematically integrated into the decision-making process, the indirect value could allow underground facilities to compete directly with above-ground solutions, making them more attractive investments. Together, the indirect value and direct value constitute the real value of UUS. Consequently, the scope of the current study is to gather and critically evaluate all existing methodologies for estimating the real value, direct value, or indirect value, including losses that may arise from UUS development. To ensure comprehensive coverage of all available techniques, the study utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. The existing methodologies were thoroughly presented, highlighting their strengths and weaknesses, and proposing ways to improve them in the future.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".