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Record W7065339017

The Economics of Adaptive Reuse of Old Buildings: A Financial Feasibility Study & Analysis

2007· dissertation· en· W7065339017 on OpenAlexaffabout

Bibliographic record

VenueUWSpace (University of Waterloo) · 2007
Typedissertation
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsRegional Municipality of Waterloo
Fundersnot available
KeywordsAdaptive reuseReuseInvestment (military)Government (linguistics)Private sectorCost–benefit analysis
DOInot available

Abstract

fetched live from OpenAlex

The debate about the financial feasibility of adaptive reuse is high among investors, planners, policy makers and heritage advocates. The old argument that it is more profitable to demolish the old brick box and replace it with a new structure have left the streets of many cities across North America and Europe with abandoned and neglected sites. Traditionally, investors and owners of such properties have shown minimal interest in investing in the rehabilitation and reuse of these buildings. Still, a growing number of successful projects featuring innovative building renovation and reuse are emerging across the province. 
\nGovernments at all levels have in fact started implementing a wide range of programs and policies to stimulate private investment in old, abandoned and underutilized buildings. Such policies have led to several innovative and successful stories across the province. However, few jurisdictions have taken full advantage of the potential economic, social, and environmental opportunities that these types of investments entail. 
\nThis study examines, from a private sector perspective, the economic costs and benefits of adaptive reuse in Ontario, and compares it with other types of new construction development scenarios with an aim to determine the characteristics of success. It investigates the potential effectiveness of various government policies and programs designed to stimulate investment in adaptive reuse in Ontario by conducting financial comparisons and analyses with other types of hypothetical new construction development options.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2007
Admission routes2
Has abstractyes

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