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Record W3014708259 · doi:10.22300/1949-8276.11.1.130

Drivers of Investment in Commercial Real Estate Sustainability: 2006–2018

2019· article· en· W3014708259 on OpenAlexaffabout
Michael Brooks, J.J. McArthur

Bibliographic record

VenueJournal of Sustainable Real Estate · 2019
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsReal estateBusinessSustainabilityInvestment (military)FinanceNatural resource economicsEconomicsPolitical science

Abstract

fetched live from OpenAlex

We investigate the factors (“drivers”) that motivated investment in energy efficiency in commercial real estate office buildings over the 2006–2011 and 2012–2017 period, and looking forward from 2018 in the context of growing concern over carbon emissions around the world. These insights were collected from large Canadian asset managers through interviews conducted in 2017 and 2018. Key findings were that (1) organizations noted an increasing number of factors driving investment decisions over the three periods; (2) cost drivers (payback period and anticipated financial returns) were the top two drivers in 2006–2017; (3) public relations factors became significantly more important looking forward, with brand (reputational impact) as the top-ranked driver and tenant attraction tied for third place; and (4) mitigation against risks such as resilience and anticipated compliance consistently increased in importance. This study contributes to a comprehensive understanding of past, present, and near-future sustainable real estate investment priorities, changing owner behaviors, and the perceived business case for building energy efficiency investments.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.238
Teacher spread0.232 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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