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Record W2765315693 · doi:10.1061/9780784481196.008

Engineers Are Telling the TBL-CBA Value Story: Financial + Social + Environmental Returns from Sustainable Infrastructure

2017· article· en· W2765315693 on OpenAlexaff
John F. Williams, James L. Grant, Peter J. Hall, Kari Hewitt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsImpact
Fundersnot available
KeywordsTriple bottom lineProcurementSustainabilityReturn on investmentWork (physics)StakeholderBusinessInvestment (military)Net present valueEnvironmental economicsBusiness caseFinanceEngineeringMarketingProcess managementEconomicsProduction (economics)Management

Abstract

fetched live from OpenAlex

This paper will describe how civil engineers are playing new roles in delivering sustainable infrastructure projects. Beyond traditional tasks including steel and concrete takeoffs, engineers are now calculating triple bottom line or TBL returns (financial/economic + social + environmental returns on investment) for design alternatives under their control. They have learned to use cost-benefit, life cycle cost, sustainable return on investment based TBL analysis to determine project value which is essential to competing in best value based procurements. They are providing data sourced from practical and accessible economic assessment tools to inform community sustainability and resilience plans. They are revealing the value of their work through compelling business cases with outputs that address returns by project stakeholder group. Finally, they have discovered the benefits (customer service and competitive) of being able to test and tune their designs as decisions are made with the goal of delivering optimal returns.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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