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Record W7162035781 · doi:10.82308/752

Water-centric approach to developing green infrastructure (framework and cost)

2014· dissertation· en· W7162035781 on OpenAlexaboutno aff
P. Beauchamp

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)WorkflowSustainabilityIdentification (biology)Bridge (graph theory)StructuringIntegrated business planning

Abstract

fetched live from OpenAlex

WATER-CENTRIC APPROACH TO DEVELOPING GREEN INFRASTRUCTURE: Framework and CostPierre Beauchamp, P. Eng., 15 avril 2014AbstractGreen infrastructure (GI) has emerged as an active term of reference in project development planning. However, elaboration and discussion of integrated frameworks to assist engineering organizations in planning the start-up of new projects are largely absent from GI research literature, particularly in the context of greening and sustainability. The present study attempts to bridge this gap by developing and proposing an integrated framework focused on the start-up development of green projects relating to storm water, water supply, and wastewater.The present study's first objective was to explore the use of fully integrated GI in the engineering design of a biophilic development incorporating sustainability principles. To achieve the desired teamwork, a clear sequence of tasks to define the workflow was required. A review of the literature led to the identification of several different approaches, from which I selected four, improved, and then employed them to build a ready-to-use framework of sequenced tasks. These tasks included all components of water management (precipitation and drainage, water supply and wastewater). A case study in China employed in testing this framework demonstrated that all GI components could be integrated into one approach. While the structuring of an integrated water-centric development (IWCD) approach was found to be applicable to a wide range of projects, appropriate capacity building was critical to its success.In support of the study's second objective, the newly proposed framework was implemented to compare, in the form of a feasibility study, the economic benefits of investment and overall cost of designing green with those of designing conventionally in the case of a new institutional pole for the city of Vaudreuil-Dorion, Quebec, Canada. While the study showed increases in the value of GI projects to mirror the construction costs of such projects, it also found that implementing GI (vs. conventional) infrastructure can result in savings in both construction and life cycle costs. Therefore, GI can provide significant economic benefits to cities.The study showed that a GI project including components from water source to wastewater disposal would cost 15 percent more, at the level of each housing unit, than a conventional infrastructure design. However, the study also demonstrated that the value of each housing unit would be 15 to 27 percent greater in a green neighborhood than in a conventionally designed neighborhood. This would provide an equivalent increase in tax revenues for the municipality. Although many frameworks have been proposed for stimulating a green urban agenda, few have offered a start-up methodology for incorporating biophilia within the engineer's design. This study served to develop a new integrated framework for storm water, wastewater, water supply, and street layout for GI projects.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.006
Science and technology studies0.0020.004
Scholarly communication0.0060.006
Open science0.0030.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2014
Admission routes1
Has abstractyes

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