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Record W2946847946

Densification of Vancouver's neighbourhoods: Energy use, emissions, and affordability

2018· article· en· W2946847946 on OpenAlexfundaboutno aff
Aaron Pardy

Bibliographic record

VenueSummit (Simon Fraser University) · 2018
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaPacific Institute for Climate Solutions
KeywordsGeographyEnvironmental planning
DOInot available

Abstract

fetched live from OpenAlex

The City of Vancouver in British Columbia has committed to use 100% renewable energy and reduce emissions by 80% by 2050. Like many cities in North America, much of the Vancouver's land area currently consists of single-family detached home neighbourhoods—a type of land use that has been associated with higher than average per capita energy use and emissions. In this study, I used an energy-economy-emissions model, CIMS, to evaluate how densifying these low-density neighbourhoods with medium-density housing forms would influence energy use, emissions, and home energy and personal transportation affordability. While densification was found to have a modest influence on reducing building emissions, zero-emission building regulations were found to be much more effective, highlighting the importance of energy-switching policy for residential building decarbonization. However, an affordability co-benefit of densification was found: smaller, more energy efficient dwellings in dense building forms reduce annual energy costs relative to detached homes, especially when coordinated with policies and actions to limit vehicle ownership.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.910

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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 designNot applicable
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
Published2018
Admission routes2
Has abstractyes

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