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Record W2538731021 · doi:10.1017/s0008423916000846

Developing Green Cities: Explaining Variation in Canadian Green Building Policies

2016· article· en· W2538731021 on OpenAlexaffabout
Elizabeth Schwartz

Bibliographic record

VenueCanadian Journal of Political Science · 2016
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMandateGreenhouse gasBusinessPoliticsSustainabilityPublic policyClimate changeEnvironmental planningEconomic growthPolitical scienceEconomicsEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Buildings produce a large proportion of Canada's greenhouse gas emissions and municipalities control a number of policy levers that can help to reduce those emissions. This article explains variation among Canadian cities regarding policies adopted to reduce greenhouse gas emissions, with a particular focus on green building standards. By applying insights from the study of the politics of public policy to urban politics, this article finds that while electoral disincentives prevent most cities from enacting high impact green building policies, the success of some cities can be attributed to the influence of independent municipal environment departments. These departments facilitate policy learning by providing information and resources. The findings suggest that policy makers could improve the effectiveness of local climate change policy by creating municipal environment departments that have organizational capacity—funding, staff, and a cross-cutting mandate—and are insulated from interference from politicians and line departments.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.264
Teacher spread0.243 · 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 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

Citations6
Published2016
Admission routes2
Has abstractyes

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