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Why is there an implementation gap in community energy planning?

2021· article· en· W3168563659 on OpenAlex
Rose Murphy, Aaron Pardy, Morgan Braglewicz, Brett Zuehlke, Mark Jaccard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Planning and Policy / Aménagement et politique au Canada · 2021
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaSimon Fraser UniversityPacific Institute for Climate Solutions
KeywordsJurisdictionGreenhouse gasGovernment (linguistics)Renewable energyBusinessLocal governmentEnergy consumptionEnergy policyConsumption (sociology)Environmental economicsEnvironmental planningPublic administrationEconomicsPolitical scienceEngineeringGeographySociology

Abstract

fetched live from OpenAlex

In community energy planning, a persistent disconnect has been observed between the targets and plans announced by local governments and the application of effective policy to reduce energy consumption and greenhouse gas (GHG) emissions. We use two methods to explore this implementation gap. First, we apply energy-economy modelling tools at the urban level to evaluate the effectiveness of various policy options available to local governments. Our case study for these exercises is the leading jurisdiction of Vancouver, British Columbia. Second, we report and analyze the results of a survey we administered to community energy practitioners in Canada. The modelling results point to jurisdictional reach as an important contributor to the implementation gap. We find that, while Vancouver can make significant progress by implementing policies that are clearly within its jurisdiction, the city is unlikely to meet its ambitious renewable energy and GHG emissions targets without the support of higher levels of government. The survey responses suggest that capacity limitations of local government also have a role in perpetuating the implementation gap.

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.

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 score0.675
Threshold uncertainty score0.912

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 head0.019
GPT teacher head0.278
Teacher spread0.259 · 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