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Record W3042747385 · doi:10.3390/en13143642

Energy Strategies, the Urban Dimension, and Spatial Planning

2020· article· en· W3042747385 on OpenAlexaboutno aff
Karishma Asarpota, Vincent Nadin

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
FundersTechnische Universiteit Delft
KeywordsUrbanizationUrban planningUrban metabolismUrban densitySpatial planningEnvironmental planningEnergy consumptionEfficient energy useDimension (graph theory)Urban climateUrban agglomerationEnergy (signal processing)Energy transitionEnergy planningEnvironmental economicsEconomic geographyGeographyBusinessEnvironmental resource managementCivil engineeringEconomic growthRenewable energyEnvironmental scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

The UN Paris Agreement of November 2016 recognises the need for a ‘cleaner and more efficient energy system’ as a core policy goal to address climate change. The spatial and urban form of cities is a key factor in achieving more efficient energy production and consumption and becomes more important with rapid urbanisation across much of the world. City urban form and planning are therefore potentially powerful levers for the energy transition. This paper examines the extent to which city ‘energy strategies’ address the critical spatial and urban form characteristics of cities as a means to achieve a more efficient energy system. We construct an assessment framework of key aspects of the spatial and urban development of cities related to transport and accessibility and urban form. The framework is used to assess the degree to which energy strategies take into consideration aspects of urban development in four cities that are taking significant action on the energy policy: Hong Kong Oakland, Oslo, and Vancouver. We conclude that in these cities there is only fragmentary consideration of the potential of shaping spatial and urban form in the interests of energy efficiency.

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 score0.704
Threshold uncertainty score0.996

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 head0.024
GPT teacher head0.270
Teacher spread0.246 · 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

Citations74
Published2020
Admission routes1
Has abstractyes

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