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Record W2001583536 · doi:10.1680/ensu.2006.159.4.155

Sustainable energy? A feasibility study for Eastside, Birmingham, UK

2006· article· en· W2001583536 on OpenAlexaboutno aff
Dexter V. L. Hunt, Ian Jefferson, Ljubomir Jankovic, K. Hunot

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Engineering Sustainability · 2006
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsRenewable energyRedevelopmentBrownfieldSustainabilityEnvironmental economicsLimitingBusinessQuarter (Canadian coin)Investment (military)Environmental planningEngineeringCivil engineeringEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

Eastside, a 130 ha brownfield site located to the eastern side of Birmingham's city centre, is undergoing social, economic and environmental changes, driven mainly through public and private investment estimated to be worth £6 billion. The regeneration programme is well under way and it aims to turn a once deprived inner-city area into the regions first ‘sustainability quarter’ Achieving a sustainable quarter, in terms of energy, will require reductions to be made in energy demands compared to typical practice, for example through more thermally efficient buildings and utilisation of low-energy technologies. In addition it will require these demands to be met through renewable technologies rather than fossil fuels. This paper presents estimates for the total energy demands from the various developments planned within Eastside assuming typical and good-practice scenarios. The paper then assesses the feasibility of introducing various renewable energy supply technologies and combined heat and power (CHP) technologies in order to meet these demands. Finally the paper presents a simplified costing scheme for assessing the potential of renewable technologies to secure energy supplies while limiting carbon emissions. The renewable technologies are compared directly, aiming at providing an independent viewpoint for decision makers when considering which technologies to adopt. While the study focuses on Eastside, the lessons learned from this study are vitally important for redevelopment programmes being undertaken elsewhere.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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.

Study designSimulation or modeling
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
Published2006
Admission routes1
Has abstractyes

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