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Record W2080585548 · doi:10.1080/14615517.2012.746836

Strategic environmental assessment in the electricity sector: an application to electricity supply planning, Saskatchewan, Canada

2012· article· en· W2080585548 on OpenAlexafffundabout
Lisa White, Bram Noble

Bibliographic record

VenueImpact Assessment and Project Appraisal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of Saskatchewan
FundersSaskPower
KeywordsElectricitySustainabilityStrategic environmental assessmentOperationalizationMains electricityEnvironmental economicsTransparency (behavior)BusinessContext (archaeology)Flexibility (engineering)Environmental resource managementEnvironmental impact assessmentEnvironmental scienceComputer scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

A strategic environmental assessment (SEA) framework for electricity sector planning is developed and applied to evaluate electricity supply scenarios for Saskatchewan, Canada. The overall goal of the SEA application was to identify a preferred future electricity production path, demonstrate the application of a quantitative SEA process that operationalizes sustainability principles through the use of assessment criteria, and examine the methodological implications resulting from the application of a structured SEA framework. Results of the application identified a renewables-focused electricity supply preference, but with several implications for electricity sector investment and sustainability, including increased infrastructure requirements and increased cost of electricity. Results also demonstrate a practical approach to the operationalization of sustainability through the application of assessment criteria that are linked to higher level principles. The use of structure in the SEA process provided for replicability, transparency and the ability to quantify issues of uncertainty in Plan, program and policy (PPP) decision-making, while at the same time maintaining flexibility to tailor the SEA framework to the electricity sector context.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.359
Teacher spread0.337 · 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 designObservational
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

Citations17
Published2012
Admission routes3
Has abstractyes

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