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Record W2166754333 · doi:10.1142/s1464333213400012

STRATEGIC ENVIRONMENTAL ASSESSMENT BEST PRACTICE PROCESS ELEMENTS AND OUTCOMES IN THE INTERNATIONAL ELECTRICITY SECTOR

2013· article· en· W2166754333 on OpenAlexaff
Lisa White, Bram Noble

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsStrategic environmental assessmentElectricityProcess (computing)BusinessBest practiceEnvironmental planningEnvironmental resource managementEnvironmental impact assessmentImpact assessmentEnvironmental economicsEnvironmental scienceEngineeringComputer scienceEconomicsPolitical scienceManagement

Abstract

fetched live from OpenAlex

This paper examines the contribution of SEA in six international electricity sector planning case studies. All cases showed some "best practice" evidence such as participation, alternatives consideration and impact assessment; however, considerable variability was found in the types of alternatives considered and the approach to impact assessment and monitoring depending on the timing of SEA application in the PPP process. Regarding substantive contributions, SEA was identified by stakeholders as improving communication during planning and informing lower-level decision making, but fared less well in influencing the nature of the PPP at hand; only two cases clearly incorporated SEA recommendations into the final PPP. Overall, results show considerable potential for SEA to support PPP assessment and decision making in the electricity sector, but also a considerable need for improvements in understanding of the importance of the timing of SEA in the PPP process and how to integrate the results of SEA into PPP development.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.325
Teacher spread0.311 · 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
Published2013
Admission routes1
Has abstractyes

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