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Record W7038430724

Integrating Social Concerns Into Regional Renewable Energy Resource Assessments: A Case Study in Rigolet, NL, Canada

2022· dissertation· en· W7038430724 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2022
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTidal powerResource (disambiguation)Marine energyRenewable energyWind powerBackupEnergy (signal processing)IndigenousEnergy planningEnergy policy
DOInot available

Abstract

fetched live from OpenAlex

This thesis demonstrates a novel approach to integrating social concerns regarding energy development into renewable energy resource assessment. An extensive literature review of the social aspects of energy identifies five perspectives (acceptability, social license, energy justice, community energy, Indigenous criticism) that define social issues in energy development. These perspectives are applied in a review of technical resource assessment and planning literature to identify the approaches taken there. The resource assessment literature demonstrates an instrumentalized approach to social license, emphasising conflict avoidance. More outcome-oriented perspectives that emphasised community ownership, distribution of project benefits, alignment with local ways of life, and similar concerns were not widely used in assessment and planning. Informed by these findings, a mixed methods approach to resource assessment was applied to the tidal energy resource near the community of Rigolet, Nunatsiavut (Labrador, Canada). Key informant interviews were conducted with participants in the community. Participants understood different energy resources well, and aligned with the outcome-focused perspectives identified in the literature. Results from these interviews were used to identify the resources most preferred by participants: wind, solar, and tidal energy. Computer modeling of the tides allowed for comparison of tidal resources near Rigolet to wind, solar, and existing diesel systems through the microgrid optimisation software HOMER. These comparisons suggest that although there is an abundant tidal resource near Rigolet, existing commercial tidal energy convertors are ill-suited to it. Future low-flow tidal energy turbines may enable Rigolet to pursue tidal energy as part of its energy future. Near term, wind energy with battery backup may be able to significantly reduce diesel consumption in Rigolet, reducing both cost of energy and pollutant emissions. The integrative approach taken to social issues as part of energy resource assessment in this study is useful to energy modelers, planners, engineers, geographers, and policymakers. Social conflict complicates energy transitions, and this thesis demonstrates a synthesising approach to considering social issues from the outset of assessment and planning. The methods demonstrated are most applicable to small geographic scales, but lessons are identified for intermediate and large-scale studies. The importance of scale in considering methods is a key finding.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0280.005
Scholarly communication0.0040.001
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes1
Has abstractyes

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