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Record W4389329824 · doi:10.36939/ir.202312051543

Strengthening energy security through community energy planning in Churchill, Manitoba

2020· dissertation· en· W4389329824 on OpenAlexfundaboutno aff
Michael Kvern

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsnot available
FundersChurchill Northern Studies Centre
KeywordsEnergy securityRenewable energyEnergy planningGovernment (linguistics)Fossil fuelEnergy consumptionTourismEnergy (signal processing)EngineeringGeographyBusinessEnvironmental economicsEnvironmental resource managementEconomicsWaste managementArchaeologyElectrical engineering

Abstract

fetched live from OpenAlex

Imagine that it costs you $100 to fill your car’s gas tank, and no one outside your isolated community is working to improve things. This scenario is a reality for many northern remote communities like Churchill, Manitoba, where traditional energy security definitions and centralized systems have left them with unaffordable, unsustainable power. This thesis begins the process of community energy planning in Churchill by creating a community energy profile, and vision statements to guide a future energy plan. It also examines, from a Northern perspective, energy security definitions, and their effectiveness in remote communities. Twenty-three semi-structured snowball interviews and a workshop (n=12) identified community priorities for an energy vision statement and future energy plan. The energy profile was constructed in Microsoft Excel with data from utility companies and government and visualized using ArcGIS 10.7.1. Interview and workshop data were analysed using Nvivo12 to identify common themes. Being a remote community, Churchill has limited diversity in its energy sources. 75% of Churchill’s energy consumption is fossil fuels, including 5.4 million litres of jet fuel. Consequently, residents consume 35%more fossil fuel than the average Canadian. A significant portion of this can be attributed to the community’s remoteness, but also the high rates of tourism with a reliance on air travel. Such high rates of fossil fuel consumption are viewed negatively in the community. All workshop participants and twenty-one interviewees mention a strong desire for Churchill to utilize more renewable energy sources. Increasing renewable energy is a seen as crucial to reducing greenhouse gases and improving sustainability in the community. Opportunities to increase the efficiency of the energy system through upgrades to building conditions, improved technology, and energy efficiency programs are also identified and viewed favourably by participants. Energy generation occurs largely outside of the control of the community. Manitoba unique monopolistic electricity market, and government ownership of a significant portion of housing in the community results in most decisions being made outside of the community with little opportunity for input. Fourteen interview participants and all workshop participants identified increased agency as crucial to the future of the energy system. Draft vison statements for Churchill’s future community energy plan, and a reconceptualized definition of energy security included the identified elements of agency and efficiency. Churchill’s energy profile also provides the foundation to an energy plan as energy consumption is more precisely known and visualized. This research illustrates an example of energy planning to other northern communities, and in partnership with the CASES project provides ongoing support for the development and implementation of the plan.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.330

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0170.004
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.026
GPT teacher head0.279
Teacher spread0.253 · 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 designQualitative
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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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