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Record W3212334567 · doi:10.11575/prism/32023

The feasibility of reducing Nunavut's diesel reliance with a transition to renewable energy technologies, primarily solar and wind energy.

2017· dissertation· en· W3212334567 on OpenAlexaboutno aff
Kiran Gurm

Bibliographic record

VenuePRISM (University of Calgary) · 2017
Typedissertation
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyWind powerEnergy transitionDiesel fuelEnvironmental scienceEngineeringNatural resource economicsWaste managementEconomicsElectrical engineering

Abstract

fetched live from OpenAlex

Residents in Nunavut have developed a complete reliance on fossil fuels for their energy needs; 100 percent of the territory's generated electricity is from diesel. Nunavut does not have a centralized grid system as all 25 communities are remote and separated by vast distances, making interconnection infeasible. Electricity needs are met with the use of diesel fuel purchased, shipped and stored in bulk. This dependency has high logistical and financial costs resulting in expensive energy that is heavily subsidized by the territorial government. There is clear motivation from an economic, technical, social and environmental standpoint to investigate the viability of alternative energy sources and decrease the widespread use of diesel in Nunavut. This study evaluates whether solar and wind energy can be part of the solution to begin transitioning Nunavut away from a complete diesel reliance. Furthermore, this study assesses the ability for renewables to reduce Nunavut's energy costs and provide social and environmental benefits for the territory. Feasibility is assessed via a social cost-benefit analysis, whereby the costs and benefits of a renewable energy project are evaluated in comparison to the costs incurred by remaining solely on diesel. By quantifying the cost saved through decreased diesel consumption, this study determines if the initial capital cost of renewable technology can be recovered in a 20-year time frame and potentially result in a cost savings for five communities. Prior to performing the costbenefit analysis the number of communities was narrowed down to the five through a community selection process. This was based on comparing key characteristics of communities already using renewable energy in Arctic communities, as well as data on the availability of renewable resources for each community in Nunavut. The results from the community selection process found that Arviat, Baker Lake, Iqaluit, Rankin Inlet and Sanikiluaq were the most promising communities. The cost-benefit analysis indicated that for all five communities, solar energy was not an economically viable option. Wind energy was found to be feasible as the net present value for all communities was positive. The wind project was able to breakeven and generated further savings the community due to decreased diesel purchases, lower diesel generator operation and maintenance costs and avoided carbon emissions. The wind-diesel system outperformed the solar-diesel systems because it was able to produce a higher level of annual renewable energy generation resulting in a greater reduction of diesel consumption. Although many barriers still exist due to the unpredictable and intermittent nature of renewables as well as the high initial capital costs, the results from this preliminary study indicate that wind energy has the potential to be economically viable in Nunavut.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.659

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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