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Record W6922137362 · doi:10.11575/prism/39584

Small Modular Reactors (SMRs) in Saskatchewan – Should SMRs be added to Saskatchewan’s energy portfolio?

2021· other· en· W6922137362 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2021
Typeother
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasNuclear powerPer capitaElectricityNatural gasPopulationCoalElectricity generationModular design

Abstract

fetched live from OpenAlex

Should Saskatchewan use Small Modular Reactors (SMRs) to help lower Greenhouse Gas Emissions (GHG) emitted by generating electricity? In this capstone, I identify and assess a number of policy considerations that are at play in answering this question. Canada committed to lowering its GHG emissions as part of the Paris Climate Agreement of 2015. Part of this commitment includes a phase out of coal-fired energy by 2030, which has a great impact on fossil fuel-dependent provinces such as Saskatchewan. Saskatchewan, in present day and in projections out to 2030, has the highest per capita GHG emissions of all provinces in Canada. While part of Saskatchewan’s 2030 lowered emissions strategy involves moving away from heavy-emitting coal to cleaner-burning natural gas, a considerable reliance on natural gas could restrict the province’s ability to further decrease its emissions. Subsequently, Saskatchewan is evaluating low-emission nuclear power use in the form of SMRs as a solution. Canada is already a world leader in nuclear power, but unlike Canada’s traditional nuclear CANDU fleet, SMRs are more suited to smaller population jurisdictions, such as Saskatchewan, due to their lesser output, as a means of decreasing emissions. Wholly imperative to Saskatchewan’s evaluation, however, is the financial cost of SMRs. Canadian governments, both Federal and Provincial, have banded together to support an SMR industry and Canadians seem receptive to its potential. For Saskatchewan, the use of SMRs as an on-grid source of electricity is foremost dependent on its costs as compared to alternatives such as natural gas. The initial wave of SMRs, termed First-of-a-kind (FOAK) reactors, have elevated Levelized Costs of Electricity (LCOE) making them uncompetitive. Given more time leading to a commercialization process, whereby costs could be driven down either by domestic production efficiency or international exports, SMRs become a formidable choice from a competitive LCOE front. The province’s utility, Saskpower, is exploring investing and integrating SMRs into its energy portfolio between 2032-2042. If SMRs implementation is rejected along this timeline, another window exists for their integration from 2042 and beyond. Playing into Saskpower’s SMR exploration are important factors such as: an outdated nuclear energy regulatory framework; financial dangers in the development of SMR technology; a nuclear waste buildup with no current long-term disposal mechanism; and geographic and climate change concerns in Saskatchewan. In the last section of this Capstone, I take a position recommending that Saskatchewan not pursue the use of SMR technology in their energy portfolio from 2032-2042, and instead adopt a policy that adds SMRs starting in 2042 and beyond. There are five reasons for this. First, by 2042 and beyond, the Deep Geological Repository (DGR) for high-level nuclear waste could be built, limiting one of the risk factors. Second, costs could realistically be driven down either from the beginnings of a domestic sales market or from international exports, meaning a reduced LCOE from commercialization. Third, natural gas power plants, the likely choice to maintain Saskatchewan’s power needs from 2032-2042, would naturally be nearing the end of their operating lives by the time SMRs are completed and running, meaning an organic transition could take place. A fourth reason is that the federal regulatory framework for SMRs has a greater chance of being streamlined by 2042. Finally, Saskatchewan will still have suitable geographic sites for SMRs in 2050 and even 2080 despite climate change.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.194
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.003

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 head0.071
GPT teacher head0.286
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicHistory of Computing TechnologiesFrench-language works237,207