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Record W6947886038 · doi:10.4224/40003207

Emissions analysis of controlled environment agriculture in the Canadian Arctic

2023· report· en· W6947886038 on OpenAlexaffvenueabout

Bibliographic record

VenueNPARC · 2023
Typereport
Languageen
FieldImmunology and Microbiology
TopicToxoplasma gondii Research Studies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsGreenhouse gasAgricultureCarbon footprintArcticFossil fuelAir quality indexCarbon dioxideThe arcticClimate change

Abstract

fetched live from OpenAlex

Evidence presented in previous studies show that far-north Canadian populations face significant food security challenges, including quality, price, and availability of fresh fruits and vegetables. To target some of these challenges, several methods of protected agriculture have been attempted in recent years in high latitude locations in Canada. Although many studies show that it is feasible to produce quality fruits and vegetables in Controlled Environment Agriculture (CEA) facilities in the Arctic, there are concerns about the energy emissions from operating such facilities. The high energy demand of CEA facilities and the high dependence on fossil fuels in the Canadian Arctic are factors that must be considered from a greenhouse gas emissions perspective. Thus, the objective of this study is to estimate the carbon dioxide equivalent emissions associated with the production of fruits and vegetables in a CEA facility located in the Canadian Arctic. Specifically, this study aims to answer the following research question: what is the relative quantity of greenhouse gases emitted for produce grown locally in the Arctic in a CEA facility compared to produce transported in from southern Canada? The carbon dioxide equivalent footprint of produce cultivated in southern Canada was quantified based on previous studies and the emissions of air transportation of food were estimated based on two transportation paths from southern Canada to locations in the Canadian Arctic. The emissions associated with food produced in the Arctic were estimated based on modelling of the energy use of a CEA facility located in Cambridge Bay, Nunavut. Considering that the production of fruits and vegetables varies significantly depending on the type of produce and the referenced study, three different yield values (low, high, and average production) were selected for performing a sensitivity analysis of the total CEA emissions. Cases of on-site renewable energy for CEA at increasing scales were modelled and compared to a baseline model with no renewable generation. The results of this study indicate that emissions from produce grown locally in the Arctic in a CEA facility could be at least eight times that of the emissions associated with products shipped from southern Canada if no local renewable generation is used. Several factors could change this result – the use of renewable energy resources and more efficient production practices would reduce the emissions associated with CEA in these locations. Further, the transportation analysis did not look at the most extreme routes, such as the northernmost communities, which would increase the transportation carbon dioxide equivalent emissions, although likely only by a factor of two or three at most. While the societal needs for the supply of fresh food is more complex than just examining the related greenhouse gas emissions, this study indicates that until there is a significant proportion of renewable energy readily available in Canadian Arctic locations, CEA operated in these locations would likely result in significantly higher emissions per mass of food produced than air transport of food from lower latitude locations.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.301
Teacher spread0.261 · 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
Published2023
Admission routes3
Has abstractyes

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