Geothermal Energy for Sustainable Food Production in Canada’s Remote Northern Communities
Bibliographic record
Abstract
The cold, remote, northern regions of Canada constitute a challenging environment for the provision of reliable energy and food supply to communities. A transition from fossil fuels to renewables-based sources of energy is one positive step in reducing the greenhouse gases from the energy supply system, which currently requires long-distance transport of diesel for electricity and heating needs. Geothermal energy can not only displace diesel for part of this energy need, it can provide a base-load source of local energy to support food production and mitigate adverse impacts of food insecurity on communities. In this proof-of-concept study, we highlight some potential benefits of using geothermal energy to serve Canada’s northern communities. Specifically, we focus on food security and evaluate the technical and economic feasibility of producing vegetables in a “controlled environment”, using ground sources of heat for energy requirements at three remote locations—Resolute Bay, Nunavut, as well as Moosonee and Pagwa in Ontario. The system is designed for geothermal district heating combined with efficient use of nutrients, water, and heat to yield a diverse crop of vegetables at an average cost up to 50% lower than the current cost of these vegetables delivered to Resolute Bay. The estimates of thermal energy requirements vary by location (e.g., they are in the range of 41 to 44 kW of thermal energy for a single greenhouse in Resolute Bay). To attain adequate system size to support the operation of such greenhouses, it is expected that up to 15% of the annually recommended servings of vegetables can be provided. Our comparative analysis of geothermal system capital costs shows significantly lower capital costs in Southern Ontario compared to Northern Canada—lower by one-third. Notwithstanding high capital costs, our study demonstrates the technical and economic feasibility of producing vegetables cost-effectively in the cold northern climate. This suggests that geothermal energy systems can supply the heat needed for greenhouse applications in remote northern regions, supplying a reliable and robust source of cost-competitive sustainable energy over the long-term and providing a basis for improved food security and economic empowerment of communities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".