Examining the Feasibility of Mine Thermal Energy Storage (MTES) in Glace Bay, Nova Scotia
Bibliographic record
Abstract
Mine Thermal Energy Storage (MTES) offers a promising solution for sustainable heating by repurposing abandoned, water-filled mines as underground thermal reservoirs. This study assesses the feasibility of implementing MTES in Nova Scotia, with a focus on the Sydney Coalfield region, particularly Glace Bay. The research combines geological analysis, residential heat demand estimation, thermal storage capacity estimation, and cost-benefit evaluation to determine whether abandoned coal mines can support district heating applications. Results show that MTES can deliver substantial heating cost reductions compared to oil-based systems, while significantly lowering greenhouse gas emissions. The study also explores the integration of MTES with local renewable energy sources, including wind and solar, to enhance energy system flexibility and reliability. International case studies from Springhill (Canada), Heerlen (Netherlands), and Bochum (Germany) are analyzed to illustrate the scalability, performance, and operational challenges of MTES systems. Key technical barriers—such as water quality management, infrastructure investment, and seasonal variability in heat demand—are discussed. Overall, the findings highlight MTES as a viable and sustainable energy storage approach for Nova Scotia and other regions with legacy mining infrastructure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 source (direct Gemma or distilled Codex), 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".