A METHODOLOGY TO QUANTIFY LOCAL CARBON-FREE THERMAL ENERGY SOURCES WITH HIGH TEMPORAL AND SPATIAL RESOLUTION
Bibliographic record
Abstract
Decarbonizing space heating is a critical challenge in Canada, where cold climates and reliance on fossil fuels contribute significantly to carbon emissions. Capturing and reusing heat that would otherwise be wasted (waste heat) presents a viable opportunity to reduce emissions associated with space heating. This heat recovery can occur internally within a single building or, when there is surplus, be distributed to neighbouring buildings through a thermal network. To effectively integrate waste heat into community energy systems, these heat sources must be identified and quantified. This enables decision-makers to assess both the quality and availability of the sources to determine how they can best meet local space heating demands. This study presents a methodology to quantify available waste heat sources across Ontario using a combination of a top-down and bottom-up approach. This study aims to enhance the understanding of heat recovery potential and support the development of thermal networks in the province. The methodology presented is also adaptable for application in other regions. Particular attention is given to nonconventional, low-temperature heat sources, which are often overlooked despite being valid options for thermal networks. These sources offer several benefits, including proximity to heating demand within communities, long-term availability, and the ability to diversify heating supply. The analysis reveals that the residual heat sources identified in this study could supply up to 128 TWh of heat annually in Ontario, which is equivalent to the province’s annual space heating demand currently being met by natural gas.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".