Life cycle assessment of solar district heating with borehole thermal energy storage in Nunavik
Bibliographic record
Abstract
Nunavik, a remote subarctic region covering the northern third of Quebec, Canada, relies heavily on diesel to meet residential heating demand. Solar district heating with borehole thermal energy storage (SDH-BTES) has been regarded as one of the most promising solutions that can break the dependence on fossil fuels and develop renewable energy resource locally. Whether to develop an SDH-BTES in Nunavik is not only a technical and economic consideration, but also an environmental deliberation. Even though SDH-BTES systems are considered as an environmentally friendly technique in other regions, it is crucial to analyze its environmental performance in Nunavik, considering the harsh weather condition, inconvenient transportation and backward infrastructure there. Therefore, in this study, a cradle-tograve life cycle assessment (LCA) of SDH-BTES in Nunavik is performed. A heating system for 20 single-family houses in Kuujjuaq, comprising a 1500 m 2 gross solar area and one hundred fifty 30–m–deep borehole heat exchangers, is modeled in SIMAPRO to analyze its environmental performance. The results are presented comparatively with the 20 conventional local household diesel furnaces. The present analyses show that SDH-BTES performs better than local diesel furnace regarding human health, climate change and resources. However, ecosystem quality impact of SDH-BTES system is remains higher than the conventional domestic diesel furnaces due to drilling process and the need to a large land occupation of underground thermal heat storage. Besides, 32418.80 kg GHG emission can be avoided per year using SDH-BTES system. In summary, the LCA results present the extent of the environmental benefits of SDH-BTES for adoption as a renewable energy shortage in Nunavik. The extent of adverse environmental impacts of the system is also characterized and estimated to provide a basis for prioritization and addressing of them.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".