In a land of cheap energy can small scale solar thermal be cost competitive? A Canadian case study
Bibliographic record
Abstract
With rising energy prices and an increased focus on environmental issues, this paper attempts to answer the question of whether solar domestic hot water (SDHW) technology can be an economically viable investment over a twenty year period for the average residential homeowner in Ottawa, Ontario by modelling different payback levels that occur through fuel savings for natural gas and electricity. Natural gas is the primary energy source for hot water heating but is not available in all jurisdictions, particularly rural areas. \n \nAverage and high consumption hot water energy demand was determined by analyzing hourly consumption data from eight sites in Ottawa for a period of between twelve and eighteen months. Three energy price forecasts were used with performance and energy savings from a typical SDHW system completed by RETScreen software. Modelling included: A) a baseline condition; B) a carbon tax; C) an increase in the harmonized sales tax (HST); and D) a combination of both. Scenarios B), C), and D) are assumed to reduce demand assuming a price elasticity of demand for electricity of -0.3 and -0.35 for natural gas. \n \nIn the absence of government incentives, all natural gas scenarios resulted in poor economic returns due mainly to the low price of natural gas relative to capital. Based on current and projected electricity prices, only the BAU policy scenario, assuming no adjustment in consumption due to rising prices resulted in the SDHW system being economically viable without incentives. Greenhouse gas (GHG) abatement costs are estimated at between $15 and $20 per tonne CO2, per year and $27 to $30 per tonne CO2 per year for natural gas and electricity, respectively and is comparable to current international prices under emissions trading.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".