A Survey of Solar Hot Water Heating Initiatives: Lessons for Policy-Makers
Bibliographic record
Abstract
Although a larger consumer of energy than the transport and electricity sectors respectively, the heating sector is often neglected in contemporary discussions surrounding the deployment of renewable energy technologies. In this article, the author surveys the approaches that some jurisdictions have taken to encourage the use of solar water heating systems, a promising renewable heating technology. The discussion focuses on solar hot water heating initiatives that are novel or representative of greater trends, namely those in Spain, the United Kingdom, Australia and the State of Hawaii in the United States. Such initiatives range from building code requirements tied to a building’s projected hot water demand in Spain to the trading of renewable energy credits by homeowners in Australia. Canadian incentives for the use of solar water heating systems are also explored as an example of a jurisdiction that has done little to encourage the use of solar water heating systems. Among other conclusions, the author makes clear that quality control measures should be launched in tandem with any solar heating initiatives and that cash incentives may not be the most effective means to support the deployment of solar water heating systems.
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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".