Barriers and opportunities for improving energy efficiency in the social housing sector : case study of E4C's division of housing and mental health
Bibliographic record
Abstract
Energy efficiency improvements in the social housing sector have the potential to produce a range of environmental and social benefits. These improvements can be produced through retrofits that deliver energy savings or new construction built to a high standard of energetic efficiency. However, implementation of these approaches is hindered by economic and organizational constraints affecting the agencies that provide society with social housing and the governments that support the provision of these services. This thesis builds on the work of other researchers studying these constraints by supplying an in-depth case study from Alberta and a discussion based on its findings. The case study focuses on E4C, a social service agency with several housing projects. Overall, findings matched important themes identified in the academic literature. The in-depth nature of the case study added additional insight to many of these themes. Most barriers are economic in nature and related to a lack of sufficient funding or the up-front costs of energy-saving retrofits. The recommendations presented are based on consideration of the multiple barriers and opportunities faced. Most of these require a considerable investment of time on the part of agencies and would be followed up by capital investments to implement energy-saving changes. Therefore it is important to note that the most significant barrier is commitment, which is one of E4C's central values. This thesis showed that commitment cannot exceed capacity to act. Greater commitment on the part of governments, agencies or society at large could have significant impacts in improving the energy efficiency of buildings in the Albertan, and Canadian, social housing sector.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".