Achieving deep-energy retrofits for households in energy poverty
Bibliographic record
Abstract
Climate change and energy poverty are two sustainability challenges that can be addressed through deep-energy retrofits for homes. This systematic review identifies which factors influence the achievement of energy retrofits for households vulnerable to energy poverty. It covers both energy-poor households and the landlords or building owners of energy-poor households. The results identify a range of influential factors across several themes: financial, policy and organizational, trust and communication, technical, attitudes and values, and health. Health and quality of life are particularly influential motivating factors among households vulnerable to energy poverty, as is the presence of trust and communication between stakeholders. Multiple financial considerations are also important, such as the availability of no-cost retrofit options and the prospect of lower energy and maintenance costs. Lastly, government requirements to retrofit and minimum energy standards are motivating, particularly in the social housing sector. These findings and the lack of focus on energy poverty within the energy retrofit literature and policies point to a need for further research on this topic, and for retrofit policies specifically targeted to households vulnerable to energy poverty. Policy relevance Energy retrofit policies targeting households vulnerable to energy poverty could be more effective if they: Improve access to low or no-cost retrofit options alongside tenant protection mechanisms Include requirements for resident consent and engagement Build capacity to collect, centralize and publicize information about building stocks to align retrofit projects with necessary upgrades Disseminate knowledge of retrofit programs through trusted communicators Increase stakeholders’ understanding of retrofit benefits Take a holistic approach by emphasizing the co-benefits of energy retrofits in energy-poor households Implement government requirements to pursue energy retrofits aligned with overarching government climate policies, particularly for publicly owned housing.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".