Co-benefits of residential retrofits: A review of quantification and monetization approaches
Bibliographic record
Abstract
Buildings must play a significant role in achieving carbon neutrality by 2050. Residential retrofits are an important part of this, but they are not being implemented at the scale and speed required to meet our goals. In this context, highlighting the co-benefits associated with energy retrofits could significantly strengthen the argument for more widespread adoption and effectively motivate building owners. Accordingly, this review utilizes a combination of qualitative and quantitative approaches to analyze various dimensions of social, economic, and environmental co-benefits associated with residential retrofits initiatives. Following PRISMA guidelines, 207 articles were reviewed which indicated that the three most common retrofit interventions include adding insulation and replacing windows and heating systems. The results highlight the importance of implementing retrofits not only to improve environmental and economic outcomes, but also to provide significant social and health benefits. Findings show that the quantification of social benefits primarily revolved around inhabitant comfort and satisfaction, with thermal comfort emerging as the most extensively studied co-benefit, followed by health impact evaluations. In the reviewed studies, economic and environmental co-benefits have been analyzed with a focus on greenhouse gas emission reduction and the generation of new direct and indirect job opportunities. However, there is a need for standardized, comprehensive, multi-scale approaches to effectively evaluate the co-benefits associated with residential retrofits for all stakeholders, including individuals, society, and governments. Such an approach enables the integration of these co-benefits into policy objectives and retrofit decision-making processes, fostering a more holistic understanding of the positive impacts of residential retrofits.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".