Unveiling the potential for decarbonization of the building sector: A comparative study of technological and non-technological low-carbon strategies
Bibliographic record
Abstract
There is an urgent need to mitigate carbon emissions in the building sector, particularly from existing buildings. The existing literature focuses predominantly on technological strategies such as low-carbon materials. This prompts the question: Can technological strategies alone drive the decarbonization of buildings, or are non-technological strategies also essential? Although recent research considers the benefits of the latter, studies assessing the potential of non-technological strategies for decarbonization of buildings are lacking because of the challenges involved in evaluating the indirect impacts and potential trade-offs associated with these strategies such as their ripple effects on mobility. This study pioneers a comparative assessment to evaluate the environmental mitigation potential of non-technological strategies (adaptation, a subset of the sharing economy, and behavioral changes) against technological strategies (low-carbon materials, retrofitting, and recycled materials) to ascertain the effectiveness of non-technological approaches. Through life cycle assessment , this study extends beyond solely evaluating the GHG reduction potential to assess the overall environmental mitigation capacity. A single-family house in Montreal was used as a reference scenario. With significant mitigation potential observed from a non-technological perspective, the results robustly reveal that the adaptation scenario surpasses all scenarios, including retrofitting, which is the primary mitigation strategy for existing buildings, by up to 50 % and 41 % at the midpoint and damage levels, respectively. Furthermore, the adaptation scenario potentially provides sufficiency by saving considerable amounts of material and energy, thereby alleviating the environmental impact of the production and use stages by up to 27 % and 15 %, respectively. This study also evaluates the combined effects of adaptation and retrofitting for existing buildings, revealing by up to 8 % greater environmental benefits at the midpoint and damage levels than in the adaptation scenario individually. These results highlight the potential of non-technological strategies that are currently overlooked in the building sector. However, their implementation requires fewer resources and less energy than technological changes. Therefore, further investigation is warranted to explore how adopting these strategies, along with technological ones, is advantageous.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".