Evaluating the Link between Low Carbon Reductions Strategies and Its Performance in the Context of Climate Change: A Carbon Footprint of a Wood-Frame Residential Building in Quebec, Canada
Bibliographic record
Abstract
The design and study of low carbon buildings is a major concern in a modern economy due to high carbon emissions produced by buildings and its effects on climate change. Studies have investigated (CFP) Carbon Footprint of buildings, but there remains a need for a strong analysis that measure and quantify the overall degree of GHG emissions reductions and its relationship with the effect on climate change mitigation. This study evaluates the potential of reducing greenhouse gas (GHG) emissions from the building sector by evaluating the (CFP) of four hotpots approaches defined in line with commonly carbon reduction strategies, also known as mitigation strategies. CFP framework is applied to compare the (CC) climate change impact of mitigation strategies. A multi-story timber residential construction in Quebec City (Canada) was chosen as a baseline scenario. This building has been designed with the idea of being a reference of sustainable development application in the building sector. In this scenario, the production of materials and construction (assembly, waste management and transportation) were evaluated. A CFP that covers eight actions divided in four low carbon strategies, including: low carbon materials, material minimization, reuse and recycle materials and adoption of local sources and use of biofuels were evaluated. The results of this study shows that the used of prefabricated technique in buildings is an alternative to reduce the CFP of buildings in the context of Quebec. The CC decreases per m2 floor area in baseline scenario is up to 25% than current buildings. If the benefits of low carbon strategies are included, the timber structures can generate 38% lower CC than the original baseline scenario. The investigation recommends that CO2eq emissions reduction in the design and implementation of residential constructions as climate change mitigation is perfectly feasible by following different working strategies. It is concluded that if the four strategies were implemented in current buildings they would have environmental benefits by reducing its CFP. The reuse wood wastes into production of particleboard has the greatest environmental benefit due to temporary carbon storage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".