Life Cycle Assessment of the Environmental Benefits of Using Wood Products and Planting Trees at an All-Electric University Laboratory
Bibliographic record
Abstract
Many countries across the globe have set targets for different economic sectors, aiming to tackle global warming by reducing the overall carbon footprint of human-related activities. Among these sectors, the building industry stands out as a major consumer of materials and energy resources, making it a key player in achieving carbon neutrality. It is one of the main contributors responsible for energy-related greenhouse gas (GHG) emissions, including both operational emissions and embodied emissions in materials and equipment manufacturing. Nature-based design solutions, such as planting trees in urban spaces, or incorporating the use of wood products, have regained attention in recent years due to their potential to mitigate carbon emissions from buildings. Within this context, this paper presents a comprehensive life cycle assessment (LCA) of a recently built research facility, with a focus on demonstrating how the benefits of nature-based solutions, specifically carbon sequestration from trees and biogenic carbon content in wood products, can be quantified and reported using the latest LCA standards and tools. The analysis is provided under three end-of-life scenarios for wood products: wood incineration with energy recovery, wood landfilling, and wood recycling/repurposing. The results indicate that the set of strategies adopted in this building, i.e., tree planting, the use of wood products, and the end-of-life treatment of materials, can potentially offset carbon emission by 37.2% up to 83.9% when included in the LCA, depending on the scenario considered. By continuing to refine LCA standards and tools, and fostering collaboration between researchers, policymakers, and industry professionals, we can advance our understanding and ultimately achieve the widespread adoption of carbon-neutral buildings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".