Feasibility of Planting Trees around Buildings as a Nature-Based Solution of Carbon Sequestration—An LCA Approach Using Two Case Studies
Bibliographic record
Abstract
In response to Canada’s commitment to reducing greenhouse gas emissions and to making pathways to achieve carbon neutral buildings, this paper presents two real case studies. The paper first outlines the potential of trees to absorb CO2 emissions through photosynthesis, and the methods used for the estimation of their annual carbon sequestration rates. The net annual carbon sequestration rate of 0.575 kgCO2eq/m2 of tree cover area is considered in our study. Then, this paper presents the carbon life cycle assessment of an all-electric laboratory at Concordia University and of a single-detached house, both located in Montreal. The life cycle assessment (LCA) calculations were performed using two software tools, One Click LCA and Athena Impact Estimator for Buildings. The results in terms of Global Warming Potential (GWP) over 60 years for the laboratory were found to be 83,521 kgCO2eq using One Click LCA, and 82,666 kgCO2eq using Athena. For the single-detached house that uses natural gas for space heating and domestic hot water, the GWP was found to be 544,907 kgCO2eq using One Click LCA, and 566,856 kgCO2eq using Athena. For the all-electric laboratory, a garden fully covered with representative urban trees could offset around 17% of the total life cycle carbon emissions. For the natural gas-powered single-detached house, the sequestration by trees is around 3% of the total life cycle carbon emission. This paper presents limits for achieving carbon neutral buildings when only the emissions sequestration by trees is applied, and discusses the main findings regarding LCA calculations under different scenarios.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".