Evaluation of aboveground forest carbon sequestration for climate change mitigation targets: a case study on McGill University properties
Bibliographic record
Abstract
Human-induced climate change is one of the biggest threats facing human-kind and the global environment today. Climate action plans at the global, regional, and local scales set C neutrality (a state of no net increase in atmospheric C achieved by balancing emissions and sequestration) as a key climate change mitigation target. Action plans to achieve C neutrality often focus on emissions reduction, with limited focus on quantifying, measuring, and increasing C sequestration. Certain forms of C sequestration include afforestation, which can remove existing C trapped in the atmosphere through photosynthesis in a cost-effective way, while also providing additional ecosystem services, such as recreation or maple syrup. Higher education institutions, particularly universities, play an important role in climate change mitigation efforts due to their size, population, and influence in sustainable education. In this case study, I focus on McGill University’s plan to become C neutral by 2040. McGill has developed an annual inventory that tracks major sources and amounts of annual GHGs emissions at McGill from travel, energy consumption, and power generation. However, missing from this inventory is a measurement of total C sequestered annually on university properties. To fill this gap in our knowledge, I measure, quantify, and evaluate the current rates of aboveground C sequestration on the two main forested properties owned by McGill University, the Morgan Arboretum (240 ha) and the Gault Nature Reserve (1000 ha). I also evaluate two different scenarios that could increase C sequestration through afforestation on the largest agricultural property at McGill University, the Macdonald Campus Farm (200 ha). To estimate C sequestration, I gathered data on tree species, tree diameter, and tree growth in 71 plots of 400 m2 from both forests (34 at the Morgan Arboretum and 37 at the Gault Nature Reserve). I inputted this data into allometric equations to calculate the C sequestration in the plots and multiplied out by forest type to estimate C sequestration across the entire area of both forests. These two forested properties are currently capturing just under 5% of the university’s annual C emissions, indicating that there needs to be significant efforts to increase C sequestration or reduce C emissions to reach C neutrality by 2040. My results show that the Morgan Arboretum (managed, with some plantations) sequesters C at a greater rate per hectare and overall than the Gault Nature Reserve (old growth and primarily unmanaged). Differences in C sequestration between the two forests appear to be primarily related to the difference in management, forest age, tree mortality, and forest density, with little influence from differences in forest composition. Afforestation at the Macdonald Campus Farm could increase C sequestration by up to 87% over current rates and bring up the capture of C emissions to just over 9% of McGill University’s current emissions. While net sequestration on campus may be small relative to emissions, the educational potential of on-campus C offsetting opportunities is large. This project provides an understanding of the potential to quantify and increase C sequestration at McGill and on other university and institutional properties in order to help reach climate change mitigation targets
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".