Evaluation of aboveground forest carbon sequestration for climate change mitigation targets: a case study on McGill University properties
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».